GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No.204

Asking about changes in happinessin a daily web survey

Yoshiro Tsutsui and Fumio Ohtake

July 2011

GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

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

Asking about changes in happinessin a daily web survey
Yoshiro Tsutsui (Osaka University)
andFumio Ohtake (Osaka University)

Abstract
This paper investigates whether the level of happiness and integrated process of changes inhappiness are the same. Using the daily data of two waves of four and six months each, we
found that the level of happiness is stationary, whereas the integrated process of changes is
non-stationary with a rising trend, implying that they are different series. An examination of thecauses of the difference indicated that although adaptation completely influences the level ofhappiness, it only partially influences the change in happiness. This may be because the latter isbased on a comparison between today and yesterday,.

JEL Classification Numbers: I31
Keywords: change in happiness, Easterlin paradox, daily web survey, adaptation

11. Introduction
In recent years, the economics of happiness, which is defined as the research area that uses dataon subjective happiness, has experienced significant development (Frey and Stutzer, 2002a, b;
Bruni and Porta, 2005; Dolan et al., 2008). However, no agreement has been reached regardingwhether subjective happiness is actually comparable between people. Moreover, we do notcompletely understand how subjective happiness relates to utility. 1 Thus, the economics ofhappiness is still at the stage where the validity of analyses that use subjective happiness is still
being examined and the relationship between subjective happiness and utility is still being
investigated.
The Easterlin paradox that average happiness in a country is stable at a constant level fora long period, while the country’s GDP grows substantially is an interesting phenomenon fromthe perspective of considering the validity of the use of happiness data (Easterlin, 1974; Clark etal., 2008). 2 This paradox suggests that economic growth is meaningless if greater happiness isthe goal (Frank, 2005). This conclusion radically contradicts ordinary intuition and common
sense. In comparison, higher income is expected to result in higher utility because utility isdefined on the basis of a comparison between two states; no one would choose a life with lower

1Kimball and Willis (2006) theoretically examined the relationship between them.
2Stevenson and Wolfers (2008) questioned the existence of the paradox in Japan and the EU.

2

income, other things being equal. Utility differs from subjective happiness in this regard.
This paper proposes a new type of question for tracking subjective happiness. 3 Instead ofasking about the level of happiness, which is the approach that was adopted by most of theprevious surveys, we ask about the change in the level of happiness from the previous day usinga daily web survey. Integrating the changes in happiness and adding the level of happiness ofthe first date, we reconstruct the level of happiness. The calculated level of happiness ought tocoincide with the data of the level of happiness obtained directly using the web survey. If they
do in fact coincide, asking regarding the change in happiness is pointless. However, the twoseries might be completely different, because in order to answer the question on changes inhappiness, respondents need to compare today’s state with yesterday’s state. The answer to thequestion on changes in happiness and utility bear similar aspects in that both are based on acomparison.
If we find that the level of happiness and the integrated process of changes in happiness
differ, then we will investigate the cause of the divergence. Our speculation is as follows: one of
the causes of the Easterlin paradox is that people soon adapt to a new level of happiness. Thisimplies that people becomes happier with an increase in income in the short-run; however, in

3Regarding developments in the measurement of subjective happiness, refer to Kahneman andKruger (2006).
3the long-run, a part of the increase in happiness is cancelled by adaptation (Easterlin, 2005; Di
Tella et al., 2007; Clark et al., 2008). 4 Nevertheless, a change in happiness, like a change inutility, may not be significantly affected by adaptation because it is based on a comparison
between today’s state and yesterday’s state. Under this supposition, this paper examines how thelevel of happiness and a change in happiness are affected by adaptation. If it is found that
adaptation affects the level of happiness more than change in happiness, the integrated processof the change in subjective happiness may be a closer variable to utility than the level ofhappiness.
The remainder of the paper is organized as follows. Section 2 explains our daily survey,
which includes questions on the level of happiness and on the daily change in happiness.
Section 3 analyzes whether the integrated process of the changes in happiness differs from thelevel of happiness. Given the result that the series differ, Section 4 investigates three possible
reasons for the difference. Section 5 discusses the implications of the obtained results to theEasterlin paradox and concludes the paper.

2. The daily survey
4

Another cause of the Easterlin paradox is that people evaluate their happiness in comparison withothers’ situations. This is called the relative income hypothesis (Duesenberry, 1949; Clark and
Oswald, 1996; Clark et al., 2008); however, this paper does not focus on this hypothesis.
4Two waves
We solicited undergraduate and graduate students at Osaka University and requested them toreport their happiness every day for several months. 5 They responded using their personal
computers and mobile phones. 6 To the best of our knowledge, administering a daily survey likethis for a long period of time is unique to this study. The survey enables us to estimate ahappiness function with panel data, which has the merit of excluding the difference in happinessbetween people with a fixed (or random) effect model. In other words, it enables us to estimate
a within-subjects happiness function, which is immune to a direct comparison of subjective
happiness between people.
Our daily survey consisted of two waves. The first (2008-survey) was from December 1,
2007 to March 31, 2008, and the number of respondents decreased slightly during this period
from 68 to 64. 7 The second wave (2009-survey) was from January 1 to June 30, 2009. During
this period, the number of respondents decreased from 52 to 41. 8

5

We also asked questions including valuations of personal and macro news (how good or bad theywere) arriving on that day, as explained below.
6

Most younger residents of Japan carry mobile phones that have the capability of connecting to theInternet and sending emails.
7

The first wave started in November 1, 2006. However, the survey did not include a question onchanges in happiness until December 2007.
8

The second phase was initially planned to conclude at the end of March; however, it was extended5

Questions and definition of variables
In the survey, we asked 13 questions; here, we explain those questions that were used in theanalysis in the current paper. 9
Q1. How happy are you now?
Choose a number between 0 and 10. 0 is “very unhappy,” 10 is “very happy.”
10

9

8

7

6

5

4

3

2

1

0

LEVEL is defined as the answer, which represents the level of happiness on a scale from 0 to 10.

