Violence, Mental Trauma, and Induced Changes in

Violence, Mental Trauma, and Induced Changes in Risk Attitudes
Among Displaced Populations in Colombia
ANDRÉS MOYA*
Shocks and traumatic experiences can alter the way in which individuals behave and
deplete their ability to make economic decisions. Using data from individuals
residing in two war-torn regions in Colombia, some of which were victimized and
displaced at different times between 2001 and 2010, I find that individuals exposed
to more severe and recent episodes of violence display higher levels of risk aversion.
Such change is explained at the psychological level by the incidence of severe
anxiety disorders. The magnitude of such change in behavior can thwart the
economic recovery of victims and have transcending impacts on poverty dynamics.
* Ph.D. Candidate, Agricultural and Resource Economics, University of California, Davis. 2158 Social Sciences & Humanities, One Shields
Avenue, Davis, CA 95616; [email protected]. I would like to thank Michael R. Carter for his advice and support throughout the different
stages of this project, as well as Alessandra Cassar, Ana María Ibañez, Carmen Elisa Flórez, Hillary Hoynes, Juan Camilo Cárdenas, Joana
Monteiro, Marc Rockmore, Laura Schechter, Travis Lybbert, Esmeralda Uribe, Andrea Velásquez, and participants at seminars at the University
of San Francisco, UC Davis, University of Wisconsin, Madison, and Universidad de los Andes, and MIEDC, NEUDC, PACDEV, and SEEDEC
conferences for useful comments and suggestions. I would also like to thank the Economics Department at Universidad de los Andes for
providing access to the ELCA data. This project would not have been possible without the support of the enumerators, public officials, and
community leaders who assisted me during the fieldwork, and without the cooperation of the displaced households who participated in the survey
and economic experiments. The Pacific Rim Research Dissertation Grant, the Programa de Dinámicas Territoriales from RIMISP, and the Henry
A. Jastro Award provided generous funding to collect the data, while the Harry Frank Guggenheim Foundation provided a dissertation
fellowship. The usual disclaimers apply.
1
Countries torn by civil violence suffer devastating consequences, including the loss of lives
and displacement of civilians, the destruction of physical capital and infrastructure, and
institutional decay, among others. The long-term economic consequences of violence are,
nevertheless, unclear. Interestingly, the few macro-oriented studies find no permanent impacts of
wars on growth and poverty, suggesting a story of post-war recovery and convergence in the
long run (Davis and Weinstein, 2002; {Brakman:2004fs} {Miguel:2011eq}).1 Macro evidence,
however, can mask worrisome micro-level dynamics that arise from civil conflicts and violence.
For instance, recent research finds that the costs of violence fall disproportionately on noncombatants, and that some of their immediate consequences, such as forced displacement, asset
losses, and schooling interruption, can drive victims into a state of chronic poverty (Brück, 2004;
Justino and Verwimp, 2006; Ibañez and Moya, 2010a).
Violence also has devastating psychological consequences. Victims, especially those who
suffered more severe or recent episodes of victimization, are vulnerable to mental trauma and
suffer a wide array of psychological disorders including anxiety, depression, and posttraumatic
disorder (Yehuda, 2002; Kienzler, 1998; Pham et al., 2004, Annan, 2006; Vinck, et al., 2007).
Such psychological disorders entail an intense emotional suffering but also affect the ability to
perform tasks, work, and maintain social relationships (Mollica et al., 1987; Vinck, et al., 2007;
Doctors Without Borders, 2010 & 2011). Under some circumstances, the psychological
consequences of violence can entail long-term consequences that include physiological changes
in the brain as well as chronic anxiety and major depression, and permanently affect individual
responses to future sources of stress (Kesler et al., 1995; Alldin et al., 1996; Davidson et al.,
1998; Gruenjar, 2000).
1
The lone exception is, to my knowledge, the paper by Acemoglu, Hassan and Robinson (2010), who find long-lasting negative consequences of
the Jewish Holocaust in Russia as a result of the impact of violence on social structures and institutions.
2
Psychological disorders can also affect economic behavior by bringing about changes in the
victims’ attitudes towards risk. In particular, the ‘Risk as Emotions’ framework has provided
evidence that risk attitudes result from a complex interplay between cognitive and emotional
processes and are often driven by the individual’s emotional state (LeDoux, 1996; Lowenstein et
al, 2001; Lowenstein and Lerner, 2003).2 Relying on experimental primes to induce different
emotions in controlled laboratory experiments, this body of work has identified that the emotions
that are prevalent among victims of violence have distinct and predictable effects on risk
attitudes. Anxiety and fear, for instance, induce individuals to consider ambiguous situations as
threatening, display pessimistic estimates of uncertain options, and exhibit high levels of risk
aversion. Anger, on the other hand, has the opposite effects (Eysenck et al., 1992; Raghunathan
and Phan, 1992; Eisenberg et al., 1995; Lerner and Keltner, 2001; Lerner and Tiedens, 2006).
The results above suggest that the populations exposed to violence, especially those affected
by severe and recent episodes of violence and who as a result are more likely to suffer an anxiety
disorder, can become more risk averse and behave in a way that has worrisome connotations for
welfare trajectories. Intuitively, in a world characterized by high levels of risk and uncertainty
and incomplete insurance mechanisms, higher levels of risk aversion will encourage individuals
to adjust consumption and investment decisions in a inefficient way, making them reluctant to
reduce their own consumption in order to make the investments required to move out of poverty.
Previous research has in fact shown that risk aversion discourages investments in physical and
2
This perspective contrasts the canonical model in economics of behavior under uncertainty, which hypothesizes that decisions are the result of a
purely cognitive process in which individuals address the desirability and likelihood of uncertain outcomes and use this information to make
choices. Behavior is therefore seemingly explained by the desirability of the outcomes, the beliefs about their likelihood, and risk preferences,
which for a long time were assumed to be exogenously determined and therefore seemingly uninteresting constructs (Lowenstein et al., 2001;
DellaVigna, 2009). While recent research has shown that risk preferences vary across individuals, cultures and circumstances, the prevailing view
is that these are driven by different factors such as long-term evolutionary processes, genetic markup and heritability, changes in beliefs,
differences in the wealth space, and frames and reference points, and therefore do not contest the cognitive-driven framework (Kahneman and
Tversky; 1979; Henrich et al., 2001; Carpenter and Cardenas, 2008; Netzer, 2008; Doss et al., 2008; Cesarini et al., 2009; Cesarini et al., 2010;
Sprenger, 2010; Carpenter et a., 2011). However, most of the individual variation in risk attitudes is still unaccounted by such factors. Research in
neuroscience and social psychology, on the other hand, has shown the variation in risk attitudes can be explained by personality traits
(Ruthischini XXX) and anticipated (Isen and Patrick, 1983; Mellers et al., 1999) and anticipatory emotions (Lowenstein et al., 2001), implying
that risk attitudes can be malleable and vary for an individual according to the emotional state at the time in which decisions are made.
3
human capital, limits technology adoption, thwarts the wage growth, and increases the
vulnerability to future poverty (Levhari and Weiss, 1974; Shaw, 1996; Brown and Taylor, 2005).
The psychological consequences of violence, and their impact on risk attitudes in particular,
could therefore constitute a different channel through which violence drives and keeps
individuals into poverty.
In this paper I analyze the impact of violence on risk aversion in Colombia, a country affected
by a long-lasting and ongoing civil conflict where more than four million civilians have been
victimized and displaced from their homelands since 1997. Using aggregate-level violence data,
recent studies have analyzed the same question in Burundi and Afghanistan and reached
seemingly contradictory results.3 In Burundi, Voors et al (2012) use the variation in the share of
deaths at the community level from the 1993-2003 period and find that individuals residing in a
community with a higher number of deaths become more risk seeking. In Afghanistan, Callen et
al. (2012) use data on violent attacks across polling districts as well as random experimental
primes to induce anxiety before the risk elicitation tasks, and find that residing in a district that
experienced at least one violent attack between 2002 and 2010 increases the preference for
certainty but only for the subsample that was emotionally primed.
While these opposing results could be explained by many factors, including the differences in
the types of violence in each country, the methodological approach taken by both Voors et al.
and Callen et al. provides a limited picture of the impact of violence on individual behavior. If
the impact of violence on risk aversion is driven by the extent of mental trauma, the impact of
violence within a conflict-torn region will likely vary across individuals according to how severe
3
Related research has analyzed the impact of violence on altruism (Bauer et al., 2011), collective action (Miguel and Bellows, 2009), political
participation (Blattman, 2010), social capital (Cassar et al., 2011; Voors et al., 2012), time preferences (Voors et al., 2012), as well as the impact
of natural disasters on risk aversion (Eckel et al., 2009; Cameron and Shah, 2011; Cassar et al., 2011a), time preferences (Cassar et al., 2011a)
and trust and pro-social behavior (Carter and Castillo, 2009; Cassar et al., 2011a).
4
and recent was their exposure to violence.4 However, by aggregating violence data over a
determined geographical-unit, individuals from a given district or community get the same
measure of violence regardless of whether they were victims, witnesses, or were not directly
exposed to any violent events. Similarly, by aggregating violence data over a ten-year period,
individuals receive the same measure of violence without distinguishing the moment at which
they were exposed to violence. As a result, the estimated effect is an average of the individual
responses to different levels of exposure to violence at different points in time.5 This is
problematic because without knowing how severe or recent was the experience of violence for
the different individuals in the sample, it is unclear what the average effect captures. More
important, however, by using aggregate-level data the above studies could fail to identify the
behavioral consequences for individuals who directly victimized, and who as a result are more
vulnerable to mental trauma and poverty.6
In this paper I build upon previous studies in three ways: First, I collected unique data from a
sample of 603 individuals who reside in two regions torn by violence in Colombia, including 285
Internally Displaced Persons (IDP) who were directly victimized and forced to flee from their
homes at different times between 2001 and 2010. I administered a household survey, a
questionnaire on household victimization, a psychological scale, and a field experiment designed
4
In Colombia, for instance, clinical reports find that in regions most affected by the civil conflict, the likelihood of suffering from chronic
anxiety is 2.6 times higher for individuals directly exposed to violence than for bystanders, and is also higher for those recently exposed to
violence (Encuesta de Salud Mental, 2003; Londoño, et al., 2005; Perez-Olmos et al., 2005; Sinisterra et al., 2007; Doctors Without Borders,
2009 & 2010).
5
Although Voors et al. and Callen et al. explicitly state that the effect on risk attitudes is permanent and therefore this would not matter, it is not
clear why this would necessarily be the case. In fact, if the behavioral response is driven by the incidence of mental trauma, the impact on risk
attitudes should not be permanent for all victims since some are able to recover psychologically over time.
6
To be fair, in the last section of their paper Callen et al. (2012) analyze the impact of violence according to the intensity of the attacks and the
time since the attacks happened. For this purpose, they first estimate regressions that include the number of attacks at the district level, and then
they separate the incidence of at least one attack from 2005 to 2007 and 2008 to 2010. Their results indicate that the Certainty Premia increases
for those individuals who were asked to recollect anxiety-inducing episodes and resided in districts with either a higher number of attacks or at
least one attack in the latter period. Nonetheless, since all of their results indicate that the district measures of violence do not explain differences
in behavior unless the subjects received an emotional prime, their results can be better understood to demonstrate the role of effective primes on
behavior rather than the one of violence and mental trauma. Voors et al. (2012) on the other hand, have individual level data on the exposure to
violence but they use it to identify the effects on time and social preferences but not on risk aversion.
5
to elicit risk attitudes. As a result, I capture the variation in the experience of violence from a
group of direct victims of violence and a group of individuals who reside in areas torn by
violence. This allows me to analyze the heterogeneous impact of violence on risk attitudes
according to the severity and temporal proximity of the episodes of violence. Second, I explore
the psychological channel through which violence affects risk attitudes using the data from the
psychological questionnaire, which measures the extent of anxiety and eight other psychological
disorders in the three months previous to the economic experiment. In particular, I test the
predictions of the ‘Risk as Emotions’ framework and analyze if individuals who were suffering
from anxiety (or anger) disorders before the experiment, display higher (lower) levels of risk
aversion in their choices during the experiment. Third, since I capture variation in mental trauma
that is not induced by experimental primes, I can also make a case for the external validity of the
results; this is, on the potentially harmful consequences that the psychological consequences of
violence have on every-day economic decisions.
