ESRI Research Note No.76

Do People Correctly Measure Their Satisfaction?

Kazuhito Higa
May 2023

Economic and Social Research InstituteCabinet Office
Tokyo, JapanThe views expressed in “ESRI Discussion Papers” are those of the authors and not those of
the Economic and Social Research Institute, the Cabinet Office, or the Government of Japan.
(Contact us: https://form.cao.go.jp/esri/en_opinion-0002.html)

ESRI Research Note No.76"Do People Correctly Measure Their Satisfaction?"

Do People Correctly Measure Their Satisfaction?∗
Kazuhito Higa
Okinawa International University
Economic and Social Research Institute, Cabinet Oﬃce

Abstract
This paper analyses people’s life satisfaction measurement. In the literature on
happiness, numerous studies have discussed the Easterlin Paradox, where Easterlin(1974) states that the increase in the reported happiness does not seem to be relatedwith the economic growth. There are several possibilities that result in observing
the paradox. The ﬁrst possibility is the recent personal news shock eﬀect thatpeople’s happiness is higher if they receive good news before the interview. Anotherpossibility is of a social norm where people may report happiness by considering
the expectation of others they interact with. The third possibility, which this paper
focuses on, is the change in one’s criteria of the happiness level. If the measurementof happiness changes over time, the level of happiness is not comparable acrossdiﬀerent time periods. This paper uses the British Household Panel Survey toestimate the criteria of people’s happiness levels. It ﬁnds that the measurement is
not constant across time.
JEL Classiﬁcation Numbers: I31, J10, D10
Keywords: Subjective Well-being; Life Satisfaction; Easterlin Paradox∗

This paper forms a part of our microdata-based research at the Economic and Social ResearchInstitute (ESRI),Japan. This paper is one of my dissertation, and I thank invaluable comments from
Daiji Kawaguchi, Naohito Abe, Yukinobu Kitamura, Takashi Oshio, and Izumi Yokoyama. The author
would like to thank Masahiro Hori, Keiko Murata, Koichiro Iwamoto, Junya Hamaaki, Takeshi Niizeki,
Fumihiko Suga, Taiyo Fukai, Kyogo Kanazawa, and other ESRI colleagues. I would also like to thankKentaro Imai for helpful comments. Special thanks go to the UK Data Archive for providing me withthe microdata employed in this study. The views expressed in this paper are those of the author and do
not represent those of the institutions with which the author is aﬃliated.

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ESRI Research Note No.76"Do People Correctly Measure Their Satisfaction?"

1

Introduction

In the literature on happiness, a paradox that the increase in the level of reported happiness does not correlate with the economic growth in Easterlin (1974) motivated further
studies to analyze happiness. The happiness and subjective well-being are related tothe policy and social changes, and thus, analyzing the well-being would be valuable forpolicymakers.
In the analysis of happiness and income relationship, two opposite results have been
observed. On the one hand, Easterlin (1974) and Easterlin (1995) stated that happinesslevel is higher in developed countries in cross-country analysis, but the positive correlation
between income and happiness was not observed within country analysis. Blanchﬂower
and Oswald (2004) support Easterlin’s views but found the positive correlation for somedemographic groups. On the other hand, Stevenson and Wolfers (2008) assessed theparadox by using more recent data of a broad array of countries and found positive relationship between income and happiness within and across countries. Stevenson and
Wolfers (2008) also used several micro-data sets and conﬁrmed the positive correlation
between the level of average subjective well-being and GDP per capita across countries.
Deaton (2008) stated there is a strong positive relationship between the log of GDP andlife satisfaction. Di Tella, MacCulloch and Oswald (2003) used several macroeconomic
variables in a happiness equation and found that GDP per capita has positive eﬀects onlife satisfaction. In addition, there are empirical studies that have found a positive correlation between these variables (for example Clark and Oswald, 1994; Frey and Stutzer,
2000; van Praag, Frijters and Ferrer-i-Corbonell, 2003; Ferrer-i-Corbonell and Frijters,
2004; Ferrer-i-Corbonell, 2005).
Several approaches have been taken to analyze the relationship between the wellbeing and income. First, we consider the literature that uses the linear and nonlinear
relationship between them (for example Deaton, 2008). This type of approach assumes
that people obtain their life satisfaction mainly from their income. Second, we consider
the literature that considers the eﬀect of others’ preferences on an individual’s preference 1(Easterlin, 1995). This means that the individual’s well-being depends on not only her
income in absolute terms but also her status in a social group. The study includingreference income considers that one’s happiness depends on one’s status and other’s.
This paper, with the aim of ﬁnding the relationship between income and happiness,
focuses on one’s personal measurement of well-being. In a survey, individuals are asked
to report their happiness or well-being on a zero-to-ten scale, based on their own criteria.
In next year, they are asked to report their respective happiness levels again. In the oneyear gap between both surveys, individuals might have had various life events such as
income increase, marriage, and moving to a big city. Then, if one’s income increased, herlife satisfaction would increase. However, the positive relationship might not be observed
due to some reasons. One possible reason is the recent personal news shock eﬀect. If she
had a temporal bad news, say, the previous night, she would report lower satisfaction
even if she had an income increase. Sano and Ohtake (2007) analyzed temporal news
shock on life satisfaction. They stated that if one had a good news, her life satisfaction
tended to be higher. However, if she had a bad news, her life satisfaction tended to belower.
Another possible reason is of a social norm (Easterlin, 1974). One may report the1

Ferrer-i-Corbonell (2005) brieﬂy summarized the interdependence of preference.

