DP2024-17

Preferences for Policies from the
Perspectives of Different Generations:
Evidence from a Stated Choice Experimentin JapanYo i c h i r o T S UJ I
Junyi SHEN

April 30, 2024

Preferences for policies from the perspectives of different
generations: Evidence from a stated choice experiment in Japan
Yoichiro Tsuji* and Junyi Shen#
Abstract
Although policy choices can impact not only the present but future generations, only
the present generation can make such choices. If a policy imposes a burden on future
generations, will the present generation consider the policy’s impact on future generations
when making a choice? In response to this intergenerational social dilemma, numerous
empirical studies have shown that when asked to imagine themselves as a future
generation, the present generation’s participation in resource distribution decisionmaking is significantly impacted for decisions that consider future generations; moreover,
local governments in Japan have made efforts that use this methodology as a social
practice (Saijo, 2022). In this study, we aim to clarify what is needed for future
generations to be considered by analyzing the respondents’ policy preferences using an
online stated choice experiment survey. The respondents were assigned different
generational standpoints and were asked to choose their favorite of three policy packages,
each of which comprised several policies. The results show that, in general, respondents
tended to avoid placing direct burdens on the present generation, regardless of thegeneration they represented. However, respondents who took their children andgrandchildren's standpoints tended to prefer policies that would not burden future
generations. In addition, respondents with prosocial tendencies made choices that focusedon future generations.
Keywords: Generational standpoint; Policy preferences; Stated choice experiment;
Present generation; Future generation
JEL codes: C25, D64, H50
1*Corresponding author. Graduate School of Economics, Kobe University, 2–1 Rokkodai,

Nada, Kobe 657–8501, Japan. Email: yoichiro.2g@gmail.com#

Research Institute for Economics and Business Administration, Kobe University, 2–1

Rokkodai, Nada, Kobe 657–8501, Japan.

2

1.

Introduction
In many countries, policies are decided indirectly and democratically throughelections and parliamentary voting at the national and local levels. However, people who
participate in elections, debates, and voting in assemblies are the present generation.
Moreover, the benefits of the policies they choose accrue to their generation, even if a
policy addresses population aging or the declining birthrate. When the question of how
the financial resources to implement a policy should be obtained arises, two options areavailable: the funds can be provided by taxes on the present generation or by issuinggovernment bonds, which places the burden of redemption on future generations.
Unsurprisingly, the choice to place the burden on the present generation has been
repeatedly avoided, instead postponing it for future unborn generations to bear (MoF,
2022 1; Miyazato, 2015 2).
These intergenerational social dilemmas extend the effect of an individual’s rational
behavior to the detriment of an entire generation. For example, the present generation’s
rational behavior, which is often detrimental to future generations, has become an
extremely important topic of discussion as the effects of climate change have increased.
Studies that have experimented with intergenerational social dilemmas have shownthat asymmetric resource distribution shifts can be regarded as customary when theprevious generation's group passes on advice to a group of succeeding generations(Schotter and Sopher, 2003). Hillis and Lubell (2015) showed that in an intergenerational
public goods game, intergenerational collaborative advice positively affected
contributions by and collaborative conversations among individuals in a subsequent
generation.
These studies have shown that passing on advice to succeeding generations
influences decision-making in those generations. However, one succeeding generation is
only a part of a continuous group linkage; the primary focus is on how the decision1 This study reported that in Japan's FY2022 budget, one-third of the revenues were financed byissuing government bonds, which, according to the report, burdens future generations.
2 Using generational accounting of Japan's redistributive policies, this study reported that from the
1990s to the mid-2000s, burdens were passed on to future generations, reducing the working-age
population’s burden.
3

making influences each succeeding generation in the linked groups. However, for realworld problems, we need to instead focus on how to influence the “previous” generation’s
decision-making (i.e., that of the present generation).
How can the present generation consider the interests of future generations?
Experimental studies in the Future Design literature have shown that participants who areassigned to play the role of future generations are more likely to deliberate and makeresource distribution decision choices that consider those generations (Kamijo et al.,2017; Shahrier et al., 2017; Hara et al., 2019; Timilsina et al., 2021; Hiromitsu, 2017). To
explore this issue more deeply, we conducted an online stated choice experiment survey
for the current study. We created policy packages comprising multiple policies that would
divide the burdens between the present and future generations. The respondents wererandomly selected to answer the choice experiment questions from the standpoint of oneof three generations (i.e., their own, their children's, or their grandchildren's generation)
before they were asked to choose one favorite package from the three policy packages ineach choice set. Investigating individuals’ preferences for different policies from thestandpoint of their children and grandchildren’s generations is a novel effort in theliterature. Studies (e.g., Graham et al., 2017; Fairbrother et al., 2021) have observed the
distribution of benefits between current and future generations; however, neither of thesestudies investigated policy preferences from the standpoint of future generations.
The policies included in the packages in this study are related to the consumption
tax rate, temporary benefit payments in extraordinary catastrophes, percentage of total
public infrastructure investment to maintain the already existing public infrastructure,social insurance premiums as a percentage of salary, and renewable energy rates in theenergy mix. To analyze respondents’ preferences for these policies from different
generational standpoints, we used both conditional logit (CL) and latent class logit (LCL)
models. The LCL approach allows us to explore the heterogeneity among respondents’preferences for different policies from each generational standpoint and categorize therespondents into different types. We then used a multinomial logit (ML) model to analyze
what respondent socioeconomic characteristics and social value orientations would affecttheir probabilities of falling into the different types obtained from the LCL results.
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The remainder of this paper is organized as follows. Section 2 presents the
hypotheses, and Section 3 describes survey issues. Section 4 provides the results anddiscussion. Finally, Section 5 presents the conclusions and implications for future work.
2.

