1
Religion and Takeovers
Hua Xin
Rutgers University
Abstract
This study examines whether religiosity at the county level will influence the firms take
over decision. I find robust evidence that firms headquartered in counties with higher levels of
religiosity exhibit lower risky take over decisions. This finding is consistent with the view that
religion, as a set of social norms, helps to curb risky activities by managers.
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Religion and Takeovers
1. Introduction
I examine the association between religiosity and a firm’s acquisition-investment decisions.
The prediction is that managers of firms located in religious counties pursue less risky
acquisitions and have more money to compensate shareholders. Our study builds on the
argument that religiosity, which has a significant impact on human behavior, also has a
significant impact on constraining opportunistic behavior by managers, particularly their
investment decisions (Ball, 2001; Watts, 2003; Ball and Shivakumar, 2005).
The impact of religiosity on human behavior was first documented in the psychological
literature (Cornwall 1989; Layman et al. 1997a, 1997b), which found its way to the economics
literature, where it is especially documented that religiosity has a significant influence on
individuals’ economic decisions (e.g. Bruce, 1993; Stern et al. 2000; Schultz et al. 2000; Gardner
et al. 2002). Lately, several studies in finance and accounting have also examined whether
religiosity has influence on finance and accounting decisions. The finance literature documents
that managers are more cautious about risky investment projects (Hilary and Hui, 2009). In
another recent study, Callen and Fang (2013) document that the financial crisis has been more
severe in areas with low religiosity, suggesting that religiosity played a significant impact on
human behavior that led to the financial crisis in certain non-religiosity areas. In the accounting
literature, McGuire et al. (2012) document that religiosity is negatively associated with earnings
manipulation through discretionary accrual, whereas Dyreng, et al.(2012) document lower
restatements by firms with headquarters in high religiosity counties. The existing literature thus
provides theoretical justification that behavioral and social norms influenced by religiosity foster
sound moral judgment and ethical behavior in firms (e.g. Weaver and Angle 2002) and also
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provides empirical evidence that firms with headquarters in high religiosity counties display few
financial reporting irregularities as measured by accounting risk and accounting restatements (e.g.
McGuire, et al. 2012; Dyreng, et al, 2012).
In this study, I extend an examination of the impact of religiosity in acquisition and show
how it influences managers investment decision making and hence dividends. I argue that higher
religious beliefs in a community in which a firm operates will have a significant impact on
human behavior of individuals associated with the firm, which in turn will have a significant
impact on mergers and acquisition decisions. I especially argue that the following factors
associated with religiosity especially reduce risk taking: First, it is argued that religiosity that
has a significant impact on individuals’ ethical behavior creates environment where managers are
more conservatism, they are less risky taking and will be less likely to onverpay for target
companies and undertake value-destroying mergers. Lower discretionary accruals by firms in
high religiosity counties is especially documented by McGuire et al. (2012), and reduction in
fraudulent accounting practices and restatements are documented by Dyreng, et al. (2012). Risk
aversion in the decision making process suggests that firms will choose project which will have
higher potential for success and lower probability for project failure. I argue that higher risk
aversion will result in high assurance for managers on the mergers.
Second, it is argued in the literatures that CEO overconfidence about their future cash flows
may create firm value along some dimensions (i.e. by counteracting risk aversion, inducing
entrepreneurship, allowing firms to make credible threats, or attracting similarly-minded
employees), and engages in mergers that do not warrant the paid premium. I therefore argue that
religiosity creates the environment in which managerial opportunistic behavior is discouraged
and this will reduce risky acquirers.
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Third, much of the literature focuses on the efficiency gains from mergers (e.g., Lang, Stulz,
and Walkling, 1989; Servaes, 1991; Mulherin and Poulsen, 1998). Religiosity, instead, is closest
to agency theory (Jensen, 1986; Jensen, 1988). Empire-building predicts heightened
acquisitiveness to the detriment of share holders, especially given abundant internal resources
(Harford, 1999). However religious CEOs, unlike traditional empire-builders, believe that they
are acting in the interest of shareholders, and are less likely to select risky projects in considering
the long term profit.
The above arguments that religiosity encourages mangers to be conservatism, less
overconfidence and more long term profit consolidation in firms with their headquarters in high
religiosity areas are also supported by the findings of a study by Grullon, et al. (2010), who
report that firms located in counties with high religiosity values show lower potential for class
action securities lawsuits, are less likely to encourage managers to engage in backdating options
and grant excessive compensation packages to managers, and discourage managers to engage in
aggressive accounting. Based on these arguments and evidence, I present lower merger premium
and thus I expect a negative association between religiosity and acquisition premium. In other
words, I expect comparatively lower acquisition premium for firms with headquarters in high
religiosity counties.
I conduct additional analyses to examine the impact of level of acquisitions on the
association between acquisition premium and religiosity. I examine whether the impact of
religiosity will differ if the level of acquisition is low or high. It is argued in the literatures that
at paying high acquisition premia is value destroying for acquirer shareholders (Laamanen 2007).
Thus, I hypothesize that the association between religiosity and acquisition premium is stronger
for firms with high acquisition premium.
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Second, I conduct the impact of religiosity on dividend payment. I argue that the managers
with high religious beliefs will challenge agency theory; religious CEOs are more likely to act in
the interest of shareholders and are willing to take the less risky project in the long run. Thus, I
expect to see the association between religiosity and acquisition premium will be moderated if a
firm pays high dividends. So I hypothesize that the association between acquisition premium and
religiosity is stronger for firms with high dividends payment.
