Deposit Insurance and LLP Discretion: Pugachev, Robin, Wang & Yang (2026)

What this is. The paper’s core results, the hypotheses it tests, the LLP prediction model used to measure accounting discretion, and the difference-in-differences specifications: enough to know what it found and how, without reading all 24 pages. To replicate or extend it, read the full source at doi.org/10.1016/j.jcorpfin.2026.102995.

TL;DR

The paper studies whether the Emergency Economic Stabilization Act (EESA) of 2008, which increased the FDIC deposit insurance ceiling from $100,000 to $250,000, changed bank accounting behavior. It uses a difference-in-differences design, exploiting the fact that 126 Massachusetts state-chartered savings banks and cooperatives were already fully insured by private state deposit insurance (DIF/SIF) and thus experienced no change in coverage. Relative to these controls, banks exposed to the EESA shock shift toward more conservative (income- and capital-reducing) discretionary loan loss provisioning. The effect is approximately 3.4 basis points of lagged loans (38% of the mean LLP level). Effects are strongest for banks that increased risk most and for those subject to the greatest regulatory scrutiny, consistent with two channels: DI-induced risk-taking prompting demand for conservative reporting, and heightened regulator incentives once the insurer’s exposure rises.

Core results

Magnitudes and significance are as reported; \*/\*\*/\*\*\* = 10%/5%/1%. All regressions include bank and quarter fixed effects with standard errors clustered by bank. DLLP is the discretionary component of LLP scaled to units where each unit = 10 basis points of lagged loan portfolio (LLP multiplied by 1,000).

#	Result	Locator	Magnitude
R1	DI coverage fraction (INSDEP) positively predicts conservative LLP in broad 75-quarter panel	Table 4, Col. 1, p. 10	INSDEP = 1.880*** (t = 7.857); n = 123,310 bank-quarters; result holds excluding crisis years and in post-EESA subsample
R2	Treated banks shift to more conservative DLLP relative to controls after EESA (main DiD result)	Table 5, Col. 1, p. 11	TREATPOST = 0.336*** (t = 2.769); equivalent to ~3.4 bps of lagged loans; 38% of the mean LLP level of 8.88 bps
R3	Banks with highest fraction of newly insured deposits shift most toward conservatism (intensive margin)	Table 7, Col. 1, p. 13; Fig. 4, p. 13	Q5NIDEPPOST = 0.577** (t = 2.323); DLLP change is monotone across DI-exposure quintiles
R4	Banks that increase nonperforming loans most post-EESA shift most toward conservatism (risk channel)	Table 7, Col. 3, p. 13	Q5NPLPOST = 2.442*** (t = 6.878); approximately seven times the full-sample baseline coefficient
R5	Banks whose z-score falls most post-EESA shift most toward conservatism (risk channel)	Table 7, Col. 2, p. 13	Q1ZSCOREPOST = 1.232*** (t = 4.400); approximately four times the full-sample baseline coefficient
R6	Least-capitalized banks shift most toward conservatism (regulatory scrutiny channel)	Table 7, Col. 4, p. 13	Q1T1CAPITALPOST = 0.635** (t = 2.028); consistent with regulators focusing on banks closest to the default boundary
R7	Among treated banks, only those provisioning opportunistically pre-EESA shift toward conservatism; pre-conservative banks do not shift or shift back	Table 6, Cols. 1 and 4, p. 12	TREATPOSTDLLP<0 = 0.440*** (t = 2.728); POSTHPRE_DLLP = -0.545** (t = -2.413); consistent with strategic constraints on banks already at or past their conservatism target

Overall (paper’s conclusion). DI expansion increases conservative LLP discretion through two non-mutually exclusive channels: the risk-taking channel (DI-induced moral hazard leads banks to take more risk, prompting creditors and regulators to demand conservative accounting) and the regulatory scrutiny channel (the insurer’s larger exposure heightens diligence). The finding contrasts with Huang (2021), who shows that deregulation reduced accounting conservatism among public bank borrowers; here the regulatory tightening from expanded DI moves bank provisioning in the opposite direction. From a policy perspective, this suggests US bank regulators provide sufficient monitoring to limit the moral hazard problem associated with DI, because the risk-taking behavior induced by DI is mitigated through the accounting channel.

Theory / model

The paper has no formal structural model. It develops two hypotheses and an identification strategy.

H1 (p. 4): Banks that experience greater DI coverage adopt more conservative LLP discretion relative to other banks. The prediction follows from two mechanisms that DI activates simultaneously:

1. Risk channel: DI reduces depositor monitoring incentives, allowing banks to take more risk, as Calomiris and Jaremski (2019) document across US banking history. Riskier banks face greater demand from creditors and equity-holders for conservative accounting (loss recognition that lowers reported income and erodes capital), as established by Beatty and Liao (2014), Kim et al. (2013), and Balakhrishnan et al. (2016).

