FEDERAL RESERVE BANK OF SAN FRANCISCO
WORKING PAPER SERIES
De-leveraging or de-risking? How banks cope with loss
Rhys M. Bidder, John R. Krainer, Adam H. Shapiro
Federal Reserve Bank of San Francisco
June 2017
Working Paper 2017-03
http://www.frbsf.org/economic-research/publications/working-papers/wp2017-03.pdf
Suggested citation:
Bidder, Rhys M., John R. Krainer, Adam H. Shapiro. 2017. “De-leveraging or de-risking? How
banks cope with loss” Federal Reserve Bank of San Francisco Working Paper 2017-03.
http://www.frbsf.org/economic-research/publications/working-papers/wp2017-03.pdf
The views in this paper are solely the responsibility of the authors and should not be interpreted as
reflecting the views of the Federal Reserve Bank of San Francisco or the Board of Governors of
the Federal Reserve System.
De-leveraging or de-risking? How banks cope with loss
Rhys M. Bidder, John R. Krainer, Adam Hale Shapiro
Federal Reserve Bank of San Francisco
Abstract
Using detailed bank balance sheet data we examine how banks respond to a net worth shock.
We make use of variation in banks’ loan exposure to industries adversely affected by the oil price
declines of 2014 and the implied variation in losses resulting from credit deterioration in those
industries. In response to these losses, exposed banks reduced the risk of their balance sheets by
shifting away from portfolio lending and towards assets with lower risk weights. Banks tightened
credit on corporate lending and on mortgages that they would ultimately hold in their portfolio.
However, they expanded credit for mortgages to be securitized and increased their holdings of
agency mortgage backed securities. Our results imply that previous work suggesting that banks
tighten credit in response to a shock (the traditional ‘bank lending channel’ view) provides only a
partial story and is in some ways misleading. It appears that banks respond to a negative shock by
de-risking rather than a uniform reduction in lending. In terms of the ultimate impact on borrowers,
we find that the shock had only a minimal impact on the overall quantity of loans supplied to firms
or households, reflecting substitution to other sources of financing. The findings have significant
implications for regulatory policy and theories of financial frictions.
I
Disclaimer: The views expressed in this document and all errors and omissions should be regarded as
those of the authors and not necessarily those of the Federal Reserve Bank of San Francisco, the Federal
Reserve Board of Governors, or the Federal Reserve System. This paper has been screened to ensure that no
confidential bank or firm-level data have been revealed. We thank Tesia Chuderewicz for excellent research
assistance and Anita Todd for editorial assistance. We also thank seminar participants at Cardiff University,
Warwick University, Bank of England, ECB, Federal Reserve Board of Governors, University College London,
the Bay Area Finance Workshop, Fed System Micro Conference (especially, Mitchell Berlin), Chicago Fed,
U.C. Davis, Econometric Society (Hong Kong).
Email addresses: [email protected] (Rhys M. Bidder), [email protected] (John R.
Krainer), [email protected] (Adam Hale Shapiro)
1. Introduction
The recent financial crisis provided a striking example of how intertwined are the fortunes
of banks and the wider economy. Even before the crisis, however, an extensive empirical literature had emerged documenting the importance of financial intermediaries and various
theories had been proffered to explain why the capital structures of firms and banks appeared to influence the financing of projects, in contrast to the stark implications of the
Modigliani-Miller theorem. The literature has mainly emphasized a tendency for credit supply to shrink following an adverse shock to a bank’s net worth or funding - often referred to
as the ‘bank lending channel’.1 In this paper we show that this traditional view is only a
partial characterization and part of a more nuanced set of responses.
Our results show that, following a shock, there are important compositional changes in
credit supply. Some types of credit do indeed contract. However, credit supply expands in
other areas, such that the overall change is ambiguous. Furthermore, we show it is fruitful
to expand analysis to look across the whole balance sheet - rather than simply examining
lending. Although we focus much of our analysis on corporate and mortgage loans, we also
identify coordinated effects outside the loan book on banks’ securities holdings — securities
and lending portfolio decisions are inextricably connected.
In response to the shock, banks ‘de-risk’ as opposed to de-leveraging in the traditional
sense of reducing their scale of operations. They move out of corporate and some types
of mortgage lending, into issuing mortgages that can be securitized and exchanged for the
less heavily risk weighted (and more liquid) agency MBS. In what follows, reflecting the
use of risk weighted assets in calculating regulatory capital ratios (risk adjusted measures of
leverage), we will refer to this behavior as ‘de-leveraging by de-risking’ since risk weighted
assets were not reduced so much through total assets but through reductions in the ‘average’
risk weight.
1
This channel is based on the observation that because of externally imposed minimal capital ratios or
constraints on bank leverage that emerge endogenously from information frictions between banks and their
investors, banks’ net worth will influence their ability to supply credit (Holmstrom and Tirole (1997), Chen
(2001) Van den Heuvel (2008), Meh and Moran (2010), Gertler and Karadi (2011), Iacoviello (2015), Bocola
(2016)). Associated with this argument is the idea that losses of liquidity by banks, regardless of whether
they initially arise from damage to their net worth, cannot frictionlessly be offset with alternative sources of
finance, again necessitating an adjustment to their lending or investment behavior (Bernanke and Blinder
(1992), Kashyap and Stein (1993), Stein (1998)).
2
We exploit a particular natural experiment that induced a shock to net worth that varied
across banks. The shock we analyze stems from the precipitous decline in the price of oil in
mid-2014, which led many firms in the oil and gas (O&G) industry to delay or cease payment
on their loans. Leading up to the oil price decline, banks held varying amounts of loans to
firms in the O&G sector so that, implicitly, the shock induced variation across banks in the
impact of the price decline.
Beginning with corporate lending, we find evidence consistent with the traditional ‘bank
lending channel’, on both the intensive and extensive margins. Lending from banks that
were particularly afflicted by the oil shock declined significantly, and the probability of their
relationship with a firm ending increased substantially. These results are consistent with
patterns elsewhere on the balance sheet and also chime with bank-level data from lending
surveys, indicating that much of this decline was due to tightened standards inducing firms
to substitute to other sources of financing.
A tightening of credit supply is also, to some extent, observed in banks’ mortgage lending.
Although in this case the story is more subtle and suggests that the pull back in lending
may more fruitfully be thought of in terms of a more general de-risking of the balance sheet.
Banks who were more exposed to the oil shock tightened credit for mortgages that they
would be holding on their balance sheet, similar to the commercial loan results. However,
simultaneously, these banks also appeared to be loosening credit supply for those mortgages
(primarily GSE-eligible) that would predominantly be securitized and thus shifted off their
balance sheet in exchange for mortgage-backed securities (MBS). The lower risk weighting
of the MBS, relative to the original underlying loans, are such that the overall risk profile of
the balance sheet is reduced - which we show is consistent with aggregate data from public
filings: affected banks’ MBS holdings increase dramatically (relative to unexposed banks),
while their retained residential loan balances do the opposite.
Turning to the ultimate impact of the banks’ decisions on borrowers, we find little evidence of a ‘credit channel’.2 Even though exposed banks seem to have tightened credit, the
2
The presence of a bank lending channel does not in itself imply that the state of banks’ balance sheets,
ceteris paribus, will influence the broader economy. For this, it must be the case that fluctuations in bank
lending cannot be offset by borrowers through substitution to alternative sources of external finance-from
other banks or the capital markets. This latter step is often referred to as the credit channel. If there are
easily accessible alternatives to bank finance in the public debt or equity markets, then the bank channel,
even if it exists, may have a limited impact on firm activity (Whited (1992), Greenspan (1999), Sufi (2009),
3
firms that were borrowing from these banks prior to the shock appear to have been able
to substitute for the vast majority of any lost financing via switching to alternative, less
exposed banks in our sample, or by drawing on existing credit lines. This appears to have
particularly been the case for firms with multiple bank relationships. Analogously, we show
that in counties with a high indirect exposure to the oil shock (via exposed banks), there is
apparently no difference in the overall level of portfolio mortgage loans (the type on which
affected banks were individually pulling back) relative to counties without exposure.
Understanding these subtleties of bank portfolio re-optimization is of enormous importance for various regulatory purposes (particularly stress testing and capital requirements),
debates over bailouts and also the role of government sponsored enterprizes (GSEs). The
policy implications of our results are clear in some respects - equity injections or ‘bailouts’
are essentially a mirror of the sort of negative net worth shock we consider here. However,
in addition to this, it is notable that even though our shock originated in the corporate loan
book, important adjustments are made through the mortgage book and related securities
(MBS) holdings. To the extent that GSEs underpin this low cost technology for adjustment
after a negative shock (thereby affecting the consequences of lending decisions), they may
have broader moral hazard effects on bank behavior, beyond those already discussed in the
literature.
The data used in this study are from the FR Y-14 (Y14) regulatory filings newly required
under post-crisis reforms. They contain loan-level information identifying both bank and
borrower, describe various characteristics of the loan and provide financial information about
the borrower. We go beyond traditional analyses by augmenting our commercial loan data
with banks’ residential lending and connecting them to the banks’ securities (MBS) holdings.
Our granular loan level data (featuring firm borrower identifiers) allows us to track the same
firm across multiple banks. This means, as in Khwaja and Mian (2008), that we can exploit
variation in lenders’ exposure to oil while controlling for credit demand and other effects
that might confound inference on credit supply movements.
The remainder of the paper is organized as follows: Section 2 relates our study to the
literature. Section 3 describes the Y14 data, the source of the net-worth shock, and the
econometric specifications. Section 4 shows the results of our analysis. Section 5 provides
Lemmon and Roberts (2010)).
4
details on the robustness of our results. Section 6 discusses policy implications. Section 7
concludes.
2. Relation to literature
While the cross-balance sheet aspect of our analysis takes us beyond (and in some ways
puts us in tension with) the standard ‘bank lending channel’ literature, our work does include
analyses of bank lending. As such, for our corporate and mortgage loan results, we draw
on tools used in existing work. In particular, to isolate bank credit-supply effects from our
shock we use the fixed-effects identification approach taken by Khwaja and Mian (2008)
(KM), but making novel use of granular data on the balance sheets of the largest U.S. bank
holding companies. This approach requires loan-level data which allows us to analyze the
same firm borrowing simultaneously from multiple banks that vary in their balance sheet’s
exposure to an exogenous shock. Firm fixed effects capture demand shifts and allow us to
separately identify the shock’s impact on credit supply. This powerful technique relies on
the (demanding) requirement that one has access to a credit-register type dataset.3
Overall, in the literature there appears to be consistent evidence of the bank lending
channel - whether in response to explicit disruptions in liquidity or via damage to bank net
worth that makes funding more difficult. With regard to the credit channel, the evidence
is less clear and more nuanced. A common theme is that if the credit channel is present, it
particularly applies to small firms and firms with relatively few alternative financing options.
3
Early tests used data at relatively high levels of aggregation—economy-wide or at the bank level
(Bernanke and Lown (1991), Kashyap, Stein, and Wilcox (1993), Hancock and Wilcox (1993), Peek and
Rosengren (1995), Stein and Kashyap (2000), Ashcraft (2005), Ashcraft (2006), Calem, Covas, and Wu
(2013)). However, as noted by Khwaja and Mian (2008), such studies suffer from the difficulty of separating
credit demand and supply effects of shocks to firms and banks. Ideally, one would wish to examine the effect
of an isolated shock to bank balance sheets but these shocks often will simultaneously influence firms’ demand
for credit, making identification difficult. Even if one can identify an exogenous shock that might plausibly
be thought only to affect credit supply (see, for example, Peek and Rosengren (1997), Paravisini (2008),
Ivashina and Scharfstein (2010), Chava and Purnanandam (2011), Greenstone, Mas, and Nguyen (2014)),
there remains the concern that correlated credit demand and supply shifts might confound estimates of the
lending channel. Notable papers that use the KM technique to avoid these problems are: Schnabl (2012),
Chodorow-Reich (2014), Jimenez, Mian, Peydro, and Saurina Salas (2014), Iyer, Peydro, da Rocha-Lopes,
and Schoar (2014), Acharya, Eisert, Eufinger, and Hirsch (2014), Cingano, Manaresi, and Sette (2016),
Bottero, Lenzu, and Mezzanotti (2016), De Jonghe, Dewachter, Mulier, Ongena, and Schepens (2016) and
Heider, Saidi, and Schepens (2017)
5
Many of these papers use data from economies that are known to be bank dependent and/or
have less developed capital markets than the United States.4 Thus their relevance to the U.S.
case, with its deep capital markets, might be questioned. Alternatively, the relevance of the
work may not be universal due to the nature of the funding being examined - trade finance
in the case of Amiti and Weinstein (2011) and exclusively syndicated loans in the case of
Acharya, Eisert, Eufinger, and Hirsch (2014), Chodorow-Reich (2014) and Chodorow-Reich
and Falato (2017). While syndicated lending is an important source of bank financing, one
might worry that the information it reveals is incomplete.5
In contrast to Chodorow-Reich (2014), we analyze a broader class of loans (not simply
syndicated) sampled from a time period in which the broader financial system was not under
stress. While it is certainly of enormous interest to know how the bank lending and credit
channels were operating during the financial crisis, and around the time of the Lehman
Brothers failure in particular (as in Chodorow-Reich (2014)), it is possible that such an
environment may limit the external validity of some of the results. A similar concern could
be raised also for Acharya, Eisert, Eufinger, and Hirsch (2014), who analyze the period of
the European sovereign debt crisis. In our case, we will analyze the effects of shocks to banks
at times when there are no worries about the collapse of the financial system. This is clearly
of great importance when assessing the credit channel as some of the effects found in extant
work may have reflected a severe but unusual lack of alternative sources of financing. Thus we
regard our work as complementary to the literature based on financial crises, in speaking to
the importance of the broad credit channel in more normal times (see also Jimenez, Ongena,
Peydro, and Saurina (2014)).
The precise alignment between our results based on our balance sheet data and those
from the lending surveys also provides important confirmation of the reliability of survey
data for tracking bank behavior, as in Ciccarelli, Maddaloni, and Peydro (2015), for example.
This enhances confidence in these series as (qualitative) observations of banks’ credit supply
schedules.
4
Jimenez et al. use Spanish data, Iyer et al. use Portuguese, Cingano et al. and Bottero et al. use
Italian, Schnabl uses Peruvian, Khwaja and Mian use Pakistani, and Acharya et al. and de Jonghe et al.
use European.
5
Our dataset is not a superset of that used by Chodorow-Reich, although the Y14 does provide information
that allows one to identify if loans are syndicated.
6
Our results on banks’ changing of their risk (weight) profile in response to the shock
provide an important extension to the traditional empirical literature testing for the importance of net worth, as predicted by financial frictions models. In a world where regulatory
capital ratios are frequently defined with respect to risk weighted, rather than total, assets,
understanding how banks’ respond along this margin is vital. Indeed, it points to interesting
extensions of models in the class of Holmstrom and Tirole (1997) to accommodate such issues and perhaps also address issues of regulatory risk-weight arbitrage (Das and Sy (2012),
Duchin and Sosyura (2014), Efing (2015)). To the extent that the agency MBS are also
highly liquid assets one can also partly regard our de-risking as encompassing an attempt
to mitigate liquidity risk, particularly since there is a powerful nexus between solvency and
liquidity (see Pierret (2015) and Bank for International Settlements (2015), for example).
Models that endogenize bank portfolio responses to shocks, particularly in the context
of stress testing are currently in high demand (Borio, Drehmann, and Tsatsaronis (2012),
Halaj (2013) and Halaj (2016)). Our results, though derived from a ‘normal shock in normal
times’, should provide some scope to help calibrate such models and reveal the subtle details
of bank adjustments. With regard to the interrelation of various regulatory ratios (capital
and liquidity, risk weighted and unweighted) in post crisis reform, the distinctions we draw
between credit tightening and de-risking as forms of adjustment also allude to important
subtleties to consider (see Cohen (2013) and Cecchetti and Kashyap (2016) for empirical
and theoretical discussions of these issues).
