GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No. 164

The Relative Utility Hypothesis With and Without
Self-reported Reference Wages

Adrián de la Garza, Giovanni Mastrobuoni, Atsushi Sannabe,
and Katsunori Yamada

November 2010; revised July 2012GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

The Relative Utility Hypothesis With and Without
Self-reported Reference Wages∗
Adrián de la Garza †Giovanni Mastrobuoni ‡Katsunori Yamada ¶Atsushi Sannabe §Abstract
This article makes three main contributions to the economics of happiness literature. First, using a novel data set of about 90,000 Japanese workers surveyed inannual cross-sections between 1990 and 2004, it demonstrates that individuals experience strong disutility when they perceive that their coworkers earn relatively higher
wages. In contrast with other tests of the relative utility hypothesis in the literature,
our estimation relies on workers’ self-reported beliefs of their peers’ wages, which we
argue are more closely aligned to the “true” reference-group benchmark than the assumed comparison income measures employed in other studies. Second, the article
shows important heterogeneous effects of both absolute and relative income on happiness. In particular, workers who are better able to accurately predict their peers’
wages seem to experience both greater utility of higher own income and greater disutility of higher relative income. Third, we assess the validity of different methodologies
that the literature has employed to construct comparison income measures and findsignificant discrepancies, particularly when reference income is derived from Mincer
equations–a common approach in other studies. We demonstrate that such discrepancies stem from the difficulty in finding valid exclusion restrictions that help identify the
relative income effect on happiness. In the absence of self-reported reference wages,
we propose a simple IV strategy that does not eliminate the lack of consistency but
delivers a lower bound of the “true” effect.
Keywords: Subjective well-being; relative utility; reference wages.
JEL classification: D00; J28.
∗

We give many thanks to Gani Aldashev, Prashant Bharadwaj, Michael Boozer, David Card, Andrew
Clark, Angus Deaton, Hank Farber, Erzo F. P. Luttmer, Guy Mayraz, Takashi Unayama, participants at theEuropean Meetings of the Econometric Society, the conference “From GDP to Well-Being” in Ancona, theHappiness and Interpersonal Relations conference, seminar participants at Yale University and Hitotsubashi
University. Of course, any remaining errors and omissions are our own. Yamada is grateful for the research
grant provided by KAKEN (24683006).
†
Banco de México, Research Department, 5 de Mayo 18 Col. Centro, Mexico DF 06500, Mexico.
‡
Corresponding author (giovanni.mastrobuoni@carloalberto.org): Collegio Carlo Alberto, Via Real Collegio 30, Moncalieri (TO) 10024, Italy.
§
Waseda Institute for Advanced Study, 1-6-1, Shinjuku Nishiwaseda 169-8050, Japan.
¶
Institute of Social and Economic Research, Osaka University, Mihogaoka 6-1, Ibaraki 567-0047, Japan.

1Introduction

The relative utility hypothesis (Duesenberry 1949) posits that individuals derive utility
not only from their own levels of consumption but also from how much they consume
relative to a well-defined benchmark group. The empirical evidence accumulated over thelast twenty years has generally found support for this theory, extensively documenting anegative relationship between an individual’s reported level of happiness and the income or
wages earned by his peers.1 However, there still seems to be less of a consensus about themagnitude and economic significance of these relative effects, among other things, becauseof the many approaches to define comparison income.
A common approach, for example, is to estimate a Mincer equation that allows the
econometrician to predict the wage of an individual with given characteristics. This predicted wage then becomes the relative measure to which individuals allegedly compare. An
alternative method is to calculate average wages by cells or groups defined by age, gender,
education level, and other particular individual characteristics. In general, all of these approaches implicitly assume that individuals compare themselves to a hypothetical “average”
individual with given characteristics and that they infer their peers’ wages the way econometricians do (Manski 1993; Sloane and Williams 2000). More specifically, each method
has potential drawbacks. For example, studies that recur to the Mincer solution must identify the reference wage effect on happiness by including additional variables in the wage
regression that are excluded from the happiness equation—exclusion restrictions that are
frequently unjustified and easily refutable.
This paper makes three main contributions to the economics of happiness literature.
First, it empirically tests the relative utility hypothesis by means of a novel data set ofabout 90,000 Japanese workers surveyed in repeated annual cross-sections between 1990 and
2004. The data contain information on workers’ reported perceptions of their peers’ wages,
which we argue are more closely aligned to the “true” reference-group benchmark than theassumed comparison income measures found in the literature. Our analysis demonstrates
the existence of significant (and negative) relative income effects on reported job satisfaction
and overall happiness. These results hold even when controlling for a rich-enough list ofcovariates—including workers’ own absolute income—and addressing the possibility of endogenous self-reported reference wages due to underlying pessimism that could, in principle,
be correlated with individual subjective well-being measures.
Second, the article shows important heterogeneous effects of both absolute and, especially, relative income on reported well-being. In particular, workers who are better able
to accurately predict their peers’ wages seem to experience both greater utility of higher
own income and greater disutility of higher relative income. We associate these results withfeelings of jealousy—as in Dupor and Liu (2003)—and with workers’ access to information
about job offers and the wage structure in their profession.
Third, we assess the validity of different methodologies that the literature has employed
to construct comparison income measures. One of the advantages of our database is thatits rich structure allows us to define reference income in ways that are similar to what
previous studies have done. We then employ three alternative definitions that are widely
1

Section 2 discusses the literature in more detail.

1

used in previous studies and re-estimate the model. Our findings show that, in all threecases, the estimated relative income effects are not consistent with results obtained whenself-reported reference wages are used as the comparison income measure. In one particularcase, when reference income is defined using Mincer-predicted wages, the estimated absoluteand relative income effects are highly unstable. We interpret this result as evidence of the
difficulty in finding valid exclusion restrictions that are helpful to identify the relative income
effect on happiness. Next, we estimate the bias in these estimates by means of a generalizedversion of the classical measurement error model and show that the direction of the bias is
ambiguous. We propose a simple instrumental-variables strategy that can be implemented
when self-reported comparison wages are not available. This approach does not eliminate
the measurement error bias, although it delivers a lower bound—in absolute value—of the
impact of reference wages on well-being.
Additionally, our paper also documents the relationship between subjective well-being
and individual characteristics of workers in Japan. Our analysis compares these results withthose in previous studies, which have focused primarily on the U.S. and the western European labor forces. Our findings confirm several standard results in the happiness literature.
For instance, women and married workers tend to report higher levels of satisfaction thanmen and single individuals, respectively. However, we also observe a U-shaped relationshipbetween satisfaction and educational attainment, which contrasts starkly with the monotonically increasing association in the U.S. and Europe found in past studies. Given that,to the best of our knowledge, our paper is one of the first happiness studies in Japan, an
additional contribution to the literature is to help in the construction of a set of stylized
facts that may be generalizable to cultures outside of the West.
The paper is organized as follows. Section 2 provides a brief synthesis of the literatureand motivates the present study. In Section 3, we present our empirical framework to test
the relative utility hypothesis, introduce our data, and explain how they differ from previous
data sets on job satisfaction and happiness. Section 4 presents our main results focusing on
the use of workers’ expectations on their peers’ wages as our primary comparison income
measure. In Section 5, we re-estimate the model employing alternative metrics used in theliterature and compare the results. We also demonstrate from a theoretical standpoint why
the bias present in estimations that employ these theoretical constructs as reference income
proxies may go in either direction and propose our instrumental variables approach to boundthe reference wage estimate. Finally, Section 6 presents some concluding remarks.

2

Motivation

2.1

Literature Overview

The last twenty years have seen an increasing interest in the economics profession to investigate the relative utility hypothesis: the idea that individuals derive negative utility
when people around them obtain a higher income. In spite of a few disputes in the earlier
literature regarding the sign and significance of this correlation, there now seems to be a
general agreement of a negative association between comparison income measures—suchas wages earned by relatives or work colleagues—and subjective well-being—for instance,2

self-reported feelings of job satisfaction or overall happiness.2 More recent studies, however,
have instead focused on providing an answer to one main question: what exactly is theappropriate comparison income measure?
There are at least two dimensions to this question. First, what is the relevant reference group? As Clark et al. (2008) have pointed out, the bulk of the literature has
defined comparison income as that earned by people with similar individual characteristics. However, it is not clear what the relevant characteristics are. Reference group definitions vary widely among different studies, from somewhat specific groups such as colleagues(Cappelli and Sherer 1988; Rizzo and Zeckhauser 2003; Brown et al. 2008; Clark et al. 2009b),
friends (Senik 2009), relatives (Senik 2009), and neighbors (Luttmer 2005; Clark et al.
2009a; Knight et al. 2009; Senik 2009) to more general ones such as “someone like you”
(Clark and Oswald 1996) and “a representative person in society” (Easterlin 2001;
Stevenson and Wolfers 2008). The difficulty in correctly defining a reference group is wellidentified in Loewenstein et al. (2003) and Luttmer (2005), when they argue that comparison groups are subject to change depending on the different circumstances that give rise
to such comparisons in the first place: “If a person buys a small house in a wealthy neighborhood in part because it has a certain status value in her apartment building, she may
not fully appreciate that her frame of references may quickly become the larger houses andbigger cars that her new neighbors have” (Loewenstein et al. 2003). Clark and Senik (2010)
find that, when it comes to income comparisons, people most often compare themselves tocoworkers—although the distribution of answers may change depending on people’s characteristics.3 For instance, individuals who are more prone to meet socially are more likely
to declare that they compare to friends, instead of colleagues. Moreover, comparisons tofriends, although less widespread, may have a greater impact on people’s utility. Nonetheless, the general conclusion is that what most people have in mind when they compare theirincome to others, is their colleagues.
Assuming the researcher chooses the appropriate reference group, the second issue that
researchers have to deal with is, how to measure comparison income? Most of the literature
has relied on three different methodologies, namely: (i) predicting individual wages from
Mincer equations (Clark and Oswald 1996; Sloane and Williams 2000;
Lévy-Garboua and Montmarquette 2004; Senik 2004); (ii) calculating cell averages according to specific individual characteristics within-sample (Ferrer-i Carbonell 2005; Akay et al.
2012), or (iii) out-of-sample from external data (Cappelli and Sherer 1988; McBride 2001;
Luttmer 2005; Clark et al. 2009a; Card et al. 2012). Consistent with several of the reference group definitions mentioned above, all of these approaches imply comparisons with2

An excellent source that documents this negative correlation and provides further detail on
the developments, validity, and applicability of happiness measures in the economics literature, see
Kahneman and Krueger (2006). A few empirical papers have shown a positive relationship between reference income and satisfaction (or happiness), which has been justified by Hirschman and Rothschild’s (1973)“tunnel effect” conjecture, whereby rising inequality may increase individual well-being if it is interpreted
as a positive signal with respect to likely future outcomes (see, for example, Senik (2004)). Nevertheless,our reading of the literature is that the consensus correlation between these two variables is negative.
3
Yamada and Sato (2010) find that coworkers are, after friends, the second most common group against
which Japanese individuals compare. However, their sample differs from ours, for example, because thedataset we employ focus exclusively on working individuals, while theirs includes students and the unemployed, who are inherently less likely to compare to work colleagues.

