Discussion Paper No. 795

ANOTHER AVENUE FOR ANATOMYOF INCOME COMPARISONS:EVIDENCE FROMHYPOTHETICAL CHOICE EXPERIMENTS

Katsunori Yamada
Masayuki Sato

September 2010
Revised December 2010
Secondly Revised November 2011
Thirdly Revised May 2012
Fourth Revised March 2013

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Another Avenue for Anatomy of Income Comparisons:Evidence from Hypothetical Choice Experiments∗
Katsunori Yamada†Institute of Social and Economic Research, Osaka University
6-1 Mihogaoka, Ibaraki 567-0047, Japan.
MOVE, Universitat Autonoma de Barcelona.
UAB Ediﬁci B (s/n) 08193 Cerdanyola del Valles, Barcelona, Spain.
Masayuki Sato
Graduate School of Human Development and Environment, Kobe University.
3-11 Tsurukabuto, Nada-ku, Kobe 657-8501, JAPAN

Abstract
We propose a new avenue for studying income comparisons eﬀects, namely hypothetical discrete choice experiments in which respondents are presented with alternative combinations of hypothetical monthly income amounts, both for themselvesand certain reference persons. With this experimental method we can avoid the
problems associated with researcher-imposed reference persons’ incomes that arefound in most of the happiness studies testing comparison eﬀects. This approach∗

We thank the associate editor and the two anonymous referees for their constructive and detailed

advice that has substantially improved the paper. We also appreciate helpful comments from Andrew
Clark, David Flath, Ronald Harstad, Arne Risa Hole, Takahiro Ito, Louis Levy-Garboua, Shigeru Matsumoto, Fumio Ohtake, Claudia Senik and Akihisa Shibata, as well as seminar participants at Academia
Sinica, Kyoto University, Nagoya University, Paris School of Economics, Universitat Pompeu Fabra, andSingapore Management University. The usual disclaimers apply. Financial support was provided by the
Global COE program at Osaka University and by the JSPS research fund KAKEN (24683006), which
was very much appreciated.
†
Corresponding author: Parmanent Address: 6-1 Mihogaoka, Ibaraki 567-0047, Japan. Tel/Fax: +816-6879-8566. Email: kyamada@iser.osaka-u.ac.jp

1

allows investigation of the diﬀerences in comparison eﬀects across types of referencegroups as well as respondents’ individual characteristics, including speciﬁc comparison benchmarks, which are the main open questions in the literature. Some resultsfrom our original, large-scale, Internet-based survey are provided.

Keywords: Relative utility; Hypothetical choice experiment; Reference group;
Comparison benchmark

JEL classiﬁcations: C9; D1; D3

2

1Introduction

Traditional economic theories focus on the role of absolute income or consumption, whereas
behavioral evidence suggests that social comparisons inﬂuence well-being and decisions(Fliessbach et al. 2007, p. 1305). Whether social comparisons do in fact aﬀect individual
utility is critically important in understanding human behavior in any social context, andin evaluating the outcomes of economic behavior.
This study provides a new way to measure income comparison eﬀects using hypothetical discrete choice experiments. A critical component in empirical studies of income comparison and relative utility is deﬁning the appropriate reference person or group for eachsubject. Our approach estimates the income comparison parameters in the utility function
through the decision utility approach, which was originally put forth by Kahneman et al.
(1997) in the ﬁeld of environmental valuation. Hence, this method is diﬀerent from thestandard experienced utility approach used in happiness studies, where respondents’ subjective reports of well-being and proxies for reference income are used to estimate therelative utility eﬀects. By using the decision utility approach, we are able to avoid the useof researcher-imposed reference persons’ incomes, which must be constructed by econometricians without knowledge of the subjects who provided data on happiness but who
were not asked with whom they made a comparison or how much they think their rivals
earn.1 In our experimental approach, we impose alternative combinations of hypotheticalmonthly income amounts on subjects, both for themselves and certain reference persons.
Using data on respondents’ choices of preferred income scenario, we can estimate the utility function parameters that capture the intensity and sign of income comparisons withcertain reference persons. As such, the methodological merits of our study include that (i)the subjects can see the characteristics of reference persons and how much these persons
earn compared with themselves in clear situation choice tasks, and (ii) the subjects canchoose discrete choices with a lower cognitive burden than when evaluating their lives
with more general happiness scores.
Exploiting the features of the choice experiment, we can investigate diﬀerences in comparison eﬀects by reference person type through changing the characteristics of reference
persons in hypothetical income scenarios. Another important issue that can be addressedwith our method is determining how individual characteristics such as age, gender, educational attainments, and respondents’ speciﬁc comparison benchmarks aﬀect attitudes in
1

Critiques of using researcher-imposed reference persons’ incomes to estimate income comparison
eﬀects in happiness regressions were detailed in Manski (1993) and Sloane and Williams (2000).

3

income comparisons. Among these variables, the comparison benchmarks are of particularinterest for researchers in the ﬁeld, because these were recently studied by Clark and Senik(2010) via the experienced utility approach. We provide empirical results on these issues
by using our original, large-scale, Internet-based survey of Japanese subjects. Our data
set is socially representative in terms of age and gender distribution, which is an importantvirtue of our sample, as many studies often rely on potentially biased student samples.
Problems of sample bias also aﬀect experimental studies that provide incentives for a small
number of subjects such as Fehr and Schmidt (1999) and Fehr and Schmidt (2006).2 Our
Internet-based survey overcomes this issue by accessing an enormous number and wide
variety of registered subjects through a reliable subcontracted research company.
While the Internet oﬀers immediate access to a large and diverse subject pool andresearch opportunities at cheaper costs, there are also caveats and potential pitfalls thatpertain to Internet-based experiments. Typical critiques include the inability to monitor the motivations and understanding of participants and uncertainty about the precise
identity of the experimental subjects, among others. On these issues, Horton et al. (2011)provided clear evidence that online experiments on social preferences through Amazon Mechanical Turk (MTurk) replicate previous experimental results from physical laboratories,
encouraging the use of Internet-based surveys.3 The fact that our experiment was hypothetical may be another source of concern, but neuroscience studies such as Bickel et al.
(2009) and Kang et al. (2011) show that incentivized and hypothetical experiments do
not generate signiﬁcant diﬀerences in human neural activity. In addition, Amir et al.
(2012) found that experimental participants in MTurk showed no diﬀerences in responses
between incentivized experiments and non-incentivized experiments in the public goods
game and in the trust game.
The remainder of the paper is organized as follows. Section 2 reviews the empiricalliterature on income comparisons and relative utility and places this study in historicalcontext. In Section 3 we explain the experimental design of hypothetical discrete choices
for income comparisons and the methods employed in our original Internet-based survey.
The questionnaire and data construction for empirical analyses are also explained in
the section. Section 4 outlines the estimation method following Train (2009). Section 5
2

Recent experimental studies on social preferences by Falk et al. (2011) and Exadaktylos et al. (2012)
report that only slight student bias can be observed, if any, and argue that experimental results fromstudent samples are useful even in designing policy for the whole population.
3
See also Paolacci et al. (2010), Suri and Watts (2011), Rand (2011), and Amir et al. (2012) for the
potential pitfalls of Internet-based experiments and surveys and rebuttals to those critiques in the literature of social preferences.

4presents the results for our benchmark task, in which the reference person is deﬁned as “thesocial average” as in the series of studies by Richard Easterlin. Section 6 provides some
additional results when diﬀerent types of reference persons are presented in hypotheticalincome scenarios. Section 7 discusses potential biases related to experimental studies andconcludes the paper.

2

Related studies on income comparisons

In the literature of happiness studies on income comparisons, reference persons are deﬁned
according to the researchers’ choices, and the list of reference groups examined in previous studies is almost endless: an average (representative) person in society (Easterlin
1974, Easterlin 1995, Easterlin 2001, Stevenson and Wolfers 2008), someone like you(Clark and Oswald 1996, Ferrer-i Carbonell 2005), colleagues (Cappelli and Sherer 1988,
Brown et al. 2008, Clark et al. 2009b), friends (Senik 2009), family (Senik 2009), neighbors (Luttmer 2005, Clark et al. 2009a, Knight et al. 2009, Senik 2009), and so forth. The
accumulated evidence on the various cases is plentiful, and a prominent area of progressin the happiness literature recently is reported by Clark and Senik (2010), who investigated the eﬀects of individual-speciﬁc comparison benchmarks on comparison attitudes
using data on who compares to whom. They found that the intensity of social comparison changes in combination with speciﬁc groups that people ascribe to their comparison
benchmarks. For example, those who think that their reference group is friends tend to
make more comparisons than those whose think the comparison benchmark group is workcolleagues.4
Despite all these evidence from ﬁeld data on happiness scores, an alternative experimental approach for testing the relative utility hypothesis is necessary (Falk and Heckman2009). One reason is that the reference income proxies used in empirical tests in previous
happiness studies were imposed on subjects by an econometrician because information
on both the direct and cardinal measures of reference income typically was missing. To
our knowledge, the only exception in the literature that has information on both the direct and cardinal measures of reference income is de la Garza et al. (2010). Knight et al.
(2009) and Senik (2009) used information on the perceptions of relative position in the
respondents’ villages or among friends and family members. However, their proxies ofrelative comparisons were ordinal, so interpretations of the magnitudes of coeﬃcients for4

See also Mayraz et al. (2009).

5

comparison eﬀects were not straightforward. Another reason is that the use of information
on subjective well-being is sometimes a cause for criticism by economists in other ﬁelds,
even though the view that subjective well-being information is valid has been well established (Hollander 2001, Ferrer-i Carbonell and Frijters 2004, Kahneman and Krueger
2006, Oswald and Wu 2010).
Aiming to overcome these issues, we present an alternative approach to hypothetical discrete choice experiments. In our experiments, similar to Clark and Senik (2010),
we investigate how people change their comparison behavior (intensity and sign of social
comparison) on the basis of their demographics, including comparison benchmarks. Moreover, similar to Senik (2009), heterogeneity of comparison eﬀects driven by diﬀerences inreference groups can be examined by changing the deﬁnition of reference persons in hypothetical situation choice tasks. This method provides a much easier way of clarifying such
heterogeneity compared with researchers who collect information in surveys on direct andcardinal measure of reference income for each reference group, together with happinessscores.
Solnick and Hemenway (1998), Johansson-Stenman et al. (2002), Alpizar et al. (2005),
Carlsson et al. (2007), and Andersson (2008) investigated the intensity of social comparisons by addressing the methodologies of hypothetical choice experiments. One issue inJohansson-Stenman et al. (2002), Alpizar et al. (2005), and Andersson (2008) is that the
choice format was designed in such a way that respondents made iterative choices to arrive at the point of indiﬀerence. This strategy is known to result in starting point bias
(Carson 1991). Solnick and Hemenway (1998) and Carlsson et al. (2007) did not use iterative choices. Instead, each respondent only made a single choice between two alternatives
related to relative income. With these strategies, we cannot apply a mixed logit framework to estimate the distribution of a parameter of relative utility. Also, the degree of
positionality inferred by these previous studies, except for Solnick and Hemenway (1998),
contained measurement error because the assigned value for the degree of positionality
was given arbitrarily. To our knowledge, Carlsson et al. (2009) is the only study that conducted hypothetical and discrete choice experiment on income comparisons with repeatedchoice questions. They considered changes in the intensity of relative utility across diﬀerent caste classes in India, but they did not provide results from factorial design analyses
with diﬀerent reference persons being tested in the same experiment. In Carlsson et al.
(2009), sample representativeness is also an issue, as they conducted in-person surveys of6498 college students.5
Finally, studies on social preferences that diﬀer from income comparison studies are
mentioned. Using game theoretical frameworks such as the dictator game, the ultimatum game, and the public goods provision game, researchers had subjects interact
in their experiments and examined the implications on reciprocity, trust and fairness.
These studies include, for example, Andreoni and Bernheim (2009), Paolacci et al. (2010),
Andreoni and Rao (2011), Suri and Watts (2011), Rand (2011), Horton et al. (2011), andAmir et al. (2012). Unlike these studies, studies on income comparisons, including happiness studies and our study of discrete choice experiments, there are no strategic interactions among subjects. This feature is actually important when we would like toestimate parameters of utility functions that can be used for macroeconomics analyses,e.g., Abel (1990), Gali (1994), Futagami and Shibata (1998), Liu and Turnovsky (2005),
and Garcia-Penalosa and Turnovsky (2008), as in macroeconomics the number of agentsis inﬁnite, which is diﬀerent from game theoretical situations.

