For FARC's sake: Demobilizing the oldest guerrilla in modern history (Preliminary work. Please do not share or cite without permission)
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For FARC’s sake:
Demobilizing the oldest guerrilla in modern history∗
(Preliminary work. Please do not share or cite without permission)
Juan P. Aparicio† Michael Jetter‡ Christopher Parsons§
April 2, 2021
Abstract
How can a state demobilize militants during a civil war? We study the effects of a messaging cam-
paign aimed at demobilizing FARC rebels, in which the Colombian government aired short commer-
cials during national football games between 2005 and 2016. Our identification strategy exploits the
exogenous timing of games (in terms of both days and time of day) and geographical variation in ex-
posure due to rain-induced signal weakness to isolate causal effects on demobilizations in all 1,122
Colombian municipalities. Our results reveal statistically powerful and quantitatively large message
effects, indicating that these messages accounted for approximately 17% of the group that signed the
2016 peace deal with the government. Applying insights from social psychology, we find messages
worked best when carrying emotional undertones, reminding rebels of their family identity.
JEL Classifications: D74, L82, N46
Keywords: Civil war, Media, Propaganda, Demobilization
∗
We are grateful for comments from seminar participants at Curtin University, The University of Western Australia, Auck-
land University, University of Vienna, University of Luxembourg, Universidad de la Sabana, Universidad del Rosario, Univer-
sidad de los Andes, The Research in Terrorism Seminar, and The Workshop on Behavioral Insights in Development and Peace
Building. We are especially thankful to Ana Arjona, Andres Zambrano, Christopher Blattman, Joanna Clifton-Sprigg, José
Gómez, Juan Camilo Chaparro, Maarten Voors, Marcela Ibañez, María del Pilar López, Santiago Tobón, and Tushar Bharati
for stimulating discussions. We also thank Juan Pablo Garcia from Lowe SSP3, for providing us with invaluable insides. Juan
P. Aparicio is grateful to the Australian Government and the University of Western Australia for funding from the Research
Training Program (RTP) scholarship.
†
Corresponding author. University of Western Australia, 35 Stirling Highway, Crawley 6009, WA. E-mail: ju-
posada93@gmail.com.
‡
University of Western Australia, IZA (Bonn), and CESifo (Munich), 35 Stirling Highway, Crawley 6009, WA. Email:
mjetter7@gmail.com.
§
University of Western Australia, 35 Stirling Highway, Crawley 6009, WA. E-mail: christopher.parsons@uwa.edu.au
1 Introduction
One in every five people on the planet has experienced the hardships of civil conflict (Blattman and
Miguel, 2010). These wars typically leave behind traces of devastation, with especially dire conse-
quences for the civil population (Collier et al., 2008; Blattman and Miguel, 2010). Families torn apart
and concerns around the legitimacy of the state among the defeated are just some of the challenges post-
conflict societies may encounter (Licklider, 1995; Mason and Fett, 1996; Walter, 1997; Fearon, 2004;
Podder, 2017; Kaplan and Nussio, 2018). What if there was a way in which a struggling state was able to
bleed insurgencies out of soldiers and bring families back together? We study how a targeted propaganda
campaign from the Colombian government was able to convince members of the world’s oldest guerrilla
to give up arms and return home.
“For me, freedom was possible. Freedom can be possible for you too. Here you have friends that
will help you make the decision. Cross the bridge, come to freedom. Think about it, there is another
life, demobilization is the way out." 1 This is just one of the hundreds of short messages the Colombian
government aired during games of the national football team, urging members of the Fuerzas Armadas
Revolucionarias de Colombia (FARC) to leave the armed group and return home.
Since 1949, the government had been fighting the guerrilla group by traditional means, but in 2005,
the Ministry of Defense decided to experiment with propaganda as an additional tool to fight the rebels
(Fattal, 2018). Messages would air during the games, when the soldiers would not be able to avoid them,
asking them to return home (Noticias Caracol, 2015). With the passing of the years, they became a
regular feature of every game of the national team and only ended with the signature of the peace deal
between the state and the armed group in September 2016 (Caracol Radio, 2015).
We take advantage of this messaging period to understand whether and, if so, how propaganda affects
demobilization dynamics in the Colombian context. As a simple motivation, Figure 1 presents how daily
average demobilizations behave for games inside and outside the said messaging period. We find that the
days following a game with propaganda present, on average, 3.5 times more demobilizations compared
to a regular day.
1
Ex-presidential candidate and former kidnappee, Íngrid Betancourt, inviting soldiers to demobilize.
1
Figure 1: National demobilization means outside and during the message period, averaging daily data
from January 1, 2003 to September 26, 2016 for all 1,122 Colombian municipalities.
In the following pages, we aim to understand how these messages affected demobilization decisions
among rebel soldiers. We use standard economics theory to formalize the rebels’ demobilization deci-
sion, provide causal estimates of demobilizations due to exposure to propagandistic messages, and apply
insights from social psychology to understand how these messages worked. We leverage our unique
set-up, in which we account for daily demobilizations in each of the 1,122 Colombian municipalities.
Beyond providing evidence on the effectiveness of propaganda, we aim to shed light on the short run
dynamics of propaganda exposure and the reasons behind its persuasive power.
Thanks to a growing literature on the effects of media in conflict settings, we now know that pro-
paganda can be a powerful driver of violence. For example, Yanagizawa-Drott (2014) and Blouin and
Mukand (2018) explain how radio stations altered the Rwandan genocide dynamics, while DellaVigna
et al. (2014) describe how, even in a post-conflict scenario, cross-border nationalistic radio still inspires
animosity among Croats against Serbs. On the other hand, and perhaps closer to us, Armand et al. (2020)
present the case of a demobilization campaign aimed at the Lord’s Liberation Army, finding sizable ef-
fects not only for demobilizations, but also in terms of violence reduction. 2
2
Outside conflict settings, media effects have been widely studied. For example, La Ferrara et al. (2012) and Kearney and
Levine (2015) show that exposure to certain TV shows can affect family forming decisions. Farré and Fasani (2013) made the
case that it could also affect migrating decisions. Olken (2009) suggests access to mass media can erode social ties altogether.
