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 happy2 Rather happy3 Not very happy4 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 Completely satisfied1 2 3 4 5 6 7 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 item-order (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

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Error Free Trait State Plot

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Time Interval (years)

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Grey=multiple itemsBlack=single items

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 1-11 12-22 Q Wives Reliability .58 [.54|.62] .64 [.61|.67] .10 Trait Variance .50 [.37|.63] .39 [.27|.51] -.15 State Variance .50 [.37|.63] .61 [.49|.73] .17 Annual Stability .87 [.83|.92] .92 [.89|.94] .26 Husbands Reliability .57 [.54|.61] .67 [.64|.70] .17 Trait Variance .43 [.30|.56] .38 [.25|.50] -.07 State Variance .57 [.44|.70] .62 [.50|.75] .08 Annual Stability .88 [.84|.92] .92 [.89|.94] .21 Similarity Trait .77 [.69|.86] Initial State .74 [.61|.87] .62 [.53|.71] -.23 New State .61 [.52|.70] .63 [.55|.72] .03 Final Statea .64 [.56|.73] .63 [.57|.69] -.02 Error .32 [.28|.36] .25 [.21|.29] -.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

1. Finland2. Netherlands3. Iceland4. Luxembourg5. Sweden6. Australia7. Norway8. Canada9. Ireland10. USA

Top 10 Bias Nations

1. Puerto Rico2. Colombia3. Venezuela4. Brazil5. El Salvador6. Malta ?7. Switzerland ?8. Denmark ?9. Mexico10. Austria ?

Happiness and Wealth (PPP)

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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 ResidualsUnhappier than PPP predicts

1. Zimbabwe2. Luxembourg3. Ukraine4. Russia5. Tanzania6. Belarus7. Moldova8. Armenia9. Pakistan10. Georgia

Top 10 ResidualsHappier than PPP Predicts

1. Indonesia2. Colombia !3. China4. El Salvador5. Mexico !6. Dominican Republic7. Nigeria8. Finland9. Malta10. 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).