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Does the Big-4 Effect Exist when Reputation and Litigation Risks are Low?
Evidence from Audit-Partner – Auditee Pair Switches
Limei Che
University College of Southeast Norway
[email protected]
Ole-Kristian Hope
Rotman School of Management
University of Toronto
[email protected]
John Christian Langli
BI Norwegian Business School
[email protected]
August 31, 2016
Abstract
This paper studies whether Big-4 firms provide higher quality audits relative to non-Big-4 firms
when the characteristics of audit partners and auditees are held constant. When an audit partner
switches her affiliation with an audit firm to a different firm, some auditees follow the partner
(hereafter, the partner-auditee pair). Employing a unique dataset of individual auditors for a large
sample of private companies in a setting with documented low litigation and reputation risk, we
analyze audit quality of the partner-auditee pairs that switch affiliations between Big-4 and nonBig-4 firms. We proxy for audit quality using measures of earnings management, deviations from
clean audit reports, and accuracy of going-concern reporting. Our evidence is consistent with a
Big-4 effect. We find less earnings management, higher going-concern accuracy, and higher audit
fees after a switch from a non-Big-4 firm to a Big-4 firm. For switches from Big-4 firms to nonBig-4 firms, we find lower going-concern reporting accuracy and lower audit fees after the switch.
We further show that the increase in going-concern accuracy and the decrease in earnings
management coincide with a lower likelihood of issuing modified audit reports; we attribute this
effect to the Big-4 firms’ ability to more accurately identify and evaluate financially-troubled
auditees and their greater competency and independence to provide higher quality financial
reporting by the auditees, resulting in less use of modified reports.
Key words: Big-4 effect, research design, audit quality, audit change, private firms, litigation risk,
reputation risk
This paper has benefited from comments from Muhammad Azim, Stephanie Cheng, Mahfuz Chy, Alastair
Lawrence, Jiri Novak, Stefan Sundgren, Aida Wahid, Marleen Willekens, and workshop participants at University
of California at Berkeley - Haas School of Business, Norwegian School of Business (BI), Norwegian School of
Economics (NHH), Umeå University, the annual meeting of the European Accounting Association (Maastricht), and
the Berlin Accounting Workshop. We are grateful to the Norwegian Tax Administration, the Financial Supervisory
Authority of Norway, Experian AS, Eniro AS, and the Center for Corporate Governance Research (CCGR) at
Norwegian Business School for providing the data. Hope gratefully acknowledges the financial support of the
Deloitte Professorship.
Does the Big-4 Effect Exist when Reputation and Litigation Risks are Low?
Evidence from Audit-Partner – Auditee Pair Switches
Abstract
This paper studies whether Big-4 firms provide higher quality audits relative to non-Big-4 firms
when the characteristics of audit partners and auditees are held constant. When an audit partner
switches her affiliation with an audit firm to a different firm, some auditees follow the partner
(hereafter, the partner-auditee pair). Employing a unique dataset of individual auditors for a large
sample of private companies in a setting with documented low litigation and reputation risk, we
analyze audit quality of the partner-auditee pairs that switch affiliations between Big-4 and nonBig-4 firms. We proxy for audit quality using measures of earnings management, deviations from
clean audit reports, and accuracy of going-concern reporting. Our evidence is consistent with a
Big-4 effect. We find less earnings management, higher going-concern accuracy, and higher audit
fees after a switch from a non-Big-4 firm to a Big-4 firm. For switches from Big-4 firms to nonBig-4 firms, we find lower going-concern reporting accuracy and lower audit fees after the switch.
We further show that the increase in going-concern accuracy and the decrease in earnings
management coincide with a lower likelihood of issuing modified audit reports; we attribute this
effect to the Big-4 firms’ ability to more accurately identify and evaluate financially-troubled
auditees and their greater competency and independence to provide higher quality financial
reporting by the auditees, resulting in less use of modified reports.
Key words: Big-4 effect, audit quality, audit change, litigation risk, reputation risk, private firms,
research design
Does the Big-4 Effect Exist when Reputation and Litigation Risks are Low?
Evidence from Audit-Partner – Auditee Pair Switches
1
Introduction
The Big-4 effect has been observed in many prior studies (see DeFond and Zhang 2014 for
an overview), but the cause of the Big-4 effect is still unclear. In addition, there is currently a debate
about whether the Big-4 effect really exists or whether it is an artifact of research-design choices,
and in particular matching-related issues (e.g., Lawrence, Minutti-Meza, and Zhang 2011; DeFond,
Erkens, and Zhang 2016). A Big-4 effect, if any, could be due to auditor-level characteristics (e.g.,
higher expertise), audit-firm factors (e.g., more stringent procedures and policies), or auditee-level
characteristics (e.g., client-risk profiles). To better understand why Big-4 audit firms differ from
non-Big-4 firms, DeFond and Zhang (2014) point to the importance of self-selection, the Big-4
firms’ ability to attract higher quality inputs, their higher litigation risk, and greater reputation
capital. DeFond and Zhang (2014, 278) further note that current research “does little to disentangle
the effects of incentives from the effects of competency.”
We use a novel research design that enables us to identify the effect of greater competence
and independence in Big-4 firms, and we employ a setting of private client firms in a country with
documented low litigation risk, thus controlling for the importance of reputation and litigation. We
exploit the fact that when an audit partner switches audit-firm affiliation, some auditees follow the
partner. Thus, we hold the pairs of the engagement partner and auditees constant while varying
their audit-firm affiliation. If the resources that are available at the new audit firm affect audit
quality, that is, if an audit-firm effect exists, we expect to observe changes in audit quality and audit
fees after a switch in affiliation. Specifically, if Big-4 firms deliver audits of higher quality than
non-Big-4 firms, we expect to see an increase (decrease) in audit quality and fees for partners
switching from non-Big 4 (Big-4) firms to Big-4 (non-Big 4) firms. In essence, we address the
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question of whether the Big-4 effect still exists when client-level and partner-level characteristics
are held constant and when both the Big-4 firms and the non-Big-4 firms face low and similar
litigation and reputational risks.
This setting and our research design offer several advantages compared to previous studies.
Auditing standards and regulatory oversight (e.g., the Public Company Accounting Oversight Board
in the U.S. and the Professional Oversight Board in the U.K.) require all audit firms to provide a
reasonable level of audit quality and thus incentivize all auditors to maintain a reasonable level of
audit quality. However, since Big-4 firms have deeper pockets and more reputational capital at stake
than smaller audit firms, Big-4 firms have greater incentives to avoid audit failures that may lead to
loss of reputation and litigation (Dopuch and Simunic 1980; DeAngelo 1981; Lennox 1999;
Khurana and Raman 2004). As such, litigation and reputational concerns may be important drivers
of higher audit quality for Big-4 firms compared to non-Big-4 firms. Because private clients are
generally considered to be of lower reputation risk to audit firms than public clients (Palmrose 1986;
Lys and Watts 1994; Bell, Bedard, Johnstone, and Smith 2002; Johnstone and Bedard 2003;
Clatworthy and Peel 2007), partners auditing private clients face lower reputation and litigation risk.
As such, the Big-4 firms’ incentives to provide audits of higher quality than those delivered by their
non-Big 4 competitors are reduced when auditing private clients.
An additional reduction in the incentives to provide high audit quality is a low likelihood of
litigation given our choice of Norway as our experimental setting. 1 In order to separate between the
effects of the Big-4 firms’ higher reputation and litigation risks versus higher competence (i.e., better
training, more in-house expertise, stronger internal-control systems, better incentive systems, better
audit technology, etc.) and independence, we have chosen a setting in which both Big-4 and non-
1
Hope and Langli (2010) document in detail the low rate of litigation in Norway.
2
Big-4 firms have low and similar litigation and reputation risks. Our tests therefore aim to capture
the potential quality effects of Big-4 firms being more competent and independent. In other words,
we assume that Big-4 firms have the potential to deliver higher audit quality than non-Big-4 firms,
and we examine whether they realize the potential when their reputation and litigation risks are
virtually the same as for the non-Big-4 firms.
Our research design has two main advantages. First, it controls for audit partners’
heterogeneous innate ability because we only compare audit quality (and audit fees) delivered by
the same partner before she switches her affiliation to that after the switch. It is important to hold
the audit-partner characteristics constant because differences among partners influence audit quality
(DeFond and Francis 2005; Francis 2011; Knechel, Vanstraelen, and Zerni 2015). We argue that
unless the audit quality delivered by a non-Big 4 partner is of sufficiently high quality before she
switches to a Big-4 firm, the Big-4 firm should be able to further increase her audit quality
independent of why the partner switches affiliation. Second, we avoid the self-selection problem
that plagues most prior tests of the Big-4 effect by requiring that the partner audits the same clients
before and after the affiliation switch (while controlling for changes in the auditees’ risk,
complexity, and size in the years surrounding the switch year). Because the auditees are held
constant, we do not need to match clients of the Big-4 firms with those of the non-Big-4 firms on
client characteristics (Lawrence et al. 2011; DeFond et al. 2016).
Our measures of audit quality capture different facets of audit quality: going-concern
accuracy (i.e., to what extent audit reports with going-concern modifications predict subsequent
financial difficulties), audit-reporting decisions (i.e., to what extent audit reports are modified), and
the auditor’s tolerance for earnings management (using discretionary accruals). If there exists a Big4 firm effect in terms of higher audit quality, we predict that the Big-4 firm would improve the
switching partner’s competence and/or align her incentives to ensure that she delivers higher audit
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quality after switching compared to before the switch. For a partner who switches from a Big-4 firm
to a non-Big-4 firm, we would expect a decline in audit quality after leaving the Big-4 firm. Because
higher quality is likely to be priced, we also examine changes in audit fees. We hypothesize an
increase (decrease) in fees when the audit partner-auditee pairs switch affiliation from a non-Big 4
(Big-4) firm to a Big-4 firm (non-Big-4 firm).
Our tests allow for a transition period of gradual increase/decrease in audit quality/fees after
the switch. Several arguments suggest such a transition period for partners switching to Big-4 firms.
For example, the Big-4 firms need time to train an incoming auditor, implement the firm’s audit
technology for the new clients, and become familiar with the new clients. With respect to the effect
on audit fees, the Big-4 firm may fear losing the incoming partner’s clients if they immediately
increase fees. In addition, a transition period is also expected for partners leaving Big-4 firms as the
benefits of being trained by a Big-4 firm do not disappear immediately; it is more likely that the
effect of not being part of the more resourceful Big-4 firms will be noticeable over time.
This paper employs a unique dataset of individual auditors for a large sample of Norwegian
private clients from 2004 to 2012. We identify a total of 120 audit partners who switch their
affiliations during the sample period and test our hypotheses using two samples: The ToBig4 sample
(i.e., the auditees of partners switching to Big-4 firms) consists of 31,418 observations while the
ToNonBig4 sample (i.e., auditees of partners switching to non-Big-4 firms) has 1,125 observations. 2
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Companies elect an audit firm (and not an individual partner) as its statutory auditor. Thus, when a partner in firm A
switches to firm B, the client normally stays with firm A. When a Big-4 firm acquires a non-Big-4 firm, the clients of
the non-Big-4 firm must elect another audit firm if the non-Big-4 firm ceases to exist and they do not want to become
clients of the Big-4 firm. When a partner in a Big-4 firm switches to a non-Big-4 firm, there is no need for the clients
of this partner to elect another audit firm because the Big-4 firm is still their elected auditor. The significant difference
in the sample sizes between the ToBig4 and ToNonBig4 sample is a reflection of this: Big-4 firms have grown through
acquisitions that bring in both clients and partners. At the same time, fewer partners leave the Big-4 firms and when
they do, the clients most often remain as clients of the Big-4 firm. As explained in Section 6, we argue that the Big-4
effect should be independent of why auditors join/leave the Big-4 firms. Nevertheless, we provide multiple tests of
endogeneity in Section 6.
