Download Auditor-Relevant Congressional Committees and Audit Quality

Auditor-Relevant Congressional Committees
and Audit Quality
Mihir N. Mehta
Temple University & MIT Sloan
[email protected]
Wanli Zhao
Southern Illinois University
[email protected]
August 24, 2014
ABSTRACT
We investigate whether the supply of audit quality is associated with auditors’ incentives to
avoid political costs from audit failure. We find that in states with powerful political
representation on U.S. congressional committees charged with auditor oversight
responsibilities, auditors supply higher quality audited earnings, issue more going concern
and internal control weakness opinions, and are less likely to be associated with fraud-related
regulatory enforcement action. In addition, two distinct exogenous shocks to auditors’
exposure to politically motivated audit failure costs affect the supply of audit quality. Our
results are robust to the inclusion of firm fixed effects, and controls for the presence of
political connections. Our paper is among the first to examine the disciplinary role of U.S.
congressional committees on auditors’ real actions, and to show that auditors’ politically
related incentives to supply audit quality are distinct and incremental to well-established
reputation and litigation related incentives.
Keywords: Political economy; Corporate governance; Audit risk; U.S. Senate; U.S.
House of Representatives
JEL Codes: G34; M42; M48
___________________________
We appreciate comments and suggestions from Sudipta Basu, Larry Brown, Robert Bushman, Jason Greene,
Jagan Krishnan, Krish Menon, Pat O’Brien, Mark Peterson, Eric Press, Luke Threinen, Joseph Weber, and
seminar participants at Southern Illinois University Carbondale and Temple University. We have benefited
from discussions with audit partners at Ernst & Young and KPMG. Mehta and Zhao acknowledge financial
support from Temple University and Southern Illinois University Carbondale respectively. Mehta is currently
visiting MIT Sloan.
!
1. Introduction
Audit failure is costly and imposes significant reputational and litigation costs on auditors. 1
Accordingly, auditors have strong incentives to supply high quality audit services and avoid audit
failure. Our goal in this paper is to examine a different incentive for auditors to supply high quality
audits: to prevent incurring politically motivated penalties imposed by powerful politicians that are
most affected by audit failure. Specifically, we investigate whether the supply of audit quality varies
with the power of a state’s political representation on the two Senate and House congressional
committees that are directly responsible for auditor regulation and oversight of the Securities and
Exchange Commission (SEC) and the Public Company Accounting Oversight Board (PCAOB). The
congressional committees are the Senate Banking, Housing, and Urban Affairs Committee and
House of Representatives Committee on Financial Services (hereafter “influential committees”). Thus,
our study is unique in that we focus on auditor behavior linked specifically to U.S. congressional
committee politicians that can impose penalties on auditors.
Following audit failure, politicians serving on influential committees have incentives to
investigate and impose penalties on auditors and/or clients for a number of reasons, including
regulatory capture, to avoid perceived responsibility for investor losses, to limit adverse effects on
constituency support, and to maintain reputability as effective politicians. The economic theory of
regulatory capture (or “capture theory”) (Stigler, 1971; Laffont and Tirole, 1991) suggests that
politicians captured by firms that provide financial and political support have incentives to be more
lenient towards transgressions by the capturer to allow the politicians to maintain those beneficial
relationships. Thus, capture theory implies that if influential committee politicians are captured by
firms, especially in their elected states, then those politicians have personal incentives to limit
See DeFond and Zhang (2013) for a detailed discussion. Examples of studies examining reputation costs include
DeAngelo (1981), Reynolds and Francis (2000), Barton (2005), Weber et al. (2008), and Skinner and Srinivasan (2012).
Examples of studies examining litigation costs include Palmrose (1988), Dye (1993), and Khurana and Raman (2004).
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penalties on connected firms following financial reporting failure, and instead, focus on imposing
penalties on auditors.
Other incentives for politicians to act following audit failure include the potential for adverse
impacts on politician reputation and voter perceptions about politician effectiveness. The economic
theory of the political process suggests politicians have incentives to avoid perceived responsibility
for investor losses from audit failures and that disciplinary action is a political attempt to escape
blame (Peltzman, 1976). In addition, politicians have incentives to increase the resources controlled
by the government via laws and regulations, since that increases the politicians’ ability to grant favors
(Meckling, 1976) and increase their own wealth (Watts and Zimmerman, 1986).
Following audit failure, disciplinary action against auditors can help a politician earn votes
and reinforce voter perception about politician effectiveness. In turn, this can increase constituency
support and improve the politician’s re-election prospects (Mayhew, 1974; Fiorina, 1977). We
assume that the incentives for an influential committee politician to discipline an auditor following
audit failure are greatest when the audit failure occurs in the politician’s home state, because these
are likely to be the most visible to the politician’s constituents, all else equal. 2 Furthermore,
politicians have incentives to discipline auditors following audit failures in order to maintain
reputations as effective and ethical legislators. Better reputations in turn increases the opportunities
for lucrative post-elective employment such as ambassadorships or non-executive corporate board
seats, or to obtain seats on powerful congressional committees that require greater legislative leeway
such as the House or Senate Appropriations Committees (Fenno, 1973; Parker, 2005).
The jurisdictional nature of the U.S. Congress requires the allocation of federal responsibility
to congressional committees that specialize in and have oversight for assigned governmental
The incumbent faces barriers to re-election if opposition candidates highlight in-state audit failures in order to attack
the incumbent’s ineffective legislative action. Such attacks must then be answered or refuted by the incumbent, thereby
raising his costs. Scrutiny of audit failures and the imposition of penalties reduce the possibility that an incumbent
politician suffers from such attacks.
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responsibilities. Our study focuses on the two congressional committees that are responsible for
auditor regulation and oversight (i.e., influential committees). Only politicians serving on influential
committees have the ability to discipline auditors following audit failures because of the
responsibilities assigned to these committees under the rules and procedures of the U.S. Senate and
the U.S House of Representatives. Disciplinary action can occur directly, via legislation sponsored
and introduced to congressional chambers by a committee member, or indirectly, through the
committees’ role as overseers of the SEC and PCAOB. For example, the Sarbanes-Oxley Act (SOX)
was co-sponsored by senior members of both the Senate and House influential committees (Paul
Sarbanes (D-MD) and Michael Oxley (R-OH), respectively). SOX resulted in substantial reforms for
auditors, including the creation of a new oversight body, and stricter auditor independence
requirements. While legislative action is relatively rare following audit failure, regulatory action by
the SEC and PCAOB is much more common. In recent years, the SEC has imposed multi-million
dollar fines on audit firms and penalized or suspended licenses for individual audit partners
following audit failures. 3 The scope and breadth of the power of influential committees over the
actions of regulatory bodies can be seen in the following comments from former SEC Chairman
Arthur Levitt: 4
“[I received] a letter from the overseers of the SEC, the congressional committee that oversees the SEC that
has a chokehold on the existence of the SEC, that can block SEC funding, that can block SEC rulemaking, that
can create a constant pressure in terms of hearings and challenges and public statements, that can absolutely make life
miserable for the commission.”
and
For example, in 2009 the SEC fined Ernst & Young (EY) $8.5m and sanctioned multiple EY partners for the audits of
Bally Total Fitness Inc. The SEC noted that there were “failures from the engagement team to the top of the firm’s
national office”. The SEC barred five current and former EY partners from auditing public companies for up to three
years (http://www.sec.gov/litigation/admin/2009/33-9096.pdf). In 2005, the SEC fined Deloitte & Touche (DT) $50m
relating to the audits of Adelphia Inc and barred the lead partner from auditing public companies for four years
(https://www.sec.gov/litigation/opinions/2008/34-57244.pdf). While only anecdotal, in both of these examples the
affected clients were located in states (IL, and PA, respectively) that had at least one congressional representative in the
top quartile of influential committee seniority.
4 http://www.pbs.org/wgbh/pages/frontline/shows/regulation/interviews/levitt.html
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“[The politicians] kept the heat on me by telephone calls, by letters, by congressional hearings, and ultimately
by threatening the funding of the agency by threatening its very existence. I mean, we were at that point struggling with
this same committee to see to it that the employees of the SEC received the same compensation as other financial
regulators. At the time, we were getting about a third less than employees for the Federal Reserve Board and other
banking entities. And certain members of this committee suggested to me that getting that pay parity was out of the
question while we were proceeding with this issue. So we were really being held, well, an attempt was made to hold us
captive.” ~ Arthur Levitt, former SEC chairman
Our study implicitly assumes that auditors are aware of heterogeneity in political influence,
and more specifically, cognizant as to which politicians serve in powerful capacities on influential
committees. Evidence from a recent report about politician action committee (PACs) lobbying and
contributions indicates that audit firms at both firm and individuals-partner levels appear to actively
lobby and make contributions to senior influential committee members. 5 For instance, politician
action committees (PACs) funded by Big 4 audit firm employees gave nearly $10m ($7m) dollars in
the 2010 (2006) congressional election cycle, with the money directly targeted to House and Senate
influential committee members. House Representative Spencer Bachus (R-AL) who is the current
chairman of the House Financial Services Committee has received $370,000 from “people in the
accounting and audit industry” over his career (which is more than any other active congressional
representative), and Senator Charles Schumer (D-NY), a senior influential committee member
during the latter half of our sample period, received over $150,000 from 156 distinct Big 4
employees in 2010. 6
We argue that if auditors believe that audit failure in states served by politicians on influential
committees is likely to result in the largest political costs for the auditor because of a politician’s
reputational or capture related incentives to minimize the affected audit client’s responsibility for
http://www.reuters.com/article/2012/03/13/us-usa-accounting-big-idUSBRE82C0JQ20120313
If auditors make political contributions to powerful influential committee members because of their ability to influence
regulation over the profession, it follows that auditors also likely understand that the same politicians can impose costs
in the event of audit failure, such as the implementation of SOX following the failure of Enron.
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audit failures, then auditors have incentives to increase the supply of audit quality in those states,
relative to other states.
An important consideration is the possibility that auditors, rather than their clients, capture
influential committee politicians via campaign and political contributions. Under this scenario
politicians may attempt to limit scrutiny of auditors and instead focus on penalizing the firm’s
managers in the event of audit failure. Thus, auditors are unlikely to have incentives to increase the
supply of audit quality, ceteris paribus, and may even have incentives to decrease the supply of audit
quality. 7
To examine the relation between the presence of influential politicians and the supply of
audit quality, we obtain data pertaining to the dates of representation and committee assignments for
all elected politicians for the years between 2000 and 2010. We identify politicians that serve on
influential committees, and for each politician, their committee seniority. Committee seniority has
been shown to be the most important determinant of a politician’s ability to have influence on a
committee (Levitt and Poterba, 1999; Cohen et al., 2010). 8 We define seniority as the ordinal ranking
of members of a given congressional committee based on committee tenure (Roberts, 1990). Senior
influential committee members have the greatest ability to impose penalties on auditors for at least
two reasons. First, senior committee members determine the committee’s agenda and legislative
focus. For instance, senior committee members and subcommittee chairmen determine which
legislative proposals are deserving of consideration by the entire House or Senate chambers. If
senior members choose not to approve a committee-relevant bill brought forward by any member in
the chamber or committee, the bill “dies in committee”. Second, senior committee members
In reality, this is unlikely to occur because auditors face reputational and litigation related incentives to prevent audit
failure irrespective of the extent to which politicians are captured.
8 Levitt and Poterba (1999) find that senior Democratic members of the House of Representatives use their positions to
positively influence the economic performance of their states. Cohen et al. (2010) provide empirical evidence that the
ascension of a congressman to a powerful committee chairmanship or ranking minority member position results in
increased federal earmark, state-level government transfers and government contract allocations to the congressman’s
state.
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determine and allocate funding to regulatory bodies overseen by the committee (as illustrated in the
aforementioned quote from Arthur Levitt).
We collect congressional committee and politician data, and merge with Compustat firm
data based on whether the firm is headquartered in the politician’s state of representation. We then
merge our dataset with auditor-relevant data from Audit Analytics. After eliminating firms that do
not use a Big 6 auditor, financial services and utilities firms, and firms missing required data, our
final sample consists of 17,017 firm-year observations. We use four constructs to capture the supply
of audit quality: (1) measures of earnings quality using accruals estimation techniques; (2) financial
statement fraud using AAER action; (3) the issuance of going concern modified audit opinions; (4)
the issuance of internal control weakness opinions. An advantage of using multiple supply-side audit
quality measures is that each of the measures have complementary strengths, which provides
valuable benefits in that we can triangulate across measures (DeFond and Zhang, 2013).
An important attribute of our research design is that as improvements in a politician’s
committee seniority can only occur via the resignation or defeat of other more senior committee
members, increases in a politician’s seniority over time is typically determined by political
circumstances in other states and thus are largely unrelated to events or activities in the politician’s
home state.
The empirical results are consistent with the argument that auditor efforts are affected by the
risk of political costs from influential committee members following audit failure. We find that the
supply of audit quality is positively associated with the seniority of a state’s representation on
influential committees across all four measures of audit quality. The findings are economically
significant: a one standard deviation increase in the seniority of a state’s representation on influential
committees results in a 4.7% to 13% increase in earnings quality for in-state firms and a 26%
decrease in the likelihood of being subject to AAER enforcement action. The issuance of going
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concern modified audit opinions and internal control weakness opinions is positively associated with
the seniority of a state’s representation on influential committees, but this effect is concentrated in
highly distressed firms. A one standard deviation increase in the seniority of a state’s influential
committee is associated with a 1.57 times (1.05 times) greater odds that highly distressed firms in the
same state receive a going concern modified audit opinion (internal control weakness opinion). The
results suggest auditors increase the ex-ante supply of audit quality to prevent political costs
following the audit failure of high-risk clients. All of our tests include auditor, year, industry, and
state fixed effects, which control for unobservable time-invariant factors or cross-sectional common
shocks that may influence our results. In addition, we find statistically and economically similar
results when we replace time-invariant industry and state fixed effects with firm fixed effects. These
results suggest that it is unlikely that our results are confounded by firm-specific omitted variables.
Furthermore, our results are concentrated in the partition of clients for which the political sensitivity
of audit failure is highest, consistent with auditors recognizing that the political costs of failure vary
among their client portfolios.
