NAREA Workshop Burlington, VT June 10, 2009 Yohei Mitani 1 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 Burlington, VT June 10, 2009 Yohei Mitani Hypothetical Bias Hypothetical Bias & Induced-values 2 Financial Incentives often reduce variance but usually have no effect on mean performance. Carmerer & Hogarth (1999) J Risk Uncrtain Experiment $ Actual Payment Questionnaire $ Hypo. Payment Hypo. Bias Control Incentives No Incentives True Value $ Need to understand the relationship to True Value Background Model Design Results Implications

NAREA Workshop 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. 3 Background Model Design Results Implications

NAREA Workshop 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. 4 Background Model Design Results Implications

NAREA Workshop 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. 5 Background Model Design Results Implications

NAREA Workshop Burlington, VT June 10, 2009 Yohei Mitani 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 c i + Value v i –Not Provided: Income y A threshold public goods experiment with continuous contribution, money back guarantee, no rebate and heterogeneous induced-values. 6 Background Model Design Results Implications

NAREA Workshop 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. 7 Background Model Design Results Implications

NAREA Workshop Burlington, VT June 10, 2009 Yohei Mitani Probability Space –Define subjective beliefs as a joint distribution of payment & provision. 8 Four Outcomes not only : {Pay, Provide}; {Not Pay, Not Provide} but also : {Pay, Not Provide}; {Not Pay, Provide} Background Model Design Results Implications

NAREA Workshop Burlington, VT June 10, 2009 Yohei Mitani Model Specification Expected Utility (if project passes: Σ j c j > PP ) 9 Risk-neutral Case {Pay, Pro}{Pay, Not Pro} {Not Pay, Pro}{Not Pay, Not Pro} Background Model Design Results Implications

NAREA Workshop 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 10 Risk-neutral case Background Model Design Results Implications

NAREA Workshop 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 ) Purely Real Purely Hypothetical Effect of Provision Effect of Payment With Equal Probabilities Background Model Design Results Implications

NAREA Workshop 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. 12 Background Model Design Results Implications

NAREA Workshop 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. 13 Background Model Design Results Implications

NAREA Workshop Burlington, VT June 10, 2009 Yohei Mitani Aggregate Level Results Average Observed Contributions 14 Our Benchmark Real Contribution Positive Effect on Contributions Negative Effect on Contributions Background Model Design Results Implications

NAREA Workshop Burlington, VT June 10, 2009 Yohei Mitani Individual Level Analysis Econometric Analysis 15 Significant Negative Effects Significant Positive Effects Background Model Design Results Implications

NAREA Workshop Burlington, VT June 10, 2009 Yohei Mitani With Equal Probabilites A Case of Ppay = Ppro 16 Observations are consistent with contributions made by risk-averse subjects in our theoretical predictions. Background Model Design Results Implications

NAREA Workshop 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. 17 Real Decision Hypothetical Decision Provision-side Uncertainty Payment-side Uncertainty Background Model Design Results Implications

NAREA Workshop 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. 18 Real Decision Hypothetical Decision Provision-side Uncertainty Payment-side Uncertainty Background Model Design Results Implications

NAREA Workshop 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. 19 Background Model Design Results Implications

NAREA Workshop Burlington, VT June 10, 2009 Yohei Mitani Thank you for your attention. 20 Yohei Mitani Contact Information Web:

NAREA Workshop Burlington, VT June 10, 2009 Yohei Mitani Risk-averse Case ( r = 0.9 ) Upper Bound Numerical Prediction 21

NAREA Workshop Burlington, VT June 10, 2009 Yohei Mitani 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. 22 Background Model Design Results Implications

NAREA Workshop Burlington, VT June 10, 2009 Yohei Mitani Experimental Design Provision Rule –Two-stage Provision Rule was employed. 23 Background Model Design Results Implications