3

Experimental design, questionnaire, and data collection

3.1

Hypothetical discrete choice question: Social average task

First we explain the experimental paradigm in the survey, using the example of a benchmark experiment called the social average task. The methods described for the social
average task are representative of the methods for all the tasks we conducted in ourstudy. We discuss the results of the other two tasks in Section 6 and provide detailed
explanations of their experimental settings and empirical results in the Appendix becauseof space constraints.
The Easterlin paradox, which suggests that “increasing the income of society as awhole will not increase the well-being of anyone,” has been discussed in the literature on
experienced utility that considers how national average income acts as a driving force ofrelative utility eﬀects. The social average task provides a useful alternative method forinvestigating the validity of the paradox and relative utility eﬀects in general. The merits
5

In terms of the representativeness of the sample, the respondents in Solnick and Hemenway (1998),
Johansson-Stenman et al. (2002), and Alpizar et al. (2005) included only students and the respondents
in Andersson (2008) were only people in academia. In contrast, the respondents in Carlsson et al. (2007)
were from a socially representative survey.

7

of our method are that subjects recognize that they are competing with the Japanesesocial average when making choices and that the economic situations are explicitly shown
to them.
Before the subjects began responding to repeated choice questions, they were shown
an instruction screen displaying the following:
The following ﬁgures show your hypothetical monthly income (before tax). Also
displayed in the same ﬁgure is Japan’s overall average monthly income (beforetax). Suppose that these are current situations of your monthly income (beforetax) and Japan’s overall average monthly income (before tax).
In the subsequent screens, we asked respondents hypothetical discrete choice questions
while showing them various ﬁgures for diﬀerent alternatives after the question as shown
below.6
Comparing situation 1 and situation 2 shown in the ﬁgures, which is more
preferable to you? Suppose that the price levels in the two situations are thesame. Please choose from the following options.

In the ﬁgure, each situation is deﬁned by two attributes, one’s own monthly pre-tax
income and the monthly pre-tax income of the reference group. The choice scenario also
provided the option “Don’t know / Cannot answer.”7 Section 4 explains how we estimate
6
In the survey information in the ﬁgures was presented in Japanese. The images for monthly income
diﬀer in terms of number of banknotes shown according to the attribute levels. Subjects repeated ﬁvequestions and they were not allowed to go back to a previous question once they had made a choice. Thissurvey format was also used in the other two tasks: the Leyden task and the “who-compares-to-whomtask.
7
We provided this no-choice option because of the suggestion by Arrow et al. (1993) and Haaijer et al.
(2001), who pointed out the importance of including a no-choice option in hypothetical choice experiments. We then removed observations in which the no-choice option was selected from our regressions.
An alternative way of coping with these observations is to interpret them as showing indiﬀerence between

8parameters for a utility function using observations of discrete choices on preferred income
scenarios. Next, we explain how we constructed our choice questions.
Considering the monthly income distribution in Japan, the levels of the attributes are
set using the following distribution: 180,000 JPY, 240,000 JPY, 400,000 JPY, 640,000
JPY, and 900,000 JPY. The two attributes (own income and reference income) and ﬁve
possible income levels for each attribute provide 25 potential variations in the income
situation scenario. In the literature, these scenarios are called alternatives.
Researchers have to make their own choices about which alternatives to use in survey
questions and which ones to discard. Following Louviere et al. (2000), we conductedorthogonal planning in choosing the alternatives to be used in choice questions. Thismethod eﬀectively pairs multi-dimensional and multiple-level attributes in alternatives,and provides an experimental plan with the greatest amount of information using theleast number of observations. Further, employing orthogonal planning, we can avoid
multicollinearity problems in the regressions of the random utility model explained in
Section 4, because the independent variables in the regressions become orthogonal. We
used SPSS Conjoint (ver. 15.0) for orthogonal design of alternatives in this study.
Next, we constructed choice sets consisting of pairs of alternatives and the no-choice
option. By the requirement of orthogonal design, we generated two diﬀerent alternative
vectors, each of which consists of 25 pairs of own income levels and reference income levels.8
Finally, choice sets, with a no-choice option, are created by pairing two alternatives, oneof which is taken from an alternative vector and the other of which is chosen from theother alternative vector. The pairing strategy is at the discretion of the researchers, but
all the variations must be exploited and same alternative cannot be used twice. Because
the orthogonality in the alternative matrix is maintained for each row permutation, we
can arbitrarily pair alternatives to meet the requirement.
As documented in Huber and Zwerina (1996) and Viscusi et al. (2008), it is ideal if the
choice design can be paired so as to balance the utility of each alternative. One diﬃculty inchoice experiments of relative utility, however, is that an increase (decrease) in a reference
the two situations, rather than a failure to understand the survey question. Unfortunately, we have no
information about the true reason why the no-choice option was chosen. Hence, following the literature,
we use the results of the ﬁrst choice out of the ﬁve questions for a robustness check and found that theresults presented are robust (not shown here due to space constraints, but available upon request).
8
As such, in the social average task, 25 alternatives out of 25 potential variations had to be used tomeet the requirement of orthogonal design. In the case of the Leyden task, 25 out of 1,000 potential
alternatives were chosen to make an alternative vector, while in the “who-compares-to-whom task, 25 out
of 125 potential variations were selected via orthogonal design. These alternative vectors were generated
from diﬀerent random seeds.

9group’s income does not necessarily mean that there is a decrease (increase) in one’s own
utility level; as such, we did not exclude the possibility of altruistic preference. Given
these constraints, our best strategy for pairing alternatives is as follows.
Suppose we have the scenario S = (x, y), where x denotes the level of one’s own income
and y is others・income. Then, qualitatively, candidates of paired scenarios consist of thefollowing 8 variations: (x, y+), (x, y−), (x+, y), (x+, y+), (x+, y−), (x−, y+), (x−, y),and (x−, y−), where x+ means some value greater than x and x− means some value
smaller than x. Since we do not exclude the possibility of altruism a priori, there are no apriori dominant choices for S from these eight alternatives. We then made pairs such thatthese eight situations appear as evenly as possible. Using the procedures discussed here,
we were able to eﬃciently obtain parameter estimates. The following table of income
levels (in 10,000 JPY) shows the set of questions we used in the survey. Each respondent
answered ﬁve randomly assigned questions out of the 25 total questions.

Alternative 1

Alternative 2

Alternative 1

Alternative 2

Q

Own income

Ref. income

Own income

Ref. incomeQ

Own income

Ref. income

Own income

Ref. income

1

64

40

40

18

14

64

90

90

90

2

90

90

40

90

15

18

64

24

40

3

90

64

64

90

16

24

90

18

64

4

90

18

90

40

17

24

18

64

24

5

90

40

90

64

18

18

90

40

40

6

90

24

90

18

19

24

24

64

18

7

24

40

64

64

20

18

24

24

90

8

64

64

90

24

21

18

40

18

24

9

40

40

64

40

22

40

64

24

64

10

64

24

18

18

23

40

18

18

90

11

40

24

24

4

24

18

18

18

40

12

64

18

40

24

25

24

64

24

18

13

40

90

40

64

3.2

Logistics of the survey and questionnaire

Our data set was created using an original, Internet-based survey.9 A Japanese consumer
monitoring company, Nikkei Research Inc., conducted the survey under the direction of
the authors. As clearly documented in Horton et al. (2011), “[t]he validity of economics
experiments depends heavily upon trust, particularly subjects’ trust that the promulgatedrules will be followed and that all stated facts about payment, · · · , are true.” In Japanesesociety, the Nihon Keizai Shimbun (NIKKEI) Group is widely viewed as a trustworthy andneutral media outlet. Nikkei Research, Inc., as a part of NIKKEI group, has establisheda high reputation among researchers and consumers. For example, in order to provide
highly reliable research data, its registered subjects are subject to monthly screenings.
9

We conducted three preliminary tests before the main test. We then took diﬀerences in referencegroups into account in designing the questions used in the choice questions.

10

The company keeps information up to date and excludes double registrations. Incentives
for respondents are provided by cash voucher, rather than by points; point incentives canlead to bias, as particular respondents with points tend to answer.
Nikkei Research, Inc., and the authors reached an agreement that the number of subjects should be over 10,000, given the volume of the research fund. Then, in considerationof the unweighted average of response rates for seven similar academic choice experiment
surveys conducted by the same company in 2008 and 2009, the company sent an invitation
email for the survey to 60,482 subjects (out of more than 160,000 total registered subjects
in the Nikkei Database). Subjects between the ages of 20 and 65 were selected using
stratiﬁed random sampling so that the cohort proﬁle of our sample mirrored the Japanesecensus statistics of age and gender distribution. Because subjects are not required todeclare their educational attainments during the registration process, we did not use suchinformation in our stratiﬁed sampling. In the email, we speciﬁed that the survey is being
conducted for research purposes and followed the disclosure requirements for research involving human beings provided with incentives as set forth by the ethics committee. We
speciﬁed our payment rules in the invitation email and stated that the incentive would be
on a lottery basis. We informed subjects that 800 winners among those who completed
the survey would be paid 500 JPY per person. The anonymity of subjects was completely
secured. If subjects wished to participate in the survey, they were instructed to follow thelink in the email that directed them to our stand-alone survey website, written in html,
that was launched in February 2010. The survey was open for one week (Feb. 18 to 25,2010), and 14,370 subjects completed the survey.10 This approach is in contrast to oneday research, which many other research companies typically adopt for cost reasons. With
one-day research, subjects are chosen on a ﬁrst-come ﬁrst-served basis, causing potential
bias as the resulting sample may well not be representative. Sampling over a one-weekperiod likely reduces this bias. The structure of the questionnaire is as follows.
3.2.1

Part 1: Introductory questions

At the beginning of the survey, respondents were asked to choose one of ﬁve possible
categories on their level of satisfaction about income. Category 1 corresponds to “Notat all satisﬁed,” while category 5 denotes “Extremely satisﬁed”. The second question
10

This response rate of 23.8% (14,370/60,482) is smaller than might have been desired. The decision ofsubjects to participate in the survey was driven by unobservable characteristics that likely diﬀer between
participants and non-participants. If the unobservable characteristics are independent of the income
comparison eﬀects, then sample selection will not bias the results.

