key: cord-0694472-ydlucaeg
authors: Dunbar, Craig; Li, Frank; Shi, Yaqi
title: CEO risk-taking incentives and corporate social responsibility
date: 2020-08-25
journal: nan
DOI: 10.1016/j.jcorpfin.2020.101714
sha: d260d25742d01cd233cf2a3f6e2dfcd2ab77972f
doc_id: 694472
cord_uid: ydlucaeg

Abstract We examine how firms adjust CEO risk-taking incentives in response to risk environments associated with their corporate social responsibility (CSR) standing. We find strong evidence that as a firm's CSR status improves (declines), increasing (decreasing) its risk-taking capacity, the firm responds by adjusting compensation contracts to increase (decrease) CEO risk-taking incentives (Vega). One channel of the adjustment is through stock option grants. Further analyses indicate that the positive CSR-Vega association is stronger in firms with better corporate governance and in industries where riskiness is more important. Our evidence indicates that firms are not passive in response to changes in CSR status and firm risk.

Stock option-based compensation is argued to give managers incentives to take risks. The literature (e.g., Guay, 1999; Coles, Daniel, and Naveen, 2006; Armstrong and Vashishtha, 2012) consistently shows a significantly positive relation between Vega, the sensitivity of CEO wealth to stock return volatility which arises from stock option compensation, and measures of firm risk.

More recent studies suggest a causal link between Vega and risk where CEO option Vega is shown to drive decisions leading to riskier firm policies and higher firm risk (e.g., Chava and Purnanandam, 2010; Shue and Townsend, 2017) . As noted by Gormley, Matsa, and Milbourn (2013) , however, very little is known regarding how firms establish and adjust risk taking incentives provided to CEOs via options. Several studies argue that option granting should be affected by a firm"s risk environment (e.g., Edmans and Gabaix, 2011) . However, firms, through proxy filings, provide little guidance on how Vega is set or adjusted. 1 In this study, we attempt to contribute to the understanding of how managerial risk-taking incentives are established and evolve by studying how a firm"s Corporate Social Responsibility (CSR) standing affects risk taking incentives provided to a CEO through their option-based compensation. Formally, we examine how CSR standing affects CEO option Vega.

For there to be a plausible connection between CSR standing and CEO Vega, we rely on the risk management theory in the CSR literature and the agency theory in the executive 7 to avoid CSR investments, all else equal. 7

Even though in our setting reverse causality concerns are not likely to be important in theory, we perform Granger causality tests and confirm that there is no significant relation between lagged Vega and future CSR. To further reduce concerns regarding endogeneity, we employ an instrumental variables approach and find that our core results are unaffected.

To check the robustness of our core evidence, we develop and test extensions of the risk capacity hypothesis. First, we consider alternative definitions of CSR standing. When we separately consider the effect of CSR strengths and weaknesses, for example, the core relation holds. However, the effect of strengths on Vega is much stronger than weaknesses. As documented by Gormley et al. (2013) , firms are sometimes slow to adjust CEO contracts in response to changes in a firm"s risk environment. However, since positive CSR outcomes are more controllable and predictable than negative outcomes, Vega response to CSR strengths should be faster and stronger. Our second set of robustness tests consider the impact of CSR on Vega for subsets of firms that are likely to be different in terms of their sensitivity to risk and CSR. For firms where risk is expected to be more important or where CSR is expected to have a larger impact on risk, we find a stronger CSR-Vega, which is consistent with our risk capacity hypothesis.

Even though our evidence supporting the risk capacity hypothesis is strong, we recognize that alternative explanations for our findings are possible. Bebchuk and Fried (2003) argue that powerful CEOs set their own pay and prefer compensation schemes with more cash and fewer options. Powerful CEOs may also be inclined to invest little in CSR so they can expropriate 8 more resources and consume more perquisites (e.g., El Ghoul, Guedhami, Wang, and Kwok, 2016) . Taken together, the entrenchment hypothesis predicts a positive CSR-Vega relation only for firms with weak governance. Our risk capacity argument assumes that firms actively adjust executive compensation according to risk capacity created by CSR to enhance firm value. Therefore, we would expect the CSR-Vega relation to be, if anything, stronger in better-governed firms. We consider a variety of governance proxies and find that our results are consistent with the risk capacity but not the entrenchment hypothesis.

It is important to emphasize key differences between our research question and the questions considered in seemingly related recent studies. Flammer, Hong, and Minor (2019), for example, study CSR-incentives in CEO compensation contracts (see also Hong, Li, and Minor, 2016, and Ikram, Li, and Minor, 2019) . They find that CEOs with CSR-linked compensation incentives pursue policies that result in stronger CSR outcomes. Although their study appears similar to ours in that they examine a relation between executive compensation and CSR outcomes, CSR contracts and Vega focus on very different incentives and have different goals. In addition, CSR contracts and Vega have little overlap in the CEO compensation structure in terms of the form of compensation. CSR-incentives are typically structured as bonuses. If the CEO meets certain CSR-related objectives, then they receive a bonus which is almost always in the form of cash or firm stock. Importantly, cash and stock have no impact on a CEO"s Vega, which is the focus of our study. While as noted above, the Vega in option compensation encourages risk taking, cash and stock compensation have been shown to discourage it. Chava and Purnanandam (2010) , for example, find that greater pay-for-performance incentives, or "delta", arising from stock-based compensation causes CEOs to pursue policies that result in lower firm risk. Existing studies, therefore, provide little insight into our central research question: how CSR standing impacts the 9 risk-taking incentives provided to CEOs.

