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Suicide kills more Americans each year than die in motor accidents. Yet its causes remain poorly understood. We suggest in this paper that the level of others’ happiness may be a risk factor for suicide (although one's own happiness likely protects one from suicide). Using U.S. and international data, the paper provides evidence for a paradox: the happiest places tend to have the highest suicide rates. The analysis appears to be the first published study to be able to combine rich individual-level data sets—one on life satisfaction in a newly available random sample of 1.3 million Americans and another on suicide decisions among an independent random sample of about 1 million Americans—to establish this dark-contrasts paradox in a consistent way across U.S. states. The study also replicates the finding for the Western industrialized nations. The paradox, which holds individual characteristics constant, is not an artifact of population composition or confounding factors (or of the ecological fallacy). We conclude with a discussion of the possible role of relative comparisons of utility.
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Final
April 2011
Dark Contrasts: The Paradox of High Rates of Suicide in Happy Places
Mary C. Daly
Federal Reserve Bank of San Francisco, USA.
Mary.C.Daly@sf.frb.org
Andrew J. Oswald
University of Warwick, UK.
andrew.oswald@warwick.ac.uk
Daniel Wilson
Federal Reserve Bank of San Francisco, USA.
Daniel.Wilson@sf.frb.org
Stephen Wu
Hamilton College, USA.
swu@hamilton.edu
Corresponding author: Andrew J. Oswald, University of Warwick, CV4 7AL, UK.
Tel: 44 2476 523510
Fax: 44 2476 523032
Email: andrew.oswald@warwick.ac.uk
Key words: Happiness, well-being, suicide, relative comparisons.
JEL codes: I 31; J 17.
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April, 2011
Dark Contrasts: The Paradox of High Rates of Suicide in Happy Places
Abstract
Suicide kills more Americans each year than die in motor accidents. Yet its causes remain
poorly understood. We suggest in this paper that the level of others' happiness may be a risk
factor for suicide (although one’s own happiness likely protects one from suicide). Using U.S.
and international data, the paper provides evidence for a paradox: the happiest places tend to
have the highest suicide rates. The analysis appears to be the first published study to be able to
combine rich individual-level data sets one on life satisfaction in a newly available random
sample of 1.3 million Americans and another on suicide decisions among an independent
random sample of about 1 million Americans to establish this dark-contrasts paradox in a
consistent way across U.S. states. The study also replicates the finding for the Western
industrialized nations. The paradox, which holds individual characteristics constant, is not an
artifact of population composition or confounding factors (or of the ecological fallacy). We
conclude with a discussion of the possible role of relative comparisons of utility.
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1.1 Introduction
Human well-being and positive affect are increasingly studied in science and social science
(Easterlin 2003, Layard 2005, Steptoe et al. 2005, Gilbert 2006, Graham 2008, Blanchflower and
Oswald 2008a, Napier and Jost 2008, White and Dolan 2009). A claim of commentators in
many countries and American states is that their areas are filled with happy and/or satisfied
people. Rankings from the World Values Survey and the U.S. General Social Survey frequently
appear in the pressand more scholarly outletswhere it is found that Danes, Swedes, and
the Swiss are among the most satisfied people in Europe and that it may be better to reside in
Alaska than in California (Christensen et al. 2006, Oswald and Wu 2010).
A little-noted puzzle is that many of these happy places have unusually high rates of
suicide. While this fact has been remarked on occasionally for individual nations, especially for
the case of Denmark, it has usually been attributed in an anecdotal way to idiosyncratic
features of the location in question (e.g., the dark winters in Scandinavia), definitional
variations in the measurement of well-being and suicide, and differences in culture and social
attitudes regarding happiness and taking one’s life. Most scholars have not thought of the
anecdotal observation as a systematic relationship that might be robust to replication or
investigation. A possible cross-country association between happiness and suicide has been
mentioned, albeit in passing, in previous research examining whether survey data on subjective
well-being might be used as tractable markers of population mental health (Bray and Gunnell
2006); other research has examined the spatial patterns in suicide (such as the important work
of Dorling and Gunnell 2003).
