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Corporate Social Responsibility and Social Capital

Anand Jha (contact author)

Assistant Professor of Finance

Texas A&M International University

Laredo, Texas

E-mail: ajha@tamiu.edu

Phone: 956-326-2581

&

James Cox

Doctoral student

Texas A&M International University

Laredo, Texas

E-mail: jocox@dusty.tamiu.edu

Phone: (956)-326-2514

Acknowledgement:

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.

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Corporate Social Responsibility and Social Capital

Abstract

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.

Key words: Corporate social responsibility; social capital; culture

JEL Classification: G30; G39

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1. Introduction

Corporations often portray themselves as socially responsible members of society.1 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.2 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.3

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, 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).

1 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).

2 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

3See the 2010 report published by The Form for Sustainable and Responsible Investment available at:

http://www.ussif.org/store_product.asp?prodid=10

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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 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 2,000 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

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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 decision-making. 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; McGuire et al., 2012b), 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

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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.

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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

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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 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

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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

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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.

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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 what is consensus in the social capital

literature—that the people in regions with high social capital are, relatively speaking, less self-

centered and more altruistic.

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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 1991–2003. 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.

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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 in-depth 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

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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.4

We consider the composite score the CSR score for the firm-year, and label it CSR_S. 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 percent like all of the other continuous variables in our

study.5 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 county-level index to measure

social capital.6 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 associations7 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).

4 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. (2012) and El Ghoul et al. (2011) include human rights in their construction of a CSR score. 5 In unreported tests, we verify that the results are robust to not winsorizing the CSR_S.

6 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 7 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.

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We present the variation in social capital at the county level in Figures 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.8 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 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

8 The correlation between the social capital index of 1997 and social capital index of 2009 is 0.88.

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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; Burt, 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 = β0 + β1SOCIAL CAPITAL+ β2 LNMV + β3MTOB + β4DEBT + β5EBITDA + β6KZ+

β7CASH + β8DIV + β9LNAGE+ β10CONTROVERSIAL+ β11INST+ β12 R&D + β13

ADVERSTISMENT + β14 INCOME + β15 RELIGION + β16RURAL+ β17LNPOP + β18POPG +

β19LNDIST + β20 REPUBLICAN+ Industry Indicators + ε (1).

Where,

CSR_S = the composite CSR score constructed as in Kim et al. (2012), McGuire et al. (2012b),

and El Ghoul et al. (2011)

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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

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

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

REL= the religiosity

RURAL= being located in a county that does not belong in the top 100 metropolitan areas based

on population

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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. (2012), 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

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percentage of Republicans9 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).10

5. Data

Our sample consists of 13,117 firms-years. It spans from 1995 to 2009 and covers 50

different industries and 2,595 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

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

9 We linearly interpolate the ratio of Democratic votes to Republican votes to fill in for those years where there was

no election. 10

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, page 7)”

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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 zero. 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 percent—the p-value is less than zero. 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 equation 1. The coefficient

of SOCIAL CAPITAL is 0.192 and significant at 5 percent. 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

20

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 percent to 2,595, but the coefficient

of SOCIAL CAPITAL hardly changes and continues to be significant at 5 percent. 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 3,227, the coefficient of SOCIAL

CAPITAL continues to be significant at 5 percent—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 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

21

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.11

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.12

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 percent. 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

11

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 equation 1. 12

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 percent of the matched sample for which the propensity density

is the lowest.

22

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 (page 84-107, Wooldridge, 2002). To do so, we run a

2SLS that uses instruments for the endogenous variable, SOCIAL CAPITAL. As instruments,

we use (1) 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 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).13

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

13

Industries cluster to reduce coordination costs and exploit spillover benefits.

23

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 0.914.14

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.15

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.

14

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.

15

The sample size for the instrumental variable tests is smaller by 63 observations because the two-digit SIC codes

are missing for some firms.

24

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.

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 subsidiaries16

, 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 percent; when the sample is limited to only those with more than six

16

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.

25

subsidiaries the coefficient of social capital is -0.016 and nonsignificant (Column 2).17

A F-test

suggests that the difference is significant at 1 percent—the p-value is 0.0002.18

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

17

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 firms.

18

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.