Third, I examine the influence of religiosity on goodwill write off. It is argued and
documented in the literatures acknowledge the possibility that management might be
appropriately exercising reporting discretion to reflect deteriorating economic conditions.
(Francis, Hanna, and Vincent (1997). Risk aversion in the decision making process suggests that
firms will choose project which will have higher potential for success and lower probability for
project failure. I argue that higher risk aversion will result in more conservative in M&A
decisions, and will trigger more timely good will write offs afterwards.
In addition to the above analyses, I also examine (1) whether the association between
acquisition premium and religiosity will be impacted within different industries. (2) the
volatility of stock returns, debt ratio; ROA will have an impact on the association between
acquisition premium and religiosity. (3) the target firm takeover factors and state takeover
defenses and target firm religiosity scores have an impact on the acquirer takeover premium.
Our main tests are based on data obtained from the American Religion Data Archive (ARDA)
for the period from 1971-2010. To evaluate the robustness of our findings, I also conduct tests
based on data compiled by the Gallup during 2008-2010. I use the latest acquisition premium
model to conduct regression analyses on the association between acquisition premium and
religiosity. In addition to controlling for different variables affecting acquisition premium that
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have been used in earlier studies (John et al. (2012)). I also include religiosity-related controls
that have been by earlier religiosity studies in the accounting and Finance literature (e.g.
McGuire et al., 2012; Dyrang, et al. 2012; Hilary and Hui, 2009).
Our results show that there is a negative association between acquisition premium and county
religiosity scores, indicating that firms are less likely to pay higher acquisition premium for the
firms that have headquarters in US counties with higher percentage of religious believers. These
results support our argument that religious beliefs of managers play an important role in the
M&A determination process. These results thus suggest that more ethical behavior and high
morals, which are reflected in more risk aversion, more conservatism, and long term
consideration , and lower possibility of class action legal suits by investors, reduce risk which is
reflected in lower acquisition premium.
Additionally, the results show that the association between acquisition premium and
religiosity are stronger for firms with high acquisition. These results confirm that religiosity
influence is stronger when risk is high.
I evaluate the robustness by conducting different tests. First, I test the robustness of our
results by using the religiosity scores based on the Gallup survey data. The results of these tests
confirm the findings based on ADA data.
I conduct supplementary tests to evaluate whether religiosity will influence the dividends
payment and good will write off, the results of these tests confirm that Risk aversion in the
decision making process suggests that firms will choose project which will have higher potential
for success and lower probability for project failure. I argue that higher risk aversion will result
in less overpay, more dividends yield and more frequent good will write off.
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Our findings make the following contributions to the literature. First, the findings confirm
that religiosity has a significant impact on risk aversion and hence it is an important determinant
of M&A value. This finding suggests that the investment value estimation models need to be
expanded to include the religiosity factor. Second, these findings supplement the findings of
agency theory to add psychology factors. Third, target firms location, incorporate law, takeover
defenses, and religiosity should also be incorporated in the decision making process. Findings
show that goodwill write-offs by firms in the high religiosity areas are timely and more
comprehensive, which make reported information more useful for investors. Fourth, acquisition
size, industries, dividend payment, debt financing have a significant influence in establishing the
relationship between M&A premium and religiosity. Religiosity has a stronger impact on the
risk aversion. This line of research can be extended to international arena and future research
studies should examine whether religiosity plays a role in the M&A premium determination
process across countries.
The remainder of the paper is designed as follows. Part II presents background and
hypotheses for the study. Research design is explained in part III. This part also provides details
on data used in the study and discusses different aspects of the acquisition premium model.
Findings are presented in part IV, and part V contains conclusion of the study.
2. Literature Review
The impact of religious beliefs and ethical values on human behavior is well recognized
in the social sciences literature (Sunstein 1996; Kennedy and Lawson 1998). It is well
documented in the psychology and religion literature that religious beliefs exert a significant
influence on individual behavior (Cornwall 1989; Layman et al. 1997a, 1997b). The results of a
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survey also confirm that individuals with strong religious beliefs show a higher level of ethical
judgment (Longenecker et al. 2004). 1
Several authors have extended research on religiosity to
evaluate the association between religious beliefs and human behavior in economics (Bruce 1993;
Shariff et al. 2007; Norenzayan 2008) and business (Stern et al. 2000; Schultz et al. 2000;
Gardner et al. 2002). Hilary and Hui (2009) extend research on religiosity and focus on human
behavior within the corporate environment, and they especially examine whether religiosity has
an effect on managerial decisions. They conclude that firms with headquarters in the US counties
with high level of religiosity exhibit lower risk exposure.
With regard to the impact of religion on business, it is argued that individuals with
strong religious beliefs are generally more ethical because they find it morally rewarding and
satisfying, and this is especially explained by the theoretical framework developed by Weaver
and Agle (2012). The authors argue that influence of religion on business ethics is determined
by the importance of religion in an individual’s life. In other words, how important is religion in
an individual’s self-identity. As religion assumes greater importance in individual’s self-identity,
his/her behavior is more and more guided by the religious and social norms (Zahn 1970). This
aspect is also explained by Parboteeah et al. (2008) in a different way; they argue that an
individual’s participation in the religious services enhances his/her interaction with other
individuals who have similar beliefs and moral values, and this strengthens their beliefs.
