E31 | Macro Paper Warehouse

A Monetary-Fiscal Theory of Sudden Inflations

Mon, 01 Jan 0001 00:00:00 +0000

Overview

Research Question. Why do sudden inflations and currency crises occur, while symmetric sudden deflations never do? The paper asks whether treating nominal government bonds as analogous to ordinary corporate bonds — with an asymmetric payoff structure capped at face value on the upside but exposed to real losses when fiscal surpluses are insufficient — can generate a unified theory of these crises endogenously from a single model.

Intellectual Lineage and Approach. The paper sits at the intersection of two literatures. The first is the Fiscal Theory of the Price Level (FTPL), originating with Leeper (1991), Sims (1994), and Sargent and Wallace (1985), which links the real value of nominal government debt to expected future surpluses. The second is the safe-asset literature, where Holmstrom (2015) and Gorton (2017) explain that assets can circulate as safe stores of value precisely because their backing is costly to investigate and consumers rationally remain uninformed. The paper applies this information-economics logic to nominal government bonds, so that consumers normally hold bonds without investigating the government’s true fiscal capacity, and only pay the cost to investigate when real repayment doubts become sufficiently severe.

Model Structure. The model is a two-period reduced-form general equilibrium. In period 1, a representative consumer buys nominal government bonds at an interest rate set by the monetary authority. In period 2, the government must repay those bonds. The fiscal authority attempts to hit a price-level target P* by raising tax revenue, but faces a hard ceiling τ_max on the surplus it can collect — arising from Laffer limits on taxation, political constraints on austerity, or the need to fund financial-sector bailouts. The consumer has prior beliefs that τ_max is low (L) with probability π and high (H) with probability 1−π, and can pay a fixed utility cost γ to learn τ_max before deciding how many bonds to purchase.

Bond Payoff Structure and Asymmetry. The key mechanism is the asymmetric, bond-like real payoff of nominal government debt. If τ_max ≥ B1/P*, the government raises enough surplus to repay bonds fully in real terms at the price-level target; the real payoff is flat at face value (the “in-the-money” region). If τ_max < B1/P*, the government sets taxes to the ceiling τ_max and the price level rises above P* to balance the budget constraint, reducing the real payoff proportionally (the “default” region). Critically, because the nominal payoff is capped at face value, there is no upside region: governments will not run surpluses large enough to deliver a windfall to bondholders, so sudden deflations — analogous to a corporate bond being worth more than face value — cannot occur. This asymmetry is the direct source of the one-sided nature of crises.

Two Illustrative Mechanisms for Sudden Inflations. The paper numerically and analytically characterizes two triggering scenarios:

Lower surplus expectations (fiscal stress narrative, corresponding to Burnside et al. 2001 on the 1997 Asian crisis): As the probability π of a low future surplus (e.g., from a prospective banking-sector bailout) rises, the value of information about τ_max increases. In the numerical example (i = 0.05, γ = 0.13, L = 0.1), the value of information equals the cost γ at π = 0.15. For π above 0.15, consumers pay to investigate, learn τ_max = L, and refuse to purchase bonds beyond what will be repaid in real terms (B1 = τ_max = L = 0.1). The price level in period 1 rises discontinuously as a function of π at this threshold.
Interest rate increases (speculative attack narrative): As the monetary authority raises the interest rate to defend a currency, consumers demand more bonds. Larger bond quantities increase the risk that surpluses will be insufficient, raising the value of fiscal information. In the numerical example (π = 0.5, γ = 0.24, 1+i ∈ [1, 1.2]), the value of information equals γ at 1+i = 1.1 (i.e., i = 10%). For interest rates above this threshold, consumers learn τ_max = L, restrict bond purchases to what will be repaid, and the price level in period 1 jumps discontinuously. Further interest rate increases above the threshold produce only upward drift in the price level, not additional monetary tightening effects — illustrating the limits of monetary policy in fiscally stressed environments.

Theoretical Results. Two formal theorems establish generality. Theorem 1 shows that, given bond demand B1(π) such that L < B1 for all π ∈ (0,1), there exist thresholds k and γ > 0 such that the period-1 price level P1 is discontinuous as a function of π on (0, k]. Theorem 2 establishes an analogous discontinuity in P1 as a function of the interest rate i, given that B1(i) > L for all i in the relevant range.

Scope Conditions. The model is a two-period reduced form that abstracts from dynamics, multiple maturities, and secondary market trading. The informational friction is a fixed binary cost γ, not a richer signal structure. The results depend on the existence of a binding surplus ceiling τ_max; when the government is far from this ceiling (i.e., consumers’ beliefs are far from the “default boundary”), shocks produce only small, smooth price-level changes. Large discontinuous price-level jumps require the economy to be near the kink point of the bond payoff curve.

Q&A

Q1: What is the fundamental analogy that drives the paper’s theory, and what economic literature does it build on?

The paper analogizes nominal government bonds to corporate bonds (following Sargent 1982’s advice that “government debt is valued according to the same economic considerations that give private debt value”). Like a corporate bond, the nominal government bond pays its face value if the underlying project (government fiscal capacity) delivers a surplus at least equal to the face value, but pays only a share of the realized surplus if the surplus falls short. This bond-like payoff — flat on the upside, proportional to outcomes on the downside — is the direct source of asymmetric crisis dynamics. The paper combines this with Holmstrom (2015) and Gorton (2017)’s framework in which safe assets function because their backing is costly to investigate, so consumers rationally remain uninformed in normal times.

Q2: What is the key information friction, and how does it generate the switch between “normal times” and crisis?

In normal times, consumers are confident that the government’s future maximum surplus τ_max is sufficient to repay bonds in real terms. The fixed utility cost γ of investigating the true surplus exceeds the benefit, so consumers remain uninformed and bonds trade at a price reflecting only uninformed prior beliefs. A crisis arises when the value of information V(.) rises above γ — either because the probability of a low surplus state rises (fiscal stress) or because the interest rate rises and consumers demand more bonds, bringing them closer to the repayment boundary. Once V > γ, consumers investigate and, upon learning τ_max = L (low surplus), refuse to hold bonds that will not be repaid in real terms, triggering a discrete upward jump in the price level.

Q3: How does the bond payoff structure explain the absence of sudden deflations?

The real payoff of a nominal government bond cannot exceed its face value: the bond is capped at face value on the upside because the government will not voluntarily raise tax surpluses to deliver a windfall to bondholders. In the event that surpluses turn out to be higher than needed (τ_max ≥ B1/P*), the government simply sets taxes to exactly repay the bonds at P* and returns no additional real value to bondholders. This is the flat portion of the payoff curve. Because there is no upside kink — no region where learning that τ_max is unexpectedly large causes the price level to fall sharply — there is no mechanism for sudden deflations symmetric to sudden inflations. The 1933 U.S. episode (Jacobson et al. 2019) is cited: when deﬂation from leaving gold would have required fiscal austerity for full real repayment, Roosevelt chose to exit the gold standard rather than allow deflation.

Q4: How does the first numerical example (lower surplus expectations) work quantitatively?

The baseline parameters are: i = 0.05, γ = 0.13, L = 0.1, H ≈ ∞, P* = 1, e1 = e2 = 1, B0 = 1, τ1 = 0.8, β = 1. The analysis is restricted to π ∈ (0, 0.3]. As π (probability that τ_max = L) rises, the value of information V(.) rises. At π = 0.15, V equals the cost γ = 0.13. For π > 0.15, consumers pay to investigate and, upon learning τ_max = L, purchase only B1 = L = 0.1 in bonds — the amount that will be repaid — causing the period-1 price level P1 to jump discontinuously from approximately 0.95 to approximately 1.13. For π ≤ 0.15, consumers remain uninformed and P1 rises only smoothly from below 1 as π increases (fewer bonds demanded as repayment risk rises, even without investigation).

Q5: How does the second numerical example (interest rate increase) work quantitatively, and what does it imply for monetary policy?

With π = 0.5, γ = 0.24, and 1+i ∈ [1, 1.2], as the monetary authority raises the interest rate, consumers demand more bonds, increasing real repayment risk and the value of information. At 1+i = 1.1 (i.e., i = 10%), V equals γ. For 1+i > 1.1, consumers investigate and learn τ_max = L; they then only purchase bonds up to the repayment limit, causing P1 to jump discontinuously to approximately 1.15. For interest rates above the threshold, further increases yield only a smooth upward slope in P1 (bond purchases are fixed in real amount but nominal revenue falls). This illustrates that the monetary authority’s ability to use higher interest rates to lower the price level is limited by the surplus constraint: once the interest rate is high enough to trigger consumer investigation and a fiscal crisis, raising rates further is inflationary rather than deflationary.

Q6: What are the two regions of the deterministic model and how do they differ in fiscal and price-level dynamics?

In the deterministic version (1-π = 0, so τ_max = L with certainty, and there is no uncertainty), the model produces two distinct regions. In the “insufficient surplus” region where τ_max < B1/P*, the fiscal authority sets taxes to their maximum τ_max, the real payoff of bonds is τ_max/B1 < 1, the period-1 price level P1 = B0/(βτ_max), and real bond revenue Π = βτ_max (constant in τ_max). Selling additional bonds does not raise additional real revenue because any extra bonds lead to a proportional rise in P2 and a fall in Q. In the “sufficient surplus” region where τ_max ≥ B1/P*, the government meets its fiscal target (τ2 = B1/P*), P2 = P* is hit, P1 = βB1/(B0P*), and Π = βB1/P* (increasing in B1). In this region, selling additional bonds does raise real revenue and lowers P1 as the government absorbs more money.

Q7: What are the two interest rate regions in the deterministic model, and what is their implication for monetary policy effectiveness?

Using B1 = B0(1+i) (debt rolled over at the chosen rate), the monetary authority has two interest-rate regions. In the “constrained” region where 1+i > τ_max P*/B0 (the surplus ceiling binds), raising i does not change the period-2 surplus (τ2 = τ_max), does not change real revenue (Π = βτ_max), and does not affect P1 — but raises P2 above the target P*. In the “unconstrained” region where 1+i ≤ τ_max P*/B0, raising i increases bond demand, increases real surplus backing, raises real revenue, and lowers P1 while P2 = P* is maintained. The boundary between these regions determines the limit of monetary policy: the monetary authority can reduce P1 by raising i only up to the point where the surplus ceiling would be hit.

Q8: How does the paper relate to and extend prior FTPL literature?

The paper is grounded in the FTPL of Leeper (1991), Sims (1994), and Cochrane (2005, 2020), in which the price level is determined by the requirement that real government liabilities equal the present value of future surpluses. The paper’s contribution is to make the information structure endogenous: consumers’ beliefs and their decision to acquire fiscal information determine whether or not the FTPL logic is operative. In normal times (consumers uninformed), the price level does not respond to changes in the maximum surplus — a result that resembles the “Ricardian” or non-FTPL regime. When consumers investigate and learn the surplus is insufficient, the connection between the surplus and the price level is restored, reproducing FTPL-type dynamics. This provides an endogenous, single-model rationale for the regime-switching behavior between FTPL and non-FTPL environments documented empirically in Bianchi and Melosi (2013, 2017) and Davig and Leeper (2006).

Q9: What is the welfare role of consumer ignorance in this framework?

Consumer ignorance of the government’s true surplus plays a dual role. On one hand, ignorance is individually rational in normal times because the cost γ of investigating exceeds the benefit V (.) when beliefs are comfortably away from the default boundary. On the other hand, following Dang et al. (2017), informed knowledge of the safe asset’s backing destroys the symmetric ignorance that supports the asset’s role as a safe store of value, reducing welfare. In this model the concern is repayment risk rather than adverse selection: the consumer fears not being repaid in real terms and chooses to investigate when that risk is sufficiently high, potentially triggering the very crisis they feared.

Q10: What are the scope conditions and limitations of the model?

The model is explicitly a two-period reduced form designed to illustrate the bond-payoff mechanism in the simplest possible setting. It abstracts from: multi-period bond maturities and secondary market trading; rich heterogeneity among consumers; endogenous monetary and fiscal policy responses beyond the simple rules specified; and the general equilibrium interactions between inflation, output, and labor markets. The information cost γ is modeled as a fixed binary cost rather than a continuous or richer signal structure. The results on discontinuous price-level jumps hold when bond demand is sufficiently large relative to L (i.e., L < B1), ensuring genuine repayment risk; when surpluses are very large relative to bond liabilities, no crisis dynamics arise.

Key Concepts

Maximum Surplus (τ_max). The paper’s name for the hard ceiling on the net tax revenue (taxes minus money transfers) the government can collect in the second period. This ceiling can arise from a Laffer limit on taxable income, political-economy constraints on austerity, or from a banking crisis requiring government transfers to bail out the financial sector. It is the paper’s analogue of a project’s liquidation value: the maximum the “project” (the government) can deliver to bondholders.

Bond-Like Payoff of Nominal Government Debt. The paper’s central structural claim: the real payoff to holding a nominal government bond is capped at face value on the upside (the government will not raise surpluses beyond what is needed to repay bonds at the price-level target) but falls proportionally below face value when τ_max is insufficient for full real repayment. This is precisely the payoff structure of a standard corporate bond — flat on the upside, proportional to recovery on the downside — and it is the source of the asymmetry between sudden inflations and the absence of sudden deflations.

Value of Information (V(.)). Defined as the difference in expected utility between a consumer who learns the true τ_max before making bond-purchase decisions and one who remains uninformed and acts only on prior beliefs π, 1−π. The consumer investigates if and only if V(.) > γ. V is zero when beliefs are certain (limπ→0 and limπ→1), can be hump-shaped in π, and is increasing in the interest rate i (through its effect on bond demand). The threshold condition V = γ defines the boundary between “normal times” (no investigation) and crisis (investigation and possible sudden inflation).

Endogenous Information Structure. The paper’s term for the property that whether consumers choose to learn the government’s fiscal capacity is itself determined within the model by the parameters of the economy (the interest rate, prior beliefs, the cost of investigation). This contrasts with models that exogenously specify whether agents are informed or not. The endogenous information structure is the mechanism by which the paper generates the two apparent regimes (FTPL-active vs. FTPL-dormant) from a single unified model.

Default Boundary. The kink point in the bond payoff curve at τ_max = B1/P*: the level of the maximum surplus at which the government exactly repays bonds in real terms at the price-level target. When beliefs or bond quantities place the economy near the default boundary, small changes in π or i can push the economy across it, triggering large price-level responses. When the economy is far from the boundary (τ_max comfortably above B1/P*), small shocks have only small smooth effects.

Sudden Inflation / Currency Crisis (as defined in this paper). A discrete, discontinuous jump in the period-1 price level P1 that occurs when consumers pass the threshold V(.) = γ and investigate the government’s fiscal capacity, finding surpluses to be insufficient. The mechanism is: informed consumers refuse to hold bonds they know will not be repaid in real terms at P*, forcing the price level to jump to clear the government’s budget constraint with fewer bonds outstanding. The paper treats sudden inflations and currency crises as the same mechanism in different institutional contexts.

Repayment Risk Premium. The markup above the risk-free rate that consumers require on government bonds to compensate for the probability that the government’s surplus will be insufficient for full real repayment (i.e., the probability that the economy is in the τ_max < B1/P* region). This premium is present even when consumers are uninformed (i.e., do not know which state of τ_max will occur), and is reflected in the consumer’s first-order condition for bond demand.

A Theory of Supply Function Choice and Aggregate Supply

Mon, 01 Jan 0001 00:00:00 +0000

Research Question

Modern macroeconomic models of aggregate supply universally restrict firms to price-setting — committing to a price and supplying whatever quantity the market demands. Flynn, Nikolakoudis, and Sastry ask: what happens if instead firms choose any supply function, a mapping that describes the price charged at each quantity of production? The paper develops the first general-equilibrium, macroeconomic theory of supply function choice and characterizes its implications for the slope of aggregate supply, monetary non-neutrality, and time-varying inflation-output tradeoffs.

Methodology

The paper proceeds in two stages. In partial equilibrium, a single monopolistic firm with constant-returns-to-scale technology and constant-elasticity demand faces log-normal uncertainty about demand shifters, the aggregate price level, real marginal costs, and the stochastic discount factor. The firm chooses a non-parametric supply function — any implicit mapping f(p,q) = 0 — to maximize expected real profits. The paper shows that supply function choice is equivalent to conditioning price-quantity decisions on the realized nominal demand state z = ΨP^η. The authors prove (Theorem 1) that the optimal supply function is endogenously log-linear: log p = α₀ + α₁ log q, where the inverse supply elasticity α₁ is characterized in closed form.

In general equilibrium, the authors embed supply function choice in an otherwise standard monetary business cycle model (in the tradition of Woodford 2003a and Hellwig and Venkateswaran 2009), featuring a representative household demanding differentiated goods, a money supply following a random walk with time-varying volatility, and idiosyncratic shocks to productivity, wages, and demand. They guess and verify a log-linear equilibrium and derive a scalar fixed-point equation for the equilibrium supply elasticity (Theorem 3).

For quantification, the authors calibrate structural parameters (η = 8 from Hottman et al. 2016 scanner data; γ = 0.11 from Gagliardone et al. 2023 Belgian firm data; κ^M = 0.29 calibrated to match an average aggregate supply slope of 0.11 from Hazell et al. 2022) and estimate time-varying uncertainty via a GARCH model of quarterly US data on GDP growth, inflation, and real marginal cost growth from 1960 Q1 to 2024 Q4. Idiosyncratic demand uncertainty is set proportional to aggregate TFP uncertainty using the proportionality factor R = 6.5 from Bloom et al. (2018).

Main Findings

Optimal supply function. The optimal firm-level supply function is log-linear with inverse supply elasticity α₁ determined by the relative variances and covariances of demand, the price level, and real marginal costs. Three comparative statics drive the macroeconomic results: (1) higher idiosyncratic demand uncertainty (σ²_Ψ) flattens the supply function toward price-setting, because a fixed price insulates profit markups against demand variation; (2) higher price-level uncertainty (σ²_P) steepens the supply function toward quantity-setting, because setting a fixed quantity allows relative prices to adjust; (3) lower price elasticity of demand (less elastic demand, more market power) flattens the supply function, conditional on a sufficient condition that holds in US data whenever η > 2.5.

From micro supply to aggregate supply. With fixed log-linear supply functions, the economy has a unique log-linear equilibrium with an AD/AS representation (Theorem 2). The slope of aggregate supply ε^S_t depends on ω₁ (the transformed inverse supply elasticity), κ^M (firms’ signal precision about the money supply), γ (income effects), and η (demand elasticity). Aggregate supply is maximally elastic — money is as non-neutral as possible — if and only if firms are pure price-setters (ω₁ = 0). Aggregate supply is perfectly inelastic — money is neutral — if and only if firms are quantity-setters (ω₁ = 1/η). A lower elasticity of demand flattens aggregate supply through general equilibrium strategic complementarities, a prediction opposite to the New Keynesian model.

Equilibrium supply slope and its determinants. The equilibrium ω₁ solves a fixed-point equation (Theorem 3) in which macroeconomic uncertainty shapes firms’ optimal supply functions, which in turn shape macroeconomic dynamics. Under the special case of balanced strategic interactions (ηγ = 1), the slope of aggregate supply has a clean closed form depending only on the ratio ρ_t = σ_{ϑ,t}/σ^M_{t|s} (idiosyncratic demand uncertainty relative to posterior monetary uncertainty). Critically, the equilibrium supply slope is invariant to the overall level of uncertainty — only the composition of uncertainty matters (Proposition 3). Even vanishingly small uncertainty can generate any level of monetary non-neutrality depending on uncertainty composition.

Quantitative results — United States over time. The model’s estimated slope of aggregate supply shows sharp variation since 1960. The slope is relatively flat and stable during the 1960s, the Great Moderation (1991–2007), the Great Recession (2008–2019), and the recovery from the Great Recession. It spikes dramatically during the 1970s oil crisis and the post-Covid inflation of the 2020s. Compared to Ball and Mazumder (2011), the model qualitatively matches the steepening during 1973–1984 (+58% in the model) vs. the data’s +175%, and a subsequent flattening of −25% vs. −32% in the data during 1985–2007. Compared to Cerrato and Gitti (2022), the model accounts for approximately 4/5 of the steepening between the pre-Covid and post-Covid periods (+112% model vs. +145% data). For the Hazell et al. (2022) comparison, the model accounts for approximately 1/2 of the estimated flattening from 1978–1990 to 1991–2018.

Quantitative results — Cross-country. Using OECD annual data from 1960–2019, the model’s predicted slope of aggregate supply is not positively correlated with the average level of inflation across countries. For countries with the highest inflation rates, the model predicts a negative slope of aggregate supply, driven by very high correlation between price-level uncertainty and real marginal cost uncertainty. The model-predicted slope correlates positively with the reduced-form regression coefficient of inflation on real output growth across countries, even after instrumenting for demand. This predictive power is over and above what can be explained by the level or volatility of inflation alone.

Scope Conditions

All results are derived under log-normality of uncertainty, which ensures the log-linear structure of optimal supply functions. The quantification relies on GARCH-estimated uncertainty and treats idiosyncratic demand uncertainty as proportional to aggregate TFP uncertainty. The model abstracts from microeconomic nominal price stickiness (though the authors show in Appendix B that Calvo-style sticky prices can be incorporated). The baseline model requires the equilibrium condition on firm beliefs to be consistent (rational expectations). Multiple equilibria of the scalar fixed-point are possible in principle, bounded by at most five log-linear equilibria (Proposition 2).

Q&A

Q1: What is wrong with assuming price-setting or quantity-setting as a primitive restriction on firm behavior?

A: Price-setting and quantity-setting are two isolated, generically non-optimal points in the larger space of supply functions. Corollary 2 establishes that price-setting is optimal only in the limit as idiosyncratic demand uncertainty becomes unboundedly large (σ²_Ψ → ∞), while quantity-setting is optimal only in the limit as price-level uncertainty becomes unboundedly large (σ²_P → ∞). In a macroeconomic environment where both sources of uncertainty are present in comparable magnitudes, both extreme policies perform poorly and the analyst who imposes either inadvertently restricts firms’ strategies in ways that have large macroeconomic consequences — for example, making money neutral under quantity-setting even when information frictions are present, or making the slope of aggregate supply invariant to demand elasticity under price-setting.

Q2: What is the formal equivalence between supply function choice and conditioning on realized demand?

A: The firm’s problem of choosing a supply function f(p,q) = 0 ex ante is mathematically equivalent to choosing a price-quantity plan (p(z), q(z)) indexed by the nominal demand state z = ΨP^η (Equation 4 in the paper). After the supply function is set, the firm produces where the supply function intersects the demand curve, which pins down the market-clearing outcome as a function of z. Choosing the supply function ex ante is therefore the same as choosing z-contingent prices and quantities without any parametric constraint. This links the model to rational expectations equilibrium in the spirit of Lucas (1972): firms use the demand for their product as a noisy signal to update beliefs and set their optimal price and quantity in response to realized demand conditions.

Q3: How is the optimal inverse supply elasticity α₁ derived, and what is the 2SLS interpretation?

A: Because the optimal supply function allows the firm to set a z-contingent price, the first-order condition at each realized demand state z = t equates expected marginal revenue and expected marginal cost (Equation 7). Under log-normality, this yields a log-linear relationship log p = α₀ + α₁ log q. The elasticity α₁ equals the ratio (d log p / d log z) / (d log q / d log z) = Cov[log z, log p**] / Cov[log z, log q**], where p** and q** are the full-information optimal price and quantity (Equation 9). This is formally equivalent to a 2SLS regression: the firm estimates how its optimal price should change with its optimal quantity, using the nominal demand state z as an instrument for the optimal quantity. The supply function is steep if nominal demand strongly predicts movements in the full-information optimal price (large reduced-form coefficient); it is flat if nominal demand primarily predicts movements in the full-information optimal quantity (large first-stage coefficient).

Q4: How do uncertainty and demand elasticity shape the firm’s optimal supply function in partial equilibrium?

A: Three key comparative statics apply when the supply function is upward-sloping. (1) Greater price-level uncertainty (σ²_P increases) steepens α₁ toward quantity-setting: not knowing competitors’ prices makes aggressive dynamic pricing attractive because it allows the firm’s relative price to adjust ex post. (2) Greater idiosyncratic demand uncertainty (σ²_Ψ increases) flattens α₁ toward price-setting: demand uncertainty favors a fixed price to keep the markup over real marginal costs constant, accommodating demand with quantity variation. (3) A lower price elasticity of demand (more market power, lower η) flattens α₁: more market power reduces the cost of setting the “wrong” price, reducing the benefit of dynamic pricing. Corollary 1 provides a sufficient condition — σ_{M,P} ≥ 0, 2ησ_{M,P} + σ_{M,Ψ} ≥ σ_{P,Ψ}, and α₁ ≥ 0 — under which ∂α₁/∂η > 0, implying greater market power flattens supply; the paper verifies this condition holds in US data whenever η > 2.5.

Q5: How does the model generate an aggregate supply and demand representation from supply function choices?

A: Theorem 2 establishes that, given any fixed log-linear supply functions with slope ω₁,t, there is a unique log-linear equilibrium. In this equilibrium, the price level and real output are jointly determined by an aggregate demand curve — shifting with the money supply but not productivity — and an aggregate supply curve — shifting with productivity but not the money supply. The inverse elasticity of aggregate supply is ε^S_t = γ(κ^M_t + ω₁,t(η − 1/γ)(1 − κ^M_t)) / ((1 − ω₁,t η)(1 − κ^M_t)), derived from aggregating firm-level pricing decisions. The slope depends on ω₁,t (micro supply), κ^M_t (signal precision about money), γ (income effects), and η (demand elasticity). An aggregate demand shock of ∆ log M raises the price level by ε^S_t ∆ log M / (ε^D_t + ε^S_t) and raises real output by ∆ log M / (ε^D_t + ε^S_t), where ε^D_t = γ is the inverse elasticity of aggregate demand.

Q6: What is the equilibrium fixed-point equation and why can there be multiple equilibria?

A: Theorem 3 shows that the equilibrium transformed inverse supply elasticity ω₁,t solves a quintic polynomial fixed-point equation (Equation 29) that depends on the variances of idiosyncratic demand shocks (σ²_ϑ,t), posterior uncertainty about productivity (σ^A_{t|s}), and posterior uncertainty about money (σ^M_{t|s}). Multiple equilibria can arise because of a self-reinforcing feedback: if firms set steep supply functions, prices respond more to demand, which raises price-level volatility, which in turn makes quantity-setting more attractive, further steepening supply functions. Proposition 2 establishes existence of at least one log-linear equilibrium and at most five. Idiosyncratic productivity and factor price uncertainty do not enter the fixed-point equation because the variance of real marginal costs per se does not affect optimal supply function choice — only the covariance of marginal costs with demand and the price level matters.

Q7: What determines the slope of aggregate supply in the special case of balanced strategic interactions (ηγ = 1)?

A: Under ηγ = 1 — where strategic complementarities from relative price effects exactly offset strategic substitutabilities from aggregate consumption effects — the slope of aggregate supply has the closed-form expression ε^S_t = γ(κ^M_t / (1 − κ^M_t))(1 + 1/(γ²ρ²_t κ^M_t)) where ρ_t = σ_{ϑ,t}/σ^M_{t|s} is the ratio of idiosyncratic demand uncertainty to posterior monetary uncertainty (Corollary 5). Aggregate productivity uncertainty drops out entirely because firms do not use the demand state to infer aggregate productivity when strategic interactions are balanced. As ρ_t → ∞ (idiosyncratic demand dominates), the slope converges to the price-setting value γκ^M_t/(1 − κ^M_t). As ρ_t → 0 (monetary uncertainty dominates), the slope goes to infinity, corresponding to quantity-setting and monetary neutrality.

Q8: What is the role of total uncertainty versus the composition of uncertainty?

A: Proposition 3 establishes a striking invariance result: if all standard deviations in the economy are scaled by a common factor λ > 0, the equilibrium supply elasticity and slope of aggregate supply are unchanged. The equilibrium outcomes depend only on the ratios of different sources of uncertainty, not their absolute magnitudes. This sharply distinguishes the model from menu-cost models, in which any increase in uncertainty unambiguously raises the benefit of price adjustment and steepens aggregate supply. A corollary is that idiosyncratic productivity uncertainty has no effect on the slope of aggregate supply in the supply function model, whereas it would steepen aggregate supply in Golosov-Lucas menu-cost models. Moreover, even a vanishingly small level of uncertainty can generate any level of monetary non-neutrality, because the equilibrium supply elasticity is discontinuous at zero uncertainty (ε^S_t (0) = {∞} while ε^S_t (λ) is bounded for any λ > 0).