3. Empirical approach
In this section we describe the novel data set that we will be using. Next, we explain
how we exploit a shock to bank net worth and then specify our econometric approach to
ascertain if this shock induced a credit supply shift (bank lending channel) and whether this
influenced borrower outcomes (credit channel).
3.1. The Y14 Data
We make use of data collected from the quarterly FR Y-14Q and monthly FR Y-14M
filings required of bank holding companies (BHCs) with more than $50 billion in assets.
The Y14 has been filed since 2012 and contains detailed data on the banks’ balance sheet
exposures, capital components and categories of pre-provision net revenue. The primary
7
purpose of the filing is to help assess the capital adequacy of banks, in support of supervisory
stress testing programs that the Federal Reserve is required to run under the Dodd-Frank
Act.
The commercial loan data are drawn from the quarterly corporate loan schedule in the
FR Y-14Q. This schedule provides a ‘credit register’ — loan-level information identifying
borrower and lender and many characteristics of the loans themselves. Since this paper is
one of the first to employ the Y14 data, we provide some perspective on how much of the
market for corporate lending we are able to capture.6 Table 1 shows that in 2014:Q2, just
prior to oil prices’ precipitous decline, the commercial loans included in the Y14 totaled $1.17
trillion, or nearly 70 percent of the $1.69 trillion in commercial and industrial loans extended
by the entire BHC population that files a Y-9C report. For perspective, the total amount
of debt owed by U.S. nonfinancial corporations at that time was $2.39 trillion. Compared
to other bank loan data sets used in the literature (e.g., syndicated loans data from the
Shared National Credits Program (SNC) or DealScan), the Y14 also captures loans that are
typically smaller in size and to smaller-scale borrowers. Thus, the dataset contains loans
that are more likely to be closer to the theoretical objects in models of financial frictions, in
that they are from smaller firms and thus more likely to be subject to credit constraints.
The FR Y-14M filings contain loan-level monthly data on banks’ mortgage positions,
including characteristics of the borrower, the mortgage and the ‘investor type’. The latter
classification allows us to determine whether a given mortgage loan in the dataset is actually
held on the balance sheet by the bank as part of its own asset portfolio (what we will refer
to as a ‘portfolio loan’), or whether it is instead a loan that is owned by some other party
perhaps after having been securitized (typically by a GSE, although some loans are also
privately securitized). In this case the bank would only be reporting the loan in the Y14 due
to its role in servicing the mortgage, for example.
Our sample of commercial loans consists of loans originated by 28 large BHCs and our
sample of residential loans consists of 26 banks. In table 2 we illustrate bank characteristics
6
Other papers that have used these novel regulatory data are Abdymomunov (2014) (operational losses),
Black, Krainer, and Nichols (2016), Johnson and Sarama (2015), Epouhe and Hall (2016), Calem and Sarama
(2016) (real estate) and, making limited use of the corporate loans data to assess the use of private information
by syndicate leaders, Balasubramanyan, Berger, Bouwman, and Koepke (2016). In contemporary ongoing
work Berrospide and Edge (2016) use loan data in examining the effects of capital regulations.
8
for each sample, based on their financial statement data reported in the FR Y-9C (Y9) prior
to the decline in oil prices. These variables will be used as bank controls in our commercial
and residential loan analysis below.
3.2. The oil price decline of 2014
Our analysis exploits a natural experiment. Specifically, we examine variation in bank
behavior induced by the dramatic oil price decline of 2014. As illustrated in figure 1, the
drop in oil prices was both sudden and significant. Furthermore, oil futures prices clearly
indicate that, although a moderate pullback in prices might have been expected, the speed
and extent of the decline was a surprise. Indeed, much commentary in the early part of 2014
was bullish on oil, such as Hamilton (2014).
Performance of loans to firms in the oil and gas (O&G) industry depend critically on
the behavior of oil prices. As shown in figure 1, the rate of loans identified as either past
due, charged-off, or a non-accrual (what we refer to as ‘problem loans’) spiked in the O&G
industry following the oil price decline. We define firms as being in the O&G industry if
the bank reported NAICS codes 211, 213111, and 213112 (oil and gas extraction, drilling
oil and gas wells, and support activities for oil and gas operations).7 Figure 2 shows that
these NAICS codes are obvious outliers in terms of being ‘net makers’ of oil-related products,
based on the ‘make’ and ‘use’ tables provided by the Bureau of Economic Analysis (BEA).8
Returning to figure 1, we see that the fraction of O&G loans that were in problem status
climbed from 0.6 percent in 2014:Q2 to 10.4 percent in 2016:Q3. The rates in other sectors,
by contrast, do not exhibit such an increase. Taking all sectors other than O&G we observe
essentially no trend. Restricting the sample to net users of O&G products (where net users
are defined as firms in industries with net trade shares less than −0.1 percent - depicted on
the left hand side in figure 2) we again observe no obvious pattern. This pattern among
net O&G users emphasizes that there was apparently little scope for passive hedging of the
7
Some banks report SIC or GICS codes, but the vast majority report NAICS codes. We map the SIC
and GICS to the NAICS industries. See the data appendix for more details.
8
BEA produces year 2007 use and make table for 369 industries, defined by three, four, and five digit
NAICS codes. They do not include a separate category for NAICS code 213112. For each industry, we
constructed the net trade with industries 324 (petroleum refineries), 211 (oil and gas extraction), 213111 (oil
and gas drilling) as a share of that industry’s total output. Specifically, the net trade share for industry i is
equal to
i
i
M AKE324,211,231111
−U SE324,211,231111
.
OU T P U T i
9
shock within the loan book. Loans are debt contracts and given the already low rates of
default expected on the typical loan, the upside from having lent to industries whose costs
may have declined with the oil price do not offset the downside from exposure to O&G.
Another concern might be the explicit hedging of borrowers’ credit risk using derivatives
(such as CDS). However, Minton, Stulz, and Williamson (2009) suggest that derivatives
positions were largely held by banks for their dealer activities, rather than to protect against
the credit risk of their loan book. In addition, we consulted the FR-Y9C and calculated
that all but one of our banks had purchased CDS protection of notional value of around 1
percent or lower of their total loan book and the number was approximately 3 percent for
the remaining bank. Thus, hedging of credit exposures via derivatives appears to be limited,
as theories of adverse and selection and moral hazard might lead us to expect in contexts
in which banks have superior information on the underlying loans relative to the broader
market (see Duffee and Zhou (2001) for discussions of banks’ informational advantage).
Based on these observations, our analysis will use the banks’ loan book exposures to O&G
as our treatment variable. Variation in this exposure implies that some banks experienced
larger shocks to their loan books than others due to the oil price shock. We calculate O&G
exposure as the share of each bank’s total committed exposure (including term loans as
well as both drawn and undrawn lines of credit) reported on the Y14 that is accounted for
by lending to firms from the O&G industry over the period 2012:Q3-2013:Q4. We start
our sample in 2012:Q3 because the data set appears still to be in flux before that point
in terms of the banks that are present, undermining comparability. In addition, since we
will be examining the effects of a natural experiment, data from earlier periods would be of
questionable relevance. Additionally, we leave a buffer before the period of the price decline
to guard against picking up simple reversion to the mean and other concerns of endogeneity.
The average exposure was 5.9 percent and, importantly for our analysis, there was considerable variation in this treatment variable across banks, implying a standard deviation of
4.9 percent. Roughly speaking, a one standard deviation increase in pre-shock O&G exposure represents a 48 basis point increase in the overall problem-loan rate for the ‘average’
bank over the course of the post period.9
9
The fraction of O&G loans in problem status increased by 9.8 percent points between 2014:Q2 and
2016:Q3. A one standard deviation increase in exposure (0.049) would cause a 0.049 ∗ 9.8 = 0.48 percentage
point increase in the overall rate.
10
It is clear, at least from the market’s perspective, that O&G exposure was important. In
figure 3, we group banks into quartile bins based on their pre-shock O&G exposure. Banks
that were more exposed to the O&G industry, defined here as those in the top quartile,
underperformed in terms of their stock prices and market capitalization following the oil price
decline, where the timing of the separation in performance is notable. This is indicative of
the repeated guidance given to investors at the time, allowing the information to be encoded
in the stock price.
The scale of the stock price response does appear striking yet further confirmation of the
importance of the impact of the oil price decline on banks comes from media and survey
evidence. The price declines of 2014 drew comment in many quarters and the implications
for banks’ performance were broadly discussed in the markets and the press (see Alloway
(2014) and Jenkins (2014)). Fairly soon after the decline in prices, concerns were raised
about its implications for banks. Furthermore, discussion of the implications of the decline
continued (and continue) beyond the period of the shock (see Noonan and McLannahan
(2014), SNC (2016) and Vickery, Thomas, and Velasquez (2016)). From the Senior Loan
Officers’ Survey in January 2015 (to be discussed further below) there is explicit evidence of
credit tightening due to the oil price decline:
Some survey respondents specifically noted their concerns about the oil and gas
sector resulting from the sharp decline in the price of oil as a reason that they
had tightened their lending policies.
Furthermore, there is some suggestion that our exposure variable correlates with exposure
to the O&G industry through other avenues. While tracking every aspect of exposure across
the balance sheet is beyond the scope of this paper, it appears that the more exposed banks
by our measure also typically made more mortgage and non-O&G commercial lending in oilproducing regions of the country. We assess this by using 2013 BEA data on the fraction of
non-farm employee compensation attributable to the O&G industry by county.10 Using the
granular Y14 data which includes the location of mortgage borrowers and the mailing address
of commercial borrowers we see there is a positive correlation between our exposure variable
and the banks’ residential lending and non-O&G commercial lending by region. While we
10
BEA Table CA6N: compensation of employees by industry.
11
found that default rates of residential and non-O&G borrowers did not rise in these regions,
one might imagine impacts on banks’ fee income and, more broadly, the demand for loans.
These correlations emphasize that our exposure variable plausibly indexes our banks based
on their broader O&G exposure.11
It is important to point out that the committed exposures reported by the banks include
both on- and off-balance-sheet amounts. In particular, undrawn or partially drawn credit
lines are reflected in the Y14 as loans with utilized exposures lower than the committed
exposure. The off-balance-sheet commitments can sum to particularly large amounts. Indeed, committed O&G loans make up a non-trivial fraction of both total on-balance-sheet
loans and total assets, averaging 4.6 and 2.3 percent over the pre-period, respectively.12 In
addition, if one alternatively expresses exposure as a fraction of equity capital, the mean and
standard deviation across banks are 19.1 and 21.0 percent, respectively, emphasizing that
multiple banks had significant net worth riding on the performance of the O&G sector and,
by implication, oil prices.
The prominence of regulatory capital requirements and the sensitivity of markets to
off-balance-sheet exposures shown by the prominent role they played in the financial crisis
give reason to believe that our exposure variable is an effective index of bank vulnerability
to the oil price decline (Cornett, McNutt, Strahan, and Tehranian (2011), Gilchrist and
Zakrajšek (2012), Acharya, Schnabl, and Suarez (2013) and Berrospide and Meisenzahl
(2015)). In section 6.2.1 we make the connection to net worth more explicit where we use
the bank’s pre-shock O&G exposure as a first stage instrument for the bank’s change in
market capitalization. In the narrowest sense, the shock implies an adverse change in the
payoffs from loans to O&G firms resulting in a mechanical effect on net worth. More broadly,
however, the shock could affect the market’s perception of bank profitability going forward,
reflecting reputational loss and endogenous responses over concern from direct losses. This
broader net worth channel could have an effect on the ability of the banks to fund their
11
There is some limited evidence that banks with higher O&G exposure held riskier O&G loans, implying
our measure could be underestimating the true risk exposure to the O&G industry. The correlation between
pre-shock O&G exposure with the post-period problem-loan rate for O&G loans is 0.15, but statistically
insignificant (p-value = 0.25).
12
While our exposure variable is committed O&G lending as a share of total committed commercial loans,
we also show in a robustness section that our results are robust to using committed O&G/total on-balancesheet loans and committed O&G/total assets.
12
existing amount and structure of assets.
Finally, we note that our shock came at a time when the financial markets were generally
not suffering from intense stress. While the oil-shock was non-trivial, it was also not of the
order that led to any real existential concern for the banks that were particularly exposed.
Thus, we regard our analysis as providing an insight into how banks behave in response to
a ‘normal shock’ in ‘normal times’. As such it is an important complement to studies that
make use of ‘crisis shocks’ that roiled broader financial markets and may even have raised
concerns about institutions’ survival (for example, U.S. banks’ exposure to Lehman Brothers
during the financial crisis in the case of Chodorow-Reich (2014), or European banks’ exposure
to sovereign debt during the Eurozone sovereign debt crises in the case of Acharya, Eisert,
Eufinger, and Hirsch (2014)).
3.3. Econometric specifications
The main specification used in our empirical analysis follows Khwaja and Mian (2008).
Specifically, we employ a fixed effects regression of the following form:
∆Yij = βj + β1 Zi + β2 Xi + εij ,
(1)
where Yij is a loan between bank i and borrower j from the pre- to the post-shock period.
βj is a borrower fixed effect, Zi is the bank’s exposure to oil (the share of its loans accounted
for by O&G firms in the pre-shock period), and Xi are bank controls. We abstract from
interactions of Zi with borrower characteristics although they will be used below.
We here describe our approach for the corporate loan dataset though it is essentially the
same for the mortgage data. As in Khwaja and Mian (2008) we define a particular loan
concept for our empirical analysis, constructed from the raw underlying loan data. Our
concept of a loan is a bank-borrower pair so that, in examining the intensive margin, ∆Yij
is the change in the mean individual loan committed balance within the pair, where the
mean is a simple average across quarters in the relevant subperiods. We focus our attention
on term loans as opposed to lines of credit to avoid complexities related to the decision to
draw upon the line or whether or not and when the line was expected to be drawn upon.
With term loans we have a clear picture of what funds were being loaned and when these
funds were made available. In fact, as discussed below, the behavior and presence of existing
lines of credit will play an important role in our analysis, so that we do not abandon their
13
information content completely. We discard loans that were 90 or more days past due and
restrict our sample only to U.S. borrowers. For all econometric specifications, we remove all
loans to firms in the oil and gas industry. Further details of how we cleaned the data are in
the data appendix.
In examining the intensive margin, we require that there be a continuous bank-firm
relationship starting before or during the pre-shock period and extending into the postshock period. In examining the extensive margin we track the existence of the bank-firm
pair in the pre- and post-shock periods, allowing us to apply a linear probability model where
Vij is an indicator capturing entry (a relationship being initiated in the post-shock period
that had not been present in the pre-shock period) or exit (a relationship that had been
present in the pre-shock period but was not present or came to an end in the post-shock
period).
The importance of the fixed effect is that it absorbs the component of the ∆Yij that is
common across i. In particular, it is intended to capture any general shift in the firm’s credit
demand. This allows us to avoid concerns that the effects of the oil price shock might induce
correlated credit demand and supply shocks, which would lead to endogeneity bias in the
regression and confound our measurement of the credit supply effect, captured in β1 . This is
the main coefficient of interest as it captures the identified effect on the lending relationship
of the credit supply shock arising from the bank’s relative exposure.13
The interpretation of β1 is somewhat subtle. Consider, for example, the intensive margin
regressions. The coefficient will indicate the association between a bank’s oil exposure and
the change in the amount borrowed from that bank by the firm in question - the response
of lending to the identified credit supply shock. However, one should not interpret the
13
If all banks systematically made adjustments in credit availability to the same firm, with the degree
uncorrelated with their own exposure to oil, this would also be absorbed in the fixed effect, along with any
credit demand effects. These could reflect the particular relevance of the oil shock for the credit-worthiness of
a borrower and may additionally include effects interpretable as ‘systemic’ that ripple across all industries’
due to the broader effect of the shock on the economy. Nevertheless, in what follows, we will adopt the
standard terminology in this literature (which focuses on separating demand from supply effects) and refer
primarily to the fixed effect as absorbing firm-specific shifts in credit demand. Our identification allows us to
examine the effects of the oil shock, controlling for credit demand effects, systemic supply effects or effects on
lending to particular firms that are common across banks. Thus β1 relates to the marginal effect arising from
banks’ relative exposure. To extrapolate from relative to systematic shocks, one must make very particular
assumptions (see Chodorow-Reich (2014) for a clear assertion of such assumptions).
