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RELIABILITY OF DISCLOSED INTERNAL CONTROL
WEAKNESS AND CHANGES IN DISCLOSURE REGULATION
by
Yanju Liu
A thesis submitted in conformity with the requirements
for the degree of Doctor of Philosophy
Joseph L. Rotman School of Management
University of Toronto
© Copyright by Yanju Liu (2013)
ii
RELIABILITY OF DISCLOSED INTERNAL CONTROL WEAKNESS
AND CHANGES IN DISCLOSURE REGULATION
Yanju Liu
Doctor of Philosophy
Joseph L. Rotman School of Management
University of Toronto
2013
ABSTRACT
This paper investigates whether changes in internal control weakness (ICW) disclosure
regulation affect the reliability of a firm’s disclosed ICW in a unique Canadian setting. In
Canada, public firms have been required to provide internal control weakness disclosures since
2006. However, the credibility enhancement mechanisms (i.e., the implementation of
effectiveness evaluation and CEO/CFO certification) were not adopted until 2008. Taking
advantage of such an evolutionary process of regulations and inferring the reliability of the
disclosed ICW from the magnitude of the negative association between disclosed ICW and
investment efficiency, I first document that in the pre-adoption period, the association between
Canadian firms’ disclosed ICW and their investment efficiency is insignificant; however, in the
post-adoption period, the disclosed ICW negatively affects firms’ investment efficiency. This
finding suggests that the credibility enhancement mechanisms improve the reliability of
disclosed ICW in Canada. In addition, using the U.S. sample as a benchmark, I find that in the
post-adoption period, the association is weaker between Canadian firms’ disclosed ICW and
their investment efficiency, which is consistent with my prediction that the external audit
requirement increases the reliability of the disclosed ICW. Overall, the study implies that
changes in disclosure regulation lead to efficient resource allocation by improving the reliability
of the information disclosed.
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ACKNOWLEDGEMENTS
I thank my supervisors, Jeffrey Callen and Franco Wong, for patiently guiding me
through my doctoral studies; my dissertation committee members Gordon Richardson and Hai
Lu for offering helpful comments on and constructive criticism of my thesis. I would also like to
acknowledge Hai Lu, Gordon Richardson and Steven Saterio for sharing the data on Canadian
ICW disclosure.
I thank my friends and colleagues in the PhD program whom I enjoyed to study with and
learn from at the University of Toronto. These include Yiwei Dou, Xiaohua Fang, Yu Hou,
Alastair Lawrence, Matt Lyle, Miguel Minutti Meza, Kevin Veenstra, Dushyantkumar Vyas, and
Youli Zou.
Finally, I deeply appreciate the love, support, and encouragement of my family. I
dedicate this thesis to them.
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TABLE OF CONTENTS
ABSTRACT ii
ACKNOWLEDGEMENTS iii
TABLE OF CONTENTS iv
LIST OF TABLES vi
CHAPTER 1 – INTRODUCTION 1
CHAPTER 2 – BACKGROUND 6
2.1 Institutional Background of the U.S. SOX 302 and SOX 404 6
2.2 Institutional Background of the Canadian Provisions 7
CHAPTER 3 – LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT 9
3.1 Literature Review 9
3.1.1 Determinants and Consequences of Internal Control Weakness Disclosure 9
3.1.2 Financial Reporting Quality and Investment 11
3.2. Hypotheses Development 12
CHAPTER 4 – SAMPLE SELECTION 18
CHAPTER 5 – RESEARCH DESIGN 20
5.1 Measures of Investment Efficiency 20
5.2 The Effects of Reliable ICW Disclosure on Investment Efficiency 21
5.3 The Effects of ICW on Investment Efficiency: The Role of Financing Constraints 24
CHAPTER 6 – EMPIRICAL RESULTS 25
6.1 Descriptive Statistics 25
6.2 Cost of Capital Change from the Pre-adoption Period to the Post-adoption Period 26
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6.3 Tests of H1a: The Negative Association between the Disclosed ICW and Investment
Efficiency Using the U.S. Sample 28
6.4 The Effects of ICW on Investment Efficiency: The Role of Financial Constraints 29
6.5 Tests of H1b: The Effects of ICW on Investment Efficiency for Canadian Firms in the
Pre- and Post-adoption Periods 30
6.6 Tests of H2 and H3: The Effects of ICW on Investment Efficiency – U.S. vs. Canada 31
6.7 Additional Analysis 34
6.7.1 Separating the effect of implementation effectiveness tests from the effect of
CEO/CFO certification 34
6.7.2 The Relation between ICW Type and Investment Efficiency in the Post-adoption
Period 35
6.7.3 The Impact of Human Capital Investment in Accounting on the Relation between
ICW and Investment Efficiency 35
6.7.4 Audit Fee 37
6.8 Sensitivity Analysis 38
6.8.1 Distressed Firms 38
6.8.2 Alternative Investment Efficiency Model 39
6.8.3 Cross-listing Firms 41
6.8.4 Investment History 41
6.8.5 Alternative Accrual Quality Measure 42
6.8.6 Potentially Biased Measures in the Investment Model 42
6.8.7 M&A and ICW 43
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LIST OF TABLES
Table 1: Internal Control Regulatory Requirements: Canada-U.S. Comparison
Table 2: Sample Selection Procedures
Table 3: Descriptive Statistics
Table 4: Cost of Capital: Canadian Sample
Table 5: Investment Efficiency and Internal Control Weaknesses: U.S. full Sample, 2004-
2009
Table 6: Investment Efficiency and Internal Control Weaknesses: U.S. Financially
Constrained and Unconstrained Subsamples, 2004-2009
Table 7: Investment Efficiency and Internal Control Weaknesses: Canadian Sample, Pre-
and Post-adoption Periods
Table 8: The Relation between ICW and Investment Efficiency: U.S. and Canadian
Matching Samples for Pre- and Post-adoption Periods Separately
Table 9: The Relation between Disclosed ICW and Investment Efficiency:
U.S. (Accelerated filers) and Canadian Matching Sample, Pooling the Pre- and Post-
adoption Periods’ Observations together
Table 10: The Relation between Disclosed ICW and Investment Efficiency:
U.S. (Non-accelerated filers) and Canadian Matching Sample, Pooling the Pre- and
Post-adoption Periods’ Observations Together
Table 11: The Relation between ICW Type and Investment Efficiency, Post-adoption Period
Table 12: The Impact of Human Capital Investment in Accounting on the Relation between
ICW and Investment Efficiency: Canadian Sample, Pre-adoption Period
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Table 13: The Reliability of ICW Disclosure and Audit Fees: Canadian Sample, Silent Group
(i.e., Firms with Zero ICW Disclosed to the Public)
Table 14: The Relation between Disclosed ICW and Investment Efficiency:
U.S. (Accelerated filers) and Canadian Matching Sample, Pooling the Pre- and Post-
adoption Periods’ Observations together, Excluding Distressed Firms
Table 15: The Relation between ICW and Investment Efficiency: Alternative Investment
Efficiency Model
Table 16: The Relation between Disclosed ICW and Investment Efficiency:
Including Canadian Cross-listing Firms, Pooling the Pre- and Post-adoption Periods’
Observations together
Table 17: Sensitivity Analysis Related to the Investment Model: Including the Investment
History
Table 18: Sensitivity Analysis Related to the Accrual Quality Measure: Modified Dechow-
Dichev Model
Table 19: The Relation between Disclosed ICW and Investment Efficiency (Excluding
Accounting-based Control Variables): U.S. (Accelerated filers) and Canadian
Matching Sample, Pooling the Pre- and Post-adoption Periods’ Observations together
CHAPTER 1 INTRODUCTION
Canadian public companies have been required to disclose internal control weakness
(ICW) since 2006. However, these disclosures are neither certified by CEO/CFO nor audited by
external auditors, which has made the reliability of such disclosures questionable. Effective
December 15, 2008, the Canadian Securities Administrators (CSA) added further requirements
for 1) implementation effectiveness evaluation and 2) management certification on ICW
disclosures. These two requirements are viewed as the credibility enhancement mechanisms to
improve the reliability of the disclosed ICW in Canada. Using this evolutionary process of the
disclosure regulation as a natural experiment, this paper examines whether changes in internal
control disclosure regulation affect the reliability of firms’ disclosed ICW.
Reliability is defined as the extent to which the accounting information represents what it
purports to represent. The reliability of the disclosed financial information has always been a
major concern for both standard-setters and investors. Prior literature has shown that reliability
of accounting information depends on the adequacy of a number of reliability factors, for
example: have the financial statements been prepared based on Generally Accepted Accounting
Principles (GAAP); have the financial statements been approved by the directors; and have the
financial statements been independently audited. An absence of reliable accounting information
impedes the flow of financial and human capital towards sectors that are expected to have high
returns and away from sectors with poor prospects (Bushman and Smith 2003). In this paper, I
focus on examining whether regulation changes affect the reliability of the information disclosed
by firms. Specifically, I investigate whether the credibility enhancement mechanisms imposed by
the new internal control disclosure regulation in Canada improve the reliability of the internal
control weaknesses disclosed. My study speaks directly to the questions that have been long
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debated in the accounting regulation literature about whether accounting regulation is effective in
achieving its aims of accurate, reliable, consistent, and comparable financial reporting. My study
also provides evidence on how management’s certificate and external auditor’s audit takes effect
in improving the efficiency of the capital resource allocation.
I use the magnitude of the negative association between a firm’s disclosed ICW and its
investment efficiency to assess the reliability of the disclosed internal control weaknesses, i.e.,
the perceived reliability. The idea underlying this is that reliable ICW disclosure reduces
information risk and increases capital investment efficiency. When disclosed ICW is a reliable
proxy for a firm’s internal control over financial reporting (ICFR), the literature shows that 1)
investors (or market) respond to ICW disclosure by avoiding investing in firms with higher
disclosed ICW; 2) in firms with higher disclosed ICW, managers are more likely to engage in
value-destroying activities (e.g., empire building). Both investors’ capital rationing and
managers’ empire-building can lead to investment inefficiency. Moreover, disclosed ICW could
also indicate a bad internal information environment that impairs managers’ ability to identify
value-creation activities. I therefore expect a strong negative association between disclosed ICW
and investment efficiency when disclosed ICW is reliable.
When firms’ disclosed ICW is not a good indicator of firms’ effectiveness of ICFR,
investors are expected to rely less on firms’ disclosed ICW in making investment decisions. In
Canada, publicly listed firms have been required to disclose ICW since 2006. In the first two
years, Canadian regulators provided very little guidance on the assessment of ICW, leaving firms
much discretion on determining what constituted ICW. This fact is similar to the U.S. Sarbanes-
Oxley Act of 2002 that required management certification of ICFR under Section 302, but
provided relatively little guidance for management to follow in determining ICW. It wasn’t until
3
the Public Company Accounting Oversight Board issued guidance on the assessment of ICFR
that managers became more knowledgeable of what constituted ICW and faced greater
incentives to disclose ICW (PCAOB 2004). Moreover, in the first two years, ICW disclosures in
Canada are neither certified by CEO/CFO nor audited by external auditors, which made the
reliability of the ICW disclosure even more questionable. Thus, I expect that the negative
association between disclosed ICW and investment efficiency is weakened when firms’
disclosed ICW is not reliable.
If the credibility enhancement mechanisms adopted at the end of 2008 take effect, I
expect the reliability of the disclosed ICW to improve in the post-adoption period relative to that
in the pre-adoption period. My first hypothesis is that the negative association between Canadian
firms’ disclosed ICW and their investment efficiency is stronger in the post-adoption period than
in the pre-adoption period.
In addition, compared to the U.S. sample, in the pre-adoption period, I expect Canadian
firms’ disclosed ICW to be less reliable due to a lack of implementation tests, CEO/CFO
certification and external auditor’s audit over the ICW disclosures. Moreover, having external
auditors is not required even in the post-adoption period. Using the U.S. sample as a benchmark,
I expect that the disclosed ICW in Canada is less reliable than the disclosed ICW in the U.S. in
both the pre- and post-adoption periods. Therefore, I hypothesize that in both the pre- and post-
adoption periods, the negative association between firms’ disclosed ICW and their investment
efficiency is weaker in Canada than in the U.S.
Using the U.S. sample, the prior literature maintains the assumption that the disclosed
ICW is a reliable proxy for a firm’s ICFR based on the fact that the ICW disclosure is both
certified by management and audited by independent auditors. I validate my reliability measure
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of the disclosed internal control weaknesses (i.e., the negative association between disclosed
internal control weaknesses and investment efficiency) using a sample of 13,222 U.S. firms
between 2004 and 2009. I restrict my U.S. sample to all the accelerated filers because the full
disclosure requirement under the Sarbanes-Oxley Act (SOX) applies only to the accelerated
filers.1,2
Consistent with my prediction and the prior literature, I find a strong negative association
between firms’ disclosed ICW and their investment efficiency in the U.S. To test my first
hypothesis, I examine whether the hypothesized negative relation between disclosed ICW and
investment efficiency holds in the Canadian sample in the periods both before and after the
credibility enhancement mechanisms of 2008 were imposed. Using a sample of 457 Canadian
firms, I show that, before the implementation effectiveness evaluations and CEO/CFO
certification were required, the predicted negative association between disclosed ICW and
investment efficiency is much weaker in 2006 compared to the same association in the U.S.
Next, I test whether the same association changed in the post-adoption period. In a sample of 359
Canadian firms, I find that the negative association was considerably strengthened in 2009, one
year after the implementation effectiveness evaluations and CEO/CFO certification became
mandatory. In addition, I employ a differences-in-differences research design to measure the
extent to which the credibility enhancement mechanisms changed the reliability of the disclosed
ICW and compare the reliability of the disclosed ICW of the Canadian sample to that of the U.S.
sample. My findings imply that in the pre-adoption period, the disclosed ICW in Canada is less
reliable than that in the U.S. Even in the post-adoption period, after the implementation of the
1Accelerated filers are those firms whose market capitalization is greater than $75 million.
2 In the additional analysis part of the paper, I use the non-accelerated filers as a control sample to further test the
effect of management certification on the reliability of the disclosed ICW.
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credibility enhancement mechanisms, due to a lack of external audits, the reliability of the
disclosed ICW in Canada is still inferior to that of the U.S.
This study makes several important contributions to the literature. First, by demonstrating
that the accounting regulation change improves the reliability of disclosed internal control
weakness, this study adds to the internal control literature. Previous studies on ICW mainly focus
on examining the economic factors that determine the ICW disclosure (Ge and McVay 2005;
Ashbaugh-Skaife, Collins and Kinney 2007; Doyle, Ge and McVay 2007b; Hoitash, Hoitash and
Bedard 2009) or on investigating the economic consequences of ICW disclosure (Ashbaugh-
Skaife, Collins, Kinney and LaFond 2008; Ashbaugh-Skaife, Collins, Kinney and LaFond 2009;
Beneish, Billings and Hodder 2008; Ogneva, Subramanyam and Raghunandan 2007; and Doyle
et al. 2007b). The maintained underlying assumption is always that the disclosed ICW is a
reliable proxy for the effectiveness of firms’ ICFR. Utilizing the unique evolutionary regulation
change in Canada, my study shows that the reliability of firms’ disclosed ICW is not always
guaranteed. It could vary with the tightness of the ICW disclosure regulation and/or other
reliability factors, which further affects the inferences drawn from previous studies.
Second, this study adds to the debate over the enhancement mechanism in ICW
disclosures. The evolutionary process of the Canadian ICW disclosure regulation provides a
unique natural experimental setting to investigate the impact of required implementation
effectiveness evaluation and CEO/CFO certification on the reliability of firms’ disclosed
information. Given the lack of control implementation testing, CEO/CFO certification, and
external audit for these ICW disclosures, it is questionable whether the disclosed ICW reflects
the firms’ true quality of internal control over financial reporting and whether such information
is equally useful to investors, compared to in the U.S. setting. Lu, Richardson and Salterio (2010)
6
provide evidence that such disclosures have some credibility as measured by the significant
association between ICW and accrual quality. However, their study does not answer whether
ICW disclosed without an enhancement mechanism is reliable in affecting real investment
decisions and whether the two new requirements introduced add any significant value. My study
suggests that the lack of an implementation effectiveness test and CEO/CFO certification impairs
the reliability of disclosed ICW.
Third, my study contributes to the accounting regulation literature. Although the legal
and regulatory environment in which firms operate has evolved rapidly in recent years, there
have been some suggestions that accounting regulations are not necessary because the market
can decide what accounting principles to demand. Others who are against accounting regulation
argue that the standard-setting process is biased in favour of the setters. Therefore, the
effectiveness of the regulation is usually hard to achieve (Menassa 2011). In this paper, by
showing that internal control disclosure regulation change does affect the reliability of firms’
disclosed ICW, I provide evidence that accounting regulation is necessary in facilitating credible
financial reporting.
The remainder of the paper proceeds as follows. Section 2 elaborates on the institutional
background of the U.S. SOX and the corresponding Canadian provisions; Section 3 describes
prior literature and hypotheses; Section 4 discusses the sample selection procedures; Section 5
introduces the research design; Section 6 presents the empirical test results, and Section 7
concludes.
CHAPTER 2 BACKGROUND
2.1 Institutional Background of the U.S. SOX 302 and SOX 404
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Prior to 2002, neither management nor the auditor of publicly listed companies3 in the
United States was required to evaluate, audit, or publicly report on firm’s ICFR. In the aftermath
of Enron, WorldCom and the various other accounting or audit scandals of the early 2000s, the
Sarbanes-Oxley Act (SOX) was enacted as an emergency legislation to restore investor
confidence in capital markets by regulating the governance of firms and improving the accuracy
and reliability of the financial information released to investors. In July 2002, four subsections of
SOX greatly expanded public information about ICFR of U.S. public companies by mandating
the ICFR disclosures and audits (U.S. Congress [2002b]). Among them, Section 302 of SOX
requires that management “assess” and “certify” the effectiveness of its disclosure controls,
which includes ICFR regarding proper preparation of financial statements. The signing officers
must certify that they are “responsible for establishing and maintaining internal controls” and
“have designed such internal controls to ensure that material information relating to the company
is made known to management.” Section 404 (a) of SOX requires management, based on a
formal assessment process, to file a management report on ICFR effectiveness. Specifically,
management is required to file an “internal control report” as part of the annual report. The
report must affirm “the responsibility of management for establishing and maintaining an
adequate internal control structure and procedures for financial reporting.” It must also “contain
an assessment of the effectiveness of the internal control structure and procedures of the
company.” Section 404 (b) requires auditors to audit management’s ICFR report, and separately,
provide the auditor’s own evaluation of ICFR.
[INSERT TABLE 1 HERE]
2.2 Institutional Background of the Canadian Provisions
3 Except for financial institutions which were required to evaluate their ICFR prior to SOX (Altamuro and Beatty
2010).
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In the spirit of SOX 302’s requirement on “management’s disclosure of control
effectiveness,” the Canadian Securities Administrators (CSA) proposed in June 2003 Multilateral
Instrument 52-109 (MI 52-109) to require that management of publicly listed companies make
internal control design effectiveness evaluations and report on those evaluations in the
company’s Management Discussion and Analysis (MD&A). Further, the CSA proposed that
management assess the effectiveness of implementation of internal controls over financial
reporting, similar to the certification requirement of SOX 302.
