WORKING PAPER SERIES 

Deposit Inflows and Outflows in Failing Banks: 

The Role of Deposit Insurance 

Christopher Martin 

Federal Deposit Insurance Corporation 

Manju Puri 

Duke University 

Federal Deposit Insurance Corporation 

National Bureau of Economic Research 

Alexander Ufier 

Federal Deposit Insurance Corporation 

May 2018 

Last Updated: July 2022 

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Federal Deposit Insurance Corporation• Center for Financial Research 
Deposit Inflows and Outflows in Failing Banks:

The Role of Deposit Insurance

Christopher Martin*, Manju Puri—, and Alexander Ufier*

July 15, 2022

Abstract

Using unique, daily, account-level balances data we investigate the drivers of deposit outflows and inflows in a distressed bank. We observe an outflow of uninsured

depositors from the bank following bad regulatory news. Both regular and temporary

deposit insurance measures reduce the outflow of deposits. We provide important new

evidence that, simultaneous with deposit outflows, deposit inflows are large and of

first-order impact — a result which is missed when looking at aggregated deposit data

alone. Outflows of uninsured deposits were largely offset with inflows of new insured

deposits as the bank approached failure, with the bank increasing term deposit interest

rates. We show this phenomenon holds more generally in a large sample of banks that

faced regulatory action. Our results suggest that inflows into insured deposits are an

important mechanism that weakens depositor discipline.

Keywords: deposit insurance, deposit inflows, funding stability, depositor discipline

JEL Classification: G21, G28, D12, G01

Views and opinions expressed in this paper reflect those of the authors and do not necessarily

reflect those of the Federal Deposit Insurance Corporation or the United States.

We thank seminar and conference participants at the Adam Smith Workshops (Paris), AFA Annual Meetings, Avoiding and Resolving Banking Crises Conference (Amsterdam), Banking Research Conference, Basel

Research Task Force Conference, Biennial International Association of Deposit Insurers Research Conference (Basel), Chicago Financial Institutions Conference, Conference for Banking Development Stability

and Sustainability (Santiago), Conference on Financial Stability: Markets and Spillovers, Duke University,

FMA Annual Meeting, Interagency Early Warning Model Workshop, EEA Conference, Federal Financial

Institutions Examination Council Conference, Federal Reserve Short-Term Funding Markets Conference,

FIRS Conference (Hong Kong), Fixed Income and Financial Institutions Conference, Johns Hopkins University, Mid-Atlantic Research Conference, NBER Corporate Finance Conference, NBER Monetary Economics

Conference, NYU, OFR, and Washington University Corporate Finance Conference for comments and suggestions. We thank Giuseppe Boccuzzi, Paul Calem, Giovanni Calice, Mark Egan, Kinda Hachem, Allen

Berger, Ali Horta ̧csu, Rustom Irani, Ed Kane, Dasol Kim, Diana Knyazeva, Carlos Noton, George Pennachi,

Raluca Roman, Richard Rosen, Glenn Schepens, Amit Seru, Philipp Schnabl, Steven L. Schwarcz, Pablo

Slutzky, G ̈unseli T ̈umer-Alkan, Guillaume Vuillemey, Larry Wall, James Wilcox, and members of the FDIC

for comments and helpful feedback.

*FDIC: chrmartin@fdic.gov, aufier@fdic.gov; —Duke University, FDIC, and NBER: mpuri@duke.edu.
1 Introduction

Bank failures are important. There were many bank failures during and after the financial

crisis of 2007-2009. The inability of financial institutions to maintain stable funding sources

was central to the crisis, which resulted in the high-profile failure or near failure of many

financial institutions and unprecedented emergency liquidity support by governments around

the world.

Since the Great Recession, a lot of attention has been paid to systemically important

institutions. However, looking closely at the roughly 500 banks that failed during the Great

Recession, most of these were smaller banks. Individually, very few of these banks were

systemic (Granja, Matvos, and Seru, 2017). However, the large number of such bank failures

during the crisis were very costly to the FDIC Deposit Insurance Fund, which backs the

insured deposit provision of banks. From 2008 to 2013 almost 500 banks failed at a cost

of approximately $73 billion to the Deposit Insurance Fund (DIF) with the DIF falling to

a negative $20.9 billion by year-end 2009 (FDIC, 2017).1 Understanding these failed banks

and the drivers of deposit flows is hence of importance in its own right.

In this paper we are able to access a unique, highly granular dataset, collected by the

FDIC from a failed US bank2, to examine the drivers of both deposit inflows and outflows.

Most of the attention in bank failures and distressed banks has been on deposit outflows. In

deposit outflows, theoretically, one of the most important factors should be deposit insurance.

Large deposits are uninsured. In principle these deposits provide discipline. However, what

about deposit inflows? While the outflow of large, uninsured depositors can impose some

depositor discipline, if a bank is able to easily attract new insured deposits then this suggests

very little depositor discipline. The theoretical model of Egan, Horta ̧csu, and Matvos (2017)

suggests that distressed banks may increase deposit rates to attract insured depositors but

there has been limited systematic evidence to suggest that distressed banks behave this

way and that depositors respond to these rates meaningfully.3 This paper fills the gap by

examining deposit inflows in depth.

To study these issues we first use our uniquely detailed dataset. These data allow us to

1The DIF went negative on an accounting basis (incorporating the contingent loss reserve for expected

future bank failures), but did not deplete its liquid assets.

2Throughout, unless otherwise noted, we use the term “bank” to refer to any insured depository institution, whether it be a commercial bank, thrift, or credit union. We use the term “financial institution”

when needed, which includes the term “bank,” as well as institutions such as non-bank finance companies,

insurance companies, hedge funds and other entities often referred to as “shadow banks.”

3The evidence has been mainly anecdotal, focusing on a single bank example and rates (see, e.g., the Ally

bank example cited in Egan et al., 2017), but no one has documented a systematic response by depositors.

2
measure daily, account-level balances and attributes for several years. Importantly, unlike

with aggregated data such as Call Reports, we are able to separately assess inflows and

outflows — a distinction that turns out to be of first-order importance. We find that simultaneous with large deposit outflows, the bank experienced large deposit inflows — just

below the deposit insurance limit — with the bank raising its interest rates. We next ask

if this phenomenon is observed more generally by studying a large panel of US banks that

faced regulatory actions. We find that large insured deposits with these banks increased

after enforcement actions, similar to the phenomenon that we observe in the failed bank we

study. We further gather data on deposit interest rates offered by large banks before and

after regulatory actions through survey data in RateWatch. We find that the larger banks

facing regulatory action raise interest rates, suggesting that the pattern of interest rates hikes

and deposit inflows that we see in the failed bank under study is a broader phenomenon.

Expanding on our analysis in greater detail, we first examine deposit outflows in our

daily-frequency data. We find that FDIC insurance and other government guarantees, including temporary measures such as the Transaction Account Guarantee program (TAG),

significantly reduce the withdrawals of insured depositors in response to ailing bank health.

Our results thus support the notion that deposit insurance — even temporary measures

which one might worry are not well-understood by depositors — improves funding stability.

Uninsured depositors are much more likely to withdraw their funds. We additionally find

account characteristics that are related to deposit stability, in that checking accounts are

more stable than savings accounts, and depositors receiving regular direct deposits such as

payroll are less likely to leave the bank. We also find that depositors who have been with the

bank longer are less likely to exit, even when faced with bad regulatory news. Finally, we

show that when uninsured depositors leave the bank under stress, they typically withdraw

a large share even of insured funds. This result is especially relevant for financial stability,

as even a substantial share of banks’ insured funding may flee in response to bad news.

Next, we study deposit inflows. Surprisingly, the failed bank we study was able to

replace about a third of its deposit base in the last year of its life, while it was publicly

known to be under supervisory scrutiny for its declining condition. About half of these

new deposits arrived in the last ninety days before failure, after public regulatory reports

showed the bank to be critically undercapitalized. The bank attracted these deposits largely

from small financial institutions around the US, most with no previous relationship, using

internet deposit listing services.4 The new deposits were almost all term deposits paying

4Listing services are firms which provide potential depositors with a list of deposit rates (one for each

3
above-market interest rates and structured to fall just under the FDIC insurance limit.

We then use a mix of public data and confidential supervisory data to see if the phenomenon of insured deposit inflows to the bank in distress holds more broadly in other US

banks. We identify over 2,000 other banks that were subject to regulatory actions similar

to the one faced by this studied bank. We then show that these banks substitute funding

away from brokered deposits and time deposits over $250,000, but increase their reliance on

listing service deposits and time deposits below $250,000. Banks especially increase reliance

on term deposits with balances above $100,000 but below $250,000, the range in which term

deposits structured to fall just under the insurance limit would be. Another question is

whether it is just small banks (which are the majority of failed banks) that raise interest

rates or whether larger banks also operate the same way. To address this question we gather

additional detailed data from RateWatch surveys of interest rates offered by the largest

banks facing regulatory action. Consistent with the behavior of our case study bank and the

predictions of Egan et al. (2017), we find that large banks facing regulatory action increase

the interest rates offered on insured term deposits. These results suggest our findings from

the detailed micro data hold more broadly in US banks facing enforcement actions related

to undercapitalization or distress.

To the best of our knowledge, we are the first to show that in the presence of government

guarantees, gross funding inflows are of first-order impact — even in imminently failing

banks. This has significant implications for depositor discipline. While large, uninsured

deposits provide depositor discipline through outflows, if banks can easily attract new insured

deposits then there is little practical depositor discipline. Our results suggest that inflows

into insured deposits are an important mechanism that weakens depositor discipline exerted

by uninsured depositors.

Further, our results from examining the source of new deposits for the failed bank we

study suggest that depositors’ knowledge of the mechanics of bank failure and deposit insurance is possibly asymmetric. Unlike in many other countries, FDIC insurance makes bank

failure essentially seamless for depositors – if the bank closes on Friday, insured depositors

participating bank) for a number of standard deposit products, often sorted from the highest rate to the

lowest and displayed on a website. For supervisory purposes, being classified as a listing service need not

imply that one is a third-party deposit placement service, though some deposits may fall in both categories.

For the sake of clarity we therefore use the terms “placed deposit” and “listing service deposit” as mutually

exclusive terms, unless otherwise noted; if a deposit could be considered both, we code it as a placed deposit.

For more detail on the supervisory definition and treatment of listed, placed, and brokered deposits, see

FDIC’s “Frequently Asked Questions on Identifying, Accepting and Reporting Brokered Deposits,” FIL-42-

2016, June 30, 2016.

4
have always been able to access their funds by or before the following Monday. However, as

our studied bank approached failure, even yields on riskless insured deposits rose, possibly

reflecting an “inconvenience yield” compensating depositors for the potential inconveniences

of bank failure. Sophisticated depositors (namely, other insured banks and credit unions)

flow into the bank to capture this premium while ordinary depositors stay away.

Our paper contributes to the existing literature in a number of ways. Much of the

empirical literature on bank runs employs aggregate data. This literature in general finds that

banks with worse fundamentals experience greater deposit withdrawals in a crisis (Gorton

(1988); Saunders and Wilson (1996); Calomiris and Mason (1997)), and that large amounts of

uninsured deposits can lead to unstable banks (Egan, Horta ̧csu, and Matvos (2017)). A small

set of papers examines responses of individual deposit(or)s to bank runs. These papers either

use snapshots of data (Davenport and McDill (2006)) or data from banks in other countries,

such as India (Iyer and Puri (2012); Iyer, Puri, and Ryan (2016)); Denmark (Iyer, Jensen,

Johannesen, and Sheridan (2019)); and Switzerland (Brown, Guin, and Morkoetter (2020)).

The theoretical model of Egan et al. (2017) provides a framework in which to understand

our results. For example, their model predicts that uninsured depositors flee distressed

banks, distressed banks pay higher rates to attract depositors, and insured depositors respond

little or even flow into distressed banks — all of which we document empirically. The

deposit flows we find are related to the causes of bank funding instability, the rationale

for deposit insurance, and the literature on panic- versus fundamentals-driven bank runs

(Diamond and Dybvig (1983), Jacklin and Bhattacharya (1988), Gorton (1988), Saunders

and Wilson (1996), Calomiris and Mason (1997)). Finally, several prominent scholars have

recently highlighted the dearth of academic empirical research on the stability and liquidity

of various funding sources, in spite of liquidity concerns in the last financial crisis (Diamond

and Kashyap (2016), Allen and Gale (2017)).

The remainder of the paper is organized as follows: Section 2 provides a brief history of

the bank to highlight our key findings and provide context for later analysis. Then, Section

3 presents regression results on the drivers of deposit liquidation; Section 4 presents results

on inflows of new depositors; and Section 5 generalizes our qualitative findings to a large

sample of US banks. Section 6 concludes.

5
2 Data Description and Background

We construct our dataset from data collected by the FDIC shortly after the bank’s failure,

building daily account balances for each deposit account. We are able to reliably construct

account-level daily deposit balances from early 2006 until the bank’s failure. We observe

all account transactions over this period at a granular level. We checked our constructed

account balances against Call Reports on total deposits and deposits by account category,

and deposits by branch against the FDIC Summary of Deposits data.5 General summary

statistics about these bank accounts are shown in Table 1, broken out to highlight periods

of special interest, discussed further in Section 2.1, below.

Until mid-2007, this bank appeared relatively healthy. The bank had approximately $2

billion in assets and primarily made residential real estate loans, but it also offered wealth

management services. The bank sourced deposits from physical branches as well as internet

depositors with a focus on savings accounts. The balances in accounts with some uninsured

funds, both transaction and term deposits6, were steadily rising (see Figure 1).7

By mid-2007, there were signs of the growing financial-system-wide stress at this bank.

Insured term balances fell as depositors shifted to other asset categories and the bank managed the size of its wholesale CD portfolio to purchase or sell residential real estate. Between

this time and August 2008, there was net run-off in uninsured balances. Figure 1 shows that

this was particularly rapid among uninsured term deposits. While less than 40% of uninsured

transaction balances ran off during the period, over 50% of uninsured term deposit balances

did so. There was comparatively little change in insured deposits. Perhaps because of the

inherently forward-looking nature of term deposits, or perhaps because term depositors are

relatively sophisticated, term deposits appear more risk sensitive than transaction deposits.

While this period excludes the worst of the financial crisis, stress was building in the financial sector8 Thus, depositors, particularly sophisticated depositors, began to react. During

5We also, for example, checked individual accounts to ensure that accounts had zero balance before

account opening and after closing, which ensures no transactions were missed.

6Throughout this paper, we use the phrase “transaction account” to refer to all non-maturity accounts,

namely, both checking and savings accounts. We acknowledge that the phrase “transaction account” has a

more precise meaning in certain contexts, such as in the Federal Reserve’s Regulation D. Additionally, we

use “term deposit” and “certificate of deposit” (“CD”) as synonyms. We classify accounts as insured or

uninsured using an approximation of the deposit insurance process, aggregating deposit accounts based on

their owners and ownership categories before applying the deposit insurance limit for each category.

7While the bank employed Federal Home Loan Bank advances (FHLBs) as funding sources, we find no

evidence that it took discount window loans or Troubled Asset Relief Program (TARP) funds.

8Bear Stearns and IndyMac failed, Northern Rock experienced a run, and some investment funds froze

withdrawals.

6
this time period, the bank significantly curtailed its residential lending activities. Thereafter

and up until failure, the bank continued to make commercial and personal loans, along with

some residential mortgage loans.

Fall of 2008 saw severe credit and liquidity risks realized across the financial system as well

as significant changes in financial policy. The most important policy change for our purpose

was the increase in the FDIC’s deposit insurance limit from $100,000 to $250,000 effective

October 3, 2008. Additionally, the FDIC’s TAG program became effective on October 14,

2008, covering all categories of checking accounts.9 The change in deposit insurance is evident

in Figure 1, where uninsured deposits drop and insured deposits jump between the Pre-Crisis

and Post-Crisis periods denoted with grey bars. Much of the sudden change in balances by

insurance status is mechanical, as deposit accounts between $100,000 and $250,000 suddenly

became insured. Much of the remaining change among transaction accounts reflects the

almost simultaneous application of TAG guarantees. In contrast, changes in term deposit

balances are partly driven by the bank’s rapid acquisition of placed deposits, as shown in

Figure 2 and discussed at greater length below. Further supporting the notion that term

depositors at the bank are more sophisticated and risk-sensitive, uninsured term balances

never increase substantially after October 2008.

The inflow of uninsured transaction deposits suggests that the time immediately after

the financial crisis was one of limited stress at this bank. The acute system-wide stress of the

crisis had receded and the bank’s health had not yet significantly deteriorated.10 About a

year before the bank’s failure, its primary federal regulator took its first publicly announced

action to address the declining health of the bank through a Cease and Desist (C&D) order.11

The C&D order cited many issues at the bank including insufficient capital as well as poor

board and management oversight, and was made public immediately, appearing in the local

press within a couple of business days.12 As can be seen in Figure 1, there is an increase in

9Initially, the deposit insurance limit increase was through the end of 2010, but it was made permanent

by the Dodd Frank Act. TAG temporarily providing unlimited deposit insurance for negotiable order of

withdrawal (NOW) accounts, non-interest-bearing demand deposit accounts, and interest on lawyer trust

accounts (IOLTAs), which cover all categories of checking accounts.

10Acharya and Mora (2015) document a similar inflow of deposits into the banking system after government

actions in late 2008, suggesting the actions reaffirmed markets’ confidence in the financial safety net and

thus the safety of the financial system (see also Pennacchi (2006), Gatev and Strahan (2006)).