3

other people that share some of the same features as the surveyed individual—“someone
like you,” in the Clark and Oswald (1996) sense. However, these methodologies also present
some potential problems. First, it is not clear that the individual traits that researchers
choose to construct reference income measures are necessarily the ones on which individuals
base their comparisons. For instance, why is it that Sloane and Williams (2000) use hours
worked but exclude the age of an individual to compute their reference income variable,
while Clark and Oswald (1996) do the exact opposite?4 Moreover, given the need to first
construct a comparison income measure and then estimating a happiness equation, thesemethodologies rely heavily on potentially questionable identification assumptions. In particular, Mincer regressions from which the predicted reference income measures are derived
must include variables that are excluded from the satisfaction equation—an exclusion restriction that is often not warranted. Finally, all of these methods presume that individuals
compare themselves to some average, which, as discussed below, it may not necessarily be thecase. In the particular case of studies favoring the Mincer approach, estimating comparison
income by imputing peers’ wages from wage equations implicitly assumes that individuals will infer peers’ wages the way econometricians do (Manski 1993; Sloane and Williams
2000). This supposes a degree of sophistication that may not accurately reflect the mental
process an individual goes through when comparing to his peers.
This paper departs from the core of the literature by employing self-reported reference
wages as an alternative comparison income measure. The only two other relevant studies
that, to the best of our knowledge, have used direct reference income comparisons—so thatthe benchmark is not chosen arbitrarily by the researcher—are by Knight et al. (2009) andSenik (2009). The first of these two papers exploits the results of an especially-designed
module on subjective well-being in China’s 2002 National Household Survey in order to showthat happiness depends on relative income, both when defined with respect to a well-definedcontemporaneous reference group and when compared to an individual’s own past income.
In particular, respondents are asked to compare their current living standard both withthe average standard in their village and with their own standard five years ago. These answers are then classified in one of five possible bins that range from “much below average” to“much above average” and then corresponding dummy variables are used in a standard OLS
regression of happiness on conventional individual characteristics and economic variables.
The results show that, under any comparison income definition, the relationship between
happiness and reference income is negative for people who consider themselves below average in any degree, while the opposite holds true for people who believe they are above
average. Analogously, Senik (2009) also provides evidence in support of the relative utility
hypothesis using the results of the Life in Transition Survey conducted in 28 post-transition
countries and Turkey in 2006. In this survey, respondents are asked to compare their current
living standard with respect to several reference groups, including their parents’, their former colleagues’, their former schoolmates’, and their past selves’ in the pre-transition period(i.e., before 1989). Although the database does not seem to allow comparisons with potentially relevant contemporaneous groups—such as current coworkers or neighbors—Senik’s
analysis concludes that “internal benchmarks” created by one’s own trajectory have the4

Table A-1 sums up how various papers that rely on different methodologies go about constructing theirreference income measures and estimating their happiness equations.

4

greatest impact on subjective well-being, followed by comparisons to former colleagues andschoolmates. Another novel finding in Senik’s paper is that unfavorable comparisons seemto exert a stronger effect on individuals’ perceived well-being than favorable ones, a resultthat the author interprets as evidence of loss aversion and a manifestation of adaptation.
Similar to the two previous studies, our approach to testing the relative utility hypothesis
also relies on a subjective measure of reference income. The data are obtained from a surveyof about 90,000 workers in Japan between 1990 and 2004, in which each respondent is asked
what he thinks is the average wage of other employees who are the same age and doing
the same job as him.5 Note that, in contrast with the ordinal nature of the comparison
income variable in Knight et al. (2009) and Senik (2009), ours is a cardinal measure, whichshould facilitate the interpretation of results regarding the economic impact of comparison
income on subjective well-being. In any case, however, we believe that the use of directcomparison income measures overcomes several of the issues that prevail in studies that rely
on researcher-based benchmarks.

2.2

Motivating the Use of Direct Comparison Income Measures

One of the evident advantages of the self-reported reference measure over the traditional
comparison income variables employed in the literature, is that individual perceptions of anevent are more closely related to the emotion that such an event may produce in a person—and hence to any related feelings of happiness or satisfaction. To be clearer, if we believethat there exists a negative relationship between the feelings of happiness of person i andthe income earned by other people surrounding such person, it must be because that person
somehow observes this. If individual i is ignorant about a given increase in other people’s
income, why would this produce in him any unhappiness, dissatisfaction, or any other suchfeeling that relates to individual disutility?6
To illustrate this with an example, suppose that there exists a society in which there aresome very rich and some very poor people. Suppose further that, in this very unequal world,
individuals do not perceive this inequality—maybe because there is a very progressive tax
system in place, or because the rich are huge philanthropists that somehow transfer resources
to the poor, or simply because people do not care: what others earn is not part of theirutility function. In this case, there is a disconnect between actual wages and perceived
wages: actual wage disparities can be large, but people may not perceive them as such andwill therefore not be emotionally or psychologically affected by them. That is, they will notderive any utility or disutility from any fluctuation in other people’s earnings.
The above example attempts to elucidate one way in which individual perceptions ofother people’s wages may be more relevant to happiness than a simple average of actual
wages of people with certain observable traits. But there are other ways in which this relationship between self-reported reference income and happiness may prove more truthful andeconomically relevant than between theoretical comparison income measures and subjective
well-being. For instance, suppose now that everybody in the world earns exactly the same
5

Further details on the sample, the specific questions asked, and their format are provided in Section 3.
Card et al. (2012) in fact show that disclosing information on peers’ salaries influences workers’ jobsatisfaction.
6

5

wage, so there is zero variation in wages. Suppose also that in this ultra-egalitarian world,
there is also a great deal of misinformation, and some people think that their peers earnmore than they do, while others think that their peers earn less. This may be, for example,
because in spite of having the same actual income, people choose different consumption
bundles, and so one person may be more frugal and save 50 percent of his income, while
another may spend this same 50 percent on a bigger house or a fancier car. In this case, even
if you know that wages do not vary across individuals, you may observe some significant
variation in perceived wages.
Now, given that there is zero variation in wages, you should never observe any correlation
between true reference wage and happiness. However, given that perceived wages do vary—and assuming happiness also varies across individuals—you may observe some correlation
between these two variables. Suppose that this correlation is negative (controlling for othervariables), as we observe in our data set. This negative correlation could arise for several
reasons. For instance, people may derive (lose) satisfaction from knowing/believing thatthey earn more (less) than their peers. An alternative explanation, which need not overrule
the previous one, is that happy/optimistic (sad/pessimistic) people may tend to think thatthey earn more (less) than others.7 Although we acknowledge the lack of good instrumentsthat may serve to identify the direction of causality from reference wages to happiness, we
argue, in agreement with the literature, that the main channel of this negative correlation
between these variables is the first one. We try and rule out the alternative direction byrunning a regression of happiness on perceived wages, controlling for pessimism variables,interacting these with perceived wages, and showing that the main effect still remains.
The above examples should help to justify the economic relationship between selfreported reference wages and happiness. If we want to learn about what makes people
happy and if we want to provide policymakers with more information about what they can
do to make citizens happier, then it becomes clear that the relationship between these twovariables is worth exploring. In the previous case, where there is zero variation in wages
but people do perceive certain differences in wages that influence their happiness, perhaps
we could say that policymakers could make greater efforts to inform people better.
To be fair, we should recognize that the world probably does not operate like eitherof the two examples above. The point of these examples, however, is to emphasize that,irrespective of the distribution of actual wages, what matters to people’s sense of happinessand overall satisfaction—in the sense of Duesenberry (1949), Dupor and Liu (2003), and thejealousy and “Keeping up with the Joneses” economics literature) is their perception of theworld. If there is a connection between such perceptions and the actual world, all the better:in this case, we can use actual data (such as actual wages) and not individual perceptions todetermine how happy or satisfied people are. But this connection is not necessarily obvious,and in this sense, we believe that a worker’s perception should be more closely aligned tohis reported level of happiness than some ad hoc comparison income measure, such as theaverage wage of other workers sharing some of his individual characteristics.
Of course, our subjective reference income variable is not perfect and there are some7

Of course it could also be that this negative correlation is due to some third variable that correlates
positively (negatively) with happiness and negatively (positively) with perceived wages. We discuss these
possibilities later in the paper.

6

caveats that should be kept in mind. In particular, these subjective data are obtained byasking workers to compare themselves to other workers of the same age and doing a similar
type of job. Workers may then provide an answer that corresponds to their belief of thewage earned by this specific reference group, but this may not correspond to the referencegroup they generally compare with, say, on a daily basis, and which may have a stronger
connection with their feelings of happiness and well-being. Nonetheless, given the longhours worked by Japanese individuals in our sample, we believe that other co-workers andcolleagues are the most natural reference group to which Japanese workers may compare.
This, in fact, is in agreement with the literature, which has consistently shown that coworkers is one of the groups to which individuals most often compare (see, for example,
Clark and Senik 2010). Thus, although important, we believe this issue is relatively minorand should not affect our results. In the next section, we introduce the model and discuss
the data and this particular issue a bit further.

3

Empirical Framework and Data Description

In order to examine the impact of absolute and relative wages on subjective well-being,researchers typically estimate the following regression:(1)

SWBi = α1 yi + β1 ȳi + x′i γ + ui ,where the dependent variable SWBi represents a measure of worker i’s subjective well-being,such as happiness or job satisfaction. This proxy for utility is assumed to depend on theworker’s (log) wage, yi ;8 a (log) reference wage that workers use as a benchmark to determine
how well they do relative to their peers, ȳi ; and a vector of individual characteristics, xi ,that includes age, tenure, gender, educational attainment, marital status, among othersocio-demographic factors. As usual, the term ui corresponds to an idiosyncratic error
term.9One advantage that we have over previous studies is that our dataset contains information on workers’ actual perceptions about their peers’ wages. As discussed in Section 2, we
8
9Unless otherwise noted, all wages are in logs.
This specification can be thought of as a reduced-form version of a standard utility function of the form:
U = U (c, c̄, h),where c is individual consumption, c̄ is the level of consumption of a comparison group, and h is hours
worked. The theoretical literature has investigated how much keeping-up-with-the-Joneses ultimately matters to consumers and what role social status plays in determining individual utility levels. These studies findthat, in addition to their own levels of consumption, individuals care about their peers’ consumption levels
and their wealth rank relative to their comparison group, which validates the use of reduced-form models
such as equation 1. For instance, see Cole et al. (1992); Corneo and Jeanne (1997); and Yamada (2008).
Boskin and Sheshinski (1978) and Oswald (1983) theoretically analyze tax policy implications of relative
utility, while Abel (1990); Bakshi and Chen (1996); Galı́ (1994); and Campbell and Cochrane (1999) examined such relative effects on asset pricing. Frank (1985) and Frank (2005) show that relative concerns are
more important for positional goods consumption than other goods such as leisure, and that the structureof utility functions with relative concerns over different types of goods is of key importance in the valuationsof social welfare.

7believe that these self-reported reference wages are more closely aligned to the true benchmark that workers use to compare themselves against their peers. We can thus estimate
equation 1 by standard methods10 and obtain a more precise measurement of the effect ofrelative wages on subjective well-being.11 We present these findings in Section 4. Our dataalso allow us to compare our results to the ones we would have obtained had we followed
the alternative methodologies that Manski (1993), Sloane and Williams (2000), and others
have deemed potentially flawed. These findings are reported in Section 5.

