DP003
Adding Nudge-based Reminders to Monetary Incentives for
Promoting Rubella Antibody Testing and Vaccination

Hiroki Kato, Osaka University
Shusaku Sasaki, Osaka University
Fumio Ohtake, Osaka University

Adding Nudge-based Reminders to Monetary Incentives for
Promoting Rubella Antibody Testing and Vaccination *Hiroki Kato † 1 , Shusaku Sasaki ‡ 2 , and Fumio Ohtake § 1, 2
1

2

Graduate School of Economics, Osaka University, Japan
Center for Infectious Disease Education and Research (CiDER), Osaka University,
Japan
First Draft: October 27, 2022

Abstract
This study examines the effects of combining financial incentives with nudges to promoterubella antibody testing and vaccination. To increase the rubella antibodies uptake rate among
40-to-57-year-old men, the Japanese government began providing vouchers for free testing andvaccination in the fiscal year 2019. During this year, the government initially mailed vouchers
only to those aged 40–46 years. Although the remaining males aged 47–57 received vouchers
via mail in FY2020, they could have obtained vouchers and received testing and vaccination
also in FY2019 by completing the application process. We focus on this policy distinction andconduct a late-FY2019 online field experiment with 40-to-57-year-old men living throughout
Japan. We randomly send reminders with nudge-based messages recommending antibody
testing and vaccination, and then track self-reported behavior until the end of FY2019. We
find that one nudge-based reminder with an altruistic message, which highlights the negative impact on the fetus caused by infection from the men to pregnant women, significantly
promotes antibody testing and vaccination among those who have already received vouchers
as a financial incentive. For the other group that must apply for vouchers, any nudge-based
reminder has no promoting impact.
JEL: D90, I12, I18
Key words: Rubella, Vaccination, Antibody Test, Text Messages, Reminders, Free Vouchers* This study is conducted as a part of the Project “Implementation of EBPM in Japan” undertaken at the ResearchInstitute of Economy, Trade and Industry (RIETI). In completing this paper, we thank participants of the EBPM Study
Group and the Discussion Paper Study Group of RIETI for their insightful comments. The research is financially supported by the Ministry of Health, Labour and Welfare (MHLW) in Japan (Grant-in-Aid for Health and Labor Administration Promotion Research Project; Grant-in-Aid for MHLW Science Research), the Japan Society for the Promotionof Science (JSPS) [JSPS Grant Number: 20H05632 (F., Ohtake)], and Japan Science and Technology Agency (JST)[JST Grant Number: JPMJPR21R4 (S., Sasaki)]. Prior to conducting a randomized controlled trial on an online survey,
this study was approved by the Institutional Review Board of the Graduate School of Economics, Osaka University(R020114).
†
Corresponding author. E-mail: h-kato@econ.osaka-u.ac.jp‡
E-mail: ssasaki.econ@cider.osaka-u.ac.jp§
E-mail: ohtake@econ.osaka-u.ac.jp1

1

Introduction

Vaccination is an essential countermeasure against infectious diseases, including COVID-19, seasonal influenza, and rubella. Vaccination enables individuals to voluntarily acquire antibodies andimmunity. They can thus prevent the onset of infectious diseases and even infection itself.
Vaccination has positive externalities and benefits for the vaccinated individuals, surrounding
community, and society. For example, vaccination lowers the risk of developing an infectious disease or becoming severely ill. It also reduces the risk of the health-care delivery system becomingstrained. Vaccination with infection-preventive effects, including the rubella vaccine, reduces therisk of infection for the vaccinated and the risk of spreading the infection. Vaccination can contribute to social stability during pandemics and the acquisition of herd immunity via these positive
externalities.
However, economic theories suggest that vaccination rates may not reach the socially optimallevel due to the positive externalities (Brito et al., 1991; Francis, 1997; Stiglitz, 2000). With theexternalities, the marginal individual benefit of vaccination becomes lower than the marginal social
benefit, and this feature discourages vaccination by those with selfish motives. Even if people
are altruistic and gain utility from the social benefits of their vaccinations reducing the infection
probability of others, they still have an incentive to free-ride on others’ vaccinations and not receive
the vaccines themselves, because the infection probability is lowered by the others’ vaccinations.
We find empirical studies reporting that others’ increased vaccination rates reduce the likelihoodof people being vaccinated (Hershey et al., 1994; Ibuka et al., 2014).
To overcome the challenge of vaccine coverage not achieving socially optimal levels, governments have used a variety of interventions, including monetary and non-monetary interventions(nudges in behavioral economics). Many countries subsidize COVID-19 vaccinations, making
them free. Some local governments set subsidies above and beyond the amount required to makevaccination free, giving vaccinated people the right to join a lottery, and recent studies have confirmed the effectiveness of these measures (Barber and West, 2022; Brehm et al., 2022). Nudgebased interventions include recommending a predetermined vaccination date, sending a reminder
message, and changing message wording (Chapman et al., 2010; Sasaki et al., 2022; Yokum et al.,2018).1
This study explores the possibility of combining financial incentives with nudges in vaccina1
In the context of vaccines for seasonal influenza and COVID-19, some studies found that messages stimulating
people’s ownership, “A vaccine dose is reserved for you,” promotes their vaccination uptake (Dai et al., 2021; Milkman
et al., 2021).

2

tion and antibody acquisition. Many previous studies estimated or compared these two as independent or substitutive. However, Richard Thaler and Cass Sunstein define nudges as “an aspectof choice architecture that alters people’s behaviour in a predictable way without forbidding any
options or significantly changing their economic incentives” (Thaler and Sunstein, 2009, p. 6). As
mentioned, “without significantly altering economic incentives,” nudges include devising wordings and expressions for existing financial incentives. They further explain that nudges adjust thesalience of financial incentives so that the incentives have the expected effect.2 In other words,
nudges and financial incentives are complementary in their original definition.
Recently, more studies have examined the two combinations in non-vaccination contexts. For
example, Burtch et al. (2018) found that combining a nudge emphasizing social norms with afinancial incentive improves the length and quality of online market reviews more than either intervention alone. Thorndike et al. (2016) reported similar results regarding healthy food choices
in a hospital cafeteria. Meanwhile, in the study of Kullgren et al. (2014), combining nudges andfinancial incentives does not increase outdoor exercise among older adults. Furthermore, Pellerano et al. (2017) reported that adding a financial incentive to a social norm nudge undermines
the energy-saving effect of the nudge-based intervention alone.3 We can expect relatively large
facilitating effects in combining nudges and financial incentives, while in practice, the effects’
direction and extent depend on contexts. Before applying the combinations to policies, we must
confirm their effects empirically.
To the best of our knowledge, no study has focused on the combination of financial incentives
and nudges in the context of vaccination. As previously stated, many previous studies have separately estimated the effects of financial incentives (Banerjee et al., 2010; Barber and West, 2022;
Barham and Maluccio, 2009; Brehm et al., 2022) and nudges (Dai et al., 2021; Chapman et al.,2010; Milkman et al., 2021; Sasaki et al., 2022) as stand-alone measures. Although some studies
focused on both, they compared the effects of the two interventions, examined which is larger,
and seemed to regard the two as substitutive. For example, Bronchetti et al. (2015) conducted arandomized controlled trial with college students in Pennsylvania and found that a financial incentive increases their seasonal influenza vaccination rate. In this experiment, a peer-related nudge
increases vaccine information exposure but does not increase vaccination rates. Campos-Mercade
2

They mention “iNcentives,” “Understand mappings,” “Defaults,” “Give feedback,” “Expect error,” and “Structurecomplex choices” as the six principles of “NUDGES” for creating a good choice architecture. The first principle,“iNcentives,” explains the complementary relationship between financial incentives and nudges.
3
Besides field experimental studies, some laboratory experimental studies have also tested the effects of the combination; Chen et al. (2021) show that combining a financial incentive with an informational nudge induces cooperative
behaviors in a prisoner’s dilemma game among intrinsically motivated individuals.

3

et al. (2021) reported that a € 20 financial incentive increases vaccination rates by 4 percentage
points (pp) in a Swedish field experiment and emphasized that some nudge-based interventions
have little promotional effect.
We add new insights to the literature on the combination of financial incentives and nudges, by
focusing on Japan’s policy on rubella antibody testing and vaccination and conducting a nationwide
online field experiment. We estimate the effect of providing nudge-based messages in each of thesituation where people can easily obtain financial incentives and where they must incur additionaltransaction costs to obtain the incentives. In the former situation, financial incentives and nudges
are more closely combined.
In Japan, men born between April 2, 1962, and April 1, 1979 (aged 40–57 years as of 2019)
have historically been excluded from routine rubella immunization (see Section 2 for details). Consequently, men in this age group have lower antibody prevalence than their female counterparts.
Therefore, Japan has yet to achieve herd immunity against rubella. Unlike flu and COVID-19 vaccinations, rubella vaccine-induced immunity is long-lasting, and people without antibodies mustreceive a single vaccination to achieve herd immunity against rubella. The Ministry of Health, Labor, and Welfare (MHLW) then initiated an additional measure for routine rubella vaccination in
FY2019 by mailing vouchers covering the costs of the rubella antibody test (approximately 5,000
JPY, which is equal to 45 US dollars) and vaccination (approximately 10,000 JPY) to men aged
40–57 years, to confirm whether they have an antibody and then encourage vaccination for those
without.
Free vouchers were distributed progressively through local governments: vouchers were mailed
to men aged 40–46 in FY2019 and those aged 47–57 years in FY2020. Men aged 47–57 years had
to apply to their local government to receive the vouchers during FY2019. That is, in FY2019,
men aged 40–46 years received the free vouchers as financial incentives by default, whereas men
aged 47–57 years had to opt-in to receive the vouchers, incurring transaction costs.
We conducted an online field experiment in February–March 2020 (at the end of FY2019)with men aged 40–57 years living throughout Japan, including the aforementioned two age groups.
In this study, we use a randomized controlled trial (RCT) and send reminders with nudge-based
messages to encourage the uptake of antibody testing and vaccination. We ascertain their intentions
to receive the test and vaccine, and their self-reported behaviors to receive them in a follow-up
survey.
By focusing on the sample aged 40–46 years, we can capture the effects of providing additional
reminders with nudged messages in one situation where they already obtained the vouchers as

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financial incentives. Meanwhile, we can capture the effects of providing reminders in anothersituation where they must incur transaction costs and opt-in to obtain financial incentives with thesample aged 47–57 years. The main finding reveals that a reminder with an altruistic message iseffective in promoting antibody testing and vaccination behaviors only in the former group. This
altruistic message emphasizes the negative impact on newborns caused by infection from the men
to pregnant women.
This study contributes to the literature exploring the conditions under which nudges work effectively. Evidence for the effectiveness of nudges has been mixed in recent years; according toa meta-analysis by DellaVigna and Linos (2022), nudges are not large in reality, and their effectiveness depends on various conditions, including topics and channels. This study focuses on the
combination of financial incentives and nudges, which have often been treated as mutually substitutive measures, and demonstrates that nudges can be effective when given to a group that has
already obtained financial incentives. This result is also consistent with the original definition of
the two being complementary.
This research also contributes to acquiring global herd immunity against rubella; according to
the World Health Organization, 101 of 194 countries have not yet eliminated rubella, accountingfor 52% (Zimmerman et al., 2022). Specifically, in the Eastern Mediterranean, South-East Asia,
and Western Pacific regions, the proportion of countries that have not yet achieved herd immunity
is high. As cross-border traffic increases toward the end of the COVID-19 pandemic, achieving
herd immunity against rubella will become increasingly important for the remaining countries.
Japan is extremely close to achieving herd immunity. The nudge-based strategy used in such a
country to acquire that last mile will be useful for many other countries in the future.
The rest of this paper is structured as follows. Section 2 provides an overview of rubella inJapan. Section 3 describes the experimental design and Section 4 presents the results. Finally,
Section 5 concludes the paper.

