1

Does Political Alignment Between Top Management and Directors Impair Board

Independence?

Jongsub Lee*

University of Florida

Kwang J. Lee

University of Pittsburgh

Nandu J. Nagarajan

University of Pittsburgh

First Draft September 2011

This version: May 2012

We acknowledge helpful comments from participants at workshops at the Tuck School of Business at

Dartmouth, University of Pittsburgh, the University of Florida, the 2012 AAA FARS Midyear Meeting

and the 2012 Midwest Finance Association Annual Meeting. In particular, we thank Don Chance (2012

MFA discussant), Willie Choi, David Denis, Diane Denis, Vicki Dickinson (2012 AAA FARS

discussant), Harry Evans, Mei Feng, Mark Flannery, Vicky Hoffman, Joel Houston, Donald Moser, Andy

Naranjo, Sugata Ray, Jay Ritter and John Wald for their insightful comments and suggestions.

*Correspondence Information: Assistant Professor, Finance, Insurance & Real Estate Department,

Warrington College of Business Administration, University of Florida, Gainesville, FL 32611 U.S.A. Tel:

(352) 273-4966. Fax: (352) 392-0301. Email: jongsub.lee@warrington.ufl.edu

2

Abstract

Using individual political donation data, we measure the extent of alignment in political

orientation between top executives and independent directors for 2,693 U.S. corporations from

1996 to 2009. We document that such political alignment is associated with lower firm

valuations and operating profitability. We further document increased internal agency conflicts

in firms with politically aligned boards, such as a reduced likelihood of dismissing a poorly

performing CEO, lower CEO pay-performance sensitivity and a greater likelihood of firms

committing accounting frauds. Importantly, these findings are robust to controlling for social ties

and demographic similarities between managers and directors. Therefore, our results shed new

light on a unique but important dimension that can impair board independence – the intangible

connections between managers and directors formed through similar political beliefs.

Key words: Political alignment, board independence

JEL Classification: G34

3

Does Political Alignment Between Top Management and Directors Impair Board

Independence?

“Similarity begets friendship”

---Plato

I. Introduction

There appears to be increasing concern that board independence is both important and difficult to

achieve.1 In addition to overt connections, the existence of intangible networks and relationships

between managers and directors could potentially impair board independence and, thus, increase

managerial entrenchment. In this paper, we provide new evidence that alignment in political

orientations between managers and directors can have such an adverse effect on board

independence. Our findings are important not only because they add to the corporate governance

literature on the effective functioning of boards, but also from a regulatory perspective.

Impairment in board independence arising from intangible connections between managers and

directors are hard to discern and difficult to eliminate. Therefore, such connections have the

potential to subvert regulations like the Sarbanes-Oxley Act that seek to enforce board

independence by eliminating any tangible connections within a corporate board.

People tend to associate and bond with others who are similar to themselves. This

tendency, often called the homophily principle, has been extensively studied by sociologists

(McPherson, Smith-Lovin, and Cook (2001)). Similarity in individual value systems, termed

1 Romano (2005) argues that requiring increased board independence may be ineffective because CEOs may have

considerable influence even over independent directors. Hermalin and Weisbach (1991) and Bhagat and Black (2000)

find no evidence that board independence improves firm performance. Larcker, Richardson, Seary and Tuna (2007)

show that cross directorships lead to higher compensation.

4

“value homophily” by sociologists, facilitates strong collaborative norms (Reynolds and

Herman-Kinney (2003), Srivastava and Banji (2011))2, and also increases connectivity within an

organization (Pachucki and Breiger (2010)). Similarity in political beliefs, being a form of value

homophily, can, therefore, also potentially lead to increased connectivity among the

organization’s members. In particular, in the corporate context, political alignment between top

managers and board members can result in increased empathy and acceptance between

management and outside directors.

While there is theoretical support for the value created by management-friendly boards

(Adams and Ferreira (2007)), recent empirical studies have documented that socio-demographic

similarities and social networks between CEOs and independent directors can have adverse value

and entrenchment consequences (Westphal and Zajac (1995), Hwang and Kim (2009) and

Fracassi and Tate (2012)). In our paper, we seek to explore whether similarity in political

orientation between top executives and independent directors can potentially have value and

entrenchment consequences that are independent of the effects documented with social ties.

Our paper makes a number of contributions. We are the first to develop a measure of

political alignment between managers and directors by measuring the similarity in their political

orientations. We also investigate the value and internal governance implications of the intangible

connections in individual value systems between managers and directors captured by our

political alignment measure. To elaborate, we extend the literature on the valuation and

entrenchment consequences of socio-demographic similarities and social networks by providing

evidence that our measure of political alignment is also associated with lower valuations, a lower

2 The literature on homophily distinguishes value homophily, in which similarity is based on attitudes, values and

beliefs from status homophily, according to which similarity is based on socio-demographic characteristics such as

race, gender, occupation and education.

5

probability of CEO turnover following poor performance, weaker compensation incentives and

greater likelihood of corporate fraud. Importantly, we document that these negative outcomes

associated with political alignment between directors and CEOs exist after controlling for

demographic similarities and social ties, thereby providing evidence that political alignment is

associated with implicit ties between directors and managers that are quite distinct and different

from those identified in the social ties literature.

Recent regulatory initiatives, including the Sarbanes–Oxley Act (SOX) of 2002, have

emphasized the importance of board independence. For instance, the Sarbanes–Oxley Act

requires that audit committees comprise only of independent directors. Our research contributes

to this discussion by showing that a less discernible but broad-based individual characteristic,

namely political orientation, can also affect board independence. Since imposing legislative

actions on individual political preferences is infeasible, our findings further suggest that

achieving complete board independence may not be possible.

We measure the similarity in political orientation between each firm’s top management

and independent directors by constructing a political homophily index (PHI) between the two

groups. We follow a two-step procedure to construct this measure. First, we measure each

individual’s political orientation by comparing the dollar amounts of political donations made by

that individual to the Republican and Democratic Parties. In particular, we measure the relative

tilt of each person’s political donations toward the Republican Party, and denote it by

Rep_index.3 Then, we calculate group-level averages of the Rep_index values separately for the

3 This individual-level Rep_index is similar to the measure used by Hong and Kostovetsky (2012) and Hutton, Jiang

and Kumar (2011), who document that U.S. money managers and corporate executives with different political

orientations exhibit different preferences towards resource allocation. Hong and Kostovetsky (2012) find that

democratic money managers tend to invest less in stocks with social responsibility concerns. Hutton, Jiang and

Kumar (2011) find that republican top executives tend to adopt more conservative corporate financial policies.

6

groups of top five executives and independent directors. Finally, we compute the PHI for each

firm by calculating the inverse Euclidean distance between the two group-level averages and

normalizing the index to be between zero and one, where a PHI value of one (zero) indicates the

greatest (least) similarity in the two groups’ political orientations.. We construct the PHI

between the firm’s CEO and the group of independent directors in a similar manner.

We find that firm values, measured by Tobin’s Q, are significantly lower when the firm’s

top five executives and independent directors have similar political orientations. Our results

indicate that a one standard deviation change in the PHI between the top five executives and

independent directors results in an economically significant 3.63% reduction in the Q ratio,

relative to its sample average. This effect is statistically significant at the 1% level even after

controlling for various board characteristics, firm-level financial variables, and the firms’

external governance index (Gindex) introduced by Gompers, Ishii, and Metrick (2003). Our

analysis holds up after several robustness checks, such as controlling for the strength of

individual- and firm-level political connections established through campaign donations and

using alternative specifications of the Rep_index.

As mentioned earlier, we examine whether the negative relation between PHI and the Q

ratio is robust after controlling for demographic similarities and social networks between top

management and independent directors. Motivated by the findings in Westphal and Zajac (1995),

we first control for age and gender dissimilarities between the two groups. Next, we further

control for social connections between CEOs and independent directors using measures

constructed as in Fracassi and Tate (2012). We find that our PHI measure continues to have both

an economically and statistically significant negative impact on Q even after controlling for these

socio-demographic similarities. Importantly, these results show that political alignment between

7

managers and directors captures a distinct and unique relationship between them which is

different from social and demographic ties.

Additional analyses show that the CEO’s political alignment with independent directors

explains approximately two thirds of the negative effect on Q of the PHI between the top five

executives and independent directors. Moreover, we find that operating profitability, measured

by return on assets, also exhibits a strong negative association both with the PHI between the top

five executives and independent directors, and also the PHI between the CEO and independent

directors. Further, the CEO-Independent directors PHI has a stronger negative relationship with

return on assets.

Given the importance of the CEO’s political alignment with directors and the possibility

that increased connectivity between the CEO and directors could result in CEO entrenchment,

we further examine whether the CEO-Director PHI is associated with a reduced likelihood of

forced CEO turnover following poor performance and weaker compensation incentives. We find

that CEOs, who are politically aligned with independent directors, are less likely to be dismissed

following poor performance, and their compensation is less performance based. For instance, a

one standard deviation increase in PHI between the CEO and independent directors reduces the

pay-performance-sensitivity (PPS, hereafter) of CEO compensation by 19.3% relative to the PPS

without the PHI effect.

Next, we investigate whether political alignment between the top management team and

independent directors affects the quality of financial reporting. Using corporate fraud data from

Dyck, Morse and Zingales (2011), we find that the marginal probability of a firm being involved

in high-profile corporate fraud goes up by 5% for a one standard deviation change in PHI

between the two groups. This effect of PHI on the likelihood of corporate fraud is roughly twice

8

as large as that explained by the degree of social connections between the CEO and independent

directors.

Finally, we report the results of robustness checks. We address the possibility that the

relation between PHI and Q is endogenous, by using a sub-sample of firm-years to identify

discrete, non-performance-related changes in PHI associated with the deaths and non-

performance-related turnover of CEOs and directors. Further, we also conduct two-stage least

squares instrumental variable regressions (2SLS IV) based on the predictive regression

specifications. By doing so, we show that our results are unlikely to suffer from reverse causality

problems, except in the extremely unlikely event that the firm’s top executives and directors are

able to perfectly forecast the firm’s future performance over longer time horizons such as 3 to 6

years.

Overall, we provide evidence suggesting that similarity in political orientation between

top executives and independent directors potentially increases connectedness within the

corporate board room, thereby exacerbating internal agency problems. We also show that our

measure has effects that are distinct from those associated with social ties or demographic

similarities between managers and directors. This suggests that similarity in less discernible

individual characteristics such as opinions and value systems, arising from political alignment,

could weaken the monitoring intensity of independent directors, even after the board’s

independence may seemingly have been achieved by eliminating other factors such as board

interlocks and any commonalities in socio-demographics between top executives and directors.

In particular, political alignment, being hard to discern and difficult to eliminate, has the

potential to subvert regulations like the Sarbanes-Oxley Act that seek to enforce board

independence.

9

The remainder of this paper is organized as follows: Section II reviews related literature

and develops our hypotheses. Section III describes our political and financial data. Section IV

describes the construction of our main political variables. The results are presented in Section V,

Section VI contains robustness analyses and Section VII presents the conclusion of this study.

II. Related Literature and Hypotheses Development

Sociologists have long studied the homophily principle that contact among similar people

occurs at a higher rate than among dissimilar people (See McPherson, Smith-Lovin, and Cook

(2001) for a comprehensive review of the literature). One dimension of similarity examined in

the literature is individual political orientation. For instance, Verbrugge (1977), Knoke (1990)

and Huckfeldt and Sprague (1995) use a person’s political orientation as a proxy for the

individual’s value system and behavioral patterns, and find a significant tendency among adults

to interact with those conforming to their own political orientations. Such voluntary associations

in social spaces, linked to similarity in political orientation, illustrate the importance of political

alignment as a catalyst in developing connections among people.

Two recent studies in behavioral finance document that people with different political

orientations exhibit different preferences. Hong and Kostovetsky (2012) find that democratic

money managers tend to hold less of their portfolios in companies with social responsibility

concerns. Hutton, Jiang and Kumar (2011) find that firms with republican top executives tend to

choose more conservative corporate financial policies. These findings support the sociologists’

view that individual political orientations could well reflect underlying value systems.

While the political orientation of executives could influence their decision choices, such

decisions are also subject to review and oversight by the board of directors. Therefore, the

10

political orientations of directors are also likely to play a role in the governance and decision

making of firms. More importantly, the alignment in political beliefs and values between

directors and top executives is likely to be important in determining the kind of oversight the

board exerts over the CEO and top executives. While shared values and belief systems between

managers and independent directors can potentially result in quicker and more efficient decision

making, and an increase in shareholder value (Adams and Ferreira (2007)), it can also result in

greater managerial entrenchment arising from weaker monitoring by the board, if such

connections impair director independence.

Existing studies have generally documented that social connections between managers

and directors reduce the monitoring intensity of independent directors, thereby allowing CEOs to

become entrenched. For instance, Westphal and Zajac (1995) find that demographic similarities

between CEOs and directors in their ages, job functions, and education levels are related to

excess CEO compensation. Hwang and Kim (2009) find that CEOs tend to be overpaid, with

lower pay-performance-elasticity, when they are connected to independent directors through

common alma maters, military service, regional origin, academic discipline and industry.

