E62 | Macro Paper Warehouse

A Temporary VAT Cut as Unconventional Fiscal Policy

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

The paper studies Germany’s temporary 3 percentage-point VAT cut from July 1 to December 31, 2020 (standard rate 19%→16%, reduced rate 7%→5%), combining two causal identification strategies with microdata and a HANK model to establish that intertemporal substitution drove a large spending response concentrated in durable goods.

Ex-ante approach (July 2020 BOP-HH survey, fielded immediately after the cut took effect): The survey distinguishes households informed about the January 2021 reversal (treated) from those who believed the cut was permanent (control). Treated households are approximately 10 percentage points more likely to increase durable purchases on the extensive margin. This is a lower bound on the intertemporal substitution effect because some “control” households likely learned about the reversal before the survey, attenuating the control group’s spending behavior toward that of the treated group.

Ex-post approach (January 2021 BOP-HH survey and GfK scanner data): Cross-household variation in perceived VAT pass-through identifies the spending effect. Households perceiving high pass-through — who saw prices actually fall at their usual stores — spent approximately 37 percent more on durables in 2020HY2 than those perceiving low or no pass-through (preferred OLS/IV specification, Table 3). GfK scanner data on semi-durables shows approximately 10 percent higher spending for high vs. low perceived pass-through (coefficient ≈ 0.093, Table 5). Non-durable spending shows no statistically significant response. The magnitude of the response increases with the durability of the good and increases over time toward the December 2020 cutoff, consistent with intertemporal substitution (a more durable good generates larger discounted savings from buying before the reversal; a later purchase locks in savings for longer until January).

Direct evidence of intertemporal pull-forward (Table 4): Households reporting high perceived pass-through in 2020HY2 planned to spend approximately 1,642 EUR less on durables in 2021 first-half relative to those with low pass-through in the GfK survey — a direct “spend now, buy less later” pattern confirming temporal shifting rather than a pure income effect.

Cross-sectional heterogeneity: The response is driven by young, low net-wealth households and price-sensitive “bargain hunters” who actively compare prices across stores. Critically, the response is NOT concentrated in financially literate households or those reporting long planning horizons, which distinguishes the VAT policy from forward guidance (which requires understanding and acting on future rate paths) and implies the policy reaches a broad spectrum of household types.

No COVID-19 confound: The paper finds no significant interaction between a household’s pandemic exposure (work disruption, income loss, health shock) and its durable spending response, confirming the intertemporal substitution mechanism operated independently of the concurrent COVID-19 environment.

HANK model (based on the Bayer, Born, Luetticke 2024a two-asset heterogeneous-agent New Keynesian framework, adapted with illiquid durable goods and a Calvo durable-adjustment friction):

Durable adjustment probability per semi-annual period: λ = 18% (Calvo friction calibrated to the spread of the durable spending response through 2020HY2)
Perceived-pass-through heterogeneity: 65% of households perceive high pass-through; perceived average cut among treated = 2.4pp (both calibrated to BOP-HH data)
Calibration targets: durable spending response elasticity = 0.32; X/Y = 0.08 (durable expenditure share); B/Y = 0.86 (liquid bond share); (B+qΠ)/Y = 1.90 (total liquid wealth); G/Y = 0.29; top-10% wealth share = 52%; fraction liquidity-constrained = 18%
Structural parameters: β = 0.92 (semi-annual discount factor); ξ = 2.0 (CRRA coefficient); ϑ = 0.5 (Frisch labor supply elasticity); ν = 0.80 (non-durable expenditure weight); τc = 17.5% (baseline VAT rate); τ = 31% (income tax rate); δ = 5% (semi-annual durable depreciation rate)
Impact effects: total consumption +4.3%; durable consumption +29.4%; the VAT-inclusive price level falls by approximately 1.0pp on impact (less than the 2.4pp perceived cut because of demand-driven upward pressure on prices)
Multipliers at ELB: impact consumption multiplier = 3.0; cumulative two-year consumption multiplier = 1.7
Multipliers with Taylor rule: impact = 2.2; cumulative two-year = 0.9 (lower because the central bank raises nominal rates in response to the demand boost, partly crowding out consumption)
Decomposition: the direct effect — computed holding GE equilibrium objects (wages, asset prices, aggregate demand) fixed — accounts for approximately 90% of the durable consumption response and approximately 4/5 of the non-durable response; the remaining indirect effect operates through positive Keynesian income spillovers
Comparison to interest rate cuts: the VAT cut delivers a larger aggregate consumption response per unit of fiscal cost than a comparable nominal interest rate reduction, because interest rate cuts create countervailing income effects for net savers (who lose interest income) that partially offset the stimulus for net borrowers

Scope conditions: Empirical estimates are local to Germany’s 2020 economic environment (near-zero ECB policy rate, partial COVID-19 demand suppression). The causal identification exploits cross-household variation in perceived pass-through, instrumented by bargain-hunting behavior; the exogeneity assumption requires that price-searching behavior affects spending through perceived prices rather than through other channels. The HANK quantitative results are conditional on the Calvo durable adjustment friction and the 65%/35% perceived-pass-through split; sensitivity to these calibration choices is explored but not the primary focus.

Note on working paper versions: This summary is based on NBER Working Paper 29442 (August 2024 revision), which uses a HANK framework and reports a 4.3% impact on total consumption. A Bundesbank Discussion Paper (24/2025, April 2025) describes the model as a “RANK” (representative-agent) framework with a 4.4% impact. The published RES version (June 2026) may differ from both working paper versions in its model specification; the core empirical findings (37% durable response, 10% semi-durable response, 10pp ex-ante effect) are unlikely to have changed.

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 ex-ante identification strategy, and what does it identify?

The July 2020 BOP-HH survey ran immediately after the VAT cut took effect and identifies the causal effect of expecting a tax cut to be temporary by comparing households informed about the January 2021 reversal (treated) with those who believed the cut was permanent (control); treated households are approximately 10 percentage points more likely to report an intention to increase durable purchases. This is a lower bound on the true intertemporal substitution effect: if some “control” households learned about the reversal through other channels between the survey date and December 2020, they would have behaved more like treated households, compressing the gap. The ex-ante design also measures the extensive-margin decision (whether to increase purchases) rather than the total spending level, so the 10pp estimate is not directly comparable to the 37% ex-post level estimate.

Q2. What is the ex-post identification strategy, and how does it address endogeneity?

The January 2021 BOP-HH survey asks respondents how their 2020HY2 spending compared to a counterfactual without the VAT cut, and instruments perceived price pass-through with bargain-hunting behavior (price comparison across stores) — a variable that predicts who notices price changes but should not directly affect intertemporal allocation decisions. OLS and IV estimates are close (Table 3), suggesting limited endogeneity bias; the IV result of 37% more durable spending for high vs. low perceived pass-through is the preferred causal estimate. GfK scanner data provides an independent corroboration using objective purchase records rather than survey recall, yielding the 10% semi-durable estimate (Table 5, coefficient ≈ 0.093 in IHS-transformed spending).

Q3. Why does the response increase with the durability of the good?

A durable good yields a flow of consumption services over multiple periods; purchasing it before the January 2021 VAT reversal locks in tax savings for the entire lifetime of the good, while purchasing a non-durable before the reversal saves taxes only on a single-period consumption unit — so the present-discounted-value gain from intertemporal substitution is proportional to the good’s durability. This prediction is confirmed empirically: durables (white goods, electronics) show the largest response (37%); semi-durables (clothing, textiles in GfK) an intermediate response (~10%); non-durables no significant response. The fact that the spending response also builds toward the December cutoff — with the largest response in November and December 2020 — further supports intertemporal substitution (households delay purchases even within the cut period, maximizing the remaining time advantage).

Q4. Why was the VAT cut effective despite the concurrent COVID-19 shock?

The paper finds no statistically significant interaction between household-level COVID-19 exposure (income loss, work disruption, health shock) and the durable spending response to the VAT cut; the intertemporal price channel operated independently of pandemic-related income and uncertainty effects. This is consistent with the bargain-hunting interpretation: price-sensitive households who actively compare prices adjusted toward durables regardless of their pandemic-specific economic circumstances. The finding also implies that the simultaneous COVID-19 shock does not confound the identification, because the cross-household variation in perceived pass-through is independent of COVID-19 exposure.

Q5. Why is a HANK model appropriate, and what does durable heterogeneity add?

A HANK model is needed because the spending response is driven disproportionately by young, low net-wealth households who face binding liquidity constraints at some frequencies — in a representative-agent model all households respond immediately to the intertemporal price signal, which would predict an immediate front-loaded response; in the HANK model with Calvo durable adjustment, constrained households adjust their durable stock only when they receive an adjustment opportunity (λ=18% per semi-annual period), spreading the response through time and matching the observed gradual build-up of durable spending through 2020HY2. The illiquid-durable extension of the Bayer-Born-Luetticke framework separately tracks liquid financial assets and illiquid durables, allowing the model to capture both the temporal dynamics of the spending response and the cross-household variation in responses across the wealth distribution.

Q6. What is the impact consumption multiplier, and why is it larger at the ELB?

The impact consumption multiplier — the increase in total consumption divided by the fiscal cost of the VAT cut (measured as the VAT rate reduction times baseline consumption) — is 3.0 at the effective lower bound (ELB) and 2.2 with an active Taylor rule. At the ELB, the demand boost from the VAT cut raises inflation expectations; since the nominal rate cannot rise, the real rate falls, providing a secondary stimulus through the inter-temporal Euler equation; with an active Taylor rule, the central bank raises the nominal rate in response to higher inflation, crowding out some consumption and reducing the multiplier. The 3.0 impact multiplier exceeds the standard Keynesian multiplier because the durable sector amplifies the effect: a 2.4pp perceived price cut induces a 29.4% jump in durable purchases, whose production generates large income spillovers.

Q7. Why does the cumulative two-year multiplier fall below the impact multiplier?

The cumulative two-year multiplier is 1.7 at the ELB (vs. 3.0 on impact) because durable purchases pulled forward into 2020HY2 create a “payback effect” — households that already upgraded their durables need fewer new purchases in 2021, reducing durable consumption below the counterfactual path for several quarters after the reversal. This is directly documented in Table 4: high perceived pass-through households planned to spend approximately 1,642 EUR less on durables in 2021H1, and the GfK data confirms a spending decline in early 2021. The cumulative multiplier remains above zero and above 1.0, confirming the policy provides net stimulus over the two-year horizon even accounting for the post-cut hangover.

Q8. Why is the VAT cut more powerful than a comparable interest rate cut?

An interest rate cut stimulates borrowers but simultaneously reduces interest income for net savers, who partially offset their reduced income by consuming less; the VAT cut lowers current prices for all households without changing the interest rate, so there is no countervailing income effect for savers, and the consumption stimulus is less diluted by redistribution. In the HANK calibration, the additional dimension is that the VAT cut operates through a perceived price channel that requires only that households notice lower prices in stores — a much lower bar than the financial sophistication required to respond to forward guidance or interest rate signals — so the policy reaches a broader share of the household distribution than monetary easing.

Q9. What does the distributional evidence imply for fiscal stimulus design?

Young, low net-wealth households respond most strongly to the VAT cut, the opposite of the pattern expected if the response required financial sophistication; combined with the bargain-hunting identification, this implies the policy’s effectiveness does not depend on forward-looking planning or consumption-smoothing capacity — it is triggered simply by noticing prices are lower at the store. This finding challenges the conventional view that temporary fiscal policies are less effective than permanent ones because households do not optimize over them; instead, the price-noticing channel bypasses the forward-looking optimization entirely and generates a large spending response among households who do not match the life-cycle model assumptions. The distributional progressivity (young, low-wealth households drive the response) also contrasts with unconventional monetary policy (which benefits asset-holders through wealth effects) and improves the equity case for temporary VAT cuts as a stimulus instrument.

