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Moral Hazard

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Bayesian Games (Games of Incomplete Information)Incentive Compatibility and Individual Rationality+1 more→→Credit Constraints in Developing MarketsFinancial Frictions and Amplification Mechanisms+5 more
contract-theory hidden-action incentives

Core Idea

Moral hazard arises when an agent's actions are unobservable to the principal. The agent may shirk or take excessive risk because consequences are shared. Classic example: insurance reduces incentive to prevent loss. The principal must design contracts (e.g., deductibles, performance pay) to align incentives. Optimal contracts balance risk-sharing with incentives.

How It's Best Learned

Analyze a simple principal-agent model with continuous effort. Solve for optimal contract. Compare to full-information benchmark to see efficiency loss from hidden action.

Explainer

From the principal-agent model, you know the basic setup: one party (the principal) delegates a task to another (the agent), and their interests may not align. Moral hazard is the specific problem that arises when the agent's *actions* are hidden — the principal can observe the outcome but cannot verify whether the agent worked hard, cut corners, or took excessive risks. The term originally comes from insurance, where it described the tendency of insured people to be less careful, but the concept applies wherever effort or behavior is unobservable.

Consider a concrete example: a restaurant owner (principal) hires a manager (agent) to run the business. The owner can observe monthly revenue, but cannot monitor whether the manager is working diligently, networking to attract customers, and maintaining food quality — or whether the manager is coasting, leaving early, and cutting corners. Revenue depends on both the manager's effort and random factors (weather, local events, economic conditions). High effort makes good outcomes more likely, but does not guarantee them — and low effort does not guarantee bad outcomes either. This randomness in the mapping from effort to outcome is what makes moral hazard so difficult. The owner sees the result, not the cause.

If the owner could observe effort directly, the solution would be simple: pay for effort. Write a contract that says "work hard, get paid well; shirk, get fired." But with hidden action, the contract can only be conditioned on observable outcomes — revenue, profit, customer ratings. The principal must design a contract that gives the agent an incentive to exert effort even when no one is watching. This typically means making the agent's pay sensitive to outcomes: performance bonuses, commissions, stock options, or profit sharing. The agent bears some risk (their pay varies with outcomes they don't fully control), but this risk exposure is the price of providing incentives.

Here lies the fundamental tradeoff at the heart of moral hazard theory: incentives versus risk-sharing. If the agent is risk-averse (as most people are), the ideal risk-sharing arrangement would fully insure the agent — pay a flat salary regardless of outcomes. But a flat salary provides zero incentive to exert effort. Conversely, making the agent the full residual claimant (keeping all profits) provides maximal incentives but loads the agent with all the risk. The optimal contract balances these two forces, providing enough pay-for-performance to motivate effort while not exposing the risk-averse agent to so much volatility that they demand a huge risk premium. This optimal contract is always less efficient than what could be achieved if effort were observable — the efficiency loss from moral hazard is the cost of information asymmetry.

Moral hazard is pervasive in economic life. Insurance deductibles and copays exist to keep policyholders careful. CEO compensation packages tie pay to stock performance to align executive and shareholder interests. Loan covenants restrict borrower behavior to protect lenders. In each case, the contract designer faces the same problem: how to motivate unobservable good behavior through the structure of observable rewards and penalties. Understanding this problem — and the tradeoff between incentives and risk-sharing that constrains its solution — is essential for analyzing contracts, regulation, and institutional design throughout economics.

Practice Questions 5 questions

Prerequisite Chain

Understanding Zero → The Number Zero → Counting to Five → Counting to 10 → Counting to 20 → Counting a Set of Objects Up to 20 → Cardinality: The Last Number Counted → Matching Numerals to Quantities → Subitizing Small Quantities → Addition Within 10 → Number Bonds to 10 → Addition Within 20 → Doubles and Near Doubles → Doubles Facts Within 10 → Near Doubles Facts Within 20 → Mental Math Strategies for Addition → Mental Math: Adding and Subtracting Tens → Addition Within 100 → Repeated Addition as Multiplication → Multiplication as Equal Groups → Multiplication: Arrays → Basic Multiplication Facts (0s, 1s, 2s, 5s, 10s) → Multiplication Facts Within 100 → Division as Equal Sharing → Division as Grouping (Measurement Division) → Division: Grouping (Repeated Subtraction) Model → Division: Fair Sharing Model → Division as Equal Sharing → Division as Grouping → Basic Division Facts → Division Facts Within 100 → Multiplication and Division Fact Families → Relationship Between Multiplication and Division → Division Facts as Inverse of Multiplication → Remainders and Quotients in Division → Division Word Problems → Multi-Step Word Problems → Solving Multi-Step Word Problems → Multiplication Word Problems → Division Word Problems → Introduction to Long Division → Factors and Multiples → Prime and Composite Numbers → Equivalent Fractions → Relating Fractions and Decimals → Decimal Place Value → Integers and the Number Line → Comparing and Ordering Integers → Absolute Value → Adding Integers → Subtracting Integers → Multiplying Integers → Dividing Integers → Unit Rates → Proportions → Percent Concept → Converting Between Fractions, Decimals, and Percents → Operations with Rational Numbers → Two-Step Equations → Solving Multi-Step Equations → Equations with Variables on Both Sides → Literal Equations → Slope-Intercept Form → Point-Slope Form → Writing Linear Equations → Parallel and Perpendicular Line Slopes → Graphing Linear Equations → Piecewise Functions → One-Sided Limits → Continuity Definition → Limits and Continuity in Multiple Variables → Functions of Several Variables → Continuity in Multiple Variables → Partial Derivatives: Definition and Computation → Differentiability in Multiple Variables → Differentiability in Multivariable Functions → Total Differential and Linear Approximation → Chain Rule for Multivariable Functions → Implicit Differentiation → Related Rates → Optimization Problems → Critical Points of Multivariable Functions → Critical Points and Classification of Extrema → Second Partial Test for Local Extrema (Hessian) → The Hessian Matrix and Second Derivative Test → Unconstrained Optimization: Finding Extrema → Optimization in Multiple Variables → Lagrange Multipliers → Constrained Optimization and Lagrange Multipliers → Utility and Preferences → Marginal Utility and Diminishing Returns → Profit Maximization → Perfect Competition → Shutdown and Breakeven Decisions → Monopoly → Monopolistic Competition → Oligopoly and Strategic Behavior → Game Theory Basics → Nash Equilibrium → Bayesian Games (Games of Incomplete Information) → Mechanism Design: Strategic Implementation → Individual Rationality (Participation Constraint) → Incentive Compatibility and Individual Rationality → Moral Hazard

Longest path: 104 steps · 557 total prerequisite topics

Prerequisites (3)

Leads To (7)