Discussion Papers In Economics
And Business

Job Satisfaction and Happiness:
New Evidence from Japanese Union Workers
Adrian de la Garza

Atsushi Sannabe

Katsunori Yamada

Discussion Paper 08-10

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANJob Satisfaction and Happiness:
New Evidence from Japanese Union Workers
Adrian de la Garza

Atsushi Sannabe

Katsunori Yamada

Discussion Paper 08-10
March 2008
この研究は「大学院経済学研究科・経済学部記念事業」
基金より援助を受けた、記して感謝する。
Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANJob Satisfaction and Happiness:
New Evidence from Japanese Union Workers
Adrian de la Garza

Atsushi Sannabe

Yale University

Kyoto University

Katsunori Yamada∗Japan Society for the Promotion of Science and Osaka University
March 2008

Abstract
This paper utilizes survey data of Japanese union workers to provide new insights to the “happiness and economics” literature. A crucial item that distinguishes our empirical analyses from previous studies is the use of data on workers’ expectations of their peers’ wages.
With our data, we confirm that individuals report higher levels of
subjective well-being (SWB) when they perceive that their wages arehigher relative to their peers’. On the other hand, the traditional approach in the literature constructs relative wages from Mincer equations, thus presuming that individuals infer their peers’ wages the way
econometricians do. We argue that this method may be inappropriate.
Moreover, we address the issue of endogeneity of our subjective reference income measure employing an instrumental variables approach,and corroborate the causality from relative income to SWB. Additionally, we study the relationship between SWB measures and workers’
individual characteristics, and compare our results with standard findings in the literature for U.S. and European workers. In agreementwith these studies, women and married individuals seem to be happierthan their counterparts, men and single workers. However, we observea U-shaped relationship between education and happiness, which contrasts with findings for U.S. and British workers. Finally, we attempt
to explain these relationships in the context of the Japanese social
background. Keywords: subjective well-being; relative utility; subjective reference income JEL classifications: C25; D00; J28.
∗

Corresponding author: kyamada@econ.osaka-u.ac.jp1

1

Introduction

Happiness research is not new in the economics literature. Ever since theseminal work of Easterlin (1974) was published, increasing empirical evidence, particularly in recent years, has reiterated that, more than their own
consumption and income absolute levels, individuals care about how muchthey earn and consume relative to their peers.1 Nonetheless, the profession
has remained skeptical of happiness studies since the key variables measuring
people’s satisfaction levels come from self-reported assessments in surveys.
As Freeman (1978) puts it, “economists [are] leery of what purport to bemeasures of individual utility,” because they only measure what people sayrather than what people actually do. Lacking any revealed preference evidence, Easterlin stood alone for about two decades in his empirical defenseof Duesenberry’s (1949) relative utility hypothesis.
In the last decade, however, happiness research in economics was revived.
There seems to be a current consensus among economists that “[subjective
well-being (SWB)] is a meaningful concept” as a measure for utility (McBride
2001) and that “the concept of utility as subjective well-being is [. . . ] measurable from survey information with sufficient precision” (Hollander 2001).
And, although sociologists and psychologists have agreed upon the validityof SWB measures for many years, economists’ skepticism has only abated in
the last ten years, bringing about an increasing interest in the topic. 2
But in spite of the upsurge in the number of happiness studies in economics, there is still ample room for improvement. One issue that has received numerous criticisms and which is standard in the literature is theuse of imputed peers’ wages from Mincer equations as a measure of the individual’s reference income. Underlying this approach lies the premise thatpeople will infer peers’ wages in the exact same way econometricians do. Several authors (Manski 1993, Sloane and Williams 2000, Lydon and Chevalier2002) have argued that this may not be the case, although they have failed
to present any conclusive evidence to support their claims. On the other
hand, one further issue that remains pervasive in happiness research is thatthe vast majority of these studies utilize U.S. and European data, and there
have been few efforts to corroborate whether the relevant empirical findings
1

For excellent surveys of the happiness and economics literature, see Frey and Stutzer(2001), Frey and Stutzer (2002), and Frey and Stutzer (2003).
2
Thorough discussions of satisfaction and well-being measures in the sociology and thepsychology literatures are found in Clark and Oswald (1996) and Alesina, di Tella, and
MacCulloch (2004). For an excellent exposition of recent developments in the economics
literature and the validity and applicability of happiness measures in economics research,see Kahneman and Krueger (2006).

2

also hold in other regions of the world.
This paper addresses these two issues by analyzing the relationship between self-reported job satisfaction measures of Japanese union workers andtheir individual characteristics. One of the novel elements of our analysis is
the use of data on workers’ actual perceptions of their peers’ wages, rather
that imputed wages from Mincer equations, in order to test the relative utility hypothesis. Our results show that individuals report higher levels ofsatisfaction, both at work and in life in general, when their individual absolute income levels are higher. More importantly, however, we observe thatworkers are happier when they perceive that their own wages are higher relative to their peers’. Moreover, we show that this result does not hold when
we employ the standard reference income measure as imputed from Mincer
equations, attempt to explain away this discrepancy, and argue that this traditional reference income measure is inadequate in happiness studies of thissort.
Even after corroborating that our preferred relative income measure issignificant across various specifications of our job satisfaction and happinessequations, there is still the concern that comparison income may be endogenous. This issue has not been explored in the literature and we attempt tofill this gap by implementing an instrumental variables approach. Our IV estimates show that subjective reference income does have a causal impact onworkers’ levels of satisfaction at work, corroborating our previous findings.
In addition to our relative utility results, we compare the relationshipbetween job satisfaction and workers’ individual characteristics to the restof the literature. Our findings confirm several results that seem to be standard in happiness research in the U.S. and Europe, e.g., that women andmarried workers tend to report higher levels of satisfaction than their respective counterparts, men and single individuals. Nonetheless, we also observe
other relationships that differ sharply from findings in the U.S. and Europe.
In particular, we observe a U-shaped relationship between SWB and educational attainment, which contrasts starkly with the monotonically increasing
association found in previous studies. In a forthcoming paper, we attempt to
explain away this relationship and conclude that our findings are consistentboth with Clark’s (1997) “aspiration hypothesis” and with the importanceof social status and hierarchy in Japan, as suggested by Benedict (1946) and
Reischauer (1950).
The paper is organized as follows. In Section 2, we introduce our dataand explain how it differs from previous data sets on job satisfaction andhappiness. Section 3 presents the model and empirical framework we will
follow in order to investigate the connection between SWB and comparison
income, as well as with workers’ individual characteristics. In our regressions,3

we first proxy reference income by our data on workers’ expectations on theirpeers’ wages. Then, we employ the traditional reference income measureobtained from Mincer equations, and compare our results. In Section 3.3
we explore the issue of endogeneity of workers’ expectations of their peers’
wages using an instrumental variables approach. This analysis shows that
endogeneity is not a serious concern in our data and that our main results
are preserved. Section 4 studies the relationship between workers’ individual
characteristics and their levels of satisfaction at work. Finally, Section 5
concludes.