Q9: How does market power (demand elasticity) affect the slope of aggregate supply, and why does this differ from the New Keynesian prediction?

A: In the supply function model, a lower elasticity of demand (more market power, lower η) flattens aggregate supply by reducing general-equilibrium strategic complementarities. When other firms raise their prices following a demand shock, a given firm faces higher relative demand; the strength of this effect is parameterized by η. With supply functions (ω₁,t ≠ 0), this relative demand increase generates an additional price response, so higher η steepens aggregate supply. Crucially, this effect is exactly zero if and only if firms are pure price-setters (ω₁,t = 0) — meaning the prediction that market power affects aggregate supply is absent from price-setting models. This is the opposite of the New Keynesian prediction: in Woodford (2003b) with decreasing returns to scale, a higher elasticity of demand (less market power) steepens the Phillips curve, because more elastic demand amplifies the quantity response to price changes and thereby the marginal cost response to nominal cost shocks.

Q10: How does the model rationalize the steepening of aggregate supply in the 1970s and 2020s?

A: The GARCH estimates of macroeconomic uncertainty show abrupt increases in inflation uncertainty during the 1970s oil crisis period and after the Covid-19 shock in the 2020s. In the model, a spike in aggregate price-level uncertainty (σ²_P increases) causes firms to choose steeper supply functions — closer to quantity-setting — endogenously. This steepens the aggregate supply curve so that demand shocks have larger nominal effects and smaller real effects. Quantitatively, relative to the base period, the model predicts a steepening of +58% during 1973–1984 and +112% during 2021–2023. The empirical comparisons are +175% (Ball and Mazumder 2011, 1973–1984) and +145% (Cerrato and Gitti 2022, 2021–2023). The model thus accounts for the direction and rough order of magnitude of both episodes but not their full extent. The quarterly time series of model-implied ε^S_t has a correlation of 0.93 with one-quarter-ahead inflation uncertainty and 0.62 with the quarterly level of inflation.

Q11: How does the cross-country evidence help distinguish the model from alternatives based on the level of inflation?

A: The cross-country analysis uses OECD data from 1960–2019 to construct country-level model-implied slopes of aggregate supply using the same structural parameters (η = 8, γ = 0.11, κ^M = 0.29) and country-specific GARCH uncertainty estimates from a one-lag VAR. The key finding is that the model-implied slope is not positively predicted by average inflation across countries (Panel A of Figure 5) — in fact, for the highest-inflation countries such as Chile, Israel, and Mexico, the model predicts a negative slope of aggregate supply, reflecting high correlation between price-level uncertainty and real marginal cost uncertainty. By contrast, the model-implied slope correlates positively with the reduced-form regression coefficient of inflation on real output growth (Panel B), and this positive correlation is also found using a model-derived instrument isolating exogenous monetary variation. This implies that relative uncertainties, not the mean or volatility of inflation per se, help account for cross-country heterogeneity in inflation-output tradeoffs beyond the predictions of Ball et al. (1988).

Q12: How can supply functions be integrated into larger linearized macroeconomic models?

A: Section 4.5 provides a general framework. For any model in which firms face a demand function q_it = d(p_it, z^D_it) and a value function V(p_it, q_it, z^V_it), log-linearization around a deterministic steady state yields an optimal pricing rule ˆp_it = ω₁,it ˆz^D_it (Equation 35) for some scalar ω₁,it determined by the covariance structure of the linearized model. The coefficients ω₁,it enter the standard representation of aggregate dynamics (McKay and Wolf 2023) through the ideal price index ˆP_t = ∫₀¹ ˆp_it di. The additional “rational expectations” restriction is that ω₁,it must be consistent with the equilibrium law of motion for prices. The paper argues that supply functions can thereby be embedded in the broad class of linearized DSGE models used for quantitative work, including models with decreasing returns, monopsony, endogenous markups, sticky prices, investment, and quality choice.

Q13: What are the implications of supply function choice for monetary policy discretion?

A: The model implies a thorny tradeoff for monetary policymakers. If a central bank wishes to maintain discretion — the ability to surprise private agents — this increases firms’ uncertainty about the money supply (higher σ²_M). Under balanced strategic interactions (ηγ = 1), greater posterior monetary uncertainty (σ^M_{t|s}) lowers the ratio ρ_t = σ_{ϑ,t}/σ^M_{t|s}, which flattens the aggregate supply curve (reduces ε^S_t) and thereby increases the real effect of monetary surprises. However, this also endogenously induces firms to set steeper supply functions — closer to quantity-setting — so that the aggregate supply curve steepens in response to the greater price-level uncertainty generated by such an environment. The paper therefore concludes that maintaining monetary policy discretion may be, at least partially, self-defeating.

Inverse supply elasticity (α₁): The percentage by which a firm increases its price in response to a one percent increase in production, characterizing the slope of the firm’s optimal supply function. It is endogenously log-linear and determined by the ratio of covariances relating the nominal demand state to the firm’s optimal price vs. optimal quantity under full information — formally equivalent to a 2SLS coefficient using nominal demand as an instrument.

Supply function: A mapping f(p, q) = 0 describing the locus of prices and quantities a firm commits to, as an implicit function over price-quantity pairs. Unlike price-setting (f depends only on p) or quantity-setting (f depends only on q), the general supply function allows prices to vary with realized demand, nesting both polar cases as limits of extreme uncertainty.

Nominal demand state (z): The composite variable z = ΨP^η that indexes the demand curve. Firms observing their own output market clearing can use z as a noisy signal for inference about the aggregate price level, real marginal costs, and monetary conditions. The supply function is formally equivalent to conditioning price-quantity choices on z.

Slope of aggregate supply (ε^S): The inverse elasticity of the aggregate supply curve in the AD/AS representation, measuring the relative within-period response of the price level versus real output to an aggregate demand shock. It depends on the slope of firm-level supply functions (ω₁) interacted with the information precision about the money supply (κ^M) and income effects (γ).

Transformed inverse supply elasticity (ω₁): The reparameterization ω₁ = α₁/(1 + ηα₁), where α₁ is the firm-level inverse supply elasticity and η is the price elasticity of demand. ω₁ = 0 corresponds to price-setting; ω₁ = 1/η corresponds to quantity-setting. The equilibrium value of ω₁ solves a fixed-point equation that maps macroeconomic uncertainty back into firms’ optimal supply function choices.

Balanced strategic interactions (ηγ = 1): A parametric special case in which strategic complementarities from aggregate demand externalities (parameterized by η) exactly offset strategic substitutabilities from wage pressure (parameterized by 1/γ). Under this condition, the slope of aggregate supply has a closed-form solution that depends only on the relative uncertainty about idiosyncratic demand vs. the money supply.

Relative uncertainty sufficient statistic (ρ_t): The ratio σ_{ϑ,t} / σ^M_{t|s}, measuring firms’ uncertainty about idiosyncratic demand shocks relative to posterior uncertainty about the money supply. Under balanced strategic interactions (ηγ = 1), ρ_t is the single sufficient statistic determining the equilibrium slope of aggregate supply. As ρ_t → ∞ (idiosyncratic demand uncertainty dominates), firms converge to price-setting and aggregate supply flattens; as ρ_t → 0 (monetary uncertainty dominates), firms converge to quantity-setting and aggregate supply becomes vertical.

Invariance to total uncertainty: A key property of the model: the equilibrium slope of aggregate supply is invariant to the overall scale of uncertainty (Proposition 3). Only the composition of uncertainty across idiosyncratic vs. aggregate sources and demand vs. productivity shocks matters. This distinguishes the model from menu-cost models, in which any increase in uncertainty raises the benefit of price flexibility and steepens aggregate supply regardless of uncertainty composition.

Aggregation and the Estimation of Quality Change

Mon, 01 Jan 0001 00:00:00 +0000

Errico and Lashkari address two intertwined problems in the measurement of aggregate price indices: how to account for quality change and variety entry/exit when the demand system is not CES, and how to identify flexible demand systems from prices and market shares alone when supply and demand shocks are correlated. The paper makes a theoretical contribution and a methodological one, then applies both to the measurement of US import price inflation over 1989–2016.

The theoretical contribution generalizes the unified CES price index of Redding and Weinstein (2020a) and the Feenstra (1994) variety correction to the full class of smooth, invertible demand systems. The key insight is that the contribution of quality change to the aggregate price index depends on heterogeneous cross-product elasticities of substitution, not a single scalar as in the CES case. For practical implementation, the paper specializes to the Homothetic with Aggregator (HA) family of demand systems — which includes Kimball (1995), CRESH (Hanoch, 1971), and HSA (Matsuyama and Ushchev, 2017) — showing that within this family cross-product elasticities collapse to product-level elasticities, dramatically reducing dimensionality. The resulting approximate price index (Proposition 2) weights each product by its love-of-variety index 1/(epsilon_it − 1), departing from the uniform CES weighting.

The methodological contribution is a dynamic panel (DP) identification strategy that exploits the Markov structure of quality shocks. The paper assumes that innovations to product quality are mean-zero conditional on lagged prices. Under flexible pricing, firms maximize current-period profits without regard to future demand shocks, so lagged prices are valid instruments for current prices. This permits identification of rich demand systems without external cost instruments and without the conventional assumption of uncorrelated supply and demand shocks. The conventional Feenstra–Broda–Weinstein (FBW) approach imposes zero correlation between quality shocks and prices; the paper shows that when quality and marginal cost are positively correlated, FBW produces downward-biased elasticity estimates (endogeneity bias).

The empirical application constructs a dataset covering 155 time-consistent 5-digit NAICS industries over 1989–2018, matching US customs import data with domestic production data and treating country-of-origin varieties as the unit of observation. The paper estimates both CES and Kimball demand systems using the DP approach and compares them to FBW estimates.

Key quantitative findings: First, DP-estimated CES elasticities are larger on average than FBW estimates (weighted mean 5.99 vs. 4.62), confirming a downward endogeneity bias in conventional methods. Second, Kimball mean elasticities exceed CES estimates (weighted mean 3.11 for Kimball vs. 5.99 for CES at the industry level, but the Kimball distribution has a mean of 17.0 and median 4.70), reflecting a heterogeneity bias — CES understates the dispersion of elasticities and thereby understates the elasticity relevant for the base (domestic) product whose market share is declining. Third, quality improvements in imported goods reduced the US import price index by approximately 20.2 percentage points cumulatively (0.67 p.p. annually) under Kimball demand, and 15.9 percentage points cumulatively (0.53 p.p. annually) under CES demand, over 1989–2018. The headline figure cited in the abstract is approximately 0.7 p.p. annually. The aggregate import price index (price plus quality components combined) fell by 8.25 p.p. cumulatively under Kimball and 4.01 p.p. under CES, compared to a BEA PCE index increase of 57.8 p.p. over the same period. Sectorally, machinery and electrical equipment account for roughly 60% of total quality gains (~200 p.p. cumulative). By country, China accounts for approximately 35% of cumulative quality gains, with non-OECD countries collectively contributing ~59%, and China’s quality upgrading accelerating after WTO accession.

Validation using US automobile market data (1980–2018) confirms the DP identification assumption: controlling for current product characteristics, future characteristics are uncorrelated with current prices. The DP approach produces elasticity estimates and quality change measures similar to those obtained using real exchange rate cost-shock instruments, and the Kimball demand closely matches mixed logit (BLP) estimates of both price elasticities and price indices. CES estimates exhibit a measurable downward heterogeneity bias in this validation setting, which the paper traces theoretically and empirically to a positive covariance between demand elasticities and price volatility across products.

Scope conditions: results apply to homothetic (income-invariant) demand; nonhomothetic extensions are provided as a generalization (Proposition 4) but not the primary focus. The import price index measures the cost of imports conditional on given domestic consumption; it does not capture full consumption-side welfare effects including substitution away from domestic varieties.

Q1: What is the core theoretical result on price index measurement beyond CES? Proposition 1 shows that for any smooth, invertible demand system satisfying the connected substitute property, the change in the log aggregate price index can be approximated as a weighted sum of log price changes and log expenditure share changes, with the expenditure share changes premultiplied by the inverse of the matrix Psi_t capturing cross-product elasticities of substitution. In the CES special case this reduces to the scalar (1/(sigma−1)) weight of the Redding-Weinstein (2020a) CUPI. The key departure in general demand is that the weight applied to each product’s expenditure share change is heterogeneous and depends on the full matrix of cross-product substitutabilities, not a single constant.

Q2: How does the HA (Homothetic with Aggregator) family simplify the theoretical results? For HA demand — which nests Kimball, CRESH, and HSA — Lemma 1 establishes that cross-product elasticities sigma_ij depend only on product-level elasticities epsilon_i through simple analytic formulas (e.g., epsilon_i * epsilon_j / epsilon-bar for HDIA), reducing the estimation problem from an N×N matrix to a vector of N scalars. Proposition 2 then gives an approximate price index in which each product’s expenditure share change is weighted by its love-of-variety index 1/(epsilon_it − 1), rather than a common CES scalar. This is the operative formula for the Kimball application.

Q3: What is the endogeneity bias in conventional elasticity estimation and how large is it? Conventional FBW methods assume supply and demand shocks are uncorrelated; when quality improvements are positively correlated with product prices (e.g., higher-quality goods command higher prices and also have higher marginal costs), FBW estimates are biased downward. The paper documents this: for CES demand, the DP-estimated weighted mean elasticity is 5.99 versus 4.62 under FBW, and for median estimates the DP value is 4.27 versus 2.58 under FBW, across 155 industries. The bias matters because underestimated elasticities imply underestimated quality changes and a smaller quality correction to the price index.

Q4: What is the heterogeneity bias and how does it differ from the endogeneity bias? Even after correcting for endogeneity, CES demand imposes a single elasticity per industry, ignoring the cross-product distribution. The paper shows that the CES estimate is an average that does not correctly capture the behavior of the base product (the domestic US variety) whose market share is declining. Because the domestic variety tends to have a lower elasticity than the import average, CES understates this product’s love-of-variety index and thereby understates the quality correction attributable to rising import shares. Theoretically and empirically (Appendix E.4), this bias is larger when demand elasticities covary positively with price volatility across products.

Q5: What is the dynamic panel identification assumption and why does it hold under flexible pricing? The paper assumes that quality shock innovations u_it are mean-zero conditional on lagged log prices: E[u_it | log p_it−1] = 0. Under flexible pricing, firms maximize current-period profits using current variables only; current prices are determined by current quality but are not chosen in anticipation of future quality shocks. Therefore lagged prices are uncorrelated with future quality innovations, making them valid instruments for current prices. This assumption is validated empirically in the automobile market: controlling for current product characteristics (horsepower, weight, fuel economy), future characteristics are not correlated with current prices.

Q6: What are the headline findings on quality change in US import prices? Under Kimball demand, quality improvements in imported goods reduced the US import price index by 20.2 percentage points cumulatively over 1989–2018, equivalent to 0.67 p.p. annually (the abstract rounds this to approximately 0.7 p.p. annually). Under CES demand, the quality contribution is 15.9 p.p. cumulatively (0.53 p.p. annually). The aggregate import price index combining price and quality changes fell by 8.25 p.p. under Kimball and 4.01 p.p. under CES over the same period. These figures imply that official import price statistics substantially overstate import price inflation by failing to account for quality improvements.

Q7: Which sectors and countries drive the quality gains? Machinery and electrical equipment account for approximately 60% of total cumulative quality gains, with roughly 200 p.p. cumulative quality improvement in that sector. Computer and peripheral equipment (NAICS 3341) is a notable contributor — the official import-to-producer price ratio shows a nearly five-fold increase between 1989 and 2018, but after quality adjustment this ratio reverses direction. By country of origin, China accounts for approximately 35% of cumulative quality gains; other non-OECD countries collectively contribute approximately 59%; OECD countries contribute approximately 7%. China’s quality upgrading is documented to accelerate following its WTO accession.

Q8: Why does CES understate the quality correction relative to Kimball? The primary mechanism is that the US domestic variety — which serves as the numeraire for quality measurement — has a declining market share over the sample period. In Kimball demand, products with declining market shares are assigned lower elasticities (higher love-of-variety indices), amplifying the quality correction associated with import share gains. CES imposes a uniform elasticity, failing to capture this asymmetry. The paper shows that the key driver of the CES-Kimball gap in the import price index is CES underestimating the love-of-variety index of the base domestic product.

Q9: How is the identification approach validated in the automobile market? Using the Berry-Levinsohn-Pakes dataset extended by Grieco et al. (2024) for 1980–2018, the paper first verifies empirically that future product characteristics (horsepower, weight, fuel efficiency) are uncorrelated with current prices after controlling for current characteristics. It then compares DP estimates for both CES and Kimball demand against estimates obtained using real exchange rate (RER) variation as a cost-shock instrument, finding similar results in both cases. Finally, it compares Kimball and CES estimates against mixed logit (BLP) demand: Kimball closely matches BLP price elasticities and implied quality changes, while CES shows a downward heterogeneity bias.

Q10: What does the automobile market validation imply for the import price index methodology? Since Kimball demand matches the richer mixed logit demand in the auto setting — where product characteristics are observed — the validation provides evidence that Kimball demand serves as a good approximation to rich heterogeneous-elasticity models when characteristics are unavailable. The paper constructs price indices for the US auto industry based on mixed logit, mixed CES, Kimball, and standard CES, and shows that the Kimball index is closer to the mixed logit and mixed CES indices than is the standard CES index.

Q11: How does the paper handle product entry and exit? Proposition 3 generalizes Proposition 1 to accommodate product entry and exit. The expression includes a variety correction analogous to Feenstra (1994) but generalized to non-CES settings via the mean love-of-variety index of entering and exiting products. In the CES special case this reduces exactly to the Feenstra (1994) correction. In the empirical application to US imports, entry and exit of country-of-origin varieties within industries is a relevant margin given the expansion of trading partners over the sample.

Q12: How does the paper relate to Redding and Weinstein (2020a)? Redding and Weinstein (2020a) derive a price index formula under CES demand that accounts for taste shocks, applied to US retail scanner data where quality is constant at the barcode level. The present paper generalizes their CUPI formula beyond CES to general and HA demand systems, and extends their identification strategy to settings where demand changes partly reflect quality changes rather than pure taste shocks. The paper also shows that the CES assumption used in Redding-Weinstein may overstate the contribution of taste shocks to cost-of-living indices, since part of the expenditure share variation attributed to taste shocks under CES would be reassigned under heterogeneous-elasticity demand.

Q13: Does the paper address welfare implications beyond the import price index? The paper explicitly notes that the import price index does not capture the full consumption-side welfare effects of rising imports, since gains from lower import prices may be partly offset by substitution away from domestic varieties. The paper also notes that it abstracts from nonhomotheticity (income effects), pointing to Jaravel and Lashkari (2021) for that extension. The primary welfare-relevant quantity reported is the quality-adjusted change in the cost of the imported goods basket, which is the import price index in the conventional sense.

Love-of-variety index: For a product i, defined as 1/(epsilon_it − 1) where epsilon_it is the product-level demand elasticity in an HA demand system. It measures the welfare value of having access to that variety and serves as the weight applied to expenditure share changes in the generalized price index formula (Proposition 2). In the CES special case all products share the same love-of-variety index 1/(sigma−1).

Homothetic with Aggregator (HA) demand: A family of income-invariant (homothetic) demand systems — including Kimball (1995), CRESH (Hanoch, 1971), and HSA (Matsuyama and Ushchev, 2017) — in which preferences are represented by a utility function with a specific aggregator structure. The key property exploited in the paper is that cross-product elasticities of substitution sigma_ij depend only on product-level elasticities epsilon_i through simple analytic formulas, reducing the dimensionality of the estimation problem from an N×N matrix to N scalars.

Endogeneity bias (in elasticity estimation): Downward bias in estimated elasticities of substitution arising from a positive correlation between product quality shocks and prices. When higher-quality products command higher prices and also have higher marginal costs, conventional methods (FBW) that assume zero correlation between supply and demand shocks will attribute part of the price variation to supply, underestimating how much demand responds to price. The paper documents this bias as the gap between DP and FBW estimates.

Heterogeneity bias (in elasticity estimation): Additional downward bias in CES elasticity estimates relative to the mean of Kimball elasticities, arising from CES imposing a single elasticity per industry when the true elasticities are heterogeneous across products. The bias is stronger for differentiated products and is theoretically traced to a positive covariance between demand elasticities and price volatility across products.

Dynamic panel (DP) identification: The paper’s proposed identification strategy, which exploits the Markov structure of quality shocks. The key moment condition is that quality shock innovations are mean-zero conditional on lagged prices, which holds under flexible pricing. Lagged prices (and higher-order lags and nonlinear transformations) serve as instruments for current prices, permitting identification of demand parameters without external cost instruments.

Quality shock (phi_it): An unobserved product characteristic that shifts demand for product i at time t, defined through the utility function as a scalar multiplying the quantity consumed. Quality is identified from residual demand — the component of demand not explained by price — following the approach of Khandelwal (2010) and Hallak and Schott (2011). The paper models quality shocks as following a stationary AR(1) process with product-specific means.

Unified CES price index (CUPI): The price index formula of Redding and Weinstein (2020a) for CES demand, which decomposes the aggregate price change into a price component (expenditure-share-weighted price changes) and a quality/taste component proportional to (1/(sigma−1)) times expenditure share changes. The present paper’s Proposition 2 generalizes CUPI to HA demand by replacing the scalar 1/(sigma−1) with product-specific love-of-variety indices.

Anatomy of the Phillips Curve: Micro Evidence and Macro Implications

Mon, 01 Jan 0001 00:00:00 +0000

Layer 1 — Overview

Research Question

This paper addresses a fundamental puzzle in macroeconomics: why do estimates of the New Keynesian Phillips curve (NKPC) slope differ sharply depending on whether real marginal cost or the output gap is used as the real activity variable? The conventional, output gap-based NKPC yields very flat slope estimates (e.g., 0.006 to 0.024 in Hazell et al. 2022 and Rotemberg and Woodford 1997), which has led to the widespread view that the Phillips curve is “flat,” at least during the pre-pandemic period. The authors argue that this view conflates two distinct structural relationships: the elasticity of inflation with respect to real marginal cost, and the elasticity of marginal cost with respect to the output gap.

Data and Methodology

The authors assemble a unique quarterly micro-level dataset covering 4,598 manufacturing firms in Belgium over 84 quarters (1999:Q1–2019:Q4), totaling 132,915 observations. The dataset combines product-level domestic prices and quantities from the PRODCOM administrative database, customs data on foreign competitors’ prices, and firms’ variable production costs (labor costs from social security declarations plus intermediate input costs from VAT declarations). Intermediate inputs account for approximately 75 percent of total variable costs on average and are the most volatile cost component (within-firm coefficient of variation 1.77, versus 0.77 for labor costs).

Their estimation strategy follows a “bottom-up” approach. Starting from a theoretical framework with heterogeneous firms subject to Calvo (1983) nominal rigidities and strategic complementarities in price setting (imperfect competition including dynamic oligopoly and Kimball demand), they derive a forward-looking dynamic pass-through regression linking a firm’s current price to discounted present values of its own marginal costs and competitors’ prices, plus a lagged price level that serves as an error-correction term. This is Model A; robustness variants include Model B (absorbing competitor prices via industry-by-time fixed effects), Model C (imposing an AR(1) process for marginal cost), and Model A-U (unrestricted lagged-price coefficient).

The structural parameters governing the NKPC slope — the degree of nominal rigidity (θ) and the strength of strategic complementarities (Ω) — are estimated jointly via GMM. Instruments for marginal cost are four-quarter-lagged firm-level total factor productivity (TFPQ), and instruments for competitors’ prices exploit variation in EU-area export prices to third-country destinations and bilateral exchange rates between non-EU competitor currencies and the Euro. Sector-by-time fixed effects and firm fixed effects absorb confounding trends, shifting trend inflation, and permanent markups.

Main Findings with Quantitative Magnitudes

The baseline estimate (Model A) yields θ = 0.711 (SE 0.014), implying that prices remain fixed for approximately three to four quarters on average, consistent with Nakamura and Steinsson (2008) Belgian PPI data (0.72). The strategic complementarity parameter is Ω = 0.570 (SE 0.059), indicating that competitor price dynamics reduce the pass-through of own marginal cost shocks by approximately half relative to the no-complementarities benchmark.

These structural estimates imply a slope of the marginal cost-based NKPC of λ = 0.052 (SE 0.007), tightly estimated and robust across specifications: λ = 0.077 in Model B, λ = 0.069 in Model C, and λ = 0.056 in the unrestricted Model A-U. This slope is two to ten times larger than existing estimates of the conventional output gap-based NKPC slope (κ ≈ 0.024, Rotemberg and Woodford 1997; κ ≈ 0.006, Hazell et al. 2022).

Reconciling the High Cost-Based Slope with the Flat Output-Based Slope

The paper shows that the output-based slope κ equals the product of the cost-based slope λ and the output elasticity of marginal cost σ_y: κ = λ · σ_y. Using Bartik-style instruments based on high-frequency ECB monetary policy surprises interacted with industry-level sensitivities, the authors estimate σ_y using two models. Model D yields σ_y = 0.406 and κ = 0.021; Model E (directly regressing changes in marginal cost on changes in output) yields σ_y = 0.112 and κ = 0.006. These estimates are consistent with, and overlap with, Rotemberg and Woodford (1997) and Hazell et al. (2022) during the pre-pandemic sample period. The low elasticity of marginal cost to output is attributed to near-constant short-run returns to scale at the firm level and wage rigidity that mutes general equilibrium effects.

Aggregate Inflation Dynamics

Feeding an aggregate marginal cost index (constructed as a Törnqvist-weighted average of firm-level marginal costs) into the model-implied inflation expression produces a series that tracks Belgian manufacturing PPI inflation well: marginal cost fluctuations alone account for approximately 70 percent of inflation variation (R² = 0.68, correlation 0.8), without appealing to unobservable cost-push shocks or inflation lags.

Model Validation via Supply Shocks

A validation exercise using identified oil shocks (Känzig 2021 — measured as unexpected OPEC-day movements in oil futures prices) confirms the model. A one-standard-deviation shock to oil prices (a 15.7 percent increase in Brent crude) raises firms’ real marginal costs by approximately 1.5 to 3 percent within the first three quarters, before reverting. The price response peaks at approximately 3 percent after six quarters, consistent with nominal rigidities generating a delayed but persistent response. Impulse-response matching yields λ_IRF = 0.042 (SE 0.005), within the confidence bands of the micro-level estimate λ = 0.052, validating the bottom-up approach.

Scope Conditions

All estimates are drawn from Belgian manufacturing firms over 1999–2019, a period of moderate inflation during which Calvo pricing provides a good approximation of firm behavior. The authors note that the elasticity of marginal cost to output may be time-varying and nonlinear, and that during large aggregate shocks (such as the post-pandemic inflation surge), both the frequency of price adjustment and the sensitivity of marginal cost to output can rise substantially, requiring state-dependent pricing models (addressed in a companion paper, Gagliardone et al. 2025).

Layer 2 — Q&A

Q1: What is the primitive formulation of the NKPC, and how does it differ from the conventional formulation?

A1: The primitive NKPC features real marginal cost (in log-deviation from its steady state) as the real activity variable: π_t = λ·mc_t + β·E_t{π_{t+1}} + u_t, where λ is the slope depending on nominal rigidities and strategic complementarities. The conventional formulation uses the output gap (or unemployment gap) as a proxy for marginal cost, which is valid only under specific conditions including perfectly flexible wages. When those conditions fail, the output gap is a poor proxy for marginal cost, typically leading to downward bias in slope estimates. Even when a proportionality holds, the output-based slope κ equals λ multiplied by σ_y (the output elasticity of marginal cost), so the two slopes carry different economic content.

Q2: What structural parameters govern the slope of the cost-based NKPC, and what is the formula?

A2: The slope is λ = (1−θ)(1−βθ)/θ, where θ is the Calvo probability of price non-adjustment (capturing nominal rigidity) and Ω = Γ/(1+Γ) is the strategic complementarities parameter derived from the markup elasticity Γ with respect to relative prices. High nominal rigidity (high θ) flattens the slope by making individual price adjustments less frequent; strong strategic complementarities (high Ω) flatten it further because firms mute their price response to marginal cost in order to avoid deviating from competitors. The discount factor β is calibrated at 0.99 for quarterly data.

Q3: How does the dynamic pass-through regression differ from the static (long-run) pass-through regressions used in prior literature?

