GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No. 360

Activity, Time and Subjective Happiness:
An Analysis Based on an Hourly Web Survey

Hideaki Sagawa, Fumio Ohtake, and Yoshiro Tsutsui

February 2015

GCOE Secretariat
Graduate School of Economics
OSAKA UNIVERSITY
1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

Activity, Time, and Subjective Happiness:
An Analysis Based on an Hourly Web Survey†

Hideaki Sakawa§
Fumio Ohtake¶Yoshiro Tsutsui‡
Abstract
This paper investigates how people’s happiness depends on their current activities andon time. We conducted an hourly web survey, in which 70 students reported theirhappiness every hour on one day every month from December 2006 to February 2008.
This method is an extension of the experience sampling method (ESM), since it uses
mobile phones and personal computers. Our new method has the same strength of ESM
in that it can measure real-time happiness data and thus avoid reflection and memorybias. Using our new method, we can obtain diurnal happiness data of respondents andalso grasp their behavior at each of their reporting times over 14 months. Analyzing thedata of our survey, we found (a) happiness significantly depends on activities, hours,
and months, (b) while most of the time-variation of happiness is attributable to the time
pattern of activities, happiness varies predictably with the hour in a day, even when
activities are controlled for, and (c) while activities affect both genders similarly, there
are gender gaps in the diurnal happiness pattern after controlling for activities.
JEL classification number: I31
Keywords: happiness, hourly web survey, ESM, mobile phone, Japan†An earlier version of this paper was presented at the MESS workshop and at annual meeting of
Association of Behavioral of Economics and Finance. We are grateful to Katherine Carman andAkiko Kamesaka for their comments. We gratefully acknowledge that this study is financially
supported by the COE and GCOE programs of Osaka University.
§
Faculty of Economics, Nagoya City University, Yamanohata 1, Mizuho-ku, Nagoya, 467-8501
Japan, e-mail: sakawa@econ.nagoya-cu.ac.jp¶
Institute of Social and Economic Research, Osaka University, Mihogaoka 6-1, Ibaraki,
567-0047 Japan, e-mail: ohtake@iser.osaka-u.ac.jp‡
Faculty of Economics, Konan University, 8-9-1 Okamoto, Higashinada-ku, Kobe, 658-8501
Japan, e-mail: tsutsui@center.konan-u.ac.jp01.

Introduction
For a long time, it has been a convention in happiness studies to ask “All things

considered, how satisfied are you with your life as a whole these days?” (The World
Values Survey) or “Taken all together, how would you say things are these days? Would
you say that you are very happy, pretty happy, or not too happy?” (The General Social
Survey). These questions ask people to evaluate their own life or make a global
retrospective judgment (Kahneman and Krueger, 2006). To answer these questions,
respondents reflect on their life from various viewpoints. Some of them might predict
their happiness, considering their home and work situations and comparing these withthose of other people, reaching a conclusion such as “considering various elements, I
should be happy, even if I don’t feel so.” In a word, it is known that memory andreflection often differ from current feelings (Kahneman, 1999).
To escape from reflection and memory bias, the experience sampling method(ESM) has been developed. ESM refers to a method of data collection in which
participants respond to repeated assessments at different moments over the course oftime while functioning within their natural settings (Scollon et al., 2003). ESM typicallyuses beepers to alert respondents to fill in questionnaires at a randomly determined time.
A merit of ESM is that it measures “point-instant utility” (Kahneman, 1999), which may

1be close to “instantaneous utility” in economics, and is free from memory bias. On theother hand, “overall” happiness is more difficult to relate to the concept of utility ineconomics.1 A demerit of ESM is that it is a costly method, and burdens respondents,
which may cause a selection bias problem.
Two methods have been developed associated with ESM. The first is ecologicalmomentary assessment (EMA), in which measurement is concerned not just with theparticipant’s momentary subjective experience, but also with elements of theenvironment related to momentary experience (Stone at al. 1999). For example, Steptoe
et al. (2005) asked 228 individuals to report their happiness every 20 minutes during aworkday, and measured the level of cortisol, an adrenal hormone related to the risk ofobesity, hypertension and autoimmune conditions, every two hours. From these data,
the authors found that subjects’ happiness ratings were significantly inversely correlatedwith their cortisol levels. This line of studies is expected to establish a foundation formeasuring objective happiness. However, EMA is more costly and puts a heavier
burden on subjects than ESM.
The other new method is the daily reconstruction method (DRM) proposed by
Kahneman et al. (2004a), in which respondents first revive their memories of the1

