Journal of Banking & Finance 60 (2015) 252–270
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Corporate social responsibility and social capital Anand Jha ⇑, James Cox 1 Texas A&M International University, Laredo, TX, United States
a r t i c l e
i n f o
Article history: Received 30 January 2015 Accepted 3 August 2015 Available online 17 August 2015 JEL classification: G30 G39 Keywords: Corporate social responsibility Social capital Culture
a b s t r a c t When corporations make an effort to be socially responsible beyond what is required by the law, this effort is often described as strategic—made mainly for the shareholders’ or managers’ benefit. A large body of literature corroborates this belief. But, could the incentives for corporate social responsibility (CSR) come from an altruistic inclination fostered by the social capital of the region in which the firm is headquartered? We investigate whether this phenomenon exists by examining the association between the social capital in the region and the firm’s CSR. We find that a firm from a high social capital region exhibits higher CSR. This result suggests that the self-interest of shareholders or mangers does not explain all of the firm’s CSR, but the altruistic inclination from the region might also play a role. Ó 2015 Elsevier B.V. All rights reserved.
1. Introduction Corporations often portray themselves as socially responsible members of society.2 These actions extend to, but are not limited to, the community where the firm operates; the environment; and the firm’s treatment of employees, suppliers, and customers. The last few decades have seen a surge in corporate social responsibility (CSR) (Callan and Thomas, 2009; Tsoutsoura, 2004). In 2014, U.S. and U.K. firms in the Fortune Global 500 spent $15.2 billion on CSR.3 Investors increasingly appear to value CSR. According to a 2010 report on the trend in socially responsible investing, $3.07 trillion of the professionally managed U.S. assets was tied to socially responsible investing.4 Given the resources devoted to CSR, it is important to understand the motivation behind CSR. There are two main views that try to explain CSR. One view, often called the stakeholder maximization view, suggests that managers practice CSR to maintain better relations with other stakeholders such as workers, suppliers, ⇑ Corresponding author. Tel.: +1 956 326 2581. E-mail addresses: [email protected]
(A. Jha), [email protected]
(J. Cox). Tel.: +1 (956) 326 2514. Corporate social responsibility (CSR) is defined as the ‘‘actions that appear to further some social good, beyond the interests of the firm and that which is required by law” (McWilliams and Siegel, 2001). 3 See the report published in the Financial Times on October 12, 2014: http://www. ft.com/intl/cms/s/0/95239a6e-4fe0-11e4-a0a4-00144feab7de.html#axzz3Ny1R3aE5. 4 See the 2010 report published by The Form for Sustainable and Responsible Investment available at: http://www.ussif.org/store_product.asp?prodid=10. 1 2
http://dx.doi.org/10.1016/j.jbankfin.2015.08.003 0378-4266/Ó 2015 Elsevier B.V. All rights reserved.
and bankers, who then reward the firm (Deng et al., 2013). This view considers CSR to be strategic. A number of recent studies support this view. For example, research shows that high CSR is associated with a lower cost of equity (El Ghoul et al., 2011), lower cost of debt (Goss and Roberts, 2011), easier access to credit (Cheng et al., 2014), lower risk of a stock price crash (Kim et al., 2014), and better access to political relations (Lin et al., 2014). Another view addresses the ulterior motive behind CSR. Often called the shareholder expense view, it suggests that managers engage is socially responsible activities at the expense of shareholders, possibly for their own benefit (Cronqvist et al., 2009; Pagano and Volpin, 2005; Surroca and Tribó, 2008). Studies arguing this viewpoint posit that the association of CSR with financial performance is mixed at best (see Margolis et al., 2009 for a review). While some studies document a positive association between CSR and financial performance (e.g., Deng et al., 2013; Erhemjamts et al., 2013; Wu and Shen, 2013), some studies do not (e.g., Di Giuli and Kostovetsky, 2014; McWilliams and Siegel, 2001). Di Giuli and Kostovetsky (2014) in fact find that higher CSR ratings are associated with declines in the return on assets and negative stock returns. But the motivation for CSR need not always be the monetary benefit of some party. Missing from the literature is the idea that nonfinancial factors such as the social capital of the firm’s location might also influence CSR. Corporations do not make decisions, managers do, and managers are likely to be influenced by the social capital in the region where they live. For example, some regions, because of their historic traditions and norms that are passed on
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from generation to generation, might be more altruistic than others. And firms headquartered in these regions might exhibit higher CSR. Two streams of literature motivate this idea. One is the classical idea, first discussed more than 2000 years ago by Aristotle in his book Nicomachean Ethics. Aristotle argued that in civilized societies, ethics plays a role in an individual’s decision (Aristotle, 2004). This ancient idea has gained traction in recent years. In his presidential address to the American Economic Association, Professor Akerlof points out that a person’s ideals affect his or her decisions—and when he or she deviates further from those ideals, it is costly. He suggests that researchers should consider this aspect to better understand how decision-makers choose between options (Akerlof, 2007). The second stream of literature is one that builds on Akerlof’s suggestion. These studies examine the role of values in decisionmaking. Often, these studies use the norms of the headquarters’ region as a proxy for the norms of the firm’s managers and examine their associations with managerial decisions. For example, Hilary and Hui (2009) show that firms headquartered in religious regions make less risky decisions; Grullon et al. (2010) show that in general the firms in religious regions are less likely to misbehave. Further, McGuire et al. (2012b) make a similar argument and show that firms headquartered in religious regions are less likely to misreport earnings. The key argument in these studies, which they borrow from the psychology literature on personnel, is that if the managers reside close to the firm’s headquarters, then the managers’ culture tends to a large extent to be congruent with the culture of the region. We build on these two streams of literature and investigate how social capital affects CSR by asking the following question: Is the extent of the firm’s CSR associated with the social capital of the region where it is headquartered? Defined as the norms and networks that encourage cooperation, social capital is the most precise social construct to capture altruistic inclinations. Following the literature (Grullon et al., 2010; Hilary and Hui, 2009; McGuire et al., 2012a,b), we use the norms in the headquarters’ region as a proxy for the corporate norm. To investigate the association between the social capital and CSR, we exploit the variations in the social capital of counties in the United States and examine their impact on the extent of a firm’s CSR. We construct a CSR score for each firm-year using the ratings in the Kinder, Lydenberg, and Domini database, KLD Stats. To measure the social capital, we construct a social capital index as in Rupasingha and Goetz (2008). We match the social capital data to the firm-level data based on the headquarters’ zip codes. We then conduct a firm-level analysis to examine the social capital’s impact on the CSR by using a multivariate framework where we control for firm-level and county-level characteristics. In line with our expectation, we find a positive association between CSR and social capital. The economic impact of CSR is quite significant: a one standard deviation change is social capital is associated 0.08 standard deviation increase in CSR, holding all other variables constant. Our results suggest that the social capital positively affects the degree of CSR in a firm. Of course, a nagging concern is whether we are simply capturing a spurious correlation or a causal relation. One concern is that rather than social capital affecting CSR, it might be that the firms that are more socially responsible might choose to headquarter in places with high social capital. Another concern is that our OLS specification might be miss-specified, because we are forcing a linear relation while the underlying relation might not be linear. Also, we might be omitting some variables from our OLS speciation whose omission might be driving the positive association between CSR and social capital.
To address these concerns, we conduct a number of tests. First, we use propensity score matching, a nonparametric method to assess the association between CSR and social capital. The advantage of this technique is that it does not assume any sort of relation between the covariates and the dependent variable. It is also a test designed to better assess a causal relation (Rosenbaum and Rubin, 1983). Our results continue to hold when we use this method. Second, we conduct an instrumental variable analysis that addresses both omitted variable bias and reverse causality. Using the average social capital of the counties within a 100-mile radius and the industry as instruments for social capital, we find that CSR is positively associated with social capital. Third, although imperfect, one way to establish causality is to see if our results are consistent with theory. We conduct two such tests. If the social norms in a high social capital region affect CSR, as we argue, then the association between social capital and CSR should be much stronger for geographically concentrated firms because the congruence between the corporate culture and the regional culture should be much more aligned. We test this idea directly by splitting the sample at the median based on the number of subsidiaries. As expected, we find the association between CSR and social capital much stronger for firms with fewer subsidiaries—the difference is statistically significant. Another test examines if the norms aspect of social capital is driving the association between social capital and CSR. As we will discuss later in the text, social capital has two components: norms and networks. The key idea of our study is that it is the norm aspect of social capital that drives the association between it and CSR. If that is case, we should find a stronger association between the norm aspect of social capital and CSR. Indeed, that is what we find. We also try to rule out a possible alternative explanation. Arguably, the positive association between social capital and CSR might not be due to an altruistic inclination, as we claim. But rather, the association might be because the benefits of CSR on the firm’s performance are greater when the firm is located in a high social capital county where the CSR might be more effective. However, that does not appear to be the case. When we split the sample into two groups based on the median level of social capital and examine the association between Tobin’s Q and CSR, we do not find that the CSR’s effect is stronger for firms in a high social capital region. Taken together, our study suggests that the altruistic norms of a high social capital region induce the firms to be more socially responsible. We do not view our results as supporting the agency view because except for the personal satisfaction that managers might derive from CSR, our study does not suggest any monetary benefit to managers. Neither do we view our results as supporting the shareholder expense view because our study does not suggest anything on whether CSR is costly to shareholders. Rather, our study offers a more neutral view on why firms indulge in CSR. The rest of the paper is organized as follows: Section 2 describes the contribution of the study; Section 3 discusses the related literature and develops the hypothesis; Section 4 describes the measurement of key variables and the construction of the empirical model; Section 5 describes the data; Sections 6 and 7 present the main and additional results, respectively; Section 8 discusses the results; and Section 9 presents the conclusion.
2. Contribution to the literature Our findings are quite important. First, our study contributes to the literature that tries to better understand the motivations behind CSR. We present empirical support for a much simpler but often neglected view. We suggest that just as some individuals are more altruistic than others depending on where they live, some
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firms might be more altruistic than others by virtue of where they are located, and these firms might exhibit higher CSR. We are not the first to suspect that the norms related to altruistic inclination might affect CSR. McGuire et al. (2012a) also attempt to do so. They argue that religion is associated with compassion and charity and hypothesize a positive association between CSR and religiosity. Surprisingly, they find a negative association. Our second contribution is to suggest that social capital, not religiosity, is the norm that captures compassion towards others and is the norm that positively influences CSR. Third, our study is among the few that suggest that there might be a direct value for managers to invest in CSR. Di Giuli and Kostovetsky (2014) show that the democratic political values in the firm’s region are associated with higher CSR. Their key idea is that its managers get a ‘‘warm glow” (Joshua and Arthur, 2005) by engaging in decisions that are closer to their ideologies. Our study suggests that this ‘‘warm glow” need not come from only adhering to political ideologies but can also come from apolitical values such as altruism. Fourth, we contribute to a recent body of research that shows that social capital affects financial decisions. Although the social capital literature is extensive in sociology, economics, politics, and management literature, we know little on how social capital affects managerial decisions. In that respect, our study complements recent studies that show that firms headquartered in high social capital regions are trustworthy in the eyes of their auditors (Jha and Chen, 2015), have better quality financial reports (Jha, 2013), and better access to credit (Guiso et al., 2004). We extend this stream of literature by showing that the social capital in the region where a firm is headquartered can also affect its CSR. More broadly, we contribute to the literature that shows that the social environment affects managerial decisions (Hilary and Hui, 2009; McGuire et al., 2012b). Fifth, and more philosophically, we contribute to the debate that centers on the very purpose of a firm. For many economists, the very idea that firms should indulge in socially responsible behavior is problematic. This idea can be best articulated by paraphrasing Milton Friedman who noted that the only valid social responsibility of a corporation is to make as much money as possible for its shareholders while conforming to the basic rules of society (Friedman, 1962). But some founders of firms disagree. According to David Packard, co-founder of Hewlett–Packard, making money is not the goal of the firm, but one of the results from a group of people that get together to make a contribution to society (Lougee and Wallace, 2008). Handy (2002) also echoes Packard’s thoughts. He states that it is a moral obligation to do something else with the profit of a business—something that will be the real justification of the business (Handy, 2002). In a similar vein, Wood, as quoted by Baron (2001), states that ‘‘business and society are interwoven rather than distinct entities; therefore, society has certain expectations for appropriate business behavior and outcomes.” He considers managers as ‘‘moral actors” who meet society’s expectations according to their discretion (Baron, 2001). We contribute to this debate because our study suggests that moral obligation, influenced by where the firm is headquartered, might indeed play a role. Some managers might indeed view their role not only as maximizing the shareholders’ profit but also as contributing to the society they are a part of.
