Well-Being and Comparisons
Arie Kapteyn
(Drawing on joint work with many co-authors, including: Maria
Björnsdotter Dahlin, Caroline Tassot, Arthur van Soest, Jim Smith,
Gema Zamarro, Jinkook Lee, Raquel Fonseca, Hanka Vonkova)
Contents
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Increasing interest in well-being
Data sources
Different well-being concepts
How they hang together
Can we explain well-being differences?
The structure of evaluative well-being
Do we compare to others?
Who are these others and what do we know about them?
And how do they influence us?
Conclusions
Interest in the measurement of Subjective Well-Being has
soared over the last decades, not just in Academia
• France: Commission on the Measurement of Economic
Performance and Social Progress*
• United Kingdom: Office of National Statistics**
• United States: Federal Reserve Chairman Ben Bernanke
declaring his interest in finding better measurements of
Americans’ well-being***
• Bhutan: Gross National Happiness****
•
•
•
•
*Stiglitz, J. E., Sen, A., & Fitoussi, J. P. (2009). Report by the commission on the
measurement of economic performance and social progress. Paris: Commission on the
Measurement of Economic Performance and Social Progress.
**Dolan, P., Layard, R., & Metcalfe, R. (2011). Measuring subjective well-being for public
policy.
***Rugaber, C. S. (2012). Are you happy? Ben Bernanke wants to know
****http://en.wikipedia.org/wiki/Gross_national_happiness
National Academy of Sciences, US
International Organizations
Various municipalities now also measure well-being
Precursors
Salvador Allende’s Chile
• Planned a centrally planned economy with continuous
measurement of happiness:
• “Beer [the designer of the system] built a device that
would enable the country’s citizens, from their living
rooms, to move a pointer on a voltmeter-like dial that
indicated moods ranging from extreme unhappiness to
complete bliss.”
• (Evgeny Morozov: “The Planning Machine”, New Yorker,
October 13, 2014)
Data Sources
• The analyses presented are based on several Internet
panels in the Netherlands and in the U.S:
– CentERpanel in the Netherlands (1996)
– American Life Panel (ALP) at RAND (2006)
– Understanding America Study (UAS) at USC (2014)
– Distinctive features:
• Recruiting not via Internet
• Provide Internet access to potential respondents
without it
• Incidentally: the most complete Internet panel in the
world is the LISS panel run by CentERdata at Tilburg
University
The American Life Panel as an
Example
The RAND American Life Panel
Population
Nationally
representative
Internet panel,
including
vulnerable
population sample
5000+
respondents aged
18+, not recruited
via internet
Research
environment
Timeliness
Access and
Usability
The RAND American Life Panel
Population
Research
environment
Nationally
representative
Internet panel,
including
vulnerable
population
sample
Internet mode
offers
visualization,
experiments etc
5000+
respondents aged
18+, not recruited
via internet
Many hours worth
of background
information (e.g.
HRS, cognitive
tests)
Timeliness
Access and
Usability
The RAND American Life Panel
Research
environment
Timeliness
Nationally
representative
Internet panel,
including
vulnerable
population
sample
Internet mode
offers
visualization,
experiments etc
Approximately
two
surveys/experime
nts per month
(about 300 since
2006)
5000+
respondents aged
18+, not recruited
via internet
Many hours worth
of background
information (e.g.
HRS, cognitive
tests)
Rapid turnaround
Population
Access and
Usability
The RAND American Life Panel
Research
environment
Timeliness
Access and
Usability
Nationally
representative
Internet panel,
including
vulnerable
population
sample
Internet mode
offers
visualization,
experiments etc
Approximately
two
surveys/experime
nts per month
(about 250 since
2006)
Data available for
download for free
5000+
respondents aged
18+, not recruited
via internet
Many hours worth
of background
information (e.g.
HRS, cognitive
tests)
Rapid turnaround
Custom interface
allows ability to
combine waves,
get data in
analysis-ready
format
Population
The panel has been used for continuous Presidential polling
Address Based Sampling
1.
2.
3.
4.
Draw zip-codes; buy addresses.
Advance notification letter.
After 1 week, 10 minute mail survey with $5 prepaid incentive.
$15 for returning completed survey; survey asks for interest in
study participation. Non-Internet respondents are offered a tablet
and Internet.
5. 2 weeks after the survey mailing, non-respondents are mailed a
reminder postcard.
6. 2 weeks after the reminder postcard is mailed, a second copy of
the survey is mailed to all sample members who have not
returned a complete survey.
7. 3 weeks after the second copy of the survey is mailed, follow-up
phone calls, up to 15 attempts
How to gauge the quality of probability
based Internet panels?
Krosnick and co-authors:
• Various studies over the last decade.
• Compare telephone, probability based
Internet, convenience Internet.
• Probability based Internet comes out very
well (in Mean Square Error sense).
Different Well-Being Concepts
• Evaluative Well-Being: Evaluation of life satisfaction/
dissatisfaction
• Experienced Well-Being: The combination of experienced
affect - range of emotions from joy to misery.
