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Who is easier to nudge?
John Beshears
James J. Choi
David Laibson
Brigitte C. Madrian
Sean (Yixiang) Wang
The appeal of defaults
•
Large effect on outcomes
•
If a default isn’t right for somebody, she
will (eventually) opt out
The downside of defaults
•
Giving some people the wrong default is
inevitable when population is heterogeneous
and only one default can be chosen
•
Sometimes it takes people a long time to opt out
Open question
Who is most vulnerable to getting stuck at a bad
default?
Default effects by income
in Madrian and Shea (2001)
Fraction at default contribution rate (3%)
and asset allocation (100% money market)
80%
70%
60%
50%
40%
30%
20%
10%
0%
Income
Why it’s difficult to
interpret that graph
•
Low-income workers could persist longer
at default because it is closer to their target
rate, so less incentive to opt out quickly
•
Relative persistence could change if we
chose a different default
What we’d like to know
Holding fixed distance between default and
target, are low-income workers are more
inertial?
Key empirical challenge
•
Distribution of target rates by income group
unobserved
– Thus, hard to control for default’s distance to target
•
In particular, target rate is unobserved for those
who are still at the default. Mixture of
– Those for whom default = target
– Those for whom default ≠ target, but they haven’t
moved there
Objectives
•
Estimate distribution of target rates by income
group separately for each company
•
Estimate per-period probability of opting out to
each target rate by income group separately for
each company
•
Estimate each income group’s probability of
remaining stuck at default 2 years after hire
when it is not target rate
Our empirical approach
Assume target rate doesn’t change over
observation period (2 years after hire)
• Two time intervals after hire
•
– Initial period (usually 2 months): Higher opt-out
activity
– Later period: Lower opt-out activity
•
Assume monthly probability of opting out to
target ci is constant across time during later
period
– But varies by target rate × company × income group
Intuition for empirical
methodology
Suppose we observe 20 people opt out to 5%
contribution rate in month 3 (start of later
period)
• Consistent with numerous possibilities
•
# people with 5%
target who haven’t
moved at beginning
of month 3
Monthly probability
of moving
# people with 5%
target who haven’t
moved at beginning
of month 4
100
20%
80
60
33%
40
40
50%
20
Intuition for empirical
methodology
Suppose we also observe 16 people opt out to
5% in month 4
• If monthly opt-out probability is constant, then
we can infer which possibility is correct
•
•
# people with 5%
target who haven’t
moved at beginning
of month 3
Monthly probability
of moving
# people with 5%
target who haven’t
moved at beginning
of month 4
40
50%
20
Above scenario implies 20 × 0.5 = 10 opt-outs in
month 4 → Inconsistent with data
Intuition for empirical
methodology
•
# people with 5%
target who haven’t
moved at beginning
of month 3
Monthly probability
of moving
# people with 5%
target who haven’t
moved at beginning
of month 4
100
20%
80
Above scenario implies 80 × 0.2 = 16 opt-outs in
month 4 → Consistent with data
Intuition for empirical
methodology
•
We know from last step how many people have
a 5% target but haven’t opted out at beginning
of month 3
•
People with 5% target at beginning of initial
period = Opt-outs in initial period + People with
5% target at beginning of later period
•
Probability of opting out to 5% during initial
period = Opt-outs in initial period / People with
5% target at beginning of initial period
High vs. low income
definition
Split employees into those above vs. below
sample-wide median income ($61,228)
Sample
Firm
Industry
Hire Dates Covered
Sample
Size
Initial
Period
Default
Rate
A
Pharma/Health
Jan 2002 – Dec 2005
14,961
14 months
3%
B
Medical Tech
Jan 2002 – Oct 2003
5,452
3 months
3%
C
Manufacturing
Oct 2008 – Dec 2010
1,931
2 months
6%
D
Manufacturing
Jan 2002 – Dec 2006
5,193
2 months
6%
E
Computer Hardware
Jan 2002 – Dec 2002
1,872
2 months
0%
F
Insurance
Aug 2003 – Dec 2006
5,819
2 months
0%
G
Business Services
Jan 2002 – Dec 2003
3,165
2 months
0%
H
IT Services
Mar 2002 – Dec 2004
8,289
2 months
0%
I
Pharma/Health
Jan 2002 – Dec 2004
5,453
12 months
0%
J
Telecom Services
Jan 2002 – Dec 2003
2,169
2 months
0%
Probability of being at default after 2
years when it’s not your target
18.6%
13.5%
6.6%
4.7%
Automatic enrollment firms
Low-income
Opt-in enrollment firms
High-income
Adjusting for differences in
target distributions
•
Less likely to opt out within 2 years if default is
close to target rate
•
Differences between low- and high-income
sticking probabilities partially driven by
differences in target rates
•
In next graph, set target rate distribution equal
to average of low- and high-income for both
income groups
Probability of being at default after 2
years when it’s not your target,
holding rate preferences fixed
15.3%
12.5%
7.2%
Automatic enrollment firms
Low-income
6.2%
Opt-in enrollment firms
High-income
Conclusion
•
Low-income individuals less likely to opt
out of default
•
Default choices should place higher
weight on low-income individuals’ needs
•
To be explored: Do defaults change target
contribution rates?