income comparisons, the easterlin paradox and public policy ape/ete masters course andrew e. clark...

Income Comparisons, the Easterlin Paradox and Public Policy

APE/ETE Masters Course

Andrew E. Clark (Paris School of Economics and IZA)http://www.parisschoolofeconomics.com/clark-andrew/

http://www.parisschoolofeconomics.com/clark-andrew/

You might think that money might help us to achieve the variety of different kinds of well-being that have been put forward as important in the literature.

Being poor certainly prevents us from doing many things that we would like to do.

If you don’t believe me, then try it…

But in addition to wishful thinking, what does the data say?

One of the first, and most original, researchers in this area is Dick Easterlin (University of Southern California).

He asked two questions:

1) Are richer individuals/countries happier than poorer individuals/countries?

2) As countries get richer, does everyone become happier?

We might expect the answer to these two questions to be the same.

I here present some of the results from:

Easterlin, R. A. (2005). “Diminishing Marginal Utility of Income? Caveat Emptor”. Social Indicators Research. pp. 243-255.

This specifically deals with the case of Japan.

Japan was a poor country in the 1950s/early 1960s, but then experienced unprecedented growth.

Fact 1. Richer countries are happier countries.

Japan was in the middle of the income distribution in the early 1960s, and had a middling level of happiness

Japan

The blue lines show the estimated relationship between income and happiness

So what happened as Japan became richer?Look at annual indices (1962=100) of life

satisfaction and real GNP per capita for Japan, 1958-1987

Between 1962 and 1987 Japan experienced unprecedented economic growth, with GNP per capita (in real terms)rising 3.5-fold: growing from 22 to 77 percent of the United States level in 1962

We might then imagine that Japan would follow the blue lines above: as Japan became richer, it would become happier.

In fact, happiness remained constant despite Japan’s remarkable economic growth

What “should” have happened

What did happen

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Happiness Real Income Per Capita

Are the Japanese strange?

Not necessarily: the same thing can be observed in 30 years of American GSS data

This is the Easterlin Paradox. Richer individuals are happier, but as countries become richer over time they do not become happier.

The challenge to social science is to try to explain why.

This finding has generated a considerable amount of controversy: it just MUST be wrong

One point to remember is that the Easterlin Paradox is a statement of why continuous GDP growth may not increase social welfare in the long-run.

It is not a theory of recessions not mattering: we shouldn’t be surprised if life satisfaction and the economic cycle are correlated in the short run

After all, unemployment is a robust significant indicator of individual subjective well-being (the blips in the US happiness line correspond to the business cycle)

Recent work has considered (and reconsidered) time–series evidence suggesting that the average well-being lines over time above are “really” flat

One well-known contribution is Stevenson and Wolfers (2008)

- There are issues with data comparability over time in Japan: the questions changed over time


- SW agree that life satisfaction in the USA follows the business cycle. But also that there is no trend in the GSS data.


- There is a positive trend in both happiness and life satisfaction scores in the World Values Survey.

- Easterlin points out that the trend in WVS happiness scores came from a change in the way the happiness question was administered.

- And one of the two positive life satisfaction slopes is entirely driven by the recovery of transition countries.

- While the other relies on two data points (and so wouldn’t survive a jackknife analysis)

United Kingdom (BHPS): Real GDP per capita and life satisfaction

1996 - 2008

West Germany (SOEP): Real GDP per capita and life satisfaction

1984 - 2010

Australia (HILDA): Real GDP per capita and life satisfaction

2001 - 2009

Life Satisfaction in Five European Countries, (World Database of Happiness)

1973-2004

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It is easy to lose the will to live when reading the attacks and counter-attacks.

There are fundamental issues with the analysis of time-series happiness data.



1) It is time-series happiness data. So we are making strong statements off of differences in slopes estimated using 20, or 10, or 4 observations.




2) There are never any control variables (age, sex, education, country of birth, urban/rural, labour force status, marital status, and so on): how much of this is a composition effect?


2) There are never any control variables (age, sex, education, country of birth, urban/rural, labour force status, marital status, and so on): how much of this is a composition effect?

3) There are never any macro control variables

It is easy to imagine macro “omitted variables” which may obscure the effect of income: inequality, crime, pollution etc.

