The Efficiency of Internal Capital Markets

and the Quality of Internal Accounting Information∗∗∗∗

Yong Zhang

School of Accounting and Finance Hong Kong Polytechnic University

Hung Hom, Hong Kong yongzhang@polyu.edu.hk

November 2014

∗

I thank Sudipta Basu, Lei Chen, Peter Chen, Kai Wai Hui, Ningzhong Li, Yinghua Li, Wei Luo, Kirill Novoselov, Guochang Zhang, Jerry Zimmerman, and seminar participants at Hong Kong University of Science and Technology and Peking University for their helpful comments and suggestions. This work has been supported by a RGC GRF grant from the Research Grants Council of the Hong Kong SAR, China (Project No. 548713). All errors are my own.

The Efficiency of Internal Capital Markets

and the Quality of Internal Accounting Information

Abstract

This study provides evidence of the impact of internal accounting information quality on the

efficiency of internal capital allocation in conglomerate firms. Empirical analysis suggests that

internal capital allocation is less efficient for firms with lower internal accounting information

quality. The relationship holds under a number robustness checks and alternative research

designs. Further analysis indicates that the detrimental effect of low internal accounting

information quality is mitigated when there are relatively informative external signals for

divisional investment opportunities. Lastly, the efficiency of internal capital allocation improves

following an increase in the quality of internal accounting information.

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1. Introduction

Accounting information plays an important role in facilitating efficient allocation of

resources (e.g., Healy and Palepu, 2001; Bushman and Smith, 2003; Kothari, Ramanna, and

Skinner, 2010). Resource allocation is implemented through both external and internal capital

markets. Numerous studies document the role of externally reported financial accounting

information in the resource allocation of external capital markets, including both equity markets

and debt markets.1 Although it is well known that an important function of the management

accounting system is to provide managers with information for making decisions such as capital

budgeting (Zimmerman, 2000), where companies allocate capital among competing investment

projects, there exists little empirical evidence. 2 This paper studies the role of accounting

information in internal capital markets, where capital resources are allocated across different

business divisions through the capital budgeting process of conglomerate firms.

Internal capital markets of conglomerate firms serve an important role in allocating

resources.3 The efficiency of internal capital allocation has been under debate by economists

since Alchian (1969) and Williamson (1975). On the one hand, some suggest that internal

capital markets may be more efficient in resource allocation than external capital markets

because corporate insiders possess information advantage over external investors (the “smarter

1 See Kothari (2001) for a comprehensive review of the role of accounting information in equity markets. And see Armstrong et al. (2010a) for a comprehensive review of the use of accounting information in private debt contracting. Easton et al. (2009) provide initial evidence on the use of accounting information in the bond market.

2 While accounting information is primarily concerned with historical performance, information provided by managerial accounting system helps corporate insiders to evaluate future prospects of investment projects, just like outputs of financial reporting system help outside stakeholder to evaluate future prospects of the whole firm.

3 Statistics provided by Guedj et al. (2009) indicate that, during the period from 1979 to 2006, the amount of equity issuance in the U.S. was about 85 billion dollars per year and the amount of bond issuance was about 536 billion dollars per year (based on information from Thomson SDC Platinum), while the amount of capital allocated through all the internal markets of conglomerate firms was about 640 billion dollars per year (based on Compustat information).

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money” effect as referred to by Stein, 2003). On the other hand, some argue that there exists

severe information asymmetry between corporate headquarters and divisional managers; this

information asymmetry, coupled with divisional managers’ rent-seeking incentives, may give

rise to potentially serious agency problems and distort capital allocation decisions in internal

capital markets (e.g., Bower, 1970; Scharfstein and Stein, 2000; Rajan et al., 2000).

While economic theories suggest an important role for information quality in the

functioning of internal capital markets, there exists little empirical evidence. A likely reason for

the lack of evidence is the difficulty in gauging the quality of information that is available for

internal decision making. While one may be tempted to use external reporting quality to proxy

for the quality of information used for internal decision making, external reporting quality is

likely a noisy, if not biased, proxy. There is a large literature on firms’ discretionary choices in

external reporting driven by incentives related to capital markets, proprietary costs or agency

conflicts (Healy and Palepu, 2001; Bens et al., 2009). Berger (2011) also points out that firm-

level accounting measures commonly used in disclosure research are the result of an inevitably

discretionary aggregation process, even though they are ultimately based internal accounting

signals. In addition, incentives that give rise to discretions in external reporting likely exhibit

large variations not only across firms but also over time.

