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

職場におけるただ乗り問題、ワークプレイス・デモクラシー、そして労働者の私的行動の抑制に関する考察（リアル・エフォート実験からの事実）
亀井 憲樹、ケイティ・タベロ
2023 年 4 月 27 日

DP2023-011
https://ies.keio.ac.jp/publications/22729/

Institute for Economic Studies, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
ies-office@adst.keio.ac.jp
27 April, 2023

職場におけるただ乗り問題、ワークプレイス・デモクラシー、そして労働者の私的行動の抑制に関する考察（リアル・エフォート実験からの事実）
亀井 憲樹、ケイティ・タベロ
IES Keio DP2023-011
2023 年 4 月 27 日
JEL Classification: C92, D02, D72, H41
キーワード: ワークプレイスデモクラシー、モラルハザード、実験、ただ乗り、チームワーク【要旨】
企業組織において「チーム」は典型的な意思決定ユニットであり、また作業ユニットでもある。本論文では、斬新なリアル・エフォートタスク実験をもとに行動データを収集することで、（A）どの程度、チームがグループ最適の実現のためにメンバーの私的行動を抑制することを選び、また（B）ワークプレイス・デモクラシーが労働生産性向上にどう寄与するか考察した。経済実験に参加した被験者は３人１組のチームに割り振られ、他の２チームとグループを構成し、グループでのレベニュー・シェアのもと、リアル・エフォー
トタスクに取り組んだ。各メンバーは、タスク期間中に、他のメンバーに知られることなく、怠業、つまり、テトリス・ゲームをプレイすることが出来た。本研究では、各グルー
プに怠業インセンティブを減らすポリシーがランダムに導入される『非民主的な』トリー
トメントと、民主的に導入の可否が決定される『民主的』トリートメントの２種類が実施された。実験結果によると、非民主的トリートメントに比べ、民主的トリートメントで統計的に有意なレベルの強い労働時間当たりの生産が実現した。この正の効果はポリシー導入の可否によらず観測された。このことは民主的カルチャーが人々の行動に直接正の作用を及ぼしたことを意味する。一方で、ワークプレイス・デモクラシーにより、（強い労働による疲労を受けた）労働者の怠業時間が増大した。しかしながら、労働時間当たり生産性の増大を受け、『民主的』トリートメントで生産が減少することはなかった。
亀井 憲樹
慶應義塾大学経済学部〒108-8345 東京都港区三田2-15-45
kenju.kamei@keio.jp
ケイティ・タベロ
ダラム大学
英国ダラム郡ミルヒル・レーン ダラム大学ビジネススクール（郵便番号 DH1 3LB）
katy.tabero@durham.ac.uk
謝辞：This project was supported by a grant-in-aid from the Murata Science Foundation. Durham
University and the Northern Ireland and North East Doctoral Training Partnership provided
additional funding support. The authors thank John Hey and Mark Wilson (an IT manager at the
University of York) for their support in recruiting subjects, and Louis Putterman and Pedro Dal Bó
for helpful comments.

Free Riding, Democracy and Sacrifice in the Workplace:
Evidence from a Real Effort Experiment
April 2023
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: Teams are increasingly popular decision-making and work units in firms. This paper
uses a novel real effort experiment to show that (a) some teams in the workplace reduce theirmembers’ private benefits to achieve a group optimum in a social dilemma and (b) suchendogenous choices by themselves enhance their work productivity (per work time production) –a phenomenon called the “dividend of democracy.” In the experiment, worker subjects are
randomly assigned to a team of three, and they then jointly solve a collaborative real effort task
under a revenue-sharing rule in their group with two other teams, while each individual worker
can privately and independently shirk by playing a Tetris game. Strikingly, teams exhibit
significantly higher productivity (per-work-time production) when they can decide whether toreduce the return from shirking by voting than when the policy implementation is randomly
decided from above, irrespective of the policy implementation outcome. This means that
democratic culture directly affects behavior. On the other hand, the workers under democracy
also increase their shirking, presumably due to enhanced fatigue owing to the stronger
productivity. Despite this, democracy does not decrease overall production thanks to theenhanced work productivity.

Keywords: workplace democracy, moral hazard, experiment, free riding, teamwork
JEL classification codes: C92, D02, D72, H41
Acknowledgement: This project was supported by a grant-in-aid from the Murata Science
Foundation. Durham University and the Northern Ireland and North East Doctoral Training
Partnership provided additional funding support. The authors thank John Hey and Mark Wilson(an IT manager at the University of York) for their support in recruiting subjects, and Louis
Putterman and Pedro Dal Bó for helpful comments.

11. Introduction
Teams are increasing popular in firms as decision-making and work units (e.g., Kamei and
Tabero, 2022). However, team decision-making and teamwork feature a coordination problem thatinvolves complexities relating to imperfect information, monitoring, and agency costs (e.g., Alchian and
Demsetz, 1972; Marschak and Radner, 1972). Thus, maintaining motivation among workers isparticularly difficult when teams are involved in the workplace, and their private interests conflict withgroup interests (e.g., Bolton and Dewatripont, 2004) — a typical example of this is moral hazard ingroups (e.g., Alchian and Demsetz, 1972; Holmstrom 1982). Democratic culture may help mitigate
conflict within and across the teams by not only enhancing their self-determination and intrinsic
motivation to cooperate (e.g., Deci and Ryan, 1985, 2000), but by also providing workers with
opportunities to signal their willingness to cooperate with their peers through democratic processes (e.g.,
Connelly et al., 2011; Bergh et al., 2014), thereby making it easy to achieve the group optimum. In suchenvironments, workers may decide to collectively decrease temptations by reducing their private gains forthe sake of group interests. But how large could the effects of workplace democracy per se on
productivity potentially be? Precisely what motivates workers’ sacrificial behaviors?
How to overcome moral hazard in groups is an important, active question in economics andmanagement. A large body of research spanning several decades has found that workers have difficulty
cooperating with each other when free riding incentives are sufficiently strong in a social dilemma (e.g.,
Ledyard, 1995; Zelmer, 2003). Specifically, prior experimental research suggests that while some people
demonstrate conditional willingness to cooperate, groups usually cannot sustain cooperation for various
reasons, e.g., their cooperation behaviors are heterogeneous (e.g., Fischbacher et al., 2001), they areeasily discouraged by seeing their peers free ride (e.g., Fischbacher and Gächter, 2010); or many tend tocooperate but by less than others (e.g., Thöni and Volk, 2018). This echoes theoretical research that
describes why moral hazard arises among workers when their effort levels are not perfectly observable(e.g., Alchian and Demsetz, 1972; Holmstrom, 1982).1 Both the theoretical and empirical literature
therefore discuss that some institutional solutions, such as corporate culture and working environments,
competition (e.g., internal job ladder, tournament), punishment and rewards, monitoring, and sorting, arerequired to assist collaboration and cooperation in the workplace (see, e.g., Prendergast 1999 forpersonnel economics, and Chaudhuri 2011 for experimental, literature). This study contributes to the large
body of literature by investigating the impact of workplace democracy, workers’ behavioral reactions to a
reduction in incentives to shirk, as well as the reasoning behind their voluntary sacrificial behaviors in theworkplace.
This study is the first to experimentally measure the so-called “dividend of democracy” when thedecision-making and work units are teams. The “dividend of democracy” is referred to an effect that
democracy directly has on the behavior of those involved. The role of democratic culture on worker
behavior has been actively studied in the literature in economics and management for the last two decades(see Dal Bó 2010 for a survey). In particular, prior experiments in economics have shown that democracy
in implementing a pro-social policy boosts cooperation in experimental games, such as public goods or
prisoner’s dilemma games, as it directly affects people’s own behavior and beliefs on their peers’
1

The difficulty in sustaining cooperation has also been widely discussed in the theoretical literature in the voluntaryprovision of public goods (e.g., Samuelson, 1954; Bergstrom et al., 1986).

1

cooperativeness (e.g., Tyran and Feld, 2006; Dal Bó et al., 2010; Sutter et al., 2010; Kamei, 2016).
Among others, Tyran and Feld (2006), Dal Bó et al. (2010) and Dal Bó et al. (2019) provide methods toisolate the dividend of democracy from selection bias, showing that the dividend of democracy is large.
Scholars have recently started to study the applicability of such a dividend of democracy in a workplacesetting by using a design with real effort tasks, but the results surprisingly showed that democracy per se
may not have strong effects in real effort settings (e.g., Dal Bó et al., 2019; Kamei and Markussen,
forthcoming; Melizzo et al., 2014). While all prior experiments on democracy used individuals as thedecision-making unit, the present study uses teams as the decision-making unit of policy-making andtask-solving for the first time, and find a significant dividend of democracy on work productivity (perwork-time production).
The policy available to workers in this study is one to reduce material incentives to shirk.
Collectively sacrificing one’s benefits through fostering customs, conventions, or rules with the aim ofresolving conflicting interests has been conceptually discussed in literature in the social sciences (such as
anthropology) and biology as key features of humans. Anecdotal evidence includes costly participation inreligious groups and rituals, or recreational activities in societies (e.g., dance and festivals), food sharing(e.g., turtle hunting by islanders for funerary rituals), holding redistributive feasts, and attending groupraids and defence (see, e.g., Smith and Bliege, 2000; Hawkes and Bliege, 2002; Sosis and Alcorta, 2003;
Sosis and Bressler, 2003; Hagen and Bryant, 2003; Lannaccone, 1992). The mechanism is described asfollows: sacrificing serves as a costly signal of one’s own quality (e.g., Gintis et al., 2001; Bliege andSmith, 2005), thus helping to coordinate with others to cooperate and bolster a cooperative atmosphere indilemma situations.2,3 Several laboratory experiments used public goods games or prisoner’s dilemma
games to study costly human sacrificing tendencies with high internal validity (e.g., Aimone et al., 2013;
Brekke et al., 2011; Grimm and Mengel, 2009). The findings are that some groups (individuals) docollectively (voluntarily) decide to reduce their private returns, thereby enhancing welfare. However, tothe best of the authors’ knowledge, sacrificing has not been studied in the workplace context using anaturally-occurring, real effort task, although recently there has been a theoretical attempt to characterize
the effects of sacrificing in the workplace (Bisetti et al., 2022).4
While sacrifice has received less attention in the workplace so far, it is becoming more and more
relevant due to a surge in remote working (potentially boosting shirking) triggered by the Covid-19 crisisand technological advances. A broad range of examples of unobserved shirking activities andcountermeasure policies are readily available in the modern workplace. For example, cyberloafing is a
In general, actors’ many decisions are characterized as costly signaling in modern societies. Examples include thejob market, in which applicants invest in education or other qualifications to indicate their quality (Spence, 1973), orat the firm level by which firms indicate their quality to other firms, the market, or other stakeholders throughinvestment in high profile board members, awards, alliances, or underpricing (see Bergh et al. 2014 for a review andexamples).
3
Empirically, people are known to choose transaction partners in dilemma situations based on factors that informthe quality of that partner. Elfenbein et al. (2012), using a novel dataset composed of more than 160,000 eBaylistings, successfully demonstrated that in online marketplaces, buyers tend to purchase products tied to charity, andthus sellers have incentives to use a charity program (e.g., eBay’s Giving Works program) as a quality signal.
4
Bisetti et al. (2022) propose a self-reporting mechanism in which a team’s pay is based on their observed joint
output and their team’s self-reported performance. They prove that a team has the incentive to under-report theirgroup’s performance (sacrifice wages for all in the team) as a punishment to free-riders, thereby enabling them toachieve higher welfare.
22

typical and costly issue whereby employees covertly use their computer or internet access for personaluse during work time. The issue is especially serious when they are not in an office. The employer may
decide to introduce measures to counter employees’ cyberloafing, for example, by monitoring their use of
the internet, imposing internet restriction policies and penalties for breaching them, or placing technicalrestrictions on employees’ access to certain non-work websites.5 While such policies can simply be
imposed from above by managerial staff or teams, the policies can also be enacted through decentralized
decision-making. For instance, a factory may produce mechanical parts by assigning workers to several
teams to take advantage of specialization. When their environment is democratic and they recognize thatcyberloafing undermines productivity, they may democratically decide to enact a restriction policy acrossthe teams, with an aim of improving the performance in the factory if they believe that productivity
impacts their material benefits such as their wages, bonuses, or rewards.6 Similar scenarios are common
across various employment relationships, e.g., a branch in a consulting firm, or a sales office for products(e.g., cars). Another related example is “moonlighting” by which employees work multiple jobs,
sometimes simultaneously and/or without the permission of their main employer.7 For example, an
employee may commit to working five days per week while secretly working for another firm to earnmore by shirking the main job. Alternatively, an employee may hold a secondary side job that takes placeoutside of their primary work hours, but spend time during those hours contributing to their secondaryjob, such as responding to e-mails, advertising, or checking their website. The increase in remote working
in recent years makes monitoring more difficult. Policies to make working on the side difficult andmaterially unbeneficial (e.g., through using a screen-capture tool and work-time tracking) may beconsidered if such free riding significantly undermines production in the main workplace.
This paper conducts an experiment with a novel “collaborative” real effort task. In theexperiment, worker subjects are randomly assigned to a team of three, and three teams constitute a group.
The real effort task requires each team to jointly calculate the number of 4s in a matrix whose cells
contain 1s, 2s, 3s, or 4s. At the onset of the experiment, each team member is assigned a number, player
1, 2, or 3, such that they have different numbers from each other. The matrix that player k is allocatedincludes only number ks while the other three numbers are blacked out. Each member counts theirassigned numbers, shares the counting outcome, and jointly calculates the final answer, on the condition
that their remuneration is based on revenue-sharing in the group. To mimic the conflict between work andshirk (or another activity) in the real workplace, each member is allowed to privately and independently
play a computer game, Tetris. A shirker can privately earn some material returns from gaming on top of
psychologically enjoying Tetris. The incentive structure is therefore similar to the so-called stag-huntgame (e.g., Hume, 1739; Rousseau, 1755): all the three members of a team must work on counting to earna reward as a team from the collaborative task, but each member has an incentive to deviate to gaming

5

Strengthening monitoring increases the probability that cyberloafing is detected and penalties are assigned, thereby
reducing workers’ incentives to cyberloaf. As will be described soon, for the sake of simplicity, the present paper
considers a policy to reduce material returns from shirking deterministically in the workplace in the experiment.
6
Knew and Simester (2001) argued that work groups may voluntarily strengthen mutual monitoring within theirgroups to obtain a bonus through achieving a firm-level goal.
7
Moonlighting is increasingly common in some countries because it is encouraged by the government. For example,
lifetime employment was a common practice in Japan traditionally. However, the Japanese Ministry of Health,Labour and Welfare published the “Guidelines for Promotion of Side Work” and deleted the description ofprohibition of subsidiary business from “The Model Rules of Employment” in 2018.

