DBJ Discussion Paper Series, No. 2201

The Real Effect of ESG Rating Agencies

Natalya Bikmetova（University of Central Florida）
Christo A. Pirinsky（University of Central Florida）

August 2022

Discussion Papers are a series of preliminary materials in their draft form. No quotations,reproductions or circulations should be made without the written consent of the authors in order toprotect the tentative characters of these papers. Any opinions, findings, conclusions or
recommendations expressed in these papers are those of the authors and do not reflect the views of
the Institute.

The Real Effect of ESG Rating Agencies*

Natalya Bikmetova and Christo A. Pirinsky
August 2022
Abstract
We examine the effect of coverage by ESG rating agencies on corporate policy. To control forthe selection in coverage, we construct a sample of firms with existing ESG ratings from someagencies and study the marginal effect of the introduction of a new coverage for a firm relative tofirms that did not receive the additional coverage. We find that when firm ESG coverageintensifies, its ESG ratings improve, its toxic emissions decline, and its board diversity increases.
More covered firms disclose more ESG information and engage more in the acquisitions oftargets with strong ESG performance. ESG coverage increases firm institutional ownership andvaluation ratios. Our results suggest that ESG rating agencies exhibit material impact on firms.
Keywords: ESG, ESG rating agencies, corporate board, mergers and acquisitions, institutional
ownership
JEL classification: G14, G40, K42*

Bikmetova is with the College of Business Administration of University of Central Florida, PO Box 161400,
Orlando, FL 32816-1400, natalya.bikmetova@ucf.edu; Pirinsky is with the College of Business Administration of
University of Central Florida, PO Box 161400, Orlando, FL 32816-1400, cpirinsky@ucf.edu, (407) 823-5962. We
thank Makoto Anayama, Jess Cornaggia, Robert Davidson, Vlad Gatchev, Masaharu Hanazaki, Jayant Kale, Kevin
Mullally, Plamen Nenov, Deyan Radev, Kazunori Suzuki, and seminar participants at the University of Central
Florida, Santa Clara University, the 2021 Annual Conference at the FEBA of Sofia University, and the ResearchInstitute of Capital Formation of the Development Bank of Japan for helpful comments.
11. Introduction
Fueled by deepening environmental problems and recent corporate governance scandals, thedemand for information on the environmental, social, and governance (ESG) performance offirms has been increasing rapidly in recent years. In response to this demand, firms, media
outlets, and independent analysts started providing ESG information to the market. However, themultidimensional nature of ESG and the lack of well-established disclosure standards
substantially decrease the usability of this information. As a result, private entities (agencies)
started aggregating ESG information into standardized ratings. In this paper, we explore theimplications of their coverage for the ESG and capital market performance of firms.
To examine the impact of ESG coverage (not ratings) on firms, we focus on the times
four major ESG rating agencies – MSCI KLD, Refinitiv, Sustainalytics, and Bloomberg –initiated coverage of large groups of firms in the market. Individual firms do not influence thecoverage decisions of ESG rating agencies. Focusing on coverage initiations of large groups ofstocks also mitigates concerns that coverage could reflect some firm-specific events – forexample, additions to major market indices or acquisitions. Agencies, however, could be biased
towards stocks with certain characteristics. To control for this selection, we compile a sample offirms already covered by some agencies and study the effect of additional coverage initiation on
firms relative to similar firms that did not receive the additional coverage. A multi-dimensional
matching procedure and numerous fixed effects further control for a broad set of covariates that
could simultaneously affect both ESG coverage and subsequent firm behavior.
Our main results could be summarized as follows. First, when firm ESG coverageintensifies, its ESG ratings improve, its toxic emissions decline, and its board diversity increases.
Second, firms with more ESG ratings disclose more ESG-related information. Third, operating in2

the ESG spotlight intensifies firm M&A activity towards targets with strong ESG performance.
Fourth, ESG coverage transfers ownership from individual investors towards financial
institutions, especially institutions with revealed preferences for ESG stocks. Finally, ESG
coverage improves firm capital market performance.
The effect of ESG coverage is persistent and robust to the inclusion of fixed effects forthe firm, the firm industry, the rating agency, and all major stock market indices. Furthermore,
the effect is not observed over pre-treatment periods (parallel trend assumption). The results arealso economically meaningful. For example, an initiation of ESG coverage by an additional
agency increases firm ESG score by around 8 percent relative to otherwise similar firms that did
not receive the additional coverage. One unit of treatment also increases firm institutional
ownership by 1.38 percent (for comparison, the average institutional ownership in our sample is61.1 percent).
The improved ESG performance of covered firms is subject to alternative interpretations.
For one, it is possible that greater coverage creates incentives for rating agencies to overstate
their ratings, the way it does for bond rating agencies (e.g., Bolton et al. 2012; Becker and
Milbourn 2011). We argue that rating inflation is less likely in the ESG space because ESG
rating agencies are neither hired nor paid by firms, but by the users of their ratings.1 There is also
evidence that high ratings could damage the reputation of an agency upon the revelation ofadverse ESG information to the market (see Section 4.2).
An alternative interpretation of the higher ratings could be greenwashing, or the tendency
of firms to overstate their ESG performance in voluntary disclosures. Several of our tests,
however, suggest that greenwashing is unlikely to explain the totality of our results. First, greater
1

See e.g., https://www.reuters.com/business/sustainable-business/regulators-turn-spotlight-company-sustainabilityratings-2021-07-26/
3

ESG coverage brings real improvements to ESG performance – more covered firms reduce their
toxic emissions and increase the diversity of their corporate boards. We also show that firms withadditional coverage experience smaller number of government enforcement actions for labor andcustomer violations. Finally, inconsistent with ESG manipulation, we find that improved ESG
ratings never reverse in the long run following the additional coverage.
It has been proposed in the literature that higher ESG ratings could lower the firm cost ofcapital (e.g., Engle et al. 2020; Goss and Roberts 2011; Chava 2014; Krueger, Sautner, andStarks 2020; Dyck et al. 2019). In this respect, greater ESG coverage could lower the cost ofcapital of firms because it provides a signal about ESG performance. Since firms with less (orno) coverage would be considered “less-compliant” with ESG, ESG-conscious investors could
withhold from investing in the firm. Consistent with this idea, we show that ESG coverageincreases firm institutional ownership. The increased investor recognition is also expected todecrease the firm cost of equity capital (Merton 1987). Consistent with a lower cost of capital,
we also find that more covered firms exhibit higher valuation ratios.
Our findings suggest that ESG rating agencies affect real economic activity. The results
are broadly consistent with Pastor, Stambaugh, and Taylor (2021), who argue that increased
demand could bid up the prices of green assets. Along the same lines, ratings are importantbecause they provide information that investors demand. In this regard, our results echo theevidence on information production in other settings. For example, an extensive literature showsthat more and better accounting disclosures can lead to tangible capital-market benefits for firms(see, e.g., Christensen, Hail, and Leuz 2019). There is also evidence that bond ratings provide
value (Hand et al. 1992; Kliger and Sarig 2000).

4We add to the growing literature on ESG. Most of the existing research evaluates the
economic implications of ESG ratings (see, e.g., Deng, Kang, and Low 2013; Ng and Rezaee
2015; Lins, Servaes, and Tamayo 2017; Hartzmark and Sussman 2019). In contrast, we showthat ESG coverage improves both firm ESG and financial performance. Our paper is related to
the work of Krueger et al. (2021) who find that mandatory ESG disclosures increase both theavailability and the quality of ESG reporting. The results also imply that firms with greater ESG
coverage, such as public firms, could be advantaged relative to firms with smaller ESG coverage.
The paper also provides an insight into aggregate M&A activity. It is well documented
that merger activity evolves in waves (Mitchell and Mulherin 1996; Harford 2005; Rhodes‐Kropf
and Viswanathan 2004). One common explanation of this fluctuation is a strategic response totechnological and regulatory shocks. Economic activity, however, is guided not only by formal
regulations but also by existing social norms. Our results suggest that systematic shifts in social
norms could influence aggregate M&A activity in the market.
The remainder of the paper is organized as follows. Section 2 discusses the real effect of
ESG rating agencies; Section 3 outlines our empirical methodology; Sections 4 and 5 report theresults on the implications of ESG coverage for firm ESG and financial performance. Section 6
concludes.

2. The Real Effect of ESG Rating Agencies
The demand for ESG information has been increasing over time. ESG disclosures,
however, face a series of challenges. Perhaps the most important one is the multidimensional
nature of ESG.2 Currently, the term ESG encompasses a wide set of activities and policies, all of2

See Christensen, Hail, and Leuz (2019) and Larcker, Tayan, and Watts (2021) for a review of the literature on
current ESG disclosures and the adoption of ESG standards.
5which could differ substantially across firms, industries, and countries. As a result, the ESG
information that exists in the market is non-standardized and fragmented, which in turnsubstantially decreases its usefulness to market participants (Kitzmueller and Shimshack 2012;
Liang and Renneboog 2017; Amel-Zadeh and Serafeim 2018).
To fulfill the need for standardized ESG information, a series of private entities(agencies) started constructing ESG ratings of firms. The coverage of these agencies has
expanded gradually over time, and currently, they constitute the primary source of ESG
information in the market (see, e.g., Gibson et al. 2020). By aggregating information fromvarious sources and standardizing it, ESG rating agencies effectively serve as a disclosure
mechanism of ESG information. While the actual content of ESG scores has been debated in theliterature (Berg, Koelbel, and Rigobon 2019; Dimson, Marsh, and Staunton 2020), the wide useof these ratings suggests that ESG rating agencies could be an important player in modern
markets.
Rating agencies could affect firm performance because they provide information about
the ESG standing of firms. Firms without coverage would be considered “non-compliant” with
ESG norms. As a result, major stakeholders such as employees and investors could withholdfrom doing business with the firm. In contrast, firms with coverage have the flexibility to comply
with ESG norms when optimal to do so because their ESG performance could be reliably
communicated to the market. This value of this flexibility would generally depend on thestrength of ESG norms in the market.
There is extensive evidence that high ESG scores could lower firm cost of capital. ESG
ratings shape the investment decisions of an increasingly large group of institutional investors(see Gibson et al. 2020; GSIA 2016; USSIF 2020). Indeed, fund investors’ preferences for ESG

