Jose M. Berrospide et al., Un-used Bank Capital Buffers and Credit Supply Shocks at SMEs during the Pandemic (2021)
Summary
In this Finance and Economics Discussion Series paper dated April 16, 2021, Federal Reserve Board economists Jose M. Berrospide, Arun Gupta and Matthew P. Seay ask whether banks curbed lending to creditworthy small and mid-sized firms during the pandemic. Using confidential loan-level data, they find that banks entering the pandemic with capital ratios close to their regulatory buffers reduced loan commitments to SME firms by an average of 1.4 percent more per quarter and were 4 percent more likely to end lending relationships than banks far from the buffers. The cuts fell hardest on private bank-dependent firms, young relationships and credit lines that matured at the start of the pandemic.
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Finance and Economics Discussion Series
Divisions of Research & Statistics and Monetary Affairs
Federal Reserve Board, Washington, D.C.
Un-used Bank Capital Buffers and Credit Supply Shocks at SMEs
during the Pandemic
Jose M. Berrospide, Arun Gupta, Matthew P. Seay
2021-043
Please cite this paper as:
Berrospide, Jose M., Arun Gupta, and Matthew P. Seay (2021). “Un-used Bank Capital
Buffers and Credit Supply Shocks at SMEs during the Pandemic,” Finance and Economics
Discussion Series 2021-043. Washington: Board of Governors of the Federal Reserve System,
https://doi.org/10.17016/FEDS.2021.043.
NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminary
materials circulated to stimulate discussion and critical comment. The analysis and conclusions set forth
are those of the authors and do not indicate concurrence by other members of the research staff or the
Board of Governors. References in publications to the Finance and Economics Discussion Series (other than
acknowledgement) should be cleared with the author(s) to protect the tentative character of these papers.
Un-used Bank Capital Buffers and Credit Supply Shocks at
SMEs during the Pandemic
Jose M. Berrospide, Arun Gupta, and Matthew P. Seay 1
April 16, 2021
Abstract
Did banks curb lending to creditworthy small and mid-sized enterprises (SME) during the COVID-19
pandemic? Sitting on top of minimum capital requirements, regulatory capital buffers introduced after the
2008 global financial crisis (GFC) are costly regions of “rainy day” equity capital designed to absorb
losses and provide lending capacity in a downturn. Using a novel set of confidential loan level data that
includes private SME firms, we show that “buffer-constrained” banks (those entering the pandemic with
capital ratios close to this regulatory buffer region) reduced loan commitments to SME firms by an
average of 1.4 percent more (quarterly) and were 4 percent more likely to end pre-existing lending
relationships during the pandemic as compared to “buffer-unconstrained” banks (those entering the
pandemic with capital ratios far from the regulatory capital buffer region). We further find heterogenous
effects across firms, as buffer-constrained banks disproportionately curtailed credit to three types of
borrowers: (1) private, bank-dependent SME firms, (2) firms whose lending relationships were relatively
young, and (3) firms whose pre-pandemic credit lines contractually matured at the start of the pandemic
(and thus were up for renegotiation). While the post-2008 period saw the rise of banking system capital to
historically high levels, these capital buffers went effectively unused during the pandemic. To the best of
our knowledge, our study is the first to: (1) empirically test the usability of these Basel III regulatory
buffers in a downturn, and (2) contribute a bank capital-based transmission channel to the literature
studying how the pandemic transmitted shocks to SME firms.
Keywords: Financial institutions, Capital Regulation, Procyclicality, COVID-19
JEL classification: G20, G21, G28
1
Federal Reserve Board (e-mail: jose.m.berrospide@frb.gov, arun.gupta@frb.gov, and matt.seay@frb.gov). The views expressed
in this paper are solely the responsibility of the authors and should not be interpreted as reflecting the views of the Federal
Reserve Board, its staff, or any other person associated with the Federal Reserve System. We are grateful to Matthew Land for
his excellent research assistance.
1. Introduction
“Since the onset of the pandemic, however, questions have arisen over banks' ability and
willingness to use the regulatory buffers available to them… in a period of stress, banks
might react with many of the same procyclical behaviors that we've seen in the past, such
as reigning back new business activity.” – S&P Global, June 11, 2020
“There has been a concern that the buffers were not being used and there was a
reluctance to use them.” - Andrea Enria, chair of the European Central Bank’s Single
Supervisory Mechanism, Financial Times, January 28, 2021
Regulatory reforms implemented after the 2008 global financial crisis (GFC) played a
central role in re-building banking system capital to the highest level in decades (nearly double
that of 2008). Despite increased capital in the banking system and significant government
support measures such as the Paycheck Protection Program (PPP), business lending during the
pandemic has remained weak. Much of the decline in business lending is attributable to loan
demand and credit quality concerns. Beyond these factors, however, a key question remains as
to whether banks used their large capital cushions built post-GFC to support lending to
creditworthy SME firms during the pandemic.
More specifically, our paper investigates a novel supply-side propagation channel related
to the “usability of regulatory capital buffers” and whether banks closer to their regulatory
capital buffers constrained credit on the margin to creditworthy SME firms during the COVID-
19 pandemic. Introduced as part of the Basel III capital reforms, regulatory capital buffers are
costly regions of “rainy day” equity capital that sit on top of minimum capital requirements, and
are designed by regulation to help absorb losses and support lending in a downturn. 2 In contrast
to minimum capital requirements, which are “hard” mandates that send a bank into resolution
when breached, regulatory capital buffers represent a “soft” mandate that limits the bank’s ability
to pay dividends and bonuses until its capital stock is rebuilt – these penalties act like a warning
signal that disincentivize the unnecessary use of buffers in normal times and allow banks time to
try to recover from unforeseen shocks.
2
As part of the Basel III capital reforms, the Basel Committee on Banking Supervision (BCBS) introduced a series of measures
to promote the buildup of regulatory capital buffers (i.e., the capital conservation buffer, the countercyclical capital buffer, and
the capital surcharge for global systemically important banks) in good times that can be drawn upon in periods of stress to
support new lending activity. See, “Consultative document: Strengthening the resilience of the banking sector,” BCBS, BIS,
December 2009. In the US implementation, the Federal Reserve introduced the stress capital buffer as a replacement for the
capital conservation buffer. Institutional details on the implementation of regulatory capital buffers in the U.S. are described in
Section 2.
At the onset of the pandemic, the Federal Reserve publicly encouraged banks to use these
buffers to support the economy during the downturn. 3 However, the prospect of large pandemic-
related losses during 2020 appear to have caused banks to take steps to reduce the likelihood that
they would have to operate near their regulatory buffers in an attempt to avoid incurring costs
associated with dipping into their regulatory buffers, despite elevated capital levels. 4 Revealed
preference appears to suggest that banks found these buffers as too costly to use. How close a
bank was to the regulatory buffer region prior to the unanticipated arrival of the pandemic can be
seen as a bank-specific measure of how binding the costs of the regulatory buffers were. Banks
that started the pandemic with a capital ratio relatively close to the regulatory buffer region are
referred to as “buffer-constrained” for ease of exposition. Consistent with the notion that banks
demonstrated a reluctance to use these buffers to absorb pandemic losses, we show that “buffer-
constrained” banks reduced loan commitments to SME firms by an average of 1.4 percent more
(quarterly) and were 4 percent more likely to end pre-existing lending relationships during the
pandemic than “buffer-unconstrained” banks.
[FIGURE 1]
As an illustrative preview, Figure 1 shows that a significant number of lending
relationships between buffer-constrained banks and private bank-dependent SME firms ended
during the pandemic (at least 2,500 relationships) as compared to that of buffer-unconstrained
banks, whose lending relationships remained relatively stable. 5 While part of these firms may
have exited due to pandemic-related bankruptcies or intentional deleveraging, the relative
difference between the two lines provides suggestive evidence that a sizeable number of SME
firms that originally banked with well-capitalized, albeit buffer-constrained, lenders may have
lost access to credit during the pandemic due to the potential un-usability of regulatory capital
buffers. 6
3 See https://www.federalreserve.gov/newsevents/pressreleases/monetary20200315b.htm for the official press release.
4 This regulatory issue expands beyond the case of the United States. In response to the concern that buffers were not be used, the
ECB even went as far as to provide pandemic capital relief by temporarily eliminating a significant portion of regulatory capital
buffers.
