Does Corporate Headquarters Location Matter for

Corporate Financial Policies?

Wenlian Gao, Lilian Ng, and Qinghai Wang1

Preliminary and Incomplete

July 2006

1Sheldon B. Lubar School of Business, University of Wisconsin-Milwaukee, Milwaukee, P.O. Box 742,WI 53201-0742. Authors’ contact information: Gao, gao@uwm.edu, (414) 229-2547; Ng, lng@uwm.edu,(414) 229-5925; and Wang, wangq@uwm.edu, (414) 229-4775.

Does Corporate Headquarters Location Matter for Corporate FinancialPolicies?

Abstract

This paper studies the impact of corporate headquarters location on corporate financial policies.

We show that firms exhibit conformity in their financial policies to those of geographically prox-

imate firms, and that the location of corporate headquarters helps explain the cross-sectional

variation of corporate policies in the United States. This location effect is robust to state regu-

lations on corporate takeover and payout and to the impact of local financial market conditions.

The results show that non-economic factors such as local culture and social interactions among

corporate executives are important determinants of corporate financial policies.

Keywords: Financial policy, state regulation, bank condition, social interaction

1 Introduction

Significant differences in corporate financial policies persist across countries. Recent studies show

that country-specific factors still play a crucial role in explaining the cross-country differences in

corporate financial policies,1 even after controlling for economic and financial factors, including

legal protection of investors. One explanation for the importance of country factors is that

differences in institutional settings across countries are an important determinant of corporate

financial policies (see Booth et al. (2000)). Another explanation is that varying non-economic

factors such as culture contribute to cross-country differences in both the institutional settings

and corporate financial policies (see Stulz and Williamson (2003)).

In this paper, we explore whether non-economic factors affect corporate financial policies

of U.S. firms. By restricting our sample to U.S. firms, we can easily control for differences in

institutional settings that can influence corporate financial policies. Using corporate headquar-

ters locations as proxies for non-economic factors that can affect corporate policies, we focus on

the local components of these factors such as local culture and social interactions among corpo-

rate executives.2 We study whether corporate headquarters locations are related to important

corporate financial policies such as capital structures, financing policies, and payout policies.

Our sample includes large U.S. corporations that have greater access to external finance

and have well established financial policies. The sample period spans from 1988 to 2003. For

the sample firms, we examine corporate financial policy, including capital structure variables

(financial leverage, interest coverage, cash holdings, the level and likelihood of net long-term

debt issues, and the level and likelihood of net equity issues) and payout policy variables (the

level and likelihood of common dividend payout, the level and likelihood of share repurchase,

the level of total payout). We use Metropolitan Statistical Area (MSA) to define the location

of corporate headquarters. In our empirical analysis, we employ the fixed effects of MSAs as a

1See Rajan and Zingales (1995) and Booth et al. (2000) for studies on capital structure, La Porta et al. (2000)on dividend policies, and Levine (2001) on corporate financing choices.

2Following standard definition, culture is “transmission from one generation to the next, via teaching andimitation, of knowledge, values, and other factors that influence behavior” (see Boyd and Richerson (1985)).Social interactions, on the other hand, can help transmit such “knowledge, values, and other factors that influencebehavior” within generation and particularly within social networks.

1

proxy for the effect of local communities on corporate policies and examine the joint significance

of these metropolitan areas on corporate policies, after controlling for firm specific and time-

varying characteristics, year fixed effects and industry fixed effects.

We find strong evidence that corporate headquarters locations have a significant impact on

corporate financial policies. Firms exhibit conformity in their financial policies to those of other

local firms, and corporate headquarters location helps explain the cross-sectional variations of

corporate policies in the United States. Corporations located in the same metropolitan areas

exhibit similar leverage ratios and have similar policies of cash holdings. These firms also tend

to follow similar patterns of issuing equity and debt. While corporate headquarters locations

have less impact on the amount of payouts by area firms, corporations located in the same

metropolitan areas show commonality in decisions on whether to pay dividends and repurchase

shares.

We control for various local economic factors that could contribute to the community effect

we document. Specifically, we incorporate state laws and banking sector development into our

specification. Given the fact that all public corporations are subject to the business corporation

statutes of the state where they are incorporated, state statutes are acknowledged to be an

important determinant in corporate policies (Bebchuk and Cohen (2003) and Wald and Long

(2006)). Following Wald and Long (2006), we create an antitakeover index and a payout re-

striction variable for each state to proxy for the variation in antitakeover statutes and payout

restriction laws across states. Antitakeover statutes have impacts on firms’ financial policy. The

state law on payout restrictions requires firms to maintain a minimum level of capital-to-debt

ratio when they pay dividend or buy back their own stocks, thus affecting both corporate capital

structure and payout policies.

Our analysis demonstrates that, despite the fact that state statutes have significant effects on

some financial policies, the fixed effects of local community remain significant. Specifically, we

find that antitakeover statutes do not reduce firms’ debt use, which is consistent with the findings

of Wald and Long (2006). Instead, we find that antitakeover statues are positively related to the

level of net long-term debt issues and negatively related to the likelihood of net equity issues. As

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expected, payout restrictions are negatively related to firm leverage, net long-term debt issues,

and the likelihood of net long-term debt issues. Also, payout restrictions are negatively related

to net equity issues. Accordingly, payout restrictions are positively related to interest coverage,

an alternative measure of capital structure. As for payout policies, payout restrictions have

positive effect on cash dividend payout and the likelihood of paying cash dividend.

The second set of local economic variables we employ is related to local financial market

conditions. If corporate financial decisions are affected by local financial market, particularly

the banking sector, local financial market conditions can contribute to the community effect.

Existing literature has documented a strong link between the functioning of financial sector and

economic growth at state level (Jayaratne and Strahan (1996), Black and Strahan (2002)). More

recent studies show local banking conditions affect individual firm’s financial and investment

policies (Zarutslie (2006)). We thus conjecture that the variation of financial sector development

at the metropolitan level may affect the fixed effects of local community on financial policies.

We construct three variables to measure the commercial bank conditions in each metropolitan

area. One is the average ratio of nonperformance loan, weighted by each bank’s commercial loans

outstanding, a proxy for bank loan quality. The second is the ratio of the sum of commercial

loans outstanding from all commercial banks in each metropolitan area to the sum of sales from

all the sample firms in the same area, a proxy for the size or depth of the financial sector. The

third is Herfindal index of commercial loans, a measure of bank competition. The results show

that, though bank conditions are significantly related to some firm policies, they do not alter

the significance of the local community fixed effects. In particular, we find that the ratio of

non-performance loan is negatively related to net long-term debt issues and the likelihood of

net equity issues, and positively related to the level of share repurchase. The financial depth

has significantly positive effect on leverage and negative effect on cash holdings. Moreover, the

financial depth has significantly negative effect on cash dividend payout but positive effect on

the likelihood of share repurchase. Under higher degree of banking market competition, firms

are more likely to issue long-term debt. We further integrate both state statutes and banking

development into the fixed effect regression specification and find that the fixed effects of local

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community are still strongly significant.

The robustness of the community effect after controlling for firm characteristics, industry ef-

fect, state regulation and local financial market condition leads us to further explore no-economic

factors as possible explanations. A number of recent studies find that personal attributes of

corporate executives and particularly their management ”styles” have significant impact on cor-

porate policies.3 Stulz and Williamson (2003) show differences in the broadly defined culture

contribute to differences in financial systems and financial policies across countries. Since cor-

porations’ decisions are constrained and shaped by managers’ embeddedness in social networks

(Granovetter (1985)), social interactions among corporate executives can influence the decisions

of corporate executives and drive the local commonality in corporate policies. Similarly, local

culture can affect the behaviors of both managers and investors and help to shape corporate

financial policies. In our empirical analysis, we focus on the two aspects of non-economic factors

and more importantly we assess the relative contribution of the two factors to the observed

evidence.

The local community effect we document could reflect the impact of social interactions

among firm managers on corporate decisions. Local community facilitates managerial interac-

tion through two channels: face-to-face information transmission and easy observational learn-

ing. Managers who work in the same geographic area normally have many opportunities to

network and build valuable relationship with peers, exchanging ideas and learning from each

other’s experience. For example, they may attend the same CEO clubs or conferences, or they

may be members of the same regional business leadership associations, such as local charitable

organizations and chambers of commerce. ”The country club cliche - that much business gossip

is traded over golf games - is in fact surprising accurate, according to discussions with directors”

(Davis and Greve (1997)).

The local community effect can also be shaped by local culture. Culture is ”transmission from

3For example, personal characteristics of CEOs and other top executives can affect acquisition or diversificationdecisions, dividend policy, interest coverage, and cost-cutting policy (Bertrand and Schoar (2003)), investmentpolicy (Malmendier and Tate (2005)), financial policy (Malmendier, Tate, and Yan (2005)), and corporate riskmanagement (Beber and Fabbri (2005)).

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one generation to the next, via teaching and imitation, of knowledge, values, and other factors

that influence behavior.” (see Boyd and Richerson (1985)) Social interactions, on the other hand,

can help transmit such ”knowledge, values, and other factors that influence behavior” within

generation and particularly within social networks. The impacts of local culture and social

interactions can overlap considerably. Culture and social factors are slow-moving components

in the social structure and they shape value and behavior of managers. Firm managers are

embedded in social structures and that their social interactions also reflect the social networks

they belong to and the social contexts in which they live.

To examine the difference of the impacts of local culture and social interactions on corporate

policies, we rely on the intuitive fact that social interactions are more important at the local

or community level, while local culture should be more important at the regional level. We

employ the nine regions defined by Census Bureau as an aggregate proxy for local culture effect.

The fixed effects of regions could further capture any possible variation with respect to income

characteristics, industrial composition of the employed labor force, and such noneconomic factors

as demographic, social, and cultural characteristics among regions. In addition, we identify

measurable component or proxies for local culture at the regional level and directly examine the

impact of those proxies on corporate policies.

