Presentation by Michael-Paul James on "Rhetoric, Reality, and Reputation: Do CSR and Political Lobbying Protect Shareholder Wealth against Environmental Lawsuits?" Paper by Chelsea Liu , Chee Seng Cheong, and Ralf Zurbruegg

1. Rhetoric, Reality, and Reputation:
Do CSR and Political Lobbying
Protect Shareholder Wealth against
Environmental Lawsuits?
Paper by Chelsea Liu , Chee Seng Cheong, and Ralf Zurbruegg
Presentation by Michael-Paul James
1

2. Table of contents
Introduction Hypothesis
Literature Review and Hypothesis
Development; CSR and Market
Reactions to Lawsuits; Lobbying and
Market Reactions to Lawsuits
Story, Questions, Context, Issues
Robustness
Robustness Analyses; Media Attention Analysis;
Falsification Test Using Subsequent Lawsuits Following
CSR Downgrades; Potential Information Leakage
Preceding Lawsuit Filings; Fama–French Estimation
Models; Alternative Proxies for CSR and Lobbying;
Lawsuit Merit Proxied by Outcomes; Propensity Score
Matching
Results
Empirical Results; Descriptive Statistics;
Univariate Analysis; Baseline Regression
Analysis; Changes in Institutional and
Active Fund Holdings Surrounding
Lawsuits
01 02
04 05
Methodology
Data and Methodology; Sample
Construction; Regression Model
Conclusion
Conclusion and Future Research
Directions
03
06
2
Michael-Paul James

3. Introduction
01
Story, Questions, Context, Issues
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Michael-Paul James

4. Environmental Concerns
● To mitigate environmental exposure
○ Develop corporate social responsibility (CSR) initiatives
○ Fund lobbying campaigns to shape environmental laws
● Direct correlation between market value and legal penalties of
environmental lawsuits
4
Michael-Paul James

5. Impact During Environmental Events
● CSR Impact
● Research offers mixed evidence of the economic value of CSR: increase
firm value or shape agency problems draining resources.
○ If CSR is positively priced, then firms with high CSR ratings will
suffer more when accused of environmental misconduct.
○ High CSR may protect firms from consequences during infractions
● Lobbying Impact
○ Lobbying influences policy to benefit firm and/or industries.
○ U-shaped curve documented: virtuous firms and frequent
offenders primarily compete in the lobbying market.
○ Lobbying builds goodwill and provides insurance-like protection
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Michael-Paul James

6. Approach
● Firm Choice
○ Financial Information from S&P (Standard & Poor) 1500 Index
○ CSR ratings from Kinder, Lydenberg, Domini (KLD) database
■ Combination of environmental strengths and concerns into a
single score
■ Alternative: Thomson Reuters ASSET4 ESG database
○ Lobbying data from Center for Responsive Politics
○ Environmental Lawsuit data from Public Access to Court Electronic
Records (PACER) database sampling US Federal courts 2000-2015
■ Important dates
● Filing date of lawsuit is the key release of public information
● Discovery date of violation or scientific testing (if known)
○ OLS regression using lagged CSR scores and lobbying efforts to
predict cumulative abnormal returns (CAR) 6

7. Findings
● Firms with superior environmental CSR scores suffer greater valuation
losses upon lawsuit filing.
○ High CSR firms suffer an average loss of -1.1% over a 5-day window.
○ Low CSR firms suffer an average loss of -0.2% over a 5-day window.
○ CSR scores are inversely related to filing-date CAR
■ One unit increase in CSR increases valuation losses of 0.3%
(average of 177 million lossed)
● Superior lobbying firms suffer smaller filing date valuation losses.
○ High Lobbying firms suffer an average loss of -0.2%
○ Low Lobbying firms suffer an average loss of -1.0%
○ Lobbying effort is positively correlated to filing-date CAR
■ A million dollar increase in Lobbying decreases valuation losses
by 0.1% (average of 59 million saved)
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Michael-Paul James

8. Robustness
● Examine changes in institutional investor holdings to verify
○ Superior CSR ratings on average suffer greater reductions in
holdings.
● Examine media attention to mitigate other confounding factors
○ High-profile lawsuits are associated with greater valuation losses
● Examine other important dates
○ Discovery of violations ○ Dates of scientific testing
○ Plaintiffs’ filling of a Notice of Intention to Sue
○ Robust to potential pre-filing information leakage.
● Falsification test using firms with previously damaged reputations
● Propensity Score matching with accounting and governance controls
● Alternative methods to measure cumulative abnormal returns (CAR)
● Alternative empirical proxies and model specifications
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Michael-Paul James

9. Contribution
● Stock market reactions to corporate misconduct
○ Valuation loss magnitude associated with environmental violations
○ Builds on research by Karpoff, adding CSR and lobbying efforts
● Economic value of pursuing CSR
○ Environmental litigation to separate perceived from actual firm
conduct
● Economic benefits of political lobbying
○ Insurance-like protection for alleged environmental infractions
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Michael-Paul James

10. Hypothesis
02
Literature Review and Hypothesis Development; CSR and Market Reactions
to Lawsuits; Lobbying and Market Reactions to Lawsuits
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Michael-Paul James