Hypothesis
To test how resources would be distributed between the current and next generations,Kamijo et al. (2017) conducted an intergenerational sustainability dilemma game where
subjects were divided into six generations. Each generation was divided into two cases,
one with only their own generation and the other with a future generation, where thefuture generation was approximated by asking the subjects to play the role of the future
generation. The results showed that subjects were more than 60% more likely to choosea sustainable resource distribution that considers the interests of future generations when
they conferred with the future generation than when they talked only with the present
generation. Treating the imaginary future generation as an approximation of the(currently) non-existent future generation, a hypothesis was established to determine
whether the policies chosen by different generations would differ and which generation
would be burdened.
In a US public opinion survey on climate change, 76% of Generation Z and 81% of
millennials said that the US should develop alternative energy sources rather than expand
fossil fuel production, whereas the percentages of Generation X and those older than thebaby boomers were 72% and 63%, respectively (Tyson et al., 2021). 3 Because younger
generations are closer in proximity to non-existent future generations, the authors statedthat future generations would make different choices than the others on issues that havesignificant externalities and attention, such as climate change.
In contrast, Shahrier et al. (2017) analyzed whether people who consider future
generations’ burdens are prosocial, where participants were categorized using a social
value orientation. They showed that when imaginary future generations were also

3 In this study, Generation Z, millennials, Generation X, baby boomers, and the silent generation

were defined as those born after 1996, between 1981 and 1996, between 1965 and 1980, between 1946
and 1964, and between 1928 and 1945, respectively.
5included, people living in rural areas were significantly more likely than those living inurban areas to sustainably distribute resources to future generations; in addition, those
who made such decisions were more likely to have prosocial tendencies.
Based on these discussions, we present the following hypotheses.
Hypothesis 1: Respondents' preferences for individual policies differ depending on theirgenerational standpoint, such as their own, their children, and their grandchildren's
generations.
Hypothesis 2: Policies that do not place burdens on future generations are more preferred
when respondents’ standpoints are their children and/or grandchildren's generations thantheir own generation.
Hypothesis 3: Prosocial respondents are more likely than respondents who are notprosocial to consider the benefits of future generations.
3.

Survey issues

3.1. Questionnaire design
The questionnaire consists of three parts, where the first part is a choice experimentwith 10 choice sets from which participants select policy packages. The second part
includes six questions related to selecting resource distributions between the respondentand an imaginary partner, which is intended to elicit respondents' social value orientation.
The questions in the third part address respondents' socioeconomic characteristics, suchas gender, age, occupation, number of people living together, number of children andgrandchildren, and annual income.
3.1.1. Choice experiment design
In a choice experiment, individuals are typically asked to repeatedly select theirpreferred alternatives from choice sets that are presented to them. In our survey, we
provided 10 choice sets. Each choice set had three alternatives: Policy packages A, B, and6

C. Each package had five common attributes (i.e., policies): the consumption tax rate,
distributing temporary benefit payment in extraordinary catastrophes, percentage of total
public infrastructure investment to maintain already existing public infrastructure, social
insurance premiums as a percentage of salary, and renewable energy rate in the energy
mix. Tables 1 and 2 present short descriptions of these policies and their levels,
respectively; detailed explanations are provided below.
Consumption tax rate
A consumption tax is a tax for social security purposes such as pensions, medicalcare, long-term care, and low fertility (Cabinet Secretariat, 2011); the tax rate is chosen
based on what is considered desirable. Generally, raising tax rates is burdensome for thepresent generation, making it less burdensome for future generations because it securesthe government's financial resources (Watanabe et al., 2015). 4 The actual consumption
tax rate in Japan has shifted step-by-step from 3%→5%→8%→10% from 1989 to 2019.
Therefore, the levels of this attribute were set at 2%, 6%, 10%, 14%, and 18%, using thepresent tax rate of 10% as the middle rate.
Distributing temporary benefit payments in extraordinary catastrophes
In Japan, temporary benefit payments are sometimes made to each citizen when adisaster that has a large impact on the whole country or society occurs (e.g., providing
disaster condolence money to the bereaved families and victims of the Great East Japan
Earthquake (Ministry of Health, Labor and Welfare, 2011) or providing the “Special Cash
Payment” as part of emergency economic measures for COVID-19 (Ministry of Internal
Affairs and Communications, 2020)). While a large benefit payment amount offers relief
to the present generation, distributing benefit payments increases fiscal expenditures. The
future tax increases planned to cover such expenditures will be a heavy burden on future
generations (Fiscal System Subcommittee of Fiscal System Council, 2020). The actual
benefit payment for the “Special Cash Payment” mentioned in the example was 100,000
4 The author determined that a consumption tax to finance the government's public investment(fiscal spending) would reduce intergenerational inequality more than an income tax.
7