Kimball and Willis (2006) dispute both the idea that happiness and utility have no relation andthe idea that they are the same, and postulate a more complex relationship between the two.
Happiness may thus be related to, but not identical to, instantaneous utility of the type found in
most economic models.
2

previous day by constructing a diary consisting of a sequence of episodes, then describe
each episode by answering questions about the situation and about the feelings that theyexperienced, as in ESM. Compared with ESM, DRM has the advantages of low costs tothe investigator and low burden on subjects. Kahneman et al. (2004a,b) show that thediurnal cycles of affect and tiredness produced by DRM and ESM are quite similar. Atz(2013) reports a low recall bias for the DRM. Thus, DRM might be a good substitute for
ESM, although more studies are necessary to confirm this (Diener and Tay 2014, Tay et
al. 2014).
This study essentially follows ESM, but has the following distinguishing features.
First, we utilize a web survey, in which subjects access a website by personal computeror mobile phone.2 The conventional ESM cannot confirm that subjects really fill informs when they receive a beep, which damages the reliability of the results of ESM(Scollon et al., 2003). In our web survey, the timing of the answers by subjects is
automatically recorded in the database, which allows no cheating. In addition, mostmembers of the younger generation in Japan always carry their mobile phones, whichhave featured internet connection and email since 2006. Thus, this method puts less of a2

Recently, papers using smartphones for ESM have been published. MacKerron and Mourato(2013) and Doherty et al. (2014) investigate how happiness relates to natural environments, using the
Global Positioning Systems (GPS) and other functions of smartphones. The former study reports thathappiness is greater in natural environments. In Atz (2013) respondents report their happiness level
using smartphones.
3

burden on respondents when they report while outside, while bringing beepers andforms is a burden to subjects (Scollon et al., 2003).
Second, we do not beep respondents at random time, but trust them to reportwhenever they want to. However, we made the following stipulations: Respondents
should start reporting within three hours after waking up and should report more thanten times during the day, and the consecutive reports should be separated more than 50
minutes. Thus, our method is “interval-contingent sampling” in a broader definition ofESM (Scollon et al., 2003).3 Making a beep would have caused a problem because our
subjects, as university students, attend classes. If we followed the conventional ESM
method with a beeper and “signaling sampling,” few students would have joined oursurvey. Using mobile phones, on the other hand, our subjects probably report while inclass. However, this removes some of the randomness of “signaling sampling.”
Third, our survey collects data for a continuous period of 14 months, and our
subjects choose one day every month and make a report on that day. Meanwhile, ESM

typically constrains respondents for a week. 4 Thus, our survey offers a unique
longitudinal dataset.
Using data from 70 Japanese students for a period of over a year, this paper

34Usual ESM uses “signal-contingent sampling.”
There are some exceptions. For example, Clark and Watson (1988) collected data for 90 days.
4examines how happiness depends on activities, and investigates patterns of variation inhappiness during a day, a week and a year. Though previous studies including
Kahneman et al. (2004a) reported that happiness changes with time during a day, they
did not adjust for the effect of the activity being undertaken at the time of datacollection. Thus, their results might merely indicate a time pattern of activity performed
in a day, which in turn affects happiness. It is interesting to know whether or nothappiness changes along with time, even controlling for subjects’ activities; weinvestigate that question in this paper.5
The rest of this paper is structured as follows. In section 2, we explain our survey.
In sections 3 and 4, we investigate how happiness depends on activity and time,
respectively, where time means hour of the day, day of the week, and month of the year.
In section 5, we run a regression to identify the effects of activities and of time. Section
6 concludes.

2. Our survey
2.1 Outline of the survey
We recruited 70 undergraduate and graduate students of Osaka University and5

In addition, panel data has to the advantage of identifying the relationship of happiness withactivity and time while adjusting for individual effects.
5requested them to report every day their happiness and evaluation of personal andmacro news (how good or bad they are) arriving on that day. We call this “daily survey.”
We also ask them to choose one day every month on which to report their happiness andwhat they were doing just before reporting, once during every hour during that day. We
call this “hourly survey.” This paper only uses the data of hourly survey on the sample
from December 1, 2006 to February 18 2008. Respondents were requested to start
reporting within three hours after waking up and to report more than ten times a day,where each two consecutive reports should be separated by more than 50 minutes.6
Those reports that did not satisfy these conditions were discarded. Students could
answer using a mobile phone or a personal computer.7
The hourly survey consists of two questions: Q1. How happy do you feel now?
Please rate it on a scale from 0 (very unhappy) to 10 (very happy).
Q2. What were you doing just before now? Choose one of the following options:
1. Studying (alone), 2. Attending a class, a seminar, or a group study, 3. Commuting to
the university, 4. Side job, 5. Club or circle, 6. Exercise, 7. Date (including by phone), 8.
Enjoying time with friends (including by phone), 9. Enjoying time with family members(including by phone), 10. Watching TV or playing a game, 11. Doing internet or email,This method is called “interval-contingent sampling” in the broader definition of ESM(Scollon et al., 2003).
7
In Japan, most mobile phones were advanced enough to connect to the internet even before
the advent of smartphones.
66

