Experimental Economics: Short Course Universidad del Desarrollo Santiago, Chile December 16, 2009

1. Experimental Economics: Short CourseUniversidad del DesarrolloSantiago, ChileDecember 16, 2009 Dr. Jonathan E. Alevy Department of Economics University of Alaska Anchorage afja@uaa.alaska.edu

2. Note on Hypothetical vs Salient Payments • Hypothetical responses • usually more noise in data • Poor publication prospects • Recent discussion on Economic Science Association Listserv

3. Economic Science Association: Listserv • Dear colleagues,Is there a classical paper (or at least well-known) paper  that specifically compares people's behavior in experiments where they are not paid for theirchoices and when they are.I googled keywords "hypothetical choice" and similar but somehow all papersthat it shows seem to be, well, too applied.Thank you in advance,Dmitry

4. Partial Response to Dmitry After doing (experimental economics) for several decades, just don't waste time on this issue. I remain astonished to see how many fine researchers still decide to waste time on this, when the evidence is so clear and has been for decades. We really have much more important issues to debate. If you or someone else insists on doing some hypthetical choices, then at least run some checks when you pay for real (and please do not do comical things like pay 1-in-3000, which one recent study did as an alleged  check on hypothetical bias). • Glenn Harrison

5. Holt & Laury, “Risk Aversion and Incentive Effects,” AER 2002

6. Holt & Laury Elicitation Results Hypothetical payments Real payments Visually: a treatment effect! Statistically: How can we be more certain?

7. Statistical Analysis: Overview • Experimental design drives the statistical analysis • What type of data? Binary, ordinal, cardinal? • HL Binary data (choose A or B) • Within or between subjects? • At what level are observations independent? • HL: Dependent across Hypothetical and Real treatments • HL: independent across subjects. (individual choice) • Two approaches: • Historically: Simple nonparametric tests provide insight on treatment effects. • Different tests used for within or between subjects designs • Current practice: Supplement nonparametric tests with conditional (regression) estimates of parameters. • Use demographic or other data to explain results. • Panel data techniques account for dependencies.

8. Statistical Analysis: HL Data • Approach 1: nonparametric statistics • If A choice = 1, B choice = 0. Define variable as sum of choices for individual iin treatmentt • Higher value implies more risk averse. • Wilcoxon test for matched data (within subjects) • Mann-Whitney test for between subjects design • See appendix slides for details or Siegel & Castellan 1988 • Note: HL protocol is used to understand behavior in other experiments (e.g. auction studies) . • Use the risk variable on right side of estimation equation is one way to do this.

9. Statistical Analysis HL Data • Approach 2: Maximum likelihood techniques • Maintain data in original binary form • Estimate probability of A choice given treatment dummy and other control variables. • Probit (or logit) specification • Multiple choices by individuals accounted for in error term (random effects model). • Can impose structure on utility • estimate Coefficient of Relative Risk Aversion and other parameters • See Harrison 2008 Maximum Likelihood in STATA on course webpage • For extensions (includes STATA code).

10. Inferring CRRA • Assume U(y) = y1-r/(1-r) for r ≠ 1 • In this case r=0 is RN, r>0 is RA, and r<0 is RL

11. Summarizing Holt Laury • Holt and Laury • Important contribution to measuring risk attitudes • Menu of choices (with real payments) provides incentive for truthful response. • Relatively easy to understand. • Criticisms • Original study confounds incentive effect by not varying order • Controlling for order, basic result holds • Salient payments important, contra Kahneman & Tversky conjecture. • Large number of applications follow this protocol. • Include extensions to non-expected utility, time preferences, valuation of goods.

12. Alternative Elicitation: BDM • Becker Degroot Marschak • Handout • A “single person auction” • Comparison to HL • Advantages • Single decision • Disadvantage • Cognitively demanding?

13. Something Completely Different

15. Asset Market Experiments • Yesterday we looked at induced value double auction (commodity market) • Smith 1962 • Quickly and reliably goes to competitive equilibrium • Asset market experiment • Smith, Suchanek, and Williams (1988) • Prices diverge from fundamental values • Price bubbles and crashes frequently observed • Why the difference?

16. Why experiment with asset markets? • Core methodological contribution: Able to induce value of the asset • Identification problem in field studies. • What is the fundamental value? • Solution: Create asset with specific payoff attributes and duration • Able to control information • Asset structure is common knowledge • Endowments are private information • Replication • Test robustness of existing findings • Systematically study new treatments

17. Core Experimental Design • Smith, Suchanek and Williams, 1988 • Nine traders in a double auction market • 15 trading periods - ‘days’ • Each trader is endowed with assets and cash • Endowments are private information • Endowments are of equal expected value for all traders • The asset traded has • State contingent dividend = {0, 8, 28, 60} • Equal probability for each state. • Expected value of 24 cents • Dividends that pay at end of each trading day • Traders can bid, offer, buy or sell or do nothing

18. Expected Price Dynamics • Rational Expectations Equilibrium • Price falls by value of expected dividend each period (-24). Tirole (1982)

19. Theory for lab experiment • Rational expectations: Backward induction  no bubbles • No trade if all are risk neutral • Price path follows the red dashes • Tirole (1982) • Rational bubbles – relax rational expectations assumption • Price rises due to: • Lack of common knowledge of bubble • Limits to arbitrage • Risk of crash exists • A coordinating device is needed to induce sales • Abreu & Brunnemeier (2003)

20. Research Question: Bubbles & Experience • Bubbles are observed in markets with new traders • Robust to many alternative treatments • Short-selling, futures markets, dividend certainty, price limits, initial endowments, informed confederates. • What works?  Experience • “…trades fluctuate around fundamental values when the same group returns for a third session.” Porter and Smith (2003 JBF) (emphasis added) • Two new results • Alevy & Price 2008 • Convergence with inexperienced traders who have received advice • Hussam Porter & Smith, 2008 • Convergence is not robust • New fundamentals  bubbles resume.

