You will hear of skirmishes and rumors of skirmishes:Mobile Communication Surrounding Political

Violence∗

Daniel Berger†1, Shankar Kalyanaraman‡2, and Sera Linardi§3

1Department of Government, University of Essex2Department of Computer Science, New York University

3Graduate School of Public and International Affairs, University of Pittsburgh

June 1, 2016

Abstract

Small-scale political violence is not well-understood, despite its insidious effect oninter-group relations and potential escalation into national-level politics and economics.Some regard it as unanticipated, but others argue that this is due to lack of information byoutside observers. This paper provides the first quantitative evidence that low-level violentincidents are preceded by widespread changes in local behavior. By combining networktraffic from Orange Telecom and peacekeeping records from the United Nations Operationsin Côte d’Ivoire, we find an increase in local mobile phone usage driven by a higher thannormal number of callers, conversations between phones that share the same provider, andactivity between geographically closer antennas. This unique communication pattern varieswith the intensity of violence and is distinct from the anticipation of positive events, suchas football matches and festivals. Our findings are consistent with psychological researchon changes in communication networks in stressful situations and suggest that some of themechanisms necessary for rumor emergence are in place.

∗The authors would like to thank Ismene Gizelis, Jenny Aker, Steven Landis, Taylor Seybolt, Burcu Savun,Michael Poznansky, and participants at the GPS Seminar (Pittsburgh), FSU BEE workshop, University of Essexseminar, EPSA 2015 panel on African politics, MPSA 2015 panel on electoral violence, ASSA 2016 panel on mobilephones, GDDA workshop (Duke University), and the UCSD Social Media Forecasting Workshop. We appreciatevaluable RA work from Erin Carbone, Matthew Wong and Linardi’s 2015 lab group. All errors are our own.†dberger@essex.ac.uk‡shankar@cs.caltech.edu§linardi@pitt.edu

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While politics in developed countries is figuratively referred to as a blood sport, in developing

democracies this can be literally true. Armed youth wings of political parties attack each

other. Opposition supporters remain targeted long after elections. These “everyday” political

conflicts (Blair et al. (2014)), which are often too small to be included in media reports or

civil-war databases,1 are not well-understood. Research, mostly through careful qualitative

studies, have yet to determine whether incidents are random (Cramer (2007)) and hence impossible

to systematically study or deter, or if the perception of randomness arises from observers’ lack

of local information (Kalyvas (2003)). It is important to make progress on this debate: these

skirmishes can have outsized impacts on national economy and politics (Balcells (2011)) by

inducing capital flight (Sambanis (2004)) and inflaming group attachments (Wilkinson (2004)).

Our study, an empirical analysis combining mobile call records in Côte d’Ivoire with United

Nations peacekeeping records, provides the first quantitative evidence that a large number of local

residents use their phones differently ahead of political violence, supporting theories that these

incidents may not be a complete suprise on the ground.

Our focus on reconstructing the experience of political violence among local residents through

their mobile usage patterns is different from, but complimentary to, the recent literature on the

causal impact of information and communication technology (ICT) on violence (Dafoe & Lyall

(2015)). The literature has found both positive and negative associations between activation of

new cellular towers and incidences of violence,2 leading to debates on whether mobile technology

help combatants organize violence or aid non-combatants prevent or escape violence. Both

interpretations rely on the yet-to-be tested assumption that ahead of violent incidents, relevant

information is present locally and is communicated through cellular networks. Cellular coverage

data cannot test this hypothesis (Shapiro & Weidmann (2015)); in general, few quantitative

sources exists on behavior ahead of violent incidents.3 This void cannot be completely filled by

1The PRIO /UCPD database includes conflicts with at least 25 battle deaths (Themnér & Wallensteen (2011)).2Pierskalla & Hollenbach (2013) and several other papers find a positive association; Shapiro & Weidmann

(2015) find the opposite. Little (January 2015) and others study the issue more theoretically.3Studies are often focused on post-violence outcomes. For example, Callen et al. (2014) looks at long term effect

of violence on risk preferences, while Kasara (2011) investigates displacement after large scale violence.

2

ex post qualitative interviews since personal reconstruction of violence is plagued by biases.4

Call record data, however, contains a continuous, spatially precise stream of passively collected

behavioral data that may finally pierce the fog around the days immediately surrounding violence.

We first investigate whether locals are surprised by violent incidents by looking for the

jump-decay pattern (a sudden spike in call volumes) that has been found to accompany surprising

events (Bagrow et al. (2011)). We then delve into the local mood surrounding the incident. To

build our framework, we use the research in psychology on communication behavior. Briefly,

negative emotions (e.g a decrease of personal control and certainty) induce a need for support

from one’s core social network. This leads to intensive, taxing communication among close,

reciprocal links that ceases soon after the crisis has passed, resulting in a narrowing of network

and an asymmetric mobilization-minimization pattern (Taylor (1991)). Positive emotions results in

the opposite communication pattern: A broadening of communication network and changes that

are more symmetric around the incident. Though specific call destinations are not available in our

data, preferential fare for within-network calls offered by the many competing mobile providers in

Côte d’Ivoire results in a market where core social networks share the same mobile provider.5 A

narrowing of network would therefore be reflected as a shift towards same-network calls.

Our results are as follows. We see a lack of jump-decay patterns in local calls on the day of

an incident. Instead, within the four days leading up to an event, daily call volumes increase

on average by 10% while the number of active mobile phones increases by 6%, an increase

of more than a thousand phones for the average subprefecture. There is no detectable influx

of phones, suggesting that these changes are driven by local residents. Characteristics of the

heightened mobile activity suggest widespread negative emotions ahead of the incident. There is

a dramatic (16%) increase in same-network calls starting at least four days ahead of violence,

directed towards geographically closer phones, and shorter conversations. The increase peaks on

the day of the incident before returning to normal shortly afterwards: an asymmetric mobilization

4Blattman (2009) notes “forgetfulness, ‘telescoping’, and simple dishonesty.” in responses to survey on violence.See also Schacter & Coyle (1997).

5These fares are indeed marketed as “ . . . ‘a welcoming offer’ to invite friends to use the . . . network”. Seehttp://www.ainewswire.com/?p=336).

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around the incident. This unique communication pattern strengthens as we consider only events

with fatalities and attenuates as geographical distance to the incident location increases. We also

show that these patterns are entirely different from anticipation of exciting but non-violent events

such as sporting matches or festivals,6 suggesting that we are not mistaking general anticipation

for call patterns unique to violence.

The behavioral changes before events implies that low-level political violence is not analogous

to terrorist bombings, which are designed to be - and are usually successful at being - an absolute

surprise to locals (Bracken et al. (2005)). Rather, many locals appear to have new reasons to use

their phones in the days preceding incidents, suggesting that they know, or sense, that something

is different. This mobile usage pattern is highly consistent with widespread negative emotions,

suggesting that the majority of local residents experience anxiety and a loss of control in the

days preceding a violent incident. Our findings are consistent with the observations of Senaratne

(1997) and Kalyvas (2003) that seemingly indiscriminate low-level violence may have actually

been predictable given local information. Though we are not able to observe the content of

conversations, the patterns we see supports the ICT hypothesis that cellular networks facilitate

information sharing ahead of violence. In fact, the urgent concentration of conversations within

primary networks compliments several strands of the literature: Horowitz & Varshney (2003)’s

observation that violence is often preceded by rumors, Bhavnani et al. (2009)’s model showing

that rumors are more likely to spread when agents with strong ties communicate, and Wilkinson

(2004)’s link between violence and increasing in-group attachments. The mobile pattern we see

may be one of an environment ripe for rumor emergence, and indeed, several news sources in

Côte d’Ivoire indeed report at the time that “. . . rumours are spreading like wildfire”.7

We should be clear what this study does not speak to. First, even though we observe evidence

strongly suggestive of widespread tension before violent events, we remain agnostic about the

details of the causal chain: the tension could be a result of observing activities related to the

6For example, antennas close to Division 1 football matches (Côte d’Ivoire Ligue 1) record more out-of-networkcalls to geographically distant antennas before and after the match.

7See http://www.abidjanlivenews.com/Ivory-Coast-hidden-battle_a214.html

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violent event, or it could be the cause of violence.8 Second, even though we do see that these

communication patterns increase in magnitude as the intensity of violence increases, we are not

comfortable extrapolating to large-scale battles with our data, which does not cover periods of

actual war. Instead, we show that these small, easily overlooked incidents may be enough to

activate the micro-level mechanisms that can spread unease and insecurity. Finally, we leave the

challenge of using mobile phone patterns to predict violence for future research.

1 Communication around Non-Neutral Events

Since Schachter (1959), psychologists have long documented that non-neutral experiences (such

as those resulting in perceptions of loss or gain in personal control) induce needs to connect with

others in particular ways. The connection between crises and the need for social support have

been replicated in circumstances as diverse as the Three Mile Island nuclear accident (Fleming

et al. (1982)), war combat, adverse changes in health, and bereavement (House et al. (1988)).

Anxiety arousing from events that induce loss of control or uncertainty (Marshall & Zimbardo

(1979)), creates the need for social support such as emotional understanding, information, and

tangible assistance. As a result, people turn to those who can empathize and whom they can rely

on: their primary network and people with similar experiences (Thoits (2011)). The presence of

negative stimuli, therefore, activates smaller communication networks with redundant links (Shea

et al. (2015)). On the other hand, positive emotions, such as those emerging from events that give

individuals the perception of personal control (Burger (1989)), momentarily broaden people’s

capacity to approach and engage with others (Fredrickson (2001)). As a result, individuals

exposed to positive stimuli activate larger and more sparsely connected social networks.

The main difference between social contacts activated by positively and negatively experienced

events can therefore be seen in two dimensions. The first contrasts the broadening or narrowing

of communication networks. Second, because negative events are taxing, ”once the threat of

the negative event has subsided, counteracting processes are initiated that reverse the responses

elicited at the initial stage of responding” (Taylor (1991)). This asymmetry around anticipated

8Both of these pathways, however, imply that low-level violence is not random and unpredictable.

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negative events is known as the mobilization-minimization hypothesis. On the other hand, the urge

for social contact in anticipation of positive events persists after the event has passed.

The framework above predicts that the occurrence of a large, surprising event results in

widespread anxiety that induces a large number of individuals to suddenly and simultaneously seek

social support. Indeed, Bagrow et al. (2011) find huge spikes in calls in the immediate aftermath

of plane crashes and earthquakes. Supporting Taylor (1991), this spike quickly attenuates in the

hours and days afterward. We follow the literature in referring to this as the jump-decay pattern.

Unlike the gradual increase ahead of scheduled events such as religious festivals and concerts,

mobile phone activity before surprising events is completely normal. The type of social contact

activated in response to events in existing research on mobile phones also appears consistent with

this psychological framework. Blumenstock et al. (2014) find that after earthquakes, people use

mobile phones to reach out to those they share strong preexisting reciprocal links with and to

those who are geographically closer to them. This is similar to what Cohen & Lemish (2005)

and Bracken et al. (2005) observe after terrorist attacks and distinct from what Pierskalla &

Hollenbach (2013) observe in rebel organizing where mobile activity is directed to geographically

distant locations.

Following Bagrow et al. (2011), this paper first looks for the jump decay pattern in call

volumes to establish whether low-level political violence is a surprise; in other words, that

no unusual activity is detectable ahead of the event. We next investigate whether changes in

calling activity associated with event indicate a negative or positive mood. An inward orientation

(narrowing of network to nearby primary contacts) and the asymmetric mobilization-minimization

pattern around the incident is consistent with the former. When accompanied by a large increase in

the number of callers and more urgent of calls (reflected in duration or timing), this pattern suggest

widespread fear and anxiety, which indicates that local residents are experiencing a loss of control

in their environment. On the other hand, a broadening of communication network to far-flung

places with heightened volume of calls without any detectable change in the number of callers is

consistent with coordination of violence, which involves intensive communication among a small

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number of plotters (Drozdova & Samoilov (2010)).9 We also explore the movement of phones as a

proxy of displacement. An outflow of phones accompanying markers of negative emotions would

suggest that the threat of violence is clearly observable and severe enough for local residents

to move out. An influx of phones (and therefore the owners of said phones) accompanying

coordination activity into an area before violence occurs would suggest that outsiders are coming

in, possibly to instigate the violence.

Note however, that pre-event communication changes can not only be a direct reaction to

the threat of violence, but also a cause of a violent incident, or a reaction to a non-violent event

that sparked both excitement and violence.10 Adjudicating between the first two has been part

of an ongoing research agenda and is not one our data is suited to undertake due to lack of

information about the content of the conversation.11 However, the third possibility suggests that

we may be mistaking general anticipation of exciting events for a communication pattern unique

to violence. We will therefore address this in two ways. First, we will examine whether mobile

usage around non-violent exciting events such as football matches and festivals are similar to

what we see around violence. Second, we will vary the intensity of violence and observe whether

the effect size changes accordingly. Finding different communication patterns around anticipation

of non-violent events and the appropriate comparative statics in coefficient magnitudes would

alleviate our concerns of omitted variable bias.