26

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 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

27

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 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)19

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 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 0.648.20

19

In unreported tests, we verify that we reach the same conclusion if we use the return on assets as another measure

of performance. 20

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 percent 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.

28

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 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.

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.

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

29

Further, we add control variables that capture the relative power of the 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.21

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 to

5 of Table 9. These results show that the effect of social capital on CSR is driven by community,

employees, and the 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.

21

Our results continue to hold if we use a continuous variable for the G-index instead of the 4 indicator variables.

30

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.

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.22

22

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

31

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.

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.

headquarters, which makes it difficult to conduct a meaningful analysis. In a sample of 5,000 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).

32

A natural concern is whether we document what Rupasingha 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.23

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

23

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.

33

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

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 are headquartered, are more altruistic than others, and

this altruism affects their socially responsible activities.

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37

Appendix

Variables used in the main model Description

CSR_S 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

SOCIAL CAPITAL 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)

LNMV The market value of the firm (ln(prcc_c*csho)). Source:

COMPUSTAT.

MTOB The market-to-book ratio ((prcc_c*csho)/ceq). Source:

COMPUSTAT.

DEBT The debt-to-assets ratio (lt/at). Source: COMPUSTAT.

EBITDA It is the EBITDA-to-assets ratio (ebitda/at).Source:

COMPUSTAT.

KZ 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.

CASH The ratio of cash to assets (che/(L.at). Source:

COMPUSTAT.

DIV The ratio of dividends to the lag of assets (div/(L.at)).

Source: COMPUSTAT.

LNAGE 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.

CONTROVERSIAL 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,

38

nuclear, and tobacco. Source: KLD

INST The percentage of shares held by institutions. Source:

Thomson Reuters

R&D The ratio of research and development expenses to sales

(xrd/sales). Source: COMPUSTAT.

ADVERSTISMENT The ratio of advertising expenses to sales ( xad/sales).

Source: COMPUSTAT.

LNINCOME The natural logarithm of the GDP per capita in the county.

RELIGION The ratio of the number of religious adherents to the total

population in the county. Source: Association of Religion

Data Archive (ARDA)

RURAL 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

LNPOP The natural logarithm of the population in the county Source: Census

POPG The percentage growth in population. Source: Census

LNDIST The natural logarithm of the distance from the nearest

regional office of the SEC calculated as in (Kedia and

Rajgopal, 2011). Source: BEA

REPUBLICAN The ratio of Republican votes in presidential elections to the

population in the county. Source:

http://uselectionatlas.org/RESULTS/ & Census

Industry Dummies

This variable is a set of binary variables constructed based

on the 17-industry grouping defined in Fama and French

(1988)

Source: COMPUSTAT

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 OF

NEIGHBOURS

The average SOCIAL CAPITAL of the counties within a

100-mile radius, excluding the county where the firm is

located and excluding the one firm in the industry for which

39

the instrumental variable is being calculated.

AVG. SOCIAL CAPITAL IN THE

INDUSTRY

The average SOCIAL CAPITAL of the firms that 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

40

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, professional organizations, and labor

organizations. Source: Rupasingha and Goetz (2008)

NGO 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)

RESPONSE TO CENSUS 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)

VOTES 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)

HIGH SOCIAL CAPITAL 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-G4 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

41

Figure 1

Social capital at the county level in 2000

Notes on Figure 1: This figure presents the social capital for the year 2000 at the county level.

42

Figure 2

Social capital at the county level in 2009

Notes on Figure 2: This figure presents the social capital for the year 2009 at the county level.

43

Table 1

Ranking of counties based on social capital

Rank Low Social capital Rank High Social capital

1 Chattahoochee, GA 1 Edgefield, SC

2 Starr, TX 2 Loving, TX

3 Hidalgo, TX 3 Thomas, NE

4 Webb, TX 4 San Juan, CO

5 Maverick, TX 5 Hinsdale, CO

6 Cameron, TX 6 Hooker, NE

7 Yuma, AZ 7 Divide, ND

8 Kings, CA 8 Lane, KS

9 Murray, GA 9 Greeley, KS

10 Imperial, CA 10 Garfield, NE

Notes on Table 1: This table presents the ten counties with the lowest social capital, and the ten

counties with the highest social capital.

