Deposit Inflows and Outflows in Failing Banks: Martin, Puri & Ufier (2026)

What this is. The paper’s core results, estimating equations, and datasets: enough to know what the paper found and how, without reading the 43-page source. To replicate or extend, read the full article at doi.org/10.1111/jofi.70007.

TL;DR

Using confidential daily, account-level deposit records collected by the FDIC from a single failing U.S. bank (roughly $2 billion in assets, failed during the financial crisis), the paper documents that gross deposit inflows are as important as outflows for understanding bank funding under distress. Granja, Matvos, and Seru (2017) document the failed banks of the Great Recession and the resulting FDIC Deposit Insurance Fund costs that motivate this question. Deposit insurance (regular FDIC coverage, the FDIC-extended $250,000 limit, and the temporary TAG program) significantly reduces outflows from insured accounts. Simultaneously, the distressed bank raised deposit rates to the 95th percentile of the industry and attracted nearly $400 million in new insured term deposits, mostly from out-of-state credit unions and banks via internet listing services, replacing roughly one-third of its departing deposit base. These inflows are generalizable: a panel of over 2,000 U.S. banks facing regulatory enforcement actions shows similar shifts in deposit composition toward small and medium-term insured deposits and away from brokered and large-uninsured deposits. The central policy implication is that insured deposit inflows substantially weaken the depositor discipline otherwise exerted by uninsured outflows.

Core results

Magnitudes and significance are as reported in the paper. ***/**/* = 1%/5%/10%.

#	Result	Locator	Magnitude
R1	In the Formal (bank-specific distress) period, uninsured transaction accounts liquidate 18 pp faster than insured accounts	Table III, col. (4), p. 656	Uninsured coeff = 0.183*** (t = 8.45); compared to near-zero baseline in Placebo period (0.0249*)
R2	TAG temporary unlimited insurance reduces transaction-account liquidation as much as ordinary deposit insurance: the two coefficients are not statistically different (p-value 0.67 for equality)	Table III, col. (3), p. 656; text p. 659	TAG/DFA Eligible coeff = -0.0944** (t = -2.00) in Postcrisis; difference from Uninsured coeff not significant
R3	Uninsured term deposits are 14.7 pp more likely to liquidate in the Formal period; brokered/placed term deposits are 55 pp more likely to liquidate	Table VI, col. (4), p. 662	Uninsured coeff = 0.147*** (t = 2.92); Brokered/Placed coeff = 0.550*** (t = 25.76)
R4	Uninsured transaction depositors draw balances well below the insurance limit under stress: in the Formal period, 20.8% liquidate to less than $1, far above the 5-7% in earlier periods	Table VIII, Panel B, p. 665	Formal period: 20.79% in < $1 bin; Placebo: 4.98%
R5	The failed bank attracted nearly $400 million in new insured term deposits in its last year, roughly one-third of its deposit base, mostly in the 90 days before failure	Figure 3, p. 666; text p. 666	~$400M new insured term deposits; nearly all from institutional depositors (81% of 2,600 new accounts in Formal period)
R6	Higher deposit rate spreads are associated with more new deposit inflows: estimated interest elasticity of demand of 0.61, in line with Egan, Hortacsu and Matvos (2017)	Tables X-XI, p. 670-671; text p. 673	Rate Spread to Market coeff = 5.549*** (t = 2.71) in Table X col. (3); elasticity of demand calculated at 0.61
R7	The Formal period is the only period where new deposit inflows are significantly elevated after controlling for macroeconomic conditions and interest rates, confirming bank-specific (not macro) drivers	Table XI, col. (4), p. 671	Formal period dummy coeff = 5.124** (t = 2.32) on log new deposits; no other period significant after controls
R8	Banks under regulatory enforcement actions in the broad U.S. panel reduce brokered deposits by 1.24 pp and increase listing-service deposits by 0.826 pp; small-term ($<$100k) deposits rise by 0.364 pp, large-term ($>$250k) fall by 0.830 pp	Table XII, p. 679	Under Reg. Action coeff: Brokered = -1.24*** (t = -22.72); Listed = 0.826*** (t = 11.40); Sm. Term = 0.364*** (t = 5.21); Lg. Term = -0.830*** (t = -12.10)
R9	The shift toward listing-service deposits begins before the enforcement action and ramps up sharply after; brokered deposits decline sharply at enforcement, consistent with regulatory restrictions	Table XIII / Figure 5, pp. 678-680	Dynamic coefficients on Listed deposits turn positive at t+2 (0.477**), reaching ~1.2 pp at t>=5 (1.21***); Brokered coefficients: ~1.2 pp before enforcement (tau-4 = 1.17***), -3.85*** by t>=5
R10	Large banks (assets > $5B) under enforcement action raise 12-month CD spreads by a statistically significant 41 bps, paying on average ~75 bps above the FDIC national average, near the regulatory rate cap	Table XV, p. 683	Under Reg. Action coeff = 0.4121*** (t = 4.48); constant = 0.3547***