Identifying the causal impact of violence on risk attitudes using individual-level data has
several methodological challenges. If the likelihood of being victimized, or the decision to
migrate are correlated to the ex-ante degree of risk aversion, the results would be biased by
unobserved selection. For this reason, I employ different strategies to control for the extent of
selective victimization and endogenous displacement decisions. First, I draw the sample of
displaced and non-displaced individuals from two regions with high levels of violence against
civilians, where the geographical variation in the intensity of violence is explained by the
proximity of rural communities to strategic corridors for the traffic of illegal drugs and arms, and
for the movement of troops. I exploit the geographical patterns of violence and sample
individuals who were displaced from municipalities located along the strategic corridors and
6
non-displaced individuals who reside in close-by municipalities but not along such corridors.
This allows me to sample from a group of displaced and non-displaced individuals that resided in
the same geographic, economic, institutional, and cultural environment and share similar
individual and household characteristics, except that the former resided in communities that were
directly in the path of illegal armed groups and were thus exposed to higher levels of violence.
Second, I address the extent of selective targeting by analyzing the determinants of victimization
and controlling for such characteristics in the econometric analysis.7 Third, to control for the
extent of endogenous displacement decisions, I take advantage of the fact that 132 out of the 285
IDP migrated with their entire community after violent combats between armed groups took
place within the village boundaries. Considering that during massive displacements the decision
to migrate was made at the community level and triggered by combats were targeting of violence
was less likely, I can analyze the robustness of the results on a segment of the sample for which
violence and displacement are arguably more exogenous to individual characteristics and risk
attitudes. Finally, I analyze the robustness of the results using the variation in violence and
mental trauma only among the sample of IDP to demonstrate that the results are not driven by
the selection of an inappropriate control group, and I can argue that if there is selection within
the sample of IDP, the source of the bias goes against the hypothesis that violence induces higher
levels of risk aversion.
Results indicate that being a victim of forced displacement induces higher levels of risk
aversion over the gains and ambiguity domains, but not over the loss domain. Such shift in
behavior is more pronounced for those individuals exposed to more severe and recent episodes of
violence, and is driven at the psychological level by the incidence of severe phobic anxiety
7
Hence if selection into displacement is on observed individual characteristics, the results would be the causal unbiased estimated of violence on
risk attitudes and not spurious relationships (Imbens, 2003).
7
disorders. The persistence of the impact of violence on risk attitudes thus hinges on whether
victims continue to suffer from anxiety disorders years after the episodes of violence.
Nevertheless, even temporary shifts in risk attitudes can have worrisome long-run consequences,
and this article therefore highlights the existence of a behavioral channel through which violence
can increase the vulnerability to poverty. Considering that approximately eight percent of the
population in Colombia has been displaced by violence, such behavioral change could reinforce
the material consequences of forced displacement, induce suboptimal economic decisions, and
leave a legacy of poverty at the micro and macro level difficult to overcome with traditional
policy interventions.
I. Civil Violence and Forced Displacement in Colombia
“I had to leave my house after my father was murdered. They [members of an armed group] shot him
in the back, a bullet to the neck. I do not know really why. I was there when this happened. I saw it all.
He fell down without even screaming, his shirt covered with blood. Immediately after, they told me:
‘Tell you family that this is a warning’. In that moment my wife and I knew we could not live there
anymore. We were terrified. We had to leave quickly, without thinking about the future, and trying not
to think about the past. […] We left everything behind – the land, animals, and the few things we had
worked to get through time – and we left immediately. […] We did not have time to think what was
happening to us. […]”8
Civil violence has torn Colombia since the late 1940’s with increasing patterns of violence
against the rural population. The origins of modern violence can be traced back to La Violencia,
a civil conflict that swept rural areas between 1948 and 1958. While the prevailing view is that
violence was driven by partisan disputes, the roots of violence lie at the unequal distribution of
lands and the poor definition of property rights. La Violencia was in fact the culminating point of
decades of tensions between large landowners, small peasants, and settlers for the control of
lands and agricultural production (Berry, 2006). It is thus not surprising that rural violence
persisted after a peace agreement was signed in 1958; aggressions towards settlers and small
8
Doctors Without Borders (2006): Testimony of a displaced household living in a slum in Sincelejo, Sucre [Own translation].
8
peasants in fact continued to occur, while peasant guerrilla groups refused to surrender and
settled in communist “independent republics” in regions of agricultural frontier. By the midsixties, the FARC9 guerrilla emerged from one of such republics and with other leftist guerrilla
groups aimed to overthrow the government (Echeverry et. al, 2001).
The rapid growth of the guerrillas in the 1970’s fostered an alliance between landlords, rural
elites, and a new class of businessmen involved in the illegal drug trade that resulted in the
creation of right-wing paramilitary groups. Paramilitaries, however, did not emerge exclusively
to protect the better-off rural segments of the rural population from the increasing harassments
by the guerrillas, but also to discourage any form of social organization and gain control of lands,
agricultural production, and local politics. As a result, during the 1970’s and 1980’s violence
against civilians was often targeted towards social leaders, members of grass roots movements,
public officials, and small landowners and squatters (Dudley, 2008; Reyes, 2009; CNRR, 2010;
CNRR, 2011).
Violence escalated from the late 1980’s onwards as guerrillas and paramilitaries clashed in
strategic regions for the control of natural resources, the production and trafficking of illegal
drugs, and mayor infrastructure and agro-industrial projects among others (Gaviria, 2000;
Thoumi, 2002; Reyes, 2009). Violence was a deliberate and effective strategy to control strategic
regions by spreading fear and uncertainty across the population. While armed groups kept
targeting specific individuals, they also exerted indiscriminate violent tactics, such as massacres,
to control, displace, or disarticulate entire communities that resided within their path of
expansion (CNNR, 2011a). As a result, civilian victimization reached unprecedented levels in
the 1990’s and early 2000’s and extended to entire communities without distinctions of race, age,
9
Spanish acronym for Armed Revolutionary Forces of Colombia (Fuerzas Armadas Revolucionarias de Colombia).
9
gender or political affiliation (Duncan, 2006; Reyes, 2009; CNRR, 2011).
In the last ten years the demobilization of paramilitary groups between 2002 and 2005 and the
intensification of military operations by the Colombian military against the FARC, has led to
even more indiscriminate patterns across certain regions. On the one hand, the neo-paramilitary
groups that emerged after the demobilization of paramilitary groups have clashed against each
other for the control of the illicit drug trade, gold mines, lands, local politics, and agro-industrial
projects in regions formerly under paramilitary control. Within these regions, civilian
victimization has been driven by the same strategies for territorial control employed by their
predecessors and by the frequent combats between the different groups in which rural
communities have often been caught in crossfire. The FARC, on the other hand, have retreated to
areas where the geographical conditions allow the movement of troops and the traffic of illegal
drugs. In the process, they have set land mines, taxed the civilian population to offset declining
incomes, employed violence to grasp control of the population, and launched indiscriminate
attacks on communities and rural towns to ease pressure from the military (Humans Right
Watch, 2010; Indepaz, 2011; Negrete, 2012).
Since 1997, over four million individuals have been displaced by violence, most of them
peasants, settlers, and laborers who were forced to migrate from rural to urban areas. While more
than 90 percent of the municipalities in Colombia report episodes of displacement, the majority
of IDP migrated from a few regions, not surprisingly those of importance for the different illegal
armed groups (Left-hand panel of Figure 1). Displacement is triggered by the exposure to
different types of violence and in some cases by an explicit order to migrate by the illegal armed
groups (Reyes, 2009; Humans Right Watch, 2010; Indepaz, 2011, CNRR, 2011 & 2011a).
Displacement is therefore perceived by the population as the only available option to protect the
10
life of the household members, as the testimony quoted at the beginning of this section portrays.
Violence and forced displacement have negative micro-level consequences that can drive and
keep victims into poverty. Displaced households lose their lands and most of their assets, and the
majority migrates to urban areas where the demand for their agricultural skills is limited. Their
ability to generate income is therefore undermined, labor income and aggregate consumption
decline by 50 and 33 percent in reception sites, and households adopt costly coping strategies
that jeopardize future welfare, including child labor and distress asset sales (Ibáñez and Moya,
2010 & 2010a). Over time, displaced households are unable to recover the levels of income and
consumption that they had before displacement and to rebuild their asset base, suggesting that
they are trapped in a state of chronic poverty (Ibáñez and Moya, 2010a).
Violence and displacement also have negative consequences on the mental health of the
population, including above normal levels of PTSD, depression, and anxiety disorders (Encuesta
de Salud Mental, 2003; Londoño, et al., 2005; Perez-Olmos et al., 2005; Sinisterra et al., 2007;
Doctors Without Borders, 2009 & 2010). Considering the strong association between anxiety and
risk aversion, this suggests that victims of forced displacement could experience a lower
tolerance for risk that would discourage important investment decisions and lead to inefficient
economic choices. Such behavioral change could therefore become a different channel trough
which violence drives victims into poverty and hinders their ability to recover.
II. Sample Design and Data
I bring together data from a household-level survey, a questionnaire on household exposure to
violence, a psychological scale, and a field experiment to elicit risk attitudes. I conducted all
experimental sessions and collected all data with the assistance of local enumerators between
November 2010 and June 2011, with the exception of the household survey data of the non-
11
displaced sample, which was collected between January and October 2010 by the Colombian
Longitudinal Survey (ELCA, for its Spanish acronym).10
A. Sample Selection and Field Protocols
The data was collected from a sample of individuals who were victimized and forced to
migrate from rural to urban areas at different times between 2002 and 2010, and a sample of nondisplaced individuals who reside in the same regions, yet in rural communities with lower levels
of violence. The sample includes individuals who reside in five different departments in two
geographic regions: the departments of Cordoba, Sucre, Bolivar and the north of Antioquia in the
Atlantic Region, and the department of Tolima in the Central Region.11 Figure 1 depicts the
geographic distribution of the municipalities where the non-displaced reside and of those where
the displaced population migrated from. It also depicts the location of the municipality heads
where I conducted fieldwork; this is, the municipality heads closest to the rural areas where the
non-displaced reside, and the municipality heads where the IDP reside.
The sample design focused on the Atlantic and Central regions for the following reasons:
First, both regions have a long history of violence against the civilian population and a high
intensity of forced displacement, explained by the strong presence of paramilitary groups and the
recent emergence of neo-paramilitary groups in the Atlantic region and by the presence of the
FARC in the Central region. Second, as I will discuss in section 5, recent conflict dynamics
suggest patterns of violence against the civilian population driven by the geographic proximity to
strategic corridors used for the shipment of illegal drugs and the movement of troops. This
10
The Colombian Longitudinal Survey conducted by the Economics Department at Universidad de los Andes is the first nationally
representative longitudinal survey in the country. The first wave of data was collected between January and October of 2010 on a sample of 6,000
urban and 4,000 rural households; the second wave of rural surveys started in April 2013. The survey is representative for all households between
social strata 1 to 4 (on a scale of 1 to 6) and for five geographical regions. The rural sample includes four geographic regions (Atlantic, Central,
Northeastern, and Western). Selection of municipalities was based on demographic and socio-economic indicators. More information is available
at http://encuestalongitudinal.uniandes.edu.co/
11
Departments are the biggest administrative-level units, similar to States in the United States, and are composed by several municipalities,
which are similar to counties in the United States. Most municipalities have a municipality (urban) head and rural areas.
12
allowed me to sample a group of individuals who were victimized and displaced because they
resided in the close proximity to such corridors, and a group of individuals who reside in the
same regions and in similar rural communities, but who nevertheless were exposed to much
lower levels of violence and were not displaced because they do not reside in the close proximity
to the strategic corridors. In doing so, I focus on episodes of violence and displacement where
the extent of selection is arguably lower.
