Natural Disasters, Loan Loss Accounting andSubsequent Lending∗
Sandra L Chamberlain

Rajesh Vijayaraghavan†

Yuxiang Zheng

October 31, 2019

Abstract
This paper examines the impact of disasters on banks’ loan loss accounting, and the impact ofloan loss accounting on banks’ ability to respond to disaster-related increases in demand for loans.
We identify banks affected by natural disasters, and match banks that are enjoying relatively
calm lending environments. We implement a difference–in–difference research strategy andfirst show loan loss provisions reflect an increasing weight on current and lagged loan loss
indicators during the four quarters that encompass the disaster. For large banks, the loan
loss provisions reflects a higher weighting on forward looking estimates of non–performing
loans post the disaster. This finding for large banks raises a concern that disasters impact loan
loss provisions in such a way that they confound the interpretation of provisioning–timelinessmeasures which have been used in prior research. We then examine whether banks that use
either more conservative, or more timely provisioning practices, in periods preceding disasters,
are able to respond more quickly to new loan demands created by disasters. Our results suggestthat smaller banks, which have conservative provisions (i.e., over-reserve) during non-disasterperiods, demonstrate greater loan growth following a disaster. However, we find that anincreased role for Tier 1 capital in the post-disaster period, subsumes this relationship between
provisioning-conservatism and lending. Finally, we fail to find evidence that provisioning–timeliness plays any role in stimulating lending in post-disaster periods. This indicates thatprior research that has suggested the existence of a positive feedback between “timeliness”
and lending during economic downturns, does not generalize to banks’ response to localized
increases in demand for loans as identified by disasters.

∗

Sandra Chamberlain is a CPA Professor of Accounting. Rajesh Vijayaraghavan is an Assistant Professor, and Yuxiang
Zheng is a PhD student. This research is partially supported by Social Sciences and Humanities Research Grant, as
well as a Sauder Exploratory Grant. Rajesh Vijayaraghavan acknowledges support from UBC Research Grant. We
are grateful to Simon Jäger, Stephen Ryan, Tom Siems (CSBS discussant), Gary Petersen (chairman, CornerstoneBank), workshop participants at the Federal Reserve/CSBS/FDIC Community Banking Research and Policy Conference,
University of Hawaii, Hong Kong PolyU, McGill Accounting Conference Workshop, UBCOW Accounting Conference,
and at the University of British Columbia for helpful comments and discussion. Comments welcome. Please do not
circulate or cite without permission.
†
Corresponding author. Email: rajesh.vijayaraghavan@sauder.ubc.ca. All three authors are at the Sauder
School of Business, University of British Columbia.

1

1

Introduction

Regulators and economists have questioned the advisability of loan loss procedures under Generally
Accepted Accounting Practices (GAAP) known as the “incurred loss model,” because of its supposed
adverse consequences for lending. Under the incurred loss model, banks typically wait until a socalled “triggering event” occurs (e.g., non-payment of interest on a loan) before recording a loan loss
provision. This practice is likely to cause loan loss reserves to lag managerial expectations of losses.
Regulators argue that this accounting practice contributed to restrained lending during economic
downturns, aka “capital crunches”. The story goes that pro-cyclicality in loan loss accounting(smaller reserves during the end of booms and at the beginning of busts, and larger provisions at thebottom of cycles and into the next boom period) can manifest, adversely, as a reduced willingness
or flexibility by bankers to extend loans at the bottom of cycles, when liquidity is in high demand.
Standard setters in the US and international have promulgated changes to loan loss provisioning
methods, partly as a response to this presumed linkage between loss accounting and lending. By
2020 under ASC 326–20, US banks will begin to recognize loan loss provisions at the date a loan isbooked, rather than waiting for triggering events. While the new standards will bring management
expectations regarding future losses to balance sheets before triggering events, they are also likely
to increase noise in loan loss provisions and could even reduce financial statement comparability.1
The CECL standard will not eliminate management discretion over the magnitude of loan loss
reserves and the timing of loan loss provisions because the original estimate of the loan loss reserves,and subsequent revisions in bad debt estimates will still be subject to judgement. In July 2019, the
FASB issued a proposal to delay the implementation of this new standard for non-SEC public banks
among others (see Maurer, 2019).
We seek additional evidence in support of the debate around accounting for credit risks. We use anovel setting – identifying banks located in areas impacted by natural disasters – to understand loan
1Chae et al. (2018) point out that small variations in assumptions that go in the provisioing model, can create big
differences in estimated reserves. Covas and Nelson (2018) apply the CECL model retroactively and suggest that the
CECL standard would not have smoothed out pro-cyclical lending.

2

loss provisioning behavior and its relation to lending. Specifically, we investigate two questions: first,
how do disasters impact bank’s loan loss provisions, and second, do banks with more conservativeand or timely loan loss provisioning policies before disasters demonstrate an increased ability tolend, as conjectured by some advocates of the ASC 326–20 standard?
A key assumption in our analysis is that banks impacted by disasters face a shifting outwardsof local demand for loans, generating capital crunch conditions for some banks. Prior research
focusing on the determinants of capital crunches links loan loss accounting to lending, using a few,
or singular, general economic downturns (e.g., Beatty and Liao (2011) or Jayaraman et al. (2018)).2 Similar to what occurs during an economic downturn, banks subject to disasters can expect anincrease in credit losses on some current loans, but also encounter profitable opportunities to lend
to local businesses, e.g., to re–build in the aftermath of the disaster. However, in our case, we canidentify such events at different points in time (i.e., using disasters that occur in different quarters
and years), and which affect only subsets of banks at these different time points.
We match disaster–treated banks in a particular quarter to contemporaneous unaffected control
banks that are not subject to disasters. We then implement a dynamic difference in difference
identification strategy by comparing treated banks to control banks, before and after disaster shocks
Because disaster-events do not cluster on all banks at one point in time, our ability to draw validcausal inferences should improve (relative to studies that focus on a single or few events).
With regard to our first research question on how bankers choose to record loan losses following
disasters, note that disasters are a form of “triggering event” that ought to stimulate bank-managers
to forecast future non-performing loans and to incorporate these estimates into loan loss accruals.
Whereas banks are often viewed (by accounting researchers) as waiting for a cash flow indication,such as a missed loan payment, that a loan’s default risk has increased, disasters allow banks torecord losses on loans when they are still performing.3 Note that academic researchers cannot
2

Beatty and Liao (2011) use samples before and after general downturns in the early 2000’s and in 2008–2009.
Jayaraman et al. (2018) use the emerging debt crisis in the late 1990’s.
3
By the word “performing” we refer to a loan where the borrower has paid according to the debt contract, principal
and interest on time.

3

observe all the triggering events that will cause revisions to credit loss estimates.4
Some research (e.g., Beatty and Liao (2011)) has assumed that contemporaneous and laggedchanges in non-performing loans are good proxies for triggering events, and these studies classify
the provisions relation to future non-performing loan changes to indicate that management has
exercised some discretion to make loan loss provisions more timely. Yet, a disaster is an event that
we, as researchers, can observe and, which is likely to make bankers update their loan loss provisions,based on their expectation of future non-performing loans, before any cash flow indicators havebeen triggered. We conjecture that in the quarters just after disasters, loan loss provisions will
display a greater weight on management’s expectations of future non-performing loans (relative toduring calmer lending environments). But, in the case of disaster-impacts on loan loss provisions,
one would be hard-pressed to say that this is an indication of high accrual quality.
As such, our analysis contributes to the collective understanding of the factors that lead bankers
to incorporate forward information in their loan loss provisions.5 Bhat et al. (2016) and Bhat
et al. (2018) provide evidence that banks differ in systematic ways in their incorporation of future
information in their loan loss provisions e.g., based on how concentrated the loan portfolio is incommercial loans, and based on the sophistication of management information systems. We suggestthat the incorporation of future information also depends on the type of triggering events a bank is
exposed to, with disasters being an example.
To examine the effect of disasters on loan loss provisioning policies, we regress loan loss
provisions on changes in non-performing loans, measured for t + 1, t, t − 1, and t − 2, along withother control variables that have been identified in prior research. At the time a bank sets its loan
loss provision, it has access to information about non-performing assets at time t and earlier. By
including the time t + 1 non-performing loan information, we are assuming the bank managers
forecast this data, even though triggering events may not yet have occurred.
4

For instance a banker could observe a Chapter 11 filing by a borrower who has not defaulted in any way on loans.
This filing would cause the banker to update loan loss estimates in advance of a cash flow indicator.
5This literature is reviewed in Beatty and Liao (2014).

4

When a bank’s operations are hit by a natural disaster, our tests of the disaster-impact on theloan loss provision is measured both by a shift in the intercept for disaster impacted banks (thedifference-in-difference average impact) and by allowing the regression coefficients for the treatedbanks to change (using a dummy interactive variable) for the four quarters encompassing thedisaster event and the three subsequent quarters. The shift in intercept allows for affected banks
to “top–up” their loan loss provisions following a disaster, and we make no prediction regarding
the sign and significance of this intercept. A positive shift would be consistent with a “generalprovisioning” increase that is not supported by current model variables. However, our expectation isthat the coefficient weight on the change in non-performing loans for period t + 1 (again, measured
ex post as a proxy for expectations) will be systematically higher for banks treated by disasters.
Our results are as follows. On average the response of loan loss provisions to period t+1
changes in non-performing loans is about twice the size of the response to period t+1 changes in
non-performing loans during non-disaster periods. The relative growth in the size of this coefficientis much larger for banks with assets greater than $500 million, and, in fact for these larger banks
the base weight on forward measures of non-performing loans (i.e., in non-disaster periods) is
statistically insignificant. In contrast, for smaller banks, the reaction to disasters in this weighting
is reserved for the fourth quarter, suggesting that management incorporates forward looking
information into the disaster related losses only in the fourth quarter.
We also investigate earnings smoothing via loan loss provisions following disasters. We find, forall banks in non-disaster periods, that loan loss provisions are higher (lower) when earnings before
provisions are higher (lower), suggesting that provisions are used to smooth income. At big banks,
during disaster periods, this evidence of earnings smoothing strengthens, suggesting that disasters
allow larger banks room to exercise discretion for this purpose.
Under our second research question, we examine how bank provisioning policies that were inplace before disasters, subsequently correlate to banks’ response to the increase in the demand forloans which have been triggered by disasters. We examine whether banks with more generous,

5and or more forward-looking, pre-disaster loan loss policies respond more flexibly to an increasein demand for loans. This inquiry is similar to the research questions addressed in Beatty andLiao (2011) and Jayaraman et al. (2018). These two papers respectively find that more timely(more smoothed) provisions allow banks more loan growth during recessions (during the emerging
market crisis). Our use of demand shocks as measured by natural disasters and our more extensive
sample of banks will speak to the generality of these findings.6
Again, we use disasters to identify economic conditions that cause banks to become more credit
constrained, similar to the more general economic downturns that are the focus of these priorstudies. We categorize banks based on two provisioning policies–provisioning conservationism
and provisioning timeliness. Our measure of conservatism is captured by the signed residual froma regression of loan loss provisions on fundamental determinants, in non-disaster periods.7 The
construct, timeliness of loan loss provisions, follows from Beatty and Liao (2011); it captures thedegree to which a bank relies on current and next period changes in non-performing loans in setting
provisions. Using a regression framework, we test whether provision-conservatism and -timelinessare positive predictors of loan growth in the period following a disaster.
Our base results suggests bank profits and capital strength support growth in lending during
normal times. In post-disaster periods, disaster-impacted banks increase their lending on average.
For small banks we find that the reliance on Tier 1 capital intensifies in the post disaster quarters,suggesting that some of these banks become capital constrained when demand for loans increases.
This suggests that natural disasters result in localized credit crunch conditions for smaller banks.
In addition, we find that conservatism in loan loss accounting associates to greater lending forsmall banks after a disaster period, but the precise reason for this relation is unclear. Specifically,small banks appear to be better able to respond to the shock to demand for loans if they have botha higher tier 1 capital ratio and if loss provisioning policies were more conservative. This finding
6

Cortés and Strahan (2017) also use natural disasters to identify shocks to demands for loans.
A positive residual is a larger income statement expense, and therefore this measures a conservative earnings
number.
7

6

could indicate that bank managers who are more conservative in general, run banks that are better
able to respond to loan growth. We find no evidence that, small banks, with more forward looking,timely, loan loss policies exhibit greater growth in loans in post disaster periods. Meanwhile, bigbanks (with assets greater than $500 million) show loan growth following disasters, but these banks
show no evidence of being capital constrained in post-disaster periods and there is no evidence
that loan loss provisioning policies impact this growth.
In prior research examining the links between provisioning and lending, Beatty and Liao (2011)
suggest that during the recent downturns, the constraints on lending appear to tighten for largerbanks (whereas in our case, following natural disasters, it is smaller banks that appear to face these
constraints). According to this prior research, large banks exhibit higher growth in loans during two
major economic downturns, if their loan loss provisions are more forward-looking. In contrast, wefind some evidence that for small banks, more conservative loan loss provisioning policies appear
to interact to loosen capital constraints in disaster periods. And, we find no evidence that more
forward-looking provisioning policies serves to alleviate capital capital constraints for either big or
small banks.8
Section 2 describes the motivation for our paper. Section 3 discusses our empirical strategy
for our analysis. Section 4 provides discussion of the key variables used in our empirical analysis,along with the descriptive statistics of the main bank and disaster sample. Section 5 discusses theempirical results, and finally Section 6 offers the conclusion.
8

Since our research suggests that capital crunches related to disasters apply to small banks, but not large banks–and
since Beatty and Liao (2011) finds that it is big banks that are constrained during economic crises–it is probably notpossible to reconcile the results from our matched–paired design using natural disasters to mimic crisis periods, withthis prior important study.

7

2

Motivation

2.1

How do Natural Disasters Impact Loan Loss Provisions?

An extensive prior literature attempts to infer how banks estimate loan loss provisions and thedegree to which these estimates are used to manage balance sheets and income statements. Thesestudies usually regress a loan loss accrual variable, such as the loan loss provision, on fundamental
indicators of loan losses, such as non-performing loans, changes in non-performing loans, loancharge–offs, and loan loss reserves. In addition, the models usually include variables to capture
financial reporting objectives such as earnings smoothing and regulatory capital management
variables. Moyer (1990) and Wahlen (1994) provide an excellent discussion to what researchers
identify fundamental indicators of normal loan losses and loan charge-offs, and Beatty and Liao(2014) review of accounting-based banking research provides a helpful summary of the various
approaches that researchers have taken to capture expected loan loss provisions in the absence of
earnings or balance sheet management.
Over time, and especially following the recent financial crisis, research-attention has shiftedfrom how banks managers use loan loss provisions to manage their financial reports, to constructing
measures of the quality of reporting. For example, the degree to which “forward-looking” fundamentals predict loan loss provisions, sometimes assumed to indicate high quality of loan loss
provisions. This view is apparent in research such as Beatty and Liao (2011), Bushman and Williams(2012), Bushman and Williams (2015), and Akins et al. (2017). The error term from a loan loss
accrual model has also been viewed as a measure of accounting quality, e.g., Jiang et al. (2016)
interpret the magnitude of the absolute value of the residual from a “normal” loan loss provisioning
model as a measure of bank opacity. Also, earnings smoothing, typically captured as the positive
correlation between the loan loss provision and earnings before provisions, is viewed by some
economists in a favorable light because it indicates countercyclical provisioning; in comparison,accounting researchers have typically viewed this correlation in a negative light as indicating some8

abuse of management discretion.
In this paper, we examine whether loan loss provisions are impacted by disasters. We contributeto the prior literature on loan loss provisioning models through our insight that the provisioning
model might change when a bank is exposed to a special kind of triggering event, namely a disaster.
This portion of our paper is in the spirit of research such as Bhat et al. (2016) and Bhat et al.
(2018). These two prior studies propose that the appropriate model of loan loss provisions is highly
contextual. Bhat et al. (2018) for example, suggest that the quality of loan loss provisions are likely
to be influenced by the sophistication of analytical models that a bank uses to understand and reporton loans. Accordingly, Bhat et al. (2018) code the disclosures made by banks regarding their use ofsophisticated modelling. They find that the information contained in these disclosures is predictiveof the degree to which loan loss provisions are forward looking. Similarly Acharya and Ryan (2016)
and Bhat et al. (2016) emphasize the need to control for loan concentrations in homogeneous(i.e., consumer loans) versus heterogenous loans (i.e.,commercial loans) as important context injudging the quality of bank’s loan loss provisions. For example commercial loans are inherently
more difficult to audit, and have more uncertainty in their credit-worthiness; hence banks thathave large concentrations of these loans can more easily exert discretion over loan loss provisions
without fear of detection.
We contribute to this stream of literature by examining the role of disasters on loan loss
accounting models. We believe that disasters are a form of triggering event that can allow banks
more latitude to smooth earnings or capital. We imagine that these events alert managers to futurechanges in non-performing loans, even though standard triggering cash flow events might not haveoccurred. To the extent that the impact of disasters on forward looking non-performing loans is
non-trivial, they provide another context in which loan loss provisions will contain forward looking
information.
Our research design, more fully described in a subsequent section, allows us to test for four
possible accounting responses to disasters. We posit that the four accounting responses are not

9mutually exclusive. First, banks in a disaster-area could choose to “top-up” the loan loss reserves,in a general manner that is not directly tied to expectations of future changes in incurred losses.
Usually under the incurred loss model for loan losses, banks are purportedly not to engage in
general-reserving, but since the future effects of natural disasters may be difficult to forecast, andto audit, our regression could detect any, undisciplined general-reserving as an increase in themodel intercept in the post disaster quarters.
Secondly, banks could re–assess, and increase, the default probabilities attached to past indicators of credit risk, which, in our model, are measured by lagged changes in non-performing loans.
We could detect this action by an increase in the slope coefficient on lagged indicators of credit
risk in the post disaster period. For example, non–payment of interest in a prior quarter might beassessed as even more likely to indicate future default shortly after a disaster.9
Thirdly, the banks could re–assess and increase estimated default risk attached to current, and to
future, credit risk indicators. We are especially interested in whether disasters cause banks to weight
future changes in non-performing loans more heavily in setting loan loss provisions. Under normal
circumstances, researchers have interpreted the weight on future changes in non-performing loans
as an indicator that a bank is more willing to reflect future losses, in a more timely manner. We
expect that banks’ loan loss provisions will be more forward-looking in the post-disaster period
because the triggering event (a disaster) naturally leads to forecasting of future credit risks, before
the usual triggering events occur (e.g., a missed loan payment). Hence disasters might associatewith more forward-looking loan loss provisions, but we don’t think this means that managers havebecome more willing in general to recognize future losses at time t, which is the general thought
behind the use of the word timeliness.
Finally, related to the first possibility, banks could exploit the uncertainty following disasters toincrease the use of the provision for smoothing income or for managing regulatory capital. We candetect this through a post–disaster change in the regression weight on earnings, or on regulatory
9

Disruption of businesses following the disaster would further constrain cash flows available to pay off loans.

