Macroprudential-Policy | Macro Paper Warehouse

Financial Stability with Fire Sale Externalities

Wed, 01 Jan 2025 00:00:00 +0000

Layer 1: Overview

Research question and motivation: Asset fire sales were a defining feature of the 2007-08 crisis, and post-crisis reforms (Basel III liquidity requirements, Money Market Mutual Fund reforms) were introduced to mitigate fire sale externalities by reducing distressed debt obligations and forcing larger liquidity buffers. The paper asks whether policies that successfully mitigate fire sale externalities actually improve financial stability, since it is not obvious how banks re-optimize in response.

Model setup (no empirical data — this is a theoretical paper): The authors build a three-period (t = 0,1,2) Diamond-Dybvig (1983) model of financial intermediation augmented with (i) cash-in-the-market pricing in a financial market as in Allen and Gale (1998), and (ii) limited commitment as in Ennis and Keister (2009), following Li (2017). A unit continuum of ex ante identical depositors have CRRA preferences with relative risk aversion γ > 1. Each depositor is impatient with known probability π. There are two assets: a short-term storage asset (1 unit yields 1 next period) and a long-term asset (1 unit at t=0 yields R > 1 at t=2). The bank invests fraction x in the long-term asset and 1−x short. Long-term assets can be sold at t=1 at an endogenous price p to risk-neutral investors who receive endowment ws (market liquidity) and have outside return R* > 0. Runs are introduced via a sunspot s ∈ {α, β} with run probability q; runs are partial (stop after fraction π is served), following Ennis and Keister. The authors assume R* = R, which implies p ≤ 1 in equilibrium. Financial fragility is measured by q-bar, the maximum run probability q for which the run strategy is an equilibrium (run condition c1 ≥ c2β).

Main analytical findings: (1) Without intervention, banks over-invest in long-term assets relative to the socially efficient level because each competitive bank takes p as given and does not internalize that selling long-term assets in a run depresses p (the fire sale externality); the equilibrium price is inefficiently low. (2) The bank’s best response is in Case I (no excess liquidity, fire sale occurs) when 0 < q < q_l, and Case II (excess liquidity held) when q_l ≤ q < 1 (Lemma 1). There is a unique q_c at which the market-clearing price p* turns from decreasing to increasing in q (Lemma 3). (3) Comparative statics on market liquidity ws (Proposition 1): when the relevant q-bar lies in Case II (low ws), q-bar is strictly increasing in ws, so a small rise in market liquidity raises fragility; when q-bar lies in Case I (high ws), q-bar is strictly decreasing in ws. The mechanism (Lemmas 4-5) is that a higher p* raises c1 via intertemporal substitution; the c2α/c2β effect is always dominant, flipping the sign of dq-bar/dws between cases. (4) The intervention: a regulator controls (x, c1), internalizing the effect on p, while the bank still chooses (c2α, c1β, c2β) taking p as given. The regulator chooses lower x and higher c1 than the bank in Case I (Lemma 6: c1 ≤ c1R, x ≥ xR), raising the market-clearing price (Proposition 2: p* ≤ pR* in Case I). (5) Key result (Proposition 3): q-bar_R ≥ q-bar when both solutions are in Case I (intervention always raises fragility); ambiguous otherwise. When ws (or R) is high, intervention raises fragility (q-bar_R > q-bar); when ws or R is low, intervention involves excess liquidity and lowers fragility (q-bar_R < q-bar). Proposition 4 gives a sufficient condition for q-bar_R > q-bar via four thresholds ws1≤ws≤ws2 and ws3<ws<ws4. When ws is sufficiently high, p = pR = 1, the externality vanishes, and q-bar = q-bar_R. (6) Welfare (Proposition 5): WR(q-bar) ≤ W(q-bar) when both in Case I, and for some parameter values otherwise — intervention does not always improve welfare and can worsen it when market liquidity is large.

Policy implication: Mitigating fire sale externalities does not necessarily increase stability. Because the regulator takes q as given, it ignores that its own intervention can raise q-bar. Policymakers must internalize the fragility effect and balance externality mitigation against increased fragility, especially when market liquidity is high.

In depth

Q1. Is there an identification strategy or empirical data? What are the threats?

No. This is a purely theoretical paper with no data, sample period, or estimation. The quantitative content consists of analytical comparative-statics results (Lemmas 1-6, Propositions 1-5) and numerical illustrations rendered as figures (Figures 4-9) for specific parameter combinations of (ws, R, q, γ, π). There is no econometric identification; the analog of robustness is the set of modeling assumptions and the parameter regions over which results hold.

Q2. What is the core economic mechanism, and how does intervention raise fragility?

The regulator internalizes the fire sale externality by reducing the bank’s long-term holdings x and holding more short-term assets, which reduces asset supply in a crisis and raises the market value p of each long-term asset (this mitigates the externality and is the intended benefit). But two competing effects act on long-term payments c2β: the higher price raises the value of remaining long-term assets, while there are fewer long-term assets left for c2β (whose period-2 return R is fixed, so the price increase does not help c2β as it does c1β). The net effect on c2β is ambiguous. Simultaneously, reducing x lowers the relative cost of t=1 consumption, optimally pushing the regulator to raise short-term payment c1. Since the run condition is c1 ≥ c2β, raising c1 while c2β may fall makes early withdrawal more attractive, raising q-bar. When market liquidity is high, the net effect always increases fragility.

Q3. What is the role of ’excess liquidity’ and how does it reverse the result at low market liquidity?

Excess liquidity (Case II: πc1 < 1−x, holding more short-term assets than needed for the first π payments) is the bank’s/regulator’s hedge against runs. When ws is low, the anticipated fire sale price is low, so the regulator chooses to hold more excess liquidity than the bank. Excess liquidity supplies additional resources to pay c1β and further reduces asset supply (raising p), leaving more resources for c2β. This makes the net effect on c2β favorable enough that q-bar falls. Thus at low market liquidity the regulator can simultaneously mitigate the externality and reduce fragility; at high market liquidity, excess liquidity is small or zero and the fragility-increasing channel dominates.

Q4. What heterogeneity / regime dependence is documented?

Results depend critically on the regime (Case I = no excess liquidity / fire sale; Case II = excess liquidity; Case III = excess liquidity, no fire sale, which never arises in equilibrium). The sign of dq-bar/dws flips between Case I (decreasing) and Case II (increasing). The intervention’s effect on fragility flips with market liquidity ws and long-term return R: low ws or low R → intervention reduces fragility; high ws or high R → intervention raises fragility; very high ws → externality vanishes (p = pR = 1) and intervention is neutral (q-bar = q-bar_R). The switch from Case I to Case II is governed by thresholds q_l (bank) and q_l,R (regulator), with q_l,R < q_l because the regulator internalizes the price and is more inclined to hold excess liquidity.

Q5. What robustness / generality checks are discussed?

