Discussion Papers In Economics
And Business

How Does Opt-in Work? A Field Experiment on Financial Incentives
for Physical Activity

Hirofumi Kurokawa and Shusaku Sasaki

Discussion Paper 23-01

February 2023

Graduate School of Economics
Osaka University, Toyonaka, Osaka 560-0043, JAPANHow Does Opt-in Work? A Field Experiment on Financial Incentives for Physical Activity*
Hirofumi Kurokawaa

Shusaku Sasakib

February 1, 2023
Abstract
We examined the effectiveness of two different schemes for providing financial incentives to promote
physical activity. We collaborated with a local government in Japan to conduct a field experiment with
498 residents randomly assigned to participate in one of three groups: an “opt-in” group (needed toapply to receive the incentive based on their daily steps), an “opt-out” group (received the incentive
by default, but could request not to receive it), and a control group (no incentive). In the opt-in group,
31.1% of the participants applied to receive the incentive, while 100% of those in the opt-out groupretained the default option and received it, indicating that their take-up rates depended heavily on thedefault settings. Our estimation results suggest that providing financial incentives in the opt-in scheme
can be effective and efficient. The opt-in group, overall, showed a statistically significant increase of
approximately 710 steps per day during the first half of the treatment period, and the number of steps
was estimated to increase by 2,280 among the 31.1% of participants who opted-in to receive the
incentive. The opt-out scheme did not show any significant increase in their number of daily steps.
JEL classification: C93, D90, I12
Keywords: field experiment, default, monetary incentive, health behavior, walking* We would like to thank Takanori Ida, Ryota Nakamura, Rei Goto, Toshiaki Iizuka, and the seminar

participants at the ABEF’s 15th annual conference 2021, 2022 Virtual Asia Pacific ESA Meeting
OSAKA, JEA’s 2022 Spring Meeting, and the 17th Annual Conference of JHEA for their helpfulcomments. We thank the city of Kobe for collaborating in this study. We declare no conflict of interest.
This study was approved by the Ethics Committee of the School of Economics and Management,
University of Hyogo, and the Research Ethics Review Committee for Kobe City Health Services (No.
1382). This study has been pre-registered through the AEA RCT Registry (RCT ID AEARCTR0006964). We gratefully acknowledge financial support from Urban Innovation Kobe (A20103). In
preparing the manuscript, Sasaki was financially supported by Japan Science and Technology Agency(JPMJPR21R4).
a School of Economics and Management, University of Hyogo. E-mail: kurokawa@em.u-hyogo.ac.jpbCenter for Infectious Disease Education and Research (CiDER), Osaka University. E-mail:ssasaki.econ@cider.osaka-u.ac.jp
11. Introduction
Most traditional policy research using randomized controlled trials has measured the causal effects
of mandatory policy assignment. However, implementing a policy intervention in a mandatory manner, as isdone in academic research, is rare in the real world. This is because mandatory implementation requires asystem that enables a policy to be applied to all individuals involved and monitors their adherence to it. In
addition, a policy must be made mandatory by law, and implementation costs tend to be extremely high. In
practice, policies are often applied to only those who choose to accept them, and the implementation costs
will be relatively small. This can be accomplished in two ways. One is the “opt-out” method, in which a
policy is applied to everyone by default, but individuals are allowed to opt-out upon request. The other is the“opt-in” method, in which a policy is not applied by default, but rather only upon request.
Under conditions with self-selection, overall policy impacts will vary depending on theheterogeneous effects across individuals and which individuals self-select to receive the policy. For example,
if a policy is widely accepted by people for whom a large (or significant) positive policy effect appears, theoverall policy impact will become larger than if the policy intervention was mandated, and thus the policy
function more efficiently due to self-selection. Conversely, if those who are likely to experience small or
negative effects choose to receive a policy intervention, the overall policy impact will become relatively
small, and self-selection will prevent it from functioning efficiently.
Further, compared with opt-out schemes, opt-in schemes require people to incur procedural costs toreceive policy interventions, and therefore generally have lower application rates (Madrian and Shea 2001;
Johnson and Goldstein 2003); thus, who choose to receive the policy intervention could differ between thetwo schemes. To accurately understand the real-world implications of policy interventions, it is essential toascertain the influence of self-selection on policy efficiency and how that influence differs across selfselection methods (e.g., opt-out and opt-in schemes). Recent field experimental studies have begun tomeasure policy intervention effects after accounting for self-selection, particularly in electricity markets(Wang et al. 2020; Fowlie et al. 2021; Ito et al. 2021; Ida et al. 2022).
The present study adds new evidence to this emerging literature stream. We collaborated with a local
government in Japan to conduct a field experiment among the general public and measure the policy
intervention effects of offering financial incentives on increasing physical activity, while taking selfselection into account. Specifically, we evaluated the impact of financial incentives on people’s daily step
counts under two schemes in which they could either opt-out of or opt-in for a program providing financial
incentives linked to their daily step counts.
From a health policy perspective, daily physical activity is important for the general population
because it helps them maintain good health (Reiner et al. 2013; Warburton and Bredin 2017; Warburton etal. 2006). Governments often employ programs that provide financial incentives to promote physical activity(Mitchell et al. 2020). The effectiveness of such financial incentive programs is supported by robust evidence,
particularly for deterring addictive behaviors such as smoking (Notley et al. 2019). Over the short-term,
financial incentives can encourage daily physical activity, such as walking; however, the linkage between
financial incentives and habit formation or long-term health improvement remains under debate (Barte and2

Wendel-Vos 2017; Mantzari et al. 2015), and thus experimental investigations continue. Further, as noted
above, forcing the general public to engage in routine physical activity or accept policy interventions toencourage such activity is difficult in the real world. Therefore, when measuring the impact of a programthat provides financial incentives for physical activity, considering whether the general population is willingto accept those incentives is essential.
Introducing a self-selection process could influence the observed effects of financial incentives on
physical activity. If individuals who would significantly and continuously increase their daily steps counts
by receiving the incentives mainly self-select to receive them, the program will have a large overallpromotion effect. Conversely, if those whose daily steps would not either increase or decrease by receiving
incentives mainly agree to receive them, the program’s effectiveness and efficiency will be deteriorated.
Investigations on how the opt-in self-selection process influences policy effectiveness and efficiency havebegun also in medical- and health-related fields (Finkelstein and Notowidigdo 2019).
This study’s novelty is that it experimentally evaluates the effects of two self-selection schemes,
opt-out and opt-in, on physical activity. We conducted a field experiment using a mobile healthcare
application during February and March 2021. Each participant was randomly assigned to one of three groups:
an opt-out group, an opt-in group, and a control group. We set a two-week treatment period, in which the
opt-out and opt-in groups had an opportunity to receive a financial incentive, while the control group did not.
The opt-out group received the incentive by default, but could decline the offer in advance. The opt-in group
was required to request the incentive in advance if they wished to receive it. No group received any financial
incentives during either the pre-treatment period or the two-week post-treatment period.
First, we found a significant difference in the take-up rates for financial incentives between the optout (100%) and opt-in (31.1%) groups. This suggests that the default options strongly influenced whether
participants received the incentives. Second, we found a statistically significant increase in daily step counts
from the opt-in scheme, but not the opt-out. Those in the opt-in group, on average, showed an estimatedincrease of approximately 710 steps per day during the first half of the treatment period (intention-to-treat[ITT]). Further, the 31.1% of participants who opted-in showed an increase of approximately 2,280 steps(local average treatment effect [LATE]). Those in the opt-out group showed no significant increase in their
number of daily steps. The results indicate that the opt-in scheme can be more efficient in offering financial
incentives. Third, we found that the positive effect of the opt-in scheme on daily step counts was short-term,
and was observed only during the first half of the treatment period. The effect did not reappear after thefinancial incentive ended. Further, we found no spillover effects, such as a treatment effect on body weight.
Our study contributes to two literature streams. First, we add evidence from the health field to theemerging literature on defaults, self-selection, and follow-on performance. Default settings are well-known
to influence various economic choices (Jachimowicz et al. 2019), such as retirement savings plans (Madrianand Shea 2001; Choi et al. 2002; Thaler and Benartzi 2004), organ donation (Johnson and Goldstein 2003;
Abadie and Gay 2006; Arshad et al. 2019), car insurance (Johnson et al. 1993), health insurance (Johnson etal. 2013), and energy saving (Wang et al. 2020; Fowlie et al. 2021; Ito et al. 2021; Ida et al. 2021). Recent
studies have explored how default settings target people through self-selection and influence consequent3

