<figure><img src="image_1.png" /></figure>

HAL
open science

# Identity conflict, ethnocentrism and social cohesion

Matteo Sestito

► To cite this version:

Matteo Sestito. Identity conflict, ethnocentrism and social cohesion. 2023. halshs-03953975

HAL Id: halshs-03953975

https://shs.hal.science/halshs-03953975

Preprint submitted on 24 Jan 2023

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.
# Identity conflict, ethnocentrism and social cohesion

Matteo Sestito

► To cite this version:

Matteo Sestito. Identity conflict, ethnocentrism and social cohesion. Journal of Development Economics, 2023, 174, pp.103426. 10.1016/j.jdeveco.2024.103426. halshs-03953975v2

HAL Id: halshs-03953975

https://shs.hal.science/halshs-03953975v2

Submitted on 15 Jan 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Identity conflict, ethnocentrism and social cohesion

Matteo Sestito

<figure><img src="image_2.png" /></figure>
Regular article

# Identity conflict, ethnocentrism and social cohesion☆

Matteo Sestito*

HEC Lausanne, Switzerland
Aix-Marseille School of Economics, France

## ARTICLE INFO

Dataset link: https://sites.google.com/view/ma-teo-sestito/research

JEL classification:

D74
N47
O55
Z13

Keywords:

Ethnic conflict
Social cohesion
Identity
Africa

## ABSTRACT

This paper uses a novel dataset on ethnic warfare to shed light on how conflict affects social identification and cohesion. A large body of anecdotal studies suggests that ethnic identities become more salient at times of conflict. Using data from thirty-six African countries, I provide econometric evidence to this notion. The relationship between ethnic conflict and various measures of social cohesion is also examined, revealing a positive link between the two. The finding is understood as a result of the ethnocentric dynamics generated by conflict: as warfare strengthens ethnic identification, prosocial behaviour increases, albeit primarily towards co-ethnics. This parochial interpretation is strengthened by the use of remote violence and the conditionality of conflict-induced prosocial behaviour on low levels of ethnic fractionalisation.

## 1. Introduction

> We kneeled down and surrendered. It was quiet, as we were all praying.
> We knew this was the end.

[Grace Githuthwa, Kikuyu woman]

After Kenya's disputed 2007 presidential elections, widespread inter-ethnic clashes claimed over 1000 lives in just two months.<fnref n="1" /> Massive protests against electoral fraud soon turned violent, with ethnic Kalenjin and Luo targeting Kikuyu presidential supporters. External observers were shocked by the intensity of violence in a country traditionally regarded as a “bastion of economic and political stability” (Rawlence, 2008, p. 2). People retreated into their ethnic community, turning against those hailing from different groups. While some feared a long-lasting legacy of inter-ethnic tensions, other observers highlighted the instrumental and opportunistic use of ethnic identities throughout and after the conflict (CIPEV, 2008).

The discussion surrounding the Kenyan crisis is symptomatic of a broader debate on the saliency and importance of social identity in times of conflict: Does ethnic conflict genuinely intensify group identities, or are these identities merely exploited for opportunistic purposes? The question is important for understanding both conflict dynamics and the post-war social landscape, with crucial repercussions for policy-making.<fnref n="2" /> This paper adds to the debate by examining the effect of ethnic conflict on social identification and cohesion across Africa. It contributes to the growing literature studying the relationship between conflict and prosociality, the “theoretical mechanisms [of which] remain elusive” (Mironova and Whitt, 2021, p. 2). This paper fills the gap by developing a new dataset on sectarian warfare in Africa, providing support to the idea that conflict triggers a genuine process of identity radicalisation and that these ethnocentric feelings are the main channel whereby conflict translates into deeper cooperation.

Large scale quantitative evidence on the subject is, indeed, still very limited. A major barrier to empirical investigation in the field is the

<footnote>☆ I would like to thank my Ph.D. advisor, Nicolas Berman, for all his insightful advices. Comments by Cecilia Garcia Peñalosa, Garance Genicot, Pauline Grosjean, and Yannick Dupraz have been of great help. This paper has benefited from useful comments by seminar participants at Aix-Marseille School of Economics, Pompeu Fabra University, 2021 Annual Southern Ph.D. Economics Conference, 2021 International Conference on Development Economics, 2021 Annual Workshop of the Households in Conflict Network, 2021 Annual meeting of the Association of Southern European Economic Theorists, 2022 Louis-André Gérard-Varet conference in public economics. It goes without saying that any error in the work is my own responsibility. This work was supported by the French National Research Agency Grant ANR-17-EURE-0020 and by the Excellence Initiative of Aix-Marseille University - A*MIDEX.</footnote>

<footnote>* Correspondence to: HEC Lausanne, Switzerland.</footnote>

<footnote>E-mail address: matteo.sestito@unil.ch.</footnote>

<footnote n="1">1 The quote is from a survivor of the 2007 post-electoral violence in Eldoret, Western Kenya. See: https://amp.theguardian.com/world/2008/jan/02/kenya1.</footnote>

<footnote n="2">2 The debate is of paramount importance for the delineation of settlement patterns in post-conflict environments. On the benefits and costs of ethnic segregation in the aftermath of sectarian wars see: Kaufmann (1996), Laitin (2004), Mironova and Whitt (2016).</footnote>
Identity conflict, ethnocentrism and
social cohesion*

Matteo Sestito†

January 23, 2023

**Abstract**

This paper uses a novel dataset on ethnic warfare to shed light on how conflict affects social identification and cohesion. A large body of anecdotal studies suggests that ethnic identities become more salient at times of conflict. Using data from eighteen sub-Saharan countries, I provide econometric evidence for such a claim. The effect of ethnic conflict on various measures of social cohesion is also investigated, uncovering a positive relationship between the two. The finding is understood as a result of the ethnocentric dynamics generated by conflict: as ethnic warfare increases ethnic identification, in-group cooperation follows suit. This parochial interpretation is further strengthened by the use of remote violence and the conditionality of conflict-induced pro-social behaviour on low levels of ethnic polarisation.

JEL codes: D74, N47, O55, Z13

Keywords: Ethnic conflict, social cohesion, identity, Africa

<footnote>*I would like to thank my advisor, Nicolas Berman, for all the insightful advises. Comments by Cecilia Garcia Peñalosa, Garance Genicot, Pauline Grosjean, and Yan-nick Dupraz have been of great help. This paper has benefited from useful comments by seminar participants at Aix-Marseille School of Economics, Pompeu Fabra University, 2021 Annual Southern PhD Economics Conference, 2021 International Conference on Development Economics, 2021 Annual Workshop of the Households in Conflict Net-work, 2021 Annual meeting of the Association of Southern European Economic Theo-rists, 2022 Louis-André Gérard-Varet conference in public economics. It goes without saying that any error in the work is my own responsibility. This work was supported by the French National Research Agency Grant ANR-17-EURE-0020 and by the Excellence Initiative of Aix-Marseille University - A*MIDEX.</footnote>

<footnote>†Aix-Marseille University, CNRS, EHESS, Centrale Marseille, AMSE, France.</footnote>
lack of comprehensive data on ethnic violence, that is, conflict activity (allegedly) carried out for the sake of a given ethnic group.<fnref n="3" /> Using geo-coded conflict data from the Armed Conflict Location and Event Data (ACLED) Project, I compile a novel dataset mapping over 1900 armed actors to various African ethnic groups. The process allows the construction of conflict measures that vary across time, space, and ethnicity. These variables are then connected to Afrobarometer surveys describing social attitudes and identification across the African continent.<fnref n="4" /> I employ three measures of social cohesion aimed at capturing prosocial behaviour at the local level: attendance at community meetings, membership in community development associations, and participation in instances of collective action. As for social identity, Afrobarometer respondents are asked whether they feel more represented by their ethnic group or by their nation-state.

The triple source of variation of the conflict variables is the essence of the identification strategy employed in this paper. The baseline model consists of an Ordinary Least Squares (OLS) regression of social behaviour on ethnic conflict, as enriched by cell-district×time and ethnic×time fixed effects. Therefore, the baseline econometric specification compares people exposed to the same conflict incident, but with different treatment status because of their ethnolinguistic affiliation. While violence does not unfold randomly, the rich set of fixed effects attenuates issues of reverse causality and omitted variable biases. It is, however, possible that estimates are biased by unobservable confounding factors operating at the ethnolinguistic-geographic-temporal level. A series of robustness tests lowers these concerns and gives credit to an interpretation of the results beyond the mere correlation. Most importantly, results hold when considering distant conflict activity, which takes place outside the cell-district of each observation and is thus orthogonal to local (ethnic-specific) shocks affecting social norms.

The estimated models substantiate a positive impact of ethnic conflict on in-group identification and social cohesion. First, ethnic conflict makes ethnic identity more salient. The result qualifies those studies reducing identity shifts to mere opportunistic calculations (Kalyvas, 2008; Mueller, 2000) and gives empirical support – the first on a cross-country level – to the large qualitative literature linking warfare to episodes of ethnic radicalisation.<fnref n="5" />

Given the limited availability of data on ethnic conflict, few studies have quantitatively explored its consequences in terms of social identification.<fnref n="6" /> Some works have analysed the responsiveness of social identity to violence, but using geographically-defined conflict and thus focusing on identity shifts due to the changing balance of power across groups (Ananyev and Poyker, 2023; Besley and Reynal-Querol, 2014). The most notable exception is the work by Rohner et al. (2013), who investigate the issue in relation to the insurgency of the Lord's Resistance Army in Uganda during the early 2000s. In an extension of their main analysis, the authors define ethnic-based measures of conflict. They find a positive impact of ethnic conflict on ethnic identification, which leads them to a conclusion similar to that of this paper: the impact of

conflict on inter-personal trust is mediated by ethnocentric dynamics. Beyond the use of a more demanding empirical specification, this paper contributes to the conflict-identity debate by: (i) generalising the results to a longer period and to the majority of the African continent;<fnref n="7" /> (ii) and by shedding light on how exposure to ethnic conflict can travel across space.

The second major finding of the paper is that ethnic warfare also has a positive effect on the three selected measures of social cohesion. The result aligns with evidence from various conflicts, such as those in: Burundi (Voors and Bulte, 2014), Sierra Leone (Bellows and Miguel, 2009), Uganda (De Luca and Verpoorten, 2015a,b), Russia (Guriev and Melnikov, 2016), and Nepal (Gilligan et al., 2014). Similar patterns have also been observed in cross-country analyses (Bateson, 2012; Grosjean, 2014). The findings of the literature are not, however, completely uncontroversial.<fnref n="8" /> Conflict type and degree of exposure do matter. On the one hand, direct combat experience appears to be unrelated to prosociality (Blattman, 2009; Kibris and Cesur, 2022). On the other hand, as shown by the Tajik civil war (1992–1997), when conflict cuts through internal cleavages, social capital tends to deplete (Cassar et al., 2013).

A conclusion that can be drawn from this literature is that *external* warfare can induce cooperation, but only parochially within each group affected by conflict.<fnref n="9" /> The focus on ethnic warfare of this paper permits a sharp definition of the groups involved in conflict, allowing a comparison of the different mechanisms relating conflict to prosocial behaviour. These can be divided into two non-exclusive categories.

First, warfare can change social norms by providing direct material incentives favouring prosocial strategies. These incentives stem from both economic and security rationales. On the one hand, amidst the insecure environment generated by conflict, communal insurance mechanisms become of greater importance, possibly paving the way to stable cooperative institutions and norms (Gilligan et al., 2014). On the other hand, conflict triggers a demand for security that is often met by the creation of local self-defence forces (Jennings and Sanchez-Pages, 2017). Cooperation within the latter can then gradually extend to the community as a whole.<fnref n="10" />

The second category of mechanisms linking conflict to cooperation, works through psychological processes that directly change individuals' social preferences. These mechanisms range from spontaneous outrage against war injustices – with the related effort to redress them – to post-traumatic growth theories (Tedeschi and Calhoun, 2004).<fnref n="11" /> One of

<footnote n="3">³ A notable exception is the dataset developed by Guarrieri and Tur-Prats (2023). Their data, however, refer exclusively to major armed conflicts.</footnote>

<footnote n="4">⁴ The choice of Africa as a setting for the analysis responds primarily to issues of data availability. Moreover, ethnic conflict is concentrated in sub-Saharan Africa (Fearon and Laitin, 2003), where sub-national identities are still strong and considerably affect the course of public policy (Easterly and Levine, 1997; Elischer, 2013).</footnote>

<footnote n="5">⁵ See, for example, Kaufmann (1996, pp. 140–145) for an overview of how ethnic identification hardened throughout many 20th century conflicts. Two of the most influential case studies in the literature are the 1994 Rwandan genocide and the 1990s Yugoslav wars. On Rwanda see Peterson (2000). The excellent anthropology documentary by Bringa and Christie (1993) remains an unrivalled window into the Yugoslavian conflict; for a broader analysis see Sambanis and Shayo (2013).</footnote>

<footnote n="6">⁶ For a theoretical analysis of the topic see: Caselli and Coleman (2013), Sambanis and Shayo (2013).</footnote>

<footnote n="7">⁷ The sample spans 36 African countries, covering over 60% of the continent.</footnote>

<footnote n="8">⁸ See Bauer et al. (2016) for a review.</footnote>

<footnote n="9">⁹ Mironova and Whitt (2018, 2021) show that in the aftermath of conflicts in Kosovo (1998–1999) and Ukraine (2014-ongoing), participants in cooperative games display a strong positive bias towards co-ethnics. This parochial interpretation, although not always explicitly spelt out, is also coherent with those studies finding a negative impact of conflict on: generalised trust (Besley and Reynal-Querol, 2014; Rohner et al., 2013; Tur-Prats and Valencia Caicedo, 2020), trust towards central political institutions (De Juan and Pierskalla, 2016; Grosjean, 2014; Voors and Bulte, 2014), and judicial impartiality (Shayo and Zussman, 2017). It is noteworthy that some of these results have been contradicted in other studies, identifying a positive effect of conflict on impersonal trust (Bellows and Miguel, 2009; De Luca and Verpoorten, 2015b) and participation in public politics (Bateson, 2012; Bellows and Miguel, 2006, 2009; Blattman, 2009; De Luca and Verpoorten, 2015a; Gilligan et al., 2014). A review of the reasons for these discrepancies is beyond the scope of this paper.</footnote>

<footnote n="10">¹⁰ There is some evidence as to the emergence of local self-defence forces in the sample. This is the case, for example, of the ‘Moorland Forces Militia (Kenya)’, a Mosop self-defence group formed to counter the Soi-led ‘SLDF: Sabaot Land Defence Force’ active in Mount Elgon (Kenya) (Simiyu, 2008).</footnote>

<footnote n="11">¹¹ Explicit empirical evidence of post-traumatic growth with respect to conflict remains, however, quite elusive. For some limited evidence concerning Yugoslavia and Liberia, see Powell et al. (2003) and Hartman and Morse (2020), respectively.</footnote>
# 1 Introduction

*Now, it is time to bring in the harvest*

Hutu radio

Frightening messages of this kind were aired by the Hutu propaganda
throughout the 1994 genocide: in less than 100 days, over half million of
cadavers dotted the green slopes of Rwanda.<fnref n="1" /> The Hutu government had
launched an all-out war against the country's Tutsi minority, with civil-
ians routinely slaughtered because of their ethnic affiliation. External
observers remained baffled at the ferocity and violence of the conflict,
which was described as a war of all against all, of neighbour against
neighbour. People sought shelter into their ethnic community, turning
against those hailing from other cultural backgrounds. While journal-
ists and politicians alike have struggled to make sense of these dynamics,
invoking ill-defined stereotypes and 'ancient hatreds' (Allen and Seaton
1999), the academia has since long recognised the role that instrumental
considerations have had in shaping these culturally-oriented behaviours
of cooperation and discrimination. Demystifying the notion of an Hobbe-
sian hell, one observer of the Rwandan conflict concluded that: "the chief
dynamic of the depredations seems to have been furnished by maraud-
ing bands of violent, opportunistic, and often drunken thugs" (Mueller
2000, p. 62).

The discussion surrounding the Rwandan genocide is symptomatic
of a broader debate on the saliency and importance of social identities
at times of conflict: are ethnocentric pulses a real by-product of conflict
or are they a mere facade used instrumentally to justify opportunistic
conduct? The question is important not only to analyse conflict dynam-
ics, but also to comprehend post-war social norms and institutions, with
crucial repercussions for policy-making.<fnref n="2" /> This paper takes part to the
debate by analysing the interplay between warfare and ethnocentrism,
and exploring its consequences in terms of cooperative behaviour. The
topic has, indeed, received growing attention in the last couple of decades
and an increasing number of studies has explored the impact of conflict
on pro-social attitudes. While establishing a positive link between the

<footnote n="1">1. For the quote see Peterson (2000, p. 273).</footnote>

<footnote n="2">2. For example, the debate is of paramount importance for the delineation of set-
tlement patterns in post-conflict environments. On the benefits and costs of ethnic
segregation in the aftermath of sectarian wars see: Kaufmann (1996), Laitin (2004),
Mironova and Whitt (2016).</footnote>
these channels operates through ethnocentric dynamics: conflict results in stronger affiliation towards co-ethnics, ultimately easing cooperation within one's own group. The very fact that warfare unfolds along ethnic cleavages makes ethnic attributes more salient: individuals perceive themselves in ethnolinguistic terms and the group identity is made thicker by the war experience.<fnref n="12" />

While these mechanisms may coexist and reinforce one another, the observed increase in social cohesion seems to be driven by the ethnocentric dynamics inherent in sectarian conflict. First, the use of ethnic-level conflict implies a cultural, rather than geographical, link between warfare and individual behaviour. Second, the positive impact on ethnic identification shows that warfare entrenches group identities. Third, Section 4 presents various empirical exercises coherent, albeit not dispositive, with a parochial understanding of the baseline results: (i) social cohesion is impacted by distant conflict activity, the effect of which is unlikely to operate through institutional channels affecting local economic and security incentives; (ii) when ethnic and non-ethnic violence are compared, only the first has a positive significant impact on prosocial behaviour; (iii) there are heterogeneous effects with respect to ethnic fractionalisation, signalling that conflict-induced cooperation fails to materialise in communities highly divided across ethnic lines.

Overall, the analysis empirically supports the idea that ethnic boundaries have a pivotal role in shaping the content and extent of prosocial behaviour during periods of conflict. While instrumental considerations have been correctly identified as important factors governing group cooperation, identity-based processes, such as those underlined here, are equally relevant in explaining post-conflict social norms.

The rest of the paper is organised as follows. The next section describes the data used in the empirical analysis, which is then presented in Section 3. Hence, I proceed to test the ethnocentric mechanism as an explanation for the conflict-induced increase in social cohesion. The final section concludes.

## 2. Data

### 2.1. Sample and outcome variables

Outcome variables on ethnic identity and social cohesion are taken from Afrobarometer surveys. These are nationally representative surveys routinely used in the economic literature (e.g. Nunn and Wantchekon (2011), Michalopoulos and Papaioannou (2013)). I employ rounds 3 to 6,<fnref n="13" /> covering 36 countries from March 2005 to September 2015.<fnref n="14" /> Fig. 1 shows these regions and their subdivision into cells of 0.5 × 0.5 degrees (~55 km × 55 km).<fnref n="15" />

Crucially, Afrobarometer provides information on the language of respondents, allowing their classification in ethnolinguistic groups. I

use the LEDA algorithm developed by Müller-Crepon and co-authors (2020) to harmonise languages across Afrobarometer rounds. The procedure yields a sample that comprehends 153,724 individuals, belonging to 448 ethnolinguistic groups and spread across 2743 cells in 36 countries.<fnref n="16" /> On average, each cell appears in three survey waves and includes about 7 different ethnolinguistic groups. Given the inclusion of cell×period and ethnicity×period fixed effects, the identifying variation stems from multiethnic cells. Fig. 1 shows that these are not geographically clustered and that they represent the vast majority (77%) of the sample.

The question capturing social identification is expressed in terms of national-vs-ethnic allegiance, with responses ranging from one (solely national identity) to five (solely ethnic identity). Political trust variables are included in robustness exercises for ensuring that shifts in social identification are due to movements in ethnic, rather than national, allegiance.

The variables describing social cohesion are: attendance at community meetings, membership in community associations, and involvement in instances of collective action. They are standard measures of prosocial behaviour at the community-level, abundantly used in the literature relating conflict to the latter. They are expressed in integers, with higher values signalling greater involvement in the local community. For clarity of exposition, I standardise the three measures and then average them into a social cohesion index. In Appendix D.3 I show that results also hold for each individual outcome. Table 1 illustrates their descriptive statistics, while Appendix A details the exact wording of all questions.

