Y. Masatlioglu and N. Uler
University of Michigan
Do Reference Points Influence Economic Outcomes?
 Standard Neoclassical Theory:
If transactions costs are small enough, reference points
should not influence rational consumers.
 In practice, defaults make an enormous difference:






Organ donation
Car insurance
Car purchase options
Consent to receive e-mail marketing
Savings
Asset allocation
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Reference-Dependent Behavior

Endowment Effect
The gap between WTA and WTP

Status Quo Bias
People often stick with their default options

Reference Effect
Reference points alter one's choices even when agents
do not stick to the reference point itself
3
The Reference Effect
Good 2
STATUS QUO BIAS
x is more likely to be
chosen from the set {x, y} when x
is the endowment,
y
x
REFERENCE EFFECT
x is more likely to be
chosen from the set {x, y, r} when
there is an endowment, r.
r
Good 1
4
Reference-Dependent Models
• Behavioral Models (Positive Approach)
Tversky and Kahneman (1991)
• Rational Models (Normative Approach)
Masatlioglu and Ok (2005, 2007, 2009)
5
Tversky and Kahneman (1991)
(Loss Aversion Model (LA))
• Reference dependence
Ur ( x)   g i [ui ( xi ) ui (ri )]
i
• Loss aversion
• Diminishing sensitivity
g i [ a ]   g i [ a ]
gi concave for a > 0, convex for a < 0
6
Shortcomings of the LA model
• Non-convex Indifference Curves
• Unusual Cyclical Choices
• Accommodates not only Status Quo Bias but also
Status Quo Aversion
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Unusual Preference Cycles
Munro and Sugden (2003) show that one can find three alternatives,
x, y and z such that
U x ( y )  U x ( x)
U y ( z)  U y ( y)
U z ( x)  U z ( z)
x z z  y y x x
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Loss Aversion Model predicts Status Quo Aversion
EXAMPLE: Consider an environment in which an agent needs to choose
among pairs (x,m), where x and m stand for the units of a private good and
money, respectively.
U ( x*,m*) ( x, m)  g x ( x  x*)  g m (m  m*)
 a if
g x (a)  
2a if
a0
a0
 a 0.8
if
g m (a)  
0.8
if
 2(a)
a0
a0
Choose between “one mug and $100” and “no mug and $103” when there is
no reference:
U(0,0) (1,100)  U(0,0) (0,103)
Now, status quo is “one mug and $100” what would you do?

U(1,100) (1,100)  U(1,100) (0,103)
STATUS QUO AVERSION !!!
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Constant Loss Aversion (CLA)
We say Constant Loss Aversion if there is no diminishing
sensitivity or constant sensitivity (gi is linear).
While Loss Aversion Model
(i) permits non-convex indifference curves and intransitive
preferences
(ii) accommodates not only status quo bias but also
status quo aversion,
While Constant Loss Aversion does not suffer from these
implications, it is much more restrictive than the LA model.
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The Multi-criteria Choice Model
Masatlioglu and Ok (2005)
Axiom of Status Quo Bias
If y is chosen when x is the status quo point, then, y
must be chosen when y is itself the status quo.
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Masatlioglu and Ok, 2005
(The SQB Model)
Sports
Assume that r is the status quo
Acceptable alternatives
when r is status quo
r2
r
u1
U = w1u1+w2u2
u2
Movie
r1
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Loss Aversion vs. Status Quo Bias

Loss Aversion Model

Status Quo Bias Model
max
xB
Ur (x)
U (x)
max
x  B  Q(r)
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The Generalized SQB Model
Masatlioglu and Ok (2009)
U (x)
max
x  S  Q(r)
Mental Constraint
such that
r  Q(r )
y  Q(r )  U ( y )  U (r )
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The Procedural RD Model
Masatlioglu and Ok (2007)
Another generalization of SQB model with two mental
constraints, Q1and Q2
max
x  S  Q (r) if {r}  Q (r)
1
1
x  S  Q (r)
otherwise
2
U (x)
such that
r  Qi (r )
y  Qi (r )  U ( y )  U (r )
y  Qi (r )  Qi ( y)  Qi (r )
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Some Facts about Experiment

Conducted at C.E.S.S. (NYU)

