1. Extracting Valuable Data from Classroom Trading Pits Extracting Valuable Data from Classroom Trading Pits Ted Bergstrom & Eugene Kwok University of California, Santa Barbara

2. The Origin of Experimental Economics The first scientific experiments in economics were classroom market experiments by Edward Chamberlin at Harvard in 1940’s.

3. Chamberlin’s experiments Assigned Buyer Values and Seller Costs. Let students mill around and trade. Recorded prices. Remarked on difference from competitive equilibrium outcome. Observed excess trading.

4. Revival at Purdue Chamberlin’s experiments went almost unnoticed until Vernon Smith revisited them in his classroom at Purdue.

5. Smith’s experiments Gave competition a better chance. Two main differences from Chamberlin. –Double oral auction, not pit trading –Ran 3-5 rounds, repeating same setup Found outcomes very close to competitive equilibrium

6. Founders of Experimental Economics Edward Chamberlin Vernon Smith

7. Our Data Classroom experiments from Experiments with Economic Principles, a principles text by Bergstrom and Miller Experiments conducted in 31 classrooms, 10 universities.

8. The Apple Market Students assigned roles as apple suppliers or apple demanders. Suppliers supply at most 1 bushel. Demanders demand at most 1 bushel.

9. Buyer Values and Seller Costs Two types of demanders –High Value—Buyer Value is $40 –Low Value—Buyer Value is $20 Two types of suppliers –High Cost—Seller Cost is $30 –Low Cost—Seller Cost is $10

10. Session 1 2/3 of Sellers have low cost, 1/3 high. 2/3 of Demanders have low value, 1/3 high.

11. Demand and Supply in Session 1

12. Session 2 2/3 of Sellers have high cost, 1/3 low. 2/3 of Demanders have high value, 1/3 low.

13. Demand and Supply in Session 2

14. Session 1: Distribution of Average Prices

15. Session 2: Distribution of Average Prices

16. Session 1: Distribution of Quantity Deviations

17. Session 2: Distribution of Quantity Deviations

18. Enough to convince crudulous students, maybe… But does the evidence show that competitive theory is empirically useful?

19. An alternative hypothesis: Profit Splitting Demanders meet suppliers chosen at random. If mutually profitable trade is available they trade, splitting the profits. –Demander with value $40 and supplier with cost $30 trade at $35, etc. –There is trading at $15, $25, and $40. If high cost seller meets low value demander, no trade..

20. Average Prices are predicted better by Profit-Splitting Session 1Session 2 Competitive$20$30 Profit-Split$20.7$29.3 Actual, Rd 1$21.2$27.0 Actual Rd 2$21.2$28.5

21. Detailed predictions Competitive theory and profit splitting theory both make detailed predictions beyond average price and total quantity. Distribution of prices –Competition implies uniform price. –Splitting implies trading at $15, $25, and $40. Both theories predict who trades with whom as well as total number of trades.

22. Demand and Supply in Session 1

23. Session 1: Detailed Price Predictions Competitive vs Profit-splitting Price Range$14-16$24-26$34-36$19-21 Competitive0% 100% Profit-splitting57%29%14%0% Actual shares, Rd 124%18%6%20% Actual shares, Rd 216%19%2%30%

24. Session 1: Distribution of All Prices

25. Demand and Supply in Session 2

26. Session 2: Detailed Price Predictions Competitive vs Profit-splitting Price Range $14-16$24-26$34-36$29-31 Competitive 0% 100% Profit-splitting 14%29%57%0% Actual shares, Rd 1 7%20%8%32% Actual shares, Rd 2 2%24%8%42%

27. Session 2: Distribution of All Prices

28. Session 1: Detailed Quantity Predictions Competitive vs Profit-Splitting Buyer Value Seller CostLow Low High High LowHigh Total Trades Competitive Prediction 19702410438 Profit-Splitting Prediction 290014573508 Actual, Round 1 221920734471 Actual Round 2 218020938465

29. Session 2: Detailed Quantity Predictions Competitive vs Profit-Splitting Buyer Value Seller CostLow Low High High LowHigh Total Trades Competitive Prediction 00241201442 Profit-Splitting Prediction 740148296518 Actual, Round 1266218211461 Actual Round 2182218213451

30. Remarks Sometimes trading environment is like Smith’s, much repetition with same environments and public trading. Sometimes more like Chamberlin’s or like ours. Seems worth understanding what happens in environments with intermediate levels of information.

31. Mining Classroom Trading Pits Data is cheap and abundant. Design is less flexible. But worth saving and studying. Remember where experimental economics started.

32. That’s all for now… Mine tailings