The Relative Income Hypothesis With and Without
Self-reported Reference Wages∗
Adrián de la Garza†

Giovanni Mastrobuoni ‡Katsunori Yamada ¶Atsushi Sannabe §Abstract
Based on self-reported reference wages of Japanese union workers we provide
new insights on the relative income hypothesis. The availability of a direct measureof peers’ wages overcomes one of main limitations of previous empirical tests of
the relative income hypothesis, i.e the subjectivity of the definition of referencegroup. Our data allows us to study the relationship between happiness and reference
wages using the following measures of reference group wages: i) based on predictedindividual wages; ii) based on cell averages from internal, as well as, iii) external
data; iv) based on colleagues’ average wage; and compare the results based onthese proxies with the results based on the “true”, self-reported, reference wage.
We show that the proxies that have been used in the literature do not introduce a
simple classical measurement error and that the bias can go in both directions. We
propose a simple IV strategy when the elicited reference wage is not available that
does not solve the bias but at least delivers an upper-bound of the true effect. We
also deal with the possible endogeneity of self-reported reference wage due to anunderlying pessimism that is correlated with life and job satisfaction.

Keywords: subjective well-being; relative utility; Mincer wage equation; comparison income

JEL classifications: D00; J28.

∗

We would like to thank Gani Aldashev, Prashant Bharadwaj, Michael Boozer, Bill Brainard, David
Card, Ray Fair, Erzo F.P. Luttmer, Hank Farber, Guy Mayraz, and seminar participants at Yale University for their useful comments. Ken Yamada is grateful for the research grant provided by the GCOE
program entitled “Human Behavior and Socioeconomic Dynamics” of Osaka University.
†
Department of Economics, Yale University, New Haven CT 06520-8281, US‡
Corresponding author: Collegio Carlo Alberto, giovanni.mastrobuoni@carloalberto.org, Via Real Collegio 30, Moncalieri (TO) 10024, Italy§
Graduate School of Management, Kyoto University, Yoshida Hon-machi, Sakyo-ku, Kyoto, 606-8501,
Japan¶
Institute of Social and Economic Research, Osaka University, Mihogaoka 6-1, Ibaraki 567-0047, Japan1

1

Introduction

Ever since the publication of Smith, economist have noticed that individuals care about
not only absolute levels of consumption and income but relative positions in their referencegroups. Using macro data the seminal empirical work of Easterlin (1974) supported therelative utility hypothesis in a framework of happiness study.1 Empirical investigation
with micro data was initiated by Clark and Oswald (1996) and summarized by Clark et al.
(2008). They argued that “the estimated coefficient on [reference income] variable in life
satisfaction equations is negative, and equal in size to the positive coefficient on [own]income, suggesting that life satisfaction is totally relative in income”.
One issue among previous studies on the relative utility hypothesis is how to construct appropriate measure of reference income. Clark et al. (2008) provided a summary of the literature, and argued that possible candidate methods include: i) basedon predicted individual wages (Clark and Oswald (1996); Sloane and Williams (2000);
Levy-Garboua and Montmarquette (2004); Senik (2004)); ii) based on cell averages frominternal (Ferrer-i Carbonell (2005)), as well as, iii) external data (Cappelli and Sherer(1988); McBride (2001); Luttmer (2005); Clark et al. (2009b)); iv) based on colleagues’
average wage (Brown et al. (2008); Clark et al. (2009a)); and finally the possibly “correct”, self-reported, reference wage (Knight et al. (2009)).2 This rich variety in proxywill be a corroborative evidence that choosing the right one is actually a difficult taskfor econometricians. Indeed, it has been authors’ arbitrary choices and a convincing
method to construct an appropriate measure has not been proposed. Also, with respect
to studies with econometrically-predicted comparison income, underlying the approach1

For excellent expositions of the validity and applicability of happiness measures in economics research,see Hollander (2001) and Kahneman and Krueger (2006).
2
Besides technical differences among different measures of comparison income, there would be a conceptual difference: some of comparison income capture the income of “people like me” while the others
capture the income of those with whom the individual comes into close daily contact such as family,
friends and work colleagues. (Clark et al. (2008)) Table 9 provides a brief summary of previous studies
for readers’ reference.

2

lies the premise that people will infer peers’ wages in the exact same way econometricians do. This presumption has been criticized by several authors (Manski (1993) and
Sloane and Williams (2000)) that this may not be the case, although they have failed topresent any conclusive evidence to support their claims.
This paper utilizes survey data of Japanese union workers to address the above mentioned methodological issue shared among the previous studies on relative utility hypothesis. Specifically, with the great merit that we have data on self-reported cardinal measureof reference income, we compare our results based on this “correct” reference wage withthe ones obtained from the other four types of “imposed” measures described above.3 We
also show that the proxies that have been used in the literature do not introduce a simple
classical measurement error and that the bias can go in both directions. We propose a
simple IV strategy when the elicited reference wage is not available that does not solve
the bias but at least delivers an upper-bound of the true effect. We also deal with thepossible endogeneity of self-reported reference wage due to an underlying pessimism thatis correlated with life and job satisfaction.
The paper is organized as follows. We introduce our dataset in the next section. In
section 3 we provide our empirical tests on the relative utility hypothesis. We also provide
a theoretical argument and related empirical results about IV strategy when the elicitedreference wage is not available. Section 4 provides concluding remarks.
3

As far as we notice, Knight et al. (2009) is the only published work with subjectively reported reference income measures. Their measures, however, are ordinal ones and cannot be used for directcomparisons with the results from Mincerian reference wages, which is one of the main topics of thepaper.

3

2

Data

2.1

Description

Our data set comes from the Comprehensive Survey of Labor Union Members, which was
designed and applied by a group of psychologists at the International Economy and Work
Research Institute. It comprises repeated cross-sections on about 130,000 Japanese 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.4 The data set also
allows us to control for individual demographic and socioeconomic characteristics, which
include age, gender, educational attainment, tenure at the current firm, annual wage level,
overtime hours worked, and workers’ expectations of their peers’ wages.
After cleaning the data and getting rid of some inconsistencies, we are left with approximately 90,000 observations. Table 1 shows some statistics that describe our data
set. Workers in our sample are young with an average age of 35 years. Their average
tenure is 14 years, which suggests relatively low mobility in the Japanese labor force.
Moreover, union workers in Japan seem to be well-educated in general, as 46 percent ofthem have graduated from high school, 10 percent have some college experience, and 34
percent have completed their university-level degree. We also observe that 58 percent ofworkers are married and work an average of 23 hours of overtime per month. All of these
individuals are regular full-time employees and union members. About one third of themhold blue-collar positions and close to one fifth of them perform some managerial rolein the company. These numbers do not intend to provide an accurate depiction of therepresentative Japanese worker since the survey was administered exclusively to union
4

The full list of question categories is available in the working paper version of this paper; seede la Garza et al. (2008), Appendix 1.

