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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 more→→Welfare Analysis: Deadweight Loss and Policy Evaluation
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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 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 → Budget Constraint → Indifference Curves → Consumer Optimum → Consumer Duality: Expenditure and Indirect Utility Functions → Hicksian Demand (Compensated Demand) → The Slutsky Equation → Compensating and Equivalent Variation: Welfare Measurement

Longest path: 98 steps · 493 total prerequisite topics

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