The initial sample strategy focused only on victims of massive displacement to further
minimize the extent of selective victimization and endogenous displacement responses to
violence. Massive displacements are episodes in which entire communities are displaced, often
after massacres or armed combats between illegal armed groups in which the community is
caught by crossfire.12 Since the episodes of violence that trigger a massive displacement are
more indiscriminate and all of the members of the community migrate, the extent of selective
targeting and endogenous displacement decisions are arguably lower. Massively displaced
communities were the identified from administrative records provided by Acción Social,13 which
contained the place of origin of each community, the date of the displacement, the number of
households, and their current location. Initially, the sample included all of the massively
displaced communities that occurred in the previous three years in both regions.14 Unfortunately,
violence escalated as I started fieldwork and I was unable to reach most of the communities since
12
The characteristics of massive displacements can be portrayed by two emblematic events that took place in the early 2000. In February of
2000, over 300 paramilitaries arrived to the municipality of El Salado in the department of Bolivar and took over the county head ordering all the
inhabitants to gather in the central plaza. Testimonies from paramilitaries who participated in the massacre and from survivors indicate that
former literally picked the victims at random, and then tortured and killed them in front of the rest of the community. The paramilitaries
abandoned the town three days later after murdering over 70 civilians, and the entire population migrated soon after (CNRR, 2009). Two years
later, guerrilla and paramilitary forces clashed in Bojayá, a town of approximately 1,000 inhabitants in the department of Chocó. Guerrilla forces
launched improvised mortars towards the buildings where the paramilitaries had taken positions. One of these mortars exploded in the local
church where over half of the population was taking shelter, killing 119 civilians and wounding 98 more. All of the survivors migrated days later
to the nearby municipality of Vigía del Fuerte (CNRR, 2010a).
13
Acción Social, now the Ministry for Social Prosperity, is the Presidential Agency for Social Programs, which administers the programs for the
IDP and the system of information on forced displacement in Colombia.
14
This sample included communities residing in the county heads of 12 different municipalities in the departments of Córdoba and Sucre
(Atlantic Region) and Tolima (Central Region).
13
the security conditions did not guarantee the safety of the enumerators and participants.
Nevertheless, under conditions far from the ideal, I managed to conduct fieldwork in the
municipalities of Tierralta and Montelíbano in the department of Córdoba, where I obtained a
sample of 132 individuals from 8 massively displaced rural communities.15
Participants were selected at a community meeting organized by the local priests and
ombudsmen in the neighborhoods and shelters where the IDP reside.16 During the meetings I
explained that broad objectives of the project, made a list of the attendants, and randomly invited
a third of them to participate.17 Enumerators then set up times during the week to conduct the
household surveys and administered them at the local church to ensure privacy and safety of
enumerators and participants. After all surveys had been conducted in the municipality,
participants were invited to a session in which the enumerators administered the victimization
questionnaire and the psychological scale, and I then conducted the activity to elicit risk
aversion.18 These sessions were conducted in groups of 10 to 15 participants. Participants were
told that the participation was voluntary and that they would be paid for the transport costs and
receive a snack. The rate of non-participation (those who answered the survey but did not
15
During my time at both of these municipalities I witnessed the arrival of two communities of over 50 members each that had been displaced
by armed combats between neo-paramilitary groups in the previous days. As I interacted with their community leaders, I extended an invitation
for them and the other members of the communities to participate in the survey and experiments, and thirty of them agreed to do so.
16
Quite different to the displacements in Africa, in Colombia there are no IDP camps. Instead, victims often settle in the outskirts of the county
heads and set improvised houses made out of cardboard and plastic. Only in a few occasions, often in the case of massive displacements,
municipal authorities set up improvised shelters in abandoned public buildings.
17
In the massively displaced communities I had a zero rejection rate, except in the case of the two communities that were displaced a few days
before. This happened because the IDP is often ignored by local authorities and most expressed that they wanted to participate so that they could
be heard and I could go on and share their stories. In the case of the communities that were displaced less than a week before I met them, a big
proportion of them expressed that they were too afraid to participate in the project, suggesting that those who participated were most likely the
relatively less traumatized.
18
Sessions were conducted during the weekends after all household surveys were completed for two reasons. First, since participants could earn
monetary prizes as part of the risk aversion experiment (on average US $10), this allowed me to minimize the amount of information about the
structure of the experiments and the monetary prizes between participants and future participants or other members of the communities. By doing
so, I also controlled the possibility that participants would self-select based on the opportunity to earn some money. Second, since the security
conditions at both groups of municipalities were far from the ideal, this was a strategy to limit the time that I spent in the different municipalities
after the community members realized that a researcher was walking around the town with a backpack full of cash.
14
participate in the activity) was less than 4%.19
Since I was unable to visit all of the massively displaced communities and the sample of
massively displaced individuals was smaller than planned, I drew a sample of IDP residing in the
nearest departmental capitals of Córdoba, Sucre, and Tolima, which are the main reception sites
for the displaced in each region. In each city, the sample was drawn from the population that
visited the Units of Assistance and Orientation for the IDP (UAO).20 Every morning, for a period
of 2 to 3 weeks, I described the project to population that was at the UAO and invited them to
participate.21 Subjects were screened with the assistance of the UAO staff to guarantee that they
were in fact displaced and that they had migrated from the same municipalities that had been
initially sampled. In these three cities I obtained a sample of 153 displaced individuals who did
not necessarily migrate with their entire community for an overall sample size of 285 displaced
households.22 Surveys were conducted during the weeks for the same reasons as before, this time
at the UAO offices. After all surveys were completed subjects were invited to participate in the
weekend sessions in which the other instruments were administered.
Finally, the group of non-displaced individuals was drawn from a stratified random sample of
rural households surveyed by ELCA in 8 neighboring municipalities to the ones where the IDP
19
20
The activities also included a module to elicit expectations of future household mobility; these are analyzed in Chapter 2.
The UAO’s are run by local authorities and by Acción Social in cities with a high influx of displaced population. Displaced households have
to declare at the UAO about the events leading to their displacement. The validity of their testimony is then analyzed using data on violent events
at the municipal level and if corroborated, they are legally registered as IDP. This is a precondition to receive assistance programs and
humanitarian aid from Acción Social. Aid packages and transfers are supposedly handed out 4 times per year, but in practice households receive
one transfer on average.
21
There could be concerns that by selecting the sample of non-massively displaced households from those who seek the assistance of the
government, the sample is biased towards those who have not recovered socio-economically and are in dire conditions. However, there are no
graduation strategies for these transfers and all displaced households, whether they have recovered socio-economically or not, are entitled to
them. What’s more, officials at the different UAO’s mentioned that better-off households who learned how the transfer system works, are the
ones who often ask for assistance, and that those who do not show up are the highly traumatized ones who are too afraid to leave their homes and
provide information about their episodes of displacement. In such case, the sample would be composed of those better of and less traumatized
households, and if anything the bias would work against the hypothesis that violence induces mental distress and risk averse behavior.
22
From here on I will refer to this group as the non-massively displaced.
15
were displaced from.23 Since this group had already answered the ELCA survey, subjects were
contacted by phone on behalf of the ELCA team and invited to participate in a follow-up activity
at the church in the nearest municipal head. Subjects were informed that they would be paid for
the transport costs to and from the municipality heads, and would receive a snack. Sessions were
conducted during the market days, when farmers travel to the municipality heads to sell their
products to make it more convenient for the subjects to assist.
B. Household Survey
The household survey followed the questionnaire used by the ELCA, which includes standard
modules on education, employment, lands and agricultural productions, social capital, assets, and
consumption, among others.24 I included a series of retrospective questions to gather information
about demographic and socio-economic conditions before the episodes of displacement. The
survey was only administered to the sample of IDP, since the sample of non-displaced
individuals had answered the ELCA survey in the six months previous to the fieldwork of this
project. For the non-displaced, I administered a short module to identify important changes or
shocks that took place since the moment in which the ELCA was administered.
C. Triggers of Displacement and Exposure to Violence
I captured information on the displacement process by including a module in the household
survey that assessed the events that triggered the displacement of the household, the municipality
where they occurred, the date of displacement, and the reception of aid at destination sites. Panel
1 of Table 1 present summary statistics for the displacement process. Almost half of the
displaced (46%) migrated less than a year before I surveyed them while 39% migrated between
23
Three of these municipalities were located in the department of Tolima, in the Central region, while four were located in the department of
Córdoba, and one in the department of Sucre, in the Atlantic region.
24
Questionnaires are available at http://encuestalongitudinal.uniandes.edu.co/index.php/es/documentacion/ronda-1
16
one and five years before. The distribution of the time since displacement is different, however,
for the massive and non-massive samples: 60% of massive displacements occurred less than a
year before compared to 34% for the non-massive, whereas only 2% of the massive
displacements migrated more than five years before vs. 28% for the non-massive.
The data on the triggers of displacement reveals, on the other hand, that both the massive and
non-massive displacements occurred after households were exposed to numerous episodes of
violence (Table 1, Panel II). On average, IDP were exposed to more than three different types of
violent events, and most of them migrated after receiving a threat or an order to migrate by an
illegal armed group, or after being exposed to combats within the community boundaries o to
random violence. Again, the scenario is different for the massive and non-massive samples.
Almost all of the massive displacements were triggered by a combat between illegal armed
groups in the proximity to the community, and to a lesser extent by threats, regular violence,
orders to migrate, and assassinations. In the case of the non-massive displacements, the majority
of the subjects migrated after a threat, but there is a more diverse exposure to violence, including
combats, random violence, orders, assassinations, recruitments, attacks, massacres, and
disappearances.
The information above provides evidence on the events that led to the displacement of the
sample of displaced individuals, but it does not capture if the subjects or members of their
households were directly exposed to such events, the number of times that each event happened,
or the exposure to violence of the non-displaced. To capture this information, during the
weekend sessions, enumerators first administered a victimization questionnaire designed to
17
measure the severity of the exposure to violence by each household.25 Each participant was
asked in private if any member of the household had been directly exposed to the same list of
violent events included in Table 1, and the number of times that each event happened during the
last year and during the last ten years. Similarly, participants responded if any members of the
household had witnessed the same events of violence and the number of events witnessed during
the last year and last ten years.
Table 2 depicts sample statistics on the exposure to violence by household for the massively
displaced, individually displaced, and non-displaced. To facilitate the interpretation, the data is
summarized and refers to whether a member of the household suffered at least one violent event
in the last ten years, and whether a member of the household suffered in the same period at least
one episode from each of the events listed before. Table 2 also includes summary statistics on
two measures for the severity of violence that I will use throughout the analysis: the total number
of violent events suffered by household members in the last ten years, and a victimization index
constructed through principal factor components using the information on the number of times
that each event happened in the same period.
The data in Table 2 reveals important differences between the three samples. First, the
incidence of violence among the displaced sample is considerably higher than for the nondisplaced sample; only 9% of the subjects from the non-massive group and 4% of the massive
group had not been directly exposed to a violent event in the last ten years, whereas only 15% of
the non-displaced were exposed to violence in the same period. While this was expected from the
way in which the sample was designed, it reveals that the majority of the IDP did not migrate as
25
Since the non-displaced did not have to answer the survey, the household victimization module was implemented during the sessions so that
all subjects, whether displaced or not, would respond to these questions at the same time. By doing so, I minimize the possibility that I could be
implicitly priming one group of respondents by asking them to think about traumatic events moments before the implementation of the risk
aversion experiment and the expectations questionnaire, while the other group answered the victimization module several days before.
18
a preventive measure fearing the escalation of violence but rather after they suffered some kind
of violence directly. Second, the data indicates once again that the massive group was displaced
by combats between illegal armed actors in the proximity to the households while the nonmassive group was displaced by a more diverse type of exposure, but more importantly that the
severity of the victimization process was much higher for the latter group. This information
points out that although the non-massive group was not necessarily displaced with their entire
communities, it suffered a more diverse and intense shock of violence than those who migrated
massively. Hence, the data suggests that even in the cased of the non-massive displacements,
displacement is not the result of an endogenous migration decision but instead that individuals
had no other choice than to migrate after being exposed to a severe shock of violence.
D. Psychological Stress
After completing the victimization module, participants answered the Symptom Checklist 90
(SCL-90-R), a psychological questionnaire that includes 90 questions on a broad range of daily
symptoms of stress, from which it is possible to obtain measures for a Global Severity Index
(GSI), PTSD, and nine different psychopathologies, including measures for anger and two
anxiety disorders. This scale was chosen over a more common PTSD questionnaire for several
reasons: First, application of the PTSD scale is only valid when the population has been exposed
to a source of trauma, which was not the case for the majority of non-displaced households. The
SLC-90 instead allows me to assess and compare the extent of psychological distress between
victimized and non-victimized individuals. Second, by providing measures for different
psychopathologies, it allows me to test the predictions from the psychological literature and
analyze the impact of anxiety and anger on risk aversion. Third, by capturing the variation on
anxiety and anger driven by the exposure to violence, rather than through experimental primes
19
that induce immediate but temporal variations in participant’s emotions, I can argue that the
choices during the field experiment revel patterns of economic behavior outside of the field
experiment. Fourth, as most psychological scales, the SCL-90 does not ask individuals directly if
they are stressed, depressed, anxious, or suffer from PTSD. Instead, it evaluates the incidence of
different symptoms, like headaches, back pains, difficulties to fall asleep, and panic attacks,
which are symptomatic for different psychological disorders, and thus minimizes the possibility
that subjects misreport the incidence of psychological stress. Finally, this scale has been
implemented in numerous studies in developing countries and it provides reliable psychometric
properties (Casullo, 2004).