3

(thereby earning some earnings privately). This setup is parallel to the real-world examples of modern
distractions at work such as cyberloafing and moonlighting. Before the task-solving phase begins, a
policy that reduces the incentive to play Tetris (“reduction policy,” hereafter) is implemented in a groupeither democratically (by voting) or autocratically (randomly by the computer without voting). The twotreatments (democratic, or autocratic) are designed using a between-subjects design.
In addition to the contribution to the literature on workplace democracy, this research is novel in
two additional aspects. First, this study provides significant methodological contributions with the newly
used “collaborative” counting task and gaming as a real activity. While much research has been
conducted using real effort tasks, a significant issue has been reported by Araujo et al. (2016) thatworkers’ incentive elasticity of outputs may be too small with the real effort tasks used. Recently, Corgnet
et al. (2015) and Kamei and Markussen (forthcoming) allowed subjects to use, respectively, internet
browsers and comedy videos, as real leisure activities. Both of the papers showed that such activities
enhance incentive elasticity in experiments. The present paper adds to the literature by using gaming as areal, but controlled, leisure activity for the first time in a computerized real effort experiment. Further, themembers of each team jointly work on a collaborative counting task. While an individual counting zeros
task is widely used in the literature (e.g., Falk et al., 2006; Abeler et al., 2009; Kamei and Markussen,
forthcoming), the use of a collaborative version is the first attempt in the literature, to the authors’knowledge. This design is meaningful as collaboration is a central aspect of teamwork in many firms andorganizations, and the new task is designed to explicitly simulate the coordination structure. Notice thestark difference in the game structure between the standard counting task and the collaborative counting
task. The new collaborative one is a coordination game: individuals earn from the team task only when all
three members work by spending time counting and communicating accurately and effectively. The newtask allows researchers to study coordination in the structure of a stag-hunt game in a natural way, evenoutside the research agenda of organizational economics and management.
Second, the experiment is the first to investigate workers’ sacrifice decisions and their reasoning
in a real effort environment. While prior research used experimental games such as public goods games topropose that some individuals will reduce their private gains in dilemma situations, showing that suchdecisions may lead to a Pareto improvement empirically (e.g., Aimone et al., 2013; Brekke et al., 2011;
Grimm and Mengel, 2009), its validity in the workplace setting is unclear as little research used naturallyoccurring, real effort in their experiments. Equally important is that no research explores what may drive
workers to sacrifice their private gains, because no data is available regarding their thinking. Subjects inthe present experiment decide whether to reduce their private gains through communication within theirteam as a team decision. This design enables us to collect a unique incentive-compatible dataset to study
the reasoning behind sacrifice decisions. A well-established coding exercise is applied to thecommunication logs in order to uncover reasoning effectively.
The experiment results reveal some teams’ preferences for sacrifice and evidence of a dividend of
democracy. 40.9% of teams voted to reduce the incentive to play the game, and as a result, the reduction
policy was enacted for 38.7% of groups. Teams that were involved in democratic decision-making
exhibited significantly higher work productivity, i.e., performance per minute of working, than those in
the regime where the computer randomly decided policy implementation, whether the reduction policy
was imposed or not. This means that the democratic culture per se directly affected behavior. Having said
that, the workers under democracy reduced work time compared to those under autocracy, presumably
4due to fatigue accumulating more quickly for the former. Nevertheless, the former did not decrease teamproduction overall thanks to the enhanced work productivity.
The present paper also provides reasoning behind workers’ sacrifice decisions based on astandard coding exercise. It reveals that the units that planned to exclusively work on task-solving,
believed that the reduction policy would deter others from shirking, or those that had supportive teamatmospheres supported the reduction policy. It also uncovers the value of signaling through sacrificial
decisions to encourage collaboration: teams who believed that other teams would complete tasks
following the vote performed strongly.
The rest of the paper proceeds as follows: Section 2 summarizes the experimental design, andSection 3 reports the results. Section 4 provides insights obtained from an analysis of communication
dialogues, and Section 5 concludes.

2. Experimental Design
The experiment is designed using a collaborative real effort task devised for this study. At theonset of the experiment, worker subjects are randomly assigned to a team of three. The three members arethen randomly assigned ID numbers, 1, 2, or 3, so that each member receives a different number from one
another. Anonymity is retained such that they do not know the identity of the other members (e.g., faces,names, gender). Let us call the player who is assigned number k {1,2,3} “player k.” The teamcomposition and the assigned ID numbers do not change for the entire experiment (partner matching).
Three teams further constitute a group (each group thus has nine members). The group composition also
does not change throughout. Section 2.1 explains the nature of the collaborative team real effort task, after
which Section 2.2 explains the structure of the experiment, a summary of treatments, the remuneration
system, and the reduction policy that could be implemented in each group. Appendix A summarizes theexperimental procedure and includes instructions used in the experiment.

2.1. A Collaborative Real Effort Task
Three members in a respective team collaboratively solve a variant of the counting task(“collaborative counting task”). The original “counting task” (e.g., Falk et al., 2006; Abeler et al., 2009;
Kamei and Markussen, forthcoming) is an individual real effort task in which subjects independently
count the number of 0s in a matrix that contains 0s and 1s. To the authors’ knowledge, no collaborative
version of the counting task has been devised and used in any prior experiments. In the new collaborative
counting task, the three team members are provided with a 15×15 matrix, each cell of which has arandomly generated integer between 1 and 4 (each integer is independently drawn with a probability of25%), and are then asked to submit the number of 4s. Collaboration is required to find the correct answer,
because only number ks appear on the computer screen of player k, while the other three numbers areblacked out – see Figure 1 for a screen image for player 1. Each team can find the correct answer if player
k counts the number of ks correctly and shares it with their teammates, and the team calculates the numberof 4s accurately after that. For example, if the numbers of 1s, 2s, and 3s are, respectively, 32, 14, and 43,
then the correct answer (the number of 4s) is: 225 – 32 – 14 – 43 = 136. A calculator is available on eachsubject’s computer screen. How to calculate the number of 4s, and by whom, is up to each team’s
discretion. When the team decides on and wants to submit the answer, all three members must submit the
team’s joint answer on their own computer screens. Hence, in the submission stage as well they must
communicate with each other about their team’s decision to answer correctly. In the case of disagreement,
5

a member can submit a different answer from the others.8 However, the answer will then be counted asincorrect. Once all three members submit an answer, a new 15×15 matrix with randomly generated 1s, 2s,
3s, and 4s in each cell is assigned to the team, and the process repeats.
Free-form communication is available using an electronic chat window during the entire tasksolving process (Figure 1; Appendix A also includes the screen image of the chat window), and messages
are recorded. This design piece helps the researchers study the reasoning behind members’ behaviors,
post-experiment. While any sort of communication, such as discussing strategy to solve the problems,
sharing the number of ks, or chatting about unrelated matters, is allowed, subjects are prohibited fromusing any kind of offensive language or sharing any information that compromises anonymity.9
The more questions a team answers correctly, the higher the earnings they can generate in theirgroup. Each correct answer is rewarded with 180 UK pence in the experiment. How the 180 pence are
distributed within the team or the group is explained in Section 2.1.

Figure 1: A Screen for Collaborative Counting Task

Notes: A screen image for player 1. The numbers of 2s, 3s and 4s are blacked out on the screen that player 1 sees.
The 15×15 matrix in this figure is for illustration only.

This very rarely happened in the experiment. All three members submitted the same answers in 96.9% of teams’
submissions in the experiment (3,176 out of 3,278 completed tasks in the 62 experiment sessions). The authors readthrough all the communication dialogues and their submitted answers, and found that almost all disagreements areerrors or typos. The mean number and the mode of disagreements across all teams that disagreed were, respectively,
1.72 and 1. The size of the error rate is unsurprising because the average number of attempts for these teams was
24.14 questions, above the average of 17.81 for the experiment, which might increase potential errors in typing.
9
The authors read through the communication dialogues and found no team to have broken the anonymity rule.
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2.2. The Experiment
There are two treatments that vary by changing the process to decide whether to enact a policy to
curb members’ shirking or not. A between-subjects design is used to avoid behavioral spillover (e.g.,
Bednar et al., 2012) or possible spill-over effects of democracy (e.g., Kamei, 2016). The experiment
begins with a practice phase, which is the same for all subjects in the experiment. The main task-solving
phase begins after the practice phase and differs by the treatment.10 The practice phase plays a role in not
only familiarizing subjects with the collaborative counting task, but also providing them with anopportunity to try the task and learn their ability to solve it.
In the practice phase, each team performs the collaborative counting task for three minutes.11
While they can answer as many questions as they wish, they are not informed whether they answer eachquestion correctly during the three-minute period. They are instead informed of the number of correct
answers at the end of the practice phase. Remuneration is based on revenue sharing in the team. Thismeans that the money a team earns is equally divided among the three team members (each member
receives 60 = 180/3 UK pence for a correct response). Each team does not interact with the other two
teams in their group in this practice; nor are they informed of the performances of the other teams.
In the main task-solving phase, each team performs the collaborative counting task for a much
longer duration – 35 minutes – with a revenue sharing rule in their group. This means that the credit ofeach correct answer (180 UK pence) is equally shared among the three teams, i.e., nine individuals as
each team has three members. The marginal per-capita return is calculated as 20 (= 180×1/9) UK pence.
There are two more distinct aspects in the main task-solving phase. First, unlike the practicephase, each member can privately shirk by playing Tetris. They can do so by simply pressing the “Game”
button (Figure 2.a). The screen is then switched to the Tetris site (Figure 2.b). No one, including theirteammates, are made aware of a member’s shirking unless the member voluntarily reports their behavior
using the electronic chat window. Further, the shirker earns a return by staying in the Game screen: 18
pence per minute spent in the Game screen.12 They can return to the work site from the Game site at any
time. Workers are not allowed to work while playing Tetris, whose requirement enables the researchers toquantify shirking versus work time as their work decisions. It should be noted here that the design of
gaming was carefully made to enhance external validity, as workers often have alternative activities
available when shirking in the workplace rather than being inactive. An advantage of using gaming overinternet browsing (Corgnet et al., 2015) as an alternative activity is the high level of control: workers may
use internet browsers differently as their preferences are heterogeneous. This feature shares similarities

The practice phase and the main task-solving phase are called “phase 1” and “phase 2” in the experiment
instructions.
11
To avoid cognitive overload, subjects are provided with instructions for the practice phase only at the beginning of
the experiment. Instructions for the main task-solving phase are distributed once the practice ends. Such graduallearning approach is often taken in experiments (e.g., Ertan et al., 2009; Kamei et al., 2015; Kamei and Tabero, 2022).
12
This return can be thought of as material returns that can be obtained from shirking in the real workplace. Shirkers
may build their social network using social media or by exchanging emails during work time, develop skills tobenefit future job prospects, complete personal tasks, or even moonlight privately as in the real-world example
described in the introduction of the paper. Such activities may not only provide intrinsic satisfaction but may also
provide material benefits. A similar designing approach was chosen in Kamei and Markussen (forthcoming) wherean activity alternative to solving a real effort task is to watch a funny video. Subjects in Kamei and Markussen(forthcoming) received a small return per minute watching the videos.
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with Kamei and Markussen (forthcoming) that adopted comedy video clips as an alternative activity.
However, using a game is better than video clips because implementation is difficult with the use of thelatter. While headsets were provided to each subject in Kamei and Markussen (forthcoming), the authors
acknowledged that even a small ripple of laughter and sounds could contaminate the data. In contrast,
gaming is a purely independent, quite leisure activity.
Notice that with the gaming option, the incentive structure of the team task in main phase is oneof the so-called “stag-hunt game” if they are highly skilled. Each team member can earn a small material
gain with certainty by deviating from collaboration. However, they earn a large team payoff when all
three team members work on the counting task, if each of them can count numbers sufficiently quickly.
Second, there is a penalty of three pence per incorrect answer in the main task-solving phase.
This penalty is imposed on the team that commits the error, not the whole group. Such penalties arecommonly used in the real workplace; for example, poor performance or mistakes can result in monetaryor social sanctions, increased threat of dismissal (through escalation procedures or informal threats), or
reduced pay where performance related wages or team bonuses are in place (see McNamara et al., 2022;
Doellgast and Marsden, 2018; Gibbons and Henderson, 2013, for examples). The penalty is equallyshared among the three members in the team (i.e., one penny is deducted from the payoff per teammember). In short, the payoff of member i in team k can be expressed as Equation (1):𝜋𝑘,𝑖 (𝑐𝑘 , 𝑖𝑐𝑘 , 𝑔𝑖 ) = 20[∑3𝑛=1 𝑐𝑛 ] − 𝑖𝑐𝑘 + 𝑟 ∙ 𝑔𝑖 ,(1)

where 𝑐𝑘 and 𝑖𝑐𝑘 are the numbers of, respectively, correct and incorrect answers by team k, 𝑔𝑖 is the time[minutes] that member i spends in the Game screen, and r is per minute return from shirking. Notice that
their work time is 35 – gi as they are not allowed to work while playing Tetris. Using the revenue sharing
rule per group and the alternative leisure opportunity, the aim is to model the work environment as atension across teams between task-solving and gaming (i.e., social dilemma). As intended, gaming was a
privately optimal option for almost all teams in the experiment sessions – see Section 3.3.
Worker subjects are not informed of how many questions their teams or other teams answer
correctly during the 35-minute task-solving phase.13 Instead, at the end of the task-solving phase theylearn (a) the total number of correct and incorrect responses of their own team and (b) the total number ofcorrect responses in their group. This setup is realistic; for example, in manufacturing, the manager willlearn how many defectives they have among mechanical parts produced in a given day, only after quality
checks at specified intervals.
At the beginning of the main task-solving phase, the return from staying in the Game screen (r)could decrease from 18 to 16 pence per minute. Notice that the size of the incentive change is very small
at only two pence. This means that the reduction policy can be thought of as a non-deterrent sanction
policy, i.e., a policy that does not alter the privately optimal behaviors of workers in a group (e.g., Tyran
and Feld, 2006; Kamei, 2016). As briefly reported in Section 3.3., this interpretation turns out to becorrect in the experiment: gaming was a privately optimal choice for almost all teams, whether the
reduction policy was in place or not, due to the strong incentives to free ride on other teams’ work efforts.