4

members employed by major public companies, as described above. This implies that theresults below may not be generalizable to the entire Japanese labor force—for instance,
our data set does not cover employees in high administrative positions as they are notallowed to take part in unions. Nonetheless, due to the large size of our sample and thebreadth of coverage of 62 firms across a variety of industries from food to electronics tofinance, we believe that this data set does reflect significant trends of the whole Japaneselabor market.
One matter to keep in mind, however, is that our data undersamples women in theJapanese labor force. According to the World Bank’s World Development Indicators data,
women represent about 40 percent of the country’s working population; in contrast, theshare of female participation in our sample is only 23 percent. This issue is important,
not particularly because of the different gender breakdown in our sample relative to thatof the Japanese labor force, but because male and female workers differ significantly alongother dimensions, as shown in Tables 2 and 3. For instance, while 39 percent of the malesubsample obtained a college degree, only 16 percent of the female group achieved thisgoal. Interestingly, the share of married men is twice the proportion of married women(66 percent vs. 33 percent). Since, as discussed below, the literature has found significant
correlations between various subjective well-being measures and individual characteristics
such as gender, educational attainment, and marital status, pooling the male and female
subsamples in our analysis may lead to results that would differ had we considered these
two groups separately. We will thus keep this distinction in mind and will bring this issue
up when necessary.

2.2

Subjective-Well Being

Each respondent is asked to provide information on his own level of life and job satisfaction
from a list of 5 categories, where 1 corresponds to “least satisfied” and 5 denotes “most5satisfied.” The first thing to note is that there are significant differences between these
two subjective well-being measures. First, the correlation between the two is a mere 27
percent. As Figure 1 suggests, the distribution of life happiness is more spread out andmore skewed to the left than that of job satisfaction. Since the literature that investigates
the relative utility hypothesis has utilized a wide variety of subjective well-being measures,the availability of these two variables makes possible to test whether our results are robust
to the use of alternate metrics for satisfaction. For brevity, our empirical analysis will
utilize life happiness as our dependent variable. However, we will provide some additional
discussion when the use of job satisfaction as an alternative proxy for subjective well-being
leads to different results.

2.3

Workers’ Own Wages and Relative 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. In the analysis that follows,
we measure individual wages as the mid-point in each of the 7 intermediate categories, anduse ad hoc values close to the minimum and maximum wage levels for the two extremes.
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.
Table 4 shows typical log wage regressions for men and women, separately. The firstthing to notice is the very high R-squared one obtains using Japanese union workers.5 For
male workers controlling for age, tenure, education, hours worked, and marital status gives
5This feature depends only marginally on wages being reported in classes. When we smooth wages
adding a disturbance term that is uniformly distributed between each cutoff point the R-squared dropby a maximum of 5 percentage points.

6

an R-squared of 66 percent. A similar regression using US data would probably generate
an R-squared which is at least half that size. Across all specification the variation of log
wages explained by the same observable characteristics for women is approximately 10
percentage points smaller than for men but still much larger than an equivalent regression
using US data would give. Adding age and tenure dummies adds approximately another5 percentage points to the R-squared, while firm dummies explain another 3 percentage
points of the variation for men and 4 percentage points for women. Explaining a largepart of the variation in wages minimizes the concern that wages might themselves dependon happiness, and thus be endogenous. The Japanese labor market is based on a very
strict seniority system. The correlation between log-wages and age for the whole sample
is 69 percent, while during the same years the Japanese surveys was conducted based on
the Current Population Survey unionized workers of a the same group show a correlation
between age and log-wages of just 21 percent. If job and life satisfaction do not vary
discontinuously across age and tenure other than through tenure one can use age andtenure dummies as an instrument for wages. We pursue this strategy in Section 3.

2.4

Different measures of Reference Wage

One of the main advantages of our data is that they allow to replicate several measuresof reference wages used in the literature and to compare them to the elicited one. For the
elicited reference wage we use the information from the question phrasing as “What do
you think the average wage of corporate employees who are your age and doing the samejob, is?” The answer to this question is also chosen from a list of 9 categories, and werefer to it is as the “truly perceived” reference wage (w̄ ∗ ) in contrast to predicted reference
wage k (w̄ k ), where k denotes the method that is used to define the reference group. We
use a continuous version of the variable like we did for the individual wage.
The reference wage from external data comes from the Basic Survey on Wage Structure7

(BSWS)” released by the Ministry of Health, Labour and Welfare.6 The cell is definedover gender (2), age category (9), education (4), and year (14). We use BSWS information
on private companies with more than 1,000 employees.
Figure 2 shows density of (log) wages, self-reported reference wages and reference
wages computed from external sources. From the figure we can see that two measuresof reference income capture the pessimistic tendency among Japanese workers that theirpeers’ are earning more than they do. Table 5 shows the descriptive statistics of thedifferent measures of reference wage that have been computed. According to the table,
the pessimistic tendency amongst Japanese workers can be estimated around 8 percent(15.484 – 15.398).7 The log average of the external reference income is even higher thanthe log average of self-reported one by 2 percent. The reason for this is that in BSWS
data information on “assistant general manager” (ka-cho in Japanese) is included whilein our data set employees are non-supervisory or assistant managers.

3

Subjective Well-Being Regressions Using Self- Reported Reference Wages

Dividing workers’ earnings into three categories, low, medium and high only 50 percent
of workers in the lowest category reports being satisfied in life, while this number goesup to approximately 60 percent for workers with medium earnings and 70 percent forworkers with high earnings. On the other hand one might assume that relative rather
than absolute wages matter. Figure 3 does indeed show that workers with higher levelsof life and job satisfaction tend to exhibit earnings that are larger than their perceived
reference wage. Whether own wages matter more or less than reference wages seems to
6

The data can be found of the following website www.mhlw.go.jp/english/database/db-l/.
The average of our individual predicted wages is slightly higher than those of the other proxies ofreference income generated from the data set. This happens because we loose about 8,000 observations
due to missing industry information.
78

be an empirical question.
In order to examine the impact of absolute and relative wages on workers’ subjective
well-being, researchers typically estimate the following regression:

SWBi = α1 yi + β1 ȳi + x′i γ + ui ,(1)

where SWBi represents a measure of worker i’s subjective well-being, such as happinessor job satisfaction. This proxy for utility is assumed to depend on the worker’s wage, yi ;8a 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 other socio-demographic factors.
As usual, the term ui corresponds to an idiosyncratic error term.9
One crucial matter that the empirical literature investigating the relative utility hypothesis has faced relates to the difficulty in obtaining an appropriate reference wage
measure. First, it is not obvious who belongs in the comparison group. Do workers
compare themselves to other workers in the same company? In the same industry? To
their relatives and friends? To their neighbors? To people of their same age and educationlevel? To workers of the opposite sex? The possibilities are endless. A second and perhaps
more subtle issue is that, ideally, the econometrician would like to have information on
workers’ perceptions about their peers’ wages but such self-reported beliefs are typically
8

In Section 2.3 we showed that most of the variability of Japanese union wages can be explained by
the typical regressors used in Mincer type regressions. Later we will show how to deal with the possibility
that wages are endogenous. Unless otherwise noted, all wages are in logs.
9This specification can be thought of as a reduced-form version of a standard utility function of theform:
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. Thesestudies find that, 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 Eq. 1. For instance, see Cole et al. (1992), Corneo and Jeanne (1997), andYamada (2008).

