Can we use RUM and don’ get DRUNK? J orge E. Araña University of Las Palmas de Gran Canaria

11

Can we use RUM anddon’ get DRUNK?

Jorge E. ArañaUniversity of Las Palmas de Gran Canaria

Collaborators: Carmelo J. León (ULPGC), W. Michael Hanemann (UC Berkeley)

FF8 FortnightAnalysis of Discrete Choice

DataSheffield, September of 2007

22

1. RUM and DC experiments2. Sources of mistakes in Citizens choices3. An Extended Frame: Bayesian Modelling4. 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

• Discussion and Further Research

Outline

33

Valuation of Health = appropriate methodsValuation of Health = appropriate methods

DCE are increasingly used and accepted.

DecisionMakingProcess

IndividualPreferences

CoherentResults forCBA or CEA

DCE and Non-Market Valuation

44

The Underlying Economic Theory The Underlying Economic Theory • Morishima (METRO,59) – Value from characteristics Lancaster (JPE, 66)

1. MEASURING PREFERENCES: (defining P) i) Experienced vs Choice Utility

ii) Absolute vs. relative utility (prospect theory)iii) …

2. LINK CHOICES AND PREFERENCES: f (.)

PfB

THE TWO MAIN ISSUESTHE TWO MAIN ISSUES

B= observed/stated choices

P= Preferences (Fundamental Value)

E= Random term (Context)

The Departing PointThe Departing Point

From the Economic theory point of view

• Lancaster (1966) – Value for characteristics

2. LINK CHOICES AND PREFERENCES: f (.)

,PfB

MAIN ISSUESMAIN 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 …

66

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”.

General

Simple

Intuitive

General

Simple

Intuitive

An accurate explanation of agents choices in a wide range of situations

An accurate explanation of agents choices in a wide range of situations

*** )( )( iff choose iiiViVi βxV(i) i where

How can we link Choices and Preferences? f (.)

77

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,…

88

However… 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;…)

99

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.

1010

Why not Bayesian?

One Elegant and Robust way of integrating Multidisciplinary contributions to DC Theory and Data Analysis: Bayesian Econometrics

1111

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?,…)

1212

EXAMPLE: Heterogeneous Decision Rules and DC

1313

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).

1414

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

Literature on Heuristics

1515

Choosing How to Choose (CHTC)

TWO STEP PROCESS

STEP 1. Choosing How to Choose. (Choice of the D.

Rule)

STEP 2. Applying the Decision Rule.

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)

**** )()EU( )()EU( iff DDDECDDECDD jjj

***** )()( iff iiiDiDi

1616

The Theoretical Model

11 ,...kj,...n; iXV ijij

For a well-behaved preference map, a general indirect utility functionof individual i, given an alternative j:

if the individual faces a multi attribute discrete choice problem, the researcher will observe that individual i chooses alternative j* if,

* *** jj XVXV ijijijij such that 1(.) ijI

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 jlVV lj

M2: Complete Ignorance jlVV lj and m =0 m

M3: Conjunctive Rule jl VV lj such that

M

m imijm γXI1

1,

M4: Satisfaction Rule jl VV lj such that

M

m imijm γXI1

1, and m =0 m

1818

Non regularityNon regularity

Problem 1:The likelihood surface for a heuristic is discontinuous, andtherefore, the global concavity can not be guaranteed.

Solution: Rewriting the probability as the product of a second step of thechoice process and a marginal heuristic probability. That is,

hhYhY ijij Prob|1Prob,1Prob

.

By adding the likelihood functions over the different decision rules,resulting in a globally concave likelihood surface,

H

hijij hhYY

1

Prob|1Prob1Prob

f(.) is a mixture distribution

1919

Evaluate an Intractable FunctionEvaluate an Intractable Function

.

From Bayes’ theorem,

|| YLY

Problem 2: The posterior distribution is intractable and difficult to evaluate

Solution:Here we deal with that complication by employing MCMC methodsas is proposed in discrete choice by Albert and Chib (1993) by combining…

GS Algorithm (Geman and Geman, 1984)

DA Technique (Tanner and Wong, 1987)

2020

Prior DistributionsPrior Distributionsi ~ MVN(

,i )

with ~ MVN(0, 109I)

~ IW(v, )

im ~ Multinomial (H, m1, É .., mD) for discrete attributes (D number of discrete attr.)

. with m ~ Dicrichlet ( ) for m=1,2,É D

im ~ N( , ) for continuously scaled attribute (e.g. cost).

with ~ N(0, 109)

~ IG(a,b)

where D is the number of discrete attributes, IW is the inverted Wishart distribution,

v=k+8, and = I. m is a vector of dimension ŌnÕ where 'n' indexes the possible values

for m, and is a conforming vector with each element set equal to 6. IG is the inverted

gamma of parameters a=10, b=10-1.

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MCMC AlgorithmMCMC Algorithm

Model 1. Linear Compensatory rule

i) ijWTP 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)

2222


Model 3. Elimination by aspects


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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Model 4. Satisfaction Rule


ii) im from equation (A2.7)

iii) from equation (A2.5)

iv) m from equation (A2.8)

v) from equation (A2.9)

vi) from equation (A2.10)

2424

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

STUDY 1: The DataSTUDY 1: The Data

Programmes

Sample Size

link

550 Individuals

Survey Design

Survey Process

(From Jun-2004

To Ap-2005)

- 2 Focus Groups

- 3 Pre-Test Questionnaires

- Final Questionnaire

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.

• D-optimal design method (Huber & Zwerina,96)

• Elicitation Technique: Choice Experiment

• Scenario were successfully tested in prior research

Testing Complexity effects on CHTCTesting Complexity effects on CHTC

TWO SPLIT SAMPLES SAMPLE I

2 pairs of alternatives + status quo

SAMPLE II

4 pairs of alternatives + status quo

Individuals emotional intensity Scale (EIS)

MEASURING EMOTIONS - Content (what we remember)

- Process (how we reason)

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)

Testing Emotional load effects on CHTCTesting Emotional load effects on CHTC

The ModelThe Model

IntroductionIntroduction

The MC The MC ExperimentExperiment

ResultsResults

ApplicationApplication

ConclusionConclusion

Results & Discussion Results & Discussion

The ModelThe Model



ResultsResults



TEST I: COMPLEXITY AND VALUATION RESULTS

Table 3. Welfare Estimation Results for M1 (€)Table 3. Welfare Estimation Results for M1 (€)

Programs 2 alter. + SQ 4 alter. + SQ

DRUGS43.45

(32.45, 54.44)

38.34

(31.65, 45.02)

DAY CARE19.51

(11.02, 27.99)

9.54

(3.24, 15.83)

HOSPITAL51.28

(39.10, 63.45)

67.88

(61.56, 74.19)

RESULT 1:RESULT 1: Complexity seems to affects absolute values of WelfareEstimations, BUT DO NOT affect programs ranking.

The ModelThe Model



ResultsResults



TEST I: COMPLEXITY AND VALUATION RESULTS

Table 3. Welfare Estimation Results for M1 (€)Table 3. Welfare Estimation Results for M1 (€)

Programs 2 alter. + SQ 4 alter. + SQ

DRUGS43.45

(32.45, 54.44)

38.34

(31.65, 45.02)

DAY CARE19.51

(15.52, 24.49)

9.54

(4.24, 14.83)

HOSPITAL51.28

(39.10, 63.45)

67.88

(61.56, 74.19)

RESULT 2: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).

The ModelThe Model



ResultsResults



TEST II: Complexity and Choosing how to Choose

Decision Rule 2 alter + SQ 4 alter + SQ

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

RESULT 3:RESULT 3: The proportion of people responding in a totally randomway is low.

The ModelThe Model



ResultsResults





Full Compensatory

44.36 28.33

Complete Ignorance

6.21 11.19

EBA (Conjunctive)

31.13 36.11



RESULT 4:RESULT 4: Deviations from M1 are extended in the sample (55%),although M1 has the larger proportion.