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This paper attempts to document the existence of a happiness-suicide paradox:
happier areas have a higher percentage of suicides. It uses micro data on well-being and on
suicide. The latter analysis is able to avoid the so-called ecological fallacy, which is the fallacy
that individual members of a group have the average characteristics of the group at large. First,
we are able to control for each individual’s differing personal characteristics. Second, we do
not argue that happier individuals are more prone to take their own life; our argument is that
there may be a form of psychological ‘externality at work in the decision to take one’s own life.
Third, we use as a key independent variable an aggregate externality characteristic that is
genuinely common to citizens of a state, namely, the level of well-being of other citizens in that
state. It certainly might be argued that different people within a state aredepending on
which sub-area they live inexposed to neighbors who are more or less cheery. But that will
result in measurement error that can be expected to make it harder, not easier, to find
statistically significant effects at the state level.
Put into everyday English, we suggest in this paper that although one’s own happiness
protects one from suicide (as shown in longitudinal data in Koivumaa-Honkanen, et al. 2001),
the level of others' happiness is a risk factor. Personal unhappiness may be at its worst when
surrounded by those who are relatively more content with their lives.
There is a precedent for such reasoning. Relative concerns are known to be important
in the domain of feelings over money: people consciously or subconsciously compare their
income to those of others (modern evidence is contained in, for example, Luttmer 2004). In
other domains of life, including those of unemployment, obesity, and crime, similar kinds of
cross-effects have been observed: Clark 2003, Clark et al. 2010, Graham 2009, Blanchflower et
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al. 2009. The results of these and similar studies suggest that human beings may construct
their norms by observing the behavior and outcomes of other people. As such, they will tend to
judge their own position less harshly when they see other people with outcomes like
themselves.
Figure 1 provides the first and simplest suggestive evidence for the paper’s suicide
paradox. It uses data on the industrialized Western nations. These are raw, unadjusted data
on subjective well-being rankings (from the World Values Survey) and suicide rates (from the
World Health Organization). Although there are variations around the average (e.g., the
Netherlands), the striking association in the scatter plot is the positive association between
happiness ranking and suicide rate. This gradient is the opposite of what might be expected,
namely a negative association. In other work, Helliwell (2007) points out that it is possible to
find a negative relationship in a much larger sample of countries. However, we suspect that
some of this result may be due to differences in cultural norms (regarding, for example, suicide
or suicide reporting), and socioeconomic and demographic differences. In this paper, we limit
our comparisons to only Western countries or to only American states, so as to minimize
variation in cultural norms; we also are able to control for major socioeconomic and
demographic differences across countries (and states).
Turning back to Figure 1, the positive slope is not driven by the Scandinavian countries
alone. Nations such as Iceland, Ireland, Switzerland, Canada, and the U.S. each display
relatively high happiness and yet high suicide rates. Moreover, the finding is not an anomaly of
the World Values survey or a result merely of raw correlations between happiness and suicide;
it emerges when multiple regression equation methods are usedas is usual in the
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epidemiology literatureto correct for confounding factors such as other differences across
individuals. For example, if instead the estimated relative happiness values across countries,
taken from another study (Blanchflower and Oswald 2008b), which employed regression-
equation methods to adjust for nations’ demographic characteristics, are used, the same
positive relationship holds between subjective national well-being and national suicide rates
(Figure 2).
The data in these scatter plots suggest the presence of a robust relationship and one
that holds in countries with harsh and less harsh winters, with more and less religious influence,
and with a range of cultural identities. Nevertheless, because of variation in cultures and
suicide-reporting conventions, such cross-country scatter plots are only suggestive.
1.2 The Paradox in U.S. Data
The central contribution of this paper is to establish the happiness-suicide paradox across space
within a single country, the United States. The scientific advantage of doing so is that cultural
background, national institutions, language, and religion are then held approximately constant
in a way that is not possible in the cross-national patterns depicted in Figures 1 and 2.
This argument should not be taken too far. The US states are not identical in cultural
norms, so our test will not be a perfect one.