11. Inconclusive Empirical Evidence on CSR
● Superior CSR ratings have been linked to:
○ Positive effects
■ Higher market valuation
■ Greater returns from operating and investment activities
■ Easier access to financing
■ Improved stakeholder relations, productivity, innovation, and
M&A success
○ Other findings
■ No change in market valuations
■ Poorer financial performance
■ Firms use CSR investment to signal better future performance
■ Agency concerns, specifically executive narcissism, political
ideology, manager-shareholder misalignment
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Michael-Paul James

12. Hypothesis 1 and Commentary
● Hypothesis 1. Environmental CSR scores are significant in predicting
capital market reactions to environmental lawsuit filings.
● Prior studies
○ Negative market reactions to corporate lawsuits in general and
environmental misconduct in particular
○ Direct legal costs impact the magnitude of valuation losses.
○ Negligible reputational penalties captured by additional losses
● Greenwashing produces over-inflated environmental reputation
divorced from firm conduct
○ Reputation effects on firm valuation has yet to be studied
○ Environmental litigation reveals reputational gaps of perception
○ Competing hypothesis: CSR may provide insurance-like protection
or adverse market reaction 12
Michael-Paul James

13. Hypothesis 2 and Commentary
● Hypothesis 2. Lobbying on environmental issues is associated with less
negative capital market reactions to environmental lawsuit filings.
● Political power and connections significantly affect firm value
● Corporate lobbying is associated with
○ Better financial performance ○ Easier access to financing
○ Lower costs of capital ○ Lower takeover risk
○ Preferential awards of government contracts and investments
● Similar lobbying research in securities fraud and labor disputes
○ Reduces litigation risks ○ Lower monetary penalties
○ SEC less likely to prosecute ○ More favorable outcomes
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Michael-Paul James

14. Methodology
03
Data and Methodology; Sample Construction; Regression Model
14
Michael-Paul James

15. Approach Differentiation
● Gather data from court records versus news sources to avoid media bias
○ Allows the addition of media attention as a control variable in
robustness check
● Legal framework
○ Clean Water Act 1972 (CWA) ○ Clean Air Act 1963 (CAA)
○ Comprehensive Environmental Response, Compensation, and
Liability Act 1980 (CERCLA)
○ Resource Conservation and Recovery Act 1976 (RCRA).
● Plaintiffs include both governmental and private advocates
○ Environmental Protection Agency (EPA)
○ Sierra Club.
15

16. Approach Differentiation
● Corporate Social Responsibility (CSR) measures
○ CSR rating as an aggregate score from KLD
■ CSR ranges from -2 (major concerns) to +2 (major strength)
○ CSR_Con captures only concerns to test asymmetric impacts
● Environmental reputation impact may vary across industries
○ Industry adjusted CSR score through deducting industry average
○ 2-digit Global Industry Classification Standard (GICS) code
● Lobbying impact impact may vary across industries
○ Firm-level lobbying expenditure on environmental and superfund
issues (LOBBY: labbed by 1 year)
○ Industry adjusted lobbying expenditure through deducting
industry average with GICS code
○ LOBBY_REP captures number of lobbyist reports sponsored
16

17. Table
1:
Sample
Selection
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Michael-Paul James
TABLE 1: Sample Selection
Table 1 outlines the sample selection process for environmental lawsuit observations. Data source:
Public Access to Court Electronic Records (PACER).
Sample Selection No. of Lawsuits
Initial sample 13,632
Less: Lawsuits not related to S&P 1500 firms -11,387
Lawsuits related to former or current S&P 1500 firms 2,245
Less: Large volumes of lawsuits filed in the same firm-year
Against IMC Global Incorporated in 2001 -941
Against ExxonMobil Oil Corporation in 2006 -143
Total 1,161
Less: Unresolved/pending lawsuits -87
Total 1,074
Less: Missing firm-year data from Compustat, CRSP, or KLD -486
Final sample 588

18. Regression Model
CARj
i,t
= α + β1
CSRi,t-1
| LOBBYi,t-1
+ γXi,t-1
+ εit
● i firm
● j lawsuit event
● t event date
● CARcumulative abnormal returns around lawsuit filings
○ Normal returns using 200 day CRSP value weighted index
○ 3 event windows: (0,+1), (-1,+1), (-2,+2)
● X firm specific characteristics
○ size, profitability, market to book ratio, leverage, size of damages sought.
● Note: Industry and year fixed effects in all regressions 18

19. Results
04
Empirical Results; Descriptive Statistics; Univariate Analysis; Baseline
Regression Analysis; Changes in Institutional and Active Fund Holdings
Surrounding Lawsuits
19
Michael-Paul James