performance. For example, in a comparison between two electricity pricing programs, time-of-use andcritical-peak pricing (CPP), Fowlie et al. (2021) found that an opt-out scheme with CPP as the default
resulted in higher acceptance of CPP than did an opt-in scheme, and reduced electricity consumption.
However, the choices accepted in opt-out schemes do not always result in subsequent optimal economic
behavior. In a comparison between flat-rate pricing and CPP, Wang et al. (2020) showed that an opt-in CPP
scheme was more effective in saving electricity, compared with an opt-out scheme with CPP as the default.1
Second, our evidence adds new insight to the literature on how to incentivize health behaviors. The
benefits of health behaviors appear in the future; however, the costs are incurred when the behavior is
performed, which will likely lead to underinvestment. Providing incentives to lower the costs of current
behaviors has been shown to be effective (Volpp et al. 2008; Charness and Gneezy 2009; Cawley and Price
2013; Acland and Levy 2015; Royer et al. 2015; Carrera et al. 2020). Some meta-analyses have shown that,at least in the short term, financial incentives are also effective for encouraging physical activity, including
daily walks (Mitchell et al. 2020). Previous studies have focused on developing incentive structures, such asincreasing price rates rather than keeping the incentive constant based on daily step counts (Bachireddy etal. 2019) and loss contracts, in which incentives are confiscated if the target number of steps is not reached(Adjerid et al. 2022). Other studies have used random and mandatory assignments of some incentive
programs: however, the present study focused on how incentives are provided, rather than on incentive
structure. We confirmed the positive effect on daily step counts of an opt-in scheme that allows people toself-select their participation in an incentive program, suggesting that introducing self-selection through an
opt-in scheme when providing financial incentives can enhance treatment effects.
The remainder of this paper is organized as follows. Section 2 describes our experimental designand estimation strategy. Section 3 presents our main results, and Section 4 reports on the tests conducted tocheck the robustness of those results. Section 5 comprises the concluding statements.

2. Methods
2.1. Experimental Design
We collaborated with the city of Kobe, Japan, to conduct a field experiment on daily walking activity
among city residents during the winter of 2020–2021. This experiment provided a treatment offering
financial incentives through either an opt-out or opt-in scheme based on daily step counts. Kobe is a largecity with a population of over 1.5 million and one of the 20 ordinance-designated cities in Japan. Several
private companies, including foreign-affiliated firms, have headquarters in Kobe.
To improve people’s health, Kobe provides a mobile healthcare management application that offers
health-related information to residents and employees in the city. Users can link the app to their smartphone
to automatically record their daily step counts. They can also record their body weight in the app for self-

Some studies suggest that providing policy interventions in an opt-out scheme can have inefficient results
for retirement savings (Beshears et al. 2022; Choukhamane 2019).
4
1

management.2 At the beginning of the experiment, approximately 6,000 people had registered to use theapp.
We targeted users of the app, and from December 1–14, 2020, we contacted existing users via app
notifications, emails, and postcards to invite them to join the experiment. In addition, to include a wide range
of individuals, we also recruited new users through Kobe’s social media accounts and invited them toparticipate. In total, 660 individuals completed the participation process. Participants also completed aquestionnaire regarding information such as socioeconomic status, health status, need for financial incentives,
whether to set daily step count goals, and frequency of app use. Our analysis excluded users who were under
19 years of age, entered nonexistent IDs, or did not record their exact number of steps in the app (see Section
2.2 for details). Following this procedure, data from 498 individuals were ultimately used for our analysis.
Our experiment was a randomized controlled trial conducted with individuals who agreed to participate in
the experiment, which is common in health behavioral studies with the general public (Charness and Gneezy
2009; Acland and Levy 2015; Royer et al. 2015; Adjerid et al. 2021; Carrera et al. 2020; Giuntella et al.
2022). Therefore, the external validity of the sample is an important question, which we explore below in
Section 2.2.
We stratified randomization based on step counts at the time of entry and need for financial
incentives, and assigned the 498 participants to one of three groups as follows:
Control group (n = 174): Participants assigned to this group received 700 JPY (approximately 6.80 USD,
based on the exchange rate at that time of 103 JPY to 1 USD) for participation and agreed to provide
step count data.
Opt-out group (n = 160): Participants assigned to this group received 700 JPY for participation and agreed
to provide step count data. Participants in this group automatically received a financial incentive
based on their daily step counts. They could decline the incentive by submitting an application inadvance; however, no participants opted out in the current experiment.
Opt-in group (n = 164): Participants assigned to this group received 700 JPY for participation and agreed
to provide step count data. Participants in this group who wished to receive the incentive wererequired to apply in advance, and only those who completed the process were eligible. The opt-in
rate in this group was 31.1% (51 out of 164 participants).
We provided financial incentives based on daily step counts from February 8–21, 2021, for allparticipants in the opt-out group and those who completed the application procedure in the opt-in group.
Specifically, 5 JPY was offered for every 1,000 steps, with a daily cap of 70 JPY (for 14,000 steps), enabling
participants to earn up to 980 JPY (approximately 9.5 USD) in rewards over the two-week treatment period.
People can earn “health points” by using this app, independent of the financial incentives provided in thisexperiment. For example, on completing 10,000 steps per day, people can earn 5 points. The points can beexchanged for rewards (e.g., 150 points = one gym ticket). In this experiment, all participants, regardless ofgroup, had the opportunity to earn these health points. We measured the effect of the financial incentive inthis experiment by offering a random financial incentive, conditioning on the impact of health points.
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Previous studies have provided incentives for walking activities themselves (e.g., daily step counts;
Bachireddy et al. 2019) or reaching a goal (e.g., 10,000 steps per day; Adjerid et al. 2022). We employed theformer method because this study’s purpose was to examine the impact of different schemes of providing
financial incentives for daily step counts rather than of goal-setting and commitment toward achieving acertain number of step counts. Among the studies employing the former method, for example, Adams et al.
(2013) provided financial incentives according to increases from each individual’s step count at baseline;
however, we did not adopt a similar approach and instead provided the incentives according to their daily
step counts during the treatment period for two reasons. First, the previous approach could lead participants
to manipulate the performance by intentionally lowering their baseline step count to obtain a higher financial
incentive. Second, when implementing financial incentive programs for the general public, providing
different monetary amounts based on individual baseline step counts is costly, and implementing a uniform
policy intervention is generally easier. Our interest is in whether combining such a simple policy intervention
and self-selection can successfully target those for whom financial incentives are more likely to increase
their step counts.
The overall period of this experiment was February 1–March 7, 2021, including the treatment periodof February 8–21. On January 26, 2021, participants in all groups were informed via the app that the walking
event would begin on February 1. On January 29, participants in the opt-in group received a message
informing them that they need to submit an application via online until January 31 to receive the financial
incentive. Participants in the opt-out group also received a message on the same day regarding the procedure
to refuse to receive the incentive. Outside of the treatment period (i.e., February 1–7 and February 22–March7), every Monday, participants in all groups received simple messages encouraging them to walk (Figure
A1a). During the treatment period, participants in the control group or those in the opt-in group who did notwish to receive the incentive received similar simple messages. Participants in the opt-in and opt-out groups
who wished to receive the incentive were sent a message regarding how they could obtain the incentive andhow the incentive was calculated (Figure A1b).3
We defined the step counts during December 22, 2020–January 3, 2021, as the baseline step counts
prior to the experiment, because the step counts from January 4–February 7, 2021, just prior to the treatment
period, may have been affected by the declaration of a state of emergency during the COVID-19 pandemic
and experimental preparations. Initially, we intended to start this experiment on January 4; however, owing
to a rapid increase in the number of people infected with COVID-19, the government declared a state of
emergency in Kobe, and residents were asked to refrain from going out unnecessarily and adhere to infection
control measures. After discussions with Kobe officials, we decided to change the original schedule of theexperiment, and communicated this change and its reason to the participants. Thereafter, we and Kobe
officials considered that physical activity to maintain and improve people’s health is essential even during