Our next set of tests builds on the associations described above by identifying two
exogenous shocks that allow us to address endogeneity concerns. The first exogenous shock is the
loss of a state’s senior representation on an influential committee. Empirical results indicate that
following the exit of a senior politician, audit quality for in-state firms decreases (relative to a
matched sample of out-of-state firms that are unaffected by shocks to their state’s influential
committee representation) across all measures of audit quality. The second exogenous shock centers
around state-level changes in firm headquarter location. For a sample of 78 firms that change
headquarter location and experience an increase (decrease) in influential committee representation,
we observe that audit quality appears to on average increase (decrease) immediately following the
firm headquarters change.
7
We also consider the possibility that our findings are attributable to increased auditor and/or
firm efforts in response to the presence of connections between firms and politicians. Prior studies
investigating the effect of political connections on earnings quality and auditor related outcomes find
mixed evidence. 9 More specifically, we expect that auditor incentives to supply greater audit quality
are likely to be greatest in clients that have captured politicians because audit failure for those clients
is likely to result in the greatest costs for auditors, ceteris paribus. In addition, we consider the
possibility that lobbying efforts by auditors may affect our findings.
The inferences from our main results are unaffected by the inclusion of variables that
capture the presence of connections between firms and politicians based on prior relationships or
political contributions, as well as political connections between politicians and auditors. Consistent
with Guedhami et al. (2014), we find some evidence that the presence of political connections
between firms and politicians is associated with lower earnings quality. We find no evidence that
lobbying by auditors affects the supply of audit quality.
We also conduct counter-factual tests using senior politicians on the most powerful Senate
and House committees that do not have jurisdiction over the accounting and auditing profession and
the affiliated regulatory bodies (hereafter “non-auditor related powerful committees”). If auditors increase
efforts in response to the possibility of political costs from any powerful politician, we should
observe differences in the supply of audit quality for firms in states served by senior politicians on
any powerful committee, not just those with membership on auditor-related committees. We find no
evidence that firms in states with senior political representation on non-auditor related powerful
committees experience differential levels of audit quality. In other words, the supply of greater audit
quality appears to be limited to firms that are headquartered in states that have senior representation
For example see Gul (2006), Ramanna and Roychowdhury (2010), Chaney et al. (2011), Yu and Yu (2011), and
Guedhami et al. (2014).
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on influential committees (as opposed to representatives on non-auditor related powerful
committees).
Our empirical results are also robust to a battery of tests and checks. We consider multiple
constructs of audit quality measures, alternative measures of committee seniority, differential effects
from either the Senate or House influential committees, differences across high business-friendly
and low business-friendly states, the use of Big 4 /Big 6 / Non-Big 6 audit firms, the use of an
extensive set of control variables, and a battery of other tests and sensitivity checks.
Our study is likely to be of interest to politicians, regulators, audit firms, and audit clients. In
addition, our study makes a number of contributions to literature in accounting, corporate
governance, and the political economy. First, by identifying auditor actions in response to the threat
of political costs from politicians that serve on auditor-relevant committees, we contribute to a
growing body of research that examines the relation between auditor outcomes and the political
economy (Gul, 2006; Chan et al., 2006). Our study is different from existing work in that we focus
on specific politicians that have jurisdiction over the auditing profession. Furthermore, we are one
of the first to provide evidence using a large sample of firms in a U.S. setting. Prior studies have
primarily used international, or country specific setting (e.g., Malaysia and China), which are subject
to very different regulatory, institutional, and political characteristics compared to the United States.
Second, our study identifies a political effect on audit outcomes and earnings quality that is
distinct and incremental to that from political connections. Our study contributes to the
understanding of the distinct mechanisms through which politicians can influence firm and auditor
actions (Leuz and Oberholzer-Gee; 2006; Gul, 2006; Chaney et al., 2011; Guedhami et al., 2014).
Third, our study contributes to a literature in political economy that examines the
consequences of political power. Prior research examines the implications of political power for
state-level federal expenditure allocations (Hoover and Pecorino, 2005; Levitt and Poterba, 1999;
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Aghion et al., 2009; Belo, Gala, and Li, 2013), IPO activity (Piotroski and Zhang, 2014), and firm
productivity and performance (Cohen et al., 2011; Amore and Bennedsen, 2013). Our paper sheds
light on the effect of political power on financial reporting and auditing outcomes.
The remainder of this paper proceeds as follows. Section 2 describes the data and
methodology. Section 3 presents descriptive evidence and the primary empirical results. Section 4
outlines additional analyses. Section 5 discusses tests for audit fees and turnover. We conclude in
Section 6.
2. Data, Variable Definitions, and Empirical Specifications
2.1 Data
We begin with Congressional committee membership data over the 2000 to 2010 period,
covering from the 106th Congress to the 111th Congress. The congressional committee membership
data allows us to identify each politician’s state of representation, the duration of each politician’s
service in the House or the Senate, the committee membership assignments, committee membership
appointment dates and service period, and party affiliation. 10 The data also identifies the duration of
each politician’s service on a committee in years, which allows us to determine committee seniority.
We specifically identify those politicians serving on the two committees responsible for oversight of
the auditing profession, the PCAOB, and the SEC: the Banking, Housing and Urban Affairs
Committee in the Senate, and the House Committee on Financial Services.
We then merge our data with firm-specific data from Compustat firms and auditor data from
Audit Analytics. We eliminate (1) utilities and financial services firms (SIC codes between 4900 and
5000; and between 6000 and 6900); (2) non-U.S. domiciled firms; (3) firms audited by Arthur
Andersen during the early years of our sample period (2000 or 2001); (4) firms that are not audited
10
We thank Charles Stewart at MIT for making this data available to us.
10
by a Big 6 auditors (where Big 6 is defined as BDO Seidman, Deloitte, E&Y, Grant Thornton,
KPMG, and PwC); and (5) firms without sufficient data to calculate required variables. 11 After these
deletions, our sample consists of 17,017 firm-year observations, representing 2,641 unique firms. 12
2.2 Measures of Politician Seniority on Influential Committees
We focus on effects associated with the most senior influential committee politicians rather
than all influential committee members because prior work shows that committee seniority is the
key determinant of committee power (Levitt and Poterba, 1999; Cohen et al., 2011). The most
senior politicians on congressional committees are responsible for the committee’s actions and
agenda and can influence the focus and actions (or inactions) of regulatory bodies under the
committee’s jurisdiction (such as the SEC and PCAOB). In extreme cases of audit failure, the most
senior committee members are typically responsible for sponsoring legislation that proceeds from
the committee to all Senate and House members. We develop multiple proxies to measure a state’s
political seniority on influential committees.
The first seniority proxy is the aggregate years of service of a state’s politicians on influential
committees by state and year (Total_Seniority). This measure is easily illustrated using an example: In
2004 Alabama had one representative on the Senate Banking Committee – Richard C. Shelby (D-AL)
– who until that year, served on the committee for 17 years. Alabama also had two representatives
on the House Financial Services Committee (Spencer Bachus (R-AL) and Artur Davis (D-AL)) who
had served on the committee for 6 and 1 year(s) respectively as of 2004. The value of Total_Seniority
Big 6 clients represent the vast majority of all Compustat firms and assets (87% and 92% respectively on average over
our sample period).
12 An important issue for our study is the link between firm headquarter location and influential committee
representation. However, a limitation of obtaining firm location from Compustat is that we can only obtain the most
current firm location data, which may result in biased estimates. In order to overcome this limitation, we obtain actual
firm-year headquarter location details from Compact Disclosure and replicate all our empirical tests. A limitation of the
Compact Disclosure data is that it is unavailable after 2006, and thus we can only use a sample period of 2001 – 2006.
The empirical results discussed below are statistically and economically significant (and in many cases, stronger) when
using this alternative measurement of firm location (untabulated).
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applied to all firms headquartered in Alabama for the 2004 year represents the aggregate years of
service across the three Alabama congressmen (17+ 6 + 1 = 24).
Our second proxy for seniority is a state-year measure of the total number of a state’s
politicians that serve on influential committees, labeled as Committee_Num. This variable captures the
possibility that committee influence may stem from “power in numbers” – the greater a state’s
representation on influential committees, the greater the ability and likelihood that those
congressmen can cohesively act to discipline auditors following audit failure. 13
The third seniority measure is an indicator variable set to 1 if the state’s aggregate politician
seniority is in the top quartile among all states for that year, and 0 otherwise (Seniority_Dum). 14
2.3 Empirical Specification
We use the following specification to test whether the seniority of a state’s political
representation on influential committees affects the supply of audit quality for client in the same
state:
Audit Qualityi,t =
+
1
* Seniorityi,t +
X
* Controlsi,t +
i,t
(1)
Where Audit Qualityi,t represents one of four sets of measures of the supply of audit quality:
(1) accruals-based earnings quality measures; (2) detected fraudulent financial reporting based on the
A possible limitation of this measure is that it potentially overstates seniority for states with a large junior
representation on influential committees. For instance, a state represented by four relatively junior congressmen each
with committee membership for five years (for a total value of Total_Seniority of 20, or total value of Committee_Num of 4)
is likely to have less power relative to another state represented by a single congressman with 20 years of committee
membership. However, an advantage of this measure is that it captures the possibility that seniority and influence on a
committee can occur through the presence of multiple state representatives on the House Committee on Financial
Services who can influence committee behavior via their cumulative voting power.
14 A number of politicians in our sample vacate their congressional seats (and thus their committee seats). Politicians
vacate their congressional seats for a number of reasons including reelection campaign defeat, death, retirement,
resignation by disgrace, transfer or reappointment to another committee, appointment to a new
government/administrative position. Our influential committee sample includes 112 politicians (29 senators and 83
congressmen) that vacate their seats. Of these 112 cases, 24 represent committee members in the top quartile (non-top
quartile) of seniority. A small number of the turnover cases occur mid-term and are treated as follows: politicians who
vacate their seats in the first half of any calendar year are deemed to have not served on the committee for that year, and
the replacement politician is coded as having served for the entire year. If a politician vacates their seat in the second half
of a calendar year, we code the outgoing politician as having served on the committee for the entire year. In untabulated
robustness tests, we find our main results are qualitatively similar if we exclude all cases where a congressional seat is
vacated mid-term.
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issuance of an Accounting and Auditing Enforcement Release (AAER); (3) the issuance of a going
concern opinion; and (4) the issuance of an internal control weakness opinion. We use a variety of
different measures because each of the measures have complementary strengths, which provides
valuable benefits in that we can triangulate across measures (DeFond and Zhang, 2013). Seniorityi,t
represents one of three measures of the power of a state’s representation on influential committees,
and Controlsi,t is a vector of control variables. We discuss these variables in more detail in the
following sections.
We use ordinary least squares for tests in which the dependent variable is earnings quality.
We use logit regressions for fraudulent financial reporting, going concern, and internal control
weakness tests. All specifications include state, year, industry, and auditor fixed effects, and standard
errors are adjusted using a Huber-White Sandwich estimator and clustered by firm.
2.3.1 Earnings Quality Measures
Our first set of measures of the supply of audit quality is based on earnings quality
constructs that rely on estimations of accruals. Caramanis and Lennox (2008) suggest that audit
effort affect the extent to which firms can report aggressively high earnings. Thus, increased audit
effort should result in reduced abnormal accruals, which reduces the risk that financial reports
contain material misstatements. The first measure (EQ1) is the absolute value of abnormal accruals
based on Hribar and Nichols (2007). The second measure (EQ2) is the error term from the
estimation of accruals calculated as the industry-adjusted absolute value of the Dechow and Dichev
(2002) residual, based on the cross-sectional adaptation of the model in Dechow et al. (2011). The
third measure (EQ3) is the Kothari et al. (2005) performance-matched signed discretionary accruals
estimate as used by Ashbaugh et al. (2003). Detailed calculations for all three measures are outlined
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in Appendix A. 15 We multiply EQ1, EQ2, and EQ3 by -100 so that larger values indicate higher
earnings quality to facilitate easier interpretation of regression coefficients.
2.3.2 Fraudulent Reporting using AAERs
Our next measure of earnings quality is the presence of fraudulent financial reporting. We
use the issuance of an Accounting and Auditing Enforcement Releases (AAERs) by the SEC to
proxy for fraudulent financial reporting (see Bonner et al., 1998; Erickson et al., 2004; among others).
AAERs are the SEC’s actions to enforce GAAP financial reporting requirements through civil
litigation and administrative proceedings. Recent studies highlight the benefits of using AAERs as an
audit quality proxy. Ball (2009) states “…a proven case of negligent or fraudulent financial reporting
is an institutional “fact”, as distinct from an error-prone academic estimate”; and DeFond (2010)
notes that AAERs “…are potentially attractive alternatives to abnormal accruals as a proxy for
earnings quality. One perceived advantage of restatements and AAERs over abnormal accruals is
that they appear to be more direct proxies for earnings quality”.
We identify fraud by collecting AAERs from the SEC website and LexisNexis between
January 1, 2000 and December 1, 2013. We delete (1) all AAERs that relate to fraud outside of our
sample period; (2) all AAERs that are unrelated to accounting fraud; and (3) duplicate AAERs
against the same firm during the sample period. The process yields 324 unique AAERs. We classify
a firm’s alleged financial misconduct based on the time period during which the alleged financial
misreporting or fraud occurred, rather than when it was detected. 16
As the Hribar and Nichols (2007) and Dichow and Dichev (2011) measures both rely on abnormal accruals, we also
conduct robustness tests using the absolute value of total accruals, scaled by average assets. Untabulated results show
that our findings are unchanged when using this alternative measure.
16 We collect AAERs that are issued after our sample end date to ensure we identify all cases of investigated fraud that
occur during our sample period. For instance, an AAER issued in 2012 may relate to a fraud in 2009. However, it is
possible that our tests include non-fraud firms (i.e., firms that are not subject to AAER action) that actually commit
fraud which is undetected by the SEC. This can occur because the SEC has limited resources and cannot investigate all
possible instances of fraud (Feroz et al., 1991; Dechow et al., 2010). Thus, our sample likely understates incidences of
accounting fraud (DeFond and Francis, 2005; Karpoff et al., 2008; Lennox et al., 2013). This measurement error and
increased noise should bias against our predictions.
