Sacrifice: An experiment on the political economy of extreme intergroup punishment

Transcription

1 Sacrifice: An experiment on the political economy of extreme intergroup punishment Catherine C. Eckel a Enrique Fatas b Malcolm J. Kass c a Department of Economics, College of Liberal Arts, Texas A&M University b School of Economics, Faculty of Social Sciences, East Anglia University c Department of Economics, College of Business, University of Texas at Arlington Abstract We analyze the behavioral determinants of extreme punishment in intergroup conflict. Individuals contribute to team production by a tedious real effort task where time in a key input for production. Teams compete for a prize in asymmetric tournaments where the value of the prize is endogenously determined. Asymmetries in real effort task times are generated by nature or by the decisions of one team, arbitrarily chosen. As the magnitude of the asymmetry is identical across conditions, we can measure the marginal effect of political inequality when only the advantaged group votes on the asymmetry size. We allow for a particular form of intergroup punishment. Individuals in the disadvantaged group may punish all individuals in the other team at an extreme price: if they decide to punish the other individuals, they lose all their individual earnings. Our results strongly support the link between political asymmetries and extreme intergroup punishment. Relative to a control treatment with no asymmetries, economic inequality has no significant effect on the likelihood of intergroup punishment. Interestingly in the political inequality treatment, we find that skilled individuals are approximately 35 percentage points more likely to sacrifice themselves to harm the other team when they are members of the politically disadvantaged group relative to these same individuals in an the economically identical situation where the political asymmetry is absent. JEL Classifications: C92, D72, D74 1

2 1. Introduction It is not uncommon to see intergroup conflict situations where inequalities exist between different competing parties. Some situations involve where one group has inherent advantages over the other group. In other situations, one advantaged party controls economic inputs and uses this control to constrain the economic productivity of other groups. These can lead to exploitative situations that range from the political, such as imperialism, to the household. (Sample 2003) (Tormey 1973) 1 However, control over resources is only part of the equation for many of these situations. With control of resources, the advantaged party can enjoy a dominant economic position over competing parties; then uses this dominant position to appropriate wealth from the disadvantaged party. This often leaves the disadvantaged group with options to either acquiesce or to punish the advantaged group in an attempt to alter the asymmetric power structure. However, punishment may leave the disadvantaged group in a worse situation than acquiesce, including loss of life and of critical resources. Historically, European colonization is an example of the parent European nations wishing for the economic success of their colonies to maximize their own welfare. However, this desire was met with violent resistance from groups such as the Algerian FLN, who resorted to acts that ended the lives of many native Algerians. With regards with current world affairs, terrorism is also a strategic act driven by retribution for unfair outcomes vs. a psychological phenomenon. (Horgan 2003, Borum 2004) For instance, suicide terrorists are often more secular than their native populations and relatively well educated individuals who are highly strategic in their actions. (Pape 2005) However, for whatever justification, these extreme instances of punishment cause massive economic and psychological costs as well as the loss of lives. Experimental research in ultimatum and dictator games have been used explain behavior given different inequalities. In these models, an advantaged party partitions an endowment between itself and another. In ultimatum games, this other party either accepts or rejects this allocated share of the endowment. If it accepts, both parties receive their endowment share. If it rejects, the entire endowment is lost. Dictator games are similar, except that once the advantaged party divides the endowment, the game ends and each party receives its share. The other party plays no active role and must accept this decision. Engel (2011) contains a good summary of this literature. (Engel 2011) While these simple models are useful to explore questions about the ties between fairness and destructive behavior (Blader 1 Goodin describes 4 conditions necessary for exploitation, There are four conditions, all of which must be present if dependencies are to be exploitable. First, the relationship must be asymmetrical Second, the subordinate party must need the resource that the superordinate supplies Third, the subordinate party must depend upon some particular superordinate for the supply of needed resources Fourth, the superordinate enjoys discretionary control over the resources that the subordinate needs from him.. Goodin, R. E. (1988). "Reasons for welfare." The political theory of the welfare state:

3 and Chen 2012) (Cappelen, Hole et al. 2005, Cappelen, Sørensen et al. 2010), they lack key components present in intergroup competition. First, the role of competition is absent in these models. Second, the endowment is an exogenous variable and not a function of economic productivity. Lastly, any potential links between ability and behavior cannot be studied in these simple games. Therefore, the relevant literature is the contest research, specifically between different groups, for this contains the necessary richer strategic environment. Intergroup conflict research has incorporated many of the interactions present in intergroup contests, such as with competing political parties or with competing firms spending research and development to offer a new product to a market first. In economic models of conflict, different parties use resources to win a prize where the victor is determined by a lottery contest success function. In these models, winning the prize of given value is socially wasteful for groups spend valuable resources in the pursuit of victory. Garfinkel and Skaperdas (2007) provide an overview of the economics of conflict. (Garfinkel and Skaperdas 2007) Experimental work generally finds that these different parties overinvest resources above the Nash prediction, driven by the competitive behavior forces. (Bornstein 2003) (Öncüler and Croson 2005) 2 The effect of punishment on cooperative behavior is contexual, for instance punishment can increase cooperation between individuals, such as with Fehr and Gachter (2000). (Fehr and Gachter 2000) However, other research finds that cooperative behavior erodes when multiple individuals can costly punish each other, especially since group punishment may be viewed by the punished party as signaling a violation of a norm. (Nikiforakis 2008) (Villatoro, Andrighetto et al. 2014) This over-expenditure of resources appears to be further exasperated by introducing intragroup punishment in intergroup competition. (Abbink, Brandts et al. 2010, Abbink, Brandts et al. 2012) (Böhm, Bornstein et al. 2014) (Yamagishi 1986) To promote competition for the prize, subjects costly punish others in the same group if they feel they are not investing enough resources in the intergroup competition. However the previous research, which has focused on rent seeking contests between symmetric individuals and/or groups, does not quite mimic the conditions necessary for our exploitative situation. Intergroup contests with resource inequalities have complexities not present in the current literature. First is the concept of production as the key variable that determines contest winners. Differences in the availability of productive resources can help maintain a superior economic position for one group. This superior position allows one group to win the contest, where the prize is a faction of the other group s productivity. Hence, victors gain when all parties are productive. However, if one group controls the availability of the productive resource, there is an interval optimum for the controlling group wants to 3