9

unobserved.
To solve this issue, researchers have followed mainly two approaches. The first one
recurs to Mincer equations to predict the wage of a worker with certain individual characteristics, and then uses this predicted value as the reference wage in an equation suchas 1. The second one computes wage averages within group cells, where the groups are
well-defined according to given workers’ characteristics. One obvious problem with thesemethodologies is that workers do not necessarily “compute” reference wages or even define
comparison groups the way econometricians do. Moreover, these approaches imply thatthe comparison wage is some sort of average of other workers’ wages that share similarsocio-demographic characteristics, and thus ignore other factors, such as a worker’s inherent pessimistic or optimistic attitude, which may influence the worker’s perception of
who her peers are and what they earn.10
In what follows, we investigate empirically the criticisms that the literature has raised
against both the use of Mincer equations and the use of cell-averages to compute reference
wages. One advantage that we have over previous studies is that our data set contains
information on workers’ perceptions about their peers’ wages. We take these self-reported
reference wages to be the “correct” benchmark that workers use to compare themselves
against their peers, and compare our results to the findings that we would have obtained
had we followed the alternative methodologies that Manski (1993), Lydon and Chevalier(2002), and others have deemed potentially flawed.
Before proceeding, a couple of remarks are in order. First, it is important to note thatreferring to this self-reported measure as the “correct” reference wage does not imply that
our approach solves all of the problems that have afflicted the aforementioned alternative
methodologies. For instance, by asking workers to report what they believe that other
employees with their same characteristics and in the same company earn, the survey
restricts workers to define their reference group to be precisely people within their same
10

See Manski (1993) and Lydon and Chevalier (2002) for a more thorough critique of these approaches.

10company. Still, on a more regular basis, workers may compare themselves to relatives,
friends, or colleagues in other firms within the same industry or even in other industries.
Moreover, the relevant reference group may not be stable over time. For instance, a recent
graduate may compare herself to other recent graduates; but a person who has been out
of school for 10 years may compare herself to former classmates and colleagues, to people
in her same cohort and within her same tenure range, to supervisees and supervisors,and even to former selves. Nonetheless, we believe that empirical tests of the relative
utility hypothesis that make use of reference wage data as provided by workers themselves
should be regarded as a superior alternative to analyses that construct comparison income
measures from Mincer equations or cell averages.
A second issue to bear in mind is that a worker’s belief of her peers’ wages may beendogenous as a relatively happier individual may have a more optimistic view of what
her colleagues earn relative to herself. Conversely, a pessimistic attitude in general may
lead a worker to think that she is underpaid with respect to her peers. With panel data,
one could difference out time-invariant characteristics that are inherent to a worker, suchas pessimism, and obtain unbiased estimates of the effect of absolute and relative wages
on happiness. Unfortunately, our data set does not allow us to track individuals over time.
However, our survey does ask two questions that are likely to capture workers’ pessimistic
attitudes along two different dimensions. The first question asks whether workers believethat their colleagues would help them if they were in need; the second question asks them
if they are satisfied about the possibility of promotion within their company. Controlling
for these two effects should capture at least some of the inherent pessimism or optimism
of a given worker, which would in turn give us a more precise estimate of the reference
wage effect on our subjective well-being measures.

11

3.1

Empirical Tests of the Relative Utility Hypothesis

This section discusses our estimation of Eq. 1 using happiness as our dependent variable
and self-reported wages as our reference wage measure. We denote this possibly “correct”
comparison measure as ȳi∗ to distinguish it from the imperfect proxy that the literature has
constructed using the Mincer and the cell-average approaches, which we denoted ȳi . As
discussed in Section 2, there are important differences between male and female workers
in almost every dimension from wages to hours worked to educational attainment, which
justifies our estimation of Eq. 1 for men and women separately. We will then show thatpooling the male and female subsamples does not have any significant qualitative impact
on our findings. Also, the errors ui are allowed to be correlated within firms and, as
will be shown in our robustness tests, different clusterings do not substantively affect ourconclusions.
The empirical results in Table 6 corroborate the validity of the relative utility hypothesis. The absolute wage coefficient is consistently positive across specifications andis always statistically significant at the 1 percent level, which implies that workers’ life
satisfaction increases as their wage goes up. This is akin to Alesina et al. (2004)’s findingthat “money buys happiness”. Holding their wages constant, however, individuals tend
to report lower levels of satisfaction when they perceive that their peers’ wages are higher.
These results hold for both the male and the female subsamples. It is important to notethat the effect of absolute wages is stronger than that of relative wages; that is, if there
were an across-the-board wage hike of, say, 10 percent, that was also reflected on worker
i’s perceptions of her peers’ wages, the positive effect of worker i’s wage increase on happiness would outweigh the negative impact that an increase on her peers’ wages has onlife satisfaction. This finding is confirmed by an F -test that rejects the null hypothesisthat the sum of the coefficients on absolute and relative wages is equal to zero. Anotherimportant result is that the absolute value of the coefficients on both absolute wages and12reference wages are between 35 to 50 percent higher for males than they are for females.
Even though we only show one “representative” specification for the female subsample.
in column 6, we observe that these gender differences are consistent when we separately
run the same specification on the male and the female group.
The results also show that individual characteristics explain a significant amount of
the variation in reported levels of life satisfaction. For instance, in the case of males, controlling for age, tenure, educational attainment, marital status, and dummies for whether
the worker has a blue-collar position and whether it performs any managerial tasks inthe company, increases the R-squared from 2.2 to 7.1 percent. As expected, given thatthese covariates have proven to be important determinants of wages, their inclusion has asignificant impact on the absolute wage effect on happiness. In particular, accounting forthese individual characteristics causes the impact of wages on life satisfaction to decrease
from 0.69 to 0.56, a 19 percent decrease. Moreover, workers that tend to be more pessimistic about the helpfulness of their colleagues and about the prospects of promotion
within their company tend to report lower levels of happiness. Thus, the inclusion ofthese two pessimism variables leads to a further decrease in the impact of wages on life
satisfaction, from 0.56 to 0.47, an additional 16 percent drop. Such pessimistic attitudes
also takes a toll on the reference wage coefficient, as the effect of this variable on happiness is reduced (in absolute value) from -0.39 to -0.30, a 23 percent drop relative tothe no-control specification in column 1. One additional thing to note is that these two
pessimism variables by themselves explain an additional 4 percent of the variation in life
satisfaction, which corroborates the relevance of those characteristics that are inherent to
the individual worker in determining his reported level of subjective well-being. Overall,
we conclude that pessimism about colleagues’ helpfulness at work and the possibilities
of job promotions has large and significant negative effects on life satisfaction, and thatthese effects are of similar magnitude for both men and women.

