Abstract
As part of the Securities and Exchange Commission’s revision of Regulation S–K, which lays out reporting requirements for publicly-listed companies, many investors proposed the mandatory disclosure of sustainability information in the form of environmental, social and governance data. However, progress is contingent on collecting evidence regarding which sustainability disclosures are financially material. To inform this issue, we examine materiality standards developed by the Sustainability Accounting Standards Board (SASB). Firms voluntarily disclosing more SASB-identified sustainability information exhibit greater price informativeness, while the disclosure of non-SASB information does not relate to informativeness. The results are robust to a changes analysis and a difference-in-differences analysis that exploits the staggered release of SASB standards across different industries over time. We also document stronger results for firms with higher exposure to sustainability issues, poorer sustainability ratings, greater institutional and socially responsible investment fund ownership, and coverage from analysts with lower portfolio complexity.
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Notes
TSC Industries v. Northway, Inc., 426 U.S. 438, 449 (1976). See also Basic, Inc. v. Levinson, 485 U.S. 224 (1988).
Though the SEC mandates governance disclosures such as executive compensation and pay ratios, the G (governance) portion of ESG disclosures typically does not cover these topics, but rather includes issues such as business ethics and transparency of payments or political lobbying. We provide more detail on this and on the validity of our measures in later sections.
In the U.S., 81% of firms on the S&P 500 Index reported on sustainability in 2015, up from 20% in 2011. Globally, close to 9000 firms disclosed ESG information in 2016, up from fewer than 20 companies in 1992.
Concept Release on Business and Financial Disclosures Required by Regulation S‐K Release Number 33‐10064; 34‐775599. Accessed from: https://www.sec.gov/rules/concept/2016/33-10064.pdf.
Letter from SEC Investor Advisory Committee, to SEC Division of Corporation Finance (June 15, 2016). Accessed from: https://www.sec.gov/spotlight/investor-advisory-committee-2012/iac-approved-letter-reg-sk-comment-letter-062016.pdf.
We use ‘ESG’ and ‘sustainability’ interchangeably, and we use ‘material sustainability’ in reference to SASB’s classification of investor-relevant sustainability issues. For an overview of the debate on the concepts and definitions of Corporate Social Responsibility, please see Van Marrewijk (2003).
Though SASB did not release its standards until 2013, our ESG disclosure data dates back to 2007, and we use the standards to construct SASB disclosure scores from 2007 onwards in order to assess whether investors integrate this information into their valuation of firms that disclose even prior to the release of the standards.
Following the recommendations of Li et al. (2014), we conduct additional analyses to examine whether the association with stock price synchronicity is indicative of news or noise in stock prices. Li et al. (2014) recommend controlling for beta in regressions that use stock price synchronicity as the dependent variable to assess whether the documented relation changes sign or disappears. In our case, we find that there is still a negative and significant association when controlling for both market and industry beta suggesting the association between material sustainability disclosures and both synchronicity and idiosyncratic volatility yields consistent results.
According to the CFA Institute: “…there remains a gap regarding how to consider ESG issues in practice. Perhaps expanding on the “how to” should now rank higher on the ESG research agenda”. See p. 38 of Environmental, Social, and Governance Issues in Investing: A Guide for Investment Professionals: https://cfainstitute.org/advocacy/policy-positions/environmental-social-and-governance-issues-in-investing-a-guide-for-investment-professionals.
Average national reporting rates for sustainability information increased from 47% in 2011 to 72% in 2017. See ‘The KPMG Survey of Corporate Responsibility Reporting 2017’: https://assets.kpmg/content/dam/kpmg/xx/pdf/2017/10/kpmg-survey-of-corporate-responsibility-reporting-2017.pdf. Moreover, in our sample, the fraction of firms issuing standalone sustainability reports tripled from 8.3% to 25.3% over the period 2007 to 2015.
Signatories to the UN Principles for Responsible Investment (PRI), launched in 2006, commit to incorporate ESG issues into their investment analysis and ownership policies and practices. As of 2016, the principles had about 1400 signatories with total assets under management of about $60 trillion. As a further sign of the institutionalization of ESG data, Bloomberg terminals integrated ESG data in 2010, dramatically increasing the diffusion of ESG information. As of 2016, more than 100 rating agencies provided ESG data, including large data providers such as Thomson Reuters and MSCI.