Considering electoral outcomes, DellaVigna and Kaplan (2007) and Enikolopov et al. (2011) provide excellent examples of the
media’s persuasive power on voting.
2
Although evidence seems conclusive, an overarching theme across the relevant literature has been
the almost exclusive use of quantitative tools, spearheaded by cross-sectional analysis (or repeated cross-
sections), to provide causal estimates of the overall effects of propaganda. These insights have allowed
us to understand that media can be a powerful decision driver in conflict settings and have been able
to show how effective propaganda is. With this paper, however, we aim to advance our understanding
of how propaganda operates in two unique ways: (i) Studying the immediate effects of propaganda
exposure in a conflict setting. Rather than providing cumulative effects, we are able to show that pro-
pagandistic messages work rather quickly and fade soon thereafter. (ii) Applying insights from social
psychology to provide, and empirically test, a theoretical model of how propaganda helps soldiers reach
a demobilization decision. We provide results consistent with the hypothesis that these messages work
by engaging the soldiers’ central route to persuasion and eliciting an intergroup comparison between
their current armed group and their families.
Moving beyond the use of cross-sectional data, we introduce an approach to study the effects of pro-
paganda that includes information from the entire set of Colombian municipalities (1,122), with daily
data from 2003 to 2016. We provide causal estimates of rebel demobilizations due to propaganda ex-
posure, by offering an approach that relies on the timing of exposure rather than signal availability. We
introduce an identification strategy based on the exogeneity of assigned game days and rain variation
across municipalities, leveraging rain’s disruptive effect on electromagnetic waves necessary for TV and
radio broadcasts (Lin, 1973; Crane, 1975; Ippolito, 1981; Ishimaru et al., 1982; Tewari et al., 1990;
Qingling and Li, 2006).
Our results suggest that messages broadcasted during football games were responsible for demobi-
lizations accounting for approximately 17% of the group that signed the peace deal with the government
in 2016 (FIP, 2017). This result emerges consistently in a range of empirical specifications, employ-
ing alternative (i) identification strategies; (ii) measures and sources of rain; (iii) econometric models;
(iv) panel data units of measure (e.g., states instead of municipalities); as well as (v) different control
variables. Additionally, we introduce a battery of placebo estimations including: (i) The exploration of
collective demobilizations instead of individual surrenders, (ii) demobilizations leading up to a game,
and (iii) randomly assigning fake games to our sample period.
3
We take advantage of the rich time dimension of our data to explore the duration of propaganda
effects. We leverage the fact that international football games are usually played in pairs to provide
evidence that re-exposure, in the form of a second game, leads to stronger demobilization effects (FIFA,
2013). We also explore how these effects behave over time, employing alternative time windows for
demobilization beyond the day after a game. We find evidence suggesting these work immediately for
1-2 days before reverting back to the sample mean, consistent with the interpretation that propaganda
works by engaging emotional, rather that logical thinking (Gleitman, 1984; Petty and Briñol, 2008).
Finally, to test the mechanisms described by our theoretical model, we exploit three different settings:
First, we compare the demobilization outcomes of two propaganda campaigns, focused around different
themes, testing the Elaboration Likelihood Model (ELM) and Intergroup Conflict Theory explanations
on how emotions and intergroup comparisons lead to the desertion of one’s group. Second, we exploit
the quasi-random outcome of football games and the expectation associated to the result as a primer
for the soldiers’ mood. Consistent with our theoretical predictions, we find that unexpected losses, that
presumably would make soldiers sad, produce stronger demobilization results than expected ones and
games won by the national team. Third, we explore how demobilizations behave after days that favor
intergroup comparisons. We find that days that trigger positive comparisons with the soldiers’ family,
such as family-related holidays and deaths of prominent FARC leaders, increase demobilizations, while
days that trigger negative comparisons, such as state-related holidays and important FARC celebrations,
decrease subsequent demobilizations.
To the best of our knowledge, we are the first to exploit the intertemporal dynamics of a propaganda
campaign lasting more than a decade. We introduce a novel identification strategy, based on the physical
properties of electromagnetic waves, to uncover media effects and advance our understanding of the
mechanisms behind propaganda’s persuasive power by providing, and empirically testing, a theoretical
model that explains the drivers behind propaganda’s persuasiveness.
Overall, this paper aims to contribute to three main fields of research. First, our methodology and
results speak to the literature on the effects of media in general and propaganda in particular, by provid-
ing a systematic approach to the study of the immediate effects and mechanisms behind it. Second, our
findings may enrich the growing literature on the dynamics of civil war and counter-insurgency efforts,
4by providing evidence on the motivations and social attitudes of rebel soldiers. We find that even highly
ideologically invested individuals, such as FARC members, are subject to the effects of well-tailored
messages, suggesting that propaganda may represent an effective anti-insurgency alternative to the over-
all traditional view on war. Our results suggest that propaganda constitute a powerful accessory in the
state-building endeavors of post-conflict societies (Blattman, 2009). Third, we contribute to the grow-
ing literature trying to empirically understand the dynamics of the Colombian conflict (Dal Bó et al.,
2006; Dube and Vargas, 2013; Arjona, 2014; Fergusson et al., 2016, 2020). This is especially impor-
tant, as Colombian media has proven to be an important catalyst for the conflict (Fergusson et al., 2013;
Aparicio and Jetter, 2020).
The reminder of this paper is structured as follows: Section two discusses relevant background in-
formation about the Colombian conflict and the demobilization campaigns. Section three combines
elements from traditional theoretical economic modeling and social psychology insights to formalize the
soldiers’ response to demobilization messages. Section four introduces our data and empirical strategy,
starting with a comparison between games with and without messages and ending with a model that
leverages the disruptive effect of rain on electromagnetic waves to uncover the effect of exposure to pro-
pagandistic messages on demobilizations. Section five presents our main findings on the demobilizing
effect of messages. Section six introduces a discussion on the mechanisms driving the demobilization
response using insights from social psychology. Finally, section seven concludes.
2 Historical Background
2.1 The Colombian Conflict Before 2003
In 1948, after the assassination of then-presidential candidate Jorge Eliécer Gaitán, Colombia entered a
period of rampant bipartisan violence known as La Violencia (The Violence; Richani, 1997, Bushnell,
2007). During these years, a group of peasants decided to form a coalition to defend themselves against
partisan guerrillas that roamed rural areas of Colombia (Bushnell, 2007; Pizarro Leóngomez, 2011).
