As a species, Homo Sapiens developed almost uniquely during the pre-agricultural era, when humans roamed the world as small bands of hunter-gatherers. For most of Sapiens existence, people lived in extended family clans numbering between 20 and 70 members (Kelly 2007). Developments in linguistic skills around perhaps 70.000 years ago, allowed the formation of tribes, regional groups reuniting several familiar clans (Gat 2006, Harari 2014). The tribe, numbering in the hundreds, shared a common language and was characterised by extensive marriage exchanges among its constituent clans. Therefore, in these prehistoric societies kinship and culture overlapped to a great extent. Beyond phenotypic traits, common language became a primary cue for kinship relations. While not all members of these regional groups were close relatives, they had far greater genetic proximity among themselves than with respect to foreigners. Language was effectively a good indicator of kinship.

Moreover, as these regional groups developed particular cultural attributes, kin-mandated cooperation was further strengthened in virtue of common cultural practices, most notably a shared language. In short, regional hunter-gatherer groups were “bound together by mutually reinforcing and overlapping ties of kinship, social co-operation, and cultural distinctiveness” (Gat 2006, p. 50).

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 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 neither increasing their biological fitness, nor that of any distant relative. Nonetheless, some of them were volunteers, enthusiastically going to risk their lives for the mother-land. This seemingly irrational behaviour shall be read as a manipulation, an extension beyond its original objective, of kin-based solidarity within small groups.<fnref n="15" /> It is this the atavistic component of contemporary xenophobia, the same often perplexing observers of ethnic cleansing and genocides.

<footnote n="15">15. 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>
# D Baseline - Robustness tests

This section reports additional robustness tests of the baseline models estimated in Section 4 of the paper. The estimating equation of interest is:

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

Where all the variables have the same meaning as in the paper. The coefficient of interest is the marginal impact of ethnic conflict ($\beta$), which is expected to be positive.

## D.1 Outlier observations

As clear from Figure 2 in the paper, ethnic conflict is spread very unevenly across sub-Saharan Africa. For each country, Figure A3 reports the cell-level average of conflict events.16 Kenya is the country with the highest density of ethnic conflict events, primarily reflecting the violent nature of its ethnic-based political system, as well as conflict spill-overs stemming from its troubled Somali and Ugandan neighbours. On the other end of the spectrum are Cape Vert, Lesotho, and Madagascar, which did not experience any conflict event during the whole period. As shown in Figure A4, baseline results are overall robust to the exclusion of each country from the sample.

A related concern is that the impact of conflict on social cohesion might be driven by few influential ethnic groups. Table A3 reports the total number of conflict events for the 64 ethnolinguistic groups that have experienced at least one conflict incident. There is, indeed, some substantial heterogeneity in warfare exposure. Kikuyu, Ijaw, Diola and Luo are the four most war-prone groups, the only experiencing at least 40 conflict incidents. The Kikuyu and Luo of Kenya have been exposed to different types of conflict, ranging from tribal clashes over cattle and land, to urban disorders in the slums of Nairobi at the hands of the Kikuyu-led ‘Mungiki Militia’ and the Luo-controlled ‘Taliban Militia’. The Ijaw of Nigeria have taken part in conflicts interesting the oil-rich Delta

$$\frac{1}{|\mathcal{K}_p|} \sum_{k \in \mathcal{K}_p} \tilde{C}_{e,k,t}$$

<footnote n="16">16. That is, the event-based definition of conflict is first aggregated at the country level and then divided by the number of cells in each country. Formally:</footnote>

<footnote>where $\mathcal{K}_p$ is the set of cells in country $p$ and $\tilde{C}_{e,k,t}$ is the event-based definition of conflict defined in the data section of the paper.</footnote>
<figure><img src="image_11.png" /><figcaption>**Figure A3:** Ethnic conflict in selected sub-Saharan countries</figcaption></figure>

Notes: Conflict events are aggregated at the country-level throughout the whole sample period.

region of the Niger river. The area has known “decades of environmental pollution, economic underdevelopment and political marginalization” (Asuni 2009, p. 3), which have ultimately resulted in fertile ground for inter-ethnic tensions. Hence, the involvement of the Ijaw in various ethnic militias and loose armed groups, such as the ‘MEND: Movement for the Emancipation of the Niger Delta’ (Hazen and Horner 2007). The Diola of Senegal have been at the forefront of the independence struggle for the Casamance region, taking the lead in rebel groups such as the ‘MFDC: Movement of Democratic Forces of Casamance’.<fnref n="17" />