Key concepts

intertemporal substitution : the mechanism by which a temporary price reduction — here a VAT cut that will be reversed — induces households to shift consumption from the post-cut period to the cut period; the paper’s primary transmission channel, more powerful for durable goods because the present-value savings scale with the good’s lifetime.

perceived pass-through : the fraction of the statutory VAT rate reduction that a household perceives as an actual reduction in the prices it faces in its usual stores; the paper’s main source of cross-sectional identification in the ex-post strategy, correlated with bargain-hunting behavior.

ex-ante approach : the identification strategy using the July 2020 BOP-HH survey; identifies the causal effect of expecting a cut to be temporary by comparing informed (reversal known) vs. uninformed (thought permanent) households on their intended durable purchase behavior.

ex-post approach : the identification strategy using the January 2021 BOP-HH survey and GfK scanner data; identifies the causal effect of perceived price changes on realized spending by comparing high vs. low perceived pass-through households and instrumenting with bargain-hunting behavior.

payback effect : the reduction in durable spending in 2021H1 among households that pulled forward purchases during the 2020 cut; documented through the 1,642 EUR planned spending gap in Table 4 and GfK scanner data; makes the cumulative two-year multiplier (1.7) substantially lower than the impact multiplier (3.0).

HANK model with durable Calvo friction : the Bayer-Born-Luetticke (2024a) two-asset heterogeneous-agent New Keynesian framework adapted with illiquid durable goods and a Calvo probability of durable adjustment (λ = 18% per semi-annual period); the Calvo friction matches the gradual build-up of the durable spending response through 2020HY2 rather than an immediate front-loaded spike.

Can Deficits Finance Themselves?

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

The paper asks whether a government can run a deficit today — issuing “stimulus checks” — and allow debt to return to its initial level without any future tax hike or spending cut. In environments combining (i) nominal rigidity and (ii) a violation of Ricardian equivalence (due to finite lives or liquidity constraints), this is possible through two complementary self-financing channels: (a) a Keynesian boom in real activity that expands the tax base and automatically raises revenue at existing tax rates; and (b) a surge in inflation that erodes the real value of outstanding nominal government debt. The paper’s headline result is that self-financing increases monotonically as fiscal adjustment is delayed, converging to full self-financing in the limit: if monetary policy does not lean too heavily against the fiscal stimulus, the initial deficit eventually returns debt to trend with no required future adjustment. Calibrated to empirical evidence on intertemporal MPCs, the speed of fiscal adjustment, the Phillips curve slope, and the monetary reaction, the model finds self-financing up to ν ≈ 0.95 — with the tax base channel dominant and inflation contributing negligibly.

Environment (Section 2): Baseline is a perpetual-youth overlapping-generations (OLG) version of the textbook New Keynesian model. Households survive from one period to the next with probability ω ∈ (0,1]; when ω=1 the model reduces to the standard PIH-RANK benchmark in which Ricardian equivalence holds and no self-financing occurs. When ω<1, two properties of consumer demand emerge: (i) consumers discount future disposable income at a rate higher than the interest rate (“discounting”), so a distant future tax hike barely affects today’s spending; (ii) consumers spend transfers relatively quickly (“front-loading”), so the Keynesian boom plays out before the promised tax hike arrives. The supply block is exactly the standard NKPC. Fiscal policy follows a rule in which taxes respond to income through a fixed tax rate τy (tax base channel) and to debt through a speed-of-adjustment coefficient τd ∈ (0,1) (with τd→0 meaning indefinitely delayed adjustment). Monetary policy keeps (expected) real rates constant in the baseline — a “neutral” benchmark that neither offsets nor amplifies the fiscal stimulus.

Self-financing result (Sections 3–4): Starting from a date-0 deficit shock ε0 (lump-sum transfer of 1% of steady-state output), define the degree of self-financing ν as the fraction of ε0 financed by the tax base and debt erosion channels; 1−ν equals the discounted present value of future tax hikes required to stabilize debt. The central results are:

Theorem 1 (baseline, φ=0): If ω<1 and τy>0, ν increases monotonically as τd→0, with ν→1 in the limit. Intuition via two-period analogy: when cumulative short-run MPC → 1, the Keynesian multiplier → 1/τy, and the induced tax revenue → 1 — exactly financing the original ε0.
Proposition 3: For any given τd or delay H, ν is strictly decreasing in ω: larger departures from permanent income (smaller ω) deliver faster and larger Keynesian booms and hence greater self-financing.
Theorem 2 (general monetary policy): Under a general real rate rule rt = φ·yt, there exists a threshold φ̄ ∈ (0, τy/(β·D^ss/Y^ss)) such that: if φ<φ̄, full self-financing is achieved in the limit; if φ>φ̄, ν is bounded strictly below 1 by ν̄(φ). If the monetary authority perfectly stabilizes output and inflation (φ→∞), ν=0 by construction.
Theorem 3 (general aggregate demand): With generalized demand ct = Md·dt + My·(yt−tt) + δ·Et[Σ(βω)^k(yt+k−tt+k)], self-financing holds whenever (i) ω<1 and (ii) Md>1−β and My·(1 + δ·βω/(1−βω)) ≥ 1. This nests the baseline OLG model, hybrid spender-OLG models, and approximately represents quantitative HANK models.

Distinction from FTPL: The Fiscal Theory of the Price Level (Cochrane) breaks Ricardian equivalence through equilibrium selection in a PIH-RANK setting; the self-financing here operates under the conventional equilibrium, with an active monetary authority and passive fiscal authority. The inflation channel is not the focal mechanism — the tax base channel is dominant.

Calibration (Table 1, hybrid OLG-spender model, quarterly frequency):

Consumer spending: share of hand-to-mouth (HtM) spenders µ = 0.073; OLG survival rate ω = 0.865; jointly matched to average MPC = 0.2 and short-run MPC slope from Fagereng, Holm, and Natvik (2021)
Fiscal adjustment: τd ∈ {0.085, 0.026, 0.004} (fast to slow; from Galí et al. 2007, Bianchi-Melosi 2017, Auclert-Rognlie 2020 respectively; equivalent to H ∈ {12, 23, 43} quarters under the non-Markovian rule)
Monetary policy: real rate feedback φ = 0 (neutral baseline)
Nominal rigidities: NKPC slope κ = 0.0062 (Hazell et al. 2022 point estimate)
Standard parameters: EIS σ=1 (log utility); β = 0.998 (1% annual real rate); tax feedback τy = 0.33 (DeLong-Summers benchmark: 33 cents of surplus per dollar of output); liquid wealth D^ss/Y^ss = 1.04 (Kaplan et al. 2018)

Quantitative results (Figure 3, Table 2):

For empirically calibrated τd range, ν reaches up to 0.95, nearly full self-financing in the most realistic (slow adjustment) specification
Virtually all self-financing (≈95–100%) occurs through the tax base channel — the flat NKPC (κ=0.0062) limits inflation and debt erosion to a negligible share; with steeper NKPC (κ=0.1), about 20% of self-financing comes through date-0 inflation
The quantitative fiscal multiplier at τd=0.085 is 1.11, consistent with Ramey (2011) empirical estimates for transfers with relatively quick adjustment
Table 2 (νmax as function of monetary ψ and NKPC κ): Full self-financing (νmax = 1) is attainable when ψ ≤ 1.25 and κ = 0.0062; drops to νmax = 0.63 at ψ=1.5 and κ=0.0062; drops to νmax = 0.22 with κ=0.1 and ψ=1; approaches 0 with both aggressive monetary and flexible prices. Key lesson: moderate monetary reaction combined with flat NKPC (consistent with evidence) supports near-full self-financing.

Robustness:

HANK model: same conclusions as hybrid spender-OLG; intertemporal MPCs nearly identical (Wolf, 2021; Auclert et al., 2023)
Distortionary fiscal adjustment: negligible impact, since the required adjustment itself vanishes in the limit
Government purchases: same self-financing logic applies (Keynesian boom raises tax revenue)
Investment: Keynesian cross applies to consumption; net of investment aggregate demand follows the same law of motion — self-financing result unchanged

Scope conditions: Self-financing requires Ricardian equivalence to fail (ω<1); in the PIH-RANK benchmark (ω=1), neither self-financing channel is operative. Monetary accommodation is assumed neutral or weak; aggressive offsetting (φ>φ̄) prevents full self-financing. The paper is purely positive: whether deficits are optimal is a separate normative question. Results are log-linearized dynamics; the quantitative conclusions depend on discipline from empirical MPC evidence, NKPC estimates, and fiscal adjustment speed. The self-financing mechanism operates through aggregate demand and is not driven by r<g or by seigniorage from a convenience yield.

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 two-period intuition for full self-financing?

In a two-period economy with fully myopic consumers (MPC=1), a date-0 transfer of ε stimulates output by y = MPC/(1−MPC·(1−τy)) · ε, generating tax revenue τy·y; with MPC→1 the output multiplier converges to 1/τy and tax revenue converges to exactly ε — full self-financing via the tax base. The infinite-horizon economy with ω<1 mirrors this intuition when fiscal adjustment is delayed far enough: the “short run” cumulative MPC approaches 1 (by discounting and front-loading), the Keynesian cross delivers a multiplier of 1/τy, and the additional tax revenue precisely repays the deficit, with no future tax hike needed.

Q2. Why does the degree of self-financing ν increase as fiscal adjustment is delayed?

As the gap H between the date-0 transfer and the promised future tax hike widens, two effects amplify the Keynesian boom: (i) near-term demand is less dampened by anticipation of the future tax hike (discounting makes far-ahead taxes nearly irrelevant to today’s spending); and (ii) the general equilibrium income feedback — the Keynesian cross — has more time to play out before being curtailed by the eventual tax hike, amplifying the total output and revenue response. The longer the delay, the larger the short-run cumulative MPC, and the larger the fraction of the deficit self-financed through the tax base.

Q3. Why does aggressive monetary policy block self-financing?

If the monetary authority raises real interest rates in response to the fiscal boom (φ>0), it discourages household spending, slowing and shrinking the Keynesian boom; above the threshold φ̄, the real rate increase is strong enough to counteract the tax base feedback before the cumulative MPC can converge to 1, meaning full self-financing becomes impossible and some future fiscal adjustment is always required. Conversely, monetary accommodation (φ<0) accelerates the boom and permits full self-financing with less delay, while perfectly stabilizing output and inflation (φ→∞) entirely shuts down both self-financing channels.

Q4. What is the role of the NKPC slope in determining which channel operates?

When the NKPC is flat (κ=0.0062, the Hazell et al. 2022 estimate), a large output boom generates negligible inflation, so debt erosion contributes almost nothing and the tax base channel carries essentially all the self-financing; when the NKPC is steep (κ=0.1, consistent with supply-constrained post-COVID), the same boom generates materially more inflation, shifting the financing split so that ~20% comes through debt erosion while ~80% still comes through the tax base. The overall degree of self-financing ν is affected only through the monetary response: a steeper NKPC triggers a more aggressive real rate response, moderating the boom, but this is captured in the analysis of Theorem 2 and Table 2.

Q5. How does this paper relate to and differ from the Fiscal Theory of the Price Level (FTPL)?

The FTPL (Cochrane) achieves deficit financing through inflation in a PIH-RANK environment by abandoning the Taylor principle and exploiting equilibrium selection; this paper requires no such departure — both monetary and fiscal policy follow conventional active/passive assignments, and the equilibrium studied is the unique bounded one. The key difference is in the consumer block: Ricardian equivalence fails here through finite lives or liquidity constraints (empirically grounded), not through equilibrium selection. Moreover, while FTPL highlights the debt erosion (inflation) channel, this paper finds the tax base (real activity) channel is dominant under empirically calibrated flat Phillips curves.

Q6. What new conditions on aggregate demand ensure self-financing extends beyond the OLG baseline?

Theorem 3 identifies two sufficient conditions: (1) “positive geometric discounting” (ω<1 in the generalized demand block), ensuring that far-ahead future taxes have negligible effect on current demand; and (2) “sufficient front-loading” (Md > 1−β and My·(1 + δ·βω/(1−βω)) ≥ 1), ensuring that income is spent quickly enough for the Keynesian feedback to deliver self-financing before debt explodes. The classical PIH-RANK fails condition (1); the spender-saver model with any margin of PIH consumers fails condition (2); the OLG baseline satisfies both; and the hybrid spender-OLG (the quantitative workhorse) satisfies both for any ω<1.