We find that the differences in culture and socioeconomic characteristics can account for part

of the variation in corporate policies. Specifically, the fixed effects of regions are significant or

marginally significant for most of the financial policies, except leverage, interest coverage, and

the likelihood of equity issues. However, the region fixed effects cannot subsume the fixed effects

of metropolitan area (except for the financial policy of interest coverage). This suggests that

most of the financial policies are affected not only by social interactions of corporate managers

but also by culture and socioeconomic factors.

We further examine the role of culture in determining corporate financial policy. The role of

culture in economics is well established (Weber (1930) and Landes (2000). We use three culture

variables, trust, church attendance, and the percentage of Protestant in each region, which are

obtained from World Value Surveys (WVS). Trust is the percentage of people who think most

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people can be trusted. Church attendance is defined as the percentage of people who attend

church at least once a week. Results suggest that cultural variables are jointly significant for

cash holdings, the likelihood of long-term debt issuance, the likelihood of paying dividend and

the likelihood of buying back shares.

Our findings contribute to the literature along several dimensions. First, our study show

significant local commonality in corporate financial policies that are unexplained by firm and

industry characteristics. Second, the study contributes to our understanding of non-economic

determinants of corporate financial policies. Existing studies show that cultural differences

across countries contribute to differences of financial systems and corporate financial policies

and corporate executives have significant impact on corporate policies. While our study follow

the insights of the above two strands of literature, our analysis provides a link (or medium)

between the two. We show that local culture and social interaction among corporate executives

are important determinants of corporate financial policies.

The remainder of the paper is organized as follows. Section 2 describes the sample selection

process and defines the main variables of interest. Section 3 discusses the empirical method,

examines the significance and magnitude of local community effects on corporate financial pol-

icy. Section 4 checks possible omitted variable problem, including statutes on antitakeover and

payout restrictions at state level and bank conditions at metropolis level. Section 5 investi-

gates interpretations to the local community effect and argues that social interactions among

firm managers can account for the local component in corporate financial policies. Section 6

concludes.

2 Data

Our sample includes publicly traded U.S. firms that are covered by Compustat over the period

1988 to 2003. We exclude financial and utility firms (with industry code 4900-4999 and 6000-

6999), because their financial policies are subject to the impact of regulation. We also delete

firms with assets less than 20 million dollars from the sample, as these smaller firms are more

6

likely to face various constraints on their financial policies. Similarly, because corporate financial

policies for younger firms are more likely to be affected by policies established before the firms

become public, we require that a firm stay in the CRSP data for at least 5 consecutive years

before entering the sample. Finally, we get 39,287 firm-year observations for 4,118 different

firms.

We define a firm’s local community as the Metropolitan Statistical Area (MSA) where the

firm is headquartered. This definition is justified by the fact that metropolitan areas are usually

clusters of firm headquarters (Davis and Henderson (2004)). According to the definition of

the Office of Management and Budget (OMB), MSAs have “at least one urbanized area of

50,000 or more population, plus adjacent territory that has a high degree of social and economic

integration with the core as measured by commuting ties”. Usually, MSAs consist of one or more

entire counties from one single state or across a couple of states. To identify the MSA where

a firm is headquartered, we obtain the current state and county of firms’ headquarters from

Compustat annual file, and then use the Compact Disclosure database to check all the firms

that have ever relocated during our sample period. Employing the state/county combination as

a link, we next merge Compustat annual file with the Metropolitan Areas and Components data

released by OMB as of 1993. To classify an MSA as a major metropolitan area in the sample,

we require that at least 20 sample firms each year are located in the metropolitan area and that

there are at least 200 firm-year observations over the whole sample period. In total, we identify

27 metropolitan areas in our sample that meet the above criterion. Our analysis focuses on

the impact of each metropolitan area on the corporate financial polices of firms located in the

area. We use the remaining firms that do not belong to any of the 27 metropolitan areas as the

reference sample in our analysis.

We employ the Compustat annual file to obtain firms’ financial information. For our sample

of firms, we construct financial policy variables and control variables. We divide the financial

policy variables into two broad categories - capital structure and payout policy. The capital

structure category includes leverage, interest coverage, cash holdings, the level and likelihood

of net long-term debt issues, and the level and likelihood of equity issues. The payout policy

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category includes the level and likelihood of common dividend, the level and likelihood of share

repurchase, and the level of total payout.4 The definition of these variables is listed in Figure 1.

Furthermore, throughout this study, our analysis controls for three firm-specific variables:

size, return on assets, and market to book ratio. Size is the logarithm of total assets. Return

on assets (ROA) is EBITDA (#18) deflated by total assets. Market to book ratio, a proxy for

growth opportunities, is defined as total assets (#6) minus the book value of common equity

(#60) plus the year-end closing price (#24) times the number of shares outstanding (#25) over

total assets (#6).

Panel A of Table 1 provides summary statistics for the financial policy variables. The data

are winsorized at 99.5 percent to reduce the impact of outliers on the findings. Firm leverage has

a median of 21.1 percent and 25th and 75th percentile values of 4.6 percent and 36.9 percent,

respectively. The median of interest coverage, an alternative measure of firm leverage, is 5.78

with a standard deviation of 206.21. While there is a large cross-sectional variation in firm

leverage, a large portion of firms use a significant amount of debt. Cash holdings have a mean of

39.1 percent and a median of 7.2 percent, suggesting a highly skewed distribution. On average,

net long-term debt issues of firms is 3.3 percent and their net equity issues is 5 percent. 34.1

percent of firms, on average, issue long-term debt and 57.8 issue equity. On payout policy, firms

have a mean dividend payout of 0.7 percent, share repurchase of 2.6 percent, and total payout

of 5.5 percent. About 31.8 percent of firms pay dividend and 38.5 percent have share repurchase

activities.

Panel B of the same table shows the statistical distribution of the 3 control variables. The

median firm asset value is $182 million (standard deviation = $3,712 million), median market to

book ratio is 1.416 (standard deviation = 1.612), and median return on assets is 0.037 (standard

deviation = 0.163). It is evident that the 3 variable vary widely across the sample firms.

4Common dividend payout, share repurchase, total payout, net long-term debt issues, and net equity issuesare set to 0 if the value is missing.

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3 Location and Corporate Financial Policies

3.1 Methodology

This subsection discusses the main methodology employed to evaluate the impact of headquarters

location on corporate financial policies. In our analysis, we perform a baseline regression by

regressing each financial policy variable on year fixed effects, industry fixed effects, and the

control variables. Next, we incorporate the fixed effects of headquarters locations and examine

the joint significance and explanatory power of firm locations. More specifically, we estimate

two regressions for each dependent variable:

yit = αt + γIND + βXit + εit (1)

yit = αt + γIND + βXit + λMSA + εit (2)

where yit represents a financial policy variable, αt is the year fixed effect, γIND are industry fixed

effects, Xit are firm-level control variables, and εit is an error term. We employ Fama-French

(1997) 43 industry classifications. λMSA in Equation (2) are fixed effects of firm locations. We

use 27 location dummy variables for the 27 metropolitan areas. All sample firms not included

in the 27 metropolitan areas form the reference sample.

It is crucial to control for the industry fixed effects in both models since the existing literature

suggests that many industries tend to cluster around a geographic area due to the consideration

of positive externalities. For example, many high technology firms tend to cluster in California

and, on average, they tend to have lower leverage. Controlling industry effects ensures that the

fixed effects of firm locations are not simply picking up sector characteristics. Finally, we use

clustered standard errors to adjust for the correlation within a firm over time in our pooled

analysis.

3.2 Baseline results

Table 2 reports F tests and adjusted R−squares from the estimation of models (1) and (2) on

financial policy variables. For each financial policy variable, the first row shows F−statistics

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from the joint significance test of industry fixed effects and adjusted R−square from (1), and the

number of observations employed in the estimation. The second row reports the F−statistics

from the joint significance tests of industry fixed effects and of metropolitan area fixed effects,

and adjusted R−square from (2). All regressions include year fixed effects and the three firm-

level control variables: logarithm of total assets, market to book ratio, and the rate of return on

assets.

We find substantial evidence that firm location has a significant impact on corporate policies,

even after controlling for industry effects. Column 4 of the table shows that the F−statistics

are large enough to reject the joint hypothesis that metropolitan areas bear no effects on all the

financial policy variables we consider. Interestingly, the significant metropolitan area effects do

not subsume the industry effects, which are also highly significant across all regressions.

Furthermore, the results show a wide variation in the explanatory power of the metropolitan

area effects, suggesting that varying location impacts on various financial policies. For capital

structure variables, firm locations strongly influence leverage, cash holdings and the decision to

issue new equity. For example, adding metropolitan area fixed effects to regression model (1)

helps improve the adjusted R−square or the pseudo R−square by 1.7 percent for leverage, 3.8

for cash holdings, 1.4 for the likelihood of net equity issues. For corporate payout variables,

the location effects of corporate headquarters have a substantial impact on the level of dividend

payout and the dividend payout dummy. Adjusted R-squares increase by 1.1 percent for the

level of cash dividend payment and by 2.5 percent for the likelihood of paying cash dividends.

Increases in the adjusted R−square, however, are less than 1 percent for all other financial

policy variables, and are particularly low for the level of share repurchase and the level of total

payout. Literature on payout policy suggests that firm managers are more concerned with the

stability of dividend payout, and hence they tend to smooth out dividend payments. By contrast,

share repurchases are more volatile and also are more sensitive to economic conditions than

dividend payout. Thus, they are less likely to exhibit any systematic pattern across metropolitan

areas.

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Finally, in Table 3, we also plot the distribution of the coefficient estimates on metropolitan

area fixed effects. Table 2 shows that firm locations in the 27 metropolitan areas jointly play

a role in corporate financial policies, while Table 3 shows how location effects on each finan-

cial policy variable vary across the metropolitan areas. It is evident that the variation in the

magnitude of location fixed effects is economically large. For example, the first row associated

with leverage shows that the range between the 25th and 75th percentile is 0.054 and the range

between the minimum and maximum is 0.134. The median leverage for all sample firms is 0.211.

For cash holdings, we observe a range of 0.031 between the 25th and 75th percentile, while the

sample median is 0.072. For payout policies, we highlight the level of cash dividend payment as

an example. The difference between the 25th and 75th percentile is 0.004, compared with the

median common dividend of 0 in our sample.

