FF8 Fortnight
Analysis of Discrete Choice Data
Sheffield, September of 2007
Can we use RUM and
don’ get DRUNK?
Jorge E. Araña
University of Las Palmas de Gran Canaria
Collaborators: Carmelo J. León (ULPGC), W. Michael Hanemann
(UC Berkeley)
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Outline
1.
2.
3.
4.
RUM and DC experiments
Sources of mistakes in Citizens choices
An Extended Frame: Bayesian Modelling
Example: Heuristics and DCE
4.1. STUDY 1: Is it really a practical problem? A Verbal Protocol
Analysis.
4.2. STUDY 2: A Bayesian Finite Mixture Model in the WTP
space. The effects of Complexity and Emotional
Load on the use of Heuristics.
4.3. STUDY 3: Heuristics Heterogeneity and Preference
Reversals in Choice-Ranking: An Alternative
Explanation.
4.4. STUDY 4: Can we use RUM and don’t get DRUNK?.
A Monte Carlo Study
5. Discussion and Further Research
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DCE and Non-Market Valuation
Valuation of Health = appropriate methods
 DCE are increasingly used and accepted.
Decision
Making
Process
Individual
Preferences
Coherent
Results for
CBA or CEA
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The Underlying Economic Theory
• Morishima (METRO,59)
Lancaster (JPE, 66)
B  f P  
– Value from characteristics
B= observed/stated choices
P= Preferences (Fundamental Value)
E= Random term (Context)
THE TWO MAIN ISSUES
1. MEASURING PREFERENCES: (defining P)
i) Experienced vs Choice Utility
ii) Absolute vs. relative utility (prospect theory)
iii) …
2. LINK CHOICES AND PREFERENCES: f (.)
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The Departing Point
From the Economic theory point of view
• Lancaster (1966) – Value for characteristics
B  f P,  
MAIN ISSUES
1. MEASURING PREFERENCES: (defining P)
i) Experienced vs Choice Utility
ii) Direct Utility
iii) Absolute vs. relative utility (prospect theory)
iv) happiness vs. utility
…
2. LINK CHOICES AND PREFERENCES: f (.)
How can we link Choices and Preferences? f (.)
Traditional Answer:
(RUM)
“Individuals have a single set of well-defined goals, and
her behavior is driven by the choice of the best way to
achieve those goals”.
choose i* iff V (i* )  V (i) i  i*
where V(i)  xi β
General
Simple
An accurate explanation of agents choices
in a wide range of situations
Intuitive
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However…
Strong and large evidence that citizens don’t
choose what make them happy?
Why?
 Failing Predicting Future Experiences
- Projection bias, Distinction bias, Memory bias, Belief bias,
Impact bias
 Failing Following Predictions
- Procrastination , Self-control bias, Overconfidence, Anchoring
Effects, Simplifyng Decision Rules,…
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However…
- Preference Reversals (Slovic and Lichtenstein, 1971,1973)
- Framing effects (Tversky and Kahneman, 1981, 1986)
-…
Do f(.) exists? or just B = ε ?
Our belief:
YES, f(.) do exists.
The Challenge:
Defining f(.) in a way that can
accommodate these deviations.
Research Strategy: Thinking in a Hyper-rationality concept
Context matters… but Fundamental values too
(McFadden, 2001; Grether & Plott, 1979, Slovic, 2002;…)
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Solutions NEED to be …
 Multidisciplinary
- Economic Theory
- Social Psychology
- Statistics
- Cognitive Psychology
- Neurology
- Political Science,…
 We need an Extended Frame that integrate
contributions from these different areas.
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Why not Bayesian?
 One Elegant and Robust way of integrating
Multidisciplinary contributions to DC Theory
and Data Analysis: Bayesian Econometrics
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Potential Bayesian Contributions to DCE
 Can use prior information (there is a lot of prior info available!.
previous research, experts, Benefit Transfer, Optimal Designs,…).
 Able to tackle more complex/sophisticated models
 More accurate results (e.g. Exact theory in finite samples)
 More informational results (reports full posterior distributions instead of
just one or two moments)
 Sample means are inefficient and sensitive to outliers (this is
especially important when studying heterogeneity in behaviour. The
role of tails have been long ignored)
 Bayesian methods can quantify and account for several kinds of
components of uncertainty.
