Why is Corporate Virtue in the Eye of The Beholder? The Case of ESG Ratings 
 
 
 
Dane Christensen 
 
 
George Serafeim 
 
 
Anywhere Sikochi* 
University of Oregon  
 
Harvard University 
 
 
Harvard University 
danec@uoregon.edu  
 
gserafeim@hbs.edu   
 
ssikochi@hbs.edu 
 
 
 
 
Abstract 
Despite the rising use of environmental, social, and governance (ESG) ratings, there is substantial 
disagreement across rating agencies regarding what rating to give to individual firms. As what 
drives this disagreement is unclear, we examine whether a firm’s ESG disclosure helps explain 
some of this disagreement. We predict and find that greater ESG disclosure actually leads to 
greater ESG rating disagreement. These findings hold using firm fixed effects, and using a 
difference-in-differences design with mandatory ESG disclosure shocks. We also find that raters 
disagree more about ESG outcome metrics than input metrics (policies), and that disclosure 
appears to amplify disagreement more for outcomes. Lastly, we examine consequences of ESG 
disagreement and find that greater ESG disagreement is associated with higher return volatility, 
larger absolute price movements, and a lower likelihood of issuing external financing. Overall, our 
findings highlight that ESG disclosure generally exacerbates ESG rating disagreement rather than 
resolving it. 
 
 
Keywords: ESG Ratings; Rating Agency Disagreement; ESG Disclosure; Corporate Social 
Responsibility; Sustainability 
JEL Classification: G24, M14, M41, Q56 
The data used in this study are publicly available from the sources cited in the text 
________________________________________________________________________ 
We thank Vishal Baloria, Mark Bradshaw, Mary Ellen Carter, Jeff Doyle, Jacquelyn Gillette, Michelle Hanlon, Amy 
Hutton, Zach Kaplan, Kevin Koharki, Brad Lindsey, Christopher Noe, Kyle Peterson, Sugata Roychowdhury, Susan 
Shu, Chad Simon, Chris Skousen, Eric So, Rodrigo Verdi, Joe Weber, and workshop participants at Boston College, 
Harvard Business School, MIT, Utah State University, and Washington University in St. Louis and the Bar-Ilan 
University New Research on Executive Compensation and on Sustainability conference (2019) for useful comments 
and suggestions. We also thank Carolyn Liu and James Zeitler for research assistance. Christensen is grateful for 
financial support from the Lundquist College of Business. Serafeim and Sikochi are grateful for financial support from 
the Division of Research and Faculty Development at Harvard Business School. All errors are our own. 
* Corresponding author: Anywhere (Siko) Sikochi, Harvard Business School, Soldiers Field Rd, 389 Morgan Hall, 
Boston, MA 02163; Telephone: (617) 496-3756; Email: ssikochi@hbs.edu.

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I. INTRODUCTION 
 
One of the biggest developments in financial markets, in recent years, has been the 
integration of environmental, social and governance (ESG) information in investment decisions. 
According to recent estimates, more than $30 trillion in assets under management are invested 
using sustainable strategies that apply ESG criteria in investment analysis and portfolio selection 
(GSIA 2018). Seeking to capture ESG information that thousands of publicly listed firms 
increasingly disclose, investor spending on ESG ratings from data providers (i.e., ESG rating 
agencies) increased from $200 million to $500 million between 2014 and 2018 (Gilbert 2019). 
In line with the increased use of ESG ratings by market participants, a growing number of 
academic studies have examined the association of ESG ratings with variables of interest, 
including stock market performance, accounting performance, financial constraints, and 
governance characteristics (e.g., Cheng, Ioannou, and Serafeim 2014; Khan, Serafeim, and Yoon 
2016; Hubbard, Christensen, and Graffin 2017).1 However, recently academic research (Chatterji 
et al. 2016; Berg, Koelbel, and Rigobon 2020) and commentators in the media have questioned 
these ratings noting the high degree of disagreement among data providers. For example, one Wall 
Street Journal article noted, “Environmental, social and governance criteria are hard to define. 
When we measure how different ESG providers rate companies in the S&P 500, there's often little 
overlap. By contrast, when ratings agencies score those same companies for their creditworthiness, 
they are much more often in agreement” (Sindreu and Kent 2018).2 Similarly, regulators have also 
voiced concerns about the substantial disagreement in ESG ratings. Commissioner Peirce of the 
Securities and Exchange Commission (SEC) mentioned in a recent speech that “the different 
                                                           
1 ESG ratings are evaluations of a company based on a comparative assessment of their quality, standard or 
performance on environmental, social or governance (ESG) issues (Brackley, Petroy, and Wong 2019). 
2 For a simple illustration of this, see the graphic in Appendix C.

2 
 
[ESG] ratings available can vary so widely, and provide such bizarre results that it is difficult to 
see how they can effectively guide investment decisions.”3 These examples highlight the lack of 
consistency in assigning ESG ratings to companies across different providers.  
This issue is of great importance because in the absence of agreement on what good ESG 
performance constitutes, market participants might be misled by ESG ratings. As an article in the 
Financial Times noted “Investors need to be clear about what the methodology they choose is 
actually measuring, and why. Otherwise ESG scoring risks creating a false sense of confidence 
among investors who don’t really understand what lies behind the numbers — and therefore don't 
really understand what they’re buying” (Allen 2018).  
While several articles highlight the fact that data providers greatly disagree on how to rate 
a company’s ESG performance, we have very little evidence on why providers disagree so much. 
Without understanding the reasons for this disagreement, it is difficult to understand not only what 
the potential remedies could be, but also the plausible consequences of this disagreement.4  
To address this, we focus on a key firm attribute that we hypothesize is likely to be of first-
order importance in causing providers to disagree on ESG ratings. Specifically, we focus on the 
extent of a firm’s ESG disclosure, as theory suggests that disagreement arises due to different 
information sets and/or different interpretations of information (e.g., Cookson and Niessner 2020). 
Conventional wisdom and a plethora of evidence in other settings, such as credit ratings and analyst 
forecasts, suggest that higher disclosure would be associated with lower disagreement (e.g., Lang 
                                                           
3 https://www.sec.gov/news/speech/speech-peirce-061819  
4 In a concurrent working paper, Kimbrough, Wang, and Wei (2019) find that a measure of ESG disagreement across 
two rating agencies is positively associated with higher annual return volatility, bid-ask spreads, and analyst forecast 
dispersion and negatively associated with the presence of a GRI report. This paper uses a substantially smaller sample 
making it difficult to compare the consistency of our findings. Moreover, although it is plausible that the presence of 
a GRI report may be associated with the quantity of disclosure, it is problematic as a proxy for the extent of ESG 
disclosure both because firms disclose significant amounts of ESG information outside GRI reports (e.g., via websites 
and sustainability or integrated reports that do not follow GRI guidelines) and because the amount of information 
included in GRI reports varies significantly.

3 
 
and Lundholm 1996; Morgan 2002; Hope 2003), as greater disclosure reduces information 
asymmetry. We argue that in our setting, due to the subjective nature of ESG information, the 
opposite would be true, where higher disclosure would be associated with higher disagreement, as 
disclosure expands opportunities for different interpretations of information.  
Specifically, in the absence of disclosure, data providers are more likely to agree because 
they use similar rules of thumb and imputation techniques. For example, they likely perceive the 
lack of disclosure on issues that are widespread and significant for a given industry (Khan, 
Serafeim, and Yoon 2016) as a bad attribute and thereby assign bad performance to the company. 
Similarly, for less significant issues where few companies in an industry disclose information, they 
likely perceive the lack of disclosure as a sign that the issue is relatively unimportant and therefore 
impute the company’s performance to be the average performance of companies in the industry 
(Kotsantonis and Serafeim 2019).5  
In contrast, for companies with higher levels of disclosure, data providers need to make a 
judgment about whether the disclosure means good or bad performance. For example, a company 
that discloses lost-time injury rates needs to be judged based on this disclosure. This gives rise to 
a level of subjectivity that leads to higher levels of disagreement. We argue that this subjectivity 
increases as firms expand their disclosures. This prediction is consistent with arguments from the 
sociology literature, which theorizes that a plurality of evaluations is likely to occur in newly 
emerging fields where rules and norms for evaluation are less developed (Lamont 2012). Higher 
disclosure also increases the likelihood that the ESG rating agencies might be able to use different 
metrics to evaluate a firm’s performance on the same issue and therefore lead to greater rating 
                                                           
5 How significant or material an issue is for a given industry is predefined according to the proprietary analysis 
framework of each rating agency. Therefore, disclosure patterns and associated imputations happen given the 
classification of whether an issue-industry pair is significant or not.

4 
 
disagreement.6 Moreover, as in financial markets, evaluators may disagree over which measures 
are more relevant to assessing ESG performance.7 Collectively, these arguments suggest that 
greater ESG disclosure would result in greater ESG rating disagreement. 
To test this prediction, we analyze data from three of the largest providers of ESG ratings 
to investors: MSCI, Thomson Reuters, and Sustainalytics. These rating agencies act as the main 
information intermediaries for ESG information in financial markets, similar to how credit rating 
agencies act as information intermediaries and gatekeepers in financial markets. We also utilize 
ESG disclosure scores from Bloomberg to proxy for the extent of firms’ ESG disclosure practices.  
Using 30,700 firm-year observations across 5,637 unique firms between the years 2004 
and 2016, we find strong support for our prediction that greater ESG disclosure leads to greater 
ESG rating disagreement. First, we provide descriptive evidence that although ESG disclosure has 
increased dramatically in the last two decades (through voluntary and mandatory disclosure 
efforts), the level of ESG disagreement for a given firm has in fact increased over the same period. 
We corroborate these results using multivariate models that control for a number of other firm 
characteristics including firm size, profitability, analyst coverage, institutional ownership, industry 
membership, and valuation multiples. Specifically, we estimate panel regressions with industry 
and year fixed effects and find a strong positive relationship between ESG disclosure and 
disagreement. We also estimate models with firm fixed effects and find similarly strong results of 
a positive association. Overall, these findings suggest that greater ESG disclosure appears to lead 
to greater ESG rating disagreement. 
                                                           
6 Agencies may use different metrics because of different ideologies or, as noted in prior literature (Merton 1987; 
Hirshleifer and Teoh 2003; Bonsall and Miller 2017), rating agencies could simply focus on different factors 
because processing all the information contained in disclosures may be too costly. 
7 In financial markets, Bloomfield and Fischer (2011) highlight that disagreement can arise from investors’ differing 
perceptions regarding the relevance of disclosures.

5 
 
Next, we consider each of the three pillars E, S, and G separately. Although substantial 
disagreement still exists across each of these areas regarding what constitutes good performance 
and what metrics to use, the environmental and social pillars have been debated for a shorter period 
of time than governance. As disclosure should contribute to greater disagreement where there is 
less of a shared understanding on what constitutes good performance, we expect disclosures on 
environmental and social issues to be relatively more likely to contribute to disagreement than 
governance disclosures. Consistent with this intuition, we find that the environmental and social 
pillars of ESG disclosure, rather than governance, primarily drive our main results on the 
relationship between ESG disclosure and disagreement. We observe similar results when we 
examine the association between pillar-specific disclosure (e.g., social disclosure) and pillar-
specific disagreement (e.g., social disagreement).  
We also perform supplemental analyses to corroborate our primary inferences. To address 
potential identification concerns, we use the staggered adoption of broad mandatory ESG 
disclosure requirements across countries as shocks to firm’s ESG disclosures. Using a difference-
in-differences design, we find that after a country or stock exchange implements mandatory ESG 
disclosure requirements, the affected firms tend to experience greater ESG rating disagreement 
(relative to the control firms). These findings provide greater confidence in our primary inferences.   
Next, to better understand what ESG ratings capture and how disclosure contributes to 
disagreement, we examine individual metrics that ESG raters use to construct their ratings. We 
categorize the metrics into inputs and outcomes. Inputs refer to efforts that a company is making 
to achieve a desired outcome (e.g., the presence of a diversity policy) and outcomes refer to actual 
performance outcomes (e.g., percentage of women in the workforce). We predict and find that 
ESG raters tend to disagree less about ESG inputs and more about ESG outcomes. This is

6 
 
consistent with the notion that the evaluation of outcomes is more subjective and relies on having 
a shared understanding of what a good versus a bad outcome might be. Further, we predict and 
find that greater ESG disclosure exacerbates these disagreements, especially in the case of 
outcomes, consistent with our argument that more pieces of information that require subjective 
evaluation should lead to greater disagreement. Moreover, we find that greater disclosure of ESG 
information at the industry-level also exacerbates ESG rating disagreement when evaluating ESG 
outcomes, suggesting that widespread increases in ESG disclosure across firms is unlikely to 
resolve ESG disagreement until there are common norms for what constitutes good and bad ESG 
performance. 
Lastly, we explore market consequences of ESG rating disagreement. Using three-day 
window tests around the release of revised ESG ratings (t-1, t+1), we find greater ESG 
disagreement is associated with higher stock return volatility and larger absolute price movements. 
These findings hold both with and without firm fixed effects, and suggest that ESG disagreement 
is relevant to market participants and influences stock prices. We also find some evidence that 
these results are becoming even stronger over time, which suggests that ESG disagreement is 
having an increasing impact on markets. In addition, we explore the influence of ESG 
disagreement on firms’ financing choices. Consistent with ESG disagreement creating market 
frictions by introducing uncertainty regarding a firm’s long-term sustainability, we find that firms 
with greater ESG disagreement are less likely to raise external financing and instead tend to rely 
more on internal financing. 
Overall, our study makes contributions to two streams of literature. First, we contribute to 
the literature that documents the presence of significant disagreement among ESG data providers 
(Chatterji et al. 2016). Our results indicate that this disagreement is most pronounced for firms

7 
 
with high levels of ESG disclosure, thereby shedding light on a key driver of ESG rating 
disagreement. More broadly, our results contribute to the growing literature that uses ESG data to 
understand their relationship with other important organizational and market outcomes (Khan, 
Serafeim, and Yoon 2016; Ferrell, Liang, and Renneboog 2016; Bereskin et al. 2018). 
Second, we contribute to the literature that investigates rating disagreement in other 
settings, such as in credit ratings or analyst forecasts (e.g., Morgan 2002; Lang and Lundholm 
1996; Bonsall and Miller 2017; Akins 2018). Contrary to evidence in these settings that disclosure 
mitigates disagreement, in our setting, disagreement is larger when firms have higher levels of 
disclosure. In our view, this highlights the importance of developing a shared understanding of a) 
what constitutes good or bad ESG performance, and b) what metrics to use to capture ESG 
performance, as preconditions for ESG disclosure to decrease disagreement.  
Our findings also illuminate some of the challenges ESG rating agencies face as 
information intermediaries. While thousands of companies now claim to integrate ESG issues in 
their business strategy and operations, it is not clear whether those claims are merely ‘cheap talk’ 
(Delmas and Burbano 2011).8 In the presence of information asymmetry and incomplete 
information about a firm’s ESG performance, ESG ratings can perform a significant information 
intermediary function, mitigating the adverse selection problem and thereby helping investors and 
other stakeholders to choose companies that exhibit their preferred ESG outcomes (Chatterji and 
Toffel 2010). In particular, having rating agencies focus on ESG outcomes might be desirable to 
mitigate ‘cheap talk’ by companies, as a company would need to show real effects (e.g., reductions 
in carbon emissions, improvements in lost time injury rates) instead of disclosing the adoption of 
a policy or initiative that might generate no real effects (Grewal and Serafeim 2020). However, 
                                                           
8 For example, many companies have adopted policies about deforestation or diversity. However, deforestation has 
not slowed down, nor have most companies become more diverse.