3.1

Data Description

Our dataset comes from the Comprehensive Survey of Labor Union Members, which was
designed and administered by a group of psychologists at the International Economy andWork Research Institute. It comprises repeated cross-sections on about 90,000 union members working in Japanese firms listed on the Tokyo Stock Exchange (TSE) from 1990 to
2004. The survey requests that respondents provide self-assessments on their individual
well-being at work and in life in general. In addition to this, other questions attempt toobtain information on workers’ perceptions of their work environment.12 The dataset also
allows us to control for individual demographic and socioeconomic characteristics, which
include age, gender, educational attainment, marital status, annual wage level, overtime
hours worked, and workers’ expectations of their peers’ wages.
After cleaning the data and removing some inconsistencies, we are left with 78,136
observations.13 Table 1 shows some statistics that describe our dataset. Workers in our
sample are young with an average age of 35 years. Their average tenure is 14 years, whichsuggests relatively low mobility in the Japanese labor force. Moreover, union workers inJapan seem to be well-educated in general, as almost all of them have graduated from highschool, close to 50 percent have some college experience, and 36 percent have completed atleast a university-level degree. We also observe that 58 percent of workers are married andwork an average of 23 hours of overtime per month. All of these individuals are regular fulltime employees and union members. About one-third of them holds blue-collar positions and
10

Given the discrete, hierarchical nature of the dependent variable, the vast majority of happiness studies
in economics have estimated equations similar to 1 using ordered logit or probit estimators. However, it is
not clear that these methods are superior to ordinary least-squares since additional assumptions required
for these estimates to be valid—such as that of parallel slopes—are often not met. Further, Luttmer (2005)
showed that results from happiness equations obtained by OLS are virtually indistinguishable from thoseobtained by ordered models. Our OLS estimates in Section 4 facilitate the interpretation of the coefficients,
although ordered probit estimates are also reported in the robustness section for completeness.
11
This of course requires that the typical exogeneity assumptions hold. Our robustness tests in Section 4
address the possibility that wages are endogenous.
12
The full list of question categories is available in an earlier version of this paper; see Appendix 1 inde la Garza et al. (2008)13
The reduction in the number of observations is mainly due to missing information for some of thevariables used in the empirical analysis. Observations that showed inconsistencies in the data, such as theworker’s tenure being greater than his age, were also dropped from the sample. To ensure that these problems
were not due to sample selection on observable characteristics, we compared the full-sample and the working
databases along different dimensions including age, education, wages, and reported levels of happiness andjob satisfaction. We are happy to report that we were unable to find any statistically significant differences
between the two datasets.

8

close to one-fifth of them performs some managerial role in the company. These numbers
do not intend to provide an accurate depiction of the representative Japanese worker since,as described above, the survey was administered exclusively to union members employedby major publicly-traded companies. Thus, the results below may not be generalizable tothe entire Japanese labor force—for instance, our dataset does not cover employees in highadministrative positions as they are not allowed to take part in unions. Nonetheless, due
to the large size of our sample and the breadth of coverage of 62 firms across a variety
of industries from food to electronics to finance, we believe that this dataset does capture
significant features of the Japanese labor market.
One additional consideration is that our database underrepresents women in the Japaneselabor force. According to the World Bank’s World Development Indicators, women represent about 40 percent of the country’s working population; in contrast, the share of female
participation in our working sample is only 22 percent. This is an important issue because
male and female workers differ significantly along several dimensions. Table 1 shows some ofthese gender differences in terms of observable characteristics, such as education and marital
status. For instance, while 40 percent of the male subsample obtained a college degree, only
20 percent of the female group achieved this goal. Interestingly, the fraction of married men
is twice the proportion of married women (66 percent vs. 33 percent). Since the literature
has found significant correlations between various subjective well-being measures and individual characteristics such as gender, educational attainment, and marital status, poolingthe male and female subsamples in our analysis may lead to results that would differ had we
considered these two groups separately. We thus keep this distinction in mind and discussthese differences accordingly.
3.1.1

Subjective-Well Being

Each respondent provides information on his own subjective well-being by choosing oneof five possible categories, from “least satisfied” (category 1) to “most satisfied” (category5). We employ primarily two distinct subjective well-being measures, life happiness andjob satisfaction, which present some significant differences.14 First, the correlation between
the two is only 27 percent. As Figure 1 suggests, the distribution of life happiness is more
spread out and more skewed to the left than that of job satisfaction. Since the literature
has utilized a wide variety of subjective well-being measures, the availability of these twovariables allows us to test the robustness of our results. For brevity, the empirical analysis
below highlights our findings utilizing life happiness as the dependent variable. However,
our results are robust to the use of job satisfaction as an alternative proxy for subjective
well-being.15
14

At the beginning of the questionnaire workers are first told “from now on, we would like to ask about
your general happiness,” and are asked to report whether they agree with the statement “I’m very happy!”
in general. Later in the questionnaire, workers are asked to report about their satisfaction with respect toall aspects of their job.
15
The literature has investigated potential “fatigue” and “question-order effects” that may arise in survey
responses (McFarland 1981; Schuman and Presser 1996). Although the number of experimental studies onthese issues remains small, the general consensus is that order effects are not pervasive in a typical attitude
survey and that, although in some cases these effects may be important, it is difficult to determine a priori

93.1.2Workers’ Own Wages

The survey also requests that workers mark down their own wage level from a list of 9
categories, where category 1 denotes annual wages of under 2 million yen and category 9
corresponds to an annual income level of over 10 million yen. We measure individual wages
as the mid-point in each of the 7 intermediate categories, and use ad hoc values for the two
extreme categories. Thus, respondents who reported categories 1, 2, . . . , 9 as their wage
level, were assigned annual wages of 1.5; 2.5; . . . ; 12 million yen, respectively. Alternative
choices do not alter the main results. Additionally, we deflate this nominal measure using
the Consumer Price Index to obtain real wages with 1990 as the base year.
One salient feature of wages in our sample of union workers in Japan is that individual
characteristics explain an atypically high fraction of the variation in wages. Table 2 shows
standard Mincer regressions for men and women, separately. Wage regressions for the malesubsample that control for age, tenure, education, hours worked, and marital status, obtain
R2 measures of about 70 percent.16 In contrast, similar specifications for a sample of U.S.
workers would generally yield an R2 of about 30-35 percent. This high coefficient of determination in the Japanese data may be partly due to the very strict seniority system thatprevails in the Japanese labor market.17 For instance, the correlation between wages andage for the full sample of Japanese workers is 69 percent. Using a comparable sample ofunionized workers from the Current Population Survey during the same 1990-2004 period,
the corresponding correlation measure for the U.S. is only 21 percent. This high coefficientof determination in the Japanese sample may play to our advantage as it reduces possible concerns that wages may depend themselves on happiness, thus minimizing potential
endogeneity issues. We nevertheless address this possibility in Section 4.
3.1.3

Self-Reported vs. Constructed Reference Wages

Survey respondents provide information on self-reported comparison wages by answering
the question, “What do you think is the average wage of corporate employees who are thesame age as you and doing the same job?” Just as in the case of workers’ own wages,
answers to this question are originally chosen from a list of 9 categories, then matched withindividual wage values according to the category mid-points described above. In contrast
with the reference wage measures employed in previous studies, the availability of selfreported comparison wages allows us to gauge how workers perceive their peers’ wages.
To better understand the differences between self-reported and constructed wage meawhat they may be. It has been argued that question-order effects may be avoided if questions on a same
topic are spread out in the survey. This and other measures have been taken by the team of psychologiststhat administered our survey. Given the robustness of our results using two distinct measures of subjective
well-being, placed in different sections of the survey, we conclude that question ordering is not an issue
within our framework.
16
This feature depends only marginally on wages being reported in categories. When we smooth out
wages by adding a disturbance term that is uniformly distributed between each cutoff point, the R2 statistic
decreases by, at most, 5 percentage points.
17
When age-related variables are omitted from an otherwise standard Mincer regression that controls
for individual characteristics such as gender, schooling, and marital status, the coefficient of determination
decreases to 30-35 percent.

10

sures, we compare our relative wage measure as reported by the worker to some of the mostcommon reference wage estimates used in other studies. A standard method consists in calculating wage averages by cells or groups defined by a set of given observable characteristics
of workers. We define cells in three different ways: by gender and age; by gender, age, andeducation level; and by gender, age, education level, and managerial experience. Following
the literature, we estimate these cell averages from two different underlying data sources:
our own dataset, described above, and an external dataset containing wage information ofworkers with similar observable characteristics. This second approach is most commonly
employed in the literature and is useful to validate our results. The external data correspond to wages for individuals working in large companies with over 1,000 employees, as
provided by the Basic Survey on Wage Structure (BSWS) released by the Japanese Ministry of Health, Labour and Welfare.18 A third method that is frequently used in relative
utility studies is to estimate Mincer regressions such as the ones showed in Table 2. The
theoretical comparison wage measure corresponds to the fitted wage value of the average
worker. In what follows we use the prediction from a wage regression that controls forgender, age, education level, and managerial experience—similar to the third cell defined
above. When using Mincer regressions to estimate reference wages researchers need to findappropriate exclusion restrictions, meaning at least one variable that influences reference
wages but not happiness. Notice that computing cell averages is equivalent to running a
Mincer wage regression where the regressors are fully interacted with each other. In thiscase the identification hinges on these interactions, which, like the exclusion restrictions,are assumed to influence reference wages but not happiness.
The bottom panel of Table 1 displays summary statistics for workers’ own wages andself-reported reference wages, as well as comparison wages defined according to the various
methods employed in the literature. The table shows that average wages estimated fromthe BSWS external database are less than 2 percent higher than self-reported reference
wages but about 10 percent higher than workers’ own wages. We attribute this significant
difference in the second case to the inclusion of workers with a general assistant manager
position (kacho in Japanese) in the BSWS data. In contrast, our dataset only includes wage
information for employees in non-supervisory, assistant manager positions. This technicalsubtlety evinces how difficult it can be to compare wage averages across different datasets,
which may pose challenges to the use of external databases in the construction of reference
wage measures. In addition, self-reported reference wages seem to be significantly moredisperse than any of the other measures utilized in the literature. Averaging wages acrossworkers with similar given characteristics may not reflect accurately, for example, howundervalued a worker feels in the labor market. This is further confirmed in Figure 2, where
we plot the kernel density of various wage measures. Comparing self-reported reference
wages and reference wages constructed from an external data source, for example, illustrates
a potential underlying pessimism among Japanese workers regarding their beliefs of what
their peers earn. If feelings like this are common among workers, studies utilizing suchtheoretical constructs would underestimate the effects of comparison wages on workers’
self-reported levels of well-being.
The data also hint at a potential underlying pessimism in workers’ perceptions of their18

The data can be obtained from the following website: http://www.mhlw.go.jp/english/database/db-l/.