### 2.2. Ethnic conflict

Data on conflict are taken from the ACLED database, which has been widely used in economics (e.g. Depetris-Chauvin et al. (2020), Manacorda and Tesei (2020)). It covers conflict incidents spanning the whole African continent from 1997 onwards, providing information on their location, date, and actors involved.<fnref n="17" /> I define ethnic conflict as conflict activity involving an actor with an ethnolinguistic affiliation. The rule for assigning an ethnolinguistic affiliation to a conflict actor depends on its type, with ACLED distinguishing between: STATE FORCES, REBEL GROUPS, POLITICAL MILITIAS, IDENTITY MILITIAS, RIOTERS, PROTESTERS, and CIVILIANS.<fnref n="18" /> The matching rules are the following:

* STATE FORCES ⇒ State forces are assigned to an ethnolinguistic group when the latter is coded by the Ethnic Power Relations (EPR) Dataset as enjoying a monopolistic or dominant access to state power during the relevant period.<fnref n="19" />

<footnote n="12">¹² Conflict is a great repertory of images and symbols shaping and substantiating a group's social identity and cohesion. Battles, even when of scarce strategic importance, are often mythologised as defining moments in the identity-building process (e.g. Poitiers in 732 for European identity, Kosovo Polje in 1389 for Serbian identity). They are often connected to feelings of victimisation and revenge, which play critical roles in cementing collective identities. Appendix D.7 shows, however, that there are not heterogeneous effects in experiencing conflict as victim or as perpetrator of violence.</footnote>

<footnote n="13">¹³ Previous rounds lack comparable questions on ethnic identification.</footnote>

<footnote n="14">¹⁴ The within-sample countries are: Algeria, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Côte d'Ivoire, Egypt, Gabon, Ghana, Guinea, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritius, Morocco, Mozambique, Namibia, Niger, Nigeria, Senegal, Sierra Leone, South Africa, Sudan, Swaziland, São Tomé and Príncipe, Tanzania, Togo, Tunisia, Uganda, Zambia, Zimbabwe.</footnote>

<footnote n="15">¹⁵ This grid cell size is standard in conflict literature (e.g. Berman et al. (2017), Manacorda and Tesei (2020)). As reported in the main text and the appendix, results are robust to the use of bigger cells measuring 1° × 1°.</footnote>

<footnote n="16">¹⁶ Note that Afrobarometer also reports the ethnicity of interviewees. Using language to define ethnolinguistic groups results in 16,030 fewer missing observations and is more coherent with the LEDA algorithm, which is based on the Ethnologue linguistic tree. The practice is also standard in the literature (e.g. Depetris-Chauvin et al. (2020)).</footnote>

<footnote n="17">¹⁷ The wide coverage of ACLED, including low-scale localised violence, is the main reason why I employ it instead of other geo-localised databases, most notably the UCDP-GED dataset. The latter includes only deadly conflict incidents belonging to a dyad crossing at least once the threshold of 25 battle-related deaths per year.</footnote>

<footnote n="18">¹⁸ There is an eighth residual category of ACLED actors (EXTERNAL/OTHER FORCES) that I do not consider because it refers mostly to international armed groups.</footnote>

<footnote n="19">¹⁹ An example of a positive match for STATE FORCES is the actor "Military Forces of Guinea (1984–2008)" that is assigned to the Susu ethnic group. Susu are coded by EPR as enjoying a position of dominance over state politics throughout the Lansana Conté regime.</footnote>
two, evidence on the actual channels is scant and “theoretical mech-
anisms remain elusive” (Mironova and Whitt 2021, p. 2). The present
paper fills this gap by exploring the effect of ethnic conflict on social iden-
tification and cohesion across sub-Saharan Africa. Thanks to a newly
created dataset on sectarian warfare, I provide empirical support to the
idea that conflict triggers a genuine process of identity radicalisation and
that these ethnocentric feelings are the main channel whereby conflict
translates into deeper cooperation.

Large scale quantitative evidence on the subject is, indeed, still very
limited. A major barrier to empirical investigation in the field is the lack
of comprehensive data on ethnic violence, that is conflict activity (al-
legedly) carried out for the sake of a given ethnic group.<fnref n="3" /> To overcome
such limitation, I compiled a novel dataset. Using geo-coded data from
the Armed Conflict Location and Event Data (ACLED) Project, I man-
ually match 1082 armed actors to various sub-Saharan ethnolinguis-
tic groups. The process allows the construction of conflict measures
that vary across time, space, and ethnicity. These variables are then
connected to several Afrobarometer surveys, describing social attitudes
and identification across the African continent.<fnref n="4" /> In particular, I em-
ploy three measures of social cohesion aimed at capturing pro-social
cooperative behaviour at the local level. These are: attendance at com-
munity meetings, membership in community development associations,
and participation in instances of collective action. As for social identity,
Afrobarometer asks respondents whether they feel more represented by
their ethnic group or by their nation-state.

The triple source of variation of the conflict variables is the essence of
the identification strategy here employed. The baseline model consists of
an Ordinary Least Squares (OLS) regression of social behaviour on eth-
nic conflict, as enriched by cell-district×time and ethnic×time fixed ef-
fects, other than various individual-level covariates taken from the Afro-
barometer. Hence, the baseline econometric specification compares peo-
ple exposed to the *same* conflict incident, but with different treatment
status because of their ethnolinguistic affiliation. While, obviously, vio-
lence does not unfold randomly, the rich set of fixed effects attenuates is-
sues of reverse causality (*e.g.* ethnicities self-selecting into conflict) and

<footnote n="3">3. A notable exception is the dataset developed by Guarnieri and Tur-Prats (2022). Their data, however, refer exclusively to major armed conflicts.</footnote>

<footnote n="4">4. The choice of sub-Saharan Africa as a setting for the analysis responds primarily to issues of data availability. Moreover, ethnic conflict is concentrated in sub-Saharan Africa (Fearon and Laitin 2003), where sub-national identities are still strong and considerably affect the course of public policy (Easterly and Levine 1997).</footnote>
<figure><img src="image_2.png" /><figcaption>Fig. 1. Countries and cells in Afrobarometer surveys 3 to 6.</figcaption></figure>

* REBEL GROUPS, POLITICAL MILITIAS & IDENTITY MILITIAS ⇒ These categories are assigned to an ethnolinguistic group when they recruit along ethnic lines.<fnref n="20" />

* RIOTERS, PROTESTERS & CIVILIANS ⇒ These actors are assigned to an ethnolinguistic affiliation when ACLED explicitly reports their ethnic identity.<fnref n="21" />

These rules are illustrated in more detail in Appendix B, which presents the novel Africa Ethnic Conflict (AEC) dataset: a cross-section of 1908 ACLED actors reporting their possible ethnic affiliations. Matches are based on a *wide* variety of sources: primarily scholarly publications, policy reports and international news agencies, but also, in the case of minor ephemeral actors, on social media posts and local newspapers. The ethnolinguistic dictionary of Olson (1996) is the main reference used to harmonise ethnic affiliations towards the Afrobarometer categories. Details on the sources used, as well as the rationale behind each actor-ethnicity link, are reported in the dataset and in its codebook.

A typical positive match is that between the 'Arrow Boys Militias' and the Ateso ethnolinguistic group. The militia was a local self-defence force in the Teso region of Uganda, which is inhabited by the Ateso-speaking Iteso people. Its formation was incentivised by the government to fight off the Lord's Resistance Army (LRA). It had a

uniquely regional character and framed the fight against the LRA in ethnic terms.<fnref n="22" />

Not all matches were so uncontroversial. Armed groups can have multiple identities and objectives, so that their ethnic dimension is sometimes difficult to ascertain. These cases have been explicitly coded and they are dropped in robustness exercises. An example of an uncertain match is that between the 'NDFF: Niger Delta Freedom Fighters' and the Ijaw ethnolinguistic group. The actor has a popular base among Ijaw people, with its membership and symbolism referring mostly to the Ijaw world. Yet, its leadership is controlled by the Urhobo ethnic group and recruits are not drawn uniquely from the Ijaw (Oriola, 2013, pp. 106, 146–147).

Another problem encountered during the matching procedure was that Afrobarometer languages do not always overlap with the ethnolinguistic group associated with the ACLED actor. Consider, for example, the 'Ilajes Ethnic Militia (Nigeria)'. The Ilajes are one of the main sub-groups of the Yoruba people (Olson, 1996, p. 239), which is the ethnic category reported in Afrobarometer surveys. As there is a fair chance that not all Yorubas are deeply concerned about Ilajes' fortunes, the baseline matching rule excludes all cases where Afrobarometer languages do not coincide with the actor's ethnolinguistic affiliation. For robustness, an alternative matching rule includes these cases.

Once conflict is associated with ethnicities, it is aggregated over time and space. Define $c_{e,l,j}$ as a dummy indicating the presence of a conflict event taking place in location $l$ during day $j$, and involving an armed actor associated with ethnolinguistic group $e$. Further define $ab_{k,l}$ as the earliest interview date conducted by Afrobarometer round $t \in \{3, 4, 5, 6\}$ in cell $k$. Hence, conflict is aggregated: temporally, over the day-unit period between two consecutive Afrobarometer surveys

<footnote n="20">²⁰ Note that this criterion is looser than that employed in the ACD2EPR Dataset, which links conflict actors to ethnolinguistic groups when they recruit along ethnic lines and publicly announce to act on their behalf (Wucherpfennig et al., 2012). I focus on ethnic recruitment because this organisational criterion is the one emphasised by ACLED in the definition of IDENTITY MILITIAS and this is the modal category in the AEC dataset.</footnote>

<footnote n="21">²¹ These categories of actors are reported in generic terms (e.g. 'Protesters (Nigeria)'). In order to assign them an ethnolinguistic affiliation, I consider the additional information contained in the *Associated Actor* variable. Where applicable, the variable specifies the ethno-religious identity of RIOTERS, PROTESTERS, and CIVILIANS involved in a given conflict event (e.g. 'Yoruba Ethnic Group (Nigeria)').</footnote>

<footnote n="22">²² This match is based on: Lomo and Hovil (2004), the data kindly shared by Rohner et al. (2013), and the actor bio provided by the Uppsala Conflict Data Program (https://ucdp.uu.se/actor/585).</footnote>
problems related to omitted variables (e.g. different local development trends). It remains, however, the possibility that estimates are biased by unobservable confounding factors operating at the ethnolinguistic-geographic-temporal level. A series of robustness tests aims at lowering these concerns, and gives credit to an interpretation of the results beyond the mere correlation. Among the various sensitivity checks, the most relevant is the use of *distant* warfare, that is, ethnic conflict taking place outside the cell-district of each observation. Conflict exposure is therefore indirect and likely orthogonal to local (ethnic-specific) shocks affecting social norms.

The models here estimated substantiate a positive impact of ethnic conflict on in-group identification and cohesion. First, ethnic conflict makes ethnic identity more salient. The result echoes popular perceptions and gives empirical support – the first on a cross-country level – to the large qualitative literature linking warfare to episodes of ethnic radicalisation.<fnref n="5" /> Moreover, the finding qualifies those studies reducing identity shifts to mere opportunistic calculations (Kalyvas 2008, Mueller 2000).

The second major finding of the paper is that ethnic conflict has also a positive effect on the three selected measures of social cohesion. The result is in line with some recent works relating warfare to social capital (Bauer et al. 2016). Importantly, the research design here employed allows to test a plausible mechanism behind this positive link. Warfare, when externally felt by a group, can, indeed, increase pro-social behaviour through several channels. These, for the purposes of this paper, can be divided into two non-exclusive categories.

First, warfare can change social norms by providing direct *material* incentives favouring pro-social strategies. These incentives stem from both economic and security rationales. On the one hand, amidst the insecure environment generated by conflict, communal insurance mechanisms acquire greater importance, possibly paving the way to stable cooperative institutions and norms (Gilligan et al. 2014). On the other hand, conflict triggers a demand for security that is, indeed, often met

<footnote n="5">5. See, for example, Kaufmann (1996, pp. 140-145) for an overview of how ethnic identification hardened throughout many 20th century conflicts. Two of the most influential case studies in the literature are the 1994 Rwandan genocide and the ethnic violence following the collapse of Yugoslavia in the early 1990s. For the first the reader is referred to Peterson (2000). On the second, the excellent anthropological documentary filmed by Bringa and Christie (1993) remains an unrivalled window into the conflict; for a more general overview of the Yugoslavian wars see Sambanis and Shayo (2013).</footnote>
<table><caption>**Table 1**
Descriptive statistics.</caption><thead><tr><th></th><th>Ethnic Identity</th><th>Social Cohesion</th><th>Attendance Community Meetings</th><th>Collective Action</th><th>Membership Community Associations</th><th>Ethnic conflict</th></tr></thead><tbody><tr><th>Mean</th><td>2.33</td><td>0.00</td><td>2.08</td><td>1.83</td><td>0.62</td><td>0.053</td></tr><tr><th>Sd</th><td>1.19</td><td>0.80</td><td>1.33</td><td>1.32</td><td>0.93</td><td>0.223</td></tr><tr><th>Min</th><td>1.00</td><td>-1.21</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>Max</th><td>5</td><td>1.88</td><td>4</td><td>4</td><td>3</td><td>1</td></tr><tr><th>N</th><td>130 997</td><td>150 804</td><td>152 739</td><td>152 387</td><td>152 178</td><td>153 724</td></tr></tbody></table>

Notes: The sample includes Afrobarometer rounds 3 to 6. Ethnic Identity captures the saliency of ethnic identification, while Social Cohesion is an index combining three dimensions of prosociality: attendance at community meetings (Attendance Community Meetings), participation in instances of collective action (Collective Action), and membership in community associations (Membership Community Associations). The Ethnic conflict dummy signals the presence of conflict events involving the ethnolinguistic group of an observation and taking place in her cell of residence during the relevant period.

$AB_{k,t} = (ab_{k,t-1}, \dots, ab_{k,t-1} - 1)$; spatially, over all the locations belonging to the cell $k$. Formally:

$$ \tilde{C}_{e,k,t} = \sum_{l \in k} \sum_{j \in AB_{k,t}} c_{e,l,j} $$

The baseline measure of conflict is an indicator function equal to one if there was at least a conflict incident $c_{e,l,j}$ in the relevant temporal-spatial-ethnic window, that is:

$$ C_{e,k,t} = 1[\tilde{C}_{e,k,t} > 0] $$

The event-based definition of conflict (i.e. $\tilde{C}_{e,k,t}$) is used in robustness checks. Another measure used for sensitivity analysis is the number of months with at least a conflict incident.<fnref n="23" />

## 2.3. Descriptive evidence

Descriptive statistics for the main outcome and treatment variables are reported in Table 1. Ethnic identification is generally high, averaging 2.33, slightly above a neutral preference for ethnic over national identity. Ethnic conflict is a relatively rare event, affecting slightly more than 5% of the sample.

The relative paucity of ethnic violence masks, however, a great variety of conflict types present in the sample. The following are anecdotal observations based on the study done during the match procedure. While not having the pretence of providing a complete description of warfare across Africa, they represent, nonetheless, a rare occasion in which the black box of ACLED conflict is actually open.

First, there are few ‘wars’: large-scale conflicts aiming at regime change or territorial conquest are quite rare. Examples of ‘wars’ unfolding in the period considered are: the violent independence pulses erupting in the Niger Delta (Nigeria) since the early 2000s; the Tuareg-cum-Islamist rebellion in northern Mali resurfacing around 2012; the al-Shabaab insurgency in Kenya (early 2010s-present), which was however largely devoid of an ethnic dimension; and the Seleka-led rebellion in Central African Republic, pitting northern groups against central authorities since the mid-2000s.

Particularly present in Nigeria and West Africa at large, is conflict between nomadic Fulani herders and various settled farmer communities. Similarly, pastoralists and agriculturalists have repeatedly clashed in Uganda, where Karamajong have often trespassed private fields. More generally, land conflict – in a continent undergoing a demographic transition – is rather widespread across Africa. The intense ethnic clashes between Mosop and Soi communities of Mount Elgon (Kenya), are among the clearest examples.

Finally, within the sample period, there has been a substantial amount of ethnically-organised political violence. Sometimes, this type of conflict engulfs the whole state apparatus. This is the case of Kenya,

where elections are ferocious contests between ethnic-based parties. In other instances, violence unfolds over local power dynamics, most notably, disputes relative to royal succession in traditional sub-national kingdoms; the Dagbani chieftaincy crisis in Ghana being a case point.

Fig. 2 maps ethnic conflict across within-sample countries, while shading these on the base of their average social identification value. There are some states, such as Botswana, where ethnic identification is relatively strong even in the absence of abundant conflict. Yet, overall, ethnolinguistic conflict tends to be concentrated in countries where ethnic animosities run higher: Nigeria and Uganda being the clearest examples.<fnref n="24" /> Obviously, the map is at most suggestive, giving a static between-country comparison that masks much sub-national and cross-temporal variation.

# 3. Econometric analysis

## 3.1. Baseline model

The baseline econometric model consists of a linear equation estimated through OLS:

$$ y_{i,e,k,t} = \mu_{k,t} + \lambda_{e,t} + \beta C_{e,k,t} + \delta' x_{i,e,k,t} + u_{i,e,k,t} \quad (1) $$

where, $y_{i,e,k,t}$ measures social behaviour of individual $i$ belonging to ethnolinguistic group $e$ and interviewed by Afrobarometer round $t$ in cell $k$. The $\mu_{k,t}$ and $\lambda_{e,t}$ terms are sets of cell-period and ethnicity-period fixed effects (FE).<fnref n="25" /> The independent variable of interest is the ethnic conflict dummy $C_{e,k,t}$, equal to 1 when ethnolinguistic group $e$ experiences conflict during $t$ within cell $k$. The vector $x_{i,e,k,t}$ controls for various individual-level characteristics reported in Afrobarometer surveys. In baseline estimations, it includes only ‘fixed’ personal attributes that are unlikely to be endogenous in Eq. (1). These are: age and its square, gender, and an urban-rural indicator. Finally, $u_{i,e,k,t}$ are standard errors clustered at the cell-ethnolinguistic-period level, that is, the level of assignment of the conflict variable.<fnref n="26" />

## 3.2. Identification

The coefficient of interest ($\beta$) captures the difference in social attitudes between individuals living in the same area and interviewed at the same time, but with different conflict exposure due to their different

<footnote n="23">²³ Appendix A.2 provides further details on the construction of conflict variables.</footnote>

<footnote n="24">²⁴ In Appendix D.1, I show that results are robust to singularly excluding each country.</footnote>

<footnote n="25">²⁵ Note that while there are two sets of fixed effects, this setting differs from the standard two-way fixed effect (TWFE) model in that there are no fixed groups whose treatment status changes across time. Hence, TWFE diagnostic and remedies (De Chaisemartin and d'Haultfoeuille, 2020; Callaway and Sant'Anna, 2021) do not apply to this context.</footnote>

<footnote n="26">²⁶ In Appendix D.1 it is shown that results are robust to the use of higher-level clustering as well as spatially correlated errors à la Conley (1999).</footnote>
by the creation of local self-defence forces (Jennings and Sanchez-Pages
2017). Cooperation within the latter can then gradually extend to the
community as a whole.<fnref n="6" />

The second category of mechanisms linking conflict to cooperation,
works through psychological processes that directly change individu-
als' social preferences. One of these channels operates through ethno-
centric dynamics: conflict results into stronger affiliation towards co-
ethnics, ultimately easing cooperation within one's own group. The very
fact that warfare unfolds along ethnic cleavages makes ethnic attributes
more salient: individuals perceive themselves into ethnolinguistic terms
and the group identity is made thicker by the war experience.<fnref n="7" /> Hence,
as ethnocentric feelings are prioritised, people become more willing to
cooperate with their co-ethnics, who are felt closer and are equally sym-
pathetic to those violent clashes investing their ethnolinguistic group as
a whole.

There are, however, many other psychological processes linking war-
fare to cooperation. These mechanisms range from spontaneous outrage
against war injustices – with the related effort to redress them – to theo-
ries explaining public engagement as an emotional value (Bateson 2012).
The one which has received the most attention is, perhaps, the so-called
post-traumatic growth theory. According to the latter, in the aftermath
of traumatic events people recast their beliefs, often in a drastic manner.
The reformation of one's own *weltanschauung* goes in the direction of a
renewed appreciation of one's own: capability, spiritual dimension, and
intimate relations (Tedeschi and Calhoun 2004); thus possibly leading
to higher involvement in social and public life.<fnref n="8" />

<footnote n="6">6. There is some evidence on the emergence of local self-defence forces within the period here considered. This is the case, for example, of the 'Moorland Forces Militia (Kenya)', a Mosop self-defence group born to counter the Soi-led 'SLDF: Sabaot Land Defence Force' active in Mount Elgon (Kenya) (Simiyu 2008).</footnote>

<footnote n="7">7. Conflict is, indeed, a great repertory of images and symbols shaping and sub-
stantiating the social identity and cohesion of a group. Beyond the mythologisation
of battles as defining moment of the identity-building process – the centuries-long
idealisation of the battle of Poitiers (732 CE) as defining moment for the definition of
a European identity, despite its marginal strategic importance at the time, is a case
point –, victimisation and, possibly, revenge appear to be of paramount importance
in cementing a common sense of identity. As an outstanding contemporary example,
consider Israel, where Holocaust martyrs are revered in the poignant Yad Vashem mu-
seum of Jerusalem and the related sense of victimisation is still today a crucial element
in the definition of Israeli national feelings (Resnik 2003).</footnote>

<footnote n="8">8. Explicit empirical evidence of post-traumatic growth with respect to conflict remains, however, quite elusive. For some limited evidence concerning Yugoslavia and Liberia, see Powell et al. (2003) and Hartman and Morse (2020), respectively.</footnote>
<figure><img src="image_3.png" /><figcaption>Fig. 2. Ethnic identity and conflict in selected sub-Saharan countries.</figcaption></figure>

ethnic background.<fnref n="27" /> Its identification exploits the triple source of variation of the ethnic conflict variable, which allows the inclusion of both cell×period and ethnicity×period FE.

While making the model relatively saturated, the rich set of fixed effects alleviates omitted variable issues. Their inclusion allows to control for many of the leading causes of (ethnic) conflict (Blattman and Miguel, 2010), ranging from income shocks and resource competition (Dube and Vargas, 2013), to inter-group inequalities (Østby, 2008; Cederman et al., 2011). In particular, the $λ_{e,t}$ FE keep constant many ethnic-specific correlates of trust and warfare: segmentary lineage organisations (Moscona et al., 2017, 2020), ethnic exclusion from power (Cederman et al., 2010), ethnic partition across national borders (Michalopoulos and Papaioannou, 2016), culture of honour (Cao et al., 2021; Fouka and Schläpfer, 2022). At the same time, through the $μ_{k,t}$ term, Eq. (1) controls for institutional elements likely to influence both conflict and social cohesion: land tenure regimes (Boone and Nyeme, 2015), national sport competitions (Depetris-Chauvin et al., 2020), elections (Posner, 2004; Eifert et al., 2010), and the overall degree of modernisation and state-building (Miguel, 2004; Robinson, 2014).

Migration might introduce selection issues that invalidate estimation. On the one hand, less prosocial people might be more likely to flee in times of conflict (Gilligan et al., 2014). On the other hand, warfare tends to displace women relatively more than men,<fnref n="28" /> ultimately altering social cohesion dynamics at the local level. In the present context, these sample selection issues are, however, unlikely to severely bias estimates. First, cell-time fixed effects should account for migration

that is not ethnic-specific. Second, the episodes of ethnic violence observed in the sample stem mostly from low-intensity conflicts, which are unlikely to trigger mass-scale migration. In Appendix D.6 I provide empirical support to these intuitions, showing that neither the population share nor the gender composition of ethnolinguistic groups were significantly affected by warfare.

Reverse causality might also threaten inference. While ethnic violence is a powerful element in the identity-building process, more cohesive and fiercer cultural groups might engage more often in conflict, having more powerful grievances and mobilisation structures. The ethnic×period fixed effects partly dispel these concerns. The model compares people with the same ethnic background and during the same period: ethnic-broad self-selection in violence is effectively taken into account.

