MobLab’s implementation of a classic lemons market (Akerlof, 19701). Students are paired. The seller knows the quality of her car, and makes a take-it-or-leave-it offer to a buyer, who knows only the range of possible car qualities. To facilitate learning, the buyer ultimately learns the value of the car, whether or not she chooses to purchase.
This game introduces students to a classic market with asymmetric information: one side has payoff-relevant information (car quality) that the other side wishes it had.
Markets where one side’s type is not observable to the other side of the market can lead to adverse selection where only the worst types (low-quality cars in this case) are transacted.
In this two-player game, one student is a buyer while the other is a seller. To view our single-player game—where each student is a buyer who makes offers to robot sellers—check out our “Lemon Market Buyer” game in the library!
If the buyer chooses not to purchase at the seller’s asking price, both buyer and seller earn zero. If the buyer purchases, buyer earnings are the buyer’s valuation minus the price, and seller earnings are the price minus seller’s valuation. Buyer’s valuation is Beta (%) of the seller’s valuation (equal to 150 by default). Setting this greater than 100 means that transacting the car is efficient.
The range of seller car valuations extends from Low Value to High Value. The default range of 500 to 5000 creates an environment where buyer losses can be significant, and thus expedites learning (perhaps after a couple of iterations) to avoid high-priced cars.
Note: If you set Periods greater than 1, pairings remain fixed across periods. Adverse selection will be most pronounced when pairings change between rounds. To change pairings, set Periods equal to 1, and after a game ends, choose Replay. With Fixed Role, a player stays in the same role, but is randomly assigned a new match, and with Random, both a player’s role and next partner are randomly assigned.
Finally, the difference between buyer and seller valuations (Beta (%)) is not reported on subject screens. This is something you may choose to announce to your subjects before the game begins.
Repeat interaction may mitigate adverse selection, as a seller can increase the likelihood of future sales by dealing fairly with a buyer today. If you set Periods greater than 1, each buyer and seller pair interacts repeatedly, with car quality randomly chosen each period. For greatest effect, make sure students know that pairings are fixed, but do not tell students the number of periods.
For the primary learning objective, you want to show that in later rounds (i.e., after learning) owners of higher-value cars have trouble selling their vehicles. Those with high-value cars need a high price in order to not sell at a loss, and a buyer purchasing at a high price earn large losses if the car turns out to be a lemon.
Use the Go To menu (Figure 1) to view a different period of a multi-period game. If you used one of the Replay options, you can compare outcomes of this game with one of these linked games using the Compare button. Alternatively, you can switch to a linked game with the View button.
The default results view presents table summarizing outcomes for each round and across all rounds (Figure 2). Decreasing values for Percent of Cars Sold is evidence of an adverse selection problem, especially if a large fraction of buyers lose money in early rounds.
Using the Go To menu (Figure 1) to view a particular period, the graph (Figure 3) displays each car and its asking price. An observation is plotted by the seller’s value of the car (horizontal axis) and asking price (vertical axis), with accepted offers depicted by a blue asterisk and rejected offers by a red diamond. We also include two reference lines. The dashed yellow line depicts the seller’s reservation value (i.e., the 45-degree line where asking price equals seller valuation). The dashed green line depicts buyer valuation (seller valuation times beta). In interpreting this graph: