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

Self-Regulatory Resources and Institutional Formation:
A First Experimental Test
Kenju Kamei

26 September, 2022
DP2022-014
https://ies.keio.ac.jp/en/publications/19162/

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

Self-Regulatory Resources and Institutional Formation: A First Experimental Test
Kenju Kamei
Keio-IES DP2022-014
26 September, 2022
JEL Classification: C92, D72, H41
Keywords: Institutional choices;social dilemma;public goods;self-control;punishment
Abstract
This study reports the result of a novel laboratory experiment that shows for the firsttime that the state of people’s self-regulatory resources influences reliance on formal
enforcement of norms in a social dilemma. In a laboratory, subjects’ self-regulatory
resources are rigorously manipulated using well-known depletion tasks. On the one
hand, when their resources are not depleted, the vast majority decide to govern
themselves through monitoring and decentralized peer-to-peer punishment in a public
goods dilemma, and then successfully achieve high cooperation norms. On the other
hand, when their resources are small, the majority vote to enact a costly formal
sanctioning institution and then construct deterrent punishment toward free riders;backed by formal punishment, the groups achieve strong cooperation. A
supplementary survey regarding the Covid-19 pandemic was also conducted toenhance the external validity of the finding, generating a similar pattern. The so-called
self-control and commitment preferences combined with inequality aversion can
explain these patterns, because the theory predicts that those with smaller selfregulatory resources are motivated to remove temptations in advance as acommitment device, thereby avoiding a large self-control cost. This underscores therole of commitment in a social dilemma context.
Kenju Kamei
Faculty of Economics, Keio University
2-15-45 Mita, Minato-ku, Tokyo
kenju.kamei@keio.jp
Acknowledgement : This study was conducted as a part of the Project: “Self-regulatory
resources and collective institutional choices in a social dilemma (an experimental
study)” undertaken at the Research Institute of Economy, Trade and Industry (RIETI).
This paper was also financially supported by a grant-in-aid from the Foundation for the
Fusion of Science and Technology. The author thanks members in the RISS at Kansai
University, especially Kazuhito Ogawa, Toshihiko Nanba, Atsuko Shimano and Hiroe
Oshima for their support in recruiting subjects, preparing for the experiment sessions
and managing the computers and the software system in the laboratory, Artem
Nesterov for his assistance in zTree programming, and Xin Fang for translating zTree
program files from English to Japanese. The author is also grateful for helpfulcomments and suggestions by Discussion Paper seminar participants at RIETI.

Self-Regulatory Resources and Institutional Formation:
A First Experimental Test

Kenju Kamei
Faculty of Economics, Keio University, 2-15-45, Mita, Minato-ku, Tokyo 108-8345, JapanRIETI, 1-3-1, Kasumigaseki, Chiyoda-ku, Tokyo 100-8901, Japan
Email: kenju.kamei@gmail.com; kenju.kamei@keio.jp

Abstract: This study conducts a novel laboratory experiment that shows that the state of people’s
self-regulatory resources influences their reliance on the formal enforcement of norms in a social
dilemma. The subjects’ self-regulatory resources are rigorously manipulated using well-known
depletion tasks. On the one hand, when their resources are not depleted, most decide to govern
themselves through decentralized, peer-to-peer punishment in a public goods dilemma, and thenachieve high cooperation norms. On the other hand, when the amount of their resources is limited, themajority enact a costly formal sanctioning institution; backed by formal punishment, groups achievestrong cooperation. A supplementary survey on the Covid-19 pandemic was conducted to enhance theexternal validity of the findings, generating a similar pattern. Self-control preference theories,
combined with inequity aversion, can explain these patterns, because they predict that those withlimited self-control are motivated to remove temptations in advance as a commitment device.
JEL codes: C92, D02, D72, D91, H41
Keywords: Institutional Choices, Social Dilemma, Public Goods, Self-Control, Punishment

Acknowledgement: This study was conducted as part of the project, “Self-regulatory resources andcollective institutional choices in a social dilemma (an experimental study),” undertaken at the ResearchInstitute of Economy, Trade and Industry (RIETI). This study was financially supported by a grant-in-aid
from the Foundation for the Fusion of Science and Technology. The author thanks the members in the
RISS at Kansai University, especially Kazuhito Ogawa, Toshihiko Nanba, Atsuko Shimano, and Hiroe
Oshima, for their support in recruiting subjects, preparing for the experiment sessions, and managing thecomputers and software system in the laboratory; Artem Nesterov, for his assistance in zTree
programming; and Xin Fang, for translating zTree program files from English into Japanese. The author
is also grateful for the helpful comments and suggestions by the participants in the discussion paper
seminar at RIETI.

1

1. Introduction
Human societies and organizations experience many conflicts between private interests andsocially optimal behaviors. Free riding problems in social dilemmas are typical examples of suchconflicts. In a social dilemma, people may recognize the value of cooperation and therefore wish toachieve the Pareto-efficient outcome(s) through mutual cooperation. However, the temptation to free ridemay be too strong for some to resist due to their self-control capacities (e.g., Gul and Pesendorfer, 2001,
2004; Baumeister et al., 1994, 2007). Societies have ways to regulate opportunistic behavior throughimplementing formal institutions (e.g., Ostrom, 1990), thus removing harmful temptations as acommitment device in advance. However, it is unclear how people’s self-control capacities are linked to
institutional formation in their community, whether in groups, societies, or organizations.
For the last few decades, experimental studies have actively examined how formal (a.k.a.
centralized) institutions can resolve social dilemmas, and when these institutions should be implemented(e.g., Falkinger et al., 2000; Tyran and Feld, 2006; Kosfeld et al., 2009; Putterman et al., 2011; Traulsen
et al., 2012; Zhang et al., 2014; Kamei et at., 2015; Nicklisch et al., 2016; Fehr and Williams, 2018;
Kamei and Tabero, 2021). Prior research suggests that not only do formal sanctioning institutions
theoretically alter people’s materially beneficial behaviors, but they also indeed induce real people to
make socially optimal choices (e.g., Falkinger, 1996; Falkinger et al., 2000; Putterman et al., 2011).
However, it at the same time, research shows that formal institutions may not always be required toresolve dilemmas, because people may successfully govern themselves through decentralized monitoringand peer-to-peer punishment (e.g., Fehr and Gӓchter, 2000, 2002; Masclet et al., 2003; Gürerk et al.,2006; Herrmann et al., 2008; Gächter et al., 2008; Casari and Luini, 2009; Ertan et al., 2009). Several
studies have investigated people’ choices between formal and informal sanctioning institutions, and havefound that groups prefer to use a formal institution to a decentralized solution only under certain
conditions, such as when the use of a formal institution does not entail a large cost (e.g., Kamei et at.,2015), when anti-social peer-to-peer punishment is more severe than possible enforcement errors by acentralized authority (e.g., Nicklisch et al., 2016), or when a normative consensus is difficult to reachthrough a decentralized mechanism (e.g., Fehr and Williams, 2018). However, no studies have explored
how people’s self-control capacities, or more precisely, self-regulatory resources, are linked to their needfor formal institutions in social dilemmas.
Self-regulatory resources are internal resources that people use to regulate their self-control, copewith stress and attention, and deal with conflicts between selfish and pro-social motivations. A large
volume of experiments in neighboring fields of economics has consistently demonstrated, since around1990, that (a) people’s decision-making is strongly influenced by the state of their self-regulatory
resources, and (b) the self-regulatory resources are limited, meaning that the resources are depleted once
used for some activities (see, e.g., Baumeister et al. [1994, 2007] and Muraven and Baumeister [2000] fora survey). Responding to the solid empirical evidence and great economic importance of self-control (e.g.,
overeating, consumption, borrowing, and procrastination), economists have joined the research andrigorously formalized people’s self-control preferences (e.g., Gul and Pesendorfer, 2001, 2004;
Fudenberg and Tirole, 2006; Dekel et al., 2009). Recently, economic experiments have also verified that2

self-control preferences are indeed prevalent, and that some people may want to remove strong
temptations in advance if they anticipate that they will succumb to them and if removing them may
improve their welfare (e.g., Bucciola et al., 2011; Burger et al., 2011; Houser et al., 2018; Toussaert,
2018; Kocher et al., 2017). While self-control and commitment theories may be applicable in the context
of institutional formation in societies or organizations, surprisingly, this possibility has not been
considered thus far.
How to implement a formal institution, such as formal punishment, is clearly a difficult but
important issue. While such questions are ubiquitous and commonly raised in modern societies,
identifying people’s institutional preferences and the effects of policies is challenging. One example isrestrictions related to the Covid-19 pandemic (which started in early 2020). Several countries, such as
those in North America, Europe and Asia, enacted lockdowns or similar restrictions when the pandemic
became serious. People’s behavioral patterns during the pandemic did resemble a self-regulatory depletion
phenomenon. For example, Japan declared a state of emergency four times in the Tokyo area, andimplemented strict restriction measures.1 Based on data on people flow, however, the impacts of such
restrictions diminished over time. For instance, on the first weekends following the declaration of thesecond, third, and fourth states of emergency, crowd numbers in Shibuya Center Street were found to be
50, 50, and 88 percentage points larger, respectively, than those on the first weekend following thedeclaration of the first state of emergency (Rei Frontier, Inc., July 2021). The news repeatedly announced
that this kind of phenomenon was due to “Jishuku zukare” (which means exhaustion from extreme selfcontrol, e.g., staying home), and emphasized that the state of emergency had become increasingly lesseffective. A survey conducted by the Cabinet Office in Spring 2021 indicated that 71.6% of the
respondents agreed that they were exhausted from self-control.2 Something similar occurred in almost
every country. In the United Kingdom (UK), many citizens strictly followed social distancing measures
and wore face coverings during the first lockdown. However, they gradually stopped following suchmeasures or government recommendations. They even tended to oppose restrictions when another wave
later came. A survey, for example, showed that the percentage of those who were willing to self-isolate if
advised decreased from 95% in April 2020 to 87% in April 2021 (Imperial College London, 2021).
Parallel to people’s attitudes, the country gradually shifted in the direction of living with the coronavirus
without (strong) restrictions.
Nonetheless, this kind of interpretation may be misleading, because the Covid-19 restriction
measures were weaker in later lockdowns/states of emergency. Thus, the pattern described above may
simply mean that people’s degrees of self-control are merely positively correlated with the strength ofrestriction measures. This opposite causation is similar to the well-known example of endogeneity,
demonstrated by Levitt (1997), for the positive correlations between crime rates and the sizes of police
forces in cities in the United States (US) (see Hoxby [2000] for another example). As policymaking indemocratic countries such as Japan and the UK reflects people’s views, the weaker restriction measures in
1