4
We find evidence consistent with a Big-4 effect. For partners switching to Big-4 firms, we
find higher going-concern reporting accuracy, less earnings management, and higher audit fees after
they switch. We observe an immediate increase in the accuracy of the going-concern reports (i.e.,
in the switching year) and a gradual increase in earnings quality (due to lower discretionary accruals)
and higher fees over time. The increase in the going-concern accuracy and the decrease in earnings
management coincide with a lower likelihood of issuing modified audit reports, including the goingconcern opinion and other types of modifications. We attribute this effect to the Big-4 firms’ ability
to more accurately identify and evaluate financially-troubled firms and their greater
competency/independence to provide fair presentation and faithful representation of the auditees’
financial situations, resulting in less use of modified reports.
Partners switching from Big-4 to non-Big-4 firms exhibit lower going-concern reporting
accuracy and lower audit fees after they leave Big-4 firms. We observe a gradual decrease in the
going-concern reporting accuracy and a substantial and continuing decrease in audit fees (a
reduction of 16% in the long run).
In all tests, we include fixed effects for partners (as well as industry and year) and 15 auditeelevel control variables to account for changes in the auditees’ size, risk, profitability, and complexity
surrounding the switch in audit-firm affiliation. We perform several sensitivity tests to validate our
findings. First, we test if switching affiliation from one non-Big-4 firm to another non-Big-4 firm
(i.e., lateral switches) causes changes in audit quality and fees. We find no changes in quality for
lateral switches. This is important as it mitigates the concern that switching affiliation per se may
induce changes in quality. Regarding audit fees, we find lower fees after lateral switches, which
might reflect strategic fee discounting (e.g., low-balling) or economies of scale that are passed on
to the clients. Second, we show that compared with non-switching partners in non-Big-4 firms, the
partners switching to Big-4 firms provide higher audit quality and charge higher audit fees prior to
5
joining the Big-4 firm, indicating that Big-4 firms are able to attract higher quality input. Combining
these findings with our main results, the evidence suggests that the Big-4 effect further improves
competencies of these incoming higher-quality partners, and thus they deliver even higher quality
audits after moving to Big-4 firms. Third, we control for partners’ age, gender, years of experience,
and level of education, as, unlike in most jurisdictions, we are able to obtain such information for
Norwegian audit partners. We show that there are no significant differences in the means for these
partner characteristics between the ToBig4 and ToNonBig4 sample, and that our findings are
invariant to the inclusion of these variables as additional control variables. Furthermore, using the
geographic location of the partners (i.e., private addresses), we show that switching affiliation does
not coincide with the partner moving to another municipality. Fourth, consistent with our arguments
that litigation and reputation risks should not drive our findings, we document that the auditees that
follow the partners from non-Big 4 to Big-4 firms are not less risky than other auditees of non-Big4 firms. 3 Finally, we show that the increase in earnings quality in the ToBig4 sample occurs because
the partners restrict the auditees’ use of income-increasing accruals.
This paper provides new insights on the determinants of audit quality, especially to
understand the difference between Big-4 firms and non-Big-4 firms. In a setting where Big-4 firms
are exposed to low and similar litigation and reputation risks as non-Big-4 firms, we find evidence
consistent with the Big-4 effect in that the Big-4 firms have higher competence and independence
than the non-Big-4 firms. As such, the Big-4 firms do not need to be incentivized by litigation and
reputational risk to provide better audit quality relative to the non-Big-4 firms. We also show that
higher-quality audits come with higher audit fees. Second, we propose a new research design that
3
Thus, the findings are not likely to be attributed to Big-4 firms favoring lower-risk clients. This also implies that the
observed increase in audit fees in the ToBig4-sample reflects the price of using higher-quality resources in audit,
alternatively that the auditors put in more effort.
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employs audit partner-auditee pairs that switch audit-firm affiliations. The main advantage of this
research design is that it allows researchers to hold audit-partner and auditee characteristics constant
to isolate the effect of audit-firm factors on audit quality. This design substantially alleviates
concerns over omitted correlated variables. Third, this study provides new evidence on audit quality
in the private-client segment of the economy. The private segment is pertinent because of its
economic significance for the overall economy and in particular for the audit industry (i.e., many
private firms produce and disclose audited financial statements either voluntarily or mandatorily). 4
Fourth, we introduce a new measure of audit quality that captures the auditors’ ability to identify
financially-troubled firms at an earlier stage than bankruptcy by using defaults on debt payments as
a yardstick to evaluate the quality of going-concern reports. By showing that going-concern reports
precede defaults of debt payment, we document that audit reports with going-concern uncertainty
provide useful signals of the auditees’ creditworthiness. Finally, we add to the literature on whether
employers contribute to the performance of their employees (e.g., Kim, Morse, and Zingales 2009;
Rozenbaum 2014).
2
Literature and Hypotheses
Following the theories proposed by DeAngelo (1981) and Dopuch and Simunic (1980),
numerous studies have investigated, both in substance and in perception, whether Big-N audit firms
provide higher quality audits than non-Big-N firms. There is ample empirical evidence to show that
Big-N clients have lower abnormal accruals, report more conservatively, have lower cost of capital,
receive more equity financing, have greater analyst forecast accuracy, and are less involved in
4
As but one example, disclosure of audited financial statements is mandatory for all private firms exceeding nationally
set firm-size thresholds in all EU-countries.
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accounting fraud (Becker, DeFond, Jiambalvo, and Subramanyam 1998; Francis and Krishnan
1999; Lennox 1999; Chang, Dasgupta, and Hilary 2009; Khurana and Raman 2004; Pittman and
Fortin 2004; Behn, Choi, and Kang 2008; Mansi, Maxwell, and Miller 2004; Lennox and Pittman
2010). Other studies, however, find inconsistent results. 5 Causholli, De Martinis, Hay, and Knechel
(2010) review audit-fee studies and conclude that Big-N auditees pay higher audit fees, but that
there are uncertainties as to why the premium exists. With regards to studies using private firms, the
empirical evidence on audit quality and audits fees is scant and the results are mixed. 6
As noted by DeFond and Zhang (2014), the findings of studies of Big-4 effects can be
confounded by audit partners’ and audit firms’ concerns for reputation losses and litigation risk.
Because our study focuses on the private-client segment in Norway, which has low litigation risk as
detailed in Hope and Langli (2010), the effects of reputation and litigation risks on audit quality and
audit fees are substantially reduced. Our paper also sheds light on the factors that may account for
some of the conflicting evidence by analyzing how different facets of audit quality and audit fees
are affected as the audit partner-auditee pairs switch audit-firm affiliation. 7
5
Jenkins and Velury (2011) find no significant difference in conservatism between clients of Big-N and second-tier
auditors in either the pre- or post-SOX periods. Boone, Khurana, and Raman (2010) find evidence suggesting that Big4 audit clients obtain similar audit quality by employing a Mid-tier firm in situations where a Mid-tier auditor is
potentially viable. Lawrence et al. (2011) do not find any evidence that Big-4 auditors supply higher audit quality than
non-Big 4 auditors and suggest that differences in the proxies for audit quality are attributable to client size (i.e., lack
of proper matching in prior research).
6
Using Belgian data, Vander Bauwhede and Willekens (2004), using earnings management as proxy for audit quality,
and Gaeremynck, Van der Meulen, and Willekens (2008), employing measures of disclosure and discretionary accruals
as proxies for audit quality, find no correlation between audit quality and Big-N auditors. Van Tendeloo and Vanstraelen
(2008), using measures of earnings management, find evidence that Big-N auditors provide higher audit quality in hightax alignment countries. Gaeremynck and Willekens (2003) find that Big-N auditors are better able to detect companies
with financial problems. Karjalainen (2011), using Finish firms, and Cano-Rodriguez and Alegria (2012), using Spanish
firms, show that Big-N auditees have lower interest rates. However, Kim, Simunic, Stein, and Yi (2011), using Korean
firms, do not find lower interest rates for Big-N auditees. Clatworthy, Makepeace, and Peel (2009) and Chaney et al.
(2004) analyze UK private firms, and only Clatworthy et al. (2009) document a fee premium. Hope and Langli (2010)
find that Big-4 firms charge higher audit fees than non-Big-4 firms in Norway. Langli and Svanström (2014) provide a
review of the literature on audits in private-firm settings.
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Jiang, Wang, and Wang (2016) test for Big-4 effects using the auditees of non-Big-4 firms that became clients of Big4 firms through the Big-4 firms’ acquisitions of non-Big-4 firms. Jiang et al. (2016), using listed U.S. firms, identify 18
8
As audit work is conducted by individual engagement partners and staff, audit firms that
supply higher audit quality should have higher quality personnel. Different audit firms provide
different incentives and resources to partners and staff to maintain their competence levels and to
influence their behavior, resulting in differences in audit quality (we revert to this in section 6.2).
Audit firms often develop their own audit procedures and create incentives that affect the behavior
of engagement-team personnel (Francis 2011). Also, larger audit offices have greater resources and
therefore a greater capacity to deliver higher quality audits due to having more collective experience,
more peers to consult, greater in-house expertise in detecting material misstatements, and economies
of scale in monitoring audit quality (Watts and Zimmerman 1981; Francis and Yu 2009; Sundgren
and Svanström 2013).
Audit firms are exposed to higher risks of reputation losses and litigation when they audit
public firms. Because Big-4 firms have more to lose, they have stronger incentives to supply high
audit quality than non-Big-4 firms. Furthermore, even if the reputational and litigation risks are low,
Big-4 firms are expected to provide higher quality due to greater expertise. Compared to non-Big-4
firms, Big-4 firms have greater in-house expertise and more internal support from legal consultants
and their valuation and tax departments. In contrast, it is likely more costly for partners in non-Big4 firms to consult specialists.
Another factor that contributes to Big-4 firms’ higher audit quality is the frequency of
practice or audit-engagement reviews by peers and authorities. All Norwegian audit firms are
required by law to have internal-control systems and periodic reviews. 8 A periodic review includes
acquisitions in the period 1976 – 2009, with the most recent merger being in 1995. Not all the partners of the acquired
non-Big-4 firms become partners in the Big-4 firms and the identities of the partners are not known. Thus, their findings
may reflect audit-partner effects as well as audit-firm effects. We hold both the partner and the auditee constant.
8
Through the European Economic Area agreement, Norway must implement all EU directives that relate to accounting
and auditing. Norwegian audit firms follow the International Auditing Standards.
9
an assessment of the firm’s internal-control system. For audit firms with private clients only, the
periodic reviews take place at a minimum of every sixth year. The inspections are undertaken by
members of the Norwegian Institute of Public Accountants (DnR) in cooperation with the Financial
Supervisory Authority of Norway (FSAN). By comparison, the inspections are undertaken by the
FSAN at least every third year for auditors of public clients. Further, for auditors of public clients
that are listed in the U.S., the inspections are undertaken at least every third year by both FSAN and
the U.S. Public Company Accounting Oversight Board (PCAOB). While the FSAN administers the
inspections, both the FSAN and PCAOB write separate reports to the inspected firms (FSAN 2009).
All Big-4 firms in Norway have some clients listed on U.S. stock exchanges and are thus subject to
PCAOB inspections. We expect that the more frequent and more stringent inspections conducted
by both FSAN and PCAOB (relative to the DnR-only inspections) create a stronger incentive for a
Big-4 firm to maintain sufficient quality in their internal-control system, which includes the
engagement-review process as well as training and supervising newly-hired auditors to meet the
required standards. Thus, when a non-Big 4 audit partner switches her affiliation to a Big-4 firm,
she will benefit from the greater resources of the Big-4 firm in terms of more expertise, better
training, and more sophisticated audit programs. In addition, she is subject to close monitoring under
the firm’s internal-control system. Therefore, we expect an increase in audit quality for auditors who
switch from non-Big-4 firms to Big-4 firms.
Big-4 firms are, to a greater extent than non-Big-4 firms, better equipped to continually
update their technologies and programs due to economies of scale and the need to adapt to the
changes in the business environment. 9 Hence, it is likely that access to ongoing sophisticated
9
The Wall Street Journal article “Auditing Firms Count on Technology for Backup” (Rapoport 2016) reports that “[t]he
Big Four are pouring hundreds of millions of dollar into new technologies, betting they will make audits more accurate
10
training programs and internal experts in tax, accounting, auditing, and valuation will be reduced
for partners switching from Big-4 to non-Big-4 firms. Non-Big-4 firms are smaller and have fewer
resources available to make similar investments, and hence partners may fall behind the
development of new technologies after they switch to a non-Big-4 audit firm.
