1 Plan of the course Income and subjective well-being I) Absolute income and the Easterlin paradox II) Relative income (comparisons) III) Adaptation- Expectations.

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Plan of the courseIncome and subjective well-being

• I) Absolute income and the Easterlin paradox

• II) Relative income (comparisons)

• III) Adaptation- Expectations

• IV) Inequality

I) The Easterlin paradoxWill raising the incomes of all raise the happiness

of all ?

• GDP is the objective of economic policy and development policy

• But the ultimate goal of public policy is welfare, not income

• So, does GDP proxy the different dimensions of welfare?

Health, life expectancy, security, capacities, freedom of choice, human rights, quality of life…

• Answer this question using Subjective Well-Being data

• This meets the discussion about the measures of well-being (e.g. the Sen, Stiglitz Report for President Sarkoy)

A) The Easterlin paradox: stylized facts

• Paradoxical relationship between income growth and subjective happiness:

Within country

Across countries

Over time at the individual level

Over time in average across countries

1) In a given country, richer individuals are happier and more satisfied with their lives

Source : WVS, China 2007.

OLS Esstimate of Happiness in USA (General Social Survey, 2006)

Happiness = 0,2 log(income). Individuals aged 25-65, earning more than 5000$.

Happiness and log household income American General Social Survey (Stevenson and

Wolfers, 2008)

Source : WVS, China 2007.

Sacks, Stevenson and Wolfers (2010)

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“In every representative national survey ever done, a significant bivariate relationship between happiness and

income has been found” (Easterlin 2005)

• Western developed countries: German Socio-Economic Panel (GSOEP), British Household Panel Survey (BHPS), Swiss household panel, Australian household survey (HILDA), General Social Survey (America), Netherlands, Denmark….

• Transition countries: Albania, Bulgaria, Latvia, Romania Russia, Estonia, Lithuania, Hungary, Belarus, Poland, Ukraine, Life in Transition Survey (LITS, 2006)....

• Asia: China, India, Shanghai, South Korea.

• Africa and Middle-East: Ethiopia, Nigeria, Peru, South-Africa (SALDRU), Tanzania, Turkey

• Latin America: Argentina, Brazil, Chile Mexico, Venezuela.

• International surveys: World Values Survey (1981- 2008, 5 waves, 105 countries), International Social Survey Program (101 countries), Gallup World Poll (2006, 105 countries), Latino Barometer (18 countries), European Social Survey (25 countries), European Values Survey, Euro-barometer.

2) People are happier and more satisfied with their lives in higher-income countries

Source : Inglehart,

Foa, Peterson, Welzel (2008)

GDP per Capita and Life Satisfaction in the World. Cross-sectional evidence

Deaton (2008, p57)

GDP and Average Life Satisfaction in the World in the 2000’s

WVS in the 2000’s. Last available year for each country.

Sacks, Stevenson et Wolfers (2010)

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3) Individuals become happier as they grow richer

• Dynamic relationship

• Individual Panel Data in Developed Countries

GSOEP, BHPS, HILDA, Netherlands, Denmark

• Individual Panel data in Low Income Countries

RLMS (Russia), ULMS (Ukraine), Peru, LSMS (Tajikistan)

4) But in average, on the long run, people do not become happier over time, when national income

increasesHappiness and Real GDP per Capita, United States, 1972-2002 (Easterlin and Angelescu, 2007)

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Japan

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USA

Europe

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In summary

• Cross-country analysis: positive relation

between Life Satisfaction and GDP per capita.

• Cross-section and panel analysis based on individual data:

strong relation between individual income and well-being.

• Aggregate time-series: no correlation between Life

Satisfaction and GDP per capita.

• The Easterlin Paradox is related to the time-dimension.

B) Maybe GDP growth increases happiness only in poor countries?

• The happiness returns to income growth are decreasing• But is there an upper bound to happiness?

“Once a country has over $15,000 per head, its level of happiness appears to be independent of its income per head” (Layard, 2005).

“Survival Societies” versus “Modern Societies” (Inglehart et al. 2009)

Satiation points (Adam Smith)?

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But the happiness-log GDP per capita gradient does not tend to zero

• New lessons from Gallup world poll 2006

Deaton (2008)

Wolfers and Stevenson (2008, 2010)

• “In logarithmic terms, there is no evidence that cross-country effects of greater income fade out or vanish as countries increase their income” (Deaton, p 3).