4 maximize productivity for all, but still maintain its favored position. 3 The role of economic superiority in arrogating wealth is not present in the contest literature nor is the endogenous nature of the contest prize, which is an increasing function of the availability of resources. Second, losing group members can resort to costly punishment in an attempt to better their overall situation, especially in situations there the source of the productive resource inequality is endogenous. For instance, research finds that nondemocratic regimes tend to have more suicide terrorism attacks than democratic regimes. (Santifort-Jordan and Sandler 2014) 4 Previous contest literature has neglected this component; thus there has been no investigation of the behavior of disadvantaged groups. Of particular interest is the potential tie between innate ability and the willingness to engage in costly punishment. Previous research has found that welleducated and analytical individuals, such as those with engineering backgrounds, are more willing to engage in extreme costly punishment, such as suicide terrorism. (Gambetta and Hertog 2009) (Benmelech and Berrebi 2007) (Krueger 2008) Third, in certain situations (specifically those that result in terrorist violence), some in the disadvantaged group heighten and alter their feelings toward this situation and radicalize, possibly by social comparison with others in their group or by condensation, where some of the exploited engage in a reaction/counter-reaction cycle against the exploiting party. (McCauley and Moskalenko 2008) This component is absent in the literature. Finally, since these inequalities are absent in the previous intergroup contest literature (rendering post-contest intergroup costly punishment an unnecessary complexity), there has not yet been an analysis of how contest winning teams can mitigate intergroup punishment via one-for-one monetary transfers and under what conditions these transfers would assuage costly punishment. This would be akin to financial aid coming from the parent state to the marginalized groups. International Aid is especially important for this reduces transnational and domestic terrorism on foreign direct investments, a key component to economic growth. (Bandyopadhyay, Sandler et al. 2013) (Booth 2012) However, aid effectiveness may be dependent on the context of the situation. Under situations where the inequality is exogenous, then aid may reduce the likelihood of costly punishment due to spite. However, when the inequality is endogenous, disadvantaged group members may be indifferent to aid. In this paper, we use laboratory methods to study how intergroup contests with costly asymmetric intergroup punishment are influenced by the introduction of asymmetries in economic and political 3 An interesting historical example is colonial American of the 1700s. Before the American Revolution, the English had a measure of political control over American Colonists, but this did not mean that the average British citizen was wealthier than the average American colonist. NORQUIST, G. G. (2012). "Tea, Taxes, and the Revolution." Retrieved September 18, 2015, from 4 Interestingly, democratic countries are more likely to face suicide terrorism on the extensive margin. The authors conclude that the freedoms inherent in the democratic society provide a measure of flexibility for terrorist groups not present in nondemocratic countries. 4

5 power, the latter of which is most akin to exploitation situations. There are a number of key research questions we wish to answer. First, are there differences in the likelihood of costly punishment by the contest losers in symmetric intergroup contests (the baseline), asymmetric intergroup contents but with a symmetric political power structure (the economic inequality treatment), and intergroup contests with an asymmetric political power structure with can lead to asymmetries in productive resources (the political inequality treatment)? Second, does inherent ability drive the likelihood of costly punishment and under what treatment conditions? Third, is costly punishment driven by the few who become radicalized and frequently engage in costly punishment or do the contest losers share the load in engaging in these extreme acts? Fourth, in the political asymmetric treatment, will advantaged groups leverage their power to maximize payoffs? Fifth, does costly punishment deter abuse of power and increase overall welfare, as found in some of the punishment literature, or do those in power decide to increase their leverage and reduce payoffs for both parties? Finally, will aid be effective in reducing costly punishment and under what situations. Our analysis is based on a zero-sum contest where team members perform a simple effort task to earn wealth. For one predetermined team, resources used to accrue wealth are either split equally, or determined exogenously or endogenously by members of the other team. The contest winner is the team with the most wealth, and this victor appropriates some of the losing team s wealth as the prize. The process is repeated for a fixed amount of iterations, after which the members of the team that lost the most contests can decide to costly punish the winning group. 5 Since costly punishment is decided ex-post, this should not change the equilibrium behavior of either team under any situation, including the exploitation environment. Our results indicate a positive relationship between ability and the willingness to costly punish under the politically unequal treatment relative to subjects in the symmetric Baseline and in Economic Inequality contests. We find that the likelihood of costly punishment and to radicalize becomes more sensitive with respect to ability under the politically unequal situation. Under this treatment, the estimated costly punishment probability of a high ability exploited subject is estimated to be 3 to 4 times larger than a low ability subject. This sensitivity is not found in the other situations. Finally, we do not have evidence that the productive resource selections by the advantaged are responsive to a history of costly punishment. 2. Experimental Design 2.1 Overall Design 5 When we examine aid effectiveness, the members of the team that won the most contests can offer monetary aid before the costly punishment decisions. 5

6 The experimental design consists of three main treatments, each with four stages. In the Baseline, subjects participate in a series of tournaments where the winner is determined by performance on a realeffort task. In the Economic Inequality treatment, inequality is imposed, creating an asymmetric tournament with one team having a reduced amount of time in which to complete the task. In the Political Inequality treatment, the degree of inequality is chosen by a randomly selected team. The general outline is shown in Table 1 below. Each row denotes a treatment, and the stages are indicated by columns. Each stage consists of a number of blocks, indicated as B1, B2, etc. Subjects accumulate earnings within a block, and are paid for one block, selected at random at the end of the experiment. Table 1: Overall Experimental Design Stage 1 Stage 2 Stage 3 Stage 4 Treatment B1 B2 B3 B4 B5 B6 B7 B8 B9 Baseline Individual Tournament Sacrifice Aid + Sacrifice Economic Inequality Individual Tournament Sacrifice Aid + Sacrifice Political Inequality Individual Tournament Sacrifice Aid + Sacrifice Baseline Each treatment consists of four stages, each comprised of blocks. Each block contains five rounds. In each round, each subject performs a timed real-effort task: adding up two, two-digit numbers. 6 Subjects earn one Experimental Currency Unit (ECU) for each correct answer. 7 At the beginning of the session, subjects are randomly assigned to six-person groups and divided further into two three-person teams, labeled Team A and Team B. Subjects remain in the same group and team for the remainder of the session. In Stage 1, subjects then perform the addition task for five, 40-second rounds. Earnings for this round are based on individual correct answers. 8 6 A closely related task has been used in a number of studies including 7 Exchange rate is 10 ECUs per 1 USD, Appendix A contains a screen shot of this effort task. 8 Hence, Stage 1 is Block 1. (B1 in the table) 6