Cemented in Marxist ideals, and reinforced by the success of the Cuban revolution, the FARC lived their
first years as the armed wing of the Colombian Communist Party (Pizarro Leóngomez, 2011).
5By 1990, not only the Soviet Union had fallen, depriving the FARC of their main ideological ref-
erence, but similar guerrilla groups, like M-19, had signed peace agreements with the government and
became part of the upcoming 1991 constitutional assembly (Pizarro Leóngomez, 2011). It was under
these circumstances that the FARC, rather than giving up arms, transitioned from a ‘party-guerrilla’ to
a ‘military-guerrilla’ to become a full-fledged army, controlling sizable parts of the Colombian territory
and launching large scale attacks against state forces (Pizarro Leóngomez, 2011). Taking advantage of
the lucrative narcotics business left by drug lords, the FARC became a powerful army with presence
all across the country (Molano, 2000; Pizarro Leóngomez, 2011). Military take-overs and mass kid-
nappings became part of daily life, as the armed group grew stronger (Castillo and Balbinotto, 2012;
Caballero Reinoso, 2013; Aparicio and Jetter, 2020).
2.2 The Colombian Conflict Since 2003
It was at the height of the FARC’s power that in 2003 the government decided to introduce a demo-
bilization program in an attempt to bleed the guerrilla out of soldiers by offering them the chance to
reintegrate into the Colombian society (Fattal, 2018). Spearheaded by the Reincorporation and Nor-
malization Agency, the demobilization program offered protection from the armed group the soldier left
behind, tertiary education, and generous financial incentives (Nussio, 2013; Fattal, 2018). Despite these
attractive benefits, the program first experienced little success. Sporadic demobilizations kept it run-
ning, but it was far from the war-ending tool the government had envisioned (Nussio, 2013, 2018; ARN,
2020). Eventually, it became clear that the reason behind the program’s apparent failure was that the
average foot soldier did not even know about the program (Fattal, 2018). Realizing this, the Ministry of
Defense began the production of a series of short messages, centered around the idea that soldiers would
demobilize if they knew about the opportunity the government was offering them (Fattal, 2018).
6Football games of the national team became the prime vessel for the delivery of the messages. On an
average campaign, the broadcasting rights to place the messages in the games would take up to 90% of
the campaign’s budget (Lowe SSP3, 2014). 3 4
The first ever message played in a game of the national football team aired in 2005, in a game
between Colombia and Paraguay. This first round of campaigns was an in-house production by the
Ministry of Defense, characterized by a somber undertone, in which messages reminded FARC members
of their lives before joining the guerrilla (Fattal, 2018). These messages ran uninterrupted in every game
of the national football team until 2010, when the government decided to recruit the help of a well-
renowned advertisement agency to overhaul the communicative strategy of the messages (Fattal, 2018).
The advisement agency contributed insights from successful experiences with consumer goods (Sokoloff,
2014; Samper, 2017) – cheerful messages, featuring members of the Colombian national army invited
FARC members to demobilize by reminding them that we were all part of the same team (Colombia)
(Lowe SSP3, 2014; Fattal, 2018). This new wave of messages began airing just in time for the 2010
World Cup (although Colombia did not qualify) and ran until the signature of the peace agreement
between the government and the FARC on September 2016 (Lowe SSP3, 2014; Caracol Radio, 2015;
Fattal, 2018).5
By 2016, the number of individual FARC demobilizations reached almost 17,000 (ARN, 2020) –
more than the total number of FARC members who surrendered after the 2016 peace agreement (ARN,
2020). Demobilizations are credited as a major reason for the FARC’s decline, as surrendering soldiers
imposed a heavy burden on the guerrilla (Nussio, 2013, 2017, 2018). Demobilizations meant not only the
loss of experienced fighters, forcing the armed group to increasingly turn to inexperienced recruits, but
also the constant leakage of sensitive military information (Bjørkhaug, 2010; McLauchlin, 2015; Hafez,
2017; Oppenheim and Söderström, 2018; Richards, 2018).
3
It is difficult to overstate how popular these games are among Colombians. As an illustration, the most popular TV show
episode ever aired on national TV would not make it on top ten list of the most watched games in the last ten years (Rating
Colombia, 2020).
4
Depetris-Chauvin et al. (2018) also study football games as unifying tools in Ivory Coast’s civil war.
5
There has been speculation about the government’s true intentions behind the messages’ change of tone. For example, Fattal
(2018) and Nussio and Ugarriza (2021) hint to the possibility that the new wave of messages served not as a demobilization
tool, but rather as a public relations campaign to engrace the army with the Colombian people.
73 Theoretical Motivation
Our focus lies on modelling the decision that rebels face on a daily basis – whether (i) to remain with
the group and stay true to the group’s identity or (ii) to leave and pursue their family identity. Staying
produces utility:
Ustay = Irebel , (1)
Where Irebel captures the cumulative benefits of the respective group identity throughout the rest of
the rebel’s life. Leaving incorporates more parameters, as it involves an irreversible decision: Once a
rebel decides to leave their group, returning is usually not possible, with group betrayal often punishable
by death. For example, in an interview with the newspaper "La Campana", an ex-soldier narrates how
threats against their families are often used to dissuade members from leaving. In this same article,
another ex-soldier describes how any attempt to escape is punishable by firing squad execution (La
Campana, 2015).
More formally, the decision to leave would yield utility:
Uleave = βIf amily − L, (2)
Where β captures the value the rebel attaches to living their family identity If amily (with −∞ <
β < ∞). β forms the crucial variable of interest in our model and distributes normally with mean β
and variance σ 2 . We will soon describe its defining features. If amily stands for the returns to living
the family identity. Parameter L describes the costs and risks associated with leaving. It constitutes the
psychological burden of betraying fellow group members, many of whom may have become friends over
time, and the more tangible consequences of leaving, i.e., group retribution, which could lead to death.