The disclosure of internal control quality on financial reporting mandated by the

Sarbanes-Oxley Act provides a unique opportunity to study the role of internal accounting

information in internal capital allocation. Although internal control quality, by definition under

the Sarbanes-Oxley Act, measures the quality of external reporting system, it also serves as a

proxy for the quality of internal accounting system. Survey evidence suggests that firms

typically use the same accounting system for both external and internal reporting (Zimmerman,

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2000).4 The internal control system acts as a part of the soft infrastructure that governs the

information-generating process for both external and internal reporting. In addition, internal

control assessment mandated by the Sarbanes-Oxley Act emphasizes specific control designs and

procedures instead of focusing on the ex post accuracy of externally reported accounting

numbers. Therefore, the internal control assessment that reflects the integrity of the external

reporting system should also be indicative of the integrity of the internal management accounting

system. Lastly, Feng et al. (2009) show that firms with weak internal controls produce less

accurate management earnings forecasts, suggesting that the quality of information provided by

internal reports for internal forecasting purposes is relatively low for firms with low internal

control quality.5 To the extent that these internal reports are also used in making decisions other

than forecasting earnings, an implication of Feng et al.’s (2009) findings is that firms with weak

internal controls use lower quality information for decisions such as internal capital allocation.

I hypothesize that high quality internal accounting information, by ameliorating the

information asymmetry between corporate headquarters and divisional managers, improves the

efficiency of internal capital allocation. Using internal control quality as a proxy for the quality

of internal accounting information, this study provides empirical evidence on the role of

accounting information in internal capital markets of conglomerate firms.6 I find that internal

4 Zimmerman (2000) suggests that a likely reason for firms to use one system for both external and internal reporting is to avoid “confusion” of having to reconcile the different numbers arising from multiple accounting systems. Using the same accounting system for multiple purposes may yield synergy benefits and increase the credibility of the financial reports for each purpose; for example, with only one accounting system, the external audit monitors the internal reporting system at little or no additional costs.

5 Feng et al. (2009) suggest that weak internal controls negatively affect the quality of financial inputs to management forecasts in at least two ways. First, internal control weaknesses can result in erroneous internal management reports. Second, internal control weaknesses can result in untimely or stale accounting information.

6 Conglomerate firms are required by segment reporting rules such as SFAS No. 131 and SFAS No. 14 to disclose capital investment at the segment level. While single-segment firms make similar decisions in allocating capital

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capital allocation is significantly more efficient for firms with high internal control quality than

for those with low internal control quality. The difference is not explained by firm

characteristics that are commonly associated with internal control quality. The results continue

to hold with propensity-score matched samples and after controlling for potential reverse

causality. Further analysis indicates that the detrimental effect of low internal information

quality on internal capital allocation efficiency is mitigated when there are relatively informative

external signals that may aid corporate headquarters in capital allocation decisions. Lastly, when

firms with weak internal controls remediate their internal control weaknesses and therefore

improve the quality of internal accounting information used for decision-making, the efficiency

of internal capital allocation is also improved.

By providing evidence on the role of internal accounting information quality in internal

capital markets, this paper makes several contributions to the literature. First, it extends the

research on the resource-allocation role of accounting information to internal capital markets.

Past studies provide extensive evidence on the role of accounting information in allocating

resources in external capital markets. For example, Biddle and Hilary (2006) and Biddle et al.

(2009) show that higher accounting quality, by reducing the information asymmetry between

firms and external capital suppliers, leads to more efficient investment at the firm level.

However, there exists little empirical evidence on the role of accounting information in

allocating resources within firm boundaries, despite the fact that internal capital markets are an

important part of the resource allocation system and that internal capital allocation poses issues

distinct from those present in external capital markets (e.g., Gertner et al., 1994; Stein 1997). I

hypothesize that higher quality internal accounting information, by alleviating information

among competing investment projects, information about capital allocation is generally not publicly available for such firms.

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asymmetry between corporate headquarters and divisional managers, leads to more efficient

internal capital allocation. 7 The empirical evidence is consistent with the hypothesis and

supports the role of accounting information in improving decision making and control in the

capital budgeting process (Zimmerman, 2000; Zimmerman, 2001). A closely related paper by

Bens and Monahan (2004) finds a positive relation between the external disclosure quality and

the excess value of diversification for conglomerate firms, supporting the monitoring effect of

external disclosure quality. This study focuses on the quality of internal accounting information,

thereby complementing the work of Bens and Monahan.

Second, this paper proposes an empirical proxy for the quality of internal information that

is used in internal decisions. While numerous studies in both economics and finance suggest a

critical role for information quality in the internal capital markets, there exists little empirical

evidence due to the lack of appropriate measures of internal information quality. This study

proposes internal control quality as a measure of the quality of internal information that is used

in corporate decision making. The proxy may help researchers to conduct more in-depth

analysis that advances our understanding of the internal capital markets.

Third, this paper adds to the internal control literature. The majority of the existing

internal control studies have focused on the association between internal control quality and the

quality of external reporting, firm risks and cost of equity (e.g., Ashbaugh-Skaife et al., 2008;

Doyle et al., 2007a).8 This paper shows that the effect of internal control system is not limited to

7 Unlike Biddle and Hilary (2006) and Biddle et al. (2009), who study the likelihood of over-investment and under-investment at the firm level, this study focuses on the allocation of capital investment across different business divisions within a firm, given the total amount of investment for the firm. Stein (1997) points out that firm-level investment and internal allocation of the investment are two distinct issues because, given any amount of firm-level investment, the CEO has incentives to achieve an optimal allocation across various divisions within the firm, although such allocation is often hindered by the information asymmetry between the CEO and divisional managers.

