Politically Connected Boards and Audit Pricing: U.S. Evidence
Chansog (Francis) Kim*
Zhifeng Yang
Oliver Zhou
Department of Accountancy
College of Business
City University of Hong Kong
March 2013
*Contact information: Chansog (Francis) Kim (Corresponding author) (Tel.:
852-3442-7962, E-mail: acckim@cityu.edu.hk), Zhifeng Yang (Tel.: 852-3442-4013,
E-mail: zhifeng@cityu.edu.hk), Oliver Zhou (Tel.: 852-3442-2912, E-mail:
yuxizhou@student.cityu.edu.hk). We thank seminar participants at the City University of
Hong Kong, Fudan University, Hong Kong Polytechnic University, Shanghai Jiaotong
University, Shanghai University of Finance and Economics, State University of New
York at Stony Brook, Yeshiva University, and Wayne State University for their helpful
comments and Jinghua Fu for his research assistance in data processing.
Politically Connected Boards and Audit Pricing: U.S. Evidence
Abstract
This study examines how clients’ personal tie-based political connections influence
auditors’ assessments of audit risk as reflected in audit fees by using a comprehensive
data set of 29,785 firm–year observations that are hand-collected from all U.S. Securities
and Exchange Commission listed firm filings from 2001 to 2009. We find that auditors
charge higher fees to politically connected firms than to non-connected ones. We also
find that this incremental effect of a firm’s political connections on audit fees is more
pronounced among firms with weaker corporate governance and more complicated
operational structures. Furthermore, this relation has become more pervasive since the
Sarbanes–Oxley Act. These findings suggest that auditors perceive politically connected
firms as riskier. Accordingly, they exercise greater effort and charge higher fees to these
connected firms. Our evidence is robust to a battery of econometric endogeneity remedies
and to exogenous events such as presidential and mid-term elections and the intense
financial crisis of 2008–2009. Our finding of higher audit fees being charged to
connected firms is also robust in the presence of other forms of political influence,
including campaign contributions and lobbying expenditures.
1
1. Introduction
This study examines how clients’ political connections influence auditors’
assessments of audit risk as reflected in audit fees in the United States. Extant studies find
that corporate political connections influence the financial reporting practices of
connected firms.1 Yu and Yu [2011] find that U.S. firms’ lobbying activities slow down
the regulatory detection of accounting fraud committed by connected firms, which
enables their managers to manipulate earnings for longer periods compared to
non-lobbying firms. Ramanna and Roychowdhury [2010] also provide evidence that U.S.
firms with connections through political contributions were more likely to manage
earnings during the 2004 elections. These findings suggest that a client’s political
connections should influence auditors’ assessments of the audit risk of those connected
firms due to their differential reporting behaviors, an issue that has received increasing
attentions from practitioners. For example, in a recent report, Deloitte [2012] calls for
more transparency about corporate political activity and the acceptance of board directors
with political backgrounds. In this regard, examination of how clients’ political
connections affect auditors’ assessments of audit risk and, in turn, audit fees is warranted.
To our best knowledge, this is the first study examining the effects of political
connections on audit pricing in the U.S. setting, a country where auditors face high
litigation risk. Ex ante it is not clear how and to what extent political connections
influence audit risk and audit fees. Political connections may help improve the financial
1
For international evidence on the effects of political connections on financial reporting, see Leuz and
Oberholzer-Gee [2006] for the pronounced income smoothing of connected firms in Indonesia and Chaney,
Faccio, and Parsley [2011] for the lower accrual quality of connected firms in their cross-country study.
2
performance of connected firms through their receipt of economic rents, including lower
costs of capital and more government procurement contracts (Goldman, So, and Rocholl
[2012], Houston et al. [2012], Karpoff, Lee, and Vendrzyk [1999]). Such political
benefits may reduce management incentives to misreport the earnings of connected firms
(Gul [2006]). Moreover, politically connected firms may face a lower failure risk because
they are more likely to be bailed out by their government when distressed (Duchin and
Sosyura [2012], Faccio, Masulis, and McConnell [2006]), which mitigates potential class
action lawsuits by shareholders due to corporate failure. As such, auditors may charge
lower fees to clients with political connections.2
However, the covert nature of corporate rent-seeking activities can increase the
opaqueness of financial statements prepared by politically connected firms. The
executives of such firms have incentives to obscure their financial statements to protect
political favors, which are often of dubious nature (Chaney, Faccio, and Parsley [2011],
Fisman [2001], Leuz and Oberholzer-Gee [2006]). These problems suggest that
accounting quality could be poorer in politically connected firms, which is confirmed by
prior research, including studies by Chaney, Faccio, and Parsley [2011], Ramanna and
Roychowdhury [2010], and Yu and Yu [2011].3
In addition, politically connected firms could be riskier because their performance
can change significantly due to changes in political power (Fisman [2001], Leuz and
2
As in Gul and Tsui [1998], we explore a supply-side argument for audit pricing. More specifically, we
investigate audit fee differences between connected and non-connected firms from the perspective of
auditors. However, we also deliberate a demand-side argument of differential audit services between
connected and non-connected firms in our discussion of alternative explanations for our findings.
3
The cross-country study of Chen, Ding, and Kim [2010] finds that analyst forecasts are less accurate for
firms with political connections, suggesting that such firms have a more opaque information environment
than firms with no such connections.
3
Oberholzer-Gee [2006]). The market values of politically connected firms in the United
States are influenced by election outcomes (Goldman, So, and Rocholl [2009]). 4
Furthermore, politically connected firms tend to commit more accounting fraud and have
a higher chance of collapse due to their riskier business models, as for Enron and
WorldCom (Yu and Yu [2011]).
The aforementioned arguments indicate higher audit risks for politically
connected clients. This perspective predicts that auditors will exercise more effort in
auditing politically connected firms and thus charge such firms higher audit fees. We also
expect that the phenomenon of higher audit fees being charged to connected firms should
be more pronounced in the United States due to its highly litigious environment. Thus,
we focus on U.S. firms from 2001 to 2009 to assess the effects of political connections on
audit fees. We use personal tie-based political connections. A firm is defined as politically
connected if one or more of its directors held a political position before sitting on the
board. Our findings suggest that auditors charge significantly higher audit fees to firms
with directors who have political experience. In addition, this finding is robust to the
presence of other measures for political connections, including campaign contributions
and lobbying expenditures.
Our findings show that auditors charge higher fees to politically connected firms
than to non-connected ones. We contend that this relation between firms’ political
connections and audit fees should be more pronounced among firms with weaker
corporate governance and more complicated operational structures. Any adverse effects
4
In the Indonesian setting and in a Malaysian study, respectively, Fisman [2001] and Johnson and Mitton
[2003] provide further evidence of the relation between corporate political risk and the political misfortune
of backers.
4
of political connections on audit risk and audit fees should be mitigated if managers are
closely monitored by directors and outside institutional shareholders. However, their
abilities to monitor are reduced as firm operations become more complicated due to
extensive geographic and product line diversification. We also argue that this relation
should be more prevalent in the post-Sarbanes–Oxley Act (SOX) era because the
litigation environment against auditors became more stringent since the passage of SOX
in 2002.
We find that this relation between firms’ political connections and audit fees is
more pronounced among firms with weaker internal governance (proxied by directors’
shareholdings) and external governance (proxied by institutional shareholdings). In
addition, we find this relation to be stronger among firms with more complicated
operational structures, including multi-segment firms and multinational corporations. The
relation also becomes more ubiquitous in the post-SOX period, since the litigation risk
imposed on auditors is intensified. On balance, these findings suggest that auditors
perceive politically connected clients to be riskier. Auditors exercise greater efforts to
reduce the audit risk of these connected firms. As a result, they charge higher fees to
politically connected clients than to non-connected firms.
However, our findings are also subject to endogeneity problems common in the
auditing literature. The first concern is the reverse causality problem, that is, firms that
have more opaque external reporting and/or are riskier in their business are more likely to
establish political connections.5 The other concern is the omitted correlated variable
5
Chaney, Faccio, and Parsley [2011] also address this concern. They show that ex ante earnings quality
does not affect the likelihood of political connections.
5
problem, that is, the audit fee model fails to control for unobservable firm characteristics
that may simultaneously increase audit risk and the likelihood of political connections.
We conduct a battery of robustness tests to address these endogeneity concerns. First,
we use an instrumental variable approach. As in Houston et al. [2012], we use two
alternative instrumental variables: (1) the distance of a firm’s headquarter from
Washington, DC, and (2) the percentage of politically connected firms in the industry to
which a firm belongs. Firms that are close to Washington may have better opportunities
to develop political connections. In addition, the propensity to establish political
connections can vary due to the differential values of connections in different industries
(Agrawal and Knoeber [2001]). However, we contend that these two instrumental
variables do not directly affect audit fees. In addition to using two-stage least squares
(2SLS) methods, we utilize the Heckman inverse Mills ratio (IMR) method to alleviate
endogeneity concerns from selection bias due to unobservables (Tucker [2010]).
Furthermore, we make use of a changes study method to alleviate endogeneity concerns.
Second, as suggested by Armstrong, Jagolinzer, and Larcker [2010], we also use a
propensity score matching (PSM) strategy to mitigate endogeneity problems from
selection bias due to observables. We first estimate the average treatment effect on the
treated (ATT) using propensity score matching methods. To alleviate the possible
imbalance problem, we also run audit fee models using both politically connected firms
and their matched non-connected counterparts (Armstrong, Jagolinzer, and Larcker
[2010], Lawrence, Minutti-Meza, and Zhang [2011]). The empirical results from utilizing
the instrumental variable method, change analysis, and propensity score matching
approach confirm our previous findings that political connections lead to higher audit
6
fees and this relation is economically and statistically significant.
Third, in addition to econometric remedies for endogeneity issues, we utilize such
exogenous events as mid-term and presidential elections to address endogeneity concerns.
We examine whether audit fees charged to connected firms differ between election and
non-election years. This test is motivated by Ramanna and Roychowdhury [2010]. These
authors argue that the incentives for connected firms to manage earnings are reinforced in
election years due to enhanced public scrutiny of such firms during the election period.
Consequently, the relation between political connections and audit fees ought to be
stronger in election years. Our results ascertain that the relation between connections and
fees is indeed more pronounced during the election period. However, this relation is also
economically and statistically significant during the non-election period. Since our
multiple robustness checks confirm our results of the adverse political connection effect
on audit fees, we conclude that our findings cannot be interpreted by endogeneity-based
arguments.
Our findings have two alternative explanations. First, one can argue that political
directors may lack expertise and incentives to monitor managers. Thus, the managers of
such firms may have more opportunities to misreport earnings. In this regard, auditors
may have to exercise greater efforts in auditing firms with political directors. Second, due
to the fact that political directors receive greater publicity than other directors, they
demand high-quality audit from auditors to protect their political reputations. To evaluate
these alternative interpretations, we partition our sample of connected firms based on
political directors sitting on audit committee. If our findings are consistent with the view
that political directors lack incentives and expertise or excessively demand higher audit
7
quality to protect their reputations, these audit fee effects of political connections should
be more pronounced among firms with political directors sitting on their audit
committees. However, we fail to find a significant difference in audit fees between firms
with and without political directors sitting on the audit committee. Therefore, this
evidence provides little support for these alternative explanations.
Our work appears to be closely related to that of Gul [2006]. This author
examines how auditors charge audit fees to politically connected firms in Malaysia
during an abnormal period, the Asian financial crisis of 1997 and 1998. However, Gul’s
findings may not directly apply to the U.S. setting, because Malaysia and U.S. have
significant differences in the strength of their economic and legal institutions (La Porta et
al. [1998]). Litigation risk faced by auditors in the United States is substantial and
significant, as opposed to the limited litigation risk in Malaysia (Choi et al. [2008]). Gul
[2006] shows that auditors charged higher audit fees to politically connected firms than to
their non-connected counterparts in the early phase of the Asian financial crisis. However,
once the Malaysian government regained its capital control, connected firms paid lower
audit fees than non-connected ones. Gul’s findings suggest that audit pricing for political
connections in Malaysia depends on whether the Malaysian government is able to make
resources available to connected firms during periods of intense financial crisis. In
contrast to his findings, we find that auditors charge higher audit fees to connected firms
than to non-connected firms in the United States. This difference can be attributed to the
disparities in the two countries’ institutional environment, litigation risk, and intensity of
financial distress.