 More interpretable inferences (probabilities, confidence?,…)
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EXAMPLE: Heterogeneous Decision Rules and DC
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The Heterogeneity in Decision Rules Argument
-Decision Making requires an Information Process
Simon (1956) Kahnemann and Tversky (1974)
Individuals have a set of decision strategies h1, h2,…, hH
at their disposal that vary in terms of:
-
Effort=EC (how much cognitive work is necessary to
make the decision using that strategy)
-
Accuracy=EU (the ability of that strategy to produce
a good outcome).
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Literature on Heuristics
The Adaptive Decision Maker (Payne, Bettman, and Johnson, 1993)
• Toolbox of possible choice heuristics in multi-attribute choice
•WADD: Weighted additive rule
•EQW: equal weight heuristic
•SAT: Satisficing Rule (Simon, 1955)
•LEX: Lexicographic Heuristics
•EBA: Elimination by Aspects (Tversky, 1972)
•ANC: Anchoring Heuristic (Tversky and Kahneman)
•MCD: Majority Confirming dimensions (Russo & Dosser, 1983)
•ADDIF: Additive difference model (Tversky, 1969)
•FRQ: Freq. of good and bad features (Alba and Marmorstein, 1987)
•AH: Affect Heuristic. Slovic (2002)
•Combined Strategies
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Choosing How to Choose (CHTC)
TWO STEP PROCESS
STEP 1. Choosing How to Choose. (Choice of the D. Rule)
D* iff
EU( D* )  EC( D* )  EU( D j )  EC( D j ) D j  D*
STEP 2. Applying the Decision Rule.
i* iff D* (i* )  D* (i) i  i*
Applications:
Manski (1977), Gensch (1987), Chiang et al (1999),
Gilbride and Allenby (2004), Beach and Potter(1992)
Swait and Adamovicz (2001), Amaya and Ryan (2004)
Araña, Hanemann and León (2005)
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The Theoretical Model
For a well-behaved preference map, a general indirect utility function
of individual i, given an alternative j:
Vij  X j    i
i  1,...n; j  1,...k
if the individual faces a multi attribute discrete choice problem,
the researcher will observe that individual i chooses alternative j* if,



Vij*  X j*    i*  Vij  X j    i

j  j * such that I ij (.)  1
Different specifications of I(.) makes the model collapse
to alternative decision rules
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Different Heuristics
Model
Specification Decision Rule to choose alternative j
M1: Full Compensatory Rule
V j  Vl l  j
M2: Complete Ignorance
V j  Vl l  j and  m =0 m
M3: Conjunctive Rule
V j  Vl
l  j such that

I X ijm , γim   1
M4: Satisfaction Rule
V j  Vl
l  j such that

I X ijm , γim   1 and  m =0 m
M
m1
M
m1
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Non regularity
Problem 1:
The likelihood surface for a heuristic is discontinuous, and
therefore, the global concavity can not be guaranteed.
Solution:
Rewriting the probability as the product of a second step of the
choice process and a marginal heuristic probability. That is,
.
ProbYij  1, h   ProbYij  1 | h  Probh 
By adding the likelihood functions over the different decision rules,
resulting in a globally concave likelihood surface,
ProbYij  1   ProbYij  1 | h  Probh 
H
h 1
f(.) is a mixture distribution
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Evaluate an Intractable Function
From Bayes’ theorem,
  | Y      LY |  
Problem 2:
The posterior distribution is intractable and difficult to evaluate
.
Solution:
Here we deal with that complication by employing MCMC methods
as is proposed in discrete choice by Albert and Chib (1993)
by combining…
 GS Algorithm (Geman and Geman, 1984)
 DA Technique (Tanner and Wong, 1987)
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Prior Distributions
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MCMC Algorithm
Model 1. Linear Compensatory rule
i)
WTPij from equation (A2.1)
ii)
i
from equation (A2.2)
iii)
i
from equation (A2.3)
iv)

from equation (A2.4)
v)

from equation (A2.5)
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MCMC Algorithm
Model 3. Elimination by aspects
i)
WTPij from equation (A2.6)
ii)
 im
from equation (A2.7)
iii)
i
from equation (A2.2)
iv)
i
from equation (A2.3)
v)

from equation (A2.4)
vi)

from equation (A2.5)
vii)
m
from equation (A2.8)
viii)

from equation (A2.9)
ix)

from equation (A2.10)
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MCMC Algorithm
Model 4. Satisfaction Rule
i)
WTPij from equation (A2.6)
ii)
 im
iii)

from equation (A2.5)
iv)
m
from equation (A2.8)
v)

from equation (A2.9)
vi)

from equation (A2.10)
from equation (A2.7)
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Different Studies that have been discussed during FF8
Study 1: Determinants of Choosing Decision Rules (task
complexity, emotional load,…)
Study 2: Heuristics and Preference Reversals in Ranking
vs Choice.