Overall, we believe our study makes several contributions to the literature. First, we contribute to the executive compensation literature on how CEO compensation contracts evolve based on firms" risk environment. Gormley et al. (2013) find that a firm"s board responds to positive risk shocks by decreasing CEO Vega. Our work complements that study by showing that firms respond to negative and positive changes in CSR-related risk environment by adjusting CEO Vega. Our study links the executive compensation literature and the CSR literature, and is the first to show that CSR standing affects executive risk-taking incentives.

Second, while the focus of our research is on executive incentives, our study also contributes to the large CSR literature. Much of that literature tests for the association between CSR standing and firm risk but does not consider the potential moderating effects of CEO incentives. Since firms respond to positive CSR outcomes by increasing risk-taking incentives, the existing literature likely understates the true risk-reducing effect of CSR, as CEO risk incentive adjustments partially undo the negative effect of CSR status on firm risk. We believe our study also makes a timely contribution to the debate on whether CSR is value maximizing. The mixed results in the large literature on the CSR-firm value relation may be driven by whether firms can identify and take actions to realize the economic benefits of CSR. "Doing good" alone is not sufficient for "doing well." Based on our findings, to maximize shareholder value, firms should actively respond by increasing Vega to encourage CEOs to take advantage of the new risk capacity created by CSR.

Finally, we contribute to the corporate governance literature. While both boards and CEOs have an impact on the setting of executive compensation contracts, the effectiveness of board oversight is still unclear (e.g., Guthrie, Sokolowsky, and Wan, 2012) . Our evidence on corporate J o u r n a l P r e -p r o o f governance reveals that some forms of board control, represented by enhanced diversity and inclusion of a sustainability/CSR committee for example, provide effective mechanisms in setting CEO risk incentives.

The remainder of the paper is organized as follows. Section 2 reviews the literature and develops our hypotheses. Section 3 discusses our data sources and variable measurements.

Section 4 presents our primary and robustness test results. Section 5 considers an alternative explanation for our findings, while Section 6 concludes with a discussion of the broader implications of our research.

Grounded in agency theory (Jensen and Meckling, 1976) , economists argue that CEOs are inherently more risk-averse than optimal for organizations. CEOs have their "human capital" and wealth tied to their firms, being less diversified than shareholders. Consequently, they seek to avoid risks as poor firm performance can have a significant bearing on their wealth (Milgrom and Roberts, 1992) . One solution to the "risk shirking" issue (Haubrich, 1994) is to give CEOs stock options. Options are argued to encourage risk-taking as their convex payoffs reward upside outcomes while having less or no downside effects. Prior research shows a significantly positive relation between Vega, the sensitivity of CEO wealth to stock return volatility, and measures of firm risk (e.g., Guay, 1999; Coles et al., 2006; Armstrong and Vashishtha, 2012; Chen, Chen, and Chu, 2014) and suggest that Vega is an effective tool for firms to encourage their executives to take risks. The literature finds a causal relation by studying exogenous shocks to Vega (e.g., Chava and Purnanandam, 2010; Gormley et al., 2013; Bakke, Mahmudi, Fernando, and Salas, J o u r n a l P r e -p r o o f 2016; Shue and Townsend, 2017). 8

How CSR affects firm outcomes, from stock performance and valuation to corporate policies, has been extensively studied. 9 One important stream of work has focused on the impact of CSR standing on firm risk. Orlitzky and Benjamin"s review paper (2001) notes that most studies find that CSR standing is significantly negatively associated with firm risk.

There are two primary theories linking CSR and firm risk. In line with stakeholder theory (see Freeman, 1984, and Jones, 1995) , the risk management theory posits that CSR engenders positive relationship-based intangible assets, or moral capital, and provides the firm with insurance-like protection (Godfrey, 2005) . Smith and Stulz (1985) and Stulz (2002) show that risk management adds value to shareholders when the "perfect" capital market assumption is violated in the real world. Specifically, risk management improves firm value by reducing any risks that would result in deadweight costs that cannot be diversified away by investors (e.g., bankruptcy costs).

Extending the theory that CSR serves as insurance against firm-specific idiosyncratic risk (Godfrey, 2005 ), Lins et al. (2017 propose that CSR generates social capital because it embraces civic engagement, shared beliefs, and trust between a firm and its stakeholders (also see Sapienza, Toldra-Simats, and Zingales, 2013) . Similarly, Borghesi, Houston, and Naranjo (2014) show that CSR investments are essentially part of an expansive strategy to create goodwill and form decent political relations. The concept of CSR generally refers to corporate policies and 12 activities that serve people, communities, and the environment in ways that go beyond shareholder interests and legal requirements (McWilliams and Siegel, 2000 & 2001) . An OECD paper (Scrivens and Smith, 2013) defines social capital along four dimensions: 1) personal relationships; 2) social network; 3) civic engagement; 4) trust and cooperative norms. Thus, CSR can directly map into at least 3) civic engagement and 4) trust and cooperative norms of the social capital definition.

The second theory linking CSR and firm risk, developed by Albuquerque et al. (2019) , is an industry equilibrium model where CSR is a technological investment to increase product differentiation. Product differentiation causes the firm to face relatively less-elastic demand, resulting in higher product prices and profit margins. The lower demand elasticity also results in lower firm risk as economic shocks have less effect on firm performance. In an equilibrium model, the negative effect of CSR on risk is greater when the firm"s industry is characterized as having greater product differentiation, greater profit margins, and/or lower elasticity of demand.

