The Real Effects of Environmental Activist Investing†

S. Lakshmi Naaraayanan‡ Kunal Sachdeva§ Varun Sharma‖

November 18, 2019

Abstract

Using a socially-motivated activist campaign by a large pension fund, we measure the realeffects of activist investing on pollution and the environment. Targeted firms reduced theirtotal toxic chemical releases, production-related emissions, cancer-causing pollution, environ-mental accidents, and legal risks. These effects do not come at the expense of lower financialperformance or returns. We rule out natural alternative hypotheses while also presenting evi-dence supporting the external validity of socially motivated activism. These findings suggestthat shareholders can delegate their pro-social preferences onto firms to maximize their totalvalue between their financial and non-pecuniary benefits.

JEL classification: G23, G34, M14Keywords: Activism, Environmental Social and Governance, Socially Responsible Investment,Corporate Social Responsibility, Pensions

†For helpful comments and suggestions, we thank Alan Crane, Arpit Gupta, Wei Jiang, Narayan Naik, KasperMeisner Nielsen, Shivaram Rajgopal, Manpreet Singh, and James Weston. This paper benefited from detailed discus-sions with Michael Garland, Assistant Comptroller of NYC, and Tian Weinberg from the Office of the Comptroller.We thank Dejan Suskavcevic for excellent research assistance. Varun Sharma thanks Wheeler Institute for funding.‡Hong Kong University of Science and Technology sln@connect.ust.hk§Jesse H. Jones Graduate School of Business at Rice University kunal.sachdeva@rice.edu‖London Business School vsharma@london.edu

Environmental activism, which involves shareholders engaging management with the goal of

improving a firm’s environmental impact, has gained popularity among both retail and insti-

tutional investors (Dyck, Lins, Roth, and Wagner (2019); Starks, Venkat, and Zhu (2018)).1 As

investors increasingly internalize the negative externalities created by firms, it is important to

understand the real effects that activism has on shareholders, targeted firms, and the broader

environment.

There has been limited research studying the real effects of shareholder activist campaigns

on pollution and the environment. On the one hand, there is a mature literature studying how

activist shareholders can affect a firm’s governance, financial, and operational performance to

increase their wealth.2 On the other hand, there is a growing literature studying investor pref-

erences for socially responsible investments, reflected by active divestment campaigns (Chava

(2014); Davies and Van Wesep (2018); Hartzmark and Sussman (2019)).3 However, between these

two literatures, there still remains an important gap in our understanding of the willingness and

ability of activist shareholders to effect a company’s environmental impact with the primary goal

of increasing the non-pecuniary benefits enjoyed by society, and not individual wealth.

This paper contributes by documenting the real effects of shareholder activist campaigns on

targeted firms and the impact they have on the environment. We focus on the quasi-experimental

setting of the Boardroom Accountability Project and exploit the unexpected nature of the activist

campaigns. We use a difference-in-difference specification to examine the effectiveness of these

shareholder engagements on reducing a firm’s environmental impact. Our core results sug-

gest that targeted firms improved their aggregate environmental scores through decreasing their

plant’s total toxic releases. These changes primarily come from a decrease in stack-air emissions

that are related to production activities. These firms also have lower levels of environment-

related accidents, and lower legal risk associated with pollution-related activities. We rule out

1These engagements aim to improve firms along environmental, social, and governance (ESG) dimensions. Since2014, 253 campaigns have been launched in the United States spanning multiple industries encouraging a vote infavor of ESG-related shareholder proposals (Factset, 2018). Additionally, sustainable funds have attracted $8.9 billionin net flows in just the first half of 2019 and appear to set another calendar-year record for flows into sustainable funds(Morningstar, 2019).

2A partial list of activism by pension funds includes Nesbitt (1994), Smith (1996), Wahal (1996), Huson (1997),Carleton, Nelson, and Weisbach (1998), and Del Guercio and Hawkins (1999). For a survey of shareholder activism,see Karpoff (2001) and Gillan and Starks (2007). Related, Brav, Jiang, and Kim (2015) studies the real effects of hedgefund activism. For a survey of hedge fund activism, see Brav, Jiang, and Kim (2010).

3Theoretical studies of impact investing include Chowdhry, Davies, and Waters (2018); Hart and Zingales (2017);Heinkel, Kraus, and Zechner (2001); Morgan and Tumlinson (2019); Oehmke and Opp (2019).

1

multiple alternative hypotheses and show that these improvements do not come at the expense of

financial performance or investor’s wealth. We also establish the external validity of our results

along other pro-social mandates, and across other activist investors. Taken together, this study

underlines the how shareholders may be able to maximize their total wealth from both financial

and non-pecuniary benefits.

We focus on the Boardroom Accountability Project (hereafter, BAP), which was initiated by

the New York City Pension System, which was intended to improve the sustainability char-

acteristics of portfolio companies. Starting in 2014, without prior announcement, the BAP began

submitting proposals requesting the inclusion of proxy access bylaws in targeted firms’ corporate

charters.4 The proxy access proposals requested new bylaws that permitted shareholders who

collectively held 3 percent of the company for at least three years to nominate up to 25 percent of

the board using the company’s proxy material. Using these proxy access proposals over several

years, the BAP pursued specific social mandates that included environmental, transparency in

political contribution, board diversity, and excess CEO pay.5

In this study we focus on firms that were targeted by the BAP through their environmen-

tal mandate, and exploit the staggered targeting of firms in a difference-in-difference empirical

setting to estimate the relationship between environmental activism and its real effects. Our

analysis compares firms that were targeted by the BAP to a counterfactual firm, constructed us-

ing propensity score matching, in the same industry and with similar financial characteristics.

We make use of the limited and staggered targeting of companies to compare between firms

that were consistently different, in parallel, along their environmental impact prior to treatment,

illustrated in Figure 1.

It is important that we emphasize that the BAP did not target firms randomly, but in con-

trast, were likely targeted because they exhibited poor sustainability characteristics and higher

capacity to implement changes (Dimson, Karakas, and Li (2015)). Thus, our empirical results

are interpreted as the treatment effect on the treated: would the same changes have occurred if

the pension system selected the identical target firms, but remained passive in their demand for

proxy access?6

4We confirmed this through a discussion with the NYC Comptroller’s office.5We group fossil fuels and climate change into an environmental mandate.6We use a similar interpretation to Brav, Jiang, and Kim (2015)

2

Using this empirical setting, we examine three questions in this paper: (1) can socially-

motivated activist investors improve the environmental impact of targeted companies; (2) do

natural alternative hypotheses drive these results, and (3) can the results be generalized to other

socially motivated campaigns to bring about real changes?

First, we test if activist investors can improve the environmental performance of the targeted

firms. Studying the real effects of activism on environmental sustainability, we hand-match

detailed data on plant-level emissions from the EPA’s Toxic Release Inventory (TRI) program and

re-test our main specification. Plants on average reduce their total chemical release, with the

reductions primarily coming from stack air and those from production waste. Less impactful

forms of waste management, recycling, energy capture, and waste treatment do not change. In

the context of the waste management hierarchy, a pecking order of environmentally preferred

waste management strategies, targeted firms are focusing their improvements along the most

impactful activities (EPA (2019b)). As a robustness, we show that the results are primarily driven

by on-site plant changes, suggesting that firms are not substituting their pollutive activity to

off-site facilities.

We further confirm our analysis by testing if firms targeted by the BAP, for the environmental

mandate, are less prone to environmental accidents. We use data from the US Coast Guard

National Response Center to measure accidental environmental incidences, which proxies for

firms taking preventative measures to reduce their impact. Using either an indicator for any

accidents or the number of accidents, we find a significant decrease in the likelihood of targeted

firms reporting incidences, consistent with firms taking preventative measures to reduce their

environmental impact.

Related to the decrease in accidents, we also consider if firms are decreasing their legal risk

associated with polluting activities. To show this, we measure the number of SEC actions and

material federal civil litigation, as reported by Audit Analytics. Using either an indicator for

a lawsuit or the number of lawsuits in a given year, we also find a significant decrease in the

likelihood of target firms to face an environmentally-related lawsuit.

Establishing that firms are responding to shareholders, we put in context the impact of the

activism on local-level changes by considering the interaction between the amount of pollution,

the toxicity of the pollution, and the size of the local pollution. We use the EPA’s Risk-Screening

3

Environmental Indicators (RSEI) Model computation methodology (EPA (2019a)), which calcu-

lates a unit-less value that accounts for the impact of a chemical release. Aggregating data at

the census block group level, our results suggest that firms are not just reducing their levels of

pollution but their overall impact, as measured by the RSEI score. Decomposing result, we find

firms reduce pollution from toxins related to causing cancer. These results provide the clearest

evidence that environmental activism has real effects on broader social benefits.

Second, we rule out natural alternative hypotheses that may be driving the environmental

activism results. A primary hypothesis is that the improvements to a firm’s environmental per-

formance substitutes for either investor wealth or financial performance. Studying the impact on

investor wealth, we conduct an event study for firms targeted through the Boardroom Account-

ability Project. Focusing on within-week changes in public equity values, we find an unexpected

1.05% increase in value.7 Focusing on the firms’ financial performance, we do not find that tar-

geted firms experience a decline in profitability, return on asset. Using the firm’s z-score, a proxy

for the riskiness implied by its accounting statement, we do not find a change for targeted firms

(Altman (1968)). These results are suggestive that the investor wealth and financial performance

of targeted firms do not act as a substitute for sustainability.

An alternative hypothesis is that firms may be responding along multiple sustainability di-

mensions, irrespective of the specific mandate put forward by an activist. That is, a firm targeted

for their environmental impact may systematically change their board diversity or governance.

Such a result would suggest that targeted firms are improving due to the attention drawn to-

wards them, and not for the precise issues brought forward by an activist. We find that this

possibility, however, is not the case. Firms are improving their sustainability performance only

along targeted mandates.

Another alternative hypothesis is that firms are responding to the treatment of being tar-

geted for proxy access bylaws, rather than the specific mandate put forward by shareholders.

We address this hypothesis by conducting a placebo test using the next largest investors that

proposed proxy access at firms, unrelated to a sustainability mandate. The results suggest that

7We also find similar results when controlling for Fama and French (1992) and Carhart (1997) risk factors. Theseresults are broadly consistent with Bhandari, Iliev, and Kalodimos (2017). We interpret these results as a confirmationthat investors assign a positive value to monitoring provided by proxy access, similar to Becker, Bergstresser, andSubramanian (2013); Cohn, Gillan, and Hartzell (2016).

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the selection, alone, of being targeted for proxy access bylaws are not driving the results.

Further, it is possibility is that firms that adopt proxy access bylaws may also simultaneously

improve their sustainability mandates due to the threat of discipline by shareholders. We disen-

tangle this possibility by focusing on the sub-sample of firms that have also adopted proxy access

but not because of the BAP campaign. Comparing targeted firms to other proxy adopting firms,

we can hold constant unobserved differences in the propensity to adopt proxy access. Estimates

from these comparisons suggest that firms do not respond simply to the threat of discipline, but

instead to specific environmental, social, and governance (ESG) activist campaigns.

Third, we test if the environmental activism results can be generalized to other socially mo-

tivated campaigns to bring about real changes. Our initial approach is to consider the efficacy

of the BAP’s other mandates, outside of environmental, and their real effects. Along the board

diversity, we find that targeted firms respond by adding new members through growing their

boards, with these new additions more likely to be female, however, this new gender diversity

substitutes for overall ethnic diversity at the board level. Focusing on the curtailment of excess

CEO pay mandate, our results suggest a significant reduction in total compensation, a decrease

in the percent of excess compensation, and an increased sensitivity to pay-for-performance. Con-

sidering the transparency of political contributions mandate, we use the CPA-Zicklin index and

find a statistically and economically significant increase in information disclosure by targeted

firms.