11The bank had previously been subject to a non-public memorandum of understanding (MOU) as well as

a later troubled condition letter (TCL). These were intended to address many of the same problems which led

to the bank’s demise. Such confidential, informal enforcement actions are a common element of regulators’

response to ailing bank health in earlier stages, when failure is relatively unlikely.

12It was described by a banking analyst quoted in the local press as unusually harsh and indicative of high

supervisory concern about the bank. Reports in the local press also remarked on the bank’s poor health as

7
aggregate run-off for transaction accounts — both insured and uninsured — unsurprisingly

given the negative attention on the bank. As noted above, there were few uninsured term

deposits left at the bank, although the few that remained still responded to the news.

Finally, three to four months before the bank failed, the banks’ public regulatory filings

(including amendments to previous filings) began showing the bank to be “significantly undercapitalized” and, within weeks, to be “critically undercapitalized.” Importantly, Prompt

Correction Action (PCA) guidelines generally require federal regulators to place a bank into

receivership or conservatorship (i.e., fail the bank) within 90 days of it becoming critically

undercapitalized.13 Depositors would expect the bank to fail soon, and uninsured deposit

run-off accelerated substantially, as shown in the far right of the top panel of Figure 1.

Ultimately, the bank failed, and its primary federal regulator concluded that its failure

was a result of heavy credit losses on the loan portfolio, especially adjustable rate mortgages.

The resolution of the bank cost the FDIC approximately 10% of the bank’s assets. For

context, of the 54 banks with assets between $1 and $10 billion which failed14 between 2007-

2014, the average cost was 18% of bank assets with a right skew, placing this bank’s losses

in the middle third of the loss rate distribution.

2.1 Defining Time Periods of Special Relevance

For the purpose of our empirical analysis, we separately analyze depositor behavior in each

of four windows of time available to us, described below. We identified these time periods

using the bank’s data and macroeconomic events in order to conduct our tests. In reverse

chronological order, the time periods are:

‹ Formal Enforcement Action. This is a period of significant bank-specific distress and

represents the primary time period of interest. This period begins with the C&D

order (a formal enforcement action) and ends with the failure of the bank. Unlike

earlier periods, the stress arose from bank-specific adverse information, rather than

from system-wide anxiety. This is generally abbreviated as the Formal period.

‹ Post-Crisis. The Post-Crisis period begins in December 2008, shortly after the government’s emergency actions in fall 2008, and runs until the end of May 2009. The

revealed by its financial ratios in a recent public regulatory report.

13The term “critically undercapitalized” is defined by law as the lowest of five ranges for bank capitalization

ratios. Banks are considered critically undercapitalized if their leverage ratio falls below 2%; nearly insolvent

in book value terms. See 12 U.S.C. §1831o for more detail on PCA guidelines.

14We exclude open bank assistance (OBA) from our definition of failure in computing this statistic.

8
Post-Crisis period was a period of considerable distress across the financial system.

Unlike in the Formal period, there were not significant revelations of bank-specific

trouble. Hence, the Post-Crisis period allows us to compare depositor behavior in response to market-wide stress. We exclude a few months in the fall of 2008 to avoid the

confounding effects of emergency actions by the US government, as well as markets’

expectations related to those actions.

‹ Pre-Crisis. The next time period we focus on is the year-long period, ending just before

September 2008, before the financial crisis. As discussed above, uninsured deposits

began running off during this period, particularly uninsured term deposits.

‹ Placebo. We utilize a period of time in 2006 as a placebo period, establishing baseline

depositor behavior when neither the bank nor the financial system were perceived to

be especially troubled.

3 Analysis of Deposit Outflows

This section presents an analysis using several regression models. We regress a dummy,

indicating whether an account liquidates15, on a variety of account and depositor characteristics in the context of OLS-estimated linear probability models (LPMs). Because the

liquidation behavior of term deposits is quite different from that of transaction deposits, we

run regressions separately on the two categories for each of the four time periods described

above: Placebo, Pre-Crisis, Post-Crisis, and Formal Enforcement Action.

All variables used in the regressions are defined in Table 2. In addition, note that TAG

and Dodd Frank Act (DFA) deposit guarantees were effective only after their implementation

in the crisis; coverage under these temporary programs was only available in our Post-Crisis

and Formal periods. That said, we include a TAG/DFA Eligible dummy in all regressions;

estimates for the Placebo and Pre-Crisis periods are for baseline/comparison purposes only.16

15An account is considered to liquidate if its balance falls by 75% or more relative to the balance as

measured at the beginning of the period, and the balance stays at or below 25% of the starting balance for

at least 61 days; see Table 2.

16Note TAG/DFA coverage was designed to effectively remove covered accounts from a depositor’s exposure

to the bank for the purposes of determining insurance coverage subject to the $250,000 limit. This means

that TAG/DFA could indirectly result in other accounts gaining insurance coverage, even if they were not

directly guaranteed under TAG or DFA. Further, TAG ends and DFA guarantees begin midway through the

Formal period, with the ultimate effect that NOW accounts are no longer covered by unlimited insurance.

Given that TAG’s expiration was known in advance, we may expect depositors in large NOW accounts to

liquidate balances prior to the scheduled end of their deposit guarantees. This would generate a positive

9
3.1 Drivers of Transaction Deposit Outflows

Focusing first on transaction deposits, Table 3 presents our baseline LPM regressions, one

for each of the four time periods of interest.17 Table 4 presents Probit and Cox proportional

hazard model estimates for the Post-Crisis and Formal periods only, demonstrating that our

results are robust to various model specifications.18 In the discussion below, we refer to the

LPM model results (Table 3) unless otherwise noted.

The Placebo period in 2006 establishes a baseline for “normal” depositor behavior with

little financial stress. First, we find that the Uninsured dummy is significant, implying

uninsured accounts liquidate about 2.5% more often than the baseline hazard, meaning

insured deposits are more stable than uninsured deposits. Second, the TAG/DFA Eligible

dummy is not statistically different from zero. This is perhaps unsurprising given that TAG

and DFA were not yet in effect. This serves as a baseline against which to assess the impacts

of TAG; during and after the crisis, this set of accounts was covered by the temporary,

unlimited FDIC insurance provided by TAG. Next, the negative, significant coefficient on

checking indicates that they are a relatively stable funding source. Fourth, we show that

accounts which are receiving direct deposits roughly every two weeks (indicative of directdeposited paychecks or other regular transactions) are also less likely to liquidate, doing so

around 10% less often than non-direct deposit accounts, as indicated by the significant Direct

Deposit dummy. Finally, the Trust dummy is insignificant, meaning that accounts held by

trusts liquidate at about the same rate as the baseline account.

We also control for other account and depositor characteristics. Because there is relatively

little variation across time periods in our coefficient estimates for these additional controls,

we will discuss them mainly with respect to the Placebo period. For instance, depositors with

a longer relationship with the bank are generally more stable as shown by the coefficients

on Log(Age). The rate at which depositors conduct transactions has a significant, nonlinear relationship with liquidation behavior, as both Prior Transactions coefficients are

significant but with opposite signs. Accounts with very infrequent transactions (unaware or

inactive depositors) and accounts with very frequent transactions (operationally important

accounts, from the depositors’ perspective) are less likely to liquidate than other accounts.

relationship between NOW status and liquidation at the same time that non-interest checking and IOLTA

accounts (both still covered by DFA) may show a negative relationship. Thus, we only mark accounts covered

by the DFA guarantees (but not TAG) with 1 for this dummy in the Formal period.

17We use asymptotically normal standard errors to measure significance, but our findings are qualitatively

robust to clustering standard errors at the branch (office) level — see Tables 22 and 23 in the Appendix.

18Figure 8 in the Appendix presents the baseline hazard rates corresponding to the Cox results, and Figure

7 presents non-parametric Kaplan-Meier survival curves for the same time periods.

10
Accounts in the middle, with moderate usage, are more likely to liquidate. While there are

statistically significant differences in this basic result across time periods, the differences are

economically insignificant. Finally, transaction accounts marked by the Institutional dummy

are not significantly more or less likely to liquidate than other accounts, and this is true in

all periods.

Moving to the Pre-Crisis period (column 2 of Table 3), we see that little changes. The

coefficient estimates for the Uninsured dummy and TAG/DFA Eligible dummy are statistically insignificant, and checking accounts and accounts receiving regular direct deposits

remain roughly as stable as in the Placebo period. The finding of similarities between the

Placebo and Pre-Crisis periods is generally consistent with the historical discussion above,

where transaction deposits did not much react to building financial weaknesses before the

peak of the crisis.

Next, in the Post-Crisis period, the Uninsured dummy point estimate is larger — such

accounts liquidate 7% more often than other accounts at the time. Similarly, the TAG/DFA

Eligible dummy shows that such accounts liquidate nearly 10% less and the estimate is

statistically significant. This reflects the fact that the accounts are actually covered by

TAG for the first time (recall that temporary insurance programs were not in place in prior

periods), and TAG dampens withdrawal behavior similarly or more effectively (per the point

estimate) than ordinary deposit insurance.

Finally, column 4 of Table 3 presents results for the Formal period. The impact of FDIC

insurance is stronger than in prior periods: uninsured accounts liquidate 18% faster than

other accounts according to the Uninsured dummy. This is statistically stronger in the

Formal period than the Placebo period at the 1% level of significance. We also find that

checking accounts; accounts receiving direct deposits every other week; and accounts held by

depositors with longer relationships with the bank continue to be statistically significantly

stickier than other accounts. Checking accounts remain sticky following bank-specific bad

news, but less so than in response to market-wide stress. In contrast, the impact of the

length of depositor relationships is stronger in the Formal period than in the Placebo period;

such accounts are more sticky according to the Log(Age) dummy. Trust accounts reverse

their behavior from the Post-Crisis period and run off 6.98% more than other accounts, as

shown by the Trust dummy. Similar results obtain in the probit and Cox regressions as can

be seen in Table 4.

We also include Table 7, which incorporates interest rate spreads over market rates as

well as account fees paid; for brevity, we report results only for the Post-Crisis and Formal

11
periods. The addition of the rate and fee variables does not meaningfully change the other

coefficients or their significance. Estimates for the rates and fees variables themselves indicate

that higher net returns on accounts (either from lower fees or higher rates) are significantly

associated with fewer liquidations. The estimates on rates and fees are potentially subject

to endogeneity bias; the left hand side is a measure of quantity, and rates and fees measure

pricing. Nonetheless, our estimated effects are consistent with the usual intuition that higher

rates make depositors more likely to stay with the bank. To the extent that high-return

accounts tend to attract rate-seeking or rate-sensitive depositors who generally turn over

more often, our estimates might arguably be lower bounds on the causal impacts of net

returns.

These results have several important implications. First, we provide clear evidence that

funds covered by deposit insurance are more stable, particularly in periods of stress. More

generally, the large impacts in periods of stress demonstrate that depositors were aware of

the bank’s declining health and the limit of deposit insurance. For outflows, absent the

gross deposit inflows we analyze in the next section, this suggests active depositor discipline.

Second, in the Post-Crisis period, when TAG was in effect, the point estimates for Uninsured

and TAG/DFA Eligible are not statistically different from one another. The point estimates

in the first two rows of column 3 of Table 3 are similar in size, and a t-test of differences

in the magnitudes of the coefficients between TAG and regular deposit insurance fails to

reject the null of no difference with a p-value of 0.67, indicating that the magnitude of the

effect of TAG is the same as that of regular deposit insurance.19 Given that TAG was new

and unconventional, the program and its operational details would have been unfamiliar to

depositors; the magnitude of the impact we estimate bodes well for the effectiveness of such

programs. We do not observe a significant impact of the DFA guarantees in the Formal

period, but there are relatively few accounts covered by DFA guarantees so we have significantly lower power than in earlier periods. Third, our finding that checking accounts and

accounts receiving regular direct deposits are relatively stable in all environments supports

assumptions made in rules such as the LCR and NSFR.20 Finally, our finding on trust accounts suggests that such depositors are more sophisticated. Trust depositors are less likely

19Given the definitions of the variables, the signs are opposite, but this implies the same effect on liquidation

behavior. We take the absolute value before conducting the t-test.

20To be considered the most stable form of funding for LCR purposes, deposit accounts must be fully

insured retail deposits and either 1) a checking account or 2) held by a depositor with other relationships

with the bank (such as loans, other accounts, bill payment services, etc.; Basel Committee (2013)). Note that

our definition of “checking account” is synonymous with the definition of “transaction account” in Federal

Reserve Regulation D.

12
than others to flee in periods of general distress (Post-Crisis) when the bank itself is not near

failure, but more likely to do so as the bank approaches failure in the Formal period; this

may reflect the fact that such depositors are typically wealthier and more financially savvy,

and thus they may be more able to determine the solvency of the bank.

3.2 Drivers of Term Deposit Outflows

Next, we conduct a parallel analysis of run-off in term deposits (see Tables 5 and 6; we

will again focus on the linear probability model estimates in Table 5).21 Focusing on the

Placebo period (Table 5, column 1) first, the Uninsured dummy is statistically insignificant,

meaning that uninsured term deposits do not liquidate more than insured accounts, probably

because bank solvency was not a concern. Turning to the Pre-Crisis period (column 2), the

Uninsured dummy is now significant with 4.3% more uninsured term deposits liquidating

than insured deposits, consistent with the fact that uninsured term deposits began running

off during this period (see Figure 1). Column 3 of Table 5 shows responses in the PostCrisis period. Point estimates for the impact of FDIC insurance are substantially higher

than in earlier periods — uninsured accounts are 8.2% less likely to liquidate than other

accounts. The results also show that brokered and placed deposits, which we expect would

be particularly risk-sensitive, run off very rapidly, with the coefficient on the Brokered/Placed

dummy indicating 28% more liquidations among these accounts. Although a longer term

to maturity (Log(Days to Maturity)) continues to be a stabilizing feature of term deposits,

relationship age (Log(Age)) becomes insignificant.

Finally, in the Formal period, the FDIC insurance dummy remains large and significant,

and it is also larger than the point estimates from prior periods; uninsured term deposits are

14.7% more likely to liquidate. Accounts with a longer relationship to the bank remain stickier than other deposits. Term deposits further from maturity are still less likely to liquidate,

as seen in the negative Log(Days to Maturity) coefficient. Like trust transaction accounts in

the Formal period, the Trust dummy demonstrates trust term accounts are more likely to

liquidate, though the estimate for term accounts is not statistically significant. Finally, the

results show a large response from wholesale accounts. Institutional - Listed/Faxed deposits,

many newly arrived at the bank, are much less likely to liquidate. The Brokered/Placed

deposits dummy, on the other hand, attests that such deposits run off 55% more than other

term deposit accounts, a high response relative to other periods and deposits, showing the

21In some time periods, some term deposit account variables are thinly populated, precluding the estimation of coefficients. When this occurs we put a – in the table corresponding to the affected variable.

13
high risk sensitivity of these wholesale deposits.

We also include Table 7 which incorporates interest rate spreads and fees paid; for brevity,

we report results only for the Post-Crisis and Formal periods. To construct the rate spreads,

we take the difference between the rate the depositor would have received had they renewed

their term deposit that day and the market average rate. Much like in transaction accounts,

these new variables do not much change the other coefficients. And higher interest rates

are again associated with fewer liquidations. Fees for CDs are rare and only appear in the

Post-Crisis period, when they are positively and significantly associated with liquidation

probability. Acknowledging the same caveat regarding endogeneity as in the transaction

account regressions, our results are consistent with the intuition that higher returns make

depositors less likely to exit the bank.

These results provide some significant insights. First and most obviously, the results for

term deposits again confirm the stabilizing impact of deposit insurance. Additionally, we

show that brokered and placed deposits exhibit a high degree of churn in all periods, but

they respond even stronger under stress; this supports the view that such deposits are hot

money. As in the transaction deposit regressions, we find that longer depositor relationships

tend to help stabilize bank funding.

3.3 Policy Change During Crisis

As we can observe depositors at a high level of frequency, we can study individual events with

good specificity. We perform an event study on unconditional liquidation propensity around

the time of the deposit insurance limit change, looking at the 36 days before and after.22

The liquidation probability among deposits uninsured under the old limit of $100,000 and

insured under the new limit of $250,000 falls from 0.0512 to 0.0465, with the drop being

particularly strong for CDs going from 0.0806 to 0.0512. The effect of this change is harder

to disentangle on transaction accounts, due to TAG being implemented approximately a

week after the insurance limit change. These results further support the efficacy of deposit

insurance in reducing withdrawals.

22We chose 36 days because this was the widest window we could use which was symmetric around the

deposit insurance limit change and did not overlap with the other time periods we study.

14
3.4 Account Liquidation and the Withdrawal of Insured Funds

Having established the increased propensity of uninsured depositors to draw down their deposits, we now investigate whether such depositors tend to draw down either to the insurance

limit or well below it.

Table 8 presents our results for withdrawals from transaction accounts.23 Results here

incorporate changes in bank conditions and macroeconomic conditions over time. Each row

represents one of our four periods. For each period we consider the set of accounts with

balances above $2,000 under insurance limit at the start of the period. The columns then

show balances of these accounts at the end of the period, in six account-balance bins. Relative

to the Placebo and Pre-Crisis period, depositors in the latter two periods, especially the

Formal period, tend to draw down well below the limit: the largest groupings in the Formal

period, relative to previous periods, are accounts with $1 or less and those between $2000

and $2000 under half the insurance limit ($2000 to $123000, in this period)24, with far fewer

accounts remaining above the deposit insurance limit than in other periods. Under stress,

uninsured depositors tend to withdraw much more than required to achieve full insurance

coverage, either drawing down to less than half of the insurance limit or liquidating all funds.