Study 3: Testing the Validity of the Model to screen out
Heuristics
Study 4: Monte Carlo Simulation Study
Study 5: Verbal Protocol and Emotional Load
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STUDY 1: The Data
Good to be valued
Valuation of a set of programs designed to
improve health care conditions for the elderly in
the island of Gran Canaria.
Programmes
link
Survey Process
- 2 Focus Groups
(From Jun-2004
- 3 Pre-Test Questionnaires
To Ap-2005)
- Final Questionnaire
Sample Size
550 Individuals
Survey Design
• D-optimal design method (Huber & Zwerina,96)
• Elicitation Technique: Choice Experiment
• Scenario were successfully tested in prior research
Testing Complexity effects on CHTC
TWO SPLIT SAMPLES
SAMPLE I
2 pairs of alternatives + status quo
SAMPLE II
4 pairs of alternatives + status quo
Testing Emotional load effects on CHTC
MEASURING EMOTIONS
- Content (what we remember)
- Process (how we reason)
Individuals emotional intensity Scale (EIS)
Emotional Intensity -------- mood experience ----- individual decision making
Def. Emotion: “ Stable individual differences in the strenght with which
individuals experience their emotions” (Larsen and Diener, 1987)
EIS-R (Geuens and Pelsmacker, 2002)
Results & Discussion
Introduction
The Model
The MC
Experiment
Results
Application
Conclusion
TEST I: COMPLEXITY AND VALUATION RESULTS
Table 3. Welfare Estimation Results for M1 (€)
Introduction
Programs
The Model
DRUGS
The MC
Experiment
DAY CARE
Results
HOSPITAL
Application
Conclusion
2 alter. + SQ
4 alter. + SQ
43.45
38.34
(32.45, 54.44)
(31.65, 45.02)
19.51
9.54
(11.02, 27.99)
(3.24, 15.83)
51.28
67.88
(39.10, 63.45)
(61.56, 74.19)
RESULT 1:
Complexity seems to affects absolute values of Welfare
Estimations, BUT DO NOT affect programs ranking.
TEST I: COMPLEXITY AND VALUATION RESULTS
Table 3. Welfare Estimation Results for M1 (€)
Introduction
Programs
The Model
DRUGS
The MC
Experiment
DAY CARE
Results
HOSPITAL
Application
2 alter. + SQ
4 alter. + SQ
43.45
38.34
(32.45, 54.44)
(31.65, 45.02)
19.51
9.54
(15.52, 24.49)
(4.24, 14.83)
51.28
67.88
(39.10, 63.45)
(61.56, 74.19)
Conclusion
RESULT 2:
Complexity makes people focus on the most appreciate attributes,
what leads to higher valuations for most valued prog. (HOSPITAL)
and lower valuations for less valued prog. (DAY CARE).
TEST II: Complexity and Choosing how to Choose
Introduction
Decision Rule
2 alter + SQ
4 alter + SQ
The Model
Full
Compensatory
44.36
28.33
Complete
Ignorance
6.21
11.19
EBA
(Conjunctive)
31.13
36.11
Satisfaction
14.63
19.45
Disjunctive
3.66
4.92
The MC
Experiment
Results
Application
Conclusion
RESULT 3:
The proportion of people responding in a totally random
way is low.
TEST II: Complexity and Choosing how to Choose
Introduction
Decision Rule
2 alter + SQ
4 alter + SQ
The Model
Full
Compensatory
44.36
28.33
Complete
Ignorance
6.21
11.19
EBA
(Conjunctive)
31.13
36.11
Satisfaction
14.63
19.45
Disjunctive
3.66
4.92
The MC
Experiment
Results
Application
Conclusion
RESULT 4:
Deviations from M1 are extended in the sample (55%),
although M1 has the larger proportion.
