# Police homicides and riots in France

Simon Varaine, Raul Magni-Berton, Sebastian Roché, Paul Le Derff

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Simon Varaine, Raul Magni-Berton, Sebastian Roché, Paul Le Derff. Police homicides and riots in France. Public Choice, 2025, 10.1007/s11127-025-01328-8. hal-05322723

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Police homicides and riots in France*

Simon Varaine¹,², Raul Magni-Berton³, Sebastian Roché⁴ & Paul Le Derff⁵

¹University of Montpellier, INRAE, CNRS, SupAgro, CEE-M, Montpellier 34960, France

²Univ. Grenoble Alpes, IEPG, CNRS, INRAE, Grenoble INP, GAEL, Grenoble 38000, France

³Anthropo-Lab-ETHICS, Université Catholique de Lille, 59000, Lille, France, 14, bd Vauban

⁴CNRS, Sciences Po Grenoble, Univ. Grenoble Alpes, Pacte, 38000, Grenoble, France

⁵Sciences Po Bordeaux, Émile Durkheim Center, 33607, Pessac, France

October 20, 2025

Abstract

Street riots often follow police killings of citizens. Yet, the majority of police killings do not lead to riots. Why do some police homicides lead to riots while others do not? This paper analyzes the intensity of riots in France using monthly data on arsons targeting private property at the department (sub-regional) level from January 1996 to August 2022 (N = 32,080), in relation to an original dataset on the occurrence and characteristics of police homicides. The results suggest that police homicides have a small overall effect on riots: fewer than 0.3% of arsons in a given department can be attributed to police killings in that department. While most police homicides do not have the potential to trigger riots, certain characteristics significantly increase the likelihood that a riot will occur: the victim has a non-European migratory background, is a French citizen, and the perpetrators belong to specific police forces. The impact also increases when there are multiple victims. We provide suggestive evidence that identification with the victim plays a key role among potential rioters, as does the social integration of the victim.

*This paper benefited from insightful discussions at the Economics Politics Workshop 2023. We are grateful to Pierre-Guillaume Méon and Emilie Van Haute for their comments. We also thank the French National Research Agency for financial support (Grant ANR-22-CE26-0014-01).
# 1 Introduction

Street riots often follow police killings of citizens. On June 27, 2023, Nahel Merzouk, a 17-year-old teenager, was shot at point-blank range by a police officer during a traffic stop in Nanterre, France. In the nights that followed, many French cities experienced a wave of destruction and arson targeting cars, public buildings, street furniture, and shops, resulting in the largest riots in recent French history. In contrast, many police killings do not lead to riots. For example, on September 6, a teenager died near Paris during an urban rodeo after refusing to obey a police officer. No increase in destruction or violence was reported in the area afterwards.

This paper addresses two questions. First, what is the causal effect of police homicides on rioting? Second, why are some police homicides conducive to rioting while others are not?

Both questions arise from the observation that several riots have been sparked by a police killing. The 2023 riots in France were explicitly triggered by the murder of Nahel Merzouk, just as, three years earlier in the United States, rioting and looting followed the murder of George Floyd by a police officer. These individual cases provide compelling evidence that, in some circumstances, police killings can cause riots. While we might be tempted to generalize from these examples, the current evidence is not strong enough to conclude that “it is not the absence of police, but the heavy and unrestrained presence of police that ignites disorder” (Klein 2012). The literature on this question offers suggestive arguments in support of the idea that repression may increase the likelihood of riots (Ives and Lewis 2020), but the specific effect of police killings on rioting has seldom been analyzed.

This information has the potential to significantly improve public debates. Governments typically tend to dismiss the role of the police in triggering destructive events. The 1967 Kerner Commission, tasked with investigating the causes of the riots at the time, ignored the findings of the team of academics it had appointed, which concluded that police brutality was a cause of the 1960s riots (Gillon 2018). Following the riots that erupted after the acquittal of the police officers accused of beating Rodney King, President George H. W. Bush stated, “It’s not about civil rights,” and added, “I will use whatever force is necessary to restore order” (Camp 2016, p.98). In the case
of the 2011 London riots, although resentment toward the police has been emphasized by scholars (Lewis et al. 2011; Drury et al. 2020), the government framed it as a gang problem: Prime Minister Cameron described the disorder as “criminality – plain and simple” (Benyon and Scarman 1991), and “the prime minister was clear in his view that an inquiry was unnecessary” (Newburn et al. 2018). In the aftermath of the 2023 national riots in France, President Emmanuel Macron summed up his response in three words: “Order, order, order.” The Prime Minister’s plan, presented to the press on November 26, 2023, failed to mention the role of the police in the analysis of the causes of the riots, and thus omitted any discussion of police tactics or the use of weapons. In this respect, the Scarman Inquiry (Scarman 1981), carried out after the 1981 Brixton riots, stands as a rare counter-example. No such independent commission can be found in Greece or France, the two European countries that have experienced the most large-scale riots. Despite the apparent reluctance of public authorities to acknowledge it, the role of police brutality – and especially lethal police violence – in triggering riots is an empirical question that deserves proper investigation.

This article focuses on the relationship between police homicides and riots. It does not attempt to identify the root causes of riots, but rather the specific events that lead potential rioters – that is, individuals who experience the root causes of rioting – to take action. Over the past twenty years, France has experienced two major waves of nationwide riots (in 2005 and 2023), both triggered by police killings. The present paper examines whether the killing of citizens constitutes an institutional cause of riots beyond these emblematic cases. Coupling comprehensive data on police homicides in France between 1996 and 2022 (N = 478) with monthly departmental (sub-regional) data on rioting intensity (N = 32,080), we show that while police homicides have, on average, a very small effect on riots, the identity of the victims and the circumstances of their deaths are important in predicting riot outbreaks. Corroborated by two surveys of French adolescents (total N = 22,771), our results strongly suggest that identification with the victim among potential rioters is a key reason why some police homicides are particularly dangerous for public order.

Our paper contributes to the public choice literature on rebellious collective action (see Courtois et al. 2024, for a recent review). Following Olson (1965) and Tullock (1971), riots can be
seen as collective action problems in which participation is costly, benefits are non-excludable, and free-riding is pervasive. One strand of this literature focuses on incentives that help resolve the “paradox of revolution,” in particular selective material benefits for rebels and the punishment of free-riders. Our study offers an empirical contribution to what Olson termed “moral incentives” (see also Opp 1994): by generating a sense of injustice, police homicides can increase the psychological cost of non-participation. Another strand emphasizes the coordination problem inherent in rebellion (e.g., Kuran 1989; Yin 1998), whereby rebelling alone is especially costly, while the cost decreases – and is eventually outweighed by the benefits – as more people join. While institutional arrangements can sometimes solve the first-mover problem in small groups (see Leeson 2010), this is harder in large groups. In this perspective, police homicides can serve as focal points that signal to potential rioters that others are likely to act (Schelling 1980), helping to solve the coordination problem. Our analysis identifies the characteristics of police homicides that make them more likely to function as focal points.

## 2 Theoretical background

A peculiarity of riot behavior is that it consists mostly – if not exclusively – of collective and simultaneous illegal acts: damage and destruction of property, physical violence, and sometimes lethal attacks. Most of the destruction targets private property, easily accessible on the street. For example, during the 21 nights of rioting in France in November 2005, it is estimated that 10,346 vehicles were burned, along with 233 public buildings (schools, sports centers, prayer rooms, post offices), 74 private buildings, 255 schools, 7 bus depots, and 22 buses or railway infrastructures (Body-Gendrot 2007, p.3). Theoretically, there are two main ways to conceptualize riots.

First, they can be understood as forms of collective action (Olson 1965). In this perspective, riots aim to achieve a public good (e.g., encouraging the state and police to reduce their severity to prevent future unrest). Participation is individually costly, and the individual contribution is negligible, since the benefit materializes only if many others also participate. As a result, large-
scale riots typically occur either when key leaders anticipate private gains (Tullock 1971), or when perceived grievances are so intense that they outweigh the expected costs of participation (Opp 1988). This collective action framework is particularly relevant for riots, which usually lack formal organization or financial support to mitigate free-riding. Historically, this political view of riots was promoted in the aftermath of the Kerner Report (1968), and Skolnick (1969) described riots "fundamentally as acts of political protest."

Second, rioting can be equated with hooliganism (Leeson et al. 2012), where violence is an end in itself rather than a means to an objective. In this perspective, participating in a riot generates utility for the participants. However, rioting is illegal and subject to punishment, with the probability of being punished decreasing as the number of people involved in the riot grows. As a result, rioters face a first-mover problem (Leeson 2010), whereby the first individuals to engage in rioting bear the greatest risk of punishment. Consequently, potential rioters are likely to join only once the riot is already well underway. This apolitical view of riots is empirically supported by the observation that there is often no political agenda or expectation of dialogue with credible interlocutors (Wieviorka and Bataille 1999).

Beyond these theoretical debates, our minimal definition of riots as a concentration in time and space of acts of destruction and violence (Roché 2006, p.34) has an empirical advantage: it allows for the measurement of riot occurrence through crime statistics. This may seem of little relevance for large riots—such as when buildings in central London are set on fire or neighborhoods in Los Angeles are burned to the ground. However, using the level of damage and destruction recorded by the police has the advantage of identifying riots that are less visible at the national level and testing their local correlates. Specifically, there appear to be many more small-scale riots within a neighborhood or community than large-scale riots across a country. Because of their small size, they may have been overlooked by previous research.

In this article, we examine the role of police homicides in the outbreak of riots. The study of the impact of police brutality on riots in the United States has largely been conducted by contemporaries of the civil rights movement (see Grimshaw 1963; Lieberson and Silverman 1965;
Downes 1968; Fogelson 1968; Feld 1970; Conyers 1981). Despite their different methodologies, these studies converged in identifying police brutality as one of the root causes of the riots of the 1960s. Surprisingly, this factor has been neglected in subsequent research (however see Schneider 2014; Hinton 2022), which has focused much more on the state's response to the initial threat of rioting (Wilkinson 2009). Clearly, the relationship between police brutality and riots is difficult to capture because of a strand of endogeneity. The conditions under which riots are likely to occur – poverty, ethnic diversity, inequality, social tension, and crime – are also likely to increase the incidence of police brutality. Police brutality is therefore often seen as part of the process leading to riots rather than as a cause in itself.

Inequality, for example, has been documented as a critical determinant of police brutality (Jacobs and Britt 1979), crime and homicide (Daly et al. 2001), and riots (Houle et al. 2022). While vertical inequality (between individuals) is mostly associated with crime, horizontal inequality (between groups) is a well-documented condition for rioting (DiPasquale and Glaeser 1998). In particular, disadvantaged ethnic groups are more likely to riot when they are permanently excluded from power (Horowitz 2023; Bodea et al. 2017) and segregated from more advantaged groups (Knopf 2017). This ethnic dimension to many riot dynamics was evident during the civil rights movement, but can also be seen in European riots such as those in the United Kingdom (Bagguley and Hussain 2016; Keith 2023) and in France (Schneider 2014; Moutselos 2020).

Poverty is also a potential factor, as rioters are more likely to come from economically disadvantaged neighborhoods, experience deprivation, and perceive the police as disrespectful (Kawalerowicz and Biggs 2015). While the true role of poverty after controlling for inequality is less clear (DiPasquale and Glaeser 1998), other indices associated with poverty – such as the presence of large public housing estates or degraded housing – are statistically related to the number and intensity of riots (Moutselos 2020).

Finally, a third important factor is the lack of associational life and organizations (Kawalerowicz and Biggs 2015; Ives and Lewis 2020). Unlike other forms of collective action, riots arise in unorganized movements and therefore face severe coordination problems. The decision to partic-
ipate in a riot is particularly influenced by the involvement of others. This typically leads to riot
dynamics similar to those of natural disasters and epidemics (Braha 2012; Bonnasse-Gahot et al.
2018).

The causes mentioned so far are root causes: the conditions that increase grievances and tensions enough to motivate people to riot. Another type of analysis focuses on precipitating causes: the events that lead people who experience the root causes of riots (hereafter: potential rioters) to actually riot. The historian of riots Stevenson (2014), in his synthesis of major episodes of rioting, observes that even in adverse contexts, outcomes often depended on certain singular events involving the authorities and the public. Waddington et al. (2021) similarly addressed the role of precipitating events in the “Flashpoints Model of Public Disorder.” However, as Newburn (2021) has suggested, one of the least developed elements of the flashpoint model is the flashpoint itself.

A lethal encounter has the potential to worsen the relationship between the public and the authorities – particularly the police –and may constitute a flashpoint in its own right. The central hypothesis of this article is that police homicides are a key precipitating cause of rioting. Two mechanisms may explain how police homicides lead to riots, depending on whether rioting is understood as a form of collective action or as a first-mover problem. First, police homicides can generate feelings of injustice and mistreatment, prompting groups of individuals to riot out of outrage. Second, police homicides may serve as focal points for potential rioters who anticipate that others will also be shocked and likely to join, thereby resolving the coordination problem (Schelling 1980). In this article, we remain agnostic about the specific mechanism through which police homicides trigger riots.

Empirically, the link between police homicides and rioting *per se* has not been studied using statistical methods, but existing research has examined several cases in which the use of deadly force was seen as a precursor to rioting, either immediately or shortly thereafter. Examples of such a relationship are numerous (see Hinton 2022). In the United States, the Harlem riots of 1964 occurred two weeks after the passage of the landmark Civil Rights Act, following the fatal shooting of a 15-year-old African American by a police officer in front of witnesses. A large protest
in downtown Baltimore turned violent after a 25-year-old African American died at the hands of police while in custody. In 2016, Tony Timpa was killed in Dallas, and marches were attended by thousands of protesters, some of whom confronted the police. After the killing of George Floyd in Minneapolis in 2020, protests took place across the country and internationally.

Schneider (2014) compared rioting in a major U.S. city (New York) and a major French city (Paris), where the 2005 national riots originated, emphasizing the role of the police in both instances. Following the national spread of the 2005 riots (Bonnasse-Gahot et al. 2018), a second wave of nationwide riots erupted in France in July 2023 after a police officer shot the young Nahel at point-blank range (Roché and Rabaté 2023). Similar examples can be found in other European countries. In Athens in 2008, the fatal shooting of a fifteen-year-old boy by a police officer sparked the most intense public order crisis since the country's transition to democracy, spreading throughout Greece and lasting more than a week (Karamichas 2009). In 1985, the police shooting of a mother during an unannounced armed raid in search of a suspected robber triggered the Brixton riot, which began with an attack on the local police station. Less than a week later, in Tottenham (London), another family lost their mother at the hands of the police, leading to the Tottenham riots (Phillips et al. 2013; Waddington 2010). In 2011, two days after the fatal shooting of 25-year-old Mark Duggan by police officers in London, a peaceful protest outside the Tottenham police station turned violent and became the starting point of four days of rioting (Newburn 2015; Jeffery and Tufail 2015; Waddington 2012).

However, not all police killings may be sufficient to trigger riots. The identity of the person killed may matter. The effect may be stronger if the person shares social characteristics with potential rioters. In particular, in the case of ethnic riots, we might expect the civilian killed to belong to the disadvantaged ethnic community. The person may also fit the profile of typical rioters, such as being male or young, because these individuals are more likely to identify with them. Based on interviews with participants in the 2011 London riots, Drury et al. (2020) found that shared identification with the victim was one of the most frequently mentioned justifications for participation, with one interviewee stating: "I would've been there because I see myself as
part of that same community. That could've been anyone instead of Mark Duggan that day, that could've been me, that has been me, that has been my friends" (Drury et al. 2020, p. 655).

Moreover, if multiple people are killed, or if the victim is perceived as innocent, the event may be seen as more tragic and unjust. If the police involved are from the same units typically deployed to suppress potential rioters, identification is also more likely. Among those arrested during the 2011 London riots, a majority expressed anti-police sentiments and reported either personal experience or close experience of unfair and humiliating treatment (Drury et al. 2020). In short, we hypothesize that police killings most likely to precipitate riots are those that both trigger feelings of mistreatment or injustice (hereafter, the *tragedy hypothesis*) and foster identification with the victim among potential rioters (hereafter, the *identity hypothesis*).

# 3 Data and methods

We employ a two-fold empirical strategy. First, we assess the general causal effect of police homicides on riot intensity. Second, we examine the characteristics of police homicides that are conducive to rioting, in order to evaluate whether they align with the characteristics of potential rioters. In the Electronic supplementary material, we present correlational analyses based on survey data to identify the relevant traits that define a potential rioter in the French context. In this section, we first describe our data sources and coding procedures. We then explain the statistical approach used.

## 3.1 Data

### 3.1.1 Criminal data: rioting intensity

We relied on monthly delinquency data at the department level in France.<fnref n="1" /> The dataset, compiled by the Ministry of the Interior, is based on *État 4001*, a tool used by the two French national

<footnote n="1">¹A department (*département* in French) is a French subnational administrative unit (in between the *région* and the *arrondissement*). There are 101 departments: 96 metropolitan and 5 overseas.</footnote>
police forces, the *Gendarmerie* and the *Police Nationale*,<fnref n="2" /> to record various types of offenses. The *État 4001* dataset covers the 96 departments of metropolitan France from January 1996 to August 2022, and the 5 overseas departments from January 2000 to August 2022. The total number of observations is *N* = 32,080.

Our dependent variable measuring rioting intensity is the monthly number of private property arsons by department. This number includes car arsons, which are one of the main types of offenses committed during riots in France (Bonnasse-Gahot et al. 2018, p.10). It also captures other types of arsons – such as those targeting forests, shops, and private buildings – which are not necessarily indicative of riots. Unfortunately, separate data for car arsons are not recorded in the *État 4001* dataset (IHEMI a).<fnref n="3" />

Figure 1 presents the overall variation in the number of private property arsons in metropolitan France (for which complete departmental data are available over the entire study period).<fnref n="4" /> The main peaks correspond to well-known waves of riots. In January 2002, incidents occurred in the *Cité des Musiciens* in Les Mureaux (Yvelines) following the death of 17-year-old Moussa, who was killed by a police officer while attempting to break through a roadblock on the ring road. The most prominent peak corresponds to the November 2005 riots, which followed the deaths of Zyed Benna and Bouna Traoré. In July 2018, riots broke out in the *Breil* district of Nantes after a 22-year-old man, Aboubacar Fofana, was shot by a CRS officer during a police check he had attempted to evade.

While riots following deadly police interventions often dominate headlines, it is important to

<footnote n="2">²The *Police Nationale* and the *Gendarmerie* are the two main police forces in France, and operate in different areas (that vary within departments): as a general rule, the *Police Nationale* operates in cities with populations above 20,000 inhabitants, while the *Gendarmerie* operates in areas with populations under 20,000 inhabitants.</footnote>

<footnote n="3">³Other sources used to more specifically measure urban violence – especially the number of car arsons – in France include victimization and self-reported delinquency surveys, car insurance data, firefighters’ interventions, and *Police-Secours* interventions (i.e., interventions by the *Police* or *Gendarmerie* following a call to the generic emergency number “17”) (IHEMI b). Yet, none of these sources are available on a regular basis at both the monthly and subnational levels, which is necessary to test variations following police homicides. Since 2016, the Ministry of the Interior has started publishing delinquency data at the city level. However, the data only report a general category of intentional destruction and damage, which includes both public and private property arsons, explosive attacks, and other types of destruction or damage. More problematically, these data are only published on a yearly basis, which does not allow us to test monthly variation following police homicides.</footnote>

<footnote n="4">⁴Figure A1 in the Electronic supplementary material presents temporal variations for all of France.</footnote>
<figure><img src="image_2.png" /><figcaption>Average monthly number of private property arsons by department in metropolitan France</figcaption></figure>

<figure><img src="image_3.png" /></figure>
note that they can also arise from a variety of other circumstances. For example, violent clashes
frequently erupt after major football matches. Political events can also spark unrest, as seen in
May 2007, when incidents broke out in several French towns – particularly in the Paris region –
following the election of Nicolas Sarkozy. Similarly, the peak of incidents in November–December
2018 corresponds to the early demonstrations of the Yellow Vest movement, with major destruction
during the third act. France also experiences recurring “ritual” riots tied to specific dates, such as
Bastille Day (July 14) and New Year’s Eve, during which public disturbances and vehicle fires
have become almost customary in certain areas. These patterns help explain the monthly increases
observed in January and July, as seen in Figure 1.

The monthly number of private property arsons varies greatly across departments (Median = 13, Mean = 25.6, Std. dev. = 37.0): the *Nord* recorded the highest average (201.4 arsons per month), while *Lozère* recorded the lowest (1.0 per month on average). The highest monthly counts were observed in November 2005 in *Nord* (1,282 private property arsons), *Seine-Saint-Denis* (1,111), and *Rhône* (810). Overall, 3.6% of department-months recorded zero private property arsons. However, all departments experienced at least some private property arsons over the study period, with *Lozère* reporting the fewest overall (a total of 225).