In the post-experiment period (i.e., March 8–21), the financial incentive was also offered to the control
group in an opt-out scheme to ensure fairness. During this period, participants in the control group were able
to earn a monetary incentive of 5 JPY for every 1,000 steps each day. They could decline the incentive bycompleting the opt-out procedure between March 8–10, but the actual opt-out rate was still 0%.
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the pandemic, decided to begin the experiment on February 1, and conveyed the updated schedule toparticipants on January 26.4 The declaration of a state of emergency and schedule changes may have affectedparticipants’ walking habits in various ways, resulting in unusual trends. In addition, on January 29, different
messages regarding the procedure for receiving financial incentives were sent to the opt-out and opt-ingroups, as mentioned above. These communications may have had different effects on daily step counts
across groups between February 1–7. For example, participants who wanted to receive higher financial
incentives during the treatment period may have increased their steps prior to the treatment period tostrengthen their walking habits and fitness level. We ruled out these effects to the extent possible by defining
the baseline step counts as those obtained from December 22, 2020–January 3, 2021.
2.2. Data
Our analysis included data from 498 participants (174 in the control group, 160 in the opt-out group,and 164 in the opt-in group). We conducted a balance check using the step counts at the time of entry andanswers to the entry survey and determined that the three groups are homogeneous. As Table 1 shows, wefound no statistically significant differences among the three groups in relation to sociodemographic
variables (e.g., age, gender, education, and income), health-related variables (e.g., height, weight, andpersonal goal), or the step counts at the time of entry.
Using the following procedure, we restricted our sample to 498 out of the 660 individuals fromwhom we obtained consent to participate. First, we excluded one underage user and two users who provided
nonexistent IDs in the entry survey. Second, we excluded 74 users whose apps were not properly linked totheir smartphones and whose steps were not recorded in the app. During the entry survey and the experiment,
we attempted to reduce the number of excluded users by guiding them several times on how to link the app
to their smartphones. Third, we excluded 85 users who, although their apps were linked to their smartphones,
were more likely to record inaccurate step counts. For example, users who did not always carry a smartphone
or wear a smartwatch, or who did not regularly open the app, were more likely to record extremely low or
missing step counts. Although we also repeatedly reminded participants of the need to consistently carry asmartphone or wear a smartwatch during the experiment, we observed that older adult users in particular did
not always do so. Therefore, we excluded older adult users and restricted our analysis sample to adults
between the ages of 20 and 64 years. Adults within that age range are expected to work during weekdays,
leading to differences in their walking habits between weekdays and weekends; therefore, we focusedparticularly on weekday data.
Even in the restricted analysis sample, daily step counts were sometimes missing for various reasons.
Missing step counts were observed also in the literature on promoting physical activity where step counts
were measured with a smartphone or smartwatch (Bachireddy et al. 2019; Aggarwal et al. 2020; Adjerid etal. 2022). In the current study, we basically imputed zero step to the missing data. This imputation approach
may undermeasure observed step counts, compared to true step counts, and underestimate the treatment

4

The state of emergency was lifted on February 28, 2021.
7

effect of financial incentives. Thus, if we found a significant promoting effect even under such a condition,
it could be strong evidence. We also used other approaches to address missing data for robustness checks.
As noted above, our experiment was a randomized controlled trial conducted with individuals who
agreed to participate; thus, the external validity of the sample was an important question. To investigate thisquestion, we compared the gender and age structure between of all the app users and of the analyzed sample.
Of the approximately 6,000 app users, 56.4% are female, while 56.6% of the analysis sample is female. The
age structures of the app users and the analysis sample, respectively, were as follows: 7.5% and 8.0% were
between 20 and 29 years old, 16.3% and 19.7% were between 30 and 39, 25.4% and 28.3% were between
40 and 49, 28.3% and 32.7% were between 50 and 59, and 16.0% and 11.2% were between 60 and 69. These
comparisons suggest that the experimental sample has a similar gender and age structure to the app user
population.
2.3. Participants Who Opted-in
No participants declined to receive the incentive in the opt-out group5; however, 51 participants inthe opt-in group completed the process to receive the incentive. Thus, the opt-out and opt-in groups had takeup rates of 100% and 31.1%, respectively. This difference in take-up rates suggests that participants’ choices
were strongly influenced by the default options available. This study’s opt-in rate (31.1%) could beconsidered reasonable, given those in previous studies and the characteristics of the incentive measures inthis study. For example, in Ito et al.’s (2021) field experiment on electricity markets, the CPP opt-in rate
ranged from 16% (without information provision) to 31% (with information provision). Comparatively, the
opt-in rate in this study was somewhat higher. Unlike with increases or decreases in electricity prices,
participants in this study received additional financial incentives based on their daily step counts, but were
not fined and did not lose money. This is thought to have contributed to the high opt-in rate.
Table 2 shows the differences in characteristics between individuals who opted-in (n = 51) and those
who did not (n = 113). No significant differences were observed in socioeconomic characteristics, such as
age and gender, or health status, such as body weight and subjective health, between both groups. However,individuals who had higher step counts at the time of entry and those who used the app more frequently weremore likely to opt-in. This may reflect that individuals who were health-conscious by nature were attracted
to the financial incentive. Those who answered that they participated in this event for obtaining the incentive
were also more likely to opt-in, suggesting that the incentive may have triggered the intention to walk amongsome participants.
2.4. Estimation Strategy
We use a difference-in-differences (DID) approach to estimate treatment effects on participants’daily step counts—our main outcome. We assume strong ignorability, in which the treatment assignment is