We also conduct external validity tests for our results using other ESG-related shareholder

proposals and find that ESG activism is broadly effective for other institutional investors. Tar-

geted firms respond only in cases where the activist investor has a firm-specific ESG mandate,

and these responses are limited to the targeted characteristics. These results, beyond our base-

line sample, establish that other activist investors can engage in sustainable investing to dis-

cipline firms on non-financial characteristics through firm-specific ESG mandates rather than

broad-based interventions.

At its core, this study pushes forward our understanding of how investors maximize their

total wealth, both through financial and private benefits. Previous research has documented the

strong preference of investors for sustainable investments resulting in investors divesting from

low sustainability firms, voting with their feet, towards high sustainability investments. At the

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same time, this preference for high sustainability firms may come at the cost of lower future

expected returns (Barber, Morse, and Yasuda (2018); Hong and Kacperczyk (2009)).8 This paper

provides a counterpoint affirming that institutional investors may be able to maximize their

total value through monitoring, voting with their voice, to bring about change.9 Further, we shed

light on our understanding of the basic tenet of economic theory that a firm should maximize

market value rather than shareholder welfare (Berle and Means (1932); Friedman (1970); Hart

and Zingales (2017)).

Our paper contributes directly to the impact investing literature by uncovering the real effects

of activist investors to improved sustainability of targeted firms.10 Previous studies have focused

on changes in aggregate ESG indexes and studying the financial changes with these firms (Aguil-

era, Bermejo, Capapé, and Cuñat (2019); Barko, Cremers, and Renneboog (2017)). However, other

research has pointed out the issue of transparency among ESG metrics data providers, which may

created inconsistencies and undermines their reliability (Kotsantonis and Serafeim (2019)). We

complement these studies by providing evidence of real effects at the plant, firm, and local level.

Further, given the considerable variation in sustainability preferences across geographic regions

and investor bases, our results provide new and important evidence for US pension investing in

public equities (Bolton, Li, Ravina, and Rosenthal (2019); Simon Glossner and Steffen (2019)).

Our results also contribute to the shareholder activism literature through uncovering the real

effects of campaigns initiated for environmental and sustainability mandates. Previous research

has studied the real effects of hedge fund activism on assets, productivity, and labor (Brav, Jiang,

and Kim (2015)), and corporate innovation (Brav, Jiang, Ma, and Tian (2018)). However, there has

been limited research studying the real effects of socially-motivated activism on targeted firms.

This paper fills this gap by systematically studying the real effects of activist investors along

non-pecuniary dimensions.

8A more recent study suggests this relationship may go the other direction, see Kim and Kim (2019).9See Appel, Gormley, and Keim (2016); Black (1990); Gillan and Starks (2000); Shleifer and Vishny (1986).

10In a cross-country context, a recent paper studies the spillover effects of environmental and social regulatoryrequirements along the global supply-chains, (Schiller (2018)).

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1 Institutional Setting and Data

1.1 The Boardroom Accountability Project

Institutional investors have called for the ability to nominate directors to the corporate ballot—

proxy access—but have historically faced opposition (Funds (2015)). In response to board failures

at Enron and WorldCom, and governance failures through the Great Recession, the SEC approved

a universal proxy access rule in 2010. Although this new rule was vacated shortly after by the

D.C. Court of Appeals on procedural grounds, it did not vacate a provision within the Dodd-

Frank Financial Reform Act enabling shareholders to start requesting proxy access mechanisms

through shareholder resolutions.

Using this new provision, the New York City pension system (NYCPS) publicly announced

the Boardroom Accountability Project (BAP) in November 2014.11 The BAP’s aim is to increase

the accountability of board members, the New York City Comptroller requested proxy access

rights for targeted portfolio companies. The proxy access proposals were made to mirror rules

previously enacted by the Securities and Exchange Commission (SEC) and requested the in-

clusion of proxy access bylaws in firms’ corporate charters permitting shareholders that who

collectively held 3 percent of the company for at least three years to nominate up to 25 percent

of the board using the company’s proxy material.

With the support of other large institutional investors, proxy access proposals were filed

simultaneously to 75 companies out of over 3000 companies that were in the US public equity

portfolio of the NYC pension fund. Although these results are non-binding proposals for proxy

access, the proposals submitted by the BAP were effective, and the majority of the engaged

companies enacted proxy access bylaws in the following year. Subsequently, the BAP re-filed

proxy access proposals for non-adopters while also added new companies to their campaign

through 2018.

Table 1 provides descriptive statistics on the companies that were targeted by BAP by year

11The New York City Pension Fund is a combination of five funds. Pension funds that are part of the NYCPS arethe New York City Employees’ Retirement System (NYCERS), the Teachers’ Retirement System of the City of NewYork (TRS), the New York City Police Pension Fund (Police), the New York City Fire Pension Fund (Fire), and the NewYork City Board of Education Retirement System (BERS). NYC’s pension system is the fourth-largest public pensionplan in the US, with approximately $199 billion in assets under management. As of May 2019, the fund had around29% of its assets allocated to US public equities. For more details, please see New York City Comptroller website.

7

(Panel A) and by industry (Panel B). Out of 181 BAP firm-mandates in our sample, 56 companies

were targeted for environmental concerns, and 65 were targeted for issues related to board di-

versity. Figure 2 shows the resolutions put forward by the NYCPS, Panel A plots all resolutions

put forward by the NYCPS while Panel B shows all proxy access resolutions that were filed.

1.2 Data

We combine data from the BAP obtained from the New York City Comptroller’s Office with

data from Sustainanlytics, CRSP-Compustat, the Environmental Protection Agency, BoardEx,

Capital-IQ Execucomp, CPA-Zicklin, Institutional Shareholder Services, Audit Analytics, the US

Coast Guard National Response Center (USCGNRS), and both Schedule 13F and 13D from the

Securities and Exchange Commission.12 Our primary data set comes from the BAP of the New

York City pension fund from 2014 through 2018, and contains the names of the firms, year of

engagement, and the categorical reason the firm was targeted. To establish a counterfactual to

the BAP firms, we merge the firms included in the S&P 500 index as well as accounting and

performance measures from CRSP-Compustat.

Our baseline environmental results rely on data from Sustainalytics’ yearly aggregate per-

formance. We use multiple alternative datasets to measure real outcomes at the firm level. We

use the EPA’s Toxic Release Inventory (TRI) program dataset from the Environmental Protec-

tion Agency (EPA) that has information on the total chemical released and pollution prevention

activities by both industrial and federal facilities.13 To measure local level changes, we use Risk-

Screening Environmental Indicators (RSEI) Model scores from the EPA. To measure environmen-

tal accidents and the legal risk associated with environmental accidents, we use data from the

UCGNRC and Audit Analytics, respectively.

We measure board-level diversity using data from BoardEx, and use standard measures of

board member compensation, age, tenure, gender ratio, and board size. We impute missing eth-

nicity, for both US census ethnicity as well as ethnic diaspora, from a third-party sociolinguistics

algorithm.14 We measure CEO compensation using data from Capital-IQ/Execucomp. We use

12Appendix A.2 provides a summary of variables used in the study.13The TRI program was created under Section 313 of the Emergency Planning and Community Right-to-Know Act

(EPCRA). See https://www.epa.gov/toxics-release-inventory-tri-program for more information.14For census ethnicities come from the Office of Management and Budget standards on race and ethnicity. These

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data from CPA-Zicklin to assess changes in performance of a company’s disclosure policy and

political spending policies. We merge additional datasets to rule out alternative hypotheses that

may be driving our results. First, we include data from Institutional Shareholder Services (ISS)

to observe all firms that received proxy access proposals. Second, we collect data for Schedule

13F filings from Thomson Reuters. Lastly, we hand-collect Schedule 13D filings from the SEC.

2 Empirical Strategy

The following section discusses our empirical strategy to uncover the real effects of environmen-

tal activist investing. We first develop a target selection model to better understand how the BAP

selected firms. Next, we use the insights from the target selection model to develop a propensity

score model. In the final subsection, we present our main specification.

2.1 Target Selection

Studying the relationship between environmental activism and real outcomes is difficult due to

the endogenous choice and possible unobserved interactions between shareholders and firms

(McCahery, Sautner, and Starks (2016)). Investors may privately convey their preference for

environmental change to a firm which would be not observable to the econometrician. Thus,

uncovering the real effects of environmental activist investing and its ability to impact pollution

mandates remains an open question.

We overcome these challenges by exploiting the quasi-random targeting of firms by the NY-

CPS. We shed light on how the BAP selected firms by estimating a multivariate logistical re-

gression, correlating characteristics with the likelihood of being targeted by the NYCPS. Using

a panel of Russell 3000 firms, the benchmark portfolio for the NYCPS from 2009 to 2018, we

estimate:

BAPi,t = β0 + β1Sizei,t−1 + β2Mk2Bki,t−1 + β3Ret12Mi,t−1 + β4Pro f itabilityi,t−1+

β5 IOi,t−1 + β61[S&P500i,t−1] + ε i,t.(1)

include: White, Black or African American, American Indian or Alaska Native, Asian, and Native Hawaiian or OtherPacific Islander. For more information see https://www.namsor.com/.

9

For all regressions, i and t subscripts are for firm and time, respectively. The dependent

variable, BAPi,t, is a dummy variable equal to one if targeted by NYCPS in the subsequent year,

and zero otherwise. We consider the following set of explanatory variables: Size is the natural

logarithm of the book value of assets. Mk2Bk is the market-to-book ratio of assets, defined

as market value of equity plus book value of debt over book value of assets. Ret12M is the

stock return in the past 12 months. Profitability is the ratio of earnings before interest, taxes,

depreciation, and amortization scaled by sales. IO is the percentage of outstanding shares held

by institutional investors. S&P 500 is an indicator that takes the value of one if the firm is

included in the S&P 500 index in that year.15

Figure 3 estimates equation (1) and plots the odds ratio for ease of interpretation.16 The

results suggest that the NYCPS is likely to target large firms, with the estimate suggesting that

the likelihood of targeting increases by a factor of 1.94. Similarly, the likelihood of targeting

increases by a factor 1.17 for firms with higher market-to-book ratios. Surprisingly, firms with

poorer performance, as measured by stock returns, are less likely to be targeted. Targeted firm

have higher institutional ownership and is likely to be included in the S&P 500 index.

2.2 Propensity Matching

Guided by the target selection model, we use a propensity score matching approach to address

the concern of selection by the BAP.17 It is, however, unclear what is the correct set of firms to

match the BAP target firms against. Motivated by the target selection model above, we use the

constituents of the S&P 500 over the same period as our candidate counterfactual firm, due to

the overwhelming overlap with BAP firms.

We construct our matched sample by starting with our pooled sample firms targeted by the

BAP and firms for propensity score matching. We match each firm targeted in the BAP in year t

with a firm not targeted from the same year, within the same SIC 1-digit industry code, and with

the closest propensity score.18 Our propensity score estimation uses the firm size measured as15Our conversations with the Assistant Comptroller of the NYCPS indicate that a third party list measuring firms

fossil fuel reserves as drives targeting for environmental concerns. We believe this is the "Fossil Free 200 Index". Re-peated requests to the data vendor were unanswered, and hence, we are unable to include this index as an explanatoryvariable in our analyses. More information can be found at http://fossilfreeindexes.com/faq/.

16Table IA2 reports estimates from the analysis.17This is a common approach in the activism literature. See Brav, Jiang, and Kim (2010, 2015).18For robustness, we also use the Fama-French twelve-industry classification approach and get qualitatively similar

10

the log of the firm’s total assets, return of assets, and market-to-book ratio.

Table IA1 summarizes the comparisons between target and matched firms’ key characteris-

tics. Panel A, columns (2)–(1) and columns (3)–(4) shows that pre-matched and matched samples

are both statistically and economically similar for the matching variables.

2.3 Main Specification: Difference-in-Difference

We use a difference-in-difference empirical strategy that compares firms that are targeted by the

BAP and a matched control firm within the same one-digit SIC industry and similar financial

performance.