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ESRI Research Note No.76"Do People Correctly Measure Their Satisfaction?"

satisfaction level considering another individual’s expectation. This would result in a bias
in the correlation between well-being and income. The third possibility is the change in
one’s criteria of the satisfaction level. If the measurement of life satisfaction was changing
over time, the level of life satisfaction is not comparable across time. If one was promoted
and got higher income, one might have a diﬀerent criteria of satisfaction. This causes abias in the correlation between income and well-being.
This paper analyzes the third possibility by using two life satisfaction variables. The
two life satisfaction variables are life satisfaction overall (overall satisfaction) and current
life satisfaction compared with that of the previous year (compared satisfaction). The
purpose of using two life satisfaction variables is that the overall satisfaction might not
be comparable over time because the life satisfaction measurement can change with time.
When a respondent was asked to answer her overall satisfaction, she would evaluate it
considering her past life. Because numerous events might have happened to her from thelast time she was asked the same question, the measurement of the overall satisfaction
might have changed over time. Thus, the criterion of the current overall satisfaction would
be diﬀerent from the criterion last time. However, the questionnaire on the comparedsatisfaction asked the individual to compare her current life satisfaction with that of theprevious year directly, and thus, this approach allows us to evaluate the satisfactions of
two periods with the same measurement. Therefore, the compared satisfaction implies
direct increase from that of the previous year.
The next section explains the data used in this study. Section three discusses an estimation model and an estimating approach of the criteria analysis. Section four discusses
the regression results and threshold analysis. Then this study concludes in Section ﬁve.

2

Data

This section explains the data set used in this study and the construction of the analysis
sample. This study used the British Household Panel Survey (BHPS) which is a panel
survey that began in 1991, conducted by the Institute for Social and Economic Researchat the University of Essex. The survey interviewed the same representative individuals
over a period of years and collected the information of social and economic changes
at the individual level and at the household level in the UK. It was also motivated tounderstand the changes, causes, and consequences related to a range of socio-economic
variables. It was an annual survey targeting each household member aged over 16 years.
More than 5,000 households were interviewed, and approximately 10,000 individuals wereinterviewed and reported information on economic activities such as income and job
status. In addition, the survey questionnaire asked about a variety of topics of policy
interest, such as household composition, education and training, health, and labor market
behavior.
This study used the data sets from Wave 6, year 1996-1997, to Wave 18, year 20082009. Wave 1 to Wave 5 and Wave 11 did not have a life satisfaction questionnaire, andthus, we dropped six waves from the analysis sample.
The key life satisfaction variables used in this study were the overall satisfaction andthe compared satisfaction. For the overall satisfaction, people are asked to answer thequestion, “How dissatisﬁed or satisﬁed are you with your life overall?,” where they haveto report one for “not satisﬁed at all” to seven for “completely satisﬁed.” Then, for the3

ESRI Research Note No.76"Do People Correctly Measure Their Satisfaction?"compared satisfaction, they answer the following question, “Would you say that you are
more satisﬁed with life, less satisﬁed or feel about the same as you did a year ago?,”where they answer one of the three options accordingly.
The description of the independent variable was as follows. The independent variable
included the real annual income, reference income, age variables, and dummy variables ofgender, household size, number of kids, education, region, housing type, and wave. The
real annual income was used as an income variable and was deﬂated by the consumer
price index, which is the 2010-based price index. We included two age variables: one’s
age and its square. For one’s education level, there are seven categories: higher degree;
1st degree; Higher National Diploma (HND), Higher National Certiﬁcate (HNC), andteaching; General Certiﬁcate of Education Advanced Level (A-level); General Certiﬁcateof Education Ordinary Level (O-level); Certiﬁcate of Secondary Education (CSE); andothers. According to Brown and Roberts (2012), this study categorized the educationlevel as following: university level for higher degree and 1st degree; some higher educationlevel for HND, HNC, teaching and A-level; high school level for O-level and CSE; andothers. The regional variable included nineteen values such as Inner London, Outer
London, Wales, Scotland, and Northern Ireland. There are eight types of housing, forexample, Owned Outright and Owned with Mortgage.

3

Model

3.1

Model of Estimation

This study follows the methodology of Ferrer-i-Corbonell (2005). It considers a satisfaction function as follows:
W = W (y, y r , X),(1)

where W is the concept of life satisfaction, y is the household income, y r represents thereference income, and X is the socio-economic and demographic characteristics. Ferreri-Corbonell (2005) considered several speciﬁcations of the reference income. This study
applies the speciﬁcation of Ferrer-i-Corbonell (2005) to include the cell-average of thereference group by age cohort, gender, education, region, and wave.
In an empirical application, this paper used the following speciﬁcation:
Wit = β0 + β1 ln(yit ) + β2 ln(ytr ) + X ′ γ + uit ,(2)

where i indicates individuals and t indicates waves.

