Week 15 — Lecture Outline · Asymmetric Information, Behavioral Economics & Inequality

Course: Principles of Microeconomics (ECON 1) · Silver Oak University (fictional sample) · Prof. Kessler
Objective 8 — market failure: asymmetric information, behavioral economics & inequality · SLO A & B
Meeting pattern: two 75-min sessions (≈150 min). Segment minutes below total ~150 — scale to your room.

The deck (E), the tutorial (C), and the workshop (P) all teach from this outline. Every number here is pre-computed and independently verified (see the verified box in §4).

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Week at a glance

Big question	When information is lopsided, when psychology hijacks rational choice, and when gains are distributed very unequally — what does economics say, and what should we do about it?
By week's end students can	(1) distinguish adverse selection (hidden info BEFORE) from moral hazard (hidden action AFTER); (2) compute the lemons buyer EV and trace market unraveling; (3) explain signaling and screening; (4) name and apply five behavioral biases; (5) read quintile shares, compute the top/bottom ratio, and separate positive from normative on inequality.
Key vocabulary	asymmetric information, adverse selection, moral hazard, signaling, screening, behavioral economics, anchoring, loss aversion, sunk-cost fallacy, present bias, framing, nudge, libertarian paternalism, income quintile, Gini coefficient, Lorenz curve, positive vs. normative
Materials	whiteboard; Week-15 readings/links; spreadsheet for quintile table; an approved chatbot
Timing note	8 segments ≈ 150 min. Trim Segment 7 if short on time.

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Segment 1 — HOOK: "Would you buy a used car from a stranger?" (12 min)

Open with: "Show of hands — who's ever worried the thing you were buying was not what the seller said it was? eBay, Craigslist, used cars, dating apps?" This is the central market-failure this week: one side knows something the other doesn't. Promise: by the end of today students will be able to compute exactly why that wrecks a perfectly reasonable market — and what economists say about fixing it.

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Segment 2 — PLAIN-LANGUAGE IDEA: asymmetric information (15 min)

Asymmetric information = one party to a transaction has private knowledge the other lacks. This creates two classic problems:

• Adverse selection — hidden info before a deal is made. The better-informed side self-selects in a way that disadvantages the other. Classic: the used-car market (Akerlof, "The Market for Lemons," 1970 — the core insight, not a quote). This is a PRE-contract information problem.
• Moral hazard — hidden action after a deal is made. Once insured (or once hired), the covered party may take more risk than they would have otherwise. This is a POST-contract behavior problem.

The single most common error to kill now: students constantly swap these. Drill the timing: adverse selection = before, moral hazard = after.

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Segment 3 — WORKED EXAMPLE: the lemons market (20 min)

Set it up on the board and do every step out loud.

The lemons market — step by step.

Imagine a used-car market with two kinds of cars: a good car worth $4,000 and a bad car ("lemon") worth $2,000. Suppose there are equally many of each (50% probability of each type) and sellers know which they have, but buyers cannot tell.

✅ VERIFIED NUMBERS (pre-computed; do not recompute live)

Step 1 — Buyer's expected value:
EV = ½ · $4,000 + ½ · $2,000 = $2,000 + $1,000 = $3,000

Step 2 — Who exits?
A seller of a good car knows it's worth $4,000. Will they accept the buyer's best offer of $3,000? No — $3,000 < $4,000. Sellers of good cars exit.

Step 3 — Market unraveling:
Only bad cars remain. Buyers figure this out → now they'll only offer $2,000 (the bad-car value) → the market for good cars has collapsed. This is adverse selection: the bad cars have "selected" into the market, the good ones have selected out.

Say it in words: information asymmetry doesn't just create a bad deal — it can destroy an entire market for the higher-quality good. That's an allocative failure: mutually beneficial trades (buyer wants a good car, seller has one) that would happen under full information never do.

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Segment 4 — SIGNALING & SCREENING (12 min)

Two private-sector fixes:

• Signaling — the informed side sends a credible, costly signal. College degrees as a signal of ability (regardless of content); warranties as a signal of product quality. For a signal to work, it must be cheaper for the high-quality type to send — otherwise the low-quality type would mimic it.
• Screening — the uninformed side designs a menu of options that induces self-selection. Insurance companies offer high-deductible / low-premium vs. low-deductible / high-premium plans so risk types sort themselves.

Government intervention: mandatory disclosure laws, required insurance (health, auto), licensing requirements, and certification all reduce information asymmetry or eliminate adverse selection by forcing pooling.

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Segment 5 — BEHAVIORAL ECONOMICS: when people systematically deviate from the model (22 min)

Standard economics assumes rational maximizers — people with stable, consistent preferences who process information correctly. Behavioral economics asks: what if they don't, and in predictable ways?