Yi,t = δi + δt + β1 I (Targeti) I (Posti,t) + β2 I (Posti,t) + ε i,t. (2)

δi and δt representing firm and year fixed effects, respectively. I (Targeti) is a dummy variable

and equals one if the firm is targeted by the BAP. Our counterfactual firms are matched using

the procedure described in the previous sub-section. The I (Posti,t) variable is a dummy variable

and equals one for the firm-year following the first target date of the BAP. For firms that were

not targeted, we use the pseudo-event year of 2015, the first year in which the BAP was initiated.

The interaction coefficient, β1 is the primary variable of interest and represents the within-firm

change in characteristics following targeting by the BAP. All estimates cluster errors at the firm

level.

We find our empirical approach satisfies the parallel trends assumption. We begin by esti-

mating equation (2), and use index levels from Sustainanlytics as our dependent variable, Yi,t.

Figure 1 plots the interaction coefficient, β1, against event time. Visual inspection of the plot

suggests that both treated and matched firms are moving in parallel in the pre-period.19 The

point estimates are statistically insignificant between groups and non-trending before time zero,

providing suggestive evidence of a pre-treatment parallel trend.

results. See https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.19Table IA4 presents the dynamic effects in table form.

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3 How Do Firms Respond to Activism?

Can activist investors reduce the environmental impact of targeted companies? In the following

section we first show that aggregate environmental score improve, and then measure the real

effects of environmental activism.

3.1 Improvements to Aggregate Environmental Scores

We begin our analysis by testing if aggregate environmental performance improves for targeted

firms. We estimate equation (2) and use the score of the firm-level environmental index as our

dependent variable, Yi,t.

The results in Table 2 suggest that activist investors can indeed improve the overall envi-

ronmental performance of targeted firms. The negative coefficients on Target Reason in column

(1) indicate that target firms were performing poorly along the sustainability dimensions before

the intervention. The interaction term estimates the relation between activism and sustainability

performance. We find statistically and economically meaningful improvements in environmen-

tal characteristics. For example, estimates from columns (2) suggest that targeted firms increase

their environmental performance by 7.7% relative to the sample mean, following the targeting by

the BAP, compared to counterfactual firms.20

We also note that the coefficients on Post are statistically insignificant, indicating that the

matched control firms do not show improvements in performance. Additional tests in Table IA3

show that the effect manifests only among firms targeted by the BAP, and firm-specific mandates

drive the results.

3.2 Real Effects of Environmental Activism Investing

To this point, our primary dependent variable has been at the aggregate environmental index

level. However, there is a concern that firms may engage in “green-washing” without making

real changes to their behaviour (Kitzmueller and Shimshack (2012); Koehn and Ueng (2010)), or

that there exists a measurement issues associated with ESG indexes (Kotsantonis and Serafeim

(2019)).

20For environment, the 7.7% increase is calculated as 13 (1SD) × 0.32 (coefficient) ÷ 54 (unconditional mean).

12

To uncover the specific environmental changes being made by a firm, we undertake a time-

intensive hand-match between plant level data from the Toxic Release Inventory (TRI) provided

by the EPA and the BAP. Using this match, we are able to re-estimate equation (2) by replacing

our primary dependent variable with the data on plant-level emissions output. We are careful

when calculating and interpreting the change in pollution at the plant-level data. Pollution is a

function of a firm’s level of both productivity and effort to reduce its environmental impact. It

is possible that a plant is decreasing their production but not reducing their production-related

emissions, thus, using raw levels of pollution will lead to misleading inferences. We solve this

problem by normalizing the pollution and waste-management with lagged COGs, as this proxies

for production in a given year.

Measuring real changes at the plant level, we confirm our initial results that firms are indeed

reducing their environmental impact.21 Table 3 presents the real effects of environmental activism

using plant-level emissions data. Column (1) provides evidence that firms, on average, decrease

their total chemical release. These improvements are consistent with the baseline results on the

enhanced aggregate performance. Columns (2) and (3) decompose these results into onsite and

offsite emissions outputs. The results are suggestive that the changes in environmental impact are

coming entirely from on-site changes to total emissions, while offsite emissions do not respond

to activist campaigns.

Table 3 decomposes the source of the decreased on-site emissions. The results suggest that

the reduction can be entirely attributed to changes in stack air emissions, those typically related

to plant production. Column (1) shows that stack air emissions—those through point source

air emissions released to air that occur through confined air streams, such as stacks, ducts or

pipes—shows a significant decrease in output. In contrast, columns (2) and (3) suggests that

both fugitive air and surface water discharges are statistically indistinguishable from zero.

We address the possibility that these results are coming from new, more efficient, plants

being built and introduced into our sample. We test this possibility by only including plants that

existed throughout our sample. Presenting our results in Table IA9, we find this main effect does

not quantitatively or qualitatively change.

21This result is robust to (1) aggregating plant level to the firm level, (2) normalizing using net sales, and (3) notnormalizing. It is also robust to matching with Fama-French industry codes.

13

The pollution reduction is entirely driven by changes in product-related waste, and are in line

with the most impactful activities. Column (1) of Table 4 shows that production-related waste,

the sum of all non-accidental chemical waste generated at a facility, prior to any form of on-site or

off-site waste management, is decreasing.22 By contrast, columns (1)–(3) show that changes waste

management efforts, such as recycling and treatment work, do not change in the post period. In

the context of the waste management hierarchy, a pecking order of environmentally preferred

waste management strategies, firms are changing their most impactful activities (Figure 4, Panel

A).

We conjecture that firms also take preventative efforts to reduce their impact on the envi-

ronment. We test this possibility by merging data from the US Coast Guard National Response

Center, which captures contact with the federal government reporting all oil, chemical, radiolog-

ical, biological and etiological discharges into the environment, anywhere in the United States.

Column (1) of Table 5 shows that the extensive margins of having an accident, in a given year, de-

creases by 25%. Examining the number of accidents, column (2) shows that there is a significant

drop in the number of accidents. Although we are unable to pin-down the exact environmental

impact, it is clear that firms are responding by reducing the likelihood of accidents, and possibly,

catastrophes.

Related to preventative measures, we also conjecture that firms are able to reduce their legal

liability related to environmental accidents. We test this by measuring the number of SEC actions

and material federal civil litigation, as reported by Audit Analytics. Table 5 shows that the overall

legal risk associated with environmental activism is going down. Column (1) shows the extensive

margin of lawsuits decreasing by 10%, while column (2) shows that the number of lawsuits

decreasing as well. While less statistically precise, the percent of lawsuits that are settled are also

decreasing, as show in column (3). Taken together, these results are consistent with firms taking

a longer-term perspective of value, reflected in efforts, and succeeding in lowering their legal risk

associated with polluting activates.

Establishing the response of firms, we measure the local level impact by consider the inter-

action between the amount of pollution, the toxicity of the pollution, and the size of the local

22It is the sum of on-site environmental releases (minus quantities from non-routine, one-time events), on-site wastemanagement (recycling, treatment, and combustion for energy recovery), and off-site transfers for disposal, treatment,recycling or energy recovery, EPA (2019b).

14

pollution. We use the EPA’s Risk-Screening Environmental Indicators (RSEI) Model computation

methodology (EPA (2019a)), which calculates a unit-less value that accounts for the size of the

chemical release, the fate and transport of the chemical through the environment, the size and

location of the exposed population, and the chemical’s toxicity (Figure 4, Panel B). Using the

broad geographic variation (Figure 5), we aggregated the RSEI scores to the census block groups

level and match the location of plants that are treated through the BAP.23

Table 6 establishes striking results that firms decrease their regional pollution intensity. Firms

that are targeted by the BAP reduce their local area environmental impact, as measured by RSEI

scores. Column (1) suggest a change of 26% for the post treated census regions. Decomposing the

RSEI score, we find that the reductions are concentrated along pollution coming from toxins that

are known to cause cancer, with a 22% decrease, while there is a statistically indistinguishable

change in non-cancer related pollution.24 These results suggest that firms are actively managing

the impact of their pollution. Further, this evidence provides the clearest evidence that changes

at the firm level have real effects on local regions and broader society.

4 Ruling Out Alternative Hypotheses

The following section rules out natural alternative hypotheses that have been proposed to be

driving the environmental activist investing results.

4.1 Return on Asset, Profitability, Riskiness

A leading alternative hypothesis is that the improvements to a firm’s environmental performance

substitutes for either investor wealth or financial performance. Focusing on the firms’ financial

performance, we find that firms, on average, do not see a deterioration in financial performance.

Panel A of Table 7 summarizes our results. When measuring profitability and return on asset,

we find that there is no significant changes on average at the firm level. Measuring the firm’s

z-score, a proxy for the riskiness implied by its accounting statement, we do not find a change

for targeted firms. These non-results, taken together, suggest that firms are not, on average,

23Using the resulting merge, we standardize the RSEI score to have a mean zero, unit standard deviation. We arecareful to re-base the RSEI score based on populations in 2010 to account for migration out of polluted regions.

24Table IA5 reports similar results at the firm level.

15

substituting sustainability performance for financial performance.

4.2 Equity Response to the BAP Campaign

How does the market respond to activist campaigns by the BAP? If the market expected envi-

ronmental activist investors were substituting environmental performance for returns, we would

expect the stock price to response accordingly.

4.2.1 Short-Term Equity Reaction

We study the market’s reaction by focusing on the within-week window around the announce-

ment date of targeting by the BAP. We adopt a two-step approach that first estimates a time series

of factor adjusted returns, and then considers a standard difference-in-difference regression to

relate excess returns to firm-specific factors.

Our first step estimates the factor exposure of the firm before targeting by the BAP. We

focus on the Fama and French (1992) and Carhart (1997) four-factor model. We estimate factor

exposures separately for each firm by running the following rolling window regression, based

on one year of daily data:

rei,t ≡ ri,t − r f ,t = β1,iRMRFt + β2,i HMLt + β3,iSMBt + β4,iUMDt + ε i,t i = 1, . . . , N. (3)

Calculating the factor exposures allow us to compute an average excess return αit per firm

for each day:

αFFCit = re

it − β1,iRMRFt + β2,i HMLt + β3,iSMBt + β4,iUMDt i = 1, . . . , N. (4)

Next, we consider a standard difference-in-difference specification to relate excess returns to

monitoring by the BAP. Our dependent variable is a panel of risk-adjusted daily returns com-

puted from equation (4). NYCPS announced about BAP on Thursday, 6 November 2014. We

define our sample period as the week (5 business days) around the initial announcement. We

define the pre-period as three days before the announcement (Monday to Wednesday) and the

16

post-period as two days, the announcement and the day after (Thursday and Friday). As the

initial announcement included 75 firms, we only define these firms as a target. We estimate the

change in unexplained performance of targeted firms in the post-period:

αFFCi,t = δi + δt + γ1 I (Targeti) I (Posti,t) + γ2 I (Posti,t) + ε i,t. (5)

This specification allows us to attributes the excess returns to the targeting of firms by the

BAP. The coefficient of interest, γ1, measures the market reaction to the target announcement.

Table 7 suggests that firms targeted by the BAP experience an increase in excess and risk-

adjusted returns, around the announcement of the targeting. Column (1) shows an increase in

excess returns of 1.05%, while columns (2) and (3) control for Fama-French, and Carhart risk-

factors, and show similar results.25 The magnitude of these results are in line with Bhandari,

Iliev, and Kalodimos (2017), which studied the initial BAP announcement, and with Becker,

Bergstresser, and Subramanian (2013); Cohn, Gillan, and Hartzell (2016), which studied the Busi-

ness Roundtable’s challenge to the SEC’s 2010 proxy access rule.

4.2.2 Long-Term Equity Reaction

We also test if there is a long-term impact of the BAP proposals on the firm’s equity returns. A

leading hypothesis that the improvements to the environmental characteristics are not coming at

the expense of long-run performance.

