Online Appendix for
“Looming Large or Seeming Small?
Attitudes Towards Losses in a Representative Sample”
by Chapman, Snowberg, Wang, and Camerer
Table of Contents
A DOSE Procedure and Survey Implementation
2
A.1 DOSE Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2
A.2 Other Survey Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4
B Choice Data
6
B.1 Choice Data From DOSE . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6
B.2 Choice Data from MPL Elicitations . . . . . . . . . . . . . . . . . . . . . . .
8
C Additional Results and Robustness
9
C.1 Alternative Utility Specifications . . . . . . . . . . . . . . . . . . . . . . . . .
9
C.2 Additional Correlations with Individual Characteristics . . . . . . . . . . . .
12
C.3 Additional Regressions with Real World Behaviors . . . . . . . . . . . . . . .
21
C.4 Additional Results from Alternative Reference Point Models . . . . . . . . . .
26
C.5 Additional Tests of Survey Fatigue and Inconsistency . . . . . . . . . . . . . .
26
C.6 Tests of Payment Schedule E
↵
ects . . . . . . . . . . . . . . . . . . . . . . . .
31
C.6.1 A Theory of Threshold Response . . . . . . . . . . . . . . . . . . . . .
34
C.6.2 Subgroups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
35
D Principal Components Analysis
38
E Screenshots
39
Online Appendix–1
A DOSE Procedure and Survey Implementation
A.1 DOSE Procedure
This subsection presents further details of the design choices for each of the two DOSE sequences
in our online survey. We start by detailing the information criterion and error specification that
we implement in both the DOSE sequences. We then explain the implementation of the question
selection in our online survey, and specify the particular design choices made for each of the
10-question and 20-question sequences. For full details of the DOSE elicitation method, see
Chapman et al. (2018).
Overview of DOSE procedure
The DOSE procedure selects a personalized sequence of
questions for each participant. Questions are selected sequentially, using a participant’s previous
answers to identify the most informative question at that point in time. In our implementa-
tion, DOSE selects each question to maximize the expected Kullback-Leibler (KL) divergence
between the prior and possible posteriors associated with each answer. That is, the question
that is picked at each point is the one with the highest expected information gain given the
initial prior and previous answers.
Formally, consider a finite set of possible parameter vectors
✓
k
for
k
=1
,...,K
,whereeach
✓
k
=(
⇢
k
,