Despite the rich set of fixed effects, Eq. (1) cannot provide estimates that are causal in the sense of exploiting (conditional) random variation. Unobserved local ethnic-specific dynamics, such as access to power or temporal resource windfalls, may still bias inference by acting on group cohesion and conflict propensity. A series of robustness tests aims at increasing confidence in baseline estimates. Particularly important is the use of distant conflict activity, which takes place far-away from Afrobarometer respondents and is less likely to be influenced by local political or economic shocks. As shown in Section 4, ethnic violence has a significant impact on ethnic identification and social cohesion even when it takes place remotely and exposure is indirect. The exercise lessens sample selection issues (respondents are not directly involved in remote conflict) and the confounding effect of local unobservables, hence strengthening a causal interpretation of the main results.

### 3.3. *Baseline results*

The top panel of Table 2 reports estimates of Eq. (1) for social identity. The first regression includes only the three sets of geographic, temporal, and ethnic fixed effects without interactions between them.

<footnote n="27">²⁷ Alternatively, the coefficient can be interpreted as a comparison between people belonging to the same ethnic cluster and interviewed at the same time, but having a different exposure to conflict in virtue of their location.</footnote>

<footnote n="28">²⁸ Globally, by the end of 2018, women and girls represented slightly more than half of the 41 million people living in a situation of internal displacement as a result of conflict and violence (Internal Displacement Monitoring Center, 2020, p. 5).</footnote>
While these mechanisms may coexist and reinforce one another, the observed increase in social cohesion seems to be driven by the ethno-centric dynamics inherent to sectarian conflict. First, the use of ethnic-level conflict implies a cultural, rather than geographical, link between warfare and individual behaviour. This, also in light of the positive impact on ethnic identification, suggests a parochial interpretation of the results: the observed increase in pro-social behaviour remains limited to one's own co-ethnics. The intuition is further tested by checking for the impact of distant conflict activity, whose effect is unlikely to operate through institutional channels affecting local economic and security incentives. Moreover, when (local) ethnic and non-ethnic violence are compared, only the first has a positive significant impact on pro-social behaviour. Finally, the models estimated at Section 5 illustrate how the effect of ethnic warfare is heterogeneous with respect to local measures of ethnic polarisation: conflict-induced cooperation fails to materialise in communities highly polarised across ethnic lines, further giving strength to a parochial understanding of the baseline results.

Overall, the analysis provides empirical evidence to the idea that ethnic boundaries have a pivotal role in shaping the content and extent of pro-social behaviour during periods of conflict. While instrumental considerations have been correctly identified as important factors governing group cooperation, identity-based processes, such as those underlined here, are equally relevant in explaining post-conflict social norms.

The rest of the paper is organised as follows. The next section reviews the existing evidence and the related literature. Section 3 describes the data used in the empirical analysis, which is then presented in Section 4. Hence, I proceed to test the ethnocentric mechanism as explanation for the conflict-induced increase in social cohesion. A final section concludes.

## 2 Existing evidence

Given the limited availability of data on ethnic conflict, few studies have quantitatively explored its consequences in terms of social identification.<fnref n="9" /> The most notable exception is the work by Rohner and co-authors (2013), who investigate the issue in relation to the resurgence of the Lord's Resistance Army insurgency in Uganda during the early 2000s. While their main model uses warfare on a geographical base, in an exten-

<footnote n="9">9. For a theoretical treatment of the topic see Sambanis and Shayo (2013).</footnote>
<table><caption>**Table 2**
OLS regressions of Ethnic identity & Social cohesion on Ethnic conflict.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td colspan="5"><strong>ETHNIC IDENTITY</strong></td></tr><tr><td>Ethnic conflict</td><td>0.059<br>(0.041)</td><td>0.088<br>(0.032)***</td><td>0.094<br>(0.033)***</td><td>0.089<br>(0.033)***<br>-0.056<br>(0.013)***</td></tr><tr><td>Urban</td><td></td><td></td><td></td><td></td></tr><tr><td>R<sup>2</sup></td><td>0.15</td><td>0.21</td><td>0.22</td><td>0.22</td></tr><tr><td>N</td><td>130,981</td><td>130,949</td><td>130,926</td><td>128,547</td></tr><tr><td colspan="5"><strong>SOCIAL COHESION</strong></td></tr><tr><td>Ethnic conflict</td><td>0.058<br>(0.019)***</td><td>0.054<br>(0.024)**</td><td>0.063<br>(0.025)**</td><td>0.050<br>(0.024)**<br>-0.143<br>(0.008)***</td></tr><tr><td>Urban</td><td></td><td></td><td></td><td></td></tr><tr><td>R<sup>2</sup></td><td>0.19</td><td>0.24</td><td>0.24</td><td>0.29</td></tr><tr><td>N</td><td>150,790</td><td>150,766</td><td>150,746</td><td>148,250</td></tr><tr><td>Period FE</td><td>Yes</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Cell FE</td><td>Yes</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Ethnic FE</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td></tr><tr><td>Cell-period FE.</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Ethnic-period FE.</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Individual cov.</td><td>No</td><td>No</td><td>No</td><td>Yes</td></tr></tbody></table>

Notes: The sample includes Afrobarometer rounds 3 to 6. Individual controls include: age and its square, gender, and an urban-rural indicator (Urban). The Ethnic conflict dummy signals the presence of conflict events involving the ethnolinguistic group of an observation and taking place in her cell of residence during the relevant period. Ethnic Identity captures the saliency of ethnic identification, while Social Cohesion is an index combining three dimensions of prosociality: attendance at community meetings, participation in instances of collective action, and membership in community associations. Standard errors are clustered at the cell-ethnic-period level and are reported in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels.

The estimated marginal impact of ethnic conflict is positive. Yet, it has a small size and is statistically indistinguishable from zero. Including cell-period dummies in the second column substantially increases the size of the coefficient, suggesting the existence of some confounding factor operating at the spatio-temporal level. Local economic development trends are a plausible explanation: while decreasing the chances of conflict, they can simultaneously correlate with both social cohesion and group identification. The third column adds ethnic-period fixed effects, further increasing the magnitude of marginal effects. The increment can again be explained in terms of omitted variables (e.g. access to political power) and underlines the importance of comparing observations belonging to the same cell×period and ethnic×period groups. The inclusion of Afrobarometer controls in column four reduces the magnitude of $\beta$ only marginally: ethnic violence does increase ethnic identification.

The estimated marginal impacts are both statistically and economically significant. In the most demanding specification, the coefficient is equal to 0.089, roughly half of the average difference between conflict-torn Uganda and peaceful Zambia. To better gauge the relevance of conflict, its coefficient can be compared to that of the rural–urban indicator. Moving from the countryside to an urban centre decreases ethnic identification by 0.056, a mere 60% of the effect triggered by ethnic violence.

The second panel of Table 2 reports results from Eq. (1) as estimated over the social cohesion index. Point estimates are positive and statistically significant throughout: ethnic conflict induces prosocial behaviour towards one's own community. Marginal effects are relatively modest when compared to the urban indicator, but comparable to the impact of conflict on ethnic identification: for both outcomes of interest, conflict exposure triggers an increase of about 8% standard deviations of the dependent variable.

### 3.4. Robustness checks

Fig. 3 reports point estimates and 90% confidence intervals of $\beta$ as estimated from various alternative specifications of Eq. (1). All regressions include the full set of cell×period and ethnic×period FE.

First, I explore sensitivity to alternative conflict definitions. Two extensive measures of ethnic violence are used: the month- and event-based definitions introduced in Section 2. Having a lot of zeros, the two variables are parametrised using the inverse hyperbolic sine function; results are identical when employing natural logs. Estimates are positive and mostly significant, signalling that ethnic identity and community engagement are also responsive to the intensity of violence.

The second battery of robustness tests deals with the matching rules used to link conflict actors to ethnolinguistic groups. The two major issues encountered during the matching procedure were that: (i) the ethnic affiliation of the actor was not always blatantly clear, primarily because of its mixed objectives and identities; (ii) the associated ethnolinguistic group was a sub-group of the reported Afrobarometer language. I specify alternative matching rules: (i) excluding uncertain matches; (ii) including matches without an exact overlap between the actor's ethnolinguistic category and the Afrobarometer language. Sign and significance of the baseline models are reproduced under these alternative matching rules.

The third series of robustness tests concerns the definition of the sample. First, I limit the sample to ethnolinguistic groups appearing throughout the four periods in order to lessen selection issues. The (dis-)appearance of a group from Afrobarometer may be related to changes in its socio-political relevance, with, for example, survey designers choosing to suddenly include a given ethnolinguistic group in light of its recent autonomy struggle. The second sample-wide modification concerns the size of the grid cells into which the sample is divided. Following Berman et al. (2017), I check robustness to bigger cells measuring 1° × 1°. For both robustness tests, results are in line with baseline estimates, albeit marginal effects on social cohesion tend to be insignificantly different than zero.

The last robustness exercises revolve around the specification of the statistical model. First, I transform outcome variables into binary indicators so as to avoid problems related to their possible subjective interpretation.<fnref n="29" /> The seventh panel of Fig. 3 reports the results of this exercise: all point estimates are statistically significant and with magnitudes similar to baseline estimates (6%–9% standard deviations). Second, I include additional covariates from Afrobarometer surveys, capturing the socio-economic profile of respondents as well as their political attitudes. The regressions labelled Additional controls in Fig. 3 are specifications of Eq. (1) cumulatively including socio-economic and political controls, beyond the usual set of fixed effects and individual-level covariates.<fnref n="30" /> While these additional controls might be endogenous, it is reassuring to see that all coefficients are positive, significant and comparable in size to baseline estimates.<fnref n="31" /> Finally, I run specifications of Eq. (1) including ethnic-time-country fixed effects. Their inclusion lowers concerns related to unobservable ethnic- and country-specific shocks (e.g. national elections, Posner (2004)), and controls for the role of political boundaries (Herbst, 2000). As shown in the last two

<footnote n="29">²⁹ The three measures of prosocial behaviour are first expressed as dummies, then standardised and finally averaged into a social cohesion index. The precise definition of the extensive margins of outcome variables is given in Appendix A.</footnote>

<footnote n="30">³⁰ Socio-economic controls include: education, employment status, and an index of household wealth. Political controls comprise: trust towards the president, trust towards the local government, and an indicator function for having voted in the last national election.</footnote>

<footnote n="31">³¹ Robustness to the inclusion of political controls is particularly relevant for the social identity outcome. Indeed, these covariates ensure that the observed increase in ethnic identification is not given by lower attachment to the nation-state, a process possibly triggered by higher conflict levels.</footnote>
sion they match conflict to ethnolinguistic groups. They find a positive impact of ethnic conflict on ethnic identification, which lead them to a conclusion similar to that of this paper: conflict impact on inter-personal trust is mediated by ethnocentric dynamics. Beyond the obvious gains in terms of external validity, this paper employs a more demanding empirical specification than that used by Rohner et al. (2013), which does not include spatio-temporal fixed effects.

Other papers analyse the responsiveness of social identity to violence, but using geographically-defined conflict and thus focusing on identity shifts due to the changing balance of power across groups. Ananyev and Poyker (2021), for example, provides another case study from sub-Saharan Africa. The authors look at the Tuareg insurgency in Mali (2012), finding that conflict-induced lower perception of state capacity is associated to decreases in national identification. The only cross-country work is that of Besley and Reynal-Querol (2014), who show that historical conflict in Africa is correlated to higher ethnic identification and more inter-ethnic mistrust. In departure from this literature, the present work deals with ethnolinguistic warfare. Thanks to the newly developed dataset linking conflict actors to ethnic groups, I can specify a cultural relationship between warfare and ethnic identity. This research design can control for local institutional dynamics, hence distinguishing changes in the saliency of ethnic affiliation from shifts in national identity.

A second body of literature to which this paper is related concerns those articles analysing the impact of conflict on pro-social behaviour. Interest in the field, at least among economists, was sparked by Bel-low and Miguel's (2009) study in Sierra Leone. Using a household sur-vey reporting also for exposure to the 1991-2002 civil war, they found that people more exposed to conflict were also more likely to partici-pate in community life once hostilities were over. Similar findings have been echoed by studies of various other conflicts around the globe, such as those in: Burundi (Voors and Bulte 2014), Uganda (De Luca and Verpoorten 2015a-b), Russia (Guriev and Melnikov 2016), and Nepal (Gilligan et al. 2014). Moving beyond the case-study literature, Gros-jean (2014) analyses the legacy of violent conflict in thirty-five Eurasian countries. She finds that personal or familiar exposure to warfare is associated to higher levels of social capital. In a similar vein, using data at the global level, Bateson (2012) concludes that crime victimisa-tion positively affects attendance at community meetings. The findings of the literature are not, however, completely uncontroversial. Conflict
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** OLS regressions of Ethnic identity & Social cohesion on Ethnic conflict – Robustness tests.</figcaption></figure>

*Notes:* The figure reports point estimates, alongside their 90% confidence intervals, of ethnic conflict. Fixed effects correspond to cell×period and ethnic×period dummies. Individual controls include: age and its square, gender, and an urban-rural indicator. Standard errors are clustered at the cell-ethnic-period level. In the eighth panel (Additional controls), regressions labelled FE comprise fixed effects, individual and socio-economic controls; regressions labelled FE and controls further add political covariates.

regressions of Fig. 3, baseline results are robust to the specification of these more saturated models.

Appendix D shows robustness of the main results to additional sensitivity exercises, including: exclusion of outlier ethnolinguistic groups, sequential exclusion of each country, alternative time windows onto which conflict is aggregated.

## 4. Mechanisms

The previous section substantiated a significant and robust link between ethnic conflict and social cohesion. People exposed to ethnic violence report higher attachment to their cultural group and exhibit greater prosocial behaviour, primarily aimed at their community fellow-s. This section delves into the mechanisms underlying these results by: (i) illustrating how ethnocentric feelings can foster cooperation; (ii) providing empirical evidence that conflict-induced ethnocentric dynamics are central for explaining the observed increase in social cohesion.

### 4.1. Ethnocentrism and prosociality

Conflict might command prosocial behaviour through ethnocentrism dynamics: when an ethnolinguistic group becomes involved in violent clashes, its members frame themselves in ethnic terms, explicitly perceive their group identity, and rally around it. This rally-around-the-flag effect is not limited to ethnic conflict and is witnessed across a wide range of settings where a group is faced by some external threat. Yet, what is remarkable about ethnic violence is the intensity of such a phenomenon, capable of prompting behavioural changes even in the absence of instrumental incentives.<fnref n="32" /> How can this powerful shift in social preferences be interpreted?

Various theories have been advanced to explain ethnocentrism, that is, the instinctive classification of the world into the superior ethnic 'us' and all the 'others' (van der Dennen, 2000). Many of these theories have an explicit evolutionary framework in order to account for the massive evidence on the human propensity to favourably treat co-ethnics (Haushofer et al., 2023). One of the most widely accepted theories understands ethnocentrism as residual, almost a manipulation, of natural selection on pro-kinship behaviour (Gat, 2006).<fnref n="33" />

Humans, as much as other animals, have a natural inclination to help their relatives. The behaviour is evolutionary rational, inasmuch as family members are repositories of one's own genes and altruistic behaviour towards them does, indeed, ensure genetic reproduction. In the late Pleistocene – a time when human societies were relatively small and composed mainly of kin mates (Kelly, 2007) – linguistic developments gradually led to ever-deepening cultural differentiation (Harari, 2014). In this setting, common language and culture were effectively good indicators of kinship: favouring one's own co-ethnics was virtually equivalent to helping one's own relatives.

This is the socio-biological essence of ethnocentrism. In the pre-agricultural world, where ethnicity predicted kinship, it is easy to see the evolutionary rationale of the us-them discrimination. Yet, as human societies grew larger and more complex, the application of kin-solidarity to co-ethnics lost any evolutionary aim. When soldiers sacrifice their lives for the *motherland*, they are not increasing their biological fitness, nor that of any distant relative. This seemingly irrational behaviour should be read as a manipulation, an extension beyond its original objective, of kin-based solidarity within small groups.<fnref n="34" /> This is the atavistic component of contemporary xenophobia, the same component often perplexing observers of ethnic cleansing and genocides.

<footnote n="32">³² The peculiarities of ethnic warfare are more evident when other conflict types are considered. For example, Peri et al. (2023) show that terrorist attacks in Europe do not have a robust effect on trust towards government institutions and on other political attitudes. Interestingly, the authors find that only *foreign* terrorist attacks have the effect of increasing the appeal of *nationalistic* parties.</footnote>

<footnote n="33">³³ A thorough treatment of ethnocentrism, also discussing alternative theories such as those advanced by Bowles (2008, 2009, 2012) and co-authors (Choi and Bowles, 2007), can be found in Appendix C.</footnote>

<footnote n="34">³⁴ The persistence of naturally selected behaviours, even if devoid of their evolutionary *raison d'être*, albeit surprising is typical of humankind. For example, in the same logic, humans have a natural love for sweet food even though sweetness no longer serves its original evolutionary aim of signalling fruit maturity.</footnote>
type and degree of exposure seem, indeed, to matter a lot. Blattman (2009) considers the consequences of abduction by the Lord's Resistance Army during the Ugandan civil war: in terms of pro-social behaviour, children who were captured and forced to fight with rebels are indistinguishable from other civilians. Kibris and Cesur (2022) study pro-social behaviour among Turkish conscripts engaged in the counter-insurgency aimed against the country's Kurdish minority. Exploiting quasi-experimental variation, they show that exposure to armed conflict does not predict community participation among former conscripts. Cassar et al. (2013) analyse the internal conflict in Tajikistan (1992-1997), finding a negative relationship between victimisation and within-community trust. The authors provides empirical support to the intuition that their results are explained by the intra-community nature of the Tajik war.

Overall, one of the conclusions that can be drawn from this literature is that *external* warfare can induce cooperation, but only limited to the group that is swamped by conflict as a ‘unitary’ actor.<fnref n="10" /> When conflict, instead, cuts through internal cleavages, social capital tends to deplete because, as a consequence of the insecure environment, mistrusting attitudes become prioritised and social networks collapse. The focus on ethnic conflict of this paper allows a sharp definition of the groups involved in conflict, thus providing clear evidence of the in-group bias of conflict-induced cooperation.<fnref n="11" /> More importantly, different mechanisms can be here compared, closing a major gap in the literature. The use of distant ethnic violence and the other tests carried out at Section 5, unambiguously identify ethnocentrism as the mechanism underlying the relationship between conflict and social cohesion.

<footnote n="10">10. Mironova and Whitt (2018 & 2021) provide explicit evidence on the parochial nature of the pro-social impact of warfare. In a series of laboratory experiments, the authors show that in the aftermath of conflicts in Kosovo (1998-1999) and Ukraine (2014-ongoing), participants in various cooperative games display a strong positive bias towards co-ethnics.</footnote>

<footnote n="11">11. This parochial interpretation, although not always explicitly spelt out, is also coherent with those articles finding a negative impact of conflict on: generalised trust (Besley and Reynal-Querol 2014, Rohner et al. 2013), central political institutions (De Juan and Pierskalla 2016, Grosjean 2014, Voors and Bulte 2014), and judicial impartiality (Shayo and Zussman 2017). It shall be noted that some of these results have been contradicted in other studies, identifying a positive effect of conflict on impersonal trust (Bellows and Miguel 2009, De Luca and Verpoorten 2015b) and participation in public politics (Bateson 2012, Bellows and Miguel 2006 & 2009, Blattman 2009, De Luca and Verpoorten 2015a, Gilligan et al. 2014). A review of the reasons for these discrepancies is out of the scope of this paper.</footnote>
<table><caption>**Table 3**
OLS regressions of Ethnic identity & Social cohesion on Distant ethnic conflict.</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 colspan="7"><strong>ETHNIC IDENTITY</strong></td></tr><tr><td>Distant conflict</td><td>0.079<br>(0.030)***</td><td>0.078<br>(0.030)**</td><td>0.064<br>(0.031)**</td><td>0.063<br>(0.031)**</td><td></td><td></td></tr><tr><td>Ethnic conflict</td><td></td><td></td><td>0.077<br>(0.034)**</td><td>0.073<br>(0.034)**</td><td></td><td></td></tr><tr><td>Distant conf. (24-cell)</td><td></td><td></td><td></td><td></td><td>0.060<br>(0.028)**</td><td>0.058<br>(0.028)**</td></tr><tr><td>R<sup>2</sup></td><td>0.22</td><td>0.22</td><td>0.22</td><td>0.22</td><td>0.22</td><td>0.22</td></tr><tr><td>N</td><td>130,926</td><td>128,547</td><td>130,926</td><td>128,547</td><td>130,926</td><td>128,547</td></tr><tr><td colspan="7"><strong>SOCIAL COHESION</strong></td></tr><tr><td>Distant conflict</td><td>0.075<br>(0.021)***</td><td>0.055<br>(0.020)**</td><td>0.065<br>(0.022)**</td><td>0.048<br>(0.021)**</td><td></td><td></td></tr><tr><td>Ethnic conflict</td><td></td><td></td><td>0.047<br>(0.025)*</td><td>0.038<br>(0.024)</td><td></td><td></td></tr><tr><td>Distant conf. (24-cell)</td><td></td><td></td><td></td><td></td><td>0.059<br>(0.021)***</td><td>0.045<br>(0.020)**</td></tr><tr><td>R<sup>2</sup></td><td>0.24</td><td>0.29</td><td>0.24</td><td>0.29</td><td>0.24</td><td>0.29</td></tr><tr><td>N</td><td>150,746</td><td>148,250</td><td>150,746</td><td>148,250</td><td>150,746</td><td>148,250</td></tr><tr><td>Fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Individual cov.</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr></tbody></table>

Notes: The sample includes Afrobarometer rounds 3 to 6. Fixed effects correspond to cell×period and ethnic×period dummies. Individual controls include: age and its square, gender, and an urban-rural indicator. The *Ethnic conflict* dummy signals the presence of conflict events involving the ethnolinguistic group of an observation and taking place in her cell of residence during the relevant period. *Distant conflict* is a dummy variable signalling the presence of ethnic conflict within national borders and within the 8-cell neighbourhood of an observation. *Distant conflict (24-cell)* is a dummy variable signalling the presence of ethnic conflict within national borders and within the 24-cell neighbourhood of an observation. *Ethnic Identity* captures the saliency of ethnic identification, while *Social Cohesion* is an index combining three dimensions of prosociality: attendance at community meetings, participation in instances of collective action, and membership in community associations. Standard errors are clustered at the cell-ethnic-period level and are reported in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels.