Q5. Recall the most important personal news or event that occurred since you answered
this questionnaire yesterday. How did you evaluate the news?
Choose a number between -5 and 5. 5 is “very good,” -5 is “very bad.”

-5

-4

-3

-2

-1

0

1

2

3

4

5

P_NEWS is defined as the answer, which represents the rating of the importance of the personal
news that the respondent received that day.

until the end of June. This is the reason why the number of respondents decreased substantially. In
fact, the number of respondents decreased from 47 in March to 41 in April.
9Questions 2, 3, 4, 6, and 8 are not used in this paper; therefore, we have omitted their explanation.

6Q7. Recall the most important news that was in the newspaper or on TV since you
answered this questionnaire yesterday. How did you evaluate the news?
Choose a number between -5 and 5. 5 is “very good,” -5 is “very bad.”

-5

-4

-3

-2

-1

0

1

2

3

4

5

M_NEWS is defined as the answer, which represents the rating of the macro news that appearedon TV and/or in newspapers that day.

Q9. Did you sleep well last night?
1. poor sleep, 2. slightly poor sleep, 3. slept well, 4. slept very well
SLEEP is defined as the answer, which represents the quality of sleep. A larger number means
better sleep.

Q10. How is your health now?
1. good, 2. generally good, 3. generally not good, 4. bad
HEALTH is defined as four minus the answer to Q10, which represents the quality of health. A
larger number means better health.

Q11. Do you feel any anxiety and stress now?

7

1. a lot, 2. a little, 3. not much, 4. none
NOANXIETY is defined as the answer, which represents the level of anxiety and stress. A larger
number means less stress.

Q12: Have you already attended a class today or are you going to attend a class today?
1. I have attended a class, 2. I will be attending a class, 3. I am attending a class now, 4.
No class today
We define NOCLASS as 1 if the respondent has no class today, and 0 otherwise

Q13: Your happiness today compared with your happiness yesterday (before) is
1. much happier, 2. reasonably happier, 3. slightly happier, 4. same as yesterday (before),
5. slightly unhappier, 6. reasonably unhappier, 7. much unhappierWe define CHANGE as four minus the answer to Q13, which represents change in happinessfrom yesterday (or the time when they answered the last survey) and ranges from -3 (muchunhappier) to 3 (much happier).
We present descriptive statistics of these variables for both waves in Table 1. In
2008-survey, the mean of LEVEL is 5.8, which is relatively lower than the level of happiness,
6.4, reported in “Kokumin Seikatsu Senkodo Chosa” (Survey on preferences in life of nations;8webpage of the Cabinet Office). The mean of CHANGE is positive, implying that the
respondents were becoming happier during the observed period. The mean of P_NEWS isslightly positive, implying that overall, they received good news, which is consistent with thefact that the mean of CHANGE is positive. In contrast, M_NEWS is slightly negative, which
implies that overall, the macro news was bad. SLEEP and HEALTH are larger than 2.5, that is,
the average on the scale of 1 to 4, suggesting that overall, respondents were in good health andslept well. However, the mean of NOANXIETY is smaller than the average on the scale of 1 to 4,
suggesting that the average respondent was bothered by stress and anxiety. The mean of
NOCLASS is 0.68. 10
The values of the variables of 2009-survey are not radically different from those of
2008-survey. However, the values of LEVEL, CHANGE, and P_NEWS in the 2009-survey werelarger than those of the corresponding variables in the 2008-survey, thereby suggesting that the
respondents of 2009-survey were happier than those of 2008-survey.

10Since Osaka University has 26 school days from January 1 to March 31 (there is a spring vacationin February and March), this number implies that respondents attended most of the school days andresponded to the questionnaire. In 2009-survey, the mean of NOCLASS is 0.58, implying that theyattended classes for approximately 76 days out of the 81 school days from January to June. However,“Class” in the question includes experiments at laboratories in natural science and technology
departments, which are conducted on days when school is not in session. Therefore, the above
assessment is crude.
9Rewards
Respondents were requested to connect to the webpage and to answer the questions every day.
They were paid 160 yen per answer for the daily survey. Those who responded to the daily
survey for over 22 days and those who answered the hourly survey more than once a month
were paid 1,300 yen as a bonus for the month, and those who responded to the daily survey forover 27 days and answered the hourly survey received 2,600 yen as a monthly bonus. 11

Response rate
Figure 1 shows the response rate of the daily survey for each month. In both 2008- and 2009surveys, the response rate is approximately 90%.