Several modeling-assumption relaxations are argued not to change results qualitatively: (i) the assumption R* = R (giving p ≤ 1) can be generalized to allow p > 1, which does not undermine findings in the p < 1 range; (ii) partial runs can be generalized to multiple waves via a richer sunspot space without changing mechanisms; (iii) depositors not observing the bank’s portfolio can be replaced by observing it only after the withdrawal decision, with identical results; (iv) the simultaneous-move game is shown equivalent to a dynamic game in which the regulator moves first, as long as depositors cannot observe regulator choices; (v) the assumption that interventions convey no information to depositors can be relaxed (justified by the complexity of post-crisis regulation, e.g., the 848-page Dodd-Frank Act) without undermining the structure.

Q6. How does this paper relate to and differ from prior work?

It builds on the fire sale externality literature (Lorenzoni 2008; Gale and Gottardi 2015; He and Kondor 2016; Davila and Korinek 2018 on over/under-investment; Acharya et al. 2011 and Gale and Yorulmazer 2020 on distorted portfolios; Perotti and Suarez 2011, Walther 2016, Kara and Ozsoy 2019 on optimal capital/liquidity regulation). It also builds on the bank-run literature (Bryant 1980; Diamond-Dybvig 1983) and on general-equilibrium / endogenous-portfolio extensions (Allen-Gale 2004; Farhi et al. 2009; Eisenbach-Phelan 2021; Cooper-Ross 1998; Ennis-Keister 2006; Li 2017). The stated novel contribution is being the first to show that policies designed to correct fire sale externalities can worsen financial fragility, achieved by jointly endogenizing the portfolio choice, the general-equilibrium asset price, and the equilibrium probability of a run.

Q7. What are the policy implications and their scope conditions?

Macroprudential interventions that regulate short-term liabilities and portfolio choice to curb fire sale externalities can increase the equilibrium probability of runs. The scope condition is market liquidity: the harmful trade-off (mitigate externality but raise fragility, and sometimes lower welfare) arises specifically when market liquidity ws is high (and/or R high); when ws is low, the regulator’s optimal excess-liquidity holding lets intervention both mitigate the externality and reduce fragility. A central caveat is that the regulator takes q as given and so does not perceive that its policy raises q-bar; the prescriptive takeaway is that policymakers must internalize q-bar (the endogenous run probability) when designing such policies, balancing externality mitigation against fragility.

Q8. Are the quantitative results exact magnitudes or signs?

The paper’s results are predominantly signs and ordinal comparisons (e.g., x ≥ xR, p* ≤ pR*, q-bar_R ≥ q-bar, monotonicity in ws and p) plus closed-form threshold expressions (q_l, p_l, p_u, the four ws thresholds in Proposition 4) given in the text and appendices. Specific numeric magnitudes appear only as illustrative figure values (e.g., the example in Figure 9 where intervention raises fragility when ws is near 0.2); the paper does not report calibrated point estimates beyond such illustrative figures.

Key Concepts

Fire sale externality: In this model, the inefficiency arising because each competitive bank takes the t=1 asset price p as given and does not internalize that its long-term holdings and crisis-time asset sales depress p, harming other banks. It leads banks to over-invest in long-term assets and sell more than the efficient amount, pushing the equilibrium price below its efficient level.

Cash-in-the-market pricing: The price of long-term assets at t=1 is set by the limited cash (endowment ws) that risk-neutral investors bring to the market rather than by fundamental value; when banks must sell, scarce market liquidity forces the price down (p ≤ 1 under the R*=R assumption).

Financial fragility (q-bar): Measured as q-bar, the maximum run probability q for which the partial-run strategy profile is part of an equilibrium, i.e., the largest q satisfying the run condition c1 ≥ c2β. Higher q-bar means the banking system is more fragile.

Excess liquidity: Short-term asset holdings beyond what is needed to pay the first π withdrawals (πc1 < 1−x; Case II). It is a precautionary buffer that supplies resources for crisis payments c1β, reduces asset supply, and raises the fire sale price; the regulator holds more of it than the bank when market liquidity is low.

Case I vs Case II vs Case III: Regimes of the bank’s best response: Case I = no excess liquidity, fire sale occurs (small q, high ws); Case II = excess liquidity held with fire sale (large q, low ws); Case III = excess liquidity so large that no fire sale occurs — shown never to be an equilibrium because it implies c2β > c2α > c1 (no run condition).

Regulator/intervention: A planner that chooses (x, c1) internalizing the effect of these choices on the asset price p, while the bank still chooses (c2α, c1β, c2β) taking p as given and the regulator cannot direct depositors’ withdrawal decisions; it represents the two policy instruments of regulating short-term liabilities and portfolio choice.

Climate Policies, Macroprudential Regulation, and the Welfare Cost of Business Cycles

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

This paper embeds a carbon pricing sector into an extended DSGE model with a financial accelerator (E-DSGE) featuring heterogeneous firms, bank monitoring, and a borrowing-constraint amplification mechanism, then compares the welfare cost of business cycles under a cap-and-trade (CAT) scheme versus a carbon tax. The central result is that, in the presence of financial frictions, CAT generates lower welfare costs than a carbon tax: under TFP and risk shocks calibrated to US quarterly data, the baseline welfare cost of business cycles is 0.6178 percent of consumption under CAT versus 1.5231 percent under a carbon tax — roughly 2.5 times larger under a tax. The mechanism is that permit prices under CAT are procyclical (they fall in downturns, reducing firms’ carbon compliance burden precisely when balance sheets are most stressed), acting as an automatic stabilizer for financial amplification, while the carbon tax holds a fixed price and provides no such buffer. A countercyclical optimal carbon tax rule that reacts vigorously to output (optimal sensitivity parameter τ = 52.2245) can mimic CAT’s stabilizing behavior, but even optimized environmental rules leave a significant welfare gap between regimes. Reserve requirement macroprudential regulation narrows this gap substantially: a static 2 percent reserve requirement brings CAT welfare costs to 0.1957 and carbon tax costs to 0.3863; an optimal dynamic rule keyed to credit growth or asset price growth brings both regimes below 0.20, effectively aligning them. A deposit interest rate subsidy can also narrow the gap when combined with a dynamic subsidy rule, but a static subsidy actually worsens welfare costs because it raises leverage and amplifies shocks around a more fragile steady state.

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 model structure and how does the environmental policy sector integrate with financial frictions?

The model is an E-DSGE built on the Christiano, Motto, and Rostagno (2014) financial accelerator framework, extended to include a carbon price instrument and heterogeneous firms that face both standard borrowing constraints and carbon compliance costs. There is a representative household and three firm sectors: a continuum of capital-producing entrepreneurs, retailers, and a goods sector. Banks extend loans to entrepreneurs at a spread over the risk-free rate; the external finance premium is endogenous because bank monitoring is costly and borrowers face costly state verification (as in Bernanke, Gertler, and Gilchrist, 1999). Environmental policy is introduced through a carbon permit or tax that enters firms’ marginal cost, so the carbon price affects both production decisions and the entrepreneur’s net worth, which in turn feeds back into the spread through the financial accelerator. Calibration uses US quarterly data (Table 1 in the paper), and the model is solved by log-linearizing around a deterministic steady state.