Q7. Is a margin of truly PIH consumers fatal for self-financing?

Yes — introducing any strictly positive mass of PIH consumers breaks self-financing entirely, creating a discontinuity: ν=0 whenever µ_PIH > 0, no matter how small. The intuition is that PIH consumers never fully spend any income received in finite time (they smooth it across their infinite horizon), so the cumulative MPC never reaches 1 and the Keynesian boom cannot fully finance the deficit. However, the discontinuity is fragile: replacing literal PIH consumers with “near-PIH” consumers (finite but large ω) restores ν→1 in the limit as H→∞ and is consistent with empirical evidence on high MPCs for liquid households.

Key concepts

fiscal self-financing : the property that a deficit-financed government transfer raises output and inflation sufficiently to replenish government revenue (via the tax base channel) and reduce the real debt burden (via the inflation/debt erosion channel), allowing debt to return to steady state without future tax increases; the degree ν ∈ [0,1] measures what fraction of the initial deficit is self-financed.

tax base channel : the mechanism by which a Keynesian boom in real activity — triggered by the deficit-financed transfer — automatically raises tax revenue (by τy dollars per dollar of additional output) without any change in tax rates; dominant over the debt erosion channel whenever the NKPC is flat (empirically, κ ≈ 0.006).

discounting and front-loading : the two consumer demand properties necessary for self-financing; “discounting” (ω<1) means far-ahead future taxes barely affect current spending, allowing the deficit to stimulate demand even with a promised future tax hike; “front-loading” means the income response is spent quickly, so the Keynesian boom plays out before the delayed tax hike arrives, raising tax revenue sufficiently to finance the deficit.

speed of fiscal adjustment (τd) : the quarterly feedback from public debt to tax revenue in the fiscal rule; τd→0 means indefinitely delayed adjustment and maximum self-financing; empirically disciplined values range from τd=0.085 (fast, Galí et al. 2007) to τd=0.004 (slow, Auclert-Rognlie 2020), with νmax ≈ 0.95 across this range under neutral monetary policy and flat NKPC.

hybrid spender-OLG model : the paper’s quantitative workhorse, combining a fraction µ of hand-to-mouth spenders with OLG perpetual-youth consumers; jointly calibrated to match the impact and short-run MPCs from Fagereng et al. (2021), while also providing a close proxy for aggregate demand in quantitative HANK models (Auclert et al. 2023; Wolf 2021).

Distorted prices and targeted taxes in the New Keynesian Network model

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

This paper asks how governments should optimally adjust sector-specific taxes in response to sectoral shocks when monetary policy cannot be tailored to individual sectors. The authors work within a variant of Rubbo’s (2023) New Keynesian Network (NKN) model, augmented to include time-varying sectoral sales taxes and production subsidies. The model features N sectors connected through input-output linkages, with Calvo-type price rigidity that is heterogeneous across sectors, and encompasses both sectoral productivity (supply) shocks and demand shocks.

The central finding, stated as Proposition 1, is that the first-best tax policy requires exactly 2N instruments—one sales tax and one production subsidy per sector—not just instruments in the shocked sector. The mechanism turns on a twofold distortion created by sticky prices. Because only a fraction of firms adjust prices at any time, relative prices are distorted both within sectors (price dispersion among firms) and across sectors (misalignment of relative prices). The production subsidy offsets the effect of shocks on marginal costs, incentivizing price-adjusting firms to leave seller prices unchanged and thereby eliminating within-sector dispersion. The sales tax—which applies to both household purchases and intermediate goods trade—steers demand across sectors so that market prices move as if fully flexible, closing sectoral output gaps even as seller prices remain constant. The optimal sales tax moves exactly one-for-one with the vector of natural prices. Crucially, budget neutrality holds to first order: the sales tax revenues fund the production subsidies.

The strength of each instrument’s response depends on network proximity rather than price rigidity. For supply shocks, adjustment propagates downstream (governed by the Leontief inverse), so sectors that intensively use inputs from the shocked sector require larger responses. For demand shocks, adjustment propagates upstream first and then back downstream, so upstream suppliers to the shocked sector face the largest responses.

Because the first-best policy requires observing sectoral shocks directly, the authors propose a simple 2N rule (Proposition 2) that responds only to observable sectoral seller-price inflation, with rule strength parameter ϕ_i per sector. As ϕ_i → ∞ the simple rule converges to the first-best. Crucially, the rule can be implemented by observing inflation only in the shocked sector and adjusting taxes and subsidies in other sectors proportionally to their input-output distance from that sector.

The quantitative assessment calibrates the model to the U.S. economy using BEA 2017 input-output accounts with N = 373 sectors at the 6-digit classification. Sectoral price flexibility is drawn from Antonova (2025), ranging from 0.052 to 0.989 with a median of 0.277 (implying a median price duration of roughly 4.3 months). Shocks follow AR(1) processes with persistence ρ = 0.97. Supply shocks hit 10 energy-related sectors (roughly 10% of total sales); demand shocks hit 22 service-related sectors (roughly 7% of total sales). The key quantitative finding is that the simple 2N policy—both subsidy and tax together—delivers substantially greater welfare improvement than a subsidy-only policy (N instruments), particularly for supply shocks. When the subsidy is not accompanied by the corresponding sales tax, welfare gains are much smaller.

The paper extends to an open economy with import-price shocks that act simultaneously as supply and demand shocks. Applied to the 2022 Ukraine war energy crisis: a 24% world-energy-price increase (IMF Global Energy Price index, 2022M1–2022M4) is used, with high-dependence Europe (energy import share γ_EU = 0.63, substitution elasticity η_EU = 1) contrasted against low-dependence U.S. (γ_US = 0.17, η_US = 4). In Europe, adverse supply effects dominate so the domestic energy sector contracts; in the U.S., demand substitution effects dominate so domestic energy expands. Simple 2N rules correlate 0.89 with the optimal policy across sectors for Europe and 0.94 for the U.S. A notable normative implication: the optimal policy raises sales taxes on energy to discourage consumption, in contrast to the actual European policy of subsidizing energy consumption during the 2022 crisis.

Q: Why can monetary policy not achieve the first-best allocation in the NKN model?

A: Monetary policy sets a single nominal interest rate that applies uniformly across all sectors, but sectoral shocks generate heterogeneous natural rates. Even if monetary policy stabilizes aggregate output, it cannot simultaneously close all sectoral output gaps and eliminate within-sector price dispersion. Rubbo (2023) shows that optimal monetary policy improves welfare but leaves a significant welfare loss remaining.

Q: What is the core tradeoff in each sector that motivates the 2N result?

A: With Calvo-type staggered pricing, adjusting a sector’s relative price to close its output gap creates price dispersion within the sector because not all firms adjust simultaneously; but holding seller prices constant to avoid dispersion leaves output gaps open due to the absence of relative price adjustment. Two instruments—production subsidy and sales tax—are required to address both sides of this distortion simultaneously, in keeping with the Tinbergen principle.

Q: How exactly do the production subsidy and sales tax each work under the optimal policy?

A: The production subsidy is paid to producers and affects the optimal seller price for a given marginal cost, incentivizing firms that can adjust prices to leave them unchanged. The sales tax is levied on buyers (households and downstream firms) and, because it is applied to both household consumption and intermediate goods trade, it steers demand across sectors to replicate the efficient allocation of expenditure. Under the optimal policy, seller prices are fully stabilized (ps_t = 0) while buyer (market) prices move as pt = τs_t = pn_t, mimicking flexible-price outcomes.

Q: What determines which sectors receive larger optimal tax and subsidy responses?

A: For supply (productivity) shocks, responses are governed by the matrix L̄ = XL, where L is the Leontief inverse measuring downstream proximity; sectors that are more intensive downstream users of the shocked sector require larger responses. For demand shocks, the relevant matrix measures upstream proximity, so sectors that supply inputs to the shocked sector face stronger responses. Critically, the level of the policy response is independent of sector-specific price rigidity; only the network structure matters.

Q: Is the optimal 2N policy budget-neutral, and why only approximately?

A: Budget neutrality holds to first order around the zero-profit steady state. The production subsidy applies to costs while the sales tax applies to sales; at the steady state these coincide, so the subsidy is exactly funded by the tax revenue. The approximation breaks down away from the zero-profit steady state because costs and sales diverge.

Q: What is the simple 2N rule and how does it relate to the first-best?

A: The simple rule sets sp_t = Iϕ · πs_t and τs_t = sp_t, where Iϕ = diag{ϕ_i} is a diagonal matrix of response coefficients for each sector’s seller-price inflation. As ϕ_i → ∞ for all i, the allocation converges to first-best; larger ϕ_i produces a stronger commitment to stabilize sectoral inflation, resulting in muted inflation rather than large tax and subsidy levels. In practice, the rule can be implemented by observing inflation only in the shocked sector and scaling responses in other sectors by their input-output distance from that sector.

Q: What does the three-sector example (Energy, Manufacturing, Services) illustrate about supply vs. demand shocks?

A: Under an adverse energy productivity shock, the optimal policy subsidizes Energy and Manufacturing (proportional to energy use in manufacturing) but not Services, since Services are not energy-intensive and thus not closely connected downstream. Under a positive manufacturing demand shock, the optimal policy subsidizes both Manufacturing and upstream Energy equally, reflecting that demand shocks propagate upstream first.

Q: What does the calibrated quantitative exercise show about the welfare gains from using both instruments versus one?

A: For both supply and demand shock scenarios, the simple 2N policy (subsidy plus tax) delivers substantially greater welfare improvement than using only monetary policy. When the subsidy is not accompanied by the corresponding sales tax, welfare gains are much smaller, confirming that both instruments together—not subsidies alone—are essential. This is identified as a key quantitative finding of the paper.

Q: How robust are results to decreasing returns to scale in production?

A: Under decreasing returns to scale, the optimal policy response is highly similar to the baseline: correlations between the two are 0.98 for supply shocks and 0.99 for demand shocks across sectors. The simple 2N rule continues to deliver significant welfare improvements. One difference is that demand shocks generate relatively higher welfare losses under decreasing returns, while productivity shocks lead to lower losses.

Q: How does the open-economy extension change the analysis for import-price shocks?

A: Import-price shocks enter the model as both supply shocks (raising input costs) and demand shocks (shifting expenditures toward domestic substitutes), so they require a policy response that accounts for both propagation channels simultaneously. The optimal open-economy policy is formally isomorphic to the closed-economy counterpart but with redefined upstream and downstream matrices and shock vectors. The relative importance of the supply versus demand channel depends on the economy’s import dependence and substitution elasticity.

Q: How does the 2022 energy crisis illustrate the difference between the optimal policy and actual European policy?

A: Using a 24% world-energy-price increase (IMF Global Energy Price index, 2022M1–2022M4), the model implies that with high European energy dependence (γ_EU = 0.63, η_EU = 1), adverse supply effects dominate and the optimal policy raises sales taxes on energy to discourage consumption and subsidizes domestic energy users proportional to downstream proximity. Actual European policy partly subsidized energy consumption, which the model identifies as welfare-reducing relative to the optimal response. For the low-dependence U.S. (γ_US = 0.17, η_US = 4), demand substitution toward domestic energy dominates, requiring additional subsidies to domestic energy producers.

Q: How does this paper relate to the Diamond-Mirrlees result on intermediate good taxation?

A: Diamond-Mirrlees (1971) recommends against taxing intermediate goods in an otherwise efficient economy to avoid introducing additional distortions. This paper considers an economy already subject to pricing frictions (Calvo staggered pricing), and shows that taxing intermediate goods through the sales tax—which applies to intermediate goods trade—is part of the optimal policy precisely because it corrects the pre-existing distortions. The paper thus does not contradict Diamond-Mirrlees but operates in a different setting where frictions are already present.

