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Subjective Well-being: Time and Context 
Andrew E. Clark (Paris School of Economics - CNRS) 
http://www.parisschoolofeconomics.com/clark-andrew/ 
OECD HLEG 
Turin 
October 30th 2014
In terms of the correlates of subjective well-being, 
I would like to ask three questions. 
Two of these have to do with time, and one with 
place (as it were). 
I think that these are fascinating research 
questions in their own right. 
But that it is also crucial to answer them if we are 
serious about thinking of a subjective well-being 
policy.
1) Do we adapt to everything? 
2) If the child makes the man, then what is the 
point of (adult) well-being interventions? 
3) Who is most affected by which well-being 
policy? (i.e. going beyond one estimated mean 
effect for the whole population).
1) Adaptation 
Adaptation to income has been proposed as one explanation 
of the Easterlin Paradox. With adaptation to income we 
have a well-being function of: 
W = W(y, yi,t-1, ....) 
We suppose that W1 > 0, as is standard, but that W2 < 0: as 
past income rises, my well-being falls (past income acts 
as a benchmark or deflator). 
If W1 + W2 = 0, then a rise in income has no long-run effect 
on well-being.
There are a number of contributions suggesting 
adaptation. 
Using SOEP data, Di Tella et al. (2010) show 
that complete adaptation to rising income occurs 
within four years. 
Wunder (2009) estimates that the improvement 
in the financial situation in Germany from 1985 
to 2006 was entirely mopped up by adaptation to 
material well-being, which he calls 
“desensitisation to the hedonic effects of 
income”
Clark (1999) suggests that adaptation to changes in labour 
income (while staying in the same job at the same 
firm) in British Household Panel Survey (BHPS) data 
occurs within one year. 
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 a g o .. 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)
If we believe these results, then rising income doesn’t 
matter for subjective well-being in the long run (in 
rich countries). 
Policy implication: 
“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 find adaptation here too?
SOEP Results: 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
These adaptation results are identical in 
BHPS data (Clark and Georgellis, 
2013). 
And in RLMS data (Clark and 
Uglanova, 2012). 
With one exception in the latter
Marriage works in Russia 
0.6 
0.4 
0.2 
0 
-0.2 
-0.4 
-0.6 
-0.8 
-1 
-4 -3 -2 -1 0 1 2 3 4 5 
Number of years before and after the event. Marriage 
Life satisfaction 
0.6 
0.4 
0.2 
0 
-0.2 
-0.4 
-0.6 
-0.8 
-1 
-4 -3 -2 -1 0 1 2 3 4 5 
Number of years before and after the event. Marriage 
Life satisfaction 
Both for men 
And for women
Rudolf and Kang (2013) also find that 
marriage is lastingly good in Korea 
(for men).
There is no adaptation to poverty in SOEP data
There is either partial (Oswald and 
Powdthavee, 2008) or little (Lucas, 2007) 
adaptation to disability (debate over method). 
Wu (2001) considers health adaptation to an 
exogenous event: heart attacks. New heart 
conditions affect emotional health less for 
individuals who have had such a condition in 
the past. 
Riis et al. (2005) uncover evidence of 
adaptation to hemodialysis. 
But there is a positive impact of cosmetic 
surgery on subjective wellbeing, which is long-lasting 
in some cases.
In the labour market, there is adaptation to 
self-employment within three years, and to 
unionization. 
On the contrary, Burchell (2011) finds that 
there is little adaptation to job insecurity in 
BHPS data 
There is adaptation to moving house in 
BHPS (Nowok et al., 2013) and HILDA 
(Frijters et al. 2011) data.
A summary table from six recent papers. 
Colonne1 
Anusic et al.(2014): 
SHP 
Clark et al. (2008): 
SOEP 
Clark and Georgellis 
(2013): BHPS 
Clark and Uglanova 
(2011): RLMS 
Rudolf and Kang (2014): 
KLIPS 
Frijters et al. 
(2011): HILDA 
Unemployment Partial None None None No Data F/None M 
Marriage Full? Full Full None Full F/Partial M Full 
Divorce/Separation Full/Over-full Full/Over-full Full Full F/None M Full 
Widowhood Partial Full Full Full None F/Full M 
Death of Spouse/Child None 
Childbirth Full/Over-full Full/Over-full Full/Over-full Full/Over-full Full 
Moved House Full 
Disability Partial 
Illness/Injury Partial 
• The empirical results thus suggest that there is adaptation 
in economic and social life, but that individuals take 
longer to become used to some states than to others. 
• This seems in particular to apply to negative events 
(unemployment, insecurity, poverty and disability): is 
there loss-aversion in adaptation?