Ethnic conflict thus plays on human ethnocentric instincts, activating feelings of solidarity and altruism even in the absence of tangible economic benefits.

## 4.2. Identifying the ethnocentric channel

The use of ethnic-varying conflict and the positive link found between this and ethnic identification, suggest that ethnocentric dynamics are key for explaining the observed increase in prosocial behaviour. The remainder of this section reports additional tests of the ethnocentric channel.

**Distant conflict.** If a process of ethnic radicalisation drives the results, people should also be responsive to distant conflict activity to which they are exposed only indirectly. Table 3 reports estimates from Eq. (1) employing $C_{e,neig(k,t)}$ as treatment variable, where $C_{e,neig(k,t)}$ is a dummy set to one if ethnolinguistic group *e* experienced conflict in period *t* over the 8-cell neighbourhood of *k* falling within national borders ($neig(k)$). We thus consider ethnic violence outside of respondents' cell, occurring up to 155km away from each observation.

The first two columns of Table 3 include only this measure of distant conflict, while the successive two also add the baseline local conflict indicator, which otherwise is a plausible omitted variable. The point estimate of distant conflict is always positive and statistically significant. Finally, the last two columns of Table 3 show that results hold even when considering ethnic warfare taking place very remotely, namely in the 24-cell neighbourhood of each observation (i.e. up to 233km from respondents).<fnref n="35" />

<table><caption>**Table 4**
OLS regressions of Ethnic identity & Social cohesion on Distant ethnic conflict — How conflict travels.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td colspan="5"><strong>ETHNIC IDENTITY</strong></td></tr><tr><td>Distant conflict</td><td>0.029<br>(0.010)***</td><td>0.029<br>(0.010)***</td><td>0.013<br>(0.011)</td><td>0.012<br>(0.011)</td></tr><tr><td>Nomadic conflict</td><td>-0.001<br>(0.008)</td><td>-0.001<br>(0.008)</td><td></td><td></td></tr><tr><td>Media ownership</td><td></td><td></td><td>-0.073<br>(0.009)***</td><td>-0.053<br>(0.009)***</td></tr><tr><td>Distant conf. × Media</td><td></td><td></td><td>0.009<br>(0.009)</td><td>0.009<br>(0.010)</td></tr><tr><td>R<sup>2</sup></td><td>0.24</td><td>0.24</td><td>0.22</td><td>0.22</td></tr><tr><td>N</td><td>86,907</td><td>85,262</td><td>130,823</td><td>128,448</td></tr><tr><td colspan="5"><strong>SOCIAL COHESION</strong></td></tr><tr><td>Distant conflict</td><td>0.021<br>(0.007)***</td><td>0.015<br>(0.007)**</td><td>0.010<br>(0.007)</td><td>0.007<br>(0.007)</td></tr><tr><td>Nomadic conflict</td><td>0.003<br>(0.006)</td><td>0.001<br>(0.005)</td><td></td><td></td></tr><tr><td>Media ownership</td><td></td><td></td><td>0.214<br>(0.005)***</td><td>0.153<br>(0.005)***</td></tr><tr><td>Distant conf. × Media</td><td></td><td></td><td>0.014<br>(0.006)**</td><td>0.010<br>(0.006)*</td></tr><tr><td>R<sup>2</sup></td><td>0.24</td><td>0.29</td><td>0.25</td><td>0.29</td></tr><tr><td>N</td><td>101,332</td><td>99,606</td><td>150,619</td><td>148,128</td></tr><tr><td>Fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Individual cov.</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr></tbody></table>

Notes: The sample includes Afrobarometer rounds 3 to 6. Fixed effects correspond to cell×period and ethnic×period dummies. Individual controls include: age and its square, gender, and an urban-rural indicator. *Distant conflict* is a dummy variable signalling the presence of ethnic conflict within national borders and within the 8-cell neighbourhood of an observation. *Nomadic conflict* considers *Distant conflict* where the ethnic group of the observation clashes with a nomadic ethnolinguistic group. *Media ownership* is a dummy variable signalling tv or radio ownership. Conflict variables have been standardised so as to have zero mean and unit standard deviation. *Ethnic Identity* captures the saliency of ethnic identification, while *Social Cohesion* is an index combining three dimensions of prosociality: attendance at community meetings, participation in instances of collective action, and membership in community associations. Standard errors are clustered at the cell-ethnic-period level and are reported in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels.

The importance of this exercise is twofold. On the one hand, it lowers the endogeneity issues mentioned in Section 3.2: local ethnic-specific shocks are unlikely to be correlated to distant clashes. On the other hand, they provide further evidence consistent with the ethno-centric mechanism. Indeed, many alternative channels are ill-suited to explain how remote ethnic violence can trigger higher social cohesion. For example, conflict-induced economic incentives to cooperation work primarily on a geographical basis: it is difficult to conceive how local insurance schemes would be adopted in response to rumours of distant conflict. While it is true that some psychological mechanisms might work with indirect exposure to conflict (e.g. mobilisation against war injustices), many other prominent channels (e.g. post-traumatic growth) are also usually understood as taking place in the midst of a direct violent trauma.

Table 4 investigates two potential mechanisms through which conflict exposure may diffuse spatially: fears of spillover related to nomadic warring groups and media coverage. All conflict variables in Table 4 have been standardised to ease comparisons.

Ethnic conflicts involving nomadic groups are inherently mobile, which may lead individuals to anticipate that warfare, even when

<footnote n="35">³⁵ Appendix D.5 explores more in detail the spatial dynamics of marginal effects, showing that they decay over space, but only after relatively large distances.</footnote>
A final strand of literature related to this paper concerns the inter-play between state-building and ethnicity in sub-Saharan Africa. Most of the countries in the continent are highly multi-ethnic; for example, the sole Nigeria is credited with more than 250 different ethnolinguistic groups. While the co-existence of different peoples shall not necessarily hamper political stability, in the African context, marked by historically weak institutions (Herbst 2000, Acemoglu and Robinson 2012, Hopkins 2019), multiculturalism has often been a source of troubles. Many states are, indeed, characterised by fierce political competition along ethnic lines. The contest often assumes a 'winner-takes-all' connotation, whereby the group in power squeezes public resources as much as possible for the sake of its fellow co-ethnics. Some of the most blatant examples include: early post-independence Ghana (Easterly and Levine 1997), Nigeria at least until 1999 (Kesselman et al. 2016, ch. 12), and Kenya since the Moi's regime (Elischer 2013, ch. 3). Moreover, given also the dis-functionality of central administrations, independence claims have abounded in the post-colonial period. Only two of them have ultimately proved successful: Eritrea gained independence from Ethiopia in 1991, and South Sudan separated from Sudan in 2011. Many more attempts have been suffocated in blood, leaving open scars in the collective memory of several peoples. Hence: the cycles of Tuareg rebellions in Mali, the Igbo struggle in Nigeria, the endless independence conflict in the Casamance region of Senegal, etc. The positive link between sectarian violence and ethnic-vs-national identification here found, unveils one of the mechanisms for the persistence of weak central administrations in sub-Saharan Africa.

# 3 Data

## A Sample and outcome variables

Outcome variables on ethnic identity and social cohesion are taken from Afrobarometer surveys. These are nationally representative surveys routinely used in the economic literature (e.g. Nunn and Wantchekon 2011, Michalopoulos and Papaioannou 2013). Here, I employ rounds 3 to 6, which cover the ten year window from March 2005 to September 2015. I keep only the eighteen countries that are present in all the surveys.<fnref n="12" />

<footnote n="12">12. In principle, the research design allows for the inclusion of any country appearing in at least two surveys. There are, indeed, other sixteen countries satisfying such criterium. Their exclusion is based on reasons of interpretability and time, with any</footnote>
<figure><img src="image_5.png" /><figcaption>Fig. 4. OLS regressions of Ethnic identity & Social cohesion on Ethnic and non-ethnic conflict.
Notes: The figure reports point estimates, alongside their 90% confidence intervals, of ethnic and non-ethnic conflict. Fixed effects correspond to cell×period and ethnic×period dummies. Individual controls include: age and its square, gender, and an urban-rural indicator. Standard errors are clustered at the cell-ethnic-period level.</figcaption></figure>

geographically distant, could spread locally. I empirically investigate this channel by specifying the following model:

$$y_{i,e,k,t} = \mu_{k,t} + \lambda_{e,t} + \beta C_{e,neig(k,t)} + \gamma C_{e,neig(k,t)}^{\text{nomadic}} + \delta' x_{i,e,k,t} + u_{i,e,k,t} \quad (2)$$

Where: $C_{e,neig(k,t)}^{\text{nomadic}}$ is a dummy for nomadic conflict, taking value of one if ethnicity e is involved in conflict against a nomadic ethnolinguistic group over the relevant spatio-temporal cluster ($neig(k,t)$)<fnref n="36" />; and all the other variables have the same meaning as in Eq. (1). The first two columns of Table 4 reveal no significant heterogeneous effects related to the nomadic nature of distant conflict: prosocial behaviour is not driven by fears of conflict spillover.

Conflict exposure might travel through media news. Coherently with the ethnocentric channel, simple knowledge of remote conflict activity may be enough to trigger changes in social identity and cohesion. I empirically investigate this issue by specifying the following model:

$$y_{i,e,k,t} = \mu_{k,t} + \lambda_{e,t} + \beta C_{e,neig(k,t)} + \phi Media_{i,e,k,t} + \alpha(C_{e,neig(k,t)} \times Media_{i,e,k,t}) + \delta' x_{i,e,k,t} + u_{i,e,k,t} \quad (3)$$

Where: $Media_{i,e,k,t}$ is a dummy equal to one if individual i owns either a radio or a tv; and all the other variables have the same meaning as in Eq. (2). Following Rohner et al. (2013), media ownership serves as a proxy for individuals' awareness of conflict.

The last two columns of Table 4 report estimates of Eq. (3). Point estimates of $\alpha$ are substantial and, in the case of social cohesion, statistically significant. While media ownership is likely endogenous and results must be taken with a grain of salt, the analysis suggests that distant ethnic warfare has a stronger impact for well-informed individuals.

Overall, the evidence in Table 4 is consistent with the ethnocentric channel. Local adaptations in social norms appear to be less influenced

by direct fears of conflict contagion, as might be expected with nomadic conflicts. Instead, distant conflict primarily affects social norms indirectly, simply travelling through media news.

**Ethnic vs. non-ethnic conflict.** To further examine the ethnocentric mechanism, I compare ethnic and non-ethnic conflict. If identity-based dynamics are at play, only ethnic conflict should have a significant impact on social identification and cohesion. Let $Z_{-e,k,t}$ be a dummy equal to one for conflict occurring in cell $k$ during period $t$ and unrelated to ethnolinguistic group e. Fig. 4 reports point estimates of $Z_{-e,k,t}$ and $C_{e,k,t}$ with their 90% confidence intervals, as retrieved from the following model:

$$y_{i,e,k,t} = \mu_{k,t} + \lambda_{e,t} + \beta C_{e,k,t} + \psi Z_{-e,k,t} + \delta' x_{i,e,k,t} + u_{i,e,k,t} \quad (4)$$

Note that baseline regressions already estimate the effect of ethnic conflict net of the total warfare present in each cell-period, as captured by the spatio-temporal fixed effects $\mu_{k,t}$. Regression (4) contrasts ethnic to non-ethnic violence by comparing cases where only one of the two is present to cases where both of them take place. The 'horse-race' between ethnic and non-ethnic conflict is clearly won by the first measure of warfare: the coefficient of $Z_{-e,k,t}$ is statistically indistinguishable from zero. The point estimate of $\beta$ remains positive and significant.

**Ethnolinguistic fractionalisation.** The final test of the ethnocentric mechanism checks heterogeneous effects with respect to an index of ethnolinguistic fractionalisation (ELF), defined as:

$$ELF_{k,t} = 1 - \sum_{e} \pi_{e,k,t}^{2}$$

Where $\pi_{e,k,t}$ is the population share of ethnic group e in cell k at time t. The index measures the probability that two randomly selected individuals in $(k,t)$ belong to different ethnolinguistic groups (Montalvo and Reynal-Querol, 2005). In areas marked by high fractionalisation levels, altruistic behaviour is more likely to benefit people hailing from different ethnolinguistic backgrounds. Hence, in these zones, if conflict affects social cohesion through ethnocentric dynamics, the estimated positive impact should be lower.

I define a new model by adding to Eq. (1) the ethnolinguistic fractionalisation index and its interaction with conflict. Formally:

$$y_{i,e,k,t} = \mu_{k,t} + \lambda_{e,t} + \beta C_{e,k,t} + \alpha(C_{e,k,t} \times ELF_{k,t}) + \delta' x_{i,e,k,t} + u_{i,e,k,t} \quad (5)$$

Note that the term $ELF_{k,t}$ is absorbed by the $\mu_{k,t}$ fixed effects. If conflict-induced prosocial behaviour remains limited to one's own coethnics, this should be harder to achieve in more fractionalised districts.

<footnote n="36">³⁶ I build this measure employing information from variable 30 of the Ethnographic Atlas (EA), which reports the prevailing type of settlement patterns of various ethnic groups. I classify Afrobarometer ethnicities as nomadic when the EA considers their settlement pattern as being SEMINOMADIC or NOMADIC. The coding decision follows McGuirk and Nunn (2024). The Ethnographic Atlas is an anthropological database widely used in both economics (Michalopoulos and Papaioannou, 2013; Fenske, 2014; Enke, 2019; Mayshar et al., 2022) and anthropology (Kelly, 2007).</footnote>
These are: Benin, Botswana, Cape Verde, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mali, Mozambique, Namibia, Nigeria, Senegal, South Africa, Tanzania, Uganda, Zambia, and Zimbabwe. They are depicted in Figure 1, which also reports their subdivision in cells of 0.5 × 0.5 degrees (~55km × 55km).<fnref n="13" />

Crucially, Afrobarometer provides information on the language of respondents, allowing their classification in ethnolinguistic groups. I use the LEDA algorithm developed by Müller-Crepon and co-authors (2020) to harmonise languages across Afrobarometer rounds.<fnref n="14" /> The procedure yields a sample that comprehends 111.428 individuals, belonging to 247 ethnolinguistic groups and spread across 1938 cells in eighteen countries.<fnref n="15" />

The question capturing social identification is expressed in terms of national-vs-ethnic allegiance. It varies discretely from one (the respondent feels only national) to five (the respondent identifies fully with her ethnic group). Note that this variable has been inverted around its median for easing the interpretation of results. The original coding of some other Afrobarometer controls has also been changed in order to harmonise them across survey rounds. The exact construction and definition of all variables, alongside their recoding, is given in Appendix A. In robustness exercises, some variables measuring for political trust are used for ensuring that shifts in social identification are due to movements in ethnic, rather than national, allegiance.

The variables describing social cohesion are: attendance at community meetings, membership in community associations, and involvement in instances of collective action. They are standard measures of social behaviour at the community-level, abundantly used in the literature relating conflict to the latter. They are expressed in integers, with higher values signalling greater involvement in the local community. For clarity of exposition, I standardise the three measures and then average

<footnote>new country included in the analysis considerably increasing the number of armed groups necessitating a match to an ethnicity.</footnote>

<footnote n="13">13. This is the standard size of grid cells typically used in the conflict literature. See for example: Berman et al. (2017), Manacorda and Tesei (2020).</footnote>

<footnote n="14">14. Few languages could not be matched using this procedure; they have been excluded from the analysis.</footnote>

<footnote n="15">15. Note that Afrobarometer also reports the ethnic group of the interviewed people. The use of language to define ethnolinguistic groups is dictated by the fact that there are 8548 less missing observations and that LEDA algorithm is based on the Ethnologue linguistic tree. The practice is also standard in the literature (e.g. Depetris-Chauvin et al. 2020). As reported in the paper and the appendix, results are robust to using ethnicity as an ethnolinguistic marker.</footnote>
<figure><img src="image_6.png" /><figcaption>**Fig. 5.** OLS regressions of Ethnic identity & Social cohesion on Ethnic conflict – Heterogeneous effects by Ethnolinguistic Fractionalisation.
*Notes:* The figure reports point estimates, alongside their 90% confidence intervals, of ethnic conflict and its interaction with an index of ethnolinguistic fractionalisation. Fixed effects correspond to cell×period and ethnic×period dummies. Individual controls include: age and its square, gender, and an urban-rural indicator. Standard errors are clustered at the cell-ethnic-period level.</figcaption></figure>

That is, the joint impact of ethnic fractionalisation and warfare (α) should be negative.

Fig. 5 reports estimates of Eq. (5). While conflict continues to positively impact community engagement and ethnic identification, the interaction term with ELF is negative. Even if not dispositive, the analysis is consistent with a parochial interpretation of the baseline results. It suggests that the positive impact of ethnic conflict on social cohesion is lower in highly fractionalised areas, where cooperation may extend beyond co-ethnics.

As shown in Appendix E, the results of Section 4.2 are robust to: employing extensive measures of ethnic conflict, different ethnicity matching rules, the use of alternative sample restrictions, a binary recoding of outcome variables, the inclusion of socio-economic and political covariates, and the addition of ethnic-country-time fixed effects.

## 5. Conclusions

This paper has investigated the impact of ethnic conflict on social identity and norms. The setting of the empirical analysis is the African continent, where sub-national identities are still strong and affect the course of public policy. The research design relies on the novel manually-compiled AEC dataset, linking conflict actors to ethnolinguistic groups. The procedure allows the construction of conflict measures varying at the spatial, temporal, and ethnic level. This triple source of variation and the ensuing rich set of fixed effects, are at the core of the identification strategy used here.

Confirming the anecdotal evidence on conflict-induced identity radicalisation, this paper substantiates the first cross-country empirical link between ethnic conflict and ethnic identification. This result is then used to shed light on the second empirical contribution of the article: ethnic violence prompts cooperative behaviour in communities exposed to it. The positive conflict-identity relationship found and the explicit use of ethnic, rather than geographical, warfare suggest that identity-based psychological processes are at the heart of the observed increase in social cohesion. This intuition is further developed and tested. First, *distant* ethnic violence positively affects civic attitudes, making it improbable that the results are driven by direct war traumas or by the effect of conflict on local institutions. Second, conflict unrelated to the ethnolinguistic group of respondents has no impact on their community engagement, making it clear that the ethnic dimension of conflict is

what really matters. Finally, conflict-induced prosocial behaviour is conditional on low levels of ethnolinguistic fractionalisation, indicating that cooperation likely remains limited to one's own group.

A socio-biological understanding of ethnocentrism provides a good theoretical framework to make sense of these findings. Conflict along ethnic cleavages taps into the innate 'us-them' ethnic division of the world, which can neither be dismissed as fully cultural nor as completely genetic. It is in light of this complex dual nature that both out-group xenophobia and in-group cooperation should be read. This paper finds evidence on the second, giving empirical support to the idea that, regardless of instrumental considerations, feelings of identification and solidarity towards one's own ethnic 'us' can lead to actual prosocial behaviour, albeit in a parochial fashion.

## Appendix A. Supplementary material

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.jdeveco.2024.103426.

### Data availability

The Africa Ethnic Conflict (AEC) dataset and the replication package for this paper are available on the author's website: https://sites.google.com/view/matteo-sestito/research.

### References

* Ananyev, Maxim, Poyker, Michael, 2023. Identity and conflict: Evidence from Tuareg rebellion in Mali. World Dev. 161, 106108.

* Bateson, Regina, 2012. Crime victimization and political participation. Am. Political Sci. Rev. 106 (3), 570–587.

* Bauer, Michal, Blattman, Christopher, Chytilová, Julie, Henrich, Joseph, Miguel, Edward, Mitts, Tamar, 2016. Can war foster cooperation? J. Econ. Perspect. 30 (3), 249–274.

* Bellows, John, Miguel, Edward, 2006. War and institutions: New evidence from Sierra Leone. Amer. Econ. Rev. 96 (2), 394–399.

* Bellows, John, Miguel, Edward, 2009. War and local collective action in Sierra Leone. J. Public Econ. 93 (11–12), 1144–1157.

* Berman, Nicolas, Couttenier, Mathieu, Rohner, Dominic, Thoenig, Mathias, 2017. This mine is mine! How minerals fuel conflicts in Africa. Amer. Econ. Rev. 107 (6), 1564–1610.

* Besley, Timothy, Reynal-Querol, Marta, 2014. The legacy of historical conflict: Evidence from Africa. Am. Political Sci. Rev. 108 (2), 319–336.
<figure><img src="image_3.png" /><figcaption>**Figure 1:** Countries and cells in Afrobarometer surveys 3 to 6</figcaption></figure>

them into a social cohesion index. In Appendix D I show that results hold also for each individual outcome. Table 1 illustrates their descriptive statistics.

## B Ethnic conflict

Data on conflict is taken from the ACLED database, which has been widely used in economics.<fnref n="16" /> It covers conflict incidents spanning the whole African continent since 1997, providing information on their location, date, and actors involved.<fnref n="17" /> In order to define ethnic conflict, I analyse all the non-state armed groups appearing in the within-sample countries and in their neighbourhood during the relevant period. There are 1082 of them in total. I associate a conflict actor to an ethnographic group when the former is organised along ethnic lines. Matches

<footnote n="16">16. To cite just some recent work: Depetris-Chauvin et al. (2020), Manacorda and Tesei (2020), Moscona et al. (2020).</footnote>

<footnote n="17">17. The wide coverage of ACLED, allowing also for the inclusion of low-scale localised violence, is the chief reason why I employ this dataset in lieu of other geo-localised databases, most notably the UCDP-GED dataset. The latter includes only deadly conflict incidents belonging to a dyad crossing at least once the 25 battle-related deaths per year.</footnote>
Blattman, Christopher, 2009. From violence to voting: War and political participation in Uganda. Am. Political Sci. Rev. 103 (2), 231-247.

Blattman, Christopher, Miguel, Edward, 2010. Civil war. J. Econ. Lit. 48 (1), 3-57.

Boone, Catherine, Nyeme, Lydia, 2015. Land institutions and political ethnicity in Africa: evidence from Tanzania. Comp. Politics 48 (1), 67-86.

Bowles, Samuel, 2008. Being human: Conflict: Altruism's midwife. Nature 456 (7220), 326-327.

Bowles, Samuel, 2009. Did warfare among ancestral hunter-gatherers affect the evolution of human social behaviors? Science 324 (5932), 1293-1298.

Bowles, Samuel, 2012. Warriors, levelers, and the role of conflict in human social evolution. Science 336 (6083), 876-879.