13One possibility that may have an impact upon the absolute and the relative wage
coefficients is the average level of wages and their dispersion across the firm. On the
one hand, higher company’s average wages may be a proxy for job amenities. They may
also have a positive effect on a worker’s satisfaction level if we believe they provide asignal about her potential to rise within the firm’s wage ladder (Clark et al. (2009a),
Senik (2004), Hirschman and Rothschild (1973), Manski (2000)). Thus, if the worker
believes she has greater possibilities of increasing her compensation in the future, thenegative impact of higher peers’ wages would decrease in absolute terms—which would
then imply that omitting average wages in the happiness regression would overestimate
the reference wage coefficient. On the other hand, greater wage dispersion within the firm
may also have a significant impact both on satisfaction and on the effect of absolute andrelative wages on happiness. For instance, if workers know that there is greater variance incompensation packages offered by their company, that may influence how they feel about
their position in the wage spectrum relative to their colleagues. We explore these twoissues by controlling for the log of average wages and the interquartile range of log wages
within the firm. We observe that higher average wages in the firm has a positive effecton reported levels of life satisfaction for male workers, but not for females. The converse
is true of the wage interquartile range—greater wage dispersion has a negative effect onhappiness for women but this effect is insignificant for men. Additionally, the inclusionof these variables does not affect substantially the impact of absolute and reference wages
on life satisfaction for either subsample; that is, the relative utility results still hold. Thisis also true for the pooled sample, although in that regression the coefficients on mean
wages and the wage interquartile range are both statistically insignificant.
In sum, the findings reported in Table 6 seem to suggest that workers who earnhigher wages are indeed happier. They also show that individuals who perceive that theircolleagues earn more tend to report lower levels of satisfaction. A crucial point that has not14been thoroughly discussed in the literature, however, is that, although both the absoluteand the relative wage coefficients on happiness are strongly statistically significant on
a consistent basis, the size of these effects is relatively small. Take for example thecoefficients reported in column 1, which are the largest in absolute value among thedifferent specifications. Even in this case, the coefficient on absolute wages of 0.69 implies
that a one-standard-deviation increase in the wage of the average worker—equivalent to
a 38 percent hike—would increase happiness by 0.27 points in a 5-point scale, or 8.2
percent increase relative to the mean. It is somewhat surprising that such a relatively
large wage increase would have such a small impact on a worker’s life satisfaction level,although this perhaps lends support to our earlier point that worker-specific characteristics
constitute some of the most pertinent determinants of happiness. We do not believe thatthese relatively small coefficients should justify the dismissal of subjective well-being andreference wage studies. Nonetheless, we do believe that the literature should pay more
attention to the magnitude of these effects.

3.2

Robustness Checks for Self-reported Reference Wages

For brevity we show our robustness check for the male sample only. Table 7 shows that
our main results, shown in column 1, are relatively similar when we use job instead of lifesatisfaction as a measure of wellbeing. Notice that the sample is slightly smaller sample
since industry information is missing for approximately 6,000 men. The coefficients are
smaller in absolute terms, but even for job-satisfaction own wages seem to matter morethan reference wages. In column 3 we add year and industry dummies and the results do
not change.
Next we address a possible endogeneity issue: workers might choose their referencegroup through residential location, and thus it might just be that people who are happy
move to poorer neighborhoods, leading to the negative relationship that we observe, or15that more wealthy neighborhoods generate positive externalities, like less crime, betterschools, etc, which has a positive effect–and thus is positively correlated–with measuresof wellbeing. Ideally we would like to control for neighborhoods fixed effects but suchinformation is not available. Instead column 4 controls for company fixed effects. Our
companies are quite large and might have more than one establishment,11 and corresponding fixed effects might average across different location. Nevertheless, it is comforting that
controlling for company fixed effects leaves our results practically unchanged.
In column 5 we perform a horse-race between self-reported reference wage and thereference wage from external data (see Section 2.4). The coefficient on the externalreference wage has the right sign but is not significantly different from zero, while again
the coefficient on the self-reported reference wages is practically unchanged. In column
6 we address the endogeneity of the wage using tenure dummies and age dummies as
instruments. The identifying assumption is that thanks to the Japanese seniority systemwages vary discontinuously over time due to either age effects or tenure effects, while age
and tenure affect happiness independently only linearly.12 The IV coefficient for both
wages tend to be larger than the OLS ones.
Column 7 concludes our robustness checks. In that column we use an ordered probit
instead of the OLS. The coefficients are not directly comparable with the OLS ones
but the relative size of the coefficient on wages and the one on reference wages can becompared and the results are basically unchanged.13 This result is in agreement withfindings by Luttmer (2005) that the use of least-squares estimators in happiness studies
does not impact negatively the general conclusions, even when ordered probit or logit
models would be methodologically preferred given the hierarchical and non-linear nature
11

We observe approximately 90,000 workers and 62 firms, which means that each company has on
average 1,500 workers.
12
Adding the squared value of age and tenure does not alter the IV results but the F-statistic of the
instruments becomes very small.
13
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.

16

of the dependent variable.

4

Testing the Relative Utility Hypothesis when SelfReported Reference Wages are Unavailable

Most times data on perceived and self-reported reference wages, denoted with a star inthis paper, are unobserved. What researchers usually use as reference wage isȳi = α0 + β0 ȳi∗ + ǫi ,(2)

a mismeasured version of the individuals’ “truly” perceived reference wage. Notice that
we are only imposing linearity, while, unlike in the classical measurement error problem,
we allow the scale of the two measures of reference wages to be different. This represents ageneralization of the classical measurement error model, where α0 = 0 and β0 = 1. If, forexample, people with low wages systematically perceive the reference wage to be larger
then actual data would suggest y ∗i > y i for low levels of y, and thus α0 would be larger
than 0 and β0 smaller than 1. Like in the classical measurement error model we assume
that the error term is uncorrelated with the true reference wage. Substituting Eq. 2 into
Eq. 1 we get that

SWBi = α1 yi + β1 (β0 ȳi∗ + ǫi ) + x′i γ + β1 α0 + µi + ui ,(3)