8 One exception is Feng et al. (2009), who show that internal control quality may also affect the accuracy of management forecasts by improving the quality of the internal management reports used to generate forecasts.

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the accuracy of performance measurement or outside investors’ perception of firm risk. The

evidence of this paper suggests that an effective internal control system, by improving the quality

of accounting information available for internal decision making, helps to improve the efficiency

of corporate investment decisions.

The remainder of the paper is organized as follows. Section 2 reviews related literature

and develops hypotheses. Section 3 contains a description of sample selection and research

design. Section 4 presents the empirical results. Section 5 concludes.

2. Related research and hypothesis development

This paper is closely related to the literature on the workings of internal capital markets.

The efficiency of internal capital markets has been under debate since Alchian (1969) and

Williamson (1975). On the one hand, Williamson (1975), Stein (1997), and Stein (2002) suggest

that a well-functioning internal capital market can create value by allocating capital to its best

use. An internal capital market may be efficient at allocating funds across projects for two

reasons (Stein, 2003). First, the CEO has incentives to become relatively well-informed about

the prospects of the firm’s divisions because of the control rights. Second, despite temptations

for either under-investment or over-investment at the firm level, the CEO has incentives to carry

out value-enhancing allocation of capital across divisions, given a certain amount of total

investment for the firm.

On the other hand, the CEO may not have access to high quality information when

making decisions involving the internal capital markets. There is ample clinical evidence in the

management literature (e.g., Bower, 1970) for information asymmetry between the users

7

(corporate headquarters) and the providers (managers of business divisions) of internal

information. 9 Theoretical studies show that the information asymmetry between corporate

headquarters and divisions, coupled with rent-seeking incentives of divisional managers, can

give rise to agency problems and induce inefficient internal capital allocation (Harris et al., 1982;

Antle and Eppen, 1985; Harris and Raviv, 1996; Harris and Raviv, 1998; Scharfstein and Stein,

2000; Rajan et al., 2000; Bernardo et al., 2001; Marino and Matsusaka, 2005; Ozbas, 2005; Wulf,

2009). A broad set of anecdotal and field-based evidence suggests that firms often do not follow

the textbook prescription of allocating capital to projects based on a simple net-present-value

(NPV) criterion but instead rely at least in part on other methods such as rationing capital to

individual division managers, imposing payback requirements, and so forth (Stein, 2003;

Graham et al., 2010). Stein (2003) interprets the anecdotal evidence as being consistent with the

view that there exists severe information asymmetry between corporate headquarters and

divisional managers and that such information asymmetry induces less than optimal internal

capital allocation.

A strong internal control system alleviates the information asymmetry between corporate

headquarters and divisional managers in two ways. First, a strong internal control system helps

to improve the accuracy of internal accounting signals generated by the internal accounting

system. Feng et al. (2009) show that firms with weak internal controls produce less accurate

management earnings forecasts, consistent with the quality of information provided by internal

reports for internal forecasting purposes being relatively low for firms with low internal control

quality. Feng et al. (2009) suggest that internal control weaknesses decrease the quality of

9 The information advantage of divisional managers may arise from divisional managers’ familiarity with the business, or from the noisiness of signals for divisional investment opportunities provided by internal accounting information system, or from divisional managers’ ability to distort internal accounting signals, which prevents corporate headquarters from accurately assessing divisional business prospects.

8

information contained in internal reports by introducing errors and/or by reducing the timeliness

of such reports. Compared to corporate headquarters, divisional managers are more familiar

with divisional business operations and less reliant on internal accounting signals; therefore, less

informative internal accounting signals increase the information asymmetry between

headquarters and divisional managers. Second, a strong internal control system, through control

procedures such as segregation of duties, curtails divisional mangers’ ability to tamper with the

internal accounting signal generation process for the purpose of obtaining private benefits. The

lower ability of divisional managers to influence the internal accounting signal generation

process in turn reduces the information asymmetry between corporate headquarters and

divisional managers. Since previously discussed economic theories suggest that information

asymmetry between corporate headquarters and divisional managers harms the efficiency of

internal capital allocation, the first hypothesis is:

H1: The efficiency of internal capital allocation is higher for firms with a strong internal control

system than for those with a weak internal control system.