11

related to social comparison and was phrased as “How much are you concerned, anxious, or jealous about the amounts of income received by other people?” The respondents
were asked to choose from ﬁve response options, where category 1 corresponded to “Notat all” and category 5 denoted “Very concerned.” The third question concerned the respondents’ deﬁnition of their reference group. They were asked to choose one category,from those applicable to them, as their reference group, with the choices being: (i) family, (ii) neighbors, (iii) friends, (iv) colleagues, (v) do not care, and (vi) others. Fromthese last two questions, we can observe “who compares to whom?” and “how much?,”which were investigated as the framework of the happiness study of European countries
by Clark and Senik (2010). The following table shows the distribution of the referencegroups chosen by the respondents.
FamilyNeighbors Friends

Colleagues

Do not compare

OthersObservations

483

578

4279

2024

2592

247

%

4.73

5.67

41.94

19.84

25.40

2.42

We can see that the most often cited reference group is friends, followed by workcolleagues. These rankings are the opposite of those in European countries, as documented
in Clark and Senik (2010), but it is interesting that in both Europe and Japan these twogroups are the two most important reference groups. Family and neighbors play minor
roles as reference groups. These ﬁndings were used in setting up our hypothetical choice
experiment in the “who-compares-to-whom” task. In our data set, one-fourth of subjects
answered they do not have comparison groups.
3.2.2

Part 2: Hypothetical discrete choice questions

1. Social average task (randomly assigned 5 questions)2. Leyden task (randomly assigned 5 questions)3. Who-compares-to-whom task (randomly assigned 5 questions)
3.2.3

Part 3: Demographic questions

The last part of the survey consisted of questions about individual characteristics, including age, gender, educational background, employment, marital status, type of residence,residence area, and annual pre-tax personal income in 2009.

12

3.3

Survey strategy and data construction

One clear deﬁciency of such web-based surveys is that researchers cannot actively monitor
and encourage subjects to participate in the survey. In particular, when subjects do nothave a clear understanding of questions, they will try to complete questions as quickly
as possible by making up answers without contemplation. Hence, there is a trade-oﬀ
between (1) better understanding of the questionnaire by subjects, which reduces the
cognitive experimenter demand eﬀect (EDE), and (2) the beneﬁts of conducting the survey“behind the veil of ignorance,” which reduces the social EDE (Zizzo 2010). In our case,given that the survey was on the Internet and that speciﬁcity was required to meet the
study’s academic purpose and the requirements of the ethics committee, we leaned toward
the former (better understanding by subjects) and explicitly stated that the survey wasa “Survey on socio-economic attitudes by Osaka University” in both the invitation email
and the top page of the survey website.11
A good way to monitor subjects’ willingness to participate in the survey is to lookat the elapsed time for completing the survey.12 If the elapsed time is extremely shortfor a subject, it is obvious that he completed the survey without contemplation, and it
is plausible that he just wanted to join the lottery for the research reward. The average
elapsed time to ﬁnish the survey was 9 min 9 s for our survey, with the median valueof 6 min 5 s.13 For subjects, the easiest way to ﬁnish the survey is to provide the sameanswers for conjoint questions documented above. We eliminated those who provided
the same number for all ﬁve questions in any tasks (2,218) after we conﬁrmed that those
who provided the same answers on ﬁve consecutive questions in a task tended to ﬁnish
11

In our survey, the questionnaire started with questions about income satisfaction and comparison
attitudes. Because these introductory questions are followed by the hypothetical choice tasks, the questionorder might make individuals conscious of making pecuniary comparisons. If this is the case, this bias
will also be related to social EDE. That said, the fact that the subjects were reminded about social
comparisons does not necessarily lead to over- or under-estimates of the true eﬀect. On the one hand,
after the instructions subjects may be motivated to “beat” the reference persons in the hypotheticalchoices, which will over-estimate the true eﬀects of social comparison. On the other hand, one canjust as easily think of mechanisms shifting the results in the other direction. Namely, many people
dislike thinking of themselves as status-seeking and they therefore underestimate the degree to which
they state that they care about social comparisons. This resembles the purchase of moral satisfactionin Kahneman and Knetsch (1992). See the concluding section for more discussion on potential biases inexperimental studies.
12
Rubinstein (2007) conducted an Internet-based survey experiment and recorded response time forsome questions. He found signiﬁcant diﬀerences in response time across types of questions, and suggested
that choices made on the basis of an emotional response require less response time than choices that
require the use of cognitive reasoning. In our case, we have information on total response time tocomplete the entire survey, while response times for individual questions are not available.
13
Observations with no time records (90) and elapsed time longer than 60 minutes (106) are excluded
from our study sample.

13

the survey very quickly, most likely without contemplation.14 We also discarded theinformation of subjects whose elapsed time is shorter than 4 min (968) on the basis offeedback from an internal company pilot test by NIKKEI. So far, we are left with 10,988
respondents.
Next, observations were dropped if they were either missing information for some ofthe variables used in the empirical analysis below (219), or contained an inconsistency inthe data, such as retirement before the age of 55 (1). Finally, we excluded observations ofrespondents who report their personal annual pre-tax income in 2009 to be higher than
12 million JPY (565).15 To ensure that this cut-oﬀ for the income variable was not a
result of sample selection, we compared the observations in the two groups along diﬀerent
dimensions including age, education, marital status, and residence area. We are happy toreport that the number of observations excluded from our working sample does not seemto be a result of any sample selection problems, and that the main results documented
below remain qualitatively unchanged when we use the whole sample as our study sample.
At the ﬁnal stage, we were left with 10,203 respondents.
The descriptive statistics of our data are shown in Table 1. Because the stratiﬁed
random sample was designed to mirror the population cohort proﬁle of Japanese census
statistics, the age and gender structures of our sample appear quite similar to national
statistics. There is however considerable under-representation of women who are divorced,
separated or widowed. This diﬀerence from national statistics comes about because thelatter include everyone aged over 15. As the average length of life for Japanese women isaround 87 (with that of men being around 78), women tend to be widowed towards theend of their lives, which is reﬂected in the rate of female divorce/separation/widowhoodin national statistics. However, as our sample only includes those who are aged up to65, the rates of divorce/separation/widowhood for both men and women are lower thanthose in national statistics.
With respect to educational attainment, in our samples of males, just 1% of the sample
completed middle school only, 18% completed high school only, 10 percent completed some
14
Rand (2011) reported that at least 80% of experiment participants in MTurk were not merely making
random selections on survey questions, which resembles the ﬁgure in our case.
15
The cut-oﬀ point, 12 million JPY, is higher than the sum of the average of personal pre-tax annual
income and three standard deviations of the income distribution. There are two major reasons for thehigh frequency of high-income level subjects. One reason is that survey participation is biased toward
persons with higher education, Internet access, and urban residence. It is natural that these individuals
have higher income than others without these characteristics. The other reason is that they tried to cheatby inﬂating their income levels. According to the exchange rate in March 2009, 12 million JPY is around
130,000 USD.

14

of college, and the remaining 70% held college or post-graduate degrees. This bias towardhigher education also holds for the female samples. This is an over-sampling of more highly
educated participants. Related to this issue, the average income levels from our sample
are greater than that in the national statistics. The survey requests that subjects indicate
their own income level from a list of 11 categories, where category 1 denotes annual wagesof less than 2 million JPY and category 11 corresponds to an annual income level of morethan 50 million JPY. When we measure individual income levels as the mid-point in eachof the 9 intermediate categories, and use ad hoc values of 1.5 and 55 million JPY for thetwo extreme categories, respectively, we obtained that the average annual income of ourwhole sample was 5.69 million JPY for males and 2.93 million JPY for females.
The diﬀerences from the national statistics for students and the unemployed do notlook severe. Information on residence location is compared. There is an over-samplingfrom the Kanto region, which includes Tokyo. Also, people from the Kansai region,
which contains Osaka, are slightly over-sampled. Overall, we ﬁnd that our data set
captures signiﬁcant features of Japanese society, except for the distribution of educational
attainment. It is diﬃcult to obtain a representative sample with small face-to-face surveys.

4

Random utility model and empirical method

In this section we introduce the econometric foundation on how subjects’ choice data canbe used to estimate their utility functions. We start by describing a discrete choice modelwith a general utility function. To analyze decisions in hypothetical choice experiments,
we use a random utility model framework. The model deals with data on repeated choices
over available alternatives. It is assumed that subjects choose an alternative since theyobtain higher utility out of the alternative than from the other available alternatives.
When there are two alternatives available (A and B, for example), and if they chose A
rather than B, then the choice data is recorded as 1 for alternative A and 0 for alternative
B, along with the levels of the explanatory variables (attributes) in alternatives A and B,
respectively. These pieces of information comprise the observation for regression analyses.
Now more speciﬁcally, there are N subjects and they answer T (≥ 1) repeated choicequestions. The utility of subject n when s/he chooses alternative i at question t ∈ T ,Uitn , consists of observable components in experiments Vitn and unobservable components²itn so that utility can be viewed as Uitn = Vitn + ²itn . Utility from observable components∑K
are assumed to be linear combinations of each attribute as Vitn =k=1 βk Xik , where15k = 1, · · · , K(K ≥ 2) represents the variety of attributes, Xk denotes the levels of kth
attributes, and βk measures marginal utility of each attribute. In the following analysis,
the vector of β ≡ (β1 , · · · , βK ) that maximizes the log likelihood function of observed
choice patterns by subject is the estimator of conditional or mixed logit model regressions. Following McFadden (1974), ²itn is distributed following independent and identical
distribution of extreme value type 1 (IIDEV1) with variance σ 2 .
The logit formula of choice probability Pitn that subject n chooses alternative i fromthe set of alternatives St (choice set) in question t ∈ T can be written as
Pitn = prob(Uitn > Ujtn , ∀j 6= i ∈ St ) = prob(²jtn − ²itn < Vitn − Vjtn , ∀j 6= i ∈ St ).
McFadden (1974) showed that Pitn = exp(λVitn )/∑j∈S exp(λVjtn ), where λ = π/

√

6σ is

the scale parameter.
Finally, a dummy variable ditn is deﬁned, taking a value of 1 if subject n choose
alternative i for question t ∈ T , and 0 otherwise. Together with the logit formula of choiceprobability Pitn , the log likelihood function of repeated choices observed in experiments
can be written as
LL(β) =∑∑∑
ntditn ln Pitn .