The first state of emergency was from April 7 to May 25, 2020; the second one was from January 8 to March 21,2021; the third was from April 25 to June 20, 2021; and the fourth was from July 12 to Sep. 30, 2021.
2
https://www5.cao.go.jp/keizai2/wellbeing/covid/pdf/result3_covid.pdf (in Japanese; accessed on Feb. 7, 2022)3

a later Covid wave may mean that, contrary to self-control theory, people do not have commitment
preferences when their self-regulatory resources are limited (i.e., when they cannot resist the temptation togo out due to, perhaps, self-regulatory depletion). Identifying people’s commitment preferences iscomplex, nevertheless; some unobserved individual characteristics, or omitted variables, might affect bothpeople’s self-control behaviors and their support for weak restriction measures through democratic
processes. Uncertainty about the fatality of the coronavirus was also gradually resolved over time, which
made comparisons of revealed behaviors between different points of time less straightforward. People’s
concerns were not limited to their health and safety, as Covid-19 restrictions also both impacted labormarkets and their incomes. Indeed, there is some indication of people’s commitment preferences:
Conducted in June 2021, an opinion survey by the Yomiuri newspaper found that (a) the percentage of
those who supported changing the Japanese constitution increased from 49% in 2020 to 56 % in 2021, and(b) 59% of the respondents agreed that the government’s control rights and power should be strengthened
in an emergency such as the Covid-19 crisis. In June 2021, an opinion survey by Jiji Press indicated that
53.7% (20.7%) of their respondents agreed (disagreed) to creating a clause in the constitution tostrengthen the government’s power in an emergency. However, it is unclear who, those with strong or
weak self-control, support such stronger formal enforcement. In addition, “emergency” in these surveys
includes not only the Covid-19 crisis, but also any other crisis, such as a possible war with a country
neighboring Japan or natural disaster.
A similar difficulty arises when this research question is examined based on an existing crosscountry dataset. For example, the World Value Survey (WVS) Wave 7 (2017-2020) collected responses
regarding what children were encouraged to learn at home, such as good manners and tolerance—see Q7
to Q17 of the survey. As people are known to build self-regulatory resources in their lifetime (e.g.,
Baumeister et al., 1994, 2007; Muraven and Baumeister, 2000), education to exercise self-control in earlystages can be a proxy for their self-regulatory strength as a nation. The WVS also collected views on
government interventions by asking respondents to rate them on a 10-point scale: 1 = The government
should take more responsibility to ensure that everyone is provided for, and 10 = People should take more
responsibility to provide for themselves. A pairwise Pearson’s correlation between the percentage ofaffirmative answers in Q7 to Q17 and the view on the government intervention was calculated as
significantly negative (correlation = -0.0247, p < 0.0001). Thus, those more educated to build self-control
in their childhood appear to ultimately support greater government responsibility. Furthermore, those whoare more educated in self-control are significantly less confident with the current level of law in theirnations.3 These patterns are again opposite to those suggested by self-control theory, as the theory
postulates that those who lack self-control want stronger interventions as a commitment device. These
interpretations, nevertheless, may be incorrect due to endogeneity issues (e.g., omitted variable bias), or
heterogeneity, typical of cross-country analyses.
An advantage of using a laboratory experiment is its control. It is possible to study people’s

The percentage of the respondents’ affirmative answers in Q7 to Q17 are significantly and negatively correlatedwith their average confidence levels on the police, courts, and government in the WVS (Q69, Q70, and Q71).
3

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preferences between formal and informal institutions without suffering from econometric issues. With acarefully constructed design, this study provides the first experimental evidence that the state of people’s
self-regulatory resources does influence their reliance on the formal enforcement of norms in a social
dilemma, as self-control and commitment theories, combined with inequity aversion, suggest. The
recruited human subjects’ self-regulatory resources are rigorously manipulated using two depletion/nondepletion tasks from the literature: the crossing-out-letters (Baumeister et al., 1998) and Stroop (1997)
tasks. When their self-regulatory resources are not depleted, most decide not to introduce a costly formal
sanctioning institution in a public goods game (“PGG,” hereafter); however, they then successfully
cooperate with one another through decentralized monitoring and peer-to-peer punishment. In contrast,
when they are forced to deplete their self-regulatory resources, the vast majority vote to implement a
costly formal institution, and then construct a deterrent punishment toward free riders. The deterrent
punishment has a strong effect in sustaining cooperation. These results, therefore, emphasize that people’s
demands for formal sanctioning institutions in a social dilemma depend on the amount of their selfregulatory resources.
While the finding is quite convincing, one may be concerned about its external validity due to theneutral framing design of the laboratory experimental approach. Although the laboratory approach isstandard and its usefulness is already well established, the present study additionally conducts a survey(opinion) to supplement the main experiment summarized above by collecting respondents’ self-control
behaviors and their opinions on the restriction measures during the Covid-19 crisis. The results obtained
about their preferences in the field are consistent with our observations in the laboratory experiment:
those who exhibit weaker self-restraint behavior during the Covid-19 pandemic prefer more to rely on
formal restrictions and sanctioning institutions to deal with the cooperation problem during the pandemic.
The rest of the paper proceeds as follows: Section 2 briefly summarizes the related literature,
while Section 3 describes the experimental design. Section 4 presents hypotheses based on theoreticalanalysis, while Section 5 reports the experimental results. Section 6 briefly explains the results of the
supplementary survey. Section 7 concludes.

2. Related Literature
Two branches of the literature in the social sciences are closely related to the present study: (a)social dilemmas, and endogenous choices of institutions, and (b) self-control and self-regulatory
resources. The branch in (a) emanates from economic experimental research, while that in (b) arises fromtheoretical suggestions and experimental evidence in economics, as well as laboratory studies inneighboring fields such as psychology.
First, there is a large volume of experimental research that examines not only the humanbehavioral tendency to cooperate, but also institutions in sustaining cooperation in social dilemmas.
People’s social dilemma behavior is often studied using a PGG —the game adopted in this study, andamong the most frequently used games in the literature. In a PGG, human subjects are randomly assigned
to a group of N, where N > 2, are given endowments, and then simultaneously decide how many points tocontribute to their group. Parameters are set such that members’ privately optimal contribution levels are5smaller than the socially optimal level. The socially optimal contribution level is often set at the fullendowment amount (i.e., linear public goods game); however, it is sometimes set at an interior level (i.e.,
non-linear public goods game). The PGG emulates many social dilemma situations, e.g., whether to litter,to comply with laws and ordinances, or to follow norms such as recycling and fulfil civic duties. Prior
research indicates that tension between cooperation and free riding is intense. For instance, while somepeople attempt to cooperate with their peers, they learn to behave uncooperatively as they gain experience(e.g., Ledyard, 1995; Chaudhuri, 2011). Thus, some institutions are required to sustain cooperation,
unless interactions are infinitely repeated.
There are two kinds of institutions that can facilitate cooperation. The first kind is to utilize acentralized or formal institution, which aligns members’ private interests with group interests using
deterrent incentives (e.g., Falkinger et al., 2000; Tyran and Feld, 2006; Kosfeld et al., 2009; Putterman etal., 2011; Traulsen et al., 2012; Zhang et al., 2014; Kamei et at., 2015; Nicklisch et al., 2016; Fehr andWilliams, 2018; Kamei and Tabero, 2021). For example, in Tyran and Feld (2006), while subjects
contributed only 30% of the endowment in a standard linear PGG with free riding being the strictly
dominant strategy, they on average contributed 93% of the endowment when a deterrent penalty scheme
changed the equilibrium behavior to full contribution. Similarly, Falkinger (1996) and Falkinger et al.
(2000) showed, theoretically and experimentally, that a redistribution mechanism (which taxes free riders
while subsidizing high contributors) can lead to almost full efficiency. Furthermore, given an option to
construct a mechanism, most subjects can build a deterrent one, thereby achieving a Pareto-efficient
outcome (e.g., Putterman et al., 2011; Kamei et al., 2015).4
An alternative to a centralized solution is to rely on decentralized, peer-to-peer monitoring andpunishment (e.g., Fehr and Gӓchter, 2000, 2002; Masclet et al., 2003; Gürerk et al., 2006; Herrmann etal., 2008; Gächter et al., 2008; Casari and Luini, 2009; Ertan et al., 2009). The standard theoreticalprediction of free riding in PGGs does not change when decentralized punishment is available, based on
agents’ self-interest and common knowledge of rationality. However, experiments have demonstrated thatmembers’ informal punishment strongly improves efficiency, for as long as the costs to the punishers are
not too high (e.g., Anderson and Putterman, 2006; Nikiforakis and Normann, 2008) and the interactions
are sufficiently long (e.g., Fehr and Gӓchter, 2000, 2002; Gächter et al., 2008). Various sets of authors
have experimentally examined the factors that may explain people’s informal punishment activities. Their
explorations have successfully found non-material motives, for example, negative emotions (e.g., de
Quervain et al., 2004), inequity aversion and beliefs in peers’ punishment (e.g., Fehr and Fischbacher,
2004; Fischbacher and Gӓchter, 2010), a conditional willingness to punish (e.g., Kamei, 2014), enjoying
punishment activities (e.g., Casari and Luini, 2009), and culture and nationality (e.g., Hermann et al.,2008). Interdependent preference models, such as inequity aversion (e.g., Fehr and Schmidt, 1999 and2010) and reciprocity (e.g., Rabin 1993, Charness and Rabin 2000), can theoretically rationalize humanpunishment behavior and its behavioral effects. The high efficiency of decentralized solutions may mean

4

Kamei and Tabero (2021) show that decision-making formats may also affect their voting behavior. They find thatas a decision-making unit, “teams” vote more efficiently than “individuals” to deter free riding.