2 Data and Methods

This section begins by describing violence data from the UN and our choice to use it. We

demonstrate why it reflects a wider and more relevant range of political violence than other

9Note that because the number of instigators will be far smaller than uninvolved civilians, signals from thegeneral population are likely to swamp any possible signal from instigators. Thus a spike in calls with no increasein callers indicate lack of observers, but a broad-based increase in mobile activity does not indicate the lack ofinstigators.

10Bhavnani et al. (2009) suggest that violence is sparked by rumors about out-group aggression. Since peoplemake choices that amplify their perceptions of risk in stressful situations (Lerner & Keltner (2001)), these types ofrumors may result in a self-fulfilling prophecy.

11For example, Trnka (2008) and De Jong (2010) illustrate how conflicts produce rumors. On the other hand,Bhavnani et al. (2009) suggest that violence is sparked by rumors.

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options, then describe how we can use mobile phone data to understand behavior at the local level.

Finally, we describe the details of our estimation strategy.

2.1 Violence in Côte d’Ivoire

Côte d’Ivoire is a West African country of approximately 22 million inhabitants with similar

numbers of Muslims and Christians; the former were concentrated in the poorer and less densely

populated North and the latter in the more developed South. After Côte d’Ivoire achieved

independence from France in 1960, it was led by Félix Houphouët-Boigny from 1960 to 1993

whom, despite leading a single-party state, maintained peace and stability in the country. After his

death he was succeeded by Henri Konan Bédié who won reelection in 1995. The country’s first

coup occurred in 1999, but civil war did not break out until 2002. The war displaced over a million

people. A ceasefire was signed in 2007 and persisted until 2011 when President Laurent Gbagbo

refused to step down after losing an election to Alassane Ouattara. Gbagbo’s unwillingness to

concede sparked the second Ivorian civil war, which caused another flood of displacement before

ending with the arrest of Gbagbo on April 11, 2011.12 While there are no incidents of high-level

violence (battles, massacres, or the like) since the arrest, significant low-level violence never went

away, and the Ivorian security situation continued to be precarious across the country (Internal

Displacement Monitoring Centre (2012, 2014)).

The most complete source of information about political violence in Côte d’Ivoire after the

second civil war can be found in reports from the United Nations Operation in Côte d’Ivoire

(UNOCI, or ONUCI in French). “ONUCI hebdo” (UNOCI weekly) is a weekly summary of

events in Côte d’Ivoire, with a focus on the “efforts, obstacles, expectations and successes” of the

UN post-conflict peacekeeping mission. Since UNOCI has a mandate to focus on the post-crisis

situation, this effectively filters for political violence. It captures a wide range of violence endemic

to post-conflict situations, such the typical type of violence in conflict databases (e.g. “6 people

killed in clashes between the army and local residents in Vavoua”), political events that do not

involve deaths (e.g. election day intimidation in Bonon), or targeted violence during political

12For an in-depth treatment of the war, see McGovern (2011).

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unrest (e.g. “clashes between herders and farmers”).13 The other natural sources of data are the

Uppsala Conflict Data Program (UCDP) and the Armed Conflict Location and Event Data Project

(ACLED). None of these conflict datasets report the vast majority of violence, which is made up

of assault, robberies, or even murders which do not relate to the political situation.14

Evidence from Ruggeri et al. (2013) suggest that the UN coverage will be more comprehensive

than other sources. The UN peacekeepers focus on areas where conflict has been historically most

explosive, hence reports are unlikely to under-represent the most violent areas. Furthermore,

events are reported up from local-level employees to district managers and then on to regional

managers which means that coverage will be significantly more uniform than datasets built from

newspaper reports, which are much more focused on the capital and large cities.15 Indeed we

observe that the UNOCI definition of violent events is similar to, but slightly more general than,

that used by UCDP. Checking the two datasets side by side for the period where they overlap, we

find that the UNOCI data is a superset of the UCDP data.16 ACLED is very different from both

UCDP and the UNOCI data. Events which both the UNOCI and UCDP place well in the outskirts

are placed in Abidjan, the economic capital of the country, and some events we observe are either

omitted or subsumed in reported events on nearby dates in other parts of the country. For these

reasons we avoid using ACLED data.

The UN usually records an exact date for each incident of violence they describe. However,

occasionally the description is ambiguous. Our core measure of violence consists of those events

where the UN reports an exact date. For robustness, we also create a second variable including

the more ambiguous events, using clues in the descriptions to infer the dates. This results in 44

13The last type, where the breakdown of order can permit the settling of scores that may not appear political tooutsiders, are exactly the types of violence that Kalyvas (2003) worries is systematically missed in studies of conflict.The UN peacekeepers heterogeneous, expanded categorization of political violence constitutes a hard test that biasesour results towards zero.

14The only source for such data would be Ivorian police records which will vary greatly in quality across time andspace.

15Dafoe and Lyall (2015) cautions agains positive measurement bias when cellular coverage is correlated withnews reported violence.

16Limiting UNOCI record of violence to fatal events produces a list identical to the UCDP data. In addition, theUCDP dataset terminates before the UNOCI dataset.

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incidents, listed in the Online Appendix.17 Some patterns are immediately obvious from a quick

examination of the data. While the events are spread over the entire time period, a few major

events affect the entire country. There were parliamentary elections on December 11, 2011 which

caused widespread violence. In late January and early February the national football team was

competing in the Africa Cup of Nations, during which violence was generally down. The coup on

March 21 and ensuing civil war in neighboring Mali did not spread violence in Côte d’Ivoire.

Violence occurs more on some days of the week than others. Sundays are by far the bloodiest day

of the week, accounting for 62% of violent events. This pattern is exacerbated by the fact that the

most violent day in our sample is election day, a Sunday. Monday is the second bloodiest, with

24% of all events.18 Figure 1, Panel A, overlays the dates and locations of these violent events

over a map of Orange Telecom mobile activity across the country.

[FIGURE 1 ABOUT HERE]

2.2 Mobile phone data

The paucity and disorganization of official data provides little help in understanding how ordinary

citizens across Côte d’Ivoire have experienced these violent incidents. Before the disputed

2014 census, the last census was in 1998.19 Administrative subdivisions of the country are

constantly changing; for example, there have been seven reorganizations of the country into

sub-preferectures just in the past fifty years. National-level estimates of displacement by the

Displacement Monitoring Centre and occasional news coverage of citizens’ reactions to violent

flare-ups exist, but there is little systematic data at finer temporal and spatial levels.

However, the period between the first and the second civil wars (2007-2011) saw rapid

adoption of mobile technology. Not only has the initial concentration of mobile phones in the

male urban middle class disappeared (Aker & Mbiti (2010), Gillwald et al. (2010)), but by 2011,

17Events without a specific location and time, such as reports of tensions along the Liberian border, are excluded.Our analysis will distinguish between events with fatalities and events with uncertain dates from our core measure.

18Our results are robust to excluding Sundays, the period around the election, and more. See the Online Appendixfor more details.

19The opposition boycotted the 2014 census claiming that it was systematically overcounting governmentsupporters. Whether or not this is true, the boycott itself introduces additional biases to the census.

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coverage has become remarkably uniform and utilization rates are high. Afrobarometer finds

in 2011 that 92% of Ivorians have a mobile phone in their household; 82% of adults make at

least one mobile phone call per day (Mitullah & Kamau (2013)). Mobile phone records have

become the most geographically and temporally comprehensive source of data on the daily lives

of Ivorians.

The many mobile service providers operating in Côte d’Ivoire are all foreign companies

without regional bases or distinct subscriber profiles.20 The network of Orange cell towers is

presented in Figure 1, Panel B. Subscribers are identified with a Subscriber Identification Module

(SIM) card, a plastic card embedded in the phone. All companies offer similar features with

deeply discounted fares for within-network calls;21 choices of providers are therefore mainly

driven by minimization of cross-network calling fees, resulting in friends and families sharing the

same network. However, because switching between carriers is as easy as swapping out SIM

cards, African subscribers on average have 1.96 SIM cards on their phone, though only one per

company. Agence de Télécommunications de Côte d’Ivoire estimated that in 2011 there were

15.8 million SIM cards operating in the country, which maps to about 8 million phones.

This paper examines reaction to incipient violence by examining two unique Orange Telecom

datasets made available as part of the Data For Development (D4D) challenge (Blondel et al.

(2012)). Orange is the service provider for 4 million SIM cards; there is one Orange SIM in every

two phones. These datasets cover a period of 150 days of call activity in Côte d’Ivoire beginning

on December 1, 2011 and ending on April 30, 2012, seven months after the second civil war.

Due to the degree of mobile adoption in the country (discussed earlier), we are fairly confident

that call records data does not overrepresent the priviledged. We also have no reasons to suspect

that Orange Telecom users are unique in any way, and note that given the practice of owning

multiple SIM cards, SIM cards can only be mapped to individual subscribers when restricted

to a single provider. However, the post-war time period could cause people to be especially

20The three largest are MTN (South Africa), Orange Telecom (France), and Moov (Emirates).21This is common across Africa; in Kenya Orange and their competitor Zain charges KES 7/min for in-network

calls and KES 14/min for out-of-network calls. See http://www.wap.ratio-magazine.com/inner.php?id=220

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responsive to signs of violence.22 In addition, lingering internal displacement from the civil war

could cause more separation among families than in normal times, leading to a higher substitution

of telephone use for face-to-face communication. Using a case where the signal may be at its

strongest might normally raise questions of external validity. However, we believe that for this

first attempt to relate mobile phone activity to small scale violence, any possible magnification of

the effect is a feature.

We turn our attention now to the two datasets. The first one, which we will refer to as the

subscriber data, is a record of all (400 million) calls made from a random sample of 500,000

Orange SIM cards.23 Each record in this dataset contains a subscriber ID, her location, and

time of call for any (in-network and out-of-network) calls made by that subscriber. For privacy

reasons, subscribers’ locations are aggregated to the subprefecture level, which follows the 2008

organization of 258 subprefectures.24

We generate several variables from the subscriber dataset. The first, All Calls, is the total

number of calls from a given subprefecture on a given day. The second, Callers, is the number of

different SIM cards which register at least one call on a given day. We also generate a measure of

mobility, Moving in, which reflects the daily net flow of phones into each subprefecture.25 This

1:8 sample of all Orange SIMs records on average 12,203 daily calls in a subprefecture. This

comes from 1,293 different subscribers who each makes about 10 calls.26 Average net mobility is,

reassuringly, approximately zero, as each subscriber who moves into one subprefecture is moving

out of another.22Post-conflict situations are tenser in general (Hartzell & Hoddie (2003)). However, it is also possible that people

become inured to conflict, which would bias against our findings.23Phones which appear to be used for business purposes (such as by people who run communications stalls) were

excluded from the dataset. However, this is still an imperfect mapping of SIMs to individuals. If someone switchesbetween two SIM cards, we only observe calls from the Orange SIM. Similarly, we observe only one subscriber iftwo people shares a phone.

24In this organization, the Ivorian economic capital Abidjan, which at a population of 7.1 million is one ofthe largest cities in West Africa, is treated as a single subprefecture. The second largest city, Bouaké, and theadministrative capital city, Yamoussoukro, have population of 650,000 and 355,573, respectively. Because Abdijan issuch an outlier in size and density that cannot be teased apart by the data, we will drop it from our analysis.

25See details about how this variable is computed and how it compares to known migration in the OnlineAppendix.

26Complete summary statistics are available in the appendix.

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The second dataset, which we will refer to as the antenna data, is not a random sample. It is

a complete hourly record of transmission between all pairs of Orange antenna towers. Unlike

the subscriber data, exact longitudes and latitudes of antennas are provided but no information

about SIM cards (and hence, subscribers) are provided. Mapping the 844 antennas to their

corresponding subprefectures (dropping units that recorded no activity) reveals that there are on

average 3.59 antennas for each subprefectures. Comparing effect sizes across the two datasets

requires care: minimization of cross-network fees implies that within-network calls make up

the majority of calls. We therefore prefer to focus on percentage changes in activity rather than

magnitudes when comparing results from the two datasets.

The first variable, In-network Calls, is the total number of calls made from a given antenna on

a given day. Again, note that since we only observe Orange to Orange transmission, this captures

within-network calls. Second, Call Duration is the average length of calls on a given day. Next,

we generate Fraction Nearby as the fraction of within-network calls made to geographically

nearby phones, which we define as being within 0.5◦ (35 miles), about one hour of driving.27

Finally, Fraction Daytime is the fraction of calls in a day made between 8 am and 5 pm. The

average Orange antenna handles 2,830 Orange-Orange calls per day, which average 137 seconds

apiece. This corresponds to a daily transmission of 96.2 hours of conversation, with 64% of the

calls made during the day. 54.5% of the calls are directed to a nearby phone.

