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DISCUSSION PAPER SERIES
Forschungsinstitut
zur Zukunft der Arbeit
Institute for the Study
of Labor
Sundays Are Blue: Aren’t They?
The Day-of-the-Week Effect on Subjective
Well-Being and Socio-Economic Status
IZA DP No. 4563
November 2009
Alpaslan Akay
Peter Martinsson
Sundays Are Blue: Aren’t They?
The Day-of-the-Week Effect on
Subjective Well-Being and
Socio-Economic Status
Alpaslan Akay
IZA and University of Gothenburg
Peter Martinsson
University of Gothenburg
Discussion Paper No. 4563
November 2009
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IZA Discussion Paper No. 4563
November 2009
ABSTRACT
Sundays Are Blue: Aren’t They?
The Day-of-the-Week Effect on Subjective Well-Being
and Socio-Economic Status*
This paper analyses whether individuals are influenced by the day of the week when
reporting subjective well-being. By using a large panel data set and controlling for observed
and unobserved individual characteristics, we find a large day-of the-week effect. Overall, we
find a ‘blue’ Sunday effect with the lowest level of subjective well-being. The day-of-the-week
effect differs with certain socio-economic and demographic factors such as employment,
marital status and age. The paper concludes with recommendations for future analyses of
subjective well-being data and design of data collections.
JEL Classification: C23, D60, I31
Keywords: subjective well-being, day-of-the-week effect
Corresponding author:
Alpaslan Akay
IZA
P.O. Box 7240
D-53072 Bonn
Germany
E-mail: akay@iza.org
* We wish to thank Andrew Oswald, Claudia Senik, Richard Easterlin, Erzo Luttmer, Alois Stutzer,
Armin Falk and participants in the 6th IZA Prize Conference on Frontiers in Labor Economics: The
Economics of Well-Being and Happiness, and seminar participants in Bonn University for their
valuable comments. Financial support from the European Science Foundation is gratefully
acknowledged.
2
‘Saturday, wait
And Sunday always comes too late
But Friday, never hesitate…’
The Cure, ‘Friday I’m in Love’
1. Introduction
Research on subjective well-being that employs a self-reported measure as a proxy for
utility has increased rapidly in economics since the 1990s (see overviews in e.g. Dolan et
al., 2008; Frey and Stutzer, 2002; van Praag and Ferrer-I-Carbonell, 2004).3 As a result of
this research, we obtain insights that are difficult to gain when using a standard neo-classic
economic approach, for example the large disutility from being unemployed (Winkelmann
and Winkelmann, 1998; Clark and Oswald, 1994; Clark, 2003), that age and subjective
well-being have a U-shaped relationship with a minimum around the age of 40 (Frey and
Stutzer, 2002), that married people have higher subjective well-being than singles (Clark
and Oswald, 1994), and that both absolute and relative income affect subjective well-being
(Easterlin, 1995; Clark et al., 2008).
Besides individual characteristics and macroeconomic factors, subjective well-being can be
explained by many other important temporary life circumstances, and these are often
linked to specific days of the week. Most people experience a rhythmic weekday-weekend
separation based on their employment status, which has implications for number of hours
of sleep, wake-up time and bedtime hours (Yang et al., 2001), different consumption
patterns on different days of a week (Cherpitel et al., 1998) and social life and stress levels
3 For overviews by psychologists, see e.g. Diener et al. (1995), Diener et al. (1998) and Kahneman et al.
(1999).
3
(see Areni and Burger, 2008, for many other related findings and a review). In the
psychological literature, it has long been discussed whether the days of the week have
different influences on the subjective well-being (as well as on many other mood
characteristics) of individuals (e.g. Farber, 1953; Snyder et al., 1977; Clark and Watson,
1988; Csikszentmihalyi and Hunter, 2003; Egloff et al., 1995; Kennedy-Moore et al.,
1992; Neale et al., 1987; Rossi and Rossi, 1977).4
The objective of the present paper is to examine whether subjective well-being, which is
used as a proxy for an individual’s utility, is influenced by the day of the week. This is
done using the German Socio-Economic Panel (GSOEP), which is one of the longest panel
data sets available. It includes a measure of subjective well-being that spans more than
twenty years and is framed as: ‘How satisfied are you at present with your life, all things
considered?’ 0 (completely dissatisfied) and 10 (completely satisfied). The measure
intends to capture overall well-being of an individual and is expected not to fluctuate in
short time intervals such as from one day to the next in the same week. Yet, the linguistic
structure of the question really pins down the degree to which respondents judge the
quality of their life at present considering all things (Veenhoven, 1991); i.e. the response is
probably affected by the momentary circumstances of the respondent when the question is
asked.5 Already Kahneman et al. (1999) argued that people assess their well-being at any
4 Biologists and psychologists identified the so-called Circaseptum rhythms in some physiological processes.
It indicates that some physiological processes show seven day cycles. For instance, immune system
responses to disease, body temperature and red-blood-cell count all exhibit a 7-day cyclic pattern (for a
review, see Larsen and Kasimatis, 1990; Larsen and Kasimatis, 1991; Croft and Walker, 2001).
5 It should be noted that the English translation of the question differs slightly between different waves, while
the question is asked exactly the same in German in all waves as “Wie zufrieden sind Sie gegenwärtig, alles
in allem, mit Ihrem Leben?” The English translation of this question is reported in text. For the details of the
survey instruments see www.diw.de.
4
given moment by examining the events and the circumstances in the short proximity of the
time that the overall subjective well-being question is prompted.
Croft and Walker (2001) suggest that there is a commonly held belief that Mondays are
blue. Supportive of this view is that well-being generally is higher on weekends since
weekends may have more pleasant daily events than other days of a week and thus
subjective well-being might also be varied between the traditional working week and the
weekend (Stone, 1987). Another view is that well-being is very low at the end of the week.
This has been coined Sunday neurosis (e.g. Mihalcea and Liu, 2006; Areni and Burger,
2008), and may be due to people planning ahead for the upcoming work week more on
Sundays than on other days (Clark and Watson, 1988). Another explanation for the same
effect is given by Csikszentmihalyi (1997), who argues that people feel the best when
occupied by tasks with clear structure, e.g. clear objectives and rules, which are mostly
lacking during leisure time.
The results from studies using the experience sampling method, which is a method where
people are asked to report where they are and what they are doing at several times during
the day on several days of the week,6 suggest that overall subjective well-being fluctuates
across the days of a week (Csikszentmihalyi and Larsen, 1987; Csikszentmihalyi and
Hunter, 2003; Larsen and Kasimatis, 1990). Some studies report a blue-Monday effect,
implying that individuals systematically report lower subjective well-being on Mondays
6 Kahneman et al. (1999) interpret this method as measuring point-instant utility of immediate environmental
circumstances as being based on recording within-individual affective experiences for the randomly assigned
short time intervals during a day to capture the dynamics of well-being in daily life and with a limited
number of observations.