Fracassi and Tate (2012) find that firm market values tend to be lower when CEOs are connected

to independent directors through ongoing and past employment, education, and other ways, such

as club memberships and social activities. These findings suggest that similarity in socio-

demographic characteristics between CEOs and independent directors promotes connectedness

within the corporate board room, thereby weakening board monitoring of the CEO and, in turn,

exacerbating internal agency problems. However, these social connections within the board

appear to be sparse, and, thus, potentially, may result in only limited managerial entrenchment.4

4 Fracassi and Tate (2012) find that only 18.3% of independent board members have at least one social tie to a CEO

and such social ties between CEOs and independent directors are found only in 15.0% of their sample firms. The

11

In contrast, in this paper, we examine whether a broader propinquity arising from similar

political orientations between managers and directors also weakens board independence.

Because political alignment can be broad-based, hard to discern and impossible to eliminate in

board rooms, it can have a particularly significant exacerbating effect on agency problems and

managerial entrenchment, if it impairs board independence. In particular, we are interested in

whether political alignment between top executives and the group of independent

directorsimpacts internal corporate governance in ways that are distinct from those associated

with social ties or demographic similarities between the two groups. Proceeding from the above

arguments, we test the following hypothesis:

H1: When corporate insiders are politically aligned with independent directors, the potential

compromise in independence results in a decrease in firm value.

Following Hypothesis 1, impairment in board independence could affect firm value

potentially through a weakening of internal governance. The main responsibilities of independent

directors are to not only monitor management but also determine executive compensation

(Murphy (1999)). Therefore, if political alignment between managers and directors weakens

board independence, it could result in a reduced likelihood of CEO turnover following poor

performance and lower pay performance sensitivity. Finally, weaker board oversight can lead to

greater laxity in financial controls, thereby resulting in a greater likelihood of fraud and earnings

manipulation. Based on these arguments, we hypothesize the following:

H2: When corporate insiders are politically aligned with independent directors, the likelihood of

a CEO’s involuntary departure following poor performance, ceteris paribus, is lower.

H3: CEOs who are politically aligned with independent directors will have lower pay

median value of the fraction of independent board members who are socially tied to the CEO is 0.

12

performance sensitivity in their compensation.

H4: When corporate insiders are politically aligned with independent directors, the likelihood of

committing a corporate fraud, ceteris paribus, is higher.

We measure a person’s political orientation using his/her campaign donations record. In

order to correctly measure individual political orientations, it is required that patterns of

individual political donations should represent the donor’s political values and beliefs rather than

an opportunistic attempt to seek political favors. Burris (2001) in the political science literature

directly addresses this issue. Using the political contributions data provided by the Federal

Election Commission (FEC), Burris (2001) investigates patterns of campaign donations made by

top executives, board members and political action committees (PACs) of 1,050 large U.S.

corporations in the 1980 election. He finds that the motivation for individual political donations

is to bolster favored candidates based on individual political ideology, whereas corporate

campaign donations through PACs are more likely to be related to political opportunism. We

measure individual political orientations using the same source of political donations data, which

is the FEC.5 To provide additional justification for measuring individual political orientations

using campaign donations data, we perform rigorous robustness checks in section VI.

Next we discuss the data and variables used in our empirical analysis.

III. Data

A. Political Donation Data

Our main measure of political alignment is the distance in political orientation between

5 Hong and Kostovetsky (2012) and Hutton, Jiang and Kumar (2011) also measure individual political orientations

using the “individual” political donations data provided by the FEC.

13

top executives and independent directors. We use the Compustat Executive Compensation

(ExecuComp) and Investor Responsibility Research Center (IRRC) databases to identify top

managers and independent directors. Our sample consists of 2,693 firms at the intersection of the

ExecuComp and IRRC databases and covers the period from 1996 to 2009, and this sample is a

broader panel dataset than those used in the existing studies such as Hwang and Kim (2009) who

use a sample of Fortune 100 firms (1996-2005) and Fracassi and Tate (2012) who use a sample

of 2,083 firms (2000-2007).

We identify the top five executives for each firm-year by ranking top executives based on

their annual compensation (salary + bonus) levels. We use annual CEO flags to identify CEOs

for given firm-years. When we identify independent directors, we use the board affiliation

variable (variable name: Classification)6 in the IRRC database. There are 24,944 top five

executives and 20,138 independent directors in our sample.

For these top five managers and independent directors, we measure their political

orientations using the individual political contributions data provided by the FEC. For political

donations made by individuals that exceed $200, the FEC has made public the identities and

contributions of donors and information about candidate or committee recipients, including their

party affiliation. The FEC’s individual political contributions database consists of more than 18

million political donations made by individuals in the U.S. since 1979. Each donation

observation in the database contains the name and occupation of the donor and the amount and

date of the contribution, which serve as the key mapping information we use to link the

ExecuComp managers and IRRC directors with the FEC donors.

6See IRRC’s classifications for the types of directors provided on the RiskMetrics Directors Definitions webpage at

http://wrds-

web.wharton.upenn.edu/wrds/support/Data/_001Manuals%20and%20Overviews/_115RiskMetrics/RiskMetrics%20

Directors%20Definitions.cfm

14

We identify approximately 72,000 political donations made by 8,349 managers for all

election cycles between 1989 and 2010. This indicates that approximately 33% of the top

executives in our sample made at least one political contribution exceeding $200 during this

period. We also find that, among the 8,349 politically active managers, 5,208 (62.4%) made

political donations to only one party. Similarly, we identify approximately 112,000 political

donations made by 8,175 independent directors during the same time period. This indicates that

approximately 40.6% of the independent directors in our sample made at least one political

contribution exceeding $200 during this period. Among the 8,175 politically active independent

directors, 4,683 (57.3%) made political donations to only one party.

After identifying political contributions made by top managers and independent directors,

we construct the political measures used in our regression analyses---both the individual and

group-level republican index (Rep_index) and the political homophily index between top

management and independent directors (PHI). We provide details about the construction of these

political measures in Section IV.

B. Financial, Executive Compensation and Board Characteristics Data

We obtain the annual accounting data for our sample of firms from Compustat and merge

this with stock return data obtained from the Center for Research in Security Prices (CRSP). The

information on top executives’ age, gender, compensation amount details, position titles, tenure,

turnover and share ownership is obtained from ExecuComp. Information on independent

directors’ age, gender, share ownership and tenure is obtained from the IRRC directors file.

Information on firm-level external governance provisions and the Gindex are obtained from the

IRRC governance file. Since the Gindex is not updated every year, we map the most up-to-date

15

values of the index to each fiscal year end date for the firms in our sample. The Gindex is

available only until 2006.

The main valuation ratio is Tobin’s Q (Q, hereafter), which is computed as the ratio of the

market value of a firm’s assets to its book value. In addition to Q, we also use the following

financial ratios: Each firm’s return on assets (roa), annual percentage net equity return

(ann_eq_ret), book leverage ratio (tda), market leverage ratio (tdm), investment in tangible

assets ratio (inv), research and development expense to assets ratio (rnd), natural logarithm of the

inflation-adjusted book value of the firm’s assets (logasset), 12-month rolling monthly net

percentage equity return volatility (eqvol) and 5-year rolling annual cash flow volatility (cfvol).

The definitions of these variables are provided in Appendix B. All financial ratios are winsorized

at the 1% and 99% levels to minimize outlier effects, and their summary statistics are provided in

Panel B of Table 1.

The summary statistics for various CEO and board characteristics are provided in Panel C

of Table 1. These variables are as follows: ceo_is_chmn_pres is a dummy variable taking a value

of one if a firm’s CEO also holds the title of either a chairman or president, and zero otherwise,

ceo_age is the age of the CEO, ceo_holding is the percentage of CEO stock ownership,

ceo_tenure is the length of the CEO’s tenure in years, board_size is the total number of directors

on the board, independent is a dummy variable which takes a value of one if the percentage of

independent directors on the board is greater than 50%, and zero otherwise, out_holding and

out_age denote the percentage of average stock ownership and the average ages of the

independent directors, respectively, and age_diff_ceo_out denotes the absolute value of the

difference between the CEO’s age and the average age of independent directors. Finally, using a

gender dummy variable, which takes a value of one when the individual is female and zero

16

otherwise, we define female_diff_ceo_out as the absolute value of the difference between the

CEO’s gender dummy and the average value of the dummy variables for the independent

directors. By focusing on the top five executives instead of the CEO, we similarly define

age_diff_top5_out and female_diff_top5_out for the groups of top five executives and

independent directors.

Using the BoardEx database, we also construct measures of social networks between the

CEO and independent directors as in Fracassi and Tate (2012). The variable Ind-Connections

denotes the number of independent directors minus independent directors with at least one social

tie to the CEO, and SNI_out is the fraction of independent directors with at least one social tie to

the CEO. Summary statistics for these two variables are reported in Panel C of Table 1.7

Panel D of Table 1 summarizes the CEO compensation data. All compensation amounts

are denominated in thousands of dollars. When these amounts are used in regressions, they are

deflated by the average annual consumer price index (CPI) values obtained from the U.S. Bureau

of Labor Statistics website that we normalize to be one in year 1992. The dependent variables

used in the PPS tests in Table 7 are the annual changes in the inflation-adjusted dollar amount of

salary and bonus payments and total compensations amounts, which are respectively denoted by

salary&bonus dollar change and total_comp dollar change. These variables are also winsorized

at the 1% and 99% levels to control for outlier effects.

IV. Main Political Variables8

7 The BoardEx database starts from 1997. However, the social network information becomes complete in the post-

2000 period. In Table 4, where we document that the negative relation between political alignment and firm

valuation is robust to the inclusion of social network variables, we only use firm-years in the post-2000 period

following the approach in Fracassi and Tate (2012). 8 All political variables are defined in the Appendix

17

A. Political Orientation Measure: Rep_index

A.1. Step 1: Individual-level Rep_index

Our measure of individual political orientation, called Rep_index, is defined as the net

donations made to republican candidates and committees divided by the total donations made to

both republican and democratic candidates and committees.9

Rep_index =

We construct Rep_index in two ways: 1) using the full time series of individual donation

histories over 11 federal election cycles from 1989 to 2010, and 2) using the donation history

from 1989 to each firm’s fiscal year end date. The former, denoted by Rep_index, is used as our

default measure of individual political orientation, and the latter, denoted by Rep_index_prior, is

used for robustness checks and constructing instruments in our causal regression analysis

(Section VI). We assign a value of zero to the Rep_index if the individual concerned made no

political contributions exceeding $200, or made donations only to candidates or committees

affiliated neither with the Republican nor Democratic Parties.10

By construction, the resulting

Rep_index ranges from -1 to 1. A Rep_index of 1 indicates that the person has donated only to

the Republican Party, thereby being classified as a “pure” republican. Similarly, a Rep_index of -

9 While the denominator of Rep_index, the total dollar amount of political donations made by an individual,

represents the person’s political activeness, Rep_index itself measures the relative tilt of the person’s political

donations toward the Republican Party. Untabulated results show that top executives and independent directors with

a small absolute value of Rep_index are more politically active than those with a large absolute value of Rep_index

in terms of dollar amount of political donations and the number of political candidates they support. The political

candidates and committees we consider include various ranks such as those who run for senate, house and

presidential elections. 10

By construction, top managers and independent directors who donated equal amounts to both Republican and

Democratic Parties have a Rep_index of zero. Among the 8,349 politically active managers and 8,175 politically

active independent directors, only 150 managers and 104 directors donated equal amounts to Democratic and

Republican Parties. The fraction of these politically neutral managers and independent directors is fairly small (only

1.83% of politically active managers and 1.27% of politically active independent directors). Overall, our approach to

constructing the Rep_index is similar to the approaches used by Hutton, Jiang and Kumar (2011) and Hong and

Kostovetsky (2012).

18

1 indicates that the person has donated only to the Democratic Party, and is classified as a “pure”

democrat.

A.2. Step 2: Group-level Rep_index

Using the individual-level Rep_index from Step 1, we compute the group-level

Rep_indexes for 1) the top five executives and 2) independent directors. First, we define the

group-level Rep_index for the top five executives as the value-weighted average of each

executive’s Rep_index with the inverse value of their annual compensation rankings as weights

(top5_index_vw, hereafter). Second, we compute an equal-weighted average of Rep_index values

and denote it by rep_omean for the group of independent directors. For a CEO, we just use the

republican index assigned to the CEO and term the index ceorep_index.

Summary statistics for these group-level Rep_index values are provided in Panel A of

Table 1. CEOs are tilted toward the Republican Party with an average ceorep_index value of

0.200. A similar tendency is found in the group of top five executives, with top5_index_vw

having an average value of 0.133. The donation pattern for the group of independent directors is

a bit more symmetric, exhibiting an average value of 0.088 (rep_omean).

