DOI: 10.5061/dryad.mcvdnckd6
title: Data from: Local competition promotes hurtful behavior towards unknown others

description: Humans frequently cooperate to achieve benefits unattainable through
 selfish behavior. Punishment of free-riders is key for sustaining
 cooperation, but also costly, raising questions about the origins of such
 behavior. Some evolutionary models suggest that individuals may be willing
 to hurt unknown others in competitive environments. However, empirical
 evidence about the effect of the scale of competition on strategies
 involving hurtful behavior in human groups, such as spite, retaliation for
 hurting, and punishment of free-riders, is missing. Using a laboratory
 experiment, we manipulate the scale of competition to investigate its
 influence on hurtful and helpful behaviors between unknown humans in an
 indirect reciprocity game. We observe distinct behavioral patterns between
 local and global competition. When competition is local and thus confined
 to take place within isolated groups, we find frequent hurting as an
 expression of spite and retaliation. In contrast, when competition extends
 globally across several groups, hurting is used for the punishment of
 free-riders primarily, whereas helping behavior is rewarded, which
 together promotes cooperation. Thus, while isolated competition fosters
 inefficient, antisocial behavior, global competition encourages
 prosociality and cooperation.
description: 156 subjects participated in the experimental sessions that were
 conducted at the CREED laboratory of the University of Amsterdam.
 Participation was voluntary, and subjects were recruited through email
 announcements from a standard university subject pool. Each experimental
 session lasted approximately 60 minutes. Subjects' total earnings in
 euros were determined by the relative ranking of the points (denoted by
 ‘francs’ in the experimental instructions) earned over all the rounds of
 the indirect reciprocity game. The only difference between the treatments
 is how the ranking is determined for these euro earnings, either within a
 local group or globally across several groups. By design, average earnings
 were 20 euros in both treatments. Each subject
 participated in only one session, and none had previously participated in
 a similar experiment. Subjects were seated in separate cubicles, which
 ensured anonymity during the experiment. We consider each group of 6
 subjects as one independent observation, because subjects only interacted
 with the other 5 subjects in their group. The
 experiment was computerized and all interactions took place anonymously
 through computers. The supplementary online materials contain the
 instructions script provided to subjects. At the end of each experimental
 session subjects were paid their euro earnings in private and in cash. In
 all treatments, subjects were informed of the matching procedure, the
 number of rounds, and how to calculate the payoff. The experiment was
 neutrally framed. The donor’s choices were called ‘blue’ (help), ‘green’
 (snub) and ‘purple’ (hurt). Each session consisted of
 100 rounds of the experimental game. In each round each six-subject cohort
 was randomly partitioned in three donor–recipient pairs. Therefore,
 pairing was independent of previous choice, distinguishing it from
 previous theoretical models. A recipient received no information about her
 donor and made no decision. A donor, however, learned the most recent
 three decisions made by her recipient. By choosing
 between the three colors the donor decided whether she would help, hurt or
 snub the recipient. The recipient learned her donor’s chosen color and her
 own earnings in each round only after all donors had made and confirmed
 all their decisions. The round number was always visible and both the
 donor and the recipient could always access the information they observed
 and their own decisions in all past rounds. In each round a pair of
 matched subjects only saw the decision of the donor in their
 pair.
description: # Local competition promotes hurtful behavior towards others \[Access this
 dataset on Dryad
 [https://doi.org/10.5061/dryad.mcvdnckd6](https://doi.org/10.5061/dryad.mcvdnckd6)] \ The raw data for the study is in CRSU_rawdata.csv. The variable definitions are shown at the bottom of this README.txt file. Information for the strategy classification (one row for each of the 156 subjects) is in Strategy_Class.csv. The variable definitions are shown at the bottom of this README.txt file. Human subjects ethics committee approval included in Ethics_approval_UvA_2019_EC_20190128040115.pdf ## Analysis code: Data processing and statistical tests use Stata version 19. Note: The labels period and round are used interchangeably. Note: Statements to "drop if round&gt;90" can be omitted to conduct analysis for all 100 rounds, as reported in section 5 of the Supplemental Online Materials. 1. Table2.do provides the calculations and statistical tests for results shown in Table 2 of the paper. It also calculates the proportion of each action by period for graphing the time series (Figures 2 and 3). 2. strategy_class_to90.do creates results to populate the worksheet columns of Strategy_Class.csv and identify the chosen strategies, through action frequencies and individual logit regressions as explained in the Supplemental Online Materials. 