As an alternative measure, we used the number of private property arsons per 100,000 inhabitants at the departmental level (Median = 2.7, Mean = 3.3, Std. dev. = 2.7). *Loire* reached the highest average relative level (7.9 arsons per month per 100,000 inhabitants), closely followed by *Nord* (7.8), while *Aveyron* recorded the lowest. Once again, the highest relative levels were observed in November 2005, particularly in *Seine-Saint-Denis* (75.3 arsons per 100,000 inhabitants), *Val-d'Oise* (54.0), and *Nord* (50.0).

**3.1.2 The police homicides dataset**

We relied on an original dataset covering police homicides in France for the entire study period.
Police homicides are defined as any death occurring following contact with a police officer on
duty, thereby excluding cases where police officers used their firearms while off duty. Our dataset
is based on an extensive search of publicly available sources. Until 2018, the primary sources used
were French media articles collected from *Europresse* and *Aday*, two databases containing a very
large number of digitized press publications. The searches were conducted through a trial-and-
error process, using various keyword combinations throughout the study period. Searches were
also alternated between terms like *Police* and *Gendarmerie*, as well as verbs frequently associated
with such events (e.g., “*abattre*”, “*tirer*”). Keyword searches proved more challenging in cases
where police action was only indirectly involved (e.g., fatal escapes, deaths in police custody), as
the role of the police was often only implied or absent from the text, making such events easier to
miss.

Recently, the *Police Nationale* and the *Gendarmerie* began recording fatal police interventions.
Annual figures have been published for both police forces since 2018. Since 2020 for the *Police
Nationale*, and 2021 for the *Gendarmerie*, the police forces have released detailed reports listing
each fatal police intervention, including the date, location, and a description of the incident. When
the *IGPN* (the internal oversight service of the *Police Nationale*) began listing civilian deaths and
injuries, the initiative was not widely known among police services. As a result, there was limited
feedback from the various police units that may have been involved in civilian deaths. The first
reports in 2018–2019 were therefore incomplete. The *IGPN* had to conduct extensive internal
communication with police services to improve reporting.<fnref n="5" />

These official reports create a statistical break in the data. They make it possible to identify
police homicides that may previously have gone undetected.<fnref n="6" /> It is reasonable to assume that police
homicides receive more attention when they are followed by riots. Therefore, this lack of compre-
hensiveness in the data should, if anything, lead to an overestimation of the causal effect of police
homicides on riots.

For the 1996–2022 period, we identified 478 police homicides. Figure 2 presents their temporal
and geographical distribution. The median yearly number of identified police homicides was 12

<footnote n="5">⁵Interview with the *IGPN* executive team conducted by one of the authors in February 2022.</footnote>

<footnote n="6">⁶Unfortunately, these “additional” police homicides cannot be individually identified, as media sources may also have mentioned certain cases based on the newly available reports.</footnote>
<figure><img src="image_4.png" /><figcaption>Figure 2: Distribution of identified police homicides in France (1996-2022)</figcaption></figure>

Geographical distribution of police homicides (colored by year of occurrence) in metropolitan France

<figure><img src="image_5.png" /></figure>
before 2018, and 38 after 2018. The increase is statistically significant according to a Wilcoxon
rank-sum test (W = 2, p = 0.001, N = 27). This likely reflects the improved comprehensiveness
of the data due to official police reports. Indeed, there is a first step in 2020 and a second in 2021-
2022, corresponding to the publication of detailed lists of fatal police interventions by police forces.
However, the increase may also partly reflect an actual upward trend in police homicides. There
is a significant positive time trend visible in the data before 2018, as confirmed by a Spearman
rank correlation between the time trend and the annual number of police homicides before 2018
(ρ = 0.75, p < 0.001, N = 22). In addition, evidence indicates that a change in the legal regulation
of firearm use by the *Police Nationale* in cases involving fleeing vehicles, enacted in 2017, led to an
increase in this specific type of police homicide (Roché et al. 2025), which may have contributed
to the overall increase at the end of the study period.

As shown in Figure 2, police homicides are not evenly distributed across French departments.
The average number of police homicides per department over the period is 4.20 (Std. dev. = 5.70,
Median = 2). Seventeen departments (16.83%) did not record any police homicides during the
study period. The highest number of police homicides (33) occurred in the department of Paris.
In general, police homicides in a given department are spread out over time: for departments
with multiple police homicides, the average number of months between two incidents is 42.48
(Median = 27).

The dataset includes several characteristics of police homicides likely to affect their conducive-
ness to riots. In particular, the literature suggests that rioters are predominantly young, male, and
from a disadvantaged ethnic group or social class (Schneider 2014; Kawalerowicz and Biggs 2015;
Moutselos 2020). According to the identity hypothesis, the death of victims with those characteris-
tics should result in larger riots. The distribution of characteristics of police homicides is presented
in Figure 3, distinguishing between police homicides that occurred before and since 2018 (when
official statistics began to be published).<fnref n="7" /> Figure 3 also provides a comparative baseline for the
French population.

<footnote n="7">⁷Summary statistics are provided in Table A1 and cross-correlations in Figure A2 in the Electronic supplementary material.</footnote>
<figure><img src="image_6.png" /><figcaption>Figure 3: Characteristics of police homicides before and after 2018.</figcaption></figure>

*Note:* For each variable, the figure shows the distribution of event-level characteristics. For categorical variables, it reports relative proportions with 95% confidence intervals. For continuous variables, it displays violin plots of the distribution, along with means and associated 95% confidence intervals. When available, French population benchmarks are indicated in dashed red for comparison.
We compiled the following indicators about the victim profile:

*   *Age* in years (Valid *N* = 427, Median = 30, Mean = 32.7, Std. dev. = 13.0). The mean age of victims is significantly lower than that of the French population – 40 years according to the French National Institute of Statistics and Economic Studies (INSEE).

*   *Male* gender (Valid *N* = 474, 97.0% male). The share of male victims is much higher than the share of males in the French population – 48.4% according to the INSEE.

*   *French citizenship* (Valid *N* = 474, 29.3% non-French). This indicator should be treated with caution. Citizenship is usually mentioned in press articles only when the person is not French. However, since French citizenship is rarely stated explicitly, we coded individuals as French based on a number of clues (French-sounding surname and first name, no mention of migratory origin suggesting otherwise). The proportion of non-French nationals among the victims is more than five times higher than that in the French population – 6.1% according to the French Institute for Demographic Studies (INED).

*   *Assessed non-European migratory origin.* Personal names are a widely used proxy to assess migratory origin when direct data is unavailable (Mateos 2007; Fiscella and Fremont 2006). We relied on Namsor, an online classification tool that infers the continent of origin from first and last names.<fnref n="8" /> Comparing Namsor's output with known individual origins, Sebo (2023) found an error rate below 20%. Due to data limitations, we distinguish only between European and non-European assessed origin (Valid *N* = 307, 57.7% non-European). Data from Mazières and Roth (2018), using a similar method on 2008 middle school students, suggest that 19.6% of French youth are of non-European migratory origin. Hence, the share among victims is much higher than in the general population.

*   *Assessed academic results.* We also used victims' first names as a proxy for social class. In France, first name choice is strongly correlated with parental education and social background (Coulmont 2022). We used the relative success rates at the *Baccalauréat* (French

<footnote n="8">⁸https://namsor.app</footnote>
high school diploma) by first name from 2012 to 2020, based on the tool developed by Coulmont (2022).<fnref n="9" /> For each individual, we computed the proportion of students with the same first name who obtained a “Very good” mention. The estimate is only available for first names with at least 40 bearers in France, excluding some cases (Valid *N* = 230, Median = 6.0, Mean = 6.4, Std. dev. = 3.7). The assessed academic profile of victims is significantly lower than that of the overall population, where the average is 9.4%.

Secondly, we compiled indicators about the event characteristics that may affect their con-
duciveness to riots:

*   **Police type.** We compiled data on the police force affiliation and, when available, the specific service of the officers involved in the police homicide (Valid *N* = 473). We distinguished the *Police Nationale* (73.6% of police homicides), the *Gendarmerie* (22.0%), and grouped together other forces (e.g. municipal police, border police, customs, railway police, etc.) amounting to 4.4%. Given its involvement in a substantial number of cases (17.3%), we also separated the *Brigade Anti-Criminalité (BAC)* from the rest of the *Police Nationale* (thus representing 56.2%). The *BAC* are specialized squads within the *Police Nationale* operating primarily in sensitive urban areas, focusing on catching offenders in *flagrante delicto*. They typically operate in plainclothes and use unmarked vehicles (Fassin and Gomme 2013). The involvement of certain forces in police homicides is disproportionately high when compared to their relative size in the overall police force: the *BAC* accounts for 1.4%, the rest of the *Police Nationale* for 42.9%, the *Gendarmerie* for 29.3%, and the other forces for 24.9% of police personnel in France.

*   *Homicide type.* The circumstances of the homicide may influence the likelihood and intensity of subsequent riots. According to the tragedy hypothesis, the killing of unarmed civilians may be particularly shocking and thus more likely to spark riots. In addition, the nature of the homicide can affect its visibility and potential to mobilize protest. We distinguish three main

<footnote n="9">⁹https://coulmont.com/bac/</footnote>
categories of police homicides (Valid *N* = 478): homicides of unarmed individuals outside police premises (60.3%), homicides of armed individuals outside police premises (25.9%), and homicides of unarmed individuals while in custody (13.8%). Custody includes all detentions supervised by the Police Nationale or Gendarmerie, including police custody and detention in Centres de Rétention Administrative (administrative holding centers). Deaths in prison are excluded, as they do not directly result from police actions. We expect the first category to be the most conducive to rioting, as it combines both strong grievances and high public visibility.

* *Multiple deaths*. The 478 police homicides identified correspond to 451 distinct events, since some events involved multiple fatalities (e.g. the deaths of Zyed Benna and Bouna Traoré). We created a binary variable identifying such cases of multiple related deaths (Valid *N* = 478, 10.1% multiple deaths), which may have a greater potential to spark riots.

Finally, we measured the *share of missing values* across the eight variables described above for each individual (Valid *N* = 478, Median = 0.12, Mean = 0.19, Std. dev. = 0.17). The amount of available information likely reflects the level of media attention given to a given homicide. The proportion of missing values is significantly higher for individuals killed after 2018, as shown by a Wilcoxon rank-sum test (*W* = 21079, *p* < 0.001). This supports the view that previously undetected homicides have been identified in the more recent data.

Beyond the proportion of missing values, only two characteristics differ significantly between the pre- and post-2018 periods: the share of non-French citizens ($\chi^2 = 5.843, p = 0.016$), and, to a lesser extent, the age of victims (*W* = 19760, *p* = 0.095). One may hypothesize that the deaths of older or non-French individuals receive less media coverage.<fnref n="10" /> This finding further corroborates the view that the dataset has become more comprehensive since 2018. Other characteristics of police homicides do not appear to be significantly affected by this change in data completeness.

<footnote n="10">¹⁰This is supported by the fact that the share of missing values is also higher for older and non-French victims (see Figure A2 in the Electronic supplementary material).</footnote>
### 3.1.3 Department-level controls

In some specifications of our analyses of the relationship between police homicides and department-level monthly arsons, we included a range of control variables to account for potential omitted variable bias. We incorporated time-varying department-level variables likely to influence the frequency of arsons. Most of these variables come from the INSEE. They include: yearly data on total population by department; yearly data on the share of youth (ages 15–24) in the department population; yearly sex ratio among youth (values above 1 indicate more young men than women); and the quarterly unemployment rate by department. We also included yearly data on average departmental income and the proportion of foreign-born residents, both estimated by Piketty and Cagé (2023). These control variables are summarized in Table A2 in the Electronic supplementary material.

## 3.2 Statistical approach

### 3.2.1 Effect of police homicides on riots

To test the effect of police homicides on riots, we exploit the quasi-random timing of police homicides. As a baseline, we use OLS to estimate the following equation:

$$Y_{dt} = \alpha + \beta_1 \times Homicide_{dt} + \sum_{i=1}^{d} \beta_d \times Department_d + \sum_{i=1}^{t} \beta_t \times Time_t + \varepsilon_{dt} \quad (1)$$

Where $Y_{dt}$ is the number of private property arsons in department $d$ during month $t$. $\sum_{i=1}^{d} \beta_t \times Time_t$ represents fixed effects for each individual month, and $\sum_{i=1}^{d} \beta_d \times Department_d$ represents department fixed effects. $Homicide_{dt}$ is a dummy variable equal to 1 if one or more police homicides occurred in department $d$ during month $t$, and 0 otherwise. Assuming that police homicides are exogenous, $\beta_1$ captures the causal effect of police homicides on private property arsons within the same month.

We tested equation (1) with standard errors clustered at the department level. Besides, we assessed the robustness of our findings to the inclusion of the time-varying department-level controls
presented above. We also tested specifications that include department-specific linear time trends, implemented by interacting department fixed effects with a linear month variable.

One might wonder whether police homicides have cumulative long-term effects on rioting activity, potentially influencing the estimated effect of subsequent police homicides within the same department. To test for such cumulative effects, we computed regression models, presented in Table A9 in the Electronic supplementary material, including a dummy variable indicating whether the department experienced a police homicide in the preceding 12 months.

A potential concern is that the dynamics of riots may differ in periods preceding police homicides compared to periods not followed by police homicides. To account for possible pre-trend differences, we conducted event-study analyses, detailing the formal model in the Electronic supplementary material and presenting the key results in the text.

In equation (1), we assumed that $\beta_1$ captures only the causal effect of police homicides on riots. However, if riots (also) cause police homicides, $\beta_1$ may also reflect this reverse causality. While event-study analyses help assess pre-treatment trends, they do not fully address this issue. Because our data are aggregated at the monthly level, and riots typically last only a few days, an increase in arson observed during the month of a police homicide could plausibly result from either direction of causality.

The most straightforward way to address this concern would be to exclude all police homicides that occurred during or shortly after riots. We are not aware of any such case within our study period,<fnref n="11" /> but we cannot fully rule out the possibility that some deaths occurred during minor unrest (e.g., isolated incidents of arson or clashes).

To strengthen our design, we adopted the inverse strategy. We identified all police homicides that were explicitly associated with riots according to French newspaper articles. The list of 52 such events is presented in Table A6 in the Electronic supplementary material. For each case, we report the date and time of the death, as well as the date and time when the riots reportedly began. In all but three cases, media sources clearly indicate that riots started after the police homicide.

<footnote n="11">¹¹One exception is the case of Mohamed Bendriss, who died during the July 2023 riots. However, this event falls outside our study period.</footnote>
In the remaining three cases – Hassan Jabiri (2004), Mohamed Benmouna (2009), and Wisam El Yamni (2012) – the official time of death occurred after the riots had begun, but the police intervention causing the death took place beforehand. Thus, riots cannot be considered the cause of any of these 52 homicides.

To rule out reverse causality, we re-estimated equation (1) in Table A10 in the Electronic supplementary material using two separate specifications. In the first, $Homicide_{dt}$ equals 1 only if one or more police homicides *with identified riots*, according to the press, occurred in department *d* during month *t* (i.e., the 52 documented cases). In the second, $Homicide_{dt}$ equals 1 only if one or more police homicides *without identified riots* occurred. Our goal is not to estimate an effect using the first group – since these cases were, by definition, followed by riots, which would constitute selection on the dependent variable – but to test whether homicides *without* such press-identified riots also show an association.

If police homicides were sometimes caused by prior riots (i.e., reverse causality), we would expect a positive association even among the cases with no identified riots according to the press. Observing such an association would not constitute definitive proof of reverse causality, as it could still reflect riots in response to homicides undetected by the media. However, finding no effect at all among these cases would be a strong argument against reverse causality. If the only homicides known to precede riots (the 52) do show an association, while those not followed by riots according to the press do not, we can confidently conclude that $\beta_1$ captures only the causal effect of police homicides on riots.

### 3.2.2 Characteristics of police homicides influencing riots

We used several complementary approaches to analyze which characteristics of police homicides are associated with a greater likelihood of triggering riots.

Our main approach relies on variations in the number of private property arsons as the dependent variable. For each police homicide, we calculated the change in the number of private property arsons in the department where the homicide occurred, comparing the period in which
the homicide took place to an earlier reference period. While these changes cannot directly be interpreted as precise measures of riot intensity induced by a given police homicide – since they may also reflect other factors – we assume that systematic differences in these variations across homicides can be attributed to the characteristics of the homicides themselves. The aim is to assess whether certain features of police homicides predict larger or smaller increases in private property arsons in the department where they occurred.

We computed several indicators to capture these variations, following a strategy similar to that of Mechoulan (2020). Our baseline indicator is the relative change in private property arsons between $t_{-1}$ (the month preceding the police homicide) and $t_0$ (the month of the homicide). However, since some homicides occur at the end of the month, riots may also spill over into the following month. We therefore computed a second indicator: the relative variation between the total number of arsons in $t_{-2} + t_{-1}$ (the two months before the homicide) and in $t_0 + t_{+1}$ (the month of the homicide and the following one). As noted earlier, riots in France display seasonal patterns, which may bias simple month-to-month comparisons. To address this, we computed analogous indicators using $t_{-12}$ and $t_{-12} + t_{-11}$ as seasonal baselines instead.

We first computed bivariate statistics to explore the relationship between individual homicide characteristics and arson variation. We then estimated OLS models to predict changes in private property arsons based on multiple homicide characteristics simultaneously. At this stage, missing data become a concern, as listwise deletion significantly reduced the sample size. To address this, we present two types of multivariate analyses. First, we report regression results based on listwise deletion – a conservative approach likely to yield null findings due to reduced power. Second, we use multiple imputation by chained equations to handle missing data. Specifically, we generated five imputed datasets, where missing values for each variable were estimated based on observed relationships with other variables. This procedure helps retain statistical power and allows us to identify significant predictors, particularly among variables with relatively few missing values, that were not estimable in the reduced sample.

We used several additional strategies as robustness checks. First, we implemented triple-
difference event-study analyses (shown in the Electronic supplementary material), interacting time relative to the homicide with its characteristics. Second, we conducted a separate set of analyses using a binary outcome indicating whether the homicide belongs to the set of 52 cases explicitly associated with riots in the press. As with the arson-based approach, we present both bivariate and multivariate analyses (with and without multiple imputation of missing values) using this alternative dependent variable in the Electronic supplementary material.

# 4 Results

## 4.1 Effect of police homicides on riots

Table 1 presents OLS regression estimates of the monthly number of private property arsons per department. Results from Model (1) indicate that the occurrence of one or more police homicides in a department during a given month is associated with 2.6 additional private property arsons during that month — a roughly 10% increase compared to the departmental monthly average. However, this effect is not statistically significant. The estimated effect remains similar in size and nonsignificant when adding demographic and economic controls in Model (2), and when further including department-specific linear time trends in Model (3).

In the Electronic supplementary material, we replicate these analyses using alternative model specifications and outcome definitions. Table A7 presents estimates from negative binomial regressions, accounting for the count nature of the dependent variable. In this case, results are statistically significant in the full specification including both controls and department-specific linear time trends (Models 3). Table A8 reports OLS regressions using an alternative dependent variable: the number of private property arsons per 100,000 inhabitants. Results mirror those of Table 1: the estimated effect of police homicides is positive but nonsignificant.
<table><caption>Table 1: Effect of police homicides on private property arsons (department × month, 1996–2022).</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Police homicide</td><td>2.639<br>(2.425)</td><td>2.789<br>(2.465)</td><td>2.249<br>(2.318)</td><td></td><td></td><td></td></tr><tr><td>Police homicide without riots<br>identified in the press</td><td></td><td></td><td></td><td>-1.189<br>(2.715)</td><td>-1.166<br>(2.782)</td><td>-1.562<br>(2.707)</td></tr><tr><td>Department fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Month fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Controls</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Department × linear time trend</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>32080</td><td>30720</td><td>30720</td><td>32080</td><td>30720</td><td>30720</td></tr><tr><td>R<sup>2</sup></td><td>0.764</td><td>0.768</td><td>0.780</td><td>0.764</td><td>0.768</td><td>0.780</td></tr></tbody></table>

*Notes:* The unit of observation is a department-month between 1996 and 2022. The dependent variable is the monthly number of private property arsons. The main predictor, *Police homicide*, is a binary variable equal to 1 if at least one police homicide occurred in that department-month. Standard errors clustered at the department level in parentheses. Columns (2) and (5) add demographic and economic controls: total population, youth share (15–24), youth sex ratio, unemployment rate, average income, and share of foreign-born residents. Columns (3) and (6) further include department-specific linear trends. All models include department and month fixed effects.

We also tested for potential cumulative effects of police homicides on arson by estimating the same set of models as in Table 1, but adding a dummy variable indicating whether at least one police homicide occurred in the same department during the previous 12 months. Results are presented in Table A9. Across all specifications, we find no evidence of a statistically significant positive effect of past police homicides on the current level of private property arsons.