3. Comparison between the level of happiness and the integrated process of the change
in happiness11

In the web survey, respondents were also requested to report their hourly happiness on one day of

their choice each month. We call this the hourly survey. The hourly survey essentially follows anexperience sampling method (Csikszentmihalyi and Hunter, 2003; Scollon et al., 2003), which is
better than Kahneman et al.’s (2004 a, b) day reconstruction method, wherein respondents answer
questions in real time, so that the responses are immune to memory biases. We do not explain thehourly survey in detail because we do not use the results in the current paper.
103.1 Comparison of the averaged data
In this section, we check if the two series of happiness, LEVEL (Q1; level of happiness) andCHANGE (Q13; change in happiness) are consistent with each other. To this aim, we define thefollowing two variables associated with LEVEL and CHANGE.
DIFFERENCE: the difference in LEVEL from the day before
INTEG: the sum of the LEVEL on the first day and CHANGE of the consecutive days until thecurrent day
Table 1 presents the descriptive statistics of INTEG and DIFFERENCE. INTEG widely
ranged from -151 to 347 in the 2008-survey and from -167 to 476 in the 2009-survey.
DIFFERENCE was very small in both the surveys and was not statistically different from zero.
Aside from the scaling of CHANGE in its definition, by mathematical definition,
DIFFERENCE and CHANGE (and therefore, LEVEL and INTEG) should follow the same series.
In order to check if they are in fact the same, we calculate the average of these four variables
over respondents for each day. Specifically, we calculate INTEG by calculating the integrated
process of each respondent, and then averaging them. 12 In Figure 2, the averages of LEVEL and

12Alternatively, we can first average CHANGE over respondents each day and then construct anintegrated process of these averages. INTEG constructed in this way is smoother than that in Figure
2, and it does not show an increase in volatility. This is because averaging CHANGE over
respondents makes the variance much smaller (i.e., the variance is denominated by the number of11

INTEG are presented for the two waves. It is apparent that although LEVEL is stabilized at aconstant level in both phases, INTEG grows throughout the periods. 13 INTEG also shows
growing volatility throughout the periods. 14 Thus, the figure reveals that the two series arecompletely different.
In Figure 3, we show CHANGE and DIFFERENCE. The figure reveals that although
DIFFERENCE is positive and negative with similar probability, CHANGE is more frequently
positive than negative. In fact, the hypothesis of the same mean for CHANGE andDIFFERENCE is rejected at the 1% level in both the waves. Although the mean of CHANGE is
significantly positive, that of DIFFERENCE is not significantly different from zero (see Table
1).

3.2 Panel unit root tests
Figure 2 gives us the impression that LEVEL is a stationary series, whereas INTEG is
non-stationary. If this is the case, the two series are certainly different. In order to check this, we
respondents). However, it increases with time as in Figure 2, so that the essential conclusions areunaltered. See footnote 14.
13

Comparing INTEG of the two phases, we found that the one in 2009-survey grows more rapidly:in the 2008-survey, it reaches 20 in four months, whereas in 2009-survey it reaches 40 in six months.
14Thisis because the disturbance term of the integrated process of an individual increases inproportion with time; therefore, its variance increases in the quadratic function of time.
12

conduct panel unit root tests for the four series LEVEL, INTEG, CHANGE, and DIFFERENCE
for the two waves.
Specifically, we employ pooled tests based on Fisher’s type statistic, as defined in Choi
(2001). Choi’s (2001) test combines p-values from a unit root test applied to each individual
under the null hypothesis that all cross section units have a unit root, against the alternative thatsome of the panel units are stationary. Choi’s (2001) test statistic, termed PN statistic in this paper,
is as follows:
1

𝑃N = 2√N ∑𝑁
𝑖=1(−2ln𝑝i − 2) →N(0, 1),

as T →∞, N →∞,(1)

where pi is the p-value associated with the unit root test statistic for individual i. We use two typesof unit root tests: the Augmented Dickey-Fuller (ADF) test where the null hypothesis is a unit root
(Dickey and Fuller, 1979) and the Kwiatkowsk-Phillips-Schmidt-Shin (KPSS) test where the null
hypothesis is stationarity (Kwiatkowski et al., 1992). Because the power of the tests of the unit
root null is low in small samples, testing the stationarity null is indispensable. We examine two
specifications: “with constant” and “with constant and time trend (TREND).”
Table 2 presents the test results. 15 The upper panel shows the results of 2008-survey. In thetwo specifications, with or without TREND, the results are almost identical. As for LEVEL,15

The number of lags of the lagged difference terms of the ADF test is selected according to Akaike

information criterion (AIC) for each regression. The number of lags truncation in the KPSS tests is
set at 12.
13

DIFFERENCE, and CHANGE, the ADF test rejects the null of non-stationarity, and the KPSS
test accepts the null of stationarity, suggesting that these series follow a stationary process. On
the other hand, as for INTEG, the ADF test accepts the null of non-stationarity, and the KPSS
test rejects the null of stationarity, suggesting that the series is non-stationary. The same results
are obtained for the 2009-survey, and they are shown in the lower panel. Thus, the results
unequivocally indicate that INTEG is non-stationary, whereas the other variables are stationary,
implying that LEVEL and INTEG cannot be the same series.
As for CHANGE and DIFFERENCE, although both series are stationary, the mean of
CHANGE is significantly positive, whereas that of DIFFERENCE is not significantly different
from zero. In addition, their correlation coefficient is only 0.456 in 2008 and 0.417 in 2009.
These results suggest that they are not the same series, even if they have some relationship.
In summary, the results of the unit root tests indicate that LEVEL and INTEG cannot be
the same series. This also applies to CHANGE and DIFFERENCE.

4. Why are the two series different?
Mathematically, the results of the integrated process of CHANGE must be identical to the level
itself. Thus, we need to question why the two series differ. We suggest and examine threepossible reasons for this.

144.1 Loss aversion
Asking a question regarding the change in happiness forces respondents to measure theirhappiness using the level of yesterday’s happiness as the reference point. Thus, loss aversion, bywhich a loss is evaluated as having twice the weight of a gain, may somehow affect the results(Kahneman and Tversky, 1979; Tversky and Kahneman, 1992).
However, since CHANGE and LEVEL themselves are the evaluation of good and badstates, loss aversion would not explain the gap between CHANGE and DIFFERENCE or
between LEVEL and INTEG.
The result that CHANGE tends to be positive simply implies that there are more good
aspects so as to make CHANGE positive even after the evaluation owing to loss aversion. 16
Indeed, since overall, our subjects get good news, maintain good health, and get good sleep (seeTable 1), it is not surprising that they tend to become happier.