12. Preparation for or cleanup after meals, 13. Household chores, 14. Meals, 15.
Drinking, 16. Smoking, 17. Shopping, 18. Driving, 19. Playing pachinko (a Japanesegambling game), 20. Going out for things to do, 21. Taking a rest, 22. Sleeping, 23.
Other

2.2 Payment schedule
Subjects were paid 160 yen (about $1.60) per answer for a daily survey. Those
who answered more than 22 days for the daily survey and answered the hourly survey
more than once a month were paid a 1300 yen a bonus for this month, and those who
answered more than 27 days and the hourly survey received a 2600 yen bonus for themonth.

2.3 Response rate
We obtained 8536 answers for the hourly survey. The response rate is 8536/
9800=87%. The response rates for the daily survey and hourly surveys were around
90% and 80%, respectively. Although selection biases are often pointed out as a demeritof ESM, the current study is therefore free from this problem

7

3. Activities and happiness
The number of observations and average happiness for each activity aresummarized in Table 1. Here, we reclassify the 23 activities into 15 categories, sincesome activities were reported too rarely to compute reliable averages. In reclassifying,
we were careful to incorporate similar categories into one, whose averages do not differ
substantially. Specifically, we incorporated class and commuting (2 and 3), side job,clubs, and exercise (4, 5, and 6), enjoying time with family and friends (8 and 9), TV
and internet (10 and 11), preparation for meals and household chores (12 and 13),drinking and pachinko (15 and 19), and shopping and driving (17 and 18).
Table 1 reveals interesting facts. First, the average happiness of all 8536 responses
is 5.947, which is lower than the usual reported values of happiness for Japanese people.
According to the annual survey conducted by Osaka University, the average happinessis around 6.2 (in 2007) and 6.32 (in 2003). The “Survey on the life preferences ofpeople in Japan” conducted by the Cabinet Office reports similar results. While
respondents are asked to evaluate their overall happiness in the annual survey, survey
respondents are asked about their current feelings in the hourly survey. “Althoughretrospective evaluations are related to the real-time reports… retrospective reports arealso susceptible to systematic biases” (Kahneman and Krueger, 2006). This may be8evidence for the existence of retrospective biases. 8
Secondly, the activities that entail considerably lower happiness than the average
are attending classes, smoking, and sleeping. The result for attending classes is
consistent with previous works, such as Csikszentmihalyi et al. (2003) and Bassi andFave (2004).9 Considering that attending classes is a kind of job for students, the result
is also consistent with Kahneman et al. (2004b). It is interesting that spontaneous study
brings about a higher level of happiness than attending classes.10 The results support
the assumption adopted in economics that job and leisure brings about negative andpositive utility, respectively.
Watching TV and connecting to the internet leads to slightly lower happiness than
the average, which is consistent with the observations of Frey et al. (2007),
Csikszentmihalyi et al. (2003) and Bassi and Fave (2004).
It is widely known that smokers are unhappier than others (Gruber andMullainathan 2005). However, the causality between happiness and smoking is not fully
known. From this viewpoint, our survey is potentially interesting, because comparinghappiness before and after smoking might have elucidated the causality. Unfortunately,

8

The result may merely imply that students of Osaka University are less happy than averagepeople. However, since students are significantly happier than the average in the annual survey,
this interpretation is questionable.
9 Csikszentmihalyi et al. (2003) examine students of 6th, 8th, 10th, and 12th graders and Bassi andFave (2004) examines adolescents aged between 15 and 18.
10
Csikszentmihalyi et al. (2003) report a similar result.
9since the number of the observations of smoking is only 6, we cannot test thishypothesis with our data.
“Sleeping” means happiness upon waking up; we observe that people are lesshappy when they have just woken up. This is consistent with the fact that depressedpeople are in a worse condition in the morning.
Students are the happiest when they are dating, for which no explanation isnecessary. Drinking makes people feel happier. Meals have a similar effect, even if not
so strong as drinking. Enjoying time with family and friends makes people happier.
In the right-hand columns of Table 1, we show the results of Csikszentmihalyi etal. (2003), Bassi and Fave (2004), and Kahneman et al. (2004a). Since theirclassifications of activities are different from ours, the figures are rough comparisons atbest. Still, one can see that the ranking of activities in our study by associated happinessis similar to those of Csikszentmihalyi et al. (2003) and Bassi and Fave (2004). It differs
only slightly from the results of Kahneman et al. (2004b).11