Japanese yen (JPY). Therefore, the levels of this attribute were set as 0 JPY, 50,000 JPY,
100,000 JPY, 150,000 JPY, and 200,000 JPY.
Percentage of total public infrastructure investment to maintain already existing
public infrastructureThis attribute refers to the government’s maintenance and renovation budget to be
used for already constructed infrastructure (e.g., public facilities, roads, bridges, ports,
and airports) as a proportion of the government’s entire budget for public infrastructure
assets. On one hand, increasing this percentage is generation-neutral in terms of benefits
because it allows the infrastructure assets currently in use to be maintained andrehabilitated; this prevents accidents due to aging and ensures long and safe use for boththe present and future generations. On the other hand, raising this ratio is expected tolower the fiscal expenditure for new infrastructure construction, so the burden will not be
as heavy for future generations (Ministry of Land, Infrastructure, Transport, and Tourism,
2018). 5 The estimated value of this ratio in Japan was approximately 57% (i.e., 5.35
trillion JPY to 9.4 trillion JPY) in FY2020.6 Using this value as a middle ratio, we set thelevels of this attribute as 37%, 47%, 57%, 67%, and 77%.
Social insurance premiums as a percentage of salaryThis attribute is the social insurance premiums employees pay for medical care,long-term care, employment, etc., which are based on a percentage of their salaries.
Increasing this ratio is a burden for the present generation but lightens the burden for
future generations because it secures the government's financial resources (Ministry of
Health, Labour, and Welfare and Welfare, 2023). The actual ratio of social insurance
premiums in Japan is approximately 18.6% (Ministry of Finance, 2021). Therefore, the
5 The report states that preventive maintenance will improve efficiency by extending service life,etc.

6 Public works-related expenditures for FY2020 were estimated in the budget as 6.1 trillion JPY

and 3.3 trillion JPY for national land resilience measures (3-year and 5-year). Maintenance andrenewal expenditures are estimated based on preventive maintenance and the average of 5.2 trillion
JPY in FY2018. A lower limit of 5.5 trillion JPY was adopted in FY2023 (Ministry of Land,
Infrastructure, Transport, and Tourism, 2018).
8

levels of this attribute were set as 5%, 10%, 15%, 20%, and 25%.
Renewable energy rate in the energy mix
This attribute indicates the share of renewable energy compared to total energy,
which consists of fossil fuels, nuclear power, and renewable energy. Increasing therenewable energy rate is a burden on the present generation because it requires investingin renewable energy facilities. However, the burden on future generations will be lighter
because energy itself will be inexpensive, diseases and economic costs will be reducedby avoiding fossil fuels, and climate change will be mitigated until 2030 (The United
Nations, 2023). The renewable energy rate target in Japan is currently 22-24% (Agency
for Natural Resources and Energy, 2021). Since the probability that the Japanese
government will reduce this rate in the future is extremely low, we set the levels as 22%,32%, 42%, 52%, and 62%.
Based on these five types of policies and the five levels of each, 3,125 (55) possible
packages could be created if we apply a full factorial design. However, this number isobviously too cumbersome for respondents to deal with. Therefore, we adopted a Doptimal design to create ten choice sets, where each includes three policy packages. 7
Table 3 presents an example of the choice sets included in the questionnaire.
3.1.2. Social value orientation
To explore the respondents’ social value orientations in the questionnaire, we
adopted Murphy et al.’s (2011) approach. The respondents were asked to distributeresources between themselves and an imaginary partner. Based on their choices, they
were classified into four orientation types: altruistic, prosocial, individualistic, andcompetitive. An example of the resource distribution question is shown in Figure 1, where
nine combinations of numbers were arranged to indicate the respondent's and imaginary
7 D-optimal designs are computer-assisted experimental designs that maintain statistical validity asmuch as possible without using orthogonal tables. In the case of discrete choice experiments, Doptimal designs are generally used to reduce the number of choice sets while maintaining statistical
validity.
9