44

Table 2

Main Result: The impact of social capital on corporate social responsibility

Panel A

mean sd p50 p25 p75 N

CSR_S -0.198 2.055 0 -1 1 13,117

SOCIAL CAPITAL -0.49 0.91 -0.451 -1.173 0.122 13,117

LNMV 7.175 1.55 6.989 6.056 8.083 13,117

MTOB 3.367 3.816 2.436 1.577 3.979 13,117

DEBT 0.479 0.222 0.48 0.306 0.623 13,117

EBITDA 0.13 0.115 0.134 0.084 0.189 13,117

KZ 0.628 1.272 0.647 0.016 1.35 13,117

CASH 0.214 0.258 0.115 0.036 0.3 13,117

DIV 0.012 0.022 0 0 0.016 13,117

LNAGE 2.933 0.715 2.89 2.398 3.611 13,117

CONTROVERSIAL 0.081 0.273 0 0 0 13,117

INST 48.627 36.53 54.579 12.561 81.096 13,117

R&D 0.06 0.121 0.007 0 0.07 13,117

ADVERSTISMENT 0.013 0.028 0 0 0.011 13,117

LNINCOME 10.669 0.29 10.651 10.474 10.838 13,117

RELIGION 0.579 0.13 0.582 0.475 0.666 13,117

RURAL 0.124 0.33 0 0 0 13,117

LNPOP 13.663 1.143 13.736 13.142 14.328 13,117

POPG 0.835 1.298 0.651 0.069 1.446 13,117

LNDIST 4.937 1.46 5.44 3.905 5.883 13,117

REPUBLICAN 0.176 0.061 0.175 0.132 0.218 13,117

45

Panel B

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21]

[1] CSR_S 1

[2] SOCIAL CAPITAL 0.08 1

[3] LNMV 0.21 0.03 1

[4] MTOB 0.14 0.03 0.28 1

[5] DEBT -0.04 0.06 0.16 0.06 1

[6] EBITDA 0.10 0.06 0.34 0.20 -0.01 1

[7] KZ -0.06 -0.02 -0.01 -0.00 0.56 -0.15 1

[8] CASH 0.04 -0.08 -0.12 0.20 -0.42 -0.20 -0.35 1

[9] DIV 0.11 0.09 0.26 0.19 0.10 0.29 -0.61 -0.08 1

[10] LNAGE 0.08 0.12 0.35 -0.05 0.22 0.13 -0.04 -0.33 0.27 1

[11] CONTROVERSIAL -0.05 0.07 0.09 -0.01 0.11 0.01 0.05 -0.09 0.04 0.12 1

[12] INST -0.04 -0.06 0.09 -0.03 0.07 0.03 0.08 -0.06 -0.03 0.02 0.04 1

[13] R&D 0.08 -0.06 -0.07 0.12 -0.21 -0.49 -0.05 0.51 -0.12 -0.20 -0.07 0.02 1

[14] ADVERSTISMENT 0.14 0.05 0.06 0.12 0.03 0.05 -0.05 0.06 0.12 -0.01 -0.02 -0.05 -0.00 1

[15] LNINCOME 0.04 0.17 0.00 0.05 -0.05 -0.08 -0.03 0.19 -0.05 -0.12 0.01 0.14 0.17 0.06 1

[16] RELIGION -0.01 0.14 0.12 0.04 0.08 0.12 -0.00 -0.15 0.12 0.15 -0.02 -0.05 -0.14 0.01 0.05 1

[17] RURAL -0.01 0.16 -0.05 -0.03 0.04 0.02 0.01 -0.07 0.03 0.04 0.02 -0.02 -0.06 -0.01 -0.31 -0.12 1

[18] LNPOP -0.01 -0.59 0.02 0.02 -0.04 -0.04 -0.01 0.11 -0.04 -0.07 -0.04 0.05 0.08 0.02 0.21 0.03 -0.41 1

[19] POPG -0.04 -0.20 0.01 -0.01 0.03 0.03 0.05 -0.05 -0.03 -0.08 0.02 0.04 -0.04 -0.05 -0.13 -0.17 0.06 -0.09 1

[20] LNDIST -0.03 -0.12 -0.03 -0.06 -0.02 -0.01 0.04 -0.02 -0.07 -0.05 -0.05 -0.01 0.01 -0.09 -0.35 -0.16 0.08 -0.14 0.28 1