11peers’ wages, as suggested by the 10-percentage point difference between self-reported reference wages and workers’ own wages. This is an important point since a worker’s belief of
his peers’ wages may be endogenous, for example, if a relatively happier individual has amore optimistic view of his own salary relative to his colleagues’. Conversely, a pessimistic
worker may think that he is underpaid with respect to his peers. With panel data, assuming there was enough variation over time in happiness, wages and relative wages, one could
difference out time-invariant worker characteristics, such as pessimism, and obtain unbiased
estimates of the effect of absolute and relative wages on happiness. As our dataset does notallow us to track individuals over time, we address this problem by using answers to twoquestions in our survey that are likely to capture workers’ pessimistic attitudes along two
different dimensions. The first question asks whether a worker believes that his colleagues
would help him in times of need; the second question asks the worker if he is satisfied about
the possibility of promotion within his company. Controlling for these two effects shouldcapture the level of inherent pessimism of a given worker. Additionally, we could interact
these proxies for pessimism with perceived reference wages to capture the potential heterogeneity in workers’ perceptions due to pessimistic attitudes. As will be discussed in the next
section, these exercises should quell some of the above-mentioned endogeneity concerns.
Having introduced the data, the next section presents the main results. It is importantto remark that, in spite of their virtues, self-reported reference wage measures are not apanacea and continue to leave some issues unresolved. For instance, by asking workers toreport what they believe that other employees with their same characteristics and in thesame job earn, the survey restricts workers to confine their reference group to individuals
within their same job. Still, on a more regular basis, workers may potentially compare
themselves to relatives, friends, or colleagues doing other jobs. Moreover, the relevant reference group may not be stable over time. For instance, a recent graduate may compare
himself to other recent graduates; but a person who has been out of school for 10 years may
compare himself to former classmates and colleagues, to supervisees and supervisors, andeven to former selves.19 Nonetheless, we believe that self-reported comparison wage data
are a superior alternative to reference wage measures constructed from Mincer equations orcell averages. First, as we mention in Section 2, workers’ perceptions of their peers wages
are more closely related to their overall feelings of happiness, which makes this variable amore suitable candidate for reference income than the theoretical constructs used in previous studies. Second, as Clark and Senik (2010) have shown, coworkers are the most frequentgroup to which workers compare, which should abate concerns about providing an explicit
reference group of coworkers to surveyed individuals. Finally, in stark contrast with thealternative comparison income measures employed in the literature, self-reported reference
wages do not limit the worker’s reference group to some cell average and do not hinge on
disputable identifying restrictions. Studies that use cell averages and Mincer-based reference income measures tacitly assume that individuals compute their peers’ wages the way
econometricians do, which, as argued in Section 2, is unlikely. Some of the disadvantagesof these alternative comparison income variables will become more evident when the model
described in equation 1 is re-estimated in Section 5 using these other measures.
19

On this and other related issues, see Senik (2009).

12

4

Results

To empirically test the relative utility hypothesis, we estimate a version of equation 1 that
substitutes our self-reported reference wage measure, ȳi∗ , for the standard proxy used in theliterature, ȳi .20 As discussed in Section 3.1, there are important differences between maleand female workers in almost every dimension from wages to hours worked to educational
attainment, which justifies our estimation of equation 1 for men and women separately.
Pooling the male and female subsamples does not have any significant qualitative impact
on our findings. The errors ui are allowed to be correlated within firms although different
clusterings do not affect our conclusions.
The results in Table 3 find strong empirical support in favor of the relative utility hypothesis: holding wages constant, individuals tend to report lower levels of satisfaction when
they perceive that their peers’ wages are higher. In particular, if a worker believes that his
peers’ wages have risen by one standard deviation, his happiness level would decrease by 0.10
standard deviations.21 On the other hand, the absolute wage coefficient is consistently positive and significant at conventional levels, which implies that a worker’s reported happinessincreases as his wage goes up. The results suggest that a one-standard-deviation increasein a worker’s own wage would lead to an increase in happiness of 0.13 standard deviations.
This finding is reminiscent of the conclusion by Alesina et al. (2004) that “money buys happiness.” Given that the impact of absolute wages on subjective well-being is stronger thanthat of relative wages, an across-the-board wage hike of, say, 10 percent would be associatedwith an overall increase in reported happiness. This finding is confirmed by an F -test that
rejects the null hypothesis that the sum of the coefficients on absolute and relative wages isequal to zero at conventional levels.
The absolute and the relative wage effects are substantially larger for males. A comparison between columns 5 and 6 shows that both the absolute and the comparison wage
coefficients are about 35 to 50 percent stronger for men. This implies that women do notappear to derive as much utility as men do either from their own labor earnings or theirperception of their peers’ wages.22 Nonetheless, in agreement with the literature, women
generally report higher levels of life satisfaction ceteris paribus, as the results for the pooled
sample in column 7 suggest.
Individual worker characteristics explain a significant amount of the variation in reported
levels of life satisfaction. For instance, in the case of males, the inclusion of age, educational
attainment, marital status, and dummies for whether the worker has a blue-collar position
and whether it performs any managerial tasks in the company, increases the coefficient ofdetermination of the regression from 2 to 7 percent.23 The inclusion of these individual
20

Section 5 discusses the consequences of estimating equation 1 using a mismeasured version of the “true”
relative wage benchmark used by workers.
21
The direction and magnitude of this estimate is consistent with the findings in Yamada and Sato (2010)
from a hypothetical choice experiment done on Japanese individuals.
22
We purposely are cautious in comparing results for men and women as Conti and Pudney (2011) haveshown that survey design might differentially influence the way men and women respondent to questions
about happiness.
23
Many previous studies control for both age and tenure in subjective well-being regressions. In our case,
we opt to exclude tenure as a regressor because of the high correlation of this variable with age due to thecharacteristic seniority tenure system in the Japanese labor force described in Section 3.1.2.

13

worker characteristics reduces the absolute wage coefficient by about 22 percent, although
the impact of wages on happiness remains positive and strongly significant. Such reductionin the magnitude of the own wage coefficient is unsurprising given that standard Mincerian
analysis has proven these individual characteristics to be important determinants of wages.
Pessimism seems to be an important individual trait that affects a worker’s sense of wellbeing.24 Pessimistic attitudes about the helpfulness of co-workers and about the possibilities
of job promotions have large and significant negative effects on life satisfaction. These effects
are of similar magnitude for both men and women. As discussed in Section 3.1, the inclusionof these two pessimism variables attempts to minimize concerns about wage endogeneity.
Controlling for pessimistic attitudes leads to small decreases (in absolute value) in themagnitudes of both the own and the comparison wage coefficients. However, in both cases,
the direction and magnitude of each of these effects are preserved. Additionally, thesetwo pessimism variables by themselves explain an extra 4 percent of the variation in life
satisfaction, which corroborates the relevance of a worker’s individual characteristics in thedetermination of subjective well-being.
To verify that the relationship between happiness and both absolute and relative wages
is not spurious, we investigate other channels through which these links may arise. Things
like a company’s wage structure, pay raises, and worker mobility within the establishment
may affect how satisfied workers are with the salary they and their co-workers perceive.
For instance, several authors have argued that high average wages within the company
may provide a signal to the worker about his ability to rise within the firm’s wage ladder (Hirschman and Rothschild 1973; Manski 2000; Senik 2004; Clark et al. 2009b). If theworker believes that he has greater possibilities of increasing his compensation in the future,
the negative impact of higher peers’ wages would decrease in absolute terms. In other words,
omitting average wages in a happiness regression would overestimate the comparison wage
effect. Moreover, greater wage dispersion within the firm may also have a significant impact
both on satisfaction and on the effect of absolute and relative wages on happiness. For
instance, if workers know that there is greater variance in compensation packages offeredby their company, that may influence how they feel about their relative rank in the wage
spectrum (Brown et al. 2008).
We explore these possibilities by controlling for the logarithm of average wages and theinterquartile range of log wages within the firm. As expected, higher average wages havea positive effect on reported levels of life satisfaction—although this effect is statisticallysignificant only in the case of male workers. In contrast, greater inequality has a negativeand significant impact on happiness for women, while it has no effect on men. Using thepooled sample, neither mean wages nor wage dispersion have a statistically significant effecton life satisfaction. In all cases, the inclusion of these variables has no discernible impact on
the own wage and the reference wage coefficients; that is, the relative utility results continue
to hold.
24
On a related issue, Stutzer (2004) found that higher income aspirations reduce individuals’ life satisfaction. See also Frey and Stutzer (2010).

14

4.1

Heterogeneity in Wage Effects

We have now confirmed that the negative correlation between reference wages and subjective
well-being holds even after accounting for a number of individual worker characteristics andcontrolling for other factors that may potentially explain away this relationship. In thissection, we now explore in further detail one additional possible mechanism that may linkthese two variables.
Perhaps the most logical or straightforward reason that may justify such relationship is
jealousy. In our case, jealous workers experience lower utility levels when they perceive that
their peers earn more than they do.25 Workers who like to compare themselves to otherworkers or who care about what their peers earn are likely to be better informed about theprevalent wage differentials at any given time. If this is true, workers who make smaller
prediction errors should suffer greater disutility from any increases in wages earned by theirpeers.
To test this, we first calculate a worker’s wage prediction accuracy and then estimate
the impacts of comparison wages on his reported levels of life satisfaction. We define suchprediction accuracy as the percentage difference (in levels) between the peer wage reportedby the worker (i.e., his belief) and the wage average within each worker group or cell
identified by the age of the worker, his gender, and his education level.26 We sort the wholesubsample of male workers by their prediction accuracy, and then group them into four
broad categories, ranging from worst (i.e., 1st quartile of workers) to best (i.e., workers in
the top 5 percent of predictors). Finally, we re-estimate the SWB equations for each of thesegroups, including the full set of controls that were accounted for in column 5 of Table 3.
The results appear in Table 4. The estimates corroborate our initial hypothesis that
better predictors are more severely affected by changes in their peers’ wages. Those who
fare worst at predicting the wage of other workers with similar characteristics, do experience
negative disutility from an increase in their peers’ wages, although this effect is smallest
relative to that of other workers who are more accurate in their predictions. The differencesin these reference wage effects over well-being are large. For instance, workers in the the
top 5 percent according to their prediction accuracy experience a negative impact on life
satisfaction that is 5 times as large as that reported by workers in the bottom 25 percent of
the sample (-0.21 for worst predictors vs. -1.06 for best predictors). Such a large effect forworkers whose reference wage is very close to the predicted one is also additional evidence
that pessimism, which would drive these two wage measures apart, cannot explain our
results.
Moreover, good predictors not only care more about their peers’ wages, but also about
their own. The differences in these own wage effects are also relatively large: about threetimes as large for best predictors compared to worst predictors. Nonetheless, these increases
in the own wage effect as workers’ prediction accuracy improves are not as marked as thoseobserved for reference wages. One possible interpretation of this finding is that better
predictors experience stronger feelings of jealousy, and so increases in their peers’ wages will25

The economics literature has traditionally defined jealousy in a similar way. See for example,
Dupor and Liu (2003).
26
Although we continue to focus on the male subsample only, in this calculation we control for genderbecause we expect male workers to compare themselves more often to other male, not female, peers.

15

outweigh any increase in well-being they may get from a similar rise in their own wage. Inturn, this result may imply that workers who care more about their peers’ and their own
wages will be more likely to be better informed about job offers and the wage structure in
their profession.
As an additional remark, the estimates in Table 4 suggest that one cannot ignore culturaldifferences when comparing reference and own wage effects on well-being across studies thatuse different samples, especially if the data employed correspond to individuals in different
countries. In Section 5.2, we compare our findings with those in previous literature keeping
this caveat in mind. Before we do that, we corroborate the robustness of our results in thefollowing section.