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our results suggest that ESG rating agencies have a more difficult time agreeing when evaluating 
a company on outcomes rather than input metrics, and that disclosure exacerbates disagreement 
on outcomes even more than it does on inputs. This lack of consensus about how to interpret 
outcomes might be an obstacle that encourages raters to focus more on inputs and thereby causes 
potential damage to the corporate accountability function that ratings could perform. In this 
context, we regard disagreement as inhibiting the accountability process. Moreover, while 
disclosure may have many positive effects, it likely needs to be placed in a framework that allows 
analysts to evaluate those outcomes with clear benchmarks.9  
Overall, given concerns over ESG rating disagreement, our findings suggest a lot of work 
still needs to be done to develop rules and norms to determine what characterizes good ESG 
performance.  
II. INSTITUTIONAL BACKGROUND AND HYPOTHESIS DEVELOPMENT 
ESG Rating Agencies 
Several ESG data providers have emerged in the last two decades, most of which provide 
aggregate ratings of a firm’s ESG performance. Responding to the need for collecting, interpreting, 
aggregating, and distributing ESG data, ESG rating agencies have become important information 
intermediaries in capital markets. The rating agencies share a common objective, which is to 
measure the ESG performance of a company (MSCI 2018; Thomson Reuters 2017; Sustainalytics 
2018). The concept of ESG performance is intended to provide an assessment of how well a 
company is managing environmental, social, and governance risks and opportunities (MSCI 2018; 
                                                           
9 A recent innovation that could serve as a useful example for framework and benchmark setting is the creation and 
adoption of science-based climate targets from more than 1,000 organizations so far. Organizations need to set their 
carbon emission reduction targets in line with the Paris Agreement based on the carbon budget remaining to avert an 
increase in the temperature by 2 degrees Celsius.

9 
 
Thomson Reuters 2017; Sustainalytics 2018). Although this captures a variety of heterogeneous 
issues, conceptually it is a summary measure of non-financial performance, which is somewhat 
analogous to how net income is a summary measure of financial performance covering a variety 
of heterogeneous areas (e.g., sales, impairments, interest and tax expenses). 
The rating agencies assess ESG performance using a wide array of metrics pertaining to a 
firm’s efforts (i.e., policies or programs) or outcomes on ESG issues. Each of the rating providers 
cover very similar ESG issues, although sometimes they label the issues with different names. The 
exact metrics that rating agencies cover can differ, and how those metrics are used is a proprietary 
part of the rating process and is not observable to outside researchers.  
Investors are the main audience of the ESG rating agencies featured in our study. The ESG 
ratings and the underlying data offered by the rating agencies are intended to help investors 
integrate ESG factors into their decisions, screen portfolios for risks and opportunities, generate 
investment ideas, conduct due diligence, determine opportunities for engagement, and support 
implementation of the UN PRI principles (MSCI 2018).10 ESG rating agencies are compensated 
by investors, and not by the companies they rate, thereby mitigating potential conflicts of interest. 
Of the ESG rating agencies, MSCI is widely considered the largest data provider to the 
investment community. It sells ESG ratings to investors and uses ESG data to construct stock 
market indices. Sustainalytics also sells its ESG ratings to investors, and provides other advisory 
and research services as well. Moreover, following its acquisition by Morningstar, Sustainalytics 
ratings form the basis for fund-level ESG ratings. Finally, ASSET4 was acquired by Thomson 
Reuters and its ESG ratings were integrated in Thomson Reuters’ platforms and provided to their 
                                                           
10 In contrast to credit ratings agencies, which often produce alphabetical ratings, historically few ESG rating 
agencies have translated their scores to alphabetical ratings. Instead they produce numeric cardinal ratings.

10 
 
subscribers.11 Each of these rating agencies employs over 150 ESG analysts who collect and 
evaluate ESG data to produce ESG ratings. ESG rating agencies collect relevant information for 
rating assessments, usually annually, while if there are major events unfolding, they might revise 
a rating before that. The latter is fairly infrequent. Different companies receive ratings on different 
months and days of the year. To collect the necessary data they analyze corporate disclosures, such 
as sustainability reports and corporate websites, administer surveys for companies to complete, 
and review information coming from other stakeholders, such as regulatory agencies, industry 
associations and NGOs.12 However, despite their best efforts, the ESG ratings they produce tend 
to differ greatly for a given firm across rating agencies, which has drawn substantial criticism from 
outside observers.  
ESG Rating Disagreement 
It should be no surprise that ESG ratings would exhibit high levels of disagreement. A large 
body of work in sociology shows that human ability to make sense of information in a common 
way occurs over time, and that both cultural and social processes define and enable the evaluation 
of knowledge (Espeland and Stevens 1998; Knorr-Cetina 1999). This can include determining 
what information to focus on, how to interpret it, and how much weight to give it.13 In areas that 
are not highly formalized or that are newly emergent, it is more likely that pluralistic evaluative 
cultures prevail, leading to higher disagreement among raters (Lamont 2012), as in the case of the 
ESG field. In comparison to financial analysis that has been taught and formalized in the last 
                                                           
11 In October 2018 Thomson Reuters sold a large stake in its data and analytics unit (Refinitiv), which distributes 
ASSET4 ratings, to Blackstone (Thomson Reuters 2018). Less than a year later, the London Stock Exchange agreed 
to acquire Refinitiv, although the deal is still awaiting regulatory approval (Isaac and Dummett 2020). 
12 We use timestamped ratings by MSCI and Sustainalytics thereby mitigating any bias arising from ex-post 
restatements of ratings. In the case of Thomson Reuters because the ratings do not have timestamps it is plausible that 
ratings are restated based on subsequent information.  
13 With that said, findings from concurrent research by Berg et al. (2020) suggest that using different weights (i.e., 
weight divergence) plays a smaller role in ESG rating disagreement than selecting different categories (i.e., scope 
divergence) and using different metrics (i.e., measurement divergence).

11 
 
century, ESG analysis has only emerged in the last two decades with most education and 
formalization occurring in the last ten years.  
Moreover, because the information flow of ESG data is less formalized than the flow of 
financial data, which is systematized by institutional arrangements such as earnings calls and 
investor presentations, it can lead to further increases in disagreement. As analysts are more likely 
to receive and evaluate ESG information in a less structured way, analysts might seek and retrieve 
different pieces of information at different points in time and in a different sequence. This in turn 
leads to formation of differential expectations about a firm’s ESG performance and therefore to 
different interpretations of subsequent information (e.g., Jones et al. 1968; Krüger and Nolte 2016). 
In other words, analysts’ prior evaluations of specific ESG data can influence their subsequent 
evaluations of other ESG data, leading to heterogeneity in judgements (Lamont 2009). For 
example, a prior assessment of a firm’s workplace practices might affect an analyst’s evaluation 
of product safety.  
In addition, evaluative practices that lead to ratings are influenced by conventions (Becker 
1982). Whether evaluators follow customary rules is associated with how strongly they are 
invested in what defines a proper evaluation and, ultimately, their self-concept as an evaluator. For 
example, accountants, financial analysts, and credit analysts have established a strong identity 
through professional associations. No such identity yet exists for ESG analysts. Such a less 
institutionalized field will be less consistent in providing clear rules and in socializing new 
evaluators, thereby giving rise to heterogeneity in judgements (Lamont 2012).  
ESG Disclosure and Disagreement  
With these things in mind, we now turn to the role that ESG disclosure may play in 
explaining ESG rating disagreement. Theory suggests that disagreement in financial markets

12 
 
generally arises due to individuals having different information sets and/or different interpretations 
of information (e.g., Cookson and Niessner 2020). Accordingly, ESG disclosure seems like a 
natural factor that could influence ESG rating disagreement. Existing literature has shown that 
disclosure improves a firm’s information environment and resolves uncertainties about the firm 
for participants in both the equity and debt markets.14 In the equity markets, Lang and Lundholm 
(1996) provide evidence that a firm’s disclosure practices lead to more accurate analyst earnings 
forecasts, less dispersion among individual analyst forecasts, and less volatility in forecast 
revisions. Moreover, their evidence suggests that disclosure reduces estimation risk and 
information asymmetry. Several subsequent studies corroborate these conclusions. For example, 
Hope (2003) finds that disclosure about a firm’s accounting policies reduces uncertainty about its 
forecasted earnings and thus reduces forecast dispersion and forecast error. Overall, evidence 
suggests that disclosure reduces disagreement (i.e., forecast dispersion) among equity analysts. 
Similarly, in the debt markets, research finds that disclosure appears to reduce 
disagreement between credit rating analysts’ assessment of credit risk of firms. Morgan (2002) 
finds that credit ratings agencies disagree more often over firms that are more opaque, and Bonsall 
and Miller (2017) find that firms with more readable disclosures are less likely to have split credit 
ratings. Additionally, Akins (2018) finds that firms with greater financial reporting quality are less 
likely to have split credit ratings. This evidence suggests that enhanced transparency through 
disclosures can mitigate rating disagreement on the premise that split ratings can arise when credit 
rating agencies have limited access to data with which to base their credit assessments. 
We argue that in the context of ESG ratings the opposite would be true, where more 
                                                           
14 Although research has also found that greater disclosure can temporarily increase disagreement over short 
windows, such as around earnings announcements (Bamber et al. 1999; 2011), this disagreement is usually resolved 
shortly thereafter, ultimately leading to lower disagreement (Amiram, Owens, and Rosenbaum 2016). In contrast, in 
our setting we examine the relation between disclosure and disagreement over a longer window.

13 
 
disclosure would be associated with higher disagreement. In contrast to financial disclosures where 
there is widespread agreement about the meaning of specific financial variables, such as leverage 
or profitability on the future creditworthiness of a company, for ESG disclosures there is no shared 
understanding yet on which exact metrics should be assessed to evaluate firm’s ESG performance, 
or how to interpret and judge their meaning about a firm’s ESG performance.  
Anecdotal evidence suggests that this might be true. For example, when Workday Inc. 
significantly increased its ESG disclosure for fiscal year 2015 by adopting the new G4 Global 
Reporting Initiative guidelines,15 its sustainability report increased from 54 to 98 pages. Its ESG 
disclosure score from Bloomberg increased significantly for both the environmental and social 
pillars. However, disagreement increased as its Thomson Reuter’s rating for both environmental 
and social issues increased, MSCI’s environmental rating decreased and its social rating increased, 
and Sustainalytics left its environmental rating unchanged while its social rating increased. We 
observed similar patterns when analyzing other companies that substantially increased their ESG 
disclosure, such as AT&T, Tech Mahindra and Altria, among others.  
The rating disagreements can arise because more disclosure is more likely to lead to rating 
agencies using different metrics in assessing a firm’s ESG performance. For example, on the issue 
of workplace safety, if a firm only discloses lost time injury rates, then all raters will likely use 
this metric to assess a company’s performance. However, if a firm also discloses additional 
information (e.g., the number of fatalities due to accidents, the number of lost workdays, time loss 
claims), raters might use different metrics to assess a firm’s safety performance or might assign 
different importance to the different metrics.16  
                                                           
15 The Global Sustainability Standards Board (GSSB) issued the G4 Global Reporting Initiative guidelines. The GSSB 
is an independent international organization whose core products are the Sustainability Reporting Standards. 
16 For example, this can occur when raters have different ideologies about which measures are relevant to assessing 
ESG performance (e.g., Bloomfield and Fischer 2011).

14 
 
In addition, analysts might disagree on how to interpret a given metric. Specifically, in the 
absence of clear rules for evaluative practices, it is likely that rules of thumb will be developed for 
the least demanding evaluative tasks. For example, while an ESG analyst would need to judge a 
specific piece of information, in the absence of that information, a simple rule can be developed. 
Therefore, in the absence of disclosure, raters are more likely to agree because they use similar 
rules of thumb and imputation techniques. For instance, as some ESG analysts shared with us, if a 
single company does not disclose information on an important issue for its industry, they perceive 
the lack of disclosure as a bad attribute and thereby assign bad performance to the company. 
Similarly, in the absence of disclosure across most companies in an industry, they likely perceive 
the lack of disclosure as a sign that the issue is relatively unimportant, thereby imputing the 
company’s performance to be the average performance of companies in the industry.  
In contrast, for companies with higher levels of disclosure, ESG analysts need to make a 
judgement about whether the information being disclosed means good or bad performance and 
assess how to aggregate the different disclosures that a firm provides. For example, a company 
that discloses cubic meters of water consumed in operations or lost time injury rates needs to be 
judged based on these disclosures. This gives rise to a level of subjectivity that leads to ratings 
disagreement. We argue that this level of subjectivity increases as firms expand their disclosure, 
since firms first tend to disclose the presence or absence of policies, practices, and strategies (e.g., 
policies to provide employees with child care support or to require a health and safety policy) and 
then eventually disclose more specific quantitative metrics (e.g., employee turnover, CO2 
emissions, water recycled). The latter are likely to give rise to the highest disagreement, as they 
need to be judged based on what constitutes “good” or “bad” performance, while at least in the 
context of policies, practices, and strategies, more disclosure might mean better performance.