Five biases (teach each with a one-sentence definition + one vivid example):

1. Anchoring — the first number seen drags estimates toward it. A $500 "suggested retail price" makes $400 look like a deal, even if the item is worth $250.
2. Loss aversion — losses loom larger than equivalent gains. Losing $50 feels roughly twice as bad as gaining $50 feels good. Drives risk-seeking to avoid losses and risk-aversion when gains are certain.
3. Sunk-cost fallacy — past irrecoverable spending influences forward-looking choices. "I've already paid for the concert ticket, so I'll go even though I'm sick." (The $40 is gone either way — irrelevant to the going vs. not-going margin.)
4. Present bias — people discount the future more steeply than the standard model predicts. Preferring $100 today over $120 in one year even if they'd prefer $120 "next year" over $100 "the year after" (time inconsistency). Ties to under-saving, over-eating.
5. Framing — the same information, differently presented, produces different choices. "90% fat-free" vs. "10% fat" — identical, but consumers rate the first product higher.

Nudges and libertarian paternalism: if biases are predictable, should policy be designed to push people toward better choices while leaving them free to opt out? Auto-enrollment in retirement savings (a nudge) vs. mandatory savings (paternalism) vs. leaving it to individuals (pure choice). Present both sides: autonomy and the risk of "who decides what's better" vs. evidence that defaults powerfully shape behavior.

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Segment 6 — INEQUALITY: the distribution of the gains (18 min)

Even when markets are efficient, they don't distribute gains equally. Economists use several tools to describe the income distribution:

• Quintile shares — divide households into five equal groups (bottom 20%, second 20%, middle 20%, fourth 20%, top 20%) and report each group's share of total income.
• The Lorenz curve — a graph of cumulative income share vs. cumulative population share; the further it bows below the diagonal, the more unequal.
• Gini coefficient — ratio of the Lorenz-curve bow to the total triangle area; ranges from 0 (perfect equality) to 1 (one household has everything).

✅ VERIFIED NUMBERS — ILLUSTRATIVE QUINTILE TABLE (engineered, not real-country data)

Quintile	Income share
Bottom 20%	4%
Second 20%	9%
Middle 20%	15%
Fourth 20%	22%
Top 20%	50%
Total	100%

Top/bottom ratio = 50 ÷ 4 = 12.5× (the top quintile holds 12.5 times the income share of the bottom quintile in this illustrative table).

Key positive vs. normative distinction (say this explicitly):
- Positive: "In this illustrative table, the top quintile holds 50% and the bottom holds 4% — a ratio of 12.5×." Economists can measure and model this. This is not real-country data — these are engineered illustrative numbers.
- Normative: "This ratio is too high / too low / just right." No amount of data settles this — it depends on what you value: equality of outcome, equality of opportunity, the total size of the pie, incentives for growth. These are value judgments, and reasonable people disagree.

Mention the positive–normative divide deliberately: causes of inequality (human capital, technology, globalization, institutions, discrimination) are empirical questions; how much inequality is acceptable is not.

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Segment 7 — INTERACTION: think-pair-share (12 min)

Pose: "A city is considering defaulting new employees into an opt-out retirement savings plan instead of the current opt-in plan. Behavioral economics suggests far more workers will save. Is this good policy? Give one argument for and one against — and label each positive or normative."

Target: FOR — default enrollment dramatically raises participation (positive: a behavioral result); workers who would have wanted to save are better off (normative). AGAINST — the government is presuming to know what workers want better than workers do (autonomy / normative); default may not match individual circumstances (positive concern about one-size-fits-all).

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Segment 8 — CALLBACKS, TEASE & THE WEEK'S WORK (9 min)

• Callback to the course arc: information, behavior, and distribution are all reasons why the idealized efficient-markets model (Week 6 surplus, Week 10 perfect competition) doesn't fully hold. With externalities (Week 14), adverse selection, behavioral biases, and inequality, markets often need some institutional support.
• Tease next week: "Week 16 is your final — cumulative, Objectives 1–8. Use Workshop 15 and Assignment 15 to lock in this week's ideas, and the exam-prep bundle to review everything."
• The week's work: Tutorial, Practice, Quiz 15, Discussion 15, Assignment 15, Workshop 15.

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Instructor FAQ — common stumbles

• "Adverse selection vs. moral hazard — which is which?" Timing is everything. Before the deal = adverse selection (the wrong types selected into the pool). After the deal = moral hazard (behavior changes because of coverage). Death: you can't swap them.
• "Why does the buyer's EV matter in the lemons example?" It's the highest price any rational buyer would pay under uncertainty. If that price ($3,000) is less than a good car's value ($4,000), no rational good-car seller accepts it — they exit. That's the mechanism.
• "Are biases really that systematic?" Yes — behavioral economics is empirical. Anchoring, loss aversion, and default effects have been replicated in many contexts. The debate is about implications for policy, not their existence.
• "Is the quintile table real data?" Explicitly no — it's an illustrative engineered table designed to demonstrate the concept clearly. Real distributions vary by country and year; link students to FRED or the Census for real data.
• "Is inequality bad?" That's a normative question and should be framed as such. Economists disagree about causes, magnitudes, and appropriate responses. The positive questions (measuring it, modeling effects) are separate from the normative ones.