To assess whether the estimates of $\beta_1$ in Models (1) to (3) of Table 1 capture only the causal effect of police homicides on riots, we conducted analyses distinguishing between the 52 police homicides followed by riots (as identified in the press) and the remaining cases. As shown in Table A10 in the Electronic supplementary material, police homicides followed by identified riots are strongly associated with additional private property arsons in the same month and department – reassuringly confirming that our measure of riots aligns with media reports. More importantly, as shown by Models (4) to (6) in Table 1, the coefficients for police homicides not associated with riots in the press are statistically insignificant and even negative in sign, suggesting a null effect in
those cases. These findings indicate that the positive (albeit nonsignificant) association found in
the baseline models can only be driven by police homicides followed by riots. This confirms the
estimates of $\beta_1$ in Models (1) to (3) of Table 1 only reflect a causal effect from police homicides to
riots.

Figure 4: Event-study model: effect of the occurrence of a police homicide on the number of private property arsons in the department of occurrence.

<figure><img src="image_7.png" /></figure>

*Note:* Coefficients from a dynamic event-study model using OLS, with department-month as the unit of observation. The dependent variable is the number of private property arsons per month. Each dot represents the coefficient for a given time relative to a police homicide (in months), with 95% confidence intervals. The omitted reference category is $t_{-1}$ (the month preceding the homicide). The model includes fixed effects for month and group (window of time by department), as well as demographic and economic controls: total population, youth share (15-24), youth sex ratio, unemployment rate, average income, and share of foreign-born residents. Standard errors are clustered by group.

Additionally, we conducted dynamic two-way fixed effects (TWFE) event-study analyses. The baseline model is formally detailed in Section 5.1 of the Electronic supplementary material. Figure 4 presents OLS event-study estimates for all police homicides, including demographic and economic controls. More specifically, it shows estimates for all coefficients corresponding to the individual effects of monthly leads and lags relative to the occurrence of a police homicide at $t_0$. The results indicate that the month during which an identified police homicide occurs ($t_0$) is associated with a significantly higher number of private property arsons in the department of occurrence compared to $t_{-1}$. No significant effect is observed in the preceding or following months (except at
$t_{-12}$).

We conducted robustness checks using alternative event-study specifications (Figures A6–A12 in the Electronic supplementary material), including standard OLS without controls, negative binomial regressions, arsons per capita as the dependent variable, alternative difference-in-differences estimators, and specifications with flexible trends. Across all models, we find that the month of a police homicide is systematically associated with a significantly higher number of private property arsons in the affected department, and, to a lesser extent, in the following month. We observe no significant differences in pre-treatment trends. Finally, distinguishing police homicides that were followed by riots reported in the press from other cases confirms that the general effect is entirely driven by the former, for which the direction of causality is unambiguous.

In summary, the overall effect of police homicides on private property arsons is small and not always statistically significant. It is important to recall that our dataset may not be exhaustive, particularly for homicides without associated riots, as these are less likely to be reported in press sources. As a result, the overall effect is likely an overestimate of the true relationship between police homicides and riots in general. To provide a sense of scale, relying on the estimate of $\beta_1$ in model (1) in Table 1, the 424 months with one or more police homicides in a given department are associated with approximately 1,243 additional private property arsons – representing just 0.15% of all recorded arsons during the study period. Even under the unlikely assumption that the true number of police homicides is twice as high as reported and that these unreported cases have similar effect sizes, the proportion would only rise to 0.3% of all arsons. These figures underscore the modest size of the overall effect. In the next section, we examine why some police homicides are associated with riots while most are not.

## 4.2 Characteristics of police homicides influencing riots

Figure 5 presents bivariate analyses of variations in private property arsons following police homicides (detailed results are reported in Table A11 in the Electronic supplementary material). The explanatory variables include, in addition to the victim's age, gender, citizenship, assessed aca-
demic achievement, and immigrant origin, the type of police involved, as well as variables related to the “tragedy hypothesis” (multiple deaths and type of homicide). As described earlier, the dependent variables are different measures of variation in private property arsons in the department where the police homicide occurred.

Most of the estimates related to victim identity are in the expected direction. The likelihood of riots is significantly higher when the victim is young, and the killing of individuals of non-European origin is associated with significantly more arsons. This finding is consistent with survey data among French teenagers presented in the Electronic supplementary material, which indicates that potential rioters are more likely to have a non-European migratory background. It also aligns with the ethnic dimension of riots observed in many countries (Kawalerowicz and Biggs 2015; Bagguley and Hussain 2016; Keith 2023; Schneider 2014; Moutselos 2020).

While survey results show that potential rioters are more likely to be male, there is no clear gender difference in the effect of police homicides. This may be because police killings of women are rare. However, the figure shows that arson activity increases significantly following the killing of men, which is not the case for women.

Similarly, in line with survey findings indicating that potential rioters tend to report lower academic achievement, victims assessed as having a low level of education are associated with larger riots. However, this association reaches statistical significance for only one measure of variation in private property arsons. This may be due to the very approximate assessment of educational attainment based on first names and the high proportion of missing values for this variable.

The only counterintuitive result concerns citizenship. Increases in arsons are significantly higher when the victim is a French citizen than when the victim is not. This finding does not align with the identity hypothesis, as French citizenship is not associated with riot propensity in the survey data. Furthermore, non-French citizenship is positively correlated with assessed non-European migratory origin (see Figure A2 in the Electronic supplementary material), which itself is associated with larger riots. This would suggest that, if anything, the French citizenship of the victim should have a negative effect on riot intensity. We return to this unexpected finding in the
<figure><img src="image_8.png" /><figcaption>Figure 5: Variation in private property arsons by department, as a function of police homicide characteristics.</figcaption></figure>

Note: Each panel shows the change in arson levels (under different temporal specifications) depending on specific characteristics of the homicide event. The unit of observation is the police homicide. Each dot represents one police homicide (jittered for readability in the case of categorical variables). The outcome variable on the y-axis is the relative change in the number of private property arsons in the department where the homicide occurred (0 = no change, 1 = 100% increase, etc.). For categorical characteristics, the figure displays violin plots of the distribution of arson variation, along with mean values and associated 95% confidence intervals. For continuous variables, it shows a fitted linear regression line with 95% confidence intervals.
conclusion.

When the officer involved in the police homicide is from the *BAC*, there is a significantly larger increase in arsons. This finding is also consistent with the identity hypothesis. Survey data detailed in the Electronic supplementary material indicate that individuals who have had prior negative interactions with the police – such as being suspected of a crime or engaging in activities like drug dealing – are significantly more likely to report a desire to riot or to have participated in arson. Additionally, residing in sensitive urban areas (*Quartiers prioritaires de la politique de la ville*) increases the likelihood of rioting. As a reminder, the *BAC* frequently operates in these areas, particularly to detect offenses committed in flagrante delicto, often related to drug trafficking. To a lesser extent, homicides perpetrated by the *Police Nationale* are also associated with significantly more private property arsons, whereas this is not the case for homicides involving the *Gendarmerie*.

Tragic events—those involving multiple victims or unarmed individuals—are likewise associated with significantly higher levels of rioting. However, this is not the case for deaths occurring in custody, likely because such deaths often remain unknown to the public.

Most of these results are attenuated in the multivariate estimates, as shown in Figure 6, which presents standardized coefficients from linear regression analyses of variations in arsons (unstandardized coefficients are reported in Tables A13 and A15 in the Electronic supplementary material). Results are particularly weak in models without multiple imputation, due to listwise deletion. In these models, only the assessed non-European migratory origin of the victim is significantly associated with riots. While multiple imputation prevents the reduction in sample size (see Table A13), it also increases noise in variables with a high proportion of missing values. The associations that remain statistically significant in some specifications are those with the involvement of the *BAC*, multiple deaths, non-European migratory origin, and the French citizenship of the victim.

To further investigate the heterogeneity of riot responses to police homicides, we implemented a triple-difference (DDD) event-study approach. As detailed in the Electronic supplementary material, we extended the standard event-study specification by interacting the treatment timing indicators with a binary variables measuring traits of the homicide (e.g., whether the victim had a
<figure><img src="image_9.png" /><figcaption>Figure 6: Effect of police homicide characteristics on changes in private property arsons by department.</figcaption></figure>

*Note:* Each dot represents the standardized coefficient from a linear regression including all police homicide characteristics as predictors. The unit of observation is the police homicide. The dependent variable is the relative change in the number of private property arsons in the department where the homicide occurred (0 = no change, 1 = 100% increase, etc.). Lines indicate 95% confidence intervals. Coefficients are shown across alternative temporal specifications and with or without multiple imputation of missing values. See Tables A12 and A14 in the Electronic supplementary material for full estimates.
non-European migratory background). The resulting interaction coefficients estimate the differ-
ential effect for events with versus without the given trait. Figures A13 to A23 in the Electronic
supplementary material present the results for each characteristic. While most differences are not
statistically significant, the estimated interaction effects generally point in the same direction as
the bivariate and multivariate results presented earlier. We find significant differences for a few
attributes. First, arson activity increases significantly more when the victim had a non-European
migratory background. Second, the increase in arsons is significantly smaller when the victim was
assessed as having high academic achievement. Third, arson levels rise significantly more when
the victim was unarmed.

Tables A19 to A21 in the Electronic supplementary material present results from additional
analyses using, as an alternative dependent variable, whether the homicide belongs to the set of
52 cases explicitly associated with riots in the press. The results are broadly consistent with those
presented earlier. In bivariate tests, homicides with identified riots in the press involved signifi-
cantly younger victims, who were more often French citizens, of non-European migratory origin,
and assessed as having low academic achievement. These cases were also more likely to involve
unarmed victims, officers from the *BAC*, and incidents with multiple victims. Multivariate analy-
ses using complete cases indicate that only age and assessed non-European migratory origin are
significantly associated with the probability that a homicide is linked to riots. When accounting for
missing data through multiple imputation, two additional variables reach statistical significance:
French citizenship and the presence of multiple deaths.

Taken together, the analyses point to a few characteristics that consistently predict the like-
lihood and intensity of riots following police homicides. The most robust predictor is the non-
European migratory origin of the victim, which is significantly associated with increased arson
activity across bivariate, multivariate, and event-study models. The involvement of the *BAC* (anti-
crime brigade) is another important factor, associated with significantly higher levels of arson in
both bivariate and some multivariate models. French citizenship appears as a positive predictor in
several bivariate and multivariate specifications.
To test whether these effects might be driven by intersectional characteristics, we explored a
series of two-way interaction terms combining non-European migratory background, non-French
citizenship, and police force type. Results are presented in Table A18 (using variations in arsons
as the outcome) and in Tables A20 and A21 (using press-identified riots as the outcome). None
of the interaction terms reach statistical significance. While this may be due to limited statistical
power, the results suggest that these characteristics operate primarily in an additive rather than
multiplicative manner.

# 5 Discussion

It is widely recognized that riots are frequently triggered by police killings of citizens (Hinton
2022; Waddington 2010, 2012; Newburn 2015; Jeffery and Tufail 2015; Stevenson 2014; Phillips
et al. 2013). In the United States, Greece (2008), the United Kingdom (1980, 2011), and France
(2005, 2023), major riots have erupted following the deaths of unarmed civilians—either because
they were killed directly by the police or because the police played an active role in circumstances
leading to their deaths. Yet, to date, no study has systematically examined the impact of the full
universe of police homicides on riot intensity.

In this article, we analyzed the effect of all documented police homicides in France over the
past 25 years on local riot intensity, far beyond the exceptional cases for which video evidence
of police misconduct exists. Our findings show that police homicides have a conditional causal
effect on riots. In general, police homicides have a limited effect, but this effect is largely driven
by specific cases in which the victim and the killer have specific characteristics.

We examined two explanatory factors. First, the *tragedy hypothesis* suggests that riots are
more likely when the event is easily interpreted as a moral outrage. This is supported by the fact
that police homicides involving unarmed victims, or resulting in multiple deaths, are more often
followed by riots. However, only the latter remains statistically significant in multivariate analyses.
Second, the *identity hypothesis* posits that riots are more likely when the victim resembles potential
rioters – namely, members of the population who experience the root causes of rioting. This hypothesis is supported by bivariate analyses using delinquency data, and confirmed with survey evidence: riots are more likely when the victim is young, of non-European migratory origin, and killed by a specialized anti-criminality unit of the French national police (BAC). In multivariate and event-study models, only the variables related to ethnic background (non-European origin) and the involvement of the BAC remain associated with increased riot intensity.

There are several possible interpretations of our findings. First, they are consistent with the hypothesis that individuals may become potential rioters when they are particularly exposed to police homicides, and perhaps more generally, to police brutality (Schneider 2014; Hinton 2022). Homicides may be seen as the tip of the iceberg, revealing recurring experiences of surveillance, humiliation, and violence. When such experiences are common, a police homicide is no longer perceived as a tragic mistake but rather as an obvious and predictable consequence of systemic police behavior toward a specific population. In this view, the overrepresentation of certain populations among police homicide victims would lead to riots when such an event occurs, especially if committed by the most repressive branch of the police. The BAC effect may reflect this, as the BAC epitomizes the most aggressive forms of policing in France. BAC officers are trained to be confrontational, operate with a high degree of autonomy from their superiors, and are frequently criticized for targeting specific populations and for using harsh and humiliating methods (Fassin and Gomme 2013).

A second interpretation is that the correlation between the profile of police homicide victims and the profile of rioters may stem from the fact that members of these populations are more likely to be involved in criminal activity. This could lead to more confrontational interactions with the police – resulting in a higher likelihood of lethal outcomes – and a greater propensity to engage in retaliatory actions, such as arson. According to this view, police killings spark riots because criminals perceive the police as an enemy group and respond when repression escalates. Our data do not allow for a clean distinction between these interpretations. However, any satisfactory explanation must account for two key facts: potential rioters and victims of police killings share
similar profiles, and when someone with such a profile is killed by the police, a riot is more likely to occur.

An unexpected result concerns citizenship. On average, the killing of French citizens leads to higher levels of rioting. This finding is probably not driven by identity-related mechanisms, but rather by dimensions of social integration. French citizenship may reflect the degree of local embeddedness of the victim: the better known the victim is, and the more social ties he or she has in the community, the more likely it is that a riot will occur. In contrast, non-French immigrants are less likely to have grown up and lived in the places where they were killed by the police. This interpretation is supported by the fact that the proportion of non-French victims is significantly higher in the post-2018 sample. This suggests that the deaths of non-French individuals receive less media coverage and are less likely to spark mobilization from people in their immediate social environment.

Several examples illustrate this point. Luigi Duquenet and Angelo Garand, both from the Roma minority, died in police operations within a few kilometers of each other, in the *Loir-et-Cher* department (central France), in 2008 and 2017 respectively. Luigi Duquenet's death led to an intense riot in the city of Saint-Aignan, whereas Angelo Garand's death did not provoke such a reaction. According to a journalist who investigated both cases, the Duquenet family was better known locally than the Garand family.<fnref n="12" /> Luigi Duquenet had grown up in the town of Onzain and was living with his partner in the neighboring town of Pontlevoy. In contrast, Angelo Garand had spent a large part of his life in prison. Between the age of 22, when he was first sentenced, and 37, when he died, he spent a total of 13 years incarcerated.

Similar comparisons can also be made with the city of Lille, in the *Nord* department. The deaths of Hakim Djelassi (24 September 2009), Selom Tonato and Matisse Ben Gabsia (15 December 2017)—all French citizens and children of immigrant parents who grew up and lived in the area—led to riots. This was not the case for Toufik Sehih, an undocumented Algerian who died in the Lille police station on 23 August 2020. By contrast, the death of Riad Hamlaoui, a 23-year-old

<footnote n="12">¹²Informal interview of a journalist specializing in fatal police incidents, conducted by one of the authors in 2020.</footnote>
Algerian who was shot in the back of the head, also in Lille on 16 April 2000, did provoke unrest: clashes erupted between local youths and the police, and around thirty cars were set on fire. Unlike Toufik Sehih, however, Riad Hamlaoui lived in the Lille-Sud neighborhood, worked as a youth worker in a neighborhood center, and had just started a new job with the Lille Urban Community.

There are, of course, counterexamples, and actors with strong local roots have occasionally succeeded in preventing violence. For instance, Yazid Kherfi, who had lived in the Val-Fourré neighborhood for 25 years, recounts that following the death of Youssef Khaïf on 9 June 1991, he persuaded the victim's friends not to riot, but instead to help organize a silent march (Kherfi and Le Goaziou 2013). Beyond nationality, it appears that victims who are socially integrated are more likely to trigger various forms of mobilization – including violent protest – especially when they have an immigrant background. Conversely, victims without strong local ties – such as homeless people or undocumented migrants – tend to elicit little public reaction.

## References

Bagguley, P. and Y. Hussain. 2016. *Riotous Citizens: Ethnic Conflict in Multicultural Britain*. Routledge.

Benyon, J. and L.G. Scarman eds. 1991. *Scarman and after: Essays Reflecting on Lord Scarman's Report, the Riots and Their Aftermath* (repr ed.). Oxford: Pergamon Pr.

Bodea, C., I. Elbadawi, and C. Houle. 2017, May. Do Civil Wars, Coups and Riots Have the Same Structural Determinants? *International Interactions* 43(3): 537–561. https://doi.org/10.1080/03050629.2016.1188093.

Body-Gendrot, S. 2007. Urban ‘riots’ or urban violence in France? *Policing: A Journal of Policy and Practice* 1(4): 416–427.

Bonnasse-Gahot, L., H. Berestycki, M.A. Depuiset, M.B. Gordon, S. Roché, N. Rodriguez, and J.P. Nadal. 2018, January. Epidemiological modelling of the 2005 French riots: A spreading
wave and the role of contagion. *Scientific Reports* 8(1): 107. https://doi.org/10.1038/s41598-017-18093-4.

Braha, D. 2012. Global civil unrest: Contagion, self-organization, and prediction. *PloS one* 7(10): e48596.

Camp, J.T. 2016, April. *Incarcerating the Crisis: Freedom Struggles and the Rise of the Neoliberal State*.

Conyers, J. 1981, January. Police Violence and Riots. *The Black Scholar* 12(1): 2-5. https://doi.org/10.1080/00064246.1981.11414151.

Coulmont, B. 2022, February. *Sociologie des prénoms* (3e édition ed.). Paris: La Découverte.

Courtois, P., R. Nessah, and T. Tazdaït. 2024. Revolutions and rational choice: A critical discussion. *Public Choice* 200(3): 497-529.

Daly, M., M. Wilson, and S. Vasdev. 2001, April. Income inequality and homicide rates in Canada and the United States. *Canadian Journal of Criminology* 43(2): 219-236. https://doi.org/10.3138/cjcrim.43.2.219.

DiPasquale, D. and E.L. Glaeser. 1998. The Los Angeles riot and the economics of urban unrest. *Journal of Urban Economics* 43(1): 52-78.

Downes, B.T. 1968. Social and political characteristics of riot cities: A comparative study. *Social Science Quarterly*: 504-520. 42858411.

Drury, J., C. Stott, R. Ball, S. Reicher, F. Neville, L. Bell, M. Biddlestone, S. Choudhury, M. Lovell, and C. Ryan. 2020. A social identity model of riot diffusion: From injustice to empowerment in the 2011 London riots. *European Journal of Social Psychology* 50(3): 646-661. https://doi.org/10.1002/ejsp.2650.

Fassin, D. and R. Gomme. 2013. *Enforcing Order: An Ethnography of Urban Policing* (English edition ed.). Cambridge, UK: Polity Press.
Feld, B.C. 1970. Police violence and protest. *Minn. L. Rev.* 55: 731.

Fiscella, K. and A.M. Fremont. 2006, August. Use of Geocoding and Surname Analysis to Estimate Race and Ethnicity. *Health Services Research* 41(4p1): 1482–1500. https://doi.org/10.1111/j.1475-6773.2006.00551.x.

Fogelson, R.M. 1968. From resentment to confrontation: The police, the Negroes, and the outbreak of the nineteen-sixties riots. *Political science quarterly* 83(2): 217–247. 2147090.

Gillon, S. 2018, June. Why the 1967 Kerner Report on Urban Riots Suppressed Its Own Expert Findings. https://www.history.com/news/race-riots-kerner-commission-findings-suppressed-lbj.

Grimshaw, A.D. 1963. Actions of police and the military in American race riots. *Phylon* (1960-24(3): 271–289. 273402.

Hinton, E.K. 2022. *America on Fire: The Untold History of Police Violence and Black Rebellion since the 1960s*. New York, NY: Liveright Publishing Corporation.

Horowitz, D.L. 2023. *The Deadly Ethnic Riot*. Univ of California Press.

Houle, C., D.J. Ruck, R.A. Bentley, and S. Gavrilets. 2022, March. Inequality between identity groups and social unrest. *Journal of The Royal Society Interface* 19(188): 20210725. https://doi.org/10.1098/rsif.2021.0725.

IHEMI. Les incendies volontaires de véhicules: éléments de compréhension et de comptage. https://www.ihemi.fr/publications/reperes/les-incendies-volontaires-de-vehicules-elements-de-comprehension-et-de-comptage.