B. The Political Homophily Index (PHI) at the Firm-level

We compute the firm-level political homophily index, PHI, which is defined as one

minus the normalized Euclidian distance between the two groups’ average Rep_index values.

Formally, the values of the political homophily index between insider and outsider groups are

defined as

top5vw_out = 1 – | top5_index_vw – rep_omean |/2

19

ceo_out = 1 – | ceorep_index – rep_omean |/2

By construction, the PHI values for top5vw_out and ceo_out, can range between zero and

one. A PHI value of zero indicates the weakest political alignment between the two groups,

thereby implying that considerable heterogeneity in political orientation exists across the two

groups and, in turn, potentially in their opinions and value systems. A PHI value of 1 implies the

strongest group-to-group political opinion similarity, thereby potentially implying the greatest

similarity in opinions and value systems between the groups.

When we use the prior-based group-level Rep_index values to construct the PHIs, the

prior-based PHIs are computed similarly, as follows:

top5vw_out_prior = 1 – | top5_index_vw_prior – rep_omean_prior |/2

ceo_out_prior = 1 – | ceorep_index_prior – rep_omean_prior |/2

The summary statistics for top5vw_out and ceo_out are provided in Panel A of Table 1.

In general, PHI is skewed to the right, which means a higher degree of alignment in political

orientation.11

The top five executives and independent directors have an average top5vw_out

value of 0.831. This indicates that insiders and outsiders in our sample firms do not differ much

in their political opinions, thereby implying that a relatively high degree of political alignment

exists between managers and directors in our sample firms. A similar pattern is found for

ceo_out. Corresponding statistics for the prior-based PHIs, top5vw_out_prior and ceo_out_prior,

are similar, which indicates that the firm-level PHI values are persistent.

The group average approach we use in this study does not capture the effect of within-

group variations in values of the Rep_index. This within-group variation, if any, will decrease the

11

This property is not driven by politically inactive observations since we find similar patterns when we calculate

the summary statistics for top5vw_out and ceo_out, using only politically active observations.

20

level of political alignment between the two groups. To capture this effect, we measure the

distance in political orientation for all possible individual-to-individual pairs between top

executives and independent directors groups as a third alternative measure of PHI. These

alternative measures are denoted by top5vw_out_dyadic and ceo_out_dyadic. The summary

statistics for these two alternative PHI measures are also reported in Panel A of Table 1. As

expected, the average value of top5vw_out_dyadic is 0.709, which is lower than 0.831 (0.852),

the average value of top5vw_out (top5vw_out_prior) based on the group average approach. We

find a similar pattern for ceo_out_dyadic.

Correlations between the political measures and their correlation to the main financial

variables are provided in Panel B of Table 2. In Panel A of the table, we also show the

correlations between the PHIs and social network index, SNI_out. Interestingly, while PHIs and

SNI_out are positively correlated with each other, the correlation is weak (0.03 for top5vw_out

and 0.02 for ceo_out), suggesting that PHI and the social network index (SNI) capture different

dimensions of relationships that can potentially affect director independence. In Panel B of the

table, Q and roa are negatively correlated with top5vw_out and ceo_out, indicating a possible

valuation discount for firms with high degrees of political alignment (PHI) between corporate

insiders and independent directors.

V. Results

A. Valuation Results: PHIs and Firm Valuation

To see the effect of PHI on firm valuation, we run the following regression for firm i in

year t:

21

We control for top5_index_vw and ceorep_index in order to capture the marginal effect

of political alignment, PHI. Following the specification used in Yermack (1996), we include

board size (board_size), the board independence dummy variable (independent), current return

on assets (roa) and 1-year and 2-year lagged return on assets (l1roa and l2roa, respectively),

investment to assets ratio (inv), research and development expense to assets ratio (rnd) and the

natural logarithm of the inflation-adjusted book value of the firm’s assets (logasset) as controls.

All standard errors are clustered at the firm level.

The results are reported in Table 3. Panel A of Table 3 focuses on the PHI between top

five executives and independent directors, top5vw_out, as the main right-hand-side (RHS)

variable.12

Column (1) shows the result of a univariate regression of Tobin’s Q on top5vw_out.

Consistent with Hypothesis 1, we find a negative correlation between Q and top5vw_out, which

is statistically significant at the 1% level. The point estimate of -0.56 indicates that, for a one

standard deviation change in top5vw_out (0.127), Q is reduced by 0.071, which corresponds to

3.63% of the sample’s average Q value (1.960). Hence, this is an economically significant effect.

When we control year- and industry13

-fixed effects (Column (2)), we get similar results. The

coefficient on top5vw_out in column (2) is negative and statistically significant at the 1% level.

The point estimate of -0.54 is very close to -0.56 in column (1).

In column (3), we repeat the same regression with additional control variables---

12

When we repeat the regression analyses in Panel A of Table 3 using the equal-weighted PHI between top five

executives and independent directors, we get both qualitatively and quantitatively similar results. This implies that

our results in Panel A of Table 3 are not driven by the weighting scheme we used to construct top5vw_out. 13

We define the industry dummy using the 2-digit standard industrial classification (SIC) code.

22

top5_index_vw, board_size, independent, roa, l1roa, l2roa, inv, rnd and logasset. We still obtain

a negative coefficient on top5vw_out with a point estimate of -0.45, which is statistically

significant at the 1% level. With this point estimate, there is a 2.92% reduction in the Q ratio for

a one standard deviation change in top5vw_out. On the other hand, the coefficient on

top5_index_vw, the value-weighted republican index for the top five executives, is insignificant,

indicating that the republican status of the top executives is not significantly associated with any

valuation feedback from shareholders. Thus, while the political affiliation of managers per se

does not appear to affect firm valuations, the alignment of political orientation between managers

and outside directors has an adverse effect on firm valuations. Though the point estimates for

other control variables, board characteristics, and financial variables, are not presented in the

table, the point estimates have the expected signs and statistical significance consistent with the

values reported in Yermack (1996).14

In column (4), we control for firm-fixed effects, instead of SIC2-level industry-fixed

effects. We get -0.22 as a point estimate for top5vw_out with a p-value of 0.061 (t-statistic (t-stat,

hereafter) of -1.88). This confirms that constant firm-level factors that may have been omitted

from the analysis in previous columns (1) to (3) do not derive our results. It further confirms that

our results come from both cross-sectional and temporal covariation between top5vw_out and Q.

This significant temporal correlation between the two variables will be confirmed again in the

causal regression results reported in robustness checks in Section VI

14

board_size is significantly negatively associated with Tobin’s Q, which is consistent with findings in Yermack

(1996). Both roa (and its lagged values), inv and rnd are significantly positively correlated with Q as expected. The

point estimate for independent is overall statistically insignificant, consistent with the results in Hermalin and

Weisbach (1991), Yermack (1996), Bhagat and Black (2000) and Fracassi and Tate (2012). However, if we replace

the independent dummy variable with the percentage of independent directors on the board (board composition

variable as in Yermack (1996)), the point estimate of the variable is negative with t-statistic of -1.49. In that

regression, we control for both year- and SIC2 level industry fixed-effects. That result is comparable to the one

reported in Table2 of Yermack (1996) where the point estimate of board composition has a negative sign with a

statistical significance at the 1% level.

23

In column (5), we add area-fixed effects using the three-digit area code of each firm’s

telephone number, in addition to year- and SIC2 level industry-fixed effects. We obtain a

coefficient of -0.48 for top5vw_out, which is significant at the 1% level, similar to the results in

column (3).

The negative impact on Q of political alignment between the top five executives and the

independent directors may arise from investor concerns that such alignment in political beliefs

could result in the firm adopting strategies that entrench the top management. For instance, a

board that is politically aligned with top management may permit the managers to protect

themselves from the disciplinary effect of the market for corporate control, or not discipline them

for poor performance. To take into account the effects of the market for corporate control, we use

the Gindex as an additional control variable in column (6)15

. We still find a negative relation

between top5vw_out and Q and, in particular, the estimated coefficient on top5vw_out, -0.44, in

this column does not change much from the value of -0.45 reported in column (3), where we do

not control for the Gindex. This indicates that PHI’s adverse impact on Q does not arise from

concerns of managerial entrenchment linked to the market for corporate control, but from

internal agency concerns. We confirm this conjecture in our subsequent analysis of CEO

turnover/compensation and corporate fraud.

In column (7), we run the regression in column (3) after excluding firm-year observations

where neither top 5 executives nor independent directors are politically active (i.e., observations

where all individual Rep_index values in each group are equal to zero, thereby being perfectly

aligned with each other). When we regress Tobin’s Q on top5vw_out using this sub-sample, we

find that the coefficient on top5vw_out is still negative and statistically significant at the 1%

15

We get similar results when we use the Eindex introduced by Bebchuk, Cohen and Ferrell (2009).

24

level.16

In columns (8) and (9), we utilize different measures of top5vw_out. First, in column (8),

we use top5vw_out_prior which is constructed using the donation information available only up

to each firm’s fiscal year end, thereby freeing our analysis from the look-ahead bias issue.17

Next,

we control for top5_index_vw_prior, which is the republican index using the same donation

information for each year, for each firm. We get a coefficient of -0.38 for top5vw_out_prior with

a t-stat of -2.24 in this regression, which is significant at the 5% level.

Finally in column (9), we replace our default PHI, top5vw_out, with a dyad-based PHI

between the top five executives and independent directors---top5_vw_out_dyadic. We get an

even stronger negative correlation between top5_vw_out_dyadic and Q (a point estimate of -0.74

with t-stat of -4.8718

), indicating that the within-group variation of Rep_index does not work

against our main findings. It actually captures an additional negative effect of political alignment

between insiders and independent directors on Q.

Given that CEOs have the highest decision rights among insiders, we also check to see if

the results in Panel A are driven primarily by CEOs rather than other insiders. We repeat the

Panel A regressions, replacing top5vw_out with the CEO-specific PHI measure, ceo_out. Results

are reported in Panel B of Table 3. In column (1), where we use a simple OLS specification

16

If we further exclude the firm-year observations where only one group’s average Rep_index is zero (13,129 firm-

year observations), we still get results that are qualitatively and quantitatively similar (-0.38 with t-stat of -2.89).

This confirms that assigning zero to the politically inactive individuals does not systematically bias our results.

Further analyses of these zero assignments to politically inactive individuals are discussed in Section VI. 17

Our main PHI measures, top5vw_out and ceo_out, are constructed using the full donation history of each

individual. Therefore, we measure each individual’s political orientation by borrowing information from the future.

This approach is intended to minimize the measurement errors that may arise with early year samples, since, without

considering the accumulated donation information, we may incorrectly identify an individual, who makes a small

initial donation to the Republican (Democratic) Party, but changes political affiliations later, as being strongly

republican (democrat)-oriented. 18

The standard deviation of top5vw_out_dyadic is 0.116. For a one standard deviation change in top5vw_out_dyadic,

the Q ratio is reduced by 4.4% from its sample average.

25

without any controls, we get -0.23 as a point estimate for ceo_out, implying that a one standard

deviation change in ceo_out (0.192) results in a decline in Q corresponding to a 2.25% reduction

from its sample average value. The result is robust to the inclusion of further controls (columns

(2) to (6)). This CEO-specific PHI explains 62% (2.25%/3.63%) of the negative effect of

top5vw_out reported in Panel A, suggesting that a significant portion of the negative effect of

political alignment between insiders and independent directors arises from the political

alignment of CEOs with independent directors.19

Once again, as was found for the group of top

five executives, the main effect of the ceorep_index is not statistically significant.

B. Valuation Results: Demographic Similarity and Social Networks vs. Political Alignment

We next establish our main valuation result that the negative association between PHI

and Q documented in Section V.A. is robust to controlling for demographic similarities and

social networks between corporate insiders and independent directors.

First, based on the evidence documented in Westphal and Zajac (1995) suggesting that

CEOs tend to be more entrenched when demographic similarities exist between CEOs and

directors, we examine whether the negative relation between Tobin’s Q and top5vw_out is robust

to the inclusion of demographic similarities, in addition to the control variables that we used in

Table 3 (Gindex, board_size, independent, roa, l1roa, l2roa, inv, rnd and logasset). In particular,

we control for similarities in ages and gender between the top five executives and independent

directors by including age_diff_top5_out and female_diff_top5_out. These two variables are

defined as the absolute value of the difference in average age and average value of a gender

19

We find a similar ratio for the CEO effect relative to that for the group of top five managers when we use the

equal-weighting scheme to define the top five executives’ group-level Rep_index, which suggests that none of the

main results reported in Table 3 is driven by the value-weighting scheme that we use.