10capital.

2.2

Do Banks with more-timely Loan Loss Provisions Respond more Flexibly
to an Increase in Loan Demand?

Our second research emphasis uses the natural disaster setting to re-examine the link between
loan loss provisioning–quality, and lending, when bank is capital constrained. Some research suchas Beatty and Liao (2011) and Jayaraman et al. (2018) has provided evidence that bank-specificapplication of standards that guide loan loss provisioning is associated with eased lending when thefirm faces capital constraints. This research provides support for the position held by policy makers
that banks delays’ of provisioning or lack of provisioning smoothing, can exacerbate downturns.10
We believe this research avenue should be further explored for two reasons. First, we think thatthe hypothesis that loan loss accounting practices caused a deepening of the last financial crisis, isa hypothesis that should be subject to considerable tension. Implicitly, regulatory criticisms thatlink provision policies to lending presume that absent forward-looking loan loss provisioning or
smoothing, bank managers, bank regulators, and (or) market intermediaries (who would pressurebank managers) fail to recognize the need to raise capital until the cost of raising capital has
increased and banks face binding liquidity and capital constraints.11 In other words, the logic that
leads to the suggestion that the application of loan loss accounting model will exacerbate downturns,
seems to give a fair degree of power to accounting numbers. It seems to rely on managers’ willfully
ignoring the underlying reality of their businesses and placing too much emphasis on accounting
translations of that reality.12
10Beatty and Liao (2011) mention the Financial Stability Form Report (2009) as well as comments made by John C.
Dugan in March of 2009. These are two examples of regulators suggesting that accounting for loan losses contributed
to restrained lending.
11
The existence of deposit insurance creates an incentive for bank shareholders to hold excess leverage. Regulatory
monitoring and capital requirements help to insure that banks raise capital if leverage becomes too high.
12
If banks face binding contracting terms that are denominated in accounting numbers, say through Tier 1 capitalconstraints, and if there is sufficient information asymmetry between bank managers and bank regulators, this could
explain how pure application of accounting numbers could lead to an exacerbation of credit crunches. One reason
to question this is that nearly all banks hold capital well in excess of regulatory minimums, suggesting that these11Secondly, while the prior research is very well-executed, the research has encountered difficult
obstacles to valid inferences. For example, Acharya and Ryan (2016) review of these studies points
out that loan loss provisioning is contextual and should be influenced by a host of variables thatare not necessarily easy to capture in a linear regression framework.. Such factors include suchbanks’ loan composition, local lending shocks, and even managerial attitudes towards risk. It is
not difficult to imagine there are omitted contextual variables that could alter interpretations of
the results that rely on major economic downturns for the research setting.13 In addition, valid
identification strategies are difficult to come by. The prior studies focus on a few crisis periods,
and this almost certainly limits the generality of conclusions that can be drawn. Relatedly, thisliterature relies on the researcher being able to convincingly separate loan demand shifts fromloan supply shifts. We believe that the use of natural disasters to identify demand shifts createsnumerous crisis periods that affect subsets of banks at different points in time, assisting us inproviding generalizable inferences. Also, the use of the matched pair design assists in controllingfor omitted variables.

3

Empirical Strategy

The first step in our research design is to tie the effects of natural disasters to the location andquarter of banks by U.S. counties. By doing this we tag banks that are simultaneously subject toa increase in the demand for loans, as well as, subject to deterioration of the loans on the books.
Section 4 describes our sample construction process where we match the affected banks (Shocked)
to unaffected control banks.
If disaster-impacted banks have more forward-looking (or more conservative) loss provisions
before the disaster occurs, and, if these provisioning attitudes drive banks to hold more real equity,
constraints are not binding.
13
Acharya and Ryan (2016), comment that the omitted variables can lead some readers to suspect an alternative
explanation that “stronger banks make both better financial reporting choices and less cyclical decisions.”(see p. 283
Acharya and Ryan, 2016).

12

then we expect that the differences between the treatment and control groups can be explainedby variation in loan loss provisioning policies. (This idea is illustrated in Figure 1 and Figure 2,
discussed below). Hence, in order to test hypotheses regarding the relation between loss policies
and bank flexibility to lend, we must first separate banks by their loan loss policies. This will be
our first set of regressions. In addition, by allowing the loan loss policy regressions to adapt in the
period following a disaster, the first set of regressions are also used to address our first researchquestion, how loan loss provisioning is altered by disasters. Our second set of regressions will
explore the relation between provisioning attitudes and lending, following demand shocks, as
captured by disasters, again, employing a control sample of unaffected banks to help identify the
impact of provisioning policies on lending growth.

3.1

Identifying Loan Loss Reactions to Disasters

We define dummy variables Shockc t that identify a county c and the quarter t, where banks arelocated and have been hit by a disaster.14 We define a second dummy variable Postd t that identifies
a particular disaster in quarter d and subsequent three quarters, d, (e.g., Hurricane Katrina). If
a bank is in a county affected by a disaster, the multiplication of Shock b and Post t is a dummy
variable for a bank that is 1 in the quarter of and the three quarters following a disaster. Supposethat the only impact of a disaster is to possibly increase the average loan loss provision for anaffected bank, we measure this average increase this using β1 in the following regression model:
L L Pbt = β1 Shock b × Post t + β2 ∆N on − Per f .Assets b,t +1 + β3 ∆N on − Per f .Assets b,t+ β4 ∆N on − Per f .Assets b,t−1 + β5 ∆N on − Per f .Assets b,t−2+ C ont r ols + Bank FE + State × Time FE(3.1)

In this regression, ∆ refers to a difference in a variable in two adjacent quarters, while Non-Perf.
14

We identify banks impacted by a disaster based on the county of the disaster in a particular quarter, and whether
the bank operates in the county.

13

Assets refers to “non-performing assets,” e.g., loans and assets that are ninety days past due and in
non–accruals. We scale both the dependent and the independent variables are one-period lagged,
gross loans. Changes in non-performing loans are likely to indicate that the risk of default haschanged, and this is the reason that these variables are often used in models of loan loss provisions.
The control variables in this equation are similar to that discussed in Beatty and Liao (2014). Given
this regression, the coefficient β1 on the Shock b × Post t dummy variable measures the averageincrease of loan loss provisions in for the four quarters encompassing all disasters in our sample.
In the prior section, we emphasize that the impact of a disaster on loan loss accounting could
manifest as an increased incorporation of future expectations on loan loss provisions. This possibility
is captured in the following regression which allows for a shift in the weight placed on the nonperforming loan changes by interacting Shock b × Post t with the non-performing loan measures. In
addition, in Equation (3.2) we add Tier 1 capital (Tier1 Ratio), measured at time t-1 and earnings
before the loan loss provisions (EBP) to see if loan loss provision behavior is consistent with capitalmanagement or earnings smoothing.

L LPbt = β1 Shock b × Post t + β2 Shock b × Post t × ∆N on − Per f .Assets b,t +1+ β3 Shock b × Post t × EBPb,t+ β4 Shock b × Post t × T ier1Rat io b,t−1 + α1 ∆N on − Per f .Assets b,t +1+ α2 ∆N on − Per f .Assets b,t + α3 ∆N on − Per f .Assets b,t−1+ α4 ∆N on − Per f .Assets b,t−2+ C ont r ols + Bank FE + State × Time FE(3.2)

A positive and statistically significant β2 indicates that in the four quarter following and including
a disaster, affected banks let their expectations of future non-performing loans to influence thelevel of the loan loss provision to a greater extent, in comparison to non-disaster periods (α1 ).
Similarly, a positive coefficient β3 (β4 ) can be interpreted as an increased use of the loan loss

14

allowance to smooth earnings (Tier 1 capital). We estimate this regression for small banks (with< 500 million assets) as well as for large banks. Our intuituion is that the weighting on futurechanges in non–performing loans will be sensitive to the sophistication of bank managers, and thatlarge banks are likely to have more sophisticated loan loss modelling abilities. We also are awarethat the relation between loan loss indicators and the provision for loan losses may change in the
fourth quarter.

3.2

Linking Loan Loss Provisioning Policies to Growth in Lending

Next we examine whether loan loss provisioning habits impact banks’ ability to lend following acrisis period. The dependent variable in our regression is loan growth, Ybt measured as the change
in loans divided by beginning period loans. Following a disaster, we expect demand for loans toshift out, relative to the matched pairs that should face the same demand curve. It is also possiblethat the supply curve for our banks shifts in because the expectation of worsening loan defaults
for the most adversely affected borrowers, should lead to additional loan loss provisioning for ourtreated banks, relative to our control banks. We specifically estimate the following model:

Ybt = β1 Shock b × Post t + β2 Shock b × Post t × L LP Polic y b,t−1+ β3 Shock b × Post t × EBPb,t+ β4 Shock b × Post t × T ier1Rat io b,t−1 + β5 L LP Polic y b,t−1+ β6 EBPb,t + β7 T ier1Rat io b,t−1+ β8 L LP Polic y b,t−1 × T ier1Rat io b,t−1+ β9 Shock b × Post t × L LP Polic y b,t−1 × T ier1Rat io b,t−1+ C ont r ols + Bank FE + State × Time FE(3.3)

where, LLPpolicy bt−1 indicates the “loan loss policy” we have assigned to the bank. We use two
measures for this variable. The first measure is conservatism and this is based on a moving average15

of the residual from Equation (3.1), over the prior 12 quarters, excluding any disaster quarters,
and provided we have least 8 quarters in the moving average. Each quarter t each bank is assigned
to either the high provisioning banks which are above the median for that quarter (HighRES12 in
our tables). This variable captures conservatism because it shows which banks set aside more thanis predicted from the normal expected loan loss provision from Equation (3.1). Tier1Ratio refers tobanks’ actual Tier 1 Capital ratio at the beginning of the quarter t.
Our second measure is captured based on the increase in r-squared which occurs when Equation (3.1) is estimated without current and future changes in NPL versus when these two terms areincluded. Hence this measure shows the extent to which a banks loan loss provision is forward
looking. This is estimated using a time series of 12 quarters for each bank in non-disaster periods.
This is the identical construct used in Beatty and Liao (2011). We label this loan loss policy as
HighR2 in our tables.
Recall that one of our important premises is that natural disasters will increase demand forloans. If this premise is correct and treated banks attempt to meet this demand, then we expect β1
to be positive. We examine this conjecture first.
If this conjecture is supported, we can analyse how banks were able to achieve the loan growth.
If meeting the increase in demand is impossible for some banks because their capital is too low, we
expect the coefficient β4 to be positive. (In other words, a positive estimate of β4 is an indicationthat there is a capital crunch during the post disaster period). Stated differently, banks that enter
disaster periods with greater regulatory capital will find it easier to respond to loan demands. Thisis illustrated in Figure 1 and Figure 2. Further if growth in lending is facilitated by loan loss policies,
then we would also expect that the growth in loans is related to our loan loss policy, perhaps
independently of capital, β2 . In other words, our understanding of regulator positions regarding
loan loss policies is that banks with more conservative (timely) loan loss policies in the pre-disasterperiods should have higher growth in loans during the disaster period. Finally, we re-estimate thisregression for big versus for small banks. Big banks may be less constrained on capital because

16they might be public with easier access to new capital. Hence, the importance of Tier 1 capital andloan loss policies might vary based on bank size.
Similar to prior research, we will define small banks as those with assets less than $500
million. One somewhat controversial result in Beatty and Liao (2011) is that the relation between
provisioning and lending seems only to hold for banks with assets greater than $500 million. Beattyand Liao point to changes in regulation which made it more difficult for large banks to assume
they were too big to fail. However, while this might explain an increased relation between the
hypothesized variables for larger banks, it does not explain why the results do not hold for smallerbanks.(This point is made by Acharya and Ryan (2016)). Our sample, which uses call report data,measuring subsidiary and independent bank income statements and balance sheets, comprises
more small banks and private banks, than does prior research.
Conceptually, we intend to address the issues raised by Acharya and Ryan (2016) by isolating
shifts in demand for loans for specific banks, using the event-dated, locational impacts of natural
disasters to separate a treated sample of banks (those exposed to disasters) from a control sample ofbanks (those not exposed), aligned in event time. We are assuming that the destruction of property
that occurs when natural disasters occur, leads to fresh demand from local individuals and businessfor financing to re–build.15 Our regression model is very similar to Beatty and Liao (2011) Table 1,
except that our post crisis period dummy separates a before versus after period and isolated treatedbanks from control banks over the same time horizon. In addition, we have considerably morecross-temporal variation in the periods that represent crisis periods.
15

Coincidentally, Cortés and Strahan (2017) use natural disasters in a similar manner to this reasoning and matchnatural disasters to loan origination in counties. Cortés and Strahan (2017) assume that natural disasters are good
indicators of a shifting up in the demand for loans. Their main interest is to document how connections between banks(e.g., banks that are located nearby and either are, or are not, part of the same bank holding company) impact theservicing of increased loan demand that accompanies a disaster.

17

4

Data and Descriptive Statistics

4.1

Sample Selection

We use quarterly data covering all banks operating in the United States between 1994Q1 – 2017Q4.
Bank’s financial information is from the Bank’s Reports of Condition and Income (the “call reports”)
submitted to the Federal Deposit Insurance Corporation (FDIC). We control for merger effects
by excluding observations when the quarterly growth rate of bank’s total assets exceeds a 10%
threshold, and when the quarterly growth rate of bank loans exceeds 15% threshold (see for e.g.,
Gatev and Strahan, 2006; Acharya and Mora, 2015). We also require that sample banks to haveat least $100 million in assets, and at least $1 million in total loans. As described in Table 1, theabove selection process produces 407, 206 bank year observations.
We collect data on natural disasters in the US from Spatial Hazard Events and Losses Databasefor the United States (SHELDUS). SHELDUS is a county–level disaster and hazard database withdifferent natural hazard events, which include thunderstorms, hurricanes, floods, wild–fires, andtornadoes. For each event, the database includes the beginning date, location (county and state),
property losses, crop losses, injuries, and fatalities that affected each county. The data are derived
from several sources, including the National Climatic Data Center’s monthly storm data publications.
Following Cortés and Strahan (2017), we identify disasters that the governor declares “state of
emergency” with a formal request for funds from the Federal Emergency Management Agency(FEMA), and include the following seven-types of disasters: coastal, wildfire, earthquake, flooding,hurricane, severe storm, and tornado. To ensure that the disaster causes a significant impact to the
local economy, we drop disasters with property damage less than $50, 000.
We obtain bank location information from the Summary of Deposit (SOD) database provided by
the FDIC. This dataset contains geographic information on all branches of depository institutions,
including street address, state, ZIP, and county. We aggregate the underlying bank-branch data to
the bank-county level. We combine the SOD data and SHELDUS data and identify banks affected18

by disaster in a particular quarter based on if it operates in a county affected by a disaster.
We also collect quarterly macro-economic information based on the state of the head quartersof the banks. The state–level unemployment rates are from the Bureau of Labor Statistics, thestate–level GDP are from the Bureau of Economic Analysis (BEA). The state level housing priceindex are from the Federal House Financing Agency (FHFA).