However, due to a lack of a national securities regulator, the CSA could not obtain the
needed unanimous agreement on MI 52-109. As a result, a phase-in approach was adopted
regarding the implementation of the Canadian version of SOX 302 and SOX 404 (a). The first
stage spanned from June 29, 2006 to December 14, 2008 and featured an internal control system
design effectiveness evaluation, in which management is required to declare that they had
“designed such internal control over financial reporting, or caused it to be designed under our
supervision, to provide reasonable assurance regarding the reliability of financial reporting and
the preparation of financial statements for external purposes in accordance with the issuer’s
GAAP” (CSA 2005b). However, because these assessments are neither certified by CEO/CFO
nor audited by an external auditor, observers have criticized the credibility of these disclosures.
The second stage started on December 15, 2008 and featured an internal control
implementation effectiveness evaluation and certification, in which management is required to
“evaluate the effectiveness of ICFR and disclose in the MD&A their conclusions on
effectiveness and a description of the process used to evaluate” (CSA 2006). These two new
requirements are viewed as the credibility enhancement mechanism to improve the reliability of
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the disclosed ICW in Canada, which left the only major difference between the final rule NI 52-
109 issued by CSA in late 2006 and SOX, i.e., the requirement of an external audit on these
control weakness disclosures. Table 1 presents the comparison of the internal control regulatory
requirements between Canada and the U.S.
CHAPTER 3 LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT
3.1 Literature Review
3.1.1 Determinants and Consequences of Internal Control Weakness Disclosure
The passage of SOX internal control provisions (Section 302 and Section 404) generated
significant research opportunities. There exist two research streams with regard to the SOX
internal control provisions. One stream focuses on the economic factors that determine internal
control weaknesses (Ge and McVay 2005; Ashbaugh-Skaife, Collins and Kinney 2007; Doyle,
Ge and McVay 2007b; Hoitash, Hoitash and Bedard 2009). Another stream investigates the
economic consequences of internal control weaknesses (Doyle et al. 2007a; Ogneva et al. 2007;
Ashbaugh-Skaife et al. 2008; Beneish et al. 2008; Hammersley et al. 2008; Ashbaugh-Skaife et
al. 2009; Feng Li and McVay 2009; Costello and Witternberg-Moerman 2011; Kim, Song and
Zhang. 2009, 2011).
The first research stream examines whether certain firm characteristics affect internal
control weaknesses (Ge and McVay 2005; Ashbaugh-Skaife, Collins and Kinney 2007; Doyle,
Ge and McVay 2007b; Hoitash, Hoitash and Bedard 2009). These researchers assume that
certain characteristics relate to internal control effectiveness. Ge and McVay (2005) find that
weaknesses in internal controls are related to an insufficient commitment of resources for
accounting controls, and that disclosing material weaknesses is positively associated with a
firm’s business complexity and is negatively associated with firm size and profitability.
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Ashbaugh-Skaife et al. (2007) find that firms disclosing internal control deficiencies after
Section 302 and before Section 404 typically exhibit more complex operations, recent
organizational changes, greater accounting risk, more auditor resignations, and fewer resources
available for internal controls. By distinguishing the internal control problems between entity
wide and account specific, Doyle et al. (2007b) document that smaller, younger, and financially
weaker firms tend to have more entity-wide control problems, while complex, diversified, and
rapidly changing operations firms have more account-specific problems. Hoitash et al. (2009)
conclude that board and audit committee characteristics also determine internal control quality.
The other research stream investigates the economic consequences of internal control
weakness disclosures. The existing empirical evidence supports the view that ineffective internal
controls negatively affect accruals quality (Doyle et al. 2007a; Ashbaugh-Skaife et al. 2008);
analyst forecast behaviour (Kim et al. 2009); cost of equity (Ogneva et al. 2007; Beneish et al.
2008; Ashbaugh-Skaife et al. 2009); cost of private debt (Costello and Witternberg-Moerman
2011; Kim et al. 2011), management forecast (Feng et al. 2009); and stock return (Beneish et al.
2008; Hammersley et al. 2008). More specifically, Doyle et al. (2007a) examine the relation
between accruals quality and internal control weakness disclosures and find that firms with
material weaknesses are generally associated with lower accruals quality as measured by the
extent to which accruals are realized as cash flows. Ashbaugh-Skaife et al. (2008) investigate
both the effect of internal control deficiencies and their remediation on accruals quality. The
authors document that firms reporting internal control deficiencies have lower accruals quality as
measured by accruals noise and absolute abnormal accruals. Using a sample of firms that
disclose auditor-attested evaluation of internal controls over financial reporting (under SOX
404), Kim et al. (2009) examine the effect of internal control quality on analyst forecast
11
behaviours. The authors argue that effective internal controls improve the quality of analysts’
forecasting decisions, and that analysts take into account the disclosed internal control
information when making forecasts. Feng et al. (2009) first investigate the effect of internal
control quality on the accuracy of management guidance. The authors find that management
guidance is less accurate among firms with ineffective internal controls over financial reporting,
which is consistent with their argument that ineffective internal control results in inaccurate
internal management reports, thus generating biased management forecasts. Literature also finds
that when a company has ICW, internal reporting usually contains more noise. Managers
therefore cannot make optimal operating, investing or financing decisions (Lambert et al. 2007
and Feng, McVay and Skaife 2012).
3.1.2 Financial Reporting Quality and Investment
In the ideal world described in Modigliani and Miller (1958), a firm should invest to the
point when the marginal cost of investment equals the marginal benefit. Without friction from
the market, firms should always invest in the projects with positive NPV. In other words, the
optimal investment level should be driven only by available opportunities. However, in reality,
problems arising from the information asymmetry between management and investors can make
it more difficult for firms to achieve its optimum investment level. For example, when managers
are better informed about a firm’s prospects, they may try to time capital issuances to sell
overpriced securities, which induces a typical lemon’s problem. Rational but uninformed
investors may respond by rationing capital, which forces the firm to give up otherwise positive
net-present-value investment opportunities, leading to an under-investment ex post. On the other
hand, managers who maximize their personal welfare may have incentives not perfectly aligned
with that of shareholders (Berle and Means 1932; Jensen and Meckling 1976). Models of moral
12
hazard suggest that managers may invest in negative net–present-value projects when there is
divergence in principal-agent incentives. The manager’s tendency to over-invest will produce
excess investment ex post if firms have resources to invest. At the same time, suppliers of capital
are likely to recognize this problem and to ration capital ex ante, which leads to under-
investment ex post (Stiglitz and Weiss 1981; Lambert et al 2007).
Prior literature suggests that higher-quality financial reporting can improve investment
efficiency by mitigating information asymmetries that cause economic frictions such as moral
hazard and adverse selection (Leuz and Verrecchia 2000; Bushman and Smith 2001; Verrecchia
2001). More specifically, if it is the case that financial reporting information is used by
shareholders to monitor managers (Bushman and Smith 2001; Lambert 2001), higher quality
financial reporting can be associated with investment efficiency by reducing moral hazard
problems. On the other hand, financial reporting quality may also mitigate the adverse selection
problem by mitigating the information asymmetry between firms and outside investors.
Consistent with this view, Chang, Dasgupta and Hilary (2009) propose a model of dynamic
adverse selection and show empirically that firms with better financial reporting have greater
flexibility to issue capital. Similarly, if financial reporting quality helps to reduce adverse
selection costs, it can thus improve investment efficiency through a reduction in external
financing costs.
Other related studies such as Biddle and Hilary (2006) found that firms with higher-
quality financial reporting exhibit higher investment efficiency proxied by lower investment
cash-flow sensitivity. Moreover, Biddle, Hilary and Verdi (2009) and Chen, Hope, Li, and Wang
(2011) show that higher quality financial reporting reduces firms’ over- and under-investment in
13
both the US and in international settings, leading to a higher level of investment efficiency and
economic performance.
3.2. Hypotheses Development
Assuming that firms’ ICW disclosure is a reliable proxy for the effectiveness of their
ICFR, the literature has shown that the disclosed ICW affects a firm’s stock returns and cost of
capital (both the debt and equity markets), and that firms with higher ICW disclosures need to
pay more to get capital from investors, which provides evidence that investors (or the market)
respond to ICW disclosures by avoiding investing in firms with higher disclosed ICW.
Hammersley et al. (2008) examine the stock price reaction to management’s disclosure of
internal control weaknesses under the Sarbanes Oxley Act 302 and to the characteristics of the
disclosed weaknesses. They find that the information content of internal control weakness
disclosures depends on the severity of the internal control weakness. In a sub-sample
uncontaminated by other announcements in the event window, they find negative price reactions
to the disclosure of internal control weaknesses and material weaknesses.
Ashbaugh-Skaife et al. (2009) use unaudited pre-Section 404 disclosures and Section 404
audit opinions to assess how changes in internal control quality affect investors’ risk assessments
and firms’ cost of equity. After controlling for other risk factors, they find that firms with
internal control problems have significantly higher idiosyncratic risk, systematic risk, and cost of
equity. Their change analyses document that auditor-confirmed changes in internal control
effectiveness are followed by significant changes in the cost of equity.
Costello and Witternberg-Moerman (2011) examine how internal control weaknesses
affect bank loan contracting terms. The authors argue that weakness in internal control is an
indicator of poor financial reporting quality. They find that after internal control weakness
14
disclosures, lenders decrease their reliance on financial covenants and financial-ratio-based
performance pricing provisions and substitute them with alternatives, such as price and security
protection and credit-rating-based performance pricing provisions. They also find that lenders are
more likely to require borrowers to provide additional collateral and that material internal control
weaknesses lead to an increase in interest rates. They show that reporting an uncorrected ICW
increases the interest rate by 29 basis points, which represents an 11% increase in the interest
rate. Taken together, ICW disclosure increases a firm’s financing costs, and firms with disclosed
ICW will have difficulties raising capital from the market when needed, leading to a lower
investment efficiency ex post.
Agency theory suggests that because managers’ interests are usually not aligned with
those of external investors, without perfect monitoring and control, managers may maximize
their personal welfare by engaging in value-destroying activities such as empire building and
management entrenchment. As part of the internal control system within a firm, the effectiveness
of the ICFR has a strong correlation with the effectiveness of the whole corporate control system.
Managers of firms with ICW, therefore, have greater opportunities to expropriate investors’
wealth due to a lack of an efficient monitoring and control system. Once ICW is disclosed to the
public, management could suffer from pay cuts and might lose their jobs. Wang (2010) examines
whether the internal control disclosures mandated by SOX have affected firms’ compensation
and retention decisions concerning their CFOs. She finds that CFOs of firms with weak internal
controls receive lower compensation and experience higher forced turnover rates after the
passage of SOX. In contrast, CFOs of firms with strong internal controls receive higher
compensation and do not experience significant changes in forced turnover rates. Hoitash,
Hoitash and Johnstone (2009) examine how CFO annual salaries and bonus compensation relate
15
to internal control material weakness disclosures. They find that CFO bonuses are negatively
associated with material weakness disclosures, and that this relationship is moderated by CFO
expertise and reputation, and board of director strength.
Cheng, Dhaliwal and Zhang (2011) examine how the disclosure of the effectiveness of a
firm’s ICFR affects its investment efficiency under the U.S. internal control disclosure
regulation--the Sarbanes Oxley Act (SOX) of 302 and 404. They find that conditional on the
availability of financial resources, weak ICFR firms over-invest or under-invest relative to
effective ICFR firms in the year prior to the initial disclosure. However, the investment
inefficiencies generally disappear after the disclosure of the weaknesses. In their paper, weak
ICFR firms are those with material weaknesses disclosed under SOX 302 and 404. They
maintain the assumption that the disclosed ICW is a reliable proxy for a firm’s ICFR, which is
also a general belief held by the prior literature using the U.S. sample based on the fact that the
ICW disclosure is both certified by management and audited by independent auditors in the U.S.
Moreover, weak internal controls will affect the accuracy and reliability of the financial
numbers provided by the internal reporting system, which in turn creates obstacles for
management to better understand a firm’s operation and to identify good investment
opportunities, leading to inferior budgeting and investment decisions. Lambert et al. (2007)
argue that ICW could have an indirect impact on firms’ cost of capital through its effect on real
decisions that impact the expected cash flow and covariances of cash flows. When the
effectiveness of the internal control system affects a firm’s cost of capital, the investment that the
firm views as optimal is also likely to change.
Similar to the requirement under SOX 302, Canadian internal control design
effectiveness assessments are required for every public company in Canada since 2006. I expect
16
that firms with disclosed ICW have lower investment efficiency when disclosed ICW is a
reliable proxy for the effectiveness of firms’ ICFR. In other words, there exists a negative
association between firms’ disclosed ICW and their investment efficiency. However, because
Canadian firms’ assessments are neither certified by CEO/CFO nor audited by external auditors
for the period from 2006 to 2008, “truth-telling” cannot be assumed (Salterio and Schmidt 2007).
In other words, firms could intentionally choose to hide the known ICW from disclosing it to the
public, causing the disclosed ICW to be less informative in serving as a proxy for firms’ ICFR.
Lu et al. (2010) examine the effectiveness of this unique Canadian disclosure approach. Using
OLS and two-stage regressions, they find evidence that the disclosures are credible as there is a
negative association between internal control weaknesses and accrual quality. In addition, their
recursive path analysis reveals an offsetting net indirect effect from internal control weakness to
accrual quality via audit effort.
In late 2006, the Canadian Securities Administrators (CSA) adopted further requirements
(effective December 15, 2008) to improve the reliability of the disclosed ICW. First, the CEO or
the CFO of a reporting issuer is required to certify in their annual certificates that she has
evaluated the effectiveness of the issuing firms’ ICFR. Second, issuing firms are required to
disclose in their annual MD&A the CEO’s (or CFO’s) conclusions about the effectiveness of the
firms’ ICFR (NI 52-109). These two requirements are viewed as the credibility enhancement
mechanisms designed to improve the reliability of the disclosed ICW in Canada. Observers have
argued that this decision of the CSA presents the management of public firms and their boards
with significant challenges to provide the same rigor in the design and evaluation of the internal
control over financial reporting and the same level of investor protection as does SOX 404
(Emerson 2006). I expect that the explicit responsibilities imposed on management should
17
provide investors with an extra layer of “assurance” of the accuracy of the information provided
by the company and also make the disclosed ICW a better proxy for firm’s ICFR. Assuming the
adopted credibility enhancement mechanisms did take effect, the reliability of disclosed ICW
should improve in the post-adoption period.
In this study, I infer the reliability of the disclosed ICW from the magnitude of the
negative association between disclosed ICW and investment efficiency. I expect that firms with
disclosed ICW have lower investment efficiency when disclosed ICW is a reliable proxy for the
effectiveness of firms’ ICFR. In other words, there exists a negative association between firms’
disclosed ICW and their investment efficiency.
Stated below in alternative form, my first hypothesis is:
H1a: There is a negative association between firms’ disclosed ICW and their
investment efficiency when disclosed ICW is a reliable proxy for the effectiveness
of firms’ ICFR.
When firms’ disclosed ICW is not a good indicator of firms’ effectiveness of ICFR,
investors are expected to rely less on firms’ disclosed ICW in making investment decisions.
Firms with no disclosed ICW are not necessarily those with effective ICFR. In fact, the silent
group (i.e., firms with no disclosed ICW) includes both the truth-telling firms and firms that have
ICW but choose not to disclose it to the public. Thus, I expect that the negative association
between disclosed ICW and investment efficiency is weakened when firms’ disclosed ICW is not
reliable.
Stated below in alternative form:
H1b: The negative association between Canadian firms’ disclosed ICW and their
investment efficiency is stronger in the post-adoption period than in the pre-
adoption period.
18
In the pre-adoption period, Canadian firms’ disclosed ICW is expected to be less reliable
compared to the U.S., due to a lack of implementation effectiveness evaluation, CEO/CFO
certification and external auditor’s audit over the ICW disclosures. Even after 2008, an
independent audit of these disclosures is still not required in Canada. Compared to the U.S., I
expect the lack of external audit requirements in Canada to have a negative effect on the
reliability of the disclosed ICW in Canada. My second and third hypotheses are:
H2: In the pre-adoption period, the negative association between firms’ disclosed
internal control weakness and investment efficiency is weaker in Canada than
that in the U.S.
H3: In the post-adoption period, the negative association between firms’ disclosed
internal control weakness and investment efficiency is weaker in Canada than
that in the U.S.
CHAPTER 4 SAMPLE SELECTION
Since the credibility enhancement mechanisms were adopted at the end of 2008, my pre-
adoption sample consists of 457 firm observations in 2006 and my post-adoption sample consists
of 359 firm observations in 2009. I chose these two years because the announcement of the
regulation change happened in late 2006, and firms may have pre-empted the regulation change
and modified their disclosure strategies accordingly in the years 2007 and 2008. In order to
minimize the noise coming from the data, I restrict my pre-adoption sample to include only
observations from 2006. Because my measure of investment requires one-year ahead Compustat
data, due to the data availability, my post-adoption sample only consists of observations from the
year 2009. For the pre-adoption sample, the internal control weaknesses data are hand collected
from annual reports downloaded from SEDAR.com for firms with year ends immediately
following June 29, 2006. For the post-adoption sample, the internal control weaknesses data are
19
hand-collected from the same source for firms with year ends immediately following Dec 15,
2008.
The 2006 Compustat data file consists of 1,230 active Canadian firms. The requirements
for all the control variables substantially reduce my sample to 603 firms. In my main tests, I
restrict my sample to non-financial firms, which further reduces the sample to 533 firms. I
deleted firms that are cross-listed in the U.S. stock exchange because these firms are subject to
the SOX 302 and SOX 404 requirement. My final sample from the year 2006 consists of 457
firms. Similarly, the 2009 Compustat data file consists of 1,112 active Canadian firms. The
requirements for all the control variables reduce my sample to 499 firms. Excluding the financial
firms further reduces the sample size to 420 firms. After deleting the cross-listed firms, my final
sample from the year 2009 consists of 359 firms. The details of the Canadian sample selection
procedure are reported in Table 2, Panel A. The rest of the dependent and independent variables
are from the COMPUSTAT (firms’ financial information), IBES (analyst coverage data),
Thomson Reuters (institutional holdings data), First Call (Management earnings forecast data),
and CRSP (firm age data) database.
[INSERT TABLE 2 HERE]
The ICW disclosure for the U.S. sample is obtained from the Audit Analytics database
where I use the ICW disclosed in the SOX 404 report as the measure of a firm’s internal control
weaknesses.4 For each firm-year, the dummy variable ICW equals 1 if the Audit Analytics 404
dataset identifies that there exist material weaknesses in a firm’s quarterly or annual reports; and
0 otherwise. The sample period is from 2004 to 2009. My U.S. sample starts from year 2004
because SOX 404 has been imposed on the accelerated filers since 2004. Table 2, Panel B
4Audit Analytics keeps two separate datasets for ICW disclosure. One is under the disclosure regulation SOX 302.
The other one is under SOX 404. Few differences have been found between these two datasets.
20
provides details concerning the U.S. sample selection procedures. The Audit Analytics database
originally contains 39,332 firm-year observations. Among them, 20,745 are accelerated filers.
The requirements for all the control variables reduce the sample to 13,222 firm-year
observations. The number of firms in each year ranges from 2,300 to 2,405 from 2004 to 2009.
CHAPTER 5 RESEARCH DESIGN
5.1 Measures of Investment Efficiency
Following the prior literature (Fazzari, Hubbard, and Petersen 1988; Hoshi, Kashyap, and
Scharfstein 1991), I measure the investment efficiency by the investment cash flow sensitivity
after controlling for investment opportunities (Tobin’s Q, which is measured by the market-to-
book equity ratio). The underlying rationale is as follows. In the ideal world described in
Modigliani and Miller (1958), a firm should invest to the point when the marginal cost of
investment equals the marginal benefit. Without frictions from the market, firms should always
invest into the projects with positive NPV. In other words, the optimal investment level should
be driven by available opportunities only and not correlate with internally generated cash flow.