5 As described in Section 3, we define a firm to be SME if the firm size is less than the median firm size as of 2019:Q4, private if
it is privately-owned, and bank-dependent if it banks with a single lender.
6 This is covered more formally in our relationship exit specifications.
More formally, we utilize a novel set of confidential, supervisory loan-level data (FR Y-
14Q) between the largest U.S. banks and their corporate borrowers. 7 This granular data provides
us with a unique advantage to observe the lending outcomes at an important, yet under-studied
segment of the economy, namely, private bank-dependent SME firms. To overcome the
identification challenge for single-bank SME firms, we extend the Khwaja and Mian (2008)
approach and compare the lending of buffer-constrained versus buffer-unconstrained banks to
groups of similar borrower firms. Specifically, these borrowers are grouped by size-x-date,
geography-x-date, and industry-x-date. 8 Additionally, we utilize the granularity of this data to
explore a second question: did buffer-constrained banks curtail lending to certain types of firms
more than others during the pandemic (in other words, which firms were impacted most by the
banks’ procyclical response to regulatory capital buffers?) Firstly, we find that buffer-
constrained banks disproportionately curtail lending to private bank-dependent SME firms while
leaving their valuable relationships with large public (“core”) clients untouched. Secondly,
buffer-constrained lenders curb lending to firms with whom its lending relationship is relatively
young (less than the median relationship age of 6 years of less). This would be consistent with
the literature on relationship lending (Bharath et al. (2011)), which attributes a larger termination
cost associated with older relationships. Finally, we also find that buffer-constrained banks
contract credit to firms whose pre-pandemic credit lines contractually matured at the start of the
pandemic. It is less costly to cut lending to these firms from a legal and contractual standpoint
since the lender does not need to break any contractual terms of a pre-existing commitment to do
so, but rather can simply decline to fund a credit line that is up for renegotiation. This third
finding also provides additional robustness for the purpose of identifying credit supply shocks, as
the selection rule for these treatment firms comes from a pre-determined variable (i.e. the
maturity of a previous contract), which was made prior to the unanticipated arrival of the
pandemic.
New to the COVID-19 literature, our paper uncovers the presence of a transmission
channel emanating from regulatory capital buffer constraints that significantly affected SME
7 This data has a minimum loan reporting threshold of $1 million USD, which excludes small business loans (according to the
thresholds defined in Call Reports). Y-14 data excludes PPP loan balances.
8
More specifically, we follow Degryse, De Jonge, Jakovlievic, Mulier and Schepens (2019) and instead of the Firm*Time fixed
effects (which would absorb single-bank firms) in the Khwaja and Mian approach, we use Firm type*Time fixed effects. Firm-
type includes firms grouped by industry, location (e.g., zip code) and size.
firms during the pandemic. Complementing studies that document the performance of SMEs
during the pandemic, our paper establishes a supply-side (feedback) transmission channel that
likely contributed to these conditions. In this way, our study contributes a new bank capital
angle to an expanding literature that studies the various effects of the COVID-19 pandemic
shock on the condition of private SME firms. For example, Bloom et al. (2021) use survey data
on an opt-in panel of around 2,500 US small businesses to assess the impact of COVID-19 and
find a significant negative sales impact that peaked with an average loss of 29 percent in sales.
Of these, almost a quarter reported losses of more than 50 percent. In addition, they find these
impacts to be persistent, as firms reporting the largest sales drops in mid-2020 were still
forecasting large sales losses a year later in mid-2021. Bloom et al. (2020) estimates that
COVID-19 would reduce Total Factor Productivity (TFP) in the private sector by up to 5 percent
in 2020Q4, falling back to a 1 percent reduction in the medium term. Gourinchas et al. (2020)
estimate the impact of the COVID-19 crisis on business failures among SMEs in seventeen
countries using a large representative firm-level database. They estimate a large increase in the
failure rate of SMEs under COVID-19 of nearly 9 percentage points, absent government support.
Alekseev et al. (2020) use survey data collected via Facebook and find that about a quarter of
small businesses had access to financing from financial institutions, and most small businesses
were reliant on personal savings and informal sources of financing during the pandemic. Kapan
and Minoiu (2021) find that despite the unexpected surge in credit line drawdowns at the onset of
the COVID-19 pandemic, banks with significant exposures to credit lines tightened their lending
standards and cut their C&I loan supply to small businesses. The authors interpret this response
as the result of a reduction in banks’ risk tolerance rather than balance sheet (liquidity and
capital) constraints. Using loan-level data from FR Y-14 as we do, Chodorow-Reich et al (2020)
document that, unlike large firms, SMEs take loans of shorter maturity, have less active maturity
management, post more collateral, pay higher spreads, and have higher utilization rates. These
facts, in their view, explain why during the pandemic SMEs did not draw down their credit lines
as much as large firms did, though PPP loans helped alleviate their curtailed access to bank
credit. Along these lines, Strahan and Li (2021) analyze the bank supply of credit under the
emergency PPP lending program and conclude that PPP loans reflect a benefit of bank
relationships as they facilitate firms’ access to government-subsidized lending that help alleviate
the rise in unemployment. Our results are consistent and complementary to the findings in these
papers, and cover a broader class of firms (those with young lending relationships as well as
credit lines maturing at the start of the pandemic). In addition, our paper contributes a novel bank
capital-based transmission channel that affected firms during the pandemic due to the procyclical
lending response of buffer-constrained banks, enabling the discussion of policy remedies.
In relation to the literature studying the credit impacts of “hard-mandate” capital
requirements (Kashyap, Stein, and Hanson, 2010; Basel Committee on Banking Supervision,
2010; Acharya, Engle, Richardson, 2012; Admati, et al., 2013; Baker and Wurgler, 2015; Sarin
and Summers, 2016; Aiyar et al., 2014; Hanson, Kashyap and Stein, 2011; Greenwood et al.,
2017; Financial Stability Board, 2020), relatively little is known about the effects of new Basel
III “soft mandate” policy tools, such as regulatory capital buffers, particularly during downturns
like the pandemic. 9 This literature can be categorized into two sets. The first set of papers
present evidence on pre-Basel III changes in capital regulation and unequivocally finds that
higher regulatory requirements reduce bank lending. Jiménez et al. (2017) studies bank lending
responses to dynamic provisioning experiments in Spain and find that countercyclical regulatory
capital buffers help to smooth credit cycles. Gropp et al. (2018) provide evidence for a similar
lending response in European banking data to the 2011 EBA capital exercise, showing that large
European banks required to maintain a higher capital ratio in the 2011 capital exercise responded
by reducing total asset size, while keeping equity capital and asset risk constant. Behn,
Haselmann and Wachtel (2016) and Fraisse, Lé, and Thesmar (2020) use German and French
loan-level data, respectively, to show that banks are more likely to cut lending when capital
charges on loans, under Basel II rules, increase. Meanwhile, the second set of papers based on
US loan-level data explore the impact of Basel III regulatory capital buffers on lending outcomes
during normal times. Specifically, Berrospide and Edge (2019) find that the introduction of
regulatory capital buffers emanating from stress test disclosures led to a lower growth in C&I
loan commitments, while Favara, Rezende, and Ivanov (2021) find that time variation in GSIB
surcharge regulatory buffers result in significant declines in C&I loan commitments by GSIBs as
well as broken lending relationships. As both of these papers concentrate on pre-pandemic boom
periods, they both contribute the important finding that soft mandate Basel III regulatory capital
9
Minimum requirements are “hard” mandates that send a bank into resolution when breached. Regulatory capital buffers, on the
other hand, are “soft” requirements that allow banks time to try to recover. If the buffer is breached, the bank’s ability to pay
dividends and bonuses is restricted until its capital stock is rebuilt.
buffers did in fact play a key role in getting bank system capital to the historically high levels
prior to the arrival of the pandemic. Our paper can be seen as a combination of both categories,
as it is the first to empirically test whether the Basel III regulatory buffers were in fact usable
during a (pandemic) downturn. We find evidence pointing to procyclical impacts of regulatory
capital buffers during the pandemic downturn, particularly on private bank-dependent SMEs and
other non-core firms for which it was relatively cheap to cut lending to.