As discussed later, the present paper uses the “weights-based identification strategy” proposed by Dal Bó et al.
(2019) to identify the dividend of democracy. The requirement to use this method is that teams’ types are
independently drawn and their behavior only depends on the team’s own type. It is therefore essential to avoid
dynamic interactions across teams including information feedback.
13

8

Figure 2: A Screen Image for Collaborative Counting Task in the Main Task-Solving Phase(a) Work site(b) Game site
The process to implement the reduction policy differs by treatment. In the EXO treatment, thepolicy is imposed in each group by the computer randomly (i.e., with a probability of 50%). By contrast,in the ENDO treatment, the policy is implemented based on majority voting by the three teams.14 The
14While another realistic voting method is a unanimity rule (consensus), this study adopted majority voting because
the interpretation of data becomes complex when the unanimity rule is in use as it possibly involves strategic voting
among voters (e.g., Battaglini et al., 2010; Kamei, 2019a).

9

voting procedure follows three steps:
Step 1. The three members in each team are given three minutes to discuss, using an electronic chatwindow (e.g., Kamei 2019b; Luhan et al., 2007), whether they want to reduce the per minute earnings
from staying in the Game screen. The communication contents are not revealed to any other team. See
Appendix A.3 for a screen image of this step.
Step 2. After the three-minute discussion, the three members each submit their preferred decisions. If thethree submit the same decision, it becomes their team vote. However, in the case of disagreement they
can submit whatever they prefer, in which case whichever receives at least two members’ support is
implemented as their team vote.
Step 3. The reduction policy is implemented in the group based on majority voting. Specifically, it is
implemented (not implemented) if it receives two or three supporting (opposing) team votes. All
subjects in the group are informed of the vote outcome and the number of supporting votes.
Notice that as the reduction policy, despite the size of the reduction being small, may encourage
teams to work harder through decreasing the material incentives to shirk, thereby leading to a higher
payoff, groups may decide to decrease such private returns by voting. As summarized in Table 1, there
are four possible institutional outcomes in this study.

Table 1: Treatments, Distribution of Votes and Institutional Outcomes
Treatment andCondition in which the# subjects
institutional outcome policy is/isn’t implemented# of subjects in proreduction teams# of subjects in antireduction teams

ENDO treatment(i) Policy was
implemented(ii) Policy was not
implemented

VotingAt least two teams votefor the policy
At least two teams voteagainst the policy

279

114

165

108

75

33

171

39

132

EXO treatment(i) Policy was
implemented(ii) Policy was not
implemented

By the computer
Randomly (50%probability)
Randomly (50%probability)

273

---

---

123

---

---

150

---

---

Total

---

552

114

165

Note: The numbers in the “# of subjects in pro-reduction teams” and “# of subjects in anti-reduction teams”
columns are based on the results of voting in the experiment.

2.3. Theoretical Predictions
Theoretical predictions on the dividend of democracy can be derived by setting a utility function
for the player and then finding their utility-maximizing behavior. As shown in online Appendix B, acalculation suggests that teams work harder with than without the reduction policy in a given institutional
condition (ENDO or EXO), and that the positive effect is stronger in the ENDO than in the EXO
treatment, for the following reasons. First, the positive effect of the reduction policy holds theoreticallyfor the EXO treatment because the policy reduces the material incentives of shirking. As the reduction
policy is imposed randomly in each group, in theory there are no differences in individual characteristics
10

between the groups where the policy is imposed or not. Thus, only the material incentives matter in thistreatment due to the lack of selection. Second, the positive effect is also applicable to the ENDO
treatment, not only due to the beneficial effects of incentive changes, but also possible selection effects
through voting. The reduction policy is enacted in the ENDO treatment only when the majority of teams
support the policy. Considering that teams who are better at solving the collaborative counting task can beassumed to incur smaller effort costs for a given effort level, the beneficial effects of the policy on hard
work exceed enhanced effort costs more easily for such higher-skilled teams. This means that higherskilled teams are more likely to enact the reduction policy by voting, and to perform strongly in the
ENDO treatment. In other words, the impact of the reduction policy is detected more strongly in the
ENDO treatment due to selection.
It should be worth remarking here that, theoretically, the positive effect of the reduction policy
does not emerge when task-solving is too costly for teams. If the return from shirking as a team is muchlarger than the marginal return from working, members in selfish teams will just stay in the Game screen
even when the sorting effects are present in the ENDO treatment.
The main hypothesis of the paper is on the dividend of democracy summarized below:
Hypothesis: Teams put more effort into task-solving in the ENDO than in the EXO treatment, evenafter controlling for possible selection effects.
The phenomenon summarized in this hypothesis is the so-called dividend of democracy. Its
mechanism lies in the democratic process that directly influences worker tendency (e.g., Dal Bó et al.,2010; Dal Bó et al., 2019; Sutter et al., 2010; Tyran and Feld, 2006; Kamei, 2016). In a workplacesetting, Kamei and Markussen (forthcoming) model this effect such that workplace democracy lowers
workers’ marginal effort costs. A model similar to Kamei and Markussen (forthcoming) supports the
hypothesis above; a decrease in the marginal effort costs driven by democracy results in hard work among
teams (see Appendix B for the detail). Part of the dividend of democracy can also be attributed tosignaling effects (e.g., Tyran and Feld, 2006; Kamei, 2019a; Jensen and Markussen, 2022).
It should be noted that identifying the dividend of democracy requires care because of thepossible selection bias already discussed (Dal Bó et al., 2010; Dal Bó et al., 2019; Tyran and Feld, 2006).
By design, pro-reduction teams are overrepresented (underrepresented) in groups where the reduction
policy was (was not) endogenously enacted. As voting behavior is likely related to teams’ skills and work
behavior, group behaviors are not comparable between the ENDO and EXO treatments unless the
distributions of votes are balanced. The present paper adopts the “weights-based identification strategy”
proposed by Dal Bó et al. (2019). This estimation method uses weights under the whole population whencalculating the average behavior in the ENDO treatment, rather than the realized vote shares in specificinstitutional outcomes. For instance, suppose that 50% of teams vote for the reduction policy and thepolicy is imposed in 50% of groups. The % of pro-reduction teams would be much more (less) than 50%
in groups where the policy is (is not) endogenously imposed because of majority voting. Instead of thehigh (low) percentage in such groups, 50% is used as a weight in calculating the average behaviors ofpro- and anti-policy units with this method. The detail of the re-weighting method along with the data will
be provided in Section 3.

11

3. Policy Preferences, and the Dividend of Democracy
552 students (279 for the ENDO treatment and 273 for the EXO treatment) at the University of York in
the United Kingdom participated in the experiment. No subjects participated in more than one session.
The experiment followed standard practices in economics, such as neutral framing. Appendix A includes
the procedure and the instructions.
Table 1 of Section 2 includes the distribution of team votes in the experiment. Consistent with theliterature on voting experiments among individuals (e.g., Aimone et al., 2013; Dal Bó et al., 2010), it
reveals that some teams vote to reduce their private returns from shirking. It indicates that 40.9% of teams(= 38/93×100%) voted for the reduction policy. As a result of majority voting, the policy was enacted in
38.7% (= 36/93×100%) of groups in the ENDO treatment. Table 1 also shows a clear pattern of selection
bias. In the ENDO treatment, the percentage of pro-reduction teams was 69.4% (= 25/36×100%) ingroups where the policy was enacted, while the percentage was only 22.8% (= 13/57×100%) in groups
where it was not enacted. Hence, pro-reduction teams were overrepresented (underrepresented) in groups
where the reduction policy was (was not) enacted in the ENDO treatment. This is a pattern similar to theselection bias discussed in Dal Bó et al. (2010) and Dal Bó et al. (2019).
In fact, teams’ support for the policy was positively correlated with their performance before
voting. In the practice phase, teams performed the task for only three minutes under individual-based
remuneration. The data indicate that teams which voted for the reduction policy on average answered
1.001 questions correctly in the practice phase; their performance was significantly better at two-sided p <0.01 (z = 4.230) than teams which voted against the policy (the average number of correct answers byanti-reduction teams was 0.414). This pattern holds regardless of the institutional outcome, i.e., whether
the policy was enacted or not (Appendix Figure C.1). This means that pro-reduction teams may havecharacteristics different from anti-reduction teams. As shown in Appendix Figure C.1, the performance ofteams in the EXO treatment was somewhere in the middle of the pro- and anti-reduction teams (wassimilar to that of anti-reduction teams) in groups where the policy was enacted (was not enacted).
In sum, selection bias must be controlled for when identifying the dividend of democracy in thedata. This paper utilizes the method proposed by Dal Bó et al. (2019) to remove selection effects. Section
3 first discusses the dividend of democracy on work productivity, after which it discusses workers’ effort
choices in detail and their welfare consequences.
Result 1: 40.9% of teams voted for reducing returns from staying in the Game screen. As a result ofmajority voting, the reduction policy was enacted in 38.7% of groups in the ENDO treatment.

3.1. Dividend of Democracy on Work Productivity
The first key result of this study is the positive effect of democracy on work productivity. The
dividend of democracy is quite strong: around 20% on average. Consider, first, groups where thereduction policy was enacted. Productivity, defined as the number of correct answers per minute of
teamwork (i.e., per average time spent in the task screen by a team member), is 0.594 in the ENDO
treatment. 0.594 means that if a team, i.e., all three members, worked the entire 35 minutes of the tasksolving phase without playing Tetris, they would be able to answer on average 20.79 (= 0.594×35) tasks
correctly. This productivity is 28.5% larger than the productivity in the EXO treatment, which is12calculated as 0.462.15 Part of the productivity increase can be attributed to selection bias as alreadydiscussed. Thus, such bias must be controlled for to isolate the dividend of democracy by adjusting the“weights,” i.e., the distribution of votes. This paper follows Dal Bó et al. (2019) calculating the reweighted productivity with the two steps:
Step 1: Calculate (a) the average number of correct answers and (b) the per member average work time,using as weights the percentage of pro-reduction teams in the population (40.9%) rather than thepercentages under the reduction regime in the ENDO treatment (69.4%).
Step 2: Calculate (a)/(b).
The re-weighted work productivity in the ENDO treatment found using these steps is still quite
large – i.e., 0.529, 14.5% larger than that in the EXO treatment.
Consider, next, groups where the reduction policy was not enacted. There is also a strong effectof democracy for these groups. First, the productivity before reweighting was modestly different between
the two conditions: 0.488 in the ENDO and 0.431 in the EXO treatment. However, this mild difference isdue to selection, in that pro-reduction teams are underrepresented in the ENDO treatment, i.e., theseaccount for only 22.8% of teams (Table 1). Productivity after reweighting was large, 0.539, in the ENDO
treatment. This means that the dividend of democracy is 0.108 (= 0.539-0.431) correct answers per min.
of teamwork, i.e., a 25.1% increase in productivity. The fact that democracy strongly affects behavior
irrespective of the policy implementation outcome suggests that being involved in the democratic process
by itself, i.e., democratic culture, affects their work motivation directly, which is consistent with the ideathat democracy directly enhances intrinsic motivations to work (e.g., Deci and Ryan, 1985, 2000).
In sum, the reweighted dividend of democracy without the reduction policy (i.e., 0.539 versus
0.431) was of almost a similar magnitude to the one in groups with the reduction policy (0.529 versus
0.462). This underscores the strong role of democracy in improving productivity. For this reason, the two
institutional outcomes (with or without the policy) are pooled to statistically test the significance of thedividend of democracy (Table 2).
Table 2 reports test results for the dividend of democracy on work productivity using all of thedata. In order to calculate each p-value, the estimates for the dividends of democracy were calculated
20,000 times based on session-level bootstrapping.16 Panel A of Figure 3 reports the distributions ofestimated dividends of democracy. These reveal that the size and the significance of the dividend of
democracy are only slightly affected by the correction of the selection bias. The overall impact is
economically large: democracy boosts productivity by 20.02% (= (0.535 – 0.445)/0.445×100%) and it issignificant at the 5% level. Hence, it can be concluded that democracy by itself strongly improves
productivity.
Readers may also be interested in knowing how the dividend of democracy persists in theworkplace. To answer this question, work productivity measures are calculated by splitting the data intoquarters of the experiment. It first shows that experience does help to improve teams’ problem-solving
skills, and hence their per-minute-of-teamwork performance. Panel B of Figure 3 indicates that, whether
in the ENDO or EXO treatment, work productivity increased from quarter to quarter. The dynamics also15

The average number of correct answers and average per member working/shirking time by institutional condition
can be found in Table 3.
16
Each estimate was calculated using 62 sessions randomly drawn from the set of the original 62 sessions.

13

reveal that higher work productivity in the ENDO treatment, relative to EXO treatment, was remarkably
stable throughout the experiment. This means that fatigue (whether physical or mental) and/or monotony
may not weaken the dividend of democracy in the workplace.17
Result 2: (a) There is strong evidence that democracy significantly boosted work productivity, defined as
the production per minute spent working. (b) The positive dividend of democracy persisted throughout thetask-solving phase.

Table 2: Dividend of Democracy in Work Productivity
A. Using original weights
Team production per minute of its three members’ working:#1(a) ENDO treatment
0.536(b) EXO treatment
0.445(c) Dividend of Democracy (= (a) – (b))
0.091
Two-sided p for H0: (a) = (b)#2
0.036**

B. Using adjusted weights
following Dal Bó et al.
0.535
0.445
0.090
0.046**

Notes: The overall productivity measures in rows a and b were calculated using the distribution of policy implementationin the EXO treatment (i.e., % of groups with policy: % of groups without policy = 123/273: 150/273). The numbers incolumn A are productivity measures calculated using the original distributions of voter types under institutional outcomes(pro- or anti-reduction teams) shown in rows i and ii of Table 1. The numbers in column B are productivity measuresusing the distribution of voter types in the population following the weights-based identification strategy proposed by Dal
Bó et al. (2019). #1 The number of correct answers per minute of teamwork #2 The p-values were calculated using thebootstrapping procedure described in Dal Bó et al. (2019). The number of bootstrap iterations was 20,000 (Figure 3).

Figure 3: Dividends of Democracy for Work Productivity(i) When using original weights(ii) When using adjusted weights according to
Dal Bó et al. (2019)
A. Distribution of bootstrapped dividends of democracy for productivity based on Dal Bó et al. (2019)17

An analysis in Section 3.2 suggests that workers in the ENDO treatment did not accumulate fatigue with a higher
work pace, as they instead increased the time spent in the Game screen.

14

0.7

0.7

0.6

0.6

0.5

0.5

0.4

0.4

0.3

0.3

0.2

0.2

0.1

0.1

0.0

0.0
1st quarter2nd quarterEXO

3rd quarter

4th quarter

1st quarter

ENDO

2nd quarter
EXO(i) When using original weights

3rd quarter

4th quarter

ENDO(ii) When using adjusted weights according to
Dal Bó et al. (2019)

B. Dividend of democracy, quarter by quarter
Notes: 1. Each distribution in panel A was drawn using 20,000 estimated dividends of democracy based on bootstrap
iterations. 2. The productivity measures of each quarter in panel B were calculated by splitting the duration of 35
task-solving phase by four (e.g., the first quarter is the first 35/4 minutes).

