Managing Stigma during a Financial Crisis
Sriya Anbil∗†
Abstract
How should regulators design effective emergency lending facilities to mitigatestigma during a financial crisis? I explore this question using data from an unex-pected disclosure of a partial list of banks that secretly borrowed from the lender oflast resort during the Great Depression. I find evidence of stigma in that depositorswithdrew more deposits-to-assets from banks included on the list in comparison tobanks left off the list. However, when all banks were simultaneously revealed to haveborrowed, there was no stigma. Overall, the results suggest that an emergency lendingfacility that reveals bank identities simultaneously will mitigate stigma. (JEL Codes:G01, G21, G28)
∗Board of Governors of the Federal Reserve, Division of Monetary Affairs, 20th and Constitution Ave.NW, Washington, DC 20551, USA. [email protected]†This paper is part of my dissertation at Yale University. I thank Heather Tookes (co-chair), Gary Gorton
(co-chair), Andrew Metrick, and Justin Murfin for valuable advice and comments. I would also like to thankseminar participants at the Yale School of Management, Board of Governors of the Federal Reserve, OlinBusiness School, Federal Reserve Bank of Cleveland, Anderson School of Management, Kelley School ofBusiness, Columbia Business School, Office of Financial Research, Federal Reserve Bank of New York, TheWharton School, Leeds Business School, McCombs Business School, and the Scheller College of Businessfor their useful comments. This paper has also benefited from discussions with Claire Brenneke, JuliaGarlick, Sean Hundofte, Wenxi Jiang, Steve Karolyi, Toomas Laarits, Margaret Leighton, Steve Malliaris,Corina Mommaerts, Marina Niessner, Andrew Sinclair, and Jacqueline Yen. I also thank Nikhil Seval, ChenWang, and Michaela Pagel for crucial data access, the Yale International Center for Finance and WhiteboxAdvisors for research funding, and Haruna Fujita and William Longinotti for excellent research assistance.All remaining errors are my own. The views expressed in this paper are solely the responsibility of the authorand should not be interpreted as reflecting the views of the Board of Governors of the Federal Reserve System,its staff or any other person associated with the Federal Reserve System.
1
1 Introduction
During the recent financial crisis, the Federal Reserve (Fed) was frustrated in its efforts
to inject liquidity into the financial system through its main emergency lending facility, the
discount window (Armantier et al. (2014)).1 At the time, the Fed Chairman Ben Bernanke
explained that “the banks’ concern was that their recourse to the discount window, if it
somehow became known, would lead market participants to infer weakness–the so-called
stigma problem (Bernanke (2009)).” The consequences of approaching the discount window
could be costly and might even lead to bank closures (Madigan (2009)). As a result, banks
are reluctant to borrow from the lender of last resort (LOLR) because they fear the public
reaction, which prevents the Fed from inserting liquidity into critical parts of the banking
sector during a financial crisis.
An important challenge for all central banks is the design of emergency lending facilities
that alleviates the “stigma problem.”2 The stigma problem is not new—arguably, it goes
back to the beginning of the 1920s. At the time, the Fed emphasized that lending from the
discount window should only be temporary, implying that a bank that borrowed from the
discount window must be in trouble (Gorton and Metrick (2013)), which has complicated
LOLR policy ever since. Although central bankers are concerned with the stigma problem,
direct evidence of its existence is limited. In this paper, I ask two related questions. First, are
banks stigmatized after news is revealed that they received emergency loans from the LOLR?
Second, if they are, how can the LOLR effectively control the information environment to1In the U.S., the Fed’s traditional means of providing emergency credit to depository institutions is
through its discount window. Lending from the discount window is in the form of promissory notes securedby adequate collateral (Furfine (2003)).
2Stigma was a central topic discussed during Federal Open Market Committee (FOMC) meetings. BenBernanke announced “...we have been looking at... how to address the stigma of the discount window. Arethere ways to provide liquidity that would help normalize money markets... but would avoid the stigma andcreate a more efficient system? (FOMC (2007))”
2
manage public reactions during a crisis? There are two requirements that must be met
in order to answer these questions. First, the researcher needs to be able to compare the
outcomes of banks that borrowed from the LOLR whose identities were revealed to similar
banks that also borrowed but whose identities were not revealed. Second, the researcher
needs to know specifically when the banks’ identities were revealed. No setting exists during
the recent crisis where both requirements are met.
I address these two requirements by using an unexpected event from the Great Depression.
On August 22, 1932, the Clerk of the House of Representatives took it upon himself to
publish a partial list of banks that had secretly borrowed from the Reconstruction Finance
Corporation (RFC), the LOLR during the Great Depression. Using a unique hand-collected
dataset of balance sheet information for 2,674 banks, I implement a difference-in-differences
approach to estimate the effect of the publication of the list on the deposits-to-assets ratio
and other bank outcomes. To do so, I compare the performance of those banks included on
the list (“the treatment group” or “revealed banks”) to banks not included on the list (“the
control group” or “non-revealed banks”). Banks were included on the list according to the
date of their loan authorization; non-revealed banks had a loan authorized by the RFC before
revealed banks. This framework meets both requirements. First, all banks in the analysis
borrowed from the RFC which eases any sampling problems by allowing me to compare
revealed and non-revealed banks that borrowed from the RFC. Second, the public learned
the identities of borrowers on August 22, 1932, via the publication of the list. Consequently,
the mechanism and timing of how the public discovered news of emergency loans is clear.
I first ask whether depositors stigmatized banks after their emergency loans were revealed
in an era before deposit insurance. I find results consistent with depositors interpreting news
of a LOLR emergency loan as financial weakness; the deposits-to-assets ratio dropped by
3
5-7% at revealed banks compared to non-revealed banks. Moreover, revealed banks were
61.4% more likely to fail after the list was published and, conditional on failing, the time-to-
failure for revealed banks was nearly five weeks sooner than non-revealed banks. Altogether,
the results provide evidence that deposits imposed a liquidity risk on revealed banks and
increased the speed and likelihood of their failure.
To the best of my knowledge, this paper is the first to identify and quantify the magni-
tude of stigma that banks experienced after their emergency loans were revealed to market
participants. Specifically, in 1932, banks on average lost 81% of their deposits before failing
(Wicker (1996)) while, at the same time, I find that the presence of stigma imposed a 5-7%
loss in the deposits-to-assets ratio at revealed banks. Accordingly, the marginal contribu-
tion of stigma towards the likelihood of failure was considerable and is significant enough to
warrant the attention of policy makers.
Having found evidence of the stigma problem, I next ask how policy makers can use this
information to improve the design of emergency lending facilities and manage the informa-
tion environment during a crisis to alleviate the problem. One possibility is to resolve a
coordination problem between banks that results from banks reluctance to borrow from the
discount window (Armantier et al. (2014)). Because of stigma, a bank’s decision to borrow
from the LOLR involves predicting the actions of other banks. If a bank believes that other
banks are reluctant to borrow, its own belief in stigma is reinforced, and, as a result, the
bank decides not to borrow from the LOLR. To identify the optimal policy design to address
this coordination problem, I must determine the conditions under which market partici-
pants did not withdraw more deposits from revealed banks than non-revealed banks. This
requires a setting that can compare market participant reactions in defined environments
where researchers can randomize how news is released from the LOLR to the public.
4
No such setting is available from the recent crisis, but it is available during the Great
Depression. Because banking markets during the 1930s were defined by city’s boundaries
(Heitield et al. (2013)), I can compare depositor reactions between two types of defined
banking markets, i.e. cities, where the important variation between each type of city was
how the information about RFC borrowers was released. The first type of banking mar-
ket includes cities where nearly all banks were publicly revealed to have borrowed from the
LOLR, while the second type includes cities where only few banks were publicly revealed. I
find that the presence of stigma was larger when information about bank borrowers was re-
vealed asymmetrically. In cities where few banks were publicly revealed, depositors withdrew
significantly more funds at revealed banks than non-revealed banks. In these cities, infor-
mation was revealed asymmetrically and depositors were more likely to interpret the news
as reflective of each bank’s financial weakness rather than reflecting an aggregate adverse
shock. However, in cities where nearly all banks were publicly revealed, depositors withdrew
their funds from all banks; indeed, they then stored their funds in the US postal savings
system (O’Hara and Easley (1979)).3 In these cities, information about bank borrowers was
revealed simultaneously.4 I find that in cities where nearly all banks were revealed (informa-
tion was revealed simultaneously), the presence of stigma was mitigated. Deposit outflows
in these cities were lower than cities where few banks were revealed. Because the presence of
stigma was larger in cities where few banks were publicly revealed (information was revealed
asymmetrically), this explains why banks are reluctant to borrow from the discount window.3The US postal savings system was the only bank to have its deposits fully insured by the federal
government, was intended to attract rural depositors, and was designed to limit its competition with privatebanks.
4In cities where more than 66% of banks were revealed, the deposits-to-assets ratio did not drop atrevealed banks compared to non-revealed banks. However, in cities where fewer than 33% of banks wererevealed, the deposits-to-assets ratio dropped by 6.89 percentage points at revealed banks. These results arerobust to using different thresholds.
5
Therefore, designing emergency lending facilities that reveal information about bank bor-
rowers simultaneously may be associated with less stigma. This can be achieved by providing
a coordination mechanism at the lending facility for banks to request loans simultaneously.5
In the case that identities are revealed, it is likely that more than one bank identity would
be simultaneously revealed, thus preventing an individual bank from being targeted when
approaching the LOLR for a loan. The design of the facility implies that revealing infor-
mation simultaneously (which can take the form of all banks being revealed or no banks
being revealed) dominates revealing information asymmetrically (where few banks are re-
vealed).6 Moreover, such lending facilities will solve the coordination problem that arises
because of stigma. Market participants would find banks receiving loans from the LOLR to
be acceptable simply because many other banks were receiving loans as well. Finally, these
conclusions also imply one rather extreme solution to the stigma problem. Central banks
could force all banks to receive capital injections, leading the public to infer that receiving
LOLR funds was acceptable since all other banks would be receiving funds as well.7
After considering depositor reactions to the August 22, 1932 publication of the list of bank
identities, I examine the behavior of those banks whose identities were revealed. An aim of
the central bank is to ease liquidity needs for solvent yet illiquid banks during a financial crisis
where, in turn, these banks use the funding to maintain loans to their consumers (Freixas
et al. (1999)). After the revelation, central banks might be concerned that revealed banks
would exhibit precautionary behavior by hoarding cash, consistent with banks taking self-
insurance (Winters (2012)). Did banks hoard cash after their identities were revealed? I find5New lending programs created by the Fed during the recent crisis provided such a coordination mecha-
nism.6Although I do not explicitly show that keeping all bank identities private dominated revealing all bank
identities at the same time, Armantier et al. (2014) infers that this was the case because banks believed instigma.