See the ‘Global Sustainable Investment Alliance 2016 Report’: http://www.gsi-alliance.org/members-resources/trends-report-2016/.
See for example, Goldman Sachs, The Metrics that Matter. A Mainstream Approach to ESG: http://www.goldmansachs.com/our-thinking/podcasts/episodes/05-08-2017-derek-bingham.html.
Corporate social responsibility measures are also likely to be discounted by evaluators when making appraisal and bonus decisions (e.g., Bento et al. 2016).
Khan et al. (2016) measure sustainability outcomes such as environmental performance (e.g., level and intensity of greenhouse gas emissions), social performance (e.g., employee satisfaction and human rights scandals) and governance performance (e.g., corruption charges), whereas we measure the level of sustainability disclosure (e.g., transparency around emissions, employment practices, human rights policies, and anti-corruption metrics).
The correlation between our disclosure score and the performance rating used in Khan et al. (2016) is approximately 0.1. To alleviate concerns that increases in SASB-identified sustainability performance ratings are driving our results, we document that firms with higher SASB-identified sustainability disclosures have more informative stock prices, even after controlling for SASB-identified sustainability performance ratings.
63% of respondents cited management of investment risk as the reason why they incorporate ESG into investment and analysis according to the CFA Institute’s (2015) survey of its members. See here.
Tim Mohin and Jean Rogers. How to approach corporate sustainability reporting in 2017. Accessed: www.greenbiz.com/article/how-approach-corporate-sustainability-reporting-2017.
See www.sasb.org.
For more information on the concepts, principles and objectives that guide SASB in setting standards for investor-relevant sustainability accounting, see SASB’s Conceptual Framework (https://www.sasb.org/standard-setting-process/conceptual-framework/). Although SASB does not state whether its standards are “principles-based” or “rules-based”, we assess the standards as being rules-based given that SASB standards provide material sustainability disclosure topics on an industry-by-industry basis.
See Framework, Behind the Terminal: Understanding the Bloomberg ESG Numbers, https://frameworkesg.com/wp-content/uploads/2019/07/Bloomberg-ESG-Infographic.pdf.
A concern when measuring disclosure levels is that not all disclosures are applicable to all firms (e.g., only if a firm chooses to have operating leases does it need to disclose future cash payments relating to those leases). A nice feature of our setting, however, is that SASB has identified sustainability topics that are relevant across all firms in a given industry; as a result, our measure reflects disclosure levels across these relevant topics and is comparable across firms.
Please see Appendix 4 for step-by-step instructions on constructing MaterialDisc, as well as an example.
We replicate all our analyses excluding industries where Bloomberg has data for fewer than 60% of the SASB issues and we find similar results.
It could, however, be the case that only the SASB metrics available in Bloomberg are financially material, while the ones that are not available (and that firms do not disclose) are immaterial. This would mean that our inferences are not generalizable to all SASB disclosures.
Bloomberg metrics in some cases are not the exact measure that SASB specifies in its standards but a proxy for that measure. We recalculate our measuring excluding all Bloomberg data items that are proxies. We find that this new disclosure metric is very highly correlated with our overall measure (0.88) and that all our results are similar to the ones we report in the paper.
Illiq reflects the average daily price impact of a trade, measured as the absolute value of returns relative to the daily value traded. A higher value of Illiq reflects a greater price change per dollar of daily trading volume. Amihud (2002) shows that this measure is strongly related to other illiquidity measures, such as microstructure estimates of illiquidity and the Amihud measure. Our second alternative dependent variable is LiqVol and it is measured as the annual standard deviation of the daily Illiq measure. A high LiqVol reflects an additional level of uncertainty and lack of information on the company. Lang and Maffett (2011) show that LiqVol is related to lower transparency and Pereira and Zhang (2010) discuss that a higher value provides more opportunity for investors to time trades, reflecting less informativeness. The third alternative dependent variable, Spread, is the yearly average of the daily bid-ask spread. A greater Spread reflects less informativeness and greater uncertainty around the underlying value. ZeroDays captures the number of zero return days in a year to the total number of trading days and provides indication on the information environment. A stock with strong information availability should experience few days without stock price movements, as investors react to information changes in the market.