[21] REPUBLICAN -0.11 0.32 -0.06 -0.06 0.04 0.04 0.04 -0.14 0.00 0.02 0.01 -0.04 -0.12 -0.08 -0.30 -0.02 0.26 -0.56 0.19 0.28 1

46

Panel C

Dependent variable = CSR_S

(1) (2) (3)

SOCIAL CAPITAL 0.192** 0.191** 0.414***

(0.044) (0.021) (0.001)

LNMV 0.250*** 0.212*** 0.097

(0.000) (0.000) (0.200)

MTOB 0.015* 0.027* 0.042**

(0.094) (0.072) (0.019)

DEBT -0.591** -0.710*** -0.316

(0.018) (0.001) (0.675)

EBITDA 0.973*** 0.711** 2.083***

(0.003) (0.042) (0.009)

KZ 0.109** 0.093* 0.037

(0.036) (0.083) (0.835)

CASH -0.173 -0.099 0.144

(0.118) (0.490) (0.709)

DIV 7.317*** 5.235* 4.231

(0.006) (0.085) (0.577)

LNAGE 0.094 0.127** 0.036

(0.123) (0.018) (0.790)

CONTROVERSIAL -0.489*** -0.405*** -0.643*

(0.002) (0.004) (0.058)

INST -0.002 -0.002 -0.002

(0.159) (0.300) (0.317)

R&D 1.073*** 0.728*** 3.181***

(0.000) (0.006) (0.003)

ADVERSTISMENT 6.103*** 5.497*** 6.920**

(0.000) (0.000) (0.011)

LNINCOME -0.129 -0.331* -0.789***

(0.485) (0.082) (0.006)

RELIGION -0.452 -0.634 -0.791

(0.322) (0.151) (0.198)

RURAL 0.042 0.009 -0.125

(0.790) (0.943) (0.489)

LNPOP -0.038 0.020 0.024

(0.514) (0.711) (0.778)

POPG 0.020 0.043 0.043

(0.544) (0.260) (0.316)

LNDIST 0.050 0.001 0.026

(0.174) (0.975) (0.627)

REPUBLICAN -4.752*** -2.851*** -6.322***

(0.000) (0.002) (0.000)

Industry Dummies YES YES N/A

Observations 13,117 2,595 3,277

R-squared 0.156 0.144 0.150

47

Notes on Table 2: Panel A reports the summary statistics of the data. Panel B reports the

correlations. The statistics in bold are significant at 5 percent. Panel C reports multivariate OLS

coefficients. Column 1 shows the results when the unit of observation is a firm-year as specified

in equation 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 percent, 5 percent, and

10 percent levels respectively. The variable descriptions are in the appendix. All of the

continuous variables are winsorized at the 1st and the 99

th percentile.

48

Table 3

Propensity score matching: Firms in high social capital regions have higher CSR

Panel A

Panel A

Treatment (N=6557) Matched (N=6557)

mean median sd mean median sd t p-value

CSR_S -0.09 0.00 2.09 -0.21 0.00 2.06 -3.21 0.001

LNMV 7.22 7.08 1.50 7.20 6.99 1.63 -0.56 0.575

MTOB 3.43 2.42 3.96 3.49 2.52 3.93 0.75 0.451

DEBT 0.50 0.50 0.22 0.50 0.49 0.22 -0.05 0.956

EBITDA 0.14 0.14 0.11 0.14 0.14 0.11 0.70 0.482

KZ 0.63 0.66 1.25 0.64 0.70 1.38 0.14 0.892

CASH 0.19 0.10 0.24 0.19 0.11 0.22 0.24 0.810

Notes on Table 3: 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.

49

Table 4

Instrumental Variable Analysis: Firms in high social capital regions have higher CSR

First Stage Second Stage

DV= SOCIAL CAPITAL DV= CSR_S

SOCIAL CAPITAL

0.600

(0.000)

LNMV 0.001 0.249

(0.928) (0.000)

MTOB 0.005 0.012

(0.009) (0.189)

DEBT 0.049 -0.608

(0.462) (0.016)

EBITDA 0.094 0.913

(0.313) (0.005)

KZ -0.005 0.110

(0.759) (0.032)

CASH -0.102 -0.114

(0.055) (0.312)

DIV 0.333 7.275

(0.675) (0.005)