3.2

Model to Estimate Threshold Points

This section explains how we estimate and test the inconsistency of the life satisfaction
measurement over time. Primary, we estimate the measurement by applying an ordered
probit estimation of equation 2. Let W be the overall life satisfaction response taking thevalues between one and seven. The value of W is derived from a latent variable model inwhich the latent variable W ∗ is determined as follows:W ∗ = xβ + e, e|x ∼ N (0, 1),(3)
4

ESRI Research Note No.76"Do People Correctly Measure Their Satisfaction?"where x indicates explanatory variables and β is the parameter vector. We deﬁne thethreshold point αkt , where k is the threshold level and t is the wave, as follows:
W =1
W =2
W =3
W =4
W =5
W =6
W =7

if
if
if
if
if
if
if

W ∗ ≤ α1t
α1t < W ∗ ≤ α2t
α2t < W ∗ ≤ α3t
α3t < W ∗ ≤ α4t
α4t < W ∗ ≤ α5t
α5t < W ∗ ≤ α6t
α6t < W ∗ .

(4)

Estimating the ordered probit for each wave cannot give a time-consistent threshold.
The survey information on people’s satisfaction in comparison with the previous year
allows us to compensate for the information on the comparable threshold over time.
Therefore, we applied the ordered probit estimation to the life satisfaction equation withthe compared satisfaction variable:q = xγ + v,

(5)where q represents the compared satisfaction variable.
Estimating these two models together, we estimate the corrected life satisfaction measurement criteria αkt . However, the regression with only these equations, equations 3 to
5, does not allow us to identify the estimates of β and γ. Thus, we estimate the following
equation together:∆y = ∆xβ + ∆e.

(6)This equation is the ﬁrst diﬀerence of equation 3. Equation 6 gives the estimate of β.
Therefore, we can estimate and identify all parameters.
We use the GMM estimation to estimate three equations at the same time. If themeasurement of life satisfaction is the same over time, the threshold points, αkt , would
not vary over time. Thus, if these thresholds changed over time, people might measure
their life satisfaction with diﬀerent criteria in each wave. This would result in the underestimation of the eﬀect of income on well-being. This implies that the Easterlin Paradox
could be observed in the well-being analysis.

4

Empirical Analysis

Table 1 shows the summary statistics of the main variables. The overall satisfaction
was about 5.2 on the one-to-seven scale. People in the UK were relatively happier.
The compared satisfaction was 0.1. Since people who reported “less satisﬁed,” “about
the same,” or “more satisﬁed” took -1, 0, or 1, respectively, the mean of the comparedsatisfaction showed that there were more people who reported “more satisﬁed” thanpeople who reported “less satisﬁed.” The number of people in a household was about
2.8, and the number of kids in a household was about 0.6. The average age was about
46 yeas. There were almost equal number of males and females in the sample. The realannual income was about £17, 326.
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ESRI Research Note No.76"Do People Correctly Measure Their Satisfaction?"

Table 2 is the key table in this sudy, which shows the comparison between the ﬁrst
diﬀerence of the overall satisfaction and the compared satisfaction. Both measures indicate the life satisfaction changes between the current time and the previous year. Thus, if
there was an increase or a decrease in the ﬁrst diﬀerence of the overall satisfaction, people
would report “more satisﬁed” or “less satisﬁed,” respectively. However, the table showsinconsistency between these life satisfaction variables. There were people whose change
in the overall satisfaction was positive, meaning their overall satisfaction improved, butthey reported that they were “less satisﬁed” than they were a year ago. There werepeople who took negative values on the change in the overall satisfaction, meaning thatthey felt less satisﬁed that they did a year ago, but they reported “more satisﬁed” thana year ago. Further, even though there were people who reported their change in life
satisfaction was positive or negative, they stated they had about the same satisfaction
compared to the previous year. The table presumably indicates that people might notcorrectly measure their satisfaction with their lives.
From the distribution of these life satisfaction variables, we constructed two life satisfaction variables using the overall satisfaction and the compared satisfaction. One isthe add-up satisfaction, which is the corrected life satisfaction upon adding -1, 0, or 1 tothe initial overall satisfaction, depending on the answer to the compared life satisfaction,“less satisﬁed,” “about the same,” or “more satisﬁed.” Thus, this is the consistent life
satisfaction variable between the overall satisfaction change and the compared satisfaction. Another is the trimmed satisfaction. The scale of the add-up satisfaction is not
one-to-seven scale. Thus the trimmed satisfaction is constructed by replacing zero andeight of the add-up satisfaction for one and seven in each wave to keep the trimmed
satisfaction to have one-to-seven scale as with the overall satisfaction. The distributiontables of both the add-up satisfaction and the trimmed satisfaction are in Appendix.
Figure 1 shows the trend of three satisfaction variables, which are the overall satisfaction, the add-up satisfaction, and the trimmed satisfaction, and the log of real income.
The dashed line, which is the log of real income over the sample period, shows continuous
increase over time, from 6.6 to 7.0. The solid line is the actual overall satisfaction. Thisline has a small blip and dip but seems decreasing over time. These lines seem to imply
the Easterlin paradox that the increase in life satisfaction does not seem to be correlatedwith the income increase. The dash-dot line is the add-up satisfaction. This line shows
continuous increase in life satisfaction, implying that the add-up satisfaction increases
depending on the increase of the log of real income. The dotted line is the trimmed
satisfaction. Since this satisfaction is bounded between one to seven, it is placed between
the overall satisfaction and the add-up satisfaction. This line seems to have moderateincrease and to have positive increase compared to the overall satisfaction. This ﬁgureshows that we could not observe a clear positive correlation between the overall satisfaction and income, but constructing consistent life satisfaction variables between the overallsatisfaction and the compared satisfaction, we might ﬁnd a clearer correlation between
constructed satisfaction variables and income.
Table 3 shows the regression result speciﬁed by equation 2. The OLS estimation results
are reported in the ﬁrst to third columns. The ﬁrst column shows the OLS estimationof the overall satisfaction. The coeﬃcient of income is negative and not statisticallysigniﬁcant. This implies that the increase in one’s income does not seem to increase
one’s life satisfaction. The second column displays the regression result for the addup satisfaction. The income eﬀect on life satisfaction was 0.22 and was positive and6

