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Compensating and Equivalent Variation: Welfare Measurement

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Consumer Duality: Expenditure and Indirect Utility FunctionsConsumer and Producer Surplus+2 moreWelfare Analysis: Deadweight Loss and Policy Evaluation
welfare-analysis consumer-theory policy

Core Idea

Compensating Variation (CV) measures the income change needed after a price change to restore original utility, while Equivalent Variation (EV) measures the income change before a price change to make the consumer indifferent to the actual change. Both are theoretically superior to consumer surplus because they account for income effects and are calculated using expenditure functions: CV = e(p₁, u₀) - m and EV = m - e(p₀, u₁).

How It's Best Learned

Draw budget lines and indifference curves before/after a price change. Identify the original bundle, new bundle, and hypothetical bundles where CV and EV apply. Calculate using expenditure functions for standard preferences like Cobb-Douglas.

Common Misconceptions

Explainer

From consumer surplus, you know how to measure welfare using the area between the demand curve and the market price. And from the Slutsky equation, you know that a price change has two distinct effects: a substitution effect (the relative price change holding utility constant) and an income effect (the change in purchasing power). The problem with ordinary consumer surplus is that it ignores this decomposition — it uses the Marshallian demand curve, which blends both effects together. When income effects are significant, consumer surplus gives an imprecise answer to the question "how much better or worse off is this consumer?" Compensating variation and equivalent variation solve this by anchoring welfare measurement to a specific utility level.

Compensating variation (CV) asks: after a price change has occurred, how much money must we give to (or take from) the consumer to restore them to their original utility level? Imagine the price of gasoline doubles. You move to a new, lower indifference curve. CV is the dollar amount that, if handed to you at the new prices, would put you back on your original indifference curve. Formally, CV = e(p₁, u₀) − m, where e is the expenditure function (the minimum cost of achieving utility u at prices p), p₁ is the new price vector, u₀ is the original utility, and m is actual income. If the price increase hurts you, CV is positive — it is the compensation you need.

Equivalent variation (EV) asks the reverse question: before the price change occurs, how much money would you be willing to give up to avoid the change, leaving you at the new utility level? EV = m − e(p₀, u₁), where p₀ is the original price and u₁ is the new utility. For a harmful price increase, EV is the amount you would pay at original prices to prevent the increase. EV is anchored to the new utility level and evaluated at old prices, while CV is anchored to the old utility level and evaluated at new prices. The distinction matters because each measure uses a different Hicksian (compensated) demand curve — one holding utility at u₀, the other at u₁.

For a normal good facing a price increase, EV < consumer surplus change < CV. The three measures converge when income effects are zero (quasilinear preferences), because the Marshallian and both Hicksian demand curves coincide. In practice, CV is the natural measure for evaluating a policy that *has been implemented* — it tells you the compensation needed to make losers whole. EV is natural for evaluating a *proposed* policy — it tells you the maximum amount people would pay to see it enacted (or to prevent it). This distinction makes CV and EV indispensable tools in cost-benefit analysis, where the choice of welfare measure can change whether a project passes or fails the test of improving social welfare.

Practice Questions 5 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 ReturnsBudget ConstraintIndifference CurvesConsumer OptimumConsumer Duality: Expenditure and Indirect Utility FunctionsHicksian Demand (Compensated Demand)The Slutsky EquationCompensating and Equivalent Variation: Welfare Measurement

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