4.2

Robustness Checks for Self-reported Reference Wages

For brevity we show our robustness checks for the male sample only. Table 5 shows that our
main results hold when we consider alternative specifications. For the sake of comparison,column 1 shows our preferred specification from Table 3, which includes individual characteristics (not shown), underlying pessimism, and firm’s average wages and wage dispersion,
with the addition of industry fixed effects. As is shown in column 2, accounting for year
fixed effects leaves our main results unchanged.
In column 3, we address the possibility of endogeneity in both workers’ own wages andreference wages. First, exploiting the Japanese seniority system, we instrument worker i ’s
own wage with the average wage of his coworkers that are of the same age; the average iscalculated using all workers of the same age, working in the same company, but excluding
the wage of worker i.27 This strategy is valid as long as the proposed instrument affects
happiness only through individual i’s own wage. The estimates in column 3 show that thecoefficient on own wage is in absolute value slightly smaller but still significantly different
from zero, and the same is true for self-reported reference wage. The own wage coefficientis still larger than the one on self-reported reference wages, but partly due to the increased
standard errors we cannot reject the two coefficients to be the same. Next, in column 4,
we contemplate that a worker’s reference group might depend on the company the worker
chooses to work for, and unobserved amenities within the company might be correlated withboth wages, reference wages, and happiness. Even though these companies are large28 andmight thus have more than one establishment, company fixed effects should also capture
average neighborhood characteristics. The results show that this is not the case and that
controlling for company fixed effects does not alter the main findings.
In column 5 we perform a horse-race between self-reported reference wage and the reference wage from external data (see Section 3.1.3). The coefficient on the external reference
wage has the right sign and is significantly different from zero, but, most importantly, thecoefficient on the self-reported reference wages is practically unchanged. One possibility for27

If we assume that the wage of individual i working in company j is given by: yij =fj (ageiP, educationi , tenurei ) + abilityi, then we can instrument this wage as fj (agei , educationi , tenurei ) ≈1
l6=i ylja .
Nja −128
We observe approximately 90,000 workers and 62 firms, which means that each company has on average
1,500 workers.

16

the negative sign on the external reference wage is that it proxies for the worker’s permanent wages. The difference between the own wage and the external reference wage will thus
proxy for unexpected positive shocks to income.29
In column 6, we use an ordered probit instead of the OLS. The coefficients are not directlycomparable with the OLS ones but the relative size of the coefficient on wages and the one
on reference wages can be compared and the interpretation remains the same.30 This result
is in agreement with findings by Luttmer (2005) that the use of least-squares estimators inhappiness studies does not impact negatively the general conclusions, even when ordered
probit or logit models would be methodologically preferred given the hierarchical and nonlinear nature of the dependent variable.
Finally, column 7 shows that using job satisfaction as our subjective well-being measuredoes not alter the main findings: the coefficient on absolute wages, although slightly smaller
in magnitude, is positive, strongly significant, and greater in absolute terms than the reference wage coefficient. As expected, the latter estimate is negative and significant at the
1 percent level. Taken together, these alternative specifications corroborate the stability of
the results emphasized throughout the section.

5

Testing the Relative Utility Hypothesis Using
Constructed Reference Wages

In the previous section, we used reference wages reported by workers to demonstrate how
robust is the negative relationship between these self-reported reference wages and wellbeing. However, self-reported reference wages are not always available. Instead, mosttests of the relative utility hypothesis found in the literature generally rely on reference
wage measures constructed as some average wage defined in various ways, as discussed
in Sections 2 and 3. In this section, we investigate whether these alternate wage measuresdeviate from the comparison wage benchmark truly perceived by workers, and if so, whether
these differences introduce any significant biases in the estimated impacts of reference wages
on well-being.
To preview our results, the findings below show that, in general, the estimated effectof reference wages on well-being is not consistent when using constructed reference wages
as proxies for the truly perceived comparison wage. Although the theory suggests thatthe direction of the bias introduced cannot be determined, our empirical estimates show
slightly smaller reference wage effects when we define this comparison wage variable using
different types of cell averages. However, when reference wages are constructed from Mincer
regressions, the estimated effects are vastly different from those suggested by all of ourprevious estimates, and highly unstable. We attribute these significant discrepancies to thedifficulty in finding valid exclusion restrictions to justify the Mincer approach.
29

We thank Angus Deaton for pointing out this possibility.
Nevertheless, the coefficients turn out to be similar in magnitude to the OLS case partly because theroot mean squared error of the baseline regression is 1.04 and thus close to unity, to which the error termin the latent model of the ordered probit is normalized.
30

17

5.1

Revisiting the Subjective Well-Being Regressions

To investigate how the use of constructed reference wage measures may affect the empiricaltests of the relative utility hypothesis, we first re-estimate the regressions in Table 3, now
substituting the self-reported reference wage variable with the alternate measures used in
the literature. The estimates are shown in Table 6. The first regression uses self-reported
reference wages and produces estimates similar to the ones shown earlier in Table 3. The
coefficients in columns 2-4 are derived from specifications that construct reference wages
using various cell definitions. Cell 1 is defined by the age and education level of the worker
and the average is computed using our own wage data. Cells 2 and 3 are both defined by
the age of the worker, his educational attainment, and whether he is responsible for any
managerial tasks within his company. The difference between these two averages is that cellaverage 2 is also computed from our own wage data, while cell average 3 uses the external
BSWS wage database described in Section 3. Finally, the reference wage in column 5 comesfrom a standard Mincer regression similar to the ones shown in Table 2.
The results suggest that life satisfaction regressions that use constructed reference wages
instead of self-reported ones do produce somewhat different results. The top panel illustrates
how the raw effects of own and reference wages change when using alternate comparison
wage measures, and so these first five specifications do not control for individual worker
characteristics. In the case of specifications that use cell averages, both the own wage andthe reference wage effects are always about 20-30 percent smaller in absolute value relative
to the benchmark estimates. However, although the differences between each of the own
wage and reference wage coefficients in columns 2-4 and the analogous estimates in column 1
are statistically significant, all of these coefficients are important in a statistical sense andthe suggested directions of these effects are preserved. By contrast, the specification thatuses Mincer-constructed reference wages tells a different story. Although the estimated own
wage effect is positive and statistically significant, as before, the coefficient is 85 percent
smaller than the one derived from a specification that uses self-reported reference wages.
Moreover, the estimated impact of reference wages on life satisfaction is more than twice
the size of the corresponding coefficient in column 1, and positive, which contrasts with thesuggested effects in all of the previous specifications.
These findings are echoed in the bottom panel of the table, where we re-estimate thesame five equations, this time controlling for worker individual characteristics. As shown in
Table 3, the inclusion of these regressors slightly lowers the magnitude of both the own wage
and the reference wage coefficients, although the general results are preserved. Once again,
the estimates derived from regressions that use constructed reference wages are somewhat
smaller in absolute value than the corresponding benchmark coefficients obtained whenself-reported reference wages are employed instead. Nonetheless, the own wage effect onlife satisfaction continues to be positive, the negative impact of reference wages on wellbeing persists, and all of the coefficients are strongly significant. The stark difference in the
results is given, once more, by the specification with Mincer-constructed reference wages.
This time, the size, direction, and statistical significance of the own wage coefficient arealmost identical to those obtained using cell averages. Similar to specification 5 in the top
panel that did not control for individual characteristics, the estimated reference wage effectunder the Mincer approach continues to be positive, although the coefficient is statistically18

indistinguishable from zero.
In sum, empirical tests of the relative utility hypothesis that employ reference wage
measures constructed by the econometrician imply reference wage effects on subjective wellbeing that deviate somewhat from those estimated when self-reported reference wages areused. In what follows, we attempt to uncover the reasons behind this bias.

5.2

Estimating the Bias

To better understand the differences described above, we utilize a generalized version of a thestandard classical measurement error model. Recall that equation 1 gives the relationshipbetween subjective well-being and the reference wage variable, ȳi . Now, suppose that thereference wage variable employed is a mismeasured version of the worker’s correct perceptionof what his peers earn, ȳi∗. That is,ȳi = α0 + β0 ȳi∗ + ǫi .

(2)This represents a generalization of the classical measurement error model, where α0 = 0 and
β0 = 1. We believe this generalization is appropriate in our case because the data suggestthat reported reference wages differ significantly depending on workers’ wage levels. For
instance, people with low wages systematically perceive that their peers earn more thanthey do; that is, ȳi∗ > ȳi for low earners, which would imply that α0 > 0 and β0 < 1. Similar
to the classical measurement error model, we assume that the error term is orthogonal to
the true reference wage, and that the relationship between the latter and the mismeasured
variable is linear.
Substituting equation 2 into equation 1 obtains:
SWBi = α1 yi + β1 (β0 ȳi∗ + ǫi ) + x′i γ + β1 α0 + ui .

(3)

In such a model, a bias arises as a function of both the variance of the ǫ residuals and the
distance between β0 and 1. To see this, assume for simplicity that subjective well-being
does not depend on worker individual characteristics.31 In this case, the probability limitof the OLS estimate of β1 is
plim βb1ols =cov(β0 ȳ ∗ + ǫ, SWB)
β0 cov(ȳ ∗ , SWB)
β0
=
=
β
1
2
2
var(β0 ȳ ∗ + ǫ)
β0 var(ȳ ∗ ) + var(ǫ)β0 + var(ǫ)/var(ȳ ∗ )The equation above suggests that, in general, βb1ols is not a consistent estimator of the true
effect of comparison wages on subjective well-being when constructed reference wages areused. This statement is true as long as β0 is different from β02 + var(ǫ)/var(ȳ ∗ ). Moreover,unless we can accurately determine whether β0 is above or below 1, the direction of the bias
is ambiguous.
31

The results below do not hinge on this assumption. Using the Frisch-Waugh-Lovell theorem we could
alternatively work with the residuals of a projection onto the space orthogonal to the individual characteristics. Alternatively we can derive the coefficient for ȳ ∗ independently from x if self-reported reference
wages are not dependent on workers’ individual characteristics. The data suggest that this is a reasonableassumption. For instance, compare columns 1 and 2 in Table 3, and note that the estimated self-reported
reference wage does not change when worker characteristics are introduced.