4.2 Asking about happiness within a certain range
The second possibility arises from the style of the question that investigates the level of
16If good and bad events happen randomly with 50% probability each, and if subjects assign

greater weight to bad events, the change in happiness tends to be negative, which contradicts
our results.
15

happiness on a range from 0 (very unhappy) to 10 (very happy). Assume that the level ofhappiness is linearly increasing with time similarly to INTEG. In this case, respondents cannot
report their actual level of happiness because the answer is limited by an upper bound of 10.
Therefore, they may report their level of happiness by transforming the linear function to afunction that is asymptotic to 10 and 0 as the level of original happiness goes to infinity andminus infinity, respectively. An example of such a function is the logistic function:
𝑈−𝐿

𝐻 = 𝐿 + 1+exp (−𝑟𝐻�) ,(2)� is the original level of happiness, U and L are thewhere H is the reported level of happiness, 𝐻
upper and lower limits, respectively, and r is a parameter determining the slope of the function.
� from H with the inverse function of (2), which is
By a simple calculation, we can recover 𝐻
called the logit function:
� = 1 (ln(𝐻 − 𝐿) − ln(𝑈 − 𝐻)) .
𝐻
𝑟

(3)

� using Equation (3) resembles INTEG.
Let us examine whether the recovered series 𝐻

� for each respondent using
Specifically, we set U = 10, L = 0, and r = 0.1, and first calculate 𝐻� using the actual values of H is
Equation (3) and then average them. 17 The recovered 𝐻

� is a kind of enlarged figure of LEVEL in
depicted in Figure 4. 18 As shown in the figure, 𝐻
17Here, r is chosen arbitrarily.
18Alternatively,

� with Equation (3). The
we can use average H over respondents to calculate 𝐻

� is not increasing and does not
results are similar to the graph in Figure 4; thus, the calculated 𝐻
16both waves, and does not increase with time as INTEG does. 19
Thus, the supposition that asking about happiness in a certain range is the cause of thegap between LEVEL and INTEG is invalid. This result is consistent with our intuition, because
LEVEL in Figure 2 fluctuates around a constant level and does not show an increasing trend. A
transformation using Equation (3) simply extends the form; therefore, it cannot be expected thata constant series will be transformed into an increasing function.
However, one may argue that the transformation using Equation (3) is biased because theextension of H is symmetrical around H = 5 in spite of the fact that the average of H isapproximately 6. Therefore the region below H=6 should be extended more intensively
compared with the region above H=6. If this is done, then the result may change. In order toaddress this concern, we estimate an ordered probit model of H in order to obtain the estimatesof the cutoff values of 0 to 10. Then, we calculate the expected value of each class by fitting astandardized normal distribution to the actual frequencies falling in these classes. These� in Equation (3).
estimates represent “standardized latent happiness,” which corresponds to 𝐻

The latent happiness thus calculated is depicted in Figure 5. 20 It is apparent from the figure that

resemble LEVEL either.
19

We also depict the case of U = 7.5, L = 5.5, and r = 0.02. The graph is extended more; however, it

does not show an increasing trend.
20In the figure, latent happiness is multiplied by 10 because the variation is too small to be observed

17these estimates do not show an upward trend. Thus, the conclusion that asking about happinessin a certain range is not the cause of the difference is confirmed.

4.3 Adaptation
The last possibility examined in this paper is that although LEVEL (and therefore,
DIFFERENCE) is affected by adaptation, CHANGE (and therefore INTEG) is not.
Although our respondents tend to become happier every day because of, for example, thereceipt of good personal news, they may adapt themselves to the happier situation brought about
by the good news and return to their original level of happiness in a few days. We speculate that
CHANGE is less affected by adaptation than DIFFERENCE. In this subsection, we compare
CHANGE and DIFFERENCE, rather than INTEG and LEVEL, because we conduct a regression
analysis, which requires that the variables be stationary.
In our questionnaire, variables that may affect the respondents’ happiness are P_NEWS,
M_NEWS, SLEEP, HEALTH, NOANXIETY, and NOCLASS. We regress CHANGE andDIFFERENCE over these variables and their lagged variables and check whether the lagged
variables have opposite effects on happiness to those of the current variables. If the lagged
variables have opposite effects to those of the current ones, the effect of the current variables isotherwise.
18

cancelled, at least partially, in the consecutive periods (Clark et al., 2008). For the exposition,
assume that happiness, H, depends on P_NEWS for four periods such that
Ht = constant + αP_NEWSt − ∑3i=1 βi P_NEWSt−i + ut ,α, βi > 0.

(4)

Then, a one-unit increase in P_NEWS raises happiness by α units in the short-run (the currentday); however, it raises happiness by only α − ∑3i=1 βi units in the long-run (three days later).
If our respondents adapt to the news, such a result will be obtained by the regression of
Equation (4).
Table 3 presents the results for 2008- and 2009-surveys estimated by random or fixed
effect models. 21 Since the two results are essentially similar, we only explain the results for the
2009-survey (lower panel) in order to save space. The results are summarized in the following
four points.
First, the coefficients of the current variables show the expected positive signs for mostcases. In particular, P_NEWS, HEALTH, and NOANXIETY have large effects on happiness.
Second, regarding the current variables, the magnitudes of the estimates are similar for
CHANGE and DIFFERENCE for all the variables except SLEEP and NOCLASS.
Third, focusing on the significant estimates, the estimates of the current variables andthose of the lagged variables take the opposite signs for P_NEWS, M_NEWS, HEALTH, and21We selected the model based on the Hausman test.