2

Data Description

Our data set comes from the Comprehensive Survey of Labor Union Members, which was designed and applied by a group of psychologists at theInternational Economy and Work Research Institute. It comprises data on
about 130,000 Japanese union members working in Japanese firms listed on
the Tokyo Stock Exchange (TSE) from 1990–2004. 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 to obtain
information on workers’ perceptions of their work environment.3 The data
set also allows us to control for individual demographic and socio-economic
characteristics, which include age, gender, educational attainment, tenure at
the current firm, annual income level, overtime hours worked, and workers’
expectations of their peers’ wages.4 It is important to note that the data set
does not track down individual workers over time, that is, the data set is nota panel. As is usual, the survey is answered anonymously.
The survey contains 238 multiple-choice questions in total. This highnumber of questions may seem both as a blessing and as a source of concern.
On the one hand, the benefit of having access to so much data on perceptions
at the workplace of so many workers is evident. On the other hand, however,
it is plausible to believe that, in their attempt to finish the questionnairequickly, respondents answer the questions without care. Also, the order inwhich the questions are posed may affect the answers provided. For instance,a worker may initially state that he is very satisfied with his work; nonetheless, upon reflecting on his relationship with his superiors and rememberinga few negative experiences, he may report a lower satisfaction level. Yet, oneof the reasons behind the many questions in the survey is precisely to ensurethat this does not happen. For example, one same question, though phrased
3
4

In Appendix 1 we provide a full list of question categories.
A complete list and description of the variables is presented in Appendix 2.

4differently, may be asked three or four times at different stages in the survey.
This and other methods employed in organizational psychology validate theresponses provided.
As is common in studies of this nature, we were unable to use the full data
set of 130,000 individuals due to missing data in some of the key variables
in our analysis, such as job satisfaction and income. Moreover, we uncovered a few inconsistencies that invalidated some of the answers provided.
For instance, 306 union members reported to have worked for their current
firm since before they were 14 years of age. Although this may very well betrue, we believe it is unlikely given that Japanese law requires individuals
to complete middle school, which in general occurs right after they turn 14.
Similarly, 751 union members reported being in managerial positions, being
under 30 years old, and having worked for the same company for less than 8
years. From our conversations with the survey administrators and firm leaders, we gathered that such situations are quite unlikely. Instead, it is possiblethat workers have been assigned to leadership positions in a few projects andhave mistakenly taken such instances as “managerial experience.” We thus
eliminated these observations from our data set. Finally, in accordance withthe definition of economically active population under current Japanese law,
we only include responses from workers who are between 15 and 60 years old.
After cleaning the data set and getting rid of the aforementioned inconsistencies, we are left with 94,504 individual observations.
It is important to highlight the potential sample selection issues that areat hand. In particular, respondents are union members who work as secretaries, salespeople, clerks, or blue-collar workers in manufacturing plants,
to name a few examples. The survey does not collect any information fromunemployed people or employees in high management positions.5

2.1

Individual Characteristics

In most dimensions, respondent groups seem to be quite homogeneous overtime. These descriptive statistics are shown in Table 1. Women represent
about 23% of the whole sample. Their average and median ages are 30
and 27, while those of men are 36 and 34 years, respectively. There is aconsiderable contrast between the proportion of married men and women in
5

Union members are regular employees working in positions like the ones describedabove as well as people in assistant management positions, which correspond to the second
lowest rank in the job ladder of corporate Japan. Under Japanese labor laws, workers inthese two positions in the hierarchy are allowed to become union members. However, once
they are promoted to assistant general manager, the next level in the chain of command,
they are required to leave their union.

5the sample. While two thirds of male workers are married, married women
only account for one third of all females. Regarding workers’ educational
attainment, an approximate 7% of the sample completed middle school, 46%
has a high school degree, 27% completed their college education, and 7%
pursued more advanced studies. The remaining 13% of the sample holds
other degrees, including technical and 2-year college programs.
As shown in Table 2, the industries with the highest representation in our
sample are the electronics manufacturing sector (38%), other manufacturing
companies (23%), the chemical/pharmaceutical industry (14%), and the foodsector (10%). The remaining 15% of workers are employed in sales, banking,and other smaller industries. Finally, the in-sample distribution of employees’
occupations is as follows: 14% work in sales/marketing, 24% have low-level
office or clerical positions, 29% are in research/design departments, and 34%
are blue-collar workers that work, for example, in manufacturing plants. It is
important to note that this distribution of workers across industries as well as
across occupations does not correspond to the actual distribution of workers
employed in all of the more than 2,200 firms listed on the TSE. For thisreason, we do not attempt to make any generalizations that would apply toall sectors of the Japanese economy. Nonetheless, given the large scope andsize of our data set,6 we believe that such lack of representativeness becomesa second-order issue. Moreover, as we will discuss later, most of our results
hold even when we confine our attention to a sub-sample of our whole dataset, which suggests that our results may be generalizable to the full set offirms under the TSE.

2.2

Subjective-Well Being and Income Data

Each respondent is asked to provide information on his own level of job satisfaction and the level of happiness in life from a list of 5 categories, where
1 corresponds to “least satisfied” and 5 denotes “most satisfied.” As can beobserved in Table 2, about half of both male and female union members report to be “moderately satisfied” (category 3). In the case of life happiness,both male and female workers report levels of happiness that are in general
6

With almost 100,000 observations and over 200 questions, our data set seems to be
one of the most comprehensive ones compiled thus far. For instance, Clark and Oswald(1996) and Clark (1997) utilize about 5,000 observations in their studies; Blanchflower and
Oswald (2004) analyze a data set containing 37,711 observations, which seems to be oneof the largest out there. The majority of the papers in the happiness and job satisfaction
literatures utilize sample sizes that vary within this range. One exception is Alesina,
di Tella, and MacCulloch (2004), which studies the relationship between inequality andhappiness in a sample of 123,668 individuals in U.S. and Europe.

6higher than their level of satisfaction at work. This difference between satisfaction at work and in life is particularly stark among women: while only
about 4% report to be “extremely satisfied” at work (category 5), 16% of female workers said to be “extremely happy” about their lives. This contrasts
with findings for British workers by Clark and Oswald (1996), which showthat about 50% of employees in the U.K. marked the highest job satisfaction
category in their 7-category scale.
The survey also requests that workers mark down their own level of income from a list of 9 categories, where category 1 denotes annual incomeof under 2 million yen (about US$17,000) and category 9 corresponds toannual income of over 10 million yen (about US$85,000).7 Table 3 shows
the distribution of SWB for each one of the 9 individual income categories.
Interestingly, this simple cross-tabulation shows that “poorer” workers inJapan tend to report lower levels of satisfaction relative to those who reporthigher income levels. This relationship is more easily observed in Figure 1.
First, we group workers reporting job satisfaction levels of 1 and 2 into a“least satisfied” bin. We do the same for individuals marking categories 4
and 5 and add them into a “most satisfied” bin. We then create three broad
income categories from the 9 different individual income levels: low (levels
1-3), middle (4-6), and high income (7-9). The figure confirms visually thispositive correlation between income and SWB. This preliminary finding isakin to Alesina, di Tella, and MacCulloch (2004)’s result that, in the caseof U.S. workers, “money buys happiness.” We shall explore these findings
further in the empirical analysis below.
An item crucial to our analysis, and which distinguishes our study frommost of the previous literature, is the information regarding the worker’s own
expectation about his peers’ wages. The question of interest is phrased asfollows, “What do you think the average wage of union members who areyour age, doing the same job, but belonging to other companies, is?”. The
answer to this question is also chosen from a list of 9 categories, and we refer
to it is as subjective reference income (SRI) in contrast to imputed wage
levels estimated from Mincer equations that are standard in the literature
and which are referred to as traditional reference income (TRI).
When plotting the distribution of SRI for each income category, we ob7

In the analysis that follows, we measure individual income as the mid-point in each of
the 7 intermediate categories, and use ad hoc values close to the minimum and maximum
income levels for the two extremes. Thus, respondents who reported categories 1, 2,. . . , 9 as their income 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.