In such a model a bias arises as a function of, both, the variance of ǫi and of the
distance between β0 and 1. In particular, assuming for simplicity that there are no other17regressors, the probability limit of the OLS estimate of β1 is
plimβb1ols =β0 COV (ȳi∗, SW Bi )COV (β0 ȳi∗ + ǫi , SW Bi)=
V AR(β0 ȳi∗ + ǫi )β02 V AR(ȳi∗) + V AR(ǫi )β0= β1 2β0 + V AR(ǫi )/V AR(ȳi∗ )(4)Thus, unless β0 = β02 + V AR(ǫi )/V AR(ȳi∗ ), βb1ols is not consistent. The direction of thebias is unclear.
What if we use two independent proxies of ȳi∗? Using an IV strategy, for example,
proxy a as an instrument for proxy b:
plimβb1iv =COV (ȳi∗, SW Bi )β1
COV (β0a ȳi∗ + ǫai , SW Bi)
=
= b
a ∗
a
b ∗
b
b
∗
COV (β0 ȳi + ǫi , β0 ȳi + ǫi )β0 V AR(ȳi )β0(5)Thus with IV the bias does not depend on the variance of the measurement error
anymore, but a bias persists. Taking the ratio between βb1iv and βb1ols one can easily seethat whenever OLS and IV give similar results the signal-to-noise ratio V AR(ȳi∗ )/V AR(ǫi )has to be large. Unfortunately β0 might still be different from one. Since workers withlow wages tend to report a reference wage that is higher than their own wage, and viceversa, the regression of predicted reference wages on self-reported ones shows a positive
constant term and a slope that is smaller than one. Given that 0 ≤ β0 ≤ 1 a first simple
rule to minimize the bias for the IV case is to choose the specification which gives thelowest coefficient βb1 .
Table 8 and 9 show our estimated βb1ols and βb1iv for a grand variety of proxies of reference
wages. Columns 1 and 2 of Table 8 show the unbiased βb1 , first with then without additional
controls. The remaining columns use two different kind of proxies. Column 3 and 4 use
the average log wage by education, age, and year extracted from Basic Survey on Wage
Structure (BSWS). The coefficient is larger (in absolute terms) than the “true” when we
do not control for other X’s, while it smaller but still significant when we do control for18other Xs. This external measure can easily be used as an instrument for proxies derived
from the survey data. Columns 5 to 8 use a simple linear prediction of individual log
wages as reference group. Following the literature we included as many regressors in the
Mincerian equation as possible in order to meet the exclusion restriction.14 Results are
not very encouraging, at least not when other regressors enter the happiness equation,
which is custom. The reason is that conditional on all other Xs the linear prediction andindividual wages are highly collinear. If on top of that we add an instrumental variable
that is conditionally not highly correlated with the mismeasured variable, and has a low
βb0 , we get such unreasonable results.15
Table 9 shows that survey-based cell averages provide more robust estimates, evenwhen instrumented. Columns 1 to 4 use gender × age cells, while columns 5 to 8 add
education, and columns 9 to 12 education and whether the workers has managerial tasks
to the variables that define the cells. Unlike the linear prediction case, βb1ols looks reasonable even when we control for other Xs and instrumenting delivers estimates that areclearly a lower-bound for the true effect. If we had to pick one proxy, based on the IV
results that control for other Xs we would clearly pick the second proxy, the one thatcomputes cell averages based on educational levels, gender, and age groups. That is the
specification with the highest βb0 (columns 7 and 8). Such a parsimonious definition ofreference group, based on just age, gender, and education is in line with the definition
used by the Leyden school. One way to pick the preferred OLS would simply be to pickthe one that corresponds to the best IV (columns 5 and 6). It is interesting to note thatwhile the IV is clearly downward biased, the measurement error variance pushes the OLS
estimate in the right direction but by too much. At the end the true lies often in between
the OLS and the IV estimates.
14

We control for the following fixed effects, gender, age, year, industry, tenure, education, occupation,
blue collar, marital status, job shock dummies, and for the log of average hours worked. See 10 for a listof papers that use wage regressions to predict reference wages.
15
Other studies have found a positive correlation between life satisfaction and linear predictions ofwages ?.

19

5

Conclusions

In this paper, we investigate the relative utility hypothesis with varieties of proxy of reference income employed in previous studies. Having data on the self-reported cardinalmeasure of reference earnings we compare the unbiased estimate based on the elicitedreference wage with estimates from proxies that have been widely used in other studies.
While we do find strong evidence that reference wages matter we cannot support Easterlin’s suggestion that life satisfaction depends negatively on reference wage as much as it
depends positively on own wages.
The proxies for reference wage that have been used in the literature do not introduce
a simple classical measurement error, with a bias that cannot be signed. Whenever
the elicited reference is not available we propose a simple IV strategy that delivers anupper-bound of the true effect. According to our results using linear predictions of wages
as reference wages perform very badly, even when instrumented, because the a multicollinearity problem generates very “weak” proxies. On the other hand, cell average wages
based on educational levels, gender, and age groups from external data sets generate themost robust results.

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23

0.50

0.50

0.40

.4663

0.40
.3511

0.30

DensityDensity

.3274

0.20

0.30

.2711

0.20
.1502
.1191

.1117

.0908

0.10

0.10

.07
.0424

0.00

0

1
2
3
4
Life happiness: =1 very unhappy... =5 very happy

0.00

5

0

1
2
3
4
Job satisfaction: =1 very unsatisfied... =5 very satisfied

Figure 1: Happiness and Job Satisfaction
Table 1: Summary statistics for the Whole Sample
Variable
Female [0,1]Age in years
Tenure in years
Middle school [0,1]=1 if educ attainment is high school
Technical school [0,1]Some college [0,1]College [0,1]Post-graduate [0,1]Other degree[0,1]Married [0,1]owhBlue-collar [0,1]Managerial tasks [0,1]
N

24

Mean
0.224
34.926
13.87
0.069
0.464
0.024
0.095
0.27
0.067
0.011
0.584
22.6790.341
0.171

Std. Dev.
0.417
9.965
10.305
0.254
0.499
0.152
0.293
0.444
0.251
0.105
0.493
19.833
0.474
0.377
91896

5

1.5
1
.5
0
14

14.5

15

15.5

16

16.5

x
log SR reference wage
External log reference wage

Individual log wage

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

1

1

2

2

3

3

4

4

5

5

0.90

0.92
0.94
0.96
Worker’s wage/subjective reference wage

0.98

0.90

Life happiness: =1 very unhappy... =5 very happy

0.92
0.94
0.96
Worker’s wage/subjective reference wage

Life satisfaction: =1 very unhappy... =5 very happy

Figure 3: Relative Wage, Happiness and Job Satisfaction

25

0.98

Table 2: Summary statistics for the Female Sample
VariableAge in years
Tenure in years
Middle school [0,1]=1 if educ attainment is high school
Technical school [0,1]Some college [0,1]College [0,1]Post-graduate [0,1]Other degree[0,1]Married [0,1]owhBlue-collar [0,1]Managerial tasks [0,1]N