7The feasibility of this policy implication may be unlikely.
6
that revealed banks increased their bonds-and-securities-to-assets ratio between 10-12.5%
compared to non-revealed banks. Moreover, revealed banks dropped their total loans-and-
discounts-to-assets ratio by 4.5-6% in comparison to non-revealed banks. My findings show
that central bank goals were circumvented when a bank’s identity was publicly revealed
because they exhibited precautionary behavior.
Because researchers face major challenges measuring stigma, the limited size of the lit-
erature reflects this. Furfine (2003) finds preliminary empirical evidence from the federal
funds market that banks were reluctant to borrow from the discount window. Armantier
et al. (2014) extend this result by showing that banks are willing to pay a premium in ex-
cess of 44 basis points on average to avoid borrowing from the discount window. Finally,
Kleymenova (2013) finds little evidence of stigma after the disclosure of discount window
borrowers on March 31, 2011. She finds that the disclosure decreased bid-ask spreads and
revealed banks’ cost of capital.8 A possible reason for the lack of stigma in 2011 is because
the disclosure occurred well after the financial crisis had ended. Asymmetric information
released in non-crisis times may have less impact because financial fragility is low. However,
the same asymmetric information released during a crisis can cause a decline in output and
consumption (Gorton and Ordonez (2014)), shedding light on why stigma may exist only
during times of economic stress.
This paper also contributes to the literature on the effect of information disclosure from
the LOLR on bank runs and on the need for banks to keep secrets. Hautcoeur et al. (2013)8Kleymenova (2013) is the first empirical paper to study the effect of the Federal Reserve’s publication
of information about banks from the recent crisis. Bloomberg News filed a lawsuit against the Fed onNovember 7, 2008 to gain access to the list of discount window borrowers. The Fed argued that doing sowould stigmatize those borrowers. The Fed lost the three-year, well-publicized lawsuit, and on March 31,2011, it released the names of the discount window borrowers. Unfortunately, it is difficult to measure thecost of stigma using this instance of disclosure because of methodological limitations due to the lack ofappropriate empirical setting during the recent crisis.
7
show that the French LOLR public bailout of the Comptoir d’Escompte, a large bank in
France, in 1889 did not turn into a general panic. Because all major banks were involved
in designing the bailout, the public interpreted banks receiving emergency loans as accept-
able. Furthermore, during the National Banking Era (1863-1913) in the U.S., private clear-
ing houses responded to severe panics by suppressing bank-specific member information in
newspapers, and published only aggregate information about the clearing house (Gorton and
Talman (2014)). My paper offers an explanation for these historical examples by showing
that the differential effects of partial and full disclosure of banks that accept emergency
credit can have strong adverse impacts on the health of the financial system.
Finally, this paper provides insights into the success of several new emergency lending
facilities introduced by the Fed during the recent crisis. The new facilities—The Term
Auction Facility (TAF), the Term Securities Lending Facility (TSLF) and the Primary Dealer
Credit Facility (PDCF)—all mitigated stigma by providing coordination mechanisms for
banks to submit bids for LOLR loans simultaneously through auctions. If leaks were to
occur, all borrowers’ names would be divulged at the same time. My results show that
such coordination and simultaneous disclosure help explain why these new facilities were
associated with less stigma than the discount window (Benmelech (2012)). By quantifying
stigma, central bankers can prioritize whether solving the stigma problem is necessary. I find
that the design of an emergency lending facility that allows banks to coordinate requests for
LOLR loans can mitigate stigma.
The remainder of the paper is organized as follows. Section 2 reviews the relevance of
stigma during the recent crisis. Section 3 presents the publication of the list on August
22, 1932, which is the main source of identification used in this paper. Section 4 describes
the data. Section 5 presents the results of testing for stigma, and Section 6 presents the
8
conclusions of how depositor reactions changed between cities in response to how information
was released about RFC borrowers. Section 7 describes how revealed banks hoarded cash
following the publication of the list, and Section 8 concludes.
2 Stigma
In the following section, I discuss how central banks attempted to manage the stigma
problem before 2008 but failed to adopt a systematic method of releasing information about
banks. This lack of policy has caused differences in how the public perceives news of gov-
ernment assistance.
2.1 Relevance during the recent crisis
I define stigma as the cost imposed on a bank after its emergency LOLR loan was revealed to
the public. The belief in stigma can arise in a rational equilibrium. Because private trades
in the interbank market are not observable by the public, information from the interbank
market, which would otherwise remain private, can flow to the public through reported
activity at the discount window (Ennis and Weinberg (2013)). In equilibrium, banks holding
bad assets cannot obtain loans in the interbank market and are forced to borrow from the
discount window. Consequently, adverse selection arises at the discount window making
stigma an equilibrium phenomenon.
During the recent crisis, central bankers believed the primary reason banks refused to
borrow from the discount window was because of stigma (Geithner (2014)).9 The Fed’s
priority of solving the stigma problem was also illustrated by its extreme reluctance to
release discount window borrowers after losing the Freedom of Information Act lawsuit filed9Geithner (2014) explains how banks’ belief in stigma hindered the effectiveness of the Fed in 2007.
9
by Bloomberg News. The Fed had argued that releasing the identities of discount window
borrowers would stigmatize banks and impede the Fed’s ability to respond to future crises
(Berry (2012)). Moreover, the Fed introduced new lending programs designed to eliminate
the perception of stigma (Armantier et al. (2014)). The Term Auction Facility (TAF) was the
first new lending program created on December 12, 2007. It seemed that banks associated
less stigma with the TAF than the discount window because total borrowings at the TAF
were much higher (Benmelech (2012)). However, the precise reason that the TAF seemed
to carry less stigma (or be preferable to banks) was unknown. The TAF and the discount
window were identical lending facilities with respect to collateral requirements and borrower
eligibility, and both also protected borrower identities. However, the borrowing rate at the
TAF was set through competitive bidding at auction whereas the discount window borrowing
rate was determined by the Fed. Under the TAF, banks were required to approach the
Fed at the same time; bids were submitted to the Fed every Monday, multiple winners
were announced on Wednesday, and the funds were received on Thursday.10 It provided
a coordination mechanism for banks to submit bids for loans simultaneously decreasing
the probability that an individual bank’s identity would be leaked. On the other hand,
the discount window required that banks approached for a loan individually providing no
coordination mechanism across banks that might mask individual identities. The relevance
of stigma in the recent crisis was significant.
How have market participants perceived news of government assistance before 2008? Cen-
tral banks have chosen different approaches to managing information about bank borrowers
with varying consequences. The first approach involved a bank identity being accidentally
leaked. On September 13, 2007, the BBC News revealed that Northern Rock bank had10Both the TSLF and PDCF also determined the borrowing rate through competitive bidding at auction
where bids were submitted by banks simultaneously.
10
secretly received assistance from the Bank of England resulting in depositors queuing to
demand their funds. A parliamentary inquiry into the leak concluded that depositors had
interpreted emergency assistance to Northern Rock as a sign of financial weakness and ran
on the bank (Treasury (2008)). The second approach involved requiring all banks to accept
LOLR capital injections while revealing their identities and loan amounts simultaneously. In
October 2008, the US Treasury simultaneously announced the identities and loan amounts
of nine banks it was investing $125 billion of TARP funding.11 This action prompted an
11% increase in the Dow Jones Industrial Average and the nine TARP recipient banks were
not stigmatized for receiving LOLR funding. From these examples, it appears that central
banks follow both asymmetric and symmetric methods of releasing information. It would be
useful to adopt a policy that best manages public reactions to news during a crisis.
3 Identification Strategy
3.1 The Reconstruction Finance Corporation
The Reconstruction Finance Corporation (RFC) was the lender of last resort to the US
banking system during the Great Depression. It was created by President Hoover in response
to a series of banking failures in 1931 after Britain left the gold standard (Friedman and
Schwartz (1963)).12 Figure 1 illustrates the steady drop in deposit levels after the stock11See the New York Times “US Investing $250 Billion in Banks” from October 13, 2008. The Troubled
Asset Relief Program (TARP) was signed into law on October 3, 2008 and was designed to purchase assetsand equity from banks to strengthen the financial sector.
1261% of banks failed between 1930 and 1933 (Wicker (1996)) while available customer deposits dropped by34% (FDIC (1920-1936)). Bank failures mostly ended after President Roosevelt declared a national bankingholiday during March 6-13, 1933. While some banks were allowed to reopen on March 13, 25% were notlicensed to reopen after the holiday. Over half of the surviving banks after the national banking holiday were
11
market crash in October 1929 and accelerations of bank suspensions in 1931. President
Hoover submitted the RFC Act to Congress on December 7, 1931 and it was passed into law
on January 22, 1932.
Figure 1: Trends of Deposits and Banks from 1921-1933
Source: Wicker (1996) p.2
This figure describes the banking sector in the US from 1921-1933. The line illustrates total deposits(in millions) across all banks in the US over time. The solid bars depict the total number of banksin the US while the dashed bars reflect the total number of suspended banks in the US. The dataare from Wicker (1996).
The Reconstruction Finance Corporation began secretly authorizing loans on February
2, 1932 to several types of institutions including commercial banks, insurance companies,
receivers of closed banks, and a variety of nonfinancial firms including state and local govern-
ments, railroads, and mortgage institutions. Loan interest rates and collateral requirements
were identical to the discount window and did not change until 1933 (Ebersole (1933); Olson
estimated to have borrowed from the RFC at least once (Todd (1992)).
12
Figure 2: Reconstruction Finance Corporation Loans Outstanding
Source: Gorton and Metrick (2013)
This figure illustrates the scale of loans from the Reconstruction Finance Corporation to banks aswell as to other institutions like state and local governments, railroads, and mortgage institutions.The vertical line depicts when borrower names were first disclosed on August 22, 1932. The dataare from Gorton and Metrick (2013).