6firms have largely contributed to the expansion of the ESG rating industry. For example,
Hartzmark and Sussman (2019) show that investors allocate more capital into funds with the best
ESG portfolios and withdraw funds from funds with the worst ESG portfolios. Similarly, Baueret al. (2021) document that pension funds’ clients prefer the funds they invest in to engage withmore sustainable companies. Some institutional investors also engage in environmental andsocial activism to improve their holdings’ EGS profile (Dimson et al. 2015; Krueger, Sautner,
and Starks 2020; Azar et al. 2021).
The fact that firm ESG disclosures are voluntary and largely unaudited has raised
concerns that firms could behave opportunistically and over-state their ESG performance, anactivity referred to as greenwashing. Consistent with this possibility, Li and Wu (2020) showthat firms could join the United Nations Global Compact (UNGC) to signal corporate social
responsibility engagement while maintaining similar ESG performance. A firm propensity foropportunistic ESG disclosures, however, could be mitigated by monitoring by the public (Kimand Lyon 2015). Marquis et al. (2016) also show that firms in countries with a higher level ofscrutiny and social norms are less likely to engage in ESG misrepresentations.
There is also anecdotal evidence that the ESG ratings issued by different agencies often
diverge (Mackintosh 2018; Wigglesworth 2018). Berg, Koelbel, and Rigobon (2019) decompose
the divergence into three sources: different scope of categories, different measurement of
categories, and different weights of categories. They find that scope and measurement
divergence are the main drivers in rating dispersion. Disagreement among information providers,
however, is expected if they indeed generate new information. Consistent with the idea that7

diverse opinions are informative, Christensen, Serafeim, and Sikochi (2021) show that greater
ESG disclosure leads to greater ESG rating disagreement across providers.3

3. Empirical Design
3.1. Methodology
Assessing the economic impact of ESG rating agencies requires comprehensive data
about firms’ exposure to ratings, independently of the information that these ratings provide. To
address this challenge, we identify the times four major ESG rating agencies – MSCI KLD,
Refinitiv, Sustainalytics, and Bloomberg – initiated coverage in the market.
Several reasons motivate us to focus on these specific data providers. First, theseagencies have been recognized as important sources of ESG information by independent third
parties. For example, ERM, the largest global sustainability consultancy, includes all four
providers in their 2020 report and notes that the ratings most favored by investors are MSCI KLD
and Sustainalytics.4 Harvard Business Review includes these agencies in the list of well-known
third-party ESG rating providers.5 Multiple advisory and consulting firms also consider theseagencies as ESG data providers of primary relevance.6 Finally, numerous institutional investors
rely on these ratings for ESG information on their portfolio companies.7

3

Gibson, Krueger, and Schmidt (2019) find that stock returns are positively related to ESG rating disagreement,suggesting that risk-averse investors could demand a risk premium for large disagreements.
4
See https://www.sustainability.com/globalassets/sustainability.com/thinking/pdfs/sustainability-ratetheraters2020report.pdf
5
See https://corpgov.law.harvard.edu/2017/07/27/esg-reports-and-ratings-what-they-are-why-they-matter/6
For example, see https://www.sicm.com/docs/who-rates.pdf , https://brokerchooser.com/how-to-invest/top-esg-rating-providers,
https://frameworkesg.com/wp-content/uploads/2019/08/RatingsRankings2019.pdf,
andhttps://investmentsandwealth.org/getattachment/fdf4d0e3-adc0-487a-bbe0-624cdefb3b2f/IWM17NovDec7
For example, many institutional investors explicitly refer to the MSCI data; Morningstar incorporates
Sustainalytics scores into their ESG fund rating computation; Bloomberg provides ESG data to the broad business
community; and ASSET4 had the largest financial firms among its clients even before its eventual acquisition by
Refinitiv. See https://www.sustainable-investment.org/Ratings/Researchkonzepte/Asset4.aspx
8

The above databases have also been used extensively in academic research. Overall, the
MSCI KLD data appears to be the most used by researchers (e.g., Lins et al. 2017; Hartzmark andSussman 2019; Ferrell et al. 2016; Di Giuli and Kostovetsky 2014; Deng et al. 2013; Engle et al.
2020). This is not surprising, given that MSCI KLD is the oldest provider of ESG information in
the market. However, multiple studies also rely on Sustainalytics (e.g., Dyck et al. 2019; Engle etal. 2020), Refinitiv (former ASSET4) (Stellner et al. 2015), and Bloomberg disclosure scores (Ngand Rezaee 2020; Gualandris et al. 2021; Dyck et al. 2019). Two recent studies, Christensen etal. (2021) and Bolton and Kacperczyk (2021), employ all four datasets in their analysis.
A few other agencies also provide ESG information, but their methodology and coverage
are not relevant to our research design. For example, RepRisk is one of the leading data science
companies focusing on ESG risks research. The business model of the company is to evaluate themedia coverage of all firms in the market, as it relates to ESG issues, and identify negative
commentaries. As such, the concept of coverage here is not well defined. Another data provider,
Corporate Knights Global 100 exhibits sparse U.S. coverage as if focuses on the top 100
sustainable firms in the world. RobecoSAM has also been focusing predominantly on European
firms. In contrast, the four agencies in our study exhibit long history of comprehensive coverage,strong reputation, and clear coverage criteria, allowing us to identify precisely shocks to ESG
exposure.
Panel A of Table 1 lists the inclusion criteria for the 10 coverage initiations of domestic
publicly traded companies analyzed in this paper (the timeline of the events is visualized in
Figure 1). We observe that rating agencies exhibit a preference towards larger firms andmembers of major market indices. As a result, a good identification strategy needs to control forthese preferences.

9

To control for the selection in coverage, we construct a sample of firms with existing
ratings from some of the four ESG rating agencies and evaluate the impact of additional
coverage. Focusing on firms that were already selected for coverage controls for the preferencesof rating agencies towards stocks with certain characteristics, as revealed in their choices. For
each event of coverage expansion, we then identify all firms that receive additional coverage andcould be matched to firms that exhibit similar probability of receiving the additional coverage
but were not selected by the rating agency. To identify such firms, we run a Propensity Score
Model (PSM) predicting the probability of additional coverage based on firm characteristics
sampled one year before coverage expansion. We then match each treated firm to five untreatedfirms with the closest propensity score (if available). All matched firms constitution the control
group.8 Figure 2 illustrates our sample construction procedure. The regions “T” and “C”
correspond to our treatment and control groups.
Table 2 reports estimated coefficients from the PSMs corresponding to all seven
expansion events used in the paper. Note that we lose the first three events because we focus
only on firms with existing coverage. The predictive variables include firm ESG score, total
assets, market-to-book ratio, number of covering agencies, return on assets (ROA), as well as
industry fixed effects. The predictive power of these variables varies across events. The mostconsistent predictor of ESG coverage is firm size (total assets). More covered firms also tend tohave larger number of institutional investors. Valuation ratios and ROAs are generally positively
correlated with ESG coverage, but the significance of these variables is less pronounced acrossevents. Interestingly, existing ESG ratings are not significantly related to the probability ofreceiving additional coverage (the only exception is the Sustainalytics coverage in 2009).

8

We exclude matches with propensity scores difference exceeding 0.002.
10Based on the above sample, we estimate a difference-in-differences model with industry
and year fixed effects, evaluating the marginal impact of additional coverage of a firm (treatedfirm) relative to a similar firm that did not receive the additional coverage (control firm):
𝑌,𝛼𝛽. 𝑇𝑟𝑒𝑎𝑡𝑒𝑑 ,𝛾. 𝑃𝑜𝑠𝑡 ,𝛿. 𝑇𝑟𝑒𝑎𝑡𝑒𝑑 , ∗ 𝑃𝑜𝑠𝑡 ,𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐹𝐸

𝑌𝑒𝑎𝑟𝐹𝐸𝜖, ,(1)

The dependent variables Yi,t measure various characteristics of firm i at time t, such as
ESG ratings and performance. Treated is an indicator variable equal to 1 for treated firms (thosewith initiated coverage by a data provider) and equal to 0 for control firms (those without
initiated coverage by the same data provider). Post is a variable indicating whether a particularfirm-year observation (for both treated and control firms) is before or after the corresponding
expansion of coverage. We also include the difference-in-differences term, the interaction term
Treated * Post.
To estimate the model in (1), we follow each firm from five years before until five years
after the initiation of additional coverage. By design, each firm can receive additional coverage
up to three times (we update the matched firms with every additional treatment). Our final
sample of matched firms contains 44,134 firm-year observations across 1,485 unique firms.
Table 1 (Panel B) reports the number of firms with initiated coverage in each of the tencases, as well as the number of firms included in our sample. These are the stocks of firms whose
ESG performance has already been ranked by another agency and that could be matched with(up to five) similar firms based on the PSM. As a result of these filters, only around 23 percent of
the initial firms are included in our final sample. Being the oldest ESG rating provider, MSCIKLD tends to lead in coverage all other agencies. As a result, MSCI KLD coverage initiations do
not enter the final sample. However, we use MSCI KLD rating information in our ESG tests.