3.1 The Decision
The rebel decides to leave the group if and only if Uleave > Ustay , which holds when:
8Irebel + L
β > β∗ = . (3)
If amily
Thus, we can visualize β with:
y
x
β β∗
Figure 2: Distribution of β
Note that we assume β ∗ > β because this realistically captures rebel groups that enjoy sustained
group membership. If β ∗ < β, for example, then group members would be likely to demobilize fre-
quently and the group would not be sustainable. Thus, the interesting case is β ∗ > β, i.e., demobilization
is less common.
3.2 Intergroup Comparisons and Emotional States
We focus on two characteristics that, following individual strains in the psychology and behavioral eco-
nomics literatures, we relate to β and its functional distribution: Intergroup comparisons and the rebel’s
emotional state.
3.2.1 Intergroup Comparisons
First, we incorporate insights from Turner et al. (1987), Tajfel and Turner (2010), and Hogg et al. (2017)
into the model, by linking the realization of β within the bounds of its distribution to intergroup com-
parisons between the soldiers’ current group (the FARC) and their families. More specifically, imagine
psychological cues that remind the rebel of their family, such as a birthday, a family holiday (e.g., Christ-
mas), or an individual item that brings back childhood memories. Such cues could drive the realization
9of β on a given day up or down, depending on whether the psychological cue triggers positive or negative
comparisons associated with the soldier’s family. For example, an increase in β might be associated with
a fond memory of childhood birthday parties or joyous family celebrations. A drop in the realization
of β might be induced by cues that refresh childhood memories of mistreatment or other traumatizing
experiences, such as psychical abuse.
Translated to our setting, we posit that positive comparisons related to the rebel’s family moves β
to the right in Figure 2. Thus, the likelihood to demobilize increases as β > β ∗ becomes more likely.
Similarly, negative comparisons would decrease the likelihood of demobilization. Later, in section 6.4,
we present evidence on how demobilizations differ after family-related and state-related holidays, for
example.
3.2.2 Emotional Sadness and Reassessing One’s Identity
The second element we incorporate comes from social psychology insights that concern the vulnerability
of one’s ruling identity: At which point is a person more likely to reconsider their identity? Petty et al.
(2003), Petty and Briñol (2008), and Petty (2013) highlight the persuasive power of sadness as an emo-
tional state that can press a person to reconsider their choices. “People might think about messages more
when in a sad state rather than a happy one because sadness signals a problem to be solved (Schwarz et al.
(1991)) or because it conveys a sense of uncertainty (Tiedens and Linton (2001)). If sadness increases
thinking over happiness, then sadness should increase persuasion." (Petty and Briñol, 2008, p. 141)
Translated to our setting, this means the likelihood to reconsider increases. We incorporate that idea
by introducing a mean-preserving spread into the distribution of β, which changes Figure 2 to Figure 3.
Everything else equal, any draw from the β distribution would now be more likely to produce β > β ∗ ,
even though β ∗ itself remains unchanged from equation (3). In section 6.3, we explore this relationship
by studying how plausible unrelated primings to the soldiers’ mood affect demobilizations dynamics.
3.3 Theoretical Implications
Taken by themselves, both concepts yield the following insights. First, the likelihood of a given rebel
to demobilize increases once positive comparisons to the person’s alternative identity (e.g., their family)
10y
x
β β∗
Figure 3: Distribution of β with increased persuasion.
are made. Second, the likelihood of demobilization increases as the rebel experiences sadness, leading
to a reassessment of their identity and intergroup comparisons.
Finally, taken together, both concepts produce a powerful proposition:
PROPOSITION 1. Rebels are particularly likely to demobilize in a combined state of sad-
ness and being presented with positive elements of their alternative family identity.
4 Data and Empirical Strategy
Data for our main analysis come from four sources: The Electronic Public Procurement System (SECOP
2), featuring all contracts between the government and the stations broadcasting the messages; the Agency
for Reincorporation and Normalization (ARN), from whom we obtain data on demobilizations; the
Colombian Football Federation game calendar, where we obtain dates and times of all games played
by the national team; and NASA’s Tropical Rainfall Measuring Mission (TRMM), providing local rainfall
data. For additional analyses, we access data from the Institute of Hydrology, Meteorology and Environ-
mental Studies (IDEAM), and the National Center for Historical Memory (CNMH) (table A1). Table 1
documents summary statistics for our main sample of daily data from January 1, 2003 until September
26, 2016, for all 1,122 Colombian municipalities. Overall, this produces 5,660,490 observations.
11Table 1: Summary statistics for all 1,122 municipalities and 5,045 days (N= 5,660,490 observations).
Variable Mean (Std. Dev.) Min. Max. Sourcea Description
Message periodt 0.855 0.352 0 1 SECOP 2 =1 if contract is active
Demobilized FARCi,t 0.003 0.122 0 50 ARN # of demobilized FARC members
Game dayt 0.035 0.184 0 1 FCF =1 if a game by the national football team is contested
Dusk gamet 0.007 0.084 0 1 FCF =1 if a game is contested between 16h-20h
Raini,t 0.326 1.059 0 67 NASA Rainfall rate (mm/h) 16h-19h
Notes:a SECOP 2= Colombia’s government contract archive, accessing the contracts between the Ministry of defense and the
stations broadcasting the games; ARN= Agencia para la Reincorporación y la Normalización (Agency for Reincorporation
and Normalization), accessing the number of demobilized FARC soldiers by day and municipality; FCF= Federación
Colombiana de Futbol, (Colombian Football Federation); NASA= National Aeronautics and Space Administration, Tropical
Rainfall Measuring Mission.
4.1 Data on Demobilizations
We rely on data provided directly by the Colombian Government through the ARN, a government agency
tasked with the reintegration process of former guerrilla soldiers into civilian life. The ARN allows
the government to keep track of every fighter’s post-demobilization development, beginning with the
location and day of their demobilization. We employ the 2019 update of ARN’s data, comprising records
of over 60,000 former fighters, demobilized between January 2001 and December 2018.