21. Reduction of bubbles with “experience”

22. Alevy & Price: Experimental Design • Control • Single session of stage game - no advice. • Do we get a bubble with our protocol? • software, subject pool, instructions etc. • Own-experience • Same cohort repeats stage game three times • Intergenerational advice • Three generations - new traders in each

23. Experimental Design:Intergenerational Treatments • Three “generations” of markets • Second and third generation receives advice from immediate predecessor. • Incentive to leave quality advice • Predecessors receive payment tied to successors performance

24. Experimental Design:Intergenerational Treatments • Full advice • All traders receive unique advice from predecessors • Partial advice • Three or six traders receive advice

25. 800 750 700 650 600 550 500 450 400 350 300 250 200 150 800 100 750 50 700 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 650 Third Generation – 9 Advised 600 550 P1G3A9a 500 450 800 400 750 350 700 650 300 600 250 550 200 500 150 450 100 400 350 50 300 0 250 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 800 200 Second Generation – 9 Advised 150 750 100 P1G2A9b 700 50 650 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 600 Third Generation – 9 Advised 550 P1G3A9b 500 450 400 800 750 350 700 300 650 600 250 550 200 500 450 150 400 100 350 300 50 250 0 200 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 800 150 Progenitor 1 100 750 50 700 0 650 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 P1G1A0 Th ird Generation – 3 Advised 600 550 P1G3A3 500 450 400 800 350 750 700 300 650 250 600 200 550 150 500 450 100 400 50 350 0 300 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 250 Second Generation – 9 Advised 200 150 P1G2A9a 100 50 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Third Generation – 6 Advised P1G3A6 Result:Bubble attenuated with advice

26. Result: Bubble Size • Bubble size declining by generation p<.05 • No significant difference between advice and experience

27. Testing the rational expectations model • Dynamic model: price depends on history : average price in session i onday t : number of offers in session i onday t : number of bids in session i onday t • Prediction under rational expectations

28. Result: Price Dynamics Table A.1. Random Effects –Advice Only ** Denotes statistical significance at the p < 0.05 level * Denotes statistical significance at the p < 0.10 level • (Models A and B) Fail to reject Ho: alpha = -24 • (Model B) Fail to reject Ho: betaBO+ beta3Gen*BO= 0  rational expectations

29. Extension: Trading Styles • Fundamentalist • If price > fundamentals, active as a seller • Definition: # offers > # bids when prices are above fundamental value • Momentum Trader • If price > fundamentals, active as a buyer • Definition: # bids > # offers when prices are above fundamental value

30. Advice and Trading Strategy • 75% of advised and 48% of unadvised are fundamentalists. • Qualitative analysis of advice shows • Little stress on fundamentals • Heuristics adopted due to advice move prices towards fundamentals • Advice is ‘sticky’ • In 2nd generation those receiving advice leave advice like their predecessor • Those without advice differ…slightly greater emphasis on fundamentals.

31. Conclusions • Prices converge rapidly to rational expectations equilibrium • A novel finding in the literature • Advice is unsophisticated but effective in changing behavior • Benefits of advice accrue at market level • Reduces variance in earnings • Advised do not earn more

32. Hussam Porter and Smith, 2008 • Achieve convergence in usual manner • Experienced group of traders • After convergence • Change fundamentals, wider distribution of dividends • Bubbles rekindle. • Would advised be more robust? • Think more deeply about the problem when giving or receiving advice. • Perhaps less brittle type of learning

33. Social Preferences • The Dictator “game” • An individual decision task on splitting a surplus with another • Stylized fact across many replications • Give none or give some (often half) two “types” • Selfish & Altruistic

34. Origin of Dictator Game • Dictator game run to better understand ultimatum game results • Ultimatum game (two person) • Player 1: Offers a division of surplus • Player 2: Accept or reject offer • If reject both players receive zero. • Dictator game • Decompose ultimatum game offers • Is a component of ultimatum offer altruistic?

35. Dictator game Ultimatum game Forsythe et al. 1994

36. Examining Robustness of Dictator giving • Innovation: The “Bully” game • Extend the action space to allow giving & taking • List 2007, Bardsley 2008

37. Give Take 1 Take 5 Earn Take 5

38. Bully Game • Behavior inconsistent with “preference based” explanation • Emphasizes importance of institutions in shaping behavior. • Including experimenter demand effects in the laboratory. • Property rights (earned endowment treatment)

39. Appendix: Nonparametric Statistics • From Andreas Lange University of Maryland