partner's shares. Different combinations of these were repeated six times to determine therespondent’s orientation type.
3.2. Data collection
An online questionnaire survey was administered in Japan by the Survey ResearchCenter Co., Ltd. at the end of August 2021. First, an email invitation was sent to a poolof respondents aged 20 to 70 years. Each respondent who agreed to participate in thesurvey accessed the link provided by the Survey Research Center and answered thequestions. Before answering the choice experiment questions, the recruited respondents
were randomly and evenly assigned the perspective of their own, their children's, or their
grandchildren's generation. After the assignment, they were asked to make choice
experiment decisions based on their assigned generational standpoints. 8 All questions in
the questionnaire, including those in the choice experiment, were the same, no matter
what generational standpoint the respondents were assigned.
A total of 500 valid responses were obtained; the average time taken to answer thequestionnaire was approximately 15 minutes. The respondents’ male/female ratio and age
composition in each assigned generational group were based on the official statistics
published by the Population and Household Statistics of the Statistics Bureau of the
Ministry of Internal Affairs and Communications.
Table 4 reports the summary of the sample’s socioeconomic characteristics. The
male-female ratio was 49.6% versus 50.4%, while the mean age of our sample was 51.10
years, which is 3.44 years older than the official mean age (i.e., 47.66 years) in 2020.
With respect to their current occupation, 37.8% of the respondents had full-time jobs,
14.4% had part-time jobs, 8.0% were self-employed or freelancers, and the remaining

8 Respondents were asked to imagine the assigned generation and answer from that standpoint, butwere not offered a strict definition of “putting yourself in every generation's shoes.” In other words,
we did not mention how respondents should perceive future generations, that is, whether they should
answer in place of their actual children or grandchildren, or whether they should answer assuming
their children or grandchildren’s general generations. We left this to the respondents' own perceptions.
In addition, since the respondents’ ages vary, their child/grandchild generation is relative to their actual
age. For example, a child for a 70-year-old respondent would be approximately 40-50 years old,
whereas a child for a 50-year-old respondent would be 20-30 years old.
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39.8% did not have jobs. The education level distribution revealed that 54.8% of the
respondents had college or advanced degrees, considerably higher than the percentage
obtained from the 2020 National Census (i.e., 25.6%). In addition, 34.8% had two family
members living together, 21.4% lived alone, and 25.4% had three family members in thehousehold. Meanwhile, about half (47.2%) of the respondents had no children, 19.4% had
one child, 24.0% had two children, and 9.4% had three or more children. Most (76.0%)
did not have grandchildren. Finally, of the 397 respondents who provided their annual
household income, 39.8% had annual incomes of less than 4 million JPY, 37.8% were
between 4 and 8 million JPY, and the remaining 22.4% reported incomes of more than 8
million JPY. 9
4.

Results and discussion

4.1. Results of the conditional logit model
Table 5 presents the results of the conditional logit model. As the table shows, thecoefficients of the consumption tax rate and social insurance premium as a percentage ofsalary are significant and negative for all generational standpoints. This suggests that all
respondents, regardless of the generational standpoint they were assigned, did not favor
increasing either of these two rates. Recall that raising the levels of these two policies
would increase the present generation’s burden but lighten that for future generations.
Hence, respondents assigned the standpoints of their children and/or grandchildren should
not be against raising the consumption tax rate or social insurance premiums.
In contrast, the coefficients of distributing temporary benefit payments inextraordinary catastrophes are significant and positive from all generational standpoints,
indicating that all respondents, regardless of their generational standpoint, are receptive
to increasing temporary benefit payments. This result is also somewhat implausible
because raising this amount would increase fiscal expenditures, and the future tax
increases planned to cover such expenditures would be a heavy burden on future
generations.
9 The official median annual income in 2021 obtained from the Japanese Ministry of Health, Labourand Welfare was 3,990,000 JPY.

11

The coefficients of the percentage of total public infrastructure investment tomaintain already existing public infrastructure are significantly positive in all three cases.
This is plausible because increasing this percentage is generation-neutral or somewhat
reduces future generations’ burden.
The coefficient of the renewable energy rate in the energy mix is significantly
negative in the sample of respondents' own generation but insignificant in the samples of
respondents’ children and grandchildren's generations. The former is plausible because
respondents whose standpoint is their own generation are not in favor of increasing thepresent generation’s burden.
Although these results imply the same preferences for four of the five policies, thepreferences for the renewable energy rate in the energy mix do reflect differences.
Therefore, Hypothesis 1 is partially supported.
4.2. Results of latent class logit model
As mentioned in the Introduction, the LCL approach allows us to explore theheterogeneity in respondents’ preferences for different policies from each generation’s
standpoint. The first issue in applying the LCL approach is determining the number oflatent classes. We calculated the Akaike information criterion (AIC) and Bayesian
information criterion (BIC) with various numbers of classes (1, 2, 3, 4, and 5) in eachgenerational standpoint sample and found that the 3-class model in each sample had theminimum values of the AIC and BIC, implying that the 3-class model was optimal.
Therefore, we selected three classes to estimate the LCL model. 10
4.2.1. Estimation of 3-class LCL model
Table 6 presents the results of estimating the 3-class LCL model from different
generational standpoints. The upper panel shows the respondents' own generation, themiddle one is their children's generation, and the bottom one is their grandchildren's
generation.