4. Time and happinessTime has cycles such as morning, afternoon, evening, and night during a day,

11

This may be because their sample is of working women instead of students, or because theyuse DRM instead of ESM.
10

weekdays and weekends during a week, and seasons (months) during a year. Subjective
happiness may fluctuate depending on these cycles; e.g., it is sometimes claimed thatpeople living in the northern part of Europe are depressed during the long winter andenjoy themselves during summer. People seem more relaxed during holidays than on
weekdays. In this section, we investigate the time patterns of happiness. Diurnal
variation in happiness, among others, is interesting in this paper, because this cannot be
investigated without an hourly survey.

4.1 Diurnal variation in happinessIn our web survey, the time an answer is sent is automatically recorded. Figure 1,
which shows the number of observations during each hour, reveals that most responses
were made during daytime, although some were made at night.
The average happiness in each hour during a day is depicted in Figure 2, alongwith 95% confidence intervals. The average happiness falls during night, reaching thelowest value at 7:00 in the morning. It then rises until 10:00. After staying at a constantlevel until 16:00, it goes up until 23:00. This pattern is similar to what Kahneman et al.
(2004a) found, although the change in happiness is not significant for most cases.
The activities of students are considerably different between weekdays and11holidays.12 Specifically, they study less on holidays and spend more time on TV, meals,
and shopping. This suggests that the diurnal variation in happiness may also be different
between weekdays and holidays.
Diurnal variation in happiness on weekdays is shown in Figure 3. While it issimilar to Figure 2, the fluctuation is clearer. In contrast, while diurnal variation inhappiness on holidays shows a similar pattern, the fluctuation is more moderate (seeFigure 4). In sum, happiness is the lowest in the morning and the highest in the evening,
and this pattern is more salient on weekdays. These results suggest that the pattern ofhappiness may reflect, at least partially, the stress of preparation for study before 9:00
and the release from stress after 17:00.
The diurnal pattern of happiness has been investigated in several studies. Amongothers, Csikszentmihalyi et al. (2003) investigated 828 elementary school students with
ESM, finding that “the first part of the day spent at school is less happy except for apeak at lunch time, followed by higher happiness in the afternoon when one is again
free.” Kanheman et al. (2004a) found that negative affect is the highest in the morningand declines after one returns home, reaching a low at 21:00. Studying 123 college
students, Clark et al. (1989) report that positive affect rises from 6:00 to noon and stays

In this paper, we define “holidays” as weekends plus holidays and define “weekdays” as
weekdays excluding holidays.
1212

at the level until 21:00, then declines consecutively until 6:00.13 Egloff et al. (1995)
report that positive affect is lowest in the morning, in the middle in the afternoon, andthe highest in the evening. The results of these studies, including ours, are all consistentin that happiness declines during the middle of the night and recovers in the morning.
They differ in the detailed movements, probably due to the difference in professions andage of the subject populations. For example, Csikszentmihalyi et al. (2003) show thathappiness declines from 21:00 to 22:30., but this time for an elementary school student
may be equivalent to the middle of the night for adults. Kahneman et al. (2004a) showthat negative affect is high during working time for workers, while the pattern is not soclear in papers such as ours that study college students.

4.2

Diurnal variation in happiness controlling for type of activity
In the previous subsection, we examined how subjective happiness depends on thehour of the day. However, we did not identify the effect of the hour of the day separatelyfrom the effect of the time pattern of activity. It might be the case that happiness merely
appears to depend on time of day because people have, by and large, a certain time
pattern of activities in a day, on which happiness depends. If this is the case, happinessdoes not truly depend on time, once we control for activities. To test this hypothesis, we
13