Weaver and Agle’s (2012) theoretical framework suggests that as religion becomes an
important element of an individual’s self-identity, individual’s outlook and behavior are
significantly molded by the religious and ethical values. Individuals do not feel comfortable
1 Barro and McCleary (2003), Guiso et al. (2003), and Lehrer (2004) have previously also examined the impact of
religion on individuals’ economic and business behavior. Whereas Lehrer (2004) focused on the religion’s impact
on economic decisions by individuals, Barro and MCleary (2003) and Guiso et al. (2003) examined the overall
economic outcome as a result of the influence of religion on human economic behavior.
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when their behavior and actions are considered to be outside the religious and social norms.
They especially try to keep their behavior within the boundaries of religious and social norms to
avoid any emotional distress and guilt feeling (Sunstein 1996; Weaver and Agle 2002). The
significant impact of religion in molding individual ethical behavior is also supported by the
evidence provided by empirical studies (Batson et al. 1993; Singelis et al. 1995; Karahanna et al.
2002). In a recent study, Longeneck et al. (2004) conclude that individuals with religious
commitments are associated with higher business ethics. Overall, their findings suggest that
religious believers generally do not engage in unethical behavior; instead they are more likely to
maintain high moral and ethical standards and their decision-making process in the business
environment is guided by their moral and ethical values.
Findings of some studies especially emphasize the role of ethics in Financial Reporting.
Based on an experimental study, Conroy and Emerson (2004) find that religiosity is negatively
associated with the use of “accounting tricks to conceal”, and their findings show that the use of
accounting manipulation is lower for individuals with higher church attendance. McGuire et al.
(2012) report that firms with headquarters in the higher religious areas engage less in financial
reporting irregularities compared to the firms with headquarters in non-religious or lower
religious area. They especially find a negative association between religiosity and abnormal
accruals. They, however, find a positive association between religiosity and real earnings
management, suggesting that managers do not consider real earnings management as unethical.
In fact, managers view real earnings management as ethical and less risky compared to the
accruals based earnings management. Dyreng et al. (2012) report that religious adherents are
associated with a lower likelihood of financial restatements and that there is a lower risk that the
financial statements will misrepresent earnings because of overstatement (understatement) of
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revenues/assets (expenses/liabilities). Calleng and Feng (2013) find that religiosity will also
constrain managers to hoard bad news, and this will result in timely, reliable, and comprehensive
disclosure of information, which will reduce potential risk for legal suits and thus there will be
reduction in risk.
Our study also makes an important contribution to the literature on takeover premium.
Considerable evidence exists on the positive takeover premium paid in acquisitions of publicly
traded targets (e.g., Schwert 1996). Fuller, Netter, and Stegemoller (2002) and Officer (2007)
suggest that the price paid by a bidder for its target is lower when the target is less liquid. Many
earlier articles also examine the role of its target is lower when the target is less liquid. Many
earlier articles also examine the role of taxes or information acquisition in explaining the
difference in takeover premium between publicly traded targets that receive cash payments and
stock payments (i.e., Huang and Walkling 1987 for the taxation explanation; i.e., Eckbo and
Langohr 1989 for the information explanation)2. Our paper contributes to this literature by
providing social norms into the takeover premium received by publicly traded targets. Our
results support the predictions of our theory, which is based on religiosity even after I control for
mode of payment, liquidity, and other considerations.
Finally, our article is related to the literature on the impact of religiosity on risk aversion.
In particular, Callen and Fang (2013) find robust evidence that firms headquartered in counties
with higher levels of religiosity exhibit lower levels of future stock price crash risk. Our main
2 Recently, Bargeron et al. (2008) find that private bidders, such as private equity funds, offer smaller premiums to
their targets compared with public bidders. They argue that the differences in managerial ownership and managerial
incentives contribute to the different premiums paid by private bidders and public bidders. Officer (2003) and Bates
and Lemmon (2003) also find the impact of target termination agreement on takeover premium, and Betton, Eckbo,
and Thorburn (2008, 2009), Jarrell and Poulsen (1989), and Ravid and Spiegel (1999) find the relation between
bidder toehold and takeover price.
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results suggest that firms headquartered in religious counties are less likely to pay high takeover
premium.
3. Hypothesis
Religiosity and Takeover premium
The existing studies on the role of religiosity in the corporate environment examine
managers’ risk aversion in investment decisions and earnings manipulations. Hilary and Hui
(2009) document that managers’ religious beliefs lead to risk aversion, meaning that religious
beliefs do not encourage managers to engage in risk-taking activities. Instead, religious believers
feel more comfortable if they can avoid risk and religion provides solace for not getting involved
in activities exposing them to risky situations (Malinowski 1925; Miller and Hoffman 1995;
Gaspar and Clore 1998).3
Additionally, it is documented that religious believers being highly ethical avoid
violating religious, morale and social norms (e.g. Boone, et al. 2013). Instead, they stay within
the boundaries of these norms and act in accordance with the guidelines provided by them. This
aspect of religiosity suggests that religion acts as a deterrent for any activity that is considered to
be outside the religious and social norms. Religious believers will feel guilty if they engage in
unreligious activities and they fear punishment by God for violating the religious guidelines. In
fact, this aspect of religion serves as a sanctioning system in the business and corporate
environment and discourages managers to engage in an opportunistic behavior. Instead, it
encourages them to behave ethically and report financial information truthfully. Recent empirical
findings confirm that religious managers avoid earnings manipulations by overstating the
3 Hilary and Hui (2009) discuss the negative association of religious beliefs with various types of risk-taking
behaviors.
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revenues and assets and understating the liabilities and expenses (McGuire et al. 2012) and they
also avoid restatements (Dyreng et al. 2012).
Recently, Callen and Fang (2013) have argued that high moral values, anti-manipulative
ethos of religion also discourage managers to withhold bad news from investors. They present
that social norms operate in the following three ways to discourage managers to hoard bad news.
