Corporate social responsibility and media coverage
Steven F. Cahan a,⇑
, Chen Chen b,1
, Li Chen a,2
, Nhut H. Nguyen c,3
aUniversity of Auckland Business School, Private Bag 92019, Auckland 1142, New Zealand
b Monash University Business School, 900 Dandenong Road, Caulfield East 3145, VIC, Australia
c School of Economics and Finance, Massey University, Private Bag 102 904, Auckland 0745, New Zealand
article info
Article history:
Received 27 January 2015
Accepted 7 July 2015
Available online 20 July 2015
JEL classification:
G10
L82
M14
Keywords:
Media management
Corporate social responsibility
Business press
abstract
In this study, we examine whether firms that act more socially responsible receive more favorable media
coverage, and we consider whether firms use CSR to actively manage their media image. We focus on all
news stories about a firm, not just those that report on specific CSR initiatives, and find that more socially
responsible firms receive more favorable news reportage overall, i.e., they have a more positive media
image. These findings are robust after controlling for potential endogeneity. Further, consistent with
firms actively managing their media image, we find a stronger relation between CSR and media favorability when incentives to improve a firm’s media image are high, e.g., among firms in sin industries, during periods of low investor sentiment, and prior to seasoned equity offerings. Finally, we find that for
firms that demonstrate superior social responsibility and receive more favorable news reporting, there
is a significant interaction between social responsibility and media favorability that increases (decreases)
a firm’s equity valuation (cost of capital). Our results are consistent with the media slanting their reporting in favor of good performing CSR firms. Overall, we contribute to the literature by showing that firms
can influence their media coverage through a relatively subtle channel, CSR performance.
2015 Elsevier B.V. All rights reserved.
1. Introduction
It is well documented that the business press plays an important role as an information intermediary and that media coverage
affects a firm’s information environment (e.g., Tetlock et al., 2008;
Fang and Peress, 2009; Engelberg and Parsons, 2011; Griffin et al.,
2011; Dougal et al., 2012; Kim et al., 2014a). Such studies assume
that media coverage is exogenous. In contrast, as Ahern and
Sosyura (2014) suggest, causality could run in the opposite direction, i.e., firms may take actions to influence the media coverage
they receive. Consistent with this notion, Gurun and Butler
(2012) find that the media respond to pressure from local advertisers. Solomon (2012) finds that media coverage is more favorable
for firms that use an investor relations firm. Ahern and Sosyura
(2014) find that firms with fixed stock exchange ratios issue fewer
negative press releases during the period when the exchange ratio
is set. We extend this limited line of research and examine whether
firms can manage their media coverage through a more subtle
channel, i.e., by performing better in terms of corporate social
responsibility (CSR).
We focus on CSR for practical and theoretical reasons. First,
investors and managers are increasingly concerned about CSR performance. For example, over 1250 institutional investors worldwide have agreed to support the United Nation’s ‘Principles of
Responsible Investment’ (PRI) project that encourages responsible
investing by institutional investors based on factors including
environmental and social performance (PRI Annual Report 2014),
and in 2010, $3.07 trillion were invested in professionally managed
US assets that ascribed to socially responsible investing
http://dx.doi.org/10.1016/j.jbankfin.2015.07.004
0378-4266/ 2015 Elsevier B.V. All rights reserved.
We thank Henk Berkman, Gary Biddle, Charl de Villiers, Fei Du, Torsten Jochem,
Charlene Lee, John Lee, Donghui Li, Dimitri Margaritas, Michaela Rankin, Irene
Tutticci, Anne Wyatt, Rencheng Wang, Xin Wang, Joyce Yu, Ying Zheng, and seminar
participants at the University of Auckland, University of Hong Kong, University of
Otago, University of Queensland, University of Technology, Sydney, Sun Yat-Sen
University, 2014 AFAANZ Conference, 2014 AAA Annual Meeting, 2015 Financial
Markets and Corporate Governance Conference, and 2015 FMA European Conference for their comments and suggestions. We also thank Thomson Reuters for
providing access to the Thomson Reuters News Analytics database, and we
appreciate the assistance of Maciej Pomalecki and Xuehui Hou in accessing the
data. We acknowledge research funding provided by the University of Auckland
Business School.
⇑ Corresponding author. Tel.: +64 9 923 7175.
E-mail addresses: [email protected] (S.F. Cahan), [email protected]
(C. Chen), [email protected] (L. Chen), [email protected] (N.H.
Nguyen).
1 Tel.: +61 3 9903 4566. 2 Tel.: +64 9 923 7484. 3 Tel.: +64 9 414 0800×43179.
Journal of Banking & Finance 59 (2015) 409–422
Contents lists available at ScienceDirect
Journal of Banking & Finance
journal homepage: www.elsevier.com/locate/jbf
(Di Giuli and Kostovetsky, 2014). In 2012, large US firms spent $28
billion on sustainability and $15 billion on corporate philanthropy
(Di Giuli and Kostovetsky, 2014), and 93% of CEOs responding to a
2010 UN survey stated that CSR was ‘important’ or ‘very important’
to the future success of their firm (Cheng et al., 2013). Second,
Fombrun and Shanley (1990) develop a model of reputation building where social responsiveness in the prior period is one factor
that can affect a firm’s media image in the current period.
However, to our knowledge, the link between CSR and a firm’s
overall media image has not been empirically examined.
For CSR to affect a firm’s wider media coverage, the media must
have incentives to report more positively about good CSR firms.
Mullainathan and Shleifer (2005) develop a demand-side model of
media slant in which the media caters to the beliefs of their readers.
They define slanting as a process where, given a set of facts, the
media portrays a subject favorably or unfavorably through selective
reporting. This model assumes that readers prefer news that is consistent with their beliefs – an assumption that is supported by
research in communications (e.g., Graber, 1984; Severin and
Tankard, 1992) and psychology (e.g., Bartlett, 1932; Klayman,
1995). Thus, slanting attracts readers, and Mullainathan and
Shleifer (2005) show that slant can persist even when readers have
homogeneous beliefs and when there are competing media sources.
Surveys of public opinion consistently show that corporate
behavior matters (e.g., Epstein-Reeves, 2010; Grant Thornton,
2011; Cone, 2013). For example, Epstein-Reeves (2010) finds that
88% of consumers think companies should try to achieve their
business goals while improving society and the environment, and
83% think companies should support charities and non-profits with
financial donations. In our context, if the public prefers companies
that are good corporate citizens and wants them to succeed,
Mullainathan and Shleifer’s (2005) catering theory predicts that
the media would provide more favorable coverage of firms with
good CSR performance. Consistent with this notion, El Ghoul
et al. (2011, 2390) suggest that ‘‘the media are more inclined to
spend time analyzing and reporting news about ‘good’ [CSR]
firms’’, although they do not test this expectation. Further, if firms
that are good CSR performers receive more favorable media coverage, managers have incentives to actively manage their media
image by being more socially responsible, as long as a positive
media image is economically beneficial.
Consequently, we consider three research questions derived
from the discussion above. First, is there a link between good corporate citizenship and favorable media coverage? Second, to the
extent there is a link, do firms actively manage CSR to gain more
favorable media coverage? Third, to consider the economic incentives that motivate such behavior, do firms with better CSR performance and more favorable media coverage benefit in terms of a
higher firm value or lower cost of capital? We divide our study into
three parts to examine each of these questions.
We rate news favorability using a measure of news tone or sentiment from Thomson Reuters News Analytics (TRNA). TRNA
employs a lexical analysis that uses a knowledge-driven neural
network to score each news item released about a firm in terms
of its positivity and negativity. Using all news stories about a firm
in a period, we compute the difference between the positive and
negative news sentiment scores from TRNA to proxy for media
favorability.4 Our media dataset includes 469,550 unique news stories during the period 2003–2011. We use CSR performance data
from the KLD (now MSCI ESG STATS) database.
In the first part of the study, we consider whether good CSR
firms have a more positive overall media image. We regress media
favorability on the firm’s CSR performance and find a positive relation, consistent with better performing CSR firms receiving more
positive news coverage from the business press. However, we
acknowledge that the endogeneity issues, such as omitted variables and reverse causality, may confound our tests.
Supplemental analyses using instrumental variables and propensity score matching support our main results. Further, we conduct
a quasi-natural experiment based on an executive order signed by
President Barack Obama on September 25, 2012 that strengthened
protections against labor trafficking by federal contractors and
subcontractors.5 This executive order would have affected CSR performance in the human rights area, but it would not have had a
direct effect on overall media favorability. The results of this analysis
support a causal relation where CSR performance affects media
favorability.
In the second part of the study, we consider whether firms
actively manage CSR in order to obtain more favorable media
image. In other words, a positive relation between CSR and media
favorability could arise if firms engage in CSR for other reasons
(e.g., altruism) in which case a positive media image is just an
attractive side-benefit. To provide evidence on active media management, we conduct three tests. First, we explore whether the
relation between CSR and media favorability is stronger in the
so-called ‘sin’ industries (i.e., alcohol, gambling, and tobacco). We
expect that sin firms would have greater incentives to promote a
more positive image, and we find a stronger relation between
CSR and media favorability for sin firms. Second, we expect that
all firms would have more incentive to increase media favorability
when the prevailing market-wide investor sentiment is more pessimistic. Since investor sentiment reflects a broad social mood that
can affect investor behavior generally, firms would be motivated to
counteract investors’ pessimistic outlook. We find that the relation
between CSR and media favorability is stronger when investor sentiment is low, consistent with firms actively managing CSR during
these periods. Third, we expect firms to use CSR to manage their
media image before a seasoned equity offerings. For a sample of
SEO firms which have less favorable media coverage before SEO,
we compare their CSR performance two years before the SEO with
their CSR performance in the year before and year of the SEO. We
find that CSR performance is higher in the SEO period, consistent
with firms trying to generate a more positive media image in the
lead-up to the SEO.
In the third part of the study, we explore the economic benefits
associated with better CSR performance and more favorable media
coverage. If there are no economic benefits, it is difficult to argue
that managers have incentives to use CSR to actively manage their
media image. El Ghoul et al. (2011) find that firms with better CSR
performance have a lower cost of capital. They speculate that
media coverage plays a role because, based on Merton (1987),
investors cannot invest in securities that they do not know about.
If the media provides more coverage, and more favorable coverage,
of good CSR firms, investors’ awareness and interest in these firms
increase. While El Ghoul et al. (2011) examine the link between
CSR performance and cost of capital, they do not explore the media
link.
We use firm value and the implied cost of capital to measure
economic benefits. We find that for firms that have both high
CSR performance and more positive media coverage, the coefficient
of the interaction between CSR performance and media favorability is positive (negative) and significant for the Tobin’s Q (cost of 4 Consistent with Gurun and Butler (2012), who examine the relation between
advertising and overall media coverage, our measure of media favorability is based on
all articles about a firm in a period, not only articles that report on CSR activity.
However, in sensitivity tests, we exclude the CSR-related articles in computing media
favorability. Our results are qualitatively unchanged.