I did want to say the word 
“multidimensional” at some point today 
Almost all of the work above has used life 
satisfaction as the dependent variable. 
But what do we know about adaptation in 
terms of positive and negative effect? 
Or in terms of eudaimonia?
2) Does the Child Make the Man? 
• Data from the British Cohort Study on children 
born in1970. 8,868 obs. 
• Aim: Explain adult life-satisfaction (0-10) at age 
34: “How dissatisfied or satisfied are you about 
the way your life has turned out so far?” 
• We explain life-satisfaction by seven adult 
outcome variables, three sets of childhood 
characteristics, and family characteristics
23 
Economic Log income (equivalised) 
Educational achievement 
Employed (measured as not 
unemployed) 
at 34 
by 34 
at 34 
Social Good conduct (= -no. of 
crimes) 
Has a partner 
at 16-34 
at 34 
Personal Self-perceived health 
Emotional health 
at 26 
at 26 
Adult outcomes
• We measure emotional health and self-perceived 
health at age 26 rather than at 34 
• This avoids the charge that these are 
synonyms of life-satisfaction rather than 
predictors of it 
• All of these seven adult outcomes can 
depend on family background and the 
childhood variables 
• And so does life satisfaction itself directly24
• Childhood variables include variables 
relating to the child and to the parents 
(“family background”) 
• Three main dimensions of child 
development – intellectual performance, 
social behaviour and emotional health. 
25
• The BCS data provide us with 
measurements on the three child variables at 
5, 10 and 16. 
• We also have measurements on the family at 
different ages 
• For simplicity we consolidate these into the 
two sets of family variables 
26
27 
Childhood variables 
Age of child 
Child characteristics 
Intellectual performance 5, 10, 16 
Good conduct 5, 10, 16 
Emotional health 5, 10, 16 
Family background 
Economic 
Father’s socio-economic group 10 
Family income 10 
Number of siblings 10 
Father in work 0, 5, 10 average 
Mother’s and father’s age on 
-- 
leaving full-time education 
Psycho-social 
Mother’s emotional health 5, 10 average 
Child conceived within marriage -- 
Both parents still together 10
Predictors of life-satisfaction at 34 
Using adult 
variables only 
Using childhood 
variables only Using both 
Log income 5.5 5.2 
Educational achievement 3.5 2.9 
Employed 8.5 8.2 
Good conduct 6.6 6.1 
Has a partner 11.6 11.3 
Self-perceived health (26) 6.8 6.5 
Emotional health (26) 20.4 18.1 
Intellectual performance (5 10 16) 4.5 -3.5 
Good conduct (5 10 16) 8.5 5.2 
Emotional health (5 10 16) 17.4 9.8 
Family Economic 5.5 2.5 
Family Psychosocial 3.0 2.4 
Female 6.8 8.2 7.2 
Observations 8,868 8,868 8,868 
28 
Partial correlation coefficients x100
29 
Structural equations for adult success
• At what stage of an individual’s 
development can we predict their adult 
outcomes? 
• The proper test of predictability is the R2s: 
these appear in the following Table 
30 
Does the child reveal the adult?
31 
Predicting adult outcomes from 
different ages of childhood 
• Apart from education, most adult variables cannot be 
well-predicted from family background. 
• We predict better and better as we add later ages 
• It is not the case that everything is fixed by age 5.
Child emotional health is the largest predictor 
of adult life satisfaction. 
So what affects child emotional health? 
Our recent work on ALSPAC has emphasised 
childhood financial difficulties, the sibship, 
moving house, and parental separation (and 
arguing). 
32
Not everyone’s well-being is affected by 
income, education, unemployment etc. in the 
same way. 
We can identify heterogeneous effects ex ante, 
or let the data decide. 
33 
3) Was it Good for You?
If we let the data decide, we can use finite 
mixture models. These divide the data up into 
a number j of different W = βj’X regressions, 
with each observation i having a probability pij 
of belonging to the jth regression (with the sum 
over all j’s of pij being one). 
This has been applied to well-being and 
income, with the marginal effect of income on 
well-being is being different across classes 
34
We can also consider quantile regression 
models, where the effect of a certain variable 
differs across the well-being distribution. 
35
Or we can ex ante cut the sample by sex, age, 
education and so on. 
I often say that men are more comparison-sensitive 
than women, for example. 
And we might expect the higher-educated to 
have a different well-being relationship to 
income, if education raises aspirations. 
36
The variable used to define heterogeneity in 
well-being can be contextual, as in the work on 
social interactions. 
Unemployment has a smaller effect on 
subjective well-being in high-unemployment 
regions, for example. 