The predicted negative effect of CSR standing on firm risk is supported by numerous empirical studies (Orlitzky and Benjamin, 2001; Lee and Faff, 2009; Jo and Na, 2012; Oikonomou, Brooks, and Pavelin, 2012; Kim, Li, and Li, 2014; Jiraporn, Jiraporn, Boeprasert, and Chang, 2014; Krüger, 2015) . Many of them have attempted to show a causal link. For example, Godfrey et al. (2009) study the shareholder reaction to 178 unexpected negative legal/regulatory actions. They find that the market reaction to a negative event is much less negative for firms with higher CSR standing. Several recent papers use instrumental variables to support a causal relation (e.g., Albuquerque et al., 2019; Becchetti, Ciciretti, and Hasan, 2015) . 10 13

Since CSR generates social capital and/or reduces a firm"s elasticity of demand, thus reducing risk, how should firms respond? Several studies argue that option granting should be affected by a firm"s risk environment (e.g. Edmans and Gabaix, 2011) . The literature also finds that firms are active in structuring option-based compensation contracts to encourage risk-taking (e.g., Core and Guay, 2002; Coles et al., 2006; Gormley et al., 2013; Dittmann, Yu, and Zhang, 2017) . As a firm"s risk environment changes due to changes in its CSR status, we expect that CEO compensation structure will be adjusted to reflect that change. Specifically, we expect risktaking incentives in compensation contracts to be higher (lower) when a firm faces lower (higher) business risks due to its higher (lower) CSR standing. As discussed previously, CSR status should affect what Gormley et al. (2013) refer to as "left-tail risk". Marginal projects become more attractive to shareholders when CSR improves and left-tail risk declines. As a result, shareholders should want to provide risk-averse CEOs with greater incentives to pursue risky projects. Also, from the shareholder"s perspective, options are less costly to grant when CSR status is high and risk is low, so greater option-based compensation (and higher Vega) is feasible. From the CEO"s perspective, options are also more attractive when CSR status is high.

Since left-tail risk decreases in that setting, the CEO should be more willing to accept a higher financial exposure to the firm through option compensation. Summing up, our risk capacity hypothesis can be formally stated as follows: 14 We gather our data from various sources. We collect CSR data using the most comprehensive database in the literature, MSCI ESG Stats (formerly known as the Kinder, Lyndenberg, and Domini (KLD) database). MSCI ESG Stats has been broadly used in scholarly research (e.g., Deng, Kang, and Low, 2013; Cheung, 2016; Chava, 2014; Giuli and Kostovetsky, 2014; Flammer and Luo, 2016; Servaes and Tamayo, 2013; Lins et al., 2017) . We collect data for CEO incentives, accounting information, stock information and institutional ownership from Execucomp, Compustat, CRSP and 13F schedules, respectively. The data on delta, Vega, and board co-option is mostly downloaded from Dr. Lalitha Naveen"s website (See Coles, Daniel, and Naveen (2006, 2014) for a detailed description of variable measurement). 11 We obtain data related to board of director attributes from ISS (formerly Riskmetrics) and BOARDEX databases, whereas analyst following data are collected from IBES. Merging different databases yields 24,496 firm-year observations for 2,610 firms for the period 1992-2016.

Following Guay (1999) and Core and Guay (2002), we use the Black-Scholes (1973) option valuation model to calculate Vega. This is consistent with many recent papers such as Anantharaman and Lee (2014 ), Coles et al. (2006 ), Low (2009 ), Hayes, Lemmon, and Qiu (2012 , and Kim and Lu (2011) , and the common practice in evaluating executive incentives.

Vega is defined as the change in the dollar value of the CEO"s wealth for a 0.01 change in the annualized standard deviation of stock returns. Here, Vega is a proxy for CEO wealth-risk sensitivity, and thus captures the executive"s risk-taking incentive (see Hagendorff and Vallascas, 2011; Croci and Petmezas, 2015) . 11 We thank Dr. Jeff Coles and his research assistant Jie Yang for updating the data of Delta and Vega for us. J o u r n a l P r e -p r o o f 15 Following previous research (e.g., Flammer and Luo, 2016), we focus on five dimensions of CSR: community activities, diversity, employee relations, environmental policies, and product development. MSCI ESG Stats is an annual data set (generated by MSCI ESG Research, a unit of MSCI) of positive and negative social performance indicators applied to publicly traded companies. In each category, MSCI ESG Research considers several possible strength and concern subcategories. See Appendix A for a detailed list of each category and subcategory. To assess social performance, MSCI ESG Research considers macro data from academic, government and NGO datasets, company disclosures, and over 1,600 media outlets. Companies are also invited to participate in a data verification process. Strength indicators consider management"s social capabilities, as captured by explicit strategy and governance statements, corporate initiatives, and corporate performance. Concern indicators are based on MSCI ESG

Research"s proprietary database on firm controversies. A firm is given a score of 1 in a strength or concern subcategory if it is judged by MSCI ESG Research to meet its proprietary criteria for that subcategory.

To formally examine the relation between CSR and Vega we begin by following the literature on CSR to construct an aggregate CSR score measure. We sum the total number of CSR strengths and subtract the total number of CSR concerns across these five categories and subcategories. 12 To mitigate concerns regarding reverse causality, we use lagged CSR scores in all models.

To test hypotheses H1, we begin by estimating the following model: 12 Given that the total number of strength and concern subcategories for most CSR categories vary greatly each year, we construct the scaled CSR measure, following Deng et al., (2013) and Lins et al., (2017) , by dividing the strength and concern scores for each dimension by the respective total number of strength and concern areas to obtain scaled strength and concern scores for that dimension and then taking the difference between the scaled strength and concern scores. We discuss robustness tests using alternative CSR measures in Section 4.2.5. J o u r n a l P r e -p r o o f Journal Pre-proof 16 VEGA t+1 = β 0 + β 1 CSR t + β 2 DELTA t + β 3 DUALITY t + β 4 TENURE t + β 5 AGE t + β 6 FEMALE t + β 7 OWNERSHIP t + β 8 CASHCOMPENSATION t + β 9 EINDEX t + β 10 INSTHOLD t + β 11 ROA t + β 12 LEVERAGE t + β 13 CAPEX t + β 14 Q t + β 15 SIZE t + β 16 

where CSR t is the aggregate CSR score in period t and all the control variables are defined below and in Appendix B.