20. Table
2:
Industry
Breakdown
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Michael-Paul James
Table 2 reports the industry
breakdown of the lawsuit
sample by 2-digit Global
Industry Classification
Standard (GICS) code
industries. Column 1 reports
the lawsuit frequency for
each industry. Columns 2
and 3 report the mean raw
CSR scores and lobbying
expenditure, respectively,
within each industry. The
raw CSR scores and lobbying
expenditure are reported
(rather than the industry-
adjusted values) for easier
interpretation. Industries in
which no firm in the sample
engages in any lobbying
(Information Technology and
Real Estate) have an average
lobbying expenditure of 0.
All variables are defined in
the Appendix.
TABLE 2: Industry Breakdown
GICS
Industry
Code
Lawsuit
Frequency
CSR_RAW
(mean)
LOBBY_RAW
(mean)
($millions)
Industry 1 2 3
Energy 10 170 -2.294 2.112
Materials 15 123 -0.821 0.489
Industrials 20 176 -1.284 2.091
Consumer discretionary 25 35 -0.257 0.575
Consumer staples 30 24 0.167 0.756
Health care 35 10 1.000 0.468
Information technology 40 5 0.000 0.000
Utilities 45 14 0.214 0.399
Real estate 50 1 0.000 0.000
Full sample 588 -1.264 1.467

21. Table
3:
Stats
and
Matrix
21
Michael-Paul James
Table 3 reports the descriptive statistics and Pearson correlation coefficients of the variables included in the baseline
regressions. N=588. All variables are defined in the Appendix. *, **, and *** indicate significance at the 10%, 5%, and
1% levels, respectively.
● Economic significance: -0.2% ~ 118 million, -0.6 ~ 354 million
● No concerns of multicollinearity in the Independent variables
TABLE 3:Descriptive Statistics and Correlation Matrix
Variables Mean Median Std. Dev. 1 2 3 4 5 6 7 8 9
Dependent Variables
CAR(0,+1) -0.002 0.000 0.025 1.000
CAR(-1,+1) -0.004 0.000 0.036 0.765*** 1.000
CAR(-2,+2) -0.006 -0.002 0.043 0.589*** 0.778*** 1.000
Independent Variables
CSR -1.087 -1.032 1.637 -0.065* -0.048 -0.010 1.000
LOBBY 9.020 0.299 16.491 0.083*** 0.080** 0.047 -0.220*** 1.000
Control Variables
DEMAND 0.000 0.000 0.005 -0.027 -0.028 -0.003 0.029 -0.020 1.000
ln(TA) 10.038 10.182 1.485 0.059* 0.018 0.003 -0.341*** 0.499*** -0.112*** 1.000
ROA 0.015 0.014 0.013 0.077** 0.025 0.086*** -0.093** 0.044 -0.005 0.236*** 1.000
MARKET_BOOK 2.469 2.242 1.466 0.036 0.080** 0.118*** 0.013 0.038 0.019 0.091*** 0.353*** 1.000
LEVERAGE 1.964 1.527 2.761 0.055* 0.058* 0.032 0.006 0.196*** -0.008 0.071** -0.090*** 0.412***

22. Table
4:
Univariate
Analysis
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Michael-Paul James
Table 4 reports the univariate analysis results. In Panel A, the sample is divided into subsamples with high corporate social
responsibility (CSR) ratings (industry-adjusted CSR scores above the sample median) and with low CSR ratings (below the sample
median). In Panel B, the sample is divided into a high-lobbying subsample (with industry-adjusted expenditure above the sample
median) and a low-lobbying subsample (below sample median). *, **, and *** indicate significance at the 10%, 5%, and 1% levels,
respectively.
● Economic significance: -0.9% ~ 531 million (59 billion mean)
TABLE 4: Univariate Analysis
Panel A. Univariate Analysis by Industry-Adjusted CSR Score (Median Split) [H1]
High-CSR Sample Low-CSR Sample Difference
Variables Mean Std. Dev. N Mean Std. Dev. N Mean p-Value
CAR(0,+1) -0.005 0.029 270.000 0.000 0.022 318.000 -0.006*** -0.006
CAR(-1,+1) -0.008 0.043 270.000 0.001 0.027 318.000 -0.009*** -0.003
CAR(-2,+2) -0.011 0.050 270.000 -0.002 0.036 318.000 -0.009** -0.019
Panel B. Univariate Analysis by Industry-Adjusted Lobbying Expenditure (Median Split) [H2]
High-LOBBY Sample Low-LOBBY Sample Difference
Variables Mean Std.Dev. N Mean Std.Dev. N Mean p-Value
CAR(0,+1) -0.001 0.021 296.000 -0.004 0.029 292.000 0.003 -0.168
CAR(-1,+1) 0.000 0.025 296.000 -0.008 0.043 292.000 0.008*** -0.007
CAR(-2,+2) -0.002 0.033 296.000 -0.010 0.051 292.000 0.009** -0.014