The part of the registration site where participants could decline the incentive was accessed 14 times intotal. Therefore, it was unlikely that participants were unable to opt out because they could not access the
site.
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independent of other observable and unobservable variables that affect daily walking, and this assumption
is satisfied by our successful random assignment. We also assume the stable unit treatment values assumption(SUTVA), in which a participant’s daily walking is affected by whether the individual participant chose the
incentive, not by whether other participants chose the incentive. If a participant walked with otherparticipants, this assumption might not be valid; however, we told participants that this was an individualcentric walking event, and the government had requested that people avoid social contact to prevent the
spread of infection. Thus, it is expected that many participants walked alone, and we can assume that the
SUTVA is validated.
First, we identify the effect of treatment assignment on daily step counts. We use data from thebaseline and treatment periods and estimate two sets of DID regression equations with each set of the control
group and one of the two treatment groups:
𝑆𝑡𝑒𝑝𝑠𝑖𝑡 = 𝛼0 + 𝛼1 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐴𝑠𝑠𝑖𝑔𝑛𝑚𝑒𝑛𝑡𝑖 × 𝑊𝑒𝑒𝑘1𝑡 + 𝛼2 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐴𝑠𝑠𝑖𝑔𝑛𝑚𝑒𝑛𝑡𝑖 × 𝑊𝑒𝑒𝑘2𝑡+𝛼3 𝐼𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙𝑖 × 𝐷𝑎𝑦_𝑜𝑓_𝑤𝑒𝑒𝑘𝑖𝑡 + 𝛼4 𝐷𝑎𝑦𝑡 + 𝜀𝑖𝑡 , (1)where 𝑖 and 𝑡 denote individual and day, respectively. 𝑆𝑡𝑒𝑝𝑠𝑖𝑡 is the number of steps walked byindividual 𝑖 on day 𝑡. The 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐴𝑠𝑠𝑖𝑔𝑛𝑚𝑒𝑛𝑡𝑖 indicator takes the value 1 for the opt-out or opt-ingroup and 0 for the control group. The 𝑊𝑒𝑒𝑘1𝑡 (𝑊𝑒𝑒𝑘2𝑡 ) indicator takes the value 1 in the first (second)week of the treatment period and 0 otherwise. The coefficients of interest are 𝛼1 and 𝛼2, which capture theaverage difference in daily steps across the treatment and control groups. 𝐼𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙𝑖 × 𝐷𝑎𝑦_𝑜𝑓_𝑊𝑒𝑒𝑘𝑖𝑡
is an interaction term between individual 𝑖 and the day of the week, which represents a fixed effect forindividual 𝑖 and the day of the week. 𝐷𝑎𝑦𝑡 is a fixed effect for each day. We use cluster standard errors at
the individual level to account for serial correlation for an individual.
All participants in the opt-out group accepted the financial incentive. Thus, the opt-out treatment
assignment effect estimated in Equation (1) is consistent with the average treatment effect (ATE). In contrast,
only 31.1% of those in the opt-in group requested the incentive. The opt-in treatment assignment is not fully
consistent with the receiving incentive status; thus, the estimated opt-in treatment assignment effect becomesthe intention-to-treat (ITT).
Second, we use the data from the opt-in and control groups, estimate a DID-instrumental variable(IV) specification, and identify the local average treatment effect (LATE) of signing up for the opportunityof receiving the financial incentive on daily step counts, which is the treatment-on-treated (TOT). Here, we
assume the exclusion restriction, where our treatment assignment does not directly affect daily step counts
while indirectly affecting the counts through the effect of signing up for the opportunity of receiving the
incentive, and our randomized control trial confirms this assumption. In addition, we assume monotonicity,where our treatment assignment weakly increases and never decreases the likelihood of receiving the
incentive, and the actual take-up rate confirms this assumption. Specifically, we use the opt-in treatment
assignment as the instrument and estimate the following DID-IV specification:
𝑅𝑒𝑐𝑒𝑖𝑣𝑖𝑛𝑔 𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒𝑖𝑡 = 𝛽0 + 𝛽1 𝑂𝑝𝑡𝑖𝑛 𝐴𝑠𝑠𝑖𝑔𝑛𝑚𝑒𝑛𝑡𝑖 × 𝑊𝑒𝑒𝑘1𝑡 + 𝛽2 𝑂𝑝𝑡𝑖𝑛 𝐴𝑠𝑠𝑖𝑔𝑛𝑚𝑒𝑛𝑡𝑖 × 𝑊𝑒𝑒𝑘2𝑡+𝛽3 𝐼𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙𝑖 × 𝐷𝑎𝑦_𝑜𝑓_𝑤𝑒𝑒𝑘𝑖𝑡 + 𝛽4 𝐷𝑎𝑦𝑡 + 𝜀𝑖𝑡, (2)9̂̂𝑆𝑡𝑒𝑝𝑠𝑖𝑡 = 𝛾0 + 𝛾1 𝑅𝑒𝑐𝑒𝑖𝑣𝑖𝑛𝑔
𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒𝑖 × 𝑊𝑒𝑒𝑘1𝑡 + 𝛾2 𝑅𝑒𝑐𝑒𝑖𝑣𝑖𝑛𝑔
𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒𝑖 × 𝑊𝑒𝑒𝑘2𝑡+𝛾3 𝐼𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙𝑖 × 𝐷𝑎𝑦_𝑜𝑓_𝑤𝑒𝑒𝑘𝑖𝑡 + 𝛾4 𝐷𝑎𝑦𝑡 + 𝜀𝑖𝑡. (3)The 𝑅𝑒𝑐𝑒𝑖𝑣𝑖𝑛𝑔 𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒𝑖𝑡 indicator takes the value 1 for participants who signed up to receive the
incentive during the treatment period and 0 otherwise. The 𝑂𝑝𝑡𝑖𝑛 𝐴𝑠𝑠𝑖𝑔𝑛𝑚𝑒𝑛𝑡𝑖 indicator takes the value
1 for the opt-in group and 0 for the control group. Equation (2) is estimated as the first step, and a two-step̂estimation is performed by fitting the estimated predicted value 𝑅𝑒𝑐𝑒𝑖𝑣𝑖𝑛𝑔
𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒𝑖 to Equation (3).
𝑆𝑡𝑒𝑝𝑠𝑖𝑡 , 𝑊𝑒𝑒𝑘1𝑡 , 𝑊𝑒𝑒𝑘2𝑡 , 𝐼𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙𝑖 , 𝐷𝑎𝑦_𝑜𝑓_𝑤𝑒𝑒𝑘𝑖𝑡 , and 𝐷𝑎𝑦𝑡 are defined as in Equation (1). The
coefficients of interest are 𝛾1 and 𝛾2 , which capture the average increase in daily step counts amongindividuals who received the incentive.
Third, we use the data from the opt-out and opt-in groups, estimate a similar DID-IV specification,and identify the LATE of receiving a financial incentive on daily step counts for passive joiners who received
it under the opt-out condition but not under the opt-in condition, which is the treatment-on-untreated (TOU).
Based on Fowlie et al. (2021), our experimental design can classify the participants as active joiners, passive
joiners, and active leavers. Active joiners are those who retained the incentive offer in the opt-out group andapplied to receive the incentive offer in the opt-in group. Passive joiners are those who did not actively
decline the incentive offer in the opt-out group and did not actively accept the incentive offer in the opt-ingroup. Active leavers are those who opted out of the incentive offer in the opt-out group and did not apply
to receive the incentive offer in the opt-in group, although we did not observe them in this experiment. Thus,
participants in the opt-out group who accepted the incentive comprise active joiners and passive joiners,
while participants in the opt-in group who applied to receive the incentive are all active joiners. Taking thedifference between the opt-out and opt-in groups, we can estimate the treatment effects for passive joiners.
We use the data from the opt-out and opt-in groups and estimate Equations (2) and (3), where all variables
are defined as above, except the IV 𝑂𝑝𝑡𝑖𝑛𝑖 . Here, the IV is 𝑂𝑝𝑡𝑜𝑢𝑡𝑖 , which is an indicator variable equal
to 1 for the opt-out group and 0 for the opt-in group.
3. Basic Analysis
Columns (1) and (2) in Table 3 show the estimation results for treatment assignments. Column (1)uses the sample comprising the control and opt-out groups. The coefficients on 𝑊𝑒𝑒𝑘1 (𝑊𝑒𝑒𝑘2 ) ×
𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐴𝑠𝑠𝑖𝑔𝑛𝑚𝑒𝑛𝑡 capture the average difference in daily steps across the opt-out and control groups
in the first (second) week of the treatment period. In the first week, the daily step count for the opt-out groupwas 541 steps higher than that for the control group, but this was not statistically significant. In the second
week, the daily step count for the opt-out group was 287 higher than that for the control group, but this was
also not statistically significant. Although all participants in the opt-out group accepted the incentive, we did
not find significant evidence that offering incentives in the opt-out scheme increased daily step counts on
average.
In contrast, we found a significant increase in daily step counts for the opt-in group. Column (2)shows that, in the first week of the treatment period, the daily step count for the opt-in group was 709 steps
higher than that for the control group (p < .05). However, this increase and its statistical significance10

