Users’ versus Preparers’ Materiality Judgments: Risk Relevance of Climate-Change Disclosures in Form 10-K

Ella Mae Matsumura University of Wisconsin–Madison

Wisconsin School of Business ellamae.matsumura@wisc.edu

Rachna Prakash University of Mississippi

Patterson School of Accountancy rprakash@olemiss.edu

Sandra C. Vera-Muñoz University of Notre Dame

Mendoza College of Business sveramun@nd.edu

Version: October 30, 2016 Preliminary manuscript for presentation at the UT-Austin Research Conference. Acknowledgements: We gratefully acknowledge the helpful comments and suggestions of Bill Kinney. We are also very grateful to Stephannie Larocque for generously sharing her cost-of-equity program with us. Professor Vera-Muñoz gratefully acknowledges financial support by the Notre Dame/Deloitte Center for Ethical Leadership, KPMG through the Department of Accountancy, and the Business Information Center, University of Notre Dame.

Users’ versus Preparers’ Materiality Judgments: Risk Relevance of Climate-Change Disclosures in Form 10-K

ABSTRACT Do firms’ Form 10-K risk disclosures meaningfully reflect the material uncertainties they face? The question is important because firms’ failure to disclose material risks may leave investors exposed to potentially significant losses. Further, investors and other stakeholders continue to raise concerns that public registrants’ 10-K filings are insufficient and boilerplate, and thus inconsistent with the requirements of Regulation S-K. We address this question with an analysis of about 3,000 climate-change risk (CCR) disclosures in 10-K filings of large public registrants over seven years. A key feature of our study is examination of users’ versus preparers’ CCR materiality judgments. Users’ judgments are proxied by the industry-based criteria for classification of CCR materiality provided by the Sustainability Accounting Standards Board’s panel of experts. Preparers’ judgments of CCR materiality are proxied by managers’ CCR disclosure choices in Form 10-K. Using cost of equity (COE) as a proxy for firm risk, we find that the COE of firms disclosing material CCR information is 21.3 basis points lower than the COE of non-disclosing firms. More importantly, and central to our materiality prediction, we find that, in industries where users judge CCR as material, the COE is 49.1 basis points lower for firms that disclose CCR information in Form 10-K than for non-disclosing firms. In contrast, we find no significant association between disclosing CCR information in Form 10-K and COE for firms in industries where users judge CCR as not-material. Our findings lend support to the concerns that managers’ failure to disclose CCR in Form 10-K imposes additional risk on firms in industries where users judge CCR as material. Keywords: Regulation S-K; Climate-change risk disclosures; Users’ versus preparers’

materiality assessments; SASB Materiality Map™; Cost of equity capital Data Availability: Data are publicly available from the sources identified in the study.

I am concerned by the fact that today many public companies are in fact providing disclosure about significant climate change-related matters through mechanisms outside of the disclosure documents they file with the Commission. [I] do not believe that public companies today are doing the best job they possibly can do with respect to their current mandated disclosures.” (Elisse B. Walter, former SEC Commissioner, 1/27/10)

I. INTRODUCTION

The escalating debate on enhanced climate-change risk disclosures in Form 10-K, has

been fueled by two conflicting views. On one hand, some investors and other stakeholders argue

that existing climate-change risk (hereafter CCR) disclosures in Form 10-K are insufficient,

boilerplate, and inconsistent across companies (Gelles 2016; SEC 2010). On the other hand,

critics argue that requiring such CCR disclosures in Form 10-K could risk burdening both

registrants and investors with costly information that is not typically material to any investment

or voting decision (SEC 2016, 212). Against this backdrop, the SEC has urged registrants to heed

Regulation S-K, which requires companies to disclose in SEC filings “the most significant

factors that make an investment in the registrant speculative or risky” (Regulation S-K, Item

503(c), SEC 2004), as well as the 2010 SEC interpretive guidance that clarifies Regulation S-K

and specifies how companies are expected to address CCR in 10-K filings (SEC 2010).1 Yet,

according to analysts, “for investors interested in managing the economic risks of climate

change, the SEC is among the least helpful places to look” (Hulac 2016), thus leading to our

research question: Do firms’ CCR disclosures in Form 10-K meaningfully reflect the material

risks they face, as measured by their cost of equity capital?

A key feature of our study is that we examine users’ (i.e., investors and other

stakeholders) versus preparers’ (i.e., managers) materiality judgments. This is theoretically and

1 The guidance became effective on February 8, 2010, and mandates companies to disclose the material effects of

CCR, typically classified into physical, regulatory, and reputation (further discussed in Section 2). For a detailed discussion of the supportive and opposing views on the guidance, see Shorter (2013).

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empirically important for our investigation for two reasons. First, we expect preparers’ choices

of whether to disclose CCR in Form 10-K to be grounded in ‘materiality.’ In its landmark

decision (Basic Inc. v. Levinson 1988), the Supreme Court defined materiality as “substantial

likelihood that the disclosure of omitted facts would have been viewed by a reasonable investor

as having significantly altered the ‘total mix’ of information made available.” Second, users’

CCR materiality judgments can be inferred from an independent, newly-created industry-based

classification of CCR materiality provided by the Sustainability Accounting Standard Board’s

(SASB) panel of experts.2 This allows us to estimate the COE-effect of preparers’ (i.e.,

managers’) choices to disclose vs. not disclose CCR information in Form 10-K, partitioned by

firms in industries where users judge CCR as either material or not material.

The importance of our inquiry is threefold. First, firms’ failure to disclose material CCR

information in 10-K filings may leave investors, who are looking for information to assess and

reduce risks in their portfolios, exposed to potentially significant losses (McCann 2016; Olson

and Viswanatha 2016; Newlands 2015). This is illustrated by two recent high-profile cases. First,

investors in Peabody Energy, the largest U.S. coal mining company, suffered millions of dollars

in losses. The investigation into Peabody by the New York attorney general’s office centered on

its failure to disclose in Form 10-K the material risk of devaluation of their coal reserves as a

result of passage of regulations to curb emissions from the combustion of coal (New York

Attorney General 2015). In the second case, the SEC launched a probe on Exxon and its auditor,

2 SASB is an independent 501(c)(3) non-profit whose mission is to develop and disseminate sustainability

accounting standards for reporting material sustainability issues in SEC filings and in compliance with SEC requirements (see http://www.sasb.org/sasb/vision-mission/). SASB’s board of directors is chaired by Michael Bloomberg, and is composed of two former SEC chairpersons, a former FASB chairperson, retired partners of CPA firms, heads of investment firms and corporate governance organizations, and lawyers, among others. In classifying sustainability issues as material for any given industry, the SASB uses the same categories of evidence used by the SEC in determining the materiality of financial information. We discuss this further in section IV and Appendix B.

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PwC, to investigate investors’ concerns about the economic feasibility of tapping future wells

that make up a significant portion of the company’s multibillion-dollar business. This is linked to

CCR information because Exxon has refused to disclose in Form 10-K the price it uses to assess

the cost of regulations to curb emissions when evaluating its future prospects (Olson and

Viswanatha 2016).

Our inquiry is also important because, in contrast to the mid-1970s, when “ethical

investors” represented two-thirds of one percent of all U.S. stock and bond holdings (SASB

2016, 2), today, mainstream investment analysts’ decisions on whether to buy, sell, or hold a

security are increasingly influenced by sustainability-related performance. For example, in 2015,

52 percent of shareholder proposals in proxy filings related to environmental and social issues

(EY 2015). In addition, in a 2015 CFA Institute survey of 1,325 institutional investors, 73

percent of respondents indicated that they take environmental, social, and governance (ESG)

issues into account in their investment analyses and decisions. The top reason investors

incorporate ESG-related information in their decisions is not to derive reputational benefit but to

determine whether a company is adequately managing risk (CFA Institute 2015).

Consistent with the above, the SEC recently issued a Concept Release seeking public

comment “to consider ways to update long-standing disclosure requirements in Regulation S-K,

including CCR, for the benefit of investors and registrants” (SEC 2016). On one hand, many of

the respondents referencing the SEC’s 2010 interpretive guidance on climate change expressed

concerns that registrants are still not following that guidance (SEC 2016, 208). On the other

hand, other commenters express concerns about burdening registrants and investors with costly

information that is not typically material to any investment decisions (Casey 2010). By

examining users’ versus preparers’ CCR materiality judgments, our study informs investors,

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regulators, and policy makers regarding the risk relevance of climate-change disclosures in Form

10-K.

Finally, understanding the financial effects of SEC regulations mandating the inclusion of

sustainability information in SEC filings is increasingly important given the recent global trends

towards requiring such disclosures. These trends include, among others, the European Union’s

(EU) recent mandate (European Parliament 2014/95/EU) on ESG disclosures and the ESG

disclosure guidance developed by the World Federation of Exchanges (WFE 2015) These global

trends highlight the need to better understand whether CCR disclosures in Form 10-K provide

meaningful information to investors.

CCR disclosures in Form 10-K can reduce a firm’s cost of capital through at least two

major channels. First, providing information to investors can reduce estimation risk in capital

markets (Botosan 1997; Easley and O’Hara 2004; Lambert et al. 2007; Leuz and Verrecchia

2000). Second, greater information transparency can mitigate adverse selection problems by

reducing transaction costs and/or information asymmetry (Graham et al. 2005; Healy and Palepu

2001; Leuz and Wysocki 2008; Verrecchia 2001).

A related body of empirical research on voluntary disclosures of financial information

generally supports the prediction that greater disclosure reduces information asymmetry (Healy

et al. 1999; Leuz and Verrecchia 2000; Lambert, Leuz, and Verrechia 2007, 2012; Heflin, Moon,

and Wallace 2015) and hence, the cost of equity capital (Botosan 1997; Botosan and Plumlee

2002; Graham et al. 2005; Healy and Palepu 2001). Our study contributes to and extends prior

research by examining the cost-of-equity effect of a Regulation S-K mandated nonfinancial

disclosure – climate-change risk – which is also publicly available through voluntary disclosure

channels, to isolate its incremental effect on the firm’s information risk.

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To address our research question, we analyze 2,996 firm-year observations of large

public registrants’ CCR disclosures in Form 10-K for years 2008 to 2014. We obtain our sample

from the intersection of the S&P 500 index firms and the Ceres, SASB, and CDP databases for

the period 2008 to 2014.3,4 We hand-collect data from the Ceres database to document CCR

disclosures of S&P 500 firms in Form 10-K. Because CCR information is, at least for the

majority of firms in our sample, available through voluntary participation in the CDP climate

change survey, we also collect data from CDP to document the firms’ participation in the CDP

survey to control for voluntary CCR disclosures. Because users’ judgments of CCR materiality

are industry-specific (Khan, Serafeim, and Yoon 2016), we rely on SASB’s industry-based

classification of CCR materiality.

We construct a composite implied cost of equity (COE) measure using the median of four

measures suggested by the accounting and finance literatures, namely, Easton’s price earnings

growth (PEG) model (2004), Gebhardt et al. (2001) (GLS), Claus and Thomas (2001) (CT), and

the price-earnings ratio. Our analyses using propensity score matching and doubly robust

regressions offer two important results. First, we find that, even after controlling for firms’

voluntary CCR disclosures in the CDP survey, COE is negatively associated with CCR

disclosures in Form 10-K. Specifically, our doubly robust regression results indicate that the

COE of disclosing firms is significantly lower, by 21.3 basis points, than the COE of non-

disclosing firms.

3 Ceres is a Boston-based nonprofit organization composed of a network of investors, companies, and public interest

groups, founded in 1989 to advance their mission of building a sustainable global economy. In 1997, Ceres launched the Global Reporting Initiative (GRI), which has been widely adopted as the standard for sustainable reporting worldwide. The first year of data availability in Ceres is 2008. See http://ceres.org/about-us/who-we-are.