LNAGE 0.036 0.061

(0.100) (0.325)

CONTROVERSIAL 0.150 -0.505

(0.052) (0.002)

INST -0.001 -0.001

(0.018) (0.355)

R&D -0.012 (1.083)

(0.944) (0.000)

ADVERSTISMENT 0.197 5.824

(0.691) (0.000)

LNINCOME 0.887 -0.519

(0.000) (0.031)

RELIGION 0.509 -0.718

(0.03) (0.129)

RURAL 0.017 0.038

(0.877) (0.828)

LNPOP -0.353 0.155

(0.000) (0.130)

POPG -0.058 0.074

0.036) (0.033)

LNDIST -0.056 0.058

(0.185) (0.149)

REPUBLICAN 1.368 -5.419

(0.123) (0.000)

AVG. SOCIAL CAPITAL OF NEIGHBOURS 0.536

(0.000)

AVG. SOCIAL CAPITAL IN THE INDUSTRY 0.183

(0.001)

50

Industry Fixed Effect YES YES

R-Squared 0.672 0.142

Observations 13054 13054

F-Statistics ( Strength of Instruments) F-stat=31.35, p-value=0.000

Hansen J Test (Overidentfication) Chi-stat=0.012, p-value=0.914

Endogenity Test Chi-stat=8.832, p-value= 0.003

Notes on Table 4: 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 percent, 5 percent, and 10 percent

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 99

th percentile.

51

Table 5

The effect of social capital on CSR is stronger for less geographically dispersed firms

Dependent variable = CSR_S

(1) (2)

# of Subsidiaries <=6 # of Subsidiaries > 6

SOCIAL CAPITAL 0.329*** -0.054

(0.001) (0.603)

LNMV 0.230*** 0.315***

(0.000) (0.001)

MTOB 0.022** 0.000

(0.014) (0.977)

DEBT -0.402 -0.583

(0.174) (0.210)

EBITDA 0.409 2.239***

(0.196) (0.002)

KZ 0.074 0.051

(0.221) (0.663)

CASH -0.135 -0.299

(0.350) (0.190)

DIV 5.677* 4.362

(0.059) (0.391)

LNAGE 0.068 0.156

(0.290) (0.144)

CONTROVERSIAL -0.582*** -0.415*

(0.000) (0.066)

INST -0.001 -0.003

(0.394) (0.160)

R&D 0.602* 1.960***

(0.065) (0.002)

ADVERSTISMENT 5.393*** 7.041**

(0.001) (0.028)

LNINCOME -0.533*** 0.417*

(0.004) (0.083)

RELIGION -0.845* -0.200

(0.066) (0.717)

RURAL 0.042 0.004

(0.802) (0.986)

LNPOP 0.068 -0.152**

(0.313) (0.044)

POPG 0.038 -0.031

(0.235) (0.419)

LNDIST 0.025 0.094**

(0.523) (0.047)

REPUBLICAN -4.705*** -3.578***

(0.000) (0.002)

Diff in Coeff of SOCIAL CAPITAL

p-value (0.0002)

Industry Dummies YES YES

Observations 7,249 5,528

52

R-squared 0.142 0.206

Notes on Table 5: 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 percent, 5 percent, and 10 percent 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 99

th percentile.

53

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) (2) (3) (4) (5)

RESPONSE TO CENSUS 1.593*

1.290

(0.098)

(0.183)

VOTE

1.789**

1.477

(0.038)

(0.124)

ASSOCIATIONS

-0.361**

-0.896***

(0.018)

(0.001)

NGO

0.000 0.010**

(0.907) (0.037)

LNMV 0.251*** 0.256*** 0.248*** 0.250*** 0.253***

(0.000) (0.000) (0.000) (0.000) (0.000)

MTOB 0.017* 0.016* 0.017* 0.017* 0.017*

(0.064) (0.080) (0.062) (0.070) (0.067)

DEBT -0.570** -0.595** -0.565** -0.580** -0.587**

(0.022) (0.017) (0.023) (0.020) (0.017)

EBITDA 1.005*** 1.002*** 1.020*** 1.011*** 1.022***

(0.002) (0.002) (0.002) (0.002) (0.002)

KZ 0.108** 0.109** 0.102** 0.107** 0.103**

(0.037) (0.033) (0.049) (0.040) (0.042)

