Climate risk and bank loan contracting - University of Hawaiʻi...firms that are able to obtain...

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Climate risk and bank loan contracting

Deniz ANGINER

Simon Fraser University

[email protected]

Karel HRAZDIL*


[email protected]

Jiyuan LI


[email protected]

and

Ray ZHANG


[email protected]

This version: October 15, 2020

* Corresponding author. WMC 3361, Beedie School of Business, 8888 University Drive,

Burnaby, B.C., V5A 1S6, Canada; Tel.: +1 778 782 6790. We thank Peter Easton, Rafael

Rogo, and Jeong Bon Kim for their helpful comments and suggestions. All errors are ours.

Hrazdil and Zhang acknowledge financial support from the Social Sciences and Humanities

Research Council of Canada. Zhang acknowledges financial support from the Chartered

Professional Accountants of British Columbia.

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Climate risk and bank loan contracting

Abstract

We investigate how a borrower’s adverse climate-related incidents affect bank loan contracting.

We construct an event-based measure of climate risk based on firm-level environmental, social,

and governance (ESG) incidents related to climate. Using a sample of 2,622 publicly traded US

firms over the period 2000–2016, this study is the first to document that loans initiated after the

occurrence of firms’ first adverse climate-related incidents have significantly higher spreads,

shorter maturities, more covenant restrictions, and higher likelihood of being secured with

collateral. In cross-sectional tests, we find that the intensity and influence of adverse climate-

related incidents are associated with the pricing of bank loans. Our results support the notion that

banks incorporate firm-specific climate risks into their lending contracts.

Keywords: Bank loan pricing; Climate finance; Climate risk; Adverse climate-related incidents;

Environmental, social, and governance (ESG) incidents; Cost of capital

JEL: G21, G22, K22, K42, Q54

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“JPMorgan Chase used its annual investor meeting on Tuesday to announce that it was banning

new lending to oil-and-gas development in the Arctic and imposing new limits on financing coal

businesses.

Outside JPMorgan’s New York offices where chief executive Jamie Dimon was speaking, the

Rainforest Action Network, 350.org, and other environmental groups demonstrated against the

bank’s financial relationships with heavily polluting businesses. The protests were the latest sign

of the increasing reputational risks climate change poses to financial companies such as

JPMorgan and BlackRock.”

Moral Money, March 23, 2020

1. Introduction

The economic costs and financial risks associated with climate change are well-documented in the

literature (National Oceanic and Atmospheric Administration 2020). Given the growing risks

associated with climate change, governments and businesses are under increasing pressure to

coordinate their efforts to reduce and limit carbon emissions, increase sustainable investments, and

build countries’ resilience to climate change (Cogan 2008, Hong et al. 2020). Banks have become

one of the focal points of sustainable climate investments as the transition to a lower-carbon

economy will require significant private capital to be mobilized over the next couple of decades.1

Historically, banks have viewed climate change through the lens of corporate social

responsibility (CSR), with a focus on their responsibilities as “corporate citizens,” in order to

safeguard their reputation. More recently, their focus has shifted to the possibility of increased

credit risk induced by changing regulations and standards. In particular, transition to a low-carbon

economy requires significant market, regulatory and technological changes to be implemented in

so called “brown” companies as they try to meet sustainability and adaptation requirements related

1 For example, the European Commission estimates that annual energy and infrastructure investments would have to

increase from today’s level of 2% of the EU’s gross domestic product (GDP) to 2.8% (an increase of about $375

billion) in order to reduce the EU’s net greenhouse gas emissions to zero by the middle of the 21st century.

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to climate change. Costs and risks associated with transition and compliance can materially impact

the future cash flows of these companies and increase credit risk for their lenders.2

There is also growing external pressure from customers, investors, and regulators to

incorporate climate change concerns into lending decisions. There are proposed regulatory

changes to incorporate climate change risks in banks’ risk management and capital allocation

frameworks (Bank of England 2018). There has also been an increase in pressure on banks from

activist groups, customers, and investors to divest from carbon-intensive and polluting industries.

This pressure is likely to grow as many new initiatives push corporates to disclose their carbon

footprint.3

Despite the growing empirical evidence that banks’ behavior is affected by considerations

related to climate change, there is relatively little research on whether banks incorporate climate

risk issues in their lending agreements. In this paper, we fill this gap in the literature by examining

how banks change their lending terms after an adverse climate incident is reported about one of

their corporate clients.

We use an event-based measure of firm-level adverse incidents related to climate for a

sample of 2,622 firms over the period from 2000 to 2016 compiled by RepRisk. RepRisk

constructs its database of environmental, social, and governance (ESG) incidents by screening over

80,000 media, regulatory, and commercial documents in 15 different languages on a daily basis

for adverse ESG incidents. They classify these incidents into 30 categories of ESG issues. We

2 Corporate assets, for example, can become stranded in an abrupt transition. The implementation of a carbon tax

could severely impact corporate profitability. Some of these transition risks are already becoming real, forcing some

companies and their lenders to write off stranded assets. British Petroleum (BP) recently, for instance, devalued its

assets by $17.5 billion partly in response to global efforts to tackle climate change.

3 The Task Force on Climate-Related Financial Disclosures is an example of such an initiative.

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focus specifically on climate-related incidents in this study. In the analyses, we show that climate

incidents are distinct from other ESG incidents in terms of their impact on bank loan contracting.

Examples of adverse climate incidents include oil spills and other pollution incidents, excess

carbon emissions, and deforestation projects to make room for pastureland.

We use these event-based data rather than data on carbon emissions or environmental

standards for two reasons. First, emissions and other environmental data reported by companies

seldom vary over time, which makes it difficult to identify and disentangle the actual effects of

climate risk from other unobservable firm-specific factors on loan pricing. Our analysis of

individual adverse incidents related to climate allows us to explore variations in the timing of

specific incidents, which enables us to infer causality and to perform more detailed analyses based

on the intensity and influence of the incidents. Second, as Li and Wu (2020) point out, outcomes

of CSR engagements are multidimensional in nature and require the collection of information

pertaining to a broad array of environmental issues, rather than only information on emissions

disclosures or adoption of environmental standards.4

The underlying assumption in our analyses is that banks will consider the adverse climate-

related incidents of their client when making decisions regarding lending contracts, and as a

consequence of such incidents, they will make loans costlier and more difficult for firms to obtain.

There are two reasons for this assumption. First, adverse incidents can reveal new information to

banks about the environmental protection and sustainability practices of their corporate clients.

This in turn can inform banks about the potential financial risk posed by these clients. For instance,

prior to the Deepwater Horizon oil spill, BP was often showcased as a role model for its exemplary

environmental practices and was ranked the “most accountable large company” by Fortune and

4 Hong et al. (2019) also note that “climate change risks need not be so narrowly confined to carbon exposures.”

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CSR Network, among others (Demos 2006). Second, adverse climate incidents implicating

corporate clients raise significant reputational concerns for banks. Banks are more likely to face

increased external pressure from activist groups, customers, and investors after an adverse climate

incident by one of their clients.

Following prior literature (e.g., Graham et al. 2008), we use loan spread, measured as the

amount the borrower pays in basis points over LIBOR (the London Interbank Offered Rate), as

our main proxy for bank loan costs. Our results suggest that after an adverse climate-related

incident is reported, the loan spread available to the firm involved increases by approximately 13

to 52 basis points (bps) relative to the pre-event average loan spread. In cross-sectional analyses,

we further show that the relationship between climate incidents and loan spreads is stronger for

incidents that are more severe in nature and those reported by more influential news sources.

We also explore the non-monetary terms of bank loan contracts by examining whether

climate incidents have effects beyond increasing loan costs. If climate incidents do indeed convey

information about the future costs of lending to a firm, lenders might incorporate this information

into loan contracts by not only adjusting the interest rate but also modifying contract terms, such

as maturity, collateral, and covenants. We also examine the impact of climate-related incidents on

loan syndicate structure and transaction fees. We document that loans provided to firms after

adverse climate-related incidents have shorter maturities, higher likelihood of being secured, and

more covenant restrictions. In addition, we find that after adverse climate-related incidents firms

borrow from syndicates that involve fewer lenders and are charged higher upfront and annual fees.