10 The AIC and BIC values of classes 1 to 5 are available upon request.

12With respect to the results from the standpoint of the respondents' own generation,
approximately 39.45% of the respondents were included in Class 1, whereas theremaining 30.86% and 29.7% were included in Classes 2 and 3, respectively.
Heterogeneity in preferences for different policies is found among the different classes.
For instance, raising the consumption tax rate and renewable energy rate in the energy
mix are undesirable for the respondents in Classes 1 and 2 but are desirable for those inClass 3, although the coefficient of the renewable energy rate in Class 1 is estimated to
be insignificant. Increasing social insurance premiums as a percentage of salary issignificantly not preferred by the respondents in Classes 1 and 2, but has a negligible
effect on the preferences of Class 3 respondents. The remaining two policies (i.e.,
distributing temporary benefit payments in extraordinary catastrophes and percentage of
total public infrastructure investment for maintaining already existing public
infrastructures) are estimated to be positive in all classes; however, the former and latterare insignificant in Classes 2 and 3, respectively.
These results indicate that the respondents in Class 1 want to avoid increases in the
consumption tax and social insurance premiums but are willing to increase temporary
benefit payments and existing public infrastructure renewal. Those in Class 2 want toavoid increases in the consumption tax, social insurance premiums, and renewable energyrate, but are willing to increase the rate of existing public infrastructure renewal. The
respondents in Class 3 are willing to accept an increase in the consumption tax, temporary
benefit payment, and renewable energy rate and are more likely to accept the burdens of
the current generation than those in Classes 1 and 2.
The preferences of three classes for different policies in the other two generational
standpoints are similar in the sense of statistical significance to those from the respondents’
own generational standpoint except: (1) the coefficient of distributing temporary benefit
payments in extraordinary catastrophes is significantly negative in Class 2 when
respondents stand for their grandchildren's generation; (2) the coefficient of thepercentage of investment used to maintain existing public infrastructure is insignificant
in Class 2 when respondents stand for their children's generation and is significantly
positive in Class 3 when they stand for either their children or grandchildren's
13generations; (3) raising insurance premiums is significantly undesirable in Class 3 when
respondents stand for either their children or grandchildren's generations; and (4) the
coefficient of the renewable energy rate in the energy mix is significantly positive in Class
1 when respondents stand for their children's generation but is insignificant in Class 2
when they stand for their grandchildren's generation. These differences can be viewed as
partially supportive evidence for Hypothesis 1.
4.2.2. Labeling the classes
These results make labelling the classes difficult because the elicited preferences foreach policy in each class are not sufficiently clear. Therefore, we use NLOGIT 6.0 andobtain individual-based willingness to accept (WTA) measures for each policy toinvestigate each class’s generational tendencies. WTA is the minimum amount of moneythat a respondent is willing to sacrifice to avoid something undesirable (i.e., decreasing
the level of a policy when they have a positive preference for it or increasing the level ofa policy that they have a negative preference for). In our study, individual-based WTA foreach policy is calculated by dividing the estimated individual-based coefficient of
temporary benefit payments by those of each policy. A larger WTA value indicates the
respondents emphasize the present generation.
Table 7 shows the average of the estimated individual WTA values, which, in mostcases, are largest for the Class 2 respondents. In contrast, the values of the Class 3
respondents are generally the smallest, especially for the policies that raise theconsumption tax rate and social insurance premiums. Combining these WTA results with
the LCL estimates, for each generational standpoint, we label the Class1, 2, and 3
respondents as generation-neutral, present generation emphasis, and future generationemphasis, respectively.
Figure 2 compares the predicted probability of the generational standpoint of the
respondents in each class. The figure illustrates that the probability respondents are
members of the group that emphasizes future generations increases from
29.7%→33.61%→40.67% when their generational standpoint changes from their own
generation→their children's generation→their grandchildren's generation. This result
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supports Hypothesis 2, as more respondents emphasize future generations when theystand for their children and/or grandchildren's generation than when they stand for theirown generation.
4.2.3. Typological distribution by social value orientation (SVO)
Based on the 500 respondents’ resource distribution choices, the results of Murphy
et al.’s (2011) social value orientation (SVO) typology are: 67.4% (337/500) of the
respondents are categorized as prosocial, 30.4% (152/500) are individualistic, 1.6%
(8/500) are competitive, and 0.6% (3/500) are altruistic.
Table 8 shows the average probabilities by the three generational standpoints ofprosocial type respondents being in each labelled class. The average probability ofprosocial respondents being in a class that emphasizes the future generation is higher thanthat of them being in a class that emphasizes the present generation, regardless of theirgenerational standpoint. This result supports Hypothesis 3.
4.3. Results of multinomial logit model
We used a multinomial logit (ML) model to investigate the types of respondents
likely to belong to each class. We first obtained each respondent's probability of being ineach class estimated by NLOGIT 6.0 (three probabilities for each respondent in the threeclasses). We then classified each respondent into the corresponding class based on thehighest probability among the three.
Table 9 reports the results obtained from the ML regression. The table shows that
respondents who choose a policy package from the standpoint of their grandchildren are
15% less likely to be in the generation-neutral class and 8.4% more likely to be in the
future generation-oriented class than those who choose a policy package from their own
generational standpoint. Male respondents are more likely to emphasize the future
generation. Respondent age has no significant effect on the probability of being in the
future generation-oriented class, while it has the opposite influence on the probabilities
of being in present generation-oriented and generation-neutral classes.
In addition, the probability of being in the present generation-oriented class is15