i∈St

In the conditional logit model, the parameters of utility function, β, can be obtained withthe ﬁrst-order condition of ∂LL(β)/∂β = 0 (McFadden 1974). To be more speciﬁc, when
we estimate the model assuming that the independence of irrelevant alternatives (IIA)
holds, we obtain a conditional logit model where all of N subjects share the same set ofparameter in β. Alternatively, when we allow for distributions of some parameters in β
across subjects, we obtain the mixed logit model. In the latter case, while we assume
that the error term is independently and identically distributed as in the conditional logit
model, non-IIA situations are allowed. In the case of the mixed logit model, we can obtain
the distribution of parameters f (β) as follows. Following Train (2009), we specify thatf (β) is either a normal or a log normal distribution function with parameters set as θ.
ML
for the mixed logit model can be written as
The choice probability function Pitn

∫
ML
=
Pitn

Pitn (β)f (β|θ)dβ,

where Pitn is the logit choice probability in the conditional logit model given β. θ can be16

obtained via simulation which maximizes the simulated log likelihood function16
SLL(θ) =

∑∑∑
n

t

ML
ditn ln Pitn.

i∈St

Next, we specify the shape of the utility function for our own purposes. Here we
present the speciﬁc theoretical framework for the social average task only because of spaceconstraints. Individuals derive utility not only from their own income X1 = y but also fromthe social average income X2 = ȳ. From textbook assumptions, we suppose that subjects
value attribute y positively. On the other hand, the social average income ȳ can be valued
positively (altruism) or negatively (jealousy). Following Johansson-Stenman et al. (2002),
Dupor and Liu (2003), Liu and Turnovsky (2005), we consider the constant relative risk
aversion-type utility function as
(1)

V =

(y ȳ γ )1−ρ,
(1 − ρ)where ρ > 0. If ρ = 1, it reduces to the log felicity function. The parameter γ regulates
the intensity and sign of relative utility and is the central topic of this study. If γ < 0,
the individual has jealousy. If γ > 0, the individual has an altruistic preference, whereas
if γ = 0, there is no relative utility.
Again, let i denote the alternative and n denote the subject. We take the logarithms
of both sides in Equation 1 to obtain(2)

ln Vni = (1 − ρ) ln yni + (1 − ρ)γ ln ȳni − ln(1 − ρ).

With an error term ²n , the probability Pin that respondent n prefers alternative i toalternative j is given by(
Pin = P rob (1 − ρ) ln yin + (1 − ρ)γ ln ȳin − ln(1 − ρ) + ²in)> (1 − ρ) ln yjn + (1 − ρ)γ ln ȳjn − ln(1 − ρ) + ²jn , for all j 6= i.
Using maximum-likelihood estimation we obtain coeﬃcients for ln y as β1 = 1 − ρ andln ȳ as β2 = (1 − ρ)γ. β1 and β2 are regarded as marginal utility in the random utility
model framework. It is noteworthy here that estimated β1 and β2 are divided by thescale parameter λ, which is unknown to researchers (Train 2009, p41). This means that16

See Section 6 of Train (2009) for details.

17

we cannot obtain true magnitudes of all the parameters in Equation 2. However, we canobtain true estimates of our interested variable γ by dividing β2 with β1 , thus canceling
λ out.17

5

Benchmark results from the social average task

Our benchmark results from social average task are shown here. Results in this section
will be informative to theoretical macroeconomists because previous theoretical studies
on relative utility eﬀects such as Abel (1990), Gali (1994), Liu and Turnovsky (2005),
and Garcia-Penalosa and Turnovsky (2008) were conducted without estimating importantparameters in the utility function. As such, they put forward various propositions inaccordance with the parameters assumed and do not necessarily reﬂect “reality.”
Table 2 provides results for the whole sample. The ﬁrst column shows the results fromthe conditional logit model. We see that a person’s own income aﬀects utility positively
and signiﬁcantly, as is expected. Next, from the coeﬃcient of the reference income term,
it is shown that relative utility exists among Japanese respondents, and that, on average,
the eﬀect appears in the form of jealousy. These two coeﬃcients provide the true magnitude for the parameter of relative utility γ, by dividing the second by the ﬁrst. From theestimates in column (1) of Table 2, we obtain that γ = −0.458. From a decision utility
framework, this result stands in the middle of two extremes in experienced utility frameworks: the fully relative utility function of Easterlin (1995) and the solely absolute utility
function of Stevenson and Wolfers (2008).18 It is noteworthy that a recent ﬁnding in thehappiness studies literature by de la Garza et al. (2010) reached the same conclusion thatmoney buys happiness to some extent in Japan, through the use of direct and cardinalmeasures of reference income. Also note that our result is perfectly in line with the otherstudies based on hypothetical choices in diﬀerent societies such as Solnick and Hemenway
17
Ida and Goto (2009) compared estimated parameters in a logit model framework by assuming thatσ = 1 through all regressions of subsamples. However, researchers cannot compare estimates from
diﬀerent subsamples without taking diﬀerences in σ into consideration. As stated in the body, thecoeﬃcients that are estimated indicate the eﬀect of each observed variable relative to the variance of
the unobserved factors. It is useful to recognize that the likelihood ratio test designed for conﬁrming
statistically signiﬁcant diﬀerence of parameters among subgroups is not suitable here. For example, alarger σ in a subsample leads to smaller coeﬃcients in its regression, even when the observed factors in
two subsamples have the same eﬀect on utility. A heteroscedastic logit model can be used in investigating
the diﬀerence of σ between the subgroups.
18
According to Figure 3 in Easterlin (1995), the Easterlin paradox evidently held for Japan in the
period from 1958 to 1987. One reason why the comparison intensity we estimated falls short of the levelvalidating the paradox reason is that the social comparison eﬀect is just one of many explanations of theEasterlin paradox. Habit formation, for example, explains the paradox as well (van de Stadt et al. 1985).

18

(1998), Johansson-Stenman et al. (2002), Alpizar et al. (2005) and Carlsson et al. (2007).

5.1

Representativeness of the result

As was documented in section 3.3, our data set under-sampled those who completed
middle school only. A simple estimation of Mincer equation with our data showed that
educational attainment was positively correlated with income levels. As was shown by
Ravallion and Lokshin (2010), it is well known in the literature that people with lower
income tend to become less jealous, and we found the same result in our data set, as
is documented below in Section 5.2. Hence, the representativeness of our results is an
issue, and it is likely that we over-estimated the relative utility eﬀects because of theoversampling of persons with higher educational attainment. In column (2) of Table 2, weshow the result when we adjust the sampling weights using a post-stratiﬁcation method.19
The estimated γ after the post-stratiﬁcation adjustment suggests, against our expectation,that the original γ in Column (1) was a slight under-estimation of the negative relative
utility eﬀect compared with the adjusted, representative data set in terms of gender, age,and educational attainment.
The reason for this result is as follows. First, the coeﬃcient of the own income term inColumn (2) is smaller in comparison with that in Column (1). Comparing coeﬃcients of
own income term from columns (1) and (2), we see that subjects with higher educational
attainment and higher income levels enjoy higher marginal utility from own income.20
Second, after the adjustment, the coeﬃcient of the reference income term in Column (2)is smaller in absolute value than the one in Column (1). The direction of the change
is hence in line with the presumption that people with higher educational attainmentand higher income levels tend to become more jealous. In our data set, the ﬁrst eﬀect
dominates the second eﬀect to provide γ of stronger jealousy after adjusting for the oversampling of persons with higher educational attainment.
Given the results, an accurate depiction of the representative relative utility eﬀects in
19
We computed post-stratiﬁcation adjustments to survey sampling weights. The sampling weights ingender and educational attainments were adjusted such that the sum of the weights equals the control
total for each stratum.
20
Because marginal utility from consumption becomes smaller as your consumption levels increases inneoclassical economics theory, at ﬁrst sight this seems odd. This observation, however, can be justiﬁed
when we allow for heterogeneity of a parameter in the utility function between the poor and the rich.
When a shift parameter of the utility function is greater for the rich than the poor, the marginal utilityof consumption at a certain level of consumption becomes higher for the rich. The heterogeneity of theutility function, reversely, could explain why some become rich while the other stay poor, even when theother demographic conditions are the same for all the subjects.

19

Japanese may not be necessary since, as described above, samples with lower educational
attainment are under-sampled. Nonetheless, because of the large size of our sample, thebreadth of coverage across Japan’s 47 prefectures, and the wide variety of job types frompublic servants to students and the unemployed, we believe that our data set does capture
signiﬁcant features of the relative utility eﬀects in Japan. Hereafter, we show results using
the unweighted sample for brevity.

5.2

Heterogeneity of preference parameters

In the last columns of table 2, we show the result from the mixed logit model in which
normal distributions of parameters across subjects are allowed.21 We ﬁnd that own incomeaﬀects utility positively, whereas the reference income has a negative impact on utility
on average. We also ﬁnd a similar ratio in the values estimated for the own income
term to that of reference income term in both the conditional logit model and the mixed
logit model, which validates the robustness of previous ﬁndings from the conditional logit
model.
It is interesting to note that the standard deviation terms estimated in the mixed logit
model are both signiﬁcant at the 1% level. Behavioral economics has provided evidence
that demographic diﬀerences lead to substantial diﬀerences in preference parameters, suchas the time discount rate and the level of risk aversion.22
Following Viscusi et al. (2008), we identify the eﬀects of individual characteristics on
preference parameters by controlling for interaction terms of attributes in the surveys anddemographic variables in conditional logit models.23 In doing so, we consider two organic
factors, six acquired individual characteristics, and three subjective variables as potential
sources of parameter heterogeneity. The two organic variables are age and gender. We
consider annual income level, educational attainment, urban residence, marital status,
21

The STATA module for mixed logit model estimation is provided by Hole (2007).
Small et al. (2005) applied the framework of a mixed logit model to investigate the distributionof commuters’ preferences for speedy and reliable highway travel, ﬁnding that there was substantial
heterogeneity in motorists’ values of travel time and reliability. Hole (2008) investigated the preferencesof patients about general practitioner appointments using standard logit, mixed logit, and latent classlogit models. He showed that there was signiﬁcant preference heterogeneity for all the attributes inthe experiment. Viscusi et al. (2008) showed that eco-conscious individuals have a lower rate of time
discounting than those who are not eco-friendly. Ida and Goto (2009) showed that smokers have with ahigher value of time discounting and a lower value of risk aversion than nonsmokers.
23
The introduction of interaction terms into conditional logit frameworks is acceptable as long as we
conﬁne our attention to the sign and signiﬁcance of the interaction terms, as is clearly explained on
page 22 in Train (2009). See Ai and Norton (2003) for interpretations of the marginal eﬀects of dummyinteraction terms in logit models. As long as one can interpret the coeﬃcients as marginal utilities, as
we do in a random utility model framework, Ai and Norton’s point is not relevant.
22

20

unemployment status, and student dummies for the six individual characteristics. The
three subjective dummy variables include a “do not compare” dummy, a “very happy”
dummy, and a “very comparison conscious” dummy.
We report the results without a detailed table to save space (the full results are available upon request). We ﬁnd that people tend to become more jealous if they are rich,female, highly educated, or married. Interestingly, urban residents do not have stronger
comparison attitudes when compared to those who do not live in major cities. It is alsointeresting that age does not aﬀect comparison intensity. In terms of the marginal utilityof own income, as previously introduced, those with higher income and higher educational
attainment tend to obtain higher utility from a certain amount of income. Regarding the
subjective variables, we ﬁnd that those who report that they do not compare have weakercomparison attitudes, which we discuss in more depth in the following section, and thatthe more they care about comparisons, the stronger their jealousy becomes. These ﬁndings are as expected. Feelings of being happier do not aﬀect comparison intensity. Ourﬁndings are robust against changes in the threshold level for the comparison conscious
group, the happy group, high income group, and elder group.
Thus, we can conﬁrm that heterogeneity plays a role in determining the intensityof social comparison, just as previous behavioral economics studies have found in otherﬁelds.