From the headquarters’ perspective, even though internal information system likely

provides the most direct and relevant information that guides capital allocation across different

business divisions, there are public signals such as Tobin’s Q for divisional investment

opportunities that may be used in capital allocation decisions (Wulf, 2009). Market-based public

signals for investment opportunities are not directly available for individual divisions of

conglomerate firms because they are not traded separately and as a result there are no readily

available market value estimates for each division that are required for estimating divisional-

level Tobin’s Q. Nevertheless, for any individual division, the headquarters may look to single-

segment firms that operate in the same industry as the division in interest and infer investment

9

opportunities from the Tobin’s Q of these industry peers. Public signals are noisy as they reflect

general industry-level information instead of firm-specific information on investment prospects,

so they are unlikely to play a first-order role in internal capital allocation decisions. However,

when internal control quality is low and internal accounting signals for divisional investment

opportunities are unreliable and/or subject to distortion by divisional managers, headquarters

may choose to rely more on public signals. Not only could public signals be used to complement

internal accounting signals, the mere existence of relatively informative public signals may also

help to deter divisional managers from distorting internal accounting signals.

Collectively, the above discussions suggest that relatively informative external signals for

investment opportunities mitigate the detrimental effect of weak internal control on internal

capital allocation efficiency. The second hypothesis is as follows:

H2: The impact of internal control quality on internal capital allocation efficiency decreases with

the informativeness of external signals for divisional investment opportunities.

Lastly, improvement in internal control quality results in both less noise in the internal

accounting signal generation process and lower ability for divisional managers to distort

accounting information, both of which would contribute to lower information asymmetry

between decision makers at corporate headquarters and divisional mangers. Previously

discussed economic theories suggest that the lower information asymmetry corporate

headquarters and divisional mangers would lead to improvement in the efficiency of internal

capital allocation. Thus, the third hypothesis is as follows:

H3: The efficiency of internal capital allocation increases when firms improve their internal

control quality.

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3. Research design

Economic theories suggest that active internal capital allocation may be harmful to firm

performance when information asymmetry between headquarters and divisional managers is high.

Theoretical work such as Harris and Raviv (1996), Harris and Raviv (1998), Marino and

Matsusaka (2005) and Ozbas (2005) predicts that, when the information asymmetry between

headquarters and divisional managers is sufficiently high, rigid capital budgets may be

performance-maximizing while active internal capital allocation hurts performance because it is

based on low quality internal information. In other words, active allocation of capital based on

low quality information hurts firm performance. Motivated by the preceding theories, Guedj et

al. (2009) conduct empirical analysis on the relation between active internal capital allocation

and firm performance. Using a comprehensive sample of U.S. conglomerate firms over the

period 1981-2006, they find that firms that actively allocate capital across divisions experience

significantly lower future operating performance than passive firms. They conclude that

although it is possible for internal capital markets to improve resource sharing across business

divisions, firms that actively allocate resources across divisions, on average, do so inefficiently.

Following the theoretical literature and Guedj et al. (2009), I measure the efficiency of internal

capital allocation with the association between activeness of internal capital allocation and future

industry-adjusted profitability of conglomerate firms. If active capital allocation decisions are

driven by high quality information inside the firms regarding investment opportunities across

different business divisions, I expect active firms to perform better on average. If, however,

conglomerate firms do not have high quality internal information in making internal capital

allocation decisions, I expect active firms to perform worse.

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3.1. Measure of internal capital allocation activeness

The activeness of internal capital allocation is measured as the Deviation from Lagged

Capital allocation (DLC), defined as the change in fractional capital allocation across business

segments over time.

����,� =12�� ����,�∑ ����,��∈� − ����,���∑ ����,����∈� ��∈�

(1)

where ����,� is the capital expenditure of segment i in firm f in year t, and the set F includes all

segments of firm f that appear in both year t-1 and t. For example, if a firm allocates 40% and

60% of its total capital expenditure across its two segments in year t-1 and changes the allocation

to 25% and 75% in year t, then the value of dlc is calculated as �� (|. 25 − .40| + |. 75 − .60|) =

0.15. Since capital expenditure is always positive, the value of the dlc measure is bounded

between 0 and 1. In order to capture the activeness of capital allocation as a long-term strategic

feature of a firm rather than as one-time transitory events, I follow Guedj et al. (2009) and define

DLC as the three-year average of dlc:

$% �,� =13 (����,� +����,��� +����,���)(2)

Therefore, DLC captures the activeness of internal capital allocation. Larger values of

DLC indicate that the internal capital allocation of a firm is frequently revised in response to

signals of investment opportunities received by the headquarters, while smaller values of DLC

indicate a long-term capital allocation strategy that is not revised frequently.10

10 In untabulated analysis, I also use a second measure of capital allocation activeness from Guedj et al. (2009), which is deviation from industry capital allocation, and obtain qualitatively similar results. The correlation coefficient between the alternative measure and the measure in the paper is larger than 0.8. A third measure of

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3.2. Measure of firm profitability

I follow the literature on conglomerate firms and measure operating performance as the

industry-adjusted profitability:

'($�$)_+,��,� = +,��,� −�-�,����∈�

+,��,�(3)

where -�,��� is the asset weight of segment j in firm f in year t-1, and +,��,� is the median ROA

of single-segment firms in j’s industry (based on Fama-French 48 industry classification). The

set F includes all segments of firm f that are used to measure the activeness of internal capital

allocation. The measure of industry-adjusted profitability is conceptually similar to the

commonly used industry-adjusted ROA except that in the setting of conglomerate firms the

industry profitability is the asset-weighted profitability of a hypothetical firm that mimics the

industrial asset composition of firm f.

