Experimental Economics: Short Course
Universidad del Desarrollo
Santiago, Chile
December 15-17, 2009
Dr. Jonathan E. Alevy
Department of Economics
University of Alaska Anchorage
afja@uaa.alaska.edu
Handbook of Experimental Economics:
Table of Contents
Handbook 1995
New in 2010
Public Goods
Industrial Organization
Auctions
Coordination Problems
Experimental Asset Markets
Bargaining Experiments
Individual Decision Making
Social preferences
Neuroeconomics
Political economy
Gender, discrimination, and
culture
Learning
Field Experiments
Market Design
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
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
Let’s do it
• Go to
• http://veconlab.econ.virginia.edu/login.htm
• Join session apr1
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
Course Outline
1. 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
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.
Precepts for induced values
1. 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
participation
4. Privacy
•
information only about own payoff
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
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
Y  f  X1,..., X n 
– Example: Let Y equal market efficiency, X 1
information condition
– Randomization and design choices  X 2 ,..., X n
held constant
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
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
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
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
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
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
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
Holt & Laury Elicitation Results
Hypothetical payments
Real payments
Critique HL Treatment Design
• Holt Laury protocol, within subjects
• Treatment
Elicitation Protocol
T1
First
Second
Third
Real 1
Hypo 20
Real 20
• Harrison et al. critique. Scale is correlated with order.
– Requires between subjects design (T1 and T2)
• Treatment
T1
T2
Elicitation Protocol
First
Second
Real 1
Real 10
Real 10
Harrision et al. result: order matters
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.
Alternative Elicitation
• BDM: See handout
• 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