26

dummy variable, which takes a value of one when the individual is female and zero otherwise,

between the groups of top five executives and independent directors. The results in column (1) of

Panel A of Table 4 support the findings in Westphal and Zajac (1995). That is, there is a positive

and statistically significant association between age_diff_top5_out and Q, while there is a

positive but statistically insignificant relation between female_diff_top5_out and Q.20

Moreover,

we find that the negative relation between top5vw_out and Q is robust to controlling for

age_diff_top5_out and female_diff_top5_out. We also find that the estimated coefficient of

top5vw_out, -0.44, is little changed from -0.44 in column (6) of Panel A of Table 3 where

age_diff_top5_out and female_diff_top5_out are not controlled. This indicates that the economic

magnitude of the impact of top5vw_out on Q is not subsumed by age_diff_top5_out and

female_diff_top5_out.

Second, Fracassi and Tate (2012) (FT (2012), hereafter) find that a firm’s market value

tends to be lower when its CEO is socially connected to independent directors. To rule out the

possibility that the results in Table 3 are driven by social ties between CEOs and independent

directors, we conduct a series of robustness checks of our results by controlling for the social

network index (SNI) introduced by FT (2012). We use the BoardEx database to construct two

social network variables following the approach described in FT (2012) --- 1) Ind-Connections

and 2) SNI_out.21

The former counts the number of directors who are both socially and

traditionally independent from the CEO, and the latter denotes the fraction of independent

directors who are socially tied to the CEO. The BoardEx database prior to the year 2000 is

incomplete, with only a small number of firm-years with at least one social tie between CEOs

20

Positive coefficients for age and gender differences imply that there is a negative effect of age/gender similarity

between top executives and independent directors on Q. 21

FT (2012) also use the BoardEx database when they construct the social network variables, Ind-Connections and

SNI_out. For more details of SNI construction, see FT (2012).

27

and the independent directors. To make our analysis meaningful, we use firm-year observations

at the intersection of our original master dataset and BoardEx in the post-2000 period. The same

post-2000 period restriction is also imposed on the test sample used in FT (2012).

Table 4 reports the results with the inclusion of social tie variables (Panel A for top five

executives and Panel B for the CEO). In column (2) of Panel A, we first replicate the results in

FT (2012) where they use board_size, independent, logasset, market leverage ratio (tdm) and

Gindex as controls in addition to the social tie variable, Ind-Connections. In this column, the

point estimate of Ind-Connections is 0.02, which is positive and statistically significant at the 5%

level. In column (3) where we replace Ind-Connections with another social tie variable, SNI_out,

the point estimate for SNI_out is -0.33 and statistically significant at the 1% level. These results

are comparable to those reported by FT (2012).22

In columns (4) and (5) of Panel A, we include top5vw_out and top5_index_vw on the

RHS of regressions reported in FT (2012). We find that the estimated coefficient on top5vw_out

is -0.70 with a t-stat of -3.43 in column (4) and -0.68 with a t-stat of -3.25 in column (5). In

column (6) of the panel where we replace top5vw_out and top5_index_vw with their look-ahead-

bias free counterparts, top5vw_out_prior and top5_index_vw_prior, respectively, we still get a

negative coefficient on top5vw_out_prior, which is statistically significant at the 1% level. From

the results reported in columns (2) to (6) of Panel A of Table 4, it appears that the negative

relationship between PHI and Tobin’s Q is unlikely to be driven by the negative impact on Q of

social ties between the CEO and independent directors previously documented in FT (2012).

Importantly, these results confirm that our political alignment variable captures implicit

relationships between managers and directors that are distinctly different from those captured by

22

The number of observations, 7,601, is also comparable to the sample size in FT (2012), 7,159.

28

social ties or demographic similarities.

Another distinct dimension of political alignment is that, in contrast to the social ties

measures, it enables us to identify firms where top managers and independent directors have

conflicting political views. A low value of PHI can be found only in firms where managers and

directors are politically active but support different political parties. As PHI is negatively

associated with Q, lower values of PHI improve Q because directors may have different value

systems compared to management and thus, potentially, may be more inclined to monitor the

management.

As additional robustness checks, in column (7) of Panel A of Table 4 we run our default

regression with the year- and industry-fixed effects together with the following additional

controls --- Gindex, board_size, independent, roa, l1roa, l2roa, inv, rnd and logasset --- for the

sub-sample where none of the firm’s independent directors are socially connected to the firm’s

CEO in the post year-2000 period. We find that the estimated coefficient on top5vw_out is -0.36

and it is statistically significant at the 10% level. Finally, in columns (8) and (9), we use the same

default regression specification once again while additionally controlling for SNI_out. We use

top5vw_out in column (8), while we use top5vw_out_prior in column (9). We continue to find

the negative relation between PHI and Q ratio in these two columns, which confirms that PHI’s

effect on Q is quite robust to the regression specifications. However, the point estimates for

SNI_out in columns (8)-(9) turn out to be statistically insignificant, indicating that SNI’s effect

on Q is sensitive to the specification used in the regressions. In Panel B of Table 4, we conduct

the same robustness check for the CEO-specific PHIs, ceo_out and ceo_out_prior. Our results

continue to be robust to the inclusion of age and gender similarities as well as social network

variables in the regression specifications. From the results reported in both Panel A and B of

29

Table 4, as before, we conclude that our political value alignment measure, PHI, captures

uniquely different effects from that captured by age and gender similarities or social network

measures.

From this point onwards in all tables, we additionally control for either Ind-Connections

or SNI_out in our regressions. However, in order not to lose any observations in our sample prior

to year 2000, we assign zero values to the social tie variables if we do not find any social

connections between the CEO and the independent directors using the BoardEx database.23

C. PHI and Firm Operating Performance

In this section, we investigate causes for the negative effect of political alignment

between managers and directors on Q, by running the following regression of return on assets

(roa) for firm i in year t:

This regression specification is similar to that used in Hutton, Jiang and Kumar (2011)

who investigate the effect of the republican status of top management team on a firm’s operating

performance. If the point estimate for PHI, b1, turns out to be significantly negative, it would

suggest that politically aligned top executives and independent directors operate firms

inefficiently, resulting in a reduction in operating profitability. This can serve as a rationale for

the lower Q ratio for the firms with high levels of PHI. Controls include logasset, Q, tda, inv,

23

This implies that our testing sample in the following analyses does not have to be restricted to 7,196 as reported in

columns (8) and (9) in Panel A of Table 4. However, the results in the later reporting tables are robust to the

exclusion of firm-years prior to year 2000. Those results are available upon request.

30

and both year- and SIC2 level industry fixed effects24

, and standard errors are clustered at the

firm level.

Results are reported in Table 5. In column (1) of the table, we find that firms with high

top5vw_out tend to have significantly lower roa. A one standard deviation change in top5vw_out

(0.127) reduces annual roa by 22 bps. To examine whether this lower roa is due to lower risk

taking by top executives who are politically aligned with independent directors, in columns (2)

and (3) of the table, we use 12-month rolling month net equity return volatility (eqvol) and 5-

year window cash flow volatility (cfvol), respectively, as left-hand-side (LHS) variables. The

significantly lower roa does not appear to arise due to lower risk taking by insiders (positive

point estimates for top5vw_out in columns (2) and (3)).

In columns (4) to (6) where we repeat the same regressions using ceo_out and

ceorep_index as the primary RHS variables, we find that ceo_out has similar relations with roa,

eqvol and cfvol. In column (4) where roa is the LHS variable, the point estimate of -0.016 for

ceo_out indicates that a one standard deviation change in ceo_out (0.192) results in a reduction

of annual roa by 31 bps, and this effect is even stronger than the effect we find with top5vw_out.

This indicates that the CEO’s political alignment with independent directors appears to exert a

first-order effect on lowering the firm’s operating performance.25

D. Managerial Entrenchment and PHI

In this section we investigate governance rationales to explain the poor valuation and

24

Following Hutton, Jiang and Kumar (2011), we do not control for the board characteristics, and thus, also drop out

SNI_out in this regression. 25

It is worth noting that the point estimates for top5_index_vw and ceorep_index have the expected signs and

significances for roa and the risk measures, which are consistent with the results reported by Hutton, Jiang and

Kumar (2011) (+ for roa and – for both eqvol and cfvol). The negative sign for the point estimate of eqvol and cfvol

are interpreted as evidence of the conservatism of republican top executives.

31

lower operating efficiency of firms whose top management and independent board members are

politically aligned. To support the argument that the negative impact on Q of greater political

alignment between directors and managers arises from weaker board independence and greater

managerial entrenchment, we examine the effect of political alignment on CEO turnover and pay

performance sensitivity.

D.1. CEO Turnover

To test Hypothesis 2, we examine whether CEOs in firms with greater political alignment

are replaced infrequently, even when the firm performs poorly over a relatively long time period.

We run the following linear regression26

for firm i in year t:

This specification is similar to the models used in Warner, Watts, and Wruck (1988) and

Yermack (1996). is a dummy variable that equals one if CEO turnover occurs for

firm i in the second half of fiscal year t or in the first half of the subsequent fiscal year t+1, and

zero otherwise. The variable cum4yr_eqret denotes the cumulative past 4-year annual stock

returns under the incumbent CEO, prior to his or her dismissal. This requires that the tenure of

the dismissed CEO be greater than or equal to 4 years. To control for demographic similarities

and social network effects, we include age_diff_top5_out (or age_diff_ceo_out),

26

To directly infer the second-order partial effect of PHI on the marginal probability change in CEO dismissal

following poor stock market performance, we use only linear probability models in this regression. When non-linear

probability models such as probit and logit models are used, the positive point estimates of the interaction terms,

top5vw_out*cum4yr_eqret and ceo_out*cum4yr_eqret, cannot be directly interpreted as net negative marginal

effects on the CEO dismissal. This is because the non-linearity in their probability density functions. See Ai and

Norton (2003) for a more detailed explanation of this issue.

32

female_diff_top5_out (or female_diff_ceo_out) and SNI_out, together with their interaction terms

with cum4yr_eqret on the RHS of the regression. We also control for ceo_holding, ceo_age and

both year- and industry-fixed effects. As in Yermack (1996), we add a dummy variable for CEO

ages of 64, 65, and 66 to incorporate possible mandatory retirement effects on the CEO

dismissals. We cluster standard errors at the firm level.

Table 6 shows the results. In column (1) of the table27

, we first run the linear probability

regression for top5vw_out. The point estimate for cum4yr_eqret (-0.107) is negative and

significant, indicating that poor past cumulative stock market performance is likely to lead to

CEO dismissals. However, this likelihood decreases when the top five executives are more

politically aligned with independent directors. The coefficient on top5vw_out*cum4yr_eqret---

the interaction term between cum4yr_eqret and the top five executives’ alignment index,

top5vw_out---is 0.094, which is positive and statistically significant at the 1% level. The

magnitude of the coefficient of the interaction term is comparable to the magnitude of the

negative coefficient on cum4yr_eqret, -0.107, implying that high political alignment between the

top management team and independent directors substantially reduces the threat of CEO

dismissal following poor firm performance.

In column (2) of Table 6, we repeat the same linear probability regression using the CEO

specific PHI measure, ceo_out.. The result is similar to that reported in column (1). We also get a

significant positive coefficient for the interaction term, ceo_out*cum4yr_eqret, indicating that

political alignment between the CEO and independent directors is associated with a reduction in

27

We report only the point estimates for the two main variables of interest, cum4yr_eqret and the interaction term,

top5vw_out*cum4yr_eqret (also, ceo_out*cum4yr_eqret in column (2)) for brevity. Even though not reported, we

control for the stand-alone PHI variables in this regression.

33

the disciplining effect of involuntary CEO turnover following poor performance.28

D.2. CEO Pay Performance Sensitivity and PHI

In this section, we investigate pay and incentive structures for CEOs who are politically

aligned with directors. We focus on the CEO specific PHI, ceo_out, to see whether this is

associated with weaker CEO pay performance sensitivity (Hypothesis 3).29

We run the following

PPS regression (Jensen and Murphy (1990) and Murphy (1999)) for firm i in year t:

The LHS variable in this regression is the annual change in the inflation-adjusted amount

of salary&bonus (or total_comp denoted as tdc1 in ExecComp) in year-1992 thousand dollars. In

the above equation, shareholders’ value change on the RHS is the annual change in the inflation-

adjusted shareholders’ value in year-1992 million dollars. We control for board_size, the number

of independent directors who are not socially connected to the CEO (Ind-Connections) 30

and

also its interaction term with shareholders’ value change. To control for the effects of

demographic similarities, we also include the age_diff_ceo_out and female_diff_ceo_out

variables, along with their interaction terms with shareholders’ value change on the RHS of the

regression. Finally, we control for independent directors’ stock ownership (out_holding) and

various CEO characteristics (ceo_is_chmn_pres, ceo_holding, ceo_tenure), the 1-year lagged

28

In untabulated regressions, we re-run the same regression, excluding non-performance related CEO turnover such

as retirement or death of the CEO. The results are similar to what is reported in Table 6, both qualitatively and

quantitatively, confirming that the results are not driven by non-disciplinary CEO departures. 29

The CEO’s political alignment with independent directors can explain a substantial fraction of the negative effect

on firm valuations of top five executives’ political alignment with independent directors. 30

When we use SNI_out, instead of Ind-Connections, the results are quantitatively and qualitatively similar.