3.3 Robustness checks

In this section, we conduct a series of robustness checks, including the significance of the

metropolitan area fixed effects over time, alternative estimation technique, and alternative in-

dustry definition. We find that our baseline evidence of location effects on financial policy is

robust.

3.3.1 Time series analysis

We first report results from the cross-sectional regressions year by year and then the pooled

regression results for three subperiods. In the cross-sectional analysis, for each year, we include

industry fixed effects, metropolitan area fixed effects, logarithm of total assets, market-to-book

ratio and ROA in the regression. We then determine the number of years in which the metropol-

itan area fixed effects are significant at 5 and 10 percent confidence levels; the respective results

are reported in Columns 2 and 3 of Table 4, with time-series average adjusted R−square in the

last column. The results show patterns similar to those of Table 2. The variables that are highly

significant tend to remain significant in most of the years.

We further examine the robustness of our results over time by estimating pooled cross-

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sectional regressions for each of the three subperiods: 1988-1992, 1993-1998, and 1999-2003.

The results are presented in Table 5. Generally, the results are fairly stable over time. For most

of the financial policy variables, the metropolitan area fixed effects are consistently significant

over different time periods. There are only a few exceptions. The metropolitan area fixed effects

are insignificant for both interest coverage and the likelihood of net long-term debt issues during

the period 1988 to 1992. For the level of share repurchase, the fixed effects of metropolitan area

are only marginally significant over the subperiods 1993 to 1998 and 1999 to 2003. Furthermore,

the metropolitan areas are not jointly significant for the level of total payout over the subperiod

1999 to 2003.

3.3.2 Alternative estimation techniques

The pooling of cross-sectional and time-series data in our regressions may create correlation of

errors at the firm level. Instead of clustering the standard errors at the firm level, we employ

an alternative technique - Newey-West (1987) specification with a lag of one. The unreported

results indicate that the F−statistics for the joint significance of metropolitan area fixed effects

are even higher than those reported in Table 2.

Another way to address the possible serial correlation at the firm level is to collapse the data

at the area-firm level. Starting with the firm-year data, we regress all financial policy variables

of interest on the year fixed effects, industry fixed effects, and control variables at the firm level.

Then, we extract firm-year residuals from the above regressions and collapse these residuals by

the area-firm level. Finally, we estimate the metropolitan area fixed effects in the collapsed

residuals. We find that our baseline evidence is robust to this alternative estimation technique.

3.3.3 Alternative industry definition

We replicate the regressions in Tables 2 and 4 by controlling for the fixed effects of 2-digit

SIC industry classifications from Compustat, instead 43 Fama-French industry classifications.

The unreported results indicate that the joint effect of 2-digit industry classifications is also

significant for all corporate policy variables. There is little variation in the significance test

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of metropolitan area fixed effects. Thus, our basic results are robust to alternative industry

definitions.

3.3.4 Alternative reference firms

Finally, we exclude all firm-year observations for which firms are located in metropolitan areas

with less than 20 firms in a year, or with less than 200 firm-year observations over the sample

period, instead of taking such firm-year observations as the reference in our previous analysis.

We get 31,136 observations for 3,316 different firms that are located across 27 metropolitan

areas. Then we re-estimate model (2) and obtain similar results.

4 State Regulation, Economic/Financial Conditions, and Loca-tion Effects

Thus far, the geographic location of corporate headquarters exhibits a substantially significant

impact on firms’ financial policies, and the magnitude of location effects is economically signifi-

cant. As discussed in the introduction, the significant location effect could represent the impact

of local “fundamentals” on corporate financial policies. In this section, we examine the impact of

these local “fundamentals” on financial policies. Particularly, we investigate whether our results

are driven by state regulations and local economic and financial conditions.

4.1 State Regulations and Financial Policies

In the U.S., all public corporations are subject to the business incorporation statutes of the

state where they are incorporated. Bebchuk and Cohen (2002) provide evidence that state

antitakeover statutes affect firms’ decision on where to incorporate. Wald and Long (2006) fur-

ther demonstrate that firms choose their incorporation state according to state laws and capital

structure needs. Therefore, we conjecture that firms headquartered in the same metropolitan

area may have incorporated in states with certain state statutes, which may partly account for

the local community effect in financial policies.

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We consider two types of state regulations in our study. One is state statutes on antitakeover,

and the other is payout restrictions. State antitakeover laws are believed to have an impact on

increasing the entrenchment level of the incumbent managers, while payout restrictions capture

the potential conflict of interest between shareholders and debtholders. The state antitakeover

laws that we consider here are referred to as “the second generation of antitakeover laws” and

most states enacted some of these laws in late 1980s.

State antitakeover statutes are recognized to have an impact on firms’ capital structure and

payout policies by increasing the managerial entrenchment level. Zwiebel (1996) and Novaes

and Zingales (1995) argue that managers use leverage to reduce the threat of a hostile takeover.

Thus, if a firm is shielded from hostile takeover, its manager will prefer a lower leverage. Garvey

and Hanka (1999) provide empirical evidence that firms issue less debt and reduce their leverage

over time after their incorporation state enacted more antitakeover statutes which are supposed

to increase managers’ entrenchment level. They further point out that the reduction in debt

funding results in the reductions in share repurchase and dividend payout. In contrast, Wald

and Long (2006) and Litov (2005) find that firms incorporated in states with more antitakeover

statutes use more debt finance and have higher leverage ratios. The two authors attribute the

difference to self-selection in firms’ decision to choose their incorporation state (Wald and Long

(2006)) and sample selection procedure (Litov (2005)).

The payout restriction statute typically requires a minimum ratio between the amount of

book capital and debt before making a dividend payment or share repurchase. Firms subject

to payout restrictions are limited in the amount of debt they can issue. Wald and Long (2006)

document that payout restriction laws have an impact on capital structure. Specifically, firms

incorporated in states with stronger payout restrictions (i.e., higher minimum capital-to-debt

ratio requirement) use less debt. Hu and Kumar (2004) document that both the likelihood

and the level of payouts are positively related to factors that increase executive entrenchment

levels, because, by committing to higher payouts, entrenched managers can protect themselves

from disciplinary sanctions by outsiders. John and Knyazeva (2006), however, find that more

antitakeover protections increase dividend payout but lower share repurchase. They attribute

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the differential effect of governance quality on repurchase to the fact that share repurchase

largely acts as a discretionary tool to distribute excessive temporary cash and that absence of

antitakeover protections will urge managers to dispense cash to shareholders through repurchase.

Information on firms’ incorporation state is available from Compustat that only provides the

current state of incorporation. We therefore gather details on historical reincorporation decision

by searching Mergent online. The information about statutes for each state is from McGum,

Pamepinto, and Spector (1989) for the late 1980s and from Gartman (2000) for the period 1990

to 2003. Following Bebchuk and Cohen (2003), we construct an antitakeover protection index

for each state by assigning one point to every specific statute in place and 0 otherwise. The

antitakeover protection index is the sum of points assigned to five state antitakeover statutes,

namely control share, fair price, no freeze-outs, poison pill endorsement, and constituencies. A

higher protection index indicates that the state provides more antitakeover protection to firms

incorporated in the state. However, most state antitakeover provisions allow companies to “opt

out” of coverage by stating their intention in their charters. For example, Romano (1993) reports

that most Pennsylvania firms choose to opt out of the Pennsylvania statute. As in Wald and

Long (2006), we use the information from IRRC to incorporate some firms’ decision to opt-out

of antitakeover statutes.

For the statutes on payout restrictions, we use the information provided in Wald and Long

(2006) and construct a variable, “Restriction”, in the same way as they do. That is, for California

and Alaska, it is equal to 1.25, for Delaware, Maine, Oklahoma, and South Dakota, it is equal

to 0, and for the remaining states, it is equal to 1.

Subsequently, we incorporate the two state regulation variables into the baseline specification

model (2) and estimate the regression model for each policy variable. The results are presented

in Table 5. Overall, the significance of the local community effect on financial policies is not

affected by state regulations. The fixed effects of metropolitan areas are still highly significant

for all the financial policy variables. Compared with the results shown in Table 2, the F values

of the joint significance of metropolitan areas are slightly reduced for all the financial policy

variables. Moreover, adding the state regulation variables does not enhance the explanatory

15

power of the regression. The adjusted R−square for each regression is almost the same as from

the estimates of model (2). For example, the adjusted R−square for the regression on leverage

is 23.3 percent for the estimate with state regulation variables, while it is 23.2 percent for the

estimate of model (2). Thus, the two state regulation variables only contribute an increase of 0.1

percentage points in the explanatory power. For the regression on cash holdings, the R−square

even keeps constant as in models (2).

The state regulation variables are significant in only a few regressions. The index of an-

titakeover statutes is statistically significant only for the likelihood of net equity issues and

the coefficient is negative. This is consistent with the findings of Harford, Mansi, and Maxwell

(2005) that firms with weak shareholder rights use less equity financing and more debt financing.

In our case, the positive coefficient on the level of net long-term debt issues is only marginally

significant. This can be attributed to the lower cost of equity associated with well-governed

firms, given the documented higher price-earnings ratio for these firms (Gompers, Ishii, and

Metrick (2003)). In contrast, weak governance firms have a lower cost of debt (Klock, Mansi,

and Maxwell (2004)) due to the alignment of interests between managers and bondholders in

these firms as both entrenched managers and bondholders dislike risky projects.

As for other variables of financial policies, state antitakeover statute has no significant effect

on them. However, they do have the expected sign. First, antitakeover statutes are negatively

related to leverage ratio, which is consistent with the findings of Wald and Long (2006) and Litov

(2005). Secondly, antitakeover statutes are negatively related to cash holdings. This is consistent

with the findings of Klock, Mansi, and Maxwell (2004) who argue that weak governance firms

dissipate their cash reserves far more quickly than do managers of firms with strong shareholder

rights, primarily through acquisitions. Thirdly, antitakeover statutes are positively related to

payout policies, which is consistent with the findings of Hu and Kumar (2004).

The variable of payout restrictions has a significantly negative coefficient in the regression on

firms’ leverage ratio, which indicates that strict state statute on payout restrictions tends to lower

firms’ leverage. This result is consistent with the evidence provided in Wald and Long (2006).