8
Our paper contributes to the literature in several aspects. First, to the best of our
knowledge, our paper is the first to investigate how firms’ personal ties between
politicians and corporate board members influence auditors’ assessments of audit risk in
the United States. We conduct our empirical study by using a large data set of 29,785
firm–year observations that are hand-collected from all listed firms in United States from
2001 to 2009. The sample of Gul [2006] contains only 740 firm–year observations
because the author focuses on an abnormal period, such as the Asian financial crisis of
1997 and 1998 in Malaysia. Furthermore, we make use of more comprehensive data by
incorporating all U.S. Securities and Exchange Commission (SEC) listed firm filing,
unlike Goldman, So, and Rocholl [2009, 2012] and Houston et al. [2012], whose studies
include only Standard & Poor’s (S&P) 500 firms. Following Miller [2004], we provide
country-specific evidence that overcomes the interpretation problems of cross-country
studies due to differences in legal/judicial systems and cultural factors.
Second, our study extends the growing literature on the relations between political
connections and financial reporting by examining the effects of such connections on audit
pricing.6 More specifically, our study contributes to the literature on how political and
institutional factors affect auditor behavior (Choi et al. [2008, 2009], Guedhami, Pittman,
and Saffar [2009, 2011], Gul [2006], Wang, Wong, and Xia [2008]). Our findings suggest
that auditors perceive politically connected clients as riskier. To the extent that auditors
exercise more effort in response to higher audit risks and charge higher audit fees, our
findings provide indirect evidence that political connections impose significant costs on
6
For more on how the political economy shapes financial reporting around the world, see Bushman and
Piotroski [2006], Bushman, Piotroski, and Smith [2004], Chaney, Faccio, and Parsley [2011], Leuz and
Oberholzer-Gee [2006], and Piotroski, Wong, and Zhang [2011]).
9
connected firms by increasing information opaqueness and business risks. By doing so,
this paper documents the additional costs of corporate political activities on top of
corporate expenditures for establishing and maintaining political connections, contrary to
the conventional political connection literature, which documents firms’ political
benefits.7
Third, we extend prior studies that examine the impacts of an independent audit
committee on firm reporting quality. Farber [2005], Karamanou and Vafeas [2005], Klein
[2002], and Krishnan [2005] document mixed empirical results. Over 90% of political
directors in our sample serve as independent directors, about 30% of whom sit on an
audit committee. Since Armstrong, Guay, and Weber [2010] call for more research on the
interactions between the auditing process, audit committees, and outside directors, among
others, we investigate the audit fee effect of independent political directors sitting on
audit committees. We find no difference in audit fees between connected firms with at
least one political director on their audit committee and those without political directors
on the committee.
Fourth, unlike prior research on political economy (Cooper, Gulen, and
Ovtchinnikov [2010], Faccio [2006], Gul [2006]), we carefully control for the
endogeneity issue of political connections. Our results are robust to econometric solutions
and exogenous events such as presidential and mid-term elections. Our findings are also
7
The majority of studies in political economy find that political connections are beneficial for both firms
around the world (Faccio [2006], Faccio, Masulis, and McConnell [2006], Ferguson and Voth [2008],
Fisman [2001], Johnson and Mitton [2003]) and U.S. firms (Cooper, Gulen, and Ovtchinnikov [2010],
Goldman, So, and Rocholl [2012], Hill et al. [2011]). However, a limited number of papers document that
political connections are costly due to rent-seeking behaviors by politicians in Canada (Morck, Stangeland,
and Yeung [2000]), France (Bertrand et al. [2008]), and China (Fan, Wong, and Zhang [2007]).
10
robust to the financial crisis period of 2008 and 2009.
Finally, this paper shows that political connections matter to auditors and
incorporates firm political connectedness in comparing the audit risk of connected and
non-connected firms. In this regard, our study is in line with those of Jensen and
Meckling [1976] and Watts and Zimmerman [1983, 1986]. These authors maintain that
auditors play a governance role in mitigating agency problems. We provide evidence that
the governance role of auditors extends to politically connected firms. Our findings are
also consistent with those of the international studies of Wang, Wong, and Xia [2008] and
Guedhami, Pittman, and Saffar [2011], who document the corporate governance role of
auditors.
The remaining sections of this paper are organized as follows. Section 2 analyzes
the positive and negative effects of political connections on audit risk and audit fees and
develops testable hypotheses. Section 3 introduces our political connection measures and
audit fee models. Section 4 reports our empirical results, followed by conclusions in
Section 5.
2. Hypothesis Development
2.1.
Political Connections and Audit Risk
Audit fees are determined by audit risk, which is, in turn, affected by managers’
incentives to misreport and by the probability of corporate failure (Choi et al. [2008,
2009], Francis and Stokes [1986], Gul [2006], Simunic [1980]). Auditors expect to
exercise more audit effort to detect potential accounting irregularities if the managers’
incentives to misreport are stronger and this extra effort is characterized by higher audit
11
fees. Furthermore, auditors may charge extra fees to clients with a higher risk of
shareholder litigation, which is often triggered by corporate failure (Dye [1993]). The
following is a detailed analysis of how clients’ political connections can influence audit
risk and consequently audit fees.
The extant political economy literature shows that political connections add value to
connected firms. In a cross-country study, Faccio [2006] finds that stock prices rise upon
the news of firms’ top officers entering politics. Goldman, So, and Rocholl [2009] show
that U.S. firms accumulate abnormal stock returns upon the nomination announcement of
politicians to their boards. These findings suggest that political connections are expected
to bring future economic benefits to connected firms. Consistent with this conjecture, the
literature shows that political connections benefit connected firms in various forms, such
as preferential treatment from banks in such countries as Brazil (Claessens, Feijen, and
Laeven [2008]), China (Du [2012]), India (Cole [2009]), Pakistan (Khwaja and Mian
[2005]), Thailand (Charumilind, Kali, and Wiwattanakantang [2006]), and the United
States (Houston et al. [2012]); financial subsidies from the government (Duchin and
Sosyura [2012], Faccio, Masulis, and McConnell [2006]); government procurement
contracts (Goldman, So, and Rocholl [2012]); lighter taxation (De Soto [1989]); and
relaxed regulatory oversight (Stigler [1971]).
Prior studies show that politically connected firms are able to gain greater access to
funds from banks in the above-mentioned developing countries and consequently such
connected firms suffer higher default rates in the future (Charumilind, Kali, and
Wiwattanakantang [2006], Claessens, Feijen, and Laeven [2008], Cole [2009], Khwaja
and Mian [2005]). Houston et al. [2012] argue that U.S. banks take into consideration
12
potential political favors in their lending decisions by showing bankers making loans at
lower interest rates to those with political connections. Regarding government subsidies,
Faccio, Masulis, and McConnell [2006] document that in 35 countries politically
connected firms are more likely to be bailed out by their governments in case of financial
distress. Duchin and Sosyura [2012] find that the allocation of government funds under
the U.S. Troubled Asset Relief Program (TARP), in response to the subprime mortgage
crisis, was biased toward politician-favored firms. For empirical evidence of government
procurement contracts, Goldman, So, and Rocholl [2012] show that U.S. firms connected
with the winning party in the 1994 mid-term and 2000 presidential elections obtained
significantly more government contracts after the elections. With respect to lighter
taxation, De Soto [1989] provides evidence that politically connected firms in Peru pay
substantially lower taxes.
To the extent that political connections can help improve the financial
performance of connected firms via preferential treatment from banks—thereby obtaining
lower costs of private debt, direct subsidies from the government, more government
procurement contracts, and lighter taxation—such connections may reduce management
incentives for misreporting. If so, auditors should face lower risks in auditing clients with
political connections. Furthermore, politically connected firms are more likely to be
rescued by the government in case of financial distress. Since corporate failure more
often triggers shareholder lawsuits against auditors, a lower bankruptcy risk of politically
connected firms should be reflected favorably in auditors’ assessments of their audit risk.
This perspective predicts that auditors should charge lower audit fees to politically
connected clients than to non-connected firms.
13
Although political connections may lead to lucrative benefits, it is by no means
costless for firms to build and maintain such connections. Politicians seek rents from
connected firms in various forms, such as political donations, lobbying expenses, and
directorships. In the United States, firms are required to disclose “hard money”
expenditures on political activities, including political contributions via political action
committees (PACs) and lobbying expenditures. However, the true amount of corporate
political spending, including “soft money,” is extremely difficult to track (Cooper, Gulen,
and Ovtchinnikov [2010], Yu and Yu [2011]). The disclosed numbers may be just the tip
of the iceberg. For example, the 2006 Center of Political Accountability (CPA) report
shows that very large amounts of political donations are made via trade associations.
Such donations are not visible to outside shareholders and are not monitored by outside
directors in most cases. The secret nature of corporate political expenditures and their
rent-seeking activities increase the opaqueness of financial statements in politically
connected firms.
Executives of politically connected firms also tend to make their financial
statements less transparent to protect the political favors they obtain, because such favors
are often of dubious legality (Leuz and Oberholzer-Gee [2006]). Leuz and
Oberholzer-Gee [2006] show that politically connected firms in Indonesia are often
reluctant to issue foreign securities, since they wish to avoid the more stringent disclosure
requirements imposed by foreign regulators. Chaney, Faccio, and Parsley [2011] argue
that the earnings quality reported by politically connected firms is lower than that
reported by their non-connected counterparts, for two potential reasons. First, insiders
have incentives to divert benefits brought about throughout political connections and thus
14
intentionally make their accounting information more opaque to avoid outside monitoring.
Second, politically connected firms have preferential access to external financing, such as
state bank loans and government subsidies, which may render accounting quality less
important to these firms. Thus, their managers may pay less attention to accrual quality,
thereby resulting in a lower quality of accruals. Chaney, Faccio, and Parsley [2011] show
that politically connected firms around the world have lower accrual quality than
non-connected firms.
Some researchers argue that politically connected firms in the United States are
more likely to manage earnings than their non-connected counterparts (Ramanna and
Roychowdhury [2010]). Yu and Yu [2011] also show that politically connected
companies are associated with higher incidences of accounting fraud. They further
indicate that the regulatory detection of fraud is significantly delayed for politically
connected firms, which enables the managers of such firms to manipulate earnings for
longer periods. Companies such as Enron and WorldCom that committed massive
accounting fraud and subsequently collapsed in the early 2000s were politically
connected. Such connections helped these firms avoid fraud detection by regulators and
thus continue their misconduct for longer, which may have partly contributed to their
eventual collapse. Kido, Petacchi, and Weber [2012] examine how political forces can
affect financial reporting quality in a somewhat different setting. They show that
incumbent governors tend to manipulate their state government’s accounting numbers to
present a strong financial picture in election years. Such findings are consistent with the
notion that political considerations tend to adversely affect accounting quality.
Politically connected firms could also be inherently riskier because their
15
performance “might vary dramatically over time depending on the political fortunes of
their backers” and “political connections can lose their value overnight when the
government fails to win an election” (Leuz and Oberholzer-Gee [2006, pp. 2–3]). Fisman
[2001] shows that the stock prices of Indonesian firms connected to then-president
Suharto moved up and down according to a string of rumors concerning his health.
Similar evidence is documented in the United States. Goldman, So, and Rocholl [2009]
find that after the 2000 presidential election, firms connected with the Republican Party
gained positive stock returns, whereas those connected with the Democratic Party lost
value. Corroborating these findings, Goldman, So, and Rocholl [2009] show that after the
1994 mid-term election and the 2000 presidential election (both elections leading to
power shifts from the Democratic to the Republican Party), companies connected with
the Republican Party obtained significantly more procurement contracts than before,
while those connected with the Democratic Party lost such contracts.
