Civil War Exposure and Competitiveness:
Experimental Evidence from the Football Field in Sierra Leone
Francesco Cecchi1*, Koen Leuveld1, Maarten Voors2 and Lizzy van der Wal1
1
Development Economics Group, Wageningen University, P.O. Box 8130, 6706 KN Wageningen,
Netherlands. Email: francesco.cecchi@wur.nl (* corresponding author),
Tel: +31 317 485286, Fax: +31 317 483482
2
Department of Land Economy, University of Cambridge, United Kingdom
Abstract: What is the link between civil war exposure and competitiveness? We use data from a street
football tournament as well as laboratory experiments in post-conflict Sierra Leone to measure the
impact of exposure to violence on competitive behavior. Our findings are in line with previous
theoretical and empirical evidence the relationship between war exposure and violence, parochial
altruism and other preferences. We find that out-group competitiveness is exacerbated by war
exposure in both the football field and lab experiments. In-group dynamics seem to be driven by
behavioural characteristics such as behindness aversion and risk propensity.
Keywords: Competitiveness, civil war, football, Africa
JEL Code:
Acknowledgments: We are grateful to seminar participants at CSAE (Oxford) and Erwin Bulte for
helpful comments and thank Esther Mokuwa for exceptional research assistance. We thank the Dutch
Organisation for Scientific Research (N.W.O.), grant nr. 452-04-333, for financial support. We
acknowledge the loyalty and hard work of the team of field enumerators and the patience and
cooperation of interviewees.
I. Introduction
Recently economists have focused on the behavioural impacts of exposure to violence in civil wars.
Predominantly these studies have examined the implications for social preferences, such as the
participation in local collective action, voting and sharing both within and across communities.
Arguably, social preferences are but one set of preferences that matter for post-conflict development.
Evolutionary theory highlights the role of inter-group conflict in shaping pro-egalitarian parochial
preferences – suppressing in-group competition while exacerbating out-group antagonism (Bowels,
2006). At shorter time-scales these findings are corroborated among others by Bellows & Miguel
(2009) and Voors et al. (2012) with respect to increased in-group cooperation, and by Miguel et al.
(2011) with respect to increased out-group antagonism. Increased antagonism matters for post-conflict
development – when it remains within the boundaries of commonly accepted “rules of the game” – as
it shapes the willingness to compete. As Bartling et al. (2011) put it, “work and career efforts are often
driven by vigorous competition [...]. Less competitive people, however, shy away from direct
competition” (Bartling et al. 2011: p.58). This in turn affects the allocative efficiency generated by
competition, as it increases the probability of non-first-best contenders winning a contest. Taste for
competitiveness is therefore not only an important non-cognitive determinant of human capital
indicators, such as adult economic achievements and productivity, but – together with the rise of local
collective action and changes in risk and time preferences – a crucial determinant of a country’s postwar political and economic recovery and development.
This study expands on previous work by explicitly measuring competitiveness both in a field
setting – in our case a football tournament – as well as a laboratory setting designed to elicit
willingness to compete. We collect data on players’ characteristics, including exposure to conflict in
an extensive survey, and implement a range of behavioral experiments – including a set of allocation
and risk games – against the opponents and their football teammates, immediately after the end of the
football game. Overall the findings in the laboratory experiments seem to mimic the outcomes
identified through the field setting and validate previous theoretical and empirical evidence. While ingroup dynamics seem to be influenced by behavioral characteristics such as behindness aversion and
risk propensity, out-group competitiveness appears to be exacerbated by war exposure: war exposed
subjects are on average 67% more likely to join a competitive tournament than the non-exposed.
The study is organized as follows. In Section II, we di scuss the existing literature on conflict
and preferences, as well as on competitiveness. In Section III, we present the context and background
to the field experiment. Section IV outlines the experimental design and presents the data. Section V
discusses our identification strategy and section VI contains our results. Section VII offers a
discussion and conclusions.
1
II. Conflict, preferences and competition
Our study combines two main fields of literature; that on the consequences of war exposure on
preferences and that on competition and willingness to compete. We apply the well-rooted strategy of
studying decision-making under the common rules and bi-lateral antagonism provided by sport (e.g.
M. D. Smith 1979; Weinstein et al. 1995; Duggan & Levitt 2002; Garicano & Palacios-huerta 2006;
Miguel et al. 2011). Closest to our work is Miguel et al. (2011) who examine the consequences of civil
war on aggressiveness of players in European football leagues.
Miguel et al. (2011) find that the
number of years the home country of a player has been in violent conflict before the player reaches the
age of eighteen is strongly and positively related to the amount of foul cards received. We build upon
their design by looking at aggression and competitive behaviour in a post-conflict country and by
looking at willingness to self-select into a tournament using a laboratory experiment.
Previously social scientists have focussed mostly on the relationship between war exposure
and post-war social preferences, and on the consequence of conflict on the working of societies as a
whole1. Bellows & Miguel (2009) study the Sierra Leonean civil war, and conclude that individuals
whose households directly experienced more intense war violence are more likely to attend
community meetings, join local political and community groups, and to vote. Blattman (2009) finds
that experiencing abduction and violence increased political engagement, voting and community
leadership among ex-combatants in Northern Uganda, while it did not appear to affect non-political
participation. Blattman & Miguel (2010) present a survey of literature on the relationship between
civil war and economic development. They claim that existing theory is omitting advances in
behavioral economics, and advocate micro-level analysis and case studies as crucial to understand
war’s causes, conduct, and consequences.
Recently, a number of studies use begun using laboratory experiments. Gilligan et al. (2011)
show that communities that suffered war-related violence during Nepal's ten-year civil war exhibit
significantly greater levels of social capital. Similarly, Bauer et al. (2012) present two case studies –
one in Georgia and one in Sierra Leone – indicating that experiencing inter-group conflict during
childhood and adolescence increases parochial egalitarianism. Finally, through a series of artefactual
field experiments carried out in Burundi, Voors et al. (2012) show that individuals exposed to violence
1
Psychological literature well documents the relationship between war exposure and trauma, focusing mostly on
post-traumatic stress disorder (PTSD), anger and anxiety. Macksoud & Aber (1996) examine the relation
between war traumas and psychosocial development, finding that the number of war traumas experienced by a
child was positively related to Post Traumatic Stress Disorder (PTSD) symptoms and differentially related to
other behavioural outcomes. P. Smith et al. (2002), Papageorgiou et al. (2000) and Layne et al. (2010) identify
similar attitudinal outcomes among conflict exposed children in Bosnia, while Dyregrov et al. (2002) find highly
time-persistent intrusive and avoidance reactions among Iraqi children exposed to a deadly aerial bombing.