This finding has significant implications for financial stability, since even some insured

funds are likely to flee banks in response to stress, and can serve to inform banking theory

models (such as Davila and Goldstein (2021)).25

4 Analysis of Deposit Inflows

The previous section focused on deposit outflows, which is traditionally the area of attention

with respect to bank funding stability. In this section, we demonstrate that deposit inflows

are also important to funding stability, even in a bank publicly known to be at high risk of

failure. After providing an overview of the deposit inflow dynamics at the bank, we use a

regression framework to establish the characteristics of new depositors; present time series

evidence that these were not solely driven by factors external to the bank; and provide

evidence that the deposit inflows were instead attracted by the combination of credible

23This is also presented as Figure 6 in the Appendix. We do not show a comparable table for term deposits

because their behavior is simpler: generally, they remain with the bank in full or exit entirely.

24$2000 was chosen based on where we observed depositors bunching and based on how much interest they

may accumulate.

25Iyer et al. (2019) find similar behavior among uninsured Danish bank depositors: in response to bad

news about the bank during the financial crisis, they tended to follow the heuristic of splitting accounts in

half between two banks to achieve full insurance coverage.

15
deposit insurance and above-market rates. We complete the section with a discussion of the

policy implications of this new finding.

In the last year of its life, the bank attracted new, insured deposits, about half of which

arrived in the last 90 days before failure — when the bank was publicly known to be critically undercapitalized. Figure 3 shows that the inflows of insured term deposits from new

depositors to the bank totaled nearly $400 million, about a third of the bank’s deposit base

and roughly equal to the volume of fleeing deposits; because of these flows, the bank’s total

deposit balances declined little as the bank approached failure. Figure 2 shows term deposits

by various wholesale funding categories in the bank over time. This figure reveals that the

inflows represented a shift in deposit composition: as placed term deposits (and transaction

accounts) fled the bank, they were replaced by institutional term deposits, attracted mostly

through internet deposit listing services.

Table 1 provides additional detail. Reflecting new deposits being structured to fall just

under the insurance limit, only 2.7% of new deposit accounts in the Formal period were uninsured, down from 6.3% in the Placebo period. Moreover, the average opening balance among

new depositors in the Formal period was $186,909. Relatedly, the share of term deposits

in new deposits is increasing over time; in the Formal period, 82% of new accounts were

term deposits. Finally, 81% of new deposits in the Formal period came from institutional

depositors (Listed, Faxed, and Other Institutional), up from 3.1% in the Placebo period.

Additional analysis of the bank’s records provides further detail. The institutional deposit

inflows were mostly CDs from out-of-state banks and credit unions. As noted above, 81% of

the 2600 new deposit accounts in the Formal period were from institutional deposits. Most

were in term deposits — 74% of the 2600 were institutional term depositors. Of those nearly

2,000 accounts, about 65% were from credit unions and 32% from banks, with just a few

percent from other businesses. Geographically, only 4% were from the same state as the

bank (as measured by the depositors’ addresses in the deposit system), and the remaining

96% were spread widely across the US. In terms of balances, about three quarters of those

institutional CDs had opening balances between $100,000 and 250,000, reflecting the fact

that they were structured to fall close to the insurance limit.26

Finally, internal bank records indicate the bank began utilizing two deposit listing services during the Formal period, and the timing of the bank’s utilization of these services

corresponds closely to the surges of institutional deposit inflows shown in Figure 2. In one

26Note that the particular dollar range of $100,000 to 250,000 is consistent with Call Report variables used

in the generalization exercise below, in Section 5.

16
case, internal emails indicate the bank began posting rates on a listing service where they

apparently had not previously advertised their deposits; in the other case, we found a signed

and dated contract with another listing service. These two events are each within a week

of either the surge at the beginning of the Formal period or the surge than began about 90

days before the bank failed.

4.1 Characteristics of New Depositors

Having shown that the bank saw substantial deposit inflows, especially late in life, we conduct

a set of classification regressions to formally characterize the differences between new and

extant depositors in each period (Table 9). We conduct a set of six regressions (one in

each column) in which the dependent variables are dummy variables capturing deposit(or)

characteristics such as Uninsured or Institutional - Listed/Faxed. Explanatory variables are

the same across all regressions and consist solely of the interaction of a dummy indicating a

new versus extant depositor with the time period dummies.27,28

The results of these regressions (Table 9) formalize the findings discussed above and

previously shown in Figure 2.29 The first column shows that, relative to the extant depositors

in the Placebo period, uninsured deposit accounts became much rarer in periods of stress.

In the Post-Crisis and Formal periods, uninsured accounts were about five percentage points

less common among existing depositors, and their prevalence among new depositors also

declined. At failure, only 1.4% of accounts had any uninsured funds in them (obtained by

summing the constant and Extant Depositors/Failure Date coefficient estimates), down from

6.5% in the Placebo period (read directly from the estimate on the constant term). These

patterns reflect the general run-off of uninsured accounts, as well as the inflows of insured

institutional CDs from listing services. Further reflecting these Institutional inflows, the

fourth column shows that Institutional - Listed/Faxed deposits were very rare at the bank

27Throughout the paper, unless otherwise noted, a depositor is considered new in any given time period if

they open an account within the period and have never previously appeared in the bank’s deposit records.

Depositors already in the bank’s deposit records are referred to as extant depositors.

28The observational unit is account × time period, and with five time periods it is possible for an individual

account to appear up to five times in this regression. The omitted category is extant depositors in the Placebo

period. Thus, the coefficient estimate for the constant term represents the share of extant depositors in the

Placebo period exhibiting that characteristic. Coefficient estimates on the other controls represent differences

relative to extant depositors in the Placebo period, in percentage points. Adding any coefficient to the

coefficient on the constant yields the absolute share of that characteristic among the relevant depositor type

(new or extant) in the relevant time period. Note that we also include a Failure Date dummy to summarize

depositors extant at the bank when it failed.

29See also Table 1 for related summary statistics.

17
until the Formal period, when they represented 71% of new depositors. Similarly, column six

shows that while CDs had generally represented 15-25% of existing depositors and a little

less than half of new depositors, they jumped to 82% of new depositors in the Formal period.

Finally, note also that the bank’s heavy reliance on brokered and placed deposits following

the crisis is reflected in the table, notably among new depositors in the Post-Crisis period.

Although total brokered/placed deposit balances were high and relatively flat throughout

the Post-Crisis period (see Figure 2), the high rate of churn among these accounts generated

many new depositors.

4.2 Drivers of Gross Depositor Inflows

While the previous section illustrates the prevalence of certain depositor characteristics

among extant and new depositors, it does not explain the overall prevalence of new compared to old depositors. This section builds upon the last by analyzing the time series of

new deposits as a share of total deposits. The left hand side of the regression is either the

proportion of deposits that are new as of that day or the log of the dollar volume of deposits

that are new that day, while the explanatory variables are time period dummies and economic controls. Among the time dummies, the omitted period is the Placebo period, with

dummies for the remaining three periods — Pre-Crisis, Post-Crisis, and Formal — as well as

dummies for the spans of time between those periods, plus a prior-to-the-placebo dummy.

Table 10 presents regression estimates explaining the share of newly arrived deposits,

while Table 11 presents results for the log of new deposit dollars. Focusing first on the

macroeconomic controls in Table 10, higher current quarter GDP growth is associated with

higher deposit inflows, consistent with wealth effects. Other macro variables such as housing

starts, stock returns, stock market volatility (VIX), OFR’s Financial Stress Index, or the

bank’s growth profile, are not statistically significant. The time series of new depositors’

share of deposits is strongly persistent at the daily frequency, as shown by the positive,

significant AR(1) term.30 In contrast, Table 11 shows that the log level of new deposits,

correlates with the VIX, the OFR Financial Stress Index, and the autoregressive term.

Now consider the time period dummies in Table 10. Even after the inclusion of macroeconomic controls, some time dummies remain significant. The dummies for the two early

time periods are significant, and we note that this was a time in which the bank was growing strategically. Depending upon how one defines a “de novo” bank, the bank we study

30Note that this is not a mechanical result of constructing the series with overlapping measurement periods,

as we define “new depositors” at the daily frequency.

18
could be considered de novo during periods before the Pre-Crisis period.31 The dummies in

the last three periods before failure also remain significant, and the point estimate for the

Formal period is especially large relative to all others. Thus, the Formal period stands out

as a period of especially high inflows.32 Table 11 shows the uniqueness of the Formal period

even more clearly, as the Formal period dummy is the only significant time dummy when all

controls are included. Given our set of controls, these results suggests that the inflows late

in the bank’s life are explained by bank-specific conditions.

Figure 4 suggests the bank-specific cause was the high deposit rates offered by the bank

in the Formal period. The figure compares the rates the bank paid on newly issued 12-month

CDs, a common deposit product that is representative of the bank’s product portfolio and

relative market position, showing both the actual rate and its relative ranking along with

market percentiles.33 The figure shows the bank paid relatively high deposit rates throughout

its life. However, as deposits flowed in over the last year of its life, the bank consistently

paid rates above the 95th percentile of the industry distribution, often approaching the top

1%. Our evidence of the distressed bank raising deposit rates to attract insured deposits

is in line with predictions of Egan et al. (2017). Including these interest rate measures

in the regressions in columns 3 and 4 of Tables 10 and 11 shows that the bank attracts

more deposits on days where it pays higher interest, although depositors do not respond to

variation in account fees. Due to potential endogeneity concerns, we view these results on

deposit pricing as merely suggestive.

Additionally, we use the regressions of Tables 10 and 11 to calculate the interest elasticities of demand, obtaining an answer very close to other estimates in the literature. Specifically, we calculate an elasticity of 0.61.34 Artavanis, Paravisini, Robles-Garcia, Seru, and

31The regulatory definition of de novo status has changed several times over the years, especially with

regard to how many years after opening a bank remains de novo.

32Because the Placebo period is omitted, all estimates are relative to the Placebo period. Nonetheless, the

Formal period’s uniqueness is not driven by the choice of omitted time period. The point estimate is much

larger than in any other period, and the Formal period estimate is statistically significantly different from

both the Pre-Placebo and Placebo-to-Pre-Crisis, as well.

33Rather than taking the average deposit rate being paid on all 12-month CDs at each date, we construct

the series as the 31-day centered moving average of rates offered on newly issued term deposits. In this way,

the rate series better reflects the rate a hypothetical depositor would have faced had they approached the

bank on that date, and there also are some days in which no new 12-month CDs are issued.

34To obtain this elasticity from the regression tables, we first calculate the elasticities of the share of

new depositors and the quantity of new deposits. These can then be leveraged to calculate the elasticity

of existing deposits. The mean share of new deposits as a share of total deposits is 4.29 basis points, the

product-weighted spread for these new products over the market interest rate on a given day averages 1.17

percentage points, and the coefficient on this interest rate in column (3) of Table 10 is 5.55. Thus, raising

rates by 1 percentage point (an 85.38% increase) raises the share of new by 5.55 basis points (a 129%

19
Tsoutsoura (2019) find an interest rate elasticity of demand for the quantity of existing depositors of 0.6, while Egan et al. (2017) find an elasticity of 0.56 for insured depositors; both

extremely close to our calculated elasticity of 0.61.

4.3 Policy Implications of Deposit Inflows

The deposit inflow phenomenon we document has several important policy implications.

First, while some depositors enforced discipline on the bank by leaving, and new depositors

were able to demand somewhat higher rates, new depositors offset the disciplining effect

by opening new accounts. This finding is important given the Basel framework considers

market (in this context, depositor) discipline of banks to be the third of three “pillars” of

financial stability. Our finding suggests insured deposit inflows are an important, hitherto

undocumented, phenomenon that undermines any such disciplining power.35 We generalize

this finding on inflows empirically in the next section, but we note here that anecdotal

evidence supporting these findings was given by our discussant at the Chicago Financial

Institutions Conference, who stated he had witnessed such deposit interest rate increases

at Indymac Bank, a $30 billion dollar bank, right before its failure in 2007-2008, and had

himself moved deposits in response to these higher interest rates. He also noted observing

that such interest rate increases were not limited to this one bank.

Second and closely related, our finding emphasizes the importance of studying gross

deposit flows rather than net deposit flows in understanding the implications of deposit

insurance. By making depositors less sensitive to bank risk, deposit insurance stabilizes

deposit funding; the inflows from new deposits makes this effect stronger.

Third, the large inflow of new deposits suggests that deposit rate restrictions placed on

increase). Taking their ratio, the interest rate elasticity of the new deposit share is 1.52. Similarly, the

coefficient on the interest rate in Table 11 is 2.61; given that the dependent variable is in logs, this coefficient

implies that the same 1 percentage point rise in interest rates (an 85.38% increase) drives a 261% increase

in new deposits. Thus, the interest elasticity of the quantity of new deposits is 3.05. Using these figures,

we can then calculate the elasticity of existing deposits. The average quantity of new deposits on any given

day is $558,000 (4.29 basis points of the $1.30 billion daily average total deposits). Using the elasticity of

3.05 from above, a 100% increase in rates therefore increases new deposit balances to $2.26 million (305%).

Using the elasticity of 1.52, we know this also increases the new deposit share to 10.79 basis points. For

the balances and shares of new deposits to match, total existing depositors’ balances must increase to $2.09

billion, an increase of 61% relative to the average such balance. Thus, the interest elasticity of the quantity

of deposits from existing depositors is 0.61.

35Clearly there are many forms that moral hazard emanating from deposit insurance can take e.g., excessive

risk taking on the lending side. Deposit insurance can also inhibit the reallocation of deposit funding away

from an insolvent bank. Our objective is not to look at all possible mechanisms - nor do we have the data to

do so - but to clearly illustrate one possible effect of deposit insurance that has been hitherto undocumented

i.e., its effect on deposit inflows which our results suggest can be large and a first order effect.

20
troubled banks are insufficient to prevent rapid insured deposit acquisition, certainly in a low

rate environment. Motivated by the concerns above, US law and regulation prohibits less

than well capitalized institutions from paying deposit rates more than 75 basis points above

the national average deposit rate on deposits solicited nationally. Egan et al. (2017) present

a theoretical model that suggests such rate caps can rule out particularly bad banking market

outcomes where failing banks attract large shares of insured deposit funds. The bank we

study was subject to these restrictions during the Formal period, and yet was able to attract

deposits equal to around a third of its deposit base. The top panel of Table 1 shows that the

bank complied with the rate restrictions; the spread to the market average on new accounts

in the Formal period was around 69 basis points.36 Because the bank was able to attract

so many new deposits while under the restrictions, we conclude the rate restrictions were

at best a minimally binding constraint on the bank’s behavior. However, it remains unclear

whether the rate caps would be more effective if short term, riskless rates were substantially

above zero; the fed funds rate was at the zero lower bound for the entire period in which the

rate caps were in place for this bank. To the extent that the dispersion of deposit rates is

reduced when average rates are low (as suggested in Figure 4), the 75bp cap on the deposit

rate spread would be less binding while at the zero lower bound compared to periods when

rates are above it. Of course, in the absence of any rate restrictions, the bank may well have

attracted even more insured funding than it actually did.

These high rates may be a sign of an “inconvenience yield” of deposits issued by a bank

close to failure. FDIC resolution of failed banks is close to seamless — if the bank closes

on Friday, insured depositors have access to their funds by or before the following Monday.

Less sophisticated depositors may be unaware of the process and have concerns about speed

and frictions of repayment, and hence refrain from putting insured deposits in a failing bank.

Meanwhile, sophisticated, informed depositors (such as other insured banks) flow into the

bank to capture this premium. Alternatively, the bank may be willing to pay higher rates

for insured deposits because the value of the bank’s “insurance put” embedded in insured

deposits is higher (Egan et al. (2017)). As the risk of bank failure increases, so does the

likelihood that the FDIC, rather than the bank, will end up bearing the costs of these new,

36Note that the spreads reported in the table are relative to our computed national average rate rather

than that defined by the FDIC. We calculate our own national average series using a method identical to

that used for the FDIC series. We use our computed series as the official data do not cover our entire sample

period, and we wish to keep series consistency across our sample. The source data underlying the official

average data changes with vintage, and we have not been able to recover the vintages used to construct the

FDIC series. As a result, our averages tend to differ slightly from the official data. Using the official data

over the supported period gives the same qualitative conclusions.

21
insured deposits. As a result, the bank might be willing to pay more for insured funds.

The final reason that the shift in deposit composition matters to policymakers is that

it transfers risk to the FDIC. In addition to fleeing insured deposits, about $150 million of

uninsured transaction deposits also left. Because the bank replaced these fleeing uninsured

deposits with insured term deposits, the share of the bank’s deposits covered by insurance

and the credit risk exposure of the FDIC increased as the bank approached failure.

5 Generalization to Other Banks

The unusually granular data for this bank allows us to identify changes in deposit composition

as it approached failure, but we unfortunately have such data for only a single bank despite

considerable effort and investment to try and secure such data for additional banks.37

To generalize our results, we conduct two additional tests. First, we use Call Report

data for all US banks to investigate whether banks that face a regulatory action see similar

changes in deposit composition. In particular, we investigate the impact of “treatment” with

a regulatory action on five funding measures. The five funding measures are the share of

each bank’s total deposit funding in the form of brokered deposits, listing service deposits,

small term deposits (those under $100,000), medium term deposits (those over $100,000 but

under $250,000), and large term deposits (those over $250,000).38 Each series is regressed

on a dummy (“treatment”) variable for whether or not the bank was facing regulatory action similar to the studied bank, as well as a set of control variables. We define a bank to

be facing regulatory action if it is under a formal enforcement action that includes capital37When we initiated this project, we asked for data from a sample of 10 banks to be recovered and put on a

secure server on which we could access the data. We then examined deposit data in each bank. Unfortunately,

for many of the banks, the deposit data is incomplete. For some of the banks, the data between systems

cannot be appropriately linked; for example, databases sometimes lack identifiers to link transactions (from

one database, and necessary for calculating account balances) with deposit accounts (from another database,

necessary for many permanent account characteristics) or customer systems (containing separate customer

characteristics.) Other banks might keep only the most recent three months of detailed transaction history.

