Decremental—Not Relative— Deprivation Motivates

Home , Disadvantaged

Title: Resisting Changes That Make Things Worse: Decremental—Not Relative—

Deprivation Motivates Political Violence

Authors: Henrikas Bartusevičius,1 Florian van Leeuwen2

Affiliations: 1Peace Research Institute Oslo, Hausmanns gate 3, 0186 Oslo, Norway.

2Department of Social Psychology, Tilburg University, Professor Cobbenhagenlaan

225, 5037 DB, Tilburg, Netherlands.

Corresponding author: Henrikas Bartusevičius. Email: [email protected]

Word Count: 10,339 (11,989 including references)

Please note that this manuscript has not yet been peer reviewed

Abstract. Despite extensive scholarly interest in the association between economic inequality and political violence, the micro-level mechanisms through which the former influences the latter remain not well understood. Drawing on pioneering theories of political violence, social psychological research on relative deprivation, and prospect theory, we examine individual-level processes underpinning the relationship between inequality and political violence. We present two arguments: despite being a key explanatory variable in existing research, lower economic status vis-à-vis other individuals (relative deprivation) is unlikely to motivate people to participate in political violence; by contrast, although virtually unexplored, a projected decrease in one’s own economic status (prospective decremental deprivation) is likely to motivate political violence. Multilevel analyses of nationally representative samples from many African countries provide evidence to support these claims. Based on this, we posit that focusing on changes in living conditions, rather than the status quo, is key to understanding political violence.

1 1 Introduction

What motivates people to participate in political violence1? Over the last decade, scholars have shown a particular interest in grievance-based explanations of political violence

(Cederman, Gleditsch, and Buhaug 2013; Østby 2013). A large portion of this research has analyzed grievances associated with economic inequality. In contrast to much prior work, more recent studies have demonstrated that economic inequality—when appropriately operationalized—relates to various forms of violence, including large-scale civil conflict (Cederman, Gleditsch, and Buhaug 2013; Østby 2013). While state- or group-level analyses have extensively analyzed the empirical association between inequality and violence, we know less about the individual-level processes through which one influences the other (see also Bartusevičius 2019; Dyrstad and Hillesund 2020; Hillesund 2015; Koos

2018; Miodownik and Nir 2016; Rustad 2016).

Here, we examine the widely held claim that inequality motivates political violence.

Ever since the publication of Why Men Rebel (Gurr 1970/2011), many scholars have assumed that economically disadvantaged individuals are predisposed to being mobilized for violent collective action. To date, this remains an undertheorized and empirically underexplored claim. Notably, extant empirical research has mainly focused on static inequalities. However, pioneering theoretical work considered changes in living conditions—not the status quo—as key to understanding violence (Davies 1962; Gurr

1970/2011). Indeed, in his seminal work, Gurr claimed that persistent relative deprivation should lead to psychological adjustment, not violence, stressing the need to account for dynamic forms of deprivation caused by changes in living conditions or people’s expectations about them (1970/2011, 46–56). This emphasis on dynamics is consistent with individual-level studies in other disciplines. Social psychological research indicates only a

1 We use “political violence” to refer to a broader construct of coalitional violence for a political cause, which subsumes various more specific types of violence, such as “civil conflict” or “non-state conflict”. We return to definitional/operational issues below.

1 weak association between individual relative deprivation and participation in collective actions (Smith et al. 2012), and behavioral economics demonstrates that risk-seeking is

related to prospective losses rather than prospective gains (e.g., resources acquired from

the advantaged) (Kahneman and Tversky 1984).

Although psychologists have extensively analyzed relative deprivation, they have

rarely linked it to participation in political violence, focusing instead on non-violent col-

lective actions (van Zomeren et al. 2008). Furthermore, psychological research has pri-

marily relied on samples from Western, educated, industrialized, rich, and developed

(WEIRD) populations (Henrich, Heine, and Norenzayan 2010). By contrast, while political scientists have extensively analyzed the empirical association between inequality and

violence, both in WEIRD and non-WEIRD countries, they have rarely tested individual-

level theories against individual-level data (for a critique of this, see Dyrstad and Hil-

lesund 2020; Hillesund 2015; Koos 2018; Miodownik and Nir 2016; Pettigrew 2015; 2016;

Rustad 2016).

Here, we present an analysis of large multinational survey data from African coun-

tries. Consistent with the theoretical focus on temporal dynamics, we find no empirical

support for the claim that perceived inequality vis-à-vis other individuals motivates vio-

lence. By contrast, we find that a projected decrease in one’s own economic status moti-

vates violence. We utilize behavioral-intentional measures to alleviate endogeneity con-

cerns, and a large set of questions to assess alternative explanations. Drawing on a long

tradition in applied economics (Altonji, Elder, and Taber 2005; Oster 2019), we also

estimate the risk of omitted-variable bias, finding that unobserved confounders are un-

likely to drive our results. Our findings also withstand various sensitivity tests, including

those addressing reporting problems and using alternative data.

Altogether, this article makes the following contributions. Theoretically, we renovate

a five-decade-old theory of relative deprivation and political violence—routinely invoked

in contemporary conflict research—with insights from recent psychological work and be-

2 havioral economics. Our study aimed both to test existing theory and to build new prop-

ositions. Specifically, we aimed to assess the general argument by Gurr (1970/2011) that

dynamic forms of deprivation, compared to static forms, are more important for violence.

Subsequently, we specified and further developed this early theoretical work by integrat-

ing insights from recent individual-level research in other disciplines, producing novel propositions that go beyond Gurr’s.

Empirically, we show that perceptions of disadvantageous economic changes, but not individual-based inequalities, significantly relate to motivations to engage in violence.

Macro-level studies have shown that inequalities between groups, compared to those between individuals, better predict civil conflict (e.g., Cederman, Gleditsch, and Buhaug

2013; Østby 2013); consequently, some scholars have called for a shift in focus from individual- to group-based grievances. Our micro-level findings suggest that group-based grievances are indeed associated with violent motivations, corroborating macro-level re-

search. However, we find that certain forms of individual-based grievances also signifi-

cantly relate to motivations to participate in violence. Instead of shifting focus away from

individual-based variables, we suggest that macro-level research re-center on temporal

economic dynamics, whether pertaining to individuals or groups. This suggestion has

implications for conflict research writ large. It points to the central role of temporal

dynamics, as contrasted to static features, in the processes leading to violence.

More generally, our study contributes to an emerging effort to integrate psychological

perspectives into the study of macro-level political phenomena (Kertzer and Tingley

2018), and the scrutiny of individual-level theories with individual-level data. Although

commonly invoking theories of individual motivations, research on civil conflict has typ-

ically focused on states or groups as units of analysis. Directly investigating individual

motivations, we find that one key theoretical account underlying a substantial portion of

conflict research needs to be revised, opening a range of new research avenues.

3 2 Assumptions in Existing Research

Early studies of political violence argued that inequalities in income or land can motivate

individuals to participate in violence challenging the status quo distribution of resources

(Nagel 1974; Russet 1964; Sigelman and Simpson 1977). Recent studies added that ine-

qualities overlapping with ethnic groups (horizontal inequalities) may also aid the mobilization of the disadvantaged for collective action (Buhaug, Cederman, and Gleditsch

2014; Cederman, Weidman, and Gleditsch 2011; Gurr 2000; Østby 2008; 2013; Stewart

2008). Ethnic groups share a common social identity and horizontal inequalities can strengthen the salience of these identities (Gurr 2000, 66–69; Østby 2008, 147). Salient identities, in turn, can facilitate the mobilization of solitary individuals for collective action (Gurr 2000, 75).

While emphasizing mobilization, studies of horizontal inequalities have not objected the claim that inequalities motivate violence. That is, this research has accentuated conditions that facilitate violence (e.g., inequality aligned with particular identity groups), while still assuming that inequalities, whether individual or group, motivate violence. As

Stewart (2008, 12) underlined, “Large scale group mobilization—particularly for violent actions—is unlikely to occur in the absence of serious grievances”, referring to grievances generated by inequalities between religion-, ethnicity-, or class-based groups.

Yet the elemental question of whether inequality-related grievances indeed motivate violence remains directly underexplored. Recent studies have shown that individual- based (vertical) inequalities are associated with a higher likelihood of non-ethnic conflict

(Bartusevičius 2014; Buhaug, Cederman, and Gleditsch 2014) and that group-level horizontal inequalities are associated with a higher likelihood of ethnic conflict (Buhaug,

Cederman, and Gleditsch 2014; Cederman, Gleditsch, and Buhaug 2013). Yet, macro- or meso-level relationships say little about individual motivations.

4 Inequality can relate to violence via multiple other pathways, unrelated to the moti-

vations of the disadvantaged. For example, vertical or horizontal inequalities may reflect

a resource concentration in the hands of the ruling elite, incentivizing intra-elite fighting

over power (Boix 2008, 400–1). To assess whether inequality generates violence via griev-

ances or via other pathways, we need to scale down. Indeed, pioneering work on relative

deprivation and political violence suggested, ideally, interviewing individuals to account

for perceived deprivations and violent motivations (Davies 1962, 18; Gurr 1970/2011,

29–30). As Weede noted: “It is hard to imagine how relative deprivation can be ade- quately assessed without recourse to survey data” (1981, 652). Several studies, following

Gurr (1970/2011), collected survey data on people’s deprivations, attitudes toward po-

litical violence, and self-reported participation (reviewed in Muller 1977). However, this early research was limited in geographic scope—focused on single or several, and mainly

Western, samples—and relied on now superseded analysis techniques (Dyrstad and Hil- lesund 2020, 1730).

Within contemporary conflict research, only a handful of studies have collected systematic data on individual participation in violence (e.g., Humphreys and Weinstein

2008) or perceptions of inequality (e.g., Langer and Mikami 2013; Langer and Smedts

2013). These studies have challenged the claim that subjective and objective inequalities correspond—a correspondence that macro-level analyses have hitherto assumed nearly unequivocally. Specifically, using surveys from five African countries, Langer and Mikami

(2013) found that members of some ethnic groups that were not the most disadvantaged perceived themselves as the most disadvantaged, whereas members of other ethnic groups perceived their status as better than it was. Furthermore, using Afrobarometer data,

Langer and Smedts (2013) found a negative association between objectively-measured and perceived horizontal inequalities (see also Miodownik and Nir 2016).

Actual and perceived inequalities might not correspond for multiple reasons, including poor quality of data and manipulation of public opinion by elites. The key point is that, contrary to the assumptions of prior research, objective measures of inequalities may not

5 reflect perceived inequalities. Hence, analysis involving objective measures of inequality at the macro level may not inform about the relationship between perceived inequalities and people’s violence motivations. We need to focus either on the factors that affect perceived inequalities, such as public opinion manipulation, or use direct measures of perceived inequalities (Pettigrew 2015, 13–4; 2016, 9–10).

Most recently, scholars have started using surveys to measure perceived inequalities and attitudes toward political violence. For example, Rustad (2016) conducted a survey in the Niger Delta measuring both objective and perceived inequalities and assessing their effects on individual attitudes toward violence. Notably, Rustad (2016) found little evidence for a correlation between objectively measured and perceived inequalities. Further- more, perceived inequalities, both individual- and group-based, were better predictors than objectively measured inequalities of attitudes toward violence. In another study in the Niger Delta, Koos (2018) found that a perception that one’s group receives an unfair share of oil revenues also predicted attitudinal support for violence.

In addition to economic inequalities, several studies have also analyzed political inequalities. For example, Hillesund (2015) found that a perception of higher status of civil and political rights among Palestinians in the Gaza Strip and the West Bank was associated with lower support for violent resistance tactics. Similarly, Dyrstad and Hillesund

(2020) found that a perception of fair distribution of political power among groups was negatively associated with support for violence in some countries but not others and that this association was moderated by perceptions of political efficacy. Using Afrobarometer data, Miodownik and Nir (2016) found another nuanced pattern: the association between perceived group-based inequalities, both economic and political, and attitudes toward violence is moderated by objectively measured inequalities.

We contribute to this emerging literature in at least three ways. First, we focus explicitly on motivations to engage in violence, as contrasted to attitudes. On the one hand, attitudes often predict actual behavior; individuals supportive of violence, ceteris paribus, should be more likely to engage in it (Hillesund 2015, 79–80). Furthermore, attitudes

6 toward violence are of key interest in themselves: armed groups often rely on local pop-

ulation support for their survival; hence, people’s support for violence is an important

risk factor for civil conflict (Dyrstad and Hillesund 2020). On the other hand, if the goal is to assess whether inequalities motivate people to engage in violence, attitudes toward violence may not be the optimal indicators. Attitudes can be idealistic and may disregard personal costs and benefits associated with participation (van Zomeren et al. 2008, 510).

Second, in addition to static inequalities, we analyze changes in living conditions, which—

as we theorize below—are likely key to understanding political violence. Finally, we uti-

lize large multinational samples. Most existing micro-level studies report results from only one or several countries, which can be confounded or moderated by country-specific factors (Dyrstad and Hillesund 2020), undermining broader generalization.

3 Theory

Drawing on pioneering theories of political violence, social psychological research on rel-

ative deprivation, and prospect theory, we develop the following overarching argument:

political violence more strongly relates to deprivation feelings arising out of temporal changes in economic conditions, rather than individuals comparing their own conditions with those of others. We develop this argument in the following way.

We first present a general theoretical framework, which draws on Gurr (1970/2011).

In contrast to much of extant research, we stress that this pioneering work emphasized the role of dynamic, rather than static, deprivations. This discussion places our more specific, subsequent arguments in a familiar theoretical framework.

These more specific arguments pertain to psychological variables. First, temporal comparisons, compared to static comparisons, produce stronger negative emotions, which in turn more strongly motivate individuals to take part in antigovernment collective action. Second, temporal comparisons promote risk-seeking, whereas static comparisons

7 promote risk-aversion. Risk-seeking/-aversion in turn relate to the probability of using

violence or joining a violent antigovernment collective action. As such, our article aims

to assess underexplored aspects of Gurr’s theoretical work and, subsequently, build new

theory by integrating insights from other disciplines.

Importantly, as we detail below, our theory centers on motivational factors. We do

not claim that opportunities for violence are less important. We merely assume that—

holding opportunity factors constant—stronger motivations for violence constitute higher

probability of actual violence.

3.1 Types of Deprivation and Political Violence

Why would inequality cause political violence? To date, Gurr (1970/2011) provides

the most elaborate theoretical model linking the two variables, still serving as the primary

reference in contemporary research. The key element of the model is the concept of rela-

tive deprivation, defined as “actors’ perception of discrepancy between their value expec-

tations and their value capabilities”. Value expectations refer to “the goods and condi-

tions of life to which people believe they are rightfully entitled” and value capabilities to

“the goods and conditions they think they are capable of getting and keeping” (ibid., 24).

In short, relative deprivation is a discrepancy between what one has and what one be-

lieves one ought to have.

As the definition implies, relative deprivation only occurs over values to which people

think they are rightfully entitled. The standards of such an entitlement are determined

by some reference point, commonly other individuals or groups,2 but may also include

own status in the past or abstract ideals (ibid., 24–25). This suggests that inequalities

2 While this is commonly overlooked in extant research, Gurr’s theory considered discontent arising out of group-based comparisons (e.g., 1970/2011, 25). Research prior to Gurr had already distinguished between “egoistical” and “fraternalist” forms of deprivation (Runciman, 1966).

8 alone can generate relative deprivation, as the presence of inequalities implies the presence of advantaged individuals or groups that constitute reference points (Yitzhaki 1979).

The actual goods or conditions over which deprivation occurs vary across cultures and include welfare (e.g., physical goods), power (e.g., to vote), and interpersonal (e.g., ability to participate in associations) values (Gurr 1970/2011, 25–26). For operational reasons, and due to their comparability across countries, subsequent empirical research has primarily focused on physical goods, such as income or land (e.g., Nagel 1974; Sigel- man and Simpson 1977). This research thus assumed that income or land constitute goods that are universally and sufficiently valued to generate deprivation among those lacking them. Indeed, some studies suggested using the income Gini coefficient not just as an indicator of income inequality but also as a measure of aggregate levels of relative deprivation (Yitzhaki 1979).

When relative deprivation occurs, individuals experience anger—commonly referred to as “grievances”—which creates a motive for violence or for joining a violent collective action. This claim assumes the existence of a psychological mechanism that generates violence-motivating anger in response to relative deprivation. For Gurr, the link between relative deprivation and violence-motivating anger was provided by the frustration-aggression mechanism (see, e.g., Berkowitz 1989). Eventually, research on aggression has developed more complex models of the mechanisms that produce aggression in response to obstructed goals (Anderson and Bushman 2002). However, it is beyond the current study whether relative deprivation produces anger via the frustration-aggression mechanism or some other psychological mechanism. What matters here is the more general assumption that some psychological mechanism generates motivation for violence in response to relative deprivation. The presence of such a mechanism has been implicitly assumed in most conflict research, including recent studies on horizontal inequalities.

The path from motivation for political violence to actual violence involves numerous mediating and moderating variables, such as justification and likely efficacy of violence, as well as political opportunity structures. These factors are discussed in detail by Gurr

9 (1970/2011; see Figure 15 for a summary). Given our focus on motivations, we omit these variables here. Gurr’s theory holds that the intensity of anger and the population share of angry individuals, ceteris paribus, positively vary with the incidence and magnitude of collective political violence (1970/2011, 319–322).

As mentioned, value capabilities are defined by a comparison to some reference point.

These points determine the types of deprivation. Static relative deprivation reflects an instantaneous discrepancy between one’s value expectations and value capabilities. Typ- ically, this discrepancy arises out of a static comparison of oneself or one’s group to others. The other types of deprivation concern changes in value expectations or capabilities over time (ibid., 46–56). Decremental deprivation arises when value expectations remain unchanged but value capabilities decline. This discrepancy arises out of losses.

Aspirational deprivation occurs when value capabilities remain unchanged but expectations increase. This happens due to a change in expectations of what one ought to have, while the value capabilities remain unchanged. Progressive deprivation is a special form of aspirational deprivation, occurring when steady and simultaneous improvement in value expectations and capabilities is followed by stabilization or decline of the latter.

This type is analogous to Davies’ J-curve, which typically reflects “…a prolonged period of objective economic and social development…followed by a short period of sharp rever- sal” (1962, 6).

Concurring with Davies, Gurr (1970/2011, 46) hypothesized that dynamic types of deprivation were more likely to generate violence:

…because RD [relative deprivation] is a psychically uncomfortable condition, men

tend over the long run to adjust their value expectations to their value capabili-

ties. Societal conditions in which sought and attainable value positions are in

approximate equilibrium consequently can be regarded as “normal,” however un-

common they may be in the contemporary world, and provide a base-line from

which to evaluate patterns of change. Three distinct patterns of disequilibrium

10 can be specified: decremental deprivation…aspirational deprivation…and progres-

sive deprivation…All three patterns have been cited as causal or predisposing fac-

tors for political violence.

Notably, this discussion (see also 46–58) seems to have been overlooked in recent conflict

research, which has primarily focused on static inequalities.

A related literature has analyzed economic downturns as causes of civil conflict, with some studies reporting a positive effect (e.g., Miguel, Satyanath, and Sergenti 2004) and others finding no effect (e.g., Ciccone 2011). At least one study has explicitly linked

downturns to decremental deprivation and found that negative economic growth is asso-

ciated with revolutions (Knutsen 2014). Subsequently, some research has examined the

association between the loss of political status and violence. Petersen (2002) argued that

members of ethnic groups that lose government control after a pro-longed period of po-

litical dominance, are likely to experience particularly strong anger, and, consequently,

that such “status reversals” produce “the highest likelihood of violent conflict” (52).

Corroborating this, Cederman, Wimmer, and Min (2010) found that recently (politically)

downgraded ethnic groups were over-represented in civil conflicts, more so than ethnic groups that generally lacked power (see also Cederman, Buhaug, and Gleditsch 2013).

