ISSN (Print) 0473-453X

Discussion Paper No. 1230

ISSN (Online) 2435-0982

OUTCOME- AND SIGN-DEPENDENTTIME PREFERENCES:
AN INCENTIVIZED INTERTEMPORALCHOICE EXPERIMENTINVOLVING EFFORT AND MONEY

Shohei Yamamoto
Shotaro Shiba
Nobuyuki Hanaki

February 2024

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, JapanOUTCOME- AND SIGN-DEPENDENT TIME
PREFERENCES: AN INCENTIVIZEDINTERTEMPORAL CHOICE EXPERIMENTINVOLVING EFFORT AND MONEY

Shohei Yamamoto1, Shotaro Shiba2, Nobuyuki Hanaki3,4,*
Graduate School of International Corporate Strategy (ICS), Hitotsubashi

1

University, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8439, Japan. Email:syamamoto@ics.hub.hit-u.ac.jp
2

Graduate School of Economics, Waseda University, 1-6-1 Nishi-waseda,

Shinjuku-ku, Tokyo 169-8050, Japan. Email: sibasyou2001@gmail.com
3

Institute for Social and Economic Research, Osaka University, 6-1 Mihogaoka,
Ibaraki, Osaka 567-0047, Japan. Email: nobuyuki.hanaki@iser.osaka-u.ac.jp
University of Limassol, Cypurs

4*

Corresponding author

1

OUTCOME- AND SIGN-DEPENDENT TIME
PREFERENCES: AN INCENTIVIZEDINTERTEMPORAL CHOICE EXPERIMENTINVOLVING EFFORT AND MONEY †Abstract
Previous research consistently identified differences in time preferences between
effort and monetary decisions. However, the root cause of this difference—
whether it stemmed from the intrinsic nature of the outcomes or the associatedpleasurable or unpleasurable experiences—remained undefined. In response, we
devised a novel two-stage experiment employing a 2 × 2 design contrastingoutcomes (money and effort) and domains (pleasant and unpleasant). Thisapproach allowed for the incentivization of all decisions, including those
involving future monetary losses. Our study reveals sign-dependent preferences,
showing varying degrees of impatience across pleasant or unpleasant experiences
in monetary or effort-related choices. We also observed outcome-dependent
preferences, particularly highlighting a higher level of impatience in unpleasantmonetary choices compared with their effort-based counterparts. However, thedegree of present bias did not differ across the four conditions. (JEL: C91, D91)
Keywords: time preferences, losses, incentivized experiment†This research has benefited from the financial support of (a) the Joint Usage/ResearchCenter, the Institute of Social and Economic Research (ISER), Osaka University, and (b)Grants-in-aid for Scientific Research Nos. 18K19954, 20H05631, and 23H00055 from theJapan Society for the Promotion of Science. The experiment reported in this paper has
been approved by IRB of ISER, Osaka University (No. 20221103). Our study waspreregistered at AsPredicted.org (#113813).

2

1. Introduction
The study of time preferences has long held a central role in the exploration ofindividual decision-making. The understanding of time preferences has been thesubject of rigorous investigation over the past decades (Abdellaoui et al., 2013;
Andreoni & Sprenger, 2012; Frederick et al., 2002; Laibson, 1997; Thaler, 1981).
The core result of these investigations is the separation of impatience (a longterm discount factor) and present bias (overvaluation of present outcomes) in time
preferences, as demonstrated by the quasi-hyperbolic discount model by Laibson(1997), which has been instrumental in elucidating aspects of human behaviour.
Empirical

investigation

of

time

preferenceshas

traditionallyconcentrated on those associated with time-dated monetary rewards (e.g., Ashraf
et al., 2006; Dohmen et al., 2010; Tanaka et al., 2010). These preferences havebeen linked to behavioural patterns, such as credit card debt (Meier & Sprenger,2010), body mass index (BMI) (Courtemanche et al., 2015), and impulse control
disorders (Brewer & Potenza, 2008; Ida, 2014; Odum et al., 2002).
Previous literature has also unveiled more nuanced dimensions of time
preferences. Evidently, they appear to be both sign-dependent (Benzion et al.,
1989; Thaler, 1981) and outcome-dependent (Odum et al., 2006; Reuben et al.,2010). This underscores the importance of broadening the scope of investigation
beyond merely monetary and pleasant outcomes to include non-monetary andunpleasant outcomes for a more comprehensive understanding of time
preferences. Moreover, the potential effect of the fungibility of money has been
discussed with an examination of the possible effect of the gap between the timingof receipt of money and its actual consumption (Cubitt & Read, 2007;
O’Donoghue & Rabin, 2015). Consequently, recent studies (e.g., Augenblick etal., 2015; Augenblick & Rabin, 2019; Carvalho et al., 2016) have begun to3

examine effort-based choices because the time dedicated to specific tasks may
serve as a more immediate reflection of consumption (Meissner & Pfeiffer, 2022).
These studies show that individuals exhibit a more pronounced present bias ineffort choices compared with monetary choices (see Imai et al., 2021, for a metaanalysis). However, monetary rewards and effort exertion are inherently different
in types of outcomes and the associated experiences, whether pleasant orunpleasant. Consequently, the observed differences in present bias could be
influenced by the type or sign of the outcomes or by a combination of both factors.
This research embarks on a methodologically rigorous experiment toinvestigate both the outcome- and sign-dependence of time preferences.
Added complexities arise, however, when considering the common
practice of estimating those preferences in the loss domain through a hypotheticalscenario (Thaler, 1981; Abdellaoui et al., 2013; Shiba & Shimizu, 2020), a
method susceptible to hypothetical bias, which has been widely recognized in theliterature (Abdellaoui et al., 2013; Shiba & Shimizu, 2020. See also Frederick etal., 2002). However, to our knowledge, no study has examined time preferences
for monetary losses using incentivized experiments due to methodological andethical challenges (detailed in the subsequent section). This study enables us toincentivize monetary losses building upon a previous elicitation method used inrisky choices, to mitigate potential biases, including the hypothetical bias (BoschDomènech & Silvestre, 2010). Through a novel two-stage incentivized
experiment, employing a 2 × 2 design contrasting outcomes (money and effort)and domains (pleasant and unpleasant), this study aims to clarify the outcomeand sign-dependence of time preferences.
Specifically, time preferences are elicited through intertemporaldecisions involving monetary gains and task exemptions for pleasant outcomes,

4

as well as monetary losses and added tasks for unpleasant outcomes. Distinct
from previous research, all decisions in our experiment, including those related
to future monetary losses, are incentivized. To streamline the transaction process,all monetary exchanges in this study were conducted through a mobile payment
system, minimizing delays and ensuring smooth transactions as in Andreoni et al.
(2018). Accordingly, this study offers significant contributions to the literature
on time preferences, specifically catering to researchers interested in exploring
loss domains through incentivized experiments.
Key findings from our research are as follows.
First, our results diverge from the conclusions drawn by Imai et al. (2021),
who argued that individuals demonstrate a more pronounced present bias when
making effort-related choices. We found no evidence of present bias in any of thefour experimental conditions. Nevertheless, we observed that participants
displayed more dynamically inconsistent preferences when making effort-related
choices.
Second, our study revealed sign-dependence in impatience irrespectiveof whether the outcome involved money or effort. Intriguingly, the direction ofthis effect was opposite between the two. For monetary decisions, greater
impatience was noted in the pleasant domain, aligning with the expected signeffect. Conversely, in decisions involving effort, a reverse sign effect wasobserved, with greater impatience in the unpleasant domain. This divergencesuggests that the sign effect may not be universally applicable across different
types of outcomes.
Third, we identified outcome-dependence in impatience. Namely, anotably higher level of impatience in unpleasant monetary choices was observed
compared with their effort-based counterparts. Furthermore, effort-related