IHEMI. Éléments de mesure des incendies volontaires de véhicules en 2015. https://www.ihemi.fr/publications/la-note/elements-de-mesure-des-incendies-volontaires-de-vehicules-en-2015.
Ives, B. and J.S. Lewis. 2020, May. From Rallies to Riots: Why Some Protests Become Violent. *Journal of Conflict Resolution* 64(5): 958–986. https://doi.org/10.1177/0022002719887491.

Jacobs, D. and D. Britt. 1979. Inequality and police use of deadly force: An empirical assessment of a conflict hypothesis. *Social Problems* 26(4): 403–412.

Jeffery, B. and W. Tufail. 2015, April. The riots were where the police were: Deconstructing the Pendelton Riot'. *Contention: The Multidisciplinary Journal of Social Protest* 2: 37–55.

Karamichas, J. 2009, August. The December 2008 Riots in Greece: Profile. *Social Movement Studies* 8(3): 289–293. https://doi.org/10.1080/14742830903024374.

Kawalerowicz, J. and M. Biggs. 2015. Anarchy in the UK: Economic deprivation, social disorganization, and political grievances in the London Riot of 2011. *Social forces* 94(2): 673–698.

Keith, M. 2023. *Race, Riots and Policing: Lore and Disorder in a Multi-Racist Society*. Taylor & Francis.

Kherfi, Y. and V. Le Goaziou. 2013. Les Émeutiers : Entre violence résignation, *Quand Les Banlieues Brûlent...: Retour Sur Les Émeutes de Novembre 2005*, 87–97. la Découverte.

Klein, A. 2012. Policing as a causal factor–a fresh view on riots and social unrest. *Safer communities* 11(1): 17–23.

Knopf, T.A. 2017. *Rumors, Race and Riots*. Routledge.

Kuran, T. 1989. Sparks and prairie fires: A theory of unanticipated political revolution. *Public choice* 61(1): 41–74.

Leeson, P.T. 2010. Rational choice, round robin, and rebellion: An institutional solution to the problems of revolution. *Journal of Economic Behavior & Organization* 73(3): 297–307.
Leeson, P.T., D.J. Smith, and N.A. Snow. 2012. Hooligans. *Revue d'économie politique* 122(2): 213-231.

Lewis, P., T. Newburn, M. Taylor, C. Mcgillivray, A. Greenhill, H. Frayman, and R. Proctor 2011, December. Reading the riots: Investigating England's summer of disorder. Monograph, The London School of Economics and Political Science and The Guardian, London, UK.

Lieberman, S. and A.R. Silverman. 1965. The precipitants and underlying conditions of race riots. *American Sociological Review*: 887-898. 2090967.

Mateos, P. 2007, July. A review of name-based ethnicity classification methods and their potential in population studies. *Population, Space and Place* 13(4): 243-263. https://doi.org/10.1002/psp.457.

Mazières, A. and C. Roth. 2018, July. Large-Scale Diversity Estimation Through Surname Origin Inference. *Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique* 139(1): 59-73. https://doi.org/10.1177/0759106318778828.

Mechoulan, S. 2020. Civil unrest, emergency powers, and spillover effects: A mixed methods analysis of the 2005 French riots. *Journal of Economic Behavior & Organization* 177: 305-326.

Moutselos, M. 2020. Estates against the state? Evidence from the 2005 wave of French riots. *Political geography* 76: 102088.

Newburn, T. 2015, January. The 2011 England Riots in Recent Historical Perspective. *British Journal of Criminology* 55(1): 39-64. https://doi.org/10.1093/bjc/azu074.

Newburn, T. 2021, January. The Causes and Consequences of Urban Riot and Unrest. *Annual Review of Criminology* 4(1): 53-73. https://doi.org/10.1146/annurev-criminol-061020-124931.
Newburn, T., T. Jones, and J. Blaustein. 2018, September. Framing the 2011 England Riots: Understanding the Political and Policy Response. *The Howard Journal of Crime and Justice* 57(3): 339–362. https://doi.org/10.1111/hojo.12268.

Olson, M. 1965. *The Logic of Collective Action*. Cambridge, MA: Harvard Univ. Press.

Opp, K.D. 1988. Grievances and participation in social movements. *American sociological review*: 853–864. 2095895.

Opp, K.D. 1994. Repression and revolutionary action: East Germany in 1989. *Rationality and society* 6(1): 101–138.

Phillips, R., D. Frost, and A. Singleton. 2013, March. Researching the riots. *The Geographical Journal* 179(1): 3–10. https://doi.org/10.1111/j.1475-4959.2012.00463.x.

Piketty, T. and J. Cagé. 2023. *Une histoire du conflit politique: élections et inégalités sociales en France, 1789-2022*. Seuil.

Roché, S., S. Varaine, and P. Le Derff. 2025. And the law relaxed the rules. A quasi-experimental study of fatal police shootings in Europe. *International Review of Law and Economics*: 106282.

Roché, S. 2006. *Le Frisson de l'émeute: Violences Urbaines et Banlieues*. Paris: Seuil.

Roché, S. and F. Rabaté. 2023, November. *La Police Contre La Rue*. Paris: Grasset.

Scarman, L.G. 1981. *The Brixton Disorders: 10 - 12 April 1981; Report of an Inquiry; Presented to Parliament by the Secretary of State for the Home Department by Command of Her Majesty* (Repr ed.). London: Her Majesty's Stationery Office.

Schelling, T.C. 1980. *The Strategy of Conflict: With a New Preface by the Author*. Harvard university press.
Schneider, C.L. 2014. *Police Power and Race Riots: Urban Unrest in Paris and New York* (1st ed.): Philadelphia: University of Pennsylvania Press.

Sebo, P. 2023. How well does NamSor perform in predicting the country of origin and ethnicity of individuals based on their first and last names? *Plos one* 18(11): e0294562.

Skolnick, J.H. 1969. *The Politics of Protest*.

Stevenson, J. 2014. *Popular Disturbances in England 1700-1832*. Routledge.

Tullock, G. 1971. The paradox of revolution. *Public Choice*: 89-99. 30022655.

Waddington, D. 2012, March. The law of moments: Understanding the flashpoint that ignited the riots: David waddington on how the shooting of Mark Duggan led to the August rioting.
*Criminal Justice Matters* 87(1): 6-7. https://doi.org/10.1080/09627251.2012.670993.

Waddington, D., K. Jones, and C. Critcher. 2021, July. *Flashpoints: Studies in Public Disorder* (1 ed.). London: Routledge.

Waddington, D.P. 2010, March. Applying the Flashpoints Model of Public Disorder to the 2001 Bradford Riot. *British Journal of Criminology* 50(2): 342-359. https://doi.org/10.1093/bjc/azp082.

Wieviorka, M. and P. Bataille. 1999. *Violence En France*. L'Epreuve Des Faits. Paris: Seuil.

Wilkinson, S.I. 2009, June. Riots. *Annual Review of Political Science* 12(1): 329-343. https://doi.org/10.1146/annurev.polisci.12.041307.075517.

Yin, C.C. 1998. Equilibria of collective action in different distributions of protest thresholds.
*Public Choice* 97(4): 535-567.
# Police Homicides and Riots in France: Electronic supplementary material

## Contents

<table><tr><td>1</td><td>Descriptive statistics</td><td>1</td></tr><tr><td>1.1</td><td>Arsons</td><td>1</td></tr><tr><td>1.2</td><td>Police homicides</td><td>2</td></tr><tr><td>1.3</td><td>Department-level controls</td><td>2</td></tr><tr><td>2</td><td>Profiles of potential rioters: survey-based analyses</td><td>4</td></tr><tr><td>3</td><td>List of police homicides with identified riots in the press</td><td>12</td></tr><tr><td>4</td><td>Effect of police homicides on riots: alternative regression specifications</td><td>14</td></tr><tr><td>5</td><td>Event study analyses</td><td>18</td></tr><tr><td>5.1</td><td>Difference-in-differences event-study analyses</td><td>18</td></tr><tr><td>5.2</td><td>Triple-difference event-study analyses</td><td>27</td></tr><tr><td>6</td><td>Additional results: characteristics of police homicides</td><td>32</td></tr><tr><td>6.1</td><td>Homicide characteristics and variation in arsons</td><td>32</td></tr><tr><td>6.2</td><td>Homicide characteristics and association with riots according to the press</td><td>40</td></tr></table>

## 1 Descriptive statistics

### 1.1 Arsons

<figure><img src="image_10.png" /><figcaption>Figure A1: Evolution of private property arsons in entire France (including Overseas departments)</figcaption></figure>
## 1.2 Police homicides

<table><caption>Table A1: Summary Statistics (police homicides dataset)</caption><thead><tr><th>Variable</th><th>Valid N</th><th>Mean</th><th>Std. dev.</th><th>Min</th><th>Median</th><th>Max</th></tr></thead><tbody><tr><td>Age</td><td>427</td><td>33</td><td>13</td><td>10</td><td>30</td><td>82</td></tr><tr><td>Male</td><td>474</td><td>0.97</td><td>0.17</td><td>0</td><td>1</td><td>1</td></tr><tr><td>Non French</td><td>239</td><td>0.29</td><td>0.46</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Assessed Non-Europe migratory origin</td><td>307</td><td>0.58</td><td>0.49</td><td>0</td><td>1</td><td>1</td></tr><tr><td>Assessed academic results</td><td>230</td><td>6.4</td><td>3.7</td><td>0</td><td>6</td><td>16</td></tr><tr><td><i>Gendarmerie</i></td><td>473</td><td>0.22</td><td>0.41</td><td>0</td><td>0</td><td>1</td></tr><tr><td><i>BAC</i></td><td>473</td><td>0.17</td><td>0.38</td><td>0</td><td>0</td><td>1</td></tr><tr><td><i>Police Nationale</i></td><td>473</td><td>0.56</td><td>0.5</td><td>0</td><td>1</td><td>1</td></tr><tr><td>Other police</td><td>473</td><td>0.044</td><td>0.21</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Armed person</td><td>478</td><td>0.26</td><td>0.44</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Death in custody</td><td>478</td><td>0.14</td><td>0.35</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Unarmed person</td><td>478</td><td>0.6</td><td>0.49</td><td>0</td><td>1</td><td>1</td></tr><tr><td>Multiple deaths</td><td>478</td><td>0.092</td><td>0.29</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Share of missing values</td><td>478</td><td>0.19</td><td>0.17</td><td>0</td><td>0.12</td><td>0.62</td></tr></tbody></table>

## 1.3 Department-level controls

<table><caption>Table A2: Summary Statistics (Department-level controls)</caption><thead><tr><th>Variable</th><th>Valid N</th><th>Mean</th><th>Std. dev.</th><th>Min</th><th>Median</th><th>Max</th></tr></thead><tbody><tr><td>Population (millions)</td><td>32,104</td><td>0.64</td><td>0.49</td><td>0.07</td><td>0.51</td><td>2.61</td></tr><tr><td>Youth share (15–24)</td><td>32,104</td><td>0.12</td><td>0.018</td><td>0.082</td><td>0.117</td><td>0.18</td></tr><tr><td>Youth sex ratio</td><td>32,104</td><td>1.06</td><td>0.062</td><td>0.85</td><td>1.07</td><td>1.36</td></tr><tr><td>Unemployment rate</td><td>31,136</td><td>0.087</td><td>0.024</td><td>0.037</td><td>0.084</td><td>0.275</td></tr><tr><td>Average income</td><td>30,720</td><td>16,073</td><td>3,024</td><td>9,577</td><td>15,774</td><td>39,426</td></tr><tr><td>Foreign-born share</td><td>30,720</td><td>0.050</td><td>0.032</td><td>0.006</td><td>0.043</td><td>0.258</td></tr></tbody></table>
<figure><img src="image_11.png" /><figcaption>Figure A2: Cross-correlogram. Results from pairwise Pearson correlation tests between variables in the police homicides dataset. The crossed-out boxes indicate a non-significant (*p* > 0.05) correlation.</figcaption></figure>
## 2 Profiles of potential rioters: survey-based analyses

To test whether the profiles of police homicides conducive to riots match with the profile of potential rioters (identity hypothesis), we relied on the French data from two international surveys on teenagers: POLIS and UPYC (Oberwittler and Roché, 2017). In France, POLIS and UPYC were respectively conducted in Fall 2012 and Spring 2015. The respondents are from junior high school only in UPYC (N = 9,092), junior and senior high school in POLIS (N = 13,979). While UPYC covers the department of Marseille, with two cities Marseille and Aix-en-Provence (an area larger than the metropolis), POLIS covers the two metropolitan areas of Lyon and Grenoble. The classes have been randomly selected based on a list of all “levels” in all relevant schools. The questionnaire is self-administered during a course time (55 minutes) with a computer, and when not available with pencil and paper.

We used two measures of teenagers' propensity to participate in riots. A question asks teenagers whether they agree or disagree with the following statement: "If there are riots against the police, I will take part". Based on this, we created a binary measure of the *desire to riot* (distinguishing people that “tend to agree” or “totally agree” from the others). Another question asks teenagers “if, in the last 12 months, they have ever set fire to a garbage can, a car or other vehicle, a building...”.16 Based on that question, we generated a binary variable distinguishing respondents who indicated that they committed *arson during the last 12 months*. As shown by Figure A3, the share of teenagers who desire to riot and reported arsons correlate with the actual number of intentional destruction and damages per inhabitants in their city of living – according to the Ministry of Interior's figures for 2016.

We used several questions from the survey that capture similar personal characteristics to those in the police homicides dataset. Summary statistics and cross-correlations are provided in Table A3 and Figure A4 for POLIS and UPYC. We included the respondents' age and *gender*. Respondents' *French citizenship* was only asked for in UPYC. For POLIS, we used the respondents' country of birth as a proxy for citizenship: we assessed that respondents' born outside France have a higher probability of being Non-French. We coded respondents as having a *non-European migratory origin* if at least one of their two parents was born in a non-European country. Both surveys include a question on self-assessed *academic results*, but with a different scales: a seven-points scale in POLIS from “poor, I’m among the weakest in my class” to “excellent, I’m probably one of the best in my class”; and a five-points scale in UPYC from “very poor student” to “very good student”. We re-scaled both variables from 0 (very bad results) to 1 (very good results).

We also included several indicators at the individual and aggregate levels to assess respondents' situation and experiences with the police. At the individual-level, we used two variables measuring previous contact with the police and involvement in delinquency. We used a binary variable measuring teenagers reporting at least one *contact with the police as suspect during the last 12 months*.17 The second binary variable distinguishes teenagers reporting having *sold drugs during the last 12 months*.18 Based on information about the city where respondents live, we distinguished

<footnote>16The formulation is slightly different in UPYC: respondents indicate “if, in the last 12 months, they have ever set fire to or tried to set fire to a garbage can, letterbox, car, moped etc...?”.</footnote>

<footnote>17The exact question is “Over the past 1 year, how many times have you personally been in contact with the Police or Gendarmerie in or around your town because they suspected you of having committed a crime (e.g. theft, assault...)?”</footnote>

<footnote>18The exact question is: “In the last 12 months, have you ever sold drugs (Hashish, Ecstasy, other products...)?”</footnote>
Figure A3: Correlation between responses to the survey (in cities with more than 50 survey respondents) and deliberate damages and destruction at the city level according to the Ministry of Interior in 2016

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>
respondents living in *Gendarmerie* or *Police Nationale* areas (based on territorial jurisdiction provided by the Ministry of the Interior).<fnref n="19" /> We also distinguished respondents living in *Quartiers prioritaires de la politique de la ville (QPPV)*<fnref n="20" />, i.e. sensitive urban areas in which units from the BAC mostly operate.

<table><caption>Table A3: Summary Statistics (POLIS and UPYC)</caption><caption>POLIS</caption><thead><tr><th>Variable</th><th>Valid N</th><th>Mean</th><th>Std. dev.</th><th>Min</th><th>Median</th><th>Max</th></tr></thead><tbody><tr><td><strong>Desire to riot</strong></td><td>13049</td><td>0.16</td><td>0.37</td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>Arson last 12 months</strong></td><td>13206</td><td>0.063</td><td>0.24</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Age</td><td>13543</td><td>15</td><td>1.4</td><td>11</td><td>14</td><td>22</td></tr><tr><td>Male</td><td>13604</td><td>0.51</td><td>0.5</td><td>0</td><td>1</td><td>1</td></tr><tr><td>Non French (assessed)</td><td>13529</td><td>0.092</td><td>0.29</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Non-Europe migratory origin</td><td>13390</td><td>0.29</td><td>0.45</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Academic results</td><td>13525</td><td>0.63</td><td>0.18</td><td>0</td><td>0.5</td><td>1</td></tr><tr><td>Contact with police as suspect last 12 months</td><td>12893</td><td>0.084</td><td>0.28</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Sold drugs last 12 months</td><td>13184</td><td>0.042</td><td>0.2</td><td>0</td><td>0</td><td>1</td></tr><tr><td><em>Police Nationale</em> area</td><td>13424</td><td>0.62</td><td>0.49</td><td>0</td><td>1</td><td>1</td></tr><tr><td>Sensitive urban area (QPPV)</td><td>13495</td><td>0.55</td><td>0.5</td><td>0</td><td>1</td><td>1</td></tr></tbody></table>

<table><caption>UPYC</caption><thead><tr><th>Variable</th><th>Valid N</th><th>Mean</th><th>Std. dev.</th><th>Min</th><th>Median</th><th>Max</th></tr></thead><tbody><tr><td><strong>Desire to riot</strong></td><td>8376</td><td>0.18</td><td>0.38</td><td>0</td><td>0</td><td>1</td></tr><tr><td><strong>Arson last 12 months</strong></td><td>9023</td><td>0.064</td><td>0.24</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Age</td><td>9043</td><td>14</td><td>1.1</td><td>10</td><td>14</td><td>19</td></tr><tr><td>Male</td><td>9055</td><td>0.49</td><td>0.5</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Non French</td><td>9074</td><td>0.067</td><td>0.25</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Non-Europe migratory origin</td><td>8943</td><td>0.28</td><td>0.45</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Academic results</td><td>9037</td><td>0.65</td><td>0.24</td><td>0</td><td>0.67</td><td>1</td></tr><tr><td>Contact with police as suspect last 12 months</td><td>8453</td><td>0.037</td><td>0.19</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Sold drugs last 12 months</td><td>9021</td><td>0.032</td><td>0.18</td><td>0</td><td>0</td><td>1</td></tr><tr><td><em>Police Nationale</em> area</td><td>8235</td><td>0.72</td><td>0.45</td><td>0</td><td>1</td><td>1</td></tr><tr><td>Sensitive urban area (QPPV)</td><td>8324</td><td>0.65</td><td>0.48</td><td>0</td><td>1</td><td>1</td></tr></tbody></table>

<footnote n="19">¹⁹https://www.data.gouv.fr/fr/datasets/competence-territoriale-gendarmerie-et-police-nationales/</footnote>

<footnote n="20">²⁰https://sig.ville.gouv.fr/atlas/ZUS</footnote>
Figure A4: Cross-correlogram. Results from pairwise Pearson correlation tests between variables in the two surveys. The crossed-out boxes indicate a non-significant (*p* > 0.05) correlation.

POLIS

<figure><img src="image_14.png" /></figure>

UPYC

<figure><img src="image_15.png" /></figure>
Figure A5 present standardized coefficients from logistic regression analyses of adolescents’ desire to riot and recent involvement in arson (see Table A5 in the appendix for unstandardized regression coefficients). We included together in the models all the variables we presented in the method section. Results confirm the relevance of almost all variables. Male youth with non-European immigrant backgrounds and lower academic achievement are more likely to both want to riot and have committed arson, consistent with findings from previous studies in other countries (Schneider, 2014; Moutselos, 2020). The coefficient related to age is not significant in most specifications. However, given that the survey is conducted among teenagers, this result is not surprising. Note also that age is negatively associated with prior arson in the POLIS survey, which includes not only young teenagers (from junior high school on) as UPYC, but also older teenagers (from senior high school on), which is a non-negligible result. Finally, despite the overrepresentation of non-French citizens among the victims of police homicide (see Figure 3), foreigners do not seem to be particularly attracted to rioting.

When we turn on the potentially conflicting relationships with the police, they are also informative. Having sold drugs and being suspected by the police increases the likelihood of both the desire to riot and prior arson in the two samples. Contextual variables are less predictive, but also seem to be associated with rioting propensity. Living in sensitive urban areas where riots are more likely to occur (Moutselos, 2020) is positively associated with both indicators, although the coefficient only reaches significance for the desire to riot in the POLIS sample. Living in areas where the *Police Nationale* (as opposed to the *Gendarmerie*) operates is not clearly associated with the dependent variables, although a positive relationship with prior arson is observed in the UPYC sample.