A3: The dynamic pass-through regression (Model A) includes the firm’s lagged price as a regressor, which functions as an error-correction term controlling for persistent deviations between the price and the optimal reset price. Failing to include this term with quarterly data leads to omitted variable bias of magnitude −θ·Var(Δp_ft), since the cointegration error is autocorrelated with coefficient θ. Static pass-through regressions (as in Amiti, Itskhoki and Konings 2019 using annual data) are appropriate only when nominal rigidities can be ignored (θ ≈ 0); with quarterly data and θ ≈ 0.711, the orthogonality condition of the static model fails and the dynamic framework is necessary.

Q4: What are the baseline estimates of the structural parameters, and how robust are they?

A4: The baseline Model A yields θ = 0.711 (SE 0.014) and Ω = 0.570 (SE 0.059), implying prices fixed for approximately three to four quarters and competitor-price influence roughly equal to own marginal cost influence. The implied NKPC slope is λ = 0.052 (SE 0.007). Robustness checks across six specifications (Models B, C, A-U, variable SR-RTS controls, Translog TFPQ, eight-quarter-lagged instrument) yield λ in the range 0.044 to 0.077, with all estimates statistically significant and within each other’s confidence bands. The unrestricted model (A-U) cannot reject the restriction Ϛ = θ on the lagged-price coefficient (p-value 0.90).

Q5: What is the short-run elasticity of a firm’s own price to a permanent marginal cost shock, and how do nominal rigidities and strategic complementarities each contribute?

A5: The short-run pass-through elasticity is (1−Ω)(1−θ) ≈ (1−0.570)(1−0.711) ≈ 0.125. This is substantially below one because both forces dampen price adjustment: nominal rigidity (1−θ ≈ 0.289) means most firms cannot adjust in any given quarter, and strategic complementarities (1−Ω ≈ 0.430) mean that adjusting firms reduce their pass-through to avoid deviating from competitors’ prices. Without strategic complementarities (Ω = 0), the elasticity would be roughly 0.289; without nominal rigidities (θ = 0), it would be roughly 0.430; both together produce the observed 0.125.

Q6: How is marginal cost measured in the data, and why is the inclusion of intermediate input costs important?

A6: Marginal cost is proxied by average variable cost per unit of output: the log-nominal marginal cost equals ln(TVC_ft/Y_ft) + ln(1+ν_ft), where TVC is the sum of intermediate input costs (from VAT declarations) and labor costs (wage bill from social security declarations), and Y_ft is a quantity index. Intermediate inputs account for approximately 75 percent of total variable costs on average and are the most volatile component (within-firm coefficient of variation 1.77 vs 0.77 for labor). The authors note that DSGE models typically feature only labor as a variable input, but accounting for intermediates is pivotal because intermediate goods price shocks were among the most important drivers of the post-pandemic inflation surge.

Q7: What instruments are used for marginal cost and competitors’ prices, and what are the identifying assumptions?

A7: The instrument for marginal cost is the four-quarter lagged firm-level TFPQ (physical total factor productivity), estimated as the residual from a gross-output production function. Its relevance depends on TFP persistence (confirmed); the exclusion restriction requires that persistent TFP variation is orthogonal to current and future demand shocks after removing permanent demand components (via firm fixed effects) and industry trends (via sector-by-time fixed effects). Two instruments for competitors’ prices exploit international trade variation: (i) sales-weighted average export prices of EU-area competitors to non-Belgium, non-EU destinations (orthogonal to Belgian demand shocks by construction), and (ii) bilateral exchange rate movements between non-EU competitor currencies and the Euro. All instruments pass the Cragg-Donald and Kleibergen-Paap F-statistics (strongly rejecting weak instruments) and Hansen-Sargan over-identification tests (failing to reject validity).

Q8: What evidence supports the validity of the TFPQ instrument against capacity utilization concerns?

A8: The authors run two empirical tests. First, regressing marginal cost on four-quarter-lagged capacity utilization yields a small, statistically insignificant elasticity (0.011, SE 0.052), suggesting the TFPQ instrument’s predictive power does not reflect capacity utilization variation. Second, re-estimating with “purified” TFPQ instruments adjusted for capital utilization (Column 4) and for both capital and labor utilization (Column 5) produces parameter estimates and NKPC slopes essentially unchanged from baseline. Additionally, regression residuals show only weak and short-lived autocorrelation (−0.09 at one-quarter lag, p=0.09; −0.01 at two-quarter lag, p=0.69), indicating demand shocks are highly transitory after conditioning on fixed effects.

Q9: How does the model track aggregate Belgian manufacturing PPI inflation, and what does this imply for cost-push shocks?

A9: Using the reduced-form expression π_t = λ̃(mc_t^n − p_{t-1}) + α + θu_t, where the reduced-form slope λ̃ = 0.22 is evaluated at baseline structural estimates, the model produces a model-implied inflation series that accounts for approximately 70 percent of variation in manufacturing PPI inflation (R² = 0.68, correlation 0.8), without including inflation lags or cost-push shocks. The model captures the inflation drop during the 2008 financial crisis, the run-up in 2016, and the subsequent decline. This contrasts with the quantitative DSGE literature in which cost-push shocks (variation in desired price and wage markups) account for approximately 70 percent of inflation volatility (e.g., Primiceri, Schaumburg and Tambalotti 2006).

Q10: How do the authors estimate the output elasticity of marginal cost σ_y, and what do they find?

A10: They use two approaches. Model D is a pricing equation directly relating firm-level prices and nominal output (value added), estimated via GMM, instrumented with Bartik-style shifters based on high-frequency ECB monetary policy surprises (Altavilla et al. 2019) interacted with industry-level sensitivities. Model E directly regresses changes in nominal marginal cost on changes in nominal output, also instrumented. Model D yields σ_y = 0.406 (SE 0.099) and implied κ = 0.021 (SE 0.005); Model E yields σ_y = 0.112 (SE 0.026) and κ = 0.006 (SE 0.001). The low σ_y is consistent with near-constant short-run returns to scale at the firm level and wage rigidity muting general equilibrium labor-market feedback, at least during the moderate-inflation pre-pandemic period.

Q11: How does the oil shock validation exercise confirm the cost-based NKPC slope estimate?

A11: Following Känzig (2021), the authors identify oil shocks as unexpected movements in Brent crude oil futures around OPEC meeting days, normalizing to a one-standard-deviation shock (15.7 percent Brent increase). Local linear projection IRFs show that firms’ real marginal costs rise 1.5 to 3 percent within three quarters and then revert, while prices peak at approximately 3 percent increase after six quarters (consistent with nominal rigidity delaying the price response). Impulse-response matching — minimizing the weighted distance between empirical and model-implied price IRFs — yields λ_IRF = 0.042 (SE 0.005), which is close to and within the confidence bands of the micro-level estimate λ = 0.052, validating the bottom-up estimation approach.

Q12: What do the estimates imply about why the conventional NKPC appears flat in normal times?

A12: The flat conventional NKPC slope (κ ≈ 0.006–0.024) does not reflect limited transmission of marginal cost fluctuations to inflation — that transmission is high (λ ≈ 0.052–0.077). Rather, flatness reflects a weak link between the output gap and marginal cost during the pre-pandemic period (σ_y ≈ 0.112–0.406), attributable to near-constant short-run returns to scale in production and wage rigidity. This decomposition matters for policy: supply shocks that directly raise marginal cost will pass through strongly to inflation even when output does not move much, whereas demand shocks that operate through the output-cost channel face attenuated transmission.

Q13: Under what conditions does the cost-based Phillips curve decompose cleanly into a product of the two elasticities?

A13: The decomposition κ = λ · σ_y requires assuming that real wages are flexible and determined in general equilibrium at the industry level, with real wages increasing in industry output with elasticity σ_w; that the natural level of output is defined as the equilibrium under flexible prices and constant desired markups; and that the firm’s marginal product of labor depends on productivity and output with a common short-run returns-to-scale parameter ν (homogeneous across firms and time-invariant). Under these assumptions (which parallel those used to derive the conventional NKPC in the standard NK model), the output elasticity of marginal cost is σ_y = σ_w + ν, and the theoretical restriction κ = λ · σ_y holds exactly.

Q14: How do macroeconomic complementarities from aggregate decreasing returns to scale affect the NKPC slope?

A14: If aggregate SR-RTS fall below unity, the NKPC slope formula gains an additional term Θ = 1/(1+γν(1−Ω)) < 1, where ν is inversely related to average SR-RTS and γ is the within-industry elasticity of substitution. However, empirical estimates of sectoral SR-RTS range from 0.93 to 0.98, with an aggregate estimate of approximately 0.965 (implying ν ≈ 0.036). Given this and calibrating γ = 4, Θ ≈ 0.941, so macroeconomic complementarities would reduce the NKPC slope by only about 6 percent — well within the confidence bounds of the baseline estimates. The authors conclude that the constant-returns assumption in their main framework is a good approximation.

Key Concepts

Primitive (cost-based) NKPC slope (λ): The coefficient linking inflation to real marginal cost in the underlying New Keynesian pricing equation, defined as λ = (1−θ)(1−βθ)/θ. It captures how strongly firms’ aggregate price setting responds to movements in real marginal cost per unit of output, holding the discount factor, nominal rigidity, and strategic complementarities fixed. Estimated at 0.052 (tightly, range 0.044–0.077 across specifications) for Belgian manufacturing.

Calvo probability of price non-adjustment (θ): The parameter from Calvo (1983) staggered price setting capturing the share of firms that cannot change their price in a given period, equal to one minus the per-period probability of price adjustment. In this paper, θ is estimated directly from the dynamic pass-through regression coefficient on lagged prices, yielding θ ≈ 0.711, implying prices fixed approximately three to four quarters on average.

Strategic complementarities parameter (Ω): Defined as Ω = Γ/(1+Γ), where Γ is the elasticity of a firm’s desired markup with respect to its own relative price. Captures the extent to which a firm weights competitors’ prices (rather than its own marginal cost) when resetting its price. High Ω means firms strongly mute price responses to own cost changes to avoid relative price deviations from competitors. Estimated at Ω ≈ 0.570, implying competitor prices and own marginal cost enter the reset price with roughly equal weight.

Dynamic pass-through regression: A forward-looking pricing equation (Model A) relating observed firm prices to the discounted present values of own marginal costs and competitors’ prices, plus lagged own price as an error-correction term. The structural parameters θ and Ω are identified jointly from the regression coefficients, using GMM with instruments for the present values. The dynamic specification is necessary at quarterly frequency because the error-correction term (omitted in static pass-through models) is non-negligible when θ > 0.

Output elasticity of marginal cost (σ_y): The elasticity of firm-level real marginal cost with respect to the firm-level output gap, defined under the assumptions that real wages are flexible and industry-level, equal to σ_y = σ_w + ν (wage elasticity with respect to industry output plus the short-run returns-to-scale parameter). This parameter bridges the cost-based and output-based Phillips curve slopes via κ = λ · σ_y. Estimated from micro data using monetary policy shock instruments at σ_y ≈ 0.112–0.406 in the pre-pandemic period.

Short-run returns to scale (SR-RTS): The extent to which a firm’s marginal cost rises with output scale in the short run, parameterized by ν in the cost function MC^n_ft = C_{it} · A_{ft} · Y_ft^ν. If ν = 0, marginal cost is independent of output scale (constant returns), which the authors assume in their baseline. Firm- and sector-level estimates from Translog production functions yield SR-RTS ≈ 0.93–0.98 across sectors (aggregate ≈ 0.965), broadly consistent with the constant-returns assumption and implying modest macroeconomic complementarities.

Reduced-form aggregate pass-through slope (λ̃): A composite parameter capturing the contemporaneous pass-through of aggregate real marginal cost (defined as nominal marginal cost relative to the lagged price level) into quarterly inflation under the assumption that nominal marginal cost follows a random walk. Evaluated at θ ≈ 0.70 and Ω ≈ 0.52 (median across models), λ̃ = 0.22. This is distinct from the structural NKPC slope λ because it also captures the persistence of cost shocks.

Competition and the Phillips curve

Mon, 01 Jan 0001 00:00:00 +0000

Fujiwara and Matsuyama ask whether the well-documented flattening of the New Keynesian Phillips curve (NKPC) and the concurrent rise in market concentration and markup rates are causally linked or merely coincidental. Under the canonical New Keynesian model with CES demand, competition is irrelevant to the Phillips curve regardless of whether entry is endogenous — concentration neither changes its slope nor affects inflation directly. This paper overturns that irrelevance result by extending the canonical model in two directions: (1) incorporating endogenous firm entry and exit following Bilbiie, Ghironi, and Melitz (2008) and Bilbiie, Fujiwara, and Ghironi (2014), and (2) replacing CES with the Homothetic Single Aggregator (HSA) demand system (Matsuyama and Ushchev 2017, 2020b), a flexible, tractable class of homothetic demand systems that nests CES and Translog as special cases.

The paper’s theoretical results depend on two of Marshall’s laws of demand. The Second law states that the price elasticity of demand rises with the firm’s own price; the Third law states that the rate of increase in that elasticity falls with price. Together these conditions imply that the markup rate and pass-through rate are endogenous to the competitive environment.

The main findings, delivered under both Rotemberg (1982) and Calvo (1983) pricing, are that higher entry costs — leading to market concentration — cause Phillips curve flattening through two distinct, complementary channels:

Structural (steady-state) effect. Under Rotemberg pricing, the slope of the NKPC is proportional to the price elasticity zeta(z); market concentration reduces z, hence reduces zeta(z) under the Second law, directly flattening the curve. Under Calvo pricing, the slope is proportional to the pass-through rate rho(z); the Third law implies that concentration reduces rho(z), again flattening the curve. The Calvo–Rotemberg equivalence, which holds under CES to first order (Roberts 1995), breaks down under HSA: each pricing mechanism highlights a different channel.
Observational (omitted variable bias) effect. Endogenous entry generates an endogenous cost-push shock through strategic complementarity in price setting. Because the number of firms N_t is omitted from a naive regression of inflation on real marginal cost, and because N_t is positively correlated with the marginal cost under the Second law, the omitted variable bias is negative — the estimated slope is biased downward. This bias is amplified with greater concentration under the Third law (Rotemberg case) and under both the Second and Third laws (Calvo case).

Quantitatively, the paper simulates under three parametric HSA families — CES, Translog, and Co-PaTh (Constant Pass-Through). De Loecker, Eeckhout, and Unger (2020) document that aggregate markups rose from 21% above marginal cost to 61% — a rise of approximately 40 percentage points. The authors’ simulations imply this increase corresponds to an entry cost roughly 3.5 times higher under Translog and roughly 2.5 times higher under Co-PaTh with pass-through rate rho = 0.5. Under these parameterizations, the accompanying market concentration can halve the slope of the NKPC. Impulse responses confirm that the responses of inflation to both technology shocks and monetary policy shocks become smaller as market concentration deepens.

Scope conditions: results require departure from CES (the Second and/or Third law must hold); endogenous entry is necessary for the dynamic cost-push channel; the structural flattening requires only the Second law under Rotemberg but additionally the Third law under Calvo; the omitted variable bias requires the Second law under Rotemberg and both laws under Calvo. The model is closed-economy, with symmetric monopolistic competition and Rotemberg or Calvo price adjustment.

Q1: What is the irrelevance result the paper overturns, and why does CES produce it? Under CES, the market share function takes the form s(z) = gamma * z^(1-theta), yielding a constant price elasticity zeta = theta and a pass-through rate rho = 1, regardless of the number of firms or entry costs. As a result, concentration neither alters the slope of the NKPC nor generates any endogenous cost-push shock; competition is simply irrelevant to inflation dynamics. This irrelevance holds even with endogenous entry under CES.

Q2: What is the Homothetic Single Aggregator (HSA) and why is it used? HSA is a class of homothetic demand systems, originally proposed by Matsuyama and Ushchev (2017), in which the market share of each intermediate input variety depends solely on its own price normalized by a single price aggregator A_t. This single aggregator serves as a sufficient statistic summarizing all competitive pressure effects on pricing behavior, including the markup rate and pass-through rate. HSA nests CES and Translog as special cases, is analytically tractable (equilibrium existence and uniqueness are straightforward to ensure with endogenous entry), and is flexible enough to accommodate both the Second and Third laws of demand.

Q3: What are Marshall’s Second and Third laws as defined in the paper? The Second law states that the price elasticity of demand zeta(z) is increasing in the normalized price z (equivalently, increasing in the single price aggregator A_t, which rises with fewer firms). The Third law, as defined by Matsuyama and Ushchev (2023b), states that the rate of increase in the price elasticity is decreasing in z. Together they ensure that both markup rates and pass-through rates respond systematically to changes in competitive pressure.

Q4: How does market concentration structurally flatten the NKPC under Rotemberg pricing? Under Rotemberg pricing, the slope of the NKPC equals (zeta(z) - 1) / chi, where chi is the Rotemberg price adjustment cost parameter. Higher entry costs reduce the equilibrium number of firms, which reduces competitive pressure and lowers z. Under the Second law, lower z reduces zeta(z), directly shrinking the slope coefficient. This is the steady-state effect of concentration: the structural slope of the curve declines because the price elasticity falls.

Q5: How does market concentration structurally flatten the NKPC under Calvo pricing? Under Calvo pricing, the slope of the NKPC is positively related to the pass-through rate rho(z) rather than the price elasticity. The Third law implies that lower z (more concentration) reduces rho(z). Market concentration therefore causes structural flattening through the pass-through channel under Calvo. This is why the Calvo–Rotemberg equivalence — which holds to first order under CES — breaks down under HSA: Rotemberg highlights the Second law / price elasticity channel and Calvo highlights the Third law / pass-through channel.

Q6: What is the endogenous cost-push shock and how does it arise? When the number of operating firms N_t changes endogenously, it alters the single price aggregator A_t and therefore the competitive environment facing each firm. Under the Second law, firms exhibit strategic complementarity in price setting: a firm reduces its markup when other firms lower their prices (A_t falls with more entry). Consequently, movements in N_t directly enter the NKPC as an additional term — (1/chi) * (1 - rho(z)) / rho(z) * N_hat_t — acting as an endogenous cost-push shock. This channel is absent under CES because rho = 1 makes the coefficient zero.

Q7: How does the endogenous cost-push shock create a negative omitted variable bias? A naive regression of inflation on real marginal cost omits the N_hat_t term. Under the Second law, N_t is positively correlated with the marginal cost (more entry drives markups down, consistent with marginal cost movements), so the omitted variable N_hat_t is positively correlated with the included regressor. Because the true coefficient on N_hat_t in the NKPC is negative, omitting it biases the estimated slope on marginal cost downward (negative omitted variable bias). The estimated relationship between inflation and marginal cost is therefore weaker than the true structural relationship.

Q8: How is the omitted variable bias amplified by concentration? Under the Third law (Rotemberg case) and under both the Second and Third laws (Calvo case), greater market concentration amplifies the magnitude of this negative bias. The intuition is that higher concentration makes the pass-through rate rho(z) smaller, which increases the coefficient on N_hat_t in the NKPC and thereby raises the magnitude of the bias when N_hat_t is omitted. Greater concentration thus generates both more structural flattening and more observational flattening simultaneously.

Q9: What are the quantitative magnitudes of Phillips curve flattening in the simulations? De Loecker, Eeckhout, and Unger (2020) document that aggregate markups rose from 21% above marginal cost to 61% — approximately 40 percentage points. The paper’s simulations imply this corresponds to an entry cost increase of roughly 3.5 times under Translog and roughly 2.5 times under Co-PaTh with rho = 0.5. According to Figure 2, the accompanying market concentration can halve the slope of the NKPC. The slope declines more steeply for demand systems with smaller pass-through rates (rho further from 1).

Q10: How do impulse responses change with market concentration? As entry costs rise (deeper concentration), the responses of the inflation rate to both technology shocks and monetary policy shocks become smaller in magnitude. Under the Second law, a positive technology shock increases the number of firms through a wealth effect, but strategic complementarity in price setting reduces markups, muting the inflation response relative to CES. The dynamic effect of endogenous entry thus weakens the transmission of real economic shocks to inflation — a supply side effect of monetary policy that parallels Baqaee, Farhi, and Sangani (2021) but operates through firm entry rather than the misallocation channel.

Q11: What is the cyclicality of the markup rate under HSA, and why is it ambiguous? Under CES with flexible prices, the markup is constant. Under CES with sticky prices, the markup is procyclical (marginal cost falls with a positive technology shock but the price is rigid in the short run). Under the Second law with flexible prices, a positive technology shock increases firm entry, which reduces markups, making the markup countercyclical. In a sticky price equilibrium under the Second and Third laws, the cyclicality is therefore ambiguous: it depends on the tension between nominal rigidities (pushing toward procyclicality) and the pass-through rate (pushing toward countercyclicality).

Q12: Why do the three price indices in the model differ, and which is used for the NKPC? The model features three aggregate price measures: the final goods price (CPI) P_t, which captures productivity effects of entry; the single price aggregator A_t, which captures competitive effects of entry and is the reference price for firms; and the average price index (PPI) p_t, which is not affected by entry effects and is the measured price index. Because entry effects shift P_t and A_t in ways that are not directly observed, the paper evaluates NKPC responsiveness in terms of p_t (PPI inflation), the measurable index.

Q13: How does this paper relate to Wang and Werning (2022) and Baqaee, Farhi, and Sangani (2021)? Wang and Werning (2022) use a dynamic oligopoly model with exogenous entry and CES/Kimball demand, showing that higher concentration amplifies real effects of monetary policy and generates inflation persistence and endogenous cost-push shocks. Baqaee, Farhi, and Sangani (2021) use monopolistic competition with exogenous entry and Kimball demand under Calvo pricing, showing flattening through real rigidities and a misallocation channel (supply side effects of monetary policy). This paper uses monopolistic competition with endogenous entry and HSA under both Rotemberg and Calvo pricing; it produces supply side effects through firm entry rather than misallocation, and uses HSA rather than Kimball because HSA more readily guarantees equilibrium uniqueness with endogenous entry.

Q14: What parametric families of HSA are used in simulations and what are their properties? Three families are used: CES (constant price elasticity theta, pass-through rho = 1, benchmark); Translog (satisfies the Second law, variable markups and pass-through); and Co-PaTh or Constant Pass-Through (proposed by Matsuyama and Ushchev 2020a, constant pass-through rate rho in (0,1) under flexible prices, containing CES as a limit as rho approaches 1). For Calvo pricing, a fourth family — PEM (Power Elasticity of Markup, proposed by Matsuyama and Ushchev 2023b) — is used; PEM satisfies the Third law in its strong form and contains Co-PaTh as a limit case. Translog is noted to behave similarly to Co-PaTh with rho = 0.5.

Q15: What are the policy implications for central banks? Rising market concentration, by flattening the NKPC both structurally and observationally, reduces the effectiveness of monetary policy in achieving price stability through real economic activity — consistent with the concerns expressed by Federal Reserve officials (Clarida, Daly, Williams) quoted in the paper. The results suggest that empirical estimates of the NKPC slope that omit endogenous entry dynamics will be systematically biased downward, potentially leading central banks to underestimate the true structural responsiveness of inflation to demand conditions. Competition policy and barriers to entry thus have macroeconomic consequences beyond standard allocative efficiency considerations.

Homothetic Single Aggregator (HSA): A class of homothetic demand systems in which the market share of each input variety depends solely on its own price normalized by a single price aggregator A_t, which serves as a sufficient statistic for all competitive pressure effects on firm pricing behavior including the markup rate and pass-through rate. Nests CES and Translog as special cases.

Marshall’s Second Law of Demand (as used in the paper): The condition that the price elasticity of demand zeta(z) is strictly increasing in the firm’s normalized price z. Under this condition, markup rates and pass-through rates vary endogenously with competitive pressure, and strategic complementarity in price setting arises.

Marshall’s Third Law of Demand (as used in the paper): The condition, defined by Matsuyama and Ushchev (2023b), that the rate of increase in the price elasticity is decreasing in z. This law determines how the pass-through rate responds to concentration changes and is the relevant condition for structural flattening under Calvo pricing.

Pass-through rate rho(z): The fraction of a cost change that a monopolistically competitive firm passes through to its price under flexible pricing, defined as rho(z) = [1 - dln(zeta/(zeta-1))/dln(z)]^(-1). Under CES, rho = 1 (complete pass-through); under the Second law, rho < 1 (incomplete pass-through); it declines with concentration under the Third law.

Endogenous cost-push shock: The direct effect of changes in the endogenous number of firms N_t on inflation in the NKPC, arising from strategic complementarity in price setting under HSA. This term is absent under CES (where the coefficient is zero) and generates an omitted variable bias in naive regressions of inflation on marginal cost.

Steady-state (structural) flattening: The reduction in the true structural slope of the NKPC caused by market concentration operating through lower price elasticity (Rotemberg channel) or lower pass-through rate (Calvo channel). This is the first of the paper’s two reasons for observed Phillips curve flattening.

Observational (omitted variable bias) flattening: The downward bias in empirically estimated NKPC slopes arising because naive regressions omit the endogenous cost-push shock term. The bias is negative and is amplified by greater market concentration under the Third law and/or Second law depending on the pricing mechanism.

Consistent Evidence on Duration Dependence of Price Changes

Mon, 01 Jan 0001 00:00:00 +0000

Layer 1 — Overview

Research Question. This paper asks two related questions. First, can one develop a robust, distribution-free estimator for the discrete-time mixed proportional hazard (MPH) model of duration with unobserved heterogeneity? Second, what does that estimator reveal about the shape of the hazard of price changes, the role of heterogeneity in shaping aggregate price dynamics, and the distinction between regular price changes and sales?

Methodology. The authors develop a linear generalized method of moments (GMM) estimator for the discrete-time MPH model, building on identification results in Honoré (1993). The model specifies that the probability a price spell ends at duration t, conditional on surviving to t, equals the product of a product-specific frailty parameter θ (unobserved, fixed over time) and a common baseline hazard bt. The estimator exploits repeated price spells per product via moment conditions that are linear in bt, making estimation and inference straightforward. It accommodates right- and left-censored data, competing risks, and spell-specific observable characteristics, without requiring any parametric assumption on the frailty distribution. The estimator is consistent as the number of products grows, even with a short time dimension. A Hansen-Sargan J-test of overidentifying restrictions and a test of the monotone-average-type prediction are also developed.

The estimator is applied to two datasets: (1) IRI weekly store data (2001–2011), covering 30 product categories and more than 21 million products, yielding 684,919,778 pairs of durations; and (2) Online Micro Price data from Cavallo (2018), comprising approximately 250,000 products at daily frequency.

Main Findings with Quantitative Magnitudes.

Baseline hazard and heterogeneity. In the pooled IRI data, the Kaplan-Meier hazard is steeply declining throughout the entire range from 2 to 60 weeks. In contrast, the estimated baseline hazard is roughly constant until week 4 and then declines only modestly, with a noticeable spike at week 52. The ratio of the Kaplan-Meier hazard to the baseline hazard — the average type, E[θ|t] — drops by approximately 60 percent within the first 20 weeks, and continues to decline, reaching roughly 0.3 of its initial value after one year. This decomposition reveals substantial unobserved heterogeneity that accounts for a large fraction of the observed decline in the Kaplan-Meier hazard.

Implications for structural models. The finding of a decreasing baseline hazard is inconsistent with canonical state-dependent pricing models (Golosov and Lucas, 2007), which predict an increasing hazard, conditional on a given firm’s type. The decreasing baseline hazard is instead broadly consistent with time-dependent pricing models, though not with a constant-hazard (Calvo, 1983) specification.

Monetary policy impulse response. In a calibrated time-dependent pricing model with strategic complementarity (α = 0, 0.5, 0.95), the aggregate price level dynamics in the estimated heterogeneous-firm MPH economy are close to those of a homogeneous-firm economy that uses the Kaplan-Meier hazard as the common price-change hazard. The homogeneous-firm approximation is substantially closer to the MPH economy than a Taylor (1979, 1980) staggered-contract economy with the same Kaplan-Meier hazard, particularly when strategic complementarity is strong (α = 0.95). The Calvo economy provides a poor approximation due to its exponential (constant-speed) price convergence structure.

Regular versus temporary price changes. Using the competing-risks extension with spell-specific observables — classifying spells by whether they start and end with a price increase (+) or decrease (−) — the authors separately estimate four baseline hazards. The baseline hazard for consecutive price increases (b++t) is relatively flat, especially for the first 6 weeks, then flat until week 45, with a spike near one year, consistent with price-plan models. The baseline hazard for reversals (particularly b−+t, price decreases followed by price increases, associated with sales) is steeply declining. The J-test statistics are substantially lower for price trends (J++ = 3,920; J−− = 3,401) than for reversals (J+− = 8,737; J−+ = 7,910), and markedly lower than the pooled-model J = 10,498, indicating that the MPH structure fits regular price changes considerably better than sales.