Based on the discussion above we hypothesize the following (all hypotheses are stated in the
alternative form):
H1a: When an audit partner switches affiliation from a non-Big-4 firm to a Big-4 firm, the
partner will deliver audits of higher quality after the switch compared to before the switch.
H1b: When an audit partner switches affiliation from a Big-4 firm to a non-Big-4 firm, the
partner will deliver audits of lower quality after the switch compared to before the switch.
It is not clear how long time it would take before audit quality increases for partners
switching to Big-4 firms. One reason is that the decision to change audit firms rests with the client.
If the client finds that the partner becomes too strict in constraining the use of accruals or starts
modifying the audit report for reasons that did not result in modified audit opinions before the
partner switched affiliation, the client may decide to elect a non-Big-4 firm. Also, if the client finds
that their audit fees (which we discuss below) increase immediately after the switch to the Big-4
firm, the client may elect a non-Big-4 firm. Both the partners switching to Big-4 firms and the Big-
and comprehensive, giving investors greater assurance that a company’s finances are sound. … Ernst & Young made
an initial capital investment of $400 million to develop its technologies, which it began using last year.” An older quote
indicates that Big-N firms always want to be in the forefront of the development of systems and procedures: “Price
Waterhouse is continually refining its audit approach, adding efficiencies and maximizing the use of new and emerging
technologies in order to meet clients’ auditing and other professional service needs... Our approach has universal
applicability and results in a tailored, effective audit of each entity’s financial statements” (Walker and Pierce 1988, 12, emphasis added).
11
4 firms themselves are aware of these possibilities. Thus, the fear of losing clients may delay the
effects on audit quality and audit fees. 10
For partners switching to non-Big-4 firms, the effects of not having access to the same
resources as those possessed by a Big-4 firm might not be evident in the short run as the switching
partner brings with her the experiences and the knowledge she obtained as a Big-4 partner. Thus,
we may not observe an immediate decline in her ability to employ these skills. As time passes, we
expect to observe a growing gap in the skills or resources at hand between what she maintains as a
non-Big 4 partner and what she would have obtained had she continued as a Big-4 partner. 11 Since
it is likely that changes in audit quality and audit fees occur gradually, we allow for a transition
period in our empirical analyses.
Next, we examine the effect of switching on audit fees. Simunic (1980) models audit fees as
the sum of a resource-cost component (cq) and an expected liability-cost component (E(d)E(θ)). q
is the quantity of resources utilized by the auditor in performing the audit, c is the per-unit factor
cost of external resources to the auditor, including opportunity costs, E(d) is the expected present
value of possible future losses due to the current period’s audit quality, and E(θ) is the likelihood
that future losses have to be paid. We argue that Big-4 firms provide higher quality audits than nonBig-4 firms because Big-4 firms have higher competence, more in-house experts, better support
systems, and stronger incentives to assure high quality due to more stringent, and usually also more
frequent, inspections (creating demand for increased investments in internal-control systems). Thus,
the unit cost of production, c, is expected to be higher in Big-4 firms. It is likely that higher c also
10
The effect may also be delayed because there is usually a portfolio of clients coming in together. In other words, the
Big-4 firms’ ability to absorb the new clients and ensure higher audit quality may not be sufficient to cover all new
clients the first year.
11
Each year, audit partners are required to document at least 105 hours of continuing education during the last three
years. This formal requirement is the same for all partners irrespective of audit-firm affiliation.
12
applies to the new clients, at least over time. We therefore expect c to increase for auditees that
switch from a non-Big-4 firm to a Big-4 firm. Conversely, when the partner-auditee pair switches
from a Big-4 firm to a non-Big-4 firm, we expect c to decrease.
The effect that a switch in affiliation may have on q is less clear. Greater collective
experience, better audit technology, and economies of scale in Big-4 firms may make it possible to
increase the efficiency of the engagements, which may decrease q. However, greater access to
expertise and more thorough internal-control systems may result in additional testing and increased
documentation, which may increase q. Thus, it is not clear how q is affected for auditees that become
clients of Big-4 firms. For the audit partner-auditees that switch to non-Big-4 firms, q is likely to
decrease due to weaker incentives to provide higher quality.
Hope and Langli (2010) provide extensive evidence that Norway has a low litigation
environment, which results in a low expected liability-loss component in audit fees for both Big-4
and non-Big-4 firms. However, even in a low litigation environment, switching to a Big-4 firm may
potentially increase the expected liability-loss component because the willingness to sue Big-4 firms
may be higher due to their “deeper pockets” compared to non-Big-4 firms and because they may be
more willing to settle cases out of court to avoid negative publicity. 12
However, there are also arguments against increases in E(d)E(θ) after an audit partner-auditee
pair switches to a Big-4 firm. First, the partner and the pre-switch non-Big-4 audit firm are liable
for audits completed prior to the switch, not the post-switch Big-4 firm. After the switch, the Big-4
firm has the means and incentives to improve audit quality, and higher audit quality in turn decreases
the expected liability-loss component. Second, before accepting a new partner and her auditees, the
12
We are not aware of studies that can shed light on how litigation risk for the same client changes with varying auditfirm affiliation. Seetharaman, Gul, and Lynn (2002) document an increase in audit fees for U.K. public companies that
cross-list on U.S. markets, consistent with an increase in the expected-loss component. They find no significant
differences in the increase in audit fees between Big-5 and non-Big 5 firms.
13
Big-4 firm has already evaluated the incoming partner’s portfolio. If some of the auditees are
deemed too risky for the Big-4 firm, the partner would not have been offered partnership or the Big4 firm would have persuaded the incoming partner to end her relationships with these clients prior
to her joining. 13 Third, from the audit-choice literature, it is well known that the demand for auditing
is increasing in agency costs, the need to raise capital, and the need for additional services from the
auditor (Carey, Simnett, and Tanewski 2000, Knechel, Niemi, and Sundgren 2008, Dedman, Kausar,
and Lennox 2014). Prior literature indicates that private companies balance the demand for credible
reporting, which will be higher by choosing a Big-4 auditor, against the possibility of extracting
private benefits, which will be lower by choosing a Big-4 auditor (Hope, Langli, and Thomas 2012).
Auditees that wish to refrain from potential scrutiny associated with Big-4 auditors will choose other
non-Big-4 firms rather than following the switching partner to a Big-4 firm. Fourth, owners, board
members, and CEOs, who may be concerned with litigation, will most likely have chosen a Big-4
firm as their company’s statutory auditor initially since the Big-4 firm provides higher audit quality
and have “deeper pockets.” This suggests that the auditees that follow the audit partner to a Big-4
firm are less likely to be candidates for litigation relative to the clients that are already audited by
the Big-4 firm.
Based on the above arguments, we expect an increase (a decrease) in the resource-cost
component of audit fees when the partner-auditee pair moves from a non-Big-4 firm (Big-4) to a
Big-4 firm (non-Big-4 firm). The effect on the expected liability-loss component is uncertain, but
given our low litigation and reputation risk setting and the discussion above, we expect the effect
13
The review of the incoming partner’s portfolio is necessary also because the audit firm needs to be reassured that the
portfolio does not contain clients that will violate the independence requirement, for instance due to family relationships.
14
on the resource-cost component to outweigh the potential effect on the expected liability-loss
component. Our second hypothesis is:
H2a: When an audit partner switches affiliation from a non-Big-4 firm to a Big-4 firm, the
partner will charge higher audit fees. 14
H2b: When an audit partner switches affiliation from a Big-4 firm to a non-Big-4 firm, the
partner will charge lower audit fees.
3
Data and Research Design
3.1
Data
We start by obtaining the names of the engagement partners from a tax form all clients must
file with the Norwegian Tax Administration (NTA) from 2004 to 2012 and a list of all licensed
auditors in Norway from FSAN. 15 To the extent possible, we manually match the names of
individual auditors in the FSAN list with the names of auditors in the NTA data file. On the forms
filed with NTA, the clients manually fill in the names of their auditors, and there may be misspelling
14
We recognize that there are alternative explanations for changes in audit fees. Firth (1993) shows that changing the
name of an audit firm to a Big-8 firm alone is sufficient to generate an audit-fee premium. If we find an increase in audit
fees that is not accompanied with an increase in audit quality, the results would support the alternative explanation of a
brand-name effect.
15
The name of the audit firm is easily obtainable through electronic open registers. The name of the engagement partner,
however, is only electronically available in a confidential register that is kept and maintained by NTA. Special
permissions are needed to obtain data from NTA. Our permission gives us access to data for the years 2004 – 2012. The
name of the partner is also stated in the audit report as the partner is required to sign the audit report. For each firm, it
is possible to obtain a pdf-file of the financial statements including the audit report from the Brønnøysund Register
Center (BRC) and providers of business-source information. However, given the very large sample size required to
identify sufficient switchers, hand collection of the names of partners from pdf-files is not feasible. Note that in order
to identify the switching partners, it is necessary to cover the population of client firms (Norwegian audit firms issued
more than 234,000 audit reports in 2010).
15
or omission errors. We are able to identify the names of the individual auditors for about 72 percent
of all limited-liability firms in Norway in the sample period. 16
Next, we merge the data from NTA and FSAN with audit-related information provided by
Experian AS and Eniro AS. The audit-related information includes the type of modified opinions,
the audit fees, and the identities of audit firms. The accounting data are provided by the Center for
Corporate Governance Research (CCGR) at BI Norwegian Business School. All private limitedliability firms are required by law to have their accounts audited, and hence our accounting data are
reliable. 17 Experian AS provides data on the dates the firms eventually were notified as having
defaulted on debt payments (see Section 3.2 for details). 18
During the years 2004-2012, we identify a total of 120 partners who have switched between
Big-4 (EY, KPMG, Deloitte, and PWC) and non-Big-4 firms. Appendix A details how we identify
switching partners. Panel A of Table 1 shows that 77 partners have switched from non-Big 4 to Big4 firms, while 43 partners have switched the other way around. Our test strategy implies that the
partners must audit the same auditees before and after the switch and that the test and control
variables can be computed. These criteria result in a final sample of 89 switching partners, of which
68 switch to Big-4 firms and 21 switch to non-Big-4 firms. 19 Panel B shows the number of clients
16
Our data do not allow us to identify to what extent the auditor’s audit team also switches when the auditor switches
affiliation. We do not regard that as a threat to our inferences as the literature shows that the beliefs and preferences of
partners can significantly affect the behaviors and attitudes of audit staff and actual audit quality (Ponemon and Gabhart
1990; DeZoort and Lord 1994; Trompeter 1994; Tan, Jubb, and Houghton 1997; Carcello, Hermanson, and Huss 2000;
Wilks 2002; Emby, Gelardi, and Lowe 2002; Ayers and Kaplan 2003; Carey and Simnett 2006). For example, Wilks
(2002) documents that partners’ views influence audit staff’ judgments while Peytcheva and Gillett (2011) show that
knowledge of superiors’ views affects audit-staff reports even when they learn their superiors’ views after they reach
their independent judgments.
17
Effective May 1, 2011, the very smallest limited-liability firms were allowed to forego having an external audit. There
is no requirement of mandatory audit rotation for private firms.
18
Part of the information from FSAN, all information from NTA, and part of the information from Experian AS are
subject to confidentiality rules set forth by the Norwegian law. We have obtained special permissions from FSAN, NTA,
the Norwegian Ministry of Finance, and Experian AS to use the data.
19
For comparison, Gul, Wu, and Yang (2013) have a sample of 85 switches in their study of auditors’ “fixed effects”
in China.
16
audited by the switching partners before and after switching affiliation; 14,932 (553) observations
before switching, and 16,486 (572) observations after switching in the ToBig4-sample (ToNonBig4sample). The limited number of auditors switching to non-Big-4 firms and the low number of clients
for these partners reflect the fact that partners in Big-4 firms rarely switch to non-Big-4 firms, and
when they do so the auditees usually remain with the Big-4 firm (as explained in footnote 2). Having
fewer switching auditors and observations in the ToNonBig4-sample reduces the power of the tests
in this sample.
3.2
Measures of Audit Quality
To test the Big-4 effect, we employ several measures of audit quality. Because no single
measure draws a complete picture of audit quality and different measures focus on different
dimensions of audit quality, we use measures that are most applicable for private clients.