3. Related literature & hypothesis development 3.1. What is social capital? Following Woolcock (2001) we define social capital as the norms and networks that facilitate collective action. This definition
incorporates the idea that regions with higher social capital encourage cooperative norms such as altruism and denser networks. It also incorporates the idea that cooperative norms induce a denser network, and a denser network induces cooperative norms. Social capital is often viewed as a norm in the economics and political science literature. For example, Guiso et al. (2004) define social capital as the levels of mutual trust and altruistic tendency in a society. Fukuyama (1997) defines social capital as ‘‘the existence of a certain set of informal values or norms shared among members of a group that permits cooperation among them.” However, in the management literature, the researchers often view social capital as a set of networks that benefits the participants (Payne et al., 2011). They posit that a strong set of networks is in and of itself a resource. For example, when these social networks are strong the agent might fear a greater cost for misbehavior (Coleman, 1990; Spagnolo, 1999). In other words, good behavior is due to strong networks. Although at first glance, the norms and networks appear to be separate concepts—they are not. Fukuyama (1997), Portes (1998), and Putnam (2001) point out that strong networks over long periods might foster norms conducive to cooperation. And people internalize these norms over generations and are intrinsically less likely to act opportunistically. In short, the differentiation of the agent’s good behavior is difficult, if not impossible to do. Is behavior good because of higher ethical norms or because of the fear of harsher punishment for misbehavior from stronger networks? Because it is difficult to disentangle between the norm and network aspect of social capital, we do not focus on this distinction. Rather, we focus on the consensus in the social capital literature—that the people in regions with high social capital are, relatively speaking, less self-centered and more altruistic. The research on social capital also suggests, unsurprisingly, that social capital is associated with greater support systems for immigrants (Janjuha-Jivraj, 2003), lower corruption (La Porta et al., 1997), and lower property crime (Buonanno et al., 2009). Overall, it is safe to say that in high social capital regions, people generally are more compassionate about the problems that others face and therefore are more altruistic.
3.2. How social capital might affect CSR The prior studies suggest that the managers of firms headquartered in high social capital regions have higher social capital. For example, research shows that firms hire and retain employees that share their values, and employees prefer to work for firms that share their values (Holland, 1976; Tom, 1971; Vroom, 1966). Hilary and Hui (2009) examine 59 CEOs that switched jobs from 1991 to 2003. They find that the religiosity of the county where the CEO moved to is positively associated with the religiosity of where CEO moved from. Jha (2013) repeats a similar exercise with social capital and shows that the social capital of where the CEOs moved to is positively associated with the social capital of where they moved from. Assuming that the employees reside close to the firm, this congruence means that the culture of the headquarters reflects the culture of its location. Therefore, if the county where the firm is located has low social capital, then the managers in the firm’s headquarters will too. The high social capital of the managers of firms in high social capital regions means that the managers of these firms are more likely to be altruistic. Because ultimately the views of the top management matter in deciding to what extent the firm should pursue CSR (Graafland and van de Ven, 2006; Joshua and Arthur, 2005), the firms in high social capital regions are likely to engage in more social responsibility, ceteris paribus.
A. Jha, J. Cox / Journal of Banking & Finance 60 (2015) 252–270
Fig. 1. Social capital at the county level in 2000. Notes: This figure presents the social capital for the year 2000 at the county level.
The top management’s social capital need not be the only way the region’s social capital affects the firm’s CSR. Even when the CEO and CFO themselves are not altruistically inclined, the corporate culture that has developed over a period of time is likely to be relatively altruistic when a firm is headquartered in a high social capital region. The firms in these regions are likely to also have suppliers, workers, lenders, and customers in the vicinity. These stakeholders might expect the firm to be socially responsible even if it comes at some cost to the firm (Bénabou and Tirole, 2010). Likely, the expectation of other stakeholders to be socially responsible might induce an otherwise not altruistically inclined manager to act altruistically. Based on these arguments, we hypothesize the following: Hypothesis. Firms in high social capital counties in the United States exhibit a higher degree of corporate social responsibility.
4. Measurement of key variables & construction of empirical model 4.1. Measuring CSR To measure CSR, we follow the approach in the recent literature (El Ghoul et al., 2011; Kim et al., 2012; McGuire et al., 2012a). We use the data from KLD Stats, an independent firm that provides research and consulting services to corporations that are interested in making socially responsible decisions. KLD Stats conducts indepth research of publicly available information from government agencies, nongovernment sources, newspapers, annual reports, regulatory filings, proxy statements, and disclosures and assigns summary statistics to how involved a firm is in these various activities. These statistics provide numerical values for the numbers of strengths and concerns of the firm for the following categories: community, diversity, human rights, employee, product, and environment. Consistent with these studies, for each firm-year in each category, we first subtract the number of concerns from the number of strengths. We then add up the score in each of these categories to construct a composite index.5 We consider the composite score the CSR score for the firm-year, and label it CSR_S. 5 Kim et al. (2012) do not include human rights in constructing the CSR score because human rights data is missing for a substantial number of years in their sample. That is not the case in our study, and so we include it. McGuire et al. (2012a, b) and El Ghoul et al. (2011) include human rights in their construction of a CSR score.
We provide further description of how the CSR_S is constructed in the Appendix. The highest CSR_S is 18 and the lowest is -9. However, to remove the possible effect of outliers we winsorize the CSR_S at 1% like all of the other continuous variables in our study.6 The CSR score also appears to be highly correlated over time. For example, there are 188 firms in our sample whose CSR_S can be calculated for both 1996 and for 2009. The correlation between their CSR_S for the two periods is 0.42. 4.2. Measuring social capital Following Rupasingha and Goetz (2008), we construct a countylevel index to measure social capital.7 As in their study, we use two measures of norms and two measures of networks. The two measures of norms are the census mail response rate and the votes cast in presidential elections. The two measures of networks are the number of associations8 and nonprofit organizations each per 10,000 people. Using these four indicators, we conduct a principal component analysis for each year (1990, 1997, 2005, and 2009). We use the first component for each year and consider it the social capital index. We linearly interpolate the data to fill in the years 1991–1996, 1998–2004, and 2005–2008 as in Hilary and Hui (2009). We present the variation in social capital at the county level in Figs. 1 and 2 for the years 2000 and 2009, respectively. These figures show that the higher social capital counties are mainly concentrated in the North and Northeast and that the social capital does not change much over time.9 This is consistent with the idea that unlike physical and human capital, social capital is ‘‘sticky” (Anheier and Gerhards, 1995). As far as we know, this is the only comprehensive index for social capital that is available for each county in the United States. This measure of social capital has been used in a number of recent 6 In unreported tests, we verify that the results are robust to not winsorizing the CSR_S. 7 We want to thank Rupasingha and Goetz (2008) for making their social capital index and its underlying data publically available at: http://aese.psu.edu/nercrd/community/tools/social-capital. 8 The types of associations include civic and social associations, physical fitness facilities, public golf courses, religious organizations, sports clubs, managers and promoters, membership sports and recreation clubs, political organizations, professional organizations, business associations, labor organizations, and other membership organizations not elsewhere classified. 9 The correlation between the social capital index of 1997 and social capital index of 2009 is 0.88.
A. Jha, J. Cox / Journal of Banking & Finance 60 (2015) 252–270
Fig. 2. Social capital at the county level in 2009. Notes: This figure presents the social capital for the year 2009 at the county level.
Table 1 Ranking of counties based on social capital. Rank
Low social capital
High social capital
1 2 3 4 5 6 7 8 9 10
Chattahoochee, GA Starr, TX Hidalgo, TX Webb, TX Maverick, TX Cameron, TX Yuma, AZ Kings, CA Murray, GA Imperial, CA
1 2 3 4 5 6 7 8 9 10
Edgefield, SC Loving, TX Thomas, NE San Juan, CO Hinsdale, CO Hooker, NE Divide, ND Lane, KS Greeley, KS Garfield, NE
Notes: This table presents the 10 counties with the lowest social capital, and the 10 counties with the highest social capital.
studies (Chetty et al., 2014; Deller and Deller, 2010; Putnam, 2007). Rupasingha and Goetz’s (2008) construction of the social capital index is consistent with the measure of social capital at the state level constructed by Robert Putnam in his seminal book ‘‘Bowling Alone” (Putnam, 2000). Putnam uses 14 different measures that he believed would be highly correlated with altruistic tendencies and community-centric attitudes. However, the limitation in Putnam’s measure is that it is at the state level and available for only one point in time. Therefore, it does not allow for the exploitation of the variation in social capital within the state or over time. In contrast, Rupasingha and Goetz’s (2008) measure of social capital is very similar to Putnam’s, but at the county level and over multiple years. Their measure allows us to take advantage of the variation in the social capital within a state and over time. Because Rupasingha and Goetz’s (2008) social capital index is at the county level, rather than at the state level, it is also likely to have greater power in testing our hypothesis. Table 1 presents the ten counties with the highest and the lowest social capital for the year 2009. The table shows that there can be quite a bit of variation in the level of social capital among the different counties in the same state. We know from Table 1 that five counties from Texas are among those with the lowest social capital, and one county is among the highest. This variation suggests the increased power of the tests. Furthermore, the county’s culture is more likely to be congruent with that of the firms, than, say the larger culture of the state. This precision can also increase the power of the tests.
We want to note here that in constructing one of the measures for the networks, we assume as in Rupasingha and Goetz (2008) that all types of association memberships increase the general altruism and propensity to honor obligations. We acknowledge that not all researchers agree with this view. Some researchers argue that groups whose memberships are more exclusive, such as professional, labor, political organizations, and business associations, might increase trust among members but reduce trust with those outside the groups (Burt, 1999, 2000). However, there are other studies that suggest that does not need to be the case (Brewer, 1999; Putnam, 2007). 4.3. Empirical model In order to investigate the association between the social capital of a headquarters’ region and CSR, we conduct a regression analysis summarized by the equation below.