5
(Stone et al., 1985; Egloff et al., 1995; Larsen and Kasimatis, 1990; McFarlane et al.,
1988; Reis et al., 2000).7 Mihalcea and Liu (2006) find that well-being is influenced by the
day of the week: the happiest day is Saturday and the bluest day is Wednesday (i.e. hump
day) and partly Sunday, possibly because people may come to realise that another long
week is about to start. Focusing on job satisfaction and mental health, Taylor (2006)
analysed the day-of-the-week effect using the British Household Panel Survey (BHPS). He
finds that individuals interviewed on a Friday report higher levels of job satisfaction and
also higher levels of mental health compared to the individuals interviewed mid-week. He
concludes that people assign a higher (lower) premium on leisure time over work and this
may result in lower (higher) level of reported job satisfaction on weekdays (weekends).
Our results suggest that overall subjective well-being is largely influenced by the day of
the week it is reported. We find that Sunday is the bluest day in Germany; i.e. this is the
day that individuals on average report the lowest level of subjective well-being. Saturday
and Friday are the other two days that individuals report lower subjective well-being.
Hence, weekends result in lower subjective well-being than weekdays. The main
advantage of the present paper is that the data set is very large and can allow us to identify
the subgroups that drive these results. A separate analysis based on different socio-
economic and demographic characteristics of individuals reveals that there are different
weekly patterns of subjective well-being, where the most pronounced effect is found
among married and middle-aged people. Moreover, we analysed the potential problem of
endogeneity since both interviewers and interviewees may self-select themselves to certain
7 It is also shown that the frequency of suicide attempts and short-term absence from work is higher and
labour productivity is lower on Mondays (Maldonado and Kraus, 1991; Nicholson et al., 1978).
6
interview days, but found that the results are quite robust to interviewer and self-selection
effects.
The remainder of the paper is organised as follows: We describe the data set in Section 2,
and in Section 3 we present the econometric methods used in the empirical analysis with a
focus on the panel aspect of the data to control for unobservable influences on subjective
well-being, which are potentially correlated with observed individual characteristics.
Section 4 contains the results. First we show descriptive analyses of subjective well-being
for each day of the week for the whole sample and then for sub-samples followed by the
results from the econometric analyses, again for the whole sample and then for sub-
samples. In Section 5 we investigate self-selection by interviewers and interviewees.
Finally, Section 6 concludes the paper.
2. The data
2.1. The sample
In this paper, we use the GSOEP data set, which has been widely used to investigate
various issues related to subjective well-being (e.g. Winkelmann and Winkelmann, 1998;
van Praag et al., 2003; Frijters et al., 2004). This panel data set originally consisted of
more than 12,000 individuals and 6,000 households in 1984, and contains detailed
information about the individuals and the households. The individuals are interviewed each
year and the data set is maintained by following all individuals aged 16 and older in the
7
household. Since the dates of the interviews, which are needed for our purposes, are not
available for the 1984 wave, we use data from 1985 to 2007.
The GSOEP data set contains detailed information on the interviews: (i) the date of the
interview (day, month and year), which enables us to calculate the day of the week the
interview was conducted, and (ii) a unique identification number for each interviewer.
Table 1 shows the descriptive statistics of the interviews listed by the day of the week for
the sample used in the analysis. First we present the total number of interviews conducted
and then the proportion of interviews conducted on each day of the week. As shown, most
of the interviews were conducted on Mondays, Tuesdays and Wednesdays, while fewer
interviews were conducted on Thursdays, Fridays, Saturdays, and in particular on Sundays.
Table 1 also presents more detailed descriptive statistics related to the proportion of
individuals who were (i) never interviewed on a particular day of the week, (ii) interviewed
at least once on a particular day of the week but not always and (iii) always interviewed on
a particular day of the week. Almost 32% of the individuals were never interviewed on a
Monday, while the same statistic for a Sunday is 67%. The proportion of individuals who
were never interviewed on a particular day of the week increases at the end of the week. It
should however be noted that only a very small fraction of individuals were always
interviewed on the same day of the week.
Table 1 about here
8
Table 2 provides a more detailed analysis of the interviews. We report average number of
interviews for each day of the week for those who were interviewed seven times or less
and those who were interviewed more than seven times in the GSOEP data set separately.
This allows us to compare the pattern between those who were interviewed more than
seven times, i.e. which in principle could mean that the individual were interviewed at least
once every day of the week, and the others. Table 2 shows that the proportion of
individuals who were never interviewed on a Monday is 50% among those who were
interviewed seven times or less, while the same proportion is only 14% for those
interviewed more than seven times. Similarly, the proportion of the individuals who were
interviewed on a particular day is very small among those interviewed seven times or
smaller and even less for those interviewed more than seven times.
Table 2 about here
There seems to be evidence that the day of the week on which an interview takes place is
not truly random. This raises the question whether there is systematic self-selection among
interviewees and/or interviewers resulting in interviews taking place on particular days of
the week. Thus, the chosen interview day may not be truly exogenous, i.e. may not be
randomly assigned. Furthermore, it is not truly endogenous to the interviewee since a
significant portion of the interview dates were decided jointly between interviewers, and
almost half of the individuals in the sample (48%) were interviewed on seven different
days of the week and this fraction constitutes 26% of the whole sample of individual-year
observations. The most natural constraint of day to be interviewed is one’s work schedule.
9
For instance, we can speculate that individuals who work weekdays may have no other
option than to be interviewed on the weekend. Table 3 shows descriptive statistics on
different interview days for different employment states. In contrast to expectations, the
distribution does not differ much between those employed and those unemployed or part-
time employed.8 This may be another indication that the day of the interview is not totally
the result of self-selection with respect to individual characteristics of the respondent.
Another factor that may influence the self-selection process is the mode of the interview.
There are different interview modes used in the collection of GSOEP data set: oral
interview, written interview with interviewer, written interview without interviewer and
computer assisted interviews. We discuss this issue in more detail in Section 6 when we
discuss endogeneity.9
Table 3 about here
3. Econometric framework
The subjective well-being in GSOEP is reported on an 11-point scale ranging from 0
(completely dissatisfied) to 10 (completely satisfied). Since the measure of subjective well-
being is an ordinal discrete variable, the econometric method used in the present study is
based on an ordered probit approach, which is the commonly used approach in this type of
studies (e.g. Clark and Oswald, 1994; Frey and Stutzer, 2002; van Praag and Ferrer-I-
8 We did the same exercise for some other subgroups such as married, single, divorced, widowed, age
categories and regions. The patterns for these subgroups are not much different from the subgroups of
employed and unemployed. The results can be provided upon request.