Q2. Why do financial frictions create a welfare advantage for cap-and-trade over carbon taxes?

Under a carbon tax, the tax rate is fixed by the regulator regardless of macroeconomic conditions; when a TFP or risk shock contracts output and reduces firm net worth, the fixed carbon cost amplifies the contraction by reducing the entrepreneur’s retained earnings, worsening the external finance premium, and deepening the financial accelerator loop. Under a CAT scheme, the equilibrium permit price is endogenous: it falls when aggregate activity and emissions decline, automatically lowering the compliance cost burden for firms at exactly the moment when balance sheets are most constrained. This procyclicality of permit prices functions as an automatic stabilizer, partially offsetting the financial accelerator’s amplification. The paper shows this via impulse response functions (Figures 1 and 2 in the paper) to TFP and risk shocks: under CAT, the responses of investment, bankruptcy, spread, and output are systematically more muted than under a carbon tax. Quantitatively, the baseline welfare cost of business cycles is 0.6178 percent of consumption under CAT versus 1.5231 percent under a carbon tax — a gap of nearly 0.91 percentage points of consumption.

Q3. How do optimal environmental policy rules affect welfare costs, and do they close the gap between regimes?

An optimal flexible CAT rule that allows permit prices to respond countercyclically to a macroeconomic indicator (net output) reduces welfare costs from 0.6178 to 0.4528 percent; an optimal flexible carbon tax rule reduces costs from 1.5231 to 1.1811 percent. In both cases, the optimal rule specifies vigorous countercyclical response: the optimal sensitivity parameter for the carbon tax rule is τ = 52.2245, meaning the tax rate must decrease sharply in recessions to mimic the automatic procyclicality of permit prices under CAT. Despite these improvements, the welfare gap between the two regimes persists even under optimal environmental rules: the optimized CAT still generates roughly 0.73 percentage points lower welfare costs than the optimized carbon tax. The paper concludes that countercyclical environmental policy can reduce but not eliminate the inherent stabilization advantage of CAT in the presence of financial frictions — because the fundamental mechanism (endogenous permit prices vs. fixed tax rate) cannot be fully replicated by a tax rule with a single output-gap indicator.

Q4. How do reserve requirement macroprudential regulations interact with the carbon pricing choice?

Introducing a static 2 percent reserve requirement (banks can loan out only 98 percent of deposits) already strongly reduces welfare costs and partially aligns the two regimes: CAT welfare costs fall from 0.6178 to 0.1957, and carbon tax costs fall from 1.5231 to 0.3863 (Table 5 in the paper). The mechanism is that reserve requirements limit bank credit expansion, lowering equilibrium leverage and reducing the severity of the financial accelerator — when firms’ balance sheets are less leveraged, adverse shocks cause smaller spirals in net worth and spreads. Dynamic reserve requirement rules — keyed to credit growth (optimal ψ_B ≈ 1.047) or asset price growth (optimal ψ_Q ≈ 0.722) — reduce welfare costs further, to 0.1207 under CAT and 0.2300 under a carbon tax with a credit-growth rule, effectively narrowing the gap to around 0.10 percentage points. The optimal policy mix (jointly optimizing both the macroprudential and environmental rules) achieves minimal additional improvement beyond the macroprudential optimum alone, suggesting the dominant stabilizing role is played by financial regulation rather than the choice of carbon pricing instrument when both are available and optimally calibrated.

Q5. How does macroprudential regulation affect the volatility of emissions and permit prices under each regime?

Table 6 in the paper reports coefficients of variation (CVE for emissions volatility, CVP_E for permit price volatility) across policy combinations. Under baseline CAT with no macroprudential regulation, CVE = 0 (the cap fixes aggregate emissions by construction) and CVP_E = 8.3578 — permit prices are very volatile. Adding a static reserve requirement reduces CVP_E to 2.5125; an optimal credit-growth rule reduces it to 1.0935, a reduction of nearly 87 percent from baseline. Under baseline carbon tax, CVP_E = 0 (the tax price is fixed by regulation) but CVE = 0.0574 — emissions are volatile. Adding a static reserve requirement reduces CVE to 0.0273; an optimal credit-growth rule reduces it to 0.0153. The paper interprets this as macroprudential regulation fostering convergence between the two instruments in their business cycle properties: it substantially stabilizes permit prices under CAT and substantially stabilizes emissions under a carbon tax, reducing the distinguishing uncertainty of each pricing approach. The optimal policy mix under a carbon tax with a dynamic subsidy achieves CVE = 0.0090 and CVP_E = 0.4956, showing that well-designed financial regulation can make a carbon tax nearly as emissions-stable as a CAT while also reducing permit price volatility.

Q6. What happens under an interest rate subsidy to depositors as an alternative macroprudential tool?

A static deposit interest rate subsidy of the welfare-maximizing level (1 percent) worsens welfare costs of business cycles — from 0.6178 to 1.1028 under CAT and from 1.5231 to 3.5597 under a carbon tax — because the subsidy moves the economy to a higher-leverage steady state, around which financial amplification is more severe (Table 7 in the paper). The intuition is that the subsidy encourages saving by raising the return on deposits, which raises equilibrium loan supply, which raises leverage; a more leveraged economy is more sensitive to adverse shocks. A dynamic subsidy rule that responds countercyclically to credit growth (optimal κ ≈ 1.319) mitigates this problem: it discourages saving when credit is expanding and encourages it when credit is contracting, partially stabilizing leverage dynamics. The dynamic subsidy reduces welfare costs substantially — to 0.2506 under CAT and 0.4706 under a carbon tax — and a joint optimization of the subsidy and the carbon pricing rule achieves 0.1926 under CAT and 0.4366 under a carbon tax with a dynamic subsidy. The authors note that the static subsidy result illustrates a general principle: macroprudential policies that move the steady state toward higher leverage can amplify cycle costs even while achieving efficiency gains around the steady state, and that distinguishing between steady-state and fluctuation welfare effects is essential when comparing such policies.

Q7. What are the main welfare and policy conclusions?

The paper establishes three conclusions. First, climate policy instrument choice has macroeconomic stabilization consequences in financially frictionous economies: CAT dominates a carbon tax for welfare when financial frictions are operative and macroprudential policy is absent or limited. Second, macroprudential regulation — particularly dynamic reserve requirement rules — is the more powerful tool for reducing the welfare cost of business cycles under both carbon pricing regimes, and can largely align the two regimes, making the instrument choice less consequential when macroprudential policy is well-calibrated. Third, the interaction between financial regulation and carbon pricing is non-trivial: the optimal sensitivity parameters for macroprudential rules differ depending on whether the economy uses CAT or a carbon tax, because the endogenous procyclicality of permit prices changes how financial shocks propagate through the economy.