19

NOANXIETY. These results imply that the long-run effect of these variables on happiness(CHANGE and DIFFERENCE) is smaller than their short-run effects, suggesting that the
respondents adapt to the level of happiness brought about with these variables.
Fourth, the absolute values of the estimates of the significant lagged variables of
DIFFERENCE are larger than those of CHANGE in all cases, suggesting that adaptation islarger for DIFFERENCE than for CHANGE.
The fourth fact, which is the most important fact for this paper to determine, is the causeof the difference between CHANGE and DIFFERENCE (and thus between LEVEL and INTEG).
In order to confirm the difference in the degree of adaptation between CHANGE andDIFFERENCE, we calculate the short-run effect, long-run effect, and adaptation ratio, which
are defined as follows: the short-run effect is the coefficients of the current variables, thelong-run effect is the sum of the significant coefficients of the current and lagged variables, andlong−run effectthe adaptation ratio is defined as �1 − short−run effect� × 100 (%). We do not calculate them if
the coefficient of the current variable is insignificant (i.e., if the short-run effect is zero).
Table 4 presents the results. The adaptation ratios are close to 100% for all the variables
for the case of DIFFERENCE for both waves, implying that none of the variables has any effecton happiness (DIFFERENCE, and therefore LEVEL) in the long-run. On the other hand, theadaptation ratios for CHANGE are approximately 50% for the 2008-survey and approximately

2030% for 2009-survey, suggesting that adaptation is not perfect for CHANGE.
The results suggest that the reason that the mean of DIFFERENCE is not statisticallydifferent from zero and LEVEL fluctuates around a constant level is that the subjects fully adapt
to the situation: although DIFFERENCE is significantly and largely affected by the current
variables, the effect is cancelled in three days. In contrast, CHANGE also adapts to the situation;
however, the adaptation is much weaker than that for DIFFERENCE. The difference in themagnitude of adaptation is the cause of the divergence between LEVEL and INTEG.

5. Discussion and conclusions
In this paper, we investigated whether the level of happiness and the integrated process ofchanges in happiness are the same process. We found that they follow different processes:
although the level is stationary, the integration of changes is non-stationary with an apparent
rising trend.
Despite the fact that mathematically, the integration of changes is the same as the level,
why do these two variables diverge? We examined three possible reasons and found that
DIFFERENCE is fully affected by adaptation, whereas CHANGE is partially affected byadaptation. Thus, in the long-run, the effects of various impacts on DIFFERENCE arecompletely cancelled in the following three days, whereas those on CHANGE are only partially21cancelled. This is the reason why INTEG, which is the integration of CHANGE, and LEVEL,
which is the integration of DIFFERENCE, diverge. Overall, the empirical outcomes are robust
across the waves.
Our results have an important implication for the Easterlin paradox, which is thephenomenon that subjective happiness, which corresponds to LEVEL in this paper, is stableirrespective of whether the standard of living (GDP) improves or deteriorates. The relative
income hypothesis and adaptation hypothesis are known to offer effective explanations of theparadox (Clark et al., 2008; Knight and Song, 2006), and they imply that if adaptation does notoccur, the Easterlin paradox should, if not completely, partially disappear. Thus, our results
suggest that if we ask about the change in happiness, CHANGE, and construct its integrated
process, INTEG, then INTEG may not exhibit the Easterlin paradox. This inference is based on
our results that adaptation affects CHANGE only partially, whereas it completely affects
DIFFERENCE.
The relationship between subjective happiness and utility is not understood fully. 22 We
believe, however, that an important distinction between the two is that decision utility isconstructed from comparisons of two ex-ante states, whereas subjective happiness is based on
22

Many economists think that comparison of subjective happiness among individuals lacks a solid

basis, whereas researchers in the field of economics of happiness estimate the happiness function
using data on subjective happiness.
22the introspection of the current feelings of an individual. Since utility is based on a comparisonof ex-ante states, it should be free from adaptation. Thus, utility is expected to increase when thestandard of living (GDP) improves.
In fact, in a survey conducted in Japan in 2008, we asked respondents whether theywould have preferred to have been born in 1910, 1950, or 1980, and many selected the later
period, suggesting that they preferred a higher standard of living when they compared theperiods. We also asked Japanese respondents whether they would have preferred to have beenborn in Italy or Indonesia, and Singapore or Mexico. These two pairs of countries differ
significantly with respect to GDP, but according to the World Value Survey, the average
subjective happiness of the nations is almost the same. Most respondents chose Italy (84%) andSingapore (68%); both these countries enjoy higher GDPs.
The Easterlin paradox means that economic growth does not lead to an improvement insubjective happiness, which raises a question regarding the role of economic growth. However,
our results suggest that INTEG (CHANGE) may be a closer concept to utility than LEVEL(DIFFERENCE) in that the former is freer from adaptation. This implies the possibility that theparadox will disappear if we measure subjective happiness by the sum of changes in happiness.
Let us examine the above speculation on the basis of our survey conducted in Japan fromfiscal years 2003 to 2009. In the survey, we investigated the level of happiness (Q1) and the23change in happiness compared with the level that existed a few years ago. From the former
question, we defined LEVEL as the average of the answer. In the latter question, respondents
were requested to select from the following options: 1. Happier than a few years ago, 2. Same asa few years ago, 3. Unhappier than a few years ago. 23 We defined CHANGE as two minus theanswer to this question, which takes the value of 1, 0, or -1. We used the average of CHANGE
over all respondents and calculate INTEG as before. Figure 6 presents the values of LEVEL and
INTEG obtained subsequently. Although LEVEL is almost constant around 6.4, INTEG increases
from 6.4 to 7.1, reflecting that CHANGE is positive for all the years. Thus, the result isessentially similar to those using the daily data in this paper. In the figure, we also show“consumption of household,” as a proxy for the standard of living, which seems to correspond
more with INTEG than with LEVEL. 24 Indeed, its correlation coefficient is -0.22 with LEVEL
and 0.74 with INTEG. These results suggest that the Easterlin paradox is seen between LEVEL
and “consumption of household,” but not between INTEG and “consumption of household.” 25
A problem of this analysis is that the data spans only seven years. In order to obtain more
23