2

Background of Rubella Vaccination in Japan

Rubella is a highly contagious disease spread through droplet transmission. The most common
symptoms are fever and rashes.In addition to serious symptoms for both men and women, women
infected with rubella during early pregnancy may have children with congenital rubella syndrome(CRS), which includes eye and ear defects. Because the spread of rubella tends to increase the CRS
incidence, the Japanese government has designated rubella as a disease requiring immunization to

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100

Antibody Prevalence (%)

80

60

40

20
0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

Age
GenderFemales

Males

Figure 1: Prevalence of Rubella Antibodies by Age and Gender. Data: NIID ”2018 National
Epidemiological Surveillance of Vaccine-Preventable Diseases (NESVPD).
prevent its spread. According to Kinoshita and Nishiura (2016), Japan can obtain herd immunity
against rubella if the antibody prevalence exceeds 90% in all generations.4
However, due to low antibody prevalence among men in their 40s and 50s, Japan has notachieved herd immunity to rubella. Fig. 1 plots the prevalence of rubella antibodies by age andgender from the National Institute of Infectious Diseases (NIID) “2018 Infectious Disease Epidemic Prediction Survey.” The antibody prevalence among men aged 39–56 years is approximately 81.5%, which is lower than that of women of the same generation (about 97.9%) and othergenerations because they have not received routine rubella vaccination and have not had less opportunity to infect naturally.5 The prevalence of antibodies in men and women aged 57 and older is
91.1% and 89.3%, respectively. Despite not having received routine vaccination, men and women
aged 57 and older grew up during a time when rubella was common and people are likely to haveantibodies from natural infection. The antibody prevalence of men and women aged 38 years andyounger is 91.3% and 94.0%, respectively. They have had at least one dose of rubella vaccine administered as part of routine immunization. We give a detailed background of routine vaccination
4

According to Plans-Rubió (2012), antibody prevalence of 83% to 95% achieves herd immunity against rubella.
Nishiura et al. (2015) found that the antibody prevalence for herd immunity is 83.6%.
5
Using this data, we predict antibody prevalence by a saturated model for three age groups (38 years and younger,
39-– 56 years, and 57 years and older) and a female dummy. The difference in antibody prevalence between men andwomen in the 39–56 age group is 0.164 (se = 0.034; p < 0.01). The difference in antibody prevalence between men
aged 39 to 56 years and men aged 57 years and older is 0.96 (se = 0.036; p < 0.01).

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against rubella in Appendix A.
To achieve herd immunity against rubella, Japan must raise the antibody prevalence among
men in their 40s and 50s from 80% to 90%. To achieve this goal, MHLW has provided the rubellavaccine as an additional free routine immunization for men aged 40–57 years (as of 2019) between
April 2019 and March 2022.6 For efficient utilization of vaccination, eligible men must first get
antibody testing. Men who have a negative test can then be vaccinated against rubella.
Following the Immunization Act, eligible men can receive free antibody testing and vaccinations. MHLW requested local governments to send free vouchers for a rubella antibody test andvaccine to eligible men over a three-year period. Concerning whether they receive the voucher
automatically in FY2019, we can divide eligible men into the following two age groups:
1. 40–46 years old: they automatically received the free voucher in FY2019;
2. 47–57 years old: they automatically received the free voucher after FY2020 but had to apply
to obtain it in FY2019.
Thus, transaction costs for monetary incentives in FY2019 differ between the two age groups.
In particular, 40-to-46-year-old men have no transaction costs because they obtained monetary
incentives by default in the form of free vouchers in FY2019. However, 47-to-57-year-old men
have high transaction costs because they must contact their local government to obtain the free
vouchers in FY2019 (opt-in incentives).
Although men aged 40–46 years automatically received monetary incentives, the uptake rateof antibody testing with vouchers remained as low as 18% as of January 2020.7 Given adequate
financial incentives, non-monetary interventions should be considered to increase antibody testing.
Therefore, we developed behavioral science-based text message reminders for those who were nottested and vaccinated. Using a nationwide online survey, we tested how well those reminders
improve antibody testing and vaccination rates among default voucher recipients (and the effecton those who incur a transaction cost to obtain them).
6

More precisely, eligible men were born between April 2, 1962, and April 1, 1979.
More than half of eligible men are 40–46 years old (6.46 million). They received the free vouchers from April 2019
to March 2020. According to interviews conducted by the MHLW, approximately 96% of local governments planned
to send them by October 2019. The cumulative number of antibody tests using vouchers through January 2019 was 1.17
million. We calculate antibody testing uptake by dividing the cumulative number of antibody tests using vouchers up
to January 2019 (1.17 million) by the population of 40-to-46-year-old men (6.46 million).
7

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3

Nationwide Online Survey Experiment

In collaboration with MHLW, we commissioned MyVoiceCome Co. Ltd. to conduct two nationwide online surveys at the end of FY2019. On February 15–17, 2020, we conducted the first survey(wave 1) of 4,200 Japanese men aged 40–59 years living throughout Japan. Wave 1 randomizes
seven text messages to test how they affect antibody testing and vaccination intentions. On March
17–25, 2020, we surveyed wave 1 respondents again for the second survey (wave 2). We received
responses from 3,963 individuals (attrition rate = 5.64%).8 Wave 2 aims to test how the randomly
assigned text messages in wave 1 affect the actual uptake of antibody testing and vaccination.
Appendix B contains detailed information about the survey. We obtained prior approval fromthe IRB of the Graduate School of Economics, Osaka University (IRB approval number: R020114)
for conducting the RCT on the online survey.

3.1

Wave 1: Treatments and Outcome Variables on Intention

We randomly sent one of the text messages in Table 1. The MHLW (Control) message is thecontrol. Using this message, MHLW promotes antibody testing and rubella vaccination against
rubella on its website (business-as-usual control). We developed six additional text messages
based on the MHLW (Control) message to explore what elements to use and how to emphasize
them. Recent behavioral science research has increasingly investigated the efficacy of multiple
candidate messages (e.g. Dai et al., 2021; Milkman et al., 2021), similar to this study.
The six treated text messages alter the MHLW (Control) message to (1) a simple age expressionand (2) behavioral economics content. In addition to the precise target age for the additional rubellameasures, the MHLW (Age) message includes the simple phrase “men in their 40s and 50s,” whichhelps the reader understand if they are eligible for vaccination and pay attention to the message.
The MHLW (Age) message only changed the age expression; the message’s content is identicalto the control. The other five messages both add simplified age expressions and change message
content based on behavioral economics.
The Altruistic message describes how one’s infection can harm others, particularly pregnant
women and their children. As previously stated, vaccination has positive externalities. In thecase of rubella, having antibodies through vaccination prevents infection in pregnant women, thus
protecting their children. The Altruistic message is the inverse of this positive externality, emphasizing the negative externality of not being vaccinated. This message is intended to help altruistic
8
The seven experimental arms have similar attrition rates. We linearly regress an attrition dummy on treatmentgroup dummies. F-test for joint null hypothesis is statistically insignificant (F-value = 1.434; p-value = 0.197).

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Table 1: List of Text Message Reminders
Age (as of Apr 2019)
Message

Contents

39

40–46

47–56

57–59

All

MHLW (Control)

Dear all men born between 1962 and 1979. Get
rubella antibody testing and be vaccinated toprotect yourself and the upcoming generation!

N

20

210

321

49

600

MHLW (Age)Dear men in their 40s and 50s (all men born
between 1962 and 1979). Get rubella antibody
testing and be vaccinated to protect yourself andthe upcoming generation!

N

23

205

309

63

600

Altruistic

Dear men in their 40s and 50s (all men born
between 1962 and 1979). If you get a pregnant
woman infected with rubella, she may give birth
to a child with a serious disability. Get rubella
antibody testing and be vaccinated!

N

24

214

296

66

600

SelfishDear men in their 40s and 50s (all men born
between 1962 and 1979). Rubella infection in
adult men may have serious complications suchas encephalitis and thrombocytopenic purpura.
Get rubella antibody testing and be vaccinated!

N

16

225

302

57

600

Social Comparison

Dear men in their 40s and 50s (all men born
between 1962 and 1979). Compared to othergenerations, more than twice as many people inyour generation can get rubella because one infive of you does not have rubella antibodies. Get
rubella antibody testing and be vaccinated!

N

18

204

321

57

600

Deadline

Dear men in their 40s and 50s (all men born
between 1962 and 1979). The voucher for a freerubella antibody test and vaccine is valid on
March 31, 2020. Get rubella antibody testing andbe vaccinated!

N

18

216

299

67

600

Convenient

Dear men in their 40s and 50s (all men born
between 1962 and 1979). You can use yourcoupon for a rubella antibody test at a growing
number of workplaces and government agencies,
in addition to your usual health examinations. Get
rubella antibody testing and be vaccinated!

N

19

213

307

61

600

9

readers imagine the negative externalities caused by the infection and change their behavior.9
The Selfish message and Social Comparison message aim to change behavior by increasing theimportance of having rubella antibodies. The Selfish one describes in detail the damage causedby the infection to the individual, making it easier for the reader to imagine it. Meanwhile, theSocial Comparison informs the reader that antibody prevalence is low and reminds them that they
are susceptible to infection. This message can help people avoid underestimating the likelihood of
infection and undervaluing vaccination.
The Deadline message and Convenient message describe the routine vaccination system. Deadline emphasizes that the FY2019 vouchers are valid until the end of March. This message aims toprevent people from postponing antibody testing and vaccination due to the present bias, which isone of behavioral economics’ key findings. Meanwhile, Convenient states that some people canget antibody testing as part of a regular health examination and emphasizes its convenience. Thismessage aims to reduce the subjective cost of antibody testing.
We employed stratified randomization. The survey firm divided respondents into four age
groups (40–44, 45–49, 50–54, and 55–59) and assigned messages to each group equally. The
sample size for each group is 1,050, with 150 for one experimental arm within each group. Thus,
the sample size for one experimental group is 600.10
In wave 1, after viewing a randomly assigned message, participants expressed their intention
to get antibody testing and vaccinated on a 5-point Likert scale (5 = definitely yes; 1 = absolutelyno). The question of willingness to take antibody testing is, “Are you willing to take an antibody
test for rubella now?” Meanwhile, the intention to get vaccinated question asks, “If the antibody
testing reveals that you have no antibodies, are you willing to get vaccinated?” We create a binary
variable taking the value of 1 if the participant responds 4 or 5 for each question and use it as theoutcome variable for intentions.