7

serve two distinct patterns, which are highlighted in Figure 2.8 The firstthing to note is that workers tend to believe that their peers earn higher
wages than they do. Although this effect seems to be prevalent across all
income levels (with the obvious exception of the highest income class), it appears to be even more apparent for low income workers as suggested by theblue bars.9 Thus, low income workers are pessimistic about how much theyearn vis-à-vis their peers. Conversely, the opposite effect operates among
the better paid. The red bars imply that, when compared to the low andmiddle income categories, a much higher proportion of workers in the highest
income brackets believe that they earn more than their peers. This implies
that richer individuals tend to be more optimistic about their earnings relative to their own reference group. Of course, other individual traits may be atplay in this relationship, so it will be interesting to observe how this alleged
pessimism or optimism of workers impacts their self-reported job satisfactionlevels once we account for their personal characteristics.10

3Empirical Analysis:
Testing the Relative Utility Hypothesis

In order to examine the impact of absolute and relative income on workers’ satisfaction levels, we follow the literature and consider an ordered logit
specification that takes the form:
0
SWBi,t = yi,t · α + yi,t
· β + hi,t · γ + Xi,t · δ + εi,t ,(1)

where SWBi,t is our measure of subjective well-being reported by worker
0
i in year t, yi,t is the log of individual income, yi,tis the log of reference
income for worker i, hi,t corresponds to log average monthly overtime hours
worked, and Xi,t is a vector of covariates that include age, job tenure, and8

For the curious reader, the complete distribution of SRI for each income category is
shown in Table 4.
9
A Marascuilo test comparing the differences among these proportions across income
levels corroborates that these shares are statistically different from each other.
10
One example of how other factors may explain the observed distribution of SRI by
income level is the effect of the age-based remuneration system in Japan. As Lazear (1979)
explains, under the current system, young workers are paid less than their marginal product, which may prevent them from quitting their jobs until they get back their “legitimate”
rewards at a later stage of the tenure cycle. Of course, this would not explain why younger,
low-paid workers would expect their peers to earn more than they do if they acknowledge
that other workers in their reference group are also subject to the age-based remuneration
system. However, if they are unaware of this fact, this may explain the aforementioned
patterns in the data.

8

dummies for gender, educational attainment, marital status, job change, andmanagerial experience. We also include occupation and industry fixed effects
in the regressions and allow the stochastic disturbances εi,t to be correlated
within these two groups.
Before proceeding, a few remarks that will help us justify our chosen
specification are in order. The happiness literature has traditionally reliedon OLS, ordered probit, and ordered logit reduced-form specifications. Given
the nature of our dependent variable, we believe that ordered probit or ordered logit specifications are more appropriate than OLS. Although there isno consensus in the happiness literature on which of the two ordered nonlinear models is more appropriate, we stick with the logistic regression because the estimates are generally easier to interpret. In any case, all of ourresults still hold when a probit model is used instead.
A more subtle (and perhaps more important) point that would concern
theorists and structural economists is the apparent lack of a theoretical framework that would support the empirical relationship described in Equation 1.
The implied utility function from which such a relationship emerges is one
that has consumption c and leisure l as its main arguments, u = u(c, l). Even
if we accept that our job satisfaction measures may act as proxies for utility,
the question that arises then is, how do we justify the inclusion of variables
0such as yi,tand Xi,t in our regression equation? Theoretical studies such asCole, Mailath, and Postlewaite (1992), Corneo and Jeanne (1998), and Corneo and Jeanne (1999) may provide an answer to this question. These papers
analyze the role of social status under a general equilibrium framework andargue that, even though individuals may ultimately care only about consumption, other variables such as wealth rank in a society or facial beauty
may be relevant to determine utility levels. Thus, the authors contend that,in such cases, these variables should be incorporated into reduced form models of utility.11 While we do not pursue this issue any longer, we deemednecessary to address briefly these potential concerns before discussing ourempirical results.

3.1

Effects of Individual Income and SRI

The first relationship we explore is that between job satisfaction, SWBi,t ,0and both absolute and relative income, yi,t and yi,t
. In Section ?? we will
explore these same relationships when we utilize the TRI measure imputed
11

Corneo and Jeanne (1997) and Yamada (2007) provide similar arguments in theirmodels of conspicuous consumption, where the level of conspicuous consumption does notaffect utility directly.

9from Mincer equations. For now, however, our relative income measure corresponds to workers’ self-reported perceptions about their peers’ wages, also
referred to as SRI.
As shown in Table 5, an increase in the worker’s own wage is associatedwith an increase in job satisfaction. In contrast, an increase in the worker’s
expectation about his peers’ wages, holding his own wage constant, is associated with a lower satisfaction level.12 The first finding should not surpriseus as it is in agreement with standard economic theory.13 The latter result
provides empirical support to Duesenberry’s (1949) relative utility hypothesis and suggests that, irrespective of their own income level, workers areless satisfied when they perceive that their peers earn more than they do.
Both income effects are preserved even after controlling for workers’ individual characteristics such as age, job tenure, gender, education, and marriage
status, as shown in Column (2). The inclusion of industry and occupation
fixed effects, however, has a small impact on the estimate of the effect of
the worker’s own income on satisfaction, although both coefficients remainstrongly significant, as observed in Columns (3) and (4). Finally, Columns (5)and (6) show that all of these effects are even stronger when we use life happiness as our SWB measure.

3.2

Measuring Comparison Income
Under the Traditional Approach

We proceed now to test the relative utility hypothesis following the conventional method employed in the happiness literature. The comparison income
measure commonly used is referred to as Traditional Reference Income (TRI)
and is calculated by imputing peers’ wages from Mincer equations to obtain ameasure of what the “average” worker with given characteristics would earn.
Since the work of Kapteyn and van Herwaarden (1980) on interdependent
welfare functions, researchers have wondered what the adequate reference income measure should be, and the implicitly agreed-upon conclusion has been
12

Note that a positive (negative) estimate does not necessarily imply an unambiguous
increase (decrease) in the level our SWB measure. Instead, a positive (negative) sign
denotes an increase (decrease) in the odds that a given satisfaction category, out of thefive available, is reported. The direction of the marginal effects of an increase in wages on
the probability of reporting a satisfaction level will depend on the different cutoff points
for the underlying latent variable. For further details, see Greene (2002) pp. 736-740. See
also Clark (1997) p. 348.
13
Most empirical studies support this positive association between income and utility.
However, Clark and Oswald (1996) find weak empirical support for this relationship in theirdata of 5,000 British workers. Perhaps surprisingly, their results show that satisfaction ismore strongly correlated with relative income than with absolute income.

10

to compute the “average” wage earned by workers of the same gender, age,education level, and other individual traits. Nonetheless, underlying thisapproach lies the premise that workers will infer their peers’ wages in theexact same way econometricians do. A few papers, notably Manski (1993),
Sloane and Williams (2000), and Lydon and Chevalier (2002), have expressed
concerns that this is indeed the case, although they have failed to provide
evidence to the contrary. In what follows, we argue that, if individuals computed reference wages in accord with econometricians’ calculations, then thechosen measure of reference income should not matter and there should not
be any discrepancies in our results. We demonstrate below that this is not
the case.
In the construction of our TRI measure, the first step is to estimate our
Mincer equations. Table 6 shows the corresponding regressions of log real
wages on various individual characteristics commonly utilized in the literature, such as gender, age, age squared, tenure, tenure squared, marital status,
educational attainment, the log of overtime hours worked, and managerial
experience, as well as occupation and industry fixed effects. Additionally,
we included a dummy that signals whether the worker has taken an administrative role in union activities, which we use later to identify the effect of
imputed wages in our job satisfaction regressions.14 The results are not surprising: for instance, we observe a greater effect of higher education on log
real wages, as well as a positive and concave effect of experience. Married
workers earn higher wages while women earn less relative to men with similar
characteristics. To show that these coefficients have the expected signs, arestrongly significant and quite stable across specifications, we report various
specifications in Table 6. However, for the sake of brevity, we do not discuss these results any further and proceed to re-estimate our job satisfaction
regressions.
The results showing the effect of absolute and relative income on job satisfaction and happiness appear in Table 7.15 In order to ease the comparison
14