disappeared in the second week of the treatment period. The take-up rate of the incentive is 31.1%; thus, theestimated result is the ITT.
Column (3) shows the estimation results for receiving the financial incentive in the opt-in group(TOT), based on the sample of opt-in and control groups. We found that, in the first week of the treatment
period, daily step count increased by 2,281 steps among those who actively applied for the incentive (p < .05).
The step counts’ increase in the second week is 744 and positive, but not statistically significant.
Column (4) shows the estimation results for the treatment effect for passive individuals who
received the incentive under the opt-out condition but did not under the opt-in condition (TOU), using thesample of opt-out and opt-in groups. The coefficients on 𝑊𝑒𝑒𝑘1 × 𝑅𝑒𝑐𝑒𝑖𝑣𝑖𝑛𝑔 𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒 and 𝑊𝑒𝑒𝑘2 ×𝑅𝑒𝑐𝑒𝑖𝑣𝑖𝑛𝑔 𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒 were negative or close to zero, respectively, implying that offering the incentives topassive individuals had no impact on increasing their daily step counts. Thus, our experimental evidence
suggests that offering incentives in an opt-in scheme can efficiently increase people’s daily step counts better
than an opt-out scheme, although this is a short-term effect.
To estimate the effects after ending the treatment, we used the same estimation strategy and datafrom the baseline and post-treatment periods. The estimation results in Table 4 provide no evidence that daily
step counts decrease during the post-treatment period. In Columns (1) – (4), the coefficients are partly
negative but not statistically significant. We further found no evidence that the financial incentive treatments
influenced participants’ body weight (see Appendix 2).
4. Further Analysis
4.1. Heterogeneity by App Use Frequency
App use frequency affects the take-up rate, which could then lead to a heterogenous treatment effect.
As shown in Table 2, which reports the association between the take-up rate and individual characteristics,
participants who frequently used the app are likely to join the incentive program. Those who less frequently
used the app may have missed messages on the app because of their inattention and may not have applied
for the incentive program in the opt-in group, while such the participants may have unknowingly accepted
the program in the opt-out group. Those who used the app more frequently may have seen the messages but
not applied for the incentive program in the opt-in group because they felt it would be too bothersome, whilein the opt-out group, they could have joined the program because they did not feel it would be a burden.
We classified participants as “heavy users” (n = 314) and “light users” (n = 184) based on the entry
survey, which asked participants how frequently they used the app. We defined “heavy users” as participants
who answered, “I keep records every day,” “I keep records almost every day (sometimes I forget to keep
records a couple of times a week),” or “I keep records a couple of times a week.” “Light users” were definedas participants who answered, “I do not keep records but I check the app every day,” “I do not keep records
but I check the app a couple of times a week,” or “I do not keep records and rarely check the app.”
Table 5 shows the estimation results for heterogeneous treatment effects, dividing the participants
by app use frequency. Among heavy users in the opt-out group, we found a weak increase in daily step countby approximately 800 in the first week of the treatment period (Column (1)), with similar findings for heavy
11users in the opt-in group (Columns (2)). The IV estimation showed that active joiners among heavy users
could further increase their step counts by approximately 2,000 (Columns (3)), while the treatment effect for
passive joiners among heavy users was close to zero and not significant (Columns (4)). We also found thatthe financial incentive treatment showed no significant effect for light users in both the opt-out and opt-ingroups (Columns (5)–(8)). Although the daily step counts for the opt-in treatment group and passive joiners
were relatively large, the standard error was also large, likely because of the small sample size. In particular,
heavy users showed a tendency in which the financial incentive contributed to an increase in daily step counts
through people’s opt-in application to receive the incentive.
4.2. Robustness Check for Missing Data
Here, we verify the robustness of our results using other approaches and re-addressing the problemof missing data for daily step counts. As explained in Section 2.2, some daily step counts were missing fromthe data. In the above analyses, we conservatively assumed that all missing daily step counts were considered
to be zero. In this subsection, we first use the missing daily step counts as missing data and analyze
unbalanced panel data. Second, we impute the average daily step counts at the baseline period for eachindividual and day of the week into the missing daily step count data during the treatment period.6
Table 6 shows the estimation results for the DID specification using data from the baseline andtreatment periods. Columns (1)–(4) report the results of the unbalanced panel estimation, and Columns (5)–(8) report the results of the second approach for missing-data imputation. We found that participants in the
opt-out group reported higher step counts per day, approximately 630 in Column (1) and 565 in Column (5),than those in the control group during the first week of the treatment period. Subsequently, we observed nostatistically significant differences in daily step counts between the control and opt-out groups during thesecond week of the treatment period. We also found that participants in the opt-in group report higher step
counts per day, approximately 718 in Column (2) and 643 in Column (6), than those in the control group,and daily step counts further increased by 2,023 (Column (3)) and 1,911 (Column (7)) among those who
actively applied to receive the incentive. We observed no evidence that the treatment also increased daily
step counts for passive individuals (Columns (4) and (8)).
In summary, we found that short-term increases in step counts may or may not be observed in the
opt-out group, depending on which approach we employ for the missing data problem. However, thecommon results across the approaches are as follows: (1) short-term step counts increased in the opt-in group,(2) step counts further increased among those who completed the opt-in procedure, and (3) even if providing
financial incentives to passive individuals who did not complete the opt-in procedure, it would not change
their step counts. Thus, all our approaches confirmed the effects on increasing daily step counts in an opt-in
scheme that allows people to self-select their participation in the incentive program, suggesting that
introducing self-selection through an opt-in scheme when providing financial incentives can enhancetreatment effects.

6

When calculating the baseline average, missing step counts at the baseline period were not included.
12

5. Discussion and Conclusions
Most traditional policy research using randomized controlled trials has measured the causal impactsof mandatory policy assignment. However, in the real world, policies are often only applied to those whochoose to accept them. Under self-selection conditions, overall policy impacts will vary depending onheterogeneous effects across individuals and which individuals self-select to receive the policy treatment.
This study aimed to maintain and improve the health status of the general public and measured thetreatment effects of providing financial incentives on their walking behavior. Self-selection can significantly
influence treatment effectiveness and efficiency in this context. While medical institutions and staff canguarantee the delivery of medical and preventive interventions to ill patients, those who remain healthy can
decide by themselves whether they engage in physical activity as well as whether they receive treatments topromote it. Based on this, we collaborated with the local government of Kobe, Japan, to conduct a field
experiment to identify the effects of providing financial incentives in two self-selection schemes: opt-in and
opt-out.
Our estimation results suggest that the opt-in scheme works efficiently. Only 31.1% of participantsin the opt-in group completed the application process to receive financial incentives, and this group overall
increased their number of steps by an average of 710 step per day compared to the control group (ITT). In
the opt-out group, none of the participants refused the financial incentives; however, no effect of increasing
step counts was observed. Among the 31.1% of participants in the opt-in group who requested the incentive,
we found that the financial incentives increased their step counts by 2,280 per day (TOT). We also foundthat even if the rest of those who did not apply had received the financial incentives, their step counts would
not have changed (TOU). In this experiment, we leveraged participants’ self-selection through an opt-in
scheme, and were then able to target and deliver the financial incentive treatments primarily to those who
experienced positive treatment effects.
Previous studies on electricity markets and retirement savings have reported that opt-in schemes
may work efficiently or opt-out schemes may not (Wang et al. 2020; Beshears et al. 2022; Choukhamane2019). Interest in self-selection has also grown in medical and health fields (Finkelstein and Notowidigdo2019). To the best of our knowledge, this study is the first to add evidence to the medical and health literatureby conducting a field experiment to identify the effects of providing financial incentives through two selfselection schemes, opt-in and opt-out, on physical activity.
The participants in our study self-selected to join the event before self-selecting to receive financial
incentives, and thus had a higher level of interest in improving their physical activity. This dual self-selection
could have contributed to targeting those likely to experience positive treatment effects and delivering
financial incentives to them. This feature is shared with Wang et al. (2020), who compared CPP with flatrate pricing and found that an opt-in CPP scheme was more effective in saving electricity, compared with an
opt-out scheme with CPP as the default. Their finding that active CPP choice was more effective than passive
CPP choice is consistent with our observation of increased step counts when incentives were offered in an
opt-in scheme.
13Our results also indicate room for improvement in the opt-in scheme used in this study. The opt-in
scheme contributed to allocating the financial incentive treatment more efficiently to increase people’s step
counts. However, considering who opted in, we notice that it failed to consider “equity,” which is a limitationof this study and an issue for future research. Specifically, we paid an average of 316 JPY per person in
incentives in the opt-out group, compared to an average of 133 JPY per person in the opt-in group. The costeffectiveness of paying incentives resulted in increases of 131 and 353 daily steps per 100 JPY for the optout and opt-in groups, respectively.7 However, the opt-in scheme may not be desirable from an equityrelated perspective. Table 2 shows that those who applied to receive the incentive in the opt-in scheme andeffectively increased their step counts were already health-conscious individuals who regularly used the app
prior to the experiment. Since the health-conscious people more walked and improved their health during
this study, the opt-in scheme may have expanded health inequalities. In public policies, physical activity
promotion is important also among those with low health consciousness to prevent their health deterioration.
However, our results indicate that those who are passive to receiving financial incentives would have shown
little change in their daily step counts even if they received the incentives. Thus, future research is needed
to develop treatments that can promote physical activity among less health-conscious individuals. To
improve the general population’s health while considering health disparities, delivering different
interventions for people with high and low health awareness will be necessary.
The opt-in scheme still has a limitation from an efficiency perspective, which is also an issue for
future research. The positive effects of the opt-in scheme were short term and did not lead to body weightloss. Those who voluntarily completed the application to receive financial incentives significantly increased
their step counts initially; however, this tendency did not persist. Future research will need to identify anothertreatment to promote continued physical activity, which can be added to financial incentives. However, we
also need to consider the possibility that the COVID-19 pandemic may have influenced this short-term effect.
Giuntella et al. (2021) provided financial incentives without any self-selection scheme for step counts during
the COVID-19 pandemic and found the incentives increased step counts to pre-pandemic levels; however,after the incentives are removed, their step counts returned to the pandemic levels. Although how to promote
their physical activity is an essential question during a situation including a pandemic when people tend to
be inactive, we need to examine whether the positive effects of the opt-in scheme would be more sustainable
after the COVID-19 pandemic has completely ended.
In this experiment, we provided financial incentives based on individuals’ daily step counts during
two treatment weeks. While the experimental features, including rules for providing incentives and lengthof treatment period, are similar to related studies conducted with the general public (Adjerid et al. 2021,
Carrera et al. 2020, Giuntella et al. 2022), the features have varied across medical studies with unhealthypeople (Mitchell et al. 2020). Follow-up studies should examine whether the efficiency of the opt-in scheme
depends on different ways of providing financial incentives and different treatment periods. Hence, this study
has great potential to extend and develop this emerging literature stream by providing the first experimental
The opt-in scheme is more efficient as measured by the incremental cost-effectiveness ratio (opt-out: 0.76
JPY/day; opt-in: 0.28 JPY/day).
14
7

evidence on the interaction between financial incentive treatments and self-selection in a medical and healthcontext, while considering effectiveness, efficiency, and equity.