The ARN mainly defines two kinds of demobilizations: First, collective demobilizations constitute
events in which a whole squadron negotiates a collective surrendering. Second, an individual demo-
bilization, when a single soldier (or a small group of them) decides to escape from their squadron to
surrender (ARN, 2018, 2019). Our main outcome variable focuses on the individual demobilizations
of FARC members, as they represent the target audience of the demobilization campaigns (Lowe SSP3,
2014; Sokoloff, 2014; Samper, 2017). Table A5, presents an additional analysis predicting collective
demobilizations for different armed groups, were we observe that collective surrendering do not seem to
be driven by messages inside football games. In a given municipality and day, we observe 0.003 individ-
ual demobilizations on average throughout our sample period, which translates to approximately three
demobilizations at the national level (0.003 × 1, 122; Table 1).
12Although the ARN constitutes the primary source for demobilization related data, it is important
to carefully evaluate the potential disadvantages of using such data. First, ARN does not share any
individual characteristics of demobilized soldiers, as these could lead to the identification of ex-soldiers.
Second, the ARN only records soldiers who entered the demobilization program, meaning that soldiers
who attempted to demobilize and their armed group captured are not part of our data.
Although these constitute relevant data characteristics, these limitations are unlikely to introduce a
systematic threat to identification in our setting. For this to bias our estimates, the chance of a successful
demobilization would have to correlate with the Colombian national team game calendar and localized
rain during that game. Although this seems unlikely, we control for rain in non-game days and find that
it has a null effect on demobilizations.
4.2 Data on Propaganda
To identify the periods during which messages where broadcasted, we rely on contractual information
between the Colombian Ministry of Defense and the two stations broadcasting the games of the national
football team: Caracol and RCN (Dinero, 2018). We access information on the individual contracts,
stored under the SECOP 2, a public repository were the Colombian government lists all contracts signed
with third parties (SECOP II, 2020).
We pay special attention to the start and end dates of a particular contract. Throughout our analysis,
the variable “Message period” indicates a binary variable that takes on the value of one if a given day
falls within these two dates, meaning that the contracts between the Ministry and the stations were active.
Importantly, we confirmed the corresponding information analyzing individual games and conducting
interviews with primary sources to guarantee the presence of demobilization messages (El Comercio,
2014; Corona, 2017; Samper, 2017). In our period of interest, 83% of the days fall inside the messaging
period (Table 1).
4.3 Data on Football Games
For our football-related data, we rely on the Colombian Football Federation (FCF) game calendar, which
contains both day and kick-off times of each game of the national team. The calendar is the result of
13several layers of negotiations between FIFA, regional football confederations, and local football federa-
tions (FIFA, 2013, 2019). For example, for the Colombian case, FIFA assigns five to seven international
football windows during the year. After FIFA sets the windows, CONMEBOL (South America’s regional
football confederation) assigns dates for specific games. Finally, after CONMEBOL confirms the dates,
local football federations can decide over the time of day for each game (FIFA, 2013, 2019). For exam-
ple, when Colombia plays at home, the FCF is famous for assigning games at 3:00 PM in the coastal
city of Barranquilla, presumably because players from other countries are not used to the intense humid
heat, providing a slight advantage to the national team (GOAL, 2015). On the other hand, when the
national team plays away, the time of the game depends on the preferences of the other team and the time
difference between the two countries.
During our period of interest, the Colombian team played 176 games, meaning 3.5% of the days
feature a game of the national team (Table 1). Furthermore, for our second empirical analysis we dis-
tinguish between ‘dusk games’, i.e., those encounters scheduled between 16:00h and 20:00h, and games
played at any other time of the day (non-dusk games) to take advantage of specific FARC activities. The
average duration of a football game is two hours, and we take these two hours as a buffer zone around
dusk (18:00h in Colombia, as the country lies on the equator and does not suffer from radical changes
in sundown time). During our period of interest, the Colombian team played 35 of such dusk games,
equivalent to 0.7% of all days.
4.4 Rain Data
We collect rainfall data from NASA’s TRMM, a joint climate research mission between NASA and the
Japan Aerospace Exploration Agency (NASA, 2020). We use the highest resolution offered by the satel-
lites (0.25 latitude × 0.25 longitude degrees; ≈ 27km2 at the equator) and record gridded rain predic-
tions based on cloud formation. These measures are available on a three-hour basis and are expressed
as average mm/h during that time, providing us with eight different measures of rain per day (NASA,
2020).6
6
We refer to https://trmm.gsfc.nasa.gov/3b42.html for an in-depth explanation of the TRMM’s work.
14For our purposes, we are particularly interested in the measure of rain that overlaps with dusk games,
which allow us to identify weather conditions for each municipality during the time of these games.
4.5 Empirical Strategy
For our main estimations, we present a sequence of analyses that build on each other to isolate causal
estimates of propaganda exposure on municipality-level demobilizations. First, we exploit the quasi-
random game dates to explore the municipality-level number of demobilizations the day after a game
played during the propaganda period. Second, we sort games by their kick-off times, distinguishing
between dusk and non-dusk games to explore whether matches that coincide with the FARC’s leisure
time feature different demobilization effects the following day. Finally, we leverage rain’s disruptive
effect on electromagnetic waves, necessary for transmission of the games, to exploit geographical and
temporal variations in the exposure to propaganda messages.
Throughout our analysis, we employ a range of control variables intended to capture unobservable
statistical variation across time and municipalities. Time-specific covariates aim to capture government
and FARC-related activities that could independently affect demobilizations, such as peace negotiations;
the inherent variation in the number of FARC members at each given time; as well as particularities
of seasons or weekdays. On the other hand, Municipality-specific controls aim to capture the inherent
differences between all Colombian municipalities, such as FARC presence, and specific socioeconomic
factors. For our preferred specification, we employ municipality-year covariates, which aim to capture
unobserved variations for each municipality in any given year, such as a change in policy of a given major
or a change in the leadership of a specific FARC squadron. Additionally, throughout all estimations we
provide two levels of standard error clustering: Municipality and municipality-year level, accounting for
potential spatial and spatiotemporal correlations.
4.5.1 Strategy 1: Exogeneity of Assigned Game Days
As mentioned in Section 4.3, national and international football federations independently schedule
games. National teams play throughout the year, with FIFA assigning five to seven international breaks.
15Within each of these breaks, regional confederations arrange schedules for competitions, while local
federations set times and places (FIFA, 2013, 2019).