Mean
30.28410.097
0.052
0.459
0.03
0.278
0.15
0.014
0.017
0.33
11.6630.227
0.02

Std. Dev.
8.839
8.771
0.223
0.498
0.171
0.448
0.357
0.116
0.128
0.47
11.139
0.419
0.141
20574

Table 3: Summary statistics for the Male Sample
VariableAge in years
Tenure in years
Middle school [0,1]=1 if educ attainment is high school
Technical school [0,1]Some college [0,1]College [0,1]Post-graduate [0,1]Other degree[0,1]Married [0,1]owhBlue-collar [0,1]Managerial tasks [0,1]
N

26

Mean
36.26614.958
0.074
0.465
0.022
0.042
0.304
0.083
0.01
0.657
25.8570.374
0.215

Std. Dev.
9.869
10.457
0.262
0.499
0.146
0.2
0.46
0.276
0.098
0.475
20.638
0.484
0.411
71322

Table 4: Mincer-type Wage Regressions(1)

VARIABLESAge in years
Tenure in years
Middle school [0,1]Technical school [0,1]Some college [0,1]College [0,1]27

Post-graduate [0,1]Other degree[0,1]
Hours worked (in logs)
Married [0,1]

(2)
(3)
(4)
Male Sample
log-wage log-wage log-wage log-wagelog-wage

(6)
(7)
(8)
Female Sample
log-wage log-wage log-wage

0.02***(0.00)
0.01***(0.00)-0.11***(0.03)-0.01(0.02)
0.06***(0.01)
0.14***(0.01)
0.16***(0.02)-0.15***(0.03)
0.05***(0.01)
0.16***(0.01)-0.06**(0.02)-0.03*(0.01)
0.05***(0.01)
0.11***(0.01)
0.14***(0.02)-0.10***(0.02)
0.04***(0.01)
0.08***(0.00)-0.06***(0.02)-0.00(0.01)
0.05***(0.01)
0.12***(0.01)
0.16***(0.01)-0.06***(0.01)
0.04***(0.00)
0.09***(0.00)

0.01**(0.00)
0.02***(0.00)-0.11**(0.05)
0.07***(0.02)
0.05***(0.02)
0.20***(0.02)
0.33***(0.03)-0.19***(0.05)
0.04***(0.01)
0.03***(0.01)-0.07(0.04)
0.05**(0.02)
0.04***(0.02)
0.19***(0.03)
0.32***(0.04)-0.13***(0.03)
0.04***(0.00)-0.01(0.01)-0.06(0.04)
0.05***(0.02)
0.06***(0.01)
0.20***(0.02)
0.34***(0.03)-0.12***(0.04)
0.04***(0.00)-0.01*(0.01)

no
no
65052
0.661

yes
no
65052
0.725

yes
yes
65052
0.753

no
no
18181
0.565

yes
no
18181
0.611

yes
yes
18181
0.650

Pessimism: No help if needy [0,1]Career: No future promotion [0,1]Age and Tenure dummies
Firm dummies
Observations
R-squared-0.06***(0.02)-0.00(0.01)
0.05***(0.01)
0.12***(0.01)
0.16***(0.01)-0.06***(0.01)
0.04***(0.00)
0.09***(0.00)-0.01***(0.00)-0.01***(0.00)
yes
yes
65052
0.754

(5)

-0.06
(0.04)
0.05***(0.02)
0.06***(0.01)
0.20***(0.02)
0.34***(0.03)-0.11***(0.04)
0.04***(0.00)-0.01**(0.01)-0.02***(0.00)
0.01**(0.00)
yes
yes
18181
0.650

Notes: All regressions additionally control for year fixed effects, industry fixed effects, a blue-collar dummy, a managerial tasks dummy, andoccupation dummies. The IV regression has a first stage F-statist for the instruments equal to 260. The partial R-squared is 11 percent.

Table 5: Summary statistics for the Reference Wages
Variablelog Self-reported reference wagelog wage
Cell av. 1 (gender, age)
Cell av. 2 (1 + education)
Cell av. 3 (2 + managerial)
Linear prediction
Firm’s average log wagelog External reference wage

28

Mean
15.48415.39815.39815.39815.39815.40615.39815.5

Std. Dev.
0.412
0.421
0.337
0.344
0.347
0.362
0.183
0.346

N
94504
94504
94504
94504
94504
83233
94504
94494

Table 6: Relative utility hypothesis tests: OLS regressions of life satisfaction with selfreported reference wages
(1)

(2)

(3)

Dep Var: Life Satisfaction
log wagelog self-reported reference wage
Hours worked (in logs)

(4)

(5)

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

0.56***(0.03)-0.38***(0.03)-0.06***(0.01)

Females

Pooled0.27***(0.04)-0.16***(0.04)-0.07***(0.01)
0.01(0.07)-0.19***(0.07)-0.41***(0.02)-0.16***(0.02)

0.42***(0.03)-0.29***(0.03)-0.05***(0.01)
0.17***(0.06)-0.37***(0.01)-0.22***(0.01)-0.37***(0.01)-0.22***(0.01)

0.42***(0.03)-0.29***(0.03)-0.05***(0.01)
0.14**(0.06)-0.07(0.08)-0.37***(0.01)-0.22***(0.01)-0.02**(0.01)
0.00(0.00)-0.02***(0.00)
0.00***(0.00)-0.03**(0.01)-0.03**(0.01)
0.04**(0.02)
0.07**(0.03)
0.02(0.02)
0.09***(0.02)
0.11***(0.03)
0.02(0.04)
0.61***(0.01)
71316
0.109-0.02*(0.01)
0.00(0.00)-0.02***(0.00)
0.00***(0.00)-0.03*(0.01)-0.04**(0.01)
0.05**(0.02)
0.07**(0.03)
0.02(0.02)
0.09***(0.02)
0.10***(0.03)
0.02(0.04)
0.61***(0.01)
71316
0.110-0.02*(0.01)
0.00(0.00)-0.02***(0.00)
0.00***(0.00)-0.03*(0.01)-0.04**(0.01)
0.05**(0.02)
0.06**(0.03)
0.02(0.02)
0.09***(0.02)
0.10***(0.03)
0.02(0.04)
0.61***(0.01)
71316
0.110

-0.02
(0.01)
0.00(0.00)-0.02***(0.01)
0.00(0.00)-0.05(0.05)-0.11***(0.03)
0.11*(0.06)
0.04(0.05)
0.16***(0.02)
0.12***(0.04)
0.10(0.08)-0.05(0.03)
0.44***(0.02)
20570
0.089