34

financial ratios (l1logasset and l1Q) and year- and SIC2-level industry-fixed effects. We cluster

standard errors at the firm level.

We report these results in Table 7. In column (1), we use the annual change in the

inflation-adjusted dollar amount of salary&bonus on the LHS and find that shareholders’ value

change has a coefficient of 0.03, implying a PPS of 0.03 for each thousand dollar increase in

shareholders’ value when the CEO is not politically aligned with independent directors. The

point estimate for the interaction term, ceo_out*shareholders’ value change is -0.03, which is

significant around the 5% level (t-stat of -2.08). Given that a one standard deviation change in

ceo_out is 0.192, we can see that the point estimate for the interaction term corresponds to a

0.0058 reduction in PPS, which, in turn, corresponds to 19.3% of the PPS for a CEO who is not

aligned with the independent directors (0.0058/0.03=19.3%). This is an economically significant

drop in PPS.

In column (2), when we use the annual change in the inflation-adjusted dollar amount of

total compensation (total_comp), we find no statistically significant relationship between PPS

and ceo_out.

Overall, it appears that cash compensation contracts have a lower emphasis on

performance related pay, and, thus, provide CEOs with more slack, when CEOs are politically

aligned with independent directors. These results confirm that political alignment has a distinct

effect on managerial compensation that is different from the results documented by Hwang and

Kim (2009) and Westphal and Zajac (1995) for social ties and demographic similarities.

D.3. Corporate Fraud

One potential consequence of the impairment in director independence arising from their

35

increased connections with managers is the likelihood of greater earnings manipulation. If firms

commit fraud and are detected, they are likely to incur increased legal liability expenses (Karpoff,

Lee and Martin (2007, 2008)). Thus, if firms with a higher degree of political alignment between

directors and managers are more likely to engage in fraud, they may experience a valuation

discount because of higher expected legal costs. To test Hypothesis 4, that managers of firms

with higher political alignment between directors and managers are more likely to commit fraud,

we run the following probability model using a corporate fraud dummy variable for firm i in year

t:

is a dummy variable taking a value of one if a given firm i is involved in an on-

going lawsuit in year t, and zero otherwise. For our analysis, we use the sample of corporate

frauds described in Dyck, Morse and Zingales (2011).31

The sample includes a total of 501 high-

profile, alleged corporate fraud cases filed against big U.S. corporations with at least $750

million in assets in the year prior to the end of class period. These cases are associated with

settlements of at least $3 million, and, thus, are likely to have a material impact on shareholder

value. The data cover the period from 1996 to 2004. Control variables for this regression include

board_size, independent, Change in SNI_out, ceo_tenure, ceo_age, ceo_holding, lagged Q (l1Q),

lagged natural logarithm of inflation-adjusted book value of assets (l1logasset), and lagged roa

(l1roa)32

. It should be noted that we use Change in SNI_out, instead of SNI_out, following the

specification used in Dey and Liu (2011), who document a higher likelihood of a firm restating

earnings when the firm’s CEO is socially connected to the directors on the board and audit

31

The data are obtained from Professor Adair Morse’s website, http://faculty.chicagobooth.edu/adair.morse/ 32

Adding age_diff_top5_out and female_diff_top5_out doesn’t change the results either qualitatively or

quantitatively. They are both statistically insignificant.

36

committee. We cluster standard errors at the firm level.

In column (1) of Table 8, we first run a linear probability model without controlling for

year- and industry-fixed effects. We find that high political alignment between top executives

and independent directors leads to more frequent corporate fraud filings (a point estimate of 0.06

for top5vw_out with a t-stat of 2.20). This result is robust to the inclusion of both year- and

single-digit SIC (SIC1) level industry fixed effects (column (2) of Table 8).

In columns (1) and (2), Change in SNI_out positively predicts the probability of corporate

fraud. The point estimate of 0.04 in column (1) is significant at the 10% level, while the

coefficient of 0.02 in column (2) is positive but insignificant , which are consistent with results

reported in Dey and Liu (2011).

In columns (3) and (4) of the same table, we run probit regressions instead of the linear

probability model. We find that the marginal probability of a firm committing fraud increases by

5% for a small increase in top5vw_out. (column (3)). This is an economically significant impact

given that Change in SNI_out increases the probablility of fraud by just 3%. In column (4),

adding both year- and SIC1 level industry fixed effects does not alter this result. Interestingly,

Change in SNI_out has an insignificant coefficient, again, when we control for year- and

industry-fixed effects in that column.

Chidambaram, Kedia and Prabhala (2010) provide evidence that different types of ties

can have different effects on the likelihood of fraud. They find that non-professional social ties

between managers and directors increase the likelihood of fraud whereas professional

connections decrease it, suggesting that the origin of the connections between managers and

directors matters in determining whether these factors impair board independence or not.

Overall, our findings indicate that when the source of the connectedness is political alignment

37

between the CEOs and directors, it weakens board independence and, thus, potentially provides

an internal governance rationale for the valuation discounts experienced by firms with high PHI

values. This effect of PHI exists even after controlling for the change in the SNI index, further

distinguishing the effects of social ties and demographics from political alignment between

directors and managers.

VI. Robustness Checks

A. Valuation Results: Political Ideology vs. Political Opportunism

It could be argued that our results reflect the value enhancement of a firm through

strategic political donations rather than the degree of alignment of each individual’s political

orientation.33

To support our argument that our individual political orientation measure,

Rep_index, indeed captures a person’s political orientation rather than strategic political

donations motivated by political opportunism, we re-run our valuation regression in Table 3

using two alternative political orientation measures for each individual. To eliminate the

possibility that individuals switch their main political orientation over different election cycles,

we construct a Rep_index for each corporate individual for each election cycle, and then average

the election cycle-specific Rep_indexes over all election cycles during which the individual made

donations. The resulting measure of individual political orientations is denoted by

Rep_index_cycle_ave, and the group-level averages of the new individual political orientation

measure are denoted by top5_index_vw_cycle_ave and rep_omean_cycle_ave. Finally, the firm-

level PHI corresponding to this new approach is denoted by top5vw_out_cycle_ave. Column (1)

33

Cooper, Gulen and Ovtchinnikov (2010) document that the firms supporting more political candidates through

PAC donations earn higher future returns, and this effect is stronger for the firms that support a greater number of

candidates who hold offices in the same state that the firm is based.

38

of Panel A of Table 9 shows the results using these new political measures. The statistically

negative relationship between the new PHI and Q is still confirmed.

Among politically active individuals, the politically polarized ones who donate only to a

single political party, either Republican or Democratic, for all election cycles, are likely to be

true republicans or democrats, respectively. Using the full donation history for each individual,

we assign 1 (-1) to the “polarized” republicans (democrats), and assign zero to all others. This

alternative measure of individual political orientation is denoted by rep_polarizer, and the group-

level averages of this measure are denoted by top5_polarizer_vw and rep_omean_polarizer.

Finally, the firm-level PHI corresponding to this polarizer-based approach is denoted by

top5vw_out_polarizer. Column (2) of Panel A of Table 9 reports the results with these new

political measures, where we still get a statistically negative relationship between the new PHI

and Q.

Lastly, we stay with our default political measures---top5vw_out and top5_index_vw, but

additionally control for political connectedness measures. In particular, we construct the

following measures of political connectedness---1) top5_connect_vw, 2) connect_omean and 3)

pac_connect. These measures capture the political connectedness of the top five executives,

independent board members and the firm’s PAC, respectively. We first measure individual-level

political connectedness by dividing the dollar amount of individual political donations during a

most recent election cycle corresponding to a given fiscal year by the median dollar value of

individual political donations in the election cycle. Based on this measure of individual-level

political connectedness, we construct group-level political connectedness measures --- the value-

weighted average of individual-level political connectedness measures for the top five executives

group (top5_connect_vw) and the equal-weighted average for the independent directors’ group

39

(connect_omean). Finally, pac_connect is a similarly constructed political connectedness

measure of a firm’s PAC. Column (3) of Panel A of Table 9 report the regression results. The

negative relation between PHI and the Q ratio is robust to controlling for political connectedness

measures.

We also confirm, though not reported in the paper, that our main results for CEO-specific

political measures (ceo_out and ceorep_index) are also robust to these additional specifications.

Overall, all these results confirm that the negative relation between PHI and Q, documented in

Table 3, are unlikely to be driven by a firm’s political connections established through political

donations.

B. Valuation Results: Causal Regressions

One concern with our valuation results is that the relationship between PHIs (i.e.,

top5vw_out, ceo_out) and value could be endogenous. To show that the negative relationship

between PHI and Q is not reverse-causal,34

and is not driven by any omitted time-invariant firm-

level characteristics that may be correlated with both PHIs and Q, we run a change-on-change

regression which is similar to what is done in Chava, Livdan and Purnanandam (2009) and

Hutton, Jiang and Kumar (2011). We use the same year- and industry-fixed effects specification

used in Column (3) of both panels in Table 3 with the full set of control variables. The annual

changes in all variables are considered in this change-on-change regression, and we cluster the

standard errors at the firm-level.

34

A lower firm valuation in a given year, for a given firm, may induce greater political alignment between the

insiders and the independent directors. For instance, if a firm’s poor performance is blamed on the fiscal, monetary

or regulatory policies of the incumbent government, managers and directors may be aligned in their desire to vote

for the other party.

40

The results are presented in columns (1) and (2) of Panel B of Table 9 for the changes in

top5vw_out (ctop5vw_out) and ceo_out (cceo_out), respectively. Both columns (1) and (2) show

statistically significant (at the 5% level) negative correlations between ctop5vw_out (cceo_out)

and changes in Q.35

In column (3), to further eliminate possibilities of any endogenous change of

board composition in expectation of poor future firm performance, we only focus on the

subsample that satisfies the following two conditions: 1) independent director departure occurs

due to mandatory retirement or death, or 2) CEO is replaced due to performance unrelated

reasons such as retirement or death. To further ensure that the composition of insiders and their

political orientations do not change in this subsample, we focus only on the CEO-specific PHI---

ceo_out.36

The director mandatory retirement and death information is obtained from the

AuditAnalytics database, and the same information for CEOs is obtained from ExecuComp.37

The subsample that satisfies the above conditions consists of only 615 firm-year observations.

Imposing further restrictions on the availability of the changes in control variables, we finally

come up with 407 firm-year observations. Even with this small sample, we get a statistically

significant negative relationship at the 10% level between the change in ceo_out and the change

in Q.

Lastly, we investigate whether poor firm performance in the current year affects

individual political donation patterns in the future. The default political measures we use are

based on the full donation history of each individual. Thus, the results in the first three columns

35

In this regression, we get strong statistical significance even without excluding zero-valued changes in all

regression variables. Given the persistence of the LHS and the main RHS variables, our result provides relatively

strong support for the temporal correlation link from the political variables, PHIs, to Tobin’s Q. 36

We restrict this analysis to ceo_out alone since it is easier for us to make sure there is no change in the republican

index level for insiders when we focus only on the CEO. 37

We include only the director departure observations where the departing reasons are clearly stated as either

“Mandatory Retirement Policy” or “Personal/Health Reasons” in AuditAnalytics. Director departures without these

specific reasons are excluded from our testing sample.

41

of Panel B of Table 9 would be still subject to this unresolved endogeneity issue. To overcome

this drawback, we run a two stage least squares regression using lagged values of prior-based

measures---top5vw_out_prior and top5_index_vw_prior---as instruments for top5vw_out and

top5_index_vw. We increase the lagging intervals from 3 years to 6 years in this test. Unless the

firm’s insiders correctly anticipate the firm’s future performance over a 3- to 6-year time

horizon38

, adjusting the composition of independent directors, and ensuring that these directors

make political donations to the same political party that the insiders contribute to, these two

instruments satisfy the exclusion conditions. Due to the persistence in individual political

donation patterns, it is unlikely that the 3- to 6-year lagged values of top5vw_out_prior and

top5_index_vw_prior are weak instruments for top5vw_out and top5_index_vw.39

However, they

are unlikely to be correlated with the firm’s performance over the 3- to 6-year horizon.

The results are reported in columns (4) to (7) of Panel B of Table 9. We confirm that all

the point estimates for top5vw_out in columns (4) to (7) are negative and statistically significant

at least at the 10% level.

VII. Conclusions

We study how the similarity in political beliefs between top executives and independent

directors affects firm value and managerial entrenchment. Using a sample of 2,693 firms from

38

In the untabulated regression results, we regress top5_vw_out on 1-year to 3-year lagged annual net percentage

equity returns, ann_eq_ret, (or roa, or Q), while controlling for board_size, independent, out_age, out_tenure,

ceo_age, ceo_tenure, tda, logasset with year- and SIC2 level industry-fixed effects. Standard errors are clustered at

the firm level. We find only 1-year lagged performance/valuation variables matter statistically for the subsequent

year’s top5_vw_out, indicating that our design with minimal 3 years lagging interval is less likely to be subject to

endogenous board composition adjustments following poor firm performance/valuation. From the autoregressive

regressions for Q and roa, we also confirm that the 3-year lag is a valid choice to prevent top management from

perfectly predicting the firm’s future valuation/performance. 39

In the first stage regression, 6-year lagged top5vw_out_prior and top5_index_vw_prior have t-stats of 4.19 and

14.47, respectively. Even though standard errors are not corrected for heteroskedasticity, these two instruments are

not likely to suffer from weak instrument problems.