3.3. Tests of H1 and H2

I measure the efficiency of internal capital allocation by the association between DLC

and future (one-year ahead) firm operating performance. Since DLC is measured over three

years preceding the year in which operating performance is measured, the underlying assumption

is that the effect of capital investment will be reflected in operating performance in one to three

years in the future.11 I test H1 by estimating the following regression model at the firm-year

capital allocation activeness used by Guedj et al. is based on deviation from segment free cash flow, i.e. the sum of segment net income and depreciation. The free cash flow, as commonly used in the finance literature, is to a large extent based on segment-level accounting performance (earnings) measures. As a result, it is affected by both internal accounting information quality and divisional managers’ influence, both of which are functions of internal control quality. Therefore, I do not have unambiguous predictions about the effect of deviation of capital allocation from segment free cash flow on operating performance or how the effect varies with internal control quality.

11 Untabulated analysis measures DLC over five years and obtains qualitatively similar results.

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level. All other explanatory variables are lagged one year compared to the dependent variable

(firm and year subscripts are omitted).

INDADJ_ROA = β0 + β1 DLC*ICW + β2 DLC + β3 ICW

+ β4 SIZE + β5 LOGAGE + β6 FOREIGNSALES + β7 INVENT + β8 MA

+ β9 RESTRUCT + β10 SALESGROW + β11 NSEG + β12 LOSS + β13 ZSCORE

+ β14 ROA

+ Year fixed effects + ε (4)

where ICW is an indicator variable that equals one for firms that report internal control

weaknesses, and zero otherwise. H1 predicts the efficiency of internal capital allocation to

increase with internal control effectiveness. Since ICW is a reverse proxy for internal control

effectiveness, I predict that β2 < 0 (i.e. active internal capital allocation hurts operating

performance more in firms with weak internal control, because allocation decisions in such firms

are more likely to be based on noisy or distorted internal information on segment investment

opportunities).

Doyle et al. (2007b) and Ashbaugh-Skaife et al. (2007) document that certain firm

characteristics are significantly different between firms reporting ICWs and those not. I include

these firm characteristics as additional explanatory variables in the regressions. LOGAGE is the

natural logarithm of the firm age. FOREIGNSALES is an indicator variable equal to one if the

firm has a non-zero foreign currency translation in year t, and zero otherwise. INVENT is

defined as inventory scaled by total assets. MA is an indicator variable equal to one if the firm

has at least one merger or acquisition activity in any of previous three years (including current

year), and zero otherwise. RESTRUCT is an indicator variable equal to one if the firm reports a

restructuring charge in any of previous three years (including current year), and zero otherwise.

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SALESGROWTH is the average sales growth over the past three years. NSEG is the number of

the firm’s operating and geographic segments. LOSS is an indicator variable equal to one if the

firm reports negative income in any of previous three years (including current year), and zero

otherwise. ZSCORE is the decile rank of Altman (1968) z-score that proxies for bankruptcy risks.

Among these control variables, LOSS and ZSCORE capture firms’ state of financial distress and

are clearly predictive of future performance, so I predict these two variables to be negatively

associated with future industry-adjusted ROA. The relation between the remaining variables and

future performance is less clear and I do not have unambiguous predictions.

In addition, I control for current operating performance ROA (measured as income before

extraordinary items divided by total assets) to address the possibility that firms with bad

performance may engage in more active capital allocation in order to improve performance.

Lastly, I include year fixed effects in the regression to control for economy-wide events that

affect the performance of all firms across the sample.

The second hypothesis predicts that the detrimental effect of internal control weaknesses

on internal capital allocation efficiency decreases with the informativeness of external signals for

divisional investment opportunities. I test H2 by estimating the following equation:

INDADJ_ROA = β0 + β1 DLC*ICW + β2 DLC + β3 ICW

+ β4 DLC*ICW*IOS_DIFF + β5 DLC*ICW*IOS_IQR

+ β6 DLC*IOS_DIFF + β7 DLC*IOS_IQR

+ β8 ICW*IOS_DIFF + β9 ICW*IOS_IQR + β10 IOS_DIFF + β11 IOS_IQR

+ β12 SIZE + β13 LOGAGE + β14 FOREIGNSALES + β15 INVENT + β16 MA

+ β17 RESTRUCT + β18 SALESGROW + β19 NSEG + β20 LOSS + β21 ZSCORE

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+ β22 ROA

+ Year fixed effects + ε (5)

where IOS_DIFF and IOS_IQR are proxies for the informativeness of external signals for

divisional investment opportunities. I infer the effect of external signal informativeness in

mitigating the detrimental impact of low internal control quality on capital allocation efficiency

using the three-way interactive terms DLC*ICW*IOS_DIFF and DLC*ICW*IOS_IQR.