To connect the UNOCI data with the mobile phone data we generate an indicator for units near

violent incidents. Again we use a distance of 0.5◦ as our initial definition of "nearby" and vary it

in robustness tests. First we collapse both cellphone datasets to the day level, the finest temporal

resolution of the UNOCI data. For each subprefecture s and date t, we define a binary variable Vst

which is 1 if a violent incident is recorded by UNOCI as occurring exactly on date t and within

0.5◦ of the centroid of subprefecture s. We then generate a set of leads and lags of Vst which we

denote as V Dst where D indicated the number of days after the violent event. Negative values of D

therefore indicate future violence, while positive values indicate past violence. Violent incidents

are rare events, affecting on average 1% of subprefecture-days, which translates to once every 3-4

27GoogleMaps estimates.

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months for a given area. For antenna data, V Dit analogously indicates violence close to antenna i

exactly D days after date t. The frequency of violence is similar in both datasets, suggesting that

there is no correlation between violence and antenna location.

These two datasets provide complementary perspectives on calling patterns. Tracking the

number of active SIM cards in the subscriber data allow us to separate whether changes in calls

are widespread or concentrated among a few. It also allows us to see how individuals move around

in response to violence. However, it is a random sample of only 12.5% of Orange subscribers, is

aggregated at a rather coarse level, and is silent on call destination. The antenna dataset fills that

gap, capturing all within-network activity of Orange antennas. From this data we can back out

average call durations and aggregate call destinations, though we are only able to map them to

antennas, not individual SIM cards. Finally, given its finer spatial resolution of longitude and

latitude, the antenna data may be more suited for examining the comparative statics of change in

calls given distance to violence.

2.3 Empirical Strategy

We regress the call variables presented in section 2.2 on the violence measures from section 2.1.

The data are nested both in time and space. Antennas, indexed by i, are nested in subprefectures,

indexed by s, which are further nested in region, r. Similarly, days are indexed t, and are nested

in w, days-of-the-week (e.g Monday, Tuesday). We subscript variables with the finest level at

which they vary.28 Call-related variables on day t are thus cit or cst , depending on whether we are

working at the antenna i or subprefecture s level.29 Our core specification of OLS with two-way

fixed effects clustering standard errors on the geographical units is thus:

28Thus, day fixed effects are δt while day-of-week fixed effects are δw, instead of referring to both as δtw.29It is initially plausible that the correct specification includes a lagged dependent variable rather than a static

model with fixed effects. To check for this, we first look at the within-unit autocorrelation of the call variables andtest for unit root. We find no higher order autocorrelation and the Dickey Fuller test rejects the null of a unit rootconvincingly. We see evidence of minor first-order autocorrelation, significantly less than 0.3, and no higher orderautocorrelation. The Dickey Fuller test returns an inverse χ2(1682) of 23,500, against a 0.001 probability level of1,867, rejecting the null of a unit root convincingly. See the online appendix for more details.

14

cit = αi ++L

∑D=−L

βDV D

it +δrt + εit (1)

cst = αs ++L

∑D=−L

βDV D

st +δrt + εst (2)

Our main independent variables are V Dit and V D

st . Recalling Section 2.2, these indicate whether

a given antenna or subprefecture centroid was near a violent event exactly D days after date t. The

parameters of interest are the β Ds, which represent changes in call activity in a given location

exactly D days after violence. Significant β D for D < 0 indicates anomalies in communication

behavior ahead of violence, significant β 0 indicates changes during an incident, and β D for D > 0

indicates changes afterwards. To determine the optimal number of leads and lags (−L,+L) for the

main independent variables, V Dit and V D

st , we start by considering an eight day window (−8,+8)

around the violence and then shrink the window one day at a time. For each specification we then

test the joint hypothesis that there is no change in the coefficient more than n days before the

violence.30 Once we include the fourth lead we cannot reject the null; all specifications therefore

include 4 leads and 4 lags of violence.

To account for time, we consider several facts about Côte d’Ivoire. First, violence is

concentrated on certain days of the week. Second, certain dates experience events common to the

entire country (e.g, elections, national team football). Third, observances like Christmas are more

important in the Christian south than the more Muslim north. We therefore use day-region fixed

effects, δrt , a complete set of date (not day-of-week) dummies interacted with regions. They

account for regional weekly patterns in addition to shocks common to the entire country while

allowing individuals in different regions to respond differently to some shocks. Time-invariant

characteristics (e.g. area) are captured by the unit fixed effects, αi or αs, which also implicitly

control for slow-changing correlates of phone use, such as population density.31

We examine the subscriber dataset before the antenna dataset. In both we look for either the30See the online appendix.31Recent estimates of the population distribution vary significantly. The last non-partisan census was conducted in

1998. We therefore prefer fixed effects to control for time-invariant local characteristics.

15

jump-decay pattern (β D = 0 for D < 0, β D 6= 0 for D≥ 0) or evidence of anticipation (β D 6= 0

for D < 0). Next we explore whether changes are wide-ranging or narrowly focused by examining

the effects on numbers of callers. We will also look for clues of whether changes are more

consistent with coordination activity from a small group of instigators or efforts from the larger

population to cope with tension. The former would be consistent with an outward orientation in

call destination (β D < 0 for In-network Calls and Fraction Nearby) without a detectable change

in the number of callers (β D = 0 for Callers). The latter would be consistent with an inward

orientation in call destination accompanied by an increase in callers. To test the asymmetry in

anticipation we do a joint F-test of the coefficients after the event (β D 6= 0 for D > 0) and one for

the coefficients before the event (β D 6= 0 for D < 0). Positively experienced events should be

somewhat symmetric (similar level of activation in anticipation of the event and afterward) while

negative events would be asymmetric (higher activation in anticipation of event, quickly ‘moving

on’ afterwards).

3 Results

Behavior changes near violence. We first show that low-level violence is preceded by local mobile

activity consistent with widespread tension. Then we investigate whether the pattern reflects

anticipation of general events, not just violence. Analyzing mobile usage around football matches

and festivals reveals entirely different communication patterns ahead of positively anticipated

events. The final subsection provides robustness tests.

3.1 Core results

Table 1 examines the subscriber dataset. There is no jump-decay pattern; in fact, the first four

columns consistently show significant increases in calls and callers preceding low-level violence.

Column 1 shows 1143 to 1927 additional daily calls (All Calls) the four days before violence,

equivalent to a 9.8%−14.2% (Column 2) increase in call volumes within 0.5◦ of the incident

location. Figure 2 Panel A plots this, showing a trend suggestive of anticipation.32

32Joint F-test for -8 to -5 and +8 and +5 reveals no change due to violence. See the online appendix.

16

Column 3 shows a similar pattern for Callers; the sample shows 71 to 112 more callers the

four days before violence, equivalent to 3.1%−7.7% increase (Column 4).33 This suggests a

change in the extensive margin, i.e. more people are using their phones.

Columns 1 to 4 show that the largest, or near largest, increases happen on the day of the

violence, before attenuating quickly afterwards. While some coefficients remain significant on the

first and fourth day after the incident, a joint F-test cannot reject the null of no post-violence

changes at the p < 0.10 level for three out of the four models. Conversely, we reject the null for

pre-violence mobile phone usage at the p < 0.05 level in all four models.34 These are inconsistent

with the jump-decay pattern but strongly suggest a pre-incident buildup of activity, with the

changes in the extensive margin suggesting that signals correlated with the incident are widely

distributed throughout the local population.

Interestingly, Column 5 reveals no detectable movement of phones. This suggests that the

newly active callers are not newcomers entering the area, but rather local subscribers that were

previously more passive.35 We can thus see that the anticipation shown in the first two columns is

not severe enough to cause people to leave. The final two columns explore the effect of controlling

for the number of people making calls. Column 6 adds Caller to the specification from Column 1,

demonstrating that each caller makes approximately 15 calls per day regardless of proximity to

violence. The pattern is identical in logs, as Column 7 demonstrates.

[TABLE 1 AND FIGURE 2 ABOUT HERE]

Using the antenna dataset, we probe the orientation, duration, and timing of calls to investigate

whether locals appear to feel more anxious or in control (Table 2). Recall that people choose their

network to match their primary contacts due to same-network discounts; shifting communication

inwards towards primary contacts would therefore manifest as more within-network calls. By

containing only Orange calls between pairs of Orange towers, it purely measures same-network

calls. Columns 1 and 2 examine the daily volume of these calls. While Column 1 is noisy,

33Given a 1 : 16 random sample of the 8 million mobile subscribers in Côte d’Ivoire, this implies 1136−1792additional unique callers ahead of an incident.

34Post-incident p-values for changes in call volumes are 0.28 (level) and 0.24 (logged); for callers they are 0.09(level) and 0.13 (logged). The corresponding pre-incident p-values are 0.02, 0.016, 0.03, and 0.02.

35It also strongly implies no major flight ahead of violence.

17

Column 2 shows a significant increase in calls before violence (14.5%-19.2%), peaking on the

day of violence (25.6%) before attenuating (7.2%). This echoes the pattern in Table 1. Column

3 shows that activity between geographically closer antennas increases by 2.9%-4.8% in the

days preceding violence; together, these columns strongly suggest narrowing of communication

networks to geographically closer primary contacts.

Column 4 reveals that call durations decreases by 3%-5.3% (becoming 6-10 seconds shorter)

before violence, slowly returning to normal afterwards, consistent with the urgency suggested by

Columns 1-3. Figure 2 Panel B plots these coefficients. Column 5 checks whether the timing

of calls shifts in proximity to violence. There are slightly more nighttime calls some days and

slightly more daytime calls on others. However, other than a shift to more daytime calls on the

days of incidents, likely reflecting immediate responses to the violence, the sizes of the effects are

substantively tiny. Taken together, Table 1 and Table 2 suggest signals of anxiety, detectable in

communication patterns but not in displacement, are widely dispersed on the ground a few days

prior to low-level violence.

Is the increase in within-network calls more than what can be explained by the higher volume

of calls? Connecting the datasets, Column 6 demonstrates that a 1% increase in call volume

translates into a 1.2% increase in same-network calls. Even accounting for the the increase in call

volumes, an additional 7.5%−9.4% shift of aggregate mobile activity towards in-network calls

occurs before violence. Figure 2 Panel D shows this inward reorientation starts 4 days before

violence, peaks on the day of violence, and returns to normal the following day.36

Does the shift to in-network calls explain the prevalence of shorter calls to closer phones?

Column 7 shows that in-network calls are 12.6% more likely to be between phones less than

0.5◦ apart. Controlling for this, calls ahead of violence are still about 3% more likely to be

directed towards geographically close phones. Column 8 investigates the relationship between

call volumes and durations. Larger call volumes may indicate more urgent or busier days and,

correspondingly, shorter calls. Further, despite generally good network coverage, higher antenna

utilization can overcrowd networks, causing dropped calls. Consistent with expectations, higher

36This figure plots the coefficients from Table 1 Column 2 and Table 2 Column 2.

18

call volume is associated with shorter calls, though the decrease in call length is 2% more than

what this relationship can explain.

[TABLE 2 ABOUT HERE]

3.2 Other Types of Events

Section 3.1 documents that behavior changes before low-level violence. Mobile usage increases,

driven by many more unique callers and a shift to conversations between phones that share

the same network and are geographically close. Interpreted through psychological research on

social network activation, these inward communication patterns suggests widespread negative

anticipation or experiences ahead of violence. We noted earlier how pre-event changes could be

the result of the threat of violence, the cause of violence, or a reaction to another exciting event

that produces both anticipation and opportunities for violence. Here we investigate call patterns

around widely anticipated positive events: Football matches and festivals. Similar patterns prior to

positive events would suggest that what we observed ahead of violence is just general anticipation,

while a distinct pattern would suggest that what we observed is specific to violence.

Table 4 repeats the specifications from Table 1, replacing violence with local football matches

as the independent variable. We use matches from Côte d’Ivore Ligue 1, the top division of

Ivorian domestic football clubs. This league contains clubs from across the country, providing

both temporal and geographical variation in match timing.37 The first four columns show a

(sometimes statistically significant) decrease in calls and callers. This is opposite the direction

of change in calling activity around violence. Column 5 shows some movement into nearby

subprefectures before matches is followed by a great deal of movement in and out afterwards,

with sign and significance switching between days. The final two columns show that unlike in the

case of violence, the change in number of calls goes well beyond that which can be explained by a

change in the number of callers. We find the same relationship between number of calls and

callers as in Table 1, but this time there is an additional 3% increase in calls on the day before

football. This implies that rather than the vast number of people making a few more calls around

37A complete list of match locations and dates is in the online appendix.

19

violence, relatively fewer people (namely football fans) each make a large number of calls around

matches.

[TABLE 4 ABOUT HERE]

The antenna data shows even clearer patterns than the subprefecture data. Table 4 exactly

follows the specifications from Table 2. Columns 1 and 2 demonstrate a decrease in same-network

calls between 6% and 14% in the four days before a match. Since price sensitivity should be

higher for less urgent events (football compared to violence), this suggests that the shift to

in-network calls observed around violence are unlikely to be due to heightened sensitivity to prices.