7 Stage 2 introduces the tournament. In this stage, subjects earn points for their team, and earnings for each team member is equal to the average number of correct answers by team members. This stage contains two blocks, Block 2 and 3, and recall that each block contains five rounds. At the end of each round, the team with the highest earnings (number of correct answers) wins the tournament. The prize is one-half of the losing team s earnings, which are then divided equally among the winning team members. Subjects are notified of both teams performance, the tournament outcome, and earnings transfer. After the fifth round in each block, the subjects are informed of the number of tournaments won and lost and the total earnings in the block. Final block earnings are the cumulative earning totals from the five rounds. After Block 2, subjects move to Block 3, which has the same procedures. Next is Stage 3, containing Blocks 4, 5, and 6, and which introduces a Sacrifice decision. At the end of each block, the number of tournaments won is compared between teams. Subjects on the team with fewer tournament victories in the block have the opportunity to make a Sacrifice decision. 9 Subjects make their Sacrifice decisions privately and simultaneously. If one subject decides to sacrifice, this cuts the preliminary block ECU earnings for winning team members by one-half. (in that specific group) However, the cost of sacrifice is severe. If only one in a group selects Sacrifice, that subject forfeits all of her earnings in that block. 10 After the Sacrifice decisions, subjects are notified on the number that decided to Sacrifice in the group and their final block earnings. All Blocks in Stage 3 are functionally equivalent. One block in Stage 3 was randomly selected for payment by the experimenter. Earnings from this block determine final Stage 3 earnings. Finally, Stage 4 contains Blocks 7, 8, and 9. Extending Stage 3, Stage 4 introduces financial aid (Aid henceforth), which are one-for-one token transfers from the team members who won the most tournaments in a block to the other team members. After seeing the number of tournaments won and before their Sacrifice decisions, winning team subjects can choose to send any amount between zero and their current earnings to members of the other team. These Aid decisions are made privately and simultaneously. The average of the individual aid choices are spread evenly to members of the other team. After the Aid decisions, subjects are notified of the amount of aid transferred and their current earnings. Then subjects in the team that lost the most tournaments made their Sacrifice decisions. All Blocks in Stage 4 are functionally equivalent. One block in Stage 4 was randomly selected for payment by the experimenter. Earnings from this block determine final Stage 4 earnings. 9 See figure 11 in appendix A for a screenshot of this decision. 10 If more than one decides to Sacrifice, the subject who pays the cost of Sacrifice is randomly determined. Hence, if two subjects in the losing team sacrifice, then there is a 50% chance that one subject will lose all of her current ECU block earnings. If 3 subjects in the losing team sacrifice, then there is a 33.33% (1/3 likelihood) that one subject will lose all of her current ECU block earnings. 7

8 The between-subject design is illustrated on Table 1 by moving from the top to bottom. There are three different sections here, a Baseline condition and two treatments, called Economic Inequality (EI) and Political Inequality (PI). The structure is similar among them, where the only differences are time constraints and how the time constraint is imposed. In the Baseline, every subject has 40 seconds to use on the effort task for every round. In the PI treatment starting in Block 2, Team A subjects endogenously choose the Team B times for the effort task. At the beginning of every block, Team A subjects privately and independently select a time between 0 and 40 seconds. These times are averaged across Team A members in a group and this average is Team B s effort task time for that block. This process is repeated for all blocks moving forward. Hence, the PI treatment has asymmetric resource quantities between teams by allowing task times to be changed and alters the source of this asymmetry by allowing times to be chosen by Team A members. 11 The EI treatment is the same as the PI treatment except that the Team B effort task times are exogenous, but otherwise identical to the Team B times from the PI treatment. 12 Hence, between the Baseline and the EI treatment, the only structural difference is the Team B time allocations. The source of the difference is controlled. Only the source of Team B time allocations is altered between the EI and PI treatment. Sacrifice Since Sacrifice is costly and does not help increase payoffs in the final block, it is not useful for any of the previous blocks. Using the standard backward induction argument, it is clear that the Sacrifice option should not be utilized. Of more interest is if the source of the inequality drives Sacrifice decisions by exploited subjects, when controlling for the magnitude of the inequality. While inequality aversion may explain differences between the Inequality treatments and the Baseline, this will not explain differences between the EI and PI treatments. (Fehr and Schmidt 1999) Hence, we ask the following research question. Question 1: Will subject s sacrifice in any treatment? Will exploited Team B subjects increase Sacrifice decisions in the PI treatment relative to the EI treatment? Next, in extending the research mentioned in the Introduction, does ability in the effort task play a role in a subject s willingness to sacrifice? Of particular note is task ability s role driving sacrifice when one group can exploit another group. Theoretically, ability should play no role regarding Sacrifice selections and the research mentioned previously is not clear on the interplay of ability and exploitation on the 11 Assuming the unlikely event doesn t occur that all Team A members choose 40 seconds for all Team B task times. 12 Therefore all the times used in the EI treatment are the exact times decided by Team A members in the PI treatment. These times are listed in Table 1 in Appendix B. 8

9 propensity of severe punishment other than it may matter. One possible argument is that higher ability is correlated with intelligence, and more intelligent subjects will understand quicker and more in-depth that sacrifice is not logically supported. This leads to the following research questions. Question 2: Is effort task ability a consistent, direct causal mechanism of sacrifice selections? Question 3: Does the political environment alter the relationship between ability and sacrifice selections? 3. Results and Discussion This Results and Discussion section is broken down into three different sections. First is an analysis of overall descriptive statistics. Second and most critical is a look into the determinants of the individual sacrifice decision and of sacrifice intensity. Third, we look into the interplay of time selections and sacrifice history, which we dub as escalation of conflict. 3.1 Aggregate stage trends and differences among treatments In Table 2 below are averages and standard deviations for the key experimental variables in this paper, separated out by stage and treatment. We have included the corresponding pvalues from the non- parametric Two-sample Wilcoxon ranksum tests comparing the different treatments to each other. From this table, some items of note. First, there are the lower levels of productivity 13 by individuals in the PI treatment. This is true across all blocks, hence this will need to be controlled for in the econometric work. Second, for many experimental variables, we fail to see statistically significant differences at the 10% level between the Baseline and the Economic Inequality treatment. Even for the variables Fraction of Team B wins and Amount destroyed by Sacrifice, these statistically significant differences are driven by differences in available economic resources (allotted Team B addition time). Third, Sacrifice is prevalent in all treatments and there is an increase in probability of Sacrifice selections in the PI treatment. While the probability of Sacrifice is similar in the Baseline and EI treatment 14, it is considerably higher in the PI treatment for both stages with incorporate Sacrifice. 15 This is true for the individual Sacrifice selection and for the probability of the group Sacrifice instance. 16 Lastly, for the 13 In correct answers per minute 14 In comparing the percentage of individual sacrifice selections between the Baseline and the Economic Inequality treatment, from Wilcoxon rank sum test, pvalve = for stage 3, pvalue = for stage 4 15 In comparing the percentages of individual sacrifice selections between the Economic Inequality treatment and Political Inequality treatment, from the Wilcoxon rank sum test, pvalue < for both stages. 16 Wilcoxon rank sum test pvalue between Baseline and Economic inequality for stage 3 is , for stage 4 is < The Wilcoxon rank sum test pvalues between Economic inequality and Political inequality for stage 4 is Between Economic Inequality and Political Inequality, the pvalue is <