We begin by regressing the number of demobilizations for municipality i on day t against football
games on day t − 1, including a binary variable indicating if the game was played during the propaganda
period, as well as an interaction term between the games variable and the propaganda period. Formally,
we estimate:
Demobi,t = β1 M essaget−1 + β2 Gamet−1 + β3 (M essaget−1 × Gamet−1 ) + β4 λi + µi,t . (4)
β3 constitutes our main coefficient of interest, while λi captures municipality-fixed effects and µi,t
constitutes the error term for municipality i and day t. If the propaganda described above was successful,
we should expect a positive and statistically significant coefficient β3 .
4.5.2 Strategy 2: Exogeneity of Timing of Games within Days
Exploiting the hour of the game as a source of exogenous variation, we focus on the matches played at
dusk (between 16:00h and 20:00h) to specifically target the time of day at which FARC members would
be more likely listening to the games (LA FM Noticias, 2016; Fattal, 2018).
We regress the number of demobilizations for municipality i on day t against dusk and non-dusk
games on day t − 1, including a binary variable indicating if the game was played during the propaganda
period, as well as an interaction term between the games variable and the propaganda period. Intuitively,
this allows us to compare the demobilizations dynamics that occurred after dusk and non-dusk games
both inside and outside the propaganda period.
With a similar specification as before, we now add the distinction between dusk and non-dusk games:
Demobi,t = β1 M essaget−1 + β2 N on − dusk gamet−1 + β3 (M essaget−1 × N on − dusk gamet−1 )
+ β4 Dusk gamet−1 + β5 (M essaget−1 × Dusk gamet−1 ) + β6 λi + µi,t .
(5)
Here, our focus lies on β5 and we expect a positive, statistically significant, and quantitatively size-
able coefficient. Note that we would also expect a positive coefficient β3 (i.e., demobilizations on the
day after a non-dusk game during the propaganda period). If FARC members were indeed more likely
16to follow games during dusk, when they have more leisure time and are therefore more likely exposed
to the propaganda messages, then demobilizations should be more frequent after such games during the
propaganda period (LA FM Noticias, 2016; Fattal, 2018).
4.5.3 Exogeneity of Rain Across Municipalities
Our third identification strategy relies on the relation between football broadcasts and municipality-level
rain conditions at the time of game. Our hypothesis is that those municipalities that experience more rain
during a game with a propaganda message are less likely to be exposed to the messages. Again, we focus
on matches played at dusk to target the time of the day during which FARC members would more likely
be listening to the games.
Our identification relies on a physical phenomenon known as “rain scattering" or “rain fade" (Lin,
1973). Essentially, rain acts as both a sponge and a mirror, absorbing and refracting the microwaves
that carry broadcasts, ultimately distorting the broadcast altogether. During a given game, guerrilla
soldiers would be differentially exposed to pro-demobilization messages, conditional on rain conditions
at their specific municipality. For example, two guerrilla camps, located in different parts of Colombia,
might experience different weather conditions during the time of the game. If it were raining at one
of those guerrilla camps, those rebels would not be able to keep up with the broadcast and thus would
not be exposed to pro-demobilization messages. We refer to Lin (1973); Crane (1975); Ippolito (1981);
Ishimaru et al. (1982); Tewari et al. (1990); Qingling and Li (2006) for in-depth explanations of the
physical phenomenon of signal attenuation due to rain. We formally estimate:
Demobi,t = β1 Raini,t−1 + β2 N on − dusk gamet−1 + β3 (Raini,t−1 × N on − dusk gamet−1 ) (6)
+ β4 Dusk gamet−1 + β5 (Raini,t−1 × Dusk gamet−1 ) + β6 λi + µi,t .
β5 constitutes our main coefficient of interest. If our intuition was correct, we should expect a
negative and statistically significant β5 , which would be consistent with the hypothesis laid out above.
Furthermore, we would expect a precisely estimated null effect for β1 because there is no indication that
rain on the previous day itself would affect demobilization rates. Note that the inclusion of Raini,t−1
also guards our estimation against concerns of potentially difficult-to-maneuver terrain after excessive
17rain the previous day influencing our estimation of β5 . In addition, β3 is expected to be statistically
insignificant because rain during dusk would likely not affect demobilizations if the game were played
outside dusk hours.
5 Empirical Findings
5.1 Main Findings
Tables 2, 3, and 4 report our main regression results, predicting demobilizations in municipality i on day
t. Column (1) presents our model with no control variables; column (2) introduces fixed effects for each
weekday and month; column (3) includes year-fixed effects; column (4) accounts for municipality-fixed
effects; and finally, column (5) presents our preferred specification, where we account for municipality-
year-fixed effects.
5.1.1 Exogeneity of Assigned Game Days
Table 2 reports the results from our first estimation strategy where we rely on the game dates. In column
(1), we find that games containing propaganda are followed by 0.0044 additional demobilizations in a
given municipality or, equivalently, 5 extra demobilizations at the national level on the subsequent day.
Once we start introducing potentially confounding covariates, the estimated effect rises marginally and
stabilizes around 0.0051, equivalent to 5.7 demobilizations at the national level. In terms of magnitude,
this evidence suggests that the day after a game we observe approximately three times the average daily
number of demobilizations. The corresponding coefficients are positive and statistically significant at the
1% level, both when clustering standard errors at the municipality and municipality-year levels.
5.1.2 Exogeneity of Timing of Games within Days
To relax the implicit assumption that periods with and without propaganda presence are comparable, we
introduce an alternative specification based on the time of day at which the national team plays a game.
In essence, this shifts our counterfactual from games outside the propaganda period to games played
within the propaganda period that FARC members are less likely to listen to (LA FM Noticias, 2016).
18Table 2: Results from OLS regressions predicting FARC demobilizations for municipality i on day t
with football games on day t − 1, employing daily data from January 1, 2003 to September
26, 2016, for all 1,122 Colombian municipalities. 176 games were played during the sample
period.
Dependent variable: Demobilized FARC soldiersi,t (Mean=0.0028)
(1) (2) (3) (4) (5)
Game dayt−1 × Messagest−1 0.004513 0.005136 0.005122 0.005122 0.005121
(0.001024)∗∗∗ (0.001112)∗∗∗ (0.001103)∗∗∗ (0.001103)∗∗∗ (0.001104)∗∗∗
[0.001046]∗∗∗ [0.001074]∗∗∗ [0.001070]∗∗∗ [0.001071]∗∗∗ [0.001070]∗∗∗
Game dayt−1 0.000341 -0.001830 -0.001842 -0.001842 -0.001842
(0.000234) (0.000457)∗∗∗ (0.000461)∗∗∗ (0.000461)∗∗∗ (0.000461)∗∗∗
[0.000621] [0.000650]∗∗∗ [0.000667]∗∗∗ [0.000667]∗∗∗ [0.000667]∗∗∗
Messagest−1 0.000720 0.000688 -0.001212 -0.001212 0.002091
(0.000339)∗∗ (0.000339)∗∗ (0.000714)∗ (0.000714)∗ (0.002696)
[0.000581] [0.000578] [0.000804] [0.000593]∗∗ [0.003785]
Day-of-week and month-fixed effects X X X X
Year-fixed effects X X X
Municipality-fixed effects X
Municipality-year-fixed effects X
N 5,629,074 5,629,074 5,629,074 5,629,074 5,629,074
Notes: Standard errors clustered at the municipality level displayed in parentheses. Standard errors clustered at
the municipality-year level displayed in brackets. * p < 0.10, ** p < 0.05, *** p < 0.01. a Period ranges from
January 1, 2003 until the signing of the peace deal between the FARC and the Colombian government on
September 26, 2016.
19Table 3 presents the corresponding results. Once again, column (1) introduces results without any
covariates, and we then subsequently introduce the same set of covariates as Table 2, with column (5)
reporting our most complete specification that accounts for the comprehensive set of regressors. We
find that a game played at dusk demobilizes approximately 8 additional FARC soldiers (0.0072 × 1, 122
municipalities), compared to 4.7 additional soldiers demobilized after a non-dusk game.
Although the coefficients for dusk and non-dusk games are not statistically different from each other,
we want to highlight that the benefits of such an analysis come from the increased focalization of treat-
ment. Since we do not directly observe exposure to demobilizing messages, our design is essentially an
intent-to-treat study, were any increase in focalization should result in higher estimates – exactly what we
see with dusk games. Furthermore, our distinction between dusk and non-dusk games plays an important
role in our final set of estimations.
5.1.3 Exogeneity of Rain Across Municipalities
Aiming towards a counterfactual that relies on the same match, Table 4 introduces our third estimation
strategy, where we leverage the disruptive effect of rain on electromagnetic waves. Table 4 focuses ex-
clusively on the period with propaganda presence and predicts the number of demobilizations, depending
on weather conditions on the day and hour of the game. Tables A3 and A4 present an alternative analysis,
where we employ a difference-in-difference-in-difference methodology instead.
Once again, column (5) presents our preferred specification, were we account for the full set of
covariates. The first row illustrates a negative and statistically significant effect of dusk rain during a
game on the number of demobilizations the next day, with a dusk game without any rain constituting the
reference point. That coefficient, associated with Dusk gamet−1 , is positive and statistically meaningful,
as expected. In terms of magnitude, a 1 mm/h increase in the average rain rate during a dusk game
decreases the demobilization power of games by 2.5 demobilizations.
Furthermore, rain at dusk does not alter the demobilization effect of games played outside dusk, as
we estimate a statistically insignificant coefficient for Non-dusk gamet−1 × Dusk raint−1 . Finally, we
identify a precisely estimated null effect associated with rain itself, i.e., rain on the previous day during
20Table 3: Results from OLS regressions predicting FARC demobilizations for municipality i on day t
with football games at different hours on day t − 1, employing daily data from January 1, 2003
to September 26, 2016, for all 1,122 Colombian municipalities. 176 games were played during
the sample period, of which 35 were dusk games, 141 were non-dusk games.
Dependent variable: Demobilized FARC soldiersi,t (Mean=0.0028)
(1) (2) (3) (4) (5)
Dusk gamet−1 × Message periodt−1 0.009826 0.007625 0.007188 0.007188 0.007186
(0.002273)∗∗∗ (0.001997)∗∗∗ (0.001929)∗∗∗ (0.001929)∗∗∗ (0.001932)∗∗∗
[0.003577]∗∗∗ [0.003530]∗∗ [0.003226]∗∗ [0.003226]∗∗ [0.003226]∗∗
Non-dusk gamet−1 × Message periodt−1 0.002820 0.004016 0.004118 0.004118 0.004118
(0.000788)∗∗∗ (0.000961)∗∗∗ (0.000982)∗∗∗ (0.000982)∗∗∗ (0.000984)∗∗∗
[0.000681]∗∗∗ [0.000701]∗∗∗ [0.000774]∗∗∗ [0.000774]∗∗∗ [0.000774]∗∗∗
Dusk gamet−1 0.003587 0.003455 0.003406 0.003406 0.003407
(0.001592)∗∗ (0.001560)∗∗ (0.001581)∗∗ (0.001581)∗∗ (0.001583)∗∗
[0.002001]∗ [0.001998]∗ [0.001609]∗∗ [0.001609]∗∗ [0.001609]∗∗
Non-dusk gamet−1 -0.000239 -0.002757 -0.002766 -0.002766 -0.002766
(0.000351) (0.000736)∗∗∗ (0.000746)∗∗∗ (0.000746)∗∗∗ (0.000747)∗∗∗
[0.000499] [0.000529]∗∗∗ [0.000627]∗∗∗ [0.000627]∗∗∗ [0.000627]∗∗∗
Message periodt−1 0.000720 0.000688 -0.001172 -0.001172 0.002043
(0.000339)∗∗ (0.000339)∗∗ (0.000717) (0.000717) (0.002696)
[0.000581] [0.000578] [0.000806] [0.000592]∗∗ [0.003785]
Day-of-week and month-fixed effects X X X X
Year-fixed effects X X X
Municipality-fixed effects X
Municipality-year-fixed effects X
N 5,629,074 5,629,074 5,629,074 5,629,074 5,629,074
Notes: Standard errors clustered at the municipality level displayed in parentheses. Standard errors clustered at
the municipality-year level displayed in brackets. * p < 0.10, ** p < 0.05, *** p < 0.01. a Period ranges from
January 1, 2003 until the signing of the peace deal between the FARC and the Colombian government on
September 26, 2016.