We test this hypothesis by conduct calendar-time portfolio regression around the initial BAP

announcement. For this test, we focus on the first set of firms were targeted by the BAP. Our

test forms portfolios that buy and hold shares of firms being targeted by the BAP during their

first round of targeting and holding them for the specific time window. For each portfolio,

we calculate the excess returns to the Fama-French and momentum factor. The key variable of

interest is the regression intercept, alpha, which measures the risk-adjusted excess returns.

The results in Table IA10 suggests that there is no change in long-term returns for the firms

targeted by the BAP. Specifically, the regression intercept, alpha, is broadly statistically indis-

25These results for the subset of firms targeted only for the environmental mandate. Results available upon request.

17

tinguishable from zero. These result is consistent between the equal-weighted portfolio and

value-weighted portfolio presented in Panel A and B, respectively.26

4.3 Are There Sustainability Mandate Spillovers Within a Firm?

An alternative hypothesis is that target firms respond across multiple ESG dimensions, irrespec-

tive of the specific mandate. If this were the case, we would expect that firms targeted for their

environmental impact would systematically change their board diversity or governance. Such a

result would suggest that targeted firms are improving due to the attention drawn to their lack

of sustainability, and not for the precise issues brought forward by an activist.

We test the possibility of treatment spillovers between the mandates put forward by the BAP:

Yi,t = γ1 I (TargetReasoni) I (Posti,t) + γ2 I (Di f f erentReasoni) I (Posti,t)

+ γ3 I (Posti,t) + δi + δt + ε i,t.(6)

I (TargetReasoni) is a dummy variable and equals one if the firm is targeted for the same ex-

planatory variable being tested, while I (Di f f erentReasoni) is a dummy variable and equals one

if the firm is targeted for some other mandate, not corresponding to the dependent variable Yi,t.

The interaction coefficient, γ1 is the primary variable of interest and represents the within firm

change in characteristics in the post-period for related targeted mandates. A positive γ1 coeffi-

cient would suggest that firms are responding to specific mandate proposed by the shareholder.

In contrast, γ2 measures the contribution of a within-firm change in non-specified targeted rea-

sons. A positive coefficient would suggest that firms are responding to targeting, but across other

sustainability dimensions.

Table 8 presents estimates of equation (6) and finds limited evidence of spillover effects of

sustainability mandates. Columns (1) and (2) show no evidence for environmental or diversity

mandates. These results are not surprising, as changes to environmental and board diversity

should not affect other mandates targeted by the BAP. Column (3) is the only estimate to show

modest spillovers, but the target reason effect is statistically and economically larger than the

non-targeted reason. We conjecture that this result is expected, as the index for governance

26This result is robust to the window size, and the inclusion of all BAP firms.

18

is coarse and captures many different components. As a result, improved governance could a

correlated proxy for other changes with the firm. However, given these results in conjunction

with the evidence on real changes at the firm level, we conclude that spillover effect are not

likely driving the main treatment effect.

4.4 Does Targeting for Proxy Access Drive the Improvements to Sustainability?

A second possibility is that an unobserved factor associated with targeting for proxy access may

be driving our results. If this were the case, the decision to target a firm for proxy access could

instead explain the documented improvements brought by ESG activism.

We conduct a placebo test that re-estimates our baseline specification for the set of firms

targeted for (i) proxy access by the top three activist investors (outside of NYCPS) and (ii) proxy

access by all investors (outside of NYCPS). This sample holds constant unobserved factors that

drive the decision to target firms for proxy access.

Table IA6 reports the estimates from this exercise. The results suggest that there is no evi-

dence of any effect on ESG performance for the placebo sample, in terms of either the economic

magnitude or the statistical significance. This indicates that our results are unlikely to be driven

by other not observable factors whose effect we spuriously attribute to targeting by the BAP.

4.5 Does the Adoption of Proxy Access Drive the Improvements to Sustainability?

Another possibility is that firms that adopt proxy access bylaws may also simultaneously im-

prove their sustainability mandates due to the threat of discipline by shareholders. Target firms

may anticipate that institutional investors can more easily discipline firms through proxy by-

laws for poor sustainability performance, and as such, they respond by improving sustainability

characteristics in advance of any proposal.

We disentangle these possibilities by focusing on the subsample of firms that adopted proxy

access, but not because of the BAP. Comparing targeted firms to other proxy adopting firms, we

can hold constant unobserved differences in the propensity to adopt proxy access. Using the

sample of firms that adopted proxy access over the same period, we re-estimate equation (2) and

use the environment, diversity, and governance aggregate scores as our dependent variable.27

27Note that here Post is an indicator for the subsequent year (and beyond) after the target by the BAP in the case

19

Comparing the interacted coefficients in Table IA7 with the coefficients in Table 2, the results

suggests that adopting of proxy access bylaws, alone, does not act as a sufficient condition for

improving sustainability performance. This suggest that engagement with the BAP drives our

baseline results, while proxy access acts as a monitoring and discipline mechanism that can be

used to bring change at a firm.

4.6 Does ESG Activism Act as a Coordinating Signal to Other Activist Investors?

Another alternative is that the BAP acted as a coordinating signal to other activist investors,

which subsequently allowed ESG activists to bring about change through coordinated activism

by traditional investors (Brav, Dasgupta, and Mathews (2019)). If this were the case, the improve-

ments to the sustainability performance of firms would incorrectly be attributed to the BAP,

rather than other traditional activist investors.

We test this possibility by measuring the change in the number of activist investors around

the BAP campaigns. To measure the level of activist investors, we match our data to Schedule

13D filings from the SEC. This regulatory form indicates a 5% block holder who may have specific

plans to affect the firm, either now or in the future. Using Schedule 13D, we compute both the

extensive margin, whether there was a new filing within a given year, and intensive margin, how

many were filings there were per year.

Table IA8 estimates equation (2) and finds no relation between ESG activism and traditional

activist investors. Columns (1) and (2) test the extensive and intensive margins of Schedule 13D

filing propensity for the target companies, respectively. The interaction term suggests that the

relationship between activism and the propensity to file a 13D is indistinguishable from zero.

This rules out coordination between activist investors as a candidate explanation for our results.

5 External Validity, Interpretation and Policy Implications

5.1 Can Activist Investing Bring About Other Real Effects?

How generalizable are the environmental activism results to other ESG campaigns? Understand-

ing this is important in an investors choice been active engaging a firm to change, voting with

of target firms and subsequent year (and beyond) after the adoption of proxy access for control firms.

20

their voice, versus broad divestment campaigns, voting with their feet.

The following subsections provides evidence that socially motivated activist investing, other

than environmental activism, is able to bring about real and important changes to targeted firms.

We focus on measuring the effectiveness of the BAP’s mandates to increase the transparency of

political contribution, increase board diversity, and curtail excess CEO pay .

5.1.1 Political Disclosure

A leading mandate of the BAP was to increase the disclosure of political contributions by firms.

To measure the change in political contributions, we use the CPA-Zicklin Index, which bench-

marks political disclosure and accountability policies and practices for election-related spending

of leading US public companies.28

The index measures three dimensions of corporate political transparency (disclosure, policy,

and oversight) each year. We standardize this variable to a zero mean unit deviation measure. It

should be noted that the index is available from 2011 and has gradually increased its coverage,

and it results in limited data coverage. As a result, our sample size considerably reduces when

analyzing changes in political transparency around BAP engagement.

Estimating our baseline specification, Table 9 shows that firms targeted for political disclosure

saw an improvement in the post-period. These results provide one of the first and clearest piece

of evidence of successful shareholder engagement improving disclosure. Overall, these results

inform the debate on corporate political spending by showing that firm-specific engagements are

useful in inducing transparency outside of regulation (Bebchuk and Jackson Jr (2013)).

5.1.2 Board Diversity and Composition

Another mandate of the BAP was to increase board diversity. We follow the BAP’s push for cor-

porate board diversity along the dimensions of gender, race, and ethnicity, and we test whether

firms targeted for diversity mandates made changes to their board composition along these di-

mensions.28The CPA-Zicklen Index is produced by the Center for Political Accountability in conjunction with the Zicklin

Center for Business Ethics Research at The Wharton School at the University of Pennsylvania. For more informationvisit https://politicalaccountability.net/.

21

Table 10 establishes a consistent narrative that targeted firms are adding new board members

and that they tend to be Caucasian females. Column (1) suggests that firms are, on average,

adding new individuals over two years. This represents a 5% increase in board size relative to

the unconditional sample mean. Moreover, estimates from column (2) suggest that the female

ratio, the fraction of females on the board, is increasing by one-third for a given year with the

increase driven by changes in composition among targeted firms as opposed to changes in the

composition among the control group.

We further uncover the changes in board diversity, by measuring the overall ethnic compo-

sition of the board. Using the new measures computed by the socio-linguistic algorithm, our

results suggest that the increase in gender diversity comes at the expense of ethnic diversity.

Column (3) suggests that the HHI of nationality diversity, measured as the board members’ na-

tionality according to US census classification, is decreasing. Column (4) suggests a similar result

when using the board members’ country of origin.

We do not find other measures of diversity changing to the corporate board. Column (6)

shows no change to the mean tenure of the average board member, while column (7) shows the

mean age of the board member. The lack of change along these characteristics provides further

confirmation that firms are indeed changing only along dimensions discussed by the BAP, and

not in other, non-targeted characteristics.

5.1.3 Excess CEO Compensation

To study a core mandate of the BAP to curtail excess CEO pay in targeted firms. We confirm that

sustainability mandates are effective in impacting a CEO’s compensation structure as well.

We follow the methodology outlined in Core, Guay, and Larcker (2008) to compute our excess

compensation metrics.29 Using our estimated model, we calculate excess compensation as total

pay as reported to SEC minus the expected payout from the benchmark model.

Table 11 shows that total CEO compensation and percent of excess compensation decreased

for firms that were targeted by the BAP.30 Moreover, the estimates suggest that the growth in

29Following prior research in this area (e.g., Smith Jr and Watts (1992); Core, Holthausen, and Larcker (1999)),our benchmark model for expected compensation is obtained by regressing the natural logarithm of compensationon proxies for economic determinants of CEO compensation. We use firm size, growth opportunities, stock returns,accounting returns, and industry controls.

30All numbers are deflated by consumer price index (2015) obtained from FRED St. Louis.

22

compensation also slowed down; however, the estimates are imprecise and hence do not allow

us to rule out that they are statistically different from zero. The estimates in column (1) suggest

that targeting leads to a decrease in total compensation by about 21% annually. Also, it leads

to a curtailment in excess CEO pay, which decreased within a firm by 26% a year (column (3)).

Furthermore, the results also suggest that targeting by the BAP better aligns CEO compensation

with the firm’s performance. Column (5) suggests that delta, the sensitivity of firm pay-to-

performance, is increasing on average for targeted firms.31.

5.2 External Validity: Are the Results Specific to the BAP?

More broadly, how generalizable are the results in the broader context of ESG activism in the

United States? A concern is that the results are specific to the size and influence of the NYCPS. If

this is the case, then our results may not generalize to other settings where investors are aiming

to improve corporate sustainability performance through engagement. To address this concern,

we show that investors targeting firms for specific ESG mandates do indeed catalyze change, on

average, for specific targeted sustainability characteristics.

We collect all proposals from Institutional Shareholder Services (ISS) shareholder proposal

database submitted to firms over the same period as the BAP.32 Each proposal is categorized

by hand if it belongs to an environmental, diversity, or CEO pay mandate.33 Using this list

of sustainability activist campaigns, we re-estimate our baseline specification and check how

effective such proposals have been.

Table 12 suggests that targeted firms respond where the activist investor has a firm-specific

ESG mandate, and these responses are limited to the targeted characteristics. Among the three

aspects, we find that the firm-specific ESG mandate is effective in improving environmental

and governance performance among targeted firms. Estimates from column 1 (3) suggest that

targeting increases the score environment score by 6.6%.34 There is an improvement in diversity

score, in terms of economic magnitude, but is statistically imprecise.