Negative affect shows the mirror pattern to positive affect.
13

run a regression that takes into account the effect of both activities and time of day.
We estimate following the equation:
Happinessi ,t   1 Dhour l i ,t    2 Dactivity l i ,t
l

l

l

l

  3 Dweekdayi ,t    4 Dmonthl  ci   it
l

(1)

l

where i stands for individual and t is the time when responses are made. Here, we added
weekday dummy and month dummies as control variables. Dweekday is a dummy
variable, which equals one if the response is made on a weekday and 0 otherwise.
Dactivityl is a dummy variable that equals one if the activity is the l-th activity shown in
Table 1 and 0 otherwise. Dhour l is a dummy variable that equals one if the response ismade at l-th o’clock and 0 otherwise. ci stands for the individual fixed effect andstands for the error term.
Estimates of equation (1) by random effects model are presented in Table 2, sincethe Hausman test does not reject the null of the random effects model at the 5% level,
and ordinary least squares (OLS) is rejected using the Breusch-Pagan test. 14 For
estimation of equations (2) and (3) we use a fixed effect model, presented in Table 2,
because the Hausman test rejects the null of the random effects model at the 1% level
and OLS is rejected by F-test of the same constants.

14

However, the estimates by fixed effects model were not substantially different from those in
Table 3.
14

In the first column of Table 2, we present the estimation results in which thevariables of Dactivity are excluded from eq. (1). The coefficients of the dummy
variables of Dhour are negative from midnight to dawn, and get larger from 9:00 to
midday. After that, they decline slightly until 17:00 and then go up rapidly until 19:00,
maintaining that high level until 23:00. Happiness drops substantially at 24:00.
Compared with 24:00, 19:00 is happier at the 1% significance level.
The coefficient of the weekday dummy is positive, but insignificant. Among themonthly dummies, March, April, May, August, and December are significantly happier.
The results are reasonable, since in March and August our respondents, students, are on
vacation, and April and May have good weather.
In the second column of Table 2, we present the estimation results of the equationthat when the variables of Dhour are excluded from eq. (1).In this case the null of theHausman test is rejected, so we adopt a fixed effects model.
In the third column of Table 2, the estimation results of equation (1) are shown.
The coefficients of Dactivity are quite similar to those shown in the second column(refer to Figure 5, where both point estimates are depicted). On the other hand, thecoefficients of Dhour are slightly different, in that they show greater happiness frommidnight to 11:00 (refer to Figure 6, where both point estimates are depicted). However,15

the general pattern is similar: happiness is low in the early morning and goes up
gradually from 9:00, then declines slightly in the evening, and becomes high again atnight. 19:00 is happier than 24:00 at the 1% level, and the hours from 20:00 to 23:00 are
happier than 24:00 at the 5% level.
These findings are summarized as follows: First, happiness depends on activities
after controlling for time. Here, activity 14 (sleeping) is excluded from the regression
and twelve activities out of 14 bring about higher happiness than “sleeping.” Small
standard errors suggest that happiness differs significantly among the various activities.
Secondly, happiness during 19:00 to 23:00 tends to be higher than happiness at
24:00 after controlling for activities. Indeed, happiness from 19:00 to 23:00 is
significantly higher at the 5% level, implying that happiness depends on time of day,even if students are doing the same activity. However, an F-test of the 23 hour dummies
reveals that they are only significant at the 10% level, implying hour dummies have
only weak additional explanatory power. Still, when we define hour dummies as adummy for every two hours, there hours, and four hours, the F-values become
significant, at the 5%, 1%, and 1% levels, respectively. Thus, we may conclude thatwhile most of the time-variation of happiness can be attributed to time pattern ofactivities, there exists a time-pattern of happiness independent of activities.

16

Third, happiness differs depending on month: happiness in five of the months ofthe year is higher than that in November. Fourth, the weekday dummy is not significant,
implying that the level of happiness is not different between weekdays and holidays,
once activities are controlled for.

5. Difference in happiness pattern between males and females
Do the happiness patterns found thus far differ between genders? In this section,
we investigate whether the activity pattern and time pattern of happiness differ between
genders.15 The sample of Kahneman et al. (2004a) is composed entirely of working
females, so these authors were unable to analyze gender differences.
Our subjects are 48 males and 21 females (total 69), so that the numbers ofobservations for each gender are 5707 and 2727, respectively.16 Estimation results arepresented in Table 3. Point estimates of the variables Dactivity for both genders are
depicted in Figure 7. Coefficients on drinking and smoking are not shown here, since
their sample size is very small. Inspection of Table 3 reveals that the pattern of how
happiness depends on activity is quite similar between males and females. Figure 7
indicates that the magnitudes of the coefficients are similar. The only differences are

