Extracting Valuable Data Extracting Valuable Data from Classroom Trading Pitsfrom Classroom Trading Pits

Ted Bergstrom & Eugene Kwok

University of California, Santa Barbara

The Origin of Experimental The Origin of Experimental Economics Economics

• The first scientific experiments in economics were classroom market experiments

by Edward Chamberlin at Harvard in 1940’s.

Chamberlin’s experimentsChamberlin’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.

Revival at Purdue Revival at Purdue

• Chamberlin’s experiments went almost unnoticed until

Vernon Smith revisited them in his classroom at Purdue.

Smith’s experimentsSmith’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

Founders of Experimental Economics

• Edward Chamberlin • Vernon Smith

Our Data Our Data

• Classroom experiments from Experiments with Economic Principles, a principles text by Bergstrom and Miller

• Experiments conducted in 31 classrooms, 10 universities.

The Apple MarketThe Apple Market

• Students assigned roles as apple suppliers or apple demanders.

• Suppliers supply at most 1 bushel.

• Demanders demand at most 1 bushel.

Buyer Values and Seller CostsBuyer 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

Session 1 Session 1

• 2/3 of Sellers have low cost, 1/3 high.

• 2/3 of Demanders have low value, 1/3 high.

Demand and Supply in Session 1Demand and Supply in Session 1

Session 2 Session 2

• 2/3 of Sellers have high cost, 1/3 low.

• 2/3 of Demanders have high value, 1/3 low.

Demand and Supply in Session 2

Session 1: Distribution of Average PricesSession 1: Distribution of Average Prices

Session 2: Distribution of Average PricesSession 2: Distribution of Average Prices

Session 1: Distribution of Quantity DeviationsSession 1: Distribution of Quantity Deviations

Session 2: Distribution of Quantity DeviationsSession 2: Distribution of Quantity Deviations

Enough to convince crudulous students, maybe…

But does the evidence show that competitive theory is empirically useful?

An alternative hypothesis: An alternative hypothesis: Profit SplittingProfit 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.

.

Average Prices are predicted Average Prices are predicted better by Profit-Splittingbetter by Profit-Splitting

Session 1 Session 2

Competitive $20 $30

Profit-Split $20.7 $29.3

Actual, Rd 1 $21.2 $27.0

Actual Rd 2 $21.2 $28.5

Detailed predictionsDetailed 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.

Demand and Supply in Session 1Demand and Supply in Session 1

Session 1: Detailed Price Predictions Session 1: Detailed Price Predictions Competitive vs Profit-splittingCompetitive vs Profit-splitting

Price Range $14-16 $24-26 $34-36 $19-21

Competitive 0% 0% 0% 100%

Profit-splitting 57% 29% 14% 0%

Actual shares, Rd 1 24% 18% 6% 20%

Actual shares, Rd 2 16% 19% 2% 30%

Session 1: Distribution of All PricesSession 1: Distribution of All Prices

Demand and Supply in Session 2

Session 2: Detailed Price Predictions Session 2: Detailed Price Predictions Competitive vs Profit-splittingCompetitive vs Profit-splitting

Price Range $14-16 $24-26 $34-36 $29-31

Competitive 0% 0% 0% 100%

Profit-splitting 14% 29% 57% 0%

Actual shares, Rd 1 7% 20% 8% 32%

Actual shares, Rd 2 2% 24% 8% 42%

Session 2: Distribution of All PricesSession 2: Distribution of All Prices

Session 1: Detailed Quantity Predictions Session 1: Detailed Quantity Predictions Competitive vs Profit-SplittingCompetitive vs Profit-Splitting

Buyer Value Seller Cost

LowLow

Low High

High Low

High High

Total Trades

Competitive Prediction

197 0 241 0 438

Profit-Splitting Prediction

290 0 145 73 508

Actual, Round 1 221 9 207 34 471

Actual Round 2 218 0 209 38 465

Session 2: Detailed Quantity Predictions Session 2: Detailed Quantity Predictions Competitive vs Profit-SplittingCompetitive vs Profit-Splitting

Buyer Value Seller Cost

LowLow

Low High

High Low

High High

Total Trades

Competitive Prediction

0 0 241 201 442

Profit-Splitting Prediction

74 0 148 296 518

Actual, Round 1 26 6 218 211 461

Actual Round 2 18 2 218 213 451

RemarksRemarks

• 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.

Mining Classroom Trading PitsMining Classroom Trading Pits

• Data is cheap and abundant.

• Design is less flexible.

• But worth saving and studying.

• Remember where experimental economics started.

That’s all for now…That’s all for now…

• Mine tailings