Lecture: Asymmetric Information, Behavioral Economics & Inequality
Lecture script · about 15–20 minutes spoken · Week 15
Opening
Picture yourself in a parking lot, about to buy a used car from a stranger. You get a short test drive. The seller has driven that car for years and knows every rattle. Now picture a new employee looking at a retirement form with one box already checked, and an analyst reading a report on how a region's income is shared. All three are living inside this week's ideas.
Here's where we are. In Week 1 you split positive claims from normative ones. In Weeks 3 and 4 you built the running market, with its equilibrium at 6 dollars and 30 units a day. In Week 6 you found its total surplus, 90 dollars a day. Week 14 showed one way a market fails, when costs spill onto bystanders.
In this lecture I'll show you three more ways real markets differ from our models. First, one side of a deal can know more than the other. Second, real people choose in predictable ways the careful chooser doesn't. Third, we'll measure how evenly income is shared. Then, because this is the last teaching week, I'll pull the whole course together.
1. Asymmetric information: who can see what
Start with a restaurant. It knows how clean its kitchen is, and you see only the dining room. That gap has a name. Asymmetric information is a situation in which one side of a deal knows something relevant that the other side does not.
The gap comes in two forms, and timing tells them apart. Adverse selection is the problem of hidden characteristics before a deal. Moral hazard is the problem of hidden actions after a deal. Hold on to that one word, timing. It sorts almost every case you'll meet.
Pause and try this. A used car's worn brakes exist before anyone buys it. Is that adverse selection or moral hazard?
Think about when the hidden thing happens.
Here's the answer. It's adverse selection. The worn brakes are a hidden trait, and they're there before the deal.
2. The used-car market
George Akerlof made this idea famous in 1970 with used cars, and we'll use a classroom version with illustrative numbers. Half the cars of one model are good, and half are lemons. A lemon is a used good, such as a car, with hidden defects that its seller knows about and a buyer cannot see before the sale.
Here are the values. A buyer would pay 10,000 dollars for a good car, and its owner would take no less than 9,000. A buyer would pay 4,000 dollars for a lemon, and its owner would take no less than 3,000. If everyone could see quality, every car would sell, because each one is worth more to a buyer than to its owner.
Pause and try this. Is a lemon in this model worthless?
Look at what a buyer would pay for one.
Here's the answer. No. A lemon is still worth 4,000 dollars to a buyer, so it's still worth trading. It's a car with hidden problems, not a car nobody wants.
3. Watching the market unravel
Now take away the buyer's eyes. You know half the cars are good, but you can't tell which one is in front of you. So you offer the average of the two values: 10,000 plus 4,000 is 14,000, and half of that is 7,000 dollars.
Follow the owners. The owner of a good car needs at least 9,000, and 7,000 is less, so that owner says no. The owner of a lemon needs only 3,000, so that owner says yes. Only lemons show up. Buyers figure that out, and they'll pay no more than 4,000, a lemon's value. The good cars leave, and each one was a 1,000-dollar gain that never happens.
Pause and try this. Why do the good cars leave if buyers value them at 10,000?
Compare the offer with what a good-car owner needs.
Here's the answer. The uninformed offer of 7,000 is below the 9,000 a good-car owner needs. Buyers can't tell good from bad, so they won't pay more.
4. Signaling and screening
Markets fight back from both sides. Signaling is an action by the better-informed side of a deal that would cost too much to fake, such as offering a warranty. Think about why that works. An owner who knows the car is a lemon would lose money promising to pay for its repairs.
Screening is an action by the less-informed side of a deal to sort the other side by hidden quality or risk, such as paying for an independent inspection. Once quality is visible, a good car can sell anywhere between 9,000 and 10,000 dollars, and the lost trade comes back.
Pause and try this. A buyer hires a mechanic to check a car. Is the buyer signaling or screening?
Ask which side knows less.