TEST II: Complexity and Choosing how to Choose
Introduction
Decision Rule
2 alter + SQ
4 alter + SQ
The Model
Full
Compensatory
44.36
28.33
Complete
Ignorance
6.21
11.19
EBA
(Conjunctive)
31.13
36.11
Satisfaction
14.63
19.45
Disjunctive
3.66
4.92
The MC
Experiment
Results
Application
Conclusion
RESULT 5:
Complexity does increase the likelihood that
Individuals follow non compensatory decision rules.
TEST II: Complexity and Choosing how to Choose
Introduction
Decision Rule
2 alter + SQ
4 alter + SQ
The Model
Full
Compensatory
44.36
28.33
Complete
Ignorance
6.21
11.19
EBA
(Conjunctive)
31.13
36.11
Satisfaction
14.63
19.45
Disjunctive
3.66
4.92
The MC
Experiment
Results
Application
Conclusion
RESULT 5:
Complexity does increase the likelihood that
Individuals follow non compensatory decision rules.
TEST III: Emotional Intensity and Choosing how to choose
Introduction
Table 5. Individuals assigned to non-compensatory rules
According to the degree of EIS (%)
The Model
The MC
Experiment
Results
Emotional Level
2 alter + SQ
4 alter + SQ
Low EIS
58.32
59.30
Avg. EIS
42.38
35.70
High EIS
71.15
77.45
Application
Conclusion
RESULT 6:
Emotional Sensitivity does affect the use of
Alternative decision rules
TEST III: Emotional Intensity and Choosing how to choose
Introduction
Table 5. Individuals assigned to non-compensatory rules
According to the degree of EIS (%)
The Model
The MC
Experiment
Results
Emotional Level
2 alter + SQ
4 alter + SQ
Low EIS
58.30
59.30
Avg. EIS
42.38
35.70
High EIS
71.15
77.45
Application
Conclusion
RESULT 7:
Extreme EIS (high or low) induces a larger departure
from M1 than average EIS.
STUDY 3: RK-Choice Preference Reversals
Summary of Results
Shows that Decision Rules are different in Choice and
in Ranking. When we take responses to ranking that
are worse than status quo out of the sample, decision
rules and mean WTP are very similar (although
variances are lower in RK since it uses more
information)
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The Data
Good to be valued
Valuation of a set of environmental actions in a
vast rural park in the island of Gran Canaria
called “The Guiniguada valley”.
Population
Gran Canaria Island Population
Survey Proccess
- 3 Focus Group
(14 months in total)
- Pre-Test Questionnaire
- 1 Focus Group
- Final Questionnaire
Sample Size
Survey Design
540 Individuals
•D-optimal design method (Huber and
Zwerina, 1996).
•Elicitation Techniques: Choice and Ranking.
•Scenario (verbal and photos) were tested in
prior research..
Results
Table 3. Welfare Estimations from M1(RUM) for Choice and Ranking
E[WTP]
PATHS
BOTGARDEN
SUSTPARK
PAINT
CAGES
RURALANDS
ENDFORESTS
Choice
Ranking
44.29
21.43
[42.11, 46.47]
[19.29, 23.57]
49.60
25.26
[47.31,51.89]
[23.12, 27.39]
38.56
34.93
[36.40, 40.72]
[32.84, 37.02]
74.33
35.74
[71.94,76.71]
[35.59, 37.89]
8.36
18.13
[6.02,10.69]
[16.00, 20.26]
56.75
41.07
[54.34, 59.17]
[38.89, 32.26]
72,07
39.52
[71.61, 72.53]
[36.98, 42.06]
Table 4. Proportion of individuals assigned to each decision rule in each model
Choice
Ranking
Ranking
Ranking
(better)
(worse)
M1: Full Compensatory Rule
26.68 %
22.96 %
27.80 %
16.46 %
M2: Complete Ignorance
9.49 %
18.57 %
9.36 %
30.93 %
M3: Conjunctive Rule
33.57 %
30.68 %
42.29 %
15.1 %
M4: Satisfaction Rule
19.17 %
19.66 %
16.63 %
23.73 %
M5: Disjunctive Rule
11.09 %
8.13 %
3.92 %
13.78 %
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Table 4. Proportion of individuals assigned to each decision rule in each model
Choice
Ranking
Ranking
Ranking
(better)
(worse)
M1: Full Compensatory Rule
26.68 %
22.96 %
27.80 %
16.46 %
M2: Complete Ignorance
9.49 %