The baseline model is thus re-estimated excluding these outlier groups. Results are reported in Table A4. The first two columns substantiate the robustness of results to the exclusion of Kikuyu respondents from the sample. Columns three and four run a more demanding specification whereby also Ijaw, Diola and Luo are excluded, limiting the sample to ethnolinguistic groups reporting less than 40 conflict incidents. Marginal impacts remain positive and significant. Finally, in the last two columns, I drop all the ethnolinguistic groups not experiencing any

<footnote n="17">17. For an overview of the conflict and the role of MFDC see: https://ucdp.uu.se/conflict/375 and https://ucdp.uu.se/actor/529.</footnote>
<figure><img src="image_12.png" /><figcaption>**Figure A4:** OLS regressions of Ethnic Identity & Social Cohesion on Ethnic conflict – Exclude countries</figcaption></figure>

*Notes*: The figure reports point estimates, alongside their 90% confidence intervals, of ethnic conflict from linear regressions sequentially excluding one country at time. 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.

conflict activity. Even though the sample size is roughly halved, the estimated coefficients are positive and always statistically different than zero.
<table><caption>**Table A3:** Conflict incidence by ethnolinguistic group</caption><thead><tr><th>ETHNOLINGUISTIC GROUP</th><th>CONFLICT INCIDENCE</th><th>ETHNOLINGUISTIC GROUP</th><th>CONFLICT INCIDENCE</th></tr></thead><tbody><tr><td>Acholi</td><td>26</td><td>Lozi</td><td>1</td></tr><tr><td>Afrikaans</td><td>1</td><td>Lugwere</td><td>1</td></tr><tr><td>Akan</td><td>4</td><td>Luhya</td><td>3</td></tr><tr><td>Alur</td><td>3</td><td>Lukhonjo</td><td>5</td></tr><tr><td>Ateso</td><td>13</td><td>Lumasaaba</td><td>1</td></tr><tr><td>Chewa</td><td>2</td><td>Luo</td><td>40</td></tr><tr><td>Dagbani</td><td>3</td><td>Lusoga</td><td>1</td></tr><tr><td>Diola</td><td>40</td><td>Mampruli</td><td>4</td></tr><tr><td>Edo</td><td>1</td><td>Ndebele</td><td>1</td></tr><tr><td>Eggon</td><td>3</td><td>Ngakarimajong</td><td>11</td></tr><tr><td>Ewe</td><td>6</td><td>Ogoni</td><td>3</td></tr><tr><td>Fulani</td><td>16</td><td>Oroma</td><td>32</td></tr><tr><td>Hausa</td><td>3</td><td>Oshiwambo</td><td>2</td></tr><tr><td>Herero</td><td>2</td><td>Pokot</td><td>13</td></tr><tr><td>Idoma</td><td>1</td><td>Pulaar/Toucouleur</td><td>1</td></tr><tr><td>Igbo</td><td>25</td><td>Rufumbira</td><td>36</td></tr><tr><td>Ijaw</td><td>81</td><td>Rukiga</td><td>7</td></tr><tr><td>Isoko</td><td>1</td><td>Rukwangali</td><td>1</td></tr><tr><td>Japadhola</td><td>2</td><td>Runyankole</td><td>1</td></tr><tr><td>Kalabari</td><td>1</td><td>Runyoro</td><td>27</td></tr><tr><td>Kalenjin</td><td>32</td><td>Shangaan/Tsonga</td><td>7</td></tr><tr><td>Kanuri</td><td>1</td><td>Shona</td><td>1</td></tr><tr><td>Karanga</td><td>1</td><td>Somali</td><td>9</td></tr><tr><td>Kikuyu</td><td>88</td><td>Sotho</td><td>1</td></tr><tr><td>Kimasai</td><td>8</td><td>Tamasheq</td><td>30</td></tr><tr><td>Kimeru</td><td>5</td><td>Tarok</td><td>6</td></tr><tr><td>Kisii</td><td>3</td><td>Tiv</td><td>3</td></tr><tr><td>Konkomba</td><td>2</td><td>Tonga</td><td>1</td></tr><tr><td>Kupsabinyi</td><td>1</td><td>Turkana</td><td>5</td></tr><tr><td>Kuria</td><td>12</td><td>Urhobo</td><td>4</td></tr><tr><td>Kusal</td><td>5</td><td>Xhosa</td><td>2</td></tr><tr><td>Langi</td><td>1</td><td>Yoruba</td><td>36</td></tr></tbody></table>