Given that both the wage and the SWB regressions contain mostly the same regressors,
we need at least one additional independent variable in our wage equation to identify theeffect of TRI on job satisfaction. In addition to this “union management” variable, we
also use firm fixed effects. We tried different specifications including parents’ income andchild status dummies. These last variables tell us what the worker’s last child’s schoollevel is: no child, before elementary school, in elementary or middle school, in high school,or married. We did not include these variables in the reported specifications due tovarious limitations in the data. For instance, we only have child status information forabout 55,000 observations. Nonetheless, all of our reported results are robust to thesealternative specifications.
15
The Mincer equation specification we utilized in order to construct our TRI measurecorresponds to that of Column (5) in Table 6, which includes occupation, industry, and11between the SRI and TRI results, in Column (1) we display our preferredjob satisfaction ordered logit specification from Table 5, utilizing SRI as our
income measure and including occupation and industry fixed effects, as well
as a time trend. Column (2) shows the exact same specification, except that
our reference wage measure corresponds to wages for the “average” worker
as imputed from Mincer equations, or TRI.
In comparing Columns (1) and (2), the most striking issue is the difference in size and magnitude between the two reference wage estimates. In
contrast with the SRI coefficient, the TRI estimates suggest that a rise in aworker’s peers’ wages would translate into an increase in his own satisfaction
at work. The first obvious source of concern is that, at an intuitive level,
this result does not make sense. If a worker perceives (or, in the case of
imputed reference wages, if he estimates the way econometricians do) thathis peers’ wages have gone up, it would be unlikely that he would feel moresatisfied. Another source of concern is that this coefficient is quite unstable
across specifications. Moreover, given that the coefficient on wages decreases
considerably once TRI is introduced, it is likely that these imputed wages are
just picking up unobservable characteristics of workers that may have little
to do with their perception of wages earned by their peers. Finally, we notethat the effects of other variables on job satisfaction are about the same,irrespective of the comparison income measure employed. The one notable
exception is the impact of age, which is U-shaped when TRI is utilized, akinto what Clark (1997) and Blanchflower and Oswald (2004) respectively findin the U.K. and in the U.S. Nonetheless, even this particular finding is notrobust across specifications.
Why are the results regarding the impact of relative wages on job satisfaction so different depending on whether we rely on actual self-reported
peers’ wages perceptions or wages constructed from standard Mincer equations? One possibility is that workers in our sample are “bad” forecasters
and for whatever reason they are unable to predict wages the way econometricians do. Using Mincer equations, econometricians may be able to predict
the wage of a worker with certain characteristics; but, what guarantees that
individuals will make “correct” predictions? Similarly, workers may consider
many other, perhaps unobservable, factors that econometricians don’t account for, which leads to “bad” forecasts. Thus, it should be the case that,
if we focus our attention on workers that make small prediction errors, theSRI and TRI results should converge.
To test whether this is true, we re-estimate the job satisfaction regressions
using a subsample of workers who make small prediction errors and display
company fixed effects, and a time trend.

12our results in Table 8. In this exercise, we consider “good” forecasters thoseworkers for which the absolute difference between their reported SRI and theimputed TRI lies within a 25% band around their own wage. We utilize thisconservative band as a first pass to see whether the estimates using either
comparison income measure look more similar. Surprisingly, as shown inColumn (1), the TRI coefficient remains positive but becomes insignificant
even at the 10% confidence level. The SRI estimate shown in Column (4),on the other hand, remains strongly significant and stable at around -0.60.
We take these results as an early indication of the instability of the TRI
estimates and the robustness of our coefficients when we utilize SRI as ourmeasure of comparison income.
To be sure, we then follow the same procedure and define “better” and“best” forecasters, except that the benchmark bands that we use to makethese distinctions are 10% and 5%, respectively. The results confirm ourearly suspicions. First, the TRI estimates switch signs, although they remain
insignificant at the 10% level. On the contrary, for the “better” forecasters,
the impact of SRI on job satisfaction remains strongly significant at the 1%
level. Even for the “best” forecasters, the SRI coefficient is still negative,
although imprecisely estimated. This is not surprising given that, when
focusing on this select group, we cut down the sample size by almost 90%,
which increases the standard errors of our estimates significantly.
What have we learned from this exercise? As expected, our estimatesusing both TRI and SRI converge. This is not surprising given that the solevariable that differed between the two regressions was our proxy for reference
income; when we focus on those observations for which the difference between
SRI and TRI is small, the results should ultimately converge. The two moreinteresting conclusions have to do with the great reduction in sample size
when we focus on the “best” forecasters and the instability of the results when
we impute wages from Mincer equations. Given that our “best” forecasters
comprise a little bit above 10% of the whole sample, the great reduction
in sample size suggests that econometricians have traditionally measured
reference income inadequately, focusing on a metric of relative income thatis foreign to about 90% of our sample. Similarly, the instability of the TRI
estimates only corroborates Manski’s (1993) and others’ criticisms about thevalidity of happiness studies that utilize imputed wages as comparison income
measures.

3.3

Endogeneity of the SRI Measure

One issue that we have not addressed is the possibility of endogeneity in
our SRI measure. Studies that explore endogeneity issues in happiness equa13

tions have been scarce, with the exceptions of Lydon and Chevalier (2002),
Brown and McIntosh (2003), and Gardner and Oswald (2007). However,these papers analyze the impact of other factors, such as absolute wages andhours worked, on satisfaction but are silent on the particular effects thatcomparison income has on this variable.
To our knowledge, no study has investigated the issue of endogeneity ofrelative income. Several possible explanations would justify such concerns.
For instance, Freeman (1978) and Akerlof, Rose, Yellen, Ball, and Hall (1988)
suggest that low satisfaction levels at work increase the probability of quitting. At the same time, when people have plans to change jobs, it is plausible
to think that they would expect better paid positions elsewhere, keeping theirhopes of finding a better job high. In addition to the reverse causality story,
it is also possible to think that omitted variables related to labor conditions
and that are hard to measure may be contaminating the estimated effect ofrelative income on job satisfaction. In this section, we attempt to fill thisgap in the literature implementing an instrumental variable (IV) approach.
Our proposed instrument is union management experience. To be eligible
as an adequate instrument, this variable should be correlated with our comparison income measure and uncorrelated with our outcome variable, job
satisfaction. First, union management experience should clearly influence
workers’ expectations of what their peers earn elsewhere since they are better informed of the actual level of wages for different workers across firms. In
Japan, union members participating in management positions within theirunion must attend shunto, a series of meetings held every spring in which
union leaders meet and discuss labor issues including wages and other factors affecting the work environment.16 Recalling that there is a tendency toreport higher SRI than own individual wages across all income categories, we
expect to see that workers in leadership positions within the union shouldreport lower levels of relative income than workers without union management experience, which would reflect their better knowledge of actual labormarket conditions.
Second, our instrumental variable should not be correlated with job satisfaction. We find no evidence that union management experience should
directly affect subjective levels of well-being since union leadership appointments are rather random instead of voluntary. Union managers are required
to step down after one or two years and there is no rent from such activities.
Hence, we believe that union management experience is a good candidate as
an instrument for SRI.
Table 9 shows the results of the first and second stages of our IV im16

For a detailed description of shunto, see Gordon (1982).