Finally, our results also point to a different interpretation of the Basel III regulatory
capital buffers. Rather than seeing the buffers as a cushion to be drawn upon during a downturn,
as originally intended by Basel III, banks seem to be treating the regulatory buffers as additional
minimum requirements. Relatedly, and in a complementary fashion, the IMF’s Global Financial
Stability Report (GFSR), in April 2021, addresses the usability of capital buffers and documents
that, despite the vital role of capital buffers to ensure continued supply of credit to the real
economy, banks remain reluctant to drawn down their buffers. 10 Using a sample of 72 large
global banks, representing 60 percent of the global banking system’s aggregate market
capitalization, the report finds that only banks accounting for 5 percent of market capitalization
clear the hurdles to use their buffers. Thus, banks seem to lack the economic incentives to dip
into their capital buffers as regulation will require them to rebuild their buffers later. Low
returns could make the usability of buffers a costly option if the additional value generated by the
new lending does not offset the negative impact from the capital shortfall resulting from using
the buffers in the first place. Using banks’ balance sheet data and a different methodology, our
paper provides evidence of additional reasons for the reluctance of banks to use their capital
buffers. Beyond the costs associated with the need to rebuild their buffers in the GFSR, we
estimate that the costs of using the buffers associated with credit downgrades and dividend cuts
are relatively large (approximately 300 basis points in a 3-day window during stressful times)
and thus may have prevented banks to use their regulatory capital buffers at the onset of the
COVID-19 crisis.
The paper is organized as follows. Section 2 provides institutional background on
regulatory capital buffers. Section 3 describes the data, empirical strategy, and summary
statistics. Section 4 presents the results. Finally, Section 5 concludes.
10
See, Chapter 1, “An Asynchronous and Divergent Recovery may put Financial Stability at Risk,” pages 22-25.
2. Capital Ratios and Basel III Regulatory Capital Buffers
This section outlines some background on the Common Equity Tier 1 (CET1) capital ratio and
the regulatory capital buffers, as implemented in the U.S. via Basel III. Bank CET1 capital ratios
can be split into three parts:
CET1 Capital Ratio = Minimum Requirement + Regulatory Capital Buffers + Excess Cushion
• A regulatory minimum requirement to prevent undercapitalization. Following the Basel III
capital rules, this is 4.5 percent for all banks and marks the (“hard” mandate) threshold below
which a bank would be deemed insolvent by regulators. If a bank enters this regime, solvency
procedures would be set in motion. 11
• Basel III regulatory capital buffers, such as the Global Systemically Important Bank (GSIB)
surcharge and the stress capital buffer. These are costly regions of “rainy day” capital that
come with payout and bonus restrictions (“soft” mandate), apply only to the largest (GSIB)
banks, and typically range from 2.5 – 7.0 percent, depending on the bank’s risk profile. These
buffers are designed to provide added resilience to absorb bank losses in the event of a stress
scenario.
• Excess cushions, which reflect the CET1 capital ratio level of bank capital in excess of
regulatory minimums plus regulatory buffers. For most large firms, this cushion is typically 3
percent or less. This excess cushion approximates the amount of capital that banks could lose
without facing potential payout restrictions or shrinking their balance sheet in order to become
compliant.
For illustrative purposes, Figure 2 depicts a hypothetical bank with a starting CET1 capital ratio
of 12 percent. The bank’s capital ratio is decomposed into a 4.5 percent Basel III minimum
11 Several papers provide theoretical rationale for why banks find it optimal to maintain an equilibrium level of capital in excess
of regulatory minimum requirements. Using a dynamic equilibrium model of relationship lending in which banks are unable to
access the equity markets every period and the business cycle determines loans' probabilities of default, Repullo and Suarez
(2013) show that banks hold endogenous capital buffers as a precaution against shocks that impair their future lending capacity.
Koch et al. (2016) compare optimal capital structure prior to the Great Depression, when no government guarantees existed,
versus that of the Great Recession, and suggest that market discipline would have induced the largest US banks to maintain
higher capital buffers prior to the 2008 crisis. Baron (2020) further provides support for the case of strengthening countercyclical
capital buffers since government guarantees can distort the incentives of banks to raise new equity and affect the dynamics of
bank capital structure over the credit cycle. Milne and Whalley (2001) suggests that banks like to keep a capital buffer above
minimum requirements, since they fear regulatory discipline from breaching such requirements. This means that changes in
capital that occur well above the requirement should trigger less of a procyclical cutback in the provision of credit to the
economy than changes that bring the level of capital close to the minimum. In line with these findings, Nier and Zicchino (2008)
provide evidence that losses lead to greater pull-back in lending for banks at a lower initial level of capital.
requirement, a 5.5 percent regulatory buffer representing the combination of the stress capital
buffer and GSIB surcharge, and an additional 2 percent cushion. As the bank’s CET1 capital
ratio declines due to the arrival of pandemic losses (downward sloping blue line), the right panel
of Figure 2 (in red) highlights an important choice the bank has to make on their lending
decisions. Specifically, the bank has two options:
• Option A: Shrink (e.g., by constraining credit) in order to remain above the regulatory
buffer. This saves the bank all costs associated with entering this buffer (payout
restrictions, bonus restrictions, etc.).
• Option B: Utilize the regulatory buffers to absorb pandemic losses and continue
supporting creditworthy firms through the provision of credit.
[FIGURE 2]
3. Empirical Approach
a. Data Description and Summary Statistics
To perform our regression analysis, we access novel loan-level information on C&I credit
lines (at the bank-firm-quarter level) sourced from the H1 Corporate Schedule of the confidential
regulatory filing FR-Y14Q, and combine this with quarterly consolidated bank balance sheet
level information at the BHC level from the regulatory filings FR Y-9C. The main balance sheet
variable of interest that separates the set of treatment and control firms in our baseline
specification is the lender’s pre-pandemic distance to the regulatory buffer (as of 2019Q4). This
is equivalent to the size of the green excess capital cushion from Figure 2. As will be elaborated
in the next section, we define a bank as being constrained by the regulatory buffer (“buffer-
constrained”) if the distance between its CET1 capital ratio and its regulatory buffer threshold is
equal to or less than that of the median bank (2.13 percent). In other words, we posit that if a
bank enters the pandemic with a relatively small cushion to absorb pandemic losses before
having to dip into its regulatory buffers (and thereby incur a variety of regulatory costs), that
bank may choose to curtail credit in order to avoid incurring any costs from regulatory buffer
usage – a less-than-desirable outcome given that the absolute level of CET1 is at historical highs,
and yet would go un-used.
The FR Y-14 Corporate Schedule is collected for the very large BHCs that participate in
the CCAR stress tests. While there are over 30 such BHCs that file, we exclude the filings of
foreign bank intermediate holding companies (IHCs), since the capital of the IHCs are internal to
the organization and thus driven by different incentives. In addition, we drop any BHCs that do
not report the FR Y-14 data during the pandemic, or those that have too little C&I loan exposure
(i.e. custodian banks). Additionally, to keep the focus on lending outcomes at nonfinancial
corporations, we exclude C&I loans to U.S. and foreign banks, other depository financial
institutions, non-depository financial institutions, and loans to financial agricultural production
and other loans to farmers. This leaves us with quarterly loan information for 16 domestic U.S.
BHCs and about 54 thousand firms between 2018Q1 and 2020Q3. The data in the FR Y-14
Corporate Schedule includes loan information at the credit facility level for loan committed
balances greater than or equal to $1 million. 12 The advantage of using loan commitments is that
they include both undrawn and drawn portions of credit facilities. This measure of commitments
(rather than on-balance sheet outstanding loan amounts) is immune to demand-driven swings in
credit line drawdowns and repayments and is thus closer to the idea of bank credit supply
decisions, compared to other studies that use outstanding loan amounts.
Table 1 provides summary statistics at the bank-firm-time level for the control variables
in our analysis. C&I commitments have grown on average at a quarterly rate of 1.36 percent at
the bank-firm level. The average CET1 capital ratio is 11.12 percent, which is nearly double the
average level of bank capital during the 2008 financial crisis. The average reporting bank
primarily funds its assets through deposit funding (65 percent), holds a sizeable amount of
illiquid assets on its books (31 percent), and has maintained a quarterly return on assets of about
28 basis points.
One appeal of the FR-Y14 data set is that it includes a wide range of firms; that is, small
and large firms and publicly traded and private firms. Our use of the FR Y-14 C&I loan level
data is quite novel as this is the closest data that we have in the United States to credit registry
data used in the prior literature – see, for example, Jimenez, et al. (2017) for banks in Spain.