Amihud (2002) states in his study that Illiq is a practical measure for informativeness as it is widely available, but acknowledges its coarse and often less accurate nature. Illiq incorporates not only firm-specific information, but also incorporates market frictions that may impact the amount traded and reflects different types of risk. Similarly, Spread not only captures informativeness for investors, but is also driven by factors such as the financial intermediaries that moderate buying and selling between transaction parties. ZeroDays is also a cruder measure for informativeness, as it does not consider the magnitude of stock price movements, but only the extent to which stock prices move at all. This binary definition neglects companies that trade actively on the market, but with little firm-specific information.
In addition, SASB has kindly provided us with a map** between its proprietary Sustainable Industry Classification System (SICS) and U.S. exchange-traded securities, allowing us to determine with precision which sustainability issues are material, per SASB, for each firm.
We note that stock returns of financial institutions in our period of study are heavily influenced by the financial crisis.
See www.sasb.org/sics.
We included time-varying industry effects and all our results were unchanged.
Dhaliwal et al. (2011) use a sample of U.S. firms from 1993 to 2007 and document that 9.14% of their sample provides a separate sustainability report. Given our sample period of 2007 to 2015 and the significant increase in ESG disclosure during this period our percentage is higher.
There are other large correlations among the variables (e.g., 0.67 between MaterialDisc and NonMaterialDisc); however, VIFs are all below 2.46, suggesting multicollinearity is not a major concern.
We note that the correlation between MaterialDisc and Integrated is difficult to predict ex-ante. Although it may seem that higher disclosure of material sustainability information will be accompanied by more integrated discussions of financial and sustainability issues, there reasons why this may not be the case. For one, high disclosure of material sustainability information does not necessarily mean that such information is provided within management discussions or disclosed within financial reports. Moreover, firms may have integrated discussions and reporting of financial and sustainability information, but the sustainability information may not be material according to SASB.
We also estimated this model separately each year and averaged across years to calculate coefficients and t-stats as in Fama and MacBeth (1973). Our results were qualitatively similar.
To further address the concern that companies with strong sustainability performance ratings may be more willing to disclose more, thereby generating a spurious relation between disclosure and stock price informativeness, we consider whether increased disclosure reduces stock price informativeness for companies with weak sustainability ratings. In untabulated tests, we also find a positive relation between material sustainability disclosure and informativeness for the subset of companies in the lowest tercile of sustainability performance ratings.
As another proxy for earnings quality, we compute the absolute value of firm accruals scaled by the absolute value of cash flow from operations (ABS_ACCR). All our aforementioned results are robust to controlling for ABS_ACCR.
In untabulated results, we also control for industry concentration (log of a revenue-based Herfindahl index of industry-level concentration). Consistent with Fernandes and Ferreira (2009), the coefficient on industry concentration is negative but insignificant, and our main results remain unchanged.
Only the first increase or decrease ‘event’ is kept for a particular firm, so as to ensure our post event indicator variable consists of unique binary values. In untabulated results, we find that our inferences are unchanged if we keep multiple increase and decrease events for a given firm so long as these events are separated by at least 2 years.
This test is well suited to the phenomenon we study given the persistence over time in material sustainability disclosure score. The first-order autocorrelation coefficient is 0.968 for the whole sample. Similarly, the first-order autocorrelation coefficient for synchronicity is also very high at 0.714. Given such time-series persistence in our dependent and independent variables, including firm-fixed effects would not lead to precise estimation.
These results suggest that by increasing SASB-identified sustainability disclosure, managers may increase the firm-specific information content in stock prices; moreover, decreases in SASB-identified disclosure are accompanied by decreases price informativeness. Given that we do not find analogous results when we examine positive and negative changes in non-SASB-identified sustainability disclosure, this indicates that changes in price informativeness, as they relate to sustainability reporting, are limited to changes in SASB-identified information.
In untabulated analysis we do not find evidence consistent with this result being driven by asymmetric timeliness of good versus bad news being reflected in stock returns.
In untabulated analysis we examine whether proprietary cost concerns positively moderate the relation between SASB disclosure and price informativeness, indicating that these firms provide less informative or more boilerplate disclosure. The results of this analysis suggest that firms with higher proprietary costs, proxied by research and development expenditures scaled by total sales and revenues from the sale of low-carbon products (using data from FTSE Russell), do not, on average, have less informative ESG disclosures.