(1977)).13 Figure 2 illustrates the scale of loans outstanding authorized by the RFC to banks
and nonfinancial firms. Prior to the August 22, 1932 publication of the list of bank identities
that had secretly borrowed from the RFC, total RFC borrowing had reached approximately
$1 billion, with about half of this total going to banks. Following the publication of the list,
net bank borrowing flattened even though nonfinancial borrowing, where stigma seems far
less of an issue, rose to more than $2 billion (Gorton and Metrick (2013)).
Most banks borrowing from the RFC were ineligible to borrow from the discount window
because they were not Federal Reserve member banks. Large reserve banks in cities tended
to be member banks (Mitchener and Richardson (2013)) and nearly 20% of my sample were
eligible to borrow from the discount window. Figure A.1 in the Online Appendix shows that
total borrowings from the Federal Reserve decreased in 1932 after the introduction of the13For more detailed information about the RFC see Olson (1977), Butkiewicz (1995); Mason (2001), Mason
and Schiffman (2003), and Calomiris et al. (2013).
13
RFC. Loans outstanding from the Fed totaled $817 million in January of 1932 and decreased
to $254 million by December.
By the end of 1932, 6,865 eligible institutions (banks and nonfinancial firms) had been
authorized over $1.6 billion in loans by the RFC (Corporation (1932)). The Reconstruc-
tion Finance Corporation’s function as a lender of last resort was large and economically
significant.
3.2 The publication of the list on August 22, 1932
The main source of identification in this paper is the unexpected publication of a list of
banks on August 22, 1932 that had secretly borrowed from the Reconstruction Finance
Corporation. I analyze the response of depositors and banks to the publication of the list
in a difference-in-differences (DID) setting. In this framework, I avoid the sample selection
problem of comparing the performance of banks that chose to borrow from the LOLR to the
performance of banks that chose not to borrow, because all banks in the estimation borrowed
from the RFC.
Initially, the identities of all RFC borrowers (banks and non-banks) were kept secret from
the public. Since its establishment, the RFC had used elaborate secret codes to transmit
messages to its loan agencies and individual banks (Olson (1977)). However, in July 1932,
an amendment was introduced to the RFC Act that extended Reconstruction Finance Cor-
poration authority into state and local relief, public works construction, slum clearance, etc.
In this amendment, a last minute clause was added requiring that all loans authorized by
the RFC be made public to Congress. President Hoover initially planned to veto the bill
due to the addition of the last-minute clause but was assured by the Senate Majority Leader
14
that RFC loans would not be revealed to the public without congressional approval. It was
decided that monthly reports of RFC loans would be of confidential nature and held by the
Clerks of the Senate and the House of Representatives until Congress resumed session in
December (Chronicle (1932b)).
Despite this, on August 22, 1932, South Trimble, the Clerk of the House of Representa-
tives, took it upon himself to release a partial list of bank identities that accepted loans from
the RFC because he felt it was his duty to inform the US public. The list was first published
in the New York Times and the Commercial & Financial Chronicle (CFC) and coverage of
this list was widespread.14 It was highly likely that the publication of the list was unexpected
given the last-minute nature of the clause and the assurances that no borrower list would
be released without congressional approval.15 The loan authorization date for a bank deter-
mined whether or not the bank identity was revealed. Mr. Trimble decided to reveal banks
that had a loan authorized between July 21 and July 31, 1932 on August 22, 1932 to coincide
with the passing of the amendment to the RFC Act on July 20, 1932. He regarded the details
of any loan authorized after July 20, 1932 to be subject to the last-minute clause (which was
then law), and required congressional approval for release (Chronicle (1932a)). Banks not
revealed on August 22 had a loan authorized on or before July 20, 1932 and therefore were
not subject to the last-minute clause. Therefore, non-revealed banks had a loan authorized
between February 2 and July 20, 1932. Figure 3 displays a timeline illustrating how banks
are assigned to the treatment and control groups. The treatment group includes 351 banks
revealed on August 22, 1932 while the control bank includes 713 banks not revealed, yielding
1,064 banks in total.14I confirm this by checking several local newspapers such as the Hartford Courant and the Ames Tribune
to determine when the list was published.15Newspaper articles do not discuss the revelation of bank borrower identities to the public until Mr.
Trimble forewarns of his intended announcement on August 18, 1932.
15
Figure 3: Timeline by Loans Authorized
This figure illustrates how the treatment group (revealed banks) and control group (non-revealedbanks) were assigned. Banks with loans authorized between July 21 and July 31, 1932 were revealedon August 22, 1932 while banks with loans authorized on or before July 20, 1932 remained notrevealed.
Since Congress was not in session, Mr. Trimble followed the August 22, 1932 list publica-
tion with five additional monthly lists finishing on January 26, 1933. In this paper, I focus on
the publication of the first list on August 22, 1932 because after this date, a bank choosing to
borrow after August 22 may have anticipated that their identity would be revealed following
their loan authorization. For the remaining discussion, I refer to banks published on the
August 22, 1932 list as “the treatment group or revealed banks.” Banks not revealed as of
this date are referred to as “the control group or non-revealed banks.” All results presented
will only include banks with loans authorized before August 22, 1932.
It is important for my identification that the list of revealed banks published on August
22, 1932 be chosen by Mr. Trimble in a way that is uncorrelated with the outcome variables
used in the estimation. There should be nothing systematically important about the dates of
loan authorizations that he chose to publish implying that the decision of when a bank chose
16
to borrow from the RFC also needs to be uncorrelated with all outcome variables. Otherwise,
the treatment and control groups may differ along a number of observable dimensions and
this will bias the results of the estimation.
I perform four robustness checks to test the validity of my identification strategy. First,
I restrict the control group to only include banks with loans authorized between July 10 and
July 20, 1932 and compare the performance of these banks to the treatment group. In this
test, I compare short symmetric windows of authorized loans. Second, I exploit differences
in the timing of loan authorization and loan application dates. Mr. Trimble revealed banks
with loans authorized between July 21 and July 31, 1932 which does not necessarily correlate
with the actual date the bank applied for the loan. Historical evidence suggests that the time
period between loan application and authorization varied across banks. For example, Olson
(1977) discusses how RFC board examiners would visit banks to discuss a loan application
and assess the bank’s financial condition. He explains that a loan authorization may be
delayed if there was not a prior relationship between the bank and the RFC.16 As long as
the period between loan application and authorization varied across banks, the treatment
and control groups were assigned quasi-randomly. Third, I restrict the treatment group to
banks with a loan authorized during the same time period as the control group, i.e. between
February 2 and July 20, 1932. I then compare the performance of these banks to the control
group. Fourth, a crucial assumption for the DID estimation to be valid is that revealed banks
and non-revealed banks should have parallel trends were it not for the publication of the list.16Olson (1977) discusses the extreme caution displayed by the RFC when distributing funds to banks.
This resulted in a delay to when a bank received a loan. The delay would sometimes result in the governor ofstate of the bank’s location intervening to request the RFC to send funds expeditiously, e.g. the governor ofTennessee intervening on behalf of the Bank of America and Trust Company in Memphis. He also describeshow the RFC would dispatch its board examiners to evaluate the conditions of banks, e.g. the UnionGuardian Trust Company in Detroit; Fidelity National Bank in Kansas City; and Hibernia Bank and TrustCompany in New Orleans. This process would obviously delay the process of when a bank received RFCfunds.
17
In the absence of the event, the observed DID estimates should be zero. In Figure 4, I plot
the average change in the deposits-to-assets ratio at revealed and non-revealed banks from
June 30, 1930 to June 30, 1933, along with 5% confidence intervals. The graph shows that
both groups follow parallel trends providing more evidence that the treatment and control
groups do not differ with respect to the deposits-to-assets ratio before the publication of
the list. The deposits-to-assets ratio then dropped sharply for revealed banks compared to
non-revealed banks following the publication of the list.
Figure 4: Trends in the deposits-to-assets ratio
This figure displays the average change in the deposits-to-assets ratio for the treatment group (351revealed banks) and control group (713 non-revealed banks), normalized by deposit levels at June30, 1930. The dashed lines denote 5% confidence intervals. The darker shaded area between June30, 1932 and December 31, 1932 denotes the period the treatment group was revealed (August 22,1932). The second lighter shaded area denotes the period the control group was revealed (January26, 1933).
18
3.3 The publication of the retroactive list on January 26, 1933
On January 26, 1933, Mr. Trimble published a final unexpected retroactive list in the New
York Times that included names of RFC borrowers with loans authorized on or before July
20, 1932. This list contained the names of the 713 banks not revealed as of August 22,
1932. Therefore, all banks in the sample were eventually revealed. Consequently, all of my
comparisons of revealed banks to non-revealed banks focus on outcomes between August 22,
1932 and January 26, 1933, before the control group was revealed. After January 26, 1933,
no obvious control group to compare the performance of banks revealed on August 22, 1932
exists. Figure 5 displays a timeline of the lists published on August 22, 1932 and January
26, 1933. See Figure A.2 in the Online Appendix for a timeline of all list publications.
Figure 5: Timeline of the August 22, 1932 List Publication
This figure displays the timeline of publications of bank borrower lists after the RFC began givingloans on February 2, 1932. 351 banks were revealed on the list published on August 22, 1932(“treatment group” or “revealed banks”) while 713 banks were not revealed (“control group” or“non-revealed banks”). However, the 713 non-revealed banks as of August 22, 1932 were retroac-tively revealed to the public on January 26, 1933. All banks in the sample were eventually revealedto the public.
19
4 Data
4.1 Reconstruction Finance Corporation loan data and bank bal-
ance sheet information
The RFC loan authorization data and bank identities are from the New York Times (Times
(1932)).17 In the original publication, bank identities were organized by state because bank
names were common across states. Loan amounts and the interest rate were also provided.
The date of loan authorization was provided for loans authorized between February 2 and
July 20, 1932. Following July 20, only loan authorization intervals were provided. The data
are hand-collected and then verified in the Commercial & Financial Chronicle.
Bank balance sheet data are from Rand McNally Bankers’ Directory which was published
every six months. I collect the amounts of paid-up capital, surplus and profits, deposits, other
liabilities, loans and discounts, bonds and securities, miscellaneous, and cash due from other
banks, and the name of the president for each bank. The coverage of the Directory seems to
be comprehensive across the U.S. as only 18 banks that had loans authorized from the RFC
were not matched in the Directory. The data are hand-collected from eight books beginning
December 31, 1929 to June 30, 1933 resulting in eight observations per bank. For roughly
5% of the data, bank balance sheet observations are repeated. I exclude this repeated data,
but in a robustness check I ensure this exclusion does not affect any of the results. I also
hand-collect the number of banks in a city as of June 30, 1932.