Each bank has different storage and retention policies. Compounding these data challenges is the fact that

the data contain large volumes of sensitive and personally identifiable information, such that access to the

data is highly restricted, and uploading the data to secure IT environments with statistical analysis software

is time-consuming. Due to the combination of these factors, it took well over a year for us to obtain access

to data for a sample of just 10 failed banks, and after analyzing their data, we determined only the single

bank we study had suitable deposit data. As reported earlier, for the bank we study, we are confident that

the deposit data are complete by matching to the Call Reports, but this is not true for the other banks.

38The average bank in our sample has 3.1% of its deposits classified as brokered, 1.6% as listed, 20.2% as

small term deposits, 13.1% as medium term deposits, and 5.5% as large term deposits.

22
related provisions or if it is less than well capitalized39 without specific written permission

from the regulator to continue taking brokered deposits, usually referred to as a brokered

deposit waiver. Less than well capitalized institutions are forbidden from taking new brokered deposits or rolling over old ones unless they have a brokered deposit waiver. Note

that any bank meeting our definition of regulatory treatment will also be subject to deposit

rate restrictions. The treatment variable is defined using data from public Call Reports and

confidential FDIC data, which provides the details of the enforcement actions and brokered

deposit waivers. The advantage of this approach is that it gives us a large panel of banks —

around 10,000 banks, of which 2,358 faced regulatory action.

The control variables are derived from Call Reports. Control variables are non-performing

assets as share of assets, to capture bank health; one-year asset growth rate, to capture the

growth and current risk profile of the bank; the natural logarithm of assets, to capture size;

deposits as a share of assets, to capture the banks’ reliance on deposits generally; and term

deposits as a share of deposits, to capture their reliance on term deposits particularly.40 We

use quarterly data for all US banks from 2000 to 2016, with 2,358 banks facing regulatory

action and 9,158 not facing them at some point during this time. However, because we rely

on the Call Report taxonomy of deposit accounts and because this taxonomy has changed

through time, some regressions use shorter samples.41 Note that regressions also include

bank and quarter fixed effects.

Finally, we conduct our analysis under three different model specifications. Two of the

specifications are simple OLS models and differ only in the specification of the treatment

dummy. In one, we have a single treatment dummy which is equal to one in any bankquarter where regulatory action was in place; in this case, the untreated (“control”) group

is all banks not contemporaneously under a regulatory action. In another, we use separate

dummy variables for each quarter from four quarters before the imposition of treatment

39See 12 U.S.C. §1831o for capital category definitions.

40We altered the raw Call Report data by correcting for apparent reporting errors and by winsorizing.

Specifically, in a handful of bank-quarters, banks appear to have reported brokered deposits in dollars,

inconsistent with the Call Report standard of thousands of dollars, requiring us to divide by 1000. When

funding shares calculated from Call Reports were a fraction of a percent above 100% or below 0%, we assume

this is due to rounding error, and we round to 100% or 0%, respectively. Finally, we bounded one-year asset

growth rates between −50% and 100%, affecting about 2% of bank-quarters, with large asset shifts usually due

to new small banks growing rapidly in the first few quarters. Importantly, none of these changes materially

affects the point estimates of the treatment variable.

41Listing service deposits were not separately identified or reported on Call Reports before the first quarter

of 2011, and data necessary to disaggregate term deposits by size is available beginning in 2010. This limited

our listing service deposit sample to 559 treated and 7,020 untreated banks and our term deposit sample to

807 treated and 7,141 untreated banks.

23
to four quarters after, plus an additional dummy for five or more quarters of continuous

treatment; in this case, the control group is banks which will not face such action for at

least the next four quarters. Finally, we estimate a third specification on propensity-scorematched banks. Banks’ propensity to be treated is determined by a logistic regression using

the same covariates as in the above regressions, plus contemporaneous term deposit size

shares. Then, banks with similar propensity scores — where one was treated and the other

was not — are compared after four quarters of continuous treatment to observe the effects

of that treatment. The results are consistent across all specifications.

Table 12 shows the results of regressions with a single regulatory action dummy variable.

Consistent with our earlier findings, banks under regulatory action reduce their reliance on

brokered deposits while increasing listing service deposits. These two compositional shifts do

not completely offset one another, but banks may also be seeking other classes of deposits.

In addition, there is an increase in the reliance on term deposits below $100,000 as well as

between $100,000 and $250,000, with a decrease in deposits above $250,000.

Table 13 and the accompanying Figure 5 demonstrate the time path of the effects of

regulatory action on the same deposit categories. Relative to banks who are more than

four quarters away from regulatory action, banks one to four quarters before an action have

statistically significantly more brokered deposits. These banks become much less likely to

source such deposits, reflecting the concurrent application of brokered deposit restrictions.

The exact opposite pattern appears with listing service deposits, with banks prior to such

events having fewer and those after having more. Presumably, this pattern reflects inflows

of listing service deposits conceptually similar to those documented previously. Small time

deposits are relatively more prevalent in banks in the last few quarters before regulatory

action than in the control group, and the difference becomes even more stark after regulatory

action. Medium time deposits make up a smaller share of funding at banks prior to regulatory

action compared to the control group, but also become more common following regulatory

action. Finally, large time deposits are equally as common among banks far from regulatory

action as well as those within only a few quarters of action, but they become much less

common among banks following regulatory action. Table 14 shows that our generalization

results are robust to using a propensity-score-matched specification.

This analysis generalizes our earlier findings along several dimensions. These regressions

consistently find that banks under regulatory action reduce reliance on brokered deposits

(due to concurrently applied brokered deposit restrictions) and deposits above the insurance

limit (reflecting the flight of uninsured depositors from the ailing bank). These regressions

24
show that banks under regulatory action increase reliance on listing service deposits, much

as the bank featured earlier in this paper did. Treated banks also increase reliance on term

deposits under the insurance limit, and especially those between $100,000 and $250,000.

Recall that the bank featured earlier in this paper structured most of its new term deposits

during the Formal period to fall just under the insurance limit — within this range.

Another question is whether these patterns hold only for small banks or also for larger

banks. The approximately 500 banks that failed during the Great Recession led to significant

depletion of the deposit insurance funds and costs to the FDIC hence this finding is important

in its own right even if it applies only to smaller banks. However, we also provide evidence

that this phenomenon is common even to larger banks. To show that this behavior generalizes

even to larger banks, we collected deposit pricing data from RateWatch for large banks which

we know faced regulatory enforcement actions similar to the bank we study. For the purposes

of this analysis, we define large banks to be those with assets over $5 billion; to avoid sample

selection issues, we use total banks assets as of 2008Q4 for this size determination. We

chose a relatively low threshold for “large banks” in order to include a sufficient number of

observations of banks which faced enforcement action; beyond the substantial right-skew in

the distribution of all banks in the US by asset size, the enforcement actions we study are

particularly uncommon against very large banks.42

We use rates offered on the 12-month retail CD with a $100,000 balance as the representative deposit rate in this analysis, and we construct the spread on these accounts relative

to the FDIC national average rate data. To measure large banks’ deposit interest rates,

we use survey data collected by RateWatch. The raw survey data collected by RateWatch

is at the weekly frequency, at the branch level, and includes rates for a large variety of

deposit products. We drop promotional rate offers, relationship-based special offers, and

business-focused offers, leaving us with regular, retail deposit offerings. We then exclude

non-bank institutions. Next, we adjust the correspondence between banks and branches to

reflect branch sales; raw survey data takes the branch as the unit of observation, and we use

a file containing changes in branch ownership to reconstruct the history of banks to which

those branches (and their survey responses) belonged. Having generated an accurate correspondence between branches’ weekly deposit rates and the bank which owns the branch, we

take an unweighted average across all branches within the bank at each point in time and

for each given product; this yields bank-level average rates by deposit product. Finally, we

42We considered higher asset size thresholds to define large banks, but cannot disclose the results, as the

sample sizes are sufficiently small that releasing those results may reveal agency sensitive information and

would violate best practices for disclosure review.

25
keep the last weekly survey report for each bank within the quarter, yielding quarter-end

deposit rates by bank and product.

Focusing only on banks facing enforcement actions, we conduct a pooled OLS regression

(Table 15). We regress these large banks’ 12-month CD rate spreads on (1) a dummy

variable indicating whether they are currently under an enforcement action and (2) a set of

quarterly (time) dummies. Here the coefficient on the enforcement action dummy represents

the difference in rate spreads offered by banks under enforcement action versus those banks

which are about to be under enforcement action (within four quarters). This regression

reveals that these larger banks raise their rates by a statistically significant 41 basis points

around the imposition of enforcement actions. Adding this result to the constant, we find

that large banks under enforcement action on average pay almost exactly 75 basis points

above the FDIC national average – that is, at or near the limit for such banks.43

6 Conclusion

In this paper we use a highly granular and unique dataset to identify important new findings

related to deposit inflows and outflows in a failing bank. Our most important finding is

that gross deposit inflows are of first order impact in failing banks’ balance sheets, despite

the banks’ elevated default risk and supervisory actions meant to prevent costly and rapid

deposit acquisition. Even though there are substantial deposit outflows in the bank under

study – the bank loses one-third of its existing deposits – these are made up by substantial

deposit inflows, with new deposits typically just below the deposit insurance limits. The new

deposit inflows arrive as the bank increases its interest rates - in line with the theoretical

predictions of Egan et. al. (2017). Though our focus is one bank, we demonstrate that our

main findings qualitatively generalize to other banks by using a combination of confidential

supervisory data and publicly available but less granular data on other US banks, and by

collecting additional data on interest rates.

We also identify a number of policy-relevant findings related to gross deposit outflows.

Most importantly, we provide further evidence that deposit insurance improves funding

stability. We also provide the first empirical evidence that temporary, crisis-era expansions

of deposit insurance, such as FDIC’s TAG, are as effective as ordinary deposit insurance in

preventing deposit outflows. Many of our findings on outflows support the intuition which is

43The above results are based on a quarterly frequency regression with data coming from a total of 19

unique banks, and the panel data are imbalanced/censored.

26
reflected in regulations, such as that checking accounts and accounts receiving regular direct

deposits are more stable. Ultimately, our results suggest that focusing on deposit outflows

alone in times of stress or bank failures is inadequate; deposit inflows are also first order

important, worthy of further attention by regulators and academicians alike.

27
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29
Table 1: Summary Statistics, by Period

(a) New Depositors

Placebo Pre-Crisis Post-Crisis Formal

(1) (2) (3) (4)

Number of New Accounts 3,028 2,146 927 2,600

Uninsured at Start of Account 0.063 0.087 0.044 0.027

TAG/DFA Eligible 0.015 0.038 0.016 0.008

0 (Covered in Post-Crisis & Formal)

Starting Balance 43,520 90,840 141,000 186,900

Term (count-weighted) 0.461 0.478 0.417 0.824

Savings (count-weighted) 0.487 0.379 0.408 0.084

Checking (count-weighted) 0.052 0.144 0.175 0.092

Starting Interest Spread to Market 2.561 1.656 0.796 0.671

Institutional - Listed/Faxed 0 0.006 0.013 0.707

Institutional - Other 0.031 0.252 0.245 0.100

Brokered/Placed 0.002 0.035 0.221

Trust 0.038 0.037 0.094 0.041

(b) Extant Depositors

Placebo Pre-Crisis Post-Crisis Formal Failure

(1) (2) (3) (4) (5)

Number of Accounts 41,366 44,910 38,374 30,342 25,141

Uninsured at Start of Period 0.065 0.087 0.009 0.021 0.014

TAG/DFA Eligible 0.006 0.008 0.004 0.004 0.005

0 (Covered in Post-Crisis & Formal)

Starting Balance 28,310 27,710 31,450 43,970 49,750

Term (count-weighted) 0.190 0.242 0.222 0.123 0.191

Savings (count-weighted) 0.734 0.690 0.700 0.768 0.678

Checking (count-weighted) 0.076 0.068 0.079 0.109 0.130

Starting Interest Rate 4.121 4.367 2.486 0.941 0.882

Starting Interest Spread to Market 2.604 2.576 1.492 0.673 0.632

Institutional - Listed/Faxed 0 0 0 0.002 0.084

Institutional - Other 0.013 0.015 0.025 0.047 0.065

Brokered/Placed 0.013 0.012 0.045 0.037 0.005

Trust 0.015 0.017 0.016 0.023 0.028

Direct Deposit 0.028 0.030 0.023 0.035 0.032

Past Month Fees -0.237 -0.061 0.084 0.174 0.171

Age of Relationship in Years 2.214 3.076 4.031 5.587 6.032

Years Since Start of Previous Period - 1.25 1.25 1.78 0.92

Panel (a) shows summary statistics across all new depositors arriving within each of the four

event periods, excluding existing depositors. Panel (b) shows corresponding statistics for

depositors who were extant at the bank at the beginning of each period as well as those at

the bank on the day it failed (Failure).

30

0
Table 2: Variable Definitions for the Regressions of Section 3

Liquidation: A dummy variable equal to 1 if the deposit account balance falls by 75% or more relative

to the balance as measured at the beginning of the period, and the balance stays at or below 25% of the

start-of-period balance for at least 61 days, which includes the account closing. This notion of account

liquidation is generally consistent with related studies (for example, Iyer and Puri (2012)). Results in this

paper are robust to different thresholds of 50% and 80%.

Uninsured: A dummy variable equal to 1 if there are any uninsured balances in the account at the start of

the period. Deposit insurance limits apply separately to different ownership types, so we account separately

for individual, corporate, municipal, joint, IRA, employee benefit plans, revocable trust, and irrevocable

trust ownership categories. An exact insurance determination can sometimes be difficult, as joint and trust

accounts have complex ownership structures which are often incompletely documented, hence we construct

this variable conservatively. Accounts we flag as insured have all funds insured. Accounts we flag as uninsured

should have some uninsured funds in them, but it is possible that they are occasionally covered because of

complex joint ownership. As a result, estimates in our regressions are lower bounds on the effects of being

over the FDIC insurance limit.

TAG/DFA Eligible: A dummy variable equal to 1 if the account is eligible for additional insurance coverage

from temporary guarantee schemes at the start of the period; 0 otherwise. The additional guarantee programs

were in effect only in our Post-Crisis and Formal periods; in earlier periods, the dummy is assigned based on

whether the account would qualify for coverage had the same programs been introduced earlier. The dummy

is used in the Placebo and Pre-Crisis periods simply to establish a basis for comparison of these accounts’

behavior; they did not have additional coverage before the financial crisis. The two additional guarantee

schemes were the Transaction Account Guarantee (TAG) program and guarantees from by the Dodd Frank

Act (DFA). TAG placed temporary but unlimited (in dollar terms) guarantees on all categories of checking

accounts at this bank from October 14, 2008 until December 31, 2010. The DFA guarantees similarly

provided unlimited insurance for non-interest-bearing demand deposit accounts and IOLTA accounts, but

not NOW accounts from December 31, 2010 until December 31, 2012. In calculating a depositor’s insurance

coverage, accounts covered by these programs (while effective) did not count toward a depositor’s $250,000

limit.

Direct Deposit: A dummy variable equal to 1 if the account is receiving an ACH deposit roughly every

two weeks as of the start of the regression period; 0 otherwise, and always 0 for term deposits.

Log(Age): The natural log of the years since the primary account holder first appears in the bank’s deposit

records, as of the start of the period. If an individual was a secondary depositor on an account before

becoming a primary account holder on another account, or closed an older account, we use the date at

which the original account was opened. Relationship age serves as a measure of the depth of the depositor

relationship. The age of the account is dated differently in the case of placed deposits; see placed deposits

below for details.

Log(Days to Maturity): The natural log of the number of days until the maturity of the account, as of

the start of the regression period. This is defined only for term accounts.

Prior Transactions: The proportion of days in the past year, at the start of the measurement period,

in which the account holder performed at least one transaction in the account. A value of 0 thus implies

no activity and 100 implies activity every day. We exclude transactions exogenous to the depositor such

as monthly interest credits or fees. This serves as another measure of depositors’ relationship depth. This

variable is always 0 for term accounts.

31
Table 2: Variable Definitions for the Regressions of Section 3

Institutional-Listed/Faxed: A dummy variable equal to 1 if the deposit is owned by a bank, savings

association, credit union, financial corporate, municipality, or non-financial corporation, or if it is a “business”

product type as marked in the bank’s records; 0 otherwise. Additionally, the deposit must have been opened

via an internet listing service or facsimile as identified by the bank’s records. We group faxed deposits

with listed because internal bank documentation, depositor behavior, and depositor types (namely, small

depository institutions making up a large portion of these deposits towards the end of the bank’s life)

all indicate that the faxed deposits were almost exclusively gathered from depositors who saw the rates on

listing services and then faxed their deposit request to the bank. This excludes third-party deposit placement

services (such as deposit brokers) as we capture these entities with a separate dummy variable.

Institutional- Other: A dummy variable exactly defined As Institutional - Listed/Faxed above, but we

have no evidence that the deposits were received from a listing service or facsimile order.