14

plementation. As expected, in the first stage union management experienceis negatively correlated with SRI. In the second stage, we observe that thenegative impact that SRI has consistently had upon job satisfaction remains.
This corroborates our earlier findings that, in addition to absolute income,how much workers earn relative to their peers is an important determinantof job satisfaction.

4

The Impact of Individual Characteristics
on Job Satisfaction

The happiness literature that has emerged in the last decade has attempted
to shed some light on the link between various SWB measures and individuals’ personal characteristics including gender, age, race, education, marital
status, among others. In this section, we investigate the effects of workers’
individual traits on their reported levels of satisfaction, and compare ourfindings with the rest of the literature. Given that the great majority ofthese other studies have utilized European and/or U.S. data, we hope that
our results will add to these studies and provide some insights regarding thegeneralization of these relationships to other cultures.
In the presentation of our findings, first we will comment on the resultsthat are in general agreement with the literature. It is not our intention
to exhibit these results as conclusive evidence of how these individual traits
influence job satisfaction. However, given that these estimates are in accord
with what other papers have found in the U.K., in the U.S., and in continental Europe, they are at least one step forward to become “stylized facts”
in the happiness literature. Next, we will present our new results. These
correspond to those individual characteristics that have not been explored in
the literature before or whose relationship with SWB measures is different
from that in other studies.

4.1

Results in Agreement With the Literature:
Happier Women and Happier Married Workers

Table 5 illustrates the results discussed in this section. The estimates inthis table correspond exactly to regressions of the form of Equation 1, which
we employed in our discussion of the impact of SRI on job satisfaction andhappiness.
First, women are considerably more satisfied at work than men. Thisfinding is much starker when we utilize life happiness as our SWB measure.
15

The results are robust across various specifications, even when we allow forindustry and occupation fixed effects. Clark (1997) derives similar conclusions for Britain. He shows that British women tend to report higher levelsof job satisfaction than men even though their jobs are generally worse in
several respects—such as pay or compensation for the same type of job. To
rationalize his finding, Clark recurs to his “aspiration hypothesis;” he argues
that women’s expectations in their workplace are generally lower than men’s,
which makes females happier, given that the gender satisfaction differential
disappears for the young, the more highly-educated, and women working
in male-dominated environments. Although there is no evidence of this in
our data set of Japanese workers, we will come back to Clark’s aspiration
hypothesis when we consider the relationship between job satisfaction andeducational attainment.
One other result that is quite robust in the literature is that married
individuals are more satisfied than unmarried ones. Again, this finding ismuch stronger when life happiness is utilized as our SWB measure. In fact,
the coefficient for “married” in the life happiness regressions is the highest
amongst all estimates, including those for the absolute and relative income
effects. This positive association between being married and satisfaction hasreceived support in Clark (1997) using British data, McBride (2001) using
U.S. data, and Alesina, di Tella, and MacCulloch (2004) who utilize datafrom the U.S. and 12 European countries. However, a couple of studies in
the psychology literature have found that, when individuals marry, happinessincreases for a year or two, and then goes back to pre-marriage levels.17 Thistransitory effect of ostensibly life-changing circumstances, like marriage, hasbeen called the hedonic treadmill. Since our data set does not track one same
individual over time, we are unable to see whether this positive impact ofmarriage on happiness is also temporary in Japan. Yet, irrespective of theduration of this effect, marriage is generally observed as providing a boost in
satisfaction at work and in life.18

4.2

The Role of Age, Tenure, Occupation,
and Education: Some New Evidence

Our results regarding the relationship between age and SWB are mixed.
As shown in Table 5, age has a positive effect on job satisfaction but has
17

See Kahneman and Krueger (2006).
To our knowledge, the only study that does not support this claim is Brown and
McIntosh (2003). This paper does not find any statistically significant differences in satisfaction levels reported by married and unmarried individuals working in the low wage
service sector in the U.K.
18

16

a negative impact on life happiness. These findings are quite stable acrossspecifications, even after controlling for industry and occupation fixed effects.
These two opposing effects of age on job satisfaction and life happiness are
surprising given that most of our conclusions derived from job satisfaction
regressions tend to be corroborated when we use happiness as our dependent
variable instead. Perhaps even more puzzling, however, is the fact that we do
not observe any non-linear effects of age on either SWB measure. This contrasts with the standard U-shaped relationship between age and satisfactionthat others have found, mainly in the U.S. and Britain (Alesina, di Tella,and MacCulloch 2004, Blanchflower and Oswald 2004). Unfortunately, we
have thus far been unable to uncover the reasons behind the discrepancies
between our results and those found in the rest of the literature.
Job tenure shows a very robust negative impact on both job satisfaction
and life happiness. However, this effect is non-linear, as suggested by the
positive coefficient of the square of tenure. Workers that have stayed in thesame company between 15 and 20 years experience the most dissatisfaction,
but after this point the negative effect of tenure slowly fades away. In fact,
for workers with more than 30 years of tenure, this effect becomes positive—
although this group represents barely 10% of our sample.19 Finally, it is a bit
surprising that, in spite of this negative effect of job tenure on satisfaction,
having changed jobs in the past also has a significantly negative effect.
Our regressions also shed some light on the relationship of SWB and otherjob characteristics such as overtime hours worked, occupation, and hierarchyin the company. As expected, longer workdays, reflected by a higher numberof overtime hours worked, has a negative impact on workers’ gratification in
their workplace. This is consistent with a worker’s utility function that is increasing in leisure. Regarding the worker’s occupation, researchers/designers
tend to report the highest satisfaction levels20 while blue-collar workers are
the most dissatisfied21 These results hold even after controlling for income
and workers’ individual characteristics. Finally, workers with an assistantmanager role are more satisfied both at work and in life. Given that wecontrol for income and other individual characteristics, we interpret this positive effect as “positional utility” that workers derive from being in a higher
19

One other paper that briefly discusses the relationship between tenure and job satisfaction is Dolan and Gosselin (1998). The authors also find a U-shaped relationshipbetween these two variables in a sample of 827 employees working in 34 car dealerships inNorthern Quebec.
20Interestingly, workers in the electronics industry—several of whom are researchers anddesigners—report the lowest satisfaction levels.
21
The occupation and industry dummies are not shown in Table 5 for the sake of brevity.
The omitted occupation category in these regressions is sales/marketing.

17echelon of the job ladder.
Finally, educational attainment seems to play an important role in SWB
regressions. A priori, if we believe that education acts as a proxy for earnings, we should expect these two variables to covary positively. Controlling for income, however, the expected association is unclear. To explore
this connection, we introduce six education dummies: middle school, technical degree, 2-year college, 4-year college, graduate degree, and other—with“high school” being the omitted category.22 Our preferred specification, Column (4) in Table 5, shows a U-shaped relationship between job satisfactionand educational attainment. Workers with a high school diploma appear tobe the most dissatisfied with their job conditions. In contrast, holding only
a middle school degree has a positive impact on workers’ satisfaction levelsin their workplace. This may seem a bit surprising given that workers witha middle school degree include high school dropouts whose families’ financial distress may have forced them to leave school early and take unwanted
positions in the labor market. But the individuals with highest satisfactionlevels are workers with 4-year college and graduate degrees, the latter being
the happiest at work. How do we explain this U-shaped relationship between
job satisfaction and educational attainment?
In the literature, the evidence regarding this relationship is mixed. For
instance, Clark (1997) finds that more educated individuals in Britain tend
to report lower levels of satisfaction. He then shows that SWB measures arenegatively correlated with the gap between aspirations and actual achievements. Thus, Clark’s “aspiration hypothesis” conjectures that individuals
with higher degrees have high expectations of themselves, which they later
find hard to fulfill.23 In contrast, Blanchflower and Oswald (2004) find thatyears of education are positively correlated with satisfaction levels in theirsample of U.S. workers.24
Hence, the relationship between education and happiness seems to be far
from a “stylized fact.” In our view, several opposing forces lie beneath thisassociation, and which of these forces exert the most influence on satisfaction
22

In our tables, we only report estimates for the middle school, 4-year college, and graduate degree dummies. We believe that, together with high school, these three categories
provide a nice coverage of the sequence followed in school by the typical Japanese worker.
Re-estimating all of our regression equations with a sub-sample of workers holding any ofthese four degrees leave our results unchanged. These workers constitute more than 85%of the whole sample.
23
For more details on the aspiration hypothesis, see also Ross and Reskin (1992) andWaldman (1994). In the psychology literature, Arvey, Carter, and Buerkley (1991) suggests that education diminishes job satisfaction by increasing occupational expectations.
24
Oswald (1997) reports similar findings, although only by means of simple descriptive
statistics.

