Skip to content

Bail-Ins, Optimal Regulation, and Crisis Resolution: Clayton & Schaab (2025)

Distilled by claude-sonnet-4-6 · extracted Jun 6, 2026, verified Jun 6, 2026

JEL (IAR-assigned): G28, G33 · assigned from the abstract, not the journal

Full structured metadata (methods, scope, relatesTo, topics, datasets): raw Markdown (.md)

paper-summarybankingfinancial-regulationcapital-structurebail-insbank-resolutionoptimal-contractingpeer-reviewedunreplicated

What this is. This is a distilled skeleton of the paper for search and navigation. Read the original at Oxford Academic to replicate or extend.

Clayton and Schaab build a tractable three-period dynamic contracting model of bank liability structure in the tradition of Innes (1990). Banks face an initial and a continuation monitoring incentive problem. In the presence of fire sales from liquidations, the privately optimal contract combines short-term standard debt (which forces liquidation in bad states and provides strong incentives) and long-term bail-in debt (which avoids resource costs of liquidation by writing down to pledgeable income). A social planner that internalizes the fire-sale externality intervenes in both the level and the composition of debt: it prefers less standard debt (a leverage cap / maximum leverage requirement) and less total debt (a TLAC requirement satisfiable with bail-in debt). The model shows that bail-ins replace bailouts as a recapitalization tool and that statutory provisions increasing the cost of bailouts improve welfare, providing a unified rationalization of postcrisis regulation. The framework connects to demand-based explanations of standard debt (Bolton and Oehmke (2019); Walther and White (2020)) and extends the macroprudential literature on pecuniary externalities from Davila and Korinek (2018) to the bank contracting setting.

#ResultLocatorMagnitude as reported
R1Privately optimal contract uses both standard and bail-in debtProposition 1, Corollary 1, pp. 2822-2825Liability structure has three regions: liquidation (R1RpR_1 \le R_\ell^p), bail-in write-down (Rp<R1RupR_\ell^p < R_1 \le R_u^p), and no write-down (R1>RupR_1 > R_u^p); implemented with short-term standard debt face value (1b)RpY0(1-b)R_\ell^p Y_0 and long-term bail-in debt face value (1b)(RupRp)Y0(1-b)(R_u^p - R_\ell^p)Y_0
R2Social optimum has same structure as private optimum but with additional wedges on standard and total debtProposition 3, Equations (24)-(28), pp. 2827-2829Socially optimal thresholds RsRpR_\ell^s \le R_\ell^p and RusRupR_u^s \le R_u^p: planner uses less standard debt and less total debt than private banks; wedges τs0\tau_\ell^s \ge 0 and τus0\tau_u^s \ge 0 reflect social cost of liquidations λsσγs\lambda^s \sigma \gamma^s
R3All three model ingredients (initial incentive problem, continuation incentive problem, costly liquidation) are needed for bail-in debt to be part of the optimal contractProposition 2, p. 2826If B0(e0)=0B_0(e_0) = 0: bail-in debt alone suffices. If B1=0B_1 = 0: long-term debt alone suffices. If γ=1\gamma = 1: standard debt alone suffices. All three ingredients are needed for the combined structure
R4With planner commitment over bailouts, bail-ins dominate bailouts: the socially optimal contract with no bailouts is Pareto efficientProposition 4, p. 2832No bailouts (T0=T1=0T_0 = T_1 = 0) is Pareto efficient; bailouts are redundant recapitalization when bail-ins are available; both instruments can achieve same state contingencies in bank debt contracts
R5Without planner commitment, Pareto-efficient debt levels still eliminate bailouts entirely; welfare is increasing in the cost of bailouts FProposition 5, p. 2834For any F0F \ge 0, Pareto-efficient debt levels (Rs,Rus)(R_\ell^s, R_u^s) result in no banks being bailed out; welfare is strictly increasing in F because higher F relaxes the no-bailout constraint (Equation 29)

Overall (paper’s conclusion). The paper provides a single contracting framework that rationalizes both a maximum leverage requirement and a TLAC requirement as jointly optimal responses to fire sale externalities. Bail-in debt is the instrument of choice because it combines the incentive properties of equity with the cash-flow transfer properties of standard debt, making it superior to outside equity as a loss-absorbing instrument. Bail-ins replace bailouts in the regulatory toolkit; statutory provisions that increase the cost of engaging in bailouts complement bail-in regulation by allowing looser regulatory constraints while still preventing bailouts in equilibrium.

The model has three periods (t=0,1,2t = 0, 1, 2), a unit continuum of banks, penniless investors, and arbitrageurs. Banks invest in a firm of variable scale Y0=A0+I0>0Y_0 = A_0 + I_0 > 0 using their own equity A0>0A_0 > 0 and investor funds I00I_0 \ge 0 (p. 2815).