Other studies explore instead positive responses to trauma––often referred to as “post-traumatic growth” (e.g.
Tedeschi & Calhoun 1996; Powell et al. 2003; Staub & Vollhardt 2008; Vollhardt 2009).
2
display more altruistic behaviour towards their neighbours, are more risk-seeking, and have higher
discount rates2.
Evolutionary theory accompanies these apparent changes in preferences brought by the
experience of conflict, identifying plausible long-term consequences on the evolution of human
societies. Experimental evidence points towards the importance of both theories of cultural evolution
as well as gene-culture co-evolution in generating a taste for parochial altruism (Bernhard et al. 2006;
Fehr & Fischbacher 2003). On a theoretical level, Henrich (2004) presents multiple behavioral
equilibria, explaining how cultural evolution endogenously favoured in-group pro-sociality. Choi &
Bowles (2007) further strengthen the gene-culture coevolutionary hypothesis by presenting a gametheoretic analysis and agent-based simulations.
Parochial altruism matters for economists because it affects the interaction between public
goods and private gains. Uncoordinated markets populated by self-interested agents are likely to
underperform in the provision of public goods (Samuelson 1954). Increased pro-sociality and in-group
altruism result in a higher provision of public goods, which in turn generate positive externalities for
private agents––i.e. cooperation pays. Yet private gains are driven by competition between agents and
by the efficiency gains generated through competition-driven creative destruction (Schumpeter 1942).
Willingness to compete – or the likelihood to engage in investments and effort – is an important noncognitive determinant of human capital indicators, such as adult economic achievements and
productivity (Niederle & Vesterlund 2007). In the context of an effort game, however, behavioral
economists found that when the type of payment was exogenously imposed on the subjects,
competitive tournaments revealed a much larger variance of effort than equivalent piece-rate schemes–
–which in turn reduced their overall efficiency (e.g. Dijk et al. 2001; Harbring & Irlenbusch 2003).
This unexpected finding may be driven by the unwillingness of some people to enter competition.
Eriksson et al. (2009) show that allowing for self-selection into a competitive tournament results in
higher average effort rates and lower between-subject variance for subjects choosing to compete.
Competitive environments are thus more efficient than non-competitive ones only if populated by a
sufficient share of agents willing to compete.
Besides the institutional and context dependent factors affecting the likelihood of competition,
willingness to compete might be correlated with individual and behavioural characteristics. Bartling et
al. (2009), for instance, find that overconfident, skilled and risk prone agents are more likely to join a
contest, whilst inequality-averse ones are less. If changes in out-group antagonism generated by war
2
Controlling for age, our study finds that war exposed subjects are robustly more likely (at the 5% level) to be of
the “generous” behavioural type towards their in-group. Similarly, in a dictator game our war exposed football
players share more than other subjects towards their in-group, but not towards the out-group. The results are in
line with theoretical and empirical evidence highlighting pro-social changes in preferences towards the in-group
brought by war exposure (see Table 6 in Appendix II for details).
3
exposure affect the willingness to compete, this has both impacts on the agents’ private gains and,
more speculatively, on the efficiency of a society as whole.
III. Civil war in Sierra Leone
In 1991 Sierra Leone had the second lowest Human Development Index ranking worldwide (UNDP
1992). That year a small group of rebels – modelled on the militias of neighbouring Liberia – started
to plunder the south-eastern frontier of the country. They found fertile ground for popular grief and
discontent towards “a decayed neo-patrimonial one-party regime” (Richards 1996; Richards 1999: p.
433) and were nurtured by Sierra Leone’s diamond wealth (Keen 2005). It was the start of a countrywide eleven year long civil war that cost over 50,000 lives, leaving over 10,000 civilians amputated
and at least 215,000 women and girls subjected to sexual violence (Doucet & Denov, 2012; Human
Rights Watch, 1999). The death toll amounted to over twelve people every thousand inhabitants, four
times as high as that of the United States during the entire Second World War, comprising a much
larger proportion of unarmed civilians.
[...]
[...]
IV. Data and experimental design
We use data collected during a youth street football tournament organised in Kenema, a regional town
in Eastern Sierra Leone. The tournament spanned several weeks between November and December
2010. For the tournament, streets within the city each assembled in a team. Matches were centrally
organised and a substantial cash reward awaited the winner. Affiliation with the team was strong as
the players took serious pride in defending their street. Referees oversaw adherence to rules and
distributed yellow and red cards in response to minor and major faults. A total of 33 cards were given
throughout the tournaments, involving 20% of the players. We carefully recoded details of the payers
and matches.
Following each football match, we invited players to participate in a survey and a series of
experiments. In total 162 players of 14 teams participated. The incentivised nature of our experiments
– and the close collaboration with tournament organisers and teams manager – minimized attrition3.
3
None of the players refused to participate. In fact, given the plein air environment in which players were
assembled, experimenters had to pay close attention to avoid the participation of non-team members. All teams
had either a clearly recognizable team kit, or a distinguishable team colour which minimized the risk of such
occurrence. The only episode of intrusion into the artefactual field experiments was identified and sturdily
denounced by the “intruded” team before the start of the experiments. There is no reason to believe that similar
episodes would not have been ostracized analogously.
4
Each participant went through a four stage process. First, we implemented a survey on personal
characteristics; second a series of allocation games and a risk game; third a competitiveness game, and
fourth an end survey related to war exposure, beliefs and trauma. The survey questions related to war
and war exposure were all asked after the behavioural games had been played, so as to not influence
their outcome or the decision making process. Table 1 presents the descriptive statistics of the players.
<< Insert Table 1 about here >>
To measure peoples willingness to self-select into competitive environments we implement a
competitiveness game based on Niederle & Vesterlund (2007) and Bartling et al. (2009). Participants
are invited to participate in a game where they throw a football into a standard sized rubbish basket
secured to the floor, from a distance of 4 meters. They could choose whether to play the game
individually – and receive 500 Leones per ball hit – or enter a competition and play the game against
an anonymous opponent. In the competition, the participant wins 1500 Leones for every ball hit if the
total number of hits was higher than the opponent––zero if lower. If the player choosing to compete
scored the same amount of hits of his opponent, he would receive 500 Leones per hit, like in the piecerate scheme. For the game, participants were randomly divided into two groups: one group (92) was
offered to play against an anonymous player of their own team (in-group) and the other (70) against an
anonymous player of the opponent team (out-group). On average, players scored 6,27 hits, with a
standard deviation of 1,82. Figure 1 shows the distribution of balls hit and relative frequency, plotted
against a normal density line. Almost 42% of the participants chose to participate in the tournament
(Table 1).