Key concepts

financial accelerator: the mechanism by which adverse shocks to entrepreneurial net worth raise the external finance premium (the spread between the loan rate and the risk-free rate), reduce investment and output, further depress net worth, and generate amplified cycles; the core friction in the E-DSGE model and the channel through which carbon pricing affects welfare costs.

procyclical permit prices: the endogenous tendency of permit prices under a CAT scheme to fall when aggregate economic activity and emissions decline; the paper’s central mechanism through which CAT acts as an automatic stabilizer for the financial accelerator — permit prices fall precisely when firms’ balance sheets are most stressed, reducing compliance costs and partially offsetting amplification.

welfare cost of business cycles (Lucas measure): the percentage of consumption that a representative household would be willing to give up to move from a world with business cycle fluctuations to one without, evaluated relative to the deterministic steady state; in the paper’s baseline calibration, this is 0.6178 percent under CAT and 1.5231 percent under a carbon tax.

reserve requirement macroprudential regulation: a regulatory constraint requiring banks to hold a fraction of deposits in reserves, limiting loan supply; implemented in the model as Φ_t ∈ (0,1] where lower Φ_t requires banks to hold more reserves; a static 2 percent reserve requirement already substantially narrows the welfare gap between carbon pricing regimes, and an optimal dynamic rule nearly closes it.

E-DSGE (Environmental DSGE): the paper’s model class — a DSGE with financial frictions (Christiano-Motto-Rostagno financial accelerator) and a carbon pricing sector; used to analyze the interaction between environmental policy instruments and macroprudential regulation in an economy with both climate and financial externalities.

coefficient of variation of emissions (CVE) / permit prices (CVP_E): volatility measures used to assess how macroprudential regulation affects the business-cycle properties of each carbon pricing instrument; macroprudential regulation substantially reduces CVP_E under CAT and CVE under a carbon tax, making each instrument’s uncertainty properties more symmetric.

Committed to flexible fiscal rules

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

A central debate in fiscal policy is whether fiscal rules—numerical constraints on budget deficits or debt levels—impair a government’s ability to respond to adverse economic shocks, creating a fundamental trade-off between debt stabilization and macroeconomic stabilization. This paper uses data on large, random natural disasters as exogenous shocks to address the endogeneity of rule adoption and provides new empirical and theoretical evidence on this trade-off. Contrary to the trade-off hypothesis, countries with fiscal rules perform significantly better following such disasters than countries without rules: GDP and private consumption are persistently higher, and fiscal policy is significantly more expansionary. The superior performance is shown to depend on the existence of prior fiscal space and the presence of escape clauses in the rules. A model of sovereign default with endogenous fiscal space and tax plans rationalizes these findings: tight rules prevent myopic governments from accumulating excessive debt in good times, which creates fiscal space for deficit spending when disasters strike, keeping sovereign spreads lower and enabling more expansionary fiscal responses.

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 identification strategy and why do natural disasters solve the endogeneity problem?

Large natural disasters serve as a source of exogenous, random adverse economic shocks; by interacting disaster exposure with the presence or absence of fiscal rules, the paper identifies the effect of rules on macroeconomic performance without confounding from the non-random adoption of rules. Endogeneity is a central concern in the fiscal rules literature because countries that adopt rules may differ in politically or economically relevant ways from those that do not (e.g., more disciplined political environments, stronger institutions). Using large disasters as quasi-experimental variation removes this concern: the timing and magnitude of natural disasters are uncorrelated with which countries happened to adopt fiscal rules, isolating the effect of rules on crisis response.

Q2. What are the main empirical findings?

Countries with fiscal rules show significantly higher output and private consumption following large natural disasters, and implement significantly more expansionary fiscal policy, compared to countries without rules—holding over a 1970Q1–2018Q4 quarterly panel—with confidence bands at the 68% and 90% levels based on 500 Monte Carlo draws. The result directly contradicts the commonly held view that fiscal rules restrict governments’ ability to respond to shocks. Moreover, the paper finds that the superior performance of rule-constrained countries is conditional on two features: the existence of fiscal space prior to the shock (low debt or deficit positions), and the presence of escape clauses that allow rules to be suspended during severe adverse events.

Q3. What is the model mechanism?

In the sovereign default model, a fiscal rule prevents a myopic government from over-borrowing in good times out of political economy considerations (e.g., electoral incentives to spend); this forced restraint creates fiscal space—lower debt, lower sovereign spreads—which allows the government to run deficits when a shock hits without triggering a default episode or a sharp rise in borrowing costs. The model predicts that, relative to a no-rule economy, when a disaster strikes in a rule-constrained economy: sovereign spreads spike by less, the fiscal policy response is more expansionary, and output and consumption are higher. Escape clauses in the rules are important: they allow the government to depart from the rule explicitly in crisis situations without destroying the credibility of the rule in normal times.

Q4. What is the policy implication for the COVID-19 fiscal response?

The paper’s findings directly address the suspension of fiscal rules during COVID-19: the theoretical and empirical results suggest that rules with escape clauses do not impair crisis response and may actually improve it, by ensuring fiscal space is available when needed. The paper’s evidence implies that the COVID-era suspension of rules in many countries (including the EU’s Stability and Growth Pact) was not necessarily required to enable expansionary fiscal responses—countries with well-designed rules including escape clauses could have responded expansively while maintaining rule credibility.

Key concepts

escape clause : a provision in a fiscal rule that explicitly permits departure from the rule’s numerical target under defined circumstances (severe recessions, natural disasters, etc.); the paper finds that the presence of escape clauses is one of the two conditions for rule-constrained countries to outperform non-rule countries after adverse shocks.

fiscal space : the buffer of low debt and deficit levels that allows a government to increase spending or cut taxes during a shock without triggering unsustainable debt dynamics or elevated sovereign spreads; the paper shows fiscal space is created by rules in good times and consumed in bad times.

How Bad Are Weather Disasters for Banks?

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

Using FEMA disaster declarations matched to SHELDUS property-damage estimates and Call Report data for 1995–2018, this paper finds that weather disasters — even at their most severe — have had modest effects on U.S. bank safety over the last quarter century. For single-county banks exposed to 95th-percentile disasters, Z-scores decline by roughly 9 percent at a five-year horizon under the panel estimates; reaching failure thresholds from sample mean Z-score levels would require a disaster approximately 6.7 standard deviations more destructive than a 95th-percentile event. Federal disaster aid does not appear to be the primary driver of this resilience, since banks exposed to weather events without FEMA declarations exhibit similar stability. Instead, the paper points to a loan demand channel — multi-county bank lending increases roughly 0.25 percentage points per standard deviation of damage at five years without an accompanying interest-rate increase — and to local banks’ apparent avoidance of mortgage lending in flood-prone areas beyond what official flood maps predict, consistent with local information about true flood risk limiting exposure before disasters strike.

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

In depth

Q1. How severe are weather disaster effects on bank safety?