3.2

Wave 2: Outcome Variables on Behavior

Wave 2 investigates actual antibody testing and vaccination behavior since wave 1. We present
exact questions and choices about antibody testing and vaccination in Appendix B. We create abinary variable taking 1 if the respondent has taken antibody testing since the end of wave 1.
To receive rubella vaccination through routine immunization, eligible men must first undergo
9

We define an altruistic person as someone who considers social benefits, including externalities.
The ages shown in Table 1 are as of April 2019, calculated using the year and month of respondents’ birth. Men aged
40–56 years as of April 2019 are eligible for the MHLW’s additional measures. Those aged 40–46 years automaticallyreceive coupons for the first year. We assume that those born in April have not yet reached their birthday. Also, somemen aged 39 years as of April 2019 were 40 years old at the time of the survey.
10

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antibody testing. However, they may have been vaccinated against rubella at their own expense
without having their antibodies tested. To eliminate this possibility, we create a binary variabletaking 1 if respondents have been tested and vaccinated since the end of wave 1.Then, we use it as
the outcome variable on vaccination coverage.

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Results

4.1

Study Population

Our study population consists of men who did not have antibody testing or vaccinations prior towave 1. We estimate the effect on intention using wave 1 data that exclude respondents who statedin wave 1 that they had already received either antibody testing or vaccination (hereinafter, Wave
1 study sample). In addition to this criterion, we estimate the effect on behavior using wave 2 data
without respondents who report in wave 2 that they had either antibody testing or were vaccinatedprior to wave 1 (hereinafter, Wave 2 study sample).11
Furthermore, we aim to estimate the effect of text messages in situations where men received
monetary incentives as vouchers by default and where they had to incur transaction costs to obtainthem. To accomplish this, we create a subsample of men aged 40–46 years and another of men
aged 47–56 years. Men in the former subsample automatically received the free vouchers (default
incentive group). Meanwhile, men in the latter subsample received no incentives or required acostly procedure to get it (opt-in incentive group). We believe that most men aged 47–56 years did
not receive free vouchers in FY2019 because 77.5% of respondents in wave 1 did not know thatrubella routine immunization began in FY2019. Thus, in the default incentive group, monetary
incentives and text message reminders are more closely combined than in the opt-in incentive
group.

4.2

Effect of Text Messages on Intentions

This subsection estimates the effect of text messages on intentions using the wave 1 study sample.
We find that individuals’ observable characteristics are balanced across experimental arms in bothsubsamples (Appendix C). Thus, we report the difference-in-mean test (t-test) here and present the
regression analysis in Appendix D. We also calculate the minimum size of the effect for the twosided hypothesis test with a statistical significance of 0.05 and statistical power of 0.8. An absolute
11

Differences could exist in responses between the first and second waves, for example, by checking one’s vaccination
history after the first survey. Hence, we exclude respondents who stated in either wave that they had received either
antibody testing or vaccination prior to wave 1.

11

A. Antibody Test (Intention)

0.208
0.229
0.351***
0.28
0.248
0.239
0.26

MHLW (Control)

Treatments

MHLW (Age)
Altruistic
SelfishSocial Comparison
Deadline
Convenient
0.00

0.25

0.50

0.75

1.00

0.75

1.00

Proportion
B. Vaccination (Intention)

0.415
0.435
0.439
0.455
0.43
0.406
0.433

MHLW (Control)

Treatments

MHLW (Age)
Altruistic
SelfishSocial Comparison
Deadline
Convenient
0.00

0.25

0.50

Proportion

Figure 2: Effect of Text Messages on Intentions for Default Incentive Group (N = 927). Data:
Men aged 40–46 years in wave 1 study sample. Note: Numbers in the figure indicate the proportionof each group. Error bars indicate the standard error of the mean. Asterisks are p-values of t-tests
for the difference-in-mean: * p < 0.1, ** p < 0.05, *** p < 0.01.
minimum difference is 6.7 pp when we use the subsample of men aged 40–46 years (N = 927).
However, it is 5 pp when using the subsample of men aged 47–56 years (N = 1, 688).
First, using a subsample of men aged 40–46 years who already have free vouchers in FY2019,
we show the proportion of intention for antibody testing (Panel A) and vaccination (Panel B) in eachexperimental arm in Fig. 2. The results show that the Altruistic message increases the intention
for antibody testing compared to the MHLW (control) message. The intention ratio of antibody
testing in the MHLW (control) message group and the Altruistic message group is about 20.8%
and 35.1%, respectively. Thus, compared to the MHLW (control) message, the Altruistic message
increases the intention for antibody testing by about 14.3 pp, which is statistically significant at the
1% level and is above the required minimum detectable effect size (6.7 pp).
Meanwhile, compared to the MHLW (control), all other text messages do not statistically andsignificantly increase the intention to get vaccinated. Note that the intention ratio of vaccination inall experimental arms is higher than that of antibody testing. This result may be explained by thestimulus of the question eliciting the vaccination intention. We asked respondents to report their
willingness to vaccinate if they did not have antibodies. This condition may strongly stimulate theneed for vaccination. Thus, when assessed by actual behavior, the results may differ.

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A. Antibody Test (Intention)

0.276
0.252
0.329
0.317
0.239
0.305
0.339

MHLW (Control)

Treatments

MHLW (Age)
Altruistic
SelfishSocial Comparison
Deadline
Convenient
0.00

0.25

0.50

0.75

1.00

0.75

1.00

Proportion
B. Vaccination (Intention)

0.528
0.462
0.5
0.5
0.446*
0.5
0.498

MHLW (Control)

Treatments

MHLW (Age)
Altruistic
SelfishSocial Comparison
Deadline
Convenient
0.00

0.25

0.50

Proportion

Figure 3: Effect of Text Messages on Intentions for Opt-in Incentive Group (N = 1, 688). Data:
Men aged 47–56 years in the first wave study sample. Note: Numbers in the figure indicate theproportion of each group. Error bars indicate the standard error of the mean. Asterisks are p-valuesof t-tests for the difference-in-mean: * p < 0.1, ** p < 0.05, *** p < 0.01.
Fig. 3 depicts the proportions of intention for antibody testing (Panel A) and vaccination (PanelB) , using a subsample of men aged 47–56 years who required costly procedures to obtain the free
vouchers in FY2019. The results indicate that in comparison to the MHLW (control), none ofthe other text messages, including the Altruistic message, significantly increase the intention toundergo antibody testing.
In contrast, the Social Comparison message may lower vaccination intention than the MHLW
(control) message. In the MHLW (control) message group and the Social Comparison message
group, the vaccination intention ratio is approximately 52.8% and 44.6%, respectively.12 Thus,
compared to the MHLW (control), the Social Comparison message reduces vaccination intention
by 8.2 pp, which is statistically significant at the 10% level. The behavior of free-riding explains
this result. The Social Comparison message emphasizes that “one in five people do not have antibodies.” Conversely, four out of five individuals have antibodies. The readers of such a message
may have believed that even if they lacked rubella antibodies, the likelihood of infection would be
low because 80% of the population possesses them. When eligible men were required to undergo
12

Similar to the results with the subsample of men aged 40–46 years who automatically received free vouchers in
FY2019, the vaccination intention ratio is higher than the antibody testing intention ratio for all experimental arms. This
result may be due to the stimulation of questions designed to elicit vaccination intent.

13

costly procedures to receive free vouchers, this belief may have made vaccination less beneficial,resulting in a lower vaccination intention than the MHLW (control).
Since age determines whether eligible men received the free vouchers automatically in FY2019,
the different effect of text messages for two subsamples is influenced by the presence or absenceof monetary incentives, and by the age group differences between the two subsamples. We estimate a linear probability model that directly controls for the effect of age. The outcome variablesconsist of the intention to get antibody testing or vaccination. The explanatory variables consist oftreatment dummies, the interaction term between treatment dummies, a binary variable indicatingage 40–46, and covariates including age. The estimated model produces the same result described
previously (see Appendix D for details).

4.3

Effect of Text Messages on Behaviors

Using wave 2 study sample, we estimate the effect of text messages on behavior. We tested abalance of individual characteristics again because a few respondents dropped out between waves
1 and 2. The results show that the observable characteristics are balanced across experimental
arms in both subsamples (see Appendix C). Therefore, we only present the difference-in-mean test
(t-test) here and the regression analysis in Appendix D. We also compute the minimum size of theeffect for the two-sided hypothesis test at 0.05 statistical significance and 0.8 statistical power. An
absolute minimum difference is 7.2 pp when the subsample of men aged 40–46 years (N = 805)
is used. Meanwhile, 5.3 pp is required when the subsample of men aged 47–56 years (N = 1, 467)
is used.
Using a subsample of men aged 40–46 years who already received the free vouchers in FY2019,
we show the uptake rate of antibody testing (Panel A) and vaccination rates (Panel B) for each experimental arm in Fig. 4.13 We find that, as in the intention case, the Altruistic message increases
the actual antibody test uptake rate compared to the MHLW (control) message. The uptake rate ofantibody testing in the MHLW (control) and Altruistic message group is 3.5% and 10.9%, respectively. Thus, the Altruistic message increases the actual uptake rate of antibody testing by 7.4 pp,
which is statistically significant at the 5% level and slightly larger than the minimum detectableeffect size (7.2 pp). Altruism may also boost vaccination rates. This arm’s vaccination rate is4.7%, which is 3.8% points higher than MHLW (control) (0.9%) at the 10% significance level.
Selfish messages may boost antibody testing uptake. Moreover, Social Comparison may in13

Vaccination is a dummy variable that takes the value of 1 if respondents have been tested and vaccinated. Thus,
the vaccination rate can be regarded as the proportion of newly acquired antibodies through vaccination. This outcome
variable matches MHLW’s policy goal.

14

A. Antibody Test (Behavior after wave 1)

0.035
0.067
0.109**
0.09*
0.087
0.042
0.072

MHLW (Control)

Treatments

MHLW (Age)
Altruistic
SelfishSocial Comparison
Deadline
Convenient
0.0

0.1

0.2

0.3

0.2

0.3

Proportion
B. Vaccination (Behavior after wave 1)

0.009
0.017
0.047*
0.027
0.049*
0.008
0.027

MHLW (Control)

Treatments

MHLW (Age)
Altruistic
SelfishSocial Comparison
Deadline
Convenient
0.0

0.1

Proportion

Figure 4: Effect of Text Messages on Behavior for Default Incentive Group (N = 805). Data: Menaged 40–46 years in wave 2 study sample. Note: Numbers in the figure indicate the proportion ofeach group. Error bars indicate the standard error of the mean. Asterisks are p-values of t-tests forthe difference-in-mean: * p < 0.1, ** p < 0.05, *** p < 0.01.
crease vaccination rates. Selfish message groups’ antibody testing uptake is 9%, 5.5 pp higher
than MHLW (control) (3.5%), which is statistically significant at the 10% level. In addition, Social Comparison’s vaccination rate is 4.9%, 4 pp higher than MHLW (control) (0.9%), which isstatistically significant at the 10% level.
Fig. 5shows the uptake rate of antibody testing (Panel A) and vaccination rates (Panel B)
for each experimental arm using a subsample of men aged 47–56 who needed costly procedures toobtain free vouchers in FY2019. Social Comparison may increase antibody testing uptake rate, but
not vaccination rate. In the MHLW (control) and Social Comparison message groups, antibody
testing uptake is 0.5% and 2.8%, respectively. However, none got vaccinated in both groups. The
Social Comparison message increases antibody testing by 2.3 pp compared to the MHLW (control)
message, which is statistically significant at the 10% level. However, the effect on vaccination rates
is zero.
We estimate a linear probability model directly controlling for age differences and obtain thesame results (see Appendix D). Among men aged 40–46, the Social Comparison message increases
antibody testing uptake by 5.7 pp compared to the MHLW (control) message, which is statisticallysignificant at the 10% level. In sum, the Altruistic message (and possibly the Social comparison

15

A. Antibody Test (Behavior after wave 1)

0.005
0.009
0.021
0.015
0.028*
0.013
0.01

MHLW (Control)

Treatments

MHLW (Age)
Altruistic
SelfishSocial Comparison
Deadline
Convenient
0.00

0.05

0.10

0.15

0.10

0.15

Proportion
B. Vaccination (Behavior after wave 1)

0
0.005
0.005
0.005
0
0.004
0

MHLW (Control)

Treatments

MHLW (Age)
Altruistic
SelfishSocial Comparison
Deadline
Convenient
0.00

0.05

Proportion

Figure 5: Effect of Text Messages on Behaviors for Opt-in Incentive Group (N = 1,467). Data:
Men aged 47–56 years in wave 2 study sample. Note: Numbers in the figure indicate the proportionof each group. Error bars indicate the standard error of the mean. Asterisks are p-values of t-tests
for the difference-in-mean: * p < 0.1, ** p < 0.05, *** p < 0.01.
message) increases antibody testing and vaccination rates among men aged 40–46 who received
free vouchers by default.

