Experimental Economics: Short Course Universidad del Desarrollo Santiago, Chile December 16, 2009 Dr. Jonathan E. Alevy Department of Economics University of Alaska Anchorage

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

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 their choices and when they are. I googled keywords "hypothetical choice" and similar but somehow all papers that it shows seem to be, well, too applied. Thank you in advance, Dmitry

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

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

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

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.

Statistical Analysis: HL Data Approach 1: nonparametric statistics – If A choice = 1, B choice = 0. Define variable as sum of choices for individual i in treatment t – 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.

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).

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

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.

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

Something Completely Different

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.

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 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 on day t : number of offers in session i on day t : number of bids in session i on day t Prediction under rational expectations

28 Result: Price Dynamics Table A.1. Random Effects –Advice Only (Models A and B) Fail to reject Ho: alpha = -24 (Model B) Fail to reject Ho: beta BO + beta 3Gen*BO = 0  rational expectations ** Denotes statistical significance at the p < 0.05 level * Denotes statistical significance at the p < 0.10 level

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 2 nd 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

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

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

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?

Dictator gameUltimatum game Forsythe et al. 1994

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

GiveTake 1 Take 5 Take 5 Earn

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)

Appendix: Nonparametric Statistics From Andreas Lange University of Maryland