Apply information economics to diagnose and remedy market failures caused by asymmetric information. Use this skill when the user needs to analyze adverse selection, moral hazard, or signaling and screening mechanisms, especially in insurance, labor, credit, or product quality markets.
npx skills add https://github.com/asgard-ai-platform/skills --skill grad-info-economics
Information economics studies how asymmetric information between parties causes market failures and shapes institutional responses. Akerlof's "market for lemons" shows adverse selection can collapse entire markets; Spence's signaling model shows informed parties can credibly convey quality through costly actions; Rothschild-Stiglitz screening shows uninformed parties can design menus that induce self-selection. Together, these frameworks explain why markets for insurance, credit, labor, and used goods systematically deviate from the competitive ideal and why institutions like warranties, credentials, and regulation exist.
IRON LAW: Information asymmetry causes market failure — without
corrective mechanisms (signals, screens, warranties), bad drives out
good. Markets with severe adverse selection can unravel completely
(Akerlof's lemons result).
Step 1 — Identify the Information Asymmetry
Classify: (a) Adverse selection (hidden type, pre-contractual) — the informed party's type affects the uninformed party's payoff; (b) Moral hazard (hidden action, post-contractual) — the informed party's effort is unobservable; (c) Both present simultaneously. Identify who is informed and who is uninformed.
Step 2 — Model the Market Failure
For adverse selection: show how pooling (offering a single contract) attracts disproportionately bad types, driving up costs, raising prices, and causing good types to exit — the unraveling dynamic. For moral hazard: show how insurance or contracting reduces the agent's incentive to exert effort or take precautions, increasing expected costs.
Step 3 — Evaluate Corrective Mechanisms
Signaling (informed party acts): identify the signal, verify the single-crossing condition (high types find the signal less costly), and check whether a separating equilibrium exists. Screening (uninformed party designs menu): design contracts that induce self-selection — typically, high types get efficient contracts while low types face quantity distortion. Other mechanisms: warranties, reputation, certification, mandatory disclosure, regulation.
Step 4 — Assess Efficiency and Policy
Compare the outcome against the full-information benchmark. Calculate welfare loss from: (a) missing trades (good types priced out); (b) signaling waste (resources spent on credentials that produce no direct value); (c) screening distortions (inefficient contracts for low types). Recommend whether market mechanisms suffice or government intervention is needed.
## Information Economics Analysis: [Market / Context]
### Information Structure
- **Asymmetry type**: Adverse selection / Moral hazard / Both
- **Informed party**: [who knows what]
- **Uninformed party**: [who lacks what information]
- **Hidden variable**: [quality / risk type / effort level]
### Market Failure Diagnosis
- **Unraveling risk**: [high / medium / low]
- **Pooling outcome**: [what happens if all types are treated identically]
- **Separating outcome**: [what happens if types are distinguished]
### Corrective Mechanisms
| Mechanism | Who Initiates | How It Works | Effective? |
|---------------|---------------|-------------------------|------------|
| Signaling | Informed | [e.g., education] | |
| Screening | Uninformed | [e.g., deductible menu] | |
| Warranty | Informed | [e.g., money-back] | |
| Regulation | Government | [e.g., mandatory disclosure] | |
### Efficiency Assessment
- **Full-information benchmark**: [first-best outcome]
- **Welfare loss sources**: [missing trades / signaling waste / screening distortion]
- **Net welfare**: [second-best outcome vs. unregulated market]
### Recommendation
[Which mechanisms to deploy; whether policy intervention is warranted]
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Take asgard-ai-platform/grad-info-economics from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.