CSR S ¼ b0 þ b1 SOCIAL CAPITAL þ b2 LNMV þ b3 MTOB þ b4 DEBT þ b5 EBITDA þ b6 KZ þ b7 CASH þ b8 DIV þ b9 LNAGE þ b10 CONTROVERSIAL þ b11 INST þ b12 R&D þ b13 ADVERSTISMENT þ b14 INCOME þ b15 RELIGION þ b16 RURAL þ b17 LNPOP þ b18 POPG
þ b19 LNDIST þ b20 REPUBLICAN þ Industry Indicators þ e ð1Þ where, CSR_S = the composite CSR score constructed as in Kim et al. (2012), McGuire et al. (2012b), and El Ghoul et al. (2011) SOCIAL CAPITAL = the social capital of the county where the firm is headquartered LNMV = the natural logarithm of the market value of the firms MTOB = the market to book ratio DEBT = the ratio of total debt to total assets EBITDA = the ratio of EBITDA to total assets KZ = the Kaplan and Zingales index for financial constraints as in (Di Giuli and Kostovetsky, 2014) CASH = the ratio of cash to total assets DIV = ratio of dividends to total assets LNAGE = the age of the firm CONTROVERSIAL = an indicator variable that is equal to one if the firm is involved in either alcohol, gambling, military, nuclear, and tobacco and zero otherwise
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INST = the percentage of institutional investors R&D = the ratio of R&D expenditure to sales ADVERTISMENT = the ratio of advertising expenditure to sales INCOME = the natural logarithm of the GDP-per-capita RELIGION = the religiosity RURAL = being located in a county that does not belong in the top 100 metropolitan areas based on population LNPOP = the natural logarithm of the population POPG = the population growth LNDIST = the natural logarithm of the distance from the SEC REPUBLICAN = the percentage of Republicans Industry Indicators = the dummies based on the 17-digit Fama– French Industry classification The firm-level control variables are based on two recent studies that investigate the determinants of the CSR_S (Di Giuli and Kostovetsky, 2014). As in McGuire et al. (2012a,b), we control for the firm’s size, profitability, debt-to-asset ratio, market-to-book ratio, and institutional ownership. We follow Di Giuli and Kostovetsky (2014) and add to our firm-level control variables the Kaplan and Zingales index for financial constraints and the ratios of R&D expenditure to sales, advertising expenditure to sales, cash to assets, and dividends to assets. We also control for whether a firm is involved in a controversial business. Because our key research variable is a regional characteristic, we need to control for a number of regional characteristics in addition to the firm-level controls. Again, we follow the literature that has attempted to examine the impact of social capital on corporate decisions (Hilary and Hui, 2009; Kedia and Rajgopal, 2011; McGuire et al., 2012b). We control for the income per capita, rural or urban environment, the natural logarithm of the population, and the population growth. In addition to these controls, we also control for religiosity because McGuire et al. (2012a) find that religiosity negatively affects CSR engagement; and for the ratio of the percentage of Republicans10 because Di Giuli and Kostovetsky (2014) find that the political affiliation of a region can affect CSR engagement. Further, we control for the distance of the firm’s headquarters from the nearest SEC office. Kedia and Rajgopal (2011) show that the firms’ decisions, such as earnings management, can be influenced by how far they are from regulatory bodies such as the SEC. It is possible that decisions such as CSR might also be influenced by how far firms are from the SEC because philanthropy might be used to cover misbehavior. We also control for the industry based on the 17-digit Fama– French industry classification and cluster the standard errors at the county level to adjust for a possible correlation in the error term that is related to county characteristics. Because we cluster the firms at the county level, the standard errors automatically cluster at the firm level (Bertrand et al., 2004).11
5. Data Our sample consists of 13,117 firms-years. It spans from 1995 to 2009 and covers 50 different industries and 2595 firms based on the two-digit SIC industrial classifications. The sample selection is as follows. We start with all nonfinancial and nonregulated firms located in the United States that have an available CSR_S. This sample represents 15,805 firm-years. From this sample, we remove 12 firms-years because SOCIAL CAPITAL is unavailable, 13 because
We linearly interpolate the ratio of Democratic votes to Republican votes to fill in for those years where there was no election. 11 Because the firms are nested in counties, we have a nested level of clustering. In such a case, ‘‘cluster-robust standard errors are computed at the most aggregate level of clustering” (Cameron and Miller, 2011, p. 7).
LNMV is unavailable, 64 because DEBT is unavailable, 58 because EBITDA is unavailable, 1,637 because KZ is unavailable, 292 because R&D is unavailable, 566 because ADVERTISMENT is unavailable, and 46 because RELIGION is unavailable. In order to remove the effect of outliers, we winsorize all of the continuous variables of the sample at the 1st and the 99th percentile. The summary statistics of the sample are presented in Panel A of Table 2. The mean and the standard deviation of the CSR_S are 0.198 and 2.055, respectively.
6. Results 6.1. Univariate results Consistent with our hypothesis, the univariate results suggest that the firms headquartered in high social capital counties exhibit higher CSR as measured by the KLD database. When we divide the sample into high and low social capital groups based on the median level of social capital, we find that the mean CSR_S for firms in high social capital counties is 0.09 but is 0.31 in the low social capital counties. A two tailed t-test for the difference between the two groups yields a p-value less than 0.001. The Pearson correlations also suggest that CSR_S and SOCIAL CAPITAL are positively associated. The correlation between these two variables is 0.08 and significant at less than 1%—the p-value is less than 0.001. The correlations are reported in Panel B of Table 2. 6.2. Multivariate results The multivariate results are also consistent with the hypothesis—firms headquartered in high social capital counties exhibit higher CSR. The results are reported in Panel C of Table 2. Column 1 presents the regression coefficient of an analysis based on Eq. (1). The coefficient of SOCIAL CAPITAL is 0.192 and significant at 5%. Based on this model, the economic significance is also quite large. One standard deviation increase in the SOCIAL CAPITAL is associated with a 0.08 standard deviation increase in the CSR_S (0.19 * 0.91/2.055). The coefficient of the control variables LNMV, DEBT, MTOB, EBTIDA, and REPUBLICAN are consistent with what Di Giuli and Kostovetsky (2014) find. The significant relation between CSR_S and SOCIAL CAPITAL does not appear to be driven by the large sample size. We know this because the results are similar when we take the mean of all of the variables by firm such that there is only one observation per firm and then conduct the regression analysis. The sample size drops by 80% to 2595, but the coefficient of SOCIAL CAPITAL hardly changes and continues to be significant at 5%. These results are reported in Column 2 of Panel C in Table 2. To mitigate this concern even further, we also conduct a county-year regression. That is, we take the mean of all of the variables based on both county and year. Again, although the sample size decreases to 3227, the coefficient of SOCIAL CAPITAL continues to be significant at 5%—this result is reported in Column 3 of Panel C in Table 2.
7. Additional results & robustness 7.1. The association between CSR and social capital are robust when using a matching technique instead of an OLS Arguably firms that have higher CSR might choose to locate in a high social capital region. If that is the case, it might not be social capital that affects CSR like we argue, but rather that firms with a greater propensity to indulge in CSR choose to locate in high social capital regions. Also, an OLS implicitly assumes that the association
Table 2 Main result: the impact of social capital on corporate social responsibility.
Panel B  CSR_S  SOCIAL CAPITAL  LNMV  MTOB  DEBT  EBITDA  KZ  CASH  DIV  LNAGE  CONTROVERSIAL  INST  R&D  ADVERSTISMENT  LNINCOME  RELIGION  RURAL  LNPOP  POPG  LNDIST  REPUBLICAN
0.198 0.49 7.175 3.367 0.479 0.13 0.628 0.214 0.012 2.933 0.081 48.627 0.06 0.013 10.669 0.579 0.124 13.663 0.835 4.937 0.176
2.055 0.91 1.55 3.816 0.222 0.115 1.272 0.258 0.022 0.715 0.273 36.53 0.121 0.028 0.29 0.13 0.33 1.143 1.298 1.46 0.061
0 0.451 6.989 2.436 0.48 0.134 0.647 0.115 0 2.89 0 54.579 0.007 0 10.651 0.582 0 13.736 0.651 5.44 0.175
1 1.173 6.056 1.577 0.306 0.084 0.016 0.036 0 2.398 0 12.561 0 0 10.474 0.475 0 13.142 0.069 3.905 0.132
1 0.122 8.083 3.979 0.623 0.189 1.35 0.3 0.016 3.611 0 81.096 0.07 0.011 10.838 0.666 0 14.328 1.446 5.883 0.218
13,117 13,117 13,117 13,117 13,117 13,117 13,117 13,117 13,117 13,117 13,117 13,117 13,117 13,117 13,117 13,117 13,117 13,117 13,117 13,117 13,117
1 0.08 0.21 0.14 0.04 0.10 0.06 0.04 0.11 0.08 0.05 0.04 0.08 0.14 0.04 0.01 0.01 0.01 0.04 0.03 0.11
1 0.03 0.03 0.06 0.06 0.02 0.08 0.09 0.12 0.07 0.06 0.06 0.05 0.17 0.14 0.16 0.59 0.20 0.12 0.32
1 0.28 0.16 0.34 0.01 0.12 0.26 0.35 0.09 0.09 0.07 0.06 0.00 0.12 0.05 0.02 0.01 0.03 0.06
1 0.06 0.20 0.00 0.20 0.19 0.05 0.01 0.03 0.12 0.12 0.05 0.04 0.03 0.02 0.01 0.06 0.06
1 0.01 0.56 0.42 0.10 0.22 0.11 0.07 0.21 0.03 0.05 0.08 0.04 0.04 0.03 0.02 0.04
1 0.15 0.20 0.29 0.13 0.01 0.03 0.49 0.05 0.08 0.12 0.02 0.04 0.03 0.01 0.04
1 0.35 0.61 0.04 0.05 0.08 0.05 0.05 0.03 0.00 0.01 0.01 0.05 0.04 0.04
1 0.08 0.33 0.09 0.06 0.51 0.06 0.19 0.15 0.07 0.11 0.05 0.02 0.14
1 0.27 0.04 0.03 0.12 0.12 0.05 0.12 0.03 0.04 0.03 0.07 0.00
1 0.12 0.02 0.20 0.01 0.12 0.15 0.04 0.07 0.08 0.05 0.02
1 0.04 0.07 0.02 0.01 0.02 0.02 0.04 0.02 0.05 0.01
1 0.02 0.05 0.14 0.05 0.02 0.05 0.04 0.01 0.04
1 0.00 0.17 0.14 0.06 0.08 0.04 0.01 0.12
1 0.06 0.01 0.01 0.02 0.05 0.09 0.08
1 0.05 0.31 0.21 0.13 0.35 0.30
1 0.12 0.03 0.17 0.16 0.02
1 0.41 0.06 0.08 0.26
1 0.09 0.14 0.56
1 0.28 0.19
A. Jha, J. Cox / Journal of Banking & Finance 60 (2015) 252–270
Panel A CSR_S SOCIAL CAPITAL LNMV MTOB DEBT EBITDA KZ CASH DIV LNAGE CONTROVERSIAL INST R&D ADVERSTISMENT LNINCOME RELIGION RURAL LNPOP POPG LNDIST REPUBLICAN
Table 2 (continued) Dependent variable = CSR_S
Panel C SOCIAL CAPITAL LNMV MTOB DEBT EBITDA KZ CASH
LNAGE CONTROVERSIAL INST R&D ADVERSTISMENT LNINCOME RELIGION RURAL LNPOP POPG LNDIST REPUBLICAN Industry Dummies Observations R-squared
0.192⁄⁄ (0.044) 0.250⁄⁄⁄ (0.000) 0.015⁄ (0.094) 0.591⁄⁄ (0.018) 0.973⁄⁄⁄ (0.003) 0.109⁄⁄ (0.036) 0.173 (0.118) 7.317⁄⁄⁄ (0.006) 0.094 (0.123) 0.489⁄⁄⁄ (0.002) 0.002 (0.159) 1.073⁄⁄⁄ (0.000) 6.103⁄⁄⁄ (0.000) 0.129 (0.485) 0.452 (0.322) 0.042 (0.790) 0.038 (0.514) 0.020 (0.544) 0.050 (0.174) 4.752⁄⁄⁄ (0.000)