Sample statistics on the incidence of psychological stress for the two groups of displaced
individuals and for the non-displaced are listed in Table 3.26 The first three columns report the
average and standard deviations of the T-Score for the GSI and nine dimensions of distress for
each group, and PTSD for the displaced.27 The last three columns report the percentage of the
population in each group that has a T-score above 63 and is considered to be at risk of suffering a
severe case of the particular psychopathology. The patterns and magnitudes of mental distress
among the displaced indicate levels of psychological trauma that are considerably higher than
those for the non-displaced and for the Colombian population (National Health Survey, 2003).
Moreover, a considerable proportion of the displaced population score above the clinical range,
26
Anxiety is a psychological state of distress, fear, and concern, and indicates general symptoms of nervousness, tension, fear and panic attacks
(Casullo, 2004). In moderate levels, it is considered a normal reaction to a stressor, but a disorder when exceeds certain levels. Anxiety can be
caused by many sources of stress, including violence, poverty, and inequality, among others. Therefore, the incidence of anxiety among the
displaced population does not indicate that it is the result of exposure to violence but instead to a broad range of difficult situations brought about
by displacement. Phobic anxiety, on the other hand, is defined as an abnormal fear and behavior of avoidance of regular object or situation. This
disorder is characterized by intense fear that is triggered by a stimulus, that is not threatening by itself, but that can bring about recalls of traumatic
episodes (Casullo, 2004). During the fieldwork I often heard participants claiming that they were unable to go out to public spaces, to places
where a large crowd of people gathered, or to be in the dark, among others, since then they would immediately remember the episodes of
victimization and this would make them afraid and anxious. Hostility refers to thoughts, feelings, and actions that characterize the negative anger
affect.
27
Each question ranges from 0 to 4 indicating no symptoms in the last three months (0), to daily symptoms in the last three months (4). T-scores
are calculated as follows: First, the scores on the relevant questions for each psychopathology are added and divided by the total number of
relevant questions that were answered. In the case of the GSI this corresponds to all 90 questions. The resulting net score is then converted into a
T-score with mean 50 and standard deviation 10 (Ti = 10 + 50 × net score).
20
whereas the non-displaced reveal moderate to normal levels of psychological stress. For instance,
over 35 and 41 per cent of the individuals in the non-massive and massively displaced sample are
at risk of suffering from severe depression disorders, 19 and 39 percent are from severe anxiety
disorders, and 24 and 48 percent from severe phobic anxiety disorders. While the levels of
hostility among the displaced are still above the levels of the non-displaced, the incidence of
hostility disorders is not as widespread as the incidence of the two anxiety disorders.28 The
distributions of GSI, anxiety, phobic anxiety, and hostility disorders across the three groups can
be observed in Figure 3.
E. Risk Aversion Elicitation
To elicit risk attitudes, I followed Binswanger’s (1982) classical ‘Choose Lottery’
experimental design, with tasks over the gains, losses, and ambiguity domains. Participants
received a booklet that contained a practice and a real round for each domain. To facilitate
understanding, each round was depicted in a graphical way with labels and pictures of the local
currency to indicate the possible payoffs. Six ovals represented the six different lotteries to
choose from, en each oval was divided into a red and blue segment indicating the two possible
payoffs for each lottery. In each round, participants had to pick one lottery, and at the end of the
activity one of the real rounds would be randomly picked by blindly selecting a white numbered
ball from a bag.29 Payoffs ranged from US$ 6.5 to US$ 17, corresponding to approximately 2 to
5 days of off-farm wages in rural areas.
28
Interestingly, while the non-massively displaced were exposed to a higher number of episodes of violence than the massively displaced, the
incidence of psychological distress is higher across the board for the latter. This is not contradictory however, as most of the massive
displacements occurred less than a year before and 20 percent of them even less than a week before they answered the psychological
questionnaire.
29
In the practice rounds the choices and outcomes were explained, each participant was asked to pick one lottery, and the experiment was played
so that participants would privately know what their payoff would have been if the lottery had been a real one.
21
In each round, the first lottery provided the lowest expected payoffs with certainty, while the
expected payoffs and as the variance of the payoffs increased gradually for the other lotteries.
Individual choice therefore reveal the willingness to bear risks; more risk averse individuals will
pick safer lotteries, the ones with lower expected payoffs and less risk, while more risk loving
individuals will choose lotteries with higher expected payoffs and higher risk.30 Table 4 provides
a description of the payoffs in each of the six lotteries and the endowments for each task.31
Figure 3 illustrates the choices in each domain, where lottery 1 corresponds to the safest lottery
and lottery 6 to the riskiest.
In the gains round, participants were instructed to pick one lottery bearing in mind that if that
round was chosen, one ball would be randomly picked out of a bag containing five red balls and
five blue balls. If a red (blue) ball was chosen participants would receive the payoff indicated in
the red (blue) semicircle of the lottery they chose. Payoffs in each choice in the gains domain
thus had equal probability of occurrence.
The second round addressed the extent of loss aversion (Kahneman and Tversky, 1976). As
before, payoffs in each choice in had equal probability of occurrence but now participants were
told that if this round was selected they would earn an upfront payment of $20,000 and that such
payment would be theirs to keep. Since the options allowed the possibility of losing money
(negative payments), they were told that they would have to use their endowment to cover any
losses. Notice that the expected payoffs in this domain are exactly the same as those in the gains
domain once we take into account the endowment (Table 3). By framing the choices in terms of
30
Other experimental designs to measure risk aversion, such as the Holt and Laury (2002) or the Tanaka et al. (2010) procedures, vary
probabilities instead of payoffs and have been used more frequently in recent research. Their appeal lies in the possibility of estimating Prospect
Theory parameters (see Carpenter and Cardenas, 2009 for a review of these methods). The ‘Choose Lottery’ design of Binswanger, however, is
more straightforward and easier to understand, particularly in a sample of rural individuals with low levels of educational attainment.
31
The experimental protocol and the graphical depiction of the lotteries are included in section I of the online appendix.
22
losses I can then address if participants display a higher willing to take risks to avoid losing their
endowment, a behavior consistent with loss aversion.32
The last round addressed the extent of ambiguity aversion. This domain resembles the gains
domain, with the exception that if this were the task chosen, participants would not know the
exact number of red and blue balls in the bag. Instead, a total of 10 balls would be introduced
into the black bag, including 3 red and 3 blue balls, as well as 4 other balls that would be
randomly and blindly chosen from a bag containing 50 blue and red balls each. The remaining
four balls could thus be all red, all blue, or a combination of blue and red balls. This meant that
at the time of the decision the first-order probability distribution of the payoffs was unknown to
both the participants and to myself, but the second-order probability distribution was known. By
introducing this source of ambiguity, I can address if participants become more risk averse under
ambiguous situations (Kahneman and Tversky, 1976).
III. Identification Strategy
To overcome the absence of longitudinal data, I first match a sample of victims of forced
displacement to a sample of non-displaced individuals, and then separately use the incidence of
displacement, the severity of violence, and the extent of anxiety and anger disorders to identify
the impact of violence on risk attitudes. As mentioned above, individuals in the IDP sample
resided in rural communities located in the proximity to three natural corridors, the Nudo del
Paramillo and Montes the María in the Atlantic region, and the Cañon de las Hermosas in the
Central region, and were forced to migrate to the municipality head or to the closest departmental
capital. Individuals in the non-displaced sample, on the other hand, reside in the same regions,
32
Note that in this experimental design the actual loss aversion parameter is not identified separately of the risk aversion parameter. However, by
introducing losses in this task, it is possible to obtain a general risk aversion behavior when losses are possible.
23
yet in municipalities farther out from these three corridors and have experiences moderate levels
of violence.
Comparing the degree of risk aversion of the displaced and non-displaced is only appropriate
if violence and displacement were exogenous to community and individual characteristics, or at
least driven by observable characteristics {Imbens:2003wj}. A priory there are two reasons by
which these conditions would not hold: One, qualitative evidence indicates that armed groups in
Colombia have targeted community leaders, entrepreneurs, and land holders more than other
segments of the population. Hence, the likelihood of being victimized could be correlated to
individual characteristics and to the ex-ante levels of risk aversion. Two, it is possible that the
decision to migrate is not exogenous or driven entirely by the exposure to violence, and could
instead be correlated with ex-ante risk attitudes. For instance, it could be that within a conflicttorn region, only those who were already more risk averse end up migrating preventively fearing
the escalation of violence in their communities, or that among those directly exposed to violence
only those that become more risk averse migrated. I argue that the way in which the sample was
designed, the data on violence and mental trauma, and the econometric strategy minimize such
concerns and allow me to identify the causal impact of forced displacement and violence on risk
attitudes.
At the municipality level, administrative level data indicates that the municipalities where the
displaced population resided are not only quite close to the municipalities where the nondisplaced reside (the distance between them is less than 60 miles in the Atlantic region and less
than 40 miles in the Central region), but are also remarkably similar across different geographic,
socioeconomic, and institutional characteristics, and are both under the influence of different
24
illegal armed groups (Table 5).33 Despite the similarity between the two groups of municipalities,
the intensity of violence, measured by the homicide and massacre rates, and the number of
attacks is considerably higher among the former, which explains the differences in intensity of
displacement. The question is of course why only some of these municipalities receive
considerable shocks of violence and whether unobserved municipality or community
characteristics explain the intensity of violence.34
A careful analysis of the dynamics of violence reveals that illegal armed groups were not
targeting specific communities. In the Atlantic region, for instance, neo-paramilitary groups have
clashed against each other for the control of the Nudo del Paramillo and the Montes the María
since they constitute natural pathways for transporting illegal drugs towards the Caribbean coast
({HumanRightsWatch:2010wr}{Indepaz:2011uh}{Negrete:wi}). In the Central region, the
FARC have retreated towards the Cañon de las Hermosas, which a natural corridor for the
movement of troops and the shipment of illegal drugs to the Pacific Ocean, and provides
geographic conditions that offer shelter from the Colombian military’s aerial bombings. This
suggests that rural communities have been victimized for no other reason than for being in the
path of expansion or retreat of the illegal armed groups, and that violence is exogenous to
community characteristics conditional on the distance to the different geographical corridors.
At the individual level, I use the retrospective questions that were included in the household
survey to show that before they were displaced, the massive and non-massively displaced
33
Unfortunately there is no available administrative data at the level of the community or at the level of the village to show that not only the
municipalities are similar, but also that within these municipalities the rural communities were similar.
34
For the non-displaced to be appropriate controls, it is not only important to show that violence was exogenous or that the patterns of
victimization are observed and can be controlled for, but also that they reside and were exposed to similar environments such that pre-violence
preferences and expectations were similar. This is not the case, however, in Voors et al (2012) where they show that violence is driven by
geographical characteristics and use the distance to the capital and the altitude as instruments for violence. This means that they end up
comparing individuals who resided in communities close to the capital, where violence was more intense, with individuals who resided farther
our communities high up in the mountains where violence was less frequent. However, there are several reasons why we would expect that such
geographic characteristics would influence preferences and expectations directly. In other words, one could argue that the exclusion restriction for
the instrumental variables estimation does not hold.
25
individuals looked similar to what the non-displaced looked like at the time of the survey over a
set of observable household and individual characteristics (Table 6). There are still some
differences between the samples, as displaced individuals were younger, slightly more educated
and had bigger lands at the time of displacement than the non-displaced.35 There are also
significant differences regarding the participation in social organizations, although this time
across the three groups. More individuals from the massive sample participated, made decisions,
and were leaders in more social organizations than the non-displaced, who in turn have higher
rates of participation than the non-massively displaced sample. The key question is now if
observable or unobservable characteristics explain the likelihood and the severity of the exposure
to violence.