We include a variety of control variables that are shown in the literature to have an influence are not available for all firm years, we report models with them included separately. Table 2 reports Pearson correlation coefficients. Firms with higher CSR scores are associated with higher VEGA, which is consistent with our risk capacity hypothesis. Further, firms with longer-tenured and higher-cash compensation CEOs, higher return on assets, higher Tobin"s Q, and larger size are more likely to offer compensation with greater risk-taking incentives (i.e., higher Vega). Conversely, firms with more spending on capital expenditure are less likely to incentivize their CEOs to take on risks.

Insert Table 2 here

J o u r n a l P r e -p r o o f in all the models. The R-squares for all models range from 42% to 77%, suggesting that these models are significant in describing variation for CEO"s risk-taking incentives, Vega. Columns

(1) -(3) report results without controlling for board characteristics, whereas columns (4) - (6) show results after controlling for board characteristics including the variables ETHINDEX,

Column (1) presents results from the industry-fixed effect model. The significantly positive coefficient on CSR supports our H1. Columns (2) and (3) Insert Table 3 here

Although the analysis in Table 3 establishes a positive link between CSR and subsequent CEO risk-taking incentives, the underlying mechanism through which firms adjust incentives in response to CSR outcomes has not been identified. One possible channel for the board to adjust 15 Motivations to include industry-fixed effect model, firm-fixed effect model and lagged dependent variables are discussed in Section 4.2.3 Granger Causality Analysis. 16 It should be noted that we scale our original VEGA score by 100 in our regression analyses to avoid reporting extremely large coefficients. 17 We also test the impact of CSR on CEO"s pay-performance sensitivity, Delta, defined as the change in dollar value of the CEO"s wealth for a one-percentage point change in stock price. While high Delta aligns the interests of executives and shareholders, its relation to firm risk is ambiguous. Prior studies demonstrate that there is no significant relation between Delta and firm risk (Coles et al., 2006; Low, 2009 ). We find the relationship between CSR and Delta is insignificant.

Journal Pre-proof 20 CEO risk-taking incentives is through option grants. Therefore, we test whether CSR status is related to subsequent CEO option grants. We assume two proxies for option grants: one is the number of options granted to CEO in year t+1 (OPTION t+1 ) and the other is the value, more specifically the grant date fair value, of options granted to CEO in t+1 (OPTIONVALUE t+1 ).

Columns (1)-(3) of Table 4 summarize the results using OPTION t+1 as the dependent variable and Columns (4)-(6) using OPTIONVALUE t+1 . The coefficients on CSR are positive and significant in all columns except column (6), indicating that CSR status is positively associated with both the number and the value of options granted to the CEO in the subsequent period.

These findings suggest that granting more options is an important mechanism that the board engages to adjust CEO compensation incentives in response to CSR outcomes.

Insert Table 4 here

In our analysis thus far, we have used different control variables and fixed effects models to address the issues of endogeneity and omitted variables. The results are robust to controlling for various observable firm and manager characteristics and unobservable time, industry, firm, and manager fixed effects. We also include a lagged dependent variable (i.e., lagged VEGA) as a 

To determine the optimal lag lengths n, we refer to the Bayesian information criterion (BIC) (Schwarz, 1978; Risannen, 1978) and the Hannan-Quinn information criterion (QIC) (Hannan and Quinn, 1979) and conclude the appropriate lengths should be 4 years. 18

Consistent with our hypothesis that a firm"s CSR standing influences its executive contracting of risk incentives, the evidence in Table 5 suggests that the causality from CSR to Vega is much stronger than the reverse causality. Based on the computed Chi-squares and their marginal significance level, Model 1 confirms that CSR Granger causes or leads Vega and Model 2 suggests that Vega leads CSR only at a marginal level.

Insert Table 5 here

To further mitigate endogeneity concerns, we use instrumental variable (IV) analysis to provide reasonable exogenous variation to identify the impact of CSR on Vega. Our first IV Table 6 reports results for our Two-Stage Least Square instrumental variable models (2SLS).

We estimate three models in which the endogenous regressor is our net CSR score. The firststage model estimates reported in Column (1) indicate that the instrument (BLUESTATE) significantly explains our CSR regressor. Columns (2) through (4) report second stage models with different dependent variables. Along with the controls considered previously, we include the predicted CSR from the first stage model. In column (2) the dependent variable is lead Vega.

Consistent with evidence in Table 3 , predicted CSR has a significantly positive effect on Vega. In

Column (3), the dependent variable is the lead number of options granted to CEO and in column (4), the dependent variable is the lead value of options granted to CEO. In both cases, the evidence is consistent with Table 4 and indicates that predicted CSR significantly positively affects option grants.

Insert Table 6 here

We statistically test the instruments for their relevance and validity. The first-stage F statistic surpasses the usual rule of thumb of 10; the over-identification test (Basmann"s test) cannot reject the null hypothesis that the instruments are valid and orthogonal to the regression residuals, and the Hausman test rejects exogeneity of the endogenous variable CSR. These results suggest that these instruments are exogenous under the usual assessment of instrumental variables, and therefore 2SLS is more efficient than OLS in this setting. 20

To check the validity of our results, we consider alternative measures of CSR standing. As argued in Servaes and Tamayo (2013) and Lins et al. (2017), the product category of CSR is comprised of a number of elements that may be less relevant to corporate social performance and therefore outside the scope of CSR. We follow Servaes and Tamayo (2013) Tables 3 and 4, employing this alternative proxy for CSR and find that the revised CSR measure is significantly positively related to Vega and option grants in all models (industry fixed effects, firm-fixed effects and lagged dependent variables). 21