15 
 
Collectively, all these arguments suggest that greater ESG disclosure increases ESG rating 
disagreement, because greater disclosure provides more information raters can disagree about, and 
thus creates more opportunities for raters to have different interpretations of information (Cookson 
and Niessner 2020). Therefore, we hypothesize: 
H1: ESG disclosure is positively associated with ESG rating disagreement. 
III. RESEARCH DESIGN AND SAMPLE CONSTRUCTION 
Empirical Model 
 
To test our hypothesis that greater ESG disclosure will lead to greater ESG rating 
disagreement, we estimate the following ordinary least squares (OLS) regression model:    
      ESG_Disagreementit
= β0 + β1 ESG_Disclosureit + ∑βk Controls + εi,t   
(1) 
where ESG Disagreement is the standard deviation of ESG ratings firm i received from rating 
agencies for its performance in year t.17 ESG_Disclosure is the firm’s ESG disclosure score 
pertaining to year t’s performance. Note that each of these ESG variables is publicly released in 
year t+1, but pertain to year t’s performance, with a firm’s ESG disclosure occurring before the 
ESG rating assessment. Thus, the model allows for an assessment of how ESG disclosures 
influence subsequent ESG disagreement.  
 
Controls consists of the following firm characteristics: the average ESG rating a firm 
received from various rating agencies for year t’s performance (ESG_Avg), firm size (Firm Size), 
firm performance (ROA), growth opportunities (BTM), capital structure (Leverage), the number of 
                                                           
17 We measure ESG disagreement using the standard deviation of ratings, as opposed to using the coefficient of 
variation, because we measure all the ESG ratings in our sample on the same scale (0 to 100) and scaling by a firm’s 
average rating would complicate inferences and induce a mechanical relationship between a firm’s average rating 
(ESG_Avg) and our dependent variable (Sørensen 2002). We also construct an alternative measure of ESG rating 
disagreement, measured as the average of absolute values of the difference between pairs of ratings that a firm receives 
for its performance in year t. Our inferences are generally similar using this alternative measure (untabulated.)

16 
 
analysts following the firm (Analyst Following), and the percentage of shares held by institutional 
investors (Inst. Ownership).18 See Appendix A for further details on how each variable is 
measured. We also include industry, year, country, and rating agency fixed effects. For 
comparison, in some specifications we replace industry and country fixed effects with firm fixed 
effects to examine within-firm variation in ESG disagreement.19  In all specifications, we winsorize 
all continuous variables at the 1st and 99th percentiles and standard errors are clustered by firm. 
Our hypothesis predicts that the estimated coefficient on ESG_Disclosure will be positively 
associated with ESG_Disagreement (i.e., β1 > 0).         
Sample Construction 
 
To construct our sample, we obtained ESG ratings from three of the world’s most 
prominent ESG rating agencies, namely Morgan Stanley Capital International’s (MSCI) Intangible 
Value Assessment, Thomson Reuters’ (TR) ASSET4, and Sustainalytics.20 These ESG rating 
agencies provide international coverage, which we later exploit to better get at identification.  
As some ESG rating agencies only provide one rating per firm each year, we construct a 
firm-year dataset by linking the ESG ratings to firm-year observations from Worldscope. When a 
rating agency release multiple ESG ratings for a given firm-year’s performance, we retain the last 
rating issued within 12 months of the firm’s fiscal year-end. This ensures that all the rating 
agencies in our sample have had an opportunity to observe any ESG disclosures a firm has made 
relating to year t’s ESG performance.21 To ensure that ratings from these agencies are comparable, 
                                                           
18 Inferences are unchanged if we also include stock return volatility as a control variable.  
19 In the models with firm fixed effects, the reported intercept captures the average of the fixed effects. 
20 We do not include MSCI’s KLD ratings in our analyses because MSCI discontinued these ratings and they pertain 
almost exclusively to U.S. firms. However, our inferences are unchanged if we also include these ratings (untabulated). 
Some studies also use membership in the Dow Jones Sustainability Index, Calvert Index, and FTSE4Good Index as 
proxies for good ESG performance (e.g., Christensen 2016; Chatterji et al. 2016). These organizations do not publicly 
release ESG ratings, so we do not view them as rating agencies. Hence we do not include them in our analyses. 
21 For example, if a firm has a December 31, 2010 year-end (i.e., fiscal year 2010), we use TR ASSET4’s 2010 rating,

17 
 
we only use the ratings for the environmental, social, and governance pillars (as some raters also 
evaluate economic performance).22 We re-scale the ratings when necessary so that they all range 
from 0 to 100. Specifically, while TR ASSSET4 and Sustainalytics scores range from 0 to 100, 
MSCI’s scores range from 0 to 10. Thus, we multiply MSCI’s scores by 10 to make them 
comparable.  
To capture the extent of firms’ ESG disclosure, we use ESG disclosure scores provided by 
Bloomberg. These disclosure scores are based on information firms disclose in various ways, such 
as via sustainability reports, annual reports, and corporate websites.23 The scores ranges from 0.1 
for firms that disclose a minimum amount of ESG data to 100 for firms that disclose every data 
point that Bloomberg collects.24 Thus, the more ESG information a company discloses, the higher 
the disclosure score. Bloomberg’s ESG disclosure coverage includes over 10,000 common stocks 
around the world and as a result is significantly larger (i.e., nearly double) than the coverage 
universe of common stocks of the ESG rating agencies. Bloomberg also tailors the disclosure 
scores for different industry sectors, so that companies are only evaluated based on data that is 
relevant to their industry. We use Bloomberg’s overall ESG disclosure score in our primary 
analyses, similar to prior studies (e.g., Baldini et al. 2018; Li et al. 2018). In supplemental analyses, 
we also employ the more granular disclosure scores Bloomberg provides for each ESG pillar. 
                                                           
and retain the last ESG rating MSCI and Sustainalytics released between January 1 and December 31, 2011. This 
approach also helps to mitigate capturing disagreement arising from differences in the information sets of rating 
agencies. Nevertheless, some differences in the information sets of each ESG rating agency may still exist. 
22 Because TR ASSET4’s composite score also includes an economic component, we exclude this and calculate the 
overall ESG score as the average environmental, social, and governance score.  
23 ESG disclosures tend to be less timely than financial disclosures. It is common for firms to disclose sustainability 
reports and ESG information on their websites several months after they release their annual reports. This likely 
reflects a combination of the difficulty of collecting some of the ESG information, the smaller human and 
information system resources dedicated to collecting ESG information compared to collecting financial information, 
and the lack of regulation mandating the timeliness of this information. 
24 Although Bloomberg’s ESG disclosure scores are used by practitioners and thus are institutionally relevant, 
Bloomberg may not capture all relevant ESG disclosures made by firms, so this proxy may measure ESG disclosure 
imperfectly. Nevertheless, the dynamic difference-in-difference analysis that we perform later in the paper provides 
some validation for this data.

18 
 
 
Our sample begins in 2004, the first year that we have data on the extent of firm’s ESG 
disclosures, and ends in 2016 based on data availability at the time we constructed our sample. We 
obtain firm financial data from Worldscope, analyst coverage from the Institutional Brokers' 
Estimate System (I/B/E/S), and institutional ownership from Factset.  
 
As we are interested in studying ESG rating disagreement, we require each firm-year to 
have ratings from more than one ESG rating agency. After requiring basic firm-level controls, 
along with ESG disclosure scores, our final sample consists of 30,700 firm-year observations from 
5,637 unique firms. Table 1 outlines a summary of our sample construction process.  
IV. DESCRIPTIVE ANALYSES 
Descriptive Statistics 
 
Table 2 Panel A provides descriptive statistics illustrating the worldwide coverage of our 
sample. The sample is comprised of firms from 69 countries, with roughly three-quarters of the 
observations coming from nine countries: the United States, Japan, the United Kingdom, China, 
Canada, Australia, France, Germany, and Switzerland.  
 
Table 2 Panel B provides descriptive statistics for the ESG ratings from each of the rating 
agencies in our sample. While our overall sample has 30,700 firm-year observations, each rating 
agency has fewer observations than this, as we only require each firm-year to be rated by at least 
two rating agencies, similar to research on analyst disagreement (e.g., Sadka and Scherbina 2007; 
Barinov 2013). In our sample, each of the raters tends to issue overall ESG scores of around 50 
(out of 100 points possible). With that said, MSCI tends to issue slightly lower overall ESG ratings 
(48.4), and Sustainalytics tends to issue slightly higher overall ESG ratings (57.2). Additionally, 
ratings provided by TR tend to have the greatest variance across firms (i.e., standard deviation of 
24.5), while ratings provided by Sustainalytics tend to have the least variance across firms (i.e.,

19 
 
standard deviation of 9.8). Similar trends exist for each of the underlying environmental, social, 
and governance pillars. 
 
Table 2 Panel C provides descriptive statistics for ESG rating disagreement that exists for 
a given firm-year. While the average overall ESG rating a firm receives for a given year’s 
performance is 52.8 (out of 100), there appears to be substantial disagreement across raters 
regarding what rating to give that firm. Specifically, the standard deviation of overall ESG ratings 
that a firm receives for a given firm-year is 12.3 (i.e., ESG_Disagreement). To provide a more 
intuitive sense for the magnitude of this disagreement, we also document the absolute difference 
in ratings that pairs of agencies provide for a given firm-year. For example, we see that the ratings 
provided by TR and MSCI for a given firm-year differ by 19.7 points, on average. Similarly, the 
ratings TR and Sustainalytics provide for a given firm year differ by 15.8, on average. While the 
ratings from MSCI and Sustainalytics tend to differ by the least amount for a given firm-year 
(12.7), that difference is also quite large. Fairly similar patterns exist for each of the underlying 
environmental, social, and governance pillars as well. Overall, this descriptive evidence suggests 
that there is considerable disagreement across rating agencies regarding how to view a given firm’s 
ESG performance.25 
 
Table 2 Panel D provides descriptive statistics for the variables used in our regression 
analyses. Here we see that the average firm’s ESG disclosure score is 28.5 (out of 100), with a 
reasonable amount of variation across firms (i.e., a standard deviation of 15.0) consistent with the 
                                                           
25 In untabulated analyses, we also examine ESG_Disagreement by industry and find that there tends to be substantial 
disagreement across all Fama French 49 industries, with the tobacco industry having slightly higher disagreement than 
the other industries. If we examine ESG_Disagreement by country, we also find there tends to be substantial 
disagreement across almost all countries (untabulated). No particular country stands out as having much higher 
disagreement than the others. The few countries without much disagreement tend to have less than 10 observations 
(e.g., Kazakhstan, Ukraine, Kenya).

20 
 
literature (e.g., Baldini et al. 2018).26 We also see that the average firm in our sample is fairly large 
(total assets of US$ 7.3 billion), profitable (ROA of 0.05), and is followed by around 11 sell-side 
equity analysts.  
 
Univariate correlations (reported in the online appendix) also illustrate that ESG 
disagreement tends to be greater for larger firms, firms with better ESG disclosure, and firms with 
greater analyst following. Another interesting observation is that although ESG_Disclosure and 
ESG_Avg are positively correlated with each other, they have very different correlations with 
ESG_Disagreement.27 
Specifically, 
ESG_Disclosure 
is 
positively 
correlated 
with 
ESG_Disagreement, while ESG_Avg is negatively correlated with ESG_Disagreement.   
Has ESG rating disagreement declined over time? 
 
While the descriptive evidence presented thus far suggests that rating agencies tend to have 
strikingly different views on a given firm’s ESG performance, it is possible that this disagreement 
has been declining over time and is not really much of an issue any more. This could occur if rating 
agencies’ views begin to converge over time regarding what constitutes good ESG performance. 
This is plausible given efforts over time by such organizations as the GRI and SASB to create 
sustainability standards.28 Thus, before moving onto the tests of our main hypothesis, we perform 
a few additional descriptive analyses.  
 
First, we construct a time trend measure where we take the fiscal year for a particular firm-
year observation and subtract the first year of our sample (Time). We then regress 
ESG_Disagreement on Time to examine whether ESG disagreement has been declining over time. 
                                                           
26 Interestingly, the disagreement for governance ratings appears to be slightly higher than for environmental and 
social ratings suggesting a significant level of disagreement even for that pillar, which has been debated for a longer 
period in rating agencies.  
27 Variance inflation factors for all the variables in our models are below 3, indicating that multicollinearity is not a 
problem (Kennedy 2008).   
28 The Global Reporting Initiative (GRI) and Sustainability Accounting Standards Board (SASB) are independent 
non-profit organizations that develop sustainability reporting standards.

21 
 
As raters have been expanding their coverage over time (e.g., picking up smaller firms), we also 
include firm fixed effects in the model to ensure that changes in the sample composition over time 
don’t confound our inferences. This effectively allows us to see how ESG disagreement for a given 
firm has been changing over time.  
   
As reported in Table 3 Panel A we see that the coefficient on Time is positive and 
statistically significant, suggesting that ESG disagreement for a given firm has actually been 
increasing over time. This finding is inconsistent with rating agencies’ views converging over time 
regarding what constitutes good ESG performance.29 To illustrate how this trend compares to 
trends in ESG disclosure, as well as average ESG performance ratings, we next replace the 
dependent variable with ESG_Disclosure and ESG_Avg, respectively, and re-run this analysis. In 
Table 3 Panel B we see that ESG disclosures also appear to be getting better over time (i.e., Time 
is again positive and statistically significant). And in Table 3 Panel C we see that average ESG 
ratings for a given firm have also been increasing over time.  
 