These findings complement our baseline results, establishing that other activist investors31For delta and vega, we extend the methodology from Coles, Daniel, and Naveen (2006, 2014); Core and Guay

(2002).32Since ISS has a broader coverage, for this test, our control group includes firms in the S&P 1500 index.33We had two independent research assistants check the veracity of the matches.34Relative to the unconditional sample mean (calculated as 13 (1SD) ×0.26 (Coeff) ÷51.19 (Mean)).

23

engaging in sustainable investing can discipline firms in non-financial characteristics through

firm-specific ESG mandates rather than broad-based interventions.

5.3 Interpretation and Policy Implications

The interpretation of our study comes with several important caveats. Specifically, our empirical

results need to be carefully interpreted in the context of our setting. Because the BAP did not

target firms randomly, any policy prescription suggesting a random targeting of firms to improve

their environmental impact would be poorly guided. Instead, the results should be interpreted

as the treatment effect on the treated: would the same changes have occurred if the BAP selected

the identical target firms, but remained passive in their demands?

In light of these caveats, we interpret our results as a potential solution to the free-rider

problem of the environment. In our setting, environmental activist investors effectively delegate

their pro-social preferences onto firms (Bénabou and Tirole (2010)). In the NYCPS and the BAP,

the ultimate beneficiaries of the pension funds vote trustees into their position to best represent

their interests and preferences. While serving in a fiduciary role, these funds may also act as

preference aggregator for the plans’ beneficiaries. Through activism, these large activist may

also help overcome the free-rider problem through providing a countervailing force to parts of

the market that are difficult to regulate (Coase (1960)).

More broadly, we interpret our results as providing novel evidence evaluating the importance

of proxy access rules. Prior studies have investigated the markets reaction of proxy access rules,

for instance (Becker, Bergstresser, and Subramanian (2013)), while more specific to the BAP, stud-

ies have focused on diversity (Barzuza (2019)) and stock reaction (Bhandari, Iliev, and Kalodimos

(2017)). Building on these works, this paper presents new empirical evidence that proxy access

rules alone do not affect firms along multiple dimensions. We instead show that it is the interac-

tion between using proxy access as a tool and a specific activist campaign that gives shareholders

the ability to effectively monitor and discipline investors.

24

6 Conclusion

This paper documents how environmental activism affects the sustainability mandate of targeted

firms. We contribute to the debate on the ability and efficacy of institutional investors to affect

the sustainability of portfolio firms. Changes measured in both aggregate index and in underly-

ing characteristics provide evidence that that firms are responding to the activist campaigns of

institutional investors. Returns and financial performance such as profitability, return on assets,

and risk of bankruptcy do not change for targeted firms. Ruling out alternative stories, we show

that the results are most consistent with the benefits of monitoring and the threat of discipline.

25

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Starks, Laura T, Parth Venkat, and Qifei Zhu, 2018, Corporate esg profiles and investor horizons,Working Paper .

Wahal, Sunil, 1996, Pension fund activism and firm performance, Journal of Financial and Quanti-tative Analysis 31, 1–23.

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-1

0

1

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-3 -2 -1 0 1 2 3

Environment score

FIGURE 1: EFFECT OF ENVIRONMENTAL ACTIVISM

This figure plots dynamic coefficients (βk) and their corresponding 95% confidence intervals of changes in z-scorearound the initiation of BAP for environmental concerns. The sample includes a panel of propensity score matchedS&P 500 firms from 2009 to 2018. We estimate the following equation:

Scoreit =−3

∑k=3

λk +−3

∑k=3

βk × 1[Target reason = Environment] + αi + θt + εit

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sponsor James McRitchieJohn Chevedden

Kenneth SteinerMyra Young

New York CityOther

(b) All Requests for Proxy Access

FIGURE 2: RESOLUTIONS FOR PROXY ACCESS

This figure shows the resolutions put forward by the New York City Pension Funds system. Panel A plots allresolutions put forward by New York City. Panel B shows all resolutions requesting access for proxy access. NewYork City Pension Funds system is the amalgamation of (i) Comptroller of the City of New York, (ii) New York CityEmployees’ Retirement System, (iii) New York City Fire Dept. Pension Fund, (iv) New York City Funds, and (v) NewYork City Teachers’ Retirement Fund.

31

Firm size

Market-to-book

12-month trailing returns

Profitability

Institutional ownership

S&P 500

0 1 2 3 4 5odds ratio

Target selection

FIGURE 3: SELECTION INTO BOARDROOM ACCOUNTABILITY PROJECT

This figure plots the odds ratio from a multivariate logit regression relating characteristics that correlate with thelikelihood of being targeted by the New York City Pension Funds system. The sample includes a panel of Russell3000 firms from 2009 to 2018. Firm Size is the natural logarithm of the book value of assets. Market-to-book is theratio of assets, defined as market value of equity plus book value of debt over book value of assets. 12-month trailingreturns is the stock return in the past 12 months. Profitability is the ratio of earnings before interest, taxes, depreciation,and amortization scaled by sales. Institutional ownership is the percentage of outstanding shares held by institutionalinvestors. S&P 500 is an indicator that takes the value of one if the firm is included in the S&P 500 index in that year.

32

(a) Waste Management Hierarchy

(b) Risk-Screening Environmental Indicators (RSEI) Model

FIGURE 4: ENVIRONMENTAL CONCEPTS FROM THE EPA

This figure presents two key environmental concepts used in this paper. Panel A describes the waste managementhierarchy, as described by the EPA (2019b), and ranks the management strategies from most to least environmentallypreferred. Source reduction and reuse refers to reducing waste at the source, and is the most preferred strategy.Recycling/Composition refers to activities related to collecting, sorting, and reprocessing items that would otherwisebe considered waste. Energy recovery refers to the conversion of non-recyclable waste materials into useable energy.Treatment and disposal refers to the reduction of the volume and toxicity of waste through processing. Panel Bdescribes the Risk-Screening Environmental Indicators (RSEI) Model computation methodology, as described by theEPA (2019a). A unitless value. the RSEI score accounts for the (i) the size of the chemical release, (ii) the fate andtransport of the chemical through the environment, and (iii) the size and location of the exposed population, and thechemical’s toxicity.

33

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Longtitude

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(a) 2010 through 2013

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Longtitude

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(b) 2014 through 2017

FIGURE 5: CHANGE IN RSEI SCORES

This figure plots the relative change in RSEI scores local Census blocks that (i) containing a targeted plant by a firms by the BAP, or (ii) belong to the propensityscore matched control firms in the sample. blocks. Figure A shows changes from 2010 through 2013, and Figure B shows changes from 2014 through 2017. Thesize of the circles represent the scaled change in the score over the time periods. Blue circle represents reduction in RSEI score, whereas the red circle representsincrease in RSEI score.

34

TABLE 1: ESG ACTIVISM BY YEAR AND INDUSTRY

This table provides descriptive statistics on ESG activism events by year (Panel A) and by industry (Panel B). Weidentify BAP targeting events through publicly available data from the Boardroom Accountability Project website.The category for Environmental includes Fossil Fuel the mandate due to the overlap mandate. Panel A uses the yeara firm is targeted as our event year, and report a total of 181 out of 190 firms due to our merging requirements. PanelB reports the number of activism events targets across the Fama-French 12 industries.

Panel A: ESG activism categories by year

Target Year # of Events Environment Diversity Pay Others

2015 75 27 21 23 42016 25 7 9 9 02017 55 19 21 14 12018 26 2 13 9 2

Full Sample: 181 55 64 55 7

Panel B: ESG activism categories by industry

# of Events Environment Diversity Pay Others

Consumer Nondurables 4 0 2 2 0Consumer Durables 5 1 2 2 0Manufacturing 7 0 5 2 0Energy 30 23 5 2 0Chemicals and Allied Products 5 0 1 4 0High Tech 29 2 14 11 2Tele and Communications 3 0 1 1 1Utilities 23 20 2 1 0Wholesale and Retail 18 5 6 5 2Healthcare, Medical Equipment, and Drugs 11 0 6 4 1Finance 32 1 14 16 1Other 14 3 6 5 0

Full Sample 181 55 64 55 7

35

TABLE 2: ESG ACTIVISM AND AGGREGATE SCORE

This table presents the impact of sustainability-mandated investor activism on firms’ environmental, board diver-sity, and governance performance. Table reports results comparing performance of targeted firms to S&P firms inpropensity score matched difference-in-differences empirical setup. The dependent variable is the standardized scoreof firms’ environmental (columns 1 and 2), board diversity (columns 3 and 4), and governance performance (columns5 and 6) each year. Target is a dummy variable indicating whether the firm is a target of BAP for the same explanatoryvariable being tested. Post is a dummy variable equal to one if the target firm (matched control firm) is within [t+1,t+3]years after the activism event year (pseudo-event year). All regressions include year fixed effects and are estimatedusing ordinary least squares (OLS) using standard errors clustered at the firm level. ∗∗∗, ∗∗, ∗ denote significance atthe 1%, 5%, and 10% level, respectively. Data source: Sustainalytics.

Dependent variable ENV Score DIV Score GOV Score

Target reason Environment Diversity Governance

(1) (2) (3) (4) (5) (6)

Target reason -0.056∗ -0.015 -0.075∗

(0.033) (0.018) (0.038)

Post -0.058 -0.073 0.110 0.132 -0.062 -0.079(0.080) (0.100) (0.113) (0.128) (0.088) (0.111)

Post × Target reason 0.345∗∗ 0.321∗∗ 0.680∗∗ 0.766∗∗ 0.315∗∗ 0.356∗∗

(0.151) (0.161) (0.288) (0.326) (0.159) (0.177)

Year fixed effects Yes Yes Yes Yes Yes YesFirm fixed effects No Yes No Yes No YesR2 0.31 0.32 0.06 0.07 0.17 0.17Observations 3,158 3,158 2,278 2,278 3,158 3,158

36

TABLE 3: REAL EFFECTS OF ENVIRONMENTAL ACTIVISM: PLANT-LEVEL EVIDENCE

This table reports results of the impact of sustainability-mandated investor activism on the release of toxic chemicalsat the plant-level. The table reports results comparing the performance of targeted firms to S&P firms in propensityscore matched difference-in-differences empirical setup. Panel A reports toxic chemical release while panel B reportstoxic chemical released to air and water. In panel A, the dependent variables are the total (column 1), onsite (column2), and offsite (column 3) toxic chemical release scaled by previous years’ cost of goods sold, respectively. In panel B,the dependent variables are the total quantity of the chemical released as stack air emissions (column 1), fugitive airemissions (column 2), and surface water discharges (column 3) scaled by previous years’ cost of goods sold, respec-tively. Environment is a dummy variable indicating whether the firm is targeted by the BAP for environmental reasons.Post is a dummy variable equal to one if the target firm (matched control firm) is within [t+1,t+3] years after the ac-tivism event year (pseudo-event year). All regressions include year and plant fixed effects and are estimated usingordinary least squares (OLS). Standard errors are clustered at the firm-year level, and are robust to heteroscedasticity.∗∗∗, ∗∗, ∗ denote significance at the 1%, 5%, and 10% level, respectively. Data source: EPA.

