Institute for Economic Studies, Keio University
Keio-IES Discussion Paper Series

The Individual-Team Discontinuity Effect on Institutional Choices:
Experimental Evidence in Voluntary Public Goods Provision
Kenju Kamei, Katy Tabero
10 November, 2022
DP2022-015
https://ies.keio.ac.jp/en/publications/21393/

Institute for Economic Studies, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
ies-office@adst.keio.ac.jp
10 November, 2022

The Individual-Team Discontinuity Effect on Institutional Choices: Experimental
Evidence in Voluntary Public Goods Provision
Kenju Kamei、Katy Tabero
Keio-IES 2022-015
10 November, 2022
JEL Classification: C92; D72; H41
Keywords: institution;public goods;experiment;punishment;discontinuity effect

Abstract
A laboratory experiment is used to show that teams as a decision-making unit behave
more efficiently than individuals in an institutional setting. Subjects make voting
choices over formal versus informal (peer to peer) sanctions in a finitely repeatedpublic goods dilemma. When a formal sanction scheme is selected in their groups,
teams vote for deterrent sanction rates much more frequently than individuals. Whenan informal sanction scheme is selected, teams inflict costly punishment more
frequently on low contributors than individuals, thereby reducing the relative
frequency of “misdirected” punishment among teams. As such, teams sustain
cooperation surprisingly better than individuals regardless of which scheme is enacted.
These behavioral patterns are consistent with the idea of “truth wins” which proposesthat teams achieve better choices than individuals through deliberation and learning.
The results underscore the effectiveness of having teams as a decision-making unit inorganizations in combating a moral hazard problem, such as free riding.
Kenju Kamei
Faculty of Economics, Keio University
2-15-45 Mita, MInato-ku, Tokyo
kenju.kamei@keio.jp
Katy Tabero
Durham University Business School
Mill Hill Lane, Durham University Business School, Durham, United Kingdom
Acknowledgement : This project was supported by a grant-in-aid from the Japan
Center for Economic Research. This project was also supported by Northern Ireland
and North East Doctoral Training Partnership. The authors thank John Hey for his
hospitality when they conducted the experiment at the University of York, and forLouis Putterman, Stefan Penczynski, Marie Claire Villeval, Daniel Li and the audience inthe research seminar at the Gate Lab in Lyon and 2022 European ESA Meeting in
Bologna for helpful comments. The authors also thank Mark Wilson (an IT manager at
the University of York) for support in managing the computers and the setup of the zTree software in the experimental sessions.

The Individual-Team Discontinuity Effect on Institutional Choices:
Experimental Evidence in Voluntary Public Goods ProvisionThis version: November 2022
Kenju Kamei#1 and Katy Tabero#2#1

Faculty of Economics, Keio University, 2-15-45, Mita, Minato-ku, Tokyo 108-8345, Japan.
Email: kenju.kamei@keio.jp.

#2

Department of Economics and Finance, Durham University, Mill Hill Lane, Durham, DH1
3LB, United Kingdom. Email: katy.tabero@durham.ac.uk.

Abstract: A laboratory experiment is used to show that teams as a decision-making unit behave
more efficiently than individuals in an institutional setting. Subjects make voting choices overformal versus informal (peer to peer) sanctions in a finitely repeated public goods dilemma.
When a formal sanction scheme is selected in their groups, teams vote for deterrent sanction
rates much more frequently than individuals. When an informal sanction scheme is selected,
teams inflict costly punishment more frequently on low contributors than individuals, thereby
reducing the relative frequency of “misdirected” punishment among teams. As such, teams
sustain cooperation surprisingly better than individuals regardless of which scheme is enacted.
These behavioral patterns are consistent with the idea of “truth wins” which proposes that teams
achieve better choices than individuals through deliberation and learning. The results underscorethe effectiveness of having teams as a decision-making unit in organizations in combating amoral hazard problem, such as free riding.
Keywords: institution, public goods, experiment, punishment, discontinuity effect
JEL classification codes: C92, D02, D72, H41
Acknowledgement: This project was supported by a grant-in-aid from the Japan Center for Economic
Research. This project was also supported by Northern Ireland and North East Doctoral Training
Partnership. The authors thank John Hey for his hospitality when they conducted the experiment at the
University of York, and for Louis Putterman, Stefan Penczynski, Marie Claire Villeval, Daniel Li and theaudience in the research seminar at the Gate Lab in Lyon and 2022 European ESA Meeting in Bologna
for helpful comments. The authors also thank Mark Wilson (an IT manager at the University of York) for
support in managing the computers and the setup of the z-Tree software in the experimental sessions.

11. Introduction
Teams have seen increasing popularity as a decision-making unit within organizations in the last
half a century; this applies to both the public and private sector, and across a breadth of industries (seeLawler et al. 1992, 1995; Devine et al. 1999; Kersley et al. 2005). For example, Eurofound (2020) foundthat just under 70% of workers in the EU27 claimed to work as part of a team, and in only the transportindustry did this fall to a low of 60%. Teams also form the basis of many decision-making units in the
public sphere, ranging from the domestic context, such as councils (and also political factions),
committees, and cabinets (ministries and agencies), to international relations, such as in internationalorganizations like the United Nations, in which each country operates as a decision-making unit thatsummarizes their citizens’ views and casts a single vote in making an organizational decision. The use ofteams and team-based structures in an organization, especially those that offer more autonomy in terms ofdecision-making and problem-solving, has been linked to improved productivity and profitability undercertain conditions (e.g., see Pfeffer 1998; Guzzo and Dickson 1996; Cohen and Bailey 1997, and Delarue
2008, for reviews and examples). Despite its importance, however, teams’ institutional formation and their
behaviors under endogenously constructed rules have not received attention in the literature on institutions.
Scholars studying workers’ performances and interactions have actively used experimental games
and human subjects in controlled laboratory settings for the last several decades. In such a setup, eachworker subject is assigned to a group, given a fixed endowment, and simultaneously decides how to use theendowment (exert costly effort) for the group. Theoretically, optimal effort provision cannot be achieved intypical environments due to workers’ free riding, whereby they pursue their own self-interest. A large
number of experiments have been conducted in the social sciences (such as economics and politicalscience) and in psychology to study worker behaviors in such voluntary provision of public goods when
individuals are the decision-making unit in a group (see, e.g., Ledyard [1995] and Chaudhuri [2011] for asurvey). It is now known that, without any institution to assist collaboration, while some individuals
initially attempt to cooperate with their peers, cooperation cannot be sustained at a high level as they learnof their peers’ opportunistic behaviors with repetition (e.g., Fischbacher and Gӓchter 2010). However,groups can sustain cooperation when the members can voluntarily monitor their peers’ contribution
behaviors (e.g., Grosse et al. 2011; Nicklisch et al. 2021), inflict costly punishment peer to peer (e.g., Fehrand Gӓchter 2000, 2002), or introduce a centralized incentive scheme regarding punishment and rewards(e.g., Falkinger et al. 2000). In particular, scholars have advanced the field during the last 15 years by
exploring individuals’ ability to construct and operate centralized governance by voting, finding thatwithout any guidance, groups can achieve high efficiency through such endogenous institution formation,
despite taking some time to learn better institutional formation (e.g., Gürerk et al. 2006; Kosfeld et al.
2009; Sutter et al. 2010; Ertan et al. 2009; Kamei et al. 2015; Fehr and Williams 2018). However,surprisingly, no attention has been paid to self-governance capacity and institutional formation when teams,
as a decision-making unit (voter), constitute a group.
2

Theoretical modeling for decision-making by teams is usually based on the same assumptions
made of the rational, self-interested individual. Hence, the neglect of teams’ self-governance possibility isnatural, and the use of individuals in a laboratory can be thought of as a simplification for experimentationin the literature. However, this assumption may not be correct according to the findings from another, but
substantial, literature on group or team decision-making. This research area proposes the so-called“individual-team discontinuity effect” (simply “discontinuity effect,” hereafter): teams may behave more
efficiently than individuals (see, e.g., Charness and Sutter [2012], Kugler et al. [2012] and Kerr et al.
[2004] for a survey). Such discontinuity effects have been detected in various setups, for example, inbeauty contest games (e.g., Kocher and Sutter 2005), ultimatum games (e.g., Robert and Carnevale 1997;
Bornstein and Yaniv 1998), signaling games (e.g., Cooper and Kagel 2005), centipede games (e.g.,
Bornstein et al. 2004), trust games (e.g., Kugler et al. 2007), coordination games (e.g., Feri et al. 2010),
and monetary policy decisions (e.g., Blinder and Morgan 2005). It is possible that teams constructinstitutions differently from individuals in the voluntary provision of public goods.
This paper provides the first experimental study by utilizing a repeated linear public goods gameand letting teams (decision-making units) govern their assigned group through building sanctioning
institutions by voting. Members of each team communicate with one another to make joint voting andcontribution decisions. The institutional formation and their behaviors under constructed institutions arecompared against the case where the units are individuals to study the following specific questions:
Question 1: Do teams utilize sanctioning institutions differently by voting from individuals?
Question 2: If yes, how does the efficiency differ between individuals and teams? For example, do teams
sustain cooperation more easily than individuals in an institutional setting?
There are two possible key hypotheses to these two questions. The first mechanism is the so-called
Condorcet’s (1785) jury theorem and behavioral public choice theorem (e.g., Ertan et al., 2009). This statesthat if the probability of an individual being correct is larger (smaller) than ½, then the probability of the
majority in a team choosing for the correct answer is larger (smaller) than that in which each individual
votes correctly. This hypothesis is valid when members do not influence each other in deciding on a teamdecision (the “independence” condition). The other mechanism is the so-called “truth wins.” Thishypothesis states that teams can achieve more efficient outcomes through communication, learning anddeliberation (Laughlin, 2015).
The experiment results are well consistent with the “truth wins” idea. First, remarkably, teams
achieve much higher efficiency than individuals thanks to the former’s effective use of the sanctioning
institutions. In particular, given an opportunity to construct a formal sanction scheme, individuals vote forinefficient, non-deterrent sanction rates much more than 50% of the time. By sharp contrast, teams vote fordeterrent sanction rates, i.e., the rates that make free riding materially unprofitable, more than 50% of thetime. This pattern is inconsistent with the Condorcet’s jury theorem, which underscores the role ofinfluence and deliberation in the team decision-making procedure. When informal (peer-to-peer)3punishment is collectively enacted in a group, its teams punish low contributors more frequently than
individuals, which helps reduce the relative frequency of “misdirected” punishment, i.e., punishment ofhigh contributors. Moral hazard in groups is a central issue in organizations as it can hurt productivity (e.g.,
Holmstrom 1982). While recent experiments suggest that it can endogenously be resolved by allowing
agents to construct institutions (e.g., Gürerk et al. 2006; Kosfeld et al. 2009; Sutter et al. 2010; Ertan et al.
2009; Kamei et al. 2015; Fehr and Williams 2018), the finding of the present study underlines the clear roleof organizational structure in strengthening a group’s ability to govern themselves, whether under formal orinformal schemes. This would open up a new research direction in the field concerning the shape of
efficient organizations.
The present paper is related to the large literature on the theory of the firm. Here, team decisionmaking is treated as a coordination problem in which the same processes involved in individual decisionmaking are used, but feature additional complexities relating to imperfect information, monitoring, andagency costs (e.g., Alchian and Demsetz 1972; Marschak and Radner 1972). Marschak and Radner (1972),
for example, build a model using teams of individuals that have homogenous preferences (that align withthe common goal), but heterogeneous information. It focuses on ways in which team members eliminate
the intra-team information gap so as to face the same situation that an individual decision-maker faces.
However, teams usually have difficulties in doing so, due to the costs of gathering information and mixed
incentives of sharing information (see also Gibbons et al. [2013] for an overview).1 By contrast, teams may
be modeled as superior decision-makers to individuals when individuals are assumed to be bounded
rational, due to the teams’ increased ability to store and process information, e.g., through shared memory(Bainbridge 2002). Unlike the assumption of these prior studies, all team members in the present
experiment have the same information described in the experiment instructions. The discontinuity effectdetected in this study therefore suggests a need to bolster existing theoretical models, perhaps explicitly
incorporating the beneficial communication, deliberation, and influence process with even symmetric
information.
Further, this paper also contributes to empirical literature on management, organizational
economics, and personnel economics that studies team decision-making and team production. First, priorresearch in management argues that managerial decision-making via top management teams can lead tobetter organizational outcomes, such as performance and innovation (e.g., Carmeli et al. 2008;
Aboramadan 2020; Certo et al. 2008). The superiority of management teams is especially strong when theteams have great heterogeneity in terms of, say, age, education, and background (e.g., Aboramadan 2020;
Certo et al. 2008). Nevertheless, it is difficult to draw causal inferences from these studies for various
reasons, for example, because there is possible selection bias in the management team formation, and manystudies rely on self-assessed/reported questionnaires. Second, team production (such as that in production
Prior research in management has thus explored effective ways to coordinate and share information held byworkers in organizations (e.g., Grant 1996).
1

4

sites) is also shown to lead to better work performance than individual production in the empirical research(e.g., Ichniowski et al. 1997), especially when teams have a greater spread in abilities across workers (e.g.,
Hamilton et al. 2003). However, the human resource practices in teams vary multiple dimensions
simultaneously, making it difficult to identify the role of the team decision process in isolation. In addition,team decision-making per se is not the prior research’s focus, and hence, scholars have not attempted to
identify its treatment effects in the past.
The rest of the paper proceeds as follows: Section 2 describes the experimental design, Section 3
discusses hypotheses, and Section 4 reports experimental results. Sections 5 and 6 report results from finite
mixture modeling and communication dialogues, respectively. Section 7 concludes. Appendix A in
supplementary materials provides a summary of the related literature.

2. Experiment Design
The experiment is built on a linear public goods game. Subjects play the games under one
treatment condition (between-subjects design).2 Six treatments are constructed by varying two dimensions(Table 1). The first dimension is the decision-making unit, either an individual or a three-person team. The
second dimension is the institutional environment; either there are no sanctioning institutions, or units canuse sanction schemes. Two different strengths of punishment are considered because the efficacy ofsanctioning mechanism can differ by its strength. The treatments are named as “I-No (Individual, No
Voting),” “I-Voting (Individual, Voting),” “I-Voting-ST (Individual, Voting, Strong),” “T-No (Team, No
Voting),” “T-Voting (Team, Voting),” and “T-Voting-ST (Team, Voting, Strong).”
The sanction scheme is designed based on Kamei et al. (2015). Each decision-making unit votes
whether to execute a formal or informal sanction scheme in their group. A novel part of the design is that
unlike all prior experimental studies on institutions (e.g., Kamei et al. 2015; Kosfeld et al. 2009; Traulsen

Table 1: Summary of Treatments

No

Cost ratio inpunishment#1
n.a.