5.3

Analysis with comparison benchmark information

A recent caveat from the happiness study of Clark and Senik (2010) is that comparison
attitudes can diﬀer depending on the reference group that people ascribe as their comparison benchmark. In our data set, similar to Clark and Senik (2010), information on speciﬁcand relevant reference groups for each subject is available. It is interesting to see how
people change the intensity of comparison on the basis of their comparison benchmarks.
Since the target reference group is based on a general concept, the diﬀerences in relative
utility intensity derived in this task reﬂect basic diﬀerences in the intensity of relative
utility across subgroups. We divide our study sample into subgroups of individually
relevant reference groups and compare the obtained true magnitudes of γ across the
subgroups.
The variances of the estimated γ by subgroup for the comparison benchmarks are obtained by using the Delta method to examine the statistical signiﬁcance of the diﬀerences.
Since γ takes the form γ = r/s, where r and s are stochastic variables, the variance of γ
21is obtained as follows:(
V ar(γ) =

∂γ
∂r

∂γ
∂s

)

(

V ar(r))( )∂γ
Cov(r, s)∂r

Cov(r, s)V ar(s)

∂γ
∂s

=

1
βr2
2βr
V
ar(r)+V ar(βs )− 3 Cov(r, s),
2
4
βs
βs
βs

where for r and s, βr and βs are the averages and V ar(r) and V ar(s) are the variances,respectively. Cov(r, s) is the covariance of r and s.
Columns (1) through (5) of Table 3 show the coeﬃcients and estimated γ acrosssubgroups for the comparison benchmarks. We exclude the subgroup of “others.” Aninteresting estimate of γ appears in column (2), where the comparison benchmark isneighbors. People who tend to compare themselves with neighbors are the most jealous
in Japan. The value of γ for the “neighbors” subgroup is signiﬁcantly diﬀerent from theother subgroups (p < 0.01).
The ﬁnding that those who compare themselves with neighbors have the strongest
intensity of relative utility requires further attention. The reference group of neighbors ischaracterized by close contact. Hence, the result seems natural if we accept that people
endogenously choose their reference groups from groups of close contacts, as Clark et al.
(2008) argued. However, it is interesting to see a weaker intensity of relative utility for
those whose comparison benchmark is work colleagues, another type of close contact,
shown in column (4). The diﬀerence of estimated γ between the two subgroups of comparison benchmark is signiﬁcant (p < 0.01). Regarding this ﬁnding, it may make sensewhen considering the tunnel eﬀect proposed by Hirschman and Rothschild (1973). He argued that an increase in work colleagues’ income could be interpreted as a positive signal
regarding likely future outcomes. However, the eﬀect seems not so strong as to provide
positive relative utility, as was found by Senik (2004) using a Russian data set.24
It is important to note that the “Do not compare” group in column (5) has a signiﬁcantly smaller estimated γ than the other groups (p < 0.01).25 However, the factthat social comparison eﬀects are observed among those who explicitly state that they do
not compare, and that diﬀerences in the estimated gamma from other subgroups appears
marginal, despite being signiﬁcant, draws our attentions.
The beneﬁts of our experimental approach are that we showed subjects clearly illustrated income comparison scenarios with information on their own income levels and24

Card et al. (2012) compared the positive eﬀects of the tunnel eﬀects and negative eﬀects of relative
utility in a social experiment setting, and showed that the negative eﬀects are dominant in the UnitedStates.
25
In the previous section we picked up the same eﬀect when we interacted a “Do not compare” dummy
variable with reference income terms.

22reference income levels, and that we elicited information on comparison benchmarks. This
procedure resolves uncertainty in existing happiness studies that do not elicit comparison benchmarks as to whether the negative coeﬃcients for the relative income proxies inhappiness regressions are in fact capturing social comparisons. Hence, a natural expectation for the results from our experimental approach is that we ﬁnd no social comparisonseﬀects among those who say they do not compare. It is also noteworthy that we did not
get the result because of a biased construction of the experiment: from the constructionof choice sets as explained in Section 3.1, we can obtain positive, negative, or no relative
utility eﬀects depending on patterns of subjects’ choices.
Table 4 shows the results when we divide the observations of the “Do not compare・group into subgroups of survey-elicited intensity of jealousy (from 1 to 5). The numberof subjects who declared the maximum intensity of jealousy (5) was too few to provide
a relevant result, as shown in Column (5) of the table. As it can be seen, the estimatedvalues of γ across subgroups of survey elicited intensity of jealousy make some sense. In
subjects who declared weaker jealousy in the questionnaire, their choice patterns in the
choice experiment provided weaker intensity of income comparisons of γ. For those who
declared the minimum intensity of jealousy (1), the estimated γ was -0.342. This ﬁgure
is a 25% reduction from the comparison eﬀect for the whole sample, but again, is signiﬁcantly diﬀerent from zero. Our interpretation of the result, considering that the result is
not found because of a biased construction of the experiment favoring negative comparisons, is that ultimately humans make comparisons, even though they themselves declarethat they do not. In support of this argument, Fliessbach et al. (2007), Takahashi et al.
(2009), and Tricomi et al. (2010) revealed a neurological basis for making comparisons inhuman brains. These studies imply that we inherently cannot escape from making comparisons. We even suggest that our study has provided stronger support for the existenceof negative relative utility eﬀects than research that relies on evaluation and rating data
can provide: it is easy for subjects to “cheat” in questionnaires to say that they do notmake comparisons, or to rate their intensity of jealousy very low, even when they indeed
are very jealous. It is not, however, very easy for them to expect what their choices
in choice experiments will indicate about their jealousy without knowing the technicalaspects of discrete choice experiments.

23

6

Discussion of results from extended tasks

A salient feature of the hypothetical discrete choice experiment on income comparisons isthat we can investigate diﬀerences in such eﬀects across various types of reference persons
in well-controlled experimental conditions. Here we introduce the main results from othersuch applications. The detailed construction of the experiments and empirical results are
given in the Appendix.

6.1

Leyden task

In the Leyden task, reference persons are characterized by speciﬁc demographic variablesof age, gender, and educational attainment, which we adapted from the Leyden Schooldeﬁnition of reference group (van Praag and Frijters 1999). We then would like to see
how the intensity and sign of relative utility change in accordance with the characteristics
of reference persons.
We found that reference groups with higher educational attainment tend to be thetarget of stronger jealousy. We also ﬁnd that if the reference person is older than thesubject, feelings of pecuniary emulation are mitigated. The altruistic attitudes toward
elderly persons, or admiration of them, are interesting since both higher age and higher
education are associated with higher income on average. As we conﬁrmed in the social
average task, reference persons with higher income levels draw stronger jealousy. These
intriguing relative utility eﬀects for the elderly may be a good reﬂection of Japaneseculture. Regarding the eﬀect of gender, it was found that males are the target of stronger
jealousy from both males and females. To sum up, from the Leyden task, we can saythat comparison attitudes change on the basis of the features of reference persons. Theseﬁndings suggest that consideration of social averages as the salient reference group is
not suﬃcient when examining the relative utility eﬀects in the whole society. Instead,researchers should pay attention to the features of the true reference groups of subjects,
since they greatly aﬀect the outcome of empirical investigations of relative utility.

6.2

Who-compares-to-whom task

In this task, we simultaneously consider two types of reference groups, friends and workcolleagues, in choice questions. The selection of these reference groups comes from theresult of our preliminary tests, which showed that these two groups were the most citedby respondents.
24

In addition to the simultaneous treatment of two external reference groups, in thisstudy we also have information on subjects’ speciﬁc comparison benchmark, and we examine the following issues: (i) if friends and colleagues are recognized as diﬀerent type ofreference groups, and if so, (ii) how diﬀerent they are; and (iii) if comparison attitudes
toward these two groups vary by speciﬁc comparison benchmarks. The answers are as
follows.
 (i) The result suggests that friends and colleagues are recognized as diﬀerent typesof reference groups and subjects frequently change their comparison benchmarks
from one to the other, rather than stick to one, in accordance with situations they
face. This result hints at the fact that reference groups are chosen endogenously.26
 (ii) The average comparison eﬀects toward the friend group and the work colleague

group are very diﬀerent in magnitude. The intensity of jealousy toward work colleague group is more than 10 times stronger than that toward the friend group.
 (iii) A noteworthy ﬁnding is the large standard deviation of the income compar-

isons eﬀect toward “friends,” compared with the small mean eﬀect. We found thata mean estimate of comparison eﬀects toward friends reﬂected diﬀering attitudes
in the subjects toward their friends. In fact, around 30% of subjects feel altruism
toward friends. Another intriguing pattern is found when we compare the estimatesof standard deviations for the relative utility eﬀects toward work colleagues by two
subgroups of subjects with friends and those with work colleagues as the comparison benchmark. On one hand, the standard deviation is found to be signiﬁcant(though it is not particularly large) in the subgroup with a comparison benchmark
of friends. On the other hand, for the subgroup with a comparison benchmark aswork colleagues, the standard deviation is found to be insigniﬁcant, implying thatthe negative relative utility eﬀect against work colleagues spikes near the group average. These outcomes suggest that reference groups of friends and work colleagues
are diﬀerent, not only in terms of the average intensities of the relative utility eﬀects,
but also in the sense that their distributions diﬀer by subsample groups of speciﬁccomparison benchmarks. We argue that this outcome reﬂects that friends encompasses many aspects of life, such as benevolent rivals, persons of understanding, andso forth, and that work colleagues tend to be regarded as rivals.
26

See Train et al. (1987) and Herriges and Kling (1996) for technical discussion on the nested logit
model that derived this implication.