Overall (paper’s conclusion). Gross deposit inflows to a failing bank are first-order in magnitude: deposit insurance both reduces outflows and enables insured inflows that offset them. Focusing only on net outflows misses this mechanism and overstates the stabilizing power of depositor discipline. Temporary crisis-era guarantees such as TAG are as effective as standard deposit insurance. These results hold for the single failed bank in the micro data and generalize to over 2,000 U.S. banks under regulatory enforcement action (pp. 683-684).

Theory / model

The paper has no formal structural model. It builds on the framework of Egan, Hortacsu, and Matvos (2017), who model a market for insured and uninsured deposits where distressed banks raise deposit rates to attract insured funds while uninsured funds flee. It also builds on Diamond and Dybvig (1983), the foundational bank-run theory that motivates the role of deposit insurance in preventing runs. The paper tests this framework empirically with granular data. The core hypotheses are:

1. Outflow hypothesis. Deposit insurance (regular, temporary TAG/DFA) reduces the probability that a covered account liquidates, because insured depositors bear no principal risk from bank failure.
2. Inflow hypothesis. Even as uninsured funds leave, distressed banks raise deposit rates above market to attract new insured deposits, especially from rate-sensitive institutional depositors using internet listing services. The inflows are concentrated just below the insurance limit.
3. Depositor-discipline hypothesis. If insured inflows offset uninsured outflows, gross outflows overstate discipline; net outflows (as reported in Call Reports) understate it. Understanding bank runs requires decomposing gross flows.

The evidence on insured inflows informs the design of optimal deposit insurance, as modeled by Davila and Goldstein (2023).

Identification. The paper exploits two quasi-natural experiments:

• The October 2008 increase in FDIC insurance from $100,000 to $250,000 and the simultaneous introduction of the unlimited-guarantee TAG program provide sharp policy changes. An event study around the insurance limit change (a 36-day symmetric window) shows the daily liquidation probability for term deposits newly insured under the $250,000 limit falls from 0.0806 to 0.0512 (p. 664).
• For generalization, the paper uses the quasi-random timing of formal regulatory enforcement actions against a large panel of U.S. banks (2000-2016), comparing treated banks to untreated controls before and after enforcement, and a propensity-score-matched specification for robustness.

Method

The paper applies linear probability models (LPM), probit, Cox proportional hazard models, OLS time-series regressions, and propensity-score matching. It does not propose a new method; the contribution is entirely empirical.

The account-level approach extends Iyer and Puri (2012), an earlier account-level bank-run study, by using finer daily data and separating inflows from outflows.

Outflow regressions (Section II). The baseline is a cross-sectional LPM for each of four time periods (Placebo, Precrisis, Postcrisis, Formal), separately for transaction and term accounts. This builds on panel-regression and linear-probability-model primitives. Robustness uses probit-regression and Cox proportional hazard models (Tables IV, VII), giving consistent results.

Inflow regressions (Section III). The characteristics of new versus extant depositors are compared via OLS (equation 2, p. 667). The daily volume of new deposits is explained with OLS and Newey-West standard errors (Tables X-XI, equations 3, p. 669).

Generalization panel (Section IV). The treatment of a formal enforcement action is identified by two OLS specifications (equations 4-5, pp. 676-677) and a propensity-score-matched comparison (equation 6 for large banks, p. 682). These build on panel-regression, event-study, and matching primitives.

Empirical specifications

Outflow regressions (Core results R1-R4)

Baseline LPM (equation 1, p. 653):

$$\mathbf{1}(\text{Liquidation}_i) = \alpha + \beta \times \text{Controls}_i + \delta_b + \epsilon_i$$

Where subscript $i$ indexes deposit accounts; $\delta_b$ is a set of branch fixed effects. $\text{Liquidation}_i$ = 1 if the account balance falls by 75% or more relative to start-of-period and stays at or below 25% of the starting balance for at least 61 days. $\text{Controls}_i$ includes: Uninsured dummy, TAG/DFA Eligible dummy, Checking dummy, Direct Deposit dummy, Log(Age), Prior Transactions, $\text{Prior Transactions}^2$, Institutional-Any dummy, Trust dummy, Brokered/Placed dummy (term only), Log(Days to Maturity) (term only). Standard errors are asymptotically normal. Run separately for each of four periods and for transaction vs. term deposits (Tables III, VI). Alternative specifications use probit (marginal effects) and Cox proportional hazard models (Tables IV, VII).