15

Though it would also be interesting to measure how these patterns depend on age, our
respondents are all students and their ages are almost the same, so age analysis is impossible.
16
Gender is not recorded for one subject, who was therefore excluded from the analysis.
17that dating (activity 4）gives extremely strong happiness to males, while only moderatehappiness to females, and that preparation for meals (activity 7) makes males unhappy,
while it increases happiness for females. In sum, our results indicate that how happiness
depends on activity does not differ much between genders.
Table 3 reveals that for males, happiness level does not change much from thelevel at 24:00.17 On the other hand, for females, many of the hours after 9:00 are
happier than 24:00. In Figure 8, we show point estimates of the coefficients of hour
dummies for males and females, which reveals that the fluctuation of happiness is muchgreater for females than for males. Still the shape of the pattern is similar between males
and females. Here, a problem is that F-test of all the hour dummies is not significant,
implying that hour dummies do not have additional explanatory power for males andfemales. However, for males, when we define hour dummies as every three hours, theF-test becomes significant at the 5% level, and when we define them for every fourhours, at the 1% level. On the other hand, for females such a treatment does not produce
a significant result, probably due to the smaller number of females in our sample. Thus,
we should be careful not to draw too strong a conclusion about the differences in thetime pattern of happiness between males and females.
Table 3 also reveals that monthly variation in happiness is much larger for females
17

The only exception is that 21:00 is happier than 24:00 at the 10% level.
18

than for males. Consequently, females are significantly happier than November in moremonths than are males.
The differences between the time pattern of happiness for males and females
are summarized as follows. First, the shape of the time pattern is similar: happinessbecomes highest at night after 18:00. Secondly, however, the variation of happiness ismuch larger for females than males. Consequently, while happiness doesn’t differ
significantly after 24:00 for males, it does for females. The subjects of Kahneman et al.
(2004a) are only female, and their results look like our results for females. Our study
suggests that for males, happiness might not depend strongly on time.

6. Conclusions
In this paper, we investigated whether or not people’s diurnal happiness variation
can be explained by the respondents’ gender and current activities. We conducted anhourly web survey, where 70 students reported their happiness every hour of one dayout of every month from December 1, 2006 to February 18, 2008. Our hourly-survey
methodology is a development of ESM, in that it uses a web survey, and subjects could
respond by mobile phone as well as personal computer.
Analyzing the survey data, we found the following: (a) happiness significantly

19depends on activities, (b) subjects are relatively unhappy around 7:00 and happy fromaround 19:00 to 23:00, (c) the diurnal variation in happiness is still observed even when
activities are controlled for, and (d) the relationship between happiness and activities
does not differ between males and females. While happiness of females depends
strongly on time of day, that of male does not.
It would be interesting to investigate the reason happiness depends on time evenafter controlling for activities. There are at least three possibilities. One is what weargued in subsection 4.1, that is, that the stress of study and release from stress makestudents unhappy and happy during a day. The second is that the diurnal pattern ofhappiness is biologically determined. The last is that our result merely indicates a lackof sufficiently detailed information on activities.

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22

Table 1. Frequency and happiness by activity
number of
Activity

happiness

Observations

average

Lower

Upper

Std.

95%

95%

error

Csikszentm

Bassi and

Kahnemanihalyi et al.

Fave

et al.

happy

happy

net affect

4 Date

97

7.371

6.939

7.803

0.218

4.74

9 drinking, pachinko

13

6.923

5.922

7.924

0.459

8 Meals

760

6.178

6.047

6.308

0.066

0.19

5 enjoying time with friends

511

6.16

5.983

6.338

0.09

0.35

12 going out for things to do

245

6.033

5.788

6.278

0.124

3 side job, club, exercise

336

5.979

5.772

6.187

0.106

11 shopping, driving

154

5.974

5.71

6.238

0.134

8536

5.947

5.623

6.27

0.15

7 preparation for meals

487

5.86

5.703

6.018

0.08

-0.21

1 study (alone)

1569

5.779

5.685

5.874

0.048

-0.11

13 Rest

475

5.773

5.593

5.952

0.091

6 TV, internet

1883

5.663

5.584

5.741

0.04

0.03

0.19

3.62

2 class, commuting

1112

5.446

5.338

5.554

0.055

-0.21

-0.36

2.65

15 Others

415

5.417

5.251

5.583

0.085

10 smoking

6

5.333

3.9

6.767

0.558

473

5.311

5.135

5.487

0.089

3.96
0.48

4.12

0.27

3.21

and familyALL

14 sleep (waking up)