4.2

Selecting Comparison GroupA key empirical challenge in our research question is finding an appropriate comparison group forbanks that are hit by natural disasters (treated banks). Specifically, the process should ensure thatbanks that experienced disasters are comparable to banks that operate in the non-disaster area. We
use a matching sampling procedure to identify the comparison group of banks that have lagged
characteristics similar to the banks in our treatment sample. We implement the propensity–scorematched sampling procedure as discussed in (Rosenbaum and Rubin, 1985; Imbens and Rubin,2015). For each treated bank, we select a control bank from the comparison group of banks, whichwere not affected by disasters, based on the propensity score calculated from banks characteristics
all measured at one–quarter lag relative to the disaster event.16 We also require the potential
matches to have no disaster in the most recent eight quarters. Banks characteristics used for thepropensity score calculation include: Size, Deposit, Loan, Homogeneous Loan(%), Head quarter
state GDP. We also require exact matches on whether banks are part of a larger holding companies.
We implement a one–to–one matching with replacement for control group.
Our matching procedure yields a sample of treatment and control with all covariates balanced
at the 1% significance level. For the successfully matched pairs, we include four–quarters before
and four–quarters after the disaster period (including the disaster quarter) to create our panel
sample. As in Table 1, the final sample include 186, 559 bank–quarter observations, which include
19, 911 bank–quarters affected by disasters.
16

See Jaravel et al. (2018); Jäger and Heining (2019) for discussion on empirical challenges to difference-indifferences in event study setting.

19

4.3

Descriptive Statistics

Table 2 Panel A reports descriptive statistics of variables used in our analysis.17 The mean size ofsample banks is $374 million (=e5.925 ) in total assets, while the median size is $276 million. A
significant portion of sample bank assets are in their loan portfolios. The mean banks’ loan share is
66% of its assets, with a standard deviation of 14. Sample banks have a significant portion of theirloans in real estate (73.4%) with a bulk of it – 37.04% – in residential real estate suggesting their
exposure to catastrophes that destroy properties.
The mean LLP to loan ratio of our sample banks is 0.12, with a standard deviation in the 0.24.
The mean non–performing loan to total loans in our sample period is 1.98%, with a standard
deviation of 2.70. This suggests there is significant variation in terms of the quality of loans acrossour sample, allowing us to identify differential responses to disasters.
A key part of our research design is built on the assumption that large banks and small banks
located in the same geographical area, having similar business models and loan compositions,react similarly to disasters, and sudden increase in credit demand. In Panel B of Table 2, we show
descriptive statistics separately for large and small banks. Not surprisingly, the average size of largebanks (1.51 billion) is substantially greater than the average size of small banks (206.34 million).
While large and small banks may appear similar, their t– statistics verify that the two populations
differ on almost every characteristics reported in the Table. Large banks hold an average of 72.54%of their total loans in real estate, while the smaller banks are little more exposed holding 73.73%.
Nevertheless, the median real estate exposure for large banks is greater than those of small banks.
Large banks have relatively higher percentage of Commercial and Industrial Loans, and Consumer
loans holding 14.64% and 7.91%, respectively. Small banks hold 13.14% and 7.7% in thesecategories. Small banks rely more on deposits as a source of funding than large banks.
Despite the differences in loan compositions and funding, the quality of the loan portfolios
between large and small banks do not differ substantially. The average non-performing loans of17

Appendix table Table A.1 describes the variable construction from call report items.

20

large banks and small banks are 2.04% and 1.96%, respectively. The LLP ratios are similar between
large and small banks. Smaller banks have higher Tier 1 capital ratios than do larger banks.
In Table 3, we report the frequency of disasters from the SHELDUS database by year. Specifically,
we report the number of unique–counties that were affected by disaster types. Note that SHELDUS
can classify one disaster across multiple categories. We follow Cortés and Strahan (2017) andidentify the following disaster types: 1) Coastal, 2) Earthquake 3) Flooding, 4) Hurricane/Tropical
Storm, 5) Severe Storm/Thunder Storm, 6) Tornado, and 7) Wildfire. The “Total” column refers
to the number of unique counties that experience any form of disaster. The number of counties
affected by disasters during the years 1994–2000 is 2109, while the number during the years
2001–2007 is 2687. During the last decade between 2008 – 2017, the number of counties subject todisasters is 3449. These suggest that natural disasters have been increasingly trend in the counties
that they destruct, which signifies the importance of our result question. In Table 2, the mean
property damage by our sample disasters is $122 million.

5

Results

5.1

How are Banks’ Loan Loss Provisions Affected by Disasters?

We begin by examining the impact of disasters on loan loss provisions in Table 4 which reports, forthe full sample, the results of estimating Equation (3.1), reporting the relation between quarterly
loan loss provisions and quarterly changes in non-performing loans. In addition, we control
for beginning period bank Size, loan growth, ∆Loan, earnings before loan loss provisions andtaxes EBP, as well as the composition of the loan portfolio residential real estate, Resid’l RE Loan;
commercial loans, Comm’l & Indus’l Loan , and commercial real estate Loans Comm’l RE Loan).
This model allows the average level of loan loss provisions to shift during disaster periods throughthe coefficient on Shock × Post. In column (1), we report results that include that only include
Shock × Post. The coefficient is positive and significant, suggesting that disasters increase loan loss
21

provisions on average.
In column (2), we report the results conditioning on bank characteristics. The coefficient ofShock × Post is negative and significant. In column (3), we repeat the analysis of column (2), but
include bank–fixed effects, to control for for un-modelled, bank-specific influences on loan loss
provisions that are constant over time. Our preferred model includes state–time fixed effects tocontrol for factors that vary by state and time.
First, note that the regression weights on changes in non-performing loans, size, loan growth,and on residential real estate, do not seem to be very sensitive to the inclusion of various forms of
fixed effects. In columns that contain changes in non-performing loans, the estimated regression
weights and significance levels are very similar, with relatively small standard errors. For example,
the coefficient on future changes in non-performing loans is 0.0211 and 0.0102 in columns 2 and3 respectively and all are reliably different from zero using standard statistical confidence levels.
These estimates suggest that for every dollar change in management’s expectation of next periods
non-performing loans scaled by loans, an additional ten to twenty cents per loan outstanding is
added to the loan loss provision. On average, the weights on lagged changes in non-performing
loans (at t − 1 and t − 2) have slightly higher weights (e.g., 0.0370 and 0.0329 in column 5) versus
the weights on current and future changes in non-performing loans (0.0289 and 0.0102 in thesame column). This suggests that on average bank-managers place greater default rates on pastchanges in non-performing loans than they do on current and expected changes in non–performing
loans.
The coefficient of Shock × Post in column (3) is not statistically significant in any of theestimations, suggesting there is no average difference in loan losses between treated and control
banks in the post disaster period. In columns (4) and (5), we repeat analysis in column (3) bysplitting our full sample in to sub samples of large banks (defined as those with assets > 500
million) and small. The coefficient of Shock × Post seems to suggest that the insignificance in thefull sample is driven by the offsetting effect between small and large banks as reported in columns

22

(4) and (5). In Appendix Table A.2, we repeat the analysis in column (3) by including bank andquarter fixed effects to show that the average effect of Shock × Post in column is robust to otherspecifications.
While the insignificant coefficient of Shock × Post in column (3) may seem to imply that disasters
have no impact on loan loss provisions, Panel B of Table 4 shows that the process by which loan loss
provisions are set, shifts in the four quarters encompassing the disaster. In this panel we allow thecoefficients on future, current and lagged changes in non-performing loans to change. In addition,
we allow the coefficients on earnings before loan loss provisions and on Tier 1 capital to shift. The
incremental coefficient on future non-performing loan changes is 0.0047 in column 1 and this,when added to the base coefficient on future non-performing loans of 0.0054 indicates that there isclose to an 87% (.0047/.0054) increase management’s assessment of default risk due to futurechanges in non-performing loans, during the four quarters that include and follow a disaster. This
increase is relative to the weight on non-performing loans as determined by before the disasterand by unaffected control banks. The weights on current, one and two period lagged changes
in non-performing loans exhibit increases of 48.3%, 39.7%, and 8.5% (but this last estimate
is not statistically significant). Hence shortly after a disaster, the average treated bank places
more emphasis on forecasts of future changes in default risk, in setting loan loss provisions and,management also increases the default risk estimates associated with current and lagged indicators,
but at somewhat lower rates of increases.
Table 4, panel B also reports how loan loss provisions are related to pre-determined Tier 1
capital ratios and to pre-provision earnings. The base coefficient on the Tier 1 ratio lies between−0.0016 and −0.0041, suggesting that banks with larger Tier 1 capital are less likely to add tothe provision for loan losses. A positive relation between these two variables would be consistent
with the manipulation of loan loss provisions to manage Tier 1 capital. The negative relation we
document likely indicates that high Tier 1 capital banks choose to invest in safer loans. Note that theinteraction of the disaster period indicator and Tier 1 capital is also negative. This means that that23

high capital banks add even less to their loan loss provisions in the post disaster periods; they areperhaps very conservative in taking on risky loans. The base relationship between earnings before
provisions (scaled by assets), and loan loss provisions (scaled by loans) is positive and significant(e.g., the point estimate is 0.2660 in column 1). This is consistent with bank’s choosing to smooth
their earnings via the loan loss provision. In Table 4, we find no incremental smoothing on averagein the four quarters following the disaster, (i.e., the coefficient on our shock term interacted withearnings before provisions is 0.0033 and this is statistically insignificant).
We suspect that the loan loss provisioning practices differ for large banks versus smaller banks.
For example, larger banks are more likely to have sophisticated modelling technologies available for
updating loan loss provisions (Bhat et al., 2018). And large bank sample is likely more dominatedby publicly traded, SEC regulated firms; hence their loan loss provisioning tactics can reflect thedemands of the SEC or shareholders. 18
Table 5 examines whether disasters differentially affect the loan loss provisioning habits of large
versus small banks. The table suggests that small banks seem to have a large weight on futurechanges in non-performing loans (the coefficient is 0.0066), but there is no change in this weighting
during the disaster period. In contrast, the larger banks show an insignificant base coefficient on
future non-performing loans in column 3 (the estimate is -0.0012) but, once a disaster occurs, thisvariable receives a positive weight of 0.0123, which is statistically significant). Hence, small banks
appear to have more forward-looking loan loss provisions at all times, while bigger banks appear
to save this until they encounter a disaster.
One explanation could be that this reflects a different loan composition for small versus largebanks. Ryan and Keeley (2013) argue for the case in difference in portfolio composition of large
versus small banks. Panel C, which further separates banks based on loan composition, suggests
this explanation is not valid.
18

For example, Craig et al. (2009) argues that publicly-traded banks are more likely to have conservative earnings.
Their research supports this conjecture; they confirm that loan loss provisions are a mechanism that form a part of thisconservatism.

24Second, note that once small and large banks have been separated, the difference in difference
average effect of a disasters is positive for large banks but is negative for small banks (column 3
versus column 1, the coefficient on Shock × Post) This is consistent with large banks increasing
the conservatism of their loan loss provisions, over and above the amount expected, based on loan
loss indicators, during disaster periods. Whereas large banks show a greater weighting on futurechanges in non-performing loans (0.0123 in column 3), the incremental weighting on this variable
is indistinguishable from zero for smaller banks. (However, small banks place a bigger weighton current and on one period, lagged changes in non-performing loans during disaster periods).
Finally there is weak evidence that large banks engage in more smoothing during disaster periods,
i.e., the coefficient on the interaction of our disaster dummy variable with earnings is positive andmarginally significant.
In Panel B of Table 5, we examine whether loan loss provisioning behavior differs in the fourth
quarter, when the audit is being completed, versus in the remaining three quarters (See for e.g.,
Liu et al., 1997). Whereas in panel A it appears that small banks do not increase their incorporationof future changes in non-performing loans after disasters, Panel B suggests that they do so, but
only in the fourth quarter. This results is only significant at the 10% level, two-tailed significance.
Large banks appear to increase their weighting on forward looking information in the first threequarters of the year, but not in the fourth quarter. However, in the fourth quarter, this is where wefind there is an increase in detected earnings smoothing.
Taken together, the results in Table 4 and Table 5 suggest first that disasters are associated,for big banks, with an increased incorporation of forward–looking information into their loan
loss provisions. This is important because prior research suggests that the incorporation of future
information in loan loss provisions is an indicator of high accounting quality. To the extent thatthe weighting on future information is simply a by-product of a bank being located in a disasterprone area, this finding casts some doubt on the idea that the weight on future changes in nonperforming loans is a reliable indicator accounting quality. Interestingly, small banks appear to25

weight forward looking information in all quarters, regardless of whether there has been a disaster.
Some would argue this means small banks have higher-quality loan loss provisions. Given thedemonstrated difference in loan loss provisioning models for big versus small banks, we estimate
loan loss provisioning policies (for our investigation of how banks respond to loan shocks) using
samples that are stratified on size.

5.2

Impact of Larger Disaster on Banks

The analysis thus far considers a binary variable for a treated bank in the difference in difference
specification. A novel aspect of our SHELDUS data is that it allows us to observe the property
damages from disasters at the county level, there by allowing us to exploit across disaster variation.
We define a continuous measure of our treatment variable Damage, which is the sum of the bankbranch–level property damage, which is then allocated damage based weights on bank’s deposits
by county. The intuition is that banks with higher damages should differentially respond on theirloan loss provision estimates relative to banks with lower damages.
Table 6 presents the results of the analysis. Our regression specification is similar to Equation (3.1), with one difference. We replace Shock × Post with Damage and repeat our regression
models for full, small and large banks. Columns (1) – (3) repeats the models presented in column(1) of Panel B, while columns (4) – (6) repeats the model presented in column (4) of Panel B.
Consistent with our results in the previous section, we continue to find that the provision estimatesof banks that suffer larger damages are more forward looking in nature. The coefficient of Damage× ∆ Non-Perf. Assets t +1 in column (1) is positively significant. We also continue to find thatlarge banks provisions are forward looking only after the disasters, while small banks tend tohave forward looking provisions only in non-disaster periods. These results are consistent after
controlling for earnings before provision and Tier 1 ratios as in columns (4) to (6). Together our
results suggest that not only do the disaster events trigger an update of banks provision estimates,
but also the magnitude of the disaster that allows banks to estimate forward looking allowances.

26

5.3

Do Banks that are more Exposed to Disasters Learn from Experience?

One concern with our prior analysis is the treatment of disaster events as a fully exogenous event
for banks. Since there are some geographic areas in the US that are repeatedly subject to disasters(such as Florida, Louisiana), these events might not be fully unanticipated. We posit that themagnitude and the timing of the disaster may be the two parameters that are fully exogenous.
Therefore, banks subject to multiple disasters across time, might learn from their prior disaster
experience, which should reflect in their provisioning policies.
In Table 7, we formally investigate whether banks that are exposed to multiple disasters, adapt
their loan loss provisioning policies. Specifically, we examine whether banks with more frequentdisasters are more forward looking in their loan provision estimates. Given the scope, we solely
focus on banks that experience disasters (treated banks) and do not include our control banks inthe analysis in Table 7. We include a variable CumDisaster, which is the banks’ disaster experiencemeasured as the number of disasters the bank experienced in the past. We interact the coefficientson loan loss indicators with CumDisaster, and examine whether the weights on loan loss indicators
are higher for banks that experience more disasters. In addition, we expect banks to react differently
to more recent disaster experience, relative to ones in longer periods in history.
In columns (1) – (3) of Table 7, we consider the entire history of disaster experience for bank.
In columns (4) – (6), we focus only on disaster experience in the past three–years. In columns(1) and (4), for the full sample, we find evidence that banks exposed to multiple disasters indeed
apply larger weights on future changes to non–performing loans. Small banks (column (3)) weightheir entire history, while large banks (column (5)) weigh based on their recent past. Across all
our columns, we find that banks subject to multiple disasters cause banks to apply larger weights
on their current changes in non–performing loans. The coefficient of CumDisaster × ∆ Non-Perf.
Assets t +1 in column (4) is larger than that of column (1) suggesting banks assign larger weights totheir recent past, than their entire history of disasters. The results in Table 7 caution us against
the idea that disasters can be treated as fully exogenous events, particularly when we examine the27relation between loan loss policies and capital, versus growth in lending during disaster periods.