5choices tended to favour balanced allocations (i.e., demonstrated a preference forsmoothness) over monetary choices.
The rest of the paper is organized as follows. The relevant literature isreviewed and the main hypotheses are formulated in Section 2. The design of theexperiment is described in Section 3 followed by the results in Section 4. Section
5 summarizes the findings and offers some discussions.
2. Literature Review and Hypotheses
In this section, we review the existing literature pertinent to our study focusing
on present bias, impatience, and their sign- and outcome-dependence. We alsopresent our main hypotheses.
2.1. Present Bias
Present bias refers to the tendency to overvalue present outcomes, and it has
become an integral concept within the realm of behavioural economics. This bias
can be traced back to models of dynamically inconsistent time preferences,
formulated by economists such as Strotz (1955), Laibson (1997), andO’Donoghue and Rabin (1999, 2001). Among these, the quasi-hyperbolic
discount model has emerged as a particularly influential model (e.g., Laibson,1997) because of its attractive analytical features (Frederick et al., 2002).
Numerous studies have reported evidence of present bias in humanbehaviour (e.g., Abdellaoui et al., 2013; Thaler, 1981). However, recent research
employing innovative methods such as the convex time budget (CTB) method(Andreoni and Sprenger, 2012) yielded differing results. The CTB method, whichallows for the simultaneous estimation of utility curvature and discountfunctions—including a parameter for present bias—has gained broad acceptance.
Notably, the CTB method has led some studies to report a reduced degree of6present bias or even an absence of it (e.g., Andreoni and Sprenger, 2012;
Andreoni et al., 2015). A meta-analysis utilizing the CTB method, conducted by
Imai et al. (2021), has shown a failure to reject the null hypothesis of no present
bias for studies based on monetary choices.
The literature suggests that present bias may fluctuate based oncontextual factors; however, precise mechanisms—such as whether it is signdependent—remain uncertain. Our literature review in the following sections
aims to develop our hypotheses around the situational determinants of present
bias. Notably, while the CTB method has been used to explore outcomedependent present bias in a few studies, its use in investigating sign-dependent
present bias is yet to be seen, to the best of our knowledge. Therefore, ourliterature review will encompass time preference studies utilizing various
methodologies.
2.1.1. Sign-dependence of Present Bias. The investigation into present bias inpleasant and unpleasant domains is challenging. Previous literature on thissubject is not only scarce but often inconsistent, thereby making conclusive
interpretations elusive.
With regard to monetary choices, the body of research offers divergent
insights. Thaler (1981) observed a marked decrease in discount rates as the timingof future gains was delayed, revealing a preference for present-biased behaviour
within gains. However, this pattern was noticeably absent in the corresponding
loss domain. Contrasting with Thaler’s findings, more recent studies conductedby Shiba and Shimizu (2020) indicated that a significant portion of their
participants exhibited present-biased preferences across both gain and loss
domains. Furthermore, Abdellaoui et al. (2013) uncovered a more pronounced
present bias in the context of monetary losses compared with gains. It is crucial
7

to note that among these studies, no study provides incentives for both gain andloss domains. 1
In the realm of effort choices, the literature is even more limited. To thebest of our knowledge, Abdellaoui et al. (2018) conducted the only study
comparing the level of present bias between gains and losses of working time anduncovering similar levels of present bias in both domains.
2.1.2. Outcome-dependence of Present Bias. Evidence suggests that individuals
exhibit varying degrees of present bias for different outcomes. Bleichrodt et al.
(2016) observed a stronger present bias for health outcomes compared with
monetary ones. Cheung et al. (2022) reported a weaker present bias for food
relative to money. 2 Studies for effort decisions (Augenblick et al., 2015;
Augenblick & Rabin, 2019) indicated a higher degree of present bias in decisions
involving effort than in those involving monetary gains. However, this body ofresearch often does not account for the pleasantness or unpleasantness of theoutcomes. Abdellaoui et al. (2018) performed the only study to compare present
bias for monetary gains and gains of time, finding a greater bias for the latter.
Furthermore, they found no significant correlation between present bias inmonetary gains and gains of time. However, the scrutiny of effort and monetary1

To the best of our knowledge, Yamamoto et al. (2020) performed the sole study that

adequately incentivized time preferences across both gain and loss domains. However,this study encountered methodological challenges in accurately estimating time
preferences.
2

Cheung et al. (2022) noted a lesser present bias for food compared with money, despite

employing the CTB method. They suggested that sample differences may influence
outcomes.

8

decisions in the domain of unpleasant outcomes remains conspicuously
unaddressed.
In summary, while there seems to be a general agreement that present
bias is more pronounced in effort choices than in monetary choices, the degree towhich this bias is influenced by sign or outcome remains unclear. Therefore, we
propose the following hypothesis.

HYPOTHESIS 1: A higher degree of present bias in the choices of effort exertion
than the choices of monetary gain is observed.

2.2. Impatience
The literature indicates that levels of impatience vary with context, yet definitive
evidence to predict specific levels of impatience in distinct scenarios remains
elusive—for example, the difference in impatience between monetary decisions
and effort-based choices in an unpleasant domain. Our literature review in thefollowing sections seeks to explore this variation to further formulate hypotheseson the contextual influences on impatience. Analogous to the discourse on present
bias detailed previously, studies employing the CTB method to investigate bothoutcome- and sign-dependent impatience are notably limited. Thus, our review
will cover a broad spectrum of time preference research employing diverse
methodologies.
2.2.1. Sign-dependence in Monetary Choices. Several researchers have identified
what is known as the sign effect, a pattern where future monetary gains tend to bediscounted more heavily than losses (Benzion et al., 1989; Myerson et al., 2017;
Thaler, 1981). This observation led to focused studies on money discounting,9

revealing that allowing the discount to change based on whether it involves gains
or losses improves the description of time preferences (Abdellaoui et al., 2013;
Scholten & Read, 2010).
The body of work related to the discounting of delayed gains issubstantial and has been described by Harris (2012) as ‘large and generally
consistent’. By contrast, studies of the discounting of delayed losses appear moreinconsistent and even contradictory.
An interesting aspect of this contrast is the tendency for zero or negative
discounting with future small losses, a pattern not commonly observed with gains.
For instance, Hardisty and Weber (2009) found varied behaviours in discounting
delayed losses. Some of their participants showed the usual pattern of discounting
delayed losses. However, others displayed negative discounting. Furthermore,
Yoon and Chapman (2016) found that nearly all participants exhibited positive
discounting of delayed monetary gains, whereas only 63% showed positive
discounting of delayed losses, and notably, 11% showed negative discounting,choosing a larger, sooner loss over a smaller, later one.
2.2.2. Sign-dependence in Non-monetary Choices. In addition to Abdellaoui et al.
(2018), who studied gains and losses of working time mentioned above, studiesof the sign effect investigated non-monetary decisions. Specifically, Baker et al.
(2003) observed that current smokers’ delay discount rates for hypotheticalcigarette outcomes were higher for gains than for losses, and these rates were also
higher when evaluating cigarette outcomes compared with monetary outcomes.
The sign effect has been identified in health outcomes as well (Baker et al., 2003;
Chapman, 1996; MacKeigan et al., 1993), with Chapman (1996) discerning amore pronounced sign effect for health than for money. Yamamoto & NavarroMartinez (2022) found a pronounced sign effect on consumer goods by10

demonstrating the absence of a discounting pattern in the future selling price of
these goods. Furthermore, Hardisty & Weber (2009) extended the investigationof the sign effect to three domains—money, the environment, and health—demonstrating that the effect is consistent across all three, but is especiallypronounced in the health domain.
In summary, the sign effect appears to manifest across different types ofoutcomes, including but not limited to monetary spheres. Furthermore, it seems
to be more pronounced for non-monetary outcomes. Driven by these insights, we
propose the following hypotheses.

HYPOTHESIS 2: A higher degree of impatience is observed for pleasant outcomesthan unpleasant outcomes in both monetary and effort choices.

HYPOTHESIS 3: The sign effect is more pronounced in effort choices comparedwith monetary choices.

2.2.3. Outcome-dependence in the Pleasant Domain. A growing body ofliterature contrasts discount rates in monetary choices with those in primary
rewards, such as food. The review by Cohen et al. (2020) indicates that thediscount rate for monetary choices is generally lower than that measured forprimary rewards, as corroborated by previous studies (Estle et al., 2007; Odum etal., 2006; Reuben et al., 2010; Tsukayama & Duckworth, 2010). Moreover,Tsukayama and Duckworth (2010) identified a positive correlation between
temporal discounting of money and consumer goods. Cheung et al. (2022) report,
once the present bias is controlled for, there is a significant difference between
the discount rate for money and foods.
11Abdellaoui et al. (2018) performed the only research specificallycomparing impatience between decisions concerning monetary gains and choices
regarding decreasing working hours. The results seemed to align with the findingof the relationship between money and consumer goods. They found that themedian discount rate was higher for gains of time than for monetary gains, and
impatience towards gains of working time and gains of money was highly
correlated.
2.2.4. Outcome-dependence in the Unpleasant Domain. We anticipate that thelevel of impatience will be more pronounced in effort choices compared with
monetary choices within the unpleasant domain. This expectation aligns with
Hypothesis 3, wherein a higher sign effect is posited in effort decisions as
opposed to monetary decisions.
Although no existing research directly contrasts time preferences
between monetary and effort choices in the unpleasant domain, previous
literature offers supporting evidence. Harris (2012) found that most participantspreferred to postpone monetary losses, but intertemporal choices for other
unpleasant experiences (such as social rejection, embarrassment, and pain)
showed highly variable responses. Some participants deferred these experiences
as long as possible, while many elected to experience them immediately. Baker
et al. (2003) reported that health losses were discounted more slowly thanmonetary losses. Some other experimental studies have also noted negative
discounting for health consequences (Ganiats et al., 2000; Van Der Pol & Cairns,
2000), as well as for electric shocks (Loewenstein, 1987; Yates & Watts, 1975).
Based on these observations, we formulate the following hypothesis.

12Hypothesis 4: In both pleasant and unpleasant domains, a higher level ofimpatience is observed in effort outcomes than in monetary outcomes.