After reviewing the role of political connections in the rise and fall of five Fortune
500 companies—Enron, Global Crossing, WorldCom, Qwest, and Westar Energy—the
2005 Center for Political Accountability report concludes that corporate political
activities were often associated with “significant issues of tax and accounting problems,
fraud, bribery, conspiracy, and other illegal actions” and that could “expose companies to
significant legal, reputational and financial risk” (p. 11). The report concludes that a
combination of these problems eventually “led to their ignominious downfall at the
expense of their shareholders” (p. 5).
To the extent that political connections increase information opaqueness and
business risk, auditors may exercise greater audit effort to discover potential accounting
16
fraud and/or charge higher fees to cover the greater legal liability costs from potential
shareholder lawsuits.
It is also possible that political connections in the United States have no impact on
audit risk or audit fees. The United States is commonly believed to have strong
institutions, such as an independent jurisdiction system and extensive media networks for
monitoring both business and politicians. These institutions may be quite effective in
curbing rent seeking through personal ties with politicians. As a result, political
connections may not generate significant value to connected firms. Public firms
employing politicians may simply be harnessing the expertise and knowledge their
business requires. Fisman et al. [2006] support this view by providing evidence that
connections with then-vice president Cheney did not add value to the companies
concerned. Bertrand et al. [2008] also document similar evidence that politicians in
France, an industrialized and democratic country much like the United States, deliver
only very limited favors to their connected firms.
2.2.
Hypothesis Development
The foregoing discussion suggests that the way political connections affect audit
risks and consequently audit fees is ultimately an empirical issue. However, we argue that
audit fees increase with firms’ political connections, contrary to Gul’s [2006] finding of
the negative relation between political connections and audit fees, for the following two
major reasons. First, Acemoglu et al. [2010] argue that a country with strong institutions,
such as the United States, has experienced a much lower probability of bailouts until
recently. This suggests that any reductions in audit risk for politically connected U.S.
firms due to potential government bailouts may not be as great as that for their
17
counterparts in developing countries such as Malaysia (Gul [2006]). Second, auditors in
the United States face the highest litigation risk in the world, which may incentivize
auditors to exert more effort in auditing politically connected clients, which are usually
more visible and riskier. The value of the Wingate [1997] litigation index, used to
measure auditors’ country-level litigation risks (Choi et al. [2008]), is 15 for the United
States but only 3.67 for Malaysia. This suggests that the litigation risk facing auditors is
much higher in the United States than in Malaysia. Reynolds and Francis [2001] also
provide evidence that the threat of shareholder lawsuits encourages auditors to be more
conservative in auditing relatively risky clients such as large public companies. Thus, we
state our first hypothesis as follows.
H1: Auditors charge higher audit fees to politically connected firms than to
non-connected ones, ceteris paribus.
Insiders’ agency problems are mitigated in well-governed firms. Any perverse
effects of political connections on audit risk and audit fees should be mitigated if
corporate managers are closely monitored by directors and outside institutional
shareholders. The strength of such monitoring is, in turn, affected by the monitors’
incentives and ability. Numerous theoretical and empirical studies show that the
incentives and abilities of directors or institutional investors to monitor company
managers increases with their equity ownership (e.g., Beasley [1996], Hartzell and Starks
[2003], Jensen and Meckling [1976]). However, their ability to monitor is reduced if firm
operations are more complicated due to extensive geographic or product-line
diversification (Bushman, Piotroski, and Smith [2004]). These discussions lead to the
following two collaborating hypotheses.
18
H2a: The adverse effect of political connections on audit fees is mitigated by
director and/or institutional shareholdings.
H2b: The adverse effect of political connections on audit fees is more pronounced
among firms with more complicated operations.
The litigation environment against auditors became more stringent after the
passage of SOX in 2002. Moreover, many massive accounting frauds and corporate
failures involved politically connected firms. This should make auditors more alert to the
corresponding risk involved in auditing politically connected firms. Thus, the perverse
effect of political connections on audit fees should be more pronounced in the post-SOX
era. The last hypothesis is stated as follows.
H3: The adverse effect of political connections on audit fees is more pronounced in
the post-SOX era than in the pre-SOX era.
3. Research Design
3.1.
Definition of Politically Connected Firms
We hand-collected biographic information about board directors from SEC filings,
including DEF 14A, 10-K, and 8-K filings. A firm is defined as politically connected if it
has one or more directors who held a political position before sitting on the board. As in
Goldman, So, and Rocholl [2009], we define political positions as follows: President,
presidential candidate, Senator, member of the House of Representatives, (assistant)
secretary, deputy secretary, deputy assistant secretary, undersecretary, governor, director
(CIA, FEMA), deputy director (CIA, OMB), commissioner (IRS, NRC, SSA, CRC, FDA,
SEC), representative to the United Nations, ambassador, staff (the White House’s, the
president’s, a presidential campaign’s), chairman of a party caucus, chairman or staff of a
presidential election campaign, and chairman or member of the presidential
19
committee/council.
In addition to using a binary variable (PC Dummy) to indicate that a firm is
politically connected, we apply several other measures that indicate not only the presence
of political connections but also their strength. The basic idea is that if political
connections matter to audit risk, then their effect should increase with their strength.
These additional measures include PC Directors, which equals the number of political
directors on the board; PC Freshness,8 determined by the number of elapsed years since
the most recent political position held by the political directors; PC Tenure, the number of
years the political directors served in the government; and PC Rank, determined by the
political power associated with the political positions held by the connected directors.9
For these additional measures, a greater value corresponds to the greater strength of a
firm’s political connections. The assigned value for these variables in non-connected
firms is zero. The Appendix shows the detailed calculation for these variables in
politically connected firms.
Such a personal tie-based definition of political connections is common in
international studies (e.g., Faccio [2006], Fan, Wong, and Zhang [2007], Fisman [2001],
Gul [2006], Leuz and Oberholzer-Gee [2006]). A similar definition is also used in U.S.
studies (e.g., Agrawal and Knoeber [2001], Goldman, So, and Rocholl [2009, 2012],
8
The variable PC Freshness intends to measure the strength of political connections, because former
politicians would be less likely to influence incumbent politicians’ policy decisions and their
implementation.
9
Our choice of PC Rank is admittedly somewhat arbitrary. Unfortunately, the extant political science
literature does not impart much guidance in terms of the hierarchical political power entailed by various
political positions. Note, however, that to the extent PC Rank does not correspond well to political power,
this should obscure our ability to determine what our hypothesis predicts. In addition, note that most of
these measures except PC Rank are also used in prior research (e.g., Houston et al. [2012]).
20
Houston et al. [2012]). 10 Prior research uses campaign contributions or lobbying
expenditures to proxy for political connections (e.g., Cooper, Gulen, and Ovtchinnikov
[2010], Yu and Yu [2011]). However, Aggarwal, Meschke, and Wang [2012] argue that
campaign contributions and lobbying expenditures tend to be transactional and short-term.
These authors further suggest that political connections result from personal ties and
represent long-term relationships. For these reasons, we adopt a human tie-based
definition of political connection. However, we do not deny that political donations and
lobbying expenditures can also capture political connections to some degree. Thus, we
also conduct a robustness test in Section 4 by controlling for political contributions and
lobbying expenditures.
3.2.
Audit Fee Model
Building on Simunic’s [1980] work, various researchers have modeled audit fees
based on audit complexity and risk. Client characteristics such as size, leverage, asset
liquidity, growth, profitability, and business and geographic diversifications are related to
audit complexity and risk and are thus found to influence audit fees (e.g., Chaney, Jeter,
and Shivakumar [2004], Choi et al. [2008, 2009], Gul [2006], Gul and Goodwin [2010]).
Accordingly, we include a number of variables related to these firm characteristics. We
also control for the peak pricing of audit services by including an indicator variable that
equals one for firms with a December fiscal year-end and zero otherwise (Gul and
Goodwin [2010]). Prior auditing research (e.g., Craswell, Francis, and Taylor [1995],
Ferguson, Francis, and Stokes [2003], Francis, Reichelt, and Wang [2005], Gul and
10
Several other studies (e.g., Chaney, Faccio, and Parsley [2010], Chen, Ding, and Kim [2010], Faccio,
Masulis, and McConnell [2006], Guedhami, Pittman, and Saffar [2011]) use Faccio’s [2006] cross-country
data for human tie-based political connections.
21
Goodwin [2010]) suggests that Big N auditing firms or auditors that are national or
city-specific industry leaders may provide higher-quality audits and thus charge fee
premiums.
Following these studies, we control for dummy variables that indicate Big 4
auditors, national industry leaders, or city-specific industry leaders. Year and industry
effects are also included. The following baseline audit fee model is used to test the audit
fee effects of political connections:
A
F
E
E
=
AFEE
=
natural logarithm of audit fees;
PCON
=
one of the political connection measures, including PC Dummy, PC
Directors, PC Freshness, PC Tenure, and PC Rank;
SIZE
=
natural logarithm of total assets;
LEV
=
ratio of total liabilities to total assets;
INVREC
=
ratio of the sum of inventory and receivables to total assets;
QUICK
=
ratio of current assets minus inventory to total assets;
CURRENT
=
ratio to current assets to total assets;
ROA
=
ratio of net income to total assets;
LOSS
=
1 for firms with negative net income and 0 otherwise;
NGS
=
number of geographic segments;
NBS
=
number of business segments;
FOREIGN
=
ratio of foreign sales to total sales;
YE
=
1 for firms with fiscal year-end December and 0 otherwise;
OPINION
=
1 for firms receiving a going-concern opinion and 0 otherwise;
BIG4
=
1 for firms audited by Big 4 auditors and 0 otherwise;
NLEADER
=
1 if the audit firm is the national leader for the client’s industry (two-digit
Standard Industrial Classification code) that year and 0 otherwise;
CLEADER
=
1 if the audit firm is the city-specific industry leader for the city where the
client’s headquarters are located that year and 0 otherwise;
VOLATILITY
=
standard deviation of daily stock returns that year;
22
MB
=
natural logarithm of the ratio of market equity to book equity;
YEAR
=
year dummies;
INDUSTRY
=
industry dummies.
4. Empirical Results
4.1.
Sample and Data
The sample period starts in 2001 (the first year U.S. firms had to publicly disclose
audit fees) and ends in 2009. We begin with 44,985 firm–years and retain 29,785
firm–years in the final sample after excluding financial institutions (9,141), utility
companies (1,032), and firms without sufficient information for the computation for the
control variables in the audit fee model (5,027).
Table 1 reports the descriptive statistics for the firms’ political connections and
financial characteristics that may affect audit fees. This table shows that about 11% of our
sample firm–year observations are politically connected, as opposed to less than 3% of
firms in Faccio’s [2006] study. We also find that about 36% of firms are connected in our
S&P 500 sub-sample, which is higher than about 31% of connected firms for Goldman,
So, and Rocholl [2009].11 Politically connected firms have, on average, 1.3 politicians on
their boards. The median number of years in government service for political directors is
five and the median political rank is three. The distributions values for firm
characteristics are roughly similar to those reported by Gul and Goodwin [2010]. Thus,
we do not discuss this in detail for the sake of brevity.
11
Compared to Goldman, So, and Rocholl [2009], who use DEF 14A for their sample construction, we
identify more connected firms because we hand-collected biographic information about board directors
from 10-K and 8-K filings, as well as DEF 14A. Not all firms disclose political background information
in DEF 14A. For example, Andrew J. Young was a congressman from 1973 to 1977. He later became the
director of Archer Daniels Midland Company from 1997 to 2000. During his directorship period, the
company did not disclose his political experiences in their DEF 14A filings.
23
[Insert Table 1 about here]
Panel A of Table 2 provides the Pearson correlations between various political
connection measures and shows that these measures are highly correlated. Panel B shows
the Pearson correlations between PC Dummy and financial characteristics, which suggest
that politically connected firms are significantly larger, use more debt financing, and are
more profitable than non-connected firms. These findings are consistent with the findings
of Goldman, So, and Rocholl [2009].
[Insert Table 2 about here]
4.2.