5 The full content of the executive order is available on the following webpage:
http://www.whitehouse.gov/the-press-office/2012/09/25/executive-order-strengthening-protections-against-trafficking-persons-fe.
410 S.F. Cahan et al. / Journal of Banking & Finance 59 (2015) 409–422
capital) test. These results suggest that firms that successfully
manage their media favorability by performing better in the CSR
arena realize economic benefits from their positive media image.
Our study contributes to the literature in two important ways.
First, we contribute to an emerging line of research that considers
how firms may proactively manage their media image (e.g., Gurun
and Butler, 2012; Solomon, 2012; Ahern and Sosyura, 2014). We
add to this limited line of research by examining a less obvious
channel that firms can use to manage their media image, namely
their CSR performance. Second, we contribute to an expansive literature on CSR performance by documenting a previously unexplored incentive for firms engaging in CSR, i.e., active media
management.6
The remainder of the paper is organized as follows. Section 2
describes the research design. Sections 3, 4, and 5 report the results
of parts 1, 2, and 3 of the study, respectively. Section 6 concludes.
2. Research design
2.1. Data and sample
We collect media sentiment data from the TRNA database from
January 2003 to December 2011. A full description of the classification and scoring process used by TRNA is provided in Appendix A.
TRNA provides three sentiment scores, ranging from 0 to 1, that
reflect the probability that a specific news item is positive, negative, or neutral (the scores sum to 1). Following Bushee et al.
(2010), we classify news items based on their source, specifically,
press-initiated or firm-initiated. We classify news items carried
on a press release wire (e.g., Business Wire, Prime Newswire, PR
Newswire) as firm-initiated. Since our objective is to study the
impact of CSR on the news media, we focus on press-initiated news
items, although we control for firm-initiated news since the press
could be influenced by press releases issued by the firm (e.g.,
Solomon, 2012; Ahern and Sosyura, 2014). Finally, we reiterate
that our measure of media favorability is based on all news reports
about a firm, not just those related to CSR activity, as we are interested in the media’s overall treatment of the firm, i.e., the firm’s
media image.7
We extract data from KLD, Compustat, CRSP and I/B/E/S databases to calculate the remaining variables in our regression models. The sample size varies depending on the analysis. We have a
maximum of 12,749 firm-year observations.
2.2. Measurement of variables
2.2.1. CSR performance
KLD evaluates firms’ CSR performance in seven qualitative issue
areas, i.e., corporate governance, community, diversity, employee
relations, environment, human rights, and product. We compute
a total CSR score (CSR) for each firm at the end of year t by summing the net scores (strengths minus concerns) of community,
diversity, employee relations, environment, human rights, and
product.8 We include the corporate governance score as a control
variable rather than as part of CSR (e.g., Kim et al., 2012; Di Giuli
and Kostovetsky, 2014).
The KLD rating is an annual measure where the rating, which is
typically released in June or July, is based on information pertaining to the prior calendar year. For example, a KLD rating issued in
June 2011 is based on CSR data from the 2010 calendar year. Thus,
the KLD rating is essentially a lagged measure relative to our measure of media favorability (which is described in below). For example, in 2011 for a firm with a December 31 year-end, we compute
media favorability using all articles from January 1, 2011 to
December 31, 2011 and we use the KLD rating released in 2011,
which is based on CSR data from January 1, 2010 to December
31, 2010. For a small subset of firms (e.g., a firm with a
September 30 year-end), there could be some overlap between
our CSR and media measures. We address this concern in
Section 3.3.
2.2.2. Media favorability
For every firm in our sample, we compute an annual score of
media favorability (Media) equal to the aggregated positive sentiment scores of press-initiated news less the aggregated negative
sentiment scores of press-initiated news for the year t scaled by
total number of press-initiated news articles in that year. We use
an annual measure of media favorability since we are interested
in whether good CSR improves the firm’s reputation among the
media in general.
3. Part 1: Effect of CSR on media favorability
3.1. Regression model
To examine the relation between CSR performance and media
favorability, we estimate the following model:
Mediait ¼ b0 þ b1CSRit þ b2NStorit þ b3ROAit þ b4BPit þ b5MVit
þ b6Levit þ b7SP500it þ b8IRiskit þ b9Retit
þ b10AdExpit þ b11CGovit þ b12FI-Newsit
þ b13MktPEit þ eit ð1Þ
where Media and CSR are defined above. If firms’ CSR behavior has a
positive impact on firms’ media favorability, we expect b1, the
coefficient of interest, to be positive.
We control for the number of news stories in year t (NStor)
because we are interested in the firm’s media image as opposed
to the level of news coverage. We also include control variables
for the firm’s fundamentals, stock performance, risk, and visibility
since they may affect media favorability. Hence, we control for
return on assets (ROA), the book-to-market ratio (BP), size (MV),
leverage (Lev), visibility (SP500), idiosyncratic risk (IRisk), and stock
performance (Ret). Following Gurun and Butler (2012), we also
include the firm’s advertising expenditure (AdExp) as they document a relation between advertising expense and media favorability. In addition, as mentioned above, we control for KLD’s rating of
the firm’s corporate governance (CGov). Following Ahern and
Sosyura (2014), we control for firm-initiated news (FI-News) which
we compute as the aggregated positive sentiment scores of
firm-initiated news less the aggregated negative sentiment scores
of firm-initiated news for year t scaled by total number of
firm-initiated news articles in year t. Finally, we control for the
general state of the market which may affect the way journalists
interpret firm-specific events. To measure market-wide investor
sentiment, we follow Conrad et al. (2002) and use the
value-weighted average of the market price-to earnings ratio of
all firms in the merged CRSP-Compustat database in year t
(MktPE). We estimate Eq. (1) with industry and year fixed effects.
We include industry fixed effects because prior research indicates
that CSR is related to industry (e.g., Di Giuli and Kostovetsky, 2014)
6 Margolis et al. (2009) conduct a meta-analysis of 214 papers from 1972–2007 just
on one aspect of CSR performance, i.e., the relation between CSR performance and
financial performance.
7 In contrast, Kruger (2014) examines how investors respond to specific
CSR-related news events.
8 Our results are robust to an alternative definition of CSR performance based on
Deng et al. (2013) where the number of strengths and concerns are normalized across
each of the six CSR dimensions.
S.F. Cahan et al. / Journal of Banking & Finance 59 (2015) 409–422 411
and press coverage is related to industry (e.g., Kothari et al., 2009).9
Appendix B contains detailed definitions of all the variables.
3.2. Descriptive statistics and correlations
Table 1 summarizes the distribution of relevant variables for
the 2003–2011 period. We winsorize all the continuous variables
at 1% and 99%. Table 1 reports the descriptive statistics for variables in Eq. (1). The mean and median for Media are positive, indicating that the news media is generally positive towards our
sample firms. The mean for CSR is 0.280 which indicates that concerns outweigh strengths on average, which is consistent with
prior studies (e.g., Kim et al., 2012; Deng et al., 2013). All the other
control variables are consistent with prior studies (e.g., Gurun and
Butler, 2012).
Table 2 reports the Pearson correlation matrix for the main variables. The correlations between media favorability (Media) and CSR
performance (CSR) are significantly positive as predicted. All of the
control variables are significantly correlated with Media. The highest correlations are, not surprisingly, between MV and SP500.
However, in our models, we are not interested in interpreting
either of these coefficients directly.
3.3. Results for the effect of CSR performance on media favorability
Table 3 reports the multivariate regression result from estimating Eq. (1). Columns (1) and (2) report the main results using the
CSR rating from the current year which is typically issued in
June. The result shows that the coefficient for CSR is significant
and positive at 1% level, which indicates that firms with high CSR
performance receive more favorable news reporting. In terms of
economic magnitude, a shift from one standard deviation below
the mean of CSR to one standard deviation above the mean of
CSR is associated with media favorability that is 8% more positive
based on the mean of Media (i.e., (0.007 (1.954 (2.514))/
0.390). This suggests that firms can use a strong CSR performance
to enhance the favorability of their media coverage, although we
acknowledge that this test does not address causality, a point that
we return to later.
As discussed in Section 2.2.1, for most firms, there will be no
overlap between CSR and Media in a temporal sense. However,
for firms that have a fiscal year-end after the KLD release date
and before December 31, there could be some overlap. For example, for a firm with a September 30 year-end, for 2011, Media is
based on articles October 1, 2010-September 30, 2011 while CSR
would be based on CSR data from January 1, 2010-December 31,
2010. This could lead to a mechanical relation between CSR and
Media. We believe this possibility is remote since (i) CSR-related
news items are a fraction of the news items we consider, (ii)
CSR-related news items are only one component of the KLD rating,
and (iii) for most firms, there is no overlap between CSR and
Media.
10 Still, we re-estimate Eq. (1) using the CSR rating from the
prior year to ensure there will be no overlap between CSR and
Media for any firm. The disadvantage of this approach is that the
CSR rating may be stale. Columns (3) and (4) provide the results
using the lagged CSR value. The results are highly similar. In fact,
the coefficient for CSR is unchanged.
Turning to our control variables, we find firms with smaller
book-to-market ratios, more visible firms, firms with lower
idiosyncratic risk, firms with more favorable firm-initiated news,
and firms with more advertising expense are treated more favorably by the media. The latter finding is consistent with Gurun
and Butler (2012), and the second to last finding is consistent with
recent research on corporate relations (e.g., Solomon and Soltes,
2011; Solomon, 2012). Finally, NStor is positively and significantly
related to media favorability. Thus, the results in Table 3 indicate
that CSR is positively and significantly related to Media after controlling for the level of news coverage, i.e., number of stories.
3.4. Endogeneity
We acknowledge that endogeneity, particularly omitted variables and reverse causality, is an issue in our setting. We conduct
three additional analyses to address these concerns.
3.4.1. Instrumental variables
First, to explicitly address omitted variables as well as possible
reverse causality (where firms’ CSR is driven by media favorability), we perform a 2SLS regression analysis using the ideological
leaning of the state in which the firm is headquartered as instrumental variables for the CSR scores. Di Giuli and Kostovetsky
(2014) find that firms which are headquartered in Democratic
rather than Republican-leaning states score higher on CSR activities. While the geographical location plays a pivotal role in firms’
CSR operations, it is unlikely that location has a significant effect
on how the news media as a whole (i.e., including national and
international media) treats firms, which satisfies the exclusion
condition of instrumental variables.11 Specifically, we use two
instrumental variables. The first one is Blue, which is a dummy variable equal to 1 if a firm’s headquarter is located in a state that is classified as a blue state. The second one is Voting, which is a continuous
variable measuring the average margin of victory/defeat in the five
presidential elections between 1992 and 2008 for the Democratic
presidential candidate in the state where firm i has its headquarters.
To provide additional support for our choice of instruments in the
2SLS regression, we perform the following two tests: (1) a Cragg
and Donald (1993) instrument relevance test to confirm the relevance of the instrumental variables (i.e., high correlations between
the instrumental variables and adjusted CSR) and (2) a Sargan
(1958) overidentification test to examine the exogeneity of the
instrumental variables (i.e., no significant correlations between the
instrumental variables and the error terms in the media favorability
regression).