10 amount of Aid sent from the winning team to the losing team, we see very little difference between the treatments. Result 1: In the Political Inequality treatment, losing Team B subjects were twice as likely to Sacrifice per block in Stage 3 and more than three times more likely to Sacrifice per block in Stage 4 than losing Team B subjects in the Economic Inequality and Baseline treatments. Table 2: Experimental Variable means, standard deviations, and corresponding Wilcoxon ranksum pvalues. Values are separated over Stage and by Treatment. Baseline Economic Inequality Political Inequality Stage Aggregate Non-parametric test: 2 sample: Wilcoxon Ranksum Stage 1: Addition task Productivity: Mean (sd) (2.76) (3.12) 9.28 (2.16) (2.76) minimum, maximum 5.70, , , , Stage 2: Tournament Productivity: Mean (sd) (3.54) (3.38) (2.62) (3.28) minimum, maximum 6.60, , , , Time Allowed for Team B subjects: Mean (sd) 40 (0) (6.672) (6.672) n/a minimum, maximum 40, 40 13, 35 13, 35 n/a Fraction of time team B B vs. EI: B vs. PI: EI vs. PI: B vs. EI: B vs. PI: EI vs. PI: wins: B vs. EI: Mean (sd) (0.340) B vs. PI: (0.447) (0.144) (0.290) EI vs. PI: minimum, maximum 0, 1 0, 0.4 0, 1 0, 1 10

11 Baseline Economic Inequality Political Inequality Stage Aggregate Non-parametric test: 2 sample: Wilcoxon Ranksum Stage 3: Sacrifice Productivity: Mean (sd) (3.96) (3.52) (3.12) (3.61) minimum, maximum 6.30, , , , B vs. EI: B vs. PI: EI vs. PI: Time Allowed for Team B subjects: Mean (sd) (0) (6.400) (6.400) n/a minimum, maximum 40, 40 15, 35 15, 35 n/a Fraction Team B wins: Mean (sd) (0.431) (0.259) (0.147) (0.338) minimum, maximum 0, 1 0, 1 0, 0.6 0, 1 B vs. EI: B vs. PI: EI vs. PI: Losing Team B Individual: Probability of choosing to sacrifice: (0.228) (0.260) (0.310) (0.273) B vs. EI: B vs. PI: EI vs. PI: Overall Group: Probability of a Sacrifice instance: (0.309) (0.214) (0.330) (0.306) B vs. EI: B vs. PI: EI vs. PI: Amount sacrificed per subject*: Mean (sd) (3.201) minimum, maximum 15.83, (4.626) 10.83, (3.089) (4.966) 6.17, , B vs. EI: B vs. PI: EI vs. PI: B vs. EI: B vs. PI: EI vs. PI: Amount destroyed by sacrifice : Mean (sd) (6.058) (5.808) (1.941) (5.692) minimum, maximum 24.0, , , , 44.3 B vs. EI: B vs. PI: < EI vs. PI:

12 Baseline Economic Inequality Political Inequality Stage Aggregate Non-parametric test: 2 sample: Wilcoxon Ranksum Stage 4: Sacrifice + Aid Productivity: Mean (sd) (4.41) (3.71) (3.11) (3.84) minimum, maximum 5.40, , , , B vs. EI: B vs. PI: EI vs. PI: Time Allowed for Team B subjects: Mean (sd) 40 (0) (6.103) (6.103) n/a minimum, maximum 40, 40 13, 35 13, 35 n/a Fraction Team B wins: Mean (sd) (0.404) (0.204) (14.7) (0.304) minimum, maximum 0, 1 0, 0.8 0, 0.6 0, 1 B vs. EI: B vs. PI: EI vs. PI: Aid given: Mean (sd) (7.839) (6.138) (5.310) minimum, maximum 0, 31 0, 27 0, 30 0, (6.515) B vs. EI: B vs. PI: EI vs. PI: Losing Individual: Probability of choosing to sacrifice: (0.169) (0.177) (0.330) (0.246) B vs. EI: B vs. PI: EI vs. PI: Overall Group: Probability of a Sacrifice instance: (0.295) (0.248) (0.398) (0.333) B vs. EI: B vs. PI: EI vs. PI: Amount sacrificed per subject*: Mean (sd) (3.530) minimum, maximum 17.50, (5.253) 13.17, (4.976) 6.33, , (5.281) B vs. EI: B vs. PI: EI vs. PI: Amount destroyed by sacrifice : Mean (sd) (3.928) (5.578) (4.035) minimum, maximum 22.6, , , , (5.239) B vs. EI: B vs. PI: EI vs. PI: <

13 Notes for Table 2: Nonparametric test key: B-Baseline, EI Economic Inequality, PI Political Inequality Productivity: Measured as the average of the correct answers per minutes per subject Time Allowed: The amount of time that Team A members selected for Team B members Fraction Team B wins: is the number of instances that Team B won a tournament over the total number of tournaments Losing Individual: Probability of choosing to sacrifice: number of times a losing subject decided to sacrifice over the number of opportunities. This is Team A and B people for Baseline, but only Team B people for Inequality treatments Overall Group: Probability of a Sacrifice instance: number of times a specific group dealt with a sacrifice instance over the 6 blocks where sacrifice could happen (blocks 4-9) Amount sacrificed per subject: For a subject that decided to sacrifice and was chosen, this is the amount that the subject game up in that block (this is the subject's block earnings before the sacrifice decision). This includes Team A and Team B losers. Amount destroyed by sacrifice (again per subject who had lost earnings): For a subject on the winning team, this is the earnings loss due to a losing team's subject deciding to sacrifice Aid given: The amount of aid given by winning team members to losing team members Amount destroyed by sacrifice: The average earnings lost per team winning team member when the losing team decides to sacrifice 3.2 Determinants of the Individual Sacrifice Decision Missing from the previous section is an examination of how different experimental and subject variables can affect a subject s decision to Sacrifice. To gain insight on losing team members types and how they come to the Sacrifice decision, we turn to investigating Sacrifice instances per block and then to econometric modeling. The next table shows the number of times individual subjects chose to Sacrifice and the number of group Sacrifice instances (whether the group underwent a sacrifice decision) per between-subject treatment and per Block. 17 Table 3: Instances that the Sacrifice option was selected per block (out of 24 Ss/8 Gr) Individual Sacrifice Selections Group Sacrifice Instances Stage Block Baseline Economic Political Economic Political Baseline Inequality Inequality Inequality Inequality sum Average per Block For all Blocks expect Block 8, there are more per Block individual sacrifice selections and more group sacrifice instances in the Political inequality treatment relative to the Baseline and relative to the 17 Group sacrifice instances are the number of groups that contained at least one subject who decided to Sacrifice in a given block. 13