9 See http://www.diw.de/documents/dokumentenarchiv/17/43529/soep_overview.pdf for detailed information
on the data collection process.
10
Carbonell, 2004).10 In this modelling approach, the actual subjective well-being is assumed
as latent, where researchers can only observe the self-reported subjective well-being on a
discrete scale. In order to test the null hypothesis of no day-of-the-week effect on
subjective well-being, we need to control for observed and unobserved individual
characteristics resulting in the ordered probit model
*
it it it it
SWB x D
β
γε
′′
=
++, (1)
where Ii ,...,1= indicates individuals and I is the total number of individuals;
IiTt i∈∀= ,,...,1 is time and i
T is the number of time periods an individual i was
interviewed (unbalanced panel data); it
x
is a vector of socio-demographic and economic
characteristics such as age, marital status and income for individuals i at time t, and
β
is
the corresponding parameter vector to be estimated; D contains seven indicator variables
for the day of the week and
γ
is the corresponding vector of parameters to be estimated.
As suggested in psychology literature (e.g. Diener et al., 1999), subjective well-being of
individuals may be explained by unobserved personality traits (such as extraversion or
neuroticism). In order to control for unobserved personality traits, the error term is
specified as itiit u+=
α
ε
, where i
α
denotes time-invariant unobserved personality traits,
which are assumed to be normally distributed with zero mean and constant variance 2
α
σ
;
and it
u is the usual error term, which is assumed to be normally distributed with zero mean
and unit variance due to identification.
10 Ferrer-I-Carbonell and Frijters (2004) find no large difference when comparing the results from using OLS
(assuming cardinality) and an ordered probit approach (assuming ordinality).
11
We assume that the unobserved individual effects i
α
are orthogonal to both observed
individual characteristics and the error terms by specifying a random-effects model.
However, the unobserved individual characteristics might be correlated with observed
individual characteristics (
[
]
|0
it i
Ex
α
≠
). A fixed-effects model could have been used to
take this correlation into account had we preferred to use an OLS regression with fixed-
effects. However, due to the nonlinear nature of the ordered probit model, the fixed-effect
model with individual dummies might be highly biased, i.e. the incidental parameters
problem (Newman and Scott, 1948). Instead, it is possible to apply a quasi-fixed-effects
model (the correlated random-effects model of Chamberlain, 1984) using an auxiliary
distribution for the unobserved individual characteristics specified as ii i
x
αφ
ν
′
=+
, where
x
is the within-means of time-varying observed characteristics of the individuals such as
age and income;
φ
is the vector of parameters to be estimated; and the new unobserved
individual effects i
ν
are assumed to be normally distributed with zero mean and variance
2
ν
σ
and orthogonal to observed characteristics and the usual error terms.
We also control the model for unobserved temporal and spatial variations, which may
influence subjective well-being. Subjective well-being can be affected by transitory
macroeconomic conditions over time, such as economic financial crises and inflation, or
different cohorts in the data set may perceive the subjective well-being question
differently. There could also be a trend effect on subjective well-being. Consequently, we
include time-specific fixed effects to control for unobserved variations over time both for
12
year effects and month effects (note that no interviews were conducted in November and
December). Germany is a large country and regional unobserved heterogeneity affecting
the subjective well-being is quite possible. Thus, we also included state-level regional
fixed-effects to capture spatial differences among the 16 German states.
The model discussed above is estimated using a maximum likelihood estimator. *
it
SWB is
latent, and we observe the subjects’ self-reported it
SWB from 0 to 10. This can be
summarised as
jitjit SWBjSWB
µµ
≤<= −
*
1
if ,10,...,0
=
=
Jj ,
−
∞
=
0
µ
,1+
≤
jj
µ
µ
,
∞
=
J
µ
, (3)
where j
µ
is the unknown upper cut-off point for category j of the ordered relationship to
be estimated. If we assume that the probability of falling into self-reported category j for an
individual i at time t is
(
)
jSWBP itj =, we can write the probability as
()
(
)
,1
()
it j j it it i i j it it i i
PSWB j xDxv xDxv
µ β γφ µ β γφ
−
′′′ ′′′
==Φ−−−−−Φ −−−−
, (4)
where Φ is the distribution function of the standard normal random variable. The
parameters of the model can then be estimated with the maximum likelihood estimator
using the Gaussian-Hermite Quadrature to integrate out the unobserved individual
heterogeneity (Butler and Moffitt, 1982).
13
4. Results
4.1. Descriptive results
We start by presenting some figures and descriptive statistics of subjective well-being on
different days. Figure 1, which shows the average values for the whole sample, indicates
that subjective well-being is fairly stable during the weekdays except for a small decrease
on Wednesdays and Fridays. However, there is a sharp 3-day continuous drop starting on
Fridays. The average level of subjective well-being over the first four days of the week is
7.04 while during the remaining three days it is 6.94. The difference is significant at the
1% level based on a chi-square test. Similarly, the difference in subjective well-being
between Mondays and Sundays is 0.185 and the null hypothesis of equality can be rejected
at the 1% level using a chi-square test.
Figure 1 about here
To gain a more detailed understanding of how subjective well-being is affected by the day
of the week, we separate the sample into several subgroups identified by socio-economic
characteristics. Figure 2 presents subjective well-being for employment-status; full-time
employed, part-time employed and unemployed. As found in previous research,
unemployment causes a significant reduction in subjective well-being, where the average
subjective well-being for full-time employed individuals is 7.12 compared to only 6.80 for
the unemployed. Subjective well-being is relatively flat for full-time empoyed individuals
14
across the weekdays with a sharp reduction on Sundays. Part-time employed people
experience a drop on Mondays and Wednesdays following a sharp and almost linear
reduction from Thursday to Sunday. The pattern of subjective well-being over the days of
the week is slightly different for the unemployed: Their subjective well-being shows a
small increase during weekdays until Thursday, but then falls dramatically. However,
overall these three figures look similar in that they all indicate a systematic reduction in
subjective well-being during weekends. Interestingly, the results seem to suggest that the
weekend blues is not related to employment status. In Section 4.3, we analyse this issue in
more detail by using a regression approach to control for socio-economic factors that may
explain differences between different employment states.
Figure 2 about here
We explore other socio-demographic characteristics that may show different patterns of
subjective well-being over the week as well: (i) marital status (Figure 3); region (former
East and West Germany) (Figure 4); (iii) natives and immigrants (Figure 5); (iv) gender
(Figure 6); and (v) age (Figure 7). The day-of-the-week effect on subjective well-being
differs depending on marital status. The subjective well-being for the married is stable over
the weekdays with a small reduction on Fridays and a sharp decrease during the weekend.