Perhaps we should stop looking at time series

We often appeal to the notion of relative utility, whereby

W = W(y, y*, …) to explain the Easterlin Paradox.

So let’s turn the question around: if we find strong and consistent evidence of a role of some benchmark or comparison income in individual well-being, then the Easterlin Paradox must at least partly hold:

- Then the time-series and cross-section relationships between subjective well-being and income cannot be the same

BROAD IDEA

A common idea across the Social Sciences: well-being or utility depends on some kind of comparison process of what you have relative to a reference level.

Comparisons can be over “things” or over money.

An example. Two people, A and B, who live next to each other, both like cars.

WA = W(CarA,.....)

WB = W(CarB,.....)

Where “W” is the individual’s well-being function.

Key question: is A more likely to buy a new car if B buys a new one?

Standard economic theory: No.Comparisons/relative utility: Yes.

If the answer is “yes”, then we could write A’s “happiness function” as WA = W(CarA/CarB,....): how good is my car relative to my neighbour’s?

This might sound like a ridiculous proposition.

It isn’t, at least not in the Netherlands.

Kuhn, P., Kooreman, P., Soetevent, A., and Kapteyn, A. (2011). "The Effects of Lottery Prizes on Winners and their Neighbors: Evidence from the Dutch Postcode Lottery". American Economic Review, 101, 2226-2247.

Each week, the Dutch Postcode Lottery (PCL) randomly selects a postal code, and distributes cash and a new BMW to lottery participants in that code.

There are 430 000 postodes in the Netherlands. An average postcode includes around 20 households.

A winning participant wins €12,500 per ticket. In addition, one participating household in the winning postcode receives a new BMW.

Households in postcodes surveyed six months after the prize was won.

PCL nonparticipants who live next door to winners have significantly higher levels of car consumption than other nonparticipants

(sig. levels are all relative to column 2, the non-participants in non-winning PCs)

Applied to Income, instead of Cars:

Income and Subjective Well-Being (SWB)

Standard model: W = W(y, ....)Comparisons: W = W(y/y*, ....)

This is analogous to the car example.

The variable y* is “comparison income”: the income to which we compare/income of the reference group.

I mostly consider evidence here resulting from direct measurement of “W”, via job satisfaction, life satisfaction, mental stress etc.

Validated by physiological/neurological studies, third-party raters, and (most importantly) future behaviours such as divorce, unemployment duration, quitting one’s job, and morbidity and mortality.

There is nothing to stop us looking at behaviours too: I think of the analysis of behaviour and proxy measures of utility as complements, not substitutes.

To whom do we compare?

Peer group/people like me Others in the same household Spouse/partner Myself in the past Friends Neighbours Work colleagues “Expectations”

We typically know nothing about the reference group. Wave 3 of the European Social Survey (22 countries) helps here.

Table1. “How important is it to you to compare your income with other people’s incomes?”

Not at all important 23.80 1 17.01 2 13.86 3 16.95 4 13.52 5 9.42 Very important 5.44

1.5

1.7

1.9

2.1

2.3

2.5

2.7

2.9

Netherl

ands

Switerla

nd

Finlan

d

German

y

Austria

Belgium

United K

ingdo

m

Portugal

Irelan

d

Danem

ark

Norway

Bulgari

a

France

Hungary

Sweden

Sloven

ia

Russia

Estonia

Poland

Ukraine

Spain

Slovak

ia

Observations Weighted % Work colleagues 6 159 38.93 Family members 929 6.03 Friends 2 382 14.94 Others 1 192 7.39 Don't compare 5 185 32.72

Observations Weighted % Work colleagues 6 159 38.93 Family members 929 6.03 Friends 2 382 14.94 Others 1 192 7.39 Don't compare 5 185 32.72

Income and comparison intensity are negatively correlated, both within and between countries

Mostly we just impose a reference group, such as people like me, neighbours or family.

I use the British Household Panel Study (BHPS) to look at the relationship between job satisfaction and labour income. Main findings:

There is some evidence that job satisfaction is an increasing function of income. However, job satisfaction falls as others’ income rises. This holds for:

The income of “people like you” (same characteristics, same type of job).