New Keynesian Network (NKN) model: A multi-sector general equilibrium framework with N sectors connected through input-output linkages, Calvo-type staggered price setting that is heterogeneous across sectors, and monopolistically competitive firms; provides the canonical system of sectoral IS curves and Phillips curves used in this paper.

2N policy: The paper’s central result that the first-best tax policy requires exactly two instruments per sector—one production subsidy and one sales tax—for a total of 2N instruments; characterized in Proposition 1 and named for this instrument count.

Production subsidy (sp_t,i): A sector-specific transfer paid to producers that affects the optimal seller price for a given marginal cost; under the optimal policy it offsets the effect of shocks on marginal costs, incentivizing price-adjusting firms to leave seller prices unchanged and thereby eliminating within-sector price dispersion.

Sales tax (τs_t,i): A sector-specific tax levied on buyers—both households and downstream firms purchasing intermediate goods—such that the buyer (market) price equals (1 + τs_t,i) times the seller price; under the optimal policy it replicates the efficient allocation of expenditure across sectors even when seller prices are fully stabilized.

Downstream proximity (Leontief inverse L̄ = XL): A measure of the total direct and indirect use of a sector’s output by other sectors, governing the propagation and optimal policy response to supply (productivity) shocks; the ij-th element of L̄ captures how strongly a shock in sector j affects policy in sector i through downstream input-output linkages.

Upstream proximity: A measure of how closely a sector supplies inputs to another sector, governing the propagation of demand shocks; demand shocks propagate first upstream (to input suppliers) before feeding back downstream.

Budget neutrality: The property that the optimal 2N policy is self-financing to first order—sales tax revenues exactly fund the production subsidies around the zero-profit steady state—so the fiscal intervention does not require net government expenditure.

Simple 2N rule: A practically implementable approximation to the first-best policy that sets subsidies and taxes proportional to observed sectoral seller-price inflation with response coefficients ϕ_i; converges to the first-best as ϕ_i → ∞ and can be implemented using only the inflation rate of the shocked sector plus network-distance weights from the input-output table.

How Do Rising U.S. Interest Rates Affect Emerging and Developing Economies? It Depends

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

Layer 1 — Overview

Research Question

This paper examines how the effects of rising U.S. interest rates on emerging market and developing economies (EMDEs) depend on the underlying source of the interest rate increase. Specifically, it asks: what mix of inflation, reaction, and real shocks has driven changes in U.S. interest rates in recent years; how do these different shock types affect EMDE financial markets, capital flows, borrowing costs, and fiscal outcomes; and how do they affect the likelihood of EMDE financial crises?

Motivation and Context

Written in late 2022 against the backdrop of the Federal Reserve’s most aggressive tightening cycle since the 1990s, the paper argues that the standard practice of treating all interest rate increases as equivalent is misleading. Whether rising U.S. rates reflect strengthening growth, rising inflation expectations, or a perceived hawkish shift in the Fed’s reaction function carries very different implications for EMDEs already burdened by post-COVID debt at record highs and scarring from the pandemic.

Methodology

Three distinct empirical approaches are used, chosen to match the data frequency and parsimony requirements of each research question.

A sign-restricted Bayesian VAR model with stochastic volatility is estimated on monthly U.S. data (January 1982 - September 2022) using four variables: 2-year Treasury yield, 10-year Treasury yield, S&P 500 index, and 5-year breakeven inflation expectations. Sign restrictions identify three shocks: (i) real shocks raise both yields, equity prices, and inflation expectations; (ii) inflation shocks raise yields and inflation expectations but lower equity prices; (iii) reaction shocks raise yields but lower both equity prices and inflation expectations.
Panel local projection models (Jorda 2005) are estimated at quarterly frequency for 17-38 EMDEs over 1997Q2-2019Q4, excluding the 2008Q4-2009Q4 global financial crisis and the COVID-19 pandemic. The models link the VAR-identified quarterly shock series (normalized to represent a 25-basis-point move in the 2-year yield) to EMDE financial, real, and fiscal variables, including local-currency bond yields, EMBI+ sovereign spreads, capital flows, real GDP components, CPI inflation, the real effective exchange rate, primary fiscal balance, government revenues, expenditures, gross debt, and debt composition.
A panel logit model with random effects is estimated on annual data for 139 EMDEs over 1985-2018, linking the three shock types to the probability of banking, currency, and sovereign debt crises (as defined by Laeven and Valencia 2020).

Key Findings

Shock decomposition: Real shocks account for the largest share of variance in 2-year U.S. yields over the full sample (39 percent at a 10-month horizon); inflation shocks explain 14 percent and reaction shocks 13 percent. However, since the start of 2022, reaction and inflation shocks together account for approximately three-quarters of the cumulative increase in yields, with real shocks playing a negligible role.

Financial market and macroeconomic spillovers: Conditional on a 25-basis-point shock, reaction shocks produce significantly adverse EMDE outcomes: widening sovereign spreads (EMBI+), declining capital flows, real exchange rate depreciation, and unlike inflation shocks, statistically significant declines in private consumption and fixed investment. Inflation shocks raise domestic EMDE CPI significantly. By contrast, real shocks are associated with declining sovereign spreads, rising capital flows, real exchange rate appreciation, and higher real exports, with other real GDP components unaffected.

Fiscal outcomes: In response to inflation and especially reaction shocks, EMDE governments improve their primary balances almost exclusively through expenditure cuts, consistent with tighter credit availability constraining fiscal space. Real shocks also improve primary balances, but through both revenue gains and expenditure reductions. Government debt declines in response to all three shock types, though the decline is statistically significant only for real shocks.

Debt composition: Reaction shocks shift debt composition toward shorter maturities and foreign-currency instruments (the latter reflecting exchange rate depreciation mechanically raising the local-currency value of foreign-currency debt). Real shocks shift composition toward longer maturities and higher external creditor participation, consistent with improved market access.

Heterogeneity by credit rating: Investment-grade and noninvestment-grade EMDEs show broadly similar responses to reaction shocks, with the exception of statistically larger yield responses for noninvestment-grade economies. The paper notes this finding contrasts with several prior studies that find stronger fundamentals buffer spillovers.

Crisis probabilities: A 25-basis-point increase in 2-year U.S. yields driven by a reaction shock almost doubles the baseline probability of financial crisis in the average EMDE, from 3.5 percent to 6.6 percent. Extrapolating the nonlinear logit relationship to the 114-basis-point reaction-shock-driven increase in 2-year yields that occurred from January through September 2022 implies the probability of financial crisis in the average EMDE rising approximately 36 percentage points, to nearly 40 percent. The paper cautions that no comparable yield episode occurred in the 1985-2018 estimation sample, so this extrapolation carries substantial uncertainty. Inflation shocks are associated with only small, statistically insignificant changes in crisis probability; real shocks reduce the probability of sovereign debt crisis while raising currency crisis probability by less than reaction shocks do.

Historical episode analysis: The 2013 taper tantrum was dominated by reaction shocks, causing 10-year yields to rise by approximately 100 basis points; sovereign spreads widened by 60 basis points in the May-June 2013 window and capital flows dropped sharply. The 2022 tightening episode was driven by reaction and inflation shocks (reaction shocks adding 114 basis points to 2-year yields through September 2022), with five-year breakeven inflation expectations breaching 3 percent for the first time in the two-decade history of the series. The 2004-2006 build-up to the global financial crisis involved a mix of all three shock types with real shocks prominent, and EMDE financial conditions remained broadly benign.

Layer 2 — Q&A

Q1: How are the three shock types identified, and what makes this identification strategy credible?

The identification uses sign restrictions imposed on a Bayesian VAR with stochastic volatility. A real shock is identified as one that simultaneously raises 2-year yields, 10-year yields, S&P 500 equity prices, and inflation expectations. An inflation shock raises all yields and inflation expectations but lowers equity prices the equity decline signals that higher rates are not accompanied by stronger growth prospects. A reaction shock raises all yields but lowers both equity prices and inflation expectations the fall in inflation expectations distinguishes it from an inflation shock and signals that markets perceive the Fed is tightening beyond what current inflation warrants. Covering both short- and long-maturity yields in the sign restrictions ensures the identified shocks capture both conventional and unconventional (e.g., quantitative easing tapering) policy moves.

Q2: What share of 2-year yield variation do the three shocks each explain over the full sample?

At a 10-month horizon, real shocks explain 39 percent of the forecast error variance in 2-year U.S. Treasury yields, making them the dominant driver over the full sample (January 1982 - September 2022). Inflation shocks account for 14 percent and reaction shocks for 13 percent. Together the three identified shocks explain roughly two-thirds of total yield variation; the remaining one-third reflects residual or unclassified movements.

Q3: How did the composition of shocks driving 2-year yields change from 2021 into 2022?

Starting in September 2021, as inflation mounted and the Fed pivoted toward aggressive tightening, reaction and inflation shocks became the dominant drivers of 2-year yield increases. By September 2022, reaction and inflation shocks together accounted for approximately three-quarters of the cumulative increase in yields from the beginning of 2022, with reaction shocks alone contributing 114 basis points to the 2-year yield.

Q4: What are the financial market effects of a 25-basis-point reaction shock on EMDEs?

Reaction shocks produce significant adverse effects on EMDE financial markets within one quarter: 10-year local-currency government bond yields rise significantly, EMBI+ sovereign spreads widen significantly, capital flows decline significantly, and the real effective exchange rate depreciates significantly. Short-term (3-month) yields and equity prices also deteriorate, but these movements are not statistically significant at conventional levels.

Q5: How do financial market effects of inflation shocks compare to reaction shocks?

Inflation shocks generate adverse directional effects similar to reaction shocks rising 10-year yields, declining capital flows, real exchange rate depreciation, and falling equity prices but with the notable difference that, except for equity prices, these effects are generally not statistically significant. The paper thus finds that reaction shocks are more potent drivers of EMDE financial market tightening than inflation shocks.

Q6: How do real shocks affect EMDE financial conditions?

Real shocks produce outcomes broadly opposite to those from inflation and reaction shocks. They are associated with significant declines in EMBI+ sovereign spreads, significant increases in capital flows, significant real effective exchange rate appreciation, and significant increases in equity prices. Ten-year government bond yields do rise consistent with global bond market integration but this occurs alongside improving risk sentiment, not financial stress.

Q7: What are the macroeconomic (real activity) effects of the three shock types?

Reaction shocks produce a statistically significant decline in real GDP components, particularly in private consumption expenditure and gross fixed capital formation (fixed investment), within one quarter. Real shocks lead to higher real exports consistent with beneficial demand spillovers from stronger U.S. activity while leaving other GDP components unchanged. Inflation shocks induce a large and statistically significant increase in domestic EMDE CPI inflation, while real shocks reduce it; neither produces significant real GDP effects beyond the export channel.

Q8: How do EMDE fiscal balances respond differently to the three shock types?

Both inflation and especially reaction shocks are followed by an improvement in the EMDE primary balance (smaller deficit or larger surplus), achieved almost exclusively through declines in government expenditure. The paper attributes this to tighter credit availability and higher borrowing costs constraining fiscal space. Real shocks also improve primary balances, but the mechanism differs: both revenue increases and expenditure decreases contribute to the improvement. Declines in gross government debt occur in response to all three shocks but are statistically significant only for real shocks.

Q9: How does the composition of government debt shift in response to the different shocks?

Following inflation and reaction shocks, debt held by external creditors declines significantly as a share of total government debt, consistent with reduced access to global credit markets. Short-term debt eventually rises following both shock types. Foreign-currency debt rises considerably following reaction shocks likely reflecting the mechanical effect of currency depreciation boosting the local-currency value of pre-existing foreign-currency obligations. Conversely, following real shocks, external creditor participation rises significantly (improved market access), foreign-currency debt shares remain broadly stable, and short-term debt declines significantly (consistent with maturity extension by fiscal authorities seeking to minimize rollover risk under favourable conditions).

Q10: Do investment-grade and noninvestment-grade EMDEs respond differently to reaction shocks?