14
coefficient as indicating the amount by which a bank reduces its desired lending, ceteris
paribus. Terms of credit are multidimensional so that even if a bank is in some sense still
willing to lend the same dollar quantity, it may nevertheless tighten credit along some other
margin, such as price or covenants. This could (and we argue below, did) induce a response
from firms, such as seeking alternative financing, that would be encoded in any reduction in
lending captured by β1 .
While regression (1) allows us to analyze the bank lending channel, we complement it
with a second type of regression that addresses the credit supply channel. Thus, we run a
specification similar to (1), but aggregated to the level of the firm:
∆Yj = β0F + β1F Z̄j + β2F Wj + β3F X̄j + ηj ,
(2)
where ∆Yj is the change (from pre- to post-oil shock) in an attribute Y of firm j. Z̄jF is the
weighted average O&G exposure of the banks from whom firm j borrowed in the pre-shock
period. The variable is meant to capture how exposed the firm was to the credit supply
effects induced by the oil price shock, via the banks with whom it was associated. Wj are
firm controls and X̄j are weighted averages of the characteristics of the banks with whom
the firm was associated.
The primary coefficient of interest in this regression is β1F as it indicates whether or not
firms were systematically affected by being exposed indirectly to the oil shock via their banks.
If we find that oil exposure typically induces a tightening of credit supply then one might
expect a non-zero coefficient if firms are unable to substitute alternative financing. However,
if the firm can substitute for any reduced financing or away from lending on worsened terms,
then one might not expect any effect.
In the case of mortgage lending, we can no longer apply the Khwaja and Mian (2008)
approach at the borrower-bank level because we cannot identify a given household across
different banks. However, this fine a level of granularity is likely unnecessary to avoid the
aforementioned concerns with confounding credit demand and supply shocks for household
borrowing. It is implausible that banks differ systematically with regard to lending to individuals in a way that is correlated with their oil shock exposure and in a way that is not
equally well-captured in their lending patterns at the county level. Thus, since our mortgage
data include borrower zip codes we then aggregate to the county-bank level before applying
15
the same fixed effects approach to absorb county-level mortgage demand effects.
Summary statistics of the log change in mortgage loans within a given county-bank pair
between the pre- and post-shock period are shown in table 4. The different categories of
loans relate to different classes of mortgages meant to roughly mimic the broad categories
included in mortgage lending questions in the Federal Reserve’s Senior Loan Officer Opinion
Survey (SLOOS), discussed below.
4. Results
In this section we discuss the results of our analysis. We begin by illustrating some
relatively coarse but suggestive evidence that the variation in banks’ exposures induced
varying responses in terms of their lending and portfolio decisions - decisions that show a
coherent cross-balance sheet pattern of de-leveraging by de-risking. We then progress to our
more rigorous regression analysis to confirm its essential message.
4.1. Aggregate analysis - balance sheet and lending tightness responses
4.1.1. FR-Y9C Data
Turning to banks’ response to the shock, in figure 4 we show how banks’ balance sheets
respond to the oil price shock over time using publicly available bank-level information from
the FR-Y9C. This aggregate bank-level analysis is meant to highlight the direction and
timing of the impact of the price shock and does not control for time-varying bank-specific
covariates or demand factors. For each bank, we regress the logarithm of the bank-level
variable on time dummies, time dummies interacted with O&G exposure, bank fixed effects,
and bank-specific time trends.14 We then plot the estimates and 90th -percentile significance
bands of the coefficients on the time dummies interacted with O&G exposure.
Panels A and B show that the more exposed banks had lower commercial and residential
lending following the shock. Panel C shows the analogous plot for agency mortgage backed
securities (MBS) and is essentially a mirror image of panel B. In panel D, we depict a simple
14
Since we include both time fixed effects and time trends we need to omit two time periods. We omit
2012:Q3 and 2014:Q2, which implies the bank-specific trends are estimated over the pre-shock period,
2012:Q3 - 2014:Q2. For ease of comparison, we normalized O&G exposure to have a mean of one-standard
deviation. To correct for a coding change in risk weights implemented by Basel III in 2015:Q1, the riskweighted asset ratio (panel D) for 2015:Q1 for each bank was spliced with its 2014:Q4 value.
16
measure of the riskiness of the bank’s balance sheet based on dividing total risk-weighted
assets by total assets and plotting the evolution of this variable around the time of the shock.
Panels E shows that there are only negligible changes to total assets. Overall, these results
show a clear de-risking approach among the more exposed banks: the more exposed banks
load up on relatively low-risk MBS and decrease their portfolio lending, while leaving total
assets relatively unchanged.
While the results of this exercise show striking portfolio responses to the oil shock, this
type of aggregate analysis is prone to identification problems. In particular, it does not
disentangle credit supply from credit demand. Firms whose credit demand might be affected
by the oil shock could systematically be distributed among banks according to bank oil
exposure. For example, firms for whom the oil shock reduces their investment opportunities
and, thus, their demand for loans, might be concentrated among banks who themselves are
damaged by the oil shock. Similarly for residential loans, it is possible that households
demanding mortgages might disproportionately be affected in a systematic way, perhaps
due to regional concentrations in the banks’ mortgage lending and the sensitivity of those
regions’ economies to the oil shock. Importantly, we account for such potential endogeneity
bias in the granular analysis that follows.15
4.1.2. Senior Loan Officer Opinion Survey
To assess whether the oil shock induced any tightening of credit among affected banks,
we use the Federal Reserve’s Senior Loan Officer Opinion Survey (SLOOS) to provide insight
on how banks’ lending standards responded during the period of the oil price shock. The
SLOOS is a quarterly survey where banks report how various terms and conditions have
changed since the previous survey. The SLOOS is qualitative: respondents indicate whether
lending standards have tightened or eased (‘somewhat’ or ‘considerably’), or remained about
15
The MBS in panel C of 4 are held on the bank balance sheets as securities, and could have been acquired
in two different ways. First, the banks may originate mortgage loans and then securitize them, effectively
swapping exposure to whole mortgage loans for exposure to MBS. Banks often retain the servicing rights to
these mortgages and, consequently, report these loans in our Y14 mortgage data set. Thus, we can apply
the loan-level granular analysis to securitized mortgages. We refer to these loans as non-portfolio mortgages
below. A second way the banks can accumulate MBS is to buy the securities outright. Changes in bank
liquidity requirements over our sample period may have given some banks incentive to increase their holdings
of MBS, independent of the oil price shock. We deal with this possibility and its implication for bias in the
robustness section.
17
the same. Importantly, the survey questions ask about lending standards for new loan
applications, and thus can be interpreted as the willingness of the banks to supply credit in
their current environment.
To be sure the SLOOS responses conform to our constructed O&G exposure variable,
we make use of the (not publicly available) underlying bank-specific data.16 In figure 5
we plot an index of tightening on loan rates and covenant requirements for commercial
and industrial (C&I) loans.17 We again average individual responses of BHCs in the top
and bottom quartiles of exposure. The index shows average cumulative tightening, so a
decreasing index depicts BHCs that have eased terms since the last survey, an increasing
index depicts tightening since the last survey, and a flat index depicts no change in lending
standards. The indexes are both normalized to zero in 2014:Q2.
BHCs in both groups were generally easing over this period of time. However, on average,
BHCs with relatively high exposure to the oil sector (red lines in figure 5) stopped easing in
late 2014 and began to tighten the terms of required loan rates and loan covenants.18 This
finding, though largely qualitative, is an important complement to our regression results.
The Y14 has very granular information on loan quantities and loan performance, but does
not contain information on covenants. Standard sample selection issues (we only observe
transacted prices) make it difficult to assess the effect of the credit supply shock on the price
(interest rate) margin because of the tendency of firms to substitute away. The results from
the SLOOS suggest that exposed banks shifted their credit supply function relative to the
less exposed BHCs, which will provide useful insights into the sources of our results below.
4.2. Granular analysis—commercial loans
The patterns discussed in the previous section are consistent with a substantial fraction
of the aforementioned reduction in corporate loans among oil-exposed banks capturing a
general desire to repair the balance sheet—a credit supply effect. However, even with the
16
The SLOOS may not be used for supervisory purposes, and the confidential bank-level survey responses
may only be used by Federal Reserve System staff in non-supervisory functions for limited-scope economic
research projects.
17
The BHCs in the indexes are ones where we could merge the bank ID in the Y14 data to the SLOOS
data. Almost every Y14 bank was also located in the SLOOS.
18
For recent work on the role of covenants in the mechanisms by which banks tighten lending in the
syndicated loans market, see Chodorow-Reich and Falato (2017).
18
support from the SLOOS, this is only suggestive evidence. Thus, we now move to a more
rigorous regression analysis.
4.2.1. Bank lending channel—intensive margin
In table 5 we illustrate results from regression analysis on the change in the intensive
margin. In our most basic fixed effects regression, shown in the first column of table 5, we
find that β1 (in the first row) is estimated to be negative and significant at the 1 percent level,
where our standard errors are two-way clustered at the bank and firm level. The coefficient
estimate of −0.83 implies that a one standard deviation increase in bank exposure to oil
(4.9 percent from table 2) is associated with a 4 percent decline on the intensive margin of
a randomly selected loan. Thus, the bank lending channel appears to be active.19
By comparing the OLS and FE estimators on the same sample we can assess the size
of any bias induced by firm credit demand. In the second and third columns of table 5 we
show the estimated OLS results using the same sample as our FE analysis (firms that borrow
from multiple banks before and after the shock) and also on the much larger sample of loans
(corresponding to 42, 111 firms instead of 3, 541) of all firms who borrow from possibly only
one bank, before and after. Details of the variables involved in the regressions are included
in table 3.
Within the same FE sample we see that the O&G exposure coefficient is broadly similar
(−0.83 in the FE specification compared to the −0.58 using OLS and firm controls), implying
that the correlation of credit demand and supply is apparently not severely biasing the OLS
coefficient. If anything, the correlation appears negative: firms that experience positive
demand shocks appear more concentrated among banks that experienced negative supply
shocks.
Thus, there is the suggestion that endogeneity bias, if there is any, may be towards attenuating the measured effect. In this case, and assuming any correlation between credit
supply and demand effects is similar in the larger sample, we may expand our sample and
treat the estimated OLS coefficients with reasonable confidence as a lower bound (in magnitude) on the credit supply effect. In this larger sample, we again obtain a highly statistically
significant coefficient on O&G exposure, although smaller in magnitude than the FE sample
19
Similar results are obtained when we weight observations by the size of loan, indicating that our results
are not being driven by the size of the loan.
19
(−0.32). This suggests that the lending channel effect is weaker for single-relationship firms
or, alternatively, that any effects of tightened credit are manifested in dimensions other than
loan size.
4.2.2. Bank lending channel—extensive margin
In the first column of table 6 we show that, under our simplest fixed effect regression,
there is a highly significant effect of the oil exposure variable on the probability of exit. In
column 1, we see that a 1 percent increase in oil exposure is associated with a 0.84 percent
increase in the probability of exit. In the second column, we have the results for an OLS
regression (again with firm controls and for the same sample but excluding O&G firms). The
coefficient (0.74) is slightly smaller than its FE equivalent, suggesting that there is little bias
within the sample. When, in the third column, we expand the sample to include single bank
firms the effect is lower in magnitude suggesting a stronger exit effect on multi-bank firms.
The remaining three columns in table 6 include the same basic specifications as in the
exit regressions, but now the dependent variable is the event of forming a new banking
relationship in the post-oil shock period where none previously existed in the pre-shock
period. New bank-firm relationship formation does not appear to be significantly related to
bank oil exposure.
Thus, our extensive margin results for firms exiting their bank relationships are broadly
in line with our intensive margin findings. The bank lending channel exists and there are
suggestions that its effects, within bank-firm pairs, are particularly noticeable among firms
with alternative bank financing. This is captured by the fact that the effects seem smaller
for our larger OLS sample, which contains firms with fewer banking relationships.
4.2.3. Bank lending channel—heterogeneous effects across firms
Using our analysis to dig somewhat deeper, we assess whether the bank lending channel
differs across types of firms. We show in table 7 results for the intensive and exit margins
where we interact exposure with a dummy indicating a ‘small firm’, where small is defined as
in Khwaja and Mian (2008) being below the 70th percentile based on average total loan size.20
Results show no differential effect on the intensive margin but a negative and significant effect
on the exit regression. This means that banks more exposed to the oil shock are more likely
20
Results did not change when we included a continuous measure of firm size.
20
to stop lending to larger firms. To dig deeper into this somewhat counterintuitive result
we delve into the characteristics of the firm that capture the firms’ access to alternative
financing.
Our results from the previous section on expanding to the single bank OLS sample already
hint at a relationship between size and availability of opportunities for substitution (see table
3). In addition to this, we create a variable (external finance) that flags whether or not the
firm has a CUSIP or a stock ticker. This is intended to capture the firm’s access to the
capital markets, which is one of the attributes that ‘size’ is implicitly taken to proxy. To
capture the ability of the firm to draw on an already existing line of credit, we construct a
variable (pre-period utilization rate) that measures the share across all of the firm’s lines of
credit (from any Y14 bank) that has been utilized in the pre-shock period. The lower the
utilization rate in the pre-period, the more the firm can borrow on their lines in the post
period. Finally, we include the number of bank relationships in our sample in the pre-shock
period, given the fact that much research (see Petersen and Rajan (1994), for example)
indicates non-trivial costs of switching from an established banking relationship.
Most of these interactions are statistically insignificant. However, there is a statistically
significant positive coefficient on the number of pre-existing banking relationships in the
exit regression on the both the FE and OLS regressions. This positive estimate suggests
that firms with existing alternative banking relationships in the pre-period were more apt to
leave their relationship with banks who were exposed to the credit supply shock. As large
firms tend to have more banking relationships, this explains the stronger exit effect on larger
firms.21 As we will show later in the paper, we find strong evidence that those firms who
exited in the post-shock period simultaneously entered into new banking relationships with
banks that were less exposed to the oil shock.
4.2.4. Credit channel—firm-level outcomes
In the previous section we demonstrated strong evidence of a bank lending channel.
Banks that were particularly affected by declining oil prices restricted their credit supply. It
is unclear if this ultimately should affect firm outcomes. The U.S. financial system is often
21
The coefficient on the small-firm interaction and number of banks interaction become insignificant when
included in the same specification. The correlation between number of banks in the pre-period on the small
size dummy is negative and statistically significant.
21
contrasted with the more bank-dependent system in Europe. It is argued that the scale and
sophistication of public debt and equity markets in the U.S. reduces the dependence of firms
on banks and allows them to circumvent the loss of credit from the banking sector. Of course,
it is also possible that firms could substitute within the banking sector, as our interactions
analysis above suggests they might. Furthermore, even if a firm’s opportunities to borrow
using term loans are reduced, it may be able to substitute to other forms of financing.
In table 8 we show the results of implementing regression (2), for various dependent variables. The first two columns of table 8 report estimates of the change in firms’ total term
loan borrowing and total borrowing from Y14 banks, respectively. Interestingly, these regressions show a statistically insignificant relationship with the aggregate exposure variable,
implying that firms that were borrowing from more exposed banks in the pre-shock period
had no net change in their term loans due to the credit supply shock. This result implies
that firms were able to substitute simply from other Y14 banks.22
In columns 3-5 we move beyond the Y14 borrowing to look at additional balance sheet
objects from Compustat.23 Here we see no statistically significant estimates, implying essentially no relationship between changes in total liabilities (or assets, or equity) and the
aggregate exposure variable. Overall, in spite of the significant adjustments that exposed
BHCs were making in the wake of the oil price shock, borrowing firms (at least in the Compustat sample) were apparently able to smooth out the effects of the shift in credit supply
using other means.
