The Impact of Legal and Normative CSR on Firm Value, Risk, and

Legal vs. Normative CSR: How Does It Work?
Maretno A. Harjoto and Hoje Jo*
Abstract. This study examines how the sell-side analysts interpret firms’ corporate social
responsibility (CSR) activities. We measure the analysts reactions from firms’ CSR based on the
analysts’ earnings forecast dispersion measure. Employing a sample of U.S. firms during 19932009, we find that overall CSR intensities do not affect analyst dispersion of earnings forecast.
This finding is consistent with the argument that the sell-side analysts have not regarded CSR
activities as valuable information that represents the long-term sustainability of the firms. When
we disaggregate CSR into legal and normative CSR, however, we find analysts’ dispersion
decreases (increases) when firms pursue the legal (normative) CSR activities. This study also
finds that firm’s stock returns volatilities decreases (increases) when it pursues legal (normative)
CSR. These findings are consistent with the information asymmetry theory. The sell-side
analysts tend to have less (greater) information asymmetry regarding the net benefits of pursuing
CSR that is (not) required by laws. Over time, however, firms that follow normative CSR also
have lower analyst disagreement and lower stock returns volatility.
Keywords Corporate social responsibility. Legal CSR. Normative CSR. Analysts Dispersion.
Information Asymmetry. Firm Risk
JUNE, 2012
For the special issue, JBE
*
Jo (corresponding author) is in the Department of Finance, Leavey School of Business, Santa
Clara University, 500 El Camino Real, Santa Clara, CA 95053-0388, (408) 554-4779, (408) 5545206 (fax), Email: hjo@scu.edu. Harjoto is in the Graziadio School of Business and
Management, Pepperdine University, 24255 Pacific Coast Highway, Malibu, CA 90263, (805)
379-5810, Email: Maretno.Harjoto@Pepperdine.edu. Harjoto acknowledges Julian Virtue
Professorship endowment for financial support, and Jo appreciates 2011 fall quarter sabbatical
support of the Leavey School of Business at Santa Clara University. The authors thank Lori
Fuller, Ye Cai, Carrie Pan, Larry Bumgardner for their helpful comments and suggestions.
Introduction
In the recent decade, corporations across the globe have made significant commitments to
become more socially responsible. There have been significant interests on quantifying the costs
and benefits of corporate social responsibility (CSR) among corporations. Yet most corporations
still view their CSR activities as “the right thing to do” rather than bringing higher profits
(Karnani, 2010). A few corporations are able to provide more tangible evidence of doing well
while doing good like Whole Foods (Whole Foods Markets Annual Report, 2010) and Patagonia
(Casadesus-Masanell, Crooke, Reinhardt, and Vasishth, 2009). Others struggle to achieve both
firm profits and social responsibilities (i.e. British Petroleum and Pfizer). Sprinkle and Maines
(2010) provide recent anecdotal evidence that the costs of CSR include immediate cash outflows
and opportunity cost of spending on CSR, whereas the benefits of CSR include tax deductions,
public image, a means of attracting, motivating and retaining talented employees, and more
importantly reducing firm risk.
Researchers have examined the benefits of CSR engagement using direct financial
measures of corporate financial performance and have found evidence that CSR is beneficial to
the firm, such as lower cost of equity (Dhaliwal, et al., 2011), higher analyst following (Hong
and Kacperczyk, 2009), receive more favorable analysts recommendation (Ioannou and
Serafeim, 2010), higher analyst forecast accuracy (Dhaliwal, et al., 2010), increased financial
communications to shareholders (Fieseler, 2011), more effective corporate governance and
higher firm value (Waddock and Graves, 1997; Blazovich and Smith, 2011; Jo and Harjoto,
2011, 2012). Margolis and Walsh (2003) conducted a meta-analysis study and found
approximately half of existing empirical studies find a positive effect of CSR on firm financial
performance.
This study examines how the sell-side analysts interpret firms CSR activities.
Specifically, we examine the analysts’ reactions toward CSR activities that are required by laws
(legal CSR), such as labor rights, antitrust, and product safety violations; and CSR activities that
follow the norms, ethics and discretionary social responsibility (normative CSR), such as
charitable giving, work life benefits, and employment of the disabled. The study empirically tests
different impact of legal CSR and normative CSR on analyst opinion and firm risk. While most
studies focus on the economic and legal aspects of corporate social responsibility, none of the
existing empirical studies examine the sell-side analysts’ reactions to the firms’ engagement in
ethical and discretionary (normative) aspect of corporate social responsibility separately.
To the best of our knowledge, this study is the first to empirically examine the
unexplored area of the differential impact of legal versus normative CSR on differences of
analyst following and firm risk. We measure the analysts’ reactions to firms’ CSR activities
based on the dispersion of analysts’ quarterly earnings forecasts following Dische (2002) who
suggest that a low analysts’ dispersion implies an absence of asymmetric information between
firms’ managers and sell-side analysts. We measure firm risk using the stock returns volatility
following extant accounting and finance literature.
Analyst coverage has been considered an important aspect of external monitoring devices
that disciplines corporate managers. Chung and Jo (1996) suggest security analysts provide a
monitoring role to reduce agency costs and act as information intermediaries between managers
and external investors. Therefore, firm value should be an increasing function of the number of
financial analysts following the firm. Knyazeva (2007) and Yu (2008) also indicate the role of
analysts is an essential part of an external monitoring mechanism. They find that analyst coverage
imposes discipline on misbehaving managers and helps align managers with shareholders, thus
2
improving managerial incentives to undertake more optimal policies. Jo and Kim (2007) further
indicate that an improved corporate transparency through frequent earnings disclosure will
reduce the information asymmetry between insiders and outsiders, discourage managerial selfdealings, and enhance firm value. Other studies have also documented that disagreements among
security analysts reduce the future stock return and firm value (Diether, et al., 2002; Dische,
2002). Johnson (2004) indicates that analyst dispersion (disagreement) is a proxy for firm’s
idiosyncratic risk and therefore is negatively related to stock returns. This study extends this
literature by focusing on the relation between CSR and dispersion of analyst earnings forecast as
well as firm risk. Specifically, it compares the impact of legal CSR versus normative CSR on
analyst dispersion, measured by standard deviation of analyst earnings estimates over the
absolute value of the mean of earnings estimates and firm risk measured by stock return
volatility.
Regarding the impact of CSR on firm risk, Spicer (1978) finds that firm social
performance reduces firm risk. Wood (1991) indicates the “Davis and Blomstrom’s Iron Law of
Responsibility” because the first principle of CSR is the institutional principle of legitimacy. It
states that society grants legitimacy and power to businesses. In the long run, firms who do not
use power in a manner at which the society considers responsible will tend to lose their
legitimacy and power. Orlitsky and Benjamin (2001) provide a meta-analysis study on the
relationship between CSR and firm risk and find a negative relation between CSR and
accounting as well as market risk. They conclude that firms who conduct CSR tend to anticipate
lower business risk, such as governmental regulation, labor unrest, and/or environmental
damage.2
2
Hillman and Keim (2001) show empirical evidence that CSR activities, that are directly tied to primary
stakeholders (customers, employees, suppliers, and communities), bring benefit not only the stakeholders but also
3
We postulate that under asymmetric information, the sell-side analysts are not able to
immediately evaluate managers’ intentions and costs and benefits of firms’ decision to engage in
corporate social responsibility. Therefore, analysts consider investments in CSR firms’ stocks as
risky investments. In equilibrium, disagreement among analysts about the value and return on
CSR firm’s stock will depend on the proportion of informed analysts. Furthermore, analysts are
more likely to experience higher information asymmetry about the costs and benefits of CSR
when firms decide to engage in normative CSR as opposed to legal CSR, although normative
CSR actions might actually be valuable signals of the CSR quality. 3 When firms pursue CSR to
fulfill regulation requirements (legal CSR), the firms and media news are more likely to disclose
the costs and benefits from legal CSR activities to the public. Therefore, analysts are more likely
to have lower search cost for information and are able to evaluate the costs and benefits of legal
CSR to follow the regulatory requirements. However, when firms decide to engage in CSR
activities that are not required by laws and regulations (normative CSR), managers are less likely
to disclose the costs and benefits of normative CSR to the public. Therefore, analysts face
relatively higher search cost and suffer greater information asymmetry when firms engage in
normative CSR compared to legal CSR.4 And greater information asymmetry will be reflected in
greater dispersion among analyst forecasts and higher firms’ risk.
increase the shareholders wealth. However, they find that participating in social issues beyond the primary
stakeholders, may adversely affect a firm’s ability to create shareholder wealth.