significantly higher (11.9%) when the respondent has a full-time job. Regarding
education, the probability of being in the generation-neutral class is significantly lower(1.6%) for those with more years of education and the probability of being in the future
generation-oriented class is significantly higher (1.6%) with more years of education.
Finally, respondents classified as prosocial types in the SVO are significantly 7.7%
less likely to be in the present generation-oriented class and 10.6% more likely to be in
the future-generation-oriented class than in the generation-neutral class, which provides
support for Hypothesis 3.
5.

Conclusion
In this study, we conducted an online stated choice experiment survey to investigate

individual policy preferences from the standpoints of their own, their children's, and their
grandchildren's generations. The CL regression results show that respondents in generaltend to avoid imposing direct burdens on the present generation (e.g., raising theconsumption tax and social insurance premium rates), no matter what generation theystand for. However, the LCL regression results provide evidence that, from eachgenerational standpoint, a number of individuals consider the burden on future
generations. The percentage of these individuals in the sample increases when
respondents’ generational standpoint changes from their own to their children andgrandchildren's generations.
In addition, based on the ML regression results, respondents who choose a policy
package from the standpoint of their grandchildren are more likely to be in the future
generation-oriented class than those who choose a policy package from their own
generational standpoint. This evidence explores the importance of people’s generational
standpoints when they make decisions. Moreover, prosocial respondents are more likely
to consider future generations, which is consistent with Shahrier et al.’s (2017) findings.
Finally, when assigning the generational standpoints to the respondents, we left how
the respondents should understand their children or grandchildren's generations up to theirown perceptions and provided no detailed explanation. This manipulation could prevent
inducing respondents’ choices. However, whether the respondents answered the questions
16

in place of their actual children or grandchildren or whether they considered their children
or grandchildren’s general generations (even if they did not currently exist) are unclear.
Therefore, this issue should be strengthened in a more refined way in future. Moreover,most previous studies of Future Design emphasize the importance of presenting an
imaginary future generation in the decision-making processes. However, individuals findit somewhat difficult to understand an imaginary future generation. Our approach ofasking people to stand for their children and/or grandchildren's generations might beviewed as an alternative to presenting an imaginary future generation because it is mucheasier to understand. The results obtained from both methods should be compared todetermine which is more suitable.
Acknowledgements
Financial support from the Japanese Ministry of Education, Culture, Sports, Science,
and Technology through Grant-in-Aid for Scientific Research (C) 19K01541 and
19K01558 is gratefully acknowledged. All views expressed in this paper and any errors
are the sole responsibility of the authors.

17

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20

Table 1. Policies and their impact on current and future generations
Policies

Description

Impact on the present
generation

Impact on future generations

Consumption tax

Tax rate for social security

Raising tax rates would

Raising the tax rate would

rate

purposes such as pensions,be a burden on the

secure the country’s financial

medical care, long-term care,present generation.

resources, so the burden would

and low fertility.

be lighter for future
generation.

Desirable amount of money to

A larger benefit package

If a special major disaster

temporary benefit

distribute to each citizen as awould be a relief for theoccurs and benefits are

payments in

temporary benefit in the event

present generation.

distributed, the financial outlay

extraordinaryof a disaster with a major

will increase, which would becatastrophes

impact on the country ora burden on the futuresociety as a whole.

generation that would have toDistributing

cover the costs.
Percentage of total

A desirable percentage of the

Increasing thisIncreasing this percentage

public infrastructure

government budget for public

percentage would allow

would reduce the proportion of

investment to

infrastructure assets, such as

us to maintain andnew infrastructure assets to be

maintain alreadypublic facilities, roads, bridges, refurbish thebuilt and allow maintaining

existing public

ports, and airports, to be usedinfrastructure assets weand rehabilitating as many ofinfrastructure

to maintain and renovate thosecurrently use.

the existing infrastructure

already constructed.

assets currently in use as
possible, reducing the burden
for future generations.