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The happiness-log GDP per capita gradient does not tend to zero

• Stevenson and Wolfers (2008, p11-12): the well-being-GDP gradient is about twice as steep for poor countries as for rich countries. That is […] a rise in income of $100 is associated with a rise in well-being for poor countries that is about twice as large as for rich countries”.

• However, the marginal utility of GDP growth is still positive in developed countries. “the Gallup results suggest that a 1 percent rise in GDP per capita would have about three times as large an effect on measured well-being in rich as in poor nations.

• Of course, a 1 percent rise in U.S. GDP per capita is about ten times as large as a 1 percent rise in Jamaican GDP per capita”.

Stevenson and Wolfers. “Subjective Well-Being and Income: Is There Any Evidence of Satiation?” (2013) AER, P&P

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The happiness-log GDP per capita gradient does not tend to zero

• Deaton (2008): “the relationship between log per capita

income and life satisfaction is close to linear. The coefficient

is 0.838, with a small standard error. A quadratic term in the

log of income has a positive coefficient: confirming that the

slope is higher in the richer countries! […]

• If there is any evidence for a deviation, it is small and is

probably in the direction of the slope being higher in the

high-income countries”.

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Deaton, 2008 Life satisfaction = 0.838 log GDP per capita

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Easterlin and Angelescu, 2007: “Rather than diminishing marginal utility of income, there is a

zero marginal utility of income”

Easterlin and Angelescu, 2007, p 24:

“The usual constancy of subjective well-being in the face of rising GDP per capita has typically been reconciled with the cross-sectional evidence on the grounds that the time series observations for developed nations correspond to the upper income range of the cross-sectional studies, where happiness changes little or not al all as real income rises.”

But “the income change over time within the income range used in the point-of-time studies do not generate the change in happiness implied by the cross-sectional pattern”.

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Easterlin and Angelescu (2007, p 24): “In 1972, the cohort of 1941-1950 had a mean per

capita income of about 12000$ (expressed in 1994 constant prices).

By the year 2000, the cohort’s average income had more than doubled, rising to almost 27000$.

According to the cross-sectional relation, this increase should have raised the cohort’s mean happiness from 2.17 to 2.27. `

In reality, the actual happiness of the cohort did not change”.

Misleading cross-sections. Actual versus predicted happiness in Japan, 1958-1987.

Easterlin, 2005, Easterlin and  Sawangfa 2005

C) The Evidence about the Long-Run Income-Happiness Nexus is Mixed and Still Controversial

• Income growth does not increase happiness over time:

Easterlin (2005a), Easterlin and Sawangfa (2005, 2009), Easterlin and Angelescu (2007), Easterlin (2009), Layard; Brockmann, Delhey, Welzel, Yuan (2009)

• Income growth does increase happiness over time:

Helliwell (2002), Stevenson and Wolfers (2008, 2010), Deaton (2008), Blanchflower (2008)

• Income growth does increase happiness over time but not always and weakly:Using GSS, Euro-barometer survey series, WVS,

Gallup World Poll, BHPSo Hagerty and Veenhoven (2000, 2003, 2006): positive and

statistically significant coefficient, but not in all countries.

o Inglehart, Peterson and Welzel (2008); Kenny (2005): positive and statistically significant coefficient, but not in all countries.

o Layard, Mayraz and Nickell (2010): positive coefficient but not always statistically significant.

o Oswald (1997): positive coefficient but not always statistically significant.

o Di Tella and MacCulloch (2008): positive coefficient but weak statistical significance.

Is the dynamic correlation “small enough to ignore”? (Hagerty and Veenhoven, 2000)

• Still controversial

• Statistical dispute: even when the data do not allow to establish a relation, this does not mean that it can be rejected.

• Less statistical power in the long run series of well-being than in the cross-section, because of the smaller variance

=> The evidence is mixed.

The Evidence about the Long-Run Income-Happiness Nexus is Mixed

Inglehart, Foa, Peterson, Welzel (2008).

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The debate continues

• Betsey Stevenson et Justin Wolfers (Wharton,

University of Pennsylvania)

“Economic growth and subjective well-being:

reassessing the Easterlin Paradox”, Brookings Papers

on Economic Activity, 2008 (see also Sacks, Stevenson

and Wolfers, 2010, IZA DP n°5230).

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Stevenson and Wolfers (2008)• Re-assess the paradox analyzing multiple rich datasets spanning

many decades. Using recent data on a broader array of countries.

• Establish a clear positive link between average levels of subjective well-being and GDP per capita across countries,

• find no evidence of a satiation point beyond which wealthier countries have no further increases in subjective well-being.