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But -- helped by the fact that we can control
within regression equations for racial and other characteristics -- the different areas of the
United States offer the potential for a more homogenous testing laboratory than a sample of
nations.
1
We thank an anonymous referee for making this point.
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Building on two channels of previous work, it has recently become possible to examine
data on, respectively, happiness and suicide risk across the 50 U.S. states and the District of
Columbia (Oswald and Wu 2010, Daly and Wilson 2009). Thus the current paper’s data are
drawn from (i) the Behavioral Response Factor Surveillance System, which uses individual
reports of subjective well-being for 1.3 million Americans, (ii) published state suicide rates, and
(iii) the National Longitudinal Mortality Study, which matches death certificate data to
individual records from the U.S. Census Bureau’s Current Population Surveys from 1978
through 2001. The paper uses these data to obtain average life satisfaction and average suicide
risk for each of the 50 U.S. states, and repeats the form of analysis performed above on
Western industrialized countries.
Spatial U.S. data allow us to address two questions related to the possible existence of a
happinesssuicide paradox. First, is it real? Since the potential biases embedded in cross-
country comparisons are minimized, any observed positive association is likely to be the result
of a true positive correlation as opposed to a spurious outcome of omitted variables. Second,
and importantly, it is possible with the paper’s two large individual-level data sets, on life
satisfaction and on US suicides, to check that the observed association between happiness and
suicide in the United States is robust to the inclusion of controls for demographic and
socioeconomic characteristics (such as marriage and joblessness) known to be correlated with
happiness and suicide risk.
The analysis first examines whether there is a correlation between reported happiness
and raw suicide rates. It then calculates adjusted correlations, where the adjustments are for a
large set of demographic and socioeconomic controls using multivariate regressions (some of
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the detailed results from the estimated equations are not given here but are available on
request from the authors). The controls in these regression equations include age, race,
gender, marital status, education, income, and employment/labor-force status, as well as year
fixed effects to control for any changes over time. (For a discussion of the data and methods,
see the section at the end of the manuscript, and the supplementary online material supplied
by Oswald and Wu (2010)).
1.3 Results
Figure 3 provides a scatter plot of raw (i.e., unadjusted) suicide rates and raw life satisfaction
scores for the 50 U.S. states plus the District of Columbia. These unadjusted suicide rates and
raw life satisfaction scores, from columns 2 and 5 of Table 1, are positively related (Pearson’s
correlation=0.249, p = 0.06; rank correlation=0.255, p = 0.05; see Appendix 2 for regression
statistics). This state-by-state association across the geography of America is consistent with
the pattern observed above for the Western industrialized nations. The states that have people
who are generally more satisfied with their lives have higher suicide rates than those that have
lower average levels of life satisfaction. For example, Utah is ranked number 1 in life-
satisfaction, but has the 9th highest suicide rate. Meanwhile, New York is ranked 45th in life
satisfaction, yet has the lowest suicide rate in the USA.
U.S. states citizens differ in important ways (such as in the proportion of people with
college degrees). A natural question is whether the happiness-suicide paradox holds when an
adjustment is made for differences in population composition across space. Figure 4 does this.
It plots the results of an analysis in which the average life satisfaction and suicide risk state-by-
state are adjusted for differences in age, gender, race, education, income, marital status and
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employment status. The Pearson correlation coefficient remains positive (correlation = 0.127,
p-value > 0.1). However, this apparently lower correlation coefficient is influenced by a tiny
number of suicide outliers such as the states of Alaska and New Hampshire.
An alternative correlation measure, which is less sensitive to outliers, is the Spearman
rank correlation. Assessing the correlation across states between their suicide rankings and
their life satisfaction rankings allows us to get a better sense of the correlation between the
two while still retaining all observations, including the states that are apparent outliers. In
Figure 4, which is based on columns 3 and 6 of Table 1, the rank correlation coefficient is 0.271,
which is positive and statistically significant at conventional levels (p-value < 0.05). (Regression
statistics are provided in Appendix 2). Hence, the paradoxical positive relationship between
state life-satisfaction and state suicides that is seen in raw, unadjusted data appears to be
genuine; it is not due to confounding caused by differences in population characteristics across
states.