ESRI Research Note No.76"Do People Correctly Measure Their Satisfaction?"

statistically signiﬁcant. The result of this estimate is noteworthy. The third column
shows the estimation result for the trimmed satisfaction. The coeﬃcient was positive;
however, it was not statistically signiﬁcant. The fourth to sixth columns report theﬁxed-eﬀect estimation results. In the ﬁxed eﬀect estimation, the coeﬃcient of income ispositive but not signiﬁcant in the overall satisfaction regression. However, with add-up
satisfaction, the coeﬃcient was 0.022. The magnitude of the income coeﬃcient in the ﬁfth
column was larger than that in the fourth column. This indicates that if life satisfaction
was corrected by arranging the original overall satisfaction for consistent change with thecompared satisfaction, the income eﬀect was larger. We might obtain this result because
the scale is diﬀerent from that of the overall satisfaction. The original life satisfaction had
the one-to-seven scale; however, the add-up satisfaction had a diﬀerent scale. This implies
that as the overall satisfaction was bounded between one and seven, the coeﬃcient in the
fourth column was smaller. If the life satisfaction were not bounded, the eﬀect of income
would be larger. The sixth column discusses if the scale was ﬁxed in the one-to-seven scale;consequently, the income coeﬃcient was 0.016, which was smaller than the coeﬃcient inthe ﬁfth column but larger than the coeﬃcient in the fourth column. These results meanthat consistently using the life satisfaction variable between the overall satisfaction andthe compared satisfaction, the income eﬀect on life satisfaction was larger than when using
the original life satisfaction. Similar to prior studies, the reference income had a negative
impact on life satisfaction; however, the ﬁxed-eﬀect estimation was not signiﬁcant for the
add-up and trimmed satisfaction variables.
Table 4 contains the regression result of the change in life satisfaction. In the ﬁrst
column, an increase in income has a positive eﬀect on the increase in the overall satisfaction. The second column shows that if a person experiences increase in income, theyreport being happier than a year before. Also, in ﬁxed-eﬀect estimation, the coeﬃcientof income was positive and signiﬁcant. These results imply that increase in income tends
to improve one’s life satisfaction.

4.1

Threshold Point Analysis

Individuals’ measurement of satisfaction could change over time because of various life
events. These events would aﬀect people’s criteria of life satisfaction. This section analyzes if the threshold points of life satisfaction were stable across the sample period.
Table 5 shows application of the ordered probit estimation to the speciﬁcation inequation 2. The income coeﬃcient was not signiﬁcant in the ﬁrst column; however,it was positive and signiﬁcant for the add-up and trimmed satisfaction variables. The
coeﬃcients of the reference income were negative, and they are signiﬁcant for the overall
and trimmed satisfaction variables.
Figure 2 shows the shifts of the threshold points of the overall satisfaction through thesample period. Applying GMM estimation for equations 3, 5, and 6 together gives thethreshold estimates. The ﬁgure shows that the cut points were almost constant during theﬁrst ﬁve waves. For example, threshold 1 was varying around -3.2 in early sample period.
However, after Wave 12, the thresholds varied with a large amount. Threshold 1 varied
between -3 and -2 during the latter period. Then, we applied a test to check whether all
the means were equal over time for each threshold point. The test result yielded that the
hypotheses were rejected for all threshold points. This means, as the table shows, thatthe thresholds were not stable across time. Thus, the measurement of the reported life
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ESRI Research Note No.76"Do People Correctly Measure Their Satisfaction?"

satisfaction was changing over time. This might result in weak correlation between life
satisfaction and income. This would be one reason why previous studies observed theEasterlin Paradox.

4.2

Robustness check

In previous sections, the estimation models include the linear terms of income and reference income. In this section, the estimation models include the linear and quadratic
terms of income and reference income. Table 6 shows the regression result speciﬁed byequation 2 including the quadratic terms. The ﬁrst column shows the OLS estimation
result of the overall satisfaction. The coeﬃcient of the squared income was positive. Thismeans that if income increased, people reported a higher level of life satisfaction. The
second column shows the OLS estimation result of the add-up satisfaction. The coeﬃcient of the squared income was larger than the respective coeﬃcient in the ﬁrst column.
The third column shows the OLS estimation of the trimmed satisfaction. The coeﬃcientof the squared income was larger than the coeﬃcient in the ﬁrst column but smaller thanthe coeﬃcient in the second column. In the fourth to sixth columns, the ﬁxed-eﬀect
estimation results are reported. If we consider a ﬁxed eﬀect, the sizes of the coeﬃcientsof the squared income were smaller than the coeﬃcients in the OLS estimation.
Table 7 gives the regression result of the change in life satisfaction. It shows that thelinear and quadratic terms of income were not statistically signiﬁcant with the change inthe overall satisfaction. However, these terms were statistically signiﬁcant when we used
the life satisfaction compared with that of the previous year.
In Table 8, the ordered probit estimation was applied to the speciﬁcation in Table
5 including the quadratic terms of income and reference income. The quadratic termsof income were positive and the linear terms of income were negative for the three life
satisfaction variables. The reference income was not statistically signiﬁcant for the overallsatisfaction, but it was signiﬁcant for the add-up and the trimmed satisfaction variables.