Combining the two literatures, Buhaug et al. (2021) analyzed the interaction between economic and political status reversals, and found that local income shocks were particularly likely to promote conflict among recently (politically) downgraded groups. Alto- gether, research on economic downturns and political status reversals has generated important insights into the causes of civil conflict, suggesting that changes in economic or political conditions, compared to status quo, may be more important for the outbreak violence. However, akin to research on the inequality-conflict nexus, these studies have focused on states or groups as units of analysis; hence, they did not directly account for individual motivations.

11 In sum, the above discussion suggests that violence should relate more strongly to deprivation caused by temporal changes in economic conditions than to deprivation resulting from comparisons between individuals. We now turn to work in psychology, which supports the general notion that people are more sensitive to disadvantageous temporal changes in their economic conditions than to disadvantageous comparisons with others.

3.2 Negative Emotions and Motivations for Political Violence

A perception that one is worse off than others (or oneself in the past) quite likely generates negative affect. But is such negative affect sufficient to motivate individuals to use violence or predispose them to being mobilized for violence? Appropriately addressing this question requires individual-level research, focused specifically on human perceptions of, and responses to, economic conditions (Pettigrew 2015, 13–4; 2016, 9–10).

Such research has been extensively conducted within social psychology, the field that pioneered the concept of relative deprivation (Stouffer et al. 1949). Unlike political scientists, psychologists have not linked individual-level factors to large-scale violence, such as civil war. However, psychologists have analyzed a range of other outcomes that likely constitute precursors of political violence, such as participation in various collective actions (e.g., riots or strikes) or intergroup attitudes (e.g., prejudice toward outgroups and outgroup hostility) (Smith et al. 2012). One insight that has emerged from social psychology is that individuals are often influenced not just by perceptions and beliefs about themselves, but also by perceptions and beliefs about the groups to which they belong

(Ellemers, 2012).

In a review of the psychological literature, Smith et al. (2012, 204) suggest that “feelings of GRD [group relative deprivation] should be associated with ingroup-serving attitudes and behavior such as collective action and outgroup prejudice, whereas IRD [individual relative deprivation] should be associated with individual-serving attitudes and behavior such as academic achievement and property crime”. Group relative deprivation

12 refers to the perception that one’s group is disadvantaged relative to other groups (corresponding to perceived horizontal inequality), and individual relative deprivation to the perception that you as an individual are disadvantaged relative to other individuals (cor-

responding to perceived vertical inequality). Subsequently, in a meta-analysis, encom-

passing 210 studies (N = 186,073), Smith et al. (2012) found evidence to support this general claim. This suggests that group relative deprivation, compared to individual relative deprivation, should be a stronger predictor of support for and participation in collective actions. But does this imply that individual-level deprivations are irrelevant for political violence?

While Smith et al.’s (2012) meta-analysis found that group relative deprivation is a stronger predictor of group-level outcomes (e.g., participation in strikes and outgroup hostility), it did not show that individual relative deprivation is unrelated to them. In fact, many studies show that individual relative deprivation is a significant (although weaker) predictor of such group-level outcomes (e.g., Pettigrew et al. 2008, 390). There are at least three plausible processes that account for this.

First, there may be spillover effects, such that a perception of a disadvantage compared to other individuals may lead to a perception that one’s whole group is disadvantaged compared to other groups (Pettigrew et al. 2008). As the correlation between in-

dividual and group relative deprivation is positive (ibid., 391), group-level outcomes may

in fact be caused by individual-based deprivations, exerting effects through group-based

deprivations. Indeed, in several European samples, group relative deprivation mediated

the association between individual relative deprivation and intergroup prejudice (ibid.).

Second, the direct association between individual-based deprivations and group-level outcomes potentially reflect that individuals compare themselves to individual outgroup members. Such comparisons can lead to evaluations of whole outgroups, and can therefore lead to group-level outcomes (Smith et al. 2012, 205). In the meta-analysis by Smith

et al., the association between individual relative deprivation resulting from comparisons

13 to outgroup members and collective behaviors was only slightly weaker than the associ-

ation between explicit group relative deprivation measures and collective behaviors, and

this difference was not significant (2012, 216).

Third, and particularly relevant for political violence, there is no difference in the

associations of individual and group deprivations with state-focused outcomes. In Smith

et al. (2012), individual relative deprivation and group relative deprivation were equally strong predictors of negative attitudes toward the “larger system”. These attitudes include trustworthiness or legitimacy of political institutions, which are most relevant for violence targeting the state. The group-focused attitudes and behaviors best predicted by group-based deprivations may actually be more relevant for communal conflicts involving violence between identity groups than for conflicts with governments (see also

Hillesund 2019).

These three processes suggest that individual-based deprivations may have an effect on participation in political violence, but also that this effect is unlikely to be strong.

Unlike participating in a strike, political violence entails the risk of injury and death.

Difficulties in recruitment to violent movements due to violence-related risks are well documented (Chenoweth and Stephan 2011). Thus, for individual-based deprivation to motivate participation in violence, it would have to generate particularly intense affect.

This is where temporal relative deprivations come into play. In particular, “when respondents compare their present with their own past—rather than comparing with other individuals or groups”, the association between individual-based deprivation and individual-based outcomes nearly doubles (Pettigrew 2015, 18). Supporting this, research on political radicalism has recently underscored the role of positional deprivation, i.e., deprivation based on a comparison of one’s own income changes (increases or decreases over time) with those of others (Burgoon et al., 2019). This implies that inward temporal comparisons are especially relevant for the deprivation effects on political violence.

The general notion that temporal comparisons strongly relate to anger is in line with a basic psychological insight that, rather than focusing on stasis, humans are attentive

14 to changes, especially those that “threaten to make things worse” (McDermott, Fowler,

and Smirnov 2008, 337). This basic tendency has been documented in research on hedonic

adaptation, showing that subjective wellbeing is strongly influenced by changes (e.g.,

becoming unemployed), but then gradually adjusts, eventually returning to pre-event

baseline levels (Diener 2000).

That people are particularly sensitive to changes for the worse aligns with Gurr’s

original prediction that dynamic forms of deprivation are key to understanding political

violence. While grievances generated by interpersonal comparisons are arguably too weak

to motivate violence, anger caused by temporal comparisons might be of greater magni-

tude and thus have sufficient motivational force to predispose individuals to political

violence.

3.3 Risk-Seeking and Political Violence

Gurr’s original claim that comparison reference points are key for understanding political violence is compatible with the emphasis on reference points in prospect theory

(Kahneman and Tversky 1979; 1984). People evaluate the outcomes of their decisions

relative to some reference point: when outcomes top the reference point, they constitute

a gain; and when outcomes come below the reference point, they constitute a loss. Re-

search has shown that “changes that make things worse (losses) loom larger than im-

provements or gains” (Kahneman, Knetsch, and Thaler 1991, 199), so that a loss of $100

has a greater impact on one’s subjective well-being than a $100 gain. Subsequently, this

research has shown that people faced with potential “gains tend to be risk averse, while

those confronting losses become much more risk seeking” (McDermott, Fowler, and

Smirnov 2008, 335). The preference for risky alternatives in the domain of losses is an

empirically robust phenomenon (Kahneman and Tversky 1984).

Tezcür (2016) invoked prospect theory to explain why people prefer participation in

violence to less risky alternatives. Rebel leaders often exaggerate threats and frame the

15 status quo as a loss. In such framing, not rebelling involves continued and certain losses

and rebelling has some prospect for gains, although at an increased risk of higher losses.

Framed in such a way, and expressed in “life or death” language, leaders’ messages can

lead followers to “take desperate gambles, accepting a high probability of making things

worse in exchange for a small hope of avoiding a large loss” (Kahneman 2011, 318–9).

The deprivation-violence link can be explained similarly. While Tezcür’s argument focuses on leaders or particular events as inducing loss frames, this function can also be performed by certain types of deprivation, corresponding to Gurr’s typology. Specifically,

(persisting) static relative deprivation corresponds to a status quo situation, where the

disadvantaged perceive likely outcomes, i.e., acquiring resources from the advantaged, as

gains. For such individuals, prospect theory predicts risk avoidance and preferences for

more certain, less risky options, such as continued status quo or non-violent activism.

By contrast, decremental deprivation corresponds to a situation where the status quo has changed, and where the disadvantaged are likely to perceive their outcomes as a loss.

Mercer (2005, 5) notes that “situational factors, such as an economic collapse, throw leaders and citizens into a domain of loss where the pre-crisis status quo becomes the standard reference point”. For such individuals, prospect theory predicts risk-seeking with the aim of averting the loss, and, by extension, higher susceptibility to being mobilized for violence.

Importantly, although Gurr’s theory does not specify whether anger relates differently to anticipated and experienced loss, prospect theory suggests that the risky choice of participating in violence should be most appealing to those who anticipate loss rather than those who have already experienced it. Specifically, compared to those who anticipate loss, those who have experienced loss are more likely to perceive the possible outcome of participating in violence as gains. When the loss has already occurred, some of the disadvantaged might take their current situation as the reference point and perceive that joining violence involves a small chance for an improved situation (e.g., doing nothing will not make things better, participating in violence might make things better). In

16 contrast, those who anticipate loss might be more likely to perceive the decision about

participating in violence in terms of loss (e.g., doing nothing will result in a loss, partic-

ipating in violence might reduce the loss). Taking such a loss frame should therefore

relate to a greater willingness to take risks. Hence, while experience of loss influences

one’s subjective well-being, prospect theory predicts that anticipation of losses or pro-

spective decremental deprivation should relate more strongly to risk-taking.

Finally, aspirational and progressive forms of deprivation correspond to situations of

status quo and improving status quo, followed by increased expectations and continuous

growth of expectations, respectively. In such cases, the disadvantaged should perceive

likely outcomes as gains, and so be risk averse. Our theoretical analysis thus suggests

that only one type of Gurr’s original deprivation should be related to risk-seeking: decremental deprivation or, more specifically, prospective decremental deprivation. Such

risk-seeking, combined with strong anger, as suggested by psychological research, should

predispose individuals to partake in violence. Thus, more formally:

H1: Prospective decremental deprivation is positively associated with motivations to

participate in political violence.

4 Research Design

4.1 Data

Instead of collecting samples from one or several counties, we attempted to identify avail-

able multinational surveys that asked questions pertaining to deprivations and political

violence. We identified two large datasets collected by Afrobarometer in 2002–2003

(Round 2) and 2011–2013 (Round 5); other rounds do not contain items on violence.

17 Afrobarometer is a multinational and multi-year project surveying nationally representative samples of citizens of voting age on topics related to governance, public services, and living standards. We first tested the hypothesis using Round 5 dataset, spanning 34 countries with a sample size of 1,200 or 2,400 per country (total N = 51,587), and then using the Round 2 dataset (Section 5.3). The surveys used clustered, stratified, multi- stage probability sampling, where random selection with a probability proportionate to the population size was applied at every stage. In addition to relevant measures of deprivations and violence, this dataset confers several advantages.

First, it enables analysis of nationally representative data that would otherwise be hardly possible. Political violence is a rare and extreme event. Hence, the number of those willing to participate in violence or actual participants, in most countries, is very small (see descriptive statistics below); thus, samples from single countries would likely fail to generate a sufficient number of people motivated to participate in violence, or self- reported participants, unless the sample size was considerably increased or the sampling non-randomly targeted such individuals, potentially leading to selection issues. Second, deprivation-related grievances may distinctly affect individual propensity to violence across different countries (Dyrstad and Hillesun 2020). Multilevel analysis of Afrobarom- eter data allows us to account for such country-level characteristics.

4.2 Outcomes

The first outcome measure is a behavioral self-report of participation in political violence (PVPARTICIPATION): “Here is a list of actions that people sometimes take as citi-

zens. For each of these, please tell me whether you, personally, have done any of these

things during the past year. If not, would you do this if you had the chance: Used force

or violence for a political cause?” (“No, would never do this”, “No, but would do if had

the chance”, “Yes, once or twice”, “Yes, several times”, “Yes, often”, and “Don’t know”).

Our primary interest was in whether people took part in violence—not whether higher

18 levels of deprivation lead to higher incidence of violence. Thus, we coded this variable as

binary, aggregating the first two responses into 0 = “no” and the last three into 1 =

“yes”. Analysis of the original, ordinal scale generates the same substantive conclusions.

There is no guarantee that all interviewees understood what “violence for a political cause” entails. We also cannot identify with certainty the exact form of violence the

interviewees had in mind. Note, however, that this item followed four other items on

political activism (“Attended a community meeting”; “Got together with others to raise

an issue”; “Refused to pay a tax or fee to government”; and “Attended a demonstration

or a protest march”). This suggests that the question about violence refers to participa-

tion in a protest-like, violent antigovernment action.

“Violence for a political cause” is a broader term that likely subsumes various more

specific types of violence. We assume that factors that increase people’s general motiva-

tions to use violence for a political cause, ceteris paribus, also increase people’s willingness

to engage in the more specific forms of violence, including civil conflicts. To rule out

participation in violence in support of governments (e.g., pro-government rallies), we

analyzed sub-samples of government supporters and non-supporters; we also controlled

for the comprehension of questions.

Individuals may not report participation in violence for many reasons, for example,

fears of repression. Thus, behavioral self-reports may underestimate the extent to which

people participate in violence. We attempted to address this concern in two ways. First, we controlled for whether interviewees were deemed suspicious or dishonest by the interviewers, and whether interviewees were concerned about repression. Second, we assessed potential nonresponse bias. We found that the average response rate to the survey was high, 78.50%, and only 1.76% of interviewees refused to answer (or replied “Don’t know” to) the question about violence. For comparison: 1.87% refused to answer (or replied

“Don’t know” to) an adjacent less sensitive question about participation in protests and

2.40% to the neutral question “What is the highest education you have completed?”

19 Non-zero nonresponse to a survey or particular questions does not necessarily imply

bias (Grooves 2006). Nonresponse would bias our results if non-responders were systematically different with respect to the variables of interest. As shown in Table S7 in SI, means of deprivation measures (operationalized below) were larger in the sub-samples of non-responders, compared to responders. This suggests that our analyses potentially underestimate the effects of deprivations on violence.

Instead of non-responding, interviewees may also provide dishonest replies. It is unlikely that interviewees report participation in violence if they had not participated in it.

By contrast, it is probable that participants in violence choose to report nonparticipation

rather than nonresponse. Once again, such dishonest reporting would bias our results if

dishonest interviewees were systematically different with respect to the variables of interest. However, the data at hand do not allow us to empirically assess this possibility.

Theoretically, one could argue that deprived individuals are also more likely to be state-

repressed; if this is true, then deprived individuals would also likely tend to dishonestly

report nonparticipation due to fears of reprisals. In such a scenario, our results would

also be underestimates of the true deprivation effects. These concerns must be considered

while evaluating our results.

The question about violence asks about participation over the last year. Given that

some of our predictors reflect interviewees’ experience over the last year or at the time

of interview, this raises reverse-causation concerns. Individuals who engaged in violence

in the past may be motivated to express their deprivations. Our analysis, therefore, may

capture the effects of past participation in violence on the voicing of deprivations. How-

ever, the question about violence is formulated in a particular way that potentially allows

us to alleviate this problem. Interviewees who have not reported participation in violence

could also indicate whether they would if they had a chance. Thus, we introduced

PVINTENTIONS, which constitutes a constrained version of PVPARTICIPATION: 0 = “No,

would never do this”, 1 = “No, but would do if had the chance”. The question explicitly

states “would you do” and—as a reply—“would do”, as contrasted to “would you have

20 done”/“would have done”. Hence, this question refers to motivations to engage in vio-

lence at the time of interview and prospectively. If individuals participated in violence in the past, this could influence their expressions of deprivations and their current motivations to engage in violence. Note, however, that PVINTENTIONS excludes those who re-

ported participation in violence over the last year. Still, it is possible that interviewees

participated in violence more than one year ago; hence, PVINTENTIONS does not fully

address reverse-causation concerns. This must be considered while evaluating our results.

PVINTENTIONS does not indicate the actual conflict behavior; however, it reflects

behavioral intentions to take part in violence. Individuals who report intentions to participate in violence if they had a chance will more likely take part in such violence once the chance arises, ceteris paribus. The use of behavioral-intentional measures as proxies of actual behavior is standard in psychology (Sheeran 2002). Studies show that expressed

intentions to engage in collective actions predict actual participation (van Zomeren et al.

2008). Gomez et al. (2017) report a convergence between stated and actual sacrifices on

the frontline among fighters against the Islamic State.

Arguably, it is most useful to consider PVINTENTIONS as reflecting motivations to

engage in violence, corresponding to our key theoretical focus:

Intentions can be inferred from participants’ responses that have the form, ‘I intend to

do X’, ‘I plan to do X’, or ‘I will do X’. In psychological terms, a behavioral intention

indexes a person’s motivation to perform a behavior. That is, behavioral intentions en-

compass both the direction (to do X vs. not to do X) and the intensity (e.g., how much

time and effort the person is prepared to expend in order to do X) of a decision (Sheeran

2002, 2).

In turn, although intentions do not directly indicate behavior, they index the probability

of it: a motivation to perform an action should relate to the likelihood of performing it.

Given that intentions to participate in violence does not constitute actual violence,

PVINTENTIONS may also suffer less from nonresponse bias. Furthermore, this measure

21 allows for assessing people’s motivations to engage in violence independent of opportuni-

ties for violence. People who have not participated in violence could have taken part in

it if they had an opportunity to do so, and some opportunities for violence may be limited

for reasons beyond the scope of this paper (e.g., sickness). As noted above, our theoretical

interest is in motivations, not opportunities that enable behavior based on motivations.

In Afrobarometer Round 5, 2.86% (N = 1.473) reported participation in violence,

with the highest figures in Uganda (9.46%) and the lowest in Botswana (0.67%); and

6.63% (N = 3,420) reported intentions to participate in violence, with the highest figures

in Mozambique (15.83%) and the lowest in Tunisia (1.08%). Table S1 in the Supporting

Information (SI) reports between-country variation in the outcomes.

4.3 Predictors

Afrobarometer data allow us to measure three types of deprivation. Static relative deprivation was measured with perceived (individual-based) economic inequality (here-

after INEQUALITY): “Let’s discuss economic conditions. In general, how do you rate your

living conditions compared to those of other [name of nationals]?” (0 = “Much better”,

4 = “Much worse”). Decremental deprivation was measured in two ways, reflecting per-

ceived retrospective and prospective losses: “Looking back, how do you rate the following

compared to twelve months ago: your living conditions” (0 = “Much better”; 4 = “Much

worse”) (RETROSPECTIVE LOSS); “Looking ahead, do you expect the following to be better

or worse: your living conditions in twelve months time” (0 = “Much better”; 4 = “Much

worse”) (PROSPECTIVE LOSS). Note that these questions were part of same battery asking

questions about economic conditions. In robustness tests, we explore several alternative

measures of predictors.

Our measures reflect perceived economic conditions as such, not those that are

deemed unfair, whereas Gurr stressed that relative deprivation occurs over values to

22 which people believe they are rightfully entitled (and hence the lack of which is consid-

ered unfair). Furthermore, our measures do not directly measure anger. The affective aspect of relative deprivation is embedded in its standard definition within social psychology: “(a) People first make cognitive comparisons, (b) they next make cognitive appraisals that they or their ingroup are disadvantaged, and finally (c) these disad- vantages are seen as unfair and arouse angry resentment” (Pettigrew 2016). Empirical research suggests that relative deprivation measures that disregard unfairness or do not tap anger, are weaker, but still significant, predictors of individual- and group-based outcomes (Smith et al. 2012). These measurement issues must be considered while evaluating our results. Tables S2–S5 present descriptive statistics for the predictors.

4.4 Modeling

Given the hierarchical structure of our data, with individuals nested within countries

and binary outcomes, we used hierarchical generalized linear models with the logit link

function. We implemented several pre-analysis steps, following the strategy recommended

by Hox, Moerbeek, and van de Schoot (2018, 42–46). First, we estimated an intercept-

only model, which revealed significant intraclass correlation in PVPARTICIPATION (ρ =

.14) and PVINTENTIONS (ρ = .10). This substantiated the modeling of random intercepts.

