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Problem 1: Buyers cannot tell if a product is good or bad, so they offer less money and good sellers may leave. Suppose 50% of used laptops are good and worth

Problem 1: Buyers cannot tell if a product is good or bad, so they offer less money and good sellers may leave. Suppose 50% of used laptops are good and worth $1,000, while 50% are bad and worth $400. Since you cannot tell which one you are buying, the average value is 0.5x1000 + 0.5x400 = $700, so you will not want to pay more than about $700. But owners of good laptops may refuse to sell for $700, so more good laptops leave the market and the chance of buying a bad one increases. And the only guy selling for $700 is the lemons. Problem 2: The theory assumes buyers already know how many bad products are in the market, but in real life they often do not. Its obvious this market for lemons can’t be true

GeneralMayhem 5d ago
I don't understand what point you're making. Your "point 1" is literally the argument of the paper. If that scenario arises, the market collapses and no further sales can be made. That's the whole problem. As someone who takes a percentage of every sale, you want to keep the lemon-sellers out even though in the short run they make you extra money. Your "point 2", if it's meant to be a rebuttal, isn't much of one. Buyers don't need to accurately know exactly what fraction of sellers are fraudulent; if they believe that it's 50-50, the same thing happens, even if the true rate is 80-20 in favor of good sellers. Conversely, if buyers are overly optimistic about quality, the market can persist despite a level of fraud that's higher than should be tolerated. But in any case, things like reviews and external reporting should eventually give them good information.