21Table 4: Results from OLS regressions, exploring the disruptive effect of rain on the demobilization
effect of football games at different hours on day t − 1, employing daily data from January 1,
2005 to September 26, 2016, for all 1,122 Colombian municipalities. 176 games were played
during the sample period, of which 35 were dusk games, 141 were non-dusk games.
Dependent variable: Demobilized FARC soldiersi,t (Mean=0.0028)
(1) (2) (3) (4) (5)
Dusk gamet−1 × Dusk raint−1 -0.002590 -0.002295 -0.002209 -0.002282 -0.002199
(0.000638)∗∗∗ (0.000595)∗∗∗ (0.000579)∗∗∗ (0.000664)∗∗∗ (0.000632)∗∗∗
[0.000610]∗∗∗ [0.000602]∗∗∗ [0.000583]∗∗∗ [0.000600]∗∗∗ [0.000579]∗∗∗
Non-dusk gamet−1 × Dusk raint−1 -0.000207 -0.000238 -0.000236 -0.000358 -0.000312
(0.000196) (0.000195) (0.000196) (0.000181)∗∗ (0.000182)∗
[0.000262] [0.000260] [0.000257] [0.000257] [0.000258]
Dusk gamet−1 0.014413 0.011588 0.011064 0.011090 0.011058
(0.003176)∗∗∗ (0.002652)∗∗∗ (0.002537)∗∗∗ (0.002573)∗∗∗ (0.002568)∗∗∗
[0.003153]∗∗∗ [0.003107]∗∗∗ [0.002980]∗∗∗ [0.002984]∗∗∗ [0.002977]∗∗∗
Non-dusk gamet−1 0.002656 0.001234 0.001329 0.001371 0.001355
(0.000507)∗∗∗ (0.000265)∗∗∗ (0.000276)∗∗∗ (0.000293)∗∗∗ (0.000286)∗∗∗
[0.000480]∗∗∗ [0.000451]∗∗∗ [0.000435]∗∗∗ [0.000442]∗∗∗ [0.000445]∗∗∗
Dusk raint−1 0.000016 0.000004 -0.000026 0.000008 0.000018
(0.000054) (0.000056) (0.000059) (0.000041) (0.000040)
[0.000048] [0.000048] [0.000048] [0.000044] [0.000043]
Day-of-week and month-fixed effects X X X X
Year-fixed effects X X X
Municipality-fixed effects X
Municipality-year-fixed effects X
N 4,800,320 4,800,320 4,800,320 4,800,320 4,800,320
Notes: Standard errors clustered at the municipality level displayed in parentheses. Standard errors clustered at
the municipality-year level displayed in brackets. * p < 0.10, ** p < 0.05, *** p < 0.01.
22dusk hours is not a statistically or quantitatively significant predictor of demobilizations. In sum, rain
only affects demobilizations by distorting the broadcasting of national football games.
5.2 Robustness
Our benchmark results, depicted in columns (5) of tables 2, 3, and 4, remain consistent throughout
a range of alternative specifications, employing alternative (i) measures and data sources for rain; (ii)
econometric models; (iii) panel data units of measure; as well as (iv) including control variables for mil-
itary actions from both the government and the FARC. Additionally, our results are robust to a series of
placebo estimations in which we: (i) Predict collective demobilizations instead of individual surrender-
ings, (ii) predict demobilizations in days leading up to a game, and (iii) assign the same number of games
to random dates in our sample (see tables A5 and A7).
In particular, we estimate models using Poisson regressions, accounting for the fact that we use a
dependent count data variable, producing consistent results (see Table A2). Re-visiting the precision of
the gridded rain estimations, we use local measures of rain, collected directly from the Institute of Hy-
drology, Meteorology and Environmental Studies (IDEAM), allowing us to obtain precise rain measures
for 73% of the Colombian municipalities where IDEAM is present. The corresponding results again con-
firm our benchmark findings (see Tables A2 and A3). We also introduce a set of estimations at a higher
level of administrative aggregation (states instead of municipalities) to alleviate concerns about whether
a guerrilla member would demobilize exactly in the same municipality they potentially witnessed the
football game on the previous day. Additionally, we introduce a range of control variables including
military activities from both FARC and the national army, as well as variables related to the games such
as scores and competition played. We present the corresponding results in Table A2.
5.3 Re-Treatment
With these baseline results in mind, we now explore whether re-treatment, i.e., the close succession of
various games with propagandistic messages influences demobilizations. Up until now, the literature on
media effects in conflict settings has focused on quantifying the overall effects of propaganda. We present
23an approach that leverages our unique setting to quantify the effect of re-exposure to propagandistic
messages on demobilizations. To do so, we access insights from social psychology.
As mentioned in Section 4.3, FIFA assigns five to seven international football windows per year
(FIFA, 2019). During these periods, FIFA often schedules pairs of games, i.e., each national team plays
two games before their players return to their respective clubs (FIFA, 2013, 2019). We build on the
specification presented on column (5) of table 2 to compare the demobilization effects of the two games
played during a given FIFA window. This comparison between succeeding games sheds light on whether
and, if so, how re-exposure to propaganda affects demobilizations. Figure 4 visualizes the corresponding
coefficients from regressions that follow the full structure from column (5) in Tables 2, 3, and 4. The
corresponding results suggest that the second game of the pair may produce even stronger demobilization
effects than the respective first game. However, confidence intervals enlarged by the reduction in the
number of games only allow us to claim a statistically significant difference at the 90% level.
Figure 4: Results from OLS regressions, predicting FARC demobilizations for municipality i on day t
with the first and second games of each FIFA calendar pair of games. The full set of control
variables include fixed effects for municipality-year, each weekday, and month. Two-sided 95
percent confidence intervals displayed.
These findings from Figure 4 are consistent with conventional social psychology theories, where ex-
perimental evidence has shown how agreement with a message first increases and then decreases with re-
peated exposure (Cacioppo and Petty, 1979; Petty and Cacioppo, 1984; Lane, 2000; Petty, 2013; Schmidt
and Eisend, 2015). Although we are not able to test for re-exposure beyond a second game because of
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