First, religious managers are more likely to internalize the social norms associated with risk
aversion, which discourage them to pay a high takeover premium. Second, managers are likely
to pay a high price in terms of social stigma if they are caught violating this social norm. Third,
religiosity will encourage potential whistle blowers to feel religion-bound to unmask
manipulators (e.g. Javers, 2011).
Grullon, et al (2010) have advanced another argument to explain the impact of
religiosity on firms. The argue and document that firms located in counties with higher levels of
religiosity are less likely to be the targets of class action securities lawsuits, engage in backdating
options, and grant excessive compensation packages to their managers. Their argument and
evidence are consistent with the argument that high religiosity environment encourage firms to
behave in ethical and moral way that does not involve risky takeovers that is important for
investors. Consequently, legal risk is reduced and there are lower class action securities law
suits.
The arguments and evidence that religiosity constrains managerial behavior of earnings
manipulation, discourages them to engage in risky projects and without bad news, which is
reflected in potential low class action suits is low, suggest high religiosity values reduce legal
risk for firms. This argument also suggests that the impact of these factors will also have a
significant moderating effect on risk taking make managers making M&A decisions. Thus, I
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argue that managers are less likely to take risky project if the client firm’s headquarters is located
in counties that associated with high religiosity values. Risk aversion will in turn have a
moderating effect on takeover price target firms received, which leads us to hypothesize that
takeover premium are lower for firms that have their headquarters in counties with high
religiosity.
On the other hand, if managers have low religious beliefs, it will have a negative impact
on their ethical and moral values, and they are likely to be in the best interest of other
stakeholders. In order to ensure the long term profit for these firms, managers will be less likely
to take risky projects. Consequently, this will result in lower takeover price.
Based on the above discussion, I develop the following hypothesis to test the impact of
managers’ religious beliefs on takeover price:
H1a: Takeover premium is lower if the parent headquarters are located in US counties
with high religiosity.
H1b: Takeover premium is higher if the target headquarters are located in US counties
with high religiosity.
Religiosity and Goodwill write off
H2: Goodwill write off will be lower if the parent headquarters are located in US
counties with high religiosity.
Religiosity and High Risk
H3: The negative association between takeover premium and religious beliefs is
especially strong if the parent belongs with high risk.
Religiosity and Dividends Yield
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H4: Dividend yield will be lower if the parent headquarters are located in US counties
with high religiosity.
Religiosity and Stock Payment
H5: Stock Payment will be lower if the parent headquarters are located in US counties
with high religiosity.
4. Sample and Research Design
4.1.Sample and Data Selection
Following Hilary and Hui (2009), I obtain religiosity data from the American Religion
Data Archive (ARDA). Once every decade, the Glenmary Research Center collects data from
surveys on religious affiliation in the U.S. (1971, 1980, 1990 and 2000). Based on the survey
results, the centre reports county-level data on the number of churches and the number of total
adherents and the number of total adherents by religious affiliation. These reports are available
on ARDA’s website under the title “Churches and Church Membership.” Our main variable of
interest is the degree of religiosity at time t (RELt) of the county in which the firm’s headquarter
is located. I calculate RELt as the number of religious adherents in the county to the total
population the county as reported by ARDA. 4 Following previous studies (e.g., Hilary and Hui
(2009) and Alesina and La Ferrara (2000)), I linearly interpolate the data to obtain the values for
missing years (1972 to 1979, 1981 to 1989, 1991 to 1999, and 2001 to 2010).
4 ARDA indicates that “for[the] purposes of this study, adherents were defined as ‘all members’, including full
members, their children and the estimated number of other regular participants who are not considered as
communicant, confirmed or full members, for example, the ‘baptized,’ ‘those not confirmed,’ ‘those not eligible for
communion’ and the like.”
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I also use another Religiosity data base, developed by Gallup organizations, to obtain
data on religious and demographic variables. The final sample consists of 1,788 observations for
678 firms with headquarters in 194 US counties from 2007-2010.
In addition, I collect takeover data from CDS. Compustat also provides information on
the location of firms’ headquarters. Following prior research (e.g., Coval and Moskowitz (1999),
Ivkovic and Weisbenner (2005), Loughran and Schultz (2004), irinsky and Wang (2006), and
Hilary and Hui (2009)), I define a firm’s location as the location of its headquarters “given that
corporate headquarters are close to corporate core business activities (Pirinsky and Wang
(2006))”.Geographic data is collected from U.S. Consensus Bureau.
The sample selection details are provided in Table 1.
------------------------------
Table 1
---------------------------
5. Empirical setting and results
5.1. Main specifications
I follow the literatures include a takeover model (Chatterjee et.al. 2012)
Takeover Premium=Religiosity+Geographic Controls+Percentageof stock+Diversify+Tender+Hostile
+Competing offer+Toehold dummy+Ratio+Target market cap.+Target market-to-book
+Target institutionalinvestor holding+Adjusted target turnover+Target merger liquidityindex
+Target's analyst dispersion in[-126,-64]+Dummyof high target analyst dispersion
+Target'ΔHold in window[-126,-64]+Target'sΔBreadth in window[-126,-64]
+Target's idiosyncratic volatility
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All variables are defined in Appendix A. In order to test our hypotheses H2 and H3, I
include the variables of acquisition size and good will writes off.