5

Conclusion

In the literature on happiness, the linear and nonlinear happiness–income analyses andthe analysis including reference income were adapted to study the correlation between
happiness and income. This study focused on estimating one’s own measurement of life
satisfaction to reveal that the life satisfaction criteria is changing over time.
Even though both the change in the overall life satisfaction and life satisfaction compared with that of the previous year might show the change in life satisfaction from theprevious year, there was inconsistency between these variables. Constructing new life
satisfaction variables that are consistent between those variables, we obtained larger relationship between income and happiness compared with previous studies. The regression
results show that the estimate of the income eﬀect on the overall life satisfaction was
positive, but it was lower than the estimates of the constructed life satisfaction variables, which were the add-up and the trimmed life satisfaction variables. In addition, thethreshold point analysis shows that one’s measurement of life satisfaction was changing
over time. This implies that since the criteria is changing with time, we might not compare the overall life satisfaction over time. Therefore, the previous literature obtained

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ESRI Research Note No.76"Do People Correctly Measure Their Satisfaction?"

low happiness–income correlation and the Easterlin paradox.
In this study, we constructed two life satisfaction variables to have consistency between
the change in the overall satisfaction and life satisfaction compared with that of theprevious year. However, there are more ways to construct life satisfaction variables.
Thus, further studies are required to examine the income eﬀect on well-being.

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ESRI Research Note No.76"Do People Correctly Measure Their Satisfaction?"

References
Blanchﬂower, D. G. and A. J. Oswald (2004) “Well-being over Time in Britain and theUSA,” Journal of Public Economics, Vol. 88, pp. 1359–1386.
Brown, H. and J. Roberts (2012) “Born to Be Wide? Exploring Correlations in Motherand Adolescent Body Mass Index Using Data from the British Household Panel Survey,” Working Paper. Sheﬃeld Economic Research Paper Series (2012019).
Clark, A. E. and A. J. Oswald (1994) “Unhappiness and Unemployment,” Economic
Journal, Vol. 104, No. 424, pp. 648–659.
Deaton, A. (2008) “Income, Health, and Well-being around the World: Evidence fromthe Gallup World Poll,” Journal of Economic Perspectives, Vol. 22, No. 2, pp. 53–72.
Di Tella, R., R. J. MacCulloch, and A. J. Oswald (2003) “The Macroeconomics of Happiness,” Review of Economics and Statistics, Vol. 85, No. 4, pp. 809–827.
Easterlin, R. A. (1974) Does Economic Growth Improve the Human Lot? Some Empirical
Evidence, pp. 89–125, New York: Academic Press.
(1995) “Will raising the incomes of all increase the happiness of all?”of Economic Behaviorand Organization, Vol. 27, No. 1, pp. 35–47.

JournalFerrer-i-Corbonell, A. (2005) “Income and Well-being: An Empirical Analysis of theComparison Income Eﬀect,” Journal of Public Economics, Vol. 89, No. 5-6, pp. 997–
1019.
Ferrer-i-Corbonell, A. and P. Frijters (2004) “The Eﬀect of Methodology on The Determinants of Happiness,” Economic Journal, Vol. 114, No. 497, pp. 641–659.
Frey, B. S. and A. Stutzer (2000) “Happiness, Economy and Institutions,” Economic
Journal, Vol. 110, No. 466, pp. 918–938.
van Praag, B. M. S., P. Frijters, and A. Ferrer-i-Corbonell (2003) “The Anatomy of
Subjective Well-being,” Journal of Economic Behavior and Organization, Vol. 51, No.
1, pp. 29–49.
Sano, S. and F. Ohtake (2007) “Roudou to Kouhukudo (Working Condition and Happiness),” The Japanese Journal of Labour studies, No. 558, pp. 4–18, in Japanese.
Stevenson, B. and J. Wolfers (2008) “Economic Growth and Subjective Well-Being: Reassessing the Easterlin Paradox,” Brookings Papers on Economic Activity, Vol. 2008,
pp. 1–87.

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ESRI Research Note No.76"Do People Correctly Measure Their Satisfaction?"

Table 1: Summary statistics of demographic variables
Variable
Overall life satisfaction
Life satisfaction compared with that of the previous year
Number of people in household
Number of children in household
Age
GenderReal annual income (in £)

Mean
5.24
0.13
2.83
0.59
46.11
1.55
17,326.332

Std. Dev.
1.28
0.65
1.37
0.97
18.25
0.50
17,466.132

Source: British Household Panel Survey.
Note: The real annual income is obtained by dividing annual income by the consumer price index
based on 2010.