Our firm-level estimates are potentially biased since they are estimated under OLS and
recall from our estimates of the intensive margin in table 5 that there appears to be some
upward endogeneity bias in the OLS estimator. Jimenez, Mian, Peydro, and Saurina Salas
(2014) suggest an adjustment (described in the appendix) to the firm level estimated co22
Since the dependent variable is the change in the log of loans, we necessarily drop those firms that entered
or exited the Y14 sample in the pre or post period in these regressions (to deal with zeros). We tested whether
this could be biasing our result by running a regression using a Y14 exit dummy as the dependent variable
in the aggregate regression. The coefficient on aggregate O&G exposure was insignificant, implying firms
that exit the Y14 were no more or less likely to be banking with more exposed banks.
23
We use the Compustat variables in this case because they are obtained from the borrowing firms’ official
financial statements and, presumably, will be more accurate than the bank-reported equivalents in the Y14. Note that the sample size declines substantially in columns 3-5 of table 8 due to the requirement that
Compustat firms be publicly traded. Note also that for clarity we do not include the coefficients on the
controls in the table.
22
efficient based on comparing the bank-firm level coefficients from FE and OLS regressions.
Making this adjustment does not materially change our insights — after the adjustment, it
appears that firms were able to substitute at least 80 percent of the decline in loan supply
simply from other Y14 banks. Indeed, this number is likely a lower bound on the true degree
of substitution, as discussed in the appendix.
Overall, the results from the firm regressions suggest a substantial ability of firms to
substitute alternative financing if the loan supply shock induced a tightening of credit supply
by the banks from whom they were previously borrowing. Importantly, if firms can cushion
themselves by borrowing from alternative sources in this way, then it breaks the vicious
circle that underpins various financial accelerator models (see Shleifer and Vishny (1992)
and Kiyotaki and Moore (1997), for example). If the shock’s effect on banks’ credit supply
has little to no first-round effect on firms (contractions, defaults, and fire-sales) that could
in turn feed back on the value of collateral and bank assets and so further damage firms and
banks not initially directly exposed to the shock, then the amplification mechanisms of these
models simply do not come into play.
4.2.5. Firm substitution of funding sources
Our interaction results in table 7 and the firm level results of the previous section suggest
substantial term-loan substitution within the set of Y14 banks. To further verify that this
type of substitution is driving the aggregate term-loan result, we run the fixed-effects entry
regressions of the type we ran in section 4.2.2 above, but on different subsamples of firms. In
table 10, we break the population of firms into groups according to whether they experienced
any relationship exit (column 1), did not experience any exit (column 2), experienced any
decline in term-loan borrowing from a BHC (column 3), or did not experience any decline in
borrowing from a BHC (column 4). Columns 1 and 3 demonstrate that firms that experienced
any reduction in lending (by any bank, for any reason) were more likely to strike up new
relationships with less-exposed banks. Conversely, firms that did not exit old relationships
or experience any measurable decline in term loan funding show no such new relationship
behavior.
As another test, we isolate those firms that switched banks—that is, those firms that
exited at least one banking relationship and entered at least one banking relationship. In
column 5, we run our firm-level regressions with the dependent variable being a dummy
23
variable equal to one if the firm switched banks. The coefficient on aggregate O&G exposure
is positive and statistically significant, indicating that those firms more exposed to the credit
supply shock were more likely to switch banks.
These results show evidence of firms substituting away from exposed banks to less exposed
banks for financing. Of course, it is not necessary that firms know which banks were affected
by oil. Instead, the result is likely driven by the fact that they were presumably aware of
the easier terms offered by the less affected banks, as evidenced in our SLOOS results.
Finally, we consider another method of financing—the firms’ undrawn credit lines. An
additional reason why we observe no effect on total utilized loans (see column 2 of table
8) could be that firms drew down from an already existing line of credit. In table 9 we
test whether firms using banks more exposed to the oil shock drew on their lines of credit.
Specifically, we regress the log change in the utilization rate (defined as total utilized amount
divided by total committed amount) between the pre and post period, on each firm’s weighted
O&G exposure with controls.24 There is apparently no effect using the sample of all firms.
However, the interaction with the pre-period utilization rate implies that there was an effect
for firms with relatively large shares of their borrowing capacity untapped.
Our credit line results emphasize the benefits of the granularity of our data as one observes
that even though we have identified an overall negative shock to the suppliers of credit, a
result of this in some dimensions (due to substitution) can be an increase in certain types of
credit. Thus, using only limited data on credit lines or unused commitments, an analyst could
easily be led astray as the data might suggest a positive credit demand shock. This alludes to
a broader point we make in this paper that the credit supply response and amount of lending
ultimately transacted is multifaceted. Traditionally, bank lending channel papers give the
impression that a bank funding shock induces a uniform reduction in credit. However, we
show that the response is more subtle. As we now illustrate, this is particularly obvious
when one looks across the balance sheet, revealing the coherence of banks’ business models
across various asset classes.
24
We restricted the sample to firms with utilization rates less than 100 percent in the pre-period.
24
4.3. Residential loan analysis
Our results using the corporate loan book give us interesting insights into how banks
respond to shocks. A significant advantage of having access to other components of banks’
balance sheets is that we can also examine how other types of lending responded. As noted
in section 4.1, panel B of figure 4 indicates that residential lending among more exposed
banks lagged behind that of the less exposed. In this section, we tease out the underlying
drivers of this relationship and explain how it relates to the behavior of MBS holdings in
panel C of figure 4.
4.3.1. SLOOS responses—residential loans
We begin by again examining responses from the SLOOS, but here in relation to residential real estate lending. Figure 6 shows how the evolution of credit terms depended on
banks’ exposure after the oil price shock. Note that since the SLOOS questions on mortgages changed in 2015:Q1 we are unable to plot pre- and post-oil shock charts as we did for
commercial loans or adjust for pre-shock trends.
In panels A and B of figure 6 we show the cumulative tightening of terms for GSE-eligible
and Government mortgages. We observe that there was a greater tendency on average to
loosen terms among the more exposed banks. In contrast, in panels C and D we repeat
the analysis for jumbo and non-jumbo, non-GSE lending and observe that the pattern is
flipped—on average, the more exposed banks seem to have tightened relative to the less
exposed.25 Immediately, therefore, we see that there are subtleties in these responses.
The latter pair of panels, are apparently more intuitive given the Y9 data on residential
loans in the banks’ portfolios, illustrated in figure 4. The (relative) relaxation of terms by the
less exposed banks, is consistent with an expansion in their lending, as borrowers presumably
25
The GSE-eligible category of residential mortgages includes loans that meet the underwriting guidelines,
including loan limit amounts, of the GSEs—Fannie Mae and Freddie Mac. The government category of
residential mortgages includes loans that are insured by the Federal Housing Administration, guaranteed
by the Department of Veterans Affairs, or originated under government programs, including the U.S. Department of Agriculture home loan programs. The Qualified Mortgage (QM) non-jumbo, non-GSE-eligible
category of residential mortgages includes loans that satisfy the standards for a qualified mortgage and have
loan balances that are below the loan limit amounts set by the GSEs but otherwise do not meet the GSE
underwriting guidelines. The QM jumbo category of residential mortgages includes loans that satisfy the
standards for a qualified mortgage but have loan balances that are above the loan limit amount set by the
GSEs.
25
are attracted to their loan products rather than those of the more exposed banks. But
what of the GSE eligible and government mortgages? As the more exposed banks loosened
their lending standards with regards to GSE-eligible and government mortgages, one might
have expected an expansion of mortgage lending, which would superficially be in tension
with the Y9 data (i.e., panel B of figure 4). However, one must recall that GSE-eligible
and government mortgages are largely intended to be securitized and shifted off the banks’
balance sheets. Thus, even if the better terms induced greater transacted originations, we
would not observe them in the Y9 data on mortgages held in the banks’ portfolios.
Returning to figure 4, panel C suggests that a higher rate of securitization of mortgages
could explain these patterns. That is, after the shock, the more exposed banks pulled back
lending (by tightening their terms) if they would ultimately have to retain those mortgages on
their balance sheet, while expanding originations of the types of mortgages that they could
securitize (and hold less capital against for regulatory purposes) or perhaps securitizing
a higher fraction of such mortgages. This hypothesis cannot be tested further using the
SLOOS, so we now turn to our Y14 data to clarify matters.
4.3.2. Residential loan regression results
In table 11 we show the results of our regression analyses, where the dependent variables
are those summarized in table 4. Importantly, we can partition residential loans into those
originated and retained on the banks’ portfolios (referred to as ‘portfolio loans’) and those
originated but either sold or securitized with only servicing rights retained (referred to as
‘non-portfolio loans’). A granular analysis at the loan level will allow us to assess whether
the time-series patterns shown in panels A and B of figure 4 are statistically significant after
properly controlling for bank characteristics and county-specific credit-demand factors.26
We use only those loans originated by the BHCs themselves, excluding those obtained from
correspondent relationships.27
The first column of table 4 relates to total non-government mortgage originations that
26
We describe how we partitioned mortgage loans from the Y14 into different types in the data appendix.
The bank level controls are the same as those in our commercial loan analysis, but we also augment them with
variables capturing trends in the banks’ mortgage lending and MBS holdings (see table 3 for a description
of the controls).
27
Results are essentially the same if the correspondent and other loans not originated by the banks themselves are included. They are available on request.
26
were retained in the banks’ portfolios. For these mortgages, the banks retain the credit risk
associated with a default. The portfolio results generally suggest that banks with exposure
to the oil shock in the corporate portfolio also reduced their balance sheet exposure to
mortgages. For both the fixed effects regression in panel A and OLS regression with county
controls in panel B of table 11, the estimated coefficient of -2.2 implies that a one standard
deviation increase in oil exposure leads to a roughly -11.0 percent decline in portfolio lending.
We estimate the balance sheet pullback to be stronger in the non-jumbo category (column
3) than in the jumbo category (column 2).28
Conversely, we see the opposite effect for the non-portfolio loans that were originated and
then securitized by the banks. In this case, banks exposed to the oil shock in the corporate
portfolio increased their origination and securitization activity (columns 4 and 5). The effect
is particularly pronounced when we limit the mortgages to non-jumbo loans, which are more
likely to be guaranteed by the GSEs. The coefficient estimate of 3.0 implies that a one
standard deviation change in O&G exposure on the corporate balance sheet is associated
with a nearly 14 percent increase in securitization. Similarly, in column 6 of the table we see a
significant positive response of changes in loan growth for FHA and VA (i.e., “government”)
mortgages—loans likely to be securitized by Ginnie Mae. The coefficient of 8.4 implies a
one standard deviation increase in O&G exposure is associated with a 41 percent increase
in these types of loans.
The magnitude of the estimate of the shock-induced change in originate-to-securitize
mortgage lending (non-portfolio mortgages) corresponds neatly with the evidence in figure
4 plotting the aggregated balance sheet data. Recall, in that figure (panel C) we observe a
substantial increase in aggregate MBS for banks according to their oil exposure. Strikingly,
we find no statistically significant change in overall residential loans (column 7). That is,
the decline in portfolio loans (again consistent with 4, panel B) was offset by the increase in
non-portfolio loans, implying no discernible change in total residential assets. The ultimate
effect was a rotation of their balance sheet away from portfolio loans towards non-portfolio
loans and, in turn, agency MBS.
Table 12 contains the aggregate regressions, where we relate changes in county-level
28
This is reminiscent of results in Calem, Covas, and Wu (2013) where jumbo, as a share of total mortgage
origination, typically rose after the liquidity shock of 2007, although our broader finding of a pull-back in
portfolio mortgage lending is somewhat in tension.
27
mortgage balances to the counties’ aggregate exposure to the oil shock via the weighted
exposures to the O&G sector of the banks active in those counties. We assess three different
aggregate categories: portfolio loans, all Y14 loans (i.e., portfolio plus non-portfolio), and
all loans from the Black Knight-McDash Analytics database, which samples loans from the
universe of mortgage lenders and not just those lenders captured in the Y14. For all three
levels of aggregation there is no statistically significant effect on loan quantity.
We point out that these results align with the SLOOS, giving important support to the
use of these surveys for insight into banks’ policies. Indeed, our results lend weight to the
approach of ‘trusting the bankers’ explored in Ciccarelli, Maddaloni, and Peydro (2015),
where aggregated SLOOS responses are used to identify credit supply policy. It appears
that the surveys accurately reflect bank behavior (at least in the dimensions we examine)
which is the assumption underlying their analysis.29
5. Robustness
While the comments and answers provided by banks in the aforementioned lending surveys give support to our regression-based analysis and our overall story, in this section we
carry out a set of robustness checks. First, we consider the implications of defining our exposure variable as a share of total on-balance-sheet loans, of total assets, and of equity capital
(all as reported in the FR Y-9C) rather than as a share of total committed commercial loan
exposure. The motivation for this (though Chodorow-Reich (2014) also scales his exposure
by loans, for example) is that the size of the loan book and capital position of banks may
differ, implying noise and possibly bias in our index. Table 14 shows that these changes do
not substantially alter our findings.
A concern is that our regression specification contains too few clusters, leading to either
‘overfitting’ of the cluster-robust variance matrix estimate or over-rejection of the critical
values (Cameron and Miller (2015)). There is no definitive measure of ‘too few’, but the
general consensus is around 30 clusters—close to the number of banks in our sample. To
address this issue, we re-assess our standard errors using the wild cluster bootstrap recom29
Ciccarelli, Maddaloni, and Peydro (2015) only use the aggregate (across the banks responding to the
SLOOS) results and also did not make use of mortgage-related responses, as the questions had not yet been
incorporated into the survey.
28
mended by Cameron and Miller (2015). Since this method is computationally intensive, we
tested only the two main fixed effects specifications in the paper (the fixed effects commercial
intensive and extensive regressions) to get a sense of whether our results would differ. For
both the intensive and extensive regressions our estimates under the wild cluster bootstrap
(clustered at the bank level) were both statistically significant at the 1 percent level.30
There is also a potential concern that the predictive power of our exposure variable was
somehow a coincidence. To test this concern, we run a type of permutation test using each
bank’s loan exposure to other industries as placebos. Analogous to the O&G exposure variable used throughout this paper, for each of the most prevalent other 49 industries (defined
by 3-digit NAICS) we create a bank-specific exposure variable analogous to our O&G exposure variable used throughout this study. We then run our main fixed-effects specification
50 times—once replicating our original specification, and 49 subsequent regressions, each
time replacing O&G exposure with one of the 49 other industry exposures. To be conservative, we collected the associated t-statistic based upon the standard errors obtained from
the wild cluster bootstrap. In figure 7 we show the distribution of t-statistics obtained from
our intensive and extensive corporate lending regressions. Clearly, there is some sampling
variability associated with this test such that, even if our O&G exposure variable were the
‘true’ exposure variable, it would not necessarily emerge as having the largest t-statistic, but
we would at least hope for it to be in the tail. In fact, the results are extremely strong. In
the intensive regressions, our exposure variable is a clear outlier in the left tail (where the
left tail is most relevant given the limited upside to a debt contract) and in the right tail in
the extensive regressions (similar relevance of the right tail).
Finally, we explore the possibility that our sample period includes other, possibly confounding shocks that may have had an impact on the portfolio adjustments that we see our
data. In particular, roughly contemporaneous to the oil price shock, U.S. banking regulators were in the process of phasing in new balance sheet liquidity requirements for many of
the banks in our sample. The new regulations stipulated that banks maintain a liquidity
coverage ratio (LCR) whereby liquid assets could be used to offset possible funding outflows
30
Although clustering should guard against an individual bank driving our results we also ran our commercial intensive and extensive fixed effect regressions repeatedly, each time removing a different bank from the
sample. We retained significance in all cases and the coefficient value was consistent (average and standard
deviation of −0.81 and 0.06, respectively, for intensive and 0.84 and 0.08 for the exit regressions).
29
in a period of stress. The rule may have influenced the demand for liquid assets for certain
banks, which include agency MBS. In the U.S., BHCs with assets over $250 billion needed
to meet 80 percent of their liquidity requirements in 2015 (i.e., in our post-shock window).