We believe that the release of SASB standards across different industries over time is a valid instrument for the following reasons. First, the timing of the release of SASB standards across industries was pre-determined in 2011, shortly after the creation of SASB and independent of companies’ existing disclosure policies. This lends plausibility to the exogeneity criterion, given that omitted variables that could also drive changes in SASB-identified disclosures (apart from the release of SASB standards) would have to coincide with the ‘as-if’ random timing of the standards releases. Second, as will be described in this section, we document a strong, positive association between our instrument (i.e., the release of SASB standards) and SASB-identified sustainability disclosure, suggesting that the instrument is relevant (Angrist and Pischke 2009).
We note that firms often discuss their use of SASB standards in sustainability reports. For example, JetBlue stated in its 2019 Environmental Social Governance Report 2019 that “JetBlue reports on ESG using recommendations from the Sustainability Accounting Standards Board (SASB)…” (see here).
While SASB defines sustainability issues that are material at the industry level, different companies within the same industry have varying degrees of disclosure of the same issues. Therefore, in this test, in this test, we examine whether firms within industries that have a SASB standards release increase (on average) the disclosure of firm-specific SASB-identified information.
In the first stage we include additional variables that have been shown to be related to ESG disclosure (e.g., Dhaliwal et al. 2011): Leverage (mean = 0.19, SD = 0.22) measured as the ratio of total debt to total assets; Financing (mean = 0.01, SD = 0.13) measured as the issuance of common and preferred stock minus the purchase of common and preferred stock, plus the long-term debt issuance minus the long-term debt reduction; Turnover (mean = 2.36, SD = 2.02) measured as the ratio of the number of shares traded to the total shares outstanding; and ROA (mean = 0.26, SD = 0.16) measured as the ratio of income before extraordinary items over total assets.
An alternative explanation for the observed increase in SASB-identified disclosures is that Bloomberg increases its coverage and/or data-collection efforts relating to SASB-identified sustainability issues following the release of SASB standards. First, we note that this would likely bias against detecting an increase in price informativeness in the second stage. Second, we examine the number of SASB-identified sustainability metrics covered by Bloomberg surrounding the release of SASB standards; we observe an increase of four metrics (from 437 to 441) over this period, suggesting that coverage remained stable over this period.
For example, firms could improve their material ESG disclosure scores by disclosing a greater amount of SASB-prescribed metrics following release of the standards, while actually improving performance along these metrics would likely be more difficult and would require greater investments (e.g., Eccles et al. 2014; Ioannou et al. 2016).
Since we do not find a strong relationship between our instrument and the alternative dependent variables of ESG performance, material ESG performance and non-material sustainability disclosure, this suggests that the instrument lacks relevance (Angrist and Pischke 2009), which is why we do not show the second stage of the IV in Panel D of Table 7.
We thank an anonymous reviewer for this suggestion.
SASB’s Navigator Research Platform (accessible on a trial basis and by subscription from: www.navigator.sasb.org) contains company-by-company information on disclosure quality for SASB’s material topics; this product is called SASB’s Disclosure Intelligence Data.
We thank an anonymous reviewer for this suggestion.
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Acknowledgements
George Serafeim acknowledges financial support from the Division of Faculty and Research Development of Harvard Business School and discloses that he has previously served on the Standards Council of SASB and as an advisor to investment organizations that use sustainability data. George Serafeim declares that he has no conflict of interest. We thank Amir Amel-Zadeh, Rob Bauer, Aiyesha Dey, Bob Eccles, Mo Khan, Jean Rogers, Ethan Rouen, Eugene Soltes and seminar participants at London School of Economics, CUNY Baruch College, Harvard Business School, Oxford University, the Alliance for Research on Corporate Sustainability 2018 conference, and the Canadian Accounting Association 2018 conference for many useful comments. Clarissa Hauptmann acknowledges financial support from the Ford Foundation. George Serafeim acknowledges financial support from the Division of Faculty and Research Development of Harvard Business School. George Serafeim has served on the Standards Council of SASB.
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This paper was previously titled “Stock Price Synchronicity and Material Sustainability Information.