Our first measure of audit quality is the accuracy of going-concern opinions (GC-opinions).
GC-opinions relay auditors’ judgments on whether there is substantial doubt about the client’s
ability to continue on a going-concern basis. Because Big-4 firms have more competence, they
should be better at identifying auditees that are likely to face financial distress and thus should be
able to issue more accurate audit reports. A stream of literature analyzes the accuracy of GCopinions by observing whether a firm declares bankruptcy subsequent to receiving a GC-modified
opinion (see Carson, Fargher, Geiger, Lennox, Raghunandan, and Willekens 2013 for a review).
It is common to classify GC-opinions as correct or wrong depending on whether the auditee
goes bankrupt within 12 months after the issuance of the audit report (Lennox 1999; Francis 2011;
Knechel et al. 2015). However, creditors of the auditee can suffer losses even if the auditee does not
declare bankruptcy. Creditors sometimes would rather write off part or all of the existing debts or
ask the courts to levy distress, rather than incurring the cost of ordering the company to be wounded
17
up in order to eventually regain some of their claims. Consistent with the definition of failure as the
“inability of a firm to pay its financial obligations as they mature” (Beaver 1966, 71), we use a new
measure, public notification of unpaid debt, as the yardstick for assessing going-concern accuracy.
The notification takes form of a payment remark. 20
Any creditor can obtain information about a firm’s payment remarks from credit-rating
companies that have permission to use such information. For the creditors, the audit report would
be useful if it provides a signal of the likelihood of the auditee’s subsequent payment remarks, as
payment remarks imply that the auditee has defaulted on its payment. Thus, instead of classifying
the accuracy of the GC-opinions using bankruptcies, we use the occurrence of payment remarks;
DefaultDebtPayit = 1 if firm i receives payment remarks within 12 months after the publication of
the annual report for year t, and 0 otherwise. We define GCAccuracyit equal to 0 for firm i in year t
if there is a Type-1 or a Type-2 error in the audit report, and 1 otherwise. 21,22 Note the use of 0 and
1: higher values indicate higher audit quality. By using payment remarks, which is a less severe
20
To collect unpaid debt, creditors may engage debt-collection firms. Debtors that do not pay their debt after receiving
reminders are registered in the Register of Mortgaged Movable Property. If there is a disagreement between the creditor
and the debtor regarding the validity of the claim, no remark is registered. The issuance of a payment remark can be
compared to court decisions in the U.S. and the U.K. where firms get a judgment due to unpaid debt. Consistent with
this argument, the credit-rating companies Experian and Dun & Bradstreet collect information about judgments in the
U.S. and U.K. and payment remarks in Scandinavia, and the information enters the debtors’ credit reports. Banks include
information on payment remarks in their default models (Carling, Jacobson, Lindé, and Roszbach 2007). The variable
“Unpaid Debt” used by Knechel et al. (2015) is based on payment remarks.
21
A Type-1 error occurs when the auditor issues an audit report due to GC uncertainty and the client does not receive a
payment remark within 12 months after the publication of the annual report. A Type-2 error occurs when the client
receives a payment remark within 12 months after the publication of the annual report and the audit report is not modified
for GC uncertainty. An increase (increase) in Type-1 (Type-2) errors is referred to as “conservative” (“aggressive”)
reporting (Knechel et al. 2015).
22
In the tabulated results, GCAccuracy is based on whether the firm has defaulted on payments or not. Untabulated
results yield consistent inferences when we define DefaultDebtPay equal to 1 if the client has defaulted on more than
5, 10, or 25 percent of total debt (0 otherwise).
18
measure of financial distress than bankruptcy, we can test for changes in audit-reporting accuracy
using the same pair of auditor-auditee before and after the switch. 23
Next, we measure how the auditors’ reporting decisions are affected using GC and NumMod,
both defined using information from the audit report. GC equals 1 if the audit report expresses
concerns over the going-concern assumption, and 0 otherwise. NumMod equals the number of
clarifications and reservations in the audit report that is not related to GC uncertainties. 24 If, for
example, the auditor includes clarifications or reservations due to disagreements with the auditee
about the use of accounting estimates and weaknesses in the internal control systems, NumMod = 2.
The advantage of using NumMod is that it is a broader measure of competence and independence
than GC, and it does not limit measuring the effect of reporting decisions to financially-distressed
clients.
We also test for changes in the partner’s views on earnings management, a more indirect but
widely used proxy for audit quality. Specifically, we use discretionary accruals (Kothari, Leone, and
Wasley 2005) and define EarningsQuality as the absolute value of the error terms multiplied by (-
23
A client that goes bankrupt prior to a switch in the partner’s affiliation does not exist after the switch. When we require
that the partners have the same clients before and after the switch in affiliation, we exclude all clients that go bankrupt
prior to the switch. The tests of changes in going concern accuracy thus suffer from measurement errors as reporting
accuracy is only based on non-bankrupt clients prior to the switch. When we use the occurrences of bankruptcies as the
yardstick for correct/wrong going concern opinions, we find that those switching to Big-4 firms show increased accuracy
after the switch (i.e. same as with the use of payment remarks, see below) while we find a negative, but insignificant
effect for those switching to non-Big-4 firms.
24
Modifications are categorized into 20 different types based on ISA 700. The most serious modification is a “negative
conclusion” (the financial statements should not be approved or the auditor is unable to conclude). The least serious
modification is “clarifications.” In between, we have “reservations” (limitations with respect to scope or disagreements
with management). Examples of reservations are modifications due to weaknesses in the internal-control system,
negative equity, uncertain values of assets or liabilities, disagreement on accounting estimates, inadequate bookkeeping
routines, and inability to verify the beginning balances. Examples of clarifications include delayed filing of the financial
statements, illegal loans to shareholders, CEOs, or board members, the company being involved in a litigation case, and
the company buying its own shares in violation of the Company Act.
19
1); thus, higher values of EarningsQuality indicate higher audit quality. 25 See Appendix B for
further details on variable definitions.
GC and NumMod intend to capture a partner’s competence and independence, and the
literature has usually interpreted higher values as indications of higher independence and higher
audit quality. However, Blay, Moon, and Paterson (2016) conclude that a higher propensity to issue
GC-opinions does not always reflect higher audit quality and Myers, Schmidt, and Wilkins (2014)
find that non-Big N firms became more conservative while Big-N firms became more accurate after
the introduction of SOX. Thus, GC and NumMod are more ambiguous measures of audit quality
compared to our other measures. Specifically, if Big-4 firms are more accurate than non-Big-4 firms
and non-Big-4 firms are more conservative than Big-4 firms, we may observe a decline in the use
of modified opinions.
3.3
Test Methodology
All tests are conducted at the auditee level and we require that the switching partners have
the same clients before and after the switch. We use the following regression, clustering standard
errors at the client level:
(1)
AQi,j,t = β0 + β1SwitchYeari,j,t + β2FirstYeari,j,t + β3AfterFirstYeari,j,t + ΣControlsi,j,t + FEyr
+ FEind + FEauditor + εi,j,t
25
Following Hope, Thomas, and Vyas (2013), we estimate discretionary accruals for each industry-year with a
minimum of 20 observations. In an untabulated sensitivity analysis we follow Dechow, Hutton, Kim, and Sloan (2012)
and estimate discretionary accruals using all the observations in a pooled regression without affecting any inferences.
20
AQ is audit quality (GCAccuracy, GC, NumMod, or EarningsQuality). We use the same
specification when the natural logarithm of audit fees (LnAF) is the dependent variable. Our
hypotheses predict increases (decreases) in audit quality and audit fees for ToBig4 (ToNonBig4)
auditors; to test them, we estimate equation (1) on the sample of partners who switch to (from) Big4 firms.
SwitchYear, FirstYear, and AfterFirstYear are the variables of interest. SwitchYear equals 1
for all clients of auditor j that have switched affiliation in the switching year (i.e., t=0), and 0
otherwise. FirstYear equals 1 for all clients of auditor j in the year after the switching year (i.e., for
t = 1), and 0 otherwise. AfterFirstYear equals 1 for all clients of auditor j in the subsequent years
after the first year (i.e., for t = 2, 3, .. T), and 0 otherwise. Thus, AfterFirstYear captures the “longterm” effect while SwitchYear and FirstYear capture the short-term effects.
For auditors switching to Big-4 firms, hypotheses 1a (increased audit quality) and 2a (higher
fees) are supported if β3 > 0; the effect should materialize in the long run. For the auditors switching
to non-Big-4 firms, hypothesis 1b (lower audit quality) and hypothesis 2b (lower fees) are supported
if β3 < 0. As the length of the transition period necessary to observe changes is uncertain in both
samples, we have no predictions regarding β1 and β2 (but assume that the long-run effect will be
captured by AfterFirstYear).
We include year (FEyr), industry (FEind), and most importantly auditor (FEauditor) fixed
effects in the regressions. Note that the auditor fixed effects control for the innate ability of the
individual partners. We further include a set of time-varying control variables (Controls) motivated
by prior research. As we compare the same pairs of auditors and auditees before and after a switch
in affiliation, the control variables are associated with the size, risk, and complexity of the auditees
that may have changed in the years surrounding the switch. Given the importance of firm size in
auditing, we measure client size by using several measures - the natural logarithm of total assets
21
(LnTA), the number of employees (LnEmployees), and the age of the client firm (LnAge). We
measure business complexity using the number of industries the clients operate in
(NumberIndustries), inventory and account receivables scaled by total assets (InvAccRec), and
intangible assets divided by total assets (Intangibles). We measure financial risk using the debt ratio
(Leverage), changes in the debt ratio (ChgLeverage), and probability of going bankrupt
(ProbBankruptcy). Operating risk is measured using return on assets (ROA), sales growth
(SalesGrowth), cash flow (CashFlow), and an indicator variable for loss (Loss). We proxy for
liquidity risk with short-term investments scaled by total assets (ShortTermInv) and the ratio of
current assets to current liabilities (CurrentRatio).
4
Summary Statistics
Table 2 presents tabulations of going-concern-modified audit opinions and firms’ default on
debt payments. The sample consists of all partner-auditee pairs with sufficient information to be
included in the multivariate tests. Panel A1 shows that the ToBig4 sample consist of 14,932 (16,486)
observations before (after) the switch. From Panel A2, we see that the percentage of firms receiving
GC-opinions is 9.0% before the switch and 7.5% after. The reduction in GC-opinions is not
consistent with Big-4 firms reporting more conservatively than non-Big-4 firms by issuing GCopinion more often. Panel A3 reports that 5.9% of the auditees defaulted on debt payments (DDP)
prior to the switch and 7.6% defaulted after the switch. Of those that received GC-opinions, 84.5%
did not default on debt payment prior to the switch while 77.4% did not default after the switch.
Panel A4 shows the changes in the error rates. We notice a decrease (7.1 percentage points) in the
Type-1 error rate and a minor increase (1.5 percentage point) in Type-2 error rates. As the
comparison of error rates pre and post the switch does not consider changes in the auditees’ financial
situation, we refrain from making inferences based on univariate statistics.
22
Panel B shows similar information as Panel A but for the ToNonBig4 sample. As noted
above, the ToNonBig4 sample is much smaller. In the ToNonBig4 sample, the percentage of firms
that receive GC-opinions decreases from 11.6% before the switch to 8.9% after the switch (Panel
B2). At the same time as fewer auditees receiving GC-opinions, there is an increase in auditees
defaulting on debt payments. Prior to the switch 4.7% of the auditees defaulted on debt payments,
while 9.1% defaulted on debt payments after the switch (Panel B3). From Panel B4, we notice a
reduction in Type-1 error rates of 15.3 percentage points and a 3.6 percentage point increase in
Type-2 error rates. Compared with Panel A4, the changes in the error rates are much higher for
partners switching to non-Big-4 firms compared to partners who switch to Big-4 firms.
Table 3 presents descriptive statistics for the sample used in the main analyses. Panel A
presents the statistics before and after the auditors switch from non-Big-4 firms to Big-4 firms. Even
though the auditors audit the same clients before and after the switch, the t-tests often reject the
hypotheses of equality of means, which provides additional support for the inclusion of the timevarying control variables. 26 Panel B presents descriptive statistics for clients of auditors who switch
to non-Big-4 firms. There is a notable difference between Panel A and Panel B as there are few
differences in the means pre and post the switch in Panel B while most variables are significantly
different in Panel A. Another notable difference between the two panels is the difference in sample
sizes.