Furthermore, Column 3 shows that activity increases between phones that are geographically

distant, with the fraction of calls that are made over a long distance increasing by 1.5%−3% on

the days before and after a match. Call durations are longer, (Column 4) especially the day before

the match (8.2%). Column 5 shows that calls shift slightly to night time before the event, but

shift to daytime on the day of the match. We now once again combine the datasets. Column 6

controls for total calls, demonstrating that unlike in response to violence, football matches are

associated with a large shift to out-of-network calls. Similarly, Column 7 demonstrates that the

shift in fraction of calls to nearby phones is not purely explained by the change in the percentage

of calls within-network. The final column demonstrates that the longer call durations are robust to

controlling for antenna utilization.

[TABLE 4 ABOUT HERE]

The football-related patterns in call volumes, callers, and movement all clearly differ from

those related to violence. The antenna data strongly suggests a broadening of communication

networks with lower within-network percentage, more calls to geographically further phones, and

longer call durations. Unlike with violence, changes persist after matches. We can reject the nulls

of no reductions in in-network and nearby calls and no increase in call duration both before and

after matches at the p < 0.01 level. (All six p-values are less than 0.01.) The outward orientation

in call destination and symmetric responses to matches are consistent with positive experiences

and entirely opposite the call pattern surrounding violence. This strongly suggests that we are not

20

confusing football or football-like exciting events with violence.38 One possible interpretation

is that even the first division of the domestic football league is not important enough to cause

major increases in call volumes or callers. However, conversations appear to be shifting to longer

conversations with non-primary contacts, consistent with discussions with faraway fans about the

local game.

The final type of event we explore is large national festivals.39 The difference between

violence and football, along with festivals can be seen visually in Figure 3. Looking at the three

panels, it is visually striking how the response to violence starts far before the event and then

decays once the it occurs. Football shows small changes in the opposite direction of violence

continuing well afterwards. Festivals have yet a third signature: call volumes surrounding festivals

spikes the day before the festival and then plateaus. Unlike around violence, there is no increase

in within-network calls in proximity to festivals.

4 Conclusion

Low-level political violence occurs in many developing and consolidating democracies. We use

mobile usage patterns to examine how individuals behave before and after such events, which

were previously too unpredictable to study. We compare communication patterns around three

kinds of events: Violence, football matches and festivals. Violent incidents exhibit many unique

patterns, one being a dramatic shift towards same-network calls that is twice as large in the days

before the incident than in the days after the incident.40 This asymmetric narrowing of network

fits closely with psychological research that has established how anxiety, tension and uncertainty

induce humans to temporarily concentrate their resources towards seeking and offering support to

close friends and family, a taxing activity that ceases after the crisis passes. This pattern is not

seen with the two other types of events, which are both positively anticipated, suggesting that we

are not mistaking anticipation of exciting events for pre-violence markers.

38This also shows that budget constraints don’t necessarily force increased calling to shift in-network.39Regression tables, discussion, and figures are in the online appendix.40In direct contrast with mobile activity surrounding football, call volumes also increase before violence, driven

by more individuals making calls, while the average duration and distance between phones of said calls decreases.

21

While we cannot observe the content of conversations,41 our findings produce new quantitative

evidence that indirectly supports and links several literature. First, it supports Kalyvas’ hypothesis

that low-level violence is not a surprise on the ground by linking it to the ICT hypothesis that

information about violence exists ahead of incidents and is communicated through local cellular

networks. Second, the shift in call destinations links theories of increased group attachment

around political violence (Wilkinson (2004)) with those identifying rumors that target group

identities as a necessary condition for violence (Bhavnani et al. (2009)). Though “tracking [these

rumors] empirically fringes on the impossible”, the existence of these rumors would result in

increased group attachment. Conversely, the intensification of in-group communication when

unsettling signals are likely to be present (as is suggested by our data) would amplify the spread

of these signals.

Anecdotes appear to confirm the above; Ivorian newspapers report rumors of bombings,

deaths of high ranking politicians, and poisoning of water spreading quickly via websites, social

media and mobile phones.42 The consequence of these types of rumors are very real and are not

unique to Côte d’Ivore. After the death of Prime Minister Indira Gandhi, false rumors of Sikhs

celebrating directly inspired communal attacks across the country. Many real atrocities in the

Rwandan Civil War were also committed in response to rumors of out-group aggression. Mobile

technology in Kenya spread insidious ethnic-centered rumors, pushing already-high levels of

intergroup animosity into impulsive, violent behavior. This leads scholars such as Stremlau &

Price (2009) to suggest that the positive role of technology on democracy can be overshadowed by

its negative effects.

The approach in this paper suggests several natural avenues for future work. Some relate

directly to communications technology. First, how generalizable is the behavioral pattern we

observe? The fact that the behavior we observe fits cleanly into psychology literature suggests that

it is driven by human nature. While this bodes well for its generalizability, we cannot be certain

41Psychological research does suggest that the conversation in crises will relate directly to the tension at handsince individuals want to collectively make sense of uncertain situations (Lewandowsky et al. (2012)), not simplyenjoy social contact (Schachter (1959)).

42http://www.abidjanlivenews.com/Ivory-Coast-hidden-battle_a214.html

22

until we obtain more data from other countries. Second, can we empirically establish causal links

between communication patterns and violence? We envision that combining the type of work

we do here with fine grained qualitative work may be fruitful in answering this question. Third,

how do the patterns change as communication technology evolves? For example, what does the

widespread use of SMS and social media mean for the way people use actual phone conversation?

The same inwards turning of communications networks may look very different in a few years’

time. Fourth, can incorporating communications pattern improve high-precision prediction? The

current state of the art, such as Weidmann & Ward (2010), is still limited to the provence-month

level of resolution. Blair et al. (2014) are able to use surveys to predict violence in Liberia at the

village level, but do not attempt to make temporal predictions. We believe that mobile activity

may be an important complement to these studies. While this is the first use of mobile phones

to study behavior around low-level political violence, we are confident it will not be the last.

By allowing us a window into an important but difficult-to-study phenomenon, we may better

understand how individuals react to one of the most common forms of violence in the world.

References

Aker, Jenny C., & Mbiti, Isaac. 2010. Mobile Phones and Economic Development in Africa.

Journal of Economic Perspectives, 24(3), 207–32.

Bagrow, James P, Wang, Dashun, & Barabasi, Albert-Laszlo. 2011. Collective response of human

populations to large-scale emergencies. PloS one, 6(3), e17680.

Balcells, Laia. 2011. Continuation of politics by two means: Direct and indirect violence in civil

war. Journal of Conflict Resolution, 55(3), 397–422.

Bhavnani, Ravi, Findley, Michael G, & Kuklinski, James H. 2009. Rumor dynamics in ethnic

violence. The Journal of Politics, 71(03), 876–892.

Blair, Robert, Blattman, Chris, & Hartman, Alexandra. 2014. Predicting local level violence.

http://dx.doi.org/10.2139/ssrn.2497153.

23

Blattman, Christopher. 2009. From violence to voting: War and political participation in Uganda.

American Political Science Review, 103(02), 231–247.

Blondel, Vincent D., Esch, Markus, Chan, Connie, Clérot, Fabrice, Deville, Pierre, Huens,

Etienne, Morlot, Frédéric, Smoreda, Zbigniew, & Ziemlicki, Cezary. 2012. Data for

Development: the D4D Challenge on Mobile Phone Data. CoRR, abs/1210.0137.

Blumenstock, J, Eagle, N, & Fafchamps, M. 2014. Risk sharing and mobile phones: Evidence in

the aftermath of natural disasters.

Bracken, Cheryl Campanella, Jeffres, Leo W, Neuendorf, Kimberly A, Kopfman, Jenifer, &

Moulla, Fani. 2005. How cosmopolites react to messages: America under attack.

Communication Research Reports, 22(1), 47–58.

Burger, Jerry M. 1989. Negative reactions to increases in perceived personal control. Journal of

personality and social psychology, 56(2), 246.

Callen, Michael, Isaqzadeh, Muhammed, Long, James, & Sprenger, Charles. 2014. Violence and

Risk Preferences: Experimental Evidence from Afghanistan. American Economic Review,

104(1).

Cohen, Akiba A, & Lemish, Dafna. 2005. When bombs go off the mobiles ring: The aftermath of

terrorist attacks. A sense of place: The global and the local in mobile communication, 117–128.

Cramer, Christopher. 2007. Violence in Developing Countries: War. Memory, Progress.

Dafoe, Allan, & Lyall, Jason. 2015. From cell phones to conflict? Reflections on the emerging

ICT–political conflict research agenda. Journal of Peace Research, 52(3), 401–413.

De Jong, Joop TVM. 2010. A public health framework to translate risk factors related to political

violence and war into multi-level preventive interventions. Social Science & Medicine, 70(1),

71–79.

24

Drozdova, Katya, & Samoilov, Michael. 2010. Predictive analysis of concealed social network

activities based on communication technology choices: early-warning detection of attack

signals from terrorist organizations. Computational and Mathematical Organization Theory,

16(1), 61–88.

Fleming, Raymond, Baum, Andrew, Gisriel, Martha M, & Gatchel, Robert J. 1982. Mediating

influences of social support on stress at Three Mile Island. Journal of Human stress, 8(3),

14–23.

Fredrickson, Barbara L. 2001. The role of positive emotions in positive psychology: The

broaden-and-build theory of positive emotions. American psychologist, 56(3), 218.

Gillwald, Alison, Milek, Anne, & Stork, Christoph. 2010. Gender Assessment of ICT access and

usage in Africa. Towards Evidence-based ICT Policy and Regulation, 1(5).

Hartzell, Caroline, & Hoddie, Matthew. 2003. Institutionalizing Peace: Power Sharing and

Post-Civil War Conflict Management. American Journal of Political Science, 47(2), 318–332.

Horowitz, Donald, & Varshney, Ashutosh. 2003. Lethal Ethnic Riots: Lessons from India and

Beyond. United States Institute of Peace Special Report, 101.

House, James S, Umberson, Debra, & Landis, Karl R. 1988. Structures and processes of social

support. Annual review of sociology, 293–318.

Internal Displacement Monitoring Centre. 2012. Global Overview 2012.

http://www.internal-displacement.org/assets/publications/

2013/2012-global-overview-corporate-en.pdf.

Internal Displacement Monitoring Centre. 2014. Global Overview 2014.

http://www.internal-displacement.org/assets/publications/

2014/201405-global-overview-2014-en.pdf.

Kalyvas, Stathis N. 2003. The ontology of “political violence”: action and identity in civil wars.

Perspectives on Politics, 1(03), 475–494.

25

Kasara, Kimuli. 2011. Electoral geography and conflict: Examining the redistricting through

violence in Kenya. Tech. rept. Mimeo: Columbia University.

Lerner, Jennifer S, & Keltner, Dacher. 2001. Fear, anger, and risk. Journal of personality and

social psychology, 81(1), 146.

Lewandowsky, Stephan, Ecker, Ullrich KH, Seifert, Colleen M, Schwarz, Norbert, & Cook, John.

2012. Misinformation and its correction continued influence and successful debiasing.

Psychological Science in the Public Interest, 13(3), 106–131.

Little, Andrew. January 2015. Communication Technology and Protest. Unpublished manuscript.

Marshall, Gary D, & Zimbardo, Philip G. 1979. Affective consequences of inadequately

explained physiological arousal. Journal of Personality and Social Psychology, 37(6),

970–988.

McGovern, Mike. 2011. Making War in Côte D’Ivoire. University of Chicago Press.

Mitullah, Winnie, & Kamau, Paul. 2013. The Partnership of Free Speech & Good Governance in

Africa. http://www.afrobarometer.org/files/documents/policy_

brief/ab_r5_policybriefno3.pdf.

Pierskalla, Jan, & Hollenbach, Florian. 2013. Technology and Collective Action: The Effect of

Cell Phone Coverage on Political Violence in Africa. American Political Science Review,

107(02), 207–224.

Ruggeri, Andrea, Gizelis, Theodora-Ismene, & Dorussen, Han. 2013. Managing Mistrust: An

Analysis of Cooperation with UN Peacekeeping in Africa. Journal of Conflict Resolution,

57(3), 387–409.

Sambanis, Nicholas. 2004. Using case studies to expand economic models of civil war.

Perspectives on Politics, 2(02), 259–279.

Schachter, Stanley. 1959. The psychology of affiliation: Experimental studies of the sources of

gregariousness. Stanford University Press.

26

Schacter, Daniel L, & Coyle, Joseph T. 1997. Memory distortion: How minds, brains, and

societies reconstruct the past. Harvard University Press.

Senaratne, J.P. 1997. Political Violence in Sri Lanka, 1977-1990: Riots, Insurrections,

Counter-Insurgencies, Foreign Intervention. Sri Lanka studies in the humanities and the social

sciences. Vu University Press Amsterdam.