Partner’s income. The income of other adults in the same household. The income that you yourself earned in the same job

one year ago.

Clark and Oswald (1996). BHPS Data on 5000 Employees

Log income (y) -0.02 0.11 -0.001

(0.039) (0.050) (0.04)

Log comparison income (y*) --- -0.20 ---

(0.062)

Log NES comparison income (y**) --- --- -0.26

(0.073)

“Comparison Income” predicted from a Mincer Earnings equation (note: requires exclusion restrictions to avoid multicollinearity);

“NES comparison income” matched in from another data set by hours of work, and thus avoids identification problems (but assumes reference group defined by hours of work).

Clark (1996). BHPS Data on 5000 Employees

Estimated only on couples where both partners are in work. Includes other standard control variables.

Log hourly pay: ln(HPi) 0.111 0.039 0.060(0.060) (0.068) (0.066)

Log Hours -0.251 -0.246 -0.250(0.061) (0.061) (0.061)

Log spouse's -0.121 -0.056 -0.047hourly pay: (ln(HPs)) (0.044) (0.052) (0.059)

Dummy: HPi > HPs --- 0.171 ---(0.074)

Log spouse's hourly pay --- --- -0.069(when HPs > HPi) (0.037)

Comparisons to the past: Clark (1999). BHPS Data

Two waves only. Estimated on individuals who did not change job or get promoted between the two waves.

Log current monthly pay 0.086 0.486 0.042 0.038(0.071) (0.166) (0.072) (0.072)

Log current monthly hours -0.081 -0.428 0.114 0.067(0.215) (0.295) (0.236) (0.219)

Log monthly pay one year ago .. -0.442 .. ..(0.163)

Log monthly hours one year ago .. 0.523 .. ..(0.288)

% Change in pay/100 .. .. 0.439 ..(0.146)

% Change in hours/100 .. .. -0.518 ..(0.285)

% Change in hourly wage/100 .. .. .. 0.450(0.126)

Therefore, when we look at the effect of own pay and others’ pay on satisfaction, we find the following kind of stylised relationships:

Income DOES bring happiness... As long as you get it and no-one else does

Sat

isfa

ctio

n

Own pay (y), holding y* constant

Sat

isfa

ctio

n

Others’ pay (y*), holding y constant

Pay rises for everybody (y/y* constant)

Sat

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n

Others have replicated these broad findings with work on life satisfaction and local area average incomes: Ferrer-i-Carbonell for Germany, and Luttmer for the US.

There are three reasons to wonder whether these comparison income results are entirely right:

• The Danish

• Tunnels

• Altruism

1) This is Denmark

• From January 1, 2007, Denmark has been split up into five Regions: two in Zealand, two in Jutland, and one covering Funen and Southern Jutland.

• Previous to this, Denmark was split up into 15 counties, and 273 municipalities.

• We use new geo-referenced data, based on a geographical grid of size 100*100 meters (i.e. 10 000 square meters, or a hectare) covering the entire country.

• Some of these grid cells are uninhabited, others are only very thinly inhabited: around two-thirds of inhabited hectare cells contain under five households.

• Data confidentiality: Statistics Denmark aggregates to produce clusters of neighbouring hectare cells with a minimum of 150 (600) households.

• Adjusted by Damm and Schultz-Nielsen (2008) to produce a classification

Constant over time Marked out by physical barriers (roads, rivers) Compact Contiguous Homogenous in terms of type and ownership of housing

(don’t mix flats and houses).

Figure 1Small neighbourhoods in the area of Taastrupgård, Høje Tåstrup

Source: Damm and Schultz-Nielsen (2008).