20 Following a large literature on CSR, we also consider three alternative instruments for CSR. We calculate the average CSR score for each state-year pair and industry-year pair. Our first alternative instrument is the average CSR rating of all the firms, except the firm itself, in the state where the firm is located. The rationale is that regional CSR practices influence a firm"s social performance (Goss and Roberts, 2011). Likewise, our second alternative instrument is based on industries because industry characteristics also determine CSR performance (Cheng, Ioannou, and Serafeim, 2014) . Meanwhile, it is unlikely that industrial CSR would directly affect a specific firm"s compensation structure (after controlling for industry and year fixed effects). Based on similar arguments, Goss and Roberts (2011) and Cheng et al. (2014) use these IVs in their studies. Following Flammer (2018), we use enactment of constituency statutes as our third alternative IV for CSR. Such statutes allow firms to consider the interests of a range of stakeholders in meeting their fiduciary responsibilities. The exogenous passage of statutes arguably increases the likelihood that firms headquartered in the state will pursue activities that have positive effects on CSR standing. Our untabulated results indicate that our primary results persist after instrumenting CSR with these three IVs. 21 The results, untabulated to conserve space in the paper, are available upon request.

J o u r n a l P r e -p r o o f (Guiso Sapienza and Zingales, 2004, 2008) . Therefore, both the civic engagement and cooperation norms perspectives imply that CSR strengths rather than concerns engender trust and J o u r n a l P r e -p r o o f Journal Pre-proof 25 social capital. An implication in our setting is that strengths should have a larger impact on a firm"s risk environment than concerns, in which case the Vega-strength relation would be stronger than the Vega-concern relation. Finally, we note that CSR measurement issues potentially influence the significance of strengths and concerns. Gormley et al. (2013) suggest that adjustments to changes in the risk environment can be slow. While our core evidence uses one-year lag, results are similar with longer lags, consistent with adjustments being slow. MSCI ESG Research"s assessment of most strength areas is based on publicly stated policies and initiatives. In contrast, the assessment of concerns is mostly based on third party assessments (e.g., media criticism by NGOs). From the firm"s perspective, their strength rating should be much more predictable than their concern rating. Since expectations of CSR changes can impact the speed of adjustment in Vega, we expect that the CSR-Vega relation should be stronger for strength measures of CSR than concerns.

Empirically, we explore the relation between CEO incentives and CSR strengths and concerns using the following model:

where STRENGTH t is the sum of total CSR strengths and CONCERN t is the sum of CSR concerns for the firm in period t. For columns (4) and (5), the STRENGTH and CONCERN variables are replaced with predicted values.

Insert Table 7 here

All models show a strong and significant relation between strengths and Vega. The relation between concerns and Vega are weaker and only occasionally statistically significant. The findings, therefore, are more consistent with civic engagement and cooperation norms perspective which suggests that strengths do more to build social capital than concerns do to destroy it. The findings are also consistent with measurement issues. Since strengths are more predictable, they should be more swiftly incorporated in board and CEO decisions regarding future risk-taking incentives. Interestingly, the instrumental variables evidence shows that predicted concerns also strongly impact future Vega, consistent with expectations playing an important role in the relation between CSR standing and firm decisions regarding CEO incentives.

We also consider models where the five separate categories of social performance are measured independently. Bouslah, Kryzanowski, and M"Zali (2013) find that the different CSR dimensions have different impacts on firm risk. In unreported results, we find that all categories of social performance have a significantly positive relation with future Vega except the product category (which is insignificant). 23

Our analysis thus far examines the impact of CSR standing on the conditional mean Vega. It is possible, however, that high risk is incompatible with CSR. In this case, the CSR would not play a role in explaining, for example, the 90 th percentile of Vega. To explore this possibility, we 23 As noted earlier, Servaes and Tamayo (2013) and Lins et al. (2017) argue that the product category of CSR is comprised of a number of elements that may be less relevant to corporate social performance. It is not surprising, therefore, that standing in product has no impact on Vega.

replicate Table 3 using quantile regressions. In unreported results, we re-estimate each model in Table 3 predicting the 25 th , 50 th and 75 th percentiles of Vega. In general, we find that the impact of CSR is more positive on the 75 th percentile of Vega than the 25 th percentile. In particular, we find that many firms with very low or zero Vega do not respond to CSR standings by granting any options to their executives. In contrast, firms with high Vega make greater adjustments according to CSR status; Vega appears to be an important incentive tool in such firms.

As discussed earlier, incentives in CEO option are different than those provided through CSR incentives which usually come in the form of cash or stock bonuses. While conceptually distinct, we recognize the possibility of a spurious relation since we find that the correlation between Vega and the presence of CSR incentives is positive (approximately 7%). In untabulated analysis, we replicate Tables 3 and 4 for different subsamples based on the presence of CSR incentives. We find that the impact of CSR on Vega remains significantly positive in both cases with coefficient magnitudes similar to what we report in our core models.

While the evidence presented thus far is consistent with our risk capacity hypothesis, we further test additional implications of this hypothesis. If risk capacity is driving our findings, the CSR-Vega relation should be more significant for firms where the potential impact of CSR on risk is expected to be largest. Albuquerque et al. (2019) find that the risk-reducing effects of CSR are larger for firms having greater product differentiation. We, therefore, expect that the CSR-Vega relation should be stronger for firms with greater product differentiation. Formally, we estimate the model:

Journal Pre-proof 28 Where DIFF t is a dummy variable equal to one if the firm exhibits high product differentiation and 0, otherwise (CONTROLS t is a vector of control variables considered previously). In this specification, we expect  2 to be significantly positive.

Estimates of equation (5) using OLS are reported in the first two columns of Table 8 . 24 In the first model DIFF t is set to 1 if the firm is in a high-tech industry, 25 and in the second model DIFF t is set to 1 if the firm is in a consumer industry. 26 The coefficient  2 is positive in both cases, and significantly so when product differentiation is defined based on high-tech status.