Shapiro, Jacob, & Weidmann, Nils. 2015. Is the Phone Mightier than the Sword? Cell Phones and

Insurgent Violence in Iraq. International Organization, 69(02), 247–274.

Shea, Catherine T, Menon, Tanya, Smith, Edward B, & Emich, Kyle. 2015. The affective

antecedents of cognitive social network activation. Social Networks, 43, 91–99.

Stremlau, Nicole, & Price, Monroe E. 2009. Media, Elections and Political Violence in Eastern

Africa: Towards a Comparative Framework. London: An Annenberg. Oxford Occasional paper

in Communications Policy Research. Retrieved December, 10, 2012.

Taylor, Shelley E. 1991. Asymmetrical effects of positive and negative events: the

mobilization-minimization hypothesis. Psychological bulletin, 110(1), 67.

Themnér, Lotta, & Wallensteen, Peter. 2011. Armed conflict, 1946–2010. Journal of Peace

Research, 48(4), 525–536.

Thoits, Peggy A. 2011. Mechanisms linking social ties and support to physical and mental health.

Journal of Health and Social Behavior, 52(2), 145–161.

Trnka, Susanna. 2008. State of suffering: Political violence and community survival in Fiji.

Cornell University Press.

Weidmann, Nils, & Ward, Michael. 2010. Predicting violence in civil war. Journal of Conflict

Resolution, 54(6), 250–270.

Wilkinson, Steven I. 2004. Votes and violence: Electoral competition and communal riots in

India. Cambridge University Press.

27

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335)

(29.

66)

(0.0

209)

(4.6

01)

(270

.2)

(0.0

127)

Day

of15

63.8∗∗∗

0.12

8∗∗∗

120.

2∗∗∗

0.09

26∗∗∗

-0.2

14-2

45.4

0.00

0793

Vio

lenc

e(5

91.4

)(0

.044

9)(4

5.22

)(0

.031

5)(2

.610

)(2

46.9

)(0

.013

2)1

Day

1211

.10.

0905∗∗

94.0

2∗∗

0.07

17∗∗

0.88

7-2

03.9

-0.0

0815

Aft

er(7

89.6

)(0

.042

9)(4

6.72

)(0

.029

5)(2

.948

)(5

07.1

)(0

.013

4)2

Day

s46

3.6

-0.0

319

35.9

2-0

.009

31-2

.848

-76.

91-0

.019

1A

fter

(496

.4)

(0.0

513)

(28.

28)

(0.0

347)

(3.4

97)

(257

.1)

(0.0

118)

3D

ays

1327

.40.

015

29.5

30.

0145

1.24

888

2.9∗

-0.0

0491

Aft

er(8

43.5

)(0

.038

8)(3

5.97

)(0

.025

9)(2

.679

)(4

93.7

)(0

.015

5)4

Day

s14

32.1∗∗

0.10

4∗∗

81.8

6∗∗

0.07

62∗∗

-2.7

3120

0.1

-0.0

0097

5A

fter

(577

.0)

(0.0

479)

(39.

57)

(0.0

343)

(2.8

27)

(285

.2)

(0.0

152)

Cal

lers

15.0

5∗∗∗

(1.5

32)

log(

Cal

lers

)1.

375∗∗∗

(0.0

295)

Adj

-R2

0.92

0.92

140.

9667

0.94

590.

0334

0.96

750.

992

N28

473

2847

328

473

2847

328

473

2847

328

473

All

Spec

ifica

tions

incl

ude

subp

refe

ctur

ean

dre

gion

-day

fixed

effe

cts.

Stan

dard

erro

rscl

uste

red

bysu

bpre

ctur

ein

pare

nthe

ses

∗p<

0.10

,∗∗

p<

0.05

,∗∗∗

p<

0.01

31

Tabl

e2:

Ant

enna

data

:mob

ileph

one

usag

ear

ound

viol

enti

ncid

ents

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

In-N

wk

log(

In-

Frac

tion

log(

Cal

lFr

actio

nlo

g(In

-Nw

kFr

actio

nlo

g(C

alls

Nw

kC

alls

)N

earb

yD

urat

ion)

Day

time

Cal

ls)

Nea

rby

Dur

atio

n)4

Day

s18

8.8

0.14

7∗∗∗

0.04

81∗∗∗

-0.0

361∗∗∗

-0.0

002

0.02

010.

0295∗∗∗

-0.0

0769

Bef

ore

(115

.0)

(0.0

428)

(0.0

0914

)(0

.011

8)(0

.001

9)(0

.033

8)(0

.005

05)

(0.0

0647

)3

Day

s20

9.5∗

0.15

0∗∗∗

0.04

70∗∗∗

-0.0

452∗∗∗

-0.0

099∗∗∗

0.01

590.

0281∗∗∗

-0.0

163∗∗

Bef

ore

(108

.0)

(0.0

441)

(0.0

0909

)(0

.011

9)(0

.003

1)(0

.034

3)(0

.004

91)

(0.0

0683

)2

Day

s24

4.6∗

0.19

2∗∗∗

0.04

65∗∗∗

-0.0

529∗∗∗

-0.0

038

0.07

51∗∗

0.02

23∗∗∗

-0.0

159∗∗

Bef

ore

(132

.3)

(0.0

497)

(0.0

0956

)(0

.013

0)(0

.003

0)(0

.037

8)(0

.004

58)

(0.0

0735

)1

Day

92.6

30.

145∗∗∗

0.02

89∗∗∗

-0.0

299∗∗∗

0.00

78∗∗

0.07

48∗∗

0.01

06∗∗∗

-0.0

0193

Bef

ore

(90.

31)

(0.0

392)

(0.0

0711

)(0

.010

0)(0

.003

1)(0

.030

9)(0

.003

35)

(0.0

0605

)D

ayof

271.

5∗∗∗

0.25

6∗∗∗

0.03

41∗∗∗

-0.0

499∗∗∗

0.01

74∗∗∗

0.09

38∗∗∗

0.00

184

-0.0

0060

4V

iole

nce

(95.

64)

(0.0

388)

(0.0

0716

)(0

.010

7)(0

.002

6)(0

.035

9)(0

.003

74)

(0.0

0665

)1

Day

61.4

10.

105∗∗∗

0.00

173

-0.0

159

0.01

28∗∗∗

0.04

41-0

.011

5∗∗∗

0.00

432

Aft

er(9

1.47

)(0

.038

2)(0

.007

69)

(0.0

106)

(0.0

031)

(0.0

297)

(0.0

0436

)(0

.006

52)

2D

ays

49.5

90.

0718∗

0.00

603

-0.0

128

0.00

49∗

0.04

38-0

.003

030.

0010

7A

fter

(88.

48)

(0.0

410)

(0.0

0816

)(0

.011

8)(0

.002

8)(0

.030

3)(0

.005

29)

(0.0

0851

)3

Day

s32

.12

0.04

17-0

.001

12-0

.005

19-0

.006

1∗∗∗

0.01

87-0

.006

380.

0028

4A

fter

(87.

46)

(0.0

359)

(0.0

0732

)(0

.009

66)

(0.0

019)

(0.0

263)

(0.0

0419

)(0

.005

85)

4D

ays

87.5

10.

0916∗∗

-0.0

0002

-0.0

0155

-0.0

013

0.02

4-0

.011

6∗∗∗

0.01

61∗∗∗

Aft

er(7

8.73

)(0

.035

9)(0

.007

47)

(0.0

104)

(0.0

030)

(0.0

290)

(0.0

0429

)(0

.006

05)

log(

All

1.20

4∗∗∗

Cal

ls)

(0.1

48)

log(

In-

0.12

6∗∗∗

-0.1

92∗∗∗

Nw

k)C

alls

(0.0

0163

)(0

.003

21)

Adj

-R2

0.75

430.

6951

0.59

450.

4345

0.59

050.

8367

0.80

520.

6963

N94

227

9422

794

227

9422

794

227

9422

794

227

9422

7A

llsp

ecifi

catio

nsin

clud

ean

tenn

aan

dre

gion

-day

fixed

effe

cts.

Stan

dard

erro

rscl

uste

red

byan

tenn

ain

pare

nthe

ses

∗p<

0.10

,∗∗

p<

0.05

,∗∗∗

p<

0.01

32

Tabl

e3:

Subs

crib

erda

ta:m

obile

phon

eus

age

arou

ndlo

calf

ootb

allm

atch

es(1

)(2

)(3

)(4

)(5

)(6

)(7

)A

llC

alls

log(

All

Cal

ls)

Cal

lers

log(

Cal

lers

)M

ovin

gin

All

Cal

lslo

g(A

llC

alls

)4

Day

s-5

76.4

-0.0

441∗∗

-59.

80∗∗

-0.0

323∗∗∗

15.1

0∗39

0.1

0.00

0983

Bef

ore

(366

.0)

(0.0

188)

(25.

18)

(0.0

123)

(8.4

07)

(340

.9)

(0.0

114)

3D

ays

-508

.3-0

.021

4-2

2.58

-0.0

0853

15.9

3-1

43.4

-0.0

0947

Bef

ore

(661

.5)

(0.0

233)

(40.

51)

(0.0

160)

(11.

13)

(216

.9)

(0.0

0978

)2

Day

s19

.08

-0.0

484∗∗∗

-58.

07∗∗

-0.0

461∗∗∗

4.59

795

7.6∗∗

0.01

59B

efor

e(3

30.8

)(0

.013

9)(2

4.30

)(0

.011

3)(5

.628

)(3

83.7

)(0

.010

8)1

Day

-993

.0-0

.028

1-9

9.49∗∗

-0.0

428∗∗

-8.4

4161

4.9∗∗

0.03

16∗∗∗

Bef

ore

(827

.6)

(0.0

269)

(50.

27)

(0.0

195)

(5.7

61)

(302

.3)

(0.0

0848

)D

ayof

-488

.40.

0265

-8.6

890.

0127

-5.3

27-3

48.0

0.00

882

Foot

ball

(506

.1)

(0.0

177)

(26.

16)

(0.0

141)

(6.8

84)

(378

.6)

(0.0

102)

1D

ay14

5.4

-0.0

304∗

1.66

9-0

.019

7∗-1

5.70∗∗

118.

4-0

.002

82A

fter

(349

.7)

(0.0

171)

(20.

22)

(0.0

119)

(6.3

86)

(196

.2)

(0.0

0831

)2

Day

s-6

70.9

-0.0

622∗∗∗

-59.

92-0

.036

0∗∗∗

15.0

4∗∗

297.

4-0

.011

9A

fter

(503

.1)

(0.0

170)

(37.

88)

(0.0

111)

(6.8

61)

(240

.2)

(0.0

0996

)3

Day

s-6

86.0

-0.1

90∗∗∗

-41.

87-0

.121∗∗∗

-14.

06∗

-9.3

50-0

.021

2∗

Aft

er(7

82.4

)(0

.030

4)(3

9.24

)(0

.024

4)(7

.535

)(3

96.2

)(0

.012

4)4

Day

s-2

86.3

0.04

02∗

-7.3

480.

0423∗∗∗

14.0

9∗-1

67.5

-0.0

190∗

Aft

er(7

05.6

)(0

.021

9)(2

4.95

)(0

.016

0)(8

.432

)(4

92.5

)(0

.010

3)C

alle

rs16

.16∗∗∗

(1.4

57)

log(

Cal

lers

)1.

397∗∗∗

(0.0

281)

Adj

-R2

0.14

340.

3782

0.18

710.

3183

0.04

190.

6515

0.92

25N

2847

328

473

2847

328

473

2847

328

473

2847

3St

anda

rder

rors

inpa

rent

hese

s∗

p<

0.10

,∗∗

p<

0.05

,∗∗∗

p<

0.01

33

Tabl

e4:

Ant

enna

data

:mob

ileph

one

usag

ear

ound

loca

lfoo

tbal

lmat

ches

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

In-N

wk

log(

In-

Frac

tion

log(

Cal

lFr

actio

nlo

g(In

-Nw

kFr

actio

nlo

g(C

alls

Nw

kC

alls

)N

earb

yD

urat

ion)

Day

time

Cal

ls)

Nea

rby

Dur

atio

n)4

Day

s-3

27.4∗∗∗

-0.2

18∗∗∗

-0.0

325∗∗∗

0.03

38∗∗∗

-0.0

252∗∗∗

-0.1

27∗∗∗

-0.0

0504∗∗∗

-0.0

0809∗

Bef

ore

(44.

79)

(0.0

160)

(0.0

0245

)(0

.005

35)

(0.0

0202

)(0

.017

8)(0

.001

85)

(0.0

0489

)3

Day

s-2

81.4∗∗∗

-0.1

11∗∗∗

-0.0

167∗∗∗

0.00

853

-0.0

0140

-0.0

855∗∗∗

-0.0

0273∗

-0.0

128∗∗∗

Bef

ore

(44.