25

7Concluding remarks

In this concluding section, we discuss biases that are normally associated with statedpreference methods.
As Hausman (1993) and Carson et al. (2001) pointed out, there are some potential
biases in stated choice methods. Bateman et al. (2002) categorized these biases into
three broad categories: (i) incentives to misrepresent responses, (ii) implied value cues,and (iii) scenario misspeciﬁcation. The ﬁrst category relates to false answers to the survey
questions. This bias arises when the questions and scenario settings are not well designed.
The second and the third biases result from respondents’ misunderstandings of the survey
questions, namely cognitive EDE as coined by Zizzo (2010). To avoid these potential
biases, researcher should carefully design choice tasks through pilot surveys, repeatedpreliminary tests and close investigation of the preliminary results. In the present study,
we conducted pilot surveys and three preliminary tests to ﬁne tune our questionnaire. The
most substantial change in our main test from the preliminary test was the introductionof visual images in the choice situation tasks. By introducing visual images, the cognitive
burden on subjects was decreased, with the average elapsed time for ﬁnishing the survey
reduced by half from the initial preliminary test without images. We also see that standard
errors in the logit model estimations were also reduced compared to the estimations
obtained from preliminary test data. By comparing our results with those from thepreliminary tests, we also ﬁnd that the order of questions and selection of questions,other than the choice questions in the present study, do not seem to seriously aﬀect themain results of our study.
Dolan and Kahneman (2008) critically summarized biases associated with stated preference methods including hypothetical discrete choice experiments. They then advocatedhappiness (or experienced utility) research for situations where researchers would liketo infer the market values of non-market goods. Note, however, that footnote 4 in
Dolan and Kahneman (2008) holds that “[their] critique is focused on the use of measures of decision utility to elicit values of this kind, rather than their usefulness in othercontexts, such as predicting behavior.” The purpose of this paper is to elicit the sign andthe intensity of comparison eﬀects which aﬀect human behavior.27
27

The other drawback inherent in stated choice methods that is often mentioned is the artiﬁcial natureof the questions and incentive incompatibility for subjects in making choices. Regarding the issue,
Lusk and Schroeder (2004) showed that stated choice methods provided similar results for marginal eﬀects
compared with the results in non-hypothetical settings. They held that careful design of the survey is
the key issue in avoiding this bias, a requirement that we argue that we have satisﬁed through the use of26

In stated choice method studies of Johansson-Stenman et al. (2002), Alpizar et al.
(2005), Carlsson et al. (2007), and Andersson (2008), respondents were asked to consider
the well-being of their oﬀspring, rather than their own well-being. This framing was usedin order to help the respondents liberate themselves from their current circumstances,disentangling their actual consumption from the hypothetical consumption choices in thesurvey. In the present study, we instead asked about the respondents’ own interests.
This design choice was made because we would like to know the current situation within
Japanese society. The biases associated with ignoring the previous strategy are not expected to be especially severe because we can control for individual ﬁxed eﬀects, as we
asked respondents to make ﬁve repeated choices in each task, unlike in the previous studies. Our strategy here was also motivated by Dolan and Kahneman (2008), who took acritical view on having subjects make hypothetical choices on the basis of future expectations and past memory.
We suggest a future research agenda as follows. The merit of the hypothetical choice
experiment framework under a random utility model is that we do not rely on informationof subjective well-being to obtain the true parameters of the (decision) utility function
for the relative utility. Because subjective well-being information is usually strongly inﬂuenced by country ﬁxed eﬀects and by social norms, hypothetical choice experimentframeworks will be useful alternative avenues in conducting international comparisons of
the relative utility eﬀects.

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34

Table 1: Descriptive Statistics

Age category
20s
30s
40s
50s
60s
Education (a)Middle school
High schoolSome college
College
Marital Status
SingleMarriedDivorced/widowed
RegionHokkaido
Tohoku
Kanto
Koshinetsu
Chubu
Kansai
Chugoku
Shikoku
KyushuFemale [0.1]Student [0,1]Annual income (b)Unemployment(1)
Our Survey (whole sample)
Male
Female(2)
Our Survey (study sample)
Male
Female

18.20
24.53
20.53
23.07
13.68

22.18
22.90
24.47
18.61
11.83

18.06
25.37
19.20
22.54
14.83

21.51
23.18
24.80
18.85
11.66

1.00
19.62
10.47
68.91

0.97
26.19
31.89
40.95

0.94
21.20
11.14
66.73

0.89
25.89
32.12
41.10

32.69
63.95
3.36

26.5
67.24
6.26

33.23
63.27
3.50

25.75
67.92
6.34
4.65
4.36
44.23
4.19
9.96
20.55
3.92
1.88
6.28
55.59
3.41
2.71
4.01

5.69

4.31
4.21
45.32
3.95
10.09
20.23
3.92
1.84
6.14
52.57
3.60
2.93
4.05

4.90

(3)
NIKKEI

13.73
36.42
30.07
13.88
5.89
N.A.

(4)
National Data (b)Male
Female
19.48
24.48
21.78
22.11
12.15

18.75
24.06
21.73
22.64
12.82

18.18
41.60
11.36
28.33

20.80
43.39
24.54
10.67

29.99
60.66
9.35

32.00
61.80
6.20

23.40
57.60
19.00

3.97
4.06
46.94
3.67
9.45
19.73
3.82
1.90
6.45
56.64
N.A.
N.A.
N.A.

4.3
7.4
32.9
6.7
11.9
16.3
6.0
3.1
11.4
51.27
7.60
4.87
1.85
4.90

All ﬁgures except for annual income (in million JPY) are percentages for each category.
(a) Those who are currently students are excluded from the ﬁgure.
(b) Demographic characteristics are from the Population Estimates by the Statistics Bureau (Sep.
2009); education attainment data are from the Employment Status Survey (Table 3; 2007) by theStatistics Bureau; marital status data are from the Population Statistics of Japan (Table 6.21; 2008) by
the National Institute of Population and Social Security Research; region data are from the Population
Statistics of Japan (Table 9.5; 2008); income information is from the Employment Status Survey (2008);
and unemployment data are from the Labour Force Survey (Feb. 2010) by the Statistics Bureau.

35

Table 2: Conditional Logit and Mixed Logit Estimates (Social Average Task)

(1)
Model
Dep. Var: Utility
Own income(2)

Conditional logit
0.048***(0.001)-0.022***(0.001)0.039***(0.002)-0.021***(0.001)

Estimated γ-0.458-0.546

Observations
Pseudo R-squared

48172
0.249

48172

Reference income(3)
Mixed logit
MeanSD
0.097*** 0.077***(0.002)(0.002)-0.044*** 0.081***(0.001)(0.002)48172∗ ∗ ∗p < 0.01, ∗ ∗ p < 0.05, ∗p < 0.1. Robust standard errors clustered by subject in parentheses.

36

Table 3: Conditional Logit Estimates across Comparison Benchmark Subgroups (Social
Average Task)

Comparison benchmark
ModelDep. Var: Utility
Own income
Reference income

Estimated γ
Estimated variance of γγ same as?
T statistics
Observations
Pseudo R-squared

(1)

(2)

familyneighbor(3)(4)

friendcolleague
Conditional logit(5)
Do not compare

0.045*** 0.056***(0.003)(0.003)-0.021*** -0.031***(0.002)(0.002)0.052***(0.001)-0.025***(0.001)0.050***(0.002)-0.022***(0.001)

0.041***(0.001)-0.017***(0.001)-0.4670.049
colleague
68.2-0.5540.018
friend16.8-0.481
0.064
family21.5-0.4400.073
Do not compare
52.4-0.415
0.026

2255
0.228

2739
0.320

20442
0.279

9581
0.253

11982
0.190

∗ ∗ ∗p < 0.01, ∗ ∗ p < 0.05, ∗p < 0.1. Robust standard errors clustered by subject in parentheses. For
estimates of γ, we report variances constructed via the Delta method.

37

Table 4: Conditional Logit Estimates in the “Do Not Compare” Subgroup by Intensityof Jealousy (Social Average Task)

Intensity of Jealousy
Model
Dep. Var: Utility
Own income
Reference income

Estimated γ
Estimated variance of γγ same as?
T statistics
Observations
Pseudo R-squared

(1)

(2)

(3)

(4)

(5)

1

2

3
Conditional logit

450.038***(0.003)-0.013***(0.002)0.041***(0.002)-0.015***(0.001)

0.044***(0.002)-0.022***(0.002)-0.342-0.3660.004
0.001
jealous=2 jealous=3
17.5
145.8

-0.5000.002
jealous=4
22.1-1.118
0.147

N.A.

3662
0.224

188
0.229

16
0.127

2181
0.162

5935
0.183

0.034*** -0.003(0.011)(0.018)-0.038*** -0.035(0.010)(0.040)∗ ∗ ∗p < 0.01, ∗ ∗ p < 0.05, ∗p < 0.1. Robust standard errors clustered by subject in parentheses. For
estimates of γ, we report variances constructed via the Delta method.

38

A

Appendices

Appendix A.1
Appendix A.1.1

Leyden task
Construction of choice tasks

The general method of constructing the choice scenarios in the Leyden task is the same as
in social average task, including orthogonal planning. However, in this task, the referencegroup is not simply the social average, but instead is characterized by the gender, age,and educational attainment of the reference person.
Hence, a total of ﬁve alternatives were deﬁned in this task. After the preliminary
tests, we determined the levels of these attributes as follows. First, as before, the income variables contain the following variations: 180,000 JPY, 240,000 JPY, 400,000 JPY,
640,000 JPY, and 900,000 JPY. For age, we included four levels – 22, 32, 45, and 58 years
old ・with the goal of reﬂecting diﬀerent stages of workers’ careers. Gender was maleand female. For the levels of educational attainment, we included ﬁve variations: middle
school, high school, technical school, undergraduate, and graduate.
One thousand potential variations in the combinations of these attributes exist. The
computer algorithm for orthogonal planning in SPSS Conjoint provided 25 sets of alternatives out of 1,000 potential variations. We replicated this procedure to obtain two setsof alternative vectors. To pair the alternatives for this task, we used the same strategy asin the social average task, with the exception that information on the three attributes ofsocioeconomic characteristics were not taken into account. We also added the no-choice
options as in the previous task. The following table shows the set of questions we used in
the survey.