2.2.5. Hypothetical Intertemporal Decisions. We argue that our research uniquely
approaches the proper incentivization of choices to elicit true underlying
preferences across both gains and losses. A prominent aspect of research into theloss domain, specifically concerning time preferences, has been the reliance on
hypothetical scenarios. This is also true for the studies introduced in Section 2.
This method, while convenient, may introduce a phenomenon known as
hypothetical bias (Frederick et al., 2002), whereby participants’ responses tohypothetical questions may differ from their choices in real situations. Thus, a reexamination of even established phenomena like sign-dependent time preferences
is warranted.
Hypothetical bias in the loss domain has been investigated in risk
preference research. Weber et al. (2004) identified varying degrees ofhypothetical bias in both gain and loss domains. If this is the case, then the extent
of hypothetical bias could differ between time preferences for gains and losses,
thereby skewing the analysis of the sign-dependency of time preference.
Abdellaoui et al. (2013) explored time preferences for both monetary gains andlosses but only incentivized the gains. Such an approach may bias themeasurement of sign-dependent preferences. Therefore, comprehensive
incentivization of all decisions, for both pleasant and unpleasant outcomes, isintegral to the validity of this study.
As previously mentioned, addressing hypothetical bias in experiments
involving potential monetary losses introduces challenges. The conventional
method of providing an initial endowment to offset potential losses can13

inadvertently trigger the house-money effect (Thaler & Johnson, 1990).
Estimating time preferences with windfall money could significantly distort theresults. To mitigate this bias, Bosch-Domènech and Silvestre (2010) introduced
an innovative approach: having participants earn money through unrelated tasks
before commencing the main experiment. The foundation of this approach lies in
the fact that money earned in this manner, and even taken home, is less likely to
be viewed as house money. Bosch-Domènech and Silvestre (2010) supported thisnotion through post-experiment surveys. Leveraging this mechanism holds thepotential to mitigate both hypothetical and house-money biases, thereby aiming
to foster a more precise evaluation of participants’ attitudes towards losses.
In light of these complex methodological challenges, our study introduces a twostage experimental design. This approach aims to minimize both hypothetical andhouse-money biases by initially allowing participants to earn money to coverpotential losses. Subsequently, participants return to engage in the main
experimental session, involving intertemporal choices.
3. Experiment
We recruited 200 university students at Osaka University (35% female, age
range: 18–36 years, with an average of 22.1 years). There were several conditions
to participate in this study: having a PayPay account (mobile payment service that
operates in Japan) and making transactions within 1 month; agreeing toparticipate in all sessions of the experiment lasting five weeks; and having adecent internet connection for participation in the experiment. The rewards
depended on their decisions and the treatments that they were assigned to, but the
average reward was 2,000 yen. Besides that, they received their preferred item,

14

which was valued at around 5,000 yen, if they completed all sessions in theexperiment. 3
3.1. Design and Procedure
At the beginning of the experiment, participants were randomly divided into twotreatments: Money and Effort. As detailed below, in the former, participantsintertemporally allocated money, while in the latter, they allocated effort.
In both treatments, the experiment lasted 5 weeks, and participants
needed to complete all sessions in Week 1 (the first day of the experiment), Week
3 (two weeks from the first day of the experiment), Week 4 (three weeks fromthe first day of the experiment), and Week 5 (four weeks from the first day of theexperiment). In Week 5, all participants answered a set of questions related totheir cognitive skills 4 and behaviour that are potentially correlated with their3

On the first day of the experiment, we presented participants with a list of 30 attractive

items. Participants were asked to rank these items in order of preference. The items werethen distributed according to these rankings. A week after the experiment, the items weredispatched to the participants’ designated locations. We opted for tangible items rather
than money as completion rewards to minimize the likelihood of participants integratingthem with the monetary rewards obtained during the experiment.
4

Cognitive skills were measured by four Matrix reasoning items and four three-dimensional rotation items from The International Cognitive Ability Resource. In thethree-dimensional rotation items, cubes were displayed at the top of the screen.
Participants were tasked with selecting, from the given cube options, the one that could
match the displayed cube when rotated. In the matrix reasoning items, a matrix featuring
different geometric patterns—with one part missing—was displayed at the top of the15

time preferences, 5 as well as demographics such as age, gender, and education.
In addition, an attention check question was included to exclude inattentive
participants from the experiment.
All sessions were conducted online, and all monetary transactions
between the experimenter and participants were made via PayPay. Figure 1 shows
the schedule over five weeks for the two treatments (for detailed experimental
instructions, see Online Appendix). Let us describe each treatment in more detail.

screen. Participants were tasked with selecting the most fitting geometric pattern from thegiven options to complete the missing part.
5

Namely, questions about their drinking habits (Bradford et al., 2017): how many days

they drink alcohol per month; how many times they drink large quantities per month (fivedrinks for men and four drinks for female), and how many glasses of alcohol they drink
on average. We also asked other questions including how many times they played sport
this year (less than once a month, 1–3 days per month, more than once a week, more than
twice a week, more than three times a week, more than five times a week); if they feelthey are usually healthy (I think very healthy, I think healthy, Not so healthy, Not
healthy); their height and weight; their GPA; and the saving proportion of their monthly
salary. In addition, we asked about their eating and shopping habits from the Shorter
PROMIS Questionnaire (Christo et al., 2003).

16

A: Money treatment

B: Effort treatment

FIGURE 1. Experimental schedule for each treatment.

17

3.1.1. Money Treatment. The participants were asked to perform the task oftranscribing 4,000 numbers on the screen in Week 1. Each page of this task
displayed ten blurred numbers that needed to be transcribed (see Figure for an
example). Thus, participants needed to complete 400 pages, and they received areward of 2,000 yen for doing so. This reward was designed to offset any potential
losses in subsequent sessions. The participants were informed that the reward is
the participants’ own payment and can be used anytime. To ensure thatparticipants understood the payment procedure and rules, they needed to correctly
answer the comprehension question before proceeding to the experiment.

FIGURE 2. Screenshot of an example for the transcription tasks (translated in
English).

In Weeks 3 and 4 (the second and third sessions), participants made a
total of 36 intertemporal allocation choices. There were two sets (Sets 1 and 2) ofnine questions for gains (pleasant domain) and nine questions for losses(unpleasant domain). In Set 1, participants faced allocation problems with abudget constraint,(1 + 𝑅𝑅)𝑥𝑥𝑠𝑠 + 𝑥𝑥𝑙𝑙 = 𝐵𝐵

where 𝑥𝑥𝑠𝑠 and 𝑥𝑥𝑙𝑙 are the amounts of outcome and B is a (future-value) budget.

The set of questions is constructed with nine different interest rates: R = –0.3, –0.18, –0.07, 0, 0.11, 0.25, 0.43, 0.67, and 1 with 𝐵𝐵 = 1000. Set 2 employed abudget constraint of18

𝑥𝑥𝑠𝑠 +

1
𝑥𝑥 = 𝐵𝐵
1 + 𝑅𝑅 𝑙𝑙

with parameters R = –0.34, –0.21, –0.09, 0, 0.07, 0.25, 0.38, 0.6, and 0.9 and
𝐵𝐵 = 800.

We based the format of our CTB questions on Cheung et al. (2022).

Namely, participants chose one option from nine equally split bundles on thebudget line as shown in Figure 2. Furthermore, to maintain equal transaction costs
between sooner and later dates, we avoided using corner bundles of zero for either
the sooner or later dates from the choice set (i.e., 𝑥𝑥𝑠𝑠 = 0 or 𝑥𝑥𝑙𝑙 = 0). The

minimum values for 𝑥𝑥𝑠𝑠 and 𝑥𝑥𝑙𝑙 were set at 5 in both Sets 1 and 2.

FIGURE 2. English translation of the screenshots of the allocation decisions in
Week 4: gain (pleasant domain) of monetary choices with R = 0 (top) and 0.07
(bottom).

The participants would receive 𝑥𝑥𝑠𝑠 and 𝑥𝑥𝑙𝑙 in the pleasant domain, andthe participants would pay 𝑥𝑥𝑠𝑠 and 𝑥𝑥𝑙𝑙 in the unpleasant domain. We did not

explicitly show the interest rate to the participants. They can allocate the budget
disproportionately more to the sooner period (e.g., the far-left options in Figure
192), disproportionately more to the later period (e.g., the far-right options in Figure2), or in between.
In the example questions shown in Figure 2, the payments on the sooner
dates always decrease from the left to the right options (refer to Figures A1, A2,
and A3 for example questions in the other treatments). To avoid the order effect,
we created another version in which the payments on the sooner dates always
increase from the left to the right options by flipping the order of the questions.
One of the two versions was randomly shown to the participants. In addition, theorder of the pleasant and unpleasant domains was randomized.
In Week 3, participants chose the allocations between Weeks 4 and 5.
The sooner date is one week after the date of the allocation decisions, making
these questions front-end-delay questions. In Week 4, they again faced the same
allocation problems between Weeks 4 and 5. However, this time the sooner datecoincided with the day of the intertemporal decisions, rendering these as no frontend-delay questions. Comparing the decisions in these two types of questions
allows us to assess their level of present bias.
Subsequently, one question was randomly selected from all questions
answered in Weeks 3 and 4. The monetary transaction was carried out based on
the participant’s decision in the selected question. If the selected question was
about the choices regarding monetary gains, they would receive 𝑥𝑥𝑠𝑠 in Week 4

and 𝑥𝑥𝑙𝑙 in Week 5. If the selected question was about the choices regarding
monetary losses, they would pay 𝑥𝑥𝑠𝑠 in Week 4 and 𝑥𝑥𝑙𝑙 in Week 5.

We again asked the comprehension quiz about the instructions to makesure participants understand how the allocation problems work before making theallocation decisions in both Weeks 3 and 4. They could not go to the next page
until they chose the right answer in each quiz.