Number of groups(sessions)
12 (2)

Number of
subjects
36

individuals

Yes

1:3

11 (2)

33

I-Voting-ST

individuals

Yes

1:5.5

11 (2)

33

Treatment name

Voting

I-No

Decision-making
unit
individuals

I-Voting
T-No

teams

No

n.a.

12 (7)

108

T-Voting

teams

Yes

1:3

11 (6)

99

T-Voting-ST

teams

Yes

1:5.5

11 (6)

99

68 (25)

408

Total
#1

Notes: The ratio (1: x) means that for each point a punisher spends in reducing another’s payoff, x points are deductedfrom the payoff of the punished. The ratio of 1:3 (1:5.5) means x = 3 and y = 5 (x = 5.5, and y = 10) – see Subsection 2.2.

2

A between-subjects design is more appropriate than a within-subjects design to avoid possible democratic spillover (e.g., Kamei 2016) or behavioral spill-over effects (e.g., Bednar et al. 2012; Cason et al. 2012).

5

et al. 2012, Zhang et al. 2014; Kamei 2019a; Fehr and Williams 2018), the present study is the first to
explore endogenous institutional choices when the decision-making units are teams. The treatments withindividuals being as the decision-making units will act as a control treatment.

2.1. Common Features in All Treatments
A partner matching protocol is used in all six treatments. At the onset of the experiment, decisionmaking units are randomly assigned to a group whose size is three (three individuals or three teams,
dependent on the treatment), and the group composition stays the same throughout the entire experiment.
The number of periods is set at 24 to allow for the evolution of institutional choice and cooperation
behavior over time. The periods are grouped into six phases of four periods each (Figure 1). The numberof periods is common knowledge to the subjects. Subject identity is kept anonymous in the experiment.
In each period, every decision-making unit will be assigned an endowment of 20 points (62.5
points = 1 pound sterling), and then simultaneously decide how many points to allocate between theirprivate and public accounts. Contribution amounts must be non-negative integers and not exceed 20. A
marginal per-capita return (MPCR) is set at 0.6. In other words, when decision-making unit i contributes
𝑐 , to the public account, she receives the following payoff 𝜋 , in that period:
𝜋,

20

0.6 ∑

𝑐,

𝑐, .

(1)

In the three treatments with teams, each member in a team i receives the team’s payoff to makethe payoff consequence the same for team members in the team treatments and individuals in theindividual treatments.3 At the end of a given period, each unit is informed of (i) their own payoff and (ii)the amounts contributed to the public account by two other units in their own group in a random order.
The structure of Phase 1 (also called “Part 1”) is the same for all six treatments. Subjects repeat the
public goods game without any sanctioning opportunities (No Sanction [NS] scheme, hereafter) four times
with the same group membership, thereby helping subjects learn the basic structure of the PGG game andthe dynamic free riding problem. Phases 2 to 6 (collectively “Part 2,” hereafter) differ by whether they canuse sanction schemes, as summarized in Sections 2.2 and 2.3. Panels A and B of Figure 1 summarize theschematic diagrams.

2.2. The Individual Treatments
Each phase of Part 2 in the I-Voting and I-Voting-ST treatments begins with each decisionmaking unit voting on the formal versus informal sanction scheme (FS and IS hereafter).4 Voting is
mandatory and does not cost subjects. As discussed below, theory predictions based on the selfishness ofplayers are different between FS and IS. At the beginning of each phase, the decision-making units decide3

The same per-subject payoff consequences for individuals and teams are usually used in the design of prior related
studies on team decision-making (e.g., Cason and Mui 1997; Kamei 2019b).
4
The formal (informal) sanctioning scheme is called group determined fines (individual reduction decisions) in theexperiment. The same wording was used in the experiment sessions of Kamei et al. (2015).

6

on which scheme they would prefer to use (Figure 1.B). Whichever scheme receives the majority of votes(i.e., more than or equal to two votes) will be enacted in that group for all four periods of the phase.
Sections 2.2.1 and 2.2.2 summarize the details of the IS and FS schemes, respectively.
In the sanction-free I-No treatment, subjects play the PGG under the NS scheme for all fivephases in Part 2 (Figure 1.A). There is a 40-second pause between the adjacent phases to control for therestart effects (Andreoni 1998; Kamei et al. 2015) that may be present in the voting treatments.

2.2.1. Informal Sanction Scheme
If IS is chosen, a punishment stage follows the allocation stage in each period of the phase. In thepunishment stage, a decision-making unit i can reduce the payoff of each of the other units (j) in theirgroup by assigning punishment points pi→j  {0, 1, 2, …, 10} at a private cost. While each punishment
point costs the recipient x points (x > 1), it costs the punisher one point. Following a prior experimental
framework (e.g., Fehr and Gächter 2000, 2002), the punishment points allocated by others cannot makethe recipients’ earnings for that period negative. However, each decision-making unit always incurs thecost of imposing punishments. The payoff for unit i in period t playing IS can be expressed as follows:𝜋,

max 20

𝑐,

0.6 ∑

𝑐,

𝑥∑

𝑝 → ,0

∑

𝑝→ .

(2)

To limit delayed revengeful punishment among members, contribution decisions of the other two
decision-making units appear in a random order in the punishment stage (e.g., Fehr and Gächter 2000,
2002; Denant-Boemont et al. 2007; Kamei et al. 2015).
At the end of the punishment stage, subjects are informed of (i) the total payoff reductions due topunishment points imposed by the other two group members (in total, not broken down by member), (ii)the total cost spent imposing punishment on other members, and (iii) their own payoffs.

2.2.2. Formal Sanction Scheme
When FS is in place, the allocation stage is followed by an automatic punishment stage in eachperiod. Specifically, groups in the FS scheme determine by voting in advance at what rate allocations to
the private account are penalized. Voting is mandatory and cost-free. The available sanction rates (SR,hereafter) are 0.0, 0.2, 0.4, 0.6, 0.8, 1.0, and 1.2. A median voting rule is used. Participants vote fourtimes, once at the onset of each period for that phase (a new rate can be selected in each period).
Imposing sanctions is costly. First, there is a fixed cost (administrative cost) imposed when using
the FS of 4 points, which is applied to each unit’s payoff for that period (See the 6-C treatment of Kamei
et al. [2015]). Second, when a member is fined, the group incurs a variable cost of imposing the
sanctions, i.e., 3/y of the amount deducted from the member’s payoff. The cost is equally shared among
the three units in the group, meaning that each unit pays (1/3)(3/y) = 1/y of the sanctions.5

5

To mirror the cost of informal punishment, the FS scheme features a proportional cost. However, unlike the IS
mechanism the variable cost will be borne by the whole group.

7

To parallel the IS scheme, the deductions resulting from formal punishment cannot result in anegative payoff, but the cost of implementing those sanctions and the administrative cost can.
Specifically, the payoff of decision-making unit i for that period is calculated first using Equation (1), andthen the sanction rate is applied to the amount that i held in the private account. If applying the sanction
rate results in a negative payoff, then it will be set at 0 (otherwise it will not be changed). The shared costof imposing the sanctions and the administrative cost are then deducted from that period’s earnings. In
short, the payoff for decision-making unit i in period t under the FS scheme is calculated as follows:𝜋,

max 20

𝑐,

0.6 ∑

𝑆𝑅 20

𝑐,

𝑐 , ,0

∑

𝑆𝑅 20

𝑐,

𝑓,

(3)

where f = 4 (administrative cost). Should the group select a sanction rate of 0.0, their payoffs would
remain effectively unchanged from that without the FS scheme (however, they still incur the fixedadministrative cost of 4 points per period). If i receives a negative payoff due to strong punishment, thenit will be deducted from their accumulated payoffs from other periods.
Equation (3) suggests that for each sanction imposed, the cost ratio between the punished and thepunishers is 1 + 1/y : 2/y (punished decision-making unit: two other units). To further make the FS
scheme comparable to the IS scheme, the cost ratio is set to be the same as the IS scheme, namely, 1
1/𝑦: 2/𝑦𝑥: 1. This reduces to the following condition for x and y.
𝑦

2𝑥

1.

(4)

Modest punishment intensity, x = 3 and y = 5, is used for the I-Voting and T-Voting, while strong
punishment intensity, x = 5.5 and y =10, is used for the I-Voting-ST and T-Voting-ST treatments.
At the end of each period, they are informed of (i) the two other units’ allocation decisions in arandom order, (ii) their own payoffs before reductions, (iii) their final payoffs in the period, and (iv) a
breakdown of reductions due to fines, the cost of administering fines, and the fixed administration cost.

2.3. The Team Treatments
The T-No, T-Voting and T-Voting-ST treatments are identical to, respectively, the I-No, I-Voting
and I-Voting-ST treatments (Figure 1), except that the decision-making units are three-person teams, notindividuals. Three subjects playing as a team will jointly make a single decision as a decision-making
unit. At the onset of the experiments, subjects are randomly assigned to a team of three, and the teamcomposition does not change throughout the entire experiment. The teams are then randomly assigned toa group of three teams (thus each group consists of nine subjects) before the experiment commences.
The team’s joint decision-making follows Kamei (2019b, 2021). Three members in a teamcommunicate with each other for 60 seconds using a computer chat screen before making each teamdecision. This enables us to perform transcript analysis post-experiment. Members are not allowed tocommunicate verbally, eliminating the risk of contamination of the experiment which may occur if
players were able to overhear another team’s discussions. The members are only able to communicate
8Figure 1: Schematic Diagram
1st Instructions

2nd instruction
about continuation ofplay as in Part 1

4 periods
no sanctions

40 secondbreak

4 periods
no sanctions

4 periods
no sanctions

4

40 secondbreak

8

40 secondbreak

4 periods
no sanctions

12

40 secondbreak

4 periods
no sanctions

4 periods
no sanctions

16

Demographic andpreference
questionnaire

20

24Periods(A) I-No and T-No treatments
2nd instruction
about formal (informal)
scheme2nd Vote
1st Instructions
1st Vote
4 periods underchosen scheme

4 periods
no sanctions

4

3rd Vote

4th Vote

Demographic andpreference
questionnaire

5th Vote

4 periods underchosen scheme

4 periods underchosen scheme

4 periods underchosen scheme

4 periods underchosen scheme

12

16

20

24

8

Periods(B) I-Voting, I-Voting-ST, T-Voting and T-Voting-ST treatments
1st communication
within teams
Communication Scheme
team
within teams
vote

Sanction rate
team vote(median)Sanctionrateoutcome

2ndcommunication
within teams

Teamallocation
decision(median)

Allocation
results andfinal payoff

1stcommunication
within teams

Sanctionrate team
vote

Scheme voteoutcome
1st period under FS scheme

2nd period under FS scheme

1st period under IS scheme

2nd period under IS scheme

FS chosen
1st decision of a given phase
IS chosen

1st communication
within teams

Teamallocation
decision(median)

2nd communication
Punishment 1st communication
Team
Allocation
within teams
punishment result andwithin teams
results
decision final payoff(median)Team
allocation
decision(C) Phase Structure in the T-Voting and T-Voting-ST treatmentswith other members of their own team.6 Anonymity is kept preserved, such that the subjects are identified
by fixed Player IDs in the chat screen, and they are instructed that disclosing any information that may

6

The use of electronic chat windows is one of the most common procedures (e.g., Charness and Sutter 2012; Kugler
et al. 2012). While some studies set the duration of each communication stage much more than 60 seconds, priorpapers such as Kagel (2018) and Kamei (2019b) set the duration of each communication stage to 60 seconds or less.
The authors read all the teams’ communication logs and counted the number of explicit agreement cases (treated acommunication log as a disagreement if there was no communication, only irrelevant communication, or one ofthree team members did not communicate, unless a pre-agreed strategy was still in play, agreements wereconsidered implicit, or teams disagree or do not try to reach consensus). Even with such strict judgment, at least
80.5% of team decisions were classified as agreed. This suggests that the 60-seconds duration was sufficient for thecommunication. A detailed analysis of communication logs will be executed in Section 6.

9

identify themselves or using offensive language in communication is prohibited.7
A team’s three joint decisions are determined using the median voting rule – see Figure 1.C. This
includes the allocation decisions in the public goods game (all team treatments), and punishment
decisions under the IS scheme and sanction rate votes under the FS scheme (T-Voting and T-Voting-ST
treatments). The specific procedure is as follows: The three members in a team first discuss strategies anddecisions with their team. After the communication stage, each member privately and simultaneously
submits their preferred decision (e.g., an amount they wish to contribute as the team’s joint contribution
decision).8 The median of three submissions becomes the team’s decision. Each team member is informedof the submissions of their two other team members, anonymously and in a random order.
A team’s joint scheme choice (FS or IS) is based on a majority rule. As in the other teamdecision-making, each team member votes on which scheme they prefer after communication, with theteam’s majority choice (an option with at least two votes) being the team’s joint voting decision.9

3. Hypothesis
Standard theory based on the assumption of agents’ self-interest and common knowledge ofrationality is straightforward when sanctioning schemes are absent (I-No and T-No treatments, and Phase
1 of the four voting treatments). Equation (1) implies that ∂𝜋 , ⁄∂𝑐 ,0.4

0, which means that

contributing nothing to the public account is the strictly dominant strategy for every decision-making unit.
Thus, mutual free riding characterizes the unique Nash Equilibrium of this game. Repetition does not alter
this prediction with the logic of backward induction.
The standard theory assumption also predicts that having IS does not alter equilibrium play fromthat in the NS scheme because punishment activities are costly. From Equation (2), ∂𝜋 , ⁄∂𝑝 →0 for all i. Thus, it is materially beneficial for each unit to not punish one another (𝑝 →10), in whichcase their payoff would be unaffected when compared to the payoff in the allocation stage (Equation (1)).
In contrast, standard theory prediction based on pure selfishness is different in the FS schemefrom that in the NS or IS scheme (e.g., Falkinger et al. 2000; Kamei et al. 2015). ∂𝜋 , ⁄∂𝑐 , is calculated
from Equation (3) as: 0.4

𝑆𝑅 1

SR is 0.0 or 0.2 as then ∂𝜋 , ⁄∂𝑐 ,1/𝑦 . This means that units contribute nothing when the enacted
0, but they contribute the full endowment amount when SR ≥ 0.4.