Brokered/Placed: A dummy variable equal to 1 if the deposit was placed by a fiduciary or deposit broker; 0

otherwise and always 0 for transaction accounts. Many term deposits at the bank are not held by individuals

but instead held by institutions acting as fiduciaries for others, and these fiduciaries do not consistently reveal

the identity of the underlying holders of the account to the bank. These deposits reflect a less personal

connection with the bank. For these accounts, the age of the account variable is dated to the start of the

individual account, not the first relationship of the reported holder of the account, as the reported holder is

only a fiduciary that may not make final renewal and withdrawal decisions. Note that we assume all placed

deposits are insured; these services advertise that they structure their deposits so as to achieve full insurance

coverage.

Trust: A dummy variable equal to 1 if the account is held by a trust and 0 otherwise. This does include

Payable on Death (POD) arrangements. Trust accounts require effort to establish, and they are a useful

legal device more for wealthier or more complex depositors. As such, we expect accounts held in trust to

represent more sophisticated customers.

Interest Rate Spread: The spread between an account’s interest rate and the market average for that

deposit product. The spread is intended to reflect the pricing offered to depositors opening a new account

or rolling over a CD. For transaction (non-maturity) accounts, which re-price continuously and thus reflect

current offered rates, we calculate the spread as the difference between the rate on an account and the

account-type-level rate average constructed from RateWatch. Term deposits reprice only periodically, so

the current rate on an existing term deposit is not generally the current rate on offer for new accounts or

funds being rolled over from a maturing CD. To construct the spread for a given term account, we take

the difference between the average rate paid on newly opened term deposits of the same maturity within a

symmetric, centered, rolling 31-day window (i.e., 15 days before to 15 days after the relevant date) and the

market average rate for that product calculated from RateWatch data. Particularly since depositors tend to

liquidate CDs at maturity, even in periods of stress, this is the relevant measure of pricing for the decisions

of both extant depositors considering leaving the bank and newly arriving depositors considering opening an

account.

Past-Month Fees: The total dollar value in the past month of all fees paid on or to the account, including

reversal of old fees and signup bonuses. For some regressions, this variable is divided by account balance to

obtain a “fee rate”.

32
Table 3: Who Withdraws? Transaction Deposits; Linear Probability Model

Placebo Pre-Crisis Post-Crisis Formal

(1) (2) (3) (4) ⁄ ⁄ ⁄⁄⁄ Uninsured 0.0249 -0.00117 0.0693 0.183

(1.94) (-0.09) (1.92) (8.45) ⁄⁄ TAG/DFA Eligible— -0.0446 -0.000624 -0.0944 -0.0412

(-1.50) (-0.02) (-2.00) (-0.95) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄ Checking -0.0901 -0.105 -0.0277 -0.0252

(-8.44) (-8.10) (-2.64) (-2.15) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Direct Deposit -0.104 -0.123 -0.0864 -0.0459

(-7.08) (-7.47) (-5.88) (-2.77) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄ ⁄⁄⁄ Log(Age) -0.0194 -0.0180 -0.00915 -0.0247

(-7.47) (-4.34) (-2.40) (-4.65) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Prior Transactions 0.00793 0.00555 0.00263 -0.00310

(14.13) (7.62) (4.37) (-4.19) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄ Prior Transactions2 -0.0000983 -0.0000746 -0.0000419 0.0000235

(-12.71) (-7.19) (-4.88) (2.34)

Institutional - Any -0.0273 0.0242 0.0218 0.0215

(-1.16) (1.01) (1.29) (1.33) ⁄ ⁄⁄⁄ Trust 0.0255 0.0248 -0.0418 0.0698

(0.93) (0.83) (-1.90) (3.38) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Constant 0.217 0.313 0.159 0.326

(13.12) (17.35) (11.62) (21.96)

Branch Controls Yes Yes Yes Yes

No. of Accounts 33498 34029 29865 26616

No. of Liquidations 6920 10795 4740 6218

R2 1.3% 0.8% 0.4% 1.8%

Model P-Value < 0.001 < 0.001 < 0.001 < 0.001

Estimates from linear probability models of the probability of account liquidation during the each

of the four periods of interest. Liquidation is defined as withdrawing 75% or more of the startof-period account balance and staying at or below 25% of the start-of-period balance for at least

61 days. T-statistics are in parentheses, calculated using asymptotically normal standard errors. ⁄⁄⁄ ⁄⁄ ⁄ Estimates significant at 99% are denoted with , 95% with , and 90% with .

— TAG/DFA coverage was only effective in the Post-Crisis and Formal periods; eligible accounts

were fully covered/insured in those periods. In earlier periods, eligibility does not imply coverage,

and is shown for comparison purposes only.

33
Table 4: Who Withdraws? Transaction Deposits; Alternative Specifications

Probit Cox P.H.

Post-Crisis Formal Post-Crisis Formal

(1) (2)⁄⁄⁄ 0.0655 0.170

(1.61) (6.69) ⁄⁄ -0.0691 -0.0164

(-2.24) (-0.42) ⁄⁄⁄ ⁄⁄ -0.0262 -0.0280

(-2.71) (-2.51) ⁄⁄⁄ ⁄⁄⁄ -0.0875 -0.0679

(-8.55) (-4.37) ⁄⁄ ⁄⁄⁄ -0.00798 -0.0231

(-2.18) (-4.49) ⁄⁄⁄ ⁄⁄⁄ 0.00290 -0.00337

(4.61) (-4.33) ⁄⁄⁄ ⁄⁄ -0.0000518 0.0000259

(-5.15) (2.36)

(3) (4) ⁄⁄ ⁄⁄⁄ 1.503 1.862

(1.99) (8.01) ⁄ 0.567 0.958

(-1.83) (-0.26) ⁄⁄⁄ ⁄⁄ 0.814 0.887

(-2.72) (-2.15) ⁄⁄⁄ ⁄⁄⁄ 0.430 0.631

(-6.04) (-4.32) ⁄⁄⁄ ⁄⁄⁄ 0.936 0.896

(-2.63) (-4.72) ⁄⁄⁄ ⁄⁄⁄ 1.025 0.985

(5.43) (-3.75) ⁄⁄⁄ ⁄ 1.000 1.000

(-5.56) (1.68)

0.0226 0.0192 1.156 1.090

(1.24) (1.18) ⁄⁄ ⁄⁄⁄ -0.0444 0.0601

(-2.30) (2.73)

(1.27) (1.24) ⁄ ⁄⁄⁄ 0.711 1.250

(-1.92) (2.71)

Yes Yes Yes Yes

29865 26616 29865 26616

4740 6218 4740 6218

-13001.0 -14236.2 -48378.0 -62351.7

< 0.001 < 0.001 < 0.001 < 0.001

Uninsured

TAG/DFA Eligible

Checking

Direct Deposit

Log(Age)

Prior Transactions

Prior Transactions2

Institutional - Any

Trust

Branch Controls

No. of Accounts

No. of Liquidations

Log Likelihood

Model P-Value

Estimates from Probit (columns 1 and 2) and Cox proportional hazard models (columns 3 and 4)

of the probability of account liquidation during the Post-Crisis and Formal Periods. Liquidation

is defined as withdrawing 75% or more of the start-of-period account balance and staying at or

below 25% of the start-of-period balance for at least 61 days. Probit estimates are expressed as

marginal effects (with a coefficient of 0 reflecting a variable having no effect on the probability

of liquidation), and Cox estimates are expressed as hazard ratios (with 1 meaning no effect).

T-statistics are in parentheses, calculated using asymptotically normal standard errors. Estimates ⁄⁄⁄ ⁄⁄ ⁄ significant at 99% are denoted with , 95% with , and 90% with .

34
Table 5: Who Withdraws? Term Deposits; Linear Probability Model

Placebo Pre-Crisis Post-Crisis Formal

Uninsured

(1)

0.00501

(0.39)

(2)⁄⁄⁄ 0.0432

(3.78)

(3) ⁄⁄ 0.0820

(2.03)

(4)⁄⁄⁄ 0.147

(2.92)

TAG/DFA Eligible—– -0.333 -0.573 -0.00332 -

Log(Age)

Log(Days to Maturity)

Brokered/Placed

Institutional - Listed/Faxed

Institutional - Other

Trust

Constant

(-1.12) ⁄ -0.00664

(-1.81)⁄⁄⁄ -0.144

(-28.94) ⁄⁄⁄ 0.254

(13.08)

-0.159

(-0.65)⁄ 0.0764

(1.80)⁄⁄⁄ -0.0745

(-3.33) ⁄⁄⁄ 0.891

(20.92)

(-1.24)⁄⁄⁄ -0.0391

(-10.60)⁄⁄⁄ -0.0695

(-21.01) ⁄⁄⁄ 0.162

(7.64)

-0.244

(-0.91)⁄ 0.0880

(1.72)

0.00318

(0.15)⁄⁄⁄ 0.815

(17.79)

(-0.02)

-0.00379

(-1.01)⁄⁄⁄ -0.200

(-54.37) ⁄⁄⁄ 0.279

(21.79)

0.0105

(0.10)

0.0180

(0.55)

-0.00600

(-0.26) ⁄⁄⁄ 1.185

(32.15)

- ⁄ -0.0124

(-1.78)⁄⁄⁄ -0.0866

(-15.90) ⁄⁄⁄ 0.550

(25.76) ⁄⁄⁄ -0.166

(-3.08)

-0.0358

(-1.15)

0.00651

(0.18)⁄⁄⁄ 0.683

(13.75)

Branch Controls Yes Yes Yes Yes

No. of Accounts 7868 10881 8509 3726

No. of Liquidations 2193 7153 2559 1736

R2 11.4% 5.4% 31.6% 34.0%

Model P-Value < 0.001 < 0.001 < 0.001 < 0.001

Estimates from linear probability models of the probability of account liquidation during the each

of the four periods of interest. Liquidation is defined as withdrawing 75% or more of the startof-period account balance and staying at or below 25% of the start-of-period balance for at least

61 days. T-statistics are in parentheses, calculated using asymptotically normal standard errors. ⁄⁄⁄ ⁄⁄ ⁄ Estimates significant at 99% are denoted with , 95% with , and 90% with .

— TAG/DFA coverage was only effective in the Post-Crisis and Formal periods; eligible accounts

were fully covered/insured in those periods. In earlier periods, eligibility does not imply coverage,

and is shown for comparison purposes only.

– Note that although TAG/DFA did not directly insure term deposits, it could do so indirectly.

Accounts covered by TAG/DFA were excluded in calculating a depositor’s total exposure to a bank

when applying standard, limited-value deposit insurance, effectively freeing up insurance coverage

to be applied to other, non-TAG/DFA-covered accounts.

35
Table 6: Who Withdraws? Term Deposits; Alternative Specifications

Probit

Post-Crisis Formal

(1) (2)

Uninsured

TAG/DFA Eligible–

Log(Age)

Log(Days to Maturity)

Brokered/Placed

Institutional - Listed/Faxed

Institutional - Other

Trust

Branch Controls

No. of Accounts

No. of Liquidations

Log Likelihood

Model P-Value

Cox P.H.

Post-Crisis Formal

(3) (4) ⁄⁄ ⁄⁄⁄ 1.637 1.746

(2.55) (2.99)

0.915 -

(-0.12) - ⁄ 1.017 1.053

(0.81) (1.92) ⁄⁄⁄ ⁄⁄⁄ 0.545 0.664

(-56.75) (-28.43) ⁄⁄⁄ ⁄⁄⁄ 3.678 8.565

(21.11) (26.58)⁄⁄ 0.788 0.465

(-0.24) (-2.14)

0.914 1.239

(-0.46) (1.50)

0.985 1.220

(-0.11) (1.31)

Yes Yes

8509 3726

2559 1736

-21333.4 -12883.1

< 0.001 < 0.001

⁄ 0.110

(1.89)

0.00446

(0.03)

-0.00498

(-0.98)⁄⁄⁄ -0.214

(-43.56) ⁄⁄⁄ 0.337

(17.44)

-0.169

(-1.18)

-0.0135

(-0.31)

-0.00735

(-0.24)

Yes

8509

2559

-3767.9

< 0.001

⁄⁄⁄ 0.167

(2.80)

-

-

-0.0156

(-1.52)⁄⁄⁄ -0.115

(-15.28) ⁄⁄⁄ 0.586

(29.93) ⁄⁄⁄ -0.233

(-3.35)

-0.0498

(-1.15)

0.0147

(0.31)

Yes

3726

1736

-1856.9

< 0.001

Estimates from Probit (columns 1 and 2) and Cox proportional hazard models (columns 3 and 4)

of the probability of account liquidation during the Post-Crisis and Formal Periods. Liquidation is

defined as withdrawing 75% or more of the start-of-period account balance and staying at or below

25% of the start-of-period balance for at least 61 days. Probit estimates are expressed as marginal

effects (with a coefficient of 0 reflecting a variable having no effect on the probability of liquidation),

and Cox estimates are expressed as hazard ratios (with 1 meaning no effect). T-statistics are in

parentheses, calculated using asymptotically normal standard errors. Estimates significant at 99% ⁄⁄⁄ ⁄⁄ ⁄ are denoted with , 95% with , and 90% with .

– Note that although TAG/DFA did not directly insure term deposits, it could do so indirectly.

Accounts covered by TAG/DFA were excluded in calculating a depositor’s total exposure to a bank

when applying standard, limited-value deposit insurance, effectively freeing up insurance coverage

to be applied to other, non-TAG/DFA-covered accounts.

36
Table 7: Who Withdraws? Linear Probability Model; Including Interest Rates

Transaction

Post-Crisis Formal

(1) (2) ⁄⁄ Uninsured 0.0723

(2.00)⁄ TAG/DFA Eligible– -0.0909

(-1.93)⁄⁄⁄ Interest Rate Spread -0.0135

(-3.05)⁄⁄ Past-Month Fees 0.00136

(2.22)⁄⁄⁄ Checking -0.0410

(-3.64)⁄⁄⁄ Direct Deposit -0.0877

(-5.96)⁄⁄⁄ Prior Transactions 0.00284

(4.69) ⁄⁄⁄ Prior Transactions2 -0.0000439

(-5.10)⁄⁄⁄ Log(Age) -0.0101

(-2.63)

Log(Days to Maturity)

Brokered/Placed

Institutional - Listed/Faxed

Institutional - Other 0.0116

(0.68)⁄ Trust -0.0407

(-1.85) ⁄⁄⁄ Constant 0.182

(11.78)

Branch Controls Yes

No. of Accounts 29865

No. of Liquidations 4740

R2 0.4%

Model P-Value < 0.001

⁄⁄⁄ 0.190

(8.72)

-0.0520

(-1.19)⁄⁄⁄ -0.0780

(-3.18)⁄⁄⁄ 0.00137

(2.60)⁄⁄⁄ -0.0487

(-3.53)⁄⁄⁄ -0.0443

(-2.67)⁄⁄⁄ -0.00297

(-4.01) ⁄⁄ 0.0000217

(2.16)⁄⁄⁄ -0.0243

(-4.57)

-0.00527

(-0.29)⁄⁄⁄ 0.0701

(3.39)⁄⁄⁄ 0.382

(16.91)

Yes

26616

6218

1.9%

< 0.001

Term

Post-Crisis Formal

(3) (4) ⁄ ⁄⁄ 0.0731 0.129

(1.75) (2.57)

0.00470 -

(0.03) - ⁄⁄⁄ ⁄⁄⁄ -0.723 -0.507

(-23.17) (-5.71) ⁄⁄⁄ 0.0382 -

(2.87) -

⁄⁄ 0.00953 -0.0109

(2.40) (-1.56) ⁄⁄⁄ ⁄⁄⁄ -0.142 -0.0849

(-28.75) (-15.54) ⁄⁄⁄ ⁄⁄⁄ 0.303 0.537

(22.04) (25.09) ⁄⁄⁄ -0.0978 -0.176

(-0.64) (-3.28)

-0.00492 -0.0429

(-0.14) (-1.38)

0.0184 0.000492

(0.77) (0.01) ⁄⁄⁄ ⁄⁄⁄ 1.706 1.048

(38.40) (13.20)

Yes Yes

7800 3717

2506 1734

35.2% 34.7%

< 0.001 < 0.001

Estimates from linear probability models of the probability of account liquidation for both transaction and

term deposits, focusing only on the Post-Crisis and Formal periods. Relative to the main specification

discussed elsewhere, this table adds interest rates and fees, at the account level, as regressors. Liquidation is

defined as withdrawing 75% or more of the start-of-period account balance and staying at or below 25% of the

start-of-period balance for at least 61 days. T-statistics are in parentheses, calculated using asymptotically ⁄⁄⁄ ⁄⁄ ⁄ normal standard errors. Estimates significant at 99% are denoted with , 95% with , and 90% with .

– Note that although TAG/DFA did not directly insure term deposits, it could do so indirectly. Accounts

covered by TAG/DFA were excluded in calculating a depositor’s total exposure to a bank when applying

standard, limited-value deposit insurance, effectively freeing up insurance coverage to be applied to other,

non-TAG/DFA-covered accounts.

37
Table 8: Uninsured Transaction Account Migration

Deposit Insurance Limit = $100,000

$2,000- $48,000- $98,000-

Bin Range <$1 $1 - 2,000 48,000 98,000 102,000 >$102,000

Placebo 4.98% 9.08% 11.42% 10.32% 11.71% 52.59%

Pre-Crisis 7.71% 9.59% 9.88% 15.59% 16.35% 40.88%

Deposit Insurance Limit = $250,000

$2,000- $123,000- $248,000-

Bin Range <$1 $1 - 2,000 123,000 248,000 252,000 >$252,000

Post-Crisis – 6.37% 14.22% 13.73% – 65.69%

Formal 20.79% 6.6% 22.3% 14.04% 8.05% 27.72%

For all transaction accounts which had a balance of $2,000 less than the current deposit

insurance limit or higher at the beginning of each period, this table shows their distribution

into various account dollar-size bins at the end of the period. Several bins are suppressed

for disclosure reasons.

