Happiness Science – Popular Topic
• What is happiness?
• An evaluation of a life. A happy life is a good life.
• How is happiness measured?
• Standard economics (Utility/$$$)
• Welfare economics (Capabilities/HDI)
• Subjective Measures (Quality of Life, Subjective
Well-Being)
Happiness Science
Book: “Well-Being: The Foundations of Hedonic
Psychology” (Kahneman, Diener, Schwarz, 1999).
• policy relevance
• development of valid indicators
• existing economic indicators are limited
• focus on traded goods
• flawed assumptions (behavioral economics)
Happiness Science
• Focus on subjective measures
• Subjective Well-Being (SWB)
• Affective Component (AWB)
• Amount of Positive Affect / Negative Affect
• Cognitive Component (CWB)
• Life Satisfaction
• Average Domain Satisfaction
Cognitive Well-Being (CWB)
• Life satisfaction judgments
• A global assessment of one’s life
• Widely used in happiness surveys
• The majority of empirical findings in happiness
science are based on these measures.
Example: World Value Survey
Taking all things together, would you say you are
1 Very happy
2 Rather happy
3 Not very happy
4 Not at all happy
All things considered, how satisfied are you with your life as a whole these
days? Using a scale on which 1 means you are “completely dissatisfied” and 10
means you are “completely satisfied” where would you put your satisfaction
with your life as a whole?
Completely dissatisfied
1
2
3
4
5
6
7
Completely satisfied
8
9
10
Promises
• subjective / evaluation based on individual’s own point
of view (not paternalistic)
• comprehensive
Problems
• requires willingness to participate
• requires cognitive abilities
• insensitive to environmental influences (set-point,
adaptation)
• may rely on inappropriate comparison standards
(satisfaction treadmill, relative vs. absolute judgments)
Participation Problems
• National representative surveys routinely include
life satisfaction questions.
• Few respondents do not answer these questions.
• Responses are not random (high correlation
between two independent questions).
Conclusion
• A general problem of survey-based indicators, but
not specific to happiness science.
(Lack of) Cognitive Abilities:
Heuristics and Biases
• Traditionally studied by social psychologists and
behavioral economists (Kahneman, Schwarz, etc.)
• The ‘heuristics and bias’ research program is itself
biased and has focused on demonstrating biases in
human judgments (Giegerenzer, Funder).
• This has lead to a biased perception of human’s
abilities.
• Individual bias may often cancel out in aggregated
measures of life satisfaction (e.g., national averages).
Example: Context-Effects
• “In a well-known example, Strack, Martin, and
Schwarz (1988) presented the following two
questions consecutively in a survey administered to
students: ‘How happy are you?’ and, ‘How many
dates did you have last month’ The correlation was
.12 when the general happiness question came
first, but when the dating question came first, the
correlation rose to .66”
(Kahneman, 1999, p. 22).
[difference between two correlations,
effect size q = .67]
Example: Context-Effects
• “Two important conclusions can be drawn from this
finding, WHICH HAS BEEN REPLICATED MANY
TIMES WITH DIVERSE POPULATIONS AND IN
A VARIETY OF LIFE DOMAINS (Schwarz &
Strack, 1999, this volume).”
• “First, people EVIDENTLY compute an answer to
the subjective happiness question on the fly, instead
of retrieving a prepared answer from memory.”
• “Second, respondents APPEAR TO anchor their
report of well-being on their satisfaction with any
significant life domain to which attention has been
drawn.” (Kahneman, 1999, p. 22).
Kahneman et al. (2006) “Would you be happier if you
were richer? A focusing illusion” SCIENCE, 312,
1908-1910.
Same example
“the dating question EVIDENTLY caused that aspect
of life to become salient and its importance to be
exaggerated when the respondents encountered the
more general question about their happiness” (p.
1908).
Schimmack and Oishi (2005)
• Meta-analysis of all studies that manipulated itemorder (no priming r = .32, priming r = .40, effect size q
= .09).
• Replication of Strack and Schwarz (1988) dating
study (no priming r = .39, priming r = .49, effect size q
= .12).
• Correlation with average domain satisfaction
(priming r = .71, no priming r = .78, effect size q =
.16).
Conclusions
• Priming effects are weak
• Satisfaction in important life domains that were not
primed is a strong predictor of global life satisfaction
judgments.
• Chronically accessible information is more
important than temporarily accessible information.
•You get a noble price for pushing a paradigm, not
for accurate reporting of empirical evidence.
Stability and Change
(Adaptation/Set Point)
• Genetic dispositions may produce stable differences
between individuals.
• Environmental influences may have short-lasting
effects due to adaptation.
• Policy implication: Even if it could be measured, it
could not be changed.
Empirical Evidence
• Meta-analyses and longitudinal panel studies
provide evidence for stability and change.
• Veenhoven (1994) – meta-analysis
• Ehrhardt et al. (2000) – SOEP
• Fujita and Diener (2005) - SOEP
• Schimmack and Oishi (2005) – meta-analysis
• Schilling (2006) – SOEP
• Schimmack and Lucas (2007) – SOEP
• Anusic and Schimmack (in prep.) – Meta
• Modeling Stability and Change
• Trait
• State
• Error / Fluctuation
• Stability of State Variance
• High – slow adaptation
• Low – fast adaptation
Trait State Error Plot
1
0.8
0.6
0.4
0.2
0
0
5
10
15
20
25
30
35
40
Error Free Trait State Plot
1
0.8
0.6
0.4
0.2
0
0
5
10
15
20
25
30
35
40
1
Grey=multiple items
Black=single items
Retest Correlation
0.8
0.6
0.4
0.2
0
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15
Time Interval (years)
Schimmack and Lucas (2007)
• A dyadic study of stability and change of
married couples.