0.40***(0.02)-0.25***(0.03)-0.05***(0.01)
0.10(0.06)-0.10(0.07)-0.38***(0.01)-0.21***(0.01)
0.43***(0.02)-0.02**(0.01)
0.00*(0.00)-0.02***(0.00)
0.00***(0.00)-0.02(0.01)-0.05***(0.01)
0.06**(0.02)
0.06**(0.03)
0.11***(0.01)
0.09***(0.02)
0.09***(0.03)-0.02(0.04)
0.57***(0.01)
91896
0.116

0.00

0.00

0.00

0.01

0.00

Firm’s log wage interquartile range

Career: No future promotion [0,1](7)0.47***(0.03)-0.30***(0.03)-0.05***(0.01)

Mean wages (in logs, by firm)

Pessimism: No help if needy [0,1](6)Female [0,1]Age in years

Observations
R-squared

71316
0.022

-0.02***(0.01)
0.00(0.00)-0.02***(0.01)
0.00***(0.00)
0.01(0.02)-0.04**(0.02)
0.04**(0.02)
0.08**(0.03)
0.02(0.02)
0.12***(0.02)
0.16***(0.03)
0.01(0.04)
0.61***(0.01)
71316
0.071

log wage + log reference wage = 0?
p-value

0.00

0.00

Age squared
Tenure in years
Tenure squaredManagerial tasks [0,1]Blue-collar [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]Married [0,1]29Table 7: Robustness checks for the relative income hypothesis

log wagelog self-reported reference wage
Hours worked (in logs)
Pessimism: No help if needy [0,1]30Career: No future promotion [0,1]Mean wages (in logs, by firm)
Interquartile range of log wages, by firm

(1)

(2)

(3)

(4)

OLS
Life-sat.
0.42***(0.03)-0.29***(0.03)-0.05***(0.01)-0.37***(0.01)-0.22***(0.01)
0.12**(0.06)-0.09(0.09)

OLS
Job-sat.
0.22***(0.04)-0.15***(0.02)-0.02**(0.01)-0.38***(0.01)-0.44***(0.01)
0.15**(0.07)-0.14(0.09)OLS

OLS

0.45***(0.03)-0.29***(0.03)-0.05***(0.01)-0.37***(0.01)-0.22***(0.01)
0.03(0.07)-0.11(0.07)

0.43***(0.03)-0.29***(0.03)-0.04***(0.01)-0.37***(0.01)-0.22***(0.01)

no
no
65052
0.111

no
no
65052
0.120

yes
no
65052
0.112

yes
yes
65052
0.115

log external reference wage 1
Year and industry dummies
Firm dummies
Observations
R-squared

(5)

(6)

(7)

OLS
IV
Ordered Probit
Life-satisfaction
0.42*** 0.79***
0.41***(0.03)(0.22)(0.03)-0.29*** -0.35***-0.29***(0.03)(0.11)(0.03)-0.05*** -0.05***-0.05***(0.01)(0.02)(0.01)-0.37*** -0.38***-0.38***(0.01)(0.01)(0.01)-0.22*** -0.22***-0.21***(0.01)(0.01)(0.01)
0.12**-0.04
0.12**(0.06)(0.08)(0.06)
-0.09
-0.11
-0.11
(0.09)(0.11)(0.09)-0.10(0.07)
no
no
no
no
no
no
65046
65046
65052
0.111
0.066
-

Notes: All regressions additionally control for year fixed effects, industry fixed effects, a blue-collar dummy, a managerial tasks dummy, andoccupation dummies.

Table 8: Self-reported vs. estimated reference wage: OLS vs. IV (PART 1)(1)
OLS
log wagelog Self-reported reference wage
31

log external reference wage
Linear prediction
log-hours worked
Other Xs
Observations
R-squared(2)
OLS

0.41*** 0.42***(0.02)(0.02)-0.29*** -0.26***(0.02)(0.02)

(3)
OLS

(4)
OLS

(5)
OLS

(6)
OLS

(7)
IV

(8)
IV

0.43***(0.01)

0.35***(0.02)

0.22***(0.02)

0.32***(0.02)

0.79***(0.03)-0.94**(0.38)-0.44*** -0.13***(0.02)(0.04)-0.21***
0.06-1.54*** 6.43***(0.03)(0.04)(0.07)(1.92)-0.09*** -0.05*** -0.08*** -0.06*** -0.07*** -0.06*** -0.01* -0.18***(0.00)(0.00)(0.00)(0.00)(0.00)(0.00)(0.01)(0.04)
no
yes
no
yes
no
yes
no
yes
91886
91886
91886
91886
83223
83223
83223
83223
0.057
0.115
0.013
0.072
0.006
0.073
.
.

Table 9: Self-reported vs. estimated reference wage: OLS vs. IV (PART 2)

log wage
Cell av. 1 (gender, age)

32

(1)
OLS

(2)
OLS

(3)
IV

(4)
IV

(5)
OLS

(6)
OLS

(7)
IV

(8)
IV

(9)
OLS

(10)
OLS

(11)
IV

(12)
IV

0.49***(0.01)-0.55***(0.02)

0.35***(0.02)-0.11***(0.04)

0.52***(0.02)-0.61***(0.02)

0.37***(0.02)-0.83***(0.23)

0.44***(0.02)

0.35***(0.02)

0.51***(0.02)

0.37***(0.02)

0.43***(0.02)

0.35***(0.02)

0.52***(0.02)

0.37*
(0.02

-0.46***(0.02)-0.16***(0.04)-0.56***(0.02)-0.60***(0.17)-0.45***(0.02)-0.08***(0.00)
no
91886
0.012

-0.14***(0.04)-0.06***(0.00)
yes
91886
0.072

-0.57***(0.02)-0.08***(0.00)
no
91886
0.012

-0.71*(0.20-0.06*
(0.00
yes
9188
0.07

Cell av. 2 (1 + education)
Cell av. 3 (2 + managerial)
log-hours worked
Other Xs
Observations
R-squared-0.08***(0.00)
no
91886
0.016

-0.06***(0.00)
yes
91886
0.072

-0.08***(0.00)
no
91886
0.016

-0.06***(0.00)
yes
91886
0.068

-0.08***(0.00)
no
91886
0.013

-0.06***(0.00)
yes
91886
0.072

-0.08***(0.00)
no
91886
0.012

-0.06***(0.00)
yes
91886
0.070

Table 10: Literature survey
Mincer wage approach

Cell average income

Authors

Clark and Oswald(1996)

SloaneandWilliams (2000)

Lévy-Garboua
andMontmarquette (2004)

Senik (2004)

Ferrer-iCarbonell(2005)

Brown
et
al.
(2008)

Clark
et
al.
(2009a)MethodOrderedprobitOLS

OrderedprobitOLS

OrderedprobitOLS

OrderedprobitHeckitOrderedprobitOrderedprobitOrderedprobitDependent
variableJob satisfaction

Expected
incomeJob satisfaction

Expected
incomeJob satisfaction

Expected
incomeHappiness

Expected
incomeIncomeReferenceinc./orig.
Referenceinc./rep.
withourdata(i)
Hours
workedAge
AgesquaredTenureTenure sq.
Sex
Education
MaritalSt./Children
Regions

Yes<, – **

Generalsatisfaction
Yes
– **

Four aspects ofjob sat.
Yes<, – **>, – ***(ii)

33

Industry
Occupation
Job class
Health status
Race
Religion
Temporarycontract
Incentive
payment
Part time
Firm sizeUnion
member
Trade
union
Other control var.