0.191⁄⁄ (0.021) 0.212⁄⁄⁄ (0.000) 0.027⁄ (0.072) 0.710⁄⁄⁄ (0.001) 0.711⁄⁄ (0.042) 0.093⁄ (0.083) 0.099 (0.490) 5.235⁄ (0.085) 0.127⁄⁄ (0.018) 0.405⁄⁄⁄ (0.004) 0.002 (0.300) 0.728⁄⁄⁄ (0.006) 5.497⁄⁄⁄ (0.000) 0.331⁄ (0.082) 0.634 (0.151) 0.009 (0.943) 0.020 (0.711) 0.043 (0.260) 0.001 (0.975) 2.851⁄⁄⁄ (0.002)
0.414⁄⁄⁄ (0.001) 0.097 (0.200) 0.042⁄⁄ (0.019) 0.316 (0.675) 2.083⁄⁄⁄ (0.009) 0.037 (0.835) 0.144 (0.709) 4.231 (0.577) 0.036 (0.790) 0.643⁄ (0.058) 0.002 (0.317) 3.181⁄⁄⁄ (0.003) 6.920⁄⁄ (0.011) 0.789⁄⁄⁄ (0.006) 0.791 (0.198) 0.125 (0.489) 0.024 (0.778) 0.043 (0.316) 0.026 (0.627) 6.322⁄⁄⁄ (0.000)
YES 13,117 0.156
YES 2,595 0.144
N/A 3,277 0.150
A. Jha, J. Cox / Journal of Banking & Finance 60 (2015) 252–270
Notes: Panel A reports the summary statistics of the data. Panel B reports the correlations. The statistics in bold are significant at 5%. Panel C reports multivariate OLS coefficients. Column 1 shows the results when the unit of observation is a firm-year as specified in Eq. (1). Column 2 shows that results are not driven by the sample size; the analysis is the same as in Column 1 except that we collapse the data so that each firm only has one observation. Column 3 also shows that results are not driven by the sample size; here we collapse the data based on county-year. The p-values based on robust standard errors clustered at the county level are in the parentheses. The ⁄⁄⁄, ⁄⁄, and ⁄ represent significance at the 1%, 5%, and 10% levels, respectively. The variable descriptions are in the Appendix. All of the continuous variables are winsorized at the 1st and the 99th percentile.
A. Jha, J. Cox / Journal of Banking & Finance 60 (2015) 252–270
between the covariates and CSR are linear. If these associations are not linear, arguably the OLS is a miss-specified model. To mitigate these concerns, we adopt propensity score matching outlined in Rosenbaum and Rubin (1983) and used in a number of studies in the finance literature (see for e.g., Drucker and Puri, 2005; Fang et al., 2014). Following the steps for propensity score matching, we divide our sample into two groups based on the median level of social capital. We consider the group with high social capital the treated group, and those with low social capital the control group. For each of the observations in the treated and control groups, we calculate the propensity score—that is, the probability of belonging to a high social capital region using a logit model. We use the following firm-level variables in constructing the propensity scores: LNMV, MTOB, DEBT, EBITDA, KZ, CASH, and industry.12 Then for each observation from the treated sample, we find the nearest neighbor, the observation from the control group for which the absolute value of the difference in propensity scores is the minimum, from the control group.13 We match with a replacement. We call this sample the matched sample. Next, we test if there is a statistically significant difference in CSR between firms in the treated group and those in the matched group. Our results remain the same. The CSR for firms in a high social capital region are quite high compared to the firms in a low social capital region. The results are reported in Table 3. The table compares the summary statistics between the treated group and the matched sample constructed from the control group. The mean CSR in the treated group (firms in high social capital region) is 0.09, compared to a mean of 0.21 in the matched sample (firms from low social capital region). The difference is significant at 1%. The comparison also shows that other firm-level variables are hardly different. While propensity score matching is not perfect, many studies suggests it helps in conducting a more accurate analysis (see for e.g., Conniffe et al., 2000; Rubin, 1997). Because our results continue to hold when we use propensity score matching, our interpretation is likely true. 7.2. The association between CSR and social capital are robust when we use the instrumental variable technique We also use an instrumental variable technique to further validate the interpretation of the result. The advantage of this technique is that it addresses reverse causality as well as omitted variable bias in the OLS at the same time (Wooldridge, 2002, pp. 84–107). To do so, we run a 2SLS that uses instruments for the endogenous variable, SOCIAL CAPITAL. As instruments, we use (i) the average SOCIAL CAPITAL of the counties within a 100-mile radius excluding the county where the firm is located and (ii) the average SOCIAL CAPITAL of the firm the industry belongs to that is based on the two-digit SIC codes, excluding the one firm in the industry for which the instrumental variable is being calculated. Theoretically our choices of instruments are sensible. For the instruments to be strong, they need to be correlated with the endogenous regressor, SOCIAL CAPITAL, in our model. It is reasonable to expect that social capital in counties within a 100-mile radius are similar. Human beings are spatially sticky, so are the norms and values that they carry. Unsurprisingly, social capital is
Table 3 Propensity score matching: firms in high social capital regions have higher CSR.
Panel A CSR_S LNMV MTOB DEBT EBITDA KZ CASH
Matching based on propensity scores constructed using these five measures results in the matched sample with the least bias—that is, the treated and the matched sample are the most alike. We confirm that the results are robust when we construct propensity scores based on the entire control variable specified in Eq. (1). 13 In untabulated results we verify that these results continue to hold if we use local linear or Gaussian kernel matching. The results are robust when we remove 2% of the matched sample for which the propensity density is the lowest.
Matched (N = 6557)
0.09 7.22 3.43 0.50 0.14 0.63 0.19
0.00 7.08 2.42 0.50 0.14 0.66 0.10
2.09 1.50 3.96 0.22 0.11 1.25 0.24
0.21 7.20 3.49 0.50 0.14 0.64 0.19
0.00 6.99 2.52 0.49 0.14 0.70 0.11
2.06 1.63 3.93 0.22 0.11 1.38 0.22
3.21 0.56 0.75 0.05 0.70 0.14 0.24
0.001 0.575 0.451 0.956 0.482 0.892 0.810
Notes: Panel A presents the summary statistics of the treatment, and the matched group. The t-stats and the p-values are test the quality of the means for the two groups.
also spatially sticky (Rutten et al., 2010). Another stream of literature shows that industries also tend to cluster in certain geographic areas (Baptista and Swann, 1998; Krugman, 1991).14 If industries cluster and social capital is spatially sticky, then it follows that the social capital of firms in an industry might be similar. Therefore, we expect both these instruments to highly correlate with SOCIAL CAPITAL. For the instruments to be valid they should also be uncorrelated with the error terms. This essentially means that the instruments should (1) not be affected by the dependent variable, (2) not affect the dependent variable except through the endogenous variables, and (3) not be correlated with omitted variables in the model. It is unlikely that the extent of the CSR in the firm affects the social capital in the counties within the 100-mile radius, or the average social capital in the industry. Also it is unlikely that the social capital of neighboring counties and the other firms in the industry affects CSR except through the social capital of where the firm is headquartered. Our instruments also pass the statistical tests for strength, validity and appropriateness. A commonly used test for the strength of the instruments is the F-test that jointly tests the significance of the instruments. The F-statistic is 31.35, which is well above the recommended minimum of 10. This number suggests that our instruments are strong. The p-value for the Hansen J-statistic for over-identification is 0.914.15 This value indicates that our instruments are sufficiently uncorrelated with the error term. The null that SOCIAL CAPITAL is exogenous is rejected at a p-value of 0.003. Together, the F-test, Hansen J-statistics, and the endogeneity test suggest that our instruments are strong, valid, and appropriate.16 The results of the instrumental variable technique are reported in Table 4. Column 1 reports the coefficients of the first-stage regression. The two instruments AVG. SOCIAL CAPITAL OF NEIGBOURS and AVG. SOCIAL CAPITAL IN THE INDUSTRY are both strongly and statistically significant. Column 2 show the results of the second-stage regressions. The coefficient of SOCIAL CAPITAL is 0.600 and is significant with a p-value of <0.001. This result more strongly supports that the social capital of where the firm is headquartered affects its CSR.
Treatment (N = 6557)
Industries cluster to reduce coordination costs and exploit spillover benefits. There is no statistical tests to ensure the perfect validity of the instruments (Roberts and Whited, 2013). The over-identification test tests the relative validity of the instruments against each other, not their absolute validity. Therefore it could suggest that both instruments are equally valid or could suggest that both are equally invalid. Therefore these tests should be interpreted with caution. 16 The sample size for the instrumental variable tests is smaller by 63 observations because the two-digit SIC codes are missing for some firms. 15
A. Jha, J. Cox / Journal of Banking & Finance 60 (2015) 252–270 Table 4 Instrumental variable analysis: firms in high social capital regions have higher CSR. First Stage DV = SOCIAL CAPITAL SOCIAL CAPITAL LNMV MTOB DEBT EBITDA KZ CASH DIV LNAGE CONTROVERSIAL INST R&D ADVERSTISMENT LNINCOME RELIGION RURAL LNPOP POPG LNDIST REPUBLICAN AVG. SOCIAL CAPITAL OF NEIGHBOURS AVG. SOCIAL CAPITAL IN THE INDUSTRY
0.001 (0.928) 0.005 (0.009) 0.049 (0.462) 0.094 (0.313) 0.005 (0.759) 0.102 (0.055) 0.333 (0.675) 0.036 (0.100) 0.150 (0.052) 0.001 (0.018) 0.012 (0.944) 0.197 (0.691) 0.887 (0.000) 0.509 (0.03) 0.017 (0.877) 0.353 (0.000) 0.058 0.036) 0.056 (0.185) 1.368 (0.123) 0.536 (0.000) 0.183 (0.001)
Industry Fixed Effect R-Squared Observations
YES 0.672 13,054
F-Statistics (Strength of Instruments) Hansen J test (Overidentfication) Endogenity test
F-stat = 31.35, p-value = 0.000 Chi-stat = 0.012, p-value = 0.914 Chi-stat = 8.832, p-value = 0.003
Second Stage DV = CSR_S 0.600 (0.000) 0.249 (0.000) 0.012 (0.189) 0.608 (0.016) 0.913 (0.005) 0.110 (0.032) 0.114 (0.312) 7.275 (0.005) 0.061 (0.325) 0.505 (0.002) 0.001 (0.355) (1.083) (0.000) 5.824 (0.000) 0.519 (0.031) 0.718 (0.129) 0.038 (0.828) 0.155 (0.130) 0.074 (0.033) 0.058 (0.149) 5.419 (0.000)
YES 0.142 13,054
Notes: This table reports the results of the instrumental variable analysis to test the association between SOCIAL CAPITAL and CSR. The instruments are AVG. SOCIAL CAPITAL OF NEIGHBOURS, and AVG. SOCIAL CAPITAL IN THE INDUSTRY based on the two-digit SIC codes. The p-values based on robust standard errors clustered at the county level are in the parentheses. The ⁄⁄⁄, ⁄⁄, and ⁄ represent significance at the 1%, 5%, and 10% levels, respectively. The p-value for the test of whether the SOCIAL CAPITAL coefficients are different between the two groups is based on a F-test. The variable descriptions are in the Appendix. All of the continuous variables are winsorized at the 1st and the 99th percentile.