To be precise, “4. Do not know” is included in the options. In 2008 and 2009, the comparison ismade with “a year ago” instead of “a few years ago.”
24

Here, “consumption of household” is normalized so that the value of the first year equals 6.38,

that is, the value of LEVEL in that year.
25

When we use GDP instead of consumption of household, its coefficient is -0.49 with LEVEL and

0.40 with INTEG.
24reliable results, it is necessary to conduct a longitudinal survey that investigates the changes inhappiness and examines if the integrated process of the change in happiness corresponds to thestandard of living (GDP). This is an important subject for future research.

25

Acknowledgments
We would like to extend our heartfelt gratitude to Masahiko Shibamoto and Frans van Winden

for their comments and acknowledge financial support from the Global Center of Excellence(GCOE) program at Osaka University. This paper was presented at MEW (Monetary Economics

Workshop) and the 2011 Spring Meeting of the Japanese Economic Association.

26

References
Bruni, L. and P. L. Porta (2005) Economics and Happiness: Framing the Analysis, New York:
Oxford University Press.
Choi, I. (2001) “Unit Root Tests for Panel Data”, Journal of International Money and Finance,
Vol. 20, No. 2, pp. 249–272.
Clark, A. E., P. Frijters and M. A. Shields (2008) “Relative Income, Happiness, and Utility: An
Explanation for the Easterlin Paradox and Other Puzzles”, Journal of Economic Literature,
Vol. 46, No. 1, pp. 95–144.
Clark, A. E. and A. J. Oswald (1996) “Satisfaction and Comparison Income”, Journal of Public
Economics, Vol. 61, No. 3, pp. 359–81.
Csikszentmihalyi, M. and J. Hunter (2003) “Happiness in Everyday Life: The Uses ofExperience Sampling”, Journal of Happiness Studies, Vol. 4, pp. 185–199.
Dickey, D. A. and W. A. Fuller (1979) “Distribution of the Estimators for Autoregressive Time
Series with a Unit Root”, Journal of the American Statistical Association, Vol. 74, No. 366,
pp. 427–431.
Di Tella, R., J. Haisken-DeNew and R. J. MacCulloch (2007) “Happiness Adaptation to Income
and to Status in an Individual Panel”, NBER Working Paper, No. 13159.
Dolan, P., T. Peasgood, and M. White (2008) “Do We Really Know What Makes Us Happy? A
Review of the Economic Literature on the Factors Associated with Subjective
Well-being”, Journal of Economic Psychology, Vol. 29, No. 1, pp. 94–122.
Duesenberry, J. S. (1949) Income, Savings, and the Theory of Consumer Behaviour, Cambridge:
Harvard University Press.
Easterlin, R. A. (1974) “Does Economic Growth Improve the Human Lot? Some Empirical
Evidence”, in P. A. David and M. W. Reder, eds., Nations and Households in Economic
Growth: Essays in Honor of Moses Abramowitz, New York: Academic Press, pp. 89–125.
____ (2005) “Building a Better Theory of Well-Being”, in L. Bruni and P. L. Porta, eds.,
Economics and Happiness, Oxford: Oxford University Press, pp. 29–64.
27

Frank, R. H. (2005) “Does Absolute Income Matter?” in L. Bruni and P. L. Porta, eds.,
Economics and Happiness, Oxford: Oxford University Press, pp. 65–90.
Frey, B. S. and A. Stutzer (2002a) Happiness and Economics, Princeton: Princeton University
Press.
____ and ____ (2002b) “What Can Economists Learn from Happiness Research?” Journal of
Economic Literature, Vol. 40, No. 2, pp. 402–435.
Kahneman, D. and A. B. Krueger (2006) “Developments in the Measurement of Subjective
Well-Being”, Journal of Economic Perspectives, Vol. 20, No. 1, pp. 3–24.
Kahneman, D., A. B. Krueger, D. Schkade, N. Schwartz and A. A. Stone (2004a) “A Survey
Method for Characterizing Daily Life Experience: The Day Reconstruction Method”,
Science, Vol. 306, pp. 1776–1780.
____, ____, ____, ____ and ____ (2004b) “Toward National Well-Being Accounts”, AEA
Papers and Proceedings, pp. 429–439.
Kahneman, D. and A. Tversky (1979) “Prospect Theory: An Analysis of Decision Making under
Risk”, Econometrica, Vol. 47, No. 2, pp. 263–292.
Kimball, M. and R. Willis (2006) “Utility and Happiness”, mimeo.
Knight, J. and L. Song (2006) “Subjective Well-Being and Its Determinants in Rural China”,
http://www.economics.ox.ac.uk/Research/wp/pdf/paper334.pdf.
Kwiatkowski, D., P. C. B. Phillips, P. Schmidt and Y. Shin (1992) “Testing the Null Hypothesisof Stationarity against the Alternative of a Unit Root: How Sure Are We That Economic
Time Series Have a Unit Root?” Journal of Econometrics, Vol. 54, Nos. 1–3, pp.
159–178.
Scollon, C. N., C. Kim-Prieto and E. Diener (2003) “Experience Sampling: Promises andPitfalls, Strengths and Weaknesses”, Journal of Happiness Studies, Vol. 4, pp. 5–34.