CASH -0.215** -0.196* -0.235** -0.204* -0.225**

(0.050) (0.070) (0.031) (0.068) (0.038)

DIV 7.412*** 7.395*** 7.376*** 7.384*** 7.249***

(0.006) (0.005) (0.006) (0.006) (0.006)

LNAGE 0.102* 0.088 0.111* 0.111* 0.075

(0.098) (0.155) (0.076) (0.072) (0.227)

CONTROVERSIAL -0.478*** -0.468*** -0.448*** -0.475*** -0.449***

(0.002) (0.003) (0.004) (0.002) (0.005)

INST -0.002* -0.002* -0.002* -0.002 -0.002

(0.067) (0.086) (0.096) (0.101) (0.138)

R&D 0.993*** 1.035*** 1.017*** 1.082*** 0.969***

(0.001) (0.001) (0.001) (0.001) (0.001)

ADVERSTISMENT 6.296*** 6.218*** 6.254*** 6.250*** 5.918***

(0.000) (0.000) (0.000) (0.000) (0.000)

LNINCOME -0.018 -0.059 0.152 0.043 -0.286

(0.914) (0.750) (0.332) (0.825) (0.190)

RELIGION -0.163 -0.227 -0.132 -0.333 0.168

(0.717) (0.617) (0.778) (0.471) (0.713)

RURAL 0.051 0.059 0.043 0.040 0.030

(0.737) (0.697) (0.771) (0.796) (0.835)

LNPOP -0.125** -0.096* -0.216*** -0.124** -0.202***

(0.017) (0.050) (0.000) (0.015) (0.000)

POPG 0.008 0.021 -0.015 -0.006 0.024

(0.798) (0.483) (0.629) (0.854) (0.390)

LNDIST 0.046 0.043 0.040 0.046 0.037

(0.220) (0.222) (0.296) (0.233) (0.280)

54

REPUBLICAN -5.135*** -5.778*** -4.560*** -4.350*** -4.959***

(0.000) (0.000) (0.000) (0.000) (0.000)

p-value for the test RESPONSE TO

CENSUS + VOTE=

ASSOCIATIONS+NGO

(0.001)

Industry Dummies YES YES YES YES YES

Observations 13,117 13,117 13,117 13,117 13,117

R-squared 0.154 0.156 0.155 0.153 0.161

Notes on Table 6: 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 percent, 5 percent, and 10 percent levels respectively. The variable

descriptions are in the appendix. All of the continuous variables are winsorized at the 1st and the

99th

percentile.

55

Table 7 The association between Tobin’s Q and CSR are not statistically different for firms in high and

low social capital counties

Panel A

Dependent variable= Q

Low Social Capital High Social capital Pooled

CSR_S 0.018 0.038** 0.026*

(0.369) (0.010) (0.058)

CSR_S*HIGH SOCIAL CAPITAL

0.009

(0.648)

HIGH SOCIAL CAPITAL

-0.112**

(0.012)

LNMV 0.261*** 0.178*** 0.222***

(0.000) (0.000) (0.000)

DEBT -1.739*** -1.389*** -1.558***

(0.000) (0.000) (0.000)

HHI -5.682** -3.847 -4.951***

(0.023) (0.105) (0.002)

INST -0.005*** -0.002*** -0.004***

(0.000) (0.002) (0.000)

KZ 0.109 0.036 0.070*

(0.169) (0.523) (0.095)

EBITDA 1.265** 2.708*** 1.943***

(0.027) (0.001) (0.000)

Diff in Coeff of CSR_S 0.331

p-value

Industry Dummies YES YES YES

Observations 6,560 6,557 13,117

R-squared 0.232 0.217 0.217

56

Panel B

(1) (2) (3)

DV=CSR_S DV=CSR_S*HIGH SOCIAL CAPITAL DV=Q

CSR_S

0.800**

(0.018)

CSR_S*HIGH SOCIAL CAPITAL

-0.524

(0.115)

HIGH SOCIAL CAPITAL 0.690 1.088** -0.274***

(0.260) (0.017) (0.009)

LNMV 0.282*** 0.110*** 0.059

(0.000) (0.000) (0.392)

DEBT -0.155 0.095 -1.373***

(0.525) (0.654) (0.000)