The ModelThe Model



ResultsResults





Full Compensatory

44.36 28.33

Complete Ignorance

6.21 11.19

EBA (Conjunctive)

31.13 36.11



RESULT 5:RESULT 5: Complexity does increase the likelihood that Individuals follow non compensatory decision rules.

The ModelThe Model



ResultsResults



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

RESULT 6:RESULT 6: Emotional Sensitivity does affect the use of Alternative decision rules

TEST III: Emotional Intensity and Choosing how to choose

Table 5. Individuals assigned to non-compensatory rulesTable 5. Individuals assigned to non-compensatory rules

According to the degree of EIS (%)According to the degree of EIS (%)

The ModelThe Model



ResultsResults



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

TEST III: Emotional Intensity and Choosing how to choose

Table 5. Individuals assigned to non-compensatory rulesTable 5. Individuals assigned to non-compensatory rules

According to the degree of EIS (%)According to the degree of EIS (%)

RESULT 7:RESULT 7: Extreme EIS (high or low) induces a larger departurefrom M1 than average EIS.

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Summary of ResultsSummary of Results

Shows that Decision Rules are different in Shows that Decision Rules are different in Choice and in Ranking. When we take responses Choice and in Ranking. When we take responses to ranking that are worse than status quo out of to ranking that are worse than status quo out of the sample, decision rules and mean WTP are the sample, decision rules and mean WTP are very similar (although variances are lower in RK very similar (although variances are lower in RK since it uses more information)since it uses more information)

STUDY 3: RK-Choice Preference Reversals

The DataThe Data

Population

Sample Size

Gran Canaria Island Population

540 Individuals

Survey Design •D-optimal design method (Huber and Zwerina, 1996).

•Elicitation Techniques: Choice and Ranking.

•Scenario (verbal and photos) were tested in prior research..

Survey Proccess

(14 months in total)- 3 Focus Group

- Pre-Test Questionnaire

- 1 Focus Group


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”.

ResultsResultsTable 3. Welfare Estimations from M1(RUM) for Choice and Ranking

E[WTP] Choice Ranking

PATHS 44.29

[42.11, 46.47]

21.43

[19.29, 23.57]

BOTGARDEN 49.60

[47.31,51.89]

25.26

[23.12, 27.39]

SUSTPARK 38.56

[36.40, 40.72]

34.93

[32.84, 37.02]

PAINT 74.33

[71.94,76.71]

35.74

[35.59, 37.89]

CAGES 8.36

[6.02,10.69]

18.13

[16.00, 20.26]

RURALANDS 56.75

[54.34, 59.17]

41.07

[38.89, 32.26]

ENDFORESTS 72,07

[71.61, 72.53]

39.52

[36.98, 42.06]

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Choice Ranking Ranking

(better)

Ranking

(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 %

Table 4. Proportion of individuals assigned to each decision rule in each model

4141


(better)

Ranking

(worse)







4242


(better)

Ranking

(worse)







4343

ResultsResultsTable 5. Welfare Estimations from Aggregated Model forChoice and Ranking

E[WTP] Choice Ranking

PATHS 49.17

[47.28, 51.05]

48.17

[44.91, 51.43]

BOTGARDEN 53.83

[51.88, 55.78]

48.25

[44.16, 52.53]

SUSTPARK 42.00

[40.13, 43.88]

38.08

[35.65, 40.51]

PAINT 83.63

[81.64, 85.62]

64.20

[57.24, 75.15]

CAGES 3.62

[1.64, 5.59]

7.79

[4.13, 8.41]

RURALANDS 61.45

[59.44, 63.46]

61.08

[38.80, 82.99]

ENDFORESTS 83.46

[81.44, 85.49]

71.90

[60.14, 83.65]

4444

ConclusionsConclusions

In this application, the EBA is the most predominantIn this application, the EBA is the most predominant

heuristic (over the FLC)heuristic (over the FLC)

A small % of subjects follows the A small % of subjects follows the Completely Random Heuristic.Completely Random Heuristic.