In measuring the informativeness of external signal, IOS_DIFF and IOS_IQR, I follow

the logic of Wulf (2009) and assume that Tobin’s Q of peer firms is an important source of

external information for segment investment opportunities in headquarters’ capital allocation

decisions. Following common practices in the literature on internal capital markets, I define peer

firms to be the single-segment firms that operate in the same industry as the business division in

interest. Stein (1997) suggests that corporate headquarters engage in “winner-picking” in

internal capital markets by shifting funds from one project from another. While external signals

for industry investment opportunities are not helpful in choosing among competing projects

within the same industry, they may provide useful information when competing projects come

from different industries. External signals are especially useful when they indicate that one

industry offers clearly superior investment opportunities. Panel A of Figure 1 illustrates the

external signals observed by the headquarters for segment-level investment opportunities with a

simple example of a conglomerate firms with only two divisions, A and B. The external signals

available for headquarters are the probability density distribution of investment opportunities

inferred from peer firms, as described by the two bell curves. Intuitively, the external signals are

more informative to the headquarters regarding the relative investment opportunities of the two

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business divisions when: (1) the two bell curves are farther apart from each other; and (2) the

two bell curves have tighter distributions.

IOS_DIFF measures the distance between the two bell curves. For each segment of a

conglomerate firm, I construct an industry Q as the median Q of peer firms. For each

conglomerate firm, I then identify the segment with the highest industry Q and the one with the

lowest industry Q and define IOS_DIFF for the firm as the difference in industry Q between the

two segments, scaled by the average Q the two industries. Intuitively, when IOS_DIFF is larger,

the two bell curves in Figure 1 are farther apart from each other and peer firms’ Q provides a

more informative external signal about the relative investment opportunities of the two segments

of the firms.

IOS_IQR measures the average tightness of the two bell curves. When the headquarter

uses the Q of peer firms to form an estimate of the segment’s investment opportunity, the larger

is the variation of Q among these peer firms, the more noisy is the signal to the headquarter. For

each segment in a conglomerate firm, I measure the inter-quartile range (the distance between

the third quartile and the first quartile) of Q among all peer firms, scaled by median Q of these

peer firms.12 I define IOS_IQR of the conglomerate firm as the average inter-quartile range of Q

between the highest-Q segment and the lowest-Q segment. Intuitively, when IOS_IQR is larger,

the two bell curves have more dispersed (less tight) distributions and peer firms’ Q provides a

12 I use the inter-quartile range instead of standard deviation because inter-quartile range is more robust to the

influence of outliers. Untabulated analysis indicates that using standard deviation produces qualitatively similar

results.

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less informative external signal about the relative investment opportunities of the two segments

of the firms.

In summary, larger values of IOS_DIFF (IOS_IQR) indicate more (less) informative

external signals for divisional investment opportunities. Panel B of Figure 1 provides an

example where IOS_DIFF is relatively large and IOS_IQR is relatively small and, as a result, the

external signals are informative about the relative investment opportunities of the two divisions,

indicating that investment projects in A’s industry are on average more promising than those in

B’s industry.13 Panel C of Figure 1 provides an example where IOS_DIFF is relatively small

and IOS_IQR is relatively large and, as a result, the external signals are not very informative

about the relative investment opportunities of the two divisions.

Since H2 predicts that the informativeness of external signal mitigates the detrimental

effect of weak internal control on internal capital allocation efficiency, I predict that β4 > 0 and

β5 < 0.

3.4. Tests of H3

The third hypothesis predicts an increase in the efficiency of internal capital allocation

when internal control quality is improved. I test H3 by estimating the following equation with a

sample of firms that report ICWs:

13 Even in this case, external signals only provide information about average investment projects in those industries. Therefore, it is important to note that external signals play a secondary role in project selection and cannot replace internal signals.

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INDADJ_ROA = β0 + β1 DLC*REMED + β2 DLC + β3 REMED

+ β4 SIZE + β5 LOGAGE + β6 FOREIGNSALES + β7 INVENT + β8 MA

+ β9 RESTRUCT + β10 SALESGROW + β11 NSEG + β12 LOSS + β13 ZSCORE

+ β14 ROA

+ Year fixed effects + ε (6)

where REMED is an indicator variable that takes a value of one when the firm reports that its

internal control weaknesses are remediated. Remediation of ICWs indicates an improvement in

internal control quality, which would result in an increase in internal capital allocation efficiency

as predicted by H3. Therefore, I predict that β1 > 0.