Economic Satisfaction, Income and Rank within Small Neighbourhoods: Panel Results

Baseline Baseline and Municipality

Baseline and Rank

Ln(HH income) 0.390** 0.390** 0.070* (0.021) (0.021) (0.028) Ln(median grid HH income) 0.228** 0.236** 0.634** (0.052) (0.055) (0.057) Ln(median municipality HH income) --- -0.062 --- --- (0.156) --- Relative rank in small grid --- --- 1.124** --- --- (0.068) See Neighbours Often -0.019 -0.019 -0.016 (0.016) (0.016) (0.016) Single -0.057* -0.057* 0.025 (0.027) (0.027) (0.028) Health problems dummy -0.023 -0.023 -0.023 (0.017) (0.017) (0.017) Age dummies (9) Yes Yes Yes Education dummies (6) Yes Yes Yes Socio-Economic Group dummies (3) Yes Yes Yes No. and Ages of children dummies (5) Yes Yes Yes No. Years in Grid dummies (5) Yes Yes Yes Regional dummies (13) Yes Yes Yes Year dummies (8) Yes Yes Yes Observations 33 870 33 870 33 870

So individuals like living with richer (very close) neighbours. This probably reflects local public goods or social capital. In this sense, were we able to hold public goods and social capital constant, then I would expect the sign on neighbours’ income to become negative.

This is what column 3 effectively does. It holds average neighbourhood income constant, and looks at rank effects. Consistent with previous work, this is positive.

People like having rich neighbours…and being on top of the pile.

2) TunnelsSenik, C. (2004). "When Information Dominates Comparison: A Panel

Data Analysis Using Russian Subjective Data.". Journal of Public Economics, 88, 2099-2123.

Clark, A.E., Kristensen, N., and Westergård-Nielsen, N. (2009). "Job Satisfaction and Co-worker Wages: Status or Signal?". Economic Journal, 119, 430-447.

A smaller number of recent papers have uncovered empirical results where some measure of individual well-being is positively correlated with reference group income or earnings: the more others earn, the happier I am.

This finding has been interpreted as demonstrating Hirschman’s tunnel effect (Hirschman and Rothschild, 1973): while others’ good fortune might make me jealous, it may also provide information about my own future prospects.

The distinction between status and signal depends on how likely you are to end up in the future with your reference group’s current income. Senik considers a very unstable labour market (Russia); Clark et al examine the average earnings of other workers within the same firm.

But how pervasive are signal effects in general?

3) AltruismDunn, E., Aknin, L., and Norton, M. (2008). "Spending Money on

Others Promotes Happiness". Science, 319, 1687-1688.

• Individuals are forced to be generous: they are given an envelope with either $5 or $20 to spend that day.

• Half are told to spend the money on themselves, and the other half on someone else.

• Spending money on others produces greater happiness than spending money on oneself.

People do not realise this ex ante.

Does this challenge the hypothesis that well-being is relative in income?

Not necessarily. We can imagine that people’s well-being is affected by both envy and altruism. The objects of these two feelings are probably not the same. When individuals buy gifts, they may not buy gifts for those in their reference group.

The results with respect to past income are interesting: the more you earned in the past, the more you need to earn now in order to be just as satisfied: wages are habit-forming.

This implies that someone who receives a pay rise will have job satisfaction over time as follows:

Job

satis

fact

ion

Time

Pay rises are good at the time, but then you get used to them. How can a firm keep its workers satisfied then? By starting them at a relatively low wage and giving them constant pay rises: profile C.

A

B

C

Time

Pay

Even a small amount of comparison to the past produces sharply-rising consumption profiles.

Conclusion: There are strong comparison effects both spatially (between

groups) and over time with respect to income.

These two phenomena can explain the Easterlin paradox

FIGURE 1: Happiness and Real Income Per Capita in the US, 1973-2004

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Subjective Well-Being Measures are not the only possible way of showing comparison effects.

1) The Leyden approach. Invert the question: Who needs more money to have a good life?

Initiated by Bernard Van Praag.Ask individuals to assign income levels (per period) to six or nine

different verbal labels (such as "excellent“,"sufficient" and "bad").

Van Praag can then estimate for each individual a lognormal “Welfare Function of Income” (the jump in income required to go from one label to another becomes increasingly large).

The resulting individual estimated means () and variances () – there is one curve for each person – were then used as dependent variables in regressions to show which types of individuals require a higher level of income to be satisfied, and which individuals have valuations that are more sensitive to changes in income.

Those with higher reference group income, and who had earned more in the past, had higher values of .

Those with higher values of μ use lower verbal well-being labels at any given level of income

2) Ask people. Preference for rising income profiles, and preferences for lower absolute incomes:

• A: Your current yearly income is $50,000; others earn $25,000.

• B: Your current yearly income is $100,000; others earn $200,000.