4 CDP (formerly Carbon Disclosure Project) is a London-based nonprofit, founded in 2000, that surveys the world’s largest companies by market capitalization on climate, forestry, water, and other sustainability topics. Its advisers consist of a network of 827 institutional investors representing over $100 trillion in assets under management. See https://www.cdp.net/en.

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More importantly, and central to our materiality predictions, we find that, in industries

where users judge CCR as material, the COE is 49.1 basis points lower for firms that disclose

CCR in Form 10-K compared to non-disclosing firms. In contrast, we find that disclosing vs. not

disclosing CCR in Form 10-K is not significantly associated with the COE for firms in industries

where users judge CCR as not material. Our results obtain after also controlling for various firm-

and industry-level variables. We conduct several robustness tests to assess the sensitivity of our

main results and our inferences are unchanged.

We contribute to and extend the growing literature on the capital markets implications of

voluntary and mandatory sustainability disclosures. Matsumura et al. (2014) find a negative

association between carbon emission levels disclosed voluntarily to the CDP and firm value.

They also find that the median market value of firms that voluntarily disclose their carbon

emissions is higher than that of their non-disclosing counterparts. Our study extends Matsumura

et al. (2014) by examining the risk relevance of CCR disclosures in 10-K filings, as measured by

the COE, incremental to voluntary CCR disclosures. Our findings lend support to the concerns

that preparers’ (i.e., managers’) failure to disclose CCR in Form 10-K imposes additional risk on

firms, but only in industries where users judge CCR as material. Our results extend Khan et al.

(2016), who find a positive association between sustainability investments and future abnormal

stock returns, but only in firms with material sustainability investments.

Finally, our study provides insights on the SEC’s recent call for public comment on

whether certain disclosure requirements in Regulation S-K need to be updated to better serve the

needs of investors and registrants (SEC 2016). The materiality principle is intended to balance

the need to provide investors with the information they need to make informed decisions against

the need to avoid burdening registrants and investors with costly information that is not typically

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material to their decisions. Our findings provide evidence that materiality plays a critical role in

the association between CCR disclosures in Form 10-K and COE.

The remainder of this paper is organized as follows. The next section provides

institutional and regulatory background on CCR and related 10-K disclosure requirements. The

third section reviews the research literature and develops our hypotheses, while the fourth section

describes our research design. The fifth section provides the results of hypotheses testing and

robustness tests. The final section briefly summarizes the findings and discusses the study’s

limitations and implications for research and practice.

II. INSTITUTIONAL AND REGULATORY BACKGROUND

The current impetus for investors’ demand for meaningful and more transparent CCR

disclosures in 10-K filings is best understood by the growing importance of climate change

issues to a fuller understanding of a company’s performance (Deloitte & Touche LLP 2016;

Gelles 2016; UBS 2012).5 Further, given the significance of climate issues for corporations’

financial position in a carbon-constrained economy, climate change represents a material risk for

many companies. To illustrate, Peabody Energy privately projected a devaluation of their coal

reserves as a result of passage of regulations to curb emissions from the combustion of coal.

However, the company withheld this information from investors, choosing instead to state in its

2011 through 2014 Form 10-K filings that “it was not possible to reasonably predict the impact

that any such laws or regulations may have on [Peabody’s] results of operations, financial

condition or cash flows” (New York Attorney General 2015). Although Peabody eventually

5 For instance, corporate board responsibility for climate change has increased from 67 percent to 95 percent from

2010 to 2015, according to a 2015 climate change report from the CDP (CDP 2015, 6). Further, as of 2016 the United Nations Principles for Responsible Investment (UNPRI) had 1,500 signatories, from over 50 countries, with $60 trillion in assets under management, who had committed to the principles recognizing the materiality of sustainability issues (see https://www.unpri.org/about).

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agreed to revise its 10-K filings to more fully disclose the potential impact of climate change

regulation on the value of its coal reserves, investors suffered millions of dollars in losses as the

company’s shares dropped from $1,000 in 2011 to around $4 four years later and the company

filed for bankruptcy in April 2016.

More recently, the SEC launched a probe on Exxon and its auditor, PwC, to investigate

what price Exxon uses to assess the “price of carbon” –that is, the cost of regulations (e.g.,

carbon tax, cap-and-trade) to reduce emissions – when evaluating certain future oil and gas

prospects. In 2014 the company determined that none of its assets were at risk of being rendered

less valuable by impacts from the global response to climate change (Olson and Viswanatha

2016). Yet, institutional investors and other energy companies cast serious doubts about Exxon’s

ability to tap future wells that make up a significant portion of the company’s multibillion-dollar

value. This is because, as the price of carbon increases, it becomes less economically feasible to

extract more of Exxon’s assets from wells in future years. Exxon, however, has refused to

disclose in Form 10-K the price it uses to assess the cost of regulations to curb emissions when

evaluating its future prospects (Olson and Viswanatha 2016).

In addition, a recent study that examined climate change disclosures of the twenty largest

publicly traded U.S. companies in their 2012 through 2014 10-K filings finds that the majority of

companies reported limited or no information (InfluenceMap 2015). For example, Boeing did not

mention climate change risks in its 10-K filings, even though its 2014 annual report stated that,

“costs incurred to ensure continued environmental compliance could have a material impact on

our results of operations, financial condition or cash flows.” Similarly, General Electric made no

mention in its 10-K filings of the risk that climate change poses to its supply chain, despite

having more than 130 manufacturing facilities in forty countries (InfluenceMap 2015, 2).

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Importantly, according to the 2010 SEC interpretive guidance on climate change,

companies’ risk exposure to climate change can be either direct or indirect: “[F]or some

registrants, financial risks associated with climate change may arise from physical risks to

entities other than the registrant itself” (emphasis added) (SEC 2010, 7). CCR is typically

classified into three categories, physical, regulatory, and reputation.6 Physical risk refers to the

impact of changes in precipitation extremes (e.g., hurricanes, tropical typhoons), which can

cause severe damage to infrastructure investments, as well as severe droughts, which can cause

disruptions to supply chains and production scheduling. Regulatory risk refers to existing or

proposed federal and state GHG emissions controls, carbon taxes, and cap-and-trade rules.

Regulation of GHG emissions and the increasing demand for clean technology and renewable

energy sources is expected to impact every sector of the economy. A mandated reduction in

emissions may lead to large expenditures to develop technologies necessary to bring companies

into compliance.7 Finally, reputation risk refers to reduced demand for goods and services – and

related loss of market share –for companies that are slow to innovate by switching to low-carbon

products, clean energy technologies, renewable energy sources, and efficient manufacturing and

shipping processes (e.g., see Gilbert 2014).

According to Item 503(c) of Regulation S-K, a registration statement filed with the SEC

must contain a discussion of the most significant factors that make the offering speculative or

risky. In addition, annual 10-K filings must also include this information, and 10-Q filings must

6 These categories are identified in the SEC interpretive Guidance issued in 2010. 7 For example, the Environmental Protection Agency (EPA) recently announced plans to issue GHG emission

standards to limit carbon emissions from aircraft by early 2017 (EPA 2016). Aircrafts are the third-largest source of GHG emissions in the U.S. transportation sector, accounting for about 3.2 percent of such emissions in the country (EPA-HQ-OAR-2014-0828, EPA 2016, 175).

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set forth any material changes to the risk factors described in the annual filings. Item 503(c) also

requires that companies explain how each risk affects the issuer or the securities being offered.

The 2010 SEC interpretive guidance clarifies Regulation S-K and specifies that

companies are expected to disclose regulatory, physical, and reputation risks that can materially

affect registrants’ business operations and financial performance. The guidance also discourages

“boilerplate” and generic CCR disclosures that could apply to any registrant. Despite this

existing regulatory framework, only some climate change information is disclosed in Form 10-K,

while much more information is disclosed outside of documents filed with the SEC through

voluntary disclosure initiatives or in response to other non-SEC regulatory requirements (Walter

2010).8

III. THEORY AND HYPOTHESES DEVELOPMENT

Risk Factor Disclosures in Form 10-K

An emerging body of research on risk factor disclosures in Form 10-K across a broad

range of risk types, time periods, and samples of firms has examined varied aspects of these

disclosures and related effects. Using firms’ risk disclosures in 10-K filings, Kravet and Muslu

(2013) find a positive association between annual increases in the number of risk sentences in a

company’s 10-K filings and the volatility of negative returns, the trading volume around the

filings, and the volatility of analyst forecast revisions surrounding the filings. They further find

that industry-level changes in risk disclosures are significantly more effective than firm-level

changes in affecting investors’ risk perceptions. Their collective findings reject the argument that

risk disclosures are boilerplate, but are consistent with the criticism that risk disclosures lack

useful firm-specific details. Consistent with Kravet and Muslu (2013), Campbell et al. (2014)

8 The CDP is the primary voluntary channel for disclosure of CCR. Other channels include corporate sustainability

reports through the Global Reporting Initiative (GRI), corporate websites, and social media.

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find little evidence that risk factor disclosures are boilerplate, as investors appear to incorporate

risk factor information disclosed in Form 10-K into market values. Brown et al. (2015) find that

managers typically use the previous year’s disclosure as a template for the current year and then

make changes to reflect updates in their operations, risk exposure, and business environment.

Bozanic et al. (2013) find an increase in qualitative risk factor disclosures in Form 10-K after

companies complete a comment letter review. Their study also reports that, after the comment

letter review, the firms’ disclosures are easier to read, less optimistic, more quantitative, and

more forward looking.

While the above literature provides evidence on the informativeness of risk factor

disclosures in 10-K filings, it is unclear from these studies whether CCR disclosures are

informative to equity investors, for several reasons. First, the extant research examines risk

disclosures in general, but not CCR disclosures in particular. CCR disclosures in 10-K filings are

more difficult to assess because they are predominantly non-quantitative in nature, inconsistent

across companies, even within the same industries, and do not distinguish between material and

immaterial issues. Second, the empirical analyses in prior studies predate the issuance of the SEC

2010 interpretive guidance, thus raising concerns about generalizing prior studies’ conclusions to

more recent periods. Further, it is unclear whether the results of studies regarding the

effectiveness of the SEC’s enforcement through the comment letter review process generalize to

CCR disclosures in 10-K filings. This is because in 2010 and 2011 combined, the Commission

issued only 49 comment letters that addressed the inadequacy of climate change disclosures,

while they issued only three comment letters in 2012, and none in 2013 (Coburn and Cook 2014,

5). Thus, our study seeks to advance our understanding of the risk-relevance of mandatory CCR

disclosures in 10-K filings.

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Voluntary Disclosure and Cost of Equity

Investors, regulators, and practitioners have long been interested in the association

between financial disclosures and the cost of capital, and prior research has examined this

relationship (Botosan 1997; Botosan and Plumlee 2002; Diamond and Verrecchia 1991; Leuz

and Verrecchia 2000). Empirical evidence on voluntary financial disclosures generally supports

the prediction that greater disclosure reduces information asymmetry (Healy et al., 1999; Leuz

and Verrecchia 2000; Lambert, Leuz, and Verrechia 2007, 2012; Heflin, Moon, and Wallace

2015) and hence, the cost of equity capital (Botosan 1997; Botosan and Plumlee 2002; Healy and

Palepu 2001). Thus, the bulk of this literature suggests that voluntary financial disclosure is

negatively associated with firms’ cost of equity.

Extant research examines the cost-of-equity effects of voluntary environmental and social

responsibility disclosures. For example, Dhaliwal, Li, Tsang, and Yang (2011) find a negative

association between voluntary disclosure of corporate social responsibility (CSR) reports and the

cost of equity capital for firms with superior CSR performance. Similarly, using a sample of U.S.

firm-year observations from 1992 to 2007, El Ghoul, Guedhami, Kwok, and Mishra (2011) find

a negative association between better CSR performance (i.e., regarding employee relations,

environmental policies, and product strategies) and cost of equity capital.