15

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17

Table 1
BALANCE CHECK

Daily step counts(at the time of entry)
Age
FemaleMarriedYears of schoolingHousehold income
Missing income
WorkerBody weight
BMI
Subjective health
No daily step target
Daily step target
App use frequency
Need for a financial incentive
Missing daily step counts(at the time of entry)

CONTROL OPT-OUT

OPT-IN

5336.555(351.197)
46.483(0.858)0.557(0.038)0.701(0.035)15.319(0.136)
701.616(24.939)0.167(0.028)0.833(0.028)61.507(1.331)22.003(0.251)
3.822(0.064)0.230(0.032)6908.046(301.841)
2.937(0.149)2.201(0.067)0.149(0.027)5305.133(355.624)
46.890(0.795)0.543(0.039)0.732(0.035)15.345(0.160)685.481(28.364)
0.195(0.031)0.878(0.026)60.118(1.452)22.490(0.261)3.689(0.074)0.256(0.034)6780.488(317.108)2.976(0.150)2.128(0.070)0.128(0.026)5171.741(373.686)
45.756(0.888)0.600(0.039)0.731(0.035)15.381(0.151)659.826(25.841)0.119(0.026)0.869(0.027)61.148(1.482)22.501(0.281)3.725(0.077)0.250(0.034)6525.000(303.277)
3.063(0.149)2.225(0.067)0.169(0.030)P-VALUE FROM THE JOINTORTHOGONALITY TEST OF
TREATMENT ARMS
0.943
0.640
0.559
0.773
0.957
0.529
0.168
0.458
0.773
0.307
0.390
0.842
0.670
0.832
0.583
0.590

Number of participants
174
160
164
NOTE. —BMI = body mass index. Standard errors clustered by individual are in parentheses. Daily step counts atthe time of entry are the mean daily step counts from December 7–15, 2020 (missing step counts are treated as zero).
Household income is defined as the average annual income after proxying the average annual income for those who
did not respond to the question about household income.

18

Table 2
DEMOGRAPHIC CHARACTERISTICS OF PARTICIPANTS WHO CHOSE TO OPT -IN
P-VALUE FROM THE JOINTORTHOGONALITY TEST OF
OPT-INNOT OPT-IN
TREATMENT ARMS
Daily step counts(at the time of entry)
Age
FemaleMarriedYears of schoolingHousehold income
Missing income
WorkerBody weight
BMI
Subjective health
No daily step target
Daily step target
App use frequency
Need for a financial incentive
Missing daily step counts(at the time of entry)

6433.667(703.982)
47.941(1.388)0.549(0.070)0.706(0.064)15.255(0.305)745.275(63.961)
0.196(0.056)0.882(0.046)61.353(1.649)22.580(0.426)3.863(0.125)0.314(0.066)6960.784(644.714)
4.843(0.219)2.451(0.102)0.059(0.033)4795.795(399.780)
46.416(0.969)0.540(0.047)0.743(0.041)15.385(0.188)658.494(29.225)
0.195(0.037)0.876(0.031)61.188(1.090)22.449(0.328)3.611(0.091)0.230(0.040)6699.115(358.411)
3.655(0.183)1.982(0.088)0.159(0.035)

0.033
0.376
0.914
0.619
0.708
0.157
0.984
0.911
0.933
0.816
0.116
0.259
0.704
0.000
0.002
0.076

Observations
51
113
NOTE. —Standard errors clustered by individual are in parentheses. Individual characteristics include age, female
dummy, married dummy, schooling, income, worker dummy, body weight, and subjective health. We omitted
participants who set a target of 0–4000 daily steps, and who do not provide input and check the app. ***, **, *indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

19

Table 3
EFFECTS OF TREATMENT ASSIGNMENT AND INCENTIVE ON DAILY WALKING DURING THE TREATMENT PERIOD
OLS

Take-up rate:

𝑊𝑒𝑒𝑘1 × 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐴𝑠𝑠𝑖𝑔𝑛𝑚𝑒𝑛𝑡
𝑊𝑒𝑒𝑘2 × 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐴𝑠𝑠𝑖𝑔𝑛𝑚𝑒𝑛𝑡IV

Opt-outOpt-inActivePassive100.0%

31.1%

Joiners

Joiners

(ATE)

(ITT)

(TOT)

(TOU)

(1)

(2)

(3)

(4)

541.099709.363**(353.849)(347.132)

286.818231.307(380.717)(374.698)
2281.090**-244.207(1115.715)(542.240)

743.81180.565(1196.032)(579.896)

𝑊𝑒𝑒𝑘1 × 𝑅𝑒𝑐𝑒𝑖𝑣𝑖𝑛𝑔 𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒
𝑊𝑒𝑒𝑘2 × 𝑅𝑒𝑐𝑒𝑖𝑣𝑖𝑛𝑔 𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒
Constant

6612.186***6654.480***(88.293)(87.161)

Fixed effect

Yes

Yes

Yes

Yes

Number of participants

334

338

338

324

Number of observations

6346

6422

6422

6156

NOTE.—OLS = ordinary least squares; IV = instrumental variable; ATE = average treatment effect; ITT = intentionto-treat; TOT = treatment effects on the treated; TOU = treatment effects on the untreated. Standard errors clusteredby individual are in parentheses. All models include fixed effects for day and individuals × day of the week. Column(1) uses the sample of the control and opt-out groups. Columns (2) and (3) use the sample of the control and opt-ingroups. Column (4) uses the sample of the opt-out and opt-in groups. Columns (3) and (4) report the IV estimation
results using opt-in and opt-out group dummies as instrument variables for receiving the incentive, respectively. ***,**, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
Column (1) uses the sample of the control and opt-out groups. The coefficients on 𝑊𝑒𝑒𝑘1 (𝑊𝑒𝑒𝑘2 ) ×
𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐴𝑠𝑠𝑖𝑔𝑛𝑚𝑒𝑛𝑡 capture the average difference in daily steps across the opt-out and control groups in thefirst (second) week of the treatment period. In the first week of the treatment period, the daily step count for the optout group is 541 steps higher than that for the control group; however, this is not statistically significant. In the second
week of the treatment period, the daily step count for the opt-out group is 287 higher than that for the control group;
however, this is also not statistically significant. Although all participants in the opt-out group accepted the incentive,
we did not find significant evidence that offering incentives in the opt-out scheme increases daily step counts on
average.
In contrast, we found a significant increase in daily step counts from the opt-in treatment assignment.
Columns (2) uses the control and opt-in groups and reports that, in the first week of the treatment period, the daily
20

step count for the opt-in group is 709 steps higher than that for the control group (p < .05), although this increase andits statistical significance disappear in the second week of the treatment period. Since the take-up rate of the incentive
is 31.1%, this estimated result is the ITT.
Column (3) shows the estimation results for receiving the financial incentive in the opt-in scheme (TOT),
which uses the sample of opt-in and control groups. We found that, in the first week of the treatment period, daily
step count increased by 2,281 steps among those who actively applied to receive the incentive (p < .05). The step
counts’ increase in the second week is 744 and positive, but not statistically significant.
Column (4) shows the estimation results for the treatment effect for passive individuals who received the
incentive under the opt-out condition but not under the opt-in condition (TOU), which uses the sample of opt-out and
opt-in groups. The coefficients on 𝑊𝑒𝑒𝑘1 × 𝑅𝑒𝑐𝑒𝑖𝑣𝑖𝑛𝑔 𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒 and 𝑊𝑒𝑒𝑘2 × 𝑅𝑒𝑐𝑒𝑖𝑣𝑖𝑛𝑔 𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒 arenegative or close to zero respectively, implying that offering the incentives to passive individuals has no increasing
impact on their daily step counts. In summary, our experimental evidence suggests that offering incentives in an optin scheme can efficiently increase people’s daily step counts better than an opt-out scheme, although its positiveeffect is short-term.