We then added the predictors with between-country variance of the corresponding slopes fixed at 0. PROSPECTIVE LOSS was a significant predictor, whereas INEQUALITY and RET-

ROSPECTIVE LOSS were not. Finally, we tested whether any of the slopes of the predictors

significantly varied across countries. Likelihood ratio tests indicated that modeling ran-

dom slopes for each predictor, save for gender (and INEQUALITY in the model of PVINTEN-

23 TIONS), significantly improved the model fit. Given this, below we include random intercepts and randomly varying slopes for all predictors that have significant between-country variance.3

To aid interpretation, we normalized all variables to [0, 1]. Normalizing variables to the same narrow range also improves convergence in the numerical integration-based

MLE.4 In the initial stage, we did not center the data. Analysis of non-centered predictors produces coefficients that are a mix of the within- and between-country effect (Raud- enbush and Bryk 2002, 139). If the between-country effect is non-zero, such blended coefficients will be biased estimates of the within-country effects. Therefore, in additional analyses we used several techniques to account for this, including specifications with country-level fixed effects.

5 Results

We first report results of parsimonious models examining associations between the key variables. We then present analyses addressing endogeneity concerns, first reverse causation and then potential omitted-variable bias. Finally, we present robustness tests assessing the sensitivity of the main results to alternative model specifications, alternative measurement of the key variables, alternative estimators, and alternative data.

3 Modeling multiple random slopes simultaneously, with large samples, is computationally intensive. Our main analysis uses Stata’s melogit, which by default calls maximum likelihood estimation with numerical integration. Stata allows for choosing between three numerical integration methods, with the mode-curva- ture adaptive Gauss-Hermite quadrature being the fastest in our setup. This method is nearly twice as fast as Stata’s default option: mean-variance adaptive Gauss-Hermite quadrature. Stata also allows for chang- ing N of integration points. A tradeoff exist between computation time and bias in parameter estimates, with every additional integration point typically slightly reducing bias, but considerably increasing processing time. We set N of integration points to 4, which produced estimates, for most parameters, identic to the third significant figure to those produced with 5 integration points, but decreased processing time up to five times. Analyses were conducted with parallel-processing Stata/MP on an 8-core computer. 4 Random effects in models with random slopes are not invariant to transformations of predictors. How- ever, our primary interest was in individual-level associations, and fixed-effects are invariant to (linear) transformations. The average predicted probabilities reported below are also invariant to the transformations.

24 5.1 Establishing Associations

Post-treatment bias is a key concern in observational data analysis. For example, the

perception of worsening economic conditions may influence the perception of one’s cur-

rent economic status, which in turn may influence violence motivations. In such a sce-

nario, a model controlling for individual economic status will underestimate the total

effect of PROSPECTIVE LOSS on violence. We are not interested in mediators through

which the three measures of deprivation influence violence. Hence, we start with basic

models that only include variables that cannot (or are very unlikely) to be outcomes of deprivations: gender (0 = man, 1 = woman) and age.

[TABLE 1 HERE]

Table 1 reports logit estimates of participation in violence as a function of the three

deprivation measures. Models 1 and 2 indicate that INEQUALITY and RETROSPECTIVE

LOSS are not significantly associated with self-reported participation in violence. By con-

trast, Model 3 indicates that PROSPECTIVE LOSS has a positive and highly significant coefficient (p < 0.001). Models 1–3 analyzed the three measures separately. Model 4 included all three variables simultaneously. The results remain similar, with INEQUALITY

and RETROSPECTIVE LOSS having non-significant coefficients and PROSPECTIVE LOSS hav-

ing a significant coefficient. Substantively, an individual who believes that economic con-

ditions will be “Much worse” is more than twice as likely to report participation in violence than an individual who believes that the conditions will be “Much better” (average predicted probabilities = 5.70% and 2.42%, respectively). This is a notable difference: analogous probabilities for men and women are 3.69% and 2.94%, respectively.

Figure 1 (upper row) depicts average predicted probabilities of participation self-reports.

[FIGURE 1 HERE]

5.2 Addressing Endogeneity Concerns

The association between PROSPECTIVE LOSS and PVPARTICIPATION may be driven by

past participation in violence causing expressions of deprivations (although it is unclear

25 why past participation in violence would only cause expressions of PROSPECTIVE LOSS

but not INEQUALITY). Hence, we analyzed PVINTENTIONS, which reflects interviewees’

motivations to participate in violence at the time of the interview, allowing us to partly

address reverse-causation concerns.

[TABLE 2 HERE]

As shown in Table 2, the coefficient of INEQUALITY was nonsignificant when regressed

separately (Model 5). In contrast, RETROSPECTIVE LOSS attained significance (Model 6).

PROSPECTIVE LOSS was also positive and highly significant (Model 7). The three variables

correlate (rs from .3 to .35); therefore, we first analyzed them separately. When all three

were regressed in one block, however, only PROSPECTIVE LOSS remained significant

(Model 8). Substantively, an individual who believes that economic conditions will be

“Much worse” is nearly twice likely to report intentions to participate in violence than an individual who believes that the conditions will be “Much better” (10.29% and 6.03%, respectively). This is a considerable difference: analogous probabilities for men and women are 8.07% and 6.50%. Figure 1 (bottom row) depicts probabilities of intentions.

We now turn to potential omitted-variable bias. In our setup, there are two main sources of such bias: level-1 and level-2 confounding. We first address the latter. The variation in our outcomes has two components: within-country variation (due to level-1 characteristics) and between-country variation (level-2 characteristics). For example, in wealthier countries, individuals may be on average less prone to violence. If in such countries individuals also feel less deprived, the covariation between country wealth and

(individual-level) violence may confound the relation between deprivation and violence.

We first attempted to address level-2 confounding by controlling for a standard set of level-2 observables, such as democracy, wealth, and population size; see Section S2 in

SI. Adding level-2 variables considerably reduced constant variance (Table S8); yet, it still remained above zero, suggesting that some level-2 influences remained unaccounted for. If such unmeasured variables correlate with both our individual-level predictors and outcomes, this can lead to biased level-1 parameter estimates.

26 To fully partition the within-group effect from the between-group effect, scholars often use fixed-effects models. However, random-effects models can analogously account for higher-level confounding, but have better statistical properties, and allow for modeling randomly varying slopes (Bell et al. 2015; 2019; McNeish and Kelley 2019). To illustrate the issue, we plotted the coefficients for each predictor by country in Figure S1 in SI.

The figure indicates considerable between-country variation in the coefficients. Likelihood ratio tests indicated that such between-country variation, for all three predictors, was unlikely due to chance. Failing to account for significant between-country heterogeneity can lead to biased estimates of level-1 coefficients and anticonservative standard errors

(Bell et al. 2019, 1062–1065). Therefore, instead of specifying standard country-level fixed effects, we continued with hierarchical models with randomly varying slopes, introducing several extensions to account for level-2 confounding.

One way to account for level-2 confounding in the random-effects framework is to add level-2 means of level-1 predictors as controls, following Mundlak (1978).5 Mundlak

specification is mathematically equivalent to group de-meaning of predictors (Bell et al.

2019, 1056), another method to obtain “pure” within-group effects (Raudenbush and

Bryk 2002, 139–141). In SI (Table S9) we replicated analyses with country-level means of level-1 predictors as controls, country demeaned level-1 predictors, and fixed-effects models (conditional maximum likelihood). All models show that unobserved level-2 variables do not confound the key relationships of interest.

We now turn to level-1 confounding. To emulate experimental research, scholars are sometimes advised to first report the so-called balance tests, indicating whether the

5 With one caveat: although Mundlak specification fully accounts for confounding by level-2 characteristics in linear models, in generalized linear models such an approach does not reliably partition between-level-2 effects from within-level-2 effects. Simulations show that this can introduce some bias in the estimates of level-1 coefficients; however, under most conditions, the remaining bias in level-1 coefficients due to level- 2 confounding is negligible (Bell et al. 2019, 1066–1067). Given that it allows for modeling randomly varying slopes, we opted to rely on Mundlak specification in our main analysis. We have crosschecked our analyses with fixed-effects estimators and found nearly identical results to those produced by the Mundlak specification (without random slopes).

27 “treatment” and “control” groups are significantly different with respect to some other

covariates. A failed balance test, however, does not tell us whether the imbalance con-

founds our key relationships of interest. Similarly, a passed balance tests does not rule

out confounding, since confounders may be among the unobserved variables, and such

tests are mute on unobservables. Further, scholars sometimes believe that covariate bal-

ance exempts them from careful consideration of confounders, especially if random as-

signment was used. However, random assignment, if unbiased, only ensures balance in

expectation. Any particular randomization, being perfectly unbiased, can generate im-

balance. Hence, as argued by Deaton and Cartwright (2018), irrespective of whether the data is observational or experimental, we must consider potential alternative explanations of our particular study’s results, drawing on our background knowledge.

Considering prior research and theory, we analyzed five sets of potential confounders: demographics; variables related to political activism/mobilization; measures of perceived repression; variables pertaining to government support; and variables highlighted in existing psychological research on political violence (including ethnic identification and dis-

crimination). We report the analyses of these variables in SI (Section S3). In short, most

of the variables have significant and sizeable coefficients, corroborating their relevance

for explaining violence. None of them, however, notably attenuate the relationships be-

tween deprivation measures and violence.

Despite our efforts to rule out alternative explanations, our estimates remain vulner-

able to unobserved confounding. While we cannot rule out this possibility, we can eval-

uate the influence that unobserved confounders would need to have to change our main

conclusions. Specifically, drawing on the idea that the amount of selection on the ob-

served controls in a model provides a guide to the amount of selection on unobservables

(Altonji, Elder, and Taber 2005; Oster 2019), we estimated the risk of omitted-variable

bias. Section S4 in SI demonstrates that unobserved confounders are unlikely to drive our results.

28 A range of alternatives exist to conventional conditioning that may enable causal

interpretation of observational results, including matching and instrumental variable (IV)

estimation. We see these alternatives as less useful or applicable in our case. Pre-pro-

cessing data with matching can be useful in many other respects; for example, to increase

the overlap between “control” and “treatment” groups or produce less model-dependent

inferences. However, matching, as a model-based method to generate causal inferences,

does not solve the unobserved confounding problem, because units are matched on ob-

servables, which is analogous to conventional conditioning. Furthermore, model depend- ency is unlikely to be a problem in our case, since our estimates essentially do not change as we move from very simple to more complex models, and we have estimated nearly 100 models.

We do not see an IV-based strategy as plausible because our predictors are psychological variables. Instrumental variables typically instrument objective characteristics rather than perceptions. As discussed, objective economic conditions of individuals do not clearly map onto their subjective perceptions; hence, instruments that correlate with, for example, objective economic downturns will not necessarily capture the perceptions of economic downturns. Indeed, in our sample, (level-2) economic growth correlated with

(level-1) RETROSPECTIVE LOSS only at r = 0.0003, and with PROSPECTIVE LOSS at r =

0.04. Given this, one might argue that we should then instrument for subjective percep-

tions. However, we can hardly envision instruments that relate to perceptions of prospec-

tive losses or inequalities and which have no direct effects on motivations for violence.

Furthermore, conventional IV-based approaches produce instrument-dependent esti-

mates of complier average causal effects, but do not allow identifying the compliers, i.e.,

individuals among whom the instrument induced changes in the variable of interest (Dea-

ton 2010; Heckman 2010). Since this variable is subjective perceptions in our case, such

compliers (i.e., individuals whom the instrument induced to perceive prospective loses)

may represent a very specific sub-set of people. Among this sub-set, deprivations may or

may not relate to violent motivations similarly to the population of interest.

29 5.3 Additional Sensitivity Tests

In addition to analyses of confounders, we conducted multiple sensitivity tests, (i) with alternative measures of predictors, (ii) using intentions to participate and self-re-

ported participation in protests as outcome variables, (iii) splitting the sample into gov-

ernment supporters and non-supporters (to rule out pro-government violence), (iv) con-

trolling for dishonesty, influenced answering, and comprehension, (v) using alternative operationalizations of outcomes and alternative estimators, (vi) and using alternative data (Afrobarometer Round 2). Section S5 in SI demonstrates that our results are not sensitive to these alternative modeling choices.

6 Discussion

Drawing on political science, psychology, and economics, we have argued that dynamic

forms of relative deprivation, specifically prospective decremental deprivation, should

predict motivations to participate in political violence, whereas static forms of relative

deprivation should not, or only weakly. Analysis of two large, multinational datasets has

provided evidence consistent with these claims. Before turning to the implications of

these results, we consider potential limitations of our analysis.

6.1 Limitations

Several macro-level studies have recently identified a positive association between

(static) vertical inequalities and civil conflict (Bartusevičius 2014; Boix 2008; Buhaug,

Cederman, and Gleditsch 2014; see also Bartusevičius 2019). Why have we not found an

association between static relative deprivation and violence? One possible reason is that

our outcome measures do not distinguish between different types of violence. The men-

tioned studies suggest that vertical inequalities should specifically relate to non-ethnic

30 conflicts; hence, our results potentially underestimate the effects of static relative deprivation on non-ethnic violence. Another possibility is that our outcome measures do not distinguish between small- and large-scale violence. Vertical inequality may specifically relate to large-scale conflicts.

However, some scholars suggest that the association between vertical inequality and civil conflict is not strong, at least when compared to the horizontal inequality-conflict link (e.g., Østby 2013). Hence, our results are not incongruent with all macro-level research. Indeed, our findings hint at a plausible explanation for the weak link between vertical inequality and civil conflict: perceived inequality does not motivate individuals to partake in violence or does so only weakly.

The fact that the measure of static relative deprivation did not predict violence can also be explained by its disregard for unfairness. In contrast to the political science literature, primarily focused on objective inequalities (exceptions include, e.g., Dyrstad and

Hillesund 2020), social psychologists have stressed perceived unfairness and anger (Pet- tigrew 2016). Although perceived disadvantage can generate negative affect, one does not automatically follow from the other. People often accept their disadvantage as appropri- ate (Pettigrew 2016). Thus, our measure of static relative deprivation may not tap anger, which may be necessary to motivate engagement in violence. However, this does not explain why our measure of temporal relative deprivation, which also disregards unfairness, does predict violence. Perhaps losing what one once possessed/attained is more often perceived as unfair.

Further, our research did not empirically account for group dynamics and political opportunity structures. Even in the presence of intense anger, people’s engagement in political violence importantly depends on contextual factors, for example, other individuals willing to engage in violence, leadership, normative and utilitarian justification of violence, and coercive capacity vis-à-vis governments (Gurr 1970/2011). Such variables may reduce the relation between motivations to engage in political violence and actual participation.

31 Since our analyses exclusively relied on African samples, a note on generalization is

also warranted. Our theory provides no rationale to suggest that deprivations relate to

violence differently among Africans, compared to others; hence, we believe the findings

should generalize beyond African populations. A possibility exists that some contextual

factors, specific to African countries, moderate the associations between deprivations and

violence. Indeed, as shown in Figure S1 (SI), the associations considerably varied across

countries. To further explore this, we conducted an analysis of cross-level interactions,

reported in SI (Section S6). These analyses suggest that most standard country-level

characteristics, such as wealth, economic growth, and population size, do not moderate

the associations between deprivation measures and violence, and hence that our findings

likely generalize to other countries varying along such characteristics.

Finally, since we used self-reports to measure predictors and outcomes, we must reit-

erate the possibility of nonresponse bias. As noted, average deprivation levels, for all

measures, were higher among interviewees who refused answering questions about vio-

lence. This suggests that deprivations potentially have stronger effects on violence than

our results indicate. Future research should therefore explore alternative techniques to

ask sensitive questions, for example, list experiments. Note, however, that the reliability

of list experiments has recently been challenged and important adjustments to the stand-

ard item-count design have been proposed (Kramon and Weghorst 2019).

6.2 Implications

Our study implies, most importantly, that temporal dynamics in economic conditions

are more critical than the economic status quo for understanding political violence (see also Buhaug et al. 2021). A broader implication is that individual-level grievances do matter for violence, but that to account for this association we need to focus on particular—and theoretically informed—grievance types (see also Dyrstad and Hillesund 2020;

Hillesund 2015; Koos 2018; Rustad 2016). Recent macro-level studies have suggested

32 shifting focus from individuals to groups. This call has been motivated by both theoretical

claims that “violent conflict is a group phenomenon, not situations of individuals ran-

domly committing violence against each other”, and findings that macro-level proxies of

individual-based grievances weakly predict civil conflict (Østby 2013, 212–213).

We do not challenge the importance of focusing on group-level attributes; but we

emphasize that all groups are made of individuals, and that it is individuals, not groups,

who make decisions about violence. An account of political violence that disregards indi-

vidual decision-making is thus incomplete. Resentful individuals can become leaders and

mobilize others or choose to become followers. Countries with larger recruitment pools

are more susceptible to violence. Given the evidence above, we argue that such recruit-

ment pools will grow particularly with the population share of individuals anticipating

economic losses, thereby increasing the overall likelihood of civil conflict.

Our study potentially explains why many attempts to associate individual-based

grievances and civil conflict were unsuccessful: the focus has been on the “wrong” type

of deprivation, which is psychologically too weak a motivator of violence. Paradoxically,

while macro research has primarily focused on static relative deprivation, neither early

theoretical work on political violence, social psychological research, nor behavioral eco-

nomics suggest that it should strongly relate to political violence.

6.3 New Research Avenues

Our study also hints at a number of unanswered questions and fruitful research ave-

nues. First, our theoretical analysis has suggested that (i) dynamic, compared to static,

forms of deprivation should be stronger predictors of violence, and that (ii) group relative deprivation, compared to individual, should be a stronger predictor of violence. Com-

bined, the two propositions hint at the importance of temporal group relative deprivation

for political violence. The temporal aspect relates to anger and risk-seeking; the group

33 aspect should further contribute to mobilization. Thus, we expect temporal group depri-

vations to be among the most powerful predictors of violence (see also Cederman, Wim- mer, and Min 2010; Petersen 2002).

While testing the effects of deprivations in the future, scholars should pay particular attention to unfairness. Overwhelming psychological evidence suggests that humans are more averse to unfairness than to inequality as such (Starmans, Seshkin, and Bloom

2017). In fact, an analysis of data from the World Values Survey shows that more unequal countries have, on average, citizens who are happier (Kelly and Evans 2017). This insight was also explicit in Gurr’s original work, referring to deprivations over “the goods and

conditions of life to which people believe they are rightfully entitled” (Gurr 1970/2011,

24). While in theory macro-level research refers to grievances generated by inequalities

that are perceived unfair, few empirical analyses have attempted to directly account for

this. Joining other recent micro-level studies (e.g., Dyrstad and Hillesund 2020), we thus

call for the study of inequity, rather than inequality per se.

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41 Table 1. Self-reported participation in political violence: A multilevel analysis (1) (2) (3) (4) FIXED EFFECTS Inequality 0.07 -0.14 [-0.25, 0.38] [-0.49, 0.22] Retrospective loss -0.04 -0.34 [-0.36, 0.27] [-0.70, 0.01] Prospective loss 0.59** 0.73**** [0.23, 0.96] [0.37, 1.10] Gender -0.28**** -0.27**** -0.26**** -0.25**** [-0.38, -0.17] [-0.38, -0.17] [-0.37, -0.15] [-0.36, -0.13] Age -0.42 -0.43 -0.41 -0.41 [-0.94, 0.10] [-0.95, 0.09] [-0.96, 0.13] [-0.96, 0.14] Constant -3.63**** -3.59**** -3.80**** -3.64**** [-3.93, -3.33] [-3.87, -3.30] [-4.07, -3.54] [-3.96, -3.32]

RANDOM EFFECTS Var(inequality) 0.31 0.40 [0.11, 0.86] [0.15, 1.06] Var(retrospective loss) 0.28 0.35 [0.10, 0.81] [0.12, 1.04] Var(prospective loss) 0.56 0.49 [0.23, 1.35] [0.19, 1.29] Var(gender) † † † †

Var(age) 0.96 1.00 1.11 1.12 [0.39, 2.39] [0.41, 2.49] [0.45, 2.71] [0.46, 2.74] Var(constant) 0.54 0.46 0.42 0.52 [0.30, 0.99] [0.25, 0.85] [0.22, 0.80] [0.27, 1.00] Observations 48686 49841 45572 44205 AIC 12351.67 12501.04 11659.85 11358.55 BIC 12413.22 12562.76 11720.94 11454.21 chi2 26.90 26.93 32.16 37.81 The table reports fixed and random effects in logit (or log odds) with 95% CIs in brackets; *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001; † = excluded randomly varying slopes due to no significant improvement in the model fit.