Appendices
Appendix 1: Examples of ESG Integration
Source: CFA Institute, Environmental, Social, and Governance Issues in Investing [URL: cfainstitute.org/advocacy/policy-positions/environmental-social-and-governance-issues-in-investing-a-guide-for-investment-professionals].
Example 1: Valuation of ESG Risks of Mining Companies
When valuing stocks in the mining sector, analysts at Citi Research analyze the management of the relevant ESG issues by the mining companies. In particular, analysts carry out environmental and social impact assessments and closure planning to gage the quality of the process that mining companies use to assess and manage the environmental and social impacts of a mine throughout its life and beyond. As part of these assessments, analysts use environmental indicators (e.g., the ISO 14001, a family of standards that provide practical tools to manage environmental responsibilities) as well as health and safety indicators (e.g., lost production time due to labor injury frequency), along with an analysis of government relations and local economic and community engagement. These analysts are of the view that effective management of ESG risks can significantly reduce mine development lead times, which they see as critical to future earnings capacity. Exercising their judgment, the analysts appropriately adjust the discount rate for mining companies that have lower ESG risks. For example, in one case, the discount rate of a mining company with better ESG management was adjusted from 10.7 to 7.5%, which increased the estimated intrinsic value of its stock by 29%.
Example 2: Valuation of ESG Risks and Opportunities of Utilities
In the United States, the Environmental Protection Agency’s emission and carbon regulations are expected to have a material impact on valuing the power sector. Analysts at ClearBridge Investments believe that these regulations will increase the operational costs of the power plants with higher emissions levels (e.g., older, less efficient coal plants) and require additional environmental spending. According to these analysts, incremental expenditures on environmental retrofits should make smaller, older coal plans uncompetitive and lead to their retirement. Implementation of mercury regulation alone could lead to retirement of an estimated 17% of the country’s coal-fired capacity. Thus, the companies owning newer plants with lower emissions (consisting of renewables, efficient coal, combined cycle gas plants, and nuclear plants) will be relative winners. The increasing penetration of distributed solar power generation and utility-scale energy storage will have a disruptive effect on utilities over the longer term. For example, NextEra Energy (NEE), the largest wind and solar energy producer in the United States, will see a higher output growth and a more efficient cost structure than some of its peers as it drives earnings growth with these low-carbon energy sources. ClearBridge analysts believe that NEE has an attractive above-average earnings growth rate of 6–8% and an attractive relative valuation.
Appendix 2: Variable Definitions
Variable | Description |
|---|---|
AltMaterialDisc i,t | Alternative disclosure score for firm i in year t that takes into account the percentage of disclosed items and the quality of the disclosures. Calculated as: (%NoDisclosureItems*0) + (%BoilerplateItems*1) + (%NarrativeItems*2) + (%Metrics*3) |
where %NoDisclosure, %BoilerplateItems, %NarrativeItems and %Metrics are obtained from SASB’s Disclosure Intelligence Data. | |
AnalystRev i,t | Natural log of number of analyst revisions for firm i in year t from I/B/E/S |
ConfCalls i,t | The natural logarithm of one plus all conference calls during the year as measured by Capital IQ |