The three bank-specific outcomes of interest in this paper are the deposits-to-assets,
loans-and-discounts-to-assets, and bonds-and-securities-to-assets ratios. I do not observe
the detailed composition of deposits, loans and discounts, or bonds and securities. The17The list also includes identities of non-banks such as railroads, state relief, and mortgage companies.
20
total book value of deposits provided in Rand McNally Bankers Directory equals demand
deposits and time deposits. The total book value of loans-and-discounts equals the book
value of loans to banks and trust companies, loans on securities (to brokers and dealers),
real estate loans and mortgages, and all other loans. Finally, the total value of bonds-and-
securities in the Directory equals the book value of US government securities, state, county,
municipal bonds, railroad bonds, all other bonds, stock of the Federal Reserve Bank, stock of
other corporations, foreign government bonds, other foreign securities, the real estate value
of the banking house, other real estate owned, and the value of furniture and fixtures within
the banking house.
In addition, I collect the date a bank failed, was suspended (in conservatorship), was
merged, or was bought from Rand McNally Bankers’ Directory. Bank failures during this
time period must be recorded carefully because suspended banks may have reemerged under
new leadership and should not be considered failed banks (Calomiris and Mason (2003)).
I supplement the information from the Directory by using Moody’s volumes from this pe-
riod which generally provided information on Federal Reserve member banks. In the data,
receiverships with a confirmed failure date and voluntary liquidations are treated as bank
failures, identical to the methodology used by Calomiris and Mason (2003). Because Rand
McNally Bankers’ Directory only reports the date a bank failed (suspended) and not its bal-
ance sheet information directly before its failure (suspension), I record these balance sheet
values as zero. However, as a robustness check, I proxy for the deposits-to-assets ratio,
the main variable of interest used in this paper, at failed banks with the total amount of
suspended deposits divided by total deposits in the state of the bank’s location.
For all lists published between August 22, 1932 and January 26, 1933, my data are 4,983
loans to 3,353 unique banks. I match 3,335 banks to balance sheet information in Rand
21
McNally Bankers’ Directory. I exclude banks which failed before August 22, 1932 (the RFC
was allowed to authorize loans to banks under conservatorship) and banks newly revealed on
January 26, 1933 that had a loan authorized in December.18 The final sample includes 2,674
unique banks. Table A.1 in the Online Appendix provides more detail about the number of
banks revealed on each subsequent list.
4.2 Additional covariates
To account for differing macroeconomic trends across each state, I include several additional
control variables in the estimation. These variables also capture the broader health of the
banking system. I use the dollar amount of total deposits and the total number of banks in
each state to account for the size, organization, and resources of the banking system. Next,
I use the dollar amount of suspended deposits and the total number of suspended banks
in each state to account for the health of the banking system. Suspended banks include
banks that closed their door to depositors for at least one business day and later resumed
operations, or banks that ceased operations, surrendered their charters, and repaid creditors
under a court-appointed receiver (Heitield et al. (2013)). The data are from the FDIC Bank
Deposit Data, 1920-1936 (Inter-university Consortium for Political and Social Research) and
are available yearly. I also use yearly state level per capita income data from the U.S. Bureau
of Economic Analysis, Survey of Current Business, May 2002. Per capita income captures
the potential cross-sectional variation in depositor income across states.
18Banks newly revealed on January 26, 1933 have no obvious comparison group since the control group isalso revealed on January 26, 1933.
22
5 Testing for stigma
5.1 Empirical specification
To compare the performance of revealed and non-revealed banks after the publication of the
list on August 22, 1932, I run the following bank-level OLS regression from June 30, 1930 to
June 30, 1933.
Yi,t = α+β1Revealedi× I{t = List}+β2Revealedi× I{t = List+ 1}+γXi,t−1 +ηt+ δi+ εi,t (1)
where Yi,t is the outcome of interest measured every six months t for bank i. I{t = List} is
a dummy equal to one following the publication of the list on August 22, 1932. December
31, 1932 is the first balance sheet date I observe for banks after the publication of the list.
I{t = List+ 1} is a dummy equal to one following the publication of the retroactive list on
January 26, 1933 revealing the control group.19 Revealed is a dummy equal to 1 if the bank
was publicly revealed on August 22, 1932. The coefficient of interest is β1, which measures the
change in Yi following the publication of the list for revealed banks compared to non-revealed
banks relative to their controls.20 The coefficient β2 measures the change in Yi following the
publication of the retroactive list for banks revealed on August 22, 1932 relative to banks
revealed on January 26, 1933 (non-revealed banks). The main outcome variable used is the
deposits-to-assets ratio. Other outcome variables are the loans-and-discounts-to-assets ratio
and the bonds-and-securities-to-assets ratio. For failed banks, I proxy for the deposits-to-19I do not use the traditional Revealed × Post specification because the control group was revealed on
January 26, 1933. Thus, the effect of stigma can only be measured between August 22, 1932 and January26, 1933.
20Note that I do not include a I{t = List} dummy, a I{t = List + 1} dummy, nor a Revealed dummysince they are not identified once I include half-year and bank fixed effects.
23
assets ratio with the total amount of suspended deposits divided by total deposits in the
state of the bank’s location so I can include failed banks in the estimation. The results are
also robust to recording zero for the deposit balance at failed banks.
A key issue that prevents the above specification from identifying the effect of the rev-
elation on Yi,t is that Yi,t may be correlated with unexplained macroeconomic conditions
and/or bank borrower characteristics in the error term εi,t. Therefore, I include controls,
Xi,t−1, to mitigate this bias and lag the covariates to ensure that Xi,t−1 does not confound
Yi,t. Xi,t−1 is a vector of controls measured six months prior to Yi,t and includes the log of
bank assets, the log of state per capita income, the log of state level deposits, the log of state
level deposits at suspended banks, the log of the number of banks at the state level, and
the log of the number of suspended banks at the state level. These covariates are intended
to capture observable proxies for macroeconomic conditions and bank characteristics that
might explain Yi,t. However, the specification may still be biased if some bank characteristics
are unobservable. Therefore, I rely on bank fixed effects, δi, to exclude biases that could
result from time-invariant bank characteristics and capture the extent to which each bank
affects Yi,t. Additionally, I include half-year fixed effects, ηt, to account for time trends in
Yi,t eliminating the concern that aggregate changes in Yi,t and the publication of the list
occurred together.
Finally, standard errors are clustered at the bank level according to Bertrand et al. (2004).
Furthermore, all continuous variables are winsorized at the 1% level to avoid outliers driving
the estimation results.
24
5.2 Restricted control group
One concern with the main specification is that some banks in the control group were revealed
before I observe any outcomes. Due to data limitations, I only observe bank-specific balance
sheet data at six-month intervals. After the first list was published on August 22, 1932, the
nearest balance sheet data are from December 31, 1932. If a bank in the control group had
another loan authorized between August 22 and November 30 and was subsequently revealed,
its December 31 balance sheet data would reflect this revelation. This characteristic of the
data makes it difficult to detect treatment effects because it biases the results towards zero
(Imbens and Angrist (1994)).21 Hence, I interpret my results as a lower bound identified
estimate of the effect of the revelation (Robins (1989)).
Figure 6 illustrates how 216 of the 713 non-revealed banks had additional loans autho-
rized. For clarity, I will refer to non-revealed banks who were not revealed as of August 22,
1932 and remained non-revealed until January 26, 1933 as the “restricted control group.”
Because I have to impose a constraint on the restricted control group of no loans authorized
between August 22 and November 30, 1932, I may be introducing a possible sample selection
bias into the results. Nevertheless, it is also worth emphasizing that banks in the restricted
control group are not limited to banks that never borrowed again from the RFC after Au-
gust 22, 1932. If a bank in the restricted control group had a loan authorized anytime in
December, its identity would not be revealed until January 26, 1933. Hence, its balance
sheet data as of December 31, 1932 would not reflect any early revelation. In the Online
Appendix, I perform a robustness check to potentially satisfy concerns about the possible21The common example used in the public health literature is a randomized drug trial. The treatment
group will receive the testable drug while the control group will receive a placebo. This assumes the con-trol group will remain compliant and not receive the testable drug elsewhere. Assuming compliance of allparticipants can be a strong assumption.
25
sample selection bias of the restricted control group.
Figure 6: Timeline Including Additional List Publications
This figure illustrates the four additional bank lists published between August 22, 1932 and January26, 1933. 216 of the 713 non-revealed banks had a loan authorized between August 22 and November30, 1932 and were revealed on a list prior to January 26, 1933. The remaining 497 banks were newlyrevealed on January 26, 1933.
5.3 Summary statistics
Table 1 displays descriptive statistics that compare revealed and non-revealed banks. Panel
A presents the summary statistics on bank-level balance sheet information on June 30, 1932,
the last balance sheet date I observe prior to the first list being published on August 22,
1932. Although the summary statistics show level differences between the treatment and
control groups, I find strong evidence that the parallel trend assumption holds with respect
to the deposits-to-assets, loans-and-discounts-to-assets, and bonds-and-securities-to-assets
ratios before the publication of the list in all the estimations and Figure 4. Non-revealed
banks were slightly larger in size and had more bonds and securities scaled by total assets.
This was offset by a smaller loan portfolio scaled by total assets. Panel B presents summary
statistics describing the loans authorized to revealed banks and non-revealed banks by the
26
RFC during 1932. Non-revealed banks received more loans from the RFC because these
banks were more likely to approach the RFC for a loan after the publication of the list
compared to revealed banks.
Table 1: Summary Statistics on Treatment and Control GroupsThis table presents summary statistics for the revealed and non-revealed banks prior to the pub-lication of the bank borrower list on August 22, 1932. There are 351 banks in the treatmentgroup (banks revealed on August 22, 1932) and 713 banks in the control group. Panel A presentsbank-level statistics as of June 30, 1932 while Panel B presents loan-level statistics for all of 1932.The loans-and-discounts-to-assets ratio is the book value of the bank’s loans outstanding scaled bytotal assets. The bonds-and-securities-to-assets ratio is the book value of the bank’s bond portfolioscaled by total assets.