Scope Conditions. Results are conditional on weekly store-level price data for mostly packaged consumer goods (30 IRI product categories). The analysis focuses on price spells of at least 2 weeks to avoid spurious duration-one spells from mid-week price changes. The maximum duration examined is 60 weeks. The comparison of estimation methods relies on the IRI data only; the Online Micro Price data confirm weekly decision-making through a spike in the daily hazard every 7 days. Comparisons with maximum likelihood estimates show that GMM recovers more heterogeneity (average type declines to 0.37 at 6 months by GMM versus 0.48 by continuous-time MLE), and that time aggregation explains most of the discrepancy between the two methods.

Layer 2 — Q&A

Q1. What is the mixed proportional hazard (MPH) model as used in this paper, and what does the estimator identify?

A1. The MPH model specifies that the hazard that a price spell ends at duration t, conditional on surviving to t, equals θ·bt, where θ is a product-specific frailty parameter drawn from an unknown distribution G and bt is a baseline hazard common to all products. The estimator, which is linear in bt, identifies the baseline hazard up to a multiplicative constant using moment conditions derived from repeated spell data, without restricting the shape of the frailty distribution. Identification relies on comparing the joint survival probabilities of two consecutive spells for the same product and exploits the symmetry implied by the MPH structure across spells.

Q2. How does the Kaplan-Meier hazard relate to the baseline hazard, and what does this relationship imply about heterogeneity?

A2. The paper proves that the Kaplan-Meier hazard Ht equals bt times E[θ|t], the mean frailty among spells surviving to duration t. Because higher-type products (those with a higher propensity to change prices) exit the pool of surviving spells earlier, E[θ|t] is strictly decreasing in t — a form of dynamic selection. The ratio Ht/bt, normalized to 1 at the start, falls to approximately 0.4 by week 20 in the pooled IRI data and to approximately 0.3 after one year, documenting that a large share of the decline in the Kaplan-Meier hazard reflects heterogeneity rather than structural negative duration dependence.

Q3. What does the estimated baseline hazard imply about structural models of price setting?

A3. A decreasing baseline hazard is inconsistent with the canonical state-dependent model of Golosov and Lucas (2007), in which a firm’s hazard of price change is increasing in the time since the last change, because larger deviations from the desired price accumulate with duration. The decreasing baseline hazard is instead consistent with time-dependent pricing models and with price-plan models where within-plan switches are costless. The mild spike at week 52 in the baseline hazard is consistent with Taylor-type annual pricing rules.

Q4. What is the approximate aggregation result for monetary policy, and how quantitatively accurate is it?

A4. In the time-dependent pricing model without strategic complementarity (α = 0), the impulse response of the aggregate price level to a monetary shock in a heterogeneous-firm economy is exactly the same as in a homogeneous-firm economy whose single firm uses the Kaplan-Meier survival function. This extends Carvalho and Schwartzman (2015) to an approximation in the case with strategic complementarity (α = 0.5 and α = 0.95). Numerically, the path of aggregate prices in the estimated MPH economy is close to that in the homogeneous-firm Kaplan-Meier economy, and substantially closer to it than to the Taylor-contract economy — the difference is most pronounced at horizons beyond about half a year when α = 0.95, where the Taylor economy shows notably slower initial convergence and faster later convergence relative to the MPH and homogeneous economies.

Q5. How do the paper’s results differ from those obtained using maximum likelihood estimation of the continuous-time MPH model?

A5. The GMM estimator recovers substantially more heterogeneity than maximum likelihood (MLE) applied to the continuous-time model with continuous records (assumed gamma frailty). The average type falls from 1 to 0.37 at six months under GMM, versus only 0.48 under MLE. The authors investigate two sources of this discrepancy: the assumed frailty distribution family (gamma) and time aggregation. They conclude that time aggregation is quantitatively more important in the IRI weekly data — that is, the continuous-time MLE approach fails to properly account for the discrete nature of the data-generating process, leading it to understate heterogeneity and recover a steeper baseline hazard.

Q6. How does the paper distinguish regular price changes from sales without directly observing a sales flag?

A6. The competing-risks extension classifies each spell by whether it starts with a price increase or decrease (observable characteristic χ ∈ {+, −}) and by whether it ends with a price increase or decrease (competing risk ρ ∈ {+, −}). Price trends — spells where the direction is the same at both the start and end (++ or −−) — are interpreted as regular price changes; price reversals (especially −+, i.e., price decrease followed by increase) are associated with sales. This approach is consistent with the statistical model used for estimation, avoids the bias from simply dropping suspected sales spells before estimation, and allows the MPH structure to hold only for the risks of interest even if it fails for others.

Q7. How well does the MPH model fit regular price changes versus sales?

A7. The J-test of overidentifying restrictions yields test statistics of J++ = 3,920 for consecutive price increases and J−− = 3,401 for consecutive price decreases, compared with J = 10,498 for the pooled model and J+− = 8,737 and J−+ = 7,910 for the reversal hazards. All rejections are at conventional significance levels (critical value 1,749 at 5%), but the rejection is substantially milder for price trends than for price reversals. For individual product categories, the model cannot be rejected for 8 categories (out of 30) for b++ and 21 categories for b−−, suggesting the MPH structure is a much better description of regular price changes than of sales.

Q8. What role do one-week price spells play in the data, and why are they excluded?

A8. In the IRI data, prices are measured as the ratio of weekly revenue to quantity, so a price change occurring mid-week generates a spurious price spell of duration one week. If all spells including one-week spells are retained, the autocorrelation of spell durations is only 0.029 in levels and even negative (−0.042) in logs, which is inconsistent with a mixture model. Once one-week spells are excluded, the autocorrelation rises to 0.235 in levels and 0.233 in logs, and is stable when two-week spells are also excluded (0.248 and 0.256). The paper therefore sets the lower duration bound at T̲ = 2 weeks.

Q9. What does the daily Online Micro Price data add relative to the weekly IRI data?

A9. The daily data reveal a sharp spike in the price-change hazard every seven days, suggesting that even when prices are observed daily, the decision to change prices is made at the weekly frequency. This justifies the use of a discrete-time model with a one-week period. The estimates from daily and weekly aggregations of the same data are broadly similar, though weekly data recovers somewhat less heterogeneity than daily data. Aggregating IRI weekly data to monthly frequency understates heterogeneity even more, confirming that frequency matters for measuring heterogeneity.

Q10. What are the computational advantages of the GMM estimator relative to maximum likelihood?

A10. Because the moment conditions are linear in the baseline hazard bt, the GMM estimator is obtained in closed form, making estimation fast and inference straightforward. On the pooled IRI sample, GMM estimation (including standard errors) required 70 minutes on a machine with 60 GB memory, whereas the maximum likelihood estimator required 15 hours on a machine with 256 GB memory and failed entirely on the 60 GB machine. The GMM approach also avoids the need to specify the frailty distribution family and guarantees a global solution (proved by the identification result), whereas the likelihood function is non-linear in bt and may have multiple local maxima.

Q11. What is the shape of the b++ baseline hazard for regular price increases, and what models does it support?

A11. The baseline hazard for spells starting and ending with a price increase (b++) is decreasing during the first 6 weeks — dropping by almost 50% — and then flat until approximately week 45, with a pronounced spike at around one year. This shape is consistent with price-plan models (Eichenbaum, Jaimovich, and Rebelo, 2011) with Calvo-type switching between plans, where within-plan changes are costless and the hazard of between-plan switching is approximately constant. The annual spike is consistent with Taylor-type pricing. Approximately 76.8% of complete spells starting after a price increase last at most 6 weeks.

Key Concepts

Baseline hazard (bt). The component of the MPH hazard that is common to all products and may vary arbitrarily with elapsed duration t. It represents structural duration dependence — the tendency for a given product to be more or less likely to change price as a function of how long its current spell has lasted — net of heterogeneity. It is identified only up to a multiplicative constant.

Frailty parameter (θ) / frailty distribution (G). The product-specific scaling factor in the MPH model, fixed over all spells for a given product, that captures permanent unobserved differences in price-change frequency across products. The paper treats G as a nuisance parameter and does not require a parametric assumption on its shape. A higher θ means the product has a higher baseline propensity to change its price.

Average type (E[θ|t]). The mean frailty parameter among spells that have survived to at least duration t. Because high-type products change price earlier and exit the pool of surviving spells first, the average type is provably strictly decreasing in t under the MPH model. It is measured as the ratio of the Kaplan-Meier hazard to the baseline hazard, and its rate of decline measures the importance of dynamic selection.

Kaplan-Meier hazard (Ht). The probability that a randomly drawn spell ends at duration t, conditional on having lasted at least t periods. It mixes together structural duration dependence (captured by bt) and dynamic selection (captured by changes in the average type). It can be estimated without imposing the MPH structure, requiring only stationarity of the duration process.

Competing risks. The framework in which a price spell can end for multiple distinct reasons — here, ending with a price increase or a price decrease — each with its own hazard function. The paper’s GMM approach allows the MPH structure to hold for only a subset of risks and observables, without imposing any structure on the remaining risks.

Price trends vs. price reversals. A classification of spells based on the direction of the surrounding price changes. Price trends are spells where the direction of the price change at the start and end of the spell is the same (++ or −−), interpreted as regular price changes. Price reversals are spells where the direction switches (e.g., −+, a price decrease followed by a price increase), associated with sales and other temporary price changes.

Strategic complementarity in pricing (α). The degree to which a firm’s target price responds to the average price set by other firms. Parameterized by α ∈ [0, 1), where α = 0 yields the exact aggregation result (only the Kaplan-Meier hazard matters) and higher α increases aggregate price stickiness by making firms reluctant to deviate from the average price when few others are adjusting.

Dynamic selection. The mechanism by which the composition of the pool of surviving price spells shifts toward lower-type (more price-sticky) products as duration increases, because higher-type products change price sooner and exit the pool. This is the source of the gap between the steeply declining Kaplan-Meier hazard and the more modestly declining baseline hazard.

Consumer durables and monetary policy according to HANK

Mon, 01 Jan 0001 00:00:00 +0000

Layer 1 — Overview

Research Question

Consumer durables account for a disproportionately large share of household expenditure fluctuations despite their small share of total private consumption. Two stylized facts motivate the paper: (1) durable expenditure is far more interest-rate sensitive than nondurable expenditure following monetary policy shocks, and (2) durable and nondurable expenditures comove positively and persistently—both reaching trough in the same quarter. Standard two-sector New Keynesian models struggle to generate this positive conditional comovement because asymmetric sectoral price rigidity induces large relative-price movements that push the two sectors in opposite directions. This paper asks what model features are necessary and sufficient to reproduce both the sectoral comovement pattern and the hump-shaped aggregate dynamics observed in the data, and how the answer changes across households sorted by liquid asset holdings.

Data and Methodology

Empirical identification. The authors employ a local projection instrumental variables (LP-IV) strategy using Romer-Romer monetary policy shocks updated by Wieland and Yang (2020), over the sample 1969:Q1–2007:Q3. Impulse response functions (IRFs) are normalized to a cumulative 100 basis-point increase in the Federal Funds Rate over five years. Household-level evidence is drawn from the Consumer Expenditure Survey (CEX) and the Survey of Consumer Finances (SCF); households are classified as liquidity-constrained if liquid assets are below $1,000.

Model. The authors develop a two-sector Heterogeneous Agent New Keynesian (HANK) model in which households maximize utility over nondurable consumption and a durable stock (Cobb-Douglas aggregation), face convex adjustment costs on durable purchases, and update expectations infrequently in the Mankiw-Reis sense (probability of not updating: Xi = 0.918 per period). The general equilibrium version features asymmetric Rotemberg price stickiness (Calvo probability 0.671 for nondurables, 0.797 for durables), nominal wage stickiness (Calvo 0.802), and a Taylor rule with inflation coefficient 1.105, output coefficient 1.440, and smoothing 0.988.

Main Findings and Quantitative Magnitudes

Sectoral magnitude gap. At trough (approximately 8 quarters after the shock), the durable expenditure response to monetary tightening is an order of magnitude larger than the nondurable response—a fact the calibrated HANK model is designed to match.
Positive comovement. Both durable and nondurable expenditures contract and reach trough in the same quarter, contradicting TANK models (Monacelli 2009) in which savers shift portfolios toward durables and generate negative comovement for that group.
Relative-price dynamics. The relative price of durables rises following monetary tightening (nondurables deflate more), but the rise is modest and cannot overturn the positive comovement result.
Role of the direct interest-rate effect. Across liquid-asset groups, the direct effect accounts for 73–87% of the cumulated durable expenditure response and 37–91% of the cumulated nondurable expenditure response. This direct channel—operating through intertemporal substitution—is quantitatively first-order for durables in a way it is not in standard single-sector HANK models where income effects dominate.
Role of sticky information. A full-information HANK variant produces a counterfactually high durable elasticity (35.24 times the baseline) and no hump-shaped dynamics. Infrequent information updating (Xi = 0.918) is essential to match the hump-shaped propagation of both sectoral and aggregate expenditures.
Income effects and fiscal policy. For a fiscal subsidy specifically targeting durable purchases, intertemporal substitution incentives generate a large shift toward durables and, without income effects, a counterfactual crowding-out of nondurable spending. Income effects are essential to protect nondurable spending, and the aggregate consumption effect of such a policy is at best modest—consistent with Mian and Sufi’s (2012) evidence on cash-for-clunkers.

Scope Conditions

All empirical results are conditional on the LP-IV sample 1969:Q1–2007:Q3 and Romer-Romer shocks as instrumented by Wieland-Yang. The household-level comovement result is established for both liquidity-constrained (liquid assets below $1,000) and unconstrained savers using CEX/SCF data. Model quantitative results are specific to the calibration targeting moments from Fagereng et al. (2021) marginal propensities and BEA depreciation data (delta = 0.054).

Layer 2 — Q&A

Q1: What is the core empirical puzzle the paper addresses, and why do standard models fail? Standard two-sector New Keynesian models predict that asymmetric sectoral price stickiness generates large relative-price movements between durables and nondurables following a monetary shock. These relative-price shifts tend to produce negative conditional comovement—when durables contract, nondurables expand—contradicting the data. The authors document that both categories exhibit positive and persistent comovement, both reaching their trough at approximately 8 quarters, which standard models cannot replicate.

Q2: What are the key empirical facts established via LP-IV? Using Romer-Romer shocks over 1969:Q1–2007:Q3, normalized to a cumulative 100bp Federal Funds Rate increase, the authors find: (1) aggregate expenditure follows a hump-shaped contraction with trough at roughly 8 quarters; (2) the durable expenditure response is an order of magnitude larger than the nondurable response at trough; (3) both categories reach their trough in the same quarter; and (4) the relative price of durables rises modestly after monetary tightening (nondurables deflate more), but not enough to reverse comovement.

Q3: How is the partial equilibrium model calibrated, and which moments does it target? Key calibrated parameters include CRRA sigma = 2.640, Cobb-Douglas weight on nondurables theta = 0.607 (implying durable expenditure share 0.193), adjustment cost alpha = 8.299, information stickiness Xi = 0.918, depreciation rate delta = 0.054, steady-state real rate r = 0.03/4, discount factor beta = 0.915 (matching a 30% share of liquidity-constrained households with liquid assets-to-income ratio of 0.26), and borrowing wedge kappa = 0.05. Moments matched include quarterly MPC on nondurables (22.94%), quarterly MPX on durables (24.15%), interest-rate elasticity of durable expenditure (3.35, within the empirical range of 1.1–5.0), price elasticity of durable demand (29.59), and durable stock skewness relative to nondurable consumption (0.695, consistent with Bertola et al. 2005).

Q4: How does the paper decompose monetary policy transmission? The paper decomposes transmission into three channels: (1) the direct effect of real interest rate changes, which operates through intertemporal substitution and accounts for the quantitatively largest share of the durable response; (2) the relative-price effect, which is modest and redistributive but cannot overturn positive comovement; and (3) pure income effects, which are key for persistence of the nondurable response but not for the sign of comovement.

Q5: What do counterfactual models reveal about the role of each model ingredient? A sticky-information RANK produces positive comovement but the dynamics are front-loaded and less inertial than in the data. A sticky-information TANK delivers results similar to RANK—income effects do not qualitatively change the story. A full-information HANK produces a counterfactually high durable interest-rate elasticity (35.24 times the baseline) and no hump-shaped dynamics, demonstrating that sticky information is the ingredient generating realistic propagation, not heterogeneity per se.

Q6: What does the household-level evidence from CEX and SCF show about comovement across the wealth distribution? Classifying households as liquidity-constrained if liquid assets are below $1,000, the LP-IV estimates show positive comovement between durables and nondurables for both constrained and unconstrained savers. This contradicts TANK models (Monacelli 2009), in which savers shift portfolios toward durables following a monetary shock, generating negative comovement for the saver group. After controlling for income and relative prices, the direct interest-rate effect operates uniformly across financial status groups.

Q7: How does the direct effect vary across liquid asset groups quantitatively? Decomposing across four liquid asset groups (below $1k, $1k–$10k, $10k–$20k, above $20k), the direct effect accounts for 73–87% of the cumulated durable expenditure response and 37–91% of the cumulated nondurable expenditure response. Income effects are more important for nondurable spending prolongation among liquidity-constrained households, but the direct channel dominates durable expenditure for all groups.

Q8: How does the general equilibrium two-sector HANK model differ from the partial equilibrium setup? The GE model adds asymmetric sectoral price stickiness (Calvo probabilities 0.671 for nondurables and 0.797 for durables), nominal wage stickiness (Calvo 0.802), a Taylor rule (inflation coefficient 1.105, output coefficient 1.440, smoothing 0.988), and fiscal lump-sum taxes responding to debt (coefficient 0.191). These features generate the relative-price dynamics observed in the data while preserving the positive comovement result.

Q9: What does the fiscal policy application reveal about the role of income effects? A fiscal subsidy targeting durable purchases generates a much larger shift in the relative price of durables than monetary policy does. Without income effects, intertemporal substitution dominates and nondurable spending falls—a counterfactual result inconsistent with the data. With income effects present, nondurable spending is protected. The aggregate consumption effect of such a durable-targeted fiscal policy is at best modest, consistent with Mian and Sufi’s (2012) evidence from the cash-for-clunkers program.

Q10: What is the broader implication for the literature on HANK versus RANK transmission? In standard single-sector HANK models, income effects (the indirect channel) typically dominate monetary transmission. The presence of consumer durables restores a quantitatively important role for the direct interest-rate channel, which operates through intertemporal substitution in durable purchases. This rebalances the direct-versus-indirect decomposition relative to the conventional HANK wisdom and shows that the durable goods sector is essential to understanding the full transmission mechanism.

Key Concepts

Sectoral comovement (conditional on monetary policy shocks) The empirical regularity that durable and nondurable expenditures both contract following monetary tightening and reach their respective troughs in the same quarter. In this paper, comovement is defined conditional on identified monetary policy shocks (LP-IV with Romer-Romer instruments), not unconditionally. Standard two-sector NK models predict negative conditional comovement due to relative-price effects; replicating positive comovement is the central discipline imposed on the model.

Direct effect (of real interest rate changes) The component of monetary transmission that operates through the intertemporal substitution incentive induced by changes in the real interest rate, holding income and relative prices fixed. Distinct from the income effect (indirect channel) and the relative-price effect. In this paper’s decomposition, the direct effect accounts for 73–87% of the cumulated durable expenditure response across liquid-asset groups.

Sticky information (Mankiw-Reis) Households update their information sets infrequently, with probability (1 - Xi) per period; Xi = 0.918 means only about 8.2% of households update each quarter. This mechanism is essential in the model for generating the hump-shaped, inertial impulse response dynamics observed in the data. Without it (full-information HANK), the durable elasticity is counterfactually large (35.24 times baseline) and dynamics are front-loaded.

MPX (Marginal Propensity to Expend on durables) Analogous to the MPC for nondurables, the MPX measures the additional durable expenditure flow induced by an income windfall. Calibrated to 24.15% quarterly, matching estimates from Fagereng et al. (2021). Distinct from the MPC because durable purchases represent investment in a stock, not immediate consumption flow.

Liquidity-constrained households Households with liquid assets below $1,000, identified in the CEX and SCF. In the model, the 30% share of such households is targeted by the discount factor (beta = 0.915) and the borrowing wedge (kappa = 0.05). The paper’s key finding is that positive comovement holds for both constrained and unconstrained households, contradicting TANK predictions.

HANK (Heterogeneous Agent New Keynesian model) A New Keynesian general equilibrium model in which households are heterogeneous in their liquid asset holdings (and thus face binding borrowing constraints), so that the distribution of assets matters for aggregate dynamics. Distinguished from RANK (Representative Agent NK) and TANK (Two-Agent NK, which approximates heterogeneity with one unconstrained and one hand-to-mouth agent). In this paper, HANK is extended to a two-sector setting with durables and nondurables.

Convex adjustment costs on durable purchases A cost of adjusting the durable stock that is convex in the size of the adjustment (calibrated parameter alpha = 8.299). This smooths the durable expenditure response and prevents counterfactually sharp jumps in durable purchases following interest rate changes, contributing to realistic propagation dynamics alongside sticky information.

Costs of Financing U.S. Federal Debt Under a Gold Standard: 1791-1933

Mon, 01 Jan 0001 00:00:00 +0000

Overview

This paper constructs a new dataset of US federal bond prices and uses it to estimate the full term structure of yields on gold-denominated US federal debt from 1791 to 1933 — the entire gold standard era. The core research question is how the costs of financing US federal debt evolved over this period and what monetary, fiscal, and financial policy changes drove that evolution, with the ultimate aim of understanding how the US built fiscal capacity and transformed its debt from a “junk bond” into a global “safe asset.”

Data and Methodology. The authors compile monthly prices, quantities, and descriptions of all US Treasury securities from 1776 to 1960 (the Hall et al. 2018 dataset). Bonds with less than one year to maturity are excluded from the main estimation due to liquidity premia. The primary estimation uses a Dynamic Nelson-Siegel (DNS) model with stochastic volatility (Diebold and Li 2006; Hautsch and Yang 2012), estimated by Bayesian MCMC. A key methodological innovation is the addition of bond-specific idiosyncratic pricing errors (Assumption 3), which allows the authors to include bonds with heterogeneous contract features — call options, indefinite maturities, conversion features — that characterize 19th-century US debt without either dropping them from the sample or having their idiosyncrasies distort the common yield curve. The data are “big” in the time-series dimension but sparse in the maturity (cross-sectional) dimension, frequently offering fewer than five price observations per month; the DNS framework pools information across time to address this sparsity.

For the greenback period (1862–1878), the authors extend the approach by modeling the greenback yield curve as a function of the gold yield curve and a time-varying VAR model of exchange rate expectations (Assumptions 4–5). Only nine greenback-denominated bonds exist in the sample, most of them short-term; the VAR is estimated jointly using exchange rate data and the relative prices of greenback and gold bonds.

Main Findings.

Long-run decline in yields. The 10-year gold-denominated zero-coupon yield fell from approximately 8% in 1800 to approximately 2% in 1900, consistent with global secular decline trends, but the trajectory stabilized near 2% after 1900 — suggesting US debt began to play a distinctive “safe-asset” role from the turn of the 20th century.
War spikes were much larger than previously understood. The paper’s estimate of the 10-year gold yield reaches a peak of approximately 16% near the end of the Civil War. This is substantially higher than the Homer and Sylla (2004) peak of 6% at the start of the war. The discrepancy arises because Homer and Sylla used bonds trading at par — which did not exist during the Civil War — while this paper uses the full universe of bonds at monthly frequency.
Yield curve slope switched sign. The term spread (10-year minus 2-year gold yield) was typically negative before the Civil War (inverted yield curve) and turned persistently positive afterward. The authors link this switch to a change in long-run inflation predictability: inflation was relatively hard to forecast before the Civil War and easier to forecast after, consistent with a negative inflation-risk premium in the pre-war period.
Default risk premium disappeared around 1905. Comparing hypothetical gold-denominated US consols to UK consols (the 19th-century benchmark safe asset), US yields were persistently above UK yields until approximately 1905, when US yields fell below UK yields. This indicates that US federal debt acquired safe-asset characteristics well before World War I, foreshadowing the shift in global reserve asset status during and after Bretton Woods.
Nominal anchor during the Civil War. Despite a 60% depreciation of the greenback against gold during the Civil War (100 greenback dollars could be purchased for as few as 40 gold dollars in summer 1864), investors expected greenbacks to eventually return to gold parity. Estimated long-run exchange rate expectations remained anchored at one-for-one parity throughout the period. This kept greenback-denominated bond yields flat at approximately 6% — bonds traded around par — explaining the “Civil War yield puzzle” noted by Friedman and Schwartz (1963).
Short-rate disconnect. Short-maturity government bonds (less than one year) traded with a premium of approximately 0.25 to 0.5 percentage points relative to model-implied yields throughout most of the 19th century, reflecting scarcity of money-like assets. This premium effectively disappeared from the 1880s until World War I — coinciding with the National Banking Era — and then reappeared in the 1920s after the Federal Reserve created a secondary market for Certificates of Indebtedness.

Q&A

Q1: Why does the paper restrict estimation to bonds with maturity greater than one year? Short-maturity Treasury notes exhibited particularly large estimated bond-specific pricing errors in preliminary analysis, which the authors attribute to a liquidity premium: short-term government debt was used for transactions and thus commanded a money-like premium that a common discount function cannot accommodate. To keep this liquidity premium from distorting estimates of the longer end of the curve, these bonds are excluded from the main estimation. Short-maturity bonds are then studied separately as an “out-of-sample” exercise (the short-rate disconnect).

Q2: How does the Dynamic Nelson-Siegel model with stochastic volatility solve the cross-sectional sparsity problem? The DNS model parameterizes the entire yield curve at each date using only three latent factors — level (L), slope (S), and curvature (C) — which follow a driftless random walk. The stochastic volatility component, captured in the covariance matrix Σt, governs how much information is pooled across adjacent time periods. When Σt → 0, the yield curve is assumed constant (full pooling); when Σt → ∞, estimates are date-by-date (no pooling). By allowing Σt to vary, the model pools more heavily in sparse periods and less during wars when yields change rapidly. The companion paper (Payne et al. 2023a) confirms via information criteria that stochastic volatility and correlated shocks improve fit without overfitting.

Q3: What is the bond-specific pricing error and why is it essential for historical data? Assumption 3 adds to each bond i a Gaussian pricing error with mean zero and bond-specific standard deviation σ(i)_m (scaled by Macaulay duration to approximate yield-space errors). This allows bonds with idiosyncratic contract features — call options, conversion clauses, ambiguous payment currency — to inform the common yield curve without unduly distorting it. Bonds with larger σ(i)_m receive less weight in estimation. In modern datasets, researchers pre-select homogeneous bonds and use time-specific pricing errors; the historical sparsity prevents that approach here.

Q4: How large were Civil War yields compared to prior estimates, and why does the discrepancy arise? The paper’s posterior median for the 10-year gold zero-coupon yield peaks at approximately 16% near the end of the Civil War. Homer and Sylla (2004) report a peak of 6% at the start of the war. The discrepancy arises because Homer and Sylla used bonds trading close to par, but during the Civil War no federal bonds traded at gold-price par (Lincoln’s re-election was uncertain in summer 1864; 100 greenback dollars could be purchased for 40 gold dollars, implying 6% coupon bonds were priced at 40% of par, implying yields in excess of 15%). This paper uses the full universe of Treasury bonds at monthly frequency and allows all bonds — regardless of trading price — to inform the yield curve.

Q5: When did US debt cease to carry a default risk premium relative to UK debt, and how is this measured? The authors compare yields-to-maturity on gold-denominated UK consols to those on hypothetical gold-denominated US consols promising the same coupon flows. Because both countries were on a gold standard for most of the period and UK consols were the 19th-century safe asset, the spread is interpreted as a risk premium on US debt. US yields fell below UK yields persistently after approximately 1905, indicating that US debt was priced as a safe asset well before World War I. US yields were temporarily close to UK yields in the 1820s but the spread re-widened after the Jacksonian era, state defaults in the 1840s, and the Civil War. The spread closed only after Civil War disruptions resolved, the National Banking System matured, and gold-greenback parity was restored in 1879.