26
After the auditor switches to a Big-4 firm, the clients are larger (LnTA and TA), default on debt payment
(DefaultDebtPay) more often, operate in more industries (NumberIndustries), and have lower short-term investments
(ShorTermInvest). As the clients’ sizes increase, it is reasonable that the auditees pay higher audit fees (AuditFee). It is
also reasonable that the sample firms are older (LnAge) following the switch.
23
5
Regression Results
Table 4 presents the results from testing changes in audit quality and audit fees before and
after a switch in audit-firm affiliation. As explained above, the partners switching affiliation have
exactly the same auditees pre and post switch. 27
In the ToBig4 sample (Panel A), we find significant support for a Big-4 effect on audit
quality. The audit reports become significantly more precise in predicting financial distress (column
1), the effect occurs immediately after the switch in affiliation (SwitchYear and FirstYear are
significantly positive) and it seems to be permanent (as AfterFirstYear also is significantly positive).
In column 4, we observe significantly higher EarningsQuality (due to lower discretionary accruals)
in the first year after the switch and in the long run (FirstYear and AfterFirstYear are significantly
positive), but no immediate effect (SwitchYear is not significant).
The results for GC (Column 2), the likelihood of issuing going-concern opinions, and
NumMod (Column 3), the number of modifications included in the audit report, show lower
likelihood of issuing modified opinions after the auditors switch to Big-4 firms (the coefficients on
the test variables are mostly significantly negative). In interpreting these findings, one should bear
in mind that both Big-4 and non-Big-4 firms face low and comparable risks related to issuing GCopinions. Thus, there is little reason to expect that Big-4 firms should report more conservatively
than non-Big-4 firms. The observed effect of lower likelihood of GC-opinion may therefore explain
how Big-4 firms are more accurate in predicting financial distress. 28 Specifically, Big-4 firms have
resources that enable them to better identify financially troubled firms (Gaeremynck and Willekens
2003; Myers et al. 2014). Better resources and more experts in Big-4 firms may also explain why
27
In Columns 1 and 2, we lose observations because the auditor fixed effects correlate perfectly with the dependent
variable. In Column 5, we lose some observations because we do not have audit fees for all sample firms.
28
Another possibility is that Big-4 firms may reject some financially-distressed firms from switching over with the
partner. We revisit this possibility in Section 6.4.
24
the auditor issues fewer modifications after becoming part of a Big-4 firm (the reduction in NumMod
after the switch in Column 3). The Big-4 firms may make the auditor more able to hinder earnings
management and otherwise provide the auditor with the tools necessary for persuading the auditees
to follow the regulations or advice given, which reduce the need to modify the audit report. Thus,
results showing increased accuracy in GC reports and reduced earnings management are consistent
with lower likelihood of GC modifications or other types of modified opinions.
The last column in Panel A of Table 4 shows that audit fees increase in the long run, but not
in the short run. The estimated coefficient of 0.047 on AfterFirstYear indicates that the audit fees
increase about five percent in the long run after the auditor switches from a non-Big-4 firm to a Big4 firm. This finding adds credence to the notion that audit quality increases; thus, we conclude that
H1a and H2a are supported. It is also reassuring that the signs of the control variables are mostly as
expected, and that the models seem to fit reasonably well (the pseudo R2s vary between 20% and
46% and the adjusted R2s vary between 12% and 61%).
Panel B reports the results for tests of audit quality and fees for auditors switching to nonBig-4 firms. An important difference, as noted before, is that the sample size with the ToNonBig4
sample is much smaller, less than 4% of the ToBig4 sample. Another (and likely related) difference
is that we find fewer significant results. However, for the two measures that have unambiguous
interpretations (GCAccuracy and LnAF), the results support our hypotheses: we find reduced
precision in going-concern reporting (consistent with H1b) and lower audit fees in the long run
(consistent with H2b). 29 Above we suggested that the increased accuracy in going-concern reporting
in the ToBig4 sample could reflect Big-4 firms being able to more accurately identify financially-
29
In addition to the arguments provided above, conversations with audit partners reveal that some firms require that
audit fees accrue to the Big-4 firm for one to two years if a Big-4 partner switches to a non-Big-4 firm and takes the
client firms with her.
25
troubled firms. The results in the ToNonBig4 are consistent with this explanation; when the auditor
no longer benefits from the resources in the Big-4 firm, she issues too many GC-reports with the
consequence of lower predictive accuracy.
Overall, the results in Table 4 provide evidence of a Big-4 effect in terms of more accurate
GC reporting, higher earnings quality, and higher audit fees.
6
6.1
Additional Analyses
Partner Switches Between Non-Big-4 Audit Firms (Placebo Analyses)
In the previous section, we attribute changes in audit quality and audit fee to changes in the
partners’ affiliation between non-Big-4 and Big-4 firms. It could be that changes in audit-firm
affiliation per see may cause changes. In order to test if the observed effect is a placebo effect due
to switches in affiliation, we rerun the tests on a sample that consists of the 79 partners who switch
affiliation from one non-Big-4 firm to another non-Big-4 firm. 30 The results are presented in Panel
A of Table 5 (we only report the test variables for brevity). For the four audit-quality measures, 11
of the 12 test variables are insignificant, which is reassuring since the effects documented in Table
4 relate to upward/downward switches and not audit-firm affiliation changes per se. When audit
fee are the dependent variable, we observe significantly lower audit fees after the switch. Lower
fees could be due to for instance low balling or economies of scale where the cost reduction is at
least partially passed on to the client.
30
Lateral changes among partners in Big-4 firms are rare events and we observe no such switches during our sample
period.
26
6.2
Endogeneity and Quality Differences Among Partners in Non-Big-4 Audit Firms
As explained, our research design holds constant both the audit partner and the client firm,
thus providing strong controls for some of the major challenges in examining potential Big-4 effects.
The design also mitigates self-selection and endogeneity concerns. This is because if Big-4 firms
truly are able to supply higher audit quality purely due to the characteristics at the audit-firm level,
as suggested by the literature, they should be able to enhance audit quality independent of why the
auditor-auditee pair becomes affiliated with the Big-4 firm and who the auditors or auditees are.
Perhaps those non-Big 4 partners who wish to work for the Big-4 firms are more motivated, better
educated, and more competent than other non-Big 4 partners. The Big-4 firms may therefore select
the most competent non-Big 4 auditors, consistent with the view that Big-4 firms are able to attract
higher quality input (DeFond and Zhang 2014). Nothing in our setting prevents these potential Big4 partners to provide outstanding audit quality prior to joining Big-4 firms. Thus, even if Big-4 firms
are able to engage the best partners available from the pool of non-Big 4 partners, these partners
have not been able to or willing to deliver audits of Big-4 quality prior to joining the Big-4 firm.
They needed the resources and/or incentives of the Big-4 firms to increase the level of quality to the
Big-4 level, that is, the Big-4 effect is necessary to increase their audit quality.
To test if Big-4 firms are able to attract higher quality inputs, primarily through better
personnel and audit-testing procedures (Francis 2011; DeFond and Zhang 2014), we observe that if
the partners shifting to Big-4 firms are of higher quality, they should deliver audits of higher quality
before joining Big-4 firms. The expected higher audit quality prior to the switch to Big-4 firms
should also correspond with higher audit fees, as argued above. We define ToBig4Pre = 1 for the
auditees of the partners switching to Big-4 firms in the years prior to the switch, and 0 otherwise.
Then, we replace the test variables in equation 1 with ToBig4Pre and rerun the models in Table 4
on a sample consisting of (i) the auditees of all partners in non-Big-4 firms that have not switched
27
to/from a Big-4 firm and (ii) all the auditees of the partners switching to Big-4 firms in the years
prior to the switch. Panel B of Table 5 presents the results (we only report the test variables for
brevity). The ToBig4Pre variable is significantly positive for GCAccuracy and LnAF and
significantly negative for GC, which provide similar results as Panel A of Table 4. For NumMod
and EarningsQuality there are no significant differences, but we note that EarningsQuality is
borderline significantly positive (i.e., the one-tailed p-value is 0.056). The results indicate that Big4 firms are able to attract higher quality inputs since the partners provide higher audit quality prior
to the switch to the Big-4 firm than other non-Big 4 partners. However, and most importantly for
our study, even if the Big-4 firms attract partners (or auditees) of higher quality, the Big-4 effect
documented in Table 4 shows that these partners deliver even higher audit quality following the
switch.
6.3
Endogeneity and Audit-Partner Characteristics
Even if our design is robust, it is important to understand and control for the underlying
factors that may explain why partners switch affiliation. To assess the effect of potentially correlated
omitted variables that relate to the partners, we make use of the rich data availability in Norway and
obtain detailed data on each partner’s gender, age, years of professional experience (measured as
the number of years since the auditor first obtained her license as an auditor), and education (whether
the auditor holds a bachelor or a master’s degree in accounting and auditing). We first test for
significant differences between those shifting to/from Big-4 firms, and second we add these
variables as additional control variables in our regressions (results not tabulated).
We observe that the partner characteristics in the two samples are almost identical.
Specifically, the partners switching from non-Big 4 to Big-4 firms are not significantly different in
terms of age, year of experience, gender, and education. The mean age is 45.6 (45.6) years and the
28
mean years of experience is 15.4 (16.2) for the partners switching to Big-4 firms (non-Big-4 firms).
The proportion of partners with a master’s degree in accounting and auditing is 80 and 75 percent,
respectively. Of those switching to Big-4 (non-Big 4) firms, 14.1 (12.5) percent are females. Next,
we add age, gender, year of experience, and education as additional controls in the regression
analyses. The inferences reported above are unchanged. Finally, using the switching partners’
private addresses, we find that none of the switching partners moves to another municipality. This
rules out the possibility that the switches are initiated by the partners’ decision to relocate.
6.4
Endogeneity and Client-Selection Bias
Measures of audit quality using financial statement information such as discretionary
accruals are subject to endogeneity as the measures are the joint product of audit quality and clients’
innate characteristics (DeFond and Zhang 2014). Because we hold both the partner and the client
firm constant, our test design is robust to such effects. However, one might argue that there is a
potential self-selection effect due to Big-4 firms favoring low-risk clients. Empirical evidence from
the U.S. suggests that Big-4 auditors tend to have less risky clients (Raghunandan and Rama 1999;
Johnstone 2000; Johnstone and Bedard 2004). As the Big-4 firms are global, it is reasonable to
assume that the practices and policies of their U.S. offices may spill over to the Norwegian offices.
Above, we have argued that the Big-4 and non-Big-4 firms face low and similar litigation
and reputation risks in our setting. Thus, there is little reason to expect that Big-4 firms should only
accept low-risk clients to protect their reputation and to keep the litigation risk low. To gain insight
into whether this assumption is supported by the data, we perform t-tests for equality of means for
the control variables used in equation 1 using a sample consisting of the auditees that follow the
switching partner (i.e., the auditees that are included in Panel A of Table 4) and the auditees of
partners not switching to Big 4. The (untabulated) t-tests show that the auditees that follow the
29
partners switching to Big-4 firms are not different from auditees of non-switching non-Big 4
partners in terms of ROA, CashFlow, and Loss, but that they are more risky as measured by
Leverage, CurrentRatio, and ProBankrupt. This adds credibility to our arguments that our results
are not likely to be driven by Big-4 firms’ concerns for risk. Thus, the incentives created by litigation
and reputation concerns are not necessary for Big-4 firms to deliver higher quality audits.
Furthermore, the observed increase in audit fees is likely not a risk premium, but a reflection of
using more competent resources and/or increased effort.
6.5
Income-Increasing and Income-Decreasing Discretionary Accruals
Finally, to assess whether partners have different opinions on income-increasing and
income-decreasing discretionary accruals, we re-conduct our main test for the subsets of firms with
positive discretionary accruals against those with negative discretionary accruals. The results are
presented in Table 6. The dependent variable is the same as in Table 4, that is, positive coefficients
on the test variables indicates better audit quality. For brevity, we only report the test variables.