The paper finds that weather disasters at any severity level produce small and often statistically insignificant effects on the key bank safety measures — charge-offs, capital ratios, return-on-assets volatility, and Z-scores — at single-county banks, with the largest measured effect being roughly a 9 percent decline in Z-scores at the 95th percentile of disaster damage at a five-year horizon. The regression framework uses bank and state-year fixed effects, with SHELDUS damage as the continuous severity measure and FEMA disaster declarations as a binary indicator. For multi-county banks, charge-offs increase by roughly 10 percent at five years, but net income also rises, suggesting disaster-area loan demand partially offsets credit losses. The paper’s calculation is that pushing a typical bank from its mean Z-score of 135.9 to the failure threshold would require a Z-score decline of 127.9 — far exceeding the estimated −9 percent impact of a 95th-percentile disaster, which would need to be approximately 6.7 standard deviations more destructive to close that gap.

Q2. Is bank resilience an artifact of federal disaster aid?

The paper presents evidence that federal disaster aid is not the primary source of bank resilience, since banks exposed to weather events that did not receive FEMA disaster declarations exhibit similarly modest effects on bank safety measures. The test is designed to separate the insurance mechanism (FEMA aid replacing household income and debt service capacity) from intrinsic bank resilience. The fact that non-FEMA disasters produce comparable stability redirects attention to the demand-side and local-knowledge channels as the more fundamental explanations for the resilience finding.

Q3. What is the loan demand channel and how large is it?

Multi-county banks experience an increase in lending of roughly 0.25 percentage points per standard deviation of SHELDUS damage at a five-year horizon, and the authors find no accompanying increase in loan interest rates, which is consistent with a demand-side shift rather than a tightening of lending standards. The demand interpretation is that disasters create a wave of borrowing demand as households and firms repair or replace damaged assets, and the increased loan volume helps offset the increase in charge-offs. The pattern is found at multi-county banks — which can serve affected and unaffected areas simultaneously — but not at single-county banks, consistent with lending capacity mattering for capturing the demand increase.

Q4. What does “local knowledge” mean in this context?

Local banks originate approximately 6.4 percent fewer log mortgage dollars per application in FEMA flood zones than would be predicted by the official flood map classifications alone, with the gap widening to 7–8 percent in areas that have experienced more than five FEMA flood declarations compared to areas with fewer than three, which is consistent with local lenders holding information about true flood risk not captured in official maps. The finding is consistent with local banks having access to community-level information — observed flooding history, property-level characteristics, local drainage and elevation — that is not incorporated into official FEMA flood zone classifications. This pre-disaster selectivity limits mortgage accumulation in the highest-risk areas before disasters occur.

Q5. What are the implications for climate risk assessment?

The paper explicitly frames the historical resilience documented for 1995–2018 as informing rather than settling assessments of physical risk to banks from future climate change, since more frequent or more severe disasters could overwhelm the demand-offset and local-knowledge mechanisms that the paper identifies as sustaining bank performance. The key qualification is temporal scope: the demand-side recovery effect requires that affected areas have the income and economic capacity to service new loans, and the local-knowledge effect requires that banks have experienced enough repeated flooding to develop accurate private flood risk assessments. Both conditions could become less reliable as climate change alters the frequency, geography, and severity of weather events relative to the historical distribution.

Key concepts

Z-score : a bank-level distance-to-insolvency measure equal to (return on assets + capital ratio) divided by return-on-assets volatility; higher values indicate greater distance from failure; used here as the primary measure of disaster impact on bank safety.

SHELDUS : the Spatial Hazard Events and Losses Database for the United States, providing county-level property damage estimates for weather events; used in this paper as the continuous measure of disaster severity in panel regressions.

single-county bank : a bank whose entire depositor base is drawn from one county, making it fully exposed to local disaster effects with no geographic diversification across other counties.

loan demand channel : the mechanism by which disasters increase demand for credit from households and firms repairing or replacing damaged assets, generating new loan volume that partially offsets credit losses at banks serving affected areas.

local knowledge : the paper’s label for the informational advantage that local banks appear to have about true flood risk beyond what official FEMA flood zone classifications capture, inferred from lower mortgage originations in areas with a history of repeated flooding.

Liquidity Traps, Prudential Policies, and International Spillovers

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

The paper develops a tractable open-economy New Keynesian model with nominal rigidities and an occasionally binding zero lower bound (ZLB) to study how monetary policy and macroprudential policy (modeled as a tax on capital flows) jointly transmit to output, capital flows, and the exchange rate, and what this implies for international spillovers and global welfare. An analytical decomposition identifies three transmission channels — intertemporal substitution, expenditure switching, and aggregate income — and the calibration finds that capital controls operate almost entirely through intertemporal substitution (about 95%), whereas expenditure switching accounts for roughly a quarter to a third of the effect of monetary policy. On the normative side, the authors show that, absent capital controls, monetary policy faces a tradeoff between stabilizing output today and curbing capital flows to lower the likelihood of a future liquidity trap, but that ’leaning against the wind’ (pre-emptively raising rates) is not necessarily optimal and can be counterproductive when tradables and non-tradables are highly substitutable. Quantitatively, adding capital controls lowers the average unemployment rate conditional on a liquidity trap from about 6% to about 1.5% and cuts the unconditional welfare cost of liquidity traps from about 0.4% to about 0.1% of permanent consumption, with an average ex-ante tax on inflows of about 0.2% and an average ex-post tax on outflows of about -0.05%. Finally, contrary to ‘currency war’ concerns, the authors argue that capital controls are not beggar-thy-neighbor: a country can use them to insulate itself from adverse foreign-policy spillovers (which operate through the world real interest rate), and coordination is beneficial only during a liquidity trap and works by stimulating rather than restricting flows. All results hold within their small-open-economy model under its calibration.

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 model, and which policies does it study?

The paper studies an infinite-horizon small open economy with nominal rigidities and an occasionally binding zero lower bound on the nominal interest rate, in which the government has two instruments — the nominal interest rate (monetary policy) and a tax on capital flows (macroprudential policy). The economy has a tradable final good and a non-tradable good with sticky prices, and features aggregate demand externalities. The authors use this setting to ask three questions: how interrelated are the transmission channels of the two policies; how should monetary policy be used jointly with macroprudential policy; and what happens to global welfare when many countries adopt prudential policies simultaneously.

Q2. What are the three transmission channels, and how much does each matter?

An analytical decomposition (extending Kaplan, Moll and Violante 2018 and Auclert 2019 to an open economy) identifies three channels — intertemporal substitution, expenditure switching, and aggregate income — and the calibration shows monetary policy and capital controls operate through very different channels. The intertemporal substitution channel accounts for about 95% of the effect of capital controls, while expenditure switching (operating through exchange-rate depreciation that shifts demand toward non-tradables) accounts for a substantial share of the effect of monetary policy — the paper states ‘about one-third’ in its introduction and ‘about one-quarter’ in its conclusion. The expenditure-switching channel and the role of the exchange rate are what distinguish the open-economy decomposition from its closed-economy antecedents.

Q3. Do open capital markets amplify or dampen monetary policy?