15
14
2.3.3 Going Concern Opinions
Our third audit quality variable is the presence of going concern modified audit opinions
(GC). A GC represents an auditor’s propensity to both detect and report problems related to a
client’s financial condition (Craswell et al., 2002; DeFond et al., 2002). An auditor’s assessment of
whether a client is a going concern is subjective and not merely the result of following a mechanical
set of rules. This suggests that auditors have to exercise significant professional judgment and use
their discretion when deciding whether it is appropriate to issue a GC (DeFond and Lennox, 2011).
All else equal, we expect to observe that clients located in states that have senior political
representation on influential committees are more likely to receive GCs. We obtain GC data from the
Audit Analytics Audit Opinion file. Our total sample consists of 410 cases of going concern
modified audit opinion issuances, representing 236 unique firms.
2.3.4 SOX 404 Internal Control Material Weakness Opinions
The fourth audit quality variable we use relies on the SOX Section 404 requirement that the
independent auditor, on an annual basis, provides an opinion on the client’s system of internal
controls over financial reporting. If the auditor discovers a “material weakness” in internal controls,
they must issue an adverse opinion (ICW). 17 Internal controls over financial reporting are defined as
the processes that provide “reasonable assurance about the reliability of a company’s financial
reporting and its process for preparing and fairly presenting financial statements in accordance with
GAAP” (PCAOB, 2004). Auditors that supply higher quality services because of concerns about
political costs of audit failure are more likely to identify internal control weaknesses and issue an
ICW. We obtain ICW data from the Audit Analytics SOX 404 Internal Controls database and
identify 11,878 firm-year observations between 2004 and 2010 for which we can obtain data about
If a client properly reports the material weakness, the auditor’s opinion on the firm’s assessment of internal controls is
unqualified. If the auditor concludes a material weakness exists but the client does not, the auditor must issue an adverse
opinion. Auditors also must report “significant deficiencies,” which are less severe than “material weaknesses” and do
not result in an adverse opinion.
17
15
auditor attestation about management’s assertion on the effectiveness of internal control. Of these
observations, 522 unique firms (representing 782 firm-year observations) receive an ICW.
2.3.5 Control Variables
Each specification of equation (1) includes a comprehensive set of control variables. We
provide detailed definitions for all variables in Appendix B. First, for tests where the dependent
variable represents one of the three accruals-based measures of earnings quality or AAER, we
include control variables commonly used in the literature on auditors and earnings quality. These
include the auditor’s city-level market share (Auditor_Share), the number of years that the auditor has
been retained (Auditor_Tenure), the (log) number of clients of the firm’s auditor office (Office_Size),
and firm-specific variables including whether the firm received a going concern opinion in the prior
year (GC_Dummy), log of total assets (Size), the ratio of long term debt to total assets (Leverage),
market value of equity divided by book value of equity (MtB), current year net income scaled by
total assets (Profit), an indicator variable set to one if the firm has issued more than 10% of the
existing total debt and equity during the prior 3 years, zero otherwise (Issuance), the standard
deviation of operating cash flows over the past five years (Stdev_Cashflow), the standard deviation of
total sales over the past five years (Stdev_Sales), and the operating cycle (Oper_Cycle). We also include
Inst_Own, the total stock ownership by institutional investors, and Analyst_Following, the log of the
number of analysts that cover the firm. In addition, we control for litigation risk (Litigation Risk),
using an indicator variable to set to one if a firm is in a high litigation risk industry, and zero
otherwise. We classify high litigation risk industries based on Kim and Skinner (2012).
For tests where the dependent variable is GC or ICW, we include controls for Auditor_Share,
Auditor_Tenure, Office_Size, Litigation Risk, Size, Leverage, MtB, Profit, Stdev_Cashflow, Stdev_Sales,
Oper_Cycle, Inst_Own, Analyst_Following, as defined above, and two additional controls from DeFond
16
and Lennox (2011): the number of days between firm’s fiscal year end and the auditor’s report date
(Reporting_Lag), and the current assets to current liabilities ratio (Current_Ratio).
2.4 Summary Statistics
We present summary statistics in Table 1. In Panel A we present descriptive information
about both influential committees (i.e., the House Committee on Financial Services and the Senate
Banking, Housing and Urban Affairs Committee) and politicians serving on these committees. Panel
B presents descriptive statistics for our primary measures of Seniority and Panel C displays summary
statistics for key variables.
First, Panel A shows that the House (Senate) influential committees have an average of 69
(21) members during our sample period, representing 29 (21) states respectively. This implies that
conditional on a state having representation on a committee, each state has an average
representation on the House (Senate) committees of about 2 (1) politicians. Politicians serving on
the House (Senate) influential committees have an average tenure of approximately 3.6 (6.9) years,
with a maximum of 19 (29) years. Next, we tabulate states representation in the top (bottom)
quartile of influential committee seniority over the sample period, where seniority is measured as the
number of consecutive years of service on a committee. Seniority does not appear to be exclusively
driven by the largest and most populated states such as New York, California, or Texas (although all
of these states do have senior representation on the House committee during the sample period).
Seniority appears to be represented in a large cross-section of states on both the House and Senate,
reducing the likelihood that our results are driven by certain states. For example the states with the
most years in the top seniority quartile on the Senate committee are Connecticut (10 years), Alabama
(10 years), Utah (8 years), and Maryland (8 years). Finally, only two states (Alaska and Maine) have
no representation on influential committees during our sample period (representing 22 firm-year
observations).
17
In Panel B, we present descriptive statistics for the seniority measures, Total_Seniority,
Committee_Num, and Seniority_Dum, based on the state-level values. In Panel C we present firm-level
seniority measures for the full sample. Differences in the state-level and firm-level seniority measure
values are simply due to an uneven distribution of firms across U.S. states. The average aggregate
seniority of state representatives on influential committees is 8.8 years, with a median of 6 years.
Each state has an average of 1.2 members on influential committees. Finally, approximately 26% of
states have a political representative in the top seniority quartile across both influential committees.
In Panel C, we present summary statistics for the variables of interest. First, we present
descriptive statistics for the primary independent variables, Total_Seniority, Committee_Num, and
Seniority_Dum. Our sample firms observe an average (median) of 18.8 (17.0) years of experience
across all their influential committee representatives in any given year. The large standard deviation
suggests the experience levels greatly vary across states. Next, the mean value for Seniority_Num of
4.56 indicates that states have an average of just over four politicians serving on influential
committees. Note that the relatively high representation on influential committees is largely driven
by political representation in the House, rather than the Senate, because of the nature of the U.S.
House of Representatives apportionment system, which allocates congressional seats to each state
based on the state’s population. Seniority_Dum displays an average value of 0.26, implying that 26%
of sample firm-year observations are located in a state that has a politician in the top seniority
quartile on at least one influential committee.
Next, the accruals based earning quality measures, EQ1, EQ2, and EQ3, display mean values
of -15.67, -0.45, and -4.19, respectively. These values are coded such that higher values represent
higher earnings quality. The mean value of 0.019 for AAER indicates that 1.9% of sample firm-year
observations are subject to AAER enforcement action. Next, approximately 2.4% of our sample
firm-year observations receive GCs and approximately 6.6% receive ICWs. Average logged audit
18
fees is 13.55, which represents average audit fees of $768,350. Finally, approximately 6.4% of sample
firms experience auditor turnover each year. In the next section, we present results from multivariate
tests.
3. Empirical Results
3.1 Influential Committee Representation and Audit Quality
In Table 2, we present the results from multivariate tests examining whether a client’s audit
quality outcomes are associated with the client’s presence in a state that has senior political
representation on influential committees. The dependent variable is one of EQ1, EQ2, EQ3, or
AAER. Panel A presents results for our primary measure of congressional seniority, and Panel B
presents results from tests in which we verify our results are robust to alternate measures of seniority.
The coefficient estimates in Panel A, column 1 indicate that when the dependent variable is
EQ1, the coefficient on Total_Seniority bears a positive (0.040) and significant at the 1% level (t =
2.60). This suggests that on average, auditors supply higher quality audits for clients located in states
with senior representation on influential committees. This difference is also economically
meaningful. A one standard deviation increase in Total_Seniority is equivalent to a 3.8% increase in
audit quality. 18 In column 2, when the dependent variable is set to EQ2, we find that the coefficient
on Total_Seniority is positive (0.002) and significant at the 5% level (t = 2.22). Economically, a one
standard deviation increase in a state’s Total_Seniority on influential committees is associated with a
6.7% increase in audit quality. We find similar statistical and economic results in column 3 when the
dependent variable is set to EQ3. Finally, the evidence in column 4 when we use AAER to measure
audit quality is consistent with the results in columns 1 to 3. The coefficient on Total_Seniority is
negative (-0.015) and statistically significant at the 5% level (t=-2.30). A negative sign on
We calculate this as the coefficient on Total_Seniority multiply by the standard deviation of Total_Seniority, scaled by the
mean value of EQ1: 0.040*15.05/15.667 = 3.8%.
18
19
Total_Seniority indicates that a firm is less likely to be subject to AAER enforcement action when the
firm is located in a state that has powerful politicians on influential committees. In economic terms,
the result indicates that a one-standard deviation increase in Total_Seniority is associated with a 20%
decrease in the odds that the firm is subject to AAER enforcement action. 19 In sum, the results in
Panel A are consistent with the notion that auditors supply higher quality audits in states served by
politicians that can impose costs on auditors following audit failure.
In Panel B, we present results from tests in which we check that our results are robust to
alternative constructs of committee seniority. We replace Total_Seniority with two alternate measures:
Committee_Num (Columns 1 to 4), and Seniority_Dum (Columns 5 to 8). Coefficient estimates suggest
similar inferences to those from the results in Panel A. Results for both alternative measures of
seniority indicate that the seniority of a state’s political representation on influential committees is
positively associated with audit quality measures. For instance, in column 1 when earning quality is
set to EQ1, the coefficient on Committee_Num is positive (0.129) and significant at the 5% level (t =
2.33). In economic terms, a one-politician increase in a state’s influential committee membership is
associated with an increase in earnings quality of between 0.8% and 8.2% for in-state firms. The
evidence in column 4 provides consistent evidence when the dependent variable is AAER. A onepolitician increase in a state’s influential committee membership is associated with a 5% decrease in
the odds ratio that in-state firms will be subject to AAER enforcement action. In column 5-8, we
find statistically and economically similar results when seniority is set to Seniority_Dum. In other
words, firms located in states with influential committee representation in the top quartile of all
politicians appear to receive a 3.4% to 28% increase in the mean earnings quality, and a 16%
reduction in the odds that the firms will be subject to AAER enforcement action.
A possible alternative explanation for this finding is that captured firms are less likely to face AAER action because of
political clout. While we cannot explicitly rule out this explanation, such an argument is inconsistent with the results in
Table 4 that provide no evidence of a relation between the presence of political connections and AAER issuances.
19
20
Coefficients on control variables are consistent with expectations. We find that auditor
market share is positively related with earnings quality, as in previous studies (e.g., Balsam et al.,
2003; Myers et al., 2003). Firm profitability is negatively related with earnings quality, and so is the
length of the firm’s operating cycle. In sum, the evidence in Table 2 is consistent with the argument
that auditors supply higher audit quality for clients located in states that have senior representation
on influential committees. Our results hold after controlling for auditor expertise, and litigation risk,
which suggests that politically related incentives (and the risk of regulatory intervention) to prevent
audit failure are an incremental, and distinct incentive to auditors’ market-based incentives related to
reputation and litigation (Dye, 1993).
3.2 Committee Seniority and Auditor Efforts: Going Concern Opinion Issuances and
Internal Control Weakness Reports
Next, we consider whether auditors are more likely to issue GCs when clients are located in
states that have senior representation on influential committees. A going concern opinion is a
serious and severe assessment of the firm’s financial and operational conditions. Auditors issue a
going concern opinion to clients whose financial conditions present doubt as to the ability to
continue as a going concern. Issuing a going concern opinion allows auditors to mitigate litigation
costs and adverse reputation effects if the client suffers failure (Kaplan and Williams, 2013). In a
similar vein, we expect that the presence of an internal control weakness report reduce the risk that
the auditor will be subject to political costs, because internal control weakness increases the chance
of financial misstatements. Thus, if auditors perceive the political costs of audit failure to be greater
than the potential costs from the loss of a client that replaces the auditor following the issuance of a
GC or ICW opinion, then we expect a positive relation between the issuance of going concern or
internal control weakness opinions and the political costs of audit failure.
21
Table 3 presents the results from logit regressions estimating Equation (1) in which the
dependent variable is an indicator variable set to one for firms that receive a GC (Panel A) or an
ICW (Panel B). We partition sample firms into terciles based on one-year lagged financial distress
risk using Altman’s Zscore. The overall evidence suggests auditors issue more GCs and ICWs to
clients located in states where audit failure is likely to result in greater political costs, but only for the
partition of firms with the highest distress risk. In untabulated analyses, we find qualitatively similar
results if we use measure financial distress using the approach in Ohlson (1980).
In Panel A, columns 1 to 3, we present regression coefficients by financial distress risk tercile.
In column 1, Total_Seniority is positive (0.027) and significant at the 5% level (t = 2.23) for the high
distress risk firm sample. Economically, a one standard deviation increase in Total_Seniority is
associated with a 1.50 times increase in the odds that an auditor issues a GC to highly distressed instate firms. 20 In contrast, Total_Seniority bears negative and statistically insignificant coefficients for
tests on the medium and low distress risk firm samples in columns 2 and 3 respectively. Our
findings are consistent with the notion that auditors issue more GCs when the political costs from
audit failure are higher, because a GC limits an auditor’s culpability in the event of firm failure.
In columns 4 and 5, we present results from tests using the high distress risk tercile in which
we replace Total_Seniority with the two alternate measures of seniority. In column 4, the coefficient
on Committee_Num is positive (0.241) and significant at the 1% level (t = 3.04). In economic terms, a
one-politician increase on influential committees is associated with a 1.27 times increase in the odds
that an auditor issues a GC to highly distressed in-state firms. We find economically and statistically
similar results in column 5 when seniority is set to Seniority_Dum.
Coefficients on control variables are consistent with expectations. Firm Size is negatively and
significantly related to the issuance of a going concern opinion across all distress risk partitions. For
20
The increase in the odds ratio is calculated as follows: e(0.027*15.053) = 1.50.
