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Extracting Valuable Data from Classroom Trading Pits

Explore the history of experimental economics through Chamberlin’s classroom market experiments at Harvard in the 1940s, their revival by Vernon Smith at Purdue, and modern data extraction and analysis from classroom trading pits. Discover how competitive theory and profit-splitting predictions align with empirical evidence in economic experiments.

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Extracting Valuable Data from Classroom Trading Pits

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  1. 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. Edward Chamberlin Vernon Smith Founders of Experimental Economics

  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

  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

  24. Session 1: Distribution of All Prices

  25. Demand and Supply in Session 2

  26. Session 2: Detailed Price Predictions Competitive vs Profit-splitting

  27. Session 2: Distribution of All Prices

  28. Session 1: Detailed Quantity Predictions Competitive vs Profit-Splitting

  29. Session 2: Detailed Quantity Predictions Competitive vs Profit-Splitting

  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

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