5.4

Impact of Disasters on Lending Constraints

In this section, we consider the effect of the disasters on banks deposits. We posit that disasters
potentially impact bank liquidity, through an outflow of deposits. As described by Kashyap et al.
(2002), banks combine loan lending and deposit–taking, with a synergy between these two activities
on both sides of the balance sheet. Banks have a natural advantage in providing liquidity, provided
deposit withdrawals and credit to borrowers are not too highly correlated.19 Acharya and Mora(2015) argue that it may make sense ex ante for banks to combine deposit-taking with loan
commitments when liquidity demands of depositors are idiosyncratic, banks may in deed experiencea shock, i.e., ex post a coincident liquidity demand from depositors and ultimately forced to adjust
to shocks by cutting back on new lending. Therefore, it is natural to understand how disasters
impact bank deposits and liquidity.
Table 8 presents the results from the regression where the dependent variable is the quarterly
change in bank deposits. Column (1) considers the full sample, and column (2) and (3) presents
for small and large banks respectively. Across the three columns, banks affected by disasters have alarger outflow of deposits, in the post period, relative to banks that were unaffected by disasters.
This is evident with the negative coefficient for difference-in-difference operator Shock × Post. The
coefficient in column (1) is significant at 10%. We find that larger banks have significant decline indeposits following the disaster relative to the control banks in the four periods after the period.
The coefficient of Shock × Post is −0.0008, suggesting a 8.4% reduction in deposit growth for largebanks. This is consistent with disasters tightening the supply of funds available to support loan
growth post-disaster for larger banks.
However, we find that for small banks there is no significant reduction in deposits in postdisaster period. This reduction in deposit growth is manifested through changes in coefficients of
19

Other works include Diamond (1984); Pennacchi (2006).

28

non–performing loans. The coefficients of ∆ Non-Perf. Assets is significant for small banks, while
insignificant for larger banks.

5.5

Do Banks with Greater (Less) Conservatism and Timeliness in Provisioning Respond More to Shifts in Demand for Loans?

Table 9 and Table 10 show the estimates of Equation (3.3). Recall this regression provides evidence
about whether pre–crisis provisioning policies correlate to a more responsive reaction to theincreased loan demand that we assume accompanies natural disasters. The regression employs two
different loan-loss policy dummy variables HighRES12 versus HighR2. As described in Section 3.2,
HighRES12 indicates banks with more conservative loan loss provisioning policies, based on thecomputation of rolling average residuals from Equation (3.1). HighR2 banks place relatively moreweight on current and future changes in non–performing loans, than do other banks. This measureof “timeliness” has been verified to be related to bank’s ability to lend during economic–wide crisis
periods in Beatty and Liao (2011), and is correlated with more discipline demonstrated over bankrisk taking in Bushman and Williams (2012).
Turning now to the results, Table 9 panel A relates bank loan growth to determinants, i.e.,
Equation (3.3), but excluding the loan loss policy variables. We do this in part to verify whether,
as we have presumed, demand for loans increases in the aftermath of disasters. Panel A supports
this conjecture, as evidenced by the positive and statistically significant coefficient on Shock × Post.
Note that both small and large banks exhibit growth in lending during disaster periods. In addition,
this panel suggests that loan growth increases in Tier 1 capital and when there is greater earnings
before loan loss provisions. Loan growth is greater for banks with a greater portfolio of commercial
loans and it is lower if banks show a lagged increase in non-performing assets.
Panel B of Table 9 explores the channels by which banks achieve post–disaster loan growth,
focusing on regulatory capital and provisioning conservatism. The first three columns of thispanel allow for disasters to impact loan growth through a shift in intercept for conservative banks
29

(HighRES12), for the post disaster period, Shock × Post, and through the interaction of these two
factors. In the first row of this table, one notices that loan growth is lower on average, among themost conservative banks (the coefficients on HighRES12 are negative and statistically significant).
In the second row of this Table (first three columns) we confirm that growth in lending is greaterfor banks in post disaster periods, in comparison to matched unaffected control banks. In the thirdrow, in column 2, for small banks, the reaction to the disaster is greater if loan loss provisions havebeen conservative before the disaster. This is indicated by the positive and significant coefficient on
Shock × Post × HighRES12. In other words, among the small bank sample there is greater loan
growth in the post disaster period for small banks that had more ample loan loss provisions in the
periods preceding disasters. In contrast, the neighbouring coefficient in column 3 for big banks, isinsignificant, suggesting that this link between loan loss policies and lending is not important forpost disaster periods.
Columns (4) through (6) expand the potential influences on loan growth to Tier 1 capital. If
some banks face increasingly tight capital constraints following disasters, we expect an increased
weighting on Tier 1 capital in predicting loan growth, after disasters; this can be detected byinspecting the coefficient on the interactions of Shock × Post with accounting policies and Tier 1
capital. To repeat, we report results separately for small versus large banks because big banks tend
to have more publicly traded holding companies, and likely have easier access to capital so they
are less likely to have exhibit binding capital constraints in post disaster periods. This distinction is
manifested by the positive and significant coefficient for Shock × Post × Tier1 Ratio for small banks,(versus the negative analogue for big banks). The positive coefficient on regulatory capital, postdisaster, for small banks is consistent with some small banks becoming more capital constrained
during crisis periods. Stated differently, it is small banks with greater Tier 1 capital that are able torespond the most to increased loan demand in the post-crisis period. Note that in column 5, thethird row, the positive influence of loan loss provisioning conservatism on post disaster lending, is
no longer evident (i.e., the coefficient on Shock × Post ×H i ghRES12 is not statistically significant,30

in contrast to in column 2). Hence, any influence of loan loss conservatism on post-disaster lending
for small banks is apparently confounded by those banks also holding higher Tier 1 capital.
The inclusion of Shock × Post × Tier1 Ratio in column 6 for big banks yields a perversely negative
influence of Tier 1 capital on growth in lending. Thus, the most aggressive loan growth in big
banks during disaster periods occurs for those with lower Tier 1 capital. This negative coefficient
suggests that large banks are not facing capital crunches during crisis periods. Given the lack ofevidence of a credit crunch in the post disaster period for large banks, this suggests that looking forevidence of a relation between loan loss policies and loan growth is not likely to be meaningful.20
Similar to column 3, the coefficient on Shock × Post is positive and significant in column six forthe big bank sample. This suggests that big banks respond positively to the shock to loan demand,
regardless of their Tier 1 capital.
Finally whether it be big or small banks, during non-disaster periods, the results suggest loan
growth is higher, when loan loss provisioning and Tier 1 capital are higher–indicated by the positivecoefficient on the Tier 1 capital × HighRES12 variable.21
Panel C repeats the analysis in Panel B, but the construct for loan loss provisioning quality is now
timeliness rather than conservatism. Again, timeliness is captured by the degree to which a loan-loss
provisioning model relies on time t and time t+1 changes in non-performing loans. As shown,
this variable (denoted HighR2) does not have a positive impact on loan growth (in some cases
the influence is estimated to be negative). This contrasts to the results in Beatty and Liao (2011)
where timeliness appears to facilitate lending for large, publicly traded entities during extreme andgeneral economic downturns. In short, the results documented by this prior paper in the context
of general economic downturns (for big banks), does not generalize to either big or small banks
during localized downturns that are the result of disasters. While we believe our research design
20

Consistent with this interpretation of our results, Beatty and Liao (2011) focus attention solely on big banks. Thisfocus is due to the lack of evidence that small banks face capital crunches during the recessionary periods.
21
Beatty and Liao (2011) examine if banks that engender high quality provisioning policies are less reliant on Tier
1 capital during economic downturns. This would manifest in our research design as a negative coefficient on theinteraction of our shock dummy variable with HighRES12 × Tier 1 Ratio. We find that this coefficient is not negative, it
is zero or positive, in the second to last row of Panel B.

31

helps to control for potential omitted variables relative to this prior study, we obviously would not
be able to refute their findings, which are based on consolidated bank holding companies and focus
on periods with perhaps greater stresses for these particular entities, than occur due to natural
disasters.
Table 9 presents our data analysis in a format that is very similar to Beatty and Liao (2011),
thereby allowing for direct comparisons for our sample versus in the prior published work. Forease with interpretation, in Table 10 we partition the samples into high and low provisioning banks.
(In this table, the dependent variable is again, loan growth). In column 1 for the full sample,
this table suggests that lending growth occurs for banks with a correspondence between highprovisions and high tier 1 capital. Column 2 shows that for small banks loan growth occurs in thepost disaster period for high provision banks with high tier 1 capital. This evidence is consistent
with the idea that high provisioning policies lead banks to raise more capital. However, the result
is also consistent with the slightly different interpretation that high provisioning policy banks are
just more conservative. That is, the relationship between capital and provision policies might wellbe the consequence of a third, omitted variable, which is management conservatism.22
Turning now to large banks, Table 10 shows that for this subsample, loan growth is higher for
low provision banks in the post disaster period. As we noted for Table 7, panel B, this evidence isinconsistent with regulator claims that ample provisioning assists in lending during crisis periods.
However, as mentioned with respect to Table 9 , our conjecture is that big banks are not constrainedon capital to the same degree as small banks in the aftermath of disasters.

5.6

Loan Growth from Loan Sales

The previous section discusses the loan loss provision constraints faced by banks in responding to
demand for loan growth during disasters. It is natural to ask how banks faced with these constraints
actually respond to disasters. One possible channel is that banks could sell existing loans on their
22

Some readers may wonder why there is not an additional Table, showing results with partitions based on HighR2.
We have suppressed the equivalent analysis based on HighR2 because it yields no additional insights to Table 9 Panel C.

32

balance sheet to fund new loans. To test this hypothesis, we examine whether treated banks have a
different tendency in loan sales. Table 12 presents the results from the analysis. The dependent
variable in column (1) to (3) is loan sales reported by banks, scaled by total loans. The dependent
variable in column (4) to (6) is the gain from sale of loans reported by banks.
We find that in our full sample, banks suffering a disaster have a direct effect on loan sales. The
coefficient of Shock × Post is positive and significant. Interestingly, we find that large and smallbanks differ in this aspect. Large banks that suffer a disaster have a positive effect on loan sales,
relative to control banks, in the post period. On the other hand, we find that treated small banks
decrease their loan sales in the post period relative to the control banks. The finding that largebanks sell loans in their balance sheet is consistent with the findings in Table 8 on deposits, which
presented large banks have a liquidity constraint in terms of outflow from deposit after the disaster.

6

Conclusions

This paper traces disasters such as wild fires, floods and hurricanes to banks that reside in counties
that are impacted by these disasters. We presume that these banks are subject to a localized increasein the demand for loans, and possibly a simultaneous decrease in the supply of loans due to theimpact of increased credit risk on capital ratios.
We use our setting to examine two questions. This first is how do banks’ loan loss provisioning
practices change when their operations and customers, become exposed to disasters? This question
is of interest because disasters give banks the license to engage in more forward-looking loan
loss provisions, in advance of the cash flow triggering events that typically accompany a loan loss
provision. Prior research often interprets a banks’ reliance on forecasted changes in credit risk, as
an indication that the bank has used high quality provisioning methods. We document that disasters
are associated with an increased use of forward-looking information, particularly for larger banks.
But we emphasize that in this instance, the increased weight on forward-looking information does
not necessarily correspond to higher quality provisions. Rather, we think the reliance on forward
33

looking information in this instance is endemic to the type of triggering event, and does not meanthat managers have improved provisioning quality. This insight contributes to work by Bhat et al.
(2016) and Bhat et al. (2018), which are two papers that present evidence that standard measuresof loan loss quality and their relation to firm results are sometimes a coincidental outcome of
un-modelled features of the bank’s operations, such as sophistication of data analytics and loan
composition.
Our second question asks whether banks’ loan loss provisioning attitudes or policies help toalleviate the constraints imposed on banks when both demand shifts out, and supply shifts out. We
see natural disasters as creating isolated crisis periods (similar to a recession) that hit differentbanks at different times. These events have the potential to create constraints on lending aka‘capital crunch’ periods. In as much as regulators and economists have asserted that loan loss
provisioning methods exacerbate cyclical downturns, we believe that that this setting allows us
to test the links between loan loss provisioning qualities, and capital constraints. Prior researchfinds support for regulator assertions that more timely (meaning more forward looking) loan loss
provisions, alleviate capital constraints (e.g., Beatty and Liao (2011)).
In this paper we propose to re-examine these ideas, but we employ a difference in difference
research design that uses the impact of natural disasters to identify banks subject to demand shifts.
In Figure 1 and Figure 2, we illustrate how a difference in difference design can be used to sharpen
inferences regarding this hypothesis. We measure loan loss conservatism as the error term from a
regression of loan loss provisions on credit loss indicators and other controls. Those banks withthe most positive residuals are classified as conservative provisioning banks. We measure a second
construct (loan timeliness) as the incremental r-squared from a regression that includes current andforward-looking loss indicators versus a regression that omits these terms. We then regress growth
in loans before, versus after, a disaster and we allow this growth to be impacted by bank’s loan loss
accounting policies. Our results find no evidence that timeliness of loan loss provisions is helpful inalleviating capital constraints. We find some evidence that small banks with more conservative

34

loan loss policies in pre-disaster periods, exhibit greater loan growth following disasters (relative tocontrol banks that are not impacted by disasters). However, this link between loan loss policies andlending is co-mingled with the impact on lending of a bank’s Tier 1 capital ratio. The evidence wefind for loan loss accounting policies impacting growth in lending for small banks, rather than big
banks, contrasts to work by Beatty and Liao (2011) which found results consistent with regulator
claims for loan loss timeliness, for big banks, but not for small banks.

35

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38

FIGURE 1: Effect of LLP on Loan Lending during Disaster

This figure shows the loan change during the disaster. In this figure, the natural disaster is modelled as an exogenous
shock that shifts the demand from D0 to D1 . The blue dashed line is the supply curve of the bank with low loan loss
provision, and the blue solid line is the supply curve of the bank with high loan loss provision. Loan loss provision
determines the kink from which bank’s further lending is restricted. For low provision banks, the kink is lower because
they don’t have enough provision ready to absorb the loan loss caused by the disaster; thus they are more likely to betangent on their tier 1 capital constraint. So their ability to respond to the increased demand will be restricted.

39

FIGURE 2: Comparison of Effect of LLP on Loan Lending Between Disaster and Non-Disaster Period

This figure shows the change of the relation between loan loss provision and banks’ lending activity before and after
the disaster. From bank A to bank D, banks’ (pre-disaster period) loan loss provision(LLP) increases monotonically. (a)shows banks’ lending activity during non-disaster period: bank B, C, and D have the same lending amount as they
are not constrained by the tier 1 capital constraint, while bank A has a lower level of lending amount because of thecapital constraint. (b) shows the lending activity during the disaster period The disaster not only shifts up the demandof loans but also shifts banks’ capital constraints towards the left. The capital constraints shift more if the bank has
lower (pre-disaster period) LLP to absorb the losses caused by the disaster. Thus, banks with higher LLP will respond
more to the increased demand. From (a) to (b), the positive correlation between the loan loss provision and banks’lending become stronger.

40

TABLE 1: This table describes the sample selection process used in the paper.

Call Report Data from 1994 Q1 to 2017 Q4
merge with FDIC deposit-weighted SHELDUS Disaster
drop if asset < $100 million
drop if total loans < $1 million
drop if asset growth > 10%
drop if loan change > 15%
drop if missing key variables
propensity score matching**1-to-1 matching with replacement

41

864,033
835,691
440,449
438,892
413,871410,253
407,206
186,558

TABLE 2: This table reports descriptive statistics of the sample used in the empirical analysis. Panel A presents descriptive statistics for the full sample. Panel B presents summary statistics
separately for small and large banks in the sample. Ln(Total Asset) is the natural logarithm of total assets. LLP is loan loss provision, scaled by lagged total loans. Deposit is deposits, scaled
total assets. Interest Income is interest incomes, scaled by total assets. EBP is earnings before loan loss provisions and taxes, scaled by lagged total assets. Tier1 Ratio is the Tier 1 capital ratioof the bank. Loan is the total loans, scaled by assets. ∆ Loan is the change in loans from last quarter to current quarter, scaled by loans in last quarter. Resid’l RE Loan is the ratio of residential
real estate loans to total loans. Comm’l RE Loan is the ratio of commercial real estate loans to total loans. Comm’l & Indus’l Loan is the ratio of commerical and industrial loans to total loans.
∆ Non-Performing Assets is banks’ Non-performing Assets scaled by lagged total loans. PropertyDamage is the average property damage experienced by a bank. ∆ GDP is the change in GDPof the head-quarter state of the bank. ∆ Unemployment is the change in unemployment of the head–quarter state of the bank. House Index is the housing price at the head–quarter state of
the bank.