22
high distress risk firm partitions, going concern opinions are positively related to the firm’s operating
cycle (Oper_Cycle), and negatively related to the firm’s current ratio. In untabulated sensitivity tests,
we find that our results are unaffected by whether a GC was also issued in the prior year or not. In
summary, the analysis in Table 3 Panel A finds that distressed firms are more likely to receive GCs
when located in states that have senior representation on the influential congressional committees.
In Panel B, we present results from tests in which the dependent variable is set to ICW. In
column 1, the coefficient on Total_Seniority is positive (0.003) and significant at the 5% level (t =
2.49). Thus for high distress risk firms, a one standard deviation increase in a state’s aggregate years
of representation on influential committees is associated with 1.05 times increase in the odds that an
auditor issues an ICW to in-state firms.
In contrast, coefficients on Total_Seniority are statistically insignificant for the mid- and lowdistress risk firm partitions in columns 2 and 3 respectively. In columns 4 and 5, the inferences are
similar for the high distress risk partition when we replace Total_Seniority with Committee_Num or
Seniority_Dum. Coefficients are consistent with prior studies (e.g., Doyle et al., 2007). Overall, the
results across both panels suggest that auditors appear to be more willing to issue GC and ICW
reports when the political costs of audit failure are highest.
3.3 Omitted Time-Invariant Firm Characteristics: Specifications with Firm Fixed Effects
A potential concern with the findings in Tables 2 and 3 is that the presence of unobserved
time-invariant firm-specific factors. For instance, a firm’s corporate governance practices may affect
the supply of audit quality (e.g., Bedard and Johnstone, 2004). To address this possibility, we
replicate our tests from Tables 2 and 3 after including firm fixed-effects in multivariate tests. 21
Untabulated results indicate that coefficients across all the seniority measures bear
economically and statistically similar results to those in Table 2 and 3 and that these results hold
A consequence of including firm fixed effects in our tests is that we must remove state fixed effects because of
homogeneity in firm headquarter state location data from Compustat.
21
23
across all measures of audit quality. Overall, the evidence from regressions that include firm fixedeffects indicates that our primary findings are not driven by unobservable time-invariant firm
characteristics.
3.4 Shocks to a State’s Senior Influential Committee Representation
Our tests thus far focus on documenting yearly associations between levels of influential
committee seniority and levels of audit quality. In this section, we present evidence from a changes
specification that regresses changes in influential committee senior representation on changes in
audit quality. We exploit changes in committee membership that occur via the exogenous exit of a
senior member. Exits can occur for a number of reasons, including defeat during a reelection
campaign, the acceptance of a more lucrative appointment (such as an ambassadorship or leadership
role in an influential government department), retirement, or resignation. A committee member’s
departure from a committee at any time results in a new political appointment to the committee but
without the incumbent’s seniority. Furthermore, all politicians with lower tenure than a resigning
committee member improve their rank following the resignation. We identify 112 exit cases (29
senators and 83 House representatives) from influential committees during our sample period. Of
these cases, 24 (11 senators and 13 House representatives) depart while they serve in the top quartile
of committee seniority.
Our central argument is that auditors have incentives to supply higher audit quality to limit
the political costs of audit failure. If the political costs of audit failure decrease then we expect that
auditor incentives to supply relatively higher audit quality decreases, ceteris paribus. First, we provide
univariate evidence. Table 4 Panel A presents figures of changes in each AQ measure for 1) firms in
states that experience a shock via the loss of an influential committee senior politician during our
sample window, and 2) a matched sample of firms in other states that do not experience a shock in
the same year and the two preceding or subsequent years. All treatment cases are coded such that
24
Year 0 represents the year of the loss of a senior influential committee member. We use propensity
score matching to identify treatment and control groups, with matching occurring in the year prior
to politician turnover. We match each firm in a state that experiences a senior influential committee
member loss with a control firm from a state that does not experience the shock in the same,
preceding or subsequent year. We match firms based on firm size, industry, state GDP growth, and
state unemployment rate. The matching process results in 1502 firms (i.e. 751 treatment and control
firms). The evidence across all the figures in Panel A systematically suggests that AQ decreases for
firms in states served by a departing influential committee senior representative in the year around
the shock. In contrast, we see no evidence of systematic changes in measures of AQ for the control
group firm sample.
Next, we perform multivariate analyses using a difference-in-differences specification to
compare the change in the supply of audit quality around the elimination of a state’s influential
committee senior representation:
Audit Qualityi,t =
+
1
* Senior_Dropi,t +
X
* Controlsi,t +
i,t
(2)
Audit Qualityi,t represents the annual change in measures of audit quality as previously
defined. For tests where the dependent variable is AAER (GC/ICW), Audit Quality is defined as an
indicator variable set to one if the audit quality variable changes such that audit quality decreases. 22
Senior_Dropi,t is an indicator variable set to one if the firm is located in a state experiencing a loss of
senior influential committee member, and zero otherwise. Controlsi,t is a vector of control variables as
previously defined.
To illustrate, consider a state that has an influential committee senior political representative who retires in year t.
Audit Quality is only set to one for in-state firms that did not receive an AAER (did receive a GC/ICW opinion) in t-1,
and did receive an AAER (did not receive an GC/ICW opinion) in t. For all other possible outcomes between t-1 and t,
Audit Quality is set to zero.
22
25
We use ordinary least squares for tests in which the dependent variable is earnings quality
and logit regressions for fraudulent financial reporting, going concern, and internal control weakness
tests. All specifications include state, industry, and auditor fixed effects, and standard errors are
adjusted using a Huber-White Sandwich estimator and clustered by firm. We present the results in
Table 4. The evidence indicates that firms located in states that lose senior representation on an
influential committee subsequently experience statistically significant decreases in the supply of audit
quality. The results are robust across all of our AQ measures: we find evidence that firms in those
states have decreases in EQ, are more likely to be subject to AAERs, and are less likely to receive a
GC or ICW opinion if they received such an opinion in the previous year. In economic terms, firms
in states that lose a senior representative from an influential committees experience a decrease in the
supply of audit quality (measured using earnings quality) of approximately 3.2% - 4.2%.
We undertake two additional tests related to shocks to influential committee member
changes. First, we examine whether the addition of a politician to an influential committee affects the
subsequent period supply of audit quality for firms in said politician’s state. We find a positive but
statistically insignificant relation across all audit quality measures (untabulated), consistent with
newly tenured committee members having relatively little political influence due to their junior status.
Second, we examine and find no evidence that the supply of audit quality is affected following the
exit of a junior influential committee member. In sum, the evidence in Table 4 provides evidence
consistent with the notion that the supply of audit quality is affected by the presence of senior
political representation on committees charged with auditor oversight.
3.5 Shocks from Client Headquarters Location Changes
In this section, we exploit whether shocks to a firm’s headquarters location affect the supply
of audit quality. While a change in the location of a firm’s headquarters naturally results in a change
in that firm’s state-level influential committee representation, headquarters changes are unlikely to be
26
directly driven by influential committee representation related reasons and more importantly, by
audit quality considerations. If our findings in Tables 2 and 3 are driven by auditor concerns about
the state-level political costs of audit failure, then changes in a firm’s headquarters location should
affect the supply of audit quality.
A limitation of our firm headquarters location data from Compustat is that historical firm
location data is back-filled using the most current headquarter location data. This is likely to result in
a data bias issue, and especially for observations in the earlier sample periods. In order to overcome
this issue, we obtain historical headquarters location information from the Compact Disclosure
database. The database contains accurate historical firm-level headquarter location data on an annual
basis, which enables us to accurately examine audit quality related effects around changes in firm
headquarters. Unfortunately, Compact Disclosure data from is only available until 2006 and so our
tests span the period from 2000 to 2006. We identify 78 unique firms in our sample that change
their headquarter location during this period. Among these cases, there are 39 (38) firms that
experience an increase (decrease) in Total_Seniority. One firm experiences no change in total seniority.
The average increase (decrease) in Total_Seniority for the respective groups is 14.28 years (14.97) years.
The maximum increase (decrease) in Total_Seniority around a headquarter change is 57 (48) years.
Figure 1 plots time-series graphs for each of our measures of audit quality centered around the year
in which sample firms change their state of headquarters (t = 0). We partition sample firms based on
whether the new headquarter state has a higher or lower value of Total_Seniority relative to the old
headquarters state in the previous year. Across cases of both increases and decreases in state
seniority, we observe a clear pattern that indicates increases (decreases) in the supply of audit quality
following headquarter changes to a state with positive (negative) change in influential committee
seniority. In sum, the results provide evidence consistent with a causal relation between committee
27
Figure 1
Changes in Measures of Audit Quality Around Changes in Firm Headquarter Location
Figure 1 presents average annual values of audit quality measures for 76 firms that change the state of firm headquarter
between the years 2001 to 2006. We split the firms based on whether the state affiliated with the new headquarters has
higher or lower Total_Seniority relative to the previous headquarters state.
EQ2
-19
-.47
-.46
-18
EQ1
-17
EQ2
-.45
-.44
-16
-.43
-15
-.42
EQ1
-3
-2
-1
0
Firms with Increasing Seniority
1
2
3
-3
Firms with Decreasing Seniority
-2
-1
0
Firms with Increasing Seniority
2
3
AAER
0
-4.8
-4.6
.01
-4.4
EQ3
AAER
.02
-4.2
.03
-4
.04
-3.8
EQ3
1
Firms with Decreasing Seniority
-3
-2
-1
0
Firms with Increasing Seniority
1
2
3
-3
Firms with Decreasing Seniority
-2
-1
0
Firms with Increasing Seniority
2
3
.065
.022
.066
ICW
.067
Going Concern
.023
.024
.068
ICW
.025
Going Concern
1
Firms with Decreasing Seniority
-3
-2
-1
Firms with Increasing Seniority
0
1
2
3
-3
Firms with Decreasing Seniority
-2
-1
Firms with Increasing Seniority
28
0
1
2
Firms with Decreasing Seniority
3
seniority and the supply of audit quality. Overall, evidence from these tests suggest that our main
findings are robust to a number of tests to address endogeneity. Nevertheless, we cannot definitively
rule out the possibility that our results are attributable to unidentified omitted factors.
4. Additional Analyses
4.1 The Effect of Political Connections
Next, we consider the possibility that our results are attributable to the presence of connections
between politicians and firms or auditors’ lobbying efforts. Existing research finds mixed evidence
about the effect of political connections on earnings quality and audit outcomes. For instance,
Chaney et al. (2011) find that in a cross-country setting, politically connected firms supply lower AQ
because of a lower need to respond to market pressures to increase the quality of disclosed
information, and Ramanna and Roychowdhury (2010) provide evidence that U.S. firms with
connections via political contributions were more likely to manage earnings downwards during the
2004 elections. Yu and Yu (2011) find that U.S. politically connected companies are associated with
higher incidences of accounting fraud, and Chan et al. (2006) find Chinese local government-owned
companies that switch from a non-local auditor to a local auditor after receiving a qualified opinion
can succeed in opinion shopping.
On the other hand, Guedhami et al. (2014) find that politically connected firms are more
likely to choose a Big 4 auditor, and less likely to engage in earnings management. Gul (2006) finds
that Malaysian auditors increase audit effort and charge higher audit fees for clients with political
connections following the Asian financial crisis due to client inefficiencies and the inability of the
government to bail out these favored clients during the crisis. We consider two channels through
which political connections are likely to occur: personal connections via employment and politically
motivated monetary contributions. First, we use BoardEx to identify firm affiliations with each
politician based on prior work experience as an executive or non-executive director, consistent with
29
the definition used in prior work (Faccio, 2006; Houston et al., 2014). We create an indicator
variable set to one if a firm in our sample is affiliated with an U.S. politician, conditional on the
politician serving in the same year, and zero otherwise (Political_Connect). Second, we collect data
about political contributions from firms to elected politicians. In the US, firms can contribute to
political parties, politicians, or candidates via 1) donations to Political Action Committees (PACs); 2)
contributions from individuals who are employed by the firm; 3) soft money donations; 4) 527
group donations; and 5) lobbying firms, which can help a firm establish political connections with
parties and individual politicians. We obtain political contribution data from CQ Roll Call’s Political
Money Line database and follow Yu and Yu (2011) to calculate a firm’s logged total political
contributions at the firm-year level (Politicial_Contrib).
We also collect data about political contributions and lobbying by Big 6 auditors. It is
possible that it is the auditors, rather than the clients, who capture politicians in order to prevent the
imposition of penalties following audit failure. 23 We collect audit firm lobbying data from Political
Money Line to measure auditor’s lobbying efforts, calculated as the log of annual total lobbying
spending annually (Auditor_Contrib).
We find that 402 unique firms are professionally connected with 376 unique politicians
during the sample period. We also find that 688 unique firms (approximately 26% of the sample
firms) contribute to politicians or political campaigns with mean (median) annual spending of $2.04
million ($357,142). 24, 25 Mean (median) annual political contributions and lobbying by Big 6 auditors
is $4.2 million ($4.35 million) during the sample period.
Note however, that if auditors capture influential committee politicians, then auditors should have decreased
incentives to supply greater audit quality for clients in states served by influential politicians. This is contrary to the
evidence in Tables 2 and 3.
24 These ratios for the number of firms that contribute are smaller than in other studies such as Houston et al. (2014)
who find approximately 43% of sample firms have a political connection. A key difference between our study and
Houston et al. (2014) that is likely to affect the difference is that the latter study focuses on Fortune 500 firms only.
25 Our firm contribution statistics are likely to be understated because of the difficulty in linking contribution data with
Compustat data. For instance, our methodology is unlikely to capture contributions made under the name of a subsidiary
23
30
In Table 5, we present results from tests of Equation (1) after including the three firm and
auditor political connection variables described above. We suppress the presentation of control
variables in the interests of brevity. Panels A, B, and C present results for each of the three seniority
measures. Coefficient estimates from all three panels indicates that the inclusion of the three political
connection variables does not change our main inferences. We find that the presence of senior
politicians on influential committees is positively and statistically associated with all of our measures
of the supply of audit quality, and that this result is unaffected by the choice of the measure of
influential committee seniority used. The results are economically similar to those presented in
Tables 2 and 3. Next, in Panel A, the evidence suggests that the presence of a prior business
connection between a firm and a politician serving on an influential committee (Politicial_Connect) is
negatively associated with all measures of the supply of audit quality, but statistically insignificant for
all three measures of earnings quality. The coefficients on Political_Contrib across all AQ measures
indicate similar inferences – the variable is negatively associated with all measures of the supply of
audit quality, but only statistically significant when audit quality is set to EQ1. We find no evidence
that Auditor_Contrib is statistically associated with any of our audit quality measures. The evidence in
Panels B and C suggest very similar inferences. Overall, the results in Table 5 suggest that although
political connections appear to be weakly associated with earnings quality, connections do not
appear to drive the relation between the presence of a senior politician on an influential committee
and the supply of audit quality for same-state firms.