Correspondingly, the statute on payout restrictions is positively related to interest coverage. In

16

addition, this law has a negative effect both on the level of long-term debt issues and the level

of equity issues, since, payout restrictions statute imposes some upper limit on firms’ liabilities.

Moreover, it has a negative effect on the likelihood of issuing long-term debt. To our surprise,

payout restrictions law is positively related to the level of common dividend payment and the

likelihood of dividend payment occurrence. In contrast, the level of total payout is negatively

associated with the law, which seems to be driven mainly by the level of share repurchase.

However, neither effect is significant.

To summarize, incorporating state statutes on antitakeover and payout restrictions into our

models has no material effect on the significance of local community effects on financial policies.

Moreover, state statutes have little contribution to the explanatory power of the model.

4.2 Bank Conditions and Corporate Financial Policies

In this subsection, we consider the impact of local economic and financial condition variables

on our finding of location effects. Corporate financial policies are likely to be affected by local

economic conditions. More important, corporate capital structure policies are more susceptible

to local financial market conditions. Up to 1978, the U.S. banking system was segregated, with

50 banking systems, one per state. The passage of the Reigle-Neal Act in 1994 made interstate

banking a bank right and the banking system became much more integrated. However, the

development of banking system may still vary in different metropolitan areas of the country.

In our analysis, we integrate banking conditions into our specification to ensure that the local

community effect is not a simple proxy for the variation in financial sector development across

metropolitan areas. In unreported results, we examine the relation between regional economic

growth and corporate financial policies and do not find any significant results.

The functioning of a local financial sector has been documented to have an impact at the firm

level. Banks have a comparative advantage in acquiring private information on borrowers and in

monitoring their actions. They can also modify the relative supply of different securities to some

extent. Peterson and Rajan (1995) provide evidence that young firms receive more institutional

finance and are more indebted in concentrated markets than in competitive markets, while

17

older firms exhibit the opposite pattern. Zarutslie (JFE forthcoming) confirms Peterson and

Rajan (1995)’s findings and further demonstrates that bank competition has significant effect

on individual firm’s financial policy. With increased competition of banking market, younger

firms would use less debt, and the pattern reverses for established firms. These studies mainly

focus on small or privately held firms, while our study examines relatively large firms. Though

large U.S. corporations shift to the securities market to fulfill their financing needs and exhibit

decreasing reliance on bank credit, banks continue to perform a critical function in providing

liquidity to large corporations, especially during economic turmoil (Saidenberg and Strahan

(1999)).

To assess the development of the financial sector in each metropolitan area, we focus on

commercial banks and obtain their balance sheet information from their Reports of Condition

and Income which is required by the Federal Deposit Insurance Corporation (FDIC). These

reports are available quarterly over our study period 1988 to 2003. For our study, we only need

the bank accounting information at the end of each calendar year. FDIC provides the Primary

Metropolitan Statistical Area (PMSA) code of bank location. We manually check the domain

of each PMSA of bank location and the domain of each MSA of firm location respectively, and

then merge the bank accounting information with our basic sample described in Section 2.

We construct three variables to denote bank condition, i.e., nonperformance loans, commer-

cial loans to sales, and Herfindal index of commercial loans. We use nonperformance loans as

an indicator for bank lending quality which is defined as the fraction of total loans classified

as ”nonperforming”. Following Jayaratne and Strahan (1996), all loans 90 days or more past

due but still accruing and non-accrual loans are classified as nonperforming loans. Next, we

compute the weighted average of nonperforming loans for all commercial banks headquartered

in the same metropolitan area, taking each bank’s commercial loans as the weight. Commercial

loans are the sum of commercial and industrial loans (C&I loans) and commercial real estate

loans. Commercial loan category measures the flows of bank credit to industries and thus is

”likely to be closely linked to commercial investment and economic conditions” (Jayaratne and

Strahan (1996)).

18

The ratio of commercial loans to sales, a proxy for bank depth or volume of bank lending,

is measured by the ratio of total volume of commercial loans in each metropolitan area to the

total volume of firm sales in each metropolitan area. It measures the size of banking sector

to the size of the economy. Total volume of commercial loans are derived by summing up the

commercial and industrial loans held by all commercial banks in each metropolitan area, whereas

total volume of firm sales are derived by summing up sales of all firms in each metropolitan area.

Herfindal index of commercial loans, a measure of the degree of bank competition, is constructed

by summing over the squared market share of commercial loans from each individual commercial

bank in a metropolitan area.

The descriptive statistics for the bank condition variables are displayed in Panel C of Table

1. The average ratio of nonperformance loans is 0.6 percent across the metropolitan areas where

the sample firms are headquartered, and the 25th and 75th percentile is 0.2 percent and 0.8

percent, respectively. The ratio of commercial loans to sales has a mean value of 0.291 and

standard deviation of 0.279. The Herfindal index of commercial loans is averaged at 0.219, with

the 25th and 75th percentile at 0.101 and 0.302. The summary statistics of the bank condition

variables suggest that banking market efficiency varies across metropolitan areas of the country.

We enhance the basic specification model (2) by incorporating the three variables of bank

conditions and present the regression estimates in Table 6. The results show that bank conditions

are significantly correlated with some firm policies. The level of net long-term debt issues and

the likelihood of net equity issues are negatively related to the ratio of nonperforming loans,

a bank health indicator, while the level of share repurchase is positively related to the ratio

of nonperforming loans. Firm leverage is positively related to the ratio of commercial loans to

sales, a measure of bank depth, since firms may be inclined to use more bank loans if the size of

the bank sector is relatively large. Firm cash holdings are negatively related to bank depth. The

precautionary incentive for cash holdings suggests that firms can use cash reserves to finance

their investment activities if other sources of funding are not available or excessively expensive

(Keynes(1934)). With abundant bank loans available, firms would find it easier to make short-

term loan arrangement and accordingly reduce their cash holdings. The variables of financing

19

sources are negatively correlated with bank depth, but none of them is significant. For payout

policies, the bank depth variable has a significant negative coefficient in the regression on the

level of common dividends, while it has a significant positive coefficient in the logit regression

on the likelihood of share repurchase. Herfindal index, a proxy for bank competition, is only

significantly related to the likelihood of net long-term debt issues, which may be interpreted as,

in more concentrated credit markets, firms are less likely to issue long-term debt.

Basically, taking into account the bank condition neither reduces the significance of the local

community effects nor increases the model’s explanatory power substantially. For instance, in

the regression on leverage, the F value of the significance of metropolitan area fixed effects

reduces only 0.65 compared with the results from the benchmark regression shown in Table 2

and the local community effect is still highly significant. The adjusted R−square remains 23.2

percent, the same as the explanatory power of the benchmark specification.

We also try altering the specification of the bank health measure and depth of financial

structure. For example, we replace the weighted-average nonperformance loans and the ratio of

commercial loans to firm sales by the weighted-average of charge-off and the ratio of total bank

assets to firm assets, respectively. These replacements yield no material change in our findings.

In a word, the evidence from Table 6 suggests that, though the variables of bank conditions

have significant impact on some financial policies, overall, bank condition does not change the

significance of local community effect.

So far, we have investigated the impact of state regulations and the impact of bank conditions

on firm policies separately. Now we consider these two factors together and integrate the two sets

of variables into one model to see if their combination has any impact on the local community

effect in firm policies. We report the regression estimates in Table 7.

In the specific regression on each policy variable, state regulations and bank conditions

have almost the same effect as when they are regressed on each policy variable independently.

To highlight an example of firm leverage, the law on payout restrictions has a significantly

negative coefficient of -0.019, and the ratio of commercial loans to sales has a significantly

20

positive coefficient of 0.031, which is exactly the same as the results shown in Tables 5 and

6. In addition, there is not much increase in the explanatory power of the specification. For

instance, the adjusted R−square is 23.4 percent, compared with 23.2 percent from the basic

specification model (2). The most important thing is that the fixed effects of metropolitan area

are still jointly significant, which indicates that the combination of the two considerations does

not explain away the local community effect in corporate financial policies.

5 Culture, Social Interaction, and Location Effects

5.1 Should non-economic factors matter?

The previous sections have documented that the location effect on corporate financial policies is

strongly statistically significant and economically important. The results are robust with respect

to state regulation variables and proxies for local financial sector condition. We now proceed to

explore non-economic explanations to this location effect.

The location effect we document could capture the influence of non-economic factors such as

culture and social interactions on corporate decision making. The role of culture in economics

is well established. The literature can be traced back to Weber (1930) who argues that cultural

changes inspired by the Protestant Reformation helped to explain the rise of capitalism in

Western Europe and America and more recently, Landes (2000) confirms Weber’s argument. U.S.

is a geographically large country. Culture and other socioeconomic factors vary substantially

across regions. For example, Southern culture has been generally more conservative than that

of the North and such cultural differences could have impact of corporate policies. The location

effect is potentially related to the immediate institutional environment or the social context

where the firm is embedded. Granovetter (1985) argues that firm managers’ embeddedness in

social networks serves as a major channel of conveying information and ideas about firm behavior.

The conformity demonstrated in financial policies among local firms, could be driven by social

interactions among corporate managers. This proposition can be further justified by two facts.

Firm managers are acknowledged to imprint their mark on a wide range of corporate policies.

21

For example, Bertrand and Schoar (2003) document that personal characteristics of CEO and

other top executives, education and birth cohort, have significant effect on firms’ investment

policy, financial policy, cost-cutting policy and performance. Beber and Fabbri (2005) find

that foreign currency risk management is also tied to CEO individual characteristics, such as

education, gender, tenure, birth cohort and previous working experience, etc. Malmendier and

Tate (2005a, 2005b) provide evidence that, distinct from observable personal characteristics,

managerial overconfidence matters in determining firms’ investment policy and financial policy.

Hence, corporate decision making is, to a large degree, rooted in firm managers’ background.