Notes: Conflict incidence refers to number of conflict events experienced by an ethnolinguistic group throughout the whole period of analysis. Only ethnolinguistic groups experiencing at least a conflict event are included in the table.
<table><caption>**Table A4:** OLS regressions of Ethnic Identity & Social Cohesion on Ethnic conflict – Outlier ethnic groups</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>Ethnic conflict</td><td>0.094<br>(0.041)**</td><td>0.088<br>(0.041)**</td><td>0.121<br>(0.043)***</td><td>0.118<br>(0.044)***</td><td>0.096<br>(0.042)**</td><td>0.094<br>(0.042)**</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>0.21</td><td>0.22</td></tr><tr><td>N</td><td>102,191</td><td>99,990</td><td>100,251</td><td>98,059</td><td>49,955</td><td>49,429</td></tr><tr><td colspan="7"><strong>SOCIAL COHESION</strong></td></tr><tr><td>Ethnic conflict</td><td>0.071<br>(0.028)**</td><td>0.072<br>(0.028)**</td><td>0.070<br>(0.032)**</td><td>0.072<br>(0.031)**</td><td>0.087<br>(0.031)***</td><td>0.079<br>(0.031)**</td></tr><tr><td>R<sup>2</sup></td><td>0.23</td><td>0.27</td><td>0.23</td><td>0.27</td><td>0.23</td><td>0.29</td></tr><tr><td>N</td><td>107,979</td><td>105,711</td><td>106,058</td><td>103,798</td><td>51,498</td><td>50,960</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><tr><td>Drop Kikuyu</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td></tr><tr><td>Drop Ijaw, Diola & Luo</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td></tr><tr><td>Drop peaceful groups</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</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. *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

## D.2 Standard error clustering

In the baseline specification standard errors are allowed to be correlated at the cell-ethnolinguistic-period level. The level of clustering was chosen so as to match the level of assignment of the conflict indicator. I here relax this assumption and allow standard errors to be correlated within each: ethnolinguistic-period pair, ethnolinguistic group, cell-period pair, and within each 0.5° × 0.5° cell. As shown in Figure A5, sign and statistical significance of the baseline estimates are always reproduced: the validity of the results does not hinge upon the level of clustering.
<figure><img src="image_13.png" /><figcaption>**Figure A5:** OLS regressions of Social identity and cohesion on Ethnic conflict – Alternative standard error clustering</figcaption></figure>

*Notes*: The figure reports point estimates, alongside their 90% confidence intervals, of ethnic conflict under different levels of standard error clustering. Fixed effects correspond to cell×period and ethnic×period dummies. Individual controls include: age and its square, gender, and an urban-rural indicator.

## D.3 Alternative treatment and outcome specifications

This section investigates robustness to alternative specifications of ethnic conflict (Table A5) and social cohesion (Table A6).

The first two columns of Table A5 check whether results are sensitive to possible measurement errors in the recording of conflict. Time-exact conflict thus considers only events whose date is identified with certainty by ACLED: baseline results remain unchanged.<fnref n="18" /> The successive pair of columns addresses concerns related to intra-ethnic conflict. When violence cuts through an ethnolinguistic group, the issue of contention is likely unrelated to ethnicity, making harder for ethnocentric feelings to materialise. Indeed, as shown in columns 3-4, when excluding these types of violent events, point estimates are greater than in the baseline models. The last two columns of Table A5, instead, check robustness to the inclusion of conflict events involving state forces. While this might make omitted variable issues more compelling – if anything because

<footnote n="18">18. Formally, time-exact conflict consider only events classified by ACLED with a time precision equal to 1.</footnote>
state involvement in conflict is more directly related to changes in overall state capacity and legitimacy –, results are robust to this inclusion.