Overall, these analyses suggest that despite improving ESG disclosures and ESG 
performance over time, ESG disagreement appears to be increasing over time. Next, we explore 
whether these trends are actually related.  
V. EMPIRICAL RESULTS 
What drives ESG rating disagreement? 
 
We now turn to examining our primary hypothesis regarding whether ESG disclosure plays 
a role in influencing ESG ratings disagreement.  
                                                           
29 After our sample period, Thomson Reuters revised its ESG rating methodology. As they released ratings using the 
new methodology that also pertain to prior years, we re-ran this analysis using these new ratings as an input into the 
calculation of ESG_Disagreement. The results are very similar (untabulated). Results from tests of our main 
hypothesis are also very similar using these new ratings (untabulated).

22 
 
Table 4 reports the results of our tests based on Equation 1 using two different 
specifications: (1) without firm fixed effects, and (2) with firm fixed effects. Consistent with our 
hypothesis that ESG disclosure will be positively associated with ESG disagreement, we find that 
the estimated coefficient on ESG_Disclosure is indeed positive and statistically significant across 
all the models. These findings suggest that when a firm increases its ESG disclosure, this appears 
to exacerbate ESG disagreement across rating agencies. In other words, greater disclosure appears 
to give rating agencies simply more information to disagree about and interpret differently. In 
terms of the magnitude of this effect, we observe that increasing ESG_Disclosure from the 25th to 
the 75th percentile is associated with a 22 to 31 percent increase in ESG_Disagreement, depending 
on the model specification. This appears to be an economically significant effect.   
In terms of other factors that help explain ESG disagreement, we find that the estimated 
coefficient on ESG_Avg is negative and statistically significant across all specifications. These 
findings suggest that ESG rating agencies tend to disagree more about firms with poor average 
ESG performance. This is similar to findings from the credit ratings literature, as there tends to be 
greater disagreement between credit raters regarding firms with lower average credit ratings 
(Cantor and Parker 1994; Bonsall et al. 2017). In terms of economic significance, we observe that 
increasing ESG_Avg from the 25th to the 75th percentile is associated with a 25 to 32 percent 
decrease in ESG_Disagreement, depending on the model specification. Again, this appears to be 
an economically meaningful effect, relative to the sample average for ESG_Disagreement. Most 
of the other firm characteristics in the model do not yield consistently significant results.30  
                                                           
30 The exceptions to this are BTM and Institutional Ownership, which are negative and significant across the 
specifications. Although these findings suggest that firms with lower growth opportunities and greater institutional 
ownership tend to have lower ESG disagreement, the economic magnitude of these findings is relatively small. 
Increasing BTM (Institutional Ownership) from the 25th to the 75th percentile is associated with a one to two (four to 
five) percent decrease in ESG disagreement, depending on the model specification.

23 
 
 
We also find that firm fixed effects appear to explain a sizable amount of the variation in 
ESG disagreement. Specifically, moving from the model without firm fixed effects, to the model 
with firm fixed effects, we see the explanatory power of the model improves dramatically (i.e., the 
adjusted R2 goes from 0.118 to 0.516) suggesting that a lot of the disagreement about a firm’s ESG 
performance is very persistent over time. 
What dimensions of ESG disclosure contribute to ESG disagreement? 
 
Next, we examine whether some attributes of the ESG disclosures play a greater role than 
others do in explaining ESG disagreement. Although substantial disagreement still exists across 
each of three pillars E, S, and G regarding what constitutes good performance and what metrics to 
use, the environmental and social pillars have been debated for a shorter period of time than 
governance. Thus, ex ante we expect disclosure to have a relatively stronger association with 
environmental and social rating disagreement compared with governance rating. As disclosure 
should contribute to greater disagreement where there is less of a shared understanding on what 
constitutes good performance, we expect disclosures on environmental and social issues to be 
relatively more likely to contribute to disagreement than governance disclosures. We 
acknowledge, however, that there is still substantial debate about the measurement and meaning 
of good corporate governance (Daines, Gow, and Larcker 2010; Guest and Nerino 2020).  
Using the separate disclosure scores Bloomberg provides for each ESG pillar, we test 
which ESG pillars contribute to the positive association between ESG disclosure and ESG 
disagreement.31 Specifically, we re-run our main analysis and replace ESG_Disclosure with its 
                                                           
31 Although we would like to perform textual analyses on firms’ ESG disclosures (e.g., sustainability reports) to 
investigate this issue, we are unfortunately unable to obtain the ESG disclosures made by the firms in our sample. 
While firms post their most recent sustainability reports on their websites, they generally do not post reports that are 
over a few years old. Thus getting access to historical reports is problematic. Although CorporateRegister.com has 
gathered a fairly comprehensive collection of sustainability reports over time, paid subscribers are only allowed to 
download up to 100 sustainability report pdfs a month. Given the large size of our sample, it is not feasible to gather 
the reports that pertain to our sample.

24 
 
underlying component scores (E_Disclosure, S_Disclosure, and G_Disclosure). As reported in 
Table 5, we find that across the specifications, E_Disclosure and S_Disclosure have a consistently 
positive and significant association with ESG_Disagreement. In contrast, G_Disclosure is only 
statistically significant in the model without firm fixed effects. These findings are consistent with 
our expectations and suggest that firms’ environmental and social disclosures appear to contribute 
to ESG rating disagreement, while firms’ governance disclosures appear to generate disagreement 
only in the cross-section but not over time for a given firm.  
Next, we corroborate this more directly by seeing whether disclosure for a particular pillar 
(e.g., environmental disclosure) helps explain disagreement for that pillar (e.g., environmental 
disagreement), while controlling for the average performance rating for that pillar (e.g., 
environmental performance). As shown in the other columns of Table 5, the results from these 
tests produce similar inferences, again suggesting that more environmental and social disclosures 
tend to contribute to environmental and social rating disagreement, with governance disclosures 
not playing as much of a role in governance disagreement.   
VI. ADDITIONAL ANALYSES 
Shocks to ESG Disclosure 
 
One potential concern regarding our results is that they may be influenced by self-selection 
or firm-level time-varying correlated omitted variables because firms can choose how much ESG 
information to disclose. Although we try to mitigate the possibility of such biases by estimating 
models with firm fixed effects, it still could be the case that our results might be influenced by 
unobservable variables. To mitigate this concern, we exploit the passage of broad mandatory ESG 
disclosure requirements, which went into effect in numerous countries during our sample period. 
These broad ESG disclosure requirements arose due to legislation at the country level or listing

25 
 
requirements from individual stock exchanges. From an identification perspective, the attractive 
features of these mandatory disclosure requirements are that they are staggered across many 
countries over time, and they should lead to exogenous increases in ESG disclosure. A summary 
of the laws/regulations that were passed in different countries is shown in Appendix B.32 Based on 
these laws/regulations, we create an indicator variable equal to one if the firm-year’s ESG 
performance was subject to mandatory ESG disclosure requirements; zero otherwise 
(Mandatory_Disclosure).  
 
We first confirm that these mandatory ESG disclosure requirements did indeed improve 
firms’ ESG disclosures. Because the firms in our sample tend to be quite large, they may have 
already been voluntarily disclosing ESG information at the level required by these mandatory 
requirements. Thus it is possible that these mandatory disclosure requirements may not have 
resulted in an increase in ESG disclosure. Therefore, we perform a validation analysis before 
performing our difference-in-differences analysis. We do this by regressing ESG_Disclosure on 
Mandatory_Disclosure, with the same control variables as before, along with firm and year fixed 
effects. As shown in Table 6, the estimated coefficient on Mandatory_Disclosure is positive and 
statistically significant, confirming that the disclosure requirements improved firm’s ESG 
disclosures, as expected.  
 
We then perform a difference-in-differences analysis with firm and year fixed effects, 
where we re-estimate Equation 1 and replace ESG_Disclosure with Mandatory_Disclosure. As 
reported in Table 6, we observe that Mandatory_Disclosure is positive and statistically significant, 
consistent with our primary results.  
                                                           
32 For further details on these disclosure requirements, see https://www.carrotsandsticks.net. Also note that our tests 
capture the first instance of broad ESG disclosure requirements in a country. Some countries have subsequently issued 
additional legislation that requires even greater ESG disclosure than the original mandate.

26 
 
To address the parallel trends assumption in this analysis, we also perform a dynamic 
difference-in-differences analysis. In this analysis, we re-estimate our models and replace the 
Mandatory Disclosure dummy with indicator variables for different periods around the adoption 
of mandatory ESG regulations. For example, Mandatory Disclosure t = -1 is an indicator variable 
equal to one in the year prior to when a firm became subject to mandatory ESG disclosure. As 
reported in Table 6, we see that the coefficients on Mandatory Disclosure in the pre-period years 
are statistically insignificant, suggesting that the parallel trends assumption holds. We also observe 
in the dynamic validation analysis that the mandatory disclosure rules appear to have increased 
ESG disclosure around the time that the laws and regulations went into effect. Importantly, we 
also observe that the ESG Disagreement, on average, appears to increase around this time, although 
with a delay. Collectively, these results suggest that the findings from our difference-in-differences 
test are not due to a pre-existing trend.33  
These findings provide further support for the notion that greater ESG disclosure leads to 
greater ESG disagreement. That being said, a few caveats are worth highlighting. First, many of 
our observations that take the value of one for Mandatory_Disclosure come from a relatively small 
number of countries thereby making these results dependent on these countries. Second, some of 
the countries, despite having disclosure mandates, have historically had very low levels of 
disclosure suggesting that not all such mandates have been effective (e.g., Greece).34 With those 
caveats in mind, we believe that these tests need to be interpreted with caution, but are additive to 
the mosaic of evidence we present in the paper.  
                                                           
33 We also perform placebo tests where we randomly assign the years that treatment firms became subject to 
mandatory disclosure requirements. We did this 1000 different times and found that in 98 percent of the regressions 
there was not a positive and significant association between the placebo Mandatory Disclosure variable and 
ESG_Disagreement (untabulated). These findings provide additional confidence in the reliability of the findings 
from our mandatory disclosure tests reported Table 6.  
34 In untabulated analyses we also find that the increase in disagreement after the mandatory disclosure requirements 
is primarily driven by UK firms. Nevertheless, UK firms do not drive the main results reported in Table 4.

27 
 
Inputs vs. Outcomes: Disagreement at the Metric Level 
 
To shed more light on what ESG ratings capture and how ESG disclosure contributes to 
ESG rating disagreement, we next examine the individual metrics underlying ESG ratings. When 
ESG rating agencies evaluate a company’s ESG performance to assign a rating, they accumulate 
and analyze a host of metrics. Some of these metrics are inputs, and others are outcomes. Inputs 
are the efforts that a company makes to achieve a desired outcome. Outcomes are the performance 
outcomes resulting from a company’s efforts. For example, the presence of a diversity policy is an 
input, whereas the percentage of women at the firm is an outcome of that policy. Similarly, setting 
a carbon emissions reduction target is an input, while the carbon intensity of a business is the 
outcome. An employee health and safety strategy is an input, while lost time injury rates is the 
outcome. These two sets of metrics are not only conceptually different but they also behave 
differently, as we show hereafter.  
Two of the three ESG ratings agencies that we examine in this paper, Sustainalytics and 
Thomson Reuters, provide firm-level data on the individual metrics they use to come up with their 
overall ratings.35 For these raters we classify each of the metrics they use as inputs or outcomes, 
and then map each metric of Sustainalytics to its corresponding metric(s) from Thomson Reuters 
based on the theme the metric is meant to reflect.36 This generates 927,352 firm-year-metric 
matches where we can examine ratings disagreement at the metric level. 
This mapping leads to three categories of metrics. First, one where both raters use input 
metrics. For example, they both may assess a company’s policies or initiatives to increase 
                                                           
35 MSCI does not provide their scoring on individual metrics for each firm. 
36 There are some topics that are covered by one rating agency and not the other. In these cases, no matching metric 
exists and we cannot calculate a disagreement score. Thus, we exclude these metrics from our analysis. 
Additionally, as the focus of our paper is on ESG disclosure, we also exclude metrics that capture controversies 
identified in the media, as these are typically not identified by firms’ disclosures.

28 
 
workforce diversity. Second, both raters assess outcome metrics, such as the percentage of women 
managers. Third, one data provider assesses an input metric while the other assesses an outcome 
metric. For example, while both rating agencies may focus on employee health and safety, one 
agency focuses on the strength of the company’s initiatives to prevent accidents and illnesses (an 
input) and the other focuses on the total number of injuries and fatalities (an outcome).37  
Our framework allows us to both predict for which metric category the level of 
disagreement will be higher and for which category disclosure might exacerbate disagreement 
more, thereby increasing our understanding of ESG ratings as a construct. First, based on our 
theory pointing to a lack of an institutionalized and shared understanding of the meaning of ESG 
metrics, we expect the lowest disagreement when both raters are assessing input metrics as it is 
much easier to agree on whether the effort is taking place. Nevertheless, disagreement may still 
exist for this category because different data providers might disagree on the extent of that effort. 
For example, data providers likely evaluate not only the presence of a policy but also how strong 
that policy is. Second, we predict that disagreement will be even higher when both raters evaluate 
outcome metrics because it is not clear what is a good or a bad outcome. Making that evaluation 
requires a shared understanding of how to evaluate whether the intention behind the efforts has 
been achieved. Third, we predict the highest disagreement when one data provider evaluates an 
input and the other evaluates an outcome, as comparing efforts and outcomes can be like 
comparing apples and oranges. 
To test these predictions, we examine ESG disagreement at the individual metric level 
                                                           
37 From a descriptive perspective (untabulated), it appears that ESG raters heavily utilize input metrics, as 81 percent 
of the observations in our matched-metric sample relate to instances where both raters are using input metrics. In 
contrast, outcome metrics appear to be far less common, as only 10 percent of the observations pertain to instances 
where both raters are using outcome metrics. The remaining 9 percent of the observations pertain to input/outcome 
matches.