On the other hand, corporate managers are deemed as a group of people who are extremely

socially active and they may be influenced by network contacts in decision making. Operating

in an uncertain environment, firm officials may look to their peers for ideas about appropriate

strategies or mimic one another’s behavior through direct contact. Recent studies have suggested

that social interaction with peers has tangible effects on a wide range of firm activities from

charitable action (Galaskiewicz and Wasserman (1989), Marquis, Glynn, and Davis (2005)),

political contributions (Mizruchi (1989) and (1992)), acquisition decision (Haunschild (1993)),

to adoption of antitakeover procedures (Davis and Greve (1997)). The social interaction effect

should be especially important for local firms because: First, geographic proximity facilitates

face-to-face interaction and makes contact/relationship easier to start and maintain. Survey

data indicates that the correlation between distance between friends and frequency of contacts

is 64 percent (Jaffe, Trajtenberg, and Henderson (1993)). Actually, many metropolitan areas

have various kinds of business leadership associations, such as the Executive Council of New

York, whose members constitute 3,500 New York metropolitan business leaders, ranging from

AT&T, AOL, to Citigroup and Lehman as well as a diverse constituency of both enterprise and

emerging growth companies. Secondly, geographic proximity facilitates observational learning

even with no direct contact. Simple exposure to the strategies of other firms may prompt firms

to adopt similar strategies and to align their activities with other firms in the local geographic

community. Naturally, we posit that social interaction matters for corporate financial policy,

i.e., firm managers may get some input from their peers and take into account when they make

22

firm financial strategies.

5.2 Culture, social interactions, and corporate financial policies

To test for the presence of culture and social interaction effect on corporate financial policies,

we first attempt to separate the effect of social interaction and the effect of culture and then use

explicitly defined culture variables in the empirical analysis. We first employ the classification

of nine census regions and identify each firm’s region as the census region where the firm’s is

headquartered. The fixed effects of regions capture any possible variation with respect to income

characteristics, industrial composition of the employed labor force, and such noneconomic factors

as demographic, social, and cultural characteristics among regions. Though both metropolitan

areas and census regions can capture some variation in socioeconomic characteristics, it is ev-

ident that census regions serve better for this purpose. For example, some metropolitan areas

even belong to one state, so it is hard to imagine that there is much variation in socioeconomic

characteristics between such two areas. In contrast, as for the capability to capture the inter-

action among managers, metropolitan areas work better than census regions. We believe that,

social interaction is really a local phenomenon, the intensity of interaction among firm managers

at regional level would decline substantially due to the distance. Thus, integrating both the

fixed effects of metropolis and the fixed effects of regions into estimation enables us to isolate

the effect of managerial interaction from the possible effect of socioeconomic factors.

To help understand if there is any pattern in corporate financial policies across regions, Panel

A of Table 8 presents, for each region, the average of residuals retrieved from the regressions

controlling for firm-specific characteristics, year fixed effects, industry fixed effects, state regu-

lation and bank conditions. It shows that regions with higher leverage, such as South Atlantic,

East South Central, and West South Central, tend to have lower interest coverage, lower cash

holdings, more long-term debt issues, and less payout, whereas regions with lower leverage, such

as New England and North West, exhibit the opposite pattern. The region of Rocky Moun-

tains is an exception. It has higher leverage, higher interest coverage ratio, and higher dividend

payment which is financed with exceptional higher long-term debt issues and net equity issues.

23

The regression results are reported in Table 9. There are several points that deserve attention.

First of all, the fixed effects of industry are still significant, which suggests that neither the fixed

effects of metropolitan nor the fixed effects of region can subsume the industry effect on firm

policies. Secondly, the integration of the fixed effects of regions does not alter the significance

of the fixed effects of metropolitan areas, though the F value of the joint test declines to some

extent. For example, the F value of the joint significance of metropolitan areas, for the variable

cash holdings, decreases from 11.55 (in the specification with no region fixed effects) to 4.51 (in

the specification with region fixed effects). Hence, the argument that the similarity in financial

policies is caused by managerial interaction has been justified. The only exception is interest

coverage. In the previous analysis, the metropolitan areas are jointly significant in the regression

on interest coverage. However, after the fixed effects of regions are controlled, the metropolitan

areas turn out to be insignificant any more, which indicates that managerial interaction is not

a significant determinant in interest coverage.

Thirdly, the significance of the join effect of regions varies with different financial policies.

In particular, the fixed effects of regions are insignificant for interest coverage, total payout, and

the likelihood of equity issuance, which means that the socioeconomic characteristics have no

significant effects on these policy variables. However, the region fixed effects are significant for

cash holdings, the level of common dividends and the likelihood of paying common dividend, the

likelihood of conducting share purchase, the level of equity issuance, the level of net long-term

debt issuance and the likelihood of net long-term debt issuance, and marginally significant for

leverage and the level of share repurchase. Accordingly, we may conclude that these financial

policies are affected by regional socioeconomic characteristics. Overall, Table 9 provides solid

support for the social interaction hypothesis by showing that the fixed effects of local community

are not a proxy for regional socioeconomic characteristics.

We have seen that regional characteristics play a significant role in some financial policies.

However, the fixed effects of region capture all kinds of variation across regions. To get a

more explicit idea about what kind of regional characteristics works, we further check the role

of culture in determining corporate financial policy. Specifically, we examine two aspects of

24

culture, trust and religion. La Porta, Lopez-de-Silanes, Shleifer and Vishny (1997) document

that trust can promote cooperation within large organizations. Guiso, Sapienza, and Zingales

(2005) examine the role of trust in the international setting and find that lower level of trust

toward a country is associated with less trade with that country, less portfolio investment and

less direct investment in that country. The claim that religious activity can affect economic

performance has been affirmed by abundant evidence (See Iannaccone (1998) for a survey.)

We obtain culture information from World Value Surveys (WVS). The surveys provide the

census region where the interviews were conducted. The surveys were conducted in 1990, 1995,

and 2000 respectively. Since culture is pretty stable over time, we use 1990 wave for the period

1988 to 1992, 1995 wave for the period 1993 to 1997, and 2000 wave for the period 1998 to

2003. We construct three variables, trust, church attendance, and the percentage of Protestant

for each region. Trust is defined as the percentage of respondents who answered ”yes” to the

question: ”Generally speaking, would you say that most people can be trusted or that you need

to be very careful in dealing with people?” The intensity of religious beliefs is proxied by the

frequency of church attendance which is coded based on the question: ”Apart from weddings,

funerals and christenings, about how often do you attend religious services these days?” We

define church attendance as the percentage of participants who attend church at least once a

week. As for religious denomination, we find the percentage of people who belong to Protestant

based on the question: ”Do you belong to a religious denomination?”

The summary statistics of cultural variables by region are displayed in Panle B of Table 8.

The cultural variables vary much across regions. West north central has the highest level of

trust, 0.451, then east north central ranks the second, while east south central has the lowest

level of trust, 0.268. East south central has the highest church attendance rate, 54 percent of

people attend church at least once a week. In New England, only 35 percent of people attend

church at least once a week. West North Central has the highest percentage of Protestants,

whereas it is only 18.6 percent for New England.

Table 10 shows the regression estimates. We use F test to examine the joint significance

of culture variables on each financial policy. Results suggest that cultural variables are jointly

25

significant for cash holdings, the likelihood of long-term debt issuance, the likelihood of paying

dividend and the likelihood of buying back shares. The level of trust is only significantly related

to the likelihood of paying dividend. Regions with high level of trust are more likely to pay

dividend. The intensity of church attendance has negative effect on cash holdings, the likelihood

of equity issues, cash dividend payment ratio, and positive effect on the likelihood of issuing

debt. But only the relation with cash holdings is significant. The percentage of Protestants is

positively related to leverage and negatively related to interest coverage. Both relationships are

only marginally significant. In addition, the percentage of Protestant is positively related to the

ratio of long-term debt issues and the likelihood of long-term debt issues, while it is negatively

related to cash holdings and the likelihood of share repurchase. It suggests that regions with more

Protestants tend to use more debt, hold less cash, and less likely to conduct share repurchase.

The fixed effects of local community are strongly significant for all the financial policies (except

for the ratio of share repurchase for which the local community effect is significant at 10 percent).

Combining with the results shown in Table 8, it is evident that, though the culture variables are

jointly significant for some financial policies, the fixed effects of region capture something not

restricted to culture. Unfortunately, we do not have enough information to identify what else

plays at the regional level other than culture.

6 Conclusion

This paper documents a significant local community effect on corporate financial policies. More-

over, the local community effect is consistently significant over different time periods and robust

to alternative industry classifications and various estimation techniques. We further demon-

strate that this local component is robust against state statutes on antitakeover and payout

restrictions and banking market development at community level. We then propose a hypoth-

esis to interpret the local community effect, i.e., the local community effect can be ascribed to

social interactions among firm managers. We argue that firm managers who work close by may

spread information to each other, observe each other’s management style, and then influence

each other’s policy making. We test this hypothesis by differentiating local community effects

26

from region fixed effects, caused by regional socioeconomic characteristics, particularly culture.

The evidence from our analysis supports the social interaction hypothesis.

Our study provides a new dimension for future studies to examine corporate policy. Our

findings suggest that, other than fundamental firm-specific characteristics or observable manage-

rial traits, social interaction among firm managers also acts as a determinant in policy making.

We believe that it would be very interesting to investigate into other corporate policies, such

as investment policy, and corporate governance structure such as board structure, managerial

compensation, to see whether and to what extent they exhibit conformity for firms located in

the local geographic community.

27

Figure 1

Variable Definition

Capital Structure Category

Leverage Sum of long-term debt (Compustat data item #9)and debt in current liabilities (#34) over total assets (#6)

Interest coverage Earnings before depreciation, interest, and tax (#13)over interest expense (#15)

Cash holdings Cash and short-term investments (#1) standardizedby total assets

Net long-term debt issues Long-term debt issuance (#111) minuslong-term debt retirement (#114) scaled by total assets

Net long-term debt issues dummy 1 if a firm’s net long-term debt issues aregreater than 0 in a year, and 0 if otherwise

Net equity issues Sale of common and preferred stock (#108)minus any purchase of common and preferred stock (#115),scaled by total assets

Net equity issues dummy 1 if a firm’s net equity issues aregreater than 0 in a year and 0 if otherwise

Payout Policy Category

Common dividends Ratio of common dividends (#21) over total assets

Common dividend dummy 1 if a firm pays common dividends in a year and 0 ifotherwise

Share repurchase Purchase of common and preferred stock (#115) minusany reduction in the redemption value of preferredstock (#56), then scaled by total assets

Share repurchase dummy 1 if a firm repurchases common stock in a year, and 0 ifotherwise

Total payout Common dividends plus purchase of common and preferredstock minus any reduction in the redemption value ofpreferred stock deflated by total assets

28

References

Bebchuk, Lucian A., and Alma, Cohen, 2003, Firms’ decisions where to incorporate, Journal

of Law and Economics 46, 383-425.