4. Empirical analysis

4.1. Sample selection

I obtain segment information as disclosed under SFAS No. 131 from Compustat segment

files. Following previous studies on internal capital markets, I exclude segments with (i) name

“other”, (ii) primary SIC code equal to zero, (iii) SIC code greater than or equal to 6000 (which

are mainly financial and services industries), (iv) incomplete accounting data (capital spending,

sales, depreciation, operating profits), (v) anomalous accounting data (zero depreciation, capital

spending greater than sales, capital spending less than zero), and (vi) sales less than $10 million.

To be included in the sample, a firm needs to have at least two segments that satisfy the above

requirements for at least two consecutive years for the purpose of constructing the measure of

internal capital allocation activeness.

Firm-level financial information is from Compustat and stock price information is from

CRSP. I obtain data on internal control quality reported under Section 302 of the Sarbanes

19

Oxley Act from Audit Analytics.14 The sample used for tests of H1 and H2 (denoted as the

cross-sectional sample) includes all firm-year observations from the time period 1999-2003 that

meet the data requirements. I classify a firm to be of weak internal control (ICW=1) for the time

period 1999-2003 if the firm reports any material internal control weakness in 2004.15 The

assumption underlying the decision to extrapolate the internal control information from 2004 to

the previous five year is that the quality of internal control is likely of a persistent nature. Since

internal control quality is a part of a firm’s soft infrastructure and is affected by a firm’s internal

control environment, it is likely persistent. The finding by Ashbaugh-Skaife et al. (2009) that

internal control quality is related to systematic risk and cost of equity also suggests the market

perceives internal control quality to be of a persistent nature. 16 After imposing the data

requirements, the sample used in the analysis includes 724 unique firms, 129 of which are

classified as having weak internal controls.

H3 predicts internal capital allocation efficiency to increase together with improvement

in internal control quality. The sample used for the tests of H3 (denoted as the time-series

sample) includes all firms reporting at least one incident of material internal control weaknesses

during 2004. To study the change in capital allocation efficiency upon improvement of internal

14 I use internal control information reported under Section 302 instead of Section 404 because Section 302 covers a larger sample of firms and therefore improves the power of empirical analysis.

15 Section 302 came into effect in 2002, but the percentage of firms reporting internal control weaknesses for year 2002 and 2003 is very low. It could have arisen from either the ambiguity in the definitions of control weaknesses or from the lack of clearly specified assessment procedures during the early years of Section 302 compliance (Ashbaugh-Skaife et al. 2007). The percentages of firms reporting internal control weaknesses for the three years starting with 2002 are 0.88%, 2.28%, 11.21%, respectively,. Therefore, I use 2004 to identify firms with internal control weaknesses.

16 Consistent with internal control quality being of a persistent nature, Hogan and Wilkins (2008) show that audit fees are significantly higher for ICW firms even before the internal control problems are initially disclosed. Doyle et al. (2007b) also use multiple years (2002-2005) to identify firms with internal control weaknesses.

20

control quality, I include in the sample all the observations from 1999 to 2008 for these firms. I

use Audit Analytics to identify the year in which internal control weaknesses are remediated.

4.2. Descriptive statistics and correlations

Table 1 reports the descriptive statistics for the cross-sectional sample. The ICW firms

account for about 18% of the firm-year observations. The percentage of ICW firms is higher

than other studies of internal control for two reasons. First, while other studies use both single-

segment and conglomerate firms, the sample used for this study includes exclusively

conglomerate firms, which are shown by past studies (e.g., Doyle et al., 2007b; Ashbaugh-Skaife

et al., 2007) to be more likely to have weak internal control than single-segment firms due to the

complexity of operations. Second, the information about internal control quality comes from

early years of SOX 302 compliance; firms are more likely to have internal control problems in

early years of compliance than in later years, because firms face pressure from both regulators

and investors to improve their internal controls once they report internal control weaknesses.

The industry-adjusted ROA for the sample firms is close to zero on average but has a large

variation. The measure of internal capital allocation activeness, DLC, has a mean of 0.127 and a

median of 0.104. 18.8% the firm-year observations have non-zero foreign sales. The inventory

of the sample firms accounts for 14.6% of total assets. During at least one of the previous three

years, 33.5% of the firms have had a merger or acquisition, 33.8% of the firms have had

restructuring activities, and 36.4% of the firms have had a loss. The total number of segments,

including both business segments and geographic segments, is on average 6.82. The average

ROA of the sample firms is about 2.5% during the sample period.

21

Table 2 reports the pair-wise correlation for the cross-sectional sample. Consistent with

past studies, firms with weak internal controls are smaller, are more likely to have foreign sales,

are more inventory-intensive and have a larger number of segments. Firms with weak internal

controls are also more likely to be loss firms, and their future performance as measured by

industry-adjusted ROA is lower. The correlation between DLC and industry-adjusted ROA is

significantly negative, confirming the finding by Guedj et al. (2009) that more active capital

allocation is associated with lower future operating performance.