While the strong role of democracy is consistent with the findings from prior research on
democracy using ‘experimental games’, such as prisoner’s dilemma and public goods games (e.g., Tyran
and Feld, 2006; Dal Bó et al., 2010; Sutter et al., 2010; Kamei, 2016), it is at odds with the finding fromthe ‘real-effort’ experiment of Kamei and Markussen (forthcoming). In Kamei and Markussen(forthcoming), subjects were assigned to a group of three and then worked individually on either the“counting task” (e.g., Falk et al., 2006; Abeler et al., 2009) or the “addition task” (e.g., Niederle and
Vesterlund, 2007; Corgnet et al., 2015) on condition that a revenue-sharing rule is in use and a funny
video is available as an alternative activity. Kamei and Markussen (forthcoming) found little evidence ofthe effects of democratic task selection. The null result was indeed a puzzle which Kamei and Markussen(forthcoming) were not able to explain. A similar null result for the dividend of democracy was also
observed and posed as a puzzle in the real effort experiment of Dal Bó, Foster and Kamei (2019) whereinternet surfing (e.g., Corgnet et al., 2015) was available as an alternative activity. So, why did we get astrong dividend of democracy in the present study? A likely reason is that each team member had stronger
shirking opportunities in the present study. Subjects in the present experiment jointly solved acollaborative counting task as a team in a group, unlike in the prior experiments where subjects
individually solved an individual real effort task in a group. Specifically, while incentives to shirk as adecision-making unit (teams in this study or individuals in the other research) in a group are the same,each team member in the present study has additional opportunities to shirk by playing Tetris privately,i.e., without notifying their other team members, whose structure features a coordination game inside theteam.18 The difference between the present and the earlier experiments suggests that the dividend of
democracy may be more important in an environment where workers have stronger incentives to shirk.

18

A team cannot complete a task while some member is shirking. Such shirking is also interpreted as maliciousness
or lack of team spirit towards members who are motivated and are waiting for the shirker’s input to find the answer.

15

3.2. Effort Choices and Welfare
The larger size of work productivity (Result 2) does not mean that democracy improves
production in the workplace. Rows I and II of Table 3 report the average numbers of attempts and correct
answers in the main task-solving phase. The average results are reported by the policy implementation
outcome because work behaviors differed substantially by the presence of the reduction policy. It showsthat teams attempted more questions and, as a result, answered more questions correctly, in the ENDO
than in the EXO treatment (Rows I and II). However, the positive effects of democracy are far fromsignificant (columns 2, 2a and 2b).
This insignificant impact, despite Result 2, was due to the workers’ effort choices. As thecollaborative counting task was a relatively challenging real effort task, shirking prevailed in theexperiment.19 Workers (although insignificantly) shirked more on average in the ENDO treatment than in
the EXO treatment – see columns 2, 2a and 2b of Row III. The higher incidence of shirking undermined
the positive impact of enhanced work productivity, which resulted in the insignificant effect on the two
effort output measures. Thus, this result suggests that a firm needs to have some mechanism to curb
workers’ effort choices beyond democracy if they want to increase production significantly, because
workers’ discretion to decide how much to work may partly cancel out the sustained positive dividend of
democracy. However, it should be emphasized here that despite the increased shirking, team production
did not decrease (instead increased although insignificantly) thanks to Result 2 under democracy. Thismeans that the same level of production can be achieved under democracy in less work time.

Table 3: Work Performance and the Dividend of Democracy
Un-weighted
With Policy(1a)W/o policy(1b)

All data(2)

I. Avg. number of attempts(a) ENDO
19.49(b) EXO
16.79
H0: (a) = (b)#1
0.151

25.28
19.49
0.043**

14.74
14.58
0.949

18.81
16.79
0.331

20.53
19.49
0.747

17.40
14.58
0.285

II. Avg. number of correct answers(a) ENDO
12.49(b) EXO
10.49
H0: (a) = (b)#1
0.170

16.61
12.12
0.060*

9.12
9.16
0.983

11.96
10.49
0.330

13.14
12.12
0.671

11.00
9.16
0.336

III. Avg per member time spent in the Game screen [min.]#1(a) ENDO
12.14
7.05
16.31
(b) EXO
11.50
8.79
13.72
H0: (a) = (b)#1
0.664
0.345
0.236

12.60
11.50
0.534

10.14
8.79
0.594

14.61
13.72
0.711

IV. Avg. payoff in the main task-solving phase [pound sterling](a) ENDO
9.62
11.09
8.41
(b) EXO
8.29
8.68
7.97
H0: (a) = (b)#2
0.065*
0.062*
0.555

9.35
8.29
0.138

9.51
8.68
0.498

9.23
7.97
0.150

All data(1)

19

Re-weighted
With Policy W/o policy
(2a)
(2b)

The high difficulty in finding answers to the real effort task is a crucial feature of the experiment, which wasintentionally designed. Notice that if the tasks were easy, worker subjects would work hard with small output
elasticity of incentive changes in this kind of real effort experiment (Corgnet et al., 2015; Erkal et al., 2018). A
challenging real effort task and an availability of alternative activities (Tetris) were thus carefully incorporated in thedesign to make the output elasticity of incentives sufficiently large.

16

Notes: The p-values were calculated using the bootstrapping procedure described in Dal Bó et al. (2019). The number ofbootstrap iterations was 20,000. The numbers in columns 1, 1a and 1b were calculated using the original distributions ofvoter types under institutional outcomes (pro- or anti-reduction teams) shown in rows i and ii of Table 1. The numbers incolumns 2, 2a and 2b were calculated using the distribution of voter types in the population following the weights-based
identification strategy developed by Dal Bó et al. (2019). The overall measures in columns 1 and 2 were calculated using
the distribution of policy implementation in the EXO treatment (i.e., % of groups with policy: % of groups without policy= 123/273: 150/273).

One may wonder why democracy worsened shirking. One possible interpretation here is that
democracy enlarged workers’ motivations to earn a high payoff in the experiment. The subjects may haveperceived it to be more payoff-enhancing if they worked harder for a shorter duration and then secured
certain gains from staying in the Game screen once exhausted. Although it cannot be verified, thispossibility may partly explain the behavior since, despite Result 2(b), subjects may quickly have feelingsof fatigue if their per-minute effort levels rise. Having said that, such a reduction in work time did notwork well for the workers, since, while democracy did increase the average payoff, the impact isinsignificant after controlling for selection (Row IV). This implies that their effort choices were not
optimal. But, if this conjecture is relevant, why did perceived fatigue play a large part in the behavioral
decisions of experiment subjects? A likely possibility is that Result 2 was still not enough to encourage
workers to choose putting in a greater effort over shirking. This possibility is quite reasonable asdiscussed carefully in Section 3.3.
Result 3: Despite Result 2, democracy did not increase team production significantly, because workers
under democracy decreased work time to some degree.

3.3. Privately versus Socially Optimal Behaviors
This experiment was designed to model a social dilemma problem, i.e., conflicts among teams, inthe workplace. Section 3.3 briefly checks the validity of this design setup, finding that its attempt was
successful as intended. This section also tries to find an answer as to why democracy was not enough toboost team production in the experiment.
Since staying in the Game screen was remunerated with 16 or 18 pence per minute, it is possible
to calculate for what percentage of teams task-solving was a socially or privately optimal strategy (in thesense of material payoff maximization). In order for task-solving to be privately optimal, a team needs tobe able to solve at least 0.80 = 16/20 (0.90 = 18/20) tasks correctly per minute when the reduction policy
is (is not) in place. A detailed look at the data (Appendix Table C1) indicates that gaming was a privately
optimal choice for almost all teams in the EXO treatment, whether the policy was in place or not.
Specifically, it is so for 95.60% of teams (87 out of 91 teams) in the EXO treatment.20 This implies thatthe reduction policy was non-deterrent in the experiment. Consistent with the prior experimental evidence
on exogenously introduced non-deterrent punishment (e.g., Tyran and Feld, 2006; Kamei, 2016), theeffect of the reduction policy was not large in the EXO treatment. Specifically, while the average numberof correct answers in the EXO treatment was larger with than without the reduction policy (12.12 versus
9.16), the difference was not significant at two-sided p = 0.109 according to the bootstrap method used inMaterial incentives did matter for workers’ effort choices. In the EXO treatment, the four teams for which tasksolving was privately optimal worked on counting on average 31.80 minutes, which is significantly larger at twosided p = 0.0015 than the average work time by the other 87 teams where gaming was privately optimal (which was
23.12 minutes) – see Appendix Table C1.
20

17

the other tests of the paper (the difference is significant but only at the 10% level, i.e., p = 0.0707 if a
two-sided Mann-Whitney test is used).21
However, as intended, the socially optimal strategy was task-solving for many teams. In order fortask-solving to be socially optimal, a team needs to be able to solve at least 0.227 ≈ 16/60 [0.30 = 18/60]
tasks correctly per minute when the reduction policy is [is not] in place. Overall, the social optimal
condition was met for 61.6% of teams (56 out of 91 teams) in the EXO treatment. Notice that task-solving
is never privately optimal for teams whose task-solving is not socially optimal. Consistent with thisincentive pattern, teams whose task-solving was not socially optimal spent significantly less time working
on the task than the other teams at two-sided p < 0.001 (15.29 versus 28.63 minutes in the EXO
treatment). The average number of correct answers per minute of working by the former was only 0.07,
but that by the latter was 0.54 in the EXO treatment.
In sum, the present experiment can be thought of as exploring workers’ voting and effort choicedecisions under social dilemmas in the workplace when the target was a non-deterrent reduction policy.
Then, one may ask whether democracy might have altered the social dilemma situation to anotherone (e.g., coordination game), as arguably democracy not only enhances work productivity (Section 3.1),
but also reduces effort costs in task-solving. Another look at the data, however, shows that the answer isnegative. Specifically, a calculation finds that gaming was a privately optimal choice for almost all teamsin the ENDO treatment, i.e., 91.40% of teams (85 out of 93 teams); and task-solving was a socially
optimal choice for 61.3% (57 out of 93 teams) in that treatment – see again Appendix Table C1. These
numbers are quite similar to those in the EXO treatment already discussed.
The reason why worker behavior was characterized by Results 2 and 3 is explained by thetheoretical analysis summarized in Appendix B. The model there assumes that, following the priorresearch findings, democracy eases a worker’s effort cost, and it also boosts their productivity (its positiveeffect on work productivity is a parameter  in that model).  > 0 was confirmed by the experiment data
as summarized in Result 2. The team’s optimal effort provision can then be determined by the relative
strength between (a) work productivity [s + in the theoretical model, where s is the marginal return ofeffort provision by team i] and (b) the material incentives to shirk by staying in the Game screen.
Theoretically, the positive value of  (Result 2) possibly changes the materially beneficial choice fromgaming to task-solving – see Appendix Figure B.2. However, the analysis in the Appendix indicates that
if the impact on work productivity is not economically large enough, gaming is still the most materiallybeneficial activity even when teams have a statistically significant dividend of democracy. This is exactly
what the above calculations on privately versus socially optimal choices in the experiment data
demonstrate. The calculations clearly reveal that democracy did not change the underlying private
incentives in the experiment. This means that additional mechanisms on top of democracy would be

21

The effect of the reduction policy was apparently strong in the Endo treatment (see Table 3 for the numbers). The
average number of correct answers in the Endo treatment was significantly larger with than without the reduction
policy at two-sided p = 0.001*** (0.0020***) according to the bootstrap method (a Mann-Whitney test). However,this strong effect is just due to selection. The difference was not significant at two-sided p = 0.388 when using thebootstrap method with the distribution of votes in the population being the weights following Dal Bó et al. (2019).
Recall that democracy enhanced work productivity in the experiment similarly regardless of whether the policy was
imposed or not (Result 2), whose aspect makes the effect of the policy in itself small.

18

required to change the incentive structure so that task-solving becomes a privately optimal choice forworkers, if the group wants to increase production.

4. Understanding Sacrifice Behavior: Communication Contents
The present experiment provides a useful opportunity to explore workers’ reasoning behind theirdecisions to reduce private returns from shirking as communication contents are available. While thedecision data not only uncovered some subjects’ preferences to reduce their private returns but also
detected a significant dividend of democracy on work productivity (Section 3), it is still unclear whatdrove such behavioral patterns.
Two independent coders were hired to read and classify the communication contents based on
their judgment of the subjects’ motives. Specifically, a list of codes was designed by the authors, based on
the theoretical predictions of the setup and related literature, that could potentially reflect a subject orteams reasoning and/or behavior. The list was given to the coders to assign whichever codes (includingnone) they deemed relevant to a given communication log. The coding procedure follows Kamei and
Tabero (2022) which utilized the standard coding approach in economics to analyze teams’ behavioral
reasoning in the context of institutional choices based on intra-team communication logs. The detail of thecoding procedure and the full lists of codes used for the present paper can be found in Appendix Sections
D.1 and D.2.
The agreement rates and Cohen’s Kappa values (Cohen, 1960) can be used to judge theconsistency of the coding process between the two coders. Overall, the agreement rates (Kappa values)
between the two coders were 96.9% (0.87) and 94.8% (0.78) in the ENDO and EXO treatments,
respectively. The Kappa values are at least 0.4 for 92.5% and 78.0% of individual codes in the ENDO andEXO treatments, respectively (Appendix Section D.3). As a Kappa value of 0.4 is usually used as a
threshold for a researcher to judge the reliability of coding, we use only the codes that exceed thisboundary in this analysis.
Table 4 summarizes the list of codes that are found to have impacted the units’ voting
significantly at least at the 10% level. Their voting is clearly linked to their intention regarding what to do
during the main task-solving phase (Code Bs): while units supported the reduction policy if they planned
to focus on task-solving, they opposed it if they were considering using the game screen. The coding
category linked to pro/anti-policy reasoning (Code Cs) reveals clear motives behind the policy
preferences. While the policy is non-deterrent, those who voted in favor of it did so to deter others fromshirking (Code C1). On the other hand, those who intended to game or believed that the policy was too
weak to alter shirking opposed its enaction. Lastly, unsurprisingly, their views on materially beneficial
behavior and team atmosphere influenced voting. Specifically, units that believed their privately-optimal
behavior was task-solving supported reducing the return from gaming. By contrast, units who experienced
discomfort or poor performance from task-solving in the practice phase opposed such a reduction. While
teams with a positive atmosphere (E2) supported the reduction policy, those with poor or lacking
communication opposed it (E5).
Result 4: The units that planned to exclusively work on task-solving, believed that the reduction policy
would deter others from shirking, or those that had supportive team atmospheres, voted for the reduction
policy. However, those who previously experienced discomfort or poor performance from working,19

considered (even only potentially) using the Game screen, believed that the policy was too weak to alter
peers’ shirking, or had poor communication with their teammates, voted against the reduction policy.