Main Analyses
Table 3 reports the results for testing H1, that auditors charge higher audit fees to
politically connected firms than to non-connected clients. The coefficient of PCON is
0.114 and significant at the 1% level when political connections are measured by PC
Dummy. This suggests that the audit fees charged to politically connected firms are 12%
higher than those charged to their non-connected counterparts (). In addition to examining
the existence of corporate political connections, we investigate the audit fee effects of the
differential strength of political connections. We report the results in columns 2–5 of
Table 3. The coefficient of PCON is 0.087 and significant at the 1% level when PC
Directors is considered. This suggests 9% () extra audit fees for politically connected
firms with one political director and 18% for those with two political directors. The
coefficients of PCON are 0.042, 0.007, and 0.064, all significant at the 1% level, when
the strength of political connections is measured by PC Rank, PC Tenure, and PC
Freshness, respectively.
24
These findings imply that auditors charge higher fees to firms with political
directors who have served more powerful positions, longer years of public service, and
more recently. The results reported in columns 2–5 are in line with the notion that audit
fees increase with the strength of political connections. These findings support H1, that
auditors charge higher audit fees to politically connected firms. The coefficients of the
control variables are generally comparable to the statistics reported in prior audit pricing
research in terms of significance levels and coefficient magnitudes (e.g., Gul and
Goodwin [2010]). Accordingly, we do not discuss them in detail for the sake of brevity.
[Insert Table 3 about here]
We next test whether the political connection effect on audit fees is mitigated by
corporate governance. We use institutional and director shareholdings to measure the
quality of external and internal governance, respectively. To test corporate governance
effects, we rank the levels of director equity (institutional) ownership each year and form
deciles. The variable GOV ranks a nine for the top decile and zero for the bottom decile
for director (institutional) shareholdings. Both GOV and the interaction term
GOV*PCON are added to the baseline audit fee model. To the extent that corporate
governance alleviates the audit fee effects of political connections, we expect a negative
coefficient for the interaction term GOV*PCON, indicating that auditors attach lower risk
assessments to connected firms with stronger corporate governance.
Panels A and B of Table 4 report the results based on director shareholdings and
institutional shareholdings, respectively. The results reveal that the coefficients of the
25
interaction term GOV*PCON are significantly negative in most columns.12 Turning to
the interaction term of PC Dummy and GOV in the first column of Panel A, the results
show an estimate of -0.015 (t-statistic = -2.58). This finding suggests that connected firms
in the highest direct shareholding decile portfolio experience 13.5% (-0.015 times nine
decile differences) lower audit fees compared to connected firms in the lowest direct
shareholdings decile group. The results in Table 4 largely suggest that even though
auditors charge higher fees to politically connected clients, they tend to reduce their fees
to those with strong internal or external monitoring. These results support H2a, that the
adverse effect of political connections on audit fees is mitigated by director and/or
institutional shareholdings.
[Insert Table 4 about here]
To test whether the political connection effect on audit fees is stronger among firms
with greater diversification, we use two diversification indexes: the revenue-based
industrial Herfindahl index (where a lower value suggests a higher degree of product line
diversification) and the revenue-based geographic Herfindahl index (where a lower value
suggests a higher degree of geographic diversification). Both indexes were developed by
Bushman et al. [2004] to proxy for operational complexity. The variable COMP equals
one for firms whose value of the industrial (geographic) Herfindahl index is below the
sample median and zero for others. We include both COMP and the interaction term
COMP*PCON in the baseline model to determine whether the political connection effect
12
We report the results of our tests in Table 4 by using the decile rank of GOV, because the use of the
decile rank lessens the adverse consequences of extreme observations. It also enables readers to calibrate
the coefficient estimates as the incremental audit fee effects for connected firms in adjacent deciles. We use
the raw values of GOV to test the audit fee effect of corporate governance. The untabulated results are
qualitatively equivalent to those reported here and are available from the authors upon request.
26
on audit fees is stronger among firms with greater industrial (geographic) diversification.
We expect that the effects of political connections on audit fees should be stronger among
firms with more complicated operations. The results, reported in Table 5, show that the
coefficients of the interaction term COMP*PCON are significantly positive for all
specifications and thus support H2b, that the adverse effect of political connections on
audit fees is more pronounced among firms with more complicated operations.13
[Insert Table 5 about here]
Table 6 reports the results for testing H3, that the effect of political connections on
audit fees is more pronounced in the post-SOX era than in the pre-SOX era. Prior studies
(e.g., DeZoort, Hermanson, and Houston [2008], Huang, Raghunandan, and Rama [2009])
consider 2002 and 2003 as the transition period from the pre-SOX to the post-SOX era
and thus exclude these years when analyzing the economic consequence of SOX.
Following these prior studies, we exclude 2002 and 2003 from the test. Thus, the
pre-SOX era includes 2001 and the post-SOX era starts in 2004. The variable SOX takes
a value of one for firm–years since 2004 and zero otherwise. We add SOX and the
interaction term SOX*PCON to the baseline model and report the results of this new test
in Table 6. The results suggest that auditors charge higher fees to politically connected
firms in both the pre- and post-SOX periods. However, the effect of political connections
on audit fees has been much stronger since the passage of SOX, supporting H3.14
13
The baseline audit fee model already controls for NGS, a proxy for geographic diversification, and NBS,
a proxy for business diversification. The results are qualitatively similar if COMP is defined based on either
NGS or NBS.
14
We also examine whether the effect of political connections on audit fees varies with firm-specific
litigation risk. We follow Shu’s [2000] approach to measure firm-specific litigation risk faced by auditors.
27
[Insert Table 6 about here]
4.3.
Endogeneity Issues
Our interpretation of the empirical results documented so far is that auditors
perceive politically connected firms to be riskier than non-connected firms and thus exert
more effort and charge higher fees for connected firms. This interpretation, however, is
subject to two related problems. The first is the reverse causality problem, where firms
with more opaque information or higher risk are more likely to establish political
connections. The second possible problem is that there may be unobservable firm
characteristics that simultaneously increase audit risks and the likelihood of establishing
political connections. Both of these scenarios could cause positive associations between
audit fees and political connections.
The endogeneity issue is a common theme in studies on the economic
consequences of political connections (e.g., Goldman, So, and Rocholl [2009], Houston
et al. [2012]). This issue, however, is partly mitigated by Chaney, Faccio, and Parsley
[2011]. These authors find that ex ante earnings quality does not affect the likelihood of
establishing political connections. To further address the endogeneity concern, we
perform five additional tests: an instrumental variable estimation method, a method based
on Heckman’s IMR approach, a propensity score matching method, change analysis, and
a method using exogenous shocks such as presidential and mid-term elections.
We first conduct an instrumental variable analysis by using a two-stage test. In the
first stage of this test we identify two instrumental variables, namely, DISTANCE
Untabulated results show that the political connection effect on audit fees is more pronounced for firms
with higher firm-specific litigation risk.
28
(defined as the distance, in thousands of kilometers, between a firm’s headquarters and
Washington, DC) and PROBPC (defined as the percentage of politically connected firms
in the industry to which each firm belongs). Because most political positions considered
in this study are at the federal government level, DISTANCE captures a measure of the
opportunities for firms to approach federal politicians and thus the likelihood of gaining
political directors.
Agrawal and Knoeber [2001] find that firms from different industries have
different incentives to build political connections. The variable PROBPC captures the
propensity for building political connections arising from industry characteristics. Neither
DISTANCE nor PROBPC is expected to affect audit fees. The dependent variable in the
first-stage model is one of the political connection measures and the independent
variables include two instruments and all the control variables of the second-stage
structural model. The predicted values of PCON from the first-stage model are used to
measure political connections in the second stage’s audit fee model. The results are
reported in Table 7.
Following Larcker and Rusticus [2010], Panel B of Table 7 also reports the results
of first-stage F-tests to detect the presence of weak instrument problems and further
report overidentification tests along with Hausman tests, because this test “is not valid if
the over-identifying restrictions test rejects the appropriateness of the instruments.” The
results of the Hausman tests are all significant at the 1% level, verifying the existence of
an endogeneity problem and the appropriateness of our instrumental variables
estimations.
The results from the first-stage model (Panel B) show that the coefficients of both
29
instrumental variables are significant at the conventional level of 1%. The coefficients of
DISTANCE are significantly negative, suggesting that the likelihood of having political
connections is reduced for firms whose headquarters are located far from Washington,
DC. The coefficients of PROBPC are positive and significant, suggesting that the
likelihood of building political connections is affected by industry characteristics.
Importantly, the results of the second-stage model (reported in Panel A) show that the
effect of political connections on audit fees remains significantly positive across all
different measures of corporate political connectedness.
[Insert Table 7 around here]
The second remedy for endogeneity problems uses Heckman’s IMR method, as
presented in Table 8. Table 8 also presents the regression results of the relation between
political connections and audit fees. As can be seen, the coefficient of political
connections (PCON) remains positive and significant at the conventional level of 1%.
The coefficient of Mill’s lambda is negative and significant for the audit fee model,
suggesting that the corresponding ordinary least squares coefficient of PCON would be
inconsistent. Correcting for the self-selection bias of corporate connectedness, the
evidence continues to support the view that auditors charge politically connected firms
higher audit fees than non-connected ones.15
[Insert Table 8 around here]
15
Following the suggestions advanced by Lennox, Francis, and Wang [2012]— who criticize our liberal
usage of Heckman procedures— for better selection model implementation, we first estimate selection
models with exclusion restrictions. We also report the results for the first-stage model in Panel B of Table 8.
As suggested by Tucker [2010] and Chaney, Jeter, and Shivakumar [2004], we compute the estimation of
counterfactual audit fees for connected firms, which are the hypothetical audit fees had these firms had no
connections. The untabulated results are available from the authors.
30
The third test relies on the propensity score matching approach. Specifically, we
run a probit model with PC Dummy as the dependent variable. The independent variables
include the two instrumental variables, DISTANCE and PROBPC, and the control
variables of the baseline audit fee model. This model predicts the propensity score for
establishing political connections. Politically connected firms are matched with
non-connected firms based on their propensity scores. We first estimate the ATT of
political connections based on nearest-neighbor and Kernel-based matching methods.16
As suggested by Armstrong, Jagolinzer, and Larcker [2010], we also include all the
control variables in our audit fee model to alleviate the possible imbalance problem.
Politically connected firms and their matched non-connected firms are then pooled to
conduct regression analyses. The results, reported in Table 9, show that politically
connected firms pay higher fees than comparable non-connected firms, suggesting that
political connections indeed matter to audit pricing.
[Insert Table 9 around here]
Goldman, Rocholl, and So [2009] document a positive abnormal return upon the
nomination announcement of former politicians to boards. Thus, we modify our baseline
audit fee model from using a level specification to using a change specification that also
serves as the fourth remedy for endogeneity problems. We test whether the addition of a
former politician to the board increases audit fees and the deletion of a political director
from the board reduces audit fees. The results of the change analysis in Table 10 show
that the coefficient of changes in political connections (∆PC) is positive and significant
16
Tucker [2010] argues that estimation of the average treatment effect on the treated using
nearest-neighbor matching can only be generalized to “common support.” Kernel-based matching, however,
can mitigate this problem.
31
for PC Dummy, PC Directors, PC Rank, and PC Freshness. These results provide
evidence consistent with the notion that changes in political connection influence audit
pricing changes.
[Insert Table 10 around here]
In addition to the econometric treatment of endogeneity problems discussed above,
we use exogenous events, such as presidential and mid-term elections, to deal with
endogeneity concerns. The fifth and last test contrasts the effects of political connections
on audit fees between election and non-election years. This test is motivated by Ramana
and Roychowdhury [2010], who argue that the incentives for earnings management in
politically connected firms are heightened during election years. This happens because
politically connected firms receive more public scrutiny during election years and thus
their managers have incentives to obscure financial statements to protect themselves and
their connected politicians. In this case, the audit risk associated with politically
connected client-firms increases during election years, which may encourage auditors to
spend more effort and charge higher fees accordingly.