Panel A (Panel B) reports results for auditors switching to Big-4 (non-Big 4) firms. The sub-sample
with positive (negative) discretionary accruals consists of firms with income-increasing (decreasing) accruals. Significant results only occur in Panel A, and the results show that the Big-4
effect is far stronger in the sub-sample of firms with income-increasing accruals. The positive and
significant coefficients for FirstYear and AfterFirstYear imply that Big-4 firms significantly curb
the auditees’ income-increasing accruals. For firms with income-decreasing accruals, we observe a
week positive effect in the long run.
30
7
Conclusion
This paper adds to the current debate about whether there is a Big-4 effect, that is, do Big-4
audit firms supply higher audit quality than non-Big-4 firms? While most evidence suggests that
Big-4 firms deliver higher audit quality than non-Big-4 firms for public clients (although some
researchers claim that such findings are due to weak research design), the evidence for the Big-4
effect in the segment of private clients is mixed and much less conclusive. When auditing public
clients that expose Big-4 firms to high risk of litigation and loss of reputation, Big-4 auditors have
stronger incentives to supply higher audit quality than non-Big-4 firms because they have more to
lose. However, much less is known about whether there are differences in audit quality between
Big-4 and non-Big-4 firms when they face low and similar litigation and reputation risks. The
question we address in this paper is whether Big-4 firms, which we argue have the potential to
deliver higher audit quality than non-Big-4 firms, actually do so even if not (or less) incentivized by
litigation and reputational concerns.
We use a new and novel research design, unique data, and a setting of private clients that
expose audit firms to low risks of litigation and loss of reputation. The research design focuses on
audit partners who have switched affiliation between Big-4 firms and non-Big-4 firms while holding
the pair of auditor-auditees constant. Thus, we control for the heterogeneous innate ability of
different audit partners and the characteristics of the auditees, which significantly alleviates
concerns of correlated omitted variables. Hence, in our setting, the only change we observe is the
change in affiliation between Big-4 and non-Big-4 firms. This enables us to isolate the effect of
audit-firm affiliation on audit quality and audit fees.
We predict that audit quality should increase when pairs of auditor-auditees switch affiliation
from non-Big-4 firms to Big-4 firms. For switches in the opposite direction, we predict a decaying
31
effect in audit quality over time. As higher audit quality is likely to be priced, we also test for
changes in audit fees.
The hypotheses are tested separately on pairs of auditor-auditees switching from non-Big 4
to Big-4 firms and from Big-4 to non-Big-4 firms. In short, the hypotheses receive support. We find
an increase in going-concern reporting accuracy, in earnings quality, and in audit fees for auditors
switching to Big-4 firms. Conversely, we find lower going-concern reporting accuracy and lower
audit fees for auditors switching from Big-4 to non-Big-4 firms. In supplementary analyses, we
provide evidence that lateral switches do not cause changes in audit quality and results that support
the view that Big-4 firms are able to attract higher-quality inputs. We further show that the results
are not caused by Big-4 firm only accepting lower-risk clients. The inferences also hold after
controlling for the partners’ level of education, gender, years of experience, and age.
Our study is conducted in a setting with low litigation and reputation risks and the findings
may not be generalizable to settings with higher litigation risk or to auditees that expose the audit
firms to higher reputational risk. We leave such examinations to future research. A limitation of our
study is the small sample of auditors switching from Big-4 to non-Big-4 firms, which is a
consequence of partners rarely leaving Big-4 firms together with “their” auditees.
32
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36
Appendix A: Identification of Auditors Switching between Big-4 and Non-Big-4 firms
We have identified the auditors who switch audit-firm affiliation using data that uniquely
identify the audit partners, the audit firms, and the client firms. We have three types of switches in
our sample. The first type of switches occurs when an auditor is affiliated with a Big-4 (non-Big
4) firm in year t-1 and a non-Big 4 (Big-4) firm in year t. In this case, year t is the switching year
(t=0 is the switching year). The second type occurs when the auditor is affiliated with only a Big-4
(non-Big 4) firm in year t-1, both types of firms in year t, and only a non-Big 4 (Big-4) firm in
year t+1. In these cases, we classify year t as the switching year for the auditees that changed
affiliation in year t. The third type of switches occurs when an auditor, over a period of several
years, is affiliated with both a Big-4 and a non-Big-4 firm, and then starts representing only one
type of audit firm. This is possible as a Big-4 auditor may have her main affiliation with a Big-4
firm, but at the same time, the auditor has some clients in her own non-Big-4 firm. For this
auditor, we define year t as the switching year if 80% or more of her auditees are clients of a Big-4
firm in year t-1 and all are clients of a non-Big-4 firm in year t. A similar rule is used for switches
in the other direction. The threshold of 80% is arbitrarily set. We have rerun the tests with
thresholds of 70% and 90% with qualitatively similar results to those we tabulate. Note that
auditors who work for both a Big-4 and a non-Big-4 firm over years without significant changes in
their client portfolios are not assumed to switch affiliation.
37
Appendix B: Variable Definitions
Variable
AfterFirstYear
=
AuditFee
Big4
CashFlow
=
=
=
ChgLeverage
CurrentRatio
EarningsQuality
=
=
=
Variable definition
1 for all clients of auditor j in the year after the switching year (i.e., for t =1),
and 0 otherwise. The switching year is t=0.
The fee for audit services in NOK 1,000.
1 if a client firm uses a Big-4 audit firm, and 0 otherwise.
Cash flows scaled by total assets. Cash flow = earnings - total accruals.
Earnings = net income after taxes before extraordinary item and taxes on
extraordinary items. Total accruals = change in current assets - change in
cash - change in short-term debt + change in short-term interest bearing debt
+ change in dividends + depreciation + amortization - change in net deferred
taxes. 31
Changes in leverage ratio = Leveraget – Leveraget-1.
Current ratio: current assets / current liabilities.
EarningsQuality is a measured of discretionary accruals using the
performance-adjusted Jones model (Kothari et al. 2005). EarningsQuality is
the absolute value of the residual from the following regression multiplied by
(-1) (subscript i indicates client firms and t indicates time period):
Accri,t = α0 + α1(1/Assetsi,t-1) +α2ΔRevi,t + α3PPEi,t + α4ROAi,t + εi,t
Accr is total accruals (defined above, see CashFlow) scaled by lagged total
assets; ∆Rev is the annual change in revenues scaled by lagged total assets;
PPE is property, plant, and equipment for firm i in year t, scaled by lagged
total assets; ROA is the net income for firm i in year t scaled by average total
assets.
FirstYear
=
1 for all clients of auditor j in the year after the switching year (i.e., for
t =1), and 0 otherwise.
GC
=
GCAccuracy
=
Intangibles
InvAccRec
Leverage
LnAF
LnAge
=
=
=
=
=
LnEmployees
LnTA
=
=
1 if audit report is modified due to going-concern uncertainty, and 0
otherwise.
1 if the audit report is correct and 0 otherwise. An audit report is considered
correct if (i) the audit report is modified for going-concern uncertainty and
the auditee defaults on debt payment within 12 months after the annual
account is filed with the Brønnøysund Register Center, or (ii) the audit report
is not modified for going-concern uncertainty and the auditee does not
defaults on debt payments with 12 months after the annual account is filed
with the Brønnøysund Register Center.
Intangible assets scaled by total assets.
The sum of inventory and accounting receivable scaled by sales.
Leverage ratio = Debt / Total assets.
The natural logarithm of audit fees = ln(AuditFee).
The natural logarithm of firm age, defined as year t less the year of
incorporation.
The natural logarithm of the number of employees.
The natural logarithm of total assets. Total assets is measured in NOK 1,000.
31
CashFlow, ChgLeverage, CurrentRatio, InvAccRec, Intangible, Leverage, SalesGrowth, and ROA, are winsorized at
the 2% and 98% levels in the regression analyses due to near-zero observations in the scaling variables (e.g., Ball and
Shivakumar 2005).
38
Loss
=
NumberIndustries =
NumMod
=
DefaultDebtPay
=
ProbBankruptcy
ROA
Sales
SalesGrowth
ShortTermInv
SwitchYear
=
=
=
=
=
=
ToBig4Pre
=
1 if a client firm has negative net income, and 0 otherwise.
The number of industries the client firm operates in.
The number of modifications included in the audit report that does not relate
to going-concern uncertainty.
1 if a client firm is registered in the Brønnøysund Register Center as having
defaulted on debt payments within 12 months after the annual account is filed
with the Brønnøysund Register Center, and 0 otherwise.
Probability of bankruptcy, estimated using model 1 in Ohlson (1980).
Return on assets = Net income / total assets.
Revenues from operations.
Sales growth = Salest /Salest-1-1.
Short term investment scaled by total assets.
1 for all clients of auditor j that has switched audit-firm affiliation in
the switching year (t=0), and 0 otherwise.
1 for auditees of partners switching audit-firm affiliation from nonBig-4 firms to Big-4 firms in the years before the switch takes place,
and 0 otherwise.
39
Table 1: Number of Auditors Switching Audit-firm affiliation and Their Clients Pre and Post
the Switch
Panel A: Number of auditors switching audit-firm affiliation by year
Year
2005
2006
2007
2008
2009
2010
2011
2012
Total
ToBig4
3
3
5
28
6
5
22
5
77
In total
ToNonBig4
5
6
6
7
10
3
4
2
43
Total
8
9
11
35
16
8
26
7
120
ToBig4
3
1
5
25
5
4
20
5
68
In sample
ToNonBig4
3
1
2
3
7
2
2
1
21
Total
6
2
7
28
12
6
22
6
89
Panel B: Number of observations by year when the auditors audit the same clients before and after
the switch in audit-firm affiliation
Year
2005
2006
2007
2008
2009
2010
2011
2012
Sum
B1: Auditors switching to Big4
Before
After
Sum
2,310
20
2,330
3,374
64
3,438
4,127
346
4,473
1,765
3,435
5,200
1,408
3,414
4,822
1,453
3,143
4,596
495
3,095
3,590
0
2,969
2,969
14,932
16,486
31,418
B2: Auditors shifting to non-Big 4
Before
After
Sum
80
28
108
103
50
153
127
47
174
104
57
161
73
80
153
65
94
159
1
121
122
0
95
95
553
572
1,125
This table presents the number of auditors that switch audit-firm affiliation between Big-4 and non-Big-4 firms and the
clients of the switching auditors. Panel A provides the number of auditors that have switched from non-Big-4 firms to
Big-4 firms (ToBig4), and the number of auditors that have switched from Big-4 to non-Big-4 firms (ToNonBig4).
Column Total is the sum of ToBig4 and ToNonBig4. “In total” shows the total number of switches identified each year.
“In sample” shows the number of switches included in the analyses. Panels B1 and B2 show the number of observations
per year before and after the switch in affiliation for the two sub-samples.
40
Table 2: Going-Concern Modified Audit Opinions and Subsequent Defaults on Debt Payments
Panel A: Audit reports modified for going-concern uncertainty (GC) or not (Non-GC) and
subsequent defaults on debt payments (DDP) or not (Non-DDP) for auditees of auditors switching
from non-Big 4 to Big-4 audit firms.
A1: Number of clients before and after the switch conditioned on default on debt payments or not
GC
Non-GC
Total
DDP
208
669
877
Before
Non-DDP
1,138
12,917
14,055
Sum
1,346
13,586
14,932
DDP
278
975
1,253
After
Non-DDP
952
14,281
15,233
Sum
1,230
15,256
16,486
A2: Percentage of clients before and after the switch conditioned on default on debt payment or not
GC
Non-GC
Sum
DDP
23.7 %
76.3 %
100.0%
Before
Non-DDP
8.1 %
91.9 %
100.0%
Sum
9.0 %
91.0 %
100.0%
DDP
22.2 %
77.8 %
100.0%
After
Non-DDP
6.2 %
93.8 %
100.0%
Sum
7.5 %
92.5 %
100.0%
A3: Percentage of clients before and after the switch conditioned on going-concern report or not
GC
Non-GC
Sum
DDP
15.5 %
4.9 %
5.9 %
Before
Non-DDP
84.5 %
95.1 %
94.1 %
Sum
100.0%
100.0%
100.0%
DDP
22.6 %
6.4 %
7.6 %
After
Non-DDP
77.4 %
93.6 %
92.4 %
Before
84.5 %
4.9 %
Change (=After - before)
-7.1 %
1.5 %
Sum
100.0%
100.0%
100.0%
A4: Levels and changes in Type-1 and Type-2 error rates
Type-1 error
Type-2 error
After
77.4 %
6.4 %
41
Panel B: Audit reports modified for going-concern uncertainty (GC) or not (Non-GC) and
subsequent defaults on debt payments (DDP) or not (Non-DDP) for auditees of auditors switching
from Big-4 to non-Big-4 audit firms.