Individuals have a marked preference for A over B.Positionality differs according to the domain. In

Alpizar et al. (2005) this is stronger for cars and housing, and weaker for vacations and insurance.

3) Experimental. a)Ultimatum game: responders frequently

rejecting offers that are under 25% of the total sum; as such the the vast majority of offers are between 40% and 50% of the sum.

b)Dictator game: non-strategic. The survey of 616 such experiments in Engel (2011) concludes that dictators give on average 28.35% of the sum of money to be divided.

c) Zizzo and Oswald (2001) report the results of an experiment whereby subjects can pay to burn each other’s money. A majority of subjects chose to do so, even though it costs them real earnings. The average subject had half of her earnings burnt, and richer subjects were burnt more often.

d) Psychological experiments. Danny Kahneman’s hand in bucket of water experiments show that the change in pain predicts overall evaluation (rather than the level).

e) McBride (2010) introduces a novel way of calculating aspirations directly in a matching pennies game, where individuals play against computers.

The computer chooses heads or tails according to (known) probability distributions (for example 80% heads, 20% tails).

After each round of playing, individuals report their satisfaction with the outcome.

Introduces social comparisons in some of the treatments (by telling the individual the outcomes of the other players).

Aspiration effect identified by varying the heads and tails probabilities played by the computer. Each subject has five pennies to play. When paired with a 80% heads, 20% tails computer, the best strategy is to always play heads, which gives an expected payoff of four pennies. When paired with a 65% heads, 35% tails computer, the best strategy is still to always play heads, but now the expected payoff is only 3.25 pennies.

Results: satisfaction is i) higher the more one winsii) lower the more others winiii) lower the higher was the aspiration level.

4) Natural Experiments

Card et al. (2012): the revelation of information on others' earnings.

The natural experiment here is a court decision that made the salary of any California state employee public knowledge.

A local newspaper set up a website making it easy to find this information.

Following this website launch, Card et al. informed a random subset of employees at three UC campuses about the site.

Some days later, all employees on the three campuses were surveyed.

Compare the treatment group (informed about the website) to others to reveal the impact of information on others' salaries.

The reference group was defined here as co-workers in the same occupation group (faculty vs. staff) and administrative unit in the university.

The survey found lower job satisfaction for those with pay below the reference group median and a greater intention to look for a new job.

The effect on both for those who were relatively well-paid was insignificant.

There is some evidence of an actual quitting effect on those who were found to be in the bottom earnings quartile in the reference group.

5) Neuro. Fließbach, K., Weber, B., Trautner, P., Dohmen, T., Sunde, U., Elger, C., & Falk, A. (2007). "Social comparison affects reward-related brain activity in the human ventral striatum". Science, 318, 1305-1308.

Payoffs vary according to whether the individual gets the task right, and also randomly when the task is correct

Brain activity measured via BOLD in various voxels.

Particular attention paid to the ventral striatum: the “neural circuitry of reward”

This kind of striatal activity has been shown to predict both hedonic outcomes (subjective well-being) and physiological outcomes (cortisol output: the body’s response to stress)

Brain activation depends on relative income: compare C6, C8 and C11 (where the individual receives 60 Euros), and C7 to C9.

What Changes in Micro Analysis if Utility is Relative?

A lot….1) Labour supply is determined by the income: leisure

trade-off. Under relative utility, higher income may not be associated with lower marginal utility (latter is also determined by y*, and higher levels of y* increase the marginal utility of own income).

There is a positive cross-partial between y and y*…Possibility of everyone working too much (with

associated tax implications).2) A taste for rising income profiles can explain why

wages rise faster than productivity. Also explains wage compression, and wage secrecy. And optimal rising consumption profiles.

3) Poverty: defined in terms of income or utility? There is no longer a monotonic relationship between the two

4) Individuals accumulate wealth over their lifetime, and productivity generally increases: reference income when ‘old’ is always higher than that when ‘young’. Savings may be too low: individuals will not postpone consumption to the future as future general consumption levels then will likely be higher due to productivity growth. This is a large (theoretical) literature: see Section 5.6 of Clark et al. Journal of Economic Literature (2008).