Beber, Alessandro, and Daniela Fabbri, 2005, Who times the foreign exchange market? Cor-

porate speculation and CEO characteristics? University of Lausanne and FAME, Working

paper.

Bertrand, Marianne, and Antoinette Schoar, 2003, Managing with style: The effect of managers

on firm policies, Quarterly Journal of Economics 118, 1169-1208.

Black, Sandra E., and Philip E. Strahan, 2002, Entrepreneurship and the availability of bank

credit, Journal of Finance 67, 2807-33.

Booth, Laurence, Varouj Aivazian, Asli Demirguc-Kunt, and Vojislav Maksimovic, 2001, Cap-

ital structures in developing countries, Journal of Finance 56, 87-130.

Boyd, Robert and Peter H. Richerson, 1985, Culture and the evolutionary process. Chicago:

University of Chicago Press.

Davis, Gerald F., and Henrich R. Greve, 1997, Corporate elite networks and governance changes

in the 1980s,American Journal of Sociology 103, 1-37.

Davis, James, and J. Vernon Henderson, 2004, The agglomeration of corporate headquarters,

Working paper, Brown University.

Fama, Eugene F., and Kenneth French, 1997, Industry costs of equity, Journal of Financial

Economics 43, 153-193.

Fama, Eugene F., and Harvey Babiak, 1968, Dividend policy: An empirical analysis, Journal

of the American Statistical Association 63, 1132-1161.

Galaskiewicz, J., and S. Wasserman, 1989, Mimetic process within an interorganizational field:

An empirical test, Administrative Science Quarterly 34, 454-479.

29

Gartman, Grant A., 2000, State Takeover Laws. Washington D.C.: Investor Responsibility

Research Center.

Garvey, Gerald T., and Gordon Hanka, 1999, Capital structure and corporate control: The

effect of antitakeover statues on firm leverage, Journal of Finance 54, 519-546.

Gompers, P., L. Ishii, and A. Metrick, 2003, Corporate governance and equity prices, Quarterly

Journal of Economics 118, 107-155.

Granovetter, Mark S., 1985, Economic action and social structure: The problem of embedded-

ness, American Journal of Sociology 91, 481-510.

Guiso, Luigi, Paola Sapienza, and Luigi Zingales, 2005, Cultural biases in economic exchange,

Working paper.

Jayaratne, Jith and Philip E. Strahan, 1996, The Finance-Growth Nexus: Evidence from Bank

Branch Deregulation, Quarterly Journal of Economics 101, 639-70.

Harford, Jarrad, Sattar A. Mansi, and William F. Maxwell, Corporate governance and a firm’s

cash holdings, Working paper, University of Washington.

Haunschild, P. R., 1993, Interorganizational imitation: The impact of interlocks on corporate

acquisition activity, Administrative Science Quarterly 38, 564-592.

Hu, A. and P. Kumar, 2004, Managerial entrenchment and payout policy, Journal of Financial

and Quantitative Analysis 39, 759-790.

Jaffe, Adam B., Manuel Trajtenberg, and Rebecca Henderson, 1993. Geographic localization of

knowledge spillovers as evidenced by patent citations, The Quarterly Journal of Economics

108, 577-98.

Jayaratne, Jith, and Philip E. Strahan, 1996, The finance-growth nexus: Evidence from bank

branch deregulation, The Quarterly Journal of Economics 111, 639-670.

John, Kose, and Anzhela Knyazeva, 2006, Payout policy, agency conflicts, and corporate gov-

ernance, New York University, Working paper.

30

Keynes, J.M., 1936, The general theory of employment, interest and money, Mcmillan, London.

Klock, Mark, Sattar Mansi, and William F. Maxwell, 2005, Does corporate governance matter

to bondholders? Journal of Financial and Quantitative Analysis 40, 693-719.

Kusnadi, Yuanto, 2004, Corporate governance mechanisms and corporate cash holdings, EFA

2005 Moscow Meetings Paper.

La Porta, Rafael, Florencio Lopez-de-Silanes, Andrei Shleifer, and Robert W. Vishny, 1997,

Trust in large organizations, American Economic Review 87, 333-338.

La Porta, Rafael, Florencio Lopez-de-Silanes, Andrei Shleifer, and Robert W. Vishny, 2000,

Agency problems and dividend policies around the world, Journal of Finance 55, 1-33.

Landes, D., 2000, Culture makes almost all the difference. In: Harrison, L.E., Huntington, S.P.

(Eds.), Culture matters. Basic books, New York, NY.

Levine, Ross, 2001, Financial structure and economic growth: A cross-country comparison of

banks, markets, and development, Cambridge, MA: MIT Press.

Lintner, John, 1956, Distribution of incomes of corporations among dividends, retained earn-

ings, and taxes, American Economic Review 46, 97-113.

Litov, Lubomir P., 2005, Corporate governance and financing policy: New evidence, Working

paper, New York University.

Malmendier, Ulrike, and Geoffrey Tate, 2005, CEO overconfidence and corporate investment,

Journal of Finance 60, 2661-2700.

Malmendier, Ulrike, Geoffrey Tate, and Jun Yan, 2005, Corporate financial policies with over-

confident managers, Working paper.

Marquis, Christopher, Mary Ann Glynn, and Gerald F. Davis, 2005, Community isomorphism

and corporate social action, Academy of Management Review, Forthcoming.

31

Mizruchi, M.S., 1989, Similarity of political behavior among large American corporations,

American Journal of Sociology 95, 401-424.

Mizruchi, M.S., 1992, The structure of corporate political action: Interfirm relations and their

consequences, Cambridge: Harvard University Press.

McGurn, Patrick S., Sharon Pamepinto, and Adam B. Spector, 1989, State Takeover Laws,

Washington D.C.: Investor Responsibility Research Center, 1989.

Morgan, Donald, Bertrand Rime, and Philip E. Strahan, 2004, Bank integration and state

business cycles, Quarterly Journal of Economics 119, 155-85.

Newey, W. K., and K. D. West, 1987, A simple positive semi-definite, heteroskedasticity and

autocorrelation consistent covariance matrix, Econometrica 55, 703-8.

Novaes, Walter, and Luigi Zingales, 1995, Capital structure choice when managers are in con-

trol: Entrenchment versus efficiency, NBER working paper No. W5384.

Petersen, Mitchell A., and Raghuram G. Rajan, 1995, The Effect of credit market competition

on lending relationships, Quarterly Journal of Economics 110, 407- 444.

Pirinsky Christo, and Qinghai Wang, 2005, Does corporate headquarters location matter for

stock returns? Journal of Finance, Forthcoming.

Rajan, Raghuram G., and Luigi Zingales, 1995, What do we know about capital structure?

Some evidence from international data, Journal of Finance 50, 1421-60.

Romano, Roberta, 1993. The Genius of American Corporate Law. Washington D.C.: AEI.

Saidenberg, Marc R., and Philip E. Strahan, 1999, Are banks still important for large busi-

nesses? Current Issues in Economics and Finance 5, Federal Reserve Bank of New York.

Stulz, Rene, and Rohan Williamson, 2003, Culture, openness, and finance, Journal of Financial

Economics 70, 313-349.

32

Wald, John K., and Michael S. Long, 2006, The effect of state laws on capital structure, Journal

of Financial Economics, Forthcoming.

Weber, Max, 1930, The Protestant Ethnic and the Spirit of Capitalism. Harper Collins, New

York.

Zarutskie, Rebecca, 2005, Evidence on the Effects of bank competition on firm borrowing and

investment, Journal of Financial Economics, Forthcoming.

Zwiebel, Jeffrey H., 1996, Dynamic capital structure under managerial entrenchment, American

Economic Review 86, 1197-1215.

33

Table 1 Summary Statistics

The sample period is 1988-2003. The full sample consists of 39,287 firm-year observations for 4,104 different firms. Assets are in millions of dollars. Variable Obs. Mean Median STD Q1 Q3

Panel A: Financial policies Leverage 39,119 0.243 0.211 0.222 0.046 0.369 Interest coverage 34,655 37.63 5.78 206.21 2.26 14.95 Cash holdings 39,271 0.391 0.072 1.105 0.019 0.284 Net long-term debt issues 39,287 0.033 0.000 0.077 0.000 0.025 Net equity issues 39,287 0.050 0.001 0.141 0.000 0.013 Net long-term debt issues dummy 39,287 0.341 0.000 0.474 0.000 1.000 Net equity issues dummy 39,287 0.578 1.000 0.494 0.000 1.000 Common dividends 39,287 0.007 0.000 0.018 0.000 0.007 Share repurchase 39,287 0.026 0.000 0.086 0.000 0.009 Total payout 39,287 0.055 0.000 0.161 0.000 0.045 Dividend dummy 39,287 0.318 0.000 0.466 0.000 1.000 Share repurchase dummy 39,287 0.385 0.000 0.487 0.000 1.000 Panel B: Control variables Assets 39,287 1218 182 3712 65 657 Market to book ratio 39,019 1.943 1.416 1.612 1.068 2.144 Return on assets 39,253 0.002 0.037 0.163 -0.012 0.077 Panel C: Bank condition variables Nonperformance loan 438 0.006 0.004 0.008 0.002 0.008 Commercial loans to sales 438 0.291 0.219 0.279 0.122 0.371 Herfindal index 438 0.219 0.197 0.146 0.101 0.302

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Table 2 Location Effects on Financial Policies

This table presents the results from fixed effects panel regressions with clustered standard errors. For each dependent variable, independent variables in row 1 include year fixed effect, industry fixed effects, logarithm of total assets, market-to-book ratio, and return on assets, while those of row 2 add metropolitan area fixed effects. Columns 2 and 4 report F-statistics for the joint significance of industry fixed effects and metropolitan area fixed effects. Columns 3 and 5 show p-values for the corresponding tests. The number of constraints is 42 for industry fixed effects and 27 for location fixed effects.