B1: Number of clients before and after the switch conditioned on default on debt payments or
not
GC
Non-GC
Total
DDP
9
17
26
Before
Non-DDP
55
472
527
Sum
64
489
553
DDP
15
37
52
After
Non-DDP
36
484
520
Sum
51
521
572
B2: Percentage of clients before and after the switch conditioned on default on debt payment or
not
GC
Non-GC
Sum
DDP
34.6 %
65.4 %
100.0%
Before
Non-DDP
10.4 %
89.6 %
100.0%
Sum
11.6 %
88.4 %
100.0%
DDP
28.8 %
71.2 %
100.0%
After
Non-DDP
6.9 %
93.1 %
100.0%
Sum
8.9 %
91.1 %
100.0%
B3: Percentage of clients before and after the switch conditioned on going-concern report or
not
GC
Non-GC
Sum
DDP
14.1 %
3.5 %
4.7 %
Before
Non-DDP
85.9 %
96.5 %
95.3 %
Sum
100.0%
100.0%
100.0%
DDP
29.4 %
7.1 %
9.1 %
After
Non-DDP
70.6 %
92.9 %
90.9 %
Before
85.9 %
3.5 %
Change (=After - before)
-15.3 %
3.6 %
Sum
100.0%
100.0%
100.0%
B4: Levels and changes in Type-1 and Type-2 error rates
Type-1 error
Type-2 error
After
70.6 %
7.1 %
This table provides the distribution of observations before and after the switch in audit-firm affiliation sorted by the
type of audit report issued (GC or Non-GC) and whether the auditees subsequently default on debt payment or not (DDP
or Non-DDP). GC is an audit report modified due to going-concern uncertainty (i.e., GC = 1). Non-GC is an audit report
not modified for going-concern uncertainty (GC = 0). DDP, default on debt payment, occurs when the firm is registered
with an payment remark within 12 months after the firm’s annual report including the audit report is filed with the
Brønnøysund Register Center (BRC) (i.e., DefaultDebtPay = 1). Non-DDP occurs when a firm does not receive a
payment remark within 12 months after filing annual and audit reports with the BRC (DefaultDebtPay = 0). Panel A
provides a breakdown of the distributions for auditees of auditors switching from non-Big-4 firms to Big-4 firms. Panel
A1 reports, for the years before and after the switch in affiliation, the number of auditees defaulting on debt payments
(DDP) or not (Non-DDP) by the type of audit report (GC or Non-GC). Panel A2 expresses the numbers in Panel A1 in
percentage of total number of firms in each column. Panel A3 expresses the number in Panel A1 in percentage of total
number of firms in each row. Panel A4 shows the levels and changes in Type-1 and Type-2 error rates. A Type-1 error
occurs when the auditor issues an audit report due to going-concern uncertainty and the auditee does not default on debt
payments (i.e., GC = 1 and DefaultDebtPay = 0). A Type-2 error occurs when the audit report is not modified for goingconcern uncertainty and the auditee defaults on debt payments (i.e., GC = 0 and DefaultDebtPay = 1). Panels B1, B2,
B3, and B4 provide the same information as the panels A1, A2, A3, and A4 except that the panels cover auditors
switching affiliation from Big-4 firms to non-Big-4 firms.
42
Table 3: Descriptive Statistics
Panel A: The sample of auditors switching affiliation from non-Big-4 firms to Big-4 firms
AfterFirstYear
AuditFee
CashFlow
ChgLeverage
CurrentRatio
DefaultDebtPay
EarningsQuality
FirstYear
GC
GCAccuracy
Intangibles
InvAccRec
Leverage
LnAF
LnAge
LnEmployees
LnTA
Loss
NumberIndustries
NumMod
ProbBankruptcy
ROA
Sales(mNOK)
SalesGrowth
ShortTermInv
SwitchYear
TA(mNOK)
#Observationsa
A1: Before the switch to Big-4 firm
Mean
SD
P25
P50
P75
0.00
0.00
0.00
0.00
0.00
20.98 24.33
8.00 15.00 25.00
0.00
0.46 -0.09
0.01
0.16
0.05
0.59 -0.08 -0.01
0.06
6.77 16.27
0.98
1.56
3.03
0.06
0.24
0.00
0.00
0.00
-0.22
0.28 -0.27 -0.11 -0.04
0.00
0.00
0.00
0.00
0.00
0.09
0.29
0.00
0.00
0.00
0.88
0.33
1.00
1.00
1.00
0.02
0.05
0.00
0.00
0.01
0.19
0.45
0.00
0.08
0.21
0.72
0.63
0.42
0.69
0.87
2.66
0.87
2.08
2.71
3.22
2.10
0.86
1.39
2.08
2.77
0.91
1.03
0.00
0.61
1.60
8.13
1.61
7.14
8.13
9.15
0.30
0.46
0.00
0.00
1.00
1.13
0.40
1.00
1.00
1.00
0.22
0.60
0.00
0.00
0.00
0.43
0.36
0.07
0.36
0.79
0.03
0.07
0.00
0.02
0.05
10.39 37.42
0.25
2.06
7.83
0.34
1.71 -0.02
0.00
0.17
0.24
0.27
0.03
0.14
0.38
0.00
0.00
0.00
0.00
0.00
12.57 46.93
1.25
3.38
9.43
14,932
43
A2: After the switch to Big-4 firm
Mean
SD
P25
P50
P75
0.37
0.48
0.00
0.00
1.00
24.93 28.92 10.00 17.00 29.00
-0.03
0.52 -0.10
0.00
0.13
0.08
0.78 -0.06
0.00
0.05
8.04 17.91
0.98
1.70
4.00
0.08
0.27
0.00
0.00
0.00
-0.19
0.25 -0.23 -0.10 -0.04
0.28
0.45
0.00
0.00
1.00
0.08
0.26
0.00
0.00
0.00
0.88
0.32
1.00
1.00
1.00
0.02
0.05
0.00
0.00
0.00
0.20
0.51
0.00
0.06
0.20
0.72
0.76
0.33
0.64
0.88
2.83
0.87
2.30
2.83
3.37
2.26
0.75
1.79
2.20
2.83
0.83
1.05
0.00
0.30
1.49
8.25
1.76
7.23
8.31
9.35
0.36
0.48
0.00
0.00
1.00
1.14
0.40
1.00
1.00
1.00
0.22
0.59
0.00
0.00
0.00
0.36
0.35
0.04
0.24
0.65
0.02
0.07
0.00
0.01
0.04
11.78 55.65
0.09
1.70
7.39
0.21
1.43 -0.05
0.00
0.11
0.25
0.28
0.03
0.13
0.41
0.35
0.47
0.00
0.00
1.00
17.42 76.29
1.39
4.06 11.50
16,486
t-value
-93.9***
-12.9***
4.6***
-4.6***
-6.6***
-6.1***
-10.2***
-76.7***
5.0***
-1.1
3.8***
-1.4
-0.4
-16.7***
-18.3***
6.8***
-6.6***
-11.4***
-01.0
1.1
17.6***
11.6***
-2.6**
7.5***
-3.2***
-85.7***
-6.7***
Panel B: The sample of auditors switching affiliation from Big-4 firms to non-Big-4 firms
AfterFirstYear
AuditFee
CashFlow
ChgLeverage
CurrentRatio
DefaultDebtPay
EarningsQuality
FirstYear
GC
GCaccuracyPR
Intangibles
InvAccRec
Leverage
LnAF
LnAge
LnEmployees
LnTA
Loss
NumberIndustries
NumMod
ProbBankruptcy
ROA
Sales(mNOK)
SalesGrowth
ShortTermInv
SwitchYear
TA(mNOK)
#Observationsa
Mean
0.00
20.56
-0.02
0.02
8.53
0.05
-0.19
0.00
0.12
0.87
0.02
0.20
0.67
2.55
2.21
0.79
8.02
0.31
1.15
0.18
0.38
0.02
15.12
0.25
0.27
0.00
11.01
553
B1: Before the switch to
non-Big-4 firm
SD
P25
P50
P75
0.00 0.00
0.00
0.00
36.00 7.00 12.80 24.00
0.47 -0.09
0.01
0.15
0.64 -0.08 -0.01
0.04
17.67 1.08
1.97
4.39
0.21 0.00
0.00
0.00
0.25 -0.24 -0.10 -0.04
0.00 0.00
0.00
0.00
0.32 0.00
0.00
0.00
0.34 1.00
1.00
1.00
0.05 0.00
0.00
0.01
0.53 0.00
0.08
0.21
0.71 0.31
0.57
0.84
0.90 1.95
2.55
3.18
0.79 1.61
2.20
2.77
1.00 0.00
0.37
1.49
1.70 6.95
8.27
9.10
0.46 0.00
0.00
1.00
0.45 1.00
1.00
1.00
0.53 0.00
0.00
0.00
0.36 0.04
0.27
0.70
0.07 0.00
0.02
0.05
78.02 0.11
1.05
5.83
1.50 -0.03
0.00
0.13
0.29 0.04
0.17
0.40
0.00 0.00
0.00
0.00
31.73 1.04
3.90
8.97
Mean
0.45
24.72
-0.02
0.06
9.10
0.09
-0.18
0.30
0.09
0.87
0.02
0.17
0.71
2.69
2.42
0.78
8.37
0.30
1.11
0.16
0.35
0.02
16.88
0.19
0.24
0.25
20.96
572
B2: After the switch to
non-Big-4 firm
SD
P25
P50
0.50
0.00
0.00
57.19
8.00 14.00
0.54 -0.07
0.02
0.66 -0.05 -0.01
18.87
0.97
1.77
0.29
0.00
0.00
0.25 -0.21 -0.09
0.46
0.00
0.00
0.29
0.00
0.00
0.33
1.00
1.00
0.06
0.00
0.00
0.47
0.00
0.05
0.82
0.29
0.59
0.87
2.08
2.64
0.65
1.95
2.40
1.04
0.00
0.22
1.86
7.31
8.50
0.46
0.00
0.00
0.35
1.00
1.00
0.52
0.00
0.00
0.35
0.03
0.24
0.07
0.00
0.02
86.13
0.01
1.12
1.42 -0.04
0.00
0.28
0.02
0.10
0.42
0.00
0.00
62.16
1.49
4.92
P75
1.00
23.50
0.14
0.04
4.27
0.00
-0.03
1.00
0.00
1.00
0.01
0.18
0.84
3.16
2.89
1.38
9.42
1.00
1.00
0.00
0.62
0.04
5.05
0.09
0.39
0.00
12.38
t-value
-21.2***
-1.4
-0.0
-0.9
-0.5
-2.9***
-0.7
-15.3***
1.5
-0.1
-0.7
1.0
-0.8
-2.6***
-4.9***
0.1
-3.3***
0.3
2.0**
0.6
1.3
1.3
-0.4
0.7
1.7*
-12.6***
-3.4***
This table provides statistics of mean, standard deviation (SD), the 25th, 50th, and 75th percentiles of all the
variables used in the regression analyses. The variables are defined in Appendix B. Panel A (B) presents
descriptive statistics for clients audited by auditors switching from non-Big 4 (Big-4) to Big-4 firms (non-Big-4
firms). Panels A1 and B1 (A2 and B2) provide descriptive statistics for the years before (after) the auditors switch
audit-firm affiliation. The last column reports the t-statistics for tests of equality of means before and after the
auditors switch affiliation. * (**) [***] indicates significance at the 1 (5) [10] percent level using two-tailed tests.
a
The number of observations for AuditFee and LnAF in the To-Big4 sample is 14,932 before the switch and 16,486
after the switch while it is 531 before the switch and 556 after the switch in the ToNonBig4 sample.