<< Insert Figure 1 about here >>
We also implement an allocation game based on Fehr et al. (2008). Participants are asked to
make four dichotomous allocation choices, allowing us to classify our respondents into types of
altruistic behaviour. All allocation choices consist of an egalitarian and a non-egalitarian allocation,
favouring (Sharing games) or disfavouring (Envy games) the decision maker (see appendix Table 3 for
choice options). Each participant made these four choices twice: once being coupled with an
anonymous player of his own team (in-group) and once with an anonymous player of the opposite
team (out-group). The distributional type of each participant is the resultant of the four choices made.
Which set of questions would be asked first was determined randomly. To avoid the insurgence of an
income-effect, before playing the game participants were notified that their final pay-off would be
5
determined by one randomly selected choice among the choices they made. Following Bartling et al.
(2009) we focus on the two broad sub-categories of egalitarian types––i.e. the aheadness-averse and
the behindness-averse subjects (see Table 4 in Appendix I). Of our participants, 78% are aheadness
averse and 52% behindness averse4.
To measure risk preferences, we develop a simple dichotomous choice game based on
Harbaugh et al. (2002). In this risk game subjects are required to choose several times between
receiving an amount of money for certain or playing a simple gamble. Six choice sets are presented;
each time we ask whether the participant prefers to toss a coin and make the chance of winning 3000
Leones (if heads) or zero (if tails), or not to toss a coin and win a certain amount of money, growing in
each choice set, from 100 Leones to 2500 Leones. The expected value of the gamble is thus kept
constant, while the certain option increases in value: the point of switch from the gamble to the certain
option is used to determine the risk preferences of the respondent––the later the switch, the less riskaverse. We construct a variable spanning from zero (i.e. never gamble) to one (i.e. always gamble)
(see Table 5 in Appendix I for details and Appendix III for a more detailed definition).
Through a series of experiments on mothers of preschool children, Bartling et al. (2009) find
that individuals with a preference for egalitarian outcomes, higher risk aversion, lower overconfidence
or ability are more reluctant to self-select into competition towards a mother of another child in the
same preschool. In our analysis we include similar variables as controls together with socio-economic
variables, such as age, expenditure on phone credit, and a dummy for first-degree relative
bereavement5. To control for overconfidence we ask the participants how good they think they will
perform relative to others. On a scale from 1 to 5 over 63% think they would be “the best” (1), with
the lowest record being “average” (3)––only 8,6% of the answers. We create a dummy for
“overconfidence”, being 1 for all players that expected to be the best and were not.
4
The two categories are not mutually exclusive, the intersection of the two representing subjects that are both
aheadness averse and behindness averse––i.e. pure inequality averse. 90% of the in-group and 87% of the outgroup participants are either aheadness-averse or behindness-averse or both (see Table 4 in Appendix I for
choice types).
5
Research about bereavement highlights trauma-related responses different from those caused by direct war
exposure. War related PTSD hyperarousal symptoms are found to correlate with aggressive impulses or urges,
perceived problems controlling violent behavior (Elbogen et al., 2010), anxiety and avoidance reactions
(Dyregrov et al. 2002) as well as an improvement in self-perception and other factors comprising the Post
Traumatic Growth Inventory scale (Powell et al. 2003). Haine et al. (2003) instead show that bereavement
significantly reduces self-esteem in children while Abdelnoor & Hollins (2004) find that bereaved subjects
underachieve significantly during secondary school years. Other typical responses to bereavement are depression
(Brent, Melhem, Donohoe, & Walker, 2009), grief, distress, and dysphoria (Dowdney, 2000). More in general,
empirical evidence appears to support the independent nature of anxiety and depression reactions to disasters and
trauma (Bonanno, Brewin, Kaniasty, & Greca, 2010). We find that bereavement is significantly negatively
correlated with self-esteem, whilst war exposure seems to be strongly correlated with an increase in self-esteem
(see Table 7 in Appendix II). We include first degree relative bereavement as a control variable in our full
model, but our results are robust to its exclusion (see Table 2 in Section VI).
6
To identify war exposure, we use a series of survey questions, covering information on
personal injury, seeing injured people, seeing combat and hearing combat. Following Bellows &
Miguel (2009), we create a victimization index as the average of responses to these violence related
questions. We decide to drop seeing combat, as we believe it only adds noise between subjects that
have witnessed war from a certain distance (captured by hearing combat), and those that have
experienced conflict indirectly but more closely – namely seeing the crude consequences of war on
another person (seeing an injured person) – or directly on themselves (personal injury)6.
V. Identification
Our empirical strategy relies on local comparisons across individuals. The key identifying assumption
is that war violence is exogenous with respect to individual characteristics. This assumption may not
hold in the presence of any systematic targeting by belligerents along some individual dimension––i.e.
religion, ethnic group etc. Literature consistently agrees that neither ethnic nor religious divisions
played a role in the Sierra Leonean conflict. Earlier work by (Bellows & Miguel, 2009) established
that individual victimization was essentially a random process. No ethnic group was
disproportionately represented among rebel victims (Conibere et al., 2004), while levels of civilian
abuse were not higher when a particular armed faction did not belong to the same ethnic groups than
the community it attacked (Humphreys & Weinstein, 2006). We test the influence of plausible
exogenous and time invariant variables on war exposure and find that all variables enter insignificantly
except the years in which the respondent experienced war, logically resulting in a higher likelihood of
being affected (see Table 10 in Appendix II for details). In our regressions below we include a variable
to capture the length of potential exposure to conflict. We also rerun the analysis for the subgroup of
respondents for which theory predicts preferences have most likely been affected by war exposure
(Camerer, 2003; Beneson et al., 2007; Fehr et al., 2008)––i.e. not older than 8 at the start of the war
and at least 8 at the end of it (see Table 11 in Appendix II for details).
Another concern is that the results could be driven by selective migration of individuals who
experienced violence (Bellows & Miguel, 2009). According to UN OCHA, from April 2001 to
November 2002, all the 223,000 registered IDPs were reintegrated within their original communities
and many more unregistered refugees have been returning home spontaneously ever since (Norwegian
Refugee Council, 2003). More recent statistics by UNHCR claim that of the almost 2.5 million people
displaced during the civil war, only 10,988 Sierra Leoneans remain refugees, asylum seekers or are in
any other way people of concern for UNHCR as of January 2012 (UNHCR, 2013). Nevertheless, if
displaced people are significantly different from people who did not migrate temporarily selective
6
The results are robust to alternative specifications of the victimization index (see Tables 8 and 9 in Appendix II
for alternative specifications of our model and Appendix III for more details about the victimization index).
7
migration might play a role in determining who experienced violence7. Below, we re-run our main
model and restrict the analysis to the individuals (82,1%) that were displaced at least once during the
war, and find that our results are robust to this sub-sample––and the R2 increases substantially8.