These results provide further evidence that lenders incorporate adverse climate events into loan

contracts.

We also examine the persistence of the impact of adverse climate incidents on loan spreads.

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We show that the greatest part of the impact occurs in the first year following the incident and that

the impact declines significantly thereafter. This short-term impact is consistent with banks’

reputational concerns being the main driver of the increase in loan spreads, as we would expect

the impact to be more persistent if the increase was due to a change in default risk of the client

firm. It is important to note, however, that our results are not dispositive of this issue, as it is

difficult to disentangle reputational risk from credit risk because similar factors influence both.5 It

is also possible that adverse climate incidents can be adequately addressed by borrowers in the

short term so that there is no significant impact on their longer-term default risk.

We conduct a number of robustness tests to address potential endogeneity concerns. In

particular, following Bertrand and Mullainathan (2003), we include lead and lag values of the

event indicator to ensure our results are not driven by pre-event trends. It is possible that firms

with adverse climate-related incidents may have had a trajectory of increasing loan spreads before

these climate incidents occurred. If these pre-event trends do exist, then we could mistakenly

attribute increases in loan spreads to these climate incidents even though loan spreads would have

increased regardless of the climate incidents. We show that the event indicator is not statistically

significant when moved backward in time, suggesting that our findings are not driven by pre-event

trends. Because we examine firms that are able to successfully obtain loans, there could be

endogeneity in our sample selection. Although this type of selection—whereby we only observe

firms that are able to obtain loans—would lower the impact of adverse climate incidents on loan

spreads, we nonetheless utilize Heckman’s (1979) two-stage correction to address this problem.

We show in the first stage that firms are indeed less likely to get loans after an adverse climate

5 For example, a negative public reaction to a climate incident may both adversely affect the borrower’s business,

increasing its default risk, and create reputational risk for the bank.

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event. In the second stage, however, the effect of the Heckman correction on loan spreads for firms

that are able to obtain loans is shown to be negligible. In the analyses, we also control for CSR

characteristics that are commonly used in the literature (e.g., Goss and Roberts 2011, Cheng et al.

2014, Francis et al. 2018). We also control for the overall tone of news in borrower-specific articles

prior to loan origination after an adverse climate-related incident, so as to ensure that our results

are not driven by general public or media sentiment about the borrower.6 We show that the impact

of climate incidents are distinct from other CSR measures and overall media sentiment.

Our results contribute to a growing literature that examines the impact of climate risk on

capital structure and cost of capital for firms (e.g., Chen and Gao 2012, Chava 2014, Bansal et al.

2016, Delis et al. 2019, Ginglinger and Moreau 2019, Hong et al. 2019). In particular, our study is

the first to use variations in the timing of specific climate-related events to infer causality, and the

first to measure the effects of intensity and influence of climate-related incidents on loan pricing.

We provide systematic evidence that banks incorporate specific climate risk considerations into

their lending agreements with corporate clients. More broadly, our paper also adds to the literature

on the determinants of bank loan contract terms (e.g., Graham et al. 2008, Roberts and Sufi 2009,

Kim et al. 2011b, Hertzel and Officer 2012, Cen et al. 2016, Campello and Gao 2017, Kim et al.

2018) and the literature on the impact of CSR on cost of capital (e.g. Hong and Kacperczyk 2009,

Cheng et al. 2014, Ge and Liu 2015, Breuer et al. 2018, Cheung et al. 2018).

The remainder of this paper proceeds as follows. Section 2 reviews related literature and

develops the hypothesis. Section 3 then describes our sample and research design. Section 4

presents the baseline results, cross-sectional analyses, and robustness checks, together with our

6 We use the media sentiment measure of Bushman et al. (2017), who analyze whether the tone of the news in

borrower-specific articles influences bank loan contracting. The authors show that as media sentiment regarding the

firm improves, non-relationship lenders have a higher probability of originating loans and their loan spreads decrease.

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discussion. Finally, section 5 concludes the paper.

2. Related literature and hypothesis development

2.1 Related literature

Our paper contributes to a growing literature that examines the impact of climate risk on firms’

capital structure and cost of capital. For example, Ginglinger and Moreau (2019) find that firms

more exposed to climate risk have lower leverage in the post-Paris Agreement period. Chang et al.

(2018) also find that firms with higher levels of environmental liabilities have lower leverage ratios.

Consistent with the above papers, Sharfman and Fernando (2008) find that improved

environmental risk management is associated with lower capital costs, which allows for higher

leverage.

Several papers examine how climate risk is priced by capital markets. Using a sample of

publicly traded US electric companies, Chen and Gao (2012) find that firms’ bonds yields are

positively correlated with carbon emission rates. Bauer and Hann (2010) use ESG ratings provided

by KLD Stats to show that environmental concerns are associated with a higher cost of debt

financing and lower credit ratings. Painter (2020) examines whether the municipal bond market

incorporates climate change risk in its pricing and finds that counties more likely to be affected by

climate change pay larger underwriting fees and initial yields to issue long-term municipal bonds.

Flammer (2020) examines corporate green bonds, whose proceeds are used to finance climate-

friendly projects, and documents that investors respond positively to issuance announcements and

that issuers improve their environmental performance post issuance. Baker et al. (2018) study the

market of municipal bonds and find that green municipal bonds are issued at a premium to

otherwise similar municipal bonds.

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Hong et al. (2019) examine climate change induced droughts and find that equity markets

do not fully price this risk. In contrast to this finding, Bansal et al. (2016) examine the effect of

long-term temperature changes on equity returns, and find that markets do price climate change

risk. Matsumura et al. (2014) and Griffin et al. (2017) use voluntary disclosures of greenhouse gas

emissions to show that markets price carbon emissions as a negative component of equity value.

In addition, a number of papers examine the impact of climate change risk on bank

financing. Delis et al. (2019) examine the risk that fossil fuel reserves will become “stranded”

during a transition to a low carbon economy and lose their economic value, using a sample of

hand-collected global data on fossil fuel reserves held by companies. They find that in the post-

Paris Agreement period, banks have increased the cost of borrowing by 16 bps for fossil fuel firms

with proven reserves. Kleimeier and Viehs (2015) and Jung et al. (2016) show that firms that

voluntarily disclose their carbon emissions through the Carbon Disclosure Project survey have a

lower cost of debt than their non-disclosing counterparts.

Most closely related to our paper is the study of Chava (2014), who analyzes the impact of

a firm’s environmental profile on the costs of both equity and debt capital, and finds that lenders

charge higher interest rates on bank loans issued to firms with environmental concerns. Chava

(2014) uses environmental scores from KLD Stats dataset, which poses difficulty for inferring

causality and for evaluating the impact of specific incidents on loan contracting, because CSR

disclosures seldom vary across years (Dhaliwal et al. 2011). Our dataset focuses on firm-specific

adverse climate-related incidents which enables us to perform a difference-in-difference analysis

that controls for all unobservable time-invariant firm characteristics.

Our study builds on the literature by disentangling the effects of climate risk from other

unobservable firm-specific factors on loan pricing, and by using variations in the timing of specific

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incidents to infer causality and determine the effects of the intensity and influence of such incidents

on loan pricing. Overall, we provide systematic evidence that banks incorporate specific climate

impact considerations into their lending agreements with corporate clients.

2.2 Hypothesis development

Adverse climate incidents can affect the pricing and terms of loan contracts through two channels.

The first is credit risk. Adverse climate incidents can reveal information about the climate-related

risks faced by client firms as well as the risk-management processes in place to mitigate such risks.

These risks can significantly affect the level and the uncertainty of the firm’s future cash flows,

which can in turn affect the firm’s ability to repay its loans and the value of the collateral that could

be recovered in the event of default. The second is reputational risk. Banks face reputational risks

from the possibility of being associated with a client firm’s environmental scandal, and may

require a premium to enter into a lending relationship with a firm that the public and the bank’s

other stakeholders may view as having socially questionable climate practices.