Panel A

42

N

Mean

SD

Min

P25

MedianP75

Max

Disaster
Dummy_4QTR
PropertyDamage_4QTR

186,558
186,558

0.343
122.1

0.475
769.2

0.000
0.0

0.000
0.0

0.000
0.0

1.000
0.0

1.000
6,748.7

Bank
Ln(Total Asset)
Tier1 Ratio
LLP(%)
Loan
Non-Perf. Assets(%)∆ Non-Perf. Assets(%)
Interest Income
Deposit∆ Deposit (%)
EBP(%)
Comm’l & Indus’l Loan(%)
Real Estate Loan(%)
Consumer Loan(%)
Comm’l RE Loan(%)
Resid’l RE Loan(%)∆ Loan_4QTR
LoanSale(%)
SaleGain(%)

186,558
176,306
186,558
186,558
186,558
186,558
186,558
186,558
186,558
186,558
186,558
186,558
186,558
167,750
186,558
73,791
167,750
186,558

5.925
14.738
0.119
0.661
1.985
0.026
0.035
0.817
1.187
0.317
13.58673.3737.756
0.262
37.045
0.046
0.591
0.063

1.141
6.284
0.236
0.143
2.701
0.636
0.018
0.085
3.863
0.200
10.42118.40510.1440.869
22.738
0.095
1.916
0.181

4.621
7.190
-0.1430.202
0.000-2.1660.008
0.396-10.226-0.341
0.000
0.268
0.000
0.000
0.020
-0.2200.000-0.003

5.096
10.790
0.013
0.579
0.453
-0.1700.019
0.782-1.0560.212
6.219
63.1151.587
0.000
20.690-0.009
0.000
0.000

5.620
12.900
0.053
0.679
1.061
-0.004
0.033
0.838
1.014
0.307
11.78876.0254.354
0.000
31.831
0.047
0.000
0.000

6.400
16.430
0.117
0.764
2.316
0.160
0.047
0.877
3.360
0.406
18.64686.9159.983
0.000
48.425
0.100
0.276
0.037

10.20546.3901.615
0.925
16.218
2.846
0.081
0.928
12.001
1.361
56.102100.000
72.8796.023
97.5550.345
13.987
1.270

State
∆ GDP
∆ Unemployment
House Index

4,998
4,998
4,998

6.17
-0.02
186.16

11.59
0.31
58.60

-61.46
-4.77
82.46

0.82
-0.20
143.182.71-0.07
184.197.39
0.07
215.82

119.47
2.63
564.62*Property Damage and ∆ GDP are in thousands.

Panel B
Small

Large

43

Mean

MedianSD

MeanMedianSD

Diff-in-meanstDisaster
Dummy_4QTR
PropertyDamage_4QTR

0.313
80.12

0.00
0.00

0.464
604.100.416
220.64

0.00
0.00

0.493
1053.38-0.103∗∗∗-140.53∗∗∗(-42.01)(-29.50)

Bank
Ln(Total Asset)
LLP(%)
Allowance(%)
Loan
Non-Perf. Assets(%)∆ Non-Perf. Assets(%)
Interest Income
Deposit
EBP(%)
Tier1 Ratio
Comm’l & Indus’l Loan(%)
Consumer Loan(%)
Real Estate Loan(%)
Comm’l RE Loan(%)
Resid’l RE Loan(%)∆ Dep(%)
LoanSale(%)
SaleGain(%)
Observations

5.331
0.109
1.422
0.656
1.962
0.027
0.035
0.834
0.311
0.153
13.1397.691
73.731
0.212
37.356
1.164
0.440
0.053
130,804

5.301
0.049
1.288
0.674
1.053
-0.007
0.034
0.850
0.303
13.40
11.5074.857
75.8400.000
32.121
1.000
0.000
0.000

0.443
0.219
0.722
0.144
2.691
0.663
0.018
0.071
0.1911
0.065
9.91519.110
17.3880.828
22.6183.84571.63870.1680

7.319
0.145
1.509
0.673
2.041
0.024
0.032
0.778
0.332
0.135
14.63617.907
72.5360.382
36.3151.24050.95220.087255,754

6.958
0.062
1.337
0.690
1.080
0.178
0.031
0.801
0.313
11.95012.604
3.132
76.4730.000
31.208
1.044
0.117
0.008

1.061
0.270
0.811
0.139
2.723
0.566
0.018
0.101
0.219
0.056
11.45312.230920.5697
0.948023.0
3.90152.41630.2061-1.988∗∗∗-0.036∗∗∗-0.0874∗∗∗-0.017∗∗∗-0.079∗∗∗0.003
0.003∗∗∗
0.057∗∗∗-0.0210∗∗∗0.018∗∗∗-1.4972∗∗∗-0.216∗∗∗
1.195∗∗∗-0.170∗∗∗1.041∗∗∗-0.0769∗∗∗-0.5121∗∗∗-0.0340∗∗∗
186,558(-426.85)(-27.84)(-22.00)(-23.39)(-5.75)(0.99)(29.65)(119.97)(-19.71)(60.23)(-26.87)(-3.74)(12.01)(-34.83)(8.99)(-3.91)(-43.18)(-34.43)∆ Loans_4QTR
HighRes
HighR2
Observations

0.04240.1936
0.502456,871

0.097
0.000
1.000

0.09480.39510.50000.05700.1976
0.4983
17,381

0.059
0.000
0.000

0.09580.39820.5000-0.0146∗∗∗-0.00410.0041
74,252(-17.54)(-1.18)(0.94)TABLE 3: This table reports the number of counties that were affected by disaster types from SHELDUS database. The Total column shows the number of unique counties that experienced
any form of disaster.

Year

CoastalEarthquake

Flooding

1994
1995
1996
1997
1998
1999
2000

0
1
5
0
1
0
0

1
0
0
0
0
0
0

115
178
305
298
362
128
91

Hurricane/
Tropical Storm
13
53
62
3
86
105
0

Severe Storm/Thunder Storm
34
65
25
20
127
37
25

TornadoWildfire

Total

12
29
21
24
112
43
17

0
0
4
0
66
8
11

149
289
394
313
554
290
120
2109

44

2001
2002
2003
2004
2005
2006
2007

3
0
2
0
5
1
0

0
0
0
0
0
0
0

182
196
281
389
225
152
235

21
49
120
121
231
0
0

9
22
71
61
52
44
31

53
65
119
104
60
64
31

0
15
8
3
30
39
8

232
306
520
582
492
281
274
2687

2008
2009
2010
2011
2012
2013
2014
2015
2016
2017

45
0
1
0
4
0
0
0
3
0

0
0
0
0
0
0
0
0
0
0

389
169
311
366
126
179
99
222
203
207

182
0
3
122
60
0
0
1
29
227

109
57
59
133
113
26
21
32
8
18

147
41
29
185
38
16
16
63
18
25

11
5
0
66
6
2
5
2
4
14

645
231
342
671
300
205
126
278
232
419
3449

TABLE 4: This table presents the effect of Disaster on LLP. Panel A of this table shows the average effect of disaster on banks’ loan loss provisioning.
Panel B shows how banks adjust the weight of their non–performing assets on their loan loss provisioning during natural disaster period. The
dependent variable is banks’ loan loss provisions in the current quarter scaled by lagged total loans. Shock is a dummy variable, which is equal to 1
for banks that are affected by a disaster. Post is the dummy variable, which is equal to 1 from 0 to 3 quarters after a disaster. Ln(Total Asset) is thenatural logarithm of total assets. Tier1 Ratio is the Tier 1 capital ratio of the bank. EBP is earnings before loan loss provisions and taxes. ∆ Loan
is the change in loans from last quarter to current quarter scaled by loans in last quarter. Residential Real Estate Loan is the ratio of residential realestate loans to total loans. Commercial Real Estate Loan is the ratio of commercial real estate loans to total loans. Commercial and Industrial Loan is
the ratio of commerical and industrial loans to total loans. ∆ Non-Performing Assets is banks’ Non-performing Assets scaled by lagged total loans.

Panel A

Shock × Post

(1)
Full
0.0000∗∗∗(0.0000)

Ln(Total Asset) t−1
Tier1 Ratio t−1
EBP∆ LoanResid’l RE Loan
Comm’l & Indus’l Loan
Comm’l RE Loan
∆ Non-Perf. Assets t +1
∆ Non-Perf. Assets∆ Non-Perf. Assets t−1∆ Non-Perf. Assets t−2
Constant
Bank FE
State-Time FE
Observations
Adjusted R2

0.0012∗∗∗(0.0000)
NoNo
186558
0.00

(2)
Full
-0.0000∗∗∗(0.0000)
0.0001∗∗∗(0.0000)-0.0018∗∗∗(0.0001)
0.2169∗∗∗(0.0029)-0.0106∗∗∗(0.0001)-0.0012∗∗∗(0.0000)-0.0010∗∗∗(0.0001)
0.0009(0.0006)
0.0211∗∗∗(0.0008)0.0408∗∗∗(0.0009)0.0544∗∗∗(0.0009)0.0499∗∗∗(0.0009)
0.0007∗∗∗(0.0000)
NoNo
1579070.15

Standard errors in parentheses∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

.tex .tex

45

(3)
Full
-0.0000(0.0000)
0.0002∗∗∗(0.0000)-0.0017∗∗∗(0.0002)
0.2523∗∗∗(0.0036)-0.0058∗∗∗(0.0001)-0.0007∗∗∗(0.0001)-0.0002∗(0.0001)-0.0006(0.0008)0.0102∗∗∗(0.0007)
0.0289∗∗∗(0.0008)0.0370∗∗∗(0.0008)
0.0329∗∗∗(0.0008)-0.0004∗∗∗(0.0001)
Yes
Yes
157772
0.40

(4)
Small
-0.0000∗∗∗(0.0000)
0.0003∗∗∗(0.0000)-0.0018∗∗∗(0.0002)0.2532∗∗∗(0.0044)-0.0052∗∗∗(0.0002)-0.0004∗∗∗(0.0001)
0.0002(0.0001)-0.0002(0.0010)0.0090∗∗∗(0.0008)
0.0232∗∗∗(0.0009)
0.0336∗∗∗(0.0009)0.0276∗∗∗(0.0009)-0.0009∗∗∗(0.0002)
Yes
Yes
110602
0.35

(5)
Large
0.0000∗∗(0.0000)
0.0002∗∗∗(0.0000)-0.0018∗∗∗(0.0004)
0.2668∗∗∗(0.0066)-0.0063∗∗∗(0.0003)-0.0011∗∗∗(0.0002)-0.0010∗∗∗(0.0002)-0.0021(0.0016)0.0066∗∗∗(0.0016)0.0427∗∗∗(0.0017)0.0397∗∗∗(0.0016)0.0422∗∗∗(0.0016)-0.0001(0.0003)
Yes
Yes
47045
0.52

Panel B

Shock × Post
Shock × Post × ∆ Non-Perf. Assets t +1
Shock × Post × ∆ Non-Perf. Assets
Shock × Post × ∆ Non-Perf. Assets t−1
Shock × Post × ∆ Non-Perf. Assets t−2(1)
LLP-0.0000(0.0000)
0.0047∗∗∗(0.0015)0.0102∗∗∗(0.0016)0.0111∗∗∗(0.0016)0.0023(0.0016)

Shock × Post × EBP(2)
LLP-0.0000(0.0000)

0.0061(0.0055)

Shock × Post × Tier1 Ratio t−1∆ Non-Perf. Assets t +1
∆ Non-Perf. Assets∆ Non-Perf. Assets t−1∆ Non-Perf. Assets t−2
EBP
Tier1 Ratio t−1
Ln(Total Asset) t−1∆ LoanResid’l RE Loan
Comm’l & Indus’l Loan
Comm’l RE Loan
Constant
Bank FE
State-Time FE
Observations
Adjusted R2(3)
LLP
0.0001∗∗∗(0.0000)

0.0054∗∗∗(0.0009)
0.0211∗∗∗(0.0009)0.0279∗∗∗(0.0009)0.0271∗∗∗(0.0009)
0.2660∗∗∗(0.0036)-0.0016∗∗∗(0.0002)0.0003∗∗∗(0.0000)-0.0048∗∗∗(0.0001)-0.0007∗∗∗(0.0001)
0.0000(0.0001)-0.0018∗∗(0.0008)-0.0006∗∗∗(0.0001)
Yes
Yes
1576730.44

Standard errors in parentheses∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

46

0.0070∗∗∗(0.0007)0.0246∗∗∗(0.0008)
0.0317∗∗∗(0.0008)
0.0280∗∗∗(0.0008)
0.2636∗∗∗(0.0041)-0.0016∗∗∗(0.0002)0.0003∗∗∗(0.0000)-0.0048∗∗∗(0.0001)-0.0007∗∗∗(0.0001)
0.0000(0.0001)-0.0018∗∗(0.0008)-0.0006∗∗∗(0.0001)
Yes
Yes
1576730.44-0.0007∗∗∗(0.0002)
0.0070∗∗∗(0.0007)0.0246∗∗∗(0.0008)
0.0317∗∗∗(0.0008)
0.0280∗∗∗(0.0008)0.2657∗∗∗(0.0036)-0.0014∗∗∗(0.0002)0.0003∗∗∗(0.0000)-0.0048∗∗∗(0.0001)-0.0007∗∗∗(0.0001)
0.0000(0.0001)-0.0018∗∗(0.0008)-0.0006∗∗∗(0.0001)
Yes
Yes
157673
0.44

(4)
LLP
0.0000(0.0000)
0.0045∗∗∗(0.0015)0.0100∗∗∗(0.0016)0.0109∗∗∗(0.0016)0.0021(0.0016)0.0033(0.0055)-0.0006∗∗∗(0.0002)0.0055∗∗∗(0.0009)
0.0212∗∗∗(0.0009)
0.0280∗∗∗(0.0009)0.0272∗∗∗(0.0009)
0.2647∗∗∗(0.0041)-0.0014∗∗∗(0.0002)0.0003∗∗∗(0.0000)-0.0048∗∗∗(0.0001)-0.0007∗∗∗(0.0001)
0.0000(0.0001)-0.0018∗∗(0.0008)-0.0006∗∗∗(0.0001)
Yes
Yes
157673
0.44

TABLE 5: This table presents the effect of natural disaster on banks’ loan loss provisioning for small banks and large banks separately. The size
cut–off for small and large banks is $500 million. Panel B shows the different effect of natural disaster on banks loan loss provisioning in the first
three–quarters and the forth–quarter. Panel C presents the effect of natural disaster on banks’ loan loss provisioning for banks with a relatively
high proportion of homogeneous loans and heterogeneous loans separately. Homogeneous loans include residential real estate loans and consumer
loans. The dependent variable is banks’ loan loss provisions is the current quarter scaled by lagged total loans. Shock is a dummy variable, whichis equal to 1 for banks that are affected by a disaster. Post is the dummy variable, which is equal to 1 from 0 to 3 quarters after a disaster. Ln(Total
Asset) is the natural logarithm of total assets. Tier1 Ratio is the Tier 1 capital ratio of the bank. EBP is earnings before loan loss provisions andtaxes. ∆ Loan is the change in loans from last quarter to current quarter scaled by loans in last quarter. Residential Real Estate Loan is the ratio of
residential real estate loans to total loans. Commercial Real Estate Loan is the ratio of commercial real estate loans to total loans. Commercial andIndustrial Loan is the ratio of commerical and industrial loans to total loans. ∆ Non-Performing Assets is banks’ Non-performing Assets scaled bylagged total loans.