42

1996 to 2009, we find lower Tobin’s Q, and a lower return on assets for firms with a high degree

of political alignment between top executives and independent directors. We also provide

evidence that CEOs, who are politically aligned with independent directors, are entrenched.

Politically aligned CEOs are less likely to be fired following long-term poor performance, are

more likely to have weaker pay-performance sensitivity, and also more likely to be involved in

high-profile corporate fraud.

Overall, our results indicate that political alignment can be a signal of the existence of

similar attitudes and beliefs between managers and directors, which can potentially impair the

independence of board members. Further, the association of manager-director political alignment

with managerial entrenchment and reduction in corporate value is robust to controlling for social

networks and demographic similarities between these groups. This result indicates that political

alignment captures implicit relationships between directors and managers that are unique and

distinctly different from previously documented results on the impact of social networks. Finally,

our finding in this paper, that political alignment, a signal of intangible connections between

managers and directors, can potentially impair director independence, raises doubts about the

effectiveness of legislation, such as SOX, which strives to ensure that directors on critical

committees, such as the Audit Committee, are independent.

43

References

Adams, R.B., and D. Ferreira, 2007. A Theory of Friendly Boards. Journal of Finance 62: 217-

250.

Ai, C., and E. C. Norton, 2003. Interaction Terms in Logit and Probit Models. Economics Letters

80: 123-129.

Bebchuk, L., A. Cohen and A. Ferrell, 2009. What Matters in Corporate Governance?. Review of

Financial Studies 22: 783-827.

Bhagat, S., and B. Black, 2002. The Non-Correlation Between Board Independence and Long-

term Firm Performance. Journal of Corporation Law 27: 231-274.

Burris, V., 2001. The Two Faces of Capital: Corporations and Individual Capitalists as Political

Actors. American Sociological Review 66: 361-381.

Chava, S., D. Livdan, and A. Purnanandam, 2009. Do Shareholder Rights Affect the Cost of

Bank Loans? Review of Financial Studies 22: 2973-3004.

Chidambaram, N.K., S. Kedia and N.R. Prabhala, 2010. CEO-Director Connections and

Corporate Fraud. Unpublished Working Paper, Fordham University, Rutgers University and

University of Maryland.

Cooper, M., H. Gulen, and A. Ovtchinnikov, 2010. Corporate Political Contributions and Stock

Returns. Journal of Finance 65: 687-724.

Dey, A., and X. Liu, 2011. Social Connections, Stock-based Compensation and Director

Oversight. Unpublished Working Paper, University of Minnesota and University of Texas,

Dallas.

Dyck, A., A. Morse, and L. Zingales, 2010. Who Blows the Whistle on Corporate Fraud?.

Journal of Finance 65: 2213-2253.

Fracassi, C., and G. Tate, 2012. External Networking and Internal Firm Governance. Journal of

Finance 67: 153-194.

44

Gompers, P.A., J.L. Ishii, and A. Metrick, 2003. Corporate Governance and Equity Prices.

Quarterly Journal of Economics 118: 107-155.

Hermalin, B., and M. Weisbach, 1991. The Effects of Board Composition and Direct Incentives

on Firm Performance. Financial Management 20:101-112.

Hong, H., and L. Kostovetsky, 2012. Red and Blue Investing: Values and Finance. Journal of

Financial Economics 103: 1-19.

Huckfeldt, R., and J. Sprague, 1995. Citizens, Politics and Social Communication: Information

and Influence in an Election Campaign. New York: Cambridge Univ. Press.

Hutton, I., D. Jiang, and A. Kumar, 2011. Conservative or Liberal? Managerial Political

Orientation and Corporate Policies. Unpublished Working Paper, Florida State University and

University of Miami.

Hwang, B., and S. Kim, 2009. It Pays to Have Friends. Journal of Financial Economics 93: 138-

158.

Jensen, M.C., and K.J. Murphy, 1990, Performance Pay and Top-Management Incentives.

Journal of Political Economy 98: 225-264.

Karpoff, J.M., S.D. Lee, and G.S. Martin, 2007. The Legal Penalties for Financial

Misrepresentation. Unpublished Working Paper, University of Washington, Texas A&M

University and American University.

Karpoff, J.M., S.D. Lee, and G.S. Martin, 2008. The Cost to Firms of Cooking the Books.

Journal of Financial and Quantitative Analysis 43: 581-612.

Knoke, D., 1990. Networks of Political Action: Toward Theory Construction. Soc. Forces 68:

1041-63

Larcker, D., S. Richardson, A. Seary, and A. Tuna, 2007. Director Networks, Executive

Compensation and Organizational Performance. Unpublished Working Paper, Stanford

University, London School of Business and Simon Fraser University.

45

McPherson, M., L. Smith-Lovin, and J.M. Cook, 2001. Birds of a Feather: Homophily in Social

Networks. Annual Review of Sociology 27: 415-444.

Murphy, K. J., 1999. Executive Compensation. Handbook of Labor Economics: 2485-2563.

Pachucki, M.A., and R.L. Breiger, 2010. Cultural Holes: Beyond Relationality in Social

Networks and Culture. Annual Review of Sociology 36:205-224.

Reynolds, L.T., and N.J. Herman-Kinney, 2003. Handbook of Symbolic Interactionism. Altamira

Press.

Romano, R., 2005. The Sarbanes-Oxley Act and the Making of Quack Corporate Governance.

Yale Law Journal 114: 1521-611

Srivastava, S.B., and M.R. Banaji, 2011. Culture, Cognition, and Collaborative Networks in

Organizations. American Sociological Review 76: 207-233.

United States Bureau of Labor Statistics, website: http://www.bls.gov/cpi/data.htm

United States Federal Election Commission, website: http://fec.gov/.

Verbrugge, L. M., 1977. The Structure of Adult Friendship Choices. Soc. Forces 56: 576-97.

Warner, J.B., R.L. Watts, and K.H. Wruck, 1988. Stock Prices and Top Management Changes.

Journal of Financial Economics 20: 461-492.

Westphal, J.D., and E.T. Zajac, 1995. Who Shall Govern? CEO Board Power, Demographic

Similarity, and New Director Selection. Administrative Science Quarterly 40: 60-83.

Yermack, D. L., 1996. Higher Market Valuation of Companies with a Small Board of Directors.

Journal of Financial Economics 40: 185-212.

46

I. Tables

Table 1. Summary Statistics

The definitions of political measures (Panel A) are provided in Section IV. Financial ratios (Panel B) and CEO/board

characteristics/Social network variables (Panel C) are defined and explained in Section III.B. More formal definitions of

the financial ratios using Compustat item names are provided in the Appendix. Political connectedness measures for top

five executives, CEO, independent directors and a firm’s PAC are denoted by top5_connect_vw, ceo_connect,

connect_omean and pac_connect, respectively. The definitions of these measures are provided in Section VI. Inflation un-

adjusted compensation amounts in thousands of dollars are reported in Panel D of this table. Section III.B explains the

definitions of these compensation variables.

Panel A: Political Measures

Group-level Average Republican Index (Rep_index) for Each Firm

Variable N mean sd min p25 p50 p75 max

top5_index_vw 26621 0.133 0.376 -1.000 -0.036 0.061 0.438 1.000

top5_index_vw_prior 26621 0.106 0.326 -1.000 0.000 0.000 0.365 1.000

ceorep_index 24624 0.200 0.637 -1.000 0.000 0.000 0.924 1.000

ceorep_index_prior 24624 0.169 0.582 -1.000 0.000 0.000 0.765 1.000

rep_omean 18683 0.088 0.278 -1.000 -0.083 0.081 0.270 1.000

rep_omean_prior 18683 0.085 0.253 -1.000 -0.043 0.068 0.250 1.000

Political Homophily Index (PHI) for Each Firm

Variable N mean sd min p25 p50 p75 max

top5vw_out 18683 0.831 0.127 0.212 0.753 0.855 0.929 1.000

top5vw_out_prior 18683 0.852 0.118 0.265 0.781 0.875 0.945 1.000

top5vw_out_dyadic 18683 0.709 0.116 0.212 0.632 0.706 0.775 1.000

ceo_out 18437 0.729 0.192 0.000 0.583 0.750 0.900 1.000

ceo_out_prior 18437 0.762 0.189 0.000 0.609 0.808 0.923 1.000

ceo_out_dyadic 18437 0.678 0.163 0.000 0.569 0.686 0.791 1.000

Political Connection Measures for Each Firm

top5_connect_vw 26621 1.574 4.477 0.000 0.000 0.000 1.131 92.61

connect_omean 18683 1.021 1.780 0.000 0.000 0.370 1.155 30

pac_connect 26621 0.848 3.115 0.000 0.000 0.000 0.000 59.78

47

Panel B: Financial Ratios

Variable N mean sd min p25 p50 p75 max

roa 25687 0.029 0.118 -0.592 0.011 0.042 0.082 0.256

ann_eq_ret 25909 0.218 0.730 -0.839 -0.183 0.086 0.406 4.068

Q 26254 1.960 1.441 0.733 1.109 1.452 2.182 9.028

tda 26498 0.229 0.194 0.000 0.055 0.209 0.350 0.865

tdm 26155 0.165 0.161 0.000 0.030 0.126 0.255 0.968

inv 26621 0.231 0.179 0.000 0.108 0.191 0.315 0.862

rnd 26621 0.028 0.056 0.000 0.000 0.000 0.030 0.297

logasset 26598 7.154 1.759 3.342 5.895 7.022 8.300 11.840

eqvol 24409 0.122 0.073 0.030 0.071 0.103 0.152 0.403

cfvol 22889 0.042 0.046 0.001 0.014 0.027 0.051 0.258

Panel C: CEO and Board Characteristics

Variable N mean sd min p25 p50 p75 max

ceo_is_chmn_pres 24624 0.923 0.266 0 1 1 1 1

ceo_age 23527 55.38 7.529 28 50 55 60 93

ceo_holding(%) 23391 2.464 5.786 0.000 0.099 0.347 1.430 34.05

ceo_tenure 23304 7.110 7.205 0 2 5 10 58

board_size 18717 9.544 2.835 3 8 9 11 39

Independent 18717 0.873 0.333 0 1 1 1 1

out_holding(%) 16229 0.200 0.479 0.001 0.018 0.056 0.157 3.354

out_age 18683 60.75 4.607 35.00 58.00 60.88 63.60 83.00

age_diff_ceo_out 17655 7.273 5.627 0.00 2.75 6.00 10.63 39.5

female_diff_ceo_out 17369 0.133 0.156 0.000 0.000 0.125 0.200 1.000

age_diff_top5_out 18159 7.853 5.200 0.00 3.67 7.25 11.2 32.8

female_diff_top5_out 17578 0.120 0.119 0.000 0.000 0.111 0.200 1.000

Ind-Connections (BoardEx Only) 15899 5.90 2.59 0 4 6 8 19

SNI_out (BoardEx Only) 15899 0.14 0.23 0 0 0 0.2 1

Panel D: CEO Compensation Variables (In thousands of dollars: Inflation un-adjusted numbers are used

here.)

Variable N mean sd Min p25 p50 p75 max

salary&bonus 24624 1236.7 1160.4 21.9 567.0 893.3 1443.0 7179.8

salary 24624 660.5 325.8 0.0 420.8 607.8 856.2 1793.8

bonus 24624 569.6 985.0 0.0 0.0 209.8 693.8 6000.0

option 17387 2133.9 4027.9 0.0 0.0 658.3 2227.3 24193.3

ltip 17518 154.7 557.4 0.0 0.0 0.0 0.0 3585.4

othcomp 24624 190.4 456.1 0.0 10.2 47.7 162.2 3200.0

total_comp (tdc1) 24441 4639.1 5911.7 170.7 1201.6 2536.3 5459.2 34004.8

48

Table 2. Correlations

For the definitions of political measures, PHIs and Rep_indexes, please refer to Section IV. Firm characteristics and CEO/board characteristics are defined and

explained in Section III, and their formal definitions using Compustat item names are provided in the Appendix. A brief summary of their definitions is as follows:

a firm’s Tobin’s Q (Q), return on assets (roa), investment in tangible assets ratio (inv), research and development expense to assets ratio (rnd) and 12-month rolling

monthly net equity return volatility (eqvol).