Q6: What is the “nominal anchor” finding during the greenback era, and what econometric method uncovers it? During 1862–1878, the federal government issued non-convertible greenback dollars alongside gold bonds. The greenback depreciated substantially (to 40 cents per gold dollar in 1864), yet greenback-paying bonds traded near par, implying greenback yields near 6%. The authors model the greenback yield curve as a product of the gold discount function and a “multiplier” z(j)_t capturing the expected future gold-to-greenback exchange rate at each horizon j (Assumption 4). The exchange rate expectations are estimated via a time-varying VAR(2) model of the gold-to-greenback and gold-to-goods exchange rates (Assumption 5), jointly constrained by the prices of greenback bonds via an interest-rate parity condition. The resulting estimates show that throughout the greenback era — even during large wartime depreciations — investors’ long-run expectations of the exchange rate remained anchored near gold parity, consistent with anticipated eventual resumption.

Q7: How did political events affect exchange rate expectations during and after the Civil War? The time-varying VAR captures shifts in exchange rate expectations associated with identifiable political events. Grant’s victory in 1869 (which resolved uncertainty about whether debts would be honored in gold) coincided with an increase in the price of greenbacks, a decrease in expected greenback appreciation, and a closing of the gap between greenback and gold 10-year yields. In the early 1870s, following the Panic of 1873 and uncertainty about resumption, investors came to expect that gold-greenback discrepancies would persist almost indefinitely, causing gold and greenback yields to converge. The Resumption Act of January 1875 then shifted 2-year and 10-year expectations back toward parity.

Q8: What is the short-rate disconnect and what does it reveal about the National Banking Era? The short-rate disconnect is the difference between observed yields-to-maturity for bonds with less than one year to maturity and the yields-to-maturity implied by the model estimated on bonds with more than one year maturity. A positive disconnect means short-maturity bonds yielded less than long-maturity bonds conditional on the model — indicating a liquidity premium on short-term debt. The authors find a persistent premium of 0.25 to 0.5 percentage points through most of the 19th century, reflecting scarcity of money-like assets when state bank notes circulated at variable discounts. The premium disappeared from approximately the 1880s to World War I, coinciding with the mature National Banking Era after greenback-gold parity was restored in January 1879. The authors interpret this as evidence that the National Banking Acts (1862–1866), which allowed National Banks to issue standardized bank notes backed by long-term US government bonds, ultimately succeeded in supplying liquid assets and equalizing the pricing of short- and long-term federal debt — but only after the currency risk from the greenback period had been resolved.

Q9: How does the composite long-term yield series (Officer-Williamson / Homer-Sylla) distort historical narratives? The composite series combines Homer and Sylla US federal yields (1798–1861), New England Municipal bond yields (1862–1899), and corporate bond yields (1900–1940). The paper shows that this composite series substantially underestimates the increase in US federal borrowing costs during Civil War deficits (peak of 6% vs. this paper’s 16%) and overstates post-Civil War borrowing costs by mixing in riskier private obligations. The authors argue that earlier findings of no strong association between 19th-century interest costs and deficits (Evans 1985, 1987) may reflect the composite series’ failure to accurately capture federal borrowing costs during large deficit episodes.

Q10: How did the yield curve slope change after the Civil War and what explains it? The term spread (10-year minus 2-year gold yield) was typically negative before the Civil War and positive after the late 1870s. Major wars caused sharp temporary decreases (inversions). The authors connect the sign switch to a change in long-run inflation dynamics documented in a companion paper (Payne et al. 2023b): long-run inflation was hard to predict before the Civil War and easier to predict after, suggesting gold bonds provided a better inflation hedge in the pre-war period (negative inflation-risk premium), which is consistent with asset pricing theory producing a downward-sloping yield curve. After the Civil War, as inflation became more predictable, the inflation-risk premium became positive and the yield curve turned upward-sloping.

Q11: What did the National Banking Acts seek to do and was the puzzle of bank note under-issuance resolved? The National Banking Acts (1862, 1863, 1865, 1866) authorized federally chartered banks to issue bank notes up to 90% of the par or market value of eligible US Treasury bonds deposited as collateral, subject to a 1% annual tax on notes outstanding (0.5% after 1900), compared to a 10% tax on state bank notes. The intended goals were to increase the supply of short-term liquid assets and to increase bank demand for long-term federal debt, thereby lowering long-term yields and eliminating the short-rate disconnect. A long-standing puzzle (Friedman-Schwartz, Cagan, Champ, Calomiris-Mason) held that yields on eligible Treasuries did not fall enough to equal the note tax rate, implying under-issuance. The paper’s analysis of the short-rate disconnect offers a resolution: if one focuses on the disconnect rather than the yield-tax spread, the National Banking Acts appear to have largely achieved their goals by the 1880s — but only after greenback-gold parity was restored, suggesting that currency devaluation risk had initially restrained bank note issuance, as hypothesized by Cagan (1965).

Key Concepts

Dynamic Nelson-Siegel (DNS) model with stochastic volatility: A parametric yield curve model (Diebold-Li 2006) parameterizing zero-coupon yields at each date as a function of three latent factors — level (L), slope (S), curvature (C) — following a driftless random walk. The paper extends this with time-varying shock volatilities (stochastic volatility) to allow the degree of information pooling across time periods to vary with institutional and wartime disruptions. Used here to handle cross-sectional sparsity in historical bond data.

Bond-specific pricing error: A Gaussian pricing error with bond-specific standard deviation σ(i)_m (scaled by Macaulay duration) added to each bond’s observed price. Allows bonds with heterogeneous and idiosyncratic contract features (call options, conversion clauses) to inform a common discount function without distorting it, by automatically down-weighting “peculiar” bonds through higher estimated σ(i)_m.

Short-rate disconnect (liquidity premium): The systematic difference between observed yields-to-maturity on bonds with less than one year to maturity and yields implied by a pricing kernel fitted on bonds with more than one year to maturity. Interpreted as a money-like convenience yield (liquidity premium) on short-term debt: when money-like assets are scarce, short-term bonds are overpriced (lower yields) relative to the term structure implied by longer maturities. Measured here as an out-of-sample fit residual from the DNS model.

Denomination risk: The risk that the unit of account in which bond payments are promised may change in value relative to gold. During the greenback era (1862–1878), bonds denominated in greenbacks carried denomination risk because greenbacks could depreciate against gold. The paper distinguishes denomination risk from default risk by estimating separate gold and greenback yield curves and modeling exchange rate expectations.

Nominal anchor: The phenomenon in which long-run market expectations of the gold-to-greenback exchange rate remained anchored near gold parity (one-for-one) even during large short-run depreciations during the Civil War. Inferred from the observation that greenback-denominated bonds traded near par (yield ~6%) while the spot greenback depreciated by up to 60% against gold, implying investors anticipated eventual full appreciation.

Default risk premium (US-UK yield spread): The difference between yields on hypothetical gold-denominated US consols and yields on UK consols. Since both were on a gold standard (so inflation expectations are similar), and UK consols were the 19th-century benchmark safe asset, the spread is interpreted as the compensation investors demanded for the risk that the US might default or alter payment terms. Persistently positive until approximately 1905, then became negative.

Convenience yield: An implicit yield that accrues to holders of money-like or safe assets because of their use in transactions or as collateral. In this paper, it emerges as the spread between yields on US federal bonds and other low-risk bonds in the late 19th century, reflecting increased demand for Treasuries as reserves under the National Banking System. Historically identified via the short-rate disconnect disappearing in the National Banking Era.

Dollar Dominance and the Transmission of Monetary Policy

Mon, 01 Jan 0001 00:00:00 +0000

Layer 1 — Summary

An emerging view in international macroeconomics contends that dollar invoicing of exports renders monetary policy ineffective for non-U.S. countries: because export prices are allegedly sticky in dollars, exchange rate depreciations cannot shift expenditure toward domestic goods, muting the classical Mundell-Fleming channel. McLeay and Tenreyro argue that this view rests on empirical assumptions that are not borne out by the data: goods priced in dollars tend to have more flexible prices and higher elasticities of substitution, not the monopoly power and sticky dollar prices assumed in dominant currency pricing (DCP) models. They propose a mixed currency pricing (MCP) framework that incorporates heterogeneous price flexibility and intra-sector international competition, and show that even with dollar pricing, depreciating the currency by loosening monetary policy can still boost exports and activity materially. The limit to any expansion is not demand, but supply capacity: after a depreciation, domestic dollar costs fall, flexible-price exporters lower prices slightly and gain large market share due to high demand elasticities, and the expansion runs until rising marginal costs offset the initial depreciation — producing limited reduced-form dollar pass-through as an equilibrium result rather than evidence of nominal stickiness. Empirical tests using monetary policy shocks in a sample of emerging and developing economies, case studies of Canada and Chile as commodity exporters, and three large devaluation episodes all find significant, material increases in exports and aggregate activity following exchange-rate depreciations, consistent with the MCP model’s predictions.

Layer 2 — Q&A

Q1. What is the specific empirical claim that DCP models rest on, and how do McLeay and Tenreyro challenge it?

DCP models (e.g., Gopinath et al. 2020) posit that exporters invoicing in dollars have monopoly power and face nominal rigidities that keep their dollar export prices sticky. The observable implication used to motivate this assumption was limited exchange rate pass-through to dollar export prices. McLeay and Tenreyro show that low pass-through is equally consistent with a flexible-price, high-elasticity equilibrium. When demand elasticities are high, firms optimally absorb exchange rate changes through quantities rather than prices; the reduced-form pass-through coefficient is small even without any nominal friction. Low pass-through is therefore not informative about the degree of nominal rigidities, and using it to calibrate sticky-price DCP models and draw normative conclusions about exchange rate policy is unwarranted.

Q2. What are the three empirical facts that motivate the MCP framework’s assumptions?

Fact 1: Homogeneous products (commodities and commodity-like goods traded on organized exchanges or reference-priced, following Rauch 1999) represent a large share of goods exports, exceeding 70% for developing economies, around 60% for emerging economies, and around 35% for advanced economies; Sub-Saharan Africa, Latin America, and the Middle East all have shares above 50%. Fact 2: Homogeneous and more competitively produced goods have more flexible prices, documented across multiple countries — for instance, Nakamura and Steinsson (2008) find a median monthly price-change frequency of 10.8% for finished-good producer prices but 98.9% for crude materials. Fact 3: Dollar (vehicle currency) invoicing is most prevalent precisely in these homogeneous, competitive-good sectors; classical work by McKinnon (1979) and Magee and Rao (1980) emphasized that vehicle-currency invoicing facilitates continuous price comparability in competitive markets, and panel regressions corroborate a positive relationship between the share of exports invoiced in dollars and the homogeneous-goods share of exports.

Q3. What is the mechanism through which depreciation boosts exports in the MCP model, and why does this generate low observed pass-through?

With sticky wages (representing non-tradable input price stickiness more broadly), a monetary policy-induced depreciation lowers the domestic cost of production when expressed in dollars. For competitive exporters facing highly elastic demand, even a small reduction in the dollar price translates into a substantial gain in export quantities. Firms therefore lower their dollar prices slightly, trading some profit margin for a large increase in market share. As exports expand, domestic marginal costs rise (firms move up the upward-sloping marginal cost curve), partially offsetting the depreciation’s effect on dollar costs. In equilibrium, the net dollar price movement is small — producing the observed limited pass-through — but the quantity response is large. In the perfectly competitive limit (relevant for commodity exporters), the dollar price is unchanged by the world market, and the entire adjustment is through an expansion of export volumes until rising domestic marginal costs absorb the depreciation. The implied observation is identical to a sticky-price model for prices, but “the implications for export quantities are diametrically opposed.”

Q4. How does the MCP model nest existing frameworks, and what does it add relative to the DCP and PCP benchmarks?

The MCP (mixed currency pricing) framework nests sticky-price DCP as a special case (by setting demand elasticities low and allowing full price stickiness) and produces behavior close to PCP (producer currency pricing) in the flexible-price, high-elasticity limit — restoring the allocative properties of the exchange rate from Obstfeld and Rogoff (1995). The distinctive addition is intra-sector international competition: domestic exporters face competition from international competitors producing highly substitutable varieties of the same good, so substitution elasticities can be high at the variety level even when macro-level elasticities between goods remain low. This follows a bottom-up approach to elasticities as in Feenstra et al. (2018). The model also allows heterogeneous nominal rigidities across producers, with exporters of dollar-invoiced homogeneous goods having flexible prices while non-tradable input prices (wages) remain sticky — the source of monetary non-neutrality and the mechanism for real exchange rate effects.

Q5. What is the role of supply capacity, and why is it “the limit” rather than demand?

In the sticky-price DCP model, the constraint on the export response is on the demand side: dollar prices do not move, so demand is unchanged, and there is no export response at all. In the MCP model, demand responds immediately to the cost reduction — the constraint that eventually stops the expansion is supply capacity, captured by the slope of the marginal cost curve and macroeconomic constraints on non-tradable inputs. With a flat marginal cost curve (plentiful supply capacity), exports expand materially; with a steep curve or hard capacity constraints, the increase in marginal cost fully offsets the depreciation before much quantity adjustment occurs. This supply-side framing reorients the policy question: the limiting factor for monetary policy’s external effectiveness is not whether dollar prices can move, but whether the domestic economy has the productive capacity to expand tradable output. This also connects the paper to the Salter-Swan two-good framework and to Schmitt-Grohé and Uribe (2021).

Q6. What do the macroeconomic empirical tests find, and how do they distinguish the MCP from sticky-price DCP?

The paper uses three empirical exercises. First, using a sample of developing and emerging economies, monetary policy expansions that generate exchange rate depreciations cause significant increases in both exports and aggregate economic activity — consistent with the MCP model’s material export response and inconsistent with the DCP prediction of no export channel. Second, focusing on Canada and Chile as commodity exporters where the MCP assumptions (competitive markets, flexible export prices) are especially applicable, the aggregate results are corroborated and sectoral evidence provides additional support. Third, three case studies of large devaluations in the sample document that they are followed by material increases in exports relative to trend. In all exercises, the direction and magnitude of export and output responses are consistent with a functioning expenditure-switching channel, even where exports are priced in dollars.

Q7. How does the paper reinterpret the pass-through evidence that motivated sticky-price DCP models, and what does this imply for normative conclusions?

Standard reduced-form pass-through regressions relate the change in dollar export prices to changes in the exchange rate. These regressions typically omit or fail to fully capture movements in marginal cost. In the MCP model, flexible-price firms fully pass through changes in marginal cost; the observed limited pass-through to export prices is an equilibrium result of the offsetting rise in marginal costs as export volumes expand, not evidence of a nominal friction. Because the standard regressions omit marginal cost dynamics, they risk attributing the equilibrium quantity-driven equilibrium to a pricing friction. This has direct normative implications: the case made by the IMF (2019, 2020) that dollar invoicing worsens the cost-benefit calculation for flexible exchange rates — and may bolster the case for capital controls — rests on interpreting low pass-through as evidence of stickiness. If low pass-through instead reflects high demand elasticities and supply-side adjustment, the normative argument for constraining exchange rate flexibility is weakened.

Q8. How does the paper relate to the purchasing power parity puzzle and the Mussa puzzle?

The MCP framework offers explanations for two classic international macro puzzles without assuming nominal rigidities in export prices. On the PPP puzzle (the volatility and persistence of the real exchange rate, Rogoff 1996): in the MCP model, exporters’ optimal reset prices move very little after exchange rate changes — not because of stickiness, but because demand is elastic and marginal costs rise quickly. This predicts limited movement in relative export prices, consistent with empirical evidence in Blanco and Cravino (2020) and Itskhoki and Mukhin (2025). On the Mussa puzzle (the large jump in nominal and real exchange rate volatility after the Bretton Woods collapse): the model’s mechanism via sticky wages is consistent with evidence that depreciations produce slow adjustment of non-tradable prices (Burstein, Eichenbaum, and Rebelo 2005), generating real exchange rate movements despite limited response in traded-good dollar prices.

Key Concepts

Dominant currency pricing (DCP): A framework in which non-U.S. exporters set and maintain prices in U.S. dollars, with sticky dollar prices. As formulated by Gopinath et al. (2020), DCP predicts that exchange rate depreciations by non-U.S. countries do not reduce dollar export prices and therefore do not stimulate export demand — muting the expenditure-switching channel of monetary policy.

Mixed currency pricing (MCP): The framework introduced in this paper. It allows heterogeneous price flexibility and market structure across export sectors, nesting both sticky-price DCP and flexible-price PCP as special cases. Dollar-priced exports face elastic demand from international competition, have flexible prices, and respond to depreciations through quantities rather than prices. Non-traded inputs (wages) remain sticky, providing the source of monetary non-neutrality.

Expenditure-switching channel: The mechanism by which exchange rate depreciations redirect spending toward domestically produced goods, boosting exports and aggregate demand. In PCP models, this works through a fall in relative export prices. In the MCP model, it works through an expansion in export quantities even when dollar prices change little.

Exchange rate pass-through (to export prices): The elasticity of dollar export prices with respect to the nominal exchange rate. In sticky-price DCP models, low pass-through reflects a nominal friction (prices cannot adjust). In the MCP model, low pass-through reflects high demand elasticities and offsetting marginal cost increases: it is an equilibrium outcome, not a friction, and therefore does not imply that export volumes are unresponsive.

Intra-sector international competition: The market structure feature central to the MCP framework. Domestic exporters of a given good compete with foreign suppliers of highly substitutable varieties, making their demand elastic at the variety level even if aggregate elasticities across different goods categories are low. This follows Armington (1969) as implemented by Feenstra et al. (2018).

Supply capacity constraint: In the MCP model, the binding constraint on how much a depreciation can boost exports. With high demand elasticities, demand for domestic exports expands freely; the limit is set by how quickly rising domestic marginal costs absorb the improvement in export profitability. The supply constraint replaces the demand constraint that operates (mechanically, via zero price response) in sticky-price DCP models.

Homogeneous goods (Rauch 1999 classification): Goods traded on organized commodity exchanges or reference-priced in trade publications, as opposed to differentiated goods. McLeay and Tenreyro use this classification to establish that dollar-invoiced exports are disproportionately homogeneous, competitive, and flexible-priced — contrary to the DCP assumption of monopoly power and price stickiness.

Summary based on published open-access version. AI-assisted, human review pending.

Inflation Expectations and the Slope of the Phillips Curve: Evidence from Firm Surveys

Mon, 01 Jan 0001 00:00:00 +0000

Do the inflation expectations of firms — rather than households or financial markets — shift the slope of the Phillips curve? Using a new panel of firm-level surveys matched to price-setting behavior, the authors find that firms with higher expected inflation adjust prices more aggressively in response to demand shocks, steepening the local Phillips curve slope. The effect is concentrated among firms that review prices frequently, suggesting a mechanism through the frequency of price adjustment rather than through the level of markups.

Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

In depth

Q1. What is the main empirical finding on expectations and the Phillips curve slope?

Firms with higher measured inflation expectations exhibit a steeper relationship between demand conditions and price adjustment — the estimated Phillips curve slope is roughly 40% larger in the high-expectations tercile than in the low-expectations tercile, conditional on the authors’ controls and sample. The authors interpret this as evidence that expectations are not merely a level shift in inflation but alter the sensitivity of prices to real activity, consistent with forward-looking pricing theories.

Q2. What is the mechanism, and how do the authors identify it?

The authors argue that expectations work through the frequency of price review: firms expecting higher inflation are more likely to be in an active review window, and so respond more to a given demand shock within that window. Identification relies on cross-firm variation in survey-measured expectations within narrow industry-time cells, so that aggregate demand shocks are held approximately fixed. The authors acknowledge this strategy absorbs industry-specific inflation trends and may understate the full expectational effect.

Q3. What does this imply for monetary policy?

If the Phillips curve slope varies with expectations, then a credible disinflation — by lowering expected inflation — flattens the curve and makes the output cost of reducing inflation larger, not smaller. The authors present this as a potential mechanism behind the observed flattening of the curve in low-inflation regimes, though they stop short of a structural welfare calculation.

Key concepts

Phillips curve slope: The coefficient linking excess demand (or unemployment gap) to inflation in the short-run Phillips curve — steeper means a given demand shortfall has a larger disinflationary effect.
price review frequency: How often a firm actively reconsiders its prices; firms that review more often are more likely to adjust in response to new information within any given period.
firm-level survey expectations: Inflation expectations measured directly from firms (rather than households or markets), which may better capture the beliefs that drive actual price-setting decisions.

Mixing It Up: Inflation at Risk

Mon, 01 Jan 0001 00:00:00 +0000

This paper introduces a Bayesian Gaussian mixture density regression framework that estimates the complete forecast distribution of inflation — not just selected quantiles — and decomposes the entire risk outlook into contributions from individual economic predictors. The methodology accommodates multimodality, skewness, and fat tails without parametric restrictions, and allows construction of risk measures calibrated to the central bank’s own loss function rather than generic percentile-based measures. Applied to the recent U.S. inflation surge, the framework finds that post-pandemic inflation risk was primarily driven by the recovery of the U.S. business cycle and surging commodity prices, while adjustments in monetary policy contributed negatively — partially mitigating the increase in right-tail inflation risk — and credit spreads also offset some risk. The Gaussian mixture structure enables fast MCMC estimation and produces well-calibrated density forecasts across a range of macroeconomic variables.

Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

In depth

Q1. What is the key methodological contribution relative to existing inflation-at-risk approaches?

Existing approaches to macroeconomic at-risk measures focus on specific quantiles of the forecast distribution — typically the 5th or 25th percentile — discarding information contained in the rest of the distribution; this paper redirects attention to the full forecast distribution while retaining the nonparametric flexibility of quantile regression. The Gaussian mixture density regression estimates a conditional distribution that is a weighted mixture of Gaussians, capturing multimodality, asymmetry, and fat tails simultaneously. The key innovation is decomposability: each predictor’s contribution to any region of the forecast distribution can be quantified, enabling a driver-level accounting of what generates tail risk in any given period.

Q2. What does the U.S. application reveal about the inflation surge?

The framework attributes the increase in right-tail U.S. inflation risk during 2021–2023 primarily to surging commodity prices and the recovery of the domestic business cycle, while monetary policy tightening contributed negatively — its effect partially offset the upward pressure from commodity and cycle drivers. Credit spreads also partially mitigated the risk. The decomposition implies that the dominant drivers of inflation risk were supply-side and aggregate-demand factors, and that monetary policy, when it tightened, reduced the right-tail risk as intended — providing quantitative support for the interpretation that policy was reactive but directionally correct.

Q3. How does the framework construct policy-relevant risk measures?

The framework allows weighting probability mass over the forecast distribution by any user-specified loss function, including asymmetric central bank preferences, yielding risk measures that integrate the full distributional information in proportion to the policymaker’s actual valuation of different inflation outcomes. A central bank that penalizes above-target inflation more heavily than below-target inflation (consistent with empirical evidence on CB loss functions) would weight the upper tail more, producing a risk statistic that is higher than a symmetric measure for the same distribution. This policy-preference-aligned risk measure could have provided a more accurate signal of the urgency of the 2021–2023 inflation risk than standard percentile measures.

Key concepts

inflation at risk : the quantile-based or distribution-based characterization of future inflation uncertainty; extended in this paper from a single quantile to the complete forecast distribution and its risk decomposition by driver.

density regression : a regression model in which the conditional distribution of the outcome — not just its mean or a specific quantile — is the object of estimation; the paper uses a Gaussian mixture density regression to capture non-standard distributional shapes.

risk decomposition : the attribution of shifts in the full forecast distribution to individual predictor variables; the paper’s key tool for identifying which economic factors drive right-tail inflation risk in any period.

CB-preference-aligned risk measure : a summary statistic constructed by weighting probability mass over the forecast distribution by the central bank’s loss function; captures asymmetric preferences and goes beyond standard percentile measures.

Monetary Policy and the Drifting Natural Rate of Interest

Mon, 01 Jan 0001 00:00:00 +0000

This paper analyzes how monetary policy should respond to a long-run natural interest rate that can drift permanently — following a bounded random walk with upper bound 3 percent and lower bound 0 percent — when the zero lower bound (ZLB) on nominal interest rates is a binding constraint. The central result is that the long-run neutral rate (the real policy rate consistent with stable inflation in long-run equilibrium) should fall more than one-for-one with the long-run natural rate as the latter approaches zero, because the mere risk of future ZLB episodes — even when the economy is currently away from the ZLB — imparts a persistent downward bias on inflation expectations that can only be offset by maintaining a pre-emptive expansionary bias. Quantitatively, the model implies that the neutral rate should be zero as soon as the long-run natural rate falls to 75 basis points — well above the near-zero estimates prevailing in the late 2010s — and that the ZLB would bind one-third of the time under optimal policy when the natural rate fluctuates between 0 and 3 percent. Price level targeting with a 10-basis-point upward drift closely approximates optimal commitment policy and has the advantage of not requiring knowledge of the natural rate level.

Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

In depth

Q1. What empirical fact motivates the model?

Empirical analyses of the long-run natural rate — the real interest rate prevailing over a long-run equilibrium in which nominal rigidities are absent — consistently find that it is time-varying in a manner best described by a random walk, meaning it can drift without reverting to a constant long-run level. The paper cites Holston, Laubach, and Williams (2017), Fiorentini et al. (2018), and Hamilton et al. (2016) as the main empirical references. Holston et al. (2017) place the long-run natural rate at between 0 and 1 percent in the U.S. and possibly slightly negative in the euro area as of 2016. The paper draws one central lesson: because the natural rate is time-varying and its future level is uncertain, a model with constant natural rate will give unreliable guidance for monetary policy, especially at low natural rate levels near zero.

Q2. What is the model and what are the key equilibrium concepts?

The paper embeds a new Keynesian model in which the long-run natural rate follows a bounded random walk with upper bound 3 percent and lower bound 0 percent, calibrated to post-WWII U.S. TFP data, and studies optimal monetary policy under commitment while imposing the zero lower bound. A critical distinction separates two notions of the long-run equilibrium interest rate: the “long-run natural rate” (denoted ¯r) is the real rate that would prevail in flexible-price equilibrium, determined by fundamentals outside the central bank’s control; the “neutral rate” (r*) is the real policy rate consistent with stable inflation in the long run, which the central bank operationally targets. The two coincide in standard models with constant ¯r, but diverge in this paper because ZLB risk drives a wedge between them.

Q3. What is the main theoretical result?

Under optimal commitment, the neutral rate r should fall more than one-for-one with the long-run natural rate ¯r — that is, the central bank should maintain a negative gap (r < ¯r) that widens as ¯r falls toward zero — because permanent downward movements in ¯r make future ZLB binding episodes permanently more likely, creating a persistent downward bias on inflation expectations that requires pre-emptive accommodation even in periods when the ZLB is not currently binding.** This result contrasts with the existing literature on optimal commitment at the ZLB, which has emphasized forward guidance — the promise to maintain low rates even after the economy recovers from a ZLB episode — as the primary stabilization tool. The paper shows that forward guidance alone is not sufficient when ¯r can permanently drift lower, because each downward drift permanently raises the probability of future ZLB episodes, reducing the central bank’s scope for fulfilling future inflation promises.

Q4. What are the quantitative implications?

The model implies that the neutral rate r reaches zero when the long-run natural rate ¯r is at 75 basis points — a level that was well above the near-zero estimates of ¯r prevailing at the end of the 2010s — and that the ZLB binds one-third of the time under optimal policy when ¯r fluctuates between 0 and 3 percent.* The 75 basis-point threshold means that a central bank operating in an environment where ¯r has declined to its estimated late-2010s levels would already be constrained to a neutral rate of zero under optimal policy. The one-third ZLB frequency is higher than what would be predicted by models with constant ¯r at typical calibrations, reflecting the permanent nature of ¯r shocks and their cumulative effect on the neutral rate.

Q5. What do the adjustment dynamics look like after a negative ¯r shock?

Following a permanent reduction in ¯r, the real policy rate adjusts gradually rather than immediately — remaining temporarily above the new long-run neutral rate during the transition — implying that monetary policy is contractionary along the adjustment path and that a permanent decline in ¯r is followed by a temporary disinflation before the economy settles at the new r.* This history-dependence of optimal commitment policy means the central bank does not immediately jump to the new, lower r* after a ¯r shock; it moves gradually, making the short-run policy stance more contractionary than the long-run position. The temporary disinflation is consistent with the general principle of history-dependence of optimal policy under commitment.

Q6. What role does price level targeting play?

Price level targeting variants — particularly a rule with an optimally chosen upward drift of 10 basis points — closely approximate the economic outcomes achieved under optimal commitment policy in the model, with the practical advantage that such rules do not require the central bank to know or estimate the current level of the long-run natural rate ¯r. The Eggertsson-Woodford (2003) price level target works well in models with constant ¯r by generating positive inflation expectations in the wake of deflationary ZLB episodes. Adding a small upward drift of 10 basis points strengthens this property under a drifting ¯r, because it provides additional buffer against the downward expectations bias that permanent ¯r drift generates. Under price level targeting rules, the neutral rate reaches the ZLB as soon as ¯r falls below 1 percent.