Industry Metropolitan Financial policy effects areas effects

Adjusted R-square

Number of Obs.

Leverage 12.55 <.0001 21.5% 38,831 8.97 <.0001 5.89 <.0001 23.2% Interest coverage 4.39 <.0001 9.0% 34,467 3.94 <.0001 2.39 <.0001 9.2% Cash holdings 17.15 <.0001 38.3% 38,982 12.41 <.0001 12.17 <.0001 42.1% Net long-term debt issues 6.75 <.0001 4.5% 38,988 5.54 <.0001 3.07 <.0001 4.7% Net equity issues 7.69 <.0001 22.1% 38,988 6.86 <.0001 3.49 <.0001 22.3% Net long-term debt issues dummy 7.02 <.0001 11.3% 38,988 5.20 <.0001 3.50 <.0001 11.9% Net equity issues dummy 8.35 <.0001 14.4% 38,988 6.66 <.0001 5.13 <.0001 15.8% Common dividends 5.80 <.0001 15.1% 38,988 5.20 <.0001 4.08 <.0001 16.2% Share repurchase 3.40 <.0001 4.6% 38,988 3.06 <.0001 1.59 0.0272 4.7% Total payout 3.56 <.0001 4.1% 38,988 3.35 <.0001 1.81 0.0062 4.2% Dividend dummy 7.38 <.0001 41.1% 38,988 6.69 <.0001 4.52 <.0001 43.6% Share repurchase dummy 3.15 <.0001 10.0% 38,988 3.04 <.0001 3.07 <.0001 10.9%

35

Table 3 Distribution of Location Effects

This table shows the distribution of the coefficients of metropolitan area fixed effects. The coefficient estimates are retrieved from regression model (2) of Table 2. The specification includes year fixed effects, industry fixed effects, metropolitan area fixed effects, logarithm of total assets, market-to-book ratio and return on assets. Variables Mean Median Std Dev Q1 Q3 Min Max Leverage -0.024 -0.013 0.032 -0.054 -0.000 -0.094 0.040 Interest coverage 1.825 1.132 15.804 -10.868 13.570 -30.632 40.491 Cash holdings 0.018 0.010 0.040 -0.007 0.024 -0.031 0.135 Net long-term debt issues -0.003 -0.003 0.005 -0.007 0.000 -0.014 0.010 Net equity issues 0.004 0.000 0.009 -0.002 0.011 -0.010 0.025 Net long-term debt issues dummy -0.004 -0.038 0.177 -0.139 0.127 -0.377 0.384 Net equity issues dummy -0.003 0.040 0.261 -0.225 0.080 -0.427 0.733 Common dividends 0.000 -0.001 0.003 -0.002 0.002 -0.004 0.006 Share repurchase -0.001 -0.001 0.006 -0.005 0.002 -0.013 0.010 Total payout -0.002 -0.003 0.011 -0.008 0.005 -0.026 0.021 Dividend dummy -0.010 0.017 0.575 -0.385 0.232 -1.260 1.076 Share repurchase dummy -0.003 0.040 0.261 -0.225 0.080 -0.427 0.733

36

Table 4 Cross-sectional Results of Location Effects on Financial Policies

This table shows the results from the cross-sectional regressions year by year. For each dependent variable, the regression specification includes industry fixed effects, metro area fixed effects, logarithm of total assets, market-to-book ratio and ROA. Column 1 reports the proportion of years in which the metro area fixed effects are significant at 5%. Column 2 shows the proportion of years in which the metro area fixed effects are significant at 10%. Column 3 shows the average adjusted R-square over time.

Num. of years in which MSA fixed effects are

significant at 5%

Num. of years in which MSA fixed effects are

significant at 10%

Average adjusted R-

square over time Leverage 16/16 16/16 23.25% Interest coverage 5/16 6/16 8.72% Cash holdings 16/16 16/16 38.81% Net long-term debt issues 7/16 11/16 3.97% Net equity issues 10/16 12/16 20.43% Net long-term debt issues dummy 3/16 5/16 14.37% Net equity issues dummy 12/16 14/16 18.21% Common dividends 16/16 16/16 17.06% Share repurchase 12/16 13/16 4.70% Total payout 12/16 14/16 4.75% Dividend dummy 16/16 16/16 43.76% Share repurchase dummy 5/16 8/16 12.90%

37

Table 5 Location Effects on Financial Policies Over Subperiods This table presents the results from fixed effects panel regressions over subperiods. For each dependent variable, the regression specification includes year fixed effects, industry fixed effects, metro area fixed effects, logarithm of total assets, market-to-book ratio and ROA. Reported in the table are F-statistics and p-values for the joint significance of metro area fixed effects. The number of constraints is 26, 27, 27 for metro area fixed effects in subperiods 1988 to 1992, 1993 to 1998 and 1999 to 2003, respectively. The number of constraints is 42 for industry fixed effects in all subperiods.

1988-1992

Industry Metro area Adjusted R-square

Number of Obs.

Leverage 5.32 <.0001 3.53 <.0001 24.4% 9,127Interest coverage 2.11 <.0001 1.34 0.1146 8.5% 8,608 Cash holdings 4.36 <.0001 4.76 <.0001 32.3% 9,146 Net long-term debt issues 3.53 <.0001 2.03 0.0015 3.7% 9,146 Net equity issues 3.74 <.0001 2.96 <.0001 21.4% 9,146 Net long-term debt issues dummy 1.95 0.0002 1.26 0.168 10.5% 9,146 Net equity issues dummy 2.23 <.0001 3.17 <.0001 15.2% 9,146 Common dividends 4.00 <.0001 3.42 <.0001 21.1% 9,146 Share repurchase 1.69 0.0038 3.02 <.0001 2.8% 9,146 Total payout 2.17 <.0001 3.11 <.0001 4.7% 9,146 Dividend dummy 10.14 <.0001 3.01 <.0001 45.9% 9,146 Share repurchase dummy 1.38 0.0531 2.44 <.0001 10.4% 9,146

1993-1998 Industry Metro area

Adjusted R-square

Number of Obs.

Leverage 7.23 <.0001 4.54 <.0001 25.3% 16,897Interest coverage 3.01 <.0001 2.26 0.0002 10.8% 15,049 Cash holdings 9.88 <.0001 9.76 <.0001 43.0% 16,946 Net long-term debt issues 5.22 <.0001 2.01 0.0015 5.8% 16,951 Net equity issues 4.64 <.0001 2.56 <.0001 19.9% 16,951 Net long-term debt issues dummy 4.19 <.0001 2.45 <.0001 12.5% 16,951 Net equity issues dummy 4.96 <.0001 3.64 <.0001 16.6% 16,951 Common dividends 4.38 <.0001 3.76 <.0001 14.3% 16,951 Share repurchase 2.39 <.0001 1.47 0.0541 5.0% 16,951 Total payout 2.87 <.0001 1.63 0.0207 4.5% 16,951 Dividend dummy 5.28 <.0001 3.91 <.0001 41.3% 16,951 Share repurchase dummy 2.86 <.0001 2.10 0.0007 11.5% 16,951

1999-2003 Industry Metro area

Adjusted R-square

Number of Obs.

Leverage 7.31 <.0001 3.86 <.0001 23.4% 12,825Interest coverage 2.60 <.0001 1.72 0.0117 8.8% 10,810 Cash holdings 10.95 <.0001 8.43 <.0001 45.0% 12,890 Net long-term debt issues 3.12 <.0001 1.78 0.008 3.9% 12,891 Net equity issues 4.48 <.0001 1.82 0.0061 27.8% 12,891 Net long-term debt issues dummy 3.12 <.0001 2.28 0.0002 12.9% 12,891 Net equity issues dummy 8.52 <.0001 2.85 <.0001 17.8% 12,891 Common dividends 4.07 <.0001 3.31 <.0001 16.7% 12,891 Share repurchase 8.90 <.0001 1.47 0.055 5.5% 12,891 Total payout 4.05 <.0001 1.29 0.1457 5.0% 12,891 Dividend dummy 10.24 <.0001 2.66 <.0001 42.2% 12,891 Share repurchase dummy 3.52 <.0001 1.64 0.0192 11.4% 12,891

38

39

Table 6 Effects of State Regulations on Financial Policies This table presents the effects of state statutes on financial policies. For each dependent variable, the regression model includes year fixed effects, industry fixed effects, metro area fixed effects, logarithm of total assets, market-to-book ratio and ROA, the index of antitakeover statutes and payout restriction variable. All the t-ratios are adjusted for clustering at firm level. Columns 3 and 4 report F-statistics and p-values respectively for the joint significance of metro area fixed effects. The number of constraints is 27 for metro area fixed effects.

Antitakeover statutes

Payout restriction

Metro area

Adjusted R-square

Number of Obs.