Table 1
Descriptive statistics.
Variable Mean Median Q1 Q3 Std. dev.
Media 0.390 0.461 0.142 0.708 0.412
CSR 0.280 1.000 2.000 1.000 2.234
NStor 36.830 27.000 18.000 42.000 40.296
ROA 0.073 0.091 0.037 0.146 0.152
BP 0.481 0.407 0.248 0.633 0.354
MV 7.099 6.888 6.001 7.999 1.525
Lev 0.190 0.154 0.003 0.301 0.194
SP500 0.199 0.000 0.000 0.000 0.399
IRisk 0.024 0.022 0.016 0.029 0.011
Ret 0.072 0.001 0.194 0.242 0.438
AdExp 0.012 0.000 0.000 0.009 0.028
CGov 0.176 0.000 1.000 0.000 0.718
FI-News 0.019 0.000 0.200 0.250 0.353
MktPE 23.193 17.715 16.198 24.153 23.103
This table reports summary descriptive statistics for all the variables used to estimate Eq. (1). See Appendix B for variable definitions. Sample size is 12,749. All
continuous variables are winsorized at 1% and 99%.
9 Although we include industry fixed effects in our main analyses, we also estimate
our regressions using an industry-adjusted CSR score and an industry-adjusted media
favorability variable to explicitly recognize that both have an industry component.
Our results are unchanged.
10 For example, 1663 of the 12,749 observations in our sample (13%) have fiscal
year-ends between July 1 and December 30.
11 Gurun and Butler (2012) find that national media slant is unrelated to a firm’
advertising expense.
412 S.F. Cahan et al. / Journal of Banking & Finance 59 (2015) 409–422
Table 4 reports the 2SLS results. As predicted, in the first stage
regression, both instrumental variables, Blue and Voting, have positive and significant coefficients at 1% and 5% levels, respectively.
The F-value for the Cragg and Donald (1993) instrument relevance
test is 13.78, rejecting the null hypothesis that the instruments are
weak and confirming the relevance of our instrumental variables.
In the second stage regression, when the media favorability is
the dependent variable and the predicted value for CSR is used as
the independent variable, we find the coefficient estimate on the
predicted value of CSR is positive and significant at the 1% level.
Further, the p-value for the Sargan (1958) overidentification test
is 0.265, suggesting that our two instrumental variables do not violate the overidentifying restriction.12
3.4.2. Propensity score matching
We further use propensity score matching to mitigate the endogeneity concern following prior literature (e.g., Gao et al., 2014). To
generate the propensity score, we construct a first-stage logistic
regression model for CSR at the firm-year level:
ProbðCSR1i;t ¼ 1Þ
¼ logitðb0 þ b1RDit þ b2Lossit þ b3Levit þ b4ROAit
þ b5Salesit þ b6Assetsit þ b7AdExpit
þ b8AvgGrowthit þ b9BPit þ b10EPit þ b11Volit
þ b12Analystit þ b13IOit þ eitÞ ð2Þ
Table 2
Pearson correlations for variables in Eq. (1).
Variable Media CSR NStor ROA BBP MV Lev SP500 IRisk Ret AdExp CGov FI-News
Media
CSR 0.153
NStor 0.239 0.347
ROA 0.071 0.089 0.019
BP 0.087 0.119 0.098 0.315
MV 0.159 0.239 0.447 0.408 0.312
Lev 0.076 0.029 0.001 0.034 0.005 0.189
SP500 0.152 0.247 0.394 0.212 0.149 0.652 0.129
IRisk 0.125 0.148 0.142 0.384 0.130 0.619 0.130 0.399
Ret 0.036 0.014 0.025 0.162 0.275 0.193 0.018 0.005 0.049
AdExp 0.124 0.112 0.009 0.122 0.082 0.049 0.012 0.083 0.017 0.015
CGov 0.045 0.031 0.121 0.031 0.027 0.289 0.092 0.168 0.126 0.021 0.022
FI-News 0.175 0.031 0.001 0.342 0.131 0.189 0.003 0.024 0.239 0.257 0.035 0.031
MktPE 0.035 0.022 0.031 0.062 0.030 0.029 0.011 0.005 0.089 0.102 0.004 0.098 0.049
This table reports Pearson correlations for variables in Eq. (1). See Appendix B for variable definitions. Bold text indicates significance at the 5% level.
Table 3
Results for Eq. (1) examining the effect of CSR performance on media favorability.
Variables (1) (2) (3) (4)
Coeff. Std. err. Coeff. Std. err.
Intercept 0.372⁄⁄ (0.162) 0.371⁄⁄ (0.223)
CSR 0.007⁄⁄⁄ (0.002) 0.007⁄⁄ (0.003)
NStor 0.001⁄⁄⁄ (0.000) 0.001⁄⁄⁄ (0.000)
ROA 0.029 (0.052) 0.052 (0.059)
BP 0.038⁄⁄ (0.019) 0.032 (0.021)
MV 0.001 (0.007) 0.005 (0.008)
Lev 0.037 (0.036) 0.043 (0.039)
SP500 0.071⁄⁄⁄ (0.021) 0.070⁄⁄⁄ (0.022)
IRisk 1.840⁄⁄⁄ (0.619) 1.783⁄⁄⁄ (0.691)
Ret 0.001 (0.008) 0.016 (0.009)
AdExp 0.668⁄⁄⁄ (0.234) 0.704⁄⁄⁄ (0.246)
CGov 0.005 (0.007) 0.005 (0.007)
FI-News 0.165⁄⁄⁄ (0.014) 0.187⁄⁄⁄ (0.016)
MktPE 0.000 (0.000) 0.000 (0.000)
Year fixed effects Yes Yes
Industry fixed effects Yes Yes
Cluster by firm Yes Yes
Adj-R2 0.189 0.207
N 12,749 10,726
This table reports the regression results for the effect of firms’ CSR on its media
favorability, i.e., Eq. (1).
Columns (1) and (2) report the results using CSRt, and columns (3) and (4) report the
results using CSRt1. See Appendix B for variable definitions. Year fixed effects and
industry fixed effects are included in the model. Robust standard errors clustered at
the firm level are used to compute t-statistics. ⁄⁄ and ⁄⁄⁄ denote significance at the
5% and 1% levels, respectively.
Table 4
Results for 2SLS test for Eq. (1) Using political leaning of home state as instruments.
Variables First stage Second stage
Coeff. Std.
err.
Coeff. Std.
err.
Intercept 5.608⁄⁄⁄ (0.882) 1.091⁄⁄⁄ (0.223)
Blue 0.305⁄⁄⁄ (0.048)
Voting 0.282⁄⁄ (0.116)
^CSR 0.116⁄⁄⁄ (0.023)
NStor 0.012⁄⁄⁄ (0.001) 0.000 (0.000)
ROA 0.449⁄⁄⁄ (0.115) 0.057⁄ (0.035)
BP 0.013 (0.057) 0.034⁄⁄ (0.013)
MV 0.309⁄⁄⁄ (0.024) 0.032⁄⁄⁄ (0.008)
Lev 0.081 (0.089) 0.012 (0.022)
SP500 0.784⁄⁄⁄ (0.074) 0.018 (0.023)
IRisk 0.806 (2.124) 1.962⁄⁄⁄ (0.518)
Ret 0.194⁄⁄⁄ (0.039) 0.022⁄⁄ (0.010)
AdExp 6.466⁄⁄⁄ (0.708) 0.047 (0.216)
CGov 0.513⁄⁄⁄ (0.033) 0.061⁄⁄⁄ (0.013)
FI-News 0.172⁄⁄⁄ (0.046) 0.182⁄⁄⁄ (0.012)
MktPE 0.004⁄⁄⁄ (0.001) 0.001⁄⁄⁄ (0.000)
Year fixed effects Yes Yes
Industry fixed effects Yes Yes
First-stage weak instrument test
(F-value)
13.78
Overidentification test (p-value) 0.265
Adj-R2 0.283 0.185
N 12,749 12,749
This table reports 2SLS regression results for Eq. (1).
In the first stage, CSR is the dependent variable, and Blue and Voting are instruments.
In the second stage, Media is the dependent variable, and the predicted value of CSR
(^CSR) is used in place of CSR. See Appendix B for variable definitions. Year fixed
effects and industry fixed effects are included in the models. Robust standard errors
clustered at the firm level are used to compute t-statistics. ⁄
, ⁄⁄, and ⁄⁄⁄ denote
significance at the 10%, 5%, and 1% levels, respectively.
12 One might argue that most national media are located in blue states. If the
national media in these states exhibit local bias favoring local firms, the exclusion
condition may not be satisfied. To address this concern, we repeated our instrumental
variable analysis after deleting firms located in California and New York, the two most
populous blue states. The results are nearly identical. For example, the coefficient on
the instrumented CSR when firms in California and New York are omitted is 0.133
compared to 0.116 reported in Table 4. Both coefficients are highly significant at the
1% level.
S.F. Cahan et al. / Journal of Banking & Finance 59 (2015) 409–422 413
We refer to firms with positive CSR scores as CSR-conscious
firms and define CSR1 equal to 1 for firm-year observations that
have a positive CSR score in year t and 0 otherwise, following
Gao et al. (2014). We include several control variables which are
found to be correlated with firms CSR performance by previous literature (e.g., Dhaliwal et al., 2012; Lys et al., 2015; Di Giuli and
Kostovetsky, 2014; Gao et al., 2014). In particular, we control for
research and development expense (RD), loss (Loss), leverage
(Lev), return on assets (ROA), sales (Sales), total assets (Assets),
firm’s advertising expenditure (AdExp), weighted average sales
growth over the past five years (AvgGrowth), book-to-market ratio
(BP), and earnings to book ratio (EP), the variance of daily stock
returns (Vol), number of analyst following (Analyst), and institutional ownership (IO). Finally, we control for industry and year
fixed effects.
Table 5 presents the propensity score matching results. Panel A
shows the first-stage logistic regression results. The regression is
estimated using 12,221 firm-year observations, among which
3897 firm-year observations are classified as CSR-conscious firms.
We find that eight out of our thirteen controlling variables are significantly related to the CSR performance and the pseudo-R2 in our
estimation is 24.6%, which is fairly high compared to Gao et al.
(2014). All the significant control variables are consistent with
our expected signs. After estimating the above logistic model, we
calculate the propensity score using the predicted probabilities
and match our treatment sample (CSR-conscious firms) to the control sample with the closest propensity score, given the distance of
the closet match is within 0.1. This procedure results in 3374
matched firms (i.e., about 92.4% of the CSR-conscious firm-year
observations are matched). The untabulated parametric t-test
and non-parametric Kolmogorov–Smirnov test show that the
mean of the propensity score in our match sample (0.375) is not
statistically different from the one in our treatment sample
(0.369). We also examine whether the independent variables used
in the first-stage prediction model are different between the treatment sample and the control sample. Untabulated results show
there are no significant differences.
After confirming our treatment sample is similar to our
matched sample except for their actual CSR performances, we then
compare the media favorability between the two samples. Panel B
of Table 5 shows the results. Both the mean and median of Media in
the treatment group (mean = 0.487, median = 0.583) are significantly higher than the matched group (mean = 0.399, median = 0.471) at the 1% level, consistent with our main findings.