More recent research has examined the association between voluntary carbon emission

disclosures in CDP and firm value (Matsumura et al 2014). This research provides empirical

evidence of a higher median firm value for S&P 500 firms that disclose their carbon emissions,

relative to firms that do not disclose. This growing body of research documents that investors

react to environmental and corporate social responsibility. However, these studies focus on

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firms’ disclosures through non-SEC mechanisms. Therefore, research is silent as to whether

CCR disclosures in Form 10-K provide incremental information about the riskiness of the firms.

We argue that CCR disclosures in Form 10-K, like voluntary financial and environmental

disclosures through non-SEC mechanisms and risk factor disclosures in Form 10-K, can reduce a

firm’s cost of capital through at least two major channels. First, enhanced disclosure can reduce

estimation risk in capital markets (Botosan 1997; Easley and O’Hara 2004; Graham et al. 2005;

Lambert et al. 2007; Leuz and Verrecchia 2000). Second, information transparency can mitigate

adverse selection problems by reducing transaction costs and/or information asymmetry (Graham

et al. 2005; Healy and Palepu 2001; Leuz and Wysocki 2008; Verrecchia 2001). Taken together,

the previous literature and the arguments above lead to our first hypothesis on the risk relevance

of mandatory CCR disclosures in 10-K filings:

H1: Ceteris paribus, cost of equity is negatively associated with disclosing climate-change

risk in Form 10-K.

One reason our hypothesis may not obtain is that firms may have already been providing

CCR information voluntarily through other mechanisms. Christensen, Floyd, Liu, and Maffett

(2016) examine the real and firm value effects of mandatory mine safety records disclosures in

SEC filings (i.e., Forms 8K, 10Q, and 10K) by SEC-registered mine owners. Importantly, this

information is already publicly available through the Mine Safety and Health Administration’s

(MSHA) website. The authors argue that one reason mandated mine safety disclosures (MSD) in

SEC filings could have an incremental effect is because SEC filings broadcast the information to

a wide range of interested parties at a low incremental acquisition cost, thus increasing

awareness of violations of the Mine Act – even if investors are not explicitly looking for them.

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Consistent with their predictions, Christensen et al. (2016) find that mining-related

citations and injuries of firms that disclose mine safety records in SEC filings are significantly

lower than those of firms that do not provide such disclosures. In addition, they provide evidence

of a negative association between MSD and firm value, consistent with the argument that safety

violations can directly decrease cash flows through fines and shutdowns. Additionally, investors

or other stakeholders might perceive policymakers’ decision to include safety information in

SEC filings as an implicit signal of that information’s importance.

Nonfinancial and ESG Disclosures and Materiality

A recent study examines a mandatory-to-voluntary regime shift caused by an SEC rule

that allows smaller reporting companies the choice to reduce disclosure on ten nonfinancial items

in the periodic SEC filings (Cheng, Liao, and Zhang 2013).9 The study finds that smaller

reporting companies that chose to continue these disclosures after the passage of the rule

experienced an increase in market illiquidity. Importantly, Cheng et al. (2013) argue that the

association between choice of disclosure and market illiquidity depends on the materiality of the

potentially reduced information. Therefore, such information provided in the SEC filings may be

especially important to smaller reporting companies’ investors because, relative to larger firms,

these companies have a poor information environment, including lower analyst following and

media coverage.

The findings from Cheng et al. (2013) point to the important role of the materiality of

nonfinancial disclosures for smaller companies’ investors, but do not address the materiality of

9 Before the SEC rule became effective in December 2007, the newly eligible smaller reporting firms (i.e., those

with a public float of less than $75 million) were required to report the same information in their SEC filings as larger firms, regardless of the information content. However, after the passage of this rule, they were not mandated to provide information on the eligible items, such as risk disclosure or compensation discussion and analysis. Instead, smaller reporting companies could voluntarily provide the information to investors on a quarterly basis.

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such disclosures, including ESG disclosures, for investors of larger companies. With one notable

exception, extant accounting research on ESG disclosures treats these issues as being equally

material to investors across industries. To our knowledge, Khan, Serafeim, and Yoon (2016) is

the first study that examines inter-industry differences in the materiality of ESG issues for

investors. Using a newly-available tool known as SASB’s Materiality Map™, their study hand-

maps sustainability investments to independent ratings of firms’ ESG performance (specifically,

KLD ratings) to measure investments on material and immaterial ESG issues for each of their

sample firms across 45 industries.10 Khan et al. (2016) report that firms with strong ratings on

material ESG issues have better future accounting performance than firms with inferior ratings

on the same issues. In contrast, firms with strong ratings on immaterial ESG issues do not

outperform firms with poor ratings on these same issues. Further, firms with strong ratings on

material ESG issues and concurrently poor ratings on immaterial ESG issues have the best future

accounting performance. The authors conclude that materiality enhances the informativeness of

ESG data for investors.

The above studies indicate that the materiality of CCR disclosures varies across

industries. Based on these arguments, our next hypothesis examines the role of materiality on the

association between disclosing CCR in Form 10-K and cost-of-equity capital:

H2: The negative association between disclosing climate-change risk in Form 10-K and cost of equity is stronger for firms in industries where users judge such disclosures as material than for firms in industries where users judge such disclosures as not material.

10 KLD statistics (currently available in the MSCI database in WRDS) provide firm-level ratings on an array of over

fifty sustainability issues and rank firms’ performance on those issues. This database and SASB’s Materiality Map™ are discussed further in the next section.

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IV. RESEARCH DESIGN

Sample and Data

We obtain our sample from the intersection of the S&P 500 index firms and the Ceres

and CDP databases for the period 2008 to 2014. In order to minimize changes in our sample over

this period we use firms that were included in the S&P 500 index on December 31, 2008. We

hand-collect data on the frequency of the firms’ CCR disclosures in Form 10-K from Ceres’ SEC

sustainability disclosure search tool.11 The tool searches the text of SEC annual filings of S&P

500, Russell 3000, and FT Global 500 firms and identifies the relevant issue of the disclosure

(e.g., climate change, water risk), categorizes the information, and identifies where in the filing

disclosure occurs. Appendix A provides two examples of excerpts of CCR disclosures in Form

10-K, for Metropolitan Life Insurance (Panel A) and Coca Cola Bottling Co. (Panel B). We

choose 2008 as the initial year of our main analyses because Ceres’ database includes SEC

filings starting in fiscal year 2008. We are able to obtain firms’ CCR disclosures in Form 10-K

from Ceres until fiscal year 2015.

SASB’s Materiality Map™ identifies the materiality of sustainability issues on an

industry-by-industry basis. To determine materiality, SASB uses an evidence-based approach

based on input from a panel of over 200 industry experts and SASB staff. At the industry level,

the map indicates whether an issue is judged to be material for companies in a given industry by

scoring sustainability issues based on three components: evidence of investor interest, evidence of

financial impact, and forward-looking impact. At the sector level, the map classifies a

sustainability issue as material if the issue is judged to be material for more than 50 percent of

industries in the sector (see Appendix B for further details).

11 Available at http://ceres.org/resources/tools/sec-sustainability-disclosure.

17

To proxy for CCR materiality based on users’ judgments, we rely on SASB’s Materiality

Map™ scoring on two sustainability issues that are directly related to CCR, namely (1) GHG

emissions, and (2) environmental and social impacts on assets and operations. We use these two

issues to classify each sample firm as belonging to an industry where CCR is judged by users

(i.e., SASB’s panel of experts) as either material or not material to investors.

We collected data on our sample firms’ participation in the CDP climate survey to control

for voluntary disclosures of this information (i.e., through channels other than the SEC filings).

The CDP survey elicits information on climate change risk and opportunities, carbon emissions

in metric tons, emission reduction targets, and managerial compensation, among others. Neither

responding to the CDP survey nor providing independent assurance on the climate data is

mandated by the CDP. We choose 2014 as the final year of our main analyses because this is the

last year of data available in the CDP database. However, in our robustness tests we examine the

sensitivity of our results by extrapolating the firms’ participation in the CDP climate survey for

2015 using 2014 data.

Table 1 provides our sample selection criteria. We start with all S&P 500 firms available

in the Ceres and CDP databases from 2008 to 2014. The result is 3,226 firm-year observations

(496 unique firms). We lose 227 firm-year observations for which we are unable to calculate

COE. This is because we exclude firms with negative book value of equity or negative earnings

forecasts for years one and two, or we are unable to obtain analyst forecasts for these firms. The

sample is further reduced by three observations for unavailable Compustat data, resulting in a

final sample of 2,996 firm-year observations (465 unique firms) for our H1 tests. We further

exclude 49 firm-year observations with missing 4-digit SIC code data necessary to match with

SASB’s industry codes for our user-based materiality classification. Thus, our final sample

18

consists of 2,947 firm-year observations (458 unique firms) for our H2 tests.

| Insert Table 1 about here |

Descriptive Statistics

Figure 1 and Table 2 show descriptive statistics of the frequencies of firms that disclosed

CCR information in Form 10-K for 2008 to 2014, and whether they participated in the CDP

climate survey. Figure 1 shows that in 2008, less than half of the firms (46.2 percent) disclosed

CCR information in Form 10-K. Notably, there was an almost ten percent increase from 2008 to

2009 in the number of CCR disclosing firms, and a further five percent increase in 2010. These

increases make intuitive sense, as the years coincide with the issuance of the SEC’s 2010

interpretive guidance on climate change disclosures, which became effective in February 2010.

Figure 1 shows a steady growth in the percentage of firms disclosing CCR in Form 10-K until

2012, but the growth then tapers off. In 2014, almost two-thirds of the firms disclosed CCR

information in Form10-K. Figure 1 also shows a nine percent growth in the firms’ participation

in the CDP climate survey over the seven-year period, from 58 percent in 2008 to 67 percent in

2014.12

Panel A of Table 2 shows that, averaged over the seven-year period, 60 percent of our

sample firms disclosed CCR information in Form 10-K. Over this period, almost two-thirds of

the firms (65.2 percent) participated voluntarily in the CDP climate survey. Further, while 40.5

percent of our sample firms both responded to the CDP climate survey and disclosed CCR

information in Form 10-K, about 15 percent provided CCR information in neither Form 10-K

nor the CDP. Notably, almost 25 percent of the firms responded to the CDP climate survey but

12 As mentioned earlier, 2014 is the last year for which CDP climate data is publicly available. However, in

robustness tests we examine the sensitivity of our results to extrapolating the firms’ participation in the CDP survey for 2015 using 2014 data.

19

chose to not disclose CCR information in Form 10-K.13 This finding is puzzling, since these

firms have voluntarily committed scarce resources to respond to the CDP survey (and some firms

may have also provided independent assurance on this information) and yet chose to not provide

this information in Form 10-K.

| Insert Figure 1 and Table 2 about here |

Panel B of Table 2 shows the frequencies of firms disclosing CCR information in Form

10-K (i.e., our proxy for preparers’ materiality judgments) and materiality as judged by users

(i.e., based on SASB’s panel of experts). Averaged over the seven-year period, the majority of

firms in our sample (61.4 percent) belong to industries where users judge CCR as not material.

Figure 2, which focuses on the firms that disclosed CCR information in Form 10-K partitioned

by user-based (SASB) materiality, shows a 20 percent growth over the seven-year period in the

number of CCR disclosing firms for both the material and not-material groups. In addition, over

the same period, the percentages of CCR disclosers in Form 10-K are consistently higher, by an

average of 20 percent, for firms in the user-based material group relative to those in the not-

material group.

| Insert Figure 2 about here |

Table 3 shows the sub-samples of firms that participated in the CDP climate survey

(Panel A) and those that did not participate (Panel B), partitioned by users’ and preparers’

materiality judgments. Notably, both panels show that the majority of firms disclosed CCR

information in Form 10-K, regardless of whether or not they participated in the CDP climate

survey.

| Insert Table 3 about here |

13 The null hypothesis of independence between disclosing CCR information in Form 10-K (our proxy for preparers’

materiality judgments) and participation in the CDP climate survey is rejected (Chi-square = 9.634; p < 0.01).