For this test we consider both general and mid-term elections. The variable
ELECTION takes the value of one for firm–years in which the company’s fiscal year-end
falls within a calendar year having a general or mid-term election. Other firm–years take
a value of zero. Both ELECTION and the interaction term ELECTION*PCON are added
to the baseline audit fee model. The results are reported in Table 11. The coefficients of
the interaction term ELECTION*PCON are significantly positive, as are those of PCON.
These results suggest that political connections positively affect audit fees and their
positive effect is more pronounced during election years.
32
[Insert Table 11 around here]
The empirical results in this subsection do not support the endogeneity argument.
Instead, they show that political connections indeed affect auditors’ assessments of audit
risk and, subsequently, audit fees.
4.4.
Political Directors on Audit Committees as a Factor in Audit Fees
One can argue that directors with political backgrounds often lack the required
expertise or incentives to monitor managers, thus giving managers more opportunities to
misreport earnings for their own benefit. Since auditors recognize the expertise or
incentive problems associated with political directors, they can exert more effort in
auditing clients with political directors to detect or prevent accounting irregularities. This
explanation is consistent with prior research showing that agency problems affect the
incidence of accounting fraud (Beasley [1996]) and therefore influence audit pricing (Gul
and Goodwin [2010], Gul and Tsui [1998]). Alternatively, one can argue that political
directors have a greater public profile than other directors and face greater reputational
loss if their affiliated firms are discovered to have accounting irregularities. Hence,
political directors may request more effort and high-quality audits from external auditors
in the hopes of minimizing financial misreporting. This demand-side argument similarly
predicts a positive association between political connections and audit fees.
To evaluate these alternative explanations, we divide politically connected firms
into two groups: one consisting of firms with at least one political director on their audit
committee and the other consisting of those without political directors on their audit
committee. The above alternative explanations are supported if the effect of political
connections on audit fees is larger for the first group than for the second group. However,
33
the empirical results, reported in Table 12, show that the difference in audit fees between
these two groups is insignificant and thus lends little support to either of these alternative
explanations.
[Insert Table 12 around here]
4.5. Alternative Forms of Political Influence
Next we conduct additional analyses by including campaign contributions and
lobbying expenditures in the baseline model to incorporate different forms of political
influence and to determine the robustness of our findings in the presence of other forms
of political connections. The variable CONTRIBUTION is the log amount of political
contributions made via Political Action Committees (PACs), and LOBBYING is the log
amount of lobbying expenditures. We first replace PCON with CONTRIBUTION and
LOBBYING, respectively, and then include both CONTRIBUTION and LOBBYING along
with PCON, to estimate PCON effects on audit fees after controlling for the effects of
campaign contributions and lobbying expenditures. The results are reported in Table 13.
The results show that the each coefficient on CONTRIBUTION and LOBBYING is
significantly positive if the other two measures are not included, suggesting that both
CONTRIBUTION and LOBBYING separately capture some aspects of political
connections. 17 However, when the model includes all three variables for political
connections, the coefficient of CONTRIBUTION becomes insignificant, suggesting that
campaign contributions effects are subsumed by the effects of lobbying and political
directors in the context of audit pricing. Above all, the coefficients of human tie-based
17
Untabulated results indicate that the Pearson correlation between PC Dummy and CONTRIBUTION is
0.23 and that between PC Dummy and LOBBYING is 0.28. Both are significant at the conventional levels.
34
political connection variables remain significantly positive after controlling for the effects
of campaign contributions and lobbying expenditures.
[Insert Table 13 around here]
4.6.
Alternative Sample (S&P 500)
Most politically connected firms are significantly larger than non-connected firms,
as demonstrated by the correlations presented in Table 2. Prior auditing research also
shows that audit pricing is different to some extent for large and small firms (e.g., Francis
and Stokes [1986], Simunic [1980]). To determine whether our findings are driven by
some unobserved firm characteristics correlated with firm size, we next run the baseline
model using only S&P 500 firms that are relatively large and comparable in size. Despite
the reduced sample size of 2,724 firm–year observations, representing a reduction of over
90% of the sample used to test the baseline model in Table 3, the coefficients of PCON
from this test are not reported here for brevity (untabulated results available upon request)
but they are all significant and positive at the 5% level.
4.7.
Distress Risk, Political Connections, and Audit Fees
Prior literature shows that politically connected firms in developing countries are
more likely to be rescued by the government in case of distress (Faccio, Masulis, and
McConnell [2006]). In Section 2, however, we argue that this type of benefit tends to be
small in the United States, because the size and frequency of bailouts there have been
minor (Acemoglu et al. [2010]). One way to evaluate this argument is to examine the
effect of political connections on the audit fees for firms close to distress. We assume that
firms showing a financial loss are close to distress and we modify the baseline model by
35
adding the interaction term LOSS*PCON. If political connections significantly increase the
probability of being bailed out by the government in case of financial distress, then the
coefficients of the interaction term LOSS*PCON should be negative. The results reported
in Table 14, however, show that the coefficients of the interaction term LOSS*PCON are
not significant. This supports our hypothesis that the net effect of political connections on
audit fees should be positive, because political connections do not bring much benefit in
the form of bailouts to politically connected firms in the United States.18
[Insert Table 14 around here]
4.8.
Audit Opinion Analysis
We have shown that auditors charge higher fees to politically connected clients,
implying that such clients are riskier. In addition, we test whether auditors tend to issue
going-concern opinions more frequently to connected firms. Prior studies (e.g., Reynolds
and Francis [2001]) show that auditors are more conservative toward clients that may
impose higher audit risks (e.g., relatively large clients). The auditors therefore tend to
lower their threshold for issuing modified opinions on such firms. Prior research suggests
that auditors should be more likely to issue going-concern opinions to politically
connected clients, ceteris paribus, if they indeed perceive such clients to be riskier. This
discussion motivates our final analysis.
For our last test, we run an audit opinion model with a dummy variable that
indicates the reception of a going-concern opinion as the dependent variable. Following
Francis and Yu [2009], we control for financial characteristics that proxy for audit risk,
The results are similar if we use other measures for financial distress, including Altman’s [1968] Z-score
and the presence of negative operating cash flows (untabulated results are available from the authors).
18
36
variables that measure auditor independence, and indicator variables for audit quality
(such as Big N, national leader, and city-specific leader). We run this audit opinion model
for both the whole sample and the sub-sample consisting of firms in financial distress.
The results are reported in Table 15. The results show that in both the whole sample and
the distressed sub-sample, auditors are more likely to issue going-concern opinions to
politically connected clients. The coefficients of PCON, based on most political
connection measures, are significantly positive. These results suggest that auditors are
more conservative toward politically connected clients.
[Insert Table 15 around here]
4.9.
Pre- and Post-Financial Crisis Periods
Table 16 presents the audit fee effects of corporate political connections for one
year prior to the financial crisis of October 2008 and for the corresponding post-financial
crisis year. Auditors charge higher audit fees to connected firms than to non-connected
ones in both the pre- and post-financial crisis periods, evidenced by the significant and
positive coefficients of the political connection variables for both periods. Table 16 also
tests the difference in the adverse audit fee effects of political connections between the
pre- and post-financial crisis periods and finds the difference to be statistically
insignificant. These results do not support the view that auditors’ perceived audit risk for
firms with political connections was changed by the financial crisis of 2008.
[Insert Table 16 around here]
5. Conclusions
Political connections significantly affect accounting quality and business risk.
37
They may help connected firms improve financial performance, which would reduce
those firms’ incentives to misreport earnings. Moreover, politically connected firms may
be less likely to fail, because they have a higher chance of being rescued by the
government in case of financial distress. These arguments suggest that auditors should
expect lower risks in auditing clients with political connections than those without.
However, managers inside politically connected firms may have greater incentives to
obscure financial statements to hide politicians’ rent-seeking activities and/or to protect
their political benefits from being revealed to outsiders. Politically connected firms also
tend to have more volatile performance, which results from the occasional changes in
political landscape. This perspective predicts a higher risk associated with auditing clients
with political connections. Audit risk determines the level of audit effort and
consequently audit fees. We thus examine empirically how clients’ political connections
affect audit fees to help resolve the issue of how political connections affect audit risk.
This issue is also important because political connections are common among
public firms in the United States. We find that about 11% of U.S. firms have politicians
on their boards. Focusing on the period from 2001 to 2009, we find that auditors charged
higher fees to clients with political connections. This finding suggests that, overall,
political connections increase audit risk and, consequently, audit fees in the United States.
We attribute this result to two major factors. First, one of the important benefits brought
about by political connections, namely, a greater likelihood of being bailed out by the
government, is relatively small in the United States. Second, the highly litigious
environment facing auditors in the United States motivates them to exert more effort in
auditing politically connected clients, which are more visible and riskier. These
38
institutional factors enable political connections to have a positive effect on audit fees and
diminish any negative effect.
We also find that the effect of political connections on audit risk and audit fees is
constrained by internal and external monitoring mechanisms and is more pronounced
among firms with more complicated operations. Moreover, the political connection effect
on audit fees has grown stronger since the passage of SOX, which has increased the
litigation risk facing auditors.
We conduct a battery of additional tests to address the endogeneity issue and
evaluate other explanations. Overall, our findings are robust to these additional tests and
to the inclusion of other measures for political connections and alternative samples.
Finally, we show that auditors are more likely to issue modified opinions to politically
connected firms. To the extent that auditors tend to set a lower threshold for issuing
modified opinions to risky clients (Reynolds and Francis [2001]), this finding helps
confirm that auditors perceive politically connected clients to be riskier than
non-connected firms.
39
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44
Appendix: Definitions of Political Connection Measures
Variable
PC Dummy
PC Directors
PC Rank
PC Tenure
PC Freshness
Definition
Equals one for a firm with one or more directors who held a
political position before sitting on the board and zero otherwise.
Following Goldman, Rocholl, and So [2009], we consider the
following political positions: president, presidential candidate,
senator, member of the House of Representative, (assistant)
secretary, deputy secretary, deputy assistant secretary,
undersecretary, associate director, governor, director (CIA,
FEMA), deputy director (CIA, OMB), commissioner (IRS, NRC,
SSA, CRC, FDA, SEC), representative to the United Nations,
ambassador, mayor, staff (the White House’s, the president’s, a
presidential campaign’s), chairman of the party caucus, chairman
or staff of a presidential election campaign, and chairman or
member of a presidential committee/council.
Total number of board directors who held a political position
before sitting on the board.
Political power of a politically connected director’s former political
position, ranging from one to five, with larger values indicating
greater political power: five for president, vice president, (vice)
presidential candidates, etc.; four for secretary of important
departments (e.g., general attorney, secretaries of state, treasury,
and defense and White House executives and SEC commissioners);
three for governors and other secretaries of departments and
senators; two for house representatives; and one for
assistant/deputy secretaries of all departments and ambassadors.
Total number of years in political positions held by all connected
directors in a firm.
For a politically connected firm, the value of this measure is
determined based on the formula PC Freshness = Ln(45) – Ln(1 +
# of elapsed years), where # of elapsed years is the number of
years between the current year and the most recent political
position held by any connected director. The largest value of # of
elapsed years within our sample is 44. The value of PC Freshness
decreases with the number of elapsed years for connected firms
and is set to zero for non-connected firms.