BHCs with assets above $50 billion but below $250 billion were subject to a modified and
less severe LCR requirement with a phase-in period in 2016 (i.e., outside our post-shock
window).
We can use the differential phase-in periods of the rule to control for possible LCR-related
asset demand that could potentially bias our regression estimates of the coefficient on O&G
exposure. We do this by constructing an indicator variable to identify banks with assets over
$250 billion and subject to the LCR phase-in in 2015, and then interacting this variable with
a proxy for exposure to the LCR in the pre oil shock period. This proxy consists of a measure
of high-quality liquid assets (Treasury Securities + Agency MBS) which is then scaled by a
measure of possible funding outflows (Total Liabilities - Deposits). The denominator of the
exposure proxy reflects the notion that the LCR treats deposit funding as more stable and
less likely to run off in a liquidity event. Thus, BHCs with large values for the proxy have
ample liquidity, or are more reliant on deposits (or both), and thus would be less constrained
by the LCR. When we include these variables among the bank controls in our regressions we
find only small quantitative differences in the estimates of the sensitivity of lending to O&G
exposure and no differences in statistical significance. Indeed, the coefficients on the LCR
controls are not statistically significant in any of the mortgage regressions. It may still be
the case that the banking sector as a whole was gradually increasing its holdings of liquid
securities in order to eventually comply with the LCR, but these changes do not account for
any of the differences in de-risking or lending behavior between banks with differing exposure
to the O&G sector.
6. De-leveraging by de-risking: Discussion
In this section we draw together the various strands of our results to emphasize the
cross-balance sheet coherence of banks’ responses to the shock. We also point out policy
implications of our findings.
30
6.1. Portfolio optimization
Together, the granular loan-level analysis corroborates the bank-level time-series patterns
observed in the Y9 and the SLOOS data. More exposed banks pulled back lending, but this
pullback was concentrated among loans that they ultimately would hold on their balance
sheet, and against which they would be required to hold regulatory capital at a relatively high
rate (depending on the LTV and other factors, the prevailing risk weights under the Basel
standardized approach over the sample period ranged from 35 percent to potentially over
100 percent for mortgages and, according to the particular agency, 0 percent or 20 percent,
for agency MBS). In contrast, lending that resulted in loans that could be securitized via
sale to GSEs was expanded - with relaxation in terms allowing this expansion, as reflected
in the SLOOS. This rebalancing would explain why in the Y9 data the MBS and residential
lending diagrams were essentially mirror images as the lending panel in figure 4 only related
to loans held on the balance sheet and the MBS apparently increased as a result of the
additional origination of securitizable loans.
The re-balancing of mortgage lending after the oil shock is an important complement to
our corporate lending channel results and goes beyond the standard findings of the bank
lending channel literature. Existing papers typically only have access to certain credit registers, rather than the broader insight into the balance sheets of banks that we possess.31
Focusing on the reduction in originations of loans to be held on the balance sheet, the results are consistent with a reduction in credit and, therefore, are in line with the corporate
results. But the offsetting increases in originations of loans that are securitizable adds an
important subtlety to the story and one that might be missed were one only looking at the
residential lending data from the Y9, especially if they were considered in isolation. Banks
are constantly solving complex optimization problems in their lending behaviors and there
is no reason to expect that a pullback in lending will be uniform across all products.
The fact that we can tie these lending book patterns to the banks’ securities holdings—
in MBS specifically—also provides an interesting perspective on the connection between
securitization and the availability of credit. Much work has been done on the influence of
securitization on the availability of credit (see Mian and Sufi (2009) and Keys, Mukherjee,
31
One exception to this is Abbassi, Iyer, Peydr, and Tous (2016) also exploit security and loan-level data in
examining a crowding out effect of banks with trading expertise shifting resources from lending to purchasing
securities the prices of which had fallen during the financial crisis.
31
Seru, and Vig (2010), for example). However, we provide a novel insight into how the ability
to securitize a broad class of mortgages allows banks to maintain their lending following
damaging shocks. Our work provides important confirmation of theories of the increased
role of securitization in recent decades, such as Loutskina (2011), which previously would
have been difficult to assess given the identification problems inherent in using aggregated
data. Indeed, quoting Loutskina (2011)
If banks can liquidate loans to finance their liquidity need, they can also do so
to finance new credit, making banks less dependent on the traditional sources of
funds. The increasing liquidity of bank loans ought to change the link from banks’
funding availability (e.g., deposits) to their willingness to supply credit. . . Because
securitization provides banks with an additional source of both loan financing
and liquidity, it should also shield banks’ willingness to supply credit from the
external cost of funds shocks.
It is an interesting question why the mortgage book was a vehicle for de-risking the
balance sheet. Intuitively, we might expect the mortgage rotation that they undertook
to have been quite cost effective. Firstly, the MBS market is highly liquid and, secondly,
it seems likely that instructing mortgage loan originators (the employees of the bank) to
switch their emphasis to conforming loans, rather than, say, jumbo is a simple switch to
make without requiring layoffs or dramatic changes in personnel or retraining. However,
this intuition is rather speculative and, of course, we do observe some reduction in corporate
lending which would free up regulatory capital (being 100% risk weighted) and resources
for expanding MBS holdings. Presumably a ‘bang-bang’ approach of effecting the de-risking
via only one margin of adjustment was not seen as optimal by the banks, with this interior
solution reflecting the complexity of their optimization problem.
6.2. Policy implications
6.2.1. Bailouts
Our results relate to the impact and desirability of ‘bailouts’ or injections of equity into
a given bank.32 Since our analysis implicitly relates to a shock to net worth we can imagine
32
Again, given our assumptions, this should be interpreted as a relative (to other banks) injection.
32
a counterfactual in which this shock did not occur, or was offset with a policy that repairs
the capital of the bank. Of course, one could argue that symmetry of effects may not be
assured, but qualitatively, there appears to be evidence that such a policy could promote
the extension of some types of credit by the bank in question and a move towards ‘riskier’
(or at least more heavily risk weighted and/or less liquid) assets.33
Since an important aspect of justifying financial support for a bank is to argue that
the broader economy will benefit, the lending channel effects we identify are ceteris paribus
apparently in favor of bailouts. However, counter to this are our findings on the credit
channel, which suggest that the effect of any credit pullback from a given bank is minimal.
Of course, the desirability of bank rescues involves a multitude of complicated factors and
may also depend on a particular institution and size of shock as well as differences in the
pool of relevant borrowers from those considered in our analysis. In addition, the credit
channel effects of systemic banks stress may also favor a policy response. Nevertheless, our
results add to the debate over this important policy tool.
In order to make this insight more formal, we recast our results explicitly in terms of a
shock to net worth. To do so, we run a two-stage instrumental variables (IV) specification
of our model, instrumenting the bank’s change in log market capitalization (between the
pre and post period) with the bank’s pre-period O&G exposure. In this way, we obtain an
elasticity of the lending measures with respect to a change in market capitalization, where
the change in market capitalization is attributable to the oil price shock. This allows us to
map the oil shock into a common scale, in terms of its ultimate effect on net worth rather
than exposure to a particular industry.
In table 13 we repeat our main regressions under the IV approach. We lose some power
through this procedure but qualitatively our results are confirmed and in most cases we
retain statistical significance. The first-stage estimates imply that those banks more exposed
to the oil-shock had reductions in their market capitalization, confirming the depiction in
figure 3. Specifically, the estimates from our commercial loans regression imply that a one33
There are some suggestions of similar risk reallocation effects in relation to liquidity provision discussed
in Drechsler, Drechsel, Marques-Ibanez, and Schnabl (2016), where it is shown that lender of last resort
provision of liquidity to Eurozone banks by the ECB during the financial crisis seemed to allow those banks
who were weakest - and for whom regulatory or endogenous capital requirements might be most binding to shift towards risky assets. Duchin and Sosyura (2014) also contains some reminiscent results in terms of
banks that received TARP aid shifting towards risky securities.
33
standard deviation increase in exposure was associated with a 5 percent decline in market
capitalization. In terms of quantitative implications, our results imply that a 1 percent hit
to net worth leads to a 0.90 percent decline in corporate lending on the intensive margin
and a 0.95 percent increase in the probability of exit. In terms of mortgage lending, the
response is a 1.8 percent decline for non-government portfolio loans, 0.6 percent increase
for non-government non-portfolio, and a 3.5 percent increase for government loans. Our
aggregate regressions again suggest that the ultimate impact on exposed borrowers was
minimal, relative to those borrowing from less exposed banks.
6.2.2. Stress testing
How banks respond to stressful conditions is also of particular interest following the
financial crisis. Our results suggest interesting opportunities to calibrate stress testing models
of balance sheet re-optimization. Halaj (2016) provides an innovative attempt to incorporate
optimizing behavior into a realistic model of bank balance sheet adjustment though most
stress testing models still rely on judgment and rules of thumb in this respect (see also Birge
and Judice (2013) for a simpler model featuring optimization).
Since a loss of net worth is in some sense a dual of facing tightened capital requirements
(in terms of a reduction in the distance of a bank from its ‘optimal’ buffer over minimum
levels of capital) our work should also aid the calibration of models attempting to establish
the appropriate regulatory requirements. Indeed, using aggregate and cross country data,
Cohen (2013) provides interesting evidence that banks have transitioned to higher capital
requirements post-crisis using some mix of a change in total assets and adjustments in riskweight profiles. Our analysis of banks’ responses to an identified shock complements these
findings and puts them on a firmer empirical footing.
6.2.3. Capital and liquidity requirements
In terms of regulatory constraints Cecchetti and Kashyap (2016) note that it is not
obvious which of the various capital and liquidity ratios now required of financial institutions
actually binds (capital relative to RWA, or simple leverage ratios, along with net stable
funding ratios and requirements for sufficient high quality liquid assets). With no discernible
effects in terms of overall quantities (commercial and portfolio mortgage lending declines,
while securitized mortgage lending expands), but a decline in the risk-weighted asset ratio,
it appears that risk weights are an important factor in a banks’ portfolio response to a net
34
worth shock and that it may not currently be the leverage ratio that binds, as opposed to risk
weighted measures of leverage. Since there is also a gain in liquidity from shifting towards
MBS, it is difficult to separate the impact of capital and liquidity requirements. Exploring
this further is beyond the scope of this paper, though interpreting de-risking somewhat more
broadly to incorporate concerns over liquidity risk (which is known to interact importantly
with solvency risk) provides an interesting dimension to our results.
It remains unclear whether regulatory constraints are what induces banks to ‘de-leverage
by de-risking’. In Holmstrom and Tirole (1997) it is shown that endogenous minimum
capital ratios for banks emerge as a result of financial frictions stemming from asymmetric
information, even in the absence of regulation. This model features risk neutral agents and
the concepts of different asset classes distinguished by risk does not feature. A richer model
with a non-trivial portfolio problem might reveal whether the market might require the
sort of de-risking patterns we observe. Without such a model, separating these endogenous
constraints from regulatory requirements would be a very difficult task, however.
6.2.4. The role of GSEs
Another interesting avenue for further exploration is the implication of our results for
understanding the role of GSEs. Our results suggest that following a shock, the more affected banks reduced their risk profile relative to the less affected, and they did so partly
by switching their mortgage origination towards loans that could be — and then were —
securitized into less heavily risk weighted asset classes. The presence of a deep and liquid
secondary market for mortgages allowed this portfolio reallocation to be effected at relatively
low cost. The involvement of the GSEs is influential in providing this cheap technology for
balance sheet reorganizations.34
One might imagine that absent this technology the banks in our paper would have
amended their risk profile by a different (more costly for them) breakdown between risk
weight adjustment and total asset adjustments. Since this ability to de-leverage by derisking partly relied on the GSEs, we note the interesting qualitative implication that the
GSEs influence the ex post consequences of bank-specific lending and investment decisions
34
One could argue that a private securitization market could allow the same opportunities although to
the extent that GSEs benefit from a special funding position (see Acharya, Richardson, Nieuwerburgh, and
White (2011) for a discussion) there is still a particular relevance to examining their role.
35
in areas other than their real estate lending. This suggests, qualitatively at least, that there
could be a moral hazard effect pervading all bank business lines and not just mortgages: if
GSEs help insure against the consequences of banks’ general investing decisions they give
them less of an incentive to be careful. Whether or not this is an important phenomenon
quantitatively is an open issue.
7. Conclusions
Banks damaged by exposure to industries adversely affected by the oil price decline made
significant adjustments to their balance sheets, as theories of the importance of bank net
worth suggest they should. We observe a significant pull-back in commercial lending by
exposed banks, both on the intensive and the extensive margins. Importantly, the balance
sheet adjustments by banks exposed to the oil price shock were not confined to the corporate
loan portfolio where the shock might be expected to have its most direct impact. Similar derisking results are obtained with regard to mortgage lending. Exposed banks tightened credit
supply for those mortgages that they would have to hold in their own portfolios. However,
there was an expansion in credit for loans that could be securitized, leading to an increase in
their MBS holdings. Thus, there are interesting connections between the banks’ lending book
and securities holdings, constituting a tendency of the damaged banks to de-leverage by derisking. This is a novel insight relative to the traditional ‘bank lending channel’ view which
emphasizes a general de-leveraging and pull back in lending. Ultimately, the relative impact
on borrowers who were particularly exposed to damaged banks was apparently limited.
It seems that the ability to substitute to alternative financing sources (largely) allowed
borrowers to smooth through any credit tightening imposed by the exposed banks. Thus,
while the bank lending channel appears to be active, the broader credit channel, at least in
this case, was weak, reflecting the richness of alternative financing sources. Significant policy
implications arise from our results and provide important avenues for future work.
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Table 1: Assessing the coverage of the Y14
$ Billions
Y-14 total utilized balances
Total commercial loans from all BHCs
Loans to U.S. nonfinancial corporates
1,170
1,691
2,386
Sources: Federal Reserve Board of Governors
Table 2: Bank summary statistics - average values pre-oil shock
Commercial Loan Analysis
Mean
Std. Dev.
Total assets ($billions)
Non-performing loan ratio (commercial loans)
Tier-1 Capital Ratio
Mean ROA (quarterly)
Equity/Total assets
On-balance-sheet total loans/Total assets
On-balance-sheet commercial loans/Total on-balance-sheet loans
On-balance-sheet residential loans/Total on-balance-sheet loans
Total charge-offs/Total on-balance-sheet loans
Deposit share of total liabilities
Foreign bank dummy
Lag ∆ log commercial loans (2012:Q3 to 2014:Q2)
Lag ∆ log residential loans (2012:Q3 to 2014:Q2)
Lag ∆ log MBS (2012:Q3 to 2014:Q2)
Committed O&G loans/Total committed commercial loans
Committed O&G loans/Total on-balance-sheet loans
Committed O&G loans/Total assets
Committed O&G loans/Equity
Observations
474
.010
.129
.002
.116
.526
.240
.285
.006
.726
.214
.326
.059
.046
.023
.194
28
675
.007
.020
.001
.023
.225
.104
.135
.005
.231
Residential Loan Analysis
Mean
Std. Dev.
466
.010
.126
.002
.118
.561
.252
.306
.006
.748
.231
694
.007
.016
.001
.022
.192
.096
.116
.005
.201
-.025
.038
.060
.045
.025
.205
26
.201
.700
.049
.041
.027
.213
.662
.049
.042
.027
.210
Notes: Data are from the Y9-C and are taken as the average from the 2012:Q3 to 2014:Q2 period. Lag
∆ log commercial loans, lag ∆ log residential loans, and lag ∆ log MBS are constructed as the change
between 2012:Q3 and 2014:Q2. “Committed O&G loans” are commercial loan commitments made to firms in
NAICS codes 211, 213111, and 213112, taken from the Y14 data. “Committed O&G loans/Total committed
commercial loans,” “Committed O&G loans/Total on-balance-sheet loans,” “Committed O&G loans/Total
assets,” and “Committed O&G loans/Equity” are calculated as the quarterly average over the 2012:Q3 to
2013:Q4 period. “Committed O&G loans/Total committed commercial loans” is the main exposure variable
used throughout the paper, referred to as “O&G Exposure.”