113.2. Data sources
We compile our data from multiple sources. As noted earlier, we focus on the ESG
coverage of four leading ESG rating providers: MSCI KLD, Refinitiv (former ASSET4 andThomson Reuters), Sustainalytics, and Bloomberg. We obtain the actual ESG data from WRDS
and Bloomberg and firm coverage criteria directly from the providers.
Table 1 (Panel A) reports the major coverage expansion times for the selected agencies
along with the coverage criteria. We observe that the inclusion criteria are similar among data
providers and tend to favor larger firms and members of major market indices, such as Russell
1000 and S&P500. MSCI KLD and Refinitiv provide precise coverage requirements.
Sustainalytics confirmed the coverage expansion years and that they cover primarily large-cap
U.S. firms. Bloomberg initiated coverage of companies that provide access to original,transparent, and publicly available data representing at least 80% of the company operations andat least 80% of the company's workforce in 2010.
Our study includes all publicly traded firms in CRSP and Compustat with fiscal years
between 1985 and 2020. We exclude highly regulated financial firms and utilities. CRSP
provides historical information on prices, returns, and shares outstanding for all stocks traded on
the U.S. major exchanges: NYSE, AMEX, and Nasdaq. Compustat is supplied by Standard &Poor’s and provides comprehensive accounting data for all publicly traded U.S. companies.
We complement the above data with mergers and acquisitions information from SDC
Platinum, keeping all deals over $1 million completed by public US-based acquirers between
1985 and 2020. We focus on deals completed within a firm’s fiscal year, excluding stock
repurchases and partial acquisitions with share owned after transactions not exceeding 50%. To

12match the Compustat with SDC data, we first employ a name and date mapping, created byPhillips and Zhdanov (2013) and updated by Ewens, Peters, and Wang (2019). We thenmanually match all residual firms based on cusips, tickers, and names.
We also obtain institutional ownership level data from the Thomson/Refinitiv Ownership(Consolidated Holdings) dataset. We focus on the ownership for a firm primary share class andselect the variables as of the last quarter available for each year to complement the Compustat
dataset.
To examine the effect of ESG coverage on firm environmental policy, we employ datafrom the Environmental Protection Agency’s (EPA) Toxics Release Inventory (TRI). The EPA
provides U.S. manufacturing firms’ facility-level disclosures of toxic chemicals emissions. To
match the sample firms with parent firms reporting their facility releases to EPA, we use thematching table provided by Xiong and Png (2019)9. To examine the effect of ESG coverage on
firm corporate governance, we obtain corporate board information from MSCI GMI Ratings.
The data on Government Enforcement Actions comes from the Violation Tracker
database provided by the non-profit organization Good Jobs First. It documents law violations
written by over 50 US federal agencies. The data classifies all violations into several primary
offense groups. Based on this classification, we identify government enforcement actions related
to environmental, social and governance irregularities.10 We then count the number of violations
within each of these three categories for a firm each year.
In Table 3, we report means and standard deviations of all variables in the sample, as well
as averages of all variables across the treatment and control groups, measured at the end of the

9

We thank Dr. Po-hsuan Hsu and Dr. Chi-Yang Tsou for providing the matching table.
Environmental violations are already classified by the data. We classify as social violations all employment andconsumer-related cases, as well as all safety-related violations related to food or product safety, mining, workplacesafety, and health issues. Finally, we classify all financial violations as governance irregularities.

1013matching year.11 Sample and control firms are similar with respect to their ESG scores and total
assets. In addition, they are also very similar with respect to their acquisition expenditures and
M&A deal characteristics. Treated firms disclose more ESG information and tend to have higher
valuation ratios and institutional ownership, more female directors, and larger corporate boards
than control firms.

4. ESG Coverage and ESG Performance
4.1. ESG ratings
We start our analysis by exploring the implications of ESG coverage for ESG
performance. Table 4 reports the estimates from our baseline model (1) for firm ESG score basedon MSCI ratings as a dependent variable. We focus on MSCI because it is the rating agency withthe longest history in the industry. Since most firms in our sample receive their first coverage by
MSCI, the paper focuses on coverage initiations from the other three agencies (see Panel B of
Table 1).
MSCI ratings consist of binary indicators for strengths and concerns along the E, S, andG dimensions, as well as miscellaneous indicators that do not fit in these three categories. Since
the number of indicators varies from year to year, we follow the existing literature to make theratings comparable across time (see, e.g., Kempf and Osthoff 2007, Halbritter and Dorfleitner2015). To this end, we first compute the total ESG score as the sum of all E, S, and G strengths

11

The Appendix presents the definitions of all variables used in the analysis. We scale most of the variables by total
assets (lagged total assets for flow variables). To ensure outliers do not skew the results, we winsorize all variables
at 1% and 99%.
14

minus the sum of all E, S, and G concerns. We also compile the miscellaneous indicators into aseparate “X” score.12 We then normalize the scores each year between 0 and 100.13
The results for the ESG score are presented in column (1) of Table 4. The dependent
variables in columns (2) to (4) are the E-, S-, and G-components of the index, respectively, while
the dependent variable in column (5) includes all components of the ESG index that cannot beclassified along the E/S/G dimensions. Our primary variable of interest, the interaction term
Treated * Post, is positive and statistically significant in models (1) thorough (4).14 The results
are also economically significant. For example, model (1) implies that initiation of additional
ESG coverage by another agency increases firm ESG score by additional 8.37 percent and the Ecomponent of the score by additional 3.91 percent relative to the control group.
Table 5 reports estimates from versions of the baseline model with different fixed effects.
As shown in Table 1, ESG rating agencies often cover (parts of) major market indices. In model(1) of Table 5, we extend our baseline model by including agency and index fixed effects.
Specifically, we include fixed effects for the Sustainalytics, Refinitiv, and Bloomberg agencies
and membership in the following indices – S&P 500, Dow Jones Industrials, Dow Jones
Transportation, and Dow Jones Utilities. Although the addition of these fixed effects reducesslightly the magnitude of the Treated * Post coefficient, the interaction term remains both
statistically and economically significant.
Models (2) and (3) of Table 4 substitute the industry and year fixed effects in the baselinemodel with firm and industry-year fixed effects. We observe that the Treated * Post coefficientremains significant in these model specifications. Model (4) of Table 4 uses as a dependent12

We calculated an alternative ESG score containing the miscellaneous indicators as additional ESG dimension.
Employing the alternative score yields quantitatively similar results.
13
The normalization formula is: (ESG – min(ESG))/(max(ESG) – min(ESG)).
14
The results hold for firms with low and high ESG ratings at the time of coverage expansion (these results are nottabulated).
15

variable the average ESG rating based on the MSCI KLD, Sustainalytics, and Bloomberg
normalized scores.15 Consistent with the MSCI results, here we also observe that the ESG
performance of a firm increases after it receives an additional coverage from an ESG rating
agency.

4.2. Are agencies inflating their ratings?
Rating agencies could exhibit conflicts of interest when their profits are linked to thefirms they rate. For example, bond rating agencies are generally paid by the issuers, which
creates incentives for an agency to provide higher ratings (Jiang et al. 2012; Bolton et al. 2012).
Becker and Milbourn (2011) contend that competition among rating agencies could increase
ratings even further. Is it possible that our results capture rating inflation in the ESG space?
We argue that rating inflation is less likely in the case of ESG given that ESG rating
agencies are neither hired nor paid by firms but by the users of their ratings.16 ESG rating
providers are in some direct or indirect way paid by investors, so they are not subject to theissuer-pays problem that continues to prevail among credit rating agencies (Walter 2020). Also
note that we measure ESG performance not with the ratings of the agency initiating theadditional coverage but with the ratings of another provider, which mitigates potential incentives
for rating manipulation even further.
ESG rating inflation could also be damaging to the reputation of the agencies, given thebad publicity surrounding corporate scandals and ESG-related missteps. For example, MSCI
acknowledges the Volkswagen (VW) emissions scandal in its ESG research reports and the fact

15

For treated firms, we do not include scores from the agency that initiated additional coverage in the mean ESG
calculation, given the absence of pre-treatment observations for a given rating
16
See e.g., https://www.reuters.com/business/sustainable-business/regulators-turn-spotlight-company-sustainabilityratings-2021-07-26/
16

that it noted a deterioration of VW’s corporate governance practices some time before thescandal and removed VW from its MSCI ACWI ESG Index.17 For all these reasons, we believethat strategic inflation of ESG ratings is not the likely explanation of our findings.

4.3. Real ESG performance
Here, we examine the implications of increased ESG coverage for real ESG performance.
We focus on two aspects of ESG – environmental and governance. To assess firm environmental
impact, we use the data from the Environmental Protection Agency’s Toxics Release Inventory(TRI).
Our main environmental variable is total toxic releases scaled by toxicity. Following
Russo and Harrison (2005), we proxy toxicity by the threshold for reporting accidental spills –the “reportable quantities” (RQ) in the EPA’s Comprehensive Environmental Response,
Compensation, and Liability Act (CERCLA). The reportable quantity varies from 1 pound to5000 pounds for relatively benign chemicals. To construct the measure, first, we divide each
toxic chemical release (in pounds) by its corresponding RQ and then aggregate all scaled
quantities across firms. We then scale the measure by total assets. We also calculate the numberof facilities as an additional dependent variable.
Table 6 reports the results. We observe that after a firm receives additional coverage by
an ESG rating agency, it reduces its toxicity-scaled releases by an additional 6.01% compared tothe control group. The last model of Table 6 shows that the change in the total number offacilities is not significantly different across the treatment and control groups, indicating thatfirms reduce their environmental footprint by cleaning up existing technologies instead ofclosing facilities.
17

See https://www.msci.com/volkswagen-scandal
17

To assess the implications of ESG coverage for the social and governance aspects offirms, we focus on corporate boards. The dependent variable in model (1) of Table 7 is the shareof outside directors. It is well accepted in the literature that outside directors are better monitors(see, e.g., Weisbach, 1988; Nguyen and Nielsen, 2010). ESG coverage, however, does not seemto exhibit a significant impact on the number of outside directors.
Model (2) of Table 7 shows that greater ESG coverage induces firms to hire more female
directors. Model (3) of Table 7 further suggests that this is not simply window dressing because
the new women directors tend to be outsiders. The greater female representation on a corporateboard is an indication that coverage by ESG rating agencies improves the social performance ofcovered firms. This finding is also consistent with the ESG results given that female directors are
more involved with ESG (McGuinness, et al. 2017; Dyck, et al. 2021). Finally, model (4) showsthat greater coverage by ESG agencies tends to reduce the size of the corporate board.
Table 8 examines the implications of coverage by ESG rating agencies for violations ofregulations imposed by 50 US federal agencies. As noted earlier, most of the violations could beclassified into and environmental, social, and governance category. The dependent variables inmodels (1) through (3) of Table 8, count the number of violations in each time by a firm eachyear. We observe that the number of social violations of a firm decrease significantly after it
receives an additional coverage by an ESG rating agency relative to the control group. The
number of environmental and governance violations does not seem significantly related to ESG
coverage.