6

that groups do not need centralized solutions under certain conditions.
For the last decade, scholars have actively examined people’s scheme preferences and theconditions under which groups enact formal, rather than informal, schemes for governance (e.g., Traulsen
et al., 2012; Andreoni and Gee, 2012; Zhang et al., 2014; Kamei et at., 2015; Nicklisch et al., 2016; Fehrand Williams, 2018; Kamei and Tabero, 2021). The findings suggest that groups do delegate sanctioning
power to a central authority by voting when formal schemes do not entail a large fixed (e.g.,administrative) cost (e.g., Kamei et at., 2015), when members’ anti-social peer-to-peer punishment ismore harmful than erroneous enforcement by the formal authority (e.g., Nicklisch et al., 2016), and whenmembers cannot reach a normative consensus regarding contribution behaviors in their group (Fehr andWilliams, 2018). The endogenous selection of institutions has additional positive effects in fostering
cooperation norms by not only allowing sorting (e.g., Dal Bó et al., 2010; Dal Bó et al., 2019), but also
by directly influencing members’ preferences for cooperation or providing them with an opportunity tosignal through voting (e.g., Tyran and Feld, 2006; Dal Bó et al., 2010; Sutter et al., 2010; Kamei, 2016,2019). Despite numerous studies in this area, all prior experiments on institutions were conducted without
considering the subjects’ self-control capacities. The present study is, to the author’s knowledge, the firstto examine how the amount of people’s self-regulatory resources influences their scheme choices andefficiency in a novel design that manipulates their regulatory resources in a laboratory.
The second closely related area involves theoretical and experimental studies on self-control andtemptation. For at least the last forty years, many scholars in psychology and its neighboring fields haveconsistently demonstrated that human self-regulatory recourses are limited, and therefore people tend to
succumb to temptation when the resources are used up—a phenomenon called “self-regulatory depletion”(see, e.g., Baumeister et al. [1994, 2007] and Muraven and Baumeister [2000] for a survey). One
important feature here is that self-regulatory resources are used to control and manage all kinds of urges
and temptations: if a person uses the regulatory resources to suppress some temptations in one dimension,
they may not be able to resist temptations in other dimensions since the resources will have diminished.
The self-regulatory hypothesis is relevant for many economic transactions because people usually faceconflicts in their economic decision-making: e.g., their individual decision-making, such as consumption
choices and borrowing, and their social decision-making, such as whether to cooperate in a social
dilemma, trust or betray others, etc.
Since around 2000, economists have followed scholars in these other social science fields on selfcontrol research due to its significant importance in economics. Gul and Pesendorfer (2001, 2004) andmany other prominent theorists first made breakthroughs by formally modeling human self-control
behaviors and people’s tendency to commit. In particular, Gul and Pesendorfer (2001) axiomatize selfcontrol preferences by introducing a new axiom, “set betweenness,” in an expected utility framework.
Their representation theorem states that an agent incurs a self-control cost in choosing an action if there
are some other tempting options in the choice set (see also Dekel et al. [2009]). The agent, therefore, has
a commitment preference, i.e., they prefer to narrow their choice set in advance by removing tempting
options from the menu. While the self-control theory by Gul and Pesendorfer (2001) provides
dynamically consistent preferences, and therefore does not explain psychologists’ idea of limited self7

regulatory resources and depletion, its variant, i.e., the addiction model (Gul and Pesendorfer, 2007;
Kamei, 2012), and multi-self models (e.g., Ozdenoren et al., 2011; Fudenberg and Levine, 2006), canexplain self-regulatory depletion.
Experimental testing of human self-control behavior and commitment preferences was conducted
relatively recently in economics (Bucciola et al., 2011; Burger et al., 2011; Houser et al., 2018;
Toussaert, 2018; Kocher et al., 2017). Houser et al. (2018) and Toussaert (2018) serve as direct tests of
Gul and Pesendorfer (2001)’s self-control theory. Indeed, both experiments revealed self-control andcommitment preferences among some individuals. First, Toussaert (2018) found that self-control
preferences were potentially dynamically consistent. In her experiment, subjects who worked on a tedioustask while facing a temptation (i.e., to read a story during a task) were classified by whether they wanted
to eliminate the temptation. A quarter to a third of the subjects were classified as the “self-control type”(those who believed in their successful self-control without such elimination), and did indeed resist thetemptation during the task. A similar finding was obtained in an experiment by Kocher et al. (2017).
Kocher et al. indicate that the stronger the self-control people have, the higher the level of cooperation
they can achieve in a PGG. Second, Houser et al. (2018) let subjects decide whether to perform a real
effort task (“counting” task) or surf the internet, with an option to commit to working by paying a fee toeliminate the internet surfing option. Some subjects did use the costly commitment option. Houser et al.
(2018) also documented that self-control behavior might potentially be dynamically inconsistent when
temptations were sufficiently strong,5 as there were some subjects who delayed a commitment decision or
succumbed to the temptation at a later stage. This is similar to the self-regulatory depletion phenomenon(Houser et al.’s experiment had a demanding, two-hour, task-solving task). The self-regulatory depletion
possibility was carefully addressed by Bucciola et al. (2011). Bucciola et al. let children (aged 6 to 13)
fold as many sheets as possible while including the so-called “Marshmallow task” in the experiment.
Their result showed that exposure to consumption temptations (e.g., a snack) significantly undermined
younger children’s productivity. In the context of the present study, using formal enforcement is linked topeople’s commitment preferences in social dilemmas as it makes free riding materially unbeneficial.
However, no study has investigated how people’s self-regulatory resources influence their voting
behavior and institutional formation outcomes. This study is the first to examine how the amount ofpeople’s self-regulatory resources affects their activation of formal enforcement, rather than theirdecentralized self-governance, in the context of endogenous institutional formation when there is tension
between contributing and free riding. The amounts of subjects’ self-regulatory resources are manipulatedusing well-established depletion tasks from the literature.

3. Experimental Design
The experiment is built on the framework of a finitely repeated linear PGG. Subjects are
randomly assigned to a group of five, and the grouping stays the same throughout the experiment (partner
matching). Each subject in a group has an endowment of 20 points in every period, and then5

It is worth acknowledging that self-control behavior may be driven by a complex mechanism, as Burger et al.
(2011) found that commitment devices might be counterproductive in the context of procrastination.

8

simultaneously decides how many points to allocate between their public and private accounts. The
marginal per capita return (MPCR) is 0.4. In other words, Subject i receives the following payoff in
Period t when they contribute ci,t:
𝜋𝑖,𝑡 (𝑐𝑖,𝑡 ) = 20 − 𝑐𝑖,𝑡 + 𝑟 ∑5𝑗=1 𝑐𝑗,𝑡 , where r = 0.4.

(1)

Four treatments are implemented as a 2×2 between-subjects design (Table 1); each subject plays
the game under only one treatment condition. This feature is important because subjects’ experience in
one environment may spill over to their behaviors in another environment—a phenomenon called“behavioral spill-over” (e.g., Kamei, 2016; Bednar et al., 2012).6 The first treatment dimension of the 2×2
design is the amount of subjects’ self-regulatory resources. The second treatment dimension is whether
subjects have an opportunity to enact sanctioning schemes by voting. In the treatments with institutional
choices, groups decide which scheme to implement, either a formal sanctioning scheme (“FS,” hereafter)or an informal sanctioning scheme (“IS,” hereafter).
Table 1: Treatments
Treatment

VotingSelf-regulatory resources

Part 2 Condition for PGG

No-N

No

Not depleted (Normal)

Six phases without sanction scheme

No-D

No

Depleted (Small)

Six phases without sanction scheme

Voting-N

Yes

Not depleted (Normal)

Six phases each with FS or IS scheme

Voting-D

Yes

Depleted (Small)

Six phases each with FS or IS scheme

All treatments comprise Parts 1 and 2. Part 1, also called Phase 1, is the same for the four
treatments. In Part 1, subjects play the PGG described above four times in sequence without institutional
choices, as in Kamei et al. (2015). Subjects’ payoffs in each period are calculated based on Equation (1).
This part plays a role in familiarizing subjects with peers’ incentives to free ride (e.g., Ledyard, 1995;
Chaudhuri, 2011). Part 2 has six phases (each comprising four periods) and differs by treatment. The six
phases are called Phases 2 to 7 (Periods 5 to 28) in the study. Having multiple phases allows us to
examine how experience affects institutional choices.
The two treatments without institutional choices are called the “No Voting, No Depletion” (NoN) and “No Voting, Depletion” (No-D) treatments. Part 2 of the No-N treatment begins with a task
without depletion, followed by six phases, each with a four-period standard PGG. By contrast, subjects in
the No-D treatment are forced to deplete their self-regulatory resources. Part 2 of the No-D treatment
begins with a task parallel to the No-N treatment; however, the task contains an element that affects theself-regulatory resources, whereafter the six phases of interactions commence (the depletion task will be
explained in Subsection 2.1). There is an additional depletion task in each period of Part 2, to maintain the6

Most research in this area used a between-subjects design (e.g., Traulsen et al., 2012; Andreoni and Gee, 2012;
Zhang et al., 2014; Kamei et at., 2015; Nicklisch et al., 2016; Fehr and Williams, 2018; Kamei and Tabero, 2021;
Tyran and Feld, 2006; Dal Bó et al., 2010; Sutter et al., 2010; Kamei, 2016).