Table A6 reports estimates of models employing the components of the social cohesion index as dependent variables. The impact of ethnic conflict on the three measures of social cohesion mimics that on the aggregated index: marginal effects are positive and become significant as cell-period fixed effects are included (columns 2-4).

<table><caption>**Table A5:** OLS regressions of Social identity and cohesion on Ethnic conflict – Time-precise, external and state 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>Time-exact conflict</td><td>0.094<br>(0.039)**</td><td>0.088<br>(0.039)**</td><td></td><td></td><td></td><td></td></tr><tr><td>External conflict</td><td></td><td></td><td>0.125<br>(0.038)***</td><td>0.121<br>(0.038)***</td><td></td><td></td></tr><tr><td>State conflict</td><td></td><td></td><td></td><td></td><td>0.086<br>(0.036)**</td><td>0.083<br>(0.036)**</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>103,588</td><td>101,378</td><td>103,588</td><td>101,378</td><td>103,588</td><td>101,378</td></tr><tr><td colspan="7"><strong>SOCIAL COHESION</strong></td></tr><tr><td>Time-exact conflict</td><td>0.091<br>(0.027)***</td><td>0.080<br>(0.026)***</td><td></td><td></td><td></td><td></td></tr><tr><td>External conflict</td><td></td><td></td><td>0.087<br>(0.028)***</td><td>0.077<br>(0.027)***</td><td></td><td></td></tr><tr><td>State conflict</td><td></td><td></td><td></td><td></td><td>0.069<br>(0.025)***</td><td>0.061<br>(0.024)**</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><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><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><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. *Time-exact conflict* includes only incidents of ethnic violence whose date is exactly identified by ACLED; *External conflict* is defined as conflict not involving two armed groups belonging to the same ethnolinguistic cluster; and *State conflict* includes also clashes between state forces and ethnic groups. *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.
<figure><img src="image_14.png" /><figcaption>**Figure A6:** OLS regressions of Social identity and cohesion on Ethnic conflict – Future conflict activity (Placebo)</figcaption></figure>

*Notes:* The figure reports point estimates, alongside their 90% confidence intervals, of ethnic conflict before (Conflict) and after (Placebo) the relevant Afrobarometer interview. 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.

Afrobarometer interview rounds 3 to 5.<fnref n="19" /> Placebo coefficients are always statistically indistinguishable from zero and smaller than the actual conflict dummy. Point estimates on the baseline conflict measure remain instead positive and significant.

## D.5 Migration

This last subsection addresses concerns related to the possible confounding effect of migration. On the one hand, population pressures

<footnote n="19">19. Formally, while the baseline conflict dummy consider events prior to $ab_{k,t}$, the earliest interview date of Afrobarometer round $t \in \{3, 4, 5, 6\}$ in cell $k$; placebo conflict is aggregated in the $X$ months successive $ab_{k,\tau}^{\text{late}}$, the latest interview date of Afrobarometer round $\tau \in \{3, 4, 5\}$ in cell $k$.</footnote>
can affect the likelihood of conflict.<fnref n="20" /> On the other hand, warfare often results into massive displacement of people.<fnref n="21" /> If these migratory patterns correlate with social cohesion, there are sample selection issues that can invalidate inference. As it has also been shown empirically in the case of the Nepalese “People’s war” (1996-2006), less pro-social people might be more likely to flee away from their communities at times of conflict (Gilligan et al. 2014). If this is the case, migration, not ethno-centrism, might actually explain the positive impact of warfare on social cohesion here found. Yet, in the present context this seems unlikely. First, cell-time fixed effects should account for migratory patterns that are not specific to some given ethnic groups. Second, most of the ethnic violence observed in the sample concerns low-intensity conflicts, which are unlikely to trigger mass-scale migration.

In order to give empirical support to these intuitions, I analyse the effect of ethnic conflict over changes in ethnic population shares. I thus collapse the sample at the ethnolinguistic-cell-period level and drop all the ethnic-cell pairs not appearing throughout the four periods. Hence, I define ethnic share change as $\Delta E_{tn\_share}_{e,k,t} = \pi_{e,k,t} - \pi_{e,k,t-1}$, where $\pi_{e,k,t}$ is the share of respondents belonging to ethnolinguistic group e in cell-period $k-t$. If ethnic conflict does not result into migration, we would expect the change in these shares to be unaffected by warfare exposure. Table A7 performs a t-test for difference in means across the two sub-samples determined by the ethnic conflict dummy. The alternative hypothesis that the mean change is different across the two groups is rejected.