14 Economic inequality treatment 18. Hence, here is evidence that the asymmetric power situation in the PI treatment drives Sacrifice selections on top of simply time allotments for team B members, as is the case in the EI treatment Determinants of the Individual Sacrifice choice Next, we turn to an econometric analysis to uncover who are the sacrificing types, focusing on the relationship between ability and the willingness to engage in this costly punishment. Skilled team B subjects appear to be the most responsive to sacrifice in the Political inequity treatment. Using the individual losing team members as our unit of observation, Table 4 contains a series of logit models were the dependent variable is the binary losing team member s decision to sacrifice. In the first model, there is a significantly significant positive PI treatment effect on sacrifice at the 0.01 level. However, once we control for experimental variables and for ability, including interacting ability with our treatment dummy variables, this effect is no longer significant in model 3. This tests the hypothesis on an interaction effect between ability and the PI treatment, the willingness to engage in severe punishment actions is increasing with ability. We use the variable 1 st Block Earnings as a proxy for ability. 19 (called FBE henceforth) Here, we find that in the PI treatment, subjects that are more skilled are more likely to engage in Sacrifice at the 0.05 level. This does not hold for the baseline and the EI treatment. In the Baseline, we find the opposite effect, the high skilled individuals may be less likely to sacrifice. In model 3, we add controls for personal characteristics. These do not greatly alter the magnitude and significance of the previous explanatory variables of interest. 20 Via the linear combination of estimators test, the interaction between ability and the PI treatment is significantly different than the interactions between ability and the Baseline and ability and the EI treatment Except block 8, where there is the same number of individual sacrifice selections between the inequality treatments and there Is the same number of group sacrifice instances between the Baseline and the PI treatment and one more group sacrifice instance in the EI treatment vs. the PI treatment. 19 Because 1 st Block Earnings are likely to be correlated with overall earnings, we control for subject preliminary earnings before the Sacrifice decision by adding Earnings before Tournament and Aid as covariates. (note that Aid=0 for Stage 3 data) 20 It has been suggested that the effectiveness of Sacrifice should be accounted for. When Sacrifice effectiveness is added or substituted into our models, it is not statistically significant and consistently provides higher Akaike information criteria values. 21 Coefficient difference between PI and the Baseline is with a pvalue = Coefficient difference between PI and EI is the is with a pvalue =

15 Table 4: Individual sacrifice choices: Stages 3 and 4. Logistic Regression Coefficients VARIABLES (1) (2) (3) coefficients coefficients coefficients Economic Inequality ** [0.969] [0.331] [0.026] Political Inequality 1.079*** ** *** [0.009] [0.026] [0.000] Earnings after Tournament *** *** [0.000] [0.000] Financial Aid to loser * [0.087] [0.102] Time trend (1/block) ** [0.154] [0.047] Baseline * FBE *** [0.124] [0.002] EI * FBE [0.493] [0.449] PI * FBE 0.057** 0.057** [0.034] [0.025] Constant *** [0.000] [0.243] [0.141] Individual N N Y a Controls Observations AIC a Risky is positively significant at the 5% level. Religiosity is negatively significant at the 5% level. Married is positively significant at the 10% level. The unit of observation is the subject level. Data for these logistic regression models are all losing team subjects for the Baseline data, but only team B losing team members for the inequality treatments. Standard errors are clusters on the individual. Explanatory variable descriptions are below. Earnings after tournaments A subject s preliminary block earnings. These are the current earnings after the last round in a given block Financial Aid to loser The amount of aid that was sent by the winning team member to a losing team member. This has a value of zero for Stage 3 data. Time Trend To account for learning. Using AIC, values from 1/(block) provided the best fit. FBE First Block Earnings. Total subject earnings from the first block. This is our measure of ability. Treatment * FBE Interaction between the treatment and the ability. Explanatory variables contained in the Individual Controls include binary terms capturing gender, race, whether the subject was an engineering student, if the student lives with parents, if the student has a foreign visa, if the student is married, and whether the student believes hard work is the most important determinant for success. Other ordinal terms are Likert scales on risk and individualism and level of religiosity, To illustrate the effect of ability more clearly, in Figure 2, we present scatter plots of each treatment of the predicted Sacrifice probabilities and the subjects 1st block earnings along with the 15

16 associated fitted line with predicted confidence intervals. Note there is a moderate downward trend in the predicted probability of sacrifice for the Baseline and the EI treatment. This is reversed in the PI treatment where the estimated sacrifice probability increases with increasing subject ability. Figure 2: Scatter plots of FBE with the predicted Sacrifice Probabilities with the fitted model and 95% Confidence Intervals. Scatter Plot of Losing Baseline Subjects Scatter Plot of EI Losing Team B subjects Scatter Plot of Losing PI Team B subjects subject earnings/correct answers from first block subject earnings/correct answers from first block subject earnings/correct answers from first block Predicted Probability of Sacrifice Fitted values 95% CI Predicted Probability of Sacrifice Fitted values 95% CI Predicted Probability of Sacrifice Fitted values 95% CI The overall effect our explanatory variables have on the estimated change in probability of a Sacrifice outcome is in Table 5. Table 5 breakdowns the treatment discrete effects and first block earnings by treatment and by the min, mean, and max of the first block earnings variable from Model 3 in Table Here, in models 1,2, and 3 are the marginal and discrete effects for the explanatory variables when the logit model is under EI treatment, respectively. 23 Models 4, 5, and 6 are the marginal and discrete effects under the PI treatment and at the minimum, mean, and maximum values of the first block earnings variable. 24 Here the discrete effect of the PI variable clearly illustrates the sensitivity of the high ability subject in this treatment. At low levels of ability, the PI treatment discrete effect is negative. Poor ability individuals under the PI treatment are less likely to Sacrifice. However for high levels of ability, the PI discrete effect is positive. Overall, the overall treatment effect is sensitive to subject task ability in the PI treatment relative to the baseline. This treatment effect for high levels of ability is not found in the EI treatment. 22 Here, we will use sample averages for the continuous explanatory variables and sample proportions for the binary variables. 23 Note that the marginal effects of the explanatory variables unrelated to the treatment variables will still change when finding these marginal effects under different treatments. These marginal effects are a function of the estimated latent model values. 24 For the PI treatment data, the minimum value for first block earnings is 14, the mean is 31, and the maximum is