Anecdotal evidence points out, on the one hand, that since armed groups do not have full
territorial control but are instead in a process of expansion or retreat in the two regions selected,
the likelihood of selective targeting is lower than in earlier periods or in other regions. In the
Atlantic region, for instance, as neo-paramilitary groups attempt to control the corridors used to
transport illegal drugs, they have relied on terror strategies to control the population rather than
selective violence, committed an increasing number of massacres, and clashed against each other
in frequent combats that involve heavy weaponry and have caught rural communities in cross
fire ({HumanRightsWatch:2010wr}{Indepaz:2011uh}{Negrete:wi}).36 This is precisely the case
for the massively displaced communities included in the sample who were displaced after major
armed combats (Tables 1.1 and 1.2). During fieldwork, victims of massive displacements pointed
35
Based on evidence from previous research in developing contexts, these differences are associated to lower levels of risk aversion, and thus
the direction of the possible bias stemming from these differences would go against the hypothesis that the displaced population becomes more
risk averse after being exposed to violence. For a review of experimental findings, see Harrison and Rudstrom, 2008 and Cardenas and Carpenter,
2010.
36
An article from El Meridiano, the local newspaper of the department of Córdoba, further supports these claims. The article describes the
different massacres that occurred in the different municipalities of the department during the second semester of 2010, and shows the increasing
levels of violence within the region as well as the indiscriminate nature of violence that took the lives of peasants, teachers, community leaders,
laborers, and children. The article can be accessed at andresmoya.weebly.com/fieldwork.html
26
out that at some point an armed group made presence in their communities, asking for supplies
and intimidating the population, until a different group arrived and engaged in wide scale
combats within the community boundaries. As armed actors took positions among the houses
and schools, households were victimized by crossfire, and after combats ceased survivors
migrated collectively to the nearest county head. In the Central region, anecdotal evidence
highlights different conflict dynamics, yet episodes of civilian victimization with low levels of
selective targeting. As the FARC have retreated, they have set land mines to keep the military
away, recruited minors, taxed and harassed civilians to offset declining incomes and grasp
control of the population, and launched indiscriminate attacks on communities and rural towns to
ease pressure from the military in neighboring municipalities {DefensoriadelPueblo:2007ve,
DefensoriadelPueblo:2008up, DefensoriadelPueblo:2009wb}.
On the other hand, I can analyze the extent of selection into violence by regressing different
measures of exposure to violence on the characteristics of the displaced and non-displaced
populations (Table 7, Columns 1-3). I use three measures of violence using the data from the
victimization module: (1) a binary variable that indicates weather the household is a victim of
violence or not, (2) the total number of violent events that the members of the households had
suffered, and (3) the victimization score. The results from this exercise suggest that households
with higher levels of education, those who own more lands, and who are involved in economic
activities different than agriculture, have a higher risk of being victimized and experienced more
of violence. I can therefore control for these characteristics during the econometric analysis, so
that under the assumption that selection into violence is based on observables, the estimated
effects will be conditionally unconfounded {Imbens:2003wj}.
27
I also argue that it is unlikely that the displaced migrated as a result of ex-ante levels of risk
aversion and that even if this is not the case, I can minimize the extent of the endogenous
displacement decisions by restricting the analysis to the subsample of massively displaced
individuals. On the one hand, the data from Tables 1 and 2 indicates that over 90 percent of the
displaced population directly suffered at least one violent episode, and in general experienced a
diverse and severe exposure to violence; the non-massively displaced suffered over 3 types of
episodes of violence while the massively displaced were victimized by crossfire between neoparamilitary groups. The data therefore indicates that the extent of preventive displacement is
negligible; this is, that within the conflict areas, individuals were not migrating preventively
fearing the escalation of violence. In fact, the data from the psychological scale (Table 3)
indicated that while the incidence of stress for the non-displaced group resembles that of the
general population – that is, population not affected by major traumatic events – the incidence of
stress for displaced population is far above normal. This provides even more evidence that the
displaced suffered an overwhelming exposure to a traumatic event, which induced abnormally
high levels of emotional distress, rather by an endogenous self-selection process.
It could still be, however, that among those exposed directly to violence, only the ones who
became traumatized and experienced a shift in their risk attitudes decided to migrate, while the
psychologically resilient did not. To control for this possibility, I first stratify the data and
conduct the analysis comparing the massively displaced individuals with the non-displaced
sample of the Atlantic region. Considering that the former migrated with their entire
communities, I can analyze the robustness of the results on a subsample for which the decision to
28
migrate is arguably exogenous to individual characteristics and risk attitudes.37 In addition, if
among the full sample of IDP, those who were ex-ante more risk averse and fearful migrated
before their neighbors or communities, the severity of exposure to violence would be inversely
correlated to the degree of risk aversion. The bias stemming from such endogenous migration
responses would bias the estimates against the hypothesis that a higher degree of victimization
induces high levels of risk aversion. The resulting estimates would thus be conservative
estimates of the true impact of violence on behavior among the displaced population.
Finally, since the environments in which the displaced reside now is quite different than the
one in which the non-displaced resides, it is possible that the variation in violence and mental
trauma is capturing some of these differences and some of the other consequences of
displacement and thus that latter are an inappropriate control group. I conduct two tests, to
further support the argument that the results are not driven by the use of an inappropriate control
group or by other circumstances related to forced displacement. First, I stratify the sample and
analyze the robustness of the results only among the displaced sample. By doing so, I ensure that
all of the individuals in the analysis were exposed to the some violence, had to migrate from
rural to urban areas, lost a considerable proportion of their assets, and experienced significant
drops in income and consumption levels, and use the variation in the severity of victimization,
time since displacement, and the incidence of psychological stress to explain risk attitudes. By
doing so, I also control for the extent of the endogenous displacement responses; this is, if the
individuals who were already more risk averse decided to migrate preventively, the sign of the
bias will go against the hypothesis that violence and displacement bring about higher levels of
37
Columns 4, 5, and 6 present the results from the analysis of the determinants of the likelihood of being victimized and the severity of the
victimization for the massively displaced sample and Atlantic controls.
29
risk aversion. Second, I use data on asset losses and current consumption levels to rule out that
the effect is explained by the extent of the material losses, or the levels of poverty of the IDP.
IV. Results
A. Forced Displacement and Risk Aversion
I first estimate the effect of forced displacement on risk aversion through Ordered Probit
estimations that do not assume a specific form for the utility function but instead order lottery
choices from the safest (lottery 1) to the riskiest (lottery 6). A positive (negative) coefficient for a
particular variable indicates that an increase in that variable has a negative (positive) effect on
the probability of choosing the riskiest lottery (Cameron and Trivedi, 2010). A negative
coefficient thus entails a shift towards the safest choice and higher levels of risk aversion.
For the gains domain, I start by estimating the index model (1) in which I regress the indicator
variable IDP for whether the individual was displaced by violence on the choice y *id without any
covariates (Table 8, Column 1).
y *i,d (gains) = β1IDPi + ε i
€
(1)
The negative and significant coefficient of the displacement status indicates that being a victim
of forced displacement €
increases the probability of choosing the safest lottery, and thus brings
about higher levels of risk aversion. Although the sample was not explicitly designed to identify
the temporal nature of the shift in behavior, I can take advantage of the fact that the IDP in the
sample were displaced at different points in time and estimate model (2) controlling for the time
that has elapsed since the episode of displacement and its quadratic term (Table 8, Column 2).
y *i,d (gains) = β1IDPi + β2 t i + β3 t i2 + ε i
(2)
The estimated impact of displacement is now almost twice as big and still significant at the 1
percent level. The€coefficients of the time terms indicate that the effect of displacement on risk
30
aversion is not permanent, although it takes over three years for it to vanish. Hence, the
estimated coefficient of Column 1 implicitly averaged the impact of displacement for individuals
who were displaced at different points in time. This results points out that the failure to consider
the heterogeneous time effects will likely underestimate the true nature of the impact of violence
on behavior, especially if the focus is on wars and conflicts that occurred several years ago, as in
the case of Voors et al. (2012). The results are robust and do not change significantly in
magnitude when I estimate model (3) in which I control for a set of pre-displacement
characteristics, including those that explain the likelihood of being victimized, the incidence of
other shocks in the previous year, the hypothetical earnings in the previous practice round X i and
a regional fixed effect υ r (Column 3).38
y *ir,d (gains) = β1IDPir + β2 t ir + β3 t ir2 + β4 X ir + υ r + ε ir
(3)
€
To gauge €
the magnitude of the impact of displacement on risk attitudes, I estimate the average
€ of being displaced on the probability of choosing each lottery following the index
marginal effect
model (3). Results indicate that displacement increases the probability of picking the safest
lottery by 12 percentage points (Table A1 of the Appendix).39 I also ran the maximum likelihood
procedure developed by Harrison and Rudstrom (2008), which estimates the average risk
aversion parameter from a constant relative risk aversion (CRRA) utility function, as well as the
contribution from the different covariates (Table A2, Columns 1-3).40 Results indicate again that
the impact of forced displacement is not permanent, and that after controlling for the time since
38
All of the results are robust when I control for the color of the ball drawn in the practice round instead of controlling for the hypothetical gains
in the practice round. Since I conducted all experimental sessions, there is no need to include an experimenter control.
39
Form now on, whenever the letter A is included in front of the number of a table, it indicates that the table is included in the appendix.
40
The MLE assumes a CRRA utility function and constructs a latent index that captures the difference in the expected utility from each lottery.
This index is then linked to the observed choices through a multinomial logistic distribution.
31
displacement and the same set of covariates as before, forced displacement increases the CRRA
coefficient by 20 percent (from 0.49 to 0.59).
I now estimate the impact of displacement on the choices at the loss domain, following the
same estimation strategy as above (Table 7, Columns 3-5). Results display a similar trend as
before when I do not include any covariates, when I control for the time since displacement, and
when I include the set of covariates. Overall, displacement has a positive effect on the likelihood
of selecting the safest lottery and the displaced seem more reluctant to take risks even in
situations when there is a chance of losing their endowments. Nevertheless, the estimated
coefficient is not significant at the 10 percent level, a result that is not entirely surprising
considering that research in neuroeconomics finds that risk attitudes are correlated to the
psychological trait of neuroticism, which is the predisposition to feel anxiety, over gains but not
over losses (Rustichini, 2010).41
Finally, I estimate the effect of forced displacement on risk attitudes in the ambiguity domain
following the same three models as before (Table 8, Columns 7-9). Notice that in this domain
individuals face a higher degree of uncertainty since they do not know the first-order probability
distribution of the payoffs which all-else equal should increase the levels of risk aversion for
both samples. Nevertheless, displacement still induces a sizeable and significant shift towards
higher levels of risk aversion, and an increase of 9 percentage points in the likelihood of
choosing the safest lottery, and a 21 percent increase in the CRRA coefficient (Tables A1 and
A2). Again, the coefficients on the time of displacement indicate that those individuals who
where displaced more recently display a more pronounced effect on behavior and that the effect
vanishes after over three years.
41
MLE estimates and Average Marginal Effects of Displacement are presented in Tables A1 and A2 of the Appendix.
32
To analyze the robustness of these results, I restrict the analysis to the sample of massively
displaced population and the non-displaced population from the Atlantic region. Recall that
massive displacements were triggered by combats between armed groups in which rural
communities were caught between crossfire and the entire community migrated soon after. The
extent of selective targeting and endogenous displacement decisions is thus much lower among
this sample. Table 9 presents the results from the estimations for each domain under the
following specification:
yid* = β1IDPi + β 2 ti + β 3 X i + ε i
∀ [i = Massive | Atl. Control]
(4).
The results are robust and even stronger in the gains task, where massive displacement brings
€ a higher level of risk aversion (Column 1), and increases the likelihood of choosing the
about
safest lottery by 14 percentage points (Table A2, Column 4).42 For the losses and ambiguity tasks
(Table 9, Columns 2 and 3), massive displacements are associated with a higher probability of
choosing the safest lottery, but the effects are smaller than before and not significant.
Overall, the results from this section point out that forced displacement induces a shift
towards higher levels of risk aversion in the gains and ambiguity domains. Moreover, the results
are robust to the inclusion of variables that explain the likelihood of selective victimization, and
for the gains domain to the stratification of the data on a sample where both the incidence of
selective targeting and endogenous displacement decisions is lower. Forced displacement,
however, entails many circumstances that could drive the effect on risk aversion, including the
exposure to violence, the migration to a different environment, the loss of all assets and lands, a
process of impoverishment, and mental trauma. In the next three sections I explore the
42
I do not include the fixed effect for the Atlantic region since all of the massively displaced and their appropriate controls reside in such region.