[TABLE 1]
For this reason, FR Y-14 does not capture very small business lending (<$1 million USD), and instead captures
12
SME as well as large public firms.
Figure 3 plots the relationship between the size of pre-pandemic distance to the
regulatory buffer, measured as of 2019Q4, versus the subsequent growth in C&I loan
commitments during the pandemic period. The figure shows that ex-post commitment growth
during the pandemic was weaker among banks that were ex-ante buffer-constrained, a.k.a. those
that entered the pandemic with CET1 capital ratios closer to the regulatory buffer. 13
[FIGURE 3]
Next, we plot time trends by comparing C&I commitment growth rates across buffer-
constrained and -unconstrained banks. Suggestive of parallel trends, Figure 4 shows the average
commitment growth rates before and after the pandemic for firms that borrow from buffer-
constrained lenders (red) versus buffer-unconstrained lenders (blue). As shown in the picture,
overall C&I commitment growth rates declined significantly after the pandemic, that is, from
2019Q4 to 2020Q3. The contraction was more severe for buffer-constrained banks (-3
percentage points) than for buffer-unconstrained banks (-1.86 percentage points).
[FIGURE 4]
b. Regression specifications
While using consolidated bank balance sheet data is less suitable for disentangling credit
supply from credit demand, to overcome this issue, we use loan-level data on C&I credit lines.
Considering that the bulk of firms in FR Y-14 data borrow from a single bank, we extend the
Khwaja and Mian (2008) approach to compare the lending of buffer-constrained versus buffer-
unconstrained banks to groups of similar borrowing firms that are likely to experience common
demand shocks (Figure 5). Specifically, our identification strategy follows Degryse, De Jonge,
Jakovlievic, Mulier and Schepens (2019) and replaces the firm*time fixed effects with firm
type*time fixed effects. Firm type includes firms grouped by industry, location (e.g., zip code)
and 10 size bins. These firm type*time fixed effects allow us to control for demand shocks that
are common to firms in the same group. Moreover, we add firm*bank fixed effects to control for
any unobserved characteristics within that specific bank-firm lending relationship. As additional
13 Please refer to Appendix for further analysis showing that this relation cannot be explained by plotting the pre-pandemic level
of the CET1 ratio versus the pandemic commitment growth. Counter to intuition, excess capital cushions are not positively
correlated with CET1 ratios.
robustness for the purpose of identifying credit supply shocks, we perform a specification that
compares lending outcomes of buffer-constrained banks for firms whose pre-pandemic credit
lines contractually matured at the start of the pandemic – the selection rule for assigning
treatment comes from a pre-determined variable (i.e. the maturity of a previous contract), which
was made prior to the unexpected arrival of the pandemic and thus unlikely to be correlated with
demand shocks in the pandemic.
[FIGURE 5]
i. Commitment Growth to Existing Borrowers
Our first set of specifications study bank response along the intensive margin. We
categorize banks as either buffer-constrained and buffer-unconstrained using a dummy variable
𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵, which takes the value of 1 for banks that had CET1 capital ratios
close to the regulatory buffer right before the onset of the pandemic and 0 for those that had
CET1 capital ratios far from it. This threshold is based on whether this distance is above or
below the median distance (2.13 percent) for CCAR banks as of 2019Q4. Equation (1) below
presents our intensive margin specification:
∆𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑏𝑏𝑏𝑏𝑏𝑏
(1)
𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑏𝑏𝑏𝑏,𝑡𝑡−1
= 𝛽𝛽0 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑡𝑡 + 𝛽𝛽1 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑏𝑏,2019𝑞𝑞4 + 𝛽𝛽2 𝜃𝜃 + ⋯ + 𝛽𝛽3 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑡𝑡
∗ 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑏𝑏,2019𝑞𝑞4 ∗ 𝜃𝜃 + 𝛽𝛽𝐵𝐵 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑏𝑏,𝑡𝑡−1
+ 𝛽𝛽𝐹𝐹 𝐹𝐹𝑖𝑖𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑓𝑓,𝑡𝑡−1 + 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑏𝑏,𝑓𝑓,𝑡𝑡−1 + 𝜀𝜀𝑏𝑏𝑏𝑏𝑏𝑏
where the “…” includes all pairwise interactions between the three interacting variables.
∆𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑏𝑏𝑏𝑏𝑏𝑏
is the quarterly growth rate in commitments from bank b to firm f at time t.
𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑏𝑏𝑏𝑏,𝑡𝑡−1
POST is a dummy variable that equals 1 starting 2020Q1 or later (pandemic period). For
regression tables 2-5, 𝜃𝜃 takes on each respective element of the following set:
𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑓𝑓,2019𝑞𝑞4 , 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑓𝑓,2019𝑞𝑞4 , 𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌ℎ𝑖𝑖𝑖𝑖𝑏𝑏,𝑓𝑓,2019𝑞𝑞4 ,
, where
𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑏𝑏,𝑓𝑓
• 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑓𝑓,2019𝑞𝑞4 is a dummy variable that equals 1 for all firms f smaller (in total assets)
than the median firm size as of 2019Q4.
• 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑓𝑓,2019𝑞𝑞4 is a dummy variable that equals 1 for all firms f that are
private, SME, and only banked with a single lender as of 2019Q4.
• 𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌ℎ𝑖𝑖𝑖𝑖𝑏𝑏,𝑓𝑓,2019𝑞𝑞4 is a dummy variable that equals 1 for all firms f that have
maintained a lending relationship with their bank b for less than or equal to the median
relationship age (6 years), as of 2019Q4.
• 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑏𝑏,𝑓𝑓 is a dummy variable that equals 1 for all firms f
whose prior credit line with bank b is set to contractually mature as of 2020Q2.
𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑏𝑏,𝑡𝑡−1 include 𝐶𝐶𝐶𝐶𝐶𝐶1𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑏𝑏,𝑡𝑡−1 , 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑏𝑏,𝑡𝑡−1 ,
∆𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑏𝑏,𝑡𝑡−1 , 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑏𝑏,𝑡𝑡−1 , 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑏𝑏,𝑡𝑡−1 , 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑏𝑏,𝑡𝑡−1 ,
𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑒𝑒𝑡𝑡𝑡𝑡ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑏𝑏,𝑡𝑡−1 , 𝑅𝑅𝑅𝑅𝑅𝑅𝑏𝑏,𝑡𝑡−1 , while 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑓𝑓,𝑡𝑡−1 include
𝐿𝐿𝐿𝐿(𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆)𝑓𝑓,𝑡𝑡−1 , 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑓𝑓,𝑡𝑡−1 , 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑅𝑅𝑅𝑅𝑅𝑅𝑓𝑓,𝑡𝑡−1 , 𝑎𝑎𝑎𝑎𝑎𝑎 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑓𝑓,𝑡𝑡−1 . Note
that 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑛𝑛𝑛𝑛𝑏𝑏,𝑓𝑓,𝑡𝑡−1 are the confidential internal ratings that bank b assigns to a
particular loan between itself and firm f at time t-1. This variable provides a way to control for
borrower risk.
According to our hypothesis, for the triple difference-in-differences specifications, we expect a
negative coefficient on the triple interaction term 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑡𝑡 *𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑏𝑏,2019𝑞𝑞4 * 𝜃𝜃.
These signs would be consistent with our prediction that buffer-constrained banks curb
commitments disproportionately to firms with particular characteristics: private bank-dependent
SMEs, those with relatively young lending relationships, and those whose pre-existing credit
lines are coming due at the start of the pandemic.
ii. Terminated Lending Relationships
Section 3.b.i captures any changes in commitment growth between a buffer-constrained
bank b and firm f assuming the bank does not reduce commitments to 0 (e.g. relationship
termination). On the other hand, our second set of specifications (equation 2) in this Section
study the bank’s decision to terminate pre-existing lending relationships. Our choice of a linear
rather than nonlinear model is motivated by two factors. First, nonlinear models tend to produce
biased estimates in panel data sets that have many fixed effects, leading to an incidental
parameters problem and inconsistent estimates. Second, nonlinear fixed effects models generate
biased estimates for interaction terms (Ai and Norton, 2003), the main coefficients of interest.
Therefore, following the recommendation of the econometrics literature (Wooldridge, 2002), we
estimate a linear model of relationship termination decisions.