23. Table
5:
Lawsuit
Responses
23
Table 5 reports the results from ordinary least squares (OLS) regressions estimating the cumulative abnormal returns (CAR)
surrounding lawsuit filings during event windows (0,+1), (-1,+1), and (-2,+2). Column 10 reports the standardized coefficients from the
regression model in column 7. All variables are defined in the Appendix. p-values in parentheses are based on a 2-tailed test. *, **,
and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
● Economic Significance: 0.02% ~ $1.467 million spent on lobbying save $12 million in market value.
TABLE 5: CSR, Lobbying, and Market Reactions to Environmental Lawsuits
CSR LOBBY CSR & LOBBY
Standardized
Coefficients
CAR(0,+1) CAR(-1,+1) CAR(-2,+2) CAR(0,+1) CAR(-1,+1) CAR(-2,+2) CAR(0,+1) CAR(-1,+1) CAR(-2,+2) Model 7
Variables 1 2 3 4 5 6 7 8 9 10
CSR -0.002*** -0.003*** -0.003** -0.002** -0.003** -0.003* -0.144**
(0.007) (0.009) (0.031) (0.014) (0.022) (0.075)
LOBBY 0.0002*** 0.0004*** 0.001*** 0.0002*** 0.0004*** 0.001*** 0.126***
(0.006) (0.000) (0.000) (0.010) (0.000) (0.000)
DEMAND -0.211*** -0.384*** -0.403*** -0.226*** -0.418*** -0.449*** -0.231*** -0.424*** -0.454*** -0.046***
(0.002) (0.000) (0.000) (0.001) (0.000) (0.000) (0.001) (0.000) (0.000)
ln(TA) -0.001 -0.001 -0.001 -0.002* -0.003** -0.003* -0.002** -0.003** -0.004** -0.134**
(0.231) (0.508) (0.677) (0.087) (0.040) (0.059) (0.028) (0.011) (0.023)
ROA 0.159* -0.036 0.118 0.140 -0.071 0.075 0.144 -0.065 0.081 0.073
(0.094) (0.759) (0.485) (0.150) (0.538) (0.659) (0.137) (0.585) (0.642)
MARKET_BOOK 0.001 0.002** 0.004*** 0.001* 0.003*** 0.004*** 0.001* 0.003*** 0.005*** 0.080*
(0.121) (0.021) (0.005) (0.075) (0.007) (0.001) (0.070) (0.007) (0.001)
LEVERAGE -0.000 -0.001** -0.001* -0.001 -0.002*** -0.002** -0.000 -0.002*** -0.002** -0.048
(0.637) (0.026) (0.088) (0.352) (0.006) (0.017) (0.427) (0.009) (0.022)
CONSTANT -0.018 -0.024* -0.032* -0.008 -0.002 -0.003 -0.006 0.001 -0.001 -
(0.132) (0.088) (0.092) (0.528) (0.906) (0.880) (0.628) (0.971) (0.959)
Year and industry
fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
N 588 588 588 588 588 588 588 588 588 588
Adj. R2 0.066 0.118 0.106 0.064 0.128 0.123 0.074 0.137 0.127 0.074

24. Table
6:
Changes
in
Holdings
24
Table 6 reports the results from ordinary least squares (OLS) regressions estimating the changes in U.S. institutional holdings of the
sued firms' stocks from the month before to the month after the lawsuit filings. ΔFUND1 measures the changes in holdings by all
institutional investors and ΔFUND2 measures the changes in holdings by active funds. Column 7 reports the standardized
coefficients from the regression model in column 5. All variables are defined in the Appendix. p-values in parentheses are based on
a 2-tailed test. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
TABLE 6: Changes in Institutional and Active Fund Holdings Surrounding Lawsuit Filings
CSR LOBBY CSR & LOBBY Std Coefficients
ΔFUND1 ΔFUND2 ΔFUND1 ΔFUND2 ΔFUND1 ΔFUND2 Model 5
Variables 1 2 3 4 5 6 7
CSR -0.001** -0.001** -0.001** -0.001** -0.127**
(0.032) (0.040) (0.035) (0.041)
LOBBY 0.000 0.000 0.000 0.000 0.018
(0.561) (0.642) (0.761) (0.851)
DEMAND -0.007 -0.049 -0.124 -0.156 -0.015 -0.054 -0.001
(0.982) (0.856) (0.668) (0.557) (0.961) (0.843)
ln(TA) -0.002** -0.001** -0.001* -0.001 -0.002** -0.002* -0.162**
(0.013) (0.019) (0.074) (0.107) (0.036) (0.058)
ROA -0.079 -0.049 -0.080 -0.050 -0.080 -0.050 -0.064
(0.423) (0.611) (0.415) (0.606) (0.421) (0.610)
MARKET_BOOK 0.001** 0.001** 0.001** 0.001** 0.001** 0.001** 0.130**
(0.017) (0.024) (0.021) (0.031) (0.018) (0.026)
LEVERAGE -0.000 -0.000 -0.000 -0.000 -0.000 -0.000 -0.074
(0.273) (0.662) (0.213) (0.543) (0.266) (0.649)
CONSTANT 0.002 0.000 0.003 0.001 0.003 0.001 -
(0.829) (0.970) (0.800) (0.955) (0.761) (0.918)
Year & Industry FE Yes Yes Yes Yes Yes Yes Yes
N 505 505 505 505 505 505 505
Adj.R2 0.050 0.034 0.041 0.025 0.048 0.032 0.048

25. Robustness
05
Robustness Analyses; Media Attention Analysis; Falsification Test Using
Subsequent Lawsuits Following CSR Downgrades; Potential Information
Leakage Preceding Lawsuit Filings; Fama–French Estimation Models;
Alternative Proxies for CSR and Lobbying; Lawsuit Merit Proxied by
Outcomes; Propensity Score Matching
25
Michael-Paul James