Insert Table 8 here

Conceptually, there are different types of risks a manager can take. "Good" risks are those arising from activities motivated by the desire to enhance firm value; "bad" risks are associated with poor management practices (e.g., investing in declining industries) or risk shifting (taking risks to expropriate bondholder wealth). The risk capacity hypothesis argues that firms adjust risk-taking incentives to enhance value. Therefore, the CSR-Vega relationship should be stronger for firms more likely to take on "good" risks rather than "bad". Formally, we estimate the model:

where GOODRISK t is a dummy variable equal to one if the firm is more likely than the typical firm to pursue good risk and 0, otherwise. In this specification, we expect  2 to be significantly positive under the risk capacity hypothesis. OLS estimates of equation (6) are reported in 24 We also estimate equation (5) (2010) show that consumer goods firms have relatively higher product differentiation. Jones (1999) also show that CSR reputation is more important for consumer goods firms.

Journal Pre-proof 29 columns (3) to (5) of Table 8 . 27 In column (3) GOODRISK t equals one if the firm is not financially distressed (Z-Score above 1.81) versus financially healthy firms. We expect that financially distressed firms are more likely to take on bad risks (Eisdorfer, 2008) . In column (4) GOODRISK t equals one if the firm has an above median credit rating. We expect that firms with lower credit ratings are more likely to take on bad risks. Finally, in column (5) GOODRISK t equals one if the firm has an above median Tobin"s Q. Firms with high Tobin"s Q are likely to have more value-enhancing growth opportunities than low-Q firms. We would expect high-Q firms to have more projects of good risks. In all three cases the coefficient is significantly positive, consistent with predictions of the risk capacity hypothesis.

Although we find strong evidence to support risk capacity theory thus far, alternative explanations for our findings are possible. For instance, Bebchuk and Fried (2003) find that powerful CEOs set their own pay and prefer compensation schemes with more cash and fewer options. Powerful CEOs are also likely to invest little in CSR so they can expropriate more resources and consume more perquisites (e.g., El Ghoul et al. 2016) . Taken together, the entrenchment hypothesis predicts a positive CSR-Vega relation only for firms with weak governance. The risk capacity hypothesis posits that firms and CEOs adjust risk-taking incentive to improve firm decision making and enhance value. Since well-governed firms are more likely to make value-enhancing decisions, a stronger CSR-Vega relation for well-governed firms would be consistent with the risk capacity hypothesis. Formally, we estimate the model:

VEGA t+1 = β 0 + β 1 CSR t + β 2 CSR t *GOODGOV t + β 3 CONTROLS t (7) 27 We also estimate equation (6) using 2SLS where we instrument CSR t and CSR t * GOODRISK t (separately) using the BLUESTATE variable as defined earlier. Results are qualitatively similar to what we report in Table 8 .

where GOODGOV t is a dummy variable equal to one if the firm is more likely than the typical firm to have strong governance and 0, otherwise. In this specification, we expect  2 to be significantly positive under the risk capacity hypothesis and significantly negative under the entrenchment hypothesis.

We consider both internal governance and external governance variables. To capture internal governance, we construct five different dummy variables that are set to one for firms with strong (board gender diversity) is above median. To capture external governance variables, we use a dummy variable (HANALYSTFOLLOWING) equal to one if the number of analysts following a firm is above median. We posit that governance should be stronger for firms with greater analyst following. Table 9 presents OLS estimates models of equation (7) using different proxies for GOODGOV as introduced above. 28 In all cases, coefficient  2 is significantly positive, consistent with risk capacity. Further, it should be noted that the coefficients for CSR ( 1 ) remain positive and are generally significant across models. Taken collectively, our results provide strong support for the risk capacity hypothesis rather than the entrenchment hypothesis.

Our results also highlight that board control is effective in setting CEO compensation even after 28 We also estimate equation (7) using 2SLS where we instrument CSR t and CSR t * GOODGOV t (separately) using the BLUESTATE variable as defined earlier. Results are qualitatively similar to what we report in Table 9 .

J o u r n a l P r e -p r o o f controlling for many variables related to CEO power (e.g., CEO tenure, duality, and percentage of ownership).

Insert Table 9 here

In this paper, we examine whether and how firms adjust CEO risk-taking incentives in response to the risk environment associated with CSR standing. Our primary analysis considers the relation between measures of aggregate CSR status and Vega. We find that as CSR status improves (declines), firms respond by adjusting CEO compensation contracts to increase (decrease) risk-taking incentives. To better understand how firms adjust Vega, we consider option grants and find that such grants vary significantly and positively with CSR changes. Taken collectively, our findings suggest that firms actively adjust CEO incentives in response to CSR standing through option grants.

We test the role of corporate governance in the CSR-Vega relation and find that the positive association is more pronounced in firms with stronger board control and more analyst following.

The results indicate that the positive association between CSR and Vega is driven by risk capacity but not CEO entrenchment.

Finally, we explore the effect of firm riskiness on CSR-Vega relation and find it stronger for the high-tech industries, the consumer goods industries, firms with sound financial condition, higher credit ratings and with higher Tobin"s Q. In sum, the further analyses indicate that in settings where riskiness is expected to be more paramount, the CSR-Vega relationship is stronger, consistent with the risk capacity hypothesis.

Since the extensive empirical literature linking CSR to firm risk does not consider the moderating effects of CEO incentives, our findings raise questions regarding the interpretation of J o u r n a l P r e -p r o o f evidence in the literature. Interestingly, while CEO risk-taking incentives dampen the riskreducing effect of CSR, they do not eliminate or reverse the effects. Overall, we believe future empirical research on the relation between CSR status and firm risk should be mindful of potential moderating effects of executive incentives.