Overall, we expect all of these characteristics to be relevant to understanding which victims of police homicide are more likely to be the cause of a riot. At the individual level, French nationality appears to be less relevant to riot motivation, while at the aggregate level, interaction with the *Police Nationale* is not strongly associated with riot behavior.
<figure><img src="image_16.png" /><figcaption>Figure A5: Standardized coefficients from logistic regression analyses of the adolescents' propensity to riot in the POLIS and UPYC surveys. Detailed estimates are presented in Table A4</figcaption></figure>

*Note:* Standardized log-odds coefficients from logistic regression models. The unit of observation is an adolescent who responded to the POLIS survey (blue) or the UPYC survey (red). The dependent variables are binary indicators of reported desire to riot (circle) and arson in the past 12 months (triangle). All predictors are entered simultaneously. Each point represents a regression coefficient; vertical bars indicate 95% confidence intervals.
<table><caption>Table A4: Adolescents' characteristics predicting the propensity to participate in riots (standardized coefficients)</caption><thead><tr><th></th><th colspan="4">Dependent variable:</th></tr><tr><th></th><th colspan="2">Desire to riot</th><th colspan="2">Arson last 12 months</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Age</td><td>0.036<br>(0.035)</td><td>0.040<br>(0.043)</td><td>-0.177**<br>(0.063)</td><td>0.080<br>(0.052)</td></tr><tr><td>Male</td><td>0.309***<br>(0.030)</td><td>0.273***<br>(0.041)</td><td>0.437***<br>(0.043)</td><td>0.523***<br>(0.061)</td></tr><tr><td>Non French (assessed in POLIS)</td><td>-0.012<br>(0.024)</td><td>0.047<br>(0.028)</td><td>-0.018<br>(0.036)</td><td>0.013<br>(0.066)</td></tr><tr><td>Non-Europe migratory origin</td><td>0.225***<br>(0.031)</td><td>0.201***<br>(0.037)</td><td>0.157***<br>(0.038)</td><td>0.186**<br>(0.058)</td></tr><tr><td>Academic results</td><td>-0.393***<br>(0.036)</td><td>-0.320***<br>(0.023)</td><td>-0.408***<br>(0.053)</td><td>-0.237***<br>(0.046)</td></tr><tr><td>Contact with police as suspect</td><td>0.346***<br>(0.019)</td><td>0.202***<br>(0.029)</td><td>0.395***<br>(0.022)</td><td>0.404***<br>(0.037)</td></tr><tr><td>Sold drugs last 12 months</td><td>0.286***<br>(0.022)</td><td>0.296***<br>(0.025)</td><td>0.394***<br>(0.027)</td><td>0.471***<br>(0.028)</td></tr><tr><td>Police Nationale area</td><td>-0.048<br>(0.050)</td><td>0.033<br>(0.049)</td><td>0.005<br>(0.072)</td><td>0.155*<br>(0.066)</td></tr><tr><td>Sensitive urban area (QPPV)</td><td>0.207***<br>(0.055)</td><td>0.062<br>(0.053)</td><td>0.127<br>(0.077)</td><td>0.077<br>(0.065)</td></tr><tr><td>Survey</td><td>POLIS</td><td>UPYC</td><td>POLIS</td><td>UPYC</td></tr><tr><td>Observations</td><td>11,829</td><td>7,747</td><td>12,007</td><td>7,897</td></tr></tbody></table>

Note: Logistic regression coefficients (standardized). The unit of observation is an adolescent who responded to the POLIS or UPYC survey. The dependent variables are binary measures of respondents' desire to riot (Models 1 and 2) and involvement in arson over the past 12 months (Models 3 and 4). Models 1 and 3 are based on the POLIS survey; Models 2 and 4 on the UPYC survey. All predictors are entered simultaneously.

Standard errors clustered by city in parentheses.

* p < 0.05, ** p < 0.01, *** p < 0.001
<table><caption>Table A5: Adolescents' characteristics predicting the propensity to participate in riots (unstandardized coefficients)</caption><thead><tr><th></th><th colspan="4">Dependent variable:</th></tr><tr><th></th><th colspan="2">Desire to riot</th><th colspan="2">Arson last 12 months</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Age</td><td>0.027<br>(0.026)</td><td>0.029<br>(0.032)</td><td>-0.131**<br>(0.047)</td><td>0.059<br>(0.039)</td></tr><tr><td>Male</td><td>0.618***<br>(0.060)</td><td>0.546***<br>(0.083)</td><td>0.875***<br>(0.087)</td><td>1.047***<br>(0.122)</td></tr><tr><td>Non French (assessed in POLIS)</td><td>-0.042<br>(0.089)</td><td>0.170<br>(0.101)</td><td>-0.067<br>(0.133)</td><td>0.047<br>(0.242)</td></tr><tr><td>Non-Europe migratory origin</td><td>0.498***<br>(0.069)</td><td>0.445***<br>(0.082)</td><td>0.349***<br>(0.084)</td><td>0.413**<br>(0.128)</td></tr><tr><td>Academic results</td><td>-1.902***<br>(0.172)</td><td>-1.547***<br>(0.111)</td><td>-1.973***<br>(0.255)</td><td>-1.146***<br>(0.221)</td></tr><tr><td>Contact with police as suspect</td><td>1.401***<br>(0.077)</td><td>0.816***<br>(0.116)</td><td>1.599***<br>(0.088)</td><td>1.634***<br>(0.150)</td></tr><tr><td>Sold drugs last 12 months</td><td>1.500***<br>(0.117)</td><td>1.553***<br>(0.134)</td><td>2.070***<br>(0.143)</td><td>2.473***<br>(0.149)</td></tr><tr><td>Police Nationale area</td><td>-0.101<br>(0.105)</td><td>0.070<br>(0.103)</td><td>0.011<br>(0.152)</td><td>0.327*<br>(0.140)</td></tr><tr><td>Sensitive urban area (QPPV)</td><td>0.421***<br>(0.112)</td><td>0.125<br>(0.108)</td><td>0.259<br>(0.156)</td><td>0.157<br>(0.133)</td></tr><tr><td>Survey</td><td>POLIS</td><td>UPYC</td><td>POLIS</td><td>UPYC</td></tr><tr><td>Observations</td><td>11,829</td><td>7,747</td><td>12,007</td><td>7,897</td></tr></tbody></table>

Note: Logistic regression coefficients (unstandardized). The unit of observation is an adolescent who responded to the POLIS or UPYC survey. The dependent variables are binary measures of respondents' desire to riot (Models 1 and 2) and involvement in arson over the past 12 months (Models 3 and 4). Models 1 and 3 are based on the POLIS survey; Models 2 and 4 on the UPYC survey. All predictors are entered simultaneously.

Standard errors clustered by city in parentheses.

* p < 0.05, ** p < 0.01, *** p < 0.001
3 List of police homicides with identified riots in the press

<table><caption>Table A6: Police homicides with identified riots based on press searches</caption><thead><tr><th>City</th><th>Death date</th><th>Moment of death</th><th>Riots starting date</th><th>Moment of riots starting</th><th>Riots before or after death?</th></tr></thead><tbody><tr><td>Fontainebleau</td><td>17/12/1997</td><td>Night</td><td>18/12/1997</td><td>Evening</td><td>After</td></tr><tr><td>Lyon</td><td>18/12/1997</td><td>Evening</td><td>19/12/1997</td><td>Evening</td><td>After</td></tr><tr><td>Toulouse</td><td>12/12/1998</td><td>Night</td><td>13/12/1998</td><td>Night</td><td>After</td></tr><tr><td>Marseille</td><td>16/03/1999</td><td>Afternoon</td><td>16/03/1999</td><td>Night</td><td>After</td></tr><tr><td>Bourg en Bresse</td><td>11/06/1999</td><td>Night</td><td>11/06/1999</td><td>Night</td><td>After</td></tr><tr><td>Lille</td><td>15/04/2000</td><td>Night</td><td>15/04/2000</td><td>Night</td><td>After</td></tr><tr><td>Combs-la-Ville</td><td>17/09/2000</td><td>Morning</td><td>17/09/2000</td><td>Night</td><td>After</td></tr><tr><td>Montigny-les-Cormeilles</td><td>19/06/2001</td><td>Afternoon</td><td>19/06/2001</td><td>Night</td><td>After</td></tr><tr><td>Metz</td><td>04/07/2001</td><td>Afternoon</td><td>04/07/2001</td><td>Night</td><td>After</td></tr><tr><td>Thonon-les-Bains</td><td>12/10/2001</td><td>Night</td><td>13/10/2001</td><td>Evening</td><td>After</td></tr><tr><td>Antibes</td><td>05/11/2001</td><td>Afternoon</td><td>05/11/2011</td><td>Evening</td><td>After</td></tr><tr><td>Neuilly-sur-Marne</td><td>26/12/2001</td><td>Morning</td><td>26/12/2001</td><td>Night</td><td>After</td></tr><tr><td>Paris</td><td>02/01/2002</td><td>Night</td><td>02/01/2002</td><td>Night</td><td>After</td></tr><tr><td>Dammarie-les-Lys</td><td>21/05/2002</td><td>Afternoon</td><td>30/05/2002</td><td>Night</td><td>After</td></tr><tr><td>Dammarie-les-Lys</td><td>23/05/2002</td><td>Evening</td><td>30/05/2002</td><td>Night</td><td>After</td></tr><tr><td>Strasbourg</td><td>18/10/2002</td><td>Night</td><td>18/10/2002</td><td>Night</td><td>After</td></tr><tr><td>Durfort</td><td>02/03/2003</td><td>Night</td><td>03/03/2003</td><td>Night</td><td>After</td></tr><tr><td>Neuville-en-Ferrain</td><td>07/01/2004</td><td>Day</td><td>12/01/2004</td><td>Night</td><td>After</td></tr><tr><td>Thiais</td><td>13/03/2004</td><td>Morning</td><td>13/03/2004</td><td>Night</td><td>After</td></tr><tr><td>Colmar</td><td>19/03/2004</td><td>Afternoon</td><td>18/03/2004</td><td>Night</td><td>Before*</td></tr><tr><td>Aubervilliers</td><td>01/04/2005</td><td>Afternoon</td><td>01/04/2005</td><td>Night</td><td>After</td></tr><tr><td>Clichy-sous-Bois</td><td>27/10/2005</td><td>Afternoon</td><td>27/10/2005</td><td>Night</td><td>After</td></tr><tr><td>Paris</td><td>02/02/2006</td><td>Evening</td><td>04/02/2006</td><td>Evening</td><td>After</td></tr><tr><td>Montpellier</td><td>16/05/2006</td><td>Night</td><td>17/05/2006</td><td>Night</td><td>After</td></tr><tr><td>Paris</td><td>17/06/2007</td><td>Night</td><td>06/07/2007</td><td>Evening</td><td>After</td></tr><tr><td>Villiers-le-Bel</td><td>25/11/2007</td><td>Afternoon</td><td>25/11/2007</td><td>Evening</td><td>After</td></tr><tr><td>Vincennes</td><td>21/06/2008</td><td>Night</td><td>22/06/2008</td><td>Afternoon</td><td>After</td></tr><tr><td>Romans-sur-Isère</td><td>29/09/2008</td><td>Night</td><td>30/09/2008</td><td>Night</td><td>After</td></tr><tr><td>Chantilly</td><td>28/11/2008</td><td>Night</td><td>28/11/2008</td><td>Night</td><td>After</td></tr><tr><td>Coutevroult</td><td>07/03/2009</td><td>Afternoon</td><td>09/03/2009</td><td>Evening</td><td>After</td></tr><tr><td>Chambon-Feugerolles</td><td>08/07/2009</td><td>Afternoon</td><td>06/07/2009</td><td>Night</td><td>Before*</td></tr><tr><td>Saint-Pierre-du-Vauvray</td><td>10/07/2009</td><td>Evening</td><td>11/07/2009</td><td>Night</td><td>After</td></tr><tr><td>Bagnolet</td><td>09/08/2009</td><td>Evening</td><td>09/08/2009</td><td>Night</td><td>After</td></tr><tr><td>Lille</td><td>26/09/2009</td><td>Morning</td><td>26/09/2009</td><td>Night</td><td>After</td></tr><tr><td>Valentigney</td><td>13/11/2009</td><td>Afternoon</td><td>15/11/2009</td><td>Evening</td><td>After</td></tr><tr><td>Woippy</td><td>19/01/2010</td><td>Night</td><td>20/01/2010</td><td>Night</td><td>After</td></tr><tr><td>Saint-Aignan</td><td>16/07/2010</td><td>Evening</td><td>18/07/2010</td><td>Morning</td><td>After</td></tr><tr><td>Grenoble</td><td>16/07/2010</td><td>Night</td><td>16/07/2010</td><td>Night</td><td>After</td></tr><tr><td>Bourg-en-Bresse</td><td>11/10/2011</td><td>Afternoon</td><td>11/10/2011</td><td>Night</td><td>After</td></tr><tr><td>Clermont-Ferrand</td><td>09/01/2012</td><td>Night</td><td>05/01/2012</td><td>Night</td><td>Before*</td></tr><tr><td>Aulnay-sous-Bois</td><td>11/01/2012</td><td>Evening</td><td>11/01/2012</td><td>Night</td><td>After</td></tr><tr><td>Montigny-en-Gohelle</td><td>28/03/2013</td><td>Morning</td><td>28/03/2013</td><td>Afternoon</td><td>After</td></tr><tr><td>Cayenne</td><td>16/12/2013</td><td>Night</td><td>16/12/2013</td><td>Night</td><td>After</td></tr><tr><td>Haguenau</td><td>02/10/2014</td><td>Night</td><td>03/10/2014</td><td>Evening</td><td>After</td></tr></tbody></table>

*Continued on next page*
<table><thead><tr><th>City</th><th>Death date</th><th>Moment of death</th><th>Riots starting date</th><th>Moment of riots starting</th><th>Riots before or after death?</th></tr></thead><tbody><tr><td>Romans-sur-Isère</td><td>15/02/2015</td><td>Morning</td><td>15/02/2015</td><td>Night</td><td>After</td></tr><tr><td>Tourcoing</td><td>01/06/2015</td><td>Night</td><td>01/06/2015</td><td>Night</td><td>After</td></tr><tr><td>Beaumont-sur-Oise</td><td>19/07/2016</td><td>Evening</td><td>19/07/2016</td><td>Night</td><td>After</td></tr><tr><td>Nouméa</td><td>29/10/2016</td><td>Night</td><td>30/10/2016</td><td>Morning</td><td>After</td></tr><tr><td>Paris</td><td>26/03/2017</td><td>Evening</td><td>27/03/2017</td><td>Evening</td><td>After</td></tr><tr><td>Lille</td><td>15/12/2017</td><td>Evening</td><td>15/12/2017</td><td>Night</td><td>After</td></tr><tr><td>Nantes</td><td>03/07/2018</td><td>Evening</td><td>03/07/2018</td><td>Night</td><td>After</td></tr><tr><td>Grenoble</td><td>02/03/2019</td><td>Night</td><td>03/03/2019</td><td>Night</td><td>After</td></tr><tr><td>Auch</td><td>13/09/2019</td><td>Afternoon</td><td>11/08/2019</td><td>Night</td><td>After</td></tr><tr><td>Saint-Denis</td><td>10/01/2020</td><td>Afternoon</td><td>10/01/2020</td><td>Evening</td><td>After</td></tr><tr><td>Sevran</td><td>26/03/2022</td><td>Afternoon</td><td>26/03/2022</td><td>Night</td><td>After</td></tr></tbody></table>

<footnote>*Riots occurred after police intervention but before the victim's death</footnote>
# 4 Effect of police homicides on riots: alternative regression specifications

<table><caption>Table A7: Effect of police homicides on private property arsons (department × month, 1996–2022). Negative binomial regression models.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>Police homicide</td><td>0.0264<br>(0.0213)</td><td>0.0334<br>(0.0208)</td><td>0.0413*<br>(0.0179)</td></tr><tr><td>Department fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Month fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Controls</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Department × linear time trend</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>32080</td><td>30720</td><td>30720</td></tr><tr><td>Pseudo R<sup>2</sup></td><td>0.250</td><td>0.251</td><td>0.263</td></tr></tbody></table>

*Notes:* The unit of observation is a department-month over the period 1996–2022. The dependent variable is the monthly number of private property arsons. The main predictor, *Police homicide*, is a binary variable equal to 1 if at least one police homicide occurred in that department-month. Column (1) is estimated without controls; column (2) adds demographic and economic controls: total population, youth share (15–24), youth sex ratio, unemployment rate, average income, and share of foreign-born residents; column (3) further includes department-specific linear trends. All models include department and month fixed effects. Standard errors are clustered at the department level.

Standard errors in parentheses.

* p < 0.05, ** p < 0.01, *** p < 0.001
<table><caption>Table A8: Effect of police homicides on private property arsons **per capita** (department × month, 1996–2022).</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>Police homicide</td><td>0.193<br>(0.137)</td><td>0.250<br>(0.141)</td><td>0.244<br>(0.127)</td></tr><tr><td>Department fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Month fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Controls</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Department × linear time trend</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>31912</td><td>30720</td><td>30720</td></tr><tr><td>R<sup>2</sup></td><td>0.595</td><td>0.601</td><td>0.624</td></tr></tbody></table>

*Notes:* The unit of observation is a department-month over the period 1996–2022. The dependent variable is the monthly number of private property arsons per 100,000 inhabitants. The main predictor, *Police homicide*, is a binary variable equal to 1 if at least one police homicide occurred in that department-month. Column (1) is estimated without controls; column (2) adds demographic and economic controls: total population, youth share (15–24), youth sex ratio, unemployment rate, average income, and share of foreign-born residents; column (3) further includes department-specific linear trends. All models include department and month fixed effects. Standard errors are clustered at the department level.
Standard errors in parentheses.

* p < 0.05, ** p < 0.01, *** p < 0.001
<table><caption>Table A9: Effect of police homicides on private property arsons (department × month, 1996–2022). **Test of cumulative effect.**</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>Police homicide</td><td>2.639<br>(2.424)</td><td>2.789<br>(2.477)</td><td>2.196<br>(2.359)</td></tr><tr><td>Police homicide past 12 months</td><td>-0.000725<br>(1.459)</td><td>0.0401<br>(1.465)</td><td>-0.971<br>(1.409)</td></tr><tr><td>Department fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Month fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Controls</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Department × linear time trend</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>32080</td><td>30720</td><td>30720</td></tr><tr><td>R<sup>2</sup></td><td>0.764</td><td>0.768</td><td>0.780</td></tr></tbody></table>

*Notes:* The unit of observation is a department-month between 1996 and 2022. The dependent variable is the monthly number of private property arsons. The main predictor, *Police homicide*, is a binary variable equal to 1 if at least one police homicide occurred in that department-month. Column (1) is estimated without controls; column (2) adds demographic and economic controls: total population, youth share (15–24), youth sex ratio, unemployment rate, average income, and share of foreign-born residents; column (3) further includes department-specific linear trends. All models include department and month fixed effects. Standard errors are clustered at the department level.

Standard errors in parentheses.

* p < 0.05, ** p < 0.01, *** p < 0.001
<table><caption>Table A10: Effect of police homicides followed or not by riots as reported in the press on arson. Falsification test for reverse causality.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Police homicide with riots<br>identified in the press</td><td>29.94***<br>(7.361)</td><td>30.66***<br>(7.479)</td><td>28.94***<br>(7.152)</td><td></td><td></td><td></td></tr><tr><td>Police homicide without riots<br>identified in the press</td><td></td><td></td><td></td><td>-1.189<br>(2.715)</td><td>-1.166<br>(2.782)</td><td>-1.562<br>(2.707)</td></tr><tr><td>Department fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Month fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Controls</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Department × linear time trend</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>32080</td><td>30720</td><td>30720</td><td>32080</td><td>30720</td><td>30720</td></tr><tr><td>R<sup>2</sup></td><td>0.765</td><td>0.769</td><td>0.781</td><td>0.764</td><td>0.768</td><td>0.780</td></tr></tbody></table>

*Notes:* The unit of observation is a department-month between 1996 and 2022. The dependent variable is the number of private property arsons in that department-month. The main explanatory variables are two binary indicators: one equal to 1 if a police homicide reported in the press as followed by riots occurred in that month and department; the other equal to 1 if a police homicide not associated with riots in the press occurred. All models include department and month fixed effects. OLS estimates with standard errors clustered by department. Columns (2) and (5) include demographic and economic controls: total population, youth share (ages 15–24), youth sex ratio, unemployment rate, average income, and share of foreign-born residents. Columns (3) and (6) add department-specific linear time trends. Standard errors in parentheses.

* p < 0.05, ** p < 0.01, *** p < 0.001
# 5 Event study analyses

## 5.1 Difference-in-differences event-study analyses

To assess the effect of police homicides on riots, we additionally conducted event-study analyses. We relied on a dynamic two-way fixed effects (TWFE) event-study design, with staggered treatments (see Clarke and Tapia-Schythe, 2021). Here, the treatment is the occurrence of one or multiple police homicide(s) in a department during a given month. We tested the effect of time (in months) relative to the occurrence of police homicide on the number of private property arsons in the given department in which the homicide took place.