45
Table 1: Descriptive Statistics
PC Dummy
PC Directors
PC Rank
PC Tenure
PC Freshness
AFEE
SIZE
LEV
INVREC
ROA
LOSS
NGS
NBS
YE
OPINION
BIG4
NLEADER
CLEADER
VOLATILITY
MB
FOREIGN
QUICK
CURRENT
# of Obs.
29,785
3,327
3,327
3,327
3,327
29,785
29,785
29,785
29,785
29,785
29,785
29,785
29,785
29,785
29,785
29,785
29,785
29,785
29,785
29,785
29,785
29,785
29,785
Mean
0.112
1.315
2.466
8.715
1.589
6.241
5.735
0.484
0.253
0.029
0.393
2.275
2.016
0.716
0.040
0.790
0.043
0.600
0.161
2.872
0.062
2.469
0.509
Std Dev
0.315
0.741
1.239
8.140
0.862
1.333
2.055
0.362
0.189
0.257
0.488
2.507
1.640
0.451
0.196
0.407
0.202
0.490
0.107
4.498
0.085
2.912
0.256
P10
0.000
1.000
1.000
1.000
0.533
4.594
3.122
0.155
0.038
-0.245
0.000
0.000
1.000
0.000
0.000
0.000
0.000
0.000
0.063
0.582
0.000
0.586
0.146
46
P25
0.000
1.000
1.000
2.000
0.995
5.216
4.238
0.268
0.098
0.000
0.000
0.000
1.000
0.000
0.000
1.000
0.000
0.000
0.089
1.149
0.000
0.944
0.312
Median
0.000
1.000
3.000
5.000
1.431
6.142
5.619
0.456
0.219
0.097
0.000
2.000
1.000
1.000
0.000
1.000
0.000
1.000
0.133
2.011
0.014
1.516
0.512
P75
0.000
1.000
3.000
14.000
2.219
7.130
7.094
0.632
0.363
0.158
1.000
4.000
3.000
1.000
0.000
1.000
0.000
1.000
0.200
3.537
0.105
2.789
0.715
P90
1.000
2.000
4.000
22.000
2.730
8.039
8.480
0.799
0.527
0.222
1.000
5.000
4.000
1.000
0.000
1.000
0.000
1.000
0.293
6.214
0.195
5.257
0.858
Table 2: Correlation Matrix
Panel A: Correlations between PC measures
[1] PC Dummy
[2] PC Directors
[3] PC Rank
[4] PC Tenure
[5] PC Freshness
[1]
[2]
[3]
[4]
0.86
0.88
0.71
0.87
0.83
0.70
0.82
0.76
0.83
0.74
Panel B: Correlations between control variables
[1] AFEE
[2] PC Dummy
[3] SIZE
[4] LEV
[5] INVREC
[6] ROA
[7] LOSS
[8] NGS
[9] NBS
[10] YE
[11] OPINION
[12] BIG4
[13] NLEADER
[14] CLEADER
[15] VOLATILITY
[16] MB
[17] FOREIGN
[18] QUICK
[19] CURRENT
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
0.20
0.79
0.31
0.00
0.29
-0.24
0.38
0.22
0.07
-0.10
0.36
0.16
0.20
-0.40
0.09
0.32
-0.22
-0.26
0.22
0.11
-0.05
0.06
-0.05
0.06
0.08
0.05
-0.02
0.07
0.07
0.06
-0.09
0.06
0.09
-0.07
-0.11
0.32
-0.09
0.43
-0.36
0.30
0.24
0.07
-0.20
0.38
0.15
0.25
-0.44
0.05
0.30
-0.27
-0.45
0.10
0.08
0.01
0.03
0.13
0.08
0.13
0.06
0.04
0.13
-0.08
-0.07
0.03
-0.68
-0.35
0.18
-0.17
0.20
0.12
-0.18
-0.03
-0.08
-0.04
-0.01
-0.05
-0.10
0.11
-0.20
0.35
-0.70
0.12
0.13
-0.03
-0.23
0.12
0.04
0.10
-0.40
0.20
0.26
-0.19
-0.28
-0.11
-0.13
0.05
0.23
-0.10
-0.04
-0.09
0.44
-0.16
-0.30
0.09
0.16
0.19
-0.02
-0.09
0.17
0.08
0.06
-0.12
0.04
0.34
0.01
0.05
-0.02
-0.05
0.05
0.03
0.08
-0.15
-0.06
0.08
-0.15
-0.16
0.02
0.05
0.02
0.02
0.06
0.02
-0.02
0.00
-0.11
-0.09
-0.02
-0.04
0.20
-0.08
-0.09
-0.14
-0.01
0.08
0.20
-0.15
0.05
0.11
0.01
-0.06
0.17
-0.05
0.01
0.08
-0.03
-0.05
-0.13
0.00
0.05
-0.10
-0.11
-0.10
-0.23
0.16
0.25
0.11
0.14
0.13
-0.01
-0.03
0.54
47
Table 3: Political Connections and Audit Fees
PCON
+
SIZE
+
LEV
+
INVREC
+
ROA
-
LOSS
+
NGS
+
NBS
+
YE
+
OPINION
+
BIG4
+
NLEADER
+
CLEADER
+
VOLATILITY
+
MB
+
FOREIGN
+
QUICK
-
CURRENT
+
Constant
Industry effects
Year effects
Observations
Adj. R2
(1)
PC Dummy
(2)
PC Directors
(3)
PC Rank
(4)
PC Tenure
(5)
PC Freshness
0.114***
[6.41]
0.499***
[98.30]
0.167***
[6.75]
0.170***
[3.20]
-0.250***
[-9.26]
0.118***
[10.08]
0.040***
[12.39]
0.040***
[10.25]
0.128***
[8.33]
0.227***
[9.84]
0.230***
[15.06]
0.304***
[11.60]
0.031***
[2.61]
0.203***
[4.20]
0.002**
[2.32]
0.814***
[6.45]
-0.042***
[-15.10]
0.482***
[11.63]
1.904***
[25.82]
0.087***
[6.89]
0.498***
[97.68]
0.167***
[6.75]
0.167***
[3.14]
-0.248***
[-9.18]
0.118***
[10.07]
0.040***
[12.36]
0.039***
[10.10]
0.128***
[8.38]
0.227***
[9.84]
0.231***
[15.09]
0.305***
[11.66]
0.030***
[2.58]
0.204***
[4.22]
0.002**
[2.29]
0.800***
[6.38]
-0.042***
[-15.10]
0.481***
[11.61]
1.914***
[25.90]
0.042***
[6.34]
0.499***
[98.25]
0.167***
[6.75]
0.170***
[3.21]
-0.251***
[-9.33]
0.117***
[10.05]
0.040***
[12.40]
0.040***
[10.30]
0.128***
[8.36]
0.227***
[9.84]
0.230***
[15.05]
0.303***
[11.60]
0.031***
[2.62]
0.203***
[4.19]
0.002**
[2.32]
0.807***
[6.39]
-0.042***
[-15.07]
0.482***
[11.63]
1.904***
[25.90]
0.007***
[4.83]
0.501***
[98.80]
0.168***
[6.74]
0.170***
[3.20]
-0.254***
[-9.43]
0.118***
[10.09]
0.040***
[12.36]
0.041***
[10.38]
0.128***
[8.36]
0.229***
[9.88]
0.229***
[14.95]
0.306***
[11.64]
0.030**
[2.53]
0.202***
[4.17]
0.003**
[2.49]
0.819***
[6.50]
-0.042***
[-15.00]
0.482***
[11.59]
1.898***
[25.66]
0.064***
[6.76]
0.499***
[98.34]
0.167***
[6.75]
0.169***
[3.18]
-0.250***
[-9.29]
0.118***
[10.12]
0.040***
[12.38]
0.040***
[10.28]
0.128***
[8.34]
0.227***
[9.83]
0.231***
[15.13]
0.304***
[11.60]
0.030**
[2.56]
0.202***
[4.17]
0.002**
[2.34]
0.818***
[6.48]
-0.042***
[-15.03]
0.481***
[11.60]
1.905***
[25.86]
Yes
Yes
29,785
0.82
Yes
Yes
29,785
0.82
Yes
Yes
29,785
0.82
Yes
Yes
29,785
0.82
Yes
Yes
29,785
0.82
All continuous variables are winsorized at the 1% and 99% levels. All t-statistics reported in
brackets are based on standard errors clustered at the firm level. The superscripts ***, **, and
* denote two-tailed significance levels of 0.01, 0.05, and 0.10, respectively.
48
Table 4: Corporate Governance, Political Connections, and Audit Fees
Panel A: Director shareholdings
PCON
+
GOV
?
GOV*PCON
-
Control variables
Industry effects
Year effects
Observations
Adj. R2
(1)
PC Dummy
(2)
PC Directors
(3)
PC Rank
(4)
PC Tenure
(5)
PC Freshness
0.147***
[3.94]
-0.006*
[-1.75]
-0.015***
[-2.58]
0.103***
[5.11]
-0.006*
[-1.86]
-0.010**
[-2.56]
0.052***
[3.86]
-0.006**
[-1.98]
-0.005**
[-2.23]
0.008***
[2.73]
-0.008**
[-2.45]
-0.001
[-1.55]
0.083***
[4.06]
-0.006**
[-1.97]
-0.008**
[-2.37]
Yes
Yes
Yes
11,940
0.78
Yes
Yes
Yes
11,940
0.78
Yes
Yes
Yes
11,940
0.78
Yes
Yes
Yes
11,940
0.78
Yes
Yes
Yes
11,940
0.78
(1)
PC Dummy
(2)
PC Directors
(3)
PC Rank
(4)
PC Tenure
(5)
PC Freshness
0.182***
[5.91]
0.013***
[5.18]
-0.013**
[-2.53]
0.115***
[4.72]
0.012***
[5.04]
-0.005
[-1.43]
0.061***
[5.26]
0.013***
[5.13]
-0.004*
[-1.94]
0.010***
[3.65]
0.012***
[5.05]
-0.001
[-1.36]
0.096***
[5.56]
0.013***
[5.11]
-0.006**
[-2.15]
Yes
Yes
Yes
29,785
0.82
Yes
Yes
Yes
29,785
0.82
Yes
Yes
Yes
29,785
0.82
Yes
Yes
Yes
29,785
0.82
Yes
Yes
Yes
29,785
0.82
Panel B: Institutional shareholdings
PCON
+
GOV
?
GOV*PCON
-
Control variables
Industry effects
Year effects
Observations
Adj. R2
All continuous variables are winsorized at the 1% and 99% levels. All t-statistics reported
in brackets are based on standard errors clustered at the firm level. The superscripts ***,
**, and * denote two-tailed significance levels of 0.01, 0.05, and 0.10, respectively.
49
Table 5: Operational Complexity, Political Connections, and Audit Fees
Panel A: Product line diversification
PCON
+
COMP
+
COMP *PCON
+
Control variables
Industry effects
Year effects
Observations
Adj. R2
(1)
PC Dummy
(2)
PC Directors
(3)
PC Rank
(4)
PC Tenure
(5)
PC Freshness
0.056*
[1.90]
0.111***
[6.63]
0.114***
[2.95]
0.052**
[2.08]
0.114***
[6.85]
0.060**
[1.96]
0.015
[1.36]
0.113***
[6.80]
0.043***
[3.03]
0.003
[1.37]
0.118***
[7.17]
0.007**
[2.14]
0.031**
[2.10]
0.113***
[6.84]
0.063***
[3.12]
Yes
Yes
Yes
25,826
0.82
Yes
Yes
Yes
25,826
0.82
Yes
Yes
Yes
25,826
0.82
Yes
Yes
Yes
25,826
0.82
Yes
Yes
Yes
25,826
0.82
(1)
PC Dummy
(2)
PC Directors
(3)
PC Rank
(4)
PC Tenure
(5)
PC Freshness
0.063**
[2.22]
0.231***
[12.07]
0.100**
[2.57]
0.054***
[2.61]
0.233***
[12.33]
0.063**
[2.46]
0.024**
[2.28]
0.234***
[12.35]
0.028**
[1.98]
0.003
[1.45]
0.235***
[12.53]
0.006**
[2.21]
0.031**
[2.07]
0.230***
[12.20]
0.065***
[3.24]
Yes
Yes
Yes
23,675
0.82
Yes
Yes
Yes
23,675
0.83
Yes
Yes
Yes
23,675
0.82
Yes
Yes
Yes
23,675
0.82
Yes
Yes
Yes
23,675
0.82
Panel B: Geographic diversification
PCON
+
COMP
+
COMP *PCON
+
Control variables
Industry effects
Year effects
Observations
Adj. R2
All continuous variables are winsorized at the 1% and 99% levels. All t-statistics reported
in brackets are based on standard errors clustered at the firm level. The superscripts ***,