Each unit obtains a payoff of 32 points (= 0.6  60 – 4) when a deterrent sanction rate is enacted, while7

A subject receives a fine of 10 pounds with an apparent violation of this rule. No one disclosed any identifiable
information, and only seven out of 306 subjects (2.28%) had to pay the fine with the rule of offensive language.
8
Where the team members agree on a decision, they can submit that decision. If they do not agree on a decision as ateam, however, they can submit whatever decision they prefer. Three team members submitted the same decisions inalmost all cases in the team treatments (2,049 out of 2,448 team allocation decisions, 581 out of 672 team sanction
rate votes, and 1,176 out of 1,296 team informal punishment decisions).
9
All three team members agreed how to vote in almost all cases in the experiment (they submitted the same vote in
278 out of 330 cases).

10

they obtain a payoff of 16 points (= 20 + 0.6  0 – 4) when a non-deterrent sanction rate is enacted
instead. Hence, the theory predicts that groups would choose FS rather than IS, and then vote for adeterrent sanction rate, after which each unit contributes the full endowment amount to the public account(trembling-hand perfect equilibrium).
However, the superiority of the FS over the IS scheme changes under certain conditions, once
people’s other-regarding preferences are considered (see, e.g., Fehr and Schmidt 2006 and Sobel 2005 fora survey). Prior experimental research has shown that real human subjects can sustain contributions athigh levels under certain conditions when the IS scheme is available (e.g., Fehr and Gächter 2000, 2002;
Anderson and Putterman, 2006; Nikiforakis and Normann, 2008) due to peer-to-peer punishment inflicted
driven by such non-selfish preferences. The costly punishment activities and the maintenance ofcontributions can be rationalized successfully by, for example, the inequity-averse preference model, as
has been proven in Fehr and Schmidt (1999). Thus, people’s institutional choices between the IS and FS
schemes are not obvious for real human subjects as they can achieve high cooperation regardless of whichscheme is selected, and they may prefer IS to FS to avoid a fixed administrative cost. Empirically, suchchoices are known to be affected by which scheme is more materially beneficial, as shown by many priorexperiments (e.g., Gürerk et al. 2006; Kosfeld et al. 2009; Sutter et al. 2010; Ertan et al. 2009; Kamei etal. 2015; Fehr and Williams 2018).
The main aim of this paper is to investigate how teams utilize sanctioning institutions differently
from individuals, and as a result how teams make contribution decisions differently from individuals
under a given sanction scheme. As described above, a theoretical analysis that does not incorporate theinternal aspects of team decision-making suggests the same behaviors for teams and individuals.
However, such analysis misses an important dimension for teams, namely, intra-team dynamics or the
preference aggregation process.
A model that may then be applicable to the present setup is that of household bargaining behavior(see, e.g., Chiappori and Mazzocco [2017] for a survey). The models in this area explicitly include intrahousehold members’ utilities to explain the household’s behavior as a whole when only observablebehavior is aggregated for the household. It is possible to treat household members and the wholehousehold as, respectively, team members and the team in the model if it is applied to the context of thepresent study. The model, however, does not predict different behaviors for individuals and teams in thepresent experiment, and its predictions are the same as those without considering the intra-team dynamics
just discussed, as explained below.
The model of household behavior considers two cases: one in which preferences are otherregarding among household members (i.e., the utility of each household member is affected by otherhousehold members’ utilities), and the other in which they are egotistic (i.e., the utility is not affected byother household members’). It is obvious that if team members are assumed to have egotistic preferences,then there will be no effects due to the difference in the decision-making units, because the utilities of11subjects, whether individuals in the individual treatments or teams or team members in the teamtreatments, are expressed by the same forms, for example, by Equations (1), (2) or (3) dependent on thetreatment condition. If it is instead assumed that people have other-regarding preferences, the utility ofmember i in a team k is expressed as: Ui(ui, uj, ul), where j and l are the two other team members of i inteam k. With s being the utility weight of regard for others outcomes, the utility is further expressed as: Ui= ui + sꞏuj + sꞏul = (1 + 2s)ꞏui, which is just a linear transformation of one’s own utility, because the threemembers of team k have the same payoff consequences. This expression clearly demonstrates that there
are no effects relating to the decision-making unit in this setup.
Hypothesis 1 (theory without considering the internal aspects of team decision-making, or the model ofhousehold bargaining behavior): The difference in the decision-making format, team- or individual
decision-making, does not have any impact in the experiment.
The approach just discussed does not consider the preference or information aggregation process
when three members in a team make a joint decision. There are two other different theories that may
explain differing behaviors between teams and individuals. The first one relates to the notion of ‘wisdomof the crowds’ and is captured by theories of preference aggregation such as the so-called Condorcet jury
theorem, or what Ertan et al. (2009) call the “behavioral public choice theorem” (also see Hauser et al.
2014). Condorcet’s (1785) jury theorem suggests that, assuming the event of individuals in a given
population choosing the correct answer is independent (unconditional independence), the probability ofthe majority of individuals voting for a correct answer hinges on the individual correctness probability.
That is, if the probability of an individual being correct exceeds ½, and that this is the same for all
individuals in the population (unconditional competence), then the probability of the majority in a groupvoting for the correct answer is larger than that in which each individual votes correctly. Similarly, if
general competence is below ½, then the probability of a majority voting for an incorrect answer is worse
in a group than by individuals and increases with group size. In terms of this experiment, it can beinterpreted that the behavior of individuals in the individual treatments would indicate the individual
correctness probability. Should individuals be able to select the strategically correct answer more than
50% of the time, then it follows that teams of three formed of similar individuals will have a greater
probability of voting for the strategically correct answer, whether a deterrent sanction rate or lack ofpunishment. This tendency is summarized as Hypothesis 2 below:
Hypothesis 2 (Condorcet jury theorem, and the so-called behavioral public choice theorem): (a) If the
majority of individuals vote for deterrent (non-deterrent) sanction rates, then teams are more likely to votefor deterrent (non-deterrent) sanction rates. As a result, teams achieve higher (lower) levels ofcontributions under the FS scheme. (b) If pro-social punishment is more (less) prevalent than anti-social
punishment among individuals, then teams inflict punishment more (less) pro-socially, thereby achieving
higher (lower) levels of contributions under the IS scheme, than individuals.
12

Hypothesis 2 does explain voting outcomes in prior experimental research where each individual
votes independently as the decision-making unit (Ertan et al., 2009; Hauser et al., 2014). It should be
worth noting, however, that Hypothesis 2 is valid only when the independence of the probability of
individuals being correct holds. This independence assumption is unlikely to hold for the present
experiment as the three team members have intra-team communication and deliberation before making
each team decision. In this sense, team decision-making may be more than the aggregation of threemembers’ preferences, and as such one can expect that Hypothesis 2 will not hold perfectly.
The second mechanism similarly explores preference combination, but unlike the models
discussed above, does not assume independence. Instead focusing on influence and learning, thismechanism is more closely related to the notion of “truth wins.” The generalization by Friedkin and Bullo(2017) of the DeGroot (1974) learning model takes the initial set of preferences or judgements for thosewithin a team and allows for teammates to influence each other over time using a weighted averaging
mechanism. This mechanism takes into account attachment to one’s own judgement as well as theinfluence of the other members’ judgements (which may be 0, as in the case of independence orindividual decision-making). As Friedkin and Bullo (2017) note, it is possible for teams to converge toboth correct and incorrect conclusions depending on the distribution of initial judgements and thecalculative logic adopted.
In the context of the present experiment, correct answers can be considered the options that lead tothe highest utilities, since, as already discussed, voting patterns revealed in prior experiments on
institutions showed the strong behavioral effects of material outcomes in driving units’ choices ofsanctioning institutions (e.g., Gürerk et al. 2006; Kosfeld et al. 2009; Sutter et al. 2010; Ertan et al. 2009;
Kamei et al. 2015; Fehr and Williams 2018). Thus, the learning model allows for one or more teammembers to persuade their teammates of the correct logic and so lead them to a better decision, i.e.,
selecting deterrent sanction rates when the FS scheme is in effect, and punishing low rather than highcontributors when the IS scheme is in effect.10 Recent experiments on problem-solving suggest asymmetry
regarding influence and persuasion. For instance, He et al. (2022) found that more cognitively able andknowledgeable members can influence less knowledgeable members more strongly if they work together,
thereby making it easier for the latter to find correct answers while the former is little affected by incorrectsuggestions made by the less able member. Similarly, Schulze and Newell (2016) found that groupmembers claimed that their group made decisions by implementing the most effective strategy proposedby one of its members when confronting probability matching bias, enabling them to outperform mostindividuals and match their best individuals. Further, Bonner et al. (2002) report that groups utilize
ranking information to assign more weight to the suggestions of high performers when completing10Well-targeted peer-to-peer punishment plays a vital role in achieving a high payoff for people in the IS scheme(e.g., Hermann et al., 2008).

13

moderately difficult problem-solving tasks. Prior experimental research on team decision-making in adifferent context also supports the role of deliberation and learning as a mechanism of improved decisionmaking. For instance, Cooper and Kagel (2022) find that teams learn to outperform individuals over timethrough careful consideration and experimentation with strategies when no singular optimal strategy isavailable. In sum, while we may still see examples of teams that converge to poor strategies when every
member, or a particularly dominant member, is incorrect, it is expected on average that deliberation andlearning will allow teams to discover the optimal strategy more quickly than individuals.
Hypothesis 3 (truth wins): (a) Under the FS scheme, teams will enact deterrent sanction rates more
frequently than individuals, and as a result, the former contribute larger amounts than the latter. (b)Under the IS scheme, teams will inflict punishment more selectively on low, rather than high,contributors, thereby achieving higher contribution norms, than individuals.

4. Experimental Results
The experiment was conducted at the University of York (see Appendix E.1 for the
implementation). Section 4.1 provides an overview of the decision-making units’ average behaviors andexamines treatment differences in contributions and payoffs. Section 4.2 investigates scheme voting
behavior, while Section 4.3 provides a comparison between units in utilizing the sanctioning institutions.

4.1. Treatment Differences in Contributions and Payoffs
Groups experienced typical free riding dynamics when sanctioning schemes were unavailable(Figure 2). The average contribution of individuals in the I-No treatment began at 62% of the endowment
and gradually decreased over time. In line with the literature, mild restart effects were seen in Phases 4 to
6 (Andreoni 1988), and end-game defection was evident in period 24. The average contribution across all
periods was 10.19 points (50.9% of the endowment) in the I-No treatment. The average contribution ofteams was also modest, 10.57 points (52.9% of the endowment), in the T-No treatment, and the dynamics
followed a declining contribution trend, similar to that of individuals in the I-No treatment.11 No
difference by the decision-making unit is unsurprising because of the floor effect, typical to the serious
free-riding dynamics in repeated PGG.
A key comparison in this study is units’ decisions to contribute under voting. Contribution trends
differ drastically between individuals and teams when they voted on the sanctioning schemes. This isclearly at odds with Hypothesis 1. The difference was especially large under the mild punishment
intensity (Figure 2.A). On the one hand, teams in the T-Voting treatment learned to cooperate graduallyfrom phase to phase. Remarkably their average contribution amounts were more than 80% of theendowment in the final three phases. On the other hand, individuals in the I-Voting treatment did not
Unlike this trend, teams cooperated much more strongly than individuals in Kamei (2019b) where the group size
was two. The difference between Kamei (2019b) and the T-No treatment, however, resonates with the idea thatcooperation is more difficult to evolve when the group size is three, rather than two (e.g., Cox and Stoddard 2018).
11

14

follow as strong a learning pattern, although they did not learn to free ride either. The individuals’
average contribution amounts hovered between 10 and 12 points. The clear difference between the TVoting and I-Voting treatments is consistent with the discontinuity-effect hypothesis, demonstrating its
application in an institutional choice setting.
When the punishment intensity was strong, cooperation evolved at a further higher level among
teams – see Figure 2.B. The average contribution in the T-Voting-ST treatment was close to the fullcontribution level in each phase of Part 2. With strong punishment, individuals (in the I-Voting-ST
treatment) were also able to gradually learn to cooperate over time. The difference between the I-VotingST and I-Voting treatments suggests that individuals’ contribution behaviors are sensitive to thepunishment effectiveness, as has been shown by Anderson and Putterman (2006) and Nikiforakis andNormann (2008). Having said this, the difference in the average contribution was consistently large
between individuals and teams even under the strong punishment intensity.
So, why did teams perform better than individuals when they voted on sanctioning schemes? Ananswer may be either by the mere structure that teams are composed of three members and aggregate thepreferences when deciding on a team decision (Hypothesis 2), or by the tendency that teams use punishment
opportunities more efficiently than individuals driven by the former’s communication, deliberation andlearning (Hypothesis 3). An analysis in Section 4.3 will reveal the validity of Hypothesis 3.
Figure 3 reports the trends of average payoffs. It shows first that individuals persistently incurred large
losses due to punishment when its intensity was modest, consistent with the idea that individuals’ failure
to learn to cooperate seen in Figure 2.A triggers negative emotional responses from their peers (e.g.,
Casari and Luini 2009; Gächter et al. 2008). As a result, individuals received lower payoffs in the IVoting than in the I-No treatment in all phases except Phase 6 (Figure 3.A). Second, teams also
experienced such negative welfare losses under the modest punishment intensity (Figure 3.A). However,the negative impact in the T-Voting treatment was limited to Phases 2 and 3. Instead, the teams achieved
higher payoffs in Phases 4 to 6, relative to the T-No treatment. Considering the teams’ increasing

Figure 2: Average Contribution Period by Period
20

16

12

8

4

T‐Voting

I‐Voting

T‐No

I‐No

0
Pd 1

Pd 2

Pd 3

Phase 1

Pd 4

Pd 5

Pd 6

Pd 7

Phase 2

Pd 8

Pd 9

Pd 10

Pd 11

Pd 12

Phase 3

Pd 13

Pd 14

Pd 15

Phase 4

Pd 16

Pd 17

Pd 18

(A) Treatments with Modest Punishment Intensity

15

Pd 19

Phase 5

Pd 20

Pd 21

Pd 22

Pd 23

Phase 6

Pd 24

20

16

12

8

4

T‐Voting‐ST

I‐Voting‐ST

T‐No

I‐No

0
Pd 1

Pd 2

Pd 3

Pd 4

Pd 5

Phase 1

Pd 6

Pd 7

Pd 8

Pd 9

Phase 2

Pd 10

Pd 11

Pd 12

Pd 13

Phase 3

Pd 14

Pd 15

Pd 16

Pd 17

Pd 18

Phase 4

Pd 19

Pd 20

Pd 21

Pd 22

Phase 5

Pd 23

Pd 24

Phase 6(B) Treatments with Strong Punishment Intensity
Note: The unit of the vertical axis in each panel is points.