Our study also informs the on-going debate on whether CSR is value maximizing. The contradicting findings in the large literature on the CSR-firm value relation may be driven by whether firms can identify the economic benefits of CSR (for example, the risk management perspectives of CSR) and more importantly, take actions to realize them. In our case, to maximize shareholder value, firms should actively respond by adjusting executive risk incentives to take advantage of the new risk capacity created by CSR.

While we believe our study shows a strong connection between CSR standing and CEO Vega, we acknowledge that some limitations remain. First, as with most empirical studies in this area, unobserved factors could explain our results. However, our results persist after including a variety of fixed effects, lagged dependent variable, IV models, and a series of robustness checks.

Also, reverse causality and the mechanical relation between Vega and risk bias against finding our results. Our analyses on CSR strengths versus concerns and other CSR measures tell a consistent story. While conceivable, it is difficult to find a self-selection or spurious relation story that generates all these results. Second, our work assumes a strong negative connection between CSR standing and firm risk. Future research should attempt to more clearly identify situations to see if implications of our risk-capacity hypothesis hold. Finally, while we provide evidence on how firms adjust CEO compensation contracts in response to risk environment, the setting of CSR is not exogenous. Future research can explore truly exogenous settings, such as regulatory changes or pandemic shocks, to further examine this question.

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This table reports the Pearson correlation coefficients among variables for 24,496 observations for 2,610 firms for the period 1992-2016. See Appendix for variable definitions. The superscripts *, **, and *** indicate significance at the 10%, 5%, and 1% levels (two-tailed test), respectively. This table presents the results for the effects of corporate social responsibility (CSR) on CEO incentive, i.e., VEGA. The dependent variable in each regression is the leading Vega (i.e., VEGA t+1 ) , where VEGA is measured as the dollar change in the value of CEO"s annual equity-based compensation associated with a 0.01 change in the annualized standard deviation of the firm"s returns (divided by 100). CSR is the net score of CSR rating (total strengths subtracting total concerns), based on five categories of KLD rating data, i.e., community, diversity, employee relations, environment, and product. Columns (1)-(3) represent models without controlling for board characteristics, whereas columns (4)-(6) represents models controlling for board characteristics (ETHINDEX, FININDEX, NUMBOARDS, PCT_FEMALE, BOARDSIZE, INDEPENDENCE, and CO-OPTED) in a reduced sample. All other variables are defined in the Appendix. Coefficient estimates (p-values) are provided in the top (bottom) row. The superscripts *, **, and *** indicate significance at the 10%, 5%, and 1% levels (two-tailed test), respectively.

Industry-fixed effect model (1) Firm-fixed effect model (2) Lagged Dependent model (3) Industry-fixed effect model (4) Firm-fixed effect model J o u r n a l P r e -p r o o f

This table presents the results exploring the effect of CSR on subsequent CEO option grants. We adopt two proxies for option grants. In model (1), (2), and (3), the dependent variable is the leading option (OPTION t+1 ), where OPTION is the log transformation of the amount of options granted to CEO. In model (4), (5), and (6), the dependent variable is lead option value (OPTIONVALUE t+ 1), where OPTIONVALUE is the log transformation of the value of options granted to CEO, determined by Black-Sholes model. All variables are defined in the Appendix. Coefficient estimates (p-values) are provided in the top (bottom) row. The superscripts *, **, and *** indicate significance at the 10%, 5%, and 1% levels (two-tailed test), respectively. Industry-fixed effect model (4) Firm-fixed effect model J o u r n a l P r e -p r o o f 

This table presents the results for the effects of CSR on CEO incentive using instrumental variable for CSR. The endogenous regressor for is CSR overall score. In first stage (model (1)), we employ BLUESTATE as the instrument variable. BLUESTATE is a dummy variable, which equals one if a firm"s headquarters is located in a blue (democratic) state and zero if otherwise. The dependent variable in the first stage is CSR. In second stage models (2)-(4), we use the predicted CSR values from the first stage as the independent variable. The dependent variable in reported second stage models are VEGA t+1 (models 1 and 2), defined as the dollar change in the value of CEO"s annual equity-based compensation associated with a 0.01 change in the annualized standard deviation of the firm"s returns (divided by 100), OPTION t+1 (model 3), defined as the log transformation of the amount of options granted to CEO, and OPTIONVALUEt +1 , defined as the log transformation of the value of options granted to CEO, determined by Black-Sholes model. Coefficient estimates (p-values) are provided in the top (bottom) row. The superscripts *, **, and *** indicate significance at the 10%, 5%, and 1% levels (two-tailed test), respectively. J o u r n a l P r e -p r o o f , VEGAt+1) , where VEGA is measured as the dollar change in the value of CEO"s annual equity-based compensation associated with a 0.01 change in the annualized standard deviation of the firm"s returns (divided by 100). STRENGTH is the sum of all strength scores, and CONCERN is the sum of all concern scores, based on five categories of KLD rating data, i.e., community, diversity, employee relations, environment, and product. Columns (1)-(3) represent represents models controlling for board characteristics (ETHINDEX, FININDEX, NUMBOARDS, PCT_FEMALE, BOARDSIZE, INDEPENDENCE, and CO-OPTED) in a reduced sample. Columns (4) and (5) report estimates from the second stage of Instrument Variables regressions. The first stage for column (4) has STRENGTH as the dependent variable and the first stage for column (5) has CONCERN as the dependent variable. The instrument is BLUESTATE, a dummy variable equal to one if the firms" headquarters is located in a blue (democratic) state and zero if otherwise. The second-stage models reported use predicted values of STRENGTHS and CONCERNS from the first stage models. All other variables are defined in the Appendix. Coefficient estimates (p-values) are provided in the top (bottom) row. The superscripts *, **, and *** indicate significance at the 10%, 5%, and 1% levels (two-tailed test), respectively.