Panel A: Toxic chemical release

Dependent variable Release/COGS t−1

Total On-site Off-site

(1) (2) (3)

Post 0.625∗∗ 0.583∗∗ -0.024(0.296) (0.229) (0.032)

Post × Environment -1.371∗∗∗ -1.147∗∗∗ -0.003(0.285) (0.243) (0.033)

Year fixed effects Yes Yes YesPlant fixed effects Yes Yes YesR2 0.22 0.25 0.29Observations 63,205 63,205 63,205

Panel B: Other emissions

Dependent variable Emissions/COGS t−1

Stack air Fugitive air Surface water discharges

(1) (2) (3)

Post 0.183∗∗∗ -0.005 0.002∗∗

(0.069) (0.006) (0.001)

Post × Environment -0.427∗∗∗ -0.005 -0.002(0.083) (0.004) (0.001)

Year fixed effects Yes Yes YesPlant fixed effects Yes Yes YesR2 0.17 0.29 0.13Observations 63,205 63,205 63,205

37

TABLE 4: SOURCES OF REDUCTION, PLANT-LEVEL EVIDENCE

This table presents the impact of environmental mandated investor activism on the sources of reduction in release ofpollutants by the firm at plant level. The table reports results comparing performance of targeted firms to S&P firmsin propensity score matched difference-in-differences empirical setup. The dependent variables are: the total quantityof production-related waste (column 1), the total quantity of the toxic chemical sent for treatment works (column 2),the total quantity of the toxic chemical recycled on site at the facility (column 3), and the total quantity of the toxicchemical sent off site for recycling (column 4) scaled by previous years’ cost of goods sold. Environment is a dummyvariable indicating whether the firm is a targeted by BAP for environmental reasons. Post is a dummy variable equal toone if the target firm (matched control firm) is within [t+1,t+3] years after the activism event year (pseudo-event year).All regressions include year and plant fixed effects and is estimated using ordinary least squares (OLS). Standarderrors are clustered at the firm-year level and robust to heteroscedasticity. ∗∗∗, ∗∗, ∗ denote significance at the 1%, 5%,and 10% level, respectively. Data source: EPA.

Dependent variable Quantity/COGS t−1

High impact Low impact

Production-related waste Treatment works On-site recycling Off-site recycling

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

Post 3.241∗∗ -0.003 -0.017 0.030(1.285) (0.004) (0.026) (0.072)

Post × Environment -2.248∗∗∗ -0.001 -0.014 -0.012(0.840) (0.002) (0.021) (0.037)

Year fixed effects Yes Yes Yes YesPlant fixed effects Yes Yes Yes YesR2 0.18 0.33 0.26 0.42Observations 63,205 63,205 63,205 63,205

38

TABLE 5: ENVIRONMENTAL ACCIDENTS, AND LEGAL RISK

This table presents the impact of sustainability-mandated investor activism on the likelihood of being reported for anaccidental event, and the likelihood of an environment-related lawsuit. The table reports results comparing perfor-mance of targeted firms to S&P firms in propensity score matched difference-in-differences empirical setup. Panel Areports results for the likelihood of being reported for an environmental accident event while panel B reports resultsfor the likelihood of an environment-related lawsuit. In Panel A (Panel B), the dependent variables are a dummy forwhether the firms was reported for for an environmental accident event (engaged in environment-related lawsuit)in column 1 while column 2 reports the log of one plus the number of accident events (total amount paid in settle-ments)Environment is a dummy variable indicating whether the firm is a target of BAP due to concerns about firms’negative impact on environment. Post is a dummy variable equal to one if the target firm (matched control firm) iswithin [t+1,t+3] years after the activism event year (pseudo-event year). All regressions include year and firm fixedeffects and are estimated using ordinary least squares (OLS). Standard errors clustered at the firm level and robust toheteroscedasticity. ∗∗∗, ∗∗, ∗ denote significance at the 1%, 5%, and 10% level, respectively. Data sources: United StatesCoastal Guard National Response Center, Audit Analytics.

Panel A: Environmental accident events

Dependent variable 1Event Log(1+ no. of events)

(1) (2)

Post 0.002 -0.025(0.024) (0.051)

Post × Environment -0.095∗ -0.459∗

(0.056) (0.255)

Year fixed effects Yes YesFirm fixed effects Yes YesR2 0.70 0.87Observations 3,158 3,158

Panel B: Environment related lawsuits

Dependent variable 1Lawsuit Log(1+ settled amount)

(1) (2)

Post 0.026 0.044(0.041) (0.054)

Post × Environment -0.108∗∗ -0.260∗

(0.053) (0.145)

Year fixed effects Yes YesFirm fixed effects Yes YesR2 0.47 0.14Observations 3,158 3,158

39

TABLE 6: LOCAL POPULATIONS’ EXPOSURE TO TOXICITY

This table presents the impact of environmental mandated investor activism on the local populations’ exposure totoxicity in a census block. Table reports results comparing toxicity of areas which has plants of the targeted firms toareas with plants of propensity score matched non-targeted firms in a difference-in-differences empirical setup. Thedependent variable is the standardised total (column 1), cancer (column 2), and non-cancer (column 3) RSEI score atthe census-block level. Environment is a dummy variable indicating whether the firm is a target of BAP due to concernsabout firms’ negative impact on environment. Post is a dummy variable equal to one if the target firm (matched controlfirm) is within [t+1,t+3] years after the activism event year (pseudo-event year). All regressions include year and blockfixed effects and are estimated using ordinary least squares (OLS) using standard errors clustered at the block level.∗∗∗, ∗∗, ∗ denote significance at the 1%, 5%, and 10% level, respectively. Data source: RSEI.

Dependent variable RSEI Toxicity Score

Total Cancer Non-Cancer

(1) (2) (3)

Post -0.053 -0.056 0.115(0.086) (0.086) (0.087)

Post × Environment -0.263∗∗ -0.224∗∗ -0.204(0.112) (0.114) (0.137)

Year fixed effects Yes Yes YesCensus-block fixed effects Yes Yes YesR2 0.03 0.04 0.02Observations 7,156 7,156 7,156

40

TABLE 7: EFFECT OF ESG ACTIVISM ON FIRM PERFORMANCE AND RETURNS

This table presents the impact of sustainability-mandated investor activism on firms’ performance (Panel A) andequity returns (Panel B). Table reports results comparing performance of targeted firms to S&P firms in propensityscore matched difference-in-differences empirical setup. In Panel A, the dependent variable is the firms’ return onassets (column 1), profitability measured as profit divided by sales (column 2), and Altman’s Z-score (column 3). InPanel B, the dependent variables are the firms’ daily excess return (column 1), the daily Fama French three factoradjusted returns (column 2) and the daily Fama French Carhart abnormal returns (column 3) during the week ofBAP announcement date. BAP is a dummy variable indicating whether the firm is a target of BAP. In Panel A,Post is a dummy variable equal to one if the target firm (matched control firm) is within [t+1,t+3] years after theactivism event year (pseudo-event year). In Panel B, Post is a dummy variable equal to zero for three days before theannouncement (Monday to Wednesday) and one for the announcement day and day after (Thursday and Friday). Allregressions include year and firm fixed effects and are estimated using ordinary least squares (OLS) using standarderrors clustered at the firm level. ∗∗∗, ∗∗, ∗ denote significance at the 1%, 5%, and 10% level, respectively. Data source:Compustat and CRSP.

Panel A: Performance

Dependent variable Return on Assets Profitability Altman’s Z-score

(1) (2) (3)

Post -0.010∗ -0.006 -0.149(0.006) (0.011) (0.122)

Post × BAP 0.010 0.045 0.177(0.010) (0.028) (0.143)

Year fixed effects Yes Yes YesFirm fixed effects Yes Yes YesR2 0.47 0.41 0.85Observations 3,156 3,156 2,747

Panel B: Equity Returns

Dependent variable Excess Returns FF3 Returns FFC Returns

(1) (2) (3)

Post × BAP 1.046∗∗∗ 1.046∗∗∗ 0.999∗∗∗

(0.286) (0.274) (0.275)

Year fixed effects Yes Yes YesFirm fixed effects Yes Yes YesR2 0.26 0.23 0.23Observations 1,690 1,690 1,690

41

TABLE 8: SPILLOVER EFFECTS OF ESG ACTIVISM

This table presents the spillover effect of sustainability-mandated investor activism. Table reports results comparingperformance of targeted firms to S&P firms in propensity score matched difference-in-differences empirical setup.The dependent variable is the standardized score of firms’ environmental (column 1), board diversity (column 2),and governance performance (column 3) each year. Target Reason is a dummy variable indicating whether the firmis a target of BAP for the same explanatory variable being tested. Different Reason is a dummy variable that takesthe value of one if the firm is targeted by BAP for some other mandate. Post is a dummy variable equal to one ifthe target firm (matched control firm) is within [t+1,t+3] years after the activism event year (pseudo-event year). Allregressions include year and firm fixed effects and are estimated using ordinary least squares (OLS) using standarderrors clustered at the firm level. ∗∗∗, ∗∗, ∗ denote significance at the 1%, 5%, and 10% level, respectively. Data source:Sustainalytics.

Dependent variable ENV Score DIV Score GOV Score

Target reason Environment Diversity Governance

(1) (2) (3)

Post -0.048 0.151 -0.144(0.108) (0.130) (0.126)

Post × Different reason -0.104 -0.136 0.260∗∗

(0.140) (0.190) (0.132)

Post × Target reason 0.298∗ 0.746∗∗ 0.417∗∗

(0.164) (0.327) (0.181)

Year fixed effects Yes Yes YesFirm fixed effects Yes Yes YesR2 0.33 0.07 0.17Observations 3,158 2,278 3,158

42

TABLE 9: ESG ACTIVISM AND DISCLOSURE SCORE

This table presents the impact of political disclosure mandated investor activism. Table reports results comparingperformance of targeted firms to S&P firms in propensity score matched difference-in-differences empirical setup.The dependent variable is the firms’ standardized CPA Zicklin Score, that measures firms’ disclosure about election-related spending. Disclosure is a dummy variable indicating whether the firm is a target of BAP due to concernsabout firms’ political disclosure. Post is a dummy variable equal to one if the target firm (matched control firm) iswithin [t+1,t+3] years after the activism event year (pseudo-event year). All regressions include year and firm fixedeffects and are estimated using ordinary least squares (OLS) using standard errors clustered at the firm level. ∗∗∗, ∗∗,∗ denote significance at the 1%, 5%, and 10% level, respectively. Data source: Center for Political Accountability.

Dependent variable CPA Zicklin Score

(1) (2)

Disclosure -0.567∗∗∗

(0.059)

Post 0.246∗∗∗ 0.242∗

(0.080) (0.135)

Post × Disclosure 0.951∗∗∗ 0.969∗∗∗

(0.166) (0.120)

Year fixed effects Yes YesFirm fixed effects No YesR2 0.23 0.31Observations 1,333 1,333

43

TABLE 10: ESG ACTIVISM AND DIVERSITY ON BOARDS

This table presents the impact of boardroom diversity mandated investor activism. Table reports results comparing performance of targeted firms to S&P firms inpropensity score matched difference-in-differences empirical setup. The dependent variable is the firms’ boardsize (column 1), male ratio (column 2), HHI indexfor board members’ ethnicity (column 3) and nationality (column 4), a dummy variable for race (column 5), tenure of board members (column 6), and averageage of the board (column 7). Diversity is a dummy variable indicating whether the firm is a target of BAP due to concerns about board members’ diversity.Post is a dummy variable equal to one if the target firm (matched control firm) is within [t+1,t+3] years after the activism event year (pseudo-event year). Allregressions include year and firm fixed effects and are estimated using ordinary least squares (OLS) using standard errors clustered at the firm level. ∗∗∗, ∗∗, ∗

denote significance at the 1%, 5%, and 10% level, respectively. Data source: BoardEx.