3
Our hypotheses are indeed based on a theoretical model of asymmetric information between managers and external
market participants, namely shareholders and analysts (Leland and Pyle, 1977; Grossman and Stiglitz, 1980; Myers
and Majluf, 1984). Gennotte and Leland (1990) present a theoretical model based on Grossman and Stiglitz (1980)
where asymmetric information plays a key role in generating price volatility and market crashes when market
participants are not completely informed about the existence of hedging activities. Attanasio (1990) argues that in
the presence of asymmetric information and risk aversion, asset prices tend to be more volatile than in full
information.
For instance, recent Johnson and Johnson’s McNeil Consumer Healthcare division product recall for consumer
(product) safety that is required by the Food and Drug Administration (FDA) indicates that both the company and
the media news continuously disclose the net impact of product recall on Johnson and Johnson sales and net
earnings to public (Loftus, 2011). In contrast, when Johnson and Johnson launched its Project Phoenix to help
4
4
Using a sample of U.S. firms from the Kinder, Lydenberg, and Domini’s (KLD’s)
database during 1993 to 2009, we find that analyst dispersion is not affected by the firms’ CSR
activities. We find supporting empirical evidence, however, that legal CSR activities reduce
disagreement (dispersion) among analyst earnings forecasts and volatility of stock returns. In
contrast, we find normative CSR contemporaneously increases stock returns volatility and
increases analyst disagreement. Our findings are consistent with information asymmetry
argument given legal CSR entails less information asymmetries among analysts compared to
normative CSR. We find, however, that the impact of normative CSR reduces volatility of stock
returns and analyst dispersion after a one year lag. The asymmetric information theory explains
this latter finding as evidence that disagreements among the sell-side analysts and stock return
volatility decrease over time because the proportion of informed analysts increases and relevant
information tends to be more available as time goes by.
There are two main implications of our findings. First, the sell-side analysts seems to be
unaffected by the firms’ overall CSR activities as documented by Condon (2005) and Ioannou
and Serafeim (2010). However, we find evidence that firms’ overall CSR reduces firms’ risk
(Oikonomou et al., 2010; Salama et al., 2011). Second, the sell-side analysts tend to experience
greater information asymmetry when managers conduct normative CSR that are above and
beyond what is required by law. However, eventually normative CSR reduces disagreement
among analysts over time. Corporate managers must be aware that the analysts are experiencing
higher search cost for information and therefore greater information asymmetry when the firm
initially engages in normative CSR. This study recommends that managers should provide
greater disclosures to the analysts, specifically about the net impact of normative CSR on firm’s
cooperatives in Brazil to enhance workers conditions, both the company and the media did not disclose any
information to public about the net impact of Project Phoenix on firm’s net sales, costs, or profit (Johnson and
Johnson Annual Repot, 2010).
5
financial performance, to reduce disagreement among analysts and stock returns volatility. This
study contributes to existing literature in two ways. First, it unveils an empirical relation between
firms’ CSR activities and the sell-side analysts’ earnings forecasts. Second, it examines the
analyst reactions to firms’ engagements in legal versus normative CSR activities. There are
certain subjectivities in our legal vs. normative CSR classifications. For robustness check, we
reclassify our CSR classifications and employ a panel of researchers to reclassify the legal and
normative CSR. We find that our main results remain robust.
Literature Review and Hypotheses
The debates about CSR continue to grow without a clear consensus on its meaning or
value. The United States Social Investment Forum (SIF) defines the social responsibility
investment as “investment practices that consider environmental, social and corporate
governance criteria to generate long-term competitive financial returns and positive societal
impact.” The European Commission defines corporate social responsibility as “a concept
whereby companies integrate social and environmental concerns in their business operations and
in their interaction with their stakeholders on a voluntary basis.” The World Bank defines CSR
as the commitment of business to manage and improve the economic, environmental and social
implications of its activities at the firm, local, regional and global levels.
Heal (2005) explains that CSR is internalizing any externalities, in terms of social costs,
that are generated by the firm. Heal argues that CSR acts as an invisible hand to reallocate
resources among stakeholders when the market fails to address social costs generated by
corporations and when disagreements between shareholders and stakeholders are strong. Bagnoli
and Watts (2003) show that CSR can be viewed as a private provision for public goods.
6
Existing literature recognizes that the role of analyst following is critical to enhance
information transparency between managers and external investors. Chung and Jo (1996)
indicate that security analysts play important roles as corporate monitors in reducing agency
costs and motivating managers. Analysts also act as information intermediaries who help expand
the breadth of investor cognizance about managerial actions. Therefore, analyst following should
have a positive impact on the market value of firms. Lang and Lundholm (1996) find that
information disclosure tends to have larger analyst following, less dispersion among analyst
forecasts and less volatility in forecast revisions.
Knyazeva (2007) and Yu (2008) also consider the potential role of analysts as an indirect,
but additional monitoring mechanism and support the notion that analyst following imposes
discipline on misbehaving managers and helps align managers with shareholders, thus improving
managerial incentives to undertake more optimal policies. Jo and Kim (2007) further indicate that
higher corporate transparency through frequent press releases reduces the information
asymmetry between insiders and outsiders, discourages managerial self-dealings, and therefore
enhances firm value. Jo and Harjoto (2011) show that security analysts provide effective external
monitoring regarding the information transparency of CSR engagement, and the CSR activities
have positive effects on firm value.
When analysts are experiencing higher asymmetric information, existing studies have
documented that disagreements among analysts reduce the future stock return and firm value.
Miller (1977) indicates that investors under uncertainty due to asymmetric information tend to
have divergence of opinions about the stock price. He further shows that the divergence of
opinions among investors is more likely to create higher risk (volatility of stock returns) and
more likely to have lower stock returns. Grossman and Stiglitz (1980) develop a theoretical
7
model to explain information asymmetry between informed and uninformed investors. They
conclude that the degree of information asymmetry is affected by the search cost of information,
the quality of information, noises of investment in risky assets, and proportion of informed
investors. Gennotte and Leland (1990) show that in a rational expectations model with
asymmetric information, if some participants are not aware of the presence of hedging trades,
then very large price drops may be misconstrued as information by traders. This information
asymmetry can cause prices to drop much further and can cause the market to crash.
Empirical finding regarding the relation among analyst dispersion, firm risk and stock
returns seems to be mixed. For instance, Dische (2002) finds that the returns are higher for stocks
with a low dispersion in analyst consensus forecasts. He further argues that a low dispersion
corresponds to a consensus among analysts due to more credible information. Diether, et al.
(2002) provide evidence that stocks with higher dispersion on analyst earnings forecast earn
significantly lower future returns than comparable stocks. They reject the notion that dispersion
in forecasts can be viewed as a proxy for risk since dispersion and future returns are negatively
related. They conclude that analyst dispersion indicates divergence in analyst opinions about the
stock value.
There has been acceleration of significant studies among researchers to examine the
relation among CSR, analyst coverage, and the role of CSR as an information disclosure.
Dhaliwal, et al. (2010) and Dhaliwal, et al. (2011) find that firms with superior social
responsibility performance attract dedicated analyst coverage. They also find that analysts have
lower forecast errors and lower dispersion for firms with better CSR disclosure. Firms exploit the
benefit of lower cost of equity and higher value associated with the CSR disclosure. Ioannou and
Serafeim (2010) provide evidence that socially responsible firms receive more favorable analyst
8
recommendations only in the recent years. They find that firms with higher visibility are more
likely to receive favorable recommendation when they engage in CSR activities. They also find
that analysts with more experience on CSR awareness are more likely to perceive CSR as valuecreation and reward firms with CSR strengths.