• show that the estimated relationship is consistent across many datasets and is similar to the relationship between subject well-being and income observed within countries.

• Finally, find that economic growth is associated with rising happiness.

• Together these findings indicate a clear role for absolute income.

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Stevenson and Wolfers (2008) - cntd

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Stevenson and Wolfers (2008) – cntd 2

Sacks, Stevenson and Wolfers (2010)

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At least 10 years between 2 dates

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W&S revisit Easterlin: Japan, 1958-1986, Average satisfaction score

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Taking into account the discontinuity in Japanese series used by Easterlin

D) GDP Fluctuations are strongly correlated with average happiness

• Recession makes people unhappy

• Macroeconomic movements exert strong effects on the happiness of nations:

unemployment,

inflation,

the volatility of output

o Di Tella, MacCulloch and Oswald (2003) , Wolfers (2003).

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Source: Stevenson and Wolfers (2008)

Life Satisfaction Follows the Business Cycle during the Russian Transition

Guriev and Zhuravskaya (2008)

Happiness and GDP Growth during Transition

Easterlin (2009)

Angus Deaton (PNAS, 2011)The Financial Crisis and the Well-Being of Americans

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Graham, Chattopadhyay and Picon (2010)

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A misleading short-term association?

• “One should avoid confusing a short-term positive

happiness-income association, due to fluctuations in

macroeconomic conditions, with the long-term

relationship”.

• “this disparity between the short and long-term

association is due to the psychological phenomenon of

loss aversion”. (Easterlin, 2009).

A Short-Term Positive Happiness-Income Association?

• Is Transition a short-term phenomenon? Or a regime change?Transition shares the essential features of development

o take-off period

o profound qualitative and institutional changes

o restructuring

Development should increase happiness… in the “short run”

It is only with the passage of time, that one will be able to observe whether the increase in subjective well-being continues with GDP growth, stagnates at a certain point, or goes down to the initial (1990) level.

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E) A measurement problem?

• Artefact linked to a limited scale?

better question « do you find that your income has

increased as compared to 10 years ago? »

or extendable satisfaction scale.

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• Deaton (2008, p12-13):  “The « best possible » life for you » is a

shifting standard that will move upwards with rising living

standards, so that we might expect the Danes to continue to

report 8 out of 10 as national income rises, provided they stay in

the same position in the global income rankings.

• Indeed it is hard to see how they could do differently faced with

a scale that has a maximum of 10.

• According to this view, average national life satisfaction will be

a useful measure in the cross-section, but not over time.”

Bounded Happiness Scales

• Satisfaction judgements are expressed on an ordinal bounded-scale.

• They express relative judgements, i.e. the relation between individuals’ attainments and the existing of possibilities (represented by the scale).

• Framing effect

Hedonic treadmill"Real" adaptation Satisfaction treadmill

(nominal adaptation)

Measures of Quality of Life in Asian Countries during Positive Episodes of GDP Growth

World Bank data, 1980-2007

Cardinal measures

Ordinal measures

Measures of Quality of Life in Rich Western Countries during Positive Episodes of GDP

Growth

World Bank data

Cardinal measures

Ordinal measures

F) SPECIFICATIONS OF THE ESTIMATES

• Co-movements between growth and quality of life indicatorsLife expectancyChild mortalitySchool enrollment…

• These channels from GDP growth to happiness should not be neutralized (controlled for) in statistical estimates.

NEGATIVE OMITTED VARIABLES

• Pollution, income inequality, work stress, extended working hours, unemployment, environmental degradation, fat intake (obesity and blood pressure), sub-urbanization…

• The influence of these “omitted variables” could hide the positive influence of GDP growth on subjective well-being in econometric estimates (Di Tella and MacCulloch, 2008).

• Many of the negative externalities of growth increase in the initial stages of development and decrease in later stages.

WHAT IS LEFT?

• The Easterlin paradox is about the TREND in life satisfaction over time, on the long run

not about cross-sectional relations

not about happiness over the business cycle

• The question is about the size of the correlation coefficient

Is is significantly different from zero? Too small to matter?

• Another formulation of the question would be: “what difference does it make in terms of well-being, whether GDP grows at a rate of 3% versus 1%?

What about the distribution of happiness? Clark, Flèche and Senik (2012)

the Great Happiness Moderation

• As GDP rises, average happiness becomes more equally distributed

• Not a negligible gain for risk-averse agents

• Attention, remettre les bons formats

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What do we do?