4.2 The Effect of Seniority on Other Powerful Congressional Committees
Our next test addresses the possibility that politicians other than those on the influential
committees funnel federal funds and earmarks to their states (Cohen et al., 2011). As a matter of fact,
other committees usually have much more power on issues such as budgeting and governmental
of a sample firm. While this is an inherent limitation in all studies investigating political contributions, our results should
be interpreted accordingly.
31
spending or contracting in certain industries. Such funding is likely to increase the financial
prospects and health of firms headquartered in those states, which in turn may reduce the need for
firms to engage in earnings manipulation or undertake aggressive accounting techniques. In other
words, it is possible that state funding from senior politicians drives our main results. To address
this possibility, we undertake a counter-factual attribution test that examines whether the supply of
audit quality differs for firms located in states with senior politicians serving on powerful nonauditor related committees (i.e., committees other than influential committees). We identify the most
powerful non-auditor related Senate and House committees from Edwards and Stewart (2006). 26 We
calculate measures of seniority that are similar to our previously defined measures but instead are
based on the aggregate seniority of a state’s political representation across these most powerful nonauditor related Senate and House committees. We add the prefix “NonAuditor_” to each measure:
NonAuditor_Total_Seniority, NonAuditor_Committee_Num, and NonAuditor_Seniority_Dum. We rerun
Equation (1) for all of our measures of audit quality after replacing the seniority-related independent
variables.
Table 6 presents coefficient estimates from multivariate tests. Panels A, B, and C present
results for each of the three new measures of non-auditor relevant committee seniority respectively.
While all specifications include a full set of control variables, we only tabulate seniority variable
coefficients in the interest of brevity. Across all audit quality measures, coefficients on each of the
seniority measures bear statistically insignificant values, consistent with the notion that the supply of
audit quality is not influenced by the seniority of a state’s political representation on powerful nonauditor related congressional committees. In sum, these results do not provide support for
Committee rankings are based on a method developed by Groseclose and Stewart (1998) that involves tracking
politician committee transfers. For instance, a politician switching from committee A to committee B implies that
politicians value the latter more highly than the former. The demand for a given committee is the proxy for committee
power. The list of the ten most powerful committees is as follows. The Senate committees are: Finance, Veterans Affairs,
Appropriations, Rules, Armed Services, Foreign Relations, Intelligence, Judiciary, Budget, and Commerce. The House
committees are: Ways and Means, Appropriations, Energy and Commerce, Rules, International Relations, Armed
Services, Intelligence, Judiciary, Homeland Security, and Transportation and Infrastructure.
26
32
arguments that our primary findings are attributable to governmental funding from senior politicians,
and instead, remain consistent with the hypothesis that auditors appear to strategically adjust the
supply of audit quality when the political costs of audit failure are high. In untabulated robustness
tests, we find similar results if we focus on the top 3 or top 5 (instead of top 10) most powerful
committees.
4.3 Domestic versus Foreign Operations and Auditor Incentives to Prevent Audit Failure
In this section, we consider whether auditors perceive the political costs of audit failure to
vary with the extent to which audit failure has political ramifications for an influential committee
representative. We proxy for the magnitude of perceived political costs using Compustat geographic
segment data and argue that political costs of failure are greater for clients that have U.S. focused
operations (Domestic Firms) relative to audit clients with both domestic and foreign operations
(International Firms). Audit failure for Domestic Firms may result in relatively greater negative domestic
publicity, and is thus potentially more politically harmful for an incumbent influential committee
politician.
We collect segment data and partition sample firms based on whether they classify their
geographic segment breakdown as purely domestic or domestic and foreign. Table 7 presents results
from tests of Equation (1) across the two groups where politician seniority is set to Total_Seniority.
While the overall evidence across both panels is largely consistent with our main results, the
magnitude of the coefficients is approximately twice as large for the domestic firm groups in Panel
A relative to the international firm sample results in Panel B. In untabulated analyses, we find similar
results for the other two measures of seniority. In sum, the findings in Table 6 suggest that auditors
recognize that firm-specific characteristics affect the potential political costs of audit failure and
adjust the supply of audit quality accordingly.
4.5. Other Tests
33
4.5.1 Do Both Senate and House Influential Committees Impose Political Costs on Auditors?
In this section, we consider whether politicians on the Senate and the House influential
committees play differential roles in affecting auditor behavior. We calculate new measures of
seniority based on a state’s seniority on each committee separately. 27 We then modify Equation 1 by
using separate Senate and House seniority variables instead of the aggregate corresponding seniority
measure. Results from untabulated tests provide no evidence that seniority on the Senate or House
committees have significantly different effects on the supply of audit quality. We find similar results
across all three measures of committee seniority
4.5.2 Potential Effects From Andersen’s Collapse
In order to eliminate the possibility that residual effects from auditor-client realignment
following Andersen’s collapse influence our results, we rerun our tests after removing all
observations for the 2000, 2001, and 2002 years. Untabulated tests across all three measures of
seniority yield results that are qualitatively similar to those presented, irrespective of the audit quality
measure used.
4.5.3 House of Representatives State Apportionment
In this section we consider the possibility that auditors and firms in states that are
disproportionately represented in the House of Representatives and the Committee for Financial
Services drive our results. This possibility exists because House seats are apportioned to a state
based on the state’s share of the aggregate national U.S. population, such that each of the 435
congressmen in the House serves an approximately equal number of constituents. Thus, the most
populous U.S. states (California, Texas, Florida, New York, Pennsylvania, and Illinois) hold the
largest number of House seats. Firms located in these six states represent 47.6% of all firms audited
Table 1 indicates that conditional on representation, the Senate Banking Committee has an average of just over one
politician per state (1.02). The sole exception represents the period between 2003 and 2005 when both the Senators
from Rhode Island served on the committee (Lincoln Chaffee (R) and Jack Reed (D)). Thus Senate_Committee_Num take
the values of 0 or 1 in all cases, except for firms headquartered in Rhode Island between 2003 and 2005.
27
34
by Big 6 auditors. Tests excluding each of these five states provide qualitatively similar results to
those presented in Tables 2 and 3.
4.5.4 Big 4 Auditors instead of Big 6 Auditors
We check that our results are robust to using just the Big 4 audit firms rather than the Big 6
firms. We rerun our analysis after removing firm observations linked to BDO Seidman or Grant
Thornton. Although Ashbaugh-Skaife et al. (2007) note that after the collapse of Andersen, both of
these firms became dominant audit suppliers in the U.S. audit market via the gain of more SEC
reporting clients, it is possible that these auditors are less subject to political pressure from audit
failure because their client portfolios, on average, represent smaller, less politically sensitive clients.
Results from untabulated tests are qualitatively similar to the results presented in Tables 2 and 3.
4.5.5 Does The Threat of Political Costs From Audit Failure Influence Non-Big 6 Auditors?
We examine whether the political costs of audit failure also affect the supply of audit quality
for clients of non-Big 6 auditors. As non-Big 6 auditors typically service small clients, audit failure
may draw less attention from a politician’s constituents, which reduces a politician’s incentives to use
political capital to impose political costs on the auditor. Thus, if non-Big 6 auditors view the threat
of political costs from audit failure as low, then for a given state, we should not observe variation in
the supply of audit quality in the seniority of the state’s representation on influential committees. In
untabulated tests using the entire sample of non-Big 6 auditors from Audit Analytics, we find no
evidence that influential committee seniority affects the supply of audit quality, or the issuance of
GC or ICW opinions.
4.5.6 The Effects of Changes in Company Performance on GC Opinions
A potential limitation of our analysis in Table 3 Panel A is that the auditor’s decision to issue
a GC opinion may be associated with shocks to company performance that are not captured in
Equation 1, which only controls for levels of client performance. We follow DeFond and Lennox
35
(2011) and rerun the GC regression in Table 3 after including variables to capture changes in
company size ( Size), profitability ( Profit), the current ratio ( Current_Ratio), and leverage
( Leverage). After including these variables, we continue to find significant positive coefficients
across all our measures of seniority when the dependent variable is GC. In addition, the coefficients
on the change in client performance variables are generally insignificant.
4.5.7 Multiple Congressional Committee Seats
Virtually all Senate committee members serve on at least one other committee at the same
time (43 out of 45 senators), and on average, serve on 1.6 other Senate committees. Among the
sample of 155 House committee members, 86 consecutively serve on 1.3 other House committees.
In an untabulated robustness check, we repeat our tests after including a control for a state’s
congressional seniority across all committees, calculated using the same methodology as used for our
primary measures of seniority. Our main findings are unaffected by the inclusion of this variable,
and furthermore, coefficients for this additional control variable are statistically insignificant across
all specifications.
4.5.8 Business Friendly States
We consider the possibility that politicians who choose to serve on influential committees
represent states that are viewed as “business-friendly”. Business friendly states are likely to attract
higher quality and more successful firms relative to other states. Under this explanation, it is the
underlying business-friendly states that have both senior political representation on influential
committees and better quality firms.
We collect state-level business-friendliness data from Forbes’ annual survey of state-level
business environments between 2005-2010. The survey ranks states along six categories of business-
36
related factors. 28 We replicate our tests using an indicator variable set to one for firms located in the
upper half of all business-friendly states and zero for firms domiciled in all other 25 states.
Untabulated tests indicate that our main results are qualitatively unchanged after including this
control variable.
5. The Threat of Political Costs, Auditor Fees, and Auditor Turnover
Our results thus far are consistent with the argument that auditors provide higher audit
quality and issue more GC and ICW opinions when the political costs of audit failure are higher. In
this section, we consider the consequences of these auditor incentives to limit the political costs of
audit failure with respect to audit fees and auditor turnover. Prior research shows that audit fees are
positively related to litigation risk (Simunic, 1980; Simunic and Stein, 1996), because auditors use
increased fee revenue to protect against the increased risk of litigation from audit failure. 29 Using
similar reasoning, it is conceivable that audit fees are a function of the risk of political costs from
audit failure. However, auditors cannot unilaterally charge higher fees for additional effort without a
corresponding increase in client demand for the additional effort (DeFond and Zhang, 2013). Thus,
the fee implication of increased risk of political costs from audit failure is an empirical question.
Second, we consider whether the political costs of audit failure affects auditor turnover. 30
Turnover can occur because a client dismisses an auditor or because an auditor resigns from an
engagement. Auditors are more likely to be dismissed following the issuance of a GC report
The six categories used in the rankings are Business Costs, Labor Supply, Regulatory Environment, Economic Climate,
Growth Prospects, and Quality of Life; http://www.forbes.com/best-states-for-business/.
29 Alternatively, auditors can reduce the risk of material misstatement by increasing audit effort, which results in higher
audit quality and reflected in higher audit fees. This suggests that even when auditors choose to reduce risk with
additional effort they may still manage this “residual”, or “client business risk” by charging a premium (Bell, Landsman,
and Shackelford, 2001; DeFond and Zhang, 2013).
30 In our setting, empirical evidence supporting this argument relies on the assumption that clients do not rationally
anticipate that all auditors face the same political cost of failure based incentives to provide higher-than-demanded levels
of audit quality, and thus appointing a new Big-6 auditor would be a futile action for a client with preferences for a
relatively lower level of audit quality.
28
37
(Craswell, 1988; Krishnan, 1994; Carcello and Neal, 2003), and when they show more skepticism
(Behn et al., 1997), suggesting auditor turnover occurs because clients replace their auditor.
Alternatively, if additional effort and/or increased fees are insufficient to reduce the political risk of
failure to a tolerable level, auditors can avoid the risk altogether by resigning from the engagement
(Menon and Williams, 1999).
Evidence from multivariate tests (untabulated) indicate that audit fees are positively and
statistically related to a client’s presence in states with senior political representation on influential
committees. Economically, a one standard deviation increase in Total_Seniority is associated with
approximately 5% higher audit fees. The results are robust across all measures of influential
committee seniority. Overall, the evidence suggests that auditors extract modest fee premiums from
clients located in states where the risk of incurring political costs following audit failure is high.
We also find that auditor turnover is positively associated with the seniority of a state’s
influential committee membership. Influential committee seniority measured using all three proxies
exhibits a statistically positive association with auditor dismissals at the 5% level. Additional analysis
indicates that our turnover results are driven by auditor resignations, rather than client dismissals.
Our findings suggest that even after controlling for litigation risk and reputation risk (Krishnan and
Krishnan, 1997; Landsman et al., 2009), political risks associated with audit failure have a distinct
effect on auditor decisions to retain clients.
6. Conclusion
In this study, we examine whether the political costs of audit failure affect auditors’
incentives to supply audit quality. Using a unique dataset for the 2000 to 2010 period, results from
empirical tests suggest an association between the seniority of a state’s political representation on
influential congressional committees and audit quality outcomes for firms located in that state. Our
38
findings hold across different audit quality proxies including accruals-based earnings quality
measures and the presence of financial fraud investigations via AAER releases. In economic terms, a
one standard deviation increase in the seniority of a state’s representation on influential committees
results in a 4.7% to 13% increase in earnings quality for in-state firms and a 26% decrease in the
likelihood of being subject to AAER enforcement action. We also find that the seniority of a state’s
representation on influential committees is positively associated with the issuance of GC and ICW
opinions. Our main results are robust to the inclusion of firm fixed effects to address omitted firmspecific correlated variable bias.
We exploit two exogenous shocks to auditors’ risk of facing political costs from audit failure,
and find that the shocks affect auditor incentives to supply audit quality. First, we find that the exit
of a senior politician from an influential committee is associated with a subsequent decrease in audit
quality for firms in the politician’s state. Second, using a sample of firms that change headquarters
locations, relocation to a state with higher (lower) seniority on influential committees is associated
with an increased (decreased) supply of audit quality.