That is, for each police homicide in a given department, we identified the month during which it took place ($t_0$), each month preceding it ($t_{-1}$, $t_{-2}$, $t_{-3}$, etc.) and each months following it ($t_1$, $t_2$, $t_3$, etc.). We thus have for each police homicide a window of time surrounding the event of a chosen size. By default, we used $T_0 = -12$ (lowest lag before the treatment is $t_{-12}$) and $T_1 = 12$ (highest lead after the treatment is $t_{+12}$). For departments in which multiple homicides took place, we assigned months to the window of time of the closest police homicide. We are thus able to test whether $t_0$, and possibly the following months, are associated with a significantly higher number of private property arsons in the department compared to previous (and following) months.

In our baseline specification, we relied on OLS to estimate:

$$Y_{gt} = \alpha + \sum_{k=T_0}^{-2} \beta_k \times \text{Homicide}_{gk} + \sum_{k=0}^{T_1} \beta_k \times \text{Homicide}_{gk} + \sum_{i=1}^{g} \beta_g \times \text{Group}_g + \sum_{i=1}^{t} \beta_t \times \text{Month}_t + \varepsilon_{gt}$$

Where: $Y_{gt}$ is the monthly number of private property arsons in a given departments during a given month. More specifically, $g$ designates each group: i.e. each departments or each window of time surrounding a given police homicide by departments in departments in which multiple police homicides occurred during the study period. $t$ designates each time period: i.e. each individual month. $\text{Homicide}_{gk}$ is a dummy variable, equaling 1 if the observation's periods relative to the group $g$'s first month with an homicide is the same value as $k$; 0 otherwise, and 0 for all departments in which no police homicides happened in the study period. As in most event studies, we used the $t_{-1}$ lag as the dropped reference. $\sum_{i=1}^{t} \beta_t \times \text{Month}_t$ corresponds to fixed effects for each individual month and $\sum_{i=1}^{g} \beta_g \times \text{Group}_g$ corresponds to fixed effects for each departments or each window of time surrounding a given police homicide by departments in departments in which multiple police homicides occurred during the study period. The fixed effects ensure that monthly variations are net of national variations in private property arsons at a specific months and net of the average variations in private property arsons across departments (and within departments at each period surrounding police homicides for those with multiple homicides). We clustered standard errors at the group level.

The upper part of Figure A6 present results from the TWFE event study for all homicides. More specifically, it presents estimates of all $\beta_k$ corresponding to the individual effects of lags and leads months relative to the occurrence of a police homicide at $t_0$. Results confirm that the month during which an identified police homicide happens ($t_0$) is related to a significantly higher number of private property arsons in the department of occurrence compared to $t_{-1}$. No significant effect is observed in any other (preceding or following) month. Results indicate that, compared to the previous month, a month with an identified police homicides is on average associated with
4.6 additional private property arsons (95% CI 0.6 to 8.5), which corresponds to an average 18% increase.

The mid and lower parts of Figure A6 correspond to the falsification test to reject reverse causality. They confirm that this effect is only driven by homicides with identified riots in the press. Virtually no effect is observed for homicides without identified riots (the majority of cases). Given that evidence about the timing of riots excludes a reverse causality mechanism in the case of homicides with identified riots (see Table A6), we can confidently conclude that the overall estimate captures the causal effect of police homicides on riots.

We additionally verified the robustness of these results by including demographic and economic controls in the event-study specification (total population, youth share, youth sex ratio, unemployment rate, average income, and share of foreign-born residents). We further included department-specific linear time trends to account for potentially heterogeneous evolutions in baseline arson levels across departments. As shown in Figures A11 and A12, the results remain unchanged.

As shown by Figure A7, we obtain similar results using a negative binomial regression models, that can be considered appropriate for our dependent variable (a count with overdispersion). Besides, we obtain similar results when using OLS on the number of private property arsons per capita (Figure A8 in the appendix).

Various studies have demonstrated that standard TWFE models can produce biased estimates when treatments occur at different times and treatment effects are heterogeneous (De Chaisemartin and d'Haultfoeuille, 2020; Callaway and Sant'Anna, 2021; Sun and Abraham, 2021a). To address this concern, we performed analyses using alternative estimators designed for dynamic difference-in-differences with heterogeneous treatment effects. Figure A9 presents results obtained with the estimator proposed by De Chaisemartin and d'Haultfoeuille (2020), while Figure A10 shows results based on the estimator developed by Sun and Abraham (2021b). In both cases, the results are consistent with our main findings.
<figure><img src="image_17.png" /><figcaption>Figure A6: Effect of police homicides on the number of private property arsons in the department of occurrence.</figcaption></figure>

*Note:* Each panel shows coefficients from a dynamic event-study model using OLS, with department-month as the unit of observation. The dependent variable is the number of private property arsons per month. Each dot represents the coefficient for a given time relative to the police homicide (in months), with 95% confidence intervals. The omitted reference category is *t*−1 (the month preceding the homicide). All models include fixed effects for month and group (window of time by department); standard errors are clustered by group. The first panel shows results for all homicides; the second panel for homicides with identified riots in the press; the third for those without.
<figure><img src="image_18.png" /><figcaption>Figure A7: Effect of police homicides on the number of private property arsons in the department of occurrence (Negative Binomial specification).</figcaption></figure>

*Note:* Each panel reports coefficients from a dynamic event-study model estimated with a negative binomial regression. The unit of observation is department-month. The dependent variable is the number of private property arsons per month. Each dot represents the coefficient for a given time relative to the police homicide (in months), with 95% confidence intervals. The omitted reference category is $t_{-1}$ (the month preceding the homicide). All models include fixed effects for month and group (window of time by department); standard errors are clustered by group. The first panel shows results for all homicides; the second panel for homicides with identified riots in the press; the third for those without.
<figure><img src="image_19.png" /><figcaption>Figure A8: Effect of police homicides on the number of private property arsons per 100,000 inhabitants in the department of occurrence.</figcaption></figure>

*Note:* Each panel shows coefficients from a dynamic event-study model using OLS, with department-month as the unit of observation. The dependent variable is the number of private property arsons per 100,000 inhabitants per month. Each dot represents the coefficient for a given time relative to the police homicide (in months), with 95% confidence intervals. The omitted reference category is $t_{-1}$ (the month preceding the homicide). All models include fixed effects for month and group (window of time by department); standard errors are clustered by group. The first panel shows results for all homicides; the second panel for homicides with identified riots in the press; the third for those without.
<figure><img src="image_20.png" /><figcaption>Figure A9: Effect of police homicides on the number of private property arsons (event-study using De Chaisemartin and d'Haultfoeuille (2020) estimator).</figcaption></figure>

*Note:* Event-study estimates using the two-way fixed effects estimator by De Chaisemartin and d'Haultfoeuille (2020), which addresses biases in TWFE with staggered treatment adoption. The unit of observation is department-month. The dependent variable is the number of private property arsons per month. Each dot represents the coefficient for a given time relative to the police homicide (in months), with 95% confidence intervals. The omitted reference category is *t*−1 (the month preceding the homicide). All models include fixed effects for month and group (window of time by department); standard errors are clustered by group. The first panel shows results for all homicides; the second panel for homicides with identified riots in the press; the third for those without.
<figure><img src="image_21.png" /><figcaption>Figure A10: Effect of police homicides on the number of private property arsons (event-study using Sun and Abraham (2021b) estimator).</figcaption></figure>

*Note:* Estimates based on the event-study estimator proposed by Sun and Abraham (2021b), accounting for heterogeneous treatment effects in staggered designs. Unit of observation: department–month. The dependent variable is the number of private property arsons per month. Each dot represents the coefficient for a given time relative to the police homicide (in months), with 95% confidence intervals. The omitted reference category is $t_{-1}$ (the month preceding the homicide). All models include fixed effects for month and group (window of time by department); standard errors are clustered by group. The first panel shows results for all homicides; the second panel for homicides with identified riots in the press; the third for those without.
<figure><img src="image_22.png" /><figcaption>Figure A11: Event-study model (TWFE with controls): effect of the occurrence of a police homicide on the number of private property arsons in the department of occurrence.</figcaption></figure>

*Note:* Same specification as in Figure A6, but including demographic and economic controls: total population, youth share (15–24), youth sex ratio, unemployment rate, average income, and share of foreign-born residents. Unit of observation is department–month. Each dot shows the coefficient for a relative month (vs. t−1), with 95% confidence intervals. Standard errors are clustered by group.
<figure><img src="image_23.png" /><figcaption>Figure A12: Event-study model (TWFE with controls and department-specific linear trends): effect of the occurrence of a police homicide on the number of private property arsons in the department of occurrence.</figcaption></figure>

*Note:* Same specification as in Figure A11, but also including department-specific linear time trends to account for heterogeneous baseline trends in arsons. Results remain robust and substantively unchanged.
## 5.2 Triple-difference event-study analyses

To explore heterogeneity in the effect of police homicides on riots, we implemented an event-study specification with interactions (triple-difference). This approach allows us to assess whether the timing and intensity of arson activity following a police homicide differs systematically depending on selected characteristics of the event.

Specifically, we estimate an extension of our standard TWFE event-study model, interacting the treatment timing dummies with a binary indicator for a given homicide attribute (e.g., whether the victim had a non-European migratory background). Formally, we estimate the following model:

$$Y_{gt} = \alpha + \sum_{\substack{k=T_0 \\ k \neq -1}}^{T_1} (\beta_k \cdot \text{Homicide}_{gk} + \gamma_k \cdot \text{Homicide}_{gk} \times \text{Trait}_g) + \sum_{i=1}^{g} \beta_g \times \text{Group}_g + \sum_{i=1}^{t} \beta_t \times \text{Month}_t + \varepsilon_{gt}$$

Where: $Y_{gt}$ is the number of private property arsons in department-month $(g,t)$. $\text{Homicide}_{gk}$ is a dummy equal to 1 if month $t$ is $k$ periods from the closest homicide in group $g$ (i.e., department × homicide window). $\text{Trait}_g$ is a binary indicator for the homicide characteristic of interest (constant within each group). $\sum_{1}^{t} \beta_t \times \text{Month}_t$ corresponds to fixed effects for each individual month and $\sum_{1}^{g} \beta_g \times \text{Group}_g$ corresponds to fixed effects for each group (i.e., department × homicide window). $\varepsilon_{gt}$ is the error term.

As in our baseline models, the reference period is $t_{-1}$, and standard errors are clustered by group. The key coefficients of interest are the $\gamma_k$ terms, which capture the differential effect of homicide timing for events with the given characteristic compared to those without.

We present separate results for each characteristic available in the police homicide dataset. For continuous characteristics — namely, Age and Assessed academic results — we use binary indicators denoting whether the individual falls below or above the sample median.

The results are displayed in Figures A13 to A23. In each case, we present the coefficients for the interaction terms $\gamma_k$ across the event window (from $t_{-12}$ to $t_{+12}$). Most characteristics show no statistically significant difference, but the estimated interaction effects generally point in the same direction than results presented in the paper. In particular, we observe significant differences at $t_0$ for several characteristics. First, arson activity increases significantly more when the victim had a non-European migratory background. Second, the increase in arson is significantly smaller when the victim was assessed as having high academic results. Third, arson levels rise significantly more at $t_0$ and $t_{+1}$ when the victim was unarmed.
<figure><img src="image_24.png" /><figcaption>Figure A13: Triple-difference event-study: interaction with **Age** of the victim (binary).</figcaption></figure>

*Note:* Each dot represents the interaction coefficient ($\gamma_k$) between the homicide timing and a binary indicator for whether the victim's age is above the median in the police homicide dataset. Lines indicate 95% confidence intervals. Reference period is $t_{-1}$; unit of observation is department-month; standard errors clustered by group.

<figure><img src="image_25.png" /><figcaption>Figure A14: Triple-difference event-study: interaction with **Male** gender of the victim.</figcaption></figure>

*Note:* Each dot represents the interaction coefficient ($\gamma_k$) between the homicide timing and a binary indicator for whether the victim was a male. Lines indicate 95% confidence intervals. Reference period is $t_{-1}$; unit of observation is department-month; standard errors clustered by group.

<figure><img src="image_26.png" /><figcaption>Figure A15: Triple-difference event-study: interaction with **Non French** citizenship of the victim.</figcaption></figure>

*Note:* Each dot represents the interaction coefficient ($\gamma_k$) between the homicide timing and a binary indicator for whether the victim was not a French citizen. Lines indicate 95% confidence intervals. Reference period is $t_{-1}$; unit of observation is department-month; standard errors clustered by group.
Figure A16: Triple-difference event-study: interaction with **Non-Europe migratory origin** of the victim.

<figure><img src="image_27.png" /></figure>

*Note:* Each dot represents the interaction coefficient ($\gamma_k$) between the homicide timing and a binary indicator for whether the victim had an assessed non-European migratory background. Lines indicate 95% confidence intervals. Reference period is $t_{-1}$; unit of observation is department-month; standard errors clustered by group.

Figure A17: Triple-difference event-study: interaction with **Assessed academic results** of the victim.

<figure><img src="image_28.png" /></figure>

*Note:* Each dot represents the interaction coefficient ($\gamma_k$) between the homicide timing and a binary indicator for whether the victim's assessed academic results are above the median in the police homicide dataset. Lines indicate 95% confidence intervals. Reference period is $t_{-1}$; unit of observation is department-month; standard errors clustered by group.

Figure A18: Triple-difference event-study: interaction with the victim's **Death in custody**.

<figure><img src="image_29.png" /></figure>

*Note:* Each dot represents the interaction coefficient ($\gamma_k$) between the homicide timing and a binary indicator for whether the victim's death in custody. Lines indicate 95% confidence intervals. Reference period is $t_{-1}$; unit of observation is department-month; standard errors clustered by group.
<figure><img src="image_30.png" /><figcaption>Figure A19: Triple-difference event-study: interaction with the victim was killed while Unarmed.</figcaption></figure>

*Note:* Each dot represents the interaction coefficient ($\gamma_k$) between the homicide timing and a binary indicator for whether the victim's was killed while unarmed. Lines indicate 95% confidence intervals. Reference period is $t_{-1}$; unit of observation is department-month; standard errors clustered by group.

<figure><img src="image_31.png" /><figcaption>Figure A20: Triple-difference event-study: interaction with involvement of the BAC.</figcaption></figure>

*Note:* Each dot represents the interaction coefficient ($\gamma_k$) between the homicide timing and a binary indicator for whether the homicide involved officers from the BAC. Lines indicate 95% confidence intervals. Reference period is $t_{-1}$; unit of observation is department-month; standard errors clustered by group.

<figure><img src="image_32.png" /><figcaption>Figure A21: Triple-difference event-study: interaction with involvement of the Police Nationale.</figcaption></figure>

*Note:* Each dot represents the interaction coefficient ($\gamma_k$) between the homicide timing and a binary indicator for whether the police force involved was the Police Nationale. Lines indicate 95% confidence intervals. Reference period is $t_{-1}$; unit of observation is department-month; standard errors clustered by group.
Figure A22: Triple-difference event-study: interaction with involvement of police forces *Other* than the *Police Nationale* / *BAC* / *Gendamerie*.

<figure><img src="image_33.png" /></figure>

*Note:* Each dot represents the interaction coefficient ($\gamma_k$) between the homicide timing and a binary indicator for whether the police force involved was not the *Police Nationale* / *BAC* / *Gendamerie*. Lines indicate 95% confidence intervals. Reference period is $t_{-1}$; unit of observation is department-month; standard errors clustered by group.

Figure A23: Triple-difference event-study: interaction with the occurrence of *Multiple deaths*.

<figure><img src="image_34.png" /></figure>

*Note:* Each dot represents the interaction coefficient ($\gamma_k$) between the homicide timing and a binary indicator for whether multiple deaths occured in the same event. Lines indicate 95% confidence intervals. Reference period is $t_{-1}$; unit of observation is department-month; standard errors clustered by group.
# 6 Additional results: characteristics of police homicides

## 6.1 Homicide characteristics and variation in arsons

<table><caption>Table A11: Average relative variation in department number of property arsons depending on homicide characteristics and bivariate statistical tests</caption><thead><tr><th rowspan="2"></th><th colspan="4">Compared months</th><th rowspan="2">Average</th></tr><tr><th>Δ<br>t<sub>0</sub><br>t<sub>-1</sub></th><th>Δ<br>t<sub>0</sub>+t<sub>+</sub>1<br>t<sub>-1</sub>+t<sub>-2</sub></th><th>Δ<br>t<sub>0</sub><br>t<sub>-12</sub></th><th>Δ<br>t<sub>0</sub>+t<sub>+</sub>1<br>t<sub>-12</sub>+t<sub>-11</sub></th></tr></thead><tbody><tr><td><strong>Age</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Pearson correlation coef</td><td>-0.115</td><td>-0.148</td><td>-0.163</td><td>-0.142</td><td>-0.142</td></tr><tr><td>p.value</td><td>0.019*</td><td>0.003**</td><td>0.001**</td><td>0.004**</td><td>0.007**</td></tr><tr><td><strong>Gender</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Female</td><td>0.122</td><td>0.054</td><td>0.305</td><td>0.086</td><td>0.142</td></tr><tr><td>Male</td><td>0.198</td><td>0.176</td><td>0.165</td><td>0.219</td><td>0.189</td></tr><tr><td>p.value</td><td>0.667</td><td>0.545</td><td>0.473</td><td>0.475</td><td>0.540</td></tr><tr><td><strong>Citizenship</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>French</td><td>0.334</td><td>0.336</td><td>0.295</td><td>0.473</td><td>0.359</td></tr><tr><td>Non French</td><td>0.124</td><td>0.169</td><td>0.090</td><td>0.069</td><td>0.113</td></tr><tr><td>p.value</td><td>0.045*</td><td>0.102</td><td>0.015*</td><td>0.004**</td><td>0.041*</td></tr><tr><td><strong>Assessed migratory origin</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Europe</td><td>0.088</td><td>0.075</td><td>0.046</td><td>0.082</td><td>0.072</td></tr><tr><td>Non-Europe</td><td>0.352</td><td>0.304</td><td>0.305</td><td>0.399</td><td>0.340</td></tr><tr><td>p.value</td><td>0.004**</td><td>0.007**</td><td>0.002**</td><td>0.016*</td><td>0.007**</td></tr><tr><td><strong>Assessed academic results</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Pearson correlation coef</td><td>-0.161</td><td>-0.118</td><td>-0.116</td><td>-0.075</td><td>-0.117</td></tr><tr><td>p.value</td><td>0.015*</td><td>0.079</td><td>0.087</td><td>0.270</td><td>0.113</td></tr><tr><td><strong>Homicide type</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Armed person</td><td>0.075</td><td>0.042</td><td>0.083</td><td>0.067</td><td>0.067</td></tr><tr><td>Death in custody</td><td>0.150</td><td>0.038</td><td>-0.014</td><td>0.016</td><td>0.047</td></tr><tr><td>Unarmed person</td><td>0.260</td><td>0.259</td><td>0.246</td><td>0.320</td><td>0.271</td></tr><tr><td>p.value</td><td>0.086</td><td>0.007**</td><td>0.010*</td><td>0.032*</td><td>0.034*</td></tr><tr><td><strong>Police type</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Gendarmerie</td><td>0.052</td><td>0.049</td><td>-0.024</td><td>0.020</td><td>0.024</td></tr><tr><td>BAC</td><td>0.344</td><td>0.476</td><td>0.398</td><td>0.761</td><td>0.495</td></tr><tr><td>Police Nationale</td><td>0.212</td><td>0.125</td><td>0.169</td><td>0.117</td><td>0.156</td></tr><tr><td>Other</td><td>0.175</td><td>0.152</td><td>0.165</td><td>0.085</td><td>0.144</td></tr><tr><td>p.value</td><td>0.097</td><td>0.000***</td><td>0.001**</td><td>0.000***</td><td>0.025*</td></tr><tr><td><strong>Multiple deaths</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Single</td><td>0.180</td><td>0.111</td><td>0.137</td><td>0.124</td><td>0.138</td></tr><tr><td>Multiple</td><td>0.360</td><td>0.783</td><td>0.456</td><td>1.075</td><td>0.668</td></tr><tr><td>p.value</td><td>0.103</td><td>0.013*</td><td>0.005**</td><td>0.021*</td><td>0.035*</td></tr><tr><td><strong>Share of missing values</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Pearson correlation coef</td><td>-0.081</td><td>-0.083</td><td>-0.061</td><td>-0.077</td><td>-0.076</td></tr><tr><td>p.value</td><td>0.080</td><td>0.076</td><td>0.190</td><td>0.103</td><td>0.112</td></tr><tr><td><strong>All police homicides</strong></td><td>0.197</td><td>0.172</td><td>0.167</td><td>0.213</td><td>0.187</td></tr></tbody></table>