(2) 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇ℎ𝑖𝑖𝑖𝑖[0/1]𝑏𝑏,𝑓𝑓,𝑡𝑡+1
= 𝛽𝛽0 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑡𝑡 + 𝛽𝛽1 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑏𝑏,2019𝑞𝑞4 + 𝛽𝛽2 𝜃𝜃 + ⋯ + 𝛽𝛽3 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑡𝑡
∗ 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑏𝑏,2019𝑞𝑞4 ∗ 𝜃𝜃 + 𝛽𝛽𝐹𝐹 𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝑓𝑓,𝑡𝑡
+ 𝛽𝛽𝑋𝑋 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑏𝑏,𝑡𝑡 + 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑏𝑏,𝑓𝑓,𝑡𝑡−1 + 𝜀𝜀𝑏𝑏𝑏𝑏𝑏𝑏
where 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅ℎ𝑖𝑖𝑖𝑖[0/1]𝑏𝑏,𝑓𝑓,𝑡𝑡+1 is a [0/1] dummy that indicates whether the bank
ends its relationship with firm f in the following period. This is determined by observing if the
bank reports an observation (in the FR Y-14Q) for this same bank b – firm f pair in the following
quarter. Thus, the interpretation of any given coefficient would be the impact of that particular
right-hand side variable on the probability that the bank ends this relationship in the following
quarter. All other variable definitions are the same as in Section 3.b.i.
According to our hypothesis, we expect a positive coefficient on the double interaction
term 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑡𝑡 *𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑏𝑏,2019𝑞𝑞4 (column 1 of Table 5). This would be consistent
with the idea that buffer-constrained banks are more likely to end pre-existing relationships
during the pandemic. Furthermore, for the triple difference-in-differences specifications, we
expect a positive coefficient on the triple interaction terms
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑡𝑡 *𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑏𝑏,2019𝑞𝑞4 * 𝜃𝜃. These signs would be consistent with our
prediction in terms of heterogeneous firm effects, i.e. the idea that buffer-constrained banks
disproportionately terminate pre-existing relationships with firms that have particular
characteristics: private bank-dependent SMEs, those with relatively young lending relationships,
and those whose pre-existing credit lines are maturing at the start of the pandemic.
4. Results
Tables 2-5 shows the regression estimates for the intensive margin specifications
(equation 1), where each column gradually add on bank controls and firm controls. Column 1 of
Table 2 has a baseline difference-in-differences estimate that shows a negative and significant
impact of BufferConstrainedBank on the growth of committed amounts of C&I loans for all
borrower firms. Columns 2 through 4 show economically and statistically significant
heterogeneous effects across firm size. Specifically, banks that entered the pandemic as buffer-
constrained disproportionately curtail lending to SME firms rather than large firms, i.e. roughly
1.4 percentage point quarterly reduction to SME firms during the pandemic. These magnitudes
are economically meaningful, given that the average growth rate of a utilized loan is 1.3 percent
per quarter as reported in Table 1. Note that these specifications control for the absolute level of
CET1 capital. 14 This would be consistent with the notion that since bank insolvency was not a
concern during the pandemic, banks were managing their capital ratios with respect to the
regulatory buffer threshold (4.5 percent minimum requirement plus regulatory buffers, e.g. the
orange plus blue regions in Figure 2), as opposed to the insolvency threshold (4.5 percent
minimum requirement, e.g. orange region in Figure 2).
Table 3 explores the impact on all SME borrowers that are also private and bank-
dependent (e.g. firms that bank with only one FR Y-14Q lender), and finds that the effect on the
triple interaction POST*BufferConstrainedBank*PrivBankdepSMEFirm has similar magnitude
of about -1.39 percentage point lower C&I commitment growth and is statistically significant.
Thus, buffer-constrained banks appear to be curtailing credit more to firms that do not have the
ability to easily substitute toward other forms of financing. Additionally, Table 6 shows
evidence that buffer-constrained banks are 4.6 percent more likely to terminate relationships with
private, bank-dependent SME firms during the pandemic.
14
In appendix A we show plots with evidence that buffer-constrained banks tend to have higher CET1 capital ratios,
implying that our regression results are not explained by the absolute level of capital.
Our results point to concerns about potential delays in the economic recovery following
the peak of the pandemic, as bank-dependent firms incur higher costs when their lender
relationship is terminated than do large firms. Notice that the double interaction coefficient is
small and statistically insignificant. This result is equally informative as it indicates that buffer-
constrained banks did not curb credit to large borrowers, which would be consistent with the
notion that banks will protect relationships with large public borrowers as they are more valuable
(e.g. multi-line products that the bank services for these firms). These findings are corroborated
in Figure 6, which shows the number of firms entering new relationships and exiting old ones
with each our two groups of banks, respectively. The top panel shows that, during the pandemic,
buffer-constrained banks appear to end more pre-existing relationships and start fewer new
relationships with private, bank-dependent SME firms than do buffer-unconstrained banks. The
bottom panel of Figure 6 shows that neither bank type appears to be making net changes to their
number of large borrowers, as would be expected.
Tables 4 and 7 provide estimates for credit supply adjustments along the intensive and
exit margins for borrowers whose lending relationships are relatively young. We define a
lending relationship as relatively young if its age is below the median relationship age for all
bank-firm pairs in the Y-14 sample (6 years or less). Along the intensive margin, Table 4 shows
that buffer-constrained banks reduce C&I commitment growth to young relationship firms by
roughly 1.2 percentage points quarterly during the pandemic. Meanwhile, Table 7 shows that
buffer-constrained banks are 0.8 percent more likely to end a pre-existing lending relationship if
that relationship qualifies as young. This result is consistent with the idea that ending younger
relationships is less costly to banks as compared to older relationships.
Finally, Tables 5 and 8 explore the set of firms who have credit lines that was originated
prior to the pandemic, namely, that contractually mature in the first major quarter of the
pandemic, 2020Q2. These are the set of firms for which it is least costly (legally and
contractually speaking) for a buffer-constrained bank to cut lending to, since the bank does not
need to break any terms of the pre-existing contract or wait for any covenants to be violated.
The bank can simply decline to renew during the contract renegotiation and allow the exposure
to roll off its books. Table 5 shows that buffer-constrained banks reduced C&I commitment
growth to these firms by 2.3 percentage points during the pandemic. Note that this effect is
economically significant. In addition to this, buffer-constrained banks are 3 percent more likely
to end relationships with those firms. Note also that this finding provides additional robustness
for the purpose of identifying credit supply shocks since the selection rule for this treatment
group of firms comes from a pre-determined variable (e.g. the contractual maturity of a pre-
pandemic credit line contract), which was determined prior to the unexpected arrival of the
pandemic downturn. This finding strongly suggests the presence of credit supply effects, as it
would be difficult to explain this result using a demand-side story. Our analysis shows that this
supply side effect expands to firms beyond just SMEs. Specifically, any firm with a young
lending relationship or loans maturing at the start of the pandemic qualify as less costly options
for buffer-constrained banks to curtail lending to in order to preserve bank capital.
In summary, we find evidence that buffer-constrained banks cut lending (and terminate
lending relationships) disproportionately with private bank-dependent SME firms, young
relationship firms, and firms whose prior credit lines were set to mature at the start of the
pandemic. Altogether, these findings are consistent with the idea that a buffer-constrained bank
optimizes how best to curtail credit by choosing firms for which it is least costly to trim lending
to or end relationships with.
iii. Why banks might find using regulatory buffers expensive?
As described in the introduction and Section 2, regulatory capital buffers are soft mandate
requirements, where banks are allowed to dip into but will incur penalties for doing so – in
particular, Basel III regulatory buffer usage means banks face market stigma when facing
regulatory restrictions on payout restrictions as well as . None of the prior studies in the literature
of regulatory buffers (Favara et al., 2021, Berrospide et al., 2019, Jimenez et al, 2017, Gropp et
al., 2018, Behn et al., 2016, Fraisse et al., 2020) explores which costs associated with the use of
the capital buffers is most binding for banks. While this task is also not empirically possible to
pin down in the context of the pandemic either, below we provide some background on the
historical costs of two possible causes for banks to avoid using their regulatory buffers,
particularly during the 2008 financial crisis, namely, the possibility of a credit rating downgrade,
and the associated limitations on capital distributions (e.g. dividend payments and share
repurchases).