Key concepts

long-run natural rate (¯r) : the real interest rate prevailing over a long-run equilibrium in which nominal rigidities are absent; in this paper modelled as a bounded random walk with upper bound 3 percent and lower bound 0 percent, calibrated to post-WWII TFP data.

neutral rate (r)* : the real policy rate consistent with stable inflation in the long run; distinct from ¯r in this paper because ZLB risk drives a negative gap (r* < ¯r) that widens as ¯r approaches zero.

zero lower bound (ZLB) : the constraint that nominal policy rates cannot fall below zero; in this model the reason that permanent reductions in ¯r create a persistent downward bias on inflation expectations even when the ZLB is not currently binding.

expansionary bias : the paper’s finding that optimal commitment policy should maintain r* < ¯r — a pre-emptive accommodation away from the ZLB — to offset the downward bias on inflation expectations created by the risk of future ZLB episodes.

price level targeting : a monetary policy rule in which the central bank targets the price level rather than the inflation rate; shown in this paper to approximate optimal commitment policy and to have the practical advantage of not requiring knowledge of ¯r.

Monetary–Fiscal Policy Interactions When Price Stability Occasionally Takes a Back Seat

Mon, 01 Jan 0001 00:00:00 +0000

The paper builds a discrete-time DSGE model with Calvo sticky prices in which the public sector has two feedback rules that can hit corners, generating endogenous shifts between an “orthodox” regime and a “fiscally-dominant” regime. Fiscal policy sets the primary surplus as s̃_t = min(ϕb̃_{t−1}, s̄): the surplus tracks real debt with coefficient ϕ = 0.1 until the limit s̄ = 0.01 (1% of output in deviation from steady state; approximately 3% in level) binds. Monetary policy follows R̂_t = min(αp̂_t, R̄): a standard Taylor rule with coefficient α = 2.5 until the nominal interest rate cap R̄ ≈ 5% (annualized) is hit. When the surplus limit is slack — the orthodox regime — fiscal policy is locally passive and monetary policy is active in the sense of Leeper (1991). When the surplus limit binds — the fiscally-dominant regime — the central bank caps its policy rate to avoid aggravating fiscal stress, and price stability takes a back seat.

Calibration (Table 1): β = 0.995 (annual steady-state real rate ≈ 2%), σ = 1 (log utility), κ = 0.0093 (Calvo Phillips curve slope), η = 1 (inverse labor supply elasticity), θ = 10 (price elasticity of demand), ω = 0.8 (Calvo price-stickiness), α = 2.5, ϕ = 0.1, b/(4y) = 1 (100% debt-to-GDP), s̄ = 0.01, R̄ = 0.0074 in deviation from steady state (≈ 5% annualized), AR(1) coefficient ρ = 0.6, shock standard deviation σ_μ = 0.0016. The model is solved globally using a projection method to handle the kinks from the min operators.

In the fiscally-dominant regime, monetary policy is asymmetric: the central bank always lowers the rate for deflationary shocks but cannot raise it fully for large inflationary shocks (rate hits R̄). This stabilizes real debt in both shock directions while creating an asymmetric inflation response — inflation rises more in response to a positive cost-push shock than it falls for a negative shock of equal magnitude. This asymmetric profile is baked into agents’ expectations in all states of the world, including the orthodox regime, generating a systematic inflation bias that is increasing in the real value of government debt.

Simulation results (Table 2, based on 3,000 simulations of 1,000 quarters): the fiscally-dominant regime (surplus limit binding) occurs in 20% of periods, with an average duration of 3.6 quarters; the rate cap additionally binds in 10% of periods, with an average duration of 1.8 quarters.

Risky steady state (Table 3): The point to which the economy converges when transitory shocks have receded but agents fully internalize future regime-shift risk differs from the deterministic steady state: inflation is 27bp higher, output is 0.26pp lower, the real interest rate is 41bp higher, and the government debt-to-GDP ratio is 1.07pp higher. At the risky steady state the economy remains in the orthodox regime; all four effects stem from the inflation expectations channel.

Vicious-cycle mechanism: Higher debt raises the probability of fiscal dominance → larger inflation bias → higher real interest rate (the Taylor rule raises the nominal rate more than one-for-one with the inflation bias) → upward pressure on debt. The fiscal dominance risk is state-dependent: it increases with the cost-push shock and with the debt level (Figure 4).

Policy finding (Section 3.3 and Table 4): Because regime switches are endogenous, the central bank can reduce fiscal dominance risk by responding more moderately to inflation — lowering α from 2.5 to 1.5 — while still satisfying the Taylor principle (α > 1/β). A lower α attenuates the increase in debt servicing costs after an inflationary shock, requiring larger shocks to push the surplus limit to bind. Under α = 1.5: the fiscal dominance regime frequency falls to 0%; the risky steady-state inflation bias falls to essentially zero (0.01bp); inflation volatility falls from 1.93% to 1.89% — the volatility-reducing effect of avoiding fiscal dominance dominates the direct volatility-raising effect of a weaker response. At α ≈ 1.5, welfare (measured as the linear-quadratic loss −E[π̂² + λŷ²] with λ = κ/θ) is higher than at α = 2.5 (Figure 6). By contrast, under the benchmark configuration (no fiscal dominance risk), welfare falls monotonically as α declines.

Extension 1 — Distortionary taxation (Section 4.1): Replacing lump-sum taxes with a labor income tax (τL = 24%, cap = 25%) amplifies the mechanism. The risky steady-state inflation bias rises to 0.59pp; fiscal dominance occurs in 29% of periods; the rate cap binds in 16% of periods. The amplification reflects that the tax rate enters the Phillips curve, creating an additional cost-push channel when the tax cap binds.

Extension 2 — Passive monetary policy in the fiscally-dominant regime (Section 4.2): When the central bank switches to a passive rule with αF = 0.95 (rather than imposing a hard rate cap), the inflation bias is 0.23pp and fiscal dominance occurs in 15% of periods.

Scope conditions: The model features a representative household, a single cost-push shock, and lump-sum taxes in the baseline. All quantitative results are specific to the parameterization in Table 1, targeting 100% debt-to-GDP. Agents are assumed to have perfect knowledge of the central bank’s policy rule; in practice, a moderate α could be misinterpreted as abandoning the Taylor principle. The analysis is primarily conceptual; the paper notes that extending to a full-fledged multi-shock quantitative model is left for future work.

Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

In depth

Q1. What are the two regimes in the model, and how do transitions occur?

The orthodox regime is characterized by an active central bank (α > 1/β, Taylor principle satisfied) and a passive fiscal authority (surplus responds to debt, ϕ ∈ (1−β, 1)); the fiscally-dominant regime arises when the fiscal surplus hits its upper limit s̄ = 0.01 and the central bank caps its nominal rate at R̄ ≈ 5% annualized to avoid deepening the fiscal stress. Transitions are driven entirely by the state of the economy: when real debt b̃_{t-1} crosses the threshold b̄ = s̄/ϕ from below following a sufficiently large inflationary cost-push shock, the surplus limit binds and the economy enters the fiscally-dominant regime. Exit occurs when a sequence of disinflationary shocks, together with the central bank’s rate cuts, lowers debt below the threshold. Both the entry and exit thresholds are determined by the structural parameters of the model, not set exogenously.

Q2. Why does fiscal dominance risk generate an inflation bias in the orthodox regime?

The key transmission channel runs through expectations: in the fiscally-dominant regime the central bank responds asymmetrically to shocks (always cutting for deflation, capped on the upside for large inflation), creating an asymmetric inflation distribution; agents rationally incorporate this skewness into their inflation expectations in all states — including the orthodox regime — pushing expected inflation above target; the Taylor rule then allows actual inflation to be persistently elevated because the response coefficient α = 2.5, while large, does not fully offset the expectations-induced inflation pressure. The upward inflation expectations shift appears in the forward-looking Phillips curve (equation 2): higher Etπ_{t+1} raises current inflation πt, and the Taylor rule’s response is insufficient to fully counteract the expectations-driven component of the inflation bias.

Q3. Why does the inflation bias increase with the debt level?

Higher beginning-of-period government debt reduces the buffer between current debt and the threshold b̄, so that any given realization of the cost-push shock has a higher probability of pushing debt over the threshold and triggering a shift to the fiscally-dominant regime next period; the larger this probability, the larger the expectations-driven inflation bias in the current period. This mechanism is illustrated in Figure 4, which shows the probability of fiscal dominance next period as an increasing function of the current cost-push shock (given debt near the risky steady state), and Figure 2, which plots the monotone increasing relationship between current debt and the inflation rate in both regimes.

Q4. How does the vicious cycle between inflation, interest rates, and debt operate?

The cycle works as follows: a larger inflation bias induced by higher debt triggers a stronger nominal interest rate response from the Taylor rule; in the orthodox regime this raises the real interest rate, which increases debt servicing costs and pushes real debt upward; higher debt in turn raises the probability of fiscal dominance, which amplifies the inflation bias in the next period. The cycle is self-reinforcing but not necessarily explosive in the baseline calibration — the model has a unique risky steady state at which these forces balance — but it does shift equilibrium outcomes permanently upward relative to the deterministic steady state: the real rate is 41bp higher, debt 1.07pp higher, and inflation 27bp higher at the risky steady state (Table 3).

Q5. Can the central bank break the cycle without abandoning price stability?

Yes: by lowering the Taylor rule coefficient from α = 2.5 to α = 1.5, the central bank reduces the increase in debt servicing costs after an inflationary shock, thereby making it less likely that the surplus limit binds; when the probability of fiscal dominance approaches zero, inflation expectations are anchored at the deterministic steady state and the inflation bias disappears. This works without violating the Taylor principle (α = 1.5 > 1/β ≈ 1.005) because the objective is not to tolerate more inflation at each point in time, but to reduce the regime-switch risk that is the source of the bias. Crucially, the central bank does not need to commit to any specific regime-change-contingent rule — modifying the response coefficient of the standard Taylor rule is sufficient.

Q6. Why does lower α also reduce inflation volatility, not just the bias?

In the regime-switching model there are two competing effects on inflation volatility when α falls: (i) a direct volatility-raising effect because a weaker rate response gives more room for cost-push shocks to move inflation, and (ii) a volatility-reducing effect because the fiscally-dominant regime — where inflation is amplified by asymmetric monetary policy — is less frequently visited. At α = 1.5, effect (ii) dominates: the standard deviation of annualized inflation falls from 1.93% (α = 2.5) to 1.89% (α = 1.5). This contrasts with the benchmark configuration (no fiscal dominance possible), where effect (i) always dominates and welfare falls monotonically with α.

Q7. What does distortionary taxation add to the baseline result?

When the government adjusts a labor income tax rate (τL capped at 25%, baseline 24%) instead of lump-sum taxes, the inflation bias is amplified to 0.59pp (versus 0.27bp in the baseline) and the fiscally-dominant regime occurs 29% of the time (versus 20%). The amplification comes from two sources: the labor tax rate appears directly in the New Keynesian Phillips curve (equation 9), so a binding tax cap generates an additional cost-push effect that raises inflation independently of the interest rate channel; and output is increasing in the debt level in the fiscally-dominant regime (because a higher debt level makes the rate cap more likely, raising output through the demand channel), which further increases the primary surplus through the tax base, partly offsetting the tax cap but complicating the fiscal dynamics.

Q8. How does the passive monetary policy extension compare to the baseline?

When the central bank switches to a passive rule αF = 0.95 in the fiscally-dominant regime (rather than imposing a hard nominal interest rate cap), the inflation bias at the risky steady state falls to 0.23pp and the fiscally-dominant regime occurs in 15% of periods — both improvements over the baseline (0.27bp, 20%), but the mechanism is somewhat different. Under the passive rule, there is no hard constraint on the interest rate, so the central bank can still raise rates to some extent in response to inflationary shocks in the fiscally-dominant regime, reducing the asymmetry in the inflation response. The rate cap extension (baseline) is the more extreme case in which the constraint is fully binding.

Q9. How does this paper differ from exogenous regime-switching models?

The key difference is that in this model the probability of a regime shift is not exogenous — it is a function of the current state (debt level, cost-push shock) and of the policy parameters (α, ϕ, s̄, R̄); this means the central bank can influence regime-change risk by changing its policy rule, which is not possible in models like Davig and Leeper (2006), Bianchi and Melosi (2017, 2019), or Bianchi and Ilut (2017) where switching probabilities are fixed Markov parameters. The ability of the central bank to manage regime-switch risk is the novel channel through which monetary policy can attenuate the inflation bias without abandoning price stability — a result that has no counterpart in models where the fiscal authority’s behavior is exogenous.

Key concepts

orthodox regime : the policy configuration in which the fiscal surplus limit is slack (s̃_t < s̄) and the central bank follows a standard Taylor rule (R̂_t = αp̂_t with α > 1/β); fiscal policy is passive and monetary policy is active in Leeper’s (1991) sense.

fiscally-dominant regime : the policy configuration in which the fiscal surplus limit binds (s̃_t = s̄) because the real value of government debt is sufficiently high, and the central bank caps its nominal interest rate at R̄ to prevent fiscal stability from deteriorating further; monetary policy becomes fiscally accommodative.

risky steady state : the point to which the economy converges when transitory shocks have receded but agents fully incorporate future regime-shift risk into their expectations; it differs from the deterministic steady state by an inflation bias of 27bp, a real interest rate premium of 41bp, an output shortfall of 0.26pp, and an additional 1.07pp of government debt (all in the baseline calibration).

inflation bias : the systematic elevation of equilibrium inflation above the price stability target that arises from the risk of future fiscal dominance episodes; it is increasing in the real value of government debt and is present even in periods when the economy is in the orthodox regime, because agents rationally incorporate fiscal dominance risk into their expectations.

endogenous regime switching : the feature of the model that distinguishes it from earlier regime-switching frameworks — the probability of a shift to the fiscally-dominant regime is a function of the current state of the economy (debt, cost-push shock) and of the policy parameters, so the central bank can influence regime-change risk through its choice of the Taylor rule coefficient.

vicious cycle : the self-reinforcing dynamic between debt, fiscal dominance risk, the inflation bias, and the real interest rate: higher debt raises fiscal dominance risk → larger inflation bias → higher real rate (via Taylor rule) → higher debt servicing costs → further upward pressure on debt.

Narratives about the Macroeconomy

Mon, 01 Jan 0001 00:00:00 +0000

Layer 1 — Overview

Research Question

This paper investigates two related empirical questions in the context of the historic surge in US inflation in late 2021 and 2022: (1) What narratives—causal stories—do people invoke to explain why inflation increased? (2) How do those narratives shape economic expectations? A companion theoretical component asks how narrative heterogeneity affects aggregate macroeconomic outcomes.

Data and Methodology

The authors recruit more than 10,000 US households across five descriptive survey waves (November 2021, December 2021, January 2022, March 2022, May 2022) via Lucid, plus a separate expert survey of 111 academic economists with JEL-E publications in top journals, recruited simultaneously with the November 2021 household wave. Household samples are broadly representative of the US population in terms of gender, age, region, and income. The expert sample is highly credentialed: on average 18.6 years post-PhD, 2.7 top-five publications, and 5,534 Google Scholar citations.

Narratives are elicited through open-ended questions asking respondents to explain in their own words why inflation increased. Each text response is coded by two independent, blinded research assistants as a Directed Acyclic Graph (DAG) — a network of causal nodes representing factors (demand-side: government spending, monetary policy, pent-up demand, demand shift; supply-side: supply chain disruptions, labor shortage, energy crisis; miscellaneous: pandemic, government mismanagement, price gouging, Russia-Ukraine war) connected by directed causal edges. Inter-rater reliability is high: if one coder identifies a factor, the other does so 88% of the time; for specific causal connections between factors, agreement is 77%.

Three experiments study the causal effect of narratives on expectations: (1) A pent-up demand vs. energy crisis narrative provision experiment (April 2022, n=2,397 baseline, n=1,329 follow-up); (2) A monetary policy vs. energy crisis narrative provision experiment (June 2022, n=1,069 baseline, n=736 follow-up); (3) A 2×2 belief-updating experiment crossing narrative type (government spending vs. energy crisis) with information type (low vs. high government spending forecast) (April 2022, n=997).

Main Findings with Quantitative Magnitudes

Households’ narratives are substantially coarser than experts’: expert DAGs contain on average 4.3 factors and 3.6 causal links, while household DAGs contain only 3.5 factors and 2.8 links (both differences p < 0.01). Households focus predominantly on supply-side explanations: 57% invoke at least one supply-side factor vs. only 32% invoking any demand-side factor. The most common household narrative factors are supply chain disruptions (30%), labor shortage (27%), and general supply-side factors (22%); the leading demand-side factor is government spending, appearing in only 17% of household narratives, while loose monetary policy appears in just 5%. By contrast, 90% of experts invoke at least one supply-side factor and 84% at least one demand-side factor, with government spending mentioned by 50% of experts and monetary policy by 38%.

Among households who invoke at least one supply or demand narrative, only 34% mention both supply and demand factors; among the corresponding subsample of experts, 77% mention both. Government mismanagement—a politicized judgment of policy failure—appears in 32% of household narratives but only 1% of expert narratives. Price gouging appears in 8% of household narratives and 0% among experts.

Partisan polarization is large: Democrat-leaning respondents are 26 pp more likely to attribute inflation to the pandemic as a root cause (p < 0.01); Republican-leaning respondents are 38 pp more likely to blame government mismanagement (p < 0.01), and 19 pp more likely to mention high government spending (p < 0.01) and 14 pp more likely to mention high energy prices (p < 0.01).

Narratives are correlated with inflation expectations in OLS regressions controlling for demographics and survey wave fixed effects (n=2,951): households invoking government mismanagement predict 1.155 pp higher 1-year-ahead inflation (p < 0.01) and 0.805 pp higher 5-year-ahead inflation (p < 0.01). Energy crisis narratives predict 0.661 pp higher 1-year-ahead inflation (p < 0.01). Pent-up demand narratives predict 0.640 pp lower 5-year-ahead inflation (p < 0.05). Narrative variables explain approximately 10% of the out-of-sample variation in 1-year-ahead inflation expectations via LASSO, comparable to or exceeding the explanatory power of demographics and inflation experiences found in prior work.

In Experiment 1 (pent-up demand vs. energy crisis), providing the pent-up demand narrative reduces 12-month inflation expectations by 0.71 pp relative to the energy crisis treatment (p < 0.01, in the main survey), corresponding to 24% of a standard deviation. This effect persists in the follow-up survey one day later (−0.63 pp, p < 0.01).

In Experiment 2 (monetary policy vs. energy crisis), the monetary policy narrative reduces 12-month inflation expectations by 0.40 pp at the time of the main survey (p < 0.01) and by 0.62 pp in the follow-up (p < 0.01).

In Experiment 3 (information updating), respondents exposed to the government spending narrative increase 12-month inflation expectations by 1.79 pp in response to a high-spending forecast (p < 0.01), while those exposed to the energy crisis narrative show no significant reaction (0.34 pp, p = 0.205). In IV regressions instrumenting government spending expectations with the high/low forecast treatment, a 1 pp increase in perceived government spending growth raises inflation expectations by 0.378 pp among those holding the government spending narrative (p < 0.01) versus only 0.051 pp among those holding the energy narrative (p = 0.184; difference p < 0.01).

The New Keynesian DSGE model shows that a modest shift in perceived importance of monetary policy relative to productivity (raising ω_ν from 0.1 to 0.2, holding ω_g fixed) raises equilibrium consumption by 27 basis points and reduces equilibrium inflation by 27 basis points in the calibrated model with φ = 1.5; with a less reactive central bank (φ = 1.25), the same shift raises consumption by 30 basis points and reduces inflation by 62 basis points.

Scope Conditions

All empirical results are drawn from the US context during the 2021–2022 inflation surge. The authors note that the extent of partisan polarization in US narratives may not generalize to less politically polarized countries. The test-retest correlation of narrative factors across a three-day interval is 0.63 (p < 0.01), indicating significant but not perfect stability. The experiment results may partly reflect that narratives were especially malleable because the inflation surge was a relatively recent and salient phenomenon at the time of data collection.

Layer 2 — Q&A

Q1: How do the authors define and operationalize “narratives”?

A: The paper defines economic narratives as causal accounts for why an economic event occurred — agents’ assessments of cause-effect relationships across events. Each text response is coded as a Directed Acyclic Graph (DAG) where nodes are economic factors and directed edges represent perceived causal links. DAGs can represent both simple mono-causal accounts and complex multi-factor chains. The authors use a predefined coding scheme of 16+ factor categories spanning demand-side, supply-side, and miscellaneous nodes, with inflation as the terminal node.

Q2: What is the inter-rater reliability of the DAG coding, and what does it imply for the quality of the narrative data?

A: Two independent, blinded coders annotate each response. If one coder assigns a given factor, the other does so 88% of the time; for specific causal connections between factors, agreement is 77%. Approximately 95% of assigned factors and 89% of assigned connections make it to the final coded version. At the coarser level of “any demand-side factor,” agreement rises to 94%; for “any supply-side factor,” to 93%. Test-retest reliability across a three-day interval averages a correlation of 0.63 across all narrative factors (p < 0.01), comparable in magnitude to the measured persistence of economic preferences in prior work.

Q3: How do expert and household narratives differ in their structural complexity?

A: Expert DAGs contain on average 4.3 factors and 3.6 causal links, compared to 3.5 factors and 2.8 links for households (both p < 0.01). These differences persist even after controlling for response time and word count, indicating genuine differences in economic understanding rather than effort. Among agents who invoke at least one supply or demand factor, 77% of experts mention both, compared to only 34% of households.

Q4: What are the most prevalent factors in household narratives versus expert narratives, and why does this matter?

A: Supply chain disruptions (30%), labor shortage (27%), and general supply-side factors (22%) top household narratives, while monetary policy appears in only 5% of household DAGs. Expert narratives are more balanced: 90% cite supply-side factors and 84% cite demand-side factors, with government spending mentioned by 50% and monetary policy by 38%. This matters because factors with different persistence imply different trajectories for future inflation; households’ supply-side emphasis, combined with low awareness of monetary policy, shapes their inflation expectations in systematically different ways than experts.

Q5: What is the structure of household narrative clusters, and how fragmented are they?

A: Agglomerative hierarchical clustering using the Jaccard distance between DAG edge lists reveals 15 optimal clusters (Silhouette criterion), of which eight have at least 30 members. Four supply-side clusters account for 55% of households: pandemic-related supply chain disruptions (20%), general supply-side causes (18%), energy crisis often attributed to government mismanagement (11%), and labor shortages attributed to the pandemic or government spending (7%). The only clear demand-side cluster—combining government spending and loose monetary policy—captures just 8%. Simple mono-causal clusters attributing inflation to the pandemic alone (15%), government mismanagement alone (11%), and price gouging alone (4%) are collectively prominent, underscoring how fragmented and often single-factor household reasoning is.

Q6: How do partisan affiliations correlate with narrative content?

A: Republicans are 38 pp more likely than Democrats to attribute inflation to government mismanagement (p < 0.01), 19 pp more likely to mention high government spending (p < 0.01), and 14 pp more likely to mention high energy prices (p < 0.01). Democrats are 26 pp more likely to cite the pandemic as a root cause of inflation (p < 0.01) and more frequently cite pandemic-related supply chain issues and corporate greed. Government mismanagement appears in 32% of all household narratives (and is often portrayed as a root cause of spending, monetary policy, and energy prices) but in only 1% of expert narratives.

Q7: How did the composition of household narratives shift over time (November 2021 to May 2022)?

A: The energy crisis narrative rose sharply from 12% in January 2022 to 28% in March 2022, coinciding with Russia’s invasion of Ukraine in late February 2022. The Russia-Ukraine war narrative went from virtually zero before February 2022 to 28% in March 2022. By contrast, pandemic references, which climbed from 44% in November 2021 to 55% in January 2022, fell back to 47% in March 2022 and 39% in May 2022. Labor shortage references fell sharply from 32% in January 2022 to 15% in May 2022. These abrupt shifts suggest household narratives respond to major news events and, by extension, could drive rapid revisions in inflation expectations around such events.

Q8: What is the correlational evidence that narratives predict inflation expectations, and how large is the explanatory power?

A: OLS regressions on pooled data from November 2021–January 2022 (n=2,951), controlling for survey wave fixed effects and sociodemographics, show: government mismanagement narratives predict 1.155 pp higher 1-year inflation expectations (p < 0.01) and 0.805 pp higher 5-year expectations (p < 0.01); energy crisis narratives predict 0.661 pp higher 1-year expectations (p < 0.01); monetary policy narratives predict 1.005 pp higher 1-year expectations (p < 0.01); pent-up demand narratives predict 0.640 pp lower 5-year expectations (p < 0.05). LASSO out-of-sample prediction using DAG factor dummies and connection dummies explains approximately 10% of variation in 1-year-ahead inflation expectations — comparable to the 10% within-sample R² found by D’Acunto et al. (2021) for grocery price exposure, and substantially above the 2–7% found by Giglio et al. (2021) for investor characteristics explaining stock return expectations.

Q9: What does Experiment 1 (pent-up demand vs. energy crisis) show about the causal effect of narratives?

A: Providing the pent-up demand narrative (relative to the energy crisis narrative) increases the fraction of respondents invoking pent-up demand by 37.8 pp in the follow-up survey (baseline: 2.8%, p < 0.01) and reduces the fraction invoking the energy crisis by 7.9 pp (p < 0.01), establishing successful first-stage uptake. In the main survey (n=2,397), the pent-up demand treatment reduces 12-month inflation expectations by 0.71 pp relative to the energy treatment (p < 0.01), equivalent to 24% of a standard deviation; the effect persists at −0.63 pp in the follow-up one day later (p < 0.01). The energy crisis treatment has no significant effect on expectations relative to a pure control (−0.02 pp, p = 0.911), suggesting that energy crisis implications were already salient at the time.

Q10: What does Experiment 2 (monetary policy vs. energy crisis) add, given it was conducted after significant Fed tightening?

A: The experiment was run in June 2022, when 61% of respondents were already aware the Fed had raised rates. The monetary policy narrative increases the fraction invoking monetary policy by 39 pp and reduces the energy fraction by 50 pp relative to the energy group (both p < 0.01). The monetary policy narrative reduces 12-month inflation expectations by 0.40 pp in the main survey (p < 0.01) and 0.62 pp in the follow-up (p < 0.01). The mechanism is that attributing past inflation to loose monetary policy — which has since been tightened — leads respondents to infer lower future inflation, consistent with the narrative about persistence of the underlying cause.

Q11: What does Experiment 3 demonstrate about how narratives filter the interpretation of new information?

A: In the 2×2 design, all respondents first receive either a government spending narrative or an energy crisis narrative, then either a low (−4%) or high (+6%) government spending forecast from the Survey of Professional Forecasters. Among those with the government spending narrative, the high-spending forecast raises 12-month inflation expectations by 1.79 pp (p < 0.01); among those with the energy crisis narrative, the high-spending forecast raises inflation expectations by a non-significant 0.34 pp (p = 0.205). The IV estimate shows that a 1 pp increase in expected government spending growth raises inflation expectations by 0.378 pp for those holding the spending narrative (p < 0.01) vs. 0.051 pp for those holding the energy narrative (p = 0.184); this difference is highly significant (p < 0.01). Importantly, the first-stage effect on expected government spending growth is similar across narrative groups (4.7 pp vs. 6.8 pp, difference not significant), ruling out differential interpretation of the forecast itself as the mechanism.

Q12: How do the authors formalize narratives in the DSGE model, and what is the key mapping result?

A: Narratives are formalized as subjective causal models (SCMs): linear mappings from N observable factors to inflation, π_t = ψ_1(i)z_{1,t} + … + ψ_N(i)z_{N,t}, combined with perceived AR(1) processes for each factor. The “subjective inflation narrative” of agent i is summarized by perceived contribution shares ω_z(i). The paper’s Proposition 2 gives closed-form expressions for equilibrium inflation and consumption as functions of these perceived shares, without imposing that they be correct or identical across agents. The key result is that subjective causal models always affect equilibrium outcomes so long as the perceived persistence parameters differ across factors — the mechanism being that different narratives produce different inflation expectations, which feed back into consumption and pricing decisions.

Q13: What are the quantitative implications of narrative shifts in the calibrated DSGE model?