Table 2. Behavioral intentions to participate in political violence: A multilevel analysis (5) (6) (7) (8) FIXED EFFECTS Inequality 0.24** 0.11 [0.08, 0.39] [-0.06, 0.27] Retrospective loss 0.21 0.03 [-0.02, 0.43] [-0.20, 0.25] Prospective loss 0.49*** 0.47*** [0.21, 0.76] [0.20, 0.74] Gender -0.30**** -0.30**** -0.28**** -0.27**** [-0.42, -0.18] [-0.41, -0.19] [-0.39, -0.16] [-0.39, -0.15] Age -1.18**** -1.21**** -1.21**** -1.23**** [-1.50, -0.85] [-1.53, -0.89] [-1.53, -0.89] [-1.56, -0.89] Constant -2.50**** -2.49**** -2.56**** -2.63**** [-2.71, -2.29] [-2.72, -2.27] [-2.77, -2.35] [-2.87, -2.39]

RANDOM EFFECTS Var(inequality) † † † †

Var(retrospective loss) 0.22 0.16 [0.09, 0.52] [0.06, 0.44] Var(prospective loss) 0.44 0.38 [0.20, 0.93] [0.17, 0.87] Var(gender) 0.07 0.06 0.06 0.07 [0.03, 0.18] [0.02, 0.16] [0.02, 0.16] [0.02, 0.19] Var(age) 0.27 0.27 0.20 0.24 [0.07, 1.07] [0.06, 1.11] [0.04, 1.11] [0.05, 1.17] Var(constant) 0.30 0.37 0.33 0.36 [0.17, 0.54] [0.20, 0.69] [0.19, 0.60] [0.19, 0.67] Observations 47247 48391 44199 42863 AIC 22949.98 23436.12 21492.28 20804.28 BIC 23011.32 23506.42 21561.85 20899.61 chi2 78.58 80.11 83.64 80.20 The table reports fixed and random effects in logit (or log odds) with 95% CIs in brackets; *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001; † = excluded randomly varying slopes due to no significant improvement in the model fit. Figure 1. The figure shows average predicted probabilities with 95% CIs (curves with shaded areas) of self-reported participation in political violence and behavioral intentions to participate in political violence as a function of INEQUAL- ITY, RETROSPECTIVE LOSS, and PROSPECTIVE LOSS. The probabilities were estimated using margins postestimation com- mand in Stata following Models 4 and 8 as reported in Tables 1 and 2. The bars at the lower part of the plots represent percentage frequency distributions of the predictors. Online Supporting Information (SI) for Resisting Changes That Make Things Worse: Decremental—Not Relative—Deprivation Moti- vates Political Violence

Contents

Section S1. Descriptive statistics ...... 3

Table S1...... 3

Table S2...... 4

Table S3...... 5

Table S4...... 6

Table S5...... 7

Table S6...... 8

Table S7...... 9

Section S2. Accounting for country-level confounding ...... 10

Table S8...... 11

Table S9...... 13

Section S3. Accounting for individual-level confounding ...... 14

Table S10 ...... 15

Table S11 ...... 17

Table S12 ...... 19

Table S13 ...... 21

Table S14 ...... 23

Section S4. Analysis of sensitivity to unobserved confounding...... 24

Section S5. Other sensitivity tests ...... 26

Table S15 ...... 27

Table S16 ...... 29

Table S17 ...... 31

1 Table S18 ...... 33

Table S19 ...... 35

Table S20 ...... 36

Section S6. Assessing cross-level interactions ...... 37

Figure S1 ...... 38

Table S21 ...... 39

Table S22 ...... 40

References ...... 41

2 Section S1. Descriptive statistics

Tables S1–S7 below report descriptive statistics for the main predictors and outcomes generated using Afrobarometer Round 5.

Table S1. Descriptive statistics for the main predictors and outcomes

N Mean Median SD Min Max PVparticipation 50641 .0290871 0 .1680524 0 1 PVintentions 49168 .0695778 0 .2544367 0 1 Inequality 49953 2.120733 2 .9847985 0 4 Retrospective loss 51172 2.074259 2 1.024831 0 4 Prospective loss 46681 1.433388 1 1.177928 0 4

3 Table S2. Afrobarometer question Q26E (“Used force or violence for a political cause”) by country Note: The table reports percentages of different replies to the question we used to construct PVPARTICIPATION and PVINTENTIONS for each of the 34 countries surveyed by Afrobarometer Round 5.

Missing No, No, but Yes, Yes, sev- Yes, Don’t Refused Total would would do if once or eral times often know never do had the twice this chance Algeria 0.00 92.52 4.82 0.08 0.17 0.00 2.41 0.00 100.00 Benin 0.00 88.00 7.92 1.83 1.17 0.92 0.17 0.00 100.00 Botswana 0.17 94.75 3.83 0.50 0.08 0.08 0.58 0.00 100.00 Burkina 0.00 88.75 7.58 0.58 0.25 0.33 2.50 0.00 100.00 Burundi 0.00 94.58 3.83 0.17 0.08 0.50 0.83 0.00 100.00 Cameroon 0.08 85.75 6.75 1.25 0.58 0.42 5.17 0.00 100.00 Cape Verde 0.00 86.67 8.36 1.16 0.17 0.58 3.06 0.00 100.00 Ivory Coast 0.00 94.42 3.08 0.92 0.42 0.67 0.50 0.00 100.00 Egypt 0.00 87.31 8.07 1.43 0.67 0.42 2.10 0.00 100.00 Ghana 0.00 92.92 4.58 1.17 0.46 0.04 0.83 0.00 100.00 Guinea 0.00 92.58 4.92 1.00 0.50 0.42 0.58 0.00 100.00 Kenya 0.13 90.83 4.79 1.33 1.13 0.42 1.38 0.00 100.00 Lesotho 0.00 86.38 6.52 1.50 3.01 1.00 1.59 0.00 100.00 Liberia 0.33 90.49 4.25 1.92 0.83 0.83 1.33 0.00 100.00 Madagascar 0.00 86.00 6.08 1.00 0.50 0.17 6.17 0.08 100.00 Malawi 0.04 92.65 5.53 1.08 0.21 0.25 0.25 0.00 100.00 Mali 0.00 91.08 7.17 0.75 0.33 0.33 0.33 0.00 100.00 Mauritius 0.00 98.50 0.50 0.17 0.17 0.33 0.33 0.00 100.00 Morocco 0.00 91.72 4.26 0.50 0.25 0.67 2.59 0.00 100.00 Mozambique 0.29 74.58 15.83 2.42 1.42 0.71 4.75 0.00 100.00 Namibia 0.00 82.75 13.58 0.75 1.08 1.08 0.75 0.00 100.00 Niger 0.00 91.16 4.50 1.00 1.17 1.00 1.17 0.00 100.00 Nigeria 0.00 87.96 8.00 1.38 0.88 0.42 1.38 0.00 100.00 Senegal 0.00 90.25 6.83 0.75 0.67 0.67 0.83 0.00 100.00 Sierra Leone 0.00 86.81 5.88 2.86 2.69 1.01 0.76 0.00 100.00 South Africa 0.00 83.53 11.42 1.79 1.00 0.33 1.92 0.00 100.00 Sudan 0.58 80.98 8.09 1.42 0.83 2.17 5.92 0.00 100.00 Swaziland 0.00 91.42 5.92 0.92 0.42 0.25 1.08 0.00 100.00 Tanzania 0.00 88.50 5.71 1.42 3.46 0.58 0.33 0.00 100.00 Togo 0.17 82.00 11.33 1.50 0.67 0.58 3.75 0.00 100.00 Tunisia 0.00 97.83 1.08 0.25 0.00 0.17 0.67 0.00 100.00 Uganda 0.25 78.88 7.71 5.50 2.96 1.00 3.71 0.00 100.00 Zambia 0.08 93.50 4.75 0.67 0.17 0.17 0.67 0.00 100.00 Zimbabwe 0.13 93.63 4.04 1.25 0.46 0.13 0.38 0.00 100.00 Total 0.07 88.68 6.63 1.38 0.95 0.52 1.76 0.00 100.00

4 Table S3. Afrobarometer question Q4 (“In general, how do you rate your living conditions compared to those of other [citizens]”) by country

Note: The table reports percentages of different replies to the question we used to construct IN- EQUALITY for each of the 34 countries surveyed by Afrobarometer Round 5.

Missing Much Worse Same Better Much Don’t Total Worse Better know Algeria 0.00 1.66 13.79 49.75 28.07 2.49 4.24 100.00 Benin 0.00 5.25 28.75 39.08 23.75 1.25 1.92 100.00 Botswana 0.25 10.08 30.25 26.00 28.92 2.17 2.33 100.00 Burkina 0.00 2.67 33.83 35.33 23.42 0.33 4.42 100.00 Burundi 0.00 9.25 31.25 44.00 14.25 1.17 0.08 100.00 Cameroon 0.42 2.92 19.92 33.08 29.17 3.25 11.25 100.00 Cape Verde 0.83 1.24 20.20 42.96 23.10 0.50 11.18 100.00 Ivory Coast 0.00 3.17 18.58 55.58 16.75 0.83 5.08 100.00 Egypt 0.00 17.06 29.33 36.81 13.45 2.18 1.18 100.00 Ghana 0.00 7.83 25.00 27.83 31.46 3.50 4.38 100.00 Guinea 0.00 7.50 34.08 39.42 15.25 2.17 1.58 100.00 Kenya 0.21 12.59 33.14 28.47 20.47 1.21 3.92 100.00 Lesotho 0.00 15.29 32.33 32.50 16.21 1.25 2.42 100.00 Liberia 1.25 3.75 19.52 20.10 37.95 15.35 2.09 100.00 Madagascar 0.17 5.67 29.08 56.75 6.92 0.58 0.83 100.00 Malawi 0.29 8.14 28.38 15.45 42.04 4.53 1.16 100.00 Mali 0.00 5.75 33.67 38.08 20.33 0.50 1.67 100.00 Mauritius 0.00 2.17 19.17 53.17 21.00 1.83 2.67 100.00 Morocco 0.00 4.01 14.80 56.44 18.56 2.34 3.85 100.00 Mozambique 0.21 9.71 27.25 30.75 19.58 2.29 10.21 100.00 Namibia 0.58 6.67 26.92 30.33 31.08 4.08 0.33 100.00 Niger 0.42 3.00 25.52 48.37 18.43 1.58 2.67 100.00 Nigeria 0.00 5.58 24.33 28.21 36.46 3.46 1.96 100.00 Senegal 0.00 2.75 24.25 54.42 16.00 0.67 1.92 100.00 Sierra Leone 0.00 4.79 20.67 17.56 40.59 15.46 0.92 100.00 South Africa 0.00 4.46 21.01 34.76 30.39 7.84 1.54 100.00 Sudan 0.00 10.84 23.85 28.27 32.19 3.75 1.08 100.00 Swaziland 0.00 8.58 23.42 35.08 29.25 2.75 0.92 100.00 Tanzania 0.00 13.71 42.67 32.54 9.29 1.13 0.67 100.00 Togo 0.08 9.17 29.25 34.67 19.42 0.75 6.67 100.00 Tunisia 0.17 7.67 21.33 46.58 22.50 1.00 0.75 100.00 Uganda 0.00 23.71 37.92 16.92 16.79 1.83 2.83 100.00 Zambia 1.17 5.33 21.50 28.75 34.75 6.83 1.67 100.00 Zimbabwe 0.17 5.83 22.54 34.79 32.29 3.38 1.00 100.00 Total 0.16 7.89 26.74 34.48 24.63 3.10 3.00 100.00

5 Table S4. Afrobarometer question Q5B (“Looking back, how do you rate the following compared to twelve months ago: Your living conditions?”) by country Note: The table reports percentages of different replies to the question we used to construct RETROSPECTIVE LOSS, for each of the 34 countries surveyed by Afrobarometer Round 5.

Missing Much Worse Same Better Much Don’t Total Worse Better know Algeria 0.00 0.50 9.22 63.46 24.09 2.33 0.42 100.00 Benin 0.00 4.42 18.33 33.83 38.17 5.25 0.00 100.00 Botswana 0.67 7.42 18.33 45.17 25.50 2.50 0.42 100.00 Burkina 0.00 1.42 24.00 30.58 42.17 1.42 0.42 100.00 Burundi 0.00 15.50 36.92 23.17 22.25 1.75 0.42 100.00 Cameroon 0.83 3.50 20.25 43.92 26.17 2.92 2.42 100.00 Cape Verde 0.58 0.58 19.12 45.86 30.79 2.07 0.99 100.00 Ivory Coast 0.00 5.92 21.08 44.67 25.92 2.00 0.42 100.00 Egypt 0.00 31.34 37.90 19.16 9.83 1.09 0.67 100.00 Ghana 0.00 6.21 20.46 29.04 38.21 5.75 0.33 100.00 Guinea 0.00 12.25 34.50 26.50 23.58 3.08 0.08 100.00 Kenya 0.17 18.13 33.93 14.80 29.14 3.04 0.79 100.00 Lesotho 0.00 15.37 21.14 35.42 21.72 3.34 3.01 100.00 Liberia 1.50 3.00 13.01 34.28 34.78 13.26 0.17 100.00 Madagascar 0.00 5.00 31.92 47.75 13.75 1.00 0.58 100.00 Malawi 0.17 14.17 31.57 16.16 33.57 3.45 0.91 100.00 Mali 0.00 8.25 27.83 17.33 39.75 6.83 0.00 100.00 Mauritius 0.00 0.92 14.83 60.33 21.58 2.33 0.00 100.00 Morocco 0.00 4.10 22.66 41.64 28.51 2.34 0.75 100.00 Mozambique 0.38 6.88 24.75 31.88 30.50 2.92 2.71 100.00 Namibia 0.50 4.00 17.50 50.42 23.75 3.58 0.25 100.00 Niger 0.17 3.50 21.60 36.70 32.86 4.84 0.33 100.00 Nigeria 0.00 4.29 17.33 27.04 44.17 6.29 0.88 100.00 Senegal 0.00 2.83 30.25 29.83 34.58 2.33 0.17 100.00 Sierra Leone 0.00 4.03 15.97 29.33 34.03 16.39 0.25 100.00 South Africa 0.00 4.00 18.26 44.73 26.43 6.09 0.50 100.00 Sudan 0.17 18.93 39.95 10.93 25.69 3.67 0.67 100.00 Swaziland 0.00 7.33 24.25 40.58 24.58 3.08 0.17 100.00 Tanzania 0.00 11.75 34.88 30.79 19.88 2.38 0.33 100.00 Togo 0.08 8.83 35.92 30.50 22.83 1.25 0.58 100.00 Tunisia 0.00 15.25 40.08 27.67 15.67 0.67 0.67 100.00 Uganda 0.04 31.25 33.67 16.25 16.71 1.58 0.50 100.00 Zambia 0.25 2.67 17.08 43.67 29.75 6.33 0.25 100.00 Zimbabwe 0.04 4.04 18.67 43.75 30.08 3.29 0.13 100.00 Total 0.15 9.03 25.13 33.06 28.13 3.84 0.66 100.00

6 Table S5. Afrobarometer question Q6B (“Looking ahead, do you expect the following to be better or worse: Your living conditions in twelve months time?”) by country Note: The table reports percentages of different replies to the question we used to construct PROSPECTIVE LOSS, for each of the 34 countries surveyed by Afrobarometer Round 5.

Missing Much Worse Same Better Much Don’t Total Worse Better know Algeria 0.00 0.25 2.66 13.79 56.06 20.85 6.40 100.00 Benin 0.00 2.08 5.33 3.58 35.92 50.42 2.67 100.00 Botswana 0.08 5.67 9.50 20.33 39.58 13.50 11.33 100.00 Burkina 0.00 0.25 9.08 8.50 54.08 11.25 16.83 100.00 Burundi 0.00 16.92 20.50 16.08 33.33 7.25 5.92 100.00 Cameroon 0.42 1.00 5.08 9.67 47.42 14.33 22.08 100.00 Cape Verde 0.17 0.25 4.22 10.60 63.25 15.48 6.04 100.00 Ivory Coast 0.00 2.58 6.00 9.33 56.42 21.83 3.83 100.00 Egypt 0.00 22.44 23.36 12.69 25.88 5.46 10.17 100.00 Ghana 0.00 1.75 5.58 8.00 41.88 35.67 7.12 100.00 Guinea 0.00 6.33 14.17 9.83 50.25 13.92 5.50 100.00 Kenya 0.29 21.88 20.18 7.92 25.09 10.25 14.38 100.00 Lesotho 0.00 5.93 9.19 16.46 31.66 18.13 18.63 100.00 Liberia 0.50 1.92 5.17 7.51 38.78 35.45 10.68 100.00 Madagascar 0.08 1.75 11.67 29.17 43.33 2.75 11.25 100.00 Malawi 0.25 8.23 17.24 8.81 38.47 8.10 18.90 100.00 Mali 0.00 2.00 6.83 2.50 51.25 31.58 5.83 100.00 Mauritius 0.00 5.50 22.67 38.17 25.83 3.75 4.08 100.00 Morocco 0.00 2.84 8.78 16.22 48.49 12.54 11.12 100.00 Mozambique 0.13 3.04 4.46 11.25 45.50 21.04 14.58 100.00 Namibia 0.83 1.00 5.25 21.33 43.67 24.42 3.50 100.00 Niger 0.08 3.34 5.42 6.92 34.61 44.62 5.00 100.00 Nigeria 0.00 3.21 3.42 6.00 37.38 47.42 2.58 100.00 Senegal 0.00 0.92 6.83 6.25 60.25 16.58 9.17 100.00 Sierra Leone 0.00 3.36 7.06 11.43 34.71 38.66 4.79 100.00 South Africa 0.00 2.96 10.92 24.80 37.93 19.72 3.67 100.00 Sudan 1.17 12.68 23.02 11.51 38.20 6.34 7.09 100.00 Swaziland 0.00 3.83 5.50 15.50 47.00 22.25 5.92 100.00 Tanzania 0.00 20.88 28.21 18.63 19.71 3.38 9.21 100.00 Togo 0.17 4.25 8.83 7.00 50.50 9.17 20.08 100.00 Tunisia 0.25 7.33 18.33 11.33 48.50 4.75 9.50 100.00 Uganda 0.04 27.38 23.29 10.75 24.67 4.42 9.46 100.00 Zambia 0.25 2.00 6.33 19.17 41.08 22.83 8.33 100.00 Zimbabwe 0.00 5.71 12.08 19.25 37.38 16.63 8.96 100.00 Total 0.13 7.12 11.66 13.15 39.92 18.63 9.38 100.00

7 Table S6. A comparison of nonresponse rates to the questions about political violence, protests or demonstrations, and education Note: The table reports percentages of Afrobarometer Round 5 interviewees who refused answering, replied “Don’t know”, or whose reply was missing for some other (unknown) reason.

“Here is a list of actions that people sometimes take as citizens. For “What is the highest each of these, please tell me whether you, personally, have done any of level of education you these things during the past year. If not, would you do this if you had have complete?” the chance?” “Used force or violence for a “Attended a demonstration or a pro- political cause” test march” 1.83% 1.94% 2.4%

8 Table S7. A comparison of means of INEQUALITY, RETROSPECTIVE LOSS, and PRO- SPECTIVE LOSS among responders and nonresponders

Note: The table reports means of INEQUALITY, RETROSPECTIVE LOSS, and PROSPECTIVE LOSS for two sub-samples: Afrobarometer Round 5 interviewees who responded to the question about participation in political violence and nonresponders, i.e., interviewees who refused answering, replied “Don’t know”, or whose reply was missing for some other (unknown) reasons. The table also reports p-values of t-tests on the equality of means.