Leverage 0.001 -0.019** 5.77 <.0001 23.3% 38,740 (0.56) (-2.58) Interest coverage -3.923* 19.079** 1.97 0.0020 9.3% 34,395 (-1.71) (2.62) Cash holdings -0.002 -0.006 11.04 <.0001 42.1% 38,891 (-0.81) (-1.10) Net long-term debt issues 0.001* -0.005** 3.12 <.0001 4.8% 38,897 (1.95) (-3.93) Net equity issues -0.001 -0.008** 3.32 <.0001 22.4% 38,897 (-0.80) (-2.89) Net long-term debt issues dummy 0.022 -0.141** 3.43 <.0001 11.9% 38,897 (1.26) (2.76) Net equity issues dummy -0.060** 0.094 4.72 <.0001 16.0% 38,897 (2.97) (1.59) Common dividends 0.000 0.002** 3.74 <.0001 16.6% 38,897 (0.52) (3.58) Share repurchase 0.001 -0.002 1.54 0.0360 4.7% 38,897 (1.26) (-1.31) Total payout 0.001 -0.001 1.77 0.0082 4.2% 38,897 (0.91) (-0.42) Dividend dummy 0.027 0.592** 3.72 <.0001 44.9% 38,897 (0.71) (5.37) Share repurchase dummy 0.010 0.053 2.77 <.0001 10.9% 38,897 (0.45) (0.87)

Table 7 Effects of State Regulations and Bank Conditions on Financial Policies

This table presents the effects of state statutes and bank conditions on financial policies. For each dependent variable, the regression model includes year fixed effects, industry fixed effects, metro area fixed effects, logarithm of total assets, market-to-book ratio and ROA, the index of antitakeover statutes, payout restriction variable, nonperforming loan, commercial loans to sales, and Herfindal index. All the t-ratios are adjusted for clustering at firm level. Columns 6 and 7 report F-statistics and p-values respectively for the joint significance of metro area fixed effects. The number of constraints is 27 for metro area fixed effects.

Antitakeover statutes

Payout restriction

Nonperformance loan

Commercial loans to

sales Herfindal

index Metro area

Adjusted R-square

Number of Obs.

Leverage 0.001 -0.019** -0.024 0.031** -0.002 5.09 <.0001 23.4% 38,740 (0.55) (-2.57) (-0.08) (2.13) (-0.10) Interest coverage -3.93* 19.085** -0.239 5.88 -5.118 1.93 0.0026 9.3% 34,395 (-1.71) (2.62) (-0.00) (0.51) (-0.26) Cash holdings -0.002 -0.007 0.362 -0.021** 0.01 10.88 <.0001 42.1% 38,891 (-0.79) (-1.12) (1.36) (-2.15) (0.64) Net long-term debt issues 0.001* -0.005** -0.150* -0.006 -0.003 3.01 <.0001 4.8% 38,897 (1.95) (-3.96) (-1.66) (-1.59) (-0.51) Net equity issues -0.001 -0.008** -0.137 -0.001 -0.01 3.39 <.0001 22.4% 38,897 (-0.80) (-2.89) (-0.79) (-0.26) (-1.20) Net long-term debt issues dummy 0.022 -0.142** -1.501 -0.087 -0.428** 3.36 <.0001 12.0% 38,897 (1.27) (2.77) (0.50) (0.78) (2.38) Net equity issues dummy -0.060** 0.094 -5.530* -0.107 0.171 4.51 <.0001 16.0% 38,897 (2.98) (1.60) (1.72) (0.82) (0.86) Common dividends 0.000 0.002** 0.031 -0.002** 0.002 3.91 <.0001 16.7% 38,897 (0.53) (3.57) (1.19) (-2.38) (1.01) Share repurchase 0.001 -0.002 0.213* 0.002 0.006 1.53 0.0387 4.7% 38,897 (1.27) (-1.33) (1.93) (0.37) (0.85) Total payout 0.001 -0.001 0.315 0.004 0.013 1.79 0.0073 4.2% 38,897 (0.91) (-0.43) (1.47) (0.57) (1.06) Dividend dummy 0.027 0.591** 2.104 -0.023 0.006 3.63 <.0001 44.9% 38,897 (0.72) (5.36) (0.54) (0.13) (0.02) Share repurchase dummy 0.009 0.053 4.05 0.271** -0.03 2.86 <.0001 10.9% 38,897 (0.45) (0.87) (1.22) (1.97) (0.15)

40

Table 8 Region Characteristics

This table shows the average of residuals from fixed effect regressions for each region (Panel A) and culture variation across regions (Panel B). For each dependent variable of financial policy, the regression model includes year fixed effects, industry fixed effects, logarithm of total assets, market-to-book ratio and ROA, the index of antitakeover statutes, payout restriction variable, nonperforming loan, commercial loans to sales, and Herfindal index. We then retrieve the residuals from the regression and find the average for each region. Culture variables are averaged over time for each region. Panel A: Residuals of financial policy variables

Region Obs. Leverage Interest

coverage Cash

holdings

Net long-term debt

issues

Net equity issues

Common dividends

Share repurchase

Total payout

New England 3,450 -0.015 2.705 0.019 -0.003 -0.001 0.000 0.003 0.002 Middle Atlantic 8,719 0.004 -7.627 -0.005 -0.002 -0.004 0.000 -0.002 0.000 South Atlantic 2,723 0.021 -9.910 -0.019 0.005 0.004 0.000 0.001 0.002 East South Central 1,189 0.018 -7.686 -0.027 0.006 0.000 -0.001 -0.004 -0.013 West South Central 4,599 0.022 -4.444 -0.023 0.004 0.002 -0.001 -0.003 -0.003 East North Central 5,758 0.010 -2.024 -0.021 -0.001 -0.005 0.002 0.001 0.005 West North Central 2,504 -0.006 13.523 -0.017 0.000 -0.003 0.002 0.005 0.011 Rocky Mountain 2,068 0.025 19.700 -0.011 0.008 0.004 -0.002 0.003 0.006 Northwest 8,277 -0.031 7.443 0.043 -0.003 0.006 -0.001 -0.001 -0.006 Panel B: Culture characteristics

Region Obs. trust attend prot New England 3,450 0.416 0.349 0.186 Middle Atlantic 8,719 0.409 0.395 0.254 South Atlantic 2,723 0.311 0.467 0.410 East South Central 1,189 0.268 0.539 0.388 West South Central 4,599 0.392 0.525 0.357 East North Central 5,758 0.432 0.474 0.279 West North Central 2,504 0.451 0.533 0.430 Rocky Mountain 2,068 0.361 0.410 0.251 Northwest 8,277 0.406 0.358 0.233

41

Table 9 Effects of Region Characteristics on Financial Policies

This table presents the effects of region characteristics on financial policies. For each dependent variable, the regression model includes year fixed effects, industry fixed effects, metro area fixed effects, region fixed effects, logarithm of total assets, market-to-book ratio and ROA, the index of antitakeover statutes, payout restriction variable, nonperforming loan, commercial loans to sales, and Herfindal index. All the t-ratios are adjusted for clustering at firm level. Columns 1 and 2 report F-statistics and p-values respectively for the joint significance of metro area fixed effects. Columns 3 and 4 report F-statistics and p-values respectively for the joint significance of region fixed effects.The number of constraints is 27 for metro area fixed effects and 8 for region fixed effects.

Metro area Region Adjusted R-square

Number of Obs.

Leverage 2.09 <.0001 0.34 0.9508 23.4% 38,740 Interest coverage 1.09 0.3452 1.30 0.2383 9.4% 34,395 Cash holdings 5.08 <.0001 1.94 0.0499 42.2% 38,891 Net long-term debt issues 1.39 0.0888 2.37 0.0155 4.8% 38,897 Net equity issues 2.64 <.0001 1.97 0.0465 22.4% 38,897 Net long-term debt issues dummy 1.74 0.0096 2.33 0.0171 12.1% 38,897 Net equity issues dummy 2.52 <.0001 1.23 0.2774 16.1% 38,897 Common dividends 2.42 <.0001 2.69 0.0059 16.9% 38,897 Share repurchase 1.64 0.0195 2.09 0.0331 4.90% 38,897 Total payout 1.40 0.0826 1.74 0.0847 4.30% 38,897 Dividend dummy 2.67 <.0001 3.33 0.0008 45.4% 38,897 Share repurchase dummy 1.98 <.0001 2.10 0.0327 11.1% 38,897

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Table 10 Effects of Culture on Financial Policies

This table presents the effects of culture on financial policies. For each dependent variable, the regression model includes year fixed effects, industry fixed effects, metro area fixed effects, logarithm of total assets, market-to-book ratio and ROA, the index of antitakeover statutes, payout restriction variable, nonperforming loan, commercial loans to sales, Herfindal index, trust, church attendance, and the percentage of Protestant. All the t-ratios are adjusted for clustering at firm level. Columns 4 and 5 report F-statistics and p-values respectively for the joint significance of metro area fixed effects. Columns 6 and 7 report F-statistics and p-values respectively for the joint significance of cultural variables. Trust

Church attendance Prot

Metro area F test for cultural variables

Adjusted R-square

Number of Obs.

Leverage 0.001 0.017 0.049* 4.45 <.0001 1.06 0.3627 23.4% 38,740 (0.01) (0.35) (1.78) Interest coverage -20.047 18.462 -46.606* 1.78 0.0076 1.23 0.2979 9.3% 34,395 (-0.50) (0.51) (-1.80) Cash holdings -0.013 -0.075** -0.046** 8.21 <.0001 3.68 0.0116 42.2% 38,891 (-0.46) (-2.23) (-2.45) Net long-term debt issues 0.010 0.0001 0.016** 2.33 0.0001 1.74 0.1571 4.8% 38,897 (0.93) (0.01) (2.18) Net equity issues -0.013 -0.001 -0.006 3.10 <.0001 0.24 0.8670 22.4% 38,897 (-0.74) (-0.05) (-0.49) Net long-term debt issues dummy 0.509 0.578* 0.477** 2.51 <.0001 3.22 0.0216 12.0% 38,897 (1.53) (1.738) (2.15) Net equity issues dummy -0.327 -0.784* 0.23 3.68 <.0001 1.56 0.1976 16.0% 38,897 (-0.85) (-1.95) (0.92) Common dividends 0.002 -0.006* -0.001 3.67 <.0001 1.02 0.3831 16.7% 38,897 (0.44) (-1.66) (-0.61) Share repurchase 0.008 -0.004 -0.004 1.45 0.0626 0.23 0.8727 4.7% 38,897 (0.55) (-0.31) (-0.41) Total payout -0.002 -0.025 -0.006 1.66 0.0175 0.42 0.7412 4.2% 38,897 (-0.06) (-0.99) (-0.33) Dividend dummy 1.583** -0.714 -0.52 9.73 <.0001 3.45 0.0159 45.0% 38,897 (2.58) (-1.09) (-1.38) Share repurchase dummy 0.109 0.023 -0.665** 2.76 <.0001 2.27 0.0787 11.0% 38,897 (0.28) (0.05) (2.55)

43