26. Media Attention
● News media constitute a crucial source of information about a firm
● Negative news coverage can significantly affect firm value
● For each lawsuit, search the Factiva News database using the following:
○ Plaintiff name
○ Defendant (company) name
○ One of the following words:
■ lawsuit ■ litigation ■ litigate ■ sue
■ sued ■ court ■ toxic ■ hazardous
■ pollution ■ pollute ■ environmental clean up
○ Include media 15 calendar days before the lawsuit filing date to 15
days after the lawsuit termination date.
26

27. Table
7:
Media
Attention
27
Table 7 reports the results from the subsample analyses, by reestimating the baseline regressions in Table 5 using 2 subsamples
with high versus low media attention. The high-media attention subsample comprises lawsuits that attract a high number of news
articles (above or equal the sample median), whereas lawsuits in the low-media attention subsample attract a low number of news
articles (below the sample median). All variables are defined in the Appendix. p-values in parentheses are based on a 2-tailed test. *,
**, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
TABLE 7: Media Attention Subsample Analysis
High Media Attention Subsample Low Media Attention Subsample
CAR(0,+1) CAR(-1,+1) CAR(-2,+2) CAR(0,+1) CAR(-1,+1) CAR(-2,+2)
Variables 1 2 3 4 5 6
CSR -0.004** -0.004** -0.004* -0.001 -0.002 -0.001
(0.015) (0.043) (0.053) (0.561) (0.256) (0.542)
LOBBY 0.0003** 0.001*** 0.001** 0.0002* 0.0004** 0.001***
(0.045) (0.008) (0.013) (0.076) (0.036) (0.003)
DEMAND -0.305*** -0.448*** -0.359** 0.363 -0.034 -1.121
(0.006) (0.000) (0.021) (0.268) (0.950) (0.332)
ln(TA) -0.004*** -0.004** -0.004 -0.001 -0.003 -0.005**
(0.007) (0.023) (0.142) (0.558) (0.203) (0.049)
ROA 0.214 0.053 0.384 0.092 -0.171 -0.183
(0.233) (0.794) (0.164) (0.433) (0.290) (0.420)
MARKET_BOOK 0.003** 0.003** 0.004* 0.001 0.004** 0.006***
(0.024) (0.040) (0.064) (0.408) (0.026) (0.008)
LEVERAGE -0.000 -0.002 -0.002* -0.001 -0.002** -0.002
(0.751) (0.146) (0.088) (0.315) (0.021) (0.165)
CONSTANT 0.005 0.014 -0.006 -0.017 -0.013 0.008
(0.759) (0.496) (0.834) (0.341) (0.553) (0.816)
Year & Industry FE Yes Yes Yes Yes Yes Yes
N 289 289 289 299 299 299
Adj. R2 0.119 0.162 0.204 0.019 0.077 0.047

28. Table
8:
Falsification
Test
28
Table 8 reports the results from a falsification test, by reestimating the baseline regressions in Table 5 using a subsample of lawsuits
filed against firms that have experienced previous declines in CSR ratings following prior environmental lawsuits. All variables are
defined in the Appendix. p-values in parentheses are based on a 2-tailed test. *, **, and *** indicate significance at the 10%, 5%, and 1%
levels, respectively.
● To be included in the sample firms must have at least one previous environmental lawsuit and a decline in CSR rating since
the previous lawsuit.
TABLE 8: Falsification Test: Subsequent Lawsuits Following CSR Score Downgrades
CSR LOBBY CSR & LOBBY
CAR(0,+1) CAR(-1,+1) CAR(-2,+2) CAR(0,+1) CAR(-1,+1) CAR(-2,+2) CAR(0,+1) CAR(-1,+1) CAR(-2,+2)
Variables 1 2 3 4 5 6 7 8 9
CSR 0.001 0.003 0.001 0.001 0.003 0.001
(0.856) (0.573) (0.864) (0.854) (0.565) (0.866)
LOBBY -0.000 -0.000 0.000 -0.000 -0.000 0.000
(0.937) (0.757) (0.917) (0.931) (0.734) (0.922)
Control variables Yes Yes Yes Yes Yes Yes Yes Yes Yes
Year & Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Yes
N 103 103 103 103 103 103 103 103 103
Adj. R2 0.199 0.145 0.130 0.199 0.140 0.130 0.189 0.135 0.119