18

4.4. Is ESG improvement reflecting omitted confounding effects?
Our design controls for a wide range of confounding effects that could affect both ESG
coverage and ESG performance. In this section, we perform a series of additional tests aimed atruling out a spurious association between ESG coverage initiation and ESG performance.
If additional ESG coverage indeed prompts firms to engage in activities that improvetheir ESG standing, we would expect treated firms to engage in strategies aimed at improving
ESG. We show that this is indeed the case. In Table 9, we examine some possible strategic
responses of firms with additional coverage. The dependent variable in model (1) of Table 9 isthe number of ESG-related words in a firm 10-K filing. To calculate this number, first, weconstruct a dictionary of ESG words based on the language used by MSCI KLD and Refinitiv todescribe the scope of ESG coverage in their handbooks.18 The complete ESG dictionary ispresented in Appendix B. We then scan each 10-K file and count the number of ESG-related
words from the dictionary mentioned in the document.19 Model (1) of Table 9 shows that ESG
coverage intensifies firm ESG disclosures.
The last three models of Table 9 focus on acquisitions. Models (2) and (3) show that
more covered firms are not more likely to engage in acquisitions. However, these firms are more
likely to acquire targets with high ESG scores (model (4)). In totality, these results are consistent
with the idea that ESG coverage triggers a strategic response in firms aimed at improving their
ESG standing.
If firms are prompted to improve their ESG performance by rating agencies, they would
be also more likely to comment on the coverage. A review of the 10K filings of firms that have18

Out of the four agencies, MSCI and Refinitiv are the only ones providing a detailed description of their ranking
criteria, with MSCI being the most complete and informative source.
19
We access each 10-K text document using the link from the complete EDGAR Daily Index dataset with all thedaily filings from 1990 till 2017 combined into one table from the James Ryans' website (available at:
http://www.jamesryans.com).
19

received an additional coverage by ESG rating agencies shows that firms discuss the increased
coverage. For example, CMS Energy Corp. states in its 2017 annual report that “[i]n a 2016
report published by Sustainalytics, a global leader in sustainability research and analysis, CMS
Energy scored the highest among 54 U.S. utilities in environmental, social, and governanceperformance.” Xylem Inc. comments in its 2020 annual report that its 2019 Credit Facility
includes a pricing grid that “determines the applicable margin based on Xylem’s credit rating,with a further adjustment depending on Xylem's annual Sustainalytics Environmental, Social andGovernance score.” These commentaries suggest that both firms and investors pay attention to
the coverage provided by ESG rating agencies.

4.6. Is ESG coverage promoting greenwashing?
Ratings could affect the propensity of firms to overstate their ESG performance, aphenomenon often referred to as “greenwashing.” While our results in Tables 6, 7 and 8 suggestthat covered firms tend to improve their real ESG performance, this does not rule out thepossibility of greenwashing.
If firms opportunistically overstate their ESG standing in response to increased coverage,
it is more likely that in the long run, a correction would follow, and the ESG score would adjust
to its true level. Model (1) of Table 12 explores the annual changes in firm ESG scores relative
to the year they receive the additional coverage (Figure 1 plots the trends in ESG scores acrossthe treatment and control groups). We observe that the ESG improvement of a newly covered
firm is concentrated over the first two years after the coverage and stabilizes after that (there issome additional increase in year 5). We do not observe any reversals of ESG ratings for years +1
to +5 following the event.

20

In sum, our analysis suggests that the increase in ESG ratings due to the additional
coverage by ESG agencies is not a result of increased greenwashing by firms. One possible
explanation of this result is that rating agencies are sophisticated enough to identify
greenwashing activities by firms, which makes greenwashing ineffective in increasing ESG
ratings. We note, however, that greenwashing could still be effective in manipulating consumers’
perceptions.

5. ESG Coverage and Firm Ownership Structure and Performance
5.1. Firm institutional ownership
Table 10 evaluates changes in the level of institutional ownership in response to
additional ESG coverage. The dependent variable in model (1) is the percentage of institutional
ownership, while the dependent variable in model (2) is the number of institutional owners.
We find that an increase in the ESG coverage tends to increase the level of institutional
ownership in terms of both percentage share ownership and number of institutional investors.
The effect is also economically meaningful. For example, one unit of treatment increases firm
institutional ownership by 1.38 percent, which is a 2.26 percent increase relative to the sample
mean of 61.1 percent.
The last two models of Table 10 evaluate the implications of ESG coverage for
institutions with a revealed preference for high- or low-ESG stocks. We classify institutions as
ESG-friendly or ESG-unfriendly as follows. Each year, we sort stocks into two groups
depending on whether they rank above (HESG) or below (LESG) the median ESG score in themarket that year. Afterward, we calculate the percentage allocation of each institutional investor

21

across the two types of stocks, HESG and LESG, normalized with the relative weight of thesegroups of stocks in the market portfolio. We refer to these adjusted weights as HESG-bias and
LESG-bias, respectively. We measure the overall bias of an institution towards high-ESG stocks
as the difference of its HESG-bias minus its LESG-bias. In other words, investors exhibit
revealed preferences for ESG stocks if they tend to over-weight high-ESG stocks and underweight low-ESG stocks. We then classify institutions in the top (bottom) 20% of their overallbias as having a revealed preference for high- (low-) ESG stocks.
We find that an increased ESG coverage by rating agencies tends to expand theownership by institutions with a revealed preference for high-ESG firms. More specifically, one
unit of treatment increases firm ownership by ESG-conscious institutions by around 1.56
percentage points. The ownership by institutions with a revealed preference for low-ESG firms
does not respond to the additional coverage.
Our institutional ownership results are consistent with the evidence that institutional
investors incorporate ESG information in their investment decisions (Dyck et al. 2019; Hong and
Kacperczyk 2009). Given that institutional investors value ESG information, they would findfirms with (more) ratings more attractive. Chen, Dong, and Lin (2020) find that institutional
ownership improves firm ESG performance through ESG-related proposals. Dimson, Karakas,
and Li (2015) further show that institutional investors are more likely to engage with companies
with inferior ESG performance. Furthermore, after successful engagements, companies
experience improved accounting performance.

225.2. Firm performance
As noted earlier, ESG coverage could improve firm performance when investors value
ESG. To shed light on the link between ESG coverage and performance, in Table 11, we
evaluate the effect of ESG coverage on a firm’s Market-to-book ratio, Tobin’s Q, Price-toearnings ratio, and 12-month return. We observe that the coefficients on the Treated * Post term
are positive and significant in all three valuation ratio regressions. The highest statisticalsignificance is for the Market-to-book ratio. The ESG coverage effect is also economicallymeaningful. For example, one unit of treatment increases exposed firms’ Market-to-book ratiowith 15.9 percent. The last column of Table 12 reveals that the increase in valuation ratios
happens during the first two years after the new coverage; this increase is also not observed overpre-treatment periods.

6. Conclusion
We find that when firm coverage by ESG rating agencies increases, its ESG ratings and
ESG performance improve. More covered firms also realize better financial performance. Our
results suggest that ESG rating agencies affect real economic activity. The economic impact of
ESG coverage would generally depend on the strength of ESG norms. When the market values
ESG, (more) covered firms would exhibit stronger incentives to improve their ESG standing
because they would be better able to communicate their ESG performance to the market. In
contrast, less covered firms would find it more difficult to signal their ESG performance to themarket.
Some researchers have expressed skepticism about the information content of ESG
ratings, citing, for example, the relatively high degree of disagreement across agencies. The

23

multidimensional and complex nature of ESG, however, could imply ratings that often disagree.
Disagreement among information providers is also a necessary condition for information
production. Our results suggest that ESG rating agencies exhibit a material impact in modern
markets.

24

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Variable

Treated

Post
Treated * Post

Appendix A
Variable Definitions
Description and Data Source
An indicator variable equal to 1 for a firm that receives an additional coverage by an
ESG rating agency and 0 for a matched firm that does not receive the additional
coverage. Both treatment and controls firms are already covered by some agencies.
Control firms are identified using propensity score matching with replacement (fivenearest neighbors with propensity score not exceeding 0.002) one year before additional
ESG coverage exposure.
An indicator variable equal to 1 for both treated and control firms after a treated firm
receives the additional ESG coverage, and 0, otherwise.
An interaction term equal to 1 for a treated firm after it experiences additional ESG
coverage and 0 otherwise.

ESG scores
ESG score

The total ESG score is calculated as the sum of all “E”, “S”, and “G” binary strengths
and concerns indicators (with concerns subtracted from one to invert them intostrengths). The aggregate indicator is further normalized each year between 0 and 100,
as follows: (ESG–min(ESG))/(max(ESG)–min(ESG)). Source: MSCI KLD

E scoreSum of all E (environmental) binary strengths and concerns indicators (with concerns
subtracted from one to invert them into strengths), normalized each year between 0 and100. Source: MSCI KLD

S scoreSum of all S (social) binary strengths and concerns indicators (with concerns subtracted
from one to invert them into strengths), normalized each year between 0 and 100.
Source: MSCI KLD

G scoreSum of all G (governance) binary strengths and concerns indicators (with concerns
subtracted from one to invert them into strengths), normalized each year between 0 and100. Source: MSCI KLD"X" score

Sum of all indicators for companies involved in controversial business sectors, namely
alcohol, firearms, gambling, military, nuclear power, and tobacco binary strengths andconcerns indicators (with concerns subtracted from one to invert them into strengths),
normalized each year between 0 and 100. Source: MSCI KLD

Mean ESG score

The average of all ESG scores available from the MSCI KLD, Bloomberg andSustainalytics rating agencies. Each score is normalized each year between 0 and 100.
For treated firms, scores from the agency initiated additional coverage are not includedin the calculation. Source: MSCI KLD, Bloomberg, Sustainalytics