The paper finds little evidence of important differences between investment-grade and noninvestment-grade EMDEs in their responses to reaction shocks across most variables. Noninvestment-grade economies do show statistically larger increases in 10-year bond yields, and larger increases in EMBI+ spreads and 3-month yields than investment-grade economies though the latter two differences are not statistically distinguishable. For fiscal, GDP, and capital flow outcomes, the two groups respond similarly. The paper notes this finding is inconsistent with several prior studies but consistent with others, concluding the role of fundamentals remains unresolved.

Q11: How does the probability of financial crisis in EMDEs respond to the three shock types?

In the baseline (explanatory variables at sample means), the average EMDE faces a 3.5 percent probability of experiencing any type of financial crisis in a given year, with currency and banking crises the most common and sovereign debt crisis the least. Reaction shocks drive by far the largest increase: a 25-basis-point increase in 2-year yields from a reaction shock almost doubles the crisis probability to 6.6 percent. Inflation shocks produce small and statistically insignificant effects. Real shocks reduce the probability of sovereign debt crisis (consistent with their benign effects on financial markets) while raising currency crisis probability by less than reaction shocks.

Q12: What does the nonlinear logit relationship imply for the 2022 tightening cycle specifically?

Because the logit function is nonlinear, a doubling of the shock size leads to a more-than-proportional increase in crisis probability. Applying the estimated model to the 114-basis-point reaction-shock contribution to 2-year yields from January to September 2022, the model implies that the probability of financial crisis in the average EMDE increased by approximately 36 percentage points, to nearly 40 percent. The paper emphasizes this estimate carries wide uncertainty because no comparable yield increase occurred during the 1985-2018 estimation period, placing this extrapolation well outside the sample’s support.

Q13: What crisis dynamics were already materializing in 2022 consistent with the model predictions?

By the time of writing (late 2022), seven EMDEs had experienced currency depreciations of at least 30 percent against the U.S. dollar meeting the Laeven and Valencia (2020) threshold for a currency crisis and 21 EMDEs had reached agreements with the IMF for additional financing. The paper notes these developments had occurred despite standard macroeconomic factors (interest rate differentials and flight-to-safety flows) not fully explaining the magnitude of depreciations.

Q14: What robustness tests were conducted, and did they alter the main conclusions?

The VAR decomposition was re-estimated using weekly rather than monthly data. The three-shock model was simplified to two shocks (real versus monetary, combining inflation and reaction). The VAR was extended to include real GDP and PCE inflation with contemporaneous exclusion restrictions to insulate shock identification from current macroeconomic conditions. Inflation expectations were replaced with the Haubrich, Pennacchi, and Ritchken (2012) model-based measure throughout, rather than only pre-2003. For the crisis probability models, panel probit with random effects and panel logit with fixed effects were estimated alongside the baseline panel logit with random effects. In all cases, the results were not materially different: inflation and reaction shocks remained more adverse than real shocks for EMDE financial and fiscal variables, and only reaction shocks produced statistically significant increases in overall crisis probability. One noteworthy robustness finding: when combining inflation and reaction into a single monetary shock, the relative importance of the inflation component appears somewhat larger than when the two are separated.

Q15: What are this paper’s main contributions relative to existing literature?

The paper makes three stated contributions. First, it is the first to decompose the evolution of U.S. interest rates over the COVID-19 pandemic recession, subsequent recovery, and 2021-22 inflation surge into the separate contributions of real, inflation, and reaction shocks. Second, it extends prior work on EMDE spillovers (e.g., Arteta et al. 2015; Hoek, Kamin, and Yoldas 2021, 2022) by showing how different shock types affect government budget balances, revenues, expenditures, and debt composition, and by expanding the EMDE country sample. Third, it is the first to examine how real, inflation, and reaction shocks differentially affect the probability of banking, currency, and sovereign debt crises in EMDEs.

Key Concepts

Reaction shock: In this paper’s framework, a change in U.S. interest rates caused by a perceived shift in the Federal Reserve’s reaction function toward a more hawkish policy stance. Identified as a shock that raises both 2-year and 10-year Treasury yields while simultaneously lowering equity prices and lowering inflation expectations. The fall in inflation expectations distinguishes this shock from an inflation shock and signals that markets believe the Fed is tightening beyond what current inflation alone would warrant.

Inflation shock: A change in U.S. interest rates caused by rising expectations of U.S. inflation. Identified as a shock that raises both yields and inflation expectations but lowers equity prices. The equity decline signals that higher rates reflect inflationary pressure rather than improved growth prospects.

Real shock: A change in U.S. interest rates driven by improved prospects for U.S. real economic activity. Identified as a shock that simultaneously raises both yields, equity prices, and inflation expectations. The equity increase distinguishes this shock from the other two and signals that higher rates are accompanied by strengthening U.S. growth.

Sign-restricted Bayesian VAR with stochastic volatility: The paper’s primary model for decomposing U.S. yield movements. Sign restrictions on four variables (2-year yield, 10-year yield, S&P 500, 5-year inflation expectations) identify the three shock types without requiring timing restrictions. Stochastic volatility is incorporated to handle the heteroskedastic financial data and the COVID-19 period’s unusual size and nature; the model covers February 1982 to September 2022 at monthly frequency.

Panel local projection (Jorda 2005): The empirical framework linking the VAR-identified shock series to EMDE outcomes at quarterly frequency. Direct estimation of impulse responses at each horizon h avoids the misspecification accumulated in iterated VAR forecasts and permits straightforward incorporation of state-dependent (investment-grade vs. noninvestment-grade) heterogeneity via a dummy-variable interaction specification.

Capital flows (as used in this paper): Defined specifically as increases in net portfolio and other investment liabilities of EMDEs, excluding foreign direct investment liabilities. This definition isolates the more volatile, financially driven flows rather than the longer-horizon FDI component.

Financial crisis typology (Laeven and Valencia 2020): The crisis classification underlying the logit analysis. Sovereign debt crises are defined as a government default or restructuring of debt owed to private creditors. Banking crises require significant distress in the banking system combined with significant policy intervention measures. Currency crises are defined as a sharp nominal depreciation of at least 30 percent against the U.S. dollar. The paper uses these definitions from Laeven and Valencia (2020), extended through 2018 in Kose et al. (2021).

Primary budget balance improvement via expenditure compression: In the paper’s framework, the fiscal adjustment mechanism triggered specifically by inflation and reaction shocks: EMDE governments improve their primary balance (reduce deficits or increase surpluses) almost exclusively by cutting expenditures, rather than raising revenues, as a response to the credit tightening and higher borrowing costs associated with adverse U.S. interest rate shocks.

Latent Heterogeneity in the Marginal Propensity to Consume

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

Lewis, Melcangi, and Pilossoph estimate the unconditional distribution of the marginal propensity to consume (MPC) using the 2008 Economic Stimulus Act (ESA) rebate payments, deploying Gaussian mixture linear regression (GMLR) — a clustering regression approach — rather than the standard practice of interacting the rebate with observable household characteristics. The key methodological departure is that households are assigned to groups not by any presupposed observable, but by how well estimated group-specific MPCs describe each household’s actual consumption response; this allows recovery of the full unconditional MPC distribution, including heterogeneity driven by latent (unobservable) factors.

Data come from the 2008 Consumer Expenditure Survey (CEX), which contains household-level expenditure data and supplemental questions on ESA payments. Identification exploits the quasi-random timing of rebate receipt, determined by the last two digits of recipients’ Social Security Numbers, following the design of Parker, Souleles, Johnson, and McClelland (2013). The specification is updated following Borusyak et al. (2024) to avoid “forbidden comparisons” in staggered treatment settings. The number of groups G is selected by BIC, which selects G = 3 for total expenditures, confirmed by K-fold cross-validation.

The main finding is substantial MPC heterogeneity. For total expenditures, the three estimated group-level MPCs are 0.04, 0.23, and 1.33, with population shares of 30%, 48%, and 23% respectively. The implied aggregate (share-weighted average) MPC is 0.42, compared to 0.24 in the homogeneous Parker et al. (2013) specification estimated on the same data. Splitting by consumption category: for nondurables, two groups have MPCs of 0.09 and 0.18, with roughly equal population shares, and the lower bound of 0.09 is statistically distinguishable from zero — evidence against strict adherence to the Permanent Income Hypothesis even among the lowest-MPC group. For durables, the MPC distribution is dichotomous: about 29% of households have a durable MPC statistically indistinguishable from zero, while 21% have an MPC of 0.67. The cross-good correlation between household-level nondurable and durable predicted MPCs is only 0.13, ruling out strong substitution but indicating weak complementarity.

Turning to observable determinants, the paper finds that many household characteristics are individually correlated with estimated MPCs — including homeownership, mortgage status, income, and the average propensity to consume (APC) — despite the fact that the same dataset and similar identification strategies previously yielded insignificant relationships. Homeowners have significantly higher MPCs than renters; households with a mortgage have even higher MPCs than outright homeowners. In salary income, households in the top tercile spend 0.17 more per rebate dollar than the baseline group; households in the top tercile of non-salary income spend 0.19 more. However, in joint regressions, only two characteristics remain robustly and positively correlated with MPCs: total income (both salary and non-salary components) and the APC. The APC relationship is particularly notable: a one-percentage-point higher prior spending rate is associated with 0.19 additional cents spent per rebate dollar in the full multivariate specification.

The paper identifies three groups in the joint income-APC space: “poor savers” (low income, low APC, lowest MPCs), an intermediate group (high income or high APC but not both), and “rich spenders” (high income and high APC, highest MPCs). The “rich spender” group has received little prior attention in consumption-savings models.

Critically, observable characteristics jointly explain at most 8% of MPC variation (adjusted R-squared from a measurement-error correction). With 92% of MPC heterogeneity unexplained by standard observables, the authors conclude that a substantial share of variation reflects latent household traits — plausibly heterogeneity in discount rates or intertemporal elasticities of substitution. This finding also limits the practical scope for government targeting of fiscal transfers: because observable characteristics predict little MPC variation, any targeting strategy can exploit only a small fraction of the overall distribution.

Scope conditions: results apply to household expenditure responses (marginal propensities to spend, not to consume in the strict sense) within one quarter of rebate receipt. The income-MPC positive correlation is confined to households within the income range eligible for the 2008 ESA (phased out above $150,000 for joint filers). The sample excludes the top and bottom 1.5% of consumption changes as outliers.

Q: What is the core methodological innovation of this paper? A: The paper applies Gaussian mixture linear regression (GMLR) to the 2008 tax rebate setting, jointly estimating group-level MPCs and household group membership probabilities without imposing any prior restriction on which observable characteristics drive heterogeneity. Because groups are determined by how well group-specific MPCs explain consumption patterns rather than by presupposed observables, the method recovers the full unconditional distribution of MPCs, including latent heterogeneity. This contrasts with sample-splitting approaches that can only recover co-variation with chosen characteristics.

Q: What are the three group-level MPCs for total expenditures, and what shares of the population do they represent? A: The three estimated MPCs are 0.04 (30% of households), 0.23 (48%), and 1.33 (23%), all with precisely estimated group shares (standard errors of 0.01). The largest MPC of 1.33 is statistically significant at the 1% level. The lowest MPC of 0.04 is not statistically different from zero even under the more favorable conditional standard errors that treat group assignment as known.

Q: How does the average MPC implied by the GMLR distribution compare to the homogeneous specification? A: The share-weighted average MPC from the three-group GMLR is 0.42, compared to 0.24 from the homogeneous (G=1) specification on the same data and identification strategy. This gap arises partly because the homogeneous estimate averages across households with very heterogeneous responses, and partly because the distribution has a right-skewed tail with a meaningful mass at MPC above 1.

Q: What are the MPC distributions for nondurable and durable goods separately? A: For nondurables, BIC selects two groups with MPCs of 0.09 and 0.18 and roughly equal population shares (48% and 52%); crucially, the lower bound of 0.09 is statistically distinguishable from zero at the 5% level, providing evidence that no household strictly follows the Permanent Income Hypothesis for nondurables. For durables, BIC selects three groups: MPCs of 0.03 (not distinguishable from zero, 29% of households), 0.15 (50%), and 0.67 (21%), reflecting the discrete, lumpy nature of durable goods purchases.