A total of 99 subjects

Money and Chocolate

Each subject answered 16 questions
On
average, earned $14 including the $7 show-up
fee and also some chocolates
17
Belgium Chocolate
Experimental Design
y
x
r1
r7
r5
r6
r0
r2
r3
r4
Money
18
A Screen Shot from the Experiment
19
Experimental Design: Theoretical Predictions
Good 2
Assume that y is preferred to x when there is no reference point.
There are four possible
cases:
x
r1
r2
r0
r1
r2
Classical
Choice
Theory
y
y
Yes
y
x
No
x
x
No
x
y
No
y
Good 1
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Loss Aversion Model
Good 2
LA model favors x
y
x
r1
r2
r1
r2
LA
CLA
y
y
Yes
Yes
y
x
Yes
No
x
x
Yes
Yes
x
y
No
No
Good 1
21
Good 2
SQB Model
y
r1
x
Acceptable
Alternatives
Choice
r1
r1 x
x
r2
r2 x y
y
SQB model favors y
u1
r2
u2
u1
r1
r2
SQB
y
y
Yes
y
x
No
x
x
Yes
x
y
Yes
Good 1
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Generalized SQB Model
Good 2
Q(r) ∩ B
Choice
r1
r1 x y
y
r2
r2 x
x
y
GSQB model is indecisive
x
r1
r2
Q(r1)
Q(r2)
r1
r2
SQB
GSQB
y
y
Yes
Yes
y
x
No
Yes
x
x
Yes
Yes
x
y
Yes
Yes
Good 1
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Good 2
Procedural R-D Model
Elimination
Q1(r) ∩ B Q2(r) ∩ B
Q1(r1)
y
Q1(r2)
Choice
r1
r1
r1 x y
y
r2
r2 x
-
x
PRD model is also indecisive
x
r1
r2
Q2(r1)
Q2(r2)
r1
r2
SQB
PRD
y
y
Yes
Yes
y
x
No
Yes
x
x
Yes
Yes
x
y
Yes
Yes
Good 1
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Theoretical Predictions for
Tversky - Kahneman
r1
Masatlioglu - Ok
CLA
LA
SQB
GSQB
PRD
-
Favors x
Favors y
Indecisive
Indecisive
r1→ r2
Belgium
Chocolate
r2
y
x
r1
r2
r0
Money
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Theoretical Predictions
Tversky Kahneman
Masatlioglu - Ok
Group 1
Group 2
CLA
LA
SQB
GSQB
PRD
r0→ r5
-
I
-
-
I
r0→ r6
-
I
-
-
I
r0→ r7
-
I
-
-
I
r1→ r2
-
x
y
I
I
r3→ r4
-
x
y
I
y
r5→ r6
-
x
-
-
I
r5→ r7
-
y
-
-
I
y
r1
r7
r5
r6
r2
x
r3
r4
r0
26
Reference Effect: Preference Reversals
No reversal
269
%73
Reversal
97
%27
# Obs
366
r1 → r2
r3 → r4
r5 → r6
r5 → r7
r1
r2
r3
r4
r5
r6
r5
r6
No reversal
40
33
42
40
32
30
25
27
Reversal
14
21
14
16
5
7
11
9
# subjects
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56
37
36
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Comparison of the Models
Explanatory Power of the Models (in percentages)
Tversky - Kahneman
Total
Masatlioglu - Ok
CLA
LA
SQB
GSQB
PRD
65
89
68
75
93
Classical
Theory
48
28
Selten’s Measure of Predictive Success
29
Conclusion
• Existence of the reference effect
• An experiment that distinguish between the
models of reference-dependence.
• R-D Models make different predictions
regarding the reference effect.
• We find that both the PRD and LA models
explain approximately 90 percent of the data.
30
THANK YOU
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A Sample Question from the
Experiment
38
Is SQB Axiom too strong?
y  c({ x, y}, x)
z  c({ y, z}, y )

z  c({x, z}, x)
Take x = CUNY, y = OSU and z = UM
OSU = c({CUNY, OSU},CUNY)
UM = c({OSU, UM},OSU)
But
CUNY = c({CUNY, UM},CUNY)
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Relaxing SQB Axiom?
Weak Status Quo Bias
implies
and
implies
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