7.3. The positive association between social capital and CSR appear stronger for less geographically dispersed firms Our key argument is that the norms in high social capital regions are such that the firms headquartered in these regions have a greater inclination to be socially responsible. If that is the case, then we should also expect that the association between social capital and CSR is much stronger when firms are less geographically dispersed. The idea being that in less geographically dispersed firms the social norms are likely to be congruent with the norms of the managers to a greater extent, and therefore the social capital’s effect is much more salient. Our results suggest that
indeed this is the case. Following McGuire et al. (2012b), we split the sample of firms by the median level of subsidiaries,17 we find that the effect of social capital is much stronger for less geographically dispersed firms. These results are reported in Columns 1 and 2 of Table 5. The median number of subsidiaries for the firms in our sample is six. When only firms with less than or equal to six subsidiaries are used, the coefficient of social capital is 0.329 (Column 1) and significant at 1%; when the sample is limited to only those with
17 In unreported tests we verify that our results are robust, if we choose 5 or 7 subsidiaries as the cutoff to distinguish whether a firm is dispersed or not.
A. Jha, J. Cox / Journal of Banking & Finance 60 (2015) 252–270
more than six subsidiaries the coefficient of social capital is 0.016 and nonsignificant (Column 2).18 A F-test suggests that the difference is significant at 1%—the p-value is 0.0002.19 Another way to test this would be to create a dummy for firms that are dispersed, construct an interaction term that is the product of this dummy and SOCIAL CAPITAL; and then include the dummy for dispersion and the interaction term in the OLS regression. In unreported tests, we do this. But we do find that the interaction term is negative and statistically significant that suggests there is no significant difference in the effect of social capital on CSR between the two groups. One reason why this might happen is because splitting the sample allows for all the covariates to differ between the two groups. But using an interaction term allows only the interacted variable to have different coefficients for the two groups. In any case, not finding a significant result when we take this approach suggests that the readers need to view these results with caution. 7.4. The association between CSR and social capital appears to be driven mainly by the norm, rather than the network The explanation we provide for why social capital affects CSR is based on the norm rather than the network aspect of social capital. We test whether the norm component of social capital indeed has a much stronger impact than the network aspect. To do so, we examine the association between CSR and social capital, not for the social capital index as we do in our main tests, but for the four different components of social capital. Two of them are network based: the numbers of associations and nonprofit organizations each per 10,000 people. Another two are norms based: the census mail response rate and the votes cast in presidential elections. The results in Table 6 show that the norms matter more. RESPONSE TO CENSUS that measures the census response rate and VOTE that measures the electoral participation rates are both positive and significant (Columns 1 and 2, respectively). The network aspects of social capital are not positive and significant. The ASSOCIATIONS is negative and significant; the NGO although positive is nonsignificant (Columns 3 and 4, respectively). In Column 5, we include all four measures in one regression and test if the sum of the coefficients of RESPONSE TO CENSUS and VOTE is equal to the sum of the coefficient of ASSOCIATIONS and NGO. The null that they are the same is rejected with a p-value of 0.001 that suggest the impact of the norms and networks are significantly different. These results further strengthen our explanation that it is the altruistic norms of the firms in high social capital regions that are inducing firms to be socially responsible. 7.5. The association between CSR and social capital is high not because CSR is more effective in high social capital regions We interpret the positive association between social capital and CSR as due to the altruistic inclination of the firm, but one could interpret this association differently—for example, one could argue that the firms in high social capital regions have greater CSR not because they have an altruistic corporate culture but because the positive impact of the CSR on the firms’ performance is higher. This line of reasoning suggests that in high social capital regions, the 18 The sample for this tests is 12,777, 340 observations less than the main model because the information on the number of subsidiaries is not available for all of the 19 In unreported tests, we split the sample into quintiles based on size. Then for the lowest and highest quintiles, we conduct a split sample analysis where we examine whether the effect of social capital is stronger when the number of subsidiaries is less. The results are qualitatively similar. The positive effect of social capital on CSR appears to be stronger for the firms with fewer subsidiaries. We repeat this exercise by splitting the sample into the lowest and highest quartiles, and the results are again similar.
Table 5 The effect of social capital on CSR is stronger for less geographically dispersed firms. Dependent variable = CSR_S
SOCIAL CAPITAL LNMV MTOB DEBT EBITDA KZ CASH DIV LNAGE CONTROVERSIAL INST R&D ADVERSTISMENT LNINCOME RELIGION RURAL LNPOP POPG LNDIST REPUBLICAN
(1) # of Subsidiaries 6 6
(2) # of Subsidiaries > 6
0.329⁄⁄⁄ (0.001) 0.230⁄⁄⁄ (0.000) 0.022⁄⁄ (0.014) 0.402 (0.174) 0.409 (0.196) 0.074 (0.221) 0.135 (0.350) 5.677⁄ (0.059) 0.068 (0.290) 0.582⁄⁄⁄ (0.000) 0.001 (0.394) 0.602⁄ (0.065) 5.393⁄⁄⁄ (0.001) 0.533⁄⁄⁄ (0.004) 0.845⁄ (0.066) 0.042 (0.802) 0.068 (0.313) 0.038 (0.235) 0.025 (0.523) 4.705⁄⁄⁄ (0.000)
0.054 (0.603) 0.315⁄⁄⁄ (0.001) 0.000 (0.977) 0.583 (0.210) 2.239⁄⁄⁄ (0.002) 0.051 (0.663) 0.299 (0.190) 4.362 (0.391) 0.156 (0.144) 0.415⁄ (0.066) 0.003 (0.160) 1.960⁄⁄⁄ (0.002) 7.041⁄⁄ (0.028) 0.417⁄ (0.083) 0.200 (0.717) 0.004 (0.986) 0.152⁄⁄ (0.044) 0.031 (0.419) 0.094⁄⁄ (0.047) 3.578⁄⁄⁄ (0.002)
Diff in Coeff of SOCIAL CAPITAL p-value
Industry Dummies Observations R-squared
YES 7,249 0.142
YES 5,528 0.206
Notes: This table reports the OLS coefficients when we split the sample based on the median number of subsidiaries. This table shows that the effect of social capital on CSR is significantly different when firms have fewer subsidiaries. The p-values based on robust standard errors clustered at the county level are in the parentheses. The ⁄⁄⁄, ⁄⁄, and ⁄ represent significance at the 1%, 5%, and 10% levels, respectively. The p-value for the test of whether the SOCIAL CAPITAL coefficients are different between the two groups is based on a F-test. The variable descriptions are in the Appendix. All of the continuous variables are winsorized at the 1st and the 99th percentile.
positive image due to socially responsible behavior travels with greater speed and force because of the dense networks, which increases the positive impact of CSR. While this logic appears plausible, there is a caveat. This logic assumes that high social capital regions value corporate responsibility to a greater extent. But that need not be the case. The socially responsible behavior of the firm might be more valuable in low social capital regions where most firms do not behave well (good behavior is likely to outshine more easily when most behave badly). So arguably, the socially responsible behavior of the firm might travel faster in a low social capital region. Regardless, we empirically examine if the impact of CSR is much stronger when the social capital is higher. We do not find evidence that in high social capital regions, CSR has a much stronger impact
A. Jha, J. Cox / Journal of Banking & Finance 60 (2015) 252–270 Table 6 The positive association between social capital and CSR is mainly due to the norm aspect of the social capital not the network aspect. Dependent variable = CSR_S (1) RESPONSE TO CENSUS
1.593⁄ (0.098) ⁄⁄
ASSOCIATIONS NGO LNMV MTOB DEBT EBITDA KZ CASH DIV LNAGE CONTROVERSIAL INST R&D ADVERSTISMENT LNINCOME RELIGION RURAL LNPOP POPG LNDIST REPUBLICAN
0.251 (0.000) 0.017⁄ (0.064) 0.570⁄⁄ (0.022) 1.005⁄⁄⁄ (0.002) 0.108⁄⁄ (0.037) 0.215⁄⁄ (0.050) 7.412⁄⁄⁄ (0.006) 0.102⁄ (0.098) 0.478⁄⁄⁄ (0.002) 0.002⁄ (0.067) 0.993⁄⁄⁄ (0.001) 6.296⁄⁄⁄ (0.000) 0.018 (0.914) 0.163 (0.717) 0.051 (0.737) 0.125⁄⁄ (0.017) 0.008 (0.798) 0.046 (0.220) 5.135⁄⁄⁄ (0.000)
0.256 (0.000) 0.016⁄ (0.080) 0.595⁄⁄ (0.017) 1.002⁄⁄⁄ (0.002) 0.109⁄⁄ (0.033) 0.196⁄ (0.070) 7.395⁄⁄⁄ (0.005) 0.088 (0.155) 0.468⁄⁄⁄ (0.003) 0.002⁄ (0.086) 1.035⁄⁄⁄ (0.001) 6.218⁄⁄⁄ (0.000) 0.059 (0.750) 0.227 (0.617) 0.059 (0.697) 0.096⁄ (0.050) 0.021 (0.483) 0.043 (0.222) 5.778⁄⁄⁄ (0.000)
0.248 (0.000) 0.017⁄ (0.062) 0.565⁄⁄ (0.023) 1.020⁄⁄⁄ (0.002) 0.102⁄⁄ (0.049) 0.235⁄⁄ (0.031) 7.376⁄⁄⁄ (0.006) 0.111⁄ (0.076) 0.448⁄⁄⁄ (0.004) 0.002⁄ (0.096) 1.017⁄⁄⁄ (0.001) 6.254⁄⁄⁄ (0.000) 0.152 (0.332) 0.132 (0.778) 0.043 (0.771) 0.216⁄⁄⁄ (0.000) 0.015 (0.629) 0.040 (0.296) 4.560⁄⁄⁄ (0.000)
0.000 (0.907) 0.250⁄⁄⁄ (0.000) 0.017⁄ (0.070) 0.580⁄⁄ (0.020) 1.011⁄⁄⁄ (0.002) 0.107⁄⁄ (0.040) 0.204⁄ (0.068) 7.384⁄⁄⁄ (0.006) 0.111⁄ (0.072) 0.475⁄⁄⁄ (0.002) 0.002 (0.101) 1.082⁄⁄⁄ (0.001) 6.250⁄⁄⁄ (0.000) 0.043 (0.825) 0.333 (0.471) 0.040 (0.796) 0.124⁄⁄ (0.015) 0.006 (0.854) 0.046 (0.233) 4.350⁄⁄⁄ (0.000)
YES 13,117 0.154
YES 13,117 0.156
YES 13,117 0.155
YES 13,117 0.153
p-Value for the test RESPONSE TO CENSUS + VOTE = ASSOCIATIONS + NGO Industry Dummies Observations R-squared
(5) 1.290 (0.183) 1.477 (0.124) 0.896⁄⁄⁄ (0.001) 0.010⁄⁄ (0.037) 0.253⁄⁄⁄ (0.000) 0.017⁄ (0.067) 0.587⁄⁄ (0.017) 1.022⁄⁄⁄ (0.002) 0.103⁄⁄ (0.042) 0.225⁄⁄ (0.038) 7.249⁄⁄⁄ (0.006) 0.075 (0.227) 0.449⁄⁄⁄ (0.005) 0.002 (0.138) 0.969⁄⁄⁄ (0.001) 5.918⁄⁄⁄ (0.000) 0.286 (0.190) 0.168 (0.713) 0.030 (0.835) 0.202⁄⁄⁄ (0.000) 0.024 (0.390) 0.037 (0.280) 4.959⁄⁄⁄ (0.000) (0.001)
YES 13,117 0.161
Notes: This table examines the effect of social capital on corporate social responsibility. But instead of using the social capital index, it uses the underlying variables used to construct the social capital index. RESPONSE TO CENSUS and VOTE measure the norms and ASSOCIATIONS and NGO measure the network. The p-values based on robust standard errors clustered at the county level are in the parentheses. The ⁄⁄⁄, ⁄⁄, and ⁄ represent significance at the 1%, 5%, and 10% levels, respectively. The variable descriptions are in the Appendix. All of the continuous variables are winsorized at the 1st and the 99th percentile.