Q: How correlated are nondurable and durable MPCs at the household level? A: The correlation between household-level posterior predicted MPCs for nondurables and durables is 0.13, statistically significant at the 1% level. This rules out substitution between goods categories, but the positive complementarity is quantitatively small. The authors interpret this as possibly reflecting a small share of “spender” types who adjust multiple consumption categories in response to transitory income shocks.

Q: Which observable characteristics are individually correlated with MPCs? A: Homeowners have significantly higher MPCs than renters; households with a mortgage display even greater MPCs than outright homeowners. Both salary and non-salary income are positively correlated: households in the top tercile of salary income have MPCs about 0.13 higher than the omitted group, and top-tercile non-salary income households have MPCs about 0.015 higher (though the latter is individually less precisely estimated). The average propensity to consume (APC) is significantly positively correlated with the MPC, with a coefficient of 0.075 in univariate regression and 0.166 in the full joint specification.

Q: Which observable characteristics remain significant in the joint (multivariate) regression? A: When all household characteristics are included jointly, only income (both salary and non-salary components) and the APC remain robustly and positively correlated with MPCs. Top-tercile salary income is associated with 0.112 higher MPCs and top-tercile non-salary income with 0.049 higher MPCs, while the APC coefficient rises to 0.166 (from 0.075 univariate). Homeownership, age, education, and most demographic controls become statistically insignificant in the joint specification.

Q: What fraction of MPC variation is explained by observable characteristics? A: The adjusted R-squared from the full multivariate regression of predicted MPCs on all observable characteristics is approximately 6%. After a measurement-error correction proposed in Supplement A.6 to account for noise in estimated posterior MPCs, the corrected R-squared rises to 8%. Either way, the vast majority — over 90% — of MPC heterogeneity is unexplained by standard observables, implicating latent household traits such as heterogeneous discount rates or intertemporal elasticities of substitution.

Q: How does the extent of MPC heterogeneity recovered by GMLR compare to sample-splitting on observables? A: Table 4 shows that splitting by age terciles yields MPC estimates ranging from 0.13 to 0.34; splitting by total income yields a range of 0.18 to 0.45; splitting by the APC yields 0.06 to 0.21. All of these ranges are far narrower than the GMLR-recovered range of 0.04 to 1.33. The authors argue that sample-splitting on individual observables, which are noisy and correlated with only a portion of MPC heterogeneity, systematically understates the true extent of heterogeneity.

Q: What is the “rich spender” finding and why is it theoretically notable? A: Households with both high total income and a high prior average propensity to consume have the largest MPCs. This “rich spender” group is poorly accommodated by standard consumption-savings models: the canonical one-asset incomplete markets model typically predicts a negative MPC-APC correlation conditional on income, and the two-asset Kaplan-Violante (2014) model can generate wealthy hand-to-mouth households with high income and high MPCs, but not necessarily high APCs. Preference heterogeneity — e.g., heterogeneous intertemporal elasticities of substitution as in Aguiar, Boar, and Bils (2019) — can rationalize the positive income-APC-MPC nexus.

Q: What explains the positive income-MPC correlation, and how does the paper relate it to the prior literature? A: The paper notes that this positive correlation is consistent with Kueng (2018), who finds higher spending propensities among high-income recipients of Alaska Permanent Fund payments, and rationalizes it via near-rationality or mental accounting: when a rebate is small relative to income, the perceived cost of deviating from consumption smoothing is low. The authors also note that low-income households still exhibit large absolute MPCs, suggesting sizable deviations from consumption smoothing at the bottom of the income distribution, even if relatively lower than for high-income households.

Q: What are the policy implications for targeting fiscal transfers? A: The paper finds that the 2008 ESA increased spending for all households in partial equilibrium (minimum group MPC of 0.04, nondurable lower bound 0.09, all statistically positive or near-positive). Among observable characteristics, targeting relatively higher-income households (including retirees and entrepreneurs via non-salary income) would maximize aggregate consumption effects. However, since observables explain only 8% of MPC variation, any targeting strategy can exploit only a small fraction of the overall heterogeneity; the government faces fundamental limits on feasible targeting. This also implies a tension between stimulus and distributional/insurance motives for transfer programs.

Q: How does the paper confirm that recovered heterogeneity is not spurious? A: The authors generate 250 Monte Carlo samples from the estimated homogeneous model, impose G=3, and re-run the GMLR and observable regressions; they find significant relationships with observable characteristics in virtually none of these samples. Additionally, applying the BIC to homogeneous Monte Carlo samples, the BIC selects G=1 in all 250 samples, confirming that the selected G=3 in actual data reflects genuine heterogeneity rather than overfitting.

Q: How does GMLR compare to quantile regression for recovering the MPC distribution? A: Quantile regression (as used by Misra and Surico (2014) on the same data) recovers relationships at percentiles of the overall conditional distribution of consumption changes, so the ranking of households is driven by all sources of variation in consumption, not just the rebate response. If factors unrelated to the rebate dominate the conditional distribution, MPC heterogeneity will be underestimated in the presence of noise. The authors illustrate this formally in Supplement B and note that Misra and Surico (2014) find a substantial share of MPCs at or below zero for nondurables, in contrast to the GMLR lower bound of 0.09 that is statistically positive.

Q: What do the longer-run (lagged) MPC estimates show? A: The specification includes up to two lags of rebate indicators, allowing measurement of spending responses in subsequent quarters after rebate receipt. The paper reports these results (Section 4.4) but the text provided does not fully detail them; the heterogeneous structure is maintained across horizons.

Gaussian Mixture Linear Regression (GMLR): A probabilistic clustering regression approach that jointly estimates group-specific regression coefficients (here, MPCs) and population group shares by maximizing an expected log-likelihood via the EM algorithm. Households receive continuous posterior weights (gamma_{jg}) reflecting uncertainty about their group membership rather than binary hard assignment, with identification from a Gaussianity assumption on within-group errors.

Unconditional MPC Distribution: The full marginal distribution of MPCs across all households in the population, capturing heterogeneity from both observable and latent (unobservable) sources. Contrasted in the paper with the conditional distributions recovered by sample-splitting on observables, which by construction can only reflect co-variation with the chosen splitting variable.

Posterior Predicted MPC: For each household, the expectation of the group-specific MPC weighted by the household’s posterior group membership probabilities (lambda-tilde_{0,j} = sum_g gamma_{jg} lambda_{0g}). This object is the optimal (MSE-minimizing) individual-level MPC prediction and is the relevant input for targeted fiscal policy design.

Latent Heterogeneity: MPC variation that cannot be attributed to any observable household characteristic and is instead driven by unobserved traits — plausibly heterogeneous discount rates, intertemporal elasticities of substitution, or other preference parameters. Operationalized as the share of MPC variance unexplained by observable regressors (approximately 92% in this paper).

Rich Spenders: A group identified jointly in the APC-income space: households with both high total income and a high average propensity to consume, displaying the largest marginal propensities to consume out of the rebate. This group is not well-accommodated by standard one-asset or two-asset incomplete markets models under homogeneous preferences.

Average Propensity to Consume (APC): Defined empirically as average lagged consumption expenditures divided by total income, intended to capture persistent preference heterogeneity — a “spender type” — by measuring how much of income a household habitually spends before receiving the rebate. A one-percentage-point higher APC is associated with 0.19 additional cents spent per rebate dollar in the full multivariate specification.

Forbidden Comparisons: A bias identified by Borusyak et al. (2024) in event-study designs with staggered treatment, arising when newly treated units are compared to previously treated units rather than true controls. The paper addresses this by regressing consumption changes on rebate receipt indicators (iota_{jl}) directly rather than on rebate amounts, and including lagged rebate indicators to account for persistent effects.

Micro MPCs and Macro Counterfactuals: The Case of the 2008 Rebates

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Layer 1 — Overview

Research question. Do the high marginal propensities to consume (MPCs) estimated in the leading household studies of the 2008 U.S. tax rebates—particularly Parker et al. (2013), which found MPCs of 50–90 percent within three months—imply plausible macroeconomic counterfactuals? And if not, what combination of micro-level bias corrections and general equilibrium forces reconciles the micro evidence with aggregate data?

Setting. The 2008 Economic Stimulus Act distributed approximately $100 billion in tax rebates, totaling eleven percent of January 2008 monthly disposable income. Among the 85 percent of households receiving a check, the average amount was $1,000. Rebates were distributed primarily from April through July 2008, with nearly half delivered in May alone. The timing of receipt was determined by the last two digits of Social Security numbers, providing quasi-random variation exploited by the household-level literature.

Methodology. The paper proceeds in two halves. In the first, the authors construct macro counterfactuals by calibrating a standard medium-scale two-good, two-agent New Keynesian (TANK) model with the micro MPCs from the literature and simulating what aggregate consumption would have been absent the rebate. The model contains life-cycle permanent income households and hand-to-mouth households whose dynamic spending propensities are calibrated directly to match the household-level estimates. General equilibrium effects—including Keynesian income multipliers, real interest rate movements, and changes in the relative price of durable goods—are incorporated. Counterfactual consumption paths are constructed by subtracting model-simulated deviations from steady state from actual NIPA consumption data.

In the second half, the authors revisit both the micro estimates and the macro model. On the micro side, they identify three upward biases in standard two-way fixed effects (TWFE) estimates applied to CEX data: (1) omitted variable bias from excluding the lagged rebate indicator; (2) “forbidden comparisons” bias arising from comparing cohorts with heterogeneous treatment effects, following Borusyak et al. (2022) and Sun and Abraham (2020); and (3) a rebate reporting bias in which households are systematically more likely to report receiving the rebate in the month that coincides with large expenditure increases, causing spurious positive correlation between reported receipt and contemporaneous spending. On the macro side, the baseline model is modified to incorporate an upward-sloping supply curve for durable goods (calibrated to a supply elasticity of 5, midway between House and Shapiro (2008) and Goolsbee (1998)), replacing the baseline assumption of frictionless conversion between nondurable and durable intermediates.

Main findings with quantitative magnitudes.

Implausibility of baseline counterfactuals. When calibrated to Parker et al.’s (2013) micro MPC of 0.9, the baseline model implies that real PCE absent the rebate would have collapsed by 6.0 percent from April through July 2008—a decline exceeded historically only by the Covid-19 lockdowns. Even the more modest micro MPC of 0.5 implies a 2.7 percent three-month PCE decline, comparable only to the 1980 Volcker disinflation with credit controls. For motor vehicle expenditures, the counterfactual drops range from 38 percent (micro MPC = 0.3) to 67 percent (micro MPC = 0.9)—larger than any historical experience, including the 30 percent Covid decline. Contemporaneous professional forecasters (Federal Reserve Greenbooks, Survey of Professional Forecasters, Goldman Sachs) predicted at most small consumption declines in summer 2008. Even the authors’ own pessimistic forecast model—incorporating actual oil price paths and a Lehman Brothers bankruptcy dummy—implies that the cumulative difference between actual and forecast consumption attributable to the rebate was at most $20 billion out of $100 billion in rebates, for an implied GE-MPC of at most 0.2.

Bias correction in micro MPC estimates. Applying all three bias corrections to CEX data (the preferred specification with lagged rebate indicator, cohort-level treatment effects, and lagged expenditure controls), the estimated three-month MPC falls from 0.50 to 0.28 in the full sample and from 0.82 to 0.34 in the rebate-recipients-only sample, with both rounding to approximately 0.3. The Borusyak-Jaravel-Spiess (BJS) imputation method yields an MPC of 0.20 in the full sample and 0.37 in the rebate-only sample, consistent with the OLS corrections.

Composition of spending. In the preferred corrected specification, essentially all of the total expenditure MPC of 0.3 is accounted for by motor vehicle spending: the MPC on motor vehicles is 0.30 in the full sample and 0.26 in the rebate-only sample, while the MPC on all other expenditures is −0.02 (full sample) and 0.08 (rebate-only sample).