20

Empirical Models and Variable Definitions

We examine the difference in cost of equity (COE) between firms that disclose and those

that do not disclose CCR information in Form 10-K (H1) (i.e., our proxy for preparers’

materiality judgments) and partitioned by user-based CCR materiality judgments based on

SASB’s panel of experts (H2) using propensity score matching (PSM). The implied COE is the

internal rate of return that equates the current stock price to the present value of all expected

future cash flows to equity. This rate is an ex ante estimate of the cost of equity capital, given

market expectations about future growth. Specifically, the value of the firm at time t is expressed

as:

𝑃𝑃𝑡𝑡 = �𝐸𝐸𝑡𝑡[𝐹𝐹𝐹𝐹𝐹𝐹𝐸𝐸𝑡𝑡+𝑖𝑖]

(1 + 𝑟𝑟𝑒𝑒)𝑖𝑖

∞

𝑖𝑖=1

where Pt is the market value of common equity on the date of the earnings forecast at time t from

the daily CRSP files, FCFEt+i is free cash flow to equity at time t+i, and re is the implied COE.

We rely on prior accounting and finance research (e.g., Hail and Leuz 2009; Hann et al.

2013) to estimate the implied COE, which is a composite COE constructed using the median of

four measures, namely: Easton’s price earnings growth (PEG) model (2004), Gebhardt et al.

(2001) (GLS), Claus and Thomas (2001) (CT), and the price-earnings ratio.14 The four models

differ in the assumptions made to forecast expected future cash flows. We follow Hann et al.

(2013) in operationalizing these models.

Following prior research, we use median analyst forecasts as our proxy for FCFE.

Analyst forecasts for year 1 correspond to the fiscal year that ends after the forecast date. That is,

14 This is consistent with prior research that aggregates various measures to calculate a composite cost-of-equity

measure (see, e.g., Hail and Leuz 2009). Aggregating across measures reduces the idiosyncratic errors that may be present in any single measure.

21

if the first-year analyst forecast (year 1 in I/B/E/S) is for the previous year because the earnings

for the previous year have not yet been announced we do not use that forecast. Instead, we use

the second-year forecast, which is the forecast for the current fiscal year, as year 1 forecast. In

residual earnings models such as CT and GLS that require an estimate of book value, we use the

book value at the end of the prior year (beginning of current year), B0. Since the first forecast is

only for part of the year, we discount only for the proportionate number of days remaining

through the year end.

Prior studies retain only one earnings forecast per year (e.g., Hail and Leuz 2009; Hann et

al. 2013). Unlike these other studies, we retain all earnings forecasts made during the year for

each firm to calculate the COE. Prior research shows that analyst forecasts tend to exhibit an

upward bias earlier in the fiscal year, but are then revised downwards over the year, and finally

exhibit a downward bias at earnings announcement (Richardson et al. 2004). Such biases in

analyst forecasts can lead to systematic biases in cost of equity calculations (Easton and

Sommers 2007). Using all available forecasts reduces this bias as well as any errors that may

arise in the COE measure from any possible errors present in the retained forecast. We take the

median of all the COE numbers for each measure for each firm-year. We then take the median

across the four measures to calculate the composite COE for each fiscal year for each firm. In

robustness analyses we also aggregate the four measures using the mean of the four measures to

calculate the composite COE.

Propensity Score Matching

We use propensity score matching (Rosenbaum 2005) to compare the COE of the firms

that disclose CCR information in Form 10-K with the COE of the non-disclosing firms. Equation

(1) shows the probit model we use to calculate the propensity scores:

22

DISC_10K = β0 + β1BETA + β2BM + β3SIZE + β4FI/PI + β5ROA + β6 EXCH + β7STRNG

+ β8CNCRN +β9CDP + ε (1)

where DISC_10K is an indicator variable that is coded as 1 if the firm discloses CCR information

in Form10-K in year t, and 0 otherwise. All independent variables are measured

contemporaneously. Next, we discuss the independent variables in Equation (1).

We match the firms that disclose CCR in Form 10-K with the non-disclosing firms on

Fama-French three factors, market beta (BETA), firm size (SIZE), and book-to-market ratio

(BM). BETA is the correlation between firm-specific returns and market returns. We use monthly

returns for the firm and CRSP value-weighted index for the market returns. We calculate betas

using returns for the five years, prior to and including fiscal year t, but require a minimum of ten

months of data. For firm-years with fewer than ten months of data, we substitute the mean beta

for the firm as beta for that fiscal year.15 Following Francis et al. (2008), we predict a positive

association between BETA and DISC_10K. We control for firm growth by including the firm’s

book-to-market ratio (BM), measured as the book value of common equity divided by the market

value of common equity at the end of the fiscal year. Because larger firms are more likely to

provide more environmental disclosures (Stanny 2013; Matsumura et al. 2014), we include the

log of firms’ total assets as our proxy for SIZE.

International product market interactions affect environmental disclosures (Matsumura et

al. 2014; Khanna et al. 2004, Stanny and Ely 2008), and European Union firms with higher

proportions of international sales are more likely to provide CCR disclosures. Therefore, to

control for international product market interactions, we include annual pre-tax foreign income

as a proportion of total pre-tax income (FI/PI) and expect a positive coefficient for this variable.

15 There are fewer than 15 firm-years (or less than 0.005 percent) in our sample for which we make this substitution.

23

Consistent with prior research that documents a positive association between firm performance

and disclosures (e.g., Miller 2002), we expect a positive coefficient on our measure of firm

performance, ROA, measured as income before extraordinary items divided by total assets.

Firms choose the exchange on which to list their securities and this choice is a function of

both firm-level characteristics and the exchange’s listing requirements, including disclosure

requirements (Corwin and Harris 2001). Therefore, we also match firms on the stock exchange

(EXCH) on which they trade. In general, larger and older firms are more likely to list on the

NYSE, but since the vast majority of our sample firms (80 percent) are listed on the NYSE (the

remaining firms trade on NASDAQ) and are likely to be among the largest global firms, we do

not predict a sign on the EXCH coefficient.

Empirical evidence indicates that firms that are more environmentally proactive are more

likely to disclose environmental information (Matsumura et al. 2014). Thus, similar to

Matsumura et al. (2014), we control for the firms’ environmentally proactive performance

ratings, measured as STRNG, and for their environmentally damaging actions ratings, measured

as CNCRN, to proxy for the firms’ environmental performance. We collect environmental

performance ratings data using the KLD database. Consistent with prior research (e.g., Cho et al.

2012; Matsumura et al. 2014) we do not aggregate STRNG and CNCRN because KLD’s

proactive dimensions are distinct from the damaging dimensions. Similar to Matsumura et al.

(2014), we expect a positive coefficient for STRNG, and do not predict a sign for CNCRN. If the

KLD score is missing for an observation, we set it equal to zero.

To address the possibility that firms may be providing CCR information through channels

other than Form 10-K, we include an indicator variable, CDP.16 If, according to the CDP, the

16 The CDP uses the following response status legend: AQ: Answered the survey; AQ(L): Answered the survey late;

AQ(SA): Answered the survey but the company is a subsidiary or has merged; NP: Answered the survey but the

24

firm answered the CDP climate survey and the response is publicly available in that year (i.e.,

CDP response status: AQ, AQ(L), AQ(SA)), then we code the firm as 1, and 0 otherwise.

Finally, as discussed earlier and in Appendix B, to proxy for CCR materiality based on users’

judgments, we rely on SASB’s Materiality Map™ scoring on two sustainability issues – GHG

emissions and environmental and social impacts on assets and operations. Thus, to test H2, we

classify each sample firm as belonging to an industry where CCR is judged by users (i.e.,

SASB’s panel of experts) as either material (coded = 1) or not material (coded = 0) to investors.

V. RESULTS

Descriptive Statistics

Table 4 provides summary statistics for the variables in Equation (1). We winsorize all

the continuous variables at the one percent level on both tails of the distribution. Panel A of

Table 4 shows that the mean (median) COE is 8.14 percent (8.05 percent).17 The firms’ mean

(median) BETA is about 1.15 (1.07), which is consistent with the relatively low risk of S&P 500

firms in general. The firms’ mean (median) BM is 0.522 (0.423), indicating that on average, the

firms are healthy and have growth opportunities. For a few firms in our sample, foreign income

represents a large proportion of their total income. The mean FI/PI is 30.2 percent, although the

median is only 12.1 percent. The first three quartiles of the EXCH variable are 1. This reflects the

composition of our sample, whereby 2,417 firm-years (80.7 percent) trade on NYSE (coded = 1),

and 19.3 percent trade on NASDAQ (coded = 3) (untabulated).18 The mean STRNG and CNCRN

response is not publicly available; IN: Information provided; DP: Declined to participate; NR: No response; X: the company did not fall into the CDP sample that year.

17 Damodaran (2015) estimates an average risk premium of 2.62 percent over the 2008−2014 period for S&P 500 firms using the dividend discounting (DD) model, and 5.50 percent using the free cash-flows-to-equity (FCFE) approach. With an average risk-free rate of 2.60 percent over this period, this translates into a cost of equity of 5.22 percent and 8.10 percent for the DD and FCFE approaches, respectively.

18 Two-thirds of the firms listed on the NYSE disclose CCR information in Form 10-K; in contrast, 37 percent of the firms listed on NASDAQ disclose this information in Form 10-K.

25

is 1.021 and 0.454, respectively. Finally, as mentioned earlier, on average, 65 percent of our

sample firms participated in the CDP climate survey.

Panel B of Table 4 shows summary statistics and univariate tests for the variables in

Equation (1), partitioned by whether the firms disclose or do not disclose CCR in Form 10-K

(DISC_10K = 1 and DISC_10K = 0, respectively), which we use as our proxy for preparers’

materiality judgments. In general, the disclosing firms are significantly different from the non-

disclosing firms on all but one of the variables, FI/PI. Both the mean and median COE are

significantly higher for the disclosing firms than for the non-disclosing firms (at p < 0.05 and p <

0.10, respectively). Although both the mean BETA and BM are higher for the disclosing firms (p

< 0.05), the median BETA is not significantly different between disclosers and non-disclosers.

The significantly higher BETA and BM for the disclosing firms suggests that, in general, these

firms are riskier on these dimensions and therefore may have a higher COE. The mean and

median SIZE of the disclosing firms are significantly higher than those of the non-disclosing

firms (p = 0.00). Contrary to expectation, the mean and median ROA are higher for the non-

disclosing firms than for the disclosing firms (p = 0.00).

Panel C of Table 4 shows summary statistics for the variables in Equation (1), partitioned

by user-based (SASB) materiality judgments. The mean and median COE are higher for firms in

the material CCR group than those of firms in the not-material CCR group. The CCR material

firms also have higher BM and are larger than the not-material CCR firms, but have lower ROA.

In addition, material CCR firms also have higher STRNG and CNCRN scores.

| Insert Table 4 about here |

Table 5 presents correlation coefficients for the variables used in Equation (1). The tables

show Pearson and Spearman rank correlations below and above the diagonal, respectively. COE

26

is significantly correlated with all the variables (at p < 0.01or better) except for FI/PI, CDP, and

DISC_10K, which are significantly correlated with COE at p < 0.10. The signs for all the

correlations are as expected, except for the positive correlation between COE and SIZE

(Spearman rank = 0.243; p < 0.01). This result is consistent with Dhaliwal et al. (2011) and may

be due to our sample firms (drawn from the S&P 500 index), which are among the largest in the

world.19 DISC_10K is significantly correlated with both STRNG and CNCRN, consistent with

our descriptive statistics in Panels B and C of Table 4. Interestingly, the correlation between

DISC_10K and CDP, although highly significant, is small (0.057, p < 0.01).

| Insert Table 5 about here |

Hypothesis 1 Tests

Table 6 presents the results of Equation (1) matching the firms that disclose CCR in Form

10K (DISC_10K = 1) with those that do not (DISC_10K = 0) on various firm-level

characteristics (Panel A), and our tests of H1 to examine the cost of equity effect of disclosing

CCR in Form 10-K after propensity score matching (PSM) (Panel B). Panel A shows that, of the

total 2,996 firm-year observations, we were able to find matches for 2,966 observations: 1,770

for DISC_10K = 1 matched with 1,196 for DISC_10K = 0. We are not able to find matches for

30 disclosing firms. As shown earlier in Panel B of Table 4, before we matched the two groups

of firms, they were significantly different on all but one of the firm-level variables included in

Equation (1). After matching, only four firm-level variables remain significantly different

between the firms, BM and SIZE (at p < 0.05), and STRNG and CDP (at p < 0.01) (Table 6, Panel

A).

| Insert Table 6 about here |

19 See also Easton (2007) for a discussion of assessing the validity of COE measures using associations/correlations

with other known risk factors, such as BETA and SIZE.