However, in reality, the existence of market frictions, such as adverse selection and moral
hazard, makes this efficient result almost impossible to achieve. According to Myers (1984),
when managers have private information that investors do not have, they may utilize this
information to time the issuance of the securities. Upon knowing this tendency, rational but
uninformed investors may respond by rationing capital ex ante. Thus, firms will need to rely on
internal funding, which increases the sensitivity of investment to cash. Moreover, under agency
theory, when the interests of managers are not perfectly aligned with investors, in order to pursue
21
perquisite consumption or other empire-building activities, managers may invest in projects with
negative net present value using excess cash left within the firm (e.g., Berle and Means, 1932;
Jensen and Meckling, 1976; Jensen 1986; Blanchard, Lopez-de-Silanes, and Shleifer 1994).
Suppliers of capital are likely to recognize this problem and to ration capital ex ante, which leads
to under-investment ex post (e.g., Stiglitz and Weiss 1981; Lambert et al. 2007). Investment will
be driven by the amount of cash on hand instead of the investment opportunities. Under this
situation, the sensitivity of investment to cash will increase as well.
The base model is as follows:
INVESTMENTt+1 = b0 +b1TOBINSQt +b2OCFt +et
(1)
where INVESTMENT is measured as the sum of research and development expenditure, capital
expenditure, and acquisition expenditure less cash receipts from sale of property, plant, and
equipment scaled by lagged total assets. TOBINSQ is a proxy for a firm’s investment
opportunity, measured as the market value of assets at the beginning of the period divided by the book
value of assets. Cash flow from operations, OCF, is measured as cash flow from operations scaled
by net property, plant and equipment. Coefficient β2 measures how sensitive a firm’s investment
level in time period t+1 is related to its cash flow from operations in time period t. The higher the
coefficient β2, the lower a firm’s investment efficiency is.
5.2 The Effects of Reliable ICW Disclosure on Investment Efficiency
The prior literature suggests that higher-quality financial reporting can improve
investment efficiency by mitigating information asymmetries that cause economic frictions such
as moral hazard and adverse selection (e.g., Leuz and Verrecchia, 2000; Bushman and Smith,
22
2001; Verrecchia, 2001). More specifically, if it is the case that financial reporting information is
used by shareholders to monitor managers (e.g., Bushman and Smith, 2001; Lambert, 2001),
higher quality financial reporting can improve investment efficiency by reducing moral hazard
problems. Financial reporting quality may also mitigate the adverse selection problem by
mitigating the information asymmetry between firms and outside investors. Consistent with this
view, Chang, Dasgupta and Hilary (2009) propose a model of dynamic adverse selection and
show empirically that firms with better financial reporting have greater flexibility to issue
capital. Similarly, if financial reporting quality helps to reduce adverse selection costs, it can
improve the investment efficiency through a reduction in external financing costs. Biddle and
Hilary (2006) find that firms with higher financial reporting quality exhibit higher investment
efficiency as measured by lower investment cash-flow sensitivity. Two related studies (Biddle et
al. 2009; and Chen et al. 2011) also show that financial reporting quality reduces firms’ over-
and under-investment in both U.S. and international settings, leading to a higher level of
investment efficiency and economic performance.
Using ICW disclosure to measure a firm’s financial reporting quality,5 the regression
model now becomes:
INVESTMENTt+1 = b0 +b1TOBINSQt +b2OCFt +b3ICWt +b4OCFt * ICWt +et
(2)
5Costello and Wittenberg-Moerman (2011) suggest that relying on internal control reports to measure reporting
quality has a number of important advantages over the reporting quality measures (e.g., accrual quality) used in prior
research. First, accruals models suffer from significant measurement error and are therefore likely to incorrectly
characterize a firm as having poor reporting quality (Dechow, Sloan and Sweeney 1995; Hribar and Collins 2002;
Hribar and Nichols 2007; Ball and Shivakumar 2008). Second, the magnitude of reported discretionary accruals is
usually strongly associated with a firm’s business model and production function. Therefore, the observed cross-
sectional differences in accruals measures are likely to be driven by firms’ fundamental properties, as opposed to
reporting quality per se. Finally, accruals quality measures usually only capture specific aspects of financial
reporting quality; internal control reports on the other hand are a more comprehensive and a direct measure of
financial reporting quality.
23
where ICW is the dummy variable that equals one if there is any internal control weakness
disclosed in a firm’s financial reports. When investment is sensitive to internally generated cash
flows, I expect a positive coefficient on the OCF variable or, in other words, the higher the
coefficient on OCF, the lower the investment efficiency. If ICW has a negative effect on
investment efficiency, I would expect b4, the coefficient on the interaction term on OCF*ICW, to
be positive.
Following the prior literature, I include control variables for other factors that are likely
to affect a firm’s investment choices. The final regression model I adopt to test my hypotheses
H1 to H3 is as follows:
INVESTMENTt+1 = b0 + b1ICWt + b2OCFt * ICWt + b3AQt + b4OCFt *AQt
+b5TOBINSQt + b6OCFt + b7INSTH t + b8COVERAGEt + b9SIZEt + b10LEVt
+b11ROEt + b12TANGIt + b13DIVIDENDt + b14STDCFt + b15STDSALEt
+b16OPCYCLEt + b17LOSSt + b18AGEt + b19FORECASTt + b20HERFt +et
(3)
The independent variables in model (3) can be classified into three groups. The first
group includes a variable that measures the availability of firms’ internally generated funds, the
cash flows from operations measure OCF. The second group includes economic factors that are
likely to affect the firm’s investment choices. Specifically, I control for Tobin’s Q (TOBINSQ),
Firm size (SIZE), Leverage (LEV), Return on equity (ROE), Standard deviation of cash flow
(STDCF), Standard deviation of sales (STDSALE), Tangibility (TANGI), Dividends
(DIVIDEND), Age (AGE), Operating cycle (OPCYCLE), Loss dummy (LOSS), and Herfindahl
indices (HERF). Among them, TOBINSQ measures the investment opportunities faced by the
firm. Prior literature has also shown that smaller firms, more levered firms, firms with lower
profitability, firms with dividend declaration, and more mature firms are expected to have fewer
investments. Herfindahl indices measure the industry competition, both for markets and
24
investments. The third group includes accounting variables. Among them, ICW is the variable of
interest. AQ controls for the general quality of accounting information disclosed in firms’
financial reports. I also include Institutional holdings (INSTH) and Analyst coverage
(COVERAGE) to control for corporate governance. In addition, Management earnings forecast
(FORECAST) is included in the model to control for firms’ voluntary disclosure.
I also include accrual quality (AQ), which is the absolute value of the discretionary
accrual estimated by a cross-sectional version of the modified Jones model with lagged ROA
included.6 A larger value of the measure indicates a lower accrual quality. I emphasize here that
efficient ICFR not only improves the quality of the accounting information disclosed through
periodic financial reports, but also improves the quality of the accounting information disclosed
through other channels. According to the Canadian Institute of Chartered Accountants’ (CICA)
report, in addition to its impact on financial reporting quality, the effectiveness of a firm’s ICFR
is highly correlated with its disclosure control and procedures (DCP) and affects the information
disclosed through conference calls, press releases, industry “road shows,” and even corporate
websites, etc. Information disclosed through all these channels could affect investors’
perceptions of the quality and reliability of the information disclosed by firms and, in turn,
affects their investment decisions. I therefore expect that disclosed ICW should have incremental
effects on investment efficiency even when an accrual quality measure is included.
5.3 The Effects of ICW on Investment Efficiency: The Role of Financing Constraints
While there is considerable debate about whether the investment-cash-flow sensitivity
actually captures investment efficiency (Fazzari, Hubbard, and Petersen 2000; Kaplan and
Zingales 2000), there is some consensus that this measure behaves differently in financially
6 I choose a modified Jones model here because it preserves the largest sample of observations. In the sensitivity
analysis section, I also use the modified Dechow-Dichev model as implemented by Francis, LaFond, Olsson and
Schipper (2005) to calculate the accrual quality.
25
constrained firms compared to how it behaves in unconstrained firms. To provide evidence on
whether financial constraints affect the relationship between investment and internal cash flows,
I partition the sample into firms that are more versus less likely to have financial constraints
using the index developed by Kaplan and Zingales (1997). Kaplan and Zingales (1997) examine
the annual reports of the 49 firms in Fazzari et al.’s (1988) “constrained” sample and rate the
firms on financial constraints scales from one to four. They run an ordered logit of this scale on
observable firm characteristics. Their index is calculated as:
KP Index = – 1.001909 * CF + 3.139193 * TLTD – 39.36780 * TDIV
– 1.314759 * CASH + 0.2826389 * Q, (4)
where CF is the ratio of cash flow to book assets, TLTD is the ratio of total long-term debt to
book assets, TDIV is the ratio of total dividends to book assets, CASH is the ratio of the stock of
cash to book assets, and Q is the market-to-book ratio. For each firm in our sample, I calculate
this index and then partition the sample into two groups at the median of the distribution. I then
rerun model (3) on both financially constrained and unconstrained groups.
CHAPTER 6 EMPIRICAL RESULTS
6.1 Descriptive Statistics
[INSERT TABLE 3 HERE]
Table 3, Panel A and Panel B provide descriptive statistics for both the U.S. and the
Canadian samples. The mean value of the internal control weaknesses disclosure dummy (ICW)
is 0.079 for the U.S. full sample, which means that 7.9% of the sample has ICW reported under
SOX 404 from the AuditAnalytics database. This mean is 24.0% and 13.1% for the Canadian
sample in the years 2006 and 2009, respectively. The differences of the means could come from
26
the actions taken by firms to remediate the disclosed ICW after it was disclosed for the first time
in 2006. It is also likely that firms with more ICW delisted or went bankrupt during this sample
period. In addition, it is worthwhile to note that the U.S. companies are in general larger in size
than the Canadian firms. The mean of the size of the firms in the U.S. sample is 6.752. The
numbers are 5.452 and 5.890 for the Canadian sample in the years 2006 and 2009, respectively.
This justifies the matching sample setting I used in testing my H2 and H3. Most of the other
variables are comparable for the U.S. and Canadian samples and for the Canadian sample across
different years. Panel C of Table 3 provides descriptive statistics of the U.S. matching sample.
For each Canadian firm, one U.S. firm is matched with it in size in the same industry (3-digit
SIC code) and year. After matching, the size difference between the U.S. and Canadian samples
is significantly reduced. The t-test on the sample mean differences shows that the difference is
not significant.
6.2 Cost of Capital Change from the Pre-adoption Period to the Post-adoption Period
The literature shows that firms with internal control deficiencies have a significantly
higher cost of capital (Ashbaugh-Skaife et al. 2009; Costello and Wittenberg-Moerman 2011). In
both the equity and debt markets, investors avoid (or expect higher returns from) investing in
firms with internal control weaknesses. This paper is investigating whether investors and
management respond to the ICW disclosure regulation change, leading to an increase in the
perceived reliability of disclosed ICW. The focus of the paper is not to disentangle the investors’
response to the regulation change from that of the management, but I first provide some evidence
showing that equity investors indeed respond to the ICW disclosure regulation change in Canada.
[INSERT TABLE 4 HERE]
27
Table 4 reports the mean of the cost of capital change in the Canadian sample from the
pre-adoption period to the post-adoption period. In each period, firms are further divided into
two sub-samples: firms with ICW (ICW sample) and firms without ICW (non-ICW sample). In
Panel A, cost of capital (CofC) is calculated following Easton (2004).
RPEG = [(eps2-eps1)/P0]1/2
(5)
where eps1 or (eps2) is the one-year or two-year ahead forecasted earnings per share, and P0 is
equal to the current price. The calculation of the cost of capital following (5) imposes the
requirements that both eps1 and eps2 are positive, and that eps2 needs to be greater than eps1,
which significantly reduces the number of observations. The mean value of CofC is 10.61% for
firms with ICW disclosed in their annual reports in the pre-adoption period, and 12.79% in the
post-adoption period. For firms with zero ICW, the mean value of CofC is 10.02% and 9.21% in
the pre- and post- adoption periods, respectively. For firms with ICW, the mean of CofC in the
post-adoption period is 2.18% higher than that in the pre-adoption period (with a t-value of 2.79,
p-value of 0.006). For firms without ICW, the mean of CofC in the post-adoption period is not
significantly different from the mean of CofC in the pre-adoption period (a difference of -0.81%
with a t-value of -1.03, p-value of 0.306). The result shows that, after the adoption of the new
rule, the cost of capital increases only for firms with ICW disclosed in their annual reports. In the
pre-adoption period, the mean difference of CofC between firms with ICW and firms without
ICW is 0.59% with a t-value of 0.42 (p-value of 0.676). In the post-adoption period, the mean
difference of CofC is 3.58% with a t-value of 4.17 (p-value < 0.001), which suggests that
investors respond more strongly to the ICW disclosure in the post-adoption period than to that in
the pre-adoption period.
28
As the requirement imposed in calculating the cost of capital following (5) significantly
reduces the sample size, I use an alternative method that imposes less stringent restrictions on
the data to calculate the cost of capital (CofC). Following Lyle, Callen and Elliott (2011), firms’
cost of capital RLCE, t+1 is calculated as follows:
RLCE, t+1 = α + η1/St + η2 (Bt/St) + η3 (xt/St) + η4 (Et [xt+1]/St) + η5 (Dt/St) + εt+1,
(6)
where St is the price per share, Bt is the book value per share, xt is the earnings before
extraordinary items, Dt is the dividends per share, Et[xt+1] is the IBES consensus forecast for one-
year-ahead earnings. Following Lyle, Callen and Elliott (2011), the coefficients α, η1, η2, η3, η4
and η5 are obtained from the Fama-Macbeth cross-sectional regression using the Canadian
sample from 1986 to 2005. RLCE is then calculated for the pre- and post-adoption periods using
the regression coefficients estimated. Panel B of Table 4 presents the results.
Similar to the results reported in Panel A, the mean difference of CofC between firms
with ICW and firms without ICW is -0.08% with a t-value of -0.03 (p-value of 0.976) in the pre-
adoption period, and 2.37% with a t-value of 3.92 (p-value < 0.001) in the post-adoption period.
The mean difference of CofC from the pre-adoption period to the post-adoption period is 2.56%
with a t-value of 4.04 (p-value < 0.001) for firms with ICW, and 0.07% with a t-value of 0.04 (p-
value of 0.968) for firms without ICW.
6.3 Tests of H1a: The Negative Association between the Disclosed ICW and Investment
Efficiency Using the U.S. Sample
[INSERT TABLE 5 HERE]
Using the U.S. sample from 2004 to 2009, I first test my hypothesis H1a: When disclosed
ICW is reliable, there exists a negative association between disclosed ICW and investment
29
efficiency. Table 5 reports the regression results. It shows that the estimated coefficient on the
interaction term OCF * ICW is significantly positive with a t value of 3.54, which is consistent
with the prediction that firms with less effective ICFR (or firms with ICW) have a higher level of
investment cash-flow sensitivity, or less investment efficiency. This result is economically
significant; on average, the investment cash-flow sensitivity for firms with ICW is about twice
the investment cash-flow sensitivity for firms without ICW.7 In this regression, I also control for
the accrual quality, AQ. The significantly positive coefficient (0.001 with a t value of 4.25) on
the interaction term AQ*ICW confirms the results in Biddle and Hilary (2006) where they find
that the investment cash-flow sensitivity is higher for firms with lower accounting quality.
However, the results also indicate that the impact of ICW on a firm’s investment efficiency are
not fully subsumed by the impact of accounting quality on investment efficiency. All the t-
statistics are calculated using clustered standard errors at the firm and year levels.
6.4 The Effects of ICW on Investment Efficiency: The Role of Financial Constraints
To mitigate the concern that the investment cash-flow sensitivity may behave differently
between financially constrained and unconstrained firms, I partition the sample into firms that
are more versus less likely to have financial constraints using the index developed by Kaplan and
Zingales (1997).
[INSERT TABLE 6 HERE]
Table 6 reports the results of the effect of ICW disclosure on investment for both
financially constrained and unconstrained firms using the U.S. sample. For both the financially
constrained and unconstrained sub-samples, ICW disclosers have lower investment cash-flow
7 I obtain this estimate by using the sum of the coefficient on OCF*ICW (0.004), the coefficient on OCF (0.003) and
the coefficient on OCF*AQ multiplied by the mean of AQ for firms with disclosed ICW (0.001*0.127) divided by
the coefficient on OCF (0.003) and the coefficient on OCF*AQ multiplied by the mean of AQ for firms without
disclosed ICW (0.001*0.193).
30
sensitivity (or higher investment efficiency) compared to non-disclosures. The coefficients on the
interaction term OCF*ICW have a value of 0.002 and 0.005 (with a t-value of 2.51 and 2.42; and
a p-value of 0.012 and 0.016) for financially constrained and unconstrained firms, respectively.
6.5 Tests of H1b: The Effects of ICW on Investment Efficiency for Canadian Firms in the
Pre- and Post-adoption Periods
Table 7 presents results of the effect of ICW on investment using the Canadian sample
starting in 2006, the first year in which all the Canadian public companies were required to
disclose their internal control design assessment in the MD&A section of firms’ financial reports.
This is the pre-adoption period in my study. At the end of 2008, the implementation
effectiveness tests and the CEO/CFO certification requirement became mandatory. I therefore
chose 2009 – the first year after the implementation effectiveness and CEO/CFO certification
were required – as my post-adoption period.
[INSERT TABLE 7 HERE]
The results in the first two columns of Table 7 show that in the pre-adoption period, the
coefficient on the interaction term OCF*ICW is positive, but not significant, which is consistent
with my prediction: since the disclosed ICW in Canada lacks credibility, the negative relation
between ICW and investment efficiency is weaker than the association documented in the U.S.
sample. In contrast, in the post-adoption period, after the credibility enhancement mechanisms
were imposed, the positive coefficient on OCF*ICW becomes significant, as shown in the third
and fourth columns of Table 7.
To test my hypothesis H1b, I first pool the Canadian firms over the pre- and post-
adoption periods together and add a year dummy D_2009, which generates a sample size of 816
(457 from the pre-adoption period and 359 from the post-adoption period). I then interact year
31
dummy D_2009 with ICW and OCF*ICW. The change in the relation between ICW and
investment efficiency between pre- and post-adoption periods should be captured by the
coefficient on D_2009*OCF*ICW. As shown in the fifth and sixth columns of Table 7, the
coefficient on D_2009*OCF*ICW is significantly positive with a value of 0.020 (t-value of 2.09,
p-value of 0.037), consistent with my prediction in H1b that the negative relation between ICW
and investment efficiency is stronger in the post-adoption period than in the pre-adoption period.
Due to the financial crisis that happened in 2007, many Canadian firms were acquired or
went bankrupt, which leads to quite a significant difference of sample firms between pre- and
post-adoption periods. In order to control for the differences of the samples, I further test H1b by
restricting my analysis to the same group of firms that appears in both the pre- and the post-
adoption samples. This restriction generates a sample of 502 firm-year observations (251 firms).
The last two columns of Table 7 present the results of this analysis. Similar to that of the fifth
and sixth columns, the positive coefficient on D_2009*OCF*ICW (0.004 with a t-value of 2.00
and p-value of 0.046) shows that the relation between ICW and investment inefficiency
strengthened in the post-adoption period.