Project quality shocks. At each of dates 1 and 2, the bank experiences a stochastic quality shock Rt[R,R]R_t \in [\underline{R}, \overline{R}] that adjusts project scale to Yt=RtYt1Y_t = R_t Y_{t-1}, so final scale is Y2=R1R2Y0Y_2 = R_1 R_2 Y_0. The project pays one unit of the consumption good per unit of final scale at date 2 but yields no dividend on date 1. Shocks RtR_t are independent and idiosyncratic with densities (p. 2815):

ft(Rtet1)=et1ftH(Rt)+(1et1)ftL(Rt).f_t(R_t | e_{t-1}) = e_{t-1} f_{tH}(R_t) + (1 - e_{t-1}) f_{tL}(R_t).

Effort and MLRP. Date 0 effort is continuous, e0[0,1]e_0 \in [0,1]; date 1 effort is binary, e1{0,1}e_1 \in \{0,1\}. Higher effort increases the quality of the return distribution. The paper assumes the monotone likelihood ratio property (MLRP): the likelihood ratio Λt(Rt)ftH(Rt)/ftL(Rt)\Lambda_t(R_t) \equiv f_{tH}(R_t) / f_{tL}(R_t) is increasing in RtR_t.

Private benefits. The banker’s date 0 private benefit is B0(e0)Y0B_0(e_0) Y_0, where B0B_0 is decreasing and concave with B0(1)=0B_0(1) = 0. The banker’s date 1 private benefit is (1e1)B1Y1(1 - e_1) B_1 Y_1 for 0<B1<10 < B_1 < 1 (p. 2816).

Resource constraints. Along a history (R1,R2)(R_1, R_2), investor repayment x1(R1)x_1(R_1), x2(R1,R2)x_2(R_1, R_2) and bank consumption c1(R1)c_1(R_1), c2(R1,R2)c_2(R_1, R_2) satisfy (p. 2817):

c_1(R_1) + x_1(R_1) = \alpha(R_1) \gamma R_1 Y_0, \tag{1}

c_2(R_1, R_2) + x_2(R_1, R_2) = (1 - \alpha(R_1)) R_1 R_2 Y_0. \tag{2}

Limited liability requires c1(R1),c2(R1,R2)0c_1(R_1), c_2(R_1, R_2) \ge 0.

Investor participation constraint. Investors must at least break even (p. 2818):

Y_0 - A_0 \le \mathbb{E}\bigl[x(R_1) \mid e_0 = e_0^*\bigr]. \tag{6}

Fire sale and liquidation price. A representative arbitrageur with borrowing constraints generates a fire sale externality. The equilibrium liquidation price γ\gamma satisfies the market-clearing condition (pp. 2820-2821):

\gamma(\Omega) = \frac{\partial \mathcal{F}(\Omega)}{\partial \Omega}, \quad \Omega = \int_{\underline{R}}^{\overline{R}} \alpha(R_1) R_1 f_1(R_1 | e_0^*) \, dR_1. \tag{16}

When 2F/Ω2<0\partial^2 \mathcal{F} / \partial \Omega^2 < 0, more liquidations reduce the price, creating the fire sale. The liquidation price elasticity is σ=(Ω/γ)(γ/Ω)\sigma = -(\Omega / \gamma)(\partial \gamma / \partial \Omega).

Date 1 incentive compatibility. High effort e1(R1)=1e_1^*(R_1) = 1 is incentive compatible if (p. 2818):

\mathbb{E}[c_2(R_1, R_2)(\Lambda_2(R_2) - 1) \mid e_1 = 0] \ge B_1 R_1 Y_0. \tag{9}

Date 0 optimal effort. The bank’s optimal date 0 effort e0e_0^* satisfies (p. 2819):

-B_0'(e_0^*) Y_0 = \mathbb{E}_0[c(R_1)(\Lambda_1(R_1) - 1) \mid e_0 = 0]. \tag{12}

This is a pure theory paper. The model is solved by characterizing the set of feasible contracts (satisfying limited liability, resource constraints, investor participation, repayment monotonicity, and incentive compatibility at both dates), then finding the contract that maximizes bank expected utility subject to those constraints.

Pledgeability reduction (Lemma 1, p. 2822). The binary date 1 effort problem reduces to a Holmstrom and Tirole (1997) style pledgeability constraint. The optimal contract sets x1(R1)=0x_1(R_1) = 0 and repays investors on date 2 at a threshold R2u(R1)R_2^u(R_1). Bank high effort on date 1 is incentive compatible if and only if c(R1)bR1Y0c(R_1) \ge b R_1 Y_0, where b=R2uR[R2R2u]f2H(R2)dR2b = \int_{\overline{R}_2^u}^{\overline{R}} [R_2 - \overline{R}_2^u] f_{2H}(R_2) dR_2 is a constant (p. 2822, Equation 18).