Performance
Total assets

Market-to-book ratio

Tobin’s Q
12-month return

Price-to-earnings ratio
Return on assets

The natural logarithm of one plus total assets (at). Source: Compustat
The natural logarithm of one plus market value of equity scaled by book value of equity.
Book value of equity equals stockholders' equity (seq, if missing pstk+ceq, if missing
at-lt) plus deferred taxes (txditc, if missing txdb+itcb) minus preferred stock (pref).
Market value of equity equals common shares outstanding (CSHO) multiplied by shareprice (PRCC_F, if missing PRCC_C) Source: Compustat, CRSPThe ratio of total assets (at) plus market value of equity (computed as above) minus
book value of equity (computed as above), to the industry total assets, scaled by total
assets (at) Source: Compustat, CRSPThe natural logarithm of one plus past 12-month compounded returns. Source: CRSPThe natural logarithm of one plus income before taxes, interest, depreciation andamortization (ib) divided by market value of equity (computed as above). Source:
Compustat, CRSPNet income (ni) divided by total assets (average at as of the beginning and the end of theyear). Source: Compustat

30

Variable

ESG words in 10-Ks

Acquisition Expenditures

Number of Deals
Target firms' ESG score
Institutional Ownership
Share of institutional ownership
Number of institutional owners

Ownership share of institutions
with high-ESG preference

Ownership share of institutions
with low-ESG preference

Variable Definitions - Continued
Description and Data Source
The natural logarithm of one plus the number of ESG-related words in a 10-K
filing in a given year. We calculate the number of words as follows. First, weconstruct ESG vocabulary used in MSCI KLD and Refinitiv handbooks. The
complete ESG vocabulary is presented in the Appendix B. Then, we access each
10-K text document using the link from the complete EDGAR Daily Index datasetwith all the daily filings from 1990 till 2017 combined into one table from theJames Ryans' website (available at: http://www.jamesryans.com). We scan each
10-K text submission and count the number of a given ESG-related word/phrase
mentioned in each line of the document. We then sum these words up to get the
final ESG words to count. Source: www.sec.gov
The natural logarithm of one plus acquisition expenditures (aqc, replaced by 0 if
missing or negative) scaled by total assets (average at as of the beginning and theend of the year). Source: Compustat
The natural logarithm of one plus the number of completed deals with theeffective date falling into the recent fiscal year. Partial deals (less than 50% held
after transaction) are excluded from the calculation. Source: SDC Platinum
Average ESG score of all firms acquired during a year
Total Institutional Ownership scaled by the firm market cap. Source: Thomson
Reuters/Refinitiv Ownership (Consolidated Holdings) dataset, CRSPThe natural logarithm of one plus number of institutional owners. Source:Thomson Reuters/Refinitiv Ownership (Consolidated Holdings) dataset, CRSPInstitutional Ownership by Institutions with revealed preference for high-ESG
stocks, scaled by the firm market cap. Each year, we sort stocks into HESG andLESG groups (above and below the median ESG score). For both groups, we
calculate market weight and each institution's holdings weight. The difference
between institution weight and market weight is HESG and LESG biases. Total
bias is the difference between HESG bias and LESG biases. We then classify
institutions in the top 20% bias as having a revealed preference for high-ESG
stocks. Source: Thomson Reuters/Refinitiv Ownership (Consolidated Holdings)
dataset, CRSPInstitutional Ownership by Institutions with revealed preference for high-ESG
stocks, scaled by the firm market cap. Each year, we sort stocks into HESG andLESG groups (above and below the median ESG score). For both groups, we
calculate market weight and each institution's holdings weight. The difference
between institution weight and market weight is HESG and LESG biases. Total
bias is the difference between HESG bias and LESG biases. We then classify
institutions in the bottom 20% bias as having a revealed preference for low-ESG
stocks. Source: Thomson Reuters/Refinitiv Ownership (Consolidated Holdings)
dataset, CRSP

Environmental Pollution

Total toxic releases to total assets

The natural logarithm of one plus the sum of toxic releases (scaled by their
toxicity) by all facilities belonging to a company, divided by total assets (at).
Before the summation, each chemical release is divided by its corresponding
reportable quantity. Lower reportable quantity signals about higher toxicity.
Source: EPA's The Toxics Release Inventory (TRI), the Comprehensive
Environmental Response, Compensation, and Liability Act (CERCLA),
Compustat31

Variable
Number of facilities

Variable Definitions - Continued
Description and Data Source
The natural logarithm of one plus the number of a company's facilities reporting to the
EPA. Source: EPA's The Toxics Release Inventory (TRI)

Corporate Governance
Share of outside directors
Share of female directors
Share of female outside
directors
Board Size

The natural logarithm of one plus share of female directors. Source: MSCI GMI Ratings
The natural logarithm of one plus share of independent directors. Source: MSCI GMI
Ratings
The natural logarithm of one plus share of female independent directors. Source: MSCIGMI Ratings
The natural logarithm of one plus number of directors in a board. Source: MSCI GMI
Ratings

Government Enforcement Actions
Environmental violations

The natural logarithm of one plus the sum of all environmental violations.
Environmental offenses are records with offense_group='environment-related offenses'.
Source: Violation Tracker

Social violations

The natural logarithm of one plus the sum of all employment- and consumer-related
violations. Employment-related offenses are records with offense_group='employmentrelated
offenses';or
offense_group='safety-related
offenses'andprimary_offense='mining violation', 'workplace safety or health violation'. Consumerprotection-related offenses are records with offense_group='consumer-related offenses';or offense_group='safety-related offenses' and primary_offense='food safety violation','product safety violation'. Source: Violation Tracker

Governance violations

The natural logarithm of one plus the sum of all consumer-related violations.
Consumer-related offenses are records with offense_group='consumer-protectionrelated offenses'. Source: Violation Tracker

32

Appendix B
ESG Dictionary
CSR , ESG , GHG , ABUSE, ACCEPTABLE STANDARDS, ACCESS TO COMMUNICATION, ACCESS TO
FINANCE, ACCESS TO HEALTHCARE, ACCOUNTING IRREGULARIT, ACRIMONIOUS CONTRACT
NEGOTIATION, ADVANCED MATERIAL, AD-WARE, ALTERNATIVE ENERGY, ANTICOMPETITIVE,
ANTI-COMPETITIVE, ANTI-UNION, BATTERY, BID RIGGING, BIODIESEL, BIODIVERS, BIOGAS,
BIOMASS, BLENDED CEMENT, BOARD-LEVEL OBJECTION, BREACH, BRIBE, CARBON, CARCINOGEN,
CAREER DEVELOPMENT, CASH PROFIT SHARING, CELLULOSIC ETHANOL, CENSORSHIP,
CERTIFICATION, CHEMICAL SAFETY, CHILD LABOR, CIVIL LIBERT, CLEAN AIR, CLEAN ENERGY,
CLEAN TECH, CLEAN WATER, CLEANER ENERGY, CLIMATE CHANGE, COLLECTIVE BARGAIN,COLLUSION, COMMUNITY, COMPACT FLUORESCENT LIGHTING, COMPLICITY IN KILLING, CONFLICTOF INTEREST, CONFLICTS OF INTEREST, CONTAMINATION, CORRUPTION, CUSTOMER RELATION,CUSTOMER TRUST, DATA PROTECTION, DATA SECURITY, DECEPTIVE, DEMOGRAPH, DEPLETION,DESALINATION, DEVELOPMENT PROGRAMS, DISCRIMINAT, DISPLACEMENT, DISPOSAL,DISTURBANCE, DIVERS, DROUGHT-RESISTANT SEED, ECOLOGICALLY SENSITIVE, ECONOMICOPPORTUNITIES, ECOSYSTEM, EFFLUENTS, ELDERLY, ELECTRICAL NETWORK EXPANSION,
ELECTRONIC WASTE, ELIMINATE CHEMICAL, EMISSION, EMPLOYEE, EMS , END-OF-LIFE
ELECTRONIC, ENERGY EFFICIENCY, ENERGY USE, ENGAGEMENT, ENVIRONMEN, EQUIPMENTCOMMERCIALIZATION, ETHIC, EXCESSIVE FEES, EXCESSIVE WORKING HOURS, FACILITY
CLOSURES, FATALIT, FIREARMS, FOOD SAFETY, FORCED LABOR, FREEDOM, FRONT-RUN, FSCCERTIFIED LUMBER, FUEL CELLS/HYDROGEN SYST, HYDROGEN, GENDER, GEOTHERMAL,
GOVERNAN, GOVERNMENT SURVEILLANCE, green, H&S , HABITAT DAMAGE, HARM, HEALTHIERPRODUCT, HIDDEN FEE, HUMAN CAPITAL, HUMAN RIGHT, ELECTRIC VEHICLES, HYBRID VEHICLES,HYDRAULIC FRACTURING, IMPROPER BILLING, IMPROVED NUTRITIONAL PROFILE, IMPROVEMENTTARGET, INADEQUATE PAY, INCENTIVE, INCLUSION, INDEPENDEN, INDIGENOUS PEOPLES,
INDUSTRIAL AUTOMATION, INFRASTRUCTURE DEVELOPMENT, INITIATIVES, INJUR, INSULATION,
INSURING HEALTH, INTERNATIONAL NORMS, JOB ACCIDENT, JOB SATISFACTION, KIDNAP, LABOR,LAND USE, LEADERSHIP, LED LIGHTING, LEGAL LIABILITY, LOCAL COMMUNIT, LOCAL ECONOMIC,
LOCK-OUTS, LONG-TERM PERFORMANCE, LOSS OF ECONOMIC VALUE, LOW-TEMP ASPHALT,MARINE, MENTAL HEALTH, MILITARY, MINORIT, MISTREAT, NATURAL, NUCLEAR POWER,
NUTRITION AND HEALTH, OFF-LABEL USE, OPPORTUNITIES IN CLEAN TECH, OPPORTUNITIES IN
HEALTH, OPPORTUNITIES IN NUTRITION, OPPOSITION TO SHAREHOLDER, OPTIMIZATION TECH &SYST, OTHER ENERGY STORAGE, OVERTIME, PACKAGING MATERIAL, PERFORMANCE AUDITING,PERFORMANCE INCENTIVES, PERSONAL DATA, PHILANTHROP, POLLUTION, PREDATORYFINANCIAL, PREDATORY PRICING, PRICE FIXING, PRISON LABOR, PRIVACY, PRODUCT QUALITY,PRODUCT RECALL, PRODUCT RECOVERY, PRODUCT SAFETY, PRODUCT TESTING, PROFESSIONAL
DEVELOPMENT, PUBLIC HEALTH, QUALITY CONCERN, RAINWATER HARVESTING, RAW MATERIAL
SOURCING, RECLAMATION COST, RECYCL, REDUCTIONS IN BENEFITS, RENEWABLE ENERGY,
RENEWABLE POWER, REPLACEMENT WORKERS, REPRESENTATION, REPRESS, REPUTABLE,
REPUTATION, RESOURCE INTENSIVE, RESPONSIBLE INVESTMENT, RESPONSIBLE MARKETING,
REUSE, RIGHTS VIOLATION, SABOTAGE, SAFETY, SCRUTINY, SECURITY, SHAREHOLDER OBJECTION,SHAREHOLDER RIGHT, SHARING PROGRAM, SLAVE LABOR, SMALL HYDRO, SMART GRID, SMART
METER, SOCIAL, SOLAR, SOVEREIGN, SPAM, SPECIALTY CEMENT, SPILL, STAKEHOLDER, STRIKE,
SUB-STANDARD TREATMENT, SUPERCONDUCTOR, SURVEILLANCE, SUSTAIN, TAKEOVER DEFENS,TAX EVASION, TENANT ENGAGEMENT, TOXIC, TRAINING, TRANSPAREN, UNDERAGE LABOR,UNDERAGE WORK, UNDER-BANKED, UNDERQUALIFICATION, UNDERREPRESENT, UNDERSERVEDPOPULATION, UNION DENSITY, UNION RELATION, UNIONIZ, UNIONS, UNSAFE WORK, VIOLENCE,
VULNERABLE POPULATION, WASTE, WATER, WAVE TIDAL, WEAPON, WORKFLOW DISRUPTION,
WORKFORCE, WORKING CONDITION, WRONGFUL TERMINATION