9

manipulated state of self-regulatory resources throughout. A schematic diagram of the two treatments isshown in Panel A of Figure 1.
The two treatments with institutional choices are called the “Voting, No Depletion” (Voting-N)
and “Voting, Depletion” (Voting-D) treatments. The structures of the Voting-N and Voting-D treatments
are the same as those of the No-N and No-D treatments, respectively, except for the opportunity to choose
institutions. The Voting-N (Voting-D) treatment has the same no-depletion (depletion) tasks as the No-N
(No-D) treatment. A schematic diagram of the voting treatments is shown in Panel B of Figure 1.
In Part 2 of the Voting-N and Voting-D treatments, subjects can use a sanctioning scheme in eachperiod of public goods interactions. The design for the institutional setting follows Kamei et al. (2015). At
the onset of each phase, groups can select an FS or an IS by voting. Whichever scheme receives at least
three votes (i.e., majority voting) will be in effect for the four periods in the given phase. Voting is costfree and mandatory. Period structures vary by scheme. When a group selects the IS scheme, each period
comprises two stages: an allocation stage, and an informal (peer-to-peer) punishment stage. The firstallocation stage is the same as the allocation stage in Phase 1: each member decides how to allocate 20
points between their private and public accounts. When all the members have made their allocation
decisions, they will be informed of each member’s contribution amount, and will then be provided with
an opportunity to assign punishment points to one another. These are costly punishment decisions; foreach punishment point assigned to a member, one point is deducted from the punisher while three points
are deducted from the punished. There are two requirements for the punishment decisions: First, thepunishment points assigned to each member must be an integer. Second, the punishment points must be
less than or equal to 10 for any one member of their group.
When a group selects the FS scheme, each period comprises two stages: a voting stage, and an
allocation stage under the enacted FS scheme. Allocations to their private accounts are penalized in the
FS scheme. The punishment strength is set such that it is equivalent to the IS scheme: the cost ratio is 1:3(punisher: punished). At the beginning of each period, the members vote on the sanction rate to be used.
There are four possible rates: {0.0, 0.4, 0.8, 1.2}. The median of the five votes will be enacted in theirgroup. The second stage is the allocation decision stage as in each period of Part 1, but subject to the FS
scheme. There are two costs under the FS scheme. First, every subject must pay an administrative fixed
cost of having the scheme, f = 5, in each period, irrespective of whether formal punishment is inflicted(hence, the aggregate fixed cost per group is large, such that 5 × 5 = 25). This means that, while thecomparative advantage of having an FS mechanism relative to an IS one is to enforce punishment
precisely on free riders, it entails a large cost. The fixed cost can be thought of as a cost to eliminate thetemptation to violate social norms in the public goods dilemma. Using the two treatments, this study asks
whether subjects prefer to commit to cooperation by collectively selecting a deterrent sanction scheme
when self-regulatory ability is dominated by the size of free riding temptations, as proposed by the selfcontrol theory (e.g., Gul and Pesendorfer, 2001 and 2004). More specifically, this study asks, do subjects
prefer using IS when they have sufficiently large self-regulatory resources? What happens to their choices
between FS and IS when their resources have been depleted?

10The second cost is variable costs. For each point lost by a member who is fined, every groupmember incurs a cost of 1/11 points to impose that punishment. This cost is interpreted as the administrative
cost of imposing the fine. The punished thus incurs a loss of 12/11 (=1+1/11) in total, while the fourpunishers incur a loss of 4/11. The ultimate cost ratio is 3:1 (12/11: 4/11). In other words, the FS and IS
schemes have the same punishment cost ratios. Note, however, that the two schemes have different aspects.
First, as already discussed, the FS scheme requires fixed administrative cost payments, in contrast to the IS
scheme. Second, punishment is only targeted at free riders with collectively agreed strength in the FS
scheme. In contrast, peer-to-peer punishment in the IS scheme depends on members’ decisions; thus, it ispossible that free riders may not be effectively punished, and that high contributors may also be punished.
Figure 1: Schematic Diagram
Instructions before Part 2 starts
The Stroop task (the color question) with/without depletion in each period of Part 2

Instructions before Part 1
startsPhase 1

Phase 2Phase 3Phase 4Phase 5Phase 6

Phase 7

PGG with noscheme for 4
periods

PGG with noscheme for 4
periods

PGG with noscheme for 4
periods

PGG with noscheme for 4
periods

PGG with noscheme for 4
periods

PGG with noscheme for 4
periods

PGG with noscheme for 4
periods

break

break

break

break

break

Crossing-out task(with/without depletion)(A) No-D and No-N treatments
Instructions before Part 2 starts
The Stroop task (the color question) with/without depletion in each period of Part 2

Instructions before Part 1
startsPhase 1

Phase 2Phase 3Phase 4Phase 5Phase 6

Phase 7

PGG with noscheme for 4
periods

PGG withchosen schemefor 4 periods

PGG withchosen schemefor 4 periods

PGG withchosen schemefor 4 periods

PGG withchosen schemefor 4 periods

PGG withchosen schemefor 4 periods

PGG withchosen schemefor 4 periods

Scheme vote
(FS or IS)

Crossing-out task(with/without depletion)

Scheme vote
(FS or IS)

Scheme vote
(FS or IS)

Scheme vote
(FS or IS)

Scheme vote
(FS or IS)

Scheme vote
(FS or IS)(B) Voting-D and Voting-N treatments#1
Notes: #1 When a group selects the FS scheme in a given phase, it decides a sanction rate by voting in each of thefour periods in that phase. In other words, they have four voting opportunities in that phase.

3.1. Depletion task
Two depletion tasks are used: one at the beginning of Part 2, and the other during the 24-period
PGG of Part 2 (again, see Figure 1). While the former depletion task is used to manipulate the amount ofself-regulatory resources before the public goods interactions begin in Part 2, the latter plays a role in11

maintaining the depleted state at low levels. The literature states that people may recover from selfregulatory depletion and regain the ability to exercise self-control if a sufficiently long time passes after
the depletion or if they experience a positive mood. For example, successful cooperation with deterrent
punishment in Part 2 may help subjects recover their resources. Thus, including the latter task can
manipulate the amount of self-regulatory resources throughout Part 2. Having the mental state depleted isalso useful for real-world relevance in modeling people’s smaller amounts of resources for a particular
temptation in some societies (e.g., people may generally have smaller amounts of resources, and thus may
tend to succumb to temptations such as littering [e.g., see the serious littering issue in the UK]; people
may be tricked by moneylending businesses in a black market, which explains why strict regulations are
required for some countries; addiction and drug use may also be related to self-regulatory resources,
although these are more complicated due to the psychopathological symptoms that occur inside the brain).
This study uses the crossing-out-letters task (“crossing-out task,” hereafter) to manipulate
subjects’ self-regulatory resources at the onset of Part 2. Hagger et al. (2010) performed a meta-analysis
of depletion tasks in the literature, suggesting that the crossing-out task is one of the most effective (Deng[2018] provides an updated meta-analysis). For example, Achtziger et al. (2016) and Gerhardt et al.
(2017) used the crossing-out task following the suggestions by Hagger et al. (2010).
In the experiment, the subjects perform the crossing-out task for eight minutes, although the rulediffers by treatment. The subjects in the no-depletion condition (i.e., the No-N and Voting-N treatments)
cross out every letter e in a paragraph (from a well-known book) appearing on the computer screen, one
by one, and then submit the number of e’s. Once a subject submits an answer, a new paragraph appears on
the computer screen. The paragraph is not short (see Appendix A.3), and thus it is not easy to answer thequestion correctly. However, self-regulatory resources are not required since the task rule is simple. By
contrast, the subjects in the depletion condition (i.e., the No-D and Voting-D treatments) cross out e’s,
except if a vowel precedes it by two letters or if it is immediately followed by a vowel (the same rule wasused in, e.g., Baumeister et al. [1998] and DeWall et al. [2011]). As in Baumeister et al. (1998), theparagraph flashes in the depletion condition, thereby requiring extra attention by the subjects (and thus
further depleting their mental resources). There are at most six paragraphs (one per screen) in this task.
Subjects will be paid one point for each paragraph they answer correctly. While making this task
incentive-compatible is crucial for encouraging subjects to seriously answer the questions (leading tosuccessful manipulation of the self-regulatory resources), the compensation is set at a minimum value to
avoid the effects of receiving compensation (if any) on subsequent behaviors.
Further, the so-called Stroop task (1992) is included during the public goods interactions tomaintain the depleted state. In each allocation decision stage of Part 2, one of the four words (“red,” “blue,”“purple,” and “black”) randomly appears on the bottom of the computer screen. In the depletion condition,
the word has a color, either red, blue, purple, or black, while the color does not necessary coincide with themeaning of the word (e.g., the word “red” appears in blue). Moreover, the word flashes, thus affecting
subjects’ attention. The coloring of the words is randomized. Subjects must answer in which color the word
appears, along with the allocation decision in the PGG (see Appendix A for a screen image). For example,
the answer is red if the word “blue” appears in red. By contrast, in the no-depletion condition, coloring12

always coincides with the meaning of the words (e.g., the word “red” appears in red), and the word does not
flash.7 Thus, subjects can answer the color questions without using any self-regulatory resources. A subject
receives one point for each correct answer in the Stroop task (the subject can earn up to 24 points as there
are 24 periods in Part 2).

4. Hypothesis
The standard theory prediction based on players’ self-interest and common knowledge ofrationality is straightforward because the experiment design uses a finitely repeated game. With the logic ofbackward induction, no one would contribute any points to their public account in each period of the novoting treatments since free riding is each player’ strictly dominant strategy in the game (𝜕𝜋𝑖 /𝜕𝑐𝑖 =−0.6 < 0). Thus, complete free riding is the unique sub-game perfect Nash Equilibrium in the No-N and
No-D treatments. In equilibrium, each player receives a payoff of 20 (= 20 + 0 × 5 × 0.4) points per period.
Having the IS scheme does not alter the free-riding equilibrium in the voting treatments, since the standard
theory predicts that no one will inflict punishment due to the cost (e.g., Fehr and Gӓchter, 2000, 2002).
However, the theoretical prediction under the FS scheme is different from that under the noscheme condition or the IS scheme in the voting treatments (e.g., Falkinger et al., 2000; Kamei and
Putterman, 2015). When the FS scheme is in effect, it is materially beneficial for each player to vote for a
sufficiently strong sanction rate, i.e., 0.8 or greater, so that contributing everything to their public accountbecomes the strictly dominant strategy (e.g., Putterman et al., 2011). Recall that the MPCR in the PGG is0.4. Note that while a median voting rule is used, the possibility of error (trembling-hand perfection)encourages all members to vote for a deterrent rate since any one’s vote can then be pivotal—see Selten(1975). By enacting a deterrent sanction rate, each player obtains a payoff of 35 (= 0 + 20 × 5 × 0.4 – 5),rather than of 15 (= 20 + 0 × 5 ×0.4 – 5). This difference in the equilibrium behavior implies that, given
an option to vote and the possibility that their votes are pivotal, all subjects would vote in favor of the FS
scheme with the aim of enforcing deterrent sanction rates thereafter.