Future cash flows at client firms can be affected by capital expenditures required to comply

with current and future environmental regulations (Sharfman and Fernando 2008). Penalties and

liabilities arising from environmental negligence can be substantial and can impact the credit

standing of companies as the BP oil spill in the Gulf of Mexico has shown (Bauer and Hann 2010).

Future cash flows can also be affected by the potential for some assets to become stranded or

unusable as a result of changing regulations (Delis et al. 2019). Moreover, environmental

regulations can sometimes subordinate the claims of debt holders, and banks may face additional

liability under the Comprehensive Environmental Response, Compensation, and Liability Act

(Balkenborg 2001, Kroszner and Strahan 2001).

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There are also indirect economic costs associated with climate incidents. These include

negative perceptions held by employees, customers, suppliers, and other stakeholders (Simnett et

al. 2009) and a detrimental impact on the firm’s ability to recruit and employ a more talented and

committed work force (Branco and Rodrigues 2006).7 For these reasons, we would expect a firm

with a higher climate risk to have a higher default risk.

Even in the absence of higher credit risk, banks may face reputational risk when they

associate themselves with polluting firms or firms that are perceived as environmentally

irresponsible by the public or the bank’s stakeholders. Corporate reputation and reputational risk

have become increasingly relevant for firms because of the growing prominence of social media,

where bad news tends to spread quickly (Lee et al. 2015). Moreover, as the level of public concern

about climate change has increased there has been growing pressure from the public for financial

institutions to practice responsible lending. An example of this is the recent protests from

environmental activists that caused J.P. Morgan Chase to end fossil fuel loans for Arctic oil drilling

(Mufson and Grandoni 2020). This pressure is likely to increase as new initiatives and regulations

will require banks to disclose the impact of their lending decisions on climate change.8 Such

reputational concerns can lead to an increase in loan spreads as banks require a premium to lend

to firms that may pose reputational risk. Alternatively, loan spreads may also increase as a

consequence of some banks ceasing to fund certain firms or business lines altogether.9

7 For example, Konar and Cohen (2001) report that a firm’s amount of legally emitted toxic chemicals is negatively

related to the value of its intangible assets.

8 One such initiative is the Partnership for Carbon Accounting Financials (PCAF). Banks will also collaborate to

establish industrywide standards for measuring the climate risk associated with lending activities. The PCAF, which

has nearly 70 members holding more than $9 trillion of assets worldwide, aims to push the financial industry to meet

the goals of the Paris Agreement.

9 In section 4.4.3, we investigate whether the probability of obtaining a loan reduces after an adverse climate event.

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We argue that adverse climate-related incidents can reveal new information about the

potential climate risks faced by bank clients as well as the processes and operations they have in

place to manage such risks. Clients’ adverse climate-related incidents can also pose a reputational

risk to banks, as discussed above. In response, banks can mitigate the impact of a borrower’s

climate risk through loan contract terms governing collateral, covenants, debt maturity, and the

price of debt. Accordingly, we postulate that lenders price the risk of adverse climate-related

incidents and charge higher interest rates on loans issued to firms with such incidents to

compensate for the increased credit and reputational risk. We also postulate that adverse climate-

related incidents will lead to loan contracts becoming less favorable to borrowers in terms of

shorter maturity, more covenants to protect the lender, and higher upfront fees as banks implement

more restrictive terms due to increased risk. We state our hypothesis in the alternative form:

H1: Borrowing costs are higher and loan terms are less favorable for clients that have

experienced adverse climate-related incidents.

3. Methodology

3.1 Sample and data

We obtain data for this study from multiple sources. We collect data on firms’ adverse climate

incidents between 2007 and 2016 from RepRisk, a data provider specializing in ESG issues.

RepRisk collates data regarding negative ESG events from over 80,000 media, regulatory, and

commercial documents in 15 different languages. Each event is assigned two proprietary scores

based on severity (the magnitude of the incident) and reach (the influence or the readership of the

source documents). RepRisk classifies the events into 30 categories. 10 We focus only on the

10 RepRisk uses the following five-step process to collect data: (1) screening, (2) identification and filtering, (3)

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climate risk category.

Our sample contains firms covered by RepRisk starting in 2007, as RepRisk began tracking

firms’ ESG performance from January of that year. Because the main purpose of our study is to

compare loan pricing before and after adverse climate-related incidents are reported, we follow

Graham et al. (2008) and expand the sample period to include the seven years prior to 2007. We

use the number of news articles about the incident (High # Articles), the severity of the incident

(Severity), and the level of influence of the reporting source about the incident (Influence) in cross-

sectional analyses. In particular, Severity is a categorical variable that refers to the severity rating

of the climate-related incident as provided by RepRisk.11 Influence is a categorical variable that

refers to the level of influence of the reporting sources in which the climate-related incident was

published.12 High # Articles is an indicator variable that equals one if the number of articles is

greater than the sample median.

We obtain companies’ financial statements data from Compustat and details of bank loan

analysis, (4) quality assurance, and (5) quantification. The first step is conducted using a proprietary computer

algorithm and the remaining procedures are conducted by a team of analysts. One of the unique features of RepRisk

is that its database is updated daily, so that incidents are screened, identified, analyzed, and updated whenever new

risk information is published.

11 RepRisk determines severity as a function of three dimensions: 1) the consequences of the risk incident (e.g., with

respect to health and safety incidents: no further consequences, injury, or death); 2) the extent of the impact (e.g., one

person, a group of people, or a large number of people); and 3) the causes of the incident (e.g., negligence, intent, or

systematic causes). These dimensions are then categorized into three severity levels: low severity, medium severity,

and high severity. In cases where there is more than one adverse climate-related incident in the month of the first

incident, the severity classification is based on the weighted severity for each incident.

12 RepRisk determines its own rating of the influence (or reach, based on readership/circulation, which is also a proxy

for credibility) of each source. Each source’s level of influence is pre-classified as low (e.g., local media, local

governmental bodies, smaller NGOs, and social media), medium (e.g., most regional and national media, international

NGOs, and state, national, and international governmental bodies), or high (e.g., a small number of truly global media

such as the BBC, New York Times, and South China Morning Post).

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contracts from Reuters–Dealscan. We merge loan-level data from Dealscan with firm data from

Compustat, following Chava and Roberts (2008) and Schwert (2018). In the analyses, we control

for a set of client and loan characteristics that may influence bank loan spreads. Specifically, we

control for client size (Firm Size), profitability (ROA), Leverage, Operating Risk, tangibility of

assets (Tangibility), market-to-book ratio (MB), financial health (Altman Z), size of the loan (Loan

Size), and loan duration (Maturity). The Appendix provides the definitions of all variables used in

our study. Our final sample comprises 20,297 unique loans issued to 2,622 publicly traded US

firms between 2000 to 2016.

3.2 Research design

We use a difference-in-difference model to capture the impact of climate incidents on bank loan

contracting. In particular, we compute changes in loan spreads and other loan characteristics

before and after an adverse climate incident for firms that experienced a climate incident and

compare these changes to firms that have not experienced a climate incident. For companies that

have more than one climate incident we use only the first incident.13

To test our hypothesis that climate incidents impact bank contracting, we estimate the

following model:

𝐿𝑜𝑎𝑛 𝑠𝑝𝑟𝑒𝑎𝑑𝑖𝑡 = 𝛽0 + 𝛽1𝑃𝑜𝑠𝑡 𝐼𝑛𝑐𝑖𝑑𝑒𝑛𝑡𝑖𝑡 + 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝐹𝑖𝑟𝑚 𝐹𝑖𝑥𝑒𝑑 𝐸𝑓𝑓𝑒𝑐𝑡𝑠 +

𝑌𝑒𝑎𝑟 𝐹𝑖𝑥𝑒𝑑 𝐸𝑓𝑓𝑒𝑐𝑠 + 𝜀𝑖,𝑡 (1)

13 We use this restriction because the main purpose of this study is to compare the cost of bank loans before and after

the climate incident. If we were to use the second incident for a firm, the pre-incident window of the second incident

would overlap with the post-incident window of the first incident. This procedure allows us to mitigate potential

confounding issues. As a robustness check, we repeat our main test using only firms with just one incident, and obtain

similar results. In addition, we examine bank loans issued within 180 and 360 days after the relevant firm’s climate

incident, and our findings are again similar.