The growth of corporate social responsibility (CSR) both in academia and practice
around the world is astounding, the impact of CSR engagement on firm risk is also examined by
many studies including McGuire et al., (1988), Feldman et al., (1997), Orlitzky and Benjamin
(2001), Husted (2005), Godfrey et al. (2009), Oikonomou et al. (2010), and Salama et al. (2011).
Our first hypothesis is based on the assumption that the analysts, on average, are fully
informed about manager’s decisions to engage in CSR activities.
H1: If analysts are fully informed about the net benefits of CSR engagement, then the impact of
firm’s engagement in overall CSR will reduce analyst dispersion and stock return volatility.
The null hypothesis is that CSR engagement does not affect analyst dispersion and stock return
volatility.
Next, we aim to sharply contrast the difference between CSR that only attempt to follow
economic profits and legal requirements versus CSR that attempt to accomplish the social norms
beyond what are required by laws. We define the legal CSR as CSR activities that satisfy
existing laws and regulations and normative CSR as CSR activities that follow social norms that
are not required by laws and regulations.
There are two representative, but opposing views about the net effect of CSR to the
society. The contractarian view (i.e. Friedman, 1970) believes that following economic and legal
responsibilities are considered maximizing social welfare. Any actions beyond the economic and
9
legal responsibilities impose “tax” to owners, employees and customers. On the other hand, the
stakeholder theory argues that CSR is not simply following the economic and legal
responsibilities. Carroll (1991) describes the pyramid of CSR and distinguishes the difference in
levels of CSR between firms that follow economic and legal responsibilities versus firms that
follow ethical and philanthropic responsibilities. He argues that total CSR of business should
comprise all four distinct components of this pyramid, if taken together, constitute a complete
CSR. Donaldson and Preston (1995) urge that examining the normative aspect of CSR is far
more important than establishing the descriptive and instrumental concepts that explain corporate
characteristics and behavior such as the nature of firm, the way managers think, how board
members think about constituencies’ interests, and how a corporation is actually managed.
Analysts are under two different levels of awareness over managers’ action to engage in
these two different CSR activities. In the first level, analysts are likely to receive more public
announcements and disclosures from managers if managers engage in CSR activities that are
consistent with the law and regulations. More importantly, the direct costs and benefits of
conforming to the law and regulation are easily measured since usually the reward or punishment
(including operational, financial, and legal risk such as litigations, lawsuits, etc.) from
conforming or not conforming to regulations are stated by local government and authorities.
These disclosures reduce the search cost for information and therefore increase the proportion of
informed analysts. Therefore, we can anticipate lower disagreement among the analysts when the
firm engages in CSR to satisfy the legal requirements.
The second level presents several possible motives for managers to engage in normative
CSR. Managers may feel morally obligated to do the right thing or CSR is a form of managers’
altruistic behavior. However, managers may engage in CSR due their own self-interests such as
10
receiving public accolades, consuming perquisites, or personal warm glow from social activities
(Cespa and Cestone, 2007). Managers may also engage in CSR to resolve conflict of interests
between investing and non-investing stakeholders (Jo and Harjoto, 2011). Regardless of the
managers’ intentions to engage in CSR, analysts have less information than the managers thus
the net impact of CSR on analyst dispersion and risk is greater. Fieseler (2011) shows that the
equity analysts in the German stock exchange perceive economic, legal, ethical and philanthropic
CSR strategies as value creations since these CSR strategies increase information disclosure and
open dialog between managers and shareholders.
If managers conduct normative CSR that is not required by laws and regulations, analysts
are facing relatively higher search costs in spite of the potential that normative CSR provides
valuable signals of the firm value because of relatively less available information.
These
activities are usually voluntary activities that conform to the societal norms, non-investing
stakeholders’ expectations, or unwritten rules. Therefore, analysts have less information and
therefore the proportion of informed analysts is lower. Consequently, we expect that
disagreements among themselves increase and the impact of normative CSR on stock returns
volatility is positive, at least initially. Over time, however, analysts are able to learn from the
observed prices and returns on CSR firms’ investments. Therefore, the proportion of informed
analysts is expected to increase over time.
Our second hypothesis is based on the premise that analysts are relatively more (less)
informed when the managers decide to engage in CSR to satisfy legal (normative) requirements.
Therefore, we expect relatively lower (higher) analyst dispersion and lower (higher) volatility of
stock returns if firms engage in legal (normative) CSR. We develop two possible hypotheses
based on whether the firm conducts legal or normative CSR.
11
H2a: If analysts are relatively more informed regarding legal CSR activities, then the legal CSR
reduces analyst dispersion and stock returns volatility.
H2b: If analysts are relatively less informed regarding normative CSR activities, then the
normative CSR increases analyst dispersion and stock returns volatility, at least initially.
The null hypothesis predicts no relationship between legal CSR (normative CSR) and analyst
dispersion as well as stock returns volatility.
Data Measurements and Empirical Model
Corporate Social Responsibility Variables
We use a combined data set from the Kinder, Lydenberg, and Domini ‘s (KLD’s) Stats
database and the institutional brokers estimation services (I/B/E/S) database for analyst
following and earnings estimates for U.S. firms during 1993 to 2009. KLD’s Stats inclusive
social rating criteria covers eighty strengths and concerns ratings in seven major qualitative areas
including community, corporate governance, diversity, employee relations, environment, human
rights and product. Detailed information about KLD Stats data is discussed in existing studies
such as Graves and Waddock (1994), Ioannou and Serafeim (2010), and Jo and Harjoto (2011,
2012). We include all seven KLD inclusionary categories and assign zero value for categories
that were not yet created or were reassigned.5
First, we calculate the net of strengths and concerns ratings consistent with Hillman and
Keim (2001). KLD strengths and concerns criteria are assigned with zero or one value. We
assign positive one (+1) value for each strength rating and assigned negative one (-1) value for
each concern criteria. Since KLD ratings change from year to year, our CSR index is constructed
5
When we exclude KLD corporate governance category from our sample, however, our main results remain
qualitatively unchanged.
12
by dividing the net of strengths and concerns with the maximum value of net strengths and
concerns in each year consistent with Baron, Harjoto and Jo (2011). Let Cijt denotes CSR index
for firm i and year t with strength j minus concern j and Ct the maximum value of KLD strengths
minus concerns for any firm in year t, the index Cit of CSR composite for firm-year observation
it is
Cit = j Cijt/Ct
(3)
where firms with higher CSR index (Cit) are considered more socially responsible firms.
Then we categorize eighty ratings across seven criteria into two major groups of CSR
activities: CSR activities that can be considered as following the laws and regulations (legal
CSR) such as tax disputes, accounting concerns, non-representation, regulatory problem, labor
rights, antitrust, product safety, etc. and CSR activities that can be considered as following social
norms (normative CSR) such as charitable giving, transparency concern, employment of the
disabled, work life benefits, benefits to economically disadvantage, etc. Appendix A lists detail
classifications of eighty ratings into legal CSR and normative CSR. Legal CSR index and
normative CSR index are constructed based on the above equation (1) and higher positive value
of the index indicates that the firm is more socially responsible. We also solicit feedback from a
panel of researchers to classify these eighty KLD ratings into legal CSR and normative CSR and
in general, our classifications are consistent with classifications from the panel.
Measurements of Dependent and Independent Variables
We use analyst following as a measure of the external market monitoring in addition to
investors (shareholders). We consider external analyst monitoring by the number of analysts
estimates of the firm’s earnings forecasts from the I/B/E/S database. Since the number of
13
analysts estimates is skewed to the right (Lim, 2001; Bushman et al., 2005), we measure analyst
following with the natural logarithm of one plus the number of analysts estimates the firm’s
earnings forecast (ANALYST). The I/B/E/S database also provides the means and medians of
analysts’ earnings forecasts and the standard deviations of earnings forecasts.