18.57 %
9.36 %
30.93 %
M3: Conjunctive Rule
33.57 %
30.68 %
42.29 %
15.1 %
M4: Satisfaction Rule
19.17 %
19.66 %
16.63 %
23.73 %
M5: Disjunctive Rule
11.09 %
8.13 %
3.92 %
13.78 %
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Table 4. Proportion of individuals assigned to each decision rule in each model
Choice
Ranking
Ranking
Ranking
(better)
(worse)
M1: Full Compensatory Rule
26.68 %
22.96 %
27.80 %
16.46 %
M2: Complete Ignorance
9.49 %
18.57 %
9.36 %
30.93 %
M3: Conjunctive Rule
33.57 %
30.68 %
42.29 %
15.1 %
M4: Satisfaction Rule
19.17 %
19.66 %
16.63 %
23.73 %
M5: Disjunctive Rule
11.09 %
8.13 %
3.92 %
13.78 %
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Results
Table 5. Welfare Estimations from Aggregated Model for
Choice and Ranking
E[WT P]
PATHS
BOTGARDEN
SUSTPARK
PAINT
CAGES
RURALANDS
ENDFORESTS
Choice
Ranking
49.17
48.17
[47.28, 51.05]
[44.91, 51.43]
53.83
48.25
[51.88, 55.78]
[44.16, 52.53]
42.00
38.08
[40.13, 43.88]
[35.65, 40.51]
83.63
64.20
[81.64, 85.62]
[57.24, 75.15]
3.62
7.79
[1.64, 5.59]
[4.13, 8.41]
61.45
61.08
[59.44, 63.46]
[38.80, 82.99]
83.46
71.90
[81.44, 85.49]
[60.14, 83.65]
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Conclusions
 In this application, the EBA is the most predominant
heuristic (over the FLC)
 A small % of subjects follows the Completely
Random Heuristic.
 Heuristics Heterogeneity is different between
Choice and Ranking (in particular between RK
below SQ).
 When the Heuristics Heterogeneity is incorporated in
the model the gap between Choice and Ranking is
drastically reduced.
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GENERAL DISCUSSION AN FURTHER RESEARCH
1. The model seems to do a good job detecting people that use
these heuristics (average efficiency 85% MC study)
2. It can be used as a test to further explore the validity of a
specific DCE are good enough to be used in PUBLIC POLICY
(friendly code will be available very soon).
3. Results from these studies can also help to decide several
aspects of the DCE design: number of attributes, levels,…)
4. First further research would be to use this information in the
DCE design using a Bayesian approach so we can improve
the accuracy of the results (respondent eficiency vs statistical
efficiency).
5. Results also have implications for Benefit Transfer. It is
possible to reduce the cost of these studies by transferring
results from previous studies to new ones. The Bayesian
framework seem to be the most adequate approach to do so.
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Thanks !!!!!
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STUDY 3: Testing the Validity of the Model to screen out
Heuristics
I. Five different treatments were assigned to the samples:
Treatment 1: All the simulated respondents follow the FLC rule
Treatment 2: All the simulated respondents follow the EBA rule
Treatment 3: All the simulated respondents follow the Completely Ignorance
Rule
Treatment 4: All the simulated respondents follow the satisfactory rule.
Treatment 5: 25 % of the simulated respondents follow the FLC rule, other
25% follow the EBA rule; other 25 % follow the satisfactory
Rule and other 25% follow the completely ignorance rule.
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STUDY 3: Testing the Validity of the Model to screen out
Heuristics
I. The true utility function was defined with values as close as possible to the ones
estimated in the current application. That is, B(DRUGS) =3; B(COST)=-0.01;
B(HOSPITAL)=3.5, B(DAY CARE) =1.5. For Treatments 2, 4 and 5 we randomly
assigned the cut-off values for each split sample.