20

3.1.2. Effort Treatment. In Week 1, participants agreed to perform the task oftranscribing 2,000 random numbers in both Weeks 4 and 5 and received 2,000
yen as compensation for doing so. As in the money treatment, they were informedthat this compensation was their own payment, and they also answered thecomprehension question about it. To underscore the commitment, we had themsign a document detailing the contract and return it to us. The purpose of thiscontractual agreement was to enhance the salience of the effort’s reference points
and encourage participants to integrate it. 6 The intertemporal decisions in thistreatment mirrored those of the money treatment. In each of Weeks 3 and 4,
participants answered a total of 36 allocation questions regarding the amount oftasks in Weeks 4 and 5. The amount of numbers to transcribe was reduced in thepleasant domain, while it was increased in the unpleasant domain. Namely,
participants had to transcribe 2000 + 𝑥𝑥𝑠𝑠 and 2000 + 𝑥𝑥𝑙𝑙 numbers in Weeks 4

and 5, respectively. In each question, participants chose one option from nine
equally split bundles on the budget line. 7
One question was later randomly selected from all questions answered

during Weeks 3 and 4 to determine the actual workload. If a pleasant one waschosen, 𝑥𝑥𝑠𝑠 and 𝑥𝑥𝑙𝑙 would be negative, while if an unpleasant one was chosen,

𝑥𝑥𝑠𝑠 and 𝑥𝑥𝑙𝑙 would be positive. The bundles of 𝑥𝑥𝑠𝑠 and 𝑥𝑥𝑙𝑙 , as well as the budget

6

The analyses presented in Appendix C indicate that this was indeed successful.

7

This contrasts with the approach of Augenblick et al. (2015), who employed a slider-

type question for effort allocation decisions. However, our analysis, transforming theexperimental data of Augenblick et al. (2015) into a format analogous to our discrete
choice framework, revealed that the results were similar. This comparative analysis isavailable upon request.

21

constraints (R and B), are identical between the two treatments, ensuring that thedecisions are comparable.
3.2.

Measurements of Time Preference

We measure the participants’ time preferences using choice-based indices. These
descriptive measures are based on simple proportions of rewards allocated tosooner versus later payment dates. By employing such descriptive measures, thestudy aims to provide evidence regarding the behaviours of interest without
relying on specific functional forms. The literature establishes that time
preference can be decomposed into three distinct properties: impatience, present
bias, and a preference for smoothing (Frederick et al., 2002). We follow Cheung
et al. (2022) to measure these properties in the four conditions: pleasant monetary
outcomes (Money-pleasant), unpleasant monetary outcomes (Money-unpleasant),
pleasant effort outcomes (Effort-pleasant), and unpleasant effort outcomes(Effort-unpleasant).
Impatience describes the extent to which individuals discount delays,
essentially measuring their willingness to wait for future outcomes. Impatiencefor pleasant outcomes is measured by the average proportions allocated to thesooner option in Week 3, the initial intertemporal decisions with front-end-delay.
𝑥𝑥𝑠𝑠
�, where 𝑥𝑥𝑠𝑠
𝑥𝑥𝑠𝑠 +𝑥𝑥𝑙𝑙

This is calculated as 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 = 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 �and 𝑥𝑥𝑙𝑙 are thesooner and later allocations at each decision problem, respectively. Impatienceequals 1 if the participant always allocates all resources (the number of exemptedtasks in Effort-pleasant) to the sooner dates (extremely impatient) and 0 if they22

always allocate them to the later dates (extremely patient). 8 For unpleasantoutcomes, the proportion of outcomes allocated to later dates serves as an
𝑥𝑥𝑙𝑙).
𝑥𝑥𝑠𝑠 +𝑥𝑥𝑙𝑙

indicator of impatience, computed as 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 = 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚(

This isbased on the rationale that an impatient individual would prefer to postpone
unpleasant outcomes to later dates.
Present bias (PB) indicates the extent to which individuals overweighpresent outcomes. Present bias is measured by the difference between allocations
at two periods, i.e., 𝑃𝑃𝑃𝑃 = 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚(

𝑥𝑥𝑠𝑠
𝑥𝑥𝑠𝑠
|
|𝑎𝑎𝑎𝑎 𝑤𝑤3 )− 𝑥𝑥 +𝑥𝑥
𝑥𝑥𝑠𝑠 +𝑥𝑥𝑙𝑙 𝑎𝑎𝑎𝑎 𝑤𝑤4
𝑠𝑠𝑙𝑙

in the pleasant

𝑥𝑥𝑙𝑙
𝑥𝑥
|
− 𝑙𝑙 |𝑎𝑎𝑎𝑎 𝑤𝑤3 ) in the unpleasant ones.
𝑥𝑥𝑠𝑠 +𝑥𝑥𝑙𝑙 𝑎𝑎𝑎𝑎 𝑤𝑤4
𝑥𝑥𝑠𝑠 +𝑥𝑥𝑙𝑙

domains and 𝑃𝑃𝑃𝑃 = 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚(

PB describes how much the allocations have changed from the questions withfront-end-delay to those without. It takes values between –1 (extremely futurebiased) and 1 (extremely present-biased).
Preference for smoothing (Smooth) describes how individuals preferequal allocations between sooner and later dates. It is defined as 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆ℎ =(𝑥𝑥𝑠𝑠 +𝑥𝑥𝑙𝑙 )−|𝑥𝑥𝑠𝑠 −𝑥𝑥𝑙𝑙 |

𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚(

𝑥𝑥𝑠𝑠 +𝑥𝑥𝑙𝑙) for both pleasant and unpleasant outcomes. It takes

values between 0 (smoothing hater) and 1 (smoothing lover).

In addition to these descriptive measures, we employ an auxiliary methodthat utilizes the quasi-hyperbolic discounted utility model of Laibson (1997) with
the Constant relative risk aversion (CRRA) utility function to yield clearer
differentiations among parameter values. Namely, we assume the utility from the8

Note that the exclusion of corner bundles from our choice sets means that ourdescriptive measures, Impatience, do not reach these theoretical extremes in our
experimental design. The same applies to PB and Smooth, which are defined in thissection.

23

alternative in the CTB questionnaire (𝑥𝑥𝑠𝑠 , 𝑥𝑥𝑙𝑙 ) is 𝑈𝑈(𝑥𝑥𝑠𝑠 , 𝑠𝑠𝑙𝑙 ) = 𝐷𝐷(𝑡𝑡𝑠𝑠 )𝑈𝑈(𝑥𝑥𝑠𝑠 ) +𝐷𝐷(𝑡𝑡𝑙𝑙 )𝑈𝑈(𝑥𝑥𝑙𝑙 ) where the discount function 𝐷𝐷(. ) and instantaneous utility 𝑈𝑈(. )are 𝐷𝐷(𝑡𝑡) = 𝛽𝛽1𝑡𝑡>0 𝛿𝛿 𝑡𝑡 and 𝑈𝑈(𝑥𝑥) = 𝑢𝑢(𝑥𝑥) for 𝑥𝑥 > 0 and 𝑈𝑈(𝑥𝑥) = −𝑢𝑢(−𝑥𝑥) for𝑥𝑥 < 0, respectively, with𝑥𝑥 1−𝛼𝛼(𝛼𝛼 ≠ 1).
(𝛼𝛼 = 1)𝑢𝑢(𝑥𝑥) = � 1−𝛼𝛼ln(𝑥𝑥)

This model allows for the joint estimation of three crucial parameters: theweekly discount factor (δ), present bias (β), and utility curvature (α), using the
maximum likelihood estimation (MLE) with the multinomial logit probability ofa specific choice
Pr(𝑐𝑐ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜) =𝑒𝑒

𝑈𝑈1 /𝑠𝑠+𝑒𝑒∗

𝑒𝑒 𝑈𝑈 /𝑠𝑠

𝑈𝑈2 /𝑠𝑠+⋯+ 𝑒𝑒𝑈𝑈9 /𝑠𝑠

where 𝑈𝑈1 , 𝑈𝑈 2 , … , 𝑈𝑈 9 are utilities of the nine alternatives in a choice set under𝛿𝛿, 𝛽𝛽, and 𝛼𝛼, and 𝑠𝑠 is a noise parameter in each choice. 𝑈𝑈 ∗ is the utility of thechosen alternative. 99In alignment with the methodology set forth by Cheung et al. (2022), we imposedbounds on the estimates for 𝛿𝛿, 𝛽𝛽, and

𝛼𝛼 within the ranges of 0.1 to 10, 0.05 to 20, and–10 to 10, respectively. The number of subjects whose estimated parameters approached
the boundary (within a distance of 0.001) was as follows: zero out of 89 for both pleasantand unpleasant domains in the monetary choices. In the effort choices, the numbers were
14 and 12 out of 76 for the pleasant and unpleasant domains, respectively. A further
breakdown in the effort condition revealed that four and three subjects approached theboundary for 𝛿𝛿, 11 and eight subjects for 𝛽𝛽, and zero and one subject for 𝛼𝛼 in thepleasant and unpleasant conditions, respectively.