Here's the answer. Screening. The buyer is the uninformed side, gathering information; the seller offering a warranty would be signaling.
5. Moral hazard: hidden actions after the deal
Now move the hidden thing to after the deal. Picture someone renting an e-bike for a weekend with full damage coverage. A scrape will cost them nothing, so they take the bumpy shortcut they'd avoid on their own bike. The rental company can't see how they ride.
That's moral hazard. The usual answer leaves some cost with the person whose care is hidden. A deductible is the part of each loss that an insured person pays before the insurer pays the rest. In the chapter's phone example, a 60-dollar deductible on a 240-dollar repair gives the owner a reason to buy a case.
Pause and try this. A driver buys full insurance and then parks more carelessly. Which problem is that?
Use the timing test.
Here's the answer. Moral hazard. The careless parking is a hidden action, and it happens after the deal.
6. Bounded rationality
Every model in this course has assumed a chooser who weighs every cost and benefit with care. On a good day, that's you. But think about the last phone plan you picked from a long page of options. You probably didn't price every plan for your own usage. You picked the one a friend had, or the one in the middle.
That's bounded rationality: the idea that people make choices with limited time, attention, and information, and so rely on rules of thumb. Behavioral economics studies the predictable ways real choices depart from the careful chooser. The key word is predictable.
Pause and try this. Does behavioral economics claim that people are stupid?
Think about why economists can study these patterns at all.
Here's the answer. No. The patterns show up in careful, educated people too, and because they repeat, economists can predict them.
7. Loss aversion
Imagine finding a 20-dollar bill on the sidewalk. Now imagine losing one from your pocket. Same amount, but the loss stings more than the find pleases.
That's loss aversion: the tendency for a loss to weigh more in a person's choices than a gain of the same size. It's a central element of prospect theory, which Daniel Kahneman and Amos Tversky published in 1979. Their insight was that people judge outcomes as gains or losses from where they stand, and losses loom larger.
Pause and try this. Which feels worse to most people: a card surcharge or a missing cash discount, when the prices come out the same?
Ask which one is framed as a loss.
Here's the answer. The surcharge. It's framed as a loss, and losses weigh more than gains of the same size.
8. Present bias and anchoring
Here's a familiar plan: start saving next month. Then next month comes, the money has a use right now, and the plan slides again. That's present bias, the tendency to weigh costs and benefits that arrive now much more heavily than the same ones later.
Now picture a jacket tagged "was 80 dollars, now 50." The 80 pulls your sense of the deal. That's anchoring, the tendency for a first number you see to pull later judgments toward it. The careful question is still the Week 6 one: is the jacket worth at least 50 dollars to you?
Pause and try this. You value the jacket at 45 dollars. Should the "was 80" tag change your choice?
Compare your value with the price you'd pay.
Here's the answer. No. Your value of 45 is below the 50-dollar price, so buying costs you 5 dollars of surplus whatever the tag once said.
9. The power of defaults
A default is the option that takes effect when a person makes no active choice. Brigitte Madrian and Dennis Shea studied one in a paper published in 2001. One large U.S. firm began enrolling new employees in its retirement plan automatically, with the right to opt out. Participation rose sharply, though everyone kept the same right to join or leave.
Keep the two kinds of claim apart. That the default raised participation is positive; you can check it against records. Whether an employer or a government should set defaults, and which ones, is normative. It rests on values.
Pause and try this. "Employers ought to enroll every worker automatically." Positive or normative?
Look for the word that signals a judgment.
Here's the answer. Normative. The word "ought" makes it a claim about values, which evidence alone can't settle.
10. The Lorenz curve
Now from choices to incomes. Line up every household in a region from the lowest income to the highest, and cut the line into five equal groups. Each group is a quintile. In our illustrative Dataset S, the five quintiles get 5, 10, 15, 25, and 45 percent of income. Those aren't any country's figures.