6.6 Tests of H2 and H3: The Effects of ICW on Investment Efficiency – U.S. vs. Canada
As shown in the descriptive statistics part, the distributions of the size of the firms are
quite different between the U.S. and Canadian samples. In order to mitigate the concern that the
investment opportunities and strategies are quite different for big firms compared to small firms,
I first match the U.S. sample to the Canadian sample to create a control group when testing my
H2 and H3. For each Canadian observation, I match a U.S. firm on size in the same industry (3-
32
digit SIC code) and year. The final sample consists of 914 firms (457 pairs) in year 2006 and 718
firms (359 pairs) in year 2009.8
[INSERT TABLE 8 HERE]
In the regression model, I add a country dummy D_CAN which equals 1 for Canadian
firms, and 0 otherwise. The coefficient on D_CAN*OCF*ICW captures the country-level
difference in the relation between ICW and investment efficiency. The first two columns of
Table 8 reports the regression coefficients for testing H2, and the third and fourth columns of the
Table report the regression coefficients for testing H3. The significantly negative coefficient on
D_CAN*OCF*ICW (-0.041 with a t-value of -3.09 and p-value of 0.002) shows that, compared
to the U.S. sample, the relation between ICW and investment efficiency is significantly smaller
in Canada in the pre-adoption period. F-test (with F-value of 1.41 and p-value of 0.249) results
show that for Canadian firms in the pre-adoption period, the relation between ICW and
investment efficiency is not significantly different from 0. On the other hand, the relation
between ICW and investment efficiency for Canadian firms in the post-adoption period is also
significantly smaller than that of the U.S. sample in the same period, as indicated by a
significantly negative coefficient on D_CAN*OCF*ICW (-0.020 with a t-value of -4.00 and p-
value < 0.001).
In order to mitigate the concern that there may be other confounding factors that lead to
the change in the relation between ICW and investment efficiency from the pre-adoption period
to the post-adoption period, I pool all the matched sample observations in Table 8 in both the
pre- and post-adoption periods together. This model simultaneously tests H1b, H2, and H3 and
also allows the use of differences-in-differences approach. Table 9 reports the regression results.
[INSERT TABLE 9 HERE]
8 Following Cram, Karan and Stuart (2007), I include a pair-wise dummy in all my matched regressions.
33
Table 9 Supplementary
Pre-adoption
Post-adoption
Difference
U.S. (1)
(1)+(3)
(3)
Canada (1)+(2)
(1)+(2)+(3)+(4)
(3)+(4)
Diff (2)
(2)+(4)
(4)
Referring to Table 9 Supplementary above, testing H1b is equivalent to testing if the
coefficient on D_CAN*D_2009*OCF*ICW, or (4) is significantly positive. Similarly, testing H2
is equivalent to testing whether the coefficient on D_CAN*OCF*ICW, or (2) is significantly
negative. Finally, testing H3 is equivalent to testing if the sum of (2) and (4), or the sum of the
coefficients on D_CAN*OCF*ICW and D_CAN*D_2009*OCF*ICW is significantly negative.
As shown in Table 9, the results are consistent with my predictions. The coefficient on
D_CAN*D_2009*OCF*ICW is 0.017 (with a t-value of 3.44, p-value < 0.001). The coefficient
on D_CAN*OCF*ICW is -0.039 (with a t-value of -5.18, p-value < 0.001). F-test results (with F-
value of 19.19; p-value < 0.001) indicate that (2)+(4) is significantly negative, implying that
even in 2009, after the implementation of the credibility enhancement mechanism, due to a lack
of an external audit, the credibility of the disclosed ICW is still inferior compared to that of the
U.S.
Taken together, the results in Table 8 and Table 9 show that in the Canadian setting, the
predicted negative association between ICW and investment efficiency is notably weaker in the
pre-adoption period, compared to that of the U.S. sample. After the implementation effectiveness
test and CEO/CFO certification of ICW disclosures became mandatory, the negative association
significantly strengthened. However, the negative association is still weaker than that of the U.S.
6.7 Additional Analysis
34
6.7.1 Separating the effect of implementation effectiveness tests from the effect of
CEO/CFO certification
Since the two new requirements (implementation effectiveness tests and CEO/CFO
certification) were introduced together by the new rule, I was not able in my previous tests to
separate the effect of each on the reliability of firms’ disclosed ICW. However, in the U.S., SOX
required non-accelerated filers to file management certification of ICFR only after December 15,
2007. In addition, no external audit on ICFR has been required, which provides a setting for
testing how each of the new requirements impacts the reliability of the disclosed ICW. I run a
test similar to that in Table 9 except that the U.S. sample now consists of only non-accelerated
filers.
[INSERT TABLE 10 HERE]
Table 10 Supplementary
Pre-adoption
Post-adoption
Difference
U.S. (1)
(1)+(3)
(3)
Canada (1)+(2)
(1)+(2)+(3)+(4)
(3)+(4)
Diff (2)
(2)+(4)
(4)
Since in the pre-adoption periods, only U.S. non-accelerated filers are required to conduct
implementation tests, while in the post-adoption periods, both the U.S. non-accelerated filers and
the Canadian firms are required to conduct implementation tests and file CEO/CFO certification
on firms’ ICFR; referring to Table 10 Supplementary above, coefficient (4) captures the effect of
implementation tests on the reliability of the disclosed ICW. In the post-adoption period, because
external audits are not required in both the U.S. non-accelerated and in the Canadian samples, I
expect no differences between the two samples in terms of the reliability of firms’ disclosed
ICW. In other words, (2)+(4) should not be significantly different from zero. As shown in Table
35
10, the results are consistent with my predictions. Coefficient (4) is significantly positive with a
value of 0.024 (t-value is 3.18, p-value is 0.002). F-test results (with F-value of 1.26 and p-value
of 0.275) indicate that (2)+(4) are insignificantly different from zero.
6.7.2 The Relation between ICW Type and Investment Efficiency in the Post-adoption
Period
When Canadian firms report their internal control assessment in the MD&A section of
the financial reports, they provide the details of the weaknesses. By hand-collecting and coding
financial reports from SEDAR.com, I classify the internal control weaknesses reported by the
Canadian firms into six types (the percentage of each type is reported in the parentheses: Lack of
segregation of duties (SD, 31.91%), Lack of accounting knowledge or expertise (LAKE,
21.28%), Lack of other knowledge or expertise (LOKE, 17.02%), Accounting system problems
(ASP, 10.64%), Information system problems other than accounting (ISP, 6.38%), and Other
problems (OTH, 12.77%). I further investigate whether the negative relation between reliable
ICW disclosure and investment efficiency depends on the types of ICW.
[INSERT TABLE 11 HERE]
Table 11 reports the regression results exploring the relation between different types of
ICW and investment efficiency using the post-adoption period Canadian sample. Among all the
six types of ICW, Lack of segregation of duties (SD) and Lack of accounting knowledge or
expertise (LAKE) are the two types of ICW that are significantly negatively related to a firm’s
investment efficiency. The coefficient on OCF*SD is 0.019 with a t-value of 1.89 (p-value is
0.060). The coefficient on OCF*LAKE is 0.010 with a t-value of 1.69 (p-value is 0.092).
6.7.3 The Impact of Human Capital Investment in Accounting on the Relation between
ICW and Investment Efficiency
36
When an external audit is not required in the disclosure of ICW, the detection of ICW
primarily depends on the internal resources devoted to the internal control process. As shown in
Table 11, the two types of ICW that significantly affect investment efficiency are those related to
a firm’s investment in obtaining and training accounting personnel. Since the detection of ICW
greatly depends on whether the company has adequate staffing with accounting knowledge and
expertise for accurately recording and monitoring the financial reporting process, a firm’s human
capital investment in accounting could be important for the credibility of the ICW disclosure in
reflecting that firm’s effectiveness of ICFR.
Here, following the prior literature (Lu et al., 2010), I use AB_ NPAP which measures the
abnormal number of professional accountants (CA, CMA, CGA) hired by the firm and as a
proxy for the resources devoted to a firm’s ICFR, where AB_NPAP is the residuals from the
regression of the logarithm of the number of professional accountants on the measures of firm
size, risk, complexity. The model used to calculate AB_NPAP is as follows:
( )
(7)
where SIZE is the logarithm of total assets. LEV is measured as long-term debt divided by total
assets. ROE is return on equity. TANGI is the tangibility of firms’ assets. INVENTORY is the
inventory scaled by total assets. RECEIVABLE is the total receivable scaled by total assets. AGE
is the logarithm of the number of years that the firm has been publicly traded. BUSSEG is the
number of business segments. GEOSEG is the number of geographic segments.
[INSERT TABLE 12 HERE]
37
I then interact AB_ NPAP with OCF*ICW. I expect that firms with more human capital
investment in accounting have a more effective ICFR. In other words, Using the Canadian data
in the pre-adoption period when the credibility of the disclosed ICW is not guaranteed, I expect
the coefficient on the interaction term OCF*ICW* AB_NPAP to be positive. Consistent with my
expectation, results shown in Table 12 report a positive and significant coefficient with a value
of 0.005 (t-value of 1.92, p-value of 0.055) on OCF*ICW* AB_NPAP.
6.7.4 Audit Fee
Hogan and Wilkins (2008) suggest that, all else equal, firms with reliable ICW
disclosures pay lower audit fees than firms withholding disclosure of ICW. Following Hogan and
Wilkins (2008), I extend my analysis to the Canadian sample. Specifically, I examine whether
Canadian firms with reliable ICW disclosures pay lower audit fees. As explained, in the pre-
adoption period, the silent group (firms with 0 ICW disclosed to the public) contains the truth-
telling firms (i.e., firms that have no ICW) and the lying firms (i.e., firms that have ICW, but
choose to withhold disclosing ICW to the public). In the post-adoption period, the silent group is
expected to have only the truth-telling firms. I therefore expect the silent group of the pre-
adoption period has more reliable ICW disclosure than the silent group of the post-adoption
period. I first calculate the mean of the audit fees for firms without ICW (i.e., ICW=0) in the pre-
and post-adoption periods. In the pre-adoption period, the mean of the audit fees is 963,893. The
number is 891,382 in the post-adoption period. The mean difference test (post – pre) has a t-
value of -5.61. It suggests that the firms with more reliable ICW disclosure pay lower audit fees,
consistent with Hogan and Wilkins (2008).
38
Pooling the silent groups of the pre- and post-adoption periods together, I then conduct a
multivariate analysis on audit fees. Following Hogan and Wilkins (2008), the estimated model is
as follows:
4
2009_
121110987
6543210
BIGLOSSMAGROWTHAQROAVAR
ROADAQUICKINVRECSIZEDLNFEE
(8)
where LNFEE is the natural logarithm of the audit fee. SIZE is the natural logarithm of total
assets. D_2009 is a dummy variable which equals to 1 for year 2009; and 0 otherwise. INVREC
is inventory and receivables divided by total assets. QUICK is the ratio of current assets minus
inventories to current liabilities. DA is long-term debt divided by total assets. ROA is operating
cash flow divided by lagged total assets. GROWTH is the one-year percentage growth in sales.
AQ is the accrual quality measured as the absolute value of the discretionary accrual estimated
by a cross-sectional version of the modified Jones model with lagged ROA included. MA is a
dummy variable which equals to 1 if firm was involved in mergers and acquisitions activities;
and 0 otherwise. LOSS is a dummy variable which equals to 1 if net loss is reported; and 0
otherwise. BIG4 is a dummy variable which equals to 1 if Big 4 auditor is used; and 0 otherwise.
The variable of interests is D_2009. The coefficient 12 is expected to be negative.
[INSERT TABLE 13 HERE]
Table 13 reports the regression results. Consistent with the expectation, 12 is significantly
negative.
6.8 Sensitivity Analysis
6.8.1 Distressed Firms
In order to mitigate the concern that the investment strategy of the distressed firms may
differ from the rest of the sample, I further exclude the distressed firms from the main tests.
39
[INSERT TABLE 14 HERE]
Similar to Table 9, Table 14 presents the relation between disclosed ICW and investment
efficiency, where distressed firms are excluded from the sample. For each Canadian firm in the
sample, the Altman Z-score is calculated as
Z=1.2*T1 + 1.4T2 +3.3T3 + 0.6T4 + 0.999T5, (9)
where T1 is defined as the working capital divided by total assets. T2 is defined as the retained
earnings divided by total assets. T3 is defined as the earnings before interest and taxes divided by
total assets. T4 is defined as the market value of equity divided by total liabilities. T5 is defined as
sales divided by total assets. The distressed firms are the firms with Z-score at the top quintile in
the pre- and post-adoption periods, respectively. After deleting all the distressed firms, for each
Canadian observation, I match a U.S. accelerated firm on size in the same industry (3-digit SIC
code) and year. The final sample consists of 732 firms (366 pairs) from year 2006 and 574 firms
(287 pairs) from year 2009. In total, the number of firm-year observations is 1,306. The results in
Table 14 are similar to those in Table 9. The coefficient on D_US*D_2009*OCF*ICW is 0.019
with a t-value of 3.78 (p-value < 0.001). The F-test result (with F-value of 15.83 and p-value <
0.001) indicates that (2)+(4) is significantly negative. Taken together, the main results stay
robust after excluding the distressed firms.
6.8.2 Alternative Investment Efficiency Model
Instead of using the investment cash-flow sensitivity to measure a firm’s investment
efficiency, following Biddle et al. (2009), I adopt an alternative method which measures
investment efficiency as deviations from expected investment using a model which predicts
investment as a function of growth opportunities. The piecewise linear regression model adopted
is as follows:
40
INVESTMENTt+1 = α0 + α1 NEGt + α2 %REVGROWTHt + α3 NEGt * %REVGROWTHt + εt
(10)
where INVESTMENT is the sum of research and development expenditure, capital expenditure,
and acquisition expenditure less cash receipts from sale of property, plant, and equipment scaled
by lagged total assets. NEG is an indicator variable which is equal to 1 for negative revenue
growth; and 0 otherwise. %REVGROWTH is the revenue growth percentage. The investment
model is estimated cross-sectionally with at least five observations in each industry by year. Both
underinvestment (negative deviations from expected investment) and overinvestment (positive
deviations from expected investment) are inefficient investments. Investment efficiency
INVESTEFF is defined as the absolute values of the residuals from the investment model (10).
The higher the variable “INVESTEFF”, the less efficient a firm’s investment. The regression
model is as follows:
t
t
ttttt
tttttt
tttttt
ICWDCAND
ICWDICWCANDDCANDHERF
FORECASTAGELOSSOPCYCLESTDSALE
STDCFDIVIDENDTANGIROELEVSIZE
COVERAGEINSTHTOBINSQAQICWINVESTEFF
*2009_*_
*2009_*_2009__
22
2120191817
1615141312
11109876
5432101
(11)
[INSERT TABLE 15 HERE]
The regression results are presented in Table 15. The coefficient on
D_CAN*D_2009*ICW is significantly positive with a value of 0.129 (t-value is 4.39, p-value <
0.001), which suggests that compared to the control group (U.S. accelerated filers), the negative
association between Canadian firms’ disclosed ICW and their investment efficiency is stronger
in the post-adoption period. The coefficient on D_CAN*ICW is significantly negative with a
41
value of -0.140 (t-value is -2.19, p-value is 0.029) which is consistent with my prediction that in
the pre-adoption period, the negative association between ICW and investment efficiency is
weaker in Canada than that in the U.S..
6.8.3 Cross-listing Firms
Canadian firms cross-listed on U.S. exchanges are required to disclose ICW under SOX
302 and SOX 404. During my sample period (from 2006 to 2009) when the Canadian ICW
disclosure regulation had a significant change, the ICW disclosure regulation (SOX 302 and
SOX 404) for these cross-listing firms stayed the same. I therefore use the cross-listing firms as
my alternative control and repeat a similar test as the one reported in Table 9. Due to the small
sample size of the cross-listing firms in both the pre- and post-adoption periods (76 and 61,
respectively), I am not able to match the cross-listing firms to my sample firms. Instead, I add an
indicator variable D_CROSS to the model, where D_CROSS equals 1 for Canadian firms that are
cross-listed in the U.S.; and 0 otherwise. The final sample consists of 533 firms from 2006 and
420 firms from 2009.
[INSERT TABLE 16 HERE]
Table 16 presents the regression results. The coefficient on D_2009*OCF *ICW is 0.027
with a t-value of 3.80, which provides evidence that the disclosed ICW of my Canadian sample
firms becomes more reliable in the post-adoption period. The F-test result (with F-value of 11.69
and p-value < 0.001) indicates that (2)+(4) is significantly positive. It suggests that, compared to
the cross-listing firms, the reliability of the disclosed ICW of my Canadian sample firms is still
inferior.
6.8.4 Investment History
42
In theory, a firm’s current and past investment activity is one of the most important
determinants of its future earnings, which in turn affects the firm’s future investment activity
(Aivazian and Callen 1979; Bar-Yosef, Callen and Livnat 1987). I therefore re-estimate the
investment efficiency model after adding current and lagged investment to Equation (1).
[INSERT TABLE 17 HERE]
Table 17 reports the regression results. The additional control (INVESTMENTt and
INVESTMENTt-1) reduces the sample size to 1,492. However, the results in Table 17 are similar
to those of the main test specifications as shown in Table 9, and no inferences are affected.
6.8.5 Alternative Accrual Quality Measure
Instead of using the modified Jones’ model to estimate a firm’s non-discretionary accrual
level, I use the modified Dechow-Dichev model as implemented by Francis, LaFond, Olsson and
Schipper (2005) to calculate the firms’ accrual quality (AQ). Specifically, the following model is
estimated for each industry (3-digit SIC code) that has at least 5 observations:
TCAi,t = α0 + α1 OCFi, t-1 + α2 OCFi,t + α3 OCFi, t+1 + α4 ΔRevi,t + α5 PPEi,t + εi,t
(12)
where TCAi,t is total accruals which is measured as the change in non-cash current assets minus
the change in current non-interest bearing liabilities, minus depreciation and amortization
expense for firm i at year t, scaled by lagged total assets. OCFi, t-1 is cash flow from operations
for firm i at year t-1. Similarly, OCFi, t (OCFi, t+1) is cash flow from operations for firm i at year t
(t+1). ΔRevi,t is the annual change in revenues. PPEi,t is property, plant, and equipment. Accrual
quality (AQ) is then calculated as the absolute residual from estimating Equation (12).
[INSERT TABLE 18 HERE]
43
The empirical results are presented in Table 18. As shown, the alternative accrual quality
measure does not change the main test results; no inferences are affected.
6.8.6 Potentially Biased Measures in the Investment Model
In Equation (3), there are several accounting-based control variables (i.e., AQ, LEV,
ROE, LOSS, TANGI and OPCYCLE) which are likely to be affected by the quality of firms’
ICFR. Including these potentially biased measures in the invest model could possibly bias the
regression results. In order to mitigate this possibility, I exclude these accounting-based control
variables.
[INSERT TABLE 19 HERE]
The empirical results are presented in Table 19. As shown, excluding these variables does
not change the main test results; no inferences are affected.
6.8.7 M&A and ICW
It is likely that some other firm-characteristics which are associated with ICW could also
potentially correlate with INVESTMENT. Prior research indicates that firms engage in M&A
activities are more likely to have ICW. M&A is also known to affect firms’ investment
decision/efficiencies. In order to mitigate concerns of such correlated omitted variable problems,
I include a dummy variable MA into the regressions. MA equals to 1 if the firm engaged in any
mergers and acquisitions activities in that year; and 0 otherwise. The untabulated results show
that including MA dummy does not change the main test results; no inferences are affected.