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where i indexes the client company and t indexes time. We use the loan spread as the main measure

for bank loan contracting costs (𝐿𝑜𝑎𝑛 𝑠𝑝𝑟𝑒𝑎𝑑) as interest costs are the most directly observable

outcome variable. In addition, we explore other lending terms such as loan securitization, loan

duration, and the number of loan covenants. Our variable of interest is Post, a time-variant

indicator variable that takes the value one for firms that have had climate incidents in the past, and

zero for firm-year observations before the firm’s first climate incident and for firms that have not

had any climate incidents. The control group in the regression is the set of firm-year observations

for firms that have never had any climate incidents and the firm-year observations for climate-

incident companies before their first climate incident. For robustness, we also use an alternative

approach whereby we exclude from the sample firms that have not had any climate incidents and

firms that have had more than one climate incident. We obtain similar results using this method.

Controls are set of client and loan characteristics that may influence bank loan spreads

described in the previous section. In the regression, we also control for potential differences across

firms by adding firm fixed effects, which effectively control for all time-invariant but firm-specific

variables that might affect climate risk and loan spread. To mitigate concerns about differences

over time, we also control for year fixed effects and cluster standard errors by firm.

4. Main results

4.1 Summary statistics

Table 1, Panel A shows the summary statistics for all variables used in the main analysis, including

the mean, median, standard deviation, and 25th and 75th percentiles. Our main variable, Loan

spread, has a mean (median) score of approximately 177 (150) bps with a standard deviation of

135. Our sample of loans has a mean (median) size of about $760 million ($321 million) with a

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standard deviation of approximately $1,615 million and a mean (median) maturity of

approximately 49 (59) months. Approximately 60% of the loans in the sample are secured by

collateral and most loans have either one or two covenants. In terms of firm characteristics, firms

in the sample have a median asset size of $4.89 billion, return on assets of 11.40%, leverage of

31.70%, market-to-book ratio of 1.92, operating risk ratio of 3.20%, tangibility ratio of 52.70%,

and Altman Z-Score of 2.431.

[Insert Table 1 about here]

Panel B of Table 1 presents the distribution of firms by their number of adverse climate-

related incidents, showing that about 76% of firms in our sample had no climate incidents. Among

the firms that had incidents, about half experienced one to two incidents, about 20% experienced

three to five incidents, and about 30% experienced more than five adverse climate-related incidents.

4.2 Baseline results

Table 2 reports the regression results for equation (1) above. Column (1) and column (2) present

the results with control variables, including firm- and loan-specific characteristics and with and

without year and industry fixed effects, respectively. Column (3) reports the results of our main

model, i.e., with firm- and loan-specific controls, and year and firm fixed effects. The coefficient

on Post is positively and statistically significant at the one percent level across all three

specifications, suggesting that banks do incorporate adverse climate-related incidents of their

clients in their loan decisions by charging higher interest rates. The magnitude of the Post

coefficient is also economically meaningful. Bank loans that originated after adverse climate-

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related incidents are associated with a 13 to 52 bps increase in loan spread, depending on whether

we control for firm- and loan-specific characteristics and firm and year fixed effects. The change

of 13 bps represents approximately a 7.6% increase relative to the average loan spread of 177 bps.


With regard to control variables, larger firms (Firm Size), firms with higher profitability

(ROA), more tangible assets (Tangibility), better financial health (Altman Z), and larger loans

(Loan Size) have statistically significantly lower loan spreads. Conversely, loan spreads are

significantly higher for firms with higher Leverage and longer loan duration (Maturity).

4.3 Cross-sectional results based on incident types

Next, we examine how the nature of adverse climate-related incidents is associated with the

magnitude of bank loan costs. Compared to mild incidents, more influential incidents pose greater

risk to the bank and are therefore expected to increase loan spreads by a larger magnitude. We

utilize three different proxies for the importance of climate incidents: the number of reports about

the incident (High # Articles), the severity of the incident (Severity), and the level of influence of

the media sources that publish news articles about the incident (Influence) as described earlier. We

include the interaction terms of Post and High # Articles / Severity / Influence in the regression

model to examine if more influential climate incidents have a greater impact on loan contracting.


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The results, reported in Table 3, indicate that the nature of climate incidents is significantly

related to loan spreads. All three interaction terms have positive coefficients and two

(Post*Influence and Post*High # Articles) are statistically significant. In terms of economic

significance, the positive and significant coefficients on Post*Influence and Post*High # Articles

indicate that the influence and intensity of adverse climate-related incidents further increase the

pricing of the loans by an additional 3–14 bps after an incident. These results support our main

finding that banks price adverse climate incidents in their loan contracts. The fact that severity of

events and influence of coverage is priced also lend further credibility to our measure of adverse

climate incidents.

4.4 Robustness

In our research setting, it is possible that some omitted firm-specific factors may be correlated with

both adverse climate-related incidents and loan spreads. We address this potential endogeneity

concern using the following four methods: controlling for alternative explanatory variables, using

alternative samples, investigating the timing of loan spread increases, and applying Heckman

correction for potential selection bias. We discuss each of these methods below.

4.4.1 Controlling for alternative explanatory variables

To rule out the possibility that our results are driven by other explanatory variables documented in

prior studies, we examine whether our model offers any incremental information after controlling

for these variables.14 First, Bushman et al. (2017) show that more positive media sentiment (i.e.,

14 We do not control for these variables (e.g., media sentiment or KLD CSR rating) in our main model because doing

so significantly reduces our sample size.

19

average sentiment across all full-length borrower-specific media articles published over a half-

year period preceding loan origination) is associated with a lower interest rate spread for the firm.

It is therefore possible that our results may be driven by negative media news sentiment. In column

(1) of Table 4, we control for Media Sentiment as defined by Bushman et al. (2017). Our results

are consistent with their findings: media sentiment is negatively associated with loan spreads.

More importantly, when controlling for media sentiment, our variable of interest, Post, remains

positive and statistically significant, which supports our main hypothesis that adverse climate-

related incidents, independently of general media sentiment, are associated with higher borrowing

costs.

Second, prior studies document that loan pricing is affected by the CSR characteristics of

the firm (e.g., Goss and Roberts 2011, Cheng et al. 2014, Chava 2014, Francis et al. 2018). To

make sure that our results are not driven by overall firm level CSR performance, we follow the

prior literature and use an index constructed using six factors related to CSR categorized by KLD

Stats (community, corporate governance, diversity, employment, environment, and production).

In particular, we calculate the CSR index as CSRstr minus CSRcon, where the CSRstr and CSRcon

are strength and concern scores reported by KLD Stats respectively. We control for firm-level

CSR performance in column (2) of Table 4. The coefficient on Post remains positive and

statistically significant even after controlling for CSR.

To ensure that our results are not driven by sample selection, we remove firms that do not

have climate incidents and firms that only have pre-incident loans or post-incident loans. In other

words, we require firms to have both pre-incident loans and post-incident loans. The results,

reported in column (3) of Table 4, are consistent with our prior findings that adverse climate-

related incidents increase loan spreads, again with statistical significance. Finally, our results could

20

be driven by a subset of firms that have multiple adverse climate-related incidents. To address this

concern, in column (4) of Table 4 we further restrict our sample to firms with only one climate

incident during the sample period. We find that the coefficient on Post remains positive and

statistically significant.


