Presentation at the HLEG thematic workshop on "Multidimensional Subjective Well-being", 30-31 October 2014, Turin, Italy, http://oe.cd/HLEG-workshop-subjective-wb-2014

1. Research on the correlates of
Subjective Well-being
HLEG Workshop on multidimensional Subjective Well-Being
Turin October 30th
Yann Algan, Sciences-Po

2. Outline
• Part 1:
Harassing the potential of Big Data to elicit
predictors/correlates of subjective well-being
• Part 2:
How can policy intervention affect well-being ?
Focus on early childhood intervention

3. Part I
What can we learn about SWB from
“Big data” ?

4. Policy motivation
• Can we use social traces let by individuals on Big Data to enhance of
our understanding of SWB ?
– What can we learn from Big Data (ex. google queries) as potential
predictors of SWB ? Learn about people’s mindset
– Traditional measures on SWB are typically low-frequency (most
often annual), while short-term movements are also important: ex
early warning for severe crisis
Challenge: how can we construct a real-time ‘early warning’
indicator of various aspects of SWB?

5. Big data at the rescue ?
• Unlike SWB surveys, big data are
– Timely and Available at the local level
– Free !
– Based on revealed behaviors and preferences instead of
imposed and decontextualized questions
– Free from question order, wording, bounded scale.. .
– Could be correlated with business-cycle or timely events
that affect SWB
• But unlike SWB, big data is also very …noisy !

6. Project
• Step 1 : Show that big data can predict subjective well-being
in a country where survey measures are available at a
high frequency (US)
– Elicit queries/behaviors that predict the best SWB from Gallup
Daily (Algan, Higa, Murtin, 14)
• Step 2: Measure alternative Big data indicators of well-being
better correlated with business cycles
– Ex. Google Pain index and Unemployment: Algan-Guyot(14)
Joint Project between OECD, Sciences Po/Medialab and Cepremap

7. Step1 - Big Data predictors of SWB
• Construct weekly predictors of 10 subjective well-being
(SWB) variables that are included in the Gallup Healthways
Well-Being survey in the US 2009-2014:
– Happiness, learning something useful, life evaluation today and in
5 years, laughing, being respected, anger, sadness, stress, and
worry
• Our Big data: keywords weekly searched on Google
available from Google Trends (Choi and Varian, 2009)

8. Methodology –
Bayesian Model Selection
• Bayesian Model Selection (BMS) approach enables to select the best
set of covariates for each SWB variable and deal with Big data
• Our approach consists of evaluating a large number of models (i.e.
selection of keywords searched):
• BMS infers the average posterior distribution of the coefficients
across all models and the covariates’ Posterior Inclusion Probability
(PIP)
• Run BMS for hundreds of keywords from the OECD Better Life
Index Online Database, Employment outlook…American Time Use
Survey
– Capture multidimensionality of well-being: positive and negative affects (Deaton et al. 2011,
Kahneman and Deaton, 2010), activities (Kahneman and Krueger, 2006)

9. Keyword Selection Procedure
1. Retain the keywords from databases that have expected signs and
PIP larger than 0.20
2. Run BMS again for all keywords selected at step 2 and pick up those
with PIP larger than 0.50 or 0.80

10. Result: Regression
• From BMS, 5-15 keywords were selected as predictors for each
SWB variable
• 15 keywords such as “drug use” and “recipe” were selected as a
predictor for at least 2 different SWB variables
• “Loans”, “Mortgages”, “Unemployment” were predictors for 10
SWB variables
• We obtained high R-squared ranging from 0.24 for anger to 0.67
for worry.

11. Correlations
• Correlations between actual and predicted series are very high
• Correlations for HP-filtered series were over 0.90 for Life evaluation
in 5 years, life evaluation, stress and worry
 Conclusion: SWB ‘big data’ indicators capture mid-run variations in
the SWB survey variables
happiness learn
life
evaluation
in 5 years
life
evaluation
laugh
being
respected
anger sadness stress worry
correlation between actual
observed and predicted SWB
variables
0.676 0.735 0.821 0.881 0.669 0.642 0.563 0.619 0.839 0.835
correlation between HP-filtered
observed and predicted SWB
variables
0.836 0.929 0.939 0.978 0.870 0.915 0.769 0.907 0.934 0.952

12. Result: Prediction
• Actual and predicted
series overlapped to a
large extent
• Our predictors
capture trends and
cycles in the actual
series very well in all
cases.