Internally valid inference requires that the cause precedes the effect; that there is a degree of
correlation between the cause and the effect; and that this correlation is nonspurious––i.e. there is no
alternative explanation to such correlation. The absence of correlation between time invariant
individual characteristics and victimization and the robustness of the results to the displaced
subsample are positive signals of the degree of internal validity of our quasi-experimental setting. Yet,
due to the absence of base-line behavioural information, we cannot verify that there was no preexisting correlation between pre-war behavioural characteristics such as willingness to compete or
social preferences and war exposure––i.e. we cannot rule out that the effect precedes the cause. Our
sample’s mean age at the beginning of the civil war was however less than one year old, eleven by the
end of it. Considering the evidence above, and the plausible quasi-experimental variation of war
exposure (see Blattman 2009 and Bellows & Miguel 2009 for a survey of such mechanisms), it is
relatively unlikely that the degree of war exposure experienced in early childhood and preadolescence
is dependent on pre-war individual level behavioral characteristics. Instead, previous experimental
evidence shows that children acquire the normative rules of the society surrounding them mostly
between the age of three and eight (Benenson et al. 2007; Fehr et al. 2008) and continue to develop
preferences till early adulthood––reaching stability around the early twenties (Sutter 2007).
Theoretical and empirical evidence therefore predict that our subjects developed most of their
normative rules and preferences throughout the war period––providing additional supporting ground
of the causal relationship between war exposure and competitiveness.
Conversely, we are unable to verify if a specific type of people have permanently migrated
away from the area – and are therefore excluded from our sample – as much as we are unable to rule
out that the subjects whose families self-selected into or out of violence are over or under represented
in our sample. Yet, our study focuses on comparisons across individuals that have experienced varying
degrees of war exposure. It does not attempt to draw conclusions on the overall intent-to-treat impact
of the Sierra Leone civil war on the competitiveness and willingness to compete of Sierra Leoneans,
nor does it expect to generalize the conclusions across countries.
7
A Pearson χ2 test rejects the null hypothesis of independence between war exposure and displacement
(p=0.004), contrarily to what is found by Bellows & Miguel (2009) for their country level sample of Sierra
Leonean households.
8
Also, counting displacement as an additional variable within the victimization index does not fundamentally
change any results, actually increasing the significance level of some key explanatory variables (see Tables 12
and 13 in Appendix II for details).
8
The core of our analysis lies in a set of regressions which seek to explain differences in
willingness to compete through individual characteristics and our measure of war exposure. We
present the results in a series of Probit models, nested in the following general equation:
C*= α + Wβ + κ (Iγ + Rδ + Aζ + Xλ) + εi
with C = 1 if C* >0 and C = 0 if C* ≤0
(1)
C is a dummy representing our measure of competitiveness; W is our individual measure of war
exposure; I a vector including the two dummies of inequality aversion––aheadness averse and
behindness averse; R represents relative risk propensity; A is a proxy of sportive ability (i.e. playing
without being substituted during the football game) and X is a vector of individual and team level
characteristics that may influence the decision to compete including age, education level, first relative
degree bereavement and self-declared measures of confidence and competitiveness. κ =0, 1; imposing
κ =0 reduces equation (1) to a Probit model with one explanatory variable––war exposure; imposing κ
= 1 instead allows for the full model to be estimated. εi is the independent error term with variance σi2.
See Appendix III for variable definitions. We expect β >0 for competitive behavior towards the outgroup and β ≤0 towards the in-group. Both Niederle & Vesterlund (2007) and Bartling et al. (2009)
find that δ >0 and ζ >0, while the latter show that γ < 0 towards the relative in-group of mothers of
pre-school children. We proceed to present the results by first testing the assumptions about the sign
and significance of the abovementioned coefficients, which we then complement with the parametric
analysis.
VI. Experimental results
Below we present our main results on the relationship between civil war exposure and competiveness
in Figure 2A-D and Table 2. Figure 2A shows the percentage of football players receiving a foul card
during the football tournament for each level of war exposure. We find that higher levels of war
expose are associated with a higher propensity of receive a foul card (at the median of all covariates).
None of the un-exposed players received a foul card. A Pearson χ2 test on the full victimization range
strongly rejects the null hypothesis of independence between war exposure and receiving a foul card
(p=0.015). While indicative of increased out-group antagonism, this result per-se is not symptomatic
of increased willingness to compete. We therefore proceed to look into our laboratory style
competitiveness experiment. We find that the results parallel the field setting: across the two
treatments, 18% of the completely war un-exposed respondents decide to join the competition,
compared to 64% of the fully war exposed respondents. (Figure 2B). A Pearson χ2 test on the pooled
dataset strongly rejects the null hypothesis of independence between war exposure and choosing to
compete (p=0.003). Figure 2C and 2D show a breakdown for subjects playing against the in-group
9
(their own team) and those playing against the out-group (an opposing team). We find that war
exposure results in out-group competition but does not significantly affect in-group competition.
<< Insert Figure 2 about here >>
In Table 2, we complement the non-parametric analysis with a set of probit regressions, where
we regress receiving a foul card and choosing to join the competition on war exposure, behindness
aversion, aheadness aversion, a measure of risk propensity, our proxy of ability in sport, and a number
of additional socio-economic and situational control variables previously mentioned (see Appendix III
for the full definitions). In column (1) and (2) we present the determinants of receiving foul cards in
the football tournament and find a positive and significant coefficient: at the median of all covariates,
war exposed subjects are 35% more likely to receive a foul card.
In columns (3)-(6) we zoom in on choosing to compete in the laboratory experiment, against
the out-group (column 3 and 4), and in-group (column 5 and 6). We find that, at the median of all
covariates, war exposed subjects are 67% more likely to join a competition against the out-group (4)
and that war exposure does not significantly change the probability of joining competition against the
in-group9.
Looking at our other covariates we find that behindness-averse respondents are 28% less
likely to join a competition against their in-group. This result corroborates the outcomes proposed by
Bartling et al. (2009) in a different context and with diverse respondents––shedding new light on
external validity of their findings10. Also, in line our hypotheses based on Niederle & Vesterlund
9
We test for the robustness of our results to group-specific characteristics. Clustering of standard errors at the
team (14) and evening (7) levels would lead to a poor approximation of the correct Wald test finite sample
critical values for rejecting the null hypothesis––correct only asymptotically. As an alternative, we introduce
team and evening level dummies in our estimation, and find that our findings are robust to such specifications
(see column (1) and (2) of Tables 8 and 9 in Appendix II for details). As a further robustness check, we assess
different specifications of the war exposure victimization index––e.g. non including hearing fighting in the
victimization index or including see combat and displaced (see column (3)-(5) of Tables 8 and 9 in Appendix II
for details). Another concern is that the age range in our sample is quite wide––i.e. between 14 and 31. It is
hardly believable that war exposure might have affected a three year old and a twenty year old in the same way.