4.4.2 Controlling for pre-event trends

It is possible that firms with adverse climate-related incidents may have had a trajectory of

increasing loan spreads before these climate incidents occurred. If these pre-event trends do exist,

then we could mistakenly attribute increases in loan spreads to these climate incidents even though

loan spreads would have increased regardless of the climate incidents. We use a lead-lag

specification, as in Bertrand and Mullainathan (2003), to test whether our results are driven by pre-

event trends. Specifically, we replace Post with four indicator variables. Before 1 (Before 2) is an

indicator variable that takes a value of one for loans issued up to one year prior to (between one

and two years prior to) the first climate incident, and zero otherwise. Post 0 is an indicator variable

that equals one for loans issued within the first year following the first adverse climate-related

incident, and zero otherwise. Post 1+ is an indicator variable that equals one for loans issued one

or more years following the first climate incident, and zero otherwise.

Column (5) in Table 4 shows the regression results using these indicator variables. The

reported coefficients on Before 1 and Before 2 are not statistically significant, whereas the

coefficients on Post 0 and Post 1+ are positive, statistically significant, and economically large in

absolute magnitude. These results suggest that there is no evidence of an increase in loan spreads

21

before the adverse climate-related incidents occur, which provides additional evidence that there

is a causal relationship between firms’ climate-related incidents and loan spreads.

4.4.3 Correcting for probability of getting a loan

Firms with adverse climate-related incidents may find it difficult to obtain loans and raise

additional funds. This can lead to potential endogeneity in sample selection when we examine the

impact of adverse climate-related incidents on loan spreads. In particular, we would be examining

firms that were able to borrow funds (and thus for whom loan spread information is available)

despite the firm experiencing an adverse climate-related incident. Although we find a statistically

significant negative impact on loan spreads even with this potential selection bias, we nonetheless

utilize Heckman’s (1979) two-stage correction for robustness in order to address selection issues.

In the first stage we start with a sample of all Compustat firms that are covered by RepRisk

and received at least one loan during the sample period. This sample includes firm-year

observations for firms that may not have received a loan in a particular year even though they have

received a loan at some point during the sample period. We then estimate the probability of

receiving a loan in a given year using a probit model and calculate the inverse Mills ratio.15 In the

second step, we add the inverse Mills ratio (Lambda) to the regression specified in model (2) as

an additional explanatory variable. If the coefficient on the inverse Mills ratio is statistically

significantly different from zero, this indicates that there is potential selection bias. The direction

of the bias is indicated by the sign of the coefficient. In particular, a positive sign on the inverse

Mills ratio coefficient indicates an upwards bias in the coefficient on Post.

In the first-step probit regression we include the same set of firm controls used in Table 2.

15 See Greene (2008) for a review of the Heckman (1979) methodology.

22

Following Faulkender and Petersen (2006), we also add a set of indicator variables indicating the

state in which the firm is headquartered. This potential exogenous variation is based on the notion

that firms whose headquarters are closer to financial centers find it easier to access bank funding.

These indicator variables are excluded in the second-stage model.


Column (1) of Table 5 reports the first-stage probit results. The coefficient on the variable

Post is negative and statistically significant, indicating that firms are less likely obtain a loan after

an adverse climate event. As expected, firms that are larger and have lower credit risk in a given

year have a higher probability of obtaining a loan. In column (2) we re-run the regression specified

in (1) but now include the inverse Mills ratio (Lambda) as an additional control. The coefficient

on the Lambda variable is positive but not statistically significant, suggesting that our estimates of

the impact of adverse climate-related incidents on loan spread without a selection correction are

not significantly biased. Comparing the coefficients on the Post variable from Table 5 (model 2)

and Table 2 (model 5), we find that with the Heckman correction, the impact of adverse climate-

related incidents on loan spread is slightly lower (9.6 bps, compared to 13.2 bps without the

correction). Overall, these additional tests show that our main findings are robust to potential

selection issues and are consistent with the main findings reported in Table 2.

4.4.4 Multiple incidents and persistence of the impact of climate events

For the main analysis, we use the entire sample period after the first climate incident to test the

effect of climate risk on bank lending costs. Although this approach has many benefits (e.g., non-

23

overlapping periods) and is used in prior studies (e.g., Graham et al. 2008), one concern is that

firms operations and financials could change significantly during the post-event period. For

example, for a climate incident that happened in 2007, the current design uses loans issued in the

years 2008–2016 to test whether lending costs are higher because of the climate event. It is possible

that a loan issued in 2016 may not price an adverse climate-related incident that took place in 2007.

We should note that including the entire period after the first climate incident is likely to bias the

results towards being insignificant. Nonetheless, we address this concern by performing an

analysis in which we control for the time between the climate incident and the issuance of the bank

loan.

Specifically, we construct an indicator variable for bank loans issued within 180 (360) days

after the climate event. Column (1) (column (2)) of Table 6 report the results for this alternative

model. The coefficient on Event is positive and statistically significant at the one percent level in

both cases, suggesting that bank loan costs increase for loans issued within the first 180 (360) days

after a climate incident. The coefficients suggest that bank loan costs rise by 21.94 (19.72) bps,

which is considerably larger than the estimate from our main model of 13.23 bps and confirms our

intuition that including entire period after the first climate incident biases the results downward.

We further examine the impact of climate incidents over time in column (3) of Table 6 by including

three indicator variables for bank loans issued within 180 days, between 180 and 360 days, and

between 360 and 720 days after a climate incident. Our results suggest that most of the impact of

climate incidents occurs during the first year after the incident, because the coefficients for bank

loans issued within 180 days and between 180 and 360 days after an incident are positive and

significant, whereas the coefficient for bank loans issued between 360 and 720 days after an

incident are not statistically significant.

24

This short-term impact is consistent with the notion that banks’ reputational concerns are

the main driver of the increase in loan spreads, as we would expect the impact to be more persistent

if the increase was due to default risk. It is important to note, however, that these results are not

dispositive of the issue, as it is difficult to disentangle reputational risk from credit risk because

similar factors influence both. For instance, borrower firm may also suffer reputational damage

which may negatively affect its profitability and so increase its default risk. It is also possible that

adverse climate incidents can be adequately addressed by borrowers in the short term so as to not

have a significant impact on default risk.


4.5 Effect of climate incidents on other loan features

A unique feature of the bank loan contract dataset we use in the analyses is that it provides

multidimensional information about loan terms. This allows us to examine the effect of climate

incidents using various additional features of loan contracts and their structure. If climate incidents

indeed convey information about the future costs of lending to a firm, lenders might incorporate

this information into loan contracts by not only adjusting the interest rate but also modifying

contract terms, such as those relating to maturity, collateral, and covenants making these contract

features costlier and stricter for higher climate risk firms. Adverse climate incidents could also

have an impact on the loan’s syndicate structure as well as transaction fees charged by the banks.

We report on tests using these loan features in the following subsections.

4.5.1 Loan contract terms

25

Following Graham et al. (2008), we begin by examining how climate incidents impact the three

major non-monetary loan contract features - collateral (Secured), loan maturity (Maturity), and

total number of covenants (# Covenants). First, collateralization is an important feature of loan

contracts. Previous research documents that in order to obtain bank loans, riskier borrowers use

more collateral (Berger and Udell 1990) and that the presence of collateral enhances efficient

monitoring (Rajan and Winton 1995). We examine whether firms that experience adverse climate-

related incidents are more likely to pledge collateral against their loans. Column (1) of Table 7

reports the results, using a linear probability model that tests the impact of adverse climate-related

incidents on loan collateral.16 The coefficient on the Post indicator variable is positive but not

statistically significant.

Second, banks may provide shorter term loan contracts to manage potential risk posed by

their clients. Column (2) of Table 7 shows that after adverse climate-related incidents, affected

firms are provided loans that have on average an approximately 4.6% shorter maturity than loans

granted before firms’ adverse climate-related incidents. This finding is consistent with prior

studies documenting that increased client risk is associated with shorter maturity (e.g., Graham et

al. 2008, Chan et al. 2013).

Third, prior studies show that lenders use loan covenants to improve ex-post monitoring of

the borrower (e.g., Kim et al. 2011a). We expect that lenders use covenants more frequently after

borrowers’ climate incidents, and test our conjecture by examining three covenant measures that

play important roles in private debt contracts. Specifically, columns (3) to (5) of Table 7 report the

impact of climate events on the total number of covenants, the number of general covenants, and

16 We estimate our regression models using an OLS specification because nonlinear models, such as logit and probit,

tend to produce biased estimates in panel datasets with many fixed effects, leading to inconsistent estimates (e.g., Ai

and Norton 2003).