Brinka, Tone, Christie, Debbie, 1993. We are all neighbours. Disappearing World Series.

Callaway, Brantly, Sant'Anna, Pedro H.C., 2021. Difference-in-differences with multiple time periods. J. Econometrics 225 (2), 200-230.

Cao, Yiming, Enke, Benjamin, Falk, Armin, Giuliano, Paola, Nunn, Nathan, 2021. Herding, Warfare, and a Culture of Honor: Global Evidence. NBER Working Papers, National Bureau of Economic Research.

Caselli, Francesco, Coleman, Wilbur John, 2013. On the theory of ethnic conflict. J. Eur. Econom. Assoc. 11 (suppl_1), 161-192.

Cassar, Alessandra, Grosjean, Pauline, Whitt, Sam, 2013. Legacies of violence: trust and market development. J. Econ. Growth 18 (3), 285-318.

Cederman, Lars-Erik, Weidmann, Nils B., Gleditsch, Kristian Skrede, 2011. Horizontal inequalities and ethnonationalist civil war: A global comparison. Am. Political Sci. Rev. 105 (3), 478-495.

Cederman, Lars-Erik, Wimmer, Andreas, Min, Brian, 2010. Why do ethnic groups rebel? New data and analysis. World Politics 62 (1), 87-119.

Choi, Jung-Kyoo, Bowles, Samuel, 2007. The coevolution of parochial altruism and war. Science 318 (5850), 636-640.

CIPEV, 2008. Report on 2007 Election. Technical Report, The Commission of Inquiry on Post Election Violence.

Conley, Timothy G., 1999. GMM estimation with cross sectional dependence. J. Econometrics 92 (1), 1-45.

De Chaisemartin, Clément, d'Haultfoeule, Xavier, 2020. Two-way fixed effects estimators with heterogeneous treatment effects. Amer. Econ. Rev. 110 (9), 2964-2996.

De Juan, Alexander, Pierskalla, Jan Henryk, 2016. Civil war violence and political trust: Microlevel evidence from Nepal. Confl. Manag. Peace Sci. 33 (1), 67-88.

De Luca, Giacomo, Verpoorten, Marijke, 2015a. Civil war and political participation: Evidence from Uganda. Econom. Dev. Cult. Chang. 64 (1), 113-141.

De Luca, Giacomo, Verpoorten, Marijke, 2015b. Civil war, social capital and resilience in Uganda. Oxf. Econ. Pap. 67 (3), 661-686.

Depetris-Chauvin, Emilio, Durante, Ruben, Campante, Filipe, 2020. Building nations through shared experiences: Evidence from African football. Amer. Econ. Rev. 110 (5), 1572-1602.

Dube, Oeindrila, Vargas, Juan F., 2013. Commodity price shocks and civil conflict: Evidence from Colombia. Rev. Econ. Stud. 80 (4), 1384-1421.

Easterly, William, Levine, Ross, 1997. Africa's growth tragedy: policies and ethnic divisions. Q. J. Econ. 1203-1250.

Eifert, Benn, Miguel, Edward, Posner, Daniel N., 2010. Political competition and ethnic identification in Africa. Am. J. Political Sci. 54 (2), 494-510.

Elischer, Sebastian, 2013. Political Parties in Africa: Ethnicity and Party Formation. Cambridge University Press.

Enke, Benjamin, 2019. Kinship, cooperation, and the evolution of moral systems. Q. J. Econ. 134 (2), 953-1019.

Fearon, James D., Laitin, David D., 2003. Ethnicity, insurgency, and civil war. Am. Political Sci. Rev. 97 (1), 75-90.

Fenske, James, 2014. Ecology, trade, and states in pre-colonial Africa. J. Eur. Econom. Assoc. 12 (3), 612-640.

Fouka, Vasiliki, Schläpfer, Alain, 2022. Culture Clash: Incompatible Reputation Mechanisms and Intergroup Conflict. Technical Report, National Bureau of Economic Research.

Gat, Azar, 2006. War in Human Civilization. Oxford University Press.

Gilligan, Michael J., Pasquale, Benjamin J., Samii, Cyrus, 2014. Civil war and social cohesion: Lab-in-the-field evidence from Nepal. Am. J. Political Sci. 58 (3), 604-619.

Grosjean, Pauline, 2014. Conflict and social and political preferences: Evidence from World War II and civil conflict in 35 European countries. Comp. Econ. Stud. 56 (3), 424-451.

Guarnieri, Eleonora, Tur-Prats, Ana, 2023. Cultural distance and conflict-related sexual violence. Q. J. Econ. 138 (3), 1817-1861.

Guriev, Sergei, Melnikov, Nikita, 2016. War, inflation, and social capital. Amer. Econ. Rev. 106 (5), 230-235.

Harari, Yuval Noah, 2014. Sapiens: A Brief History of Humankind. Random House.

Hartman, Alexandra C., Morse, Benjamin S., 2020. Violence, empathy and altruism: evidence from the Ivorian refugee crisis in Liberia. Br. J. Political Sci. 50 (2), 731-755.

Haushofer, Johannes, Lowes, Sara, Musau, Abednego, Ndetei, David, Nunn, Nathan, Poll, Moritz, Qian, Nancy, 2023. Stress, ethnicity, and prosocial behavior. J. Political Econ. Microecon. 1 (2), 225-269.

Herbst, Jeffrey, 2000. States and Power in Africa. Princeton University Press.

Internal Displacement Monitoring Center, 2020. Women and girls in internal displacement. Online available at: https://www.internal-displacement.org/sites/default/files/publications/documents/202003-twice-invisible-internally-displaced-women.pdf. (Accessed 30 January 2022).

Jennings, Colin, Sanchez-Pages, Santiago, 2017. Social capital, conflict and welfare. J. Dev. Econ. 124, 157-167.

Kalyvas, Stathis N., 2008. Ethnic defection in civil war. Comp. Political Stud. 41 (8), 1043-1068.

Kaufmann, Chaim, 1996. Possible and impossible solutions to ethnic civil wars. Int. Secur. 20 (4), 136-175.

Kelly, Robert L., 2007. The Lifeways of Hunter-Gatherers: The Foraging Spectrum. Cambridge University Press.

Kibris, Arzu, Cesur, Resul, 2022. Does War Foster Cooperation or Parochialism? Evidence from a Natural Experiment among Turkish Conscripts. NBER Working Papers, National Bureau of Economic Research.

Laitin, David D., 2004. Ethnic unmixing and civil war. Secur. Stud. 13 (4), 350-365.

Lomo, Zachary, Hovil, Lucy, 2004. Behind the violence. The war in northern Uganda. Inst. Secur. Stud. Monogr. 2004 (99), 87.

Manacorda, Marco, Tesei, Andrea, 2020. Liberation technology: Mobile phones and political mobilization in Africa. Econometrica 88 (2), 533-567.

Mayshar, Joram, Moav, Omer, Pascali, Luigi, 2022. The origin of the state: Land productivity or appropriability? J. Polit. Econ. 130 (4), 1091-1144.

McGuirk, Eoin F., Nunn, Nathan, 2024. Transhumant pastoralism, climate change, and conflict in Africa. Rev. Econ. Stud. rdae027.

Michalopoulos, Stelios, Papaioannou, Elias, 2013. Pre-colonial ethnic institutions and contemporary African development. Econometrica 81 (1), 113-152.

Michalopoulos, Stelios, Papaioannou, Elias, 2016. The long-run effects of the scramble for Africa. Amer. Econ. Rev. 106 (7), 1802-1848.

Miguel, Edward, 2004. Tribe or nation? Nation building and public goods in Kenya versus Tanzania. World Politics 56 (3), 327-362.

Mironova, Vera, Whitt, Sam, 2016. The evolution of prosociality and parochialism after violence. J. Peace Res. 53 (5), 648-664.

Mironova, Vera, Whitt, Sam, 2018. Social norms after conflict exposure and victimization by violence: Experimental evidence from Kosovo. Br. J. Political Sci. 48 (3), 749-765.

Mironova, Vera, Whitt, Sam, 2021. Conflict and parochialism among combatants and civilians: Evidence from Ukraine. J. Econ. Psychol. 86, 102425.

Montalvo, José G., Reynal-Querol, Marta, 2005. Ethnic polarization, potential conflict, and civil wars. Am. Econ. Rev. 95 (3), 796-816.

Moscona, Jacob, Nunn, Nathan, Robinson, James A., 2017. Keeping it in the family: Lineage organization and the scope of trust in sub-Saharan Africa. Amer. Econ. Rev. 107 (5), 565-571.

Moscona, Jacob, Nunn, Nathan, Robinson, James A., 2020. Segmentary lineage organization and conflict in sub-Saharan Africa. Econometrica 88 (5), 1999-2036.

Mueller, John, 2000. The banality of “ethnic war”. Int. Secur. 25 (1), 42-70.

Müller-Crepon, Carl, Pengl, Yannick I., Bormann, Nils-Christian, 2020. Linking ethnic data from Africa. Unpublished Manuscript. ETH Zurich. https://yannickpengl.com/src/research/ethnic_matchung.pdf.

Nunn, Nathan, Wantchekon, Leonard, 2011. The slave trade and the origins of mistrust in Africa. Amer. Econ. Rev. 101 (7), 3221-3252.

Olson, James Stuart, 1996. The Peoples of Africa: an Ethnohistorical Dictionary. Greenwood Publishing Group.

Oriola, Temitope B., 2013. Criminal Resistance?: The Politics of Kidnapping of Oil Workers (Interdisciplinary Research Series in Ethnic, Gender and Class Relations). Ashgate Publishing Group.

Østby, Gudrun, 2008. Polarization, horizontal inequalities and violent civil conflict. J. Peace Res. 45 (2), 143-162.

Peri, Giovanni, Rees, Daniel I., Smith, Brock, 2023. Terrorism and political attitudes: Evidence from European social surveys. Reg. Sci. Urban Econ. 99, 103864.

Peterson, Scott, 2000. Me Against My Brother: at War in Somalia, Sudan and Rwanda. Routledge.

Posner, Daniel N., 2004. The political salience of cultural difference: Why Chewas and Tumbukas are allies in Zambia and adversaries in Malawi. Am. Political Sci. Rev. 98 (4), 529-545.

Powell, Steve, Rosner, Rita, Butollo, Willi, Tedeschi, Richard G., Calhoun, Lawrence G., 2003. Posttraumatic growth after war: A study with former refugees and displaced people in Sarajevo. J. Clin. Psychol. 59 (1), 71-83.

Rawlence, Ben, 2008. Ballots to Bullets: Organized Political Violence and Kenya's Crisis of Governance, (no. 1), Human Rights Watch.

Robinson, Amanda Lea, 2014. National versus ethnic identification in Africa: Modernization, colonial legacy, and the origins of territorial nationalism. World Politics 66 (4), 709-746.

Rohner, Dominic, Thoenig, Mathias, Zilibotti, Fabrizio, 2013. Seeds of distrust: Conflict in Uganda. J. Econ. Growth 18 (3), 217-252.

Sambanis, Nicholas, Shayo, Moses, 2013. Social identification and ethnic conflict. Am. Political Sci. Rev. 107 (2), 294-325.

Shayo, Moses, Zussman, Asaf, 2017. Conflict and the persistence of ethnic bias. Am. Econ. J. Appl. Econ. 9 (4), 137-165.

Simiyu, Robert Romborah, 2008. Militianisation of resource conflicts: the case of land-based conflict in the Mount Elgon region of western Kenya. Inst. Secur. Stud. Monogr. (152), 80.
have been based on a *wide* variety of sources: primarily scholarly publications, policy reports and international news agencies, but also, in the case of minor ephemeral actors, on social media posts and local newspapers. The ethnolinguistic dictionary of Olson (1996) has been the main reference used to harmonise ethnic affiliations towards the Afrobarometer categories. Details on the sources used as well as the rationale behind each actor-ethnicity link, are reported in the dataset and in its codebook. Appendix B gives a more accurate overview of the database.

A typical positive match is for example that between the 'Arrow Boys Militias' and the 'Ateso' ethnolinguistic group. The militia was a local self-defence force in the Teso region of Uganda, which is inhabited by the Ateso-speaking Iteso people. Its formation was incentivised by the government to fight off the Lord's Resistance Army (LRA). It had a uniquely regional character and framed the fight against the LRA in ethnic terms.<fnref n="18" />

Not all matches were so uncontroversial. Armed groups have often multiple identities and objectives, so that their ethnic dimension is sometimes difficult to ascertain. These cases have been explicitly coded and they are dropped in robustness exercises. An example of uncertain match is that between the 'NDFF: Niger Delta Freedom Fighters' and the 'Ijaw' ethnolinguistic group. The actor has a popular base among Ijaw people, with its membership and symbolism referring mostly to the Ijaw world. Yet, its leadership is controlled by the Urhobo ethnic group (Oriola 2013, pp. 106, 146-147).

Another problem encountered during the matching procedure was that Afrobarometer languages can sometimes encompass the ethnolinguistic group represented by the ACLED actor. As example of the latter, consider the 'Ilajes Ethnic Militia (Nigeria)'. The Ilajes are a sub-group of the Yoruba people (Olson 1996, p. 239), which is the ethnic category reported in the Afrobarometer surveys. There is a fair chance that not all Yorubas are deeply concerned about Ilajes' fortunes. To account for this, an alternative matching rule excludes all the cases whereby Afrobarometer languages engulf actor's ethnolinguistic affiliation.

Once conflict is associated to ethnicities, it is aggregated over time and space. Define $c_{e,l,j}$ as a dummy indicating the presence of a conflict event taking place in location $l$ during day $j$, and involving an armed

<footnote n="18">18. This match is based on: Lomo and Hovil (2004), the data kindly shared by Rohner et al. (2013), and the online description of the actor provided by the Uppsala Conflict Data Program at: https://ucdp.uu.se/actor/585.</footnote>
Tedeschi, Richard G., Calhoun, Lawrence G., 2004. Posttraumatic growth: conceptual foundations and empirical evidence. Psychol. Inq. 15 (1), 1–18.

Tur-Prats, Ana, Valencia Caicedo, Felipe, 2020. The long shadow of the spanish civil war. CEPR Discussion Paper No. DP15091.

van der Dennen, Johan M.G., 2000. Of badges, bonds and boundaries: Ingroup/outgroup differentiation and ethnocentrism revisited. In: Thienpont, K., Cliquet, R. (Eds.), In-Group/Out-Group Behaviour in Modern Societies. an Evolutionary Perspective. NJCM Boekerij, pp. 37–74.

Voors, Maarten J., Bulte, Erwin H., 2014. Conflict and the evolution of institutions: Unbundling institutions at the local level in Burundi. J. Peace Res. 51 (4), 455–469.

Wucherpfennig, Julian, Metternich, Nils W., Cederman, Lars-Erik, Gleditsch, Kris-
tian Skrede, 2012. Ethnicity, the state, and the duration of civil war. World Politics
64 (1), 79–115.
actor associated to ethnolinguistic group e.<fnref n="19" /> I exclude conflict incidents involving state forces to ease the interpretation of results and to avoid endogeneity problems related to changes in state capacity and legitimacy.<fnref n="20" /> Further define $ab_{k,t}$ as the earliest interview date conducted by Afrobarometer round $t \in \{3, 4, 5, 6\}$ in cell k. Hence, conflict is aggregated: temporally, over the period between two consecutive Afrobarometer surveys, that is, over the day-unit intervals $AB_{k,t} = \{ab_{k,t-1}, \dots, ab_{k,t-1}\}$; spatially, over all the locations belonging to the cell k. Formally:

$$ \tilde{C}_{e,k,t} = \sum_{l \in k} \sum_{j \in AB_{k,t}} c_{e,l,j} $$

The baseline measure of conflict is an indicator function equal to one if there was at least a conflict incident $c_{e,l,j}$ in the relevant temporal-spatial-ethnic window, that is:

$$ C_{e,k,t} = 1[\tilde{C}_{e,k,t} > 0] $$

The event-based definition of conflict (i.e. $\tilde{C}_{e,k,t}$) is used, instead, in robustness checks. Another measure used for sensitivity analysis is the count of months with at least a conflict incident.

## C Descriptive evidence

Descriptive statistics for the main outcome and treatment variables are reported in Table 1. As shown in the first column, ethnic identification is on average relatively high: its mean (2.36) is slightly above the value indicating a preference of national over ethnic attributes (2). Summary statistics for ethnic conflict are in the last column. This is a relatively rare event, with slightly more than 4% of the sample belonging to an ethnic group involved in a conflict in the relevant spatial-temporal window. The majority of warfare in sub-Saharan Africa does not, indeed, unfold along ethnic lines: while 37% of the cells experienced at least a conflict incident, only 12% of the total witnessed ethnic violence.

<footnote n="19">19. Note that some of these conflict incidents involved armed actors with the same ethnolinguistic background. Intra-ethnic conflict – which is experienced by only a small fraction (0.8%) of the sample – cuts through the group and thus is potentially unrelated to the ethnocentric dynamics here analysed. In Appendix D I show that, indeed, when excluding intra-ethnic conflict, the estimated marginal impacts are generally larger.</footnote>

<footnote n="20">20. In Appendix D it is shown that results are nearly identical when including clashes between ethnic actors and national armies. Note that, anyway, the baseline definition of conflict does not exclude violent events pitting civilians & rioters against armed groups with an ethnolinguistic affiliation.</footnote>
<table><caption>**Table 1:** Descriptive statistics</caption><thead><tr><th></th><th>Ethnic Identity</th><th>Social Cohesion</th><th>Attendance Community Meetings</th><th>Collective Action</th><th>Membership Community Associations</th><th>Ethnic conflict</th></tr></thead><tbody><tr><td>Mean</td><td>2.36</td><td>0.00</td><td>2.15</td><td>1.86</td><td>0.61</td><td>0.043</td></tr><tr><td>Sd</td><td>1.20</td><td>0.79</td><td>1.30</td><td>1.29</td><td>0.93</td><td>0.203</td></tr><tr><td>Min</td><td>1</td><td>-1.25</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Max</td><td>5</td><td>1.89</td><td>4</td><td>4</td><td>3</td><td>1</td></tr><tr><td>N</td><td>103656</td><td>109426</td><td>110772</td><td>110520</td><td>110387</td><td>111428</td></tr></tbody></table>

*Notes:* The sample includes Afrobarometer rounds 3 to 6. *Ethnic Identity* captures the saliency of ethnic identification, while *Social Cohesion* is an index combining three dimensions of pro-sociality: attendance at community meetings (*Attendance Community Meetings*), participation in instances of collective action (*Collective Action*), and membership in community associations (*Membership Community Associations*). The *Ethnic conflict* dummy signals the presence of conflict events involving the ethnolinguistic group of an observation and taking place in her cell of residence during the relevant period.

The relative paucity of ethnic violence masks, however, a great variety of conflict types present in the sample. The following are anecdotal observations based on the study done during the match procedure. While not having the pretence of providing a complete description of conflict in sub-Saharan Africa – if anything because clashes between state forces and civilians were not analysed – they are, nonetheless, a rare occasion in which the black box of ACLED conflict is actually open.

The first remark is that there are few ‘wars’, at least in the meaning popularly attached to the word. Conflicts aiming at regime change or territorial conquest, are extremely rare. Examples of ‘wars’ unfolding in the period considered are: the violent independence pulses erupting in the Niger Delta (Nigeria) since the early 2000s; the Tuareg-cum-Islamist rebellion in northern Mali resurfacing around 2012; and al-Shabaab’s insurgency in Kenya (early 2010s-present), which was however largely devoid of an ethnic dimension.

Particularly present in Nigeria and West Africa at large, is conflict between nomadic Fulani herders and various settled farmer communities. Similarly, pastoralists and agriculturalists have repeatedly clashed in Uganda, where Karamajong have often trespassed private fields. More generally, land conflict – in a continent undergoing a demographic transition – is rather widespread across Africa. The intense ethnic clashes between Mosop and Soi communities of Mount Elgon (Kenya), are among the clearest examples.

Finally, within the sample period, there has been a substantial amount of ethnically-organised political violence. Sometimes, this type of con-
<figure><img src="image_4.png" /><figcaption>**Figure 2:** Ethnic identity and conflict in selected sub-Saharan countries</figcaption></figure>

conflict engulfs the whole state apparatus. This is the case of Kenya, where elections are ferocious contests between ethnic-based parties. In other instances, violence unfolds over local power dynamics, most notably, disputes relative to royal succession in traditional sub-national kingdoms; the Dagbani chieftaincy crisis in Ghana being a case point.

Figure 2 gives an overview of the geographical distribution of ethnic conflict. The map also shades countries in base of their average social identification value. There are some states, such as Botswana, where ethnic identification is relatively strong even in the absence of abundant conflict. Yet, overall, ethnolinguistic conflict tends to be concentrated in countries where ethnic animosities run higher, Nigeria and Uganda, but also Mali, being the clearest examples.<fnref n="21" /> Obviously, the map is at most suggestive, giving a static between-country comparison that masks much sub-national and cross-temporal variation.

<footnote n="21">21. In Appendix D, I show that results are robust to singularly excluding each country.</footnote>
# 4 Econometric analysis

## A Baseline model

The baseline econometric model consists of a simple linear equation estimated through OLS:

$$y_{i,e,k,t} = \mu_{k,t} + \lambda_{e,t} + \beta C_{e,k,t} + \delta' x_{i,e,k,t} + u_{i,e,k,t} \quad (1)$$

Where, $y_{i,e,k,t}$ measures social behaviour of individual i belonging to ethnolinguistic group e and interviewed by Afrobarometer round t in cell k. The $\mu_{k,t}$ and $\lambda_{e,t}$ terms are sets of cell-period and ethnicity-period fixed effects.<fnref n="22" /> The independent variable of interest is the ethnic conflict dummy $C_{e,k,t}$, equal to 1 when ethnolinguistic group e experiences conflict during t within cell k. The vector $x_{i,e,k,t}$ controls for various individual-level characteristics reported in Afrobarometer surveys. In the baseline estimations, it includes only 'fixed' personal attributes that are unlikely to be endogenous in equation (1). These are: age and its square, gender, and an urban-rural indicator. Finally, $u_{i,e,k,t}$ are standard errors clustered at the cell-ethnolinguistic-period level, that is, the level of assignment of the conflict variable.<fnref n="23" />

## B Identification

The identification strategy behind the model exploits the triple source of variation of the ethnic conflict variable. Its coefficient ($\beta$) captures the difference in social attitudes between individuals living in the same area and interviewed at the same time, but with different conflict exposure due to their different ethnic background.<fnref n="24" /> The inclusion of cell×period and ethnicity×period fixed effects, means that we are using variation within these groups to estimate the marginal impact of interest. While making the model relatively saturated, the rich set of fixed effects allows to control for many ethnic-specific, possibly time-varying, factors, which

<footnote n="22">22. Note that while there are two sets of fixed effects, this setting differs from the standard two-way fixed effect (TWFE) model in that there are not fixed groups whose treatment status changes across time. Hence, TWFE diagnostic and remedies (De Chaisemartin and d'Haultfoeuille 2020, Callaway and Sant'Anna 2021), are here irrelevant.</footnote>

<footnote n="23">23. In Appendix D it is shown that results are robust to the use of higher-level clustering.</footnote>

<footnote n="24">24. Alternatively, the coefficient can be interpreted as a comparison between people belonging to the same ethnic cluster and interviewed at the same time, but having a different exposure to conflict in virtue of their residence.</footnote>
have been shown to correlate with trust attitudes, conflict propensity,
and socio-economic outcomes.<fnref n="25" /> At the same time, through the $\mu_{k,t}$ term,
equation (1) controls for institutional elements likely to influence both
conflict and social cohesion: land tenure regimes (Boone and Nyeme
2015), national sport competitions (Depetris-Chauvin et al. 2020), elec-
tions (Posner 2004, Eifert et al. 2010), and the overall degree of mod-
ernisation and state-building (Miguel 2004, Robinson 2014).