<,
***

Yes
<,– **

+

>,
***

Yes
+
+

>,
***

Yes
<,
***
<,
***

+

+
+

Cell av. income from external source

Cappelli
andSherer(1988)
OLS

Clark
and
Oswald(1996)
OrderedprobitMcbride(2001)

Job satisfaction

Job satisfaction

Yes<, + *>, – ***(iii)

Subjecref. icome
Kingthet(2009)

Clark
et
al.
(2009b)OrderedprobitBlanchflowerLuttmer
and Os(2005)
wald(2004)
OrderedOLS
logit

Job satisfaction

Happiness

Happiness

Happiness

Yes<, – ***

Yes<, – **

Yes<, – **

Yes>, + **

Yes<, – **

Economic
Satisfaction
Yes>, + **<,+*** (iv)>, – ***(v)<,+*** (vi)<, – ***(vii)>, – ***(viii)

N.A.(ix)

N.A.(ix)>, – *
(x)

OLS

OLS/
ProbitOrdereprobitHappi

Yes<, – *

Yes

No

Yes

Yes

No

Yes

No

No

No

Yes

Yes

No

Yes

No

No

Yes

No

Yes

Yes
Yes

Yes (6)

Yes
No

No
No

No
No

No
No

No
No

Yes
No

Yes
Yes

Yes
No

Yes (3)

Yes
No

Yes
Yes

No
No

Yes
Yes

Yes
Yes

Yes (9)

Yes
Yes

No
No
Yes
Yes (3)
No

Yes
Yes
Yes
Yes (12)
Yes (5)

Yes
Yes
Yes
Yes (5)
Yes

Yes
Yes
Yes
Yes (5)
Yes (2)NoNo
Yes
Yes (4)
Yes

Yes
Yes
Yes
Yes (4)
Yes (2)

No
No
Yes
No
No

Yes
No
Yes
No
No

No
No
Yes
Yes
Yes

No
No
Yes
Yes
Yes

No
No
Yes
Yes(5)
Yes

Yes
No
Yes
No
No

No
No
Yes
No
No

No
No
Yes
No
Yes

No
No
Yes
Yes
Yes (6)

No
No
Yes
Yes
No

No
No
No
Yes(6)
Yes(6)

No
No
Yes
Yes
Yes (3

Yes (18)

Yes (18)

Yes (5)

No

Yes

Yes

Yes(7)

Yes(7)Yes

Yes

Yes(13)No

Yes (18)

No

Yes (44)

Yes(13)

Yes (2

Yes (10)No
Yes
Yes (3)

Yes (61)
Yes (77)
Yes (2)
Yes (5)No
Yes
Yes (2)No

Yes (10)
Yes (8)
Yes
No

No
No
No
Yes

No
Yes (2)
No
Yes

No
No
No
No

Yes(8)
Yes(10)
No
No

No
Yes
No
No

Yes
Yes
No
No

Yes(6)
No
Yes
Yes

No
Yes (3)No
No

Yes (10)No
No
Yes (3)No
No
No
Yes (3)

No
No
No
No

Yes (U.S
State)
No
No
No
No

No
No
No
Yes

No
No
No
Yes (2

Yes (3)
No
Yes

No
No
Yes

Yes
No
Yes

Yes
No
Yes

No
Yes
No

No
Yes
No

No
No
No

No
No
No

No
No
No

No
Yes
Yes

No
No
No

No
No
No

Yes (3)
No
No

Yes
No
No

Yes (3)No
No

Yes (5)
Yes (13)
No

No
No
No

Yes
No
No

No

Yes

No

Yes

No

No

No

No

No

No

No

No

No

No

No

No

No

No

No
No
No

Yes
Yes (11)Yes

Yes
Yes (3)Yes

Yes
Yes (3)
Yes

No
No
No

Yes
No
No

No
No
No

No
No
No

No
No
No

No
Yes
No

No
Yes(3)
No

Yes
No
Yes

No
No
No

No
No
No

No
No
No

No
No
No

No
No
No

No
No
No

No

Yes

Yes

Yes

No

No

No

No

No

Yes

No

No

No

No

No

No

No

No

Renterand Second job
statusOrganizationWorking
type(7),
conaccident
dition
(3),
(14),
when
sex mix
work
at work(9), sex(4),
mix atmanual,work
and(5), andtraining
pensionperiodmemberTraining
period,when
work,and selectivity

Current
and past
wage
gap,
birth
place,
language,and satisfaction
withleisureBirthplace,
language,and satisfaction
withleisureYear
fixedeffects,
familiy
size (2),familiy
condition,andsocialmean(4)

Averagewage
inthe
workplace
and
wage
range

SubordinatesWorking
and yearcondifixed eftions
fects(7)(6),
wage
system,andlayoffParentallivingstandard(4)

See
Unemployed,Value of
retired,home,neighstudent,
renter,bors
often,
Keeping
populahome,tion, jobsocioandstatuseconomicothers(2),groupshouse(3), No.
years
hold
size, gein Gridography(5), andyear (8)(2), andfraction
black in
PUMA(i) Replication with our dataset; < when the absolute value of the coefficient on Ref. income is greater than that of own income, – otherwise; + the sign is positve, – the sign is negative; *** p<0.01, ** p<0.05, * p<0.1.
(ii) Categories over education, age, and region (50)(iii) Comparison income as the field wage rank in the workplace enters in the happiness regression: negative sign confirms jelousy effects.
(iv) Proxy of comparison income as the average income in the workplace(v) Categories over occupation and seniority; comparison made inside/outside of workplace(vi) Categries over working hours (28) and sex; Data from 1991 New Earnings Survey. When education is contorlled, the sign turns negative significantly at 1% level.
(vii) The average income of everyone from 5 years younger than him to 5 years older than him; Data from 1994 current population survey(viii) The average level of income in each of the different income quintiles within the person’s State; Data from BEA state per capita income data(ix) Comparison income with geometric information. Unfortunately, we do not have regional information in our dataset.
(x) Comparison income from survey; dummies of household income above/below village average (4)Unempasset,
community
variables(6), aattitudinalvariables(6)