contribution trend, this implies that, in later phases, teams did not need to discipline their group members
through costly punishment, because the group successfully cooperated then (Figure 2.A).
Third, likewise, when the punishment intensity was strong, having the sanctioning schemes led tosimilar negative welfare consequences in groups. However, the duration in which groups suffered fromlosses was shorter relative to the treatments with modest punishment (Figure 3.B). In other words, theavailability of strong punishment induced the members to learn to cooperate smoothly, thereby helping
reduce the welfare loss due to punishment activities.
A series of non-parametric tests were performed to judge treatment differences statistically (Table2), which confirms most of the patterns seen in Figures 2 and 3. First, without the sanctioning schemes, units(whether individuals or teams) had a significantly lower level of contribution in Part 2 (Phases 2 to 6) than inPart 1 (Phase 1) of the experiment. Second, in both the T-Voting and T-Voting-ST treatments, teams’contribution behaviors were significantly stronger in Part 2 than in Part 1. As a result, the teams did notexperience a drop in payoffs after Part 1, unlike in the T-No treatment. An across-treatment comparison inPart 2 further demonstrates that teams contributed larger amounts when the sanctioning schemes were

Figure 3: Average Payoff Period by Period
35

30

25

20

I‐Voting

T‐Voting

I‐No

T‐No

15
Pd 1

Pd 2

Pd 3

Phase 1

Pd 4

Pd 5

Pd 6

Pd 7

Phase 2

Pd 8

Pd 9

Pd 10

Pd 11

Pd 12

Pd 13

Phase 3

Pd 14

Pd 15

Phase 4

Pd 16

Pd 17

Pd 18

(A) Treatments with Modest Punishment Intensity

16

Pd 19

Phase 5

Pd 20

Pd 21

Pd 22

Pd 23

Phase 6

Pd 24

35

30

25

20

T‐Voting‐ST

I‐Voting‐ST

T‐No

I‐No

15
Pd 1

Pd 2

Pd 3

Pd 4

Phase 1

Pd 5

Pd 6

Pd 7

Phase 2

Pd 8

Pd 9

Pd 10

Pd 11

Pd 12

Pd 13

Phase 3

Pd 14

Pd 15

Phase 4

Pd 16

Pd 17

Pd 18

Pd 19

Phase 5

Pd 20

Pd 21

Pd 22

Pd 23

Pd 24

Phase 6(B) Treatments with Strong Punishment Intensity
Notes: The unit of the vertical axis in each panel is points.

available than otherwise (see H0: (c) = (d) in Table 2).12 Third, individuals earned significantly less in Part
2 than in Part 1 of the experiment in the I-Voting treatment, but not in the I-Voting-ST treatment.13
Lastly, a closer look at the data by scheme uncovers three further patterns. First, in the I-Voting
and I-Voting-ST treatments, while cooperation did not evolve when FS was in place, individuals
maintained strong cooperation norms when IS was instead in effect (panels (a) and (b) of Appendix
Figure C.1). Hence, the individuals’ overall failure to learn cooperation (Figure 2) is partly attributable totheir selection of sanction rates and/or contribution behaviors under the FS scheme. Second, due the low
cooperation norms and administrative cost payments, the individuals persistently earned much less under
the FS scheme, relative to the I-No treatment (panels (a) and (b) of Appendix Figure C.2), and thedifference is significant (Table 2). Under the IS scheme, individuals in the I-Voting (I-Voting-ST)
treatment successfully cooperated with each other in Phase 6 (from Phase 4),14 but they received lower
payoffs than those in the I-No treatment in Phases 2 to 5 (Phase 2 to 3). The low payoffs in the earlier
phases were due to losses from intensive punishment activities. Hence, learning to cooperate withinformal punishment requires a sufficiently long length of interactions in the experiment, as Gächter et al.
(2008) demonstrated.
12

The same positive effect can be found even if the two team treatments are not pooled (Panel C of Appendix B).
A regression was also performed as a supplementary analysis to analyze the contribution trend in Part 2(Appendix Table C.1). It confirms that when the sanctioning schemes were unavailable, units, whether individuals
or teams, decreased contribution amounts significantly over time. By contrast, teams increased contribution amounts
significantly from phase to phase in both the T-Voting and T-Voting-ST treatments. A regression also confirms thatthe contribution trend differs by punishment intensity when the units are individuals: an increasing (somewhat
decreasing) contribution trend in the I-Voting-ST (I-Voting) treatment. It further shows that the payoff trend issimilar to the contribution trend: declining trends for the I-No and T-No treatments versus an increasing trend in the
T-Voting treatment (the maintenance of high payoff in the T-Voting-ST treatment) – see Appendix Table C.2.
14
A group-level Mann-Whitney test finds that the average contribution in Phase 6 (Phases 4-6) under the IS scheme in
the I-Voting (I-Voting-ST) treatment is different from that in the I-No treatment at two-sided p = 0.0709 (0.0196).
Likewise, the average payoff in Phase 6 (Phases 4-6) under the IS scheme in the I-Voting (I-Voting-ST) treatment isdifferent from that in the I-No treatment at two-sided p = 0.0709 (0.0245). Note that there were only three groups
playing the IS scheme in Phase 6 for the I-Voting treatment, making statistical significance difficult to obtain.
13

17

Table 2: Average Contribution and Payoff
I. Contribution
Avg. contribution based on all data(i) Phase 1 (ii) Phases 2- p-value for6
H0: (i) = (ii)[Individual treatments:](a) I-No(b) Indiv Voting (I-Avg. contribution under a given sanction scheme in Phases 2-6(iv) ISp-value for p-value for H0:(iii) FS p-value for
H0: (i) = (iv)H0: (i) = (iii)(iii) = (iv)#1

12.92

9.64

0.0414**

---

---

---

---

---

10.60

12.68

0.291410.24

0.831315.04

0.27900.2330(b1) I-Voting(b2) I-Voting-ST

11.92
9.27

11.57
13.80

0.92920.15499.69
10.88

0.48380.8590

13.66
16.23

0.95940.20260.73530.1614[Team treatments:](c) T-No(d) Team Voting (T-

13.26

10.04

0.0096***

---

---

---

---

---

12.53

17.67

0.0001***18.02

0.0002***17.30

0.0166**0.1054(d1) T-Voting
12.81(d2) T-Voting-ST
12.24[Across-treatment comparisons:]
p for H0: (a) = (b)0.1882p for H0: (c) = (d)0.7051p for H0: (a) = (c)0.9310p for H0: (b) = (d)0.2007

16.53
18.80

0.0128**0.0033***

16.87
18.81

0.0209**0.0051***

16.28
18.78

0.0827*0.1282

0.0966*0.75320.22730.0000***0.68610.0074***---------------0.0003***---------------0.0554*------------------

Voting, I-Voting-ST)

Voting, T-Voting-ST)

II. Payoff
Avg. payoff based on all data(i) Phase 1 (ii) Phases 2- p-value for6
H0: (i) = (ii)[Individual treatments:](a) I-No(b) Indiv Voting (I-Avg. payoff under a given sanction scheme in Phases 2-6(iii) FS p-value for(iv) ISp-value for p-value for H0:
H0: (i) = (iv)H0: (i) = (iii)(iii) = (iv)#1

30.34

27.71

0.0414**

---

---

---

---

---

28.48

25.27

0.0575*23.38

0.0086***27.09

0.0304**0.1252(b1) I-Voting(b2) I-Voting-ST

29.54
27.42

23.79
26.75

0.0208**0.7897

22.88
23.97

0.0357**0.1731

24.81
29.07

0.0218**0.50760.0280**0.8886[Team treatments:](c) T-No(d) Team Voting (T-

30.61

28.03

0.0096***

---

---

---

---

---

30.02

29.90

0.935329.71

0.809230.10

0.17010.1252(d1)T-Voting
30.25(d2) T-Voting-ST
29.79[Across-treatment comparisons:]
p for H0: (a) = (b)--p for H0: (c) = (d)--p for H0: (a) = (c)--p for H0: (b) = (d)
---

29.07
30.73

0.53370.4236

28.38
30.63

0.37430.5076

29.56
30.89

0.18230.73530.13860.91650.23430.0661*0.6861
0.0514*---------------0.0004***---------------0.3061------------------

Voting, I-Voting-ST)

Voting, T-Voting-ST)

Notes: All p-values are based on two-sided tests. Group-level Wilcoxon signed rank (Mann-Whitney) tests were conducted
for within(across)-treatments comparisons. See Panel A of Appendix B for the standard errors. See Panel C of Appendix B
for more detailed across-treatment comparisons. “Indiv Voting” includes the I-Voting and I-Voting-ST treatments. “TeamVoting” includes the T-Voting and T-Voting-ST treatments. To supplement the nonparametric tests (Table 2) and theregression analyses (Appendix Tables C.1 and C.2), additional regression was conducted using group-level averagecontribution or payoff as the dependent variable (Appendix Table C.3). The results consistently confirm strong
discontinuity effects under voting. #1 Only groups that had experienced both the FS and IS schemes in Part 2 were used. *,**, and *** indicate significance at the 0.10 level, at the 0.05 level, and at the 0.01 level, respectively.

18

Third, the picture is markedly different in the team treatments. Whether in the FS or IS scheme,
cooperation was sustained at high levels (panels (c) and (d) of Appendix Figure C.1). Regardless of whichscheme was in effect, the teams’ contribution amounts with the sanction schemes were significantly
larger, relative to the T-No treatment (Table 2). Teams also quickly responded to the informal punishment
received from their peers. Although payoff losses due to punishment were large in Phases 2 and 3 (inPhase 2) with the IS scheme in the T-Voting (T-Voting-ST) treatment, they achieved high payoffs after
these phases. Despite administrative cost payments, teams in the T-Voting-ST treatment did earn morethan those in the T-No treatment across all phases (panels (c) and (d) of Appendix Figure C.2). The
clearly better outcomes among teams than individuals reject Hypothesis 1, and underscore the effects thatthe internal aspects of team decision-making (intra-team dynamics, and/or the preference aggregation
process in team decision-making) have under the sanctioning institutions.
Result 1: (a) Decision-making units (whether individuals or teams) reduced their contributions over time
when sanctioning schemes were unavailable. (b) With the sanctioning schemes, individuals in the IVoting treatment sustained their initial level of cooperation, and individuals in the I-Voting-ST gradually
increased cooperation further. (c) Inconsistent with Hypothesis 1, the impact of voting was much stronger
for teams: Under each punishment intensity, teams increased their cooperation more strongly and quickly
than individuals regardless of which sanction scheme was chosen.

4.2. Scheme Choice
The strong efficiency under the IS scheme was not driven by a small number of groups. Despite
standard theory based on selfishness of players predicting the superiority of the FS scheme, on average
47.3%, 63.0%, 53.3%, and 46.1% of decision-making units voted for the IS scheme in the I-Voting, TVoting, I-Voting-ST, and T-Voting-ST treatments, respectively (Table 3.I). As a result of majority voting,groups adopted the IS scheme similar percentages of the time, i.e., 47.3%, 58.2%, 54.6%, and 40.0% of
the time in the corresponding treatments (Table 3.II). Group-level Wilcoxon signed rank tests confirm
that units’ voting for the IS scheme and the vote outcomes were not the result of error. Group-level MannWhitney tests also indicate that scheme choice behaviors did not differ between individuals and teams(Panel K of Appendix B). A look at the across-phase trend also indicates that the popularity of the IS
scheme was quite stable.
Realized relative effectiveness of FS and IS schemes affected voting. Seven, nine, eight, and six
groups experienced both the FS and IS schemes at least once as a result of voting. Using these groups,
Figure 4 demonstrates that decision-making units were more likely to vote for the scheme under which
they had previously experienced higher payoffs on average. This resonates with the idea that people’s
institutional choices are guided by material outcomes (e.g., Ertan et al. 2009; Kamei et al. 2015),15 and it
15

To supplement this finding, a regression analysis was also conducted regarding how decision-making units’
voting in Phase 6 (the final phase) may be influenced by relative payoff ratios they experienced before that phase.
As shown in Appendix Table C.4, the relative payoff ratio is a significantly positive predictor for their selection of19

may be a general phenomenon as the role of realized payoffs has been demonstrated in another setup, e.g.,
voting on leadership in PGG (e.g., Güth et al. 2007).

Table 3: Scheme Choice and Voting Outcome
I. Percentages of Times that Decision-Making Units Voted for the IS Scheme
Phase 2Phase 3Phase 4Phase 5Phase 6Overall

I-Voting

48.5%

63.6%

42.4%

51.5%

30.3%

47.3%

p-value for Wilcoxon
signed rank tests#1
0.0022***

I-Voting-ST

54.5%

48.5%

45.5%

60.6%

57.6%

53.3%

0.0017***

T-Voting

48.5%

84.8%

63.6%

66.7%

51.5%

63.0%

0.0016***

T-Voting-ST

33.3%

48.5%

48.5%

54.5%

45.5%

46.1%

0.0017***

Average

46.2%

61.4%

50.0%

58.3%

46.2%

52.4%

0.0000***II. Percentages of Times that the IS Scheme was Selected in Groups
Phase 2Phase 3Phase 4Phase 5Phase 6Overall

I-Voting

54.5%

63.6%

36.4%

54.5%

27.3%

47.3%

p-value for Wilcoxon
signed rank tests #1
0.0021***

I-Voting-ST

54.5%

54.5%

36.4%

63.6%

63.6%

54.5%

0.0021***

T-Voting

45.5%

81.8%

54.5%

63.6%

45.5%

58.2%

0.0015***

T-Voting-ST

27.3%

36.4%

36.4%

54.5%

45.5%

40.0%

0.0197**

Average

45.7%

59.2%

41.1%

59.2%

45.7%

50.0%

0.0000***Notes: #1 p-values here are one-sided as the theory predicts a specific direction. The null hypothesis is that the percentageof the time that units or groups select the IS scheme is less than or equal to 5%, assuming that errors happen with a 5%
probability. In order to perform Wilcoxon signed rank tests, the overall percentage of decision-making units that voted for
IS was calculated for each group in panel I (the percentage of times when IS was enacted was calculated for each group inpanel II). After that, using the group-average observations Wilcoxon signed rank tests were performed. *, **, and ***indicate significance at the 0.10 level, at the 0.05 level, and at the 0.01 level, respectively.