We also undertake a series of additional tests. Our main results persist after considering the
effects of political connections; do not hold for tests examining powerful politicians that serve on
non-auditor relevant congressional committees. Furthermore, our results are concentrated in the
partition of clients for which the political sensitivity of audit failure is highest, consistent with
auditors recognizing that the political costs of failure vary among their client portfolios. Our results
are also robust to a battery of alternative specifications and sensitivity checks.
Our findings are likely to be of interest to politicians, regulators, audit firms, and clients of
audit firms. In addition, our study makes a number of contributions to literature in accounting,
corporate governance, and the political economy. First, by identifying auditor actions in response to
the threat of political costs from politicians that serve on auditor-relevant committees, we build on
39
recent literature that examines the relation between auditor outcomes and the political economy, but
in a US setting (Gul, 2006; Chan et al., 2006). Second, our study identifies a political effect on audit
outcomes and earnings quality that is distinct and incremental to that from political connections.
Our findings help to understand the different mechanisms through which politicians can influence
firm and auditor actions; either via the presence of political connections (Leuz and Oberholzer-Gee;
2006; Gul, 2006; Chaney et al., 2011; Guedhami et al., 2014).
Third, our study contributes to a literature in political economics that examines the
consequences of political power. Prior research examines the implications of politician power for
state-level federal expenditure allocations (Hoover and Pecorino, 2005; Levitt and Poterba, 1999;
Aghion et al., 2009; Belo, Gala, and Li, 2013), IPO activity (Piotroski and Zhang, 2014), and firm
productivity and performance (Cohen et al., 2011; Amore and Bennedsen, 2013). Our paper sheds
light on the effect of political power on financial reporting and auditing outcomes.
40
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Appendix A. Earnings Quality Measures
EQ1: Unsigned Abnormal Accruals (Hribar and Nichols, 2007)
We first estimate the following regression for each year and Fama-French industry:
TACC = + 1 REV + 2PPE +
where TACC is total accruals, defined as income before extraordinary items minus cash from
operations divided by lagged total assets. REV is the change in sales adjusted for the change in
receivables, divided by lagged total assets. PPE is gross property, plant, and equipment, scaled by
lagged total assets. We then calculate the abnormal accruals as the residual term in the regression, i.e.,
TACC – ( + 1 REV + 2PPE), and Hribar is the absolute value of the residual (abnormal
accruals).
EQ2: Industry-Adjusted Absolute Value of DD Residual (Dechow et al., 2011)
We first regress working capital accruals (WC_ACC) on operating cash flows in the current
year (CFOt), the preceding year (CFOt-1), and the following year (CFOt+1):
WC_ACCi,t = 0,i + 1,i CFOi,t-1 + 2,i CFOi,t + 3,iCFOi,t+1 + i,t
current assets between year t-1 and t
t-1 and t,
-Term Investments between year t-1 and t
in short-term debt between year t-1 and t,
t-1
and t.
All variables are scaled by average total assets and winsorized at the 1 percent and 99 percent
levels. We estimate equation (6) by year for each of the two-digit SIC industry groups. EQ3 is the
absolute value of each firm’s residual less the average absolute value for the corresponding industry.
EQ3: Performance-Matched Discretional Accruals (Kothari et al., 2005)
We estimate abnormal accruals for each firm-year and subtract the value from the
discretionary-accruals of the performance-matched firm. The modified Jones model of abnormal
accruals model is estimated cross-sectionally each year using all firm-year observations in the same
Fama-French industry.
TAi,t = 0 + 1(1/ASSETSi,t-1) + 2( SALESi,t – ARi,t) + 3PPEi,t + i,t
where TA (total accruals) is the change in non-cash current assets minus the change in current
liabilities excluding the current portion of long-term debt, minus depreciation and amortization,
scaled by lagged total assets; SALESi,t is change in sales; ARi,t is change in account receivable; and
PPEi,t is gross property, plant and equipment, all scaled using lagged total assets, ASSETSi,t-1. We use
total assets as the deflator to mitigate heteroscedasticity in the residuals.
Residuals from the annual cross-sectional industry regression model in the modified Jones
model are used to measure estimated abnormal accruals. We then match each firm-year observation
with another firm from the same Fama-French industry and year with the closest return on assets in
the current year, ROAi,t (net income divided by total assets). We define the DGLS for firm i in year t
as the abnormal accrual in year t minus the performance-matched abnormal accrual for year t.
44
Appendix B. Variable Definitions
Dependent Variables
EQ1: Unsigned Abnormal Accruals from Hribar and Nichols (2007) and detailed in Appendix B
EQ2: Industry-Adjusted Absolute Value of DD Residual from Dechow et al. (2011) and detailed in
Appendix B
EQ3: Performance-Matched Discretional Accruals from Kothari et al. (2005) and detailed in
Appendix B
Fraud: an indicator variable set to one if the firm is the subject of a fraud-related AAER in the
current year, and set to zero otherwise;
GC_Dummy: an indicator variable set to one in years that the firm receives a qualified audit
opinion with a going concern modification, and set to zero otherwise;
ICW_Dummy: an indicator variable set to one in years that the firm receives a qualified audit
opinion with a going concern modification, and set to zero otherwise;
Turnover: an indicator variable set to one if a firm switches auditors during the year, and set to zero
otherwise;
Audit fees: log of current year audit fees.
Seniority Variables
Total_Seniority: for each firm in a given state, the aggregate tenure of that state’s current political
representation for both influential committees (in years);
Committee_Num: for each firm in a given state, the total number of politicians from that state
serving on influential committees;
Seniority_Dum: for each firm in a given state, an indicator variable set to one if that state’s political
representation on influential committees is in the top quartile of seniority on at least one of the
influential committees, and zero otherwise;
NonAuditor_Total_Seniority: for each firm in a given state, the aggregate tenure of that state’s
current political representation for the top ten most powerful committees from Edwards and
Stewart (2006) (in years);
NonAuditor_Committee_Dum: for each firm in a given state, the total number of politicians from
that state serving on the top ten most powerful committees from Edwards and Stewart (2006);
NonAuditor_Seniority_Dum: for each firm in a given state, an indicator variable set to one if that
state’s political representation on the top ten most powerful committees from Edwards and Stewart
(2006) is in the top quartile of seniority on at least one of the influential committees, and zero
otherwise;
Senate_Total_Seniority: for each firm in a given state, the aggregate tenure of that state’s current
political representation on the Senate Financial Services Committee (in years);
House_Total_Seniority: for each firm in a given state, the aggregate tenure of that state’s current
political representation on the House Financial Services Committee (in years).
Control Variables
Size: log of total assets;
MtB: market value of equity divided by book value of equity;
Profit: earnings before extraordinary items divided by total assets;
Stdev_Cashflow: standard deviation of cashflow from operations between t-4 and t;
Stdev_Sales: standard deviation of sales between t-4 and t;
Oper_Cycle: log (days in account receivables + days in inventory);
45
Auditor_Share: the auditor’s national industry share, measured by the proportion of the total assets
of all firms in the same Fama-French industry;
Auditor_Tenure: number of years that firm has received an audit opinion from the current auditor;
Office_Size: log number of clients of auditor office;
Report_Lag: number of days after the fiscal year-end that the 10-k report is released;
Current_Ratio: current assets divided by current liabilities;
Litigation_Risk: an indicator variable set to one if the firm is in one of the following industries:
biotech (SIC codes 2833-2836 and 8731-8734), computer (3570-3577 and 7370-7374), electronics
(3600-3674), retail (5200-5961), and zero otherwise;
Disc_Accrual: discretional accruals based on Francis et al. (2005);
Leverage: long-term debt divided by total assets;
Loss_Dummy: an indicator variable set to one if the firm experiences a loss during the prior fiscal
year, and set to zero otherwise;
Merger: an indicator variable set to one if the firm has made an acquisition in the previous three
years, and set to zero otherwise;
Sales_Growth: geometric growth rate in sales over the five years prior to the current year (year t-6
to t-1);
Inv+Acr: inventory plus accounts receivable, scaled by total assets;
Cash: cash divided by total assets;
Issuance: an indicator variable set to one if the firm has issued new long-term debt or stock worth
more than ten percent of the prior year’s long-term debt or common equity in the prior three years,
and set to zero otherwise;
ZScore: a measure of bankruptcy risk from Altman (1968);
CEO/CFO_Change: an indicator variable set to 1 if the target firm experiences a CEO/CFO
change in the prior year, and set to 0 otherwise;
Political_Connect: an indicator variable set to one for each year that a firm in our sample that is
affiliated with an U.S. politician, conditional on the politician serving in the same year, and zero
otherwise;
Politicial_Contrib: log of total dollar amount of a firm’s political contributions during a year;
Auditor_Contrib: log of total lobbying spending of an auditor firm during a year;
Inst_Own: year-end institutional ownership as a percentage of common stock;
Analyst_Following: (log) of the number of analysts that cover the firm during the year.
46
Table 1 Summary Statistics
This table presents descriptive statistics. Panel A presents statistics about the House Financial Services Committee
(House Committee), and the Senate Banking, Housing and Urban Affairs Committee (Senate Banking Committee)
characteristics. Panel B displays seniority measure statistics. Panel C provides descriptive statistics for our primary
independent and dependent variables from the full sample.
Panel A: Influential Committee Descriptive Statistics
Average size (in number of members)
Average # of states represented on committee
Average # of state representatives
Max # of state representatives
Average politician tenure on committee (in years)
Maximum politician seniority on committee (in years)
House
Committee
69.25
29.05
2.28
11
3.62
19.00
Senate
Committee
21.25
20.67
1.02
2
6.94
29.00
States with the greatest number of years of representation (and corresponding
duration) in the top quartile of influential committee between 2001 and 2010:
House Committee: CA, PA, NY, MA, AL, NC, IL, LA, DE (10 years); VT, IA, OK, (8 years); OK,
KS, TX, NE (6 years); IN, OH, NJ (4 years); OR, MN, MO, FL (2 years);
Senate Committee: CT, AL (10 years); UT, MD (8 years); SD, TX, RI, (4 years); ID, ID, NE, MA,
FL, WY, IN, CO, NY, KY (2 years)
States with the number of years of representation (and corresponding duration) in
the bottom quartile of influential committee between 2001 and 2010:
House Committee: ME, AK (10 years); KY, WI (8 years); MN, MS (6 years); AR, AZ, CO, CT, MI,
MO, NH, NJ, NM, SC, TN, UT, WV (4 years); GA, ID, NV, VA, WA (2 years)
Senate Committee: ME, AK (10 years); HI, NH, NJ (6 years); DE, FL, GA, MI, MT, NC, OH, PA,
TN (4 years); CO, ID, IN, KY, LA, NE, NV, NY, OR, SC, TX, VA, WI (2 years)
States with no representation on influential committees during sample period: AK, ME
Total # of sample firm-year observations from these states: 22
47
Panel B: Descriptive Statistics for Seniority Measures (n = 17,017)
Mean
Median
Total_Seniority
8.76
6
Committee_Num
1.19
1
Seniority_Dum
0.26
0
Standard Deviation
9.98
2.08
0.44
Panel C: Descriptive Statistics for Primary Variables (n = 17,017)
Mean
Median
Standard
Deviation
Independent Variables
Total_Seniority
Seniority_Num
Seniority_Dum
18.824
4.559
0.263
17.000
4.000
0.000
15.053
3.072
0.440
Dependent Variables
EQ1
EQ2
EQ3
AAER
GC
ICW
Audit fees (log)
Turnover
-15.667
-0.447
-4.194
0.019
0.024
0.066
13.552
0.064
-11.997
-0.489
-3.995
0.000
0.000
0.000
13.553
0.000
14.633
6.670
15.442
0.137
0.153
0.248
1.263
0.245
Low Risk Medium Risk
GC and ICW partitioned by Firm Risk Tercile
GC
ICW
0.010
0.057
48
0.025
0.067
High Risk
0.051
0.074
Table 2
Congressional Committee Seniority and Audit Quality
This table presents coefficients from OLS and logit regressions examining the relation between the seniority of a state’s
representation on influential congressional committees and the supply of audit quality for in-state firms. We measure
congressional committee seniority using one of three proxies: Total Seniority (Panel A), Committee_Num, or Seniority Dum
(Panel B). We measure audit quality using four different proxies from prior research based on accruals estimates or the
presence of SEC enforcement action following the revelation of fraud. All variables are defined in Appendix A. t-values
are reported in parentheses. Standard errors are Huber-White Sandwich estimator clustered at firm level. All
specifications include fixed effects for state, industry, year, and auditor. Statistical significance at the 1%, 5%, and 10%
level is denoted by ***, **, and *, respectively.