Note: numbers in normal font are the average variation in private property arsons in the Department for police homicides of a given characteristics. For instance, deaths of female individuals are related on average with a 0.112 (11.2%) increase in private property arsons between the month of their death and the previous month. P-values are computed based on Pearson correlation test for age, assessed academic results and share of missing values; Student t-test for gender, citizenship, assessed migratory origin and multiple deaths; Anova for homicide type and police type.
<table><caption>Table A12: Homicide characteristics predicting variation in private property arsons (standardized coefficients, complete cases)</caption><thead><tr><th></th><th colspan="4">Dependent variable:</th></tr><tr><th></th><th>Δ<br>t<sub>0</sub><br>t<sub>-1</sub></th><th>Δ<br>t<sub>0</sub>+t<sub>+</sub>1<br>t<sub>-1</sub>+t<sub>-2</sub></th><th>Δ<br>t<sub>0</sub><br>t<sub>-12</sub></th><th>Δ<br>t<sub>0</sub>+t<sub>+</sub>1<br>t<sub>-12</sub>+t<sub>-11</sub></th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Age</td><td>-0.030<br>(0.103)</td><td>-0.051<br>(0.065)</td><td>-0.054<br>(0.076)</td><td>-0.009<br>(0.110)</td></tr><tr><td>Male</td><td>0.037<br>(0.104)</td><td>0.045<br>(0.066)</td><td>0.061<br>(0.076)</td><td>0.040<br>(0.111)</td></tr><tr><td>Non French</td><td>-0.121<br>(0.103)</td><td>-0.0004<br>(0.065)</td><td>-0.080<br>(0.074)</td><td>-0.156<br>(0.110)</td></tr><tr><td>Assessed Non-Europe migratory origin</td><td>0.228*<br>(0.114)</td><td>0.097<br>(0.073)</td><td>0.194*<br>(0.083)</td><td>0.213<br>(0.123)</td></tr><tr><td>Assessed academic results</td><td>-0.068<br>(0.104)</td><td>-0.032<br>(0.065)</td><td>-0.031<br>(0.076)</td><td>-0.023<br>(0.110)</td></tr><tr><td>Homicide type (armed person as ref.)</td><td></td><td></td><td></td><td></td></tr><tr><td>Death in custody</td><td>0.061<br>(0.131)</td><td>0.001<br>(0.082)</td><td>0.030<br>(0.096)</td><td>0.027<br>(0.139)</td></tr><tr><td>Unarmed person</td><td>0.168<br>(0.100)</td><td>0.077<br>(0.063)</td><td>0.080<br>(0.073)</td><td>0.097<br>(0.106)</td></tr><tr><td>Police type (Gendarmerie as ref.)</td><td></td><td></td><td></td><td></td></tr><tr><td>BAC</td><td>0.065<br>(0.106)</td><td>-0.023<br>(0.066)</td><td>0.099<br>(0.078)</td><td>0.172<br>(0.111)</td></tr><tr><td>Police Nationale</td><td>-0.024<br>(0.115)</td><td>-0.050<br>(0.072)</td><td>0.058<br>(0.084)</td><td>-0.016<br>(0.122)</td></tr><tr><td>Other</td><td>0.046<br>(0.085)</td><td>0.014<br>(0.054)</td><td>0.033<br>(0.063)</td><td>-0.012<br>(0.090)</td></tr><tr><td>Multiple deaths</td><td>-0.103<br>(0.122)</td><td>-0.075<br>(0.083)</td><td>0.016<br>(0.090)</td><td>-0.069<br>(0.140)</td></tr><tr><td>Observations</td><td>143</td><td>141</td><td>142</td><td>140</td></tr><tr><td>R<sup>2</sup></td><td>0.122</td><td>0.081</td><td>0.155</td><td>0.098</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.049</td><td>0.002</td><td>0.083</td><td>0.020</td></tr></tbody></table>

Note: Each column reports standardized coefficients from an OLS regression. The unit of observation is the police homicide. The dependent variable is the relative change in private property arsons in the department where a police homicide occurred (with different temporal specification in each model). A value of 0 indicates no change; 1 indicates a 100% increase. Only homicides with complete information on all predictors are included. All listed variables are included simultaneously in the model.

Standard errors in parentheses.

* p < 0.05, ** p < 0.01, *** p < 0.001
<table><caption>Table A13: Homicide characteristics predicting variation in private property arsons (unstandardized coefficients, complete cases)</caption><thead><tr><th></th><th colspan="4">Dependent variable:</th></tr><tr><th></th><th>Δ<br>t<sub>0</sub><br>t<sub>-1</sub></th><th>Δ<br>t<sub>0</sub>+t<sub>+</sub>1<br>t<sub>-1</sub>+t<sub>-2</sub></th><th>Δ<br>t<sub>0</sub><br>t<sub>-12</sub></th><th>Δ<br>t<sub>0</sub>+t<sub>+</sub>1<br>t<sub>-12</sub>+t<sub>-11</sub></th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Age</td><td>-0.002<br>(0.008)</td><td>-0.004<br>(0.005)</td><td>-0.004<br>(0.006)</td><td>-0.001<br>(0.008)</td></tr><tr><td>Male</td><td>0.215<br>(0.617)</td><td>0.268<br>(0.390)</td><td>0.362<br>(0.451)</td><td>0.234<br>(0.655)</td></tr><tr><td>Non French</td><td>-0.265<br>(0.225)</td><td>-0.001<br>(0.143)</td><td>-0.175<br>(0.162)</td><td>-0.343<br>(0.241)</td></tr><tr><td>Assessed Non-Europe migratory origin</td><td>0.460*<br>(0.230)</td><td>0.195<br>(0.148)</td><td>0.393*<br>(0.169)</td><td>0.430<br>(0.248)</td></tr><tr><td>Assessed academic results</td><td>-0.018<br>(0.028)</td><td>-0.008<br>(0.018)</td><td>-0.008<br>(0.020)</td><td>-0.006<br>(0.029)</td></tr><tr><td>Homicide type (armed person as ref.)</td><td></td><td></td><td></td><td></td></tr><tr><td>Death in custody</td><td>0.177<br>(0.379)</td><td>0.003<br>(0.239)</td><td>0.086<br>(0.277)</td><td>0.077<br>(0.402)</td></tr><tr><td>Unarmed person</td><td>0.342<sup>+</sup><br>(0.204)</td><td>0.157<br>(0.128)</td><td>0.163<br>(0.149)</td><td>0.198<br>(0.217)</td></tr><tr><td>Police type (Gendarmerie as ref.)</td><td></td><td></td><td></td><td></td></tr><tr><td>BAC</td><td>0.171<br>(0.279)</td><td>-0.062<br>(0.174)</td><td>0.262<br>(0.205)</td><td>0.455<br>(0.294)</td></tr><tr><td>Police Nationale</td><td>-0.047<br>(0.231)</td><td>-0.101<br>(0.145)</td><td>0.116<br>(0.169)</td><td>-0.032<br>(0.246)</td></tr><tr><td>Other</td><td>0.222<br>(0.414)</td><td>0.066<br>(0.260)</td><td>0.158<br>(0.303)</td><td>-0.059<br>(0.438)</td></tr><tr><td>Multiple deaths</td><td>-0.356<br>(0.422)</td><td>-0.258<br>(0.287)</td><td>0.055<br>(0.309)</td><td>-0.239<br>(0.483)</td></tr><tr><td>Observations</td><td>143</td><td>141</td><td>142</td><td>140</td></tr><tr><td>R<sup>2</sup></td><td>0.122</td><td>0.081</td><td>0.155</td><td>0.098</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.049</td><td>0.002</td><td>0.083</td><td>0.020</td></tr></tbody></table>

Note: Each column reports unstandardized coefficients from an OLS regression. The unit of observation is the police homicide. The dependent variable is the relative change in private property arsons in the department where a police homicide occurred (with different temporal specification in each model). A value of 0 indicates no change; 1 indicates a 100% increase. Only homicides with complete information on all predictors are included. All listed variables are included simultaneously in the model.

Standard errors in parentheses.

* p < 0.05, ** p < 0.01, *** p < 0.001
<table><caption>Table A14: Homicide characteristics predicting variation in private property arsons (standardized coefficients, multiple imputation)</caption><thead><tr><th></th><th colspan="4">Dependent variable:</th></tr><tr><th></th><th>Δ<br>t<sub>0</sub><br>t<sub>-1</sub></th><th>Δ<br>t<sub>0</sub>+t<sub>+</sub>1<br>t<sub>-1</sub>+t<sub>-2</sub></th><th>Δ<br>t<sub>0</sub><br>t<sub>-12</sub></th><th>Δ<br>t<sub>0</sub>+t<sub>+</sub>1<br>t<sub>-12</sub>+t<sub>-11</sub></th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Age</td><td>-0.028<br>(0.048)</td><td>-0.030<br>(0.042)</td><td>-0.025<br>(0.041)</td><td>-0.019<br>(0.054)</td></tr><tr><td>Male</td><td>0.017<br>(0.038)</td><td>0.028<br>(0.034)</td><td>-0.013<br>(0.034)</td><td>0.037<br>(0.043)</td></tr><tr><td>Non French</td><td>-0.062<br>(0.048)</td><td>-0.017<br>(0.041)</td><td>-0.103<br>(0.053)</td><td>-0.086<br>(0.050)</td></tr><tr><td>Assessed Non-Europe migratory origin</td><td>0.018<br>(0.051)</td><td>0.057<br>(0.065)</td><td>0.120**<br>(0.046)</td><td>0.117<br>(0.081)</td></tr><tr><td>Assessed academic results</td><td>-0.079<br>(0.050)</td><td>0.015<br>(0.078)</td><td>0.017<br>(0.043)</td><td>0.068<br>(0.121)</td></tr><tr><td>Homicide type (armed person as ref.)</td><td></td><td></td><td></td><td></td></tr><tr><td>Death in custody</td><td>0.038<br>(0.044)</td><td>0.024<br>(0.039)</td><td>-0.025<br>(0.039)</td><td>0.006<br>(0.051)</td></tr><tr><td>Unarmed person</td><td>0.060<br>(0.045)</td><td>0.051<br>(0.041)</td><td>0.026<br>(0.041)</td><td>0.009<br>(0.052)</td></tr><tr><td>Police type (Gendarmerie as ref.)</td><td></td><td></td><td></td><td></td></tr><tr><td>BAC</td><td>0.046<br>(0.050)</td><td>0.100*<br>(0.044)</td><td>0.119**<br>(0.044)</td><td>0.178**<br>(0.057)</td></tr><tr><td>Police Nationale</td><td>0.052<br>(0.050)</td><td>0.026<br>(0.044)</td><td>0.079<br>(0.044)</td><td>0.048<br>(0.057)</td></tr><tr><td>Other</td><td>0.005<br>(0.040)</td><td>0.011<br>(0.036)</td><td>0.019<br>(0.036)</td><td>0.013<br>(0.047)</td></tr><tr><td>Multiple deaths</td><td>0.037<br>(0.040)</td><td>0.161***<br>(0.036)</td><td>0.038<br>(0.036)</td><td>0.231***<br>(0.048)</td></tr><tr><td>Share of missing</td><td>-0.042<br>(0.044)</td><td>-0.054<br>(0.038)</td><td>-0.014<br>(0.041)</td><td>-0.039<br>(0.050)</td></tr><tr><td>Observations</td><td>447</td><td>447</td><td>447</td><td>447</td></tr></tbody></table>

Note: Each column reports **standardized** coefficients from an OLS regression. The unit of observation is the police homicide. The dependent variable is the relative change in private property arsons in the department where a police homicide occurred (with different temporal specification in each model). A value of 0 indicates no change; 1 indicates a 100% increase. Missing values on predictors were handled using multiple imputation by chained equations. All listed variables are included simultaneously in the model.
Standard errors in parentheses.

* p < 0.05, ** p < 0.01, *** p < 0.001
<table><caption>Table A15: Homicide characteristics predicting variation in private property arsons (unstandardized coefficients, multiple imputation)</caption><thead><tr><th rowspan="3"></th><th colspan="4">Dependent variable:</th></tr><tr><th>Δ</th><th>Δ</th><th>Δ</th><th>Δ</th></tr><tr><th>t<sub>0</sub><br>t<sub>-1</sub></th><th>t<sub>0</sub>+t<sub>+</sub>1<br>t<sub>-1</sub>+t<sub>-2</sub></th><th>t<sub>0</sub><br>t<sub>-12</sub></th><th>t<sub>0</sub>+t<sub>+</sub>1<br>t<sub>-12</sub>+t<sub>-11</sub></th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Age</td><td>-0.002<br>(0.004)</td><td>-0.002<br>(0.003)</td><td>-0.003<br>(0.003)</td><td>-0.002<br>(0.005)</td></tr><tr><td>Male</td><td>0.065<br>(0.239)</td><td>0.179<br>(0.214)</td><td>-0.083<br>(0.211)</td><td>0.261<br>(0.274)</td></tr><tr><td>Non French</td><td>-0.205*<br>(0.100)</td><td>-0.076<br>(0.089)</td><td>-0.218<br>(0.106)</td><td>-0.172<br>(0.135)</td></tr><tr><td>Assessed Non-Europe migratory origin</td><td>0.126<br>(0.126)</td><td>0.127<br>(0.119)</td><td>0.258*<br>(0.109)</td><td>0.204<br>(0.145)</td></tr><tr><td>Assessed academic results</td><td>-0.010<br>(0.015)</td><td>0.004<br>(0.021)</td><td>0.008<br>(0.017)</td><td>0.010<br>(0.031)</td></tr><tr><td>Homicide type (armed person as ref.)</td><td></td><td></td><td></td><td></td></tr><tr><td>Death in custody</td><td>0.108<br>(0.129)</td><td>0.069<br>(0.114)</td><td>-0.071<br>(0.114)</td><td>0.024<br>(0.147)</td></tr><tr><td>Unarmed person</td><td>0.112<br>(0.093)</td><td>0.106<br>(0.083)</td><td>0.051<br>(0.083)</td><td>0.019<br>(0.108)</td></tr><tr><td>Police type (Gendarmerie as ref.)</td><td></td><td></td><td></td><td></td></tr><tr><td>BAC</td><td>0.123<br>(0.126)</td><td>0.259*<br>(0.113)</td><td>0.294*<br>(0.113)</td><td>0.455**<br>(0.147)</td></tr><tr><td>Police Nationale</td><td>0.102<br>(0.099)</td><td>0.046<br>(0.088)</td><td>0.147<br>(0.090)</td><td>0.082<br>(0.114)</td></tr><tr><td>Other</td><td>0.036<br>(0.203)</td><td>0.046<br>(0.179)</td><td>0.098<br>(0.180)</td><td>0.059<br>(0.232)</td></tr><tr><td>Multiple deaths</td><td>0.076<br>(0.136)</td><td>0.542***<br>(0.124)</td><td>0.109<br>(0.128)</td><td>0.803***<br>(0.163)</td></tr><tr><td>Share of missingings</td><td>-0.192<br>(0.268)</td><td>-0.281<br>(0.255)</td><td>-0.055<br>(0.254)</td><td>-0.218<br>(0.350)</td></tr><tr><td>Observations</td><td>447</td><td>447</td><td>447</td><td>447</td></tr></tbody></table>

Note: Each column reports unstandardized coefficients from an OLS regression. The unit of observation is the police homicide. The dependent variable is the relative change in private property arsons in the department where a police homicide occurred (with different temporal specification in each model). A value of 0 indicates no change; 1 indicates a 100% increase. Missing values on predictors were handled using multiple imputation by chained equations. All listed variables are included simultaneously in the model.
Standard errors in parentheses.

* p < 0.05, ** p < 0.01, *** p < 0.001
<table><caption>Table A16: Homicide characteristics predicting variation in private property arsons (including only predictors significant in bivariate analyses, unstandardized coefficients, complete cases)</caption><thead><tr><th rowspan="3"></th><th colspan="4">Dependent variable:</th></tr><tr><th>Δ</th><th>Δ</th><th>Δ</th><th>Δ</th></tr><tr><th>t<sub>0</sub><br>t<sub>-1</sub></th><th>t<sub>0</sub>+t<sub>+</sub>1<br>t<sub>-1</sub>+t<sub>-2</sub></th><th>t<sub>0</sub><br>t<sub>-12</sub></th><th>t<sub>0</sub>+t<sub>+</sub>1<br>t<sub>-12</sub>+t<sub>-11</sub></th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Age</td><td>-0.004<br>(0.006)</td><td>-0.003<br>(0.006)</td><td>-0.007<br>(0.005)</td><td>-0.001<br>(0.009)</td></tr><tr><td>Non French</td><td>-0.241<br>(0.155)</td><td>-0.081<br>(0.148)</td><td>-0.234<br>(0.127)</td><td>-0.354<br>(0.243)</td></tr><tr><td>Assessed Non-Europe migratory origin</td><td>0.330*<br>(0.146)</td><td>0.166<br>(0.142)</td><td>0.308*<br>(0.120)</td><td>0.378<br>(0.233)</td></tr><tr><td>Homicide type (armed person as ref.)</td><td></td><td></td><td></td><td></td></tr><tr><td>Death in custody</td><td>0.105<br>(0.277)</td><td>0.146<br>(0.265)</td><td>-0.058<br>(0.227)</td><td>0.086<br>(0.434)</td></tr><tr><td>Unarmed person</td><td>0.207<br>(0.155)</td><td>0.098<br>(0.148)</td><td>0.127<br>(0.127)</td><td>0.097<br>(0.244)</td></tr><tr><td>Police type (Gendarmerie as ref.)</td><td></td><td></td><td></td><td></td></tr><tr><td>BAC</td><td>0.178<br>(0.212)</td><td>0.117<br>(0.201)</td><td>0.282<br>(0.176)</td><td>0.566<br>(0.333)</td></tr><tr><td>Police Nationale</td><td>0.056<br>(0.182)</td><td>-0.114<br>(0.173)</td><td>0.177<br>(0.151)</td><td>-0.107<br>(0.289)</td></tr><tr><td>Other</td><td>0.123<br>(0.320)</td><td>0.031<br>(0.304)</td><td>0.105<br>(0.263)</td><td>-0.006<br>(0.501)</td></tr><tr><td>Multiple deaths</td><td>-0.453<br>(0.270)</td><td>0.834**<br>(0.276)</td><td>-0.214<br>(0.221)</td><td>1.545***<br>(0.451)</td></tr><tr><td>Observations</td><td>201</td><td>198</td><td>198</td><td>196</td></tr><tr><td>R<sup>2</sup></td><td>0.072</td><td>0.102</td><td>0.118</td><td>0.156</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.028</td><td>0.059</td><td>0.076</td><td>0.116</td></tr></tbody></table>

Note: Each column reports unstandardized coefficients from an OLS regression. The unit of observation is the police homicide. The dependent variable is the relative change in private property arsons in the department where a police homicide occurred (with different temporal specification in each model). A value of 0 indicates no change; 1 indicates a 100% increase. The model includes only those predictors that were significantly associated with arson variation in previous bivariate analyses. Only homicides with complete information on those predictors are included. Standard errors in parentheses.

* p < 0.05, ** p < 0.01, *** p < 0.001
<table><caption>Table A17: Homicide characteristics predicting variation in private property arsons (including only predictors significant in bivariate analyses, unstandardized coefficients, multiple imputation)</caption><thead><tr><th rowspan="3"></th><th colspan="4">Dependent variable:</th></tr><tr><th>Δ</th><th>Δ</th><th>Δ</th><th>Δ</th></tr><tr><th>t<sub>0</sub><br>t<sub>-1</sub></th><th>t<sub>0</sub> + t<sub>+</sub>1<br>t<sub>-1</sub> + t<sub>-2</sub></th><th>t<sub>0</sub><br>t<sub>-12</sub></th><th>t<sub>0</sub> + t<sub>+</sub>1<br>t<sub>-12</sub> + t<sub>-11</sub></th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Age</td><td>-0.003<br>(0.004)</td><td>-0.002<br>(0.003)</td><td>-0.002<br>(0.003)</td><td>-0.001<br>(0.004)</td></tr><tr><td>Non French</td><td>-0.140<br>(0.118)</td><td>-0.039<br>(0.096)</td><td>-0.219*<br>(0.104)</td><td>-0.211<br>(0.113)</td></tr><tr><td>Assessed Non-Europe migratory origin</td><td>0.162<br>(0.094)</td><td>0.141<br>(0.077)</td><td>0.218*<br>(0.083)</td><td>0.181<br>(0.102)</td></tr><tr><td>Homicide type (armed person as ref.)</td><td></td><td></td><td></td><td></td></tr><tr><td>Death in custody</td><td>0.118<br>(0.127)</td><td>0.086<br>(0.114)</td><td>-0.058<br>(0.115)</td><td>0.046<br>(0.148)</td></tr><tr><td>Unarmed person</td><td>0.116<br>(0.093)</td><td>0.107<br>(0.082)</td><td>0.070<br>(0.082)</td><td>0.035<br>(0.106)</td></tr><tr><td>Police type (Gendarmerie as ref.)</td><td></td><td></td><td></td><td></td></tr><tr><td>BAC</td><td>0.095<br>(0.125)</td><td>0.243*<br>(0.113)</td><td>0.303**<br>(0.112)</td><td>0.458**<br>(0.145)</td></tr><tr><td>Police Nationale</td><td>0.087<br>(0.099)</td><td>0.029<br>(0.089)</td><td>0.164<br>(0.089)</td><td>0.087<br>(0.115)</td></tr><tr><td>Other</td><td>0.003<br>(0.200)</td><td>0.036<br>(0.179)</td><td>0.115<br>(0.182)</td><td>0.079<br>(0.234)</td></tr><tr><td>Multiple deaths</td><td>0.082<br>(0.139)</td><td>0.544***<br>(0.122)</td><td>0.126<br>(0.127)</td><td>0.786***<br>(0.159)</td></tr><tr><td>Share of missing</td><td>-0.250<br>(0.262)</td><td>-0.324<br>(0.224)</td><td>-0.002<br>(0.244)</td><td>-0.140<br>(0.286)</td></tr><tr><td>Observations</td><td>447</td><td>447</td><td>447</td><td>447</td></tr></tbody></table>

Note: Each column reports unstandardized coefficients from an OLS regression. The unit of observation is the police homicide. The dependent variable is the relative change in private property arsons in the department where a police homicide occurred (with different temporal specification in each model). A value of 0 indicates no change; 1 indicates a 100% increase. The model includes only those predictors that were significantly associated with arson variation in previous bivariate analyses. Missing values on those predictors were handled using multiple imputation by chained equations. Standard errors in parentheses.