Figure 4 also indicates two more interesting patterns. First, there is a large variation for decisionmaking units’ voting behaviors: the correlations between units’ scheme votes and experienced relative
payoff ratios are far from perfect. This implies strong heterogeneity in subjects’ cooperation andpunishment tendencies (e.g., Fischbacher et al. 2001; Fischbacher and Gächter 2010; Kamei 2014).
Second, the punishment intensity influenced the relative effectiveness of informal sanctions. Under themodest punishment intensity, a considerable majority of the units – i.e., 95.24% and 66.66% of the units
in the I-Voting and T-Voting treatments, respectively, had lower payoffs on average under the IS than the
FS scheme due to punishment losses. However, the informal punishment became more effective under thestrong punishment intensity. The percentages of the units who on average earned less under the IS thanthe FS scheme are a minority, i.e., 41.67% and 38.89% in the I-Voting-ST and T-Voting-ST treatments,
respectively. Around 32% of groups exclusively selected one of the schemes across the five phases in Part
2. Except for one group in the I-Voting treatment, the groups’ persistence in one scheme can be explained

the IS scheme both in the individual voting and team voting treatments (when data are pooled irrespective of thepunishment intensity). The role of experience is also supported by an analysis of communication logs (Section 6).

20

by their success in cooperation under that scheme. The average contributions of groups that always
selected IS were 19.93 and 19.21 points in the I-Voting-ST and T-Voting-ST treatments, respectively.16
The average contributions of groups that always selected FS were 15.16, 19.54, 18.29, and 19.67 points in
the I-Voting, I-Voting-ST, T-Voting, and T-Voting-ST treatments, respectively.17

1.2

1.2

1.0

1.0

0.8
0.6
Freq.

y = 0.272x + 0.181(0.109) (0.083)

0.4

Linear (Freq.)

0.2% of times voted for IS% of times voted for IS

Figure 4: Scheme Choice and Relative Payoff Ratio

y = 0.340x + 0.154(0.095) (0.113)

0.8
0.6

Freq.
0.4

Linear (Freq.)

0.2

0.0

0.0
0.0

0.2

0.4

‐0.2

0.6

0.8

1.0

1.2

‐0.5

0.0

0.5

‐0.2

Scheme Payoff Ratio(b) I-Voting-ST treatment
y = 0.353x + 0.271(0.088) (0.085)

1.0% of times voted for IS

0.8% of times voted for IS

2.0

1.2

1.0

0.6
Freq.
0.4

Linear (Freq.)

0.2
0.0

y = 0.474x + 0.208(0.116) (0.119)

0.8
0.6

Freq.
0.4

Linear (Freq.)

0.2
0.0

0.0
‐0.2

1.5

Scheme Payoff Ratio(a) I-Voting treatment
1.2

‐0.3

1.0

0.3

0.6

0.9

1.2

1.5

1.8

2.1

0.0
‐0.2

Scheme Payoff Ratio

0.3

0.6

0.9

1.2

1.5

1.8

2.1

Scheme Payoff Ratio(d) T-Voting-ST treatment(c) T-Voting treatment

Notes: The figures were depicted based on the data from the groups that experienced both the FS and IS schemes in Part 2.
The horizontal axis (x-axis) is calculated by a given unit’s average payoff under the IS scheme divided by their average
payoff under the FS scheme across all periods. The vertical axis (y-axis) is the percentage of times the unit voted for IS
and takes a value between 0 and 1. The size of each point indicates its frequency. The numbers in parentheses in the linear

16

The numbers of groups that selected the IS (FS) scheme for all phases were 1(3), 1(2), 0(2), and 4(1) in the IVoting, I-Voting-ST, T-Voting, and T-Voting-ST treatments, respectively. The average contribution of the groupthat exclusively selected IS in the I-Voting treatment was 11.2 points.
17
Factors other than realized payoffs also affected the groups’ scheme choice outcomes (Appendix Table C.5). First,
subjects’ preferences for fairness drove their scheme choices. Subjects provided their views on the fairness of eachscheme in the post-experiment questionnaire. A calculation found that the fairer its members on average perceived
the IS scheme relative to the FS scheme, the more likely a group was to implement the IS scheme. Second, subjects’levels of trust in others also drove their selection of the IS scheme. Specifically, the more strongly group members
believed that people were trustworthy, the more frequently a group implemented the IS scheme. Third, cognitive
ability affected voting, especially when the punishment strength was modest. A calculation shows that a moremathematically able group was more likely to select the IS scheme in the I-Voting and T-Voting treatments. Recall
that sustaining cooperation with informal punishment was difficult when punishment strength was modest.
However, more cognitively able groups might have attempted to build cooperative relationships without relying on
the alternative centralized mechanism since the FS scheme entailed an administrative cost.

21equation (OLS) in each panel are robust standard errors clustered by group ID. The slopes in the linear lines in panels a, b,
c, and d are significantly positive at two-sided p = 0.046, 0.009, 0.004, and 0.009, respectively.

Result 2: (a) Despite standard theory based on selfish preferences predicting the superiority of the FS
scheme, around half of the groups adopted the IS scheme. (b) Decision-making units voted for the scheme
under which they had previously experienced higher payoffs. (c) Almost all groups that selected one
scheme (FS or IS) for all phases achieved successful cooperation in that scheme.

4.3. Discontinuity Effects in Utilizing the Sanctioning Institutions
While the analysis in Section 4.2 usefully uncovered the motives behind the units’ scheme
choices (FS or IS), it does not help to determine which hypothesis, Hypothesis 2 or Hypothesis 3, drove
Result 1. Section 4.3 will explain that the higher efficiency of teams relative to individuals was driven by
the “truth wins” mechanism: in line with Hypothesis 3, there were clear differences in the ways in which
decision-making units utilize the sanctioning institutions.

4.3.1. Voting and Contribution Behaviors in the FS Scheme
Units’ decisions to contribute under the FS scheme were strongly influenced by their group’s
sanction rate. A regression analysis finds that units were significantly more likely to contribute large
amounts, the higher the sanction rate their group had implemented (Appendix Table C.7). Having adeterrent sanction rate effectively improves units’ decisions to contribute (again see Appendix Table C.7).
The larger impact of having stronger punishment is consistent with prior research on formal sanctioning
institutions (e.g., Falkinger et al. 2000; Kamei et al. 2015), suggesting that a centralized solution of thefree riding problem is to enforce an incentive mechanism in a society or organization.
Result 1 was driven by the difference in voting. As shown in Figure 5, the popularity of sanction
rates differs markedly between individuals and teams. First, the sanction rate of 0.0 was the focal point
among the individuals. Strikingly, individuals in the I-Voting and I-Voting-ST treatments voted for thezero sanction rate on 63.79% and 54.00% of the occasions, respectively (Figure 5.A). As a result of the
majority rule applied, the regime without any sanctions, the same regime as in Phase 1, was implemented
on 70.69% and 57.00% of the occasions, respectively, in these two treatments (Figure 5.B).18,19
Given this revealed voting patterns of individuals, Hypothesis 2 predicts that if team decisionmaking were a mere aggregation process of three members’ preferences, then teams would vote for nondeterrent sanction rates, and therefore collectively enact non-deterrent formal schemes in their groups,
more frequently than individuals. However, clearly contrary to this hypothesis, teams voted for the zero
18

In Kamei et al. (2015), almost all individuals successfully constructed deterrent schemes. The difference between
this study and Kamei et al. could be due to the difference in the research site: the USA versus England. Alternatively
it could be due to the difference in the group size – three in this study versus five in Kamei et al. (2015).
19
The outcome of the zero sanction rate is somewhat larger than the percentage of the voters who preferred it (e.g.,
70.69% > 63.79%). This is due to the majority voting system because it tends to outnumber the preferences ofminorities – a phenomenon called the Condorcet’s (1785) jury theorem or the behavioral public choice theorem(e.g., Ertan et al. 2009; Hauser et al. 2014).

22

sanction rate only 34.06% and 28.03% of the time in the T-Voting and T-Voting-ST treatments,
respectively. Instead, consistent with Hypothesis 3, teams used voting much more efficiently than
individuals: teams voted for deterrent sanction rates (0.4 or above) on 56.88% and 66.67% of the occasions
in the T-Voting and T-Voting-ST treatments, respectively. In particular, teams’ preferences for the highest
sanction rate – 1.2 per point allocated to the private account – were strikingly strong (Figure 5.A). In the TVoting-ST treatment, teams voted for the highest rate on 53.54% of the occasions. With the majority rule,31.52% (26.09%) and 62.12% (14.39%) of the vote outcomes were the highest (zero) sanction rate in the
T-Voting and T-Voting-ST treatments, respectively.20 The average realized group sanction rates were 0.64
and 0.89, both of which are deterrent, in the T-Voting and T-Voting-ST treatments, respectively. However,average realized sanction rates were much smaller in the individual treatments, i.e., 0.11 and 0.39 in the IVoting and I-Voting-ST treatments, respectively.21,22 The difference in the severity of selected sanction

Figure 5: Voting on Sanction Rates and Vote Outcome
80%

60%

0.0
0.2
0.4

40%

0.6
0.8
1.0

20%

1.2

0%
I‐Voting

I‐Voting‐ST

T‐Voting

T‐Voting‐ST(A) Distributions of Decision-Making Units’ Voting
80%

60%

0.0
0.2
0.4

40%

0.6
0.8
1.0

20%

1.2

0%
I‐Voting

I‐Voting‐ST

T‐Voting

T‐Voting‐ST(B) Distributions of Vote Outcomes

The percentages of cases in which a group selected the zero (highest) sanction rate in Phases 2 to 6 are
significantly different between individual and team voting at two-sided p = 0.0080 (p = 0.0319), according to agroup-level Mann-Whitney test, when pooled data are used – see Panel F of online Appendix B.
21
The average realized sanction rates are significantly different at two-sided p = 0.0116 between individual versus
team voting when pooled data are used (see Panel F of Appendix B).
22
Figure C.3 reports the popularity of sanction rates, period by period. It indicates that teams’ strong preferences fordeterrent sanction rates were stable across all periods, while individuals’ preferences for non-deterrent sanction rates
were strong from earlier periods and became even stronger gradually as the experiment progressed.
20

23

Table 4: Average Contribution by Sanction Rate under the FS scheme
Sanction rate(a) Individual Voting(i) All(ii) I(iii) Idata
VotingVoting-ST(i) All
data

0.0 or 0.2 (nondeterrent)
0.4 or above(deterrent)

7.52(4.08)
17.67(5.38)

15.12(5.87)
19.10(1.51)

7.86(4.66)
16.72(6.29)

7.04(3.59)
18.34(4.27)(c) Mann-Whitney tests#1(i) H0:(ii) H0:(iii) H0:
a.i = b.i a.ii = b.ii a.iii = b.iii(b) Team Voting(ii) T(iii) TVoting
Voting-ST
14.09(6.93)
18.50(2.14)

16.42(4.99)
19.42(0.72)

0.0110**0.14150.0274**0.0268**0.1467

0.0949*#1

Notes: Numbers in parenthesis are standard errors based on group averages. Two-sided p for group-level Mann-Whitney
tests. *, **, and *** indicate significance at the 0.10 level, at the 0.05 level, and at the 0.01 level, respectively.

rates well explains the stronger contribution behaviors of teams in the voting treatments (Figure 2,
Appendix Figure C.1), and supports Hypothesis 3 – the truth wins mechanism.23
Result 1 was also partly affected by decision-making units’ decisions to contribute in the FS
scheme, which differs between individuals and teams even when the same sanction rates prevailed.
Strikingly, on average, teams contributed significantly more than individuals, whether sanctions were
deterrent or not (see columns a.i, b.i, and c.i of Table 4). The difference was especially large under nondeterrent sanction rates (i.e., rates of 0.0 or 0.2). This difference cannot be explained by a selectivity bias.
Notice that more cooperative groups can be assumed to select stronger sanction rates, making mutual
cooperation easier (Appendix Table C.7). If this assumption is correct, the least cooperative units would beoverrepresented in groups that enacted non-deterrent sanction rates for the T-Voting (T-Voting-ST) rather
than the I-Voting (I-Voting-ST) treatment, because such weak sanction rates were realized only in a small
fraction of groups in the team treatments (Figure 5, Table 4).
As the maintenance of group cooperation norms leads to large long-term payoffs, the teams’
stronger behavioral responses to sanction rates suggest that, with the FS being enacted, teams may be
more far-sighted and less myopic loss averse than individuals (Sutter 2007, 2009; Bougheas et al. 2013),
and these responses are again consistent with the positive effects of deliberation and learning that the truth
win mechanism proposes.
Result 3: (a) While individuals voted for the zero sanction rate more than 50% of the time in the two
individual treatments, teams did so much less than 50% of the time in the two team treatments,
inconsistent with Hypothesis 2. Instead, teams voted for deterrent sanction rates more than 50% of thetime, and this more efficient voting by teams is consistent with Hypothesis 3. As a result, (c) teams
enacted significantly stronger sanction rates than individuals. Specifically, the average sanction rate in
the T-Voting (T-Voting-ST) treatment was 0.64 (0.89), while the average sanction rate in the I-Voting (IAs was done for the groups’ scheme choices in Section 4.2, bivariate correlations between a group’s average
sanction rate and their attribute variables were calculated to explore what non-material factors might have affected
their selection of sanction rates (Appendix Table C.6). The calculation reveals (a) female subjects’ possible dislikeof using punishment, (b) economics students’ rational voting, i.e., voting for strong sanction rates, (c) a positive
impact of perceived fairness under the FS scheme on voting, and (d) subjects’ intention to encourage others’
contributions through centralized punishment, for both individuals and teams.
23

24

Voting-ST) treatment was 0.11 (0.39). (b) Teams contributed significantly more than individuals for given
sanction rates.