27

Panel B of Table 6 shows the t-tests of differences in the COE of DISC_10K = 1 versus

DISC_10K = 0 firms using the propensity scores calculated in Panel A. For the unmatched

sample, the difference in COE is positive and significant (p < 0.05); that is, the COE of the

DISC_10K = 1 firms is higher than the COE of the DISC_10K = 0 firms. However, for the

matched firms, the COE for the DISC_10K = 1 firms is lower than that of the DISC_10K = 0

firms, but the difference is not statistically significant (p > 0.10).

As discussed above, even after propensity score matching, our matched sample is

significantly different on four dimensions. Additionally, the standard errors from the PSM may

not be unbiased. Therefore, to remove any residual misspecification that may remain after

matching, we estimate a doubly robust regression, clustering the standard errors on firm

identifier (Permno) (Imbens and Wooldridge 2007). Panel C of Table 6 shows that the difference

in COE between the DISC_10K = 1 and the DISC_10K = 0 firms is negative and significant (p <

0.01, one-tailed): the COE of disclosing firms is approximately 21.3 basis points lower than the

COE of the non-disclosing firms, thus providing support for H1.

Hypothesis 2 Tests

Our tests of H2 examine the role of CCR materiality, as judged by users (i.e., SASB’s

panel of experts), on the association between disclosing CCR information in Form 10-K and

COE. The PSM results on Table 7, Panel A show that the matched sample for the material CCR

firms shows differences along the three risk dimensions, BETA, BM, and SIZE, as well as for the

two environmental performance measures, STRNG, and CNCRN. We are able to find matches for

1,095 firm-year observations (out of the total 1,138): 809 for DISC_10K = 1firms to 286

DISC_10K = 0 firms. We are not able to find matches for 43 disclosing firms.

Panel B of Table 7 shows the tests of differences in COE differences between DISC_10K

28

= 1 and DISC_10K = 0 firms using the propensity scores calculated in Panel A, partitioned by

user-based (SASB) materiality. For the matched sample of the material CCR firms, the COE of

DISC_10K = 1 firms is lower than the COE of DISC_10K = 0 firms, but the difference is not

significant (p > 0.10). However, the doubly robust regression results in Panel C show that, for

the material CCR group, the difference in COE between the disclosing and non-disclosing firms

is negative and significant (p < 0.05, one-tailed): the COE of disclosing firms is 49.1 basis points

lower than the COE of non-disclosing firms.

Next, we discuss the results for the not-material CCR firms. Panel A of Table 7 shows that

firms in the matched sample differ along three dimensions after matching: BETA, ROA, and

STRNG. We are able to find matches for 1,793 firm-year observations (out of the total 1,809):

912 DISC_10K = 1 firms to 881 DISC_10K = 0 firms. We are not able to find matches for 16

disclosing firms. Panel B of Table 7 shows, for the not-material CCR group of matched firms,

that the COE of DISC_10K = 1 firms is higher than the COE of DISC_10K = 0 firms, but the

difference is not statistically significant (p > 0.10). Similarly, the doubly-robust regression

results (Panel C, Table 7) show no statistical difference in the COE of disclosing versus non-

disclosing firms for the not-material CCR group. Taken together, our results support H2.

| Insert Table 7 about here |

In summary, our H1 results are consistent with the conclusion that the COE of firms that

disclose CCR information in Form 10-K is lower than the COE of firms that do not disclose

CCR. In addition, our H2 results indicate that disclosing CCR in Form 10-K is associated with

lower COE only for firms in industries where CCR is judged by users as material. For firms

where CCR is judged not material by users, we find no association between disclosing CCR in

Form 10-K and COE.

29

Sensitivity and Robustness Tests

Although our hand-collected data on firms’ CCR disclosures in Form 10-K includes

2015, the time period for our main analyses ends in 2014 because we do not have 2015 data

available on firms’ participation in the CDP climate survey. However, firms’ participation in the

survey is sticky; that is, once a firm starts participating in the CDP survey, it is likely to continue

to do so in subsequent years. In our sample period, less than 10 percent of the firms change their

reporting status from one year to the next. In addition, the correlation between CDP reporting

status in 2013 and 2014 is 0.85 (p < 0.01). Consequently, we test H1 and H2 by extrapolating

firms’ CDP reporting status for 2015 using CDP 2014 data. Our sample size increases to 3,395

firm-year observations (i.e., an increase of 399 observations relative to our main results), of

which we are able to match 3,376.

Panels A through C of Table 8 present our results. We first present the results for the full

sample (i.e., before partitioning on user-based materiality). As shown in Panel A of Table 8, we

are able to match 2,045 DISC_10K = 1 firms to 1,331 DISC_10K = 0 firms (we are unable to

find a match for 19 observations). After matching, five firm-level variables remain significantly

different between the disclosing and non-disclosing firms: BETA, BM, ROA, STRNG, and CDP.

Panel B of Table 8 shows that, similar to our main results, the difference in COE

between DISC_10K = 1 and DISC_10K = 0 for the matched firms is still not significant (Table 8,

Panel B). However, as shown in Panel C, the results from the doubly robust regressions are

stronger, relative to our main results. The difference in COE between the disclosing and non-

disclosing firms is negative and highly significant (p < 0.01, one-tailed): the COE for the

disclosing firms is 24.4 basis points lower than the COE for the non-disclosing firms, thus

consistent with H1.

30

| Insert Table 8 about here |

Table 9 shows PSM results with our firms partitioned on user-based materiality

judgments to test H2. For firms in the CCR material group, shown in Panel A, we are able to

match 927 DISC_10K = 1 firms with 316 DISC_10K = 0 firms. The doubly robust regression

results for the CCR material group (Panel C of Table 9) show that the COE for the disclosing

firms is 54.6 basis points lower than the COE for the non-disclosing firms, and this difference is

significant (p < 0.01, one-tailed). The results for firms in the not-material CCR group remain

unchanged (see Panel A through C of Table 9). The difference in COE between disclosing and

non-disclosing firms is not significant in either the PSM or the doubly robust regressions. Taken

together, our results support H2. In conclusion, our results based on extrapolating firms’ CDP

participation status for 2015 using 2014 CDP data are stronger than our main results.

| Insert Table 9 about here |

We also test H1 and H2 after including industry fixed effects in our models (untabulated).

We use Fama-French five industry classification for industries. Although we are able to find

matches for more observations for the full sample we are unable to match on six of the nine firm-

level variables. Our results remain unchanged. The COE coefficient for the full sample in the

doubly robust regression shows that the COE for disclosing firms is 18.3 basis points lower than

the COE for non-disclosing firms (p < 0.05, one-tailed). Our results after partitioning on user-

based materiality judgments also remain unchanged. For firms in the CCR material group, the

COE of disclosing firms is 73.7 basis points lower than the COE of non-disclosing firms, and the

difference is significant (p < 0.01, one-tailed). In contrast, for firms in the CCR not-material

group, we find no significant difference in the COE of disclosing versus non-disclosing firms.

These results are consistent with our main results.

31

In our next robustness test (untabulated), instead of matching firms on firm performance

using ROA, we match on whether a firm suffered a loss during the year. We include an indicator

variable equal to 1 if the firm reported negative income before extraordinary items during the

year, and 0 otherwise. Our results are inferentially similar to our main results. Finally, we

calculate implied COE as the average of the four COE measures, instead of the median of the

four measures (untabulated). Our results are inferentially similar to our main results.

VI. CONCLUSION

We examine whether firms’ disclosures of climate-change risk (CCR) information in

Form 10-K meaningfully reflect the material risks they face, as measured by their cost of equity

capital (COE). We focus on CCR disclosures because there is a lack of consensus as to whether

CCR is material to any investment or voting decisions. As our evidence indicates, there is

considerable variation in CCR disclosures in Form 10-K. Specifically, over the seven-year period

ending in 2014, 40 percent of our sample (i.e., 1,196 firm-year observations of the total 2,996) do

not disclose CCR information in their Form 10-K.

In addition, preparers’ judgments regarding the materiality of CCR information may be

different from those of users. Thus, a key feature of our study is that we examine the role of

materiality of CCR disclosures – as judged by preparers versus users – on the association

between disclosing CCR in Form 10-K and COE. Users’ CCR materiality judgments are proxied

by the industry-based criteria for classification of CCR materiality provided by the Sustainability

Accounting Standard Board’s (SASB) panel of experts, as displayed in the SASB Materiality

Map.™ Preparers’ CCR materiality judgments are proxied by managers’ CCR disclosure choices

in Form 10-K.

Using hand-collected data on S&P 500 firms’ CCR disclosure choices for the period

32

2008 to 2014, we find that the COE of firms that disclose material CCR information in Form 10-

K is 21.3 basis points lower than the COE of non-disclosing firms. However, we find that this

negative association is restricted to firms in industries where users judge CCR as material. In

particular, for this group of firms we find that the COE of disclosing firms is 49.1 basis points

lower than the COE of non-disclosing firms. In contrast, we find no significant association

between disclosing CCR in Form 10-K and COE for firms in industries where users judge CCR

as not material. Our results obtain even after we control for the firms’ voluntary disclosures of

CCR information in the CDP climate survey.

Our results should be of interest to a broad range of decision-makers inside and outside of

the firm. Consistent with the increasing global interest in global climate-change risk and, more

broadly, nonfinancial sustainability disclosures, we provide evidence on the association between

disclosing these risks in Form 10-K and the firms’ cost of equity. In addition, our findings

provide important insights regarding the SEC’s concern that registrants are still not following

Regulation S-K and the SEC’s 2010 interpretive guidance on climate change disclosures.

Although Regulation S-K requires firms to disclose all material risks in Form 10-K, our findings

show that there is considerable variation in firms’ CCR disclosure practices and little consensus

as to whether CCR is a material risk.

As mentioned earlier, almost 25 percent of the sample firms participated in the CDP

climate survey – the main channel for such voluntary disclosures – but chose to not disclose

CCR information in Form 10-K. This finding is puzzling, given that these firms have voluntarily

committed scarce resources to respond to the CDP survey and yet, they chose to not disclose this

information through the regular SEC mechanisms. Our findings lend support to the concerns that

managers’ failure to disclose CCR in Form 10-K imposes additional risk on firms in industries

33

where users judge CCR as material. Thus, our results should be useful to managers considering

disclosing CCR in Form 10-K when they are already providing this information voluntarily

through non-SEC channels.

Collectively, our results indicate that demands by investors and other stakeholders for

enhanced CCR disclosures in 10-K filings are not without merit. Our findings are consistent with

the conclusion that firms’ Form 10-K climate-change risk disclosures meaningfully reflect the

material uncertainties they face. At the same time, since the negative association between CCR

disclosures and COE is obtained only for firms where users judge CCR to be material, the

demand for expanded CCR disclosures needs to be tempered by an understanding of the firms’

business models and users’ needs.