29 
 
using the following OLS regression model: 
ESG Metric Disagreementit 
= β0 + β1 Both Outcomesit + β2 Input/Outcomeit 
+ ∑βk  Controls + εi,t   
(2) 
where ESG Metric Disagreement is the standard deviation of the Sustainalytics rating and 
Thomson Reuters rating for the matched metrics.38 Our variables of interest are indicator variables 
that flag each of the three combinations of inputs and outcomes. As we expect the least 
disagreement when both raters evaluate inputs (Both Inputs), we exclude this category from the 
model and use it as the baseline category. Both Outcomes is an indicator variable set equal to one 
where both rating agencies evaluate outcome metrics. Input/Outcome is an indicator variable set 
equal to one where one rating agency assesses an input and the other an outcome metric. Controls 
captures the same firm-level characteristics as in our prior tests. We expect β1 > 0 and β2 > β1. 
 
As predicted, in columns (1) and (2) of Table 7 Panel A we find that the estimated 
coefficients on Input/Outcome and Both Outcomes are both positive and significant. Further, the 
estimated coefficient on Input/Outcome is statistically larger than the coefficient on Both 
Outcomes (p < 0.01, untabulated), as expected.  
Having established these baseline predictions, we next consider the role of disclosure in 
this process. We expect that disagreement across all the three categories should increase as a 
function of disclosure because disclosure provides more pieces of information to evaluate thereby 
leading to more disagreement. To test this, we add an indicator for above median ESG Disclosure 
(High ESG Disclosure) to Equation 2 and interact it with each of the metric categories. As reported 
in columns (3) and (4) of Table 7 Panel A, we find that the coefficient on High ESG Disclosure is 
positive and significant, consistent with our primary findings that greater disclosure is associated 
                                                           
38 In other words, ESG Metric Disagreement is measured the same way ESG Disagreement is, but now at the metric 
level, rather than at the firm level. The results are similar if we instead measure ESG metric disagreement by taking 
the absolute value of the difference in the metric scores (untabulated).

30 
 
with greater disagreement. More interestingly, we find that the effect of disclosure on disagreement 
is the largest for the category when both data providers evaluate outcomes (i.e., High ESG 
Disclosure × Both Outcomes is positive and significant). Again, this is consistent with our theory 
that the evaluation of outcomes relies on having an institutionalized understanding of what a good 
vs. a bad outcome might be. Thereby, we find a stronger effect on disagreement for the category 
of metrics that one would expect the largest need for development of a shared understanding of 
what the metrics might mean.  
These latter results allow us to make some inferences about the ‘desirability’ of the 
disagreement. While we cannot pursue a general equilibrium evaluation of the social welfare 
implications from ESG disagreement, these findings suggest that to improve accountability among 
firms there needs to be heavy emphasis on outcomes. In the absence of focusing on outcome 
metrics we could have extended ‘goodwashing’ among companies. Yet our results also provide 
evidence that getting agreement on outcomes is much more difficult and that disagreement 
increases sharply with more disclosure. In this context, we regard disagreement as inhibiting the 
accountability process and that disclosure needs to be shaped around a consistent set of rules and 
a framework that allows analysts to evaluate those outcome metrics with clear benchmarks.  
To provide further insight into this phenomenon, we also examine the role of ESG 
disclosure at the industry level. A high degree of ESG disclosure at the industry level might help 
create a common benchmark against which rating agencies can assess a given firm’s ESG 
performance, thereby decreasing disagreement. However, due to the highly subjective nature of 
ESG information and the lack of common norms for evaluating ESG information, we predict that 
the opposite would occur, as greater industry disclosure likely provides even more complex 
information for raters to try to interpret, and even more information to disagree about. To test this,

31 
 
we create an indicator variable set to one if the average level of ESG disclosure for a firm’s industry 
in year t is above the sample median industry ESG disclosure (Industry High ESG Disclosure), 
and replace our firm-level ESG disclosure variable with this measure.39 Consistent with our 
prediction, in columns 1 and 2 of Table 7 Panel B we find that greater industry-wide ESG 
disclosure appears to exacerbate disagreement for outcome metrics.40 This finding holds in 
columns 3 and 4 when we add back firm level ESG disclosure to the model.41 Overall, these 
findings suggest that widespread increases in ESG disclosure across firms is unlikely to resolve 
ESG disagreement until a shared understanding is developed regarding what constitutes good and 
bad ESG performance.      
Consequences of ESG Disagreement 
 
To better understand how ESG rating disagreement influences markets, we next examine 
stock market consequences of ESG disagreement. Specifically, we perform short-window tests 
using 3 day windows (-1,+1) centered on the date when a rating agency comes out with a new ESG 
rating. These tests allow us to understand whether the issuance of a new rating that disagrees more 
with the existing rating from another agency has implications for market prices. In these tests we 
use data from MSCI and Sustainalytics as their data reveal the exact day when the new rating 
comes out, and their ratings are released in a staggered fashion throughout the year. (In contrast, 
Thomson Reuters only provides the year their annual ratings pertains to.)42 We perform these 
                                                           
39 Industries such as chemicals, tobacco, and electrical equipment tend to have high ESG disclosure, whereas 
industries like personal services, entertainment, and trading tend to have low ESG disclosure (untabulated).  
40 We speculate that the negative and significant coefficient on the base term for Both Outcomes in Panel B is due to 
the fact that in industries with low ESG disclosure raters might rely more on inputs in how they rate companies as 
evaluations of outcomes might be even more difficult to perform.  
41 Moreover, in untabulated analyses, when we examine the combination of high firm-level ESG disclosure with 
high industry-level ESG disclosure, we find that the effect of high firm-level ESG disclosure on ESG outcome 
disagreement manifests in industries with greater ESG disclosure. 
42 Sustainalytics and MSCI often update their ratings more than once a year. We exclude observations where rating 
agencies issue updated ratings that do not change their prior rating (i.e., rating reiterations), as prior research on

32 
 
market consequence tests by estimating the following OLS regression model: 
             Market Outcomei(t-1,t+1) 
= β0 + β1 ESG_Disagreementit + ∑βk  Controls + εi,t   
(3) 
where Market Outcome is measured using three different variables: Absolute CAR, Return 
Volatility, and Bid-Ask Spread.  Absolute CAR captures absolute market-adjusted returns 
cumulated over the announcement window. We evaluate unsigned instead of signed returns as 
disagreement is unsigned and therefore we do not have ex ante a view about whether disagreement 
would represent good or bad news from an equity holder perspective. Return Volatility is the 
standard deviation of market-adjusted returns during the announcement window; and Bid-Ask 
Spread is the average daily bid-ask spread during the announcement window. ESG Disagreement 
is measured the same as before, except now it is based on the new ESG rating released on day t 
(e.g., from MSCI) and the most recently available ESG rating from the other rater (e.g., 
Sustainalytics). Controls includes ESG_Avg, which is also measured based on the most recently 
available ESG ratings to help control for any new economic events that may have occurred since 
the last rating update. The other controls are the same variables as in our prior tests, measured as 
of the most recent fiscal year-end prior to the rating release date. The models are estimated with 
(1) industry, nation, and year fixed effects, or (2) firm and year fixed effects.  
Table 8 Panel A reports results of these tests. As shown in columns (1) and (2), we find 
that ESG Disagreement is positively associated with Absolute CAR. In economic terms, increasing 
ESG Disagreement from the 25th to the 75th percentile is associated with an eight to ten basis point 
increase in absolute market-adjusted returns, depending on the specification. Given that the sample 
is comprised of very large firms, this effect size seems reasonable. In columns (3) and (4) we find 
                                                           
other information intermediaries has found dampened market reactions to reiterations (e.g., Mikhail, Walther, and 
Willis 2004). If we also include rating reiterations in these tests, we observe similar, although economically weaker, 
results as expected (untabulated). Also note that the sample for these consequences tests contains ratings released up 
through the end of 2017, consistent with our determinants tests.

33 
 
that ESG Disagreement is also positively associated with Return Volatility. Here, increasing ESG 
Disagreement from the 25th to the 75th percentile is associated with a three to four basis point 
increase in return volatility. Lastly, when we examine bid-ask spreads in columns (5) and (6), we 
do not observe a significant relation with ESG Disagreement. Collectively, these findings that ESG 
disagreement is positively associated with return volatility and absolute price movements suggests 
that ESG disagreement is relevant to market participants and influences stock prices.43  
As the use of ESG data has been increasing over time (Gilbert 2019), we next examine 
whether these market consequences are stronger in the later part of our sample. To test this, we 
interact a Time trend variable with ESG Disagreement and re-run these analyses.44 As reported in 
Table 8 Panel B, we find that the interaction term (ESG Disagreement × Time) is positive and 
significant in one of the two specifications when Absolute CAR is the dependent variable, and in 
both specifications when Return Volatility is the dependent variable. These results provide some 
evidence that ESG disagreement is having an increasing impact on markets as time goes on. 
Lastly, to shed more light on the capital market implications of ESG disagreement, we 
explore the influence of ESG disagreement on firms’ financing choices. Prior research has found 
that greater ESG performance tends to increase firms’ access to external capital (Cheng, Ioannou, 
and Serafeim 2014). As our prior findings illustrate that ESG disagreement appears to introduce 
uncertainty regarding the sustainability of a firm, this may serve as a market friction that impedes 
a firm’s access to external capital. Thus, firms facing greater ESG disagreement may be less likely 
to obtain external financing and instead may rely more on internal financing, as internal financing 
tends to be less costly. To test this, we utilize our firm-year dataset and examine the following 
                                                           
43 The results are similar if we also drop (or control for) observations where firms announced earnings or released 
management guidance during the 3 day window (untabulated). 
44 The main effect of the time trend variable is subsumed by the year fixed effects and thus is excluded in the model.

34 
 
three dependent variables: Equity Issuance, Debt Issuance, and Cash Holdings. Equity Issuance is 
an indicator variable equal to one if the firm issues equity in year t+1; Debt Issuance is an indicator 
variable equal to one if the firm issues debt in year t+1; Cash Holdings is cash divided by total 
assets in year t+1. We predict that ESG Disagreement will be negatively associated with the two 
external financing measures (Equity Issuance and Debt Issuance) and positively associated with 
the internal financing measure (Cash Holdings).  
As reported in Table 9 Panel A, the results from OLS regressions generally support these 
predictions, as we find statistically significant results in the predicted directions in four of the six 
specifications. These findings suggest that firms facing greater ESG disagreement are less likely 
to raise external financing and instead tend to rely more on internal financing.45 In economic terms, 
increasing ESG Disagreement from the 25th to the 75th percentile is associated with a 0.2 to 1.1 (0 
to 2.2) percent lower likelihood of issuing equity (debt) in year t+1, depending on the model 
specification. Similarly, increasing ESG Disagreement from the 25th to the 75th percentile is 
associated with a 1.4 to 5.1 percent increase in a firm’s cash holdings in year t+1, depending on 
the model. Additionally, in Panel B, when we perform time trend tests for these financing 
outcomes, we find some evidence that ESG disagreement is having an increasing impact on these 
financing outcomes as time goes on. 
VII. CONCLUSION 
The use of environmental, social, and governance (ESG) ratings in financial markets has 
risen dramatically, and so has the disagreement across ESG rating agencies regarding what rating 
to give to an individual firm. The media has highlighted the existence of ESG rating disagreement, 
but there is a dearth of evidence on what drives this disagreement. In this paper, we document 
                                                           
45 For the binary dependent variables, we find similar results if we instead use probit models (untabulated).

35 
 
empirical evidence on the extent to which a firm’s ESG disclosure helps explain this disagreement.  
Our tests reveal several interesting findings. In contrast to evidence in other settings where 
greater disclosure helps reduce disagreement among information intermediaries, we find that 
greater ESG disclosure leads to greater ESG disagreement across ESG rating agencies. These 
findings are robust to including firm fixed effects and using a difference-in-differences design with 
staggered mandatory ESG disclosure shocks. These findings also appear to be primarily driven by 
the environmental and social disclosures, rather than governance disclosures. We also find that 
ESG disclosure appears to amplify disagreement about ESG metrics, particularly for ESG 
outcomes. Overall, our results show that greater ESG disclosure does not appear to help resolve 
ESG rating disagreement.  
We note that over time as analysts develop a consensus both on the metrics to use to assess 
a firm’s performance on a specific ESG issue and how to interpret the information reflected in 
each metric, the relation between disclosure and disagreement might diminish or even become 
negative. In other words, our study is likely to be reflective of the early stages of institutional 
innovation around ESG disclosures.  
As rating disagreements threaten to dampen confidence in ESG ratings, the findings in our 
study are important because without understanding why providers disagree, it becomes difficult to 
understand not only what the potential remedies could be, but also the plausible consequences of 
this disagreement. Although we also document some of the consequences of ESG disagreement 
(e.g., higher return volatility and larger absolute price movements), there is still much to learn 
about the consequences of disagreement, which we encourage future research to explore.