Social insurance

A desirable percentage paid

Increasing the premium

premiums as aout of the salaries of those who percentage would be

would secure the government’s

percentage of salary

work for a company to be usedburdensome for thefinancial resources, so thefor social insurance premiums

present generation.

burden would be lighter forfor medical care, long-term

Increasing the percentage

future generation.

care, employment, etc.
Rare of renewable

A desirable percentage of the

Increasing the renewable

Increasing the percentage

energy in the energy

total power source mix, whichenergy rate would

would ease global warming bymix

consists of fossil fuels, nuclear

require investment ineliminating the use of fossil

power, and renewable energy,

renewable energy

fuels, thus lightening the social

for renewable energy.

facilities, which would

burden for future generation.

be a burden on thepresent generation.

21

Table 2. Levels of each policy
Policies
Consumption tax rateReal-world values

Unit

LevelsTransition from 3 to 5 to 8 to 10%.

%

2

6

10

14

18

0

5

10

15

20

%

37

47

57

67

77

%

5

10

15

20

25

%

22

32

42

52

62

Distributing temporary

The amount of the “Special Cash

10,000benefit payment inPayment” as part of emergency

yenextraordinary catastrophes

economic measures for the new-type
coronavirus infection was 100,000 yen.

Percentage of total public

According to the MLIT White Paper,

infrastructure investment tomaintenance and renewal costs weremaintain already existing

56.6% of the 8.3 trillion yen in social

public infrastructure

capital investment in FY2020, or 4.7
trillion yen.

Social insurance premiums

About 15% on average.

as a percentage of salary
Renewable energy rate in the According to the Agency for Naturalenergy mix

Resources and Energy, the target
renewable energy rate for 2030 is 22%24%.

22

Table 3. Example of a choice set
Policy packages
A

B

C

Consumption tax rate

8%

10%

3%

Distributing temporary benefit payment in extraordinary

0 yen

200,000 yen

150,000 yen

57%

47%

67%

Social insurance premiums as a percentage of salary

25%

10%

5%

Renewable energy rate in the energy mix

32%

52%

42%

Please choose the most desirable package with a ✔ in □

□

□

□

catastrophes
Percentage of total public infrastructure investment tomaintain already existing public infrastructure23

Table 4. Sample socioeconomic characteristics
Characteristicsn%

Gender/Ages

Characteristicsn%

Occupation

Male/20-29 years old

32

6.0

Managers and executives

9

1.8

Male/30-39 years old

39

8.0

Company employee

153

30.6

Male/40-49 years old

51

10.0 Civil servants, Organization employees,

27

5.4

Teachers
Male/50-59 years old

45

9.0

Self-employed or Freelancer

40

8.0

Male/60-69 years old

42

8.0

Part-time job, including temporary and

72

14.4

Male/70-79 years old

39

8.0

Student

11

2.2

Female/20-29 years old

30

6.0

Housewife/Househusband

89

17.8

Female/30-39 years old

36

7.0

Unemployed including pensioners

96

19.2

Female/40-49 years old

48

10.0 Other3

0.6

Female/50-59 years old

45

9.0

Educational level

Female/60-69 years old

45

9.0

High school

149

29.8

Female/70-79 years old

48

10.0 Vocational school

55

11.0

243

48.6

None

236

47.2 Graduate school

31

6.2

One

97

19.4 In school

2

0.4

Two

120

24.0 Other

7

1.4

Three or more

47

9.4

13

2.6

contract employees

University / Junior college

Number of children

Number of grandchildren

I don’t want to answer
Household annual income

None

380

76.0 Less than 2 million yen

56

11.2

One

34

6.8

2 million yen – less than 4 million yen

102

20.4

Two

40

8.0

4 million yen – less than 6 million yen

88

17.6

Three or more

46

9.2

6 million yen – less than 8 million yen

62

12.4

8 million yen – less than 10 million yen

41

8.2

Household size
One (solitude)107

21.4 10 million yen or more

48

9.6

Two

174

34.8 I don’t want to answer

103

20.6

Three

127

25.4 Respondents’ generational standpoint

Four

63

12.6 Their own generation

167

34.0

Five

21

4.2

Their children’s generation

167

34.0

Six or more

8

1.6

Their grandchildren’s generation

166

33.0

24

Table 5: Conditional logit model results
Policies

Own generation

Children’s generation

Grandchildren’s
generation

Consumption tax rate
Distributing temporary benefit payment in
extraordinary catastrophes
Percentage of total public infrastructure
investment to maintain already existing
public infrastructure
Social insurance premiums as apercentage of salary-0.0702***0.0497***-0.0559***
0.0422***-0.0599***
0.0283***0.0065**0.0092***0.0119***-0.0203***-0.0302***-0.0288***

Renewable energy rate in the energy mix-0.0059**-0.00020.0023

Predictive Power (%)
Log-likelihood
Observations

54.31

49.04

46.11

-1656.38-1703.99-1731.30

1670

1670

1660

Notes: Predictive power refers to the proportion of choices correctly predicted by the model. *, **, and *** denote thatthe estimated parameter is significantly different from zero at the 10%, 5%, and 1% levels, respectively. Standard errors
and z values are omitted to save space.