34

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40

FIGURE 1

Percentages of Sample S&P 500 Firms that Disclosed Climate Change Risk in Form 10-K and Firms that Participated in the CDP Climate Survey (n = 2,996)

FIGURE 2

Percentages of S&P 500 Firms that Disclosed Climate Change Risk (CCR) in Form 10-K,

Partitioned by Users’ Materiality Judgments (SASB) (n = 1,780)

40.00%

45.00%

50.00%

55.00%

60.00%

65.00%

70.00%

2008 2009 2010 2011 2012 2013 2014

CDP 10K

30%

40%

50%

60%

70%

80%

90%

2008 2009 2010 2011 2012 2013 2014

Not material Material

APPENDIX A

Excerpts of Climate-Change Risk Disclosures in Form 10-K20 Panel A. Metropolitan Life Insurance, Form 10-K for FYE 12/31/15

Page Item Excerpt

19 item 1a. Risk Factors Catastrophes can be caused by various events, including hurricanes, windstorms, earthquakes, hail, tornadoes, explosions, severe winter weather (including snow, freezing water, ice storms and blizzards), fires and man-made events such as terrorist attacks. Historically, most of our property & casualty catastrophe-related claims have related to homeowners’ coverages. However, catastrophes may also affect other property & casualty coverages. Due to their nature, we cannot predict the incidence, timing and severity of catastrophes. In addition, changing climate conditions, primarily rising global temperatures, may increase the frequency and severity of natural catastrophes such as hurricanes, tornadoes and floods. We have hurricane exposure in coastal sections of the northeastern U.S. (including lower New York, New Jersey, Connecticut, Rhode Island and Massachusetts), the south Atlantic states (including Virginia, North Carolina, South Carolina, Georgia and Florida) and the Gulf Coast (including Alabama, Mississippi, Louisiana and Texas).

34 item 7. Management's Discussion and Analysis of Financial Condition and Results of Operations

In addition, in our property & casualty businesses, catastrophe-related losses increased due to severe storm activity in 2014. Non-catastrophe related claim costs also increased as a result of severe winter weather in 2014.

35 item 7. Management's Discussion and Analysis of Financial Condition and Results of Operations

In our property & casualty business, catastrophe-related losses increased by $21 million as compared to 2013, mainly due to severe storm activity in 2014. In addition, severe winter weather in 2014 increased non-catastrophe claim costs by $18 million, which was the result of higher frequencies in both our auto and homeowners businesses, as well as higher severities in our homeowners business, partially offset by lower severities in our auto business.

42 item 7. Management's Discussion and Analysis of Financial Condition and Results of Operations

See Note 1 of the Notes to the Consolidated Financial Statements for further information about tax credit and renewable energy partnerships, funds withheld and operating joint ventures. Our private placement unit originated $9.7 billion and $8.3 billion of private investments, comprised primarily of certain privately placed fixed maturity securities and tax credit and renewable energy partnerships, during the years ended December 31, 2015 and 2014, respectively.

59 item 8. Financial Statements and Supplementary Data

Tax credit and renewable energy partnerships which derive a significant source of investment return in the form of income tax credits or other tax incentives.

20 Source: Ceres Sustainability Disclosure Search database (http://ceres.org/resources/tools/sec-sustainability-

disclosure).

42

APPENDIX A - Excerpts of Climate-Change Risk Disclosures in Form 10-K (continued)

Panel A. Metropolitan Life Insurance, Form 10-K for FYE 12/31/15 (continued)

Page Item Excerpt 42 item 7. Management's

Discussion and Analysis of Financial Condition and Results of Operations

See Note 1 of the Notes to the Consolidated Financial Statements for further information about tax credit and renewable energy partnerships, funds withheld and operating joint ventures. Our private placement unit originated $9.7 billion and $8.3 billion of private investments, comprised primarily of certain privately placed fixed maturity securities and tax credit and renewable energy partnerships, during the years ended December 31, 2015 and 2014, respectively.

59 item 8. Financial Statements and Supplementary Data

Tax credit and renewable energy partnerships which derive a significant source of investment return in the form of income tax credits or other tax incentives.

70 item 8. Financial Statements and Supplementary Data

Other Invested Assets Other invested assets is comprised primarily of freestanding derivatives with positive estimated fair values (see Note 9), tax credit and renewable energy partnerships, and leveraged and direct financing leases.

Panel B. Coca Cola Bottling Co., Form 10-K for FYE 01/31/16

Page Item Excerpt 22 item 1. Business If we were convicted of a violation of the federal Clean Air Act or Clean

Water Act, the facility or facilities involved in the violation would be ineligible to be used in performing any U.S. Government contract we are awarded until the Environmental Protection Agency thereafter certifies that the condition giving rise to the violation had been corrected. In addition, we could be affected by future laws or regulations imposed in response to concerns over climate change. Changes in climate change concerns, or in the regulation of such concerns, including greenhouse gas emissions, could subject us to additional costs and restrictions, including compliance costs and increased energy and raw materials costs.

55 item 1a. Risk Factors

Changes in environmental and climate change laws or regulations, including laws relating to greenhouse gas emissions, could lead to new or additional investment in product designs and could increase environmental compliance expenditures. Changes in climate change concerns, or in the regulation of such concerns, including greenhouse gas emissions, could subject us to additional costs and restrictions, including increased energy and raw materials costs.

43

APPENDIX B

Climate-Change Risk and Users’ Materiality Judgments: SASB’s Materiality Map™

The Sustainability Accounting Standards Board (SASB) is an independent 501(c)3 non-

profit whose mission is to develop and disseminate sustainability accounting standards for reporting

material sustainability issues in SEC filings and in compliance with SEC requirements.21 SASB’s

Materiality Map™ is based on tests designed to prioritize issues on behalf of “reasonable investors.”

To determine materiality, SASB uses an evidence-based approach based on input from a panel of

over 200 industry experts and SASB staff. SASB’s board of directors is chaired by Michael

Bloomberg, and is composed of two former SEC chairwomen, a former FASB chairman, retired

partners of CPA firms, heads of investment firms and corporate governance organizations, and

lawyers, among others.

The Materiality Map™ identifies the materiality of sustainability issues on an industry-by-

industry basis. The map is comprised of 10 sectors, and each sector is comprised of several

industries.22 For each sustainability issue, SASB uses a three-component evidence of materiality

test: evidence of investor interest, evidence of financial impact, and forward-looking impact.23 At

the sector level, the map classifies a sustainability issue as material if the issue is judged to be

material for more than 50 percent of industries in the sector. At the industry level within each

sector, the map indicates whether an issue is likely to be material for companies in the industry by

21 SASB is not affiliated with other accounting standards boards. 22 The sectors (number of industries within each sector) are: health care (5), financials (7), technology and

communications (6), non-renewable resources (8), transportation (8), services (10), resource transformation (5), consumption (15), renewable resources and alternative energy (6), and infrastructure (6). The Materiality Map™ is available at http://materiality.sasb.org/?__hssc=105637852.1.1475597119066&__hstc=105637852.ecb493eb82c7b42b7b418ab8e3a13325.1415210680741.1475100716540.1475597119066.6&__hsfp=3853461716&hsCtaTracking=28ae6e2d-2004-4a52-887f-819b72e9f70a%7C160e7227-a2ed-4f28-af33-dff50a769cf4.

23 A detailed description of the process appears in the SASB Conceptual Framework, available at http://www.sasb.org/wp-content/uploads/2013/10/SASB-Conceptual-Framework-Final-Formatted-10-22-13.pdf.

44

scoring the issues based on the three components mentioned above. Specifically, evidence of

investor interest uses two scores. The first, the heat map (HM) score, is out of 100 points, and

indicates the relative importance of the issue among SASB’s list of 43 sustainability issues. The

score is based on the frequency of relevant keywords in documents (i.e., SEC filings, shareholder

resolutions, legal news, key newswires, and CSR reports) that are available in the Bloomberg

terminal for the industry’s publicly listed companies. The second score, industry working group

score (IWGS) represents the percentage of IWG participants that found the issue to be material.

The third score, forward-looking impact, is used in some cases to adjust issues to raise their

importance if management or mismanagement of the issue could potentially create positive or

negative externalities that other stakeholders, industries, or future generations will deal with.

We focus on two issues from SASB’s Materiality Map™ to capture materiality from the

users’ perspective, namely, (1) GHG emissions, and (2) environmental, social impacts on assets

and operations. We select these two issues to classify each sample firm as belonging to an industry

where climate change risk is judged by users (i.e., SASB’s panel of experts) as either material or

not material to investors. We rely on the scoring described above to classify each sample firm as

material (coded = 1) if either or both of the two issues is classified as material in SASB’s

Materiality Map™, and as not-material (coded = 0) otherwise.

Finally, the industry categories in SASB’s Materiality Map™ correspond to SASB’s

Sustainable Industry Classification System (SICS), which classifies industries “in accordance with

their resource intensity and sustainability impact as well as their sustainability innovation

potential” (SASB 2013, 10). Using the 4-digit SIC codes (SICDESC) and, if needed, additional

information obtained from web resources, we identified the industry to which each firm belongs.

Next, for each firm we matched the SIC industry description to a SASB industry classification.

APPENDIX C

Variable Definitions

Variable Definition DISC_10K = an indicator variable equal to 1 if the firm discloses CCR information in

Form 10-K, 0 otherwise; COE = the composite implied cost of equity capital constructed using the median of

four measures, namely: Easton’s PEG model (2004), Gebhardt, Lee, and Swaminathan (2001) (GLS), Claus and Thomas (2001) (CT), and the price-earnings ratio;

BETA = the correlation of firm returns to market returns calculated using monthly returns and the value-weighted market index for the past five years;

BM = book value of equity / market value of equity; SIZE = the log of the firm’s total assets at the end of the fiscal year; FI/PI = pre-tax foreign income/pre-tax total income; ROA = income before extraordinary items / total assets; EXCH = stock exchange membership, namely NYSE/AMEX (coded = 1), NASDAQ

(coded = 3); STRNG = the number of proactive environmental ratings (strengths) for the firm

identified in KLD; CNCRN = the number of damaging environmental ratings (concerns) for the firm

identified in KLD; CDP

= an indicator variable equal to 1 if the firm participates in the CDP climate survey and the response is publicly available, and 0 otherwise.

46

TABLE 1

S&P 500 Firms Sample Selection (2008 – 2014)

Firm-years Unique

Firms Data available in the Ceres and CDP databasesa 3,226 496 Less:

Number of observations with missing data necessary for cost of equity calculationsb (227) (31) Number of observations with missing Compustat data (3) (0)

Final sample for H1 tests 2,996 465 Less:

Number of observations with missing SIC code data necessary for industry-based (SASB) materiality classificationc (49) (7)

Final sample for H2 tests 2,947 458

a We hand-collect data on the frequency of the firms’ CCR disclosures in Form 10-K from Ceres’ SEC sustainability

disclosure database. We collect data on our sample firms’ participation in the CDP climate survey to control for voluntary disclosures of this information through channels other than the SEC filings.

b We are unable to calculate COE for these firms because we exclude firms with negative book value of equity or negative earnings forecasts for years one and two, or we are unable to obtain analyst forecasts for these firms.

c We use two sustainability issues that are directly related to CCR from SASB’s Materiality Map™, namely (1) GHG emissions, and (2) environmental and social impacts on assets and operations, to classify each sample firm as belonging to an industry where CCR is judged by users (i.e., SASB’s panel of experts) as either material or not material to investors.

47

TABLE 2

Frequencies of Climate Change Risk (CCR) Disclosures in Form 10-K: CDP Climate Survey

Participation and Users’ Materiality Judgments, 2008 – 2014a

Panel A. Frequencies of CCR Disclosures in Form 10-K and Participation in the CDP Climate Survey

Preparers’ Materiality Judgments c

Disclosed CCR Information in Form 10-K?