24

TABLE 1. The summary table of demographic and behavioural variables.
Money
Mean
Age
22.00
Female0.39
College
0.36
BMI
21.52
GPA
2.93
Saving_rate
28.99
Matrix_score
1.96
Rotation_score 0.82
Variables

EffortSD
Mean(2.15) 22.32(0.49) 0.30
(0.48) 0.25
(4.89) 21.23(0.53) 2.95
(24.91) 33.75(1.12) 1.74
(1.14) 0.86

tSD
(3.06) –0.77(0.46) 1.21
(0.44) 1.52
(2.58) 0.46
(0.64) –0.20(24.22) –1.24(1.16) 1.23
(1.05) –0.20

pN(0.44)(0.23)(0.13)(0.64)(0.84)(0.22)(0.22)(0.84)

87
89
89
88
83
89
89
89

Notes: ‘Saving_rate’ refers to the proportion of the monthly salary thatparticipants saved. ‘Matrix_score’ represents the scores derived from four matrix
reasoning items. ‘Rotation_score’ captures the scores from four assessments
involving three-dimensional rotation tasks.

4. Results
A total of 184 participants took part in Week 1. Of these, 13 participants withdrew
during the course of the experiment, and an additional five failed to complete
transactions correctly. One participant who did not pass our attention check wasalso excluded. Consequently, our analysis includes 165 participants who
successfully completed all sessions.

25

Table 1 outlines the demographic and other pertinent characteristics ofthe participants between the two treatments, highlighting selected variables; acomprehensive list of other variables is available in Table A.2 in the Appendix.
The values of the variables do not differ between the treatments (t-test, all pvalues greater than 0.1).
4.1. Descriptive Analysis
Figurereports the mean proportions of allocations (Money-pleasant, Money-

unpleasant, Effort-pleasant, and Effort-unpleasant) and their standard errors tosooner dates for each condition. 10 The left side of Figure
Set 1 and the right side of Figure

shows the results ofshows the results of Set 2. The overall patterns

between the two sets are similar, implying that preferences were not affectedmuch by the two different budget sets. The proportion of allocation to sooner
dates decreases as R increases in pleasant conditions, while the proportion to later
dates increases as R increases in unpleasant conditions, consistent with the lawof demand.

10

The proportion of the allocation is calculated by 1 − X𝑙𝑙 /𝐵𝐵

Set 2.

26

for Set 1 and Xs /𝐵𝐵forFIGURE 4. Proportions of allocations in the four conditions for Set 1 (left) and
Set 2 (right).
NOTE: The solid lines indicate the proportion of the allocations in Week 3, andthe dotted lines indicate the proportion of the allocations in Week 4. Each bar
describes the standard error.

Table 2 reports the descriptive measures in the four conditions. The mean
Impatience ranges from 0.39 to 0.44 in all conditions, indicating that ourparticipants allocated a significant amount to the later (sooner) dates whenallocating pleasant (unpleasant) outcomes. The median value of Impatience formonetary gains in Cheung et al. (2022) is 0.39, which is close to our value,indicating our results in this condition are compatible with the previous finding.
The solid lines in Figure 4 describe the allocations in Week 3 (with front-enddelay), and the dotted lines describe the allocations in Week 4 (no front-enddelay). In the pleasant condition, present-biased subjects are expected to allocate
27

more pleasant outcomes to the sooner date in Week 4; thus, the dotted lines would
be above the solid lines. In the unpleasant conditions, present-biased subjects are
expected to allocate more unpleasant outcomes to the later date in Week 4; thus,
the dotted lines would be below the solid lines. However, we do not clearly see
such patterns in either treatment. Indeed, the mean PB is nearly zero in all four
conditions (t-test, all p-values greater than 0.1), indicating that our participants’
allocation remained unchanged from Week 3 to Week 4 on average. This outcome,highlighting the absence of present bias in monetary choices, aligns withpreceding studies. However, the lack of present bias in effort choices countersestablished literature on the topic (Imai et al., 2021).

RESULT 1: Present bias is not observed in either the effort or monetary choices.

TABLE 2. The summary statistics of descriptive measures, by condition.

Impatience
PB
SmoothN(1)
MoneyPleasant
mean/sd/p50
0.41(0.04)
0.39–0.00(0.04)
0.00
0.10(0.07)
0.12
89(2)
MoneyUnpleasant
mean/sd/p50
0.39(0.04)
0.39
0.00(0.07)
0.00
0.11(0.10)
0.12
89(3)
EffortPleasant
mean/sd/p50
0.42(0.16)
0.39–0.01(0.22)
0.00
0.31(0.28)
0.15
76(4)
EffortUnpleasant
mean/sd/p50
0.44(0.17)
0.41–0.02(0.21)
0.00
0.35(0.30)
0.30
76

By contrast, the standard deviations for PB in the Effort treatment are
significantly larger in both pleasant and unpleasant domains than in the Moneytreatment (F-test, p < 0.001 in both domains), indicating considerable
heterogeneity among participants’ PB in effort choices. Moreover, Table 3
describes the frequencies of present-biased, future-biased, and neutral (PB = 0)28

participants. In the Effort treatment, only 21% of observations are categorized as
neutral in both pleasant and unpleasant domains, whereas in the Money treatment,
58% of observations in the pleasant domain and 50% in the unpleasant domain
are classified as neutral. These suggest a higher level of dynamic inconsistency
in effort choices. In the pleasant domain, the proportion of present-biased
participants is noticeably higher in Effort than in Money. Conversely, in theunpleasant domain, the proportion of present-biased individuals does not show asubstantial difference between the two treatments.

TABLE 3. Present-biased and future-biased individuals.

Money

Pleasant
UnpleasantEffortPleasant
UnpleasantPresentbiased
19(21.35%)26(29.21%)

Neutral(PB = 0)52
(58.43%)45(50.56%)

Futurebiased
18(20.22%)18(20.22%)37(48.68%)24(31.58%)16(21.05%)16(21.05%)23(30.26%)36(47.37%)Total
89(100%)
89(100%)
76(100%)
76(100%)

Next, we examine the preference for smoothness. As depicted in Figure ,almost all outcomes were allocated to the sooner date when (1 + R) was less than
1 and allocated to the later date when (1 + R) was more than 1 in the Moneytreatment. However, a less extreme pattern is found for the Effort treatment, withthe allocation to the sooner date gradually decreasing as R increased. A similar
pattern was observed for the unpleasant conditions. The sooner allocation
progressively increased as R rose, while in the Money treatment, nearly all
monetary losses were allocated to the later date when 1 + R was less than 1 andto the sooner date when 1 + R was more than 1. This trend is further supportedby the smoothness index. The mean value of Smooth was approximately 0.1 in29

Money and 0.3 in Effort (see Table 2). This indicates a higher inclination towards
balanced allocations in the effort choices compared with the monetary ones,
providing evidence of a greater preference for smoothness in the former.

TABLE 4. The mean differences of the three descriptive measures.

Impatience
PB
SmoothN(1)
PleasantUnpleasant(Money)(2)
PleasantUnpleasant(Effort)0.02***(0.01)
0.00(0.01)–0.01(0.01)
178–0.02**(0.01)
0.01(0.02)–0.04***(0.02)
152(3)
MoneyEffort(Pleasantdomain)–0.01(0.02)
0.01(0.02)–0.21***(0.03)
165(4)
MoneyEffort(Unpleasant domain)–0.06**(0.02)
0.03(0.02)–0.24***(0.03)
165

(5)
Gains
of
MoneyAdditional
Effort–0.04*(0.02)
0.02(0.02)–0.25***(0.03)
165

Notes: Standard errors in parentheses; *, **, and *** denote statisticalsignificance at the 10%, 5%, and 1% levels, respectively.

4.1.1. Sign-dependent Preferences. To examine sign-dependent preferences, weused our descriptive measures to compare the pleasant and unpleasant domains
within the monetary or effort choices to avoid any unnecessary confounds. The
findings are presented in Columns (1) and (2) of Table 4.
Participants displayed greater impatience for pleasant outcomescompared with unpleasant ones in the Money treatment (paired t-test, p = 0.002),
consistent with the sign effect (Frederick et al., 2002).
Sign-dependence was also observed in the Effort treatment (p = 0.03),
but surprisingly, participants exhibited greater impatience in the unpleasantdomain compared with the pleasant domain. This finding contradicts the signeffect and does not support Hypotheses 2 or 3. By contrast, no evidence of signdependent preferences in PB was found in either treatment (p = 0.62 in the Moneytreatment and 0.58 in the Effort treatment). Smooth in the unpleasant domain was30larger than in the pleasant domain in the Effort treatment (p = 0.006), but thedifference was not significant in the Money treatment (p = 0.22).

A: Money treatment
Impatience (ρ = 0.09)

PB (ρ = −0.14)

Smooth (ρ = 0.39)

PB (ρ = 0.77)

Smooth (ρ = 0.89)

B: Effort treatment
Impatience (ρ = 0.83)

FIGURE 5. Indices of time preferences based on descriptive measures in money(top) and effort (bottom).
Note: The values in the pleasant domain are shown on the x-axis, and the values
in the unpleasant domain are shown on the y-axis. The dots represent observations.
The red lines describe the fitted lines

Given that participants answered the questions in both pleasant andunpleasant domains, we can examine the relationship between these domains at
the individual level. Figure 5 shows the correlation between the two domains foreach index in both treatments. In the Money treatment, there is no clear
correlation for any of the indices between the two domains, suggesting that time31

preferences for monetary gains and losses are almost independent of each other.
By contrast, each index in the Effort treatment shows a strong positive correlation
between the two domains, indicating that preferences are similar across both
domains when it comes to effort choices. Furthermore, the values for each index
are much more dispersed for Effort, reflecting considerable heterogeneity in theeffort choices as noted above.