Previous experimental evidence shows that children acquire the normative rules of the society surrounding them
mostly between the age of three and eight (Camerer, 2003; Beneson et al., 2007; Fehr et al., 2008). We therefore
re-run the regressions including only the subjects that were not older than 8 at the start of the war but at least 8 at
the end of it. None of our results is fundamentally changed in this sub-sample (see Table 11 in Appendix II for
details).
10
Bartling et al (2009) find that aheadness-aversion drives their result; we instead find it relates to behindness
aversion. This dissimilarity might be the result of a substantial variation in the two experiments. Contrary to their
study, our subjects’ score does not affect the opponent’s pay-out unless the opponent choses to play the
tournament as well. Aheadness-averse subjects therefore have to worry less about the monetary outcome of their
opponent and can focus strictly about their own preferences. Bauer et al. (2012) suggest that war exposure might
10
(2007) and Bartling et al. (2009), we find that more able subjects – i.e. playing without being
substituted during the football game – are more likely to enter a competition. Risk prone participants
are significantly more likely to join a competition only against the in-group11.
<< Insert Table 2 about here >>
VII. Discussion and conclusions
In this study we explore whether war exposure affects the competitiveness of youth
participating in a local street football tournament in Sierra Leone. Previous economic literature on the
consequences of civil war on preferences had focused on increased in-group cooperation, political
activeness and altruism. Our main contribution to this literature is to provide suggestive evidence that
increased parochial altruism is a two-fold process. To study war exposure driven out-group dynamics
we expand the design by Miguel et al. (2011) by not only looking at aggressiveness during the football
game, but also at behavior in laboratory experiments. Subjects more exposed to war during early
childhood are not only robustly more likely to commit card deserving fouls during a football game, but
self-select more often into a competition against an out-group.
We apply the well-rooted strategy of studying decision-making under the common rules and
bi-lateral antagonism provided by sport. Our football tournament setting, in which people were
assigned to teams depending on the street of residence, provides a unique opportunity to quasiexogenously impose credible group dynamics on our subjects. It is likely that people living in the
same street share common grounds, or even strong friendship ties, but other bonding factors like
having studied in the same school or sharing the same ethnic origins where highly prevalent across
teams too. Under other circumstances, our players might have felt as belonging to the same in-group
against a common adversary. The concept of in-group vs. out-group – and the differential set of
behavioural outcomes it entails – is therefore the result of agonistic framing, rather than ubiquitous.
be correlated with behavioral types. This could bring along the estimation problems typical of severe
multicollinearity. We test the correlation between our victimization variable and the two behavioural types,
aheadness-averse and behindness-averse. The highest correlation, found between war exposure and behindness
aversion in the in-group sample, is 0.26–– way below the standard rule of thumb of 0.80 for severe collinearity.
We also test multivariate collinearity by measuring the Variance Inflation Factors (VIF). We find the highest
VIF to be 1.56 << 5, the usual rule of thumb. Also, we test for the endogeneity of the behavioural types by
performing the standard Smith-Blundell test for exogeneity in Probit models. We cannot reject the null of
exogeneity for any of the four tests––the models seem to be appropriately specified with all behavioural
variables exogenous.
11
Yet, the sign of risk propensity in columns (3) and (4) is positive, as expected. An analysis of more coherent
subsamples reveals significant correlations, at the 10% level, between risk propensity and competitiveness both
towards the out-group and the in-group (see Tables 11 and 12 for details). A Pearson χ2 test on the pooled
sample (162) rejects the null hypothesis of independence between risk propensity and choosing to compete
(p=0.032).
11
Our out-group subjects may well have been in-group to each other in different contexts, yet making
decisions immediately after the football game made them clearly identify with the colours of the team
they defended. If parochial altruism is frame dependent, then it might differentially influence
dynamics spanning from intra-household to nation-wide contexts, including within village and
regional changes.
The relevance of frame depended increases in competitiveness towards the out-group becomes
clear when accompanied by increased in-group political activeness and engagement. Civil war does
not only seem to foster cooperation towards perceived in-groups, but seems to curb the normative
distaste for free competition against perceived out-groups. Being more prone to cooperate and engage
in public debates may affects the community level provision of public goods, potentially promoting
economic development (Bellows & Miguel 2009). Similarly, accepting inequality-averse outcomes
driven by a fair and regulated competition – and accepting to be part of it – is a fundamental element
of economic growth (Bartling et al. 2011). Competitiveness is not only a well-known non-cognitive
determinant adult economic achievements and productivity (Niederle & Vesterlund 2007), but a direct
driver of growth through the efficiency gains generated by competition-led creative destruction
(Schumpeter 1942; Acemoglu & Robinson 2012).
Obviously, these statements should be interpreted as speculative at this point; different types
of conflicts could have varying legacies, and the human cost of conflict may never be justified by its
plausible “externalities”. Yet, a growing body of evidence about war violence victims’ profound
changes in individual beliefs, values, and preferences poses new challenges to policy makers and postconflict recovery strategists, as it profoundly rejects the notion of development in reverse. Not only
has war historically promoted state formation and nation building – ultimately strengthening
institutional capacity and promoting economic development (Tilly & Ardant, 1975) – but it also may
foster inclusive and dynamic societal transformation by an otherwise unimaginable extent.
[...]
[...]
12
Bibliography
Acemoglu, D., & Robinson, J. A. (2012). Why Nations Fail: The Origins of Power, Prosperity, and
Poverty. New York: Crown Business.
Andersen, S., Harrison, G. W., Lau, M. I., & Rutstrom, E. E. (2008). Eliciting risk and time
preferences. Econometrica, 76(3), 583–618.
Bartling, B., Fehr, E., Maréchal, M. A., & Schunk, D. (2009). Egalitarianism and Competitiveness.
American Economic Review, 99(2), 93–98.
Bartling, B., Fehr, E., & Schunk, D. (2011). Health effects on children’s willingness to compete.
Experimental Economics, 15(1), 58–70.
Bauer, M., Cassar, A., Chytilová, J., & Henrich, J. (2012). Warfare Experience during Ontogeny
Increases Egalitarian and Parochial Motivations.
Bellows, J., & Miguel, E. (2009). War and local collective action in Sierra Leone. Journal of Public
Economics, 93(11-12), 1144–1157.
Bernhard, H., Fischbacher, U., & Fehr, E. (2006). Parochial altruism in humans. Nature, 442(7105),
912–5.