General equilibrium dampening via inelastic durable supply. In the model with a calibrated durable supply elasticity of 5, rebate-induced demand for motor vehicles raises the relative vehicle price by approximately 1.1 percent in July 2008. This price increase crowds out durable expenditure by optimizing households through intertemporal substitution. At the preferred micro MPC of 0.3, the general equilibrium MPC (GE-MPC) for total PCE is only 0.07, well below the 0.3 micro estimate. At a micro MPC of 0.5, the GE-MPC is 0.22. The combination of the bias-corrected micro MPC and dampening general equilibrium forces implies a general equilibrium consumption multiplier below 0.2 for the 2008 rebates.

Importance of durable goods composition for HANK models. A model that abstracts from durable goods and calibrates the full expenditure micro MPC to nondurable spending predicts a GE-MPC of 0.36 when the micro MPC is 0.30—five times larger than the 0.07 implied by the model with durable goods. This contrast illustrates that the distribution of spending across nondurable and durable goods is a key determinant of the aggregate fiscal multiplier, in addition to heterogeneity in wealth and income emphasized by the existing HANK literature.

Layer 2 — Q&A

Q1. What is the central empirical puzzle the paper addresses? A. The leading household studies of the 2008 rebates estimate very high three-month MPCs (50–90 percent). When these estimates are plugged into a standard New Keynesian model to construct counterfactual consumption paths absent the rebate, the model implies that PCE would have collapsed by 2.7–6.0 percent from April through July 2008 and then sharply recovered just as Lehman Brothers failed in September. No contemporaneous forecaster or narrative evidence suggests such extreme, short-lived macroeconomic stress was present. The Lehman collapse itself caused only a 1.1 percent three-month PCE decline—smaller than all three counterfactual declines implied by micro MPCs of 0.3, 0.5, or 0.9.

Q2. What are the features of the TANK model used to construct the counterfactuals? A. The model is a two-good (nondurable and durable), two-agent (optimizing life-cycle and hand-to-mouth) New Keynesian model calibrated at monthly frequency, building on Ramey (2021) and Galí et al. (2007). Intermediate goods can, in the baseline, be frictionlessly converted into either nondurable or durable goods (implying a fixed relative price of one). Durable goods (interpreted as motor vehicles) enter household utility, with optimizing households facing a Calvo-type adjustment friction motivated by Evans and Ramey (1992) calculation costs. The fraction of hand-to-mouth consumers and their dynamic propensities to spend are calibrated directly to match the micro MPC estimates from the household literature. The model incorporates a Calvo-style price-adjustment structure for nondurables, sticky wages set by unions, capital with adjustment costs and variable utilization, and an inertial monetary policy rule.

Q3. How does the model translate micro MPCs into macro counterfactuals, and why does it amplify rather than dampen the micro estimates in the baseline? A. The model’s GE-MPC equals the micro MPC’s direct demand effect plus Keynesian income multiplier effects. Because the rebate is highly transitory, there is little movement in the real interest rate (the Phillips curve is flat and monetary policy is inertial), so the dominant general equilibrium force is the income multiplier. This amplifies, rather than dampens, the micro MPCs. As a result, the GE counterfactuals exhibit even sharper V-shapes than the pure micro counterfactuals.

Q4. What narrative and forecast evidence do the authors use to argue the baseline counterfactuals are implausible? A. Contemporary forecasts from the Federal Reserve Greenbooks, the Survey of Professional Forecasters, and Goldman Sachs all predicted at most small consumption declines in summer 2008—Goldman Sachs forecast only −0.125 percent (not annualized) per quarter in Q2–Q3 2008. The authors also construct their own “pessimistic” time-series forecast that incorporates actual oil price paths (which rose from $98 to $140 per barrel by July 2008) and a Lehman Brothers bankruptcy dummy; even this forecast lies above all three model counterfactuals in summer 2008 and displays no V-shape. Furthermore, the cumulative difference between actual PCE and the pessimistic forecast over April–October 2008 totals only $20 billion—implying a GE-MPC of at most 0.2 even if the entire gap were attributed to the rebate.

Q5. What is the first bias in standard TWFE estimates of the MPC, and how large is its effect? A. The first bias is omitted variable bias from excluding the lagged rebate indicator. In a first-differenced panel regression, lagged treatment enters the error term. Because current treatment reduces the probability of past treatment, current and lagged treatment are negatively correlated, and omitting the lag inflates the OLS estimate of the contemporaneous effect. Including a lagged rebate indicator reduces the contemporaneous spending response by $40 in the full CEX sample (from $470 to $434) and by approximately $237 in the rebate-only sample (from $764 to $527).

Q6. What is the “forbidden comparisons” bias and how is it corrected? A. When treatment effects are heterogeneous across cohorts (e.g., the June rebate cohort has a larger MPC than the September cohort), standard homogeneous TWFE estimates use later-treated cohorts as control groups for earlier-treated cohorts even after accounting for average mean-reversion. Because the mean-reversion of the earlier (larger-effect) cohort is larger than that of the later cohort, this comparison is contaminated, inflating the estimate. The authors correct for this by allowing cohort-specific treatment effects, following Sun and Abraham (2020). This reduces the contemporaneous effect by a further $90 in the full sample; in the rebate-only sample the correction raises the estimate slightly (by $70) because later treatment effects are larger in that sample.

Q7. What is the rebate reporting bias and what mechanism underlies it? A. The rebate reporting bias arises because households in the CEX are systematically more likely to report receiving the rebate in the interview month that coincides with high expenditure. Although the true timing of rebate checks is determined by Social Security number last-digits (and is thus random), the reported timing may reflect recall issues: households more readily remember and report receiving the rebate when it was accompanied by a large purchase. The empirical signature is a statistically significant negative effect of future rebate receipt on current expenditure (−$863 in the full sample, −$575 in the rebate-only sample at the 10% level), indicating that rebate reporters had unusually low spending in the period prior to reporting receipt. Controlling for lagged expenditure and income decile fixed effects corrects for this bias, reducing the three-month MPC in the full sample from 0.37 to 0.28.

Q8. What are the authors’ preferred bias-corrected MPC estimates, and how do they compare across specifications and estimators? A. After correcting for all three biases (preferred specification, column 4 of Table 3), the implied three-month MPC is 0.28 in the full sample and 0.34 in the rebate-only sample, both approximately 0.3. The Borusyak-Jaravel-Spiess imputation method, which imposes weaker assumptions and overcomes the first two biases by construction, yields an MPC of 0.20 (full sample) and 0.37 (rebate-only sample), with an average consistent with the OLS-corrected estimates. Both methods point to an MPC around 0.3, substantially below the 0.5–0.9 range from the baseline Parker et al. (2013) approach.

Q9. How is almost all of the total expenditure MPC concentrated in motor vehicles? A. After bias correction, the MPC on motor vehicles is 0.30 in the full sample and 0.26 in the rebate-only sample. The MPC on all other PCE is −0.02 (full sample) and 0.08 (rebate-only sample), neither statistically significant. This concentration in durables is consistent with Adams et al. (2009) and Aaronson et al. (2012), and is corroborated by CEX vehicle-expenditure data showing a car-purchase response concentrated in the three months surrounding receipt of the rebate.

Q10. How does introducing an upward-sloping supply curve for durable goods change the model’s general equilibrium predictions? A. In the modified model, durable goods producers face a production externality (or fixed factor) that makes the short-run supply of motor vehicles upward-sloping, with supply elasticity calibrated to 5. When rebate recipients increase demand for motor vehicles, the relative price of motor vehicles rises by approximately 1.1 percent in July 2008 (consistent with the observed 1.5 percent spike in the BLS new vehicle price index relative to core CPI around the rebate distribution). This price increase induces optimizing households to intertemporally substitute away from durable goods. Because durable demand is highly price-elastic (long-run elasticity of −1 to −15 depending on the study), even a modest relative price increase generates substantial crowding out of durable expenditure by non-recipients.

Q11. What are the GE-MPC estimates in the modified model with less elastic durable supply, and how do they decompose? A. At the preferred micro MPC of 0.3, the GE-MPC for total PCE is 0.07—general equilibrium forces dampen the micro effect. At micro MPC of 0.5, GE-MPC is 0.22 (modest dampening). At micro MPC of 0.9, the GE-MPC rises to 1.42 (amplification). Decomposing by good type at micro MPC of 0.3: the GE-MPC on motor vehicles is 0.09 and the GE-MPC on nondurables is −0.03. The dampening is concentrated almost entirely in durable expenditure.

Q12. How sensitive are the GE-MPC results to the calibration of durable demand elasticity? A. The baseline calibration uses a long-run vehicle demand elasticity of −15, based on household-level evidence from Bachmann et al. (2021). When the authors instead use the lower-bound estimate of −6.4 from Baker et al. (2019), the GE-MPC at micro MPC of 0.3 rises from 0.07 to 0.12. Even at this lower demand elasticity there is substantial crowding out in general equilibrium, so the qualitative conclusion is robust.

Q13. Why does a nondurables-only model with the same overall MPC substantially overstate the fiscal multiplier? A. When abstracting from durable goods and calibrating a nondurable MPC of 0.30 (to match the overall expenditure MPC), the model predicts a GE-MPC of 0.36—five times larger than the 0.07 from the two-good model. This occurs because nondurable demand is far less price-elastic than durable demand, and the nearly-flat Phillips curve makes nondurable supply very elastic, so there is no relative-price-driven crowding out channel. The comparison illustrates that the distribution of spending across nondurable and durable goods is a quantitatively important determinant of the fiscal multiplier, independent of the level of the MPC.

Q14. What evidence is provided that the control group in the household regressions is itself affected by the rebate in general equilibrium? A. Figure 9 in the paper plots motor vehicle spending per household by rebate-receipt status using CEX data. When rebate recipients begin reporting receipt in June 2008, motor vehicle expenditure in the rebate group rises while simultaneously falling in the never-rebate group. This pattern is consistent with the model’s prediction that the rebate-induced rise in relative motor vehicle prices crowds out purchases by non-recipient households. This general equilibrium spillover means the difference-in-differences micro MPC estimate remains valid as a micro estimate (the symmetric crowding out does not affect the treated-versus-control difference), but the aggregate GE-MPC is less than the micro MPC.

Q15. How do the authors verify that their preferred corrected specification recovers true MPCs? A. In Appendix C.6 the authors simulate household-level data from the modified Section 5 model and apply both the original Parker et al. (2013) specification (Equation 1) and their preferred corrected specification (Equation 5). The Parker et al. specification produces upward-biased MPC estimates in the simulated data, consistent with Kaplan and Violante’s (2014) theoretical argument. The preferred corrected specification recovers the true MPCs from the model, validating the correction methodology.

Key Concepts

GE-MPC (General Equilibrium Marginal Propensity to Consume). The paper’s term for the aggregate increase in total consumer spending per dollar of tax rebate, incorporating both the direct micro-level demand effect of the rebate on hand-to-mouth households’ consumption and the induced macroeconomic income effects from Keynesian multipliers and relative price changes. Distinct from the micro MPC, which captures only the household-level spending response before any general equilibrium feedbacks.

Micro MPC. The causal effect of receiving a temporary lump-sum transfer on a household’s own consumer expenditure, expressed as a fraction of the transfer amount, estimated from household panel data via difference-in-differences event studies. In the paper’s usage, this is a partial equilibrium concept that excludes any impact of the policy on prices, wages, or other households’ incomes.

Forbidden comparisons bias. A form of bias in two-way fixed effects event study estimates that arises when treatment effects are heterogeneous across cohorts and later-treated units are used as control groups for earlier-treated units whose outcomes are still reverting after treatment. Named and formalized in Borusyak and Jaravel (2017) and Borusyak et al. (2022); in this paper it manifests because cohorts receiving rebates in June have systematically larger spending responses than those receiving in September, so using September recipients as a “clean” control for June reversal yields contaminated estimates.