The subjective well-being among single people is quite stable over the week, and there is
only a small decline towards the end of the week. In absolute terms, married people show a
sharper decline in subjective well-being on Sundays than do singles. Those who are
divorced and those who are widowed show a lower absolute level of subjective well-being,
15
but the level is more stable over all days of the week compared to for those who are
married.
Figure 3 about here
Figure 4 shows that the former East and West Germany are very different in terms of the
day-of-the-week effect on subjective well-being. People in the former West Germany show
a fairly stable weekday effect on subjective well-being and a sharp decline on weekends,
while people in former East Germany report an almost constant level of subjective well-
being throughout the week including Sundays. However, the latter group’s absolute level
of subjective well-being is remarkably lower than that of the former West Germans.
Figure 4 about here
Figure 5 shows that natives and immigrants are similar in absolute terms on weekdays in
terms of subjective well-being, but that immigrants are much less affected by weekends in
terms of decreased subjective well-being. This might be related to lifestyle differences
between the two groups. Figure 6 shows the gender effect on subjective well-being over
the weeks. The general trend is the same except that males experience a sharper drop on
Sundays than do females. Figure 7 shows that there is a U-shaped relationship between age
and subjective well-being. Every age group from 26 to 65 reports declining subjective
well-being on weekends. There is no effect among those 25 and younger, and it tends to
decrease again in the older ages as we observe a lower weekend effect for those above age
65.
16
Figure 5 about here
Figure 6 about here
Figure 7 about here
4.2. Regression results
The descriptive statistics suggest that subjective well-being is affected by the day of the
week, but that there are differences between subgroups of individuals. However, the
descriptive analyses of subjective well-being are not conditioned on observed and
unobserved individual characteristics. Thus, we apply econometric analysis by regressing
subjective well-being on individual characteristics as well as on the days of the week,
month, year and regional fixed-effects. The results of this regression are reported in Table
4. We present the results from three models: (i) the pooled ordered probit model, which
does not control unobserved individual effects; (ii) the random-effects ordered probit
model, which controls for unobserved individual characteristics; and (iii) the quasi-fixed-
effects model, which controls for unobserved individual characteristics that are correlated
with observed individual characteristics.
The overall results are in line with previous findings, e.g. the healthier, richer, married and
employed have higher subjective well-being (see e.g. Frey and Stutzer, 2002; Dolan et al.,
2008). The difference between the first two models is that the second accounts for
unobserved individual characteristics such as personality traits on subjective well-being,
17
and we can reject the hypothesis of homogeneity at the 1% significance level. The quasi-
fixed-effect model shows almost the same results as those obtained by using a random-
effects approach, with the difference that the magnitudes of the parameters are smaller with
the quasi-fixed-effects model, especially for Fridays, Saturdays and Sundays. They are also
less significant. Overall, the regression results support the descriptive findings in Figure 1,
showing a sharp decline in subjective well-being during the weekend. In all three
regressions, we control for time fixed-effects both for years and months (in addition to day
of the week). The estimation results, which for space reasons are not reported in the tables,
suggest that there is a decline in subjective well-being over time. This effect is less
apparent when we control for unobserved individual heterogeneity using a random-effect
or quasi-fixed-effects specifications. There was no interview in November and December,
and we only use nine month dummies (January is the base category) in the analyses.
Compared to January, individuals reported significantly higher levels of subjective well-
being in May and June.
Table 3 about here
4.3. Day-of-the-week and socio-economic characteristics
To better understand how the days of the week affect different sub-groups, we run separate
regressions models for sub-groups within employment status, marital status, place of
residence, nationality and age. Table 5a and 5b show the quasi-fixed-effects estimates of
these models. For brevity, we only report the day-of-the-week effect.11 As already could be
11 Full results are available upon request.
18
read in figures in the previous section, the effect of the day of the week, especially for
weekends, differs substantially between different sub-groups. We find similar results with
econometric analysis when we condition on observed and unobserved individual
characteristics. Estimating different models for employment status reveals that there is a
relationship between employment status and the day-of-the-week effect. The full-time
employed experienced a significantly lower subjective well-being on Fridays, Saturdays
and Sundays compared to on Mondays, while for part-time employed only Saturdays and
Sundays are significant and for unemployed only Sundays. It is also observed that the
slope of the decrease in subjective well-being on weekends is the steepest for the full-time
employed (Table 5a). However, only the Sunday dummy is statistically significant for the
unemployed. Married individuals reported a significantly lower level of subjective well-
being on Fridays, Saturdays and Sundays, while single, widowed and divorced individuals
do not show a significant day-of-the-week effect (except Mondays for single people). The
results for people living in areas of the former West and East Germany are substantially
different (Table 5b). Compared to on Mondays, West Germany residents reported a
significantly higher subjective well-being on other weekdays and significantly lower well-
being on Sundays. The regression does not show the same pattern for East Germany
residents, who only report a significantly lower subjective well-being on Fridays than on
Mondays. Females and males experience almost the same day-of-the-week effect and
report lower subjective well-being during Saturdays and Sundays (Table 5b). Finally,
among those 25 years old or younger, there is no day-of-the-week effect, while there is a
significant Sunday effect in the 26-35 group. In the 36 to 55 age range, there are significant
19
Saturday and Sunday effects, whereas there is no day-of-the-week effect for those older
than 55 (Table 5c).
Table 4a about here
Table 4b about here
Table 4c about here
5. Interviewer effect and self-selection to day of interview
One important issue that can substantially affect the results presented above is that the
choice of interview day may not be random. It might be the case that individuals with
higher or lower subjective well-being are self-selected to be interviewed on some specific
days of the week. For instance, based on the results presented above, the individuals who
already have lower subjective well-being may systematically choose to be interviewed
during weekends. Failing to take this self-selection problem into account may bias the
results. In our case, both the interviewer and the respondent may self-select themselves to
be interviewed on a certain day. First we test for interviewer selection of the day of the
interview and second for the subject (respondent) selection of the day of the interview.
The actual decision regarding on which day the interview will take place is based on a
phone call from the interviewer, who suggests a date for the interview. In case the
respondent cannot be interviewed on this specific date, they jointly try to find another day
for the interview. Thus, unobserved interviewer characteristics may affect which day is
20
scheduled, especially the initial day suggested by the interviewer. To control for this effect,
we specify an interviewer fixed-effects model for both the whole sample and for different
sub-groups. The results are reported in Table 6. We only show the parameters for the day-
of-the-week effect for space reasons, and the results for the other variables are pretty very
much in line with what we reported above. Using an F-test, we cannot reject the hypothesis
of an interviewer fixed-effect at the 5% significance level. This indicates that the day-of-
the-week effect does not seem to be caused by the unobserved characteristics of the
interviewers.