In order to test target state takeover defenses law, I follow Barzuza’s (2009) classification
of state antitakeover laws, summarized in table .Based on data on a firm’s state of incorporation
taken from Compustat, I generate qualitative variables describing the antitakeover laws to which
firms in each state are subject. In order to simplify the analysis, I create a poison pill
endorsement indicator variable equal to one if a firm is incorporated in a state with a strong or
intermediate poison pill endorsement statue and equal to zero otherwise. Similarly, I create
another constituency statue dummy equal to one if the firm is incorporated in state with a strong
or intermediate other constituency statue and zero otherwise. I also create binary variables for
firms in states that apply Unocal, this time equal to 1 if the state follows the enhanced fiduciary
standard and 0 if they reject it. As described in the introduction, I omit Delaware firms. First,
courts in states with weak statues- Illinois and Wisconsin- applied Unocal. Second, courts in
states that had no statues at all when the case was decided- Arkansas, Florida, Indiana, Kansas,
Maryland, and Minnesota- applied unocal, except for one case applying New York law (before
the original statue was enacted), where the court suggested it would not recognize enhanced
duties.
5.2.Main results
Descriptive Statistics
Descriptive statistics on religiosity are provided in Table 2.
-------------------------------------
Insert Table 2 Here
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-------------------------------------
Association between Religiosity and Takeover premium
The results are contained in Table 3.
-------------------------------------
Insert Table 3 here
-------------------------------------
I first evaluate the association between takeover premium and religiosity based on county
scores of religiosity. I validate the above results based on the religiosity scores developed for the
Gallup religiosity data base. I use five different measures for calculating the religiosity scores.
Overall, the results (untabulated) of these analyses are consistent with the results reported in
Table 3. The results show that the RELIGIOSITY coefficient is negative and statistically
significant. The results based on individual elements show that the element of “belonging to a
religious group” especially plays an important role in one’s life, which in turn has an impact on
the individual’s behavior in the business environment.
In addition, I incorporate target firms’ religiosity in the regression. I find that target firms’
religiosity is positively correlated with acquisition premium, which means that target firms’
manager will charge a higher premium in the religiosity areas. The results are consistent with the
theory that religiosity reduce the agency problems.
The impact of religiosity and dividend payment
Then I put the number of dividend payment in the model. I find that there is a negative
and significant relation between religiosity and dividend payment.
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-------------------------------------
Insert Table 4 Here
-------------------------------------
Impact of Goodwill Write-off and Religiosity
I also put the goodwill like a measure of acquisition premium. I find the religiosity is
significantly negative with goodwill write off for acquirer, but significantly positively correlated
with targets’ goodwill write off, which means the target firms located in religious areas will write
down their assets.
-------------------------------------
Insert Table 5 Here
-------------------------------------
Impact of Percentage Stock Payment and Religiosity
I also put the percentage of stock payment in the model and find that acquirer’s
religiosity is significantly negative with stock percentage payment, but not targets’ religiosity,
which means the acquirer located in religiosity areas will be more likely to select cash payments
to avoid the takeover risk.
-------------------------------------
Insert Table 6 Here
-------------------------------------
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Robustness Tests
I conduct tests to evaluate the robustness of our findings. First, I conduct a test using the
religiosity score calculated based on the Metropolitan Statistical Area (MSA). The number of
observations is reduced to because some sample observations are not in any MSA. The results of
this test are also contained in Panel B of Table 3 (Column 1 and 2). Column 1 contains the
results based on the religiosity score calculated using the factor analysis, whereas Column 2
contains the results based on the average religiosity score. The results for both columns show
that the coefficients are significantly negative, and thus these results are similar to the main
results.
The impact of industry on the association of takeover price and religiosity
I also find in different industries, the religiosity effect is different. Especially in mining
and construction, there is a positive correlation between religiosity and acquisition premium.
-------------------------------------
Insert Table 7 Here
-------------------------------------
The impact of debt ratio on the association of takeover price and religiosity
Our results show that if the firm is financed by debt, I find a negative and significant
correlation between religiosity and acquisition premium.
-------------------------------------
Insert Table 7 Here
-------------------------------------
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6. Conclusion
It is well documented in the social sciences literature that religious beliefs have a significant
impact on individual behavior. Consistent with this argument, the business and accounting
studies also document that managers with strong religious beliefs do not engage in earnings
manipulations and misrepresentations that may distort information and make it unreliable for
users. These arguments suggest that the quality of information prepared by managers with high
religious values is generally of a higher quality compared to information disclosed by managers
with lower religious values. In this study, I extend this line of research and argue that high
quality of information issued by firms that are located in counties with high religious beliefs and
values will also have an impact on takeover price the target firms received. In view of risk
aversion, CEOs will be less likely to take risky projects. This in turn will result in lower
takeover premium for firms with headquarters in counties with high religious values. The results
of our empirical tests confirm that there is a negative association between religiosity and
takeover premium.
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24
Appendix A: List of Variables
Test Variable:
Religiosity: The ratio of the number of religious adherents in the county (as reported by ARDA)
to the total population of the county (as reported by the US Census Bureau).
Demographic control variables:
Population : the size of the population in the county.
Education: educational attainment, defined as the percentage of people 25 years and over having
a bachelor’s, gradulate, or professional degree.
Male: the male-to-female ratio in the state.
Married: the percentage of married people in the state.
Money: average state money income.
Minority: the percentage of minorities in the state.
Age: the median age of each state.
Dependent variables:
Takeover Premium: the percentage premium of the value of the bidder’s offer over the market
value of the target’s equity on the trading day -64(with adjustments).
Return-based premium: the cumulative target abnormal return in the trading day window [-
63,126].
Percentage of stock: the percentage of target’s shares owned by the bidder.