29. Table
9:
Robustness
Tests
29
TABLE 9: Robustness Tests: Controlling for Potential Information Leakage Preceding Lawsuit Filings
CSR LOBBY CSR & LOBBY
Panel A. Aggregated CAR for All Pre-Filing Lawsuit Events and Lawsuit Filing
PREFILE_FILE_
CAR(0,+1)
PREFILE_FILE_
CAR(-1,+1)
PREFILE_FILE_
CAR(-2,+2)
PREFILE_FILE_
CAR(0,+1)
PREFILE_FILE_
CAR(-1,+1)
PREFILE_FILE_
CAR(-2,+2)
PREFILE_FILE_
CAR(0,+1)
PREFILE_FILE_
CAR(-1,+1)
PREFILE_FILE_
CAR(-2,+2)
Variables 1 2 3 4 5 6 7 8 9
CSR -0.003** -0.004** -0.003* -0.003** -0.003** -0.003
(0.013) (0.015) (0.093) (0.019) (0.027) (0.163)
LOBBY 0.0002** 0.0004*** 0.001*** 0.0002* 0.0004*** 0.001***
(0.039) (0.003) (0.004) (0.056) (0.005) (0.005)
Control variables Yes Yes Yes Yes Yes Yes Yes Yes Yes
Year & Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Yes
N 588 588 588 588 588 588 588 588 588
Adj. R2 0.042 0.079 0.068 0.038 0.083 0.079 0.047 0.091 0.081
Panel B. Aggregated CAR for the Filing of Notice of Intent / Notice of Violations and Lawsuit Filing
NOTICE_FILE_
CAR(0,+1)
NOTICE_FILE_
CAR(-1,+1)
NOTICE_FILE_
CAR(-2,+2)
NOTICE_FILE_
CAR(0,+1)
NOTICE_FILE_
CAR(-1,+1)
NOTICE_FILE_
CAR(-2,+2)
NOTICE_FILE_
CAR(0,+1)
NOTICE_FILE_
CAR(-1,+1)
NOTICE_FILE_
CAR(-2,+2)
Variables 1 2 3 4 5 6 7 8 9
CSR 0.002*** -0.003*** -0.003** -0.002** -0.003** -0.003*
(0.008) (0.008) (0.036) (0.015) (0.020) (0.085)
LOBBY 0.0002*** 0.0004*** 0.001*** 0.0002*** 0.0004*** 0.001***
(0.006) (0.000) (0.000) (0.009) (0.000) (0.000)
Control variables Yes Yes Yes Yes Yes Yes Yes Yes Yes
Year & Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Yes
N 588 588 588 588 588 588 588 588 588
Adj. R2 0.066 0.119 0.108 0.064 0.129 0.126 0.074 0.137 0.130
Table 9 reports the results from ordinary least squares (OLS) regressions estimating the aggregated cumulative abnormal returns (CAR) surrounding lawsuit
filings and other pre-filing lawsuit-related events over windows (0,+1), (-1,+1), and (-2,+2). In Panel A, PREFILE_FILE_CAR is calculated by aggregating the CAR
surrounding all pre-filing events and the lawsuit filing date. In Panel B, NOTICE_FILE_CAR is calculated by aggregating the CAR surrounding the filing date of the
plaintiff(s)’ Notice of Intention to Sue (Notice of Intent) or Notice of Violations and the lawsuit filing date. All variables are defined in the Appendix. p-values in
parentheses are based on a 2-tailed test. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

30. Table
10:
Robustness
Tests
30
Table 10 reports the results from ordinary least squares (OLS) regressions estimating filing-date cumulative abnormal returns (CAR) computed using the
Fama–French (1993) 3-factor model (Panel A) and Carhart (1997) 4-factor model with momentum (Panel B) for event windows (0,+1), (-1,+1), and (-2,+2). All variables are
defined in the Appendix. p-values in parentheses are based on a 2-tailed test. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
TABLE 10: Robustness Tests: Fama-French and Carhart Estimation Models
CSR LOBBY CSR & LOBBY
Panel A. Fama French (1993) 3-Factor Model
CAR_FF3(0,+1) CAR_FF3(-1,+1) CAR_FF3(-2,+2) CAR_FF3(0,+1) CAR_FF3(-1,+1) CAR_FF3(-2,+2) CAR_FF3(0,+1) CAR_FF3(-1,+1) CAR_FF3(-2,+2)
Variables 1 2 3 4 5 6 7 8 9
CSR -0.003*** -0.004*** -0.003** -0.002** -0.003*** -0.003*
(0.006) (0.004) (0.036) (0.012) (0.009) (0.082)
LOBBY 0.0002*** 0.0004*** 0.001*** 0.0002** 0.0004*** 0.001***
(0.008) (0.001) (0.000) (0.012) (0.001) (0.000)
Control variables Yes Yes Yes Yes Yes Yes Yes Yes Yes
Year & Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Yes
N 584 584 584 584 584 584 584 584 584
Adj. R2 0.072 0.120 0.105 0.069 0.126 0.122 0.080 0.136 0.126
Panel B. Carhart (1997) 4-Factor Model with Momentum
CAR_FF4(0,+1) CAR_FF4(-1,+1) CAR_FF4(-2,+2) CAR_FF4(0,+1) CAR_FF4(-1,+1) CAR_FF4(-2,+2) CAR_FF4(0,+1) CAR_FF4(-1,+1) CAR_FF4(-2,+2)
Variables 1 2 3 4 5 6 7 8 9
CSR -0.002** -0.003** -0.003* -0.002** -0.003** -0.002
(0.028) (0.019) (0.087) (0.047) (0.038) (0.175)
LOBBY 0.0002** 0.0004*** 0.001*** 0.0002** 0.0004*** 0.001***
(0.010) (0.001) (0.000) (0.015) (0.002) (0.000)
Control variables Yes Yes Yes Yes Yes Yes Yes Yes Yes
Year & Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Yes
N 584 584 584 584 584 584 584 584 584
Adj. R2 0.055 0.101 0.099 0.056 0.110 0.118 0.062 0.116 0.120