Table 6 about here
We test for self-selection among interviewees. To test this hypothesis, we use a two-step
modelling approach where we instrument the probability of being interviewed in a
particular day. In the first step, we estimate the probability of being interviewed on a day
using a quasi-fixed-effects probit model. In the second step, we use the predicted
probability of the first model in the subjective well-being regression using a quasi-fixed-
effects approach to test for self-selection. We apply a two-step approach for Saturdays and
Sundays, which are the days with the fewest interviews. Additionally, we combine these
two days and test the endogeneity of weekends (Saturdays and Sundays together)
The two-step estimation approach requires instruments, i.e. identification (or exclusion)
restrictions, to be able to identify the day-of-the-week effect in the subjective well-being
equation. Thus, we need to find robust instruments that affect the probability of being
interviewed on a particular day without affecting the respondent’s subjective well-being.
21
However, this is not an easy task. We use three instruments: (i) change of interviewer, (ii)
the mode of the interview and (iii) mean number of interviews on a specific day. The
change of interviewer from one wave of the panel to the next can be seen as an external
exogenous shock and should not be correlated with subjective well-being. With respect to
mode of the interviews, a non-negligible proportion of interviews are not oral interviews
but rather conducted using other modes such as computer assisted telephone interviews
and written interview with and without an interviewer. The mode of interview may affect
the chosen interview day, but it is not expected to be correlated with subjective well-being.
The third set of instruments is based on the mean number of interviews on each day of the
week. These variables can be interpreted as an indicator for the underlying self-selection
process by individuals to a particular day of the week. In summary, change of interviewer,
mode of the interview and mean number of interviews are assumed not to be correlated
with subjective well-being, but may affect the chosen day of the week when the interview
takes place.
The two-stage regression results (not reported here but available upon request) suggest that
the parameters for change of interviewers have a significant and negative impact on the
probability of being interviewed on a Saturday, a Sunday or on a weekend. Compared to
oral interviews, written interviews with and without an interviewer decreased the
probability of being interviewed on Saturday, Sunday or on a weekend, while computer
assisted telephone interviews had the opposite effect. Table 7 shows the results of the two-
stage regressions. The results show that our main findings regarding the day-of-the-week
effect are not driven by subject self-selection. Saturdays, Sundays and weekends have a
22
significant and negative effect on subjective well-being, which is in line with the main
results presented above.12
Table 7 about here
6. Discussion and conclusion
Over the last decade there has been an increased interest in subjective well-being analysis
in economics. By using GSOEP, which is one of the longest panel data sets measuring
subjective well-being, we investigated whether there is a day-of-the-week effect on
reported subjective well-being. We find a strong day of the week effect on subjective well-
being even after controlling for the observed and unobserved characteristics of the
individuals which are assumed to be correlated. In more detailed analyses, we investigated
the effects of the day of the week on subjective well-being among people in different sub-
groups. The results suggest that the day-of-the-week effect is primarily explained by
employed and married natives living in the former West Germany. We also tested for
potential self-selection to the day of the interview among interviewers and interviewees,
but could not reject the hypothesis of self selection in either case.
Both descriptive statistics and results from econometric analyses show lower and declining
subjective well-being on weekends compared to on weekdays. Sundays are found to be the
bluest day, although this result differs somewhat among different socio-demographic
12 We also experimented with different combinations of exclusion restrictions. The results were very
sensitive to the instrument used in the analysis. However, the results were in line with our main findings for
most of the cases, i.e. negative and significant weekend effects, although the difference in magnitude of the
estimated parameters is sometimes very large.
23
groups. Thus, our results are in line with previous results that some day or days of the
week are blue (Csikszentmihalyi and Larsen, 1987; Csikszentmihalyi and Hunter, 2003;
Larsen and Kasimatis, 1990). However, in contrast to e.g. Taylor (2006), who pointed to a
positive effect of Fridays on subjective well-being (measured as job satisfaction and
mental health), we find a ’blue weekend’ effect, supporting the so-called Sunday neurosis.
The results are transparent in the sense that people cannot isolate themselves from the
actual present events in life when asked to report their subjective well-being.
Our findings provide initial evidence of the day of the week effect on subjective well-being
in economics research. The paper suggests three important conclusions regarding survey
design and analysis of subjective well-being data. First, the fact that subjective well-being
is affected by the day of the interview suggests that controls for the day-of-the-week effect
should always be included. In our case, however, we do not find that the estimates of the
socio-economic variables are affected, but this is not surprising given the large panel data
set used. Second, both interviewers and interviewees may self-select themselves to certain
days of the week. In the case of new data collection on subjective well-being, there should
be an a priori strategy to handle the potential day-of-the-week effect, especially when
working with small sample sizes. This is ideally done by using randomisation. If secondary
data is used, the potential problem of self-selection should be considered. Finally, the exact
formulation of the subjective well-being question may influence how much individuals
focus on the moment in time when the question is asked (see discussion in Kahneman et al,
1999). Clearly, further work is needed to better understand which particular factors
generate the day-of-the-week pattern observed in the present paper.
24
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29
Table 1. Descriptive statistics on the different interview days (whole sample).
Day of interview Total number
of interviews
Average
number of
interviews
per individual
Proportion
never been
interviewed
on that day
Proportion been
interviewed at
least once but
not always on
that day
Proportion
always been
interviewed on
that day
Monday 58,401 1.505 0.317 0.678 0.005
Tuesday 58,890 1.514 0.309 0.686 0.005
Wednesday 58,489 1.506 0.314 0.679 0.007
Thursday 52,224 1.344 0.349 0.646 0.004
Friday 49,832 1.281 0.367 0.625 0.004
Saturday 48,219 1.241 0.454 0.541 0.004
Sunday 22,223 0.575 0.673 0.325 0.001
30
Table 2. Descriptive statistics on the different interview days separated between whether
the respondent was interviewed more than 7 times or 7 times or less.
Day of interview Total
number of
interviews
Total
number of
interviews
Average
number of
interviews
per
individual
Proportion
never been
interviewed
on that day
Proportion
been
interviewed at
least once but
not always on
that day
Proportion
always
been
interviewed
on that day
7≤T 12,384 0.697 0.505 0.494 0.011
Monday 7>T 46,017 2.255 0.143 0.858 0.000
7≤T 12,748 0.717 0.493 0.507 0.011
Tuesday 7>T 46,142 2.256 0.138 0.861 0.000
7≤T 12,571 0.711 0.497 0.503 0.015
Wednesday 7>T 45,918 2.246 0.145 0.855 0.000
7≤T 11,007 0.621 0.539 0.461 0.008
Thursday 7>T 41,217 2.014 0.174 0.825 0.000
7≤T 10,386 0.591 0.555 0.445 0.008
Friday 7>T 39,416 1.923 0.193 0.807 0.000
7≤T 8,929 0.514 0.631 0.368 0.008
Saturday 7>T 39,290 1.916 0.289 0.710 0.000
7≤T 4,311 0.245 0.807 0.192 0.003
Sunday 7>T 17,912 0.881 0.549 0.451 0.000
Note: T denotes number of periods that an individual is observed.