Control i | An indicator variable equal to one for firms in the industries that did not have SASB standards released in the sample period |
ESGExposure i,t | A variable equal to 1 if firm i has above-average exposure to ESG-driven risks and opportunities in year t, as defined by MSCI IVA, 0 otherwise |
ESGPerf i,t | The ESG performance score of firm i in year t from MSCI IVA that captures the weighted average score on the environment, social, and governance dimensions |
ESGPerfMaterial i,t | The difference between the sum of KLD strengths and KLD concerns for items that are material according to SASB standards, defined in Khan et al. (2016) |
Financing i,t | Issuance of common and preferred stock minus the purchase of common and preferred stock, plus long-term debt issuance minus long-term debt reduction, in year t, computed using CRSP |
GRICompl i,t | Bloomberg variable equal to 1 if firm i complies with GRI guidelines in year t, 0 otherwise |
Illiq i,t | Natural logarithm of the yearly average daily price impact of a trade, measured as the absolute value of stock price returns times 100 relative to the stock price times trading volume scaled by 1000: |Return × 100|/(Price × Volume/1000) |
IndustryBeta i,t | Coefficient estimate of industry returns from the regression of daily firm returns on value-weighted market and industry returns, for firm i in year t |
InsiderTrades i,t | Natural logarithm of the absolute value of net trading by insiders scaled by annual trading volume for firm i in year t, computed using CRSP |
InstOwn i,t | The percentage of firm i’s ownership by institutional shareholders in year t, from Thomson Reuters Ownership |
Integrated i,t | A variable equal to 1 if firm i has high integration of sustainability across core business in year t, from Thomson Reuters Asset4, 0 otherwise |
LiqVol i,t | Natural logarithm of the annual standard deviation of daily illiquidity, Illiqi,t |
MarketBeta i,t | Coefficient estimate of market returns from the regression of daily firm returns on value-weighted market and industry returns, for firm i in year t. |
MarketCap i,t | Natural logarithm of market capitalization for firm i in year t, from Compustat |
MaterialDisc i,t | Ratio of the number of disclosed SASB ESG metrics to the total number of SASB ESG metrics available in Bloomberg, for firm i in year t |
MTB i,t | Market to book value for firm i in year t, computed using Compustat |
MgmtGuide i,t | The natural logarithm of one plus all guidance events during the year as measured by Capital IQ |
NegChangeYrs i,t | An indicator variable taking the value of one in the year of, and directly after, (i.e., year t and t + 1) a decrease in material sustainability disclosure (MaterialDisc) from year t − 1 to year t for firm i, 0 otherwise |
NonMaterialDisc i,t | Ratio of the number of disclosed non-SASB ESG metrics to the total number of non-SASB ESG metrics in Bloomberg, for firm i in year t |
NumComp i,t | The average number of companies in analyst coverage across all analysts covering firm i in year t, computed using I/B/E/S |
PoorEQ i,t | Absolute value of firm-specific residual from cross-sectional annual industry regression of working capital accruals on lagged, contemporaneous, and leading cash flows, scaled by lagged total assets (Dechow and Dichev 2002) |
PoorESGPerf i,t | An indicator variable taking the value of one for firms with below-average ESGPerf in year t |
PosChangeYrs i,t | An indicator variable taking the value of one in the year of, and directly after, (i.e., year t and t + 1) an increase in material sustainability disclosure (MaterialDisc) from year t − 1 to year t for firm i, 0 otherwise |