CHAPTER 7 CONCLUSION
This paper investigates whether changes in internal control disclosure regulation affect
the reliability of firms’ disclosed ICW. Using the magnitude of the negative association between
a firm’s disclosed ICW and its investment efficiency to infer the reliability of the disclosed
44
internal control weaknesses, I find strong evidence indicating that the credibility enhancement
mechanisms (i.e., the implementation effectiveness evaluation and CEO/CFO certification)
adopted at the end of 2008 take effect and improve the reliability of Canadian firms’ disclosed
ICW. Using the U.S. sample as a benchmark, I find that the negative association between
Canadian firms’ disclosed ICW and their investment efficiency is weaker in the pre-adoption
period due to a lack of implementation effectiveness tests, CEO/CFO certification and external
auditors’ audit over firms’ ICW disclosures., Even in the post-adoption period, because external
auditors are never required to audit firms’ disclosed ICW, I find that the negative association
between Canadian firms’ disclosed ICW and their investment efficiency is still weaker than that
of the U.S. sample, consistent with my prediction that the external audit requirement increases
the reliability of the disclosed ICW.
This paper contributes to the accounting regulation literature. First, this study adds to the
internal control literature. Utilizing the unique evolutionary regulation change in Canada, my
study shows that the reliability of firms’ disclosed ICW is not always guaranteed. It could vary
with the tightness of the ICW disclosure regulation and/or other reliability factors, which could
affect the inferences drawn from previous studies. This paper also adds to the debate over the
enhancement mechanism in ICW disclosures. The evolutionary process of the Canadian ICW
disclosure regulation provides a unique natural experimental setting for investigating the impact
of required implementation effectiveness evaluation and CEO/CFO certification on the reliability
of firms’ disclosed information. Given the lack of control implementation testing, CEO/CFO
certification, and external audit for these ICW disclosures, it is questionable whether the
disclosed ICW reflect the firms’ true quality of internal control over financial reporting and
whether such information is equally useful to investors, as compared to in the U.S. setting. My
45
study suggests that the lack of implementation effectiveness test and CEO/CFO certification
impairs the reliability of disclosed ICW. Finally, by showing that internal control disclosure
regulation change does affect the reliability of firms’ disclosed ICW, I provide evidence that
accounting regulation is necessary in facilitating credible financial reporting.
46
APPENDIX: Variable Definitions
Variable Names Definitions and Estimations
INVESTMENT
The sum of research and development expenditure, capital expenditure, and
acquisition expenditure less cash receipts from sale of property, plant, and
equipment scaled by lagged total assets.
ICW
An indicator variable, for the Canadian sample, 1 if there is at least one internal
control weakness self-reported by the firm in its Management Discussion &
Analysis, 0 otherwise. These data are collected by hand from annual reports. For
the U.S. sample, 1 if AuditAnalytics 404 data reports at least one material
weakness in the firm’s quarterly or annual reports, 0 otherwise.
AQ
The absolute value of the discretionary accrual estimated by a cross-sectional
version of the modified Jones model with lagged ROA included. The larger the
value, the lower the accrual quality.
TOBINSQ
Market value of assets at the beginning of the period divided by the book value of
assets.
OCF
Cash flow from operations scaled by the book value of assets.
INSTH
Percentage of institutional holdings reported by the Thomson Reuters Ownership
Database at the beginning of the year.
COVERAGE
Log of the number of analysts following the firm as reported by IBES.
SIZE
Logarithm of total assets.
LEV
Long-term debt divided by total assets.
ROE
Return on equity. Net income divided by average total equity.
TANGI
The tangibility of the firm's assets, measured as the ratio of PPE to total assets.
DIVIDEND
An indicator variable, 1 if the firm paid a dividend, 0 otherwise.
STDCF
Standard deviation of cash flow from operations deflated by average total assets
from year t-5 to year t-1.
STDSALE
Standard deviation of sales deflated by average total assets from year t-5 to year t-
1.
OPCYCLE
The operating cycle, measured as the log of receivables to sales plus inventory to
COGS multiplied by 360.
LOSS
An indicator variable, 1 if the average of earnings before extraordinary items in
fiscal years 2005 and 2006 is negative, 0 otherwise.
AGE
The logarithm of the number of years that the firm has been publicly traded.
FORECAST
An indicator variable, 1 if the management make earnings forecast, 0 otherwise.
HERF
Herfindahl index, measured as the sum of squared market shares over all firms in
an industry, where market share is measured by firms’ sales as a proportion of total
47
sales in the industry.
D_CAN
An indicator variable, 1 for Canadian firms, 0 otherwise.
D_2009
An indicator variable, 1 for year 2009, 0 otherwise.
SD
An indicator variable, 1 if the internal control weakness is due to a lack of
segregation of duties, 0 otherwise.
LAKE
An indicator variable, 1 if the internal control weakness is due to a lack of
accounting knowledge or expertise, 0 otherwise.
LOKE
An indicator variable, 1 if the internal control weakness is due to a lack of other
knowledge or expertise, 0 otherwise.
ASP
An indicator variable, 1 if the internal control weakness is due to accounting
system problems, 0 otherwise.
ISP
An indicator variable, 1 if the internal control weakness is due to information
system problems other than accounting, 0 otherwise.
OTH
An indicator variable, 1 if the internal control weakness is due to other problems, 0
otherwise.
AB_NPAP
Abnormal number of professional accountants. The residuals of the regression of
the logarithm of the number of professional accountants on the measures of firm
size, risk, and complexity.
CF
The ratio of cash flow to book assets.
TLTD
The ratio of total long-term debt to book assets.
TDIV
The ratio of total dividends to book assets.
CASH
The ratio of the stock of cash to book assets.
Q
The market-to-book ratio, measured as the market value of total assets to book
value of total assets.
INVENTORY
Inventory scaled by total assets.
RECEIVABLE
Accounts receivable scaled by total assets.
BUSSEG
The number of business segments collected from the financial statements.
GEOSEG
The number of geographic segments collected from the financial statements.
INVESTEFF
A measure of investment efficiency, which is measured as the absolute values of
the residuals from the investment model:
INVESTMENTi, t= α0 + α1 * NEGi, t-1 + α2 * %RevGrowthi,t-1+α3* NEGi, t-
1* %RevGrowthi,t-1+ εi,t.
NEGi, t-1is the indicator variable which takes the value of one for negative revenue
growth, and zero otherwise. %RevGrowthi,t-1 is the revenue growth percentage.
48
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53
Table 1: Internal Control Regulatory Requirements: Canada-U.S. Comparison
U.S. accelerated filers U.S. non-accelerated filers Canadian TSX companies
Assess design effectiveness of
internal controls
Quarterly after August 14,
2003 (SOX 302)
Quarterly from 2003 (SOX
302)
Year ends after June 29, 2006
Effectiveness of implementation of
internal controls
Quarterly after August 14,
2003 (SOX 302)
Quarterly from 2003 to
date (SOX 302)
Quarterly after December 15,
2008 (52-109, revised 2008)
Management certification of
evaluation of ICFR
Year ends and quarterly after
June 15, 2004 (SOX 404 Part
a)
Year ends and quarterly
after December 15, 2007
(SOX 404 Part a)
Year ends and quarterly after
December 15, 2008 (52-109,
revised 2008)
Audit of ICFR
Year ends after June 15,
2004 (SOX 404 Part b)
N/A N/A
Source: Lu, Richarson, and Salterio (2010).
54
Table 2: Sample Selection Procedures This table presents the sample selection procedures for the U.S. and Canadian samples.
Panel A: Canadian Sample
Pre-adoption (2006) Post-adoption (2009)
All firm-years from Compustat data file
1,230
1,112
Less: firm-years without required control variables
(627)
(613)
Firm-years with required control variables
603
499
Less: financial firms with SIC code 6000-6999
(70)
(79)
Less: cross-listed firms
(76)
(61)
Final sample for the main tests 457 359
Panel B: U.S. Sample
2004-2009 2004 2005 2006 2007 2008 2009
All firm-years in the AuditAnalytics database with
SOX 404 ICW disclosure
39,332
2,765 3,951 4,517 8,133 9,445 8,182
Less: Non-accelerated filers
(18,587)
(526) (735) (935) (4,391) (5,722) (4,548)
Firm-years of accelerated filers with SOX 404 ICW
disclosure
20,745
2,239 3,216 3,582 3,742 3,723 3,634
Less: firm-years without required control variables
(7,523)
(778) (916) (1,249) (1,337) (1,351) (1,283)
Final sample for the main tests 13,222 1,461 2,300 2,333 2,405 2,372 2,351
55
Table 3: Descriptive Statistics
Panel A: U.S. Sample, 2004-2009
This panel provides the descriptive statistics for the U.S. sample from 2004 to 2009. See the Appendix for variable definitions.
N Mean Std Dev P25 Median P75
INVESTMENTt+1
13,222
0.132
0.131
0.047
0.089
0.182
ICWt
13,222
0.079
0.270
0.000
0.000
0.000
AQt
13,222
0.178
0.422
0.021
0.051
0.131
TOBINSQt
13,222
2.133
2.184
1.220
1.603
2.357
OCFt
13,222
0.082
0.103
0.014
0.069
0.133
INSTHt
13,222
0.659
0.314
0.469
0.732
0.895
COVERAGEt
13,222
1.095
1.120
0.000
0.693
2.079
SIZEt
13,222
6.752
1.695
5.531
6.647
7.864
LEVt
13,222
0.177
0.207
0.000
0.105
0.287
ROEt
13,222
-0.021
0.312
-0.009
0.041
0.064
TANGIt
13,222
0.328
0.158
0.218
0.323
0.434
DIVIDENDt
13,222
0.421
0.494
0.000
0.000
1.000
STDCFt
13,222
0.073
0.160
0.024
0.043
0.078
STDSALEt
13,222
0.165
0.201
0.055
0.108
0.202
OPCYCLEt
13,222
4.414
1.085
4.086
4.582
4.998
LOSSt
13,222
0.273
0.445
0.000
0.000
1.000
AGEt
13,222
2.566
0.992
2.079
2.639
3.258
FORECASTt
13,222
0.374
0.484
0.000
0.000
1.000
HERFt
13,222
0.106
0.101
0.047
0.069
0.112
56
Table 3: Descriptive Statistics (cont’d)
Panel B: Canadian Sample
This panel provides the descriptive statistics for the Canadian sample in both the Pre- and Post-adoption periods. See the Appendix for variable definitions.
Pre-adoption (2006)
Post-adoption (2009)
N Mean Std Dev P25 Median P75
N Mean Std Dev P25 Median P75
INVESTMENTt+1 457 0.172 0.233 0.021 0.099 0.221
359 0.120 0.148 0.025 0.077 0.172
ICWt
457 0.240 0.429 0.000 0.000 0.000
359 0.131 0.340 0.000 0.000 0.000
AQt
457 0.189 0.201 0.035 0.074 0.195
359 0.162 0.420 0.013 0.051 0.119
TOBINSQt
457 2.061 1.653 1.171 1.544 2.240
359 2.001 2.742 1.072 1.402 2.106
OCFt
457 0.091 0.137 0.020 0.081 0.172
359 0.061 0.102 0.009 0.056 0.100
INSTHt
457 0.061 0.169 0.000 0.000 0.007
359 0.072 0.191 0.000 0.000 0.002
COVERAGEt
457 0.871 0.691 0.693 0.693 1.386
359 0.869 0.677 0.693 0.693 1.386
SIZEt
457 5.452 2.028 4.037 5.455 6.833
359 5.890 2.049 4.359 5.970 7.345
LEVt
457 0.147 0.202 0.000 0.062 0.217
359 0.151 0.197 0.000 0.077 0.252
ROEt
457 -0.024 0.201 -0.030 0.034 0.077
359 -0.065 0.331 -0.043 0.033 0.080
TANGIt
457 0.401 0.183 0.282 0.403 0.498
359 0.405 0.150 0.316 0.431 0.500
DIVIDENDt
457 0.370 0.483 0.000 0.000 1.000
359 0.424 0.495 0.000 0.000 1.000
STDCFt
457 0.081 0.091 0.028 0.053 0.101
359 0.081 0.120 0.026 0.051 0.087
STDSALEt
457 0.155 0.195 0.032 0.094 0.212
359 0.144 0.181 0.044 0.096 0.179
OPCYCLEt
457 4.048 1.761 3.966 4.508 5.019
359 4.141 1.638 3.920 4.604 5.024
LOSSt
457 0.382 0.487 0.000 0.000 1.000
359 0.489 0.507 0.000 0.000 1.000
AGEt
457 2.724 1.103 2.190 2.565 3.044
359 2.732 2.069 2.194 2.565 3.044
FORECASTt
457 0.028 0.162 0.000 0.000 0.000
359 0.017 0.104 0.000 0.000 0.000
HERFt
457 0.118 0.105 0.033 0.071 0.125
359 0.103 0.101 0.038 0.069 0.120
57
Table 3: Descriptive Statistics (cont’d)
Panel C: Matched U.S. Sample This panel provides the descriptive statistics for the matched sample in both the Pre- and Post-adoption periods. U.S. firms are matched with Canadian firms on
size in the same industry (3-digit SIC code) and year. See the Appendix for variable definitions.
Pre-adoption (2006)
Post-adoption (2009)
N Mean Std Dev P25 Median P75 N Mean Std Dev P25 Median P75
INVESTMENTt+1 457 0.213 0.215 0.072 0.145 0.276
359 0.137 0.148 0.045 0.094 0.172
ICWt
457 0.069 0.254 0.000 0.000 0.000
359 0.065 0.247 0.000 0.000 0.000
AQt
457 0.148 0.163 0.031 0.084 0.209
359 0.160 0.174 0.031 0.094 0.233
TOBINSQt
457 2.517 2.500 1.327 1.866 2.899
359 1.917 1.984 1.035 1.444 2.182
OCFt
457 0.099 0.193 0.026 0.082 0.183
359 0.059 0.251 0.011 0.044 0.158
INSTHt
457 0.516 0.378 0.159 0.509 0.838
359 0.511 0.346 0.185 0.519 0.820
COVERAGEt
457 1.820 0.723 1.386 1.792 2.303
359 1.818 0.786 1.099 1.792 2.485
SIZEt
457 5.053 2.155 3.564 4.953 6.525
359 5.378 2.173 3.912 5.361 6.810
LEVt
457 0.089 0.145 0.000 0.012 0.126
359 0.106 0.171 0.000 0.006 0.156
ROEt
457 -0.050 0.202 -0.074 0.009 0.051
359 -0.086 0.282 -0.110 0.000 0.058
TANGIt
457 0.343 0.184 0.210 0.327 0.456
359 0.361 0.177 0.231 0.353 0.480
DIVIDENDt
457 0.268 0.443 0.000 0.000 1.000
359 0.264 0.441 0.000 0.000 1.000
STDCFt
457 0.148 0.797 0.036 0.065 0.126
359 0.114 0.469 0.030 0.056 0.104
STDSALEt
457 0.229 0.412 0.072 0.138 0.256
359 0.191 0.222 0.074 0.129 0.226
OPCYCLEt
457 4.796 0.965 4.423 4.884 5.301
359 4.725 0.908 4.334 4.829 5.252
LOSSt
457 0.437 0.496 0.000 0.000 1.000
359 0.467 0.499 0.000 0.000 1.000
AGEt
457 2.327 0.971 1.946 2.485 2.996
359 2.479 0.906 1.946 2.639 3.091
FORECASTt
457 0.221 0.415 0.000 0.000 0.000
359 0.183 0.387 0.000 0.000 0.000
HERFt
457 0.102 0.096 0.038 0.069 0.128
359 0.099 0.106 0.041 0.056 0.129
58
Table 4: Cost of Capital: Canadian Sample
Panel A: Cost of Capital Calculated Following Easton (2004)
This panel presents the cost of capital of the Canadian sample in the pre- and post-adoption periods. In each period, firms are further divided into two sub-
samples: firms with ICW (ICW sample) and firms without ICW (non-ICW sample). Cost of Capital is calculated following Easton (2004). RPEG = [(eps2-
eps1)/P0]1/2
, where eps1 (eps2) is the one- (two-) year ahead forecasted earnings per share, and P0 equal to the current price. The number of observations is further
reduced by the requirements imposed that both eps1 and eps2 are positive, and eps2 needs to be greater than eps1.
Pre-adoption (2006) Post-adoption (2009)
Difference t-value
ICW
Mean [1] 10.61% [2] 12.79% [2]-[1] 2.18% 2.79
Median 9.16%
11.82%
N 58 27
Non-ICW
Mean [3] 10.02%
[4] 9.21% [4]-[3] -0.81% -1.03
Median 8.71%
8.33%
N 207 165
Difference [1]-[3] 0.59%
[2]-[4] 3.58%
t-value 0.42 4.17
59
Table 4: Cost of Capital: Canadian Sample (Cont’d)
Panel B: Cost of Capital Calculated following Lyle, Callen and Elliott (2011)
This panel presents the cost of capital of the Canadian sample in the pre- and post-adoption periods. In each period, firms are further divided into two sub-
samples: firms with ICW (ICW sample) and firms without ICW (non-ICW sample). Cost of Capital is calculated following Lyle, Callen and Elliott (2011). RLCE, t+1 = α + η1/St + η2 (Bt/St) + η3 (xt/St) + η4 (Et [xt+1]/St) + η5 (Dt/St) + εt+1, where Stis the price per share, Bt is the book value per share, xt is the earnings
before extraordinary items, Dt is the dividends per share, Et[xt+1] is the IBES consensus forecast for one year ahead earnings. Following Lyle, Callen and Elliott
(2011), the coefficients α, η1, η2, η3, η4and η5is obtained from the Fama-Macbeth cross-sectional regression using the Canadian sample from the years 1986 to
2005. RLCE is then calculated for the pre- and post-adoption periods using the regression coefficients estimated.
Pre-adoption (2006) Post-adoption (2009)
Difference t-value
ICW
Mean [1] 12.82% [2] 15.38% [2]-[1] 2.56% 4.04
Median 10.28%
12.10%
N 92 40
Non-ICW
Mean [3] 12.94%
[4] 13.01% [4]-[3] 0.07% 0.04
Median 10.33%
11.60%
N 310 257
Difference [1]-[3] -0.08%
[2]-[4] 2.37%
t-value -0.03 3.92
60
Table 5: Investment Efficiency and Internal Control Weaknesses: U.S. full Sample, 2004-2009
This table presents the regression coefficients for the model examining the relation between internal control
weakness and investment efficiency using the U.S. full sample. INVESTMENT is the sum of research and
development expenditure, capital expenditure, and acquisition expenditure less cash receipts from sale of property,
plant, and equipment scaled by lagged total assets. ICW is an indicator variable, which equals 1 if AuditAnalytics
404 data reports at least one material weakness in the firm’s quarterly or annual reports; and 0 otherwise. OCF is the
cash flow from operations scaled by the book value of assets. AQ is the absolute value of the discretionary accrual
estimated by a cross-sectional version of the modified Jones model with lagged ROA included. TOBINSQ is the
market value of assets at the beginning of the period divided by the book value of assets. INSTH is the percentage of
institutional holdings reported by the Thomson Reuters Ownership Database at the beginning of the year.
COVERAGE is the log of the number of analysts following the firm as reported by IBES. SIZE is the logarithm of
total assets. LEV is the long-term debt divided by total assets. ROE is the net income divided by average total equity.