28

Stevenson, B. and J. Wolfers (2008) “Economic Growth and Subjective Well-being:
Reassessing the Easterlin Paradox”, Brookings Papers on Economic Activity, Spring, pp.
1–102.
Tversky, A. and D. Kahneman (1992) “Advances in Prospect Theory: Cumulative
Representation of Uncertainty”, Journal of Risk and Uncertainty, Vol. 5, No. 4, pp.
297–323.

29

Table1 Descriptive statistics
2008-survey

MeanStandard

standard

95% lower 95% upper

Deviation

error

band

band

Min.

Max.

LEVEL

5.875

2.036

0.024

5.829

5.922

0

10

CHANGE

0.115

1.223

0.014

0.088

0.143

-3

3

INTEG

10.58540.639

0.473

9.659

11.512-151

347

DIFFERENCE

0.003

1.656

0.019

-0.035

0.041

-10

10

P_NEWS

0.544

2.297

0.027

0.492

0.597

-5

5

M_NEWS

-0.4

1.981

0.023

-0.445-0.354

-5

5

SLEEP

2.645

0.984

0.011

2.622

2.667

1

4

HEALTH

2.737

0.813

0.009

2.718

2.756

1

4

NOANXIEY

2.021

0.952

0.011

1.999

2.043

1

4

NOCLASS

0.684

0.465

0.005

0.673

0.695

0

1

Number of

7389

Standard

standard

95% lower 95% upper

Min.

Max.

Deviation

error

band

band

observations

2009-survey

Mean

LEVEL

6.216

2.036

0.024

6.170

6.263

0

10

CHANGE

0.199

1.141

0.013

0.173

0.225

-3

3

INTEG

23.83656.9390.666

22.53125.140-167

476

DIFFERENCE-0.003

1.573

0.018

-0.039

0.033

-9

10

P_NEWS

0.794

2.577

0.030

0.735

0.853

-5

5

M_NEWS-0.064

2.068

0.024

-0.112-0.017

-5

5

SLEEP

2.652

0.988

0.012

2.629

2.674

1

4

HEALTH

2.817

0.872

0.010

2.797

2.837

1

4

NOANXIEY

2.271

1.061

0.012

2.247

2.295

1

4

NOCLASS

0.580

0.494

0.006

0.569

0.591

0

1

Number of

7319

observations

30

Table 2 Results of panel unit root tests
2008-survey

without TREND

with
ADF

KPSS

TREND
ADF

KPSS

PN

p valuePN

p valuePN

p valuePN

p value

LEVEL

4.745

0.000

0.096

0.924

4.473

0.000

-0.867

0.386

INTEG

-0.6750.500

10.513

0.000

0.545

0.586

10.513

0.000

CHANGE

4.745

0.000

-0.196

0.845

2.713

0.007

-0.617

0.537

DIFFERENCE

8.210

0.000

-0.928

0.354

8.210

0.000

-0.9720.331

2009-survey

without TREND

with
ADF

KPSS

TREND
ADF

KPSS

PN

p valuePN

p valuePN

p valuePN

p value

LEVEL

8.210

0.000

-0.605

0.545

8.210

0.000

-0.299

0.765

INTEG

-0.2490.804

10.513

0.000

0.310

0.757

10.513

0.000

CHANGE

8.210

0.000

-0.697

0.486

5.725

0.000

-0.876

0.381

DIFFERENCE

8.210

0.000

-1.000

0.317

8.210

0.000

-1.0000.317

Note: PN is a Fisher’s statistic as defined in Choi (2001) based on a p value of the individual augmented Dickey and Fuller (1979) ofnull of a unit root and the individual Kwiatkowski et al. (1992) test of the null of no unit root. The lag length of the lagged differenceterms to be added to the individual ADF test was selected according to Akaike information criterion (AIC) for each regression, andtruncation lags in the KPSS test was set at 12. A Fisher’s statistic PN as defined in Choi (2001) has a N(0, 1) distribution under thenull hypothesis.
31

Table 3

Estimation results on adaptation hypothesis
2008

CHANGE

DIFFERENCE

fixed effectrandom effectVariable

Coefficient

P-value Coefficient P-value

P_NEWS

0.302[.000]

0.404[.000]

P_NEWS(-1)-0.084[.000]-0.363[.000]

P_NEWS(-2)-0.039[.000]-0.033[.000]

P_NEWS(-3)-0.025[.000]-0.011[.165]

M_NEWS

0.007[.250]

0.050[.000]

M_NEWS(-1)-0.009[.174]-0.052[.000]

M_NEWS(-2)-0.011[.091]

0.001[.889]

M_NEWS(-3)-0.020[.002]-0.001[.918]

SLEEP

0.000

[.999]

0.020[.311]SLEEP(-1)-0.033[.013]-0.008[.704]SLEEP(-2)

0.011[.418]

0.013[.525]SLEEP(-3)

0.005[.680]-0.027[.162]HEALTH

0.179[.000]

0.206[.000]HEALTH(-1)-0.047[.011]-0.165[.000]HEALTH(-2)-0.035[.061]