Toehold dummy: equals one if, prior to the takeover announcement, the bidder owns more than 5%
of the target’s shares.
Ratio: the log ratio of the target’s market value of equity to the bidder’s market value of equity
on the trading day -64.
Target market capitalization: the log of the target’s market value of equity on the trading day -64,
i.e., the day before my measurement window of the total takeover premium.
Target’s market to book ratio: the ratio of the target’s market value divided book value.
25
Target’s institutional investor holding: the percentage of the target’s shares hold by institutional
investors in the first reporting date reported by CDA/Spectrum prior to the trading day -63.
Adjusted target turnover: the ratio of stock trading turnover to the market average turnover,
where stock turnover is trading volume in shares divided by shares outstanding -63.
Target liquidity index: the ratio of the value of mergers in the target’s industry to the total book
value of assets of the firms in the target’s industry. The target’s industry is defined by the two-
digit SIC code.
CDA Variables:
Percentage of stock: fraction of the value of stock component in the whole payment package.
Dummy of all-cash offers
Dummy of all-stock offers
Tender: tender offer.
Hostile: Dummy of hostile takeovers. following the SDC’s definition.
Competing offers: 1 if there is more than one bidder who bids for the target.
Diversify: 1, if the bidder and the target operate in different industries, as defined by the two-
digit SIC codes.
26
Appendix B:
Variation in State Law This table presents the distribution of state law as classified and reported by Barzuza (2009). Year the
statute became effective is in parentheses.
Pill Endorsement
Statutes
Strong GA (1989), MD (1999), VA (1990), CO
(1989),
Intermediate CT (2003), FL (1989), HI (1988), ID (1988),
IL (1989), IN (1986), IA (1989), KY (1984),
ME (2003), MI (2001), NV (1999), NJ (1989),
OH (1986), OR (1989), PA (1989), RI (1990),
SD (1990), SC (2001), TN (1989), UT (1989),
WA (1998), WI (1987)
Weak NY (1988), NC (1990)
Other Constituency
Statutes
Strong IN (1989), MD (1999), NV (1991), NC
(1993), OH (1984), PA (1990), VA (1988)
Intermediate AZ (1987), CT (1997), HI (1989), ID (1988),
IL (1985), IA (1989), KY (1989), LA (1988),
MA (1989), MN (1987), MI (1990), MO
(1989),
NJ (1989), NM (1987), ND (1993), OR
(1989),
RI (1990), SD (1990), TN (1988), TX (2006),
VT (1998)
Weak
FL (1989), GA (1989), ME (1986), NE
(2007),
NY (1987), WI (1987)
Unocal Yes AR,CA,DE,FL,IL,KS,MI,MN,MO,OR,TX,WI
Yes(language) FL (1989), GA (1989), ME (1986), NE
No IN, MD,MA,NV,NJ,NY,NC,OH,PA,VA,
No (language)
AZ, CT (1997), HI (1989), ID (1988),
IL (1985), IA (1989), KY (1989), LA (1988),
MA (1989), MN (1987), MI (1990)
NJ (1989), NM (1987), ND (1993), OR
(1989),
RI (1990), SD (1990), TN (1988), TX (2006),
VT (1998)
28
Table 2 Description Statistics Panel A: Univariate Statistics
Variable N Mean SD. Q1 Median Q3
value 632 6.00 48.79 0.05 0.12 0.63
Religiosity 609 0.53 0.18 0.40 0.50 0.62
Population 609 11.15 1.37 10.10 11.20 12.20
Education 609 23.07 5.00 20.00 20.00 30.00
Male 609 96.86 2.27 94.90 96.44 98.70
Married 630 58.64 2.27 57.00 58.28 60.00
Income 630 10.65 0.13 11.00 11.00 11.00
Minority 609 21.44 8.17 15.10 20.90 26.70
Age 609 33.67 2.32 30.00 30.00 40.00
Stock 632 0.17 0.38 0.00 0.00 1.00
Percentage of Stock 632 2.29 11.40 -0.01 0.00 0.02
Diversify 632 0.12 0.33 0.00 0.00 0.00
Tender 632 0.21 0.41 0.00 0.00 0.00
Competing offer 632 0.01 0.10 0.00 0.00 0.00
Toehold dummy 632 0.06 0.24 0.00 0.00 0.00
Ratio 632 -0.80 1.63 -1.02 0.00 0.00
Target market cap. 632 5.33 2.01 3.88 5.36 6.76
Target market-to-book 632 1.97 16.81 1.05 1.64 2.54
Target institutional
investor holding
632 10.92 13.96 3.84 7.47 13.10
Adjusted target turnover 632 1.18 2.00 0.17 0.61 1.43
Target merger liquidity
index
632 0.00 0.00 0.00 0.00 0.00
29
Panel B: Correlation among Religiosity, M&A, and Control Variables
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
(1) value -0.03 0.00 0.16 0.21 0.13 0.06 -0.10 -0.01 0.26 -0.02
(2) Religiosity -0.14 -0.11 -0.09 0.18 -0.28 -0.19 -0.15 -0.01 0.05
(3) Population 0.24 -0.30 -0.44 0.33 0.05 0.06 -0.10 0.12
(4) Education 0.00 -0.26 0.84 -0.08 0.52 0.25 0.12
(5) Male 0.58 -0.03 0.05 -0.45 0.08 -0.14
(6) Married -0.37 -0.37 -0.15 0.01 -0.10
(7) Income -0.04 0.43 0.16 0.07
(8) Minority -0.32 -0.04 -0.10
(9) Age 0.18 0.11
(10) Stock 0.08
(11) Percentage of Stock
(12) Diversify
(13) Tender
(14) Competing offer
(15) Toehold dummy
(16) Ratio
(17) Target market cap.