Table 1 shows more details on the data points behind these scatter plots and allows a
focus on the patterns across states. The table reveals, for example, that New Jersey ranks near
the bottom in adjusted life satisfaction (47th) and has one of the lowest adjusted suicide risks
(coincidentally, also the 47th highest rate), while at the other end of the spectrum Hawaii ranks
#2 in adjusted average life satisfaction and has the 5th highest suicide rate in the country.
2.1 Discussion
We have found that the happiness-suicide paradox holds in data for Western nations and
across the intrinsically more homogeneous setting of the U.S. states (in both raw correlations
and regression-adjusted correlations). It is this latter finding that makes the existence of a
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paradox empirically persuasive. To our knowledge, the cross-state finding has not been
discussed in the earlier literatures on suicide or well-being.
Future research may have to examine how frequently, and in what settings, it recurs in
spatial data in other countries. Such efforts will need to be especially cognizant of one pitfall
for the future that we wish to highlight here. In future work, it will typically be necessary to
control for individuals’ personal characteristicsespecially inside nations that have some small,
distinct, highly disadvantaged regions. The reason is that otherwise the socio-economic
disadvantage of those regions’ citizens (which our calculations, like the literature, such as
Agerbo et al. 2007, find to be predictors of individual suicide risk) will likely swamp the cross-
regional pattern and thereby lead analysts using aggregated data to conclude erroneously that
there is no happiness-suicide paradox.
A detailed conceptual explanation for the paradox must await future research.
However, looking beyond culture and any differences in the reporting of well-being and suicide
scores, one natural possible account, as implied earlier, draws on ideas about the way that
human beings rely on relative comparisons (for example, within the happiness literature, in
Easterlin 2003 and Stutzer 2004, and, more broadly, in Crosby 1976).
Discontented people in a happy place may feel particularly harshly treated by life.
Those dark contrasts may in turn increase the risk of suicide (our results are reminiscent of the
interesting finding by Platt et al 1992 that suicide rates by the unemployed seem to be higher in
low-unemployment regions). If humans are subject to mood swings, the lows of life may thus
be most tolerable in an environment in which other humans are unhappy. Whether such
relative comparisons work by producing discord due to unmet aspirations (e.g. Daly, Wilson and
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Johnson 2009), or reflect a real inability to integrate into broader society and gain access to key
supports, remains to be understood.
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Appendix 1:
Data and Methods
To measure state-level life satisfaction, we draw upon data collected under the auspices of the
Behavioral Risk Factor Surveillance System (BRFSS). The BRFSS is a state-based system of health
surveys that gathers information on risky behaviors, preventive health practices, and access to
health care. The BRFSS was established in 1984 by the Centers for Disease Control and
Prevention (CDC); currently data are collected monthly in all 50 states, the District of Columbia,
Puerto Rico, the U.S. Virgin Islands, and Guam. The data set is meant to “identify emerging
health problems, establish and track health objectives, and develop and evaluate public health
policies and programs.” More than 350,000 adults are interviewed each year; the BRFSS is the
largest telephone health survey in the world.
We study a sample of respondents between the ages of 18 and 85 with non-missing
information. The data set’s annual samples provide statistically representative
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(1) cross-
sectional snapshots of the U.S. Information on individual life-satisfaction was collected in
BRFSS for the first time in 2005. Hence there has been little published research on life-
satisfaction using this data set.
In addition to questions on health behaviors, access to health care, and physical health
status, the survey also contains questions about mental health and subjective well-being. We
rely on one particular survey question. It provides information about how satisfied people feel
about the quality of their lives. The exact wording of the BRFSS life-satisfaction question is: “In
general, how satisfied are you with your life?” Here people are able to answer one of the
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following: Very Satisfied, Satisfied, Dissatisfied, or Very Dissatisfied (Questionnaire line code
206).
