Risk Attitudes of Children and
Adults: Choices Over Small and
Large Probability Gains and Losses
WILLIAM T. HARBAUGH University of Oregon
KATE KRAUSE University of New Mexico
LISE VESTERLUND University of Pittsburgh
We look at decisions under risk in people ranging in age from 5
to 64.
• Our primary objective is descriptive: see how risk preferences
change with age.
• In particular, we ask how the tendency to behave in ways that
are consistent w/ EUT changes.
• There’s lots of evidence that college age subjects violate EUT
in a variety of ways.
• If these violations increase with age and experience, this
suggests these violations are “real” and important,
 they might result from the application of learned rules of thumb or
habits.
• If they decrease, it suggests that the violations are more in the
nature of mistakes – which is not to say they are not important.
We use the SEU model to organize our empirical
results.
Under SEU, people weight outcomes using subjective,
rather than objective weights.
This model can accommodate a wide range of
deviations from EUT.
One familiar example is Cumulative Prospect Theory:
(Tversky and Kahneman, 1992)
CPT Behavior:
1. Risk-seeking over small-probability gains
2. Risk-aversion over high-probability gains
3. Risk-seeking over high-probability losses
4. Risk-aversion over small-probability losses
Subjective Probability
Weighting for CPT behavior:
value
subjective probability
1
0.8
change
0.6
0.4
0.2
0.2
0.4
0.6
0.8
1
objective probability
Straightforward Protocol:
• People were offered a choice between a gamble and a
sure payoff equal to the gamble’s expected value.
(Except for 4 checks for monotonicity.)
• 14 different choice pairs, 7 over gains, 7 over losses.
• Choices differed in the probability of a gain (or loss.)
• Some variation in the specific gambles used.
For gains, proportion choosing the gamble, by age.
1.00
0.90
Age 5-8
0.80
Proportion choosing gamble
Age 9-13
0.70
Age 14-20
0.60
Age 21+
Age 5-8 OLS fit
0.50
Age 9-13 OLS fit
Age 14-20 OLS fit
0.40
Age 21+ OLS fit
0.30
0.20
0.10
0.00
0.00
0.20
0.40
0.60
probability of winning
0.80
1.00
For losses, proportion choosing the gamble, by age.
1.00
0.90
Age 5-8
Proportion choosing gamble
0.80
Age 9-13
0.70
Age 14-20
0.60
Age 21+
Age 5-8 OLS fit
0.50
Age 9-13 OLS fit
0.40
Age 14-20 OLS fit
0.30
Age 21+ OLS fit
0.20
0.10
0.00
0.00
0.20
0.40
0.60
probability of losing
0.80
1.00
% BY AGE CATEGORIES
PATTERN
(LOW P. GAIN, HIGH
P. GAIN, LOW P.
LOSS, HIGH P. LOSS)
5-8
9-13
14-20
21-64
(g,g,g,g)
20.3
10.8
6.9
4.3
11.1
(g,g,g,c)
10.9
9.2
15.5
8.5
11.1
(g,c,c,g)*
3.1
7.7
6.9
21.3
9.0
(c,g,g,g)
10.9
15.4
10.3
6.4
11.1
(c,g,g,c)+
17.2
20.0
13.8
12.8
16.2
(c,c,c,c)
6.3
3.1
5.2
6.4
5.1
Number of participants
64
65
58
47
234
ALL
•indicates the choice pattern consistent with a reflection point at 1/3 and a regressive weighting function;
•+ indicates the choice pattern least consistent with a reflection point at 1/3 and a regressive weighting function.
•Bold indicates the modal pattern for each age group.
Results:
Children tend to underweight low-probability events
and overweight high-probability events.
Adults are closer to w(p) = p than are children, and
more so over gains than over losses.
Differences between choices over losses and choices
over gains diminish with age.
Within-subject analysis: CPT pattern is modal choice
among adults; rare among younger subjects.
Conclusion:
We examined choices between a simple gamble and a
certain outcome for children and adults and we
found large age-related differences in choices.
About 70% of the youngest children chose a fair gamble
when the chance of the gain was 0.8, while only
43% of the oldest adults did.
Over losses, about 75% of these children took a fair
gamble when the chance of the loss was 0.1,
compared to 53% of the adults.
These differences are even more dramatic if we limit the
sample to those who pass the rationality tests.
Conclusions:
Most experimental work on adults finds that they tend to
overweight low-probability outcomes and
underweight high-probability ones, a pattern of
behavior that can be explained by a regressive
probability weighting function.
In children, we find the opposite of regressive
weighting: on average they choose as if they
underweight low-probability events and overweight
high-probability ones. This result is statistically
significant and it is large.
Conclusions:
In situations with a small probability of a large loss they will
be more likely to take the risky than the safe option.
While our data come from financial decisions, this pattern
seems to fit the stylized facts of adolescent decisionmaking in such areas as risky sex, smoking, and
impaired driving.
If children respond differently than adults do to a risk, then
policies that aim at informing children of the risks that
they face are unlikely to achieve a response that adults
will find satisfactory.
The fact that our experiments do not find this same tendency
to underweight low probability outcomes among adults
suggests a justification for constraints on adolescent
decisions in situations that present low probability risks.