TANGI is the tangibility of the firm's assets, measured as the ratio of PPE to total assets. DIVIDEND is an indicator
variable which equals 1 if the firm paid a dividend; and 0 otherwise. STDCF is the standard deviation of cash flow
from operations deflated by average total assets from year t-5 to year t-1. STDSALE is the standard deviation of sales
deflated by average total assets from year t-5 to year t-1. OPCYCLE is the operating cycle, measured as the log of
receivables to sales plus inventory to COGS multiplied by 360. LOSS is an indicator variable which equals 1 if the
average of earnings before extraordinary items in fiscal years 2005 and 2006 is negative; and 0 otherwise. AGE is
the logarithm of the number of years that the firm has been publicly traded. FORECAST is an indicator variable
which is equal to 1 if the management make earnings forecast; and 0 otherwise. HERF is the Herfindahl index,
measured as the sum of squared market shares over all firms in an industry, where market share is measured by
firms’ sales as a proportion of total sales in the industry. The sample period is from 2004 to 2009. T-statistics are
calculated using clustered standard errors at the firm and year level. *, ** and *** indicate significance at the 0.10,
0.05, and 0.01 levels, respectively, using two-tailed tests.
Dependent Variable = INVESTMENTt+1
Estimate
t Value
ICWt
-0.021*** -4.18
OCFt*ICWt
0.004*** 3.54
AQt
-0.018*** -6.27
OCFt*AQt
0.001*** 4.25
TOBINSQt
0.012*** 4.48
OCFt
0.003*** 5.87
INSTHt
0.010
1.54
COVERAGEt
0.010*** 6.28
SIZEt
-0.013*** -8.37
LEVt
-0.089*** -9.60
ROEt
-0.011*** -2.57
TANGIt
0.010
0.77
DIVIDENDt
-0.025*** -6.55
STDCFt
0.042* 1.96
STDSALEt
-0.040*** -3.63
OPCYCLEt
0.001
0.66
LOSSt
0.031*** 6.61
AGEt
-0.003
-1.39
FORECASTt
0.009*** 2.75
62
Table 6: Investment Efficiency and Internal Control Weaknesses: U.S. Financially Constrained and
Unconstrained Subsamples, 2004-2009
This table presents the regression coefficients for the model examining the relation between internal control
weakness and investment efficiency by separating the U.S. full sample into two subsamples (according to the
Kaplan and Zingales index): financially constrained and financially unconstrained samples. INVESTMENT is the
sum of research and development expenditure, capital expenditure, and acquisition expenditure less cash receipts
from sale of property, plant, and equipment scaled by lagged total assets. ICW is an indicator variable, which equals
1 if AuditAnalytics 404 data reports at least one material weakness in the firm’s quarterly or annual reports; and 0
otherwise. OCF is the cash flow from operations scaled by the book value of assets. AQ is the absolute value of the
discretionary accrual estimated by a cross-sectional version of the modified Jones model with lagged ROA included.
TOBINSQ is the market value of assets at the beginning of the period divided by the book value of assets. INSTH is
the percentage of institutional holdings reported by the Thomson Reuters Ownership Database at the beginning of
the year. COVERAGE is the log of the number of analysts following the firm as reported by IBES. SIZE is the
logarithm of total assets. LEV is the long-term debt divided by total assets. ROE is the net income divided by
average total equity. TANGI is the tangibility of the firm's assets, measured as the ratio of PPE to total assets.
DIVIDEND is an indicator variable which equals 1 if the firm paid a dividend; and 0 otherwise. STDCF is the
standard deviation of cash flow from operations deflated by average total assets from year t-5 to year t-1. STDSALE
is the standard deviation of sales deflated by average total assets from year t-5 to year t-1. OPCYCLE is the
operating cycle, measured as the log of receivables to sales plus inventory to COGS multiplied by 360. LOSS is an
indicator variable which equals 1 if the average of earnings before extraordinary items in fiscal years 2005 and 2006
is negative; and 0 otherwise. AGE is the logarithm of the number of years that the firm has been publicly traded.
FORECAST is an indicator variable which equals 1 if the management make earnings forecast; and 0 otherwise.
HERF is the Herfindahl index, measured as the sum of squared market shares over all firms in an industry, where
market share is measured by firms’ sales as a proportion of total sales in the industry. The sample period is from
2004 to 2009. T-statistics are calculated using clustered standard errors at the firm and year level. *, ** and ***
indicate significance at the 0.10, 0.05, and 0.01 levels, respectively, using two-tailed tests.
Dependent Variable = INVESTMENTt+1
Financially Constrained Firms
Financially Unconstrained Firms
Estimate
t Value
Estimate
t Value
ICWt
-0.026*** -3.62
-0.006
-1.10
OCFt*ICWt
0.002** 2.51
0.005**
2.42
AQt
-0.014*** -3.60
-0.021*** -6.00
OCFt*AQt
0.001*** 4.15
0.001**
2.54
TOBINSQt
0.010*** 3.27
0.015*** 4.77
OCFt
0.002*** 4.19
0.003*** 6.25
INSTHt
0.009
0.90
0.010
1.37
COVERAGEt
0.013*** 5.27
0.007*** 3.68
SIZEt
-0.017*** -6.67
-0.009*** -5.50
LEVt
-0.126*** -8.66
-0.081*** -7.13
ROEt
-0.016*** -2.94
-0.004
-0.95
TANGIt
0.016
0.79
0.019
1.24
DIVIDENDt
-0.016*** -3.15
-0.025*** -4.99
STDCFt
0.033
1.51
0.056*
1.90
STDSALEt
-0.061*** -4.19
-0.019
-1.35
63
OPCYCLEt
0.000
0.10
0.003
1.24
LOSSt
0.040*** 6.51
0.013**
2.17
AGEt
-0.001
-0.18
-0.005**
-2.33
FORECASTt
0.009* 1.79
0.009**
2.10
HERFt
0.016* 1.65
0.027**
2.00
Intercept
0.172*** 7.27
0.081*** 4.62
Firm/Year Cluster Yes
Yes
N
6,611
6,611
R-Square
0.185
0.101
64
Table 7: Investment Efficiency and Internal Control Weaknesses: Canadian Sample, Pre- and Post-adoption Periods
This table presents the regression coefficients for the model examining the relation between internal control weakness and investment efficiency using the
Canadian sample for the pre- and post-adoption periods. Pooling two years’ data together generates a sample of 953 observations. Choosing firms that are in both
the pre- and post-adoption periods generates a sample of 502 observations (251 firms). INVESTMENT is the sum of research and development expenditure,
capital expenditure, and acquisition expenditure less cash receipts from sale of property, plant, and equipment scaled by lagged total assets. ICW is an indicator
variable, which equals 1 if there is at least one internal control weakness self-reported by the firm in its Management Discussion & Analysis, 0 otherwise. These
data are collected by hand from annual reports. OCF is the cash flow from operations scaled by the book value of assets. AQ is the absolute value of the
discretionary accrual estimated by a cross-sectional version of the modified Jones model with lagged ROA included. TOBINSQ is the market value of assets at
the beginning of the period divided by the book value of assets. INSTH is the percentage of institutional holdings reported by the Thomson Reuters Ownership
Database at the beginning of the year. COVERAGE is the log of the number of analysts following the firm as reported by IBES. SIZE is the logarithm of total
assets. LEV is the long-term debt divided by total assets. ROE is the net income divided by average total equity. TANGI is the tangibility of the firm's assets,
measured as the ratio of PPE to total assets. DIVIDEND is an indicator variable which equals 1 if the firm paid a dividend; and 0 otherwise. STDCF is the
standard deviation of cash flow from operations deflated by average total assets from year t-5 to year t-1. STDSALE is the standard deviation of sales deflated by
average total assets from year t-5 to year t-1. OPCYCLE is the operating cycle, measured as the log of receivables to sales plus inventory to COGS multiplied by
360. LOSS is an indicator variable which equals 1 if the average of earnings before extraordinary items in fiscal years 2005 and 2006 is negative; and 0 otherwise.
AGE is the logarithm of the number of years that the firm has been publicly traded. FORECAST is an indicator variable which equals 1 if the management make
earnings forecast; and 0 otherwise. HERF is the Herfindahl index, measured as the sum of squared market shares over all firms in an industry, where market
share is measured by firms’ sales as a proportion of total sales in the industry. D_2009 is an indicator variable which equals 1 for year 2009; and 0 otherwise. *,
** and *** indicate significance at the 0.10, 0.05, and 0.01 levels, respectively, using two-tailed tests.
Dependent Variable = INVESTMENTt+1
Pre-adoption
Post-adoption
Pooling
Same firm
Estimate
t Value
Estimate
t Value
Estimate
t Value
Estimate
t Value
ICWt
0.014
0.60
-0.025
-1.14
-0.213
-1.00
-0.283
-0.97
OCFt*ICWt 0.004
1.00
0.005***
3.41
-0.012
-0.35
-0.002
-0.25
AQt
-0.037
-0.28
0.074*
1.89
0.356
1.23
0.536
1.47
OCFt*AQt
0.016*
1.92
0.001*
1.92
0.002*
1.80
0.113
0.88
TOBINSQt 0.012
1.63
0.016***
3.91
0.044
1.00
0.084
0.90
OCFt
0.008***
2.86
0.001*
1.89
0.012
1.24
0.002
0.08
INSTHt
0.022
0.33
0.059
0.99
-0.062
-0.12
0.026
0.04
COVERAGEt 0.102**
2.09
-0.014
-0.63
0.033
0.14
0.041
0.14
SIZEt
0.004
0.53
0.002
0.32
0.047
0.93
0.008
0.11
LEVt
-0.122**
-2.17
-0.071*
-1.81
1.209***
3.15
1.470***
2.95
ROEt
0.042
0.67
-0.028
-1.18
-0.735**
-2.50
-1.316***
-2.94
TANGIt
0.216***
3.58
0.027
0.54
0.464
1.08
0.748
1.21
65
DIVIDENDt -0.054**
-2.20
-0.011
-0.58
-0.224
-1.29
-0.159
-0.74
STDCFt
0.372***
2.70
0.194**
2.48
-0.179
-0.27
-0.475
-0.39
STDSALEt
0.060
1.05
0.021
0.55
-0.236
-0.66
-0.209
-0.37
OPCYCLEt 0.000
-0.04
-0.003
-0.74
0.065
1.53
0.113*
1.89
LOSSt
-0.032
-1.11
-0.003
-0.18
0.016
0.09
0.010
0.04
AGEt
-0.002*
-1.86
-0.002
-0.14
-0.061
-0.56
-0.102
-0.73
FORECASTt -0.029
-0.46
-0.038
-0.52
-0.091
-0.17
-0.126
-0.20
HERFt 0.029
1.38
0.010
1.20
0.017
1.02
0.024
1.53
D_2009
-0.350**
-2.27
-0.386**
-2.00
D_2009*ICWt
0.189
0.50
0.312
0.54
D_2009*OCFt*ICWt
0.020**
2.09
0.004**
2.00
Intercept
0.102
1.57
0.073*
1.70
-0.521
-1.19
-0.826
-0.84
N
457
359
816
502
R-Square
0.117
0.140
0.105
0.073
66
Table 8: The Relation between ICW and Investment Efficiency: U.S. and Canadian Matching
Samples for Pre- and Post-adoption Periods Separately
The table presents the regression coefficients of the matched sample analysis for the pre- and post-adoption periods,
separately. For each Canadian observation, I match a U.S. firm on size in the same industry (3-digit SIC code) and
year. The final sample consists of 914 firms (457 pairs) in 2006 and 718 firms (359 pairs) in 2009. INVESTMENT is
the sum of research and development expenditure, capital expenditure, and acquisition expenditure less cash receipts
from sale of property, plant, and equipment scaled by lagged total assets. ICW is an indicator variable. For the
Canadian sample, it equals 1 if there is at least one internal control weakness self-reported by the firm in its
Management Discussion & Analysis, 0 otherwise. These data are collected by hand from annual reports. For the
U.S. sample, ICW equals 1 if AuditAnalytics 404 data reports at least one material weakness in the firm’s quarterly
or annual reports, 0 otherwise. OCF is the cash flow from operations scaled by the book value of assets. AQ is the
absolute value of the discretionary accrual estimated by a cross-sectional version of the modified Jones model with
lagged ROA included. TOBINSQ is the market value of assets at the beginning of the period divided by the book
value of assets. INSTH is the percentage of institutional holdings reported by the Thomson Reuters Ownership
Database at the beginning of the year. COVERAGE is the log of the number of analysts following the firm as
reported by IBES. SIZE is the logarithm of total assets. LEV is the long-term debt divided by total assets. ROE is the
net income divided by average total equity. TANGI is the tangibility of the firm's assets, measured as the ratio of
PPE to total assets. DIVIDEND is an indicator variable which equals 1 if the firm paid a dividend; and 0 otherwise.
STDCF is the standard deviation of cash flow from operations deflated by average total assets from year t-5 to year
t-1. STDSALE is the standard deviation of sales deflated by average total assets from year t-5 to year t-1. OPCYCLE
is the operating cycle, measured as the log of receivables to sales plus inventory to COGS multiplied by 360. LOSS
is an indicator variable which equals 1 if the average of earnings before extraordinary items in fiscal years 2005 and
2006 is negative; and 0 otherwise. AGE is the logarithm of the number of years that the firm has been publicly
traded. FORECAST is an indicator variable which equals 1 if the management make earnings forecast; and 0
otherwise. HERF is the Herfindahl index, measured as the sum of squared market shares over all firms in an industry,
where market share is measured by firms’ sales as a proportion of total sales in the industry. D_CAN is an indicator
variable which equals 1 for Canadian firms; and 0 otherwise. *, ** and *** indicate significance at the 0.10, 0.05,
and 0.01 levels, respectively, using two-tailed tests.
Dependent Variable = INVESTMENTt+1
Pre-adoption
Post-adoption
Estimate
t Value
Estimate
t Value
ICWt
-0.032
-0.05
-0.002
-0.06
OCFt*ICW t (1)
0.040***
3.29
0.041***
3.21
AQt
0.289
0.61
0.482
1.04
OCFt*AQt
0.112**
2.10
0.092*
1.71
TOBINSQt
0.031
1.41
0.021***
2.73
OCFt
0.010
1.28
0.081***
5.29
INSTHt
-0.182
-0.63
-0.219
-0.75
COVERAGEt
0.046
0.19
0.036
1.21
SIZEt
0.058
1.30
0.062**
1.99
LEVt
1.162***
2.88
0.829***
2.72
ROEt
-1.827***
-3.61
-1.721**
-2.40
TANGIt
0.040
0.58
0.011
0.35
DIVIDENDt
-0.021
-1.28
-0.039
-1.16
STDCFt
0.134
0.41
0.162
0.28
67
STDSALEt
-0.061
-0.34
-0.051*
-1.90
OPCYCLEt
0.073
1.19
0.020**
2.01
LOSSt
-0.123
-1.36
0.019
0.38
AGEt
-0.031
-0.36
-0.019
-0.29
FORECASTt
-0.027
-1.28
-0.035
-0.48
HERFt
0.017*
1.85
0.009
1.12
D_CAN
0.210**
2.45
0.181***
5.92
D_CAN*ICWt
-0.022
-0.01
-0.018
-1.00
D_CAN*OCFt*ICWt (2)
-0.041***
-3.09
-0.020***
-4.00
Intercept
-0.212***
-7.32
0.172*
2.12
N
914
718
R-Square
0.245
0.241
F-test: (1) + (2) = 0
1.41
14.96
68
Table 9: The Relation between Disclosed ICW and Investment Efficiency:
U.S. (Accelerated filers) and Canadian Matching Sample, Pooling the Pre- and Post-adoption
Periods’ Observations together
The table presents the regression coefficients of the matched sample analysis by pooling the pre- and post-adoption
periods’ observations together. For each Canadian observation, I match a U.S. accelerated firm on size in the same
industry (3-digit SIC code) and year. The final sample consists of 914 firms (457 pairs) from year 2006 and 718
firms (359 pairs) from year 2009. In total, the number of firm-year observations is 1,632. INVESTMENT is the sum
of research and development expenditure, capital expenditure, and acquisition expenditure less cash receipts from
sale of property, plant, and equipment scaled by lagged total assets. ICW is an indicator variable. For the Canadian
sample, it equals 1 if there is at least one internal control weakness self-reported by the firm in its Management
Discussion & Analysis, 0 otherwise. These data are collected by hand from annual reports. For the U.S. sample,
ICW equals 1 if AuditAnalytics 404 data reports at least one material weakness in the firm’s quarterly or annual
reports, 0 otherwise. OCF is the cash flow from operations scaled by the book value of assets. AQ is the absolute
value of the discretionary accrual estimated by a cross-sectional version of the modified Jones model with lagged
ROA included. TOBINSQ is the market value of assets at the beginning of the period divided by the book value of
assets. INSTH is the percentage of institutional holdings reported by the Thomson Reuters Ownership Database at
the beginning of the year. COVERAGE is the log of the number of analysts following the firm as reported by IBES.
SIZE is the logarithm of total assets. LEV is the long-term debt divided by total assets. ROE is the net income
divided by average total equity. TANGI is the tangibility of the firm's assets, measured as the ratio of PPE to total
assets. DIVIDEND is an indicator variable which equals 1 if the firm paid a dividend; and 0 otherwise. STDCF is the
standard deviation of cash flow from operations deflated by average total assets from year t-5 to year t-1. STDSALE
is the standard deviation of sales deflated by average total assets from year t-5 to year t-1. OPCYCLE is the
operating cycle, measured as the log of receivables to sales plus inventory to COGS multiplied by 360. LOSS is an
indicator variable which equals 1 if the average of earnings before extraordinary items in fiscal years 2005 and 2006
is negative; and 0 otherwise. AGE is the logarithm of the number of years that the firm has been publicly traded.
FORECAST is an indicator variable which equals 1 if the management make earnings forecast; and 0 otherwise.
HERF is the Herfindahl index, measured as the sum of squared market shares over all firms in an industry, where
market share is measured by firms’ sales as a proportion of total sales in the industry. D_2009 is an indicator
variable which equals 1 for year 2009; and 0 otherwise. D_CAN is an indicator variable which equals 1 for Canadian
firms; and 0 otherwise.*, ** and *** indicate significance at the 0.10, 0.05, and 0.01 levels, respectively, using two-
tailed tests.
