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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: [email protected]
Phone: 956-326-2581
&
James Cox
Doctoral student
Texas A&M International University
Laredo, Texas
E-mail: [email protected]
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.