3.4.3. Quasi-natural experiment
We also conduct a quasi-natural experiment utilizing a group of
firms that were forced to improve their performances in the area of
employee relations. On September 25, 2012, President Obama
signed an executive order to prohibit human trafficking-related
activities that applies to all federal contractors and subcontractors.
This executive order requires compliance measures for government contracts and subcontracts and provides federal agencies
with additional tools to foster compliance. We expect that the
executive order, which forced the federal contractors and subcontractors to mandatorily improve their performances in the
employee relations area, would have a positive impact on the
CSR performance of firms affected by the executive order; on the
other hand, the executive order would have had no direct effect
on overall media favorability of these firms.13
Using a difference-in-difference research design, we test
whether the media image for firms affected by the executive order
increases after the executive order was implemented. Specifically,
we identify 713 firm-year observations in the treatment sample
during the period between 2010 and 2013 where the firm’s major
customer is the US government. As the US government is their
major customer, they are required to follow the executive order
starting from year 2012. We then form a one-to-one matched sample by choosing the firm that had the closest market capitalization
in the same 2-digit SIC industry but did not have the US government as its major customer. We estimate the following regression
model:
Mediait ¼ b0 þ b1Treatmentit Postit þ b2Treatmentit
þ b3Postit þ b4NStorit þ b5ROAit þ b6BPit þ b7MVit
þ b8Levit þ b9SP500it þ b10IRiskit þ b11Retit
þ b12AdExpit þ b13CGovit þ b14FI-Newsit
þ b15MktPEit þ eit ð3Þ
We define two dummy variables in the model. Post equals to 1 if
year is 2012 or 2013 and 0 otherwise. Treatment equals to 1 for the
firm-year observations whose major customer is the US government. The coefficient, b1, of the interaction item between these
two dummy variables is our interest.
Table 6 tabulates the results of the estimation of Eq. (3). We find
that the interaction item is significantly positive at the 5% level,
indicating that the treatment firms, which were forced to improve
their employee relations after 2012, received more favorable media
reporting compared to the control group in the post-executive
order period. It is worth mentioning that the coefficient on
Treatment is found to be insignificant, meaning that there is no significant difference between our treatment sample and matched
sample in terms of media favorability before the regulation (i.e.,
2010 and 2011). The coefficient on Post is also found to be
Table 5
Results for Eq. (2) using a propensity score matched sample.
Variables Coeff. Std. err.
Panel A: Logistic regression to CSR
Intercept 5.378⁄⁄⁄ (0.662)
RD 0.000⁄⁄ (0.000)
Loss 0.088 (0.070)
Lev 0.813⁄⁄⁄ (0.199)
ROA 0.689⁄⁄ (0.346)
Sales 0.037 (0.053)
Assets 0.427⁄⁄⁄ (0.058)
AdExp 1.445⁄ (0.814)
AvgGrowth 0.004⁄ (0.003)
BM 0.003 (0.006)
EP 0.027⁄⁄ (0.013)
Vol 0.001 (0.011)
Analyst 0.343⁄⁄⁄ (0.054)
IO 0.050 (0.141)
Year fixed effects Yes
Industry fixed effects Yes
Cluster by firm Yes
Pseudo-R2 0.246
N 12,221
N of CSR1 = 1 3897
N Mean Median
Panel B: Propensity score matching results
CSR-conscious firms 3654 0.487 0.583
Matched firms 3378 0.399 0.471
Difference 0.001 0.000
This table reports results of propensity score matching. Panel A reports the
regression results for the first-stage CSR determinant model, i.e., Eq. (2).
See Appendix B for variable definitions. Year fixed effects and industry fixed effects
are included in the model. Robust standard errors clustered at the firm level are
used to compute t-statistics. Panel B reports the mean and median of media
favorability for CSR-conscious firms vs. propensity-matched non-CSR-conscious
firms. ⁄
, ⁄⁄, and ⁄⁄⁄ denote significance at the 10%, 5%, and 1% levels, respectively.
13 The t-test shows the mean of CSR performance for our treatment sample
increases by 0.415 after 2012, and it is significant at the 5% level.
414 S.F. Cahan et al. / Journal of Banking & Finance 59 (2015) 409–422
insignificant, indicating that the control firms’ media favorability
does not change after 2012.
Together, the results in Tables 4–6 address concerns about
endogeneity and provide support for a causal relationship where
CSR performance influences a firm’s media favorability.
3.5. Alternative measures of media favorability
As our objective is to examine the effect of CSR on the firm’s
overall media image, we use all articles about the firm to compute
Media. This includes CSR and non-CSR related articles. Based on
prior research which examines only CSR-related news, we expect
the number of CSR-related articles to be small relative to the
non-CSR related articles.14 However, even if the number of
CSR-related news items is small, it is possible that they may be driving media favorability. For example, if all non-CSR news is neutral
and CSR-related articles are positive, overall media favorability
would be positive, although in terms of the magnitude the effect
of the CSR-related articles would be small since Media is scaled by
the total number of articles in a year for that firm.15
To address this concern, we compute two alternative measures
that exclude CSR-related articles. However, identifying
‘‘CSR-related’’ articles is subjective. We use two definitions. First,
Reuters provides a topic code for every article. We identify eight
topic codes16 that broadly relate to CSR issues: crime (Reuters code
CRIM), disasters/accidents (DIS), environment/nature (ENV), genetically modified (GMO), health/medicines (HEA), labor/employment
(JOB), and judicial (JUDIC). Based on these codes, 51,083 of the
469,550 articles in our sample (10.9%) are classified as CSR-related.
Second, we identify 67 category labels based on definitions used
by KLD. These labels are more specific that the Reuters code, e.g.,
‘charity’, ‘clean energy’, ‘employee benefits’, ‘support for education’, and ‘workplace safety’. We search all articles and classify
an article as CSR-related if one or more of the 67 category labels
appear in the headline of the article. Using this approach, we classify 8834 of the 469,550 articles (1.9%) as CSR-related. Thus, we
view the Reuters-based approach as providing an upper bound of
CSR-related articles while the KLD-based approach provides a
lower bound.
We re-estimate Eq. (1) after deleting the CSR-related articles.
When the Reuters-based CSR articles are omitted, the coefficient
for CSR is 0.006 (t-stat. = 2.29, p-value = 0.02). When the
KLD-based CSR articles are omitted, the coefficient for CSR is
0.006 (t-stat. = 2.37, p-value = 0.018). To summarize, our results
do not change if we exclude the CSR related articles from our
sample.
A related concern is that if firms have a greater propensity to
disclose positive CSR performance than negative CSR performance,
the media’s coverage could be overly optimistic. However, we do
not expect this to bias our tests in favor of finding a result for
two reasons. First, the firm’s own disclosures are not the media’s
only source of information about CSR performance. For example,
the media can access reports and data from governmental and
industry sources, talk to activist groups and other engaged stakeholders (e.g., community leaders, union officials, employees, charities), and observe actual activity (e.g., an oil spill and the firm’s
clean-up efforts). Second, if the media’s coverage is asymmetrically
positive, it would reduce the correlation between actual CSR performance and media coverage, biasing the tests against finding a
significant result. One implication from the asymmetrical reportage of positive and negative events is that firms with the highest
CSR performance should have greater media favorability.
Consequently, we divide our sample into quartiles based on CSR
performance and run results separately for subsets of firms in
the top and bottom quartiles.17 We find that the positive relation
between Media and CSR is concentrated among the firms with the
highest values for CSR performance as CSR is positive and significant
for the top quartile but not the bottom quartile. This provides further
support for the notion that firms actively manage CSR since it is positive events that they are most likely to actively manage.
4. Part 2: Evidence on active media management
As documented in the previous section, our evidence supports a
causal relation between firms’ CSR performance and media image.
However, it is possible that firms do not actively manage their CSR
to improve their media image; instead, they may invest in CSR for
other reasons (e.g., altruism), and favorable media coverage may be
an attractive side-benefit. We next consider whether managers
proactively engage in media management. We provide three tests
for different settings where firms have different incentives to manage media image.
4.1. Sin vs. non-sin industries
First, we consider ‘sin’ industries (i.e., alcohol, gambling, and
tobacco). Hong and Kacperczyk (2009) argue firms in sin industries
are viewed negatively by the public because they can be addictive
and have undesirable social consequences when consumed excessively. For example, in the US, the tobacco industry has had to
Table 6
Results for Eq. (3) using quasi natural experiment based on presidential executive
order on human-trafficking.
Variables Coeff. Std. err.
Intercept 0.345⁄⁄⁄ (0.080)
Treatment Post 0.021⁄⁄ (0.010)
Treatment 0.002 (0.022)
Post 0.004 (0.009)
NStor 0.000⁄⁄ (0.000)
ROA 0.076⁄ (0.044)
BP 0.007 (0.048)
MV 0.012 (0.016)
Lev 0.006 (0.067)
SP500 0.044⁄ (0.025)
IRisk 3.623⁄ (2.005)
Ret 0.030 (0.042)
AdExp 0.925⁄⁄ (0.423)
CGov 0.009 (0.025)
FI-News 0.047 (0.049)
MktPE 0.001⁄ (0.000)
Industry fixed effects Yes
Cluster by firm Yes
Adj-R2 0.275
N 1426
This table reports the regression results for the effect of executive order signed by
President Obama in Sep 2012 on the affected firms’ media favorability, i.e., Eq. (3).
See Appendix B for variable definitions. Industry fixed effects are included in the
model. Robust standard errors clustered at the firm level are used to compute tstatistics. ⁄
, ⁄⁄, and ⁄⁄⁄ denote significance at the 10%, 5%, and 1% level, respectively.
14 For example, Kruger (2014) examines CSR-related news events over a seven-year
period. He identifies 2116 such events. By contrast, our measure of media favorability
is based on 469,550 news articles over a nine-year period.
15 We thank an anonymous reviewer for raising this issue. 16 There are well over 1000 topic codes in TRNA database and a news story usually
has multiple topic codes assigned to it. Therefore, if an article has one of the
CSR-related news codes but one or more non-CSR news codes, the article may actually
focus on the non-CSR topic(s) and the CSR portion could be relatively small. Thus, our
classification approach is extremely conservative as we classify an article as
CSR-related even if CSR is the focus of only a small part of the article. 17 We thank an anonymous reviewer for suggesting this analysis.
S.F. Cahan et al. / Journal of Banking & Finance 59 (2015) 409–422 415
include health warnings on cigarette packages since 1965, and
broadcast advertising of tobacco products has been banned since
1971. Hong and Kacperczyk (2009) find that a broad set of institutions that are constrained by social norms (e.g., pensions, universities) invest less in sin industries.