Dependent variable Boardsize Male Ratio HHI (Ethnicity) HHI (Nationality) Race Mean Tenure Mean Age

(1) (2) (3) (4) (5) (6) (7)

Post -0.160 -0.023∗∗ 0.000 0.001 0.017 -0.324 -0.772∗∗∗

(0.192) (0.009) (0.002) (0.003) (0.013) (0.255) (0.292)

Post × Diversity 0.457∗∗ -0.058∗∗∗ -0.008∗∗ -0.011∗∗ -0.010 -0.165 0.213(0.199) (0.018) (0.004) (0.005) (0.017) (0.417) (0.432)

Year fixed effects Yes Yes Yes Yes Yes Yes YesFirm fixed effects Yes Yes Yes Yes Yes Yes YesR2 0.76 0.75 0.76 0.79 0.85 0.77 0.81Observations 1,607 1,607 1,607 1,607 1,444 1,607 1,599

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TABLE 11: ESG ACTIVISM AND CEO COMPENSATION

This table presents the impact of excess CEO compensation mandated investor activism. Table reports results com-paring performance of targeted firms to S&P firms in propensity score matched difference-in-differences empiricalsetup. The dependent variables are the natural logarithm of total compensation (column 1), growth in total com-pensation (column 2), excess total compensation (column 3), growth in excess compensation (column 4), and pay-for-performance measure of compensation delta (column 5) and vega (column 6). Governance is a dummy variableindicating whether the firm is a target of BAP due to concerns about firms’ CEO compensation. Post is a dummyvariable equal to one if the target firm (matched control firm) is within [t+1,t+3] years after the activism event year(pseudo-event year). All regressions include year and firm fixed effects and are estimated using ordinary least squares(OLS) using standard errors clustered at the firm level. ∗∗∗, ∗∗, ∗ denote significance at the 1%, 5%, and 10% level,respectively. Data source: Execucomp.

CEO compensation Excess compensation Pay-for-performance

Dependent variable Log (total) % (total) Total % (total) Delta Vega

(1) (2) (3) (4) (5) (6)

Post -0.043 -0.009 -531.761 -0.037 21.576 -81.349∗∗

(0.067) (0.050) (526.272) (0.068) (142.105) (33.322)

Post × Governance -0.216∗∗ -0.022 -2097.012 -0.261∗∗ 652.931∗ -35.178(0.107) (0.106) (1582.367) (0.118) (337.833) (66.779)

Year fixed effects Yes Yes Yes Yes Yes YesFirm fixed effects Yes Yes Yes Yes Yes YesR2 0.77 0.08 0.64 0.71 0.75 0.67Observations 2,393 2,060 2,182 2,182 2,787 2,787

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TABLE 12: ESG ACTIVISM AND PERFORMANCE, EXTERNAL VALIDITY

This table presents the impact of investor activism by other investors (Non-NYCPS) on firms’ sustainability perfor-mance. Table reports results comparing performance of firms targeted by any investor to S&P 1500 firms in propensityscore matched difference-in-differences empirical setup. The dependent variable is the standardized score of firms’environmental (column 1), board diversity (column 2), and governance performance (column 3) each year. Target isa dummy variable indicating whether the firm is a target for the same explanatory variable being tested. Post is adummy variable equal to one if the target firm (matched control firm) is within [t+1,t+3] years after the activism eventyear (pseudo-event year). All regressions include year fixed effects and are estimated using ordinary least squares(OLS) using standard errors clustered at the firm level. ∗∗∗, ∗∗, ∗ denote significance at the 1%, 5%, and 10% level,respectively. Data source: Sustainalytics.

Dependent variable ENV Score DIV Score GOV Score

Target reason Environment Diversity Governance

(1) (2) (3)

Post -0.123∗∗ -0.051 0.000(0.057) (0.076) (0.065)

Post × Target reason 0.261∗ 0.065 0.332∗∗

(0.141) (0.219) (0.130)

Year fixed effects Yes Yes YesFirm fixed effects Yes Yes YesR2 0.20 0.06 0.11Observations 9,367 5,124 9,367

46

Appendix A.1 Additional tables

TABLE IA1: SUMMARY STATISTICS FOR THE TARGET FIRMS AND THE MATCHEDCONTROL SAMPLE.

This table reports firm characteristics at the firm-year level for firms targeted by the Boardroom Accountability Projectand the control samples. Panel A uses the annual portfolio of the New York City pension system a control sample.Panel B uses the composition of the SP 500 as a control sample. The control samples are formed by matching targetfirms to non-target firms in year t with the same SIC 1-digit industry code, and with the closest propensity score. Weestimate propensity scores using the firms’ previous year’s log total assets, return on assets, and market to book ratio.We report the differences in mean values between the targeted and the Non-targeted firms.

Before matching After matching

Targeted Non-targeted Difference Targeted Non-targeted Difference(1) (2) (1) - (2) (3) (4) (3) - (4)

Ln (firm assets) 9.57 9.57 0.00 9.57 9.63 -0.06Return on assets 0.14 0.13 0.02 0.14 0.13 0.01Market-to-book 1.75 1.67 0.08 1.76 1.70 0.05

47

TABLE IA2: TARGET SELECTION

This table analysis the determinants that influenced the selection of a firm by NYCPS for investor activism. Tablereports the results using Russell 3000 sample from 2009 to 2018. The dependent variable is a dummy variable thattakes the value of one if the firm was part of BAP. Firm size is the log of firm’s asset. Market-to-book is the ratio ofmarket value to book value of equity. Returns is the equity return for the previous twelve months. Profitability is theearnings before interest, depreciation, taxes, and amortization relative to bookvalue of assets. IO is the proportion ofshares held by institutional investors. 1S&P500 is the dummy that takes the value of one if the firm was part of theS&P. All regressions include industry fixed effects and standard errors are clustered at the firm level. ∗∗∗, ∗∗, ∗ denotesignificance at the 1%, 5%, and 10% level, respectively. Data source: Compustat, CRSP, Thomson Reuters 13F.

Dependent variable 1BAP

Specification OLS Logit Odds ratio

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

Firm size 0.004∗∗∗ 0.003∗∗∗ 0.662∗∗∗ 1.939∗∗∗

(0.001) (0.001) (0.085) (0.165)

Market-to-book 0.001∗∗∗ 0.001∗ 0.163∗∗∗ 1.177∗∗∗

(0.000) (0.000) (0.041) (0.048)

Returns -0.003∗∗∗ 0.001 -0.573∗∗∗ 0.564∗∗∗

(0.001) (0.001) (0.202) (0.114)

Profitability -0.000 0.000 0.986∗∗ 2.681∗∗

(0.000) (0.000) (0.418) (1.121)

Institutional ownership -0.004 -0.005∗ 0.771∗ 2.163∗

(0.003) (0.003) (0.463) (1.001)

1S&P500 0.034∗∗∗ 0.037∗∗∗ 1.171∗∗∗ 3.226∗∗∗

(0.005) (0.005) (0.335) (1.082)

Year fixed effects No Yes No NoIndustry fixed effects Yes Yes Yes YesObservations 28,655 28,655 28,655 28,655

48

TABLE IA3: ESG ACTIVISM AND AGGREGATE SCORE, TARGETED FIRMS

This table presents the impact of sustainability-mandated investor activism on firms’ environmental, board diversity,and governance performance. Panel A reports results comparing performance of targeted firms to BAP firms targatedfor different reason in difference-in-differences empirical setup. Panel B conditions on firms targeted for the samereason and compares their pre-period performance with the post-period. The dependent variable is the standardizedscore of firms’ environmental, board diversity, and governance performance each year. Target is a dummy variableindicating whether the firm is a target of BAP for the same explanatory variable being tested. Post is a dummy variableequal to one if the target firm (matched control firm) is within [t+1,t+3] years after the activism event year (pseudo-event year). All regressions include year fixed effects and are estimated using ordinary least squares (OLS) usingstandard errors clustered at the firm level. ∗∗∗, ∗∗, ∗ denote significance at the 1%, 5%, and 10% level, respectively.Data source: Sustainalytics.

Panel A: Target firms only

Dependent variable ENV Score DIV Score GOV Score

Target reason Environment Diversity Governance

(1) (2) (3)

Post -0.130 0.029 0.131(0.149) (0.215) (0.136)

Post × Target reason 0.400∗∗ 0.864∗∗ 0.157(0.196) (0.370) (0.197)

Year fixed effects Yes Yes YesFirm fixed effects Yes Yes YesR2 0.34 0.06 0.21Observations 1,225 872 1,225

Panel B: Target reason firms only

Dependent variable ENV Score DIV Score GOV Score

Target reason Environment Diversity Governance

(1) (2) (3)

Post 0.419∗ 0.946∗∗ 0.111(0.238) (0.371) (0.237)

Year fixed effects Yes Yes YesFirm fixed effects Yes Yes YesR2 0.32 0.09 0.17Observations 392 290 328

49

TABLE IA4: ESG ACTIVISM AND AGGREGATE SCORE, DYNAMIC EFFECTS

This table presents the impact of sustainability-mandated investor activism on firms’ environmental, board diversity,and governance performance. Panel A reports results comparing performance of targeted firms to BAP firms targetedfor different reason in difference-in-differences empirical setup. The dependent variable is the standardized score offirms’ environmental, board diversity, and governance performance each year. Target is a dummy variable indicatingwhether the firm is a target of BAP for the same explanatory variable being tested. Before−k ( After+k) is a dummyvariable equal to one if the target firm (matched control firm) is targeted k years before (after) the activism eventyear (pseudo-event year). All regressions include year fixed effects and are estimated using ordinary least squares(OLS) using standard errors clustered at the firm level. ∗∗∗, ∗∗, ∗ denote significance at the 1%, 5%, and 10% level,respectively. Data source: Sustainalytics.

Dependent variable ENV Score DIV Score GOV Score

Target reason Environment Diversity Governance

(1) (2) (3)

Before−2 0.138 -0.112 0.073(0.132) (0.168) (0.132)

Before−1 -0.010 0.010 -0.003(0.070) (0.082) (0.079)

After+1 -0.092 0.161 -0.193∗∗

(0.083) (0.116) (0.085)

After+2 -0.223 0.196 -0.205(0.137) (0.194) (0.158)

Before−2 × Target reason 0.241 0.164 -0.004(0.187) (0.203) (0.202)

Before−1 × Target reason 0.101 -0.127 -0.011(0.149) (0.158) (0.143)

After+1 × Target reason 0.079 0.114 0.263(0.153) (0.268) (0.169)

After+2 × Target reason 0.505∗∗ 0.870∗∗ 0.401∗

(0.202) (0.347) (0.211)

Year fixed effects Yes Yes YesFirm fixed effects Yes Yes YesR2 0.33 0.07 0.17Observations 3,158 2,278 3,158

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TABLE IA5: REAL EFFECTS OF ESG ACTIVISM, FIRM-LEVEL EVIDENCE

This table presents the impact of environmental mandated investor activism on the release of pollutants by the firm.Table reports results comparing performance of targeted firms to S&P firms in propensity score matched difference-in-differences empirical setup. The dependent variable is the log of firms’ total, onsite, and offsite pollutant release.Environment is a dummy variable indicating whether the firm is a target of BAP due to concerns about firms’ negativeimpact on environment. Post is a dummy variable equal to one if the target firm (matched control firm) is within[t+1,t+3] years after the activism event year (pseudo-event year). All regressions include year and firm fixed effectsand are estimated using ordinary least squares (OLS) using standard errors clustered at the firm level. ∗∗∗, ∗∗, ∗ denotesignificance at the 1%, 5%, and 10% level, respectively. Data source: EPA.

Dependent variable Log (1+...)

Total emissions Onsite emissions Offsite emissions

(1) (2) (3)

Post -0.080 -0.059 -0.050(0.337) (0.187) (0.589)

Post × Environment -0.410∗∗ -0.151 -1.185(0.187) (0.209) (0.731)

Year fixed effects Yes Yes YesFirm fixed effects Yes Yes YesR2 0.95 0.98 0.86Observations 1,069 1,069 1,069

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TABLE IA6: ESG ACTIVISM AND PERFORMANCE, PLACEBO

This table explores if the firms that get targeted for proxy access improves their sustainability characteristics. Thetable reports results comparing performance of firms that got targeted for proxy access to S&P 500 firms in propensityscore matched difference-in-differences empirical setup. The dependent variable is the standardized score of firms’environmental, board diversity, and governance performance each year. Target is a dummy variable indicating whetherthe firm is targeted by investor(s) for proxy access. Post is a dummy variable equal to one if the target firm (matchedcontrol firm) is within [t+1,t+3] years after the activism event year (pseudo-event year). All regressions include yearand firm fixed effects and are estimated using ordinary least squares (OLS) using standard errors clustered at the firmlevel. ∗∗∗, ∗∗, ∗ denote significance at the 1%, 5%, and 10% level, respectively. Data source: Sustainalytics.