38
Table 9: Comparison of Depositors Across Periods

Var. Type

Placebo

Uninsured TAG/DFA

Eligible—

Dummy Dummy

Extant Depositors

Brokered/ Institutional:

Placed Listed/Faxed

Dummy Dummy

Omitted Dummy

Checking

Dummy

CD

Dummy

Pre-Crisis

Post-Crisis

Formal

Failure Date

⁄⁄⁄ 0.0212

(15.29)⁄⁄⁄ -0.0558

(-38.71)⁄⁄⁄ -0.0445

(-28.91)⁄⁄⁄ -0.0509

(-31.27)

⁄⁄⁄ 0.00205

(3.82)⁄⁄⁄ -0.00226

(-4.04)⁄⁄⁄ -0.00188

(-3.15)⁄⁄ -0.00155

(-2.46)

-0.000476

(-0.47)⁄⁄⁄ 0.0325

(30.92)⁄⁄⁄ 0.0242

(21.64)⁄⁄⁄ -0.00707

(-5.97)

-0.00000572

(-0.01)

0.000240

(0.29)⁄⁄ 0.00197

(2.23)⁄⁄⁄ 0.0843

(90.07)

⁄⁄⁄ -0.00803

(-4.17)

0.00254

(1.27)⁄⁄⁄ 0.0328

(15.37)⁄⁄⁄ 0.0543

(24.02)

⁄⁄⁄ 0.0521

(19.08)⁄⁄⁄ 0.0315

(11.11)⁄⁄⁄ -0.0674

(-22.27)

0.00108

(0.34)

Placebo

Pre-Crisis

Post-Crisis

Formal

New Depositors ⁄⁄⁄ -0.00160 0.00903

(-0.42) (6.08) ⁄⁄⁄ ⁄⁄⁄ 0.0221 0.0316

(4.91) (18.09) ⁄⁄⁄ ⁄⁄⁄ -0.0208 0.0100

(-3.07) (3.83) ⁄⁄⁄ -0.0377 0.00230

(-9.16) (1.44)

⁄⁄⁄ -0.0105

(-3.78)⁄⁄⁄ 0.0220

(6.70)⁄⁄⁄ 0.209

(42.42)⁄⁄⁄ -0.0125

(-4.18)

-0.0000725

(-0.03)⁄⁄ 0.00552

(2.13)⁄⁄⁄ 0.0129

(3.31)⁄⁄⁄ 0.707

(298.88)

⁄⁄⁄ -0.0242

(-4.55)⁄⁄⁄ 0.0674

(10.77)⁄⁄⁄ 0.0987

(10.51)⁄⁄⁄ 0.0158

(2.77)

⁄⁄⁄ 0.271

(35.92) ⁄⁄⁄ 0.287

(32.42) ⁄⁄⁄ 0.227

(17.09) ⁄⁄⁄ 0.634

(78.30)

Constant ⁄⁄⁄ 0.0650

(64.97)

⁄⁄⁄ 0.00616

(15.90)

⁄⁄⁄ 0.0125

(17.20)

0.0000725

(0.13)

⁄⁄⁄ 0.0761

(54.73)

⁄⁄⁄ 0.190

(96.59)

No. of Obs.

R2

Model P-value

F-Statistic

188834

2.4%

< 0.001

572.9

188834

0.3%

< 0.001

61.22

188834

1.9%

< 0.001

463.5

188834

35.0%

< 0.001

12708.7

188834

0.7%

< 0.001

161.3

188834

5.2%

< 0.001

1299.1

Each column corresponds to a separate account-level OLS regression of an account characteristic on the

interaction of time period dummies and an indicator for extant or new depositors. The observational unit is

account-time period, and with five time periods it is possible for an individual account to appear up to five

times in this regression. Extant depositors are those with an existing deposit relationship at the beginning

of each time period, and new depositors are those who arrive during the period. The omitted category is

extant depositors in the Placebo period, so all estimates in other rows reflect differences relative to that

category. T-statistics are in parentheses, calculated using asymptotically normal standard errors. Estimates ⁄⁄⁄ ⁄⁄ ⁄ significant at 99% are denoted with , 95% with , and 90% with .

— TAG/DFA coverage was only effective in the Post-Crisis and Formal periods; eligible accounts were fully

covered/insured in those periods. In earlier periods, eligibility does not imply coverage, and is shown for

comparison purposes only.

39
Table 10: What Explains the Share of Deposits Which Are New?

(1) (2) (3) (4)

Time Period Dummies:

Pre-Placebo

Placebo to Pre-Crisis

Pre-Crisis

⁄⁄⁄ 5.439

(2.90)⁄⁄ 2.027

(2.01)⁄⁄⁄ -1.442

(-2.72)

1.337

(1.13)⁄⁄ 2.054

(2.21)

0.378

(0.25)

⁄ 1.925

(1.65)⁄⁄⁄ 3.079

(3.05)

2.907

(1.58)

⁄ 1.883

(1.65)⁄⁄⁄ 2.889

(2.84)

2.735

(1.49)

Crisis 0.188 2.069 3.417 3.157

Post-Crisis

Post-Crisis to Formal

Formal

(0.25)

-0.393

(-0.60)⁄⁄ -1.254

(-2.18) ⁄⁄⁄ 5.821

(2.79)

(0.92)

3.044

(0.98)

2.230

(0.74)⁄⁄ 7.495

(2.14)

(1.40)⁄⁄ 8.875

(2.20)⁄⁄ 9.264

(2.24)⁄⁄⁄ 14.91

(2.99)

(1.30)⁄⁄ 8.685

(2.15)⁄⁄ 8.919

(2.16)⁄⁄⁄ 14.48

(2.90)

Economic Controls:

Log(VIX) 1.137 -1.263 -0.806

GDP Growth

(0.63)⁄⁄ 0.339

(2.21)

(-0.51) ⁄⁄⁄ 0.543

(2.60)

(-0.32)⁄⁄ 0.514

(2.46)

Housing Starts 0.00442 0.00415 0.00430

(1.57) (1.46) (1.47)

Daily S&P 500 Return 18.19 13.93 15.04

AR(1)

OFR Financial Stress Index

Rate Spread to Market (Dollar-Weighted Average)

(1.34)⁄⁄⁄ 0.390

(7.05)

0.139

(0.95)

(1.02)⁄⁄⁄ 0.379

(7.04)⁄ 0.380

(1.74)⁄⁄⁄ 5.549

(2.71)

(1.10)⁄⁄⁄ 0.378

(7.02)

0.328

(1.48)⁄⁄⁄ 5.742

(2.77)

Past-Month Fees/Deposit Balances (in Aggregate) 1083.8

Constant ⁄⁄⁄ 3.218

(6.62)

-8.348

(-1.62)

⁄⁄ -12.01

(-2.55)

(0.96)⁄⁄⁄ -13.45

(-2.62)

N 2079 2078 2078 2078

Model P-Value < 0.001 < 0.001 < 0.001 < 0.001

Estimates from daily-frequency regressions of the share of deposit dollars at the bank which

are new (as of that day) on various controls. All models are OLS with

o

Newey-West standard

errors of lag length 9, chosen by the Newey-West rule of thumb (.75 3

T) (Stock and Watson,

2019). Estimates significant at 99% are denoted with ⁄⁄⁄, 95% with ⁄⁄, and 90% with ⁄.

40
Table 11: What Explains the Log-Level of New Deposits?

(1) (2) (3) (4)

Time Period Dummies:

Pre-Placebo 1.152 0.410 0.673 0.662

(1.57) (0.54) (0.90) (0.89)

Placebo to Pre-Crisis 0.991 0.616 1.084 1.039

Pre-Crisis

(1.46)⁄⁄ -1.822

(-2.57)

(0.88)

-0.123

(-0.11)

(1.51)

1.064

(0.95)

(1.39)

1.023

(0.90)

Crisis -1.167 1.337 1.963 1.902

Post-Crisis

Post-Crisis to Formal

Formal

(-1.30)

-1.066

(-1.17)⁄⁄⁄ -2.225

(-3.60)

0.671

(0.95)

(0.78)

0.718

(0.38)

-0.840

(-0.46)

1.798

(0.98)

(1.15)⁄ 3.440

(1.67)

2.447

(1.16)⁄⁄ 5.229

(2.44)

(1.09)

3.394

(1.63)

2.364

(1.09)⁄⁄ 5.124

(2.32)

Economic Controls:

Log(VIX) ⁄⁄⁄ 4.182

(4.20)

⁄⁄⁄ 3.055

(2.72)

⁄⁄⁄ 3.160

(2.62)

GDP Growth -0.0225 0.0706 0.0639

(-0.25) (0.74) (0.64)

Housing Starts 0.00178 0.00163 0.00167

(1.11) (1.01) (1.02)

Daily S&P 500 Return -8.577 -10.40 -10.14

AR(1)

OFR Financial Stress Index

Rate Spread to Market (Dollar-Weighted Average)

(-0.65) ⁄⁄⁄ 0.276

(19.61) ⁄⁄⁄ -0.266

(-2.81)

(-0.78) ⁄⁄⁄ 0.274

(19.48)

-0.155

(-1.39) ⁄⁄⁄ 2.606

(3.15)

(-0.75) ⁄⁄⁄ 0.274

(19.42)

-0.167

(-1.38) ⁄⁄⁄ 2.649

(3.18)

Past-Month Fees/Deposit Balances (in Aggregate) 251.3

Constant ⁄⁄⁄ 11.09

(21.17)

⁄⁄ -6.523

(-1.97)

⁄⁄ -8.184

(-2.41)

(0.30)⁄⁄ -8.515

(-2.38)

N 2079 2078 2078 2078

Model P-Value < 0.001 < 0.001 < 0.001 < 0.001

Estimates from daily-frequency regressions of the logarithm of new deposit dollars at the

bank (as of that day) on various controls. All models are OLS with

o

Newey-West standard

errors of lag length 9, chosen by the Newey-West rule of thumb (.75 3

T) (Stock and Watson,

2019). Estimates significant at 99% are denoted with ⁄⁄⁄, 95% with ⁄⁄, and 90% with ⁄.

41
Table 12: Impact of Regulatory Action on Funding Shares For US Banks

Brokered Listed Sm. Term Med. Term Lg. Term

(1) (2) (3) (4) (5)

−1.24⁄⁄⁄ 0.826⁄⁄⁄ 0.364⁄⁄⁄ 0.466⁄⁄⁄ −0.830 Under Reg. Action ⁄⁄⁄

(−22.72) (11.40) (5.21) (6.25) (−12.10)

NPLt~At 0.0102⁄⁄ 0.00224 0.117⁄⁄⁄ −0.00903 −0.108⁄⁄⁄

(2.38) (0.42) (22.32) (−1.62) (−20.97)

%ΔAt 0.0238⁄⁄⁄ 0.0127⁄⁄⁄ −0.0282⁄⁄⁄ 0.0215⁄⁄⁄ 0.00668⁄⁄⁄

(54.53) (18.82) (−41.97) (29.95) (10.13)

ln(At) 2.83⁄⁄⁄ 0.354⁄⁄⁄ 1.27⁄⁄⁄ −0.837⁄⁄⁄ −0.432⁄⁄⁄

(137.87) (8.31) (31.87) (−19.66) (−11.05)

4.29⁄⁄⁄ 1.62⁄⁄⁄ 1.79⁄⁄⁄ 1.81⁄⁄⁄ −3.60 Deposits ⁄⁄⁄ t~At

(35.62) (7.44) (8.33) (7.92) (−17.11)

14.4⁄⁄⁄ 16.2⁄⁄⁄ 29.1⁄⁄⁄ 39.3⁄⁄⁄ 31.6 Term Dep ⁄⁄⁄ t~At

(173.14) (112.02) (211.50) (267.68) (234.54)

−41.9⁄⁄⁄ −11.6⁄⁄⁄ −3.58⁄⁄⁄ 4.15 Constant ⁄⁄⁄ −0.575

(−176.09) (−22.44) (−7.40) (8.04) (−1.21)

Bank & Quarter FE Yes Yes Yes Yes Yes

N 554180 162123 193306 193306 193306

R2 13.6% 8.3% 68.8% 33.8% 24.1%

Model P-Value < 0.001 < 0.001 < 0.001 < 0.001 < 0.001

Estimates from regressing funding shares on a regulatory action dummy and bank-level

controls for all US banks from 2000 to 2016. The regulatory action dummy is based on

public and confidential supervisory data; other variables are from the regulatory filings of

all US banks. Dependent variables are expressed in percentage points. Observational units

are bank-quarters, with brokered deposit data available from 2000 to 2016, listing service

deposits from 2011 to 2016, and all other term deposit data from 2010 to 2016. Small term

deposits are those with balances of $100,000 or less; medium term deposits are between

$100,001 and $250,000; and large term deposits are those over $250,000. T-statistics in

parentheses, calculated using asymptotically normal standard errors. Estimates significant

at 99% are denoted with ⁄⁄⁄, 95% with ⁄⁄, and 90% with ⁄.

42
Table 13: Impact of Regulatory Action on Funding Shares For US Banks, Quarterly Dummies

Brokered Listed Sm. Term Med. Term Lg. Term

(t − 4)

(t − 3)

(t − 2)

(t − 1)

(t)

(t + 1)

(t + 2)

(t + 3)

(t + 4)

(t + i),i C 5

NPLt~At

%ΔAt

ln(At)

Depositst~At

Term Dept~At

Constant

Bank & Quarter FE

(1) (2) ⁄⁄⁄ ⁄⁄⁄ 1.17 −0.958

(11.71) (−5.00) ⁄⁄⁄ ⁄⁄⁄ 1.07 −0.951

(11.12) (−5.63) ⁄⁄⁄ ⁄⁄⁄ 0.951 −0.633

(10.20) (−4.01) ⁄⁄⁄ ⁄⁄⁄ 0.704 −0.535

(8.03) (−3.78) ⁄⁄⁄ ⁄ 0.729 −0.260

(8.33) (−1.91) ⁄⁄⁄ 0.512 0.00124

(4.03) (0.01) ⁄⁄⁄ ⁄⁄⁄ −0.667 0.477

(−4.50) (2.72) ⁄⁄⁄ ⁄⁄⁄ −1.48 0.594

(−9.13) (3.54) ⁄⁄⁄ ⁄⁄⁄ −2.13 0.669

(−12.53) (4.10) ⁄⁄⁄ ⁄⁄⁄ −3.85 1.21

(−41.34) (12.85) ⁄⁄⁄ 0.0192 0.00499

(4.43) (0.94) ⁄⁄⁄ ⁄⁄⁄ 0.0233 0.0126

(53.55) (18.66) ⁄⁄⁄ ⁄⁄⁄ 2.79 0.383

(135.80) (8.98) ⁄⁄⁄ ⁄⁄⁄ 4.33 1.55

(36.05) (7.13) ⁄⁄⁄ ⁄⁄⁄ 14.4 16.2

(173.11) (112.00) ⁄⁄⁄ ⁄⁄⁄ −41.4 −11.8

(−174.03) (−22.96)

Yes Yes

(3)⁄⁄ 0.443

(2.49)⁄⁄⁄ 0.725

(4.68)⁄⁄⁄ 0.759

(5.29)

⁄⁄⁄ 0.653

(5.03)⁄⁄⁄ 0.639

(5.14) ⁄⁄⁄ 1.22

(7.90) ⁄⁄⁄ 1.40

(9.10) ⁄⁄⁄ 1.14

(7.63)⁄⁄⁄ 0.759

(5.06)

−0.000122

(−0.00)

⁄⁄⁄ 0.114

(21.74)⁄⁄⁄ −0.0280

(−41.63) ⁄⁄⁄ 1.22

(30.38) ⁄⁄⁄ 1.86

(8.69) ⁄⁄⁄ 29.0

(211.38) ⁄⁄⁄ −3.01

(−6.22)

Yes

(4) ⁄⁄⁄ −0.857

(−4.51)

⁄⁄⁄ −0.863

(−5.22)

⁄⁄⁄ −0.838

(−5.46)

⁄⁄⁄ −0.663

(−4.77)

⁄⁄⁄ −0.559

(−4.21)

⁄⁄⁄ −0.743

(−4.51)

⁄⁄⁄ −0.457

(−2.77)

−0.0210

(−0.13)

⁄ 0.284

(1.77) ⁄⁄⁄ 1.14

(11.19)

−0.00804

(−1.43)

⁄⁄⁄ 0.0213

(29.65)⁄⁄⁄ −0.761

(−17.78) ⁄⁄⁄ 1.65

(7.21) ⁄⁄⁄ 39.3

(267.89) ⁄⁄⁄ 3.40

(6.56)

Yes

(5)⁄⁄ 0.414

(2.37)

0.138

(0.91)

0.0785

(0.56)

0.00987

(0.08)

−0.0797

(−0.65)

⁄⁄⁄ −0.476

(−3.14)

⁄⁄⁄ −0.945

(−6.25)

⁄⁄⁄ −1.12

(−7.62)

⁄⁄⁄ −1.04

(−7.08)

⁄⁄⁄ −1.14

(−12.18)

⁄⁄⁄ −0.106

(−20.58)

⁄⁄⁄ 0.00668

(10.12)⁄⁄⁄ −0.455

(−11.58) ⁄⁄⁄ −3.52

(−16.70) ⁄⁄⁄ 31.6

(234.61)

−0.384

(−0.81)

Yes

N 554180 162123 193306 193306 193306

R2 13.9% 8.5% 68.8% 33.9% 24.1%

Model P-Value < 0.001 < 0.001 < 0.001 < 0.001 < 0.001

Estimates from regressing funding shares on quarterly regulatory action dummies and banklevel controls for all US banks from 2000 to 2016. The regulatory action dummies are based

on public and confidential supervisory data; other variables are from the regulatory filings

of US banks. Dependent variables are expressed in percentage points. Observational units

are bank-quarters, with brokered deposit data available from 2000 to 2016, listing service

deposits from 2011 to 2016, and all other term deposit data from 2010 to 2016. T-statistics

in parentheses, calculated using asymptotically normal standard errors. Small term deposits

are those with balances of $100,000 or less; medium term deposits are between $100,001 and

$250,000; and large term deposits are those over $250,000. Time period t is the quarter in

which the regulatory action began. Estimates significant at 99% are denoted with ⁄⁄⁄, 95%

with ⁄⁄, and 90% with ⁄.