Panel A (at June 30, 1932)Revealed Banks (351) Non-revealed Banks (713)
Obs. Mean Std. Dev Obs Mean Std. Dev Diff. in Meanstotal assets 351 2597.3 5539.0 713 4664.2 6348.3 ***
log(total assets) 351 6.74 1.43 713 7.95 0.92 ***DepositsAssets
351 0.70 0.12 713 0.69 0.12LoansAssets
351 0.69 0.17 713 0.65 0.15 ***BondAssets
351 0.31 0.16 713 0.34 0.14 ***
Panel B (all of 1932)Revealed Banks (351) Non-revealed Banks (713)
Obs. Mean Std. Dev Obs Mean Std. Dev Diff. in MeansLoan Amount 351 370.3 1233.4 713 530.04 1064.5 **(in thous.)
Number of Loans 351 1.7 1.21 713 2.09 1.35 ***
Table 2 displays summary statistics describing the number of bank failures amongst
the treatment and control groups. Because the control group was eventually revealed, the
period of interest to study bank failures was between August 22, 1932 (when the first list
was published) and January 26, 1933 (when the retroactive list was published). Therefore,
Table 2 illustrates that 9.2% of revealed banks failed during this period while only 6% of
non-revealed banks failed. This provides preliminary evidence that revealed banks were
27
more likely to fail after the publication of the list. Overall, most bank failures occurred after
January 26, 1933 because of the national banking holiday between March 6 and March 13,
1933 when President Roosevelt closed all banks across the US.
Table 2: Summary Statistics on Failed BanksThis table presents summary statistics for the number of bank failures amongst the 1,064 banks inthe treatment and control groups. Failed banks are banks that did not emerge from conservatorshipor were liquidated. Percentages of bank failures are provided in parentheses.
1064 banks 351 banks 713 banksTotal Revealed Banks Non-Revealed Banks
Failed 315 (29.6%) 105 (29.9%) 210 (29.5%)Failed before August 22, 1932 0 0 0Failed btw. August 22, 1932 75 (7.0%) 32 (9.2%) 43 (6.0%)and January 26, 1933Failed after January 26, 1933 240 73 (20.8%) 167 (23.4%)
5.4 The effect of the August 22, 1932 list on deposits
I start with the DID estimation of the effect of the August 22, 1932 publication of the
list of RFC borrowers on the deposits-to-assets ratio. Table 3 presents the main results.
Revealed banks experienced a highly statistically significant drop in their deposits-to-assets
ratios by 3.3 to 4.8 percentage points compared to non-revealed banks. This translates to a
5-7% relative drop in the deposits-to-assets ratios with respect to the pre-publication level,
and a $2.1 to 3.6 million loss in deposits in 2014 dollars relative to deposit levels before
the disclosure.22 Column (2) adds bank-level and state-level controls which slightly lowers
the coefficient yet remains highly economically and statistically significant. Columns (1)
and (2) also include one additional interaction term with Revealed. I find no differential22$134,000-$223,000 loss in deposits in 1932 dollars.
28
effect with respect to the deposits-to-assets ratio between treated and control firms prior
to the publication of the list. This provides evidence that the parallel trend assumption,
which is crucial for the validity of the DID estimate, holds in the data. The drop in the
deposits-to-assets ratio starts immediately after the list was published on August 22, 1932,
but disappears after the control group was revealed on January 26, 1933. The coefficient on
Revealedi × I{t = List + 1} indicates no differential effect in deposit withdrawals between
treated and control groups after all RFC borrowers were revealed. Indeed, after all borrowers
were revealed, the deposits-to-assets ratio for non-revealed banks caught up to the revealed
banks (see Figure 4), suggesting that depositors withdrew from all RFC borrowers equally
after all banks were revealed. Finally, Table A.2 in the Online Appendix provides evidence
that this drop in the deposits-to-assets ratio due to the August 22, 1932 list being published
was not driven by the amount borrowed from the RFC. Depositors do not withdraw more
from revealed banks that borrowed larger amounts.
29
Table 3: Effect of the Published List on LOLR Borrower DepositsThis table presents the DID estimates of the effect of the list published on August 22, 1932 on thedeposits-to-assets ratio. Revealed is a dummy that equals 1 if the bank was revealed on August 22,1932 (revealed banks). There are 351 revealed banks in the treatment group and 713 non-revealedbanks in the control group. Revealedi × I{t = List} is a dummy equal to one for revealed banksafter the August 22 list was published, and zero otherwise. Revealedi× I{t = List+1} is a dummyequal to one for banks revealed after the retroactive January 26, 1933 list was published, and zerootherwise. Revealedi × I{t = List − 1} is a dummy equal to one for revealed banks before theAugust 22 list was published, and zero otherwise. Xi,t−1 are controls measured six months prior tothe deposits-to-assets ratio and include the log of bank assets, the log of state per capita income,the log of state level deposits, the log of state level deposits at suspended banks, the log of thenumber of banks at the state level, and the log of the number of suspended banks at the statelevel. Standard errors are clustered at the bank level and presented in parentheses. All continuousvariables are winsorized at the 1% level. ***, **, and * indicate significance at the 1%, 5%, and10% respectively.
deposits-to-assets(1) (2)
Revealedi × I{t = List− 1} -0.0049 -0.0002(0.0072) (0.0070)
Revealedi × I{t = List} -0.0482*** -0.03295**(0.0163) (0.0157)
Revealedi × I{t = List+ 1} 0.0007 0.0166(0.0264) (0.0236)
Xi,t−1 No YesBank Fixed Effects δi Yes Yes
Half-Year Fixed Effects ηt Yes Yes
Obs. 6303 6303R2 0.6255 0.6626
The main concern of the identification strategy is that the treatment and control groups
may differ along a number of observable dimensions which might be correlated with the
outcome variables and bias the estimation. I address this concern through two robustness
checks (mentioned in the identification strategy section). First, I restrict the loan autho-
rization window for the control group to a short window of time by only including banks
with loans authorized between July 10 and July 20, 1932. Table 4 presents the results of
30
this test that uses a narrow time window of authorized loans. Revealed banks experienced
a 5.5 percentage point drop in the deposits-to-assets ratio compared to non-revealed banks.
This amounts to an 8% relative drop in deposits-to-assets with respect to the pre-publication
level. The drop in deposits at revealed banks continues to be economically and statistically
significant. In addition, Table 4 provides evidence that the parallel trend assumption still
holds for banks that had loans authorized in July.
Table 4: Effect of the Published List on LOLR Borrower Deposits for LoansAuthorized in JulyThis table presents the DID estimates of the effect of the list published on August 22, 1932 onthe deposits-to-assets ratio at banks with loans authorized in July. Revealed is a dummy thatequals 1 if the bank was revealed on August 22, 1932 (revealed banks). These revealed banks hada loan authorized from the RFC between July 21 and 31, 1932 while non-revealed banks had aloan authorized from the RFC between July 10 and July 20, 1932. Revealedi × I{t = List} is adummy equal to one for revealed banks after the August 22 list was published, and zero otherwise.Revealedi × I{t = List + 1} is a dummy equal to one for banks revealed after the retroactiveJanuary 26, 1933 list was published, and zero otherwise. Revealedi × I{t = List− 1} is a dummyequal to one for revealed banks before the August 22 list was published, and zero otherwise. Xi,t−1is a vector of controls identical to those used in Table 3. Standard errors are clustered at the banklevel and presented in parentheses. All continuous variables are winsorized at the 1% level. ***,**, and * indicate significance at the 1%, 5%, and 10% respectively.
deposits-to-assetsRevealedi × I{t = List− 1} -0.0139
(0.0124)Revealedi × I{t = List} -0.0546**
(0.0250)Revealedi × I{t = List+ 1} 0.0231
(0.0514)Xi,t−1 Yes
Bank Fixed Effects δi YesHalf-Year Fixed Effects ηt Yes
Obs. 2400R2 0.6213
Second, I restrict the treatment group to only include banks that had a loan authorized
31
Table 5: Effect of the List Publication on Deposits for Revealed Banks withEarlier Authorized LoansThis table presents the DID estimates of the effect of the list published on August 22, 1932 on thedeposits-to-assets ratio at banks with earlier authorized loans. Revealed is a dummy that equals1 if the bank was revealed on August 22, 1932 (revealed banks). These revealed banks had a loanauthorized between July 21 and 31, 1932 and between February 2 and July 20, 1932. Revealedi ×I{t = List} is a dummy equal to one for revealed banks after the August 22 list was published, andzero otherwise. Revealedi × I{t = List+ 1} is a dummy equal to one for banks revealed after theretroactive January 26, 1933 list was published, and zero otherwise. Revealedi× I{t = List− 1} isa dummy equal to one for revealed banks prior to the August 22 list, and zero otherwise. Xi,t−1 isa vector of control variables and is identical to those used in Table 3. Standard errors are clusteredat the bank level and presented in parentheses. All continuous variables are winsorized at the 1%level. ***, **, and * indicate significance at the 1%, 5%, and 10% respectively.
deposits-to-assetsRevealedi × I{t = List− 1} -0.0119
(0.0118)Revealedi × I{t = List} -0.0716**
(0.0354)Revealedi × I{t = List+ 1} -0.0362
(0.0547)
Xi,t−1 YesBank Fixed Effects δi Yes
Half-Year Fixed Effects ηt Yes
Obs. 4771R2 0.6588
during the same time period as the control group, i.e. between February 2 and July 20,
1932. Table 5 presents the results of this test. The deposits-to-assets ratio dropped by 7.2
percentage points at revealed banks compared to non-revealed banks. This translates to an
11.5% relative drop in the deposits-to-assets ratio with respect to the pre-publication level.
I then estimate the effect of the publication of the list on August 22, 1932 on the deposits-
to-assets ratio after constraining the control group to banks who remained non-revealed until
January 26, 1933, i.e. the restricted control group. Because some of the banks in the control
32
group receive treatment, i.e. revealed at a later date but before I observe its balance sheet,
the DID estimates are biased towards zero. Comparing revealed banks to banks in the
restricted control group in the following test will resolve this bias. The results are presented
in Table 6. The deposits-to-assets ratio significantly dropped by 5.5-7.2 percentage points
at revealed banks compared to non-revealed banks. This translates to an 8-10.5% relative
drop in the deposits-to-assets ratio with respect to the pre-publication level. I show that the
magnitude of the drop in deposits is larger after restricting the control group to banks who
remained non-revealed until January 26, 1933. If a bank in the control group had another
loan authorized after August 22, 1932 and was subsequently revealed before January 26,
1933, its December 31, 1932 balance sheet data would reflect this revelation, biasing the
estimation result towards zero.
