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Monopoly

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Price Elasticity of DemandProfit Maximization+2 moreMonopolistic CompetitionMonopoly Market Power and Barriers to Entry+4 more
monopoly market power MR < P deadweight loss markup

Core Idea

A monopolist is the sole seller in a market, facing a downward-sloping demand curve, and is therefore a price-maker. Marginal revenue is less than price (MR < P) because selling an additional unit requires lowering price on all units. The monopolist maximizes profit at MR = MC and then reads the price off the demand curve, producing less and charging more than the competitive equilibrium. This results in deadweight loss (foregone surplus from trades that would have been mutually beneficial). The Lerner Index (P − MC) / P measures the degree of monopoly power and equals 1/|PED|.

How It's Best Learned

Derive MR algebraically from a linear demand curve, then solve the monopoly optimum both graphically and numerically. Compare the monopoly solution to the competitive one to quantify deadweight loss.

Common Misconceptions

Explainer

You know that a competitive firm is a price taker: it sells as much as it wants at the market price, and its marginal revenue equals that price. A monopolist breaks this assumption entirely. As the sole seller, it faces the entire downward-sloping market demand curve — if it wants to sell more, it must lower its price. This single difference transforms everything about how profit maximization works.

The key insight is why MR < P for a monopolist. Suppose the current price is $60 and you sell 40 units. To sell a 41st unit, you must drop the price to, say, $59 — but that new price applies to all 41 units, not just the last one. You gain $59 from the new sale but lose $1 on each of the 40 existing sales. MR = $59 − $40 = $19, far below the $59 price. More generally, for a linear demand curve P = a − bQ, the marginal revenue is MR = a − 2bQ — same intercept, twice the slope downward. The monopolist's MR curve lies below the demand curve everywhere (except the first unit).

Given this, profit maximization still follows the MR = MC rule — produce until the revenue gained from the last unit equals its cost. But because MR < P, the monopolist stops at a lower quantity than the competitive outcome (where P = MC). Having found the profit-maximizing quantity, the monopolist reads the price off the demand curve (not off MR). This is the two-step monopoly solution: (1) find Q where MR = MC, (2) find P from the demand curve at that Q. The gap between price and marginal cost is where monopoly profit comes from — and where social harm originates.

That social harm takes the form of deadweight loss: transactions that would have been mutually beneficial (for consumers willing to pay above MC but below the monopoly price) never occur. Resources that could have been used productively are not. The Lerner Index (P − MC) / P = 1 / |PED| quantifies the markup as a fraction of price and connects it to demand elasticity — a monopolist with inelastic demand can charge a large markup; one facing elastic demand cannot. This explains why pharmaceutical companies (with patented drugs that have few substitutes) earn larger margins than, say, gasoline retailers.

A common misconception is that the monopolist charges "the highest possible price." In fact, raising price beyond the optimum reduces quantity so much that total profit falls — the lost sales more than offset the higher margin per unit. The monopolist is constrained by demand, not unconstrained. It is a price-maker, not a price-ignorer. Understanding this distinction prepares you for studying price discrimination, where a monopolist extracts more surplus by charging different prices to different buyers — and potentially eliminates deadweight loss in the extreme case of perfect price discrimination.

Practice Questions 3 questions

Prerequisite Chain

Understanding ZeroThe Number ZeroCounting to FiveCounting to 10Counting to 20Counting a Set of Objects Up to 20Cardinality: The Last Number CountedMatching Numerals to QuantitiesSubitizing Small QuantitiesAddition Within 10Number Bonds to 10Addition Within 20Doubles and Near DoublesDoubles Facts Within 10Near Doubles Facts Within 20Mental Math Strategies for AdditionMental Math: Adding and Subtracting TensAddition Within 100Repeated Addition as MultiplicationMultiplication as Equal GroupsMultiplication: ArraysBasic Multiplication Facts (0s, 1s, 2s, 5s, 10s)Multiplication Facts Within 100Division as Equal SharingDivision as Grouping (Measurement Division)Division: Grouping (Repeated Subtraction) ModelDivision: Fair Sharing ModelDivision as Equal SharingDivision as GroupingBasic Division FactsDivision Facts Within 100Multiplication and Division Fact FamiliesRelationship Between Multiplication and DivisionDivision Facts as Inverse of MultiplicationRemainders and Quotients in DivisionDivision Word ProblemsMulti-Step Word ProblemsSolving Multi-Step Word ProblemsMultiplication Word ProblemsDivision Word ProblemsIntroduction to Long DivisionFactors and MultiplesPrime and Composite NumbersEquivalent FractionsRelating Fractions and DecimalsDecimal Place ValueIntegers and the Number LineComparing and Ordering IntegersAbsolute ValueAdding IntegersSubtracting IntegersMultiplying IntegersDividing IntegersUnit RatesProportionsPercent ConceptConverting Between Fractions, Decimals, and PercentsOperations with Rational NumbersTwo-Step EquationsSolving Multi-Step EquationsEquations with Variables on Both SidesLiteral EquationsSlope-Intercept FormPoint-Slope FormWriting Linear EquationsParallel and Perpendicular Line SlopesGraphing Linear EquationsPiecewise FunctionsOne-Sided LimitsContinuity DefinitionLimits and Continuity in Multiple VariablesFunctions of Several VariablesContinuity in Multiple VariablesPartial Derivatives: Definition and ComputationDifferentiability in Multiple VariablesDifferentiability in Multivariable FunctionsTotal Differential and Linear ApproximationChain Rule for Multivariable FunctionsImplicit DifferentiationRelated RatesOptimization ProblemsCritical Points of Multivariable FunctionsCritical Points and Classification of ExtremaSecond Partial Test for Local Extrema (Hessian)The Hessian Matrix and Second Derivative TestUnconstrained Optimization: Finding ExtremaOptimization in Multiple VariablesLagrange MultipliersConstrained Optimization and Lagrange MultipliersUtility and PreferencesMarginal Utility and Diminishing ReturnsProfit MaximizationPerfect CompetitionShutdown and Breakeven DecisionsMonopoly

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