43
Table 14: Matched Effects of Regulatory Action on Funding Shares for All US Banks, Four

Quarters after Treatment

Brokered Listed Sm. Term Med. Term Lg. Term

(1) (2) (3) (4) (5)

Untreated 4.07 3.12 17.54 14.21 5.27

Matched Untreated 3.65 4.37 19.96 16.77 5.17

Treated

T-Stat on Differences

1.95

−3.46

9.98

3.90

23.41

3.88

22.78

4.73

4.30

−2.63

N Untreated 29611 29597 29597 29597 29597

N Treated 142 142 142 142 142

Estimates from propensity-score-matching for a regulatory treatment based on bank covariates to measure the effects on certain deposit products as a share of total deposits for all US

Banks. The regulatory action dummy is based on a combination of public and confidential

supervisory data; all other variables are from the regulatory filings of all US banks. Dependent variables are expressed in percentage points. Observation units are bank-quarters,

with brokered deposit data available from 2000 to 2016, listing service deposits from 2011 to

2016, and all other term deposit data from 2010 to 2016. Only banks that at some time come

under an enforcement action between 2000 and 2016 are used for matching. T-statistics in

parentheses, calculated using asymptotically normal standard errors. Matching is done using

a logistic model to generate a propensity score for being treated with regulatory action based

on the covariates used in the above regressions (1 year asset growth, natural log of assets,

nonperforming loans as share of assets, deposits as a share of assets) as well as all outcome

variables (brokered deposits, listing service deposits, all three categories of term deposits; as

shares of deposits). Then treated banks in one quarter are matched to untreated banks in

the same quarter at the time of treatment based on this score. Matches are done based on

normal kernel weighting, so that close matches are weighted proportionally more. We then

observe the difference between treated and untreated four quarters after treatment.

44
Table 15: Large Bank Deposit Rate Responses to Enforcement Actions

$100K, 12m CD spread

Under Reg. Action

Constant

0.4121⁄⁄⁄

(4.48)

0.3547⁄⁄⁄

(2.99)

Quarter FEs Yes

N 65

R2 6.9%

Model P-Value < 0.001

Estimates from pooled OLS regression of 12-month, $100,000 CD rate spreads (measured

against FDIC-published national average rates) on a dummy indicating the bank is currently

under regulatory enforcement action, time fixed effects, and a constant. Units are in percentage points: a value of 0.01 corresponds to 1 basis point (0.01%). The sample of banks

is constrained to those over $5 billion in assets (as of 2008Q4) which are under enforcement

action or will be within four quarters. The coefficient on Under Reg. Action can therefore

be interpreted as large banks’ average change in deposit rates around/following the imposition of enforcement actions. T-statistics are in parentheses, calculated using asymptotically

normal standard errors. Estimates significant at 99% are denoted with ⁄⁄⁄, 95% with ⁄⁄, and

90% with ⁄.

45
Figure 1: Deposit Balances

(a) Transaction

(b) Term

Total balances in transaction (top panel) and term (bottom panel) deposit accounts. Grey

bars denote the time periods analyzed in the regressions of Section 3, and overlaid text identifies the name of each period. Note that the dramatic, brief spike in uninsured transaction

deposits between the Post-Crisis and Formal periods reflects a single transaction in which

another subsidiary of the bank’s holding company passed funds through the bank in such a

manner that they remained within the bank for a few days.

46

800 

Formal Enforcement 

700 

600 

500 

Placebo Pre-Crisis 

---Insured Transaction Deposits 

- Uninsured Transaction Deposit s 

Post-Crisis Action -------; I .,.._. 

,J --...... /-..,. ;-L'' "'°"'1 I 

I "'-•...I~ ,1 I 

... ~ ,, '~ 

v,' 

~ 400 C 

.. 

ni 

cc 

~ <I> 

.,,-

QI 

u 

C 

.. 

ni 

cc 

300 

200 

100 

0 

Dec-OS 

800 

Dec-06 Dec-07 Dec-08 Dec-09 Dec-10 

Formal Enforcement 

700 +--- Placebo _. Pre-Crisis Post-Crisis ~ --------------1 Action 

600 

500 

400 

300 

200 

100 

, 

,: I 1• I I 

Dec-OS 

I ---Insured Term Deposits 

r,, - Uninsured Term Deposits I ---:-\, J 

: II• I \. ,'~'I 

: --: : : , .... , i 

r"J •: + 'l.✓ • .,,.-.. •~•-,.+,-------,,. - ,J-} : I 

, • ._ I 1, '"" t \ ..,.,,-',,, I 1_ ,1-...... _: I '; r" , •,,' ~ I \ -- I 'ti "I r' I I I \\ II ,..,.,: '• 

I ) ~ '. : \J \, ,._.__,: \.: 

'\;' Y. : ..__1 "1'.{ r 

-----

Dec-06 Dec-07 Dec-08 Dec-09 Dec-10 
Figure 2: Term Deposit Balances in Brokered, Placed, and Institutional Accounts

This figure shows term deposit account balances in brokered accounts (dash-dotted red),

placed accounts (dotted green), institutional deposits obtained via rate listing services and

faxes (bold blue), and other institutional deposits (solid, thin purple). Placed deposits are

those placed by a third party on behalf the underlying depositor, where that third party

does not meet the definition of deposit broker. Note that this is a different notion of placed

deposits relative to that used in the regressions; here, we split placed and brokered deposits

into two categories, whereas both were grouped as “placed” in the regressions. Among

placed and brokered deposits, the underlying depositors are often not identified to the bank

accepting the deposits. Grey bars denote the time periods analyzed in the regressions of

Section 3, and overlaid text identifies the name of each period.

47

500 ~--~ 

450 +------t Pre-Crisis 

400 +----I 

350 +----I 

~ okered Total 

~ ·····Plc1ced Total 

Post-Crisis 

Formal Enforcement 

Action 1----1 1--------------t 

j I 

·'· ........... 

~ 300 +----I 

V> 

I:. 

~--- - ~ln=stitutional- Listed/Faxed .. ._: ••• 

v,' 

~ 250 +----I 

C: 

l'O 

~ 200 -------1 

150 --t--'-'~ ~---t 

I 

~ I 

100 +---~r 

I. 

1 I - Institutional - Other 

I ·I ------h ,__ I ________ -< 

..•

. ., ................................. . 

\1 

50 -+.-•• -••• -•• -••• -. ---1 ........ •··· •.• • •••• • 

0 1;~t;;;~:::~=::::::~~::::::::::::;:::;:::::::;:~=::::;;;;;;;;;;;;;{J_~~'~-:.-~·~:.,:·..:::....:~ 

Dec-05 Dec-06 Dec-07 Dec-08 Dec-09 Dec-10 
Figure 3: Term Deposit Balances From New Depositors

Balances in term deposit accounts from depositors who opened their first deposit account

with the bank after the formal enforcement action — new depositors.

48

450 

400 

350 

300 

~ 

vi- 250 

.,,-

Cl) 

u 

lii 200 

iii 

co 

150 

100 

50 

0 

,., ,., 

,. , , 

337 

,., _,., 

---New Insured Term Deposits 

- New Uninsured Term Deposits 

_.,.--J- ,. ., -------

277 217 

I-

,., _.,----------------

157 

Days to Failure 

, .. , 

,., .. ,.,. 

,, 

-

,- , ., 

,., 

I 

I 

I ,. 

I 

I 

I 

I 

I 

-' ____ ., 

97 37 
Figure 4: 12-Month CD Rates Relative to the Market Distribution

31-day moving average (fifteen days before, day of, and fifteen days after) of all rates offered

by the bank on newly issued 12-month term deposits with balances below $100,000 (“Bank

Average;” solid red line) is shown on the left axis, while the percentile relative to the distribution of banking industry rates (from RateWatch) for the same product is shown on the

right axis. The bank average series is a measure of the rate which would have been faced by

a depositor considering depositing funds at the bank that day. “Newly issued” term deposits

include newly established term deposit accounts as well as rollovers of existing term deposits

upon the expiration of the previous product.

49

6 

5 

4 

QI 

1u 

a: 

~ 3 ... 

QI 

c 

2 

1 

0 

Jan-06 Dec-06 Dec-07 Dec-08 

Industry, 5th - 95th Pct I 

- Industry, 25th - 75th Pct l 

- · ·Industry, M edian 

- Bank Average 

--Bank Percentile (Right Axis) 

Dec-09 Nov-10 

80 

70 

60 -2! 

·.;; 

50 C: 

QI 

u 

... 

QI 

40 0.. 

30 

20 

10 

0 
Figure 5: Impact of Regulatory Action on Funding Shares

Graphical presentation of the impact of regulatory action on funding shares, using the estimates from Table 13. Small term deposits are those with balances of $100,000 or less;

medium term deposits are between $100,001 and $250,000; and large term deposits are those

over $250,000. Time period t is the quarter in which the enforcement action was issued.

Error bands represent 95% asymptotic confidence intervals

50

2 

1 

- 0 e 

:i 

·;;; 

&.-1 .. 

C 

~ 

~ -2 

0 

.. 

:. 

.l: 

Ill -3 

-4 

-5 

2 

1.5 

1 

~ :i 

·;;; 0.5 

0 

c.. 

.. 

C 

ni 

... 0 

~ 

0 

.. -0.5 

:. 

.l: 

Ill 

-1 

-1.5 

-2 

-4 -3 

O Brokered 

■ Listed 

+ 

Quarters Before(-) /After(+) Enforcement Action 

• Small Term Deposits ($100k or less) 

■ M edium Term Der osit s {$100k-$250k) 

• Large Term Deposits ($250k or more) 

I 

,, 

,1 t I I t 

+ 

' 

+ -4 -3 -ii -it 

! 

+ I;) f ! 

' I . I I I I ' I • • • • I 

. 

I .i. • • I I I • ! I ♦ 1 

I I 1 

-

Quarters Before (-)/After(+) Enforcement Action 

3 4 

I 

' • ' 4 

I 

I • I 

- • - 3 4 ~5 I 

r 
7 Appendix

Figure 6: Uninsured Transaction Account Migration

For all transaction accounts which had a balance of $2,000 less than the current deposit

insurance limit or higher at the beginning of each period, this figure shows their distribution

into various account dollar-size bins at the end of the period. Several bins are suppressed

for disclosure reasons.

51

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

. o 0.1 

0 

<1 (l,2000) [2000,.S"D-2000) (.5 • D-2000, D-2000) [D-2000,D->2000) (>0.2000) 

-' aceoo 1-'ret:rlSIS 1-'og: t:rlSIS ormc'J L>=-t:urrent Ueposrt insurance umit 
Figure 7: Kaplan-Meier Survival Curves

All Transaction Deposits

Post-Crisis Formal

Uninsured Transaction Deposits

Post-Crisis Formal

Each X-axis tick represents 50 days.

52

0 

'< 

Kaplan-Meier survival estimate 0 

'< 

Kaplan-Meier survival estimate 

"' ": "' ": 0 0 

fi1 fi1 

"' "! "' "! 0 0 

0 0 

'< 0 

0 0 

analysis time analysis time 

0 

'< 

Kaplan-Meier survival estimate 0 

'< 

Kaplan-Meier survival estimate 

"' ": "' ": 0 0 

0 

""! 0 ""! 0 0 

"' N "'! 0 0 

0 

'< 0 '< 0 0 

analysis time analysis time 
Figure 8: Cox P.H. Baseline Hazard Curves

All Transaction Deposits

Post-Crisis Formal

Each X-axis tick represents 50 days.

53

Cox proportional hazards regression Cox proportional hazards regression 

analysis time analysis time 
- -

- -

Table 16: Who Withdraws? Probit Model; Including Interest Rates

Transaction

Post-Crisis Formal

(1) (2)

Uninsured

TAG/DFA Eligible–

Interest Rate Spread

Past-Month Fees

Checking

Direct Deposit

Prior Transactions

Prior Transactions2

Log(Age)

Log(Days to Maturity)

Brokered/Placed

Institutional - Listed/Faxed

Institutional - Other

Trust

Branch Controls

No. of Accounts

No. of Liquidations

Log Likelihood

Model P-Value

⁄ ⁄⁄⁄ 0.0697 0.179

(1.69) (6.96) ⁄⁄ -0.0667 -0.0270

(-2.12) (-0.71) ⁄⁄⁄ ⁄⁄⁄ -0.0137 -0.0794

(-3.06) (-3.19) ⁄⁄ ⁄⁄⁄ 0.00148 0.00134

(2.52) (2.82) ⁄⁄⁄ ⁄⁄⁄ -0.0384 -0.0498

(-3.91) (-4.01) ⁄⁄⁄ ⁄⁄⁄ -0.0882 -0.0665

(-8.67) (-4.26) ⁄⁄⁄ ⁄⁄⁄ 0.00316 -0.00326

(4.98) (-4.18) ⁄⁄⁄ ⁄⁄ -0.0000550 0.0000238

(-5.42) (2.17) ⁄⁄ ⁄⁄⁄ -0.00915 -0.0228

(-2.49) (-4.44)

0.0123 -0.00759

(0.68) (-0.45) ⁄⁄ ⁄⁄⁄ -0.0438 0.0602

(-2.26) (2.73)

Yes Yes

29865 26616

4740 6218

-12993.0 -14226.8

< 0.001 < 0.001

Term

Post-Crisis Formal

(3) (4)⁄⁄ 0.0881 0.143

(1.40) (2.34)

0.0408 -

(0.21) - ⁄⁄⁄ ⁄⁄⁄ -0.841 -0.735

(-21.49) (-5.85)

⁄⁄⁄ 0.0210 -0.0161

(3.54) (-1.55) ⁄⁄⁄ ⁄⁄⁄ -0.152 -0.115

(-24.90) (-15.08) ⁄⁄⁄ ⁄⁄⁄ 0.409 0.576

(19.52) (28.40) ⁄⁄⁄ -0.190 -0.252

(-1.08) (-3.73)

0.0123 -0.0663

(0.24) (-1.53)

0.0264 0.00249

(0.74) (0.05)

Yes Yes

7798 3717

2506 1734

-3395.6 -1832.1

< 0.001 < 0.001

Estimates from probit models of the probability of account liquidation for both transaction and term deposits,

focusing only on the Post-Crisis and Formal periods. Liquidation is defined as withdrawing 75% or more of

the start-of-period account balance and staying at or below 25% of the start-of-period balance for at least

61 days. T-statistics are in parentheses, calculated using asymptotically normal standard errors. Estimates ⁄⁄⁄ ⁄⁄ ⁄ significant at 99% are denoted with , 95% with , and 90% with .

– Note that although TAG/DFA did not directly insure term deposits, it could do so indirectly. Accounts

covered by TAG/DFA were excluded in calculating a depositor’s total exposure to a bank when applying

standard, limited-value deposit insurance, effectively freeing up insurance coverage to be applied to other,

non-TAG/DFA-covered accounts.

54
- -

- -

Table 17: Who Withdraws? Cox P.H. Model; Including Interest Rates

Transaction

Post-Crisis Formal

(1) (2)

Uninsured

TAG/DFA Eligible–

Interest Rate Spread

Past-Month Fees

Checking

Direct Deposit

Prior Transactions

Prior Transactions2

Log(Age)

Log(Days to Maturity)

Brokered/Placed

Institutional - Listed/Faxed

Institutional - Other

Trust

Branch Controls

No. of Accounts

No. of Liquidations

Log Likelihood

Model P-Value

⁄⁄ ⁄⁄⁄ 1.538 1.915

(2.10) (8.30) ⁄ 0.581 0.921

(-1.75) (-0.50) ⁄⁄⁄ ⁄⁄⁄ 0.914 0.737

(-2.91) (-2.65) ⁄⁄ ⁄⁄⁄ 1.009 1.004

(2.57) (2.86) ⁄⁄⁄ ⁄⁄⁄ 0.744 0.813

(-3.67) (-3.19) ⁄⁄⁄ ⁄⁄⁄ 0.427 0.638

(-6.10) (-4.21) ⁄⁄⁄ ⁄⁄⁄ 1.026 0.986

(5.72) (-3.66) ⁄⁄⁄ 1.000 1.000

(-5.77) (1.52) ⁄⁄⁄ ⁄⁄⁄ 0.931 0.897

(-2.82) (-4.70)

1.088 0.993

(0.72) (-0.09) ⁄ ⁄⁄⁄ 0.718 1.255

(-1.87) (2.76)

Yes Yes

29865 26616

4740 6218

-48371.0 -62344.9

< 0.001 < 0.001

Term

Post-Crisis Formal

(3) (4) ⁄⁄ ⁄⁄⁄ 1.480 1.655

(2.01) (2.69)

0.944 -

(-0.08) - ⁄⁄⁄ ⁄⁄⁄ 0.114 0.245

(-17.70) (-4.27)

⁄⁄⁄ ⁄ 1.184 1.053

(7.26) (1.94) ⁄⁄⁄ ⁄⁄⁄ 0.651 0.669

(-28.68) (-27.66) ⁄⁄⁄ ⁄⁄⁄ 4.612 8.203

(21.53) (25.84)⁄⁄ 1.406 0.448

(0.34) (-2.24)

1.061 1.212

(0.30) (1.34)

1.065 1.180

(0.45) (1.09)

Yes Yes

7800 3717

2506 1734

-21340.1 -12850.8

< 0.001 < 0.001

Estimates from Cox proportional hazard models of the probability of account liquidation for both transaction

and term deposits, focusing only on the Post-Crisis and Formal periods. Liquidation is defined as withdrawing

75% or more of the start-of-period account balance and staying at or below 25% of the start-of-period balance

for at least 61 days. T-statistics are in parentheses, calculated using asymptotically normal standard errors. ⁄⁄⁄ ⁄⁄ ⁄ Estimates significant at 99% are denoted with , 95% with , and 90% with .