We calculate standard deviations of earnings forecasts over the absolute value of the mean
of earnings forecasts among analysts (DISPERSION) as a measure of the disagreements among
financial analysts from I/B/E/S. This measure of analyst dispersion is consistent with Diether, et al.
(2002), Dische (2002), and Johnson (2004). Similarly, based on the accounting and finance
literature, we measure firm’s idiosyncratic risk from the volatility (standard deviation) of monthly
stock returns (VOLATILITY) from the Center for Research in Security Prices (CRSP) database.
The other financial variables that measure firms’ characteristics are taken from Compustat. Table
1 provides descriptions of constructing the variables used in this study.
[Table 1 about here]
Empirical Models and Estimations
Ioannou and Serafeim (2010) acknowledge that company may choose to adopt CSR to
receive more optimistic analyst coverage. In other words, a firm’s choice to engage in CSR is
endogenous. To address this issue properly, we conduct an endogeneity correction for the
treatment effects. Without correcting the endogeneity problem in which firms with higher
number of analysts following choose to invest in CSR to begin with, the CSR involvement’s
contribution to analyst dispersion and firm risk can therefore be estimated incorrectly (Greene,
2008). The choice of CSR engagement is related to the number of analysts following
14
(ANALYST). Firms’ engagement in CSR may also draw more analysts’ attention and therefore
increases analyst following.
Existing studies have also documented that there is a causal relationship between CSR and
corporate governance measures including analyst following and firm value (Nelling and Webb,
2009; Jo and Harjoto, 2011; Baron et al., 2011). To address the causality between CSR, analyst
following, and analyst dispersion, we use three different regression estimations. First, we use the
ordinary least square (OLS) using the recursive estimation approach by using the predicted value of
CSR based on analyst following as a predictor for analyst dispersion and stock returns volatility.
Second, we utilize the fixed effects panel data regression to account for fixed effects within each
firm in the sample. In the fixed effects estimation, we also use the recursive approach. And third, we
use the two-stage simultaneous equations regression method to estimate the effect of CSR on
analyst dispersion and stock returns volatility. We adopt Jo and Harjoto (2012) to correct for
endogeneity and causality based on the Granger causality test (Granger, 1969). Since the main
purpose of this study is not to examine the endogeneity and causality relationship between CSR
and analyst following, we do not report the results of endogeneity and causality estimations in
this study.6
This study empirically examines the impact of firm’s overall CSR activities on the firm’s
analyst dispersion and firm risk. We define two main structural equations as follows:
DISPERSIONit = a + b(CSRit) + c(CSRit-1) + k k(Zit) + eit
VOLATILITYit = a + b(CSRit) + c(CSRit-1) + k k(Zit) + eit
6
(1)
(2)
This study addresses the endogeneity and causality issues between CSR engagement and analyst following based
on Granger causality by regressing CSR with lag CSR and lag analyst following and vice versa (Granger, 1969).
The regression results to address the endogeneity and causality between CSR and analysts following for this study
are available upon request.
15
where DISPERSIONit is measured by the dispersion of analyst earnings forecast (DISPERSION) as
a measure of disagreement among analysts. VOLATILITYit is measured by standard deviation of
monthly stock returns (stock returns volatility) which can be considered as a measure of firm risk.
Other control variables are closely following Jo and Harjoto (2011, 2012). Zit are firm’s financial
characteristics such as firm’s size (LOGASSET), total debt ratio (DEBTR), research and
development ratio (RNDR), advertising expense ratio (ADVR), capital expenditure ratio (CAPXR),
one year sales growth rate (SALEGRW), KLD DUMMY, and also the Fama-French (1997) 48
industry dummy variables (Fama and French, 1997). KLD DUMMY is a dummy variable to
represent firms that do not have any KLD ratings but listed in KLD database. CSRit and CSRit-1
are the contemporaneous and one year lag of CSR index for firm i. We include the lag effect of CSR
to capture any lag effect of CSR on firm risk and dispersion. Then, we classify overall CSR
activities into legal and normative CSR and re-estimate equations (1) and (2) by consecutively
replacing CSR with legal CSR (CSRLEGAL) versus normative CSR (CSRNORM).
Empirical Results
Sample Statistics
The sample from KLD database is merged with the data for analyst earnings forecast and
analyst following from the I/B/E/S database. We also require that firms are covered in the
Compustat and the Center for Research in Security Prices (CRSP) databases for their financial
information, stock prices and volatility of monthly stock returns. After matching across all these
four databases and accounting for lags and changes in CSR, analyst earnings forecasts and volatility
of monthly stock returns variables, the combined sample consists of approximately 11,932 firm-
16
year (2,789 firms) observations from 1993 to 2009 (presented at the regression results on Table
4). Actual samples used in the regression analyses are slightly different than the combined
sample depending on data availability because variables vary across different regression models.
Descriptive statistics of sample data for this study are presented in Table 2. The mean of
CSR index is -0.00306, indicating that the firms in our samples during 1993 to 2009 have more
CSR concerns scores than strengths scores. The mean of legal CSR index is -0.13578 and the
mean of normative CSR index is 0.07843. Legal CSR index is negative since 20 out of 22 total
ratings that we consider comes from satisfying legal and regulatory requirements raised by
concerns items. On the other hand, the average of normative CSR index is positive because 42
out of 56 ratings come from strengths and only 16 ratings come from concerns ratings. The
average natural log of the number of analysts following plus one is 1.79849 indicating that the
average number of analysts following for each firm in the sample is approximately 6 analysts.
[Table 2 about here]
The average analyst dispersion, measured by standard deviation of analyst earnings
forecasts over the absolute value of the mean of earnings forecasts is 4.33%; which is slightly
lower than the median dispersion of 5.3% reported by Diether et al. (2002). The averages of
firms’ financial characteristics reported in Table 2 are comparable with those in Ioannou and
Serafeim (2010), Baron, et al. (2011), Dhaliwal et al. (2011), and Jo and Harjoto (2011, 2012).
The KLD dummy variable that represents the percentage of firms used in this study that do not
have any ratings in KLD Stats is approximately 10% of the sample.
Table 3 provides bivariate correlation matrices for variables of our main interest that are
used in this study. We find that CSR is positively correlated with measures of analysts. Legal
CSR (CSRLEGAL) is negatively correlated with normative CSR (CSRNORM), stock return
17
volatility (VOLATILITY), and analyst dispersion (DISDPERSION) while CSRNORM is
positively correlated with VOLATILITY and DISPERSION. As anticipated, DISPERSION is
positively correlated with VOLATILITY. Although unreported to conserve space, firm size
(LOGASSET) is positively correlated with both analysts and CSR measures. In contrast,
research and development expense ratio (RNDR) is generally negatively correlated with both
analysts and CSR measures.
[Table 3 about here]
Multivariate Regression Analysis
The first regression analysis examines the impact of overall CSR activities on analyst
dispersion and firm risk. The dependent variables are measures of asymmetric information
between managers and analysts and firm risk. We utilize the standard deviation of analyst
earnings forecast divided by absolute value of mean for analyst earnings forecast and the
standard deviation of monthly stock returns as measures of analyst dispersion and firm risk,
respectively. The independent variables are contemporaneous CSR index, one year lag CSR
index, and firm’s characteristics as control variables. The one year lag of CSR is included in the
regression to capture any time delay (lag effect) of CSR on analyst dispersion and stock returns
volatility. Table 4 presents the regression results from ordinary least square (OLS), fixed effects
panel data, and two-stage least square (2SLS) estimations. 7
[Table 4 about here]
7
In the ordinary least square (OLS) and fixed effects panel regressions, we employ the recursive model by using the
predicted value of contemporaneous and lag of CSR index determined by the analyst following from the regression
that addresses the endogeneity and simultaneity issues.