36 
 
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39 
 
APPENDIX A 
Variable Definitions
Variable Name
Description
Source
ESG Variables
ESG_Score 
The firm's overall weighted environmental, social, 
and governance rating from a unique ESG rating 
agency for year t on a scale of 0 to 100. 
MSCI IVA,  
TR ASSET4, 
Sustainalytics
E_Score 
The firm's environmental rating from a unique 
ESG rating agency for year t on a scale of 0 to 
100.
MSCI IVA,  
TR ASSET4, 
Sustainalytics
S_Score 
The firm's social rating from a unique ESG rating 
agency for year t on a scale of 0 to 100. 
MSCI IVA,  
TR ASSET4, 
Sustainalytics
G_Score 
The firm's governance rating from a unique ESG 
rating agency for year t on a scale of 0 to 100. 
MSCI IVA,  
TR ASSET4, 
Sustainalytics
ESG_Avg 
The average environmental, social, and 
governance rating a firm received for year t's ESG 
performance.
MSCI IVA,  
TR ASSET4, 
Sustainalytics
E_Avg 
The average environmental rating a firm received 
for year t's environmental performance.  
MSCI IVA,  
TR ASSET4, 
Sustainalytics
S_Avg 
The average social rating a firm received for year 
t's social performance.  
MSCI IVA,  
TR ASSET4, 
Sustainalytics
G_Avg 
The average governance rating a firm received for 
year t's governance performance.  
MSCI IVA,  
TR ASSET4, 
Sustainalytics
ESG_Disagreement 
The standard deviation of ESG ratings that a firm 
received for year t's ESG performance.  
MSCI IVA,  
TR ASSET4, 
Sustainalytics
E_Disagreement 
The standard deviation of environmental ratings 
that a firm received for year t's environmental 
performance. 
MSCI IVA,  
TR ASSET4, 
Sustainalytics
S_Disagreement 
The standard deviation of social ratings that a firm 
received for year t's social performance.  
MSCI IVA,  
TR ASSET4, 
Sustainalytics
G_Disagreement 
The standard deviation of governance ratings that 
a firm received for year t's governance 
performance. 
MSCI IVA,  
TR ASSET4, 
Sustainalytics
Diff_TR_MSCI 
The absolute value of the difference between the 
rating Thomson Reuters and MSCI gave a firm for 
year t's ESG performance. 
MSCI IVA,  
TR ASSET4, 
Sustainalytics
Diff_TR_SUST 
The absolute value of the difference between the 
rating Thomson Reuters and Sustainalytics gave a 
firm for year t's ESG performance. 
MSCI IVA,  
TR ASSET4, 
Sustainalytics

40 
 
APPENDIX A (Continued) 
Variable Definitions
Variable Name
Description
Source
Diff_MSCI_SUST 
The absolute value of the difference between the 
rating MSCI and Sustainalytics gave a firm for 
year t's ESG performance. 
MSCI IVA,  
TR ASSET4, 
Sustainalytics
ESG_Disclosure 
The firm's ESG disclosure score for the ESG 
report pertaining to year t's performance. 
Bloomberg 
E_Disclosure 
The firm's environmental disclosure score for the 
ESG report pertaining to year t's performance. 
Bloomberg 
S_Disclosure 
The firm's social disclosure score for the ESG 
report pertaining to year t's performance. 
Bloomberg 
G_Disclosure 
The firm's governance disclosure score for the 
ESG report pertaining to year t's performance. 
Bloomberg 
High_ESG_Disclosure An indicator variable equal to one if the firm-
year’s ESG disclosure was above the sample 
median; zero otherwise.
Bloomberg 
Industry High  
ESG Disclosure 
An indicator variable equal to one if the average 
ESG disclosure in the firm’s industry during year t 
was above the sample median industry disclosure; 
zero otherwise.
Bloomberg, 
Worldscope 
Mandatory_Disclosure
An indicator variable equal to one if the firm-
year’s ESG performance was subject to broad 
mandatory ESG disclosure requirements; zero 
otherwise.
Carrots & Sticks
ESG_Metric_Disagree The standard deviation of scores that a firm 
received for year t's performance for the matched 
metrics. 
TR ASSET4, 
Sustainalytics 
Control Variables
Firm Size 
The natural log of total assets (in millions of US$) 
as of the end of year t.
Worldscope 
ROA 
Return on Assets, defined as net income for year t 
divided by total assets at the end of year t.
Worldscope 
BTM 
The book value of equity divided by the market 
value of equity, as of the end of year t. 
Worldscope 
Leverage 
Total liabilities divided by total assets, as of the 
end of year t.
Worldscope 
Analyst Following 
The natural log of the number of analysts 
following the firm as of the end of year t.
Worldscope 
Inst. Ownership 
The percentage of the firm's shares owned by 
institutional investors at the end of year t, 
multiplied by 100. Set to zero if missing. Max 
value capped at 100.
Factset 
Industry Fixed Effects 
Industry indicators are based on the Fama French 
49 industry classifications.
Ken French’s 
website

41 
 
APPENDIX A (Continued) 
Variable Definitions
Variable Name
Description
Source
Time 
The fiscal year for a particular firm-year 
observation minus the first year in the sample.
Worldscope 
Input/Outcome Variables
Both Inputs 
An indicator equal to 1 if both ESG raters measure 
the issue using input metrics. 
TR Asset4, 
Sustainalytics
Both Outcomes 
An indicator equal to 1 if both ESG raters measure 
the issue using outcome metrics.
TR Asset4, 
Sustainalytics
Input/Outcome 
An indicator equal to 1 if one ESG rater measures 
the issue using an input metric, and the other rater 
uses an outcome metric.
TR Asset4, 
Sustainalytics 
Consequence Variables
Absolute CAR 
The firm’s cumulative absolute market-adjusted 
return during the 3-day window (-1,+1) around the 
ESG rating release date, multiplied by 100 for 
ease of exposition.  
Datastream 
Return Volatility 
The standard deviation of the firm’s market-
adjusted returns during the 3-day window (-1,+1) 
around the ESG rating release date, multiplied by 
100 for ease of exposition.  
Datastream 
Bid-Ask Spread 
The average daily relative bid-ask spread during 
the 3-day window (-1,+1) around the ESG rating 
release date, multiplied by 100 for ease of 
exposition. The relative bid-ask spread is 
measured as (Ask-Bid)/((Ask+Bid)/2).  
Datastream 
Equity Issuance 
An indicator equal to 1 if proceeds from equity 
issuances in year t+1 are greater than zero.
Worldscope 
Debt Issuance 
An indicator equal to 1 if net proceeds from debt 
issuances in year t+1 are greater than zero.
Worldscope 
Cash Holdings
Cash, scaled by total assets at time t+1.
Worldscope

42 
 
APPENDIX B 
Mandatory ESG Disclosure Requirements (by Country) 
Country 
Source of 
Requirement 
Year 
Regulation  
Portugal 
Government 
1985 
Law No 141/85 and Decree-Law No 9/92 
  
  
  
  
Australia 
Stock Exchange 
1996 
Australian Stock Exchange Listing Rule 4.10.3  
  
  
  
  
Finland 
Government 
1997 
The Finnish Accounting Act 
  
  
  
  
France 
Government 
2003 
New Economic Regulations Act  
  
  
  
  
Greece 
Government 
2007 
Law 3487 
  
  
  
  
Malaysia 
Stock exchange 
2007 
Bursa Malaysia Listing Requirement (Appendix 9C, Part A, 
29)
  
  
  
  
Argentina 
Government 
2008 
Law 2594 
  
  
  
  
China 
Stock exchange 
2008 
Guidelines for Corporate Social Responsibility of Shenzhen 
Stock Exchange Listed Companies; Shanghai Stock Exchange 
Guidelines on Listed Companies’ Environmental Information 
Disclosure   
Sweden 
Government 
2008 
Guidelines for external reporting by state-owned companies 
  
  
  
  
Austria 
Government 
2009 
Corporate Law Amendment Act 2008 - URÄG 2008 
  
  
  
  
Denmark 
Government 
2009 
Act amending the Danish Financial Statements Act. (Report 
on social responsibility for large businesses).
  
  
  
  
Netherlands 
Government 
2010 
Guideline 400 (RJ-Uiting 2009-8) and Parliamentary Paper 
26485, no.86
 
 
 
 
Pakistan 
Government 
2010 
S.R.O. 983(I)/2009 Companies (Corporate Social 
Responsibility) General Order, 2009 
  
  
  
  
South Africa 
Stock exchange 
2010 
Johannesburg Stock Exchange Listing Requirement 8.63 and 
King Code of Governance Principles (King III)
  
  
  
  
India 
Government 
2012 
Securities and Exchange Board CIR/CFD/DIL/8/2012 
  
  
  
  
Indonesia 
Government 
2012 
Decision of the Chairman of the Capital Markets Supervisory 
Agency No.KEP-431/BL/2012 
Israel 
Government 
2012 
Public Reporting Directives [1] (10/11) Section 620-E

43 
 
APPENDIX B (Continued) 
Mandatory ESG Disclosure Requirements (by Country) 
Country 
Source of 
Requirement 
Year 
Regulation  
Nigeria 
Government 
2012 
Nigerian Sustainable Banking Principles 
 
 
 
 
Norway 
Government 
2013 
Statute 44; LOV-2013-04-19-15 
  
  
  
  
Qatar 
Government 
2013 
Sustainable Development Industry Reporting (SDIR) Program 
  
  
  
  
United 
Kingdom
Government 
2013 
UK Statutory Instruments 2013 No. 1970 
  
  
  
  
Taiwan 
Stock exchange 
2014 
Taiwan Stock Exchange Corporation Rules Governing the 
Preparation and Filing of Corporate Social Responsibility 
Reports by TWSE Listed Companies 
  
  
  
  
Thailand 
Government 
2014 
Rules, Conditions and Procedures for Disclosure regarding 
Financial and Non-financial Information of Securities Issuers 
  
  
  
  
Turkey 
Government 
2014 
Communiqué on Corporate Governance 
  
  
  
  
Chile 
Government 
2015 
General Standard N° 385 
  
  
  
  
Peru 
Government 
2015 
Resolution SMV No 033-2015-SMV/01 
  
  
  
  
Hong Kong 
Stock exchange 
2016 
HKEx Environmental, Social and Governance Reporting 
Guide
  
  
  
  
This appendix summarizes staggered adoptions of broad mandatory ESG disclosure requirements through the end of 
our sample period. Some ESG disclosure requirements are initiated by individual countries, and others are initiated 
by stock exchanges in those countries. The “year” column captures the first fiscal year-ends that became subject to 
the disclosure requirements. The “regulation” column captures the name of the regulation identified by Carrots and 
Sticks and/or by internet searches. During our sample period, in a few countries the disclosure requirements only 
applied to a subset of firms. The requirements in Israel and Nigeria applied to banks, and in Qatar they applied to oil, 
energy, and transportation firms. In India they applied to firms on the BSE and National India stock exchanges. In 
Sweden they applied to state owned enterprises.

44 
 
APPENDIX C 
 
Although some disagreement in ratings is to be expected when different agencies rate a 
given firm, the following graphic illustrates how stark the differences can be between ESG 
raters. Rating disagreement between credit rating agencies is provided as a comparison.  
 
This image comes from Sindreu and Kent (2018). It has been included in this document in preparation for final 
publication with permission for the final publication through a license for re-use of an article and graphics in 
publication via Copyright Clearance Center. The original interactive graphic is available at: 
https://www.wsj.com/articles/why-its-so-hard-to-be-an-ethical-investor-1535799601.

45 
 
TABLE 1
Sample Construction 
  
  
Firm-Years
Unique Firms
Worldscope firm-years with ESG ratings (2004 - 2016)
      65,995 
        11,217 
Require ESG ratings from 2+ raters for each firm-year
    (26,892)
         (4,268)
      39,103 
          6,949 
Require control variables
         (511)
              (91)
      38,592 
          6,858 
Require ESG disclosure scores
      (7,892)
         (1,221)
      30,700 
          5,637 
The table presents the summary of our sample construction. The sample covers the period from 2004 to 2016 
for firm-years with at least two ESG ratings from MSCI, Thomson Reuters ASSET4, and Sustainalytics. 
Control variables and ESG disclosure scores are as described in Appendix A.

46 
 
TABLE 2 
Descriptive Statistics 
Panel A: Country Composition 
  
  
  
  
Nation 
Freq.
%
  
Nation 
Freq.
%
United States 
9,704 
31.61
  
Belgium 
172 
0.56
Japan 
3,444 
11.22
  
Turkey 
165 
0.54
United Kingdom 
2,413 
7.86
  
Bermuda 
163 
0.53
China 
2,187
7.12
 
Philippines 
147
0.48
Canada 
1,848 
6.02
  
Poland 
136 
0.44
Australia 
1,561 
5.08
  
Austria 
134 
0.44
France 
852 
2.78
  
Chile 
130 
0.42
Germany 
728 
2.37
  
Israel 
97 
0.32
Switzerland 
561 
1.83
  
New Zealand 
97 
0.32
India 
534 
1.74
  
Greece 
90 
0.29
South Africa 
516 
1.68
  
Portugal 
81 
0.26
Brazil 
481 
1.57
  
Colombia 
77 
0.25
Sweden 
470 
1.53
  
United Arab Emir. 
58 
0.19
Spain 
362 
1.18
  
Luxembourg 
56 
0.18
Italy 
339 
1.10
  
Jersey 
46 
0.15
Netherlands 
332 
1.08
  
Qatar 
38 
0.12
Singapore 
316 
1.03
  
Hungary 
28 
0.09
Malaysia 
266 
0.87
  
Czech Republic 
25 
0.08
Indonesia 
222 
0.72
  
Macau 
21 
0.07
Finland 
218 
0.71
  
Peru 
21 
0.07
Norway 
217 
0.71
  
Egypt 
19 
0.06
Ireland 
215 
0.70
  
Puerto Rico 
12 
0.04
South Korea 
215 
0.70
  
Cayman Islands 
11 
0.04
Russia 
206 
0.67
  
Malta 
9 
0.03
Mexico 
202 
0.66
  
Papua New Guinea 
9 
0.03
Denmark 
198 
0.64
  
Isle of Man 
8 
0.03
Thailand 
187 
0.61
  
Other 
56 
0.18
  
 
 
 
  
30,700 
100.00

47 
 
TABLE 2 (continued) 
  
  
  
  
  
  
  
  
  
  
Panel B: ESG Ratings from each Rating Agency 
Standard 
  
N 
  
Mean 
  
Median 
  
Deviation 
MSCI IVA 
  
  
  