Dependent Variable = INVESTMENTt+1
Estimate
t Value
ICWt -0.141
-1.00
OCFt*ICWt (1) 0.038***
3.71
AQt 0.172
1.21
OCFt*AQt 0.001*
1.92
TOBINSQt 0.018**
2.22
OCFt 0.041***
3.18
INSTHt -0.018
-1.29
COVERAGEt 0.031
1.21
SIZEt 0.026**
2.07
LEVt 0.730
0.99
ROEt -0.918
-1.16
TANGIt 0.022
1.10
69
DIVIDENDt -0.011
-1.11
STDCFt 0.029
0.61
STDSALEt -0.018
-0.39
OPCYCLEt 0.030
1.00
LOSSt -0.004
-0.17
AGEt -0.018
-0.88
FORECASTt -0.013
-0.32
HERFt 0.016
1.37
D_CAN 0.104***
3.19
D_2009 -0.161
-1.49
D_CAN*ICWt -0.041
-0.28
D_2009*ICWt 0.038
0.92
D_CAN*D_2009*ICWt 0.031
0.08
D_CAN*OCFt*ICWt (2) -0.039***
-5.18
D_2009*OCFt*ICWt (3) 0.001
0.19
D_CAN*D_2009*OCFt*ICWt (4) 0.017***
3.44
Intercept -0.280
-0.39
N 1,632
R-Square 0.030
F-test:
(2) + (4) = 0 19.19
70
Table 10: The Relation between Disclosed ICW and Investment Efficiency:
U.S. (Non-accelerated filers) and Canadian Matching Sample, Pooling the Pre- and Post-adoption
Periods’ Observations Together
The table presents the regression coefficients of the matched sample analysis by pooling the pre- and post-adoption
periods’ observations together. For each Canadian observation, I match a U.S. non-accelerated firm on size in the
same industry (3-digit SIC code) and year. The final sample consists of 914 firms (457 pairs) from 2006 and 718
firms (359 pairs) from 2009. In total, the number of firm-year observations is 1,632. INVESTMENT is the sum of
research and development expenditure, capital expenditure, and acquisition expenditure less cash receipts from sale
of property, plant, and equipment scaled by lagged total assets. ICW is an indicator variable. For the Canadian
sample, it equals 1 if there is at least one internal control weakness self-reported by the firm in its Management
Discussion & Analysis, 0 otherwise. These data are collected by hand from annual reports. For the U.S. sample,
ICW equals 1 if AuditAnalytics 404 data reports at least one material weakness in the firm’s quarterly or annual
reports, 0 otherwise. OCF is the cash flow from operations scaled by the book value of assets. AQ is the absolute
value of the discretionary accrual estimated by a cross-sectional version of the modified Jones model with lagged
ROA included. TOBINSQ is the market value of assets at the beginning of the period divided by the book value of
assets. INSTH is the percentage of institutional holdings reported by the Thomson Reuters Ownership Database at
the beginning of the year. COVERAGE is the log of the number of analysts following the firm as reported by IBES.
SIZE is the logarithm of total assets. LEV is the long-term debt divided by total assets. ROE is the net income
divided by average total equity. TANGI is the tangibility of the firm's assets, measured as the ratio of PPE to total
assets. DIVIDEND is an indicator variable which equals 1 if the firm paid a dividend; and 0 otherwise. STDCF is the
standard deviation of cash flow from operations deflated by average total assets from year t-5 to year t-1. STDSALE
is the standard deviation of sales deflated by average total assets from year t-5 to year t-1. OPCYCLE is the
operating cycle, measured as the log of receivables to sales plus inventory to COGS multiplied by 360. LOSS is an
indicator variable which equals 1 if the average of earnings before extraordinary items in fiscal years 2005 and 2006
is negative; and 0 otherwise. AGE is the logarithm of the number of years that the firm has been publicly traded.
FORECAST is an indicator variable which equals 1 if the management make earnings forecast; and 0 otherwise.
HERF is the Herfindahl index, measured as the sum of squared market shares over all firms in an industry, where
market share is measured by firms’ sales as a proportion of total sales in the industry. D_2009 is an indicator
variable which equals 1 for year 2009; and 0 otherwise. D_CAN is an indicator variable which equals 1 for Canadian
firms; and 0 otherwise.*, ** and *** indicate significance at the 0.10, 0.05, and 0.01 levels, respectively, using two-
tailed tests.
Dependent Variable = INVESTMENTt+1
Estimate
t Value
ICWt -0.101
-0.78
OCFt*ICWt (1) 0.020***
2.88
AQt 0.244
1.07
OCFt*AQt 0.007*
1.69
TOBINSQt 0.023***
2.99
OCFt 0.022***
4.51
INSTHt -0.017
-1.18
COVERAGEt 0.040
1.42
SIZEt 0.019*
1.88
LEVt 0.618
0.86
ROEt -0.821
-0.53
TANGIt 0.152
0.41
DIVIDENDt -0.211
-1.38
71
STDCFt 0.061
1.30
STDSALEt -0.027
-0.75
OPCYCLEt 0.017*
1.84
LOSSt -0.008
-0.29
AGEt -0.028
-1.11
FORECASTt -0.012
-0.41
HERFt 0.022
1.55
D_CAN 0.109**
2.16
D_2009 -0.280*
-1.89
D_CAN*ICWt -0.084
-0.64
D_2009*ICWt 0.019
0.14
D_CAN*D_2009*ICWt 0.006
0.08
D_CAN*OCFt*ICWt (2) -0.022***
-5.29
D_2009*OCFt*ICWt (3) 0.018**
2.20
D_CAN*D_2009*OCFt*ICWt (4) 0.024***
3.18
Intercept -0.055*
-1.67
N 1,632
R-Square 0.017
F-test:
(2) + (4) = 0 1.26
72
Table 11: The Relation between ICW Type and Investment Efficiency, Post-adoption Period
This table presents the regression coefficients for the model examining the relation between internal control
weakness and investment efficiency using the Canadian sample for the post-adoption period. Internal control
weaknesses have been classified into six types: segregation of duties (SD), lack of accounting knowledge or
expertise (LAKE), lack of other knowledge or expertise (LOKE), accounting system problems (ASP), information
system problems other than accounting (ISP), and other problems (OTH). INVESTMENT is the sum of research and
development expenditure, capital expenditure, and acquisition expenditure less cash receipts from sale of property,
plant, and equipment scaled by lagged total assets. ICW is an indicator variable. For the Canadian sample, it equals 1
if there is at least one internal control weakness self-reported by the firm in its Management Discussion & Analysis,
0 otherwise. These data are collected by hand from annual reports. For the U.S. sample, ICW equals 1 if
AuditAnalytics 404 data reports at least one material weakness in the firm’s quarterly or annual reports, 0 otherwise.
OCF is the cash flow from operations scaled by the book value of assets. AQ is the absolute value of the
discretionary accrual estimated by a cross-sectional version of the modified Jones model with lagged ROA included.
TOBINSQ is the market value of assets at the beginning of the period divided by the book value of assets. INSTH is
the percentage of institutional holdings reported by the Thomson Reuters Ownership Database at the beginning of
the year. COVERAGE is the log of the number of analysts following the firm as reported by IBES. SIZE is the
logarithm of total assets. LEV is the long-term debt divided by total assets. ROE is the net income divided by
average total equity. TANGI is the tangibility of the firm's assets, measured as the ratio of PPE to total assets.
DIVIDEND is an indicator variable which equals 1 if the firm paid a dividend; and 0 otherwise. STDCF is the
standard deviation of cash flow from operations deflated by average total assets from year t-5 to year t-1. STDSALE
is the standard deviation of sales deflated by average total assets from year t-5 to year t-1. OPCYCLE is the
operating cycle, measured as the log of receivables to sales plus inventory to COGS multiplied by 360. LOSS is an
indicator variable which equals 1 if the average of earnings before extraordinary items in fiscal years 2005 and 2006
is negative; and 0 otherwise. AGE is the logarithm of the number of years that the firm has been publicly traded.
FORECAST is an indicator variable which equals 1 if the management make earnings forecast; and 0 otherwise.
HERF is the Herfindahl index, measured as the sum of squared market shares over all firms in an industry, where
market share is measured by firms’ sales as a proportion of total sales in the industry. *, ** and *** indicate
significance at the 0.10, 0.05, and 0.01 levels, respectively, using two-tailed tests.
Dependent Variable = INVESTMENTt+1
Estimate t Value
SDt
0.012
0.40
LAKEt
-0.060
-1.50
LOKEt
0.051
0.86
ASPt
-0.061
-1.47
ISPt
-0.029
-0.40
OTHt
-0.005
-0.24
OCFt*SDt
0.019*
1.89
OCFt*LAKEt
0.010*
1.69
OCFt*LOKEt
0.038
1.56
OCFt*ASPt
0.018
0.41
OCFt*ISPt
0.004
0.04
OCFt*OTHt
0.000
-0.01
AQt
0.030*
1.66
OCFt*AQt
0.001**
2.00
TOBINSQt
0.013***
3.27
OCFt
0.005**
2.29
73
INSTHt
0.060
1.02
COVERAGEt
-0.012
-0.62
SIZEt
0.002
0.31
LEVt
-0.068*
-1.79
ROEt
-0.028
-1.19
TANGIt
0.026
0.52
DIVIDENDt
-0.011
-0.57
STDCFt
0.189**
2.42
STDSALEt
0.029
0.60
OPCYCLEt
-0.004
-0.97
LOSSt
-0.002
-0.14
AGEt
-0.002
-0.15
FORECASTt
-0.035
-0.49
HERFt
0.017
1.31
Intercept
0.095**
2.11
N
359
R-Square
0.142
74
Table 12: The Impact of Human Capital Investment in Accounting on the
Relation between ICW and Investment Efficiency: Canadian Sample, Pre-adoption Period
The table presents the regression coefficients of the model examining the impact of human capital investment in
accounting on the relation between disclosed ICW and investment efficiency. AB_ NPAP measures the abnormal
number of professional accountants (CA, CMA, CGA) hired by the firm and is the residuals from the regression
employing the logarithm of the number of professional accountants on the measures of firm size, risk, and
complexity. The model used to calculate AB_NPAP is as follows:
Log (NPAP) = α0 + α1SIZE + α2 LEV + α3 ROE + α4 TANGI + α5 INVENTORY + α6 RECEIVABLE + α7 AGE + α8
BUSSEG + α9 GEOSEG + εt (2)
SIZE is the logarithm of total assets. LEV is measured as long-term debt divided by total assets. ROE is the net
income divided by average total equity. TANGI is the tangibility of the firm's assets, measured as the ratio of PPE to
total assets. INVENTORY is the inventory scaled by total assets. RECEIVABLE is the total receivable scaled by total
assets. AGE is the logarithm of the number of years that the firm has been publicly traded. BUSSEG is the number of
business segments. GEOSEG is the number of geographic segments. NPAP is the total number of professional
accountants (CA, CMA, CGA) hired by the firm. INVESTMENT is the sum of research and development
expenditure, capital expenditure, and acquisition expenditure less cash receipts from sale of property, plant, and
equipment scaled by lagged total assets. ICW is an indicator variable, which equals 1 if there is at least one internal
control weakness self-reported by the firm in its Management Discussion & Analysis, 0 otherwise. These data are
collected by hand from annual reports. OCF is the cash flow from operations scaled by the book value of assets. AQ
is the absolute value of the discretionary accrual estimated by a cross-sectional version of the modified Jones model
with lagged ROA included. TOBINSQ is the market value of assets at the beginning of the period divided by the
book value of assets. INSTH is the percentage of institutional holdings reported by the Thomson Reuters Ownership
Database at the beginning of the year. COVERAGE is the log of the number of analysts following the firm as
reported by IBES. ROE is the net income divided by average total equity. TANGI is the tangibility of the firm's
assets, measured as the ratio of PPE to total assets. DIVIDEND is an indicator variable which equals 1 if the firm
paid a dividend; and 0 otherwise. STDCF is the standard deviation of cash flow from operations deflated by average
total assets from year t-5 to year t-1. STDSALE is the standard deviation of sales deflated by average total assets
from year t-5 to year t-1. OPCYCLE is the operating cycle, measured as the log of receivables to sales plus inventory
to COGS multiplied by 360. LOSS is an indicator variable which equals 1 if the average of earnings before
extraordinary items in fiscal years 2005 and 2006 is negative; and 0 otherwise. FORECAST is an indicator variable
which equals 1 if the management make earnings forecast; and 0 otherwise. HERF is the Herfindahl index,
measured as the sum of squared market shares over all firms in an industry, where market share is measured by
firms’ sales as a proportion of total sales in the industry. *, ** and *** indicate significance at the 0.10, 0.05, and
0.01 levels, respectively, using two-tailed tests.
Dependent Variable = INVESTMENTt+1
Estimate
t Value
ICWt
0.015
0.68
OCFt*ICWt
0.001
0.27
OCFt*ICWt*AB_NPAPt
0.005*
1.92
AB_NPAPt
0.030**
2.48
OCFt*AB_NPAPt
-0.001
-0.94
AQt
-0.040
-0.75
OCFt*AQt
0.016*
1.77
TOBINSQt
0.012*
1.65
OCFt
0.007**
2.15
75
INSTHt
0.022
0.31
COVERAGEt
0.109**
2.18
SIZEt
0.005
0.61
LEVt
-0.131**
-2.27
ROEt
0.043
0.68
TANGIt
0.222***
3.59
DIVIDENDt
-0.056**
-2.30
STDCFt
0.361***
2.88
STDSALEt
0.062
1.10
OPCYCLEt
0.001
0.09
LOSSt
-0.029
-1.02
AGEt
-0.003**
-2.24
FORECASTt
-0.030
-0.46
HERFt
0.028
1.37
Intercept
0.102*
1.65
N
457
R-Square
0.132
76
Table 13: The Reliability of ICW Disclosure and Audit Fees: Canadian Sample, Silent Group (i.e.,
Firms with Zero ICW Disclosed to the Public)
The table presents the regression coefficients of the model examining the relation between the reliability of ICW
disclosure and audit fees. Following Hogan and Wilkins (2008), the estimated model is as follows:
4
2009_
1211109
876543210
BIGLOSSMAGROWTH
AQROAVARROADAQUICKINVRECSIZEDLNFEE
(8)
where LNFEE is the natural logarithm of the audit fee. D_2009 is a dummy variable which equals to 1 for year
2009; and 0 otherwise. SIZE is the natural logarithm of total assets. INVREC is inventory and receivables divided by
total assets. QUICK is the ratio of current assets minus inventories to current liabilities. DA is long-term debt
divided by total assets. ROA is operating cash flow divided by lagged total assets. GROWTH is the one-year
percentage growth in sales. AQ is the accrual quality measured as the absolute value of the discretionary accrual
estimated by a cross-sectional version of the modified Jones model with lagged ROA included. MA is a dummy
variable which equals to 1 if firm was involved in mergers and acquisitions activities; and 0 otherwise. LOSS is a
dummy variable which equals to 1 if net loss is reported; and 0 otherwise. BIG4 is a dummy variable which equals
to 1 if Big 4 auditor is used; and 0 otherwise. Equation (8) is estimated by pooling the silent groups (i.e., firms with
zero ICW disclosed to the public) of the pre- and post-adoption periods together. *, ** and *** indicate significance
at the 0.10, 0.05, and 0.01 levels, respectively, using two-tailed tests.
Dependent Variable = LNFEE
Estimate
t Value
D_2009
-0.277***
-3.68
SIZE
0.361***
28.18
INVREC
0.562***
6.37
QUICK
-0.030**
-2.17
DA
-0.012
-1.47
ROA
-0.140***
-3.75
GROWTH
-0.016*
-1.81
AQ
0.124*
1.90
MA
0.007**
2.19
LOSS
0.122***
4.31
BIG4
0.209***
3.59
Intercept
2.419
23.19
N
659
R-Square
0.297
77
Table 14: The Relation between Disclosed ICW and Investment Efficiency:
U.S. (Accelerated filers) and Canadian Matching Sample, Pooling the Pre- and Post-adoption
Periods’ Observations together, Excluding Distressed Firms
The table presents the regression coefficients of the matched sample analysis by pooling the pre- and post-adoption
periods’ observations together, excluding distressed firms. Altman Z-score is calculated as Z=1.2*T1 + 1.4T2 +3.3T3
+ 0.6T4 + 0.999T5 for each Canadian firm in the sample. T1 is defined as the working capital divided by total assets.
T2 is defined as the retained earnings divided by total assets. T3 is defined as the earnings before interest and taxes
divided by total assets. T4 is defined as the market value of equity divided by total liabilities. T5 is defined as the
sales divided by total assets. The distressed firms are the firms with Z-score at the top quintile in the pre- and post-
adoption periods, respectively. After deleting all the distressed firms, for each Canadian observation, I match a U.S.
accelerated firm on size in the same industry (3-digit SIC code) and year. The final sample consists of 732 firms
(366 pairs) from the year 2006 and 574 firms (287 pairs) from the year 2009. In total, the number of firm-year
observations is 1,306. INVESTMENT is the sum of research and development expenditure, capital expenditure, and
acquisition expenditure less cash receipts from sale of property, plant, and equipment scaled by lagged total assets.
ICW is an indicator variable. For the Canadian sample, it equals 1 if there is at least one internal control weakness
self-reported by the firm in its Management Discussion & Analysis, 0 otherwise. These data are collected by hand
from annual reports. For the U.S. sample, ICW equals 1 if AuditAnalytics 404 data reports at least one material
weakness in the firm’s quarterly or annual reports, 0 otherwise. OCF is the cash flow from operations scaled by the
book value of assets. AQ is the absolute value of the discretionary accrual estimated by a cross-sectional version of
the modified Jones model with lagged ROA included. TOBINSQ is the market value of assets at the beginning of the
period divided by the book value of assets. INSTH is the percentage of institutional holdings reported by the
Thomson Reuters Ownership Database at the beginning of the year. COVERAGE is the log of the number of
analysts following the firm as reported by IBES. SIZE is the logarithm of total assets. LEV is the long-term debt
divided by total assets. ROE is the net income divided by average total equity. TANGI is the tangibility of the firm's
assets, measured as the ratio of PPE to total assets. DIVIDEND is an indicator variable which equals 1 if the firm
paid a dividend; and 0 otherwise. STDCF is the standard deviation of cash flow from operations deflated by average
total assets from year t-5 to year t-1. STDSALE is the standard deviation of sales deflated by average total assets
from year t-5 to year t-1. OPCYCLE is the operating cycle, measured as the log of receivables to sales plus inventory
to COGS multiplied by 360. LOSS is an indicator variable which equals 1 if the average of earnings before
extraordinary items in fiscal years 2005 and 2006 is negative; and 0 otherwise. AGE is the logarithm of the number
of years that the firm has been publicly traded. FORECAST is an indicator variable which equals 1 if the
management make earnings forecast; and 0 otherwise. HERF is the Herfindahl index, measured as the sum of
squared market shares over all firms in an industry, where market share is measured by firms’ sales as a proportion
of total sales in the industry. D_2009 is an indicator variable which equals 1 for the year 2009; and 0 otherwise.
D_CAN is an indicator variable which equals 1 for Canadian firms; and 0 otherwise. *, ** and *** indicate
significance at the 0.10, 0.05, and 0.01 levels, respectively, using two-tailed tests.