33

Appendix C
Empirical evidence on ESG ratings and firm performance
A large body of work has examined the connection between actual ESG ratings (not coverage) andorganizational performance. Here, we review some of the literature.
The term ESG was introduced in 2006. ESG issues were first mentioned in the 2006 United Nation's
Principles for Responsible Investment (PRI) report consisting of the Freshfield Report and “Who Cares Wins.” ESG
criteria were, for the first time, required to be incorporated in the financial evaluations of companies. Not
surprisingly, the early studies did not consider ESG as an aggregate concept; but focused on some specific aspects of
ESG performance. For example, Dowell, Hart, and Yeung (2000) find that firms with more stringent global
environmental standards are associated with higher market valuations. Hong and Kacperczyk (2009) show thatpublicly traded companies involved in alcohol, tobacco, and gaming (“sin” stocks) have higher expected returns thanotherwise comparable stocks, consistent with a higher cost of equity. Along the same lines, Ng and Rezaee (2015)
document that higher levels of both economic sustainability disclosure and higher ESG score result in a lower costof equity. Edmans (2011) focuses on the relationship between employee satisfaction and long-run stock returns. He
finds that the “100 Best Companies to Work for in America” significantly outperformed both the market and theirindustry peers throughout the 80s and 90s. Servaes and Tamayo (2013) document that corporate social responsibility(CSR) and firm value are positively related for firms with high customer awareness.
In a more recent paper, Krüger (2015) studies how the stock markets react to positive and negative events
concerned with a firm’s corporate social responsibility. He finds that investors respond strongly negatively tonegative events and weakly negatively to positive events. He attributes the negative response to positive CSR news
to agency problems.
Some studies have also examined the implications of ESG for firm performance in different settings andsituations. Deng, Kang, and Low (2013) find that acquirer ESG performance is an important determinant of merger
performance. Specifically, more socially responsible acquirers realize higher merger announcement returns andlarger increases in post-merger long-term operating performance. Lins, Servaes, and Tamayo (2017) also find that
more socially responsible firms performed better during the 2008–2009 financial crisis. They attribute the better
performance of socially responsible firms to lower cost of capital.

34

Table 1
ESG Coverage Expansion Chronology
Panel A describes the criteria for the ESG coverage initiations and expansions of large groups of stocks for the four
major ESG rating agencies analyzed in the paper. Inclusion year indicates the year of initiation or expansion, while
inclusion criteria lists the criteria used for the coverage of a firm. Panel B reports the number of firms that wereadded for coverage in each instance, the number of firms that were added for coverage and could be matched with afirm with similar industry and size and for which both the included firm and the matched firm have outstanding
coverage from another agency at the time of the inclusion, and the percentage of matched firms relative to all added
firms. We do not consider coverage expansion criteria for international firms.
Panel A: ESG inclusion criteria

MSCI KLD

Inclusion
year
(1)
1991

MSCI KLD

2001

MSCI KLD
Refinitiv

2003
2003

Sustainalytics

2009

Bloomberg

2010

Sustainalytics
Refinitiv

2010
2011

Sustainalytics

2016

Refinitiv

2017

Database

Inclusion criteria(2)"Universe A": MSCI KLD 400 Social Index & the S&P 500 (as of August)"Universe C": MSCI KLD 400 Social Index and the top 1000 U.S. companies bymarket capitalization (as of December)"Universe D": top 3000 U.S. companies by market capitalization (as of December)
S&P500 and Nasdaq100 index members
Undisclosed. Rating methodology was launched. Coverage is based on tracking
several indices
Rating methodology was launched. Companies need to have publicly available
data representing at least 80% of the company operations and at least 80% of thecompany's workforce. Full data transparency and access to the original documents.
Undisclosed
Russell 1000 index constituents
Undisclosed. Coverage expanded primarily from large cap US names. For North
America, current coverage roughly aligns with the R1000, S&P500 and TSXCI.
Russell 2000 index constituents with Market Cap of at least $400 million
Panel B: Number of included firms

Database
MSCI KLDMSCI KLDMSCI KLD
Refinitiv
Sustainalytics
Bloomberg
Sustainalytics
Refinitiv
Sustainalytics
Refinitiv
Total

Inclusion
year
(1)
1991
2001
2003
2003
2009
2010
2010
2011
2016
2017

Number of Inclusion Firms

Number of Matched Firms

(2)

(3)

Percentage ofMatched Shocks
(4)

492
324
1199
385
387
1919
202
252
1242
780
7182

0
0
0
66
92
797
56
51
294
308
1003

0
0
0
17.14
23.77
41.53
27.72
20.24
23.67
39.49
1664

35

Table 2
Propensity Score Models for the Probability to Receive Additional Coverage by an ESG Rating Agency
The table reports coefficient estimates and standard errors from logit regressions. Treated firms are firms receiving new coverage by an ESG-rating agency
next year; all sample firms have at least one existing coverage by an ESG rating agency. All models include industry Fama-French 17 industries fixed
effects. All variables are computed one year before the coverage and are detailed in the Appendix. The last two rows report the total number of observations
in each regression. (***), (**), and (*) indicate statistical significance at the 0.01, 0.05, and 0.10 levels, respectively.

Intercept
ESG score
Total Assets
Market-to-book ratio
Number of Institutional Owners
Number of Covering
ESG rating agencies
Return on Assets
Industry Fixed Effects(# of significant)Observations

Bloomberg
2009

Refinitiv
2002

Refinitiv
2010

Refinitiv
2016

Sustainalytics
2008

Sustainalytics
2009

Sustainalytics
2015

(1)

(2)

(3)

(4)

(5)

6

(7)

-2.4977**(1.2039)0.2352(0.2916)0.2609***(0.0897)
0.1934*(0.1033)
0.1118(0.1707)-30.5531***(3.4406)-0.2721(0.5178)
0.6358***(0.2127)
0.6167**(0.2759)4.6285***(0.597)-31.1005***(11.7114)
0.2799(0.4011)0.9329***(0.2235)
1.2441***(0.2425)
2.3075***(0.5746)

3.0036(17.9458)-0.2605(0.2555)-0.2749**(0.1386)
0.098(0.1556)
0.0285(0.3992)-39.6996***(3.9155)-0.1417(0.4365)0.8872***(0.1945)0.7073***(0.2264)5.0604***(0.7461)-25.475***(2.9773)-0.9201**(0.4674)
0.3353*(0.1914)-0.2804(0.2083)5.3243***(0.6559)

24.9785(1019.0131)-2.4096(3.8676)-1.9067***(0.3091)-1.0272***(0.3147)
0.0994(0.6586)0.844*(0.4436)0.7687*(0.4461)

0.0589(1.6282)

8.1385***(0.8897)
2.636*(1.5852)

1.7858***(0.4718)
2.32***(0.7511)2.6102***(0.7337)-2.0969*(1.2082)-6.0988***(1.0989)-1.6282(1.0726)

1.7842(1.1692)
2.161(2.3307)

5
1346

2
543

0
1129

0
626

2
1295

2
1036

0
652

36

Table 3
Summary Statistics
This table reports summary statistics for variables reflecting firm ESG exposure, capital budgeting, mergers andacquisitions activity, and performance. Columns 1 and 2 presents the mean and standard deviation for the fullsample. Columns 3 and 4 provide the means for the treated and control group calculated over the matching year.
Columns 5 and 6 present the difference between them and the associated t-statistic. (***), (**), and (*) indicate
statistically significant differences between treated and control firms at the 0.01, 0.05, and 0.10 levels,
respectively. Exact definitions of all variables are provided in the Appendix.
Matched sample
across all years

Treated
Post
Treated*Post
ESG scores
ESG score
E scoreS scoreG score"X" score
Mean ESG score
Performance
Total assets
Market-to-book ratio
Tobin’s Q
12-month return
Price-to-earnings ratio
ESG words in 10-Ks
Acquisition Expenditures
Number of Deals
Target firms' ESG score
Institutional Ownership
Share of institutional ownership
Number of institutional owners
Ownership share of institutions
with high-ESG preference
Ownership share of institutions
with low-ESG preference

Matched sample one year prior to the shock

Mean
(1)

STD
(2)

Treated
Mean
(3)

0.36
0.39340.16990.48
0.48850.375510.00180.001800.000800.0018*(1.7333)

3.4872
3.84963.40364.00554.129
3.38080.41970.38420.41590.30450.28490.38733.52643.77643.46564.07584.19063.4711

3.53793.86143.43434.0871
4.197
3.4746-0.0115-0.0849***
0.0313***-0.0113-0.0064-0.0035(-0.7754)(-5.7256)(3.0789)(-1.2716)(-1.0059)(-0.3153)