4.4

Mechanism of Difference of Uptake Rate between Antibody Testing and Vaccination

Vaccination rates are lower than antibody testing uptake rates in all experimental arms, regardlessof whether eligible men already received free vouchers. This may not be due to the large numberof people who tested negative for antibodies but were not vaccinated, but rather to an exogenous
factor in which fewer people should be vaccinated because the majority of those who took the test
already had antibodies. To check this point, we compute the number of people who had antibody
testing, who had a negative antibody test, and who were vaccinated in each experimental arm(Table 2).
This table shows that most of those with negative antibody test results were vaccinated in all
experimental arms, regardless of whether eligible men were automatically given free vouchers.14
14

The vaccination rate of negatives is 87.5% (= 21/24) among men who received free vouchers automatically in
FY2019. The 95% confidence interval using 1,000 bootstrap samples is [75.0%, 100.0%]. Similarly, the vaccination
coverage of the negatives is 66.7% (= 4/6) among men who require expensive procedures to receive the free coupons16

Table 2: Classification of Antibody Test Takers
Default Incentive Group
Text messages
MHLW (Control)
MHLW (Age)
Altruistic
SelfishSocial Comparison
Deadline
Convenient
Fisher’s exact test
(p-value)

Opt-in Incentive Group

Antibody
test

Negatives

Vaccination

Antibody
test

Negatives

Vaccination

4
8
14
10
9
5
8

1
2
7
3
5
1
5
0.53

1
2
6
3
5
1
3
0.66

1
2
4
3
6
3
2

0
2
1
1
1
1
0
0.47

0
1
1
1
0
1
0
1.00

Note: Fisher’s exact test was used to test the null hypothesis that the number of negative antibody tests
does not differ between experimental arms. It was also used to test the null hypothesis that the number ofvaccinations would not differ between experimental arms.

The negatives who received a monetary incentive automatically have been vaccinated in all messages except the Altruistic and Convenient messages. In the Altruistic and Convenient messages,
a few negatives have not been vaccinated. Similarly, the negatives who required expensive procedures to obtain the incentive were vaccinated in all message groups except MHLW (age) andSocial Comparison.
Furthermore, the number of negative antibody tests varied between experimental arms. The
25% (= 1/4) antibody tests are negative in eligible men in the MHLW (Control) group who received the free vouchers automatically. In contrast, given men who automatically received the free
vouchers, the negative ratios of antibody tests for the Altruistic and Social comparison messages,
which have a positive effect on vaccination, are 50% (= 7/14) and 55% (= 5/9), respectively.
The negative ratio of antibody testing for the Selfish message, which is only effective for antibody
test uptake, is 30% (= 3/10). This value is similar to the MHLW (control) message. Thus, the experimental arms with higher vaccination rates have a greater negative ratio, resulting in a positiveeffect on vaccination.
However, the variation in the negative ratio of antibody tests across experimental arms is most
likely due to statistical error. A Fisher’s exact test is used to test the null hypothesis that the numberof negative antibody tests does not differ across message groups. Consequently, regardless ofwhether men automatically received free vouchers or not, we cannot reject this null hypothesis.
Our data show that the negative ratio of antibody tests varies across experimental arms but not in
the population.15
in FY2019. Its 95% confidence interval, calculated using 1,000 bootstrap samples, is [33.3%, 100.0%].
15
A competing hypothesis is that after reading the Altruistic or Social Comparison messages, men who automaticallyreceived the free vouchers believed they did not have antibodies against rubella and thus had their antibodies tested. We

17

4.5

Monetary Value of Text Messages

Altruistic (and possibly Social comparison) messages encourage men who received free vouchers
by default to obtain antibody testing and be vaccinated. This implies that text messages complement financial incentives. If the MHLW only uses financial incentives without text messages, howmuch should it pay to eligible men? We use willingness to pay (WTP) for rubella vaccination to
calculate monetary values.16
We elicit the WTP for vaccination in the first wave before participants read messages. If thevaccination costs 5,000 JPY, we ask respondents if they will get it if the local government pays sj .
The subsidy amounts are sj ∈ {0, 1000, 2000, . . . , 10000}. Let sminbe the lowest subsidy at whichi
respondents indicate that they would vaccinate. Let smax
be the highest subsidy that respondentsi
indicate they would not vaccinate. We can identify the WTP for vaccination within the range
min 17 Thus, assuming that the true WTP is uniformly distributed within[5000 − smax
i , 5000 − si ).
min
the range [5000 − smax
i , 5000 − si ), we draw the demand curve for vaccination (see Appendix Efor details).
We calculate the monetary value of the messages by examining the change in WTP when thevaccination rate increases from baseline by the message’s effects. Baseline immunization rate is
the sum of free vaccinations and antibody testing in MHLW (Control) message group. We examine
estimate the uptake rate of antibody testing among the negatives to test this hypothesis. We cannot directly recover itfrom the data since some negatives did not get antibody testing. Using Bayes’ theorem, we can thus estimate the uptake
rate of antibody testing among the negatives indirectly. Consider the negative event A, and the antibody test uptake
event B. The overall uptake rate of antibody testing is P (B), and the negative ratio of antibody testing is P (A|B),
which we can directly estimate from the data. By Bayes’ theorem, the negative rate of antibody testing is as follows:
P (A|B) =P (B|A) · P (A),P (B)where P (A) is the proportion of the negatives, which is 0.2 from NIID’s data on antibody prevalence. The probability
P (B|A) is the antibody testing uptake rate conditional on the negatives, the parameter of interest. Thus, we can estimate
the update rate of antibody testing conditional on the negatives as follows:P̂ (B|A) =P̂ (A|B) · P̂ (B).
0.2

In the Altruistic message group, P̂ (A|B) = 0.5 and P̂ (B) = 0.109, so P̂ (B|A) = 0.273 (95% confidence interval
constructed on 1,000 bootstrap samples is [0.117, 0.469]). Furthermore, we test the null hypothesis that the event ofbeing negative (event A) is independent of the event of taking the antibody test (event B) to determine whether theselection for antibody testing is dependent on being negative. If the 95% confidence interval for P (B|A) − P (B)does not contain zero, the null hypothesis can be rejected at the 5% significance level. Therefore, we can reject the null
hypothesis because the 95% confidence interval for P (B|A) − P (B) is [0.016, 0.336]. Similarly, the 95% confidence
interval for P (B|A) − P (B) in the Social Comparison message group is [0.000, 0.350]. Thus, the negatives in the
Altruistic and Social Comparison messages are more likely to get antibody testing.
16
Several studies calculate the monetary value of text messages. For example,Bursztyn et al. (2019) calculated itusing the two relative effects of financial incentives and text message treatment groups. Moriwaki et al. (2020) also
surveyed WTP amounts and calculated monetary value. The latter study is similar to our approach.
17
If respondents indicated that they would not vaccinate at all subsidy amounts, then smax
= 10000. However, weicannot define sminin the data. Therefore, we assume smin
= 11000. This assumption does not affect the monetary valueiiof the messages.

18

Table 3: Estimated Monetary Value of Text Message Reminders
Monetary value (JPY)
Text messages
MHLW (Age)
Altruistic
SelfishSocial Comparison
Deadline
Convenient

Monetary value (USD)

Size of effectBaseline + size of effect

pp

total

pp

total

0.032
0.075
0.055
0.053
0.008
0.037

0.732
0.774
0.755
0.752
0.707
0.737

367.8542037.553
744.045596.335
86.059422.789

1.946
10.779
3.936
3.155
0.455
2.237

3.344
18.523
6.764
5.421
0.782
3.844

17.69097.98835.78228.678
4.139
20.332

Note: We use the effect size of each text message reminder on antibody testing. Baseline is the sum of the rate ofantibody test in the control and the free vaccination rates. The monetary value is the amount per person multipliedby the number of people who received the coupon in 2019 but did not use it until January 2020 (5.29 million). We
valued it in Japanese Yen and US Dollars (1USD = 110JPY). The unit of monetary value per person is 1 JPY and 1
USD, respectively. The unit of total monetary value is 1 billion JPY and 1 million USD, respectively.

the message’s impact on men who received free vouchers. Since they were vaccinated for free, thevaccine supply curve is horizontal at zero, and the equilibrium is part of the baseline. Adding the
MHLW(Control) antibody testing uptake rate to the baseline vaccination rate removes the effect ofthe message in our survey. Baseline vaccination rate is 70%, and the corresponding WTP is −394
JPY.
We use the text messages’ effects on antibody test uptake. As shown in Table 2, most people
with negative antibody tests are vaccinated. This suggests that antibody test takers are willing to
be vaccinated. Text messages’ effect on antibody test uptake can be viewed as an effect on (true)
vaccination intention inferred from behavior.
Table 3 shows the estimated message value. The per capita value in the fourth column is theabsolute change in WTP when vaccination rate is increased from baseline by the message effect(third column). Therefore, the Altruistic and Social Comparison message, which promotes antibody testing, value about 2,000 JPY (about 18 USD) and 600 JPY (about 5.5 USD), respectively.
The total monetary value is the product of the per capita value and the number of people who have
not yet used the free vouchers issued in FY2019 (5.29 million as of January 2020). In the fifth column, the Altruistic and Social Comparison messages are worth 10 billion JPY (about 98 million
USD) and 3 billion JPY (about 28 million USD), respectively.