26

the number of financial covenants, respectively.17 We find that banks use a greater number of

covenants (both general and financial) after adverse climate related incidents. Overall, our results

suggest that, in addition to loan interest rates, banks use non-monetary contractual terms to manage

climate-related risks of their clients.


4.5.2 Lender structure and transaction fees

Next, we investigate the relationship between climate risk and loan syndicate structure. We predict

that climate incidents increase credit risk and heighten lenders’ need to monitor firms after an

adverse event, which may reduce the number of lenders extending a loan. This prediction is

motivated by prior research suggesting that banks attempt to enhance the efficiency of monitoring

by adjusting the loan’s syndicate structure — fewer lenders facilitates decision-making and thus

enhances the efficacy of monitoring (Lee and Mullineaux 2004, Graham et al. 2008).

Column (1) of Table 8 reports the results of our regression for the number of lenders. The

coefficient on Post is negative and statistically significant, indicating that the number of lenders

decreases after adverse climate-related incidents. These results support our conjecture that lenders

consider climate incidents to denote an increased level of risk that requires greater monitoring, and

therefore firms can only borrow from syndicates that involve fewer lenders.

17 Covenants in bank loan contracts take various forms, including general covenants, financial covenants, and other

covenants. General covenants include limits on prepayments, dividends, and voting rights. Financial covenants place

financial restrictions, which must be maintained while the debt is outstanding, on accounting variables and other ratios,

such as debt-servicing ratios.

27


In a syndicate lending arrangement, the lead bank establishes and maintains a relationship

with the client and acts as an agent for the syndicate by collecting and processing information

about the client and enforcing the terms and covenants of the contract. Prior to committing to

provide funding, the lead bank conducts due diligence and it charges an upfront fee at the time of

closing the deal and an annual fee for subsequent services. Underwriters of the syndicate typically

receive lower fees than the lead bank and general syndicate members are likely to receive fees

determined based on their commitment. The amounts of the upfront and annual fees are usually

correlated with the complexity and riskiness of the loan. We conjecture that adverse climate-

related incidents increase the level of risk of the client and thus result in the lead bank conducting

more detailed and costly due diligence. As a consequence, we expect the lead bank to demand

higher upfront and annual fees to compensate for higher costs.

Columns (2) and (3) of Table 8 present the results of our tests of this conjecture. We show

that after adverse climate-related incidents, there is a significant increase in both upfront and

annual fees. These results suggest that lenders charge higher fees to compensate for the higher

costs of due diligence. These results are also consistent with the findings in Graham et al. (2008)

who show that firms that have restated their financial statements are charged higher upfront and

annual fees.

5. Conclusion

Our study is the first to show that adverse climate-related incidents play an important role in bank

loan contracting. We employ an event-based measure of climate risk using data compiled by

28

RepRisk, which screens over 80,000 media, regulatory, and commercial documents in 15 different

languages on a daily basis for adverse ESG incidents related to climate. Unlike emissions and

environmental standards adoption reported by firms, our event-based measure of climate risk

allows us to explore variations in the timing of specific incidents in order to infer causality and to

perform more detailed analysis based on the intensity and influence of the incidents.

Using a sample of 20,297 unique loans granted to 2,622 publicly traded US firms over the

period from 2000 to 2016, we document that loans initiated after firms’ first adverse climate-

related incidents have statistically significantly higher spreads, shorter maturities, and more

covenant restrictions. In terms of economic significance, after an adverse climate incident, the loan

spread increases by approximately 13 to 52 bps relative to a pre-event average loan spread. In

further cross-sectional tests, we find that the intensity and influence of adverse climate-related

incidents further increase the pricing of the loans.

We conduct a number of robustness tests and show that our results are robust to controlling

pre-event trends, correcting for potential selection bias and controlling for overall media sentiment

and CSR measures commonly used in the literature. In the context of climate change risks

becoming increasingly important in financial markets, our results provide evidence of banks

incorporating firm-specific climate risk in their lending contracts, and contribute to a growing

literature that examines the impact of climate risk on capital structure and cost of capital for firms.

29

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Appendix

Variable Definitions

Variable Definition and construction

Climate risk

Post An indicator variable that equals one if the loan is initiated after the date of the firm’s first

adverse climate-related incident, and zero otherwise.

Severity A categorical variable that refers to the severity of the news on a climate-related incident.

Where there is more than one climate-related incident in the first month in which a firm has

climate-related news, Severity is the weighted severity of each incident.

Influence A categorical variable that refers to the level of influence of the reporting sources in which

the climate-related incident was published. Where there is more than one article in the first

month in which a firm has climate-related incident, Influence is the weighted influence for

each article.

High # Articles An indicator variable that equals one if the number of news articles is greater than the sample

median, and zero otherwise.

Loan characteristics

Loan Spread The amount that the borrower pays in basis points (bps) over LIBOR for each dollar drawn

down.

Loan Size Natural logarithm of the amount of the loan facility in million USD.

Maturity Natural logarithm of the number of months to maturity at the start of the loan term.

Secured An indicator variable that equals one if the loan facility is secured by collateral, and zero

otherwise.

# Financial covenants The number of financial ratios the loan contract restricts (based on Bradly and Roberts

2015).

# General covenants The total number of covenants with respect to dividend restrictions, secured debt, and

restrictions on asset, debt, and equity sales (based on Bradley and Roberts 2015).

# Covenants The number of the following six types of covenants: dividend restriction, secured debt,

accounting ratios, asset sweep, debt sweep, and equity sweep (based on Bradley and Roberts

2015).

# Lenders Total number of lenders in a single loan.

Upfront Fee Natural logarithm of the fee paid by the borrower upon the agreement of a loan (measured

in bps).

Annual Fee Natural logarithm of the annual charge against the entire loan commitment amount, whether

used or unused (measured in bps). Also commonly called a facility fee.

Firm-level variables

Firm Size Natural logarithm of total assets in million USD.

ROA Earnings before interest, tax, depreciation, and amortization scaled by total assets.

Leverage Current debt and long-term debt scaled by total assets.

Operating Risk The standard deviation of yearly cash flows from operations divided by total assets over the

previous five fiscal years.

Tangibility Gross property, plant, and equipment scaled by total assets.

Altman Z Modified Altman (1968) Z-score = (1.2 x working capital + 1.4 x retained earnings + 3.3 x

income before extraordinary items + 0.999 x sales) / total assets.

MB Market-to-book ratio.

36

Table 1

Summary Statistics

Panel A: Summary statistics

Variables N Mean SD Median Q1 Q3

Loan Spread 20,297 176.699 135.374 150.000 75.000 250.000

Loan Size 20,297 707.440 1096.778 321.000 126.161 800.000

Maturity 20,297 49.012 26.693 59.000 35.000 61.000

Secured 12,771 0.610 0.488 1.000 0.000 1.000

# Financial covenants 34,893 2.166 0.989 2.000 1.000 3.000

# General covenants 10,993 1.528 1.382 1.000 0.000 3.000

# Covenants 9,859 3.069 1.748 3.000 2.000 5.000

Ln (# Lenders) 20,297 1.614 0.904 1.386 0.693 2.303

Annual Fee 20,297 0.172 0.808 0.000 0.000 0.000

Upfront Fee 3,936 5.169 0.886 5.364 4.615 5.861

Severity 20,297 0.486 0.746 0.000 0.000 1.000

Influence 20,297 0.523 0.808 0.000 0.000 1.000

High # Articles 20,297 0.340 0.474 0.000 0.000 1.000

Firm Size 20,297 8.494 1.722 8.432 7.310 9.676

ROA 20,297 0.123 0.071 0.114 0.080 0.159

Leverage 20,297 0.324 0.174 0.317 0.200 0.441

Operating Risk 20,297 0.046 0.051 0.032 0.019 0.054

Tangibility 20,297 0.576 0.402 0.527 0.229 0.876

MB 20,297 2.878 3.321 1.923 1.224 3.198

Altman Z 20,297 2.921 2.143 2.431 1.458 3.772

Panel B: Distribution of # of Incidents

# of Incidents Frequency Percentage Cumulative Percentage

0 2,002 76.35 76.35

1 217 8.28 84.63

2 96 3.66 88.29

3 56 2.14 90.43

4 38 1.45 91.88

5 29 1.11 92.98

> 5 184 7.02 100.00

Total 2,622 100.00

Panel A provides summary statistics for loan characteristics and firm characteristics for our sample. The sample

period is from 2000 to 2016. Panel B describes the distribution of the number of climate-related incidents for

firms in our sample. All variables are as defined in the Appendix.