2. Regulatory scrutiny channel: DI shifts the monitoring role from depositors to bank regulators (the FDIC). Regulators are known to prefer conservative accounting (Qiang (2007), Lobo and Zhou (2006)), and DI expansion increases their incentive to enforce conservative practices as their exposure grows.

Identification strategy. The paper exploits the EESA of 2008, which increased the FDIC deposit insurance ceiling from $100,000 to $250,000 per account. Diamond and Dybvig (1983) established that DI stabilizes banking systems by converting deposits to a risk-free asset; the EESA shock re-prices the coverage for roughly all US banks. The key identification assumption is that Massachusetts state-chartered savings banks and cooperatives are unaffected by EESA: their deposits were already fully insured by private state schemes (DIF/SIF) initiated in the 1930s. Assignment to treated vs. control status is thus pre-determined by a decision to incorporate in Massachusetts as a state-chartered savings or cooperative institution, typically 200 years before EESA (p. 2), satisfying the exogeneity requirement for DiD.

To improve comparability, the paper applies propensity score matching (PSM) on 22 pre-shock bank characteristics, selecting treated banks that resemble controls on observable traits. Pre-shock parallel trends in DLLP are documented in Figure 1 (p. 8), supporting DiD validity. A battery of placebo tests and three alternative control samples (non-Massachusetts state-chartered savings banks; Massachusetts federally chartered savings banks; Massachusetts state-chartered commercial banks) confirm robustness.

Method

The method has two steps: constructing the discretionary LLP measure, then running the DiD.

Step 1: LLP discretion (Eq. 1, p. 4). Following Nicoletti (2018) with bank fixed effects added, the paper estimates a predicted LLP for each bank-quarter from its loan portfolio fundamentals:

$$\text{LLP}_{b,t} = \alpha_1 \text{DNPL}_{b,t+1} + \alpha_2 \text{DNPL}_{b,t} + \alpha_3 \text{DNPL}_{b,t-1} + \alpha_4 \text{DNPL}_{b,t-2} + \alpha_5 \text{EBLLP}_{b,t}$$ $$+ \alpha_6 \text{TIER1}_{b,t-1} + \alpha_7 \text{LSIZE}_{b,t-1} + \alpha_8 \text{DLOAN}_{b,t} + \mu_b + \tau_t + \varepsilon_{b,t} \tag{1}$$

where LLP is scaled to 1,000 bps of lagged loans; subscripts $b$ and $t$ index bank and quarter; $\text{DNPL}$ captures changes in nonperforming loans; $\text{EBLLP}$ is earnings before LLP and taxes; $\text{TIER1}$ is the Tier 1 capital ratio; $\text{LSIZE}$ is log assets; $\text{DLOAN}$ is loan growth; $\mu_b$ are bank fixed effects; $\tau_t$ are quarter fixed effects. Appendix B (p. 22) reports coefficient estimates. The residuals from Eq. (1), denoted $\text{DLLP}_{b,t}$, measure discretionary LLP: positive (negative) values indicate more conservative (opportunistic) provisioning than fundamentals predict.

Step 2: Broad-sample panel regression (Eq. 2, p. 8). To establish the motivating association before the DiD, the paper estimates:

$$\text{DLLP}_{b,t} = \beta_1 \text{INSDEP}_{b,t} + \gamma' \text{Controls} + \mu_b + \tau_t + \varepsilon_{b,t} \tag{2}$$

where $\text{INSDEP}_{b,t}$ is the fraction of bank $b$‘s deposits below the FDIC insurance limit. $\beta_1$ measures the contemporaneous relationship between DI coverage and LLP conservatism over the full 75-quarter broad panel (n = 123,310 bank-quarters). Because $\text{DLLP}$ is a residual used as a dependent variable, the paper follows Chen et al. (2018) and includes all first-stage controls in the second-stage regression to mitigate bias from using residuals as dependent variables.

Empirical specifications

Main DiD specification. In Eq. (2), $\text{INSDEP}$ is replaced by the treatment indicator:

$$\text{DLLP}_{b,t} = \beta_1 \text{TREATPOST}_{b,t} + \gamma' \text{Controls} + \mu_b + \tau_t + \varepsilon_{b,t} \tag{3}$$

where $\text{TREATPOST}_{b,t} = 1$ for all banks in the PSM-matched treated sample in every quarter after 3Q2008 (the EESA passage date). Controls include past, present, and future changes in nonperforming loans ($\text{DNPL}$), earnings before LLP ($\text{EBLLP}$), Tier 1 capital ($\text{TIER1}$), log assets ($\text{LSIZE}$), and loan growth ($\text{DLOAN}$). All continuous variables are winsorized at 1% tails. Standard errors are clustered by bank. Bank and quarter fixed effects are included in all specifications.