31
Table 3. Descriptive statistics of interviews by days and subgroups
Day of the week
Sub Groups Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Part-time employed
Never interviewed 0.448 0.447 0.452 0.489 0.498 0.536 0.726
Interviewed at least once 0.555 0.553 0.548 0.511 0.502 0.464 0.273
Full-time employed
Never interviewed 0.619 0.618 0.622 0.649 0.660 0.688 0.937
Interviewed at least once 0.381 0.382 0.378 0.351 0.340 0.312 0.163
Unemployed
Never interviewed 0.482 0.471 0.473 0.503 0.527 0.610 0.791
Interviewed at least once 0.519 0.529 0.527 0.497 0.473 0.389 0.208
32
Table 4. Regression results.
Pooled Ordered
Probit Model
Random-effects Ordered
Probit Model
Quasi
Fixed-effects Ordered
Probit Model
Coefficient Std.err Coefficient Std.err Coefficient Std.err
Age -0.030 ***0.007 -0.052 ***0.002 -0.119 ***0.002
Age-squared/100 0.038 ***0.007 0.060 ***0.002 0.070 ***0.002
Male (=1) -0.074 ***0.038 -0.152 ***0.016 -0.141 ***0.017
Single (=1) -0.128 ***0.064 -0.179 ***0.015 -0.159 ***0.015
Widowed (=1) -0.200 ***0.085 -0.465 ***0.019 -0.435 ***0.019
Divorced (=1) -0.273 ***0.074 -0.253 ***0.016 -0.229 ***0.163
Household size 0.002 0.002 -0.007 *0.004 -0.006 0.004
#Household member aged [0,1] 0.106 ***0.009 0.263 ***0.018 0.263 ***0.018
#Household member aged [2,4] -0.030 ***0.006 -0.041 ***0.011 -0.039 ***0.011
#Household member aged [5,7] -0.032 ***0.006 -0.032 **0.011 -0.030 *0.011
#Household member aged [8,10] -0.012 **0.005 0.009 0.012 0.010 0.011
#Household member aged [11,12] -0.024 ***0.006 0.004 0.013 0.007 0.013
#Household member aged [13,14] -0.018 ***0.005 0.017 *0.010 0.019 *0.010
#Household member aged [15,18] 0.044 ***0.005 0.100 ***0.009 0.010 ***0.010
Full-time employed (=1) 0.174 ***0.011 0.174 ***0.012 0.188 ***0.022
Part-time employed (=1) 0.019 ***0.007 -0.019 0.014 -0.007 0.014
Log (household income) 0.016 ***0.001 0.028 ***0.004 0.023 ***0.001
Average weekly working hours -0.003 ***0.001 -0.002 ***0.001 -0.002 ***0.001
Employed in second job (=1) -0.181 ***0.049 -0.183 *0.099 -0.194 *0.100
Log (income from second job) 0.024 ***0.006 0.024 *0.013 0.027 *0.013
High school education (=1) 0.064 ***0.004 0.011 0.011 -0.004 0.011
University degree (=1) 0.182 ***0.006 0.299 ***0.016 0.257 ***0.016
Health very good (=1) 1.538 ***0.008 2.289 ***0.016 2.273 ***0.016
Health good (=1) 1.077 ***0.006 1.607 ***0.012 1.602 ***0.012
Health satisfactory (=1) 0.620 ***0.006 0.953 ***0.011 0.952 ***0.011
West Germany (=1) 3.279 ***0.025 8.315 ***0.064 7.022 ***0.086
Migrant (=1) -0.082 ***0.006 -0.367 ***0.237 -0.111 ***0.024
Mean (Age) - - - - 7.404 ***0.171
Mean (Income) - - - - 0.037 ***0.004
Mean (Weekly work hours) - - - - -0.001 0.001
Mean (Income from second job) - - - - -0.010 **0.003
Tuesday (=1) 0.006 0.012 0.003 0.012 0.001 0.011
Wednesday (=1) -0.002 0.011 -0.006 0.011 -0.007 0.012
Thursday (=1) 0.006 0.012 0.004 0.012 0.003 0.012
Friday (=1) -0.015 **0.012 -0.025 **0.012 -0.021 *0.012
Saturday (=1) -0.037 ***0.012 -0.047 ***0.012 -0.031 **0.012
Sunday (=1) -0.061 ***0.015 -0.075 ***0.015 -0.053 ***0.016
1
µ
0.241 ***0.005 0.703 ***0.016 0.703 ***0.161
2
µ
0.599 ***0.004 1.687 ***0.187 1.689 ***0.187
3
µ
0.981 ***0.003 2.672 ***0.193 2.674 ***0.194
4
µ
1.295 ***0.003 3.445 ***0.193 3.448 ***0.194
5
µ
1.924 ***0.003 4.939 ***0.199 4.944 ***0.199
6
µ
2.315 ***0.002 5.850 ***0.200 5.856 ***0.201
7
µ
2.937 ***0.002 7.275 ***0.201 7.282 ***0.201
8
µ
3.892 ***0.003 9.484 ***0.205 9.489 ***0.206
9
µ
4.557 ***0.004 11.125 ***0.209 11.133 ***0.209
Year dummies Yes Yes Yes
Month dummies Yes Yes Yes
State dummies Yes Yes Yes
33
Standard dev. of random-effects - - 1.616 ***0.006 1.593 ***0.006
Log likelihood -637774.7 -575829.9 -575095.0
McFadden Pseudo R-squared
Number of observations 344,351 344,351 344,051
Note: *, **, and *** indicate the 10%, 5% and 1% levels of statistical significance, respectively.
34
Table 5a. Quasi-fixed-effects ordered probit estimates for different sub-groups.
