Phillips Curve Dynamics in Modern Models

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inflation unemployment expectations price-setting

Core Idea

The modern Phillips curve relates inflation to expected future inflation and current economic slack, with the slope reflecting nominal rigidities and firms' pricing behavior. This forward-looking specification—derived from New Keynesian models—shows that inflation depends on firms' expectations about future demand and costs, not just current conditions. The Phillips curve forms a key constraint on monetary policy trade-offs and crucially depends on the anchoring of inflation expectations.

Explainer

The original Phillips curve you studied — a negative relationship between inflation and unemployment — was an empirical regularity that broke down in the 1970s when high inflation and high unemployment coexisted (stagflation). Friedman and Phelps had already predicted this: once workers and firms adjust their inflation expectations upward, the short-run tradeoff between inflation and unemployment shifts, and there is no permanent tradeoff to exploit. The modern New Keynesian Phillips Curve (NKPC) incorporates this lesson by making expectations the central driver of inflation dynamics.

The NKPC takes the form: π_t = βE_t[π_{t+1}] + κx_t, where π is inflation, E_t[π_{t+1}] is expected future inflation, x is the output gap (or marginal cost), β is a discount factor close to 1, and κ is the slope parameter. This equation emerges from the microeconomics of staggered price setting — the Calvo model where each period only a fraction of firms can adjust their prices. A firm that gets the chance to reset its price must think ahead: it sets a price that is optimal not just for today but for the entire expected duration until it can adjust again. If the firm expects higher inflation in the future, it sets a higher price today to avoid being stuck below its competitors. This forward-looking behavior is what makes the NKPC fundamentally different from the backward-looking, expectations-augmented Phillips curve.

The slope parameter κ encodes the degree of nominal rigidity in the economy. When prices are very sticky (few firms adjust each period), κ is small, and inflation responds weakly to the output gap — the central bank must engineer large output fluctuations to move inflation. When prices are flexible (many firms adjust frequently), κ is large, and inflation responds readily to demand conditions. Empirically, κ appears to be quite small in advanced economies, which explains why the Phillips curve has looked "flat" in recent decades: large swings in unemployment during the Great Recession produced only modest declines in inflation.

The critical policy implication involves expectations anchoring. If the central bank is credible — agents believe it will keep inflation near target — then E_t[π_{t+1}] stays close to the target, and inflation fluctuations are small and transient. The Phillips curve becomes a constraint the central bank can work with. But if credibility erodes and expectations become unanchored, the feedback loop turns vicious: higher expected inflation causes higher actual inflation (firms set higher prices today expecting higher costs tomorrow), which further raises expectations. This self-fulfilling dynamic explains why central banks guard their inflation-fighting credibility so fiercely, and why re-anchoring expectations after a period of high inflation — as Volcker did in the early 1980s — requires a costly recession to convince agents that the central bank will follow through on its commitment.

Practice Questions 5 questions

Prerequisite Chain

Counting to 10Counting to 20Understanding ZeroThe Number ZeroCounting to FiveOne-to-One CorrespondenceCombining Small Groups Within 5Addition Within 10Addition Within 20Two-Digit Addition Without RegroupingTwo-Digit Addition with RegroupingAddition Within 100Repeated Addition as MultiplicationMultiplication 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 100Two-Digit by One-Digit DivisionDivision with RemaindersRemainders and Quotients in DivisionDivision Word ProblemsIntroduction to Long DivisionFactors and MultiplesPrime and Composite NumbersEquivalent FractionsRelating Fractions and DecimalsDecimal Place ValueReading and Writing DecimalsComparing and Ordering DecimalsAdding and Subtracting DecimalsMultiplying DecimalsDividing DecimalsDividing FractionsMixed Number ArithmeticOrder of OperationsInteger Order of OperationsVariable ExpressionsCombining Like TermsOne-Step EquationsTwo-Step EquationsSolving Multi-Step EquationsEquations with Variables on Both SidesAngle Pairs: Complementary, Supplementary, and VerticalParallel Lines and TransversalsCorresponding AnglesAlternate Interior AnglesTriangle Angle Sum TheoremExterior Angle TheoremTriangle Inequality TheoremSimilar Triangles: AA SimilaritySimilar Triangles: SSS and SAS SimilarityProportions in Similar TrianglesRight Triangle Trigonometry IntroductionTrigonometric Ratios ReviewRadian MeasureConverting Between Degrees and RadiansThe Unit CircleGraphing Sine and CosineGraphing Tangent and Reciprocal Trigonometric FunctionsDerivatives of Trigonometric FunctionsAntiderivativesIndefinite IntegralsBasic Integration RulesRiemann SumsDefinite Integral DefinitionFundamental Theorem of Calculus Part 1Fundamental Theorem of Calculus Part 2U-SubstitutionIntegration by PartsSeparable Differential EquationsIntegrating Factor Method for First-Order Linear ODEsFirst-Order Linear Ordinary Differential EquationsSecond-Order Linear Homogeneous Differential EquationsCharacteristic Equation Method for Linear ODEsComplex Roots and Oscillatory SolutionsSpring-Mass Systems and Mechanical VibrationsResonance and Damping in Forced VibrationsRLC Circuit Applications of Differential EquationsIntroduction to Differential EquationsEuler's Method for Numerical SolutionsLinearization of Nonlinear SystemsBaseline New Keynesian ModelPhillips Curve Dynamics in Modern Models

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