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. 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Yu, Fang, 2008, Analyst coverage and earnings management, Journal of Financial Economics 88, 245-271. 28 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