56)

(0.0

139)

(0.0

0165

)(0

.005

90)

(0.0

0188

)(0

.012

9)(0

.001

57)

(0.0

0480

)2

Day

s-2

79.0∗∗∗

-0.1

65∗∗∗

-0.0

233∗∗∗

0.02

66∗∗∗

-0.0

0731∗∗∗

-0.1

11∗∗∗

-0.0

0251∗

-0.0

0515

Bef

ore

(33.

07)

(0.0

114)

(0.0

0182

)(0

.005

08)

(0.0

0162

)(0

.012

3)(0

.001

50)

(0.0

0456

)1

Day

-153

.3∗∗∗

-0.1

66∗∗∗

-0.0

208∗∗∗

0.08

24∗∗∗

-0.0

137∗∗∗

-0.0

930∗∗∗

0.00

0107

0.05

04∗∗∗

Bef

orw

(51.

04)

(0.0

141)

(0.0

0199

)(0

.007

87)

(0.0

0214

)(0

.013

8)(0

.001

63)

(0.0

0608

)D

ayof

55.0

8-0

.029

9∗∗∗

-0.0

135∗∗∗

-0.0

0625

0.04

63∗∗∗

-0.0

530∗∗∗

-0.0

0972∗∗∗

-0.0

120∗∗

Foot

ball

(36.

92)

(0.0

0984

)(0

.001

68)

(0.0

0567

)(0

.003

24)

(0.0

0972

)(0

.001

56)

(0.0

0553

)1

Day

-208

.0∗∗∗

-0.0

978∗∗∗

-0.0

148∗∗∗

0.00

435

0.00

0176

-0.0

839∗∗∗

-0.0

0249∗

-0.0

145∗∗∗

Aft

er(2

9.77

)(0

.012

0)(0

.001

75)

(0.0

0542

)(0

.001

78)

(0.0

108)

(0.0

0144

)(0

.005

18)

2D

ays

-207

.7∗∗∗

-0.1

41∗∗∗

-0.0

210∗∗∗

0.02

07∗∗∗

-0.0

115∗∗∗

-0.0

697∗∗∗

-0.0

0326∗

-0.0

0637

Aft

er(3

3.82

)(0

.011

4)(0

.001

91)

(0.0

0567

)(0

.001

92)

(0.0

139)

(0.0

0169

)(0

.005

20)

3D

ays

-315

.7∗∗∗

-0.2

60∗∗∗

-0.0

260∗∗∗

0.07

24∗∗∗

0.00

556∗∗∗

-0.0

795∗∗∗

0.00

678∗∗∗

0.02

24∗∗∗

Aft

er(4

9.44

)(0

.016

1)(0

.002

13)

(0.0

0798

)(0

.002

10)

(0.0

247)

(0.0

0211

)(0

.006

62)

4D

ays

-296

.3∗∗∗

-0.1

07∗∗∗

-0.0

226∗∗∗

0.00

782

-0.0

0126

-0.1

11∗∗∗

-0.0

0910∗∗∗

-0.0

128∗∗

Aft

er(5

1.59

)(0

.015

1)(0

.002

23)

(0.0

0635

)(0

.001

83)

(0.0

161)

(0.0

0199

)(0

.005

98)

log(

All

1.20

0∗∗∗

Cal

ls)

(0.1

47)

log(

In-

0.12

6∗∗∗

-0.1

92∗∗∗

Nw

kC

alls

)(0

.001

62)

(0.0

0320

)A

dj-R

20.

2643

0.40

090.

3862

0.19

990.

5691

0.67

670.

7021

0.56

91N

9422

794

227

9422

794

277

9422

794

227

9422

794

227

Stan

dard

erro

rsin

pare

nthe

ses

∗p<

0.10

,∗∗

p<

0.05

,∗∗∗

p<

0.01

34

Tabl

e5:

In-n

etw

ork

calli

ngre

spon

seto

vary

ing

viol

ence

and

dist

ance

(1)

(2)

(3)

(4)

(5)

(6)

(7)

0.25◦

0.50◦

0.75◦

1.00◦

Incl

udin

gda

teE

xact

Dat

eFa

tal

Unc

erta

inO

nly

Onl

y4

Day

s0.

0267

0.07

69∗∗

0.06

54∗∗∗

0.05

90∗∗∗

0.05

56∗∗

0.14

7∗∗∗

0.09

30∗∗∗

Bef

ore

(0.0

323)

(0.0

346)

(0.0

202)

(0.0

191)

(0.0

249)

(0.0

428)

(0.0

291)

3D

ays

0.02

490.

0647∗∗

0.05

77∗∗∗

0.05

04∗∗∗

0.03

630.

150∗∗∗

0.11

6∗∗∗

Bef

ore

(0.0

273)

(0.0

255)

(0.0

206)

(0.0

184)

(0.0

226)

(0.0

441)

(0.0

278)

2D

ays

0.07

910.

0717∗∗

0.05

76∗∗∗

0.05

00∗∗

0.06

68∗∗∗

0.19

2∗∗∗

0.21

4∗∗∗

Bef

ore

(0.0

611)

(0.0

341)

(0.0

217)

(0.0

203)

(0.0

239)

(0.0

497)

(0.0

320)

1D

ay0.

0178

0.03

070.

0536∗∗

0.02

850.

0440∗∗

0.14

5∗∗∗

0.24

7∗∗∗

Bef

ore

(0.0

259)

(0.0

209)

(0.0

224)

(0.0

197)

(0.0

214)

(0.0

392)

(0.0

327)

Day

of0.

017

0.09

26∗∗∗

0.11

5∗∗∗

0.07

94∗∗

0.15

7∗∗∗

0.25

6∗∗∗

0.31

4∗∗∗

Vio

lenc

e(0

.027

8)(0

.031

5)(0

.029

6)(0

.031

3)(0

.022

4)(0

.038

8)(0

.038

0)

1D

ay0.

0426

0.07

17∗∗

0.06

25∗∗

0.03

650.

0715∗∗∗

0.10

5∗∗∗

0.17

0∗∗∗

Aft

er(0

.030

4)(0

.029

5)(0

.029

4)(0

.028

6)(0

.022

9)(0

.038

2)(0

.040

4)

2D

ays

-0.0

0096

2-0

.009

310.

0214

0.02

84-0

.057

0∗0.

0718∗

-0.0

386

Aft

er(0

.021

1)(0

.034

7)(0

.026

4)(0

.023

4)(0

.030

2)(0

.041

0)(0

.055

3)

3D

ays

-0.0

0391

0.01

450.

0187

0.00

433

0.00

187

0.04

17-0

.035

3A

fter

(0.0

300)

(0.0

259)

(0.0

232)

(0.0

189)

(0.0

307)

(0.0

359)

(0.0

396)

4D

ays

0.03

350.

0762∗∗

0.03

53∗

0.02

690.

251∗∗∗

0.09

16∗∗

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er(0

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0.10

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p<

0.05

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p<

0.01

35

You will hear of skirmishes and rumors of skirmishes:

Online Appendix

May 29, 2016

1

1 Data

1.1 Summary Statistics

Table 1 presents summary statistics for all the variables we use in the paper, aggregated to the

subprefecture or antenna level as appropriate.

Table 1: Summary StatisticsPanel 1: Subscriber Data

mean sd min maxDays with calls 141 9.45 72 144All calls 12203 22154 138 197486log(All calls) 8.28 1.47 4.73 12.1Callers 1293 1831 19.7 12237log(Callers) 6.15 1.42 0 9.26Moving in 0.696 2.72 -7.13 22.7Fraction days near violence 0.0104 0.0111 0 0.0534n=233

Panel 2: Antenna Data

mean sd min maxIn-network calls 2830 2625 44.8 21012log(In-network calls) 7.30 0.917 3.05 9.83Call duration (sec) 137 21.7 82.5 242Fraction calls nearby 0.545 0.119 0.103 0.861Fraction calls daytime 0.642 0.0366 0.443 0.899Fraction days near violence 0.0108 0.0132 0 0.0625n=844

1.2 Violent events

We code all violent events discussed in ONUCI Hebdo during the period we examine. Our coding

begins two weeks before we have cell phone data and continues for two weeks afterwards. This

allows us to consider all leads and lags of violent events. The process of coding consists of

reading the descriptions of violent events and extracting the date, location and severity.

A complete list of violent events is presented in Table 2.

2

Table 2: Violent EventsReport date Exact Date Fatal Location Brief Summary12/9/2011 No Yes Tengrela Skirmishes and intimidation U 5 deaths12/9/2011 No No Zouan-Hounien Armed men around candidates12/16/2011 Yes No Agbovile Severe Election-day irregularities12/16/2011 Yes No Zanzan Severe Election-day irregularities12/16/2011 Yes No Bouna Severe Election-day irregularities12/16/2011 Yes No Fresco Severe Election-day irregularities12/16/2011 Yes No Grand-Lahou Severe Election-day irregularities12/16/2011 Yes No Duekoue Severe Election-day irregularities12/16/2011 Yes No Kouibly Severe Election-day irregularities12/16/2011 Yes No Boufale Severe Election-day irregularities12/16/2011 Yes No Dikodougou Severe Election-day irregularities12/16/2011 Yes No San Pedro Severe Election-day irregularities12/16/2011 Yes No Oungolodougougu Severe Election-day irregularities12/16/2011 Yes No Biankouma Severe Election-day irregularities12/16/2011 Yes No Man Severe Election-day irregularities12/16/2011 No Yes Duekoué Politically motivated rape12/16/2011 No Yes Logoualé Candidate killed12/23/2011 Yes Yes Vavoua Army kills 6 civilians12/23/2011 No No Guepahouo Army/civilian skirmishes12/23/2011 No No Songon-Agban Army/civilian skirmishes1/13/2012 Yes Yes Bahé Blaon Intercommunal violence1/20/2012 No Yes Bazre Clashes between communities1/27/2012 No Yes Yopougon. Opposition party violently disbanded1/27/2012 Yes Yes Bateguedia 4 killed in clashes between communities1/27/2012 No Yes Koonan Expulsion of fulani2/3/2012 No Yes Logoualé RDR candidate death2/10/2012 No Yes Bouake Gender based violence2/17/2012 Yes Yes Baoule Fight between groups of teens2/17/2012 Yes Yes Arrah Clashes between communities2/17/2012 No Yes Bouake Attacks on the roads2/17/2012 No No Tiebissou Clashes between communities2/17/2012 No No Beoumi Clashes between communities2/17/2012 No No Katiola Clashes between communities2/24/2012 Yes Yes Sikensi Clashes between communities3/2/2012 Yes Yes Bonon Violence (no other description)3/2/2012 Yes Yes Facobly Violence (no other description)3/16/2012 Yes Yes Fouenan Clashes between communities3/16/2012 Yes Yes Touba Clashes between communities3/16/2012 Yes Yes Ouaninou Clashes between communities4/6/2012 Yes Yes Agnibilekro Civilians attacked by soldiers4/6/2012 Yes Yes Fronan Civilians attacked by soldiers4/6/2012 Yes Yes Niakaramandougou Civilians attacked by soldiers4/27/2012 Yes Yes Sakré Violence on western border5/3/2012 Yes Yes Tai Violence on western border

3

1.3 Football Matches

The top league of the Fédération Ivoirienne de Football is Ligue 1. Most years about half of

the teams in the league hail from parts of Abidjan, with the remainder distributed across the

country. In most years, the season stretches from November to May, but an abbreviated season

with a smaller number of teams was instituted for the 2010-2011 and 2011-2012 seasons due to

uncertainty in the aftermath of the conflict. Therefore there are only matches from March to May

in these seasons. The league returned to its normal format for the 2012-2013 season.

During the two months for which our cellphone data overlaps with the league season there

are matches at four stadiums outside of Abidjan. These are the Stade Henri Konan Bédié in

Abengourou, the Stade El Hadj Mamadou Coulibaly in Odienné, the Stade de Yamoussoukro in

Yomoussoukro and the Stade Municipal d’Issia in Issia. These are well spread around the country,

with one in the Northwest, one in the southwest, one in the middle and one in the east.

Table 3: Football MatchesDate Location3/2/2012 Stade de Yamoussoukro3/3/2012 Stade de Yamoussoukro3/11/2012 Stade Municipal d’Issia3/15/2012 Stade El Hadj Mamadou Coulibaly3/18/2012 Stade de Yamoussoukro3/18/2012 Stade El Hadj Mamadou Coulibaly3/18/2012 Stade Henri Konan Bédié3/24/2012 Stade Municipal d’Issia3/31/2012 Stade de Yamoussoukro4/1/2012 Stade de Yamoussoukro4/10/2012 Stade El Hadj Mamadou Coulibaly4/11/2012 Stade Henri Konan Bédié4/15/2012 Stade de Yamoussoukro4/15/2012 Stade El Hadj Mamadou Coulibaly4/29/2012 Stade El Hadj Mamadou Coulibaly5/6/2012 Stade de Yamoussoukro5/6/2012 Stade El Hadj Mamadou Coulibaly5/6/2012 Stade Henri Konan Bédié

4

1.4 Festivals

Unlike football matches and violence, which occur in specific places at specific times, festivals

tend to be celebrated across the country. Even many of those that originated in a specific location,

such as the Fete de Masques in Man, have spread to be significantly observed away from their

historical home. We therefore code the days for festivals but do not map them to a specific

location.