Post i,t | An indicator variable equal to one in the years following the release of SASB standards. Varies depending on the year of the release of the standards for a given industry |
PseudoPost i,t | An indicator variable equal to one in the three years before SASB standards were released for the treated firm |
ReturnVariability i,t | The natural logarithm of the standard deviation of daily returns over year t |
ROA i,t | Ratio of income before extraordinary items over total assets in year t, computed using Compustat |
Spread i,t | The natural logarithm of the annual average of the daily absolute difference between bid and ask spread, scaled by the stock price |
SRIOwn i,t | The percentage of firm i’s ownership by socially responsible investment funds in year t, calculated using Thomson Reuters Ownership data and Bloomberg |
StdDevROA i,t | Standard deviation of quarterly ROA, measured over the three years preceding and including t for firm i, computed using Compustat |
SustReport i,t | A variable equal to 1 if firm i issues a sustainability report in year t, otherwise, from Thomson Reuters Asset4, CorporateRegister and Bloomberg |
Synchronicity i,t | Firm i’s stock price synchronicity in year t, as \( \log \left( {\frac{{R^{2} }}{{1 - R^{2} }}} \right) \) from annual regression of daily firm returns on value-weighted market and industry returns |
Treat i | An indicator variable equal to one for firms in the industries that had SASB standards released in the sample period, i.e., firms in the healthcare, non-renewable resources, technology, transportation and services sectors |
Turnover i,t | The natural logarithm of the average of daily trading volume divided by shares outstanding over year t, computed using CRSP |
ZeroDays i,t | Natural log of ratio of zero return days to total trading days within a year |
Appendix 3: Correlation Matrix
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
(1) Synchronicity | 1.00 | |||||||||||
(2) MaterialDisc | 0.24 | 1.00 | ||||||||||
(3) NonMaterialDisc | 0.24 | 0.67 | 1.00 | |||||||||
(4) ESGExposure | 0.26 | 0.29 | 0.35 | 1.00 | ||||||||
(5) Integrated | − 0.02 | − 0.33 | − 0.33 | − 0.18 | 1.00 | |||||||
(6) SRIOwn | 0.08 | 0.00 | 0.01 | 0.03 | − 0.01 | 1.00 | ||||||
(7) NumComp | − 0.01 | 0.04 | 0.07 | 0.07 | − 0.19 | 0.03 | 1.00 | |||||
(8) MarketBeta | − 0.01 | − 0.17 | − 0.16 | − 0.15 | 0.14 | 0.00 | − 0.07 | 1.00 | ||||
(9) IndustryBeta | 0.13 | 0.07 | 0.12 | 0.18 | − 0.20 | − 0.02 | 0.17 | − 0.32 | 1.00 | |||
(10) MarketCap | 0.50 | 0.60 | 0.64 | 0.46 | − 0.43 | 0.03 | 0.10 | − 0.30 | 0.23 | 1.00 | ||
(11) GRICompl | 0.16 | 0.46 | 0.69 | 0.24 | − 0.22 | 0.01 | 0.03 | − 0.10 | 0.09 | 0.43 | 1.00 | |
(12) SustReport | 0.22 | 0.55 | 0.72 | 0.31 | − 0.29 | 0.01 | 0.03 | − 0.12 | 0.08 | 0.55 | 0.65 | 1.00 |
(13) InstOwn | 0.28 | 0.11 | 0.07 | 0.07 | − 0.15 | 0.07 | 0.10 | − 0.09 | 0.11 | 0.28 | 0.00 | 0.03 |
(14) AnalystRev | 0.20 | 0.21 | 0.23 | 0.27 | − 0.12 | 0.05 | 0.04 | − 0.11 | 0.17 | 0.32 | 0.14 | 0.18 |
(15) MTB | 0.02 | 0.04 | 0.05 | 0.03 | − 0.05 | − 0.01 | − 0.02 | − 0.04 | 0.04 | 0.11 | 0.04 | 0.03 |
(16) StdDevROA | − 0.25 | − 0.19 | − 0.17 | − 0.04 | 0.13 | − 0.03 | 0.06 | 0.11 | 0.07 | − 0.31 | − 0.09 | − 0.14 |
(17) InsiderTrades | − 0.03 | − 0.03 | − 0.07 | − 0.06 | 0.03 | 0.00 | − 0.06 | 0.01 | − 0.10 | − 0.02 | − 0.06 | − 0.07 |
(18) PoorEQ | − 0.09 | − 0.10 | − 0.09 | − 0.01 | 0.08 | − 0.02 | 0.03 | 0.04 | 0.02 | − 0.16 | − 0.06 | − 0.08 |
(19) MgmtGuide | 0.04 | − 0.03 | 0.00 | 0.02 | 0.03 | 0.03 | − 0.01 | − 0.01 | − 0.02 | − 0.04 | 0.00 | − 0.01 |
(20) ConfCalls | 0.05 | − 0.06 | 0.03 | 0.04 | 0.05 | 0.03 | 0.00 | 0.01 | 0.02 | 0.01 | 0.07 | 0.01 |
(21) Illiq | − 0.41 | − 0.14 | − 0.15 | − 0.16 | 0.12 | − 0.04 | − 0.01 | 0.05 | − 0.10 | − 0.36 | − 0.08 | − 0.10 |