Sub-groups
Employed
Full-Time Part-Time Unemployed Married Single Widowed Divorced
Tuesday 0.001
(0.017) 0.020
(0.024) 0.013
(0.018) -0.009
(0.014) 0.041
(0.024)* 0.016
(0.046) -0.001
(0.046)
Wednesday -0.011
(0.017) -0.011
(0.025) 0.005
(0.018) -0.007
(0.014) 0.007
(0.024) 0.036
(0.046) -0.042
(0.048)
Thursday -0.002
(0.018) 0.001
(0.025) 0.015
(0.018) -0.004
(0.014) 0.041
(0.025) -0.002
(0.046) -0.044
(0.048)
Friday -0.031*
(0.018) -0.018
(0.026) -0.018
(0.019) -0.037**
( 0.015) -0.011
(0.025) 0.006
(0.048) 0.003
(0.049)
Saturday -0.063***
(0.017) -0.062**
(0.026) -0.041
(0.030) -0.057***
(0.015) -0.006
(0.025) -0.024
(0.052) -0.069
(0.050)
Sunday -0.087***
(0.022) -0.076**
(0.032) -0.081***
(0.026) -0.102***
(0.019) -0.038
(0.032) -0.008
(0.068) 0.024
(0.061)
Socio-economic
variables Yes Yes Yes Yes Yes Yes Yes
Year dummies Yes Yes Yes Yes Yes Yes Yes
Month dummies Yes Yes Yes Yes Yes Yes Yes
State dummies Yes Yes Yes Yes Yes Yes Yes
St. dev. Of RE 1.620***
(0.008) 0.816***
(0.011) 1.689***
(0.009) 1.710***
(0.008) 1.479***
(0.012) 1.662***
(0.026) 1.629***
(0.024)
Pseudo R-
squared 0.099 0.078 0.121 0.114 0.079 0.088 0.088
# observations 129,119 68,568 141,006 222,892 79,790 21,834 21,852
Note. All specifications are based on the quasi-fixed-effects ordered probit model. Subjective well-being is
the dependent variable taking values from 0 to 10. Standard errors are given in parentheses. *, **, and ***
indicate the 10%, 5% and 1% levels of statistical significance, respectively.
35
Table 5b. Quasi-fixed-effects ordered probit estimates for different sub-groups.
Sub-groups
West
Germany East
Germany Natives Immigrants Female Males
Tuesday 0.072***
(0.012) 0.031
(0.025) 0.004
(0.012) 0.020
(0.030) 0.019
(0.016) -0.019
(0.017)
Wednesday 0.059***
(0.012) 0.035
(0.025) -0.009
(0.012) 0.022
(0.032) 0.012
(0.016) -0.029
(0.017)
Thursday 0.076***
(0.013) 0.028
(0.026) 0.003
(0.013) 0.024
(0.032) 0.016
(0.017) -0.012
(0.017)
Friday 0.054***
(0.013) -0.048*
(0.026) -0.025*
(0.013) 0.010
(0.032) -0.016
(0.017) -0.027
(0.018)
Saturday 0.011
0.013 0.014
(0.026) -0.047***
(0.013) -0.003
(0.030) -0.032*
(0.017) -0.031*
(0.018)
Sunday -0.031*
(0.018) 0.026
(0.029) -0.064***
(0.017) -0.060*
(0.031) -0.045**
(0.021) -0.062**
(0.023)
Socio-economic
variables Yes Yes Yes Yes Yes Yes
Year dummies Yes Yes Yes Yes Yes Yes
Month dummies Yes Yes Yes Yes Yes Yes
State dummies Yes Yes Yes Yes Yes Yes
St. dev. of RE 1.696***
(0.007) 1.675***
(0.014) 1.628***
(0.007) 1.417***
(0.016) 1.589***
(0.009) 1.590***
(0.009)
Pseudo R-squared 0.096 0.138 0.226 0.067 0.220 0.227
#observations 272,543 75,816 294,713 48,982 178,148 166,203
Note. All specifications are based on the quasi-fixed-effects ordered probit model. Subjective well-being is
the dependent variable taking values from 0 to 10. Standard errors are given in parentheses. *, **, and ***
indicate the 10%, 5% and 1% levels of statistical significance, respectively.
36
Table 5c. Quasi-fixed-effects ordered probit estimates for different age groups.
Age groups
25Age ≤ 25 35Age<≤ 35 45Age
<
≤ 45 55Age
<
≤ 55 65Age<≤ 65Age >
Tuesday 0.052
(0.031) 0.057**
(0.028) -0.043
(0.027) 0.004
(0.031) -0.022
(0.030) 0.006
(0.030)
Wednesday 0.024
(0.032) 0.011
(0.028) -0.042
(0.027) 0.004
(0.031) 0.012
(0.030) -0.029
(0.030)
Thursday 0.053
(0.032) -0.011
(0.030) -0.017
(0.028) 0.025
(0.032) 0.013
(0.031) -0.030
(0.030)
Friday 0.023
(0.032) -0.033
(0.030) -0.032
(0.029) -0.020
(0.032) -0.029
(0.031) -0.039
(0.033)
Saturday -0.014
(0.032) -0.032
(0.030) -0.065**
(0.029) -0.059*
(0.033) -0.048
(0.033) -0.053
(0.035)
Sunday -0.051
(0.041) -0.113**
(0.037) -0.083**
(0.036) -0.069*
(0.041) -0.038
(0.042) -0.058
(0.049)
Socio-
economic
variables
Yes Yes Yes Yes Yes Yes
Year
dummies Yes Yes Yes Yes Yes Yes
Month
dummies Yes Yes Yes Yes Yes Yes
State
dummies Yes Yes Yes Yes Yes Yes
St. dev. of
RE 1.372***
(0.014) 1.626***
(0.015) 1.768***
(0.014) 1.847***
(0.017) 1.881***
(0.016) 1.713***
(0.017)
Pseudo R-
squared 0.097 0.165 0.166 0.154 0.207 0.142
#observations 50,116 62,470 67,970 53,334 54,849 48,996
Note. All specifications are based on the quasi-fixed-effects ordered probit model. Subjective well-being is
the dependent variable taking values from 0 to 10. Standard errors are given in parentheses. *, **, and ***
indicate the 10%, 5% and 1% levels of statistical significance, respectively.
37
Table 6. Interviewer (within) fixed-effects estimate for different sub-groups.