Blattman, C. (2009). From Violence to Voting: War and Political Participation in Uganda. American
Political Science Review, 103(02), 231.
Blattman, C., & Miguel, E. (2010). Civil War. Journal of Economic Literature, 48(1), 3–57.
Choi, J. K., & Bowles, S. (2007). The coevolution of parochial altruism and war. Science, 318(5850),
636–640.
Conibere, R., Asher, J., Cibelli, K., Dudukovich, J., Kaplan, R., & Ball, P. (2004). Statistical Appendix
to the Report of Truth and Reconciliation Commission, Report of Sierra Leone.
Doucet, D., & Denov, M. (2012). The power of sweet words: Local forms of intervention with waraffected women in rural Sierra Leone. International Social Work, 55(5), 612–628.
Duggan, M., & Levitt, S. D. (2002). Winning Isn’t Everything: Corruption in Sumo Wrestling.
American Economic Review, 92, 1594–1605.
Eriksson, T., Teyssier, S., & Villeval, M.-C. (2009). Self-Selection and the Efficiency of Tournaments.
Economic Inquiry, 47(3), 530–548.
Fehr, E., Bernhard, H., & Rockenbach, B. (2008). Egalitarianism in young children. Nature,
454(7208), 1079–83.
Fehr, E., & Fischbacher, U. (2003). The nature of human altruism. Nature, 425(6960), 785–91.
Garicano, L., & Palacios-huerta, I. (2006). Sabotage in Tournaments: Making the Beautiful Game a
Bit Less Beautiful.
Gilligan, M. J., Pasquale, B. J., & Samii, C. D. (2011). Civil War and Social Capital: BehavioralGame Evidence from Nepal.
Harbaugh, W. T., Krause, K., & Vesterlund, L. (2002). Risk Attitudes of Children and Adults: Choices
Over Small and Large Probability Gains and Losses. Experimental Economics, 84, 53–84.
13
Harbring, C., & Irlenbusch, B. (2003). An experimental study on tournament design. Labour
Economics, 10(4), 443–464.
Henrich, J. (2004). Cultural group selection, coevolutionary processes and large-scale cooperation.
Journal of Economic Behavior & Organization, 53(1), 3–35.
Human Rights Watch. (1999). Sierra Leone: Getting Away with Murder, Mutilation, and Rape. New
York: Human Rights Watch.
Humphreys, M., & Weinstein, J. M. (2006). Handling and Manhandling Civilians in Civil War:
Determinants of the Strategies of Warring Factions. American Political Science Review, 100(3),
429–447.
Keen, D. (2005). Conflict and Collusion in Sierra Leone. New York: Palgrave.
Miguel, E., Saiegh, S. M., & Satyanath, S. (2011). Civil War Exposure and Violence. Economics &
Politics, 23(1), 59–73.
Niederle, M., & Vesterlund, L. (2007). Do women shy away from competition? do men compete too
much? Quarterly Journal of Economics, 122(3), 1067–1101.
Norwegian Refugee Council. (2003). Profile of Internal Displacement: Sierra Leone (pp. 0–138).
Geneva.
Richards, P. (1996). Fighting for the Rainforest: War, Youth and Resources in Sierra Leone. London:
James Currey.
Richards, P. (1999). New political violence in Africa: Secular Sectarianism in Sierra Leone.
GeoJournal, 47, 433–442.
Samuelson, P. A. (1954). The Pure Theory of Public Expenditure. The Review of Economics and
Statistics, 36(4), 387–389.
Schumpeter, J. A. (1942). Capitalism, Socialism and Democracy. (Anonymous, Ed.)Economica (Vol.
11, p. 40). Routledge.
Smith, M. D. (1979). Hockey Violence: A Test of the Violent Subculture Hypothesis. Social
Problems, 27(2), 235–247.
Tilly, C., & Ardant, G. (1975). The Formation of national States in Western Europe. Princeton, N.J.:
Princeton University Press.
UNDP. (1992). Human Development Report. Oxford University Press.
UNHCR. (2013). Statistical Snapshot - Sierra Leone. Retrieved February 22, 2013, from
http://www.unhcr.org/pages/49e485186.html
Van Dijk, F., Sonnemans, J., & Van Winden, F. (2001). Incentive systems in a real effort experiment.
European Economic Review, 45, 187–214.
Voors, M. J., Nillesen, E. E. M., Verwimp, P., Bulte, E. H., Lensink, R., & Soest, D. P. Van. (2012).
Violent Conflict and Behavior: A Field Experiment in Burundi. American Economic Review,
102(452), 941–964.
Weinstein, M. D., Smith, M. D., & Wiesenthal, D. L. (1995). Masculinity and hockey violence. Sex
Roles, 33(11-12), 831–847.
14
Tables and Figures
Table 1: Summary statistics
Variable
Foul Card in Football Game
Ability
Self-reported Competitiveness
Overconfidence
Tournament Participation
Observations
162
162
162
162
162
Mean
0.204
0.457
0.856
0.611
0.420
Std. Dev.
0.404
0.500
0.231
0.489
0.495
Age
Education
Mende
Left Footed
Muslim
162
162
162
162
162
19.747
2.636
0.543
0.185
0.790
Behindness Aversion
Aheadness Aversion
Risk Propensity
First Degree Relative Bereavement
War Exposure
162
162
162
162
162
0.519
0.784
0.568
0.210
0.570
Figure 1: Balls hit in the competitiveness game
15
Min
0
0
0
0
0
Max
1
1
1
1
1
3.441
0.744
.500
0.390
0.408
14
1
0
0
0
31
4
1
1
1
0.501
0.413
0.363
0.408
0.257
0
0
0
0
0
1
1
1
1
1
1
Tournament -- ALL
.25
.5
.75
1
.75
0
Foul Card -- ALL
.25
.5
0
None
Remote
Indirect
Direct
None
Remote
War Exposure
Indirect
Direct
War Exposure
Mean
95% C.I.
Mean
95% C.I.
B
0
Tournament -- Ingroup
.25
.5
.75
Tournament -- Outgroup
0
.25
.5
.75
1
1
A
None
Remote
Indirect
Direct
None
War Exposure
Remote
Indirect
Direct
War Exposure
Mean
95% C.I.
Mean
95% C.I.