18
We find that the impact of CSR on analyst dispersion of earning forecasts is not
statistically significant both in contemporaneous and one year lag effect. This provides evidence
that the sell-side analysts seems to be unaffected by the firm CSR activities. One can also
interpret our results as evidence to support the argument that the sell-side analysts neither have
personal preference nor incentives toward CSR firms (Condon, 2005). However, we find that
CSR activities reduce the firms’ risk measured by the stock returns volatility. Therefore, our first
hypothesis (H1) is only partially supported.
Next, we re-estimate the regression models stated in equations (1) and (2) for legal CSR
and normative CSR separately. Table 5 shows the results for legal and normative CSR on
dispersion of analysts forecast. Table 5 indicates that legal CSR reduces analyst dispersion. One
unit increase in legal CSR reduces contemporaneous dispersion by 13.2% to 19.4% and the lag
effect of legal CSR reduces dispersion by 11.22% to 16.22%. These findings are economically
and statistically significant. Analysts are fully informed when firms engage in legal CSR
activities and therefore support the hypothesis H2a. On the contrary, analysts are not fully
informed when firms conduct normative CSR. The contemporaneous effect of normative CSR
increases analyst dispersion of earnings forecast. However, after one year, the lag of normative
CSR more than offsets the positive contemporaneous effect of normative CSR on analyst
dispersion. We believe that over time, the search cost of information for analysts decreases and
the proportion of informed analysts increase. Therefore, dispersion among participants decreases
over time. Overall, we find supporting empirical evidence that market participants are relatively
less informed when firms initially engage in normative CSR activities compared to engaging in
legal CSR.
[Table 5 about here]
19
Table 6 presents the regression results for legal and normative CSR on firm volatility of
stock returns. We find that legal CSR has immediate (contemporaneous) and lag effect on
reducing firm’s volatility of stock returns. Following legal requirements reduces the firms’
current risk indicated by the significance of current negative effect of legal CSR on firm stock
returns volatility. Based on hypothesis H2a, these results indicate that analysts are relatively
more informed about reduced risk when firms follow legal CSR. Therefore, the hypothesis H2a
is supported. Analysts are relatively more informed about the impact of legal CSR on stock
returns volatility. The information asymmetry theory indicates that when the proportion of
informed investors increases and the search cost for information decreases, disagreement among
the analysts and volatility of stock returns (firm risk) both decrease.
Next, we find that normative CSR increases stock returns volatility. On average, analysts
are not better informed about the impact of normative CSR activities. Therefore, Hypothesis H2b
is supported since investors are relatively less informed about the impact of normative CSR on
firm risk at least initially. While the normative CSR increases stock return volatility
contemporaneously, it reduces the volatility in one year lag effect. This finding indicates that
there is disagreement among analysts about the impact of normative CSR on the current period.
It takes time (one year lag) for analysts to receive full information about the impact of normative
CSR. This finding is consistent with the information asymmetry theory which indicates that over
time, rational analysts learn from observed returns of normative CSR activities. Therefore,
search cost for information decreases and proportion of informed analysts increases over time.
The empirical finding supports the notion that it takes time to reduce the search cost and to
increase the proportion of informed investors, leading to reduced stock return volatility.
[Table 6 about here]
20
Robustness Check
To check the robustness of our results, we conduct the difference regressions of each
variable, based on the change of CSRLEGAL, change of CSRNORM, change of DISPERSION,
change of VOLATILITY, and changes of other control variables and repeat our regressions
similar to Tables 5 and 6. Our results reported in Table 7 based on difference in DISPERSION
provide qualitatively similar results reported in Table 5, although the coefficients on
CSRLEGAL are somewhat weaker. Our regression results based on the change in VOLATILITY
are reported in Table 8 and are also qualitatively similar to the results reported in Table 6.
[Table 7 about here]
Even though KLD’s “corporate governance” (CG) is included in our legal vs. normative
CSR classification, KLD’s corporate governance is not a main category of CSR and quite
different from conventional CG measures used in accounting and finance literature. Thus, we
examine the robustness of our results after taking this KLD CG category out from CSR
classification and find our main results remain intact. Furthermore, because our classification of
the two types of CSR activities is based on our subjective judgments, we compiled a panel of
researchers to classify the KLD criteria into CSR legal and normative categories. Thus, to
further check the robustness of our main results, we reexamine our regression results after
switching various KLD variables in diversity, employee relations, environment, and product to
alternative category based on the panel if those selected variables are subject to certain
ambiguities in legal vs. normative classification and find that our main results remain
qualitatively quite robust in various alternative classifications.8
8
In addition, although our focus is not on firm value, for confirmation purpose in our unreported results, we check
the separate impact of legal vs. normative CSR on firm value using Tobin’s Q measure that is widely used in
accounting, economics, and finance areas. Our untabulatd results suggest that lagged value of CSRLEGAL
21
Discussion
There are two main implications of this study. First, our result confirms existing
arguments that the sell-side analysts are not affected by the firms’ CSR activities (Condon,
2005). However, we find evidence that stock returns volatility is lower for firms with higher
CSR activities. Second, the analysts are relatively better informed when firms conduct CSR
activities that are according to the laws (legal CSR). This argument is supported by lower
dispersion of analysts forecast and lower volatility of stock returns when firms conduct legal
CSR. In contrast, when firms pursue CSR activities that are not required by laws, analysts are
relatively less informed. This is shown by increased dispersion of analysts and increased
volatility of stock returns. Eventually, however, the impact of following the norms reduces
disagreement among analysts and lowers the stock returns volatility because the search cost of
information decreases, and therefore, the proportion of informed analysts increases over time.
Managers cannot expect immediate favorable reactions from the analysts when they
decide to conduct CSR to follow the norms that are not required by laws. We believe that
managers should provide more disclosure for the analysts to reduce asymmetric information
especially when they pursue CSR activities that are not required by laws.9 Our results are
consistent with recent APCO Global CSR Study Report which shows that society is listening, but
still does not feel well informed on socially responsible activities of companies. People who are
positively influences Tobin’s q, but contemporaneous value of CSRLEGAL does not affect Tobin’s Q. In contrast,
while the lagged value of CSRNORM does not affect Tobin’s Q, contemporaneous value of CSRNORM influences
Tobin’s Q positively in all three regressions of OLS, fixed effect, and 2SLS. Thus, firm value results are also
consistent with our main results of analyst dispersion and firm risk and the notion that normative CSR actions are
perceived by market participants as valuable signals of the CSR quality.
9
For instance, General Mills, Intel Corporation, Starbucks, and many other corporations listed and have improved
their rankings in Corporate Responsibility Magazine (CRO) 100 Best Corporate Citizens when they continuously
provide news and media releases about their progresses in corporate social responsibility activities especially those
which are not required by laws.
22
listening believe corporate CSR communication is both credible and important. Additionally,
third-party verification of CSR practices from non-governmental organizations (NGOs), media,
government, employees, customers and other key stakeholders enhances credibility of the firm’s
CSR activities (APCO, 2004).10
Ioannou and Serafeim (2010) find evidence of an inter-temporal shift in analyst
recommendations for socially responsible firms in recent years. They find that CSR firms receive
more favorable analyst recommendations in recent years relative to earlier periods. Therefore, we
believe that future study can explore the inter-temporal analysis to examine the impact of legal
and normative CSR on analyst dispersion and volatility of stock returns.
Conclusions
Researchers have defined that corporate social responsibility into two main categories.
Friedman (1970) defines CSR as conducting the business in accordance with shareholders’
desires, which generally is to make as much money as possible while conforming to the basic
rules and laws of society. Carroll (1979) defines the hierarchical CSR as economic, legal, moral,
and philanthropic actions of firms that influence the quality of life of relevant stakeholders.
Donaldson and Preston (1995) classify CSR based on the stakeholders theory, which consists of
descriptive and instrumental CSR and normative CSR.