Industry-fixed effect model (1) Firm-fixed effect model (2) Lagged Dependent model J o u r n a l P r e -p r o o f 0000-0999, 2000-2399, 2500-2599, 2700-2799, 2830-2869, 3000-3219, 3420-3429, 3523, 3600-3669, 3700-3719, 3751, 3850-3879, 3880-3999, 4813, 4830-4899, 5000-5079, 5090-5099, 5130-5159, 5220-5999, 7000-7299, and 7400-999) . Column (3) reports results for financially distressed versus financially healthy firms, where financially distressed firms have Z-Score below 1.81 (Eisdorfer, 2008) and financially healthy firms have Z-Score equal to or above 1.81. Column (4) presents results for firms based on credit rating (CREDITRATING equals 1 if the firm rating is above the median value of 11, where rating values range from 1 for S&P D rating to 24 for S&P AAA rating). Column (5) reports results for high Tobin"s Q vs. low Tobin"s Q, where high Q firms are those with Tobin"s Q equal to or above the median (1.5), and low Q firms are those below 1.5. The dependent variables in all models are VEGA t+1. All variables are defined in the Appendix. Coefficient estimates (pvalues) are provided in the top (bottom) row. The superscripts *, **, and *** indicate significance at the 10%, 5%, and 1% levels (two-tailed test), respectively. 

The sum of all concern scores, based on five categories of KLD rating data, i.e., community, diversity, employee relations, environment, and product; Net score of CSR rating (total strengths subtracting total concerns), based on one category of KLD rating data, i.e., community; Net score of CSR rating (total strengths subtracting total concerns), based on one category of KLD rating data, i.e., diversity; Net score of CSR rating (total strengths subtracting total concerns), based on one category of KLD rating data, i.e., diversity; Net score of CSR rating (total strengths subtracting total concerns), based on one category of KLD rating data, i.e., environment; Net score of CSR rating (total strengths subtracting total concerns), based on one category of KLD rating data, i.e., product; 

Net score of CSR rating (total strengths subtracting total concerns), based on one category of KLD rating data, i.e., human rights.

DELTA Dollar change in the value of CEO"s annual equity-based compensation for a 1% change in the stock price (in $000s);

Coles, Daniel and Naveen (2006) and Dr. Naveen"s website https://sites.temple.edu/lnaveen/data/ VEGA Dollar change in the value of CEO"s annual equity-based compensation associated with a 0.01 change in the annualized standard deviation of the firm"s returns (in $000s).

Coles, Daniel and Naveen (2006) The firm-year Tobin"s Q, which is computed as the sum of the book value of total assets plus the market value of common stock less the book value of equity over the book value of assets;

Percentage of institutional share ownership Return on assets Total liabilities over total assets Capital expenditures over total assets Log of total assets at the end of the fiscal period Altman"s Z score = 1.2 (working capital/total assets)+1.4(retained earnings/total assets)+3.3 (earnings before interest and taxes/total assets)+0.6(market value of equity/book value of total liabilities)+0.999(sales/total assets) The S&P credit rating from AAA (24) 

Corporate social responsibility and firm risk: Theory and empirical evidence

Managerial risk-taking incentives and corporate pension policy

Executive stock options, differential risk-taking incentives, and firm value

Corporate social responsibility and credit ratings

The causal effect of option pay on corporate risk management

The extensiveness of corporate social and environmental commitment across firms over time

What Matters in Corporate Governance?

Executive compensation as an agency problem

Corporate social responsibility, stakeholder risk and idiosyncratic volatility

The pricing of options and corporate liabilities

Corporate socially responsible investments: CEO altruism, reputation and shareholder interests

The impact of the dimensions of social performance on firm risk

CEO duality and firm performance: A contingency model

Corporate social performance and stock returns: UK evidence from disaggregate measures

Corporate social responsibility, firm value ad influential institutional ownership

Commitment to build trust by socially responsible firms: evidence from cash holdings

Environmental externalities and cost of capital

 Table 9 The Effect of Corporate Governance This table presents the results for the effects of board characteristics and external governance mechanism on VEGA, using OLS regressions. The dependent variable is the leading Vega (VEGA t+1 ) for all models. Internal corporate governance variables include LETHINDEX (equals one if ETHINDEX is below median 9.965, meaning more diversity in ethnicity, zero otherwise), HFININDEX (one if FININDEX is greater than median 9.567, meaning more financial experts, zero otherwise), SUSTAINABILITY (one if a firm has a sustainability/CSR committee, zero otherwise), HNUMBOARDS (one if NUMBOARDS is greater than the median 10.056, zero otherwise), and HFEMALE (one if PCT_FEMALE is greater than the median 10.039, zero otherwise). The external governance mechanism considered is HANALYSTFOLLOWING (one if ANALYSTFOLLOWING is greater than the median 9, zero otherwise). All variables are defined in Appendix B. Coefficient estimates (p-values) are provided in the top (bottom) row. The superscripts *, **, and *** indicate significance at the 10%, 5%, and 1% levels (two-tailed test), respectively. 

Highlights:(1) As a firm"s CSR status improves (declines), increasing (decreasing) its risk-taking capacity, the board responds by adjusting compensation contracts to increase (decrease) CEO risk-taking incentives (Vega);(2) Corporate boards use option grants to adjust CEO incentives in response to their CSR standing;(3) The positive CSR-Vega association is stronger in firms with better corporate governance and in industries where riskiness is more important;(4) To maximize shareholder value, firms should actively respond by increasing Vega to encourage CEOs to take advantage of the new risk capacity created by CSR.J o u r n a l P r e -p r o o f