Banks that dip into their regulatory capital buffers may fear potential risks associated
with credit ratings downgrades. For example, during April 2020, Moody’s released a statement
that global investment banks are expected to maintain solid capital buffers at or above 2019
levels. To test the impact of a potential downgrade under stress, we use Moodys and S&P ratings
downgrades data from 1990 – 2020 and daily stock price data, to conduct an event study using a
Fama French 3 factor model. For each event i, we estimate coefficients for the Fama French 3
factor model in a 120-day estimation window (130 days before to 10 days before event).
𝑅𝑅𝑖𝑖𝑖𝑖 = 𝛽𝛽𝑖𝑖 + 𝛾𝛾𝑖𝑖𝑖𝑖 (𝑀𝑀𝑀𝑀𝑀𝑀 − 𝑅𝑅𝑅𝑅)𝑡𝑡 + 𝛼𝛼2 𝐻𝐻𝐻𝐻𝐻𝐻𝑡𝑡 + 𝜏𝜏3 𝑆𝑆𝑆𝑆𝑆𝑆𝑡𝑡 + 𝜀𝜀𝑖𝑖𝑖𝑖
We then use these coefficients to extract the abnormal stock return of bank i using a (-1,1) 3-day
event window around the ratings downgrade. We find that credit rating downgrades are
associated with negative abnormal stock returns for banks, and the magnitude of the decline is
more severe during stress events such as the Global Financial Crisis.
Ratings (-1,1) CAR percent
Downgrade
Events
All 122 -1.29 percent***
Normal Times 73 -0.43 percent
GFC Crisis 48 -2.65 percent***
Firms that breach regulatory capital buffers may face limitations on capital distributions
imposed by regulators. 15 We conduct a second event study, using dividend cuts from 1990-
present, and employing a framework similar to that described in the previous section. Overall,
we find that dividend cuts reduce cumulative abnormal returns about 2 percent during the 3-day
event window. The effects are larger when firms are under stress.
Events (-1,1) CAR percent
ALL 42 -2.34 percent**
Normal Times 12 -1.07 percent
GFC Crisis 28 -2.88 percent**
15
For example, Capital conservation buffer, countercyclical capital buffer amount, and GSIB surcharge, 12 C.F.R.
§ 217.11.
In both cases, the costs associated with credit downgrades and dividend cuts during the GFC are
relatively similar and close to 300 basis points during the 3-day event window. Despite the
potential caveats associated with the limited number of these events, our estimates seem
statistically and economically significant and point to the magnitude of the costs banks may fear
they could have faced had they used their regulatory capital buffers at the onset of the COVID-
19 crisis. As discussed above, our cost estimates for potential market penalties associated with
the use of capital buffers add to the costs estimates provided by the IMF’s GFSR associated with
the need to rebuild the buffers if they were to be used in the first place.
5. Conclusion
Sitting on top of minimum capital requirements, regulatory capital buffers introduced
after the 2008 financial crisis are costly regions of “rainy day” equity capital designed to allow
banks to absorb losses and support the economy through lending in a downturn. Although the
implementation of these Basel III regulatory buffers played a key role in helping build banking
system capital (nearly double that of 2008), it appears this stockpile of capital went effectively
unused during the pandemic. Our results suggest that banks were reluctant to use their
regulatory buffers to absorb pandemic losses, and instead curtailed lending to SME firms that
were deserving of credit during the pandemic.
To explore this, we employ a novel set of confidential, supervisory loan-level data
between the largest U.S. banks and their corporate borrowers during the pandemic. This
comprehensive coverage of this data provides us with a unique advantage to observe the lending
outcomes at an important, yet under-studied segment of the economy, namely, private SME
firms, whose survival was particularly dependent on financing from their relationship lenders.
Controlling for borrower risk, we find that during the pandemic, “buffer-constrained
banks” (e.g. lenders that entered the pandemic with a capital ratio relatively close to the
regulatory buffer region) curtailed commitments and terminated lending relationships with
private SME firms significantly more than “buffer-unconstrained” banks (e.g. lenders that
entered the pandemic with a capital ratio relatively far to the regulatory buffer region). We
further find heterogeneous effects across firm type. Specifically, our results show that buffer-
constrained banks disproportionately curtailed lending to and ended lending relationships with:
(1) private bank-dependent SME firms (leaving their valuable relationships with large public
clients untouched), (2) firms that had a relatively young lending relationship with their bank, and
(3) firms whose pre-pandemic credit lines contractually matured at the start of the pandemic (and
thus were up for renegotiation). These results are consistent with banks making cost-minimizing
decisions on which firms would be least costly to curtail credit to.
Our study brings a new angle to the literature on how the pandemic transmitted shocks to
SME firms – specifically, these findings uncover a novel transmission channel emanating from
constraints related to bank capital which led to credit supply shocks, potentially delaying the
economic recovery for private SME firms. Rather than seeing the buffers as a cushion to be
drawn upon during a downturn, as intended by Basel III, banks seem to be treating the regulatory
buffers as additional minimum requirements.
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Figure 1: Buffer-Constrained Banks vs. Number of Borrowers
Note: This plot shows the number of lending relationships between SME firms and buffer-constrained banks (those that start
the pandemic with a capital ratio relatively close to the regulatory buffer region). Domestic BHCs, Source: Y-14Q H1
Schedule.
Figure 2: Visualizing the Bank’s Decision Whether to Avoid or Use Regulatory Buffers
Figure 3. Buffer-Constrained Banks and C&I Commitment Growth in the Cross-Section
Source: Public FR Y-9C reports
Figure 4: Buffer-Constrained Banks and C&I Commitment Growth through Time
Domestic BHCs. Source: FR Y-14Q H1
Figure 5: Empirical Setup
Figure 6: Extensive Margin (Relationship Entry and Exit Flow
Note: Domestic BHCs. Source: FR Y-14Q H1
Table 1: Summary Statistics
This table provides summary statistics for all balance sheet variables in the regression analysis using bank-firm
observations for the lending by the 16 BHCs in our sample to nonfinancial firms in the FR Y-14Q data. The table
reports the number of observations (at the bank-firm-time level), 10th percentile, mean, 90th percentile, and standard
deviation for both BHC variables and firm variables. There are 530,904 bank-firm-time observations, which are
spread across 16 lenders and 11 quarters. Source: FR Y-9C, FR Y-14.
Variable p10 Mean p90 Std Dev
Growth in Commitments (perc) -6.68 1.36 4.46 20.53
CET1 Ratio (perc) 9.35 11.12 12.87 1.49
Bank Undrawn CL Exposure (perc) 8.86 13.69 20.73 4.94
ln(Bank Assets) 18.76 20.44 21.69 1.17
Bank Deposit / Assets (perc) 56.07 65.61 75.70 10.36
Bank Liq Assets / Assets (perc) 22.31 31.53 39.10 7.03
Bank Provisions / Assets (perc) -0.01 0.06 0.26 0.13
Bank ROA (perc) 0.13 0.28 0.38 0.12
Borrower Debt / Assets (perc) 0 34 72 28
Borrower ROA (perc) -0.02 0.10 0.24 0.19
Borrower Net Sales / Assets (perc) 0.31 2.24 4.34 2.06
ln(Borrower Size) 15.31 18.35 22.42 2.73
Borrower Rating B BB BBB .