18

will heavily depend on particular conditions of a country, such as the composition of the workforce by education level and even individual idiosyncracies
and traditions. Our preliminary analyses suggest that at least two main
forces coexist in Japanese labor markets and help to explain the association
between education and satisfaction: the aspiration hypothesis (Clark 1997),
on the one hand, and a strong emphasis on social status and hierarchy inJapanese culture (Reischauer 1950) on the other. Since we believe that theseissues are too complex to cover satisfactorily in this article, we explore themin detail in a forthcoming paper.

5Conclusions

The role of relative income in individuals’ utility functions has not been
researched thoroughly in the economics literature. Our results provide empirical support for Duesenberry’s (1949) relative utility hypothesis, thus confirming that individuals care not only about what they earn in absoluteterms, but about what they earn relative to their peers. Perhaps mainly due
to the lack of available data, other studies that have attempted to investigate the role of comparison income in job satisfaction or happiness equations
have traditionally calculated this reference metric as wages for the “average”
worker with given individual characteristics, imputing them from standard
Mincer equations. Our data on workers’ actual perceptions about their peers’
wages allows us to empirically corroborate the critics’ numerous objections
to the standard methodology. When we utilize imputed wages as a proxyfor comparison income, the effect of this variable on job satisfaction becomes
unstable across specifications. In contrast, the impact of subjective reference
income on happiness is robustly negative, which adheres to the economic
theory furthered by Duesenberry and others. Moreover, we conclude that
only about 10% of our sample of almost 100,000 workers predicts wages close(±5%) to what econometricians would forecast.
An additional contribution of our paper is to investigate the role of individual characteristics on job satisfaction and happiness among union workers
in Japan. Since the extant literature focuses almost exclusively on the U.S.,
the U.K., and continental Europe, our study of Japanese workers complements these papers nicely and sheds some light on what findings may beconsidered generally applicable to other cultures and which ones deserve further attention. For instance, our estimates confirm that women and married
individuals tend to be happier both at work and in life, results that are inagreement with what other studies have found in other countries. On thecontrary, we observe a U-shaped relationship between education and satis19

faction, high school graduates being the most dissatisfied among the various
education groups. Given the mixed evidence regarding the link between education and happiness in the literature, we believe that this issue needs tobe studied further.
In conclusion, we hope that our analyses make other researchers aware of
the need of collecting better data that allow us to investigate the factors that
contribute to individuals’ happiness. The possibility to amend old results anddiscover new ones should keep economists motivated to continue research inthis area. Even after we have all agreed that certain factors, such as educationor marital status, may have an important effect on happiness, we should makean effort to go beyond these simple associations and uncover the links thatmake these relationships work.

Acknowlegdement
We would like to thank the seminar participants at Yale University for theirhelpful comments. Of course, any remaining errors are the authors’. The
preliminary version of the paper was published at Economics Bulletin, 4(17),pp.1-21, 2005. Part of the research was financially supported by Japan Society for the Promotion of Science Research Fellowships for Young Scientists.

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23

FIGURE 1
Shares of Workers Reporting Low and High Job Satisfaction by Income Level
80

70

60

50
Least satisfied%

40

Most satisfied

30

20

10

Low

MiddleIncome LevelHighFIGURE 2
Shares of Workers Reporting SRI One Category Above and Below Their Own Income Levels
40
3530% reporting SRI one
category above own income

%

25
20

% reporting SRI one
category below own income

15
10
5
0
Low

MiddleIncome levelHighTABLE 1
Descriptive Statistics
Frequency%

Gendermale
female73,213
21,291

77.5
22.5

Marital status
singlemarrieddivorced/widowed

38,81754,946
741

41.1
58.1
0.8

Education
middle school
high school
technical school
2-year college
4-year college
graduate
other

6,511
43,7412,240
9,189
25,423
6,350
1,050

6.9
46.3
2.4
9.7
26.9
6.7
1.1

Industryfoodelectronics
chemistryother manufacturing
finance
sales
other

8,660
32,604
11,582
19,534
2,951
4,239
6,251

10.1
38.0
13.5
22.8
3.4
4.9
7.3

Occupation
sales/marketing
designer/researcher
office work
blue collar

13,10727,084
22,204
32,109

13.9
28.7
23.5
34.0

Income (in million yen)less than 2
2-3
3-4
4-5
5-6
6-7
7-8
8-9
more than 10

2,213
11,327
15,827
15,428
16,832
12,604
9,964
8,787
1,522

2.3
12.0
16.8
16.3
17.8
13.3
10.5
9.3
1.6

TABLE 2
Subjective-Well Being Shares by Gender (%)
Job satisfaction

Male
Femaleless satisfied
1
7.0
7.0

2
14.6
16.6

3
46.1
48.4

more satisfied
4
5
27.9
4.5
24.5
3.5

Total
100.0
100.0

more satisfied
5
9.9
15.5

Total
100.0
100.0

Life happiness

Male
Femaleless satisfied
1
10.0
5.8

2
12.8
8.7

3
33.7
29.5

4
33.6
40.4

TABLE 3
Job Satisfaction Shares by Income Level (%)
Job satisfaction
Income (in million yen)less than 2
2-3
3-4
4-5
5-6
6-7
7-8
8-9
more than 10
Total

less satisfied
1
2
10.6
16.4
9.8
17.3
9.2
16.8
8.0
15.6
6.6
15.1
5.2
14.1
4.8
12.9
3.3
11.6
4.1
11.2
7.0

15.0

3
50.3
48.3
46.0
46.2
47.4
46.8
46.3
45.5
39.1

more satisfied
4
5
18.2
4.6
20.5
4.1
23.7
4.3
25.7
4.5
27.0
4.0
29.6
4.2
32.0
4.0
35.1
4.5
41.7
4.0

Total
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0

46.6

27.1

100.0

4.2

TABLE 4
Distribution of Subjective Reference Income by Income Level (%)
Income (in million yen)less than 2
2-3
3-4
4-5
5-6
6-7
7-8
8-9
more than 10

1
39.2
2.2
0.3
0.2
0.2
0.2
0.1
0.1
0.3

2
44.3
49.0
6.8
1.1
0.3
0.2
0.2
0.1
0.1

3
9.9
37.9
47.3
11.0
2.8
1.1
0.7
0.6
0.7

4
1.6
7.1
32.5
33.5
11.4
3.5
1.3
0.5
0.5

5
0.9
1.7
9.0
35.9
31.8
16.2
8.3
3.7
1.4

6
0.5
0.4
1.8
12.0
32.2
28.4
18.3
9.6
4.6

7
0.3
0.2
0.6
3.0
14.8
29.8
27.6
19.8
11.5

8
0.5
0.1
0.3
1.3
5.1
18.1
37.4
45.0
33.4

9
0.6
0.2
0.2
0.3
0.7
2.3
5.9
20.3
47.0

Total
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0

TABLE 5
Ordered Logit Regressions of Job Satisfaction (1-4) and Happiness (5-6)
Dependent Variable
Job satisfaction
Log of real wage
Log of real reference wage