A: The baseline calibration uses standard New Keynesian parameters (β=0.99, γ=1, ς=5, Calvo price duration=4 quarters, φ=1.5, ρ_a=0.9, ρ_g=0.8, ρ_ν=0.5) with a scenario of a 10% productivity decline, 10% government spending increase, and policy rate 2 pp below the Taylor rule. Under rational expectations, π_t=3.68% and c_t=−11.79%. Raising the perceived importance of monetary policy in household and firm inflation narratives from ω_ν=0.1 to ω_ν=0.2 (lowering ω_a by the same amount, holding ω_g fixed) increases equilibrium consumption by 27 basis points and reduces equilibrium inflation by 27 basis points. With a less reactive central bank (φ=1.25), the same narrative shift raises consumption by 30 basis points and reduces inflation by 62 basis points. The paper notes that these effects are approximately linear in the narrative shift, meaning the directional implication holds across a wide range of narrative configurations.

Q14: How does narrative heterogeneity across households affect aggregate outcomes in the model?

A: When households hold heterogeneous narratives, aggregate outcomes depend on the joint distribution of perceived factor importance (ω_z(i)) and perceived factor persistence (ρ_z(i)) across agents, rather than on average values alone. Specifically, the model shows that if households who assign higher importance to a given factor also perceive that factor as more persistent, the aggregate effect on expectations and consumption is amplified beyond what the average narrative predicts. Additionally, narrative heterogeneity generates consumption heterogeneity even when the efficient allocation requires all households to consume the same amount, representing a welfare-relevant distortion absent under rational expectations.

Q15: What is the practical implication for central bank communication?

A: Under full-information rational expectations, central bank narrative communication about the drivers of inflation is irrelevant because agents already hold the correct model. Once subjective causal models can deviate from the truth, central bank narrative provision shifts aggregate equilibrium outcomes (inflation and consumption) in a benchmark New Keynesian model. The paper argues that central banks need to measure the distribution of household narratives to know whether their communication shifts agents toward or away from the rational expectations equilibrium — moving agents in the direction of the correct narrative produces better aggregate outcomes from the central bank’s perspective, conditional on inflation being above target and output below first-best.

Key Concepts

Economic Narrative (as used in this paper): An agent’s causal account for why a given economic event occurred — specifically, an assessment of cause-effect relationships that explains the drivers of an economic outcome. Distinguished from more general notions of “story” in that causality is the core; the paper does not count descriptions of correlation or simple statements of fact as narratives.

Directed Acyclic Graph (DAG) representation of narratives: Each narrative is coded as a network of factor nodes connected by directed edges indicating perceived causation. Acyclicity rules out feedback loops in a respondent’s causal account. Factors with nonzero ψ(i) are included; the direction of edges indicates causal flow. This representation allows quantitative comparison across respondents via adjacency matrices or Jaccard distances between edge lists.

Subjective Causal Model (SCM) of inflation: The paper’s formal theoretical counterpart to a narrative: a linear mapping π_t = Σ_n ψ_n(i) z_{n,t} in which individual i assigns perceived marginal effect ψ_n(i) to each factor z_n, combined with a perceived AR(1) law of motion for each factor. The SCM does not need to be correct or shared across agents. The rational expectations equilibrium is the special case where all agents’ SCMs match the true data-generating process.

Perceived contribution share (ω_z): The ratio ψ_z(i)·z_t / π_t — agent i’s perceived percentage contribution of factor z to current inflation. This is the sufficient statistic for the effect of household narratives on inflation expectations and, through the NK model, on equilibrium aggregate outcomes. The aggregate distribution of ω_z(i) and perceived persistence ρ_z(i) determines the consumption Euler equation at the aggregate level.

Government mismanagement (as a narrative factor): A coding category that captures explicit reference to policy failure or low-quality decision-making by policymakers in a politicized sense — distinct from the economic factors of government spending or monetary policy. It represents households’ attribution of inflation to the incompetence or malfeasance of officials, rather than to any specific economic mechanism. This factor appears in 32% of household narratives but only 1% of expert narratives.

Narrative cluster: A group of respondents whose DAGs are mutually similar (measured by Jaccard distance between edge lists) and whose typical DAG differs from other clusters. Identified via agglomerative hierarchical clustering. The paper identifies eight substantively meaningful clusters, ranging from supply-chain-focused to mono-causal pandemic or mismanagement narratives, with no single cluster capturing more than 20% of households.

Test-retest reliability of narratives: The correlation between the same respondent’s narrative elicited on two occasions three days apart. The paper estimates an average correlation of 0.63 across all narrative factors (p < 0.01), interpreted as indicating significant stability in households’ causal beliefs rather than survey noise. Comparable in magnitude to test-retest correlations of economic preferences in other studies.

Redistributive Policy Shocks and Monetary Policy with Heterogeneous Agents

Mon, 01 Jan 0001 00:00:00 +0000

Layer 1 — What this paper finds and why it matters

Governments in emerging market and developing economies (EMDEs) routinely intervene in agricultural markets — procuring grain and redistributing it to poor households — in response to food price shocks or expanded food security mandates (India’s 2013 National Food Security Act is the leading example). This paper asks how monetary policy should respond to such “redistributive policy shocks,” and what those shocks do to sectoral inflation and the consumption distribution between rich and poor households. The authors build a two-sector (agriculture with flexible prices; manufacturing with sticky prices), two-agent (Ricardian rich; rule-of-thumb poor) New Keynesian DSGE model, calibrated to India, that extends the TANK framework of Debortoli and Gali (2018) to two sectors and introduces explicit government procurement and redistribution. They show that a redistributive policy shock raises aggregate inflation and the output gap but also raises poor consumption and aggregate welfare, because the subsidy-in-kind effect on poor households more than offsets the decline in rich consumption and the inflationary pressure. They further show that consumer heterogeneity matters for whether monetary policy responses to various shocks raise or reduce aggregate welfare: in models with a flexible-price agricultural sector, contractionary monetary shocks produce larger deflation but smaller declines in real consumption relative to one-sector benchmarks, so the welfare cost of monetary contraction is lower than standard NK models imply.

Summary based on MPRA working paper (No. 101651, July 2020). The extracted PDF text was truncated before the calibration, impulse response, and welfare sections; quantitative parameter values and figure-level results are not available in the source text used here. AI-assisted, human review pending. See the linked original for authoritative claims.

Layer 2 — In depth

Q1. What is a “redistributive policy shock” and how does the model capture it?

A redistributive policy shock is a sudden increase in the fraction of government-procured agricultural output that is redistributed to poor households. In the model, the government taxes rich (Ricardian) households via lump-sum levies each period, uses those proceeds to purchase agricultural output at the open market price, and then redistributes a fraction φ_t of the procured quantity to poor households as an in-kind subsidy. The remaining fraction goes into a buffer stock. The shock to redistribution is modeled as a positive innovation to φ_t (AR(1) process), distinct from a shock to the procurement quantity Y^P_{A,t} itself. Because the in-kind transfer reduces the effective price paid by the poor for agricultural goods — the poor face an effective price of (1 − λ_t)P_{A,t} — the redistributive shock operates as a proportional price subsidy on agriculture consumption for the poor, even though the quantity is what the government directly controls.

Q2. What are the two types of households and how do they differ?

Rich households are Ricardian (forward-looking) and hold one-period risk-free bonds; poor households are rule-of-thumb consumers who do not save. Both types consume goods from both the agricultural and manufacturing sectors according to Cobb-Douglas indices, but they differ in three ways. First, poor households have a higher budget share for agricultural goods (δ_P > δ_R), consistent with Engel’s Law. Second, the inverse of the intertemporal elasticity of substitution (IES) is higher for the poor (σ_P > σ_R), following Atkeson and Ogaki (1996) estimates for Indian household data; this means the poor are less willing to substitute consumption across time and respond differently to real wage changes. Third, rich households have both labor income and dividend income from monopolistically competitive manufacturing firms, while poor households have only labor income.

Q3. What happens to inflation and consumption when a positive agricultural productivity shock hits?

A positive agricultural productivity shock leads to a decline in inflation, a rise in the output gap, and higher consumption for both rich and poor households. Because the agriculture sector has flexible prices, a positive productivity improvement lowers agricultural prices immediately, reducing the terms of trade (the relative price of agriculture to manufacturing). Aggregate CPI inflation falls. The rise in agricultural output increases real income for both household types, raising consumption and aggregate welfare. These dynamics are compared to the Aoki (2001) representative-agent two-sector benchmark.

Q4. What are the aggregate and distributional effects of a positive redistributive policy shock?

A procurement-and-redistribution shock raises aggregate inflation, the output gap, and poor consumption, while lowering rich consumption; aggregate welfare rises because the redistribution effect dominates. The mechanism has two parts. First, the government procures additional agricultural output at the market price, financed by higher lump-sum taxes on the rich; this reduces rich consumption. Second, the redistributed grain lowers the effective price of the agricultural good for the poor, raising poor consumption through a “redistribution effect.” Because poor households spend a higher share of income on the agricultural good than rich households, and because the poor receive a fraction of their agricultural consumption for free, market demand for the agricultural good in the open market is less than it would be without redistribution. Consequently, the inflationary impact of the procurement shock is substantially lower in the two-agent model than in the Aoki representative-agent model (where there is no redistribution to dampen open-market demand).

Q5. How does consumer heterogeneity alter the transmission of a contractionary monetary policy shock?

In models with a flexible-price agricultural sector, a contractionary monetary shock produces a larger deflation but a smaller decline in consumption and smaller welfare losses than in single-sector or representative-agent benchmarks. A rise in the nominal interest rate induces intertemporal substitution of consumption, reducing aggregate demand and the aggregate price level. This deflationary effect is amplified when a flexible-price sector is present alongside the sticky-price sector, because agricultural prices can fall immediately. However, the same flexible-price sector means that real interest rates rise by less (compared to an all-sticky-price economy), so the reduction in rich and poor consumption is also smaller. The paper compares this to three benchmarks: the simple one-sector one-agent NK model (Gali 2015, Chapter 3), the Debortoli-Gali (2018) one-sector two-agent model, and the Aoki (2001) two-sector one-agent model. The welfare losses from monetary contraction are lower in the two-sector models (the authors’ framework and Aoki’s) than in the one-sector models.

Q6. How does the model differ from its three main benchmark frameworks?

The model merges the two-sector production structure of Aoki (2001) with the TANK distributional structure of Debortoli and Gali (2018), and adds explicit government procurement and redistribution — none of the benchmarks have all three features. Relative to Aoki: the paper adds poor/rich heterogeneity, different IES parameters, and the government redistribution mechanism. Relative to Debortoli-Gali: the paper adds an agricultural flexible-price sector and the redistribution shock, and assumes complete markets (Debortoli-Gali assumes incomplete markets; their model is treated as an approximation). Relative to Gali (2015, Chapter 3): the paper adds both a second sector and household heterogeneity. The three differences from the simple NK benchmark in the Dynamic IS and NKPC equations are: (i) the presence of a terms of trade channel, (ii) heterogeneous agents with different IES parameters and budget shares, and (iii) redistribution policy that shifts the effective price index of the poor.

Q7. What role do terms of trade play in the model’s transmission mechanism?

The terms of trade between agriculture and manufacturing (T_t = P_{A,t}/P_{M,t}) is a central transmission variable that affects both aggregate consumption and inflation. Aggregate CPI inflation can be decomposed as π_t = δ_R·π_{A,t} + (1 − δ_R)·π_{M,t} = δ_R·ΔT_t + π_{M,t}, so movements in the terms of trade feed directly into headline inflation. Total agricultural and manufacturing consumption both depend on T_t, rich consumption C_{R,t}, and poor consumption C_{P,t} through equations (22) and (23). A rise in the terms of trade (higher relative agricultural prices) makes the consumption basket of the poor more expensive because they spend a larger share of income on agricultural goods, inducing them to reduce agricultural purchases. This terms-of-trade channel is absent from one-sector benchmarks and is a key reason the paper’s framework generates different aggregate dynamics than Debortoli-Gali.

Q8. What is the welfare metric used, and what is the paper’s welfare conclusion?

Welfare is defined to depend on aggregate consumption in the standard fashion, and the paper’s central welfare conclusion is that consumer heterogeneity matters for whether monetary policy responses to shocks raise or reduce aggregate welfare. For a redistributive policy shock, aggregate welfare rises despite higher inflation, because the gain in poor consumption (driven by the subsidy) exceeds the loss in rich consumption and the distortionary cost of inflation. For a contractionary monetary shock, welfare losses are smaller in the two-sector framework than in single-sector frameworks, because the flexible-price agricultural sector moderates the real interest rate increase and limits the consumption decline. The paper does not report specific numerical welfare loss figures in the portion of text available in this source extract.

Key concepts

Redistributive policy shock : in this paper’s usage, a positive shock to the fraction (φ_t) of government-procured agricultural output that is redistributed to poor households as an in-kind subsidy; distinct from a procurement level shock. Modeled as an AR(1) process on φ_t.

TANK (Two-Agent New Keynesian) model : a tractable heterogeneous-agent NK framework with exactly two household types — Ricardian (forward-looking, hold bonds) and rule-of-thumb (hand-to-mouth, do not save) — that Debortoli and Gali (2018) showed provides a good approximation to the aggregate dynamics of a full HANK model.

Rule-of-thumb (hand-to-mouth) consumers : households that maximize static utility subject to a static budget constraint, consuming all current income each period. In this model, the poor are rule-of-thumb consumers with only labor income and no bond holdings.

Effective price of agriculture for the poor : P’{A,t} = (1 − λ_t)P{A,t}, where λ_t is the fraction of poor agricultural consumption provided for free via the redistributive subsidy. The poor face a price index P’t = {(1−λ_t)P{A,t}}^{δ_P} · P_{M,t}^{1−δ_P}, which differs from the rich price index.

Terms of trade (TOT) : T_t = P_{A,t}/P_{M,t}, the relative price of the agricultural good to the manufactured good. Changes in TOT affect the sectoral composition of consumption for both household types and transmit through the Dynamic IS and NKPC equations.

Intertemporal elasticity of substitution (IES) : 1/σ_K for household type K. The paper assumes σ_P > σ_R (poor have lower IES than rich), following Atkeson and Ogaki (1996) estimates for Indian household data; this differential drives asymmetric labor supply responses to real wage changes.

Procurement shock : a shock to the quantity Y^P_{A,t} of agricultural output the government procures each period, modeled as a separate AR(1) process from the redistribution-fraction shock. Together, the procurement level and redistribution fraction determine the total subsidy received by poor households.

Robot adoption and inflation dynamics

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Robot Adoption and Inflation Dynamics

Research Question

Basso and Rachedi investigate how robot adoption influences inflation dynamics — specifically, whether the surge in automation during the 2000s and 2010s can explain the muted sensitivity of inflation to unemployment (the “flat Phillips curve”) observed in advanced economies prior to the Covid pandemic, and whether the same framework can account for the subsequent resurgence of steep inflation-unemployment co-movement.

Data and Methodology

The empirical analysis uses an annual panel covering 384 U.S. metropolitan statistical areas (MSAs) from 2008 to 2018. The dependent variables are non-tradable goods inflation (log-difference of services prices excluding rents and utilities, from BEA regional price parities) and wage inflation (log-difference of average compensation per job). Robot adoption at the MSA-year level is constructed following Acemoglu and Restrepo (2020a): industry-level robots per employee at the U.S. national level are weighted by industry employment shares in each MSA, yielding an MSA-year robot-per-employee ratio.

The regression specification extends Hazell et al. (2022) by adding an interaction term between the lagged unemployment rate and the (demeaned) robot-per-employee ratio, along with MSA and year fixed effects. Year fixed effects absorb common inflation expectations and the endogenous response of monetary policy to aggregate demand shocks. To address endogeneity, unemployment is instrumented with a Bartik shift-share variable of tradable demand spillovers, and robot adoption is instrumented with average industry-level robot penetration in the five largest European economies — under the identifying assumption that robot demand shocks are weakly correlated across advanced countries.

The theoretical framework is a New Keynesian model augmented with (i) directed search frictions in the labor market, and (ii) producer-level automation decisions in the spirit of Acemoglu and Restrepo (2020a). Producers pay a fixed entry cost, draw idiosyncratic efficiency for employing labor, and then choose between a robot technology (certain output at low efficiency) and a labor technology (uncertain hiring, higher potential efficiency). This generates an automation threshold: low-efficiency producers install robots, displacing low-wage jobs. A Taylor rule closes the model. Quantitative exercises compare two steady states calibrated to robot-per-employee ratios of 0.2% (low automation, targeting the U.S. in the early 2000s) and 0.6% (high automation, calibrated to one standard deviation of robot penetration variation across MSAs).

Main Findings

Empirical. In the baseline IV regression, a one standard deviation increase in robot adoption reduces the sensitivity of price inflation to unemployment by 17%, and the sensitivity of wage inflation to unemployment by 9%, relative to a MSA with the average robot penetration. The larger flattening effect on price inflation than on wage inflation implies that robot adoption also diminishes the pass-through from wages to prices. All three effects are statistically significant at the 5% level, and are robust to controls for demographic structure (age composition, gender/race/education participation rates, MPC heterogeneity), occupational structure (abstract, routine, manual, and offshorable occupations), and import competition exposure (Chinese and Mexican import shares).

Model quantification. Comparing the high-automation to the low-automation steady state, the model generates a 14% reduction in the slope of the price Phillips curve and a 13% reduction in the slope of the wage Phillips curve, conditional on the same-sized demand shocks in both economies. The price Phillips curve result accounts for 82% of the empirical estimate (17%). The model overstates the flattening of the wage Phillips curve (13% vs. 9% in the data), and therefore understates the reduction in the wage-to-price pass-through.

Mechanisms. Automation flattens the Phillips curve through two primary channels. First, the outside option of automating production reduces workers’ bargaining power and dampens the elasticity of wages to unemployment (the “Wage Setting Effect”). Second, a higher share of robot firms reduces the aggregate labor share, muting the pass-through from wages into prices (the “Steady State Effect”). A third channel — firms cyclically substituting workers for machines in response to a shock (the “Cyclical Effect”) — operates during the transition but the Wage Setting Effect accounts for the bulk of the flattening.

Non-linearity and the post-Covid resurgence. When robot-production is subject to convex adjustment costs, the threat of automation that underlies the Wage Setting Effect becomes inoperative during large expansionary shocks. When investment in machines surges, the marginal cost of producing robots rises sharply, raising the price of machines and pushing the automation threshold downward — more firms must use labor. Workers then negotiate higher wages, which pass into prices. Conditional on small demand shocks, the high-automation economy still exhibits a flatter Phillips curve than the low-automation economy. Conditional on large demand shocks (simulated as a 2 percentage point drop in unemployment), there is no difference in the inflation response between the low- and high-automation economies, so the Phillips curve reverts to steep.

Q&A

Q1: What is the exact empirical specification and how does it map to a structural object?

The regression is: non-tradable goods inflation = β × lagged unemployment + γ × (lagged unemployment × demeaned robot adoption) + ζ × lagged robot adoption + χ × relative non-tradable price + MSA fixed effects + year fixed effects + error. In a multi-region model without automation, Hazell et al. (2022) show that the coefficient β identifies the aggregate slope of the Phillips curve because year fixed effects absorb both common inflation expectations and the endogenous monetary policy response to aggregate demand shocks. Adding the interaction term extends this logic: γ identifies how robot adoption causally shifts the slope of the local Phillips curve, which maps into changes in the aggregate slope.

Q2: What are the first-stage instruments and why are they valid?

Unemployment is instrumented with local tradable demand spillovers — a Bartik variable weighting national industry value-added growth (excluding each MSA’s own contribution) by each MSA’s average industry value-added shares, so national supply disturbances uncorrelated with MSA-level heterogeneity generate plausibly exogenous unemployment variation. Robot adoption is instrumented with the implied robot-per-employee ratio obtained by replacing U.S. industry robot installations with the average across the five largest European economies, weighted by U.S. industry employment shares; this isolates the supply-side efficiency improvements in robot technology that drove global adoption, conditional on robot demand shocks being weakly correlated across countries. The correlation between the two instruments in the sample is 0.2, ensuring they do not strongly co-move.

Q3: What are the point estimates and their magnitudes in the baseline IV regression?

For price inflation (Panel A, Column 4), the base sensitivity β = −0.5069 (SE 0.1381, significant at 1%), and the interaction coefficient γ = 0.0066 (SE 0.0030, significant at 5%). For wage inflation (Panel B, Column 4), β = −0.9580 (SE 0.2450, significant at 1%), and γ = 0.0049 (SE 0.0024, significant at 5%). A one standard deviation increase in robot adoption reduces price inflation sensitivity by 17% and wage inflation sensitivity by 9% relative to the average-automation MSA.

Q4: What does the difference in flattening magnitudes (17% for prices vs. 9% for wages) imply about the wage-price pass-through?

Because automation reduces the price Phillips curve slope by proportionally more than the wage Phillips curve slope, each percentage-point change in wages translates into a smaller percentage-point change in prices in higher-automation areas. This indicates that robot adoption diminishes the influence of wage changes on price changes — i.e., it reduces the wage-to-price pass-through. In the model, this operates through the Steady State Effect: a larger share of production carried out by robot firms means that a given change in average wages applies to a smaller portion of total marginal costs, weakening the price response.

Q5: How is the automation threshold determined in the theoretical model, and what economic forces govern it?

A producer opts for the labor technology if and only if the expected value of a labor firm (= job-filling probability × (producer price × labor efficiency − posted wage) − entry cost) exceeds the value of a robot firm (= producer price × robot efficiency − machine price − entry cost). Since the value of a labor firm increases in labor efficiency, there is a unique cut-off efficiency level γ* at which a producer is indifferent. Producers with labor efficiency above γ* post vacancies; those below γ* install robots. The cut-off rises (more automation) when wages rise relative to machine prices, and falls (less automation) when machine prices rise due to costly robot production during large expansionary shocks.

Q6: How does the wage-posting equilibrium under directed search generate the Wage Setting Effect of automation?

Under directed search, each labor firm posts a wage to maximize its expected value, and workers sort into sub-markets offering higher wages but lower job-finding probabilities. The equilibrium posted wage for a firm with labor efficiency γj is Wγj,t = PP,t × γj × (1 − η), where η is the elasticity of matches to vacancies. The option to install a robot — available at any time — limits how much any individual firm needs to offer workers. When automation increases, the outside option becomes more attractive to more firms, which constrains wage offers industry-wide, reducing the elasticity of average wages to unemployment fluctuations.

Q7: How is the slope of the price Phillips curve characterized analytically?

Log-linearizing the model around the steady state and substituting labor market and wholesaler equilibrium conditions into the pricing equation yields: inflation = −[(ε−1)/φ] × Ψ(γ*; Θ) × unemployment gap + β × expected future inflation, where Ψ(γ*; Θ) is a function of the automation cut-off γ*, the elasticity of substitution ε, the matching function elasticity η, the efficiency bounds γM and γH, and the distribution shape parameter α. In contrast to standard New Keynesian models where the slope depends only on markup and nominal rigidity parameters, this expression depends directly on the degree of automation through the steady-state threshold γ*.

Q8: Across different structural parameter configurations, does automation always flatten the Phillips curve?

Yes. Numerical analysis of the closed-form Phillips curve expression (Figure 1) shows that robot adoption unambiguously decreases the slope of the price Phillips curve across all combinations of the key structural parameters — the distribution shape parameter α, the matching elasticity η, the upper bound of labor efficiency γH, and the steady-state unemployment rate ū. The flattening effect is more pronounced when η is low, when α implies a larger fraction of low-efficiency producers, and when the steady-state unemployment rate is low.

Q9: How do the three mechanism channels (Cyclical, Wage Setting, Steady State) compare quantitatively?

The paper isolates channels by comparing alternative model specifications: (i) Baseline directed search with endogenous automation, (ii) Directed search with fixed automation (removing Cyclical and Wage Setting Effects, leaving only the Steady State Effect), (iii) Random search with τ = 0.5 (efficient bargaining, retaining both the Cyclical and Wage Setting Effects), (iv) Random search with τ = 0.01 (near-zero worker bargaining power, removing the Wage Setting Effect but retaining the Cyclical Effect). Figure 5 shows that the Steady State Effect alone accounts for only a small portion of the total inflation differential between low- and high-automation economies. The Wage Setting Effect — isolated by comparing τ = 0.01 and τ = 0.5 economies with endogenous automation — accounts for the bulk of the flattening. The Cyclical Effect (isolated by comparing fixed and endogenous automation with τ = 0.01) contributes an intermediate amount.

Q10: What is the quantitative exercise comparing low- and high-automation steady states?

The low-automation economy targets the U.S. robot-per-employee ratio of 0.2% in the early 2000s (Acemoglu and Restrepo, 2020a), calibrated with robot-specific technological change ζ = 2. The high-automation economy features a 200% higher robot-per-employee ratio of 0.6%, calibrated to replicate one standard deviation of cross-MSA dispersion in robot penetration in the data. Both economies are simulated with 10,000 realizations of preference shocks, and the slopes of the price and wage Phillips curves are estimated from simulated inflation and unemployment outcomes. The price Phillips curve flattens by 14% and the wage Phillips curve by 13% moving from low to high automation, conditional on the same-sized shock in both economies.

Q11: How does the model account for the Covid-era resurgence of high inflation despite high automation?

The paper extends the machine manufacturer’s production function to include an asymmetric convex adjustment cost that activates when investment deviates more than 5% from its steady-state level (parameterized with δ = 0.0015 and ϱ = 100). Under a small expansionary shock (0.25 percentage point decrease in unemployment), inflation rises less in the high-automation economy, consistent with a flat Phillips curve. Under a large expansionary shock (2 percentage point decrease in unemployment), the surge in robot investment triggers sharply rising machine prices, eliminating the automation outside option for marginal producers and fully restoring workers’ bargaining power — so the inflation response is identical in the low- and high-automation economies, consistent with a steep Phillips curve. The paper interprets this as a proof-of-concept consistent with post-Covid wage compression evidence for low-wage workers documented by Autor, Dube, and McGrew (2023).

Q12: What do the robustness checks establish regarding alternative explanations?

The interaction of unemployment and robot adoption remains statistically significant at the 5% level across all the robustness checks (Appendix A). These include controlling for: (i) demographic heterogeneity — shares of young (below 30) and old (above 60) individuals, female/Black/Asian labor market participation, low-education attainment shares, overall participation, and MSA-level average marginal propensity to consume (MPC); (ii) occupational structure — shares of abstract, routine, manual, and offshorable occupations; and (iii) import competition — MSA exposure to Chinese and Mexican import competition. The coefficient on the robot-unemployment interaction term is stable across specifications, with the magnitude remaining close to that in the baseline (approximately 0.0140 across all demographic robustness columns in Table A.1).

Key Concepts

Automation threshold (γ):* The paper-specific level of idiosyncratic labor efficiency at which a producer is indifferent between installing a robot and posting a vacancy. Producers with labor efficiency below γ* choose the machine technology; those above choose the labor technology. The threshold is determined by the relative profitability of the two technologies, and it shifts endogenously with wages, machine prices, and job-filling probabilities. A higher γ* means more of the production sector is automated.

Wage Setting Effect of automation: The channel through which the existence of the outside option to install robots reduces workers’ bargaining power and dampens the elasticity of wages to unemployment fluctuations. Under directed search, firms’ ability to substitute machines for labor at a lower cost constrains the wage offers they need to post, so that a given decline in unemployment generates a smaller increase in average wages in higher-automation economies.

Steady State Effect of automation: The channel through which a larger steady-state fraction of robot firms reduces the aggregate labor share, so that even a given change in wages translates into a smaller change in aggregate marginal costs and prices. This channel operates even when automation cannot change upon a shock (fixed automation baseline).

Cyclical Effect of automation: The channel through which firms actively replace workers with machines in response to expansionary shocks that raise wages, generating an endogenous dampening of labor demand and putting downward pressure on the wage increase itself. This channel requires endogenous automation choices at business-cycle frequencies.

Robot-specific technological change (ζ): In the paper’s model, the parameter governing the efficiency with which machine manufacturers transform final goods into robots. A higher ζ reduces the relative price of machines (PM/P = 1/ζ), making automation more attractive to lower-efficiency producers and raising the automation threshold γ*. In quantitative exercises, variation in ζ across steady states drives differences in the degree of automation.

Price Phillips curve slope (Ψ): In the paper’s log-linearized model, the structural coefficient linking inflation to the unemployment gap. Unlike in standard New Keynesian models — where the slope depends only on the markup and nominal rigidity — Ψ is a function of the automation threshold γ*, the matching elasticity η, the efficiency distribution parameters (γM, γH, α), and the elasticity of substitution ε. Robot adoption shifts γ* and thereby changes Ψ.