Sub-groups
Employed
Part-Time Full-Time Unemployed Married West
Germany Native All
Sample
Tuesday 0.007
(0.021) -0.013
(0.014) 0.015
(0.016) -0.014
(0.012) 0.003
(0.011) 0.003
(0.010) 0.003
(0.009)
Wednesday -0.022
(0.022) -0.013
(0.014) -0.007
(0.017) -0.022
(0.012) -0.021
(0.011) -0.012
(0.010) -0.014
(0.010)
Thursday 0.003
(0.022) -0.006
(0.014) 0.024
(0.017) -0.010
(0.012) 0.003
(0.011) 0.002
(0.010) 0.003
(0.010)
Friday -0.020
(0.022) -0.024
(0.014) 0.020
(0.017) -0.023*
(0.012) -0.004
(0.011) -0.005
(0.011) -0.010
(0.010)
Saturday -0.053**
(0.023) -0.023*
(0.014) -0.050***
(0.018) -0.042***
(0.013) -0.047***
(0.012) -0.028***
(0.011) -0.036***
(0.010)
Sunday -0.062**
(0.029) -0.047***
(0.018) -0.044*
(0.024) -0.089***
(0.024) -0.077***
(0.016) -0.026*
(0.016) -0.049***
(0.011)
Socio-economic
variables Yes Yes Yes Yes Yes Yes Yes
Year dummies Yes Yes Yes Yes Yes Yes Yes
Month dummies Yes Yes Yes Yes Yes Yes Yes
State dummies Yes Yes Yes Yes Yes Yes Yes
ρ
0.212 0.167 0.198 0.202 0.183 0.183 0.176
),( iti xCorr
α
0.050 0.091 0.060 0.077 0.039 0.039 0.083
Overall R-squared 0.145 0.158 0.184 0.160 0.142 0.142 0.165
# observations 68,168 128,819 140,603 221,424 273,844 292,615 346,166
Notes: All specifications are based on the interviewer fixed-effects model assuming subjective well-being is
measured on a continuous metric. There is a total of 1868 interviewer in the data. Subjective well-being is the
dependent variable taking values from 0 to 10.
ρ
is the variation in dependent variable explained by the
unobserved fixed-effects. Standard errors are given in parentheses. *, **, and *** indicate the 10%, 5% and
1% levels of statistical significance, respectively.
38
Table 7. Specifications with self-selection.
Two-stage regressions
Saturday Sunday Saturday or Sunday
All sample -0.060***
(0.012) -0.093***
(0.013) -0.062***
(0.011)
Part-time -0.068***
(0.011) -0.076***
(0.012) -0.075***
(0.010)
Employed Full-time -0.022
(0.016) -0.091***
(0.017) -0.039***
(0.015)
Unemployed -0.064***
(0.016) -0.097***
(0.018) -0.070***
(0.016)
Married -0.051***
(0.014) -0.148***
(0.014) -0.070***
(0.013)
West
Germany -0.066***
(0.013) -0.180***
(0.013) -0.071***
(0.012)
Native
-0.112***
(0.013) -0.166***
(0.013) -0.128***
(0.012)
Notes: All specifications are based on the two-stage method and each stage is estimated with the quasi-fixed-
effects approach. The first stage includes three exogenous instruments: whether the respondent experienced a
change of interviewer, mode of the interview (four dummies: oral, written with interviewer, written without
interviewer, computer-assisted telephone interview), and mean number of interviews conducted on a
particular day (6 dummies). These instruments are excluded from the second-stage estimation and predicted
values of first stage are included in the second-stage subjective well-being equation. Standard errors are given
in parentheses. *, **, and *** indicate the 10%, 5% and 1% levels of statistical significance, respectively.
39
Figure 1. Average subjective well-being levels by days of the week for the whole sample.
6.70 6.80 6.90 7.00 7.10
SWB
Monday Tuesday WednesdayThursday Friday Saturday Sunday
Days of a Week
All Sample
40
Figure 2. Average subjective well-being levels by days of the week for full-time
employed, part-time employed and unemployed.
6.60 6.70 6.80 6.90 7.00 7.10 7.20
SWB
Monday Tuesday WednesdayThursday Friday Saturday Sunday
Days of a Week
Full-Time Employed
6.60 6.70 6.80 6.90 7.00 7.10 7.20
SWB
Monday Tuesday WednesdayThursday Friday Saturday Sunday
Days of a Week
Part-Time Employed
41
6.60 6.70 6.80 6.90 7.00 7.10 7.20
SWB
Monday Tuesday WednesdayThursday Friday Saturday Sunday
Days of a Week
Unemployed
42
Figure 3a. Average subjective well-being levels by days of the week for married, single,
divorced and widowed.
6.40 6.60 6.80 7.00 7.20
SWB
Monday Tuesday WednesdayThursday Friday Saturday Sunday
Days of a Week
Married
6.40 6.60 6.80 7.00 7.20
SWB
Monday Tuesday WednesdayThursday Friday Saturday Sunday
Days of a Week
Single
43
6.40 6.60 6.80 7.00 7.20
SWB
Monday Tuesday WednesdayThursday Friday Saturday Sunday
Days of a Week
Divorced
6.40 6.60 6.80 7.00 7.20
SWB
Monday Tuesday WednesdayThursday Friday Saturday Sunday
Days of a Week
Widowed
44
Figure 4. Average subjective well-being levels by days of the week for West and East
Germans.
6.40 6.60 6.80 7.00 7.20
SWB
Monday Tuesday WednesdayThursday Friday Saturday Sunday
Days of a Week
West Germany
6.40 6.60 6.80 7.00 7.20
SWB
Monday Tuesday WednesdayThursday Friday Saturday Sunday
Days of a Week
East Germany
45
Figure 5. Average subjective well-being levels by days of the week for native Germans
and immigrants .
6.70 6.80 6.90 7.00 7.10 7.20
SWB
Monday Tuesday WednesdayThursday Friday Saturday Sunday
Days of a Week
Native German
6.70 6.80 6.90 7.00 7.10 7.20
SWB
Monday Tuesday WednesdayThursday Friday Saturday Sunday
Days of a Week
Immigrants
46
Figure 6. Average subjective well-being levels by days of the week for female and males.
6.70 6.80 6.90 7.00 7.10 7.20
SWB
Monday Tuesday WednesdayThursday Friday Saturday Sunday
Days of a Week
Females
6.70 6.80 6.90 7.00 7.10 7.20
SWB
Monday Tuesday WednesdayThursday Friday Saturday Sunday
Days of a Week
Males
47
Figure 7. Average subjective well-being levels by days of the week for age categories.
6.60 6.80 7.00 7.20 7.40
SWB
Monday Tuesday WednesdayThursday Friday Saturday Sunday
Days of a Week
Age<=25
6.60 6.80 7.00 7.20 7.40
SWB
Monday Tuesday WednesdayThursday Friday Saturday Sunday
Days of a Week
25<Age<=35
6.60 6.80 7.00 7.20 7.40
SWB
Monday Tuesday WednesdayThursday Friday Saturday Sunday
Days of a Week
35<Age<=45
6.60 6.80 7.00 7.20 7.40
SWB
Monday Tuesday WednesdayThursday Friday Saturday Sunday
Days of a Week
45<Age<=55
6.60 6.80 7.00 7.20 7.40
SWB
Monday Tuesday WednesdayThursday Friday Saturday Sunday
Days of a Week
55<Age<=65
6.60 6.80 7.00 7.20 7.40
SWB
Monday Tuesday WednesdayThursday Friday Saturday Sunday
Days of a Week
Age>65