5

Discussion and Conclusion

This study uses RCTs to investigate effective messages for promoting rubella antibody testingand vaccination. The main results reveal that the Altruistic message, which emphasizes the negative externality of infection, increases antibody testing uptake by 7.5 pp, equating to a 2,000 JPY
19

subsidy (about 18 USD if 1USD = 110JPY) among men who received free coupons by default
in FY2019. However, this message is less effective for men aged 47–57 years who need costly
procedures to obtain financial incentives.
This finding suggests that text message reminders are effective when financial incentives andreminders are closely combined; that is, text message reminders and financial incentives are complementary. The regression analysis also shows that the positive difference in the effect of the
Altruistic message on antibody testing by proximity to financial incentives is statistically significant, albeit at the lower end of the scale at 10% level (see Appendix D).
Our messages are ineffective among men who required costly procedures to obtain financial
incentives because there is low awareness of additional rubella routine immunization beginningin FY2019. Before presenting the messages, we inquire about the MHLW’s policy in the wave 1
survey. As a result, approximately 77.5% are unaware of the rubella immunization program. Even
if they read the messages and realized the importance of rubella antibody testing and vaccination,
they would believe they had to pay for these preventive actions. Our messages may not increase
the value of antibody testing and vaccination sufficiently to outweigh their cost. In addition, thefact that many people are unaware of the additional routine rubella vaccinations precludes thepossibility that they stopped getting antibody testing and were vaccinated in the first year (FY2019)because they expected to automatically receive the voucher the following year.
Furthermore, we discover that the Selfish and the Social comparison message may encourage
antibody testing among men who received vouchers by default. A simple difference-in-means test
reveals that, albeit with low statistical significance, the Selfish message encourages antibody testing. A linear probability model that accounts for individual characteristics shows that the Selfish
and Social comparison messages promote antibody testing, although the statistical significance isweak (see Appendix D). Furthermore, we estimate the message effect in comparison to the Altruistic message using a linear probability model (also see Appendix D). The findings reveal that theantibody testing uptake rates for the Selfish and Social comparison messages are not statisticallyand significantly different from the Altruistic message. In this sense, the Selfish and Social Comparison message may also encourage antibody testing. However, the effect size is not large enoughto maintain statistical power, requiring a reexamination with a larger sample size.
The fact that all behavior-related outcomes were self-reported limits the scope of this study.
The estimated effects are biased if the outcome variables contain recall bias or incorrect responses.
We assume no misreporting of whether respondents receive antibody testing and get vaccinatedor not. This assumption becomes more valid as the timing of antibody tests and vaccination ap-

20

proaches the time of wave 2 responses. Therefore, we can have some confidence in the results of
the antibody testing and vaccination decisions made after wave 1. We also estimate the effects on
non-timing-related behaviors. The results show that the Altruistic message encourages antibody
testing among default coupon recipients in FY2019, although the magnitude, monetary value, andstatistical significance vary (see Appendix F for details). Objective indicators, such as administrative data, must be used to completely eliminate self-reporting issues. This solution is one future
research direction.

21

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Appendix A

Background on Routine Rubella Vaccination in Japan

Vaccinations are of two types: “voluntary vaccination” and “routine vaccination.” Voluntary vaccinations incur costs, but routine vaccinations are free under the Immunization Act.
Since August 1977, Japan has vaccinated pregnant women against rubella. Junior high girls
were required to receive a single routine rubella vaccination. Meanwhile, male and female infants
aged 12–72 months received the measles, mumps, and rubella (MMR) vaccine in April 1989. In
April 1993, routine MMR vaccination was suspended due to aseptic meningitis.
The 1994 Immunization Act revision made routine vaccination voluntary. In April 1995, men
and women aged 12–90 months became the target population for routine rubella vaccination. Thischange aimed to end the rubella epidemic and achieve herd immunity. The government also madetransitional measures for generations who had not received rubella or MMR vaccines. Transitional
measures apply to (1) boys and girls under 90 months and first- and second-graders in 1995, (2)first-graders in 1996–1999, and (3) junior high school students who were born between April 2,
1979, and October 1, 1987, from April 1995 to September 2003. Two routine Measles and Rubella(MR) vaccinations have been given since 2006. The first dose is administered to 12-to-24-montholds, and the second is one year before elementary school.
In 2007, there was a nationwide measles outbreak among teens and people in their 20s. From
April 2008 to March 2013, the government gave first-year junior high and third-year high school
students the second dose of the MR vaccine.
Due to Japan’s history of routine vaccinations, two generations lack the rubella vaccine: (1)men and women born before April 2, 1962; (2) men born between April 2, 1962, and April 1, 1979.
The first generation graduated from junior high school before the routine rubella vaccination started
in 1977. The second generation’s rubella vaccination was limited to junior high girls. In 1995, thisgeneration’s men were not eligible for transitional measures. Men and women born after April 2,
1979, are eligible for routine vaccination, including transitional measures.
Rubella antibodies can be acquired through vaccination and infection. The first generation had
lived through a period when rubella was widespread and thus possessed antibodies from natural
rubella infection. Rubella antibodies are less common in men born between April 2, 1962, andApril 1, 1979.
As described in the main manuscript, in April 2019, MHLW in Japan started additional measures for routine rubella vaccination for the second group, men born between April 2, 1962, andApril 1, 1979. This additional measure aims to increase this group’s antibody coverage from 80%

24

Figure A.1: Sample of Voucher for Free Rubella Antibody Test and Vaccination
to 90% by March 2022. If this goal is achieved, Japan can achieve herd immunity against rubella.
Under this additional measure, local governments mail vouchers to men in the target generation
vouchers for free antibody testing and vaccination against rubella (Fig. A.1). They first use thevoucher to receive a free antibody test. Those without antibodies will receive a free vaccine.

25Appendix B

Additional Tables and Figures about Online Survey ExperimentThis section overviews our online surveys. We commissioned an Internet research firm, MyVoiceCom Co. Ltd., and conducted two experiments. Fig. B.1 shows experiment flow.
Wave 1 experiment was conducted on February 15–17, 2020. For the first survey, we surveyed
4,200 Japanese men ages 40–59 years residing throughout Japan. Wave 1 experiment randomly
assigns nudge-based reminders to test how they influence rubella antibody testing and vaccination
intentions. Before viewing nudge-based reminders, first-wave participants will be asked about
daily health behaviors, rubella knowledge, rubella infection history, and vaccination history (Questionnaire A). Table B.1 shows covariates used in regression analysis and balance checks.
Table B.1: List of Covariatesagecoupon2019
marriededucation
incomenoinfo_income
exercise_w1
health_check

flushot
handwash

temp_checkavoid_out
avoid_crowd

wear_mask

Description

Mean

Std.Dev.

(Wave1) Age as of April 2019 based on year of birth and month of birth.
(Wave1) Dummy variable taking one if 40 to 46 years old as of April 2019.
(Wave1) Dummy variable taking one if a respondent is married.
(Wave1) Years of education.
(Wave1) Household income. For those who did not respond withhousehold income, the overall average was substituted.
(Wave1) Dummy variable taking one if a respondent did not answer
household income.
(Wave1) Dummy variable taking one if a respondent exercises or plays
sports more than once a week.
(Wave1) Dummy variable taking one if a respondent has had a medical
examination at his/her city or place of employment in the past year fromthe time of the wave 1.
(Wave1) Dummy variable taking one if a respondent is vaccinated against
influenza every year.
(Wave2) Five-point Likert scale for the question ”I wash my hands andgargle frequently during the period from the end of the previous
questionnaire response to today.”(Wave2) Five-point Likert scale for the question ”I take my temperature
frequently during the period from the end of the previous questionnaire
response to today.”(Wave2) Five-point Likert scale for the question ”I am refraining fromgoing out during the end of the previous questionnaire response to today.”(Wave2) Five-point Likert scale for the question ”I avoid crowded places
when I go out from the end of the previous questionnaire response to
today.”(Wave2) Five-point Likert scale for the question ”I always wear a medical
mask when I go out or meet people during the period from the end of theprevious questionnaire response to today.”

48.66
0.35
0.58
14.75
684.90

5.69
0.48
0.49
2.31
375.74

0.15

0.36

0.22

0.42

0.68

0.46

0.27

0.45

3.91

1.04

2.26

1.22

2.96

1.20

3.38

1.10

3.14

1.38

Next, one of seven text messages is randomly displayed. Participants complete Questionnaire
B after viewing the message. This questionnaire asks about willingness to receive rubella antibody
testing, willingness to receive a rubella vaccine, birth year and month, marital status, and years of26

Figure B.1: Overview of Online Survey Experiment27

600 males read
MHLW (Control) message

600 males read
MHLW (Age) message

600 males read
Altruistic message

3963 males participated in Wave 2

600 males read
Deadline message

600 males read
Convenient message

Questionnaire A investigated daily health behaviors,
knowledge of rubella, infection history, and vaccination history.
Questionnaire B investigated the intention to be tested for antibody torubella and to be vaccinated, as well as socioeconomic attributes.
Wave 2 surveyed the behavior of antibody testingand vaccination against rubella since Wave 1.

600 males readSocial comparison message

4200 males answered Questionnaire B

600 males read
Selfish message

4200 males answered Questionnaire A

4200 males participated in Wave 1

education. Table B.1 contains descriptive statistics, including marital status and age from birthyear and month.
Wave 2 is a follow-up to wave 1. From March 17, 2020, to March 25, 2020, we surveyed
all first-wave participants and received 3,963 responses. The attrition rate is 5.64% across allseven groups. Also, the seven experimental arms have similar attrition rates. We linearly regress
an attrition dummy on treatment group dummies. F-test for joint null hypothesis is statisticallyinsignificant (F-value = 1.434; p-value = 0.197). Wave 2 tests how randomly assigned nudgebased reminders affect the actual behaviors of receiving rubella antibody testing and vaccination.
In wave 2, participants are asked if they have had antibody testing and vaccination since wave
1. The antibody testing question asks, “Have you had rubella antibody testing since the end of thelast survey?” Participants were given the following choices:• (a) Yes, I have taken antibody testing;• (b) No, I have not taken antibody testing;• (c) I have taken antibody testing before the last survey.
Meanwhile, the vaccination question is “Have you been vaccinated against rubella since the endof the last survey?” Respondents were given one of five options:• (a) I have been vaccinated;• (b) I do not need the vaccine due to a positive test or infection experience;• (c) I have taken antibody testing but have not been vaccinated yet;• (d) I have not taken antibody testing or gotten vaccinated;• (e) I have been vaccinated before the last survey.
In the main manuscript, we create a binary variable taking 1 if the respondent chooses option (a)and use it as an outcome variable for the uptake rate of antibody testing. Moreover, we createa binary variable taking 1 if the participant chooses option (a) for both the antibody testing andvaccination questions. Then, we use it as the outcome variable on vaccination.
Wave 2 also includes questions on daily infection prevention, such as handwashing and avoiding crowds, in light of the 2020 COVID-19 outbreak. Balance test and regression analysis also use
daily infection prevention behaviors. Table B.1 shows variable descriptive statistics.

28

Appendix C

Results of Balance Test

Each covariate’s linear model was estimated as a balancing test. The MHLW message group is thereference group in this model. The F-test tests whether all estimated coefficients are zero.
Table C.1 shows the balance test results for men aged 40 –-46 in the wave 1 study sample(default incentive group). Table C.2 shows the result of a balance test for men aged 47–57 in
the wave 1 study sample (opt-in incentive group). Table C.3 shows the default incentive group’sbalance test using the wave 2 study sample. Table C.4 shows the opt-in incentive group’s second
wave balance test. Each table’s right column shows p-values of the F-test.