The primary purpose of this paper is to examine and contrast the level of information
asymmetries among the sell-side analysts by examining the analyst reactions when firms conduct
CSR that follow the laws (legal CSR) versus when firms engage in CSR that follow social norms
(normative CSR). This study relies on the asymmetric information theory to explain the impact
10
APCO Worldwide is a consulting firm that addresses firms' interests and objectives through communication and
public affairs and consulting that combines a global perspective with local expertise to understand the issues, events
and trends that impact businesses and organizations around the world (http://www.apcoworldwide.com/).
23
of CSR engagement (legal and normative) on dispersion of analyst earnings forecasts and stock
returns volatility. We argue that analysts are more likely to face greater asymmetric information
about firms’ engagement in CSR activities to the extent that analysts are not properly and timely
informed. If analysts are fully informed about the rationales behind CSR engagements, then CSR
will reduce analyst dispersion and stock returns volatility. However, when analysts are not fully
informed, then CSR activities will increase analyst dispersion and stock returns volatility - at
least in the short term.
Using a sample of U.S. firms from the KLD Stats database during 1993 to 2009, this
study finds that analysts are not affected by firms’ overall engagement in CSR activities. The
impact of CSR activities on analyst dispersion is not statistically significant. However, we find
that stock return volatility decreases as the firms CSR activities increase. When we classify CSR
into legal and normative CSR, we find evidence that suggest analysts are fully informed about
firms’ engagement in CSR activities to fulfill legal requirements (legal CSR). In contrast, when
firms engage in CSR activities that are not required by laws (normative CSR), analysts are not
fully informed. The analyst dispersion and stock returns volatility increase contemporaneously
with firms’ engagement in normative CSR. Over one year lag, the normative CSR also reduces
analyst dispersion and stock returns volatility.
24
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Appendix A Categories for Legal and Normative CSR
KLD VARIABLES
LEGAL
NORMATIVE
STRENGTH
Community:
Charitable Giving
Yes
1
Innovative Giving
Yes
1
Support for Housing
Yes
1
Support for Education
Yes
1
Non-US Charitable Giving
Yes
1
Volunteer Programs
Yes
1
Yes
1
Community Other Strength
CONCERN
Investment Controversies
Yes
-1
Negative Economic Impact
Yes
-1
Tax Disputes
Yes
-1
Community Other Concerns
Yes
-1
Corporate Governance:
Limited Compensation
Yes
1
Ownership Strength
Yes
1
Transparency Strength
Yes
1
Political Accountability Strength
Yes
1
Public Policy Strength
Yes
1
Corp Gov Other Strength
Yes
1
High Compensation
Yes
-1
Ownership Concern
Yes
-1
Accounting Concern
Yes
-1
Transparency Concern
Yes
-1
Political Accountability Concern
Yes
-1
Public Policy Concern
Yes
-1
Corp Gov Other Concerns
Yes
-1
Diversity:
CEO
Yes
1
Promotion
Yes
1
Board of Directors
Yes
1
Work Life Benefits
Yes
1
Women and Minority Contracting
Yes
1
Employment of the Disabled
Yes
1
Gay and Lesbian Policies
Yes
1
Diversity Other Strength
Yes
1
Controversies
Yes
-1
Non Representation
Yes
-1
Diversity Other Concerns
Yes
29
-1
KLD INCLUSIONARY VARIABLES
LEGAL
NORMATIVE
STRENGTH
Employee Relations:
Union Relations
Yes
1
No Layoff Policy
Yes
1
Cash Profit Sharing
Yes
1
Employee Involvement
Yes
1
Retirement Benefits Strength
Yes
1
Health and Safety Strength
Yes
1
Emp Relations Other Strength
Yes
1
Union Relations
Health and Safety Concern
Yes
-1
Yes
-1
Workforce Reductions
Retirement Benefits Concern
CONCERN
Yes
-1
Yes
-1
Emp Relations Other Concerns
Yes
Environment:
Beneficial Products and Services
Yes
1
Pollution Prevention
Yes
1
Recycling
Yes
1
Clean Energy
Yes
1
Property Plant Equipment
Yes
1
Management Systems Strength
Yes
1
Yes
1
Environment Other Strength
-1
Hazardous Waste
Yes
-1
Regulatory Problems
Yes
-1
Ozone Depleting Chemicals
Yes
-1
Substantial Emissions
Yes
-1
Agriculture Chemicals
Yes
-1
Climate Change
Yes
-1
Environment Other Concerns
Yes
-1
Human Rights:
Positive Record in South Africa
Yes
1
Indigenous Peoples Relations Strength
Yes
1
Labor Rights Strength
Yes
1
Human Rights Other Strength
Yes
1
South Africa Concern
Yes
-1
Northern Ireland Concern
Yes
-1
Burma Concern
Yes
-1
Yes
-1
Mexico Concern
Labor Rights Concern
Yes
-1
Indigenous Peoples Concern
Yes
-1
Human Rights Other Concerns
Yes
30
-1
KLD INCLUSIONARY VARIABLES
LEGAL
NORMATIVE
STRENGTH
Product:
Quality
Yes
1
R & D Innovation
Yes
1
Benefits to Economically Disadvantage
Yes
1
Yes
1
Product Other Strengths
CONCERN
Product Safety
Yes
-1
Marketing Contracting Concern
Yes
-1
Antitrust
Yes
-1
Product Other Concerns
Yes
-1
Notes: If a rating is categorized as legal CSR, the same rating cannot be categorized as normative CSR.
KLD variable descriptions are available at http://www.kld.com/research/stats/indicators.html
31
Table 1 Variable descriptions
Variables
CSR(t)
CSR(t-1)
 CSR(t)
 CSR(t-1)
CSRLEGAL
CSRNORM
ANALYST(t)
ANALYST(t-1)
DISPERSION
 DISPERSION
VOLATILITY
 VOLATILITY
LOGASSET
DEBTR
RNDR
ADVR
CAPXR
SALEGRW
KLD DUMMY
Descriptions
CSR Index in current year. It is derived from the sum of strengths minus
concerns scores over the maximum score of strengths minus concerns scores
in each year as described in equation (1).
One year lag in CSR Index (one year lag of CSR(t)).
Change in CSR index in current year ( CSR(t) = CSR(t) – CSR(t-1))
One year lag of change in CSR Index (one year lag of  CSR(t))
CSR Index based on what are required by laws (legal characteristics of CSR)
defined in Appendix A
CSR Index based on what are required by norms that are not required by laws
(normative characteristics of CSR) defined in Appendix A
Analyst following. ANALYST(t) = Log(1+Number of Analysts Estimates)
Analyst following in the previous year (one year lag of ANALYST(t)).
Standard deviation of analysts’ earnings estimates relative to the absolute
value mean of earnings estimates stated in % [Diether, Malloy, and
Scherbina, 2002]
Change in DISPERSION in current year
Standard deviation of monthly stock returns
Change in VOLATILITY in current year
Firm size. It is the log(total asset) where total asset is in $ million unit.
Long-term debt divided by total asset
Research and development expense divided by total sales
Advertising expense divided by total sales
Capital expenditure expense divided by total sales
Sales growth rate from t-1 to t stated in %
A dummy variable to indicate firms that do not have any KLD ratings but
listed in KLD database
32
Table 2 Descriptive statistics
Variables
CSR
CSRLEGAL
CSRNORM
ANALYST
DISPERSION
Observations
29487
29487
29487
23151
16895
VOLATILITY
24404
LOGASSET
26062
DEBTR
25990
RNDR
26062
ADVR
26062
CAPXR
25197
SALEGRW
25877
KLD DUMMY
29487
Notes: See variable definitions in Table 1.