25

Table 6. Latent class logit model results
Standpoints

Policies

Own generation Consumption tax rate
Distributing temporary benefit payment in
extraordinary catastrophes
Percentage of total public infrastructure
investment to maintain already existing
public infrastructure
Social insurance premiums as a percentage
of salary
Renewable energy rate in the energy mix
Estimated latent class probability (%)

Children’s
generation

Class 1

Class 2

Class 3-0.2724***0.1219***-0.0527**
0.00880.0374**0.0443***0.0142**0.0098*0.0054-0.0319*-0.0336***-0.0039-0.0148-0.0144*0.0188***0.39450.3086

0.2970Predictive power (%)
Log-likelihood

71.68-1469.48

Observations
Consumption tax rate

1670
-0.2074***-0.0407**0.0474**

Distributing temporary benefit payment in
extraordinary catastrophes

0.1070***-0.0055

0.0413***

Percentage of total public infrastructure
investment to maintain already existing
public infrastructure

0.0110*0.00410.0175***Social insurance premiums as a percentage
of salary-0.0931***-0.0403***-0.0204*

Renewable energy rate in the energy mix

0.0241***-0.0194***

0.0229***Estimated latent class probability (%)
Predictive power (%)

0.406478.44

0.25750.3361-0.2325***-0.0935***

0.0272*0.1265***-0.0232*

0.042***

Percentage of total public infrastructure
investment to maintain already existing
public infrastructure

0.00840.0112**0.0304***Social insurance premiums as a percentage
of salary-0.1162***-0.029***-0.0335***

Renewable energy rate in the energy mix

0.0177-0.00430.0206***

Estimated latent class probability (%)

0.26930.32400.4067Predictive power (%)

79.34

Log-likelihood
Observations-1497.01
1670

Grandchildren’s Consumption tax rate
generation
Distributing temporary benefit payment in
extraordinary catastrophes

Log-likelihood-1541.46

Observations

1660

Notes: Predictive power refers to the proportion of choices correctly predicted by the model. *, **, and *** denote thatthe estimated parameter is significantly different from zero at the 10%, 5%, and 1% levels, respectively. Standard errors
and z values are omitted to save space.

26

Table 7. Average values of estimated individual WTA (JPY)Standpoints

Policies

Class 1

Class 2

Class 3

Own generation

Consumption tax rate

22,58040,268

6,922

Percentage of total public infrastructure investment tomaintain already existing public infrastructure
Social insurance premiums as a percentage of salary

1,237

7,207

1,446

2,884

23,434

1,764

Renewable energy rate in the energy mix
Consumption tax rate

1,322
19,428

9,558
154,284

3,687
10,340Percentage of total public infrastructure investment tomaintain already existing public infrastructure

1,082

16,463

4,101

Social insurance premiums as a percentage of salary
Renewable energy rate in the energy mix

8,926
2,180

146,372
60,136

5,484
5,238

Consumption tax rate

19,262

76,9386,148

Percentage of total public infrastructure investment tomaintain already existing public infrastructure824

11,6697,497

Social insurance premiums as a percentage of salary

9,586

30,364

8,862

Renewable energy rate in the energy mix

1,424

2,662

4,892

Children’s
generation

Grandchildren’s
generation

27

Table 8. Average probabilities of prosocial respondents being in each classStandpoints
Own generation
Children's generation
Grandchildren's generation

Generation-neutral

Emphasizing present
generation

Emphasizing future generation

0.56250.54760.70370.72550.70180.76470.57350.73530.704528

Table 9. Multinomial logit model results (marginal effects)
Generation-neutral

Emphasizing

Emphasizing future

present generation

generation

Standpoint of children’s generation

0.0316-0.0273-0.0043

Standpoint of grandchildren’s generation-0.1499***

0.06590.0839*Male-0.0405-0.0678

0.1084**Age-0.0035**

0.0030**0.0004

Occupation (regular job DUMMY)-0.07560.1190**-0.0434

Occupation (irregular job DUMMY)-0.0172

0.0844-0.0671

Education（converted to years of education)-0.0165*0.00100.0155*

SVO（prosocial DUMMY)-0.0296-0.0765*

0.1061**

Log-likelihood-510.86

Observations

487

Notes: *, **, and *** denote that the estimated parameter is significantly different from zero at the 10%, 5%, and 1%
levels, respectively. Standard errors and z values are omitted to save space.

29

Figure 1. Resource distribution

30

100
Generation-neutral
Emphasizing present generation

80

Emphasizing future generation

60

40

40.67

40.64

39.45
30.86 29.7

33.61
25.75

26.93

32.4

20

0

Own generation

Chindren's generation

Grandchildren's generation

Figure 2: Predicted probability of respondents in each class31