• Spousal similarity in trait variance
• Assortative mating (genetic similarity)
• Stable environmental factors
• Spousal similarity in state variance
• Mutual social influence
• Shared environmental factors
Table 3
Parameter Estimates for Life Satisfaction.
Parameter
Wives
Reliability
Trait Variance
State Variance
Annual Stability
Husbands
Reliability
Trait Variance
State Variance
Annual Stability
Similarity
Trait
Initial State
New State
Final Statea
Error
1-11
12-22
Q
.58 [.54|.62]
.50 [.37|.63]
.50 [.37|.63]
.87 [.83|.92]
.64 [.61|.67]
.39 [.27|.51]
.61 [.49|.73]
.92 [.89|.94]
.10
-.15
.17
.26
.57 [.54|.61]
.43 [.30|.56]
.57 [.44|.70]
.88 [.84|.92]
.67 [.64|.70]
.38 [.25|.50]
.62 [.50|.75]
.92 [.89|.94]
.17
-.07
.08
.21
.77 [.69|.86]
.74 [.61|.87]
.61 [.52|.70]
.64 [.56|.73]
.32 [.28|.36]
.62 [.53|.71]
.63 [.55|.72]
.63 [.57|.69]
.25 [.21|.29]
-.23
.03
-.02
-.09
Conclusion
• Evidence for a stable trait component, presumably
due to genetic dispositions.
• Evidence for a slowly change state component. No
evidence for quick adaptation.
• Both components contribute about equally to the
error free variance in life satisfaction.
• Evidence for spousal similarity in both
components.
• Change may be due to changing circumstances
rather than simple adaptation to stable
circumstances.
Environmental factors that produce change in life
satisfaction?
• Unemployment (down, up after reemployment)
• Disability (down, adaptation evidence mixed)
• Widowhood (down, slow adaptation)
• Divorce (down, then up in new relationship)
• Marriage (up and down, no adaptation)
• Having children (on average up, adaptation unknown)
• Bigger house (up, adaptation unknown)
Source. Several articles by Rich Lucas, review article by Diener et al. 2006);
children effect based on poster German Sociological Society 2007;
house effect based on preliminary unpublished results of SOEP data.
Relative versus Absolute:
National Differences in Happiness
• Studies of individuals within a nation fail to reveal
causes that produce differences across nations.
• Changes within nation may be caused by absolute
or relative judgments of well-being.
• Large survey studies of national representative
samples show marked differences between nations.
• Last year, researchers published a world map of
happiness.
Theoretically Important Questions
• What is the correlation between per capita GDP in
Purchasing Power Parity $ and happiness?
• Is the relation linear or non-linear (log-function,
diminishing marginal utility)?
• What predicts discrepancies between these two
measures of nations’ well-being (welfare)?
• standard economics (error in happiness
measures)
• happiness economics (false assumptions of
standard economics)
Schimmack, Oishi, Diener (in preparation)
• used two WVS items (N = 80 nations)
• avoid computation of average
• estimate correlations separately for frequencies of
different response categories
• modeling shows that indicators are not
unidimensional.
• one dimension shows high loadings of categories
7,8, and 9, other dimension has high loading of 10s.
• GDP predicts frequencies of 7s, 8s, and 9s.
• Latin America predicts frequencies of 10s.
Top 10 Happy Nations
Top 10 Bias Nations
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Finland
Netherlands
Iceland
Luxembourg
Sweden
Australia
Norway
Canada
Ireland
USA
Puerto Rico
Colombia
Venezuela
Brazil
El Salvador
Malta ?
Switzerland ?
Denmark ?
Mexico
Austria ?
Happiness and Wealth (PPP)
6
5.5
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
0
5000
10000
15000
20000
25000
30000
35000
40000
Results
• Linear correlation with PPP, r = .83
• Correlation with Log-PPPP, r = .82
• Multiple correlation, r = .85
• unique linear, beta = .51
• unique log, beta = .35
Lowest 10 Nations Residuals
Unhappier than PPP predicts
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Zimbabwe
Luxembourg
Ukraine
Russia
Tanzania
Belarus
Moldova
Armenia
Pakistan
Georgia
Top 10 Residuals
Happier than PPP Predicts
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Indonesia
Colombia !
China
El Salvador
Mexico !
Dominican Republic
Nigeria
Finland
Malta
Philippines !
Human Development Index
(Education, Longevity, Log (PPP)
• Correlation with happiness, r = .73
• Controlling for PPP, beta = .17, n.s.
Gini
(Income Inequality)
• Correlation with happiness, r = -.24
• Controlling for PPP, beta = .13
• Correlation with bias, r = .55
• Controlling for Latin America, beta = .27
CO2 Emissions
• Correlation with happiness, r = .57
• Controlling for PPP, beta = -.16, n.s.
Electricity Consumption
• Correlation with happiness, r = .66
• Controlling for PPP, beta = -.03
Conclusion
• Life satisfaction judgments are – at least partially –
based on absolute information.
• PPP predicts life satisfaction beyond the fulfillment
of basic needs (proxy for utility).
• Other national indicators do not explain
discrepancies between happiness and PPP.
• Measurement error in PPP may account for some of
the discrepancies?
Hedonic indicators (AWB)?
• Less empirical evidence, but often highly correlated
with CWB.
• Life satisfaction more responsive to unemployment
than affective well-being (Schimmack, Schupp, &
Wagner, in press) (“hedonic treadmill, “bread and
circuses”).
Happiness Science
• Important research area
• Wealth of data
• Remaining problems
• cardinality
• bounded measure (problem?)
• More empirical (positive happiness science) work
needed before it can be used in public policy
(normative happiness science).