Panel A
Small

Shock × Post
Shock × Post × ∆ Non-Perf. Assets t +1
Shock × Post × ∆ Non-Perf. Assets
Shock × Post × ∆ Non-Perf. Assets t−1
Shock × Post × ∆ Non-Perf. Assets t−2(1)
LLP-0.0001∗∗∗(0.0000)-0.0002(0.0018)0.0036∗∗(0.0018)0.0054∗∗∗(0.0018)-0.0014(0.0018)

Shock × Post × EBP
Shock × Post × Tier1 Ratio t−1∆ Non-Perf. Assets t +1
∆ Non-Perf. Assets∆ Non-Perf. Assets t−1∆ Non-Perf. Assets t−2
EBP
Tier1 Ratio t−1
Ln(Total Asset) t−1∆ LoanResid’l RE Loan
Comm’l & Indus’l Loan
Comm’l RE Loan
Constant
Bank FE
State-Time FE
Observations
Adjusted R2Standard errors in parentheses∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

0.0066∗∗∗(0.0010)0.0184∗∗∗(0.0010)0.0275∗∗∗(0.0010)0.0243∗∗∗(0.0010)0.2750∗∗∗(0.0044)-0.0016∗∗∗(0.0002)0.0003∗∗∗(0.0000)-0.0044∗∗∗(0.0002)-0.0005∗∗∗(0.0001)
0.0005∗∗∗(0.0001)-0.0017(0.0011)-0.0010∗∗∗(0.0002)
Yes
Yes
47
110367
0.38

(2)
LLP
0.0001(0.0000)-0.0003(0.0018)0.0034∗(0.0018)0.0052∗∗∗(0.0018)-0.0016(0.0018)0.0005(0.0069)-0.0008∗∗∗(0.0002)0.0067∗∗∗(0.0010)0.0185∗∗∗(0.0010)0.0276∗∗∗(0.0010)0.0244∗∗∗(0.0010)0.2748∗∗∗(0.0050)-0.0013∗∗∗(0.0003)0.0003∗∗∗(0.0000)-0.0044∗∗∗(0.0002)-0.0005∗∗∗(0.0001)
0.0005∗∗∗(0.0001)-0.0017(0.0011)-0.0010∗∗∗(0.0002)
Yes
Yes
110367
0.38

Large
(3)
LLP
0.0001∗∗∗(0.0000)
0.0123∗∗∗(0.0032)
0.0168∗∗∗(0.0033)
0.0203∗∗∗(0.0032)
0.0020(0.0032)-0.0012(0.0021)0.0312∗∗∗(0.0021)0.0252∗∗∗(0.0020)0.0339∗∗∗(0.0021)0.2630∗∗∗(0.0067)-0.0016∗∗∗(0.0004)
0.0002∗∗∗(0.0000)-0.0048∗∗∗(0.0003)-0.0013∗∗∗(0.0002)-0.0011∗∗∗(0.0003)-0.0036∗∗(0.0016)-0.0003(0.0003)
Yes
Yes
46749
0.57

(4)
LLP
-0.0000(0.0001)
0.0121∗∗∗(0.0032)
0.0168∗∗∗(0.0033)
0.0203∗∗∗(0.0032)
0.0020(0.0033)
0.0193∗(0.0099)0.0004(0.0004)-0.0012(0.0021)0.0312∗∗∗(0.0021)0.0252∗∗∗(0.0020)0.0339∗∗∗(0.0021)0.2547∗∗∗(0.0079)-0.0017∗∗∗(0.0005)
0.0002∗∗∗(0.0000)-0.0048∗∗∗(0.0003)-0.0014∗∗∗(0.0002)-0.0011∗∗∗(0.0003)-0.0036∗∗(0.0016)-0.0002(0.0003)
Yes
Yes
46749
0.57

Panel B
Small

Shock × Post
Shock × Post × ∆ Non-Perf. Assets t +1
Shock × Post × ∆ Non-Perf. Assets
Shock × Post × ∆ Non-Perf. Assets t−1
Shock × Post × ∆ Non-Perf. Assets t−2
Shock × Post × EBP
Shock × Post × Tier1 Ratio t−1∆ Non-Perf. Assets t +1
∆ Non-Perf. Assets∆ Non-Perf. Assets t−1∆ Non-Perf. Assets t−2
EBP
Tier1 Ratio t−1
Ln(Total Asset) t−1∆ LoanResid’l RE Loan
Comm’l & Indus’l Loan
Comm’l RE Loan
Constant
Bank FE
State-Time FE
Observations
Adjusted R2(1)
Q4-0.0001(0.0001)
0.0086∗(0.0051)0.0148∗∗∗(0.0048)-0.0008(0.0051)-0.0039(0.0050)
0.0211(0.0166)-0.0006(0.0006)
0.0140∗∗∗(0.0029)0.0134∗∗∗(0.0027)
0.0357∗∗∗(0.0029)0.0387∗∗∗(0.0028)0.2813∗∗∗(0.0114)-0.0025∗∗∗(0.0007)0.0005∗∗∗(0.0001)-0.0084∗∗∗(0.0005)-0.0007∗∗(0.0003)0.0012∗∗∗(0.0004)-0.0042(0.0027)-0.0017∗∗∗(0.0006)
Yes
Yes
26582
0.40

Standard errors in parentheses∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

48

(2)
Non-Q40.0001∗(0.0000)-0.0011(0.0018)0.0014(0.0019)0.0068∗∗∗(0.0019)-0.0028(0.0019)-0.0010(0.0075)-0.0008∗∗∗(0.0002)0.0033∗∗∗(0.0010)0.0191∗∗∗(0.0011)0.0251∗∗∗(0.0011)0.0219∗∗∗(0.0011)0.2672∗∗∗(0.0056)-0.0013∗∗∗(0.0003)0.0002∗∗∗(0.0000)-0.0036∗∗∗(0.0002)-0.0004∗∗∗(0.0001)
0.0003∗(0.0002)-0.0007(0.0011)-0.0007∗∗∗(0.0002)
Yes
Yes
82888
0.37

Large
(3)
Q4
-0.0001(0.0002)0.0054(0.0089)0.0354∗∗∗(0.0083)
0.0211∗∗(0.0085)-0.0282∗∗∗(0.0088)0.0447∗∗(0.0225)0.0007(0.0010)-0.0001(0.0059)
0.0384∗∗∗(0.0052)
0.0341∗∗∗(0.0053)
0.0563∗∗∗(0.0056)0.1340∗∗∗(0.0170)-0.0025∗∗(0.0011)0.0004∗∗∗(0.0001)-0.0091∗∗∗(0.0008)-0.0016∗∗∗(0.0005)-0.0004(0.0006)-0.0008(0.0041)-0.0004(0.0008)
Yes
Yes
11188
0.54

(4)
Non-Q4-0.0000(0.0001)
0.0159∗∗∗(0.0034)
0.0081∗∗(0.0037)
0.0215∗∗∗(0.0035)
0.0129∗∗∗(0.0035)0.0100(0.0110)0.0004(0.0004)-0.0050∗∗(0.0022)0.0297∗∗∗(0.0023)0.0212∗∗∗(0.0022)0.0283∗∗∗(0.0022)0.3275∗∗∗(0.0092)-0.0018∗∗∗(0.0005)
0.0002∗∗∗(0.0000)-0.0039∗∗∗(0.0003)-0.0012∗∗∗(0.0002)-0.0012∗∗∗(0.0003)-0.0048∗∗∗(0.0017)-0.0001(0.0004)
Yes
Yes
35162
0.58

Panel C
Small

Shock × Post
Shock × Post × ∆ Non-Perf. Assets t +1
Shock × Post × ∆ Non-Perf. Assets
Shock × Post × ∆ Non-Perf. Assets t−1
Shock × Post × ∆ Non-Perf. Assets t−2
Shock × Post × EBP
Shock × Post × Tier1 Ratio t−1∆ Non-Perf. Assets t +1
∆ Non-Perf. Assets∆ Non-Perf. Assets t−1∆ Non-Perf. Assets t−2
EBP
Tier1 Ratio t−1
Ln(Total Asset) t−1∆ LoanResid’l RE Loan
Comm’l & Indus’l Loan
Comm’l RE Loan
Constant
Bank FE
State-Time FE
Observations
Adjusted R2(1)
Homogenous-0.0001(0.0001)-0.0053∗(0.0028)0.0012(0.0028)0.0062∗∗(0.0028)-0.0052∗(0.0028)0.0056(0.0097)-0.0001(0.0003)
0.0059∗∗∗(0.0015)0.0135∗∗∗(0.0016)0.0244∗∗∗(0.0016)0.0211∗∗∗(0.0016)0.2533∗∗∗(0.0073)-0.0009∗∗(0.0003)0.0005∗∗∗(0.0001)-0.0044∗∗∗(0.0003)-0.0008∗∗∗(0.0002)-0.0005∗(0.0003)-0.0018(0.0016)-0.0015∗∗∗(0.0004)
Yes
Yes
49385
0.41

Standard errors in parentheses∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

49

(2)
Heterogenous
0.0001(0.0001)
0.0034(0.0024)0.0049∗∗(0.0024)0.0049∗∗(0.0024)-0.0005(0.0025)
0.0038(0.0101)-0.0011∗∗(0.0004)
0.0066∗∗∗(0.0014)0.0193∗∗∗(0.0014)0.0273∗∗∗(0.0014)0.0246∗∗∗(0.0014)0.2902∗∗∗(0.0072)-0.0014∗∗∗(0.0004)
0.0002∗∗∗(0.0001)-0.0040∗∗∗(0.0002)0.0001(0.0003)0.0012∗∗∗(0.0002)-0.0020(0.0015)-0.0007∗∗(0.0003)
Yes
Yes
60162
0.37

Large
(3)
Homogenous
0.0000(0.0001)
0.0159∗∗∗(0.0053)0.0158∗∗∗(0.0056)
0.0167∗∗∗(0.0052)
0.0049(0.0052)-0.0149(0.0138)0.0009∗(0.0005)-0.0095∗∗∗(0.0034)
0.0287∗∗∗(0.0036)
0.0239∗∗∗(0.0033)
0.0269∗∗∗(0.0033)
0.2967∗∗∗(0.0114)-0.0002(0.0006)
0.0003∗∗∗(0.0001)-0.0018∗∗∗(0.0004)-0.0018∗∗∗(0.0003)-0.0012∗∗∗(0.0004)-0.0024(0.0027)-0.0008(0.0005)
Yes
Yes
20675
0.66

(4)
Heterogenous-0.0001(0.0001)
0.0157∗∗∗(0.0043)0.0180∗∗∗(0.0044)0.0228∗∗∗(0.0044)-0.0024(0.0044)0.0385∗∗(0.0156)-0.0003(0.0008)
0.0006(0.0028)0.0318∗∗∗(0.0028)0.0229∗∗∗(0.0028)0.0359∗∗∗(0.0028)0.2467∗∗∗(0.0122)-0.0027∗∗∗(0.0008)
0.0003∗∗∗(0.0001)-0.0063∗∗∗(0.0004)
0.0000(0.0004)-0.0005(0.0004)-0.0017(0.0023)-0.0007(0.0005)
Yes
Yes
24931
0.51

TABLE 6: This table presents the effect of natural disaster on banks’ loan loss provisions for full, small and large banks. Damage(by Deposit) andDamage(by Branch) is the sum of the branch-level property damage, which is the allocated damage based on deposit for Damage(by Deposit). The
size cut–off for small and large banks is $500 million. The dependent variable in columns (1) to (3) is loan sale, while the dependent variable forcolumns (4) to (5) is the gain reported by banks. Shock is a dummy variable, which is equal to 1 for banks that are affected by a disaster. Post is
the dummy variable, which is equal to 1 from 0 to 3 quarters after a disaster. Ln(Total Asset) is the natural logarithm of total assets. Tier1 Ratio is
the Tier 1 capital ratio of the bank. EBP is earnings before loan loss provisions and taxes. ∆ Loan is the change in loans from last quarter to current
quarter scaled by loans in last quarter. Residential Real Estate Loan is the ratio of residential real estate loans to total loans. Commercial Real Estate
Loan is the ratio of commercial real estate loans to total loans. Commercial and Industrial Loan is the ratio of commerical and industrial loans tototal loans. ∆ Non-Performing Assets is banks’ Non-performing Assets scaled by lagged total loans.

DamageDamage × ∆ Non-Perf. Assets t +1
Damage × ∆ Non-Perf. Assets
Damage × ∆ Non-Perf. Assets t−1
Damage × ∆ Non-Perf. Assets t−2

(1)
Full
-0.0000(0.0000)
0.0008∗∗∗(0.0001)
0.0011∗∗∗(0.0001)
0.0010∗∗∗(0.0001)
0.0002(0.0001)(2)
Small-0.0000∗∗∗(0.0000)
0.0002(0.0002)0.0004∗∗(0.0002)0.0004∗∗(0.0002)-0.0001(0.0002)(3)Large
0.0000∗∗∗(0.0000)
0.0018∗∗∗(0.0003)
0.0021∗∗∗(0.0003)
0.0022∗∗∗(0.0003)0.0001(0.0003)

0.0049∗∗∗(0.0009)
0.0216∗∗∗(0.0010)0.0289∗∗∗(0.0009)0.0275∗∗∗(0.0009)
0.2764∗∗∗(0.0038)-0.0018∗∗∗(0.0002)0.0003∗∗∗(0.0000)-0.0051∗∗∗(0.0002)-0.0007∗∗∗(0.0001)
0.0000(0.0001)-0.0017∗(0.0009)-0.0006∗∗∗(0.0002)
Yes
Yes
1459430.44

0.0065∗∗∗(0.0010)0.0190∗∗∗(0.0011)0.0289∗∗∗(0.0011)0.0246∗∗∗(0.0011)0.2863∗∗∗(0.0047)-0.0017∗∗∗(0.0003)0.0003∗∗∗(0.0000)-0.0045∗∗∗(0.0002)-0.0004∗∗∗(0.0001)
0.0004∗∗∗(0.0002)-0.0018(0.0011)-0.0008∗∗∗(0.0002)
Yes
Yes
1035000.38-0.0033(0.0021)0.0313∗∗∗(0.0022)0.0249∗∗∗(0.0021)0.0346∗∗∗(0.0022)0.2707∗∗∗(0.0072)-0.0019∗∗∗(0.0005)
0.0002∗∗∗(0.0000)-0.0053∗∗∗(0.0003)-0.0013∗∗∗(0.0002)-0.0010∗∗∗(0.0003)-0.0035∗∗(0.0018)0.0000(0.0004)
Yes
Yes
41934
0.56

Damage × EBP
Damage × Tier1 Ratio t−1∆ Non-Perf. Assets t +1
∆ Non-Perf. Assets∆ Non-Perf. Assets t−1∆ Non-Perf. Assets t−2
EBP
Tier1 Ratio t−1
Ln(Total Asset) t−1∆ LoanResid’l RE Loan
Comm’l & Indus’l Loan
Comm’l RE Loan
Constant
Bank FE
State-Time FE
Observations
Adjusted R2Standard errors in parentheses∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

50

(4)
Full
0.0000(0.0000)
0.0008∗∗∗(0.0001)
0.0011∗∗∗(0.0001)
0.0010∗∗∗(0.0001)
0.0002(0.0001)
0.0009∗(0.0005)-0.0001∗∗∗(0.0000)
0.0050∗∗∗(0.0009)
0.0217∗∗∗(0.0010)0.0289∗∗∗(0.0009)0.0275∗∗∗(0.0009)
0.2731∗∗∗(0.0043)-0.0016∗∗∗(0.0002)0.0003∗∗∗(0.0000)-0.0051∗∗∗(0.0002)-0.0007∗∗∗(0.0001)
0.0000(0.0001)-0.0017∗(0.0009)-0.0006∗∗∗(0.0002)
Yes
Yes
145943
0.44

(5)
Small
0.0000(0.0000)
0.0002(0.0002)0.0004∗∗(0.0002)0.0004∗∗(0.0002)-0.0001(0.0002)0.0013∗∗(0.0006)-0.0001∗∗∗(0.0000)
0.0066∗∗∗(0.0010)0.0191∗∗∗(0.0011)0.0290∗∗∗(0.0011)0.0247∗∗∗(0.0011)0.2814∗∗∗(0.0052)-0.0015∗∗∗(0.0003)0.0003∗∗∗(0.0000)-0.0045∗∗∗(0.0002)-0.0004∗∗∗(0.0001)
0.0004∗∗∗(0.0002)-0.0018(0.0011)-0.0008∗∗∗(0.0002)
Yes
Yes
103500
0.38

(6)
Large
0.0000(0.0000)
0.0018∗∗∗(0.0003)
0.0021∗∗∗(0.0003)
0.0022∗∗∗(0.0003)0.0001(0.0003)0.0010(0.0009)
0.0000(0.0000)-0.0033(0.0021)0.0313∗∗∗(0.0022)0.0248∗∗∗(0.0021)0.0346∗∗∗(0.0022)0.2663∗∗∗(0.0084)-0.0020∗∗∗(0.0005)
0.0002∗∗∗(0.0000)-0.0053∗∗∗(0.0003)-0.0013∗∗∗(0.0002)-0.0010∗∗∗(0.0003)-0.0035∗∗(0.0018)0.0000(0.0004)
Yes
Yes
41934
0.56

TABLE 7: This table presents the effect of Disaster on LLP based on banks’ past experience with disasters. The sample includes banks that have been
subject to a disaster in our sample period. CumDisaster is banks’ disaster experience measured as the number of disasters the bank experienced in
the past. Columns 1 to 3 measures from the beginning of our sample period. Columns 4 to 6 measures in the past 3 years. The dependent variable
is banks’ loan loss provisions in the current quarter scaled by lagged total loans. Ln(Total Asset) is the natural logarithm of total assets. Tier1 Ratio
is the Tier 1 capital ratio of the bank. EBP is earnings before loan loss provisions and taxes. ∆ Loan is the change in loans from last quarter tocurrent quarter scaled by loans in last quarter. Residential Real Estate Loan is the ratio of residential real estate loans to total loans. Commercial
Real Estate Loan is the ratio of commercial real estate loans to total loans. Commercial and Industrial Loan is the ratio of commerical and industrial
loans to total loans. ∆ Non-Performing Assets is banks’ Non-performing Assets scaled by lagged total loans.