INEQUALITY RETROSPECTIVE LOSS PROSPECTIVE LOSS Responders Nonresponders Responders Nonresponders Responders Nonresponders 2.12 2.22 2.07 2.21 1.43 1.63 p = 0.004 p < 0.001 p < 0.001

9 Section S2. Accounting for country-level confounding

As discussed in the main text, we addressed country-level (or level-2) confounding using several techniques.

First, we controlled for country-level confounding by adding a number of level-2 variables to the specifications: democracy, indexed by Electoral Democracy Index from the Varieties of Democ- racy project (Coppedge et al. 2021; Pemstein et al. 2021); instability, measured by the proportion of interviewees indicating “political instability/ethnic tensions” among the three “most important problems facing [country name]”; violent crime, measured by intentional homicide rate per 100,000 (The World Bank 2019); social conflict, proxied by the count of events coded in the

Social Conflict Analysis Database (SCAD) in the year before the surveys (adjusted to population; per 1,000,000 people) (Salehyan et al. 2012); economic status, measured with GDP/cap.

(constant 2010 US$) (The World Bank 2019); economic growth, measured with GDP/cap. annual growth (ibid.); peace years since the last internal armed conflict coded in the UCDP/PRIO

Armed Conflict Dataset (Gleditsch et al. 2002; Pettersson and Eck 2018); and population size

(The World Bank 2019). As shown in Table S8 below, observed level-2 variables do not notably attenuate the level-1 coefficients of interest.

10 Table S8. Analogous to Table 1 but controlling for country-level variables

Self-reported participation Intentions (1) (2) FIXED EFFECTS (LEVEL 1)

Inequality -0.11 0.07 [-0.45, 0.24] [-0.11, 0.24] Retrospective loss -0.33 0.05 [-0.67, 0.02] [-0.17, 0.27] Prospective loss 0.79**** 0.48*** [0.43, 1.14] [0.21, 0.76] Gender -0.24**** -0.28**** [-0.35, -0.13] [-0.40, -0.15] Age -0.37 -1.23**** [-0.93, 0.19] [-1.58, -0.88] Constant -3.89**** -2.26**** [-4.83, -2.96] [-2.96, -1.55] COUNTRY LEVEL (LEVEL 2)

Democracy 0.19 -0.29 [-0.84, 1.21] [-1.10, 0.52] Instability -0.84 -0.53 [-2.34, 0.67] [-1.65, 0.58] Violent crime 1.86*** 1.44*** [0.89, 2.83] [0.65, 2.24] SCAD events -0.35 -0.87 [-1.63, 0.93] [-1.84, 0.10] Economic status -1.72** -1.04* [-2.84, -0.60] [-1.91, -0.17] Economic growth 0.29 -0.83* [-0.73, 1.30] [-1.64, -0.01] Peace years -0.17 -0.01 [-0.94, 0.60] [-0.62, 0.59] Population size 0.82 0.37 [-0.35, 1.98] [-0.59, 1.34] RANDOM EFFECTS

var(inequality) 0.31 † [0.11, 0.87] var(retrospective loss) 0.29 0.14 [0.10, 0.91] [0.05, 0.39] var(prospective loss) 0.42 0.36 [0.15, 1.15] [0.16, 0.83] var(gender) † 0.07 [0.02, 0.20] var(age) 1.16 0.29 [0.49, 2.74] [0.07, 1.28] var(cons) 0.24 0.20 [0.10, 0.56] [0.10, 0.40] Observations 43200 41899 AIC 11009.38 20192.85 BIC 11174.18 20357.07 chi2 61.79 100.39 Note: The table reports fixed and random effects in logit (or log odds) with 95% CIs in brackets; *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. † = excluded randomly varying slopes due to no significant improvement in the model fit.

11 Subsequently, we conducted analogous analyses but without country-level controls and with:

• Randomly varying intercepts and country-level demeaned individual-level predictors

(Table S9: Models 1 and 2);

• Randomly varying intercepts and country-level means of individual-level predictors add-

ed as controls (i.e., the Mundlak specification) (Table S9: Models 3 and 4);

• Standard country-level fixed-effects models (conditional maximum likelihood) (Table S9:

Models 5 and 6);

• Randomly varying intercepts and slopes, and country-level means of individual-level pre-

dictors added as controls (Table S9: Models 7 and 8).

These analyses indicate that unobserved level-2 variables also do not notably confound the level-1 relationships of interest.

In the subsequent tests, i.e., those reported in Tables S10–S18, we used the specification reported in Table S8 as a baseline, extending it with various individual-level controls.

12 Table S9. Controlling for country-level confounding via alternative techniques

Random effects: Random effects: Fixed effects: Random effects: Mundlak specification demeaned predictors conditional ML Mundlak specification (without random slopes) (without random slopes) (with random slopes) Participation Willingness Participation Willingness Participation Willingness Participation Willingness (1) (2) (3) (4) (5) (6) (7) (8) Inequality -0.18 0.09 -0.18 0.09 -0.18 0.08 -0.16 0.10 [-0.42,0.06] [-0.08,0.25] [-0.42,0.06] [-0.08,0.25] [-0.42,0.06] [-0.08,0.25] [-0.52,0.20] [-0.07,0.27] Retrospective -0.19 0.07 -0.19 0.07 -0.19 0.07 -0.33 0.03 loss [-0.43,0.04] [-0.09,0.23] [-0.43,0.04] [-0.10,0.23] [-0.43,0.04] [-0.10,0.23] [-0.68,0.01] [-0.19,0.26] Prospective loss 0.87**** 0.49**** 0.87**** 0.49**** 0.87**** 0.49**** 0.72*** 0.49*** [0.66,1.08] [0.33,0.64] [0.66,1.08] [0.33,0.64] [0.66,1.08] [0.34,0.64] [0.36,1.09] [0.23,0.74] Gender -0.25**** -0.25**** -0.25**** -0.25**** -0.25**** -0.25**** -0.25**** -0.27**** [-0.36,-0.14] [-0.33,-0.18] [-0.36,-0.14] [-0.33,-0.18] [-0.36,-0.14] [-0.33,-0.18] [-0.36,-0.13] [-0.39,-0.15] Age -0.21 -1.11**** -0.21 -1.11**** -0.21 -1.11**** -0.41 -1.23**** [-0.55,0.14] [-1.36,-0.87] [-0.55,0.14] [-1.36,-0.87] [-0.56,0.14] [-1.36,-0.86] [-0.96,0.14] [-1.56,-0.89] Constant -35.62 -15.91 -3.82**** -2.78**** -62.60 -11.40 [-101.69,30.45] [-61.65,29.83] [-4.08,-3.56] [-2.99,-2.57] [-134.68,9.48] [-61.07,38.27] Observations 44205 42863 44205 42863 44205 42863 44205 42863 AIC 11415.77 20838.99 11416.75 20850.95 11110.21 20496.21 11360.22 20793.66 BIC 11520.13 20942.98 11477.62 20911.61 11153.69 20539.54 11499.37 20932.31 chi2 100.00 181.07 87.54 154.31 86.58 157.21 47.82 109.95 Note: The table reports fixed effects in logit (or log odds) with 95% CIs in brackets; *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Mundlak specifications contain country-level means of predictors as controls (not reported). Coefficients from the random part for all random-effects models are also not reported.

13 Section S3. Accounting for individual-level confounding

Drawing on our background knowledge within political science and psychology, we analyzed five sets of potential level-1 confounders.

First, we analyzed additional demographics (i.e., in addition to gender and age): education (0 =

“No formal schooling”, 9 = “Post-graduate”) and subjective socioeconomic status (SES) (“Let’s discuss economic conditions. In general, how would you describe your own present living conditions?”; 0 = “Very bad”, 4 = “Very good”). As an alternative measure of SES, we also used employment status (0 = unemployed; 1 = employed). High-education individuals may be better aware of their disadvantage and concurrently be less prone to violence (e.g., Østby, Urdal, and

Dupuy 2019), which may suppress the INEQUALITY effects on violence. One the other hand, low-education individuals may see fewer economic prospects and concurrently be more prone to violence (ibid.), which potentially confounds the PROSPECTIVE LOSS effects on violence. Further, low-income or unemployed individuals may see few economic prospects and concurrently have low opportunity costs to joining violence (Collier and Hoeffler 2004), which potentially confounds the effects of PROSPECTIVE LOSS on violence. As shown in Table S10 below, the additional demographics do not notably attenuate the coefficients associated with the key predictors.

14 Table S10. Analogous to Table S8 but controlling for additional demographics Self-reports Intentions Self-reports Intentions Self-reports Intentions Self-reports Intentions (1) (2) (3) (4) (5) (6) (7) (8) LEVEL 1

Inequality -0.11 0.07 -0.14 0.05 -0.01 0.09 -0.09 0.08 [-0.45,0.24] [-0.11,0.24] [-0.48,0.20] [-0.13,0.22] [-0.36,0.34] [-0.09,0.28] [-0.42,0.25] [-0.09,0.25] Retrospective loss -0.33 0.05 -0.33 0.05 -0.28 0.06 -0.30 0.05 [-0.67,0.02] [-0.17,0.27] [-0.67,0.02] [-0.17,0.28] [-0.62,0.06] [-0.16,0.28] [-0.65,0.04] [-0.17,0.27] Prospective loss 0.79**** 0.48*** 0.79**** 0.49*** 0.83**** 0.49*** 0.80**** 0.48*** [0.43,1.14] [0.21,0.76] [0.44,1.14] [0.22,0.77] [0.47,1.18] [0.22,0.76] [0.44,1.15] [0.21,0.75] Gender -0.24**** -0.28**** -0.26**** -0.29**** -0.24**** -0.27**** -0.23*** -0.27**** [-0.35,-0.13] [-0.40,-0.15] [-0.38,-0.15] [-0.41,-0.16] [-0.35,-0.12] [-0.40,-0.15] [-0.34,-0.11] [-0.40,-0.15] Age -0.37 -1.23**** -0.43 -1.31**** -0.36 -1.22**** -0.38 -1.24**** [-0.93,0.19] [-1.58,-0.88] [-1.00,0.14] [-1.67,-0.95] [-0.92,0.20] [-1.57,-0.87] [-0.93,0.18] [-1.59,-0.89] Edu -0.21 -0.17 [-0.60,0.18] [-0.42,0.09] SES 0.17 0.04 [-0.15,0.50] [-0.16,0.24] Employment 0.02 0.02 [-0.21,0.25] [-0.07,0.10] LEVEL 2

Democracy 0.19 -0.29 0.36 -0.15 0.23 -0.23 0.38 -0.29 [-0.84,1.21] [-1.10,0.52] [-0.79,1.51] [-1.03,0.73] [-0.75,1.21] [-1.04,0.57] [-0.62,1.39] [-1.10,0.53] Instability -0.84 -0.53 -0.87 -0.41 -0.95 -0.52 -0.96 -0.52 [-2.34,0.67] [-1.65,0.58] [-2.49,0.76] [-1.58,0.76] [-2.40,0.49] [-1.62,0.57] [-2.42,0.50] [-1.64,0.59] Violent crime 1.86*** 1.44*** 2.08*** 1.54*** 2.22**** 1.36*** 1.82*** 1.45*** [0.89,2.83] [0.65,2.24] [0.99,3.17] [0.68,2.39] [1.30,3.14] [0.58,2.15] [0.88,2.76] [0.66,2.25] SCAD events -0.35 -0.87 -0.68 -0.93 -0.35 -0.91 -0.30 -0.86 [-1.63,0.93] [-1.84,0.10] [-2.13,0.76] [-1.96,0.11] [-1.58,0.87] [-1.87,0.05] [-1.56,0.97] [-1.83,0.11] Economic status -1.72** -1.04* -1.86** -1.05* -1.74** -1.03* -1.71** -1.05* [-2.84,-0.60] [-1.91,-0.17] [-3.11,-0.60] [-1.98,-0.12] [-2.81,-0.67] [-1.90,-0.17] [-2.81,-0.60] [-1.92,-0.18] Economic growth 0.29 -0.83* 0.31 -0.80 0.14 -0.89* 0.26 -0.82* [-0.73,1.30] [-1.64,-0.01] [-0.83,1.44] [-1.67,0.07] [-0.83,1.12] [-1.70,-0.08] [-0.73,1.24] [-1.64,-0.01] Peace years -0.17 -0.01 -0.34 -0.04 -0.11 0.08 -0.34 -0.01 [-0.94,0.60] [-0.62,0.59] [-1.19,0.51] [-0.68,0.61] [-0.84,0.62] [-0.52,0.68] [-1.09,0.41] [-0.61,0.60] Population size 0.82 0.37 1.03 0.20 0.97 0.30 0.65 0.38 [-0.35,1.98] [-0.59,1.34] [-0.27,2.34] [-0.84,1.23] [-0.11,2.05] [-0.65,1.26] [-0.47,1.78] [-0.59,1.35] Constant -3.89**** -2.26**** -3.82**** -2.26**** -4.17**** -2.33**** -3.94**** -2.28**** [-4.83,-2.96] [-2.96,-1.55] [-4.86,-2.78] [-3.02,-1.50] [-5.09,-3.24] [-3.04,-1.61] [-4.85,-3.02] [-2.99,-1.57] Observations 43200 41899 43130 41830 43105 41807 43088 41801 AIC 11009.38 20192.85 10987.84 20143.04 10974.25 20154.29 10880.72 20143.99 BIC 11174.18 20357.07 11169.96 20324.51 11156.35 20335.74 11062.82 20316.80 chi2 61.79 100.39 67.28 102.53 70.00 98.90 61.34 101.51 Note: The table reports fixed effects in logit (or log odds) with 95% CIs in brackets; *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. All models include random intercepts and slopes for predictors with significant between-country variance (not reported in the table).

15 Second, we analyzed variables related to political activism and mobilization. Specifically, we controlled for whether individuals were active supporters of a political party (“Thinking about the last national elections in [year], did you: Work for a candidate or party?”; 0 = “No”, 1 =

“Yes”), “active members” of a “voluntary association or a community group” (0 = “No”, 1 =

“Yes”), and “attended a demonstration or a protest march” (analogous to PVPARTICIPATION).

Politically active or mobilized individuals may be better aware of their disadvantage, and concurrently have increased opportunities to engage in violence, which may confound the effects of

PROSPECTIVE LOSS on violence. As shown in Table S11 below, the variables related to political activism and mobilization do not notably attenuate the coefficients associated with the key predictors.

16 Table S11. Analogous to Table S8 but controlling for political activism and mobilization Self-reports Intentions Self-reports Intentions Self-reports Intentions Self-reports Intentions (1) (2) (3) (4) (5) (6) (7) (8) LEVEL 1

Inequality -0.11 0.07 -0.06 0.09 -0.05 0.08 0.04 0.09 [-0.45,0.24] [-0.11,0.24] [-0.41,0.30] [-0.08,0.26] [-0.39,0.30] [-0.10,0.25] [-0.26,0.33] [-0.09,0.26] Retrospective loss -0.33 0.05 -0.29 0.04 -0.33 0.04 -0.23 0.03 [-0.67,0.02] [-0.17,0.27] [-0.64,0.05] [-0.18,0.27] [-0.68,0.02] [-0.19,0.28] [-0.49,0.04] [-0.19,0.26] Prospective loss 0.79**** 0.48*** 0.82**** 0.48*** 0.86**** 0.44*** 0.74**** 0.48*** [0.43,1.14] [0.21,0.76] [0.49,1.14] [0.21,0.75] [0.52,1.21] [0.18,0.71] [0.42,1.05] [0.21,0.76] Gender -0.24**** -0.28**** -0.18** -0.26**** -0.10 -0.23*** -0.01 -0.25**** [-0.35,-0.13] [-0.40,-0.15] [-0.30,-0.07] [-0.39,-0.13] [-0.22,0.01] [-0.36,-0.09] [-0.13,0.12] [-0.38,-0.12] Age -0.37 -1.23**** -0.68* -1.37**** -0.50 -1.29**** 0.08 -1.18**** [-0.93,0.19] [-1.58,-0.88] [-1.28,-0.08] [-1.74,-1.01] [-1.10,0.10] [-1.65,-0.93] [-0.48,0.64] [-1.53,-0.83] Community 1.22**** 0.24** [0.95,1.49] [0.07,0.41] Party 1.07**** 0.56**** [0.87,1.27] [0.40,0.72] Protest 3.18**** 0.73**** [2.83,3.53] [0.46,1.01] LEVEL 2

Democracy 0.19 -0.29 -0.41 -0.47 0.22 -0.35 -0.21 -0.30 [-0.84,1.21] [-1.10,0.52] [-1.45,0.64] [-1.25,0.31] [-0.77,1.21] [-1.21,0.50] [-1.02,0.60] [-1.15,0.54] Instability -0.84 -0.53 -1.63* -0.53 -1.30 -0.45 -1.42* -0.45 [-2.34,0.67] [-1.65,0.58] [-3.25,-0.01] [-1.62,0.55] [-2.78,0.18] [-1.63,0.73] [-2.59,-0.25] [-1.62,0.72] Violent crime 1.86*** 1.44*** 2.16**** 1.39*** 2.17**** 1.52*** 1.74**** 1.41*** [0.89,2.83] [0.65,2.24] [1.21,3.12] [0.63,2.16] [1.23,3.10] [0.67,2.37] [1.00,2.48] [0.58,2.24] SCAD events -0.35 -0.87 -0.22 -0.84 -0.89 -0.90 -0.23 -0.96 [-1.63,0.93] [-1.84,0.10] [-1.51,1.07] [-1.78,0.10] [-2.20,0.42] [-1.94,0.14] [-1.27,0.82] [-1.97,0.05] Economic status -1.72** -1.04* -1.43* -0.85* -1.70** -0.92* -1.78*** -0.98* [-2.84,-0.60] [-1.91,-0.17] [-2.55,-0.31] [-1.68,-0.01] [-2.77,-0.63] [-1.83,-0.00] [-2.68,-0.88] [-1.89,-0.07] Economic growth 0.29 -0.83* 0.06 -0.94* 0.39 -0.83 -0.16 -0.91* [-0.73,1.30] [-1.64,-0.01] [-0.97,1.08] [-1.72,-0.15] [-0.58,1.35] [-1.70,0.03] [-0.98,0.65] [-1.77,-0.06] Peace years -0.17 -0.01 -0.65 -0.02 -0.09 0.03 -0.84** 0.08 [-0.94,0.60] [-0.62,0.59] [-1.44,0.13] [-0.60,0.57] [-0.82,0.64] [-0.61,0.67] [-1.44,-0.25] [-0.55,0.72] Population size 0.82 0.37 0.17 0.26 0.28 0.49 -0.25 0.46 [-0.35,1.98] [-0.59,1.34] [-0.98,1.32] [-0.66,1.18] [-0.83,1.40] [-0.57,1.56] [-1.13,0.63] [-0.55,1.47] Constant -3.89**** -2.26**** -3.89**** -2.25**** -4.26**** -2.44**** -4.30**** -2.39**** [-4.83,-2.96] [-2.96,-1.55] [-4.84,-2.94] [-2.93,-1.57] [-5.17,-3.36] [-3.18,-1.69] [-5.04,-3.57] [-3.12,-1.65] Observations 43200 41899 42904 41611 42020 40750 42898 41607 AIC 11009.38 20192.85 10491.60 19965.40 10458.84 19386.55 8046.78 19922.10 BIC 11174.18 20357.07 10673.60 20146.76 10640.40 19567.47 8220.11 20103.45 chi2 61.79 100.39 141.17 111.79 171.80 138.54 364.24 119.87 Note: The table reports fixed effects in logit (or log odds) with 95% CIs in brackets; *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. All models include random intercepts and slopes for predictors with significant between-country variance (not reported in the table).