Happiness

(1)

(2)

(3)

(4)

(5)

(6)

0.870
[31.66]***-0.473[16.47]***0.742[23.53]***-0.522[16.31]***
0.091[4.88]***
0.024[2.67]***
0.000[1.05]-0.034[7.24]***
0.001[6.59]***-0.036[4.65]***
0.137[8.51]***-0.121[7.11]***
0.150[8.64]***

0.606[16.95]***-0.515[15.23]***0.078[3.63]***
0.023[2.38]**
0.000[0.55]-0.033[6.63]***
0.001[5.15]***-0.042[4.90]***
0.150[8.88]***-0.101[5.61]***
0.155[8.24]***

0.640[18.57]***-0.517[15.37]***0.076[3.59]***
0.021[2.19]**
0.000[0.47]-0.033[6.63]***
0.001[5.16]***-0.054[6.54]***
0.150[8.85]***-0.101[5.62]***
0.161[8.66]***

0.896[28.09]***-0.563[17.55]***0.822[44.21]***-0.029[3.15]***
0.000[1.64]-0.044[9.40]***
0.001[8.95]***-0.094[12.46]***1.014[64.56]***-0.066[3.84]***
0.013[0.79]

0.842[24.28]***-0.555[16.54]***0.775[36.92]***-0.030[3.04]***
0.000[1.70]*-0.044[8.85]***
0.001[7.74]***-0.096[11.95]***1.022[61.97]***-0.058[3.16]***
0.025[1.38]

0.046[1.94]*
0.206[10.09]***0.574[16.89]***0.052[1.92]*
0.095[4.07]***
0.359[9.25]***

0.071[2.74]***
0.090[3.85]***
0.360[9.31]***
0.017[11.28]***0.078[2.98]***
0.236[11.87]***0.283[8.99]***

0.123[4.33]***
0.188[8.29]***
0.281[7.85]***
0.007[4.57]***

FemaleAge in years
Age squared
Tenure in years
Tenure squared
Log overtime
MarriedJob changeManagerial experience
Education variables:
Middle school
Undergraduate degree
Graduate degree
Time trend
Occupation FE
Industry FE
Year FE

No
No
No

No
No
No

Yes
Yes
Yes

Yes
Yes
No

No
No
No

Yes
Yes
No

Observations

94504

91896

83233

83233

91896

83233

Robust z statistics in brackets* significant at 10%; ** significant at 5%; *** significant at 1%

(1)TABLE 6
Mincer Equations
Dependent Variable: Log real wage
(2)
(3)
(4)

(5)

(6)

0.069
[55.00]***-0.001[46.34]***0.022[33.01]***0.000[16.80]***0.069[53.28]***-0.001[44.87]***
0.021[30.78]***0.000[16.60]***0.068[51.76]***-0.001[44.24]***0.022[32.15]***0.000[18.53]***0.066[54.96]***-0.001[45.23]***0.018[29.14]***0.000[17.27]***0.066[54.96]***-0.001[45.23]***0.018[29.14]***0.000[17.27]***0.066[54.96]***-0.001[45.23]***0.018[29.14]***0.000[17.27]***-0.089[23.05]***
0.187[77.63]***0.251[68.18]***-0.230[96.07]***0.076[39.64]***0.029[32.18]***-0.077[19.35]***0.169[62.67]***0.231[57.41]***-0.232[89.30]***0.074[37.16]***0.025[25.93]***0.070[33.65]***0.005[2.93]***-0.081[21.25]***0.152[54.70]***0.193[45.70]***-0.242[87.93]***0.075[37.25]***0.033[34.36]***0.091[41.99]***-0.001-0.660-0.063[17.14]***0.114[44.51]***
0.131[33.23]***-0.237[91.87]***0.073[40.59]***0.042[42.56]***0.099[50.31]***
0.012[7.89]***-0.063[17.14]***0.114[44.51]***
0.131[33.23]***-0.237[91.87]***0.073[40.59]***0.042[42.56]***0.099[50.31]***
0.012[7.89]***

13.552[680.99]***13.594[660.29]***13.595[641.05]***13.484[521.95]***-0.063[17.14]***0.114[44.51]***
0.131[33.23]***-0.237[91.87]***0.073[40.59]***0.042[42.56]***0.099[50.31]***
0.012[7.89]***-0.010[8.00]***
33.323[13.43]***13.469[417.21]***

No
No
No
No

Yes
No
No
No

Yes
Yes
No
No

Yes
No
Yes
No

Yes
No
Yes
No

Yes
No
Yes
Yes

Observations
94504
89214
R-squared
0.71
0.71
Robust t statistics in brackets* significant at 10%; ** significant at 5%; *** significant at 1%

80573
0.72

80573
0.78

80573
0.78

80573
0.78

Age in years
Age squared
Tenure in years
Tenure squared
Education variables:
Middle school
Undergraduate degree
Graduate degree
FemaleMarriedLog overtime
Managerial experience
Union management experience
Time trend
Constant
Occupation FE
Industry FE
Company FE
Year FE

TABLE 7
Job Satisfaction Regressions Using Traditional Reference Income

Log of real wage
Log of real reference wage
FemaleAge in years
Age squared
Tenure in years
Tenure squared
Log overtime
MarriedJob changeManagerial experience

Dependent Variable:
Job satisfaction
(1)
(2)
SRI
TRI
0.640
0.282
[18.57]***[8.51]***-0.5170.557[15.37]***[6.86]***
0.076
0.230
[3.59]***[8.75]***
0.021-0.025[2.19]**[2.37]**
0.000
0.000
[0.47][3.09]***-0.033-0.048[6.63]***[9.23]***
0.001
0.001
[5.16]***[7.22]***-0.054-0.074[6.54]***[8.50]***
0.150
0.107
[8.85]***[5.96]***-0.101-0.109[5.62]***[5.92]***
0.161
0.124
[8.66]***[6.16]***

Education variables:
Middle school
Undergraduate degree
Graduate degree
Time trend
Observations

0.071[2.74]***
0.090[3.85]***
0.360[9.31]***
0.017[11.28]***0.130[4.80]***
0.000[-0.01]
0.252[6.20]***
0.015[9.79]***

83233

80573

Robust z statistics in brackets* significant at 10%; ** significant at 5%; *** significant at 1%
Note: All specifications include occupation and industry fixed effects.