Interest rate controls added in Table V (Postcrisis and Formal periods only): rate spread to market and past-month fees are added to equation 1 for both transaction and term deposits.

New-depositor characteristics (equation 2, p. 667)

$$\mathbf{1}(\text{Characteristic}_{i,t}) = \alpha + \sum_{t=1}^{5} \beta_t \times \text{Time Period Dummy}_t \times \mathbf{1}(\text{Extant Depositor}_{i,t}) + \epsilon_{i,t}$$

OLS, N = 188,834, six separate regressions (one per account characteristic as dependent variable). Omitted category is extant depositors in the Placebo period.

Time-series inflow regressions (equations 3, p. 669)

$$y_t = \alpha + \beta \times \text{Time Period Dummy}_t + \gamma \times X_t + \epsilon_t$$

$y_t$ is either the share of deposits that are new that day (Table X) or the log of dollar volume of new deposits (Table XI). $X_t$ includes Log(VIX), GDP Growth, Housing Starts, Daily S&P 500 Return, AR(1) term, OFR Financial Stress Index, Rate Spread to Market (dollar-weighted average). OLS with Newey-West standard errors (lag length 9, Newey-West rule of thumb).

Generalization: panel with regulatory action dummy (equations 4-5, pp. 676-677)

$$y_{j,t} = \alpha + \beta \times \text{Under Reg. Action}_{j,t} + \gamma \times X_{j,t} + \delta_j + \zeta_t + \epsilon_{j,t} \tag{4}$$ $$y_{j,t} = \alpha + \sum_{i=-4}^{\geq 5} \beta_i \times \text{Under Reg. Action}_{j,t=\tau+i} + \gamma \times X_{j,t} + \delta_j + \zeta_t + \epsilon_{j,t} \tag{5}$$

$y_{j,t}$ is a funding-share outcome for bank $j$ at quarter $t$. Bank and quarter fixed effects ($\delta_j$, $\zeta_t$). $X_{j,t}$ includes NPL/Assets, one-year asset growth rate, log assets, deposits/assets, term deposits/assets. Panel covers ~10,000 U.S. banks, 2000-2016, quarterly. Equation (4) produces Table XII; equation (5) produces Table XIII and Figure 5. A propensity-score- matched specification (Table XIV) and a pooled OLS for large banks (equation 6, p. 682, Table XV) round out Section IV.

Datasets used

Dataset	Role in paper	Wiki page
FDIC confidential account-level deposit microdata	Daily account balances and transactions for the one failed bank (early 2006 to failure); source of all Section II-III results	No page yet
FDIC Call Reports (public)	Panel of ~10,000 U.S. banks for the generalization analysis (Section IV); funding-share outcomes and bank-level controls	No page yet
FDIC confidential supervisory data	Identifies enforcement actions (C&D orders, less-than-well-capitalized status) and brokered-deposit waivers for the generalization panel	No page yet
RateWatch deposit rate surveys	12-month CD rate spreads for large banks under enforcement action (Section IV.B); branch-level weekly survey data	No page yet

Sample (single bank): daily, from early 2006 to bank failure (approximately late 2010-early 2011). Generalization panel: quarterly, 2000-2016 (brokered deposits); 2011-2016 (listing-service deposits); 2010-2016 (term deposits by size). Total observations in panel regressions: up to 554,180 bank-quarters.

When to read the full paper

Read the original if you are: studying bank-run dynamics and need account-level evidence on the gross inflow-outflow decomposition; designing deposit insurance policy (insurance limit levels, temporary guarantees, rate caps); testing or calibrating models of depositor behavior under distress (the EHM 2017 framework empirically); or analyzing the stability of different deposit categories (checking vs. savings vs. CDs, insured vs. uninsured, brokered/placed vs. listing-service). The Internet Appendix (Appendix S1, referenced p. 685) contains additional robustness tables cited throughout.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 81(2), April 2026. This article is a U.S. Government work and is in the public domain in the USA (stated on PDF p. 643). This distillation was extracted by an LLM on 2026-06-01 and is not human-verified or independently reproduced. The underlying data are confidential FDIC supervisory records; replication requires FDIC data access.

Martin, Christopher, Manju Puri, and Alexander Ufier. “Deposit Inflows and Outflows in Failing Banks: The Role of Deposit Insurance.” The Journal of Finance 81, no. 2 (April 2026): 643-685. DOI: 10.1111/jofi.70007. U.S. Government work; public domain in the USA. This page is an extract-only distillation by the Institute for Automated Research; it is not a verbatim reproduction of the article.