37
Along these lines, we can ask why some 
individuals are less affected than others by 
negative life shocks. 
This is the analysis of resilience. 
Which brings us back to cohort analysis. Are 
there specific events in childhood that make 
the individual more able to bounce back from 
adverse events in adulthood? 
38

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HLEG thematic workshop on "Multidimensional Subjective Well-being", Andrew Clark

  • 1. Subjective Well-being: Time and Context Andrew E. Clark (Paris School of Economics - CNRS) http://www.parisschoolofeconomics.com/clark-andrew/ OECD HLEG Turin October 30th 2014
  • 2. In terms of the correlates of subjective well-being, I would like to ask three questions. Two of these have to do with time, and one with place (as it were). I think that these are fascinating research questions in their own right. But that it is also crucial to answer them if we are serious about thinking of a subjective well-being policy.
  • 3. 1) Do we adapt to everything? 2) If the child makes the man, then what is the point of (adult) well-being interventions? 3) Who is most affected by which well-being policy? (i.e. going beyond one estimated mean effect for the whole population).
  • 4. 1) Adaptation Adaptation to income has been proposed as one explanation of the Easterlin Paradox. With adaptation to income we have a well-being function of: W = W(y, yi,t-1, ....) We suppose that W1 > 0, as is standard, but that W2 < 0: as past income rises, my well-being falls (past income acts as a benchmark or deflator). If W1 + W2 = 0, then a rise in income has no long-run effect on well-being.
  • 5.
  • 6. There are a number of contributions suggesting adaptation. Using SOEP data, Di Tella et al. (2010) show that complete adaptation to rising income occurs within four years. Wunder (2009) estimates that the improvement in the financial situation in Germany from 1985 to 2006 was entirely mopped up by adaptation to material well-being, which he calls “desensitisation to the hedonic effects of income”
  • 7. Clark (1999) suggests that adaptation to changes in labour income (while staying in the same job at the same firm) in British Household Panel Survey (BHPS) data occurs within one year. 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 a g o .. 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)
  • 8. If we believe these results, then rising income doesn’t matter for subjective well-being in the long run (in rich countries). Policy implication: “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 find adaptation here too?
  • 9. SOEP Results: there is little adaptation to unemployment Unemployment starts bad, and stays bad
  • 10. We get used to marriage
  • 11. And we get used to divorce
  • 12. Even widowhood is worse at the beginning than afterwards
  • 13. And we can’t even count on our children
  • 14. These adaptation results are identical in BHPS data (Clark and Georgellis, 2013). And in RLMS data (Clark and Uglanova, 2012). With one exception in the latter
  • 15. Marriage works in Russia 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1 -4 -3 -2 -1 0 1 2 3 4 5 Number of years before and after the event. Marriage Life satisfaction 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1 -4 -3 -2 -1 0 1 2 3 4 5 Number of years before and after the event. Marriage Life satisfaction Both for men And for women
  • 16. Rudolf and Kang (2013) also find that marriage is lastingly good in Korea (for men).
  • 17. There is no adaptation to poverty in SOEP data
  • 18. There is either partial (Oswald and Powdthavee, 2008) or little (Lucas, 2007) adaptation to disability (debate over method). Wu (2001) considers health adaptation to an exogenous event: heart attacks. New heart conditions affect emotional health less for individuals who have had such a condition in the past. Riis et al. (2005) uncover evidence of adaptation to hemodialysis. But there is a positive impact of cosmetic surgery on subjective wellbeing, which is long-lasting in some cases.
  • 19. In the labour market, there is adaptation to self-employment within three years, and to unionization. On the contrary, Burchell (2011) finds that there is little adaptation to job insecurity in BHPS data There is adaptation to moving house in BHPS (Nowok et al., 2013) and HILDA (Frijters et al. 2011) data.
  • 20. A summary table from six recent papers. Colonne1 Anusic et al.(2014): SHP Clark et al. (2008): SOEP Clark and Georgellis (2013): BHPS Clark and Uglanova (2011): RLMS Rudolf and Kang (2014): KLIPS Frijters et al. (2011): HILDA Unemployment Partial None None None No Data F/None M Marriage Full? Full Full None Full F/Partial M Full Divorce/Separation Full/Over-full Full/Over-full Full Full F/None M Full Widowhood Partial Full Full Full None F/Full M Death of Spouse/Child None Childbirth Full/Over-full Full/Over-full Full/Over-full Full/Over-full Full Moved House Full Disability Partial Illness/Injury Partial • The empirical results thus suggest that there is adaptation in economic and social life, but that individuals take longer to become used to some states than to others. • This seems in particular to apply to negative events (unemployment, insecurity, poverty and disability): is there loss-aversion in adaptation?