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ESRI Research Note No.76"Do People Correctly Measure Their Satisfaction?"

Table 2: Life satisfaction comparison matrix∆Life satisfaction(overall)
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
Total

Life satisfaction compared with that of the previous year
Less satisﬁed
About the same
More satisﬁed
Obs.
%
Obs.
%
Obs.
%
49
0.05
42
0.04
10
0.01
81
0.08
53
0.05
24
0.02
287
0.27
149
0.14
47
0.04
879
0.82
642
0.60
142
0.13
2,351
2.18
2,639
2.45
628
0.58
4,798
4.45
12,583
11.67
4,113
3.81
5,377
4.99
31,28729.02
13,805
12.80
2,027
1.88
11,620
10.78
6,707
6.22
540
0.50
2,739
2.54
1,944
1.80
152
0.14
783
0.73
612
0.57
53
0.05
235
0.22
196
0.18
9
0.01
69
0.06
58
0.05
10
0.01
65
0.06
21
0.02
16,61315.41
62,90658.34
28,307
26.25

Source: British Household Panel Survey.

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ESRI Research Note No.76"Do People Correctly Measure Their Satisfaction?"

Table 3: Life satisfaction regression (level)

Ln incLn ref. inc
Age
Age2
Other controls
Observations
R-squaredLS
overall
OLS-0.001(0.005)-0.095***(0.015)-0.043***(0.003)0.001***(0.000)Yes
147,283
0.057

Add-upLS
OLS
0.022**(0.009)-0.017(0.023)-0.078***(0.004)0.001***(0.000)Yes
147,283
0.051

Trimmed
LS
OLS
0.011
(0.007)-0.045**(0.020)-0.067***(0.003)0.001***(0.000)Yes
147,2830.048

LS
overallFE
0.005(0.004)-0.041***(0.011)-0.012(0.012)-0.000(0.000)Yes
147,283
0.008

Add-upLS
FE0.022***(0.006)0.020(0.017)0.125***(0.017)-0.001***(0.000)Yes
147,283
0.041

TrimmedLS
FE
0.016***(0.005)-0.003(0.014)0.068***(0.014)-0.000***(0.000)Yes
147,283
0.021

Source: British Household Panel Survey.
Note: The add-up satisfaction is the life satisfaction constructed by adding compared satisfaction toinitial overall life satisfaction. The trimmed satisfaction is the life satisfaction variable that isconstructed by replacing zero and eight in the add-up satisfaction for one and seven, respectively. The
reference income is the cell-average of the annual real income by age cohort, gender, education, region,
and wave. The additional dependent variables are log of number of kids + 1, log of number of adult,
education dummy variables, gender dummy variable, regional dummy variables, time dummy variables,
house type dummy variables, and marital status dummy variables. Standard errors robust against
individual-level clustering are reported in parentheses. ∗ indicates signiﬁcance at the 10% level. ∗∗indicates signiﬁcance at the 5% level. ∗∗∗ indicates signiﬁcance at the 1% level.

13

ESRI Research Note No.76"Do People Correctly Measure Their Satisfaction?"

Table 4: Life satisfaction regression (FD)∆LS(overall)

Ln incLn ref. inc
Age
Age2
Other controls
Observations
R-squared

OLS
0.008**(0.003)-0.006(0.009)0.004***(0.001)-0.000***(0.000)Yes
107,8260.003

LScomparedwith theprevious year
OLS
0.007***(0.002)-0.017***(0.006)-0.025***(0.001)0.000***(0.000)Yes
147,2830.066∆LS(overall)FE
0.012**(0.006)0.001(0.015)-0.007(0.017)-0.000**(0.000)Yes
107,8260.004

LScomparedwith theprevious yearFE
0.012***(0.003)-0.008(0.007)-0.018***(0.006)0.000***(0.000)Yes
147,283
0.013

Source: British Household Panel Survey.
Note: The reference income is the cell-average of the annual real income by age cohort, gender,
education, region, and wave. The additional dependent variables are log of number of kids + 1, log of
number of adult, education dummy variables, gender dummy variable, regional dummy variables, time
dummy variables, house type dummy variables, and marital status dummy variables. Standard errors
robust against individual-level clustering are reported in parentheses. ∗ indicates signiﬁcance at the
10% level. ∗∗ indicates signiﬁcance at the 5% level. ∗∗∗ indicates signiﬁcance at the 1% level.

14

ESRI Research Note No.76"Do People Correctly Measure Their Satisfaction?"

Table 5: Life satisfaction regression (Ordered probit estimation)

Ln incLn ref. inc
Age
Age2
Other controls
Observations
Pseudo R-squaredLS(overall)-0.004(0.005)-0.081***(0.013)-0.036***(0.002)0.000***(0.000)Yes
147,283
0.018

Add-upLS
0.015***(0.005)-0.011(0.013)-0.043***(0.002)0.000***(0.000)Yes
147,283
0.013

TrimmedLS
0.009*(0.005)-0.026*(0.014)-0.046***(0.002)0.000***(0.000)Yes
147,283
0.013

Source: British Household Panel Survey.
Note: The add-up satisfaction is the life satisfaction constructed by adding compared satisfaction toinitial overall life satisfaction. The trimmed satisfaction is the life satisfaction variable that isconstructed by replacing zero and eight in the add-up satisfaction for one and seven, respectively. The
reference income is the cell-average of the annual real income by age cohort, gender, education, region,
and wave. The additional dependent variables are log of number of kids + 1, log of number of adult,
education dummy variables, gender dummy variable, regional dummy variables, time dummy variables,
house type dummy variables, and marital status dummy variables. Standard errors robust against
individual-level clustering are reported in parentheses. ∗ indicates signiﬁcance at the 10% level. ∗∗indicates signiﬁcance at the 5% level. ∗∗∗ indicates signiﬁcance at the 1% level.