23

3.24
-0.27
3.91

Table 2 Estimates of Equation (1)Happiness(1)
Dhour 1
Dhour 2Dhour 3Dhour 4
Dhour 5
Dhour 6
Dhour 7
Dhour 8Dhour 9Dhour 10Dhour 11
Dhour 12Dhour 13Dhour 14Dhour 15
Dhour 16
Dhour 17
Dhour 18
Dhour 19
Dhour 20
Dhour 21Dhour 22Dhour 23
Dactivity 1
Dactivity 2
Dactivity 3
Dactivity 4
Dactivity 5
Dactivity 6

0.042(0.25)
0.179(0.95)
0.146(0.67)-0.049(-0.21)-0.147(-0.60)-0.145(-0.74)-0.152(-0.93)-0.116(-0.79)0.006(0.04)
0.024(0.18)
0.078(0.60)
0.147(1.14)
0.113(0.88)
0.094(0.73)
0.144(1.12)
0.013(0.10)
0.059(0.46)
0.147(1.13)
0.363(2.77)
0.292(2.21)
0.296(2.22)
0.324(2.37)
0.315(2.22)

Happiness(2)

Happiness
(3)

0.456
(5.81)
0.381(4.44)
0.706(6.67)
1.655(9.92)
0.924(9.75)
0.565(7.41)

0.018(0.11)
0.188(1.01)
0.206(0.95)
0.100(0.43)-0.024(-0.10)0.089(0.45)-0.047(-0.29)0.033(0.23)
0.105(0.77)
0.139(1.06)
0.168(1.31)
0.160(1.25)
0.109(0.86)
0.142(1.12)
0.185(1.46)
0.054(0.42)
0.113(0.89)
0.169(1.31)
0.337(2.59)
0.263(2.01)
0.297(2.24)
0.324(2.39)
0.305(2.17)
0.407(5.02)
0.353(4.04)
0.632(5.82)
1.619(9.61)
0.857(8.75)
0.510(6.45)

**
*
*
*
*

24

***
***
***
***
***
***

**
*
*
*
*
***
***
***
***
***
***

Dactivity 7
Dactivity 8
Dactivity 9
Dactivity 10
Dactivity 11
Dactivity 12
Dactivity 13
Dactivity 15
Weekday

Observation

0.009(0.22)
0.068(0.90)
0.065(0.80)
0.382(4.38)
0.197(2.21)
0.282(3.21)
0.120(1.35)-0.167(-1.89)0.186(2.07)
0.119(1.34)
0.097(1.11)
0.209(2.78)
5.525(28.83)8434

Adjusted R2

0.003

Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Dec
Constant

***
*
**

+
*

**
***

F(ci=c)Chi2 Test

124.50***F test (Hour=0)

60.55

***

56.44

***

82731.60***

F test (Active=0)
Breusch-Pagan
Test
Hausman Test
Estimation Model

***
***
**

***
**
***
***

***
*
**
+
+
*

**
***

0.022

F testF test (Month=0)

0.628(6.56)
0.826(9.59)
1.127(2.76)-0.066(-0.11)
0.849(6.25)
0.306(2.65)
0.531(5.52)
0.425(4.28)
0.054(1.26)
0.077(1.02)
0.080(1.00)
0.395(4.57)
0.202(2.29)
0.276(3.17)
0.149(1.70)-0.145(-1.66)0.215(2.42)
0.132(1.50)
0.106(1.22)
0.215(2.90)
5.048(54.71)8434

0.583(6.01)
0.766(8.65)
1.019(2.48)-0.104(-0.17)0.802(5.82)
0.265(2.26)
0.488(4.96)
0.383(3.79)
0.052(1.21)
0.070(0.93)
0.077(0.96)
0.388(4.49)
0.200(2.26)
0.266(3.06)
0.137(1.56)-0.152(-1.74)0.206(2.32)
0.124(1.41)
0.104(1.20)
0.209(2.81)
4.939(33.94)
8434

***
***
*

***
*
***
***

***
*
**

+
*

**
***

0.023

10.89

***

6.47

***

80.35

***

80.46

***

1.41

+

11.32

***

10.45

***

5.46

***

5.42

***

38.76

82.99

***

114.80***Random

Fixed

Fixed

Note: In the regression, Dactivity_14, Dhour_24, and November are excluded fornormalization. t-values are in parentheses.
+ p<0.10, * p<0.05, ** p<0.01, *** p<0.001

25

Table 3 Estimates of Equation (1) for male and female samples

Dhour 1
Dhour 2Dhour 3Dhour 4
Dhour 5
Dhour 6
Dhour 7
Dhour 8Dhour 9Dhour 10Dhour 11
Dhour 12Dhour 13Dhour 14Dhour 15
Dhour 16
Dhour 17
Dhour 18
Dhour 19
Dhour 20
Dhour 21Dhour 22Dhour 23
Dactivity 1
Dactivity 2
Dactivity 3
Dactivity 4
Dactivity 5
Dactivity 6