**, and * denote two-tailed significance levels of 0.01, 0.05, and 0.10, respectively.
50
Table 6: SOX, Political Connections, and Audit Fees
PCON
+
SOX
+
SOX*PCON
+
Control variables
Industry effects
Observations
Adj. R2
(1)
PC Dummy
(2)
PC Directors
(3)
PC Rank
(4)
PC Tenure
(5)
PC Freshness
0.058**
[2.05]
0.942***
[75.78]
0.106***
[3.59]
0.067***
[3.82]
0.950***
[77.40]
0.033*
[1.69]
0.024**
[2.39]
0.946***
[77.14]
0.032***
[2.95]
0.004
[1.58]
0.947***
[78.46]
0.005**
[2.00]
0.043***
[2.92]
0.947***
[77.44]
0.047***
[2.92]
Yes
Yes
22,452
0.82
Yes
Yes
22,452
0.82
Yes
Yes
22,452
0.82
Yes
Yes
22,452
0.81
Yes
Yes
22,452
0.81
All continuous variables are winsorized at the 1% and 99% levels. All t-statistics reported
in brackets are based on standard errors clustered at the firm level. The superscripts ***,
**, and * denote two-tailed significance levels of 0.01, 0.05, and 0.10, respectively.
51
Table 7: Political Connections and Audit Fees (Instrumental Variables Using 2SLS)
Panel A: Second-stage regression: Political connections and audit fees
PCON
+
Control variables
Industry effects
Year effects
Observations
Overidentification test (p-value)
1st-stage F-test (p-value)
Hausman test
Adj. R2
(1)
PC
Dummy
(2)
PC
Directors
0.902***
[3.70]
Yes
Yes
Yes
29,785
0.3533
0.0000
0.0000
0.79
(3)
PC Rank
(4)
PC
Tenure
(5)
PC
Freshness
0.655***
[3.38]
0.361***
[3.56]
0.095***
[3.25]
0.510***
[3.66]
Yes
Yes
Yes
29,785
0.7080
0.0000
0.0000
0.79
Yes
Yes
Yes
29,785
0.3323
0.0000
0.0000
0.79
Yes
Yes
Yes
29,785
0.2351
0.0000
0.0000
0.74
Yes
Yes
Yes
29,785
0.2612
0.0000
0.0000
0.77
Panel B: First-stage model: Probability of establishing political connections
DISTANCE
-
PROBPC
+
SIZE
+
LEV
?
INVREC
?
ROA
?
LOSS
?
NGS
+
NBS
+
YE
?
OPINION
?
BIG4
?
FOREIGN
?
QUICK
?
(1)
PC Dummy
(2)
PC Directors
(3)
PC Rank
(4)
PC Tenure
(5)
PC Freshness
-0.000***
[-2.75]
0.846***
[7.84]
0.048***
[14.48]
0.010
[1.17]
-0.033
[-1.33]
-0.090***
[-5.16]
0.002
[0.36]
-0.001
[-0.65]
0.011***
[3.52]
0.007
[0.83]
0.027**
[2.24]
-0.016**
[-2.05]
0.091
[0.94]
-0.000
[-0.35]
-0.000***
[-3.21]
1.181***
[6.92]
0.075***
[11.71]
0.005
[0.48]
-0.045
[-1.22]
-0.136***
[-5.57]
0.008
[0.78]
-0.001
[-0.12]
0.023***
[3.38]
0.002
[0.17]
0.038**
[2.31]
-0.030***
[-2.79]
0.294*
[1.66]
-0.002
[-0.95]
-0.000**
[-2.29]
2.372***
[7.64]
0.139***
[13.11]
0.018
[0.88]
-0.115*
[-1.65]
-0.206***
[-4.35]
0.022
[1.29]
-0.005
[-0.92]
0.028***
[2.79]
0.007
[0.27]
0.086***
[2.64]
-0.047**
[-2.27]
0.445
[1.54]
-0.003
[-1.05]
-0.001*
[-1.88]
8.063***
[4.99]
0.472***
[10.55]
0.014
[0.15]
-0.710**
[-2.52]
-0.619***
[-2.89]
0.066
[0.85]
-0.021
[-0.83]
0.098**
[2.26]
0.087
[0.79]
0.243*
[1.75]
-0.181**
[-1.99]
1.202
[0.92]
-0.032**
[-2.25]
-0.000**
[-2.44]
1.300***
[7.03]
0.084***
[13.28]
0.005
[0.36]
-0.082*
[-1.88]
-0.134***
[-3.93]
-0.000
[-0.04]
-0.001
[-0.27]
0.019***
[3.20]
0.011
[0.73]
0.057**
[2.57]
-0.046***
[-3.32]
0.071
[0.38]
-0.003
[-1.49]
52
Table 7 (Cont’d.): Political Connections and Audit Fees (Instrumental Variables
Using 2SLS)
CURRENT
?
NLEADER
?
CLEADER
?
VOLATILITY
?
MB
?
Constant
Industry
effects
Year effects
Observations
Adj. R2
0.059***
[2.69]
0.021
[1.05]
-0.003
[-0.51]
0.016
[0.73]
0.003***
[4.02]
-0.282***
[-10.12]
0.089***
[2.62]
0.028
[0.79]
-0.003
[-0.31]
0.025
[0.82]
0.004***
[3.78]
-0.442***
[-8.82]
0.176***
[2.71]
0.095
[1.55]
-0.011
[-0.60]
0.094
[1.53]
0.007***
[3.82]
-0.845***
[-10.42]
0.871***
[2.97]
0.262
[0.93]
0.068
[0.79]
0.291
[1.07]
0.016**
[2.01]
-2.857***
[-7.65]
0.114***
[2.96]
0.050
[1.25]
0.002
[0.16]
0.078*
[1.90]
0.004***
[3.64]
-0.480***
[-9.61]
Yes
Yes
Yes
Yes
Yes
Yes
27,393
0.10
Yes
27,393
0.10
Yes
27,393
0.11
Yes
26,356
0.06
Yes
26,356
0.04
All continuous variables are winsorized at the 1% and 99% levels. All t-statistics reported
in brackets are based on standard errors clustered at the firm level. The superscripts ***,
**, and * denote two-tailed significance levels of 0.01, 0.05, and 0.10, respectively.
53
Table 8: Political Connections and Audit Fees (Heckman IMR Method)
Panel A: Second-stage regression: Political connections and audit fees
PC Dummy
PCON
+
MILLS
?
0.100***
[5.70]
-0.135***
[-3.08]
Variance inflation factors (VIFs)
PCON
MILLS
1.11
16.54
Control variables
Industry effects
Year effects
Observations
Adj. R2
Yes
Yes
Yes
27,393
0.83
Panel B: Four-stage model: Probability of establishing political connections
PC Dummy
DISTANCE
-
PROBPC
+
SIZE
+
LEV
?
INVREC
?
ROA
?
LOSS
?
NGS
+
NBS
+
YE
?
OPINION
?
BIG4
?
FOREIGN
?
QUICK
?
54
-0.001***
[-5.89]
4.412***
[8.72]
0.263***
[32.04]
0.076**
[2.51]
-0.164
[-1.82]
-0.475***
[-7.87]
0.014
[0.47]
-0.017***
[-3.51]
0.045***
[7.08]
0.072***
[2.75]
0.169***
[2.72]
-0.039
[-1.24]
0.068
[0.26]
-0.003
[-0.51]
Table 8 (Cont’d.): Political Connections and Audit Fees (Heckman IMR Method)
CURRENT
?
NLEADER
?
CLEADER
?
VOLATILITY
?
MB
?
Constant
Industry effects
Year effects
Observations
Adj. R2
0.259***
[3.80]
0.012
[0.25]
-0.023
[-0.98]
-0.059
[-0.43]
0.013***
[5.50]
-3.698***
[-23.67]
Yes
Yes
27,393
0.13
All continuous variables are winsorized at the 1% and 99% levels. All t-statistics
reported in brackets are based on standard errors clustered at the firm level. The
superscripts ***, **, and * denote two-tailed significance levels of 0.01, 0.05, and
0.10, respectively.
55
Table 9: Political Connections and Audit Fees (Propensity Score Matching)
Panel A
AFEE (mean value)
PCON=1
(# of obs. 3212)
Near Neighbor
(n=1)
Kernel-based
(Gaussian)
Kernel-based
(Epanechnikov)
7.03
6.93
6.76
6.91
3.95***
16.12***
7.31***
t-Stat. of differences
Panel B
PCON
+
Control PSCORE portfolio dummies
Control variables
Industry effects
Year effects
Observations
Adj. R2
Near Neighbor (n=1)
Whole sample
0.089***
0.078***
[4.41]
[4.33]
No
Yes
Yes
Yes
5,868
0.87
Yes
Yes
Yes
Yes
27,393
0.83
Panel A presents the differences in audit fees between politically connected firms
and matched outcomes calculated by nearest-neighbor and kernel-matching methods,
respectively. Panel B presents the results of ordinary least squares regressions on
audit fees. We construct 20 portfolios based on propensity matching scores
(PSCORE). Then we assign dummy variables to indicate each portfolio and include
them in the regression to control for the effects of the probability of being politically
connected. All continuous variables are winsorized at the 1% and 99% levels. All
t-statistics reported in brackets are based on standard errors clustered at the firm
level. The superscripts ***, **, and * denote two-tailed significance levels of 0.01,
0.05, and 0.10, respectively.
56
Table 10: Audit Fees and Political Connections (Change Analysis)
∆PC
∆Size
∆Lev
∆InvRec
∆ROA
∆LOSS
∆NGS
∆NBS
∆Busy
∆Opinion
∆Big4
∆Foreign
∆Quick
∆Current
∆NLeader
∆CLeader
∆Volatility
∆MB
Constant
Observations
Adj. R2
(1)
(2)
(3)
Political Rank
(4)
Political
Tenure
PC Dummy
PC Directors
0.034*
[1.91]
0.278***
[30.77]
0.035***
[3.31]
0.133***
[3.15]
-0.135***
[-6.82]
0.036***
[6.14]
0.004
[1.43]
0.003
[0.93]
0.249***
[4.50]
0.056***
[4.02]
0.165***
[14.26]
0.021
[0.30]
-0.006***
[-3.61]
-0.148***
[-5.17]
0.127***
[8.40]
0.077***
[11.04]
0.039
[1.48]
-0.000
[-0.29]
-0.028**
[-2.23]
24,366
0.16
(5)
PC Freshness
0.033***
[2.97]
0.277***
[30.71]
0.035***
[3.30]
0.132***
[3.15]
-0.134***
[-6.79]
0.036***
[6.14]
0.004
[1.42]
0.003
[0.94]
0.249***
[4.52]
0.056***
[4.04]
0.164***
[14.23]
0.023
[0.33]
-0.006***
[-3.62]
-0.149***
[-5.18]
0.127***
[8.39]
0.077***
[11.03]
0.039
[1.49]
-0.000
[-0.30]
-0.028**
[-2.22]
0.010*
[1.70]
0.278***
[30.77]
0.035***
[3.31]
0.133***
[3.15]
-0.135***
[-6.81]
0.036***
[6.14]
0.004
[1.42]
0.003
[0.94]
0.249***
[4.50]
0.056***
[4.02]
0.165***
[14.25]
0.021
[0.31]
-0.006***
[-3.61]
-0.149***
[-5.17]
0.127***
[8.40]
0.077***
[11.04]
0.039
[1.49]
-0.000
[-0.30]
-0.028**
[-2.24]
0.001
[0.71]
0.278***
[30.78]
0.035***
[3.31]
0.133***
[3.16]
-0.135***
[-6.80]
0.036***
[6.14]
0.004
[1.39]
0.003
[0.94]
0.249***
[4.50]
0.056***
[4.02]
0.165***
[14.25]
0.021
[0.30]
-0.006***
[-3.62]
-0.148***
[-5.16]
0.127***
[8.39]
0.077***
[11.04]
0.039
[1.48]
-0.000
[-0.30]
-0.028**
[-2.26]
0.042**
[2.02]
0.278***
[30.75]
0.035***
[3.31]
0.133***
[3.15]
-0.134***
[-6.78]
0.036***
[6.16]
0.004
[1.42]
0.003
[0.93]
0.249***
[4.51]
0.057***
[4.05]
0.164***
[14.25]
0.021
[0.30]
-0.006***
[-3.61]
-0.148***
[-5.16]
0.127***
[8.39]
0.077***
[11.03]
0.039
[1.49]
-0.000
[-0.31]
-0.028**
[-2.23]
24,366
0.16
24,366
0.16
24,366
0.16
24,366
0.16
The dependent variable is the annual change in the log of audit fee. Columns 1 to 5 present
57
the results of variant measures of firm human-tie political connections. Here ∆PC is the
annual change in the respective PC measure. The analysis is for observations with changes in
their respective PC measures. The analysis sample also includes unchanged observations. All
continuous variables are winsorized at the 1% and 99% levels. The analysis controls for
industry and year fixed effects. All statistics are adjusted by clustering within firms. The
superscripts ***, **, and * denote two-tailed significance levels of 0.01, 0.05, and 0.10,
respectively.