CumDisaster
CumDisaster × ∆ Non-Perf. Assets t +1
CumDisaster × ∆ Non-Perf. Assets
CumDisaster × ∆ Non-Perf. Assets t−1
CumDisaster × ∆ Non-Perf. Assets t−2
CumDisaster × EBP
CumDisaster × Tier 1 Ratio t−1∆ Non-Perf. Assets t +1
∆ Non-Perf. Assets∆ Non-Perf. Assets t−1∆ Non-Perf. Assets t−2
EBP
Tier 1 Ratio t−1
Ln(Total Asset) t−1∆ LoanResid’l RE Loan
Comm’l & Indus’l Loan
Comm’l RE Loan
Constant
Bank FE
State-Time FE
Observations
Adjusted R2Standard errors in parentheses∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

(1)
Full
0.0000∗∗(0.0000)
0.0014∗∗∗(0.0003)
0.0024∗∗∗(0.0003)
0.0025∗∗∗(0.0003)0.0009∗∗∗(0.0003)-0.0000(0.0009)
0.0000∗∗(0.0000)-0.0004(0.0019)0.0138∗∗∗(0.0019)0.0208∗∗∗(0.0019)0.0177∗∗∗(0.0019)0.2975∗∗∗(0.0082)-0.0000∗∗∗(0.0000)
0.0003∗∗∗(0.0000)-0.0040∗∗∗(0.0002)-0.0005∗∗∗(0.0002)0.0003(0.0002)-0.0007(0.0015)-0.0011∗∗∗(0.0003)
Yes
Yes
53723
0.48

All Past(2)
Big
0.0000∗∗(0.0000)
0.0006(0.0004)
0.0009∗∗(0.0004)
0.0019∗∗∗(0.0004)
0.0007∗(0.0004)
0.0026∗∗(0.0012)-0.0000∗(0.0000)
0.0065∗(0.0039)0.0374∗∗∗(0.0041)0.0259∗∗∗(0.0040)0.0238∗∗∗(0.0039)0.2409∗∗∗(0.0146)0.0000(0.0000)
0.0003∗∗∗(0.0001)-0.0042∗∗∗(0.0005)-0.0021∗∗∗(0.0003)-0.0013∗∗∗(0.0005)-0.0031(0.0028)-0.0005(0.0006)
Yes
Yes
18732
0.58

51

(3)
Small
-0.0000(0.0000)
0.0012∗∗(0.0005)
0.0024∗∗∗(0.0005)
0.0019∗∗∗(0.0005)-0.0002(0.0005)-0.0007(0.0020)0.0000∗∗∗(0.0000)-0.0011(0.0024)0.0085∗∗∗(0.0025)0.0208∗∗∗(0.0025)0.0178∗∗∗(0.0025)
0.3279∗∗∗(0.0118)-0.0000∗∗∗(0.0000)
0.0004∗∗∗(0.0001)-0.0035∗∗∗(0.0003)0.0003(0.0003)0.0010∗∗∗(0.0003)
0.0030(0.0021)-0.0020∗∗∗(0.0005)
Yes
Yes
34410
0.41

(4)
Full
0.0000(0.0000)
0.0034∗∗∗(0.0008)
0.0084∗∗∗(0.0009)
0.0086∗∗∗(0.0009)0.0051∗∗∗(0.0009)
0.0079∗∗∗(0.0030)-0.0000(0.0000)
0.0002(0.0021)0.0101∗∗∗(0.0021)0.0170∗∗∗(0.0022)0.0128∗∗∗(0.0021)0.2817∗∗∗(0.0089)-0.0000∗(0.0000)
0.0003∗∗∗(0.0000)-0.0039∗∗∗(0.0002)-0.0004∗∗(0.0002)0.0004∗(0.0002)0.0000(0.0015)-0.0011∗∗∗(0.0003)
Yes
Yes
53723
0.48

Past 3 year
(5)Big
0.0000(0.0000)
0.0023∗(0.0013)0.0043∗∗∗(0.0014)0.0085∗∗∗(0.0014)0.0031∗∗(0.0014)0.0150∗∗∗(0.0042)-0.0000(0.0000)
0.0045(0.0043)0.0331∗∗∗(0.0046)
0.0183∗∗∗(0.0045)0.0208∗∗∗(0.0044)0.2215∗∗∗(0.0162)-0.0000(0.0000)
0.0002∗∗∗(0.0001)-0.0042∗∗∗(0.0005)-0.0019∗∗∗(0.0003)-0.0012∗∗∗(0.0004)-0.0020(0.0027)-0.0003(0.0006)
Yes
Yes
18732
0.58

(6)
Small
-0.0001∗∗(0.0000)
0.0009(0.0012)0.0075∗∗∗(0.0013)0.0061∗∗∗(0.0013)0.0050∗∗∗(0.0014)0.0196∗∗∗(0.0051)0.0000(0.0000)
0.0027(0.0026)
0.0066∗∗(0.0026)
0.0186∗∗∗(0.0027)0.0092∗∗∗(0.0027)0.2936∗∗∗(0.0120)-0.0000(0.0000)
0.0003∗∗∗(0.0001)-0.0034∗∗∗(0.0003)0.0003(0.0003)0.0011∗∗∗(0.0003)
0.0032(0.0021)-0.0017∗∗∗(0.0005)
Yes
Yes
34410
0.42

TABLE 8: This table presents the effect of natural disaster on banks’ deposits. The sample includes banks that have been subject to a disaster in our
sample period. The dependent variable is quarterly change in banks’ deposits scaled by total assets. Shock is a dummy variable, which is equal to 1
for banks that are affected by a disaster. Post is the dummy variable, which is equal to 1 from 0 to 3 quarters after a disaster. Ln(Total Asset) is thenatural logarithm of total assets. Tier1 Ratio is the Tier 1 capital ratio of the bank. EBP is earnings before loan loss provisions and taxes. ∆ Loan
is the change in loans from last quarter to current quarter scaled by loans in last quarter. Residential Real Estate Loan is the ratio of residential realestate loans to total loans. Commercial Real Estate Loan is the ratio of commercial real estate loans to total loans. Commercial and Industrial Loan is
the ratio of commercial and industrial loans to total loans. ∆ Non-Performing Assets is banks’ Non-performing Assets scaled by lagged total loans.

Shock × Post
Ln(Total Asset) t−1
Tier1 Ratio t−1
EBP∆ LoanResid’l RE Loan
Comm’l & Indus’l Loan
Comm’l RE Loan
∆ Non-Perf. Assets t +1
∆ Non-Perf. Assets∆ Non-Perf. Assets t−1∆ Non-Perf. Assets t−2
Constant
Bank FE
State-Time FE
Observations
Adjusted R2

(1)
Full
-0.0003∗(0.0002)-0.0093∗∗∗(0.0003)-0.0232∗∗∗(0.0032)
1.0384∗∗∗(0.0575)0.1361∗∗∗(0.0023)0.0006(0.0015)0.0131∗∗∗(0.0019)-0.0137(0.0134)
0.0662∗∗∗(0.0119)0.0372∗∗∗(0.0122)0.0180(0.0121)0.0217∗(0.0122)0.0611∗∗∗(0.0022)
Yes
Yes
1577720.12Standard errors in parentheses∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

52

(2)
Small
-0.0002(0.0002)-0.0237∗∗∗(0.0006)-0.0203∗∗∗(0.0040)1.2015∗∗∗(0.0730)
0.1098∗∗∗(0.0027)-0.0016(0.0021)0.0070∗∗∗(0.0024)0.0001(0.0175)
0.0826∗∗∗(0.0140)0.0479∗∗∗(0.0142)0.0262∗(0.0142)0.0328∗∗(0.0143)
0.1334∗∗∗(0.0037)
Yes
Yes
110602
0.12

(3)
Large
-0.0008∗∗∗(0.0003)-0.0068∗∗∗(0.0006)-0.0392∗∗∗(0.0059)
0.9270∗∗∗(0.0966)
0.1788∗∗∗(0.0042)-0.0005(0.0026)
0.0149∗∗∗(0.0035)-0.0308(0.0232)
0.0346(0.0236)0.0350(0.0247)
0.0291(0.0236)
0.0188(0.0238)
0.0573∗∗∗(0.0045)
Yes
Yes
47045
0.14

TABLE 9: This table presents the effect of natural disaster on banks’ lending. The dependent variable is bank’s loan change from last quarter to
3 quarters later. Panel A presents on average how bank’s loans change in the disaster period. Panel B measures banks’ provision policy as theaverage residual in the past 12 quarters. Panel C measures banks’ provision policy as the difference in R-squares between regression (1) L LPt =α0 + α1 ∆N PA t−1 + α2 ∆N PA t−2 + α3 T ier1Rat io t + α4 EBPt + e t and regression (2) L LPt = α0 + α1 ∆N PA t +1 + α2 ∆N PA t + α3 ∆N PA t−1 +α4 ∆N PA t−2 + α5 T ier1Rat io t + α6 EBPt + e t . All policy measures are dummy variables that equals one if the corresponding measure is higher
than the median of all banks in the same quarter and same size group. Shock is a dummy variable, which is equal to 1 for banks that are affectedby a disaster. Post is the dummy variable, which is equal to 1 from 0 to 3 quarters after a disaster. Ln(Total Asset) is the natural logarithm of total
assets. Tier1 Ratio is the Tier 1 capital ratio of the bank. EBP is earnings before loan loss provisions and taxes. ∆ Loan is the change in loans fromlast quarter to current quarter scaled by loans in last quarter. ∆ Non-Performing Assets is banks’ Non-performing Assets scaled by lagged total loans.

Panel A

Shock × Post
EBP t−1
Tier1 Ratio t−1
Ln(Total Asset) t−1
Comm’l & Indus’l Loan t−1
Loan t−1
Deposit t−1∆ Non-Perf. Assets t−1
Constant
Observations
Adjusted R2

(1)
Full
0.0039∗∗∗(0.0010)1.5693∗∗∗(0.2336)0.0011∗∗∗(0.0002)-0.0581∗∗∗(0.0021)0.0900∗∗∗(0.0078)-0.2517∗∗∗(0.0061)-0.0126(0.0096)-0.3656∗∗∗(0.0421)0.5206∗∗∗(0.0170)
73327
0.49

Standard errors in parentheses∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

53

(2)
Small
0.0031∗∗∗(0.0011)1.0823∗∗∗(0.2687)
0.0014∗∗∗(0.0002)-0.0794∗∗∗(0.0031)0.0543∗∗∗(0.0091)-0.2875∗∗∗(0.0071)-0.0056(0.0123)-0.3591∗∗∗(0.0454)0.6277∗∗∗(0.0227)
56057
0.50

(3)
Big
0.0051∗∗(0.0022)1.2779∗∗(0.5053)
0.0022∗∗∗(0.0004)-0.0460∗∗∗(0.0049)
0.1367∗∗∗(0.0203)-0.2076∗∗∗(0.0148)-0.0634∗∗∗(0.0188)-0.2807∗∗(0.1103)0.5126∗∗∗(0.0432)
16545
0.55

Panel B - High Average Residual

HighRES12
Shock × Post
Shock × Post × HighRES12
EBP t−1
Tier1 Ratio t−1
Ln(Total Asset) t−1
Comm’l & Indus’l Loan t−1
Loan t−1
Deposit t−1∆ Non-Perf. Assets t−1

(1)
Full
-0.0209∗∗∗(0.0008)
0.0030∗∗∗(0.0010)0.0038∗∗(0.0017)1.4968∗∗∗(0.2322)0.0008∗∗∗(0.0002)-0.0628∗∗∗(0.0021)0.0863∗∗∗(0.0078)-0.2562∗∗∗(0.0061)-0.0087(0.0096)-0.4145∗∗∗(0.0419)(2)
Small-0.0203∗∗∗(0.0009)
0.0019∗(0.0012)0.0051∗∗∗(0.0019)
1.1013∗∗∗(0.2672)0.0011∗∗∗(0.0002)-0.0875∗∗∗(0.0031)0.0496∗∗∗(0.0090)-0.2939∗∗∗(0.0071)0.0005(0.0123)-0.3978∗∗∗(0.0451)(3)
Big-0.0207∗∗∗(0.0017)0.0051∗∗(0.0023)-0.0013(0.0035)
1.0671∗∗(0.5022)0.0019∗∗∗(0.0004)-0.0497∗∗∗(0.0049)
0.1334∗∗∗(0.0201)-0.2103∗∗∗(0.0147)-0.0691∗∗∗(0.0187)-0.3416∗∗∗(0.1096)0.5574∗∗∗(0.0170)
Yes
Yes
73327
0.50

0.6820∗∗∗(0.0227)
Yes
Yes
56057
0.51

0.5548∗∗∗(0.0431)
Yes
Yes
16545
0.55

Shock × Post × Tier1 Ratio t−1
HighRES12 × Tier1 Ratio t−1
Shock_t × Post_t × HighRES12 × Tier1 Ratio t−1
Constant
Bank FE
State-Time FE
Observations
Adjusted R2Standard errors in parentheses∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

54

(4)
Full
-0.0306∗∗∗(0.0021)0.0009(0.0024)-0.0069(0.0044)1.4697∗∗∗(0.2322)0.0005∗∗∗(0.0002)-0.0628∗∗∗(0.0021)0.0868∗∗∗(0.0078)-0.2571∗∗∗(0.0061)-0.0074(0.0096)-0.4136∗∗∗(0.0419)0.0001(0.0001)
0.0006∗∗∗(0.0001)
0.0007∗∗∗(0.0003)
0.5617∗∗∗(0.0170)
Yes
Yes
73327
0.50

(5)
Small
-0.0328∗∗∗(0.0023)-0.0062∗∗(0.0028)
0.0027(0.0050)
1.0943∗∗∗(0.2671)0.0006∗∗∗(0.0002)-0.0882∗∗∗(0.0031)0.0499∗∗∗(0.0090)-0.2953∗∗∗(0.0071)
0.0025(0.0123)-0.3969∗∗∗(0.0451)0.0005∗∗∗(0.0002)0.0008∗∗∗(0.0001)
0.0002(0.0003)
0.6912∗∗∗(0.0227)
Yes
Yes
56057
0.51

(6)
Big
-0.0297∗∗∗(0.0048)0.0214∗∗∗(0.0055)-0.0204∗(0.0108)0.9900∗∗(0.5023)0.0018∗∗∗(0.0004)-0.0498∗∗∗(0.0049)
0.1337∗∗∗(0.0201)-0.2107∗∗∗(0.0147)-0.0692∗∗∗(0.0187)-0.3370∗∗∗(0.1096)-0.0012∗∗∗(0.0004)
0.0007∗∗(0.0003)0.0014∗(0.0008)
0.5569∗∗∗(0.0431)
Yes
Yes
16545
0.55

Panel C - High ∆R2

HighR2
Shock × Post
Shock × Post × HighR2
EBP t−1
Tier1 Ratio t−1
Ln(Total Asset) t−1
Comm’l & Indus’l Loan t−1
Loan t−1
Deposit t−1∆ Non-Perf. Assets t−1

(1)
Full
0.0007(0.0006)
0.0049∗∗∗(0.0012)-0.0020(0.0014)1.5736∗∗∗(0.2337)0.0011∗∗∗(0.0002)-0.0581∗∗∗(0.0021)0.0899∗∗∗(0.0078)-0.2517∗∗∗(0.0061)-0.0128(0.0096)-0.3654∗∗∗(0.0421)(2)
Small
0.0006(0.0007)0.0047∗∗∗(0.0014)-0.0031∗(0.0016)
1.0883∗∗∗(0.2688)0.0014∗∗∗(0.0002)-0.0794∗∗∗(0.0031)0.0541∗∗∗(0.0091)-0.2876∗∗∗(0.0071)-0.0058(0.0123)-0.3590∗∗∗(0.0454)(3)
Big
0.0020(0.0014)0.0070∗∗∗(0.0026)-0.0039(0.0030)
1.2904∗∗(0.5053)
0.0022∗∗∗(0.0004)-0.0460∗∗∗(0.0049)
0.1362∗∗∗(0.0203)-0.2076∗∗∗(0.0148)-0.0635∗∗∗(0.0188)-0.2789∗∗(0.1103)0.5205∗∗∗(0.0170)
Yes
Yes
73327
0.49

0.6276∗∗∗(0.0227)
Yes
Yes
56057
0.50

0.5115∗∗∗(0.0433)
Yes
Yes
16545
0.55

Shock × Post × Tier1 Ratio t−1
HighR2 × Tier1 Ratio t−1
Shock × Post × HighR2 × Tier1 Ratio t−1
Constant
Bank FE
State-Time FE
Observations
Adjusted R2Standard errors in parentheses∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

55

(4)
Full
-0.0017(0.0017)0.0036(0.0029)-0.0103∗∗∗(0.0039)
1.5789∗∗∗(0.2337)0.0010∗∗∗(0.0002)-0.0581∗∗∗(0.0021)0.0898∗∗∗(0.0078)-0.2518∗∗∗(0.0061)-0.0130(0.0096)-0.3658∗∗∗(0.0421)0.0001(0.0002)0.0002(0.0001)
0.0006∗∗(0.0002)0.5228∗∗∗(0.0170)
Yes
Yes
73327
0.49

(5)
Small
-0.0025(0.0019)-0.0010(0.0034)-0.0099∗∗(0.0045)1.0999∗∗∗(0.2688)0.0012∗∗∗(0.0002)-0.0793∗∗∗(0.0031)0.0541∗∗∗(0.0091)-0.2879∗∗∗(0.0071)-0.0063(0.0123)-0.3591∗∗∗(0.0454)0.0004∗(0.0002)0.0002∗(0.0001)
0.0004(0.0003)
0.6311∗∗∗(0.0227)
Yes
Yes
56057
0.50

(6)
Big
-0.0058(0.0042)0.0223∗∗∗(0.0066)-0.0124(0.0090)
1.2845∗∗(0.5052)
0.0020∗∗∗(0.0005)-0.0462∗∗∗(0.0049)
0.1349∗∗∗(0.0203)-0.2086∗∗∗(0.0148)-0.0638∗∗∗(0.0188)-0.2838∗∗(0.1102)-0.0011∗∗(0.0004)
0.0006∗(0.0003)0.0006(0.0006)
0.5164∗∗∗(0.0433)
Yes
Yes
16545
0.55

TABLE 10: This table presents the effect of natural disaster on banks’ lending by splitting the subsample as banks with high and low provision
measure. The dependent variable is bank’s loan change from last quarter to 3 quarters later. Panel A measures banks’ provision policy as theaverage residual in the past 12 quarters. Panel B measures banks’ provision policy as the difference in R-squares between regression (1) L LPt =α0 + α1 ∆N PA t−1 + α2 ∆N PA t−2 + α3 T ier1Rat io t + α4 EBPt + e t and regression (2) L LPt = α0 + α1 ∆N PA t +1 + α2 ∆N PA t + α3 ∆N PA t−1 +α4 ∆N PA t−2 + α5 T ier1Rat io t + α6 EBPt + e t . All policy measures are dummy variables that equals one if the corresponding measure is higher
than the median of all banks in the same quarter and same size group. Shock is a dummy variable, which is equal to 1 for banks that are affectedby a disaster. Post is the dummy variable, which is equal to 1 from 0 to 3 quarters after a disaster. Ln(Total Asset) is the natural logarithm of total
assets. Tier1 Ratio is the Tier 1 capital ratio of the bank. EBP is earnings before loan loss provisions and taxes. ∆ Loan is the change in loans fromlast quarter to current quarter scaled by loans in last quarter. ∆ Non-Performing Assets is banks’ Non-performing Assets scaled by lagged total loans.