HHI -19.303*** -12.382*** 3.327

(0.000) (0.000) (0.439)

INST -0.002* -0.001 -0.003***

(0.082) (0.177) (0.000)

KZ -0.019 -0.044 0.064

(0.611) (0.145) (0.174)

EBITDA 0.771** 0.575** 1.747***

(0.010) (0.048) (0.000)

RELIGION -0.704 -0.136

(0.244) (0.434)

REPUBLICAN -1.961 0.664*

(0.142) (0.089)

RELIGION*HIGH SOCIAL CAPITAL 0.181 -0.162

(0.811) (0.797)

REPUBLICAN*HIGH SOCIAL CAPITAL -2.906** -5.755***

(0.050) (0.000)

F-Statistics (Strength of Instruments) F-stat=13.17, p-value=0.000 F-stat=12.10, p-value=0.000

Hansen J Test (Overidentfication) Chi-stat=1.207, p-value=0.5469

Endogenity Test (Appropriateness) Chi-stat=6.460, p-value= 0.003

Industry Dummies YES YES YES

Observations 13,117 13,117 13,117

R-squared 0.142 0.087 -0.358

57

Notes on Table 7: 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 percent, 5 percent, and 10 percent levels respectively. The variable descriptions are in the appendix. All of the

continuous variables are winsorized at the 1st and the 99

th percentile.

58

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) (2)

HIGH SOCIAL CAPITAL INDICATOR 0.274**

(0.011)

SOCIAL CAPITAL

0.205**

(0.042)

G1

0.119

(0.445)

G2

0.148

(0.191)

G3

0.344**

(0.013)

G4

0.315

(0.122)

LNMV 0.251*** 0.261***

(0.000) (0.000)

MTOB 0.016* 0.014

(0.093) (0.207)

DEBT -0.602** -0.716*

(0.016) (0.090)

EBITDA 0.995*** 1.851***

(0.002) (0.004)

KZ 0.110** 0.149*

(0.032) (0.077)

CASH -0.170 -0.257

(0.122) (0.259)

DIV 7.440*** 11.060**

(0.005) (0.011)

LNAGE 0.094 -0.070

(0.125) (0.420)

CONTROVERSIAL -0.495*** -0.472**

(0.001) (0.011)

INST -0.002 -0.002

(0.129) (0.160)

R&D 1.062*** 1.695***

(0.001) (0.005)

ADVERSTISMENT 6.185*** 5.737***

(0.000) (0.004)

59

LNINCOME -0.081 -0.181

(0.639) (0.458)

RELIGION -0.419 -0.350

(0.333) (0.523)

RURAL 0.020 0.029

(0.899) (0.892)

LNPOP -0.089* -0.038

(0.075) (0.619)

POPG 0.015 0.024

(0.598) (0.651)

LNDIST 0.046 0.052

(0.192) (0.310)

REPUBLICAN -4.907*** -5.875***

(0.000) (0.000)

Industry Dummies YES YES

Observations 13,117 7,596

R-squared 0.156 0.185

Notes on Table 8: 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

controldummies 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 percent, 5 percent, and 10 percent levels respectively. The variable

descriptions are in the appendix. All of the continuous variables are winsorized at the 1st and the

99th

percentile.

60

Table 9 The association between different types of CSR and social capital

DV

= C

SR

_C

OM

M

DV

= C

SR

_D

IV

DV

= C

SR

_E

MP

DV

= C

SR

_H

UM

AN

DV

= C

SR

_P

RO

DU

CT

DV

= C

SR

_E

NV

DV

= C

SR

_S

TR

EN

GT

HS

DV

= C

SR

_C

ON

CE

RN

S

(1) (2) (3) (4) (5) (6) (7) (8)

SOCIAL CAPITAL 0.037** 0.054 0.066** -0.004 0.035* -0.007 0.104 -0.101**

(0.034) (0.191) (0.035) (0.542) (0.070) (0.725) (0.145) (0.047)

LNMV 0.052*** 0.284*** 0.095*** -0.040*** -0.081*** -0.041*** 0.583*** 0.329***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.005) (0.000) (0.000)

MTOB 0.002 -0.006* -0.002 0.003*** 0.013*** 0.005* -0.019*** -0.036***

(0.369) (0.086) (0.652) (0.000) (0.000) (0.058) (0.006) (0.000)