Given a negative public image, we expect that firms in sin
industries would have more incentives to engage in CSR to improve
their media image. We follow Hong and Kacperczyk (2009) classification scheme to identify firms in sin industries. Sin firms include
the following: (1) firms with SIC codes 2100–2199 which are beer
and liquor producers, (2) firms with SIC codes 2080–2085 which
are tobacco firms, and (3) firms with NAICS codes 7132, 71312,
713210, 71329, 713290, 72112, and 721120 which are gambling
firms. We create an indicator variable, Sin, that is equal to 1 for
firms that are members of a sin industry, and 0 otherwise, and
we estimate the following model:
Mediait ¼ b0 þ b1CSRit Sinit þ b2CSRit þ b3Sinit þ b4NStorit
þ b5ROAit þ b6BPit þ b7MVit þ b8Levit þ b9SP500it
þ b10IRiskit þ b11Retit þ b12AdExpit þ b13CGovit
þ b14FI-Newsit þ b15MktPEit þ eit ð4Þ
In Eq. (4), the interaction, CSR Sin, represents the incremental
effect in CSR-Media relation for sin firms relative to non-sin firms.
We expect that b1 will be positive and significant if firms in sin
industries are more likely to engage in CSR in order to manage
their media image.
Table 7 contains the results for this analysis. CSR remains significant with a coefficient of 0.007. More importantly, the interaction
variable, CSR Sin, has a positive and significant coefficient of
0.023. This indicates that the impact of a unit increase in CSR is
4.29 times (0.030/0.007) higher if the firm is in a sin industry, consistent with these firms undertaking activities that will improve
their media image. Of course, this does not mean sin firms have
better media images. The coefficient of Sin is 0.130 and is significant, indicating that sin firms have much less favorable media
images on average.
4.2. Impact of investor sentiment
We examine whether our findings vary with the level of
market-wide investor sentiment. Investor sentiment is attractive
because it is exogenous to the firm and because it can affect investor behavior. For example, Baker and Stein (2004) find that investor
sentiment affects trading activity, while Shiller (2000) and
Nofsinger (2005) find investor sentiment affects expectations
about future firm performance. Mian and Sankaraguruswamy
(2012) provide evidence that investor sentiment is associated with
the stock market’s response to unexpected earnings around the
earnings announcement date, leading investors to overreact to
bad earnings news during low sentiment periods. Not surprisingly,
evidence suggests that firms take action to counteract investors’
pessimistic outlook. For example, Bergman and Roychowdhury
(2008) find that walk-up forecasts are more frequent during periods of low investor sentiment, consistent with managers correcting
investors’ overly low expectations. Thus, we expect that all firms
would have more incentives to increase media favorability when
the prevailing investor sentiment is more pessimistic.
We use two market sentiment proxies. Following Conrad et al.
(2002), MktPE is the value-weighted average of market priceto-earnings ratio of all the firms in the merged CRSP-Compustat
database in year t (MktPE is used as a control variable in our earlier
tests). BW is the average monthly Baker-Wurgler Sentiment Index
(Baker and Wurgler, 2006).18 We interact both measures with CSR
and rerun our main regression.
Table 8 provides the estimation results. When we use MktPE as
a proxy for market sentiment, we find that the interaction item
CSR Sentiment is significantly negative at the 5% level. When
we use BW as a proxy for market sentiment, we also find that
the interaction item CSR Sentiment is significantly negative at
5%.19 These two results mean that during periods when the
market-level investor sentiment is less favorable, firms’ CSR performance is more correlated with media favorability. For example, for a
firm at the 75th percentile of CSR (a firm with superior CSR performance), a move from one standard deviation below the mean of
BW to one standard deviation above the mean of BW decreases the
effect of CSR performance on media favorability by 2.37 times compared to when sentiment is neutral. This supports our hypothesis
that when market-level investor sentiment is pessimistic, firms have
more incentives to perform well in the CSR area to improve their
media image.
4.3. Change around SEO offering
Next, we conjecture that one important incentive for firms to
use media management is to lower their financing costs. Prior
studies provide evidence that firms use various mechanisms to
lower their financing cost around SEOs (e.g., Teoh et al., 1998;
Lang and Lundholm, 2000; Kim and Park, 2005; Shroff et al.,
2013), and media-related studies show that the tone of news stories can affect firms’ cost of capital (e.g., Kothari et al., 2009). As
such, we expect that firms with poor media images prior to an
SEO would have incentives to engage in active media management
in order to maximize the proceeds they obtain from the offering.
We use all SEO announcements between 2005 and 2011.
Because Media has an upper bound, firms that already have high
media favorability have less ability (and incentives) to further
improve their media image. Accordingly, we divide the sample
based on the median media favorability two years before the SEO
Table 7
Results for Eq. (4) examining the effect of CSR performance and sin industry
membership on media favorability.
Variables Coeff. Std. err.
Intercept 0.379⁄⁄ (0.165)
CSR Sin 0.023⁄⁄ (0.011)
CSR 0.007⁄⁄⁄ (0.003)
Sin 0.130⁄⁄ (0.059)
NStor 0.001⁄⁄⁄ (0.000)
ROA 0.028 (0.052)
BP 0.040⁄⁄ (0.020)
MV 0.001 (0.007)
Lev 0.037 (0.036)
SP500 0.071⁄⁄⁄ (0.021)
IRisk 1.804⁄⁄⁄ (0.615)
Ret 0.002 (0.008)
AdExp 0.712⁄⁄⁄ (0.236)
CGov 0.006 (0.008)
FI-News 0.165⁄⁄⁄ (0.014)
MktPE 0.000 (0.000)
Year fixed effects Yes
Industry fixed effects Yes
Cluster by firm Yes
Adj-R2 0.194
N 12,749
This table report the regression results for the sin industry effect on the association
between firms’ CSR and its media favorability, i.e. Eq. (4).
See Appendix B for variable definitions. Year fixed effects and industry fixed effects
are included in the model. Robust standard errors clustered at the firm level are
used to compute t-statistics. ⁄
, ⁄⁄, and ⁄⁄⁄ denote significance at the 10, 5, and 1%
levels, respectively.
18 We thank Jeffrey Wurgler for making the data available on his website,
http://pages.stern.nyu.edu/ ~jwurgler/. 19 The sample size for the market-wide investor sentiment measure developed by
Baker and Wurgler is smaller as their measure ends in 2010.
416 S.F. Cahan et al. / Journal of Banking & Finance 59 (2015) 409–422
announcement (year T-2) and conduct our tests using firms in the
lower half. The final sample consists of 1100 observations. Since
more positive media reports can lower financing costs (e.g.,
Kothari et al., 2009), we expect that firms will use CSR to improve
their media image leading up to SEO.
To examine this possibility, we estimate the following regression model using the observations from year T-2 to year T:
CSRit ¼ b0 þ b1Post1it þ b2RDit þ b3Lossit þ b4Levit þ b5ROAit
þ b6Salesit þ b7Assetsit þ b8AdExpit þ b9AvgGrowthit
þ b10BPit þ b11EPit þ b12Volit þ b13Analystit þ b14IOit þ eit
ð5Þ
We define a dummy variable Post1 equal to 1 if the firm-year
observation falls into the period between T-1 and T year, and 0 if
it belongs to T-2 year. We expect the coefficient of Post1 to be significantly positive. The control variables are identical to Eq. (2).
Table 9 presents the estimation results. The coefficient of Post1
variable is significantly positive at 1% level, consistent with these
firms increasing their CSR performance prior to the SEO to lower
their financing costs.
In sum, the three tests in Section 4 provide support for active
media management.
5. Part 3: Economic benefits of media management
Our tests assume that firms would have an economic motivation to manage their media image. That is, firms that make a positive effort in the CSR arena and that receive favorable media
coverage realize economic benefits. Consequently, to provide evidence on this underlying assumption, we examine the effect of
the interaction between CSR performance and media favorability
on the firm’s value and cost of capital.
5.1. Models and variables
To measure firm value, we calculate TobinQi,t as the log of
market-to-book ratio for firm i at the end of year t. We also
calculate four measures of firms’ implied cost of capital based on
the prior literature. The first measure (COCOJ) is developed by
Ohlson and Juettner-Nauroth (2005). Our second measure is developed by Easton (2004). Our third measure is from Gebhardt et al.
(2001), and our fourth measure is based on Claus and Thomas
(2001). The control variables are from Dhaliwal et al. (2005),
Naiker et al. (2013), Gurun and Butler (2012).
Following Dhaliwal et al. (2011), we focus on the economic benefits accruing to firms that had favorable media coverage and high
CSR performance. In other words, we consider whether the economic benefits are greater for firms that have a good media image
and that image is more credible (i.e., supported by superior CSR
performance). Specifically, we estimate:
TobinQit ¼ b0 þ b1CSRit Mediait H-Hit þ b2CSRit Mediait
þ b3Mediait H-Hit þ b4CSRit H-Hit þ b5H-Hit
þ b6Mediait þ b7CSRit þ b8FI-Newsit þ b9CGovit
þ b10RD1it þ b11CAPXit þ b12IOit þ b13SGrowthit
þ b14ROAit þ b15MVit þ b16Levit þ b17Analystsit
þ b18IRiskit þ b19Retit þ b20AdExpit þ eit ð6Þ
or
COCit ¼ b0 þ b1CSRit Mediait H-Hit þ b2CSRit Mediait
þ b3Mediait H-Hit þ b4CSRit H-Hit þ b5H-Hit
þ b6Mediait þ b7CSRit þ b8FI-Newsit þ b9CGovit
þ b10I-COCit þ b11BPit þ b12Disperseit þ b13MVit þ b14LTGit
þ b15Beta SMBit þ b16Beta Mktit þ b17Beta-HMLit þ eit
ð7Þ
where H-H equals 1 if firm i receives more favorable media reporting and has strong CSR performance in year t, and 0 otherwise. We
define high media favorability as firm-year observations where
Media exceeds the sample median and the high CSR performance
as firm-year observations where CSR is above the sample median.
Thus, the interactions with H-H allow us to estimate separate coefficients for the high favorability/high CSR performing firms. All
Table 8
Results for regressions examining the effect of CSR performance and investor
sentiment on media favorability.
Variables Sentiment = MktPE Sentiment = BW
Coeff. Std. err. Coeff. Std. err.
Intercept 0.371⁄⁄ (0.163) 0.378⁄⁄ (0.149)
CSR Sentiment 0.000⁄⁄ (0.000) 0.012⁄⁄ (0.005)
CSR 0.009⁄⁄⁄ (0.003) 0.006⁄⁄ (0.002)
Sentiment 0.000 (0.000) 0.016 (0.081)
NStor 0.001⁄⁄⁄ (0.000) 0.001⁄⁄⁄ (0.000)
ROA 0.030 (0.052) 0.048 (0.055)
BP 0.038⁄ (0.019) 0.054⁄⁄ (0.021)
MV 0.001 (0.007) 0.005 (0.011)
Lev 0.037 (0.037) 0.039 (0.039)
SP500 0.071⁄⁄⁄ (0.021) 0.070⁄⁄⁄ (0.021)
IRisk 1.831⁄⁄⁄ (0.619) 2.000⁄⁄⁄ (0.634)
Ret 0.002 (0.008) 0.002 (0.009)
AdExp 0.668⁄⁄⁄ (0.234) 0.697⁄⁄⁄ (0.246)
CGov 0.005 (0.007) 0.005 (0.008)
FI-News 0.165⁄⁄⁄ (0.014) 0.166⁄⁄⁄ (0.014)
Year fixed effects Yes Yes
Industry fixed effects Yes Yes
Cluster by firm Yes Yes
Adj-R2 0.190 0.280
N 12,749 11,280
This table report the regression results for the effect of market sentiment on the
association between firms’ CSR and its media favorability. See Appendix B for
variable definitions. Year fixed effects and industry fixed effects are included in the
model. Robust standard errors clustered at the firm level are used to compute tstatistics. ⁄
, ⁄⁄, and ⁄⁄⁄ denote significance at the 10, 5, and 1% levels, respectively.