13. Correlations: Out-of-sample Prediction
• Out-of-sample prediction yields high
correlations for happiness, life evaluation,
stress and worry.
happiness learn
life
evaluation
in 5 years
life
evaluation
laugh
being
respected
anger sadness stress worry
correlation between actual
observed and latest-1-year-predicted
SWB variables
0.687 0.603 0.353 0.696 0.688 0.384 0.460 0.396 0.848 0.732
correlation between actual
observed and latest-2-year-predicted
SWB variables
0.400 0.561 0.535 0.484 0.544 0.408 0.506 0.453 0.831 0.742

14. Result: Out-of-sample Prediction
• Out-of-sample
predictions at both a
one and two-year
horizon moved
similarly to the actual
series.

15. Step 2 – Analysis of Big Data
measures of Well-Being
• Are big data indicators measuring alternative
dimensions than SWB survey ?
• Research goal here: not to match SWB, but exploit
big data to measure determinants out-of reach of
survey:
– Example: high-frequency business cycles (unemployment, job
creation), local socioeconomic environment (inequalities..), event
studies(news, holidays …)
– Seminal work with traditional survey by Deaton (2012) and
Aghion et al. (2014)
Deaton (2012): Gallup SWB higly correlated with stock markets, but less so with the
rise in unemployment : "in a world of bread and circus, such measure may catch the
circus but miss the bread."

16. EX. Google Stress/Pain Index
• Indicators only based on
pain-related keywords from
NCHS and related treatments
• Strong correlation at metropolitan
level with:
- Unemployment (+), Hiring Rates (-), Firing rates (+)…
- Local Inequalities (+)
- Day-off and holidays (-)
- Temperature variations (+)
- …and losses by the local basketball team ?

17. Part II
Policy intervention and Well-Being

18. Intervention and Causal inference ?
• Causal factors: intervention with long-term follow-up
MTO (Katz et al., 2013 ) , Abecederian (Heckman et al., 2014)
• How can we avoid fragmented analysis and solutions ?
Ex. if SWB is affected by crime, health, education and inequality: hire
more police? More doctors ? and give cash transfers ?
• Focus on early childhood intervention:
- Provide capabilities: capacity to function in a multitude of life tasks
- Low and core set of capabilities in childhood predicts and causes a
variety of later outcomes Heckman Kautz (2014)
- Capabilities are skills and can be acquired, but gaps emerge
early and widen in absence of intervention

19. Early childhood intervention
• Most early childhood program targeted at cognitive skills, or
pyshiological stimulation, and at preschool
– Abecederian (Campbel et al. , 2014)
– Perry Preschool program (Heckman)
– Jamaican study (Campbel et al., 2014)
– Project Star (Krueger 99, Chetty et al., 14)
• But most of the impact flies through a residual: non-cognitive skills
• What are those non-cognitive skills? How do they relate to well-being
?
• What is the optimal timing of intervention ? Is school entry too late
for bosting non-cognitive skills ?

20. Early childhood intervention
Campbel et al., Science 2014

21. Example of early childhood training in
non-cognitive skills
• Motivation for those skills in non-experimental studies:
– Self-control strong predictor of health, education, earning.. (Mofitt et al. 2011;
Duckworth, 2005, 2014)
– Social skills (Trust, Perspective taking) highly correlated with favorable adult
ouctomes and well-being (Algan and Cahuc, 2014; Chetty et al., 2014)

22. Example of early childhood training in
non-cognitive skills
• Evaluation of the program from adolescence to early adulthood:
Algan et al (2014)
– Strong impact on crime (11 pp), employment (19pp), education
(12pp), health and smoking behavior
– Impact on adult emotional-well being (8 pp)
• Channels
– Boost in self-control and trust in early adolescence, no initial impact
on IQ, grades or other non-cognitive skills
– Trust and Self-control explain later academic achievement and
favourable adult outcomes
– Self-control explains most of the health outcomes (32%)
– Trust explains most of the boost in emotional-well-being (26%)