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NAREA Workshop 2009 @ Burlington, VT June 10, 2009 Yohei Mitani
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Yohei Mitani Institute of Behavioral Science
University of Colorado, Boulder
Nicholas Flores Department of Economics & IBS University of Colorado, Boulder
A New Explanation of Hypothetical Bias: Subjective Beliefs about
Payment and Provision Uncertainties
NAREA Workshop 2009 @ Burlington, VT June 10, 2009 Yohei Mitani
Hypothetical Bias
• Hypothetical Bias & Induced-values
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Financial Incentives often reduce variance but usually have no effect on mean performance.
Carmerer & Hogarth (1999) J Risk Uncrtain
ExperimentExperiment
$ Actual Payment
QuestionnaireQuestionnaire
$ Hypo. Payment
Hypo. Bias
Control Incentives No IncentivesTrue Value
$
Need to understand the relationship to True Value
Background Model Design Results Implications
NAREA Workshop 2009 @ Burlington, VT June 10, 2009 Yohei Mitani
Previous Findings
• Meta-analysis by Murphy et al. (2005) ERE– Hypo. Payment > Real Payment– Note that these studies compare only b/w
payments, do not compare them to true value.– Values of public goods are unobservable
• Induced-value Test of Hypo. Bias– Induced-value experimental design allows us to
observe/control true value.– No Evidence of Positive Hypo. Bias.
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Background Model Design Results Implications
NAREA Workshop 2009 @ Burlington, VT June 10, 2009 Yohei Mitani
Motivation
• No Systematic Explanation– Underling causes are not sufficiently
understood.– Clarifying the causes is needed for mitigation.
• This Paper Aims– To provide a systematic explanation for the
results of hypo. bias.
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Background Model Design Results Implications
NAREA Workshop 2009 @ Burlington, VT June 10, 2009 Yohei Mitani
Our Contributions• Payment and Provision Uncertainties
– Introducing the probabilities of payment and provision to a threshold public goods game.
• Investigate the Relative Probabilities– Providing a closer look at how the upper bound of a
subject’s contribution changes depending on those probabilities.
• Induced-value Experimental Test– Using a lab exp. design that varies the probabilities of
payment and provision as treatments.
• Finding– Relative probabilities explain the causes of hypo. bias.
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Background Model Design Results Implications
NAREA Workshop 2009 @ Burlington, VT June 10, 2009 Yohei Mitani
Discrete Public Project
• Discrete Public Project– Voluntary contribution for a public project.– A threshold level of total contributions is
required to provide the project.
• Payoffs (PPM)– Provided: Income y – Contribution ci + Value vi
– Not Provided: Income y• A threshold public goods experiment with continuous
contribution, money back guarantee, no rebate and heterogeneous induced-values.
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Background Model Design Results Implications
NAREA Workshop 2009 @ Burlington, VT June 10, 2009 Yohei Mitani
Subjective Beliefs
• Key Economic Issue: Payment & Provision
• Hypothetical Natures in Stated Preference– Payment Uncertainty: whether payment is coercive
– Provision Uncertainty: whether the project is provided
• Subjective Beliefs– Respondents might form their subjective belief
about payment & provision uncertainty when stating their values.
– Decision-makings could be made based on their subjective beliefs.
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Background Model Design Results Implications
NAREA Workshop 2009 @ Burlington, VT June 10, 2009 Yohei Mitani
Probability Space
• Probability Space– Define subjective beliefs as a joint distribution
of payment & provision.
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Four Outcomesnot only: {Pay, Provide}; {Not Pay, Not Provide} but also: {Pay, Not Provide}; {Not Pay, Provide}
Background Model Design Results Implications
NAREA Workshop 2009 @ Burlington, VT June 10, 2009 Yohei Mitani
Model Specification
• Expected Utility (if project passes: Σj cj > PP)
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Risk-neutral Case
{Pay, Pro} {Pay, Not Pro}
{Not Pay, Pro} {Not Pay, Not Pro}
Background Model Design Results Implications
NAREA Workshop 2009 @ Burlington, VT June 10, 2009 Yohei Mitani
Theoretical Predictions
• Upper Bound of a Subject’s Contribution– Option Price (ex ante WTP for project)
• Effect of Subjective Probability
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Risk-neutral case
Background Model Design Results Implications
NAREA Workshop 2009 @ Burlington, VT June 10, 2009 Yohei Mitani
• Under our Experimental Setting– Standard constant relative risk-aversion utility function
Upper Bound Numerical Prediction 11
Risk-neutral (r=0)
PurelyReal
PurelyHypothetical
Effect of ProvisionEffect of
Payment
With Equal Probabilities
Background Model Design Results Implications
NAREA Workshop 2009 @ Burlington, VT June 10, 2009 Yohei Mitani
Experimental Design• Laboratory Designed for Economic Experiments
– Subject Pool: 90 general public individuals
• Induced-values– Induced-value was assigned to each subject.