To measure state-level suicide risk, we first obtain crude suicide rates from the report
"Ranking America's Mental Health: An Analysis of Depression Across the States." We then
estimate adjusted average suicide risk by state using data from the National Longitudinal
Mortality Study, which is constructed by and housed at the U.S. Census Bureau. The adjusted
suicide risks are the hazard ratios corresponding to the estimated coefficients on state dummy
variables in a suicide Cox Proportional Hazards model which includes age, race, gender,
education, income, marital status, and employment/labor-force status the same set of
controls used to adjust the life satisfaction estimates. See Daly, Wilson, and Johnson (2008)
and Daly and Wilson (2009) for more details about the NLMS and the Hazards model we use
here.
For the cross-country comparisons, suicide rates are taken from the WHO:
http://www.who.int/mental_health/prevention/suicide_rates/en/. Country happiness
coefficients are taken from Table 3 of Blanchflower and Oswald (2008b). Controls in the
underlying regressions include age, gender, education, marital and employment status and age
left schooling.
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Appendix 2: Fitted Regression Equations Depicted in Figures 1-4
Standard errors are in parentheses
Figure 1. Unadjusted Suicide Rates vs. Happiness Rankings across Western Countries
[SUICIDE RATE] = 17.737 + 0.250*[HAPPINESS RANK] , R2 = 0.126, N = 21
(4.174) (0.151)
Figure 2. Unadjusted Suicide Rates vs. Adjusted Happiness Scores across European Countries
[SUICIDE RATE] = 24.912 + 8.255*[HAPPINESS SCORE COEF] , R2 = 0.248, N = 15
(2.311) (3.992)
Figure 3. Unadjusted Suicide Rates vs. Unadjusted Life Satisfaction across U.S. States
[SUICIDE RISK RANK] = 19.347 + 0.255*[HAPPINESS SCORE RANK] , R2 = 0.065, N = 51a
(4.128) (0.138)
Figure 4. Adjusted Suicide Risk vs. Adjusted Life Satisfaction across U.S. States
[SUICIDE RISK RANK] = 18.953 + 0.271*[HAPPINESS SCORE RANK], R2 = 0.073, N = 51 a
(4.108) (0.138)
a Includes District of Columbia.
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Figure 1. Unadjusted Suicide Rates vs. Happiness Rankings across Western Countries
Unadjusted Suicide Rates per 100,000 (y-axis); 2002 WVS Happiness Rankings (x-axis)
Italy
Greec e
Portugal
Germany
Spain
Austria
Netherlands
Belgium
Ireland
Luxe mbourg
Sweden
Finland
Denmark
Switzerland
New Zealand
Iceland
Norway
Canada
United States
Australia
0
5
10
15
20
25
30
35
40
0 5 10 15 20 25 30 35 40 45
Note: The 2002 WVS Rankings were reordered from a descending level of happiness rank to an ascending ranking (higher rank =higher level of happiness).
Rank
Per 100,000
Correlation: 0.355
18
Figure 2. Unadjusted Suicide Rates vs. Adjusted Happiness Scores across European Countries
Unadjusted Suicide Rates per 100,000 (y-axis); Happiness Score Regression Coefficients (x-axis)
Greec e
Portugal
East Germany
Germany
Spain
France
UK
Austria
Netherlands
Belgium
Ireland
Luxe mbourg
Sweden
Finland
Denmark
0
5
10
15
20
25
30
35
40
-1.5 -1 -0.5 0 0.5 1
Coefficients
Per 100,000
Correlation: .4975
19
Figure 3. Unadjusted Suicide Rates vs. Unadjusted Life Satisfaction across U.S. States
Unadjusted Suicide Rates per 100,000 (y-axis); Unadjusted Life Satisfaction (x-axis)
AL
AK
AZ
AR
CA
CO
CT
DE
DC
FL
GA
HI
ID
IL
IN
IA
KS
KY
LA
ME
MD
MA
MI
MN
MS
MO
MT
NE
NH
NV
NJ
NM
NY
NC
ND
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VT
VA
WA
WV
WI
WY
0
5
10
15
20
25
-0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08
Coefficients
Per 100,000
Rank Correlation: 0.255
20
Figure 4. Adjusted Suicide Risk vs. Adjusted Life Satisfaction across U.S. States