II. In order to simulate responses to the “Monte Carlo survey”, we employed the same
experimental designs that were used in the field data experiment. Then, 100 samples
were simulated for each treatment and for each condition (e.g. Condition A: 2
options +SQ; and Condition B: 4 options +SQ). In total 1000 samples were
simulated (100 samples for 5 treatments in the 2 conditions).
III. After the final responses were collected for each sample, the proposed Bayesian
mixture model was estimated for each one of them, and therefore the probability
that each individual follows each decision rule. Results on the average proportion of
individuals correctly assigned to each decision rule among the samples are presented
in the Table R1 in this reply.
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STUDY 3: Testing the Validity of the Model to screen out
Heuristics
Table R1. Proportions of individuals correctly assigned to their decision rule by using
the Bayesian Mixture Model
Condition A
Condition B
2 options + SQ
4 options + SQ
Treatment 1: FLC
92 %
95 %
Treatment 2: EBA
69 %
74 %
Treatment 3: Completely
Ignorance
58 %
64 %
Treatment 4: Satisfaction
70 %
76 %
Treatment 5:
Mixture Model
82 %
85 %
Average efficiency: 85%
Notes: No prior info and no respondent efficient design
have been applied
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STUDY 4: Monte Carlo Study. People follow alternative
heuristics…. So what are the consequences?
• A conventional Conditional Logit model and a
Hierarchical Bayes Model are estimated in 900
samples following same idea that study 2.
• Samples differ in terms of the % of citizens following
each decision rule (e.g. 10, 20, 30, 40, 50, 60, 70, 80,
90%).
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STUDY 4: Monte Carlo Study. People follow alternative
heuristics…. So what are the consequences?
Bias
E(WT P)
40
35
30
25
EBA
CI
Sat
20
15
10
5
0
10%
30%
50%
70%
% people following Heuristics
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STUDY 4: Monte Carlo Study. People follow alternative
heuristics…. So what are the consequences?
•It is found that for the most predominant heuristics (EBA, Satisficing),
the % of individuals that would generate a significant bias in welfare
results (10%) is 70% or higher (what is unusual in practice).
•However, a 20 % of people following the COMPLETELY IGNORANCE
heuristic is enough to seriously bias the results.
•When we use a Hierarchical Bayes Model, we get smaller bias for any
% of people following alternative heuristics.
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STUDY 5:EXP. I: Valuation of Externalities
Good to be valued
Valuation of a set of policy proposals to ameliorate
externalities of a Stone Mining Facility in the
suburbs of Las Palmas de Gran Canaria (Gran
Canaria).
Population
8000 individuals (total surrounding population)
Survey Process
- 2 Focus Groups
- 2 Pre-Test Questionnaires
- Final Questionnaire
Sample Size
288 Individuals (very familiar with the externalities)
Survey Design
• D-optimal design method (Huber & Zwerina,96)
• Elicitation Technique: Choice Experiment
• Scenario (verbal and photos) where tested in prior
research
EXPERIMENT I: Valuation of Externalities
MEASURING HEURISTICS
Verbal Protocol (Ericsson and Simon, 1980)
-DCCV (Hanemann, 92, Schkade and Payne, 93)
Concurrent Protocol Approach:
“Respondents are asked to verbalize their
thoughts and explain how they arrive at the final
choice while they are completing the task”.
Evaluation Process
Responses were recorded, transcribed and
evaluated by 2 judges who where unaware of our
hypotheses. (3rd judge for disagreements)
EXPERIMENT I: Valuation of Externalities
MEASURING EMOTIONS
- Content (what we remember)
- Process (how we reason)
Individuals emotional intensity Scale (EIS)
Emotional Intensity -------- mood experience ----- individual decision making
Def. Emotion: “ Stable individual differences in the strenght with which
individuals experience their emotions” (Larsen and Diener, 1987)
EIS-R (Geuens and Pelsmacker, 2002)
EXPERIMENT I: Valuation of Externalities
Attribute Negative Emotional Load Scale (ANEL)
This scale indicates the amount of affect involved in making trade-offs between
an specific attribute and money.
The ANEL scale is generated as a confirmatory analysis of the following
measures adapted from Lazarus (1991):
1. Severity of the worst potential consequence (scale 0 to 100)
2. Likelihood of negative outcomes (scale 0 to 100)
3. Degree of Threat (scale 0 to 100)
Results
Introduction
The Model
The MC
Experiment
TEST I: Effects of the Verbal Protocol approach
 Swait and Louviere (1993)
EQUAL PARAMETER TEST:
-2 [312.8172-148.5683-160.4279] = 7.642  X8 .