(22) LiqVol | − 0.44 | − 0.12 | − 0.14 | − 0.15 | 0.11 | − 0.04 | 0.01 | 0.03 | − 0.10 | − 0.35 | − 0.07 | − 0.09 |
(23) Spread | − 0.41 | − 0.22 | − 0.24 | − 0.18 | 0.22 | − 0.04 | − 0.06 | 0.11 | − 0.11 | − 0.47 | − 0.13 | − 0.17 |
(24) ZeroDays | − 0.44 | − 0.18 | − 0.18 | − 0.17 | 0.15 | − 0.05 | 0.03 | 0.09 | − 0.08 | − 0.46 | − 0.11 | − 0.15 |
(25) ESGPerf | 0.13 | 0.33 | 0.38 | 0.21 | − 0.23 | 0.04 | 0.05 | − 0.14 | − 0.01 | 0.43 | 0.25 | 0.31 |
(26) ESGPerfMaterial | − 0.11 | 0.09 | 0.15 | 0.03 | − 0.25 | 0.00 | − 0.05 | − 0.06 | 0.01 | 0.07 | 0.17 | 0.17 |
(13) | (14) | (15) | (16) | (17) | (18) | (19) | (20) | (21) | (22) | (23) | (24) | (25) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
(13) InstOwn | 1.00 | ||||||||||||
(14) AnalystRev | 0.13 | 1.00 | |||||||||||
(15) MTB | − 0.02 | − 0.03 | 1.00 | ||||||||||
(16) StdDevROA | − 0.23 | − 0.03 | 0.10 | 1.00 | |||||||||
(17) InsiderTrades | − 0.08 | − 0.02 | 0.04 | − 0.05 | 1.00 | ||||||||
(18) PoorEQ | − 0.11 | 0.00 | 0.10 | 0.41 | 0.00 | 1.00 | |||||||
(19) MgmtGuide | 0.07 | 0.02 | 0.01 | − 0.07 | 0.00 | − 0.04 | 1.00 | ||||||
(20) ConfCalls | 0.04 | 0.07 | 0.02 | − 0.01 | − 0.01 | − 0.01 | 0.39 | 1.00 | |||||
(21) Illiq | − 0.38 | − 0.13 | − 0.02 | 0.19 | 0.04 | 0.11 | − 0.04 | − 0.04 | 1.00 | ||||
(22) LiqVol | − 0.39 | − 0.14 | − 0.01 | 0.20 | 0.05 | 0.10 | − 0.04 | − 0.05 | 0.72 | 1.00 | |||
(23) Spread | − 0.44 | − 0.16 | − 0.01 | 0.28 | 0.07 | 0.16 | − 0.08 | − 0.05 | 0.63 | 0.66 | 1.00 | ||
(24) ZeroDays | − 0.40 | − 0.18 | 0.00 | 0.35 | − 0.05 | 0.17 | − 0.08 | − 0.06 | 0.38 | 0.41 | 0.46 | 1.00 | |
(25) ESGPerf | 0.14 | 0.19 | 0.05 | − 0.14 | 0.03 | − 0.09 | 0.07 | 0.06 | − 0.15 | − 0.15 | 0.24 | 0.14 | 1.00 |
(26)ESGPerfMaterial | 0.01 | − 0.05 | 0.08 | − 0.03 | 0.01 | − 0.02 | 0.00 | 0.05 | − 0.01 | − 0.01 | − 0.04 | 0.00 | 0.19 |
Appendix 4: Steps to construct material sustainability disclosure variable (MaterialDisc)
- Step 1:
-
Enter “XLTP XESG” in Bloomberg. An excel worksheet will open on the screen
- Step 2:
-
Select “SASB” from the drop-down menu under Ticker
- Step 3:
-
Select the Sector and Industry from the drop-down menu under SASB SICS Sector and SASB SICS Industry to obtain the corresponding SASB topics
- Step 4:
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Under “View”, check “Headings” and unhide the columns in the worksheet to the right of the SASB topics
- Step 5:
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The columns to the right of the SASB topics will display the Bloomberg ESG fields that correspond to SASB metrics. These fields represent the Bloomberg ESG data items that were mapped to SASB topics
- Step 6:
-
Copy these Bloomberg fields and paste them as column headers into an excel worksheet that is linked to Bloomberg (i.e., on the Bloomberg terminal). Download data for these fields for the firm-years in your sample that are in the relevant Sector and Industry (from Step 3) using unique identifiers (e.g., ISIN). Follow the instructions in “Formulas → Insert Formula” to construct the download formula
- Step 7:
-
Compute firm-year material sustainability disclosure (MaterialDisc) as the number of non-missing fields as a fraction of the total number Bloomberg fields that correspond to SASB metrics
Example: SASB SICS Sector: Non-Renewable Resources; SASB SICS Industry: Oil & Gas – Exploration & Production
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Grewal, J., Hauptmann, C. & Serafeim, G. Material Sustainability Information and Stock Price Informativeness. J Bus Ethics 171, 513–544 (2021). https://doi.org/10.1007/s10551-020-04451-2
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DOI: https://doi.org/10.1007/s10551-020-04451-2