17 Table 5: Individual Sacrifice choices: Logistic Regression models. Shown are the estimated changes in the probability of Sacrifice, ΔPr[Sacrifice=1], using Model 3 in Table 4. For EI and PI, these are the changes in the probability of Sacrifice from the Baseline. Individual controls are not shown. VARIABLES (1) (2) (3) (4) (5) (6) EI and EI and PI and PI and FBE=mean FBE=max FBE = min FBE=mean EI and FBE=min PI and FBE=max EI or PI *** *** *** [0.004] [0.196] [0.410] [0.000] [0.850] [0.000] Earnings after Tournament ** *** ** *** *** *** [0.015] [0.000] [0.044] [0.003] [0.000] [0.000] Financial Aid to loser * [0.207] [0.154] [0.221] [0.176] [0.105] [0.094] Time trend 0.117* 0.087** * 0.139** 0.178* [0.100] [0.041] [0.129] [0.061] [0.043] [0.050] FBE *** 0.007** 0.009* [0.544] [0.443] [0.249] [0.000] [0.034] [0.063] Robust pvalues in brackets *** p<0.01, ** p<0.05, * p<0.1 In Appendix B, Table 2 contains these results and the estimated probability changes of all explanatory variables, including under the baseline condition. As expected from Figure 2, under the Baseline treatment, first block earnings decreases the probability of Sacrifice. While the previous tables provide a glimpse into behavior differences between symmetric intergroup contests and intergroup contests with resource inequalities, we complete this section by focusing on behavioral differences when only source of the resource inequality varies. Next, we turn to a comparison between political inequality and economic inequality behavior. Similar to the previous Table 5, which incorporated data from the control and the two treatments, Table 6 uses the same model structure, but only incorporating team B loser data from the two inequality treatments. Here we again breakdown the marginal effects for model 3 in the previous table 4 by the min, max, and the mean value of the first block earnings for the Political inequality treatment. Here we have the similar result as before, that the desire to sacrifice is more sensitive with respect to ability in the political inequality treatment vs. the economic inequality treatment. 25 To further illustrate this point, the estimated probability of sacrifice for a low ability subject and a high ability subject in the Economic inequality treatment is 15.48% (pvalue = 0.059) and 6.96% (pvalue = 0.111) respectively. The difference between estimated sacrifice probabilities between a low ability subject and a high ability subject in the Political inequality treatment are 10.18% (pvalue 0.008) and 32.85% (pvalue <0.0001) respectively. 25 One final observation of note, these results are inconsistent with the idea that engineers have a proclivity to engage in destructive behavior. We find that, ceteris paribus, engineers may be less likely to engage in sacrifice. 17

18 Table 6: Individual Sacrifice choices: Logistic Regression models. Shows are the estimated changes in the probability of Sacrifice, ΔPr[Sacrifice=1], for Model 3 in Table 4, except this is Inequality Data only. Individual controls are not shown. VARIABLES (1) (2) (3) PI and FBE=mean PI and FBE = min PI and FBE=max PI *** 0.349*** [0.682] [0.001] [0.000] Earnings after Tournament ** *** *** [0.012] [0.001] [0.001] Financial Aid to loser [0.517] [0.499] [0.498] Time trend 0.137** 0.233** 0.277** [0.043] [0.020] [0.024] FBE 0.006*** 0.011*** 0.013*** [0.000] [0.003] [0.005] Robust pvalues in brackets *** p<0.01, ** p<0.05, * p<0.1 Note, the variables Engineer and Religiosity were negatively significant at the 10% level. Visa at the 5% level. Risky and Married are positively significant at the 10% level. This leads to our second result. Result 2: Different that the Baseline and the Economic Inequality treatments, higher ability individuals are more likely to Sacrifice in the Political Inequality treatment. The estimated sacrifice probability of a high ability Team B subject is estimated to be 3 to 4 times larger than a low ability Team B subject. This increase is not present in the Baseline or the Economic Inequality Treatment Determinants of Sacrifice Intensity (Radicalization) One potential confound with the previous section is that losing team B subjects could potentially be coordinating on when to Sacrifice, thus spreading the cost of the punishment. 26 Therefore, we turn to analyzing how often a given subject decides to Sacrifice in the experiment, or an intensity of Sacrifice. We deem this Sacrifice intensity as having a real world parallel in radicalization. Table 7 contains a partial list of the aggregate number of Sacrifice decisions per subject, separated by treatment. In this list are subjects that Sacrificed three or more instances, highest to lowest. Note that the five most radicalized subjects are in the Political Inequality treatment and that these five 26 An example of this could be losing Team B subjects alternating who sacrifices for a given Block. 18

19 (out of 24) account for 56.4% (22 out of 39) of the total number of sacrifice instances. 27 One key observation is that the most radicalized subject in the Baseline or the Economic Inequality treatment only decided to sacrifice a maximum of three times. Therefore, radicalization may be phenomena magnified by the political inequality treatment. Table 7: Subjects with the most instances of selecting Sacrifice. # of Sacrifice instances 5 times 4 times 3 times Subject Group Team Treatment Sacrifice Opportunities Sacrifice Intensity % B Pol In % B Pol In % B Pol In % B Pol In % B Pol In % 35 6 B Base % 40 7 B Base % B Pol In % B Econ In % However, to account for subject differences in game play and different personal characteristics, we turn to an econometrics analysis. Below in Table 8 are a series of ordered probit regression models where the dependent variable is the count of sacrifice selections per subject over Stages 3 and Observations are Baseline subjects that had at least three opportunities to Sacrifice and team B subjects in the inequality treatments. 29 Here we find much of the same story as the analysis on the individual sacrifice decision. The PI treatment is strongly significantly positive in the first model as compared to the Baseline and to the EI treatment. (pvalue = for the linear combination of coefficients test between the two inequality treatments) As seen previously, once we account for ability by incorporating treatment interactions with FBE, the PI coefficient is strongly negatively significant and the interaction coefficients have similar signs as Table 4. However the pvalues, while significant at the 10% level, are considerably larger. Nonetheless, Table 8 is evidence that higher ability subjects are more prone to radicalize in the PI treatment vs. others. In addition, radicalization is sensitive with respect to ability in the Baseline and PI 27 In addition, only 30.7% (12 out of 39) of sacrifice instances in the political inequality treatment were subjects who only sacrificed once. However for the economic inequality treatment, the percentage is 43.8% and in the Baseline treatment, it is 41.2%. (the p value from fisher exact test between the two inequality treatments is 0.153) 28 Therefore, unlike the previous section on the individual sacrifice decision which used a longitudal data set, this section using a cross sectional data set 29 In the inequality treatment, every team B subject had at least 3 opportunities to sacrifice. Hence the restriction on allowable observations for the Baseline. 19