58
Table 11: Elections, Political Connections, and Audit Fees
(1)
PC Dummy
PCON
+
ELECTION
+
ELECTION*PCON
+
Control variables
Industry effects
Year effects
Observations
Adj. R2
(2)
PC Directors
(3)
PC Rank
(4)
(5)
PC Tenure PC Freshness
0.077***
[4.23]
0.031***
[4.31]
0.045***
[3.83]
0.061***
[5.02]
0.032***
[4.42]
0.029***
[3.97]
0.028***
[4.22]
0.031***
[4.34]
0.017***
[4.05]
0.005***
[3.47]
0.033***
[4.65]
0.002**
[2.26]
0.043***
[4.49]
0.031***
[4.38]
0.025***
[3.70]
Yes
Yes
Yes
29,785
0.82
Yes
Yes
Yes
29,785
0.82
Yes
Yes
Yes
29,785
0.82
Yes
Yes
Yes
29,785
0.82
Yes
Yes
Yes
29,785
0.82
All continuous variables are winsorized at the 1% and 99% levels. All t-statistics reported in
brackets are based on standard errors clustered at the firm level. The superscripts ***, **, and
* denote two-tailed significance levels of 0.01, 0.05, and 0.10, respectively.
Table 12: Connected Directors on the Audit Committee and Audit Fees
(1)
PC
Dummy
(2)
PC
Directors
PC Rank
0.114***
[3.67]
0.114***
[5.70]
0.081***
[4.74]
0.093***
[5.77]
0.038***
[3.80]
0.045***
[5.73]
0.005**
[2.49]
0.008***
[4.79]
0.064***
[4.04]
0.064***
[5.87]
PCON_AUD = PCON_NUAD
(p-value)
0.986
0.566
0.538
0.170
0.987
Control variables
Industry effects
Year effects
Observations
Adj. R2
Yes
Yes
Yes
29,785
0.82
Yes
Yes
Yes
29,785
0.82
Yes
Yes
Yes
29,785
0.82
Yes
Yes
Yes
29,785
0.82
Yes
Yes
Yes
29,785
0.82
PCON_AUD
+
PCON_NAUD
+
(3)
(4)
(5)
PC
PC Tenure Freshness
The variable PCON_AUD equals one for firms with at least one political director sitting on
the audit committee and zero otherwise and PCON_NAUD equals one for those firms that are
politically connected but do not have a political director on the audit committee and zero
otherwise. All continuous variables are winsorized at the 1% and 99% levels. All t-statistics
reported in brackets are based on standard errors clustered at the firm level. The superscripts
***, **, and * denote two-tailed significance levels of 0.01, 0.05, and 0.10, respectively.
60
Table 13: Political Contributions, Lobbying Expenditure, and Audit Fees
(1)
PCON
+
CONTIBUTION
+
LOBBYING
+
Control variables
Industry effects
Year effects
Observations
Adj. R2
(2)
(3)
PC Dummy
(4)
PC Directors
(5)
PC Rank
(6)
PC Tenure
(7)
PC Freshness
0.010***
[6.88]
0.097***
[5.42]
-0.000
[-0.13]
0.009***
[6.22]
0.075***
[6.02]
-0.001
[-0.31]
0.009***
[6.15]
0.035***
[5.35]
-0.000
[-0.09]
0.009***
[6.16]
0.005***
[3.93]
0.000
[0.07]
0.009***
[6.44]
0.055***
[5.80]
-0.001
[-0.17]
0.009***
[6.22]
Yes
Yes
Yes
29,785
0.82
Yes
Yes
Yes
29,785
0.82
Yes
Yes
Yes
29,785
0.82
Yes
Yes
Yes
29,785
0.82
Yes
Yes
Yes
29,785
0.82
Yes
Yes
Yes
29,785
0.82
0.008***
[2.84]
Yes
Yes
Yes
29,785
0.82
Continuous variables are winsorized at the 1% and 99% levels. All t-statistics reported in brackets are based on standard errors
clustered at the firm level. The superscripts ***, **, and * denote two-tailed significance levels of 0.01, 0.05, and 0.10, respectively.
61
Table 14: Political Connections and Audit Fees for Financially Distressed Firms
PCON
+
LOSS
+
PCON*LOSS
?
Control variables
Industry effects
Year effects
Observations
Adj. R2
(1)
PC Dummy
(2)
PC Directors
(3)
PC Rank
(4)
PC Tenure
(5)
PC Freshness
0.058**
[2.32]
0.093***
[6.34]
-0.011
[-0.32]
0.048***
[3.15]
0.092***
[6.37]
-0.005
[-0.22]
0.015
[1.63]
0.091***
[6.28]
0.002
[0.17]
0.003
[1.64]
0.092***
[6.42]
-0.000
[-0.09]
0.025*
[1.88]
0.093***
[6.44]
-0.007
[-0.39]
Yes
Yes
Yes
29,785
0.73
Yes
Yes
Yes
29,785
0.73
Yes
Yes
Yes
29,785
0.73
Yes
Yes
Yes
29,785
0.73
Yes
Yes
Yes
29,785
0.73
All continuous variables are winsorized at the 1% and 99% levels. All t-statistics reported in
brackets are based on standard errors clustered at the firm level. The superscripts ***, **, and *
denote two-tailed significance levels of 0.01, 0.05, and 0.10, respectively.
62
Table 15: Political Connections and Audit Opinions
Panel A: Whole sample
PCON
+
SIZE
-
DACC
+
LEV
+
ROA
-
LOSS
+
BIG4
-
LIT_SHU
+
OFFICESIZE
+
INFLUENCE
+
NLEADER
+
CLEADER
+
TENURE
+
REPORTLAG
+
MB
+
VOLATILITY
+
PRIOGC
+
Constant
Industry effects
Year effects
Observations
Pseudo-R2
(1)
(2)
PC Dummy PC Directors
(3)
PC Rank
(4)
(5)
PC Tenure PC Freshness
0.239***
[3.22]
-0.247***
[-10.14]
0.008
[0.73]
0.214*
[1.93]
-1.107***
[-12.33]
0.858***
[11.57]
-0.128*
[-1.80]
0.222***
[8.27]
0.050**
[2.30]
0.026
[0.33]
-0.198
[-1.28]
0.044
[0.89]
0.006
[0.11]
0.004***
[6.53]
-0.000
[-0.41]
0.865***
[5.25]
1.745***
[19.93]
-0.923**
[-2.03]
0.112**
[2.55]
-0.245***
[-10.07]
0.008
[0.72]
0.216*
[1.94]
-1.110***
[-12.40]
0.859***
[11.62]
-0.129*
[-1.81]
0.222***
[8.28]
0.051**
[2.36]
0.029
[0.36]
-0.201
[-1.31]
0.042
[0.86]
0.008
[0.15]
0.004***
[6.53]
-0.000
[-0.39]
0.865***
[5.25]
1.745***
[19.97]
-0.938**
[-2.06]
0.085***
[3.23]
-0.247***
[-10.13]
0.008
[0.71]
0.215*
[1.94]
-1.110***
[-12.36]
0.857***
[11.56]
-0.128*
[-1.80]
0.222***
[8.26]
0.050**
[2.32]
0.026
[0.33]
-0.200
[-1.29]
0.043
[0.87]
0.007
[0.13]
0.004***
[6.53]
-0.000
[-0.40]
0.868***
[5.26]
1.743***
[19.94]
-0.930**
[-2.05]
0.015***
[2.81]
-0.245***
[-10.11]
0.008
[0.75]
0.214*
[1.93]
-1.111***
[-12.42]
0.857***
[11.59]
-0.128*
[-1.80]
0.223***
[8.29]
0.050**
[2.33]
0.027
[0.34]
-0.196
[-1.28]
0.041
[0.83]
0.009
[0.17]
0.004***
[6.53]
-0.000
[-0.39]
0.866***
[5.25]
1.746***
[19.99]
-0.929**
[-2.04]
0.122***
[2.97]
-0.247***
[-10.13]
0.008
[0.72]
0.215*
[1.94]
-1.105***
[-12.29]
0.860***
[11.57]
-0.124*
[-1.74]
0.222***
[8.27]
0.050**
[2.30]
0.024
[0.30]
-0.196
[-1.28]
0.042
[0.85]
0.006
[0.12]
0.004***
[6.53]
-0.000
[-0.39]
0.864***
[5.23]
1.743***
[19.94]
-0.917**
[-2.02]
Yes
Yes
22,387
0.48
Yes
Yes
22,387
0.48
Yes
Yes
22,387
0.48
Yes
Yes
22,387
0.48
Yes
Yes
22,387
0.48
63
Table 15 (Cont’d.): Political Connections and Audit Opinions
Panel B: Financially distressed sub-sample
(1)
(2)
PC Dummy PC Directors
PCON
Control variables
Industry effects
Year effects
Observations
Pseudo-R2
+
(3)
PC Rank
(4)
(5)
PC Tenure PC Freshness
0.270***
[3.46]
0.130***
[2.63]
0.094***
[3.28]
0.016***
[2.83]
0.140***
[3.18]
Yes
Yes
Yes
8,528
0.37
Yes
Yes
Yes
8,528
0.37
Yes
Yes
Yes
8,528
0.37
Yes
Yes
Yes
8,528
0.37
Yes
Yes
Yes
8,528
0.37
This table reports the results of the probit model, with an indicator variable for going-concern
opinions as the dependent variable. Among independent variables, LIT_SHU is firm-specific
litigation risk, based on Shu [2000]; OFFICESIZE is the log value of audit fee revenues for the
city-based practice office that provides audit services for the sample firm; INFLUENCE is the
percentage of total fees (including both audit and non-audit fees) charged to the sample firm
relative to the total revenue for the city-based practice office that provides audit services for the
firm; TENURE equals one if auditor tenure is three years or shorter and zero otherwise;
REPORTLAG equals the number of days between the fiscal year-end and the earnings
announcement date; VOLATILITY is the standard deviation of monthly stock returns for 12
months of the current fiscal year; PRIORGC equals one if the company received a going-concern
audit opinion in the last year and zero otherwise; and DACC is unsigned discretionary accruals
estimated from the modified cross-sectional Jones model. All other variables are defined in
Section 3.2. All continuous variables are winsorized at the 1% and 99% levels. The analysis
controls for industry and year fixed effects. All statistics are adjusted by clustering within firms,
that is, standard errors are estimated by maximum likelihood under the assumption that residuals
are clustered within firms. The superscripts ***, **, and * denote two-tailed significance levels
of 0.01, 0.05, and 0.10, respectively.
64
Table 16: Political Connections and Audit Fees (Before and After the Financial Crisis)
PC Dummy
One year before Oct. 2008
PCON
Difference
(p-value)
Control variables
Industry effects
Observations
R-Squared
+
0.062**
[2.30]
One year after Oct. 2008
0.072***
[2.79]
0.010
[0.25]
Yes
Yes
3,288
0.81
Yes
Yes
3,020
0.83
All continuous variables are winsorized at the 1% and 99% levels. All t-statistics reported in
brackets are based on standard errors clustered at the firm level. The superscripts ***, **, and *
denote two-tailed significance levels of 0.01, 0.05, and 0.10, respectively.
65