Panel A: Tests Using Total_Seniority to measure Seniority
Dependent Variable:
Constant
Total_Seniority
Auditor_Share
Auditor_Tenure
Office_Size
GC_Dummy
Litigation Risk
Size
Leverage
MtB
Profit
Issuance
Stdev_Cashflow
Stdev_Sales
Oper_Cycle
Inst_Own
Analyst_Following
State, Industry, Year, Auditor Fixed Effects
Observations
Adjusted R2
(1)
EQ1
-40.198***
(-6.49)
0.040***
(2.60)
3.315**
(1.97)
-0.020
(-1.18)
0.465**
(2.48)
-3.228***
(-2.68)
1.006**
(2.10)
0.350***
(2.98)
-1.229
(-1.52)
-0.072**
(-2.26)
-0.263**
(-2.05)
-1.437***
(-5.97)
-0.304
(-1.18)
-1.578**
(-2.08)
-0.750***
(-3.05)
0.693
(1.13)
0.428***
(2.73)
Yes
17,017
0.214
49
(2)
EQ2
-1.029
(-1.03)
0.002**
(2.22)
0.064
(1.10)
-0.008
(-1.52)
0.071
(1.11)
-0.262
(-1.43)
0.154***
(4.89)
0.028
(0.72)
-0.486*
(-1.69)
-0.020
(-1.27)
-0.189***
(-4.48)
-0.629***
(-5.89)
-0.141
(-1.21)
-0.218
(-0.73)
-0.189**
(-2.05)
0.218
(1.05)
0.136**
(2.51)
Yes
17,017
0.035
(3)
EQ3
-13.336***
(-4.43)
0.011**
(2.11)
3.352**
(2.03)
-0.035**
(-2.56)
0.475***
(2.72)
-2.542*
(-1.80)
0.755***
(5.22)
0.453**
(2.27)
-0.188
(-1.23)
-0.032*
(-1.81)
-1.227***
(-7.80)
-0.356
(-1.47)
-3.639**
(-2.34)
-1.122
(-1.42)
-0.296
(-1.13)
1.317**
(2.30)
0.152
(1.04)
Yes
17,017
0.146
(4)
AAER
-8.820***
(-8.36)
-0.015**
(-2.30)
0.876
(1.01)
-0.016
(-1.19)
-0.308**
(-2.46)
-1.104
(-1.05)
0.040
(1.01)
0.426***
(5.44)
-1.103
(-1.56)
0.022
(0.88)
0.055
(1.00)
0.133
(0.80)
0.665
(0.52)
1.358***
(2.75)
0.631***
(3.89)
-0.133
(-1.24)
-0.131
(-0.96)
Yes
17,017
0.067
Panel B: Tests Using Committee_Num or Seniority_Dum to measure Seniority
Dependent Variable:
Constant
Committee_Num
Seniority_Dum
Auditor_Share
Auditor_Tenure
Office_Size
GC_Dummy
Litigation Risk
Size
Leverage
MtB
Profit
Issuance
Stdev_Cashflow
Stdev_Sales
Oper_Cycle
Inst_Own
Analyst_Following
State, Industry, Year, Auditor Fixed Effects
Observations
Adjusted R2
(1)
EQ1
-40.246***
(-6.50)
0.129**
(2.33)
-
(2)
EQ2
-1.005
(-1.00)
0.037**
(2.51)
-
(3)
EQ3
-13.357***
(-4.44)
0.134**
(2.28)
-
(4)
AAER
-9.110***
(-9.06)
-0.052**
(-2.16)
-
3.315**
(1.97)
-0.019
(-1.14)
0.468**
(2.49)
-3.197***
(-2.66)
1.009
(1.14)
0.350***
(2.97)
-1.217
(-1.51)
-0.074**
(-2.30)
-0.260**
(-2.03)
-1.442***
(-5.99)
-0.292**
(-2.18)
-1.557**
(-2.06)
-0.741***
(-3.01)
0.962
(1.57)
0.575***
(3.63)
Yes
17,017
0.209
0.063
(1.09)
-0.008
(-1.57)
0.057*
(1.84)
-0.282
(-1.46)
0.152***
(4.84)
0.028*
(1.72)
-0.482*
(-1.68)
-0.020
(-1.23)
-0.187***
(-4.46)
-0.632***
(-5.91)
-0.136**
(-2.20)
-0.226
(-0.76)
-0.194**
(-2.10)
0.176*
(1.86)
0.070**
(2.28)
Yes
17,017
0.033
3.355**
(2.03)
-0.035**
(-2.55)
0.436**
(2.34)
-2.570*
(-1.82)
0.753***
(4.19)
0.453***
(4.27)
-0.187
(-1.23)
-0.031
(-0.79)
-1.225***
(-7.80)
-0.358
(-1.48)
-3.643**
(-2.34)
-1.117
(-1.41)
-0.301
(-1.15)
1.388**
(2.37)
0.329**
(2.22)
Yes
17,017
0.114
-0.589
(-0.44)
0.013
(0.97)
-0.335**
(-2.42)
0.641
(0.59)
-0.012
(-0.29)
-0.508***
(-6.15)
0.880
(1.24)
0.027
(1.30)
0.053
(0.83)
0.142
(0.89)
0.258
(1.18)
1.304***
(2.58)
0.950***
(3.66)
-0.203
(-1.35)
-0.149
(-1.14)
Yes
17,017
0.067
50
(5)
EQ1
-40.210***
(-6.49)
-
(6)
EQ2
-1.022
(-1.02)
-
(7)
EQ3
-13.376***
(-4.44)
-
(8)
AAER
-8.938
(-8.99)
-
0.537**
(2.30)
3.315**
(1.96)
-0.019
(-1.15)
0.473**
(2.52)
-3.244***
(-2.69)
1.001
(1.01)
0.351***
(2.98)
-1.241
(-1.54)
-0.072**
(-2.25)
-0.265**
(-2.07)
-1.435***
(-5.96)
-0.320**
(-2.19)
-1.567**
(-2.07)
-0.747***
(-3.04)
0.933
(1.53)
0.570***
(3.60)
Yes
17,017
0.209
0.127**
(2.44)
0.064
(1.10)
-0.008
(-1.55)
0.075
(1.17)
-0.255
(-1.42)
0.156***
(4.98)
0.028*
(1.73)
-0.493*
(-1.71)
-0.021
(-1.28)
-0.190***
(-4.52)
-0.628***
(-5.87)
-0.150**
(-2.22)
-0.223
(-0.74)
-0.191**
(-2.07)
0.163*
(1.79)
0.072**
(2.32)
Yes
17,017
0.033
0.271**
(2.43)
3.349**
(2.03)
-0.034**
(-2.52)
0.478***
(2.74)
-2.552*
(-1.81)
0.752***
(11.19)
0.453***
(4.27)
-0.170
(-1.21)
-0.031
(-0.78)
-1.225***
(-7.80)
-0.360
(-1.49)
-3.654**
(-2.35)
-1.102
(-1.39)
-0.302
(-1.16)
1.404**
(2.40)
0.331**
(2.23)
Yes
17,017
0.114
-0.098**
(-2.47)
-0.584
(-0.44)
0.013
(0.95)
-0.339**
(-2.46)
-0.583
(-0.54)
-0.005
(-0.11)
-0.513***
(-6.13)
0.865
(1.22)
0.025
(1.21)
0.059
(0.92)
0.144
(0.91)
0.325
(0.23)
1.269**
(2.49)
0.941***
(3.62)
-0.244
(-1.42)
-0.156
(-1.18)
Yes
17,017
0.067
Table 3
Congressional Committee Seniority and the Issuance of Going Concern and Internal
Control Weakness Reports
This table presents coefficients from OLS and logit regressions examining the relation between the seniority of a state’s
representation on influential congressional committees and the likelihood that an auditor issues a going concern audit
opinions or internal control weakness report to in-state firms. We measure congressional committee seniority using one
of three proxies: Total_Seniority (Panel A), Committee_Num, or Seniority_Dum (Panel B). We partition sample firms into
terciles based on the risk of distress using the Altman (1968) Z-Score. All variables are defined in Appendix A. t-values
are reported in parentheses. Standard errors are Huber-White Sandwich estimator clustered at firm level. All
specifications include fixed effects for state, industry, year, and auditor. Statistical significance at the 1%, 5%, and 10%
level is denoted by ***, **, and *, respectively.
Panel A: Going Concern Audit Opinion Tests
Distress Risk tercile:
Dependent variable:
Constant
Total_Seniority
Committee_Num
Seniority_Dum
Auditor_Share
Auditor_Tenure
Office_Size
Litigation Risk
Size
Leverage
MtB
Profitability
Stdev_Cashflow
Stdev_Sales
Oper_Cycle
Current_Ratio
Report_Lag
Inst_Own
Analyst_Following
State, Industry, Year, Auditor Fixed Effects
Observations
Pseudo R2
(1)
(2)
(3)
High Risk
Mid Risk
Low Risk
-11.004**
(-2.17)
0.027**
(2.23)
-
-10.765***
(-4.43)
0.009
(0.65)
-
GC
-6.281***
(-6.65)
-0.013
(-1.57)
-
-
-
1.896
(0.81)
0.087***
(2.82)
-0.055
(-0.14)
-0.352
(-0.45)
-1.301*
(-1.70)
3.115
(1.39)
0.050
(1.06)
-0.018
(-0.17)
0.685
(0.37)
0.024
(0.02)
0.998*
(1.75)
-0.533
(-0.98)
0.964
(1.43)
0.027
(0.01)
0.383
(1.05)
Yes
5,672
0.269
1.968
(0.81)
0.016
(0.39)
-0.152
(-0.48)
-0.297
(-0.44)
-0.214
(-1.10)
-0.183
(-0.06)
0.015
(0.11)
-0.127
(-1.46)
1.124
(1.18)
-0.197
(-1.46)
0.516
(0.73)
-0.774
(-1.40)
1.846***
(4.64)
-2.407
(-1.55)
-0.436
(-1.28)
Yes
5,672
0.255
51
(4)
High Risk
Tercile
(5)
High Risk
Tercile
-11.597**
(-2.19)
-
-10.605**
(-2.16)
-
-
0.241***
(3.04)
-
1.056
(1.38)
0.019
(1.46)
0.054
(0.60)
-0.155
(-0.86)
-0.151**
(-2.04)
-1.749***
(-3.46)
-0.049***
(-3.02)
-0.182***
(-5.64)
1.365***
(2.77)
-0.863**
(-2.35)
0.288***
(2.77)
-1.550***
(-9.44)
1.053***
(5.93)
-0.679
(-1.27)
-0.386***
(-2.88)
Yes
5,673
0.251
1.779
(0.80)
0.086***
(2.81)
-0.071
(-0.17)
-0.369
(-0.46)
-1.357*
(-1.71)
3.497
(1.63)
0.045
(1.08)
0.011
(0.09)
0.077
(0.04)
-0.078
(-0.05)
1.083*
(1.77)
-0.692
(-1.26)
0.944
(1.26)
0.084
(0.03)
0.445
(1.13)
Yes
5,672
0.301
0.411**
(2.49)
2.065
(0.86)
0.084**
(2.56)
-0.068
(-0.17)
-0.252
(-0.33)
-1.258*
(-1.76)
2.708
(1.11)
0.052
(1.05)
-0.034
(-0.33)
0.880
(0.51)
0.053
(0.04)
0.964*
(1.76)
-0.481
(-0.87)
0.955
(1.59)
0.177
(0.06)
0.327
(0.95)
Yes
5,672
0.259
Panel B: Internal Control Weakness Opinion Tests
Distress Risk tercile:
Dependent variable:
Constant
Total_Seniority
Committee_Num
Seniority_Dum
Auditor_Share
Auditor_Tenure
Office_Size
Litigation Risk
Size
Leverage
MtB
Profitability
Stdev_Cashflow
Stdev_Sales
Oper_Cycle
Current_Ratio
Report_Lag
Inst_Own
Analyst_Following
State, Industry, Year, Auditor Fixed Effects
Observations
Pseudo R2
(1)
(2)
(3)
High Risk
Mid Risk
Low Risk
-16.480***
(-9.78)
0.003**
(2.49)
-
-18.371***
(-8.05)
-0.006
(-1.10)
-
ICW
-17.264***
(-10.50)
-0.002
(-0.51)
-
-
-
0.428
(0.49)
-0.049***
(-2.95)
-0.102
(-1.23)
0.128
(0.70)
0.082
(0.96)
-3.640**
(-1.97)
-0.007
(-0.21)
0.019
(0.36)
0.925
(1.30)
0.141
(0.40)
0.188
(1.64)
-0.096
(-0.56)
3.030***
(8.72)
0.138
(1.34)
0.009
(1.08)
Yes
3,959
0.178
0.240
(0.29)
-0.021
(-1.56)
-0.052
(-0.54)
0.401**
(2.16)
0.086
(1.05)
-0.698
(-1.14)
0.046**
(2.40)
0.038
(0.68)
1.813**
(2.41)
-0.041
(-0.09)
0.285*
(1.88)
-0.087
(-0.45)
3.215***
(7.15)
1.912*
(1.88)
-0.388*
(-1.93)
Yes
3,959
0.140
52
(4)
(5)
High Risk High Risk
Tercile
Tercile
-16.461***
(-9.77)
-
-16.461***
(-9.73)
-
-
0.008**
(2.27)
-
-0.052
(-0.07)
-0.018
(-1.60)
-0.079
(-1.01)
0.282*
(1.73)
0.246***
(4.68)
-0.220
(-0.67)
0.011
(0.97)
0.120**
(2.07)
0.325
(0.57)
-0.002
(-0.01)
0.120
(1.08)
-0.047
(-0.39)
3.014***
(9.08)
0.468
(1.42)
-0.092
(-1.19)
Yes
3,960
0.144
0.443
(0.51)
-0.049***
(-2.95)
-0.100
(-1.19)
0.132
(0.73)
0.081
(0.95)
-3.636**
(-1.97)
-0.006
(-0.20)
0.019
(0.36)
0.919
(1.30)
0.141
(0.40)
0.189
(1.64)
-0.092
(-0.53)
3.026***
(8.70)
0.139
(1.35)
0.009
(1.08)
Yes
3,959
0.178
0.192**
(2.03)
0.429
(0.49)
-0.050***
(-2.97)
-0.103
(-1.24)
0.115
(0.62)
0.081
(0.95)
-3.589*
(-1.96)
-0.008
(-0.24)
0.018
(0.34)
0.915
(1.29)
0.130
(0.36)
0.193*
(1.67)
-0.110
(-0.64)
3.031***
(8.70)
0.133
(0.33)
0.006
(0.06)
Yes
3,959
0.178
Table 4 Shocks to Influential Committee Membership via Senior Politician Exits
This table presents evidence from analysis examining the association between measures of audit quality around the exit
of a state’s senior representation (top quartile) from an influential congressional committee. Panel A presents figures of
the level of measures of audit quality in the years around a politician exit centered at t = 0. Panel B presents results from
regressions of audit quality on shocks to a state’s influential committee senior representation. t-values are reported in
parentheses. Standard errors are Huber-White Sandwich estimator clustered at firm level. All specifications include fixed
effects for state, industry, year, and auditor. Statistical significance at the 1%, 5%, and 10% level is denoted by ***, **,
and *, respectively. All variables are defined in Appendix A.