4.1. The Self-Control Model
The present experiment manipulates subjects’ self-regulatory resources by adopting the crossingout and Stroop tasks. The effect of the self-control aspect can easily be incorporated into the theoreticalanalysis using the well-known self-control preference model developed by Gul and Pesendorfer (2001,
2004). The self-control model in itself, however, does not change the standard theory predictions justdiscussed. To see this, assume the following utility functional form (Gul and Pesendorfer, 2001):
𝑈𝑖 (𝑆) = 𝑚𝑎𝑥𝑥𝑖∈𝐴 [𝜋𝑖 (𝑥𝑖 ) − 𝑓 ∙ 1𝐹𝑆 − 𝑆𝑅 ∙ (20 − 𝑥𝑖 ) ∙ 1𝐹𝑆 − 𝑐𝑖 (𝑥𝑖 )],7

An alternative to the Stroop task could be an attention control task (e.g., Gilbert et al., 1988; Masicampo andBaumeister, 2008; DeWall et al., 2011; Ainsworth et al., 2014). In the attention control task, (neutral) unrelated
words, such as tree, forest, and water, appear randomly for 10 seconds each on the subjects’ computer screens. The
subjects in the depletion condition are instructed not to see the words and will be reminded during the experiment
whether they see them, while, in the no-depletion condition, the subjects are not given any instructions for thewords. Implementing this task would be more difficult than using the Stroop task because it is often difficult forexperimenters to judge whether subjects see the words during the experiment. Therefore, the attention control task
was not adopted in the present study.

13where 𝑐𝑖 (𝑥𝑖 ) = max𝑦∈𝐴 𝑣𝑖 (𝑦) − 𝑣𝑖 (𝑥𝑖 ) ≡ 𝜌𝑖,𝑠 (max𝑦∈𝐴 [𝜋𝑖 (𝑦) + 𝑆𝑅 ∙ 𝑦 ∙ 1𝐹𝑆 ] −𝜋𝑖 (𝑥𝑖 ) − 𝑆𝑅 ∙ 𝑥𝑖 ∙ 1𝐹𝑆 ).

(2)

Here, i indexes individual players, S {FS, IS}, A is the choice set in the PGG, i.e., [0, 20], 𝜋𝑖 (𝑥𝑖 ) is
given by Equation (1), f is the fixed administrative cost (= 5), SR is the sanction rate enacted in the group,and 1𝐹𝑆 = 1(0) when the FS (IS) scheme is chosen. 𝑐𝑖 (𝑥𝑖 ) is Player i’s self-control cost and 𝜌𝑖,𝑠 indicatesthe state of i’s self-regulatory resources. The specific form of the self-control cost with 𝜌𝑖,𝑠 was used in
Kamei (2012), and 𝜌𝑖,𝑠 is called the “temptation index.” The subscript, s, in 𝜌𝑖,𝑠 indicates the state of selfregulatory resources, i.e., D (Depleted) or N (Non-depleted). As self-regulatory depletion renders a playersusceptible to temptation, such depletion is modelled by allowing the temptation index to enlarge (seealso Ozdenoren et al. [2011]). Therefore, in this theoretical framework, it can reasonably be assumed that:
𝜌𝑖,𝐷 > 𝜌𝑖,𝑁 .

(3)

In this self-control framework, Player i behaves the same under the FS scheme as the standard
theoretical prediction described above. Notice that i and their group members would vote to enact
deterrent sanction rates (SR = 0.8 or 1.2) for material reasons in the FS scheme. This means that
individual interests are aligned with group interests in that scheme, by which they contribute fullendowment amounts. Thus, the self-control cost is zero in a deterrent FS scheme. More formally, Player
i’s payoff is calculated as 40 – f:𝑈𝑖 (𝐹𝑆) = max𝑥𝑖∈𝐴 [𝜋𝑖 (𝑥𝑖 ) − 𝜌𝑖,𝑠 (𝜋𝑖 (20) + 20𝑆𝑅 − 𝜋𝑖 (𝑥) − 𝑥 ∙ 𝑆𝑅) − 𝑓 − 𝑆𝑅 ∙ (20 − 𝑥𝑖 )]= 𝜋𝑖 (20) − 𝑓 = 40 − 𝑓.
By contrast, individual private interests conflict with group interests in the IS scheme. Player i
incurs a self-control cost accordingly: 𝑐𝑖 (𝑥𝑖 ) = 𝜌𝑖,𝑠 (𝜋𝑖 (0) − 𝜋𝑖 (𝑥𝑖 )). This cost strengthens their motives
to contribute nothing to their group (i.e., 𝑥 = 0) under the IS scheme. In equilibrium, each player obtains
20 points as their payoff in the IS scheme, as follows:
𝑈𝑖 (𝐼𝑆) = max𝑥𝑖∈𝐴 [𝜋𝑖 (𝑥𝑖 ) − 𝜌𝑖,𝑠 (𝜋𝑖 (0) − 𝜋𝑖 (𝑥𝑖 ))]= max𝑥𝑖∈𝐴 [(1 + 𝜌𝑖,𝑠 )𝜋𝑖 (𝑥𝑖 )] − 𝜌𝑖,𝑠 𝜋𝑖 (0)= (1 + 𝜌𝑖,𝑠 )𝜋𝑖 (0) − 𝜌𝑖,𝑠 𝜋𝑖 (0) = 𝜋𝑖 (0) = 20.
In sum, the self-control model predicts that all groups in the Voting-N and Voting-D treatmentschoose the FS scheme, whereafter they enact deterrent sanction schemes by voting, and then contributethe full endowment amount in the allocation stage, as is the case for the standard theory prediction.

4.2. Incorporating Inequity-Averse Preferences in the Self-Control Model
For the last few decades, however, experiments have shown that human subjects behave
differently from predictions based on self-interest and rationality (see Section 2). Especially, they cansustain cooperation with peers at a high level when peer-to-peer punishment is available (e.g., Fehr andGӓchter, 2000, 2002), for as long as the punishment costs to the punishers are not too large (e.g.,Anderson and Putterman, 2006; Nikiforakis and Normann, 2008). The positive effects of the IS scheme
14

can be explained by other-regarding preference models (see, e.g., Fehr and Schmidt [2006] and Sobel[2005] for a survey). For example, the inequity-averse preference model (Fehr and Schmidt, 1999, 2010)
can successfully rationalize members’ punishment behaviors and reactions to punishment received insocial dilemma settings. The prevalence of inequity-averse preferences among people can also be seen in
altruistic punishment by bystanders (e.g., Fehr and Fischbacher 2004; Kamei, 2020). Prior research on
institutional choices further indicates that, given an option to vote, people do choose the IS scheme rather
than the FS, and then sustain cooperation well under certain conditions, whose pattern is consistent withinequity aversion (e.g., Zhang et al., 2014; Kamei et al., 2015; Fehr and Williams, 2018; Kamei and
Tabero, 2021).
This subsection shows that groups’ scheme choices are theoretically affected by the members’
states of self-regulatory resources, once their inequity-averse preferences are incorporated in the selfcontrol model. Assume the following utility functional form, instead of Equation (2):𝑈𝑖 (𝑆) = 𝑚𝑎𝑥𝑥𝑖∈𝐴 [𝜋𝑖 (𝑥𝑖 ) − 𝑓 ∙ 1𝐹𝑆 − 𝑆𝑅 ∙ (20 − 𝑥𝑖 ) ∙ 1𝐹𝑆 − 𝜇𝑖 ∑𝑗≠𝑖(𝜋𝑖 (𝑥𝑖 ) + 𝑆𝑅 ∙ 𝑥𝑖 ∙ 1𝐹𝑆 − 𝜋𝑗 (𝑥𝑗 ) −2𝑆𝑅 ∙ 𝑥𝑗 ∙ 1𝐹𝑆 ) − 𝑐𝑖 (𝑥𝑖 )],where 𝑐𝑖 (𝑥𝑖 ) = 𝜌𝑖,𝑠 (max𝑦∈𝐴 [𝜋𝑖 (𝑦) + 𝑆𝑅 ∙ 𝑦 ∙ 1𝐹𝑆 ] − 𝜋𝑖 (𝑥𝑖 ) − 𝑆𝑅 ∙ 𝑥𝑖 ∙ 1𝐹𝑆 ).

(4)

Here, 𝜇𝑖 is Player i’s utility weight on income inequality.8 Obviously, the inclusion of members’ inequity
aversion does not alter the prediction under the FS scheme, because individual and group interests arealigned with deterrent sanction rates; and 𝑈𝑖 (𝐹𝑆) = 40 − 𝑓. Thus, the rest focuses on an analysis underthe IS scheme while considering, for an illustrative purpose, a symmetric contribution situation. The
assumption of symmetry significantly simplifies the analysis because no punishment is expected due tomembers’ inequity concerns.9
The optimal behavior under the IS scheme is analyzed by finding the optional control 𝑥𝑖 , i.e., thecontribution level that maximizes the inside of the squared bracket of Equation (4). The first-ordercondition here is written as follows:𝜕𝑈𝑖 (𝐼𝑆)= −1 + 𝑟 − 2𝜇𝑖 (−1) ∑𝑗≠𝑖(𝜋𝑖 (𝑥𝑖 ) − 𝜋𝑗 (𝑥𝑗 )) + 𝜌𝑖,𝑠 (−1 + 𝑟)𝜕𝑥𝑖

= (−1 + 𝑟)(1 + 𝜌𝑖,𝑠 ) + 2𝜇𝑖 ∑𝑗≠𝑖(−𝑥𝑖 + 𝑥𝑗 ).