Heuristics Heterogeneity is different Heuristics Heterogeneity is different between Choice and Ranking (in particular between Choice and Ranking (in particular between RK below SQ).between RK below SQ).

When the Heuristics Heterogeneity is incorporated inWhen the Heuristics Heterogeneity is incorporated in

the model the gap between Choice and Ranking isthe model the gap between Choice and Ranking is

drastically reduced.drastically reduced.

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GENERAL DISCUSSION AN FURTHER RESEARCHGENERAL DISCUSSION AN FURTHER RESEARCH

1.1. The model seems to do a good job detecting people The model seems to do a good job detecting people that use these heuristics (average efficiency 85% MC that use these heuristics (average efficiency 85% MC study)study)

2.2. It can be used as a test to further explore the validity It can be used as a test to further explore the validity of a specific DCE are good enough to be used in of a specific DCE are good enough to be used in PUBLIC POLICY (friendly code will be available very PUBLIC POLICY (friendly code will be available very soon).soon).

3.3. Results from these studies can also help to decide Results from these studies can also help to decide several aspects of the DCE design: number of several aspects of the DCE design: number of attributes, levels,…)attributes, levels,…)

4.4. First further research would be to use this information First further research would be to use this information in the DCE design using a Bayesian approach so we in the DCE design using a Bayesian approach so we can improve the accuracy of the results (respondent can improve the accuracy of the results (respondent eficiency vs statistical efficiency).eficiency vs statistical efficiency).

5.5. Results also have implications for Benefit Transfer. It Results also have implications for Benefit Transfer. It is possible to reduce the cost of these studies by is possible to reduce the cost of these studies by transferring results from previous studies to new transferring results from previous studies to new ones. The Bayesian framework seem to be the most ones. The Bayesian framework seem to be the most adequate approach to do so. adequate approach to do so.

4646

Thanks !!!!!

4747

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.

4848


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.

4949


Average efficiency: 85%

Notes: No prior info and no respondent efficient design have been applied

Table R1. Proportions of individuals correctly assigned to their decision rule by using the Bayesian Mixture Model

Condition A

2 options + SQ Condition B

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 %

5050

• A conventional Conditional Logit model and a A conventional Conditional Logit model and a Hierarchical Bayes Model are estimated in 900 Hierarchical Bayes Model are estimated in 900 samples following same idea that study 2.samples following same idea that study 2.

• Samples differ in terms of the % of citizens Samples differ in terms of the % of citizens following each decision rule (e.g. 10, 20, 30, 40, following each decision rule (e.g. 10, 20, 30, 40, 50, 60, 70, 80, 90%).50, 60, 70, 80, 90%).

STUDY 4: Monte Carlo Study. People follow alternative heuristics…. So what are the consequences?

5151


0

5

10

15

20

25

30

35

40Bias E(WTP)

10% 30% 50% 70%% people following Heuristics

EBACISat

5252


•It is found that for the most predominant heuristics (EBA, Satisficing),It is found that for the most predominant heuristics (EBA, Satisficing),

the % of individuals that would generate a significant bias in welfarethe % of individuals that would generate a significant bias in welfare

results (10%) is 70% or higher (what is unusual in practice).results (10%) is 70% or higher (what is unusual in practice).

•However, a 20 % of people following the COMPLETELY IGNORANCEHowever, a 20 % of people following the COMPLETELY IGNORANCE

heuristic is enough to seriously bias the results.heuristic is enough to seriously bias the results.

•When we use a Hierarchical Bayes Model, we get smaller bias for any When we use a Hierarchical Bayes Model, we get smaller bias for any

% of people following alternative heuristics. % of people following alternative heuristics.