If conflict does not affect the ethnic composition of the various cell-period units, it might still be the case that it changes the internal composition of ethnic groups. In particular, conflict might lead to gender-specific migration, with women disproportionally leaving war-torn areas. The different gender balance might then influence the social cohesion dynamics at the local level. I thus analyse changes in male ratios, which are defined as $\Delta Male\_ratio_{e,k,t} = Male\_ratio_{e,k,t} - Male\_ratio_{e,k,t-1}$, where $Male\_ratio_{e,k,t} = \#male\_respondents_{e,k,t}/\#respondents_{e,k,t}$. The second row of Table A7 substantiates that gender composition is relatively unaffected by conflict: while male ratios are slightly higher among ethnic groups

<footnote n="20">20. For example, Hendrix and Salehyan (2012) and Seter (2016) discuss how weather calamities can result into migration towards areas spared by the climatic disaster. The resulting population pressures can then put a strain on local resources, ultimately leading to open conflict.</footnote>

<footnote n="21">21. According to the World Bank, in 2016 across sub-Saharan Africa, there were more than 12 million internally displaced people for reasons related to conflict and violence.</footnote>
<table><caption>**Table A7:** T-test for differences in population share and male ratio changes across peaceful and violent ethnolinguistic groups</caption><thead><tr><th></th><th>Mean Peace</th><th>Mean War</th><th>p-value</th><th>N peace</th><th>N war</th></tr></thead><tbody><tr><td>Δ Ethnic share</td><td>0.00</td><td>-0.01</td><td>0.47</td><td>2265</td><td>81</td></tr><tr><td>Δ Male ratio</td><td>0.00</td><td>0.03</td><td>0.20</td><td>2265</td><td>81</td></tr><tr><td>Ethnolinguistic groups</td><td>130</td><td>33</td><td>.</td><td>3019</td><td>109</td></tr><tr><td>Cells</td><td>467</td><td>61</td><td>.</td><td>3019</td><td>109</td></tr><tr><td>Periods</td><td>4</td><td>4</td><td>.</td><td>3019</td><td>109</td></tr></tbody></table>

*Notes:* The sample is a balanced panel with each ethnolinguistic-cell pair observed in all Afrobarometer rounds 3 to 6.

exposed to conflict, this difference is statistically insignificant.

Further empirical support to the average differences shown at Table A7, is given in Figure A7, reporting point estimates and 90% confidence intervals from the regressions:

$$ \Delta \text{Mig}_{e,k,t} = \alpha + \theta_{e,k} + \Phi_t + \gamma C_{e,k,t} + \varepsilon_{e,k,t} $$

Where: $\Delta \text{Mig}_{e,k,t} \in \{\Delta \text{Etn\_share}_{e,k,t}, \Delta \text{Male\_ratio}_{e,k,t}\}$, $\theta_{e,k}$ are cell-ethnic fixed effects, $\Phi_t$ are period dummies, and $\varepsilon_{e,k,t}$ are robust standard errors. All the estimated coefficients are small and statistically indistinguishable from zero.
<figure><img src="image_15.png" /><figcaption>**Figure A7:** OLS regressions of Ethnic share and Male ratio changes on Ethnic conflict</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 allowed to be heteroskedastic. The sample is a balanced panel of cell-ethnic units.

# E Mechanisms - Robustness tests

## E.1 Distant conflict

The model investigating the impact of remote conflict activity on social identity and cohesion is:

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

Where $C_{e,n(k),t}$ is the ethnic conflict indicator computed over the eight-cell neighbourhood of cell $k$. The meaning of the other variables is the same as in the paper. The coefficient of interest is $\phi$, which is expected to be positive.