Sample period: 4Q2005-4Q2011 (12 quarters before and 12 quarters after EESA). Final PSM sample: 9,568 bank-quarters (314 treated, 74 control banks). Observations require both periods to exist (no survivorship-biased exits), total assets above $25 million, no quarterly asset growth above 10% (to exclude acquisition-driven changes), and at most 25% missing values.

Intensive-margin specification. To test whether effects scale with DI exposure, $\text{TREATPOST}$ is replaced by $\text{Q5NIDEPPOST}$, an indicator for banks in the highest quintile of the fraction of 3Q2008 deposits in the $100K-$250K range (the newly insured band). Following Lambert et al. (2017), the paper estimates newly insured deposits as the value of 3Q2009 deposits above the $250K ceiling minus the value of 3Q2008 deposits above the $100K ceiling, scaled by 3Q2008 total deposits. Table 7 Column 1 (p. 13) reports results from the treated-bank sample only; control banks are replaced by lower-exposure treated banks.

Channel specifications. To decompose mechanisms, $\text{TREATPOST}$ is interacted with quintile indicators for four cross-sectional variables measured pre-to-post EESA: change in z-score ($\text{Q1ZSCOREPOST}$), change in NPL ($\text{Q5NPLPOST}$), Tier 1 capital pre-EESA ($\text{Q1T1CAPITALPOST}$), and bank size pre-EESA ($\text{Q5SIZEPOST}$). These indicators equal one for banks in the highest risk-change or regulatory-scrutiny quintile. Table 7 Columns 1-5 (p. 13) report each interaction separately; Column 6 includes all interactions simultaneously.

Pre-shock heterogeneity. Table 6 (p. 12) adds $\text{TREATPOSTDLLP<0}$, which interacts $\text{TREATPOST}$ with an indicator for banks with negative average pre-EESA DLLP (opportunistic provisioners). This tests whether the shift toward conservatism is concentrated in banks that had room to become more conservative.

Robustness. Three alternative DLLP constructions (Kanagaretnam et al. 2010; Bushman and Williams 2012; Basu et al. 2020) and three alternative control samples yield similar $\beta_1$ estimates (Table 8, p. 15-16). Placebo tests using randomly assigned treatment or placebo event windows yield insignificant coefficients. The crisis period (2007-2010) is also excluded in one robustness column to address the concern raised by Huizinga and Laeven (2012) that LLP discretion systematically rises during financial crises; the TREATPOST coefficient remains similar in the non-crisis subsample.

Datasets used

Dataset	Role in paper	Wiki page
FDIC Statistics on Depository Institutions (SDI)	Main bank financial data: LLP, nonperforming loans, earnings, Tier 1 capital, total assets, loan balances, deposit composition (core, large, demand deposits), securities, write-offs; also TAG participation	no page yet
Census Bureau	County-level unemployment rate (UNEMP) for bank’s main office county; matching covariate	no page yet
Federal Housing Finance Agency (FHFA)	County-level housing price index (LHPI) for bank’s main office county; matching covariate	FHFA House Price Index
U.S. Treasury	TARP Capital Purchase Program participation indicator; matching covariate	no page yet

Sample: 4Q2005-4Q2011 (PSM baseline). Broad panel for motivating regressions: 1999-2018 (75 quarters, 123,310 observations). Data availability statement: “Data are available from the public sources cited in the text” (p. 22).

When to read the full paper

Use the original if you are: studying how banking regulation affects accounting discretion; designing DiD studies using EESA as a natural experiment for DI-related hypotheses; comparing the Nicoletti (2018) and Beatty and Liao (2014) LLP discretion constructions; or investigating whether regulatory intervention can attenuate moral hazard from deposit insurance. The exact PSM procedure (22 matching variables, Table 2, p. 7) and the placebo designs (Table 8, p. 15-16) are important for replication.

Attribution and rights

Source: peer-reviewed, Journal of Corporate Finance 99, 2026, article 102995. This distillation was extracted by an LLM on 2026-06-26 and is not human-verified or independently reproduced. The paper is paywalled (© 2026 Elsevier B.V. All rights reserved); only textual extract is provided here.

Pugachev, Leo, Ashok Robin, Dilin Wang, and Rong Yang. “Deposit insurance and discretion in loan loss provisioning.” Journal of Corporate Finance 99 (2026): 102995. DOI: 10.1016/j.jcorpfin.2026.102995. © 2026 Elsevier B.V. All rights reserved. Extract-only; not reproduced.