Table 2: Impact of Buffer-constrained Banks on Commitment Growth to SME Firms –
Intensive margin
This table reports the regression results for equation (1) capturing the relative differences in quarterly loan
commitment growth rates to SME firms from buffer-constrained banks after the 2020Q1 arrival of the
pandemic. BufferConstrainedBank is a 0/1 variable denoting if the firm banks with a lender whose CET1
capital ratio was relatively close to the costly regulatory capital buffer region ex-ante to the pandemic
(2019Q4). SMEFirm is a 0/1 variable denoting if the firm’s size is less than the median. Controls include
lagged firm and bank-level characteristics. All specifications are at the bank-firm-date level and include
bank-firm, firmindustry-date, firmzip-date, and firmsize-date fixed effects. Standard errors are clustered
at the firm level. *, **, and *** denote statistical significance at 10 percent, 5 percent, and 1 percent,
respectively. Source: Y-14Q H1
C&I Loan Commitment
Growth Rate percent (Quarterly)
VARIABLES (1) (2) (3) (4)
POST * BufferConstrainedBank -0.397** 0.172 -0.0394 0.0192
POST * BufferConstrainedBank * SMEFirm -1.215*** -1.304*** -1.400***
POST * SMEFirm 0.693 0.766* 0.530
Borrower Size -0.385*** -0.315***
Borrower ROA 1.370*** 1.727***
Borrower Leverage -2.839*** -2.867***
Borrower Sales Ratio 0.0840 0.0734
Rating_BtoAAA 0.0712 0.0490
CET1 capital ratio 0.219** 0.248** 0.213**
Undrawn Credit Line Exposure -0.170*** -0.167*** -0.196***
Bank Log Assets -1.667*** -1.863*** -1.614***
Bank Deposit Ratio -0.0536* -0.0577* -0.0495
Bank Provisions to RWA -0.437 -0.523 -0.516
Bank Liquid Asset Ratio 0.0603** 0.0524* 0.0620**
Bank ROA -0.952* -0.790 -0.847
Constant 44.98*** 1.534*** 41.47*** 42.88***
Observations 530,904 517,391 517,391 480,102
R-squared 0.272 0.260 0.260 0.268
Bank-Firm FE Y Y Y Y
Industry-Date FE Y Y Y Y
Zip-Date FE Y Y Y Y
Size-Date FE Y Y Y Y
No. of Banks 16 16 16 16
No. of Firms 54849 45209 45209 43023
Table 3: Impact of Buffer-constrained Banks on Commitment Growth to Private, Bank-
Dependent SME Firms – Intensive margin
This table reports the regression results for equation (2) capturing the relative differences in quarterly loan
commitment growth rates to private bank-dependent SME firms from buffer-constrained banks after the
2020Q1 arrival of the pandemic. BufferConstrainedBank is a 0/1 variable denoting if the firm banks with
a lender whose CET1 capital ratio was relatively close to the costly regulatory capital buffer region ex-
ante to the pandemic (2019Q4). PrivBankdepSMEFirm is a 0/1 variable denoting if the firm is an
SMEFirm, private, and banks with only one Y-14 lender. Controls include lagged firm and bank-level
characteristics. All specifications are at the bank-firm-date level and include bank-firm, firmindustry-date,
firmzip-date, and firmsize-date fixed effects. Standard errors are clustered at the firm level. *, **, and ***
denote statistical significance at 10 percent, 5 percent, and 1 percent, respectively. Source: Y-14Q H1
C&I Loan Commitment
Growth Rate percent (Quarterly)
VARIABLES (1) (2) (3)
POST * BufferConstrainedBank 0.168 -0.0338 0.0131
POST * BufferConstrainedBank * PrivBankdepSMEFirm -1.219*** -1.308*** -1.389***
POST * PrivBankdepSMEFirm 0.219 0.288 0.153
Borrower Size -0.345***
Borrower ROA 1.731***
Borrower Leverage -2.806***
Borrower Sales Ratio 0.0678
Rating_BtoAAA 0.0477
CET1 capital ratio 0.246** 0.215**
Undrawn Credit Line Exposure -0.163*** -0.197***
Bank Log Assets -1.827*** -1.565***
Bank Deposit Ratio -0.0631** -0.0524
Bank Provisions to RWA -0.514 -0.502
Bank Liquid Asset Ratio 0.0477* 0.0581*
Bank ROA -0.825 -0.845
Constant 1.576*** 41.25*** 42.74***
Observations 525,208 525,208 486,114
R-squared 0.261 0.261 0.268
Bank-Firm FE Y Y Y
Industry-Date FE Y Y Y
Zip-Date FE Y Y Y
Size-Date FE Y Y Y
No. of Banks 16 16 16
No. of Firms 46971 46971 44342
Table 4: Impact of Buffer-constrained Banks on Commitment Growth to Young
Relationship Firms – Intensive margin
This table reports the regression results for equation (2) capturing the relative differences in quarterly loan
commitment growth rates to SME firms from buffer-constrained banks after the 2020Q1 arrival of the
pandemic. BufferConstrainedBank is a 0/1 variable denoting if the firm banks with a lender whose CET1
capital ratio was relatively close to the costly regulatory capital buffer region ex-ante to the pandemic
(2019Q4). YoungLendingRelationship is a 0/1 variable denoting if the firm’s relationship with its lender is
less than the median age (6 years or less). Controls include lagged firm and bank-level characteristics. All
specifications are at the bank-firm-date level and include bank-firm, firmindustry-date, firmzip-date, and
firmsize-date fixed effects. Standard errors are clustered at the firm level. *, **, and *** denote statistical
significance at 10 percent, 5 percent, and 1 percent, respectively. Source: Y-14Q H1
C&I Loan Commitment
Growth Rate percent (Quarterly)
VARIABLES (1) (2) (3)
POST * BufferConstrainedBank 0.364* 0.133 0.212
POST * BufferConstrainedBank * YoungLendingRelationship -1.110*** -1.083*** -1.197***
POST * YoungLendingRelationship -0.411** -0.400** -0.258
Borrower Size -0.378***
Borrower ROA 1.359***
Borrower Leverage -2.796***
Borrower Sales Ratio 0.0848
Rating_BtoAAA 0.0624
CET1 capital ratio 0.239** 0.213**
Undrawn Credit Line Exposure -0.129*** -0.168***
Bank Log Assets -1.939*** -1.635***
Bank Deposit Ratio -0.0673** -0.0503
Bank Provisions to RWA -0.517 -0.460
Bank Liquid Asset Ratio 0.0440 0.0570*
Bank ROA -0.875* -0.928*
Constant 1.472*** 43.46*** 44.14***
Observations 574,053 574,053 530,904
R-squared 0.264 0.264 0.272
Bank-Firm FE Y Y Y
Industry-Date FE Y Y Y
Zip-Date FE Y Y Y
Size-Date FE Y Y Y
No. of Banks 16 16 16
No. of Firms 58466 58466 54849
Table 5: Impact of Buffer-constrained Banks on Commitment Growth to Firms with Prior
Credit Lines Set to Mature at the Start of the Pandemic – Intensive margin
This table reports the regression results for equation (2) capturing the relative differences in quarterly loan
commitment growth rates to SME firms from buffer-constrained banks after the 2020Q1 arrival of the
pandemic. BufferConstrainedBank is a 0/1 variable denoting if the firm banks with a lender whose CET1
capital ratio was relatively close to the costly regulatory capital buffer region ex-ante to the pandemic
(2019Q4). FirmCreditLineMaturinginPandemic is a 0/1 variable denoting if the firm has a previous credit
line that is maturing in 2020Q2. Controls include lagged firm and bank-level characteristics. All
specifications are at the bank-firm-date level and include bank-firm, firmindustry-date, firmzip-date, and
firmsize-date fixed effects. Standard errors are clustered at the firm level. *, **, and *** denote statistical
significance at 10 percent, 5 percent, and 1 percent, respectively. Source: Y-14Q H1
C&I Loan Commitment
Growth Rate percent (Quarterly)
VARIABLES (1) (2) (3)
POST * BufferConstrainedBank -0.0610 -0.299* -0.290*
POST * BufferConstrainedBank * FirmCreditLineMaturinginPandemic -2.459*** -2.454*** -2.323***
POST * FirmCreditLineMaturinginPandemic -0.280 -0.278 -0.266
Borrower Size -0.305**
Borrower ROA 1.717***
Borrower Leverage -2.896***
Borrower Sales Ratio 0.0732
Rating_BtoAAA 0.0270
CET1 capital ratio 0.243** 0.205*
Undrawn Credit Line Exposure -0.152*** -0.187***
Bank Log Assets -1.991*** -1.720***
Bank Deposit Ratio -0.0583* -0.0474
Bank Provisions to RWA -0.623 -0.650
Bank Liquid Asset Ratio 0.0447 0.0552*
Bank ROA -0.888* -0.866
Constant 1.675*** 44.39*** 45.06***
Observations 511,393 511,393 473,170
R-squared 0.260 0.260 0.267
Bank-Firm FE Y Y Y
Industry-Date FE Y Y Y
Zip-Date FE Y Y Y
Size-Date FE Y Y Y
No. of Banks 16 16 16
No. of Firms 46719 46719 44038
Table 6: Impact of Buffer-constrained Banks on Commitment Growth to SME Firms –
Lending Relationship Exit
This table reports the regression results for linear probability model in equation (2), capturing the change
in the probability that a buffer-constrained banks will renew its relationship with a private, bank-
dependent SME firm during the pandemic. BufferConstrainedBank is a 0/1 variable denoting if the firm
banks with a lender whose CET1 capital ratio was relatively close to the costly regulatory capital buffer
region ex-ante (2019Q4). PrivBankdepSMEFirm is a 0/1 variable denoting if the firm is an SMEFirm,
private, and only banks with one Y-14 lender. Controls include lagged firm and bank-level characteristics.