Table 9
Results for Eq. (5) examining CSR performance prior to seasoned equity offerings.
Variables Coeff. Std. err.
Intercept 1.884 (1.093)
Post1 0.200⁄⁄⁄ (0.073)
RD 0.010⁄ (0.006)
Loss 0.341 (0.355)
Lev 0.134 (0.605)
ROA 0.456 (0.848)
Sales 0.219⁄ (0.132)
Assets 0.165 (0.157)
AdExp 9.574⁄ (5.796)
AvgGrowth 0.166 (0.265)
BM 0.736⁄⁄ (0.283)
EP 1.389⁄⁄⁄ (0.423)
Vol 0.046 (0.394)
Analyst 0.708⁄⁄⁄ (0.221)
IO 0.327 (0.471)
Year fixed effects Yes
Industry fixed effects Yes
Cluster by firm Yes
Adj-R2 0.321
N 1100
This table reports the regression results for the CSR performance leading up to an
SEO, i.e., Eq. (5).
See Appendix B for variable definitions. Year fixed effects and industry fixed effects
are included in the models. Robust standard errors clustered at the firm level are
used to compute t-statistics. ⁄
, ⁄⁄, and ⁄⁄⁄ denote significance at the 10%, 5%, and 1%
percent level, respectively.
S.F. Cahan et al. / Journal of Banking & Finance 59 (2015) 409–422 417
other variables are defined above. We expect b1 to be positive (negative) and significant if firms with good CSR and favorable media
coverage realize a higher firm valuation (lower cost of capital).
5.2. Results for the economic benefits of media management
Table 10 reports the regression results from estimating Eq. (6)
that considers the joint effect of media favorability and firms’
CSR performance on firm value. Before estimating Eq. (6), we estimate two reduced models. In the first reduced model, we regress
TobinQ on CSR and the control variables, i.e., we exclude Media
and H-H and the interactions between those variables and CSR.
We use this as a baseline model because an extensive body of
research examines the effect of CSR on firm attributes, such as firm
value, with mixed results (e.g., see Margolis et al., 2009, for a
review). In the second reduced model, we add Media and
CSR Media to the model, but exclude H-H and the interactions
with H-H. In this way, we are able to link Eq. (6) to the prior
research and can assess whether it is likely that the omission of
media-related variables impacts the results of those earlier studies.
In columns (1) and (2) which reports the results for the first
reduced model, we find an insignificant coefficient for CSR, indicating that better CSR performance does not increase firm value.
Further, in the second reduced model, shown in columns (3) and
(4), we find insignificant coefficients for CSR, Media, and the interaction CSR Media, indicating that CSR and media favorability do
not individually or jointly affect firm value. However, when Eq.
(6) is estimated, columns (5) and (6) show that the coefficient for
the three-way interaction, CSR Media H-H, is positive and statistically significant (coefficient = 0.086, p-value < 0.05), indicating
that, for firms with superior CSR performance and superior media
favorability (i.e., when H-H = 1), better CSR performance and more
positive media favorability are jointly associated with a higher firm
value. In other words, superior CSR performance by itself is insufficient to generate a lower cost of capital. Instead, superior CSR
performance is rewarded only if it is supported by favorable media
coverage, which is consistent with favorable media coverage
enhancing the overall reputation of good CSR firms.
We assess the incremental economic effect of CSR Media
H-H by considering how a shift from below the mean of Media to
one standard deviation above the mean of Media affects firm value
at the 75th percentile of CSR (i.e., a firm with superior CSR performance). We find such a shift is associated with an increase in
Tobin’s Q of 6.79% (7.15%) based on the mean (median) of
Tobin’s Q. Thus, the incremental economic effect of CSR Media
H-H on Tobin’s Q is economically significant.
Table 11 reports the results for Eq. (7) which uses cost of capital
as the dependent variable. We only tabulate the results for the
measure COCOJ as we obtain similar results for the other three cost
of capital measures. Similar to Table 10, we estimate two reduced
models before estimating the full model. In columns (1) and (2), we
find no evidence that CSR affects cost of capital. In columns (3) and
(4), when we include CSR, Media, and the interaction CSR Media,
we find that their coefficients are not significant. On the other
hand, in columns (5) and (6), when Eq. (7) is estimated, the coefficient for CSR Media H-H is negative and significant (coefficient = 0.011, p-value < 0.05), indicating that cost of capital is
lower for firms that have both better CSR performance and higher
media favorability scores. In terms of economic significance, for a
firm at the 75th percentile of CSR, a shift from one standard deviation below the mean of Media to one standard deviation above
the mean of Media is associated with an average decrease in cost
of capital of 97.5 basis points across our four cost of capital measures. Based on the mean (median) of the four measures, this is
equivalent to a 9.39% (10.59%) reduction in cost of capital, which
is economically significant.
To summarize, Tables 10 and 11 provide consistent results that
CSR performance and media favorability can jointly lower a firm’s
cost of capital and increase its firm value when its CSR performance is relatively good and its media image is relatively
Table 10
Results for Eq. (6) Examining the Effect of Media Favorability and CSR Performance on Tobin’s Q.
Variables (1) (2) (3) (4) (5) (6)
Coeff. Std. err. Coeff. Std. err. Coeff. Std. err.
Intercept 0.705⁄⁄⁄ (0.187) 0.716⁄⁄⁄ (0.187) 0.721⁄⁄⁄ (0.194)
CSR Media H-H 0.086⁄⁄ (0.041)
CSR Media 0.008 (0.014) 0.045 (0.039)
Media H-H 0.032 (0.119)
CSR H-H 0.077⁄⁄ (0.033)
H-H 0.025 (0.079)
Media 0.001 (0.030) 0.013 (0.029)
CSR 0.007 (0.006) 0.011 (0.010) 0.005 (0.009)
FI-News 0.057⁄⁄ (0.023) 0.054⁄⁄ (0.024) 0.054⁄⁄ (0.023)
CGov 0.052⁄⁄⁄ (0.015) 0.051⁄⁄⁄ (0.015) 0.053⁄⁄⁄ (0.015)
RD1 0.097⁄⁄⁄ (0.016) 0.113⁄⁄⁄ (0.018) 0.095⁄⁄⁄ (0.016)
CapX 0.255⁄⁄ (0.113) 0.267⁄ (0.154) 0.254⁄⁄ (0.113)
IO 0.207⁄⁄⁄ (0.063) 0.198⁄⁄⁄ (0.063) 0.212⁄⁄⁄ (0.063)
SGrowth 0.263⁄⁄⁄ (0.029) 0.249⁄⁄⁄ (0.027) 0.260⁄⁄⁄ (0.028)
ROA 0.375⁄⁄ (0.151) 0.457⁄⁄⁄ (0.157) 0.377⁄⁄ (0.151)
MV 0.112⁄⁄⁄ (0.015) 0.122⁄⁄⁄ (0.021) 0.122⁄⁄⁄ (0.021)
Lev 0.347⁄⁄⁄ (0.086) 0.332⁄⁄⁄ (0.086) 0.351⁄⁄⁄ (0.085)
Analysts 0.102⁄⁄⁄ (0.026) 0.102⁄⁄⁄ (0.026) 0.099⁄⁄⁄ (0.025)
IRisk 3.765⁄⁄⁄ (1.381) 4.122⁄⁄⁄ (1.394) 3.863⁄⁄⁄ (1.375)
Ret 0.401⁄⁄⁄ (0.017) 0.401⁄⁄⁄ (0.017) 0.399⁄⁄⁄ (0.017)
AdExp 0.049⁄⁄⁄ (0.018) 0.053⁄⁄⁄ (0.016) 0.053⁄⁄⁄ (0.015)
Year fixed effects Yes Yes Yes
Industry fixed effects Yes Yes Yes
Cluster by firm Yes Yes Yes
Adj-R2 0.367 0.367 0.369
N 12,332 12,332 12,332
This table reports the regression results for the joint effect of superior CSR performance and high media favorability on firm value, i.e., Eq. (6).
Results for the full model are reported in columns (5) and (6). Columns (1)–(4) report results for two reduced models that exclude the media-related variables. See Appendix B
for variable definitions. Year fixed effects and industry fixed effects are included in the models. Robust standard errors clustered at the firm level are used to compute tstatistics. ⁄
, ⁄⁄, and ⁄⁄⁄ denote significance at the 10%, 5%, and 1% levels, respectively.
418 S.F. Cahan et al. / Journal of Banking & Finance 59 (2015) 409–422
favorable. Said differently, a firm with good CSR performance cannot realize economic benefits unless its overall media image is positive. This suggests that good CSR firms rely on the media in
building its reputation, consistent with Fombrun and Shanley’s
(1990) reputation-building model. Indeed, our results suggest that
a superior media image may be a missing link in prior studies that
find insignificant or mixed results for the effect of CSR performance
on firm attributes such as firm value.
6. Conclusion
We examine whether firms manage the favorability of their
news coverage through better CSR performance. We use data from
TRNA where news items are rated based on their positive or negative tone and data on CSR performance from KLD.
In part 1 of the study, we find that firms that perform better in
CSR areas are viewed more favorably in the media. In terms of the
economic consequences, a shift from one standard deviation below
to one standard deviation above the mean of CSR performances
increases media favorability by 8%. These results are robust after
controlling for the endogeneity issues and eliminating any temporal overlap between our media favorability and CSR measures.
Results from a quasi-natural experiment support a causal relationship from CSR performance to media favorability. Thus, we provide
support for Mullainathan and Schleifer’s (2005) catering theory of
the media where slants its news coverage to coincide with the
beliefs and preferences of its readers. In our setting, the public’s
preference for socially responsible business practices provides
the media with incentives to provide more favorable coverage of
good performing CSR firms. Our empirical results support El
Ghoul et al.’s (2011, 2390) conjecture that the media spends more
time ‘‘analyzing and reporting news about ‘good’ CSR firms’’.
In part 2 of the study, consistent with the notion that managers
actively manage their CSR activity to affect their media image, we
find our results are more pronounced in firms in sin industries and
when market-wide investor sentiment is more negative. For
example, the effect of CSR performance on media favorability is
4.29 times greater for sin firms compared to non-sin firms, suggesting that sin firms have more incentives to manage their CSR
performance to improve their media image. Similarly, for a firm
at the 75th percentile of CSR performance, a move from one standard deviation below to one standard deviation above the mean of
investor sentiment decreases the effect of CSR performance on
media favorability by 2.37 times compared to when investor sentiment is neutral. Further, we document a significant increase in CSR
performance in the two-year period prior to a SEO for a sample of
low media favorability firms, suggesting that these firms use CSR
performance to improve their media favorability and lower their
financing costs.