  • 21. I did want to say the word “multidimensional” at some point today Almost all of the work above has used life satisfaction as the dependent variable. But what do we know about adaptation in terms of positive and negative effect? Or in terms of eudaimonia?
  • 22. 2) Does the Child Make the Man? • Data from the British Cohort Study on children born in1970. 8,868 obs. • Aim: Explain adult life-satisfaction (0-10) at age 34: “How dissatisfied or satisfied are you about the way your life has turned out so far?” • We explain life-satisfaction by seven adult outcome variables, three sets of childhood characteristics, and family characteristics
  • 23. 23 Economic Log income (equivalised) Educational achievement Employed (measured as not unemployed) at 34 by 34 at 34 Social Good conduct (= -no. of crimes) Has a partner at 16-34 at 34 Personal Self-perceived health Emotional health at 26 at 26 Adult outcomes
  • 24. • We measure emotional health and self-perceived health at age 26 rather than at 34 • This avoids the charge that these are synonyms of life-satisfaction rather than predictors of it • All of these seven adult outcomes can depend on family background and the childhood variables • And so does life satisfaction itself directly24
  • 25. • Childhood variables include variables relating to the child and to the parents (“family background”) • Three main dimensions of child development – intellectual performance, social behaviour and emotional health. 25
  • 26. • The BCS data provide us with measurements on the three child variables at 5, 10 and 16. • We also have measurements on the family at different ages • For simplicity we consolidate these into the two sets of family variables 26
  • 27. 27 Childhood variables Age of child Child characteristics Intellectual performance 5, 10, 16 Good conduct 5, 10, 16 Emotional health 5, 10, 16 Family background Economic Father’s socio-economic group 10 Family income 10 Number of siblings 10 Father in work 0, 5, 10 average Mother’s and father’s age on -- leaving full-time education Psycho-social Mother’s emotional health 5, 10 average Child conceived within marriage -- Both parents still together 10
  • 28. Predictors of life-satisfaction at 34 Using adult variables only Using childhood variables only Using both Log income 5.5 5.2 Educational achievement 3.5 2.9 Employed 8.5 8.2 Good conduct 6.6 6.1 Has a partner 11.6 11.3 Self-perceived health (26) 6.8 6.5 Emotional health (26) 20.4 18.1 Intellectual performance (5 10 16) 4.5 -3.5 Good conduct (5 10 16) 8.5 5.2 Emotional health (5 10 16) 17.4 9.8 Family Economic 5.5 2.5 Family Psychosocial 3.0 2.4 Female 6.8 8.2 7.2 Observations 8,868 8,868 8,868 28 Partial correlation coefficients x100
  • 29. 29 Structural equations for adult success
  • 30. • At what stage of an individual’s development can we predict their adult outcomes? • The proper test of predictability is the R2s: these appear in the following Table 30 Does the child reveal the adult?
  • 31. 31 Predicting adult outcomes from different ages of childhood • Apart from education, most adult variables cannot be well-predicted from family background. • We predict better and better as we add later ages • It is not the case that everything is fixed by age 5.
  • 32. Child emotional health is the largest predictor of adult life satisfaction. So what affects child emotional health? Our recent work on ALSPAC has emphasised childhood financial difficulties, the sibship, moving house, and parental separation (and arguing). 32
  • 33. Not everyone’s well-being is affected by income, education, unemployment etc. in the same way. We can identify heterogeneous effects ex ante, or let the data decide. 33 3) Was it Good for You?
  • 34. If we let the data decide, we can use finite mixture models. These divide the data up into a number j of different W = βj’X regressions, with each observation i having a probability pij of belonging to the jth regression (with the sum over all j’s of pij being one). This has been applied to well-being and income, with the marginal effect of income on well-being is being different across classes 34
  • 35. We can also consider quantile regression models, where the effect of a certain variable differs across the well-being distribution. 35
  • 36. Or we can ex ante cut the sample by sex, age, education and so on. I often say that men are more comparison-sensitive than women, for example. And we might expect the higher-educated to have a different well-being relationship to income, if education raises aspirations. 36
  • 37. The variable used to define heterogeneity in well-being can be contextual, as in the work on social interactions. Unemployment has a smaller effect on subjective well-being in high-unemployment regions, for example. 37
  • 38. Along these lines, we can ask why some individuals are less affected than others by negative life shocks. This is the analysis of resilience. Which brings us back to cohort analysis. Are there specific events in childhood that make the individual more able to bounce back from adverse events in adulthood? 38