37

Table 2

Climate Risk and Loan Spread

Dependent Variable: Loan Spread

(1) (2) (3)

Post 51.373*** 51.635*** 13.234***

(17.27) (17.63) (4.49)

Firm Size −30.801*** −23.014*** −22.809***

(−55.07) (−34.97) (−31.30)

ROA −248.176*** −242.987*** −261.158***

(−15.95) (−15.84) (−17.24)

Leverage 147.132*** 140.982*** 154.068***

(24.11) (23.46) (23.26)

Operating Risk 259.266*** 266.267*** 201.763***

(15.29) (15.98) (12.66)

Tangibility −16.949*** −15.487*** −43.262***

(−7.53) (−7.00) (−13.09)

MB −0.495* −0.191 −0.171

(−1.74) (−0.68) (−0.62)

Altman Z −10.495*** −9.849*** −8.257***

(−16.51) (−15.77) (−12.68)

Ln (Loan Size) −12.327*** −14.814***

(−17.22) (−21.48)

Ln (Maturity) 28.647*** 16.403***

(22.51) (12.86)

Intercept 445.877*** 341.036*** 363.325***

(70.11) (41.53) (40.61)

Fixed Effects None Year, Industry Year, Firm

F 769.03 713.00 387.41

Adjusted R2 0.232 0.260 0.418

N 20,297 20,297 20,297

This table shows the effect of climate risk on loan spread. The sample period is from 2000 to 2016. All variables

are as defined in the Appendix. Each of the continuous variables is winsorized at the 1% and 99% levels to

mitigate the effect of outliers. T-statistics are reported in parentheses. ***, **, and * indicate statistical significance

at the 1%, 5%, and 10% levels, respectively.

38

Table 3

Effects of Number of Articles, Severity of Incidents, and Influence of Incidents on Loan Spread


(1) (2) (3)

Post 5.345 3.233 −8.684

(0.78) (0.40) (−1.13)

Post*High # Articles 3.443**

(2.21)

Post*Severity 6.513

(1.27)

Post*Influence 13.814***

(3.04)

Firm Size −27.254*** −27.299*** −27.114***

(−14.16) (−14.19) (−14.09)

ROA −263.129*** −262.529*** −263.368***

(−13.90) (−13.87) (−13.92)

Leverage 156.943*** 157.222*** 156.736***

(16.33) (16.37) (16.32)

Operating Risk 75.451*** 75.370*** 75.037***

(3.66) (3.66) (3.64)

Tangibility −63.126*** −63.027*** −62.936***

(−10.55) (−10.52) (−10.52)

MB −1.027*** −1.028*** −1.037***

(−3.02) (−3.02) (−3.05)

Altman Z −3.450*** −3.476*** −3.459***

(−3.86) (−3.89) (−3.87)

Ln (Loan Size) −13.312*** −13.271*** −13.298***

(−18.60) (−18.56) (−18.60)

Ln (Maturity) 7.224*** 7.234*** 7.190***

(5.78) (5.79) (5.75)

Intercept 428.368*** 428.399*** 427.465***

(23.32) (23.32) (23.27)

Fixed Effects Year, Firm Year, Firm Year, Firm

F 177.54 177.55 177.91

Adjusted R2 0.563 0.563 0.563

N 20,297 20,297 20,297

This table shows the results for the impact of the number of articles related to climate incidents, severity of

incidents, and the influence of incidents on the relationship between climate risk and loan spread. The sample

period is from 2000 to 2016. Post*High # Articles is the interaction term of Post and the number of news articles.

Post*Severity is the interaction term of Post and Severity. Post*Influence is the interaction term of Post and

Influence. All variables are as defined in the Appendix. Each of the continuous variables is winsorized at the 1%

and 99% levels to mitigate the effect of outliers. T-statistics are reported in parentheses. ***, **, and * indicate

statistical significance at the 1%, 5%, and 10% levels, respectively.

39

Table 4

Robustness Tests: Alternative Explanations


(1) (2) (3) (4) (5)

Before 2 8.785

(1.52)

Before 1 5.742

(1.12)

Post 0 32.366***

(4.09)

Post 1+ 13.049***

(3.72)

Post 14.164*** 8.310** 15.742*** 17.066**

(4.04) (2.34) (3.11) (2.08)

Firm Size −17.620*** −10.692*** −23.879*** −28.313*** −27.183***

(−7.81) (−4.22) (−7.38) (−5.31) (−15.83)

ROA −172.030*** −163.561*** −229.814*** −453.538*** −218.704***

(−11.79) (−9.40) (−6.43) (−7.78) (−18.46)

Leverage 172.927*** 152.302*** 117.895*** 159.050*** 173.373***

(18.28) (14.45) (6.38) (5.42) (22.96)

Operating Risk 45.932*** 47.554** 176.606*** −43.622 27.538***

(3.95) (2.35) (2.75) (−0.45) (3.00)

Tangibility −40.773*** −41.559*** −80.892*** −32.543** −52.385***

(−6.89) (−6.39) (−8.55) (−1.97) (−10.81)

MB −0.171** −0.191*** −0.348 −3.365*** −0.192***

(−2.53) (−2.58) (−0.46) (−3.39) (−2.92)

Altman Z 0.002 −0.415 −5.363** 2.431 −0.013

(0.13) (−0.65) (−2.58) (0.74) (−0.88)

Ln (Loan Size) −12.253*** −9.796*** −16.898*** −21.661*** −12.184***

(−15.91) (−11.81) (−15.39) (−9.98) (−18.13)

Ln (Maturity) 7.051*** 6.081*** 7.762*** 13.816*** 6.888***

(5.34) (4.41) (4.29) (3.87) (5.98)

Media Sentiment −40.241***

(−3.73)

CSR −0.094

(−0.23)

Intercept 301.955*** 230.317*** 445.312*** 473.211*** 357.431***

(15.08) (9.72) (12.19) (8.43) (23.24)

Fixed Effects Year, Firm Year, Firm Year, Firm Year, Firm Year, Firm

F 116.45 123.54 74.12 26.82 160.66

Adjusted R2 0.573 0.594 0.539 0.592 0.558

N 14,927 11,069 5,624 2,075 20,297

40

Table 4 continued

This table shows the results of parallel trend analyses. In column (1) we control for media sentiment. Following

Bushman et al. (2017), we obtain media sentiment scores from RavenPack. Media Sentiment is estimated as the

average news sentiment over the 180 days prior to a loan’s initialization date. News sentiment is provided as a

score ranging from 0 to 100 in the database, where a high score means positive sentiment. We apply a linear

transformation to the news sentiment score and define Media Sentiment = (sentiment score – 50) / 50). In column

(2) we control for the firm’s CSR performance. Following Cho et al. (2013), the CSRstr (CSRcon) index is

constructed by adding strength (concern) scores from six factors categorized by KLD STAT: community,

corporate governance, diversity, employment, environment, and production. CSR is equal to CSRcon minus

CSRstr. In column (3) we exclude firms that only have either pre-incident loans or post-incident loans, which

facilitates the comparison of loan pricing before and after climate-related incidents. In column (4) the sample is

restricted to firms that have only one climate-related incident over the sample period. Column (5) presents the

trend analysis. Before 2 is an indicator variable that equals one if the facility is issued between one and two years

before the first climate-related incident, and zero otherwise. Before 1 is an indicator variable that equals one if

the facility is issued up to one year before the first climate-related incident, and zero otherwise. Post 0 is an

indicator variable that equals one if the loan is issued within one year following the first climate-related incident,

and zero otherwise. Post 1+ is an indicator variable that equals one if the loan facility is issued more than one

year after the first climate-related incident, and zero otherwise. All other variables are as defined in the Appendix.