In part 3 of the study, we show that firms with good CSR performance only realize a higher firm value or lower cost of capital if
they also have favorable media coverage, consistent with these
firms benefiting from the positive tone in the media’s general
reportage about the firm. We show that for a firm at the 75th percentile of CSR performance, a shift from one standard deviation
below to one standard deviation above the mean of media favorability increases Tobin’s Q by 6.79% and decreases cost of capital by
9.39%. These findings suggest that the media can play an active role
in the reputation building process. Further, our analyses that link
our models that incorporate media favorability to reduced models
that exclude media favorability suggest that a firm’s overall media
image is a missing link in prior studies that examine the effect of
CSR performance on firm attributes such as firm value.
Our study contributes to the literature in two important ways.
First, we extend the growing literature on the business press.
While prior research on the business press generally treats news
as exogenous, we examine whether firms can actively influence
their own media coverage. Our evidence suggests that firms can
manage their media image through their CSR performance, a channel that is more subtle and indirect than the direct channels documented by Gurun and Butler (2012), Solomon (2012), and Ahern
and Sosyura (2014). Second, we contribute to an expansive
Table 11
Results for Eq. (7) examining the effect of media favorability and CSR performance on cost of capital.
Variables (1) (2) (3) (4) (5) (6)
Coeff. Std. err. Coeff. Std. err. Coeff. Std. err.
Intercept 0.046⁄⁄⁄ (0.006) 0.057⁄⁄⁄ (0.001) 0.049⁄⁄⁄ (0.006)
CSR Media H-H 0.011⁄⁄ (0.005)
CSR Media 0.000 (0.000) 0.000 (0.000)
Media H-H 0.021 (0.023)
CSR H-H 0.006 (0.004)
H-H 0.022 (0.021)
Media 0.001 (0.001) 0.001 (0.001)
CSR 0.000 (0.000) 0.011 (0.010) 0.005 (0.009)
FI-News 0.007⁄⁄⁄ (0.001) 0.006 (0.001) 0.005⁄⁄⁄ (0.001)
CGov 0.000⁄⁄⁄ (0.000) 0.000 (0.000) 0.001 (0.000)
I-COCOJ 0.620⁄⁄⁄ (0.041) 0.621⁄⁄⁄ (0.041) 0.095⁄⁄⁄ (0.016)
BP 0.008⁄⁄⁄ (0.014) 0.007⁄⁄⁄ (0.001) 0.009⁄⁄ (0.002)
Disperse 0.176⁄⁄⁄ (0.016) 0.194⁄⁄⁄ (0.017) 0.173⁄⁄⁄ (0.016)
MV 0.002⁄⁄⁄ (0.000) 0.003⁄⁄⁄ (0.000) 0.002⁄⁄⁄ (0.000)
LTG 0.050⁄⁄⁄ (0.002) 0.051⁄⁄⁄ (0.002) 0.049⁄⁄⁄ (0.002)
Beta-SMB 0.005⁄⁄⁄ (0.001) 0.005⁄⁄⁄ (0.001) 0.005⁄⁄⁄ (0.001)
Beta-Mkt 0.004⁄⁄⁄ (0.001) 0.005⁄⁄⁄ (0.001) 0.005⁄⁄⁄ (0.001)
Beta-HML 0.003⁄⁄⁄ (0.001) 0.003⁄⁄⁄ (0.001) 0.003⁄⁄⁄ (0.001)
Year fixed effects Yes Yes Yes
Industry fixed effects Yes Yes Yes
Cluster by firm Yes Yes Yes
Adj-R2 0.446 0.450 0.452
N 9983 9983 9983
This table reports the regression results for the joint effect of superior CSR performance and high media favorability on cost of capital, i.e., Eq. (7).
Results for the full model are reported in columns (5) and (6). Columns (1)–(4) report results for two reduced models that exclude the media-related variables. Cost of capital
is estimated following Ohlson and Juettner-Nauroth (2005). See Appendix B for variable definitions. Year fixed effects and industry fixed effects are included in the models.
Robust standard errors clustered at the firm level are used to compute t-statistics. ⁄⁄ and ⁄⁄⁄ denote significance at the 5% and 1% levels, respectively.
S.F. Cahan et al. / Journal of Banking & Finance 59 (2015) 409–422 419
literature on CSR performance. Although this literature is at least
40 years old, academic interest in CSR has not abated. For example,
recent studies examine the relation between CSR and cost of capital (El Ghoul et al., 2011), cost of bank loans (Goss and Roberts,
2011), earnings management (Kim et al., 2012), analyst forecast
errors (Becchetti et al., 2013), access to finance (Cheng et al.,
2013), stock price crash risk (Kim et al., 2014b), and local political
orientation (Di Giuli and Kostovetsky, 2014). We add to this literature by documenting a previously unexplored incentive for firms
engaging in CSR, i.e., active media management. Overall, our
results suggest that the media slants their reporting in favor of
good CSR firms, giving firms an opportunity to enhance their media
image by improving CSR performance.
Appendix A
A.1. TRNA Rating Procedure
TRNA analyzes news items in real-time to determine the sentiment of the item using a lexical analysis that uses a
knowledge-driven neural network to rate each news item released
about a firm in terms of the tone of the news coverage. The output
of this analytical process is three probability scores that reflect the
positive, negative, and neutral tones of the news item. According to
Thomson Reuters (2013, 6), these scores capture the ‘‘sentiment
expressed by the author about the subject matter being discussed’’.
Thus, TRNA ratings are useful in our context as they capture the
journalistic slant embedded in the news reports.
TRNA uses a three-stage process to assign sentiment scores:
pre-processing, feature extraction, and classification. In the
pre-processing phase, the news item is divided into sentences,
and each sentence is broken down by part of speech, e.g., noun,
verb, or adjective. In the feature extraction phase, TRNA identifies
the words or phrases that convey sentiment. These words and
phrases are compared to a dictionary of 16,000 words and 2500
phrases convey sentiment based on the ratings of three human
annotators. In this stage, the words and phrases are not just analyzed alone but are analyzed in conjunction with each other. For
example, TRNA recognizes negation (e.g., ‘‘not well’’ or ‘‘did not
go well’’) as well as intensification where favorability can differ
for the subject and object of the same verb (e.g., ‘‘X outperformed
Y’’ which is positive for X and negative for Y).
In the classification phase, a sentiment score is determined for
each entity named in the news item. Specifically, TRNA considers
the sentiment attached to the individual words and phrases in
the news item and assigns three overall sentiment scores that capture the positive, negative, and neutral tones of the entire news
item. Importantly, these overall sentiment scores are not just a
count of the number of positive, negative, and neutral elements
in the news item since some elements carry more weight than
others in terms of setting the tone. To assess the relative weights
of different elements, TRNA employs a three-layer
back-propagation neural network with weight relaxation that has
been trained using 5000 news articles that have been scored by
three humans.
According to Thomson Reuters (2013), 75% of the ratings
assigned by TRNA agree with ratings made by humans, which is
only slightly lower than the 82% agreement rate when two humans
rate the same articles. Aside from its classification accuracy, a key
feature of TRNA is the speed at which the process takes place. The
entire rating process takes 0.1 s per news item.
If the item refers to more than one firm, each firm receives its
own set of sentiment scores. In addition, TRNA provides a relevance score for each news item that reflects the degree to which
the item focuses on a particular firm. Relevance is scored from 0
(low) to 1 (high). A news item that mentions several firms may
not be equally relevant for all the firms if the item focuses on just
one of those firms. Consistent with Sinha (2011), we use the relevance score to filter news items and exclude news items that have
a relevance score of less than 1.
Appendix B
Variable definitions
Variables in Eq. (1)
Media = The aggregated positive sentiment
scores of press-initiated news less
the aggregated negative sentiment
scores of press-initiated news for
year t scaled by total number of
press-initiated news articles in year t
CSR = Total CSR score for firm i at the end of
year t, calculated by aggregating the
net scores (strengths minus
concerns) for the community,
diversity, employee relations,
environment, human rights, and
product categories in KLD
NStor = The total number of news articles for
firm i during year t
ROA = Ratio of the operating income after
depreciation to the total assets for
firm i at year t
BP = Ratio of the book value of equity to
the market value of equity for firm i
at the end of year t
MV = Log of market value of equity for firm
i at the end of year t
Lev = Ratio of debt to total assets for firm i
at the end of year t
SP500 = Indicator variable being 1 if firm i is
in the S&P500 index at year t, and 0
otherwise
IRisk = Standard deviations of the
market-model residuals of daily
stock returns measured over the year
Ret = Market adjusted buy-and-hold stock
returns measured over year t
AdExp = The total advertising expense scaled
by total sales for firm i at the end of
year t
CGov = Total score for firm i at the end of
year t, calculated by aggregating the
net scores (strengths minus
concerns) for the corporate
governance in KLD
FI-News = The aggregated positive sentiment
scores of firm-initiated news less the
aggregated negative sentiment
scores of firm-initiated news over
year t scaled by total number of
press-initiated news articles in year t
MktPE = The value weighted average of
market price-to-earnings ratio of all
the firms in the merged
CRSP-Compustat database in year t,
following Conrad et al. (2002)
420 S.F. Cahan et al. / Journal of Banking & Finance 59 (2015) 409–422
Appendix B (continued)
Variables in Eq. (1)
Instrumental
variables in 2SLS
tests
Blue = 1 if the state where firm i’s
headquarter is located is defined as a
Blue state that supports President
Obama in 2008 presidential election
and 0 otherwise. The Blue state is
defined by the following webpage:
http://www.electoral-vote.com/
evp2010/Info/red-blue.html
Voting = The average margin of victory in the
five presidential elections between
1992 and 2008 for the democratic
president candidate in the state
where firm i’s headquarter is located
in. The statistics is from the following
webpage:
http://en.wikipedia.org/wiki/File:
Red-and-Blue-States-Map-(AverageMargins-of-Presidential-Victory).svg
Additional variables in Eq. (2)
CSR1 = 1 if a firm has positive CSR score in
year t and 0 otherwise
RD = 1 if a firm has positive R&D expenses
in year t and 0 otherwise
Loss = 1 if a firm reports negative earnings
before extraordinary items in year t
and 0 otherwise
Sales = Sales revenue in year t divided by
total assets at the end of year t
Assets = The natural logarithm of total assets
at the end of year t
AvgGrowth = The weighted average sales growth
over the past five years
EP = The ratio of net income before
extraordinary items for fiscal year to
market value of equity in year t
Vol = The variance of daily stock returns
over year t
Analyst = The natural logarithm of 1 plus the
number of analysts following the
firm in year t
IO = The percentage of institutional
holdings at the end of year t
Additional variables in Eq. (3)
Treatment = 1 if a firm’s major customer is the US
government and 0 otherwise
Post = 1 if year is 2012 or 2013 and 0
otherwise
Additional variables in Section 3.3
Sin = 1 if firms with SIC codes 2100–2199
or SIC codes of 2080–2085 or NAICS
codes: 7132, 71312, 713210, 71329,
713290, 72112, and 721120 and 0
otherwise
BW = Average monthly Baker-Wurgler
Sentiment Index during one fiscal
year
Appendix B (continued)
Variables in Eq. (1)
Post1 = 1 if the firm-year observation is one
year before or the year when SEO
announcement is made, and 0 if it is
two year before the SEO is
announced
Additional variables in Eqs. (6), (7)
TobinQ = The log of market-to-book ratio for
firm i at the end of year t
COCOJ = Implied cost of capital calculated
following Ohlson and
Juettner-Nauroth (2005)
I-COCOJ = The mean of implied cost of capital
calculated following Ohlson and
Juettner-Nauroth (2005) for the
industry that the firm belongs to
using 2-digit SIC industry
H-H = Dummy variable and it equals to 1
when the firm-year observation is
both above the sample median of
media favorability and above the
sample median of CSR performance
and 0 otherwise
RD1 = Spending on research and
development expenses as reported
by firm i in year t scaled by sales
CapX = Capital expenditure as reported by
firm i in year t scaled by sales
SGrowth = The percentage change in sales
compared to prior year’s sales
LTG = Analyst long-term growth in
earnings forecast from I/B/E/S
Disperse = Forecast dispersion defined as the
coefficient of variation of I/B/E/S
earnings forecasts
Beta-Mkt = Market factor loading from Fama and
French (1996) three-factor model on
risk factors
Beta-SMB = Size factor loading from Fama and
French (1996) three-factor model on
risk factors
Beta-HML = Book-to-market factor loading from
Fama and French (1996) three-factor
model on risk factors for firm i at the
end of year t
References
Ahern, K., Sosyura, D., 2014. Who writes the news? Corporate press releases during
merger negotiations. Journal of Finance 69, 241–291.