17 Third, we analyzed three measures of perceived political repression (“During electoral cam- paigns in [country name], how much do you personally fear becoming a victim of political intimidation or violence?”; 0 = “Not at all”, 3 = “A lot”. “How likely do you think it is that powerful people can find out how you voted, even though there is supposed to be a secret ballot in [country name]?”; 0 = “Not at all likely”, 3 = “Very likely”. “In your opinion, how often in

[country name] do people have to be careful of what they say about politics?”; 0 = “Never”, 3 =

“Always”). Individuals who see high levels of repression may also believe that they are economically disadvantaged and concurrently be afraid to challenge governments (e.g., Young 2019), which may suppress the INEQUALITY effects on violence. On the other hand, those who see high levels of repression may see fewer economic prospects and concurrently be motivated to use an- ti-government violence (e.g., Sullivan and Davenport 2017), which potentially confounds the

PROSPECTIVE LOSS effects on violence. Controlling for perceived repression may also address the potential reporting bias (interviewees may not honestly report participation in violence if they fear state reprisals). As shown in Table S12 below, the variables related to repression do not notably attenuate the coefficients associated with the key predictors.

18 Table S12. Analogous to Table S8 but controlling for repression Self-reports Intentions Self-reports Intentions Self-reports Intentions Self-reports Intentions (1) (2) (3) (4) (5) (6) (7) (8) LEVEL 1

Inequality -0.11 0.07 -0.08 0.04 -0.14 0.02 -0.12 0.06 [-0.45,0.24] [-0.11,0.24] [-0.43,0.26] [-0.13,0.21] [-0.50,0.22] [-0.16,0.19] [-0.48,0.23] [-0.11,0.24] Retrospective loss -0.33 0.05 -0.34 0.05 -0.30 0.04 -0.29 0.07 [-0.67,0.02] [-0.17,0.27] [-0.69,0.01] [-0.17,0.28] [-0.64,0.03] [-0.19,0.26] [-0.64,0.06] [-0.15,0.29] Prospective loss 0.79**** 0.48*** 0.76**** 0.46*** 0.79**** 0.47*** 0.80**** 0.48*** [0.43,1.14] [0.21,0.76] [0.40,1.13] [0.21,0.71] [0.41,1.16] [0.22,0.72] [0.44,1.15] [0.22,0.74] Gender -0.24**** -0.28**** -0.25**** -0.26**** -0.23*** -0.25**** -0.22*** -0.27**** [-0.35,-0.13] [-0.40,-0.15] [-0.36,-0.13] [-0.37,-0.15] [-0.34,-0.11] [-0.36,-0.14] [-0.34,-0.11] [-0.39,-0.15] Age -0.37 -1.23**** -0.32 -1.16**** -0.29 -1.14**** -0.34 -1.23**** [-0.93,0.19] [-1.58,-0.88] [-0.89,0.24] [-1.50,-0.82] [-0.88,0.30] [-1.47,-0.82] [-0.90,0.23] [-1.58,-0.88] Intimidation 0.42** 0.26** [0.17,0.66] [0.08,0.44] Non-anonymous 0.82**** 0.38*** [0.44,1.21] [0.16,0.60] Restricted -0.13 -0.01 [-0.44,0.18] [-0.19,0.18] LEVEL 2

Democracy 0.19 -0.29 -0.03 -0.19 0.16 -0.15 0.21 -0.39 [-0.84,1.21] [-1.10,0.52] [-1.13,1.08] [-1.00,0.63] [-0.89,1.22] [-0.94,0.64] [-0.91,1.33] [-1.18,0.40] Instability -0.84 -0.53 -0.81 -0.49 -0.52 -0.32 -0.67 -0.81 [-2.34,0.67] [-1.65,0.58] [-2.42,0.80] [-1.61,0.63] [-2.07,1.02] [-1.40,0.77] [-2.38,1.04] [-1.92,0.30] Violent crime 1.86*** 1.44*** 1.81*** 1.38*** 1.94*** 1.50*** 1.80*** 1.45*** [0.89,2.83] [0.65,2.24] [0.77,2.85] [0.58,2.18] [0.95,2.93] [0.73,2.27] [0.76,2.84] [0.69,2.20] SCAD events -0.35 -0.87 -0.42 -0.77 -0.60 -0.79 -0.58 -1.08* [-1.63,0.93] [-1.84,0.10] [-1.79,0.95] [-1.74,0.20] [-1.95,0.74] [-1.74,0.16] [-1.96,0.80] [-2.02,-0.14] Economic status -1.72** -1.04* -1.64** -1.19** -1.88** -1.23** -1.64** -1.00* [-2.84,-0.60] [-1.91,-0.17] [-2.83,-0.45] [-2.06,-0.32] [-3.03,-0.73] [-2.07,-0.38] [-2.84,-0.43] [-1.83,-0.17] Economic growth 0.29 -0.83* 0.46 -0.88* 0.14 -0.84* 0.79 -0.87* [-0.73,1.30] [-1.64,-0.01] [-0.65,1.56] [-1.71,-0.05] [-0.87,1.16] [-1.63,-0.05] [-0.34,1.92] [-1.67,-0.06] Peace years -0.17 -0.01 -0.21 0.06 -0.22 0.04 -0.22 0.01 [-0.94,0.60] [-0.62,0.59] [-1.04,0.61] [-0.55,0.67] [-1.00,0.56] [-0.55,0.63] [-1.05,0.62] [-0.58,0.59] Population size 0.82 0.37 0.72 0.44 0.77 0.58 0.56 0.39 [-0.35,1.98] [-0.59,1.34] [-0.53,1.98] [-0.54,1.41] [-0.41,1.95] [-0.37,1.52] [-0.69,1.82] [-0.52,1.31] Constant -3.89**** -2.26**** -3.91**** -2.39**** -4.05**** -2.47**** -3.92**** -2.16**** [-4.83,-2.96] [-2.96,-1.55] [-4.91,-2.91] [-3.10,-1.68] [-5.00,-3.10] [-3.16,-1.78] [-4.93,-2.91] [-2.85,-1.48] Observations 43200 41899 42533 41245 40950 39700 42341 41051 AIC 11009.38 20192.85 10843.36 19800.27 10285.88 19015.34 10871.51 19805.66 BIC 11174.18 20357.07 11025.18 19981.44 10466.90 19195.71 11053.24 19986.73 chi2 61.79 100.39 67.52 104.90 74.10 113.76 58.64 105.07 Note: The table reports fixed effects in logit (or log odds) with 95% CIs in brackets; *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. All models include random intercepts and slopes for predictors with significant between-country variance (not reported in the table).

19 Fourth, we analyzed variables pertaining to government support. Specifically, we controlled for whether interviewees “trust” state leaders (presidents or prime ministers) and parliaments (0 =

“Not at all”, 3 = “A lot”), and whether they “approve” of the way state leaders (presidents and prime ministers) “performed their jobs over the past twelve months” (0 = “Strongly disapprove”, 4 = “Strongly approve”). Individuals who distrust or disapprove of their governments may see fewer economic prospects and concurrently may be prone to antigovernment violence, which may confound the PROSPECTIVE LOSS effects on political violence. As shown in Table S13 below, the variables related to government support do not notably attenuate the coefficients associated with the key predictors.

20 Table S13. Analogous to Table S8 but controlling for government support Self-reports Intentions Self-reports Intentions Self-reports Intentions Self-reports Intentions (1) (2) (3) (4) (5) (6) (7) (8) LEVEL 1

Inequality -0.11 0.07 -0.12 0.05 -0.12 0.04 -0.11 0.09 [-0.45,0.24] [-0.11,0.24] [-0.47,0.24] [-0.12,0.23] [-0.47,0.22] [-0.14,0.21] [-0.46,0.25] [-0.09,0.26] Retrospective loss -0.33 0.05 -0.33 0.06 -0.32 0.03 -0.32 0.03 [-0.67,0.02] [-0.17,0.27] [-0.68,0.02] [-0.17,0.29] [-0.67,0.04] [-0.20,0.26] [-0.66,0.03] [-0.20,0.27] Prospective loss 0.79**** 0.48*** 0.69**** 0.46** 0.77**** 0.41** 0.71**** 0.46*** [0.43,1.14] [0.21,0.76] [0.35,1.03] [0.18,0.73] [0.43,1.12] [0.14,0.68] [0.35,1.06] [0.19,0.73] Gender -0.24**** -0.28**** -0.21*** -0.26**** -0.24**** -0.23**** -0.20*** -0.24*** [-0.35,-0.13] [-0.40,-0.15] [-0.32,-0.09] [-0.39,-0.14] [-0.35,-0.12] [-0.35,-0.12] [-0.32,-0.09] [-0.37,-0.12] Age -0.37 -1.23**** -0.31 -1.08**** -0.36 -1.04**** -0.33 -1.11**** [-0.93,0.19] [-1.58,-0.88] [-0.90,0.28] [-1.41,-0.75] [-0.94,0.23] [-1.36,-0.73] [-0.90,0.25] [-1.46,-0.75] Trust pres. -0.26 -0.41**** [-0.56,0.03] [-0.59,-0.24] Trust parl. -0.24 -0.34**** [-0.57,0.09] [-0.51,-0.17] Approve pres. -0.38* -0.54**** [-0.74,-0.02] [-0.80,-0.29] LEVEL 2

Democracy 0.19 -0.29 0.05 0.06 -0.13 -0.51 0.06 -0.76 [-0.84,1.21] [-1.10,0.52] [-1.41,1.51] [-0.86,0.98] [-1.34,1.09] [-1.34,0.33] [-1.19,1.31] [-1.58,0.06] Instability -0.84 -0.53 -1.05 -0.31 -0.83 -0.60 -0.94 -1.12* [-2.34,0.67] [-1.65,0.58] [-3.04,0.94] [-1.45,0.83] [-2.57,0.91] [-1.72,0.52] [-2.75,0.86] [-2.22,-0.02] Violent crime 1.86*** 1.44*** 2.01** 1.18** 2.58**** 1.66**** 1.73** 0.93* [0.89,2.83] [0.65,2.24] [0.77,3.25] [0.40,1.97] [1.41,3.74] [0.85,2.47] [0.53,2.93] [0.13,1.74] SCAD events -0.35 -0.87 -0.20 -1.62** -0.70 -1.13* -0.62 -0.82 [-1.63,0.93] [-1.84,0.10] [-2.09,1.69] [-2.81,-0.43] [-2.18,0.78] [-2.12,-0.13] [-2.12,0.88] [-1.77,0.14] Economic status -1.72** -1.04* -1.99** -1.32** -2.10** -1.24** -2.06** -1.09* [-2.84,-0.60] [-1.91,-0.17] [-3.38,-0.59] [-2.17,-0.46] [-3.42,-0.78] [-2.12,-0.36] [-3.44,-0.68] [-1.99,-0.19] Economic growth 0.29 -0.83* 0.00 -1.02* -0.41 -1.17** -0.04 -0.97* [-0.73,1.30] [-1.64,-0.01] [-1.26,1.27] [-1.80,-0.24] [-1.64,0.83] [-2.02,-0.32] [-1.30,1.21] [-1.78,-0.16] Peace years -0.17 -0.01 0.12 -0.17 0.54 0.22 0.38 0.04 [-0.94,0.60] [-0.62,0.59] [-0.85,1.09] [-0.77,0.42] [-0.40,1.48] [-0.42,0.86] [-0.57,1.33] [-0.58,0.65] Population size 0.82 0.37 0.88 0.32 1.03 0.17 0.86 -0.02 [-0.35,1.98] [-0.59,1.34] [-0.59,2.35] [-0.60,1.24] [-0.33,2.39] [-0.80,1.13] [-0.57,2.29] [-0.94,0.91] Constant -3.89**** -2.26**** -3.69**** -1.92**** -3.78**** -1.87**** -3.60**** -1.46*** [-4.83,-2.96] [-2.96,-1.55] [-4.87,-2.51] [-2.64,-1.21] [-4.88,-2.68] [-2.60,-1.15] [-4.76,-2.43] [-2.20,-0.73] Observations 43200 41899 40947 39697 40575 39329 40922 39658 AIC 11009.38 20192.85 10502.51 19217.24 10459.37 19119.48 10569.95 19176.61 BIC 11174.18 20357.07 10683.53 19397.61 10640.20 19299.65 10750.96 19356.96 chi2 61.79 100.39 52.38 123.27 65.35 112.85 52.21 106.28 Note: The table reports fixed effects in logit (or log odds) with 95% CIs in brackets; *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. All models include random intercepts and slopes for predictors with significant between-country variance (not reported in the table).

21 Fifth, we analyzed variables highlighted in psychological literature on political violence (for review, see Becker and Tausch 2015): group-based injustice, contempt toward state authorities, and group or political efficacy (to influence political decisions). Group-based injustice is also a key variable highlighted in the macro-level research on horizontal inequalities and civil conflict.

We proxied this variable with “How often is [interviewee’s ethnic group] treated unfairly by the government?” (0 = “Never”, 3 = “Always”). Political efficacy was measured with “How much of the time do you think…members of parliament…try their best to listen to what people like you have to say?” (0 = “Never”, 3 = “Always”). Contempt toward state authorities was proxied with voting intention. Deliberate refusal to vote, as contrasted with other reasons (e.g., lack of time), is an expression of disaffection with state authorities. Becker and Tausch report a negative correlation between voting and an explicit measure of contempt toward state authorities (Becker and Tausch 2015). We coded voting in the most recent national elections (0 =

“Decided not to vote”, 1 = all other replies). Members of disadvantaged groups or individuals with low perceived efficacy or contempt may see themselves as relatively deprived, and concurrently deem opportunities to challenge governments too low, which may suppress the Inequality effects on violence. On the other hand, such individuals may see fewer economic prospects, and concurrently be motivated to use violence, either due to group-based grievances or a perception that non-violent forms of political activism are ineffective, which may confound the PROSPEC-

TIVE LOSS effect on violence. Tables S14 below shows that none of these controls notably atten- uates the coefficients associated with the key predictors.

22 Table S14. Analogous to Table S8 but controlling for variables highlighted in psychological research Self-reports Intentions Self-reports Intentions Self-reports Intentions Self-reports Intentions (1) (2) (3) (4) (5) (6) (7) (8) LEVEL 1

Inequality -0.11 0.07 -0.11 0.07 -0.06 0.07 -0.06 0.03 [-0.45,0.24] [-0.11,0.24] [-0.45,0.24] [-0.10,0.24] [-0.41,0.29] [-0.11,0.24] [-0.45,0.33] [-0.16,0.22] Retrospective loss -0.33 0.05 -0.35* 0.05 -0.27 0.04 -0.37 0.00 [-0.67,0.02] [-0.17,0.27] [-0.70,-0.00] [-0.17,0.28] [-0.61,0.07] [-0.18,0.27] [-0.74,0.01] [-0.23,0.24] Prospective loss 0.79**** 0.48*** 0.80**** 0.48*** 0.82**** 0.42** 0.88**** 0.36* [0.43,1.14] [0.21,0.76] [0.45,1.14] [0.21,0.75] [0.46,1.18] [0.17,0.68] [0.53,1.22] [0.08,0.64] Gender -0.24**** -0.28**** -0.25**** -0.28**** -0.20*** -0.25**** -0.16* -0.20*** [-0.35,-0.13] [-0.40,-0.15] [-0.36,-0.13] [-0.40,-0.15] [-0.32,-0.09] [-0.35,-0.15] [-0.28,-0.04] [-0.31,-0.09] Age -0.37 -1.23**** -0.35 -1.22**** -0.41 -1.11**** -0.24 -1.02**** [-0.93,0.19] [-1.58,-0.88] [-0.91,0.21] [-1.56,-0.87] [-1.00,0.17] [-1.43,-0.78] [-0.89,0.41] [-1.39,-0.65] No vote 0.22 0.20 [-0.20,0.64] [-0.04,0.44] Heard MPs 0.66** 0.31** [0.16,1.16] [0.08,0.55] Eth. disc. 0.50* 0.40*** [0.12,0.88] [0.20,0.61] LEVEL 2

Democracy 0.19 -0.29 0.27 -0.25 -0.10 -0.40 0.06 0.52 [-0.84,1.21] [-1.10,0.52] [-0.76,1.29] [-1.06,0.57] [-1.02,0.83] [-1.20,0.39] [-1.23,1.36] [-0.62,1.67] Instability -0.84 -0.53 -0.77 -0.47 -1.04 -0.46 -0.89 0.11 [-2.34,0.67] [-1.65,0.58] [-2.27,0.73] [-1.59,0.64] [-2.39,0.31] [-1.53,0.60] [-2.65,0.87] [-1.28,1.49] Violent crime 1.86*** 1.44*** 1.89*** 1.43*** 1.88**** 1.58**** 2.42**** 1.45** [0.89,2.83] [0.65,2.24] [0.93,2.86] [0.63,2.22] [1.02,2.73] [0.81,2.35] [1.37,3.47] [0.47,2.44] SCAD events -0.35 -0.87 -0.42 -0.93 -0.24 -0.90 -2.65 -2.37 [-1.63,0.93] [-1.84,0.10] [-1.70,0.86] [-1.91,0.04] [-1.43,0.95] [-1.85,0.05] [-6.64,1.35] [-5.68,0.93] Economic status -1.72** -1.04* -1.75** -1.03* -1.87*** -1.31** -2.04** -1.52* [-2.84,-0.60] [-1.91,-0.17] [-2.87,-0.63] [-1.90,-0.16] [-2.88,-0.85] [-2.15,-0.47] [-3.43,-0.66] [-2.72,-0.32] Economic growth 0.29 -0.83* 0.28 -0.82 -0.18 -1.05** 0.37 -0.81 [-0.73,1.30] [-1.64,-0.01] [-0.73,1.29] [-1.63,0.00] [-1.07,0.72] [-1.84,-0.25] [-0.56,1.29] [-1.64,0.01] Peace years -0.17 -0.01 -0.17 -0.03 0.01 0.20 -0.47 -0.06 [-0.94,0.60] [-0.62,0.59] [-0.94,0.59] [-0.64,0.58] [-0.67,0.69] [-0.40,0.81] [-1.19,0.26] [-0.73,0.60] Population size 0.82 0.37 0.74 0.40 0.32 0.32 0.41 0.51 [-0.35,1.98] [-0.59,1.34] [-0.42,1.90] [-0.57,1.37] [-0.65,1.28] [-0.59,1.24] [-0.61,1.43] [-0.49,1.51] Constant -3.89**** -2.26**** -3.94**** -2.30**** -3.78**** -2.24**** -3.79**** -2.67**** [-4.83,-2.96] [-2.96,-1.55] [-4.87,-3.00] [-3.01,-1.59] [-4.62,-2.94] [-2.92,-1.56] [-4.85,-2.73] [-3.62,-1.72] Observations 43200 41899 43082 41787 40342 39086 35525 34353 AIC 11009.38 20192.85 10945.60 20114.38 10409.16 18915.59 9614.62 16924.45 BIC 11174.18 20357.07 11127.69 20295.83 10589.87 19095.64 9792.66 17101.78 chi2 61.79 100.39 65.71 102.44 63.28 109.31 71.17 78.17 Note: The table reports fixed effects in logit (or log odds) with 95% CIs in brackets; *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. All models include random intercepts and slopes for predictors with significant between-country variance (not reported in the table).

23 Section S4. Analysis of sensitivity to unobserved confounding

We draw on Altonji, Elder, and Taber’s (2005) idea that the amount of selection on the observed controls in a model provides a guide to the amount of selection on unobservables. In applied economics, coefficient stability across models with and without observed controls is commonly taken as evidence against omitted variable bias (Gonzales and Miguel 2015, 30). The robustness of the PROSPECTIVE LOSS coefficient to inclusion of 19 theoretically relevant controls is a first piece of evidence against omitted variables driving our results (Bellows and Miguel 2009,

1152). We now proceed to a more formal evaluation of sensitivity to unobserved confounding, following the approach introduced by Altonji, Elder, and Taber (2005) and further developed by

Oster (2019).