Rebate reporting bias. A bias specific to the CEX survey data in which the timing of a household’s self-reported rebate receipt is correlated with unusually high contemporaneous expenditure (and correspondingly low prior-period expenditure), likely due to recall effects. Because the true rebate timing is random but the reported timing is not, this correlation inflates the difference-in-differences estimate of the spending effect.

Two-good, two-agent New Keynesian (TANK) model. A medium-scale New Keynesian model containing two types of households (optimizing life-cycle consumers and hand-to-mouth consumers who exhaust current income) and two goods (nondurables and durable goods interpreted as motor vehicles). The model is used in this paper as a framework to translate micro MPC estimates into aggregate general equilibrium counterfactuals, calibrated at monthly frequency.

Durable supply elasticity. The elasticity of real durable goods production with respect to the relative price of durable goods, calibrated in the paper to 5. In the baseline model, this elasticity is infinite (the relative price is fixed at one because intermediates convert frictionlessly). With a finite supply elasticity of 5, rebate-induced durable demand causes the relative vehicle price to rise, generating crowding out of optimizing households’ durable expenditure.

Calvo durable adjustment friction. An adjustment friction imposed on optimizing households’ durable goods purchases, motivated by Evans and Ramey’s (1992) calculation cost model. Only a fraction 1−θd of households reoptimize their durable stock each period (with probability drawn randomly), producing a Calvo-type reduced form. This friction limits both the extensive and intensive margins of durable adjustment and prevents unrealistically large intertemporal substitution of durable purchases in response to price changes.

Macro counterfactual. In this paper’s usage, the simulated path of aggregate consumption that would have occurred in the absence of the 2008 tax rebate, constructed by subtracting the model-implied impulse response to the rebate from the actual observed NIPA consumption series. Plausibility of the counterfactual is assessed by comparison to contemporaneous forecasts and to historical episodes of large consumption declines.

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.

Rent Guarantee Insurance

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

Abramson and Van Nieuwerburgh study Rent Guarantee Insurance (RGI), a product in which an insurer pays the landlord on behalf of a tenant who defaults on rent due to a negative income or health expenditure shock, in exchange for a monthly premium proportional to rent. The central question is whether RGI can be designed to be both welfare-improving and financially viable, given the frictions of moral hazard and adverse selection.

The authors develop a dynamic overlapping-generations equilibrium model of the rental market that features endogenous rent default, security deposits, evictions, and homelessness. Households face idiosyncratic persistent and transitory income risk, idiosyncratic medical expenditure risk, and aggregate (cyclical) income risk. Rental contracts are non-contingent, households face borrowing constraints, and housing is indivisible with a minimum quality floor. Landlords set deposits to break even in expectation given observed tenant characteristics. An insurance agency can offer RGI and must also break even in the long run. The model is calibrated to the United States at monthly frequency. Income dynamics are estimated from CPS data (1994–2023) and incorporate transitions among employment, unemployment, out-of-labor-force, and retirement states along with transfer income (unemployment insurance, disability, food stamps) and a progressive tax system. Key moments targeted by Simulated Method of Moments include a delinquency rate of 12.15% (model: 12.69%), average security deposit of $984 (model: $992, from approximately 500,000 Craigslist listings across the 100 largest MSAs), homelessness rate of 1.43% (model: 1.42%), and home-ownership rate of 63.6% (model: 63.2%).

The model’s pre-RGI analysis establishes that persistent income shocks — not transitory shocks or medical shocks — are the primary driver of rent defaults. Default risk remains elevated for 3–6 months following a persistent shock, implying that short-duration RGI coverage is insufficient to prevent eviction; coverage must span multiple months.

The paper’s main policy experiments introduce RGI under different access rules and provider types. Unrestricted RGI (available to all renters) generates large welfare gains through improved risk-sharing and lower security deposits — because insured tenants pose less default risk, landlords lower deposit requirements — but is not financially viable for either a public or private insurer due to moral hazard and adverse selection. Even a public insurer that internalizes the fiscal savings from reduced homelessness cannot break even under unrestricted access.

Restricting access changes the viability calculus sharply. A publicly provided RGI targeted to households at the bottom of the wealth distribution can achieve financial viability: these households are precisely those most prone to homelessness, so the reduction in homelessness expenses — which the public insurer internalizes — offsets the insurance deficit. This restricted public RGI generates substantial welfare gains for the most vulnerable households.

A privately provided RGI must instead target higher-wealth renters to break even, because these households have low default risk (limiting claim payouts) while remaining sufficiently risk averse to pay the premium. The intersection of financial viability and take-up is small, yielding a limited target audience. The private program has minimal impact on housing insecurity, and the most vulnerable households derive little benefit. This pattern matches observed private RGI markets, where providers restrict access to renters in good financial condition.

An RGI mandate — requiring all renters to purchase coverage — mitigates adverse selection by improving the pool of insured tenants, dramatically increasing financial viability and allowing the insurer to reduce the premium substantially while still breaking even. Mandated RGI is highly effective at preventing housing insecurity and generates welfare gains concentrated among the most financially vulnerable households.

Scope conditions: results are calibrated to U.S. income, medical, and housing market parameters as of 2019. The insurer’s borrowing cost matters: the public insurer faces lower, counter-cyclical municipal bond spreads, whereas private insurers face higher, pro-cyclical corporate spreads, which constrains the generosity of private contracts in recessions.

Q: What is Rent Guarantee Insurance and how does it work mechanically in the model? A: RGI is a contract under which a tenant pays a flat monthly premium equal to a fraction kappa of rent. When the insured tenant defaults, the insurer pays the landlord directly and deducts one period from the tenant’s stock of “insurance credit.” The tenant remains housed. Once insurance credit is exhausted, the insurer no longer covers defaults. The insurer sets the premium and the maximum coverage duration to break even in the long run.

Q: Why do most rent defaults arise from persistent rather than transitory shocks? A: The model shows that the renter population is disproportionately exposed to persistent unemployment and labor-force-exit spells, and that negative persistent income shocks are harder to smooth through savings than transitory ones. Default risk remains elevated for 3–6 months after a persistent shock but dissipates quickly after a transitory shock. This implies that RGI coverage periods of only a few months would fail to prevent eviction for the majority of defaulting tenants.

Q: How does RGI affect security deposits in equilibrium? A: Because landlords observe the tenant’s insurance status at lease signing and deposits are set to make landlords break even in expectation, insured tenants pose lower default risk and thus face lower upfront deposit requirements. This deposit reduction is a key welfare channel of RGI, as large deposits tie up a disproportionate share of poor households’ wealth and price the most vulnerable out of housing entirely.

Q: Why is unrestricted RGI financially non-viable even for the public insurer? A: Unrestricted access induces both adverse selection — riskier households self-select into coverage — and moral hazard — insured households alter their default and savings behavior. These effects cause the insurer to run a persistent deficit. Even a public insurer that internalizes the fiscal cost savings from reduced homelessness cannot recoup enough to break even, implying that an unrestricted program would require an ongoing subsidy.

Q: How does publicly provided restricted RGI achieve financial viability? A: By targeting households at the bottom of the wealth distribution — precisely those most prone to homelessness — the public RGI program produces large reductions in homelessness. Because the public insurer internalizes the fiscal expenses associated with shelters, health services, and policing that accompany homelessness, these savings are passed through to the insurer and are sufficient to offset the insurance deficit. No such mechanism is available to a private insurer.

Q: Why must private RGI target higher-wealth renters, and what are the consequences? A: Private insurers must break even using only premium revenue, without access to homelessness cost savings. Higher-wealth renters have lower default probabilities, which limits claim payouts, while remaining sufficiently risk averse to demand coverage and pay the premium. The viable target audience is small given these competing requirements. As a result, private RGI covers few households, has minimal effect on housing insecurity, and provides essentially no benefit to the most vulnerable renters. This pattern is consistent with observed private RGI markets.

Q: What are the two differences between public and private insurers in the model? A: First, the public insurer internalizes the fiscal costs of homelessness (shelters, health services, policing), raising its net benefit from offering coverage. Second, the public insurer borrows at municipal bond spreads — which are lower than corporate spreads and counter-cyclical — whereas the private insurer faces higher, pro-cyclical corporate spreads. Counter-cyclical borrowing costs allow the public insurer to extend more generous coverage precisely when aggregate conditions deteriorate and claims rise.

Q: How does an RGI mandate improve financial viability? A: Mandatory enrollment forces all renters, including low-risk ones, into the insurance pool, which counteracts adverse selection. The expanded and higher-quality pool dramatically reduces per-insured expected claim costs, allowing the insurer to lower the premium substantially while still breaking even. The low-premium mandated policy is then both affordable and effective at preventing housing insecurity, with welfare gains concentrated among the most financially vulnerable renters.

Q: What novel data does the paper use for calibration of security deposits? A: The authors construct a dataset of approximately 500,000 Craigslist rental listings scraped across the 100 largest U.S. metropolitan statistical areas between November 2022 and March 2024 to measure the cross-sectional distribution of security deposits. The average deposit in this dataset is $984, which the model matches closely at $992. The data also reveal that the deposit-to-rent ratio is decreasing in house quality, reflecting the higher default risk of low-income renters in lower-quality units.

Q: What is the paper’s definition of homelessness and what rate does the model match? A: Homelessness is defined broadly to include sheltered homeless, unsheltered homeless (0.6% of households), and doubled-up families (0.83% of households), for a total of 1.43% of U.S. households. The model matches this rate closely at 1.42%.

Q: What is the paper’s key implication for the design of housing policy? A: The central implication is that financial viability and impact on housing insecurity are in tension for private insurers, and cannot both be achieved simultaneously. Only a publicly provided program that internalizes homelessness fiscal costs and faces counter-cyclical borrowing spreads can target the most vulnerable renters, break even, and materially reduce housing insecurity. Private RGI, while viable for a narrow segment, cannot substitute for public provision as a tool against homelessness.

Q: How does RGI relate conceptually to rental assistance programs? A: The paper distinguishes RGI from rental assistance on a structural basis: insurance contracts require tenants to pay premiums, making them potentially self-financing for private providers, whereas rental assistance is a net transfer that can never be self-financing. This conceptual distinction motivates studying whether RGI can be designed to eliminate the need for ongoing fiscal transfers, though the analysis ultimately shows that a public subsidy or mandate is required to serve the most vulnerable renters.

Rent Guarantee Insurance (RGI): A contract under which an insured tenant pays a monthly premium equal to a flat percentage of rent; when the tenant defaults, the insurer pays the landlord directly, preserving tenancy, for a limited number of periods governed by the tenant’s stock of insurance credit.

Insurance Credit: An endowment of periods of RGI coverage that households receive upon entry into the model; each time the insurer pays on behalf of a defaulting tenant, one unit of credit is consumed, and no further coverage is available once credit is exhausted.

Housing Insecurity: In the paper’s framework, the set of outcomes — rent delinquency, eviction, and homelessness — arising from the combination of non-contingent rental contracts, borrowing constraints, and idiosyncratic or aggregate income and medical shocks.

Security Deposit: An upfront payment from tenant to landlord, set by the competitive landlord to break even in expectation given the tenant’s characteristics and insurance status; a key channel through which RGI affects welfare by reducing the upfront cost barrier to obtaining housing.

Moral Hazard (in RGI context): The change in a tenant’s default, savings, and housing choices induced by the presence of insurance coverage, which increases expected claim costs for the insurer relative to a world where behavior is held fixed.

Adverse Selection (in RGI context): The tendency of renters with higher default risk to self-select into RGI when access is unrestricted, worsening the insurer’s risk pool and driving up expected payouts relative to premiums.

Homelessness Externality: The fiscal costs borne by government — for shelters, health services, and policing — that accompany homelessness; the public insurer internalizes these costs, creating a net benefit from RGI that private insurers cannot capture.

Counter-cyclical Borrowing Spread: The feature of public (municipal bond) financing whereby borrowing costs fall during recessions, allowing the public insurer to expand coverage when claims are highest; contrasted with private insurers’ pro-cyclical corporate bond spreads that tighten precisely when aggregate conditions worsen.