Since massively displaced individuals migrated at the same time with the other members of their communities and there are only seven massively
displaced communities in the sample, there is not enough variation in the time since displacement to identify its impact.
33
mechanisms that explain the shift in risk attitudes using data on the severity of violence, the
incidence of mental trauma, asset losses, and expenditure levels.
B. Victimization and Risk Aversion
I address if the impact on risk attitudes varies according to the severity of the exposure to
violence by estimating the following model:
y *ir,d = β1Violenceir + β2 t ir + β3 t ir2 + β4 X ir + υ r + ε ir
(5)
I control for the matrix of pre-displacement household and individual characteristics used before
€
and for a regional
fixed effect, and separately use two measures for the severity of violence: the
standardized total number of violent events suffered by the household in the previous ten years
(Table 10, Column 1), and a victimization score that was constructed through principal factor
analysis (Table 10, Column 2).43
The results from Column 1 of Panel A indicate that the exposure to a higher number of
episodes of violence brings about higher levels of risk aversion during the gains domain. The
average marginal effect indicates that an increase of one standard deviation in the number of
violent events raises the probability of choosing the safest lottery by almost 4 percentage points,
which is one third of the effect of displacement estimated before (Table A3a, Column 1 of the
Online Appendix). To put this figure into context, the mean value of the standardized measure of
violence for the displaced population is one standard deviation higher than the mean value for
the non-displaced. Violence thus not only explains a considerable portion of the effect of
displacement on risk attitudes, but there is a heterogeneous effect across the displaced according
to the severity of the exposure, and a sizeable impact for the individuals at the right tail of the
violence distribution. A similar picture emerges when I use the victimization index, although the
43
The coefficients for the controls are not included for space considerations, but the full results are included in Table A5 of the online appendix.
34
overall and marginal effects are now significant only at the 15% (Column 2, Panel A, Table
A3b). Notice that the sign of the time coefficients still indicate that the effect on risk attitude is
not permanent although not statistically significant in this case.
As in the gains domain, violence has no effect on risk attitudes in the loss task (Table 10,
Panel B and Tables A3a and A3b, Column 2), but it does have a sizeable effect on choices during
the ambiguity task. In fact, a more severe exposure to violence induces an even bigger shift
towards higher levels risk aversion in the ambiguity domain than in the gains domain (Table 10,
Panel C, Columns 1 and 2). Marginal effects indicate that a one standard deviation increase in
the severity of violence (victimization score) increases the likelihood of choosing the safest
probability by 6.46 (7.46) percentage points, almost twice the size of the effect in the gains
domain (Tables A3a and A3b, Column 3).44
The validity of the results above hinges on the argument that violence was indiscriminate at
the community level, conditional on the distance to the strategic corridors, and that along with
more indiscriminate patterns of civilian victimization in the two regions chosen I can control for
the characteristics that explain the likelihood of being targeted by an illegal armed group. To
analyze the robustness of the results under a less restrictive set of assumptions, I drop the sample
of non-displaced individuals and conduct the analysis only using the variation in the severity of
violence among the displaced. The underlying assumption in such case is that conditional on
being displaced, the severity of the exposure to violence is random. This assumption falls in line
with anecdotal evidence that points out that in contested territories the extent of selection is
lower as illegal armed groups use strategies of terror to gain control of the civilian population
and that I can still control for the observable characteristics that explain any remaining targeting
44
Since for the massive displacements there is not enough variation in the exposure to violence within each community, this could provide an
explanation as to why the estimated coefficients of Model 4 (Table 8) where not robust during the ambiguity domain.
35
of violence. In addition, if there were any extent of endogenous displacement decisions within
the IDP sample, the ex-ante levels of risk aversion would be negatively correlated with the
severity of violence. In other words, if risk averse individuals decided to migrate fearing the
escalation of violence while the less risk averse stayed, the severity of the exposure to violence
would be higher among the latter who only migrated after being exposed to more violence. In
such case, the sign of the bias would lead to conservative estimates of the true impact of violence
on behavior.
Columns 3 and 4 of Table 4 present the results of the estimation of model (5) for each
domain using the data from the displaced population only.45 The results are still significant and
do not change considerably in magnitude across each domain, indicating that a more severe
exposure to violence induces higher levels of risk aversion in the gains and in the ambiguity
domains, but not in the loss domain. Marginal effects indicate that a one standard deviation
increase in the number of violent events suffered by the household members increases the
probability of selecting the safest lottery by 3.23 and 5.48 percentage points in the gains and
ambiguity domains respectively, and similar effects for the victimization score (Table A3a and
A3b). The statistically significant effects for the two time terns indicate once more that the effect
of violence on risk aversion is not permanent.
C. Mental Trauma and Risk Aversion
To further explore the channels that drive the differences in individual risk attitudes, I use the
data on mental trauma, which I measured using the SCL-90. Following the results of the ‘Risk as
Emotions’ framework, I estimate an ordered probability model in which I include measures for
anxiety and phobic anxiety, which are expected to induce higher levels of risk aversion, and a
45
Table A6 of the Online Appendix presents the full estimation results.
36
measure for hostility, which is expected to have the opposite result. I include the two measures of
anxiety at the same time because the while former captures the general feeling of dread and fear
that could be explained by different stressful circumstances in addition to violence, the latter
refers to abnormal fears to everyday situations, which in the context of the IDP relate to
situations and places that evoke the episodes of violence.
In the main specification model 6), I include three indicator variables for whether an
individual suffered levels of anxiety, phobic anxiety, or hostility above the critical T-score
threshold. The reason for this is that the psychology literature points out that while moderate
levels of psychological stress are normal, once they exceed certain levels they predispose
individuals to behave in a particular way and individuals become incapable to regulate or
overcome such predispositions and even experience physiological changes in the brain that will
permanently affect their responses to future sources of stress (Kesler et al., 1995; Alldin et al.,
1996; Davidson et al., 1998; Gruenjar, 2000).46
y *ir,d = β1 Anxiety ir + β2 PhobicAnxiety ir + β3 Hostility ir + β4 X ir + υ r + ε ir
(6)
Columns 1, 2, and 3 of Table 11 display the results of the impact of mental trauma at each
€
domain
over the full sample of displaced and non-displaced individuals. The coefficients for the
three disorders have the expected signs, although only the incidence of severe phobic anxiety
disorders has a significant effect on risk attitudes in the gains and ambiguity domains. As in the
two previous sections, behavior in the loss domain is not explained by the variation in mental
trauma. Severe hostility disorders, on the other hand, have a significant effect on behavior in the
46
Nevertheless, the results are robust, albeit slightly less significant when the continuous score of each psychopathology is included and are
included in Table A9 of the online appendix.
37
ambiguity domain.47 Average Marginal Effects indicate that the probability of selecting the
safest lottery is 14 percentage points higher in the gains domains, and 10 percentage points
higher in the ambiguity domain for an individual who is at risk of suffering severe phobic
anxiety disorders (Table A4). This entails an effect higher than the one of forced displacement
(Table A2) and five times as high as the one from a one-standard deviation increase in the
number of violent events (Table A3). Although hostility has an effect of a similar magnitude and
opposite direction in the ambiguity domain, considering that anxiety disorders are more
prevalent among the displaced individuals, both in my sample and in the sample of other studies
(Doctors Without Borders, 2010), the results suggest that the psychological consequences of
violence often bring about negative consequences on economic behavior.
Notice that I did not include control for the time since the displacement episode and its
quadratic term. The reason for this is that on average the main observable variables that explain
the incidence of mental trauma are the severity and temporal proximity to the episodes of
violence. The variation in the incidence of different psychological disorders should then already
capture the effect of time. Still, when I estimate the same model and include controls for the time
since displacement (Table A10), the coefficients do not change in magnitude opposite to what
happened when I estimated the effect of displacement or violence with and without the time
controls. One interpretation for these results is that while the vulnerability to mental trauma is
explained to a large extent by how recent and severe were the episodes of violence, it also
depends on the psychological resources and resilience that an individual has, which are
unobserved to us. Yet by measuring the incidence of mental trauma, I implicitly capture some of
this variation, and this allows me to identify that the behavioral consequences of violence are
47
These results are robust when I only include variables for the incidence of Phobic Anxiety and Hostility, but not when I include Anxiety and
Hostility.
38
more intense and can persist with time for those individuals who become more traumatized and
are unable to recover psychologically. The effects on welfare trajectories and on the vulnerability
to poverty would thus be stronger for this group than for the rest of the displaced population.
Since the variation in mental trauma between the displaced and non-displaced samples could
capture different circumstances brought about by forced displacement, I conduct two tests to
analyze the robustness of the results. First, I drop the sample of non-displaced and estimate
equation 6 on the sample of displaced population. By doing so, I can guarantee that all
individuals were exposed to relatively similar circumstances, in relation to being victimized and
having to migrate to an urban setting. The results of these estimations do not change
considerably in magnitude or significance, and provide further evidence on the channel that
explains the differences in risk attitudes during the economic experiment. Second, in Table A12
I find that the variation in the Global Severity Index, which captures general psychological stress
and aggregates over different psychopathologies, does not explain the variation in behavior. I
thus argue that the effect of violence on risk attitudes are precisely driven by the incidence of
specific psychological disorders, rather than other circumstances that could be correlated with
general levels of stress.
D. Alternative Mechanisms
Since forced displacement entails a wide variety of experiences, and at the time of the field
experiments the living conditions of the displaced and non-displaced populations were
considerably different, the variation in the severity of violence or the incidence of mental trauma
could be capturing other consequences of displacement, for instance the extent of asset losses or
the poor material conditions of the IDP. First, to rule out that the magnitude of the asset losses is
underlying the effects on risk attitudes, I use data on land holdings before and after displacement
39
and estimate a serried of ordered probit models in which I include different measures the of asset
(land) losses. Table A12 presents the coefficients of the different measures of asset losses that I
employed, and suggests that if anything, individuals who lost more lands or a higher proportion
of their lands exhibit lower levels of risk aversion. Second, Gollier and Pratt (1996) show that
adding mean-zero background risk to a CRRA utility function will lead to lower investments
across independent decisions, and that a concave utility function is therefore vulnerable to risk.
Under standard assumptions, poorer individuals will thus chose less risky gambles. This could be
problematic considering that available evidence indicates considerable drops in income and
consumption as a consequence of displacement. The variation in violence and mental trauma
between the displaced and non-displaced samples could therefore be correlated to underlying
differences in income and expenditure levels. Addressing the impact of poverty on risk attitudes
is a difficult task since it would require solving the simultaneous nature of the relation. To
provide some evidence that the differences in wellbeing are not driving the differences in
behavior I present sample statistics of the exposure to violence, mental trauma, and choices
across quartiles of the severity of violence (Table A13). Although there are somewhat linear
patterns across the quartiles of violence for mental trauma and choices during the experiment,
consumption levels do not follow the differences in the severity of violence.
V. Discussion
I collected individual-level data from populations residing in two conflict-torn regions of
Colombia, some of which were displaced by violence at different times in the last ten years,
analyze the impact of violence on risk attitudes and explore the underlying psychological
channel. The results from a different set of estimations of the impact of forced displacement,
violence, and mental trauma fall in line with the findings of a series of studies in psychology that
40
analyze how emotions affect risk attitudes. These studies, however, rely on experimental primes
to temporally induce different emotions in controlled laboratory experiments. To date, it is
unclear how these effects map outside of the lab.
In this paper, I take a different approach and rely on a psychological scale to measure the
incidence of different psychological disorders in the three months previous to the economic
experiment. By doing so, I not only provide a test of the predictions of the psychological
literature in a clinical setting but more important I can argue on the external validity of the
results. In particular, since those individuals who were suffering from severe phobic anxiety
disorders in the three months previous to the economic experiment display higher levels or risk
aversion during the economic experiment, it is likely that the psychological consequences are
also inducing a lower tolerance for risk on every-day economic decisions.