– Note that although TAG/DFA did not directly insure term deposits, it could do so indirectly. Accounts

covered by TAG/DFA were excluded in calculating a depositor’s total exposure to a bank when applying

standard, limited-value deposit insurance, effectively freeing up insurance coverage to be applied to other,

non-TAG/DFA-covered accounts.

55
Table 18: Who Withdraws? Transaction Deposits; Linear Probability Model; Lower (50%)

Liquidation Threshold

Placebo Pre-Crisis Post-Crisis Formal

(1) (2) (3) (4) ⁄⁄ ⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Uninsured 0.0295 0.0301 0.109 0.219

(2.12) (2.28) (2.78) (9.51) ⁄⁄ ⁄ TAG/DFA Eligible— -0.0226 -0.0165 -0.106 -0.0764

(-0.70) (-0.55) (-2.09) (-1.66) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Checking -0.130 -0.152 -0.0595 -0.0525

(-11.19) (-11.25) (-5.24) (-4.21) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Direct Deposit -0.109 -0.123 -0.111 -0.0507

(-6.76) (-7.21) (-6.99) (-2.89) ⁄⁄⁄ ⁄⁄ ⁄⁄⁄ Log(Age) -0.0111 -0.00936 -0.00235 -0.0187

(-3.93) (-2.17) (-0.57) (-3.31) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Prior Transactions 0.0130 0.0122 0.00791 0.00350

(21.34) (16.08) (12.13) (4.46) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Prior Transactions2 -0.000159 -0.000160 -0.000105 -0.0000603

(-18.85) (-14.79) (-11.32) (-5.65)

Institutional - Any -0.0164 0.0358 0.0167 0.00351

(-0.64) (1.43) (0.92) (0.20) ⁄⁄ ⁄⁄⁄ Trust -0.00403 0.0231 -0.0613 0.0601

(-0.13) (0.74) (-2.57) (2.74) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Constant 0.262 0.369 0.205 0.382

(14.56) (19.61) (13.82) (24.23)

Branch Controls Yes Yes Yes Yes

No. of Accounts 33498 34029 29865 26616

No. of Liquidations 8911 12949 5825 7499

R2 1.8% 1.2% 0.7% 2.0%

Model P-Value < 0.001 < 0.001 < 0.001 < 0.001

Estimates from linear probability models of the probability of account liquidation during the each of

the four periods of interest. Liquidation is defined as withdrawing 50% or more of the start-of-period

account balance and staying at or below 50% of the start-of-period balance for at least 61 days.

Recall that in most of the paper, a threshold of 75% was used for defining liquidation. Estimates are

expressed as hazard ratios. T-statistics are in parentheses, calculated using asymptotically normal ⁄⁄⁄ ⁄⁄ ⁄ standard errors. Estimates significant at 99% are denoted with , 95% with , and 90% with .

— TAG/DFA coverage was only effective in the Post-Crisis and Formal periods; eligible accounts

were fully covered/insured in those periods. In earlier periods, eligibility does not imply coverage,

and is shown for comparison purposes only.

56
Table 19: Who Withdraws? Transaction Deposits; Linear Probability Model; Higher (80%)

Liquidation Threshold

Placebo Pre-Crisis Post-Crisis Formal

(1) (2) (3) (4) ⁄⁄⁄ ⁄⁄ ⁄⁄⁄ Uninsured 0.0325 -0.00282 0.0780 0.177

(2.60) (-0.22) (2.20) (8.27) ⁄ ⁄⁄ TAG/DFA Eligible— -0.0525 0.00259 -0.101 -0.0245

(-1.81) (0.09) (-2.18) (-0.57) ⁄⁄⁄ ⁄⁄⁄ ⁄ Checking -0.0750 -0.0996 -0.0168 -0.0217

(-7.19) (-7.76) (-1.64) (-1.87) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄ Direct Deposit -0.0989 -0.116 -0.0872 -0.0412

(-6.86) (-7.12) (-6.06) (-2.53) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Log(Age) -0.0208 -0.0178 -0.0108 -0.0260

(-8.22) (-4.34) (-2.89) (-4.95) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Prior Transactions 0.00687 0.00392 0.00164 -0.00414

(12.54) (5.45) (2.78) (-5.68) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Prior Transactions2 -0.0000865 -0.0000525 -0.0000295 0.0000383

(-11.44) (-5.11) (-3.51) (3.86)⁄ Institutional - Any -0.0314 0.0241 0.00881 0.0273

(-1.36) (1.02) (0.53) (1.70)⁄⁄⁄ Trust 0.0266 0.0259 -0.0312 0.0742

(0.99) (0.88) (-1.45) (3.64) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Constant 0.206 0.305 0.153 0.310

(12.75) (17.09) (11.44) (21.18)

Branch Controls Yes Yes Yes Yes

No. of Accounts 33498 34029 29865 26616

No. of Liquidations 6500 10338 4502 5978

R2 1.2% 0.8% 0.4% 1.8%

Model P-Value < 0.001 < 0.001 < 0.001 < 0.001

Estimates from linear probability models of the probability of account liquidation during the each of

the four periods of interest. Liquidation is defined as withdrawing 80% or more of the start-of-period

account balance and staying at or below 20% of the start-of-period balance for at least 61 days.

Recall that in most of the paper, a threshold of 75% was used for defining liquidation. Estimates are

expressed as hazard ratios. T-statistics are in parentheses, calculated using asymptotically normal ⁄⁄⁄ ⁄⁄ ⁄ standard errors. Estimates significant at 99% are denoted with , 95% with , and 90% with .

— TAG/DFA coverage was only effective in the Post-Crisis and Formal periods; eligible accounts

were fully covered/insured in those periods. In earlier periods, eligibility does not imply coverage,

and is shown for comparison purposes only.

57
Table 20: Who Withdraws? Term Deposits; Linear Probability Model; Lower (50%) Liquidation Threshold

Placebo Pre-Crisis Post-Crisis Formal

(1) (2) (3) (4) ⁄⁄⁄ ⁄ ⁄⁄⁄ Uninsured 0.00660 0.0445 0.0768 0.154

(0.51) (3.90) (1.90) (3.05)

TAG/DFA Eligible—– -0.338 -0.583 -0.00402 -

(-1.13) (-1.26) (-0.03) - ⁄ ⁄⁄⁄ ⁄ Log(Age) -0.00619 -0.0389 -0.00353 -0.0130

(-1.68) (-10.58) (-0.94) (-1.86) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Log(Days to Maturity) -0.148 -0.0701 -0.202 -0.0876

(-29.64) (-21.24) (-54.81) (-16.08) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Brokered/Placed 0.253 0.160 0.278 0.546

(13.01) (7.58) (21.65) (25.58) ⁄⁄⁄ Institutional - Listed/Faxed -0.159 -0.245 0.0102 -0.169

(-0.65) (-0.92) (0.09) (-3.13) ⁄ Institutional - Other 0.0684 0.0843 0.0148 -0.0399

(1.61) (1.66) (0.45) (-1.28) ⁄⁄⁄ Trust -0.0699 0.000762 -0.00178 0.00645

(-3.12) (0.04) (-0.08) (0.18) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Constant 0.940 0.826 1.206 0.707

(22.06) (18.07) (32.71) (14.22)

Branch Controls Yes Yes Yes Yes

No. of Accounts 7868 10881 8509 3726

No. of Liquidations 2212 7176 2574 1743

R2 11.7% 5.5% 31.8% 33.9%

Model P-Value < 0.001 < 0.001 < 0.001 < 0.001

Estimates from linear probability models of the probability of account liquidation during the each of

the four periods of interest. Liquidation is defined as withdrawing 50% or more of the start-of-period

account balance and staying at or below 50% of the start-of-period balance for at least 61 days.

Recall that in most of the paper, a threshold of 75% was used for defining liquidation. Estimates are

expressed as hazard ratios. T-statistics are in parentheses, calculated using asymptotically normal ⁄⁄⁄ ⁄⁄ ⁄ standard errors. Estimates significant at 99% are denoted with , 95% with , and 90% with .

— TAG/DFA coverage was only effective in the Post-Crisis and Formal periods; eligible accounts

were fully covered/insured in those periods. In earlier periods, eligibility does not imply coverage,

and is shown for comparison purposes only.

– Note that although TAG/DFA did not directly insure term deposits, it could do so indirectly.

Accounts covered by TAG/DFA were excluded in calculating a depositor’s total exposure to a bank

when applying standard, limited-value deposit insurance, effectively freeing up insurance coverage

to be applied to other, non-TAG/DFA-covered accounts.

58
Table 21: Who Withdraws? Term Deposits; Linear Probability Model; Higher (80%) Liquidation Threshold

Placebo Pre-Crisis Post-Crisis Formal

(1) (2) (3) (4) ⁄⁄⁄ ⁄⁄ ⁄⁄⁄ Uninsured 0.00470 0.0429 0.0821 0.147

(0.37) (3.76) (2.03) (2.92)

TAG/DFA Eligible—– -0.333 -0.573 -0.00336 -

(-1.11) (-1.24) (-0.02) - ⁄ ⁄⁄⁄ ⁄ Log(Age) -0.00668 -0.0388 -0.00380 -0.0124

(-1.82) (-10.52) (-1.01) (-1.78) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Log(Days to Maturity) -0.144 -0.0695 -0.200 -0.0866

(-28.96) (-20.99) (-54.36) (-15.90) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Brokered/Placed 0.254 0.162 0.279 0.550

(13.09) (7.67) (21.80) (25.76) ⁄⁄⁄ Institutional - Listed/Faxed -0.159 -0.244 0.0107 -0.166

(-0.65) (-0.91) (0.10) (-3.08) ⁄ ⁄ Institutional - Other 0.0777 0.0882 0.0181 -0.0358

(1.83) (1.73) (0.56) (-1.15) ⁄⁄⁄ Trust -0.0740 0.00143 -0.00588 0.00651

(-3.31) (0.07) (-0.25) (0.18) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Constant 0.885 0.815 1.185 0.683

(20.77) (17.79) (32.14) (13.75)

Branch Controls Yes Yes Yes Yes

No. of Accounts 7868 10881 8509 3726

No. of Liquidations 2192 7150 2558 1736

R2 11.4% 5.4% 31.6% 34.0%

Model P-Value < 0.001 < 0.001 < 0.001 < 0.001

Estimates from linear probability models of the probability of account liquidation during the each of

the four periods of interest. Liquidation is defined as withdrawing 80% or more of the start-of-period

account balance and staying at or below 20% of the start-of-period balance for at least 61 days.

Recall that in most of the paper, a threshold of 75% was used for defining liquidation. Estimates are

expressed as hazard ratios. T-statistics are in parentheses, calculated using asymptotically normal ⁄⁄⁄ ⁄⁄ ⁄ standard errors. Estimates significant at 99% are denoted with , 95% with , and 90% with .

— TAG/DFA coverage was only effective in the Post-Crisis and Formal periods; eligible accounts

were fully covered/insured in those periods. In earlier periods, eligibility does not imply coverage,

and is shown for comparison purposes only.

– Note that although TAG/DFA did not directly insure term deposits, it could do so indirectly.

Accounts covered by TAG/DFA were excluded in calculating a depositor’s total exposure to a bank

when applying standard, limited-value deposit insurance, effectively freeing up insurance coverage

to be applied to other, non-TAG/DFA-covered accounts.

59
Table 22: Who Withdraws? Transaction Deposits; Linear Probability Model; Errors Clustered at Branch Level

Placebo Pre-Crisis Post-Crisis Formal

(1) (2) (3) (4) ⁄⁄⁄ ⁄⁄⁄ Uninsured 0.0249 -0.00117 0.0693 0.183

(0.91) (-0.07) (4.57) (4.36) ⁄⁄ ⁄⁄ TAG/DFA Eligible— -0.0446 -0.000624 -0.0944 -0.0412

(-4.97) (-0.06) (-3.82) (-1.13) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄ ⁄⁄⁄ Checking -0.0901 -0.105 -0.0277 -0.0252

(-43.36) (-4.93) (-3.15) (-3.86) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Direct Deposit -0.104 -0.123 -0.0864 -0.0459

(-6.69) (-6.61) (-6.85) (-7.49) ⁄⁄⁄ ⁄⁄ ⁄⁄⁄ Log(Age) -0.0194 -0.0180 -0.00915 -0.0247

(-12.21) (-4.34) (-11.27) (-1.70) ⁄⁄⁄ ⁄⁄ ⁄⁄⁄ Prior Transactions 0.00793 0.00555 0.00263 -0.00310

(10.59) (3.38) (1.96) (-5.78) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄ ⁄ Prior Transactions2 -0.0000983 -0.0000746 -0.0000419 0.0000235

(-15.68) (-7.11) (-2.80) (2.32)

Institutional - Any -0.0273 0.0242 0.0218 0.0215

(-2.24) (1.28) (0.83) (0.87) ⁄⁄ ⁄⁄⁄ ⁄⁄ Trust 0.0255 0.0248 -0.0418 0.0698

(4.65) (1.10) (-5.34) (2.50) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Constant 0.217 0.313 0.159 0.326

(69.66) (50.15) (29.80) (24.64)

Branch Controls Yes Yes Yes Yes

No. of Accounts 33498 34029 29865 26616

No. of Liquidations 6920 10795 4740 6218

R2 1.3% 0.8% 0.4% 1.8%

Estimates from linear probability models of the probability of account liquidation during the each

of the four periods of interest. Liquidation is defined as withdrawing 75% or more of the start-ofperiod account balance and staying at or below 25% of the start-of-period balance for at least 61

days. Estimates are expressed as hazard ratios. T-statistics are in parentheses, calculated using

⁄⁄⁄ standard errors clustered at the branch level. Estimates significant at 99% are denoted with , ⁄⁄ ⁄ 95% with , and 90% with .

— TAG/DFA coverage was only effective in the Post-Crisis and Formal periods; eligible accounts

were fully covered/insured in those periods. In earlier periods, eligibility does not imply coverage,

and is shown for comparison purposes only.

60
Table 23: Who Withdraws? Term Deposits; Linear Probability Model; Errors Clustered at

Branch Level

Placebo Pre-Crisis Post-Crisis Formal

(1) (2) (3) (4) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Uninsured 0.00501 0.0432 0.0820 0.147

(7.12) (13.17) (0.86) (3.73) ⁄⁄⁄ ⁄⁄⁄ TAG/DFA Eligible—– -0.333 -0.573 -0.00332 -

(-365.06) (-76.33) (-0.04) - ⁄⁄⁄ ⁄⁄⁄ ⁄ ⁄⁄ Log(Age) -0.00664 -0.0391 -0.00379 -0.0124

(-11.16) (-126.27) (-2.35) (-2.96) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Log(Days to Maturity) -0.144 -0.0695 -0.200 -0.0866

(-18.68) (-131.29) (-141.89) (-20.26) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Brokered/Placed 0.254 0.162 0.279 0.550

(24.71) (90.76) (96.46) (49.76) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Institutional - Listed/Faxed -0.159 -0.244 0.0105 -0.166

(-21.30) (-312.72) (4.55) (-17.62) ⁄⁄⁄ ⁄ Institutional - Other 0.0764 0.0880 0.0180 -0.0358

(13.31) (2.34) (0.55) (-1.03) ⁄ Trust -0.0745 0.00318 -0.00600 0.00651

(-3.16) (1.61) (-0.53) (0.13) ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ ⁄⁄⁄ Constant 0.891 0.815 1.185 0.683

(30.25) (129.02) (46.77) (24.20)

Branch Controls Yes Yes Yes Yes

No. of Accounts 7868 10881 8509 3726

No. of Liquidations 2193 7153 2559 1736

R2 11.4% 5.4% 31.6% 34.0%

Estimates from linear probability models of the probability of account liquidation during the each

of the four periods of interest. Liquidation is defined as withdrawing 75% or more of the startof-period account balance and staying at or below 25% of the start-of-period balance for at least

61 days. T-statistics are in parentheses, based on standard errors clustered at the branch level. ⁄⁄⁄ ⁄⁄ ⁄ Estimates significant at 99% are denoted with , 95% with , and 90% with .

— TAG/DFA coverage was only effective in the Post-Crisis and Formal periods; eligible accounts

were fully covered/insured in those periods. In earlier periods, eligibility does not imply coverage,

and is shown for comparison purposes only.

– Note that although TAG/DFA did not directly insure term deposits, it could do so indirectly.

Accounts covered by TAG/DFA were excluded in calculating a depositor’s total exposure to a bank

when applying standard, limited-value deposit insurance, effectively freeing up insurance coverage

to be applied to other, non-TAG/DFA-covered accounts.

61