C
D
Figure 2: Foul cards and tournament participation and war exposure
Table 2: Probit regression models
Foul Card
(1)
War Exposure
0.266**
(0.125)
Behindness Aversion
Aheadness Aversion
Risk Propensity
Ability
Individual Controls
N
Pseudo R2
(2)
0.346***
(0.116)
0.0685
(0.0555)
-0.0328
(0.0664)
-0.105
(0.104)
0.129**
(0.0637)
Tournament out-group
(3)
(4)
Tournament in-group
(5)
(6)
0.485**
(0.222)
0.274
(0.227)
0.665**
(0.274)
0.0669
(0.182)
0.0223
(0.164)
0.378
(0.264)
0.410***
(0.138)
0.077
(0.264)
-0.282**
(0.115)
-0.131
(0.152)
0.325*
(0.195)
0.206*
(0.116)
no
yes
no
yes
no
yes
162
0.025
162
0.193
70
0.055
70
0.260
92
0.011
92
0.158
Notes: Probit marginal effect estimates at the medians of all covariates; robust standard errors in parentheses.
Self-reported competitiveness opinion, age, a dummy for first degree relative bereavement, overconfidence,
higher than average phone credit, Muslim, left-footed, football game victory and a dummy for no lottery choice
in the risk game are included as additional individual controls. * p<0.1, ** p<0.05, *** p<0.01.
16
Appendix I: Additional Tables
Table 3: Overview of the allocation games
Allocation A
Game
Allocation B
Self
Other
Self
Other
(1)
Costless Sharing
1000
1000
1000
0
(2)
Costly Sharing
1000
1000
2000
0
(3)
Costless Envy
1000
1000
1000
2000
(4)
Costly Envy
1000
1000
2000
3000
Table 4: Egalitarian types12
(1)
Costless Sharing
(2)
Costly Sharing
(3)
Costless Envy
(4)
Costly Envy
Inequality-averse
(1000, 1000)
(1000, 1000)
(1000, 1000)
(1000, 1000)
Behindness-averse
Any
Any
(1000, 1000)
(1000, 1000)
Aheadness-averse
(1000, 1000)
(1000, 1000)
Any
Any
Table 5: Risk game choice sets and pay-offs
Coin Tossing
CRRA13
No Coin Tossing
Choice set
If heads
If tails
For certain
High
Low
(1)
3000
0
100
1
0,80
(2)
3000
0
500
0,80
0,61
(3)
3000
0
1000
0,61
0,37
(4)
3000
0
1500
0,37
0
(5)
3000
0
2000
0
-0.71
(6)
3000
0
2500
-0.71
-2,80
12
The other three types presented by Fehr et al. (2008) are the Selfish, the Spiteful and the Generous type. The
last two are actually embedded in the Behindness-averse and Aheadness-averse types respectively. The Selfish
type would always choose the best solution for himself and be indifferent if the two solutions present the same
outcome––i.e. chose allocation B for the Costly games, and be indifferent for the Costless games. We find that
90% (87%) of the participants are either aheadness-averse or behindness-averse towards their in-group (outgroup). These two categories thus overarch the vast majority of types present in our sample.
13
The coefficient of Constant Relative Risk Aversion (CRRA) is calculated using 0.5[(3000)^(1-r)]/(1-r) =
[(NCT)^(1-r)]/(1-r) (Andersen et al., 2008). Subjects that never participate to the lottery have a coefficient
between 1 (infinite risk aversion) and 0,80. Plain gamblers have a CRRA between -2,80 and -∞.
17
Appendix II: Additional Results
Table 6: War exposure and other regarding preferences
(1)
War Exposure
Age
N
(Pseudo) R2
(2)
Dictator Game
in-group
out-group
(3)
(4)
Generous Type=1
in-group
out-group
82.56**
(35.06)
10.85
(54.57)
0.309**
(0.137)
0.141
(0.0867)
-5.692***
(2.112)
7.732***
(2.613)
-0.00485
(0.00909)
0.00467
(0.00528)
162
0.048
162
0.033
162
0.038
162
0.050
Notes: OLS regressions coefficients for (1) and (2); Probit marginal effect estimates at the medians of all
covariates for (3) and (4); robust standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.
Table 7: Determinants of self-declared self-esteem as a football player
Model
N
War
Exposure
Bereavement Overconfidence
Risk
Propensity
OLS
153
0.24***
(0.07)
-0.11**
(0.05)
-0.08**
(0.04)
-0.14***
(0.05)
Heckman
Selection
162
0.23***
(0.08)
-0.11**
(0.05)
-0.08**
(0.04)
-0.13**
(0.05)
Inverse
Mills Ratio
0.086
(0.17)
Notes: (Robust) standard errors in parenthesis. The selection variables in Heckman’s two-step consistent
estimator are Number of meals per day, Foul Card and Not Played, all p<0.1. * p<0.1, ** p<0.05, *** p<0.01.
Table 8: Alternative model specifications for foul-card in the football games
Group dummies
evening level
team level
War Exposure
Behindness Aversion
Aheadness Aversion
Risk Propensity
Ability
N
Pseudo R2
0.323***
(0.119)
0.0677
(0.0597)
-0.0346
(0.0666)
-0.122
(0.104)
0.140**
(0.0629)
162
0.204
0.209***
(0.0726)
0.0362
(0.0366)
-0.00878
(0.0393)
-0.112*
(0.0642)
0.0885**
(0.0414)
162
0.238
Alternative War Exposure Measures
(1)
(2)
(3)
0.470***
(0.127)
0.0611
(0.0554)
-0.0260
(0.0655)
-0.116
(0.103)
0.131**
(0.0639)
0.206**
(0.104)
0.0647
(0.0569)
-0.0230
(0.0654)
-0.0997
(0.104)
0.125*
(0.0643)
0.309**
(0.121)
0.0599
(0.0564)
-0.0307
(0.0665)
-0.110
(0.104)
0.128**
(0.0632)
162
0.213
162
0.171
162
0.184
Notes: Probit marginal effect estimates at the medians of all covariates; robust standard errors in parentheses. (1)
removes “seen injured person” from the victimization index; (2) removes “hear fighting” from the victimization
index; (3) adds “see combat” and “displaced” to the victimization index. * p<0.1, ** p<0.05, *** p<0.01.
18
Table 9: Alternative model specifications for out-group tournament participation
Group dummies
evening level team level
War Exposure
Behindness Aversion
Aheadness Aversion
Risk Propensity
Ability
N
Pseudo R2
Alternative War Exposure Measures
(1)
(2)
(3)
0.936***
(0.349)
0.124
(0.199)
-0.134
(0.182)
0.356
(0.275)
0.391**
(0.155)
0.805***
(0.312)
0.0464
(0.242)
-0.0215
(0.175)
0.388
(0.267)
0.505***
(0.159)
0.705***
(0.273)
0.0560
(0.178)
-0.0132
(0.168)
0.371
(0.258)
0.400***
(0.138)
0.641***
(0.239)
0.0981
(0.187)
0.0262
(0.163)
0.410
(0.266)
0.417***
(0.138)
0.542**
(0.268)
0.0230
(0.180)
0.0189
(0.153)
0.319
(0.264)
0.385***
(0.134)
70
0.358
70
0.345
70
0.262
70
0.269
70
0.235
Notes: Probit marginal effect estimates at the medians of all covariates; robust standard errors in parentheses. (1)
removes “seen injured person” from the victimization index; (2) removes “hear fighting” from the victimization
index; (3) adds “see combat” and “displaced” to the victimization index. * p<0.1, ** p<0.05, *** p<0.01.