RESULT 2: While the sign effect finds empirical support in monetary choices, asurprising reversal of the sign effect emerges in effort choices, partially
supporting Hypothesis 2 but not supporting Hypothesis 3. No evidence of signdependent present bias was found in either type of choice.

4.1.2. Outcome-dependent Preferences. Next, we compare indices in monetary
choices with those in effort choices within either the pleasant or unpleasantdomains as shown in Columns (3) and (4) of Table 4.
Impatience between monetary and effort choices is not significantly
different in the pleasant domain (t-test, p = 0.4). By contrast, impatience inmonetary choices is significantly higher than in effort choices within theunpleasant domain (p = 0.003).

RESULT 3: While impatience levels in monetary and effort choices are statisticallyindistinguishable in the pleasant domain, monetary choices in the unpleasantdomain exhibit significantly higher impatience levels compared with effort
choices. Consequently, these findings do not lend support to Hypothesis 4, which
had posited greater levels of impatience in effort-based choices.

32

In addition, Smooth is higher for effort choices than for monetary choices(p < 0.001 in both domains). Importantly, we do not identify outcome-dependent
preferences in terms of PB in either the pleasant or unpleasant domains (p = 0.58
and 0.26, respectively).
In Column (5) of Table 4, we additionally compare preferences between the
Money-pleasant and Effort-unpleasant conditions as in Augenblick et al. (2015).
PB is not significantly different between these conditions, thereby failing tosupport Hypothesis 1. Impatience is higher (p = 0.04) and Smooth is significantly
higher (p < 0.001) in the Effort-unpleasant than in the Money-pleasant condition.
These results do not match the previous findings that there is a lower estimatedpresent bias β and a higher discount factor δ in the Effort-unpleasant condition

compared with the Money-pleasant condition (Augenblick et al., 2015).
4.2. Parameter Estimations

Next, we report the results of our parameter estimations of the quasi-hyperbolicmodel with CRRA utility function at the individual level. 11 Overall, the findings
are consistent with those based on descriptive measures reported above.

11

We conducted an aggregate analysis as well, which is presented in Appendix A.

33

FIGURE 6. Boxplots for each parameter from MLEs.
Note: The left and right edges of each box indicate the first (Q1) and third
quartiles (Q3), respectively. The line within the box represents the median.
Outliers, identified as values falling below Q1 minus 1.5 times the interquartile
range (IQR) or above Q3 plus 1.5 times the IQR, have been excluded inaccordance with Tukey’s rule.

The results are summarized in Figure . We found the medians of δ andβ to be one across the four conditions. The interquartile ranges are larger in the

Effort treatment than the Money treatment for all parameters, indicating a more
pronounced heterogeneity in the Effort treatment than in the Money treatment(Brown–Forsythe test; p < 0.001 in both the pleasant and the unpleasant domains).
The results suggest a greater prevalence of time-inconsistent behaviours indecisions involving effort.
In both treatments, the median value of α is negative in the unpleasantdomain, indicating that the utility function of our participants is concave (sign34

rank test; p = 0.02 in the Money treatment and p = 0.001 in the treatment Effort).
In the pleasant domain, the median values of α are positive but not significantly
different from 0 in both treatments (p = 0.21 in the Money treatment and p = 0.15

in the Effort treatment). The small interquartile range of α in the monetary
choices indicates little heterogeneity regarding utility curvature. These results forthe utility curvature are consistent with Abdellaoui et al. (2013) and Abdellaoui(2018).
We also tested for sign-dependent preferences using the sign-rank test.
First, δ is significantly different between the two domains for monetary choices(p = 0.02) and marginally different for effort choices (p = 0.07). However, β isnot significantly different between the two domains in both treatments (p = 0.97
in the Money treatment and 0.42 in the Effort treatment). The difference in α

between the two domains is not significant for monetary choices (p = 0.5), but
the difference is marginally significant for effort choices (p = 0.08).
The correlation between domains, in monetary choices, δ and β is notstrong ( ρ = 0.1 and –0.02, respectively). However, in effort choices, theseparameters show a moderate positive correlation ( ρ = 0.53 for δ and ρ =0.69 for β).

Next, we examine outcome-dependent preferences using the Mann–

Whitney test. α is significantly different between the two types of outcomes inboth domains (p = 0.003 in the Money treatment and p < 0.001 in the Effort
treatment), thereby confirming that utility curvature varies between monetary andeffort choices in the pleasant domain. δ shows a significant difference between
outcomes only in the pleasant domain (p = 0.02 for pleasant and p = 0.18 for
unpleasant). Finally, β exhibits no significant difference between outcomes ineither domain (p = 0.72 in the pleasant domain and 0.15 in the unpleasant domain).

35

4.3. Preference as a Predictor of Economic Behaviours
We now shift our focus to the relationship between specific indicators of time
preference—for both money and effort across pleasant and unpleasant domains—and behaviours outside of the experimental setting. These behaviours wereelicited through the questionnaire administered in Week 5, across varying
conditions and domains.
For our analysis, we selected those behaviours that have previously beenshown to correlate with experimentally measured time preferences, such as
alcohol use (Bradford et al., 2017; Sutter et al., 2013; Vuchinich & Simpson,1998), savings (Angeletos et al., 2001; Bradford et al., 2017; Sutter et al., 2013),
educational attainment (Duckworth & Seligman, 2005; Falk et al., 2018; Reed &Martens, 2011), and exercise (Bradford, 2010; Bradford et al., 2017). We also
included behaviours intuitively linked to time preferences but with nonsignificant correlations, such as the percentage of income saved (Chabris et al.,2008), as well as those with mixed results, BMI (Barlow et al., 2016; Cheung etal., 2022).
Bartels et al. (2023) examine a wide range of behaviours and suggest
their correlations with experimentally measured discount rates are frequently
modest and sometimes even negligible. A plausible reason for this limited
correlation is the methodology used both by Bartels et al. (2023) and many earlier
studies listed above. Specifically, these studies predominantly employ choices
involving monetary gains to measure time preferences. However, most real-worldintertemporal choices encompass diverse outcomes and domains, for example,
when choosing to exercise involves short-term effort for long-term health benefits.
As our analyses have demonstrated, time preferences are both outcomeand sign-dependent, and they display significant heterogeneity at the individual36

level. Therefore, some behaviours may correlate with experimentally measured
time preferences only when considering particular outcome choices within
specific domains.
We employ multiple linear regression to analyse behaviour, using theestimated time preference parameters δ and β as predictors and demographic
variables as controls. 12
Our analysis reveals notable correlations between measured time
preferences and behaviours such as eating habits, BMI, and academic
performance. Table 5 indicates that the patience parameter, δ, among Effortpleasant conditions, is negatively correlated with BMI. That is, individuals witha higher level of patience have smaller BMI. However, these parameters frommonetary choices show no significant correlation with BMI. This contrasts with
Cheung et al. (2022), who found that for money and unhealthy food, less patientindividuals, and those less present biased towards healthy food, had lower BMIs.
Furthermore, β in the Money-unpleasant condition is negatively correlated withbinge eating, a correlation not observed for β s estimated under the otherconditions. This suggests that individuals who are more inclined to under weighfuture payments are also prone to binge eating.
We also found that δ in effort-based decisions is positively correlatedwith grade point average (GPA), whereas its monetary counterpart is not. Thisobservation suggests that a greater level of patience in effort-related decisions isindicative of higher academic performance. This conclusion is not entirely

12

We employed age, age squared, and two dummy variables indicating female andgraduate students as our control variables.

37

aligned with previous results (e.g., Cheung et al., 2022) that show a positive
correlation between patience in monetary choices and grades.
Some behaviours, such as saving rates and health behaviours, showed
weak or no correlation with measured time preferences in our study. In addition,some findings are counterintuitive. For example, both δ and β in the unpleasant-monetary condition are negatively correlated with sports engagement. Moreover,
we observe a positive correlation between δ in the Effort-pleasant condition andalcohol-related behaviours. This result is incongruent with the study ofVuchinich and Simpson (1998), who demonstrated a significant relationshipbetween temporal discounting rates for financial rewards and heavy drinking.
These results imply that time preference parameters may not be robust predictors
for certain behaviours, aligning with the findings of Bartels et al. (2023), evenwhen outcome- and sign-dependence in time preferences are taken into account.