Table 4: Significant Code Meanings and Its Impact on Voting for the Reduction Policy
Code
B1
B2
B3

Meaning
Direction
Agree/Imply to count as primary behavior(+)***
Agree/Imply to game as primary behavior(-)*
Agree to hybrid behavior e.g. so many tasks/minutes before switching to the game screen(-)**
Agree to discuss, decide and/or alter behavior during the counting task later (35-minuteB4(-)**
phase) based on performance/needs in Phase 2
Pro-policy to deter others from switching to the game screen by reducing the return
C1(+)***(monetary deterrence)
C8
Anti-policy as they intend to game for at least some of the task-solving period(-)***
Express that the policy is not strong enough to deter others from switching to the game
C11
(-)**
screen (monetary)
D2
Believe they as a team make the most money from counting(+)***
D5
Discuss their performance or comfort in Phase 1 (weak/negative)(-)**
E2
Positivity towards teammates e.g. attempts to encourage others or being supportive(+)*
E5
No communication from just 1 or 2 team members(-)***
Notes: +(-) in “Direction” means the reasoning en(dis)courages voting for the policy. * p < 0.1; ** p < 0.05; *** p < 0.01.

As summarized in Result 4, units’ commitment to task-solving and their intention to affect others’
shirking were the drivers behind their votes in favor of the reduction policy. To explore how policy
implementation outcomes affected units’ behaviors, coding analyses were further performed using thecommunication logs of the 35-minutes task-solving phases (Table 5). Three similar tendencies wereobserved for both the ENDO and EXO treatments. First, those who reacted negatively to theimplementation outcome tended to work less (F1, I1). Such negative reciprocal tendencies were
unsurprising considering the large findings of other-regarding preferences– see, e.g., Sobel (2005) andFehr and Schmidt (2006). Second, a units’ plan to work on counting or engage in gaming affects
performance (F3, F4, F5, F6, I4, I5, I6), similar to Result 4. Third, units’ positive and negative
experiences of task-solving, respectively, improve and hurt performance (G4, G5, D4, D5).
The results reveal signaling effects of voting on task-solving, and some nuanced evidence about
the teams’ dividends of democracy seen in Result 2. First, units that considered the distribution of votes topredict others’ task-solving or discussed changing behavior worked longer (F9, F15). Second, units whobelieved that other teams would complete tasks following the vote performed strongly (F7), resonatingwith the idea that voting has a signaling value, thereby encouraging collaboration. Further, even units
who thought that others would not respond to the reduction policy improved their performance (F8),which implies democracy directly affects behavior beyond signaling. Nevertheless, its effects arecancelled out if an anti-policy team is present in a group and units have a negative view on the tasksolving behavior of the anti-policy team (F13).

Table 5: Reasoning behind Work Choice and Productivity
Code

Meaning
Direction
Codes related to reactions to vote outcome in ENDO (Code Fs) or policy outcome in EXO (Code Is)[ENDO treatment:]
F1
Express negative emotions (e.g., upset, anger) about the outcome of the vote(wt-)***
F3
Agree/Imply to count as primary behavior(wt+)***, (p+)***
F4
Agree/Imply to game as primary behavior(wt-)***, (p-)***

20

F5

Agree to hybrid behavior e.g. so many tasks/minutes before switching to the game screen(wt-)**, (p-)*
Agree to discuss, decide and/or alter behavior during the counting task later (35-minute(p-)*
phase) based on performance/needs in Phase 2
F7
Express belief/hope that other teams will complete tasks following the vote(wt+)***, (p+)**F8
Express belief that teams will not complete tasks following the vote(wt+)***, (p+)***
F9
Discuss the distribution of votes and predict how each team may respond to one another(wt+)***
F13 Belief on other teams’ responses: anti-policy teams will work little(p-)***
F15 Discuss whether to change behavior based on the vote outcome(wt+)***[EXO treatment:]
I1
Express negative emotions (e.g., upset, anger) about the policy outcome(wt-)***, (p-)***
I4
Agree/Imply to game as primary behavior(wt-)***, (p-)***
I5
Agree to hybrid behavior e.g. so many tasks/minutes before switching to the game screen(wt-)***, (p-)**Agree to discuss, decide and/or alter behavior during the counting task later (35-minuteI6(wt-)*
phase) based on performance/needs in Phase 2
Other Codes [The same codes were used for the ENDO and EXO treatments]
G4 Expression of strong negative emotion e.g. frustration, anger, disappointment(p-***, Exo)
G5 Expression of strong positive emotion e.g. enjoyment, things are going well
(wt+***, p+** , Exo)
D4 Discuss their performance or comfort in Phase 1 and/or so far in Phase 2 (strong/positive)(wt+***, p+***, Endo)(wt-***, p-***, Endo),
D5 Discuss their performance or comfort in Phase 1 and/or so far in Phase 2 (weak/negative)(wt-**, Exo)
D8 Discuss uncertainty surrounding other teams’ work choices or abilities(wt-***, p-**, Exo)
D9 Suggest distrust of other teams e.g. expect them to take advantage(wt-***, p-***, Endo)
F6

Notes: wt and p in the “Direction” column indicates two work performance measures: work time (minutes) and productivity
defined as the number of correct answers divided by the work time. +(-) means the reasoning in(de)creases the performancemeasures. All significant codes are listed for Code Fs and Is, while only some key codes are included for the other coding
categories to conserve space (Appendix D4 includes the full estimation results). * p < 0.1; ** p < 0.05; *** p < 0.01.

5. Conclusion
Teams are popular decision-making and work units in organizations that feature a complex
coordination problem. Overcoming moral hazard among teams in the workplace plays a crucial role inmaintaining productivity in the firm, whether in the traditional work environment or in a remote working
setting, such as that triggered for many by the Covid-19 crisis. The present paper investigated how
frequently groups reduce the return from shirking by enacting a formal non-deterrent sanction policy, andhow such endogenous choices per se improve work productivity. To achieve this, a novel real effort
experiment was designed, equipped with (a) a collaborative counting task featuring an intra-teamcoordination game and (b) gaming (Tetris) as a real leisure activity. The experiment results showed thataround 40% of teams voted to reduce the return from staying in the Game screen. A contents analysis
using teams’ communication logs showed that such voting was driven by not only their commitment towork on counting but also their belief that the reduction policy would deter others from shirking.
The decision data uncovered a significant and strong dividend of democracy on work
productivity. Strikingly, whether the policy was enacted or not, teams in the ENDO treatment displayed
significantly higher per-work-time production than those in the EXO treatment. This means that
democratic culture directly affects behavior positively. However, the workers under democracy also
experienced higher levels of shirking, i.e., the time spent on the Game screen was larger in the ENDO
than in the EXO treatment, presumably driven by their enhanced fatigue due to the more intensive
working in the former. This implies that while additional mechanisms that affect incentives besides
democracy may be required to increase production, democracy may improve efficiency. What kinds ofmechanisms would work best to instead increase production further remains for future research. Having
said that, it should be emphasized here that the average production of the workers under democracy did
not decrease (it increased, although insignificantly) thanks to their strong per-work-time production.
21

The findings on the positive dividend of democracy on work productivity have a policy
implication for effective human resource and management practices. While prior research suggests that
innovative human resource management involving worker participation (such as that in production sites)
lead to better work performance (e.g., Ichniowski et al., 1997), it is unclear how democracy affects
behavior, as earlier real effort experiments failed to detect strong dividends of democracy (e.g., Dal Bó etal., 2019; Kamei and Markussen, forthcoming). Using an environment with strong shirking incentives,
the present experiment suggests that organizations with a shared goal can benefit from introducing
participatory decision-making with their employees or group members, by potentially improving theirwork productivity. Even when democracy induces the workers to work less, the improvement toproductivity allows for achieving a production goal with fewer working hours.
The effect of democratic culture in achieving the same goal in less work time collaborates recent
work style reform. There is a trend to transform the traditional workplace to an employee-centeredworkplace in many countries. For example, in the United Kingdom, some firms recently tested four-day
work weeks to make working conditions flexible to meet the different needs of employees.22 Having
higher work productivity in a democratic environment certainly helps firms to achieve the same orpotentially better outcomes with fewer working hours. This boost to productivity is achieved throughenhanced self-determination and signaling effects; workplace democracy provides the workers with theability to foster trust with each other and to indicate their intentions or desire to cooperate throughdemocratic procedures such as voting, and the recipients can then respond to these signals. Such a social
exchange may be fundamental for workers to achieve collaboration through reducing uncertainty
surrounding each other’s behavior in a democratic workplace environment. The firm may create apositive and collaborative atmosphere and improve productivity by designing democratic systems in
multiple layers and activities across the organization.

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For example, see the following BBC news: https://www.bbc.co.uk/news/business-64669987 (last accessed on
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25

Not for Publication

Supplementary Online Appendix to: Kamei and Tabero:“Free Riding, Sacrifice and Democracy in the Workplace:
A Real Effort Experiment”#

Table of Contents

Appendix A: Experiment Procedure and Instructions Used in the Experiment . . . . . . . . . p. 2
Appendix B: The Dividend of Democracy in a Theoretical Model . . . . . . . . . . . . . . . . . . . p. 17
Appendix C: Additional Figures and Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . p. 20
Appendix D: Coding Procedure and Analysis Results for the Communication Contents . . . p. 23#

Kamei: Keio University (kenju.kamei@gmail.com; kenju.kamei@keio.jp); Tabero: Durham University(katy.tabero@durham.ac.uk).
1

Appendix A: Experiment Procedure and Instructions Used in the Experiment
Sixty-two sessions (thirty-one sessions per treatment) were conducted online using the oTree software(Chen et al., 2016) and Zoom from May 2021 through January 2022, following the same procedure as astandard laboratory experiment. All subjects’ cameras were on during the session to make sure that they
were alone, were paying attention to the experiment, and did not cheat when making decisions. Whilesubjects were visible by the experimenter, they were unable to see the researcher on Zoom during theexperiment. They also remained anonymous during the entire session without seeing other participants’faces, names, etc. Each session consisted of nine subjects. This means that each session consists of one
group. However, full anonymity was retained in the experiment since, as already mentioned, subjects did
not see the other students’ names or faces, and they were recruited from a very large student population in
the university. All experiment sessions were conducted using the subject pool and the experiment systemin the EXEC (Centre for Experimental Economics) at the University of York. As all standard experiment
protocols (such as the no deception rule) have been rigorously adopted for any experiment in thislaboratory for more than 30 years, it can be assumed that subjects believed in the explanation provided in
the experiment, although subjects do not see the presence of any subject physically.
All subjects were recruited using hroot (Bock et al., 2014). Subjects did not participate in morethan one session. A total of 552 students in the University of York participated in the experiment.23 The
instructions shown below were neutrally framed. Terms with positive or negative connotations, such as
shirk, free ride or cooperate, were not used. Each session took between 90-120 minutes.
This part of the Appendix includes the instructions for both the ENDO and EXO treatments asfollows:
A.1: Instructions for phase 1 (identical for the ENDO and EXO treatments)
A.2: Instructions for phase 2 (the EXO treatment)
A.3: Instructions for phase 2 (the ENDO treatment)

References:
Bock, Olaf, Ingmar Baetge, and Andreas Nicklisch, 2014. “hroot: Hamburg Registration andOrganization Online Tool.” European Economic Review, 71, 117-120.
Chen, Daniel, Schonger, Martin, and Wickens, Chris, 2016. “oTree - An open-source platform forlaboratory, online and field experiments.” Journal of Behavioral and Experimental Finance, 9, 88-97.

Two teams’ observations (six subjects) were omitted from the study. The first one was due to a subject’s computer
experiencing technical problems, meaning that their whole team could not participate in the task-solving phase, andthe second was due to suspected cheating which was identified in the chat dialogue.
232A.1: Instructions: Slides for the Practice Phase (identical for the ENDO and EXO Treatments)
At the onset of the experiment, the following instructions (PowerPoint file) were shown on Zoom andwere made available on the subjects’ experiment screens; they were read aloud to subjects by the
researcher.
Slide 1:

Slide 2:

3

Slide 3:

Slide 4:

4

Slide 5:

Slide 6:

5

Slide 7:

A.2: Instructions: Slides for the Main Task-Solving Phase in the EXO Treatment
Once Phase 1 was over, the following instructions were shown on Zoom and were made available on thesubjects’ experiment screens; they were read aloud to subjects by the researcher.
Slide 1:

6

Slide 2:

Slide 3:

7

Slide 4:

Slide 5:8

Slide 6:

Slide 7:

9

Slide 8:

Slide 9:10

A.3: Instructions: Slides for the Main Task-Solving Phase in the ENDO Treatment
Once Phase 1 was over, the following instructions were shown on Zoom and were made available on thesubjects’ experiment screens; they were read aloud to subjects by the researcher.
Slide 1:

Slide 2:11

Slide 3:

Slide 4:

12

Slide 5:

Slide 6:

13

Slide 7:

Slide 8:14

Slide 9:

Slide 10:15

Slide 11:16

Appendix B: The Dividend of Democracy in a Theoretical Model
This part of the appendix illustrates how sacrifice helps improve effort provision. It also studies how
democracy in decision-making helps improve productivity further. The analysis can be made using asimilar framework to the one used in Kamei and Markussen (forthcoming) except changing the variables.
In the present paper, a group consists of three teams, and each team consists of three individual members.
As the likelihood to answer a collaborative counting task depends on a team’s joint effort provision, it isreasonable to assume that the payoff a team receives depends on the team’s degree of effort provision𝑒𝑖 ∈ [0,1]. For example, if all three members put their highest effort without any shirking, 𝑒𝑖 = 1.
However, e would be considerably smaller if just one member puts in very little effort, as then the numberof 4s cannot be answered accurately. On the other hand, e may be at an adequate level when all three
execute adequate effort with some shirking. Each team’s decision can be expressed as below:
max {𝜋𝑖 (𝑒𝑖 |𝑒−𝑖 ) = ∑3𝑛=1(𝑠𝑛 + 𝜇𝐷) ∙ 𝑒𝑛 + 𝑟𝑘∈{S,NS} ∙ 𝑔𝑖 − 𝑓(𝑒𝑖 )}, where𝑒𝑖 ∈[0,1]𝑓(𝑒𝑖 ) = (𝑐𝑖 − 𝛿𝐷)𝑒𝑖 2 [cost function]; and𝑒𝑖 + 𝑔𝑖 = 1 [allocation of effort and shirking activities].