4.3.2. Contribution and Punishment Behaviors in the IS Scheme
Decision-making units inflicted costly punishment based on the distribution of contributions intheir group (Appendix Table C.8). First, the smaller the amount a decision-making unit j contributed tothe public account relative to i, the more strongly i punished j. Second, contributing more than anothermember also attracted punishment by that member to some degree, but such anti-social punishment issignificantly weaker than pro-social punishment. These two patterns, which hold for all treatments, are inline with the prior research (e.g., Fehr and Gächter 2000; Kamei and Putterman 2015). But then, what
may explain the difference in efficiency between individuals and teams in the IS scheme? The differencein the overall punishment distribution provides the answer.
Figure 6 reports the relative strength and frequency of anti-social (perverse) punishment to prosocial (non-perverse) punishment. It reveals that (a) pro-social (non-perverse) punishment was moreprevalent than anti-social (perverse) punishment among individuals, and that (b) pro-social (non-perverse)
punishment was even more dominant among teams than individuals.24 The teams’ better targetedpunishment behaviors are consistent with both Hypothesis 2 and Hypothesis 3.
In addition, behavioral responses to punishment received differ by the decision-making unit

Figure 6: Relative Strength and Frequency of Perverse/Anti-Social Punishment
0.20

0.45
Individuals

Teams

0.40

0.18

Individuals

Teams

0.16

0.35

0.14

0.30

0.12
0.25
0.10
0.20

0.08

0.15

0.06

0.10

0.04

0.05

0.02
0.00

0.00

relative frequency relative strength relative frequency relative strength

relative frequency relative strength relative frequency relative strength(a) Perverse Punishment(a) Perverse Punishment(b) Anti‐social Punishment(A) I-Voting versus T-Voting treatments(b) Anti‐social Punishment(B) I-Voting-ST versus T-Voting-ST treatments

Notes: Following Herrmann et al. (2008), (i) punishment from i to j in period t is defined as anti-social if j
contributed more than i or when both i and j are 20-contributors in that period, and (ii) punishment that is not antisocial is called pro-social. Following Cinyabuguma et al. (2006), (iii) punishment from i to j in period t is defined asperverse if j contributed more than their group average or when all in their group contributed the full endowment
amount in that period, and (iv) punishment that is not perverse is called non-perverse.

Due to the small sample size, the difference is only significant at p = 0.0544 for the relative frequency if a onesided group-level Mann-Whitney test is used for pooled data. However, a finite mixture modeling analysis in
Section 5 reveals significant different punishment strategies between individuals and teams.
24

25

(see again Table C.8). Individuals were insensitive to punishment received: Pro-social punishment did notencourage individuals to contribute larger amounts in the following periods. Anti-social punishment also
did not significantly discourage the recipients’ subsequent cooperative behaviors. Instead, individuals
tended to conform to group norms, forming contribution decisions based on their group’s last-period
contribution behavior. By contrast, teams strongly responded to peers’ anti-social punishment: the larger
the anti-social punishment teams received, the more strongly they reduced their own contributions in thefollowing periods. In addition, pro-social punishment helped teams boost cooperation for the T-VotingST treatment. These differences in the behavioral responses are more in line with the “truth wins”
hypothesis. Notice that simple preference aggregation predicts that teams would be more insensitive than
individuals to punishments received, considering that individuals tended not to respond to punishment.
Unlike this prediction, teams responded more strongly than individuals to punishments received so as tomake punishment better targeted and to sustain contributions at high levels.

5. Structural Estimations of Punishment Types under the IS Scheme
The main experimental finding of the previous section was that (a) teams are able to sustain
cooperation at a higher level than individuals when they can vote on sanctioning institutions, and (b) theteams’ high efficiency is driven by their effective use of punishment. This is consistent with the “truthwins” mechanism summarized as Hypothesis 3.
In the previous analysis, while the percentages of types as a voter, i.e., rational or irrational, wereprecisely compared among units in the FS scheme (Figure 5), it is still unclear what percentages of units
punished anti-socially or pro-socially in the IS scheme. Section 5 analyzes the differences in punishment
type in the IS scheme more accurately by using finite mixture modeling.25,26
Finite mixture modeling assumes a set of possible behavioral types in advance and then assigns aprobability measure over the types to each subject so that the likelihood is maximized (McLachlan andPeel 2000; Moffatt 2016). Table 5 reports the estimation results.27 Two models were estimated byassuming different sets of three punishment types, as there are two approaches to define punishment
patterns (Herrmann et al. 2008; Cinyabuguma et al. 2006). The first model assumes the pro-social
punisher, the anti-social punisher, and the selfish type (Herrmann et al. 2008), while the second model

25

Some units voted on sanction rates in an indecisive manner (e.g., voted for deterrent rates in some periods and fornon-deterrent rates in the other periods). To explain their behavior, finite mixture modeling analysis was conducted
for these units by assuming possible types (e.g., a type who votes based on their punishment received in the lastperiod). However, almost all models were unable to be estimated (failed to converge) due to a small sample size.
26
As an additional analysis, units’ contribution types were structurally estimated using the finite mixture modelingapproach. The result uncovered that the distribution of contribution types under the FS scheme differs largely fromthose under the IS scheme, as the decision-making units decided contribution amounts in response to the sanction
rate collectively enforced in the current period under the FS scheme (Appendix Table C.9).
27
Typical to a maximum likelihood method, estimation results may depend on what starting values are assumed. In
each model of Table 5, starting values were chosen to achieve the highest log likelihood. The selected starting
values in some models coincide with the starting values based on the method suggested by Moffatt (2016).

26

assumes the non-perverse punisher, the perverse punisher, and the selfish type (Cinyabuguma et al. 2006).
The pro-social and anti-social punishers, and the perverse and non-perverse punishers, are defined thesame as in Section 4.3.2. The selfish type is defined as a player who does not inflict punishment
throughout.
Consider, first, Models A.i and B.i to see behavioral differences between individuals and teams
with a larger dataset. The results show that a larger percentage of teams, relative to individuals, inflictedpunishment on low contributors (60.0% versus 49.4% in panel I, and 65.6% versus 48.2% in panel II).
The difference in the classified type is especially large in panel II: According to a two-sided KolmogorovSmirnov test, the percentage of non-perverse punishers is significantly larger among teams than
individuals at p = 0.025. On the other hand, types that engage in “misdirected” punishment are regularlypresent regardless of the decision-making format.28 This implies that the issue of misdirected punishment
is ubiquitous whether among individuals or teams.
The estimation results by the respective treatment provide further nuanced explanations for the
discontinuity effects detected in Section 4. First, strikingly, the percentage of anti-social (perverse)
punishers is only 9.1% (12.4%) in the T-Voting treatment, which is less than one fourth (a half) of thepercentage in the I-Voting treatment, while the percentages of pro-social (non-perverse) punishers do notdiffer much between the two treatments.29 Hence, under weak punishment intensity, there is strong
evidence that team decision-making effectively prevents units from engaging in misdirected punishment.
Result 4: (a) On average, a significantly larger percentage of teams, relative to individuals, inflictedpunishment on low contributors, while “misdirected” punishment was observed both for individuals andteams. (b) Under the weak punishment intensity, team decision-making effectively prevented decisionmaking units from engaging in misdirected punishment.
Stronger punishment intensity makes individuals reluctant to anti-socially punish members(compare Models A.ii and A.iii of Table 5), perhaps being afraid of inviting blind revenge in thefollowing periods (e.g., Ostrom et al., 1992). This is consistent with the higher efficiency of the I-VotingST relative to the I-Voting treatment seen in Result 1.b and Figures 2 and 3. Reflecting this, teams cannot
be judged superior to individuals for their punishment type choices under the strong punishment intensity.
In particular, as seen in Models A.iii and B.iii, the differences of the estimated percentages of pro-social
versus anti-social punishers (non-perverse versus perverse punishers) are large for both the individuals
and teams: 44.4% (44.5%) in the T-Voting-ST treatment, and 27.8% (38.8%) in the I-Voting-ST
treatment. So, why did the T-Voting-ST treatment perform much better, compared with the I-Voting-ST
Regarding misdirected punishment, no consistent patterns were seen between unit types across definitions: “antisocial” (“perverse”) punishment was less (more) frequent among teams than individuals.
29
A two-sided Kolmogorov-Smirnov test finds that the percentages of anti-social (perverse) punishers are
significantly different between the T-Voting and I-Voting treatments at p < 0.001 (p = 0.018), while the percentagesof pro-social (non-perverse) punishers are not significantly different between the T-Voting and I-Voting treatments
at p = 0.391 (p = 0.148).
28

27

treatment, at sustaining cooperation (Figures 2 and 3)? The answer to this may be due to the difference inthe percentages of pro-social or non-perverse punishers per group. Notice that, remarkably, around 6768% of teams, i.e., on average two out of three units per group, are classified as pro-social/non-perverse
punishers in the T-Voting-ST treatment (Model B.iii). The corresponding percentage is around 45-52% in
the I-Voting-ST treatment (Model A.iii), which means that there is often only one pro-social/non-perverse
punisher per group. It might have been challenging for a single member to discipline two members of hergroup in the I-Voting-ST treatment as punishment is privately costly.

Table 5: Estimated Percentages of Punishment Types in the IS Scheme
Treatment:

A. Individual Voting(i) All data(ii) I-Voting

B. Team Voting(iii) I-Voting-ST(i) All data(ii) T-Voting(iii) T-Voting-ST

I. Pro-social versus Anti-social punishment
Classified types [%]Pro-social

49.4% (7.7)*** 44.2% (10.1)*** 52.9% (10.9)***Anti-social

25.5% (6.0)*** 41.5% (9.3)***

25.1% (8.2)***

19.1% (5.7)***25.1% (7.0)***22.0% (9.4)**

20.9% (7.7)*** 42.6% (12.9)***Selfish14.3% (7.8)*

60.0% (8.2)*** 48.3% (12.9)*** 67.9% (11.6)***
9.1% (5.0)*

23.5% (9.3)**
8.6% (8.1)# of obs.

1,344

624

720

1,296

768

528

Wald χ2

118.77103.8943.70

162.02

128.48

41.56

Prob > Wald χ2< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001

II. Perverse versus Non-perverse punishment
Classified types [%]Non-perverse 48.2% (7.1)*** 49.8% (10.3)*** 46.1% (10.4)***
Perverse
16.7% (5.3)*** 26.8% (9.7)*** 26.5% (8.1)***
Selfish

35.2% (6.6)*** 23.4% (8.9)***27.4% (9.6)***

65.6% (8.1)*** 60.3% (10.3)*** 67.4% (11.6)***
20.3% (5.5)***

12.4% (5.8)**

23.9% (9.4)**

14.2% (6.8)**

27.3% (9.7)***

8.8% (8.2)# of obs.

1,344

624

720

1,296

768

528

Wald χ2

120.31

61.92

14.56

123.22

70.73

39.78

Prob > Wald χ2< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001

Notes: The numbers in parentheses are standard errors. All models were estimated by having a tremble term. Estimation
results in each model occasionally varied dependent on their starting values, due to multiple local equilibria of thelikelihood function. As such, starting values were initially set based on the method suggested by Moffatt (2016), and thensystematically varied to achieve the global maximum log likelihood. The selected starting values coincide with the starting
value based on the method suggested by Moffatt (2016) for models A.i, A.ii and B.ii of panel I and models A.i, A.ii, andA.iii of panel II. *, **, and *** indicate significance at the 0.10 level, at the 0.05 level, and at the 0.01 level, respectively.

6. Team Communication Dialogues
Teams’ communication dialogues contain richer information that may explain the reasoning behindteam decisions, although a look at the communication dialogues do not help explain the behavioral
differences between individuals and teams as such contents are unavailable for individuals that could be
used for comparison. As discussed in Cooper and Kagel (2022), analysis using behavioral data is based onassumptions imposed by each estimation model. Human reasoning and responses are usually complex andcan differ from those assumed in economic models; as such, it is still meaningful to investigate teams’28communication dialogues.30 As a final supplementary analysis, communication dialogues were analyzed
following the standard coding procedure in the experimental literature (e.g., Cooper and Kagel 2005, 2022;
Cason and Mui 2015; Kagel and McGee 2016; Leibbrandt and Sääksvuori 2012).
Two research assistants (RAs) were hired as independent coders. They were not informed about any
substance of the research, such as the research aim, to avoid demand effects. They were simply provided
with the instructions, communication logs, and the list of codes, and were then asked to assign as many
relevant codes as possible to each log. The full list of codes is available in online Appendix D.2. Once thetwo RAs finished coding, the researchers checked for discrepancies between the two coders’ classifications
and highlighted any differences. After that, each coder was given the other coder’s assigned codes andcould reconsider their own coding, with the knowledge that the other coder would independently follow thesame reconsideration process. This reconsideration process was first used, and confirmed effective incatching any errors in initial coding, by Elten and Penczynski (2020). Appendix D.1 includes the detail ofthe coding procedure adopted in the present paper.
Cohen’s Kappa (Cohen, 1960) is the most popular form of agreement analysis and is hence used tojudge the reliability of coding (e.g., Cason and Mui 2015; Leibbrandt and Sääksvuori 2012). The final
Kappas after reconsideration were 0.88, 0.90, and 0.87 in the T-No, T-Voting, and T-Voting-ST treatments,
respectively. Appendix D.3 includes the Kappas for individual codes, showing that almost all codes havehigh Kappa values. Regression analyses below use codes whose Kappa is above 0.4. 0.4 is often used as acriterion for the reliability of codes (e.g., Landis and Koch 1977, Bougheas et al. 2013, Cason et al. 2012).

6.1. Voting on Sanction Rates in the FS Scheme
As discussed in Section 4.3.1, a large fraction of decision-making units, even teams (34.06% and28.03% of occasions in the T-Voting and T-Voting-ST treatments, respectively), voted for the zero
sanction rate. Two codes were considered to capture this inefficient voting behavior:
C1: Suggests 0.0 sanction rate/desire to have effectively no fine due to ideological reasons (e.g., dislike of
coercive measures) or simply due to their tastes against the cost.
C2: Suggests 0.0 sanction rate/desire to have effectively no fine due to confusion of the incentive structure.
The earlier analysis in Section 4.3.1 at the same time found that teams selected stronger sanction
rates much more frequently than individuals (Figure 5). Thus, two additional codes were also considered
to explain possible sources for this efficient voting behavior as follows:
C5: Discusses rate based on deterrence, i.e., deterrent (non-deterrent) if it is ≥ (<) 0.4.
C6: Discusses effects of a strong sanction rate, other than deterrence (e.g., why 1.2 is preferred to 0.8).
The key difference between C5 and C6 is whether members recognize the relationship between
30

Cooper and Kagel (2022) demonstrated that the distributions of subjects’ strategy choices in an IRPD are
estimated significantly differently between coding exercises from communication logs and the Strategy Frequency
Estimation Method, because subjects often employ a strategy which is incompletely specified at the outset.