10,169
22.9
(47.0)
-.05
(.47)
48,739
10.8
(41.2)
-.07
(.35)
0.39
(0.49)
0.41
(0.49)
Loan Variables
20,006
77,928
18.9
9.9
(41.4)
(36.7)
0.26
(0.44)
17,132
0.28
(0.45)
74,407
over the pre-period.
total utilized commercial loan exposure in the pre-period (2012:Q3 to 2014:Q2) divided by total committed commercial loan exposure
respectively. Access to external finance = dummy for whether the firm has either a CUSIP or a ticker; Share of credit lines utilized =
A “loan” is defined as a bank-firm pair. The pre and post data are averaged over 2012:Q3 to 2014:Q2 and 2015:Q1 to 2015:Q3,
Notes: Listed are means and standard deviations in parentheses of variables used in the six main commercial loan samples.
Entry in post-shock period
Exit in post-shock period
∆ Log loan size
Number of loans
Loan size ($ millions, pre-shock period)
Intensive Margin
Exit Margin
Entry Margin
FE Sample OLS Sample FE Sample OLS Sample FE Sample OLS Sample
Firm Variables
Number of firms
3,541
42,111
7,802
65,724
5,901
60,121
Assets ($ millions)
720
179
569
178
619
182
(745)
(455)
(711)
(454)
(722)
(479)
Access to external finance
.56
.11
.44
.11
.47
.10
(.49)
(.31)
(.50)
(.31)
(.50)
(.30)
Share of credit lines utilized (pre-shock period)
.72
.89
.74
.88
(.28)
(.22)
(.29)
(.23)
Number of bank relationships (pre-shock period)
4.5
1.5
3.6
1.4
(3.2)
(1.5)
(2.7)
(1.4)
Table 3: Regression samples - firm and loan characteristics
Table 4: Regression samples - residential loans
Variable
Observations
Bank-County Level Variables
∆ Log Total Loans
32,832
∆ Log Non-Government Portfolio Loans
19,929
∆ Log Non-Government Portfolio Jumbo Loans
5,555
∆ Log Non-Government Portfolio Non-Jumbo Loans
18,913
∆ Log Government Portfolio Loans
3,639
∆ Log Non-Government Non-Portfolio Loans
24,366
∆ Log Non-Government Non-Portfolio Non-Jumbo Loans
22,892
County Level Variables
∆ Log Total LPS Loans
∆ Log Total Y-14 Loans
∆ Log Total Portfolio Y-14 Loans
2,938
2,943
2,943
Mean
Std. Dev.
-0.18
-0.16
0.16
-0.12
0.02
-0.09
-0.06
0.54
0.64
0.61
0.67
0.87
0.52
0.55
-0.11
-0.16
-0.17
0.14
0.12
0.41
Notes: Listed are the summary statistics of dependent variables used in the residential loan regression
analysis. For each category, we aggregate the data by county-bank-quarter, and then average over the preshock and post-shock periods (defined as 2012:Q3 to 2014:Q2 and 2015:Q1 to 2015:Q3, respectively), then
take the difference of the log value. County-level variables are constructed in an analogous fashion at the
county level.
Table 5: The bank lending channel - commercial loans, intensive margin
FE
O&G Exposure
-0.825***
(0.162)
Log Total Assets
0.060***
(0.009)
ROA
-14.229***
(3.994)
Equity/Total Assets
-1.308***
(0.486)
Lag ∆ Log Commercial Loans
-0.054
(0.040)
NPL/Total Assets
-4.523**
(2.227)
Tier-1 Capital/RWA
1.145
(0.729)
Total Loans/Total Assets
0.457***
(0.116)
Charge Offs/Total Loans
-7.117***
(1.435)
Commercial Loans/Total Loans -0.615***
(0.116)
Residential Loans/Total Loans
-0.713***
(0.181)
Deposits/Total Liabilities
0.123**
(0.048)
Foreign Bank Dummy
0.230***
(0.038)
Number of Observations
10169
R-squared
0.59
Fixed Effects
Firm
∆ Log loan Size
OLS
OLS
-0.578***
-0.323***
(0.170)
(0.058)
0.040***
0.035***
(0.009)
(0.004)
-6.447*
-7.502***
(3.780)
(1.437)
-0.219
-0.979***
(0.534)
(0.145)
-0.041
-0.009
(0.032)
(0.016)
-3.805**
-1.790**
(1.794)
(0.746)
0.410
1.328***
(0.658)
(0.205)
0.191*
0.207***
(0.109)
(0.042)
-2.727**
-1.851**
(1.217)
(0.736)
-0.477***
-0.309***
(0.107)
(0.042)
-0.542***
-0.509***
(0.172)
(0.053)
0.092*
0.150***
(0.048)
(0.027)
0.174***
0.143***
(0.034)
(0.012)
10169
48739
0.18
0.06
Firm Controls Firm Controls
Notes: The dependent variable is the change in log term-loan size. All quarterly data for a given loan were
collapsed to a single pre and post period, defined as 2012:Q3 to 2014:Q2 and 2015:Q1 to 2015:Q3, respectively.
We removed from the sample loans to firms in the oil and gas industry and data was restricted to term loans
for consecutive quarters throughout both the pre- and post period (i.e., no exiting and re-entering). O&G
exposure is constructed as total committed loans to oil and gas firms divided by total committed commercial
loans in 2012 and 2013. Column 1 is run on the sample of firms that borrow term loans from multiple banks
and include firm fixed effects. Column 2 is run on the same sample of firms as column 1 (but excluding
firms in the O&G industry) and includes firm controls instead of firm fixed effects. Column 3 includes firms
that borrow from single banks and includes firm controls. All bank controls listed are measured in the
pre-shock period. Firm controls in columns 2 and 3 include industry-times-region fixed effects (across 322
industries—3-digit NAICS codes, and 9 regions—the first digit of firm’s zip code) and a dummy indicating
whether the firm borrows from multiple banks. Standard errors in parentheses are two-way clustered at the
bank (28 banks in total) and firm level. ∗ p < 0.10,∗∗ p < 0.05,∗∗∗ p < 0.01.
Table 6: The bank lending channel- commercial loans, extensive margin
Exit?
FE
OLS
O&G Exposure
0.838***
0.739***
(0.158)
(0.193)
Log Total Assets
-0.027**
0.005
(0.011)
(0.011)
ROA
8.047
4.836
(6.041)
(7.436)
Equity/Total Assets
0.270
-0.326
(0.749)
(0.859)
Lag ∆ Log Commercial Loans
0.064*
0.059*
(0.036)
(0.036)
NPL/Total Assets
3.159
2.233
(1.993)
(2.412)
Tier-1 Capital/RWA
1.241**
2.392***
(0.579)
(0.792)
Total Loans/Total Assets
-0.215
0.047
(0.152)
(0.125)
Charge Offs/Total Loans
0.737
-1.640
(1.971)
(1.939)
Commercial Loans/Total Loans 0.475***
0.477***
(0.138)
(0.154)
Residential Loans/Total Loans
0.486***
0.295
(0.160)
(0.187)
Deposits/Total Liabilities
-0.112
-0.119
(0.068)
(0.077)
Foreign Bank Dummy
-0.132***
-0.095**
(0.038)
(0.038)
Number of Observations
20006
20006
R-squared
0.60
0.11
Fixed Effects
Firm
Firm Controls
OLS
0.109
(0.072)
0.012*
(0.007)
1.044
(2.932)
-0.419
(0.327)
0.011
(0.022)
3.080**
(1.511)
1.247***
(0.417)
0.028
(0.060)
-0.010
(1.255)
0.143*
(0.075)
0.075
(0.094)
0.034
(0.051)
-0.011
(0.021)
77928
0.04
Firm Controls
FE
-0.106
(0.176)
-0.050***
(0.012)
6.584
(5.414)
0.718
(0.622)
0.053
(0.033)
7.356***
(1.942)
-0.352
(0.885)
-0.129
(0.139)
-0.874
(1.815)
-0.191
(0.142)
0.163
(0.177)
-0.141**
(0.060)
-0.039
(0.042)
17132
0.56
Firm
Entry?
OLS
0.042
(0.210)
-0.039**
(0.017)
1.796
(6.627)
-0.254
(0.741)
0.060
(0.045)
9.295***
(2.385)
0.312
(1.340)
0.065
(0.155)
-2.440
(2.564)
-0.157
(0.177)
0.179
(0.194)
-0.215**
(0.097)
-0.037
(0.044)
17132
0.12
Firm Controls
OLS
0.199
(0.278)
-0.053**
(0.026)
6.187
(7.792)
0.783
(0.840)
0.039
(0.060)
12.714***
(2.850)
-0.277
(1.658)
-0.172
(0.190)
-3.206
(2.837)
0.154
(0.253)
0.468**
(0.183)
-0.339***
(0.121)
-0.128***
(0.042)
74407
0.11
Firm Controls
Notes: The dependent variable in columns 1-3, “exit,” is an indicator whether the term loan is not renewed
and the firm exits its banking relationship in the post-shock period. The dependent variable in columns 4-6,
“entry,” is an indicator if the term loan was made for the first time in the post-shock period. We removed
from the sample loans to firms in the oil and gas industry. O&G exposure is constructed as total committed
loans to oil and gas firms divided by total committed commercial loans in 2012 and 2013. Column 1 is run
on the sample of firms that borrow term loans from multiple banks in the pre-period and include firm fixed
effects. Column 2 is run on the same sample of firms as column 1 (but excluding firms in the O&G industry)
and includes firm controls instead of firm fixed effects. Column 3 includes firms that borrow from single
banks and includes firm controls. Column 4 is run on the sample of firms that borrow from multiple banks in
the post-period and includes firm fixed effects. All bank controls listed are measured in the pre-shock period.
Firm controls include industry-times-region fixed effects (across 322 industries—3-digit NAICS codes, and 9
regions—the first digit of firm’s zip code) and a dummy indicating whether the firm borrows from multiple
banks. Standard errors in parenthesis are two-way clustered at the bank (28 banks in total) and firm level.
∗
p < 0.10,∗∗ p < 0.05,∗∗∗ p < 0.01.
Table 7: The bank lending channel - commercial loans, interaction effects
O&G Exposure
O&G Exposure*(Small Firm)
FE
-0.904***
(0.195)
0.190
(0.217)
Small Firm
O&G Exposure*(External Finance Firm)
∆ Log loan size
OLS
FE
-0.376*
-1.085**
(0.207)
(0.482)
0.065
(0.228)
-0.097***
(0.015)
0.043
(0.244)
External Finance Firm
O&G Exposure*(Pre-period # of Banks)
-0.017
(0.026)
Pre-period # of Banks
O&G Exposure*(Pre-period Utilization Rate)
0.496
(0.483)
Pre-period Utilization Rate
Number of Observations
R-squared
Fixed Effects
10169
0.59
Firm
48739
0.06
Firm Controls
10169
0.59
Firm
Exit?
OLS
-0.129
(0.365)
0.138
(0.125)
-0.003
(0.010)
-0.025
(0.034)
0.007***
(0.003)
-0.143
(0.354)
-0.092***
(0.023)
48739
0.06
Firm Controls
FE
1.038***
(0.209)
-0.396*
(0.226)
OLS
0.688**
(0.278)
-0.635**
(0.297)
0.157***
(0.017)
FE
0.930**
(0.435)
OLS
0.327
(0.310)
0.044
(0.292)
0.273
(0.339)
-0.033*
(0.019)
0.054**
(0.027)
-0.024***
(0.002)
-0.378
(0.339)
-0.153***
(0.023)
77928
0.05
Firm Controls
0.047*
(0.026)
-0.585
(0.426)
20006
0.60
Firm
77928
0.05
Firm Controls
20006
0.60
Firm
Notes: The dependent variable for columns 1 through 4 is the change in log term-loan size and for columns 5
through 8 is an indicator whether the term loan is not renewed and the firm exits its banking relationship in
the post-shock period (i.e., “exit.”). All quarterly data for a given term loan were collapsed to a single preand post-shock period, defined as 2012:Q3 to 2014:Q2 and 2015:Q1 to 2015:Q3, respectively. We removed
from the sample loans to firms in the oil and gas industry. Data for the fixed-effects regressions were restricted
to term loans in the sample for consecutive quarters throughout both the pre- and post-shock period (i.e., no
exiting and re-entering). O&G exposure is constructed as total committed loans to oil and gas firms divided
by total committed commercial loans in 2012 and 2013. Columns 1 and 3 are run on the sample of firms with
term loans from multiple banks and include firm fixed effects. OLS regressions include firms that borrow from
single banks and includes firm controls. Firm controls include industry-times-region fixed effects (across 322
industries—3-digit NAICS codes, and 9 regions—the first digit of firm’s zip code) and a dummy indicating
whether the firm borrows from multiple banks. Small firm = total borrowing by firm from all banks is in
bottom 70%, external finance firm = dummy for whether the firm has either a CUSIP or a ticker; pre-period
utilization rate = total utilized commercial loan exposure in the pre-period (2012:Q3 to 2014:Q2) divided by
total committed commercial loan exposure over the pre-period; pre-period # of banks = number of banks the
firm has at least one loan with in the pre-shock period; length of relationship = years between date of first
origination and 2014:Q2. Bank controls include pre-shock period ROA, equity/total assets, NPL/total assets,
tier-1 capital/risk-weighted assets, total loans/total assets, charge offs/total loans, commercial loans/total
loans, residential loans/total loans, deposits/total liabilities, lag change in log commercial loans (defined
between 2012:Q3 and 2014:Q2), and a foreign bank dummy. Standard errors in parentheses are two-way
clustered at the bank and firm level (28 banks in total). ∗ p < 0.10,∗∗ p < 0.05,∗∗∗ p < 0.01.
Table 8: The credit channel - firm regressions
Agg. O&G Exposure
Number of Observations
R-squared
Bank Controls
Firm Controls
Y14
Y14 & Compustat
Term Loans All Loans
Liab
Eqty
Assets
0.090
0.058
-0.122 -0.333
0.141
(0.448) (0.565) (0.396)
(0.066)
(0.074)
1460
1426
1460
47167
47150
0.06
0.07
0.43
0.37
0.42
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Notes: These regressions are run at the firm level. We removed from the sample loans to firms in the oil
and gas industry. The dependent variable is the change in log total term-loan borrowing (column 1), the
change in log total-loan borrowing (column 2), the change in log total liabilities (column 3), the change
in log total equity (column 4), and the change in log total assets (column 5) by firm across the pre- and
post-shock period. Total term loan borrowing includes all term loans across all banks in the Y14 sample.
Total-loan borrowing includes utilized loans of all types (i.e., not just term loans) across all banks in the
Y14 sample. Liabilities, equity, and assets are taken from Compustat, limiting the sample of firms. All
quarterly data for a given loan were collapsed to a single pre and post period, defined as 2012:Q3 to 2014:Q2
and 2015:Q1 to 2015:Q3, respectively. Firm controls include industry-times-region fixed effects (across 322
industries—3-digit NAICS codes, and 9 regions—the first digit of firm’s zip code) and a dummy indicating
whether the firm borrows from multiple banks. Agg-exposure is the loan-size weighted O&G exposure
across all banks by the firm. Bank controls include pre-shock period loan-weighted average levels of ROA,
equity/total assets, NPL/total assets, tier-1 capital/risk-weighted assets, total loans/total assets, charge
offs/total loans, commercial loans/total loans, residential loans/total loans, deposits/total liabilities, lag
change in log commercial loans (defined between 2012:Q3 and 2014:Q2), and a foreign bank dummy. Robust
standard errors in parentheses. ∗ p < 0.10,∗∗ p < 0.05,∗∗∗ p < 0.01.