(B1)

Here, 𝑔𝑖 is team i’s average shirking level in the phase 2 task-solving stage. Note that 𝑒𝑖 + 𝑔𝑖 = 1
because there are only two possibilities for simplicity: work or shirk. 𝑠𝑖 is the marginal return of effort
provision by team i, and it is assumed to be a constant (reflecting the number of tasks answered, thelikelihood to answer correctly, and the earnings from correct answers and from mistakes), and 𝑟𝑘 ismarginal return of shirking which depends on their group’s sacrifice decisions, i.e., k = S (Sacrifice) or
NS (Not Sacrifice), and 𝑟NS > 𝑟S.
As in Kamei and Markussen (forthcoming), it can be assumed that being involved in democratic decisionmaking eases workers’ effort cost (𝛿 > 0) such that 𝛿 < 𝑐𝑖 , due to either enhanced intrinsic motivation orsignaling. It can also be assumed that democracy boosts team i’s work productivity, defined as per effort
productivity, from 𝑠𝑖 to 𝑠𝑖 + 𝜇, where 𝜇 > 0. 𝐷 is an indicator variable which equals 1 in the ENDO
treatment.24
Team i’s optimal effort level can be derived merely using the first-order condition for (B1) as thecost function is quadratic. The optimality condition is summarized in Equation (B2) below.
𝑠 +𝜇−𝑟𝑖𝑘𝑒𝑖 ∗ = 2(𝑐if 𝑠𝑖 > 𝑟𝑘 − 𝜇; and 𝑒𝑖 ∗ = 0 otherwise, for 𝑘 ∈ {S, NS}.
−𝛿𝐷)
𝑖

(B2)

It is clear from (B2) and the figure on the next page that sacrifice has a positive impact on teams’
effort provision since 𝑟NS > 𝑟S:

24

It can further be assumed that 𝛿 and 𝜇 depend on the policy outcome such that these parameters are larger when
the policy is selected than is not selected: 𝛿|imposed > 𝛿|not imposed > 0 and 𝜇|imposed > 𝜇|not imposed > 0. This Appendix
provides the theoretical result when these effects of democratic decision-making do not depend on the outcome forsimplicity as the theoretical implication is similar regardless of the assumption.

17𝑠 +𝜇−𝑟𝑠 +𝜇−𝑟

𝑖

𝑖

𝑖
S
𝑖
NS
𝑒𝑖,S ∗ = 2(𝑐≥ 𝑒𝑖,NS ∗ = 2(𝑐
.
−𝛿𝐷)
−𝛿𝐷)

(B3)

Figure B.1: The Reduction Policy and Worker’s Optimal Effort Scheduleei,S*, ei,NS*

Slope =

𝟏
𝟐(𝒄𝒊 −𝜹𝑫)with sacrifice

without sacrificesirNS − Dr S − D

Further, as 𝛿 > 0 and 𝜇 > 0, teams in the ENDO treatment work harder than those in the EXO treatment– see the figure below:
𝑠 +𝜇−𝑟𝑠 −𝑟𝑖𝑘𝑒𝑖,𝑘 ∗ |𝐸𝑛𝑑𝑜 = 2(𝑐≥ 𝑒𝑖,𝑘 ∗ |𝐸𝑥𝑜 = 𝑖2𝑐 𝑘 for given k{S,NS}.
−𝐷)
𝑖

𝑖

(B4)

Figure B.2: Effects of Democracy on Worker’s Optimal Effort Schedule
Slope =

ei*

𝑒𝑖,S ∗ |𝐸𝑛𝑑𝑜

𝟏
𝟐(𝒄𝒊 −𝜹)in the Endo condition𝑒𝑖,NS ∗ |𝐸𝑛𝑑𝑜𝑒𝑖,S ∗ |𝐸𝑥𝑜𝑒𝑖,NS ∗ |𝐸𝑥𝑜rNS− 

Slope =

𝟏
𝟐𝒄𝒊

in the Exo condition

rS− 
si
rS

rNS

These analyses can be summarized as in Summary 1:
Summary 1: (a) Teams work harder with than without the reduction policy. (b) Democracy induces theworkers to work harder.
Does the sacrifice benefit teams? This question may not be obvious because a rise in effort
provision means not only an increased return from work but also a rise in the effort cost. To make thefurther analysis simple, assume the homogeneity in skills (𝑠𝑖 = 𝑠 for all i) and the following condition:
Assumption 1: 𝑠 > 𝑐𝑖 .
18

Assumption 1 means that the unit effort cost is not too large compared to the material return fromtask-solving. A calculation suggests that under Assumption 1, teams earn more when the reduction policy
is imposed in their group than otherwise if 𝑠 > 𝑟𝑘∈{S,NS}, irrespective of whether they are in the ENDO or
EXO treatment. However, if 𝑠 < 𝑟𝑘∈{S,NS}, sacrifice is purely harmful to welfare.
Summary 2: Suppose Assumption 1 holds. Regardless of whether they are in the ENDO or EXO
treatment, if the material benefit from working is high (low) enough that 𝑠 > 𝑟𝑘∈{S,NS} (𝑠 < 𝑟𝑘∈{S,NS} ),teams earn more (less) when the reduction policy is imposed than is not imposed.
Proof: Suppose first that 𝑠 > 𝑟𝑘∈{S,NS} , i.e., the situations are characterized by interior solutions. Then, we can show
the beneficial effect of sacrifice by simply calculating the difference in the payoff between the two conditions.
𝜋𝑖 S = 𝜋𝑖 (𝑒𝑖,S ∗ |𝑒𝑖,S ∗ ) = 3𝑠𝜋𝑖 NS = 𝜋𝑖 (𝑒𝑖,NS ∗ |𝑒𝑖,NS ∗ ) = 3𝑠
Then, 𝜋𝑖 S − 𝜋𝑖 NS = 3𝑠𝑟NS −𝑟S2(𝑐𝑖 −𝛿𝐷)+ 𝑟S ∙ (1 −=4(𝑐𝑖 −𝛿𝐷)2(𝑐𝑖 −𝛿𝐷)+ 𝑟S ∙ (1 −𝑠+𝜇𝐷−𝑟NS𝑠+𝜇𝐷−𝑟S2(𝑐𝑖 −𝛿𝐷)𝑟NS −𝑟S𝑠+𝜇𝐷−𝑟S2(𝑐𝑖 −𝛿𝐷)𝑠 +𝜇𝐷−𝑟S2(𝑐𝑖 −𝛿𝐷)+ 𝑟NS ∙ (1 −) − 𝑟NS ∙ (1 −)−𝑠+𝜇𝐷−𝑟NS2(𝑐𝑖 −𝛿𝐷)𝑠+𝜇𝐷−𝑟NS2(𝑐𝑖 −𝛿𝐷))−(𝑠+𝜇𝐷−𝑟S )24(𝑐𝑖 −𝛿𝐷)

)−

.

(B5)

(𝑠+𝜇𝐷−𝑟NS )24(𝑐𝑖 −𝛿𝐷)(𝑠+𝜇𝐷−𝑟S )24(𝑐𝑖 −𝛿𝐷)+.

(𝑠+𝜇𝐷−𝑟NS(B6))24(𝑐𝑖 −𝛿𝐷){6𝑠 − 4𝑐𝑖 − (𝑟NS + 𝑟S ) + 4𝛿𝐷} > 0.

(B7)

Consider next the case of corner solutions both with and without sacrifice (i.e., 𝑠 < 𝑟𝑘∈{S,NS} ). In this case,∗𝑒𝑖,S = 𝑒𝑖,𝑁S ∗ = 0; written differently, 𝑔𝑖,S ∗ = 𝑔𝑖,𝑁S ∗ =1. Then, from Equation (B1), 𝜋𝑖 S − 𝜋𝑖 NS < 0.
The remaining case is the situation with an interior solution under sacrifice but a corner solution without
sacrifice (𝑟NS > 𝑠 > 𝑟S ,). Then, 𝑒𝑖,S ∗ =𝜋𝑖 S = 𝜋𝑖 (𝑒𝑖,S ∗ |𝑒𝑖,S ∗ ) = 3𝑠𝑠𝑖 −𝑟S, and 𝑒𝑖,𝑁S ∗ = 0), and we have the following:2(𝑐𝑖 −𝛿𝐷)𝑠+𝜇𝐷−𝑟S2(𝑐𝑖 −𝛿𝐷)+ 𝑟S ∙ (1 −𝑠 +𝜇𝐷−𝑟S2(𝑐𝑖 −𝛿𝐷))−(𝑠+𝜇𝐷−𝑟S )24(𝑐𝑖 −𝛿𝐷).

𝜋𝑖 NS = 𝑟NS .
In this case, it is not obvious which is larger, 𝜋𝑖 S or 𝜋𝑖 NS .
□Notice that 𝜋𝑖 S − 𝜋𝑖 NS in Equation (B7) is decreasing in 𝑐𝑖 . This means that teams who are betterat solving the collaborative counting task (i.e., teams with smaller 𝑐𝑖 ) have larger gains from the reduction
policy through its strong positive impact on their effort provision.
It should be emphasized here that the beneficial effects of the reduction policy emerge whenteams have sufficiently low effort costs as expressed by Assumption 1. However, introducing the policy is
oppositely harmful if they are not skilled and therefore incur large costs from effort provision. This
theoretical implication suggests that teams who are skilled at solving the collaborative counting task votein favor of the reduction policy in phase 2.
Summary 3: Those who are better at solving the collaborative counting task in phase 1 are more likely
to vote for the reduction policy in phase 2.

19

Appendix C: Additional Figure and Tables
Figure C.1. Cumulative Distribution of Performance by Voting in the Threeminutes Practice Phase(A) Pro- and anti-reduction teams in the groups where the policy was endogenously imposed(B) Pro- and anti-reduction teams in the groups where the policy was not endogenously imposed
Note: The cumulative distribution of teams’ performance in the EXO treatment was also drawn as a reference.

20

Table C.1: Privately and Socially Optimal Choices between Task-Solving and Gaming[1. Which activity is privately optimal, task-solving or gaming?]
A. ENDO treatment

i. # of teamii. Avg # of correct answer
per minuteiii. Avg work time (min)i. # of teamii. Avg # of correct answer
per minuteiii. Avg work time (min)A1: Under the reduction policya. Task-solving is a b. Gaming is a
privately optimal
privately optimal
828

A2: Without reduction policyc. Task-solving is a d. Gaming is a
privately optimal
privately optimal
0
57

0.97

0.38

---

0.33

33.33

26.42

---

18.69

A3: All data in the ENDO
e. Task-solving is a f. Gaming is a
privately optimal
privately optimal
8
85
0.97

0.35

33.33

21.24

B. EXO treatment

i. # of teamii. Avg # of correct answer
per minuteiii. Avg work time (min)i. # of teamii. Avg # of correct answer
per minuteiii. Avg work time (min)B1: With reduction policya. Task-solving is a b. Gaming is a
privately optimal
privately optimal
338

B2: Without reduction policyc. Task-solving is d. Gaming is aa privately optimal privately optimal
1
49

0.94

0.37

1

0.30

30.74

25.86

35.00

21.00

B3: All data in the EXO
e. Task-solving is a f. Gaming is a
privately optimal
privately optimal
4
87
0.96

0.33

31.80

23.12

[2. Which activity is socially optimal, task-solving or gaming?]
A. ENDO treatment

i. # of teamii. Avg # of correct answer
per minuteiii. Avg work time (min)A1: Under the reduction policya. Task-solving is a b. Gaming is asocially optimalsocially optimal
28
8

A2: Without reduction policyc. Task-solving is a d. Gaming is asocially optimalsocially optimal
29
28

0.64

0.08

0.58

0.07

32.10

13.43

26.98

10.11

A3: All data in the ENDO

21

i. # of teamii. Avg # of correct answer
per minuteiii. Avg work time (min)e. Task-solving is asocially optimal
57

f. Gaming is asocially optimal
36

0.61

0.07

29.50

10.85

B. EXO treatment

i. # of teamii. Avg # of correct answer
per minuteiii. Avg work time (min)i. # of teamii. Avg # of correct answer
per minuteiii. Avg work time (min)B1: With reduction policya. Task-solving is a b. Gaming is asocially optimalsocially optimal
32
9

B2: Without reduction policyc. Task-solving is d. Gaming is aa socially optimal socially optimal
24
26

0.51

0.07

0.58

0.07

28.50

18.09

28.81

14.33

B3: All data in the EXO
e. Task-solving is a f. Gaming is asocially optimalsocially optimal
56
35
0.54

0.07

28.63

15.29

22

Appendix D: Coding Procedure and Analysis Results for the Communication Contents
D.1. Coding Procedure
Two coders were hired to judge each team’s communication content for both the 3-minutecommunication segment prior to voting in the ENDO treatment and the 35-minute communication
segments in the main task-solving phase for both the ENDO and EXO treatments, by assigning therelevant codes (summarized in section D.2). The treatments were presented as ‘Treatment A’ andTreatment B’ to the coders, which alternated as coders completed the treatments in different orders. The
coders were provided with a copy of the experiment instructions. Each coder was provided with three
Excel files, termed “Coding Sheet – Treatment XY,” where X indicates either “A” or “B” to designate thetreatment and Y indicates the communication length, either 3 or 35 minutes (e.g., Coding Sheet –TreatmentA35). Each file had separate sections for each code type and only the relevant codes for that
communication type were available. In the columns, the files contained a list of the team numbers inascending order, which corroborated with “Segment” numbers found in the “Communication Files.”Six Communication files were also provided, comprising a sample set of ten communication
segments and the remaining set of communication segments for each of the three communication
combinations. Coders were instructed to first read the entire communication segment of a team and thenassign as many codes as deemed appropriate in the Coding Sheet in a given teams column.
The files consisted of data from 93 teams in the ENDO treatment, each having one 3-minute andone 35-minute dialogue segment, and 91 teams from the EXO treatment, with just a 35-minute dialogue
segment each, resulting in 277 dialogue segments to be coded. Coding was conducted by treatment andcommunication type, and further broken into four blocks as detailed below. While the coders were awarethat there were two coders, they were kept anonymous from each other for the entire process and so wereunable to communicate with each other.
The first block (first nine days):
The coding sheet, experiment instructions, and 10-segment sample communication file for the 3minute communication in Treatment A (35-minute communication in Treatment B for the other coder)were provided on the first day. A meeting was scheduled for the same day, separately for each coder, toallow one of the researchers to explain the coding process and treatment in more detail. Coders were notmade aware of the purpose of the research, subject details, or any of the analysis/results throughout thecoding process.
After the sample set had been coded, a researcher met with each coder to discuss any problems ordifficulties. This initial practice and feedback process took two days. After that, the researchers sent thecommunication file with the remaining 83 (81) dialogue segments for that block to be completed over thefollowing seven days.
Once all 93 or 91 dependent on the coder (including the sample set) of the dialogue segments had
been coded, the Coding Sheet was returned to the researchers and no further changes could be made(unless there had been some misunderstanding about the coding practice). Feedback was not given to thecoders regarding their coding practice.
The second block (next nine days):23

Once the first block was completed, the coders were given the coding sheet and 10-segment
sample communication for the 35-minute communication in Treatment A (3-minute communication in
Treatment A for the other coder, along with the instructions for Treatment A), and a meeting wasscheduled for that day, separately for each coder, to go through the instructions and codes. The remaining
procedure is the same as in the first block. Two days were given to complete the sample set, after which
there was a meeting to clarify any questions. After that, seven days were given to code the remaining 83
dialogue segments. As before, no feedback was given to the coders regarding their coding practice.
The third block (next nine days):
As in the first and second block, the coders were provided with the instructions, coding sheet and
10-segment sample communication for the 35-minute communication in Treatment B (35-minutecommunication in Treatment A for the other coder), and a meeting was held to discuss the instructions
and codes. Two days were given to complete the 10-segment sample set, before a further meeting washeld to clarify any questions. The coders were allowed a further seven days to code the remaining 81 (83)dialogue segments. No feedback was given regarding the coders’ coding practices.
The fourth block (final seven days):
The coding results were compared for discrepancies between the two coders’ coding results. The
discrepancies were then highlighted in the Excel spreadsheets and a copy was given to each coder. The
coders were given a further seven days to re-evaluate these discrepancies, with the additional knowledgeof one another’s codes, and to either confirm or alter their initial findings. Each coder was informed that
their codes would be sent to the other coder, and that they would simultaneously re-evaluate thediscrepancies. Coder identity remained anonymous throughout the process (and also after the codingwork) and no communication was permitted.