Migration is another obvious confounding factor: in 2016, across
sub-Saharan Africa, there were more than 12 million internally dis-
placed people for reasons related to conflict and violence.<fnref n="26" /> These mi-
gratory patterns can correlate to social cohesion, with, for example, less
pro-social people more likely to flee away at times of conflict (Gilligan
et al. 2014). Similarly, warfare tends to displace women relatively more
than men,<fnref n="27" /> ultimately altering social cohesion dynamics at the local
level. In the present context, these sample selection issues are, how-
ever, unlikely to severely bias estimates. First, cell-time fixed effects
should account for migration that is not ethnic-specific. Second, the
episodes of ethnic violence observed in the sample stem mostly from low-
intensity conflicts, which are unlikely to trigger mass-scale migration. In
Appendix D I provide empirical support to these intuitions, showing that
neither the population share nor the gender composition of ethnolinguis-
tic groups, were significantly affected by warfare.

Another obvious threat to inference stems from reverse causality. While
ethnic violence is a powerful element in the identity-building process, it
might well be the case that more cohesive and fiercer cultural groups
engage more often in conflict, having more powerful grievances and mo-
bilisation structures. The ethnic×period fixed effects partly dispel these
concerns. The model compares people with the same ethnic background
and during the same period: ethnic-broad self-selection into violence is
effectively taken into account.

Despite the rich set of fixed effects, equation (1) can not provide es-

<footnote n="25">25. For example, Moscona and co-authors (2017 and 2020) find that ethnic groups with segmentary lineage social structures have lower levels of generalised trust and are more prone to conflict in sub-Sharan Africa. Exclusion from power, separation across national borders and traditional reputation mechanisms have also been pointed out as predictors of violence (Cederman et al. 2010, Michalopoulos and Papaioannou 2016, Cao et al. 2021, Fouka and Schläpfer 2022).</footnote>

<footnote n="26">26. The statistics come from the World Bank: https://data.worldbank.org/indicator/VC.IDP.TOCV?locations=ZG.</footnote>

<footnote n="27">27. Globally, by the end of 2018, women and girls represented slightly more than half of the 41 million people living in a situation of internal displacement as a result of conflict and violence (Internal Displacement Monitoring Center 2020, p. 5).</footnote>
estimates that are causal in the sense of exploiting (conditional) random variation. Unobserved local ethnic-specific dynamics – such as access to power or temporal resource windfalls – may still bias inference by acting on the group social cohesion and its conflict propensity. A series of imperfect measures is adopted to lower these concerns. First, additional socio-economic and political variables are included in equation (1), thus partially controlling for the above mentioned confounding factors. Second, various subsamples are considered so as to make observations more comparable and to lower sample selection concerns. Most notably, as shown in Appendix D.1, all non-violent ethnolinguistic groups are excluded from the analysis, thus ruling out that results are driven by some omitted characteristic radically differing between peaceful and conflict-prone ethnolinguistic groups. Finally, but perhaps most importantly, results still hold when we consider far-away distant conflict, which is definitely less likely to be influenced by local political or economic shocks. As shown in Section 5, ethnic violence has a significant impact on ethnic identification and social cohesion even when exposure is only indirect. The exercise effectively lessens sample selection issues (respondents are not directly involved in remote conflict) and the confounding effect of local unobservables, hence strengthening a causal interpretation of the main results.

## C Baseline results

Table 2 reports the estimation of the model over social identity and cohesion. The first regression includes only the three sets of geographic, temporal, and ethnic fixed effects not interacted between themselves. The estimated marginal impact of conflict on ethnic identification is positive. Yet, it has a small size and is statistically indistinguishable from zero. Including cell-period dummies in the second column increases the size of the coefficient by more than 50%, suggesting the existence of some confounding factor operating at the spatio-temporal level. Local economic development trends seem a likely culprit: while decreasing the chances of conflict, they can simultaneously positively correlate with both social cohesion and group identification. The third column adds ethnic-period fixed effects. The estimated coefficient almost doubles in size to 0.113. The increase of magnitude can again be explained in terms of omitted variable (e.g. access to political power) and underlines the importance of comparing observations belonging to the same cell×period and ethnic×period groups. The inclusion of Afrobarometer controls in column four only slightly decreases the magnitude of the co-
half of the effect triggered by ethnic violence.

The second panel of Table 2 reports results from equation (1) as estimated over the social cohesion index. The control of cell-period fixed effects seems particularly relevant in this context. Indeed, their inclusion substantially increases the magnitude of the coefficient, signalling the presence of some unobservable variable positively affecting community engagement and negatively correlating with conflict (e.g. local development trends). The point estimates of the last three columns are positive and statistically significant: ethnic conflict induces pro-social behaviour towards one's own community. Marginal effects are relatively modest when compared to the urban indicator, but comparable to the impact of conflict on ethnic identification: for both outcomes of interest, conflict exposure trigger an increase of about 9% standard deviations of the dependent variable.

## D Robustness checks

The models at equation (1) are perturbed over several dimensions to test their robustness.

First, sensitivity to alternative conflict definitions is explored. Two extensive measures of ethnic violence are used: the month- and event-based definitions introduced in Section 3. Having a lot of zeros, the two variables are parametrised using the inverse hyperbolic sine function; results are identical when natural logs are instead employed. Figure 3 shows their point estimates alongside their 90% confidence interval from regressions employing cell-period and ethnic-period fixed effects as well as the set of controls including the fixed personal characteristics of respondents. Estimates are positive and significant, signalling that ethnic identity and community engagement are responsive also to the intensity of violence.

A second battery of robustness tests concerns the match between ethnolinguistic groups and conflict actors. The two major issues encountered during the matching procedure were that: (i) the ethnic affiliation of the actor was not always blatantly clear, primarily because of its mixed objectives and identities; (ii) the associated ethnolinguistic group was a sub-group of the reported Afrobarometer language. Figure 3 reports point estimates and 90% confidence intervals from a version of equation (1) excluding these types of controversial matches. Two regressions for each match-outcome pair are run, one including only fixed effects, the other adding individual covariates. Results are overall robust to alternative matching procedures, the only exception being the marginal impact
<figure><img src="image_5.png" /><figcaption>**Figure 3:** OLS regressions of Ethnic identity & Social cohesion on Ethnic conflict – Robustness tests</figcaption></figure>

*Notes:* The figure reports point estimates, alongside their 90% confidence intervals, of ethnic conflict. Fixed effects correspond to cell×period and ethnic×period dummies. Individual controls include: age and its square, gender, and an urban-rural indicator. Standard errors are clustered at the cell-ethnic-period level. In the last panel (Additional controls), regressions labelled FE comprehend fixed effects, individual and socio-economic controls; regressions labelled FE and controls further add political covariates.

on ethnic identity that is no longer statistically different than zero when more imprecise matches are excluded.

A third series of robustness tests concerns the definition of the sample. First, ethnolinguistic groups not appearing in all four periods are dropped from the analysis. Indeed, their (dis-)appearance from the sample may be related to changes in their socio-political relevance. For example, Afrobarometer survey designers might have chosen to suddenly include a given ethnolinguistic group in light of its recent autonomy struggle. By excluding transient cultural entities, we avoid this type of selection issues. The second sample-wide modification concerns the dimension used for defining ethnolinguistic groups. The Afrobarometer question on ethnicity, rather than language, is thus employed.<fnref n="28" />

<footnote n="28">28. On the reasons why the linguistic marker is preferable to ethnic's, see footnote 15.</footnote>
shown in Figure 3, sign and significance of the baseline models are re-
produced under these alternative samples.

The last robustness exercises concern the coding of outcome vari-
ables and the inclusion of additional covariates. In the literature, social
cohesion measures are often transformed in binary indicators so as to
avoid problems related to their possible subjective interpretation. I thus
create dummy variables for ethnic identity and the three measures of
pro-social behaviour, whose standardised score is then averaged into an
alternative social cohesion index. The seventh panel of Figure 3 repeats
this exercise: all point estimates are statistically significant and with
same order of magnitude relatively to baseline estimates (7% standard
deviations). I then include further covariates taken from Afrobarome-
ter surveys. These variables control for the socio-economic profile
of respondents as well as their political attitudes. In the first group
are included: education, employment status, and an index of house-
hold wealth. Political controls comprehend: trust towards the president,
trust towards the local government, and an indicator function for hav-
ing voted in the last national election. Their inclusion aims at lowering
the risks stemming from omitted variable issues; yet, they themselves
might be endogenous in equation (1), and these results shall be read
bearing this in mind. The last two regressions of Figure 3 are specifica-
tions of equation (1) cumulatively including socio-economic and political
controls, beyond the usual set of fixed effects and individual-level covari-
ates. All coefficients are positive, significant and comparable in size to
the baseline estimates. Robustness to the inclusion of political controls
is particularly relevant for the social identity outcome. Indeed, these
covariates ensure that the observed increase in ethnic identification is
not given by lower attachment to the nation-state, a process possibly
triggered by higher conflict levels.

Appendix D shows robustness of the main results to additional sen-
sitivity exercises, including: placebo analysis using future conflict activ-
ity, exclusion of the most peaceful and the most violent ethnolinguistic
groups, sequential exclusion of each country.

5 Mechanisms

The empirical analysis in the previous section substantiated a signifi-
cant and robust link between ethnic conflict and social cohesion. People
exposed to ethnic violence reports higher attachment to their cultural
group and exhibit greater pro-social behaviour, primarily aimed at their
community fellows. As seen in Section 1, the perhaps surprising result
that conflict can prompt cooperative behaviour has been variously ratio-
nalised in the academic world. This section provides empirical support
to the idea that conflict-induced ethnocentric dynamics are central for
explaining the observed increase in social cohesion. Indeed, as one's
own ethnolinguistic group is involved in some violent clash, the individ-
ual frames herself in ethnic terms, explicitly perceives her group identity,
and rallies around it. This rally-around-the-flag effect is not limited to
ethnic conflict and is witnessed across a wide range of settings where a
group is faced by some external threat. Yet, what is remarkable about
ethnic violence is the intensity of such phenomenon, capable of bringing
about changes in actual behaviour even in the absence of instrumental
incentives.<fnref n="29" /> How can we make sense of this powerful shift in social
preferences?

Various theories have been advanced to explain ethnocentrism, that
is, the instinctive classification of the world into the superior ethnic ‘us’
and all the ‘others’ (Dennen 2000). Many of these theories have an ex-
plicit evolutionary framework in order to account for the massive evi-
dence on human propensity to favourably treat co-ethnics (Haushofer
et al. 2022). One of the most widely accepted theory understands eth-
nocentrism as residual, almost a manipulation, of natural selection on
pro-kinship behaviour (Gat 2006).<fnref n="30" />

Humans, as much as other animals, have a natural inclination to
help their relatives. The behaviour is evolutionary sensible, inasmuch
as family members are repositories of one's own genes and altruistic be-
haviour towards them does, indeed, ensure genetic reproduction. In the
late Pleistocene – a time when human societies were relatively small and
composed mainly of kin mates (Kelly 2007) – linguistic developments
gradually led to ever-deepening cultural differentiation (Harari 2014).
In this setting, common language and culture were effectively good in-
dicators of kinship: favouring one's own co-ethnic was virtually equiva-
lent to helping one's own relative. This is the socio-biological essence of
ethnocentrism. In the pre-agricultural world, where ethnicity predicted

<footnote n="29">29. The peculiarities of ethnic warfare are more evident when other conflict types are considered. For example, Peri et al. (2021) empirically show that terrorist attacks in Europe have not a robust effect on trust towards government institutions and on other political attitudes. Interestingly, the authors find that only *foreign* terrorist attacks have the effect of increasing the appeal of *nationalistic* parties.</footnote>

<footnote n="30">30. A thorough treatment of ethnocentrism, discussing also alternative theories such as those advanced by Bowles (2008, 2009, 2012) and co-authors (Choi and Bowles 2007), is relegated to Appendix C.</footnote>
kinship, it is easy to see the evolutionary rationale of the us-them dis-
crimination. Yet, as human societies grew larger and more complex, the
application of kin-solidarity to culturally related people (i.e. co-ethnics)
definitely lost any evolutionary aim. When during the Second World War
Japanese kamikazes committed suicide against US ships, they were nei-
ther increasing their biological fitness, nor that of any distant relative.
Nonetheless, some of them were volunteers, enthusiastically going to
risk their lives for the motherland. This seemingly irrational behaviour
shall be read as a manipulation, an extension beyond its original objec-
tive, of kin-based solidarity within small groups.<fnref n="31" /> It is this the atavistic
component of contemporary xenophobia, the same often perplexing ob-
servers of ethnic cleansing and genocides.

Ethnic conflict thus plays onto human ethnocentric instincts, acti-
vating feelings of solidarity and altruism even in the absence of tangible
economic benefits.

A *Identifying the ethnocentric channel*

The use of ethnic-varying conflict and the positive link found between
this and ethnic identification, already suggest that the ethnocentric chan-
nel is central for explaining the observed increase in pro-social behaviour.
The remainder of the paragraph carries out additional tests of this mech-
anism.

First, if a process of ethnic radicalisation drives the results, we would expect that people are responsive also to distant conflict involving their co-ethnics. Table 3 reports estimates of a model employing conflict spatially aggregated over the eight-cell neighbourhood of each respondent. We are thus considering ethnic violence outside of the cell of the respondent, taking place up to 155km away from each observation. The first two columns include only this external measure of conflict, while the last two add also the baseline local conflict indicator, which otherwise is a plausible omitted variable. The point estimate of distant conflict is always positive and mostly statistically significant, albeit having a magnitude lower than that of local violence. Interestingly, as shown in Appendix E, ethnic conflict has still a positive impact on social identity and cohesion, when it is spatially aggregated over the 24-cell neighbourhood of respondents.

<footnote n="31">31. The persistence of naturally selected behaviours even if devoid of their evolutionary *raison d'être*, albeit surprising, is typical of humankind. For example, in the same logic, as humans we have a natural love for sweet food, even though sweetness does not serve any more its original evolutionary aim of signalling fruit maturity.</footnote>
<table><caption>**Table 3:** OLS regressions of Ethnic identity & Social cohesion on Distant ethnic conflict</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td colspan="5"><strong>ETHNIC IDENTITY</strong></td></tr><tr><td>Distant conflict</td><td>0.058<br>(0.030)*</td><td>0.057<br>(0.030)*</td><td>0.049<br>(0.030)*</td><td>0.049<br>(0.030)</td></tr><tr><td>Ethnic conflict</td><td></td><td></td><td>0.107<br>(0.037)***</td><td>0.101<br>(0.037)***</td></tr><tr><td>R<sup>2</sup></td><td>0.22</td><td>0.22</td><td>0.22</td><td>0.22</td></tr><tr><td>N</td><td>103,588</td><td>101,378</td><td>103,588</td><td>101,378</td></tr><tr><td colspan="5"><strong>SOCIAL COHESION</strong></td></tr><tr><td>Distant conflict</td><td>0.059<br>(0.021)***</td><td>0.044<br>(0.020)**</td><td>0.053<br>(0.021)**</td><td>0.039<br>(0.020)*</td></tr><tr><td>Ethnic conflict</td><td></td><td></td><td>0.069<br>(0.027)***</td><td>0.063<br>(0.026)**</td></tr><tr><td>R<sup>2</sup></td><td>0.22</td><td>0.27</td><td>0.22</td><td>0.27</td></tr><tr><td>N</td><td>109,371</td><td>107,094</td><td>109,371</td><td>107,094</td></tr><tr><td>Fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Individual cov.</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr></tbody></table>

*Notes:* The sample includes Afrobarometer rounds 3 to 6. Fixed effects correspond to cell×period and ethnic×period dummies. Individual controls include: age and its square, gender, and an urban-rural indicator. The *Ethnic conflict* dummy signals the presence of conflict events involving the ethnolinguistic group of an observation and taking place in her cell of residence during the relevant period. *Distant conflict* is a dummy variable signalling the presence of ethnic conflict in the 8-cell neighbourhood of an observation. *Ethnic Identity* captures the saliency of ethnic identification, while *Social Cohesion* is an index combining three dimensions of pro-sociality: attendance at community meetings, participation in instances of collective action, and membership in community associations. Standard errors are clustered at the cell-ethnic-period level and are reported in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels.

The importance of this exercise is twofold. On the one hand, it lowers the endogeneity issues mentioned in Section 4B: local ethnic-specific shocks are unlikely to be correlated to distant clashes. On the other hand, it pins down ethnocentrism as the causal force behind the results of the main empirical analysis. Indeed, alternative mechanisms are ill-suited to explain how remote ethnic violence can trigger higher social cohesion. For example, conflict-induced economic incentives to cooperation work primarily on a geographical base: it is hard to see how local insurance schemes are adopted in response to rumours of distant conflict. Other psychological mechanisms, such as post-traumatic growth,
are also usually understood as taking place in the midst of a direct violent trauma.

A second strategy to test the relevance of the ethnocentric channel is to compare ethnic and non-ethnic conflict, whereby only the first should have a significant impact on social identification and cohesion if identity-based dynamics are at play. Define $Z_{-e,k,t}$ as a dummy taking value of one for conflict activity taking place in cell $k$ at period $t$ and unrelated to ethnolinguistic group $e$. Figure 4 reports point estimates of $Z_{-e,k,t}$ and $C_{e,k,t}$, with their 90% confidence intervals, as retrieved from the following model:

$$y_{i,e,k,t} = \mu_{k,t} + \lambda_{e,t} + \beta C_{e,k,t} + \psi Z_{-e,k,t} + \delta' x_{i,e,k,t} + u_{i,e,k,t} \quad (2)$$

Note that baseline regressions are already estimating the effect of ethnic conflict net of the total warfare present in each cell-period, as captured by the spatio-temporal fixed effects $\mu_{k,t}$. Regression (2) contrast ethnic to non-ethnic violence by comparing cases where only one of the two is present to cases where both of them are taking place. The ‘horse-race’ between ethnic and non-ethnic conflict is clearly won by the first measure of warfare: the coefficient of $Z_{-e,k,t}$ is always negative and insignificantly different than zero. The point estimate of $\beta$, instead, remains positive and significant.

Finally, a more direct test of the ethnocentric channel can be easily carried out using an index of ethnolinguistic polarisation. This is defined as:

$$ELP_{k,t} = 1 - \sum_{e} \left( \frac{\frac{1}{2} - \pi_{e,k,t}}{\frac{1}{2}} \right)^2 \pi_{e,k,t}$$

Where $\pi_{e,k,t}$ is the population share of ethnic group $e$ in cell $k$ at time $t$. The index measures how far the distribution of ethnic groups is from the bipolar distribution. As $ELP_{k,t}$ moves to one, polarisation increases, signalling that the cell-period is evenly divided between two groups. In areas marked by a high ethnolinguistic polarisation, local public goods are contested between equally powerful entities and cooperation at the community level goes benefiting people hailing from different cultural backgrounds. Hence, in these zones, if conflict affects social cohesion through ethnocentric dynamics, the estimated positive impact should be lower.

We thus define a new model by augmenting equation (1) with the ethnolinguistic polarisation index and its interaction with conflict. Formally:

$$y_{i,e,k,t} = \mu_{k,t} + \lambda_{e,t} + \beta C_{e,k,t} + \alpha(C_{e,k,t} \times ELP_{k,t}) + \delta' x_{i,e,k,t} + u_{i,e,k,t} \quad (3)$$
<figure><img src="image_6.png" /><figcaption>**Figure 4:** OLS regressions of Social identity and cohesion on Ethnic and non-ethnic conflict</figcaption></figure>

*Notes:* The figure reports point estimates, alongside their 90% confidence intervals, of ethnic and non-ethnic conflict. Fixed effects correspond to cell×period and ethnic×period dummies. Individual controls include: age and its square, gender, and an urban-rural indicator. Standard errors are clustered at the cell-ethnic-period level.

Note that the term *ELP*<sub>*k*,*t*</sub> is absorbed by the *μ*<sub>*k*,*t*</sub> fixed effects. If conflict-induced pro-social behaviour remains limited to one's own co-ethnics, such a cooperation should be harder to achieve in districts more polarised. That is, the joint impact of ethnic polarisation and warfare (α) shall be negative.

Figure 5 reports estimates of equation (3). While conflict still has a positive impact on community engagement and ethnic identification, its interaction with the *ELP* is always significantly negative. There are, indeed, heterogeneous effects with respect to the ethnolinguistic composition of the various cell-period observations. In areas with large levels of ethnolinguistic polarisation, the positive impact of ethnic conflict decreases for the very simple fact that within-community cooperation goes benefiting also people from other cultural units. Put it differently, the pro-social impact of conflict is *parochial*, being directed only towards one's own co-ethnics.