3. earnings_by_role.do calculates mean earnings by donor (A) and recipient (B) roles, and cumulative earnings, through period 90 for the variables ave_as_A, ave_as_B and earn_at90 of Strategy_Class.csv. 4. strategy_earnings_bySub.dta is the Stata dataset taking the relevant variables from Strategy_Class.csv to conduct statistical tests comparing strategy frequencies (Figure 4 and Table S1) and strategy earnings (Figure 5 and Table S3), using strategy_comparison.do. This dataset is also provided in open access format strategy_earnings_bySub.csv. --- ### Variable definitions for raw data CRSU_rawdata.csv: ida: ID number of donor (A) idb: ID number of recipient (B) round: round number adecision: Donor's (A's) decision acumearn: A cumulative earnings bcumearn: B cumulative enarings numblbya: Number of helps in A's history numgrbya: Number of snubs in A's history numpubya: Number of hurts in A's history numblbya2rds: Number of helps by A in past 2 rounds numgrbya2rds: Number of snubs by A in past 2 rounds numpubya2rds: Number of hurts by A in past 2 rounds numblbyb: Number of helps in B's history numgrbyb: Number of snubs in B's history numpubyb: Number of hurts in B's history numblbyb3rds: Number of helps by B in past 3 rounds numgrbyb3rds: Number of snubs by B in past 3 rounds numpubyb3rds: Number of hurts by B in past 3 rounds clickbl1: Did A click to see B's info when B helped in period -1 clickbl2: Did A click to see B's info when B helped in period -2 clickbl3: Did A click to see B's info when B helped in period -3 clickgr1: Did A click to see B's info when B snubed in period -1 clickgr2: Did A click to see B's info when B snubed in period -2 clickgr3: Did A click to see B's info when B snubed in period -3 clickpu1: Did A click to see B's info when B hurt in period -1 clickpu2: Did A click to see B's info when B hurt in period -2 clickpu3: Did A click to see B's info when B hurt in period -3 see1onbl1: If clickbl1=yeslook, B's recipient action in period -1 see2onbl1: If clickbl1=yeslook, B's recipient action in period -2 see3onbl1: If clickbl1=yeslook, B's recipient action in period -3 see1onbl2: similar to above if clickbl2=yeslook see2onbl2 see3onbl2 see1onbl3: similar to above if clickbl3=yeslook see2onbl3 see3onbl3 see1ongr1: similar to above if clickgr1=yeslook see2ongr1 see3ongr1 see1ongr2: similar to above if clickgr2=yeslook see2ongr2 see3ongr2 see1ongr3: similar to above if clickgr3=yeslook see2ongr3 see3ongr3 see1onpu1: similar to above if clickpu1=yeslook see2onpu1 see3onpu1 see1onpu2: similar to above if clickpu2=yeslook see2onpu2 see3onpu2 see1onpu3: similar to above if clickpu3=yeslook see2onpu3 see3onpu3 group: group number within session round10: Indicates blocks of 10 rounds session: session code R1dum: dummy variable to indicate GLO treatment R2dum: dummy variable to indicate LOC treatment cohort: Unique identifier for cohort (group) indivIDa: Unique ID number for A (donor) indivIDb: Unique ID number for B (recipient) bluedum: Dummy variable to indicate help decision purpledum: Dummy variable to indicate hurt decision numclicksgrpur: not used multposs: not used ### Variable definitions for strategy classification (one row for each of the 156 subjects) in Strategy_Class.csv: (Note that help_rate_helpers is n/a if donor never meets a recipient who helped frequently) subjectID: Subject ID number punish_logit: Indicator for significance of others helps in punish logit punishGT0.1: Indicator for punish greater than 10 percent punisher_def: Indicator for discriminate punisher hurt_hurters: Indicator for hurting past hurter greater than 50 percent hurt_rate: Hurting frequency recip_hurts: Frequency recipient has 2 or 3 hurts in history hurts_hurtful: Frequency of hurts for hurtful recipient punish_logit_pun: Indicator for significance of others hurts in punish logit retaliator: Indicator for retaliator punish_logits_both: Indicator for both punish logit variables significant help_logit: Indicator for significance of others helps in helping logit passLT0.6: Indicator for snub less than 60 percent helpGT0.35: Indicator for help more than 35 percent help_rate: Overall helping rate help_rate_helpers: Helping rate for recipients who help 2 or 3 times (can be n/a if never meet a recipient who helped 2 or 3 times) recip_helps: Frequency recipient has 2 or 3 helps in history helps_helpful: Helping frequency when recipient has 2 or 3 helps in history rewarder: Indicator for discriminate rewarder help_logit_own: Indicator for significance of own helps in helping logit not_discrim: Indicator for not discriminate no_actionGT0.85: Indicator if no action taken more than 85 percent cautious: Indicator for cautious strategy helpGT0.8: Indicator if helps greater than 80 percent altruist: Indicator for altruist strategy pastGT0.8: Indicator if snub greater than 80 percent defector: Indicator for indiscriminate defector punishGT0.5: Indicator if hurt greater than 50 percent punisher: Indicator for indiscriminate punisher unclassified: Indicator if strategy unclassified lookupGT0.2: Indicator if looks up pass or punish history greater than 20 percent GLOtreatID: Indicator for GLO treatment i_rewarder: Indicator for image rewarder s_rewarder: Indicator for standing rewarder i_punisher: Indicator for image punisher s_punisher: Indicator for standing punisher i_retaliator: Indicator for image retaliator s_retaliator: Indicator for standing retaliator class: Strategy classification identifier poolclass: Pooled strategy classification identifier earn_at90: Earnings accumulated through period 90 euro_pay: Payoff earned in Euros rank: Earnings ranking within group norm_earn: Earnings normalized for equal role frequency A_times: Frequency in donor role through period 90 ave_as_A: Average earnings as donor ave_as_B: Average earnings as recipient normearn2: Average earnings if both role frequency is equal norm_euros: Earnings in Euros for equal role frequency norm_rank: Earnings ranking within group for equal role frequency ## Human subjects data Consent was provided by subjects in accordance with regulations at the University of Amsterdam (reference EC 20190128040115; ethics committee approval included in Dryad files).