D.2. Full List of Codes(a) ENDO 3-minute communication immediately before voting
CodeInterpretation (notshown to coders)

Description

Note: Coders may assign as many codes as appropriate, including assigning no codes,
to a dialogue segment.
Codes related to voting decision
A1
Agreement among the three team members explicitly to vote for the policy
A2
Agreement among the three team members explicitly to vote against the policy
A3
The team’s majority favored decision changes over the course of the discussion
The team’s majority favored decision does not change over the course of the
A4
discussion
Disagreement on what to vote for at the beginning of communication which is
A5then resolved
One or more teammates decide to cast their own preference (leaving the
A6majority rule to decide the team vote)
There is an unresolved split in opinion about whether to vote for or against theA7
policy due to strong preferences on both sides
Teammates do not reach a consensus by the end of the 3-minute
A8communication period for reasons other than A6 or A7
A9
Discuss how the other two teams may vote
A10 Confusion about the voting rule

24

Consensus
Consensus
Learning
Learning
Disagreement
Disagreement
Disagreement
Lack of time
Strategic
ConfusionCodes related to deciding what to do during the task-solving phase
B1
Agree/Imply to count as primary behavior
B2

Agree/Imply to game as primary behavior

Agree to hybrid behavior e.g. so many tasks/minutes before switching to thegame screen
Agree to discuss, decide and/or alter behavior during the counting task later
B4(35-minute phase) based on performance/needs in Phase 2
Suggest altering and/or discussing their behavior for the task-solving phaseB5
depending on the vote outcome
B6
Confusion about the rules in phase 2 (e.g., the revenue-sharing rule)
Codes related to why they are pro/anti the policy
Pro-policy to deter others from switching to the game screen by reducing the
C1
return (monetary deterrence)
B3

C2

Pro-policy to signal intention to complete the tasks to other teams

C8

Pro-policy for a normative reason e.g. it is the right thing to do, it is desirablesocially for their group, it is morally good
Pro-policy as the policy is perceived as fair, i.e., reduces income inequality
among subjects in a given groupPro-policy out of spite or enjoyment of punishment
Pro-policy out of anticipated anger should other teams not complete tasks
Pro-policy for strategic reasons (e.g., induce other teams to complete the task
while they themselves do not complete the task)
Anti-policy as they intend to game for at least some of the task-solving period

C9Anti-policy as they like unfair distribution of income

C3
C4
C5
C6
C7

Anti-policy as they dislike punishment philosophically (e.g., dislike coercive
punishment), and/or do not perceive other teams' gaming as negative
Anti-policy as they are uncertain about whether they will want to access the
C11game screen
Express that the policy is not strong enough to deter others switching to theC12game screen (monetary)
C13 Express that the policy is unlikely to affect other teams' working behavior
Codes related to why they chose a certain behavior
D1
Discuss ability to complete X tasks in Y minutes
D2
Believe they as a team make the most money from counting
D3
Believe they as a team make the most money from gaming
D4
Discuss their performance or comfort in Phase 1 (strong/positive)
D5
Discuss their performance or comfort in Phase 1 (weak/negative)
Discuss behavior in terms of guaranteed pay (game screen) versus uncertain
D6
pay (tasks)
C10

D7

Discuss use of the game screen as a break

D8
D9Discuss uncertainty surrounding other teams' work choices or abilities
Suggest distrust of other teams e.g. expect them to take advantage
Discuss expected fatigue from performing the task in Phase 2 based on their
D10experience in Phase 1
Discuss how total earnings for the whole group are maximized if all 3 teams
D11
work on the counting task
D12 Discuss how their vote affects the vote outcome
Codes related to team behavior
E1

Negativity towards teammates e.g. being critical of others or discouraging

25

Cooperative
Uncooperative/freeriding
Somewhat cooperative
Flexible strategy
Rational
Confusion
Monetary
incentive/punishment
Signaling/Information
Effects
Normative reasoning
Fairness preference
Spitefulness
Emotive reasoning/anger
Strategic
SelfishFairness preference(reverse)
Dislike of punishment on
private activity
Uncertainty
Punishment strength
Policy insensitivity
Skills/Ability
Monetary incentive
Monetary incentive
Incentives/Rationality
Incentives/Rationality
Uncertainty
Team behavior:
Strategizing
Uncertainty
Group behavior: Distrust

Pareto Efficiency
Pivotal voting
Team behavior: Negative
communication

E2
E3

Positivity towards teammates e.g. attempts to encourage others or being
supportive
Discusses or suggests task-related behavior e.g. double-counting, waiting foreach other, a specific method, etc.

E4

No communication of the entire team

E5

No communication from just 1 or 2 team members

E6

Chat about topics unrelated to the experiment

E7

Resolve some confusion about the experiment through communication

Team behavior: Positive
communication
Team behavior:
Strategizing
Team behavior: No
communication
Team behavior:
Limited/poor
communication
Team behavior: team
identity
Team behavior:Communication(b) ENDO 35-minute communication during the main task-solving phase
Interpretation (notCode Description
shown to coders)Note: Coders may assign as many codes as appropriate, including assigning no codes, to a dialogue segment.
Codes related to voting outcome
Group identity
F1
Express negative emotions (e.g., upset, anger) about the outcome of the vote(negative)
F2
Express positive emotions (e.g., happiness) about the outcome of the voteGroup identity (positive)
F3
Agree/Imply to count as primary behavior
Cooperative
Uncooperative/freeF4
Agree/Imply to game as primary behavior
ridingAgree to hybrid behavior e.g. so many tasks/minutes before switching to theF5Somewhat cooperative
game screen
Agree to discuss, decide and/or alter behavior during the counting task later
F6
Flexible strategy(35-minute phase) based on performance/needs in Phase 2
F7
Express belief/hope that other teams will complete tasks following the vote
Positive expectations
F8
Express belief that teams will not complete tasks following the vote
Negative expectations
Discuss the distribution of votes and predict how each team may respond to one
F9
Signaling
another
F10 Belief on other teams' responses: pro-policy teams will work hard
Belief on voter typeF11 Belief on other teams' responses: anti-policy teams will work hard
Belief on voter type
F12 Belief on other teams' responses: pro-policy teams will work little
Belief on voter typeF13 Belief on other teams' responses: anti-policy teams will work little
Belief on voter typeDiscuss some confusion (e.g., they voted based on some misunderstanding of
F14
Confusion
the experiment)
Conditional
F15 Discuss whether to change behavior based on the vote outcome
cooperation/rational
Codes related to task performance
Discuss wanting to switch to the game screen some time during the taskG1
Fatigue/inability
solving phase
G2
Discuss difficulty/unpleasantness of task e.g. being slow, tired, bored, etc.
Fatigue/inability
Enact hybrid behavior e.g. set a given number of tasks/minutes before
G3
switching to the game screen and back
G4
Expression of strong negative emotion e.g. frustration, anger, disappointment
Emotive: Negative
G5
Expression of strong positive emotion e.g. enjoyment, things are going well
Emotive: Positive
Codes related to why they chose/are choosing a certain behavior
D1
Discuss ability to complete X tasks in Y minutes
Rationality

26

D2
D3
D4
D5
D6

Believe they as a team make the most money from counting
Believe they as a team make the most money from gaming
Discuss their performance or comfort in Phase 1 and/or so far in Phase 2(strong/positive)
Discuss their performance or comfort in Phase 1 and/or so far in Phase 2(weak/negative)
Discuss behavior in terms of guaranteed pay (game screen) versus uncertainpay (tasks)

Monetary Incentive
Monetary Incentive
Rationality
Rationality
Uncertainty

D7

Discuss use of the game screen as a break

D8Discuss uncertainty surrounding other teams' work choices or abilities

Team behavior:
Strategizing
Uncertainty

D9

Suggest distrust of other teams e.g. expect them to take advantage

Group behavior: Distrust

Compare their estimated earnings so far from task-solving and forgone
earnings from not staying in the Game screen
Discuss how total earnings for the whole group are maximized if all 3 teams
D11
work on the counting task
Codes related to team behavior
D10

Evident mismatch in behavior e.g. one teammate switches to the game screen
against others' wishes
H1
1 or more players discuss being trapped in the waiting screen
H2
H3
H4
H5
H6
H7
H8
H9

H10
H11
H12
H13
H14

Positivity towards teammates e.g. attempts to encourage others or being
supportive
Negativity towards teammates e.g. being critical of others or discouraging
Discusses or suggests task-related behavior e.g. double-counting, waiting foreach other, a specific method, etc.
Checking whether their teammates refrain from using the Game screen
Communicate about their Tetris score or enjoying playing Tetris
Disagreement on what to do (count or gaming) at the beginning ofcommunication
No communication of the entire team
No communication from just 1 or 2 team members; or ignore messages fromtheir teammates
A team exclusively communicates numbers (for the counting task) throughout
the 35-minute phase
A player/s states that they have not or do not spend any time in the game screen
Chat about topics unrelated to the experiment
Resolve some confusion about the experiment through communication

Rationality
Pareto Efficiency

Team behavior:
miscommunication/selfis
hnessTeam behavior:
miscommunication/poor
planning
Team behavior: Negative
communication
Team behavior: Positive
communication
Team behavior:
Strategizing
Team behavior:
Monitoring
Team behavior: Enjoy
shirking
Team behavior:
Disagreement
Team behavior: No
communication
Team behavior:
Limited/poor
communication
Team behavior
Strategic/Lying
Team behavior: team
identity
Team behavior:Communication(c) EXO 35-minute communication during the main task-solving phase
CodeInterpretation (notshown to coders)

Description

27

Note: Coders may assign as many codes as appropriate, including assigning no codes, to a dialogue segment.
Codes related to policy outcome
I1
Express negative emotions (e.g., upset, anger) about the policy outcome
Group identity (negative)
I2
Express positive emotions (e.g., happiness) about the policy outcome
Group identity (positive)
I3
Agree/Imply to count as primary behavior
Cooperative
I4
Agree/Imply to game as primary behavior
Uncooperative/free-riding
Agree to hybrid behavior e.g. so many tasks/minutes before switching to theI5
Somewhat cooperative
game screen
Agree to discuss, decide and/or alter behavior during the counting task later
I6
Flexible strategy(35-minute phase) based on performance/needs in Phase 2
Express belief/hope that other teams will complete tasks following the policy
I7
Positive expectations
outcome
I8
Express belief that teams will not complete tasks following the policy outcome Negative expectations
Discuss whether the method that the computer randomly decides whether the
I9policy is implemented is fair
Discuss whether the method (computer's random choice) is accurate as
I10
described by the instructions (e.g., the computer's choice may not be random)
I11
Discuss some confusion
Codes related to task performance
Discuss wanting to switch to the game screen some time during the taskG1
Fatigue/inability
solving phase
G2
Discuss difficulty/unpleasantness of task e.g. being slow, tired, bored, etc.
Fatigue/inability
Enact hybrid behavior e.g. set a given number of tasks/minutes before
Team behavior:
G3switching to the game screen and backStrategizing
G4
Expression of strong negative emotion e.g. frustration, anger, disappointment
Emotive: Negative
G5
Expression of strong positive emotion e.g. enjoyment, things are going well
Emotive: Positive
Codes related to why they chose/are choosing a certain behavior
D1
Discuss ability to complete X tasks in Y minutes
Rationality
D2
Believe they as a team make the most money from counting
Rationality
D3
Believe they as a team make the most money from gaming
Rationality
Discuss their performance or comfort in Phase 1 and/or so far in Phase 2
D4
Rationality(strong/positive)
Discuss their performance or comfort in Phase 1 and/or so far in Phase 2
D5
Rationality(weak/negative)
Discuss behavior in terms of guaranteed pay (game screen) versus uncertain
D6
Uncertaintypay (tasks)
Team behavior:
D7
Discuss use of the game screen as a break
Strategizing
D8
Discuss uncertainty surrounding other teams' work choices or abilities
Uncertainty
D9
Suggest distrust of other teams e.g. expect them to take advantage
Group behavior: Distrust
Compare their estimated earnings so far from task-solving and forgone
D10
Rationality
earnings from not staying in the Game screen
Discuss how total earnings for the whole group are maximized if all 3 teams
D11
Pareto Efficiency
work on the counting task
Codes related to team behavior
Team behavior:
Evident Mismatch in behavior e.g. one teammate switches to the game screen
H1
miscommunication oragainst others' wishes
selfishness
Team behavior:
H2
1 or more players discuss being trapped in the waiting screen
miscommunication/poor
planning
Positivity towards teammates e.g. attempts to encourage others or being
Team behavior: Negative
H3
supportive
communication

28

H4

Negativity towards teammates e.g. being critical of others or discouraging

H5

Discusses or suggests task-related behavior e.g. double-counting, waiting foreach other, a specific method, etc.