As shown in Appendix E, the results of this section are robust to:
employing extensive measures of ethnic conflict, different actor-ethnicity
<figure><img src="image_7.png" /><figcaption>**Figure 5:** Heterogeneous effects by Ethnolinguistic Polarisation</figcaption></figure>

*Notes:* The figure reports point estimates, alongside their 90% confidence intervals, of ethnic conflict and its interaction with an index of ethnolinguistic polarisation. Fixed effects correspond to cell×period and ethnic×period dummies. Individual controls include: age and its square, gender, and an urban-rural indicator. Standard errors are clustered at the cell-ethnic-period level.

matching rules, the use of alternative sample restrictions, a binary re-
coding of outcome variables, and the inclusion of socio-economic and
political covariates.

**6 Conclusions**

This paper has enquired the impact of ethnic conflict on social identity
and norms. The setting of the empirical analysis is sub-Saharan Africa,
where sub-national identities are still very alive and affect the course of
public policy. The research design relies on a novel manually-compiled
dataset, linking conflict actors to ethnolinguistic groups. The procedure
allowed the construction of conflict measures varying at the spatial, tem-
poral, and ethnic level. This triple source of variation and the ensuing
rich set of fixed effects, are at the core of the identification strategy here
employed.

Confirming the anecdotal evidence on conflict-induced identity radi-
calisation, this paper substantiates the first cross-country empirical link
between ethnic conflict and ethnic identification. This result is then used
to shed lights on the second empirical contribution of this article: eth-
nic violence prompts cooperative behaviour in communities exposed to
it. The positive conflict-identity relationship found and the explicit use
of ethnic, rather than geographical, warfare suggest that identity-based
psychological processes are at the base of the observed increase in social
cohesion. This intuition is further developed and tested. First, *distant*
ethnic violence positively affects civic attitudes, making improbable that
results are driven by direct war traumas or by the effect of conflict on lo-
cal institutions. Second, conflict unrelated to the ethnolinguistic group
of respondents has not any impact on their community engagement,
making clear that the ethnic dimension of conflict is what really mat-
ters. Finally, conflict-induced pro-social behaviour is conditional on low
levels of ethnolinguistic polarisation, indicating that cooperation likely
remains limited to one's own group.

A socio-biological understanding of ethnocentrism provides a good
theoretical framework to make sense of these findings. Conflict along
ethnic cleavages plays, indeed, onto the innate 'us-them' ethnic division
of the world, which can neither be dismissed as fully artificial nor as
completely genetic. It is, indeed, in light of this complex dual nature that
both out-group xenophobia and in-group cooperation shall be read. This
paper found evidence on the second, giving empirical support to the idea
that, regardless of instrumental considerations, feelings of identification
and solidarity towards one's own ethnic 'us' can lead to actual pro-social
behaviour, albeit in a parochial fashion.
References

Acemoglu, Daron, and James A Robinson. 2012. *Why nations fail: The origins of power, prosperity and poverty*. Profile London.

Allen, Tim, and Jean Seaton. 1999. *The media of conflict: War reporting and representations of ethnic violence*. Zed Books.

Ananyev, Maxim, and Mikhail Poyker. 2021. "State Capacity and Demand for Identity: Evidence from Political Instability in Mali." *Working paper*, ISSN: 1556-5068. https://doi.org/10.2139/ssrn.3419294.

Asuni, Judith Burdin. 2009. *Understanding the Armed Groups of the Niger Delta*. Working Paper. Council on Foreign Relations.

Barbero, Alessandro. 2009. *I conflitti d'identita nella storia*. Online available at: https://www.youtube.com/watch?v=h6C_qI347Bo&ab_channel=Festiva lEconomia [Accessed 30/01/2022].

Bateson, Regina. 2012. "Crime victimization and political participation." *American Political Science Review* 106 (3): 570–587.

Bauer, Michal, Christopher Blattman, Julie Chytilová, Joseph Henrich, Edward Miguel, and Tamar Mitts. 2016. "Can war foster cooperation?" *Journal of Economic Perspectives* 30 (3): 249–74.

Bellows, John, and Edward Miguel. 2006. "War and institutions: New evidence from Sierra Leone." *American Economic Review* 96 (2): 394–399.

———. 2009. "War and local collective action in Sierra Leone." *Journal of public Economics* 93 (11-12): 1144–1157.

Berman, Nicolas, Mathieu Couttenier, Dominic Rohner, and Mathias Thoenig. 2017. "This mine is mine! How minerals fuel conflicts in Africa." *American Economic Review* 107 (6): 1564–1610.

Bernhard, Helen, Urs Fischbacher, and Ernst Fehr. 2006. "Parochial altruism in humans." *Nature* 442 (7105): 912–915.

Besley, Timothy, and Marta Reynal-Querol. 2014. "The legacy of historical conflict: Evidence from Africa." *American Political Science Review* 108 (2): 319–336.

Blattman, Christopher. 2009. "From violence to voting: War and political participation in Uganda." *American political Science review* 103 (2): 231–247.

Boone, Catherine, and Lydia Nyeme. 2015. "Land institutions and political ethnicity in Africa: evidence from Tanzania." *Comparative Politics* 48 (1): 67–86.
Bowles, Samuel. 2008. "Being human: Conflict: Altruism's midwife." *Nature* 456 (7220): 326-327.

———. 2009. "Did warfare among ancestral hunter-gatherers affect the evolution of human social behaviors?" *Science* 324 (5932): 1293-1298.

———. 2012. "Warriors, levelers, and the role of conflict in human social evolution." *Science* 336 (6083): 876-879.

Brewer, Marilynn B. 2007. "The importance of being we: Human nature and intergroup relations." *American psychologist* 62 (8): 728.

Bringa, Tone, and Debbie Christie. 1993. *We are all neighbours*. Disappearing World Series.

Callaway, Brantly, and Pedro HC Sant'Anna. 2021. "Difference-in-differences with multiple time periods." *Journal of Econometrics* 225 (2): 200-230.

Cao, Yiming, Benjamin Enke, Armin Falk, Paola Giuliano, and Nathan Nunn. 2021. *Herding, Warfare, and a Culture of Honor: Global Evidence*. NBER Working Papers. National Bureau of Economic Research.

Cassar, Alessandra, Pauline Grosjean, and Sam Whitt. 2013. "Legacies of violence: trust and market development." *Journal of Economic Growth* 18 (3): 285-318.

Cederman, Lars-Erik, Andreas Wimmer, and Brian Min. 2010. "Why do ethnic groups rebel? New data and analysis." *World politics* 62 (1): 87-119.

Choi, Jung-Kyoo, and Samuel Bowles. 2007. "The coevolution of parochial altruism and war." *science* 318 (5850): 636-640.

De Chaisemartin, Clément, and Xavier d'Haultfoeuille. 2020. "Two-way fixed effects estimators with heterogeneous treatment effects." *American Economic Review* 110 (9): 2964-96.

De Juan, Alexander, and Jan Henryk Pierskalla. 2016. "Civil war violence and political trust: Microlevel evidence from Nepal." *Conflict Management and Peace Science* 33 (1): 67-88.

De Luca, Giacomo, and Marijke Verpoorten. 2015a. "Civil war and political participation: Evidence from Uganda." *Economic Development and Cultural Change* 64 (1): 113-141.

———. 2015b. "Civil war, social capital and resilience in Uganda." *Oxford Economic Papers* 67 (3): 661-686.
Dennen, Johan M G van der. 2000. "Of badges, bonds and boundaries: In-group/outgroup differentiation and ethnocentrism revisited." In *In-group/out-group behaviour in modern societies. An evolutionary perspective*, edited by K. Thienpont and R. Cliquet, 37-74. NJCM Boekerij.

Depetris-Chauvin, Emilio, Ruben Durante, and Filipe Campante. 2020. "Building nations through shared experiences: Evidence from African football." *American Economic Review* 110 (5): 1572-1602.

Easterly, William, and Ross Levine. 1997. "Africa's growth tragedy: policies and ethnic divisions." *The quarterly journal of economics*, 1203-1250.

Eifert, Benn, Edward Miguel, and Daniel N Posner. 2010. "Political competition and ethnic identification in Africa." *American journal of political science* 54 (2): 494-510.

Elischer, Sebastian. 2013. *Political parties in Africa: Ethnicity and party formation*. Cambridge University Press.

Fearon, James D, and David D Laitin. 2003. "Ethnicity, insurgency, and civil war." *American political science review* 97 (1): 75-90.

Fouka, Vasiliki, and Alain Schläpfer. 2022. *Culture Clash: Incompatible Reputation Mechanisms and Intergroup Conflict*. Technical report. National Bureau of Economic Research.

Gat, Azar. 2006. *War in human civilization*. Oxford University Press.

Gilligan, Michael J, Benjamin J Pasquale, and Cyrus Samii. 2014. "Civil war and social cohesion: Lab-in-the-field evidence from Nepal." *American Journal of Political Science* 58 (3): 604-619.

Grosjean, Pauline. 2014. "Conflict and social and political preferences: Evidence from World War II and civil conflict in 35 European countries." *Comparative Economic Studies* 56 (3): 424-451.

Guarnieri, Eleonora, and Ana Tur-Prats. 2022. "Cultural distance and conflict-related sexual violence." *Working paper*.

Guriev, Sergei, and Nikita Melnikov. 2016. "War, inflation, and social capital." *American Economic Review* 106 (5): 230-35.

Hamilton, Annette. 2006. "Reflections on the 'disappearing Sakai': a tribal minority in southern Thailand." *Journal of Southeast Asian Studies* 37 (2): 293-314.

Harari, Yuval Noah. 2014. *Sapiens: A brief history of humankind*. Random House.
Hartman, Alexandra C., and Benjamin S. Morse. 2020. "Violence, empathy and altruism: evidence from the Ivorian refugee crisis in Liberia." *British Journal of Political Science* 50 (2): 731–755.

Haushofer, Johannes, Sara Lowes, Abednego Musau, David Ndetei, Nathan Nunn, Moritz Poll, and Nancy Qian. 2022. *Stress, Ethnicity, and Prosocial Behavior*. NBER Working Papers. National Bureau of Economic Research.

Hazen, Jennifer M., and Jonas Horner. 2007. *Small arms, armed violence, and insecurity in Nigeria: The Niger Delta in perspective*. JSTOR.

Hendrix, Cullen S., and Idean Salehyan. 2012. "Climate change, rainfall, and social conflict in Africa." *Journal of peace research* 49 (1): 35–50.

Herbst, Jeffrey. 2000. *States and power in Africa*. Princeton University Press.

Hopkins, Antony G. 2019. *An economic history of West Africa*. Routledge.

Internal Displacement Monitoring Center. 2020. *Women and Girls in Internal Displacement*. Online available at: https://www.internal-displacement.org/sites/default/files/publications/documents/202003-twice-invisible-internally-displaced-women.pdf [Accessed 30/01/2022].

Jennings, Colin, and Santiago Sanchez-Pages. 2017. "Social capital, conflict and welfare." *Journal of Development Economics* 124:157–167.

Kalyvas, Stathis N. 2008. "Ethnic defection in civil war." *Comparative Political Studies* 41 (8): 1043–1068.

Kaufmann, Chaim. 1996. "Possible and impossible solutions to ethnic civil wars." *International security* 20 (4): 136–175.

Kelly, Robert L. 2007. *The foraging spectrum: Diversity in hunter-gatherer life-ways*. Smithsonian Inst Press.

Kesselman, Mark, Joel Krieger, and William A Joseph. 2016. *Introduction to comparative politics: political challenges and changing agendas*. 7th ed. Cengage Learning.

Kibris, Arzu, and Resul Cesur. 2022. *War Does not Foster Cooperation when Neoclassical Mechanisms are Absent! Evidence from a Natural Experiment among Turkish Conscripts*. NBER Working Papers. National Bureau of Economic Research.

Laitin, David D. 2004. "Ethnic unmixing and civil war." *Security Studies* 13 (4): 350–365.

Lee, Richard Borshay. 1979. *The !Kung San: men, women and work in a foraging society*. Cambridge University Press.
Lomo, Zachary, and Lucy Hovil. 2004. "Behind the violence. The war in north-ern Uganda." *Institute for Security Studies Monographs* 2004 (99): 87.

Manacorda, Marco, and Andrea Tesei. 2020. "Liberation technology: Mobile phones and political mobilization in Africa." *Econometrica* 88 (2): 533–567.

Michalopoulos, Stelios, and Elias Papaioannou. 2013. "Pre-colonial ethnic in-stitutions and contemporary African development." *Econometrica* 81 (1): 113–152.

———. 2016. "The long-run effects of the scramble for Africa." *American Eco-nomic Review* 106 (7): 1802–48.

Miguel, Edward. 2004. "Tribe or nation? Nation building and public goods in Kenya versus Tanzania." *World politics* 56 (3): 327–362.

Mironova, Vera, and Sam Whitt. 2016. "The evolution of prosociality and parochial-ism after violence." *Journal of Peace Research* 53 (5): 648–664.

———. 2018. "Social norms after conflict exposure and victimization by vio-lence: Experimental evidence from Kosovo." *British Journal of Political Sci-ence* 48 (3): 749–765.

———. 2021. "Conflict and parochialism among combatants and civilians: Ev-idence from Ukraine." *Journal of Economic Psychology* 86:102425.

Moscona, Jacob, Nathan Nunn, and James A Robinson. 2017. "Keeping it in the family: Lineage organization and the scope of trust in sub-Saharan Africa." *American Economic Review* 107 (5): 565–71.

———. 2020. "Segmentary Lineage Organization and Conflict in Sub-Saharan Africa." *Econometrica* 88 (5): 1999–2036.

Mueller, John. 2000. "The banality of "ethnic war"." *International Security* 25 (1): 42–70.

Müller-Crepon, Carl, Yannick I Pengl, and Nils-Christian Bormann. 2020. "Link-ing ethnic data from Africa." *Unpublished Manuscript. ETH Zurich*. https://yannickpengl.com/src/research/ethnic_matchings.pdf.

Nunn, Nathan, and Leonard Wantchekon. 2011. "The slave trade and the ori-gins of mistrust in Africa." *American Economic Review* 101 (7): 3221–52.

Olson, James Stuart. 1996. *The peoples of Africa: an ethnohistorical dictionary*. Greenwood Publishing Group.

Oriola, Temitope B. 2013. *Criminal Resistance?: The Politics of Kidnapping of Oil Workers (Interdisciplinary Research Series in Ethnic, Gender and Class Relations)*. Ashgate Publishing Group.
Peri, Giovanni, Daniel I Rees, and Brock Smith. 2021. *Terrorism and Political Attitudes: Evidence from European Social Surveys*. NBER Working Papers 28662. National Bureau of Economic Research.

Peterson, Scott. 2000. *Me against my brother: at war in Somalia, Sudan and Rwanda*. Routledge.

Pinker, Steven. 2012. *The false allure of group selection*. Online available at: https://www.edge.org/conversation/stevenpinker-the-false-allure-of-group-selection [Accessed 30/01/2022].

Pohl, Walter. 2015. "Political uses of ethnicity in early medieval Europe." In *Ethnicity as a Political Resource: Conceptualizations across Disciplines, Regions, and Periods*, edited by University of Cologne Forum, 201-208. Transcript Verlag.

Posner, Daniel N. 2004. "The political salience of cultural difference: Why Chewas and Tumbukas are allies in Zambia and adversaries in Malawi." *American Political Science Review* 98 (4): 529-545.

Powell, Steve, Rita Rosner, Willi Butollo, Richard G Tedeschi, and Lawrence G Calhoun. 2003. "Posttraumatic growth after war: A study with former refugees and displaced people in Sarajevo." *Journal of clinical psychology* 59 (1): 71-83.

Resnik, Julia. 2003. "'Sites of memory' of the Holocaust: shaping national memory in the education system in Israel." *Nations and nationalism* 9 (2): 297-317.

Robinson, Amanda Lea. 2014. "National versus ethnic identification in Africa: Modernization, colonial legacy, and the origins of territorial nationalism." *World Politics* 66 (4): 709-746.

Rohner, Dominic, Mathias Thoenig, and Fabrizio Zilibotti. 2013. "Seeds of distrust: Conflict in Uganda." *Journal of Economic Growth* 18 (3): 217-252.

Sambanis, Nicholas, and Moses Shayo. 2013. "Social identification and ethnic conflict." *American Political Science Review* 107 (2): 294-325.

Scott, James C. 2009. *The art of not being governed*. Yale University Press.
—. 2017. *Against the grain*. Yale University Press.

Seter, Hanne. 2016. "Connecting climate variability and conflict: Implications for empirical testing." *Political Geography* 53:1-9.

Shayo, Moses, and Asaf Zussman. 2017. "Conflict and the persistence of ethnic bias." *American Economic Journal: Applied Economics* 9 (4): 137-65.
Simiyu, Robert Romborah. 2008. "Militianisation of resource conflicts: the case of land-based conflict in the Mount Elgon region of western Kenya." *Institute for Security Studies Monographs*, no. 152, 80.

Tedeschi, Richard G, and Lawrence G Calhoun. 2004. "Posttraumatic growth: conceptual foundations and empirical evidence." *Psychological inquiry* 15 (1): 1–18.

Tilly, Charles. 1994. "States and nationalism in Europe 1492-1992." *Theory and society*, 131–146.

Voors, Maarten J, and Erwin H Bulte. 2014. "Conflict and the evolution of institutions: Unbundling institutions at the local level in Burundi." *Journal of Peace Research* 51 (4): 455–469.

Welsh, David. 1996. "Ethnicity in sub-saharan Africa." *International Affairs* 72 (3): 477–491.

Wrangham, Richard. 2019. *The goodness paradox: The strange relationship between virtue and violence in human evolution*. Vintage.

Yamagishi, Toshio, and Nobuhiro Mifune. 2016. "Parochial altruism: Does it explain modern human group psychology?" *Current Opinion in Psychology* 7:39–43.
Online Appendix to

Identity conflict, ethnocentrism and social
cohesion

Matteo Sestito†

<footnote>†. Aix-Marseille University, CNRS, EHESS, Centrale Marseille, AMSE, France.</footnote>
# Contents

<table><tr><td>A Data</td><td>1</td></tr><tr><td>A.1 Outcome and control variables</td><td>2</td></tr><tr><td>A.2 Conflict variables</td><td>5</td></tr><tr><td>B Africa ethnolinguistic armed groups Dataset</td><td>6</td></tr><tr><td>B.1 Dataset coverage</td><td>7</td></tr><tr><td>B.2 Descriptive statistics</td><td>9</td></tr><tr><td>C Ethnocentrism</td><td>11</td></tr><tr><td>D Baseline - Robustness tests</td><td>14</td></tr><tr><td>D.1 Outlier observations</td><td>14</td></tr><tr><td>D.2 Standard error clustering</td><td>18</td></tr><tr><td>D.3 Alternative treatment and outcome specifications</td><td>19</td></tr><tr><td>D.4 Placebo</td><td>21</td></tr><tr><td>D.5 Migration</td><td>22</td></tr><tr><td>E Mechanisms - Robustness tests</td><td>25</td></tr><tr><td>E.1 Distant conflict</td><td>25</td></tr><tr><td>E.2 Ethnic vs non-ethnic conflict</td><td>26</td></tr><tr><td>E.3 Heterogeneous effects</td><td>27</td></tr></table>
# A Data

This section provides further details on the data employed in the empirical analysis of the paper. Table A1 displays summary statistics for all the variables used, while the subsections below report further information for outcome, control, and conflict variables.

<table><caption>**Table A1:** Descriptive statistics</caption><thead><tr><th></th><th>Mean</th><th>Sd</th><th>Min</th><th>Max</th><th>N</th></tr></thead><tbody><tr><td colspan="6"><strong>CONFLICT</strong></td></tr><tr><td>Ethnic conflict</td><td>0.043</td><td>0.203</td><td>0</td><td>1</td><td>111428</td></tr><tr><td>Distant conflict</td><td>0.096</td><td>0.295</td><td>0</td><td>1</td><td>111428</td></tr><tr><td>Remote conflict</td><td>0.135</td><td>0.342</td><td>0</td><td>1</td><td>111428</td></tr><tr><td>Non-ethnic conflict</td><td>0.340</td><td>0.474</td><td>0</td><td>1</td><td>111428</td></tr><tr><td>Conflict-ELP</td><td>0.022</td><td>0.119</td><td>0</td><td>1</td><td>111428</td></tr><tr><td>Events</td><td>0.134</td><td>1.161</td><td>0</td><td>33</td><td>111428</td></tr><tr><td>Months</td><td>0.045</td><td>0.223</td><td>0</td><td>1</td><td>111428</td></tr><tr><td>Precise match</td><td>0.034</td><td>0.181</td><td>0</td><td>1</td><td>111428</td></tr><tr><td>No sub-groups</td><td>0.035</td><td>0.183</td><td>0</td><td>1</td><td>111428</td></tr><tr><td colspan="6"><strong>OUTCOMES</strong></td></tr><tr><td>Ethnic Identity</td><td>2.362</td><td>1.196</td><td>1</td><td>5</td><td>103656</td></tr><tr><td>Social Cohesion</td><td>0.002</td><td>0.791</td><td>-1.25</td><td>1.89</td><td>109426</td></tr><tr><td>Att. Comm. Meetings</td><td>2.150</td><td>1.302</td><td>0</td><td>4</td><td>110772</td></tr><tr><td>Collective Action</td><td>1.855</td><td>1.293</td><td>0</td><td>4</td><td>110520</td></tr><tr><td>Community Associations</td><td>0.609</td><td>0.928</td><td>0</td><td>3</td><td>110387</td></tr><tr><td colspan="6"><strong>CONTROLS</strong></td></tr><tr><td>Age</td><td>36.810</td><td>14.772</td><td>18</td><td>130</td><td>110350</td></tr><tr><td>Male</td><td>0.498</td><td>0.500</td><td>0</td><td>1</td><td>111427</td></tr><tr><td>Urban residence</td><td>0.385</td><td>0.487</td><td>0</td><td>1</td><td>110158</td></tr><tr><td>Education</td><td>3.298</td><td>1.997</td><td>0</td><td>9</td><td>111173</td></tr><tr><td>Employment status</td><td>1.277</td><td>1.157</td><td>0</td><td>3</td><td>111019</td></tr><tr><td>Household wealth</td><td>6.039</td><td>4.617</td><td>0</td><td>20</td><td>110015</td></tr><tr><td>Voted</td><td>0.724</td><td>0.447</td><td>0</td><td>1</td><td>109664</td></tr><tr><td>Trust president</td><td>1.826</td><td>1.127</td><td>0</td><td>3</td><td>107208</td></tr><tr><td>Trust local govt.</td><td>1.566</td><td>1.074</td><td>0</td><td>3</td><td>102953</td></tr></tbody></table>

Notes: The *Events* and *Months* specifications of conflict are in levels.
## A.1 Outcome and control variables

Individual-level information on social norms comes from Afrobarometer surveys.<fnref n="1" /> Here is reported the exact text of Afrobarometer variables alongside the meaning of their values.<fnref n="2" />

*   **Ethnic identity**

- *Afrobarometer question:* "Let us suppose that you had to choose between being a [enter nationality] and being a [Respondent's ethnic group]. Which of the following best expresses your feelings?"