Add them up as you go: 5, then 15, then 30, then 55, then 100 percent. A Lorenz curve plots those running totals against the share of households. Picture a square chart. Households run along the bottom, income up the side. A straight diagonal from corner to corner is the line of perfect equality. Our curve starts and ends at the same corners but sags below that diagonal.
Pause and try this. At 60 percent of households, how much income has the curve reached?
Read the running total for the first three quintiles.
Here's the answer. 30 percent. The poorest three fifths of households together get 30 percent of income in this example.
11. The Gini coefficient
A curve is useful, but one number is easier to compare. The Gini coefficient summarizes how far a Lorenz curve falls below the line of perfect equality. It runs from 0, perfect equality, to 1, where one recipient or group gets all the income. That's how the Census Bureau describes its Gini index, and it's named for Corrado Gini, from 1912.
To compute it, find the area under our curve by cutting it into five strips. For Dataset S that area is 0.31. The Gini coefficient is one minus twice that area, so it's 0.38.
Pause and try this. A classmate says a Gini of 0 means one family gets everything. Is that right?
Think about which end of the scale means equal shares.
Here's the answer. No, it's backwards. A Gini of 0 means every household gets the same share; one family getting everything would be a Gini of 1.
12. Inequality as a named debate
The curve and the number describe how income is shared. They don't say why, or what to do about it. Those questions are a debate, and in this course we present both sides.
Some questions are positive. How much do skills, technology, trade, and family background explain? How do taxes and transfers change choices to work and save? Economists measure these and disagree about the sizes. One side argues that moving income toward households with little eases real hardship and widens opportunity. The other argues that it can weaken incentives, and that a fair process matters more than an equal result. How much equality to aim for is normative, and this course gives no verdict.
Pause and try this. Is "a Gini of 0.38 is too high" a positive or a normative claim?
Ask whether evidence alone could settle it.
Here's the answer. Normative. The 0.38 is a measurement; calling it too high is a judgment about values.
13. Pulling the course together
Let's step back. Markets allocate well when buyers and sellers face the full costs and benefits of their choices, when no one sets the price alone, and when both sides can see what they're trading. Then the running market reached its largest total surplus, 90 dollars a day.
Every later week broke one condition. A binding ceiling cost 40. Market power cost the ferry's riders a deadweight loss of 32. Week 14's smoke cost 10, until a 2-dollar corrective tax fixed it. That same 2-dollar tax caused a loss of 10 in Week 7. A policy isn't good or bad on its own; its effect depends on the market it lands in.
Pause and try this. Why do positive analysis before stating a judgment?
Think about that 2-dollar tax.
Here's the answer. Because the same policy can help one market and hurt another, so you need the model's prediction before you can weigh it.
14. The classic wrong turns
Let me name the slips that trip people up this week. Adverse selection and moral hazard get swapped; use the timing test. A Gini of 0 gets read as perfect inequality, when it's perfect equality. A bias gets read as foolishness, when it's a predictable pattern. And the lemons model gets read as "all used cars are bad," when it says good cars can be pushed out of a market when buyers can't tell them apart.
Pause and try this. A friend says the lemons model proves you should never buy a used car. What's wrong with that?
Remember how many of the cars were good.
Here's the answer. Half the cars in the model are good. The model shows that hidden quality can drive good cars out, which is why warranties and inspections matter.
Closing
Let me pull it together. Asymmetric information means one side knows more. Adverse selection is hidden traits before the deal, moral hazard is hidden actions after it, and signals and screens close the gap. Bounded rationality, loss aversion, present bias, anchoring, and defaults are predictable patterns. The Lorenz curve and the Gini coefficient measure how income is shared, and the debate over what to do keeps positive and normative claims apart. Across the whole course, the model tells you what a policy does, and your values tell you whether it's worth it.
Next in this module are the slide deck, the Readings & Resources page, the lecture tutorial, the practice set, and the quiz. After that comes the final exam, where every model from the frontier to the Lorenz curve comes back.
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