Male

Female0.031(0.18)
0.056(0.30)
0.186(0.83)
0.003(0.01)-0.109(-0.40)0.048(0.22)-0.026(-0.15)-0.056(-0.35)-0.032(-0.22)0.037(0.27)
0.045(0.33)
0.051(0.38)-0.017(-0.13)0.026(0.20)
0.118(0.89)-0.071(-0.53)0.023(0.18)
0.093(0.69)
0.150(1.12)
0.171(1.26)
0.263(1.90)
0.183(1.28)
0.172(1.17)
0.420(4.69)
0.272(2.86)
0.614(5.11)
2.182(10.40)0.793(7.32)
0.484(5.55)

0.070(0.17)
0.670(1.39)
0.368(0.70)
0.513(0.85)
0.437(0.88)
0.422(0.99)
0.219(0.61)
0.446(1.37)
0.632(1.99)
0.580(1.88)
0.718(2.33)
0.678(2.19)
0.653(2.13)
0.647(2.12)
0.607(1.98)
0.574(1.89)
0.557(1.79)
0.593(1.90)
1.057(3.33)
0.742(2.33)
0.646(2.04)
0.894(2.78)
0.786(2.36)
0.307(1.83)
0.517(2.72)
0.557(2.50)
0.913(3.11)
0.922(4.57)
0.471(2.87)

+

***
**
***
***
***
***

26

*
+
*
*
*
*
*
+
+
+
***
*
*
**
*
+
**
*
**
***
**

Dactivity 7

Observation

0.375(3.25)
0.703(7.15)
0.727(1.70)
0.448(0.67)
0.827(5.33)
0.239(1.79)
0.379(3.34)
0.350(2.97)-0.023(-0.50)0.000(0.00)-0.108(-1.21)0.168(1.74)
0.115(1.16)
0.011(0.11)-0.128(-1.30)-0.186(-1.91)
0.195(1.97)
0.149(1.54)
0.017(0.17)
0.125(1.50)
5.121(33.21)
5707

Adjusted R2

0.031

F testDactivity 8
Dactivity 9
Dactivity 10
Dactivity 11
Dactivity 12
Dactivity 13
Dactivity 15
Weekday
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Dec
Constant**

0.820(4.55)
0.813(4.50)
1.822(1.85)-1.569(-1.31)0.694(2.53)
0.213(0.94)
0.638(3.37)
0.392(2.05)
0.277(2.98)
0.198(1.33)
0.430(2.66)
0.784(4.52)
0.322(1.83)
0.814(4.69)
0.642(3.67)-0.106(-0.60)0.225(1.25)-0.009(-0.05)0.234(1.36)
0.306(2.07)
4.391(12.89)
2727

***
+

***
+
***
**

+

+
*

***

***
***
+

*

***
*
**

**
***
+
***
***

*
***

0.037

5.716

***

3.536

***

F(ci=c)

106.4

***

45.17

***

F test (Hour=0)

1.07

F test (Active=0)
F test (Month=0)

1.00

10.79

***

3.238

***

4.08

***

5.948

***

Hausman Test

2666.7

***

679.2

***

Estimation Model

Fixed

Fixed

Note: In the regression, Dactivity_14, Dhour_24, and November are excluded fornormalization. t-values are in parentheses.
+ p<0.10, * p<0.05, ** p<0.01, *** p<0.001

27

Figure 1. Number of observations by hour28Figure 2. Happiness during the day29

Figure 3.

Happiness during the average weekday

30

Figure 4.

Happiness during the average holiday day

31

Figure 5.

Point estimates of the coefficients of Dactivity when Hour is controlled for (Table 2 (3)) and not controlled for (Table 2 (2))

1.800
Table2-Action(2)Table2-Action(3)

1.300

0.800

0.300

1

2

3

4

5

6

7

-0.200
32

8

9

10

11

12

13

15

Figure 6.

Point estimates of the coefficients of Dhour when Activity is controlled for (Table 2 (3)) and not controlled for (Table 2 (1))

Table2-Happy(1)1.000Table2-Happy(3)

0.500

0.000

1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23-0.500-1.00033

Figure 7.

Point estimates of Dactivity for males and females

Table3-Male-Action

34

Table3-Female-Action

Figure 8.

Point estimates of Dhour for males and females

1.500

Table3-Male

Table3-Female

1.000

0.500

0.000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23-0.50035