5) Conspicuous consumption introduces externalities between individuals, which should be corrected by a tax. Taxing luxury cars will make no difference to utility if they are only consumed for rank reasons.

6) Migration. Now no longer only for income, but also for relative standing reasons. Anti brain-drain conclusion (Mexican Doctors).

7) Relative utility might imply following behaviour… or it might not. Consider an additive comparisons model:

8) Worker behaviour, such as quitting and effort (productivity) will depend not only on own income, but also income of others. See Clark, A.E., Masclet, D., and Villeval, M.-C. (2010). "Effort and Comparison Income". Industrial and Labor Relations Review, 63, 407-426.

The rank-dependence of effort (Random-effect Tobit model)

Macro Implication. As a result of the Easterlin Paradox:“Money/possessions aren’t making us any happier: we

should spend our time concentrating on X instead”

Candidates for X:

A (good) job Marriage/Family Social Activities Freedom/Democracy Health Religion

But what if we found the same phenomena of adaptation and comparisons there too? This is only rarely tested.

Public PolicyImportant to note that policy can’t do anything about

some of the things we know are important from 20 years of subjective well-being research.

Gender

Women often report higher life satisfaction scores than men...

but also higher psychological stress scores

Nolen-Hoeksema, S., and Rusting, C.L. (1999). "Gender differences in well-being". In D. Kahneman, E. Diener, and N. Schwartz (Eds.), Well-being: The foundations of hedonic psychology. New York: Russell Sage Foundation.

Policy can’t do anything about some of the things we know are important from 20 years of subjective well-being research.

Age6.

77.

25(m

ean)

sat

life

20 40 60 80Age of Individual

female male

well-being by age and sex

The “happiness smile” (or grimace) is almost ubiquitous. But we still are not sure what is behind it

Policy can’t do anything about some of the things we know are important from 20 years of subjective well-being research.

Country

The happiest country in the world is …

Australia!!

Or Denmark, or Nigeria.

Policy question: How do we make the French Australian?

Well-being and the Labour Market They’re right! Unemployment really is important.....ECHP: Satisfaction Scale 1-6. 500 000 individuals.

2

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4.5

Life Satisfaction

Self-Employed Employed Unemployed NLF

Labour Force Status

ECHP: Labour Force Status and Life Satisfaction

But do you adapt to it, and is it relative?

Main results:

The psychological impact of unemployment is lower

When the regional unemployment rate is higher (estimated: no impact for regional unemployment of 20-25%).

When there is more unemployment in the household (estimated: no impact if all other adults in the household are unemployed too).

When the individual’s past unemployment is greater (estimated: no impact if the individual has been unemployed for 2 out of the past 3 years).

All of these effects are far stronger for men, especially prime-age men (16-50), than for women.

Social Comparisons with respect to Unemployment?

GHQ

Diffe

renc

e

Regional Unemployment Rate5 10 15

-1

0

1

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3

GL91

GL92GL93

GL94

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SW93SW94

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YH93

YH94

YH95

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YH97

NT91

NT92

NT93

NT94

NT95

NT96

NT97

WA91

WA92

WA93

WA94

WA95WA96

WA97

SC91SC92

SC93

SC94

SC95

SC96SC97

The well-being gap between employees and the unemployed is smaller in regions with greater unemployment.

Unemployment hurts less when I share it with other household members

Social Comparisons with respect to Unemployment?

60.257.6

30.8

38.4

57.955.6

30.9

40

0

10

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E. UReg

=5%

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=10%

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=5%

U. UReg

=10%

E. Par

tner E

E. Par

tner U

U. Part

ner E

U. Part

ner U

But there is little adaptation to unemployment

Unemployment starts bad, and stays bad

We get used to marriage

And we get used to divorce

Even widowhood is worse at the beginning than afterwards

And we can’t even count on our children

Social Comparisons with respect to Divorce?

Can these two possibly be related?

Own divorce hurts less in a high-divorce region

The estimated effect of divorce is zero in a region with a 25% divorce rate. Is this a “plenty more fish in the sea” effect?

Not only: the largest effect is from the regional percentage of divorced men on male divorce

Adaptation to Poverty

We may adapt to income in general, but what about adaptation to poverty as a specific low-income state?