Capital flows may either amplify or attenuate the output effects of monetary policy, depending on the relative sizes of the elasticity of substitution over time and the elasticity across sectors. If the intertemporal elasticity exceeds the intratemporal one, an open capital account amplifies monetary policy (a monetary expansion raises total consumption more than output, so households borrow from abroad); the result reverses when the intratemporal elasticity is larger, in which case a closed capital account produces the larger output expansion.

Q4. Is ’leaning against the wind’ the optimal prudential use of monetary policy?

Contrary to a widespread policy view, leaning against the wind is not necessarily optimal: when the elasticity of substitution across sectors is higher than across time, raising the interest rate ahead of a liquidity trap can be counterproductive. In that case a rate hike generates a large negative expenditure-switching effect and a sharp income drop while only modestly reducing consumption, so in general equilibrium it leads to capital inflows and more external debt — exacerbating the aggregate demand externality and making a future contraction more likely. The implication is that a prudential monetary policy may require lowering, not raising, the interest rate ahead of a liquidity trap.

Q5. How should monetary and macroprudential policy be combined, and how pre-emptively?

When capital controls are available, the central bank uses monetary policy to stabilize output and uses the capital-flow tax to manage flows, with the macroprudential tax on debt positive only if the ZLB is likely to bind next period; monetary policy, by contrast, must be used prudentially even when the ZLB binds only in some distant future. Because monetary policy is a blunter instrument, it has to be used more pre-emptively than capital controls. The authors also show the central bank may restrict outflows during a liquidity trap when that trap is either temporary or very severe.

Q6. What are the quantitative welfare and unemployment gains from capital controls?

Adding capital controls substantially improves macroeconomic stabilization: average unemployment conditional on a liquidity trap falls from about 6% to about 1.5%, and the unconditional welfare cost of liquidity traps falls from about 0.4% to about 0.1% of permanent consumption — more than a fourfold reduction. The average ex-ante prudential tax on inflows is about 0.2% and the average ex-post tax on outflows is about -0.05%. The authors also note that, with capital controls, liquidity traps are less frequent and less severe but — perhaps surprisingly — tend to last longer.

Q7. Are capital controls beggar-thy-neighbor, and how do international spillovers work?

The authors argue that, contrary to emerging policy concerns, capital controls are not beggar-thy-neighbor and can enhance global macroeconomic stability; international spillovers operate through the world real interest rate, and a country can use capital controls to insulate itself from adverse foreign policies. In their multi-country extension, a country can remain insulated from negative spillovers of a change in the foreign monetary stance through capital controls, which can help prevent the outbreak of a currency war.

Q8. When is international policy coordination desirable?

The authors provide conditions under which a regime of uncoordinated capital controls can dominate laissez-faire, and they find that coordination is desirable only during a liquidity trap — where, notably, it calls for stimulating capital flows rather than preventing them. This stands against the view that uncoordinated capital-control policies necessarily produce a global paradox of thrift.

Q9. How do these results differ from prior open-economy liquidity-trap models?

The paper’s more benign view of spillovers contrasts with contributions such as Caballero, Farhi and Gourinchas (2021), Eggertsson et al. (2016), and Fornaro and Romei (2019), and the authors trace the difference to two features of their model: positive liquidity and the presence of ex-post capital controls. Because goods subject to nominal rigidities are consumed only domestically, foreign policies that favor savings (lowering the world interest rate) raise demand for domestic goods through asset markets and can be stabilizing at the ZLB; and ex-post controls let the central bank actively manage flows during a trap to offset adverse spillovers.

Key concepts

aggregate demand externality : the externality (as in Schmitt-Grohe and Uribe 2016 and Farhi and Werning 2016) by which an individual agent’s borrowing raises external debt and, given nominal rigidities and the ZLB, makes the economy more vulnerable to a future demand-driven contraction; it is the market failure that prudential policy targets in this model.

expenditure switching channel : the open-economy transmission channel through which an exchange-rate depreciation makes non-tradables relatively cheaper, shifting demand toward domestically produced goods; the paper finds it accounts for a substantial share (roughly a quarter to a third) of monetary policy’s effect.

intertemporal substitution channel : the channel through which a change in the intertemporal price shifts consumption between present and future; it accounts for about 95% of the effect of capital controls in the calibration.

liquidity trap / occasionally binding ZLB : a state in which the zero lower bound on the nominal interest rate binds, so conventional monetary policy cannot stabilize output; the risk of entering such a state in the future is what makes pre-emptive prudential policy valuable here.

capital controls (prudential tax on flows) : the macroprudential instrument in the model — a tax on capital inflows (ex ante) or outflows (ex post) — used to manage the level and timing of capital flows and to insulate the economy from foreign spillovers.

beggar-thy-neighbor : a policy that improves one country’s outcomes at others’ expense; the paper argues capital controls are, contrary to common concern, not beggar-thy-neighbor in its setting and can raise global stability.

Redemption Fees and Gates in the Lab

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

Layer 1: Overview

This paper uses laboratory experiments to evaluate the effectiveness of two liquidity management tools — redemption fees and redemption gates — in reducing runs on money market funds (MMFs), explicitly accounting for preemptive run behavior where investors withdraw before a fee or gate is triggered to avoid being harmed by its imposition. The experimental design is based on a Diamond–Dybvig framework modified following Engineer (1989), in which four investors must decide whether to withdraw before learning their own liquidity type (patient or impatient), generating a setting where preemptive runs are theoretically possible even without fear of fund default. Three treatments are compared: a laissez-faire baseline, a gates treatment (withdrawals suspended after cash reserves are exhausted), and a fees treatment (a redemption fee charged on withdrawals once cash reserves are exhausted). Across 15-period session halves, redemption fees produce significantly lower withdrawal rates than both the baseline and gates treatments, with the gap emerging primarily after the first ten periods as participants adapt to the tool; gates, contrary to the theoretical prediction that they reduce the risk factor of the no-run equilibrium, do not lower withdrawal rates relative to the baseline — and in the full-session analysis, gates actually generate significantly higher withdrawal rates than the baseline, consistent with preemptive runs accelerating when investors fear losing access to their funds. The overall finding is that neither tool eliminates fund fragility, but fees offer a modest and delayed stabilizing effect while gates are counterproductive, lending empirical support to the SEC’s 2023 regulatory shift away from gates and toward fees in MMF regulation.

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 experimental design and why does it explicitly study preemptive runs?