29sanction rates and material incentives in the game. The sanction rate should be set equal to or greater than
0.4 to induce other units to contribute fully to the public account. A rational decision-making unit would
be indifferent between the sanction rates of, for example, 0.4 and 0.8. The two coders assigned Codes C1,
C2, C5, and C6 at least once for 28.1%, 43.9%, 63.2%, and 26.3% of the teams playing FS, respectively.
Table 6.A reports key estimation results of a regression where the dependent variable is teamvoting on a sanction rate in the FS scheme. The results first indicate that C1 and C2 are both significantly
negative predictors for units’ sanction rate preferences. This confirms that some subjects’ dislike of using
centralized punishment and/or confusion does harm efficient institutional formation. Second, C6 is asignificant positive predictor for their preferred sanction rates. C5 has also a significant and positivecoefficient for the T-Voting-ST treatment, but not when all data are used (column (1)). A close look by
the authors at the coding results for Code C6 and the teams’ communication logs indicate that teams often
had negative reactions and intolerance towards low contributions, and therefore had preferences for the
maximum sanction rate to punish such acts. This result collaborates with the fact that the sanction rate of
1.2 was the most popular among the deterrent sanction rates (Figure 5).
In summary, it can be concluded that voting for deterrent sanction rates was driven by their
negative reactions and intolerance towards low contributions and their learning about its impact.

6.2. Informal Punishment Decisions in the IS Scheme
Both individuals and teams inflicted punishment not only pro-socially but also anti-socially(Section 4.3.2). Four codes are considered to investigate motives behind these punitive behaviors:
F1: Suggests punishment for a contribution higher than their own (anti-social).
F2: Suggests no punishment for a contribution higher than their own (pro-social).
F3: Suggests punishment for a contribution lower than their own (pro-social).
F4: Suggests no punishment for a contribution lower than their own.
Codes F1 to F4 are defined using the anti- or pro-social punishment classification (Hermann et al.
2008). As in the earlier analyses, four more codes (F5 to F9) are also considered based on the perverse or
non-perverse punishment definition (Cinyabuguma et al. 2006). The result shown in Section 6.2 is basedon Codes F1 to F4, and is similar when Codes F5 to F9 are instead used (Appendix D.4.b).
In order to control for factors related to confusion, errors, and mistakes evident in thecommunication, Code F19 is also considered:F19: Confusion, errors, mistakes (e.g., failing to understand the punishment cost).
Table 6.B reports key regression results. It first shows that Code F19 is a positive predictor forunits’ punishment decisions. Thus, some units’ punishment activities are due to their low cognitive
ability. However, even after controlling for Code F19, Codes F1 and F3 are positive predictors for units’
decisions to punish (and the size of the absolute values of the coefficient estimates are much larger thanfor F2 and F4, respectively). This means that punishment motives are heterogeneous (Kamei 2014), and30

units have clear intentions to punish pro-socially, or anti-socially, parallel to the observations from thedecision data.
The results reveal three further patterns. First, emotion (Code F16: Suggests punishment as anemotional response) drives punishment. Second, some units inflict punishment on those whose
contribution is less than a certain threshold (Code F9: Suggests punishment based on absolute contribution
e.g. below or above a specific number). Third, costs for punishment (Code F11: Expresses desire to avoid
punishment regardless of contribution due to the cost in imposing punishment) and the fear of retaliation(Code F13: Expresses desire to avoid punishment to prevent retaliation) discourage punishment.

Table 6: Reasoning behind Units’ Use of Punishment
A. Team votes on a sanction rate in the FS scheme
Dependent variable: a sanction rate voted by team i in period t

C1 dummy
C2 dummy
C5 dummy
C6 dummy# of observations
Wald χ2
Prob > Wald χ2(1) Pooled data
Coefficient
S.E.
-1.475***0.270-1.565***
0.229
0.226
0.182
1.161***
0.339
672--136.33--0.000---(2) T-Voting
Coefficient
S.E.
-1.319***0.284-1.041***
0.256-0.1640.215
0.747*
0.403
276--75.53--0.000---(3) T-Voting-ST
Coefficient
S.E.
-1.557***0.525-1.862***0.391
0.991***
0.314
1.299**
0.651
396--78.49--0.000---

Notes: Tobit regressions. Decision-making unit random effects were included to control for the panel structure. The regression
includes all C codes and G codes with Kappa being above 0.4, phase dummies, and the Period within phases variable as
independent variables. The full estimation result can be found in online Appendix Section D.4.a. *, **, and *** indicate
significance at the 0.10 level, at the 0.05 level, and at the 0.01 level, respectively.

B. Team informal punishment decisions in the IS scheme
Dependent variable: total punishment points assigned from team i to the other two teams in i’s group in period t

F1 dummy
F2 dummy
F3 dummy
F4 dummy
F9 dummyF11 dummy
F13 dummy
F16 dummy
F19 dummy# of observations
Wald χ2
Prob > Wald χ2(1) Pooled data
Coefficient
S.E.
6.349***
1.284-3.610***1.308
8.835***
1.101-2.909**1.233
3.576***
1.116-3.259**1.443-3.736**1.592
6.103***
2.100
7.964***
1.741
648--172.55--0.000---(2) T-Voting
Coefficient
S.E.
6.747***
1.506-3.916**1.538
10.352***
1.381-6.107***1.497
4.569***
1.368-0.0191.990-5.046*2.741-5.132
3.805
6.153***
2.065
384--150.91--0.000---(3) T-Voting-ST
Coefficient
S.E.
3.946***
1.473-3.409**1.529
7.524***
1.116
1.621
1.074
9.646***
1.773-8.875***
1.507
0.775
1.076
9.854***
1.519
12.468***2.736
264--n.a.
--n.a.
---

Notes: Tobit regressions with decision-making unit random effects. Codes associated with the definition of anti-social/pro-social
punishment were used. The regression includes all F and G codes (except F5 to F8) with Kappa being above 0.4, phase dummies,
and the Period within phases variable as independent variables. The full estimation result can be found in online Appendix
Section D.4.b. *, **, and *** indicate significance at the 0.10 level, at the 0.05 level, and at the 0.01 level, respectively.

31

6.3. Contribution Decisions
Coding analyses, summarized in Table 7, suggest qualitatively similar, and important motives forcontributions for all team treatments. First, units with unconditional willingness to cooperate contributed
large amounts (variable i). Apart from such altruistic motives, some units aimed to encourage other units
to cooperate, or to avoid discouraging already cooperative teams, through contributing large amounts(variable ii). Second, however, there were some units who discussed unconditional free riding in thecommunication stage, and did so as their team contribution decisions (variable iii), consistent with theprevalence of such free rider types in public goods dilemmas (e.g., Fischbacher et al. 2001; Fischbacher
and Gächter 2010). Those who had inclinations to cooperate tended to decrease contributions out of
distrust for the other teams or safety (variable iv).
There is one interesting pattern for the units’ reasoning under the FS scheme. The result showsthat units’ desire to avoid receiving fines (code D9), rather than material calculations in the public goods
game (D10), drove their strong contribution behaviors. This implies that positive effects of formal
sanctioning institutions widely documented in prior research, such as in Falkinger et al. (2000) and Kamei
et al. (2015), may emerge merely from people’s dislikes of receiving formal punishment, regardless of
their cognitive ability or understanding of the material incentives within the game.

Table 7: Reasoning behind Units’ Contribution Decisions
Dependent variable: contribution amount of team i in period t
Codes included in the regression:i. Contribute high always (Codes A2, D1, E1
dummies)ii. Contribute high to encourage others to cooperate(Codes A3, D3, E3 dummies)iii. Contribute low always (Codes A4, D2, E2
dummies)iv. Contribute low out of distrust (Codes A5, D4,
E4 dummies)
vi. Contribute to avoid fines (Code D9 dummy)vii. Contribute based on material payoff
maximization (Code D10 dummy)# of observations
Wald χ2
Prob > Wald χ2(1) No scheme
Coefficient
S.E.

(2) Under FS scheme
Coefficient
S.E.

(3) Under IS scheme
Coefficient
S.E.

4.954***

0.617

8.916***

2.049

3.015***

1.201

5.428***

0.612

2.380

2.594

5.558***

1.570-4.098***0.625-6.524***

2.107-7.058***1.143-4.429***0.714-12.415***

2.700-7.788***

1.608

---

---

9.203***

2.388

---

---

---

---

-2.624

2.046

---

---

1,128
749.450.000-------

672
212.5
0.000

-------

648
254.420.000-------

Notes: Tobit regressions with decision-making unit random effects. The regressions include all relevant codes (all A codes, D
codes, and E codes in columns (1), (2) and (3), respectively) and G codes with Kappa being above 0.4, phase dummies, and the
Period within phases variable as independent variables. The full estimation results can be found in online Appendix Section
D.4.c. *, **, and *** indicate significance at the 0.10 level, at the 0.05 level, and at the 0.01 level, respectively.

6.4. Scheme Choice
The remaining analysis is on communication dialogues related to scheme choices. The same kindof regression analysis using classification codes was performed. However, a relatively large number ofthe codes were omitted in the analysis due to collinearity. Nevertheless, four patterns are worth
32

mentioning. First, units’ support for the FS scheme is driven by their dislike of the unpredictable/variable
nature of the IS scheme (Code B2). Second, however, some units voted for the FS scheme in theexperiment with a clear intention to construct the NS by selecting the zero sanction rate (Code B3). Third,some units voted against the FS scheme to avoid the fixed administrative charge of operating the scheme(Code B4). Lastly, consistent with the results summarized in Figure 4, members discussed priorexperiences/contributions/behaviors under IS and FS schemes in order to decide which sanctioning
scheme to vote for (Code B11). Online Appendix D.4.d includes the detail of the estimation results.

7. Conclusion
Team decision-making is ubiquitous whether in the public or private sphere. The literature in thetheory of the firm has so far assumed that team decision-making is inferior to individual decision-making
due to imperfect information, monitoring issues, and agency costs. In their theoretical context, teamdecision-making is just identical to individual decision-making when complexities in teams are resolved(e.g., Alchian and Demsetz 1972; Marschak and Radner 1972). Furthermore, team decision-making hasreceived no attention in the experimental literature in an institutional setting to date either. While during
the last two decades numerous scholars have studied members’ institutional choices and self-governancepossibilities by letting them vote in experiments (e.g., Gürerk et al. 2006; Kosfeld et al. 2009; Sutter et al.
2010; Ertan et al. 2009; Kamei et al. 2015; Fehr and Williams 2018), no studies used teams as thedecision-making unit (voter). Using individuals as the decision-making unit could be a nice simplification
if the following assumption is correct: teams make the same institutional choices as individuals on thecondition that the former hold the same information and face the same incentive structure as the latter.
However, to the authors’ knowledge, there is no research to compare institutional formation andbehaviors under the selected institutions between individuals and teams. Moreover, little research has
been conducted to study the role of team decision-making in the empirical literature in management andorganizations.
This paper demonstrated, for the first time, that teams may be more able than individuals to form
efficient institutions by voting and therefore overcome free riding in groups more effectively. In theexperiment, decision-making units, teams or individuals, were given a voting opportunity to either
construct a formal sanction scheme or to use informal punishment in a public goods dilemma. The results
showed that teams achieved surprisingly higher levels of group contributions than individuals in the
public goods game. The strong effects of team decision-making were driven by teams’ effective use of the
sanctioning institutions. When the formal scheme was selected, teams voted to enact deterrent sanction
rates much more frequently than individuals. The difference in voting is remarkable: while the majority of
individuals in the individual treatments voted for the zero sanction rate, teams voted for deterrent sanction
rates more than 50% of the time. When peer-to-peer punishment was instead selected, teams inflicted
costly punishment more frequently on low contributors than individuals. These results are consistent with33

the “truth wins” hypothesis. This hypothesis explains that teams achieve better choices than individuals
through deliberation and learning.
While the results obtained from the present experiment are sufficiently clear, this study is only the
first step in researching the individual-team discontinuity effect on institutional choices in dilemma
situations. There are many directions for further research. For example, this study set both the team sizeand group size to three. The sizes of teams and/or groups could be much larger in real organizations,however. The design setup chosen in this study was necessary because with larger team and group sizes
the experiment would have been too costly in terms of payment size and the difficulty in implementing
the experiment. However, it would be a useful robustness check to study the same research questions bychanging the group size and/or team size. For another example, the three team members communicatedwith each other anonymously, i.e., without being allowed to disclose their identifiable information, tojointly make a single decision in the experiment. This design setup is standard in the current experimental
literature (e.g., Charness and Sutter [2012], Kugler et al. [2012] and Kerr et al. [2004]) and is useful toidentify the effects of team decision-making in isolation while controlling for any effect of teamcomposition. In the typical workplace environment (excluding some anonymous online work), however,members of a team are fully or partially aware of the identity of each other. It would therefore be
worthwhile studying how the discontinuity-effect phenomenon differs by the anonymity condition withinteams. Another important direction of further research is to study possible discontinuity effects whenconflicts among team members prevail (e.g., Glaetzle-Ruetzler et al., 2021). The present study assumes
for simplicity that the three members in a team received the same payoffs, but there are many real-world
situations where team members receive different payoffs. Lastly, of course, the finding of this researchalso opens up further avenues for theoretical research, for example, in the theory of the firm, as according
to the finding of the present experiment, teams, as decision-making units, make different choices throughdeliberation and learning compared with individuals, even if they face the same incentive structure.