• Subjects were told the amount varies across individuals but not told the range & the frequency of values.
• Subjects know only their own values.
• Probabilities Pairs (experimental treatments)– A pair of two prob. was assigned to the group.
• Two prob. were common knowledge.
– 19 experiment treatments• 19 pairs were used from combinations of {0, .25, .5, .75, 1}• Within-subjects: Every subject participated in 11or14 choices.
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Background Model Design Results Implications
NAREA Workshop 2009 @ Burlington, VT June 10, 2009 Yohei Mitani
Experimental Design
• Provision Rule– Two-stage Provision Rule was employed.– Stage 1:
• A contribution decision like “how much would you contribute for a public project that provides you a value shown in your value card?” after the probabilities of payment and provision were announced to the group. If total contributions exceed the preannounced threshold, the project passes and Stage 2 comes.
– Stage 2:• A computer decided whether subjects had to pay their
contributions stated in stage 1 and whether subjects could receive their value, depending on the preannounced probability pair.
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Background Model Design Results Implications
NAREA Workshop 2009 @ Burlington, VT June 10, 2009 Yohei Mitani
Aggregate Level Results
• Average Observed Contributions
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Our BenchmarkReal Contribution
Positive Effect on Contributions
Negative Effect on Contributions
Background Model Design Results Implications
NAREA Workshop 2009 @ Burlington, VT June 10, 2009 Yohei Mitani
Individual Level Analysis
• Econometric Analysis
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Significant NegativeEffects
Significant PositiveEffects
Background Model Design Results Implications
NAREA Workshop 2009 @ Burlington, VT June 10, 2009 Yohei Mitani
With Equal Probabilites
• A Case of Ppay = Ppro
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Observations are consistent with contributions made by risk-averse subjects in our theoretical predictions.
Background Model Design Results Implications
NAREA Workshop 2009 @ Burlington, VT June 10, 2009 Yohei Mitani
Explanation of Hypo. Bias
• Positive Hypothetical Bias occurs– if the relative probability satisfies that prob. of
payment is greater than prob. of provision in the hypothetical payment decisions.
• Many previous studies succeed to control whether payment is coercive; whereas, they often fail to control the provision-side uncertainty.
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Real Decision
HypotheticalDecision
Provision-sideUncertainty
Payment-sideUncertainty
Background Model Design Results Implications
NAREA Workshop 2009 @ Burlington, VT June 10, 2009 Yohei Mitani
Explanation of Hypo. Bias
• No Hypothetical Bias occurs– if the relative probability satisfies that prob. of
payment equals prob. of provision in the hypothetical payment decisions.
• Well-controlled experiments like induced-value experiments wherein experimenters could control both payment & provision sides equally.
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Real Decision
HypotheticalDecision
Provision-sideUncertainty
Payment-sideUncertainty
Background Model Design Results Implications
NAREA Workshop 2009 @ Burlington, VT June 10, 2009 Yohei Mitani
Implications for Mitigation
• Ex Ante Mitigation of Hypo. Bias– It will be important to control both payment &
provision sides in the same way.– It should be designed so as not to have the
worst & best outcomes.– Consequentiality is of course critical.
• Ex Post Mitigation of Hypo. Bias– Measuring the subjective probabilities might
allow us to calibrate ex-post hypothetical & real values.
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Background Model Design Results Implications
NAREA Workshop 2009 @ Burlington, VT June 10, 2009 Yohei Mitani
Thank you for your attention.
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Yohei Mitani
Contact Information Email: mitani@colorado.edu Web: http://homepage3.nifty.com/ymitani/
NAREA Workshop 2009 @ Burlington, VT June 10, 2009 Yohei Mitani
• Risk-averse Case (r = 0.9)
Upper Bound Numerical Prediction 21
NAREA Workshop 2009 @ Burlington, VT June 10, 2009 Yohei Mitani
Subjective Beliefs
• Subjective Beliefs– Respondents might
form their subjective belief about payment & provision uncertainty when stating their values.
– Decision-makings could be made based on their subjective beliefs.
• Probability Space– Define subjective
beliefs as a joint distribution of payment & provision.
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Background Model Design Results Implications
NAREA Workshop 2009 @ Burlington, VT June 10, 2009 Yohei Mitani
Experimental Design
• Provision Rule– Two-stage Provision Rule was employed.
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Background Model Design Results Implications