Mapping to promised liabilities. The paper maps actual-repayment contracts to promised “face value” liabilities L(R1)L(R_1): if L(R1)(1b)R1Y0L(R_1) \le (1-b)R_1 Y_0 the bank avoids liquidation; if L(R1)>(1b)R1Y0L(R_1) > (1-b)R_1 Y_0 the bank is liquidated with actual repayment γR1Y0\gamma R_1 Y_0 (p. 2822).

First-order conditions for the private optimum (Proposition 1, pp. 2822-2823). The privately optimal liquidation threshold RpR_\ell^p and total debt threshold RupR_u^p satisfy:

\underbrace{\frac{1 - \Lambda_1(R_\ell^p)}{(1 - e_0^*) + e_0^* \Lambda_1(R_\ell^p)} \frac{1}{|B_0''(e_0^*)|}}_{\text{Incentive Provision}} b \lambda G = \underbrace{b + \lambda(1 - b - \gamma)}_{\text{Liquidation Costs}}, \tag{20}

\underbrace{\frac{F_{1L}(R_u^p) - F_{1H}(R_u^p)}{|B_0''(e_0^*)|}}_{\text{Incentive Provision}} \lambda G = \underbrace{(\lambda - 1)(1 - F_1(R_u^p | e_0^*))}_{\text{Investor Repayment}}, \tag{21}

where λ>1\lambda > 1 is the Lagrange multiplier on the investor participation constraint and G=RRpγR1(f1H(R1)f1L(R1))dR1+RpR(1b)min{R1,Rup}(f1H(R1)f1L(R1))dR1G = \int_{\underline{R}}^{R_\ell^p} \gamma R_1 (f_{1H}(R_1) - f_{1L}(R_1)) dR_1 + \int_{R_\ell^p}^{\overline{R}} (1-b) \min\{R_1, R_u^p\} (f_{1H}(R_1) - f_{1L}(R_1)) dR_1.

Social planner’s first-order conditions (Proposition 3, pp. 2827-2828). The planner’s optimum satisfies the same structural equations as the private optimum but with wedge terms +τs+\tau_\ell^s and τus-\tau_u^s on the right-hand sides of Equations (24) and (25):

\tau_\ell^s = \left(1 - \frac{1 - \Lambda_1(R_\ell^s)}{(1 - e_0^s) + e_0^s \Lambda_1(R_\ell^s)} \frac{1}{|B_0''(e_0^s)|} b L^s\right) \lambda^s \sigma \gamma^s \ge 0, \tag{26}

\tau_u^s = \frac{F_{1L}(R_u^s) - F_{1H}(R_u^s)}{|B_0''(e_0^s)|} L^s \sigma \gamma^s \ge 0, \tag{27}

where σ\sigma is the liquidation price elasticity and γs\gamma^s is the equilibrium liquidation price. The wedge τs\tau_\ell^s reflects the social cost of additional liquidations induced by standard debt; τus\tau_u^s reflects the social cost of more total debt through the indirect effort channel.

No-bailout constraint (Proposition 5, p. 2833). In the case without planner commitment, the planner does not bail out banks if the total losses from liquidations are high enough:

(1 - \gamma(\Omega)) \Omega Y_0 \le F. \tag{29}

This is a pure theory paper with no empirical component. There are no regressions, no estimation equations, and no data. The paper characterizes optimal contracts and regulatory instruments analytically. The special case of linear private benefits (B0(e0)=b0(1e0)B_0(e_0) = b_0(1-e_0)) is worked out in Internet Appendix B.3 as a closed-form tractable illustration.

DatasetRole in paperWiki page
NonePure theory paper; no data usedN/A

Read Clayton and Schaab (2025) if you are designing or evaluating:

  • Optimal bank capital structure rules combining leverage caps and TLAC requirements
  • The comparison between bail-in debt and outside equity as loss-absorbing instruments (Proposition 2 and Section 2.2.1), extending Dewatripont and Tirole (1994)
  • The rationale for statutory provisions that increase the cost of bailouts, such as Dodd-Frank Act restrictions (Section 4.2); connects to Farhi and Tirole (2012) and Chari and Kehoe (2016)
  • The good bank / bad bank approach to resolving large banks with partial liquidations (Section 5.2)
  • The connection between the incentive-based explanation and the result of Keister and Mitkov (2023) that anticipated bailouts suppress bail-in debt issuance
  • The connection between the incentive-based and demand-based (safety premium) explanations for the coexistence of standard and bail-in debt (Section 5.3)

Clayton, C., and A. Schaab. 2025. “Bail-Ins, Optimal Regulation, and Crisis Resolution.” The Review of Financial Studies 38(9): 2810-2843. https://doi.org/10.1093/rfs/hhaf002.

Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For commercial re-use contact reprints@oup.com. This page is an LLM-distilled summary (extract-only); it is not human-verified and has not been reproduced. Read the original at the DOI above.

Found an error or want a topic covered? Open an issue, use the Edit page link above, or email contact@instituteforautomatedresearch.org. Edits are reviewed before publishing; provenance and accuracy are the point.