• We uncover a declining spread in happiness over time during

periods of positive income growth

In cross-sections of countries

In panels of countries

In time series within-country

• An “augmented Easterlin paradox” (mean-preserving declining

spread in happiness)

A Mean-Preserving Decline in Happiness Spread

This applies to life satisfaction and to all the domains of satisfaction

available in the data.

The fall in happiness variance comes about because there are fewer

responses at both ends of the subjective well-being scale.

We find no such movement in the countries that have had zero growth,

and the opposite effect in countries that have undergone recessions.

This does not reflect demographics

The extremes of the happiness scale shrink

Data (Respondents 18-65 years old)

• World Values Survey: 5 waves, 1981-2008. Life satisfaction: 1-10

Periods of positive income growth, at least 5 years 60

countries

• Great-Britain: BHPS: 1996-2008. Life satisfaction: 1-7

• Germany: GSOEP: 1984-2009. Life satisfaction: 0-10

• Australia: HILDA: 2001-2009. Life satisfaction: 0-10

• United-States: GSS: 1972-2010. Happiness: 1-3

The American General Social Survey (1972-2009)

• The only long run survey containing a happiness or life satisfaction question in the United-States.

• But only 3 modalities: very happy, pretty happy, not too happy

• Obviously not fit to the analysis of the variance (although W&S, Dutta and Foster)

• We consider the results with greater caution.

Household Income

• Ideally, use the net disposable income after tax and transfers. • GSOEP and HILDA: measure of the annual disposable net

combined income after taxes and public transfers (Government pensions and benefits).

• BHPS: combination of labor income, non-labor income and pensions for all household members, in the previous year, before taxes.

• GSS: “total family income”, i.e. all types of income from all sources, for all members of the household, before taxes, in the previous year.

• PPP converted GDP per capita, in constant prices (2000), US dollars: Penn Table

Standard deviation / mean happiness

• Self-declared happiness is a choice on a proposed scaleEquality all respondents choose the same rating

• Standard deviation of self-declared happiness / mean happiness for each country*yearNo scale dependenceCardinalizationIndex of Ordinal Variance Wolfers and Stevenson (2008), Dutta and Foster (2011)

device sophisticated measures of happiness inequality o which lead to exactly same results as ours.

Happiness inequality and GDP per capita cross-countries,

WVS, last available year (2000s)

A doubling of GDP per capita is associated with a 10% reduction in happiness inequality.

Same result withsd(happy)/ mean(happy)

Happiness Inequality Over Time in Growing Countries, Western Countries (WVS)

Happiness Inequality Over Time with Some Periods of Negative or Zero Growth (WVS)

Happiness Inequality Over Time during Periods of Decreasing GDP (WVS)

Happiness Inequality Over Time during Periods of Decreasing GDP (Eurobarometer)

Happiness Inequality Over Time during Periods of Negative or Zero Growth (WVS)

Happiness Inequality Over Time during Periods of Decreasing GDP (Eurobarometer)

Trends in income growth, average happiness and happiness inequality

Great Britain (BHPS)

Same result with sd(happy)instead of sd/mean; with or without controls

Trends in income growth, average happiness and happiness inequalityWest Germany (GSOEP)

Same result with sd(happy)instead of sd/mean; with or without controls

Trends in income growth, average happiness and happiness inequality

Australia (HILDA)

Same result with sd(happy)instead of sd/mean; with or without controls

Trends in income growth, average happiness and happiness inequality

USA (GSS)

Same result with sd(happy)instead of sd/mean; with or without controls

Trends in income growth, average happiness and happiness inequalityOther OECD countries

(10 years, positive growth), flat happiness trend)

Trends in income growth, average happiness and happiness inequalityOther OECD countries

(10 years, positive growth), flat happiness trend)

The Extreme Levels of Happiness are Vanishing

The Extreme Levels of Happiness are Vanishing

Trends in average Satisfaction and Satisfaction Inequality by Domain, Great Britain

Happiness Inequality and Income Inequality

• Starting in the 1980s, in Australia, Great-Britain, Germany,

United-States, general rise in income inequality

Dustmann, Ludsteck and Schönberg (2008)

Atkinson, Piketty and Saez (2011)

• General fall in the spread of happiness

although in Germany and the US, this trend breaks in the

1990s.