Panel A: PHIs, Firm-level Rep_indexes and Socio-demographic Similarities & Social Ties

top5vw_out top5_index_vw ceo_out ceorep_index rep_omean age_diff female_diff SNI_out

top5vw_out 1.00

top5_index_vw -0.11 1.00

ceo_out 0.67 -0.11 1.00

ceorep_index -0.07 0.85 -0.12 1.00

rep_omean 0.07 0.25 0.06 0.21 1.00

age_diff_top5_out 0.03 -0.07 0.04 -0.06 -0.03 1.00

female_diff_top5_out -0.02 -0.02 -0.02 -0.02 -0.07 -0.08 1.00

SNI_out 0.03 0.00 0.02 0.00 -0.03 -0.07 0.01 1.00

Panel B: Firm Characteristics and PHIs

Q roa inv rnd eqvol board_size top5vw_out ceo_out

Q 1.00

roa 0.31 1.00

inv 0.35 0.06 1.00

rnd 0.36 -0.21 0.27 1.00

eqvol 0.07 -0.36 0.19 0.26 1.00

board_size -0.16 0.04 -0.24 -0.26 -0.27 1.00

top5vw_out -0.08 -0.06 -0.04 0.03 0.03 0.00 1.00

ceo_out -0.04 -0.06 -0.03 0.06 0.04 -0.05 0.67 1.00

49

Table 3. Firm Valuation and PHI

The dependent variable is Tobin’s Q. There are two PHIs on the RHS of the regressions: top5vw_out (Panel A) and ceo_out (Panel B). Section IV provides the

definitions of the PHIs, top5vw_out and ceo_out, together with those of top5_index_vw and ceorep_index. Gindex is the external governance index introduced by

Gompers et al. (2003). Year, SIC2 and Area denote dummy variables defined using fiscal year, 2-digit standard industrial classification(SIC) code and the area

code of each firm’s telephone number. Control variables include board size, independent dummy, roa, l1roa, l2roa, inv, rnd and logasset. See Appendix for the

definition of these variables. In all columns of the panels in Table 3, standard errors are clustered at the firm level, and t-stats are shown in parentheses. *, **, and

*** denote the statistical significance at the 10%-, 5%-, and 1%-levels, respectively.

Panel A: Top Five Executives Value-weighted PHI

No Control Variables Control Variables Gindex

Non-missing

Rep_index PHI_prior PHI_dyadic

(1) (2) (3) (4) (5) (6) (7) (8) (9)

top5vw_out -0.56*** -0.54*** -0.45*** -0.22* -0.48*** -0.44*** -0.44***

(-3.39) (-3.62) (-3.65) (-1.88) (-3.66) (-2.94) (-3.54)

top5_index_vw

-0.02 0.03 -0.004 -0.03 -0.02 -0.02

(-0.49) (0.50) (-0.09) (-0.58) (-0.48) (-0.53)

Gindex

-0.023***

(-3.36)

top5vw_out_prior -0.38**

(-2.24)

top5_index_vw_prior 0.034

(0.55)

top5vw_out_dyadic -0.74***

(-4.87)

control variables No No Yes Yes Yes Yes Yes Yes Yes

fixed effects No Year, SIC2 Year, SIC2 Year, Firm Year, SIC2,

Area Year, SIC2 Year, SIC2 Year, SIC2 Year, SIC2

Observations 18,660 18,660 17,493 17,493 13,523 12,363 16,940 17,493 17,493

Adj. R-sqrd 0.003 0.194 0.398 0.699 0.472 0.410 0.406 0.397 0.400

50

Table 3 Continued. : LHS Variable is Tobin’s Q

Panel B: CEO PHI

No Control Variables Control Variables Gindex

Non-missing

Rep_index PHI_prior PHI_dyadic

(1) (2) (3) (4) (5) (6) (7) (8) (9)

ceo_out -0.23** -0.22** -0.21*** -0.21*** -0.19** -0.25** -0.21**

(-2.09) (-2.29) (-2.61) (-2.61) (-2.17) (-2.41) (-2.49)

ceorep_index

-0.005 -0.005 -0.0004 -0.009 -0.006

(-0.20) (-0.20) (-0.01) (-0.31) (-0.26)

Gindex

-0.023***

(-3.35)

ceo_out_prior -0.16*

(-1.69)

ceorep_index_prior 0.008

(0.22)

ceo_out_dyadic -0.32***

(-3.26)

control variables No No Yes Yes Yes Yes Yes Yes Yes

fixed effects No Year, SIC2 Year, SIC2 Year, Firm Year, SIC2,

Area Year, SIC2 Year, SIC2 Year, SIC2 Year, SIC2

Observations 18,426 18,426 17,439 17,439 13,523 12,312 16,630 11,476 17,439

Adj. R-sqrd 0.001 0.192 0.398 0.398 0.470 0.410 0.408 0.438 0.398

51

Table 4. Demographic Similarity, Social Network vs. Political Alignment

This table shows that the results reported in Table 3 are robust to the inclusion of demographic similarities and social ties between CEOs and independent directors.

age_diff_top5 (or ceo)_out and female_diff_top5 (or ceo)_out measure age and gender dissimilarities between corporate insiders (or a CEO) and independent

directors, respectively. The social network index (SNI) is constructed in the same way as in Fracassi and Tate (2012). Ind-Connections is the number of

independent directors minus independent directors with at least one social tie to the CEO. SNI_out is the fraction of independent directors with at least one social

tie to the CEO. For a meaningful comparison, in columns (2) to (9) of both panels, we use the sub-sample that satisfies the conditions used in Fracassi and Tate

(2012). This sub-sample starts from year 2000 and consists of firm-year observations available in the Boardex database. In columns (2) to (6) of both panels,

control variables include board_size, independent, logasset, tdm and Gindex, which are the control variables used in Fracassi and Tate (2012). In columns (1), (7),

(8) and (9) of both panels, control variables include Gindex, board_size, independent, roa, l1roa, l2roa, inv, rnd and logasset. All standard errors are clustered by

firm, and t-stats are shown in parentheses. *, **, and *** denote the statistical significance at the10%-,5%-, and 1%-levels, respectively.

Panel A: Top Five Executives Value-weighted PHI

PHI vs. Age and

Gender dissimilarities

PHI vs. Social Network Index

Replication of Regressions in Fracassi and Tate (2011)

with and without PHI

Firm-years with

SNI_out = 0

Our default specification used

in Table 3 with Gindex

(1) (2) (3) (4) (5) (6) (7) (8) (9)

top5vw_out -0.44***

-0.70*** -0.68***

-0.36* -0.38**

(-3.24)

(-3.43) (-3.25)

(-1.65) (-2.21)

top5_index_vw -0.013

-0.15** -0.15**

-0.07 -0.09

(-0.29)

(-2.19) (-2.19)

(-0.87) (-1.53)

age_diff_top5_out 0.008**

(2.32)

female_diff_top5_out 0.049

(0.38)

Ind-Connections 0.02** 0.02**

(2.48) (2.45)

SNI_out -0.33*** -0.32*** -0.31*** -0.05 -0.04

(-3.32) (-3.24) (-3.19) (-0.50) (-0.46)

top5vw_out_prior

-0.76***

-0.52**

(-2.69)

(-2.32)

top5_index_vw_prior

-0.11

-0.04

(-1.13)

(-0.48)

control variables Yes Yes Yes Yes Yes Yes Yes Yes Yes

fixed effects Year, SIC2 No No No No No Year, SIC2 Year, SIC2 Year, SIC2

Observations 13,332 7,601 7,601 7,597 7,597 7,597 4,371 7,196 7,196

Adj. R-sqrd 0.418 0.228 0.230 0.235 0.237 0.233 0.443 0.457 0.456

52

Table 4 Continued.

Panel B: CEO PHI

PHI vs. Age and

Gender dissimilarities

PHI vs. Social Network Index

Replication of Regressions in Fracassi and Tate (2012)

with and without PHI

Firm-years with

SNI_out = 0

Our default specification used

in Table 3 with Gindex

(1) (2) (3) (4) (5) (6) (7) (8) (9)

ceo_out -0.24***

-0.31** -0.29**

-0.32** -0.22**

(-2.58)

(-2.30) (-2.18)

(-2.08) (-1.99)

ceorep_index 0.001

-0.07* -0.07*

-0.04 -0.04

(0.05)

(-1.69) (-1.68)

(-0.89) (-1.19)

age_diff_ceo_out 0.003

(0.85)

female_diff_ceo_out 0.052

(0.49)

Ind-Connections 0.02** 0.02**

0.02**

(2.48) (2.54)

(2.51)

SNI_out -0.33*** -0.32*** -0.05 -0.05

(-3.32) (-3.28) (-0.50) (-0.51)

ceo_out_prior

-0.25*

-0.20*

(-1.68)

(-1.65)

ceorep_index_prior

-0.06

-0.03

(-1.04)

(-0.69)

control variables Yes Yes Yes Yes Yes Yes Yes Yes Yes

fixed effects Year, SIC2 No No No No No Year, SIC2 Year, SIC2 Year, SIC2

Observations 12,942 7,601 7,601 7,542 7,542 7,542 4,354 7,166 7,166

Adj. R-sqrd 0.418 0.228 0.230 0.235 0.230 0.232 0.444 0.456 0.455

53

Table 5. Operating Performance and PHI

This table provides results on how PHI relates to the return on assets of a firm (roa). It further shows the relation of PHI to a firm’s risk measured by 12-month

rolling monthly net equity return volatility (eqvol), and 5-year rolling annual cash flow volatility (cfvol). In the first three columns (1) to (3), we report the results

for top5vw_out, whereas the results for ceo_out are reported in the remaining columns (4) to (6). Both year- and industry-fixed effects are controlled in all columns.

Industry-fixed effects are defined at 2-digit SIC (SIC2) level. All standard errors are clustered at the firm level, and t-stats are shown in parentheses. *, **, and ***

denote the statistical significance at the 10%-, 5%-, and 1%-levels, respectively.

Top Five Executives CEO

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

roa eqvol cfvol roa eqvol cfvol

top5vw_out -0.017** 0.005 0.002 ceo_out -0.016*** 0.005 0.002

(-2.24) (1.13) (0.55) (-2.81) (1.40) (1.02)

top5_index_vw 0.01*** -0.01*** -0.002* ceorep_index 0.004** -0.004*** -0.0009

(4.60) (-6.09) (-1.83) (2.52) (-3.80) (-1.23)

logasset 0.01*** -0.01*** -0.008*** logasset 0.010*** -0.01*** -0.008***

(7.43) (-19.38) (-16.54) (7.57) (-19.60) (-16.52)

Q 0.02*** 0.0006 0.005*** Q 0.02*** 0.0006 0.005***

(14.24) (0.96) (9.21) (14.07) (0.93) (9.11)

tda -0.11*** 0.032*** 0.02*** tda -0.11*** 0.033*** 0.01***

(-8.67) (6.36) (3.23) (-8.54) (6.35) (3.17)

inv -0.02**

inv -0.02**

(-2.25)

(-2.24)

Year Yes Yes Yes Year Yes Yes Yes

SIC2 Yes Yes Yes SIC2 Yes Yes Yes

Cluster Firm Firm Firm Cluster Firm Firm Firm

Observations 17,970 18,232 17,515 Observations 17,757 18,013 17,324

Adj. R-sqrd 0.187 0.351 0.267 Adj. R-sqrd 0.186 0.349 0.268

54

Table 6. CEO Turnover and PHI

This table shows how PHI relates to the likelihood of a CEO’s dismissal. Turnover, the dependent variable, is defined as a variable taking the value of one if a

CEO is dismissed between the second half of a given fiscal year and the first half of the subsequent fiscal year, and zero otherwise. A linear probability model is

used to directly interpret the second-order partial effect of PHI on the marginal probability change in CEO dismissal following poor stock market performance.

cum4yr_eqret denotes the cumulative stock returns from the past four fiscal years prior to a given fiscal year end date, and thus CEO tenure is required to be at

least four years to ensure that the CEO considered is responsible for the past four years’ stock return performance. top5vw_out (or ceo_out), top5_index_vw (or

ceorep_index), board_size, independenct, ceo_age, CEO retirement age dummies for the CEO’s age of 64, 65 and 66, and ceo_holding are additionally controlled

on the RHS of the regressions. Moreover, age_diff_top5_out (or age_diff_ceo_out), female_diff_top5_out (or female_diff_ceo_out) and SNI_out and these

variables’ interaction terms with cum4yr_eqret are further controlled. The coefficients on all these additional control variables are not reported for brevity. SIC2

represent the industry-fixed effects defined at 2-digit SIC levels. All standard errors are clustered at the firm level, and t-stats are shown in parentheses. *, **, and

*** denote the statistical significance at the 10%-, 5%-, and 1%-levels, respectively.

(1) (2)

Top Five Executives Value-

weighted PHI

CEO PHI

cum4yr_eqret -0.107*** cum4yr_eqret -0.074**

(-3.15) (-2.36)

top5vw_out * cum4yr_eqret 0.094*** ceo_out * cum4yr_eqret 0.061*

(2.62) (1.73)

Controls Yes Controls Yes

Year Yes Year Yes

Industry SIC2 Industry SIC2

Observations 9,690 Observations 9,690

Adj. R-sqrd 0.032 Adj. R-sqrd 0.033

55

Table 7. CEO Pay-performance- sensitivity (PPS) and PHI

The dependent variables are the annual changes in the inflation-adjusted dollar amount of salary&bonus (column (1)) and total compensation (column (2) - named

tdc1 in the ExecuComp database) that includes stock/option grants and other compensation amounts. The dollar change amounts are denominated in thousands of

year-1992 dollars using the annual average CPI. From the definition of PPS, change in inflation-adjusted shareholders’ value (shareholders' value change) is used

on the RHS of the regressions. In both columns, we control for Ind-Connections and its interaction term with shareholders’ value change, and we also include

age_diff_ceo_out and female_diff_ceo_out, with their interaction terms with shareholders’ value change on the RHS of the regression. We further control for

ceorep_index, board_size, independent, ceo_holding, ceo_is_chmn_pres, ceo_tenure, out_holding, 1-year lagged natural logarithm of inflation-adjusted book

value of assets (l1logasset), and 1-year lagged Q (l1Q) in all columns. Coefficients of these control variables are not reported for brevity. In column (2) where the

dollar amount of change in total compensation is used as the dependent variable, lagged 12-month rolling monthly net percentage equity return volatility (l1eqvol)

is additionally controlled. Year- and industry-fixed effects are controlled in all columns (1) to (2), and the industry-fixed effects are defined at 2-digit SIC (SIC2)

level. All standard errors are clustered at the firm level, and t-stats are shown in parentheses. *, **, and *** denote the statistical significance at the 10%-, 5%-, and

1%-levels, respectively.