6.73610.79911.98540.0511-0.0056
6.40750.028
0.17873.56391.37980.73971.36540.51430.25760.85870.06490.36180.45536.99020.75891.91320.177-0.0069
6.41120.02510.1606
3.5985

6.63750.70721.74060.187-0.01276.288
0.022
0.17473.66140.3527***0.0517**0.1726***-0.01
0.00580.1232***0.0031*-0.0141-0.0629(8.9555)(2.3311)(4.7344)(-0.7352)(0.9259)(3.9471)(1.7374)(-1.365)(-0.6832)

0.61125.29990.11720.62620.61695.44470.61145.29570.0054*0.149***(1.653)(8.8579)

0.48840.14590.50190.484

0.15530.129

0.15820.166437

Control
Mean(4)Treated- Control
Difference
t-stat(5)(6)

0.0179***-0.0082**(4.675)(-2.4855)

Table 3 – Continued
Matched sample one year prior to the shock
Matched sample
across all years
TreatedControl
Treated- Control
Mean
STD
Mean
Mean
Difference
t-stat
(1)
(2)
(3)
(4)
(5)
(6)
Environmental Pollution
Total toxic releases to total
assetsNumber of facilities
Corporate Governance
Share of outside directors
Share of female directors
Share of female outside directors
Board size
Government Enforcement Actions
Environmental Violations
Social Violations
Governance Violations

0.24180.48630.23810.21530.0228(0.7543)1.47740.83851.72131.28770.4336***(8.7232)

0.80790.09550.10870.10520.10250.11850.81520.10970.12420.81040.07940.09020.00480.0303***0.034***(1.6205)(10.0633)(9.8061)8.2982

1.89148.59148.10040.4909***(8.4008)

0.04330.03530.00190.216
0.19020.039

0.08090.05960.00390.01620.01610.00280.0647***0.0435***0.0011(8.3076)(7.2095)(0.5891)38

Table 4
Firm ESG Coverage and ESG Performance
Our initial sample consists of all firms with existing coverage by at least one ESG rating agency. Afterwards, we
identify the subset of firms that receive additional coverage by another agency and could be matched to firms fromthe initial sample that did not receive the additional coverage and exhibits similar propensity scores. This group offirms constitutes our treatment group, while their five closest matched counterparts – our control group. The table
reports coefficient estimates and t-statistics from OLS regressions of firm ESG performance variables on Treated, an
indicator variable equal to 1 for treated firms; Post, an indicator variable equal to 1 for the inclusion year and all
subsequent years; and Treated * Post. The dependent variables are the natural log of one plus normalized ESG, E, S,
G, or "X" score based on MSCI KLD. All firms are followed from 5 years before until 5 years after coverage
expansion. All models include industry and year fixed effects. Standard errors are clustered at the firm level. Allvariables are detailed in the Appendix. The last two rows report the total number of observations and adjusted Rsquared of each regression. (***), (**), and (*) indicate statistical significance at the 0.01, 0.05, and 0.10 levels,
respectively.

Treated
Post
Treated * Post
Year and Industry fixed
effects
Observations
Adjusted R-squared

ESG score

E scoreS scoreG score"X" score(1)-0.0053(-0.3961)
0.0133(0.4064)0.0837***(3.0946)(2)-0.03***(-3.6325)
0.0129(0.8227)
0.0391*(1.9117)(3)0.0207(1.3992)
0.0778***(3.2111)0.0466**(2.3461)(4)-0.016(-1.3284)-0.0298*(-1.9485)0.0403**(2.3242)(5)-0.0073(-1.1282)
0.0089(0.6854)0.0134(0.9767)

Yes

Yes

Yes

Yes

Yes

37957
0.563137957
0.727137957
0.499437957
0.359437957
0.531139

Table 5
Firm ESG Coverage and ESG Performance: Additional Evidence
Our initial sample consists of all firms with existing coverage by at least one ESG rating agency.
Afterwards, we identify the subset of firms that receive additional coverage by another agency and could bematched to firms from the initial sample that did not receive the additional coverage and exhibits similar
propensity scores. This group of firms constitutes our treatment group, while their five closest matchedcounterparts – our control group. The table reports coefficient estimates and t-statistics from OLS
regressions of firm ESG performance variables on Treated, an indicator variable equal to 1 for treated firms;
Post, an indicator variable equal to 1 for the inclusion year and all subsequent years; and Treated * Post, aninteraction of Treated and Post. In models (1) - (3), the dependent variable is the natural log of one plus
normalized firm ESG score based on MSCI KLD, while in model (4), the dependent variable is the mean
ESG score based on MSCI KLD, Sustainalytics, and Bloomberg normalized scores. All firms are followedfrom 5 years before until 5 years after coverage expansion. All models include industry and year fixed
effects. Standard errors are clustered at the firm level. All variables are detailed in the Appendix. The last
two rows report the total number of observations and adjusted R-squared of each regression. (***), (**), and(*) indicate statistical significance at the 0.01, 0.05, and 0.10 levels, respectively.

Intercept
Treated
Post
Treated * Post
Year fixed effects
Industry fixed effects
Agency fixed effects
Index fixed effects
Firm fixed effects
Year-Industry fixed effects
Observations
Adjusted R-squared

ESG score

ESG score

ESG score

(1)

(2)

(3)

3.8518***(93.0891)-0.0189(-1.397)-0.0274(-0.8691)
0.0755***(2.9272)
Yes
Yes
Yes
Yes

3.3588***(57.15)-0.04810***(-3.50)-0.09925**(-2.77)
0.0938***(3.51)
Yes

3.4603***(299.59)
0.0060(0.49)
0.0468*(2.03)
0.0482*(2.36)

Mean ESG
score(4)
3.0643***(47.445)0.0096(0.6715)0.1501***(4.9627)
0.1432***(5.9856)
Yes
Yes

Yes
37957

37957

Yes
37957

0.59950.76800 .6477

4040755
0.4248

Table 6
Firm ESG Coverage and Environmental Pollution
Our initial sample consists of all firms with existing coverage by at least one ESG rating agency.
Afterwards, we identify the subset of firms that receive additional coverage by another agency andcould be matched to firms from the initial sample that did not receive the additional coverage andexhibits similar propensity scores. This group of firms constitutes our treatment group, while theirfive closest matched counterparts – our control group. The table reports coefficient estimates and tstatistics from OLS regressions of firm toxic releases variables on Treated, an indicator variableequal to 1 for treated firms; Post, an indicator variable equal to 1 for the inclusion year and all
subsequent years; and Treated * Post. The dependent variables are natural log of one plus firm
total toxic releases scaled by toxicity divided by total assets and number of reporting facilities in acompany. All firms are followed from 5 years before until 5 years after coverage expansion. All
models include industry and year fixed effects. Standard errors are clustered at the firm level. Allvariables are detailed in the Appendix. The last two rows report the total number of observations
and adjusted R-squared of each regression. (***), (**), and (*) indicate statistical significance atthe 0.01, 0.05, and 0.10 levels, respectively.

TreatedTotal toxic releases
to total assets

Number of facilities

(1)

(2)

Year and Industry fixed effects

0.1003*(1.9206)-0.1128*(-1.909)-0.0601**(-1.9684)
Yes

0.3236***(3.3812)0.3585***(3.0549)-0.0611(-0.7171)
Yes

Observations
Adjusted R-squared

10083
0.249310083
0.2684

Post
Treated * Post

41

Table 7
Firm ESG Coverage and Corporate Board Structure
Our initial sample consists of all firms with existing coverage by at least one ESG rating agency. Afterwards, we
identify the subset of firms that receive additional coverage by another agency and could be matched to firms
from the initial sample that did not receive the additional coverage and exhibits similar propensity scores. This
group of firms constitutes our treatment group, while their five closest matched counterparts – our control group.
The table reports coefficient estimates and t-statistics from OLS regressions of firm toxic releases variables on
Treated, an indicator variable equal to 1 for treated firms; Post, an indicator variable equal to 1 for the inclusion
year and all subsequent years; and Treated * Post. The dependent variables are natural log of one plus the share ofoutside directors, the share of female directors, the share of female outside directors, and board size. All firms arefollowed from 5 years before until 5 years after coverage expansion. All models include industry and year fixed
effects. Standard errors are clustered at the firm level. All variables are detailed in the Appendix. The last two
rows report the total number of observations and adjusted R-squared of each regression. (***), (**), and (*)
indicate statistical significance at the 0.01, 0.05, and 0.10 levels, respectively.

Treated
Post
Treated * Post
Year and Industry fixed effects
Observations
Adjusted R-squared

Share of outside
directors

Share of female
directors

Share of female
outside directors

Board Size

(1)

(2)

(3)

(4)

0.0085(1.1135)
0.0192(1.4333)-0.0023(-0.3241)
Yes

0.0171***(2.7613)0.0127(1.6089)0.0145**(2.2005)
Yes

0.0186***(2.6162)
0.0116(1.2558)
0.0175**(2.1216)Yes

0.4568***(4.3793)0.8571***(5.2462)-0.2273*(-1.8546)
Yes

41161
0.139941161
0.160241161
0.159441161
0.161842

Table 8
Firm ESG Coverage and Government Enforcement Actions
Our initial sample consists of all firms with existing coverage by at least one ESG rating agency.
Afterwards, we identify the subset of firms that receive additional coverage by another agency andcould be matched to firms from the initial sample that did not receive the additional coverage andexhibits similar propensity scores. This group of firms constitutes our treatment group, while theirfive closest matched counterparts – our control group. The table reports coefficient estimates andt-statistics from OLS regressions of firm government enforcement actions on Treated, an indicatorvariable equal to 1 for treated firms; Post, an indicator variable equal to 1 for the inclusion year
and all subsequent years; and Treated * Post. The dependent variables are natural log of one plus
the number of government enforcement actions related to firm environment, social, andgovernance violations. All firms are followed from 5 years before until 5 years after coverage
expansion. All models include industry and year fixed effects. Standard errors are clustered at thefirm level. All variables are detailed in the Appendix. The last two rows report the total number ofobservations and adjusted R-squared of each regression. (***), (**), and (*) indicate statisticalsignificance at the 0.01, 0.05, and 0.10 levels, respectively.
Environmental
violations