Table 9: Substitute financing - credit line utilization
Agg O&G Exposure
Agg O&G Exposure*(Pre-Period Utilization Rate)
Pre-Period Utilization Rate
Number of Observations
R-squared
Bank Controls
Firm Controls
∆ Utilization Rate
OLS
OLS
0.018
0.417**
(0.056)
(0.173)
-0.521**
(0.213)
-0.132***
(0.014)
14205
14205
0.09
0.14
Yes
Yes
Yes
Yes
Notes: The dependent variable is the change in the utilization rate by firm. The utilization rate is defined as
total utilized commercial loan exposure divided by total committed commercial loan exposure. We removed
from the sample loans to firms in the oil and gas industry and restricted the sample to firms with utilization
rates less than 100 percent in the pre-period. Firm controls include industry-times-region fixed effects (across
322 industries—3-digit NAICS codes, and 9 regions—the first digit of firm’s zip code) and a dummy indicating
whether the firm borrows from multiple banks. Agg-exposure is the loan-size weighted O&G exposure across
all banks by the firm. Bank controls include pre-shock period loan-weighted averages of ROA, equity/total
assets, NPL/total assets, tier-1 capital/RWA, total loans/total assets, charge offs/total loans, commercial
loans/total loans, residential loans/total loans, deposits/total liabilities, lag change in log commercial loans
(defined between 2012:Q3 and 2014:Q2), and a foreign bank dummy. Robust standard errors in parentheses.
∗
p < 0.10,∗∗ p < 0.05,∗∗∗ p < 0.01.
Table 10: Substitute financing - alternative banks
O&G Exposure
FE
-0.513**
(0.247)
FE
0.284
(0.200)
Agg. O&G Exposure
Number of Observations
R-squared
Fixed Effects
Bank Controls
Sample
7277
0.41
Firm
Yes
Firms that
exit
8447
0.42
Firm
Yes
Firms with
no exit
Entry?
FE
-0.346*
(0.183)
FE
0.421
(0.268)
Switch?
OLS
0.070***
(0.02)
12017
5115
77187
0.15
0.36
0.63
Firm
Firm
Firm Controls
Yes
Yes
Yes
Firms with Firms without
All Firms
loan declines loan declines
Notes: Fixed-effects (FE) regressions are run at the loan level. The dependent variable of the FE regressions
is an indicator if the term loan was made for the first time in the post-shock period (i.e., entry). All FE
regressions are run with multiple banking relationships using firm fixed effects. Column 1 is run on the
sample of firms that exited from at least one banking relationship in the post period. Column 2 is run on the
sample of firms that did not exit any banking relationship. Column 3 is run on a sample firms with at least
one loan that declined in size. Column 4 is run on a sample of firms without any loans that decreased in size.
Bank controls include pre-shock period ROA, equity/total assets, NPL/total assets, tier-1 capital/RWA,
total loans/total assets, charge offs/total loans, commercial loans/total loans, residential loans/total loans,
deposits/total liabilities, lag change in log commercial loans (defined between 2012:Q3 and 2014:Q2), and a
foreign bank dummy. Column 5 is a regression run at the firm level, where the dependent variable is a dummy
equal to one if the firm exited at least one banking relationship and entered at least one banking relationship
between the pre- and post-shock period. Bank controls are loan-size weighted in the OLS specification. We
removed from the sample loans to firms in the oil and gas industry. Standard errors in parentheses are
two-way clustered at the bank (28 banks in total) and firm level for FE regressions and robust standard
errors reported for the OLS regression. ∗ p < 0.10,∗∗ p < 0.05,∗∗∗ p < 0.01.
Table 11: The bank lending channel - residential loans
All
Non-Government
Government
Portfolio Loans
Non-Portfolio Loans
Jumbo Non-Jumbo
All
Non-Jumbo
O&G Exposure
-2.247***
(0.538)
Number of Observations
19929
R-squared
0.23
Bank Controls
Yes
Fixed Effects
County
O&G Exposure
-2.223***
(0.531)
Number of Observations
19928
R-squared
0.09
Bank Controls
Yes
Fixed Effects
State
County Controls
Yes
Panel A: County Fixed Effects
1.073
-1.451
-2.521**
(0.920)
(1.019)
(0.933)
5555
18913
23353
0.29
0.26
0.23
Yes
Yes
Yes
County
County
County
-1.522*
(0.911)
5555
0.09
Yes
State
Yes
Panel B: OLS
0.965
-2.512**
(0.924)
(1.039)
18912
23351
0.13
0.11
Yes
Yes
State
State
Yes
Yes
All Loans
3.042**
(1.185)
23247
0.26
Yes
County
8.426***
(1.190)
13712
0.40
Yes
County
-1.502
(0.944)
29056
0.24
Yes
County
2.941**
(1.232)
23245
0.14
Yes
State
Yes
8.295***
(1.133)
13710
0.23
Yes
State
Yes
-1.518
(0.955)
29053
0.16
Yes
State
Yes
Notes: The dependent variable is the change in log residential loans by bank-county. All quarterly data for
loans was aggregated by bank-county and then collapsed to a single pre- and post-shock period, defined as
2012:Q3 to 2014:Q2 and 2015:Q1 to 2015:Q3, respectively. O&G exposure is constructed as total committed
loans to oil and gas firms divided by total committed commercial loans in 2012 and 2013. In columns 1-4 the
dependent variables are log changes in BHC portfolio loans, while columns 5-6 are BHC non-portfolio loans.
Portfolio loans are than divided between non-government jumbo loans, non-government non-jumbo loans, and
government loans (i.e., FHA and VA loans). We include only loans originated by the BHC (i.e., we removed
mortgages with loan source marked as correspondent, servicing rights purchased, and bulk purchased).
Bank controls include pre-shock period ROA, equity/total assets, NPL/total assets, tier-1 capital/RWA,
total loans/total assets, charge offs/total loans, commercial loans/total loans, residential loans/total loans,
deposits/total liabilities, lag change in log residential loan (defined between 2012:Q3 and 2014:Q2), lag
change in log MBS (defined between 2012:Q3 and 2014:Q2), and a foreign bank dummy. County controls
include pre-period log population, log population density, percent veterans, log housing density, percent
urban housing units, percent occupied housing units, percent vacant housing units, log median house value,
log median rental value, fraction of housing with 3 or more individuals, percent in poverty, unemployment
rate, percent disabled, log median household income. Standard errors in parentheses are two-way clustered
at the bank (26 banks in total) and county level. ∗ p < 0.10,∗∗ p < 0.05,∗∗∗ p < 0.01.
Table 12: The credit channel - county regressions
Y-14
LPS
Portfolio
All
Aggregate O&G Exposure
-0.477
-0.263 -0.105
(0.901) (0.428) (0.532)
Number of Observations
2951
3010
3003
R-squared
0.15
0.10
0.08
Yes
Yes
Bank Controls
Yes
Fixed Effects
State
State
State
Yes
Yes
County Controls
Yes
Notes: The dependent variable is the change in log residential loans by county. All quarterly data for loans
were aggregated by county and then collapsed to a single pre- and post-shock period, defined as 2012:Q3 to
2014:Q2 and 2015:Q1 to 2015:Q3, respectively. In column 1, we include only portfolio loans, and in column 2
all Y14 mortgages. In column 3, we include all loans in the LPS McDash Analytics database. Agg-exposure
is the loan weighted O&G exposure across all banks in the county. Bank controls include pre-shock period
(weighted to county level) ROA, ROE, NPL/total assets, tier-1 capital/RWA, total loans/total assets, charge
offs/total loans, commercial loans/total loans, residential loans/total loans, deposits/total liabilities, lag
change in log residential loan (defined between 2012:Q3 and 2014:Q2), lag change in log MBS (defined between
2012:Q3 and 2014:Q2), and a foreign bank dummy. County controls include pre-period log population, log
population density, percent veterans, log housing density, percent urban housing units, percent occupied
housing units, percent vacant housing units, log median house value, log median rental value, fraction of
housing with 3 or more individuals, percent in poverty, unemployment rate, percent disabled, log median
household income. Robust standard errors in parentheses. ∗ p < 0.10,∗∗ p < 0.05,∗∗∗ p < 0.01.
Table 13: The bank lending channel- instrumental variables
∆ Log Market Cap
O&G Exposure
Number of Observations
R-squared (second-stage)
Bank Controls
Fixed Effects
∆ Log Market Cap
O&G Exposure
Number of Observations
R-squared (second-stage)
Bank Controls
Fixed Effects
A: Commercial Loans
∆ Log
Exit?
Entry?
loan size
FE-IV
FE-IV
FE-IV
Second Stage
0.901*** -0.953**
0.056
(0.328)
(0.410)
(0.209)
First Stage
-0.956*** -0.907** -0.907**
(0.368)
(0.353)
(0.367)
9719
19222
16412
0.60
0.61
0.57
Yes
Yes
Yes
Firm
Firm
Firm
B: Residential Loans
Non-Government
Government
Portfolio Non-Portfolio
FE-IV
FE-IV
FE-IV
Second Stage
1.812***
-0.581*
-3.453***
(0.630)
(0.316)
(0.436)
First Stage
-1.427***
-2.772***
-2.679***
(0.448)
(0.316)
(0.159)
19451
22830
13514
0.24
0.24
0.41
Yes
Yes
Yes
County
County
County
All Loans
FE-IV
1.181
(0.775)
-1.749***
(0.486)
28378
0.25
Yes
County
Notes: All regressions were run under two-stage least squares using O&G exposure as an instrument for the
change in bank’s market capitalization—defined as the difference in log average quarterly market capitalization in the post-period (2015:Q1 and 2015:Q3) and pre-period (2012:Q3 and 2014:Q2). The dependent
variables are the change in log term-loan size (column 1, panel A), an indicator whether the term loan is
not renewed and the firm exits its banking relationship in the post period (column 2, panel A), and an
indicator if the term loan was made for the first time in the post-period (column 3, pane A), and the change
in log residential loans (originated by the BHC) by county and type of loan (panel B). Bank controls include
pre-shock period ROA, equity/total assets, NPL/total assets, tier-1 capital/RWA, total loans/total assets,
charge offs/total loans, commercial loans/total loans, residential loans/total loans, deposits/total liabilities,
and a foreign bank dummy. Residential loan regressions in include the lag change in log residential loan
(defined between 2012:Q3 and 2014:Q2) and lag change in log MBS (defined between 2012:Q3 and 2014:Q2)
as controls, and the commercial loan regressions include the lag change in log commercial loans (defined
between 2012:Q3 and 2014:Q2) as a control. Standard errors in parentheses are two-way clustered at the
bank and firm level. ∗ p < 0.10,∗∗ p < 0.05,∗∗∗ p < 0.01.
Table 14: Robustness - alternative exposure measures
A: Commercial Loans
∆ Log loan size
Exit?
Committed O&G/Total Loans -1.015***
1.282***
(0.239)
(0.197)
Committed O&G/Total Assets
-1.649***
2.076***
(0.358)
(0.317)
0.270***
Committed O&G/Equity
-0.190***
(0.054)
(0.040)
20006
20006
20006
Number of Observations
10169
10169
10169
0.60
0.60
0.60
R-squared
0.59
0.59
0.59
Fixed Effects
Firm
Firm
Firm
Firm
Firm
Firm
Yes
Yes
Yes
Bank Controls
Yes
Yes
Yes
All
Committed O&G/Total Loans
Number of Observations
R-squared
Bank Controls
Fixed Effects
Committed O&G/Total Assets
Number of Observations
R-squared
Bank Controls
Fixed Effects
Committed O&G/Equity
Number of Observations
R-squared
Bank Controls
Fixed Effects
B: Residential Loans
Non-Government
Portfolio Loans
Non-Portfolio Loans
Jumbo Non-Jumbo
All
Non-Jumbo
Government
All Loans
-5.103*** -1.536
(0.730)
(1.148)
19929
5555
0.24
0.28
Yes
Yes
County County
-7.316***
(1.544)
18913
0.27
Yes
County
4.384***
(1.701)
23353
0.24
Yes
County
4.621**
(2.067)
23247
0.26
Yes
County
12.507***
(1.204)
13712
0.41
Yes
County
1.287
(1.672)
29056
0.24
Yes
County
-6.335*** -3.150
(1.271)
(2.096)
19929
5555
0.23
0.28
Yes
Yes
County County
-8.726***
(2.617)
18913
0.27
Yes
County
4.077
(2.814)
23353
0.23
Yes
County
6.669*
(3.606)
23247
0.26
Yes
County
21.341***
(1.606)
13712
0.41
Yes
County
0.255
(2.077)
29056
0.24
Yes
County
-0.865*** -0.342
(0.180)
(0.279)
19929
5555
0.23
0.28
Yes
Yes
County County
-1.163***
(0.397)
18913
0.27
Yes
County
0.847***
(0.295)
23353
0.24
Yes
County
1.164***
(0.319)
23247
0.26
Yes
County
2.787***
(0.264)
13712
0.41
Yes
County
0.169
(0.251)
29056
0.24
Yes
County
Notes: These tables show the main estimates under three alternative O&G exposure variables: (1) committed
O&G commercial loans divided by total on-balance sheet loans, (2) committed O&G commercial loans
divided by total assets, and (3) committed O&G commercial loans divided by total equity capital. The
dependent variables are the change in log term-loan size (columns 1 and 2, panel A), an indicator whether
the term loan is not renewed and the firm exits its banking relationship in the post period (columns 3
and 4, panel A), and the change in log residential loans (originated by the BHC) by county and type of
loan (panel B). Bank controls include pre-shock period ROA, equity/total assets, NPL/total assets, tier1 capital/RWA, total loans/total assets, charge offs/total loans, commercial loans/total loans, residential
loans/total loans, deposits/total liabilities, and a foreign bank dummy. Residential loan regressions in
include the lag change in log residential loan (defined between 2012:Q3 and 2014:Q2) and lag change in log
MBS (defined between 2012:Q3 and 2014:Q2) as controls, and the commercial loan regressions include the
lag change in log commercial loans (defined between 2012:Q3 and 2014:Q2) as a control. Standard errors in
parentheses are two-way clustered at the bank and firm level. ∗ p < 0.10,∗∗ p < 0.05,∗∗∗ p < 0.01. Standard
errors in parentheses are two-way clustered at the bank (28 banks in total) and firm level. ∗ p < 0.10,∗∗ p <
0.05,∗∗∗ p < 0.01.
Figure 1: Oil Prices and Problem Loans by Industry
Dollars per barrel
100
12.0%
WTI Price
10.0%
80
8.0%
60
6.0%
40
4.0%
O&G Firms
Non O&G Firms
O&G Net-using Firms
20
2.0%
0
0.0%
Percentage of Loans Past Due, Charged Off, or in Non-accrual Status
120
Notes: The red dashed line represents the price per barrel of crude West Texas Intermediate oil, collapsed
to the quarterly level. Blue bars represent the percentage of loans (i.e., including undrawn commitments)
to oil and gas firms (defined by NAICS codes 211, 213111, and 213112) that are either past due, charged
off, or in non-accrual status (referred to as “problem loans”). Gray bars represent the percentage of loans
to all non oil and gas firms that are problem loans. Orange bars represent the percentage of loans to all
net users of the oil and gas industry—defined as firms in industries with less than -0.10 in net trade (i.e.,
make minus use) with the oil and gas industry, as defined by the 2007 BEA make and use tables—that are
problem loans.
Figure 2: Net Trade with NAICS 324, 211, and 213111 as a Share of Total Output by Industry
1
0.8
0.6
Net Trade Ratio
0.4
0.2
0
-0.2
-0.4
369 industries between -0.10 and 0
-0.6
-0.8
Notes: Bars represent net trade (that is, make minus use) of each industry with NAICS 324, 211, 213111
(petroleum refineries, oil and gas extraction, and drilling oil and gas wells, respectively), based on the 2007
Bureau of Economic Analysis (BEA) make and use tables (https://www.bea.gov/industry/io_annual.
htm). BEA provides make and use matrix tables for 389 industries, where each industry is mapped into a
three, four, or five digit NAICS code. NAICS code 213112 (support activities for oil and gas operations),
included in our measure of oil and gas exposure, is not separately included in the BEA input-output table.