Here, we shift to linear probability models (LPM) with country-level fixed effects, because such sensitivity analyses are not yet applicable to hierarchical models. The coefficient from an LPM that regresses PVPARTICIPATION on PROSPECTIVE LOSS, with no controls, is bnc = .0305. This effect is similar to the marginal effect on probabilities (3.28%) estimated based on the main hierarchical model (Model 4). The coefficient estimated in an LPM that regresses PVPARTICIPA-

TION on PROSPECTIVE LOSS, with all 19 controls, is bc = .0270. Hence, the observed level-1 controls attenuate the effect of PROSPECTIVE LOSS by bnc – bc = .0305 – .0270 = .0035. To reduce this coefficient to zero, selection on unobservables would therefore have to be 7.7 times stronger

(.0270/.0035) than the selection on the 19 observed controls combined. An analogous ratio in the case of PVINTENTIONS, is bc/(bnc – bc) = .0227/(.0237 – .0227) = 22.7. Given this, and because we have considered a large set of theory-informed confounders (not a random set), we see it as unlikely that the bias due to unknown variables—which are accounted for in neither prior research nor in our theory—would explain away the entire effects.

This analysis assumed that the observed and unobserved confounders have equal variances. Os- ter (2019, 189–190) has shown that if these variances are unequal, coefficient stability across

24 models with and without observed controls may fail to detect existing omitted variable bias.

Accordingly, Oster argues, we have to account for the variance in the outcome due to observed controls, and suggests scaling coefficient movements by the change in R-squared when controls are included. Accordingly, she derives the following formula:

* b = bc – (bnc – bc) × (Rmax – Rc) / (Rc – Rnc) (1)

Where bc and Rc are from the model including observed controls, and bnc and Rnc are from the model with no controls. Rmax represents R-squared from a model with all observable and unobservable controls; hence, it is an unknown parameter.

If observables and unobservables have the same explanatory power in the outcome, (1) is a consistent estimator of the bias-adjusted treatment effect. All coefficients, besides Rmax, can be estimated from regressions with and without controls. For Rmax, however, we can only derive some plausible bounds. Rc is, naturally, a lower bound on Rmax, and 1 is a hypothetical upper bound on Rmax. However, given the likely measurement error, Rmax = 1 is highly unrealistic (Gonzales and Miguel 2015). Based on randomized experiments as empirical reference, Oster suggests the upper bound of 1.3Rc, above which we can consider results robust. If we set Rmax to 1.3Rc the bias-adjusted coefficients of PROSPECTIVE LOSS on PVPARTICIPATION and PVINTENTIONS are

0.026 and 0.016, respectively. If we use a more conservative value of 2.2Rc, suggested by Oster in her earlier work, the coefficients drop to 0.021 and 0.015. Hence, to reduce the coefficients to zero, selection on unobservables would still have to be many times stronger than the selection on observables. Oster also suggest to report the upper bound of Rmax under which the coefficient would be 0, which are 0.68 and 0.75 in our case. Overall, these analyses provide evidence against omitted variables driving our results.

25 Section S5. Other sensitivity tests

(i) First, we analyzed alternative measures of the key variables. Afrobarometer Round 5 con- tains the following question pertaining to RETROSPECTIVE LOSS: “Looking back, how do you rate the following compared to twelve months ago: economic conditions in [country name]” and “your living conditions” (0 = “Much better”, 4 = “Much worse”). Assuming that one’s own status is more important than the state’s status for violence motivations, we used the response for personal living conditions as the main measure. Results for the state-referenced measure are reported in robustness tests below. Similarly, PROSPECTIVE LOSS was proxied with: “Looking ahead, do you expect the following to be better or worse: economic conditions in [country name] in twelve months time; your living conditions in twelve months time” (0 = “Much better”; 4 =

“Much worse”). We used individual-referenced measure in the main analyses and the state-referenced alternative in robustness tests below. Table S15 below reports results analogous to those in Table S8 when RETROSPECTIVE LOSS and PROSPECTIVE LOSS are substituted with state-referenced measures. The estimates remain virtually the same.

26 Table S15. Analogous to Table S8 but with state-referenced RETROSPECTIVE LOSS and PROSPECTIVE LOSS Self-reports Intentions Self-reports Intentions Self-reports Intentions (1) (2) (3) (4) (5) (6) LEVEL 1

Inequality -0.11 0.07 -0.15 0.07 0.02 0.10 [-0.45,0.24] [-0.11,0.24] [-0.49,0.18] [-0.10,0.24] [-0.30,0.34] [-0.07,0.27] Retrospective loss -0.33 0.05 -0.27 0.02 [-0.67,0.02] [-0.17,0.27] [-0.61,0.07] [-0.21,0.24] Prospective loss 0.79**** 0.48*** 0.76**** 0.49*** [0.43,1.14] [0.21,0.76] [0.42,1.09] [0.21,0.76] Gender -0.24**** -0.28**** -0.23**** -0.27**** -0.21*** -0.28**** [-0.35,-0.13] [-0.40,-0.15] [-0.35,-0.12] [-0.39,-0.15] [-0.32,-0.09] [-0.40,-0.15] Age -0.37 -1.23**** -0.36 -1.22**** -0.29 -1.20**** [-0.93,0.19] [-1.58,-0.88] [-0.92,0.20] [-1.57,-0.87] [-0.84,0.27] [-1.53,-0.87] Retrospective loss_state -0.21 -0.01 [-0.53,0.12] [-0.22,0.20] Prospective loss_state 0.71**** 0.54*** [0.36,1.06] [0.27,0.81] LEVEL 2

Democracy 0.19 -0.29 0.16 -0.24 0.26 -0.23 [-0.84,1.21] [-1.10,0.52] [-0.84,1.15] [-1.04,0.56] [-0.76,1.29] [-1.09,0.62] Instability -0.84 -0.53 -0.83 -0.46 -0.89 -0.39 [-2.34,0.67] [-1.65,0.58] [-2.29,0.63] [-1.56,0.63] [-2.38,0.61] [-1.56,0.79] Violent crime 1.86*** 1.44*** 1.97**** 1.33*** 1.82*** 1.29** [0.89,2.83] [0.65,2.24] [1.03,2.91] [0.55,2.11] [0.85,2.80] [0.45,2.12] SCAD events -0.35 -0.87 -0.35 -0.97* -0.35 -0.95 [-1.63,0.93] [-1.84,0.10] [-1.61,0.91] [-1.93,-0.01] [-1.62,0.93] [-1.97,0.07] Economic status -1.72** -1.04* -1.73** -0.96* -1.61** -0.89 [-2.84,-0.60] [-1.91,-0.17] [-2.81,-0.65] [-1.82,-0.11] [-2.73,-0.49] [-1.80,0.03] Economic growth 0.29 -0.83* 0.38 -0.90* 0.46 -0.80 [-0.73,1.30] [-1.64,-0.01] [-0.59,1.35] [-1.70,-0.10] [-0.56,1.48] [-1.66,0.06] Peace years -0.17 -0.01 -0.16 -0.03 -0.16 -0.05 [-0.94,0.60] [-0.62,0.59] [-0.89,0.58] [-0.63,0.57] [-0.93,0.61] [-0.68,0.59] Population size 0.82 0.37 0.98 0.42 0.97 0.39 [-0.35,1.98] [-0.59,1.34] [-0.13,2.10] [-0.54,1.39] [-0.21,2.14] [-0.63,1.40] Constant -3.89**** -2.26**** -3.97**** -2.22**** -4.12**** -2.33**** [-4.83,-2.96] [-2.96,-1.55] [-4.87,-3.07] [-2.91,-1.52] [-5.06,-3.18] [-3.07,-1.60] Observations 43200 41899 42821 41525 42830 41531 AIC 11009.38 20192.85 10951.58 20075.11 10986.84 19988.30 BIC 11174.18 20357.07 11116.21 20239.16 11151.47 20152.35 chi2 61.79 100.39 63.66 98.95 54.27 99.94 Note: The table reports fixed effects in logit (or log odds) with 95% CIs in brackets; *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. All models include random intercepts and slopes for predictors with significant between-country variance (not reported in the table).

27 (ii) Subsequently, we performed analogous analyses with PDPARTICIPATION and PDINTENTIONS as outcomes, which are identic to PVPARTICIPATION and PVINTENTIONS but focus on whether interviewees “attended a demonstration or a protest march”. Table S16 shows that the coefficient of RETROSPECTIVE LOSS remains insignificant, whereas that of INEQUALITY becomes significant; however, the coefficient is negative, suggesting that economically disadvantaged individuals are less likely to participate in protests. By contrast, the coefficient of PROSPECTIVE LOSS remains positive; however, the coefficient is only significant at p < 0.10 in the model of PDPAR-

TICIPATION (and significant at p < 0.05 in the model of PDINTENTIONS). Notably, thus, the associations are weaker for PROSPECTIVE LOSS in the protest model, indicating that projected decremental deprivations more reliably predict motivations to engage in violent than non-violent collective actions.

28 Table S16. Analogous to Table S8 but with PDPARTICIPATION and PDINTENTIONS as outcomes Self-reports Intentions (1) (2) LEVEL 1

Inequality -0.31** -0.13* [-0.50,-0.12] [-0.25,-0.02] Retrospective loss -0.04 0.09 [-0.26,0.18] [-0.04,0.23] Prospective loss 0.25 0.22* [-0.02,0.52] [0.01,0.43] Gender -0.45**** -0.35**** [-0.52,-0.38] [-0.44,-0.25] Age -1.09**** -1.67**** [-1.47,-0.71] [-1.89,-1.45] LEVEL 2

Democracy 1.22** 0.53 [0.32,2.12] [-0.35,1.42] Instability -0.13 -0.00 [-1.45,1.19] [-1.29,1.29] Violent crime 0.56 1.17** [-0.33,1.45] [0.29,2.05] SCAD events 1.06* -0.83 [0.04,2.08] [-1.84,0.17] Economic status -0.31 -0.13 [-1.27,0.65] [-1.06,0.80] Economic growth -0.30 -0.71 [-1.26,0.65] [-1.65,0.23] Peace years -0.09 0.30 [-0.80,0.61] [-0.39,0.99] Population size -0.01 0.01 [-1.14,1.13] [-1.13,1.14] Constant -2.60**** -1.27** [-3.41,-1.80] [-2.05,-0.49] Observations 43166 39234 AIC 25342.56 38206.72 BIC 25507.35 38369.69 chi2 220.64 291.71 Note: The table reports fixed effects in logit (or log odds) with 95% CIs in brackets; *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. All models include random intercepts and slopes for predictors with significant between-country variance (not reported in the table).

29 (iii) The question about violence does not explicitly mention the target of the violence. Citizens might be part of a pro-government movement engaging in violence against opposition. Hence, we cannot rule out the possibility that our results were driven by individuals fighting in support of governments. We therefore conducted an analysis differentiating between self-reported participation in pro- or antigovernment violence. We used questions about approval of government to identify interviewees who are very unlikely to take part in pro-government violence: “Do you approve or disapprove of the way that the following people have performed their jobs over the past twelve months, or haven’t you heard enough about them to say?: President/prime minis- ter; Your member of parliament”. Individuals who disapprove of governments are very unlikely to take part in (costly and risky) pro-government violence. Table S17 below reports estimates analogous to those reported in Table S8 but splitting the sample into supporters of PMs

/Presidents vs. non-supporters of PMs/Presidents and supporters of MPs vs. non-supporters of

MPs. As shown in the table, the associations between the predictors and outcomes remain substantively the same in the sub-sample of government non-supporters.

30 Table S17. Analogous to Table S8 but splitting the sample into government supporters vs. non-supporters Self-reports Intentions Support PM/Pres.? Support MP? Support PM/Pres.? Support MP? No Yes No Yes No Yes No Yes (1) (2) (3) (4) (5) (6) (7) (8) LEVEL 1

Inequality -0.18 -0.17 -0.39 -0.03 -0.03 0.16 -0.01 0.13 [-0.67,0.32] [-0.54,0.19] [-0.87,0.09] [-0.39,0.34] [-0.31,0.24] [-0.07,0.39] [-0.25,0.24] [-0.12,0.39] Retrospective loss -0.02 -0.57** -0.27 -0.34 -0.10 0.07 0.07 0.00 [-0.52,0.48] [-0.95,-0.18] [-0.75,0.21] [-0.76,0.08] [-0.42,0.22] [-0.22,0.36] [-0.21,0.35] [-0.27,0.28] Prospective loss 0.54* 0.90**** 0.84**** 0.58* 0.45** 0.40* 0.62**** 0.21 [0.07,1.02] [0.61,1.20] [0.44,1.25] [0.05,1.12] [0.16,0.75] [0.02,0.78] [0.31,0.92] [-0.15,0.56] Gender -0.11 -0.28*** -0.08 -0.35**** -0.21** -0.27*** -0.18** -0.25*** [-0.28,0.07] [-0.43,-0.12] [-0.25,0.08] [-0.52,-0.18] [-0.34,-0.08] [-0.41,-0.12] [-0.30,-0.07] [-0.37,-0.12] Age -0.51 -0.29 -0.57 -0.35 -0.77*** -1.33**** -0.96**** -1.23**** [-1.40,0.38] [-0.93,0.34] [-1.44,0.29] [-1.00,0.30] [-1.19,-0.34] [-1.80,-0.87] [-1.34,-0.58] [-1.78,-0.69] LEVEL 2

Democracy -0.04 0.39 0.18 -0.07 -0.40 0.37 -0.09 -0.27 [-1.34,1.25] [-0.56,1.33] [-1.03,1.39] [-1.01,0.87] [-1.24,0.44] [-0.55,1.29] [-0.94,0.76] [-1.17,0.63] Instability -1.29 -0.84 -0.75 -1.00 -0.72 -0.20 -0.41 -0.50 [-3.13,0.55] [-2.24,0.57] [-2.64,1.13] [-2.32,0.31] [-1.89,0.45] [-1.43,1.03] [-1.61,0.79] [-1.64,0.65] Violent crime 1.96** 1.60*** 2.05*** 1.68*** 1.25** 1.14* 1.21** 1.41** [0.71,3.20] [0.72,2.47] [0.90,3.21] [0.79,2.58] [0.37,2.13] [0.25,2.03] [0.35,2.07] [0.54,2.28] SCAD events -0.79 -0.52 -0.48 -0.65 -0.89 -1.01 -0.79 -0.99 [-2.32,0.75] [-1.83,0.80] [-1.94,0.99] [-2.01,0.70] [-1.87,0.10] [-2.21,0.19] [-1.81,0.23] [-2.12,0.14] Economic status -1.95* -1.87*** -1.93** -1.72*** -1.43** -0.99* -1.11* -1.01* [-3.44,-0.46] [-2.86,-0.89] [-3.27,-0.58] [-2.73,-0.71] [-2.48,-0.37] [-1.93,-0.05] [-2.06,-0.16] [-1.94,-0.09] Economic growth -0.23 0.10 0.22 -0.08 -0.94* -0.96* -0.71 -1.11** [-1.52,1.06] [-0.79,0.99] [-0.99,1.43] [-0.93,0.78] [-1.80,-0.07] [-1.84,-0.08] [-1.60,0.18] [-1.95,-0.27] Peace years 0.25 -0.47 -0.08 -0.28 0.26 -0.28 0.28 -0.18 [-0.75,1.26] [-1.14,0.20] [-0.97,0.82] [-0.97,0.41] [-0.41,0.94] [-0.94,0.37] [-0.38,0.95] [-0.81,0.45] Population size 0.61 0.61 0.64 0.53 0.05 0.67 0.37 0.47 [-0.76,1.98] [-0.46,1.68] [-0.63,1.91] [-0.54,1.60] [-0.96,1.06] [-0.36,1.71] [-0.66,1.41] [-0.53,1.48] Constant -3.50**** -3.61**** -3.76**** -3.36**** -1.80**** -2.62**** -2.42**** -2.13**** [-4.75,-2.25] [-4.45,-2.77] [-4.87,-2.65] [-4.23,-2.49] [-2.60,-1.00] [-3.40,-1.84] [-3.20,-1.65] [-2.89,-1.37] Observations 13707 27215 18298 20767 13140 26518 17668 20161 AIC 4363.89 6198.33 5131.71 5197.87 7361.06 11838.09 9308.22 9374.39 BIC 4506.88 6346.14 5280.18 5340.81 7488.28 11993.62 9440.48 9524.70 chi2 23.45 82.58 38.21 48.14 49.86 71.08 60.52 62.17 Note: The table reports fixed effects in logit (or log odds) with 95% CIs in brackets; *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. All models include random intercepts and slopes for predictors with significant between-country variance (not reported in the table).

31 (iv) Given the potentially sensitive nature of questions related to violence, we also controlled for whether interviewees’ answers were deemed by interviewers to have been influenced by third parties and whether interviewees appeared suspicious or dishonest: “Did the respondent check with others for information to answer any question?” (0 = “No”, 1 = “Yes”); “Do you think anyone influenced the respondent’s answers during the interview?” (0 = “No”, 1 = “Yes”);

“What was the respondent’s attitude towards you during the interview? E. Was he or she at ease, in between, suspicious?” (1 = “At ease”, 2 = “In between”, 3 = “Suspicious”); “What was the respondent’s attitude towards you during the interview? F. Was he or she honest, in between, misleading?” (1 = “Honest”, 2 = “In between”, 3 = “Misleading”). We also controlled for whether interviewees correctly understood the interviewers’ questions with the following:

“What proportion of the questions do you feel the respondent had difficulty answering?” (0 =

“None”, 1 = “Few”, 2 = “Some”, 3 = “Most”, 4 = “All”). Table S18 below shows that the results remain substantively the same.