Participated in CDP Climate Survey?b No (Not Material) Yes (Material) Total

No

456

(15.2)

587

(19.6)

1,043 (34.8)

Yes

740

(24.7)

1,213 (40.5)

1,953 (65.2)

Total 1,196 (40.0)

1,800 (60.0)

2,996 (100)

Panel B. Frequencies of CCR Disclosures in Form 10-K and Users’ Materiality Judgments

Preparers’ Materiality Judgments

Disclosed CCR Information in Form 10-K?

Users’ Materiality Judgments (SASB)d No (Not Material) Yes (Material) Total

Not Material

881

(29.9)

928

(31.5)

1,809 (61.4)

Material

286 (9.7)

852

(28.9)

1,138 (38.6)

Total 1,167 (39.6)

1,780 (60.4)

2,947 e (100)

a Cell frequencies are number of firm-year observations (% of total). b Participation in the CDP climate survey is voluntary. c We use preparers’ (i.e., managers’) CCR disclosure choices in Form 10-K as a proxy for their climate-change risk

materiality judgments. d To proxy for CCR materiality based on users’ judgments, we rely on SASB’s Materiality Map™ scoring on two

sustainability issues that are directly related to CCR: (1) GHG emissions; and (2) environmental and social impacts on assets and operations (see Appendix B).

e We lose 49 firm-year observations (7 unique firms) due to missing SIC code information.

48

TABLE 3

Frequencies of Climate Change Risk (CCR) Disclosures in Form 10-K: CDP Climate Survey

Participation and Users’ Materiality Judgments, 2008 – 2014 (n = 2,947)a Panel A: Sub-Sample of Firms that Participated in the CDP Climate Surveyb

Preparers’ Materiality Judgmentsd Disclosed CCR information in Form 10-K?

Users’ Materiality Judgments (SASB)c No (Not Material) Yes (Material) Total

Not Material

535

(27.6)

645

(33.2)

1,180 (60.8)

Material

201

(10.4)

559

(28.8)

760

(39.2) Total 736

(37.9) 1,204 (62.1)

1,940 (100)

Panel B: Sub-Sample of Firms that Did Not Participate in the CDP Climate Survey

Preparers’ Materiality Judgments Disclosed CCR information in Form 10-K?

Users’ Materiality Judgments (SASB) No (Not Material) Yes (Material) Total

Not Material

346

(34.4)

283

(28.1)

629

(62.5)

Material

85

(8.4)

293

(29.1)

378

(37.5) Total 431

(42.8) 576

(57.2) 1,007 (100)

a Cell frequencies are number of firm-year observations (% of total). b Participation in the CDP climate survey is voluntary. c To proxy for CCR materiality based on users’ judgments, we rely on SASB’s Materiality Map™ scoring on two

sustainability issues that are directly related to CCR: (1) GHG emissions; and (2) environmental and social impacts on assets and operations (see Appendix B).

d We use managers’ (i.e., preparers) CCR disclosure choices in Form 10-K as a proxy for their climate-change risk materiality judgments.

TABLE 4

Descriptive Statistics

Panel A. Full Sample (n = 2,996)

Full Sample

Variable Mean Q1 Median Q3 Std Dev COE 0.0814 0.0679 0.0805 0.0934 0.0237 BETA 1.1476 0.7440 1.0686 1.4682 0.5642 BM 0.5218 0.2520 0.4226 0.6950 0.3996 SIZE 9.7393 8.7698 9.6113 10.5375 1.3352 FI/PI 0.3016 0.0000 0.1210 0.5469 0.5570 ROA 0.0548 0.0183 0.0505 0.0882 0.0644 EXCH 1.3865 1 1 1 0.7898 STRNG 1.0210 0 0 2 1.2894 CNCRN 0.4539 0 0 0 0.9358 CDP 0.6519 0 1 1 0.4765

Panel B. Firms Partitioned by CCR Disclosers (DISC_10K = 1) and Non-Disclosers

(DISC_10K = 0) in Form 10-Ka

DISC_10K = 0 (Not Material)

n = 1,196 DISC_10K = 1 (Material)

n = 1,800

t-stat Wilcoxon

Variable Mean Median Std Dev Mean Median Std Dev p-value p-value

COE 0.0801 0.0802 0.0236 0.0822 0.0810 0.0254 0.0148 0.0601 BETA 1.1225 1.0277 0.5717 1.1642 1.1017 0.5460 0.0421 0.1333 BM 0.5036 0.3790 0.3982 0.5340 0.4481 0.4067 0.0462 0.0000 SIZE 9.5144 9.2790 1.3617 9.8887 9.7714 1.2666 0.0000 0.0000 FI/PI 0.3030 0.1331 0.5794 0.3006 0.1103 0.4986 0.9091 0.7194 ROA 0.0610 0.0590 0.0677 0.0506 0.0458 0.0596 0.0000 0.0000 EXCH 1.6321 1 0.8459 1.2233 1 0.6397 0.0000 0.0000 STRNG 0.8161 0 1.2252 1.1572 1 1.2331 0.0000 0.0000 CNCRN 0.1355 0 0.5705 0.6656 0 1.1775 0.0000 0.0000 CDP 0.6187 1 0.4859 0.6739 1 0.4689 0.0010 0.0019

a We use firms’ choices to disclose (DISC_10K = 1) or to not disclose (DISC_10K = 0) CCR information in Form

10-K as our proxy for preparers’ materiality judgments.

50

TABLE 4 (continued)

Descriptive Statistics

Panel C. Breakdown of Sample by User-Based (SASB) Materiality Judgmentsb

CCR Judged NOT MATERIAL

n = 1,809 CCR Judged MATERIAL

n = 1,138

t-stat Wilcoxon

Variable Mean Median Std Dev Mean Median Std Dev p-value p-value

COE 0.0816 0.0806 0.0227 0.0820 0.0814 0.0249 0.3184 0.0601 BETA 1.1677 1.0516 0.5773 1.1355 1.1044 0.5407 0.0627 0.1333 BM 0.4698 0.3593 0.3984 0.6077 0.5444 0.3904 0.0000 0.0000 SIZE 9.6214 9.4504 1.3585 9.9695 9.8962 1.2678 0.0000 0.0000 FI/PI 0.3557 0.2339 0.5825 0.2221 0.0000 0.5122 0.0000 0.7194 ROA 0.0632 0.0617 0.0673 0.0399 0.0326 0.0561 0.0000 0.0000 EXCH 1.4655 1 0.8454 1.2285 1 0.6365 0.0000 0.0000 STRNG 0.9989 0 1.2916 1.0870 1 1.2976 0.0362 0.0000 CNCRN 0.2294 0 0.6023 0.8155 0 1.2218 0.0000 0.0000 CDP 0.6523 1 0.4764 0.6678 1 0.4712 0.1927 0.0019

b To proxy for CCR materiality based on users’ judgments, we rely on SASB’s Materiality Map™ scoring on two

sustainability issues that are directly related to CCR: (1) GHG emissions; and (2) environmental and social impacts on assets and operations (see Appendix B).

For variable definitions see Appendix C.

TABLE 5

Correlation Coefficients

COE DISC_10K BETA BM SIZE FI/PI ROA EXCHG STRNG CNCRN CDPCOE 0.034 0.249 0.489 0.243 -0.039 -0.280 -0.148 0.105 0.121 0.044DISC_10K 0.044 0.027 0.081 0.155 -0.007 -0.089 -0.254 0.143 0.291 0.057BETA 0.268 0.036 0.229 -0.073 0.087 -0.266 -0.023 -0.072 -0.064 -0.083BM 0.467 0.037 0.282 0.361 -0.231 -0.567 -0.154 -0.030 0.103 -0.024SIZE 0.256 0.137 -0.011 0.376 -0.099 -0.309 -0.174 0.178 0.206 0.253FI/PI -0.009 -0.002 0.065 -0.114 -0.056 0.350 0.104 0.187 0.037 0.166ROA -0.273 -0.079 -0.274 -0.461 -0.230 0.116 0.162 0.093 -0.049 0.052EXCHG -0.122 -0.254 -0.034 -0.113 -0.176 0.080 0.147 -0.097 -0.224 -0.063STRNG 0.074 0.130 -0.073 -0.076 0.181 0.141 0.071 -0.103 0.346 0.323CNCRN 0.147 0.278 -0.050 0.056 0.202 0.050 -0.065 -0.205 0.309 0.087CDP 0.040 0.057 -0.077 -0.049 0.253 0.105 0.026 -0.063 0.322 0.098 Pearson (Spearman rank) correlation coefficients are below (above) the diagonal. Coefficients in boldface are significant at p < 0.01. Coefficients in italics are significant at p < 0.10. Coefficients in grey are not significant (p > 0.10). n = 2,996 for COE model. For variable definitions see Appendix C.

TABLE 6

Cost-of-Equity Effects of Decision to Disclose Climate-Change Risk in Form 10-K: Propensity-Score Matching

Panel A: Probit Regression Model for Propensity Score Matchinga

D.V. is DISC_10K Estimates Covariate Bal (Nearest Neighbor)

Variables Coeff Z-stat DISC_10K = 1 DISC_10K = 0 t-stat

BETA 0.1319 *** 2.79 1.1613 1.1611 0.01 BM -0.1669 ** -2.22 0.5369 0.5046 2.42 ** SIZE 0.0697 *** 3.30 9.8582 9.9483 -1.97 ** FI/PI 0.0026 0.06 0.2938 0.3088 -0.83 ROA -0.6338 -1.48 0.0510 0.0535 -1.10 EXCH -0.3023 *** -9.71 1.2271 1.2418 -0.68 STRNG 0.0289 1.31 1.1056 1.2542 -3.29 *** CNCRN 0.4602 11.65 0.6090 0.5915 0.52 CDP 0.0009 0.02 0.6695 0.7130 -2.80 *** n (Total) 2,996 n (DISC_10K = 1) 1,770 n (DISC_10K = 0) 1,196 n (Matched) 2,966 Pseudo-R2 0.1065

*, **, *** Denote significance at p < 0.10, < 0.05, and < 0.01, respectively. a We match firms that disclose climate-change risk in Form10-K (DISC_10K = 1) with firms that do not (DISC_10K

= 0), using the probit model below. We use the nearest neighbor matching algorithm.

DISC_10K = β0 + β1BETA + β2BM + β3SIZE + β4FI/PI + β5ROA + β6 EXCH + β7STRNG + β8CNCRN + β9CDP + ε (1)

We report covariate balance means and Z-stats to test how equal (balanced) the disclosing and non-disclosing firms are for each covariate after matching. For variable definitions see Appendix C.

53

TABLE 6 (continued)

Cost-of-Equity Effect of Decision to Disclose Climate-Change Risk in Form 10-K: Propensity-Score Matching

Panel B: Difference in COE of Propensity Score-Matched Firmsb DISC_10K = 1 DISC_10K = 0 Diff. t-stat Unmatched

Matched 0.0824 0.0822

0.0804 0.0840

0.0021 0.0015

2.25 -1.18

**

Matched n 1,770 1,196 *, **, *** Denote significance at p < 0.10, < 0.05, and < 0.01, respectively. b After matching our disclosing (DISC_10K = 1) and non-disclosing (DISC_10K = 0) firms using the propensity

scores calculated in Panel A, we examine the difference in the COE between these two groups of firms. COE is the composite cost of equity constructed using the median of four measures, namely: Easton’s PEG model (2004), Gebhardt, Lee, and Swaminathan (2001) (GLS), Claus and Thomas (2001) (CT), and the price-earnings ratio.