(5)

Suppose that all j but i choose 𝑐 ∗ as their contribution amounts: 𝑥𝑗 = 𝑐 ∗ . Then, if 𝜌𝑖,𝑠 issufficiently small such that

2𝜇𝑖 (𝑁−1)− 1 > 𝜌𝑖,𝑠 , i also chooses 𝑥𝑖 = 𝑐 ∗ as their optimal response (thus,(1−𝑟)𝑥𝑘 = 𝑐 ∗ for all k holds as an equilibrium outcome).10 In equilibrium, 𝑈𝑖 (𝐼𝑆) = (1 + 𝜌𝑖,𝑠 )𝜋𝑖 (𝑐 ∗ ) −𝜌𝑖,𝑠 𝜋𝑖 (0) = (1 + 𝜌𝑖,𝑠 )(20 − 𝑐 ∗ + 0.4 ∙ 5 ∙ 𝑐 ∗ ) − 𝜌𝑖,𝑠 (20 + 0.4 ∙ 4 ∙ 𝑐 ∗ ) = 20 + (1 − 0.6𝜌𝑖,𝑠 )𝑐 ∗ . Thus,
8This quadratic functional form was used in Kamei (2018)’s theoretical analysis.
The strategic situation is the one with multiple equilibria when other-regarding motives are added to the model,thus making the analysis quite complex if we also consider the cases with asymmetric equilibria.
10
If 𝑥𝑖 = 𝑐 ∗ + 1, the right-hand side of Equation (5) = (−1 + 𝑟)(1 + 𝜌𝑖,𝑠 ) − 2𝜇𝑖 (𝑁 − 1) < 0. If 𝑥𝑖 = 𝑐 ∗ − 1, the9right-hand side of Equation (5) = (−1 + 𝑟)(1 + 𝜌𝑖,𝑠 ) + 2𝜇𝑖 (𝑁 − 1) > 0, provided that152𝜇𝑖 (𝑁−1)(1−𝑟)− 1 > 𝜌𝑖,𝑠 .

whether the Pareto-dominant equilibrium (𝑐 ∗ = 20) also maximizes their utility level depends on the sizeof 𝜌𝑖,𝑠 , as the c* that maximizes 𝑈𝑖 depends on 𝜌𝑖,𝑠 as follows:
if 𝜌𝑖,𝑠 < 5/3, then 𝑐 ∗ = 20 maximizes 𝑈𝑖 and 𝑈𝑖 (𝐼𝑆) = 40 − 12𝜌𝑖,𝑠 ;if 𝜌𝑖,𝑠 > 5/3, 𝑐 ∗ = 0 maximizes 𝑈𝑖 and 𝑈𝑖 (𝐼𝑆) = 20.
Combined with the optimal behaviors in the FS scheme, it can be concluded that players prefer
the FS (IS) scheme if they have sufficiently small (large) amounts of self-regulatory resources, i.e., 40 −𝑓 > (<)40 − 12𝜌𝑖,𝑠 , meaning that 𝜌𝑖,𝑠 > (<)5/12. Note that when 𝜌𝑖,𝑠 > 5/3, 𝑈𝑖 (𝐼𝑆) = 20, which isalways less than 𝑈𝑖 (𝐹𝑆) = 35. These analyses can be summarized in Proposition 1 as the main
hypothesis of this study. Since the amount of subjects’ self-regulatory resources in the Voting-D
treatment is small, the FS scheme is predicted to be more prevalent in the Voting-D treatment than in the
Voting-N one.
Proposition 1: The smaller the amounts of self-regulatory resources people have, the more strongly they
rely on law enforcement. In the context of the present study, members vote for enacting the FS scheme
more frequently in the Voting-D treatment than in the Voting-N treatment.

5. Experiment Results
The experiment sessions were conducted face-to-face at the Experimental Economics Laboratory,
Research Institute of Socionetwork Strategies at Kansai University, in November and December 2020 andJuly and August 2021.11 A total of 175 students (45, 45, 40, and 45 subjects in the No-N, No-D, VotingN, and Voting-D treatments, respectively), recruited through the ORSEE (developed by Greiner, 2015),
participated in the experiment. No subjects participated in more than one session.
Appendix Table B.1 reports the performances in the crossing-out and Stroop tasks. It shows thatthe average scores in both the crossing-out and Stroop tasks are significantly better in the non-depletion
than in the depletion treatments. While this is the expected pattern, however, the scores in the two tasks
are economically very similar for the four treatments. This is also an expected result. Recall thatanswering the color questions correctly is not difficult even in the depletion condition, although additional
effort and attention are required; moreover, accurately counting the number of e’s in the crossing-out task
is difficult even in the non-depletion condition, since each paragraph is lengthy (Appendix A.3). This
helps remove the possibility of wealth effects gained from the task as a confounding factor in examining
the effects of the manipulation on subjects’ institutional formation.
This section first describes the treatment differences in contribution and payoffs (Section 5.1),
whereafter it examines subjects’ scheme choice behaviors (Section 5.2). Lastly, their behavior under theenacted schemes is examined (Section 5.3).

5.1. Contribution and Payoff
Figure 2 reports the contribution and payoff dynamics in each treatment. It shows that the11This is a standard laboratory with three tall partitions in each desk: one for the front and two for the sides.

16efficiencies are very similar in Part 1 for all four treatments. For example, the average contribution is lessthan 50% of the endowment in each treatment. A Mann-Whitney test finds that the differences in the
average contribution or payoff are not significant for any comparison (Panel I.i of Appendix Table B.2). It
follows that the random assignment in the experiment was successful, and that there was a large degree offree riding in each treatment without sanction schemes.
Figure 2: Efficiency by Treatment
20

No-N

No-D

Voting-N

Voting-D

16

12

8

4

0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Period(A) Average Contribution
36

No-N

No-D

Voting-N

Voting-D

33
30
27
24
21

18
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Period(B) Average Payoff

The treatment effects of voting and self-regulatory resources on efficiency can be examined using
the observations in Part 2. It shows first that, regardless of whether members’ self-regulatory resourceswere depleted, free riding was serious when the sanction schemes were absent. Specifically, in both the
No-N and No-D treatments, the average contributions were consistently less than 40% of the endowment,
while the levels gradually declined over time. The difference in the average contribution in Part 2 is not17

significant between the two no-voting treatments (two-sided p = 0.2475, Mann-Whitney test). A
regression analysis finds a qualitatively similar result – see Part II of Appendix Table B.2.
Second, free riding was clearly deterred by the availability of sanction schemes (Panel I of Figure
2, Table 2). The effectiveness of punishment was not undermined by the state of members’ self-regulatory
resources. While in Part 1 the subjects in the Voting-N and Voting-D treatments experienced similarlevels of free riding to those in the No-N and No-D treatments, the former achieved much higher levels ofcontributions in Part 2, thereby receiving larger payoffs, than the latter. The difference in the level of theaverage contribution or the average payoff is significant between the no-voting and voting treatments(Table 2 – see Part I of Appendix Table B.2 for more detailed results). A regression analysis, whether alinear or tobit regression model is used, finds a qualitatively similar result (Part II of Appendix Table
B.2). This suggests that prior findings on the strong positive effects of punishment are robust to theamount of people’s self-regulatory resources.
Nonetheless, a close look at the data indicates that the impact of voting on payoffs is somewhat
weaker than that on contributions due to members’ punishment loss. The impact is not significant for the
Voting-N treatment (Panels I.iii and I.vi of Table B.2). The negative welfare effects of punishment areconsistent with prior research: (a) punishment activities may be too intense under the IS scheme (e.g.,Fehr and Gӓchter, 2000, 2002), and (b) a small number of groups may fail to construct a deterrent scheme
and may therefore perform extremely poorly under the FS scheme (e.g., Group 13 of Putterman et al.
[2011]). In contrast, such negative effects seem to be milder for the Voting-D treatment. For example, asignificantly larger percentage of groups in Voting-D still received 30 points or greater as a payoff,
compared with the No-D treatment. Here, the 30 points is the average payoff assuming that the averagecontribution in a group is 50% of the endowment and no punishments are inflicted. As will be explainedlater, this difference in efficiency between the Voting-D and Voting-N treatments is driven by (a) the
large difference in the scheme choice outcome, and (b) the significantly stronger informal punishment
activities seen in the Voting-D treatment relative to the Voting-N treatment.
Result 1: (i) Free riding was serious in both the No-D and No-N treatments where the sanction schemes
were absent. (ii) Voting on sanction schemes significantly improved cooperation regardless of whether
the amounts of subjects’ self-regulatory resources were small. The positive effects of voting were
somewhat stronger in the Voting-D treatment than in the Voting-N treatment, nevertheless.
Table 2: Treatment Effects of Voting on Contributions and Payoffs(i) No voting#1(ii) Voting#2

Two-sided p-value for H0: (i) = (ii)(a) Avg. contribution in Part 1#3

7.739 points

7.362 points

0.916(b) Avg. contribution in Part 2

5.836 points

14.809 points

0.0001***(c) Avg. payoff in Part 2

25.836 points

30.259 points

0.0349**(d) % of groups whose Part 1 avg.
contributions were ≥ 10 points#3

37.778%38.529%1.000(e) % of groups whose Part 2 avg.
contributions were ≥ 10 points

28.472%76.912%0.0005***18

(f) % of groups whose Part 2 avg.
payoffs were ≥ 30 points

27.454%65.343%0.0016***

Notes: p-values were calculated based on group-level Mann-Whitney tests for Rows (a) to (c) and Fisher exact tests
for Rows (d) to (f). No significant differences are found between the No-D and No-N treatments, as well as between
the Voting-D and Voting-N treatments, in each of the six performance measures ((a) to (f)) – see Appendix Table B.2.
#1
“No voting” includes the No-D and No-N treatments. #2 “Voting” includes the Voting-D and Voting-N treatments.
#3
The average payoffs in Part 1 are monotonic transformations of the Part 1 average contributions based on Equation(1) for all treatments. *, **, and *** indicate significance at the 0.10, 0.05, and 0.01 levels, respectively.

5.2. Scheme Choice
In contrast to the similar efficiencies in the two voting treatments, however, the popularity of the
FS scheme and its vote outcomes differ markedly by the amount of self-regulatory resources. On the one
hand, the majority of depleted subjects consistently preferred to use the FS scheme in the Voting-D
treatment (Panel A of Figure 3). On the other hand, strikingly, only approximately 30% of non-depleted
subjects voted for the FS scheme in the Voting-N treatment. These institutional preferences remained
quite stable even after the subjects gained experience. The difference in the vote share for the FS scheme
is large: the vote share in the Voting-D treatment is approximately double that in the Voting-N treatment.
This voting pattern is indeed consistent with the prediction from the self-control and inequity-averse
preference models summarized in Proposition 1.
Table 3 reports the results from the regression analysis of the treatment difference in the subjects’
scheme votes. Model 1 of the table includes only the “Depleted” dummy (which equals 1 [0] for the
Voting-D [Voting-N] treatment) to identify the treatment difference using all observations. It indicates thatthe depleted subjects’ stronger preference for the FS scheme, relative to that of the non-depleted subjects,
is strongly significant. Model 2 includes available demographic variables as additional independent
variables to control for possible differences in subjects’ individual characteristics. It confirms that the
impact of self-regulatory depletion remains significant by almost the same magnitude. Last, Models 3 and4 add the vote number variable (which equals the phase number minus 1) and its interaction with
“Depleted” to evaluate whether the effects of depletion vary as the subjects gain experience. The results
show that both the vote number and the interaction are far from significant. This suggests that depleted(non-depleted) subjects’ preferences for the FS (IS) scheme persist in the experiment.
A majority rule was applied in the experiment to determine a group’s scheme. Panel B of Figure 3
reports the scheme choice outcomes by treatment. It indicates that the FS scheme was implemented much
more frequently in the Voting-D treatment than in the Voting-N treatment. Regression analysis suggeststhat parallel to the sustained differences in the popularity of sanction schemes (Panel A of Figure 3), thestrong effect of self-regulatory resources on voting was not only significant, but it also persisted fromphase to phase (Panel B of Figure 3, Models 5 to 8 of Table 3).
It is worth noting here that the difference in the scheme choice outcome (Panel B) is much larger
than that in the scheme vote share (Panel A). This is due to the so-called “behavioral public choicetheorem”—a key feature of the majority voting rule (e.g., Ertan et al., 2009; Hauser et al., 2014). Under
majority voting, minorities tend to be outnumbered by the majority, thus allowing the latter’s preference19

to be more easily enacted in the group.
Result 2: Consistent with Proposition 1 of Section 4, subjects with smaller amounts of self-regulatory
resources relied more on the FS scheme. Strikingly, the vote share of the FS scheme in the Voting-D
treatment was approximately double that in the Voting-N treatment.
Figure 3: Popularity of the FS Scheme and Vote Outcomes
Voting-N