31. Table
11:
Robustness
Tests
31
Table 11 reports the results from
robustness tests using alternative
proxies for CSR reputations and
lobbying. Panel A reports the
results from ordinary least
squares (OLS) regressions that
employ an alternative CSR
measure, CSR_CON, which
captures the number of
environmental concerns only.
Panel B reports the results from
OLS regressions that employ
CSR_MONITOR, which represents
an alternative CSR rating sourced
from Thomson Reuters ASSET4
database. Panel C reports the
results from OLS regressions that
employ an alternative proxy for
lobbying activities, LOBBY_REP,
which captures the
industry-adjusted number of
lobbyist reports on
environmental issues sponsored
by the firm. All variables are
defined in the Appendix. p-values
in parentheses are based on a
2-tailed test. *, **, and *** indicate
significance at the 10%, 5%, and
1% levels, respectively.
TABLE 11: Robustness Tests: Alternative Proxies for CSR and Lobbying
CSR | LOBBY CSR & LOBBY
CAR(0,+1) CAR(-1,+1) CAR(-2,+2) CAR(0,+1) CAR(-1,+1) CAR(-2,+2)
Variables 1 2 3 4 5 6
Panel A. Alternative Proxy for CSR Using CSR Concerns
CSR_CON 0.003*** 0.005*** 0.006*** 0.003*** 0.004*** 0.005**
(0.002) (0.003) (0.002) (0.006) (0.010) (0.011)
LOBBY 0.0002** 0.0004*** 0.001***
(0.017) (0.001) (0.001)
Control variables Yes Yes Yes Yes Yes Yes
Year & Industry FE Yes Yes Yes Yes Yes Yes
N 588 588 588 588 588 588
Adj. R2 0.073 0.126 0.117 0.079 0.141 0.135
Panel B. Alternative Source of CSR Ratings Using ASSET4 Data (Emission: Thomson Reuters ASSET4 ESG database)
CSR_MONITOR -0.003 -0.003** -0.005** -0.003 -0.003** -0.005**
(0.175) (0.034) (0.031) (0.172) (0.026) (0.033)
LOBBY -0.000 0.001** -0.000
(0.805) (0.180) (0.887)
Control variables Yes Yes Yes Yes Yes Yes
Year & Industry FE Yes Yes Yes Yes Yes Yes
N 312 312 312 312 312 312
Adj. R2 0.023 0.006 0.041 0.020 0.006 0.038
Panel C. Alternative Proxy for Lobbying Using Number of Reports
LOBBY_REP 0.0004 0.001*** 0.001*** 0.0003 0.001*** 0.001***
(0.142) (0.000) (0.004) (0.279) (0.001) (0.007)
CSR -0.002*** -0.003** -0.003*
(0.009) (0.019) (0.058)
Control variables Yes Yes Yes Yes Yes Yes
Year & Industry FE Yes Yes Yes Yes Yes Yes
N 588 588 588 588 588 588
Adj. R2 0.055 0.119 0.109 0.066 0.128 0.113

32. Table
12:
Propensity
Score
32
Table 12 reports the results from
ordinary least squares (OLS)
regressions estimating the
cumulative abnormal returns
(CAR) surrounding lawsuit filings
during event windows (0,+1),
(-1,+1), and (-2,+2) by employing
propensity score matched
samples based on CSR_MED
(reported in models 1–3) and
LOBBY_MED (reported in models
4–6), respectively. All variables are
defined in Appendix. p-values in
parentheses are based on a
2-tailed test. *, **, and *** indicate
significance at the 10%, 5%, and
1% levels, respectively.
TABLE 12: Propensity Score Matching
CSR_MED LOBBY_MED
CAR(0,+1) CAR(-1,+1) CAR(-2,+2) CAR(0,+1) CAR(-1,+1) CAR(-2,+2)
Variables 1 2 3 4 5 6
CSR_MED -0.005* -0.010*** -0.011***
(0.064) (0.003) (0.010)
LOBBY_MED 0.009*** 0.010** 0.009
(0.009) (0.017) (0.146)
Accounting control
variables Yes Yes Yes Yes Yes Yes
Corporate governance
control variables Yes Yes Yes Yes Yes Yes
Year & Industry FE Yes Yes Yes Yes Yes Yes
N 322 322 322 176 176 176
Adj. R2 0.090 0.114 0.122 0.088 0.088 0.070

33. Conclusion
06
Conclusion and Future Research Directions
33
Michael-Paul James

34. Reputation, Lobbying, and Lawsuits
● Environmental lawsuit filings (misconduct announcements) hurt the
value of firms with high CSR ratings more than those with low
● Higher lobbying expenditure is associated with smaller firm value
losses.
○ Investors positively price political capital to mitigate economic
losses
● Contributions
○ Bridges corporate misconduct with CSR research
○ Impact of lobbying on firm value during environmental lawsuits
○ Evidence that Investors positively priced CSR into firm valuation
34
Michael-Paul James

35. You are Amazing
Ask me all the questions you desire. I will do my best to answer honestly
and strive to grasp your intent and creativity.
35
Michael-Paul James