(18) Target market-to-book
(19) Target institutional investor holding
(20) Adjusted target turnover
(21) Target merger liquidity index
(12) (13) (14) (15) (16) (17) (18) (19) (20) (21)
(1) value -0.04 -0.06 -0.01 -0.03 -0.40 -0.35 -0.01 -0.04 -0.04 0.13
(2) Religiosity 0.07 -0.10 -0.05 0.03 -0.03 0.02 0.08 -0.02 -0.06 0.05
(3) Population -0.04 -0.10 0.00 0.05 0.03 -0.02 -0.03 0.09 0.05 0.00
(4) Education 0.01 0.02 -0.04 0.04 -0.26 0.00 -0.03 -0.04 0.01 0.11
(5) Male -0.01 -0.01 0.05 -0.07 -0.07 -0.13 0.03 -0.02 0.09 0.10
30
(6) Married -0.01 0.04 -0.05 -0.06 0.00 -0.10 0.05 0.01 -0.03 0.09
(7) Income 0.04 0.00 0.00 0.02 -0.24 0.05 -0.05 -0.02 0.04 0.05
(8) Minority 0.04 0.03 0.09 -0.03 0.09 0.02 -0.01 0.00 0.08 0.03
(9) Age -0.01 0.08 -0.06 0.04 -0.14 0.10 -0.03 -0.04 -0.10 0.05
(10) Stock 0.20 -0.14 0.07 0.04 -0.35 -0.03 -0.04 -0.10 0.00 0.13
(11) Percentage of Stock 0.04 0.12 0.01 0.77 -0.11 0.03 0.00 -0.08 -0.02 -0.04
(12) Diversify -0.12 0.01 0.07 -0.28 -0.04 -0.07 -0.02 0.04 0.12
(13) Tender 0.06 0.08 0.10 0.01 0.01 -0.03 -0.09 -0.08
(14) Competing offer 0.04 -0.02 -0.01 -0.01 -0.03 0.01 0.04
(15) Toehold dummy -0.14 0.02 0.00 -0.09 0.01 0.01
(16) Ratio 0.25 0.11 -0.01 -0.06 -0.09
(17) Target market cap. 0.01 -0.40 0.07 -0.03
(18) Target market-to-book 0.05 -0.25 0.00
(19) Target institutional investor holding -0.05 -0.05
(20) Adjusted target turnover 0.05
(21) Target merger liquidity index
31
Table 3 Value premium and religiosity
Panel A: Acquiror's Religiosity Score
Parameter Coefficient t Value Pr > |t|
Intercept 134.13 1.35
Religiosity -32.06 -4.03 ***
Population 0.00 1.91 *
Married -0.59 -0.58
Male 1.20 1.1
Minority -0.58 -3.11 ***
Age -3.26 -3.48 ***
Income 0.00 -4.45 ***
Education 2.36 4.15 ***
Stock 22.99 7.8 ***
Percentage of Stock -0.32 -1.86 *
Diversify -18.62 -4.61 ***
Tender 1.93 0.58
Competing offer -3.14 -0.25
Toehold dummy -14.64 -1.68 *
Ratio -11.01 -12.32 ***
Target market cap. -6.71 -9.2 ***
Target market-to-book 0.03 0.31
Target institutional investor holding 0.00 -4.93 ***
Adjusted target turnover -1.97 -2.93 ***
Target merger liquidity index 941.71 1.19
N
1106
R Square
0.35
Note: value premium, relative to the target's market value on the trading day -64
Panel B: Target's Religiosity Score
Parameter Coefficient t Value Pr > |t|
Intercept 606.60 5.7 ***
Religiosity -159.75 -5.06 ***
Religiosity_Target 118.34 3.74 ***
Population 0.00 4.02 ***
Married 3.48 3.3 ***
Male -4.60 -4.12 ***
Minority -0.90 -4.67 ***
Age -7.66 -7.16 ***
Income 0.00 -3.49 ***
Education 2.25 3.92 ***
Stock 36.11 10.8 ***
Percentage of Stock -0.66 -2.76 ***
Diversify -75.74 -11.39 ***
Tender 5.97 1.87 *
Competing offer -1.01 -0.08
Toehold dummy -50.03 -3.92 ***
Ratio -47.11 -45.47 ***
Target market cap. -8.48 -10.68 ***
32
Target market-to-book 0.42 4.63 ***
Target institutional investor holding 0.00 -6.41 ***
Adjusted target turnover -2.84 -4.09 ***
Target merger liquidity index 290.59 0.32
N
1146
R Square 0.91
33
Table 4: Dividend Payment
Panel A: Acquirer Dividend Payment
Parameter Coefficient t Value Pr > |t|
Intercept 200.43 9.5 ***
Religiosity -3.32 -0.53
Religiosity_Target -15.13 -2.41 **
Population 0.00 2.52 **
Married 1.29 6.16 ***
Male -2.08 -9.42 ***
Minority -0.18 -4.67 ***
Age -2.18 -10.31 ***
Income 0.00 2.24 **
Education 0.02 0.18
Stock 21.27 32.09 ***
Percentage of Stock -0.23 -4.86 ***
Diversify -0.50 -0.38
Tender -1.53 -2.41 **
Competing offer 0.42 0.16
Toehold dummy 7.37 2.91 ***
Ratio 3.05 14.85 ***
Target market cap. 0.60 3.8 ***
Target market-to-book -0.02 -0.92
Target institutional investor holding 0.00 -3.15 ***
Adjusted target turnover 0.14 1.02
Target merger liquidity index 679.89 3.81 ***
N
1146
R Square
0.82
Panel B: Target Dividend Payment