Adjusted Suicide Hazard Ratios (y-axis); Adjusted Life Satisfaction (x-axis)
AL
AK
AZ
AR
CA
CO
CT
DE
DC
FL
GA
HI
ID
IL
IN
IA
KS
KY
LA
ME
MD
MA
MI
MN
MS
MO
MT
NE
NH
NV
NJ
NM
NY
NC
ND
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VT
VA
WA
WV
WI
WY
-0.5
0
0.5
1
1.5
2
2.5
3
-0.1 -0.08 -0.06 -0.04 -0.02 0 0.02 0.04
Coefficients
Hazard Ratio
Rank Correlation: 0.271
21
____________________________________________________________________________________________________________
State Suicide Rate Adjusted Suicide Adjusted Raw Coeffici ents Adjuste d Coeffi cients Adjusted Life
(Unadjusted) Hazard Ratio Suicide Rank on Life Satisfaction on Life Satisfaction Satisfaction Rank
Alabama 11.98 0.134 45 0 0 9
Alaska 23.6 1.955 2 0.0185221 -0.0130092 12
Arizona 15.32 0.702 16 0.0493716 0.0017149 5
Arkansas 13.14 0.522 23 0.0099526 -0.0173219 18
Californi a 9.4 1.634 4 -0.010424 -0.0758851 46
Colorado 17.33 0.742 13 0.0594714 -0.0270579 23
Connecticut 8.41 0.326 36 0.0124054 -0.0811446 50
Delaware 11.22 0.527 22 0.0454979 -0.0268907 22
District of Columbia 5.69 -0.033 49 0.0253698 -0.0480357 37
Florida 13.76 0.688 18 0.0405791 0.0041158 3
Georgia 10.89 0.455 29 0.0269513 -0.0214549 19
Hawaii 9.21 1.554 5 0.0530756 0.0111783 2
Idaho 16.92 0.705 15 0.0302691 -0.0141471 16
Illi nois 8.09 0.348 35 0.0046879 -0.0712013 45
Indiana 11.31 0.215 42 -0.0485675 -0.0785005 48
Iowa 11.61 0.661 19 0.0238617 -0.0408878 31
Kansas 13.51 0.172 44 0.0262973 -0.0433084 32
Kentucky 13.53 0.215 43 -0.0522251 -0.0450508 35
Louisiana 11.94 0.291 40 0.0618289 0.0328016 1
Maine 13.01 0.960 10 0.0264101 -0.0060124 10
Maryland 9 0.756 12 0.0355865 -0.0663173 40
Massachusetts 6.6 -0.155 50 -0.0220967 -0.0700712 44
Michigan 10.88 0.298 39 -0.0213069 -0.078772 49
Minnesota 10.29 0.307 37 0.0552504 -0.0304928 26
Mississippi 12.1 0.532 21 -0.0090073 0.0008344 7
Missouri 12.43 0.224 41 -0.0416866 -0.0637721 38
Montana 18.89 0.474 26 0.0344648 0.0007023 8
Nebraska 9.5 0.472 27 0.0044156 -0.0439223 33
New Hampshire 10.25 1.656 3 -0.0064468 -0.06437 39
Nevada 18.86 2.824 1 0.039696 - 0.033304 28
New Jerse y 6.88 0.067 47 0.0032406 -0.0784707 47
New Mexico 18.73 0.713 14 0.0079173 -0.0287865 24
New York 6.15 1.000 9 -0.0285934 -0.0877726 51
North Carolina 12.04 0.432 32 0.0165689 -0.0132164 13
North Dakota 11.48 0.300 38 0.0230207 -0.0296804 25
Ohio 11.51 0.486 25 -0.0323678 -0.0694797 43
Oklahoma 14.36 0.465 28 -0.009718 -0.026477 21
Oregon 15.46 1.307 6 0.012761 -0.0400734 30
Pennsyl vania 11.39 0.438 31 -0.0511653 -0.0670964 41
Rhode Island 7.88 -0.018 48 -0.01163 -0.0683337 42
South Carolina 11.49 0.691 17 0.0335299 0.0014269 6
South Dakota 14.54 0.597 20 0.0213743 -0.0133957 14
Tennesse e 13.46 0.448 30 0.010376 0.0026267 4
Texas 10.21 0.867 11 0.0309428 -0.0139671 15
Utah 15.57 0.488 24 0.0629503 -0.0256149 20
Vermont 14.98 0.086 46 0.0377624 -0.0171947 17
Virgini a 11.08 0.381 34 0.038553 -0.0332978 27
Washington 13.38 1.155 7 0.0217956 -0.0456155 36
West Virgini a 15.74 -0.189 51 -0.0514003 -0.0440579 34
Wisconsin 12.04 0.428 33 0.0003551 -0.0374646 29
Wyoming 17.41 1.061 8 0.0551404 -0.0126597 11
Pearson Correlation between Unadjusted Sui cide Rate and Unadjusted Life Sat Coeff icients 0.249
Spearman Rank Correlation betwee n Unadjusted Suicide Risk and Unadjusted Life Sat Coeffi cients 0.255
Pearson Correlation between Adjusted Suicide Risk and Adjusted Life Sat Coefficients 0.127
Spearman Rank Correlation betwee n Adjusted Suicide Risk and Adjusted Life Sat Coeff icients 0.271
____________________________________________________________________________________________________________
Notes: Crude suicide rates are taken from the report "Ranking America's Mental Health: An Analysis of Depression Across the States."