– Positive Affect
– Negative Affect
• Eudemonic measures refer to the existence of underlying
psychological needs, encompassing various dimensions of
wellness, such as autonomy, personal growth, or purpose
in life.
Examples
• Evaluative (SHARE)
– How satisfied are you with your life in general? Very satisfied / Somewhat
satisfied / Somewhat dissatisfied/ Very dissatisfied
• Experienced Questions – Gallup Well-Being Index
–
–
–
–
–
–
–
Did you experience anger during a lot of the day yesterday?
Did you experience depression during a lot of the day yesterday?
Did you experience enjoyment during a lot of the day yesterday?
Did you experience happiness during a lot of the day yesterday?
Did you experience sadness during a lot of the day yesterday?
Did you experience stress during a lot of the day yesterday?
Did you experience worry during a lot of the day yesterday?
• Eudemonic (Office of National Statistics, UK):
– Overall, to what extent do you feel that the things you do in your life are
worthwhile? (Not at all) 0/1/2/3/4/5/6/7/8/9/10 (Completely)
By combining experienced well-being with time use, one can
paint a picture of well-being during the day and by activity
• Day Reconstruction Method*:
– A self-administered time use diary with ratings of
positive and negative affect for each period.
• What do we like most and what do we like least?
– Most liked: Intimate relations; Socializing; Relaxing;
– Least liked: Taking care of children; computer/email/internet;
housework; working; commuting.
*Kahneman, D., Krueger, A. B., Schkade, D. A., Schwarz, N., & Stone, A. A.
(2004b). A survey method for characterizing daily life experience: The day
reconstruction method. Science, 306(5702), 1776-1780.
Diurnal Patterns (from Kahneman et al. 2004)
Fig. 1. Comparison of diurnal patterns of tiredness (A) and negative affect (B) for DRM and ESM
studies.
D Kahneman et al. Science 2004;306:1776-1780
Published by AAAS
Relation of the Different Measures
• It appears that evaluative and eudemonic measures are
hard to distinguish empirically (they form one factor in
factor analysis)
• Experienced well-being can be decomposed in a positive
and a negative factor
• So we have three factors in total
Kapteyn, A., Lee, J., Tassot, C., Vonkova, H., and Zamarro, G., “Dimensions of Subjective
Well-Being”, Social Indicators Research, forthcoming (DOI 10.1007/s11205-014-0753-0;
available online at: http://link.springer.com/article/10.1007/s11205-014-07530?sa_campaign=email/event/articleAuthor/onlineFirst ). Based on ALP data
Example of Experienced Well-Being Scale
(Kapteyn, et al. 2014)
Happy
Interested
Frustrated
Sad
Enthusiastic
Content
Angry
Tired
Stressed
Lonely
Worried
Bored
Pain
Depressed
Joyful
Negative Affect
-0.32
-0.10
0.80
0.79
-0.13
-0.26
0.76
0.62
0.79
0.68
0.78
0.54
0.57
0.78
-0.21
Positive Affect
0.83
0.82
-0.26
-0.24
0.83
0.76
-0.16
-0.15
-0.23
-0.20
-0.19
-0.25
-0.06
-0.31
0.84
Can we explain well-being differences?
Life Satisfaction (SHARE) and Income
0.25
0.2
0.15
Life Satisfaction (SHARE)
0.1
0.05
0
<$25,000$
$25,000-$49,999
$50,000-$74,999
$75,000-$99,999
>$100,000
Life Satisfaction and Age
Life Satisfaction (SHARE)
0.35
0.3
0.25
0.2
Life Satisfaction (SHARE)
0.15
0.1
0.05
0
<25
25-34
35-44
45-54
55-64
65+
Life Satisfaction and Employment Status
Life Satisfaction (SHARE)
0.45
0.4
0.35
0.3
0.25
Life Satisfaction (SHARE)
0.2
0.15
0.1
0.05
0
Retired
Other work
Working now
Disabled
Unemployed
Experienced Well-Being and Age
0.5
0.45
0.4
0.35
0.3
Negative Affect
0.25
Positive Affect
0.2
0.15
0.1
0.05
0
<25
25-35
35-45
45-55
55-65
65+
Experienced Well-Being and Income
(Nothing Significant)
0.2
0.18
0.16
0.14
0.12
Negative Affect
0.1
Positive Affect
0.08
0.06
0.04
0.02
0
<$25,000$
$25,000-$49,999
$50,000-$74,999 $75,000-$100,000
>$100,000
Experienced Well-Being and Employment Status
0.7
0.6
0.5
0.4
Negative Affect
Positive Affect
0.3
0.2
0.1
0
Unemployed
Retired
Disabled
Other work
Working now
More on Retirement (SHARE and HRS): Correlations
Depression, Life
Retirement, Retirement,
Depression Life Satisfaction Satisfaction
Austria
Belgium
Denmark
France
Germany
Greece
Italy
Netherlands
Spain
Sweden
Switzerland
United States
0.09
0.04
0.01
0.05
0.06
0.15
0.09
0.09
0.17
0.08
0.07
0.08
-0.05
-0.01
-0.04
-0.08
-0.07
-0.15
-0.09
-0.04
-0.07
-0.04
-0.04
0.02
-0.41
-0.28
-0.29
-0.3
-0.32
-0.26
-0.35
-0.26
-0.38
-0.28
-0.31
-0.34
Total
0.1
-0.03
-0.31
However, this is not causal*
• A structural model, using country institutions as
instrumental variables shows:
– Retirement reduces depression
– Retirement increases life satisfaction
• And as a bonus:
– A strong effect of unemployment replacement rates on
the life satisfaction of the unemployed.
