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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)

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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.

iii

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.

iv

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

v

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

vi

CHAPTER 7 – CONCLUSION 43

APPENDIX: VARIABLE DEFINITIONS 46

REFERENCES 48

vii

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

4

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

7

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).

8

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

9

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.

10

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

61

HERFt

0.018* 1.91

Intercept

0.122*** 6.27

Firm/Year Cluster Yes

N

13,222

R-Square

0.153

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

88

D_2009 -0.160

-1.47

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) -0.038***

-5.00

D_2009*OCFt*ICWt (3) 0.001

0.18

D_CAN*D_2009*OCFt*ICWt (4) 0.016***

3.33

Intercept 0.310

0.26

N 1,632

R-Square 0.030

F-test:

(2) + (4) = 0 17.36