Panel A: Audit Quality Around Senior Influential Committee Politician Exit Shock
2: EQ2
-.72
-15.9
-.7
-15.8
-.68
EQ2
EQ1
-15.7
-.66
-15.6
-.64
-.62
-15.5
1: EQ1
-3
-2
-1
0
Firms with Shock
3
2
1
-3
-2
Firms without Shock
-1
0
Firms with Shock
2
3
4: AAER
-5
.026
-4.8
.028
EQ3
-4.6
AAER
.03
-4.4
.032
-4.2
.034
3: EQ3
1
Firms without Shock
-3
-2
-1
0
Firms with Shock
1
2
3
-3
-2
Firms without Shock
-1
0
Firms with Shock
2
3
6: ICW
.02
.02
.04
ICW
.06
Going Concern
.025
.03
.08
.1
.035
5: GC
1
Firms without Shock
-3
-2
-1
Firms with Shock
0
1
2
3
-3
-2
-1
Firms with Shock
Firms without Shock
53
0
1
2
Firms without Shock
3
Panel B: Change in Audit Quality Around Influential Committee Politician Exit Shock
Dependent variable:
Constant
Senior_Drop
Auditor_Share
Auditor_Tenure
Office_Size
GC_Dummy
Litigation Risk
Size
Leverage
MtB
Profit
Issuance
Stdev_Cashflow
Stdev_Sales
Oper_Cycle
Current_Ratio
Report_Lag
Inst_Own
Analyst_Following
State, Industry, Year,
Auditor Fixed Effects
Observations
Adjusted/Pseudo R2
(1)
(2)
(3)
(4)
(5)
(6)
-4.473
(-0.95)
-0.022*
(-1.90)
0.421
(0.10)
-0.041
(-0.97)
0.418
(0.95)
-1.955
(-1.42)
-0.403
(-1.50)
0.033
(0.11)
-4.256*
(-1.94)
-0.108
(-0.87)
-0.053
(-0.34)
0.424
(1.52)
-6.168
(-1.40)
-2.479
(-1.03)
-0.414
(-0.46)
-
-6.820
(-0.96)
-0.020**
(-2.17)
0.042
(0.76)
-0.006
(-0.26)
0.067
(0.25)
-5.248*
(-1.83)
-0.026
(-1.15)
0.086
(0.40)
-2.120*
(-1.70)
-0.068
(-0.94)
-0.064
(-0.89)
1.134**
(2.20)
-1.860
(-0.80)
-4.172*
(-1.71)
-0.686
(-1.40)
-
1.289
(0.87)
-0.027**
(-2.46)
0.451
(0.47)
-0.008
(-0.16)
0.021
(0.04)
-3.477***
(-3.18)
-0.445
(-1.51)
0.141
(0.36)
-4.079
(-1.55)
-0.126
(-0.74)
-0.081
(-0.34)
2.661***
(2.63)
-2.512
(-0.45)
-1.061
(-0.36)
-0.338
(-0.34)
-
-6.112***
(-3.30)
0.389**
(2.40)
-0.166
(-1.22)
0.032
(1.10)
-0.155
(-1.02)
0.229
(1.39)
0.152
(1.30)
-0.125
(-0.88)
-0.233
(-0.82)
0.033
(0.90)
0.211
(0.59)
-0.042
(-0.90)
-5.283
(-1.50)
-0.772
(-1.20)
-0.650*
(-1.80)
-
-
-
-
-
-0.164
(-0.10)
-0.129
(-0.31)
-1.428
(-1.18)
-0.408
(-1.27)
-0.979
(-0.43)
-0.140
(-0.25)
0.211
(1.03)
0.528
(1.02)
-8.739**
(-2.20)
-0.270**
(-2.50)
-2.273
(-0.71)
-0.011
(-0.23)
-0.066
(-0.33)
-1.000
(-1.02)
-0.309*
(-1.70)
0.089
(1.01)
0.011
(0.55)
-0.133*
(-1.90)
-1.666
(-0.90)
1.621
(0.92)
1.033*
(1.90)
2.020***
(3.00)
0.792
(1.21)
-0.455*
(-1.89)
1.029
(1.39)
-0.221
(-1.02)
-0.320
(-0.89)
-13.390***
(-5.58)
-0.123**
(-2.26)
-2.402
(-1.40)
-0.033
(-1.11)
0.255
(1.23)
-0.355
(-0.82)
-0.211**
(-2.02)
0.529
(0.75)
-0.005
(-0.28)
-0.062
(-1.39)
-0.777
(-0.89)
0.672
(1.03)
0.014
(1.39)
0.528
(1.28)
1.394*
(1.90)
-0.466
(-1.39)
1.210**
(2.50)
-0.332
(-1.09)
-0.293
(-1.23)
Yes
Yes
Yes
Yes
Yes
Yes
1,502
0.105
1,502
0.080
1,502
0.091
1,502
0.065
489
0.311
338
0.140
54
Table 5
Political Connections and Audit Quality
This table presents coefficients from OLS and logit regressions examining whether the seniority of a state’s
representation on auditor related powerful congressional committees is associated with the supply of audit quality. We
measure auditor related powerful congressional committee seniority using one of three proxies: Total_Seniority (Panel A),
Committee_Num (Panel B), or Seniority_Dum (Panel C). We control for three types of political connections.
Politicial_Connect is an indicator variable set to one for firms that have had a active politician previously serve as in an
executive or non-executive capacity in the firm. Politicial_Contrib is the log of the total annual monetary political
expenditures of the firm. Auditor_Contrib is log of the auditor’s annual lobbying spending. All variables are defined in
Appendix A. t-values are reported in parentheses. Standard errors are Huber-White Sandwich estimator clustered at firm
level. Statistical significance at the 1%, 5%, and 10% level is denoted by ***, **, and *, respectively.
Panel A: Tests Using Total_Seniority to measure Seniority
Dependent variable:
Total_Seniority
Political_Connect
Political_Contrib
Auditor_Contrib
Controls?
State, Industry, Year, and
Auditor Fixed Effects?
Observations
Adjusted/Pseudo R2
(1)
(2)
(3)
(4)
(5)
GC (High Risk
Tercile)
(6)
ICW (High Risk
Tercile)
EQ1
EQ2
EQ3
AAER
0.038**
(2.30)
-0.441
(-1.10)
-0.095**
(-2.10)
0.082
(0.66)
Yes
Yes
0.002**
(2.15)
-0.185
(-1.30)
-0.006
(-1.42)
0.080
(1.19)
Yes
Yes
0.011*
(1.90)
-0.239
(-0.77)
-0.034
(-1.23)
0.020
(0.11)
Yes
Yes
-0.011**
(-2.31)
0.427*
(1.70)
0.035*
(1.67)
0.069
(0.80)
Yes
Yes
0.026**
(2.20)
-0.912
(-0.69)
-0.221
(-1.00)
0.008
(0.43)
Yes
Yes
0.002**
(2.45)
0.359
(1.48)
-0.070
(-1.52)
-0.066
(-0.49)
Yes
Yes
17,017
0.214
17,017
0.035
17,017
0.146
17,017
0.067
5,672
0.269
3,959
0.178
(5)
(6)
ICW (High Risk
Tercile)
0.008**
(2.25)
0.343
(1.39)
-0.072**
(-2.20)
-0.055
(-0.69)
Yes
Panel B: Tests Using Committee_Num to measure Seniority
Dependent variable:
Committee_Num
Political_Connect
Political_Contrib
Auditor_Contrib
Controls?
State, Industry, Year, and
Auditor Fixed Effects?
Observations
Adjusted/Pseudo R2
(1)
(2)
(3)
(4)
EQ1
EQ2
EQ3
AAER
0.122**
(2.20)
-0.440
(-1.15)
-0.085*
(-1.90)
0.105
(0.70)
Yes
0.035**
(2.12)
-0.187
(-1.32)
-0.005
(-0.50)
0.082
(1.20)
Yes
0.130**
(2.19)
-0.229
(-0.77)
-0.033
(-1.29)
0.024
(0.29)
Yes
-0.051**
(-2.18)
0.390*
(1.79)
0.031
(1.26)
0.088
(0.93)
Yes
GC (High Risk
Tercile)
0.237***
(2.93)
-1.111
(-0.90)
-0.199
(-0.43)
0.060
(0.39)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
17,017
0.209
17,017
0.033
17,017
0.114
17,017
0.067
5,672
0.301
3,959
0.178
55
Panel C: Tests Using Seniority_Dum to measure Seniority
Dependent variable:
Seniority_Dum
Political_Connect
Political_Contrib
Auditor_Contrib
Controls?
State, Industry, Year, and
Auditor Fixed Effects?
Observations
Adjusted/Pseudo R2
(1)
(2)
(3)
(4)
EQ1
EQ2
EQ3
AAER
0.513**
(1.99)
-0.421
(-1.02)
-0.091*
(-1.95)
0.100
(0.99)
Yes
0.119** 0.258**
(2.26)
(2.06)
-0.138
-0.249
(-1.30) (-0.63)
-0.004
-0.037
(-0.53) (-1.29)
0.095
0.031
(1.30)
(0.30)
Yes
Yes
(5)
(6)
-0.087**
(-2.28)
0.435*
(1.75)
0.033
(1.55)
0.072
(0.97)
Yes
GC (High Risk
Tercile)
0.387**
(2.33)
-1.349
(-1.33)
-0.229
(-0.93)
-0.007
(-1.30)
Yes
ICW (High Risk
Tercile)
0.181**
(2.12)
0.329
(1.30)
-0.062**
(-2.03)
-0.065
(-0.80)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
17,017
0.209
17,017
0.033
17,017
0.114
17,017
0.067
5,672
0.301
3,959
0.178
56
Table 6
Tests using Non-Auditor Related Congressional Committee Seniority
This table presents coefficients from OLS and logit regressions examining whether the seniority of a state’s
representation on non-auditor related powerful congressional committees affects the supply of audit quality. We measure
non-auditor related powerful congressional committee seniority using one of three proxies: NonAuditor_Total_Seniority
(Panel A), NonAuditor_Committee_Num (Panel B), or NonAuditor_Seniority_Dum (Panel C). All variables are defined in
Appendix A. t-values are reported in parentheses. Standard errors are Huber-White Sandwich estimator clustered at firm
level. All specifications include fixed effects for state, industry, year, and auditor. Statistical significance at the 1%, 5%,
and 10% level is denoted by ***, **, and *, respectively.
Panel A: Total Seniority of Non-Auditor Related Powerful Committee Members
(1)
(2)
(3)
(4)
Dependent variable:
EQ1
EQ2
EQ3
AAER
NonAuditor_Total_Seniority
-0.602
(-1.45)
-0.018
(-0.10)
-0.440
(-0.97)
Yes
Yes
Yes
Yes
Controls?
State, Industry, Year, and
Auditor Fixed Effects?
Observations
Adjusted/Pseudo R2
(5)
GC (High Risk
Tercile)
(6)
ICW (High Risk
Tercile)
-0.028
(-0.21)
1.613
(0.83)
0.259
(1.61)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
17,017
0.068
5,672
0.338
3,959
0.140
17,017 17,017 17,017
0.190 0.013 0.079
Panel B: Number of Non-Auditor Related Powerful Committee Members
(1)
(2)
(3)
(4)
Dependent variable:
EQ1
EQ2
EQ3
AAER
NonAuditor_Committee_Num
0.130
(1.14)
0.032
(1.11)
0.014
(0.19)
Yes
Yes
Yes
Yes
Controls?
State, Industry, Year, and
Auditor Fixed Effects?
Observations
Adjusted/Pseudo R2
Yes
0.009
(0.99)
Yes
(5)
GC (High Risk
Tercile)
0.108
(1.63)
Yes
(6)
ICW (High Risk
Tercile)
0.011
(1.49)
Yes
Yes
Yes
Yes
Yes
17,017
0.068
5,672
0.360
3,959
0.138
17,017 17,017 17,017
0.190 0.013 0.079
Panel C: Dummy Variable for High Total Seniority of Non-Auditor Related Powerful
Committee Members
(1)
(2)
(3)
(4)
Dependent variable:
EQ1
EQ2
EQ3
AAER
NonAuditor_Seniority_Dum
1.154
(1.07)
Yes
0.109
(0.89)
Yes
1.503
(1.12)
Yes
Yes
Yes
Yes
Controls?
State, Industry, Year, and
Auditor Fixed Effects?
Observations
Adjusted/Pseudo R2
17,017 17,017 17,017
0.047 0.011 0.017
57
-0.232
(-0.89)
Yes
(5)
GC (High Risk
Tercile)
1.677
(1.49)
Yes
(6)
ICW (High Risk
Tercile)
0.258
(1.39)
Yes
Yes
Yes
Yes
17,017
0.068
5,672
0.321
3,959
0.137
Table 7
Domestic and International Firms
This table presents coefficients from OLS and logit regressions examining whether the relation between the seniority of
a state’s representation on influential congressional committees and the supply of audit quality varies across firms. We
present results from tests in which we measure influential committee seniority using Total_Seniority. All variables are
defined in Appendix A. t-values are reported in parentheses. Standard errors are Huber-White Sandwich estimator
clustered at firm level. All specifications include fixed effects for state, industry, year, and auditor. Statistical significance
at the 1%, 5%, and 10% level is denoted by ***, **, and *, respectively.
Panel A: Domestic Firms
Dependent variable:
Total_Seniority
Controls?
State, Industry, Year, and
Auditor Fixed Effects?
Observations
Adjusted/Pseudo R2
(1)
(2)
(3)
EQ1
EQ2
EQ3
(4)
(5)
Domestic Firms
0.054** 0.003** 0.014**
(2.45)
(2.32)
(2.33)
Yes
Yes
Yes
(6)
AAER
GC
(High Risk Tercile)
-0.020**
(-2.42)
Yes
0.025**
(2.20)
Yes
ICW
(High Risk
Tercile)
0.004**
(2.22)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
6,620
0.248
6,620
0.047
6,620
0.134
6,620
0.075
2,206
0.230
1,540
0.273
Panel B: International Firms
Dependent variable:
Total_Seniority
Controls?
State, Industry, Year, and
Auditor Fixed Effects?
Observations
Adjusted/Pseudo R2
(1)
(2)
(3)
EQ1
EQ2
EQ3
0.023* 0.002* 0.008*
(1.82) (1.73) (1.77)
Yes
Yes
Yes
(4)
(5)
International Firms
(6)
AAER
GC
(High Risk Tercile)
-0.011
(-1.55)
Yes
0.015*
(1.78)
Yes
ICW
(High Risk
Tercile)
0.002*
(1.82)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
10,397
0.233
10,397
0.037
10,397
0.141
10,397
0.065
3,466
0.154
2,419
0.155
58