19

Can we use instrumental variables (IV) to consistently estimate βb1ols ? In the classicalmeasurement error model, an IV estimator would normally get rid of the bias introduced by
the mismeasurement of the independent variable. By contrast, in the generalized version of
the model, an IV approach does not lead to a consistent estimate of βb1ols , although it does
allow us to bound the estimated effect. To see this, suppose that there are two independent
proxies of ȳ ∗ , proxy a and proxy b. We can then use proxy a as an instrument for proxy b:(4)plim βb1iv =cov(β0a ȳ ∗ + ǫa , SWB)
cov(ȳ ∗ , SWB)
β1
=
= b.
a ∗
b ∗
b
a
b
∗
cov(β0 ȳ + ǫ , β0 ȳ + ǫ )β0 var(ȳ )β0The expression above implies that an IV estimate of β1 would still be biased, although
now this bias depends only on the distance between β0b and 1, not on the variance of the ǫ
residuals. Taking the ratio between βb1iv and βb1ols one can see that whenever OLS and IV give
similar results, the signal-to-noise ratio var(ȳ ∗)/var(ǫ) will be large. Unfortunately, β0 might
still be different from one and bias both estimates. Since workers with low wages tend toreport a reference wage that is higher than their own wage, and vice versa, the regression ofpredicted reference wages on self-reported ones shows a positive constant term and a slopethat is smaller than one. The IV estimate will thus tend to be larger, in absolute value,than the true coefficient.
Table 7 compares the OLS and the IV estimates. The OLS regressions are identical tothe ones that appear in Panel A of Table 6 and are shown to facilitate the comparison with
the IV estimates. The four OLS regressions are exactly the same, except that each uses
a different reference wage measure. The analogous results for Panel B are not reproduced
for brevity. The reported IV regressions instrument for the same reference wage measureemployed in the corresponding OLS regression, using a different cell wage average definition
as instrument.32 We focus on estimations that use self-reported and constructed reference
wages since, as discussed above, these coefficients are stable across specifications. Resultsthat use reference wages derived from Mincer regressions are explored further in Table 8
below.
The findings confirm the corresponding effects that own wages and reference wages haveon life satisfaction. In all cases, each of the IV estimates is statistically indistinguishable
from the OLS estimate obtained when self-reported reference wages are employed. Looking
at equation 4, the direct implication of this is that βb0 is close to 1, and that the IV coefficients
do provide a consistent estimate of the effect of reference wages on subjective well-being.
Of course, it is possible that this result may not be generalizable to other similar databases
measuring well-being and wages for different populations in other regions or countries. A
generalized result, though, is that the IV procedure gets rid of one of the sources of bias. In
our case, this is shown by the significant differences between each of the IV coefficients andthe OLS regression that employs the corresponding mismeasured (constructed) reference
wage measure. Moreover, with respect to the own wage effects, we observe that the IV
coefficient is always somewhat larger than the corresponding OLS estimate. We attribute
this result to the construction of the reference wage measure used in the OLS regressions as
32

The first-stage estimations and the results from several underidentification and weak-instruments tests
are not reported for brevity. These results confirm the validity of our instruments and are available uponrequest.

20

some average of the own wage variable. In this case, the inclusion of another variable that
accounts for the mean wage of some subsample of the population that has similar characteristics to the worker’s would most likely absorb some of the effect that would otherwisebe (correctly) attributed to the own wage variable.

5.3

Understanding Mincer-Predicted Reference Wages

Thus far, our findings have been quite robust to different specifications and subsamples.
They have also been relatively stable when using alternate definitions of comparison wages,
except in the case of Mincer-predicted reference wages. In this section, we further investigate this discrepancy and argue that one of the main reasons behind the instability of the
Mincer reference wage effects on subjective well-being is the indiscriminate use of exclusion
restrictions that may not necessarily help accurately identify the reference wage effect. A
proper instrument should be able to predict a reference wage, or define a reference group
without influencing life satisfaction.33
Table 8 displays results for various tests of the relative utility hypothesis according toother papers in the literature that construct reference wages following a Mincer approach.
Each column shows the own wage and the reference wage coefficients we obtain by runninga specification as close as possible to the one employed in each of the papers, but using
our own data. We also report the direction and significance of the reference wage effect on
well-being that the respective authors obtain in their paper using their own data.
The differences in the estimated reference wage effects, both across specifications andacross samples, are striking. First, the coefficients vary significantly depending on the specification, going from -0.41 to 0.64. Thus, the discrepancies arise not only in the magnitudeof the coefficients, but also in the direction of the estimated effect. Such large variation in
the implied impact of reference wages on well-being is surprising given that the underlying sample is exactly the same and the only thing that changes is the exclusion restriction
assumed to identify the reference wage effect. Second, note how the same (or nearly thesame) specification produces very different results from those reported in the original papers
for their respective samples. For instance, while Sloane and Williams (2000) find a positiveand insignificant effect of reference wages on well-being when they control for variables suchas hours worked, tenure, marital status, and union management experience in the Mincer
equation, we obtain a negative and very significant coefficient using the same specificationfor our sample.
We believe that what gives rise to such disparate results is the indiscriminate use ofdifferent exclusion restrictions and the unjustified categorization of variables as belonging ineither the Mincer or the well-being equation. As noted earlier, the fact that our findings arequite robust to different specifications, including those that use various constructed reference
wages, suggests that something not inherently related to the estimation of the subjective
well-being equation may be driving the Mincer results. Under the Mincer framework, it is
up to the econometrician to decide whether a given variable belongs in one or the otherequation. For example, some of the authors that follow the Mincer approach have used33

A recent paper by Card et al. (2012) does this in a clever way by using randomized manipulation ofaccess to information on peers’ wages.

21different combinations of variables including age, education, and marital status in their
exclusion equations, even when these variables have shown to be both economically andstatistically relevant in the subjective well-being equation.
Of course, it is also possible that the discrepancies outlined above are due to other reasons. For instance, different papers use different samples. It is possible that the estimatedreference wage effects on well-being are stronger or weaker, depending on the working population. It is also entirely possible that the “tunnel effects” described in Section 2 are moreprevalent in other populations. Moreover, note that, for the sake of comparison, we are
restricting our estimations to the same sample of male workers with which we have worked
throughout the paper. Other papers in the literature have pooled males and females andused individuals that differ in their characteristics from our sample of unionized workers
employed by large, publicly-listed firms in Japan. Nonetheless, estimations using different
working subsamples (not reported for brevity) produce results that are, in some cases, significantly different from the ones reported for each of the specifications in the table, whichfurther underlines the instability of the Mincer estimates.
Summing up, the results above suggest that the use of constructed reference wages in
empirical tests of the utility hypothesis produces inconsistent estimates of the effects ofcomparison wages on subjective well-being. Our theoretical framework demonstrates that,in the presence of several mismeasured proxies of the reference wage that is truly perceived
by the worker, an instrumental variables approach may help bound the comparison wage
estimate, although it does not eliminate the bias entirely. An evaluation of the various
empirical constructs that the literature has previously used shows that various cell wage
averages, computed both within our own dataset and from an external wage data source,
perform best in the estimation of the reference wage effects compared to the self-reported
reference wage measure. However, the use of Mincer-predicted reference wages obtains reference wage effects that are unstable due to multicollinearity that ad hoc exclusion restrictions
typically do not solve. It is thus advised that these Mincer-predicted reference wages are
employed with care.

6

Conclusions

The results in this article find strong support for the relative utility hypothesis. Using dataon self-reported reference wages, we observe that individuals report lower levels of both lifeand job satisfaction when they perceive that their co-workers earn higher salaries. We findthat the association between absolute wage effects and subjective well-being is economicallystronger than that between the latter and comparison wages. Moreover, our results demonstrate that standard methods employed in the literature to estimate reference income yieldinconsistent estimators of the reference income effect. In particular, we show that linear
predictions of benchmark wage measures obtained by Mincer estimations perform poorly.
This is mainly due to weak identifying assumptions of the comparison wage effect and astrong multicollinearity problem between predicted wages and the rest of the individual
worker characteristics. In contrast, cell averages based on age, gender, and education levels
and estimated using external datasets generate more reliable results.
We believe that the findings in this paper can be extended in three main directions.
22

First, having acknowledged the existence of important heterogeneous relative utility effects
among workers, it would be relevant to better understand the source of such heterogeneity.
If, for example, better informed individuals tend to exhibit stronger feelings of jealousy and astronger negative correlation between their peers’ wages and their own happiness, employers
may find it beneficial to enhance the secrecy of its workers’ pay information to preserve
aggregate utility within the firm. Second, the disutility that workers experience from higher
wages among their peers may translate into more tangible outcomes than mere answers in asurvey. For instance, potential feelings of jealousy may impact worker behavior in the formof reduced teamwork and, hence, lower levels of productivity at the workplace. Alternatively,
affected workers may change job search intensity, as suggested by Card et al. (2012). Third,
it is plausible that workers experience loss aversion in the sense of Kahneman and Tversky(1979). Understanding workers’ asymmetric responses to variations in their peers’ wages
may improve the design of wage setting policies and pay incentive schemes.
From a practical point of view, our findings strengthen the case to increase resourcesthat improve the quality of surveys and data collection on job satisfaction and life happiness. Relative wage effects have obvious consequences in redistributive policies, both withina firm in the form of salary increases, as within a country in the shape of tax considerations.
Through a better understanding of these potential externalities on workers, firms and governments will be able to ameliorate the design of welfare-enhancing pay schedules and fiscal
programs.

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26

Table 1: Summary statisticsa

Worker characteristics
Age (years)
Tenure (years)
Middle school [0,1]High school [0,1]Technical school [0,1]Some college [0,1]College [0,1]Post-graduate [0,1]Married [0,1]Blue collar [0,1]Managerial tasks [0,1]
Hours worked (monthly overtime)
Life satisfaction (1=lowest. . . 5=highest)
Job satisfaction (1=lowest. . . 5=highest)

Wage datab
Own wages
Self-reported reference wages
Cell wage average from our datac
Cell wage average from external datac
Mincer-predicted wagesd

Observationsa

Whole
sampleAll

Working subsamples
Males
Females

34.926(9.96)
13.870(10.3)
0.069(0.25)
0.464
(0.5)
0.024
(0.15)
0.095(0.29)
0.270(0.44)
0.067(0.25)
0.581(0.493)0.341(0.47)
0.171(0.38)
22.679(19.83)
3.274
(1.1)
3.066
(0.93)

34.676(9.64)
13.525(9.94)
0.044
(0.2)
0.474
(0.5)
0.025
(0.16)
0.095(0.29)
0.283(0.45)
0.067(0.25)
0.583(0.493)0.336(0.47)
0.181(0.39)
23.235(20.38)
3.270
(1.1)
3.070
(0.93)

35.921(9.58)
14.554(10.11)
0.048(0.21)
0.480
(0.5)
0.024
(0.15)
0.042
(0.2)
0.315
(0.46)
0.081(0.27)
0.653(0.476)0.369(0.48)
0.225(0.42)
26.404(21.16)
3.202(1.11)
3.085(0.94)

30.153(8.46)
9.786
(8.3)
0.029
(0.17)
0.454
(0.5)
0.030
(0.17)
0.286(0.45)
0.167(0.37)
0.015(0.12)
0.329(0.469)0.219(0.41)
0.023(0.15)
11.718(11.34)3.516(1.04)
3.017(0.91)

15.401(0.42)
15.486(0.41)
15.401(0.35)
15.501(0.35)
15.406(0.26)

15.405(0.42)
15.491(0.41)
15.405(0.34)
15.508(0.34)
15.405(0.26)

15.508(0.37)
15.583(0.37)
15.508(0.28)
15.612(0.29)
15.508(0.16)

15.028(0.37)
15.155(0.38)
15.028(0.25)
15.133(0.23)
15.028(0.21)

91896

78136

61278

16858

For each worker characteristic, first number corresponds to sample average; second number (inparentheses) corresponds to standard deviation.
b
Wage figures are in 2005 Japanese yen and are reported in logs.
cCells defined as gender × age × education level.
dPredicted wages from a Mincer regression that controls for age, tenure, gender, education level,
managerial task, blue collar, job shock, occupation, industry, hours worked, marital status, and year
fixed effects.