- *Values (recoded):* 1=I feel only [enter nationality]; 2=I feel more [enter nationality] than (Respondent's ethnic group); 3=I feel equally [enter nationality] and (Respondent's ethnic group); 4=I feel more (Respondent's ethnic group) than [enter nationality]; 5=I feel only (Respondent's ethnic group)

- *Values in binary recoding:* 0=Otherwise, 1=I feel only (Respondent's ethnic group)

- *Afrobarometer question number (rounds 3 to 6):* Q82, Q83, Q85b, Q88b

- *Further comments:* The original coding of the variable is inverted around the median value of 3 (e.g. value 1 in the original coding corresponds to value 5 in the recoded variable used in the analysis)

*   **Attendance community meetings**

- *Afrobarometer question:* "Here is a list of actions that people sometimes take as citizens. For each of these, please tell me whether you, personally, have done any of these things during the past year. If not, would you do this if you had the chance: Attended a community meeting?"

- *Values:* 0=No, would never do this; 1=No, but would do if had the chance; 2=Yes, once or twice; 3=Yes, several times; 4=Yes, often

- *Values in binary recoding:* 0=Otherwise; 1=Yes, often

- *Afrobarometer question number (rounds 3 to 6):* Q31a, Q23a, Q26a, Q20a

*   **Collective action**

- *Afrobarometer question:* "Here is a list of actions that people sometimes take as citizens. For each of these, please tell me whether you, personally, have done any of these things during the past year. If not,

<footnote n="1">1. The surveys are publicly available at: http://afrobarometer.org/data/geocoded-data.</footnote>

<footnote n="2">2. The exact wording of the question is taken from the sixth Afrobarometer round.</footnote>
would you do this if you had the chance: Got together with others to raise an issue?”

- Values: 0=No, would never do this; 1=No, but would do if had the chance; 2=Yes, once or twice; 3=Yes, several times; 4=Yes, often

- Values in binary recoding: 0=Otherwise; 1=Yes, often

- Afrobarometer question number (rounds 3 to 6): Q31b, Q23b, Q26b, Q20b

* **Membership community associations**

- Afrobarometer question: “Let’s turn to your role in the community. Now I am going to read out a list of groups that people join or attend. For each one, could you tell me whether you are an official leader, an active member, an inactive member, or not a member: Some other voluntary association or community group?”

- Values: 0=Not a member; 1=Inactive member; 2=Active member; 3=Official leader

- Values in binary recoding: 0=Not a member & Inactive member; 1=Otherwise

- Afrobarometer question number (rounds 3 to 6): Q28d, Q22b, Q25b, Q19b

- Further comments: The question in the third round is phrased slightly differently. Moreover, in this survey, contrarily to the others, respondents were previously asked also for their membership in trade unions and business associations

* **Age**

- Afrobarometer question: “How old are you?”

- Values: 18-130

- Afrobarometer question number (rounds 3 to 6): Q1, Q1, Q1, Q1

* **Gender**

- Afrobarometer question: “Respondent’s gender”

- Values (recoded): 0= Female; 1= Male

- Afrobarometer question number (rounds 3 to 6): Q101, Q101, Q101, Q101

* **Urban**

- Afrobarometer question: “Urban or Rural Primary Sampling Unit”

- Values (recoded): 0=Rural; 1=Urban

- Afrobarometer question number (rounds 3 to 6): Urbrur

* **Education**
- Afrobarometer question: "What is your highest level of education?"

- Values: 0=No formal schooling; 1=Informal schooling only (including Koranic schooling); 2=Some primary schooling, 3=Primary school completed; 4=Intermediate school or Some secondary school / high school; 5=Secondary school / high school completed, 6=Post-secondary qualifications, other than university e.g. a diploma or degree from a polytechnic or college; 7=Some university; 8=University completed; 9=Post-graduate

- Afrobarometer question number (rounds 3 to 6): Q90, Q89, Q97, Q97

* **Employment status**

- Afrobarometer question: "Do you have a job that pays a cash income? If yes, is it full-time or part-time? If no, are you presently looking for a job?"

- Values (recoded): 0=No (not looking); 1=No (looking); 2=Yes part time; 3=Yes, full time

- Afrobarometer question number (rounds 3 to 6): Q94, Q94, Q96, Q95

- Further comments: In Afrobarometer rounds 3 and 4 respondents can distinguish between “not looking” and “looking” also in reference to the part- and full-time categories. This distinction is eliminated to express the variables as in the value-scale reported above

* **Household wealth**

- The covariate is equal to the average of five standardised variables measuring household wealth

- Afrobarometer question: "Over the past year, how often, if ever, have you or anyone in your family gone without: X?"
  Where X ∈ {enough food to eat, enough clean water for home use, medicines or medical treatment, enough fuel to cook your food, a cash income}

- Values: 0=Never; 1=Just once or twice; 2=Several times; 3=Many times; 4=Always

- Afrobarometer question number (rounds 3 to 6): Q8a-e, Q8a-e, Q8a-e, Q8a-e

* **Trust president**

- Afrobarometer question: "How much do you trust each of the following, or haven't you heard enough about them to say: The President?"

- Values: 0=Not at all; 1=Just a little; 2=Somewhat; 3=A lot

- Afrobarometer question number (rounds 3 to 6): Q55a, Q49a, Q59a, Q52a

* **Trust local government**
- *Afrobarometer question:* "How much do you trust each of the following, or haven't you heard enough about them to say: Your Metropolitan, Municipal or District Assembly?"

- *Values:* 0=Not at all; 1=Just a little; 2=Somewhat; 3=A lot

- *Afrobarometer question number (rounds 3 to 6):* Q55d, Q49d, Q59e, Q52e

• Voted

- *Afrobarometer question:* "Understanding that some people were unable to vote in the most recent national election in [20xx], which of the following statements is true for you?"

- *Values (recoded):* 0=Did not vote; 1=Voted

- *Afrobarometer question number (rounds 3 to 6):* Q30, Q23d, Q27, Q21

- *Further comments:* The original question list various reasons for not having voted, which change from one Afrobarometer round to another. They all have been coded within the same no-vote category

## A.2 Conflict variables

Ethnic conflict variables are built starting from the ACLED dataset, which provides information on the location, date, and actors involved in conflict incidents spanning the whole African continent since 1997. I focus only on non-state armed groups. These are the actors defined by ACLED as rebel groups, political and identity militias: interaction codes 2, 3, and 4, respectively. I thus assign, where relevant, an ethnic affiliation to these armed groups (more details on the *Africa ethnolinguistic armed groups Dataset* in the next section). The procedure yields a cross-section of conflict incidents $c_{e,l,j}$ taking place in location $l$, at day $j$, and involving an armed group associated to ethnolinguistic group $e$. The baseline measure excludes conflict incidents where a state force is involved.

Then conflict incidents are aggregated over space and time. Spatially, I consider all the locations $l$ belonging to a cell $k$. Temporally, I aggregate over the period between two consecutive Afrobarometer surveys, that is, over the day-unit intervals $AB_{k,t} = \{ab_{k,t-1}, \dots, ab_{k,t-1}, ab_{k,t}\}$; where $ab_{k,t}$ is the earliest interview date conducted by Afrobarometer round $t \in \{3, 4, 5, 6\}$ in cell $k$.<fnref n="3" /> Note that $ab_{k,2}$ is not present in the data, so that it is arbitrarily

<footnote n="3">3. When the interview date is missing – because the cell was not surveyed in that period – it is replaced by the country-specific average interview date, i.e. $|K_{p,t}|^{-1} \sum_{k \in K_{p,t}} ab_{k,t}$ where $K_{p,t}$ is the set of cells in country $p$ surveyed during $t$.</footnote>
set so as to impose a length of the first period approximately equal to the average of the others. Formally:

$$ab_{k,2} = ab_{k,3} - \frac{1}{3} \left( \frac{1}{K_6} \sum_{k \in K_6} ab_{k,6} - \frac{1}{K_3} \sum_{k \in K_3} ab_{k,3} \right)$$

where $K_3$ and $K_6$ are the sets of cells appearing in period 3 and 6, respectively.

The baseline measure of conflict is thus

$$C_{e,k,t} = 1\{\tilde{C}_{e,k,t} > 0\}$$

Where $\tilde{C}_{e,k,t}$ is the event-based definition of ethnic conflict. Formally:

$$\tilde{C}_{e,k,t} = \sum_{l \in k} \sum_{j \in AB_{k,t}} c_{e,l,j}$$

The third measure of conflict employed is the count of events with at least a conflict incident. Define $M_{k,t}$ as the set of unique months belonging to the interval $AB_{k,t}$, hence, the month-based measure of conflict is defined as:

$$\hat{C}_{e,k,t} = \sum_{m \in M_{k,t}} 1[\sum_{l \in k} \sum_{j \in m} c_{e,l,j} > 0]$$

Finally, the non-ethnic conflict variable employed at equation (2) is defined as:

$$Z_{-e,k,t} = 1[\sum_{l \in k} \sum_{j \in AB_{k,t}} c_{l,j} - \tilde{C}_{e,k,t} > 0]$$

Where $c_{l,j}$ is a conflict incident in location $l$ at day $j$, which may or may not be related to a given ethnic group.

# B Africa ethnolinguistic armed groups Dataset

This section introduces the Africa ethnolinguistic armed groups dataset, linking conflict actors to ethnolinguistic groups. This is the dataset used for the definition of ethnic conflict in the paper. Additional details on its construction and on the sources employed are available in the relevant codebook.

The dataset is a cross-section of armed actors active in sub-Saharan Africa, reporting information on their ethnolinguistic affiliation. In particular, a conflict actor is assigned to an ethnolinguistic group when the
former is organised along ethnic lines. Ethnic organisation primarily
means that recruitment practices and actual composition of the armed
actor overlap with ethnolinguistic cleavages. Occasionally, when the or-
ganisational structure was ambiguously defined, the assignment has
also been made with reference to its conduct and targets. A note in the
dataset explains the rationale behind each actor-ethnicity match. The
list of armed actors comes from the Armed Conflict Location and Event
Data (ACLED) Project. The cross-section contains all the non-state mili-
tias involved in conflicts taking place in various sub-Saharan countries
over the first decade of the 21st century.

**B.1 Dataset coverage**

Spatially the dataset covers the eighteen countries present throughout Afrobarometer surveys rounds 3 to 6, as well as some neighbour areas. The countries included are: Benin, Botswana, Cape Verde, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mali, Mozambique, Namibia, Nigeria, Senegal, South Africa, Tanzania, Uganda, Zambia, and Zimbabwe. More precisely, countries are defined by the ensemble of grid cells intersecting the GADM polygon of the in-sample countries.<fnref n="4" />,<fnref n="5" /> Grid cells measure 0.5° × 0.5°, corresponding to roughly 55km × 55km at the equator. Additionally, the dataset covers the conflict activity taking place in some of the cells bordering within-sample countries. In the specific, these are the cells falling within the 24-cell neighbourhood of an Afrobarometer respondent.<fnref n="6" />

The period considered depends on the interview dates of Afrobarometer surveys, with conflict activity analysed between Afrobarometer rounds "two" and six.<fnref n="7" /> Figure A1 gives a visual representation of the zones and

<footnote n="4">4. GADM is a portal providing administrative country divisions for the whole world. Access is free at the following link: https://gadm.org/</footnote>

<footnote n="5">5. A cell belongs to a country when the two intersect. In case of cells spanning two or more within-sample countries, the cell was assigned to the country to which it belongs the most.</footnote>

<footnote n="6">6. The *X*-cell neighbourhood of a given cell is the collection of *X* cells which surround it. Hence, the 24-cell neighbourhood of a cell *c* is the 5×5 square of cells centred around *c* and excluding it.</footnote>

<footnote n="7">7. To be more precise, countries were divided into three macro-regions (i.e. West Africa, South-East Africa, and Madagascar), each having a different period in which conflict was considered. Formally, given a macro-region *r*, the upper bound of its period is given by $U_r = \max_{k \text{ in } r} ab_{k,6}$ where $ab_{k,6}$ is the earliest Afrobarometer 6 interview in cell *k*. Similarly, the lower limit of the period is given by $L_r = \max_{k \text{ in } r} \tilde{a}b_{k,2}$ where $\tilde{a}b_{k,2}$ is the virtual</footnote>
<figure><img src="image_8.png" /><figcaption>**Figure A1:** Dataset coverage</figcaption></figure>

periods where ACLED conflict was taken into account.

Ethnolinguistic affiliation was possibly assigned to the following types of armed actors: rebel groups, political and identity militias. These are the actors identified by ACLED with interaction code 2, 3 and 4, respectively. Globally, they are involved in roughly 57% of the conflict incidents taking place across the 18 selected countries between May 2002 and November 2014. The dataset is muted, instead, on the possible ethnic identity of other ACLED actor categories. These are: state forces, rioters, protesters, civilians and other/external forces. The latter are an ephemeral actor category, appearing in very few conflict incidents related mostly to the French intervention in Mali (2012-2022) and various diplomatic events connected to international peacekeeping and monitoring operations. Rioters, protesters and civilians are, instead, involved in the vast majority of conflict incidents: jointly they appear in 69% of the conflict events considered. However, they are recorded by ACLED in general categories, such as “Protesters (Nigeria)” or “Civilians (Uganda)”.

<footnote>interview date of Afrobarometer round 2. That is, inasmuch as $ab_{k,2}$ is not in the data, $\tilde{ab}_{k,2}$ is arbitrarily set so as to impose a length of the first period approximately equal to the average of the others, that is: $\tilde{ab}_{k,2} = ab_{k,3} - \frac{1}{3}\left(\frac{1}{K_6}\sum_{k \in K_6} ab_{k,6} - \frac{1}{K_3}\sum_{k \in K_3} ab_{k,3}\right)$, where $K_3$ and $K_6$ are the sets of cells appearing in period 3 and 6, respectively. The upper and lower interval limits of those cells not belonging to any within-sample country, are given by $\max_r U_r$ and $\min_r L_r$, respectively.</footnote>
Clearly, this generic designation prevents the assignment of an ethnic identity. State forces are involved in as much as 34% of the conflict events. While being a significant actor in sub-Saharan Africa, the assignment of an ethnic affiliation is complicated by the multi-cultural nature of many African polities. Different state agencies have different ethnic allegiances, which moreover are prone to mutate over-time. Given these difficulties and the mole of research implied (there are 125 state actors active in the relevant sample), I preferred not to include them in the dataset, leaving this to future work.

## B.2 Descriptive statistics

The dataset therefore has a good, if yet incomplete, coverage of sub-Saharan ethnic conflict throughout the 2000s and early 2010s. Table A2 provides summary statistics for ethnic violence, comparing it to all conflict activity (All conflict) and warfare involving any kind of non-state militias (Non-ethnic & ethnic conflict): between May 2002 and November 2014, across the 18 sub-Saharan countries sampled, the majority of violence did not follow ethnic lines. As shown in Figures A2a and A2b there is, however, significant heterogeneity across time and space. While ethnic conflict hovers around 250 events per quarter, other types of violence spiked after 2012 as a consequence of: political instability in Mali, the reignition of Boko Haram's attacks in Nigeria, the intensification of al-Shabaab activity in Kenya, a small-lived resurgence of the Renamo rebel group in Mozambique, as well as a surge in riots and protests across both Ghana and South Africa.<fnref n="8" /> Spatially, instead, ethnic conflict tends to be concentrated in Nigeria, Uganda, and Kenya, with clusters also in Mali, Ghana, and Senegal. These countries experience also a lot of non-ethnic conflict activity, albeit other states, most notably South Africa and Zimbabwe, have equally massive presence of non-ethnic conflict.

<table><caption>**Table A2:** Conflict descriptive statistics (May 2002 - November 2014)</caption><thead><tr><th></th><th>All conflict</th><th>Non-ethnic & ethnic conflict</th><th>Ethnic conflict</th><th>Inter-ethnic conflict</th></tr></thead><tbody><tr><th scope="row">N Events</th><td>27422</td><td>14093</td><td>4947</td><td>4771</td></tr><tr><th scope="row">N Cells</th><td>1401</td><td>1041</td><td>442</td><td>432</td></tr><tr><th scope="row">Fatalities</th><td>56832</td><td>49730</td><td>21405</td><td>20661</td></tr></tbody></table>

*Notes*: Data refer to the 18 sampled sub-Saharan countries.

<footnote n="8">8. The UCDP website provides summary accounts of these conflicts.</footnote>
<caption>**Figure A2:** Conflict trend and distribution (May 2002 - November 2014)</caption>

<figure><img src="image_9.png" /><figcaption>(b) Conflict events across selected sub-Saharan countries</figcaption></figure>

<figure><img src="image_10.png" /></figure>
C Ethnocentrism

Ethnocentrism indicates the instinctive classification of the world into
the superior ethnic 'us' and all the 'others'. As such, the concept refers
to cultural mutable objects: peoples, nations, ethnicities.<fnref n="9" /> Yet, for how
much artificial ethnicity is, ethnocentrism seems to be a fairly constant
feature of human societies throughout the whole (pre-)history. It is par-
ticularly revealing to look at the names that many hunter-gatherer peo-
ples used to refer to themselves. Consider the Eskimos of the Arctic
zone, among which the Inuit are perhaps the most well-known eth-
nic group. All these societies called themselves by many names usu-
ally meaning the 'real people' (Gat 2006, pp. 50-51). Moving south-
wards in the New World, many American Indians also self-defined them-
selves as the sole legitimate humans. The native American tribes of
Niimiipuu (Nez Perce), Lenape (Delaware), Lakota, Illiniwek referred in-
variably to themselves as 'men', 'people', or 'genuine Indians' (Barbero
2009). Similarly, Zhu/twasi, the ethnonym of a !Kung sub-group living
across northern Botswana and Namibia, is a self-appellation translating
as 'genuinue people' (Lee 1979, p. 38). Even more telling is the case of
the Yanomamo horticulturalists, still inhabiting the fluvial zone between
Brazil and Venezuela. "Yanomamo in fact means 'humanity', or at least
the most important segment of humanity. All other peoples are known by
the term naba, a concept that implies an invidious distinction between
'true' man and 'sub-human' man."<fnref n="10" /> These examples are symptomatic of
a broader pattern that extends to many other tribal societies, including
Aboriginal Australian groups (Hamilton 2006, footnote 8).

If foraging peoples tended to deny the very humanity of other ethnic
groups by considering themselves as the only 'legitimate people', the pic-
ture did not change much with the rise of states. As it is well known,
ancient Greeks pejoratively defined all the rest of the world as barbar-
ians. The very term barbarian comes from the 'bar-bar' sound whereby
Greeks mimicked what they considered grotesque and incomprehensi-
ble non-Greek languages. In the classical world, the term will be later
enlarged to indicate all those people living outside of the Roman Empire.
But this was not a Mediterranean peculiarity. From Chinese to Persians
and Indians, much of the pre-modern states have applied the derogative
barbarian category to the outer world (Scott 2009 & 2017).

<footnote n="9">9. For some historical examples on the malleability of ethnic identities see: Barbero (2009), Pohl (2015), Tilly (1994), and Welsh (1996).</footnote>

<footnote n="10">10. Napoleon Chagnon (1968), *Yanomamo social organization and warfare*, Ann Arbor (MI): University Microfilms, as quoted in Gat (2006, p. 51)</footnote>
As the brief discussion above has shown, the ethnic-based ‘us-them’ demarcation of the world is deeply in-grained in human psyche. The overwhelming force of ethnocentrism, as well as its omnipresence in human societies, are difficult to explain with exclusive reference to social factors. Socio-biology is, instead, on a much firmer foot to explain the ubiquitousness of this phenomenon. Various evolutionary theories have been advanced to explain humans’ peculiarly strong in-group bias.<fnref n="11" /> The most widely accepted one understands ethnocentrism as residual, almost a manipulation, of natural selection on pro-kinship behaviour.<fnref n="12" />

From an evolutionary perspective, purely altruistic behaviour – diminishing one's own biological fitness at no personal benefit – can naturally emerge only towards relatives. The cold mechanics of natural selection plainly imply that a given behaviour can be naturally selected if the genes mandating for it are passed on to the next generation. Genetic material is transmitted both through offspring and kin mates. Relatives share some of their genes among themselves, in diminishing proportion as the kin distance increases.<fnref n="13" /> Humans, as much as other animals,<fnref n="14" /> have thus developed a natural inclination to help their relatives, with greater sacrifices easily exerted for closer kin.

<footnote n="11">11. See Dennen (2000) and Wrangham (2019, ch. 7) for a review of some of these theories. The most popular among economists is the so-called parochial altruism theory (Bernhard et al. 2006; Bowles 2008, 2009, 2012; Choi and Bowles 2007), whereby in-group altruism emerged because it gave a competitive edge in conflict among hunter-gatherer bands: more cohesive groups could subjugate the others, hence the natural selection of cooperative traits. For how appealing and elegant the argument is, it: rests on the controversial notion of group-selection (Pinker 2012); does not fit well with the raid-and-ambush nature of prehistoric warfare (Gat 2006); can not be extended to other animal species characterised by intensive group-conflict (Wrangham 2019, ch. 7); implies a close relationship between in-group favouritism and out-group aggressiveness, which is not however always observed (Brewer 2007; Yamagishi and Mifune 2016). More robust are the arguments connecting human altruism to a millennial process of self-domestication (Wrangham 2019). These theses are, however, not antithetical to the one here illustrated; they are, indeed, probably complementary inasmuch as they deal with generic human cooperation, rather than limited to co-ethnics.</footnote>

<footnote n="12">12. The following discussion relies heavily on the treatment in Gat (2006), particularly at chapters 3 and 7.</footnote>

<footnote n="13">13. For example, siblings share, on average, 50% of their genes, while cousins just 12.5%.</footnote>

<footnote n="14">14. The case of bees is emblematic. They live in colonies whose vast majority of members are siblings or even clones. It is not surprise then to observe incredibly altruistic behaviours: individual bees are ready to sacrifice their lives for the survival of the whole colony, which is a huge repository of the genes present in a single bee (Gat 2006, p. 44).</footnote>