Panel C: Doubly Robust Regression Estimates of COE

Coefficient Z-stat

DISC_10K -0.00213*** -2.46 Matched n 2,966

*** denotes significance at p < 0.01, one-tailed. We report the Z-statistic based on robust standard errors.

TABLE 7

Cost-of-Equity Effects of Decision to Disclose Climate-Change Risk in Form 10-K: Propensity-Score Matching/ Breakdown of Sample by User-Based (SASB) Materiality Judgments

Panel A: Probit Regression Model for Propensity Score Matchinga

CCR Judged NOT MATERIAL by Users (SASB) CCR Judged MATERIAL by Users (SASB)

D.V. is DISC_10K Estimates Covariate Bal (Nearest

Neighbor)

Estimates Covariate Bal (Nearest

Neighbor)

Variables Coeff Z-

stat DISC_10K

= 1 DISC_10K

= 0 t-stat

Coeff Z-

stat DISC_10K =

1 DISC_10K =

0 t-stat

BETA 0.1329 ** 2.22 1.1835 1.1182 2.34 ** 0.1147 1.29 1.1356 1.1820 -1.77 *

BM -0.2841 *** -2.92 0.4795 0.4798 -0.02 -0.0471 -0.34 0.6029 0.5065 5.54 ***

SIZE 0.1751 *** 6.64 9.8489 9.8532 -0.07 -0.1434 *** -3.62 9.9000 9.5936 4.87 ***

FI/PI 0.0035 0.06 0.3563 0.3779 -0.82 0.1964 * 1.89 0.2173 0.2227 -0.23

ROA -0.6083 -1.17 0.0602 0.0659 -1.89 * -0.2484 -0.27 0.0387 0.0389 -0.06

EXCH -0.2142 *** -5.82 1.3224 1.3092 0.39 -0.5255 *** -7.69 1.1162 1.0890 1.24

STRNG 0.0624 ** 2.28 1.1272 1.2445 -1.87 * -0.0151 * -0.38 1.0952 0.9703 1.89 *

CNCRN 0.2564 *** 4.06 0.2763 0.2818 -0.19 0.4945 *** 8.26 0.8826 0.7553 2.17 **

CDP 0.0288 0.42 0.6897 0.7061 -0.76 -0.1504 -1.45 0.6514 0.6700 -0.79 n (Total) 1,809 1,138 n (DISC_10K = 1) 912 809 n (DISC_10K = 0) 881 286 n (Matched) 1,793 1,095 Pseudo-R2 0.0683 0.1837

55

TABLE 7 (continued)

Cost-of-Equity Effects of Decision to Disclose Climate-Change Risk in Form 10-K: Propensity-Score Matching Breakdown of Sample by User-Based (SASB) Materialitya

Panel B: Difference in COE of Propensity Score-Matched Firmsb CCR Judged NOT MATERIAL by Users (SASB) CCR Judged MATERIAL by Users (SASB) DISC_10K = 1 DISC_10K = 0 Diff. t-stat DISC_10K = 1 DISC_10K = 0 Diff. t-stat

Unmatched 0.08350 0.08016 0.00334 2.98 *** 0.0821 0.0828 0.0821 -0.39

Matched 0.08333 0.08242 0.00092 0.54 0.0814 0.0845 0.0814 -1.12

Matched n 912 881 809 286 *, **, *** Denote significance at p < 0.10, < 0.05, and < 0.01, respectively. a We match firms that disclose climate-change risk in Form 10-K (DISC_10K = 1) with firms that do not (DISC_10K = 0), using the probit model below. We use

the nearest neighbor matching algorithm.

DISC_10K = β0 + β1BETA + β2BM + β3SIZE + β4FI/PI + β5ROA + β6 EXCH + β7STRNG + β8CNCRN ++ β9CDP + ε (1) We report covariate balance means and Z-stats to test how equal (balanced) the disclosing and non-disclosing firms are for each covariate after matching. For variable definitions see Appendix C. b After matching our disclosing (DISC_10K = 1) and non-disclosing (DISC_10K = 0) firms using the propensity scores calculated in Panel A, we examine the

difference in the COE between these two groups of firms. COE is the composite cost of equity constructed using the median of four measures, namely: Easton’s PEG model (2004), Gebhardt, Lee, and Swaminathan (2001) (GLS), Claus and Thomas (2001) (CT), and the price-earnings ratio.

56

TABLE 7 (continued)

Cost-of-Equity Effects of Decision to Disclose Climate-Change Risk in Form 10-K: Propensity-Score Matching Breakdown of Sample by User-Based (SASB) Materialitya

Panel C: Doubly Robust Regression Estimates of COE

CCR Judged NOT MATERIAL by Users (SASB) CCR Judged MATERIAL by Users (SASB) Coefficient Z-stat Coefficient Z-stat DISC_10K 0.00673 0.680 -0.00491** -2.080 Matched n 1,793 1,095

** denotes significance at p < 0.05, one-tailed. We report the Z-statistic based on robust standard errors.

TABLE 8

Cost-of-Equity Effects of Decision to Disclose Climate-Change Risk in Form 10-K/ Propensity-Score Matching Using Extrapolated CDP Data for 2015

Panel A: Probit Regression Model for Propensity Score Matchinga

D.V. is DISC_10K Estimates Covariate Bal (Nearest Neighbor)

Variables Coeff Z-stat DISC_10K = 1 DISC_10K = 0 t-stat

BETA 0.1066 ** 2.38 1.1538 1.1843 -1.72 * BM -0.1818 *** -2.63 0.5299 0.4595 5.90 *** SIZE 0.0784 *** 4.01 9.8955 9.8351 1.46 FI/PI 0.0409 0.96 0.3015 0.3128 -0.68 ROA -1.0038 ** -2.52 0.0493 0.0534 -2.07 ** EXCH -0.3071 *** -10.59 1.2318 1.2484 -0.82 STRNG 0.0024 0.11 0.9760 1.0484 -1.77 * CNCRN 0.4437 *** 11.32 0.5487 0.5462 0.08 CDP 0.0250 0.49 0.6734 0.7056 -2.23 ** n (Total) 3,395 n (DISC_10K = 1) 2,045 n (DISC_10K = 0) 1,331 n (Matched) 3,376 Pseudo-R2 0.0967 *, **, *** Denote significance at p < 0.10, < 0.05, and < 0.01, respectively. a We match firms that disclose climate-change risk in Form 10-K (DISC_10K = 1) with firms that do not

(DISC_10K = 0), using the probit model below. We use the nearest neighbor matching algorithm. DISC_10K = β0 + β1BETA + β2BM + β3SIZE + β4FI_PI + β5ROA + β6 EXCH + β7STRNG + β8CNCRN + β9CDP + ε (1)

We report covariate balance means and Z-stats to test how equal (balanced) the disclosing and non-disclosing firms are for each covariate after matching. For variable definitions see Appendix C.

58

TABLE 8 (continued) Panel B: Difference in COE of Propensity Score-Matched Firmsb DISC_10K = 1 DISC_10K = 0 Diff. t-stat Unmatched

Matched 0.0809 0.0808

0.0793 0.0822

0.0016 -0.0014

1.87 -1.05

*

Matched n 2,045 1,331 *, **, *** Denote significance at p < 0.10, < 0.05, and < 0.01, respectively. b Using the propensity scores from the probit regressions in Panel A, we match our disclosing firms (DISC_10K = 1)

with the non-disclosing firms (DISC_10K = 0). This table provides the difference in COE for our matched firms, i.e., the average effect on the disclosing firms, using the nearest neighbor matching algorithm. COE is the composite cost of equity constructed using the median of four measures, namely: Easton’s PEG model (2004), Gebhardt, Lee, and Swaminathan (2001) (GLS), Claus and Thomas (2001) (CT), and the price-earnings ratio.

Panel C: Doubly Robust Regression Estimates of COE

Coefficient Z-stat

DISC_10K -0.00244*** -3.07 Matched n 3,376

*** denotes significance at p < 0.01, one-tailed. We report the Z-statistic based on robust standard errors.

TABLE 9

Cost-of-Equity Effects of Decision to Disclose Climate-Change Risk in Form 10-K: Propensity-Score Matching/ Breakdown of Sample by Users’ Based (SASB) Materiality Judgments Using CDP Extrapolated Data for 2015

Panel A: Probit Regression Model for Propensity Score Matchinga

CCR Judged NOT MATERIAL by Users (SASB) CCR Judged MATERIAL by Users (SASB) D.V. is DISC_10K Estimates

Covariate Bal (Nearest Neighbor)

Estimates

Covariate Bal (Nearest Neighbor)

Variables Coeff Z-

stat DISC_10K

= 1 DISC_10K

= 0 t-stat

Coeff Z-

stat DISC_10K =

1 DISC_10K =

0 t-stat

BETA 0.1372 ** 2.39 1.1835 1.1182 2.34 ** 0.0641 0.78 1.1296 1.1431 -0.57

BM -0.3575 *** -3.88 0.4795 0.4798 -0.02 -0.0223 -0.18 0.6032 0.5011 5.69 ***

SIZE 0.1875 *** 7.66 9.8489 9.8532 -0.07 -0.1437 *** -3.95 9.9206 9.6938 3.97 ***

FI/PI 0.0325 0.65 0.3563 0.3779 -0.82 0.2695 *** 2.70 0.2235 0.2579 -1.54

ROA -0.9532 * -1.94 0.0602 0.0659 -1.89 * -0.6765 -0.84 0.0357 0.0370 -0.38

EXCH -0.2195 *** -6.37 1.3224 1.3092 0.39 -0.5238 *** -8.32 1.1143 1.1014 0.62

STRNG 0.0411 1.57 1.1272 1.2445 -1.87 * -0.0503 -1.32 0.9525 1.0032 -0.84

CNCRN 0.2436 *** 3.88 0.2763 0.2818 -0.19 0.4739 *** 7.96 0.7745 0.7077 1.23

CDP 0.0605 0.94 0.6897 0.7061 -0.76 -0.1212 -1.25 0.6526 0.7541 -4.81 *** n (DISC_10K = 1) 1,052 927 n (DISC_10-K = 0) 982 316 n (Matched) 2,034 1,243 Pseudo-R2 0.0673 0.1672

*, **, *** Denote significance at p < 0.10, < 0.05, and < 0.01, respectively. a We match firms that disclose their climate change risk in their 10-K reports, DISC_10K = 1, with firms that do not, DISC_10K = 0, using the probit model below. We use

the nearest neighbor matching algorithm. DISC_10K = β0 + β1BETA + β2BM + β3SIZE + β4FI_PI + β5ROA + β6 EXCH + β7STRNG + β8CNCRN ++ β9CDP + ε (1)

60

TABLE 9 (continued) We report covariate balance means and t-stats to test how equal (balanced) the disclosing and non-disclosing firms are for each covariate after using nearest neighbor matching. For variable definitions see Appendix C. Panel B: Difference in COE of Propensity Score-Matched Firmsb

CCR Judged NOT MATERIAL by Users (SASB) CCR Judged MATERIAL by Users (SASB) DISC_10K = 1 DISC_10K = 0 Diff. t-stat DISC_10K = 1 DISC_10K = 0 Diff. t-stat

Unmatched 0.08186 0.07914 0.00272 2.61 *** 0.08061 0.08165 -0.00105 -0.64

Matched 0.08169 0.08202 0.00033 -0.21 0.07990 0.08247 0.00256 -0.96

Matched n 982 1,052 927 316 b Using the propensity scores from the probit regressions in Panel A, we match our disclosing firms (DISC_10K = 1) with the non-disclosing firms (DISC_10K = 0). This

table provides the difference in COE for our matched firms, i.e., the average effect on the disclosing firms, using the nearest neighbor matching algorithm. COE is the composite cost of equity constructed using the median of four measures, namely: Easton’s PEG model (2004), Gebhardt, Lee, and Swaminathan (2001) (GLS), Claus and Thomas (2001) (CT), and the price-earnings ratio.

Panel C: Doubly Robust Regression Estimates of COE

CCR Judged NOT MATERIAL by Users CCR Judged MATERIAL by Users Coefficient Z-stat Coefficient Z-stat DISC_10K 0.00038 0.42 -0.00546*** -2.59 Matched n 2,034 1,243

** denotes significance at p < 0.05, one-tailed. We report the Z-statistic based on robust standard errors.