H6

Checking whether their teammates refrain from using the Game screen

H7

Communicate about their Tetris score or enjoying playing Tetris

H8

Disagreement on what to do (count or gaming) at the beginning ofcommunication

H9

No communication of the entire team

H10

No communication from just 1 or 2 team members; or ignore messages fromtheir teammates

H11
H12

A team exclusively communicates numbers (for the counting task) throughout
the 35-minute phase
A player/s states that they have not or do not spend any time in the gamescreenH13

Chat about topics unrelated to the experiment

H14

Resolve some confusion about the experiment through communication

Team behavior: Positive
communication
Team behavior:
Strategizing
Team behavior:
Monitoring
Team behavior: Enjoy
shirking
Team behavior:
Disagreement
Team behavior: No
communication
Team behavior:
Limited/poor
communication
Team behavior
Strategic/Lying
Team behavior: team
identity
Team behavior:Communication

D.3. Agreement rates and Kappas
The average Cohen’s Kappas for the initial coding were 0.67, 0.60, and 0.45 for the ENDO 3minute dialogue segments, ENDO 35-minute dialogue segments, and EXO 35-minute dialogue segments,
respectively. The reconsideration step improved the Kappas. After the independent reconsideration
process, the Kappas became 0.87, 0.87, and 0.78 for the ENDO 3-minute dialogue segments, ENDO 35minute dialogue segments, and EXO 35-minute dialogue segments, respectively.
Remark: The overall agreement rates of coding between the two coders after (before) the reconsideration
process were 96.3% (90.5%), 97.5% (93.0%), and 94.9% (88.5%) for the ENDO 3-minute dialogue
segments, ENDO 35-minute dialogue segments, and EXO 35-minute dialogue segments, respectively.
The following summarizes the agreement rates and the Kappas before and after the reconsideration stepfor each code:(a) ENDO 3-Minute Dialogue Segments[Agreement Rate:]

29

[Cohen’s Kappa:](b) ENDO 35-Minute Dialogue Segments[Agreement Rate:]30

[Cohen’s Kappa:](c) EXO 35-Minute Dialogue Segments[Agreement Rate:][Cohen’s Kappa:]

31

Note: Cohen’s Kappa cannot be calculated where a code that is not used by either coder; these are marked with “n.a.”

D.4. Regression Analysis
This section reports a regression analysis to explore subjects’ reasoning behind their voting
decisions regarding the reduction policy and their task-solving behavior, utilizing the classified codes (seeSection D.2 for the full list of codes). Following the convention in the experimental literature on teamdecision-making, the codes with Kappa values greater than 0.4 were used in each model.
As listed in Section D.2, five kinds of coding categories (Code As, Bs, Cs, Ds and Es) were used
to classify the subjects’ reasoning behind voting (the ENDO 3 Minutes Dialogue). Four kinds of coding
categories (Code Fs, Gs, Ds, and Hs for the ENDO treatment; Code Is, Gs, Ds, and Hs for the EXO
treatment) were used to classify their reasoning in task-solving. As each coding category classifies thesame behavior just from a different angle, having all codes altogether in a regression leads to serious
collinearity. The codes of one coding category are therefore included as independent variables in eachmodel in the following analyses. Note that as shown in Section D.3, the Kappa values of almost allclassified codes are more than 0.4.
(a) Voting whether to implement the reduction policy (An analysis for the ENDO 3-Minute Dialogue
Segments)
The following table reports results when using Code Bs, Cs, Ds, and Es as independent variables
in Models 1, 2, 3 and 4, respectively. Code As (Codes related to the voting decision), such as “A1:
Agreement among the three team members explicitly to vote for the policy,” have almost the same
information as the teams’ voting decisions. Unsurprisingly, collinearity is strong and no meaningful results
are obtained when using Code As as independent variables. The results are omitted to conserve space.
Dependent variable: A dummy which equals 1 if team i voted for (against) the reduction policy
Model 1: Using codes
related to deciding what to
do during task-solving as
independent variables

Model 2: Using codes
related to why they are
pro/anti the policy as
independent variables

Model 3: Using codes
related to why they chosea certain behavior as
independent variables

32

Model 4: Using codes
related to team behavior
as independent variables

Independent Coefficientvariableestimates
0.31***
Code B1(0.11)-0.26*
Code B2(0.13)-0.27**
Code B3(0.11)-0.27**
Code B4(0.12)
0.02
Code B5(0.11)
0.40***
Constant(0.12)

Independent
variable

Coefficientestimates
0.57***(0.10)
0.06(0.05)
0.06(0.05)-0.21(0.16)
0.26(0.17)
0.13(0.11)-0.41***(0.09)-0.10(0.13)-0.27**(0.13)-0.30(0.23)
0.05(0.14)
0.37***(0.09)

Independent
variable# of obs.

93

Constant

R-squared

0.72# of obs.
R-squared

Code C1
Code C3
Code C4
Code C5
Code C6
Code C7# of obs.

93

Code C8

R-squared

0.34

Code C10
Code C11
Code C12
Code C13
Constant

Code D1
Code D2
Code D3
Code D4
Code D5
Code D6
Code D7
Code D8
Code D9
Code D10
Code D11
Code D12

Coefficientestimates
0.07(0.11)
0.41***(0.11)
0.04(0.13)
0.28(0.17)-0.26**(0.10)-0.21(0.13)-0.16(0.15)-0.03(0.12)
0.19(0.19)
0.14(0.12)
0.05(0.14)
0.19(0.17)
0.22***(0.08)
93
0.40

Independent
variable
Code E1
Code E2
Code E3
Code E5
Code E6
Code E7
Constant

Coefficientestimates-0.21(0.28)
0.30*(0.15)-0.11(0.11)-0.31***(0.08)
0.19(0.24)-0.18(0.21)
0.42***(0.08)# of obs.

93

R-squared

0.07

Notes: Linear regressions with robust standard errors. The numbers in parentheses are standard errors. Codes whose Kappa
values are equal to or above 0.4 are used as independent variables. * p < 0.1; ** p < 0.05; *** p < 0.01.

(b) Task-solving (An analysis for the ENDO/EXO 35-Minute Dialogue Segments)
Subjects’ behaviors in the main task-solving phase can be characterized as (i) their work time(minutes), i.e., the duration in which they work on counting, rather than staying in the Game screen, and(ii) work productivity, i.e., the number of correct answers per minute of work time. The regression
analysis below reports two versions for each coding category: one with the work time as the dependent
variable, and the other with the work productivity as the dependent variable.
Subsections b1, b2, b3, and b4 below each include regression results of the ENDO treatment(Models 1 and 2) and of the EXO treatment (Models 3 and 4), side by side, to make comparison easier.
b1: Codes related to voting outcome (Code Fs) or policy outcome (Code Is))ENDO (Code Fs)#1
Dependent Var.: Work
Dependent Var.: Work
time (minutes)
productivity
Model 1Model 2
Independent Coefficient
Independent Coefficientvariableestimates
variableestimates-14.77***-0.11
Code F1
Code F1(1.71)(0.17)-3.01-0.03
Code F2
Code F2(6.47)(0.21)
Code F3
11.27***
Code F3
0.24***EXO (Code Is)
Dependent Var.: Work
Dependent Var.: Work
time (minutes)
productivity
Model 3Model 4
Independent Coefficient
Independent Coefficientvariableestimates
variableestimates-8.94***-0.24***
Code I1
Code I1(2.98)(0.07)-21.71***-0.41***
Code I4
Code I4(2.60)(0.05)
Code I5-9.28***
Code I5-0.19**

33

Code F4
Code F5
Code F6
Code F7
Code F8
Code F9
Code F13
Code F15
Cons.
# of obs.
R-squared(2.70)-16.15***(2.90)-3.98**(1.85)-3.93(6.04)
6.83***(0.53)
5.25***(1.03)
13.64***(0.48)
2.46(2.99)
4.80***(1.15)
18.47***(2.73)
93
0.46

Code F4
Code F5
Code F6
Code F7
Code F8
Code F9
Code F13
Code F15
Cons.
# of obs.
R-squared(0.07)-0.25***(0.09)-0.09*(0.05)-0.28*(0.15)
0.24**(0.12)
0.28***(0.04)
0.17(0.10)-0.41***(0.15)
0.07(0.17)
0.32***(0.06)
93
0.31

Code I6
Code I11
Cons.

(3.09)-7.47*(3.92)
2.08(1.85)
27.33***(0.98)

Code I6
Code I11Cons.

(0.08)-0.11(0.07)
0.07(0.06)
0.43***(0.04)

b2: Codes related to task performance (Code Gs)
ENDO (Code Gs)
Dependent Var.: Work
Dependent Var.: Work
time (minutes)
productivity
Model 1Model 2
Independent Coefficient
Independent Coefficientvariableestimates
variableestimates
-1.80
-0.11
Code G1
Code G1(3.43)(0.08)
5.32*-0.01
Code G2
Code G2(3.01)(0.07)
2.13
0.06
Code G3
Code G3(3.07)(0.07)-1.64-0.16
Code G4
Code G4(3.05)(0.08)
5.60
0.03
Code G5
Code G5(5.62)(0.11)
21.86***
0.46***
Cons.
Cons.
(2.42)(0.06)# of obs.
93# of obs.
93
R-squared
0.03
R-squared
0.06

EXO (Code Gs)
Dependent Var.: Work
Dependent Var.: Work
time (minutes)
productivity
Model 3Model 4
Independent Coefficient
Independent Coefficientvariableestimates
variableestimates-11.08***-0.16**
Code G1
Code G1(2.64)(0.07)
5.93**
0.16***
Code G2
Code G2(2.41)(0.06)
6.66**
0.14**
Code G3
Code G3(2.67)(0.07)-3.86-0.22***
Code G4
Code G4(2.91)(0.06)
7.66***
0.30**
Code G5
Code G5(2.63)(0.13)
26.76***
0.39***
Cons.
Cons.
(1.43)(0.04)# of obs.
91# of obs.
91
R-squared
0.32
R-squared
0.29

b3: Codes related to why they chose/are choosing a certain behavior (Code Ds)
ENDO (Code Ds)
Dependent Var.: Work
Dependent Var.: Work
time (minutes)
productivity
Model 1Model 2
Independent Coefficient
Independent Coefficientvariableestimates
variableestimates
19.24
0.64**
Code D1
Code D1(11.86)(0.31)
4.13-0.28***
Code D2
Code D2(2.63)(0.06)
Code D3-20.20***Code D3-0.42***EXO (Code Ds)
Dependent Var.: Work
Dependent Var.: Work
time (minutes)
productivity
Model 3Model 4
Independent Coefficient
Independent Coefficientvariableestimates
variableestimates
4.44*
0.09
Code D1
Code D1(2.32)(0.10)
9.16***
0.23***
Code D2
Code D2(2.07)(0.08)
Code D3-8.45**
Code D3-0.11

34

Code D4
Code D5
Code D6
Code D7
Code D8
Code D9
Code D10
Code D11Cons# of obs.
R-squared(1.69)
7.95***(2.42)-18.53***(4.44)
3.98**(1.69)
3.13(2.18)
0.59(7.80)-19.21***(4.76)
11.06(11.34)-7.74(5.09)
22.42***(1.69)
93
0.14

Code D4
Code D5
Code D6
Code D7
Code D8
Code D9
Code D10
Code D11Cons# of obs.
R-squared(0.04)
0.27***(0.06)-0.43***(0.10)
0.07(0.04)-0.01(0.08)
0.03(0.21)-0.37***(0.12)
0.19(0.29)-0.35***(0.12)
0.42***(0.04)
93
0.11

Code D5
Code D6
Code D7
Code D8
Code D10
Code D11Cons(3.72)-11.07**(4.71)-5.78(4.24)-0.97(2.24)-14.89***(4.36)-1.72(2.74)
9.25(6.54)
24.51***(1.28)

Code D5
Code D6
Code D7
Code D8
Code D10
Code D11
Cons(0.09)-0.13(0.10)-0.12(-0.12)0.06(0.07)-0.23**(0.09)-0.22**(0.09)
0.15(0.17)
0.36***(0.04)# of obs.

91# of obs.

91

R-squared

0.29

R-squared

0.19

b4: Codes related to team behavior (Code Hs)
ENDO (Code Hs)
Dependent Var.: Work
Dependent Var.: Work
time (minutes)
productivity
Model 1Model 2
Independent Coefficient
Independent Coefficientvariableestimates
variableestimates
-5.16
-0.15
Code H1
Code H1(5.89)(0.10)
10.79**
0.17*
Code H2
Code H2(4.70)(0.10)
3.01
0.02
Code H3
Code H3(3.38)(0.09)
0.26-0.02
Code H4
Code H4(6.48)(0.13)
6.70**
0.04
Code H5
Code H5(2.72)(0.09)
3.90
0.25**
Code H6
Code H6(5.88)(0.12)-5.09-0.30*
Code H7
Code H7(7.91)(0.16)-7.07-0.13
Code H8
Code H8(4.98)(0.09)-22.78***-0.46***
Code H9
Code H9(1.91)(0.05)-7.97-0.14
Code H10
Code H10(4.91)(0.09)
10.19***
0.26***
Code H11
Code H11(1.95)(0.08)
10.95***
0.30***
Code H12
Code H12(1.99)(0.05)
2.07-0.27**
Code H13
Code H13(5.42)(0.10)-3.98-0.17*
Code H14
Code H14(3.08)(0.10)
23.27***
0.46***
Cons.
Cons.
(1.90)(0.05)EXO (Code Hs)
Dependent Var.: Work
Dependent Var.: Work
time (minutes)
productivity
Model 3Model 4
Independent Coefficient
Independent Coefficientvariableestimates
variableestimates-1.87-0.20**
Code H1
Code H1(5.98)(0.08)
5.63
0.00
Code H2
Code H2(6.58)(0.07)
2.62
0.03
Code H3
Code H3(2.09)(0.07)-2.12-0.12
Code H4
Code H4(4.41)(0.08)
3.64*
0.09
Code H5
Code H5(2.03)(0.06)
2.76
0.04
Code H6
Code H6(2.50)(0.10)-2.57-0.06
Code H7
Code H7(1.60)(0.07)-5.23-0.09
Code H8
Code H8(3.32)(0.06)-8.60*-0.13*
Code H10
Code H10(4.80)(0.08)
8.17***
0.07
Code H11
Code H11(2.62)(0.07)
4.67***-0.17***
Code H12
Code H12(1.62)(0.06)-2.85-0.06
Code H13
Code H13(3.44)(0.08)
0.13-0.10
Code H14
Code H14(2.36)(0.08)
22.97***
0.43***
Cons.
Cons.
(2.56)(0.06)# of obs.

35

91# of obs.

91

# of obs.
R-squared93
0.68# of obs.
R-squared93
0.56

R-squared

0.57

R-squared

0.50

Notes: Linear regressions with robust standard errors. The numbers in parentheses are standard errors. Work productivity is
calculated as the number of correct answers divided by the duration to stay on the work site (minutes). Codes whose Kappa
values are equal to or above 0.4 are used as independent variables. #1 Code F10 was omitted despite the Kappa values being
above 0.4, because only one chat dialogue was categorized as this code (making estimating its coefficient estimate impossible). *p < 0.1; ** p < 0.05; *** p < 0.01.

36