Adjusted suicide risks are the hazard ratios corresponding to the estimated coeffici ents on state fixe d effe cts from a Cox Proportional
Hazards regression using the National Longitudinal Mortality Study data. Coeffi cients for dissatisfaction with li fe are taken from regressions
using data from the Behavioral Risk Factor Surveil lance System (surve y years betwee n 2005-2008). Controls for income, education, age, race,
gender, and marital and employment status are included in both the NLMS and BRFSS regressions.
Table 1: Suicide Rates and Happiness Across U.S. States
... Why do we need to understand trends? There is an inconclusive, but growing, literature examining the relationship between population happiness and mental illness, mental disorder and self-harm (Daly et al., 2011). The Covid-19 pandemic has been linked with increased rates of certain mental illnesses in certain populations, but mental illness affects a minority of populations at any given time (O'Connor et al., 2020). ...
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Objective To identify levels and key correlates of happiness across Europe in 2018, prior to the Covid-19 pandemic. Methods We used data from the European Social Survey to determine levels of happiness in individuals (n = 49,419) from 29 European countries and identify associations between happiness and age, gender, satisfaction with income, employment status, community trust, satisfaction with health, satisfaction with democracy, religious belief and country of residence. Results In 2018, self-rated happiness varied significantly across the 29 European countries, with individuals in Denmark reporting the highest levels of happiness (8.38 out of 10) and individuals in Bulgaria reporting the lowest (5.55). Ireland ranked 11 th (7.7). Happiness had significant, independent associations with younger age, satisfaction with health, satisfaction with household income, community trust, satisfaction with democracy and religious belief. These factors accounted for 25.4% of the variance in happiness between individuals, and, once they were taken into account, country of residence was no longer significantly associated with happiness. Conclusions Self-rated happiness varied significantly across pre-pandemic. At individual level, happiness was more closely associated with certain variables than with country of residence. It is likely that the Covid-19 pandemic had significant impacts on some or all of these variables. This highlights the importance of further analysis of correlates of happiness in Europe over future years, when detailed happiness data from during and after the pandemic become available.
... Although the WHI clearly captures elements of this referential process at a macro-level, future studies with a more fine-grained resolution are needed to complement the current work with findings from micro-level contexts (i.e., social comparison of happiness as a result of immediate social interactions). Previous work on the prevalence of suicide in happy places has shown that both perspectives do not always converge in their results 58,59 , and other paradoxical patterns described in the happiness literature (e.g., the Easterlin Paradox 60 ) have highlighted the critical importance of explicitly clarifying the level of analysis when interpreting results. In this regard, future studies could also benefit from explicitly distinguishing between different potential sources that shape people's perceived social pressure to be happy and not sad (e.g., macro-versus micro-level, implicit versus explicit, objective antecedents versus subjective appraisals, etc.). ...