(1) (2)

salary&bonus dollar change total_comp(tdc1) dollar change

ceo_out -16.39 -27.86 (-0.94) (-0.26)

ceo_out*shareholders' value change -0.03** 0.049 (-2.08) (0.57)

shareholders' value change 0.03** 0.057 (2.41) (0.72)

Controls Yes Yes

Year Yes Yes

SIC2 Yes Yes

Observations 14,566 14,127

Adj. R-sqrd 0.095 0.014

56

Table 8. Corporate Fraud and PHI

This table shows how the top five executives’ value-weighted based PHI (top5vw_out) relates to the firm’s likelihood of

committing corporate fraud. The main dependent variable is a dummy variable taking a value of one if a firm-year is

associated with an alleged corporate fraud, and zero otherwise. This corporate fraud dummy variable is used in Dyck,

Morse and Zingales (2011), and the data on this variable are taken from the Adair Morse website

(http://faculty.chicagobooth.edu/adair.morse). The data starts from year-1996 and ends in year-2004. In all columns (1) to

(4), board_size, independent, Change in SNI_out, ceo_tenure, ceo_age, ceo_holding, lagged Q (l1Q), lagged natural

logarithm of inflation-adjusted book value of assets (l1logasset), lagged roa (l1roa) are controlled. For the firm-year

observations that are not available in the BoardEx database, we assign zero to SNI_out. Year- and industry-fixed effects

are controlled in columns (2) and (4), and the industry-fixed effects are defined at the 1-digit SIC (SIC1) level. All

standard errors are clustered at the firm level, and t-stats (z-stats for Probit models) are shown in parentheses. *, **, and

*** denote the statistical significance at the 10%-, 5%-, and 1%-levels, respectively.

(1) (2) (3) (4)

Corporate Fraud Dummy

Coeff. Coeff. Coeff. Marginal Prob. Coeff.

top5vw_out 0.06** 0.06** 0.85** 5%** 0.88**

(2.20) (2.29) (2.11)

(2.20)

top5_index_vw 0.01 0.01 0.09 1% 0.14

(0.75) (1.27) (0.75)

(1.04)

board_size -0.00 -0.00 -0.01 -0% -0.02

(-0.61) (-0.56) (-0.70)

(-0.83)

Independent 0.01 0.01 0.14 1% 0.15

(1.03) (1.27) (0.91) (0.92)

Change in SNI_out 0.04* 0.02 0.48** 3%** 0.29

(1.77) (1.06) (2.33)

(1.35)

ceo_tenure 0.00 0.00 0.02 0% 0.01

(1.15) (1.17) (1.38)

(1.35)

ceo_age -0.00** -0.00* -0.02** -0%** -0.02*

(-2.18) (-1.65) (-2.20)

(-1.69)

ceo_holding 0.00 0.00 0.00 0% 0.00

(0.54) (0.41) (0.19)

(0.11)

l1q 0.01** 0.01** 0.11*** 1%*** 0.10***

(2.50) (2.05) (3.58) (2.69)

l1logasset 0.02*** 0.02*** 0.24*** 1.5%*** 0.27***

(4.37) (4.42) (5.27)

(4.89)

l1roa -0.11 -0.11 -1.03** 6%** -0.99*

(-1.55) (-1.47) (-2.09)

(-1.76)

Model Linear Linear Probit Probit

Year No Yes No Yes

SIC1 No Yes No Yes

Cluster Firm Firm Firm Firm

Observations 4,873 4,873 4,873 4,873

Adj. R-sqrd 0.026 0.041 N.A. N.A.

57

Table 9. Robustness Checks

Panel A: Political Ideology vs. Political Opportunism

This table shows additional results supporting the claim that Rep_index captures a person’s political orientation rather

than strategic political donations, and PHIs capture similarity in political ideologies rather than a firm’s political

connections established through political donations. The first two columns report the same Q regression results as done in

Table 3, but using two new alternative measures of Rep_index and the new PHI measures corresponding to them:

top5_index_vw_cycle_ave (top5_polarizer_vw) and top5vw_out_cycle_ave (top5vw_out_polarizer). In the third column,

we control for each firm’s political connections using political donations made by the firm’s top five executives,

independent directors, CEO and political action committee (top5_connect_vw, connect_omean, ceo_connect and

pac_connect, respectively) See Section VI.A for the definitions of these variables. Control variables include Gindex,

board size, independent roa, l1roa, l2roa, inv, rnd, logasset, SNI_out, age_diff_top5_out and female_diff_top5_out. All

standard errors are clustered by firm, and t-stats are shown in parentheses. *, **, and *** denote the statistical significance

at the10%-,5%-, and 1%-levels, respectively.

(1) (2) (3)

Alternative Political Measures Political Connections

Controlled

top5vw_out_cycle_ave -0.38**

(-2.38)

top5_index_vw_cycle_ave -0.04

(-0.70)

top5vw_out_polarizer -0.39**

(-2.18)

top5_polarizer_vw -0.09

(-1.34)

top5vw_out

-0.44***

(-2.80)

top5_index_vw

-0.04

(-0.77)

top5_connect_vw 0.01**

(2.53)

connect_omean

0.03***

(3.38)

pac_connect

0.01*

(1.68)

Year, SIC2 dummies Yes Yes Yes

Observations 11,208 11,208 11,208

Adj. R-sqrd 0.413 0.413 0.417

58

Table 9 Continued.

Panel B: Causal Relationship Between Tobin’s Q and PHI

This table shows the results for causal regressions. The first three columns of this table show the results when the change

in Tobin’s Q is regressed on the change in the independent variables used in the regressions in column (3) of both panels

in Table 3. Change in SNI_out is further controlled in these regressions. In column (3) of this panel, our testing sample is

restricted to the firm-years that experience either of the following two cases: case 1) independent director departure occurs

due to mandatory retirement or decease, or case 2) CEO departure occurs due to non-performance related reasons such as

retirement or decease. In column (4)-(7) of this panel, lagged political measures using only past donation history---lagged

top5vw_out_past and top5_index_vw_past---are used as instruments for top5vw_out and top5_index_vw, respectively. All

standard errors are clustered at the firm level. t-stats are shown in parentheses, and *, **, and *** denote the statistical

significance at the10%-,5%-, and 1%-levels, respectively.

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

Change to Change 2SLS IV 6-year No Sample Restrictions Director Retirement/Decease

OR

CEO Turnover due to

Retirement/Decease

3-year 4-year 5-year 6-year

ctop5vw_out -0.19**

(-2.39)

ctop5_index_vw -0.06

(-1.46)

cceo_out

-0.12** -0.22*

(-2.01) (-1.82)

cceorep_index

-0.01 -0.04

(-0.69) (-0.93)

top5vw_out

-1.31* -1.41* -1.97* -2.32*

(-1.75) (-1.65) (-1.77) (-1.66)

top5_index_vw

-0.06 -0.08 -0.12 -0.099

(-0.60) (-0.71) (-1.03) (-0.75)

Year Yes Yes Yes Yes Yes Yes Yes

SIC2 Yes Yes Yes Yes Yes Yes Yes

Cluster Firm Firm Firm Firm Firm Firm Firm

Observations 14,541 14,464 407 11,054 9,423 7,975 6,677

Adj.R-sqrd

(or R-sqrd) 0.109 0.108 0.188 0.415 0.426 0.421 0.402

59

II. Appendices

Appendix A. Formal Definitions of Political Measures

A.1 Variables Indicating the Dollar Amount of Individual Political Donations

: Total dollar amount of political donations made by individual-i to the Republican Party over 11

election cycles from 1989 to 2010

: Total dollar amount of political donations made by individual-i to the Democratic Party over 11

election cycles from 1989 to 2010

: Total dollar amount of political donations made by individual-i to the Republican Party up to

the end of fiscal year-t since the first election cycle in 1989

: Total dollar amount of political donations made by individual-i to the Democratic Party up to

the end of fiscal year-t since the first election cycle in 1989

: Total dollar amount of political donations made by individual-i to the Republican Party in

election cycle-c

: Total dollar amount of political donations made by individual-i to the Democratic Party in

election cycle-c

A.2 Individual-level Republican Indices

where |C(i)| denotes the total number of election cycles for which individual-i has donation

records, and C(i) denotes a set of such election cycles.

60

A.3 Group-level Republican Indices

a) Top Five Executives Group: i denotes an executive whose pay rank based on salary and bonus

is i for i=1, 2, .., 5

+

+

+

+

+

+

+

+

b) Independent Directors Group: j denotes an independent director of a board for j=1, 2, ..., N

where N is the total number of independent directors of the board

c) CEO: k denotes the CEO of a firm

61

A.4 Firm-level Political Homophily Indices (PHIs)

top5vw_out = 1 – | top5_index_vw – rep_omean |/2

ceo_out = 1 – |ceorep_index – rep_omean |/2

top5vw_out_prior = 1 – |top5_index_vw_prior – rep_omean_prior |/2

ceo_out_prior = 1 – |ceorep_index_prior – rep_omean_prior |/2

top5vw_out_cycle_ave = 1 – |top5_index_vw_cycle_ave – rep_omean_cycle_ave |/2

ceo_out_cycle_ave = 1 – |ceorep_index_cycle_ave – rep_omean_cycle_ave |/2

top5vw_out_polarizer = 1 – | top5_polarizer_vw – rep_omean_polarizer |/2

ceo_out_polarizer = 1 – |ceorep_polarizer – rep_omean_polarizer |/2

top5vw_out_dyadic =

|

ceorep_out_dyadic =

62

Appendix B. Definitions of Financial Variables

Variables Definitions

Q Average Tobin’s Q, which is the ratio of market value of a firm’s asset to its book value.

The market value of a firm’s asset is computed as the book value of a firm’s total assets

(AT) minus the book value of common equity plus market value of common equity

(PRCC_F*CSHO) minus deferred taxes and investment credits (TXDITC) (if available).

The book value of common equity is computed as the total stockholders’ equity (SEQ) or

if this is missing, the first available of total common equity (CEQ) plus total preferred

stock (PSTK) or total assets (AT) minus total liabilities (LT)] minus the liquidating value

of preferred stock (PSTKL) [or, if that is missing, the first available of the redemption

value of preferred stock (PSTKRV) or total preferred stock (PSTK)].

roa The ratio of income before extraordinary items (IB) to the book value of a firm’s total

assets (AT).

tda The ratio of the book value total debt (DLC+DLTT) to the book value of a firm’s total

assets (AT).

tdm The ratio of the book value total debt (DLC+DLTT) to the market value of a firm’s total

assets. See the definition of Q above for the definition of the market value of a firm’s total

assets.

inv The ratio of capital expenditure (CAPX) to total net property, plant, and equipment

(PPENT). For a missing CAPX, we replace it with zero.

rnd The ratio of research and development expense (XRD) to the book value of a firm’s total

assets (AT). For a missing XRD, we replace it with zero.

logasset The natural logarithm of the inflation adjusted book value of a firm’s total assets

(AT/Average annual CPI).

eqvol The volatility of last 12-month monthly net equity returns. There are estimated at each

fiscal year end date of the firms in the sample using CRSP monthly stock file.

cfvol Cash flow volatility measured in the 5-year period prior to the fiscal year end date for a

given fiscal year. Cash flow is defined as the ratio of operating income before depreciation

(OIBDP) to the book value of a firm’s total assets (AT).