References
Aboramadan M (2020) Top Management Teams Characteristics and Firms Performance: Literature
Review and Avenues for Future Research. Int. J. Organ. Anal. 29(3):603-628.
Ahn T.K., Ostrom E, Schmidt D, Shupp R, Walker J (2001) Cooperation in PD games: Fear, greed, andhistory of play. Public Choice 106(1/2): 137-55.
Alchian A, Demsetz H (1972) Production, Information Costs, and Economic Organization. Am. Econ.
Rev. 62(5): 777-795.
Anderson C, Putterman L (2006) Do non-strategic sanctions obey the law of demand? The demand forpunishment in the voluntary contribution mechanism. Games Econ. Behav. 54(1):1-24.
Andreoni J (1988) Why Free Ride? Strategies and Learning in Public Goods Experiments. J. Pub. Econ.
37:291-304.
Appelbaum E, Batt R, 1994. The New American Workplace: Transforming Work Systems in the UnitedStates, ILR Press, Ithaca, NY.
34

Bainbridge S (2002) Why a Board? Group Decisionmaking in Corporate Governance. Vanderbilt Law
Rev. 55(1).
Bednar J, Chen Y, Liu T, Page S (2012) Behavioral Spillovers and Cognitive Load in Multiple Games: anExperimental Study. Games Econ. Behav. 74:12-31.
Blinder A, Morgan J (2005) Are Two Heads Better than One? Monetary Policy by Committee. J. Money
Credit Bank. 37(5):789-811.
Bornstein G, Kugler T, Ziegelmeyer A (2004) Individual and Group Decisions in the Centipede Game:Are Groups More ‘‘Rational’’ Players? J. Exp. Soc. Psychol. 40:599-605.
Bornstein G, Yaniv I (1998) Individual and Group Behavior in the Ultimatum Games: Are Groups More
“Rational” Players? Exp. Econ. 1:101-108.
Bougheas S, Nieboer J, Sefton M (2013) Risk-taking in Social Settings: Group and Peer Effects. J. Econ.
Behav. Organ. 92:273-283.
Carmeli A, Sheaffer Z, Halevi M Y (2009) Does Participatory Decision-Making in Top Management
Teams Enhance Decision Effectiveness and Firm Performance? Pers. Rev. 38(6):696-714.
Casari M, Luini L (2009) Cooperation under Alternative Punishment Institutions: An Experiment. J.
Econ. Behav. Organ. 71(2):273-282.
Cason T, Mui V (1997) A Laboratory Study of Group Polarisation in the Team Dictator Game. Econ. J.
107:1465-83.
Cason T, Mui V (2015) Rich communication, social motivations, and coordinated resistance against
divide-and-conquer: A laboratory investigation. Euro. J. Polit. Econ. 37:146-159.
Cason T, Savikhin A, Sheremeta R (2012) Behavioral Spillovers in Coordination Games. Eur. Econ. Rev.
56:233-245.
Cason T, Sheremeta R, Zhang J (2012) Communication and efficiency in competitive coordination
games. Games Econ. Behav. 76:26-43.
Certo T, Lester R, Dalton C, Dalton D (2006) Top Management Teams, Strategy and Financial
Performance: A Meta-Analytic Examination. J. Manag. Stud. 43(4):813-839.
Charness G, Sutter M (2012) Groups Make Better Self-Interested Decisions. J. Econ. Perspect. 26:157-76.
Chaudhuri A (2011) Sustaining Cooperation in Laboratory Public Goods Experiments: A Selective
Survey of the Literature. Exp. Econ. 14(1):47-83.
Chiappori P-A, Mazzocco M (2017) Static and Intertemporal Household Decisions. J. Econ. Lit. 55(3):985-1045.
Cinyabuguma M, Page T, Putterman L (2006) Can second-order punishment deter perverse punishment?
Exp. Econ. 9:265-279.
Cohen J (1960) A Coefficient of Agreement for Nominal Scales. Educ. Psychol. Meas. 20:37-46.
Cohen S, Bailey D (1997) What Makes Teams Work: Group Effectiveness Research from the Shop Floor
to the Executive Suite. J. Manag. 23(3): 230-290.
Condorcet M (1785) Essai sur l'application de l'analyse à la probabilité des décisions rendues à la pluralitédes voix, de l'Impr. royalede l'Impr. royale, Paris.
Cooney R (2004) Empowered self‐management and the design of work teams. Pers. Rev. 33(6):677-692.
Cooper D, Kagel J (2005) Are two heads better than one? Team versus individual play in signalinggames. Am. Econ. Rev. 95(3):477-509.
35

Cooper D, Kagel J (2022) Using Team Discussions to Understand Behavior in Indefinitely RepeatedPrisoner’s Dilemma Games. Working Paper.
Cox C, Stoddard B (2018) Strategic thinking in public goods games with teams. J. Pub. Econ. 161:31-43.
DeGroot M (1974) Reaching a Consensus. Journal of the American Statistical Association. 69(345):118121.
Denant-Boemont L, Masclet D, Noussair C (2007) Punishment, counterpunishment and sanction
enforcement in a social dilemma experiment. Econ. Theory 33:154-167.
Delarue A, Van Hootegem G, Procter S, Burridge M (2007) Teamworking and organizational
performance: A review of survey-based research. Int. J. Manag. Rev. 10(2):127-148.
Devine D, Clayton L, Philips J, Dunford B, Melner S (1999) Teams in Organizations: Prevalence,
Characteristics, and Effectiveness. Small Group Res. 30(6):678-711.
Elten J, Penczynski S (2020) Coordination games with asymmetric payoffs: An experimental study withintra-group communication. J. Econ. Behav. Organ. 169:158-188.
Ertan A, Page T, Putterman L (2009) Who to punish? Individual decisions and majority rule in mitigating
the free rider problem. Eur. Econ. Rev. 53:495-511.
Eurofound, Cedefop (2020). European Company Survey 2019: Workplace practices unlocking employee
potential. European Company Survey 2019, Publications Office of the European Union, Luxembourg.
Falkinger J, Fehr E, Gächter S, Winter-Ebmer R (2000) A Simple Mechanism for the Efficient Provisionof Public Goods: Experimental Evidence. Am. Econ. Rev. 90(1):247-264.
Fehr E, Gächter S (2000) Cooperation and Punishment in Public Goods Experiments. Am. Econ. Rev.
90(4):980-994.
Fehr E, Gächter S (2002) Altruistic punishment in humans. Nature 415:137-140.
Fehr E, Williams T (2018) Social Norms, Endogenous Sorting and the Culture of Cooperation. Universityof Zurich Department of Economics Working paper No. 267.
Feri F, Irlenbusch B, Sutter M (2010) Efficiency Gains from Team-based Coordination—Large-Scale
Experimental Evidence. Am. Econ. Rev. 100:1892-912.
Fischbacher U, Gächter S, Fehr E (2001) Are People Conditionally Cooperative? Evidence from a Public
Goods Experiment. Econ. Lett. 71(3):397-404.
Fischbacher U, Gächter S (2010) Social Preferences, Beliefs, and the Dynamics of Free Riding in Public
Goods Experiments. Am. Econ. Rev. 100(1):541-56.
Gächter S, Renner E, Sefton M (2008) The Long-Run Benefits of Punishment. Science 322(5907):1510.
Gibbons R, Matouschek N, Roberts J (2013) Decisions in Organizations (Ch. 10) in The Handbook of
Organizational Economics (ed. by R. Gibbons and J. Roberts), 373-431, Princeton University Press.
Gillet J, Schram A, Sonnemans J (2009) The Tragedy of the Commons Revisited: the Importance ofGroup Decision-Making. J. Pub. Econ. 93:785-797.
Glätzle-Rützler D, Lergetporer P, Sutter M, 2021. Collective intertemporal decisions and heterogeneity ingroups. Games Econ. Behav. 130:131-147.
Grant R (1996) Toward a Knowledge-based Theory of the Firm. Strateg. Manag. J. 17:109-122.
Grosse S, Putterman L, Rockenbach B (2011) Monitoring in Teams: Using Laboratory Experiments to
Study a Theory of the Firm. J. Eur. Econ. Assoc. 9(4):785-816.

36

Gunnthorsdottir A, Houser D, McCabe K (2007) Disposition, History and Contributions in Public Goods
Experiments. J. Econ. Behav. Organ. 62(2):304-15.
Gürerk O, Irlenbusch B, Rockenbach B (2006) The Competitive Advantage of Sanctioning Institutions.
Science 312(5770):108-111.
Güth W, Levatia V, Sutter M, der Heijden E, 2007. Leading by Example with and without Exclusion
Power in Voluntary Contribution Experiments, J. Pub. Econ. 91:1023-1042.
Guzzo R, Dickson M (1996) Teams in Organizations: Recent Research on Performance andEffectiveness. Annu. Rev. Psychol. 47:307-338.
Hamilton B, Nickerson J, Owan H (2003) Team Incentives and Worker Heterogeneity: An Empirical
Analysis of the Impact of Teams on Productivity and Participation. J. Polit. Econ. 111(3):465-497.
Hauser O, Rand D, Peysakhovich A, Nowak M (2014) Cooperating with the future. Nature 511:220-223.
He Y, Lien J, Zheng J (2022) Making Use of the Wisdom of Crowds: Stuck in the Majority Rule. SSRN
Working Paper.
Herrmann B, Thöni C, Gächter S (2008) Antisocial Punishment across Societies. Science 319:1362-1367.
Holmstrom B (1982) Moral Hazard in Teams. Bell J. Econ. 13(2):324-340.
Ichniowski C, Shaw K, Prennushi G (1997) The Effects of Human Resource Management Practices on
Productivity: A Study of Steel Finishing Lines. Am. Econ. Rev. 87:291-313.
Isenberg D (1986) Group Polarization: A Critical Review and Meta-Analysis. J. Pers. Soc. Psychol.
50(6): 1141-1151.
Kagel J (2018) Cooperation through Communication: Teams and Individuals in Finitely Repeated Prisoners’
Dilemma Games. J. Econ. Behav. Organ. 146:55-64.
Kagel J, McGee P (2016) Team versus Individual Play in Finitely Repeated Prisoner Dilemma Games.
Am. Econ. J. Micro 8(2):253-76.
Kamei K (2014) Conditional Punishment. Econ. Lett. 124(2):199-202.
Kamei K (2016) Democracy and Resilient Pro-social Behavioral Change: An Experimental Study. Soc.
Choice Welf. 47(2):359-378.
Kamei K (2019a) Cooperation and Endogenous Repetition in an Infinitely Repeated Social Dilemma. Int.
J. Game Theory 48(3):797-834.
Kamei K (2019b) The Power of Joint Decision-Making in a Finitely-Repeated Dilemma. Oxford Econ.
Pap. 71(3):600-622.
Kamei K (2021) Teams do Inflict Costly Third Party Punishment as Individuals do: Experimental
Evidence. Games 12(1):22.
Kamei K, Putterman L (2015) In Broad Daylight: Fuller Information and Higher-Order Punishment
Opportunities can Promote Cooperation. J. Econ. Behav. Organ. 120:145-159.
Kamei K, Putterman L, Tyran J-R (2015) State or Nature? Endogenous Formal versus Informal Sanctionsin the Voluntary Provision of Public Goods. Exp. Econ. 18:38-65.
Kerr N, Tindale S (2004) Group Performance and Decision Making. Annu. Rev. Psychol. 55:623-655.
Kersley B, Alpin C, Forth J, Bryson A, Bewley H, Dix G, Oxenbridge S (2005) Inside the Workplace:Findings from the 2004 Workplace Employment Relations Survey. WERS.
Kocher M, Sutter M (2005) The Decision Maker Matters: Individual versus Group Behavior inExperimental Beauty-Contest Games. Econ. J. 115:200-223.
37

Kosfeld M, Okada A, Riedl A (2009) Institution Formation in Public Goods Games. Am. Econ. Rev.
99(4):1335-55.
Kugler T, Kausel E, Kocher M (2012) Are Groups More Rational than Individuals? A Review ofInteractive Decision Making in Groups. WIREs Cognitive Science 3:471-482.
Kugler, T, Bornstein G, Kocher M, Sutter M (2007) Trust between Individuals and Groups: Groups are
Less Trusting than Individuals but Just as Trustworthy. J. Econ. Psych. 28:646-57.
Landis R, Koch G (1977) An Application of Hierarchical Kappa-type Statistics in the Assessment ofMajority Agreement among Multiple Observers. Biometrics 33(2):363-74.
Laughlin, PR, (2015) Social Combination Processes of Cooperative Problem-Solving Groups. Progress insocial psychology. Psychology Press.
Lawler E, Mohrman S A, Ledford G (1992) Employee Involvement and Total Quality Management:
Practices and Results in Fortune 1000 Companies. San Francisco: Jossey-Bass.
Lawler E, Mohrman S A, Ledford G (1995) Creating High Performance Organizations: Impact of
Employee Involvement and Total Quality Management. San Francisco: Jossey-Bass.
Ledyard J (1995) Public Goods: A Survey of Experimental Research, pages 111-194 in J. Kagel and A.
Roth (eds.), Handbook of Experimental Economics. Princeton University Press.
Leibbrandt A, Sääksvuori L (2012) Communication in Intergroup Conflicts. Eur. Econ. Rev. 56(6):1136-47.
Marschak J, Radner R (1972) Economic Theory of Teams. New Haven: Yale University Press.
McLachlan G, Peel D (2000) Finite Mixture Models. New York: Wiley.
Moffatt P (2016) Experimetrics: Econometrics for experimental economics, Macmillan International
Higher Education.
Müller W, Tan F (2013) Who Acts More Like a Game Theorist? Group and Individual Play in a
Sequential Market Game and the Effect of the Time Horizon. Games Econ. Behav. 82:658-74.
Nicklisch A, Putterman L, Thöni C (2021) Trigger-Happy or Precisionist? On Demand for Monitoring in
Peer-based Public Goods Provision. J. Pub. Econ. 200:104429.
Nikiforakis N, Normann H-T (2008) A Comparative Statics Analysis of Punishment in Public-Good
Experiments. Exp. Econ. 11:358-369.
Ostrom E, Walker J, Gardner R (1992) Covenants With and Without a Sword: Self-Governance is
Possible. Am. Polit. Sci. Rev. 86(2):404-417.
Pfeffer J (1998) Seven Practices of Successful Organizations. Calif. Manage. Rev. 40(2):96-124.
Robert C, Carnevale P (1997) Group Choice in Ultimatum Bargaining. Organ. Behav. Hum. Decis.
Process. 72:256-279.
Sunstein C (2007) Republic.com 2.0, Princeton University Press.
Sutter M (2007) Are Teams prone to myopic loss aversion? An Experimental Study on Individual Versus
Team Investment Behavior. Econ. Lett. 97:128-132.
Sutter M (2009) Individual Behavior and Group Membership: Comment. Am. Econ. Rev. 99:2247-2257.
Sutter M, Haigner S, Kocher M (2010) Choosing the Carrot or the Stick? – Endogenous Institutional
Choice in Social Dilemma Situations. Rev. Econ. Stud. 77(4):1540-1566.
Traulsen A, Röhl T, Milinskim M (2012) An Economic Experiment Reveals that Humans Prefer Pool
Punishment to Maintain the Commons. Proc. R. Soc. B: Biol. Sci. 279:3716-3721.

38

Tyran J-R, Feld L (2006) Achieving Compliance when Legal Sanctions are Non-deterrent. Scan. J. Econ.
108(1):135-156.
Zhang B, Li C, Silva H, Bednarik P, Sigmund K (2014) The Evolution of Sanctioning Institutions: anExperimental Approach to the Social Contract. Exp. Econ. 17(2):285-303.

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