Dr. Jonathan E. Alevy Department of Economics University of Alaska Anchorage afja@uaa.alaska

1. Experimental Economics: Short CourseUniversidad del DesarrolloSantiago, ChileDecember 15-17, 2009 Dr. Jonathan E. Alevy Department of Economics University of Alaska Anchorage afja@uaa.alaska.edu

2. Handbook of Experimental Economics: Table of Contents Handbook 1995 New in 2010 Social preferences Neuroeconomics Political economy Gender, discrimination, and culture Learning Field Experiments Market Design Public Goods Industrial Organization Auctions Coordination Problems Experimental Asset Markets Bargaining Experiments Individual Decision Making

3. 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

4. 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

5. Let’s do it • Go to • http://veconlab.econ.virginia.edu/login.htm • Join session apr1

6. 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

7. Course Outline • Methods and Methodology • Controlling and/or measuring preferences • Treatment design & analysis • Lab & field experiments • Substantive areas • Individual choice • Auction & Asset Markets • Entrepreneurship • Purposes • Testing theory, looking for facts, policy • Resources for experimentalists • Research • Teaching

8. 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.

9. Precepts for induced values • Non-satiation • more of the reward is better • Salience • payoff depends on actions • difference between alternatives are significant • Dominance • rewards dominate any subjective costs of participation • Privacy • information only about own payoff

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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

18. 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

19. Holt & Laury Elicitation Results Real payments Hypothetical payments

20. 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 10 T2 Real 10

21. Harrision et al. result: order matters

22. 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.

23. Alternative Elicitation • BDM: See handout

24. 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