dr. jonathan e. alevy department of economics university of alaska anchorage [email protected]
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Experimental Economics: Short Course Universidad del Desarrollo Santiago, Chile December 15-17, 2009. Dr. Jonathan E. Alevy Department of Economics University of Alaska Anchorage [email protected]. Handbook of Experimental Economics: Table of Contents. Handbook 1995. New in 2010. - PowerPoint PPT PresentationTRANSCRIPT
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Experimental Economics: Short CourseUniversidad del Desarrollo
Santiago, ChileDecember 15-17, 2009
Dr. Jonathan E. AlevyDepartment of Economics
University of Alaska [email protected]
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Handbook of Experimental Economics: Table of Contents
Handbook 1995
Public GoodsIndustrial OrganizationAuctionsCoordination ProblemsExperimental Asset MarketsBargaining ExperimentsIndividual Decision Making
New in 2010
Social preferencesNeuroeconomics Political economyGender, discrimination, and
culture LearningField ExperimentsMarket Design
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Why Experiment?• Experimental economics has been the
protagonist of one of the most stunning methodological revolutions in the history of science.– Francesco Guala, New Palgrave Dictionary of Economics
• Core of the methodological advance– Making the unobservable (latent variables) observable
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Example: Inducing Supply and Demand
• The study of…suitably motivated individuals in laboratory settings has important application to the … verification of theories of the economic system– Vernon Smith, 1976, AER
• Application of induced values to supply and demand Vernon Smith, Nobel prize 2002
• “Just do it” – Vernon Smith
Rasmuson Chair Emeritus University of Alaska Anchorage
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Let’s do it
• Go to • http://veconlab.econ.virginia.edu/login.htm• Join session apr1
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Double Auction Results
• Contrast to textbook treatment– Competitive market assumptions not met• Small number of buyers & sellers• Price makers• Limited information
– Teaching and research tool
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Course Outline1. Methods and Methodology– Controlling and/or measuring preferences – Treatment design & analysis– Lab & field experiments
2. Substantive areas– Individual choice– Auction & Asset Markets– Entrepreneurship
3. Purposes– Testing theory, looking for facts, policy
4. Resources for experimentalists– Research – Teaching
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Methods and Methodology I: Fundamentals
• Treatment and Control– Comparison allows identification of causal effect• Comparison either to theory or baseline experiment
• What motivates behavior? – “homegrown values” subjects bring to experiment• May need to measure • Relevant in lab and field settings
– Induced values: created by researcher • For example, the value of a fictitious good. • Researcher knows the value for each subject.
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Precepts for induced values1. Non-satiation • more of the reward is better
2. Salience • payoff depends on actions• difference between alternatives are significant
3. Dominance • rewards dominate any subjective costs of
participation4. Privacy • information only about own payoff
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Conclusion on Induced values• Compensation can be a treatment variable– Real versus hypothetical payments
• However: Standard practice for publication– Pay your subjects!
• Payment differences must depend on behavior– Differences large enough to focus attention
• Amount must exceed opportunity cost of time – At least “in expectation”
• Economists view contrasts with some psychologists– More evidence on this below
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Methods and Methodology II
• Randomization of subjects to treatment & role– Equalize distribution of observable &
unobservable characteristics across treatments• Fundamental to valid statistical inference
– All causes model
– Example: Let equal market efficiency, information condition
– Randomization and design choices held constant
nXXfY ,...,1
1XY
nXX ,...,2
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Methods and Methodology III
• Replication– Support or dispute previous results– Extend previous results
• Knowledge accumulates– A strength of laboratory experiments
• Literatures we will examine– Risk elicitation – Asset markets
• Can be a challenge for field experiments– But extremely important contributions
» Especially in combination & contrast with lab results
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Methods and Methodology IV:Experimental Design Choices
• Within vs. Between subject design– Within design has subjects participating in more than one
treatment. • Confound treatment effect with learning.
– Between subject design has subjects participating in only one treatment. • Clean comparison
• Other issues– No deception!!
• Loss of control.• Contamination of subject pool.• Unable to publish
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Control: Elicitation of homegrown values
• Elicitation– What’s in there?• Risk attitudes, time preferences, belief, valuation (WTP
& WTA)
– Psychologists question preference stability and other aspects of economic rationality• Anchoring, preference reversals
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Summary: Clean design
• What practices reduce (not eliminate), so that we can plausibly say we have controlled environment– Randomization to treatment – Clear instructions– Control for experience & order effects– Ceteris Paribus: Change one thing only
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Risk Elicitation• “Risk attitudes are confounding unobservables that
have remained latent in a wide range of experiments.”– Cox and Harrison, 2008– E.g. auction theory for risk neutral bidders, but bidders risk
preferences are unknown.
• Risk Elicitation methods– Multiple Price List (MPL)
• Holt & Laury 2002 – BDM
• Becker DeGroot & Marschak 1963– Tradeoff method
• Wakker & Deneffe, 1996
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Let’s do it
• Google veconlab & find participant login• http://veconlab.econ.virginia.edu/login.htm• Group is split across two sessions, jev3 and
jev4– If your participant number is odd • join jev3
– If your particpant number is even• join jev4
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Risk Elicitation• Risk Aversion & Incentive Effects– Holt Laury, AER, 2002
• Research Question– Impact of hypothetical vs. salient payments on risk
attitudes– Tversky & Kahneman: hypothetical payments are
ok.• People know how they would behave in actual situations• Have no reason to disguise their true preferences
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Holt & Laury Elicitation Results
Hypothetical payments Real payments
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Critique HL Treatment Design• Holt Laury protocol, within subjects• Treatment Elicitation Protocol
First Second Third
T1 Real 1 Hypo 20 Real 20
• Harrison et al. critique. Scale is correlated with order. – Requires between subjects design (T1 and T2)
• Treatment Elicitation Protocol First Second
T1 Real 1 Real 10T2 Real 10
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Harrision et al. result: order matters
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Importance
• Holt and Laury– Confound order & scale effect– Result: Overstate the importance of scale
• Stastical note: – Harrison et al. use ordered probit• Choices are naturally ordered (1-10)
– However, choices are not independent (within subjects)• Use error components model to control for repeated
choices.
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Alternative Elicitation
• BDM: See handout
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• Resources– Working paper listserv distributed by Dan Houser – Software cites– Teaching materials– Charlie Holt’s webpage to run experiments and
get impression of different instructions: http://veconlab.econ.virginia.edu/admin.htm