38

TABLE 5. Relationship between 𝛿𝛿 and 𝛽𝛽, and self-reported everyday behaviours.
Moneyunpleasant

Money-pleasant

Saving_rate

BMI

EffortunpleasantEffort-pleasant

35.22
1.67

(41.39)(20.60)

85.83
15.23

(81.05)(14.41)

2.04
-0.46

(1.60)(0.50)

0.30-0.16(2.15)(0.53)

-3.99
-2.63

(5.07)(1.92)-6.96
1.16(6.77)(1.69)-0.28***
0.06(0.10)(0.05)

-0.15
0.10*

(0.09)(0.05)

7.16
2.89(4.58)(1.97)-5.23-1.62**(3.95)(0.71)

0.35**-0.09(0.16)(0.06)

-0.18
-0.11

(0.25)(0.08)

𝛿𝛿
𝛽𝛽

1.02
-0.00

(1.96)(0.94)-3.06
0.80(4.62)(0.75)

0.33***-0.01(0.12)(0.03)

0.06-0.01(0.13)(0.02)

2.76-0.10(4.44)(2.59)

-5.75
-2.70

(6.36)(2.28)

0.63**
0.02(0.27)(0.04)

0.10-0.00(0.15)(0.04)

𝛿𝛿
𝛽𝛽

-1.46
-0.64

(1.26)(0.72)

0.14
0.05(0.89)(0.22)

0.06***-0.01(0.02)(0.01)

0.06*
-0.00

(0.03)(0.01)

𝛿𝛿
𝛽𝛽
𝛿𝛿
𝛽𝛽

Alcohol_days𝛿𝛿𝛽𝛽

Alcohol_ave

Alcohol_lot

GPA

𝛿𝛿
𝛽𝛽

Binge_eat

39

𝛿𝛿
𝛽𝛽

-1.82
-1.19

(2.51)(1.30)-1.31-0.88***(1.36)(0.21)

0.06-0.02(0.05)(0.02)

-0.04
-0.04

(0.07)(0.03)

0.38
0.18(1.08)(0.47)

2.13*
0.33(1.17)(0.33)

0.04-0.00(0.06)(0.02)

0.02-0.04(0.09)(0.02)-0.23
0.56(5.01)(2.75)

2.10-0.86(5.02)(1.26)-0.05
0.00(0.18)(0.04)-0.29
0.03(0.31)(0.05)

10.46
8.72(7.21)(5.95)-7.10**-2.12*(3.55)(1.17)

0.17-0.04(0.14)(0.05)-0.04
0.00(0.17)(0.05)

Binge_shop𝛿𝛿
𝛽𝛽Unhealthy𝛿𝛿𝛽𝛽Sport_often𝛿𝛿𝛽𝛽

Notes: Standard errors in parentheses; *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. The behavioural variables
were obtained from the survey conducted in Week 5. The term ‘Saving_rate’ refers to the proportion of the monthly salary saved; ‘Alcohol_days’ specifies
the number of days alcohol is consumed per month; ‘Alcohol_ave’ indicates the average number of glasses of alcohol consumed; ‘Alcohol_lot’ signifies
whether large quantities of alcohol are consumed per month (defined as five drinks for men and four for women); ‘Binge_eat’ and ‘Binge_shop’ represent
the average scores for binge eating and binge shopping, respectively, as measured by the Shorter PROMIS Questionnaire (Christo et al., 2003); ‘Unhealthy’
denotes whether respondents are unhealthy; ‘Sport_often’ indicates engagement in sports at least once per week. Ordinary Least Squares regressions are
conducted for all variables except for the dummy variables ‘Alcohol_lot’, ‘Unhealthy’, and ‘Sport_often’, for which logit regression analysis is performed.

40

5. Summary and Concluding Discussions
To explore the foundational differences in time preferences between monetaryand effort-related choices, we undertook a methodologically rigorous experiment.
To test our hypotheses related to outcome- and sign-dependence of time
preferences, this research employed a 2 × 2 experimental design that contrasts
two types of outcomes—money and effort—and two domains of experience—
pleasant and unpleasant. Drawing on the framework by Bosch-Domènech &Silvestre (2010), our design permitted the incentivization of all decision-making,even those involving future monetary losses. For operational efficiency, allmonetary transactions were done through a mobile payment system, thereby
minimizing delays and ensuring seamless transactions, akin to the methodology
employed by Andreoni et al. (2018).
Our detailed analysis using descriptive measures led to several key
findings. In divergence from the conclusions reached by Imai et al. (2021), ourstudy found no evidence of present bias on average across any of the four
experimental conditions. However, we did find a greater propensity for timeinconsistent behaviours in decisions involving effort. Our study also identified
sign-dependence in impatience regardless of the monetary or effort-related natureof the outcome. Interestingly, the direction of this effect differed between the two.
For monetary choices, we noted greater impatience in the pleasant domain,
consistent with the sign effect. Conversely, in the realm of effort-based choices,a reverse sign effect was observed, characterized by a higher level of impatiencein the unpleasant domain. The results imply that the sign effect may not beuniversally applicable across disparate types of outcomes.
Furthermore, our results indicate outcome-dependence, highlighting ahigher level of impatience in unpleasant monetary choices compared with their41

effort-based counterparts. Effort choices also demonstrated a proclivity forbalanced allocations—or a preference for smoothness—over monetary choices.
At the individual level, the correlation between pleasant and unpleasant domains
was notably stronger for effort choices than for monetary choices.
5.1. Absence of Present Bias Across Monetary and Effort Decisions
Our analysis revealed an absence of present bias in both monetary and effort
choices, which merits further discussion. The composition of our sample ofJapanese students might be a reason for this absence. Various studies haveindicated that individuals from Eastern cultures, such as Japan, tend to discountfuture rewards less than their Western counterparts (Du et al., 2002; Ishii et al.,2017; Kim et al., 2012; Takahashi et al., 2010). This implies a greater tendency
among Japanese individuals to wait for delayed rewards. However, the estimatedpresent bias in monetary choices in Sawada & Kuroishi (2015) aligns closely withthe average bias observed in a meta-analysis by Imai et al. (2021). 1 Consequently,
we did not find clear evidence to suggest that present bias is different in ourJapanese sample from that observed in other samples.
One might consider whether the initial amount of money or effort (2,000
units in Weeks 4 and 5) given to participants was disproportionately large
compared with the values in the subsequent options. The highest value among theoptions was 1,510, which is nearly the maximum possible value of 2,000.

1

The participants in Sawada and Kuroishi’s (2015) study resided in Iwanuma city,

Miyagi prefecture. The city suffered damage from the 11th March 2011 Great East Japan
Earthquake. Their research was carried out in May 2014. They found that the impact of
the earthquake-induced disaster damage on present bias was not statistically significant.

42

Therefore, we do not believe that the relative differences between the initialamounts and the values in the options significantly influenced the results. This isfurther substantiated by Abdellaoui et al. (2018) investigating time preferences inworking time, which detected a significant present bias even when the initialvalue was set at 4 hours (with options varying by up to ±3 hours). Moreover, ouradditional analysis supports the assumption that participants updated theirreference points, indicating they did not consider the initial 2,000 units in theirintertemporal decision-making process (refer to Appendix C for details).
5.2. Reverse Sign Effect in Effort Choices
In effort choices, participants exhibited greater impatience in the unpleasantdomain compared with the pleasant domain. This outcome contradicts the signeffect. Prior literature has consistently identified the sign effect across four
distinct outcomes—money, environment, health (Hardisty & Weber, 2009), andtime (Abdellaoui et al., 2018).
One theoretical framework that may shed light on the sign effect is
Molouki et al. (2019)’s contemplation-emotion explanation. This framework
attributes the sign effect to the heightened emotional impact that individuals
experience while anticipating losses, which serves as a catalyst for the effect. A
plausible explanation for our observation of a reverse sign effect may lie in the
distinct emotional weight carried by immediate, unpleasant tasks. These tasks are
vividly conceptualized, and the future emotional burden they impose may not be
as onerous for participants.
In addition, the emotional burden associated with immediate unpleasanttasks may be more potent than the more abstract notion of environmental
degradation, health deterioration, or monetary loss, leading participants to delaythese concrete aversive tasks. Notably, past research examining time preferences43

in environmental and health-related choices was not incentivized (Baker et al.,
2003; Chapman, 1996; Hardisty & Weber, 2009; MacKeigan et al., 1993).
Furthermore, participants may perceive immediate monetary losses as
recoverable through future adjustments in consumption, a notion corroborated by
Cohen et al. (2020), who emphasized the delayed nature of consumption inmonetary contexts. By contrast, our study required participants to exert actual
effort at certain points to complete the experiment.