Each of the continuous variables is winsorized at the 1% and 99% levels to mitigate the effect of outliers. T-

statistics are reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels,

respectively.

41

Table 5

Robustness Tests: Heckman Two-stage Correction Test

(1)

First stage

(2)

Second stage

Post −0.137*** 9.608**

(−4.80) (2.52)

Firm Size 0.272 −22.384***

(81.12) (−3.71)

ROA 0.533*** −225.072***

(28.11) (−12.32)

Leverage 0.755*** 175.006***

(29.08) (9.46)

Operating Risk −0.034 30.524***

(−1.33) (3.14)

Tangibility 0.058*** −56.746***

(3.71) (−10.33)

MB −0.000 −0.179***

(−0.13) (−2.76)

Altman Z 0.000 0.004

(0.91) (0.26)

Ln (Loan Size) −10.890***

(−14.03)

Ln (Maturity) 5.290***

(4.11)

Lambda 0.520

(0.02)

Intercept −3.571*** 357.536***

(−27.03) (4.37)

Fixed Effects Year, Industry, State Year, Firm

Chi-squared 23,353.87 150.91

Adjusted /Pseudo R2 0.265 0.569

N 92,705 17,327

This table provides results for Heckman two-stage regressions. The sample period is from 2000 to 2016. All

variables are as defined in the Appendix. Each of the continuous variables is winsorized at the 1% and 99%

levels to mitigate the effect of outliers. T-statistics are reported in parentheses. ***, **, and * indicate statistical

significance at the 1%, 5%, and 10% levels, respectively.

42

Table 6

Robustness Tests: Including All Climate Incidents


(1) (2) (3)

Event0-180 21.940*** 19.051***

(3.74) (3.22)

Event0-360 19.723***

(4.19)

Event180-360 16.409***

(2.65)

Event360-720 −1.391

(−0.26)

Firm Size −24.453*** −24.279*** −27.197***

(−13.55) (−13.44) (−14.17)

ROA −227.590*** −227.052*** −262.067***

(−17.94) (−17.90) (−13.89)

Leverage 173.792*** 174.269*** 156.557***

(21.86) (21.92) (16.32)

Operating

Risk 28.286*** 28.620*** 75.584***

(2.90) (2.93) (3.74)

Tangibility −57.168*** −57.350*** −63.375***

(−11.16) (−11.20) (−10.67)

MB −0.193*** −0.193*** −0.987***

(−2.91) (−2.91) (−2.93)

Altman Z 0.001 0.001 −3.518***

(0.04) (0.05) (−3.96)

Ln (Loan Size) −12.551*** −12.558*** −13.825***

(−18.21) (−18.22) (−18.92)

Ln (Maturity) 6.470*** 6.482*** 7.015***

(5.41) (5.42) (5.60)

Intercept 379.314*** 377.828*** 431.549***

(23.37) (23.27) (23.57)


F 177.78 177.95 171.74

Adjusted R2 0.559 0.559 0.563

N 20,297 20,297 20,297

In the first two columns, we replace our main independent variable with Event180/360, an indicator variable that

equals one if there are climate incidents between 180 and 360 days prior to a loan contract, and zero otherwise.

In column (3) the independent variables of interest are Event0-180, Event180-360, and Event360-720, indicator variables

that respectively equal one if there are climate incidents within the 180 days prior to a loan contract, between

180 days and 360 days prior to a loan contract, and between 360 days and 720 days prior to a loan contract, and

zero otherwise. The sample period is from 2000 to 2016. All variables are as defined in the Appendix. Each of

the continuous variables is winsorized at the 1% and 99% levels to mitigate the effect of outliers. T-statistics are

reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

43

Table 7

Climate Risk and Other Loan Contract Terms

(1) (2) (3) (4) (5)

Secured Ln_Maturity # Covenants # General

covenants

# Financial

covenants

Post 0.012 −0.046** 0.224*** 0.203*** 0.089***

(0.92) (−2.39) (3.06) (2.58) (2.90)

Firm Size −0.053*** −0.087*** −0.227*** −0.233*** −0.112***

(−7.11) (−7.57) (−6.88) (−7.25) (−6.40)

ROA −0.443*** 0.641*** −1.405*** −1.744*** −0.508***

(−5.90) (5.53) (−4.38) (−5.59) (−2.95)

Leverage 0.242*** 0.036 1.354*** 1.393*** 0.477***

(6.49) (0.62) (8.49) (8.98) (5.61)

Operating Risk 0.186** −0.104 0.011 −0.146 0.176

(2.46) (−0.86) (0.03) (−0.47) (1.04)

Tangibility −0.107*** −0.096*** −0.599*** −0.576*** −0.265***

(−4.56) (−2.68) (−5.82) (−5.75) (−4.78)

MB −0.001 −0.005** 0.000 0.000 −0.003

(−1.01) (−2.31) (0.05) (0.00) (−1.16)

Altman Z −0.012*** −0.014** −0.063*** −0.057*** −0.021***

(−3.32) (−2.56) (−4.30) (−4.02) (−2.76)

Ln (Loan Size) −0.049*** 0.012*** 0.000 0.003 −0.026***

(−15.09) (2.81) (0.00) (0.20) (−3.11)

Ln (Maturity) 0.080*** 0.004 −0.027 0.103***

(14.67) (0.17) (−1.19) (8.43)

Intercept 0.974*** 3.977*** 5.086*** 4.347*** 3.050***

(14.03) (37.17) (17.21) (15.11) (19.29)

Fixed Effects Year, Firm Year, Firm Year, Firm Year, Firm Year, Firm

F 34.02 110.67 35.02 34.86 58.97

Adjusted R2 0.686 0.331 0.666 0.668 0.620

N 12771 20297 9859 10859 20297

This table shows the results of regressions of climate risk on other loan contract terms. The sample period is

from 2000 to 2016. All variables are as defined in the Appendix. Each of the continuous variables is winsorized

at the 1% and 99% levels to mitigate the effect of outliers. T-statistics are reported in parentheses. ***, **, and *

indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

44

Table 8

Climate Risk and Lender Structure

(1) (2) (3)

# Lenders Annual Fee Upfront Fee

Post −0.062*** 0.102*** 0.090*

(−2.69) (3.72) (1.89)

Firm Size 0.067*** −0.017 −0.213***

(5.40) (−1.16) (−8.82)

ROA −0.294*** −0.243** −1.455***

(−3.42) (−2.39) (−7.71)

Leverage 0.174*** −0.013 0.965***

(3.15) (−0.20) (8.61)

Operating Risk 0.154** 0.084 0.751***

(2.31) (1.07) (3.29)

Tangibility −0.021 −0.106** −0.297***

(−0.60) (−2.52) (−4.31)

MB 0.001 −0.000 −0.004**

(1.39) (−0.43) (−2.20)

Altman Z 0.000 0.000 −0.007

(0.40) (0.30) (−1.45)

Ln (Loan Size) 0.069*** 0.032*** −0.061***

(14.10) (5.58) (−8.21)

Ln (Maturity) 0.061*** 0.037*** 0.123***

(7.23) (3.71) (8.33)

Intercept 0.215* 0.304** 6.007***

(1.91) (2.29) (27.15)


F 10.61 4.21 19.30

Adjusted R2 0.173 0.020 0.797

N 20297 20297 3936

This table shows the results of regressions of climate risk on lender structure. The sample period is from

2000 to 2016. All variables are as defined in the Appendix. Each of the continuous variables is winsorized

at the 1% and 99% levels to mitigate the effect of outliers. T-statistics are reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

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