0.006[.827]HEALTH(-3)-0.038[.037]-0.053[.046]NOANXIETY

0.293[.000]

0.187[.000]NOANXIETY(-1)-0.087[.000]-0.166[.000]NOANXIETY(-2)-0.020[.284]

0.026[.348]NOANXIETY(-3)-0.064[.000]-0.039[.142]NOCLASS

0.078[.004]

0.034[.394]NOCLASS(-1)-0.035[.227]-0.093[.034]NOCLASS(-2)-0.039[.173]-0.010[.823]NOCLASS(-3)-0.016[.541]

0.030[.459]

0.036[.637]

constant
adjusted R

2

Number of

0.535

0.392

7249

7249

observations
Hausman test[.027][1.000]32

2009

Variable

CHANGE

DIFFERENCE

random effectrandom effectCoefficient P-value

Coefficient P-value

P_NEWS

0.260[.000]

0.295[.000]

P_NEWS(-1)-0.050[.000]-0.274[.000]

P_NEWS(-2)-0.023[.000]-0.019[.015]

P_NEWS(-3)-0.015[.001]-0.011[.129]

M_NEWS

0.047[.000]

0.045[.000]

M_NEWS(-1)-0.017[.001]-0.042[.000]

M_NEWS(-2)

0.004[.405]

0.004[.622]

M_NEWS(-3)

0.000[.978]-0.003[.684]

SLEEP

0.036

[.001]

0.001[.959]SLEEP(-1)

0.011[.351]

0.015[.420]SLEEP(-2)-0.014[.210]-0.038[.041]SLEEP(-3)

0.006[.601]

0.024[.182]HEALTH

0.172[.000]

0.174[.000]HEALTH(-1)-0.048[.006]-0.176[.000]HEALTH(-2)-0.014[.404]

0.008[.773]HEALTH(-3)-0.012[.466]-0.003[.923]NOANXIETY

0.228[.000]

0.288[.000]NOANXIETY(-1)-0.059[.000]-0.301[.000]NOANXIETY(-2)-0.021[.191]

0.043[.106]NOANXIETY(-3)-0.052[.001]-0.036[.163]NOCLASS

0.055[.016]

0.008[.838]NOCLASS(-1)-0.038[.119]-0.076[.061]NOCLASS(-2)-0.001[.969]

0.059[.145]NOCLASS(-3)

0.016[.489]-0.015[.689]

constant-0.564[.000]

0.018[.812]

adjusted R

2

Number of

0.445

0.311

7211

7211

observations
Hausman test[.532][1.000]33

Table 4 Adaptation ratio of CHANGE and DIFFERENCE

2008

P_NEWS

M_NEWSSLEEPHEALTHANXIETY

NOCLASS2009

CHANGE

DIFFERENCECHANGE

DIFFERENCE

short-run effect

0.302

0.404

0.260

0.295

long-run effect

0.153-0.003

0.172

0.003

adaptation ratio (%)

49.3

100.8

34.1

99.1

short-run effect

0

0.050

0.047

0.045

long-run effectNA-0.003

0.030

0.003

adaptation ratio (%)

NA

105.2

36.0

93.2

short-run effect

0

0

0.036

0

long-run effect

NA

NA

0.036

NA

adaptation ratio (%)

NA

NA

0.0

NA

short-run effect

0.179

0.206

0.172

0.174

long-run effect

0.094-0.0120.125-0.002

adaptation ratio (%)

47.6

105.7

27.6

101.1

short-run effect

0.293

0.187

0.228

0.288

long-run effect

0.143

0.021

0.117

-0.012

adaptation ratio (%)

51.4

88.8

48.6

104.2

short-run effect

0.078

0

0

0

long-run effect

0.078

NA

NA

NA

0.0

NA

NA

NA

adaptation ratio (%)

34

Figure 1 Response rate

35

Figure 2 LEVEL and INTEG
2008-survey
25

20

15

10

5
LEVEL
INTEG

0
2007/12/01

2008/01/01

2008/02/01

2008/03/01

2009-survey
50
45
40
35
30
25
20
15

LEVEL

10

INTEG

5
0
2009/01/01

2009/02/01 2009/03/01

2009/04/01

36

2009/05/01

2009/06/01

Figure3 CHANGE and DIFFERENCE
2008-survey
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6

DIFFEREENCECHANGE-0.8
2007/12/02

2008/01/02

2008/02/02

2008/03/02

2009-survey
0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
2009/1/1

2009/2/1

2009/3/1

2009/4/1

2009/5/1

2009/6/1
DIFFERENCECHANGE37� using equation (3)Figure 4 Recovered 𝐻

2008-survey
8
7
6
5
4
3
2

LEVEL

1

recovered0
2007/12/01

2008/01/01

2008/02/01

2008/03/01

2009-survey
10
9
8
7
6
5
4
3
2

LEVEL

1

recovered0
2009/01/01

2009/02/01 2009/03/01

2009/04/01

38

2009/05/01

2009/06/01

Figure 5 Latent happiness2008-survey
8
6
4
2
0
-2
H

-4
2007/12/01

2008/01/01

2008/02/01

latent happiness x 10
2008/03/01

2009-survey
8
6
4
2
0
-2
H

latent happiness x 10

-4
2009/01/01

2009/02/01

2009/03/01

2009/04/01

39

2009/05/01

2009/06/01

Figure 6 The Easterlin paradox
7.2
7
6.8
6.6
6.4
6.2
LEVEL

6

INTEG

5.8

Consumption of Households

5.6
2003

2004

2005

2006

40

2007

2008

2009