33
Table 6: Effect of the Published List on LOLR Borrower Deposits using the“Restricted Control Group”This table presents the DID estimates of the effect of the list published on August 22, 1932 on thedeposits-to-assets ratio using the “restricted control group”. Revealed is a dummy that equals 1if the bank was revealed on August 22, 1932 (revealed banks). There are 351 revealed banks inthe Revealed group and 497 non-revealed banks in the restricted control group. Bank identitiesbelonging to the “restricted control group” remained secret until January 26, 1933. Revealedi ×I{t = List} is a dummy equal to one for revealed banks after the August 22 list was published, andzero otherwise. Revealedi × I{t = List+ 1} is a dummy equal to one for banks revealed after theretroactive January 26, 1933 list was published, and zero otherwise. Revealedi × I{t = List − 1}is a dummy equal to one for revealed banks before the publication of the August 22 list, and zerootherwise. Xi,t−1 is a vector of controls identical to those used in Table 3. Standard errors areclustered at the bank level and presented in parentheses. All continuous variables are winsorizedat the 1% level. ***, **, and * indicate significance at the 1%, 5%, and 10% respectively.
deposits-to-assets(1) (2)
Revealedi × I{t = List− 1} -0.0106 -0.0063(0.0075) (0.0070)
Revealedi × I{t = List} -0.0719*** -0.0547***(0.0166) (0.0157)
Revealedi × I{t = List+ 1} -0.035 -0.0070(0.0280) (0.0249)
Xi,t−1 No YesBank Fixed Effects δi Yes Yes
Half-Year Fixed Effects ηt Yes Yes
Obs. 4967 4967R2 0.6161 0.6582
One potential concern regarding comparisons of the performance of the treatment group
to the restricted control group is that I am imposing a restriction onto control group banks
thereby introducing a possible sample selection bias. A bank in the restricted control group
had no loans authorized between August 22 and November 30, 1932 whereas banks in the
treatment group could have had additional loans authorized during this period. I address this
concern in a robustness check by restricting the treatment group to only include banks that
had no additional loans authorized between August 22 and November 30, 1932, identical
34
to the restricted control group. Table A.3 in the Online Appendix displays the results
showing that restricting the treatment group does not alter the effect of the disclosure on
the deposits-to-assets ratio.
5.5 Empirical specification to analyze bank failure rates
The performance of a bank can also be measured by whether or not the bank failed. I examine
the effect of the revelation on bank failure rates between August 22, 1932 and January 26,
1933 using a duration model to isolate the effect of stigma before the control group was
revealed on January 26, 1933. The benefit of using a duration model is that it incorporates
the history of bank failures over time where I can exploit knowing the exact date a bank
suspended. I define a failed bank as a bank that did not emerge from conservatorship or was
liquidated, identical to the methodology used by Calomiris and Mason (2003). Furthermore,
I also test whether the failure of revealed banks was accelerated due to the revelation.
I estimate the duration model using maximum likelihood estimation (MLE). The like-
lihood can be written as a function of the hazard and survival functions. Let i index all
banks that were operating as of June 30, 1932, i.e. all banks in both the treatment and
control groups. If a bank did not suspend operations by June 30, 1933, it is treated as a
right-censored observation in the analysis. Let t index the duration time in weeks since the
August 22, 1932 list was published. Let θ(t;X) be the hazard function of the probability
that bank i failed at time t, conditional on surviving until t, with time-invariant covariates
X. For a discrete time duration model, the likelihood function is:
L =p∏p−1
[θ(tp; X)
tp−1∏s=0
(1− θ(s; X))]
(2)
35
To estimate the hazard function, θ(t; X), I use a parametric hazard function following a
Weibull distribution. Under the Weibull assumption, the hazard function takes the form:
θ(t; X) = ρλ(X)(tλ(X))ρ−1 (3)
λ(X) = exp {Xβ} (4)
The parameter ρ is the scale parameter of the hazard function. For example, if ρ̂ is estimated
to be between 0.5 and 1, then the hazard function is increasing at a decreasing rate. The
shape of the baseline hazard denotes the average bank failure rate over time.
The specification used in the analysis is estimated on a weekly basis from August 22,
1932 to January 26, 1933. However, the ideal hazard specification would also incorporate
bank-level time-varying covariates to account for time-varying differences in the baseline
hazard each week. Because I only observe bank balance sheet information every six months,
I estimate the baseline hazard using time-invariant covariates fixed as of June 30, 1932, the
first balance sheet date I observe prior to the publication of the list on August 22, 1932.
Therefore, the following specification estimates the probability a revealed bank failed by
January 26, 1933 compared to a non-revealed bank. I estimate ρ and β1 under the following
parameterization:
ln(λ) = α + β1Revealedi + γXi,June 30, 1932 + σu (5)
where u follows a Type-1 extreme value distribution and σ is a shape parameter that is
equivalent to 1ρ. Hence, β1, the coefficient of interest, measures the logged probability that
a revealed bank failed by January 26, 1933 compared to non-revealed banks. Revealed is a
dummy equal to 1 if the bank was publicly revealed on August 22, 1932. Xi,June 30, 1932 is a
36
vector of controls measured at June 30, 1932 and includes the log of bank assets and the log
of the loan amount bank i received from the RFC in 1932.
5.6 The effect of the August 22, 1932 list on bank failure rates
Table 7 shows that revealed banks were more likely to fail after the list was published
compared to non-revealed banks. The exponentiated regression coefficients for the hazard
model and the accelerated time-to-failure model are displayed. Revealed banks were 61.4%
more likely to fail by January 26, 1933 (when the control group was revealed) compared
to non-revealed banks. Moreover, conditional on failure, revealed banks failed 21.4% faster
than non-revealed banks after the publication of the list (1 - 0.7859). This translates to an
acceleration of nearly five weeks. For a graphical representation of this test, Figure A.3 in
the Online Appendix displays the cumulative hazard estimates using a Weibull distribution
for revealed banks and non-revealed banks.
Although it is difficult to estimate the marginal contribution of the revelation on deposit
withdrawals and likelihood of failure given the poor macroeconomic conditions during the
Great Depression, the hazard analysis provides some perspective on the economic importance
of the main results. Taken alone, it is difficult to assess the importance of a 5-7% drop in
the deposits-to-assets ratio at revealed banks compared to non-revealed banks. However, in
1932 banks, on average, lost 81% of their deposits before failing (Wicker (1996)). Taking this
fact into consideration, along with the 61.4% increase in probability that a revealed bank
would fail after the publication of the list, suggests that the marginal contribution of the
revelation was considerable. Given the many problems banks faced during this period, the
stigma introduced by the revelation was a problem that could have been fixed by Congress.
37
Table 7: Effect of the Published List on LOLR Borrower Failure RatesThis table presents the parametric hazard function following a Weibull distribution estimates ofthe effect of the list publication on August 22, 1932 on bank failure rates. Revealed is a dummythat equals 1 if the bank was revealed on August 22, 1932 (revealed banks) conditional on survivinguntil August 21, 1932. Column (1) provides the exponentiated hazard coefficients presenting theprobability of failure by June 30, 1933 while Column (2) provides the exponentiated acceleratedtime-to-failure coefficients. Xi,June 30, 1932 are fixed bank-level controls measured prior to August22, 1932 including the log of bank assets and the log of the loan amount accepted from the RFC.Standard errors are presented in parentheses. All continuous variables are winsorized at the 1%level. ***, **, and * indicate significance at the 1%, 5%, and 10% respectively.
(1) (2)Hazard Accelerated Time-to-Failure
Treatmenti 1.6137* 0.7859*(0.4361) (0.1089)
Xi,June 30, 1932 Yes Yes
σ 0.5036 0.5036Obs. 1064 1064
Wald chi2(3) 4.58 4.58
38
5.7 Banks that did not borrow from the Reconstruction Finance
Corporation
After all RFC borrowers were revealed, it would interesting to observe if depositors differ-
entially withdrew from banks that borrowed from the RFC compared to banks that never
borrowed. However, this comparison introduces a sample selection bias into the analysis.
For example, suppose that banks with loans from the RFC experienced larger deposit with-
drawals than banks without loans. Is this result driven by depositors withdrawing more from
the banks with loans because they had borrowed from their LOLR, or because those banks
were somehow unobservably worse?
Nonetheless, I collected balance sheet data for 130 randomly selected banks that never
approached the RFC for a loan.23 First, Figure 7 displays the trends in bank size (total
assets) for banks that never borrowed from the RFC, revealed banks and non-revealed banks.
Initially, banks that would receive an RFC loan were similar in size to banks that would
receive no loans. However, after 1932, the size of the borrowing banks dropped faster than
the non-borrowing banks, despite receiving loans from the RFC.23These balance sheet data are also from Rand McNally Bankers’ Directory.
39
Figure 7: Trends in Bank Size
This figure illustrates the average percent change in total bank assets for banks that borrowed fromthe RFC (revealed and non-revealed banks) and banks that did not borrow from the RFC (neverborrowed banks), normalized by June 30, 1930 total asset levels. Total assets for each banks is thesum of its loans and discounts, bonds and securities, cash due from other banks, and real estateowned. The darker shaded area between June 30, 1932 and December 31, 1932 denotes the periodthe Revealed group was revealed (August 22, 1932). The second lighter shaded area denotes theperiod the control group was revealed (January 26, 1933).
Second, Figure 8 illustrates the trend in the deposits-to-assets ratio for banks that never
borrowed from the RFC, revealed banks and non-revealed banks. Initially, banks that re-
ceived no loans had less funding from deposits than banks that received loans. Therefore,
these non-borrowing banks had larger alternate sources of funding. As the Depression wore
on, banks that received no loans were able to maintain higher deposit funding compared
to banks that received loans. Third, Figure 9 displays the trends in total liabilities minus
deposits over total assets (the remaining sources of funding for banks). We can see that
banks that did not receive RFC loans had a larger share of alternate funding sources. These
alternate funding sources included paid-up capital, bank surplus and profits, and loans from
40
Figure 8: Trends in Deposit Levels
This figure illustrates the average ratio of total deposits over assets for banks that borrowed fromthe RFC (revealed and non-revealed banks) and banks that did not borrow from the RFC (neverborrowed banks). The darker shaded area between June 30, 1932 and December 31, 1932 denotesthe period the treatment group was revealed (August 22, 1932). The second lighter shaded areadenotes the period the control group was revealed (January 26, 1933).