Table 4: FestivalsDate Festival Religious Affiliation12/25/11 Christmas Day Christian01/01/12 New Year’s Day02/05/12 Birth of the Prophet Muslim04/01/12 Bouake Carnival04/08/12 Easter Sunday Christian04/09/12 Easter Monday Christian04/16/12 Fete du Dipri05/01/12 Labor Day

2 Specifications

2.1 Dynamics

The core specifications in the correlates of violence section are all static models with two-way

fixed effects. It is initially plausible that the correct specification includes a lagged dependent

variable rather than fixed effects. This would be a major problem if the process, in fact, contained

a unit root. In this section we see that this is not the case.1 We further see that time trends are not

important in our data.

The steps we take to explore this possibility are as follows. First, we look at the within-unit

autocorrelation in All Calls. Figure 1 shows the results from a representative subprefecture.2 We

can see clear evidence of minor first-order autocorrelation, significantly less than 0.3, and no

1While dynamic panel models which include fixed effects and lagged dependent variables exist, we cannot defendtheir assumptions in this context.

2The figure presents the results from Dabouyo, which hosts the first antenna in the antenna dataset. The graphsfrom the other subprefectures are very similar.

5

Table 5: Coefficients on Lagged Dependent VariableOne Lag 0.969 0.731 0.645 0.605 0.578

(0.00244) (0.0193) (0.0237) (0.0201) (0.0145)Two Lags 0.245 0.0446 0.0338 -0.0197

(0.0188) (0.0126) (0.0115) (0.0115)Three Lags 0.295 0.234 0.226

(0.0136) (0.0285) (0.0293)Four Lags 0.113 -0.00958

(0.0226) (0.0115)Five Lags 0.214

(0.0282)

higher order autocorrelation. This is clearly inconsistent with there being a unit-root, our largest

worry, with a Dickey Fuller test rejecting the null of a unit root convincingly, with an inverse

χ2(1682) of 23,500, against a 0.001 probability level of 1,867. Recall that we always cluster on

the units, which will correct the error terms for this minor autocorrelation.

When we replace the fixed effects with lagged dependent variables in Table 5, the coefficient

on the lagged dependent variable is greater than 0.96. This is because it is picking up the

differences in average levels between the units (antennas or subprefectures). Intuitively this makes

sense. An antenna in Bouaké (a prosperous city) is likely to have more calls than one in Odienné

(a Sahelian town) on both days t and t +1. However, it is not that a larger number of calls on one

day caused the greater number on the next day, but rather that differences in the two areas lead to

fundamentally different levels of calling behavior. If we add additional lags, the coefficients on

the lags move up and down, with sign and significance of multiple lags flipping when another is

added, as would be expected given the scenario just described. We take this to be clear evidence

that the correct specification is fixed effects rather than lagged dependent variables.

Finally, we considered whether we were mistaking a trend for an effect. It is important to note

that a time trend (or region specific time trends) are simply restrictions on the region-day fixed

effects. Therefore, if time trends are correct, then the coefficients on violence estimated with fixed

effects will still be consistent, just slightly less powerful. However, if the fixed effects are correct

the estimates using time trends will likely be inconsistent due to omitted variable bias. An F-test

of the restriction gives a F(260, 27668)= 52.54, again well above the 0.001 critical value of 1.3.

6

Figure 1: Serial Correlation in Dabouyo

2.2 Number of Lags

In the main body of the paper, we use a consistent 4 day window around violent events. Table

7 considers why we made this choice. It starts with an eight day window around the violence

and steps down a day at a time to a three day window. For each specification we then test the

hypothesis that there is no change in All calls more than n days before the violence, for all values

of n between 3 and the largest lag in the specification. For leads of more than 4 days, we can

reject the null hypothesis that the coefficients are jointly 0 in 9 of the 10 tests. However, once we

include the fourth lead it becomes hard to reject the null. This implies that four days before the

event is when behavior starts to change.3 Figure 2 presents the first column of Table 7 visually.

The reader can see the common increase in calls starting four days before the events which then

persists for some time afterward.

3Readers who are concerned about multicollinearity among our variables can also note that the removal ofvariables in the restricted model leaves the remaining coefficients and standard errors almost untouched, which wouldbe unlikely were multicollinearity a problem here.

7

Table 6: Subscriber data: mobile phone usage around violent incidents(1) (2) (3) (4) (5)

ncalls logncalls nuniquecaller lognuniquecaller movein8 Days 449.5 -0.0352 56.35 0.0113 -5.367Before (569.1) (0.0443) (43.68) (0.0288) (4.887)7 Days 2553.2∗ 0.366∗∗∗ 192.0∗∗ 0.212∗∗∗ 0.267Before (1390.6) (0.101) (95.72) (0.0573) (6.657)6 Days 1047.4 0.0428 105.4 0.0392 11.54Before (965.8) (0.0607) (80.41) (0.0383) (9.099)5 Days 376.0 -0.343∗∗∗ 44.08 -0.222∗∗∗ 8.897Before (1317.0) (0.116) (101.6) (0.0832) (7.151)4 Days 2158.3∗∗ 0.150∗∗ 109.2 0.0825∗∗ -9.055Before (988.9) (0.0613) (66.40) (0.0399) (7.368)3 Days 1292.8∗∗ 0.117∗∗ 82.48∗ 0.0675∗∗ -1.889Before (579.9) (0.0453) (42.81) (0.0286) (4.697)2 Days 1494.8∗ 0.0883 112.5∗ 0.0642∗ -1.173Before (801.6) (0.0582) (65.11) (0.0379) (3.473)1 Day 1545.9∗∗ 0.0684 116.1∗∗ 0.0539∗ 0.0213Before (689.8) (0.0479) (48.94) (0.0309) (3.238)Day of 2384.1∗∗ 0.170∗∗∗ 183.4∗∗ 0.116∗∗∗ 0.0139Violence (970.1) (0.0649) (74.48) (0.0442) (3.240)1 Day 1804.1 0.0914 137.7∗ 0.0770∗ -2.042After (1275.6) (0.0646) (77.13) (0.0417) (3.671)2 Days 879.2 -0.0567 71.70 -0.0214 -5.343After (779.8) (0.0807) (52.04) (0.0543) (4.712)3 Days 1454.1 0.00781 33.70 0.00587 1.128After (920.8) (0.0451) (43.23) (0.0306) (2.634)4 Days 1525.7∗∗ 0.103∗ 88.31∗ 0.0785∗ -3.042After (647.2) (0.0558) (46.97) (0.0406) (2.807)5 Days 1545.8∗ 0.139∗∗ 87.15 0.0866∗ -3.156After (793.0) (0.0642) (61.13) (0.0457) (3.245)6 Days 409.0 0.0248 62.95 0.0388 -6.509∗∗∗

After (689.8) (0.0456) (46.14) (0.0308) (2.521)7 Days -1251.3 -0.112∗∗ -52.35 -0.0376 -4.014After (832.8) (0.0569) (57.79) (0.0348) (2.824)8 Days 722.6 -0.0126 51.51 0.0140 0.874After (819.4) (0.0731) (67.02) (0.0495) (3.988)N 25889 25889 25889 25889 25889Standard errors in parentheses∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

8

Table 7: How large a temporal window is best?(1) (2) (3) (4) (5) (6)

Dependent Variable Always All Calls8 Days Bef. 449.5Violence (569.1)7 Days Bef. 2553.2∗ 2505.5∗

Violence (1390.6) (1361.1)6 Days Bef. 1047.4 1089.1 1076.7Violence (965.8) (915.1) (829.1)5 Days Bef. 376.0 380.4 357.9 295.5Violence (1317.0) (1206.9) (1202.9) (1184.3)4 Days Bef. 2158.3∗∗ 2108.0∗∗ 2016.5∗∗ 1963.9∗∗ 1927.2∗∗

Violence (988.9) (944.8) (917.8) (888.9) (862.9)3 Days Bef. 1292.8∗∗ 1279.3∗∗ 1225.9∗∗ 1175.1∗∗ 1143.2∗∗ 1065.3∗∗

Violence (579.9) (570.0) (535.3) (512.2) (497.0) (508.4)2 Days Bef. 1494.8∗ 1519.2∗∗ 1488.6∗∗ 1433.8∗∗ 1471.3∗∗ 1814.8∗

Violence (801.6) (758.0) (728.3) (692.3) (711.4) (968.9)1 Day Bef. 1545.9∗∗ 1529.2∗∗ 1489.7∗∗ 1447.3∗∗ 1331.8∗∗ 1294.2∗∗

Violence (689.8) (679.2) (656.6) (635.1) (583.4) (576.9)Day of 2384.1∗∗ 2356.5∗∗ 2404.8∗∗ 1589.2∗∗∗ 1563.8∗∗∗ 1528.4∗∗∗

Violence (970.1) (920.6) (943.1) (604.2) (591.4) (584.5)1 Day Post 1804.1 1707.7 1247.8 1241.1 1211.1 1207.6Violence (1275.6) (1245.7) (804.9) (804.3) (789.6) (790.5)2 Days Post 879.2 470.6 477.1 479.3 463.6 448.3Violence (779.8) (531.8) (521.6) (505.3) (496.4) (490.2)3 Days Post 1454.1 1375.8 1352.5 1339.4 1327.4 1322.0Violence (920.8) (897.9) (875.6) (857.5) (843.5) (864.6)4 Days Post 1525.7∗∗ 1480.2∗∗ 1499.2∗∗ 1450.9∗∗ 1432.1∗∗

Violence (647.2) (618.5) (609.1) (589.4) (577.0)5 Days Post 1545.8∗ 1505.4∗∗ 1468.4∗∗ 1463.2∗∗

Violence (793.0) (754.9) (725.8) (708.4)6 Days Post 409.0 377.8 363.0Violence (689.8) (667.0) (650.8)7 Days Post -1251.3 -1265.1Violence (832.8) (821.3)8 Days Post 722.6Violence (819.4)Standard errors in parentheses∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

9

Figure 2: Eight Leads and Lags of Violence

10

3 Additional Results

3.1 Sundays and the Election

Violence is heavily concentrated on Sundays, with an extra large increase on election Sunday. Here

we show that neither of these factors are driving the results. It is important to remember that there

are day-region fixed effects, which will pick up any common increases in the independent variables

on Sundays or around the election (which are just a combination of individual region-days). Here

we confirm that this is sufficient. In Table 8 we can see that the correlations between violence and

calls are general. This table presents the results on call duration: As to eliminate fixed effects

without adding in lagged dependent variables, as we do in the final columns, we need a dependent

variable which is generally the same order of magnitude across units, which duration satisfies.

Column 1 is a reminder of the shortening of call duration around violence from the main body of

the paper. Column 2 shows that even when excluding the weeks before and after the election (thus

accounting for both prospective and retrospective effects of the election) calls are significantly

shorter before violence. Because of the infrequence of violence, this also excludes all cases of

multiple violent events in the same location within a week of each other. Column 3 excludes

Sundays from the regression, but not the leads or lags of events which occurred on them. Again,

nothing substantively changes. Next, Column 4 removes all events which occurred on Sundays

from the dataset. Again, there is a (statistically and substantively) significant decrease in the

length of calls, despite cutting the effective number of events in half. In Column 5, we replace the

day-region fixed effects with day-of-the-week effects. Column 6 replaces the fixed effects with

the sum of previous violent events, and Column 7 combines the sum of previous violent events

with day-of-the-week fixed effects. Finally, Column 8 restricts to only the more violent days of

the week, also replacing the fixed effects with the sum of previous violent events as in Column

7. In sum, the general result of shortening call duration before violence persists both through

changes in the fixed effect strategies and through excluding various subsets of the data.

11

Tabl

e8:

Rel

axin

gfix

edef

fect

san

dda

tasu

bset

s(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)A

llN

oFi

rst

No

No

Sun

Day

-of-

Wee

kN

oA

nten

naFi

xed

Eff

ects

Dat

es2

wee

ksSu

nday

sV

iole

nce

Fixe

dE

ffec

tsA

llD

ay-o

f-w

eek

FEs

Onl

yT

hurs

day-

Sund

ay4

Day

s-3

.951

∗∗∗

-3.6

59∗∗

∗-4

.166

∗∗∗

-19.

47∗∗

∗-2

.671

∗∗∗

-4.2

82∗∗

∗-2

.988

∗∗1.