44
Table 4: Regression Results of Changes in Audit Quality and Audit Fees after the
Auditor Switches Audit-firm affiliation
Panel A: Auditors switching affiliation from non-Big 4 to Big-4 audit firms
SwitchYear
FirstYear
AfterFirstYear
LnTA
LnAge
LnEmployees
NumberIndustries
ROA
SalesGrowth
CashFlow
Leverage
ChgLeverage
CurrentRatio
InvAccRec
Intangibles
ShortTermInv
Loss
ProbBankruptcy
Fixed effects:
Auditor
Industry
Year
Constant
Observations
Adjusted R2
Pseudo R2
(1)
GCAccuracy
0.115*
(1.74)
0.353***
(3.75)
0.401***
(3.13)
0.068***
(2.94)
0.015
(0.43)
-0.302***
(-8.00)
-0.129*
(-1.76)
0.939**
(1.99)
-0.042***
(-3.81)
0.052
(1.26)
-0.693***
(-11.49)
0.228***
(5.50)
0.000
(0.06)
-0.210***
(-5.32)
-0.766*
(-1.87)
0.854***
(6.37)
-0.483***
(-8.45)
-1.482***
(-12.71)
(2)
GC
-0.025
(-0.28)
-0.321**
(-2.40)
-0.751***
(-3.87)
-0.152***
(-4.44)
-0.165***
(-3.26)
0.092*
(1.67)
0.165
(1.50)
-0.335
(-0.50)
0.055***
(3.87)
0.008
(0.15)
1.034***
(10.30)
-0.365***
(-5.65)
0.007*
(1.81)
0.228***
(3.77)
1.495***
(2.73)
-1.300***
(-6.14)
1.083***
(13.68)
3.744***
(17.61)
(3)
NumMod
-0.047***
(-3.97)
-0.028*
(-1.90)
-0.049**
(-2.42)
-0.029***
(-8.14)
-0.014**
(-2.11)
0.007
(1.31)
-0.007
(-0.55)
-0.134
(-1.61)
0.008***
(3.49)
0.006
(0.54)
0.137***
(9.03)
-0.022**
(-2.21)
0.001***
(5.00)
0.015
(1.58)
-0.185*
(-1.65)
-0.176***
(-9.92)
0.095***
(9.49)
0.106***
(4.91)
(4)
EarningsQuality
0.005
(1.04)
0.020***
(3.03)
0.024***
(2.76)
0.005***
(2.98)
0.039***
(15.89)
0.025***
(10.27)
-0.019***
(-4.43)
-0.721***
(-15.83)
-0.016***
(-10.52)
0.025***
(3.68)
-0.085***
(-13.43)
-0.002
(-0.37)
-0.000***
(-3.49)
-0.034***
(-7.47)
-0.075*
(-1.85)
-0.079***
(-10.12)
-0.047***
(-10.83)
-0.012
(-1.36)
(5)
LnAF
-0.010
(-0.91)
0.024*
(1.72)
0.047**
(2.42)
0.158***
(31.81)
0.117***
(16.00)
0.401***
(52.38)
-0.089***
(-5.99)
-0.330***
(-4.13)
-0.008***
(-3.74)
-0.037***
(-3.84)
0.132***
(11.10)
-0.054***
(-6.00)
-0.003***
(-9.33)
0.073***
(7.49)
0.276***
(2.92)
0.095***
(4.26)
0.014
(1.36)
-0.006
(-0.28)
Yes
Yes
Yes
2.295***
(4.67)
31,331
Yes
Yes
Yes
-4.116***
(-6.88)
31,327
Yes
Yes
Yes
0.522***
(4.38)
31,418
0.119
Yes
Yes
Yes
-0.221***
(-7.45)
31,325
0.142
Yes
Yes
Yes
0.915***
(9.85)
30,812
0.610
0.208
0.463
45
Panel B: Auditors switching affiliation from Big-4 to non-Big-4 audit firms.
SwitchYear
FirstYear
AfterFirstYear
LnTA
LnAge
LnEmployees
NumberIndustries
ROA
SalesGrowth
CashFlow
Leverage
ChgLeverage
CurrentRatio
InvAccRec
Intangibles
ShortTermInv
Loss
ProbBankruptcy
Fixed effects:
Auditor
Industry
Year
Constant
Observations
Adjusted R2
Pseudo R2
(1)
GCAccuracy
0.725
(1.20)
0.067
(0.10)
-1.532***
(-2.76)
0.172
(1.23)
0.008
(0.04)
-0.767***
(-3.08)
-0.097
(-0.32)
-3.936
(-1.26)
-0.120*
(-1.94)
0.026
(0.10)
-0.730***
(-3.13)
0.103
(0.62)
-0.016
(-1.43)
-0.142
(-0.47)
-0.892
(-0.31)
-0.562
(-0.91)
-1.327***
(-4.34)
-2.617***
(-3.43)
(2)
GC
-0.211
(-0.29)
-0.016
(-0.02)
1.675*
(1.82)
-0.553***
(-2.92)
-0.148
(-0.44)
0.311
(0.87)
0.332
(0.71)
-1.716
(-0.32)
0.205*
(1.73)
0.680
(1.45)
2.221*
(1.89)
-0.897
(-1.45)
0.003
(0.14)
0.521
(1.52)
7.950**
(2.34)
1.568*
(1.73)
1.902***
(4.86)
4.168**
(2.54)
(3)
NumMod
-0.113**
(-2.02)
-0.002
(-0.03)
-0.011
(-0.13)
-0.027
(-1.55)
-0.009
(-0.33)
-0.031
(-1.23)
-0.005
(-0.13)
-0.051
(-0.11)
-0.008
(-1.20)
0.012
(0.25)
0.172***
(2.95)
0.002
(0.05)
0.001
(1.58)
0.077**
(2.19)
0.298
(0.64)
-0.053
(-0.77)
0.091**
(2.41)
0.228**
(2.09)
(4)
EarningsQuality
0.011
(0.39)
-0.032
(-1.08)
-0.002
(-0.04)
0.009
(1.26)
0.013
(0.94)
0.025**
(2.24)
0.022
(1.05)
-0.550*
(-1.96)
-0.035***
(-4.45)
0.051
(1.64)
-0.067***
(-2.92)
0.018
(0.57)
-0.001*
(-1.89)
0.015
(1.07)
0.112
(0.58)
-0.103***
(-2.77)
-0.055**
(-2.31)
-0.024
(-0.45)
(5)
LnAF
0.108
(1.59)
-0.036
(-0.56)
-0.163**
(-2.15)
0.174***
(5.93)
0.016
(0.38)
0.418***
(7.25)
-0.071
(-0.99)
-0.624
(-1.29)
0.012
(1.42)
-0.039
(-0.70)
0.166***
(3.29)
-0.022
(-0.63)
-0.003**
(-2.14)
0.199***
(5.88)
1.160
(1.54)
0.127
(1.30)
0.083
(1.59)
-0.195
(-1.57)
Yes
Yes
Yes
14.245***
(5.19)
1,079
Yes
No
Yes
-11.989**
(-2.30)
1,003
Yes
Yes
Yes
1.274***
(7.31)
1,125
0.292
Yes
Yes
Yes
-0.604***
(-5.22)
1,120
0.209
Yes
Yes
Yes
1.387***
(3.60)
1,087
0.651
0.412
0.668
This table presents results of regressing measures of audit quality and audit fee against test and control variables.
Panel A reports results for auditors that have switched affiliation from non-Big-4 firms to Big-4 firms. Panel B
reports results for auditors that have switched from Big-4 firms to non-Big-4 firms (fixed effects for industry
affiliation is excluded in column 2 due to singularity). The variables are defined in Appendix B. The z-values
(logit) and t-values (OLS) are adjusted for within-cluster correlation at the client-firm level using the Huber-White
Sandwich Estimator. * (**) [***] indicates significance at the 1 (5) [10] percent level using two-tailed tests.
46
Table 5: Regression Results of Differences in Audit Quality and Audit Fees for Partners
who Switch between Non-Big-4 audit firms (Panel A) and between Non-Big 4 Partners
that do not Switch and those who Switch to Big-4 audit firms (Panel B).
Panel A: Regression Results of Changes in Audit Quality and Audit Fees after the Auditor
Switches Affiliation from one Non-Big-4 audit firm to another Non-Big-4 audit firm.
SwitchYear
FirstYear
AfterFirstYear
Observations
Adjusted R2
Pseudo R2
(1)
GCAccuracy
0.020
(0.28)
0.133
(1.40)
0.090
(0.73)
24965
(2)
GC
-0.108
(-1.13)
-0.179
(-1.31)
-0.212
(-1.19)
24532
0.227
0.488
(3)
NumMod
-0.000
(-0.00)
-0.005
(-0.30)
-0.048**
(-2.34)
25305
0.149
(4)
EarningsQuality
-0.002
(-0.52)
-0.000
(-0.00)
0.001
(0.11)
24151
0.167
(5)
LnAF
-0.017**
(-1.98)
-0.025**
(-2.15)
-0.043***
(-2.78)
24955
0.625
Panel B: Regression Results of Differences in Audit Quality and Audit Fees between NonSwitching Non-Big 4 Audit Partners and Non-Big 4 Audit Partners who Switch to Big-4
firms
ToBig4Pre
Observations
Adjusted R2
Pseudo R2
(1)
GCAccuracy
0.054*
(1.76)
483549
(2)
GC
-0.085**
(-2.20)
528844
0.184
0.418
(3)
NumMod
-0.002
(-0.35)
528859
0.131
(4)
EarningsQuality
0.003
(1.56)
494665
0.172
(5)
LnAF
0.056***
(9.67)
519416
0.525
This table presents in Panel A results of regressing measures of audit quality and audit fee against test and control
variables for a sample consisting of the auditees of audit partners that switch audit-firm affiliation from one nonBig-4 audit firm to another non-Big-4 firm. Panel B presents results of regressing measures of audit quality and
audit fee against test and control variables for a sample consisting of the auditees of non-switching non-Big 4
audit partners and the auditees of audit partners switching from non-Big-4 audit firms to Big-4 firms. For the
switching partners, only observations from the year prior to the switch are included. ToBig4Pre = 1 for auditees
of partners switching affiliation from non-Big-4 firms to Big-4 firms in the years before the switch takes place,
and 0 otherwise. The variables are defined in Appendix B. The z-values (logit) and t-values (OLS) are adjusted
for within-cluster correlation at the client-firm level using the Huber-White Sandwich Estimator. * (**) [***]
indicates significance at the 1 (5) [10] percent level using two-tailed tests.
47
Table 6: Regression Results using Sub-samples of Client Firms with Income-Increasing
and Income-Decreasing Discretionary Accruals
Dependent variable
Sub-sample:
SwitchYear
FirstYear
AfterFirstYear
Observations
Adjusted R2
Panel A: Auditors switching from
non-Big 4 to Big-4 audit firms
EarningsQuality
Positive disc.
Negative disc.
accruals
accruals
0.011
(1.64)
0.030***
(3.67)
0.036***
(3.32)
16256
0.371
0.007
(1.06)
0.010
(1.38)
0.019*
(1.87)
15069
0.405
Panel B: Auditors switching from
Big-4 to non-Big-4 audit firms
EarningsQuality
Positive disc.
Negative disc.
accruals
accruals
0.025
(0.59)
-0.003
(-0.08)
-0.010
(-0.19)
576
0.484
0.041
(1.38)
0.004
(0.12)
0.063
(1.43)
544
0.531
This table presents OLS results of regressing EarningsQuality against test and control variables for sub-samples
of client firms with positive (i.e., income increasing) and negative (i.e., income decreasing) discretionary accruals.
For brevity, only coefficients and t-values for the test variables are reported. The control variables are as in Table
4. Panel A reports results for auditors that have switched affiliation from non-Big-4 firms to Big-4 audit firms.
Panel B reports results for auditors that have switched from Big-4 firms to non-Big-4 firms. The t-values (OLS)
are adjusted for within-cluster correlation at the client-firm level using the Huber-White Sandwich Estimator. *
(**) [***] indicates significance at the 1 (5) [10] percent level using two-tailed tests.
48