Panel A – Residual provision as the Measure of Provision Policy
Full

Shock × Post
Tier1 Ratio t−1
Shock × Post × Tier1 Ratio t−1
EBP t−1
Ln(Total Asset) t−1
Comm’l & Indus’l Loan t−1
Loan t−1
Deposit t−1∆ Non-Perf. Assets t−1
Constant
Bank FE
State-Time FE
Observations
Adjusted R2(1)
High Prov’n-0.0097∗∗∗(0.0030)0.0015∗∗∗(0.0002)0.0009∗∗∗(0.0002)
1.9878∗∗∗(0.2921)-0.0672∗∗∗(0.0033)
0.0784∗∗∗(0.0117)-0.2822∗∗∗(0.0090)0.0445∗∗∗(0.0150)-0.2968∗∗∗(0.0511)0.5271∗∗∗(0.0257)
Yes
Yes
39539
0.56

Small

(2)
Low Prov’n
0.0014(0.0031)0.0001(0.0003)-0.0001(0.0002)-0.3531(0.4199)-0.0742∗∗∗(0.0036)
0.0564∗∗∗(0.0128)-0.3231∗∗∗(0.0095)-0.0308∗∗(0.0139)-0.4258∗∗∗(0.0736)0.7158∗∗∗(0.0276)
Yes
Yes
32921
0.50

Standard errors in parentheses∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

56

(3)High Prov’n-0.0100∗∗∗(0.0034)0.0014∗∗∗(0.0003)0.0010∗∗∗(0.0002)1.5348∗∗∗(0.3395)-0.0999∗∗∗(0.0047)
0.0423∗∗∗(0.0131)-0.3304∗∗∗(0.0104)
0.0372∗(0.0192)-0.3006∗∗∗(0.0553)0.7176∗∗∗(0.0342)
Yes
Yes
30092
0.58

(4)
Low Prov’n-0.0035(0.0036)0.0004(0.0003)0.0003(0.0002)-0.4121(0.4721)-0.1190∗∗∗(0.0054)
0.0266∗(0.0152)-0.3547∗∗∗(0.0113)-0.0009(0.0177)-0.4261∗∗∗(0.0795)
0.9203∗∗∗(0.0376)
Yes
Yes
25107
0.51

Large
(5)
High Prov’n
0.0098(0.0079)
0.0049∗∗∗(0.0006)-0.0004(0.0005)
1.1914∗(0.6299)-0.0741∗∗∗(0.0086)
0.1793∗∗∗(0.0388)-0.2198∗∗∗(0.0235)-0.0033(0.0312)-0.0385(0.1368)
0.6144∗∗∗(0.0748)
Yes
Yes
8466
0.62

(6)
Low Prov’n
0.0194∗∗∗(0.0072)
0.0002(0.0008)-0.0017∗∗∗(0.0005)
0.7072(1.0567)-0.0213∗∗∗(0.0081)0.0412(0.0330)-0.2845∗∗∗(0.0250)-0.1420∗∗∗(0.0304)-0.1608(0.2100)
0.5141∗∗∗(0.0706)
Yes
Yes
6828
0.54

Panel B – R-Square Difference as the Measure of Provision Policy
Full

Shock × Post
Tier1 Ratio t−1
Shock × Post × Tier1 Ratio t−1
EBP t−1
Ln(Total Asset) t−1
Comm’l & Indus’l Loan t−1
Loan t−1
Deposit t−1∆ Non-Perf. Assets t−1
Constant
Bank FE
State-Time FE
Observations
Adjusted R2(1)
High Prov’n-0.0086∗∗∗(0.0031)0.0007∗∗∗(0.0002)0.0008∗∗∗(0.0002)2.4289∗∗∗(0.3421)-0.0529∗∗∗(0.0033)
0.0723∗∗∗(0.0119)-0.2901∗∗∗(0.0092)-0.0268∗(0.0145)-0.4687∗∗∗(0.0600)
0.5315∗∗∗(0.0257)
Yes
Yes
36161
0.53

Small

(2)
Low Prov’n
0.0056∗(0.0032)0.0012∗∗∗(0.0002)-0.0001(0.0002)0.6444∗(0.3416)-0.0647∗∗∗(0.0032)
0.1130∗∗∗(0.0118)-0.2407∗∗∗(0.0091)0.0093(0.0143)-0.2585∗∗∗(0.0611)0.5328∗∗∗(0.0255)
Yes
Yes
35996
0.52

Standard errors in parentheses∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

57

(3)High Prov’n-0.0133∗∗∗(0.0035)0.0010∗∗∗(0.0003)0.0009∗∗∗(0.0002)1.8008∗∗∗(0.3905)-0.0695∗∗∗(0.0046)
0.0299∗∗(0.0139)-0.3273∗∗∗(0.0107)-0.0108(0.0188)-0.4387∗∗∗(0.0645)0.6099∗∗∗(0.0339)
Yes
Yes
27571
0.55

(4)
Low Prov’n
0.0010(0.0037)0.0015∗∗∗(0.0003)0.0002(0.0002)0.6816∗(0.3991)-0.0923∗∗∗(0.0048)0.0869∗∗∗(0.0136)-0.2865∗∗∗(0.0109)
0.0134(0.0184)-0.2829∗∗∗(0.0667)0.6783∗∗∗(0.0349)
Yes
Yes
27420
0.53

Large
(5)
High Prov’n
0.0162∗∗(0.0082)
0.0026∗∗∗(0.0007)-0.0007(0.0005)
1.9909∗∗(0.8100)-0.0557∗∗∗(0.0083)
0.0485(0.0332)-0.2898∗∗∗(0.0242)-0.0939∗∗∗(0.0299)-0.5399∗∗∗(0.1738)
0.6635∗∗∗(0.0716)
Yes
Yes
7611
0.59

(6)
Low Prov’n
0.0250∗∗∗(0.0076)
0.0032∗∗∗(0.0007)-0.0014∗∗∗(0.0005)-0.6282(0.7552)-0.0397∗∗∗(0.0077)0.2058∗∗∗(0.0336)-0.1830∗∗∗(0.0229)-0.0098(0.0301)-0.1727(0.1637)
0.3891∗∗∗(0.0684)
Yes
Yes
7634
0.59

TABLE 12: This table presents the effect of natural disaster on banks’ loans held for sale, and net gains/losses on sales of loans. The dependent
variable in columns (1) to (3) is loan sale scaled by lagged total loans, while the dependent variable for columns (4) to (6) is the gain on sales ofloans scaled by lagged total loans reported by banks. Shock is a dummy variable, which is equal to 1 for banks that are affected by a disaster. Post
is the dummy variable, which is equal to 1 from 0 to 3 quarters after a disaster. Ln(Total Asset) is the natural logarithm of total assets. Tier1 Ratio is
the Tier 1 capital ratio of the bank. EBP is earnings before loan loss provisions and taxes. ∆ Loan is the change in loans from last quarter to current
quarter scaled by loans in last quarter. Residential Real Estate Loan is the ratio of residential real estate loans to total loans. Commercial Real Estate
Loan is the ratio of commercial real estate loans to total loans. Commercial and Industrial Loan is the ratio of commerical and industrial loans tototal loans. ∆ Non-Performing Assets is banks’ Non-performing Assets scaled by lagged total loans.

LoanSale

Shock × Post
Ln(Total Asset) t−1
Tier1 Ratio t−1
EBP∆ LoanResid’l RE Loan
Comm’l & Indus’l Loan
Comm’l RE Loan
Constant
Bank FE
State-Time FE
Observations
Adjusted R2

(1)
Full
0.0202∗∗∗(0.0047)
0.6032∗∗∗(0.0072)-0.3147∗∗∗(0.0654)6.5043∗∗∗(1.1462)
0.5665∗∗∗(0.0456)
1.3608∗∗∗(0.0314)0.3119∗∗∗(0.0388)
1.1927∗∗∗(0.2712)-3.4760∗∗∗(0.0463)
Yes
Yes
145944
0.78

(2)
Small
-0.0133∗∗∗(0.0034)
0.2823∗∗∗(0.0077)-0.4586∗∗∗(0.0487)
9.1316∗∗∗(0.8475)0.4540∗∗∗(0.0320)0.9038∗∗∗(0.0250)0.1784∗∗∗(0.0289)
0.4112∗∗(0.2060)-1.5452∗∗∗(0.0440)
Yes
Yes
103501
0.69

SaleGain(3)
Large0.0426∗∗∗(0.0135)0.9300∗∗∗(0.0205)-0.3990∗(0.2037)4.6529(3.0703)0.6846∗∗∗(0.1347)
2.3770∗∗∗(0.0895)
0.3256∗∗∗(0.1226)
1.8762∗∗(0.7643)-6.2446∗∗∗(0.1596)
Yes
Yes
41934
0.80

Standard errors in parentheses∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

58

(4)
Full
0.0132∗∗∗(0.0026)
0.2886∗∗∗(0.0039)0.1043∗∗∗(0.0357)
12.2138∗∗∗(0.6286)-0.0245(0.0249)
0.5900∗∗∗(0.0172)0.3032∗∗∗(0.0212)0.7908∗∗∗(0.1481)-1.7984∗∗∗(0.0253)
Yes
Yes
145477
0.69

(5)
Small
-0.0022(0.0014)0.1017∗∗∗(0.0032)-0.0170(0.0200)
9.1335∗∗∗(0.3480)-0.0484∗∗∗(0.0131)0.2305∗∗∗(0.0103)
0.0267∗∗(0.0119)0.1608∗(0.0845)-0.5727∗∗∗(0.0181)Yes
Yes
103470
0.66

(6)
Large
0.0141∗(0.0079)
0.4295∗∗∗(0.0121)-0.0393(0.1201)23.7297∗∗∗(1.8281)-0.0627(0.0796)1.3063∗∗∗(0.0528)0.5069∗∗∗(0.0730)
1.6088∗∗∗(0.4501)-3.2004∗∗∗(0.0940)
Yes
Yes
41500
0.72

A
A.1

Appendix

Variable Definitions

TABLE A.1: This table reports the variable definitions and the call report item.

Variable
Total Asset
Deposit
LLP
Tier1 Ratio
Resid’l RE Loan
Comm’l & Indus’l Loan
Comm’l RE Loan
EBP
Loan
Non-Perf. Assets

Call Report ItemRCFD2170
RCFD2200
RIAD4230
RCFD8274(RCON1797 + RCON5367 +RCFD5368 + RCFD1460)
RCFD1766
RCFD2746(RIAD4300 + RIAD4230)
RCFD2122(RCFD1403 + RCFD1407)

59

A.2

Supplementary Tables and Figures

TABLE A.2: This table presents the effect of Disaster on LLP. Panel A of this table shows the average effect of disaster on banks’ loan loss provisioning.
Panel B shows how banks adjust the weight of their non–performing assets on their loan loss provisioning during natural disaster period. The
dependent variable is banks’ loan loss provisions in the current quarter scaled by lagged total loans. Shock is a dummy variable, which is equal to 1
for banks that are affected by a disaster. Post is the dummy variable, which is equal to 1 from 0 to 3 quarters after a disaster. Ln(Total Asset) is thenatural logarithm of total assets. EBP is earnings before loan loss provisions and taxes. Tier1 Ratio is the Tier 1 capital ratio of the bank. ∆ Loan
is the change in loans from last quarter to current quarter scaled by loans in last quarter. Residential Real Estate Loan is the ratio of residential realestate loans to total loans. Commercial Real Estate Loan is the ratio of commercial real estate loans to total loans. Commercial and Industrial Loan is
the ratio of commerical and industrial loans to total loans. ∆ Non-Performing Assets is banks’ Non-performing Assets scaled by lagged total loans.

Shock × Post
Ln(Total Asset) t−1
Tier1 Ratio t−1
EBP∆ LoanResid’l RE Loan
Comm’l & Indus’l Loan
Comm’l RE Loan(1)
LLP
-0.0000(0.0000)
0.0004∗∗∗(0.0000)-0.0021∗∗∗(0.0002)0.2640∗∗∗(0.0037)-0.0061∗∗∗(0.0001)-0.0010∗∗∗(0.0001)-0.0003∗∗∗(0.0001)-0.0005(0.0008)(2)
LLP-0.0000(0.0000)
0.0004∗∗∗(0.0000)-0.0019∗∗∗(0.0002)0.2748∗∗∗(0.0036)-0.0050∗∗∗(0.0001)-0.0009∗∗∗(0.0001)-0.0001(0.0001)-0.0019∗∗(0.0008)(3)
LLP-0.0000(0.0000)
0.0002∗∗∗(0.0000)-0.0017∗∗∗(0.0002)
0.2523∗∗∗(0.0036)-0.0058∗∗∗(0.0001)-0.0007∗∗∗(0.0001)-0.0002∗(0.0001)-0.0006(0.0008)0.0102∗∗∗(0.0007)
0.0289∗∗∗(0.0008)0.0370∗∗∗(0.0008)
0.0329∗∗∗(0.0008)-0.0012∗∗∗(0.0001)
Yes
Yes
No
1577880.38-0.0012∗∗∗(0.0001)
Yes
No
Yes
1576890.42-0.0004∗∗∗(0.0001)
Yes
Yes
No
1577720.40∆ Non-Perf. Assets t +1
∆ Non-Perf. Assets∆ Non-Perf. Assets t−1∆ Non-Perf. Assets t−2
House Index∆ GDP∆ Unemployment
Constant
Bank FETime FE
State-Time FE
Observations
Adjusted R2Standard errors in parentheses∗
p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01

60

(4)
LLP
-0.0000(0.0000)
0.0003∗∗∗(0.0000)-0.0019∗∗∗(0.0002)0.2568∗∗∗(0.0036)-0.0055∗∗∗(0.0001)-0.0007∗∗∗(0.0001)-0.0003∗∗∗(0.0001)-0.0008(0.0008)0.0103∗∗∗(0.0007)
0.0288∗∗∗(0.0008)
0.0367∗∗∗(0.0008)
0.0327∗∗∗(0.0008)-0.0000∗∗∗(0.0000)-0.0000∗∗∗(0.0000)
0.0002∗∗∗(0.0000)
0.0014∗∗∗(0.0001)
Yes
Yes
No
157772
0.41

(5)
LLP
-0.0000(0.0000)
0.0003∗∗∗(0.0000)-0.0016∗∗∗(0.0002)0.2658∗∗∗(0.0036)-0.0048∗∗∗(0.0001)-0.0007∗∗∗(0.0001)
0.0000(0.0001)-0.0018∗∗(0.0008)
0.0070∗∗∗(0.0007)0.0246∗∗∗(0.0008)
0.0317∗∗∗(0.0008)
0.0280∗∗∗(0.0008)-0.0006∗∗∗(0.0001)
Yes
No
Yes
1576730.44