Dependent Variable:
Job satisfaction
Log of real wage
Log of real reference wage
FemaleAge in years
Age squared
Tenure in years
Tenure squared
Log overtime
MarriedJob change
Assistant manager

TABLE 8Job Satisfaction Regressions Using TRI vs SRI for Workers Making Small Prediction Errors
TRI
SRI
(1)
(2)
(3)
(4)
(5)
Good
BetterBest
Good
Better
0.563
0.797
0.781
0.798
0.876
[11.44]***[9.92]***[6.74]***[16.42]***[11.26]***0.033-0.220-0.145-0.620-0.478[0.30][1.22][0.55][9.21]***[3.18]***
0.147
0.131
0.119
0.053
0.092
[4.31]***[2.49]**[1.58][1.80]*[1.86]*
0.011
0.020
0.024
0.040
0.032
[0.79][0.94][0.78][3.03]***[1.53]-0.000-0.000-0.000-0.000-0.000[0.14][0.25][0.04][1.89]*[0.70]-0.051-0.061-0.065-0.043-0.058[7.41]***[5.80]***[4.32]***[6.39]***[5.52]***
0.001
0.001
0.001
0.001
0.001
[6.21]***[4.92]***[3.04]***[5.46]***[4.69]***-0.074-0.060-0.052-0.062-0.055[6.88]***[3.76]***[2.26]**[5.89]***[3.46]***
0.118
0.093
0.064
0.144
0.105
[5.24]***[2.74]***[1.32][6.60]***[3.15]***-0.113-0.144-0.166-0.111-0.144[4.93]***[4.06]***[3.33]***[4.83]***[4.07]***
0.161
0.141
0.204
0.190
0.157
[6.65]***[3.91]***[4.02]***[8.30]***[4.48]***

(6)
Best
0.843
[7.34]***-0.368[1.48]
0.082[1.12]
0.035[1.15]-0.000[0.35]-0.062[4.12]***
0.001[2.92]***-0.048[2.09]**
0.075[1.56]-0.166[3.34]***
0.219[4.35]***

Education variables:
Middle school
Undergraduate degree
Graduate degree
Time trend
Observations

0.126[3.66]***
0.026[0.82]
0.300[6.05]***
0.015[7.31]***

0.162[3.03]***
0.056[1.15]
0.246[3.33]***
0.015[5.07]***

0.268[3.49]***
0.033[0.48]
0.233[2.25]**
0.016[3.74]***

0.093[2.77]***
0.080[2.64]***
0.363[7.55]***
0.016[8.08]***

0.145[2.74]***
0.080[1.69]*
0.274[3.78]***
0.016[5.39]***

0.253[3.31]***
0.056[0.83]
0.261[2.54]**
0.017[3.95]***

52460

23386

11821

52460

23386

11821

Robust z statistics in brackets* significant at 10%; ** significant at 5%; *** significant at 1%
Note: All specifications include occupation and industry fixed effects.

TABLE 9
Instrumenting for Comparison Income
Dependent Variable:
Log of real reference wage

Job satisfaction
Original-0.517[15.37]***SRI
1 stagest

Linear prediction of log of real reference wage

-6.187[3.09]***

Instrument: Union management experienceLog of real wage
FemaleAge in years
Age squared
Tenure in years
Tenure squared
Log overtime
MarriedJob changeManagerial experience

Job satisfactionnd
2 stage

0.640[18.57]***0.076[3.59]***
0.021[2.19]**
0.000[0.47]-0.033[6.63]***
0.001[5.16]***-0.054[6.54]***
0.150[8.85]***-0.101[5.62]***
0.161[8.66]***-0.007[4.60]***
0.512[117.62]***-0.085[32.26]***0.034[27.95]***0.000[20.13]***0.008[13.73]***0.000[13.24]***0.011[11.18]***0.007[3.80]***
0.010[4.63]***
0.005[2.65]***

3.543[3.45]***-0.397[2.34]**
0.217[3.15]***-0.002[2.91]***
0.012[-0.71]0.000[-1.04]0.008[-0.35]
0.187[8.40]***-0.048[1.73]*
0.199[9.31]***

Education variables:
Middle school
Undergraduate degree
Graduate degree
Time trend

0.071[2.74]***
0.090[3.85]***
0.360[9.31]***
0.017[11.28]***-0.022[6.19]***
0.047[17.50]***0.049[11.82]***-0.003[17.26]***12.769[37.01]***-0.040[-0.81]
0.349[3.63]***
0.627[6.00]***
0.000[-0.02]

83233

80573

80573

Constant
Observations

Robust z statistics (ordered logit) and t statistics (OLS) in brackets* significant at 10%; ** significant at 5%; *** significant at 1%
Note: All specifications include occupation and industry fixed effects.

Appendix 1
Question Categories in the Survey
Individual characteristics
Individual characteristics that the survey requests from respondents include:
sex, age, marital status, status of children, occupation, class of job, job
tenure, job change, experience in union management positions, educational
attainment, yearly income, households’ yearly income, monthly overtime
hours worked, and commuting time. In addition, workers are asked to distribute 100 points among five aspects of their lives according to the priority
or importance they give to each of these aspects. The five options are: family,
religion, work, friends/neighbors, hobbies and entertainment.

Attitude towards group activities
About 11 questions deal with workers’ attitudes towards group activities.
Workers answer all of these questions by providing a number from 1 to 5,
where 5 corresponds to being in complete agreement with the proposed statement. Examples of such questions are:• I want to actively participate in various groups and organization activities.
• I enjoy activities in groups and organizations.

Attitude towards union activities
This category includes about 10 questions asking respondents to evaluate
their interest in union activities. Examples of these questions include:• I am not interested in the labor union activities.
• The labor union is useful to me.

Sense of belonging
About 3 questions are included in this category. Respondents provide information regarding their sense of belonging in their company, the union, andtheir own family.

32

Relationship between workers and their companies
This category includes 3 questions and asks workers about their relationship with their company/factory and how much they care about the firm’s
productivity, efficiency and general success. Some questions in this categoryare:• I always think about the productivity of the company/factory and theefficiency of the management.
• I dream about the future of my company/factory and its work.

Social environment at the workplace and happinessThis category includes 9 questions on workers’ perceptions of their relationships with their work colleagues and about how satisfied they are with theirlives. For example,• When I feel troubled, I can reach out to my co-workers for help.
• In general, I am currently very happy with my life.
This last question is what we employ as our life happiness measure.

Union’s usefulness
This category includes about 40 questions that ask respondent to evaluate
the usefulness of the union with respect to (i) its ability to provide advice onvarious issues (legal, work-related, etc.); (ii) its ability to provide opportunities that showcase workers’ skills; (iii) helping workers achieve their goals;
and (iv) improving communication with management.

General impression on union activities
About 9 questions in the survey request that respondents give their general
impression regarding union activities. Questions include:• The labor union activity is not active.
• The labor union doesn’t have any influence on management’s decisions.

36Communication between company and union members
This category, including 4 questions, deals with the relationship between
workers and their firms, and how they feel about their company. For instance:• Workers’ opinions do not influence the company/factory’s activities.
• The workers really follow the company/factory’s management policy.

Communication between union and union members
Four questions in this category ask respondents to assess the amount andquality of communication between union members and union management.

General impression of workers’ activities in the workplace
Questions in this category (about 22 of them) ask workers to provide theirinsights on their activities at their workplace, including the visibility of theirachievements and their desirability to continue working for the same company
in the long run. For example:• My accomplishments at work are clearly visible to others.
• I want to be employed by this company/factory as long as possible.

Satisfaction
This category contains about 17 questions that ask workers how satisfied
they are with respect to: their supervisor’s leadership skills; the level andquality of social interaction with their supervisor and colleagues; their assessments of their own work by supervisors and colleagues; the ability to workin groups with colleagues; general work environment; promotion prospects;
their current wages; office equipment and infrastructure; working hours, benefit packages; social perceptions of the worker’s occupation; social perceptionsof the company. In addition, the survey asks workers to rate their generalexperience at work considering all work-related factors; we use the answers
to this question as our measure of job satisfaction.

Self-evaluations
About 7 questions in the survey ask workers to evaluate themselves and thework they do.
37

Fairness
Four questions in this category deal with the sense of impartiality at theworkplace and workers’ assessments on fairness of their wages and promotion
prospects. As our subjective-reference income measure, we utilize answers
about workers’ expectations of their peers’ wages.

Transparency
This category, including 3 questions, deals with transparency at the workplace in terms of the firms’ evaluations of the workers’ activities and therecruiting process. Questions include:• I know the results of my company/factory’s evaluations of my work.
• Through the labor union, I can opine on company’s decisions about
personnel changes and evaluations.

38