DEBT -0.039 0.501*** -0.347*** -0.066*** -0.240*** -0.355*** 0.657*** 1.261***

(0.490) (0.000) (0.000) (0.005) (0.001) (0.000) (0.001) (0.000)

EBITDA 0.016 -0.311 0.589*** 0.095*** 0.259*** 0.245** -0.802*** -1.799***

(0.819) (0.128) (0.000) (0.001) (0.001) (0.016) (0.002) (0.000)

KZ 0.018 -0.023 0.030 0.015*** 0.014 0.039*** -0.036 -0.139***

(0.147) (0.414) (0.178) (0.004) (0.320) (0.009) (0.358) (0.000)

CASH -0.041* 0.011 -0.058 -0.011 -0.010 -0.091*** -0.078 0.098

(0.084) (0.872) (0.280) (0.324) (0.768) (0.009) (0.496) (0.331)

DIV 1.617*** 0.795 2.194** 0.313 -0.172 2.142*** 4.213** -2.779

(0.005) (0.593) (0.036) (0.261) (0.818) (0.002) (0.044) (0.101)

LNAGE 0.022 0.181*** 0.003 -0.017*** -0.030* -0.054** 0.317*** 0.212***

(0.122) (0.000) (0.900) (0.004) (0.062) (0.011) (0.000) (0.000)

61

CONTROVERSIAL 0.005 -0.268*** -0.057 -0.028 -0.107** -0.031 -0.182 0.337***

(0.876) (0.002) (0.237) (0.140) (0.027) (0.538) (0.190) (0.006)

INST -0.001** -0.001* -0.001 0.001*** -0.000 -0.000 -0.001 0.001**

(0.021) (0.095) (0.162) (0.000) (0.167) (0.866) (0.488) (0.025)

R&D 0.119** 0.050 0.596*** 0.031 0.173** 0.097 0.289 -0.818***

(0.036) (0.762) (0.000) (0.187) (0.032) (0.196) (0.290) (0.000)

ADVERSTISMENT 0.956*** 3.590*** 0.915* -0.202 -0.142 0.611* 4.472*** -1.536*

(0.003) (0.000) (0.078) (0.137) (0.740) (0.083) (0.000) (0.053)

LNINCOME -0.073** 0.306*** -0.267*** -0.035* -0.134*** 0.107* 0.250* 0.408***

(0.046) (0.000) (0.000) (0.053) (0.001) (0.091) (0.063) (0.005)

RELIGION 0.043 -0.634*** 0.058 0.078** -0.012 -0.096 -1.061*** -0.522*

(0.554) (0.003) (0.720) (0.028) (0.894) (0.374) (0.001) (0.065)

RURAL -0.019 0.037 0.046 -0.005 -0.005 0.015 0.049 -0.034

(0.552) (0.601) (0.415) (0.666) (0.877) (0.768) (0.684) (0.745)

LNPOP 0.003 -0.030 -0.006 -0.011** 0.005 0.002 -0.062 -0.032

(0.781) (0.308) (0.796) (0.040) (0.681) (0.920) (0.193) (0.413)

POPG 0.003 -0.017 0.021** -0.001 0.005 0.004 -0.033 -0.050**

(0.729) (0.253) (0.048) (0.705) (0.514) (0.526) (0.155) (0.015)

LNDIST 0.011* 0.011 0.006 0.005 0.001 0.010 0.062** 0.009

(0.093) (0.495) (0.653) (0.195) (0.909) (0.310) (0.029) (0.771)

REPUBLICAN -0.775*** -2.080*** -1.258*** -0.120* -0.484** 0.081 -4.139*** 0.688

(0.000) (0.000) (0.001) (0.099) (0.025) (0.763) (0.000) (0.307)

Industry Dummies YES YES YES YES YES YES YES YES

Observations 13,117 13,117 13,117 13,117 13,117 13,117 13,117 13,117

R-squared 0.100 0.269 0.096 0.154 0.165 0.173 0.345 0.263

Notes on Table 9: 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

62

county level are in the parentheses. The ***, **, and * represent significance at the 1 percent, 5 percent, and 10 percent levels

respectively. The variable descriptions are in the appendix. All of the continuous variables are winsorized at the 1st and the 99th

percentile.