Fonseca, R., Kapteyn, A., Lee, J., Zamarro, G. (2014), “Does Retirement Make you Happy? A
Simultaneous Equations Approach”, Working Paper, CESR, University of Southern California.
Based on SHARE and HRS data
The structure of evaluative well-being
• We can explain overall life satisfaction by satisfaction with
life domains*. Here are the relative weights:
0.45
0.4
0.35
0.3
0.25
Netherlands
0.2
U.S.
0.15
0.1
0.05
0
Relations
•
Job/daily
activities
Health
Income
*Kapteyn, A., Smith, J., and Van Soest, A., “Life Satisfaction,” in: Ed Diener, John F.
Helliwell, Daniel Kahneman (eds.) International Differences in Well-Being, Oxford
University Press, 2010, 70-104. Using CentERpanel and ALP
Comparisons
To evaluate how we are doing, we need a frame of
reference
• That frame can be our own past, or we can compare with
others.
• Who are these others, what do we know about them, and
how do they influence us?
• So we* asked:
– Who do you compare yourself to (in various life domains)
– Where do you think you stand?
– Where do you think others (friends, people in your street, etc.)
stand?
• Next we used that information to explain individual
happiness and satisfaction in several domains
– Today I am mainly talking about the income domain.
Dahlin M.B., Kapteyn A., Tassot, C. (2014), “Who are the Joneses?” CESR Working Paper
2014-004; using ALP
Data
• American Life Panel (5,475 respondents)
• Questions about
– Happiness
– Satisfaction with life domains
– Comparison groups
– Comparison intensity
• Zip-code data from American Community Survey (about 3
million households annually)
• IRS (tax) data
• Information on crime, local and state taxes
Dependent Variables
How happy are you?
• Very Happy
• Happy
• Neither happy nor
unhappy
• Unhappy
• Very unhappy
How satisfied are you with
the total income of your
household?
• Very satisfied
• Satisfied
• Neither satisfied nor
dissatisfied
• Dissatisfied
• Very dissatisfied.
Subjectively measured own income
Perceived Income of Others
Who Matters Most?
Happiness
100%
90%
22.4
80%
70%
60%
50%
57.2
40%
30%
20%
15.8
10%
0%
Very unhappy
Neither happy nor unhappy
Very happy
4.1
0.5
Unhappy
Happy
Satisfaction with Domains
100%
6.7
90%
28.1
14.7
14.8
48.0
47.8
20.2
17.6
13.5
16.6
3.7
3.3
Job / daily activities
Health
27.1
80%
36.0
70%
60%
50%
23.0
50.3
46.0
40%
30%
20%
26.7
16.6
12.8
10%
7.6
0%
HH income
7.5
1.4
Family life
Very dissatisfied
Neither satisfied nor dissatisfied
Very satisfied
8.8
1.5
Number of friends
Dissatisfied
Satisfied
Percentage of sample reporting comparison intensity 6-10
Who do we compare to?
70
60
50
40
30
20
10
0
Family, friends
and
acquaintances
Coworkers and
colleagues
Health
People my age People living on People living in People in the US
my street
my town
Friends
Family
Job
Household income
People in the
world
Findings
• Those who compare most have the highest estimate of
other people’s income (and hence they rank their relative
position worst)
• If we consider comparisons with geographic groups
(street, postal code, county, etc.) we generally find that
higher incomes in these groups raise happiness or the
satisfaction with own income. This suggests a public goods
interpretation.
• Own rank raises income satisfaction and happiness
• Comparison with the own age group suggests a strong
relative component (if others of my age make more
money that reduces my happiness or income satisfaction)
Conclusions (some more tentative than others)
• Subjective Well-Being can be succinctly summarized by
three dimensions:
• One evaluative dimension
• Two experienced dimension (one positive, one negative)
• Determinants of experienced dimensions are less easy to
characterize by demographics, policy variables, etc.
• Evaluative well-being is partly relative (comparisons to
others matter), shows stable relations with demographics
(e.g. age), and is amenable to policy (e.g. income
maintenance policies)
• Nevertheless, more work needs to be done to gain better
understanding who individuals compare themselves with.