36. Variable Definitions
Dependent Variables
● CAR
○ Cumulative abnormal returns calculated based on the CRSP value-weighted index over a 200-day
estimation period (−231, −31) using the market model. CAR(0,+1) is estimated over a 2-day event
window (0, +1), where t = 0 is the day of the lawsuit filing. CAR(−1,+1) and CAR(−2,+2) are estimated over
a 3-day event window (−1, +1) and a 5-day event window (−2, +2), respectively.
● CAR_FF3
○ Cumulative abnormal returns calculated using the same methodology as CAR above, with the
exception that the Fama–French (1993) 3-factor model is used when estimating normal returns.
● CAR_FF4
○ Cumulative abnormal returns calculated using the same methodology as CAR above, with the
exception that the Carhart (1997) 4-factor model with momentum is used when estimating normal
returns.
● ∆FUND
○ Changes in the number of common shares in a firm held by U.S. institutional investors from the
month before to the month after the lawsuit filing, scaled by the firm’s total number of common
shares outstanding. Specifically, ∆FUND1 measures the changes in holdings by all institutional
investors, and ∆FUND2 measures the changes in holdings by active funds.
36
Michael-Paul James

37. Variable Definitions
● NOTICE_FILE_CAR
○ Aggregated cumulative abnormal returns surrounding the lawsuit filing date and the filing date of
any Notice of Intention to Sue or Notice of Violations by the plaintiff(s) prior to the lawsuit filing, if the
latter date is reported in the lawsuit complaint. Each CAR is calculated using the same methodology
as previously explained in relation to CAR.
● PREFILE FILE CAR
○ Aggregated cumulative abnormal returns surrounding the lawsuit filing date and other identifiable
pre-filing event(s) related to the lawsuit, including the date of the discovery of violations, the date of
any scientific testing, and the date of the filing of the Notice of Intention to Sue or Notice of Violations
by the plaintiff(s), as recorded in the lawsuit complaint. Each CAR is calculated using the same
methodology as previously explained in relation to CAR.
Independent Variables of Interest
● CSR
○ Industry-adjusted environmental CSR score in year t-1 based on the Kinder, Lydenberg, Domini (KLD)
ratings, calculated as the aggregated number of environmental strengths less the number of
environmental concerns, adjusted by deducting the industry mean within each 2-digit Global Industry
Classification Standard (GICS) code industry.
● CSR CON
○ Industry-adjusted number of environmental concerns recorded by KLD in year t-1, adjusted by
deducting the industry mean within each 2-digit GICS code industry.
37
Michael-Paul James

38. Variable Definitions
● CSR MED
○ Dummy variable which equals 1 if the industry-adjusted CSR score for year t-1 is above or equal the
sample median, and 0 otherwise.
● CSR RAW
○ Raw (unadjusted) environmental CSR score in year t-1 based on KLD ratings, calculated as the
aggregated number of environmental strengths less the number of environmental concerns.
● CSR MONITOR
○ Industry-adjusted CSR emission monitoring score (out of 100) in year t-1 based on Thomson Reuters
ASSET4 ESG ratings, which relates to a firm’s monitoring of its emission reduction performance, scaled
by the industry mean within each 2-digit GICS code industry.
● LOBBY
○ Industry-adjusted lobbying expenditure on environmental and superfund issues in year t-1 ($millions),
scaled by the industry mean within each 2-digit GICS code industry.
● LOBBY MED
○ Dummy variable which equals 1 if the industry-adjusted lobbying expenditure in year t-1 is above or
equal the sample median amongst lobbying firms, and 0 otherwise.
● LOBBY RAW
○ Raw (unadjusted) lobbying expenditure on environmental and superfund issues in year t-1 ($millions).
● LOBBY REP
○ Industry-adjusted number of lobbyist reports on environmental and superfund issues sponsored by
the firm in year t-1, scaled by the industry mean within each 2-digit GICS code industry.
38
Michael-Paul James

39. Variable Definitions
Control Variables
● DEMAND
○ The amount of monetary compensation demanded by the plaintiff(s) in the lawsuit, scaled by firm’s
total assets.
● DISMISSED
○ Dummy variable equals 1 if the lawsuit is terminated by dismissal, and 0 otherwise.
● FACTIVA
○ Number of news articles related to a lawsuit, generated by conducting the following keyword search
in the Factiva database: “Firm (Defendant) Name,” “Plaintiff Name,” and one of the
lawsuit/environmental keywords (“lawsuit” or “litigation” or “litigate” or “sue” or “sued” or “court” or
“toxic” or “hazardous” or “pollution” or “pollute” or “environmental clean up”), dated from-15 days
before the lawsuit filing date to +15 days after the lawsuit termination.
● FACTIVA MED
○ Dummy variable equals 1 if the number of Factiva news articles is above the sample median, and 0
otherwise.
● LEVERAGE
○ Financial leverage proxied by debt-to-asset ratio in year t-1, calculated as the book value of total
liabilities divided by the book value of total assets.
39
Michael-Paul James

40. Variable Definitions
Control Variables
● ln(TA)
○ Firm size proxied by the natural logarithm of total assets of the firm in year t-1.
● MARKET BOOK
○ Market-to-book ratio in year t-1, calculated as the market capitalization divided by the book value of
total equity.
● ROA
○ Firm performance proxied by return on assets, calculated as the net income (loss) divided by total
assets in year t-1.
40
Michael-Paul James