These results contradict recent evidence from studies in Burundi (Voors et al., 2012) and
Afghanistan (Callen et al., 2012). I argue, however, that the methodological approach followed
by both studies provides a limited picture of the effect of violence on individual behavior. In
particular, by relying on violence data aggregated over a determined region unit and over several
years, they estimate an average behavioral response for individuals who in a determined region
could have been exposed to different levels of violence at different moments. As a result,
previous studies overlook the heterogeneous responses to violence and can underestimate the
consequences for those individuals who experienced violence directly, and who are at risk of
suffering mental trauma and more vulnerable to poverty. This highlights the limitations of using
aggregate-level data for the analysis of the microeconomic and behavioral consequences of
violence, and the need to move forward to collect detailed individual-level data. Of course, the
shortcoming of this latter approach is that it raises potential selection problems that would affect
41
the validity of the results. Nonetheless, considering the implications of violence on individual
behavior and welfare trajectories, and the policy implications that follow from our research, it is
important to devise methodological strategies that allow us to rely on individual data while
controlling for such potential endogeneity.
In this paper I take into account the dynamics of violence within the two regions from where I
drew the sample, and use municipal and individual data to argue that the extent of selective
targeting or endogenous displacement decisions are not a concern. I also take advantage of a
special type of displacement episode, one in which entire communities migrate after being
caught in the crossfire of armed combats and thus were the severity of the exposure to violence
and the decision to migrate are exogenous to individual decisions, to analyze the robustness of
the results. Finally, I stratify the sample in different ways and address the sign of the possible
selection bias to provide further evidence on the robustness of the results.
Results highlight a considerable variation across a sample of individuals residing in two
regions that for several decades have been torn by violence, and considerable effects for
individuals exposed to a higher number of episodes of violence. In fact, the impact on behavior
when I take into account the variation in the experience of violence is considerably higher than
when I pool together all victims of displacement and compare their risk attitudes with those of
the non-displaced sample. I also find that the effect of violence on behavior is not necessarily
permanent and depends on whether individuals recover psychologically over time, and thus on
the unobserved levels of psychological resilience. Nevertheless, some individuals continue to
suffer from severe cases of mental trauma and display high levels of risk aversion years after
their exposure to violence.
42
From a development economics perspective, the relevant question is of course how big or
how permanent should the effects on behavior be to matter and affect welfare trajectories. Microeconomic models indicate that individual decisions not only depend on behavioral parameters,
but also on the level of endowments, available options, and probability distributions, which vary
across different contexts. We are therefore unable to predict with certainty how these behavioral
consequences will play out. Nonetheless, victims of violence, especially in developing countries,
are exposed to a considerable amount of risk. As they lack formal or informal insurance
mechanisms, a shock that lowers their tolerance to bear risks will raise their vulnerability to
poverty. The psychological consequences of violence, even if temporary, can thus hinder
investments and induce inefficient decisions such as distress asset sales and the interruption of
child schooling that are not only costly in the short run but can also have irreversible
consequences over time and across generations. At a theoretical level, for instance, in an
economy characterized by the existence of a poverty trap, even temporary shifts in behavior can
condemn individuals to a state of chronic poverty from which they are unable to move out from
(Moya, 2013b).
Consequently, while countries like Germany, Japan, and Vietnam can recover as a whole and
exhibit rates of growth similar to those of their war-free neighbors, while others like Colombia
can be immersed in a civil conflict and still thrive economically, violence can drive a subset of
the population into poverty and bring about different circumstances or channels that will prevent
them to recover. In this paper, I highlight one of such channels driven by the psychological
consequences of violence. In the context of Colombia, where 8% of the population has been
displaced by violence, the psychological consequences of violence can therefore reinforce the
material consequences of forced displacement and have disturbing consequences at the micro
43
and macro level. However, and despite a comprehensive set of laws and policies that aim to
repair the material losses caused by violence and displacement, the psychological consequences
have received little or no attention. For instance, among all South American countries, Colombia
devotes the least amount of resources as a percentage of the national health budget to the
diagnostic and treatment of mental disorders (Doctors Without Borders, 2010). The findings
from this paper highlight the need to design, fund, and incorporate mental health interventions
for the displaced population and for other victims of the civil conflict.
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46
Tables and Figures
Table 1. Violence and Displacement
IDP
Non-Massive
I. Displacement Process
# of violent events leading to the displacement
IDP
Massive
3.56
3.47
[3.657]
[2.310]
Time since displacement episode (years)
Less than one year
Between a year and five years
Between five and ten years
34%
39%
28%
60%
39%
2%
II. Triggers of Displacement
Threats
Combats within the municipality
Random Violence
Order to migrate
Assasinations
Recruitment
Attacks
Massacres
Dissapearances
Extorsion
Kidnapings
Mines
Sexual Violence
31%
93%
33%
24%
25%
7%
14%
16%
7%
8%
5%
3%
2%
88%
26%
31%
33%
28%
29%
26%
20%
20%
18%
14%
15%
11%
153
132
Observations
Standard errors in brackets
Table 2. Exposure to Violence in Previous Ten Years
Household member suffered:
At least one violent event
Threats
Combats
Order to Migrate
Indiscriminate Violence
Assasinations
Attacks
Recruitments
Extorsion
Dissapearances
Massacres
Land Mines
Kidnappings
Sexual Violence
Sum of Violent Events (Standarized)
Victimization Score
Observations
Standard errors in brackets
IDP
Non-Massive
IDP
Massive
Non IDP
91%
72%
34%
48%
25%
31%
24%
16%
15%
17%
16%
13%
11%
8%
96%
28%
87%
38%
30%
20%
7%
5%
7%
5%
3%
2%
0%
0%
15%
9%
2%
1%
1%
5%
2%
0%
2%
1%
0%
0%
0%
0%
0.37
0.26
-0.34
[1.613]
[0.971]
[0.105]
0.39
0.08
-0.23
[1.91]
[0.66]
[0.11]
153
132
318
47
Table 3. Mental Trauma
T Score (Mean & S.D.)
IDP
IDP
Non IDP
NonMassive
Massive
Global Severity Index
Somatization
Obsession
Sensitivity
Depression
Anxiety
Hostility
Phobic Anxiety
Paranoid
Psicotic
PTSD
Observations
% Above Clinical Cutoff (T>63)
IDP
IDP
Non IDP
NonMassive
Massive
58.22
60.38
53.73
0.15
0.28
0.01
[5.162]
[5.483]
[2.792]
[0.360]
[0.450]
[0.100]
60.19
61.91
55.69
0.28
0.42
0.08
[6.371]
[7.447]
[4.856]
[0.452]
[0.495]
[0.278]
58.41
61.55
53.93
0.22
0.33
0.02
[6.577]
[6.415]
[3.868]
[0.418]
[0.473]
[0.141]
55.90
56.84
52.32
0.11
0.15
0.01
[5.835]
[5.330]
[2.912]
[0.308]
[0.356]
[0.100]
61.08
62.63
55.79
0.35
0.41
0.06
[5.938]
[6.107]
[4.286]
[0.478]
[0.494]
[0.245]
58.28
62.34
52.80
0.19
0.39
0.02
[6.458]
[8.204]
[3.128]
[0.394]
[0.489]
[0.129]
55.32
55.15
52.40
0.14
0.15
0.02
[6.166]
[6.633]
[3.546]
[0.346]
[0.356]
[0.141]
58.45
63.06
52.86
0.24
0.48
0.03
[7.721]
[9.088]
[3.917]
[0.427]
[0.502]
[0.162]
58.09
59.79
53.01
0.25
0.32
0.03
[6.870]
[5.745]
[3.781]
[0.434]
[0.467]
[0.172]
54.72
56.86
51.71
0.09
0.10
0.00
[5.142]
[5.775]
[2.256]
[0.281]
[0.302]
[0]
58.59
61.32
-
0.17
0.37
-
[5.757]
[6.834]
[0.378]
[0.485]
153
132
153
132
318
318
Standard errors in brackets
Table 4. Elicitation of Risk Attitudes
Payoffs
Choice 1
Choice 2
Choice 3
Choice 4
Choice 5
Choice 6
Gains
1
Losses
Ambigutiy
2
Red
Blue
Red
Blue
Red
Blue
13,000
10,000
7,000
4,000
2,000
0
13,000
19,000
25,000
31,000
36,000
38,000
-7,000
-10,000
-13,000
-16,000
-18,000
-20,000
-7,000
-1,000
5,000
11,000
16,000
18,000
13,000
10,000
7,000
4,000
2,000
0
13,000
19,000
25,000
31,000
36,000
38,000
Endowment
0
20,000
0
# of balls
5
5
5
5
3:7
3:7
1
All payoffs are in Colombian Pesos (COP). At the time of the experiments, the exchange rate was approximately
COP$1,700 = US$1; 2 In the Ambiguity task, a total of ten balls were included in the black bag, 3 of them were red,
three were blue, and the remaining 4 were blindly chosen from a bag containing 50 red and 50 blue balls.
48
Table 5. Municipal Characteristics
Atlantic Region Municipalities
IDP
IDP
Non-IDP
Massive
Non-Massive
A. Geographic
Area (km)
Altitude (meters above sea level)
Land quality index
Distance to main economic centers (km)
B. Socio-economic
Rural population
Rurality index
Poverty rate
Unmet Basic Needs - Rural Area (% of pop.)