Table 10: Plausible determinants of War Exposure
DV
N
Years
of War14
Muslim
Mende
Fula
War
Exposure
162
0.030***
(0.010)
0.042
(0.081)
0.068
(0.04)
-0.250
(0.166)
Mandingo
Temne
Left
Footed
-0.168
(0.017)
-0.123
(0.172)
0.092
(0.091)
Poisson regression coefficients; robust standard errors in parenthesis. * p<0.1, ** p<0.05, *** p<0.01.
Table 11: Probit regression models (not older than 8 at the start of the war and at least 8 at the end)
(1)
Foul Card
War Exposure
Behindness Aversion
Aheadness Aversion
Risk Propensity
Ability
N
Pseudo R2
(2)
Tournament out-group
0.326**
(0.133)
0.0640
(0.0608)
-0.0499
(0.0735)
-0.102
(0.109)
0.129*
(0.0679)
0.603**
(0.306)
0.0371
(0.201)
0.0865
(0.174)
0.530*
(0.293)
0.425***
(0.154)
152
0.171
65
0.319
(3)
Tournament in-group
-0.0255
(0.301)
-0.272**
(0.121)
-0.108
(0.165)
0.359*
(0.195)
0.195*
(0.115)
87
0.142
Notes: Probit marginal effect estimates at the medians of all covariates; robust standard errors in parentheses.
* p<0.1, ** p<0.05, *** p<0.01.
14
The variable Years of War is a linear combination of Age, namely Age minus 8, i.e. the approximate timespan between the end of the war and the experiments.
19
Table 12: Probit regression models (displaced sub-sample)
(1)
Foul Card
War Exposure
Behindness Aversion
Aheadness Aversion
Risk Propensity
Ability
N
Pseudo R2
(2)
Tournament out-group
0.410***
(0.148)
0.0504
(0.0636)
-0.0720
(0.0812)
-0.147
(0.116)
0.199***
(0.0768)
0.697**
(0.308)
0.198
(0.219)
-0.220
(0.199)
0.566*
(0.315)
0.615***
(0.148)
133
0.241
57
0.331
(3)
Tournament in-group
-0.154
(0.329)
-0.302**
(0.130)
-0.143
(0.182)
0.411*
(0.238)
0.0868
(0.132)
76
0.200
Notes: Probit marginal effect estimates at the medians of all covariates; robust standard errors in parentheses.
* p<0.1, ** p<0.05, *** p<0.01.
Table 13: Probit regression models (with displacement included in the victimization index)
(1)
Foul Card
War Exposure
Behindness Aversion
Aheadness Aversion
Risk Propensity
Ability
N
Pseudo R2
(2)
Tournament out-group
0.370***
(0.137)
0.0647
(0.0545)
-0.0319
(0.0657)
-0.114
(0.104)
0.126**
(0.0634)
0.882***
(0.320)
0.0712
(0.181)
0.00754
(0.163)
0.345
(0.264)
0.407***
(0.138)
162
0.193
70
0.267
(3)
Tournament in-group
-0.0291
(0.279)
-0.287**
(0.116)
-0.120
(0.147)
0.341*
(0.196)
0.205*
(0.116)
92
0.157
Notes: Probit marginal effect estimates at the medians of all covariates; robust standard errors in parentheses.
* p<0.1, ** p<0.05, *** p<0.01.
20
Appendix III: Data definitions
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Foul Card in Football Game. Individual level dummy variable indicating if the respondent i had
received at least one yellow/red card up to that stage of the tournament.
Ability. As a proxy for sportive ability we take an individual level dummy variable taking unity if
the i-th respondent had respondent positively to the question “did you play the whole football
game?”, zero if else. The answer was crosschecked with the control questions “how many minutes
did you play in this game” and “How many minutes did the game last in total?”; the dummy would
take a value of zero if the ratio of their responses differed from unity.
Self-reported Competitiveness. Individual level index constructed as the answer to the question
“Compared to your team mates, how competitive would you say you are?”, on a scale of 1 (not
competitive at all) to 5 (the most competitive), standardized between 0 and 1.
Overconfidence. Individual level dummy variable taking value of unity if the answer to the
question “Compared to the other people participating to our games, how well do you think you
will perform in putting the pall into the basket?”, on a scale from 1 (the best) to 5 (the worst),
equaled one, and the participant scored less than 10 hits in the competitiveness game ––the best
result achieved by the participants to that game; zero if else.
Tournament Participation. Individual level dummy variable taking value of unity if the i-th
subject decides to enter the competition in the competitiveness game, zero if else.
Age. Age of respondent i as measured in years, rounded down to the age at the last birthday.
Education. Individual level variable taking value 1 if the respondent had not completed primary
school, 2 if he had not completed junior secondary school, 3 if they had not completed senior
secondary school, 4 if else.
Mende. Individual level dummy taking value of unity if the i-th respondent self-declared to be
ethically Mende, zero if else.
Left Footed. Individual level dummy taking value of unity if the i-th respondent self-declared to be
left-footed, zero if else.
Muslim. Individual level dummy taking value of unity if the i-th respondent self-declared to be
Muslim by religion, zero if else.
Behindness Aversion. Individual level dummy taking value of unity if the respondents choose the
egalitarian option in both the envy games of the allocation game, zero if else (see Tables 3 and 4 in
Appendix I).
Aheadness Aversion. Individual level dummy taking value of unity if the respondents choose the
egalitarian option in both the sharing games of the allocation game, zero if else (see Tables 3 and 4
in Appendix I).
Risk Propensity. Individual level variable based on the respondents’ six choices in the risk game,
spanning from zero (i.e. never gamble) to one (i.e. always gamble), and allowing for indifference
by taking the last switch point as significant.
First Degree Relative Bereavement. Individual level dummy taking value of unity if the i-th
respondent declares that one of his first degree relatives – including mother, father and siblings –
deceased as a consequence of war-related violence.
War Exposure. Average of responses to these violence related questions: “during war time...” “did
you ever hear combat, shooting and explosions?”, “did you ever see a person injured because of
war-related violence?” and “did you personally suffer from physical injury because of war-related
violence?”.
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