41
other banks. Paid-up capital is a function of the bank president’s own capital. The share
of alternate funding sources for borrowing banks increased after 1932 because they received
loans from the RFC.
Figure 9: Trends in Remaining Liabilities
This figure illustrates the average ratio of total liabilities minus deposits over assets for banks thatborrowed from the RFC (revealed and non-revealed banks) and banks that did not borrow from theRFC (never borrowed banks). The darker shaded area between June 30, 1932 and December 31,1932 denotes the period the treatment group was revealed (August 22, 1932). The second lightershaded area denotes the period the control group was revealed (January 26, 1933).
Finally, Table 8 displays summary statistics comparing banks that never borrowed from
the RFC to non-revealed banks as of June 30, 1932, the last balance sheet date I observe
prior to the list being published on August 22, 1932. It shows that non-revealed banks and
never-borrowed banks are statistically different with regards to size, loans, and bonds. In
summary, banks that never borrowed from the RFC were fundamentally different from banks
that borrowed. This suggests that any analysis comparing the performance of banks that did
not borrow from their LOLR to banks that did borrow would suffer from a sample selection
42
bias.
Table 8: Summary Statistics for Never Borrowed BanksThis table presents summary statistics for the sample of non-revealed and never borrowed banksprior to the publication of the bank borrower list on August 22, 1932. There are 713 non-revealedbanks that borrowed from the RFC and 130 randomly selected banks that never approached fromthe RFC. The panel presents bank-level statistics as of June 30, 1932. The loans-and-discounts-to-assets ratio is the book value of the bank’s loans outstanding scaled by total assets. The bonds-and-securities-to-assets ratio is the book value of the bank’s bond portfolio scaled by total assets.The last ratio represents how each type of bank, on average, funds its assets adjusting for depositlevels.
Non-Revealed Banks (713) Never Borrowed (130)Obs. Mean Std. Dev Obs Mean Std. Dev Diff. in Means
total assets 713 4664.2 6348.3 130 15812 27079 ***log(total assets) 713 7.95 0.92 130 8.44 1.63 ***
DepositsAssets
713 0.69 0.12 130 0.72 0.23 *LoansAssets
713 0.65 0.15 130 0.58 0.17 ***BondAssets
713 0.34 0.14 130 0.42 0.17 ***Liabilities−Deposits
Assets713 0.31 0.12 130 0.28 0.22 ***
5.8 Further robustness
In the Online Appendix, I provide supplementary tables that present alternative specifica-
tions which test for stigma. First, Table A.4 in the Online Appendix shows that adding
bank-level loan variables controlling for the length of time since the bank had a loan autho-
rized, the number of times the bank approached the RFC for a loan, and the amount the bank
borrowed do not alter the effect of the disclosure on the deposits-to-assets ratio. Second,
Table A.5 in the Online Appendix illustrates that subtracting the bank’s total borrowings
from the RFC from its total assets to control for the change in the balance sheet after the
bank borrowed has little effect on the results. The effect of the revelation for revealed banks
after the list was published continues to be economically large and statistically significant.
43
Third, the results qualitatively hold when the outcome variable is changed to the log level of
deposits. Fourth, Table A.6 in the Online Appendix shows that revealed banks were quali-
tatively more likely to fail by June 30, 1933 compared to non-revealed banks but the effect
is mitigated due to the control group disclosure on January 26, 1933. Fifth, a condition of
the analysis is that the sample only consists of banks that chose to borrow from the LOLR.
It could be that the deposits-to-assets ratio for all RFC borrowers was decreasing at a rate
different to banks that never borrowed during this period. However, this is also a strength
of the analysis because it highlights the potential significance of the sample selection bias of
comparing banks that chose to borrow from the LOLR to banks that chose not to borrow.
Finally, because the deposits-to-assets ratio for non-revealed banks meets the levels of the
revealed banks after all RFC borrowers were revealed, it is difficult to raise a theory that
creates a drop in the deposits-to-assets ratio for the revealed banks compared to the non-
revealed banks, and then sees the ratios merge after the non-revealed banks were revealed,
that is uncorrelated with the August 22, 1932 list being published.
6 Managing the information environment: inter-city
comparisons
6.1 Empirical specification
Having found evidence of the stigma problem, I next ask how policy makers can use this in-
formation to improve the design of emergency lending facilities and manage the information
environment during a crisis to alleviate the problem. One possibility is to resolve a coordina-
44
tion problem between banks that results from banks reluctance to borrow from the discount
window (Armantier et al. (2014)). Because of stigma, a bank’s decision to borrow from the
LOLR involves predicting the actions of other banks. If a bank believes that other banks are
reluctant to borrow, its own belief in stigma is reinforced, and, as a result, the bank decides
not to borrow from the LOLR. To identify the optimal policy design to address this coordi-
nation problem, I must determine the conditions under which market participants did not
target withdrawals at revealed banks in comparison to non-revealed banks. Because banking
markets during the Great Depression were mostly defined by city boundaries (Heitield et al.
(2013)), I can compare depositor reactions between two types of defined banking markets,
i.e. cities, where the important variation between each type of city was how the informa-
tion about RFC borrowers was released. The first type of banking market includes cities
where nearly all banks were revealed to have borrowed from the LOLR, while the second
type includes cities where only few banks were revealed to have borrowed. If information
was revealed that nearly all banks in a city borrowed from the RFC, did depositors react
differently than depositors in cities where few banks in a city were revealed?
To test depositor reactions across cities, I create two dummy variables: I{Revealed ≤
1−x} and I{Revealed ≥ x} where x ε[ 50%, 100%] is the percentage of revealed banks within
a city as of June 30, 1932, prior to the publication of the list. I{Revealed ≤ 1 − x} equals
1 when the percentage of revealed banks within that city was beneath 1 − x. Similarly,
I{Revealed ≥ x} equals 1 when the percentage of revealed banks within that city was above
x. Using the nearly identical OLS regression from above, I now add two additional terms,
two triple interactions, that incorporate the percentage of revealed banks within a city. For
bank i in city c at half-year t:
45
Deposits
Assets i,t= α + β1 Revealedi × I{t = List} × I{Revealed ≤ 1− x}c
+β2 Revealedi × I{t = List} × I{Revealed ≥ x}c
+β3 Revealedi × I{t = List} + αRemaining Interaction Terms
+γXi,t−1 + ηt + δi + εi,t
(6)
β1 measures the change in the deposits-to-assets ratio following the publication of the
list for revealed banks in cities where the percentage of revealed banks was beneath 1 − x.
Similarly, β2 measures the change in the deposits-to-assets ratio following the publication of
the list in cities where the percentage of revealed banks exceeded x. Finally, the coefficient
β3 measures the change in the deposits-to-assets ratio following the publication of the list for
revealed banks compared to non-revealed banks relative to their controls.24 I also include
lagged control variables Xi,t−1, bank fixed effects δt, and time fixed effects ηt for the same
reasons explained in Section 5.1 although the results are robust to using fixed effects at the
city level.25 Standard errors are clustered at the bank level according to Bertrand et al.
(2004) and all continuous variables are winsorized at the 1% level to avoid outliers driving
the estimation results.
24Note that I do not include the remaining terms of Revealed × I{Revealed ≤ 1 − x}, Revealed ×I{Revealed ≥ x}, Dec. 31, 1932 and Revealed since they are not identified once I include half-year and bankfixed effects.
25The estimation results are also robust to including triple interaction terms for when the control groupis revealed.
46
6.2 Summary statistics
Table 9 displays summary statistics describing the distribution of revealed and non-revealed
banks across cities as a function of the total number of banks in each city as of June 30, 1932.
The percentage of revealed banks within a city equals the number of revealed banks divided
by the total number of banks in that city. The total number of banks within a city includes
the number of revealed banks, the number of non-revealed banks, and the number of banks
that never borrowed from the RFC. The 1,064 banks with loans authorized before August
22, 1932 were located in 817 cities where the average percentage of revealed banks within
a city was 22%. Although there were cities where all banks were revealed, those locations
were limited to cities with a single bank. Requiring a city to have at least two banks reduces
the maximum percentage of revealed banks within a city to 75%. The average percentage of
revealed banks within a city with at least two banks was 12%.
Table 9: Summary Statistics across CitiesThis table presents summary statistics describing the distribution of revealed and non-revealedbanks across cities as a function of the total number of banks in each city as of June 30, 1932 (priorto the publication of the list on August 22, 1932). The percentage of revealed banks within a cityequals the number of revealed banks in that city divided by the total number of banks in that city.The total number of banks within a city includes the number of revealed banks, the number ofnon-revealed banks, and the number of banks that never borrowed from the RFC. The 1,064 banksin the data (351 + 713) were in 817 cities.
As of June 30, 1932 % of revealed banks within a cityNo. of cities mean max
All cities 817 22% 100%Cities with 1 bank 252 53% 100%
Cities with at least 2 banks 565 12% 75%Cities with at least 3 banks 318 11% 75%Cities with at least 4 banks 204 10% 75%Cities with at least 5 banks 140 11% 60%
47
6.3 The effect of the August 22, 1932 list on deposits across cities
First, I consider depositor reactions in cities where all banks were revealed. According to
Table 9, cities where all banks were revealed were in cities with a single bank. Table 10
displays the DID estimates of comparing revealed banks to non-revealed banks in cities
with a single bank. The coefficient on Revealedi × I{t = List} is insignificant implying
no difference in the deposits-to-assets ratio for revealed banks compared to non-revealed
banks. Examining depositor reactions in cities with a single bank is analogous to examining
depositor reactions in cities where all banks were revealed. Unfortunately, it is difficult to
distinguish if depositors were processing information differently or if their actions resulted
from their inability to move their funds.