1

Alternative 1

Alternative 2

Reference person’s characteristics

Reference person’s characteristics

Q

Own income

Ref. income

Gender

EducationAge

Own income

Ref. income

Gender

Education

Age

1

18

90

femaleunder graduate

32

40

40

male

junior high

32

2

40

18

male

graduate

32

64

40

male

tech school

45

3

24

24

male

graduate

45

40

24

male

tech school

22

4

90

24

femalejunior high

32

64

64

femalejunior high

22

5

18

40

femalegraduate

58

24

40

male

under graduate

22

6

40

40

male

under graduate

22

64

24

male

graduate

22

7

40

24

femalehigh school

22

90

24

male

high school

32

8

40

64

male

junior high

58

24

64

male

high school

45

9

64

40

femalejunior high

45

24

24

femalejunior high

58

10

90

90

male

high school

58

64

90

femaleunder graduate

32

11

24

64

femaleunder graduate

22

18

64

male

graduate

32

12

90

40

male

tech school

22

90

90

male

junior high

45

13

64

90

male

graduate

22

24

18

femaletech school

32

14

64

24

male

under graduate

58

24

90

male

graduate

22

15

18

24

male

tech school

22

40

64

male

under graduate

58

16

64

18

femalehigh school

22

90

18

male

under graduate

22

17

18

64

male

high school

45

18

40

femalehigh school

22

18

40

90

femaletech school

45

18

18

male

junior high

22

19

90

64

femalegraduate

22

40

18

femalegraduate

45

20

90

18

male

under graduate

45

40

90

femalehigh school

22

21

24

40

male

high school

32

18

90

male

tech school

58

22

64

64

male

tech school

32

64

18

male

high school

58

23

18

18

male

junior high

22

18

24

femaleunder graduate

45

24

24

90

male

junior high

22

90

40

femalegraduate

58

25

24

18

femaletech school

58

90

64

femaletech school

22

Appendix A.1.2

Instructions in the survey

Before the subjects started the repeated choice questions, they were shown an instruction
screen saying that:
The next ﬁgure shows your hypothetical monthly income (before tax). It also
shows the monthly income (before tax) of a certain other person. As in the previous question [social average task], suppose that the current situation of yourmonthly income (before tax) and the other person’s monthly income (beforetax) are both as shown.
This certain other person might, for example, be a 28-year-old woman witha university degree, or a 58-year-old man with a high school diploma. The
characteristics of this other person vary in each question.
In the subsequent screens, we asked respondents to answer the following question while
showing them various ﬁgures for diﬀerent alternatives after the question, as shown below.28
Comparing situation 1 and situation 2 shown in the ﬁgures, which is more
preferable to you? Suppose that the price levels in the two situations are the28

In the survey information in the ﬁgures was presented in Japanese. The images for monthly income
diﬀer in terms of number of banknotes shown according to the attribute levels. Also, images for reference
person diﬀer, depending on his or her characteristics. Subjects repeated ﬁve questions and they were notallowed to go back to previous questions once they had made their choices.

2

same.

As we documented above, we prepared in a total of 25 choice sets consisting of speciﬁchypothetical amounts for a person’s own pre-tax monthly income and for the reference
person. Each respondent answered ﬁve randomly assigned questions out of the 25 totalquestions.
Appendix A.1.3

Empirical results

We identify the eﬀects of the reference person’s characteristics on marginal utility byadding interaction terms for reference income levels and reference person characteristic
dummy variables. We created these dummy variables as follows. Regarding gender, we
made a diﬀerent gender dummy variable, with information of the subjects’ own genderand that of the reference group in the choice scenario (0=“same gender”). With respect
to age, we created dummy variables for higher age and younger age (0=“same age”)
using information on the subjects’ own age and that of the reference group in the choice
scenarios. Finally, using information on the subjects’ own level of educational attainment
and that of the reference group in the choice scenarios, we created dummy variables forhigher education and lower education (0=“same education”). These interaction terms areadded into the conditional logit model estimation, thereby examining how this additional
information on reference groups aﬀects social comparison. Table 5 presents the results.
In column (1) of Table 5, we show the results of a conditional logit model estimation
when we do not control for the eﬀects of subjects’ own individual characteristics on theirown income, reference income, and reference group type dummy interactions. Column (2)of Table 5 shows the results when controlling for these eﬀects.29 The columns show thatone’s own income eﬀect is positive and signiﬁcant, as is the case in the previous task. The
29

Our ﬁndings are robust against changes in the threshold level for the comparison-conscious group,
the happy group, the high-income group, and the elder group. These results are available upon requests.

3main eﬀect of reference income identiﬁed is negative and signiﬁcant.
As columns (1) and (2) show, reference groups with higher educational attainment
tend to be the target of stronger jealousy. We also ﬁnd that if the reference person isolder than you are, feelings of pecuniary emulation are mitigated. The altruistic attitudes
toward elderly persons, or admiration of them, are interesting since both higher age andhigher education are associated with higher income on average. As it was conﬁrmed in thesocial average task, reference persons with higher income levels draw stronger jealousy.
These intriguing relative utility eﬀects for the elderly may be a good reﬂection of Japaneseculture.
While columns (1) and (2) do not provide strong evidence showing eﬀects of reference
persons’ gender on comparisons, we actually have signiﬁcant eﬀects when we divide oursample into subgroups of male and females. Column (3) (male) and column (4) (female)suggest that males have stronger jealousy toward people of the same gender than they dotoward females, whereas females have weaker jealousy toward people of the same genderthan they do toward males. Hence, we conclude that males are the target of stronger
jealousy in Japanese society. Columns (3) and (4) also suggest that the previous resultson the eﬀects of age and educational backgrounds of reference groups remain unchanged.
To sum up, from the Leyden task we can say that comparison attitudes change on thebasis of the features of reference persons. These ﬁndings suggest that consideration ofsocial averages as the salient reference group is not suﬃcient when examining relative utility eﬀects. Instead, researchers should pay attention to the features of the true referencegroups of subjects, since they can greatly aﬀect the outcome of empirical investigations
on relative utility.

Appendix A.2
Appendix A.2.1

Who-compares-to-whom task
Construction of choice tasks

In the descriptive statistics from the pre-test, we could see that the most often citedreference group is friends, followed by work colleagues. Now these two groups are treatedas reference persons.30 We thus use three attributes, one’s own income, reference incomeof friends, and reference income of colleagues. While we can elicit the intensity and signsof relative utility for friends and colleagues using a two-situation-choice framework as in30

In the Japanese social context, the two reference groups (friends/classmates and work-related) may
not be mutually exclusive. The nested-logit regressions, however, show that respondents distinguishedthese two reference groups clearly. We thank Charles Yuji Horioka for pointing out this potential ﬂaw inthe structure of the choice experiment.

4the previous tasks, the framework of this choice task has ﬁve options: (i) situation 1, (ii)situation 2, (iii) situation 3, (iv) situation 4, and (v) do not know/cannot answer.
We created this expanded framework so as to use a tree structure for the choice options.
Our purpose is to investigate whether people perceive two diﬀerent reference groups as
actually diﬀerent. More speciﬁcally, we would like to exclude the possibility that people
deﬁne their comparison benchmark to simply be others and that the exact characterizations of others are not important.
As before, the levels of the three attributes have ﬁve variations: 180,000 JPY, 240,000
JPY, 400,000 JPY, 640,000 JPY, and 900,000 JPY. Given that we have three attributes
in this task, there are 125 potential variations of alternatives. Again, orthogonal design
was used to pick up 25 out of the 125 variations to make a vector of alternatives. We
repeated this procedure four times to form the four-situation choice task used in thesurvey. We paired these four situations to form a choice set such that we can make useof a tree structure in the hypothetical choice experiments. Two attributes for situation 1
and situation 2 are characterized by the same level of income for colleagues, while one’s
own income and income levels of friends are randomly chosen. Regarding the attributesof situation 3 and situation 4, the income level of friends is ﬁxed, while one’s own income
and income levels of colleagues are randomly chosen. We call the nest of situation 1 andsituation 2 C-ﬁxed, while the second nest of situation 3 and situation 4 is called F-ﬁxed.
For respondents who consider that only the reference income of friends matters, the Fﬁxed nest exhibits the similarity of the choice options in the nest. Also, for respondents
who consider that only reference income of work colleagues matters, the C-ﬁxed nestshows the equivalence of the choice options in the nest. With this tree structure, if
subjects think that there is no diﬀerence between the reference group of friends andthat of work colleagues– in other words, if they think of both reference groups of friends
and work colleagues as being simply “others”–the tree structure of the choice options
becomes irrelevant. If this is the case, from the nested logit model estimation, we would
obtain that Inclusive Value (IV) parameters related to respective nests are estimated tobe signiﬁcantly diﬀerent from one. The following table shows the set of questions we usedin the survey.

5

Alternative 1

Alternative 2

Ref. income

Alternative 3

Ref. income

Alternative 4

Ref. income

Ref. income

Q

Self

colleaguefriendSelfcolleaguefriendSelfcolleaguefriendSelfcolleaguefriend

1

90

40

64

18

40

90

90

64

40

64

18

40

2

90

64

24

64

64

40

64

24

90

18

90

90

3

64

64

64

18

64

24

90

40

18

24

24

18

4

40

90

64

40

90

40

40

64

90

64

40

90

5

18

24

64

24

24

90

24

64

18

18

18

18

6

64

18

90

90

18

64

64

90

18

40

40

18

7

64

40

18

40

40

64

90

18

24

40

64

24

8

40

40

40

64

40

18

40

90

24

24

44

24

9

24

64

40

90

64

94

18

40

90

24

18

90

10

18

40

24

90

40

40

24

44

64

64

24

64

11

24

18

64

64

18

24

40

24

18

64

64

18

12

64

24

40

90

24

18

90

28

64

24

90

64

13

64

90

24

18

90

64

64

40

24

18

24

24

14

90

90

90

24

90

18

64

64

64

40

18

64

15

24

90

18

90

90

24

64

18

40

90

24

40

16

40

64

18

24

64

64

24

18

90

90

68

90

17

40

24

90

64

24

64

18

18

18

90

90

18

18

24

40

90

24

40

24

18

24

40

40

90

40

19

18

64

90

40

64

18

40

40

40

24

68

40

20

18

18

18

40

18

90

24

24

24

64

90

24

21

90

18

40

18

18

18

90

90

90

40

24

90

22

40

18

24

24

18

40

24

90

40

18

40

40

23

24

24

24

18

24

40

18

90

64

18

64

64

24

18

90

40

64

90

90

18

64

24

90

18

24

25

90

24

18

40

24

24

40

18

64

90

40

64

Appendix A.2.2

Instructions in the survey

Before the subjects started the repeated choice questions, they were shown an instruction
screen saying that:
The next ﬁgure shows your hypothetical monthly income (before tax). And in
the same way as before, it pairs that amount with the monthly income (beforetax) of certain other persons. Suppose that the current situations for thesesets are as shown.
This time for the question, imagine that the certain other persons as (1) aco-worker, (2) a friend.
In the subsequent screens, we asked respondents to answer the following question while
showing the various ﬁgures for diﬀerent alternatives, as shown below.
footnoteIn the survey everything in the ﬁgures was presented in Japanese. Subjects
repeated ﬁve questions and they were not allowed to go back to previous questions once
they had made their choices.
Comparing situations 1 through 4 as shown in the ﬁgures, which would be themost preferable to you? Suppose that the price levels in the four situations are
all the same.