Results
EQUAL SCALE TEST:
Application
-2 [312.5553 - 312.8172] = 0.5238  X1
Conclusion
RESULT 1:
The use of verbal protocol in this context seems that
would not affect individuals’ behaviour.
TEST II: Explaining the use of Compensatory D. Rules
Introduction
The Model
The MC
Experiment
Results
Application
Conclusion
Table 3. Results of the Probit model
Estimations
Covariates
Coefficient
(s. e.)
p-value
Constant
-0.1623 (0.2455)
0.5084
Income
0.0297 (0.0284)
.2960
Age
0.1489 (0.0375)
0.0875
Gender
0.0392 (0.0421)
0.3514
Education
-0.0703 (0.0137)
0.0000
EIS
0.5291 (0.1094)
0.0000
EIS^2
-0.1791 (0.0040)
0.0000
ANEL
-0.6491 (0.1094)
0.0000
Log-likel.
-2554.651
TEST II: Explaining the use of Compensatory D. Rules
Introduction
The Model
The MC
Experiment
Results
Application
Conclusion
Table 3. Results of the Probit model
Estimations
Covariates
Coefficient
(s. e.)
p-value
Constant
-0.1623 (0.2455)
0.5084
Income
0.0297 (0.0284)
.2960
Age
0.1489 (0.0375)
0.0875
Gender
0.0392 (0.0421)
0.3514
Education
-0.0703 (0.0137)
0.0000
EIS
0.5291 (0.1094)
0.0000
EIS^2
-0.1791 (0.0040)
0.0000
ANEL
-0.6491 (0.1094)
0.0000
Log-likel.
-2554.651
TEST II: Explaining the use of Compensatory D. Rules
Estimations
Introduction
The Model
The MC
Experiment
Results
Application
Conclusion
Covariates
Coefficient
(s. e.)
p-value
Constant
-0.1623 (0.2455)
0.5084
Income
0.0297 (0.0284)
.2960
Age
0.1489 (0.0375)
0.0875
Gender
0.0392 (0.0421)
0.3514
Education
- 0.0703 (0.0137)
0.0000
EIS
0.5291 (0.1094)
0.0000
EIS^2
-0.1791 (0.0040)
0.0000
ANEL
-0.6491 (0.1094)
0.0000
Log-likel.
-2554.651
RESULT 2:
Educated people are more likely to use non compensatory
decision rules (which raise doubts about the cognitive ability explanation:
Swait and Adamowicz, 2001)
TEST II: Explaining the use of Compensatory D. Rules
Estimations
Introduction
The Model
The MC
Experiment
Results
Application
Conclusion
Covariates
Coefficient
(s. e.)
p-value
Constant
-0.1623 (0.2455)
0.5084
Income
0.0297 (0.0284)
.2960
Age
0.1489 (0.0375)
0.0875
Gender
0.0392 (0.0421)
0.3514
Education
-0.0703 (0.0137)
0.0000
EIS
0.5291 (0.1094)
0.0000
EIS^2
-0.1791 (0.0040)
0.0000
ANEL
-0.6491 (0.1094)
0.0000
Log-likel.
-2554.651
RESULT 3:
Extreme bounds of EIS are less likely to the choice of
compensatory decision rules (related with the evidence that EIS has on
task performance – ”Yerkes-Dodson Law”, 1908)
TEST II: Explaining the use of Compensatory D. Rules
Estimations
Introduction
The Model
The MC
Experiment
Results
Application
Conclusion
Covariates
Coefficient
(s. e.)
p-value
Constant
-0.1623 (0.2455)
0.5084
Income
0.0297 (0.0284)
.2960
Age
0.1489 (0.0375)
0.0875
Gender
0.0392 (0.0421)
0.3514
Education
-0.0703 (0.0137)
0.0000
EIS
0.5291 (0.1094)
0.0000
EIS^2
-0.1791 (0.0040)
0.0000
ANEL
-0.6491 (0.1094)
0.0000
Log-likel.