* p < 0.05, ** p < 0.01, *** p < 0.001
<table><caption>Table A18: Homicide characteristics predicting variation in private property arsons (unstandardized coefficients, complete cases)</caption><thead><tr><th></th><th colspan="4">Dependent variable: Δ arsons between t<sub>0</sub> and t<sub>-1</sub></th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Age</td><td>-0.002<br>(0.008)</td><td>-0.002<br>(0.008)</td><td>-0.003<br>(0.008)</td><td>-0.003<br>(0.008)</td></tr><tr><td>Male</td><td>0.215<br>(0.617)</td><td>0.164<br>(0.623)</td><td>0.258<br>(0.628)</td><td>0.182<br>(0.623)</td></tr><tr><td>Non French</td><td>-0.265<br>(0.225)</td><td>-0.072<br>(0.378)</td><td>-0.338<br>(0.567)</td><td>-0.240<br>(0.230)</td></tr><tr><td>Assessed Non-Europe migratory origin</td><td>0.460*<br>(0.230)</td><td>0.533*<br>(0.257)</td><td>0.448<br>(0.235)</td><td>-0.051<br>(0.498)</td></tr><tr><td>Assessed academic results</td><td>-0.018<br>(0.028)</td><td>-0.016<br>(0.028)</td><td>-0.015<br>(0.029)</td><td>-0.018<br>(0.028)</td></tr><tr><td>Homicide type (armed person as ref.)</td><td></td><td></td><td></td><td></td></tr><tr><td>Death in custody</td><td>0.177<br>(0.379)</td><td>0.208<br>(0.383)</td><td>0.161<br>(0.386)</td><td>0.168<br>(0.382)</td></tr><tr><td>Unarmed person</td><td>0.342<br>(0.204)</td><td>0.339<br>(0.205)</td><td>0.344<br>(0.209)</td><td>0.360<br>(0.206)</td></tr><tr><td>Police type (Gendarmerie as ref.)</td><td></td><td></td><td></td><td></td></tr><tr><td>BAC</td><td>0.171<br>(0.279)</td><td>0.145<br>(0.283)</td><td>0.212<br>(0.312)</td><td>-0.012<br>(0.363)</td></tr><tr><td>Police Nationale</td><td>-0.047<br>(0.231)</td><td>-0.054<br>(0.232)</td><td>-0.071<br>(0.251)</td><td>-0.192<br>(0.278)</td></tr><tr><td>Other</td><td>0.222<br>(0.414)</td><td>0.215<br>(0.415)</td><td>0.179<br>(0.448)</td><td>0.156<br>(0.629)</td></tr><tr><td>Multiple deaths</td><td>-0.356<br>(0.422)</td><td>-0.371<br>(0.424)</td><td>-0.364<br>(0.428)</td><td>-0.385<br>(0.426)</td></tr><tr><td>Non French</td><td></td><td>-0.303<br>(0.475)</td><td></td><td></td></tr><tr><td>Non Europe migratory origin</td><td></td><td></td><td>-0.129<br>(0.737)</td><td></td></tr><tr><td>Non French</td><td></td><td></td><td></td><td></td></tr><tr><td>Non BAC</td><td></td><td></td><td></td><td></td></tr><tr><td>Non French</td><td></td><td></td><td>0.170<br>(0.737)</td><td></td></tr><tr><td>Non Police Nationale</td><td></td><td></td><td>(0.629)</td><td></td></tr><tr><td>Non French</td><td></td><td></td><td></td><td></td></tr><tr><td>Non Other</td><td></td><td></td><td>0.371<br>(1.266)</td><td></td></tr><tr><td>Non-Europe migratory origin</td><td></td><td></td><td></td><td>0.653<br>(0.615)</td></tr><tr><td>Non BAC</td><td></td><td></td><td></td><td></td></tr><tr><td>Non Europe migratory origin</td><td></td><td></td><td></td><td>0.585<br>(0.530)</td></tr><tr><td>Non Police Nationale</td><td></td><td></td><td></td><td></td></tr><tr><td>Non Europe migratory origin</td><td></td><td></td><td></td><td>0.452<br>(0.889)</td></tr><tr><td>Non Other</td><td></td><td></td><td></td><td></td></tr><tr><td>Observations</td><td>143</td><td>143</td><td>143</td><td>143</td></tr></tbody></table>

Note: Each column reports unstandardized coefficients from an OLS regression. The unit of observation is the police homicide. The dependent variable is the relative change in private property arsons (compared to the previous month) in the department where a police homicide occurred. A value of 0 indicates no change; 1 indicates a 100% increase. Only homicides with complete data are included. All predictors are included simultaneously, along with interaction terms capturing combinations of migratory origin, citizenship, and type of police force. Standard errors in parentheses.

<footnote>* p < 0.05, ** p < 0.01, *** p < 0.001</footnote>
## 6.2 Homicide characteristics and association with riots according to the press

<table><caption>Table A19: Summary descriptive statistics on characteristics of homicide associated or not with riots according to the press.</caption><thead><tr><th></th><th>No identified riots</th><th>Identified riots</th><th>p.overall</th></tr></thead><tbody><tr><td>Age</td><td>34.1 (13.0)</td><td>22.8 (8.12)</td><td>&lt;0.001</td></tr><tr><td>Gender</td><td></td><td></td><td>1.000</td></tr><tr><td>Female</td><td>3.16%</td><td>1.61%</td><td></td></tr><tr><td>Male</td><td>96.8%</td><td>98.4%</td><td></td></tr><tr><td>Citizenship</td><td></td><td></td><td>0.003</td></tr><tr><td>French</td><td>65.6%</td><td>87.5%</td><td></td></tr><tr><td>Non French</td><td>34.4%</td><td>12.5%</td><td></td></tr><tr><td>Assessed migratory origin</td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>Europe</td><td>48.4%</td><td>15.8%</td><td></td></tr><tr><td>Non-Europe</td><td>51.6%</td><td>84.2%</td><td></td></tr><tr><td>Assessed academic results</td><td>6.80 (3.82)</td><td>4.76 (2.84)</td><td>&lt;0.001</td></tr><tr><td>Homicide type</td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>Armed person</td><td>28.1%</td><td>11.3%</td><td></td></tr><tr><td>Death in custody</td><td>15.4%</td><td>3.23%</td><td></td></tr><tr><td>Unarmed person</td><td>56.5%</td><td>85.5%</td><td></td></tr><tr><td>Police type</td><td></td><td></td><td>0.007</td></tr><tr><td>Gendarmerie</td><td>23.7%</td><td>10.0%</td><td></td></tr><tr><td>BAC</td><td>15.5%</td><td>30.0%</td><td></td></tr><tr><td>Police Nationale</td><td>56.7%</td><td>53.3%</td><td></td></tr><tr><td>Other</td><td>4.12%</td><td>6.67%</td><td></td></tr><tr><td>Multiple deaths</td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>Single</td><td>93.8%</td><td>71.0%</td><td></td></tr><tr><td>Multiple</td><td>6.25%</td><td>29.0%</td><td></td></tr><tr><td>Share of missing values</td><td>0.20 (0.17)</td><td>0.08 (0.11)</td><td>&lt;0.001</td></tr><tr><td>Δ arsons between t<sub>0</sub> and t<sub>-1</sub></td><td>0.11 (0.63)</td><td>0.77 (1.34)</td><td>&lt;0.001</td></tr><tr><td>Δ arsons between t<sub>0</sub> + t<sub>+</sub>1 and t<sub>-2</sub> + t<sub>-1</sub></td><td>0.09 (0.49)</td><td>0.71 (1.43)</td><td>0.001</td></tr><tr><td>Δ arsons between t<sub>0</sub> and t<sub>-12</sub></td><td>0.09 (0.59)</td><td>0.69 (1.09)</td><td>&lt;0.001</td></tr><tr><td>Δ arsons between t<sub>0</sub> + t<sub>+</sub>1 and t<sub>-12</sub> + t<sub>-11</sub></td><td>0.10 (0.71)</td><td>0.95 (2.17)</td><td>0.003</td></tr></tbody></table>

*Note:* Each row reports descriptive statistics for police homicides with or without press-identified riots. The unit of observation is the police homicide. Means and standard deviations are shown for continuous variables; proportions are shown for categorical variables. Reported *p*-values correspond to bivariate tests: two-sided *t*-tests for continuous variables, Fisher's exact tests for binary variables, and chi-squared tests for categorical variables. Differences are assessed between cases with and without identified riots. Relative variations in arson levels are coded as 0 for no change, 1 for a 100% increase, etc.
<figure><img src="image_35.png" /><figcaption>Figure A24: Characteristics of police homicides associated or not with riots according to the press.</figcaption></figure>

*Note:* For each variable, the figure shows the distribution of event-level characteristics. For categorical variables, it reports relative proportions with 95% confidence intervals. For continuous variables, it displays violin plots of the distribution, along with means and associated 95% confidence intervals. Detailed results are reported in Table A19. When available, French population benchmarks are indicated in dashed red for comparison.
<table><caption>Table A20: Homicide characteristics predicting association with riots in the press (unstandardized coefficients, complete cases)</caption><thead><tr><th></th><th colspan="4" style="text-align:center;">Dependent variable: riots according to press</th></tr><tr><th></th><th style="text-align:center;">(1)</th><th style="text-align:center;">(2)</th><th style="text-align:center;">(3)</th><th style="text-align:center;">(4)</th></tr></thead><tbody><tr><td>Age</td><td style="text-align:center;">-0.095*<br>(0.043)</td><td style="text-align:center;">-0.096*<br>(0.043)</td><td style="text-align:center;">-0.098*<br>(0.044)</td><td style="text-align:center;">-0.101*<br>(0.044)</td></tr><tr><td>Male</td><td style="text-align:center;">14.490<br>(1,243.006)</td><td style="text-align:center;">16.583<br>(3,380.468)</td><td style="text-align:center;">16.697<br>(3,345.827)</td><td style="text-align:center;">14.607<br>(1,237.121)</td></tr><tr><td>Non French</td><td style="text-align:center;">-1.109<br>(0.774)</td><td style="text-align:center;">-15.548<br>(1,917.065)</td><td style="text-align:center;">-0.226<br>(1.505)</td><td style="text-align:center;">-1.154<br>(0.798)</td></tr><tr><td>Assessed Non-Europe migratory origin</td><td style="text-align:center;">1.595*<br>(0.762)</td><td style="text-align:center;">1.522*<br>(0.770)</td><td style="text-align:center;">1.552*<br>(0.773)</td><td style="text-align:center;">1.739<br>(1.309)</td></tr><tr><td>Assessed academic results</td><td style="text-align:center;">-0.098<br>(0.107)</td><td style="text-align:center;">-0.097<br>(0.106)</td><td style="text-align:center;">-0.081<br>(0.112)</td><td style="text-align:center;">-0.085<br>(0.109)</td></tr><tr><td>Homicide type (armed person as ref.)</td><td></td><td></td><td></td><td></td></tr><tr><td>Death in custody</td><td style="text-align:center;">1.578<br>(1.208)</td><td style="text-align:center;">1.513<br>(1.208)</td><td style="text-align:center;">1.494<br>(1.224)</td><td style="text-align:center;">1.694<br>(1.254)</td></tr><tr><td>Unarmed person</td><td style="text-align:center;">0.729<br>(0.736)</td><td style="text-align:center;">0.718<br>(0.735)</td><td style="text-align:center;">0.649<br>(0.740)</td><td style="text-align:center;">0.731<br>(0.746)</td></tr><tr><td>Police type (Gendarmerie as ref.)</td><td></td><td></td><td></td><td></td></tr><tr><td>BAC</td><td style="text-align:center;">-0.590<br>(0.828)</td><td style="text-align:center;">-0.528<br>(0.835)</td><td style="text-align:center;">-0.274<br>(0.896)</td><td style="text-align:center;">-0.499<br>(1.373)</td></tr><tr><td>Police Nationale</td><td style="text-align:center;">-0.715<br>(0.751)</td><td style="text-align:center;">-0.684<br>(0.751)</td><td style="text-align:center;">-0.505<br>(0.827)</td><td style="text-align:center;">-1.087<br>(1.393)</td></tr><tr><td>Other</td><td style="text-align:center;">1.170<br>(1.175)</td><td style="text-align:center;">1.184<br>(1.166)</td><td style="text-align:center;">1.345<br>(1.205)</td><td style="text-align:center;">2.043<br>(1.573)</td></tr><tr><td>Multiple deaths</td><td style="text-align:center;">1.107<br>(1.022)</td><td style="text-align:center;">1.102<br>(1.019)</td><td style="text-align:center;">1.055<br>(1.026)</td><td style="text-align:center;">1.116<br>(1.061)</td></tr><tr><td>Non French × Non-Europe migratory origin</td><td></td><td style="text-align:center;">14.555<br>(1,917.065)</td><td></td><td></td></tr><tr><td>Non French × BAC</td><td></td><td></td><td style="text-align:center;">-16.615<br>(2,304.590)</td><td></td></tr><tr><td>Non French × Police Nationale</td><td></td><td></td><td style="text-align:center;">-0.827<br>(1.728)</td><td></td></tr><tr><td>Non French × Other</td><td></td><td></td><td style="text-align:center;">-15.593<br>(6,522.639)</td><td></td></tr><tr><td>Non-Europe migratory origin × BAC</td><td></td><td></td><td></td><td style="text-align:center;">-0.170<br>(1.745)</td></tr><tr><td>Non-Europe migratory origin × Police Nationale</td><td></td><td></td><td></td><td style="text-align:center;">0.381<br>(1.653)</td></tr><tr><td>Non-Europe migratory origin × Other</td><td></td><td></td><td></td><td style="text-align:center;">-1.585<br>(2.191)</td></tr><tr><td>Observations</td><td style="text-align:center;">146</td><td style="text-align:center;">146</td><td style="text-align:center;">146</td><td style="text-align:center;">146</td></tr></tbody></table>

Note: Logistic regression predicting whether a police homicide was followed by riots according to press reports. The unit of observation is the police homicide. All predictors are included simultaneously, along with interaction terms capturing combinations of migratory origin, citizenship, and police force. Only complete cases are included. Reported coefficients are unstandardized log-odds. Standard errors in parentheses.

* p < 0.05, ** p < 0.01, *** p < 0.001
<table><caption>Table A21: Homicide characteristics predicting association with riots in the press (unstandardized coefficients, multiple imputation)</caption><thead><tr><th></th><th colspan="4" style="text-align:center;">Dependent variable: riots according to press</th></tr><tr><th></th><th style="text-align:center;">(1)</th><th style="text-align:center;">(2)</th><th style="text-align:center;">(3)</th><th style="text-align:center;">(4)</th></tr></thead><tbody><tr><td>Age</td><td style="text-align:center;">-0.088**<br>(0.030)</td><td style="text-align:center;">-0.089**<br>(0.030)</td><td style="text-align:center;">-0.090**<br>(0.032)</td><td style="text-align:center;">-0.092**<br>(0.031)</td></tr><tr><td>Male</td><td style="text-align:center;">0.696<br>(1.272)</td><td style="text-align:center;">0.710<br>(1.275)</td><td style="text-align:center;">0.716<br>(1.284)</td><td style="text-align:center;">0.623<br>(1.267)</td></tr><tr><td>Non French</td><td style="text-align:center;">-1.358**<br>(0.490)</td><td style="text-align:center;">-12.236<br>(821.974)</td><td style="text-align:center;">-0.321<br>(1.405)</td><td style="text-align:center;">-1.320**<br>(0.492)</td></tr><tr><td>Assessed Non-Europe migratory origin</td><td style="text-align:center;">1.305*<br>(0.549)</td><td style="text-align:center;">1.260*<br>(0.557)</td><td style="text-align:center;">1.262*<br>(0.559)</td><td style="text-align:center;">1.541<br>(1.061)</td></tr><tr><td>Assessed academic results</td><td style="text-align:center;">-0.057<br>(0.082)</td><td style="text-align:center;">-0.057<br>(0.081)</td><td style="text-align:center;">-0.062<br>(0.089)</td><td style="text-align:center;">-0.059<br>(0.083)</td></tr><tr><td>Homicide type (armed person as ref.)</td><td></td><td></td><td></td><td></td></tr><tr><td style="padding-left: 20px;">Death in custody</td><td style="text-align:center;">0.048<br>(0.914)</td><td style="text-align:center;">0.035<br>(0.914)</td><td style="text-align:center;">0.031<br>(0.918)</td><td style="text-align:center;">0.067<br>(0.916)</td></tr><tr><td style="padding-left: 20px;">Unarmed person</td><td style="text-align:center;">0.525<br>(0.496)</td><td style="text-align:center;">0.527<br>(0.496)</td><td style="text-align:center;">0.523<br>(0.498)</td><td style="text-align:center;">0.568<br>(0.498)</td></tr><tr><td>Police type (Gendarmerie as ref.)</td><td></td><td></td><td></td><td></td></tr><tr><td style="padding-left: 20px;">BAC</td><td style="text-align:center;">0.089<br>(0.609)</td><td style="text-align:center;">0.109<br>(0.610)</td><td style="text-align:center;">0.298<br>(0.657)</td><td style="text-align:center;">-0.175<br>(1.118)</td></tr><tr><td style="padding-left: 20px;">Police Nationale</td><td style="text-align:center;">0.515<br>(0.551)</td><td style="text-align:center;">0.523<br>(0.549)</td><td style="text-align:center;">0.664<br>(0.606)</td><td style="text-align:center;">0.779<br>(0.945)</td></tr><tr><td style="padding-left: 20px;">Other</td><td style="text-align:center;">0.795<br>(0.860)</td><td style="text-align:center;">0.792<br>(0.857)</td><td style="text-align:center;">1.346<br>(0.985)</td><td style="text-align:center;">2.209<br>(1.512)</td></tr><tr><td>Multiple deaths</td><td style="text-align:center;">1.636**<br>(0.521)</td><td style="text-align:center;">1.629**<br>(0.527)</td><td style="text-align:center;">1.626**<br>(0.520)</td><td style="text-align:center;">1.608**<br>(0.525)</td></tr><tr><td>Share of mismissions</td><td style="text-align:center;">-6.302***<br>(1.703)</td><td style="text-align:center;">-6.318***<br>(1.692)</td><td style="text-align:center;">-6.279***<br>(1.685)</td><td style="text-align:center;">-6.320***<br>(1.715)</td></tr><tr><td>Non French × Non-Europe migratory origin</td><td></td><td style="text-align:center;">10.932<br>(821.976)</td><td></td><td></td></tr><tr><td>Non French × BAC</td><td></td><td></td><td style="text-align:center;">-1.390<br>(1.921)</td><td></td></tr><tr><td>Non French × Police Nationale</td><td></td><td></td><td style="text-align:center;">-0.949<br>(1.564)</td><td></td></tr><tr><td>Non French × Other</td><td></td><td></td><td style="text-align:center;">-16.072<br>(1,128.266)</td><td></td></tr><tr><td>Non-Europe migratory origin × BAC</td><td></td><td></td><td></td><td style="text-align:center;">0.253<br>(1.322)</td></tr><tr><td>Non-Europe migratory origin × Police Nationale</td><td></td><td></td><td></td><td style="text-align:center;">-0.395<br>(1.147)</td></tr><tr><td>Non-Europe migratory origin × Other</td><td></td><td></td><td></td><td style="text-align:center;">-1.871<br>(1.820)</td></tr><tr><td>Observations</td><td style="text-align:center;">447</td><td style="text-align:center;">447</td><td style="text-align:center;">447</td><td style="text-align:center;">447</td></tr></tbody></table>

Note: Logistic regression predicting whether a police homicide was followed by riots according to press reports. The unit of observation is the police homicide. All predictors are included simultaneously, along with interaction terms capturing combinations of migratory origin, citizenship, and police force. Analyses are based on multiple imputation of missing values. Reported coefficients are unstandardized log-odds.

Standard errors in parentheses.

* p < 0.05, ** p < 0.01, *** p < 0.001