21

Table 4
EFFECTS OF TREATMENT ASSIGNMENT AND INCENTIVE ON DAILY WALKING DURING THE POST -TREATMENTPERIODOLS

Take-up rate:

𝑃𝑜𝑠𝑡 𝑊𝑒𝑒𝑘1 × 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐴𝑠𝑠𝑖𝑔𝑛𝑚𝑒𝑛𝑡
𝑃𝑜𝑠𝑡 𝑊𝑒𝑒𝑘2 × 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐴𝑠𝑠𝑖𝑔𝑛𝑚𝑒𝑛𝑡IV

Opt-outOpt-inActivePassive100.0%

31.1%

Joiners

Joiners

(ATE)

(ITT)

(TOT)

(TOU)

(1)

(2)

(3)

(4)

184.203-19.113(359.059)(358.129)-548.622-367.538(371.251)(381.833)-61.462295.078(1152.182)(567.774)-1181.887-262.813(1242.285)(541.151)

𝑃𝑜𝑠𝑡 𝑊𝑒𝑒𝑘1 × 𝑅𝑒𝑐𝑒𝑖𝑣𝑖𝑛𝑔 𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒
𝑃𝑜𝑠𝑡 𝑊𝑒𝑒𝑘2 × 𝑅𝑒𝑐𝑒𝑖𝑣𝑖𝑛𝑔 𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒
6725.666***6759.484***(81.442)(84.476)

Fixed effect

Yes

Yes

Yes

Yes

Number of participants

334

338

338

324

Number of observations

6346

6422

6422

6156

Constant

NOTE.—OLS = ordinary least squares; IV = instrumental variable; TOT = treatment effects on the treated; TOU =treatment effects on the untreated; =; ITT = intention-to-treat. Standard errors clustered by individual are inparentheses. All models include the fixed effects for day and individuals × day of the week. Column (1) uses the
sample of the control and opt-out groups. Columns (2) and (3) use the sample of the control and opt-in groups.
Column (4) uses the sample of the opt-out and opt-in groups. Columns (3) and (4) report the IV estimation results
using opt-in and opt-out group dummies as instruments for receiving the incentive, respectively. ***, **, * indicate
statistical significance at the 1%, 5%, and 10% levels, respectively.

22

Table 5
EFFECTS OF TREATMENT ASSIGNMENT AND INCENTIVES ON DAILY STEP COUNTS DURING THE TREATMENT PER IOD BY INDIVIDUAL APP USE FREQUENCYHEAVY USERS

LIGHT USERS

OLS
Take-up rate:

𝑊𝑒𝑒𝑘1 × 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐴𝑠𝑠𝑖𝑔𝑛𝑚𝑒𝑛𝑡
𝑊𝑒𝑒𝑘2 × 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐴𝑠𝑠𝑖𝑔𝑛𝑚𝑒𝑛𝑡IV

Opt-outOpt-in100.0%IV

Passive
Joiners

Opt-outOpt-in39.3%

ActiveJoiners

100.0%

(ATE)

(ITT)

(TOT)

(TOU)

(1)

(2)

(3)

(4)

814.919*

15.8%

ActiveJoiners

Passive
Joiners

(ATE)

(ITT)

(TOT)

(TOU)

(5)

(6)

(7)

(8)

782.453*

100.268

634.653(442.640)(443.965)(590.584)(554.106)433.951384.72145.384-41.448(494.736)(476.554)(598.855)(616.833)

𝑊𝑒𝑒𝑘1 × 𝑅𝑒𝑐𝑒𝑖𝑣𝑖𝑛𝑔 𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒
𝑊𝑒𝑒𝑘2 × 𝑅𝑒𝑐𝑒𝑖𝑣𝑖𝑛𝑔 𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒
Constant

OLS

1993.393*

53.4434019.468-634.582(1124.176)(765.223)(3550.989)(744.498)

980.12281.040-262.501

103.112(1208.161)(817.720)(3926.251)(806.289)

7259.673***7314.691***5554.430***5505.681***(113.307)(115.428)(141.860)(132.076)

Fixed effect

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Number of participants

207

214

214

207

127

124

124

117

Number of observations

3933

4066

4066

3933

2413

2356

2356

2223

NOTE.—OLS = ordinary least squares; IV = instrumental variable; TOT = treatment effects on the treated; TOU = treatment effects on the untreated; ITT = intention-to-treat. Standard
errors clustered by individual are in parentheses. All models include the fixed effects for day and individuals × day of the week. We define heavy users as participants who answered“I keep records every day,” “I keep records almost every day (sometimes I forget to keep records a couple of times a week),” and “I keep records a couple of times a week,” and lightusers as participants who answered “I do not keep records but I check the app every day,” “I do not keep records but I check the app a couple of times a week,” and “I do not keep
records and rarely check the app.” Columns (1) and (5) use the sample of the control and opt-out groups. Columns (2), (3), (6), and (7) use the sample of the control and opt-in groups.
Columns (4) and (8) use the sample of the opt-out and opt-in groups. Columns (3) and (7) report the IV estimation results using opt-in dummies as instruments for receiving the incentive.
23Columns (4) and (8) report the IV estimation results using the opt-out dummy as the instrument for receiving the incentive. ***, **, * indicate statistical significance at the 1%, 5%, and
10% levels, respectively.

24

Table 6
EFFECTS OF TREATMENT ASSIGNMENT AND INCENTIVE ON DAILY WALKING WITH ADDRESSED MISSING VALUES DURING THE TREATMENT PERIOD
UNIMPUTED DAILY WALKING STEPS
OLS
Take-up rate:

𝑊𝑒𝑒𝑘1 × 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐴𝑠𝑠𝑖𝑔𝑛𝑚𝑒𝑛𝑡
𝑊𝑒𝑒𝑘2 × 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐴𝑠𝑠𝑖𝑔𝑛𝑚𝑒𝑛𝑡IV

Opt-outOpt-in100.0%

OLS

IV

Passive
Joiners

Opt-outOpt-in34.5%

ActiveJoiners

100.0%

(ATE)

(ITT)

(TOT)

(TOU)

(1)

(2)

(3)

(4)

630.451**

31.1%

ActiveJoiners

Passive
Joiners

(ATE)

(ITT)

(TOT)

(TOU)

(5)

(6)

(7)

(8)

717.655**

565.338**642.877**(303.838)(306.399)(282.452)(285.867)

394.944308.473317.255

260.412(342.422)(337.210)(314.059)(312.557)

𝑊𝑒𝑒𝑘1 × 𝑅𝑒𝑐𝑒𝑖𝑣𝑖𝑛𝑔 𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒
𝑊𝑒𝑒𝑘2 × 𝑅𝑒𝑐𝑒𝑖𝑣𝑖𝑛𝑔 𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒
Constant

IMPUTED DAILY WALKING STEPS

2023.418**-129.298

1911.237**-115.933(840.905)(500.268)(824.176)(449.671)

811.164131.658716.982

81.744(934.526)(547.408)(907.478)(484.525)

7513.984***7615.263***7441.752***

7550.744***(76.372)(76.443)(73.602)(74.167)

Fixed effect

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Number of participants

321

323

323

312

329

334

334

323

Number of observations

5525

5552

5552

5307

5832

5862

5862

5656

NOTE.—OLS = ordinary least squares; IV = instrumental variable; TOT = treatment effects on the treated; TOU = treatment effects on the untreated; ITT = intention-to-treat. Standard
errors clustered by individual are in parentheses. All models include the fixed effects for day and individuals × day of the week. Columns (1) and (5) use the sample of the control and
opt-out groups. Columns (2), (3), (6), and (7) use the sample of the control and opt-in groups. Columns (4) and (8) use the sample of the opt-out and opt-in groups. Columns (3) and (7)report the IV estimation results using the opt-in dummy as the instrument for receiving the incentive. Columns (4) and (8) report the IV estimation results using the opt-out dummy as
the instrument for receiving the incentive. The dependent variables in Columns (1)–(4) are treated as missing when the daily step count is 0. The dependent variables in Columns (5)–(8) are imputed as the individual-level mean value for each day of the week prior to the treatment period for missing values. If the daily step counts were missing before the treatment
25

period for which the mean is calculated, they are not included in the calculation of the mean. ***, **, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

26

Appendix 1. Messages
Figure A1a. Messages during the experiment (control conditions).