959

Bef

ore

(1.1

75)

(1.3

28)

(1.2

11)

(2.7

70)

(0.9

97)

(1.4

23)

(1.4

85)

(1.7

01)

3D

ays

-2.8

09∗∗

∗-2

.132

∗-2

.654

∗∗-3

.670

∗∗-8

.527

∗∗∗

-3.1

81∗∗

-8.8

68∗∗∗

-5.3

06∗∗∗

Bef

ore

(1.0

79)

(1.2

49)

(1.0

84)

(1.7

88)

(0.8

87)

(1.4

23)

(1.4

85)

(1.6

98)

2D

ays

-3.6

10∗∗

∗-3

.255

∗∗-3

.576

∗∗∗

-2.4

97-2

.352

∗∗-4

.129

∗∗∗

-2.8

04∗

-8.6

68B

efor

e(1

.165

)(1

.343

)(1

.158

)(1

.904

)(1

.101

)(1

.418

)(1

.478

)(6

.394

)1

Day

-1.7

41-2

.094

2.40

2∗∗

-4.7

67∗∗

∗-4

.264

∗∗∗

-2.4

06∗

-4.8

90∗∗

∗-1

9.76

∗∗∗

Bef

ore

(1.1

39)

(1.4

68)

(1.0

71)

(1.4

74)

(1.0

10)

(1.3

08)

(1.3

54)

(4.3

13)

Day

of-4

.657

∗∗∗

-5.5

13∗∗

∗-3

.699

∗∗-4

.243

∗∗∗

-2.7

59∗∗

∗-5

.759

∗∗∗

-3.7

19∗∗

∗-1

6.79

∗∗∗

Vio

lenc

e(1

.085

)(1

.383

)(1

.459

)(1

.539

)(0

.942

)(1

.317

)(1

.361

)(1

.994

)1

Day

-2.5

82∗∗

-3.2

41∗∗

-2.5

32∗∗

-1.0

28-5

.163

∗∗∗

-3.7

17∗∗

∗-6

.196

∗∗∗

-12.

51∗∗∗

Aft

er(1

.048

)(1

.324

)(1

.043

)(1

.737

)(0

.951

)(1

.318

)(1

.361

)(1

.997

)2

Day

s0.

184

0.03

14-6

.135

∗∗∗

-2.4

270.

658

-0.9

31-0

.408

5.19

0∗∗∗

Aft

er(1

.273

)(1

.609

)(1

.440

)(1

.571

)(1

.249

)(1

.324

)(1

.368

)(1

.497

)3

Day

s-0

.751

-1.3

48-0

.642

-1.1

930.

0123

-1.8

33-1

.044

4.64

0∗∗∗

Aft

er(1

.220

)(1

.448

)(1

.239

)(1

.483

)(1

.121

)(1

.332

)(1

.377

)(1

.741

)4

Day

s-1

1.67

∗∗∗

-14.

08∗∗

∗-1

1.73

∗∗∗

-3.4

99∗∗

1.65

9-1

2.80

∗∗∗

0.60

29.

270∗

∗∗

Aft

er(1

.957

)(2

.283

)(1

.962

)(1

.540

)(1

.513

)(1

.332

)(1

.377

)(1

.741

)Pr

evio

us0.

516∗

∗∗0.

356∗

∗∗-0

.336

∗

Vio

lenc

e(0

.116

)(0

.122

)(0

.179

)A

nten

naFE

sX

XX

XX

Day

-reg

ion

FEs

XX

XX

XX

Day

-of-

Wee

kFE

sX

XN

9422

787

274

8052

026

7894

227

9422

794

227

4183

1St

anda

rder

rors

clus

tere

dby

ante

nna

inpa

rent

hese

s∗

p<

0.10

,∗∗

p<

0.05

,∗∗∗

p<

0.01

12

3.2 Additional Robustness

In the paper, we vary the severity and distance to violence in the antenna data. Here we demonstrate

that the same robustness checks on the subscriber data produces equivalent results

13

Tabl

e9:

Var

ying

the

dist

ance

tovi

olen

ce(1

)(2

)(3

)(4

)(5

)(6

)(7

)0.

25◦

0.50

◦0.

75◦

1.00

◦In

clud

ing

date

Exa

ctD

ate

Fata

lU

ncer

tain

Onl

yO

nly

4D

ays

0.02

570.

147∗

∗∗0.

124∗

∗∗0.

0684

∗∗∗

0.03

74∗

0.07

69∗∗

0.04

17∗∗

∗

Bef

ore

(0.0

927)

(0.0

428)

(0.0

318)

(0.0

253)

(0.0

197)

(0.0

346)

(0.0

156)

3D

ays

0.03

110.

150∗

∗∗0.

108∗

∗∗0.

0390

0.03

45∗∗

0.06

47∗∗

0.06

94∗∗

∗

Bef

ore

(0.0

944)

(0.0

441)

(0.0

330)

(0.0

255)

(0.0

147)

(0.0

255)

(0.0

167)

2D

ays

0.07

440.

192∗

∗∗0.

156∗

∗∗0.

104∗

∗∗0.

0490

∗∗0.

0717

∗∗0.

0700

∗∗∗

Bef

ore

(0.1

00)

(0.0

497)

(0.0

362)

(0.0

283)

(0.0

206)

(0.0

341)

(0.0

217)

1D

ay0.

0931

0.14

5∗∗∗

0.14

4∗∗∗

0.12

1∗∗∗

0.01

420.

0307

0.04

07∗∗

Bef

ore

(0.0

614)

(0.0

392)

(0.0

283)

(0.0

246)

(0.0

118)

(0.0

209)

(0.0

167)

Day

of0.

171∗

∗∗0.

256∗

∗∗0.

257∗

∗∗0.

199∗

∗∗0.

0587

∗∗∗

0.09

26∗∗

∗0.

0715

∗∗∗

Vio

lenc

e(0

.060

5)(0

.038

8)(0

.031

0)(0

.030

9)(0

.017

7)(0

.031

5)(0

.019

2)

1D

ay0.

0084

40.

105∗

∗∗0.

104∗

∗∗0.

0642

∗∗0.

0376

∗∗0.

0717

∗∗0.

0537

∗∗

Aft

er(0

.062

2)(0

.038

2)(0

.029

2)(0

.028

3)(0

.015

3)(0

.029

5)(0

.022

0)

2D

ays

0.00

856

0.07

18∗

0.06

55∗∗

0.08

23∗∗

∗-0

.061

5∗∗∗

-0.0

0931

-0.0

758∗

∗

Aft

er(0

.063

7)(0

.041

0)(0

.029

9)(0

.026

5)(0

.023

1)(0

.034

7)(0

.036

2)

3D

ays

-0.0

438

0.04

170.

0510

∗0.

0656

∗∗∗

-0.0

338∗

∗0.

0145

-0.0

689∗

∗∗

Aft

er(0

.063

7)(0

.035

9)(0

.026

4)(0

.023

5)(0

.016

8)(0

.025

9)(0

.026

7)

4D

ays

0.02

880.

0916

∗∗0.

0783

∗∗∗

0.07

33∗∗

∗0.

129∗

∗∗0.

0762

∗∗0.

189∗

∗∗

Aft

er(0

.062

5)(0

.035

9)(0

.026

4)(0

.024

1)(0

.027

0)(0

.034

3)(0

.048

1)

Adj

-R2

0.69

470.

6951

0.69

540.

6952

0.94

60.

9459

0.94

59N

9422

794

227

9422

794

227

2847

328

473

All

spec

ifica

tions

incl

ude

ante

nna

orsu

bpre

fect

ure

and

regi

on-d

ayfix

edef

fect

s.St

anda

rder

rors

clus

tere

dby

ante

nna

orsu

bpre

fect

ure

inpa

rent

hese

s∗

p<

0.10

,∗∗

p<

0.05

,∗∗∗

p<

0.01

14

3.3 Festivals

Here we present the results of the effect of festivals on calling behavior. This demonstrates a

third signature, neither matching that of violence or football. People are clearly able to anticipate

national festivals, and respond to them by activating wider, positive social networks. Figure 3 in

the main text compares violence, football, and festivals.

This table explores how calling patterns change ahead of major festivals. These are national

events including religious observances such as Christmas and Eid and secular holidays such as

New Year’s Day and Labour Day. We are forced, by the fact that most major festivals take place

nationwide, to relax the day-region fixed effects, instead including a complete set of day-of-week

dummies in conjunction with week fixed effects. We observe a large increase in calls starting

the day before a festival and continuing for 4 days after (Column 1), driven by an increase in

callers (Column 2). Column 3 demonstrates that this same pattern can be seen in within-network

calls. Interestingly, Column 4 shows a significant shortening of these calls, of up to 10%. In

conjunction with Columns 5 and 6, which show that the day before the festival is marked by a

significant (7%) increase in calls per caller and out-of-network calls (8%), we conclude that this

behavior is consistent with calling acquaintances to pass on holiday greetings

15

Tabl

e10

:Mob

ileph

one

usag

ear

ound

natio

nalf

estiv

als

(1)

(2)

(3)

(4)

(5)

(6)

log(

All

log(

log(

In-N

wk

log(

Cal

llo

g(A

lllo

g(In

-Nw

kC

alls

)C

alle

rs)

Cal

ls)

Dur

atio

n)C

alls

)C

alls

)4

Day

s-0

.156

∗∗∗

-0.0

754∗

∗∗-0

.229

∗∗∗

-0.0

0811

∗∗∗

-0.0

412∗

∗∗-0

.046

8∗∗∗

Bef

ore

(0.0

100)

(0.0

0646

)(0

.004

28)

(0.0

0222

)(0

.002

53)

(0.0

168)

3D

ays

0.02

12∗

0.03

53∗∗

∗-0

.003

72-0

.020

6∗∗∗

-0.0

352∗

∗∗-0

.022

4∗∗∗

Bef

ore

(0.0

121)

(0.0

0898

)(0

.006

68)

(0.0

0303

)(0

.001

94)

(0.0

0453

)2

Day

s-0

.271

∗∗∗

-0.1

89∗∗

∗-0

.267

∗∗∗

0.06

11∗∗

∗0.

0130

∗∗0.

0408

Bef

ore

(0.0

129)

(0.0

0855

)(0

.007

72)

(0.0

0245

)(0

.005

31)

(0.0

306)

1D

ay0.

363∗

∗∗0.

204∗

∗∗0.

358∗

∗∗-0

.034

8∗∗∗

0.06

96∗∗

∗-0

.081

3∗∗

Bef

ore

(0.0

150)

(0.0

116)

(0.0

109)

(0.0

0446

)(0

.005

18)

(0.0

379)

Day

of0.

414∗

∗∗0.

264∗

∗∗0.

464∗

∗∗-0

.031

5∗∗∗

0.02

17∗∗

∗-0

.006

05Fe

stiv

al(0

.016

7)(0

.013

2)(0

.008

75)

(0.0

0459

)(0

.006

54)

(0.0

416)

1D

ay0.

373∗

∗∗0.

247∗

∗∗0.

449∗

∗∗-0

.060

1∗∗∗

0.01

09∗

0.01

53A

fter

(0.0

139)

(0.0

110)

(0.0

0665

)(0

.003

82)

(0.0

0659

)(0

.038

7)2

Day

s0.

413∗

∗∗0.

282∗

∗∗0.

501∗

∗∗-0

.100

∗∗∗

-0.0

0566

0.03

07A

fter

(0.0

118)

(0.0

0926

)(0

.006

60)

(0.0

0388

)(0

.007

47)

(0.0

421)

3D

ays

0.43

3∗∗∗

0.27

1∗∗∗

0.48

3∗∗∗

-0.0

907∗

∗∗0.

0302

∗∗∗

-0.0

0273

Aft

er(0

.018

7)(0

.014

1)(0

.010

7)(0

.005

24)

(0.0

0691

)(0

.043

0)4

Day

s0.

546∗

∗∗0.

264∗

∗∗0.

581∗

∗∗-0

.082

1∗∗∗

0.16

6∗∗∗

-0.0

459

Aft

er(0

.020

2)(0

.014

2)(0

.008

46)

(0.0

0465

)(0

.006

62)

(0.0

559)

log(

1.47

8∗∗∗

Cal

lers

)(0

.025

4)lo

g(A

ll1.

192∗

∗∗

Cal

ls)

(0.1

06)

Adj

-R2

0.16

860.

1256

0.27

740.

1061

0.90

460.

3405

N28

473

2847

394

227

9422

794

227

9422

7A

llsp

ecifi

catio

nsin

clud

ean

tenn

aor

subp

refe

ctur

efix

edef

fect

s,da

y-of

-wee

k,an

dw

eek

fixed

effe

cts.

Stan

dard

erro

rscl

uste

red

byan

tenn

aor

subp

refe

ctur

ein

pare

nthe

ses

∗p<

0.10

,∗∗

p<

0.05

,∗∗∗

p<

0.01

16

Figure 3: Comparison of behavioral responses to violence, football, and festivalsPanel A: Percent Change t days after violent incidents

Panel B: Percent Change t days after football matches

Panel C: Percent Change t days after national festivals

17