70%

Voting-D

60%

50%
40%
30%
20%
10%
0%
All phases

Phase 2Phase 3Phase 4Phase 5Phase 6

Phase 7

Phase by phase(A) Popularity of the FS Scheme
Voting-N

Voting-D

70%
60%
50%
40%
30%

20%
10%
0%
All phases

Phase 2Phase 3Phase 4Phase 5Phase 6

Phase 7

Phase by phase(B) Scheme Choice Outcome (% in Which the FS Scheme was Enacted)

Table 3: Members’ Amounts of Self-Regulatory Resources and Scheme Choices
Dependent
variable:
Estimation
Method:Independent
variables:

A dummy that equals 1(0) if Subject i voted for
the FS (IS) scheme

The vote share of the FS schemein Group j  {0.0, 0.2, 0.4, 0.6, 0.8, 1.0}

Subject random effects probit regressions withrobust bootstrapped S.E. clustered by group ID.

Group random effects ordered probit regressions

(1)

(2)

(3)

(4)

20

(5)

(6)

(7)

(8)

(a) Depleted dummy {=1.322***
1 for the Voting-D(0.401)
treatment; 0 otherwise}

1.241**(0.529)1.787***(0.664)1.702**(0.712)2.013***(0.761)2.036**(0.823)

---

---

0.082
(0.074)0.081(0.082)

---

---

0.151
(0.096)0.150(0.096)-------0.133(0.120)-0.132(0.125)-------0.249*(0.131)-0.248*(0.131)-0.968***(0.289)-1.065(0.865)-1.257***(0.485)-1.361(0.996)

---#2

---

---

---

# of observations

510

510

510

510

102

102

102

102

Control variable#1

No

Yes

No

Yes

No

Yes

No

Yes

Wald χ2

10.88

64.33

8.09

78.42

7.00

10.99

10.20

13.89

Prob > Wald χ20.00100.00000.04430.00000.00820.05150.01700.0532(b) Vote number variable{= 1, 2, …, 6}(c) Interaction (a)  (b)(d) Constant

2.937*** 2.958***(0.925)(0.982)

Notes: The numbers in parentheses are robust standard errors (S.E.). The units of observations are individuals in
Models 1 to 4, and groups in Models 5 to 8. Group-level clustering was included in Models 1 to 4 as eachindividual’s voting may be correlated within their group. Subject random effects linear regressions with robust
standard errors (clustered by group ID) generate qualitatively similar results—see Appendix Table B.5.
#1
The control variables include gender dummies, an economics major dummy, university year dummies, andpolitical preferences in Models 2 and 4 [the percentage of female subjects, the percentage of economics majors, thepercentage of the first-year undergraduate students, and the average political preference in a given group in Models
6 and 8]. #2 The cut points were omitted to conserve space.
*, **, and *** indicate significance at the 0.10, 0.05, and 0.01 levels, respectively.

5.3. Performance Differences between the FS and IS Schemes
Both the FS and IS schemes are strong deterrents against free riding, consistent with priorresearch. Part I of Appendix Table B.4 reports the regression results of examining how formal andinformal punishment improved contributions in Part 2 relative to the no scheme condition of the novoting treatments. It shows that, regardless of the scheme imposed, the average contribution was
significantly higher in the Voting-D (Voting-N) treatment than in the No-D (No-N) treatment. The strong
effects of sanctioning schemes are not affected by the manipulation of self-regulatory resources (neither
the interaction term between the Depleted and FS dummies nor that between the Depleted and IS
dummies is significant). This again underlines the robustness of the role of punishment in social
dilemmas.
Panel A of Figure 4 reports the contribution dynamics based on whether the sanction rate in the FS
scheme is set at a deterrent level. It indicates that once a deterrent sanction rate was collectively enacted, thesubjects contributed almost the full endowment, irrespective of the state of their self-regulatory resources.12
The contribution level was much higher than that under the IS scheme (see Models I and II of Table 4).
However, this is not a surprise as full contribution is the unique Nash Equilibrium under the deterrent FS
scheme. In contrast to the strong deterrence with high sanction rates, the subjects failed to sustain

Appendix Table B.6 reports a regression analysis to explain subjects’ decisions to contribute as a function of the
sanction rates enforced. The analysis found that the size of the sanction rate enacted in a group was a significantly
positive predictor of the members’ contribution amounts.
1221

cooperation when non-deterrent sanction rates were instead enacted (again, see Table 4 and Panel A of
Figure 4). While this is also expected, since free riding is clearly the unique Nash Equilibrium outcome withmild sanctions, the contribution levels in the Voting-D treatment were persistently extremely low (theconnected dotted lines in Figure 4). This was not the case for the Voting-N treatment, although nondeterrent cases were observed only in Phases 2 and 5 here. The extremely low contribution level with nondeterrent FS suggests that depleted subjects in Voting-D could not resist the temptation to free ride with
only mild law. On average, approximately 70% of the subjects voted for deterrent sanction rates in both the
Voting-D and Voting-N treatments (Panel C of Figure 4).
One interesting difference was observed between the states of self-regulatory resources: theaverage contribution under the IS scheme exhibits a significantly increasing trend in the Voting-D
treatment. This is in contrast to the trend in the Voting-N treatment, where it remains stable or issomewhat in a decreasing trend (Panel A of Figure 4, Part II of Appendix Table B.4). The same isobserved for the subjects’ payoffs (Panel B of Figure 4). This difference in the efficiency trend can be
explained by the significantly strong pro-social punishment of the depleted subjects in the Voting-D
treatment relative to that in the Voting-N treatment (Panel D of Figure 4, Appendix Table B.8). Here,punishment received by Subject i in Period t is classified as pro-social (anti-social) when i’s contribution
amount ci,t is less than (not less than) their group’s average contribution amount 𝑐̅𝑡 in Period t. While prosocial punishment was found to be significantly stronger than anti-social punishment in both the VotingD and Voting-N treatments (Appendix Table B.9), the punishment activities among the depleted subjects
were more intense, much more than double those among the non-depleted subjects (Panel D of Figure4).13 This feature matters in fostering cooperation norms because those who had been pro-sociallypunished in Period t increased contribution amounts in Period t + 1, ceteris paribus (Appendix Table
B.10).
Note that subjects have two conflicting sources of temptation in the punishment stage under the
IS scheme: one is to free ride on peers’ punishment acts, while the other is to inflict justice driven by
negative emotions. The observed punishment patterns suggest that depleted individuals succumbed to the
hot temptation to respond to their negative emotions, rather than to their cool temptation to free ride onothers’ punishment acts. This interpretation supports the view that (a) punishment decisions may be
driven by negative emotional states (e.g., de Quervain et al., 2004), and (b) such motives may be hotter
and stronger than their material motives to free ride on others’ punishment. There are, of course, manyother possible interpretations; however, the bottom line here is that, despite the possibly better effects of
the IS scheme in the Voting-D treatment, depleted subjects still preferred to rely on the FS scheme evenafter gaining experience. The welfare loss due to punishment activities and administrative cost payments
was much smaller in the IS scheme than in the FS scheme in a later phase—see Appendix Figure B.2.
Result 3: (a) Voting on sanction rates and contribution decisions under the FS scheme was similar for the
Voting-D and Voting-N treatments. Especially, deterrent FS schemes sustained contributions at almost
the full efficiency level. (b) The IS scheme was effective in boosting contributions because pro-social
13

These punishment patterns hold for each of the six phases in Part 2 (Appendix Figure B.1).

22

punishment was stronger than anti-social punishment for both voting treatments. Interestingly, pro-social
punishment by depleted subjects was significantly stronger than that by non-depleted subjects, and thus
depleted subjects more effectively strengthened cooperation norms over time in the IS scheme.
Figure 4: Performances under Selected Schemes
20

20

16

16

12

12

FS (deterrent sanction rate)8

FS (deterrent sanction rate)8

FS (non-deterrent sanction rate)

FS (non-deterrent sanction rate)
IS

4

IS

4

0

0

Phase 2Phase 3Phase 4Phase 5Phase 6

Phase 7

Phase 2Phase 3i. Voting-N treatment#1

Phase 4Phase 5Phase 6

Phase 7ii. Voting-D treatment

A. Average Contribution by Sanction Scheme
40

40

35

35

30

30

25

25

FS (deterrent sanction rate)
FS (non-deterrent sanction rate)20

FS (deterrent sanction rate)
FS (non-deterrent sanction rate)

20

IS

IS
15

15

10

10
Phase 2Phase 3Phase 4Phase 5Phase 6

Phase 7

Phase 2i. Voting-N treatment#1

Phase 3Phase 4Phase 5Phase 6

Phase 7ii. Voting-D treatment

B. Average Payoff by Sanction Scheme
100%

Voting-N

Points15.0

Voting-D

80%

12.0

60%

9.0

40%

6.0

20%

3.0

Voting-N

Voting-D

0.0

0%Vote share for deterrent rates

When contributed less than average

Vote outcome

C. % of Voting on Deterrent Sanction Rates (≥ 0.8) under FS

When contributed average or more

D. Avg. per Period Loss due to Punishment Received
under IS#2

Notes: #1 The number of cases in which a group selected the FS scheme was much lower in the Voting-N treatment
than in the Voting-D treatment. Non-deterrent rates were enacted only in Phases 2 and 5 in the Voting-N treatment.
#2 See Appendix Figure B.1 for the trend of average loss due to punishment, phase to phase. See Appendix Figure
B.2 for the trend of average per subject punishment loss by sanction scheme.