20 treatment, but in opposite directions. Finally, subjects who identify as engineering students are less likely to radicalize vs. other students as shown in Model 3. Table 8: Total Individual Sacrifice Counts, Ordered Probit models. Coefficient vales are for the latent linear equation in this model. (1) (2) (3) VARIABLES coefficients coefficients coefficients EI * [0.743] [0.288] [0.060] PI 0.818*** ** *** [0.008] [0.024] [0.001] Mean of Aid given [0.508] [0.172] Mean of Earnings after Tournament ** [0.033] [0.151] Baseline * FBE * *** [0.054] [0.001] EI * FBE [0.509] [0.422] PI * FBE 0.040* 0.051* [0.064] [0.064] Individual N N Y a Controls Observations AIC Robust pval in brackets *** p<0.01, ** p<0.05, * p<0.1 a Engineer was negatively significant at the 5% level. The unit of observation is the subject level. Data for these ordered probit regression models are all losing team subjects for the Baseline data with 3 or more Opportunities for Sacrifice and Team B members for the inequality treatments. Standard errors are clustered on the group level. Variable descriptions for the new explanatory variables not listed in the previous tables are below. Mean of Aid given The total amount of aid in Stage 4 that was sent by the winning team members to a losing team member. This is to account for the total aid given to losing subjects Mean of Earnings after Tournament The sum of the subject s preliminary block earnings for stages three and four. (blocks 4 to 9) This is to account for total performance. The above table shows the coefficient vales for the latent equation for the ordered probit model. While the FBE variable may not be a consistent significant driver of Sacrifice, there may not be a straightforward relationship between FBE and Sacrifice intensity that is not conditional on the treatment environment. However, to get estimates on whether the PI treatment will cause subjects to radicalize more than Baseline and EI treatment, we need to determine a sacrifice intensity cutoff to classify 20

21 subjects as radicalized or not. Table 9 contains the changes in the probability of Sacrifice using model 3 in Table 8. These changes are with respect to whether the individual sacrificed three times or more in the experiment. We chose a cutoff of three because of the amount of data available. 30 However, this does require altering the dependent variable slightly for the five Political Inequality subjects who decided to sacrifice more than three times. Therefore, sacrifice counts of 3 or more all have a value of 3. These estimated changes of the predicted probabilities for the inequality treatment and the minimum, mean, and maximum FBE value to capture what these changes are for low, average, and high ability people. All other explanatory variables are at the mean value. The first three models show estimated change in the probability of Sacrifice under the EI treatment. The last 3 models are under the PI treatment. 31 Table 9: Sacrifice Intensity, Ordered Probit Regression models. Shown are the discrete changes in the predicted probabilities of a subject choosing to sacrifice three or more times. Probability estimates of a sacrifice outcome, ΔPr[Sacrifice>=3] for Model 3 in Table 9. VARIABLES (1) (2) (3) (4) (5) (6) EI and EI and PI and PI and FBE=mean FBE=max FBE = min FBE=mean EI and FB =min PI and FBE=max EI or PI ** *** 0.151*** 0.400* [0.026] [0.770] [0.608] [0.007] [0.006] [0.075] Mean of Aid given [0.336] [0.250] [0.583] [0.315] [0.161] [0.201] Mean of Earnings after Tournament FBE [0.313] [0.207] [0.577] [0.157] [0.254] [0.295] ** [0.603] [0.361] [0.242] [0.017] [0.212] [0.240] Robust pvalues in brackets *** p<0.01, ** p<0.05, * p<0.1 Unlike the previous breakdown of the marginal effects associated with the binary sacrifice decision, the evidence is weak that ability directly induces radicalization. We see a much more powerful PI treatment effect. Here the average and high ability individuals in the PI treatment are more likely to radicalize vs. the Baseline. Low ability individuals in the PI treatment are less intense relative to individuals in the baseline. 32 Lastly, there is little evidence outside of low ability individuals to suggest differences between the EI treatment and the Baseline setting. Differences in the resource level are not driving radicalization. The previous analysis contains changes in the probability of radicalization relative to the baseline treatment. The crucial question is if the source of the inequality plays a role in inducing radicalization. By 30 There was only one baseline subject and one EI subject who sacrificed 3 times 31 For the PI treatment data, the minimum value for first block earnings is 14, the mean is 31, and the maximum is Note that our design may suffer from type II errors due to a lack of data. Nevertheless, the results in Table 9 hold using the same model without the personal characteristics explanatory variables. 21

22 controlling for unaccounted time differences that are present in the Baseline data, we can attribute that the differences in radicalization behavior are from differences in the source of the resource inequality. The changes in the probability of Sacrifice using only EI and PI data are shown in Table 10 broken down by the maximum, mean, and minimum value FBE value. Table 10: Sacrifice Intensity, Ordered Probit Regression models. Shown are the discrete changes in the predicted probabilities of a subject choosing to sacrifice three or more times. Probability estimates of a sacrifice outcome, ΔPr[Sacrifice>=3] for Model 3 in Table 9. Inequality Treatment data only. VARIABLES (1) (2) (3) PI and FBE=mean PI and FBE = min PI and FBE=max Political Inequality *** 0.492* [0.753] [0.006] [0.076] Mean of Aid given [0.980] [0.980] [0.980] Mean of Earnings after Tournament [0.158] [0.311] [0.263] 1 st block earnings 0.004* [0.064] [0.218] [0.141] Robust pvalues in brackets *** p<0.01, ** p<0.05, * p<0.1 Many of the results from these two tables mirror the findings from the above work. The PI treatment is more likely to induce individuals to radicalize, but the evidence is weak that radicalization is directly dependent on subject ability in the PI treatment. This leads to our 3 rd result. 33 Result 3: Losing Team B subjects in the Political Inequality treatment are more likely to radicalize compared to the Baseline and the EI treatment, as defined by subjects who decided to sacrifice 3 or more instances. We do not find radicalization differences between the EI treatment and the Baseline, it is the source of the time constraint vs. the constraint itself radicalizes subjects. However, unlike the binary sacrifice decision, we do not have strong evidence that ability interacted with the PI treatment affects radicalization Conflict Escalation While we have examined determinants of the individual and aggregate Sacrifice decision(s), now we look to see how responsive team A and team B subjects are to decisions by the opposing team. Using winning 33 In Appendix B, Table 3, we duplicate these results using a different dependent variable as a measure of radicalization, the fraction of sacrifice instances given a subject s opportunities to sacrifice. We find similar results. 22