15

ESRI Research Note No.76"Do People Correctly Measure Their Satisfaction?"

Table 6: Robustness check: life satisfaction regression (level)

Ln inc(Ln inc)2
Ln ref. inc(Ln ref. inc)2
Other controls
Observations
R-squaredLS
overall
OLS-0.093***(0.013)0.009***(0.001)-0.002(0.058)-0.008*(0.005)
Yes
147,283
0.057

Add-upLS
OLS-0.100***(0.021)0.012***(0.002)0.186**(0.085)-0.017**(0.007)
Yes
147,283
0.051

TrimmedLS
OLS-0.092***(0.018)0.010***(0.002)0.103(0.071)-0.013**(0.006)
Yes
147,2830.049

LS
overallFE-0.026**(0.015)0.003***(0.001)-0.058(0.043)0.001(0.003)
Yes
147,283
0.008

Add-upLS
FE-0.020(0.011)0.005***(0.002)0.035(0.066)-0.002(0.005)
Yes
147,283
0.041

TrimmedLS
FE-0.018(0.012)0.004***(0.001)
0.011(0.050)-0.002(0.004)
Yes
147,283
0.022

Source: British Household Panel Survey.
Note: The add-up satisfaction is the life satisfaction constructed by adding compared satisfaction toinitial overall life satisfaction. The trimmed satisfaction is the life satisfaction variable that isconstructed by replacing zero and eight in the add-up satisfaction for one and seven, respectively. The
reference income is the cell-average of the annual real income by age cohort, gender, education, region,
and wave. The additional dependent variables are log of number of kids + 1, log of number of adult,
education dummy variables, gender dummy variable, regional dummy variables, time dummy variables,
house type dummy variables, and marital status dummy variables. Standard errors robust against
individual-level clustering are reported in parentheses. ∗ indicates signiﬁcance at the 10% level. ∗∗indicates signiﬁcance at the 5% level. ∗∗∗ indicates signiﬁcance at the 1% level.

16

ESRI Research Note No.76"Do People Correctly Measure Their Satisfaction?"

Table 7: Robustness check: life satisfaction regression (FD)∆LS(overall)

Ln inc(Ln inc )2
Ln ref. inc(Ln ref. inc)2
Other controls
Observations
R-squared

OLS
0.002
(0.010)0.001(0.001)-0.082*(0.046)0.006*(0.003)
Yes
107,8260.003

LScomparedwith theprevious year
OLS
-0.020***(0.006)0.003***(0.001)0.045*(0.027)-0.005**(0.002)Yes
147,2830.066∆LS(overall)FE-0.000(0.015)0.001(0.001)-0.067(0.058)0.005(0.004)
Yes
107,8260.004

LScomparedwith theprevious yearFE
-0.006(0.007)0.002***(0.001)-0.010(0.026)-0.000(0.002)Yes
147,283
0.013

Source: British Household Panel Survey.
Note: The reference income is the cell-average of the annual real income by age cohort, gender,
education, region, and wave. The additional dependent variables are log of number of kids + 1, log of
number of adult, education dummy variables, gender dummy variable, regional dummy variables, time
dummy variables, house type dummy variables, and marital status dummy variables. Standard errors
robust against individual-level clustering are reported in parentheses. ∗ indicates signiﬁcance at the
10% level. ∗∗ indicates signiﬁcance at the 5% level. ∗∗∗ indicates signiﬁcance at the 1% level.

17

ESRI Research Note No.76"Do People Correctly Measure Their Satisfaction?"

Table 8: Robustness check: life satisfaction regression (Ordered probit estimation)

Ln inc(Ln inc )2
Ln ref. inc(Ln ref. inc)2
Other controls
Observations
Pseudo R-squaredLS(overall)-0.063***(0.011)0.006***(0.001)-0.013(0.050)-0.006(0.004)
Yes
147,283
0.018

Add-upLS-0.047***(0.012)0.006***(0.001)0.106**(0.049)-0.010**(0.004)
Yes
147,283
0.013

TrimmedLS-0.046***(0.012)0.005***(0.001)
0.073(0.048)-0.008**(0.004)
Yes
147,283
0.013

Source: British Household Panel Survey.
Note: The add-up satisfaction is the life satisfaction constructed by adding compared satisfaction toinitial overall life satisfaction. The trimmed satisfaction is the life satisfaction variable that isconstructed by replacing zero and eight in the add-up satisfaction for one and seven, respectively. The
reference income is the cell-average of the annual real income by age cohort, gender, education, region,
and wave. The additional dependent variables are log of number of kids + 1, log of number of adult,
education dummy variables, gender dummy variable, regional dummy variables, time dummy variables,
house type dummy variables, and marital status dummy variables. Standard errors robust against
individual-level clustering are reported in parentheses. ∗ indicates signiﬁcance at the 10% level. ∗∗indicates signiﬁcance at the 5% level. ∗∗∗ indicates signiﬁcance at the 1% level.