The experiment models a fund with four investors, each holding a demandable claim worth 1 ECU in period 1; the fund holds 2 ECUs in cash and a project that pays 2R ECUs in period 2 if allowed to mature but only 1 ECU if liquidated early, and investors must make their period-1 withdrawal decision before learning whether they are impatient (need period-1 funds) or patient (can wait), mirroring the Engineer (1989) setup where preemptive runs arise from the risk of being locked in rather than from fundamental insolvency concerns. The key feature is that an investor who expects fees or gates to be imposed faces an incentive to withdraw early to avoid either losing access (gates) or paying a fee (fees) at precisely the moment their liquidity need arises, which is exactly the preemptive run mechanism observed empirically during the COVID-19 MMF turmoil of spring 2020. Investors are sequentially asked whether they wish to withdraw in a random order without observing others’ choices, and they learn their type only in the evening of period 1 after having already made the morning withdrawal decision. In the treatment with gates, the fund suspends payouts entirely once its 2 ECU cash reserve is exhausted (i.e., after two withdrawals), forcing the third and fourth investors to wait for period 2 regardless of their type. In the treatment with fees, the fund charges a redemption fee on the third and fourth period-1 withdrawals instead of suspending them.

Q2. How does the theoretical risk factor framework generate the paper’s main hypothesis?

The paper uses the concept of the “risk factor of the no-run equilibrium” — defined as the probability p at which an investor becomes indifferent between staying invested and withdrawing when all other investors stay with probability p — to rank the three treatments by their predicted effectiveness: fees should generate the lowest risk factor and thus the highest tendency toward the no-run equilibrium, followed by gates, with the baseline highest. Fees dominate gates on the risk factor because fees still permit withdrawal in period 1 (albeit at a cost), meaning an impatient investor who remained invested can still access funds when needed, whereas under gates an impatient investor who is locked out has no recourse. This additional flexibility of fees means that the downside of remaining invested is smaller under fees than under gates, making the no-run equilibrium relatively more attractive under fees. The paper’s design tests whether this theoretical ranking carries through to actual investor behavior in the lab, where cognitive limitations, learning dynamics, and strategic uncertainty may produce deviations from the prediction.

Q3. What are the main experimental results on withdrawal rates?

Pooled over the first 15 periods of the first session halves (where no spillovers from prior experience occur), withdrawal rates are 30.3% in the baseline, 31.7% in gates, and 27.6% in fees; proportion tests confirm that fees produce significantly lower withdrawal rates than both baseline and gates at the 5% level, but no significant difference is found between baseline and gates — gate withdrawal rates are actually slightly higher than the baseline, contradicting the directional hypothesis. In the robustness check using both session halves (full 30 periods), the pattern sharpens: overall withdrawal rates are 30.3% (baseline), 33.4% (gates), and 25.4% (fees), with gates now significantly higher than baseline (p = 0.000) as well as significantly higher than fees, indicating that gates actively encourage preemptive withdrawal rather than deterring it. Withdrawal rates in the fees treatment exhibit a distinctive time pattern: they start higher than the other treatments in the first 5 periods (the Fees × Period interaction in the regression is negative and significant, while the main Fees coefficient is positive and significant, indicating an initially elevated but steeply declining trajectory), with the fee benefit materializing only from period 11 onward — consistent with the European Commission’s (2023) observation that European MMF investors more familiar with fees show less preemptive behavior than U.S. investors.

Q4. What does the regression analysis reveal about the treatment effects and dynamics?

Random-effects panel linear probability models of the binary withdrawal decision confirm that the fees treatment produces a significantly negative trend (Fees × Period coefficient negative and statistically significant) while the baseline shows no time trend and gates show no significant deviation from baseline trends, and that prior round experience — specifically the number of withdrawal requests in the immediately preceding round — is a strong positive predictor of withdrawal (approximately 6 percentage points per additional prior-round withdrawal request), while longer-run experience before the last round carries no significant predictive power. The inclusion of individual-level controls in Model (4) shows that higher risk tolerance is associated with significantly lower withdrawal rates (a surprising finding relative to prior experimental literature, which the authors suggest may reflect the preemptive nature of the decision making risk tolerance relevant through attitudes toward liquidity timing risk rather than through classic strategic risk). The regression analysis confirms that gates’ ineffectiveness is not explained by observable participant characteristics: the gates dummy is never significant and the gates-period interaction is not significantly different from the baseline, ruling out the possibility that session-level composition differences drive the null result for gates.

Q5. Does switching regulatory regime across session halves generate behavioral change?

Switching from the baseline to fees in the second session half produces a significant reduction in withdrawal rates in the final 5-period block consistent with Hypothesis 2, and switching from fees to gates in the second half produces a significant increase in withdrawal rates in the final 5-period block; however, switching from baseline to gates and from gates to fees produce no significant differences between session halves at the 5% level. The modest switching effects suggest that the fee benefit takes time to emerge regardless of prior regime experience — a finding consistent with the general pattern that fee effectiveness materializes only after participants have had multiple rounds of exposure. This regime-switching analysis also rules out a strong order effect as an explanation for the observed fee benefit: the fee advantage over baseline is present even when comparing within the same session halves and is not driven by participants carrying in stabilizing prior knowledge from the fees treatment.

Q6. What are the regulatory implications and how do the findings connect to the 2020 MMF turmoil?

The experimental findings directly inform the ongoing regulatory overhaul of MMF liquidity management tools, supporting the SEC’s 2023 decision to move away from gates toward mandatory swing pricing (which functions similarly to a fee) as the primary tool for U.S. MMFs, and providing micro-level behavioral evidence for why the 2014 fees-and-gates provisions failed to prevent the spring 2020 MMF runs even though they were in force. The preemptive run mechanism is empirically identified in the lab as a real and substantial phenomenon: withdrawal rates in the first round are if anything higher under fees than under the baseline, and the fee benefit only consolidates after participants have repeatedly experienced the tool, suggesting that investor familiarity is necessary for fee effectiveness — a condition that was likely not met in 2020. The finding that gates actively worsen run propensity in the full-session analysis provides the starkest regulatory implication: gates may be self-defeating by compressing investors’ effective option to wait, creating a focal first-mover advantage that accelerates exactly the run the gate is meant to stop.

Key Concepts

preemptive run : a run in which investors withdraw from a fund before their immediate liquidity need arises, driven by the strategic risk that fees or gates will be imposed at the exact moment they need liquidity; modeled here following Engineer (1989) and experimentally documented as a significant behavioral phenomenon that undermines both fees and gates.

risk factor of the no-run equilibrium : a measure based on risk dominance (Harsanyi and Selten 1988) defined as the probability p at which an investor becomes indifferent between withdrawing and remaining when all others stay with probability p; lower risk factor means the no-run equilibrium is more robust to coordination failure, and the paper predicts fees < gates < baseline in this ranking.

redemption gate : a liquidity management tool that suspends fund withdrawals once cash reserves are depleted, theoretically preventing fire sales but experimentally found to be ineffective and potentially counterproductive due to the preemptive run incentive it creates for investors who fear losing access to their funds.

redemption fee : a liquidity management tool that charges a cost on fund withdrawals during periods of redemption stress, internalizing liquidation losses into the withdrawing investor’s payoff; experimentally found to significantly reduce withdrawal rates relative to both baseline and gates, but only after a learning period of approximately 10 periods.