Socialviolations

Governanceviolations

(1)

(2)

(3)

Year and Industry fixed effects

0.0544***(5.7596)0.0377(1.5428)
0.0026(0.1657)
Yes

0.0892***(5.9463)0.0898**(2.1202)-0.033***(-3.1256)
Yes

0.0009(0.7736)-0.0011(-0.8551)
0.0009(0.6784)
Yes

Observations
Adjusted R-squared

44134
0.134544134
0.037944134
0.0051Treated
Post
Treated * Post

43

Table 9
Firm Strategic Responses to ESG Coverage
Our initial sample consists of all firms with existing coverage by at least one ESG rating agency. Afterwards,
we identify the subset of firms that receive additional coverage by another agency and could be matched tofirms from the initial sample that did not receive the additional coverage and exhibits similar propensity
scores. This group of firms constitutes our treatment group, while their five closest matched counterparts –our control group. The table reports coefficient estimates and t-statistics from OLS regressions of firm
strategic response variables on Treated, an indicator variable equal to 1 for treated firms; Post, an indicatorvariable equal to 1 for the inclusion year and all subsequent years; and Treated * Post, an interaction ofTreated and Post. The dependent variables are the natural log of one plus the number of ESG-related words
in firm 10-K filing, acquisition expenditures, the number of deals, and average target firms’ ESG score. Allfirms are followed from 5 years before until 5 years after the coverage expansion. All models include
industry and year fixed effects. Standard errors are clustered at the firm level. All variables are detailed in the
Appendix. The last two rows report the total number of observations and adjusted R-squared of each
regression. (***), (**), and (*) indicate statistical significance at the 0.01, 0.05, and 0.10 levels, respectively.

Treated
Post
Treated * Post
Year and Industry fixed effects
Observations
Adjusted R-squared

ESG words in
10-Ks

Acquisition
expenditures

Number of
Deals

Target firms'
ESG score

(1)

(2)

(3)

(4)

0.0052(0.1415)0.1273***(2.876)0.1233**(2.4999)Yes
24150

0.0024(1.1547)-0.0015(-0.6102)
0.0029(1.1656)
Yes
44134

0.0259*(1.8619)0.0411**(2.2849)
0.0117(0.8029)Yes
44134-0.0503(-1.194)-0.0643(-0.9202)
0.161*(1.8107)Yes
1909

0.55440.04780.071

0.268844

Table 10
Firm ESG Coverage and Institutional Ownership
Our initial sample consists of all firms with existing coverage by at least one ESG rating agency. Afterwards, we
identify the subset of firms that receive additional coverage by another agency and could be matched to firms fromthe initial sample that did not receive the additional coverage and exhibits similar propensity scores. This group offirms constitutes our treatment group, while their five closest matched counterparts – our control group. The table
reports coefficient estimates and t-statistics from OLS regressions of firm institutional ownership variables on
Treated, an indicator variable equal to 1 for treated firms; Post, an indicator variable equal to 1 for the inclusion
year and all subsequent years; and Treated * Post. The dependent variables are the natural log of one plus thepercentage of institutional ownership, the number of institutional investors, and the ownership share for institutions
with a preference for high-/low-ESG stocks. All firms are followed from 5 years before until 5 years after coverage
expansion. All models include industry and year fixed effects. Standard errors are clustered at the firm level. Allvariables are detailed in the Appendix. The last two rows report the total number of observations and adjusted Rsquared of each regression. (***), (**), and (*) indicate statistical significance at the 0.01, 0.05, and 0.10 levels,
respectively.

Treated
Post
Treated * Post
Year and Industry fixed
effects
Observations
Adjusted R-squared

Share ofinstitutional
ownership

Number of
institutional
owners

(1)

(2)

(3)

Ownership % of
institutions with
low-ESGpreference(4)0.0028(0.3806)
0.0085(0.6507)0.0138*(1.895)0.1322***(3.8893)0.21***(3.6408)
0.1075**(2.2658)

0.0244***(2.9067)
0.0359**(2.3354)0.0156**(2.0458)-0.0196***(-2.6086)-0.0269*(-1.8488)-0.0044(-0.647)

Ownership % of
institutions withhigh-ESG preference

Yes

Yes

Yes

Yes

43058
0.0826

43282
0.159

43079
0.258443102
0.408345

Table 11
Firm ESG Coverage and Firm Performance
Our initial sample consists of all firms with existing coverage by at least one ESG rating agency.
Afterwards, we identify the subset of firms that receive additional coverage by another agency and could bematched to firms from the initial sample that did not receive the additional coverage and exhibits similar
propensity scores. This group of firms constitutes our treatment group, while their five closest matchedcounterparts – our control group. The table reports coefficient estimates and t-statistics from OLS
regressions of firm operating performance variables on Treated, an indicator variable equal to 1 for treatedfirms; Post, an indicator variable equal to 1 for the inclusion year and all subsequent years; and Treated *Post. The dependent variables are percentage natural log of one plus Market-to-book ratio, Tobin’s Q, Priceto-earnings ratio, and 12-month return. All firms are followed from 5 years before until 5 years after
coverage expansion. All models include industry and year fixed effects. Standard errors are clustered at thefirm level. All variables are detailed in Appendix A. The last two rows report the total number ofobservations and adjusted R-squared of each regression. (***), (**), and (*) indicate statistical significanceat the 0.01, 0.05, and 0.10 levels, respectively.

Treated
Post
Treated * Post
Year and Industry fixed effects
Observations
Adjusted R-squaredMarket-tobook ratio

Tobin’s Q

Price-toearnings ratio

12-month return(1)
0.0422(0.897)-0.1325(-1.5063)
0.1591**(2.1023)
Yes
44102
0.1673(2)
0.157*(1.8312)-0.1757(-1.2758)
0.1479*(1.9002)Yes
44134
0.1481(3)-0.0088(-0.684)-0.0636**(-2.0423)
0.0707*(1.9024)Yes
44133
0.1452(4)
0.0249***(2.9167)-0.052***(-2.9782)
0.0238(1.3581)Yes
44109
0.257746

Table 12
Firm ESG Coverage Effects: Lead-and-Lags Regressions
Our initial sample consists of all firms with existing coverage by at least one ESG rating agency.
Afterwards, we identify the subset of firms that receive additional coverage by another agency and could bematched to firms from the initial sample that did not receive the additional coverage and exhibits similar
propensity scores. This group of firms constitutes our treatment group, while their five closest matchedcounterparts – our control group. The table reports coefficient estimates and t-statistics from OLS
regressions of firm outcome variables on Treated, an indicator variable equal to 1 for treated firms; a series
of indicators t for each year in the sample relative to the coverage expansion, excluding t0 (coverage
expansion year), which is taken as the baseline; a series of interactions of treated and years relative to thecoverage expansion Treated * t, excluding Treated*t0, which is taken as the baseline. The dependent
variables are the MSCI KLD ESG score, the mean ESG score based on MSCI KLD, Sustainalytics, andBloomberg normalized scores, the natural log of one plus the number of institutional owners, and thenatural log of one plus Market-to-Book ratio. All firms are followed from 5 years before until 5 years after
coverage expansion. All models include industry and year fixed effects. Standard errors are clustered at thefirm level. All variables are detailed in Appendix A. The last two rows report the total number ofobservations and adjusted R-squared of each regression. (***), (**), and (*) indicate statistical significanceat the 0.01, 0.05, and 0.10 levels, respectively.

Treated * t-4
Treated * t-3
Treated * t-2
Treated * t-1
Treated * t+1
Treated * t+2
Treated * t+3
Treated * t+4
Treated * t+5
Year and Industry
fixed effects
Observations
Adjusted R-squared

ESG scoreMean
ESG score

Number of
institutional owners(1)-0.0226(-1.2503)-0.0028(-0.1938)
0.0032(0.1978)-0.0165(-1.4068)
0.1300***(3.1679)
0.0734**(2.2843)0.0005(0.0185)0.0426(1.3399)
0.0554*(1.838)(2)-0.0153(-0.8107)-0.0027(-0.1135)-0.0226*(-1.8511)-0.0235**(-2.2275)
0.1035***(3.8371)
0.0712**(2.5517)0.1304***(4.6074)
0.1866***(3.5507)0.1790***(4.0556)(3)
0.0135(0.3718)
0.0316(1.3342)-0.0072(-0.3704)-0.005(-0.4073)
0.0677***(3.1148)0.0959***(2.7683)0.0896**(2.4098)
0.1855**(2.0427)0.1612(1.5613)

Yes

Yes

Yes

Yes

37957
0.571640755
0.442943282
0.165544102
0.171547

Market-to-book
ratio
(4)
-0.0206(-0.4484)-0.0175(-0.3941)
0.0102(0.2131)-0.0057(-0.1551)
0.1236***(2.7511)0.1031*(1.9147)0.0884(1.0826)
0.1551(1.6042)0.3613(1.351)

Figure 1 – ESG Coverage Expansion Timeline
This figure presents the timeline for ESG coverage initiations and expansions of the four major ESG rating providers analyzed in the paper. The circle indicatesthe year of coverage initiation or expansion for a specific group of firms, while the description states the inclusion criteria used.

48

Firms with new coverage

T
C

Existing coverage byanother ESG rating
agencyMatchedfirms withnewcoveragetMatchedfirms
without
newcoverage

Firms without new coverage

Figure 2 – Construction of the Treatment and Control Groups
This figure visualizes the sample construction procedure. Out of the CRSP-Compustat universe, we select all firms
with existing coverage by at least one ESG rating agency. Afterwards, we identify the subset of firms that receive
additional coverage by another agency and could be matched to firms from the initial sample that did not receive theadditional coverage and exhibits similar propensity scores. This group of firms constitutes our treatment group,
while their five closest matched counterparts – our control group.

49

Figure 3 – Leads and Lags Plot of ESG coverage effect 5 years around the initiation
This figure presents the average ESG score for treated and control firms around the time an additional coverage was initiated by an ESG rating agency (time 0).
Coefficients and 95% confidence intervals are from the Table 12 regressions. The vertical line separates the pre- from the post- ESG coverage initiation period.

50