29

Table C.1: Balance Test for Default Incentive Group in First Wave Study Sample
MHLW(Control)age
education
exercise_w1
flushot
health_checkincomemarriednoinfo_income

MHLW
(Age)

Altruistic

SelfishSocialComparison

Deadline

Convenient

p-value

42.862 43.046 43.135 43.045 42.909 42.906 42.866
14.654 14.473 14.595 14.205 14.099 14.348 14.575
0.246
0.176
0.277
0.189
0.165
0.217
0.213
0.238
0.260
0.203
0.144
0.140
0.239
0.236
0.654
0.626
0.696
0.538
0.603
0.674
0.614
557.562 645.556 613.156 623.542 569.530 590.422 633.487
0.408
0.458
0.412
0.417
0.455
0.478
0.480
0.162
0.168
0.203
0.197
0.157
0.130
0.181

0.874
0.446
0.285
0.055
0.150
0.149
0.785
0.706

Note: Description of variables is shown in Table B.1. The wave 1 study sample includes men aged
40–46 years who received free vouchers in FY2019. Columns 2–8 show sample averages for eachexperimental arm. Column 9 shows p-value of the joint null hypothesis (F-test).

Table C.2: Balance Test for Opt-in Incentive Group in First Wave Study Sample
MHLW(Control)age
education
exercise_w1
flushot
health_checkincomemarriednoinfo_income

MHLW
(Age)

Altruistic

SelfishSocialComparison

Deadline

Convenient

p-value

51.632 51.408 51.226 51.657 51.582 51.545 51.502
14.572 14.655 14.530 14.830 14.566 14.634 14.393
0.156
0.193
0.239
0.230
0.183
0.203
0.218
0.228
0.244
0.197
0.270
0.275
0.228
0.251
0.632
0.664
0.701
0.683
0.653
0.659
0.644
712.622 707.190 687.764 677.141 656.419 707.708 710.713
0.600
0.588
0.628
0.657
0.602
0.549
0.619
0.184
0.164
0.145
0.117
0.155
0.163
0.205

0.712
0.578
0.252
0.433
0.742
0.540
0.334
0.211

Note: Table B.1 describes variables. The wave 1 study sample includes men aged 47–57 who needed
expensive procedures to get free vouchers in FY2019. Columns 2–8 show sample averages for eachexperimental arm. Column 9 shows p-value of the joint null hypothesis (F-test).

30

Table C.3: Balance Test for Default Incentive Group in Second Wave Study Sample
MHLW(Control)age
avoid_crowd
avoid_out
education
exercise_w1
flushot
handwashhealth_checkincomemarriednoinfo_income
temp_checkwear_mask

MHLW
(Age)

Altruistic

SelfishSocialComparison

Deadline

Convenient

p-value

42.861 43.059 43.102 43.036 42.893 42.898 42.964
3.296
3.336
3.273
3.234
3.350
3.305
3.324
3.096
3.034
3.047
2.793
2.932
3.025
2.928
14.496 14.471 14.547 14.126 14.010 14.407 14.595
0.252
0.185
0.266
0.171
0.165
0.195
0.225
0.235
0.261
0.227
0.135
0.146
0.246
0.207
3.861
3.916
3.797
3.757
3.767
3.915
3.829
0.643
0.639
0.680
0.532
0.631
0.661
0.640
548.244 649.778 614.512 599.124 555.083 591.597 637.056
0.391
0.454
0.391
0.360
0.437
0.466
0.477
0.174
0.126
0.203
0.207
0.146
0.136
0.171
2.139
2.235
2.414
2.126
2.204
2.203
2.117
2.930
3.076
3.109
3.009
3.010
3.144
3.207

0.953
0.990
0.544
0.474
0.375
0.082
0.835
0.391
0.102
0.467
0.522
0.535
0.794

Note: Description of variables is shown in Table B.1. The wave 2 study sample includes men aged
40–46 years who received free vouchers in FY2019. Columns 2–8 show sample averages for eachexperimental arm. Column 9 shows p-values of the joint null hypothesis (F-test).

Table C.4: Balance Test for Opt-in Incentive Group in Second Wave Study Sample
MHLW(Control)age
avoid_crowd
avoid_out
education
exercise_w1
flushot
handwashhealth_checkincomemarriednoinfo_income
temp_checkwear_mask

MHLW
(Age)

Altruistic

SelfishSocialComparison

Deadline

Convenient

p-value

51.695 51.394 51.179 51.662 51.421 51.605 51.512
3.295
3.361
3.447
3.239
3.313
3.309
3.433
2.886
2.889
2.932
2.866
2.855
2.964
2.941
14.505 14.620 14.553 14.876 14.593 14.610 14.345
0.159
0.194
0.232
0.229
0.173
0.211
0.202
0.223
0.245
0.189
0.264
0.280
0.215
0.241
3.823
3.889
3.926
3.751
3.836
3.861
3.867
0.632
0.667
0.684
0.677
0.645
0.673
0.631
712.165 707.809 686.355 671.407 644.798 699.289 718.575
0.591
0.560
0.611
0.652
0.598
0.547
0.596
0.173
0.157
0.137
0.114
0.159
0.166
0.222
2.095
2.204
2.221
2.100
2.136
2.085
2.182
3.082
3.176
3.116
3.144
2.977
2.942
3.010

0.564
0.437
0.960
0.472
0.432
0.376
0.769
0.849
0.370
0.407
0.142
0.841
0.533

Note: Description of variables is shown in Table B.1. The wave 2 study sample includes men aged
47–57 years who needed expensive procedures to get free vouchers in FY2019. Columns 2–8 show
sample averages for each experimental arm. Column 9 shows p-values of the joint null hypothesis(F-test).

31

Appendix D

Estimation Results of Linear Probability Model

As age determines whether eligible men automatically got the free vouchers, the different effect of
the text messages for the two subsamples (men aged 40–46 years and aged 47-56 years) is influenced by whether to receive the vouchers automatically or not and by the age difference between
the two subsamples. After eliminating this issue, we will test whether the message’s effect differs
between age groups. We estimate a linear probability model of intent:
Yij = α +X

βj Messagej +jXγj (Messagej × Couponi ) + δCouponi + λXij0 + ij ,(1)jwhere where Messagej is a treatment dummy (the control group is MHLW(Control) message),
Couponi is a binary variable indicating 40-46 years old (who automatically received the free vouchers), and X is a set of covariates including age.
Our parameter of interest is βj and γj . The parameter βj represents a text message effect among
men who needed a costly procedure to get the free vouchers. The linear combination of parameters,
βj +γj , is a text message effect among men who already obtained monetary incentives as vouchers.
The parameter γj shows a difference in the message effect between the two age groups.
Table D.1 is the estimated result of the linear probability model of intentions. The linear probability model is used to represent a text message effect for two age groups in Table D.2. The
altruistic message effect on the intention to get antibody testing is statistically significant among
men who automatically received the vouchers in FY2019, similar to the t-test results. However,among men who required costly procedures to obtain incentives, its effect is statistically insignificant. It should be noted that the difference in effect between the two groups is not statisticallysignificant (see Table D.1).
Similar to the t-test results, the effect of Social Comparison messages on vaccination intention is statistically non-significant among men who received free vouchers in FY2019. The Social
Comparison message discourages men who underwent expensive procedures to get vaccinated. It
is -9.8 pp. This is bigger than difference-in-mean. Furthermore, Table D.1 shows that the difference between the two effects is statistically significant at the 10% level.
Table D.3 compares the effect of the message with that of the Altruistic message. In FY2019,the message effect among men who required a costly procedure to receive free vouchers is a linear
combination of parameters βj − βAltruistic . The message effect among men who received vouchers
automatically represents a linear combination of parameters (βj + γj ) − (βAltruistic + γAltruistic ).
Among men who have already received monetary incentives, the intention to get antibody testing32

Table D.1: Linear Probability Model of Intentions
Antibody Test

Vaccination

(1)

(2)

−0.033(0.038)0.044(0.041)0.014(0.040)−0.050(0.038)0.024(0.039)0.056(0.041)−0.080(0.051)
0.057(0.063)0.105(0.065)0.093(0.065)0.120*(0.063)0.005(0.062)−0.002(0.064)−0.075*(0.043)−0.040(0.044)−0.058(0.044)−0.095**(0.043)−0.033(0.043)−0.035(0.044)−0.075(0.062)0.102(0.075)0.066(0.073)0.135*(0.075)0.138*(0.075)0.023(0.073)0.059(0.075)

2615
0.337
0.331
X

2615
0.502
0.497
X

MHLW (Age)
Altruistic
SelfishSocial Comparison
Deadline
Convenient
CouponCoupon×MHLW (Age)
Coupon×Altruistic
Coupon×Selfish
Coupon×Social Comparison
Coupon×Deadline
Coupon×Convenient
Num.Obs.
R2
R2 Adj.
Covariates

Note: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard
errors. We use the wave 2 study sample and control for covariates. The list of covariates is presented in Table B.1.

33

Table D.2: Effects of Text Message on Intentions for Two Groups Using Linear Probability Model
Estimates
Antibody testing

VaccinationGroup

Text messages

estimate

std.error

p.valueestimate

std.error

p.value

Opt-inincentive

MHLW (Age)−0.033

0.038

0.380

−0.075

0.043

0.084

Altruistic
SelfishSocial Comparison
Deadline
Convenient
MHLW (Age)

0.044
0.014
−0.050
0.024
0.056
0.024

0.041
0.040
0.038
0.039
0.041
0.050

0.292
0.727
0.186
0.536
0.165
0.630

−0.040−0.058−0.095−0.033−0.035
0.027

0.044
0.044
0.043
0.043
0.044
0.061

0.360
0.189
0.026
0.440
0.421
0.660

Altruistic
SelfishSocial Comparison
Deadline
Convenient

0.149
0.107
0.070
0.030
0.055

0.050
0.051
0.051
0.048
0.050

0.003
0.034
0.172
0.541
0.272

0.026
0.077
0.043
−0.011
0.024

0.058
0.061
0.062
0.059
0.060

0.662
0.209
0.483
0.855
0.688

default
incentive

Note: We estimate the effect for the default incentive group (men aged 40–46) and the opt-in incentive
group (men aged 47–57 years) using Table D.1. The effect for the opt-in incentive group is the estimate
βj . The effect for the default incentive group is a linear combination of the estimates, βj + γj . F-test
is used for linear combination null hypothesis. Robust standard errors.

Table D.3: Effects of Text Message on Intentions for Two Groups Using Linear Probability Model
Estimates (Baseline: Altruistic Message)
Antibody testing

VaccinationGroup

Text messages

estimate

std.error

p.valueestimate

std.error

p.value

Opt-inincentive

MHLW (Age)−0.077

0.041

0.059

−0.035

0.045

0.440

SelfishSocial Comparison
Deadline
Convenient
MHLW (Age)−0.030−0.093−0.0190.013−0.125

0.043
0.040
0.042
0.043
0.052

0.490
0.021
0.648
0.766
0.017

−0.018−0.055
0.007
0.005
0.001

0.046
0.044
0.045
0.045
0.058

0.697
0.216
0.875
0.907
0.982

SelfishSocial Comparison
Deadline
Convenient−0.042−0.079−0.119−0.094

0.053
0.054
0.051
0.052

0.435
0.142
0.020
0.072

0.051
0.018
−0.036−0.001

0.058
0.059
0.056
0.057

0.377
0.762
0.518
0.983

default
incentive

Note: We estimate the effect for the default incentive group (men aged 40–46) and the opt-in incentive
group (men aged 47–57 years) using Table D.1. The effect for the opt-in incentive group is a linear
combination of the estimates, βj − βAltruistic . The effect for the default incentive group is a linear combination of the estimates, βj + γj − (βAltruistic + γAltruistic ). F-test is used for linear combination null
hypothesis. Robust standard errors.