27

Table 2: Mincer regressions(1)
Dependent variable:
Annual income

Age in years
Age squared
Tenure in years
Tenure squared
Middle school [0,1]Technical school [0,1]Some college [0,1]College [0,1]Post-graduate [0,1]Other degree [0,1]
Hours worked (monthly overtime)
Married [0,1]Managerial tasks [0,1]Blue collar [0,1]Observations
Adjusted R-squared

(2)
Males

(3)

(4)

(5)
Females(6)

Industry+Year

Fixed effects:
Company+Year

Industry+Year

Company+Year

0.08***(0.01)-0.00***(0.00)
0.02***(0.00)-0.00***(0.00)-0.14***(0.03)-0.00(0.03)
0.07***(0.02)
0.15***(0.01)
0.20***(0.02)-0.06(0.04)
0.02*(0.01)
0.09***(0.01)
0.07***(0.02)-0.01(0.02)

0.08***(0.01)-0.00***(0.00)
0.02***(0.00)-0.00***(0.00)-0.08***(0.02)-0.02(0.02)
0.06***(0.01)
0.12***(0.01)
0.15***(0.02)-0.08***(0.02)
0.04***(0.00)
0.09***(0.00)
0.08***(0.01)-0.01(0.02)

0.08***(0.01)-0.00***(0.00)
0.02***(0.00)-0.00***(0.00)-0.06***(0.01)-0.01(0.01)
0.05***(0.01)
0.11***(0.01)
0.12***(0.01)-0.05***(0.01)
0.04***(0.00)
0.09***(0.00)
0.08***(0.01)-0.01(0.02)

0.06***(0.01)-0.00***(0.00)
0.03***(0.01)-0.00**(0.00)-0.19***(0.03)
0.09***(0.03)
0.09***(0.02)
0.25***(0.04)
0.42***(0.04)-0.08**(0.03)
0.03**(0.01)
0.01(0.01)
0.11***(0.03)-0.09***(0.02)

0.04***(0.01)-0.00***(0.00)
0.03***(0.01)-0.00***(0.00)-0.11***(0.02)
0.07***(0.02)
0.07***(0.02)
0.21***(0.03)
0.35***(0.04)-0.12***(0.03)
0.04***(0.01)-0.00(0.01)
0.13***(0.02)-0.10***(0.01)

0.04***(0.01)-0.00***(0.00)
0.03***(0.01)-0.00***(0.00)-0.08***(0.02)
0.06***(0.02)
0.06***(0.02)
0.19***(0.03)
0.33***(0.04)-0.11***(0.03)
0.04***(0.00)-0.01*(0.01)
0.15***(0.02)-0.09***(0.01)

61278
0.664

61278
0.723

61278
0.755

16858
0.538

16858
0.598

16858
0.636

*** p<0.01, ** p<0.05, * p<0.1
Notes: Standard errors clustered by firm in parentheses. All wage measures and hours worked reported inlogs. High school education dummy excluded from regression.

28

Table 3: Testing the relative utility hypothesis using self-reported reference wages
Dependent variable:
Life satisfaction
Own wage
Self-reported reference wage
Hours worked (monthly overtime)

(1)

(2)

Males
(3)

(4)

(5)

Females
(6)

All
(7)

0.69***(0.04)-0.39***(0.03)-0.02(0.02)

0.54***(0.02)-0.38***(0.03)-0.06***(0.01)

0.46***(0.02)-0.30***(0.03)-0.06***(0.01)

0.42***(0.02)-0.29***(0.03)-0.05***(0.01)
0.11*(0.06)-0.37***(0.01)-0.22***(0.01)-0.37***(0.01)-0.22***(0.01)

0.43***(0.02)-0.29***(0.03)-0.05***(0.01)
0.09(0.06)-0.09(0.08)-0.37***(0.01)-0.22***(0.01)

0.26***(0.05)-0.16***(0.04)-0.08***(0.01)-0.03(0.09)-0.19**(0.08)-0.41***(0.02)-0.15***(0.02)-0.05***(0.01)
0.05***(0.01)
0.61***(0.02)
0.08***(0.03)
0.09***(0.03)
0.04*(0.02)
0.17***(0.03)
0.22***(0.04)
0.06(0.04)
0.01(0.02)-0.04**(0.02)-0.04***(0.01)
0.04***(0.01)
0.60***(0.02)
0.07**(0.03)
0.08***(0.03)
0.03(0.02)
0.13***(0.03)
0.17***(0.04)
0.05(0.04)-0.03*(0.02)-0.03*(0.02)-0.04***(0.01)
0.04***(0.01)
0.61***(0.02)
0.08***(0.03)
0.08**(0.03)
0.03(0.02)
0.13***(0.03)
0.16***(0.04)
0.05(0.04)-0.03(0.02)-0.03*(0.01)-0.04***(0.01)
0.04***(0.01)
0.61***(0.02)
0.08***(0.03)
0.08**(0.03)
0.03(0.02)
0.13***(0.03)
0.16***(0.04)
0.04(0.04)-0.03(0.02)-0.03*(0.01)-0.05***(0.01)
0.06***(0.02)
0.43***(0.02)
0.08(0.11)
0.04(0.05)
0.17***(0.03)
0.18***(0.04)
0.22***(0.07)-0.05(0.04)-0.06(0.05)-0.10***(0.03)

0.40***(0.03)-0.26***(0.03)-0.06***(0.01)
0.05(0.06)-0.12*(0.07)-0.38***(0.01)-0.21***(0.01)
0.43***(0.02)-0.04***(0.01)
0.04***(0.01)
0.56***(0.02)
0.08**(0.03)
0.07**(0.03)
0.13***(0.02)
0.14***(0.03)
0.17***(0.04)
0.01(0.03)-0.03*(0.02)-0.04***(0.02)

Firm’s average wages
Firm’s wage interquartile range
Pessimism aboutcolleagues’ helpfulness
Pessimism about
future promotions
Female [0,1]Age (years)
Age squared (/100)
Married [0,1]Middle school [0,1]Technical school [0,1]Some college [0,1]College [0,1]Post-graduate [0,1]Other degree [0,1]Managerial tasks [0,1]Blue collar [0,1]Observations
Adjusted R-squared

61278
0.022

61278
0.074

61278
0.111

61278
0.111

61278
0.111

16858
0.087

78136
0.117

Own wage + reference wage = 0?
p value

0.000

0.000

0.000

0.000

0.000

0.113

0.001

*** p<0.01, ** p<0.05, * p<0.1
Notes: Standard errors clustered by firm in parentheses. All wage measures and hours worked reported inlogs. High school education dummy excluded from regression. Except for column (1), all specifications
account for industry fixed effects.

29

Table 4: Effects of own wages and self-reported reference wages on life satisfaction by
accuracy of workers’ prediction of peers’ wages
Dependent variable:
Life satisfaction

Own wage

(1)
(2)
(3)
(4)
Prediction accuracy (quartile)
1
2+3
4
Top 5%
0.24***(0.05)-0.21***(0.04)

0.66***(0.05)-0.58***(0.04)

0.63***(0.05)-0.91***(0.13)

0.65***(0.13)-1.06***(0.32)

Observations
Adjusted R-squared

14216
0.060

30174
0.077

17588
0.088

3702
0.089

Own wage + reference wage = 0?
p value

0.678

0.260

0.028

0.171

Self-reported reference wage*** p<0.01, ** p<0.05, * p<0.1
Notes: Standard errors clustered by firm in parentheses. All wage measures and hours worked reported inlogs. Prediction accuracy defined as the percentage difference (in levels) between peer reference wage
reported by worker and wage average by cell defined as age × gender × education. All specifications
control for individual worker characteristics including age, age squared, marital status, education level,
managerial and blue collar dummies, as well as firm’s average wages, firm’s wage interquartile range, andindustry fixed effects.

30

Table 5: Robustness tests
(1)

(2)

(3)

(4)

BenchmarkYear

Own wage IV

Company

0.43***(0.02)-0.29***(0.03)-0.05***(0.01)
0.09(0.06)-0.09(0.08)-0.37***(0.01)-0.22***(0.01)

0.43***(0.03)-0.30***(0.03)-0.05***(0.01)
0.13*(0.07)-0.14*(0.08)-0.37***(0.01)-0.22***(0.01)

0.32***(0.12)-0.25***(0.05)-0.04***(0.01)
0.09(0.06)-0.09(0.08)-0.37***(0.01)-0.22***(0.01)

0.41***(0.03)-0.29***(0.03)-0.04***(0.01)

Fixed effects

IndustryYear

Industry

Observations
Adjusted R-squared

61278
0.111

61278
0.111

Own wage + reference wage = 0?
p value

0.000

0.000

Self-reported reference wage
Hours worked (monthly overtime)
Firm’s average wages
Firm’s wage interquartile range
Pessimism about colleagues’ helpfulness [0,1]31Pessimism about future promotions [0,1](6)OrderedprobitLife satisfaction

Dependent variable:
Own wage(5)Externalreference(7)

Job sat.
0.43***(0.03)-0.29***(0.03)-0.06***(0.01)
0.09(0.06)-0.09(0.08)-0.37***(0.01)-0.22***(0.01)-0.14*(0.07)

0.42***(0.03)-0.30***(0.03)-0.05***(0.01)
0.09(0.06)-0.10(0.08)-0.38***(0.01)-0.21***(0.01)

0.25***(0.04)-0.17***(0.02)-0.03**(0.01)
0.15*(0.08)-0.13(0.08)-0.38***(0.01)-0.44***(0.01)

Company&Year

Industry

Industry

Industry

61278
0.111

61278
0.115

61278
0.111

61278
0.040a

61278
0.121

0.423

0.000

0.000

0.000

0.029

-0.37***(0.01)-0.22***(0.01)

Cell average reference wage*** p<0.01, ** p<0.05, * p<0.1
Notes: Standard errors clustered by firm in parentheses. All wage measures and hours worked reported in logs. All specifications control for age,
age squared, education level, marital status, manager and blue collar positions. In column 3 Own wage is instrumented by the average wage of his
coworkers that are of the same age; the first stage F-statistic of the excluded instrument is larger than 200.
a
Adjusted R-squared not available; pseudo R-squared reported instead.

Table 6: Testing the relative utility hypothesis using alternate reference wage measures
Panel A. Without individual characteristics(1)
Dependent variable:
Life satisfaction
Own wage
Reference wage

Observations
Adjusted R-squared

Self-reported

(2)

(3)

Reference wage:
Cell average 1 Cell average 2

(4)

(5)

Cell average 3

Mincer

0.67***(0.03)-0.39***(0.03)

0.49***(0.03)-0.22***(0.07)

0.48***(0.03)-0.20***(0.05)

0.52***(0.03)-0.26***(0.05)

0.10**(0.04)
0.85***(0.13)

61278
0.025

61278
0.021

61278
0.019

61278
0.020

61278
0.024

32

Panel B. With individual characteristics

Own wage
Reference wage

Observations
Adjusted R-squared

(6)

(7)

(8)

(9)

(10)

0.54***(0.02)-0.38***(0.03)

0.37***(0.03)-0.18***(0.05)

0.37***(0.03)-0.15***(0.05)

0.37***(0.03)-0.28***(0.07)

0.36***(0.03)
0.06(0.14)

61278
0.074

61278
0.069

61278
0.069

61278
0.069

61278
0.069

*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by firm in parentheses. All wage measures reported in logs. All specifications control for(log) hours worked and industry fixed effects. Specifications in bottom panel additionally account for individual worker characteristics including
age, age squared, marital status, education level, managerial and blue collar dummies. Cell averages 1 and 2 calculated from our own data set anddefined as (age × gender × education) and (age × gender × education × managerial tasks), respectively. Cell average 3 calculated using BSWS
data and defined as (age × gender × education × managerial tasks). Mincer reference wage predicted from a standard regression of own wages on
age and tenure dummies, hours worked, education level, marital status, manager, blue collar, job shock, and occupation dummies, as well as
industry and year fixed effects.