We omit the (many) industries for which this net position is negligible.
Figure 3: Stock price and market capitalization - dependence on oil exposure
B: Market Capitalization
A: Stock Price
30%
30%
Percent Deviation from 2014:2
20%
Exposure Below
25th Percentile
20%
10%
10%
0%
0%
-10%
-10%
-20%
-30%
Exposure Below
25th Percentile
-20%
Exposure Above
75th Percentile
-30%
Exposure Above
75th Percentile
-40%
-40%
-50%
-50%
-60%
2012:01 2012:07 2013:01 2013:07 2014:01 2014:07 2015:01 2015:07 2016:01
-60%
2012:01 2012:07 2013:01 2013:07 2014:01 2014:07 2015:01 2015:07 2016:01
Notes: This figure illustrates the impact of a bank’s oil and gas extraction (O&G) exposure to bank stock
prices (panel A) and market capitalization (panel B). For each bank in each month, we calculate the deviation
of its stock price or market capitalization from 2014:M6. The red dashed line represents the average deviation
for banks in the upper-quarter in terms of exposure to the O&G sector, the blue dashed line represents the
average deviation for banks in the bottom quartile.
Figure 4: Behavior of bank balance sheet variables
C: Agency MBS
.75
.12
B: Residential Loans
.5
.25
0
0
-.12 -.08 -.04
-.25
0
-.12 -.08 -.04
.04
.04
.08
.08
.12
A: Commercial & Industrial Loans
D: Risk-Weighted Asset Ratio
E: Total Assets
.08
0
-.12 -.08 -.04
.04
.08
.04
0
-.12 -.08 -.04
2012.3 2013.1 2013.3 2014.1 2014.3 2015.1 2015.3 2016.1
2012.3 2013.1 2013.3 2014.1 2014.3 2015.1 2015.3 2016.1
.12
2012.3 2013.1 2013.3 2014.1 2014.3 2015.1 2015.3 2016.1
.12
2012.3 2013.1 2013.3 2014.1 2014.3 2015.1 2015.3 2016.1
2012.3 2013.1 2013.3 2014.1 2014.3 2015.1 2015.3 2016.1
Notes: This figure illustrates the impact of a bank’s oil and gas extraction (O&G) exposure to banks balance
sheet variables. All data are from the FR-Y9. For each bank, we regress the logarithm of the bank-level
variable on time dummies, time dummies interacted with O&G exposure, bank fixed effects, and bankspecific time trends. Shown are the coefficients and 90 percent confidence bands of the coefficients on the
time dummies interacted with O&G exposure. Omitted periods in the regression are 2012:Q3 and 2014:Q2,
implying bank-specific time trends capture pre-shock period trends. O&G exposure was normalized to have
a mean of one standard deviation. Standard errors are clustered by bank. To correct for a coding change
in risk weights implemented by Basel III in 2015:Q1, the risk-weighted asset ratio (panel D) for 2015:Q1 for
each bank was spliced with its 2014:Q4 value.
Figure 5: Bank Lending Standards for Commercial Loans- dependence on oil exposure.
6
A: Loan Covenants
Index of Cumulative Tightening Relative to 2014:2
5
4
3
2
Exposure Above
75th Percentile
1
0
-1
-2
Exposure Below
25th Percentile
-3
6
B: Loan Rates
Index of Cumulative Tightening Relative to 2014:2
5
4
3
2
1
Exposure Above
75th Percentile
0
-1
-2
-3
Exposure Below
25th Percentile
2012:3 2012:4 2013:1 2013:2 2013:3 2013:4 2014:1 2014:2 2014:3 2014:4 2015:1 2015:2 2015:3 2015:4 2016:1 2016:2 2016:3
Notes: This figure illustrates the impact of a bank’s oil and gas extraction (O&G) exposure to banks lending
standards for commercial loans. All data are from the Senior Loan Officer Opinion Survey (SLOOS). For
each bank we measured the cumulative amount of survey responses in which the response was a tightening
of the specified variable—loan covenants (panel A) or loan rates (panel B)—relative to 2014:Q2. The reddotted line represents the average for banks in the upper-quarter in terms of exposure to the O&G sector,
the blue-dotted line represents the average for banks in the bottom quartile.
Figure 6: Bank Lending Standards for Mortgages- dependence on oil exposure.
A: GSE Eligible
Exposure
Below 25th
Percentile
-0.4
-0.6
-0.8
-1
-1.2
Exposure
Above 75th
Percentile
-1.4
-1.6
-1.8
Index of Cumulative Tightening Relative to 2015:1
Index of Cumulative Tightening Relative to 2015:1
-0.2
-2
Exposure
Below 25th
Percentile
-0.2
-0.4
-0.6
-0.8
Exposure
Above 75th
Percentile
-1
-1.2
-1.4
C: Non-Jumbo, Non-GSE Eligible
0.2
Exposure
Above 75th
Percentile
0.1
0
-0.1
Exposure
Below 25th
Percentile
-0.2
-0.3
D: Jumbo
0
-0.4
-0.5
Index of Cumulative Tightening Relative to 2015:1
0.3
Index of Cumulative Tightening Relative to 2015:1
B: Government
0
0
-0.2
Exposure
Above 75th
Percentile
-0.4
-0.6
Exposure
Below 25th
Percentile
-0.8
-1
-1.2
-0.6
2015q1
2015q2
2015q3
2015q4
2016q1
2016q2
2016q3
2015q1
2015q2
2015q3
2015q4
2016q1
2016q2
2016q3
Notes: This figure illustrates the impact of a bank’s oil and gas extraction (O&G) exposure to bank lending
standards for mortgages. All data are from the Senior Loan Officer Opinion Survey (SLOOS) questions about
qualifying mortgages. A positive slope indicates tightening. For each bank we measured the cumulative
amount of survey responses in which the response was a tightening of the specified variable relative to
2015:Q1, the first date available. The GSE-eligible category of residential mortgages includes loans that
meet the underwriting guidelines, including loan limit amounts, of the GSEs—Fannie Mae and Freddie
Mac. The government category of residential mortgages includes loans that are insured by the Federal
Housing Administration, guaranteed by the Department of Veterans Affairs, or originated under government
programs, including the U.S. Department of Agriculture home loan programs. The non-jumbo, non-GSEeligible category of residential mortgages includes loans that satisfy the standards for a qualified mortgage
and have loan balances that are below the loan limit amounts set by the GSEs but otherwise do not meet
the GSE underwriting guidelines. The jumbo category of residential mortgages includes loans that satisfy
the standards for a qualified mortgage but have loan balances that are above the loan limit amount set by
the GSEs. The red dashed line represents the average for banks in the upper-quarter in terms of exposure
to the O&G sector, the blue dashed line represents the average for banks in the bottom quartile.
Figure 7: Wild Cluster Bootstrap t-Statistic Permutation Test by Industry
5
4
Frequency
3
2
2
Frequency
3
4
5
6
B: Commercial Loans, Extensive Margin with Fixed Effects
6
A: Commercial Loans, Intensive Margin with Fixed Effects
KŝůĂŶĚ'ĂƐ
1
0
0
1
KŝůĂŶĚ'ĂƐ
-4
-3
-2
-1
0
1
2
Wild Bootstrap t-Statistic by Industry
3
4
-4
-3
-2
-1
0
1
2
Wild Bootstrap t-Statistic by Industry
3
4
Notes: This figure shows histograms of wild cluster bootstrap t-statistics for each of the 50 most common
industries (defined by 3-digit NAICS code) in the commercial loan Y14 data. Analogous to the O&G exposure
variable used throughout this paper, for each of the other 49 industries we create a bank-specific exposure
variable. We then run the fixed fixed-effects specification 50 times—once as our original specification, and 49
subsequent regressions, each time replacing O&G exposure with one of the 49 other industry exposures. tstatistics for each regression were constructed using the wild cluster bootstrap resampling method (clustered
by bank) as described in Cameron and Miller (2015). This sequence of steps was performed twice: producing
two histograms, one for the intensive margin (Panel A) and one for the extensive margin (Panel B). The
wild cluster bootstrap t-statistic for the O&G exposure variable is marked in each panel.
Appendix A. Data Appendix
In this appendix, we discuss our methods and assumptions involved in constructing the
data set of commercial loan and mortgage loan observations from the Y-14 and Y-9 databases
maintained by the Federal Reserve Board of Governors (FR). The disaggregated commercial
and mortgage loans are obtained from the FR Y-14Q and FR Y-14M database, respectively.
Appendix A.1. Commercial Loans
Appendix A.1.1. Firm ID Cleaning
We use only loans made to U.S. firms. Firms are identified in Y14 by “tax identification
number” (TIN), some of which had missing values. We used the field “obligorname” to help
fill in those observations with missing TIN information or to identify potential problem TIN
information. If an obligorname is associated with a missing TIN for some entry we look to
see if, for an exact obligorname match, a TIN is provided anywhere else in the data set.35 If
so, we fill in the missing TINS for that obligorname with the modal provided TIN.
We corrected the remainder of the missing TINS by the following:
• Standardize all obligornames using the STATA command stnd command (Wasi and
Flaaen (2015)).
• Flag “good TIN”—TINS uniquely associated with a standardized obligorname by a
single bank.
• Replace missing TINS with matched (by standardized obligorname) good TIN.
• Replace remaining missing TINS with a newly created TIN assigned to standardized
obligorname.
We then corrected TINs associated with more than one standardized obligorname. We
replaced these TINS with a matched (by standardized obligorname) “good TIN.” The remaining multiple-obligorname TINs we replaced with a newly created TIN assigned by its
standardized obligorname.
35
Obligorname was set to missing if individual, trust, confidential, NA etc.
Appendix A.1.2. Loan Level Cleaning
We dropped loans if a duplicate loan ID (“internalcreditfacilityid”) was found within a
quarter. Any IDs that are inadvertently used by more than one bank were replaced with
unique IDs.
We define term loans as those loans with credit facility type marked in the Y14 as “Term
Loan,” “Term Loan-A,” “Term Loan-B,” “Term Loan-C,” “Term Loan-Bridge,” “Term LoanAsset Based,” and “Term Loan-DIP.”
We define loans as ”Line of Credit” as those with facility type marked in the Y14 as
“Revolving Credit,” “Revolving Credit Converting to Term Loan,” “Revolving Credit-Asset
Based,” “Revolving Credit-DIP,” “Non-revolving Line of Credit,” “Non-revolving Line of
Credit Converting to Term Loan,” and “Standby Letter of Credit.”
We then cleaned the above definitions via the following: We changed loans labeled as
“Term Loan” in Y14 to “Line of Credit” if the loan had a zero interest rate and had any
undrawn commitments over the life of loan—these are likely credit lines. We changed loans
labeled as “Line of Credit” in Y14 to “Term Loan” if the loan had a non-zero interest rate
and had no undrawn commitments over any part of the life of the loan—these are likely term
loans.
Appendix A.1.3. Industry Codes
We focus on three-digit level NAICS codes. If multiple NAICS codes are used for a given
TIN, we imposed the modal code. Some banks use SIC or GICS codes, which were converted
to NAICS codes using a crosswalk.
NAICS code 211 (oil and gas extraction) was augmented with two six-digit codes 213111
(drilling oil and gas wells) and 213112 (support activities for oil and gas operations). To
ensure that oil and gas extraction sector firms were labeled correctly we did the following
sequence of steps.
• Flag suggestive obligornames names—those with any of the following words in the name
“gas”, “drill”, “energy”, “oil”, “propane”, “pipe”, “power”, “resource”, “material”,
“petrol”, “technologies”, “operating.”
• Assign firm to NAICS code 211 if at least 10 percent of its observations have NAICS
code 211 and obligorname features any of the suggestive energy related words above.
• Assign firm to 211 if at least 10 percent of its observations have a suggestive energy
related obligorname and at least one entry has a NAICS code 211.
O&G exposure bank level variable is then created.
Appendix A.1.4. Final Cleaning
The Y14 imposes a $1m materiality threshold from 2012:Q2 onwards. To allow for
inflation and strategic behavior around the threshold, we left a buffer thus dropping those
loans less than $1.2m. We dropped those observations still missing a TIN. We keep data
between 2012:Q3 and 2015:Q3—data before 2012:Q3 include 19 BHCs. Log loan change
variables are then created.
Appendix A.2. Residential Loans
A 10 percent random sample was drawn from the Y-14M. We dropped loans with missing
zip codes, origination amounts, and principal balance amounts. We then winsorized principal
balances at the 1 percent level. All loans past due were dropped.
Appendix A.2.1. Defining Types of Mortgages
Government, Portfolio, Non-Portfolio, and BHC origination categorization:
• Government Loans: Loan Type (1 = FHA Residential or 2 = VA Residential)
• Non-Government Portfolio Loans: Investor Type (7 = Portfolio); Loan Type (NOT 1
= FHA Residential, NOT 2 = VA Residential)
• Non-Government Portfolio Loans Originated by BHC: Investor Type (7 = Portfolio);
Loan Type (NOT 1 = FHA Residential, NOT 2 = VA Residential); Loan Source (NOT
3 = Correspondent, NOT 4 = Servicing Rights Purchased, NOT 5 = Bulk Purchased)
• Non-Government Non-Portfolio Loans: Investor Type (NOT 7 = Portfolio); Loan Type
(NOT 1 = FHA Residential, NOT 2 = VA Residential)
• Non-Government Non-Portfolio Loans Originated by BHC: Investor Type (NOT 7 =
Portfolio); Loan Type (NOT 1 = FHA Residential, NOT 2 = VA Residential); Loan
Source (NOT 3 = Correspondent, NOT 4 = Servicing Rights Purchased, NOT 5 =
Bulk Purchased)
Appendix B. Descriptive Appendix
Appendix B.1. Adjusting the firm-level corporate regressions
A way to correct for endogeneity bias in the firm-level OLS estimator entails directly
comparing the FE and OLS estimates of the loan-level regressions for term loans and adjusting the initial aggregate OLS estimate accordingly (see Jimenez, Mian, Peydro, and
Saurina Salas (2014)):36
V ar(Zi )
F
− (β̂1,OLS − β̂1,F E ) ∗
,
βˆ1F = β̂1,OLS
V ar(Z̄i )
(B.1)
F
where β̂1,OLS , β̂1,F E , and β̂1,OLS
are aggregate OLS, loan-level FE, and loan-level OLS estii)
represents the ratio of
mates of the coefficient on O&G exposure, respectively, and VV ar(Z
ar(Z̄i )
the in-sample variance of the loan-level and firm-level O&G exposure variables. Plugging in
the coefficients from the intensive FE and OLS estimates gives a correction of -0.25, generating a coefficient of -0.16 on the aggregate term-loan regression coefficient.37 Therefore, the
) implying
bias-corrected coefficient is one-fifth the size of the FE intensive effect (= −0.16
−0.83
that firms were able to substitute 80 percent of the decline in loan supply simply from other
Y14 banks.
The 80 percent number is likely a lower bound, for several reasons. First, even if there
was not complete substitution for financing using funds from other Y14 banks, for some
firms the shortfall was likely to be partly made up from loans from non-Y14 banks. Second,
because firms’ total change in term loans also includes extensive effects (where the bias is
apparently less severe), using the coefficients on the intensive regressions likely implies an
over correction.38 Finally, we note that the availability of commercial paper facilities and
other modes of external finance reinforce the sense in which our adjusted estimate is only a
bound.
36
Since our FE loan-level analysis dealt with term loans, this adjustment is not suitable for the other
dependent variables in table 8.
37
From table 5, β̂1,F E = −0.83 and β̂1,OLS = −0.58. The standard deviations of O&G exposure in the
ar(Zi )
OLS and FE aggregate regressions are both equal to 0.37 implying VV ar(
=1
Z̄i )
38
While one could combine both the intensive and extensive effects by implementing a Tobit model, a fixed
effects Tobit estimator does not exist - there is no sufficient statistic allowing fixed effects to be conditioned
out of the likelihood function. Including unconditional fixed effects is also not suitable, since it suffers from
the incidental parameters problem and is biased. See Greene (2004).