34

Table D.4: Linear Probability Model of Behaviors
Antibody Test

Vaccination

(1)

(2)

0.003
(0.008)0.016(0.011)0.007(0.010)0.022*(0.013)0.009(0.009)0.007(0.009)0.019(0.020)0.026(0.030)0.057*(0.034)0.054(0.033)0.034(0.035)−0.003(0.026)0.031(0.031)

0.004(0.005)0.005(0.005)0.005(0.005)0.000(0.001)
0.005(0.005)0.000(0.001)
0.003(0.011)
0.002(0.015)0.032(0.021)0.013(0.018)0.039*(0.023)−0.006(0.013)0.018(0.018)

2272
0.080
0.069
X

2272
0.040
0.028
X

MHLW (Age)
Altruistic
SelfishSocial Comparison
Deadline
Convenient
CouponCoupon×MHLW (Age)
Coupon×Altruistic
Coupon×Selfish
Coupon×Social Comparison
Coupon×Deadline
Coupon×Convenient
Num.Obs.
R2
R2 Adj.
Covariates

Note: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust Standard
errors. We use the wave 2 study sample. We use waves 1 and2 covariates. The list of covariates is presented in Table B.1.

for the Selfish and Social Comparison messages is not statistically significantly different from thatof the Altruistic message group. In this sense, the Selfish message and Social Comparison may
encourage people to test for antibodies. However, these differences are insufficient to maintainsufficient power. We should conduct another study with a larger sample size.
As with intention, we estimate a linear probability model of behavior. Table D.4 shows linear
probability model results. Table D.5 shows message effects using linear probability model estimates. Similar to a difference-in-mean test, statistically significant at the 10% level, the Social
Comparison message increased antibody testing uptake by 5.7 pp among men who received free
vouchers automatically in FY2019.Table D.4 also shows that the Altruistic message effect on antibody test uptake and the Social Comparison message effect on vaccination uptake are statisticallysignificant at the 10% level.
35

Table D.5: Effects of Text Messages on Behaviors for Two Groups Using Linear Probability Model
Estimates
Antibody testing

VaccinationGroup

Text messages

estimate

std.error

p.valueestimate

std.error

p.value

Opt-inincentive

MHLW (Age)

0.003

0.008

0.755

0.004

0.005

0.436

Altruistic
SelfishSocial Comparison
Deadline
Convenient
MHLW (Age)

0.016
0.007
0.022
0.009
0.007
0.029

0.011
0.010
0.013
0.009
0.009
0.028

0.159
0.501
0.082
0.346
0.430
0.307

0.005
0.005
0.000
0.005
0.000
0.006

0.005
0.005
0.001
0.005
0.001
0.015

0.381
0.301
0.920
0.318
0.876
0.702

Altruistic
SelfishSocial Comparison
Deadline
Convenient

0.073
0.061
0.056
0.005
0.038

0.032
0.032
0.032
0.025
0.029

0.023
0.055
0.084
0.833
0.202

0.037
0.018
0.040
−0.002
0.018

0.020
0.017
0.023
0.012
0.018

0.071
0.292
0.084
0.897
0.311

Default
incentive

Note: We estimate the effect for the default incentive group (men aged 40–46 years) and the opt-in
incentive group (men aged 47–57) using results of the linear probability model presented in D.4. The
effect for the opt-in incentive group is the estimate βj . The effect for the default incentive group isa linear combination of the estimates, βj + γj . F-test is used for linear combination null hypothesis.
Robust standard errors.

Table D.6: Effects of Text-Based Nudges on Behaviors for Two Groups Using Linear Probability
Model Estimates (Baseline: Altruistic Message)
Antibody Test

VaccinationGroup

Text messages

estimate

std.error

p.valueestimate

std.error

p.value

Costlyprocedure

MHLW (Age)−0.013

0.012

0.287

−0.001

0.007

0.898

SelfishSocial Comparison
Deadline
Convenient
MHLW (Age)−0.0090.006−0.007−0.009−0.044

0.013
0.015
0.013
0.012
0.036

0.501
0.693
0.569
0.476
0.218

0.001
−0.0050.000−0.005−0.031

0.008
0.006
0.007
0.005
0.022

0.927
0.405
0.999
0.369
0.158

SelfishSocial Comparison
Deadline
Convenient−0.012−0.017−0.068−0.035

0.038
0.039
0.033
0.036

0.749
0.662
0.039
0.330

−0.0180.003−0.038−0.019

0.024
0.028
0.020
0.024

0.438
0.923
0.058
0.429

Automaticreceiving

Note: We estimate the effect for the default incentive group (men aged 40–46) and the opt-in incentive
group (men aged 47–57) using results of the linear probability model presented in D.4. The effect forthe opt-in incentive group is a linear combination of the estimates, βj − βAltruistic . The effect for thedefault incentive group is a linear combination of the estimates, βj + γj − (βAltruistic + γAltruistic ). F-test
is used for linear combination null hypothesis. Robust standard errors.

36

Table D.6 compares the effect of the message with that of the Altruistic message. Other text
message groups’ antibody testing uptake rates do not differ significantly from those of the Altruistic
message group. In this sense, text messages other than the Altruistic message may also promote
antibody test uptake; however, the difference is insufficient to maintain sufficient power.

37

Appendix E

Elicitation of WTP for Rubella Vaccination and Estimation of Demand Curve

Figure E.1: Elicitation of Willingness-to-Pay for Rubella Vaccination.
To evaluate the effect of the text messages in monetary terms, we use the rubella vaccination
willingness to pay. Before presenting text messages, the first wave survey elicits the willingnessto-pay amount using the multiple price list method (see Figure E.1 for the survey screen). We ask
respondents to indicate their intention to get vaccinated when the local government’s subsidy is
5,000 JPY. sj . The subsidy amounts are sj ∈ {0, 1000, 2000, . . . , 10000}. Let sminbe the lowesti
subsidy at which respondents indicate that they would vaccinate. Let smax
be the highest subsidy
i
38

5000

WTP (Price of vaccination=5,000JPY)

4000
3000
2000
1000
0
−1000
−2000
−3000
−4000
−5000
−6000
0.2

0.4

0.6

0.8

1.0

Cumulative fraction

Figure E.2: Demand Curve of Rubella Vaccination for Default Incentive Group. Data source: Menaged 40–46 years in the second wave analysis sample. Note: Black triangles indicate the sum of
the percentage of vaccination when vaccination costs are free and the percentage of antibody test
uptake in the MHLW message combined, and the corresponding WTP.
at which respondents indicate they would not vaccinate. If respondents indicated that they would
not vaccinate at all subsidy amounts, then smax
= 10000. However, we cannot define sminin theiidata. Therefore, we assume smin
= 11000.
i
We can identify the willingness to pay for vaccination within the range [5000 − smax
i , 5000 −smini ). Thus, without additional assumptions, the demand curve is step-wise, and we estimate themonetary value of the message effect with bounds.
To estimate the monetary value, we assume true willingness to pay for vaccination is uniformly
max
distributed between [[5000 − smin
i , 5000 − si ). The vaccination demand curve can then be lin-

early interpolated. Figure E.2 shows a linear-interpolated demand curve for men aged 40–46 whoreceived vouchers in FY2019 but had not gotten antibody testing or vaccination at the wave 1
survey.

39

Appendix F

Analysis to Address Recall Bias Associated with SelfReporting of Behavior

In this section, we show results that account for recall bias in second-wave antibody testing andvaccination behaviors. The second wave asks about antibody testing and vaccinations before wave
1. If these responses are subject to recall bias, we should not exclude those who got antibody testingor were vaccinated before wave 1 in the second wave (criterion of the wave 2 analysis sample). We
estimate the effect of text messages on behavior by excluding men who have already been tested
or vaccinated at response of wave 1 (the same criterion as in the wave 1 analysis sample). Thus,
we include those who report in wave 2 getting antibody testing or vaccinated only before wave 1.
Table F.1: Balance Test for Default Incentive GroupMHLW(Control)age
avoid_crowd
avoid_out
education
exercise_w1
flushot
handwashhealth_checkincomemarriednoinfo_income
temp_checkwear_mask

MHLW
(Age)

Altruistic

SelfishSocialComparison

Deadline

Convenient

p-value

42.869 43.063 43.099 43.016 42.948 42.901 42.893
3.328
3.331
3.261
3.211
3.339
3.336
3.273
3.082
3.047
3.028
2.805
2.896
3.038
2.926
14.598 14.457 14.592 14.236 14.130 14.267 14.603
0.262
0.181
0.289
0.179
0.165
0.198
0.215
0.238
0.268
0.211
0.130
0.148
0.244
0.215
3.885
3.866
3.824
3.764
3.748
3.954
3.744
0.656
0.638
0.683
0.528
0.617
0.664
0.620
556.952 652.347 611.214 625.226 564.594 588.881 640.231
0.402
0.465
0.408
0.415
0.452
0.473
0.479
0.164
0.157
0.190
0.187
0.157
0.130
0.182
2.180
2.260
2.380
2.179
2.226
2.145
2.157
2.951
3.063
3.113
3.033
2.965
3.115
3.174

0.948
0.958
0.509
0.530
0.161
0.040
0.624
0.236
0.096
0.765
0.840
0.735
0.852

Note: Description of variables is shown in Table B.1. We use men aged 40–46 received free vouchers
in FY2019. Columns 2–8 show sample averages for each experimental arm. Column 9 shows p-valuesof the joint null hypothesis (F-test).