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Happiness inequality and social expenditures

Two-Periods in Germany and USA

• In the late 1990’s happiness inequality rises again in Germany and in the USA, probably due to the rise in income inequality

Interpretation

• Explain: (1) the rise in average income per capita over time, (2) the stability of average happiness over time, (3) the fall in happiness inequality over time.

• Not the evolution of income inequality • Not the effect of demographic change

Two Lines of Interpretation

• Actual Homogeneization of Happiness• Rescaling

In both cases: qualification of the Easterlin paradox

1) Social harmonization, public goods and

modern growth

• Externalities of economic growth and modernization

Welfare system

Improvement in education, health, life expectancy, child

mortality

Infrastructure

Political rights, private liberties, gender equality, capabilities

• Extension of public goods fall in the spread of happiness

2) Income as Buffer Stock

• Higher revenue allows buffering income shocks and other various shocks of life, such as job loss, divorce etc…Happiness dispersion is lower within richer groupsRich deciles of the population experience less volatility

of happiness over time

The spread of Happiness is lower within higher deciles of the income distribution

Individuals are assigned to the average decile they belong to over the whole period

The spread of happiness over time is lower in higher deciles of the income distribution

Individuals are assigned to the average decile they belong to over the whole period.

3) Rescaling

• Satisfaction treadmill rather than hedonic treadmill

Relationship between latent happiness and self-declared happiness o “The ‘best possible life for you’ is a shifting standard that will move upwards

with rising living standards” (Deaton, 2008)

Unequal growth: people “rescale” more at the top of the ladder than at the bottom, because their world of opportunities expands more than that of less fortunate people.

This would create a convergence movement whereby the self-declared happiness of the poor would rise whereas that of the rich would not

• Consistent with “augmented Easterlin paradox”, but less negative message concerning growth

Some Usual Interpetations or Discussions of the Easterlin Paradox that Do Not Hold

1. Happiness as a log function of (absolute) income and nothing else? (W&S)

• Possible if income distribution concentrates around the mean or medianNot really what’s happening

• Test:Estimate happiness function in first year:

o Happiness= a0 + a1 age + a2 age2 + a3 log income + a4 gender +

Predict distribution of happiness in end year

Actual versus predicted distribution of happiness

BHPS

2. Social comparisons

• A priori, in the presence of rising income inequality, income comparisons should lead to an increase in the standard deviation in happiness, not to a fall.

• Comparisons are mostly upward• Top income are diverging• Not easy to imagine how comparisons could lead to a

reduction in the happiness spread.

3. Adaptation

• Simple negative influence of lagged income? • Bliss point, satiation point? • Would not lead to the fall of the happiness spread

• More sophisticated notions of AdaptationMaslow Rescaling

• Higher needs may be much more difficult to fulfill than basic needs. • Survival versus life; Survival societies versus modern societies• “Economic development increases people’s sense of existential security, leading them to shift

their emphasis from survival values towards self-expression values and free choice…” (Inglehart 2010).

• Development the share of the population that feels totally deprived (the bottom of the scale) and totally satisfied (the top of the scale) both shrink.

• Consistent with “augmented Easterlin paradox”, but less negative message concerning growth.

Adaptation à la Maslow (1943, 1954)

Conclusions

• Actual concentration of happiness:Positive message to developing countries: promise of

greater social homogeneityMore optimistic interpretation of the Easterlin paradox

• Rescaling: Questions the pessimistic message of Easterlin

Appendix

Index of Ordinal Variance vs Standard deviation

Index of Ordinal Variance vs Standard deviation

Income inequality and happiness inequality in Germany

Sd(income) and sd(happiness) Income level by quintile

Average happiness by quintile Sd(happiness) by quintile

Happiness inequality between and within income, groups, Germany: 2 periods

Average happiness by quintile Sd(happiness) by quintile

Income inequality and happiness inequality in Australia

Sd(income) and sd(happiness) Income level by quintile

Average happiness by quintile Sd(happiness) by quintile

Income inequality and happiness inequality in the U.S.

Sd(income) and sd(happiness) Income level by quintile

Happiness inequality between and within income, groups, USA: 2 periods

Average happiness by quintile

Happiness equalization is not due to demographic change

• RIF regressions of the variance in happiness (based on the WVS) show that GDP per capita and income inequality affect happiness inequality beyond the impact of demographic change both in cross-section estimates (controlling for year fixed-

effects) and over time (controlling for country fixed-effects). • Common trend: happiness inequality declines in all countries

within age, education, gender, marital status and employment status categories,

• RIF: Recentered Influence Function regressions (Firpo , Fortin and Lemieux, 2009)