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Happiness is a valuable experience, and societies want their citizens to be happy. Although this societal commitment seems laudable, overly emphasizing positivity (versus negativity) may create an unattainable emotion norm that ironically compromises individual well-being. In this multi-national study (40 countries; 7443 participants), we investigate how societal pressure to be happy and not sad predicts emotional, cognitive and clinical indicators of well-being around the world, and examine how these relations differ as a function of countries’ national happiness levels (collected from the World Happiness Report). Although detrimental well-being associations manifest for an average country, the strength of these relations varies across countries. People’s felt societal pressure to be happy and not sad is particularly linked to poor well-being in countries with a higher World Happiness Index. Although the cross-sectional nature of our work prohibits causal conclusions, our findings highlight the correlational link between social emotion valuation and individual well-being, and suggest that high national happiness levels may have downsides for some.
... These ten countries were Norway, Denmark, Finland, Australia, New Zealand, Sweden, Canada, Switzerland, the Netherlands, and the United States. Accordingly, the authors concluded that the countries most highlighted as happy were simultaneously those with the highest suicide rates 9 . ...
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Objective The aim of this study was to use a wavelet technique to determine whether the number of suicides is similar between developed and emerging countries. Methods Annual data were obtained from World Health Organization (WHO) reports from 1986 to 2015. Discrete nondecimated wavelet transform was used for the analysis, and the Daubechies wavelet function was applied with five-level decomposition. Regarding clustering, energy (variance) was used to analyze the clusters and visualize the clustering process. We constructed a dendrogram using the Mahalanobis distance. The number of groups was set using a specific function in the R program. Results The cluster analysis verified the formation of four groups as follows: Japan, the United States and Brazil were distinct and isolated groups, and other countries (Austria, Belgium, Chile, Israel, Mexico, Italy and the Netherlands) constituted a single group. Conclusion The methods utilized in this paper enabled a detailed verification of countries with similar behaviors despite very distinct socioeconomic, geographic and climate characteristics.
... Several economic studies have recently found evidence of a significant and empirically large downturn in human well-being during the mid-life years -so-called "happiness curves" (Rauch, 2018). Early work was based on life satisfaction and happiness data (Blanchflower and Oswald, 2008); the research now extends to trends in unhappiness, stress, lack of sleep, depression, and even suicide (Daly et al, 2011) and across multiple data sets (Blanchflower, 2020a(Blanchflower, , 2020c. Blanchflower and Oswald (2020) show a dramatic rise in extreme misery among prime age less educated whites in the United States. ...
... Several economic studies, including our own 1 , have recently found evidence of a significant and empirically large downturn in human well-being during the mid-life years -so-called "happiness curves" (Rauch, 2018). Early work was based on life satisfaction and happiness data; the research now extends to trends in unhappiness, stress, lack of sleep, depression, and even suicide (Daly et al, 2011) and across multiple data sets (Blanchflower, 2020a(Blanchflower, , 2020b. There is within-person evidence of a U-shape from longitudinal surveys which focuses on changes in life satisfaction as a linear function of individual age (Cheng, Powdthavee and Oswald, 2017). ...
... Well before the pandemic, several economic studies, including several of our own, 1 found evidence of a significant and empirically large downturn in human well-being during the mid-life years -so-called "happiness curves" (Rauch, 2018). Early work was based on life satisfaction and happiness data; the research now extends to trends in unhappiness, stress, lack of sleep, depression, and even suicide (Daly et al, 2011) and across multiple data sets and 145 countries (Blanchflower, 2020a). There is also evidence that unhappiness reaches a zenith in midlife (Blanchflower, 2020b;Graham and Ruiz-Pozuelo (2017). ...
... Daly, Oswald, Wilson, Wu, 2011 [11] believe that the decision to commit suicide is influenced by relative comparisons. It may be said in different words that "people may find it particularly painful to be unhappy in a happy place". ...
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