All specifications are at the bank-firm-date level and include bank-firm, firmindustry-date, firmzip-date,
and firmsize-date fixed effects. Standard errors are clustered at the firm level. *, **, and *** denote
statistical significance at 10 percent, 5 percent, and 1 percent, respectively. Source: Y-14Q H1
Pr (End Lending Relationship)
VARIABLES (1) (2) (3) (4)
POST * BufferConstrainedBank 0.00969*** -0.000267 -0.00216 -0.00332*
POST * BufferConstrainedBank *PrivBankdepSMEFirm 0.0461*** 0.0460*** 0.0459***
POST * PrivBankdepSMEFirm -0.0158*** -0.0158*** -0.0173***
Borrower Size -0.000216 -0.000979
Borrower ROA -0.0159*** -0.0108**
Borrower Leverage -0.000145 0.000333
Borrower Sales Ratio 0.000497 -6.80e-05
Rating_BtoAAA -0.00512*** -0.00353***
CET1 capital ratio 0.00237** 0.00180** 0.00190**
Undrawn Credit Line Exposure -0.00189*** -0.000826** -0.000674*
Bank Log Assets -0.0107** -0.0140*** -0.0158***
Bank Deposit Ratio 0.00173*** 0.00133*** 0.00124***
Bank Provisions to RWA -0.00522 -0.00285 -0.00390
Bank Liquid Asset Ratio -0.000348 -0.000169 -0.000110
Bank ROA -0.0244*** -0.0182*** -0.0205***
Constant 0.177 0.0319*** 0.232** 0.290***
Observations 570,369 547,962 547,962 516,982
R-squared 0.398 0.369 0.369 0.376
Bank-Firm FE Y Y Y Y
Industry-Date FE Y Y Y Y
Zip-Date FE Y Y Y Y
Size-Date FE Y Y Y Y
No. of Banks 16 16 16 16
No. of Firms 58222 47599 47599 45593
Table 7: Impact of Buffer-constrained Banks on Commitment Growth to Young
Relationship Firms – Lending Relationship Exit
This table reports the regression results for linear probability model in equation (2), capturing the change
in the probability that a buffer-constrained banks will renew its relationship with a young relationship
firm during the pandemic. BufferConstrainedBank is a 0/1 variable denoting if the firm banks with a
lender whose CET1 capital ratio was relatively close to the costly regulatory capital buffer region ex-ante
to the pandemic (2019Q4). YoungRelationshipFirm is a 0/1 variable denoting if the firm’s relationship with
its lender is less than the median. Controls include lagged firm and bank-level characteristics. All
specifications are at the bank-firm-date level and include bank-firm, firmindustry-date, firmzip-date, and
firmsize-date fixed effects. Standard errors are clustered at the firm level. *, **, and *** denote statistical
significance at 10 percent, 5 percent, and 1 percent, respectively. Source Y-14Q H1
Pr (End Lending Relationship)
VARIABLES (1) (2) (3)
POST * BufferConstrainedBank 0.0134*** 0.0104*** 0.0102***
POST * BufferConstrainedBank * YoungRelationshipFirm 0.00980*** 0.0109*** 0.00846**
POST * YoungRelationshipFirm 0.0250*** 0.0242*** 0.0263***
Borrower Size -0.000808
Borrower ROA -0.0145***
Borrower Leverage -0.00202
Borrower Sales Ratio 0.000380
Rating_BtoAAA -0.00510***
0.00226** 0.00244**
CET1 capital ratio -0.00191*** -0.00154***
Undrawn Credit Line Exposure -0.00982* -0.0128**
Bank Log Assets 0.00198*** 0.00173***
Bank Deposit Ratio -0.00396 -0.00481
Bank Provisions to RWA -0.000415 -0.000361
Bank Liquid Asset Ratio -0.0223*** -0.0239***
Bank ROA 0.0455*** 0.136 0.223*
Constant
604,770 604,770 570,369
Observations 0.394 0.394 0.399
R-squared Y Y Y
Bank-Firm FE Y Y Y
Industry-Date FE Y Y Y
Zip-Date FE Y Y Y
Size-Date FE 16 16 16
No. of Banks 61021 61021 58222
No. of Firms 0.0134*** 0.0104*** 0.0102***
Table 8: Impact of Buffer-constrained Banks on Commitment Growth to Firms with Prior
Credit Lines Set to Mature at the Start of the Pandemic – Lending Relationship Exit
This table reports the regression results for linear probability model in equation (2), capturing the change
in the probability that a buffer-constrained banks will renew its relationship with a firm whose prior credit
line is contractually set to mature in 2020Q2. BufferConstrainedBank is a 0/1 variable denoting if the firm
banks with a lender whose CET1 capital ratio was relatively close to the costly regulatory capital buffer
region ex-ante (2019Q4). FirmCreditLineMaturinginPandemic is a 0/1 variable denoting if the firm has a
pre-pandemic credit line that is set to mature in 2020Q2. Controls include lagged firm and bank-level
characteristics. All specifications are at the bank-firm-date level and include bank-firm, firmindustry-date,
firmzip-date, and firmsize-date fixed effects. Standard errors are clustered at the firm level. *, **, and ***
denote statistical significance at 10 percent, 5 percent, and 1 percent, respectively. Source: Y-14Q H1
Pr (End Lending Relationship)
VARIABLES (1) (2) (3)
POST * BufferConstrainedBank 0.0122*** 0.0107*** 0.00959***
POST * BufferConstrainedBank * FirmCreditLineMaturinginPandemic 0.0359*** 0.0355*** 0.0331***
POST * FirmCreditLineMaturinginPandemic 0.0155*** 0.0158*** 0.0156***
Borrower Size -0.00171
Borrower ROA -0.0113**
Borrower Leverage 0.00417
Borrower Sales Ratio 7.13e-05
Rating_BtoAAA -0.00345***
CET1 capital ratio 0.0023*** 0.00236***
Undrawn Credit Line Exposure -0.0011*** -0.000947**
Bank Log Assets -0.00991** -0.0121**
Bank Deposit Ratio 0.0013*** 0.00130***
Bank Provisions to RWA -0.00342 -0.00418
Bank Liquid Asset Ratio -0.000104 -5.82e-05
Bank ROA -0.0175*** -0.0195***
Constant 0.0247*** 0.139 0.213*
Observations 532,424 532,424 502,187
R-squared 0.367 0.368 0.374
Bank-Firm FE Y Y Y
Industry-Date FE Y Y Y
Zip-Date FE Y Y Y
Size-Date FE Y Y Y
No. of Banks 16 16 16
No. of Firms 47336 47336 45285
Appendix A: Distance to the Regulatory Buffer and CET1 capital ratios
The literature on bank capital typically uses the level of CET1 capital ratios when analyzing credit
effects. Thus, a natural question is whether banks with high excess capital cushions also tend to have
high CET1 capital ratios. Figure 7 demonstrates this is not the case, as there appears to be a negative
relation during the pandemic episode. If one were to evaluate solely the relationship between pre-
pandemic CET1 capital ratios and pandemic credit growth, a negative relation appears (Figure 8), where
higher capitalized banks appear to have weaker commitment growth during the pandemic. This is
counterintuitive to what the traditional bank capital literature would predict. This picture is resolved once
the excess capital cushion portion is broken out separately, as in Figure 3. Comparing Figures 3 and 8, the
key question is: what threshold are banks responding to when managing capital ratios? Given that bank
CET1 capital ratios are so high (and thus the 4.5 percent minimum capital requirements are nowhere close
to binding), insolvency concerns are unlikely to be driving bank capital management decisions. However,
the costs associated with utilizing regulatory buffers are closer to binding during the pandemic. As
evidenced in section 3, banks are managing capital ratios with regards the regulatory buffer threshold, i.e.
to avoid costs incurred by utilizing the GSIB surcharge and stress capital buffers.
Figure 7: CET1 capital ratios and Distance to the Regulatory Buffer
Source: Public Y-9C reports
Figure 8: Pre-Pandemic CET1 capital ratios vs. Pandemic Growth in C&I Commitments
Source: Public Y-9C reports
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