We estimate the message effects using two subsamples, dividing the sample by automaticallyreceiving vouchers in FY2019 (aged 40-46 years or not). Tables F.1 and F.2 show balance tests.
We confirm that treatment arms have balanced observables.
We calculate the smallest effect size required to maintain 80% power and 5% significance.
When using the subsample of men who received free vouchers automatically in FY2019, we require at least a 6.8 percentage point difference. When we use the subsample of men who required
costly procedures to obtain monetary incentives such as vouchers, the minimum difference is 5.2
percentage points.
We alter the outcome variables. The outcome variable of antibody test uptake in the main paper
is a binary variable that takes one if the respondent received antibody testing after the first wave
40

Table F.2: Balance Test for Opt-in Incentive GroupMHLW(Control)age
avoid_crowd
avoid_out
education
exercise_w1
flushot
handwashhealth_checkincomemarriednoinfo_income
temp_checkwear_mask

MHLW
(Age)

Altruistic

SelfishSocialComparison

Deadline

Convenient

p-value

51.664 51.396 51.210 51.602 51.454 51.567 51.536
3.307
3.378
3.429
3.250
3.306
3.296
3.455
2.903
2.917
2.919
2.884
2.825
2.966
2.982
14.542 14.652 14.533 14.833 14.576 14.609 14.378
0.160
0.196
0.248
0.231
0.188
0.206
0.216
0.223
0.243
0.200
0.264
0.284
0.223
0.248
3.803
3.883
3.900
3.778
3.817
3.833
3.892
0.634
0.661
0.690
0.685
0.651
0.670
0.649
709.184 711.202 688.773 673.323 645.225 712.130 713.246
0.588
0.578
0.624
0.662
0.603
0.554
0.608
0.185
0.165
0.129
0.111
0.157
0.163
0.212
2.139
2.248
2.210
2.083
2.192
2.086
2.270
3.071
3.191
3.157
3.148
2.961
2.966
3.068

0.722
0.354
0.848
0.589
0.304
0.453
0.827
0.872
0.326
0.337
0.076
0.490
0.447

Note: Description of variables is shown in Table B.1. We use men aged 40–46 years who needed
expensive procedures to get free vouchers in FY2019. Columns 2–8 show sample averages for eachexperimental arm. Column 9 shows p-values of the joint null hypothesis (F-test).

survey. The vaccination outcome variable is a dummy variable that takes one if the respondent
received antibody testing after the first wave and also received the vaccination. In contrast, in this
supplement, the antibody test uptake is a dummy variable that takes one if the respondent reported
getting antibody testing in the second wave, regardless of timing. Furthermore, vaccination uptake
is a dummy variable that takes one if the participant responded in the second wave that they received
antibody testing and vaccination regardless of timing.
Using a subsample of men who received free vouchers in FY2019, we present in Fig. F.1 theuptake rate of antibody taking (Panel A) and vaccination (Panel B) for each experimental arm. The
Altruistic message encourages antibody testing. MHLW (Control) and Altruistic antibody testing
uptake rates are 6.6% and 14.1%, respectively. The Altruistic message increased antibody testingby 7.5 percentage points, which is statistically significant at the 5% level. The result is consistent
with the main paper. The altruistic message effect on vaccination is insignificant.
Fig. F.2 shows the uptake rate of antibody testing (Panel A) and vaccination (Panel B) for
each experimental arm among men who required costly procedures to obtain vouchers in FY2019.
Altruistic and Convenient messages encourage antibody testing, but only Convenient boosts vaccination rates. The MHLW (Control) message group’s antibody testing uptake rate is 2.5%, while
the Altruistic and Convenient messages are 5.7% and 6.8%, respectively. Thus, the Altruistic andConvenient messages increase antibody testing uptake by 3.2 and 4.3 percentage points, respectively. MHLW (Control) and Convenient have 1.7% and 5% vaccination coverage, respectively.
The Convenient message effect on vaccination coverage is 3.3
41

A. Antibody Test (Behavior)

0.066
0.102
0.141**
0.122
0.113
0.099
0.099

MHLW (Control)

Treatments

MHLW (Age)
Altruistic
SelfishSocial Comparison
Deadline
Convenient
0.0

0.1

0.2

0.3

0.2

0.3

Proportion
B. Vaccination (Behavior)

0.025
0.047
0.049
0.057
0.052
0.053
0.05

MHLW (Control)

Treatments

MHLW (Age)
Altruistic
SelfishSocial Comparison
Deadline
Convenient
0.0

0.1

Proportion

Figure F.1: Effect of Text Messages on Behavior for Default Incentive Group (N = 881). Note:
Numbers in the figure indicate the proportion of each group. Error bars indicate the standard errorof the mean. Asterisks are p-values for t-tests of the difference in means from the MHLW message
group: * p < 0.1, ** p < 0.05, *** p < 0.01.
A. Antibody Test (Behavior)

0.025
0.039
0.057*
0.051
0.048
0.039
0.068**MHLW (Control)

Treatments

MHLW (Age)
Altruistic
SelfishSocial Comparison
Deadline
Convenient
0.00

0.05

0.10

0.15

Proportion
B. Vaccination (Behavior)

0.017
0.03
0.043
0.037
0.013
0.021
0.05*

MHLW (Control)

Treatments

MHLW (Age)
Altruistic
SelfishSocial Comparison
Deadline
Convenient
0.00

0.05

0.10

0.15

Proportion

Figure F.2: Effect of Text Messages on Behaviors for Opt-in Incentive Group (N = 1,578). Note:
Numbers in the figure indicate the proportion of each group. Error bars indicate the standard errorof the mean. Asterisks are p-values for t-tests of the difference in means from the MHLW message
group: * p < 0.1, ** p < 0.05, *** p < 0.01.

42

Table F.3: Linear Probability Model of Behaviors
Antibody Test

Vaccination

(1)

(2)

0.010
(0.017)0.028(0.019)0.020(0.018)0.020(0.018)0.013(0.016)
0.041**(0.020)0.019(0.028)0.025(0.038)0.046(0.041)
0.042(0.041)
0.031(0.041)
0.019(0.038)−0.006(0.040)0.012(0.014)0.023(0.016)0.019(0.015)−0.005(0.011)
0.005(0.013)0.031*(0.017)−0.014(0.021)0.010(0.027)0.000(0.028)0.015(0.029)0.032(0.027)0.023(0.027)−0.006(0.029)

2459
0.096
0.086
X

2459
0.052
0.042
X

MHLW (Age)
Altruistic
SelfishSocial Comparison
Deadline
Convenient
CouponCoupon×MHLW (Age)
Coupon×Altruistic
Coupon×Selfish
Coupon×Social Comparison
Coupon×Deadline
Coupon×Convenient
Num.Obs.
R2
R2 Adj.
Covariates

Note: * p < 0.1, ** p < 0.05, *** p < 0.01. Robust standard
errors.. We also control for covariates obtained in waves 1
and 2. The list of covariates is presented in Table B.1.

43

Table F.4: Effects of Text Messages on Behaviors for Two Groups Using Linear Probability Model
Estimates
Antibody testing

Vaccination

How to get
coupons

Text-based nudges

estimate

std.error

p.valueestimate

std.error

p.value

Opt-inincentive

MHLW (Age)

0.010

0.017

0.530

0.012

0.014

0.404

Altruistic
SelfishSocial Comparison
Deadline
Convenient
MHLW (Age)

0.028
0.020
0.020
0.013
0.041
0.035

0.019
0.018
0.018
0.016
0.020
0.035

0.146
0.270
0.275
0.436
0.039
0.308

0.023
0.019
−0.005
0.005
0.031
0.022

0.016
0.015
0.011
0.013
0.017
0.023

0.152
0.213
0.668
0.688
0.065
0.354

Altruistic
SelfishSocial Comparison
Deadline
Convenient

0.074
0.062
0.051
0.032
0.035

0.036
0.037
0.037
0.034
0.035

0.043
0.088
0.166
0.347
0.322

0.023
0.034
0.027
0.028
0.025

0.023
0.025
0.025
0.024
0.024

0.306
0.175
0.278
0.246
0.296

Default
incentive

Note: We estimate the effect for the default incentive group (men aged 40–46) and the opt-in incentive
group (men aged 47–57) using F.3. The effect for the opt-in incentive group is the estimate βj . The
effect for the default incentive group is a linear combination of the estimates, βj + γj . F-test is used forlinear combination null hypothesis. Robust standard errors.

We estimate a linear probability model because the difference in effects between two subsamples is affected not only by whether or not the free vouchers are automatically distributed but also
by differences in age groups. Regression analysis produces the same results. Furthermore, Table
F.4 shows that the effect of the Selfish message on antibody testing uptake rate is 6.2 percentage
points among men who automatically received coupons in FY 2019, which is statistically significant at the 10% level. Even though the effect size remains unchanged, the effect of Altruistic
messages on antibody testing is statistically insignificant among men who required a costly procedure to receive the free vouchers. Furthermore, Table F.3 shows that the heterogeneous message
effects are statistically insignificant.
Table F.5 shows the number of people who had antibody testing, the number of people who hada negative antibody test, and the number of people who were vaccinated in each experimental arm.
As stated in the main paper, most negatives receive vaccination in all experimental arms, regardlessof whether eligible men received vouchers automatically. As a result, the message effects on
vaccination are strongly influenced by the negative ratio of antibody testing. The Convenientmessage has a negative rate of 87% (= 13/15) among eligible men who needed costly proceduresto get vouchers. As a result of this, the Convenient message has a statistically significant positiveeffect on vaccination rates. The variation in negative rates across experimental arms, on the other
hand, is most likely a statistical error. A Fisher’s exact test is used to test the null hypothesis that44

Table F.5: Classification of Antibody Test Takers
Default incentive group
Text messages
MHLW (Control)
MHLW (Age)
Altruistic
SelfishSocial Comparison
Deadline
Convenient
Fisher’s exact test
(p-value)

Opt-in incentive group

Antibody
test

Negatives

Vaccination

Antibody
test

Negatives

Vaccination

8
13
20
15
13
13
12

3
6
8
7
7
7
8
0.83

3
6
7
7
6
7
6
0.76

6
9
12
11
11
9
15

4
9
9
8
5
6
13
0.12

4
7
9
8
3
5
11
0.31

Note: Fisher’s exact test was used to test the null hypothesis that the number of negative antibody tests
and vaccinations does not differ between experimental arms.

Table F.6: Estimated Monetary Value of Text Message Reminders
Monetary value (JPY)

Monetary value (USD)

Text-based nudge

Size of effectBaseline + size of effect

pp

total

pp

total

MHLW (Age)
Altruistic
SelfishSocial Comparison
Deadline
Convenient

0.037
0.075
0.056
0.047
0.034
0.034

0.766
0.805
0.786
0.777
0.763
0.763

1528.377
3925.285
3285.074
2200.534
1331.690
1327.720

8.085
20.76517.37811.641
7.045
7.024

13.89435.68429.86420.00512.10612.07073.501
188.771
157.982105.826
64.04263.851

Note: We use the effect size of each text message reminder on antibody testing. Baseline is the sum of the rate ofantibody test in the control and the free vaccination rates. The monetary value is the amount per person multipliedby the number of people who received the coupon in 2019 but did not use it until January 2020 (5.29 million). We
valued it in Japanese Yen and US Dollars (1USD = 110JPY). The unit of monetary value per person is 1 JPY and 1
USD, respectively. The unit of total monetary value is 1 billion JPY and 1 million USD, respectively.

the number of negative antibody tests is the same across experimental arms. As a result, in two
subsamples, we cannot reject the null hypothesis.
In terms of monetary value, we attempt to assess the impact of text messages among those who
automatically received free vouchers in FY2019. Fig. F.3 draws the demand curve for rubellavaccinations using men who automatically received monetary incentives as vouchers in FY2019,using the method presented in the main paper and Appendix E. The vaccination rate is 0.664 whenthe vaccination price is zero.
Using the message effects on the uptake rate of antibody testing, Table F.6 shows the economic
value of text messages. The per capita value displayed in the fourth column represents the absolutechange in WTP when vaccination rate is increased from baseline by the message effect (thirdcolumn). The value per person of the Altruistic message is approximately 3,900 JPY (about 35
USD). The total value is equal to the product of the per capita value and the number of people who

45

5000

WTP (Price of vaccination=5,000JPY)

4000
3000
2000
1000
0
−1000
−2000
−3000
−4000
−5000
−6000
0.2

0.4

0.6

0.8

1.0

Cumulative fraction

Figure F.3: Demand Curve of Rubella Vaccination for Default Incentive Group. Note: Black
triangles indicate the sum of the percentage of vaccination when vaccination costs are free and thepercentage of antibody test uptake in the MHLW message combined, and the corresponding WTP.
have not yet redeemed their free vouchers for FY2019 (5.29 million as of January 2020). The totalmonetary value of the Altruistic message is 20 billion JPY in the fifth column (about 189 million
USD).

46