18

4.5

LS−Level
Trimmed LS
510Add−up LS
Income15

6.6

5Life Satisfaction (mean)
5.5
6

6.7
6.8
6.9
Loqui g of Income (mean)

7

6.5

ESRI Research Note No.76"Do People Correctly Measure Their Satisfaction?"

20

Wave

Figure 1: Life satisfaction comparisons
Source: British Household Panel Survey.
Note: The red solid line is the overall life satisfaction. The orange dashed line is the log of real total
income in the previous month. The blue dash-dot line is the add-up satisfaction. The green dotted lineis the trimmed satisfaction.

19

−3

Threshold (mean)
−2
−1
0

1

ESRI Research Note No.76"Do People Correctly Measure Their Satisfaction?"

5

10

15
Wave

Threshold 1
Threshold 3
Threshold 5Threshold 2Threshold 4Threshold 6

Figure 2: Threshold points transitions
Source: British Household Panel Survey.

20

20

ESRI Research Note No.76"Do People Correctly Measure Their Satisfaction?"

Appendix
Table 9: Constructed life satisfaction distribution (in %, add-up life satisfaction)
Add-up satisfaction

wave

-3
-2
-1
0
1
2
3
4
5
6
7
8
9
10
11

6
0.0
0.0
0.0
0.0
1.5
2.4
5.8
14.4
28.3
31.5
16.1
0.0
0.0
0.0
0.0

7
0.0
0.0
0.0
0.4
1.7
3.1
6.4
13.4
23.0
28.5
19.9
3.6
0.0
0.0
0.0

8
0.0
0.0
0.2
0.7
1.8
3.5
6.6
12.1
20.2
25.2
21.2
6.9
1.7
0.0
0.0

9
0.0
0.1
0.2
0.6
2.3
3.2
6.4
12.2
20.5
25.1
20.0
6.2
2.5
0.6
0.0

10
0.1
0.1
0.4
1.1
2.2
3.2
6.6
11.8
18.6
23.7
19.4
7.7
3.5
1.4
0.4

-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
9
10
11
12
13

12
0.0
0.0
0.0
0.0
0.0
0.0
1.5
1.9
5.9
13.5
29.2
32.6
15.5
0.0
0.0
0.0
0.0
0.0
0.0

13
0.0
0.0
0.0
0.0
0.0
0.5
1.3
2.9
6.2
13.2
23.4
29.5
19.5
3.6
0.0
0.0
0.0
0.0
0.0

14
0.0
0.0
0.0
0.0
0.3
0.5
1.8
3.3
6.4
12.4
21.0
26.2
19.8
6.9
1.5
0.0
0.0
0.0
0.0

15
0.0
0.0
0.0
0.2
0.3
0.8
2.1
3.6
6.2
11.6
20.0
24.2
18.4
8.2
3.6
0.8
0.0
0.0
0.0

16
0.0
0.0
0.1
0.3
0.5
1.2
2.4
3.4
6.5
10.8
18.2
23.2
17.4
8.9
4.6
2.0
0.5
0.0
0.0

Source: British Household Panel Survey.

21

17
0.0
0.1
0.2
0.3
0.8
1.4
2.3
3.8
6.2
10.6
16.7
21.9
17.3
8.7
5.5
2.8
1.2
0.3
0.0

18
0.0
0.1
0.2
0.6
0.9
1.5
2.4
3.8
6.1
10.5
16.8
20.7
16.4
8.8
5.2
3.5
1.6
0.7
0.2

Total
0.0
0.0
0.1
0.1
0.3
0.8
1.9
3.2
6.3
12.2
21.2
25.9
18.4
5.9
2.4
1.0
0.3
0.1
0.0

ESRI Research Note No.76"Do People Correctly Measure Their Satisfaction?"

Table 10: Constructed life satisfaction distribution (in %, trimmed life satisfaction)
Trimmed satisfaction
0
1
2
3
4
5
6
7
8

6
−
1.48
2.36
5.82
14.42
28.34
31.53
16.07
−

7
0.38
1.67
3.12
6.41
13.43
23.02
28.49
19.92
3.56

8
0.76
1.91
3.46
6.62
12.08
20.2
25.45
22.32
7.2

wave
9
0.7
2.43
3.24
6.44
12.24
20.61
25.56
22.03
6.75

10
1.3
2.43
3.2
6.65
11.85
18.76
24.31
22.71
8.79

0
1
2
3
4
5
6
7
8

12
−
1.45
1.93
5.85
13.52
29.2
32.56
15.49
−

13
0.51
1.26
2.87
6.15
13.19
23.38
29.52
19.54
3.59

14
0.67
1.88
3.34
6.37
12.39
21.03
26.31
21.22
6.8

15
1.03
2.27
3.7
6.22
11.57
20
24.75
21.03
9.43

16
1.6
2.7
3.53
6.52
10.85
18.35
24.09
22.21
10.15

Source: British Household Panel Survey.

22

17
2
2.77
3.93
6.3
10.69
16.94
23.16
23.46
10.75

18
2.42
3.02
4.05
6.14
10.65
17.07
22.73
23.67
10.26