Sample Next three activist Targeted for Proxy Access

Dependent variable ENV Score DIV Score GOV Score ENV Score DIV Score GOV Score

(1) (2) (3) (4) (5) (6)

Post 0.046 -0.138 -0.297 0.205∗ -0.005 -0.054(0.140) (0.211) (0.196) (0.119) (0.146) (0.160)

Post x Targeted -0.133 -0.311 0.084 -0.006 -0.077 -0.126(0.229) (0.276) (0.213) (0.122) (0.197) (0.150)

Year fixed effects Yes Yes Yes Yes Yes YesFirm fixed effects Yes Yes Yes Yes Yes YesR2 0.32 0.07 0.17 0.32 0.06 0.17Observations 3,158 2,278 3,158 3,158 2,278 3,158

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TABLE IA7: ESG ACTIVISM AND AGGREGATE SCORE, PROXY ACCESS SAMPLE

This table presents the impact of proxy access bylaws on firms’ sustainability characteristics. The table reports resultscomparing performance of targeted firms to firms that adopted proxy access in propensity score matched difference-in-differences empirical setup. The dependent variable is the standardized score of firms’ environmental, boarddiversity, and governance performance each year. Target is a dummy variable indicating whether the firm is a targetof BAP for environmental, board diversity, and governance concerns. Post is a dummy variable equal to one if thetarget firm (matched control firm) is within [t+1,t+3] years after the activism event year (pseudo-event year). Allregressions include year and firm fixed effects and are estimated using ordinary least squares (OLS) using standarderrors clustered at the firm level. ∗∗∗, ∗∗, ∗ denote significance at the 1%, 5%, and 10% level, respectively. Data source:Sustainalytics.

Dependent variable ENV Score DIV Score GOV Score

Target reason Environment Diversity Governance

(1) (2) (3)

Post -0.058 0.153 0.033(0.083) (0.106) (0.092)

Post × Target reason 0.386∗∗∗ 0.747∗∗ 0.307∗

(0.148) (0.320) (0.182)

Year fixed effects Yes Yes YesFirm fixed effects Yes Yes YesR2 0.33 0.08 0.19Observations 2,632 1,857 2,632

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TABLE IA8: ESG ACTIVISM AND 13D FILING PROPENSITY

This table explores if sustainability-mandated investor activism acts as coordinating signal to other activist investors.The table reports the results comparing level of activist investors in targeted firms to S&P 500 firms in propensity scorematched difference-in-differences empirical setup. The dependent variable is a dummy variable that takes a value ofone if a new filing tool place within a given year for a firm and number of filings in a year. BAP is a dummy variableindicating whether the firm is a target of BAP. Post is a dummy variable equal to one if the target firm (matchedcontrol firm) is within [t+1,t+3] years after the activism event year (pseudo-event year). All regressions include yearand firm fixed effects and are estimated using ordinary least squares (OLS) using standard errors clustered at the firmlevel. ∗∗∗, ∗∗, ∗ denote significance at the 1%, 5%, and 10% level, respectively. Data source: Schedule 13D filings.

Dependent variable Recieved 13D No. of 13Ds

(1) (2)

Post 0.012 0.023(0.014) (0.018)

Post × BAP -0.002 -0.006(0.018) (0.023)

Year fixed effects Yes YesFirm fixed effects Yes YesR2 0.14 0.12Observations 3,162 3,162

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TABLE IA9: PLANT-LEVEL EVIDENCE (RESTRICTIVE PLANT SAMPLE)

This table reports results of the impact of sustainability-mandated investor activism on the release of toxic chemicals atthe plant-level, but excludes the plants that were added to the sample after 2010. The table reports results comparingthe performance of targeted firms to S&P firms in propensity score matched difference-in-differences empirical setup.Panel A reports toxic chemical release while panel B reports toxic chemical released to air and water. In panel A, thedependent variables are the total (column 1), onsite (column 2), and offsite (column 3) toxic chemical release scaledby previous years’ cost of goods sold, respectively. In panel B, the dependent variables are the total quantity of thechemical released as stack air emissions (column 1), fugitive air emissions (column 2), and surface water discharges(column 3) scaled by previous years’ cost of goods sold, respectively. Environment is a dummy variable indicatingwhether the firm is targeted by the BAP for environmental reasons. Post is a dummy variable equal to one if thetarget firm (matched control firm) is within [t+1,t+3] years after the activism event year (pseudo-event year). Allregressions include year and plant fixed effects and are estimated using ordinary least squares (OLS). Standard errorsare clustered at the firm-year level, and are robust to heteroscedasticity. ∗∗∗, ∗∗, ∗ denote significance at the 1%, 5%,and 10% level, respectively. Data source: EPA.

Panel A: Toxic chemical release

Dependent variable Release/COGS t-1

Total On-site Off-site

(1) (2) (3)

Post 0.707∗∗ 0.664∗∗∗ -0.029(0.325) (0.249) (0.034)

Post × Environment -1.472∗∗∗ -1.239∗∗∗ -0.002(0.297) (0.253) (0.035)

Year fixed effects Yes Yes YesPlant fixed effects Yes Yes YesR2 0.22 0.25 0.29Observations 59,893 59,893 59,893

Panel B: Other emissions

Dependent variable Emissions/COGS t-1

Stack air Fugitive air Surface water discharges

(1) (2) (3)

Post 0.210∗∗∗ -0.006 0.003∗∗

(0.075) (0.006) (0.001)

Post × Environment -0.458∗∗∗ -0.006 -0.002(0.087) (0.005) (0.001)

Year fixed effects Yes Yes YesPlant fixed effects Yes Yes YesR2 0.17 0.28 0.13Observations 59,893 59,893 59,893

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TABLE IA10: ESG ACTIVISM AND AGGREGATE SCORE, TARGETED FIRMS

This table reports regression estimates and standard errors from equal- and value-weighted calendar-time portfolioregressions. "Window" indicates the buying time relative to the event (the first year the BAP targeted a company) andholding period in months. "Alpha" is the estimate of the regression intercept from the factor models. The Beta arethe factor loading on the concurrent market excess return. SMB and HML are estimates of factor loading from theFama-French size and book-to-market factors. MOM is the Carhart momentum factor. ***, **, * denote significance atthe 1%, 5%, and 10% level, respectively. Data source: CRSP and Ken Frenchs website at Dartmouth College.

(a) Panel A: Long-Term Returns of Target Company (Equal-Weighted)

Window (Months)[-24,-18) [-18,-12) [-12, -6) [ -6, 0) [ 0, 6) [ 6, 12) [ 12, 18) [ 18, 24]

Alpha 0.035∗ 0.009 0.020 -0.020 -0.030 -0.031 -0.033 -0.010(0.019) (0.016) (0.018) (0.019) (0.022) (0.023) (0.033) (0.023)

Beta 1.025∗∗∗ 1.161∗∗∗ 0.962∗∗∗ 1.075∗∗∗ 1.059∗∗∗ 1.019∗∗∗ 1.038∗∗∗ 1.130∗∗∗

(0.028) (0.028) (0.034) (0.032) (0.028) (0.023) (0.034) (0.036)

SMB 0.148∗∗∗ 0.024 0.058 0.056 0.113∗∗ 0.190∗∗∗ 0.173∗∗ -0.070(0.055) (0.043) (0.050) (0.039) (0.046) (0.048) (0.071) (0.055)

HML 0.198∗∗∗ 0.079 0.020 0.005 0.102∗ -0.135∗∗ 0.293∗∗∗ 0.458∗∗∗

(0.063) (0.049) (0.060) (0.065) (0.059) (0.057) (0.065) (0.048)

MOM -0.212∗∗∗ -0.165∗∗∗ 0.045 -0.084 -0.571∗∗∗ -0.551∗∗∗ -0.619∗∗∗ -0.243∗∗∗

(0.049) (0.047) (0.051) (0.065) (0.035) (0.034) (0.037) (0.039)

R2 0.209 0.249 0.180 0.158 0.197 0.266 0.239 0.177

(b) Panel B: Long-Term Returns of Target Company (Value-Weighted)

Window (Months)[-24,-18) [-18,-12) [-12, -6) [ -6, 0) [ 0, 6) [ 6, 12) [ 12, 18) [ 18, 24]

Alpha -0.001 -0.001 0.003 -0.014 -0.007 0.014 0.0002 0.008(0.015) (0.013) (0.015) (0.015) (0.017) (0.016) (0.022) (0.017)

Beta 1.063∗∗∗ 1.099∗∗∗ 0.998∗∗∗ 1.064∗∗∗ 1.101∗∗∗ 1.035∗∗∗ 1.095∗∗∗ 1.073∗∗∗

(0.022) (0.023) (0.027) (0.025) (0.021) (0.016) (0.023) (0.026)

SMB -0.152∗∗∗ -0.092∗∗∗ -0.097∗∗ -0.092∗∗∗ -0.032 0.011 -0.040 -0.157∗∗∗

(0.044) (0.035) (0.040) (0.030) (0.035) (0.034) (0.048) (0.039)

HML 0.350∗∗∗ 0.237∗∗∗ 0.196∗∗∗ 0.154∗∗∗ 0.234∗∗∗ 0.021 0.336∗∗∗ 0.503∗∗∗

(0.050) (0.040) (0.048) (0.049) (0.045) (0.040) (0.044) (0.034)

MOM -0.185∗∗∗ -0.105∗∗∗ 0.046 -0.030 -0.422∗∗∗ -0.298∗∗∗ -0.264∗∗∗ -0.089∗∗∗

(0.039) (0.038) (0.041) (0.050) (0.027) (0.024) (0.025) (0.028)

R2 0.304 0.316 0.239 0.230 0.293 0.389 0.323 0.245

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Appendix A.2 Data Definitions

TABLE IA11: VARIABLE DEFINITIONS AND DATA SOURCES

This table summarizes the variable construction and data sources of the main variables used in this paper.

Variable Definition Data source

13D Fillings Number of 13D fillings for a firm in a given year. Schedule 13DBoardsize Number of members on a firm’s board. BoardExCompensation Overall and excess compensation of a firm’s CEO. ExecucompCPA Zicklin Score Standardised scores, with mean of zero and standard deviation of one. Center for Pol. Acc.COGS Cost of goods sold. CompustatEnvironmental Accidents Incidents reported to United States Coast Guard. USGCESG Scores Standardised scores, with mean of zero and standard deviation of one. SustainalyticsEmissions Emissions in stack and fugitive air and surface water discharge. EPAExcess Returns Equity returns minus risk free rate. CRSPFF3 Abnormal returns computed using Fama French three factor model. CRSP & WRDSFFC Abnormal returns computed using Fama French Carhart model. CRSP & WRDSHHI (Ethnicity) HHI index of the of board members’ ethnicity. BoardExHHI (Nationality) HHI index of the of board members’ nationality. BoardExLawsuit Number of lawsuits filled against a firm. Audit AnalyticsMale Ratio Number of male board members divided by board size. BoardExMean Tenure Average duration for which members are on a firms board. BoardExMean Age Average age of the board members. BoardExProfitability Earnings before interest, depreciation, taxes, and amortization relative

to book value of assets.Compustat

Race Dummy variable for a board member’s race. BoardExReturn on Assets Profit after tax relative to book value of assets. CompustatSettled Amount Amount paid by a firm to settle the lawsuits. Audit AnalyticsSales Growth Measured as the annual growth rate of sales. CompustatSize Measured as the log of book value of assets. CompustatSource of reduction Production related, treatment, on-site, and off-site recycling. EPATotal Assets Total book value of assets. CompustatToxic chemical release Total, on-site, and off-site chemical release. EPAToxicity Score Total, cancer, non-cancer score, adjusted for base year population

(2010).RSEI

57