Parameter Coefficient t Value Pr > |t|
Intercept 220.16 10.53 ***
Religiosity -4.39 -0.71
Religiosity_Target -12.08 -1.94 *
Population 0.00 1.62
Married 0.94 4.54 ***
Male -2.06 -9.41 ***
Minority -0.21 -5.49 ***
Age -2.25 -10.73 ***
Income 0.00 3.21 ***
Education -0.10 -0.88
Stock 20.47 31.16 ***
Percentage of Stock -0.18 -3.83 ***
Diversify -3.87 -2.96 ***
Tender -1.13 -1.8 *
Competing offer 0.12 0.05
Toehold dummy 5.74 2.29 **
Ratio 2.86 14.03 ***
Target market cap. 0.49 3.11 ***
Target market-to-book -0.09 -4.94 ***
Target institutional investor holding 0.00 -3.49 ***
Adjusted target turnover 0.26 1.88 *
35
Table 5: Goodwill Write Off
Panel A: Acquier's goodwill write off
Parameter Coefficient t Value Pr > |t|
Intercept 19020.59 2.85 **
Religiosity -1538.14 -2.52 **
Population 0.00 1.32
Married 31.23 0.54
Male -137.92 -1.98 *
Minority -12.39 -1.29
Age -167.74 -2.59 **
Income -0.04 -1.13
Education 35.20 1.14
Stock -255.63 -1.57
Percentage of Stock 1.18 0.33
Diversify 304.17 1.89 *
Tender -388.09 -1.83 *
Toehold dummy 370.39 1.34
Ratio 102.70 2.52 **
Target market cap. 10.87 0.39
Target market-to-book 27.60 3.66 ***
Target institutional investor holding 0.04 1.84 *
Adjusted target turnover 64.39 0.88
Target merger liquidity index -28951.73 -0.99
N
32
R Square
0.84
Panel A: Target's goodwill write off
Parameter Coefficient t Value Pr > |t|
Intercept -1762.38 -2.41 **
Religiosity 83.57 2.83 **
Population 0.00 -0.16
Married 8.08 1.33
Male 6.97 0.79
Minority -0.05 -0.09
Age 9.44 1.52
Income 0.01 5.81 ***
Education -10.76 -3.84 ***
Stock 12.94 0.83
Percentage of Stock -0.47 -1.06
Diversify 0.10 0.01
Tender -85.43 -4.19 ***
Toehold dummy 94.03 4.96 ***
Ratio 1.31 0.54
Target market cap. 1.34 0.52
Target market-to-book -16.73 -4.49 ***
Target institutional investor holding 0.01 4.66 ***
Adjusted target turnover -5.34 -1.36
Target merger liquidity index -9791.29 -4.27 ***
N
28
R Square 0.94
36
Table 6 Percentage of Stock
Panel A: Acquiror's Religiosity Score
Parameter Coefficient t Value Pr > |t|
Intercept 9.22 10.97 ***
Religiosity -0.69 -9.89 ***
Population 0.00 -6.17 ***
Married 0.01 1.54
Male -0.07 -8.16 ***
Minority -0.01 -8.08 ***
Age -0.08 -9.68 ***
Income 0.00 1
Education 0.01 1.41
Percentage of Stock 0.01 6.7 ***
Diversify 0.00 0.13
Tender -0.25 -9.04 ***
Competing offer 0.16 1.55
Toehold dummy -0.40 -5.08 ***
Ratio -0.10 -14.22 ***
Target market cap. 0.04 6.17 ***
Target market-to-book 0.00 0.17
Target institutional investor holding 0.00 -5.37 ***
Adjusted target turnover -0.01 -2.41 **
Target merger liquidity index 67.01 9.83 ***
N
1146
R Square
0.55
Note: value premium, relative to the target's market value on the trading day -64
Panel B: Target's Religiosity Score
Parameter Coefficient t Value Pr > |t|
Intercept 11.65 13.19 ***
Religiosity -0.54 -1.92
Religiosity_Target -0.13 -0.44
Population 0.00 -6.52 ***
Married 0.02 1.97 **
Male -0.09 -9.7 ***
Minority -0.01 -8.74 ***
Age -0.11 -12.38 ***
Income 0.00 4.71 ***
Education -0.01 -1.55
Percentage of Stock 0.01 4.42 ***
Diversify 0.19 3.28 ***
Tender -0.16 -5.51 ***
Competing offer 0.28 2.37 **
Toehold dummy -0.52 -4.62 ***
Ratio -0.14 -17.43 ***
Target market cap. 0.03 4.14 ***
37
Target market-to-book 0.00 1.53
Target institutional investor holding 0.00 -5.37 ***
Adjusted target turnover 0.00 -0.39
Target merger liquidity index 48.57 6.15 ***
N
1146
R Square 0.55
38
Table 7 Within Industry Risk and Outside Industry
Coeff. Of Religiosity Sig. N
1 Agriculture 1
2 Miningandconstruction -0.36
20
3 Food -0.17
36
4 Textile 1.36 * 53
5 Chemicals 0.88
29
6 Pharma -0.13
22
7 Extractive 120.57
47
8 Durable -0.14
145
9 Transportation -0.8
32
10 Utilities -1.74 *** 25
11 Retail 0.34
70
12 Services 0.76
48
13 Computer 0.7 81