you will hear of skirmishes and rumors of skirmishes: mobile communication surrounding ... · 2016....
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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.†[email protected]‡[email protected]§[email protected]
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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.
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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.
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Figu
re1:
Vio
lenc
ean
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ellT
ower
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ates
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28
Figu
re2:
Beh
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tovi
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nelA
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29
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
30
Tabl
e1:
Subs
crib
erda
ta:m
obile
phon
eus
age
arou
ndvi
olen
tinc
iden
ts(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
s19
27.2∗∗
0.14
2∗∗∗
92.1
9∗0.
0769∗∗
-7.7
0953
9.6
0.03
62∗∗
Bef
ore
(862
.9)
(0.0
532)
(55.
01)
(0.0
346)
(7.5
80)
(435
.1)
(0.0
160)
3D
ays
1143
.2∗∗
0.11
0∗∗∗
71.4
8∗∗
0.06
47∗∗
-2.2
2667
.41
0.02
1B
efor
e(4
97.0
)(0
.040
6)(3
5.14
)(0
.025
5)(4
.770
)(2
87.0
)(0
.015
6)2
Day
s14
71.3∗∗
0.09
78∗
112.
2∗∗
0.07
17∗∗
-1.5
88-2
17.2
-0.0
0084
2B
efor
e(7
11.4
)(0
.052
3)(5
5.87
)(0
.034
1)(3
.618
)(2
71.3
)(0
.015
6)1
Day
1331
.8∗∗
0.04
6478
.84∗∗∗
0.03
075.
384
145.
20.
0042
Bef
ore
(583
.4)
(0.0
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∗∗
0.26
5∗∗∗
Aft
er(0
.031
6)(0
.034
3)(0
.020
2)(0
.018
0)(0
.035
5)(0
.035
9)(0
.045
2)A
dj-R
20.
9468
0.94
690.
9459
0.94
590.
6953
0.69
510.
6952
N28
473
2847
328
473
2847
394
227
9422
794
227
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
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