32 Table S18. Analogous to Table S8 but controlling for dishonesty and comprehension Self-reports Intentions Self-reports Intentions Self-reports Intentions Self-reports Intentions Self-reports Intentions (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) LEVEL 1 Inequality -0.13 0.06 -0.12 0.05 -0.11 0.07 -0.11 0.07 -0.12 0.07 [-0.48,0.22] [-0.12,0.23] [-0.47,0.23] [-0.12,0.22] [-0.45,0.23] [-0.10,0.24] [-0.45,0.22] [-0.10,0.24] [-0.46,0.22] [-0.11,0.24] Retrospective loss -0.31 0.05 -0.32 0.04 -0.33 0.05 -0.33 0.04 -0.33 0.05 [-0.65,0.03] [-0.18,0.27] [-0.67,0.02] [-0.18,0.26] [-0.68,0.02] [-0.18,0.27] [-0.68,0.02] [-0.18,0.26] [-0.67,0.02] [-0.17,0.27] Prospective loss 0.78**** 0.48*** 0.78**** 0.49*** 0.78**** 0.49*** 0.79**** 0.49*** 0.78**** 0.48*** [0.44,1.13] [0.21,0.75] [0.43,1.14] [0.21,0.76] [0.43,1.13] [0.22,0.76] [0.43,1.14] [0.22,0.75] [0.43,1.13] [0.21,0.75] Gender -0.25**** -0.28**** -0.25**** -0.28**** -0.25**** -0.28**** -0.24**** -0.28**** -0.26**** -0.27**** [-0.36,-0.14] [-0.41,-0.16] [-0.36,-0.14] [-0.41,-0.16] [-0.36,-0.13] [-0.40,-0.16] [-0.35,-0.13] [-0.40,-0.16] [-0.37,-0.14] [-0.40,-0.15] Age -0.37 -1.24**** -0.35 -1.23**** -0.36 -1.22**** -0.37 -1.22**** -0.38 -1.23**** [-0.92,0.19] [-1.59,-0.89] [-0.91,0.21] [-1.58,-0.89] [-0.92,0.19] [-1.56,-0.88] [-0.93,0.20] [-1.56,-0.88] [-0.94,0.17] [-1.58,-0.88] Checked -0.21 0.21 [-0.90,0.48] [-0.11,0.54] Influenced -0.30 0.35* [-1.10,0.49] [0.05,0.64] Suspicious 0.20 0.17 [-0.14,0.54] [-0.09,0.43] Misleading 0.00 0.27 [-0.40,0.40] [-0.04,0.59] Comprehension 0.40 -0.29 [-0.16,0.96] [-0.64,0.07] LEVEL 2 Democracy 0.12 -0.31 0.11 -0.29 0.20 -0.26 0.17 -0.21 0.14 -0.27 [-0.90,1.15] [-1.12,0.50] [-0.92,1.14] [-1.10,0.51] [-0.85,1.24] [-1.12,0.59] [-0.86,1.21] [-1.06,0.64] [-0.87,1.15] [-1.06,0.52] Instability -0.81 -0.52 -0.82 -0.52 -0.91 -0.51 -0.86 -0.44 -0.80 -0.51 [-2.32,0.70] [-1.63,0.59] [-2.34,0.70] [-1.63,0.59] [-2.45,0.62] [-1.68,0.67] [-2.38,0.66] [-1.61,0.74] [-2.27,0.68] [-1.59,0.56] Violent crime 1.87*** 1.45*** 1.87*** 1.44*** 1.85*** 1.44*** 1.85*** 1.48*** 1.87*** 1.44*** [0.90,2.85] [0.66,2.24] [0.89,2.85] [0.65,2.23] [0.86,2.84] [0.60,2.29] [0.87,2.82] [0.64,2.31] [0.92,2.82] [0.67,2.21] SCAD events -0.33 -0.87 -0.32 -0.88 -0.34 -0.85 -0.32 -0.89 -0.39 -0.86 [-1.61,0.95] [-1.84,0.10] [-1.61,0.97] [-1.84,0.09] [-1.65,0.96] [-1.87,0.17] [-1.62,0.97] [-1.91,0.12] [-1.66,0.88] [-1.80,0.09] Economic status -1.70** -1.03* -1.71** -1.04* -1.73** -1.09* -1.66** -1.10* -1.72** -1.09* [-2.82,-0.58] [-1.89,-0.16] [-2.84,-0.58] [-1.91,-0.18] [-2.87,-0.59] [-2.01,-0.17] [-2.79,-0.52] [-2.01,-0.19] [-2.82,-0.61] [-1.94,-0.24] Economic growth 0.23 -0.80 0.22 -0.83* 0.18 -0.91* 0.35 -0.87* 0.30 -0.89* [-0.78,1.25] [-1.62,0.01] [-0.80,1.25] [-1.64,-0.01] [-0.86,1.22] [-1.78,-0.05] [-0.68,1.37] [-1.73,-0.01] [-0.69,1.29] [-1.68,-0.09] Peace years -0.11 -0.02 -0.11 -0.00 -0.22 0.03 -0.23 0.02 -0.19 0.03 [-0.88,0.66] [-0.62,0.58] [-0.88,0.67] [-0.61,0.60] [-1.01,0.56] [-0.61,0.67] [-1.01,0.54] [-0.62,0.65] [-0.94,0.56] [-0.56,0.62] Population size 0.82 0.36 0.84 0.38 0.78 0.45 0.75 0.43 0.78 0.37 [-0.35,1.98] [-0.60,1.33] [-0.33,2.01] [-0.58,1.34] [-0.41,1.97] [-0.58,1.47] [-0.42,1.92] [-0.58,1.45] [-0.35,1.91] [-0.56,1.31] Constant -3.89**** -2.26**** -3.88**** -2.26**** -3.85**** -2.31**** -3.87**** -2.36**** -3.89**** -2.24**** [-4.83,-2.95] [-2.96,-1.55] [-4.83,-2.94] [-2.96,-1.56] [-4.81,-2.90] [-3.06,-1.57] [-4.82,-2.93] [-3.10,-1.62] [-4.82,-2.97] [-2.93,-1.56] Observations 43173 41872 43146 41845 43174 41873 43160 41859 43159 41858 AIC 10955.85 20158.96 10959.35 20162.65 10998.19 20145.22 11004.84 20135.02 10970.45 20167.75 BIC 11137.98 20340.45 11141.47 20344.13 11180.32 20326.71 11186.96 20316.50 11152.57 20349.23 chi2 63.72 101.78 62.41 105.49 62.43 101.23 61.43 103.99 66.16 103.81 Note: The table reports fixed effects in logit (or log odds) with 95% CIs in brackets; *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. All models include random intercepts and slopes for predictors with significant between-country variance (not reported in the table). 33 (v) Our main outcome measure was dichotomized. We also analyzed the original ordinal scale

(coded 0–4) and an alternative ordinal scale with the category “No, but would do if had the chance” coded 0. We analyzed these categories in hierarchical models with the cumulative logit link function and the identity link function, as well as fixed-effects OLS. As shown in Table S19, the results remain substantively the same.

34 Table S19. Analogous to Table S8 but using alternative outcome measures and estimators Political violence, original scale [0, 4] Political violence, adjusted original scale [0, 3] Random effects: Random effects: Fixed effects: Random effects: Random effects: Fixed effects: linear ordered logit Linear linear ordered logit Linear (1) (2) (3) (4) (5) (6) LEVEL 1

Inequality -0.00 -0.01 -0.01 -0.01 -0.17 -0.01 [-0.04,0.03] [-0.16,0.13] [-0.03,0.01] [-0.03,0.01] [-0.42,0.08] [-0.03,0.00] Retrospective loss -0.02 -0.04 -0.01 -0.02 -0.29 -0.01 [-0.05,0.01] [-0.24,0.16] [-0.03,0.01] [-0.03,0.00] [-0.61,0.03] [-0.02,0.00] Prospective loss 0.11**** 0.62**** 0.11**** 0.05*** 0.79**** 0.05**** [0.06,0.15] [0.41,0.83] [0.09,0.13] [0.02,0.08] [0.43,1.14] [0.04,0.06] Gender -0.03**** -0.27**** -0.04**** -0.01**** -0.24**** -0.01**** [-0.04,-0.02] [-0.36,-0.18] [-0.05,-0.03] [-0.02,-0.01] [-0.36,-0.13] [-0.02,-0.01] Age -0.06* -0.96**** -0.07**** 0.00 -0.38 -0.00 [-0.11,-0.01] [-1.29,-0.62] [-0.10,-0.04] [-0.03,0.04] [-0.94,0.19] [-0.02,0.01] LEVEL 2

Democracy 0.01 -0.20 0.01 0.28 [-0.09,0.11] [-0.89,0.48] [-0.05,0.07] [-0.61,1.18] Instability -0.09 -0.75 -0.03 -1.01 [-0.23,0.05] [-1.69,0.20] [-0.11,0.06] [-2.31,0.29] Violent crime 0.21**** 1.57**** 0.09** 1.88**** [0.11,0.30] [0.91,2.23] [0.03,0.14] [1.03,2.73] SCAD events -0.03 -0.70 0.00 -0.29 [-0.14,0.08] [-1.53,0.12] [-0.06,0.07] [-1.42,0.84] Economic status -0.12* -1.17** -0.05 -1.76*** [-0.22,-0.01] [-1.91,-0.44] [-0.11,0.01] [-2.75,-0.78] Economic growth -0.01 -0.58 0.01 0.05 [-0.11,0.09] [-1.27,0.10] [-0.04,0.07] [-0.83,0.93] Peace years -0.02 -0.19 -0.00 -0.22 [-0.10,0.06] [-0.70,0.33] [-0.05,0.04] [-0.90,0.46] Population size 0.06 0.45 0.04 0.67 [-0.06,0.18] [-0.36,1.25] [-0.03,0.10] [-0.35,1.69] Constant 0.13** 0.16**** 0.03 0.05**** [0.05,0.22] [0.14,0.17] [-0.02,0.08] [0.04,0.06] Observations 43200 43200 44205 43200 43200 44205 AIC 66284.96 34173.76 68620.88 22211.72 13672.31 23997.48 BIC 66458.43 34364.58 68673.06 22385.19 13845.78 24049.66 chi2 96.76 130.32 46.13 68.08 Note: The table reports fixed effects in logit (or log odds) with 95% CIs in brackets; *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Random-effects models include random intercepts and slopes for predictors with significant between-country variance (not reported in the table).

35 (vi) Finally, we replicated our main analyses (as reported in Table 1) with Afrobarometer

Round 2 data, spanning 16 countries (N = 24,301). Given the smaller N of level-2 units, we used standard fixed-effects logit models instead of hierarchical models; hence, these models do not include level-2 variables. Table S20 shows that the results remain substantively the same.

Table S20. Analogous to Table 1 but with Afrobarometer Round 2 data Self-reports Intentions (1) (2) Inequality -0.23 0.15 [-0.53,0.08] [-0.07,0.36] Retrospective loss -0.10 0.13 [-0.40,0.20] [-0.08,0.34] Prospective loss 0.32* 0.25* [0.04,0.61] [0.05,0.46] Gender -0.52**** -0.19*** [-0.66,-0.37] [-0.29,-0.09] Age -0.93**** -0.96**** [-1.39,-0.48] [-1.29,-0.63] Observations 19143 18289 AIC 6700.91 11239.39 BIC 6740.21 11278.46 chi2 69.50 57.57 Note: The table reports logit (or log odds) with 95% CIs estimated using standard fixed-effects models (i.e., conditional maximum likelihood with country-level fixed effects); *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

36 Section S6. Assessing cross-level interactions

To assess generalization beyond African countries, we tested whether the individual-level associations between PROSPECTIVE LOSS and political violence measures were moderated by any of the country-level characteristics analyzed above. As shown in Figure S1, the associations between PROSPECTIVE LOSS and political violence measures considerably varied across the 34 countries. We thus attempted to identify country-level variables that accounted for this variation.

As shown in Table S21 below, none of the country-level variables significantly moderated the association between PROSPECTIVE LOSS and PVPARTICIPATION; although, the coefficient associated with PROSPECTIVE LOSS*economic status was significant at the 10% level (p = 0.064). As shown in Table S22, only democracy significantly moderated the association between PROSPEC-

TIVE LOSS and PVINTENTIONS; although, the coefficient associated with the PROSPECTIVE

LOSS*population size was significant at the 10% level (p = 0.059). These results thus suggest that the association between prospective loss and political violence may not generalize to demo- cratic countries, but should generalize to countries with varying levels of instability, violent crime, incidence of social conflict events, economic status, economic growth, conflict history, and population size.

Figure S1. The figure shows logit (with 95% CIs) of self-reported participation in political violence, as a function of three measures of deprivation. The vertical red lines indicate zero. Dashed vertical lines indicate 95% CIs of a logit estimated across all countries (Table 2, Model 4)

38 Table S21. Country-level variables as moderators of the association between PRO- SPECTIVE LOSS and PVPARTICIPATION (1) (2) (3) (4) (5) (6) (7) (8) LEVEL 1

Prospective loss 0.78* 0.70*** 0.84*** 0.74*** 0.91**** 0.55 1.02*** 0.67** [0.01,1.56] [0.29,1.10] [0.35,1.33] [0.33,1.15] [0.51,1.32] [-0.02,1.11] [0.50,1.55] [0.23,1.10] Gender -0.25**** -0.25**** -0.25**** -0.25**** -0.25**** -0.25**** -0.25**** -0.25**** [-0.37,-0.14] [-0.37,-0.14] [-0.36,-0.14] [-0.37,-0.14] [-0.36,-0.14] [-0.37,-0.14] [-0.36,-0.14] [-0.37,-0.14] Age -0.39 -0.39 -0.39 -0.39 -0.38 -0.39 -0.39 -0.39 [-0.94,0.16] [-0.94,0.16] [-0.94,0.16] [-0.94,0.16] [-0.93,0.17] [-0.94,0.16] [-0.94,0.17] [-0.94,0.16] LEVEL 2

Democracy 0.29 0.24 0.24 0.26 0.26 0.23 0.25 0.24 [-0.58,1.15] [-0.58,1.06] [-0.57,1.06] [-0.56,1.08] [-0.56,1.08] [-0.58,1.05] [-0.57,1.06] [-0.58,1.06] Instability -0.93 -0.88 -0.93 -0.93 -0.93 -0.93 -0.94 -0.93 [-2.12,0.27] [-2.15,0.38] [-2.12,0.25] [-2.12,0.26] [-2.12,0.27] [-2.12,0.26] [-2.13,0.25] [-2.12,0.26] Violent crime 1.74**** 1.75**** 1.85**** 1.74**** 1.66**** 1.74**** 1.74**** 1.75**** [0.96,2.52] [0.97,2.53] [1.04,2.65] [0.96,2.52] [0.88,2.45] [0.96,2.52] [0.96,2.51] [0.97,2.52] SCAD events -0.36 -0.36 -0.37 -0.21 -0.34 -0.36 -0.36 -0.36 [-1.39,0.67] [-1.39,0.67] [-1.39,0.65] [-1.37,0.96] [-1.36,0.68] [-1.39,0.66] [-1.38,0.66] [-1.39,0.66] Economic status -1.70*** -1.72*** -1.74*** -1.72*** -1.39** -1.71*** -1.69*** -1.71*** [-2.61,-0.80] [-2.62,-0.81] [-2.64,-0.84] [-2.62,-0.81] [-2.35,-0.42] [-2.61,-0.81] [-2.58,-0.79] [-2.61,-0.81] Economic growth 0.15 0.15 0.16 0.16 0.18 0.08 0.17 0.15 [-0.65,0.96] [-0.65,0.95] [-0.64,0.96] [-0.65,0.96] [-0.63,0.99] [-0.75,0.92] [-0.63,0.97] [-0.65,0.95] Peace years -0.07 -0.07 -0.07 -0.07 -0.08 -0.07 0.07 -0.07 [-0.68,0.54] [-0.68,0.54] [-0.68,0.53] [-0.68,0.54] [-0.69,0.54] [-0.68,0.53] [-0.56,0.70] [-0.68,0.54] Population size 0.76 0.77 0.76 0.77 0.73 0.76 0.78 0.76 [-0.17,1.70] [-0.17,1.70] [-0.17,1.69] [-0.16,1.71] [-0.21,1.67] [-0.17,1.69] [-0.15,1.71] [-0.19,1.72] CROSS-LEVEL INTERACTIONS

Prospective loss#democracy -0.22 [-1.57,1.13] Prospective loss#instability -0.25 [-2.47,1.97] Prospective loss#violent crime -0.57 [-1.80,0.66] Prospective loss#SCAD events -0.55 [-2.45,1.34] Prospective loss#econ. status -1.30 [-2.67,0.08] Prospective loss#econ. growth 0.40 [-1.01,1.81] Prospective loss#peace years -0.83 [-1.84,0.18] Prospective loss#population 0.03 size [-1.50,1.55] Constant -4.05**** -4.03**** -4.05**** -4.05**** -4.08**** -3.99**** -4.09**** -4.02**** [-4.81,-3.30] [-4.76,-3.29] [-4.79,-3.32] [-4.79,-3.31] [-4.82,-3.34] [-4.74,-3.25] [-4.83,-3.36] [-4.76,-3.29] Observations 44555 44555 44555 44555 44555 44555 44555 44555 AIC 11293.54 11293.59 11292.87 11293.32 11290.29 11293.34 11291.04 11293.64 BIC 11432.81 11432.86 11432.14 11432.59 11429.56 11432.61 11430.31 11432.91 chi2 63.71 63.94 64.93 64.68 70.71 64.50 68.90 63.67 Note: The table reports fixed effects in logit (or log odds) with 95% CIs in brackets; *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. All models include random intercepts and slopes for predictors with significant between-country variance (not reported in the table).

39 Table S22. Country-level variables as moderators of the association between PRO- SPECTIVE LOSS and PVINTENTIONS (1) (2) (3) (4) (5) (6) (7) (8) LEVEL 1

Prospective loss 1.09*** 0.44** 0.73*** 0.44** 0.59** 0.55 0.76*** 0.30 [0.49,1.68] [0.12,0.75] [0.34,1.12] [0.11,0.76] [0.23,0.94] [-0.02,1.11] [0.31,1.21] [-0.04,0.63] Gender -0.28**** -0.28**** -0.28**** -0.28**** -0.28**** -0.25**** -0.28**** -0.28**** [-0.40,-0.17] [-0.40,-0.17] [-0.40,-0.17] [-0.40,-0.17] [-0.40,-0.17] [-0.37,-0.14] [-0.40,-0.17] [-0.40,-0.17] Age -1.22**** -1.22**** -1.22**** -1.22**** -1.22**** -0.39 -1.22**** -1.22**** [-1.56,-0.89] [-1.56,-0.88] [-1.55,-0.88] [-1.56,-0.89] [-1.56,-0.88] [-0.94,0.16] [-1.55,-0.88] [-1.55,-0.88] LEVEL 2

Democracy 0.00 -0.16 -0.18 -0.18 -0.15 0.23 -0.16 -0.15 [-0.76,0.76] [-0.92,0.59] [-0.93,0.58] [-0.94,0.57] [-0.90,0.60] [-0.58,1.05] [-0.90,0.59] [-0.90,0.59] Instability -0.51 -0.58 -0.54 -0.53 -0.52 -0.93 -0.51 -0.53 [-1.53,0.51] [-1.63,0.46] [-1.58,0.50] [-1.57,0.50] [-1.55,0.50] [-2.12,0.26] [-1.54,0.51] [-1.55,0.48] Violent crime 1.35*** 1.40*** 1.50**** 1.41*** 1.36*** 1.74**** 1.39*** 1.39*** [0.62,2.07] [0.66,2.13] [0.76,2.25] [0.67,2.14] [0.63,2.10] [0.96,2.52] [0.66,2.11] [0.67,2.11] SCAD events -0.83 -0.89* -0.89* -0.98* -0.87 -0.36 -0.88* -0.87* [-1.70,0.04] [-1.78,-0.01] [-1.77,-0.00] [-1.93,-0.03] [-1.75,0.01] [-1.39,0.66] [-1.75,-0.00] [-1.74,-0.00] Economic status -1.02* -1.06** -1.10** -1.07** -0.98* -1.71*** -1.05* -1.05** [-1.82,-0.23] [-1.87,-0.26] [-1.91,-0.30] [-1.87,-0.26] [-1.81,-0.16] [-2.61,-0.81] [-1.85,-0.25] [-1.85,-0.26] Economic growth -0.95* -0.97* -0.96* -0.97* -0.96* 0.08 -0.96* -0.97* [-1.69,-0.20] [-1.72,-0.21] [-1.72,-0.21] [-1.73,-0.22] [-1.71,-0.21] [-0.75,0.92] [-1.71,-0.21] [-1.71,-0.22] Peace years 0.06 0.06 0.06 0.06 0.06 -0.07 0.14 0.05 [-0.49,0.61] [-0.50,0.62] [-0.50,0.62] [-0.50,0.62] [-0.49,0.62] [-0.68,0.53] [-0.43,0.70] [-0.49,0.60] Population size 0.09 0.10 0.11 0.10 0.09 0.76 0.12 0.02 [-0.80,0.97] [-0.79,0.99] [-0.79,1.01] [-0.79,1.00] [-0.80,0.98] [-0.17,1.69] [-0.77,1.01] [-0.87,0.90] CROSS-LEVEL INTERACTIONS

Prospective loss#democracy -1.14* [-2.18,-0.11] Prospective loss#instability 0.53 [-0.99,2.05] Prospective loss#violent crime -0.87 [-1.92,0.19] Prospective loss#SCAD events 0.44 [-0.97,1.85] Prospective loss#econ. status -0.47 [-1.59,0.66] Prospective loss#econ. growth 0.40 [-1.01,1.81] Prospective loss#peace years -0.62 [-1.45,0.22] Prospective loss#population 1.22 size [-0.05,2.49] Constant -2.29**** -2.19**** -2.21**** -2.17**** -2.21**** -3.99**** -2.24**** -2.18**** [-2.93,-1.64] [-2.84,-1.53] [-2.87,-1.56] [-2.83,-1.52] [-2.86,-1.56] [-4.74,-3.25] [-2.89,-1.59] [-2.82,-1.53] Observations 43225 43225 43225 43225 43225 44555 43225 43225 AIC 20870.09 20874.21 20872.18 20874.31 20874.00 11293.34 20872.63 20871.21 BIC 21017.55 21021.67 21019.64 21021.77 21021.47 11432.61 21020.09 21018.68 chi2 114.12 108.46 111.17 108.02 109.02 64.50 111.44 113.56 Note: The table reports fixed effects in logit (or log odds) with 95% CIs in brackets; *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. All models include random intercepts and slopes for predictors with significant between-country variance (not reported in the table).

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