on the firm’s value. These results are based on the following analysis. We split the sample into two groups based on the median level of social capital and test if the association between the firms’ performance (measured by Tobin’s Q)20 and CSR differs between these two groups. The results are reported in Panel A of Table 7. Although the coefficient of CSR is much larger for the group that is headquartered in high social capital counties, the difference is not statistically significant according to the F-test—the p-value is 0.331. Rather than split the sample, we also run a pooled test and reach the same conclusion. The pooled test includes an interaction
20 In unreported tests, we verify that we reach the same conclusion if we use the return on assets as another measure of performance.
term that is the product of HIGH SOCIAL CAPITAL (a dummy that is one for firms in high social capital and zero otherwise) and CSR. We find that the interaction term, though positive, is not statistically significant—the p-value is 0.648.21 21 In unreported tests, we split the sample into quintiles based on social capital. Then for the lowest and highest quintiles, we conduct a split sample analysis where we examine whether the effect of CSR on Tobin’s Q is much stronger when the firm is in a high social capital region. We do not find that to be the case. However, the interaction term HIGH SOCIAL CAPITAL and CSR is positive and significant. We repeat the same exercise by splitting the firms into the lowest and highest quartiles. The results are similar except for the interaction terms HIGH SOCIAL CAPITAL and CSR. These terms are no longer statistically significant at the 10% level. Overall, we view these results to be qualitatively consistent with the idea that CSR is not necessarily more effective in a high social capital region.
A. Jha, J. Cox / Journal of Banking & Finance 60 (2015) 252–270
Table 7 The association between Tobin’s Q and CSR are not statistically different for firms in high and low social capital counties. Dependent variable = Q
Panel A CSR_S
Low Social Capital
High Social capital
0.261⁄⁄⁄ (0.000) 1.739⁄⁄⁄ (0.000) 5.682⁄⁄ (0.023) 0.005⁄⁄⁄ (0.000) 0.109 (0.169) 1.265⁄⁄ (0.027) 0.331
0.178⁄⁄⁄ (0.000) 1.389⁄⁄⁄ (0.000) 3.847 (0.105) 0.002⁄⁄⁄ (0.002) 0.036 (0.523) 2.708⁄⁄⁄ (0.001)
0.026⁄ (0.058) 0.009 (0.648) 0.112⁄⁄ (0.012) 0.222⁄⁄⁄ (0.000) 1.558⁄⁄⁄ (0.000) 4.951⁄⁄⁄ (0.002) 0.004⁄⁄⁄ (0.000) 0.070⁄ (0.095) 1.943⁄⁄⁄ (0.000)
YES 6560 0.232
YES 6557 0.217
YES 13,117 0.217
(1) DV = CSR_S
(2) DV = CSR_S⁄HIGH SOCIAL CAPITAL
(3) DV = Q
CSR_S*HIGH SOCIAL CAPITAL HIGH SOCIAL CAPITAL LNMV DEBT HHI INST KZ EBITDA Diff in Coeff of CSR_S p-value Industry Dummies Observations R-squared
Panel B CSR_S CSR_S*HIGH SOCIAL CAPITAL
F-Statistics (Strength of Instruments) Hansen J Test (Overidentfication) Endogenity test (Appropriateness)
0.690 (0.260) 0.282⁄⁄⁄ (0.000) 0.155 (0.525) 19.303⁄⁄⁄ (0.000) 0.002⁄ (0.082) 0.019 (0.611) 0.771⁄⁄ (0.010) 0.704 (0.244) 1.961 (0.142) 0.181 (0.811) 2.906⁄⁄ (0.050) F-stat = 13.17, p-value = 0.000 Chi-stat = 1.207, p-value = 0.5469 Chi-stat = 6.460, p-value = 0.003
1.088⁄⁄ (0.017) 0.110⁄⁄⁄ (0.000) 0.095 (0.654) 12.382⁄⁄⁄ (0.000) 0.001 (0.177) 0.044 (0.145) 0.575⁄⁄ (0.048) 0.136 (0.434) 0.664⁄ (0.089) 0.162 (0.797) 5.755⁄⁄⁄ (0.000) F-stat = 12.10, p-value = 0.000
Industry Dummies Observations R-squared
YES 13,117 0.142
YES 13,117 0.087
HIGH SOCIAL CAPITAL LNMV DEBT HHI INST KZ EBITDA RELIGION REPUBLICAN RELIGION⁄HIGH SOCIAL CAPITAL REPUBLICAN⁄HIGH SOCIAL CAPITAL
0.800⁄⁄ (0.018) 0.524 (0.115) 0.274⁄⁄⁄ (0.009) 0.059 (0.392) 1.373⁄⁄⁄ (0.000) 3.327 (0.439) 0.003⁄⁄⁄ (0.000) 0.064 (0.174) 1.747⁄⁄⁄ (0.000)
YES 13,117 0.358
Notes: Panel A of this table examines the association between Tobin’s Q and CSR in an OLS framework for two groups based on the median level of social capital. Column 1 represents the regression when only firms headquartered in counties with less than or with the median level of social capital are included. Column 2 represents the regression when only firms located in more than the median level of social capital are included. Panel B of this table examines the association between Tobin’s Q using an instrumental variable technique. Columns 1 and 2 of this panel present the first-stage regression, and Column 3 presents the second-stage regression. The p-values based on robust standard errors clustered at the firm level are in the parentheses. The ⁄⁄⁄, ⁄⁄, and ⁄ represent significance at the 1%, 5%, and 10% levels, respectively. The variable descriptions are in the Appendix. All of the continuous variables are winsorized at the 1st and the 99th percentile.
One could still argue that our model suffers from omitted variable bias and that is why we find no significant difference in the effect of CSR on Tobin’s Q. To address this concern we conduct
an instrumental variable analysis. We use the religiosity and political leaning of the county that the firm is headquartered in as instruments for CSR. Our instruments are motivated by
A. Jha, J. Cox / Journal of Banking & Finance 60 (2015) 252–270 Table 8 The association between social capital and CSR is robust when we use a dichotomous measure of social capital, rather than continuous, and when we control for the G-index. Dependent variable = CSR_S (1) HIGH SOCIAL CAPITAL INDICATOR
0.251⁄⁄⁄ (0.000) 0.016⁄ (0.093) 0.602⁄⁄ (0.016) 0.995⁄⁄⁄ (0.002) 0.110⁄⁄ (0.032) 0.170 (0.122) 7.440⁄⁄⁄ (0.005) 0.094 (0.125) 0.495⁄⁄⁄ (0.001) 0.002 (0.129) 1.062⁄⁄⁄ (0.001) 6.185⁄⁄⁄ (0.000) 0.081 (0.639) 0.419 (0.333) 0.020 (0.899) 0.089⁄ (0.075) 0.015 (0.598) 0.046 (0.192) 4.907⁄⁄⁄ (0.000)
0.205⁄⁄ (0.042) 0.119 (0.445) 0.148 (0.191) 0.344⁄⁄ (0.013) 0.315 (0.122) 0.261⁄⁄⁄ (0.000) 0.014 (0.207) 0.716⁄ (0.090) 1.851⁄⁄⁄ (0.004) 0.149⁄ (0.077) 0.257 (0.259) 11.060⁄⁄ (0.011) 0.070 (0.420) 0.472⁄⁄ (0.011) 0.002 (0.160) 1.695⁄⁄⁄ (0.005) 5.737⁄⁄⁄ (0.004) 0.181 (0.458) 0.350 (0.523) 0.029 (0.892) 0.038 (0.619) 0.024 (0.651) 0.052 (0.310) 5.875⁄⁄⁄ (0.000)
YES 13,117 0.156
YES 7596 0.185
G1 G2 G3 G4 LNMV MTOB DEBT EBITDA KZ CASH DIV LNAGE CONTROVERSIAL INST R&D ADVERSTISMENT LNINCOME RELIGION RURAL LNPOP POPG LNDIST REPUBLICAN Industry Dummies Observations R-squared
This finding of no statistical difference between the firms in high and low social capital regions demonstrates that the positive association between CSR and social capital is caused by something else, such as altruistic inclination, and not because CSR is more effective in high social capital regions.
Notes: This table presents the robustness tests. It examines the effect of social capital on corporate social responsibility. In Column 1, instead of using the continuous social capital index, it uses the indicator variable that is equal to 1 for firms that have above median social capital and 0 for those that have below median social capital. In Column 2, additional control dummies based on the G-index are introduced. The p-values based on robust standard errors clustered at the county level are in the parentheses. The ⁄⁄⁄, ⁄⁄, and ⁄ represent significance at the 1%, 5%, and 10% levels, respectively. The variable descriptions are in the Appendix. All of the continuous variables are winsorized at the 1st and the 99th percentile.
Deng et al. (2013) who use religiosity and political leaning in examining whether CSR affects firm performance. The results of the instrumental variable analysis reported in Panel B of Table 7 confirm that that there is no significant difference in the association of CSR with Tobin’s Q for firms in a high social capital region, compared to those in a low one.