Gini coefficient
Land Gini coefficient
Index of land informality
C. Institutional
Fiscal performance index
Agrarian bank - number of offices
Commercial banks - number of offices
Number of instititutions
Violence in 1948 - 1953
D. Violence & Civil Conflict
Homicide rate
Massacre rate
Violent attacks (number)
Displaced Population (number)
FARC (presence of)
Paramilitaries (presence of)
Criminal Bands (presence in 2010) 2
Central Region Municipalities
IDP
Non-IDP
Non-Massive
National
Averages 1
3,314
738
547
884
752
910
[1437.1]
[644.8]
[287.3]
[600.4]
[231.3]
[2708.9]
95
108
82
970
352
1,234
[40.64]
[79.47]
[55.09]
[726.6]
[35.51]
[1173.2]
2.47
3.18
4.49
2.65
2.66
2.65
[0.112]
[1.494]
[0.808]
[0.767]
[0.0780]
[1.223]
371
410
437
232
227
342
[16.42]
[93.38]
[7.461]
[22.59]
[8.009]
[153.4]
33,864
15,678
31,252
14,504
16,790
9,294
[10598.8]
[11917.1]
[12255.9]
[6916.0]
[8079.9]
[8430.8]
1.07
1.54
1.10
3.01
1.98
3.15
[0.409]
[1.834]
[0.272]
[2.104]
[1.490]
[3.906]
0.53
0.68
0.66
0.66
0.57
0.61
[0.126]
[0.0837]
[0.106]
[0.0939]
[0.118]
[0.0547]
79.74
76.24
73.66
62.54
62.61
55.42
[12.27]
[10.89]
[7.209]
[16.59]
[13.98]
[20.45]
0.43
0.45
0.46
0.47
0.47
0.46
[0.0293]
[0.0347]
[0.0122]
[0.0200]
[0.0132]
[0.0364]
0.69
0.67
0.72
0.74
0.74
0.69
[0.0176]
[0.0597]
[0.0346]
[0.0399]
[0.0448]
[0.108]
0.60
0.19
0.08
0.27
0.24
0.20
[0.182]
[0.135]
[0.0129]
[0.141]
[0.0492]
[0.228]
60.05
55.16
56.18
55.47
57.32
57.05
[5.899]
[7.104]
[8.017]
[6.577]
[6.938]
[8.044]
1.00
1.25
1.00
1.00
1.00
1.20
[0]
[0.434]
[0]
[0]
[0]
[0.529]
2.50
0.83
2.00
1.78
2.00
1.06
[1.539]
[1.145]
[1.107]
[1.036]
[0]
[1.342]
46.50
29.71
46.80
22.11
31.00
21.53
[3.591]
[21.83]
[17.79]
[14.46]
[10.86]
[18.48]
No
No
No
Yes
Yes
-
38.61
58.47
18.47
70.24
41.31
62.06
[22.75]
[79.75]
[10.41]
[48.26]
[24.22]
[124.9]
2.43
3.26
-
3.30
-
[1.982]
[2.721]
[2.636]
3.61
[5.410]
5.476
12.86
3.392
8.861
8.268
8.813
[4.460]
[21.32]
[4.703]
[10.15]
[9.985]
[20.14]
2278.70
745.50
31.20
432.50
356.80
191.70
[2405.4]
[1642.4]
[24.77]
[517.4]
[415.1]
[749.2]
0.77
0.54
0.07
0.69
0.75
0.30
[0.431]
[0.499]
[0.258]
[0.466]
[0.440]
[0.458]
0.35
0.20
0.02
0.12
0.16
0.10
[0.485]
[0.398]
[0.152]
[0.323]
[0.367]
[0.304]
1.00
1.00
1.00
0.00
0.00
0.33
# of Municipalities
2
24
5
9
3
1,077
All variables in Panels B,C, and D refer to 1993-2010 averages unless indicated. Source: Base de Datos Municipales 1993 - 2010. Universidad de
los Andes, Facultad de Economía; 1 Excluding departamental capitals; 2 Indepaz (2011) "Sexto informe sobre la presencia de grupos
narcoparamilitares. Primer Semestre 2011"; Standard errors in brackets
49
Table 6. Individual and Household Characteristics
IDP 1
Age
Male (=1)
Household size
Literate (=1)
Years of education
Off-farm laborer (=1)
Occupation - Peasant (=1)
Occupation - Domestic (=1)
Hh member participates in at least one organization (=1)
Non-Massive
Massive
(I)
(II)
Non-IDP
Mean
Difference
Mean
Difference
(III)
(III-I)
(III-II)2
8.877***
43.64
38.86
47.38
3.740***
[12.87]
[13.33]
[12.93]
(-2.87)
(5.52)
0.605
0.760
0.648
0.0422
-0.0890
[0.490]
[0.429]
[0.479]
(-0.87)
(-1.52)
4.830
4.985
4.923
0.0929
0.248
[2.383]
[2.120]
[2.263]
(-0.4)
(0.93)
0.850
0.785
0.755
-0.0953**
-0.0620
[0.358]
[0.413]
[0.431]
(-2.32)
(-1.20)
5.986
5.038
5.622
-0.364
0.207
[3.964]
[4.049]
[3.744]
(-0.94)
(0.42)
0.400
0.373
0.383
-0.0174
0.0533
[0.492]
[0.486]
[0.487]
(-0.35)
(0.85)
0.633
0.692
0.634
0.00158
-0.0407
[0.484]
[0.464]
[0.482]
(-0.03)
(-0.70)
0.293
0.222
0.272
-0.0207
0.0100
[0.456]
[0.418]
[0.446]
(-0.46)
(0.19)
0.253
0.790
0.399
0.146***
-0.506***
[0.436]
[0.409]
[0.491]
(-3.05)
(-9.05)
Hh member makes decisions in at least one organization (=1)
0.260
0.730
0.379
0.119**
-0.465***
[0.539]
[0.802]
[0.486]
(-2.34)
(-5.96)
0.151
0.210
0.379
0.229***
0.0545
[0.413]
[0.456]
[0.486]
(-4.88)
(0.95)
8.521
8.716
1.800
-6.722***
-6.852***
[14.44]
[16.31]
[3.749]
(-7.55)
(-5.09)
5.350
5.950
1.208
-4.142***
-4.718***
[12.91]
[13.75]
[3.098]
(-5.25)
(-4.17)
153
132
318
445
285
Hh member is leader in at least one organization
Lands (ha)
Lands owned (ha)
Observations
1
All variables refer to household and respondents' characteristics at origin sites; for displaced households these thus corrspond
to characteristics before displacement. 2 I compare only the massively displaced with their appropriate controls, this is the nondisplaced in the Atlantic Region. Standard errors in brackets; t-statistics in parentheses; * p<0.05 ** p<0.01 *** p<0.001
50
Table 7. Selection into Violence
Male-headed household
Indigenous
Literate
Member participated in decision processes
Household devoted to agriculture
Land size (Ha)
Constant
Victim (=1)
Number of
Events
Victimization
Score
Victim (=1)
Number of
Events
Victimization
Score
[1]
[2]
[3]
[4]
[5]
[6]
0.138
1.544
0.120
0.182
-1.284
0.019
[0.154]
[1.225]
[0.079]
[0.267]
[0.823]
[0.042]
-0.053
0.763
0.091
-0.036
1.070
0.004
[0.138]
[0.836]
[0.081]
[0.201]
[0.815]
[0.028]
0.311**
0.222
-0.059
-0.120
-0.180
-0.008
[0.133]
[0.873]
[0.088]
[0.192]
[0.610]
[0.026]
0.414***
0.991
0.013
1.323***
3.028***
0.121***
[0.115]
[0.723]
[0.062]
[0.177]
[0.630]
[0.026]
-0.245
-1.415
-0.211***
-0.282
0.838
-0.051
[0.151]
[1.277]
[0.079]
[0.251]
[0.710]
[0.040]
0.044***
0.236**
0.026**
0.036***
0.035
0.000
[0.010]
[0.106]
[0.013]
[0.008]
[0.042]
[0.001]
-0.527***
-0.768***
[0.148]
[0.216]
Sample
Observations
R-squared
Full
Full
Full
Massive & Ctrls
Massive & Ctrls
Massive & Ctrls
567
567
0.33
567
0.45
265
265
0.13
265
0.10
Table 8. Forced Displacement and Risk Aversion
Ordered Probit Estimates
Displacement Status
[1]
Gains
[2]
[3]
-0.276*** -0.461*** -0.368***
[0.088]
[0.111]
[0.123]
Time since Displacement (Years)
0.121*** 0.119***
[0.042]
[0.044]
Time since Displacement Squared (Years)
-0.007**
-0.007**
[0.003]
[4]
Losses
[5]
[6]
[7]
-0.001
-0.206*
-0.153
-0.144*
[0.088]
[0.109]
[0.115]
[0.086]
0.135*** 0.117***
[0.038]
[0.037]
-0.007*** -0.006***
[0.003]
[0.002]
[0.002]
Ambiguity
[8]
[9]
-0.264** -0.293***
[0.108]
[0.113]
0.085**
0.107***
[0.039]
[0.039]
-0.005**
-0.006**
[0.003]
[0.003]
Male
-0.062
-0.036
[0.091]
[0.092]
[0.091]
Literate
-0.214*
0.036
-0.053
[0.111]
[0.110]
[0.114]
Land Size
0.002*
0.000
-0.001
[0.001]
[0.001]
[0.003]
0.072
-0.062
0.111
[0.095]
[0.095]
[0.092]
-0.019
-0.015
-0.005
[0.020]
[0.019]
[0.019]
Death of Family Member - Past 12 months
0.016
0.014
-0.042
[0.078]
[0.074]
[0.073]
Atlantic region
-0.124
0.019
-0.047
Participated in at least one social organization
Economic Shock - Past 12 months
Earnings in previous (practice) round
Sample
Full
Full
Observations
603
603
Robust standard errors in brackets; * p<0.1; ** p<0.05; *** p<0.01
-0.036
[0.090]
[0.094]
[0.093]
0.019***
0.016***
0.026***
[0.005]
[0.003]
[0.005]
Full
600
Full
603
Full
603
Full
600
Full
603
Full
603
Full
600
51
Table 9. Robustness: Massive Displacements and Risk Aversion
Gains
[1]
Losses
[2]
Ambiguity
[3]
-0.406**
-0.072
-0.061
[0.190]
[0.183]
[0.190]
0.13
0.10
0.03
[0.108]
[0.091]
[0.186]
-0.058
-0.173
-0.05
[0.126]
[0.131]
[0.128]
-0.452***
0.101
-0.065
[0.152]
[0.149]
[0.144]
0.006
-0.008
0.003
[0.005]
[0.006]
[0.006]
Ordered Probit Estimates
Displacement Status
Time since Displacement (Years)
Male
Literate
Land Size
Participated in at least one social organization
Economic Shock - Past 12 months
Death of Family Member - Past 12 months
Earnings in previous (practice) round
0.121
-0.015
-0.196
[0.150]
[0.147]
[0.137]
0.013
0.008
0.024
[0.051]
[0.044]
[0.025]
-0.085
0.157
0.062
[0.150]
[0.145]
[0.150]
0.018**
0.015***
0.028***
[0.007]
[0.005]
[0.007]
296
296
Observations
296
Robust standard errors in brackets; * p<0.1; ** p<0.05; *** p<0.01
Table 10. Severity of Victimization and Risk Attitudes
Ordered Probit Estimates
1
A. Gains Domain
Violence
Full Sample
# of Violent Victimization
Events
Score
[1]
[2]
-0.112**
0.051
-0.070
[0.045]
0.045
[0.036]
[0.036]
[0.044]
[0.044]
-0.004
-0.003
-0.007**
-0.007**
[0.003]
[0.003]
[0.003]
[0.003]
-0.010
0.031
0.026
[0.054]
[0.056]
0.089***
0.009
[0.051]
0.087***
0.111***
0.109***
[0.030]
[0.030]
[0.037]
[0.037]
-0.005***
-0.005***
-0.006***
-0.006***
[0.002]
[0.002]
[0.002]
[0.002]
-0.202***
-0.167***
-0.176***
0.070*
-0.230***
[0.088]
0.069*
[0.036]
[0.037]
[0.041]
[0.041]
-0.004
-0.004
-0.006**
-0.007**
[0.003]
[0.003]
[0.003]
[0.003]
Full
Yes
596
Full
Yes
596
IDP
Yes
280
IDP
Yes
280
[0.045]
Time since displacement - Years
Time since displacement squared - Years
B. Losses Domain
Violence
[0.048]
Time since displacement - Years
Time since displacement squared - Years
C. Ambiguity Domain
Violence
[0.055]
Time since displacement - Years
Time since displacement squared - Years
Sample
Household controls & regional fixed effect
Observations
IDP Sample
# of Violent Victimization
Events
Score
[3]
[4]
-0.089**
-0.067
[0.045]
[0.041]
0.101**
0.105**
[0.057]
[0.065]
0.109***
0.116***
1
Each column reports the coefficients for the time controls and the measure of violence used, which is
indicated at the top of each column. Household and region controls are included in all regressions, but not
reported for space considerations. They can be found in Table A5 of the online Appendix. Robust standard
errors in brackets; * p<0.1; ** p<0.05; *** p<0.01
52
Table 11. Mental Trauma and Risk Attitudes
Ordered Probit Estimates
Anxiety
Hostility
Phobic Anxiety
Gains
[1]
Full Sample
Losses
[2]
Ambiguity
[3]
Gains
[4]
IDP Sample
Losses
[5]
0.027
-0.086
[0.180]
[0.179]
-0.025
0.063
-0.125
0.167
[0.180]
[0.193]
[0.194]
[0.200]
0.469**
Ambiguity
[6]
0.211
0.224
0.353*
0.302
0.262
[0.174]
[0.210]
[0.195]
[0.195]
[0.227]
[0.221]
-0.409**
-0.056
-0.303**
-0.338*
-0.030
-0.427**
[0.163]
[0.157]
[0.148]
[0.183]
[0.176]
[0.174]
Sample
Full
Full
Full
IDP
IDP
IDP
Time controls
No
No
No
No
No
No
Hh and regional controls
Yes
Yes
Yes
Yes
Yes
Yes
Observations
597
597
597
281
281
281
All stress variables are entered as dummy variables indicating if the individual is at risk of suffering mental distress for each
dimension (Ti>63=1); Robust standard errors in brackets; * p<0.1; ** p<0.05; *** p<0.01
Figure 1. Intensity of Displacement and Geographic Distribution of the Sample
*Source: Observatorio Nacional de Desplazamiento Forzado, Registro Único de Población Desplazada. Sept, 2010
53
Figure 2. Psychological Distress by Group
Non-Massive Massive Non-Displaced
80
50
60
70
T Score
80
70
60
50
50
60
70
80
90
Hostility
90
Phobic Anxiety
90
Anxiety
Non-Massive Massive Non-Displaced
Non-Massive Massive Non-Displaced
Box-plot figures depict the distribution of the Global Severity Index, Posttraumatic Stress Disorder, Anxiety, and
Depression for the non-massively displaced, massively displaced, and non-displaced samples. The white line in the box
depicts the median, the lower and upper borders of each box depict the 25th and 75th percentiles, while the dotted line
depicts the critical level (T>63) above which individuals are considered to be at risk of developing severe cases of stress
for each disorder.
Figure 3. Choices by Group
5
6
Ambiguity
4
4
5
6
Losses
Non-Massive Massive Non-Displaced
3
1
2
3
2
1
1
2
3
Lottery Choice
4
5
6
Gains
Non-Massive Massive Non-Displaced
Non-Massive Massive Non-Displaced
54