Mean
-0.00306
-0.13578
0.07843
1.79849
4.33426
Median
0
0
0
1.75882
0.755
Min
-0.28238
-1
-0.66667
0.69315
0
Max
0.32778
0.33333
1
3.84541
226.46
0.12339
7.30377
0.18953
0.04288
0.01195
0.05189
15.07913
0.10065
0.10485
7.28450
0.13867
0
0
0.03273
8.06683
0
0.00279
-5.29832
0
0
0
-0.15178
-5615.1
0
2.05239
14.61450
4.98233
201.4
1.944686
12.8
25500
1
33
Table 3. Bivariate correlation coefficients
No
1
2
4
5
Variables
CSR(t)
CSR(t-1)
ANALYST(t)
ANALYST(t-1)
No
6
7
8
9
CSRLEGAL
CSRNORM
VOLATILITY
DISPERSION
1
1
0.754*
0.182*
0.201*
2
3
4
5
1
0.171*
0.199*
0.030*
0.021*
1
0.922*
1
6
7
8
9
1
-0.138*
-0.124*
-0.1771*
1
0.164*
0.0735*
1
0.0257*
1
* significant at 5% level or lower.
Total sample size for these correlation matrices is 17,835 firms-years across 3,527 firms during 1993 through 2009.
34
Table 4 Relationship between CSR, analyst dispersion, and volatility of stock returns
OLS
OLS
PANEL
FIXED
PANEL
FIXED
2SLS
2SLS
DISPERSION
VOLATILITY
DISPERSION
VOLATILITY
DISPERSION
VOLATILITY
0.22890
-0.05548
-16.08408
-0.08956
-0.36385
-0.04479
(0.50)
(1.98)*
(1.73)
(4.70)**
(0.71)
(2.54)*
-0.13310
-0.02781
-3.24451
-0.03838
0.21071
-0.00684
(1.00)
(4.02)**
(1.91)
(5.08)**
(0.41)
(0.39)
-0.02711
-0.01018
-0.02489
-0.01421
-0.02598
-0.01046
(3.32)**
(25.07)**
(0.59)
(8.94)**
(2.94)**
(35.85)**
DEBTR
0.11828
0.03618
0.25010
0.03468
0.11522
0.03666
(1.77)
(11.34)**
(1.36)
(5.27)**
(1.62)
(16.24)**
RNDR
0.40318
0.08355
0.25213
0.10116
0.40929
0.08163
(1.89)
(3.13)**
(0.43)
(4.63)**
(2.35)*
(13.78)**
ADVR
-0.07056
0.07432
-0.47503
0.02468
-0.05463
0.07299
(0.71)
(5.14)**
(0.40)
(0.56)
(0.15)
(6.30)**
CAPXR
-0.40449
-0.04247
-0.59876
-0.10167
-0.37222
-0.04960
(1.65)
(3.84)**
(0.98)
(4.26)**
(1.27)
(5.10)**
SALEGROWTH
-0.00047
0.00000
-0.00038
-0.00004
-0.00048
-0.00000
(1.13)
(0.00)
(0.81)
(2.23)*
(1.34)
(0.18)
INTERCEPT
0.24939
0.16305
0.32392
0.22269
0.22948
-0.08822
(3.17)**
(24.88)**
(0.96)
(17.60)**
(0.85)
(9.14)**
11932
14430
11932
14430
11932
14430
CSR(t)
CSR(t-1)
Control Variables:
LOGASSET
Observations
Number of firms
Adj. R-squared
2789
2985
2789
2985
2789
2985
0.0049
0.2686
0.0015
0.2317
0.0050
0.1962
Notes: See variable definitions in Table 1. To address the causality between CSR, analyst following, and firm value, we use three
different regression estimations. First, we use the ordinary least square (OLS) using the recursive estimation approach by using the
predicted value of CSR based on analyst following as a predictor for analyst dispersion and stock returns volatility. Second, we utilize
the fixed effects panel data regression to account for fixed effects within each firm in the sample. In the fixed effects estimation, we
also use the recursive approach. And third, we use the two-stage simultaneous equations regression method to estimate the effect of
CSR on analyst dispersion and stock returns volatility. Fama and French (1997) 48 industry dummy variables are not reported to
conserve space. Robust t statistics in parentheses. * significant at 5%; ** significant at 1%
35
Table 5 The Impact of legal CSR and normative CSR on analyst dispersion
CSRLEGAL(t)
CSRLEGAL(t-1)
OLS
FIXED
2SLS
OLS
FIXED
2SLS
DISPERSION
DISPERSION
DISPERSION
DISPERSION
DISPERSION
DISPERSION
-19.37310
-15.60702
-13.19768
(6.32)**
(1.96)*
(4.25)**
-11.21768
-16.22019
-13.00079
(4.31)**
(0.82)
(2.66)**
18.11709
13.50305
12.37338
(3.10)**
(2.17)*
(6.45)**
-23.01098
-17.31796
-16.86681
(4.44)**
(2.06)*
(6.43)**
CSRNORM(t)
CSRNORM(t-1)
Control Variables:
LOGASSET
0.73991
1.36167
1.98847
4.23251
5.79496
3.57725
(4.03)**
(0.65)
(25.75)**
(14.42)**
(1.82)
(14.53)**
2.01061
-2.19289
1.89665
-0.53299
3.06434
-0.35177
(3.30)**
(1.20)
(3.70)**
(0.78)
(1.76)
(0.41)
3.87155
1.11489
4.09363
12.81202
0.57954
8.27435
(2.76)**
(0.21)
(3.25)**
(7.95)**
(0.01)
(4.15)**
ADVR
2.25085
-0.80879
1.85738
11.44599
17.47730
12.44122
(1.09)
(0.07)
(0.72)
(4.84)**
(1.44)
(2.93)**
CAPXR
5.69653
-5.35805
-0.26737
9.18269
10.19410
9.57859
(1.60)
(0.61)
(0.13)
(2.34)*
(0.26)
(2.71)**
SALEGRW
0.00011
-0.00905
-0.00352
-0.00775
0.00631
-0.00996
(0.05)
(0.93)
(1.43)
(3.41)**
(0.19)
(2.59)**
INTERCEPT
-6.09090
-16.81364
-9.89978
-20.35951
-12.05948
-22.82144
(1.89)
(1.13)
(4.41)**
(5.55)**
(2.83)**
(5.80)**
Observations
14104
14104
14104
14104
14104
14104
Number of firms
3228
3228
3228
3228
3228
3228
DEBTR
RNDR
Adj. R-squared
0.1187
0.0407
0.1075
0.1174
0.0595
0.1430
Notes: See variable definitions in Table 1. To address the causality between CSR and analyst dispersion, we use three
different regression estimations. First, we use the ordinary least square (OLS) using the recursive estimation approach by
using the predicted value of CSR based on analyst following as a predictor for analyst dispersion. Second, we utilize the
fixed effects panel data regression to account for fixed effects within each firm in the sample. In the fixed effects
estimation, we also use the recursive approach. And third, we use the two-stage simultaneous equations regression
method to estimate the effect of CSR on analyst dispersion. Fama and French (1997) 48 industry dummy variables are
not reported to conserve space. Robust t statistics in parentheses. * significant at 5%; ** significant at 1%.