7.6. The results are robust using a dichotomous measure of social capital In order to mitigate the concerns that our results might be biased because of measurement error in calculating the index for social capital, we also use a dichotomous measure instead of a continuous variable. We create an indicator variable, HIGH SOCIAL CAPITAL, that is equal to one when the firm is headquartered in a county with more than the median level of social capital, and zero when the firm is located in a county with less than or equal to the median level of social capital. We replace the SOCIAL CAPITAL variable with this variable, and the results continue to hold. These results are reported in Column of 1 of Table 8. 7.7. The results are robust to controlling for the G-index Further, we add control variables that capture the relative power of the firm’s management compared to the shareholders by using the G-index as in Jo and Harjoto (2011). The results continue to hold. The advantage of using the G-index as a measure of corporate governance is that it is comprehensive. It is constructed so that a smaller value means that the shareholders have greater rights. Consistent with the literature (Bergstresser and Philippon, 2006; Jiang et al., 2010), we construct four indicator variables that capture the corporate governance based on the G-index: G1 equals one if the governance index is less than or equal to six and zero otherwise, G2 equals one if the index is more than six but less than or equal to nine and zero otherwise, G3 equals one if the index is more than nine but less than or equal to 12 and zero otherwise, and G4 equals one if the index is more than 13 and zero otherwise.22 The results are reported in Column 2 of Table 8. 7.8. What sort of CSR activity is driven more by social capital? In order to get a better idea of what type of CSR is driven most by social capital, we also examine the different components of CSR separately. These results are reported in Columns 1–5 of Table 9. These results show that the effect of social capital on CSR is driven by community, employees, and product. It does not seem to be driven by CSR for human rights, or the environment. In Columns 6 and 7 of Table 9, we examine whether it is CSR strength that is driving the result, or CSR concerns. Rather than use CSR_S as the dependent variable as in our main model, this time we use CSR_STRENGTH and CSR_CONCERNS separately. These results show that firms in high social capital regions have lower CSR concerns, and that is what drives the result. This finding is consistent with the prospect theory that suggests that people’s (by extension the firm’s) judgments reflect a reference dependence (Kahneman and Tversky, 1979) and that loss looms larger than gains (Einhorn and Hogarth, 1981). Put differently, psychologically, the impact of 1 unit of loss is larger than 1 unit of gain. It appears that psychologically the cost of doing harm to the society is greater than the benefit the managers derive by doing good.
22 Our results continue to hold if we use a continuous variable for the G-index instead of the 4 indicator variables.
Table 9 The association between different types of CSR and social capital.
SOCIAL CAPITAL LNMV MTOB DEBT EBITDA KZ
DIV LNAGE CONTROVERSIAL INST R&D ADVERSTISMENT LNINCOME RELIGION RURAL LNPOP POPG LNDIST REPUBLICAN Industry Dummies Observations R-squared
DV = CSR_DIV (2)
DV = CSR_EMP (3)
DV = CSR_HUMAN (4)
DV = CSR_PRODUCT (5)
DV = CSR_ENV (6)
DV = CSR_STRENGTHS (7)
DV = CSR_CONCERNS (8)
0.037⁄⁄ (0.034) 0.052⁄⁄⁄ (0.000) 0.002 (0.369) 0.039 (0.490) 0.016 (0.819) 0.018 (0.147) 0.041⁄ (0.084) 1.617⁄⁄⁄ (0.005) 0.022 (0.122) 0.005 (0.876) 0.001⁄⁄ (0.021) 0.119⁄⁄ (0.036) 0.956⁄⁄⁄ (0.003) 0.073⁄⁄ (0.046) 0.043 (0.554) 0.019 (0.552) 0.003 (0.781) 0.003 (0.729) 0.011⁄ (0.093) 0.775⁄⁄⁄ (0.000)
0.054 (0.191) 0.284⁄⁄⁄ (0.000) 0.006⁄ (0.086) 0.501⁄⁄⁄ (0.000) 0.311 (0.128) 0.023 (0.414) 0.011 (0.872) 0.795 (0.593) 0.181⁄⁄⁄ (0.000) 0.268⁄⁄⁄ (0.002) 0.001⁄ (0.095) 0.050 (0.762) 3.590⁄⁄⁄ (0.000) 0.306⁄⁄⁄ (0.000) 0.634⁄⁄⁄ (0.003) 0.037 (0.601) 0.030 (0.308) 0.017 (0.253) 0.011 (0.495) 2.080⁄⁄⁄ (0.000)
0.066⁄⁄ (0.035) 0.095⁄⁄⁄ (0.000) 0.002 (0.652) 0.347⁄⁄⁄ (0.000) 0.589⁄⁄⁄ (0.000) 0.030 (0.178) 0.058 (0.280) 2.194⁄⁄ (0.036) 0.003 (0.900) 0.057 (0.237) 0.001 (0.162) 0.596⁄⁄⁄ (0.000) 0.915⁄ (0.078) 0.267⁄⁄⁄ (0.000) 0.058 (0.720) 0.046 (0.415) 0.006 (0.796) 0.021⁄⁄ (0.048) 0.006 (0.653) 1.258⁄⁄⁄ (0.001)
0.004 (0.542) 0.040⁄⁄⁄ (0.000) 0.003⁄⁄⁄ (0.000) 0.066⁄⁄⁄ (0.005) 0.095⁄⁄⁄ (0.001) 0.015⁄⁄⁄ (0.004) 0.011 (0.324) 0.313 (0.261) 0.017⁄⁄⁄ (0.004) 0.028 (0.140) 0.001⁄⁄⁄ (0.000) 0.031 (0.187) 0.202 (0.137) 0.035⁄ (0.053) 0.078⁄⁄ (0.028) 0.005 (0.666) 0.011⁄⁄ (0.040) 0.001 (0.705) 0.005 (0.195) 0.120⁄ (0.099)
0.035⁄ (0.070) 0.081⁄⁄⁄ (0.000) 0.013⁄⁄⁄ (0.000) 0.240⁄⁄⁄ (0.001) 0.259⁄⁄⁄ (0.001) 0.014 (0.320) 0.010 (0.768) 0.172 (0.818) 0.030⁄ (0.062) 0.107⁄⁄ (0.027) 0.000 (0.167) 0.173⁄⁄ (0.032) 0.142 (0.740) 0.134⁄⁄⁄ (0.001) 0.012 (0.894) 0.005 (0.877) 0.005 (0.681) 0.005 (0.514) 0.001 (0.909) 0.484⁄⁄ (0.025)
0.007 (0.725) 0.041⁄⁄⁄ (0.005) 0.005⁄ (0.058) 0.355⁄⁄⁄ (0.000) 0.245⁄⁄ (0.016) 0.039⁄⁄⁄ (0.009) 0.091⁄⁄⁄ (0.009) 2.142⁄⁄⁄ (0.002) 0.054⁄⁄ (0.011) 0.031 (0.538) 0.000 (0.866) 0.097 (0.196) 0.611⁄ (0.083) 0.107⁄ (0.091) 0.096 (0.374) 0.015 (0.768) 0.002 (0.920) 0.004 (0.526) 0.010 (0.310) 0.081 (0.763)
0.104 (0.145) 0.583⁄⁄⁄ (0.000) 0.019⁄⁄⁄ (0.006) 0.657⁄⁄⁄ (0.001) 0.802⁄⁄⁄ (0.002) 0.036 (0.358) 0.078 (0.496) 4.213⁄⁄ (0.044) 0.317⁄⁄⁄ (0.000) 0.182 (0.190) 0.001 (0.488) 0.289 (0.290) 4.472⁄⁄⁄ (0.000) 0.250⁄ (0.063) 1.061⁄⁄⁄ (0.001) 0.049 (0.684) 0.062 (0.193) 0.033 (0.155) 0.062⁄⁄ (0.029) 4.139⁄⁄⁄ (0.000)
0.101⁄⁄ (0.047) 0.329⁄⁄⁄ (0.000) 0.036⁄⁄⁄ (0.000) 1.261⁄⁄⁄ (0.000) 1.799⁄⁄⁄ (0.000) 0.139⁄⁄⁄ (0.000) 0.098 (0.331) 2.779 (0.101) 0.212⁄⁄⁄ (0.000) 0.337⁄⁄⁄ (0.006) 0.001⁄⁄ (0.025) 0.818⁄⁄⁄ (0.000) 1.536⁄ (0.053) 0.408⁄⁄⁄ (0.005) 0.522⁄ (0.065) 0.034 (0.745) 0.032 (0.413) 0.050⁄⁄ (0.015) 0.009 (0.771) 0.688 (0.307)
YES 13,117 0.100
YES 13,117 0.269
YES 13,117 0.096
YES 13,117 0.154
YES 13,117 0.165
YES 13,117 0.173
YES 13,117 0.345
YES 13,117 0.263
Notes: This table reports the results of an OLS test where the dependent variable is not the aggregate CSR score, but rather the breakdowns. KLD provides the strength and the weakness for each categories of social responsibility. Columns 1–5 report the coefficients of the regression analysis where the dependent variables are the different types of CSR. Columns 6–7 report the results of the aggregate score for CSR strengths and CSR weakness separately. The p-values based on robust standard errors clustered at the county level are in the parentheses. The ⁄⁄⁄, ⁄⁄, and ⁄ represent significance at the 1%, 5%, and 10% levels, respectively. The variable descriptions are in the Appendix. All of the continuous variables are winsorized at the 1st and the 99th percentile.
A. Jha, J. Cox / Journal of Banking & Finance 60 (2015) 252–270
DV = CSR_COMM (1)
A. Jha, J. Cox / Journal of Banking & Finance 60 (2015) 252–270
8. Discussion on the results 8.1. The positive association between social capital and CSR is not simply a spurious correlation As discussed earlier, an important concern is whether the association we capture is simply a spurious correlation, or whether the altruistic inclination that is fostered by social capital indeed affects CSR as we argue. Additional tests suggest that the association we find is unlikely to be a spurious correlation. To reiterate the points made earlier, the positive association between social capital and CSR continue to be robust when we use propensity score matching, or the instrumental variable analysis. Additional tests are also consistent with our argument that the norms at the county level affect the firm’s behavior. For example, it is the norms aspect, rather than the network aspect of social capital that is driving the association between CSR and social capital. This consistency with theory is an indicator that the explanation put forth is correct—that is, the relation is causal. A similar idea has been used in the literature (Guiso et al., 2004, 2008) to develop the case for causality. Also, it does not seem like firms in high social capital have higher CSR because it is more effective in generating higher value for its shareholders.23 8.2. The way we measure altruism is perhaps the best available option One of the limitations of our study is that while we claim that the altruistic inclination of the managers affects CSR, we do not measure the altruistic inclination of the managers directly. So, our evidence is indirect. While we acknowledge that this is a concern, we believe our approach might be the best option at hand to gauge how altruistic the firm is. One option would be to conduct a survey of the CEOs on how altruistic they are personally, and then examine how those scores of altruism affect CSR. There are a number of problems with this approach. One is that the survey captures the perception of the managers on how altruistic they are. Worse still, a manager coming from a high CSR firm is by design likely to espouse how moral it is for a firm to consider the society and not just the shareholders. Also, the CEO survey approach assumes wrongly that it is only the CEO that decides the CSR level of the firm. Possibly, it is not as much the CEO’s altruism, but largely the corporate culture that is already in place that drives the altruistic inclination of the firm.