STUDY 5:EXP. I: Valuation of ExternalitiesSTUDY 5:EXP. I: Valuation of Externalities

Population

Sample Size

8000 individuals (total surrounding population)

288 Individuals (very familiar with the externalities)

Survey Design

Survey Process- 2 Focus Groups

- 2 Pre-Test Questionnaires


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).

• D-optimal design method (Huber & Zwerina,96)

• Elicitation Technique: Choice Experiment

• Scenario (verbal and photos) where tested in prior

research

EXPERIMENT I: Valuation of ExternalitiesEXPERIMENT I: Valuation of Externalities

Responses were recorded, transcribed and

evaluated by 2 judges who where unaware of our

hypotheses. (3rd judge for disagreements)

Evaluation Process

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”.

MEASURING HEURISTICS Verbal Protocol (Ericsson and Simon, 1980)

-DCCV (Hanemann, 92, Schkade and Payne, 93)


Individuals emotional intensity Scale (EIS)

MEASURING EMOTIONS - Content (what we remember)

- Process (how we reason)

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)


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)

The ModelThe Model



ResultsResults



ResultsResults

TEST I: Effects of the Verbal Protocol approach

Swait and Louviere (1993) Swait and Louviere (1993)

EQUAL PARAMETER TEST:

-2 [312.8172-148.5683-160.4279] = 7.642 X8 .

EQUAL SCALE TEST:

-2 [312.5553 - 312.8172] = 0.5238 X1

RESULT 1:RESULT 1: The use of verbal protocol in this context seems thatwould not affect individuals’ behaviour.

The ModelThe Model



ResultsResults



TEST II: Explaining the use of Compensatory D. Rules

Table 3. Results of the Probit modelTable 3. Results of the Probit model

Covariates

Estimations

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

The ModelThe Model



ResultsResults




Covariates

Estimations

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


RESULT 2: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)

The ModelThe Model



ResultsResults




Covariates

Estimations

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


RESULT 3: 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)

The ModelThe Model



ResultsResults




Covariates

Estimations

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


RESULT 4: 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

Covariates

Compensatory heuristic

Non-compensatory heuristics

Pooled

Coefficient(s. e.)

Coefficient(s. e.)

Coefficient(s. e.)

Explosions0.8084***

(0.1527)0.3077*

(0.1699)0.4744***

(0.1068)

Noise1.1555***

(0.1153)0.0822

(0.1219)0.5874***

(0.0747)

Airdust1.3352***

(0.1354)0.5138*** (0.1247)

0.7871***

(0.0825)

Smokes0.5775***

(0.1137) 0.2987**

(0.1227)0.2911***

(0.0767)

Odours1.2385***

(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

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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: 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)

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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

6666

EXPERIMENT II: EMOTIONS MANIPULATIONEXPERIMENT II: EMOTIONS MANIPULATION

TREATMENTS

Sample Size

Lerner, Small and Loewestein (2004; Psych. Science)

-Sadness

-Disgust

-Neutral

129 Participants randomly assigned to treatments

Overall Experiment Details

Why a 2nd experiment? -Check results out in a more controlled setting.

-Testing effects of alternative emotional states.

2 unrelated studies with 2 different researchers.

STUDY 1 “imagination study” by a psychologist

STUDY 2 “Externalities Valuation study” by an

economist.

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Sample Size 129 Participants randomly assigned to treatments

Experiment Details

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”

5. Writing down how they would feel in the clip situation

6. Collecting materials and going to another room

----------------------------------------------------------------------------

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)


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Figure 3. Self-reported emotion in the three emotion conditions

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Z-s

core

sel

f re

po

rted

Em

oti

on

DISGUST NEUTRAL SAD

Disgust Sad



0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

Neutral Sadness Disgust

Choice Decision Rules under the Alternative Emotion Induction

Conpensatory Non-Compensatory

Decision Rule Neutral Sadness Disgust

% % %

Conpensatory 63.98 58.23 74.17

Non-Compensatory 36.02 41.77 25.83

Can we use RUM and don’ get DRUNK? J orge E. Araña University of Las Palmas de Gran Canaria

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