The Cost of Consumer Collateral: Evidence From Bunching

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

Layer 1: Overview

This paper estimates the shadow cost that consumers assign to pledging their primary residence as collateral, using administrative loan application and performance data from the U.S. Federal Disaster Loan (FDL) Program, which offers low-interest loans to households following natural disasters. A loan amount threshold — set at $10,000 from 2005–2007, $14,000 from 2008–2013, and $25,000 from 2014–2018 — separates uncollateralized from collateralized borrowing, with no other loan terms changing at the threshold; this sharp, discontinuous design allows the paper to use bunching estimation to identify collateral aversion. Roughly one-third of all program borrowers, and 38% of those with losses above the threshold, choose exactly the maximum uncollateralized loan amount, generating sharp mass at the threshold. Traditional bunching estimates, corroborated by two alternative approaches using household-level damage data and originally requested loan amounts, consistently find that the median borrower is willing to forgo 40–47% of their potential loan amount to avoid pledging their home as collateral, equivalent in demand terms to a 200 basis point interest rate increase. The paper also exploits threshold variation over time as an instrument for collateralization and finds that posting collateral causally reduces default rates by approximately 35%, an effect comparable in magnitude to a 100-point increase in borrower credit score, establishing that collateral substantially mitigates moral hazard in consumer lending.

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 setting and why does it cleanly identify the collateral shadow cost?

The Federal Disaster Loan Program creates a quasi-experimental threshold because collateral is the only loan term that changes at the $25,000 boundary — interest rate, maturity, approval probability, and all other terms remain identical on both sides. Households with uninsured disaster damages (median $51,000) can borrow up to their loss amount at a fixed 2.5% rate; those requesting above the threshold must post their home as collateral. Unlike mortgage or auto loan markets, which always require collateral, and credit card markets, which never do, this program generates a setting where the binary collateral requirement is the borrower’s own choice subject only to the threshold, eliminating the standard endogeneity between contract terms and borrower risk. The authors use administrative data covering over 1 million applications from 2005 to 2018 across all 50 states.

Q2. How do the bunching estimates identify the distribution of collateral aversion?

The traditional bunching estimator fits a polynomial to the distribution of loan amounts below the threshold, extrapolates it above the threshold to construct a counterfactual without the collateral requirement, and attributes the excess mass at the threshold (the “missing” mass above) to collateral aversion. The bunching region spans from the threshold to the upper loan amount beyond which essentially no borrower would give up to avoid collateral; for the $25,000 threshold, this region extends to $49,900. Across all three threshold regimes, 73–78% of borrowers within the bunching region move to the threshold, and the estimated mean private value of collateral ranges from $7,944 (at the $10,000 threshold) to $18,268 (at the $25,000 threshold), representing 37–44% of ideal loan amounts. The median borrower’s collateral aversion — 39–47% depending on the threshold — is robust across all three estimation methods.

Q3. What alternative bunching methods does the paper develop and what do they find?

Two alternative estimation approaches — a difference-in-bunching (DiB) estimator that compares borrowers with identical damage levels across different threshold regimes, and an originally-requested-loan estimator that uses the amount households requested before collateral salience increased — both yield median collateral aversion estimates consistent with the traditional method (40–47%), while suggesting considerably wider heterogeneity in the tails. The DiB approach exploits the fact that for a household with $20,000 in damages, the same loan amount was uncollateralized under the $25,000 regime but required collateral under the $10,000 regime, enabling consumer-level identification. The originally-requested-loan method shows that 70% of eventual bunchers initially requested an amount above the threshold, with the majority of the shift occurring after meeting with a loan officer when the collateral requirement became salient. Both methods are immune to the standard counterfactual mis-specification concern of the traditional bunching approach, and they suggest the traditional estimator substantially under-predicts the proportion of highly collateral-averse borrowers at the upper end of the distribution.

Q4. What is the mechanism behind collateral aversion and what does heterogeneity reveal?

Collateral aversion in this setting is driven by both financial incentives and behavioral/preference factors, as evidenced by the finding that roughly 30% of borrowers already underwater on existing mortgages — who have no real equity to lose — still bunch at the threshold to avoid adding a lien on their home. More creditworthy borrowers (higher credit scores, higher incomes) are actually more likely to bunch, consistent with an “advantageous selection” interpretation in which borrowers who are confident in their repayment capacity are especially averse to the stigma and risk of pledging their home. Interest rates also matter: borrowers bunch more when program interest rates are higher, suggesting that financial incentives amplify the existing aversion. The magnitude of bunching — giving up thousands of dollars of subsidized low-interest disaster recovery loans — indicates that the perceived cost of a lien on the primary residence extends far beyond the financial value of potential foreclosure.

Q5. How does collateral causally reduce default rates?

Using time variation in the collateral threshold as an instrument for whether a borrower’s loan is collateralized — borrowers are more likely to collateralize when the threshold is low (so their ideal loan amount exceeds the low threshold) than when it is high — the paper estimates that collateral causally reduces default rates by about 35%. This local average treatment effect applies to borrowers who would collateralize under the $10,000 threshold but not under the $25,000 threshold, and the magnitude is comparable to a 100-point FICO score improvement. The finding implies that collateral requirements address genuine moral hazard in consumer lending: when a primary residence is pledged, borrowers take repayment obligations substantially more seriously, reducing the probability of strategic or precautionary default. This provides causal evidence for the classic prediction of models like Bester (1985) and Chan and Thakor (1987) that collateral mitigates information asymmetries and expands efficient credit access.

Q6. What are the aggregate implications and contribution to methodology?

In aggregate, borrowers in the program have given up more than $1.1 billion in disaster recovery loans to avoid posting collateral, indicating that collateral requirements — standard in most large consumer credit markets — impose large implicit costs that are not captured in stated interest rates. The methodological contribution is threefold: the paper is among the first to apply bunching to consumer (rather than corporate) collateral; it develops two consumer-level alternative estimators that relax assumptions of the standard method and reveal greater heterogeneity in collateral aversion; and it separately identifies the moral hazard effect of collateral from adverse selection by exploiting threshold variation, extending bunching beyond tax compliance and into household finance.

Key Concepts

collateral shadow cost : the implicit cost a borrower assigns to pledging collateral beyond the direct financial cost; measured in this paper as the maximum loan amount a borrower forgoes to avoid posting their home, identified from bunching mass at the collateral threshold in the FDL program.

bunching estimator : an estimation strategy that infers a structural parameter — here collateral aversion — from the excess density of agents at a policy threshold, using a polynomial-extrapolated counterfactual distribution to identify the missing mass above the threshold.

difference-in-bunching (DiB) estimator : a consumer-level alternative to traditional bunching estimation that uses borrowers with identical damage levels but facing different threshold regimes over time to construct a within-person counterfactual for the ideal loan amount, avoiding assumptions about the counterfactual distribution shape.

advantageous selection : the pattern in which more creditworthy, higher-income borrowers are the ones most likely to avoid collateral requirements, the reverse of the adverse selection typically assumed in collateral models; consistent with these borrowers having strong repayment intent independent of the lien.