Poverty starts bad and stays bad

Social Comparisons and Social Capital?

Research on BHPS data shows that:

Individuals are happier when their levels of social capital (measured by social activities) are higher (but beware of causality)

1) Individuals are also happier when they live with other household members who are active socially

2) But, given own social capital and others’ social capital, there is a happiness boost from being the most active individual in the household.

Well-Being and Others’ Social Capital: WVS Evidence Spillovers are defined at the regional (NUTS2) level. We

drop regions with fewer than 50 observations. Others’ social capital measured by the regional median level

Own SK Activity 0.075**(.011)

Regional SK Activity (median) -0.098+(.059)

It is still good to be active oneself: but well-being is higher when others’ social activity is lower, as if there were comparisons in social capital. This can be seen in the coefficient on the continuous measure of others’ median social capital above.

Well-Being and Others’ Social Capital: ESS Evidence

All All Women MenMeet Friends 0.156** 0.158** 0.167** 0.145**

(0.004) (0.004) (0.006) (0.006)Regional Average Meet Friends

-0.164** -0.210** -0.112*

(0.037) (0.050) (0.057)Observations 86555 86555 45700 40855Log likelihood -164054.3 -164044.7 -86998.9 -76927.6

Social Comparisons and Health?

Work on European data has shown that :

1) My own health problems have less effect on my own well-being when the problems are shared by others in the same household.

2) Individuals feel less overweight as the average weight in the region rises

3) Within the household, couples where both are obese have similar mental stress levels to couples where neither is obese

Women like their partner to have the same BMI as they do (life

satisfaction in the BHPS)

And… so do men

Obese -0.101** -0.092* -0.162**(0.031) (0.039) (0.045)

Spouse Obese -0.035 -0.104*(0.039) (0.045)

Both Obese 0.263**(0.099)

Observations 10648 6035 6035

Note: Plus controls for Age, Sex, Education, Marital Status,

Labour Force Status, and Income.

GHQ

Well-Being and BMI: Household Results. BHPS Wave 14

And for those of you who like it semi-parametrically, the “Happiness Hills”

The hill peaks are on a 45-degree line: your own weight doesn’t matter if it matches your partner’s

Social Comparisons and Religion?

Recent work on European Social Survey Data shows that:

Religious individuals are happier when they live in religious regions

1) But atheists are happier when they live in religious regions too

2) Catholics get a happiness boost from being in a Catholic majority region

The religious “spillovers” are mostly positive

Spillover effects of specific religious denominations: Life satisfaction regressions

Spillover effects of specific religious denominations: Life satisfaction regressions

The religious like being with other religious; they don’t like being with atheists.

Atheists are more satisfied when they live in more religious regions too.

Atheists don’t like living with other atheists either.

Conclusions

1)Let’s switch from the extensive to the intensive margin. How much does the EP hold?2)Are comparisons cardinal or ordinal? If the latter, then the EP holds fully3)Who is in the reference group?4)Is this a rich country result?5)What is well-being anyway

- Life satisfaction?- Happiness?- Psychological functioning?- Eudaimonia?

A Summary

We need to find domains of life that are non-rival to which we do not adapt…

Horizontal IntertemporalComparisons Comparisons(Status) (Adaptation)

Income Yes Yes

Unemployment Yes (but small) No

Poverty ? No

Marriage/Divorce Yes Yes

Health Perhaps? Partial?

Social Activities Perhaps? No?

Freedom ? ?

Religion Perhaps? ?

Is everything really lost for GDP per capita?

Almost all discussion in the literature has looked at mean happiness.

We might not be so utilitarian though, in that we care about the distribution of happiness too.

Could give more weight to those at the bottom of the well-being distribution: misery-aversion.

Here GDP per capita does seem to play a role

Happiness inequality and GDP per capita across countries, WVS

Last available year (2000s)

Happiness inequality within Country over Time in Growing Countries, Selected 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)

Single Country Results

United Kingdom (BHPS) West Germany (SOEP)

Single Country Results

Australia (HILDA) United States (GSS)

Income growth reduces happiness inequality, as do “social programmes”; income inequality increases happiness inequality

income comparisons, the easterlin paradox and public policy ape/ete masters course andrew e. clark...

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