-2554.651
RESULT 4:
Individuals are more likely to avoid trade-offs when negative
emotional load is high among the task attributes (exploring levels
of trade-offs and ANEL levels)
Table 4. Valuation functions for compensatory and non compensatory heuristics
Compensatory
heuristic
Non-compensatory
heuristics
Pooled
Coefficient
(s. e.)
Coefficient
(s. e.)
Coefficient
(s. e.)
Explosions
0.8084***
(0.1527)
0.3077*
(0.1699)
0.4744***
(0.1068)
Noise
1.1555***
(0.1153)
0.0822
(0.1219)
0.5874***
(0.0747)
Airdust
1.3352***
(0.1354)
Smokes
0.5775***
(0.1137)
0.2987**
(0.1227)
0.2911***
(0.0767)
Odours
1.2385***
(0.1252)
0.4775***
(0.1134)
0.7327***
(0.0752)
Cost
-0.0135***
(0.0022)
-0.0006
(0.0027)
-0.0066***
(0.0016)
Log-likel.
-558.4755
-394.9577
-997.5038
% of
individuals
68
32
100
Covariates
0.5138***
(0.1247)
0.7871***
(0.0825)
Welfare Estimates for compensatory and non compensatory heuristics
Pooled
Compensatory
Non
Compensatory
Attribute
Mean WTP
Mean WTP
Mean WTP
Explosions
71.2448
59.4739
512.83
Noise
88.214
85.0077
137.00
Airdust
118.189
98.2298
856.33
Smokes
43.7179
42.4851
497.83
Odours
110.02
91.1173
795.83
RESULT 5:
The validity of SPM results for guiding public policy is affected
by the proportions of individuals using non compensatory
decision rules. (Therefore affected by the levels of EIS and ANEL)
64
64
Welfare Estimates for compensatory and non compensatory heuristics
Pooled
Compensatory
Non
Compensatory
Attribute
Mean WTP
Mean WTP
Mean WTP
Explosions
71.2448
59.4739
512.83
Noise
88.214
85.0077
137.00
Airdust
118.189
98.2298
256.33
Smokes
43.7179
42.4851
49.83
Odours
110.02
91.1173
195.83
65
65
EXPERIMENT II: EMOTIONS MANIPULATION
Why a 2nd experiment?
-Check results out in a more controlled setting.
-Testing effects of alternative emotional states.
TREATMENTS
Lerner, Small and Loewestein (2004; Psych. Science)
-Sadness
-Disgust
-Neutral
Sample Size
129 Participants randomly assigned to treatments
Overall Experiment
Details
2 unrelated studies with 2 different researchers.
STUDY 1 “imagination study” by a psychologist
STUDY 2 “Externalities Valuation study” by an
economist.
66
66
EXPERIMENT II: EMOTIONS MANIPULATION
PROCEDURE
1. Welcome and Introduction by researcher in Psycho.
2. Signing Consent Form for STUDY 1.
3. Asking EIS questions
4. Watching a film clip (Lerner et al, 2004)
SAD
– “The Champ”
DISGUST – “Trainspotting”
NEUTRAL – “National Geographic”
Sample Size
5. Writing down how they would feel in the clip situation
129 Participants randomly assigned to treatments
6. Collecting materials and going to another room
----------------------------------------------------------------------------
Experiment Details
7. Welcome by the researcher in economics.
8. Signing the Consent form for STUDY 2.
9. Replicating experiment I.
10. Emotion Manipulation check (10 affective states)
11. What do you think is the aim of the study?
12. Subjects get paid (≈15€ for ≈ 45-50 minutes)
67
67
EXPERIMENT II: EMOTIONS MANIPULATION
Figure 3. Self-reported emotion in the three emotion conditions
Z-score self reported Emotion
Disgust
Sad
1.2
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
DISGUST
NEUTRAL
SAD
68
68
EXPERIMENT II: EMOTIONS MANIPULATION
Choice Decision Rules under the Alternative
Emotion Induction
100.00
90.00
80.00
70.00
60.00
50.00
40.00
30.00
20.00
10.00
0.00
Neutral
Sadness
Conpensatory
Disgust
Non-Compensatory
Neutral
Sadness
Disgust
%
%
%
Conpensatory
63.98
58.23
74.17
Non-Compensatory
36.02
41.77
25.83
Decision Rule