6

Each respondent answered ﬁve randomly assigned questions out of the 25 total questions.
Appendix A.2.3

Empirical results

In the questionnaire for this task, we provided four choice options and a “Don’t know/
Cannot answer” option, since we aim to use a tree structure for the choice options. The
ﬁrst two options are the F-ﬁxed nest, while the third and the fourth options are the Wﬁxed nest in this task. Our purpose in making these nests is to test if people perceive thefriend and colleague groups as independent from each other. If people deﬁne their rivals
as being merely “others”, then the characterization of reference persons is not important,nullifying the nested structure of the four options.
The results from the nested logit model are as follows (not shown in tabular form).
First, the IV parameter for the F-ﬁxed nest becomes 1.556, while that for the W-ﬁxed nest
becomes 1.627. Both of these results are signiﬁcantly diﬀerent from 1 at the 1% conﬁdence
level. These ﬁgures indicate that respondents perceive the two reference groups as diﬀerent
from each other. Secondly, an interesting ﬁnding here is that the estimated IV parameters
exceed 1. According to Train et al. (1987), from a purely statistical perspective, thevalues of IV parameters indicate the relative substitutability within and among nests,
and if they are greater than 1, it means that choice substitutability among nests are7

more frequent.31 In our choice setting, the outcome suggests that subjects frequently
change their comparison benchmarks from one to the other, rather than stick to a single
benchmark group, in accordance with situations they face. This result hints at the factthat reference groups are chosen endogenously.
To investigate the diﬀerence in relative utility eﬀects toward friends and colleagues,
we ﬁrst employ a conditional logit model framework as is shown in Table 6. In this task,
the true parameters of relative utility, γf and γw , are calculated by dividing the estimatesof the reference income terms for friends and for work colleagues by the estimates of theown income term.
The ﬁrst column of Table 6 shows the result of conditional logit estimation for thewhole study sample. Firstly, it shows that the own income eﬀect is found to be positiveand signiﬁcant, which validates the framework of the choice task in this study. Secondly,
the relative utility eﬀects toward the friend group and colleague group are both estimatedto be signiﬁcantly negative, as is the case when the reference group is the social average.
The diﬀerence in magnitudes for the terms of these two reference groups, however,warrants attention. Looking at the true estimates of relative utility parameters, theintensity of jealousy toward work colleague group is more than 10 times stronger thanthat of the friend group. Another interesting ﬁnding is that from columns (2) to (6),where estimation results of subgroups of individual-speciﬁc comparison benchmark areprovided, the relative utility eﬀect toward certain types of friends disappears in somecases.
Especially, in column (4), for those who state that their reference group is friends,
the relative utility eﬀect toward friends is not signiﬁcantly diﬀerent from zero; whereas incolumn (5), for those whose reference group is work colleagues, the relative utility eﬀect
toward friends is signiﬁcantly negative.32 At ﬁrst glance, this outcome is puzzling.
A mixed logit model framework helps to understand the issue of the weak intensityof comparison attitude toward friends. Column (7) of Table 6 shows that we obtainvery similar magnitudes for the mean eﬀects of one’s own income, the reference incomeof friends, and that of work colleagues, as is the case in the conditional logit model incolumn (1). The column, on the other hand, shows that the relative magnitudes of the31

See Herriges and Kling (1996) for the relationship between the magnitude of IV parameters andthe global necessary and suﬃcient condition of utility maximization behavior in a random utility model
framework.
32
We point out that the intensity of jealousy toward the reference group of work colleagues by those
who answered that they do not compare is the weakest among subgroups (column 6). Together with thesame ﬁnding in the social average task, this result validates our data set.

8

standard deviation terms compared to their mean estimates are very diﬀerent from eachother.
A noteworthy ﬁnding is the large standard deviation for the reference income level forfriends compared to the mean. With this ﬁnding, we conclude that a mean estimate ofreference income for friends that is close to zero reﬂects diﬀering attitudes in the subjects
toward their friends. In order to visualize the intuition of this point, we provide ﬁgure 1
in which the cumulative distribution functions (CDFs) of the true parameters of relative
utility for the social average (γa ), for the friend group (γf ), and for the work colleague
group (γw ) are illustrated.33 The ﬁgure clearly shows that the distributions of these true
magnitudes of relative utility eﬀects exhibit diﬀerent patterns from each other. The CDF
of γw shows that all the subjects in our study sample have negative relative utility toward
work colleagues, whereas the CDF of γf shows that around 30%of subjects feel altruism
toward friends. We also see from the CDF of γa that the distribution of γa has the largest
variance.
It is also interesting to note the diﬀerences in the distribution of relative utility eﬀects,
in addition to those in the intensity of comparisons, by separately regressing subsamplesof speciﬁc comparison benchmarks using the mixed logit model framework. Results areshown in Table 7.
The ﬁrst noteworthy ﬁnding is that in each subgroup of speciﬁc comparison benchmarks, the means of the eﬀect of one’s own income provides quite similar magnitudes toeach other. The means of relative utility eﬀects for friends are found to be signiﬁcantly
negative, except for subgroups with neighbors as the comparison benchmark. For the
subgroups with family as the comparison benchmark, the mean relative utility eﬀect offriend’s income is estimated to be signiﬁcant, but it is only at the 10% conﬁdence level.
In all subgroups, the absolute values of the mean estimates of relative utility eﬀects forfriends are close to zero. Another noteworthy observation is that the estimated standard deviation terms of the reference income terms for friends are large compared to themeans. Notice that those terms are signiﬁcant for all subgroups, including the subgroupwith work colleagues as the comparison benchmark.
The mean relative utility eﬀects of work colleagues’ income are found to be signiﬁcantly
negative for all subsample regressions. The magnitudes of the mean estimates comparedto the own income terms are larger than they were in the case of using friends as thereference group.
33

γa is obtained in the social average task. We obtained individual parameters of relative utility using
the inverse Bayesian formula after the mixed logit model estimation (Train 2009).

9

An intriguing pattern is found when we compare the estimates of standard deviations
for the relative utility eﬀects toward work colleagues for the two subgroups of subjects withfriends (column 3) and with work colleagues (column 4) as the comparison benchmark. On
one hand, the standard deviation is found to be signiﬁcant (though it is not particularly
large) in the friend subgroup. On the other hand, for the work colleague subgroup, thestandard deviation is found to be insigniﬁcant, implying that the negative relative utility
eﬀect against work colleagues spikes near the average point among those subsamples. Thisoutcome suggests that reference groups of friends and work colleagues are diﬀerent, notonly in terms of the average intensities of the relative utility eﬀects, but also in the sensethat their distributions diﬀer by subsample group for speciﬁc comparison benchmarks.
We argue that this outcome reﬂects that friends encompass many aspects of life, such as
benevolent rivals, persons of understanding, and so forth, and that work colleagues tend
to be regarded as rivals.

10

1

Figure 1: Distributions of Relative Utility Parameters (Social Average, Friends, and Colleagues)

0

.2

.4

CDF

.6

.8

Social average
Friends
Colleagues

−4

−2
0
relative utility parameters112

Table 5: Conditional Logit Estimates with Characterized Reference Groups (Leyden Task)(1)
Dep. Var: Utility
Own income
Reference income
Interactions:
Reference person’s demographic * reference income
Diﬀerent sex ∗ȳHigher age ∗ȳ
Lower age ∗ȳHigher education ∗ȳ
Lower education ∗ȳInteractions of own individual characterstics
and income levels (a)Observations
Pseudo R-squared

0.028***(0.001)-0.001(0.001)(2)(3)
Conditional logit model
0.028***
0.029***(0.001)(0.002)-0.001-0.002(0.001)(0.002)(4)0.020***(0.001)0.001(0.002)0.001(0.001)0.005***(0.001)-0.004***(0.001)-0.004***(0.001)-0.000(0.001)0.001(0.001)0.005***(0.001)-0.004***(0.001)-0.004***(0.001)-0.000(0.001)0.003***(0.001)0.007***(0.002)-0.005***(0.001)-0.004***(0.001)-0.000(0.001)-0.007***(0.001)0.007***(0.002)-0.003***(0.001)-0.001(0.001)-0.000(0.001)

No
45554
0.194

Yes
45554
0.213

Yes
20328 (male)
0.253

Yes
25226 (female)0.186∗ ∗ ∗p < 0.01, ∗ ∗ p < 0.05, ∗p < 0.1. Standard errors clustered by subject in parentheses. Omitted
categories are “Same age ∗ȳ” and “Same education ∗ȳ.”(a) If Yes, individual characteristics are controlled with interaction terms for the own income term, andfor reference income. The same set of individual characteristic variables controlled in the social averagetask regressions are taken into account.

12

13
47180
0.140

Observations
Pseudo R-squared

2226
0.143

0
0.039
-0.4290.047

0.028***(0.001)-0.001(0.001)-0.012***(0.001)(2)
family

2664
0.168

0
0.030
-0.3870.036

0.031***(0.001)0.000(0.001)-0.012***(0.001)

20131
0.147

0
0.013
-0.429
0.015

9387
0.158

-0.1000.017-0.3670.019

0.028*** 0.030***(0.000)(0.001)0.000-0.003***(0.000)(0.001)-0.012*** -0.011***(0.000)(0.001)

(3)
(4)
(5)
neighborfriendcolleague
Conditional logit

11639
0.111

-0.0800.017-0.2800.019

0.025***(0.001)-0.002***(0.000)-0.007***(0.000)(6)
Do not compare

47180

Mixed logit
MeanSD
0.035*** 0.029***(0.000)(0.001)-0.002*** -0.011***(0.000)(0.000)-0.011*** 0.006***(0.000)(0.001)(7)∗ ∗ ∗p < 0.01, ∗ ∗ p < 0.05, ∗p < 0.1. Robust standard errors clustered by subject in parentheses. For estimates of γ, we report variances constructed via
the Delta method.
Result omitted for the case of “other comparison benchmarks”.

-0.393-0.036

Estimated γf
Estimated variance of γf
Estimated γw
Estimated variance of γw

0.028***(0.000)
Reference income (Friend)-0.001***(0.000)
Reference income (Colleague) -0.011***(0.000)Dep. Var: Utility
Own income

Comparison benchmarks(1)

Table 6: Conditional Logit and Mixed Logit Estimates (Who-Compares-to-Whom Task)

14SD
0.031***(0.003)0.013***(0.002)0.010***(0.003)2226

Mean
0.036***(0.002)-0.002*(0.001)-0.013***(0.001)(1)
familySD
0.024***(0.002)0.006*(0.003)0.006**(0.003)2664

Mean
0.036***(0.002)-0.000(0.001)-0.012***(0.001)(2)neighbor∗ ∗ ∗p < 0.01, ∗ ∗ p < 0.05, ∗p < 0.1. Robust standard errors in parentheses.
Result omitted for the case of “other comparison benchmarks”.

Observations

Reference income (Colleague)

Reference income (Friend)

Dep. Var: Utility
Own income

Comparison benchmark

20131

(3)
friendMixed logit
MeanSD
0.035*** 0.028***(0.001)(0.001)-0.001** 0.012***(0.000)(0.001)-0.013*** 0.007***(0.000)(0.001)SD
0.028***(0.001)0.011***(0.001)
0.003(0.005)9387

Mean
0.038***(0.001)-0.004***(0.001)-0.011***(0.001)(4)colleagueSD
0.030***(0.001)0.008***(0.001)
0.003(0.002)11639

Mean
0.032***(0.001)-0.003***(0.000)-0.007***(0.000)(5)
Do not compare

Table 7: Mixed Logit Estimates for Subgroups of Comparison Benchmarks (Who-Compares-to-Whom Task)