  
ESG_Score
  
  26,116 
  
48.36
  
48.00
12.41
E_Score
  
  26,108 
  
50.20
  
50.00
21.28
S_Score
  
  26,116 
  
46.35
  
46.00
17.87
G_Score
  
  26,102
  
56.13
  
55.00
20.58
  
  
  
  
TR ASSET4 
  
  
  
ESG_Score
  
  29,767 
  
54.13
  
56.15
24.53
E_Score
  
  29,767 
53.67
  
56.85
32.04
S_Score
  
  29,767 
  
54.18
  
57.21
31.03
G_Score
  
  29,767 
  
54.52
  
60.92
29.58
  
  
  
  
SUSTAINALYTICS 
  
  
  
ESG_Score
  
  25,786 
  
57.21
  
56.00
9.82
E_Score
  
  25,786 
54.28
  
53.00
13.82
S_Score
  
  25,786 
  
56.99
  
56.00
11.06
G_Score
  
  25,786 
  
62.21
  
62.00
10.89

48 
 
TABLE 2 (continued) 
  
  
  
  
  
  
  
  
  
  
Panel C: Disagreement for each ESG category 
Standard 
  
N 
  
Mean 
  
Median 
  
Deviation 
ESG Overall  
  
  
  
ESG_Avg 
  
   30,700  
  
52.80
  
52.95
13.93
ESG_Disagreement 
   30,700  
  
12.32
  
11.65
6.75
ESG_Diff_TR_MSCI 
   25,183  
  
19.67
  
17.90
13.01
ESG_Diff_TR_SUST 
   24,853  
  
15.75
  
14.34
10.61
ESG_Diff_MSCI_SUST 
   21,202  
  
12.71
  
11.00
9.08
  
  
  
  
  
Environmental 
E_Avg 
  
   30,700  
52.08
  
52.23
19.11
E_Disagreement 
   30,700  
  
17.03
  
16.60
8.38
E_Diff_TR_MSCI 
   25,175  
  
27.47
  
26.05
17.29
E_Diff_TR_SUST 
   24,853  
  
21.37
  
21.92
11.44
E_Diff_MSCI_SUST 
   21,194  
  
17.28
  
15.00
12.28
  
  
  
  
  
Social 
  
  
  
S_Avg 
  
   30,700  
  
52.08
  
52.34
16.90
S_Disagreement 
   30,700  
  
17.45
  
17.22
8.52
S_Diff_TR_MSCI 
   25,183  
  
27.10
  
25.49
17.09
S_Diff_TR_SUST 
   24,853  
  
22.64
  
22.05
12.93
S_Diff_MSCI_SUST 
   21,202  
  
18.06
  
15.67
13.16
  
  
  
  
Governance 
  
  
  
G_Avg 
   30,700  
  
57.12
  
58.78
15.92
G_Disagreement 
   30,696  
  
17.53
  
16.64
9.68
G_Diff_TR_MSCI 
   25,169  
  
26.74
  
24.16
18.14
G_Diff_TR_SUST 
   24,853  
  
23.46
  
20.62
15.89
G_Diff_MSCI_SUST 
   21,192  
  
18.59
  
16.00
13.23

49 
 
TABLE 2 (continued) 
  
  
  
  
  
  
  
  
  
  
  
Panel D: Descriptive Statistics of Regression Variables (n = 30,700) 
Standard 
Variables 
Mean 
  
Deviation
  
25% 
  
Median 
75% 
ESG_Disagreement 
12.32
  
6.75
  
7.12
  
11.65
  
16.92
E_Disagreement 
17.03
  
8.38
  
11.14
  
16.60
  
22.33
S_Disagreement 
17.45
  
8.52
  
11.19
  
17.22
  
23.12
G_Disagreement 
17.53
  
9.68
  
10.06
  
16.64
  
24.09
ESG_Avg 
  
52.80
  
13.93
  
41.43
  
52.95
  
64.01
E_Avg 
  
52.08
  
19.11
  
35.71
  
52.23
  
68.36
S_Avg 
  
52.08
  
16.90
  
38.09
  
52.34
  
65.90
G_Avg 
  
57.12
  
15.92
  
45.11
  
58.78
  
69.44
ESG_Disclosure 
28.50
  
14.97
  
14.91
  
25.62
  
40.50
E_Disclosure 
19.20
  
18.79
  
0.00
  
13.95
  
34.88
S_Disclosure 
26.00
  
19.32
  
8.77
  
23.33
  
38.60
G_Disclosure 
51.78
  
9.03
  
48.21
  
51.79
  
57.14
Firm Size 
  
8.89
  
1.64
  
7.77
  
8.73
  
9.87
ROA 
  
0.04
  
0.08
  
0.01
  
0.04
  
0.08
BTM 
  
0.64
  
0.50
  
0.30
  
0.52
  
0.85
Leverage 
  
0.58
  
0.23
  
0.43
  
0.58
  
0.74
Analyst Following 
2.43
  
0.73
  
2.08
  
2.56
  
2.94
Inst. Ownership 
48.03
  
31.28
  
19.70
  
41.97
  
78.26
  
  
  
The table presents the sample descriptive statistics. Panel A shows the sample distribution by country (China 
includes observations from Taiwan and Hong Kong). Panel B reports summary statistics of ESG ratings from each 
rating agency. Panel C reports summary statistics of ESG rating disagreement for each ESG category. Panel D 
reports summary statistics of the control variables. All continuous variables have been winsorized at the 1st and 99th 
percentiles. See Appendix A for variable definitions.

50 
 
TABLE 3
Time Trend Analyses 
 
Panel A: ESG rating disagreement over time 
Dependent Variable: 
ESG_Disagreement
Coef.
t-stat.
Time
0.121
(5.98)
***
Intercept
11.283
(65.12)
***
Firm F.E.
Yes
Adj. R2
0.486
N
30,700
Panel B: ESG disclosure over time 
Dependent Variable: 
ESG_Disclosure
Coef.
t-stat.
Time
1.388
(51.89)
***
Intercept
16.609
(72.45)
***
Firm F.E.
Yes
Adj. R2
0.870
N
30,700
Panel C: Average ESG performance over time 
Dependent Variable: 
ESG_Avg
Coef.
t-stat.
Time
0.764
(29.85)
***
Intercept
46.256
(210.9)
***
Firm F.E.
Yes
Adj. R2
0.872
N
30,700
This table reports the trends over time of ESG rating disagreement in Panel A, ESG disclosure 
in Panel B, and Average ESG performance in Panel C. Time is the difference between the 
fiscal-year for a particular firm-year observation and 2004, which is the first year in the 
sample. All t-statistics are based on two-tailed tests and are calculated based on standard 
errors that are clustered by firm. All continuous variables have been winsorized at the 1st and 
99th percentiles. See Appendix A for variable definitions. *, **, *** indicate statistical 
significance at the 0.10, 0.05, and 0.01 levels, respectively.

51 
 
TABLE 4 
Is better ESG Disclosure or Performance associated with greater ESG Rating 
Disagreement?
Dependent Variable: ESG_Disagreement 
OLS 
  
OLS w/  
Firm F.E. 
Coef.
t-stat.
Coef.
t-stat.
ESG_Disclosure 
0.127
(16.27)
***
0.094
(10.23)
***
ESG_Avg
-0.158
(-19.32)
***
-0.206
(-18.09)
***
Firm Size
0.928
(12.57)
***
-0.012
(-0.06)
ROA
1.723
(2.23)
**
-0.500
(-0.64)
BTM
-0.438
(-2.87)
***
-0.290
(-1.97)
**
Leverage
-0.142
(-0.35)
1.710
(2.82)
***
Analyst Following
-0.411
(-3.34)
***
0.072
(0.53)
Inst. Ownership
-0.010
(-2.60)
***
-0.012
(-1.86)
*
Intercept
14.835
(3.66)
***
13.806
(4.15)
***
ESG Rater F.E.
Yes
Yes
Year F.E.
Yes
Yes
Industry F.E.
Yes
No
Country F.E.
Yes
No
Firm F.E.
No
Yes
Adj. R2
0.118
0.516
N
30,700
30,700
This table reports the results of tests based on Equation 1 examining the effects of ESG disclosure on ESG 
disagreement. There are two specifications: (1) ordinary least squares (OLS) without firm fixed effects, and (2) 
OLS with firm fixed effects. All t-statistics are based on two-tailed tests and are calculated based on standard 
errors that are clustered by firm. All continuous variables have been winsorized at the 1st and 99th percentiles. 
See Appendix A for variable definitions. *, **, *** indicate statistical significance at the 0.10, 0.05, and 0.01 
levels, respectively.

52 
 
TABLE 5 
Is better Pillar-specific Disclosure associated with greater Rating Disagreement? 
 
 
 
 
 
 
 
 
Dependent Variable: 
ESG_Disagreement 
 
E_Disagreement 
 
S_Disagreement 
 
G_Disagreement 
OLS 
OLS w/ Firm 
F.E. 
 
OLS 
OLS w/ Firm 
F.E. 
OLS 
OLS w/ 
Firm F.E. 
OLS 
OLS w/ 
Firm F.E. 
E_Disclosure 
0.075 
*** 
0.047 
*** 
 
0.008 
0.026 
*** 
 
 
(10.14) 
(5.95) 
 
(1.14) 
(2.90) 
S_Disclosure 
0.018 
*** 
0.036 
*** 
 
0.08 
*** 
0.058 
*** 
(2.83) 
(5.20) 
 
(13.01) 
(7.22) 
 
G_Disclosure 
0.039 
*** 
-0.003 
 
 
0.036 
*** 
-0.013 
(3.77) 
(-0.34) 
 
 
(3.25) 
(-1.06) 
Intercept 
14.729 
*** 
15.003 
*** 
 
11.258 
** 
13.534 
*** 
 
17.414 
*** 
19.033 
*** 
 
28.029 
*** 
23.929 
*** 
(3.63) 
(4.49) 
 
(1.96) 
(3.14) 
(3.66) 
(5.10) 
(4.17) 
(5.33) 
Controls 
Yes 
Yes 
 
Yes 
Yes 
Yes 
Yes 
Yes 
Yes 
ESG Rater F.E. 
Yes 
Yes 
 
Yes 
Yes 
 
Yes 
Yes 
 
Yes 
Yes 
Year F.E. 
Yes 
Yes 
 
Yes 
Yes 
 
Yes 
Yes 
 
Yes 
Yes 
Industry F.E. 
Yes 
No 
 
Yes 
No 
 
Yes 
No 
 
Yes 
No 
Country F.E. 
Yes 
No 
 
Yes 
No 
 
Yes 
No 
 
Yes 
No 
Firm F.E. 
No 
Yes 
 
No 
Yes 
No 
Yes 
No 
Yes 
Adj. R2 
0.119 
0.517 
 
0.066 
0.432 
0.083 
0.435 
0.283 
0.504 
N 
30,700 
30,700 
 
30,700 
30,700 
 
30,700 
30,700 
 
30,700 
30,700 
F-test (p-value) 
 
 
 
E_Disc=S_Disc 
0.000 
*** 
0.382 
 
- 
- 
 
- 
- 
 
- 
- 
E_Disc=G_Disc 
0.010 
*** 
0.000 
*** 
 
- 
- 
 
- 
- 
 
- 
- 
S_Disc=G_Disc 
0.108 
0.002 
*** 
 
- 
- 
- 
- 
- 
- 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
This table reports the effects of disclosure on disagreement for each respective ESG pillar: Environment (E), Social (S), or Governance (G). For each pillar, there 
are two specifications: (1) ordinary least squares (OLS) without firm fixed effects, and (2) OLS with firm fixed effects. Controls are the same as before, except 
when an individual pillar is examined (e.g., E_Disagreement), then ESG_Avg is replaced with the average rating for the pillar being examined (e.g., E_Avg). All 
t-statistics are based on two-tailed tests and are calculated based on standard errors that are clustered by firm. All continuous variables have been winsorized at the 
1st and 99th percentiles. See Appendix A for variable definitions. *, **, *** indicate statistical significance at the 0.10, 0.05, and 0.01 levels, respectively.

53 
 
TABLE 6 
Mandatory ESG Disclosure 
 
  
Validation 
  
Diff-in-Diff 
  
 
Dynamic 
Validation
 
Dynamic  
Diff-in-Diff
 
Dependent Variable: 
ESG_Disclosure 
  
ESG_Disagreement 
  
 
ESG_Disclosure
 
ESG_Disagreement
 
  
Coef. 
t-stat. 
 
Coef.
t-stat.
  
Coef.
t-stat.
  
Coef.
t-stat.
 
Mandatory_Disclosure 
1.435 
(5.24) *** 
0.448
(2.20)
** 
 
 
 
Mandatory_Disclosure t = -2 
 
  
 
 
0.233
(0.85)
 
-0.206
(-1.05)
 
Mandatory_Disclosure t = -1 
 
  
 
 
0.323
(1.07)
 
-0.077
(-0.33)
 
Mandatory_Disclosure t = 0 
 
  
 
 
1.768
(4.89)
*** 
-0.366
(-1.30)
 
Mandatory_Disclosure t > +1 
 
  
 
 
1.387
(3.48)
*** 
0.826
(2.72)
** 
ESG_Avg 
0.274 (21.45) *** 
-0.181
(-15.82)
***  
0.273
(21.47)
*** 
-0.181
(-15.82)
*** 
Firm Size 
0.759 
(3.34) *** 
0.054
(0.28)
  
 
0.752
(3.30)
*** 
0.070
(0.36)
  
ROA 
0.201 
(0.24)   
-0.463
(-0.59)
  
 
0.198
(0.23)
  
-0.430
(-0.54)
  
BTM 
0.244 
(1.29)   
-0.272
(-1.84)
* 
 
0.240
(1.27)
  
-0.274
(-1.85)
* 
Leverage 
0.089 
(0.11)   
1.753
(2.84)
***  
0.088
(0.11)
  
1.787
(2.90)
*** 
Analyst Following 
0.725 
(4.34) *** 
0.145
(1.05)
  
 
0.722
(4.31)
*** 
0.154
(1.13)
  
Inst. Ownership 
-0.004 
(-0.52)   
-0.012
(-1.89)
* 
 
-0.004
(-0.50)
  
-0.012
(-1.91)
* 
Intercept 
-17.676 
(-3.57) *** 
12.127
(3.56)
***  
-17.601
(-3.56)
*** 
11.913
(3.50)
*** 
ESG Rater F.E. 
Yes   
  
Yes
  
  
 
Yes
 
  
Yes
 
  
Year F.E. 
Yes   
  
Yes
  
  
 
Yes
 
  
Yes
 
  
Firm F.E. 
Yes   
  
Yes
  
  
 
Yes
 
  
Yes
 
  
Adj. R2 
0.885   
  
0.511
  
  
 
0.885
 
  
0.512
 
  
N 
30,700   
  
30,700
  
  
 
30,700
 
  
30,700
 
  
  
  
  
  
  
  
  
 
 
 
This table reports the results using mandatory ESG disclosure requirements, which went into effect in different countries and at different times. The validation 
columns confirm that mandatory disclosure requirements improve firms’ ESG disclosures. The Diff-in-Diff columns show the difference-in-differences analysis. 
All t-statistics are based on two-tailed tests and are calculated based on standard errors that are clustered by firm. All continuous variables have been winsorized at 
the 1st and 99th percentiles. See Appendix A for variable definitions. *, **, *** indicate statistical significance at the 0.10, 0.05, and 0.01 levels, respectively.