Dependent Variable = INVESTMENTt+1
Estimate
t Value
ICWt -0.120
-0.72
OCFt*ICWt (1) 0.036***
3.41
AQt 0.162
1.15
OCFt*AQt 0.001*
1.88
TOBINSQt 0.020**
2.41
OCFt 0.039***
3.00
INSTHt -0.016
-1.17
COVERAGEt 0.025
1.19
SIZEt 0.024**
2.05
78
LEVt 0.716
0.92
ROEt -0.906
-1.15
TANGIt 0.020
1.09
DIVIDENDt -0.016
-1.20
STDCFt 0.031
0.63
STDSALEt -0.023
-0.41
OPCYCLEt 0.031
1.03
LOSSt -0.001
-0.01
AGEt -0.012
-0.52
FORECASTt -0.011
-0.29
HERFt 0.014
1.24
D_CAN 0.100***
3.04
D_2009 -0.169
-1.51
D_CAN*ICWt -0.027
-0.17
D_2009*ICWt 0.036
0.91
D_CAN*D_2009*ICWt 0.036
0.07
D_CAN*OCFt*ICWt (2) -0.035***
-4.56
D_2009*OCFt*ICWt (3) 0.001
0.13
D_CAN*D_2009*OCFt*ICWt (4) 0.019***
3.78
Intercept -0.317
-0.89
N 1,306
R-Square 0.031
F-test:
(2) + (4) = 0 15.83
79
Table 15: The Relation between ICW and Investment Efficiency: Alternative Investment Efficiency
Model
The table presents the regression coefficients of the matched sample analysis with alternative investment efficiency
model specifications. For each Canadian observation, I match a U.S. accelerated firm on size in the same industry
(3-digit SIC code) and year. The final sample consists of 914 firms (457 pairs) from year 2006 and 718 firms (359
pairs) from the year 2009. In total, the number of firm-year observations is 1,306. INVESTEFF is a measure of
investment efficiency, which is measured as the absolute values of the residuals from the investment model:
INVESTMENTt+1 = α0 + α1 NEGt + α2 %REVGROWTHt + α3 NEGt * %REVGROWTHt + εt (3)
INVESTMENT is the sum of research and development expenditure, capital expenditure, and acquisition expenditure
less cash receipts from sale of property, plant, and equipment scaled by lagged total assets. NEG is an indicator
variable which equals 1 for negative revenue growth; and 0 otherwise. %REVGROWTH is the revenue growth
percentage. The investment model is estimated cross-sectionally with at least five observations in each industry by
year. ICW is an indicator variable. For the Canadian sample, it equals 1 if there is at least one internal control
weakness self-reported by the firm in its Management Discussion & Analysis, 0 otherwise. These data are collected
by hand from annual reports. For the U.S. sample, ICW equals 1 if AuditAnalytics 404 data reports at least one
material weakness in the firm’s quarterly or annual reports, 0 otherwise. AQ is the absolute value of the
discretionary accrual estimated by a cross-sectional version of the modified Jones model with lagged ROA included.
INSTH is the percentage of institutional holdings reported by the Thomson Reuters Ownership Database at the
beginning of the year. COVERAGE is the log of the number of analysts following the firm as reported by IBES.
SIZE is the logarithm of total assets. LEV is the long-term debt divided by total assets. ROE is the net income
divided by average total equity. TANGI is the tangibility of the firm's assets, measured as the ratio of PPE to total
assets. DIVIDEND is an indicator variable which equals 1 if the firm paid a dividend; and 0 otherwise. STDCF is the
standard deviation of cash flow from operations deflated by average total assets from year t-5 to year t-1. STDSALE
is the standard deviation of sales deflated by average total assets from year t-5 to year t-1. OPCYCLE is the
operating cycle, measured as the log of receivables to sales plus inventory to COGS multiplied by 360. LOSS is an
indicator variable which equals 1 if the average of earnings before extraordinary items in the fiscal years 2005 and
2006 is negative; and 0 otherwise. AGE is the logarithm of the number of years that the firm has been publicly
traded. FORECAST is an indicator variable which equals 1 if the management make earnings forecast; and 0
otherwise. HERF is the Herfindahl index, measured as the sum of squared market shares over all firms in an industry,
where market share is measured by firms’ sales as a proportion of total sales in the industry. D_2009 is an indicator
variable which equals 1 for the year 2009; and 0 otherwise. D_CAN is an indicator variable which equals 1 for
Canadian firms; and 0 otherwise. *, ** and *** indicate significance at the 0.10, 0.05, and 0.01 levels, respectively,
using two-tailed tests.
Dependent Variable = INVESTEFFt+1
Estimate
t Value
ICWt 0.138***
3.26
AQt 0.027***
2.71
INSTHt 0.058***
5.21
COVERAGEt 0.041
1.59
SIZEt -0.371***
-2.91
LEVt 0.268
1.25
ROEt 0.396
1.29
TANGIt 0.182**
2.37
DIVIDENDt -0.271
-1.42
80
STDCFt 0.018
0.51
STDSALEt -0.080
-0.36
OPCYCLEt -0.074
-2.91
LOSSt -0.001
-0.52
AGEt -0.023*
-1.83
FORECASTt 0.028
0.28
HERFt 0.018
1.10
D_CAN 0.053***
4.02
D_2009 0.018
1.29
D_CAN*ICWt -0.140**
-2.19
D_2009*ICWt 0.004
0.27
D_CAN*D_2009*ICWt 0.129***
4.39
Intercept -0.291
-0.83
N 1,306
R-Square 0.092
81
Table 16: The Relation between Disclosed ICW and Investment Efficiency:
Including Canadian Cross-listing Firms, Pooling the Pre- and Post-adoption Periods’ Observations
together
The table presents the regression coefficients of the matched sample analysis by pooling the pre- and post-adoption
periods’ observations together. The final sample consists of 533 firms from the year 2006 and 420 firms from the
year 2009. In total, the number of firm-year observations is 953. INVESTMENT is the sum of research and
development expenditure, capital expenditure, and acquisition expenditure less cash receipts from sale of property,
plant, and equipment scaled by lagged total assets. ICW is an indicator variable, which equals 1 if there is at least
one internal control weakness self-reported by the firm in its Management Discussion & Analysis, 0 otherwise.
These data are collected by hand from annual reports. OCF is the cash flow from operations scaled by the book
value of assets. AQ is the absolute value of the discretionary accrual estimated by a cross-sectional version of the
modified Jones model with lagged ROA included. TOBINSQ is the market value of assets at the beginning of the
period divided by the book value of assets. INSTH is the percentage of institutional holdings reported by the
Thomson Reuters Ownership Database at the beginning of the year. COVERAGE is the log of the number of
analysts following the firm as reported by IBES. SIZE is the logarithm of total assets. LEV is the long-term debt
divided by total assets. ROE is the net income divided by average total equity. TANGI is the tangibility of the firm's
assets, measured as the ratio of PPE to total assets. DIVIDEND is an indicator variable which equals 1 if the firm
paid a dividend; and 0 otherwise. STDCF is the standard deviation of cash flow from operations deflated by average
total assets from year t-5 to year t-1. STDSALE is the standard deviation of sales deflated by average total assets
from year t-5 to year t-1. OPCYCLE is the operating cycle, measured as the log of receivables to sales plus inventory
to COGS multiplied by 360. LOSS is an indicator variable which equals 1 if the average of earnings before
extraordinary items in the fiscal years 2005 and 2006 is negative; and 0 otherwise. AGE is the logarithm of the
number of years that the firm has been publicly traded. FORECAST is an indicator variable which equals 1 if the
management make earnings forecast; and 0 otherwise. HERF is the Herfindahl index, measured as the sum of
squared market shares over all firms in an industry, where market share is measured by firms’ sales as a proportion
of total sales in the industry. D_2009 is an indicator variable which equals 1 for the year 2009; and 0 otherwise.
D_CROSS is an indicator variable which equals 1 for Canadian firms that are cross-listed in the U.S.; and 0
otherwise. *, ** and *** indicate significance at the 0.10, 0.05, and 0.01 levels, respectively, using two-tailed tests.
Dependent Variable = INVESTMENTt+1
Estimate
t Value
ICWt -0.236
-1.59
OCFt*ICWt (1) -0.008
-0.27
AQt 0.206
0.88
OCFt*AQt 0.002*
1.79
TOBINSQt 0.044
1.01
OCFt 0.092***
2.73
INSTHt -0.061
-0.42
COVERAGEt 0.034
0.15
SIZEt 0.052
1.14
LEVt 1.126**
2.18
ROEt -0.821**
-2.22
TANGIt 0.427
1.19
DIVIDENDt -0.224
-1.29
STDCFt -0.163
-0.17
STDSALEt -0.237
-0.66
OPCYCLEt 0.066*
1.64
82
LOSSt 0.015
0.11
AGEt -0.062
-0.57
FORECASTt -0.091
-0.17
HERFt 0.023
1.57
D_CROSS 0.092***
3.64
D_2009 -0.196**
-2.10
D_CROSS*ICWt -0.029
-0.39
D_2009*ICWt 0.010
0.07
D_CROSS*D_2009*ICWt 0.001
0.00
D_CROSS*OCFt*ICWt (2) 0.031***
5.48
D_2009*OCFt*ICWt (3) 0.027***
3.80
D_CROSS*D_2009*OCFt*ICWt (4) -0.026***
-3.62
Intercept -0.180***
-2.89
N 953
R-Square 0.121
F-test:
(2) + (4) = 0 11.69
83
Table 17: Sensitivity Analysis Related to the Investment Model: Including the Investment History
The table presents the regression coefficients of the matched sample analysis by pooling the pre- and post-adoption
periods’ observations together. For each Canadian observation, I match a U.S. accelerated firm on size in the same
industry (3-digit SIC code) and year. The final sample consists of 902 firms (451 pairs) from the year 2006 and 590
firms (295 pairs) from the year 2009. In total, the number of firm-year observations is 1,492. INVESTMENT is the
sum of research and development expenditure, capital expenditure, and acquisition expenditure less cash receipts
from sale of property, plant, and equipment scaled by lagged total assets. ICW is an indicator variable. For the
Canadian sample, it equals 1 if there is at least one internal control weakness self-reported by the firm in its
Management Discussion & Analysis, 0 otherwise. These data are collected by hand from annual reports. For the
U.S. sample, ICW equals 1 if AuditAnalytics 404 data reports at least one material weakness in the firm’s quarterly
or annual reports, 0 otherwise. OCF is the cash flow from operations scaled by the book value of assets. AQ is the
absolute value of the discretionary accrual estimated by a cross-sectional version of the modified Jones model with
lagged ROA included. TOBINSQ is the market value of assets at the beginning of the period divided by the book
value of assets. INSTH is the percentage of institutional holdings reported by the Thomson Reuters Ownership
Database at the beginning of the year. COVERAGE is the log of the number of analysts following the firm as
reported by IBES. SIZE is the logarithm of total assets. LEV is the long-term debt divided by total assets. ROE is the
net income divided by average total equity. TANGI is the tangibility of the firm's assets, measured as the ratio of
PPE to total assets. DIVIDEND is an indicator variable which equals 1 if the firm paid a dividend; and 0 otherwise.
STDCF is the standard deviation of cash flow from operations deflated by average total assets from year t-5 to year
t-1. STDSALE is the standard deviation of sales deflated by average total assets from year t-5 to year t-1. OPCYCLE
is the operating cycle, measured as the log of receivables to sales plus inventory to COGS multiplied by 360. LOSS
is an indicator variable which equals 1 if the average of earnings before extraordinary items in the fiscal years 2005
and 2006 is negative; and 0 otherwise. AGE is the logarithm of the number of years that the firm has been publicly
traded. FORECAST is an indicator variable which equals 1 if the management make earnings forecast; and 0
otherwise. HERF is the Herfindahl index, measured as the sum of squared market shares over all firms in an industry,
where market share is measured by firms’ sales as a proportion of total sales in the industry. D_2009 is an indicator
variable which equals 1 for the year 2009; and 0 otherwise. D_CAN is an indicator variable which equals 1 for
Canadian firms; and 0 otherwise. *, ** and *** indicate significance at the 0.10, 0.05, and 0.01 levels, respectively,
using two-tailed tests.
Dependent Variable = INVESTMENTt+1
Estimate
t Value
ICWt -0.140
-1.00
OCFt*ICWt (1) 0.037***
3.68
AQt 0.181
1.29
OCFt*AQt 0.001*
1.92
TOBINSQt 0.020**
2.47
OCFt 0.021**
2.02
INSTHt -0.033
-1.51
COVERAGEt 0.032
1.28
SIZEt 0.029**
2.10
LEVt 0.264
0.52
ROEt -0.826
-1.08
TANGIt 0.024
1.17
DIVIDENDt -0.011
-1.11
STDCFt 0.029
0.60
84
STDSALEt -0.015
-0.35
OPCYCLEt 0.030
1.00
LOSSt -0.004
-0.17
AGEt -0.018
-0.88
FORECASTt -0.012
-0.30
HERFt 0.014
1.29
D_CAN 0.115***
3.42
D_2009 -0.128
-1.35
D_CAN*ICWt -0.052
-0.47
D_2009*ICWt 0.039
1.09
D_CAN*D_2009*ICWt 0.030
0.08
D_CAN*OCFt*ICWt (2) -0.038***
-5.15
D_2009*OCFt*ICWt (3) 0.000
0.02
D_CAN*D_2009*OCFt*ICWt (4) 0.017***
3.46
INVESTMENTt 0.163**
2.37
INVESTMENTt-1 0.058*
1.92
Intercept -0.361*
-1.66
N 1,492
R-Square 0.032
F-test:
(2) + (4) = 0 18.86
85
Table 18: Sensitivity Analysis Related to the Accrual Quality Measure: Modified Dechow-Dichev
Model
The table presents the regression coefficients of the matched sample analysis by pooling the pre- and post-adoption
periods’ observations together. For each Canadian observation, I match a U.S. accelerated firm on size in the same
industry (3-digit SIC code) and year. The final sample consists of 848 firms (424 pairs) from the year 2006 and 578
firms (289 pairs) from year 2009. In total, the number of firm-year observations is 1,426. INVESTMENT is the sum
of research and development expenditure, capital expenditure, and acquisition expenditure less cash receipts from
sale of property, plant, and equipment scaled by lagged total assets. ICW is an indicator variable. For the Canadian
sample, it equals 1 if there is at least one internal control weakness self-reported by the firm in its Management
Discussion & Analysis, 0 otherwise. These data are collected by hand from annual reports. For the U.S. sample,
ICW equals 1 if AuditAnalytics 404 data reports at least one material weakness in the firm’s quarterly or annual
reports, 0 otherwise. OCF is the cash flow from operations scaled by the book value of assets. AQ is the absolute
residual of the modified Dechow-Dichev model as implemented by Francis et al. (2005). TOBINSQ is the market
value of assets at the beginning of the period divided by the book value of assets. INSTH is the percentage of
institutional holdings reported by the Thomson Reuters Ownership Database at the beginning of the year.
COVERAGE is the log of the number of analysts following the firm as reported by IBES. SIZE is the logarithm of
total assets. LEV is the long-term debt divided by total assets. ROE is the net income divided by average total equity.
TANGI is the tangibility of the firm's assets, measured as the ratio of PPE to total assets. DIVIDEND is an indicator
variable which equals 1 if the firm paid a dividend; and 0 otherwise. STDCF is the standard deviation of cash flow
from operations deflated by average total assets from year t-5 to year t-1. STDSALE is the standard deviation of sales
deflated by average total assets from year t-5 to year t-1. OPCYCLE is the operating cycle, measured as the log of
receivables to sales plus inventory to COGS multiplied by 360. LOSS is an indicator variable which equals 1 if the
average of earnings before extraordinary items in the fiscal years 2005 and 2006 is negative; and 0 otherwise. AGE
is the logarithm of the number of years that the firm has been publicly traded. FORECAST is an indicator variable
which equals 1 if the management make earnings forecast; and 0 otherwise. HERF is the Herfindahl index,
measured as the sum of squared market shares over all firms in an industry, where market share is measured by
firms’ sales as a proportion of total sales in the industry. D_2009 is an indicator variable which equals 1 for the year
2009; and 0 otherwise. D_CAN is an indicator variable which equals 1 for Canadian firms; and 0 otherwise. *, **
and *** indicate significance at the 0.10, 0.05, and 0.01 levels, respectively, using two-tailed tests.
Dependent Variable = INVESTMENTt+1
Estimate
t Value
ICWt -0.133
-0.88
OCFt*ICWt (1) 0.036***
3.64
AQt 0.421
1.02
OCFt*AQt 0.013**
2.18
TOBINSQt 0.024**
2.53
OCFt 0.014*
1.92
INSTHt -0.032
-1.50
COVERAGEt 0.028
1.17
SIZEt 0.029**
2.10
LEVt 0.262
0.51
ROEt -0.712
-0.75
TANGIt 0.021
1.14
DIVIDENDt -0.011
-1.11
STDCFt 0.027
0.53
86
STDSALEt -0.017
-0.39
OPCYCLEt 0.031
1.00
LOSSt -0.003
-0.16
AGEt -0.012
-0.89
FORECASTt -0.010
-0.27
HERFt 0.019
1.40
D_CAN 0.281***
5.27
D_2009 -0.271
-1.51
D_CAN*ICWt -0.047
-0.36
D_2009*ICWt 0.029
0.77
D_CAN*D_2009*ICWt 0.029
0.08
D_CAN*OCFt*ICWt (2) -0.037***
-4.96
D_2009*OCFt*ICWt (3) 0.000
0.01
D_CAN*D_2009*OCFt*ICWt (4) 0.018***
3.28
Intercept -0.651***
-2.63
N 1,426
R-Square 0.035
F-test:
(2) + (4) = 0 16.21
87
Table 19: The Relation between Disclosed ICW and Investment Efficiency (Excluding Accounting-
based Control Variables): U.S. (Accelerated filers) and Canadian Matching Sample, Pooling the
Pre- and Post-adoption Periods’ Observations together
The table presents the regression coefficients of the matched sample analysis by pooling the pre- and post-adoption
periods’ observations together. For each Canadian observation, I match a U.S. accelerated firm on size in the same
industry (3-digit SIC code) and year. The final sample consists of 914 firms (457 pairs) from year 2006 and 718
firms (359 pairs) from year 2009. In total, the number of firm-year observations is 1,632. INVESTMENT is the sum
of research and development expenditure, capital expenditure, and acquisition expenditure less cash receipts from
sale of property, plant, and equipment scaled by lagged total assets. ICW is an indicator variable. For the Canadian
sample, it equals 1 if there is at least one internal control weakness self-reported by the firm in its Management
Discussion & Analysis, 0 otherwise. These data are collected by hand from annual reports. For the U.S. sample,
ICW equals 1 if AuditAnalytics 404 data reports at least one material weakness in the firm’s quarterly or annual
reports, 0 otherwise. OCF is the cash flow from operations scaled by the book value of assets. TOBINSQ is the
market value of assets at the beginning of the period divided by the book value of assets. INSTH is the percentage of
institutional holdings reported by the Thomson Reuters Ownership Database at the beginning of the year.
COVERAGE is the log of the number of analysts following the firm as reported by IBES. SIZE is the logarithm of
total assets. DIVIDEND is an indicator variable which equals 1 if the firm paid a dividend; and 0 otherwise. STDCF
is the standard deviation of cash flow from operations deflated by average total assets from year t-5 to year t-1.
STDSALE is the standard deviation of sales deflated by average total assets from year t-5 to year t-1. AGE is the
logarithm of the number of years that the firm has been publicly traded. FORECAST is an indicator variable which
equals 1 if the management make earnings forecast; and 0 otherwise. HERF is the Herfindahl index, measured as the
sum of squared market shares over all firms in an industry, where market share is measured by firms’ sales as a
proportion of total sales in the industry. D_2009 is an indicator variable which equals 1 for year 2009; and 0
otherwise. D_CAN is an indicator variable which equals 1 for Canadian firms; and 0 otherwise.*, ** and ***
indicate significance at the 0.10, 0.05, and 0.01 levels, respectively, using two-tailed tests.
Dependent Variable = INVESTMENTt+1
Estimate
t Value
ICWt -0.140
-1.00
OCFt*ICWt (1) 0.037***
3.68
TOBINSQt 0.018**
2.22
OCFt 0.038***
3.01
INSTHt -0.016
-1.13
COVERAGEt 0.030
1.15
SIZEt 0.031**
2.38
DIVIDENDt -0.011
-1.11
STDCFt 0.029
0.61
STDSALEt -0.028
-0.40
AGEt -0.027
-0.97
FORECASTt -0.013
-0.32
HERFt 0.021
1.61
D_CAN 0.104***
3.18