et al. (2006) already suggest in their study. We do not think that is the case. True, their study suggests social capital might affect CSR, but the suggestion is tangential—never in their study do they discuss the possible impact of CSR on the firm. What is more, it is unclear whether what they observe is associated with social capital, is an input of social capital as they suggest, or the outcome of social capital. Their study does not try to disentangle the issue of causality. Recent studies suggest that some of the association they document might not hold up in rigorous testing, raising concerns on whether they are inputs.24 In any case, we view Rupashinga et al.’s (2006) study as complementing our study. Rupashinga et al. (2006) suggest that higher social capital is associated with fairer treatment of women and greater attachment to the community. Our study suggests that it is the altruistic inclination that might be driving the treatment of woman and what we see as the attachment to the community. More importantly, we suggest, and to our knowledge for the first time, that the positive effect of altruistic inclinations need not to be limited to the treatment of woman and attachment to place, but that it can extend to the socially responsible actions of managers. 8.4. Limitations of the study & future research While our results are consistent with the idea that the altruistic norms of a high social capital region induces firms to be socially responsible, we do not investigate what the origin of these norms are. Are managers acting altruistically because of their intrinsic nature? Or, are they acting altruistically because the stakeholders of the firm who live in the area expect these managers to be altruistic? It is hard to disentangle whether a person’s good behavior is because of his or her intrinsic nature or because he or she lives in an environment where certain behavior would not be approved of. Therefore, we are careful in our choice of words and only claim that, social capital, a social environment that fosters altruism, is positively associated with CSR. We refrain from making a claim on whether it is due to the intrinsic nature of the managers, or because of the societal expectation of the place where the managers, workers, suppliers and possibly the customers reside. We leave it to future research to examine whether the intrinsic norm or societal expectation is driving the association between CSR and social capital. 9. Conclusion
8.3. The idea that social capital of where firm is headquartered can affect CSR is novel Rupasingha et al. (2006) find that social capital is associated with ethnic homogeneity, income inequality, attachment to the place, literacy, age, and female labor force participation across U.S. counties. In their list one can observe factors that can increase CSR. For example, a higher female participation in the workforce increases the chance that the firm will have female friendly policies, which will increase the CSR score. Similarly, an attachment to one’s place might encourage the managers to give back to the community. A natural concern is whether we document what Rupasingha
The last few decades have seen an increase in firms trying to be socially responsible. Exactly why they do so is not well understood. Often, the argument is that firms do so because they benefit from acting socially responsibly—a large body of literature corroborates this suggestion. Little attention is paid to the idea that the social capital that inculcates civic duties might also play a role. Our study investigates if indeed, social capital, an environment that induces people to be altruistic, affects the extent of CSR We find that a firm headquartered in a high social capital county in the United States has greater CSR. This association appears to be more than just a spurious correlation. The economic significance is also quite large. Overall, our study suggests that some firms, because of where they
23 Another way to address the issue of causality even further would be to examine those firms that moved their headquarters and test how their level of CSR is affected by the change. However, very few firms move their headquarters, which makes it difficult to conduct a meaningful analysis. In a sample of 5000 firms that spans 15 years, Pirinsky and Wang (2006) find only118 examples of relocation. It is for this reason, that many studies that have examined the role of culture measured by the county in which the firm is located have refrained from taking this route to assess causality (for example, Hilary and Huang, 2013; Hilary and Hui, 2009; McGuire et al., 2012b).
24 For example, more recent studies show that the idea that the heterogeneity in ethnicity and income reduces social trust does not hold up to rigorous tests. You (2012) examine survey results of about 170,000 individuals spanning 80 countries and finds, in a multivariate regression analysis, that if the system is fair, then the heterogeneity in ethnicity and income does not reduce social trust, the key element of social capital. In a country like the United States where the institutions are well developed and the system is comparatively fair, the heterogeneity in income and ethnicity should not affect the social capital.
A. Jha, J. Cox / Journal of Banking & Finance 60 (2015) 252–270
are headquartered, are more altruistic than others, and this altruism affects their socially responsible activities.
Appendix (continued) Variables used in the main model
The natural logarithm of the distance from the nearest regional office of the SEC calculated as in (Kedia and Rajgopal, 2011). Source: BEA The ratio of Republican votes in presidential elections to the population in the county. Source: http://uselectionatlas.org/RESULTS/ & Census This variable is a set of binary variables constructed based on the 17-industry grouping defined in Fama and French (1988) Source: COMPUSTAT
We thank Carol Alexander (editor) and an anonymous referee for their valuable feedback. We thank George Clarke, Collins Okafor, Siddharth Shankar, and the participants at the seminar series at Texas A&M International University. We also thank Jonathan Moore for copyediting our manuscript.
Additional variables used in robustness tests Tobin’s Q Measures the Tobin’s Q (prcc_c*csho + at ceq txdb)/at). Source: COMPUSTAT AVG. SOCIAL CAPITAL The average SOCIAL CAPITAL of the counties OF NEIGHBOURS within a 100-mile radius, excluding the county where the firm is located and excluding the one firm in the industry for which the instrumental variable is being calculated AVG. SOCIAL CAPITAL IN The average SOCIAL CAPITAL of the firms that THE INDUSTRY belong to an industry that is determined by the two-digit SIC code, excluding the one firm in the industry for which the instrumental variable is being calculated COMM_STRENGTH The number of strengths in terms of community. Source: KLD COMM_CONCERNS The number of concerns in terms of community. Source: KLD DIV_STRENGTH The number of strengths in terms of diversity. Source: KLD DIV_CONCERNS The number of concerns in terms of diversity. Source: KLD EMP_STRENGTH The number of strengths in terms of employee relations. Source: KLD EMP_CONCERNS The number of concerns in terms of employee relations. Source: KLD HUMAN_STRENGTH The number of strengths in terms of human rights. Source: KLD HUMAN_CONCERNS The number of concerns in terms of human rights. Source: KLD PRODUCT_STRENGTH The number of strengths in terms of products. Source: KLD PRODUCT_CONCERNS The number of concerns in terms of products. Source: KLD ENV_STRENGTH The number of strengths in terms of environment. Source: KLD ENV_CONCERNS The number of concerns in terms of environment. Source: KLD CSR_STRENGTHS COMM_STRENGTH + DIVERSITY_STRENGTH + EMP_STRENGTH + HUMANRIGHT_STRENGTH + PRODUCT_STRENGTH + ENVI_STRENGTH. Source: KLD CSR_CONCERNS COMM_CONCERNS + DIVERSITY_CONCERNS + EMP_CONCERNS + HUMANRIGHT_CONCERNS + PRODUCT_CONCERNS + ENVI_CONCERNS. Source: KLD CSR_COMM COMM_STRENGTH COMM_CONCERNS. Source: KLD CSR_DIV DIV_STRENGTH DIV_CONCERNS. Source: KLD CSR_EMP EMP_STRENGTH EMP_CONCERNS. Source: KLD CSR_HUMAN HUMAN_STRENGTH HUMAN_CONCERNS. Source: KLD CSR_PRODUCT PRODUCT_STRENGTH PRODUCT_CONCERNS. Source: KLD CSR_ENV ENV_STRENGTH ENV_CONCERNS. Source: KLD HHI Herfindahl index of the firm based on the 17-digit industry grouping defined in Fama and French (1988). Source: COMPUSTAT ASSOCIATIONS It is the number of associations in the county normalized by the population. Bowling centers, public golf courses, membership sports and recreation clubs, religious organizations, civic and social associations, and physical fitness facilities, political organizations, business associations,
Variables used in the main model
Measure for corporate social responsibility constructed as in El Ghoul et al. (2011). It is the sum of CSR_STRENGTHS and CSR_CONCERNS. The detailed descriptions of how CSR_STRENGTHS and CSR_CONCERNS are calculated are described later in this table. A higher number indicates greater social responsibility. Source: KLD The social capital of the county where the firm is headquartered constructed as in Rupasingha and Goetz (2008). A higher number indicates greater social capital. Source: Rupasingha and Goetz (2008) The market value of the firm (ln(prcc_c*csho)). Source: COMPUSTAT The market-to-book ratio ((prcc_c*csho)/ceq). Source: COMPUSTAT The debt-to-assets ratio (lt/at). Source: COMPUSTAT It is the EBITDA-to-assets ratio (ebitda/at). Source: COMPUSTAT Calculated as in (Di Giuli and Kostovetsky, 2014), it is the Kaplan and Zingales (1997) index of financial constraint. Specifically, it is calculated as follows: 1.002 * (CF/L.at) 39.368 * (div/L.at) 1.315 * (C/L.at) + 3.139 * lev + 0.283 * Qraj where Qraj = ((prcc_f*csho)+at (ceq+txdb))/at. Source: COMPUSTAT The ratio of cash to assets (che/(L.at). Source: COMPUSTAT The ratio of dividends to the lag of assets (div/(L. at)). Source: COMPUSTAT The natural logarithm of the age of the firm. It is the difference in the current year and the first year the firm appears in COMPUSTAT. An indicator variable that is equal to 1 if the firms belong to controversial industries and 0 otherwise. These controversial industries are alcohol, gambling, military, nuclear, and tobacco. Source: KLD The percentage of shares held by institutions. Source: Thomson Reuters The ratio of research and development expenses to sales (xrd/sales). Source: COMPUSTAT The ratio of advertising expenses to sales (xad/ sales). Source: COMPUSTAT The natural logarithm of the GDP per capita in the county The ratio of the number of religious adherents to the total population in the county. Source: Association of Religion Data Archive (ARDA) An indicator variable that equals 1 if the firm does not belong to the top 100 metropolitan areas based on population and 0 otherwise. Source: Census The natural logarithm of the population in the county Source: Census The percentage growth in population. Source: Census
LNMV MTOB DEBT EBITDA KZ
CASH DIV LNAGE
INST R&D ADVERSTISMENT LNINCOME RELIGION
A. Jha, J. Cox / Journal of Banking & Finance 60 (2015) 252–270 Appendix (continued) Variables used in the main model
RESPONSE TO CENSUS
HIGH SOCIAL CAPITAL
Description professional organizations, and labor organizations. Source: Rupasingha and Goetz (2008) This is the number of nongovernment organizations divided by the population of the county times 10,000, calculated as in Rupasingha and Goetz (2008). Source: Rupasingha and Goetz (2008) Percentage of people who cooperated with the census and mailed back the forms, calculated as in (Rupasingha and Goetz, 2008). Source: Rupasingha and Goetz (2008) The percentage of votes cast in the presidential elections for 1996, 2004, and 2008; and we fill in the rest of the years by linear interpolation, except for 2009 that we replace with 2008. Source: Rupasingha and Goetz (2008) An indicator variable that is equal to 1 if the social capital in the county is above the median, and 0 if it is below the median. Source: Rupasingha and Goetz (2008) G1 equals 1 if the g-index constructed by (Gompers et al., 2003) is less than or equal to 6 and 0 otherwise, G2 equals 1 if the index is more than 6 and less than or equal to 9 and 0 otherwise, G3 equals 1 if the index is more than 9 and less than or equal to 12 and 0 otherwise, and G4 equals 1 if the index is more than 13 and 0 otherwise. Source: EXECUCOMP
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