54 
 
TABLE 7
Multivariate analyses of ESG Metric Disagreement
 
Panel A: Firm-level ESG Disclosure
Dependent variable: ESG_Metric_Disagreement
Baseline
High ESG Disclosure
Input/Outcome
2.869*** 
2.726*** 
2.877***
2.728***
(33.11) 
(31.47) 
(22.87)
(21.88)
 
 
Both Outcomes
0.585*** 
0.451*** 
-0.023
-0.128
(5.00) 
(3.87) 
(-0.13)
(-0.73)
 
 
High ESG_Disclosure
1.553*** 
0.616*** 
(13.21) 
(5.76) 
 
 
High ESG_Disclosure × Input/Outcome 
-0.160 
0.005 
(-0.99) 
(0.03) 
 
 
High ESG_Disclosure × Both Outcomes 
0.748*** 
0.855*** 
(3.41) 
(3.91) 
Intercept
14.651***
10.320***
15.868***
10.329***
(39.64)
(10.77)
(41.89)
(10.80)
Controls
Yes
Yes
Yes
Yes
Year F.E.
Yes
Yes
Yes
Yes
Industry F.E.
Yes
No
Yes
No
Country F.E.
Yes
No
Yes
No
Firm F.E.
No
Yes
No
Yes
Adj. R2
0.017
0.056
0.020
0.056
N
927,352
927,352
927,352
927,352

55 
 
TABLE 7 (continued)
Panel B: Industry-level ESG Disclosure
 
Dependent variable: ESG_Metric_Disagreement
Industry  
ESG Disclosure
Industry & Firm  
ESG Disclosure
Input/Outcome
2.952***
2.757***
2.936***
2.744***
(27.00)
(25.40)
(22.24)
(20.95)
Both Outcomes
-0.495***
-0.660***
-0.800***
-0.919***
(-3.03)
(-4.11)
(-4.04)
(-4.67)
High ESG_Disclosure
1.588***
0.651***
(13.47)
(6.07)
High ESG_Disclosure × Input/Outcome
-0.113
0.023
(-0.67)
(0.14)
High ESG_Disclosure × Both Outcomes
0.363
0.458**
(1.63)
(2.07)
Industry High ESG_Disclosure
-0.075 
-0.066 
-0.116 
-0.070 
(-0.95) 
(-0.88) 
(-1.48) 
(-0.92) 
 
 
 
 
Industry High ESG_Disclosure × 
Input/Outcome
-0.151 
-0.050 
-0.173 
-0.053 
(-0.99) 
(-0.33) 
(-1.09) 
(-0.34) 
 
 
 
 
Industry High ESG_Disclosure ×  
Both Outcomes
1.831*** 
1.879*** 
1.753*** 
1.787*** 
(8.56) 
(8.83) 
(8.04) 
(8.28) 
Intercept
14.705***
10.215***
15.925***
10.252***
(39.37)
(10.63)
(41.66)
(10.69)
Controls
Yes
Yes
Yes
Yes
Year F.E.
Yes
Yes
Yes
Yes
Industry F.E.
Yes
No
Yes
No
Country F.E.
Yes
No
Yes
No
Firm F.E.
No
Yes
No
Yes
Adj. R2
0.018
0.056
0.020
0.056
N
927,352
927,352
927,352
927,352
This table shows the regression results of disagreement for each matched-metric from Sustainalytics and ASSET4. 
The variables of interest are Input/Outcome (e.g., where the Sustainalytics metric for the topic is an input and the 
corresponding ASSET4 metric is an outcome, or vice versa), Both Outcomes (i.e., both Sustainalytics and ASSET4 
use an outcome metric), and the interaction terms between these variables and ESG disclosure (High 
ESG_Disclosure). The comparison group in the model is Both Inputs (i.e., both Sustainalytics and ASSET4 use an 
input metric). Panel A illustrates the role of firms’ ESG disclosure and Panel B adds industry-level ESG disclosure. 
Controls consists of the same variables used in the prior tests, except now ESG_Avg is calculated at the metric level, 
rather than at the firm-level. Similarly, ESG_Metric_Disagreement is calculated at the metric level. See Appendix A 
for variable definitions. ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels, respectively.

56 
 
TABLE 8
Consequences of ESG Rating Disagreement using 3-Day Windows
Panel A: Market Consequences 
Dependent Variable:
Absolute CAR
Return Volatility
Bid-Ask Spread
ESG_Disagreement
0.011*** 
0.008*** 
0.004*** 
0.003** 
0.001 
0.000 
(5.24) 
(3.42) 
(4.68) 
(2.57) 
(1.01) 
(0.05) 
ESG_Avg
-0.012***
-0.007***
-0.005***
-0.002**
0.000
0.001***
(-7.56)
(-2.63)
(-6.82)
(-2.21)
(0.34)
(2.91)
Firm Size
-0.341***
-0.494***
-0.141***
-0.210***
-0.063***
-0.068***
(-26.27)
(-8.07)
(-25.02)
(-7.45)
(-14.19)
(-6.41)
ROA
-4.025***
-1.691***
-1.577***
-0.630***
-0.306***
-0.096*
(-15.11)
(-5.21)
(-13.88)
(-4.34)
(-4.98)
(-1.76)
BTM
0.589***
0.562***
0.232***
0.195***
0.093***
0.077***
(13.81)
(8.33)
(13.44)
(7.32)
(8.52)
(5.45)
Leverage
0.774***
1.532***
0.315***
0.614***
0.122***
0.233***
(8.96)
(7.10)
(8.35)
(6.10)
(5.70)
(5.76)
Analyst Following
0.106***
-0.062
0.040***
-0.026
-0.058***
-0.073***
(3.94)
(-1.32)
(3.30)
(-1.26)
(-6.81)
(-7.66)
Inst. Ownership
-0.002***
-0.004***
-0.001***
-0.001***
-0.001***
-0.001***
(-3.84)
(-6.18)
(-2.92)
(-4.09)
(-7.02)
(-7.73)
Intercept
5.665***
6.759***
2.362***
2.876***
0.915***
0.864***
(45.66)
(11.98)
(43.93)
(11.23)
(26.60)
(8.94)
Year F.E.
Yes
Yes
Yes
Yes
Yes
Yes
Industry F.E.
Yes
No
Yes
No
Yes
No
Country F.E.
Yes
No
Yes
No
Yes
No
Firm F.E.
No
Yes
No
Yes
No
Yes
Adj. R2
0.135
0.213
0.109
0.179
0.412
0.612
N
74,032
74,032
74,032
74,032
74,032
74,032
Panel B: Market Consequences over Time 
Dependent Variable:
Absolute CAR
Return Volatility
Bid-Ask Spread
ESG_Disagreement
0.004
0.000
-0.000
-0.002
0.000
-0.000
(0.92)
(0.01)
(-0.24)
(-1.13)
(1.63)
(-0.55)
ESG_Disagreement
0.001
0.002*
0.001**
0.001***
-0.000
0.000
× Time 
(1.49) 
(1.90) 
(2.42) 
(2.71) 
(-1.31) 
(0.70) 
Intercept
5.684***
6.742***
2.375***
2.865***
0.912***
0.863***
(45.38)
(12.00)
(43.59)
(11.25)
(26.45)
(8.93)
Controls
Yes
Yes
Yes
Yes
Yes
Yes
Year F.E.
Yes
Yes
Yes
Yes
Yes
Yes
Industry F.E.
Yes
No
Yes
No
Yes
No
Country F.E.
Yes
No
Yes
No
Yes
No
Firm F.E.
No
Yes
No
Yes
No
Yes
Adj. R2
0.135
0.213
0.109
0.179
0.412
0.612
N
74,032
74,032
74,032
74,032
74,032
74,032
This table reports the results on the market consequences of ESG rating disagreement using tests during the 3-day 
window (-1, 0, +1) around the ESG rating release date. Panel A reports the main consequences tests, and Panel B 
reports the impact of ESG rating disagreement over time. All t-statistics are based on two-tailed tests and are calculated 
based on standard errors that are clustered by firm. All continuous variables have been winsorized at the 1st and 99th 
percentiles. See Appendix A for variable definitions. *, **, *** indicate statistical significance at the 0.10, 0.05, and 
0.01 levels, respectively.

57 
 
TABLE 9
Consequences of ESG Rating Disagreement on Firm-level Financing Outcomes
Panel A: Financing Outcomes 
Dependent Variable:
Equity Issuance
Debt Issuance
Cash Holdings
ESG_Disagreement
-0.0002 
-0.0011** 
-0.0022*** 
-0.0000 
0.0007*** 
0.0002* 
(-0.3465) 
(-2.1515) 
(-4.4612) 
(-0.0186) 
(3.8483) 
(1.8137) 
ESG_Avg
0.0006
-0.0001
-0.0004
0.0003
-0.0001
-0.0001
(1.4189)
(-0.1166)
(-1.0990)
(0.6435)
(-0.3824)
(-1.3991)
Firm Size
0.0134***
0.0115
0.0778***
0.0450***
-0.0206***
-0.0270***
(2.7317)
(1.1401)
(19.1637)
(4.1696)
(-11.9057)
(-9.5946)
ROA
-0.2020***
0.1445***
-0.0969*
0.0854
-0.0927***
0.0006
(-3.8529)
(2.7812)
(-1.8560)
(1.5593)
(-3.6930)
(0.0512)
BTM
-0.0884***
-0.0745***
-0.0126
-0.0581***
-0.0414***
-0.0049**
(-8.4021)
(-6.6218)
(-1.3729)
(-6.6726)
(-12.0585)
(-2.5095)
Leverage
-0.0531*
0.0272
0.3382***
-0.1142***
-0.1122***
-0.0298***
(-1.9212)
(0.7102)
(13.2584)
(-2.9389)
(-10.0653)
(-2.9573)
Analyst Following
0.0163**
0.0043
-0.0291***
0.0225***
0.0244***
-0.0009
(2.0216)
(0.4962)
(-4.4479)
(2.7661)
(8.8754)
(-0.5056)
Inst. Ownership
0.0018***
0.0000
0.0005**
-0.0003
-0.0006***
-0.0000
(6.5315)
(0.0700)
(2.2628)
(-0.8191)
(-6.0933)
(-0.2141)
Intercept
0.4067***
0.5438***
-0.0818**
0.2994***
0.3760***
0.4045***
(10.4977)
(5.4594)
(-2.3413)
(2.8977)
(26.2289)
(15.8940)
ESG Rater F.E.
Yes
Yes
Yes
Yes
Yes
Yes
Year F.E.
Yes
Yes
Yes
Yes
Yes
Yes
Industry F.E.
Yes
No
Yes
No
Yes
No
Country F.E.
Yes
No
Yes
No
Yes
No
Firm F.E.
No
Yes
No
Yes
No
Yes
Adj. R2
0.227
0.565
0.154
0.450
0.302
0.835
N
30,700
30,700
30,700
30,700
27,084
27,084
Panel B: Financing Outcomes over Time 
Dependent Variable:
Equity Issuance
Debt Issuance
Cash Holdings
ESG_Disagreement
-0.0017
-0.0024**
0.0010
0.0016
0.0001
-0.0000
(-1.3578)
(-2.0097)
(0.9111)
(1.4683)
(0.1846)
(-0.2043)
ESG_Disagreement
0.0002 
0.0002 
-0.0004*** 
-0.0002 
0.0001* 
0.0000 
× Time
(1.2709) 
(1.1796) 
(-3.0591) 
(-1.6414) 
(1.7687) 
(1.0437) 
Intercept
0.4086***
0.5404***
-0.0858**
0.3035***
0.3768***
0.4040***
(10.5578)
(5.4216)
(-2.4559)
(2.9404)
(26.2745)
(15.8550)
Controls
Yes
Yes
Yes
Yes
Yes
Yes
ESG Rater F.E.
Yes
Yes
Yes
Yes
Yes
Yes
Year F.E.
Yes
Yes
Yes
Yes
Yes
Yes
Industry F.E.
Yes
No
Yes
No
Yes
No
Country F.E.
Yes
No
Yes
No
Yes
No
Firm F.E.
No
Yes
No
Yes
No
Yes
Adj. R2
0.227
0.565
0.154
0.450
0.302
0.835
N
30,700
30,700
30,700
30,700
27,084
27,084
This table reports the results of ESG rating disagreement on firm-level financing outcomes. Panel A reports the 
main financing consequences tests, and Panel B reports the impact over time. All t-statistics are based on two-
tailed tests and are calculated based on standard errors that are clustered by firm. See Appendix A for variable 
definitions. *, **, *** indicate statistical significance at the 0.10, 0.05, and 0.01 levels, respectively.