36
Table 6 The impact of legal CSR and normative CSR on stock returns volatility
OLS
VOLATILITY
CSRLEGAL(t)
CSRLEGAL(t-1)
VOLATILITY
VOLATILITY
VOLATILITY
2SLS
VOLATILITY
-0.03440
-0.03919
-0.00822
(3.45)**
(11.77)**
(2.12)*
-0.00962
-0.07454
-0.00293
(1.04)
(0.84)
(0.65)
CSRNORM(t)
CSRNORM(t-1)
Control Variables:
LOGASSET
PANEL FIXED
VOLATILITY
0.03255
1.73675
0.0335
(2.11)*
(2.01)*
(2.23)*
-0.01195
-0.64521
-0.01335
(4.73)**
(2.06)*
(2.19)*
-0.01137
-0.01166
-0.02651
-0.01140
-0.01115
-0.01171
(16.27)**
(13.77)**
(17.69)**
(2.57)*
(31.99)**
(39.04)**
0.03802
0.03819
0.03845
-0.02310
0.03684
0.03376
(10.58)**
(11.55)**
(9.40)**
(0.68)
(16.81)**
(16.02)**
0.12070
0.07960
0.07159
0.12994
0.08125
0.08533
(11.89)**
(2.98)**
(6.79)**
(2.10)*
(13.91)**
(15.45)**
ADVR
0.06213
0.06572
-0.00805
0.40325
0.07047
0.07329
(4.37)**
(4.52)**
(0.29)
(1.85)
(6.26)**
(6.67)**
CAPXR
-0.04366
-0.05706
-0.12311
-0.15453
-0.04925
-0.04341
(3.89)**
(4.89)**
(9.24)**
(2.94)**
(5.21)**
(4.99)**
SALEGRW
-0.00001
0.000002
-0.00002
-0.00004
-0.00000
0.00004
(0.53)
(0.03)
(2.14)*
(0.84)
(0.03)
(4.19)**
INTERCEPT
0.15915
0.17704
0.29049
0.09820
0.17152
0.17916
(27.78)**
(22.67)**
(30.02)**
(1.56)
(18.64)**
(20.70)**
Observations
17835
11587
17835
11587
17835
11587
Number of firms
3527
2566
3527
2566
3527
2566
DEBTR
RNDR
Adj. R-squared
0.2731
0.2700
0.2815
0.1012
0.2697
0.2987
Notes: See variable definitions in Table 1. To address the causality between analyst following, firm value, and risk, we
use three different regression estimations. First, we use the ordinary least square (OLS) using the recursive estimation
approach by using the predicted value of CSR based on analyst following as a predictor for firm value and risk. Second,
we utilize the fixed effects panel data regression to account for fixed effects within each firm in the sample. In the fixed
effects estimation, we also use the recursive approach. And third, we use the two-stage simultaneous equations
regression method to estimate the effect of CSR on firm value and risk. Following Chung and Pruitt (1994), Fama and
French (1997) 48 industry dummy variables are not reported to conserve space. Robust t statistics in parentheses. *
significant at 5%; ** significant at 1%.
37
Table 7 The impact of changes in legal CSR and normative CSR on the change of analyst dispersion
OLS

DISPERSION
FIXED

DISPERSION
2SLS

DISPERSION
OLS

DISPERSION
FIXED

DISPERSION
2SLS

DISPERSION
CSRLEGAL(t)
-7.09410
-5.84923
-3.23640
(1.17)
(2.35)**
(1.86)*
CSRLEGAL(t-1)
0.52387
-1.87229
-5.03077
(0.09)
(2.05)*
(1.49)
CSRNORM(t)
11.26856
7.94542
3.71312
(2.15)*
(2.04)*
(0.58)
CSRNORM(t-1)
-15.02055
-16.70508
-44.11079
(2.10)*
(2.17)*
(0.69)
Control Variables:
LOGASSET
DEBTR
RNDR
ADVR
CAPXR
SALEGRW
INTERCEPT
Observations
-0.13524
-0.47008
-0.11996
-0.22239
-0.70103
-0.15278
(1.16)
(1.77)
(0.97)
(1.64)
(2.51)*
(1.10)
0.60525
1.81480
0.83586
0.82457
2.68261
0.71051
(1.00)
(1.49)
(1.12)
(1.17)
(1.68)
(0.95)
0.00799
0.18113
-0.03168
-0.01453
0.01146
-0.00068
(0.13)
(0.27)
(0.08)
(0.21)
(0.02)
(0.00)
-3.78488
-7.87385
-4.35038
-3.98222
-8.95992
-2.10242
(1.23)
(1.01)
(0.93)
(1.16)
(0.96)
(0.41)
-0.11265
5.45697
1.06799
0.05805
-0.79532
1.03703
(0.05)
(1.07)
(0.43)
(0.02)
(0.17)
(0.41)
0.00045
-0.00073
0.00024
0.00027
-0.00090
-0.00017
(0.59)
(0.33)
(0.17)
(0.24)
(0.36)
(0.11)
-0.35576
-2.39218
-1.78276
-0.41142
1.53608
-0.65235
(0.11)
(0.64)
(0.52)
(0.09)
(3.43)**
(0.19)
8165
8165
8165
8165
8165
8165
Number of firms
2299
2299
2299
2299
2299
2299
0.0061
0.0446
0.0401
0.0072
0.0602
0.0505
R-squared
Notes: See variable definitions in Table 1. The dependent variable is the change in DISPERSION from year t-1 to t.
To address the causality between CSR and analyst dispersion, we use three different regression estimations. First, we use
the ordinary least square (OLS) using the recursive estimation approach by using the predicted value of CSR based on
analyst following as a predictor for analyst dispersion. Second, we utilize the fixed effects panel data regression to
account for fixed effects within each firm in the sample. In the fixed effects estimation, we also use the recursive
approach. And third, we use the two-stage simultaneous equations regression method to estimate the effect of CSR on
analyst dispersion. Fama and French (1997) 48 industry dummy variables are not reported to conserve space. Robust t
statistics in parentheses. * significant at 5%; ** significant at 1%.
38
Table 8 The impact of changes in legal CSR and normative CSR on the change of volatility
OLS

VOLATILITY
FIXED

VOLATILITY
2SLS

VOLATILITY
OLS

VOLATILITY
FIXED

VOLATILITY
2SLS

VOLATILITY
CSRLEGAL(t)
-0.00791
-0.00394
-0.00825
(1.97)*
(3.11)**
(1.70)*
CSRLEGAL(t-1)
-0.00339
-0.01103
-0.01102
(1.10)
(0.87)
(0.87)
CSRNORM(t)
0.05299
0.02955
0.00349
(1.87)*
(5.22)**
(1.48)
CSRNORM(t-1)
-0.06033
-0.07633
-0.00268
(2.15)*
(8.59)**
(1.19)
Control Variables:
LOGASSET
DEBTR
RNDR
-0.00097
-0.00095
0.02230
-0.00095
0.00081
-0.00092
(2.93)**
(3.31)**
(7.55)**
(2.87)**
(1.95)
(3.23)**
0.01557
0.01656
-0.06096
0.01554
0.01419
0.01252
(6.70)**
(9.65)**
(3.57)**
(6.70)**
(5.50)**
(5.98)**
0.00131
0.00148
0.00663
0.00138
0.00479
0.00137
(2.96)**
(1.56)
(0.70)
(3.06)**
(3.54)**
(2.25)*
0.00099
0.00074
-0.03711
0.00132
0.00230
0.00092
(0.11)
(0.07)
(0.34)
(0.15)
(0.14)
(0.12)
-0.01532
-0.01314
0.29915
-0.01535
-0.01121
-0.00802
(1.95)*
(2.32)*
(5.30)**
(1.95)
(1.42)
(1.28)
0.000001
0.000001
0.000001
0.000001
-0.000002
0.000001
(0.65)
(1.00)
(0.22)
(0.64)
(0.79)
(1.04)
-0.00210
-0.00266
0.04470
-0.00098
0.00512
-0.00303
(0.65)
(0.43)
(0.73)
(0.32)
(7.05)**
(0.87)
Observations
10129
10129
10129
9267
9267
9267
Number of firms
0.0522
0.0537
0.0205
0.0525
0.0512
0.0183
ADVR
CAPXR
SALEGRW
INTERCEPT
R-squared
2431
2431
2431
2399
2399
2399
Notes: See variable definitions in Table 1. The dependent variable is the change in VOLATILITY from year t-1 to t.
To address the causality between CSR and analyst dispersion, we use three different regression estimations. First, we use
the ordinary least square (OLS) using the recursive estimation approach by using the predicted value of CSR based on
analyst following as a predictor for volatility. Second, we utilize the fixed effects panel data regression to account for
fixed effects within each firm in the sample. In the fixed effects estimation, we also use the recursive approach. And
third, we use the two-stage simultaneous equations regression method to estimate the effect of CSR on volatility. Fama
and French (1997) 48 industry dummy variables are not reported to conserve space. Robust t statistics in parentheses. *
significant at 5%; ** significant at 1%.
39