Experimental Economics: Short CourseUniversidad del Desarrollo

Santiago, ChileDecember 15-17, 2009

Dr. Jonathan E. AlevyDepartment of Economics

University of Alaska Anchorageafja@uaa.alaska.edu

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

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 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

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 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

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

– Example: Let equal market efficiency, information condition

– Randomization and design choices held constant

nXXfY ,...,1

1XY

nXX ,...,2

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

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

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