①
②

③

①

④

⑤

②

Translations:① We are pleased to bring you “Information from the City of Kobe.”② Let’s beat COVID-19! Health Promotion Event 2021
③ Walk keeping distance between yourself and others!
④ Check out last week’s step counts. Walk this week, wearing a mask and keeping distance between
yourself and others.
⑤ Check out last week’s step counts. Would you like to walk at a different time of day this week?
Even on your usual path, you may see a different view at a different time.

27

Figure A1b. Messages during the experiment (treated conditions).

①

②

④
③

Translations:① For two weeks, from February 8 to February 21, you will earn an additional 5 JPY gift certificate
for every 1,000 step counts you take.
② If you make more effort than usual and walk 10,000 steps every day, you will earn an additional
700 JPY gift certificate!
③ From today through February 21, you can earn an additional 5 JPY Amazon gift certificate foryour every 1,000 steps (the daily upper limit is 14,000 steps). Check out last week’s step counts.
Walk this week, wearing a mask and keeping distance between yourself and others.
④ For the remaining week until February 21, you can earn an additional 5 JPY Amazon gift
certificate for your every 1,000 steps (the daily upper limit is 14,000 steps). Check out last week’s
step counts. Would you like to walk at a different time of day this week? Even on your usual path,
you may see a different view at a different time.

28

Appendix 2. Effects on Body Weight
Here, we examine the treatment effects on body weight. An increase in daily steps could result in a loss of bodyweight. Our outcome measure is body weight recorded in the app. The app automatically recorded participants’ daily step
counts; however, they were required to enter their body weight themselves. To encourage participants to provide their bodyweight information, we paid an additional incentive of 100 JPY for each participant who entered their weight at least once
during each of the following three periods: January 4–14, February 22–28 (first week of the post-treatment period), and March
1–7 (second week of the post-treatment period).
We estimate a DID specification using average body weight per week as the outcome variable. Participants did notenter their body weight every day; therefore, we first conduct the estimation by calculating the average using only the enteredbody weight. Second, if a body weight is not recorded, we assume no changes in body weight up to that date and impute theweight entered in the past for the analysis.
Tables A2a and A2b show the treatment effects on body weight during the treatment and post-treatment periods,
respectively.8
We observe no evidence of decreasing body weight from any treatment assignment group, either during or after thetreatment period (Columns (1)–(4) in Tables A2a and A2b). This finding does not change when we analyze the results by
imputing the most recently entered weight for the missing weight (Columns (5)–(8) in Tables A2a and A2b). Although wefind providing a financial incentive increased daily step counts in the opt-in group, this increase was insufficient to cause
weight loss. This finding is consistent with the fact that the effect of the financial incentive on increasing step counts was
short-term.

Tables A2a and A2b show the results of the analysis using body mass index instead of body weight, which show nodifferences from the results using body weight.
29
8

Table A2a.
EFFECTS OF TREATMENT ASSIGNMENT AND INCENTIVE ON BODY WEIGHT DURING THE TREATMENT PERIOD
UNIMPUTED BODY WEIGHTOLS

Take-up rate:

𝑊𝑒𝑒𝑘1 × 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐴𝑠𝑠𝑖𝑔𝑛𝑚𝑒𝑛𝑡
𝑊𝑒𝑒𝑘2 × 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐴𝑠𝑠𝑖𝑔𝑛𝑚𝑒𝑛𝑡

IV

OLS

IV

Opt-outOpt-inActivePassive

Opt-outOpt-inActivePassive100.0%

41.6%

Joiners

Joiners

100.0%

35.8%

Joiners

Joiners

(ATE)

(ITT)

(TOT)

(TOU)

(ATE)

(ITT)

(TOT)

(TOU)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

0.105

0.093

(0.102)(0.089)

0.140

0.115

(0.118)(0.095)

𝑊𝑒𝑒𝑘1 × 𝑅𝑒𝑐𝑒𝑖𝑣𝑖𝑛𝑔 𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒
𝑊𝑒𝑒𝑘2 × 𝑅𝑒𝑐𝑒𝑖𝑣𝑖𝑛𝑔 𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒

Constant

IMPUTED BODY WEIGHT-0.066-0.1540.105-0.053-0.1320.093(0.089)(0.204)(0.102)(0.080)(0.184)(0.089)-0.006-0.0100.140-0.009-0.0090.115(0.092)(0.207)(0.118)(0.082)(0.190)(0.095)60.664***60.693***60.361***60.587***(0.033)(0.029)(0.030)(0.028)Fixed effect

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Number of participants

203

212

212

193

269

283

283

260

Number of observations

575

603

603

548

745

775

775

718

NOTE.—OLS = ordinary least squares; IV = instrumental variable; TOT = treatment effects on the treated; TOU = treatment effects on the untreated; ITT = intention-to-treat. Standard
errors clustered by individual are in parentheses. All models include the fixed effects for day and individuals × day of the week. Columns (1) and (5) use the sample of the control and
opt-out groups. Columns (2), (3), (6), and (7) use the sample of the control and opt-in groups. Columns (4) and (8) use the sample of the opt-out and opt-in groups. Columns (3) and (7)report the IV estimation results using the opt-in dummy as the instrument for receiving the incentive. Columns (4) and (8) report the IV estimation results using the opt-out dummy as30

the instrument for receiving the incentive. Columns (1)–(4) and (5)–(8) use the unimputed body weight and imputed body weight for missing data to the latest body weight, respectively.
***, **, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

31

Table A2b.
EFFECTS OF TREATMENT ASSIGNMENT AND INCENTIVE ON BODY WEIGHT DURING THE POST - TREATMENT PERIOD
UNIMPUTED BODY WEIGHTOLS

Take-up rate:

𝑊𝑒𝑒𝑘1 × 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐴𝑠𝑠𝑖𝑔𝑛𝑚𝑒𝑛𝑡
𝑊𝑒𝑒𝑘2 × 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡 𝐴𝑠𝑠𝑖𝑔𝑛𝑚𝑒𝑛𝑡

IV

OLS

IV

Opt-outOpt-inActivePassive

Opt-outOpt-inActivePassive100.0%

41.6%

Joiners

Joiners

100.0%

35.8%

Joiners

Joiners

(ATE)

(ITT)

(TOT)

(TOU)

(ATE)

(ITT)

(TOT)

(TOU)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

0.143

0.153

(0.119)(0.106)

0.119

0.114

(0.128)(0.111)

𝑊𝑒𝑒𝑘1 × 𝑅𝑒𝑐𝑒𝑖𝑣𝑖𝑛𝑔 𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒
𝑊𝑒𝑒𝑘2 × 𝑅𝑒𝑐𝑒𝑖𝑣𝑖𝑛𝑔 𝐼𝑛𝑐𝑒𝑛𝑡𝑖𝑣𝑒

Constant

IMPUTED BODY WEIGHT

0.017

0.051

0.143

-0.027-0.0400.153(0.096)(0.212)(0.119)(0.090)(0.205)(0.106)-0.079-0.1830.119-0.081-0.2040.114(0.101)(0.221)(0.128)(0.096)(0.221)(0.111)60.635***60.904***60.603***60.868***(0.039)(0.033)(0.036)(0.033)Fixed effect

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Number of participants

223

234

234

217

289

304

304

283

Number of observations

621

651

651

602

785

817

817

764

NOTE.—OLS = ordinary least squares; IV = instrumental variable; TOT = treatment effects on the treated; TOU = treatment effects on the untreated; ITT = intention-to-treat. Standard
errors clustered by individual are in parentheses. All models include the fixed effects for day and individuals × day of the week. Columns (1) and (5) use the sample of the control and
opt-out groups. Columns (2), (3), (6), and (7) use the sample of the control and opt-in groups. Columns (4) and (8) use the sample of the opt-out and opt-in groups. Columns (3) and (7)report the IV estimation results using the opt-in dummy as the instrument for receiving the incentive. Columns (4) and (8) report the IV estimation results using the opt-out dummy as32

the instrument for receiving the incentive. Columns (1)–(4) and (5)–(8) use unimputed body weight and imputed body weight for missing data to the latest body weight, respectively.
***, **, * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

33