48
Table 10: Effect of the Published List on LOLR Borrower Deposits in Cities with1 BankThis table presents the DID estimates of the effect of the list published on August 22, 1932 onthe deposits-to-assets ratio in cities with a single bank. Revealed is a dummy that equals 1 if thebank was revealed on August 22, 1932 (revealed banks). There are 252 cities with a single bankwhere 143 revealed banks are in the treatment group and 109 non-revealed banks in the controlgroup.Revealedi × I{t = List} is a dummy equal to one for revealed banks after the August 22list was published, and zero otherwise. Revealedi × I{t = List + 1} is a dummy equal to onefor banks revealed after the retroactive January 26, 1933 list was published, and zero otherwise.Revealedi × I{t = List− 1} is a dummy equal to one for revealed banks before the August 22 listwas published, and zero otherwise. Xi,t−1 is a vector of controls identical to those used in Table3. Standard errors are clustered at the bank level and presented in parentheses. All continuousvariables are winsorized at the 1% level. ***, **, and * indicate significance at the 1%, 5%, and10% respectively.
deposits-to-assetsRevealedi × I{t = List} -0.0196
(0.0296)
Xi,t−1 YesBank Fixed Effects δi Yes
Half-Year Fixed Effects ηt Yes
Obs. 1361R2 0.6815
I address this concern in two ways. First, I only examine cities with a minimum of two
banks and I use a threshold of revelation. The benefit of using a threshold is that if some
banks in a city were revealed, then alternative banking options in that city were available
such as the US postal savings system (O’Hara and Easley (1979)). I start with x = 66%
although the results are robust to using alternate thresholds. Table 11 presents the DID
estimates of comparing the performance of revealed banks in cities where more than 66%
of banks were revealed and revealed banks in cities where fewer than 33% of banks were
revealed to non-revealed banks. Revealed banks, in cities where at least 66% of banks were
49
revealed, did not experience a drop in the deposits-to-assets ratio compared to non-revealed
banks (-0.0827 + 0.1148). In these cities, information was revealed simultaneously. However,
revealed banks, in cities where fewer than 33% of banks were revealed, experienced a drop
in the deposits-to-assets ratio of 6.89 percentage points compared to non-revealed banks (-
0.0827 + 0.0138). This translates to a 10% relative drop in the deposits-to-assets ratio with
respect to the pre-publication level. In these cities, information was revealed asymmetrically.
Moreover, from Table 11, we can also see that revealed banks, in cities where at least 66% of
banks were revealed, had higher deposit-to-assets ratios than revealed banks in other cities.
By construction, this signifies that deposit outflows in cities where nearly all banks were
revealed were less than deposit outflows in cities where few banks were revealed.26 This
implies that banks were worse off with respect to the deposits-to-assets ratio in cities where
few banks were publicly revealed. In these cities, the presence of stigma was larger, consistent
with the rationale behind banks’ reluctance to borrow from the discount window. Hence,
revealing nearly all bank identities simultaneously mitigated the effect of stigma.26If deposit outflows at revealed banks in cities where few banks were revealed were greater than deposit
outflows at revealed banks where nearly all banks were revealed, it must follow that the sum of depositoutflows across all banks in cities where few banks were revealed must be greater than the sum of depositoutflows across all banks in cities where nearly all banks were revealed.
50
Table 11: Effect of the Published List on LOLR Borrower Deposits in Cities with≥2 BanksThis table presents the DID estimates of the effect of the list published on August 22, 1932 on thedeposits-to-assets ratio across cities with at least two banks. Revealedi×I{t = List} × Revealed ≥66% is a dummy equal to one for revealed banks after the August 22 list was published in cities whereat least 66% of banks were revealed, and zero otherwise. Revealedi×I{t = List} ×Revealed ≤ 33%is a dummy equal to one for revealed banks in cities where fewer than 33% of banks were revealed,and zero otherwise. Revealed is a dummy that equals 1 if the bank was revealed on August 22,1932 (revealed banks). There are 351 revealed banks in the treatment group and 713 non-revealedbanks in the control group. Revealedi × I{t = List} is a dummy equal to one for revealed banksafter the August 22 list was published, and zero otherwise. Remaining interactions include alladditional interactions from the triple interactions. Xi,t−1 is a vector of controls identical to thoseused in Table 3. Standard errors are clustered at the bank level and presented in parentheses. Allcontinuous variables are winsorized at the 1% level. ***, **, and * indicate significance at the 1%,5%, and 10% respectively.
deposits-to-assetsRevealedi × I{t = List} × Revealed ≥ 66% .1148**
(0.0603)Revealedi × I{t = List} × Revealed ≤ 33% 0.0138
(0.0440)Revealedi × I{t = List} -0.0827***
(0.0301)
Remaining Interactions YesXi,t−1 Yes
Bank Fixed Effects δi YesHalf-Year Fixed Effects ηt Yes
Obs. 4942R2 0.6594
The potential mechanism explaining these results is the following. Depositors, in cities
where nearly all banks were revealed, were more likely to assume that the remaining non-
revealed banks in their city also borrowed from the RFC. Revealing nearly all banks was
suggestive of an aggregate adverse shock rather than an idiosyncratic shock to a specific bank.
On the other hand, depositors, in cities where few banks were revealed, were more likely to
51
interpret the news as reflective of each bank’s financial weakness rather than reflecting an
aggregate adverse shock. I make two assumptions for this mechanism to hold. First, assume
banks lent locally implying the default risk to their loan portfolios was limited to businesses
within the city of the bank’s location (Hautcoeur et al. (2013)). Second, assume that some
cities were hit harder than others during the Great Depression (Wicker (1996)). Table A.6
in the Online Appendix presents the DID estimates of the effect on the deposits-to-assets
ratio for alternative threshold levels and the results are robust for values of x ≥ 60% and
qualitatively robust for x = 50%. The effect is also qualitatively robust when increasing the
minimum number of banks within a city.
Finally, these results imply that emergency lending facilities that reveal information about
bank borrowers simultaneously can mitigate stigma. This can be achieved by providing a
coordination mechanism at the lending facility that allows banks to jointly request loans
simultaneously from the LOLR. In the case that identities are revealed, it is likely that more
than one bank identity would be simultaneously revealed, thus preventing an individual
bank from being targeted when approaching the LOLR for a loan. The design of the facility
implies that revealing information simultaneously (which can take the form of all banks
being revealed or no banks being revealed) dominates revealing information asymmetrically
(where few banks are revealed). Although the results do not explicitly show that keeping all
bank identities private dominated revealing all bank identities simultaneously, we can make
this inference based on the result that stigma exists and assumptions made in the existing
literature. Ideally, central banks should prevent the release of bank borrower identities during
a financial crisis but this may not be possible given the idiosyncratic leakage of discount
window loans during the recent crisis.27 Finally, these conclusions shed light on why banks27Armantier et al. (2014) identify some media articles that leaked discount window borrowing of large
banks and other idiosyncratic channels.
52
may have expected less stigma from TAF loans rather than loans from the discount window.
Because I show that the presence of stigma was worse for the aggregate system, from a policy
maker perspective, forcibly requiring banks to accept LOLR funding also alleviated stigma.
The public believed that borrowing from the LOLR was acceptable because it seemed that
many banks had chosen to borrow, solving the coordination problem between banks. The
stigma associated with receiving emergency loans was mitigated.
7 Ex-post bank behavior
Thus far, I have only considered the reactions of depositors to the August 22, 1932 publication
of the list. However, an aim of the central bank is to ease liquidity needs for solvent yet
illiquid banks during a crisis where, in turn, these banks may use the funding to maintain
loans to their consumers (Freixas et al. (1999)). Did banks behave differently after their
identities were revealed? Answering this question with these data, however, is difficult. The
lack of detail regarding a bank’s loan and bond portfolio makes it difficult to say precisely
how revealed banks adjusted their balance sheet in response to the withdrawal of deposits
after the list was published due to data limitations. For example, I do not observe if revealed
banks stopped issuing new loans after the publication of the list or if they wrote down the
value of existing loans.28 Nonetheless, in the Online Appendix, I examine the impact to28I can somewhat address this concern by exploring call reports from Federal Reserve member banks that
also borrowed from the RFC. Anecdotally, revealed banks reduced their real estate mortgages and calledin loans from other banks in response to depositors withdrawing their funds. Moreover, revealed banksincreased their bond portfolios by purchasing more US government securities and state or municipal bonds.A few also bought railroad bonds. However, it is difficult to be precise about how revealed banks modifiedtheir balance sheet after the publication of the list due to data limitations. I intend to address this limitationin future work.
53
loans and bonds at revealed banks compared to non-revealed banks but do not observe the
detailed compositions of loans/discounts or bonds/securities.
Table A.7 in the Online Appendix presents the results of the DID estimation of the
effect of the August 22, 1932 publication of the list on loans/discounts and bonds/securities
scaled by total assets. Column (1) shows a qualitatitive drop in the loans-and-discounts-to-
assets ratio (that the coefficient on Revealedi × I{t = List}is not statistically significant).
However, Column (2) presents the result for loans when the performance of revealed banks
is compared to the restricted control group. The loans-and-discounts-to-assets ratio dropped
by 4.2 percentage points at revealed banks compared to non-revealed banks. This translates
to a 6.2% relative drop in the loans-to-assets ratio with respect to the pre-publication level.
Columns (3) and (4) show the results of the DID estimation on bonds and securities.
The bonds-and-securities-to-assets ratio increased by 3-4 percentage points at revealed banks
compared to non-revealed banks after the publication of the list. The increase in the bonds-
and-securities-to-assets ratio translates to a 10-12.5% relative increase at revealed banks
compared to non-revealed banks with respect to the pre-publication level, a significantly
large increase.
8 Conclusion
In this paper, I provide evidence of the stigma problem. Using data from an unexpected
disclosure of a partial list of banks that secretly borrowed from the LOLR during the Great
Depression, I find evidence of the stigma problem in that depositors withdrew more deposits-
to-assets from banks included on the list in comparison to banks left off the list. This result
54
sheds light on why banks wish to avoid approaching the discount window. However, if the
identities of nearly all banks in a given banking market were simultaneously revealed to have
borrowed from the LOLR, the presence of stigma was mitigated. Symmetric information
revelation motivated depositors to interpret borrowing from the LOLR as reflecting an ag-
gregate negative shock rather than reflecting those banks’ financial weakness. Thus, cities
where information was revealed simultaneously were associated with lower deposit outflows
than cities where information was revealed asymmetrically. These conclusions provide ev-
idence that it is in the policy maker’s interest to prevent runs on banks associated with
stigma to support the financial system during a crisis. Revealing the identities of all banks
simultaneously dominates revealing the identities of few banks asymmetrically.
This paper helps to explain how the LOLR’s management of the information environment
can have strong adverse impacts on the health of the financial system. Designing emergency
lending facilities that allow banks to coordinate requests for LOLR loans will mitigate stigma,
solve the coordination problem between banks, and implicitly encourage banks to borrow
from the LOLR. Solving the stigma problem is crucial to effectively fight future financial
crises.
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