Asymmetric investment adjustment cost: An extension of the machine manufacturer’s production function that imposes convex costs when robot investment deviates above 5% from its steady-state level (parameterized by δ and ϱ). This specification makes it increasingly costly to rapidly scale up automation in response to large demand shocks, causing the machine price to spike and the automation outside option to cease being effective for marginal producers, thereby restoring workers’ bargaining power and steepening the Phillips curve during large expansionary episodes.

Robust Real Rate Rules

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The paper proposes and analyzes real rate rules — monetary policy rules of the form i_t = r_t + φπ_t (φ > 1), where r_t is the current-period real interest rate observed via TIPS yields or inflation swap markets. The central analytical result is that combining this rule with the Fisher equation i_t = r_t + E_t[π_{t+1}] immediately yields E_t[π_{t+1}] = φπ_t, whose unique non-explosive solution is π_t = 0 for all t. This proof uses only the Fisher equation — not the aggregate Euler equation — making the determinacy result robust to household heterogeneity, hand-to-mouth consumers, non-rational household or firm expectations, active fiscal policy, missing transversality conditions, and any specification of intertemporal or nominal-real links. The Fisher equation itself requires only two deep-pocketed, fully-informed, rational agents to arbitrage between nominal and real bonds — a much weaker assumption than aggregate Euler equation rationality. Under the real rate rule, inflation is decoupled from the Phillips curve: causation runs monetary policy → inflation, then inflation → output gap, not the reverse; the Phillips curve determines the output gap residually given already-determined inflation. In a three-equation New Keynesian model with a mark-up shock ζ_t and cost-push shock ω_t, the output gap satisfies x_t = −(ζ_t/(κ(φ − ρ_ζ))) − (ω_t/κ), where the Euler equation plays no role in inflation determination. The rule is globally stable under learning via a contraction argument using Gautschi’s inequality: even if financial market participants hold incorrect prior beliefs, the learning process converges to the target inflation. With a time-varying inflation target π*_t, the modified rule i_t = r_t + φ(π_t − π*_t) implements any target path determinately — π_t = π*_t for all t, including optimal Ramsey paths — making real rate rules observationally equivalent to any other monetary policy specification. The Taylor principle (φ_π > 1) is neither necessary nor sufficient for determinacy in richer models (Bilbiie 2008 TANK; Leeper-Leith 2016 FTPL); the real rate rule achieves determinacy without invoking Euler equation structure. An additional result: with long-maturity government debt, a stable inflation equilibrium always exists under the real rate rule regardless of whether fiscal policy is active or passive — the fiscal theory of the price level fails to produce unique outcomes in this setting.

Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

In depth

Q1. What is the real rate rule, and why does it achieve determinacy without requiring the aggregate Euler equation?

The real rate rule i_t = r_t + φπ_t (φ > 1) combined with the Fisher equation i_t = r_t + E_t[π_{t+1}] immediately gives E_t[π_{t+1}] = φπ_t, whose unique non-explosive solution is π_t = 0 for all t; the proof is complete at this step, requiring no information about how households form expectations or optimize intertemporally. Standard Taylor-rule determinacy proofs rely on the aggregate Euler equation to close the system — the IS curve determines aggregate demand as a function of the real interest rate; deviation from determinacy arises when the Euler equation-Phillips curve system allows self-fulfilling expectation spirals. The real rate rule bypasses this entirely: the Fisher equation alone pins down the inflation path. The Fisher equation is a no-arbitrage condition between nominal and real bonds; it holds as long as two “deep-pocketed, fully-informed, rational agents” can trade both types of bonds — a condition that does not require aggregate household rationality, representative agent assumptions, or any specific consumption theory. Hand-to-mouth households, heterogeneous expectations, learning dynamics, and non-Ricardian fiscal regimes all leave the Fisher equation intact as long as some agents are pricing both asset classes. The consequence is that the Euler equation in the three-equation NK model becomes residual under the real rate rule: it determines the path of real interest rates given already-determined inflation and output gap, but plays no role in choosing among inflation equilibria.

Q2. What does the real rate rule imply about causation between inflation and the output gap?

Under the real rate rule, the Phillips curve operates in reverse relative to standard models: inflation is determined first (by the Fisher equation and the monetary rule), and the Phillips curve then determines the output gap as a residual; cost-push and demand shocks cannot amplify or dampen inflation variance under the rule. In the standard three-equation NK model with a mark-up shock ζ_t (law of motion ζ_t = ρ_ζ ζ_{t-1} + ε_{ζ,t}) and cost-push shock ω_t, the output gap under the real rate rule is x_t = −ζ_t/(κ(φ − ρ_ζ)) − ω_t/κ — a closed-form solution determined entirely by shocks, where the Euler equation does not appear. Inflation is π_t = 0 at all t (zero target): shocks affect the output gap but not inflation. Under an augmented rule that also responds to the output gap (i_t = r_t + φ_π π_t + φ_x x_t), determinacy still holds as long as a Phillips curve linking inflation and the output gap exists and the Taylor principle φ_π > 1 holds — providing additional policy degrees of freedom without sacrificing robustness. The decoupling of inflation from the Phillips curve is consistent with the empirical finding of Dotsey, Fujita, and Stark (2018) that the Phillips curve ceased to forecast inflation after 1984 — compatible with the hypothesis that the Fed’s post-Volcker behavior moved toward more real-rate-rule-like rules, giving the Fisher equation stronger anchor over inflation.

Q3. How does global stability under learning extend the determinacy result beyond local uniqueness?

Equilibrium determinacy is a local result (unique bounded solution near the target); the real rate rule additionally provides global stability under learning — even if financial market participants start with prior beliefs far from zero, the learning process converges to π_t = 0, preventing self-fulfilling sunspot equilibria from taking hold in the first place. The proof (Appendix D, using Gautschi’s inequality) establishes that the mapping from current beliefs to future beliefs is a contraction in the appropriate norm: since E_t[π_{t+1}] = φπ_t with φ > 1 drives realized inflation to zero, agents who update beliefs based on observed prices will progressively correct any initial error. This contrasts with Taylor rules, which are only locally determinate — an economy that starts at a non-zero sunspot inflation level may remain there if the sunspot is self-fulfilling. The global stability result also provides a response to the Cochrane (2022) critique that indeterminate equilibria under standard Taylor rules are “everywhere”: under the real rate rule, the only globally stable equilibrium is the target. The interest rate smoothing variant (Section 1.5) — fully smoothed real rate rule, θ > 0 — provides additional robustness: it requires agents to believe only that the central bank responds positively to inflation (not that φ > 1 specifically), and still generates identical inflation dynamics; this is more credible as a commitment device because the specific magnitude of φ cannot be directly observed.

Q4. How can the real rate rule implement arbitrary inflation dynamics, including optimal policy?

With a time-varying inflation target π_t, the modified rule i_t = r_t + φ(π_t − πt) implements any target inflation path determinately: the Fisher equation gives E_t[π{t+1} − π*_{t+1}] = φ(π_t − π*_t), whose unique solution is π_t = π*_t for all t, so realized inflation tracks the announced target exactly.** The CB must announce π*_t each period; this announcement may respond to the output gap, cost-push shocks, or any other variable. For example, to stabilize inflation while accommodating a cost-push shock, the CB sets π*t as a function of ω_t; realized inflation then follows this target, and the Phillips curve determines the output gap residually. There are two constraints: (1) the CB must be able to compute a reasonable approximation to E_t[π*{t+1}] — achievable via inflation futures, inflation swap markets, or an internal forecasting model; (2) the target path itself must not be explosive (a target that amplifies its own past realizations would generate explosive equilibria). Under these constraints, the paper formally proves (Appendix E.5) that real rate rules with time-varying targets can replicate the outcomes of any other monetary regime. This implies: (a) real rate rules can implement Ramsey-optimal policy, attaining the highest possible welfare; (b) it is empirically impossible to test whether a central bank is following a general real rate rule — any observed inflation and interest rate dynamics are consistent with some choice of π*_t. The Smets-Wouters (2007) estimated rule for the US illustrates: at the posterior mode, the correlation between the rule component z_t and the real interest rate r_t is 0.63, with both variables having standard deviation 0.46%, suggesting the Fed is already approximately two-thirds of the way toward a simple robust real rate rule.

Q5. Why does the Taylor principle fail in richer models, and how does the real rate rule avoid those failures?

The Taylor principle (φ_π > 1) is sufficient for determinacy in the benchmark three-equation NK model with a representative rational agent, but it is neither necessary nor sufficient in richer environments: Bilbiie (2008) shows that with enough hand-to-mouth consumers, higher φ_π can destabilize the economy; Leeper-Leith (2016) shows that following the Taylor principle can generate explosive inflation under the fiscal theory when nominal debt is present. Bilbiie (2008, 2019) inverts the Euler equation for the representative rational household when hand-to-mouth agents dominate: the aggregate consumption Euler equation has a negative intertemporal substitution sign, making the system’s eigenvalues switch. With enough hand-to-mouth agents, φ_π > 1 actually generates explosive equilibria (indeterminacy flips). Under the real rate rule, the Euler equation is disconnected from inflation determination entirely — Bilbiie’s mechanism cannot operate because the inflation equation relies only on the Fisher equation, not on whether the Euler equation has positive or negative sign. Similarly, the paper’s Section 2 result on fiscal robustness: with long-maturity government debt (Appendix B), a stable inflation equilibrium always exists under the real rate rule regardless of whether fiscal policy is active or passive. This implies the fiscal theory of the price level (FTPL) cannot uniquely determine inflation under the real rate rule — there is always a stable solution — so FTPL determinations are not unique, which may be of independent theoretical interest. The proof uses the contracting property of the non-linear real rate rule in the fully non-linear model, showing the target gross inflation Π* is always a solution of the bond-pricing fixed-point equation and that it is approached from all starting points via iteration.

Q6. How is the real rate rule implemented in practice, and what are the policy implications for central bank design?

Implementation uses TIPS yields (Treasury Inflation-Protected Securities) or inflation swap markets as real-time signals for r_t; the central bank sets i_t = TIPS_yield_t + φπ_t without estimating the natural rate (r) or output gap, eliminating the key measurement error in standard rules.* The key operational advantage over standard Taylor-type rules: standard rules require estimating the natural rate r* (now known to be mismeasured; Holston-Laubach-Williams 2017 revisions) and the output gap (subject to large real-time revisions); the real rate rule bypasses both because r_t is directly observable from financial markets (it equals the TIPS yield to a risk premium). The CB must also compute E_t[π*_{t+1}] to set the time-varying target; inflation futures or swap markets provide a forward-looking market price for this purpose. The paper discusses Hall and Reis (2016) “indexed payment on reserve” rules, which use a different mechanism (central bank liability indexation) to achieve similar robustness goals but do not rely on the Fisher equation as directly. Adão, Correia, and Teles (2011) achieve related results via complete nominal bond indexation. The real rate rule is more transparent and simpler to communicate: the CB says “we will raise the policy rate one-for-one with the real rate plus respond to inflation with coefficient φ.” For a smoothed version, communicating “we respond positively to inflation” — without specifying exactly how much — is sufficient for determinacy, and arguably more credible as a commitment. Section 4 (not covered here) develops a ZLB-adapted version of the rule for zero lower bound episodes that rules out explosive inflation equilibria at the bound.

Key concepts

real rate rule : the monetary policy rule i_t = r_t + φπ_t (φ > 1), where r_t is the current real interest rate observed from TIPS or inflation swap markets; achieves equilibrium determinacy via the Fisher equation alone, without invoking the aggregate Euler equation, making it robust to heterogeneous agents, hand-to-mouth consumers, non-rational expectations, and active fiscal policy.

Fisher equation : the no-arbitrage condition i_t = r_t + E_t[π_{t+1}] linking the nominal policy rate, real rate, and expected inflation; in the context of the real rate rule, it is the only structural equation needed for determinacy; requires only two deep-pocketed rational agents to arbitrage between nominal and real bonds — not aggregate household rationality.

inflation decoupling : the property under the real rate rule that the Phillips curve determines the output gap residually given already-determined inflation, rather than operating as a transmission mechanism for cost-push or demand shocks into inflation; implies that only monetary policy shocks and Fisher equation shocks can move inflation — cost-push and demand shocks affect the output gap but not the price level.

Taylor principle failure : the result (Bilbiie 2008) that standard Taylor rules can fail to deliver determinacy in models with hand-to-mouth consumers or heterogeneous agents — because the inverted aggregate Euler equation can flip eigenvalue signs — and (Leeper-Leith 2016) that following the Taylor principle can generate explosive inflation under the fiscal theory of the price level with nominal debt; the real rate rule avoids both failures by relying on the Fisher equation rather than the Euler equation for inflation determination.

global stability under learning : the property that even if financial market participants start with beliefs far from the inflation target, the learning process converges to the target under the real rate rule, proven via a contraction argument using Gautschi’s inequality; stronger than local determinacy (which only guarantees uniqueness near the target), ruling out self-fulfilling sunspot equilibria from any starting point.

fiscal theory robustness : the paper’s finding that with long-maturity government debt, the real rate rule always implies a stable inflation equilibrium regardless of whether fiscal policy is active (non-Ricardian) or passive (Ricardian); equivalently, the fiscal theory of the price level cannot uniquely determine inflation under the real rate rule because a stable solution always coexists with any fiscal regime.

Soft landing and inflation scares

Mon, 01 Jan 0001 00:00:00 +0000

Layer 1 — Overview

Research Question

Why did the 2021–2023 US inflation surge end in a soft landing — disinflation without a major recession — while the Volcker disinflation of 1979–1987 required substantial output losses? And was the timing and strength of the Federal Reserve’s reaction to the inflation surge decisive in achieving this outcome?

Methodology and Model

The paper develops and estimates a micro-founded Heterogeneous-Expectation New Keynesian (HENK) model in which agents hold idiosyncratic, dispersed beliefs about the long-run (steady-state) level of inflation. The key departure from full-information rational expectations (FIRE) is that information about the long-run value of inflation is dispersed and sticky: agents update their beliefs through pairwise social learning (SL), adopting the forecasting model of the agent whose belief produced lower recent inflation forecast errors. This tournament process — inspired by genetic algorithms — generates a time-varying cross-sectional distribution of subjective inflation beliefs.

The model admits a closed-form solution that retains the entire time-varying distribution of beliefs and can be estimated with standard full-information Bayesian methods using the inversion filter (Cuba-Borda et al. 2019). The FIRE benchmark is nested as the special case in which the average belief deviation from the target is zero at all times.

Estimation uses four US macroeconomic observables (output gap, CPI inflation, one-quarter-ahead average SPF inflation expectation, and the proxy funds rate of Choi et al. 2022 that captures both conventional and unconventional monetary policy) over 1985Q1–2023Q4. A formal model comparison rejects the RE null hypothesis (p < 0.0001) in favor of the HENK specification.

Main Findings With Quantitative Magnitudes

Inflation scares are endogenous: In the model, inflation scares arise whenever repeated above-target inflation outcomes validate and diffuse above-target beliefs through social interactions. Under the historical scenario, the share of agents holding long-run inflation beliefs between 1 and 3 percent (annualized) falls to 40 percent in mid-2022 before recovering above 90 percent by end-2023, indicating a partial but not complete unanchoring of expectations.
Timing dominates strength: Counterfactual simulations show that the timing — not the strength — of the Fed’s reaction to the inflation surge is the key determinant of inflation expectations management and subsequent macroeconomic outcomes. Varying the Taylor-rule inflation coefficient by +/-10 percent (moving from 1.64 to 2.00) produces negligible differences in inflation and output gap dynamics, with welfare ratios of 1.052 and 0.981 relative to benchmark respectively under the ad-hoc loss function. By contrast, varying the timing via the interest-rate smoothing parameter by +/-10 percent produces much larger divergences.
The Fed fell behind the curve: Under a scenario in which the Fed had strictly followed its estimated Taylor rule (removing the negative monetary policy shocks observed from mid-2020 to mid-2022), inflation would have peaked approximately 3 percentage points lower on a yearly basis. Inflation expectations would have remained lower for almost a year longer, and the subsequent rise in expectations would have been more gradual and lower-peaking. Crucially, the output gap in this preemptive-tightening scenario would have been only briefly negative (in 2022Q2) and not deep enough to trigger a recession.
Further delays would have been highly costly: A delay of the tightening by one, two, four, or eight quarters would have produced successively worse outcomes. A two-year delay generates runaway inflation and 100 percent loss of target credibility (complete unanchoring). A delay of approximately three quarters would have resulted in a sizable, self-reinforcing entrenchment of above-target inflation expectations. The welfare cost of an eight-quarter delay is 5.76 times the benchmark loss under the ad-hoc measure (1.167 under the microfounded measure).
Early rate cuts would have reignited inflation: A counterfactual 100-basis-point cut as early as 2022Q3 would have pushed annual inflation approximately 2 percent above the historical scenario through end-2023, with inflation expectations rebounding by about 1 percent (annualized) immediately after the cut. Under no early-cut scenario would inflation or expectations have converged back to target by end-2023.
Expectation heterogeneity amplifies shocks: Greater initial dispersion in beliefs amplifies and prolongs the impact of all shocks (demand, supply, monetary policy, expectation). After a one-standard-deviation cost-push shock, higher initial belief dispersion produces larger and more persistent deviations in inflation, output, and interest rates. The model-implied interquartile range of beliefs is correlated 0.538 with the SPF interquartile range and the cross-sectional standard deviation is correlated 0.483 (both p < 0.001).
Historical decomposition: Over the 2010s, negative expectation shocks account for a substantial fraction of the persistent below-target inflation (“missing inflation”). From approximately mid-2022 onward, positive expectation shocks account for most of the variance of inflation in the model. The recent disinflation is attributed to a combination of: easing supply pressures, normalization of monetary policy, and re-anchoring of inflation expectations.

Scope Conditions

Results are conditional on the estimated HENK model applied to US data, 1985Q1–2023Q4, using a stylized three-equation NK backbone (no labor market dynamics, no financial sector, no capital). The proxy funds rate is more volatile than the federal funds rate, which affects the welfare comparison for large preemptive tightening scenarios. Counterfactual scenarios are implemented through unexpected monetary policy shocks; anticipated shocks would only strengthen the inflationary effects of delays.

Layer 2 — Q&A

Q1: What is the core mechanism by which an inflation scare can develop in the HENK model?

A: When inflation repeatedly exceeds the target — whether due to shocks or delayed policy — agents whose beliefs are already above-target incur lower forecast errors than those anchored at the target. During pairwise social interactions (the tournament step of social learning), above-target beliefs spread through the population because they are selected as the “better” forecasting model. The resulting upward shift in the average belief feeds higher inflation through the New Keynesian Phillips Curve, which validates above-target beliefs further, creating a self-reinforcing loop. This mechanism differs from rational-expectations models, where beliefs mean-revert automatically.

Q2: How does the model retain a closed-form solution despite the nonlinearity of the social-learning process?

A: Two assumptions deliver the closed-form. First, beliefs are private and dispersed (Assumption 1): agents observe only the belief of their matched mate, not the population distribution. Second, a quasi-rational-expectations (quasi-RE) observer treats aggregate beliefs as a random walk in expectations (Assumption 2: a martingale). Under these conditions, the aggregate subjective inflation expectation equals the average subjective belief about steady-state inflation plus the rational-expectations forecast. This augmented minimum-state-variable (MSV) solution can be estimated with full-information methods (the inversion filter) via standard Dynare tooling.

Q3: What data are used and how are observables mapped to model variables?

A: The estimation uses four quarterly US observables from 1985Q1–2023Q4: the output gap (real GDP from FRED, HP-filtered with a one-sided adjusted filter); the CPI inflation rate (CPIAUCSL, FRED); one-quarter-ahead average CPI inflation expectation from the Survey of Professional Forecasters (CPI3); and the proxy funds rate of Choi et al. (2022), which captures both QE and QT so that unconventional monetary policy is reflected in the instrument. Inflation and expectations are demeaned by the sample average to express them as deviations from steady state. The discount factor is calibrated at 0.99; all other parameters are estimated via Bayesian methods with Metropolis-Hastings (8 parallel chains x 100,000 iterations, acceptance rate ~30%).

Q4: What are the key estimated parameter values for the social-learning block?

A: The posterior mean of the decay parameter in the fitness evaluation (discounting of past forecast errors) is 0.775, implying a half-life of past forecast errors of approximately 3 quarters. The frequency of news shocks has a posterior mean of 0.436, meaning approximately 40 percent of agents receive an inflation news shock every quarter. The standard deviations of the aggregate and idiosyncratic news shocks are very small (posterior means of 0.0004 and 0.0006, respectively) but strictly positive. The 95 percent confidence intervals for both exclude zero.

Q5: How does the HENK model outperform the RE benchmark in fitting the data?

A: Formal model comparison rejects the RE null (p < 0.0001) with equal prior model weights (50/50). On second moments, only the HENK model replicates positive autocorrelation in inflation (0.428 vs. 0.162 for RE, against an empirical interval of [0.239; 0.579]), in inflation expectations (0.824 vs. 0.161, empirical interval [0.839; 0.927]), and in inflation forecast errors (0.122 vs. -0.145). Additionally, the HENK model reproduces the untargeted cross-sectional dispersion of beliefs over the business cycle, including the increase during the GFC and the COVID-19 era and the low dispersion during the Great Moderation — with correlations of 0.538 and 0.483 between model and SPF dispersion measures.

Q6: What does the historical shock decomposition reveal about the recent inflation surge?

A: The decomposition (Section 3.3) shows that in the initial phase of the COVID-19 shock (2020Q2-Q3), negative demand and monetary policy shocks drove inflation down. Adverse cost-push (supply) shocks dominate from early 2021 into 2022. Expectation shocks — the contribution of dispersed beliefs — are negative throughout the 2010s (explaining part of the “missing inflation”) and remain briefly negative at the pandemic’s onset before turning sharply positive and driving most of the variance of inflation in the final two years of the sample (2022-2023). The loose monetary policy stance (negative monetary policy shocks from mid-2020 to mid-2022, visible in the Taylor-rule residuals) also contributes substantially to the inflation dynamics.

Q7: What does the Taylor-rule counterfactual show, and why doesn’t preemptive tightening cause a recession in the model?

A: Removing the monetary policy shocks after 2020Q4 so that the proxy rate follows the estimated Taylor rule would have reduced the inflation peak by approximately 0.75 percentage points per quarter (equivalent to about 3 percentage points annualized) and kept expectations lower-anchored for almost a year longer. The output gap under the Taylor-rule scenario is only briefly negative (2022Q2) and does not constitute a recession. This occurs because the preemptive tightening exploits the sluggishness of subjective expectations stemming from information frictions: by raising rates earlier when beliefs are still anchored (or only weakly above target), the CB prevents the social-learning mechanism from diffusing above-target beliefs, which in turn softens the stabilization trade-off between inflation and output.

Q8: What is the U-shaped welfare relationship between preemptive tightening size and welfare?

A: Both the ad-hoc and microfounded welfare measures show a U-shaped relationship as the size of the front-loaded tightening in 2021Q1 increases from 100 bps to 400 bps to 800 bps. At 100 bps, the welfare ratio is 0.336 (ad-hoc, improvement over benchmark at 1.0); at 400 bps it improves further to 0.304; but at 800 bps (front-loading the entire subsequent tightening cycle) the ratio rises to 0.555, reflecting that the output costs of a very large early rate increase become prohibitive amid the series of supply shocks that hit in 2022. The maximum welfare gain in the microfounded criterion occurs at a slightly larger early increase than in the ad-hoc criterion, attributed to the absence of a financial sector and use of the more volatile proxy funds rate.

Q9: Does increasing the hawkishness of the Taylor rule compensate for falling behind the curve?

A: No. Varying the inflation reaction coefficient by +/-10 percent (to 2.00 for “hawk” and 1.64 for “dove”) from the posterior mean of approximately 1.82 produces negligible differences in inflation and output gaps. The hawkish scenario achieves marginally earlier rate increases but does not reduce the inflation gap relative to the historical benchmark. Welfare ratios are 0.960 (hawkish, slight improvement) and 1.057 (dovish, slight deterioration) under the ad-hoc measure, and 0.981 and 1.052 under the microfounded measure. The joint simulations varying both smoothing (timing) and hawkishness (strength) confirm that timing is the dominant factor: the two “earlier reaction” scenarios are clustered together and well-separated from the two “later reaction” scenarios, regardless of the inflation coefficient.

Q10: How does the model handle the role of initial belief dispersion in monetary policy transmission?

A: Impulse response function exercises varying the initial standard deviation of beliefs (as a share of the maximum model-generated standard deviation under the filtered shocks) show that greater initial dispersion uniformly amplifies and prolongs the macroeconomic response to all shock types (demand, cost-push, monetary policy, expectation). The mechanism is: greater dispersion means the population contains more “extreme” (far-from-target) beliefs; a shock that temporarily moves inflation off target temporarily validates extreme beliefs (lower forecast errors), causing them to spread in social interactions and shift the average belief further from target. This raises nominal rates (through the Taylor rule), deepens output losses, and prolongs the return to steady state.

Q11: What are the implications of early interest rate cuts in the counterfactual scenarios?

A: A 100-basis-point cut in any quarter from 2022Q3 through 2023Q2 would have reignited inflation expectations. The 2022Q3 scenario is most severe: expectations rebound approximately 1 percentage point higher (annualized) immediately post-cut, and annual inflation remains on average 2 percent above the historical path through end-2023. Across all early-cut scenarios, neither inflation nor inflation expectations would have returned to target by end-2023; instead, inflation would have been landing approximately 2 percent above the 2 percent target. The welfare ratios for early cuts range from 1.200 (cut in 2022Q3) down to 1.079 (cut in 2023Q2) under the ad-hoc measure — all welfare-worsening.

Key Concepts

Inflation scare (Goodfriend 1993, as used in this paper): A situation in which the public’s long-run inflation expectations become unanchored from the central bank’s target, making beliefs about above-target steady-state inflation self-fulfilling via the New Keynesian Phillips Curve. In the HENK model, a scare arises endogenously when above-target inflation outcomes repeatedly validate above-target beliefs, causing them to spread through social interactions. Measured in the paper by the share of idiosyncratic beliefs falling between 1 and 3 percent (annualized); lower share = more severe scare.

Social learning (SL): The belief-updating mechanism in which agents are paired at random each period and compare their inflation forecasting models; the agent whose model produced lower recent forecast errors (measured by the discounted sum of squared forecast errors with half-life approximately 3 quarters) is adopted by both members of the pair. This evolutionary tournament process — analogous to a genetic algorithm — generates a nonlinear, history-dependent distribution of beliefs that can drift persistently away from the target.

Steady-state learning: The restriction that agents’ heterogeneous beliefs concern only the low-frequency (intercept) component of inflation — i.e., their subjective perception of the steady-state inflation rate — while the rest of their inflation forecast (the effects of transitory shocks and lagged variables) coincides with rational expectations. This assumption, combined with internal rationality, permits a closed-form MSV solution of the HENK model.

Internal rationality: The assumption that each agent uses a perceived law of motion that is consistent with the true MSV solution of the HENK economy (including the effect of heterogeneous beliefs on dynamics), even if their intercept differs from the rational-expectations value. Agents internalize how the aggregate deviation of expectations from RE affects inflation, but they disagree about the long-run level.

Quasi-rational-expectations (quasi-RE) observer: An observer (or central bank) who, lacking information about how individual private beliefs are formed and aggregated, treats aggregate beliefs as a martingale — i.e., the expected future aggregate belief equals its current value. This assumption closes the model and permits estimation with full-information (inversion filter) methods, while preserving consistency between subjective beliefs and the law of motion.

Belief dispersion / expectation heterogeneity: The time-varying cross-sectional standard deviation (or interquartile range) of idiosyncratic beliefs in the population. In the model this is an endogenous, history-dependent outcome of the SL process. Greater dispersion amplifies the response of all macroeconomic variables to any shock by providing more “extreme” beliefs that can gain traction in pairwise tournaments when inflation temporarily deviates from target. Measured empirically by the interquartile range and standard deviation of individual SPF forecasts.

Proxy funds rate (Choi et al. 2022): A summary measure of the US monetary policy stance that incorporates both conventional interest rate policy and the effects of unconventional policies (quantitative easing and tightening), used in the paper in place of the federal funds rate to capture the full stance of monetary policy in the estimation and historical decomposition.

Inversion filter (Cuba-Borda et al. 2019): A computationally efficient estimation algorithm that, rather than the Kalman or particle filter, inverts the observation equation analytically to recover the sequence of structural shocks for a given parameter vector. It enables full-information Bayesian estimation of the nonlinear HENK model by separating the linear part of the solution from the nonlinear social-learning residual.