Figure A8 checks robustness to: (i) alternative conflict measures, namely, the count of conflict events and of months with at least one event; (ii) alternative actor-ethnicity match procedures, namely, that excluding imprecise matches and that dropping matches involving subgroups of Afrobarometer languages; (iii) alternative samples, namely,
that dropping ethnolinguistic groups not present in all Afrobarometer surveys and that using the Afrobarometer question on ethnicity as marker defining ethnolinguistic groups; (iv) a binary recoding of the outcome variables; (v) the inclusion of further individual-level covariates, namely, two sets of socio-economic and political controls. Results are overall robust, albeit the impact of distant conflict on social cohesion becomes at time marginally insignificant.

The last panel (Remote conflict) of Figure A8 explores alternative distances over which external conflict is aggregated. In particular, I consider ethnic conflict aggregated over the 24-cell neighbourhood of each observation. This measure includes conflict activity taking place very far away from Afrobarometer respondents (up to 233km). Unsurprisingly coefficients are smaller and statistical significance is not always achieved. Overall, however, the consistency of the sign is indicative that identity dynamics are at play: it is hard to see how conflict can directly affect security and economic incentives when taking place over 200km away.

## E.2 Ethnic vs non-ethnic conflict

The model comparing ethnic and non-ethnic conflict is the following:

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

Where all the variables have the same meaning as in the paper. The coefficients of interest are $\beta$ and $\psi$, whereby we expect the former to be greater than the latter.

Figure A9 checks robustness to: alternative actor-ethnicity match procedures, sample-wide restrictions, binary recording of outcome variables, and the inclusion of additional controls.<fnref n="22" /> All the regressions include the usual set of fixed effects and individual-level covariates. The point estimates for ethnic conflict ($\beta$) are always positive and, with few exceptions, statistical significant. The coefficient on non-ethnic conflict is instead mostly negative and statistically indistinguishable from zero.

<footnote n="22">22. Note that we can not check robustness to the event-based definition of conflict inasmuch as ethnic conflict events and non-ethnic conflict events are collinear to the cell-period fixed effects.</footnote>
<figure><img src="image_16.png" /><figcaption>**Figure A8:** OLS regressions of Social identity and cohesion on Distant ethnic conflict – Robustness tests</figcaption></figure>

Notes: The figure reports point estimates, alongside their 90% confidence intervals, of distant 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. *Distant conflict* is spatially aggregated on the 8-cell neighbourhood of each observation. *Remote conflict* is instead spatially aggregated on the 24-cell neighbourhood of each observation. In the eight panel (Additional controls), regressions labelled *FE* comprehend fixed effects, individual and socio-economic controls; regressions labelled *FE and controls* further add political covariates. Standard errors are clustered at the cell-ethnic-period level.

## E.3 Heterogeneous effects

The model investigating heterogeneous effects with respect to cell-period ethnic polarisation is:

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

Where all the variables have the same meaning as in the paper. Interest lies in the joint effect of conflict and ethnolinguistic polarisation (ELP): the parameter $\alpha$, which we expect to be negative.

Figure A10 checks robustness to: alternative conflict measures, alternative actor-ethnicity match procedures, sample-wide restrictions, binary recording of outcome variables, and the inclusion of additional
<figure><img src="image_17.png" /><figcaption>**Figure A9:** OLS regressions of Social identity and cohesion on Ethnic and non-ethnic conflict events – Robustness tests</figcaption></figure>

*Notes:* The figure reports point estimates, alongside their 90% confidence intervals, of ethnic and non-ethnic conflict. All regressions include: cell×period and ethnic×period fixed effects, as well as individual controls for age and its square, gender, and urban residence. In the last panel (Additional controls), socio-economic and political covariates are added. Standard errors are clustered at the cell-ethnic-period level.

controls. All the regressions include the usual set of fixed effects and individual-level covariates. Apart for the regressions employing the alternative marker for ethnolinguistic groups, the ELP-conflict interaction is always significantly negative.
<figure><img src="image_18.png" /><figcaption>**Figure A10:** Heterogeneous effects by ELP – Robustness tests</figcaption></figure>

*Notes:* The figure reports point estimates, alongside their 90% confidence intervals, of ethnic conflict and its interaction with the index of ethnolinguistic polarisation. All regressions include: cell×period and ethnic×period fixed effects, as well as individual controls for age and its square, gender, and urban residence. In the last panel (Additional controls), socio-economic and political covariates are added. Standard errors are clustered at the cell-ethnic-period level.