44

Appendix
A. Pooled-Data Parameters Estimated by Maximum Likelihood Estimations(MLEs)
Table A.1 reports the MLEs for the Money and Effort treatments, using pooleddata. The four parameters of the time preferences are estimated for each model.
The estimated present bias β is not statistically different from 1 in all models in
the two tables (as shown by the Wald test reported in the last row). The result is
consistent with the findings based on our descriptive measures.
The models in the table present regression results of MLEs. Models 1 and
4 are results in the pleasant domain, Models 2 and 5 are the results in theunpleasant domain, and Models 3 and 6 include both domains. Models 3 and 6
include the ‘unpleasant’ dummy variable associated with unpleasant outcomes,
to assess sign-dependent preferences.
We find the discount factor 𝛿𝛿 is less than 1 in the pleasant condition in

the Money treatment (p =0.10, Model 1), but the parameter is not significantly
different from 1 in the Effort treatment (p = 0.30, Model 4). The utility curvature
α dose not significantly differ from 0 for both pleasant and unpleasant outcomesin the Money treatment (p = 0.20, Model 1; p = 0.11, Model 2, respectively). In
the Effort treatment, the value is not significantly different from 0 in theunpleasant condition (p = 0.11, Model 5), but is significantly negative in thepleasant condition (p = 0.02, Model 4)This suggests that, our subjects exhibited a linear utility function, except
for pleasant outcomes in the Effort treatment where we observed a convex utility

function. This result in this particular condition is not in line with previous studies,representing a unique feature of our methodology using CTB and MLE. It is45critical, however, to note that this deviation occurs at the aggregate level, and we
found that the majority of individuals indeed possess concave utility functions
when considering individual-level data as we discussed in the main analysis.
In the Money treatment, δ is significantly larger for the unpleasant outcomesthan for the pleasant ones (p = 0.002, Model 3). The results are consistent withthe findings of the descriptive measures. However, no sign-dependent preferencein terms of δ is found in the Effort treatment, whereas Impatience wassignificantly higher in the unpleasant domain in the descriptive measures (p =0.87). In addition, we do not find a significant difference for β between thepleasant and unpleasant domains in both treatments Money and Effort (p = 0.58
for both treatments). This finding is again consistent with the results of ourdescriptive measure.
TABLE A.1. The four parameters of time preferences by MLEs.
Money
(1)
Pleasant
0.98***(0.01)(2)
Unpleasant
1.00***(0.01)

β

0.99***(0.01)

1.01***(0.01)

α-0.10(0.08)

0.05(0.03)

σ

0.02**(0.01)

0.01**(0.01)

δ

Unpleasant

Unpleasant

Unpleasant

Unpleasant

LogLikeliho -2059.30
od
Cluster
89
N3204
p-value:0.103
δ=1p-value:0.404
β=1Effort(4)
Pleasant
1.16***(0.16)(5)
Unpleasant
1.04***(0.06)

1.15***(0.20)

1.09***(0.11)-0.86**(0.35)

0.21(0.13)

0.14*(0.08)

0.17**(0.07)-5068.75-5028.18(6)All
1.05***(0.09)-0.01(0.04)
1.10***(0.16)-0.03(0.05)-0.49***(0.12)
0.72***(0.15)
0.12**(0.06)
0.06(0.04)-10256.98-2053.95(3)All
0.98***(0.01)
0.02***(0.01)
1.00***(0.01)-0.00(0.01)-0.08*(0.05)
0.15***(0.06)
0.01***(0.00)
0.00(0.01)-4264.67

89
3204
0.532

89
6408
0.035**

76
2736
0.298

76
2736
0.495

76
5472
0.577

0.368

0.856

0.468

0.393

0.517

46

Notes: Clustered standard errors at the individual level are shown in parentheses;*, **, and *** denote statistical significance at the 10%, 5%, and 1% levels,
respectively.
TABLE A.2. The summary table of demographic and behavioural variables.
Variables

Money

Efforttp(4.31)–0.67(0.51)

1.29(3.92)–0.85(0.39)(1.43)

7.87(1.99)–1.48(0.14)

0.31(0.47)

0.42(0.50)–1.42(0.16)

Unhealthy

0.31(0.47)

0.25(0.44)

0.91(0.36)

Binge_eat

3.30(0.91)

3.34(0.98)–0.25(0.80)

Binge_shop

3.50(0.76)

3.52(0.88)–.13(0.89)

Mean

SD

Mean

SD

Alcohol_days

2.56(3.62)

2.97

Alcohol_lot

0.88(2.16)

Alcohol_ave

7.47

Sport_often

Notes: The term ‘Alcohol_days’ specifies the number of days alcohol isconsumed per month; ‘Alcohol_lot’ signifies whether large quantities of alcohol
are consumed per month (defined as five drinks for men and four for women);‘Alcohol_ave’ indicates the average number of glasses of alcohol consumed;‘Sport_often’ indicates engagement in sports at least once per week; ‘Unhealthy’
denotes whether respondents are unhealthy; ‘Binge_eat’ and ‘Binge_shop’
represent the average scores for binge eating and binge shopping, respectively, as
measured by the Shorter PROMIS Questionnaire (Christo et al., 2003).

47

B. Choice Consistency
Our participants demonstrated consistency to a similar degree as seen in previous
studies (e.g., Cheung et al., 2022). The pass rate of the Generalized Axiom of
Revealed Preference (GARP) test for the monetary decisions was extremely highin Weeks 3 and 4 (higher than 0.97 and 0.96 in the pleasant and unpleasantdomains, respectively). Afriat’s critical cost efficiency index (AEI), a measure ofrationalizability, was more than 0.99 in all cases (the AEI of a random chooser isabout 0.8). Cheung et al.’s experiment found the AEI for monetary gain decisions
to be 0.98, a result similar to ours.
By contrast, the pass rate for effort decisions was relatively low, generally around0.75. However, the AEI was above 0.96 in the two domains, suggesting that ourparticipants’ choices were generally consistent. Moreover, in the common choice
set between Sets 1 and 2 (R = 0.25 in both sets), more than 94% of participantsin the Money treatment and more than 82% in the Effort treatment chose the sameor a neighbouring bundle. The figure ranged between 80% and 83% in the study
by Cheung et al., 2 further supporting the conclusion that our participants’choices were sufficiently consistent. 3

2

Cheung et al. (2022) tested this with three outcomes (money and healthy and unhealthy

foods) in the two sets.
3

Our participants demonstrated a tendency to frequently select extreme endpoints.

Specifically, in pleasant (unpleasant) scenarios, they often chose options with the
maximum sooner (later) values at negative interest rates and the maximum later (sooner)
values at positive rates. In fact, a significant proportion of participants consistently madesuch choices: 79% (83% for unpleasant) in monetary choices and 28% (22% for
unpleasant) in task choices. This pattern of selection could potentially have influenced
the high pass rates observed in the GARP tests.

48

C. Robustness Check
Analysis without the assumption of updated reference points
In Week 1 of the experiment, participants in the Effort treatment committed totranscribing 2,000 numbers during Weeks 4 and 5. The intent behind this design
was to offer flexibility in task quantity, thereby creating a pleasant domain ineffort choices.
In the analysis presented in the main text, we operated under theassumption that participants used this 2,000-number commitment as a reference
point during their allocation decisions. Such an assumption is well-founded inexisting literature (see Augenblick et al., 2015 and Abdellaoui et al., 2018).
Furthermore, the seminal work by Kahneman and Tversky (1979) suggests that
individuals updated their reference points in choices involving risk.
However, it is possible that participants did not update their reference
points when making allocation decisions. That is, participants might haveconsidered the entire 2,000 numbers as the baseline for all tasks. For example, inthe unpleasant domain, the allocation question presented the options of adding
675 numbers in Week 4 and 250 in Week 5. However, participants could haveintegrated these quantities into the initial 2,000-number commitment, effectively
considering tasks of 2,675 numbers in Week 4 and 2,250 in Week 5.
To assess the robustness of our initial assumptions, we re-estimate thetime preference parameters using MLEs without assuming that reference points
have been updated. We then compare the model fit of this revised approach withthe original model to gauge the validity of our initial assumptions.
Table C.1 presents model-fitting indices, namely the Akaike information
criterion (AIC) and Bayesian information criterion (BIC), for regression models
49both with and without the reference point assumption. Across all model fittings,
the indices favour the model that incorporates the reference point assumption.
This lends credence to our initial assumption, suggesting that participants arelikely to have updated their reference points when making allocation decisions.

TABLE C.1. Regression analysis with and without the assumption of reference
point.
RP
RP
RP
No
RPNo
RPNo
RP
Assumption Assumption Assumption Assumption Assumption Assumption-0.42***
0.25***-0.42***
0.90***
1.41***
0.39***
α(0.10)(0.06)(0.10)(0.16)(0.43)(0.11)
Unpleasant
0.67***(0.15)
1.04***
1.03***
1.04***
1.05***
1.03***
1.04***
β(0.07)(0.03)(0.07)(0.07)(0.03)(0.05)
Unpleasant-0.02(0.05)
1.00***
1.00***
1.00***
1.02***
1.00***
1.01***
δ(0.04)(0.02)(0.04)(0.04)(0.02)(0.03)
Unpleasant-0.01(0.03)
DomainPleasant
Unpleasant Both
Pleasant
Unpleasant Both
LogLikeliho -5226.71-5116.24-10342.94 -5314.36-5204.94-10698.05
odCluster
76
76
76
76
76
76
N
2736
2736
5472
2736
2736
5472
AIC
10461.41
10240.47
20701.88
10636.71
10417.88
21404.10
BIC
10485.0710264.1320754.74
10660.37
10441.53
21430.53

Notes: Standard errors in parentheses; *, **, and *** denote statisticalsignificance at the 10%, 5%, and 1% levels, respectively.

50

FIGURE A.1. English translation of the screenshots of the allocation decisions in
Week 4: unpleasant-effort condition with R = 0 (top) and 0.07 (bottom).

51

FIGURE A.2. English translation of the screenshots of the allocation decisions in
Week 4: pleasant-effort condition with R = 0 (top) and 0.07 (bottom).

52

FIGURE A3. English translation of the screenshots of the allocation decisions in
Week 4: unpleasant-money condition with R = 0 (top) and 0.07 (bottom).

53

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