Table 7: Use of IV estimates when self-reported reference wages are not available(1)

Dependent variable:
Life satisfaction

Own wage
33

Reference wage

Self-reported
OLS

0.67***(0.032)-0.39***(0.025)(2)

Cell average 1
OLS
IV

0.49***(0.025)-0.22***(0.049)

(4)

61278
0.025

61278
0.021

0.56***(0.033)-0.34***(0.067)

61278
0.021

(5)

Reference wage:
Cell average 2
OLS
IV

0.48***(0.027)-0.20***(0.052)Cell av. 3

Instrumental variable:
Observations
Adjusted R-squared(3)

0.57***(0.035)-0.35***(0.069)(6)

Cell average 3
OLS
IV

0.52***(0.026)-0.26***(0.047)

Cell av. 3
61278
0.019

61278
0.019

(7)

0.53***(0.031)-0.28***(0.061)
Cell av. 1

61278
0.020

61278
0.020

** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered by firm in parentheses. All wage measures reported in logs. Cell averages 1 and 2
calculated from our own data set and defined as (age × gender × education) and (age × gender × education × managerial tasks), respectively. Cellaverage 3 calculated using BSWS data and defined as (age × gender × education × managerial tasks). All specifications control for (log) hours
worked and industry fixed effects.

Table 8: Impact of different exclusion restrictions on own wage and reference wage coefficients

Dependent variable:
Life satisfaction
Own wage
Mincer-predicted reference wage

Effect of reference income
in original paper
Exclusion restriction

34
Observations
Adjusted R-squared
Own wage + reference wage = 0?
p value(1)
Clark & Oswald (1996)a
0.39***(0.02)
0.64***(0.10)(2)
Sloane & Williams (2000)b
0.20***(0.02)-0.41***(0.06)(3)Lévy-Garboua & Montmarquette (2004)c
0.28***(0.03)
0.45***(0.06)(4)
Senik (2004)d
0.22***(0.04)
0.18**(0.08)

Negative andsignificant

Positive andinsignificant

Negative andsignificant

Positive andsignificant

Age categories
Tenure, Tenure squared
Education level
Marital status
Union management
Firm and year fixed effects
61278
0.03

Hours worked
Tenure, Tenure squared
Marital status categories
married female
married male
Union management
61278
0.14

Age
Hours worked
Tenure, Tenure squared
Marital status
Managerial tasks
Occupation fixed effects
61278
0.12

Age, Tenure
Education level
Marital status categories
Managerial tasks
Industry andoccupation fixed effects
55801
0.06

0.000

0.001

0.000

0.000

Standard errors clustered by firm in parentheses. All wage measures reported in logs. *** p<0.01, ** p<0.05, * p<0.1aAdditional variables in life satisfaction equation include hours worked, age, age squared, managerial tasks, industry and occupation fixed effects.
bAdditional variables in life satisfaction equation include hours worked, age, educational attainments, managerial tasks, company fixed effects, six
subjective variables regarding working conditions.
cAdditional variables in life satisfaction equation include age categories, educational attainment, job satisfaction, satisfaction with leisure.
dAdditional variables in life satisfaction equation include age categories, marital status, family income.

Table A-1: Literature survey

35

Authors
Data characteristics
Dependent variable
Own-income effect
Reference income effect| Ref. income | > | Own income | ? (vi)
Hours worked
Age
Age squared
TenureTenure squared
Sex
Education
Marital Status/Children
Industry
Occupation
Job class/ supervisor
RegionTable and equation number
MethodClark and Oswald (1996) (i)U.K. workers
Job satisfaction
IncomePositive
Negative
Yes ∗∗
Yes
No
Yes
Yes
Yes
No
No
Yes
No
Yes
Yes
Yes
No
Yes
No
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
4(1)
A
Ordered Probit

Mincer wage approach
Sloane and Williams (2000) (ii)Lévy-Garboua and Montmarquette (2004) (iii)
U.K. workers
Canadian workers
Job satisfaction
IncomeJob satisfaction
IncomePositive
Positive
Positive
Negative (v)
No
No
Yes
Yes
No
Yes
Yes
No
Yes
Yes
No
No
No
No
No
Yes
No
Yes
No
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
No
No
No
No
No
No
No
Yes
Yes
Yes
No
Yes
Yes
No
No
Yes
8(1)
7
1,2(1)
A.1
Ordered Probit
Ordered Probit

Senik (2004) (iv)
RussiaHappiness
IncomePositive
Positive
No ∗ ∗ ∗
No
No
Yes
Yes
No
No
No
Yes
No
No
Yes
Yes
No
Yes
Yes
Yes
No
Yes
No
Yes
No
Yes
Yes
Yes
3(3)
A.1
Ordered Probit(i) Other controls in happiness regression; Renter, Has second job, Temporary contract, Health status. Other controls in Mincer equation; Health dummies, Establishment

size dummies, Accident dummies, When work dummies, Sex mix at work dummies, Organization type dummies, Trade Union recognized, Pension member,Incentive
payment, Union member, Part time, Temporary contract.
(ii) Other controls in happiness regression; Temporary contract, Firm size dummies, Part time, Owner-occupier, Promotion prospects, Frequent overtime, Perceived career,
14 Working condition variables, Manual dummy, Union dummy, Training period, Sex mix at work dummies, Race dummies. Other controls in Mincer equation; Female
married, Male married, Length of training period, Shift work, Merit-related payment, Relevant trade union present, Trade union member, Selectivity, Part time, Temporary
contract, Firm size dummies.
(iii) Other controls in happiness regression; Job related satisfactions, Country of birth, Mother tongue, Satisfaction with leisure, Religion. Other controls in Mincer equation;
Canadian borne, Bilingual, Religion, Satisfaction with health, Satisfaction with leisure, Part time.
(iv) Other controls in happiness regression; Lagged individual income, Household income, Household size, Mother tongue, Believer, Round, Health.
(v) Current wage gap (own income minus reference income) and reference income controlled. The difference of the estimate of reference income and that of current wage gap
is negative, indicating negative effect of reference income.
(vi) *** p < 0.01, ** p < 0.05, * p < 0.1

Table A-1: Literature survey (continued)

36

Authors
Data characteristics
Dependent variable (i)Own-income effect
Reference income effect| Ref. income | > | Own income | ? (v)Hours worked
Age
Age squared
TenureTenure squared
Sex
Education
Marital Status/Children
Industry
Occupation
Job class/ supervisor
RegionTable and equation number
Method (vi)

Cell average income from dataset
(1)
(2)
(3)
Germany
U.K.
Denmark
H.
J.S.
J.S.
Positive
Positive
Positive
Negative
Negative (ii)
Positive
No ∗∗N.A. ∗∗
No ∗∗
No
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
2 (1)
6A (2)
1 (1)
O.P.
O.P.
O.P.

(4)
U.S.
J.S.
Positive
Negative (iii)
Yes ∗
No
Yes
No
Yes
No
Yes
No
No
No
Yes
No
No
3 (2)
OLS

Cell average income from external source
(5)
(6)
(7)
(8)
U.K.
U.S.
U.K. and U.S.
U.S.
J.S.
H.
H.
H.
Positive
Positive
Positive
Positive
Negative
Negative
Negative
Negative
Yes ∗∗Yes ∗∗
N.A. ∗∗Yes ∗∗
Yes
No
No
Yes
Yes
No
Yes
Yes
Yes
No
Yes
Yes
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
No
No
Yes
Yes
aNo
Yes
Yes
No
Yes
No
No
No
Yes
No
Yes
Yes
Yes
No
No
No
Yes
No
Yes
Yes
3 (3)
2 (1)
8 (4-8)
1 (1)
O.P.
O.P.
O. L.
OLS

(9)
Denmark
H.
Positive
Positive
No ∗ ∗ ∗
No
Yes
No
No
No
No
Yes
Yes
No
No
No
Yes
1 (1)
OLS

Subjective reference income
(10)
(11)
China
29 countries
H.
H.
Positive
Positive
Negative
Negative (iv)
N.A. ∗ ∗ ∗N.A.∗ ∗ ∗
Yes
No
Yes
Yes
Yes
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
No
Yes
No
Yes
Yes
Yes
6 (4)
1 (1)
OLS
OLS

(1) Ferrer-i-Carbonell (2005), Other controls; Year fixed effects, Familiy income, Number of children at home, Number of adults at home, Living together, Family condition,
Social means (familiy income, years of education, number of children at home, and adults at home), Cell average income defined by (education, age, and region).
(2) Brown et al. (2008), Other controls; Employer size, Race, Temporary job, Mean pay, Pay range, Union recognition at the work place, Cell defined by work place.
(3) Clark et al. (2009b), Other controls; Year fixed effects, Health problem, Plant size, Cell average wages define by plant.
(4) Cappelli and Sherer (1988), Other controls; Layoff, Compare mkt, Compare outside, Compare carrier, Manage info, Union info, Union dues, Participations, Part time, Job
influence, Change work, Change time.
(5) Clark and Oswald (1996), Other controls; Health dummies, Race dummies.
(6) McBride (2001), Other controls; Consumption habituation norm dummies, Race, Health dummies.
(7) Blanchflower and Oswald (2004), Other controls; Year fixed effects, Unemployed, Regional house price index, Household size, Race, Retired, Student, Keeping home.
(8) Luttmer (2005), Other controls; Value of home, Renter, Household size, Population, Non-metropolitan area, Fraction black in PUMA, Religion dummies, Race dummies.
(9) Clark et al. (2009a), Other controls; See neighbors often, Socio-economic groups, Number and ages of children, Years in Grid, Year fixed effects, Health problem.
(10) Knight et al. (2009), Other controls; Net wealth, Health status, Unemployed, Comparison variables, Community variables, Attitude variables, Race dummies.
(11) Senik (2009), Other controls; Family size variables, Owner ship of firms, Member of communist party.
(i) Abbreviations: J.S.; Job satisfaction, H; Happiness.
(ii) Reference income as “wage rank.”(iii) Reference income as “market wage.”(iv) Subjective comparison perceptions towards neighbors, family members, friends, and work colleagues.
(v) N.A. when magnitudes are not comparable. *** p < 0.01, ** p < 0.05, * p < 0.1(vi) Abbreviations: O.P.; Ordered probit, O.L.; Ordered logit.

0.50

0.50
.4647

0.40

0.40
.3496

DensityDensity

.3266

0.30

0.20

0.30

.273

0.20
.1499
.1198

.112

.0919

0.10

0.10

.0695
.043

0.00

0

1

2

3
Life happiness

4

0.00

5

0

1

2

3
Job satisfaction

4

5

Figure 1: Distributions of life happiness and job satisfaction (1=lowest.. 5=highest)

Workers’ own wages
Self−reported reference wages
Cell average wages (external source)

14

14.5

15

15.5

16

16.5

Log wages

Figure 2: Density of (log) wages, self-reported reference wages andreference wages computed from external sources37