23

Table 4: Deterrence of the FS Scheme and Efficiency
Dependent variable: Contribution of Subject i in Period t, where t >4
Independent
variables:(a) Deterrent FS dummy(b) Non-deterrent FS
dummy(c) IS dummy(d) Constant# of observations
Reference Group
Control variable#1
R-squared

I. Voting-D(1)(2)

II. Voting-N(1)(2)

Payoff of Subject i in Period t, where t > 4
III. Voting-D(1)(2)

14.688*** 14.781*** 10.598*** 11.066*** 10.668*** 11.277***(1.338)(1.227)(2.505)(2.444)(1.803)(1.894)-3.504** -3.477** 7.640*** 8.115*** -6.569** -6.586**(1.742)(1.652)(2.732)(2.564)(2.882)(2.838)6.965***(2.427)
6.447***(1.142)
2,160
No-D
No
0.5481

6.989***(2.366)7.555***(1.798)
2,160
No-D
Yes
0.55988.084***(2.759)5.225***(1.565)
2,040
No-N
No
0.2726

IV. Voting-N(1)(2)
4.556*(2.636)0.167(2.852)3.626(2.654)-0.806(2.693)8.563***-0.821-0.856
4.683
3.715(2.593)(4.746)(4.592)(2.861)(2.714)6.539*** 26.447*** 27.236*** 25.225*** 24.738***(2.948)(1.142)(2.095)(1.565)(3.122)
2,040
2,160
2,160
2,040
2,040
No-N
No-D
No-D
No-N
No-N
Yes
No
Yes
No
Yes
0.3062
0.23630.2626
0.06430.1195

Two-sided p-values for Wald test:
H0: (a) = (b)0.0000*** 0.0000*** 0.0025*** 0.0018*** 0.0000*** 0.0000*** 0.0039*** 0.0030***
H0: (a) = (c)0.0001*** 0.0001*** 0.0058*** 0.0040*** 0.0085*** 0.0054*** 0.9414
0.9583H0: (b) = (c)0.0000*** 0.0000***
0.19890.18360.0821*0.0761* 0.0000*** 0.0000***

Notes: Subject random effects linear regressions with robust standard errors (S.E.s) clustered by group ID. The
numbers in parentheses are robust S.E.s. Observations from the No-D and Voting-D (No-N and Voting-N)
treatments are used for Models I and III (II and IV). #1 The control variables include gender dummies, an economics
major dummy, university year dummies, and political preferences. *, **, and *** indicate significance at the 0.10,
0.05, and 0.01 levels, respectively.

6. Supplementary Opinion Survey
While the laboratory experiment revealed strong effects of people’s self-regulatory resources on
their policy preferences, one concern is its external validity, as is sometimes the case for a neutrally
framed laboratory experiment. To supplement the main laboratory experiment, an opinion survey wasadditionally conducted regarding people’s self-control behaviors and their policy preferences during therecent Covid-19 pandemic. The survey was conducted in July 2022 by recruiting third- and fourth-year
undergraduate students at Kansai University.14 As explained below, it was found that those with weaker
self-control were more likely to support the strengthening of the formal enforcement of self-restraint
behavior, consistent with the main result from the laboratory experiment.
A challenge in collecting the information of self-control behaviors from respondents is thepresence of a possible social desirability bias. Considerable prior experimental research has shown thatpeople are reluctant to accept their socially undesirable behaviors when directly asked in a survey. For
14

Considering that the survey includes some questions on their behaviors as university students under thegovernment’s self-restraint requests, only third- or fourth-year undergraduates (as of July 2022) were recruited. Notethat while Japan has declared a state of emergency four times thus far, third- or fourth-year undergraduates
experienced all the four self-restraint requests as university students.

24

example, in the context of an election, respondents are reluctant to accept their vote-buying experiences(e.g., Gonzalez-Ocantos et al., 2012). To avoid such a bias, the respondents were provided with ten
concrete examples in the survey, among which seven were on weak self-control behaviors (e.g., “I saw
my relatives (and/or your parents if you lived all by yourself), as normal. The frequency of seeing them
was not much affected by the declaration of the state of emergency.”) and three were on careful and highself-control behaviors (e.g., “I tried avoiding using public transportation (such as trains and buses) as
much as possible.”); the respondents were then asked to answer, in integers, the question of how many
examples applied to their behaviors under the state of emergency (see Appendix C.1.1 for the detail).
There are two benefits of using the approach just stated: First, it is possible to let respondents consider
more concrete behaviors than when a question asks about their self-control in an abstract manner (e.g.,
did you comply with almost all the restriction measures imposed in the region?), thereby making it
possible to have a more precise measure of their self-control. Second, the ten examples include bothsocially desirable and undesirable behaviors, whose aspects make it difficult for respondents toimmediately notice what indicator the experimenter wants to see from the question. As seven (three) outof the ten examples were on weak (strong) self-restraint behavior, the respondents’ answers were
expected to range from 3 to 7, such that a larger number would correspond to a weaker self-control type.15
The respondents were also asked a different question with ten examples, each of which described
how the formal enforcement of restrictions could be strengthened (e.g., “The police should strengthen thepatrol duties to monitor people’s self-restraint behaviors during the periods when the government’s
request for self-restraint is in effect.”); they were asked how many examples they agreed with—see
Appendix C.1.3. The responses are used as the respondents’ preferences for strong formal restrictions in
the regression analysis.
In addition to these two key variables, the questionnaire asked about the subjects’ perceptions ofothers’ self-control (Appendix C.1.2). Not only people’s preferences for strong formal restrictions andpenalty, but also their own self-control behaviors may be affected by their beliefs about others’ behaviors,
in which case an omitted variable bias may influence the result. Note that conditional cooperation is quite
common in the context of a social dilemma (e.g., Fischbacher et al., 2001; Fischbacher and Gächter,2010). The questionnaire also asked questions on a variety of respondents’ demographic and backgroundinformation as control variables. These questions are included in Appendix C.1.4.
Appendix Section C.2 summarizes the results of the regression analysis. The results show thatthose who lack self-control to a larger degree are more likely to support the strengthening of the formal
enforcement of restrictions. This significant correlation is not affected by whether people’s perceptions ofothers’ self-control or any other control variable are added. The result is also robust to the regression
method used—a linear or tobit regression. The supplementary survey therefore confirms, in a realistic
context, the key result of a significant relationship between people’s self-control types and their

15

Indeed, most respondents selected numbers between 3 and 7. The percentages of those who selected the numbers
0, 1, 2, 8, 9, and 10 were 0.00%, 3.36%, 8.39%, 2.35%, 0.34%, and 1.01%, respectively.

25

commitment behaviors that was obtained in the main laboratory experiment.

7. Conclusion
Social dilemmas are ubiquitous in both our private and economic lives, while people’s free riding
in such dilemmas is known to be harmful to societies and organizations. During the last few decades,
economic research has documented that the dilemmas can be overcome when people’s incentives arechanged by enforcing a formal institution. While a formal institution can effectively alter individuals’material interests such that these are aligned with their group’s common interests, enacting it usuallyentails a cost, such as fixed administrative charges. Thus, the question remains unsettled as to when
implementing a formal institution is desirable, as groups can instead use decentralized peer-to-peer
monitoring and punishment (e.g., Ostrom, 1990). The present study is the first to show that the need for acentralized solution may depend on the state of the self-regulatory resources of a given group’s members.
In a novel laboratory experiment that rigorously manipulated their self-regulatory resources, most of thesubjects preferred to govern themselves using monitoring and informal punishment when their resourceswere not depleted. However, when their resources were depleted, the majority of subjects preferred to rely
on costly formal punishment. A survey on the Covid-19 pandemic revealed a similar relationship: those
who had weaker self-control attitudes were more likely to support stronger restriction measures.
This study is closely related to a large research area on self-control and self-regulatory resources.
The scheme choice preference found in the experiment is consistent with the well-known self-control
model formalized by Gul and Pesendorfer (2001, 2004) when combined with social preferences. The
theory suggests that people with small amounts of self-regulatory resources incur a large self-control costwhen they exercise self-control, such that they do not succumb to the temptation to free ride. The
presence of disutility causes them to remove such temptations by voting in advance as a commitment
device. In contrast, people can easily resist temptation when their resources are abundant. Thus, suchstrong self-control types sustain cooperation with informal punishment, rather than enact costly formal
punishment. The present experiment underlines the presence of such human self-control preferences andthe predictive power of the commitment theory in the context of endogenous institutional formation.
While the result obtained from the experiment is sufficiently clear, it is worth emphasizing thatthe present study is only the first step in exploring the role of self-regulatory resources in an institutionalsetting. For example, this study adopted experimental parameters frequently used in the literature, such as
the group size of five, MPCR of 0.4, and 24-period interactions in Part 2. These settings are desirable andstandard, and satisfy the usual requirements for a fixed laboratory size and an experiment duration of
approximately two hours. However, there are numerous other possible parameter values, such as different
group sizes, in experiments. It is certainly a useful robustness test to study the same question by
conducting experiments with different game parameters. For another example, the accuracy of
enforcement and noise may affect people’s institutional formation. The present study assumes, forsimplicity, that not only do the subjects accurately observe their peers’ contributions, but that thepunishments are also inflicted on the targets as intended. Such perfect observability and the absence oferrors are typically assumed in the experimental literature for simplicity (e.g., Falkinger et al., 2000;
26

Tyran and Feld, 2006; Kosfeld et al., 2009; Putterman et al., 2011; Traulsen et al., 2012; Zhang et al.,2014; Kamei et at., 2015; Fehr and Williams, 2018; Kamei and Tabero, 2021). However, Type I or II
errors sometimes occur in a real authority or society. A novel experiment by Nicklisch et al. (2016)
demonstrated that the imperfect observability of peers’ contributions (hence some noise in punishment)
raised the attractiveness of formal mechanisms when anti-social decentralized punishment was severe in agroup. It can be imagined that self-regulatory resources may be more important for people to behave insuch a complex, risky environment; however, how the resource amount affects their institutional choices
is unclear. Further experimental research would certainly be useful before the role of self-control is
generalized in the context of institutions and social dilemmas.

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