Baker, M., Stein, J., 2004. Market liquidity and a sentiment indicator. Journal of
Financial Markets 7, 271–299.
Baker, M., Wurgler, J., 2006. Investor sentiment and the cross-section of returns.
Journal of Finance 61, 1645–1680.
Bartlett, F., 1932. Remembering: A Study in Experimental and Social Psychology.
Cambridge University Press, Cambridge.
Becchetti, L., Ciciretti, R., Giovannelli, A., 2013. Corporate social responsibility and
earnings forecasting unbiasedness. Journal of Banking and Finance 37, 3654–
3668.
Bergman, N., Roychowdhury, S., 2008. Investor sentiment and corporate disclosure.
Journal of Accounting Research 46, 1057–1083.
Bushee, B., Core, J., Guay, W., Hamm, S., 2010. The role of the business press as an
information intermediary. Journal of Accounting Research 48, 1–19.
S.F. Cahan et al. / Journal of Banking & Finance 59 (2015) 409–422 421
Cheng, B., Ioannou, I., Serafeim, G., 2013. Corporate social responsibility and access
to finance. Strategic Management Journal 35, 1–35.
Claus, J., Thomas, J., 2001. Equity premia as low as three%? Evidence from analysts’
earnings forecasts for domestic and international stock markets. Journal of
Finance 56, 1629–1666.
Cone, 2013. Cone Communications Social Impact Study. Available at: http://
www.conecomm.com/stuff/contentmgr/files/0/e3d2eec1e15e858867a5c2b1a2
2c4cfb/files/2013_cone_comm_social_impact_study.pdf.
Conrad, J., Cornell, B., Landsman, W., 2002. When is bad news really bad news?
Journal of Finance 57, 2507–2532.
Cragg, J., Donald, S., 1993. Testing identifiability and specification in instrumental
variable models. Econometric Theory 9, 222–240.
Deng, X., Kang, J., Low, B., 2013. Corporate social responsibility and stakeholder
value maximization: evidence from mergers. Journal of Financial Economics
110, 87–109.
Dhaliwal, D., Krull, L., Li, O., Moser, W., 2005. Dividend taxes and implied cost of
equity capital. Journal of Accounting Research 43, 675–708.
Dhaliwal, D., Li, O., Tsang, A., Yang, Y., 2011. Voluntary nonfinancial disclosure and
the cost of equity capital: the initiation of corporate social responsibility
reporting. The Accounting Review 86, 59–100.
Dhaliwal, D., Radhakrishnan, S., Tsang, A., Yang, Y., 2012. Nonfinancial disclosure
and analyst forecast accuracy: international evidence on corporate social
responsibility disclosure. The Accounting Review 87, 723–759.
Di Giuli, A., Kostovetsky, L., 2014. Are red or blue companies more likely to go
green? Politics and corporate social responsibility. Journal of Financial
Economics 111, 158–180.
Dougal, C., Engelberg, J., Garcia, D., Parsons, C., 2012. Journalists and the stock
market. Review of Financial Studies 25, 639–679.
Easton, P., 2004. PR ratios, PEG ratios, and estimating the implied expected rate of
return on equity capital. The Accounting Review 79, 73–95.
El Ghoul, S., Guedhami, O., Kwok, C., Mishra, D., 2011. Does corporate social
responsibility affect the cost of capital? Journal of Banking and Finance 35,
2388–2406.
Engelberg, J., Parsons, C., 2011. The causal impact of media in financial markets.
Journal of Finance 66, 67–97.
Epstein-Reeves, J., 2010. Consumers overwhelmingly want CSR. Forbes. Available at:
http://www.forbes.com/sites/csr/2010/12/15/new-study-consumers-demandcompanies-implement-csr-programs/.
Fama, E., French, K., 1996. Multifactor explanations of asset pricing anomalies. The
Journal of Finance 51, 55–84.
Fang, L., Peress, J., 2009. Media coverage and the cross-section of stock returns.
Journal of Finance 64, 2023–2052.
Fombrun, C., Shanley, M., 1990. What’s in a name? Reputation building and
corporate strategy. Academy of Management Journal 33, 233–258.
Gao, F., Lisic, L., Zhang, I., 2014. Commitment to social good and insider trading.
Journal of Accounting and Economics 57, 149–175.
Gebhardt, W., Lee, C., Swaminathan, B., 2001. Toward an implied cost of capital.
Journal of Accounting Research 39, 135–176.
Goss, A., Roberts, G., 2011. The impact of corporate social responsibility on the cost
of bank loans. Journal of Banking and Finance 35, 1794–1810.
Graber, D., 1984. Processing the News: How People Tame the Information Tide.
Longman Press, New York.
Grant Thornton, 2011. Public opinion driving CSR activity. Available at: http://
www.internationalbusinessreport.com/Press-room/2011/CSR.asp.
Griffin, Hirschey, J.N., Kelly, P., 2011. How important is the financial media in global
markets? Review of Financial Studies 24, 3941–3992.
Gurun, U., Butler, A., 2012. Don’t believe the hype: local media slant, local
advertising, and firm value. Journal of Finance 67, 561–597.
Hong, H., Kacperczyk, M., 2009. The price of sin: the effects of social norms on
markets. Journal of Financial Economics 93, 15–36.
Kim, Y., Park, M., 2005. Pricing of seasoned equity offers and earnings management.
Journal of Financial and Quantitative Analysis 40, 435–463.
Kim, Y., Park, M., Wier, B., 2012. Is earnings quality associated with corporate social
responsibility? The Accounting Review 87, 761–796.
Kim, J.-B., Zhang, H., Li, L., Tian, G., 2014a. Press freedom, externally-generated
transparency, and stock price informativeness: international evidence. Journal
of Banking and Finance 46, 299–310.
Kim, Y., Li, H., Li, S., 2014b. Corporate social responsibility and stock price crash risk.
Journal of Banking and Finance 43, 1–13.
Klayman, J., 1995. Varieties of confirmation bias. In: Busemeyer, J., Hastie, R., Medin,
D. (Eds.), Decision Making from a Cognitive Perspective: The Psychology of
Learning and Motivation, vol. 32. Academic Press, San Diego, pp. 365–418.
Kothari, S., Li, X., Short, J., 2009. The effect of disclosure by management, analysts,
and business press on cost of capital, return volatility, and analysts forecasts: a
study using content analysis. The Accounting Review 84, 1639–1670.
Kruger, P., 2014. Corporate goodness and shareholder wealth. Journal of Financial
Economics, forthcoming.
Lang, M., Lundholm, R., 2000. Voluntary disclosure and equity offerings: reducing
information asymmetry or hyping the stock? Contemporary Accounting
Research 17, 623–662.
Lys, T., Naughton, J., Wang, C., 2015. Signaling through corporate accountability
reporting. Journal of Accounting and Economics, forthcoming.
Margolis, J., Elfenbein, H., Walsh, J., 2009. Does it pay to be good and does it matter?
A meta-analysis of the relationship between corporate social and financial
performance. Working paper, Harvard University.
Merton, R., 1987. A simple model of capital market equilibrium with incomplete
information. Journal of Finance 42, 483–510.
Mian, G., Sankaraguruswamy, S., 2012. Investor sentiment and stock market
response to earnings news. The Accounting Review 87, 1357–1384.
Mullainathan, S., Shleifer, A., 2005. The market for news. American Economic
Review 95, 1031–1053.
Naiker, V., Navissi, F., Truong, C., 2013. Options trading and the cost of equity
capital. The Accounting Review 88, 261–295.
Nofsinger, J., 2005. Social mood and financial economics. Journal of Behavioral
Finance 6, 144–160.
Ohlson, J., Juettner-Nauroth, B., 2005. Expected EPS and EPS growth as determinants
of value. Review of Accounting Studies 10, 349–365.
Sargan, J., 1958. The estimation of economic relationships using instrumental
variables. Econometrica 26, 393–415.
Severin, W., Tankard Jr., J., 1992. Communication Theories: Origins, Methods, and
Uses in Mass Media, third ed. Longman Group Ltd, New York.
Shiller, R., 2000. Irrational Exuberance. Princeton University Press, Princeton, NJ.
Shroff, N., Sun, A., White, H., Zhang, W., 2013. Voluntary disclosure and information
asymmetry: evidence from the 2005 securities offering reform. Journal of
Accounting Research 51, 1299–1345.
Sinha, N., 2011. News articles and momentum. Working paper, University of Illinois,
Chicago.
Solomon, D., 2012. Selective publicity and stock prices. Journal of Finance 67, 599–
638.
Solomon, D., Soltes, E., 2011. The determinants of coverage in the business press.
Working paper, University of Southern California.
Teoh, S., Welch, I., Wong, T., 1998. Earnings management and the
underperformance of seasoned equity offerings. Journal of Financial
Economics 50, 63–99.
Tetlock, P., Saar-Tsechansky, M., Mackassy, S., 2008. More than words: quantifying
language to measure firms’ fundamentals. Journal of Finance 63, 1437–1467.
Thomson Reuters, 2013. Thomson Reuters News Analytics: Sentiment analysis,
relevance, novelty. White paper, Thomson Reuters, New York.
422 S.F. Cahan et al. / Journal of Banking & Finance 59 (2015) 409–422