23 team A members as our unit of observation, Table 11 contain 3 models where the dependent variable is the real effort task (RET) time selected by team A members in Stages 3 and 4. Table 11: Team B time selections by Team A members. Random effects GLS panel models. Only Political Inequality data from Stages 3 and 4. Coefficients shown. (1) (2) (3) VARIABLES coefficients coefficients coefficients Lag Group Sacrifice [0.366] [0.373] [0.522] Lag Team A time selection 0.407*** 0.371*** 0.279** [0.000] [0.000] [0.016] Time trend (1/block) [0.443] [0.472] [0.402] Lag Earnings After Tournaments [0.791] [0.847] Lag Financial Aid Sent [0.999] [0.686] First Block Earnings 0.295** [0.016] [0.848] Constant *** *** [0.000] [0.319] [0.001] Individual N N Y a Controls Within R AIC b Observations Number of subjects Robust pval in brackets *** p<0.01, ** p<0.05, * p<0.1 a Engineer is positively significant at the 10% level. Risky is negatively significant at the 5% level. Religiosity is positively significant at the 1% level. Married is negatively significant at the 1% level. b AIC measure is from maximum likelihood estimated Random effect panel models The unit of observation is the subject level. Data for these Random effects GLS panel models are all Team A subjects whose team won 3 or more tournaments in each block in stages 3 and 4. This was to keep the data set balanced. Standard errors are clustered on the group level. Explanatory variable descriptions are below. Lag Group Sacrifice Binary variable indicating whether a Team A subject was punished in the previous block Lag Team A time selection Team A member time selection in the previous block Lag Earnings After Tournaments Subject preliminary earnings after the 5 round in the block Lag Financial Aid Sent Team A member time selection in the previous block The key variable in each of these models is whether team A members are responding to the sacrifice decisions by team B members in the previous block. The first model includes the explanatory variable of interest as well as an autoregressive term for the time selection and a time trend. There is no evidence that team A members are responding to a sacrifice selection in the previous block. Even when controlling 23

24 for experimental variables (model 2) and for individual controls (model 3), we see no evidence of a response. In addition, ability level for team A members is not a consistent predictor of time selections. Next we move to a discussion on the responsiveness of losing Team B members. Similar to the previous discussion, we do not have consistent evidence that Team B members are responding to RET times chosen by Team A member in their group. Using losing Team B members as our unit of observation, Table 12 contain 3 models where the dependent variable is the Sacrifice decision by Team B members in stages 3 and 4. Table 12: Team B Sacrifice decisions by Team B members. Random effects Probit Panel models. Only Political Inequality data from Stages 3 and 4. Coefficients shown. 34 VARIABLES (1) (2) (3) coefficients coefficients coefficients Lag Sacrifice [0.109] [0.153] [0.172] Team A time selection *** [0.003] [0.771] [0.728] Time trend (1/block) [0.242] [0.667] [0.433] Earnings After Tournaments * ** [0.073] [0.043] Financial Aid Received [0.505] [0.692] First Block Earnings 0.098* 0.166** [0.083] [0.012] Constant 2.127* ** [0.085] [0.551] [0.020] Individual N N Y a Controls AIC Observations Number of subjects Robust pval in brackets *** p<0.01, ** p<0.05, * p<0.1 a Risky is positively significant at the 5% level. Religiosity is positively significant at the 10% level. Married is positively significant at the 10% level. The unit of observation is the subject level. Data for these Random effects Probit panel models are all Team B subjects whose team won 3 or more tournaments in each block in stages 3 and 4. This was to keep the data set balanced. The key variable in each of these models is whether Team B members are responding to the RET time decisions by team A members in the same block. The first model includes the explanatory variable of 34 These same models are tested using bootstrapped standard errors in Table X in Appendix B. 24

25 interest as well as an autoregressive term for the Sacrifice selection and a time trend. While there is some evidence in this model for Team B member responsiveness, this is not a statistically significant factor when including experimental and individual controls. Similar to above findings, Team B member ability is a driver of sacrifice selections along with preliminary Team B member earnings. 4. Concluding remarks The overarching conclusion for this novel experiment is that the politically unequal environment increases the detrimental effects for all parties. While sacrifice instances certainly occurred under all situations, these instances were fueled by the Political inequality treatment, where the source of the resource inequality was from the competing party and not from the experimenter. The key finding from the experiment is the interaction of ability with the politically unequal contest environment. There is a positive relationship between the ability of the disadvantaged group and the probability of punishment. This finding is surprising since this implies that individuals that are more economically productive and therefore often have more to lose are also more likely to decide to sacrifice what is earned. This was not found in the other situations where the economic standing was equal or where the differences in economic standing were exogenously generated by the experimenter. While we do not find evidence of the Engineers of Yihad hypothesis, our results are not necessarily contradictory to this empirical result. Engineers tend to have some of the highest Marginal Revenue Products across professions reflected by high and increasing starting salaries and increasing employment numbers. (BLS 2015) These individuals are some of the most economically productive individuals in a society and they may be the ones most sensitive to politically unequal environments. Our results fit this narrative. This is the first experiment to the authors knowledge that explores the behavioral consequences of economic and political inequalities in the intergroup contest environment. We find many of the same consequences of exploitative behavior in this abstract lab environment as is found in many conflicts in the political arena. While this paper provides a glimmer into these outside political conflicts, it is still just a piece of the larger work to be explored. For instance, given the experimental design, we can also test if advantaged subjects best respond with their time selections when they realize the ability of their counterparts. Therefore, more work is to be done, but the results from this paper do pave the way for future work on this topic. 25

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29 Appendix A: Figure 1: Example of an addition problem in each round Figure 2: Example of the sacrifice decision 29

30 Figure 3: Transfer decision 30