4.3. Tests of H1: Internal control quality and internal capital allocation efficiency

4.3.1. Tests of H1 using all ICW firms and non-ICW firms

Table 3 reports estimates of equation (4) based on the sample including both ICW firms

and non-ICW firms. Throughout the empirical tests, the reported T-statistics are based on

heteroskedasticity-consistent Huber-White sandwich standard errors (White, 1980) that allow for

firm-level clustering (Peterson, 2009). For presentational clarity, I also multiply all coefficient

estimates by 100 in all regressions throughout the paper. In Table 3, I first estimate the equation

separately on the non-ICW subsample and the ICW subsample and then estimate it with the

pooled sample. Columns (1) and (2) reports estimates based on the two subsamples with the

explanatory variables including only DLC and the year fixed effects. Consistent with the

prediction, column (1) reports that more active capital allocation is not significantly associated

with worse future operating performance when internal control quality is high. However,

column (3) suggests that more active capital allocation is associated with significantly worse

future operating performance for firms with weak internal control, where accounting information

22

of relatively low quality is used in allocating capital among different business divisions. Column

(3) indicates that the coefficient on DLC*ICW is significantly negative, suggesting that active

capital allocation is more negatively associated with future operating performance for firms with

relatively weak internal controls than those with relatively strong internal controls. In columns

(4)-(6), I add all the control variables to the regressions and the inferences are unchanged.

Columns (4) and (5) show that active internal capital allocation associated with lower future

operating performance for firms with low internal control quality, but such association does not

exist for firms with high internal control quality. Column (6) continues to show that the relation

between active internal capital allocation and future performance is more negative for firms with

low internal control quality. Consistent with firms’ operating performance being somewhat

persistent over time, ZSCORE (ROA) tends to be negatively (positively) associated with future

operating performance. In summary, the results reported in Table 3 are consistent with H1 that

the efficiency of internal capital allocation is negatively associated with the existence of internal

control weaknesses. Intuitively, when the quality of internal accounting information is low,

active capital allocation that utilize such information results in lower future operating

performance.

4.3.2. Tests of H1 based on samples matched on propensity score

Previously reported correlation statistics suggests that, consistent with findings of past

studies, ICW firms are significantly different from non-ICW firms in characteristics such as size,

presence in foreign markets, inventory intensity, loss frequency and number of segments. To the

extent that such characteristics are also correlated with firm internal operations and future

performance, it may give rise to spurious differences in internal capital allocation efficiency

23

between ICW and non-ICW firms. While regression analysis in the preceding section controls

for firm characteristics commonly associated with the existence of internal control quality, it

produces unbiased parameter estimates only if strict assumptions about the functional

relationship between the control variables and the outcome variable are met. Larcker and

Rusticus (2010), Armstrong et al. (2010b) and Jagolinzer et al. (2011) suggest that the use of

propensity-score matched samples helps to address such concerns in empirical accounting

research. 17 I follow these studies and conduct additional analysis using propensity-score

matched samples, which ensures comparisons are carried out between samples of firms similar

along all observable dimensions except the treatment effect (internal control quality).

Specifically, for each ICW firm used in the analysis, I choose the non-ICW firm that is closest in

characteristics based on propensity score. The propensity score is generated from a prediction

model with the dependent variable being ICW and the explanatory variables being determinants

indentified by previous studies (SIZE, LOGAGE, FOREIGNSALES, INVENT, MA, RESTURCT,

LOSS, ZSOCRE, SALESGROW and NSEG).18 The matching algorithm produces 117 pairs of

firms corresponding to a total 708 firm-year observations (373 for ICW firms and 335 for non-

ICW firms).

Panel A of Table 4 suggests that the matching is reasonably successful as the matched

non-ICW firms exhibit similar characteristics as the ICW firms. With the exception of

SALESGROW, the variables used in generating the propensity score are not statistically different

between the ICW sample and the matched non-ICW sample. SALESGROW is statistically

different between the ICW sample and the matched sample based on the rank sum test, but it is

17 The method of propensity-score matching was first proposed by Rosenbaum and Rubin (1983) and Rosenbaum (2002).

18 In order to obtain matched firm pairs, I estimate the ICW determination model based on the average characteristics of each firm over the five-year sample period.

24

not statistically different between the two subsamples by the t-test. However, capital allocation

activeness is significantly different between the two samples, with the ICW firms being

significantly less active in capital allocation. I address this potential issue using an alternative

matching algorithm in section 4.3.3.

Panel B of Table 4 reports estimates of equation (4) based on the propensity score-

matched sample. The results reported in columns (1) and (4) show that, for the propensity score

matched non-ICW firms, capital allocation activeness is not significantly associated with future

operation performance. Consistent with H1, not only is the association between capital

allocation activeness and future operating performance significantly negative for ICW firms

(columns 2 and 5), it is also significantly more negative for ICW firms than for the matched non-

ICW firms, as indicated by the significantly negative coefficient on DLC*ICW in columns (3)

and (6).

4.3.3. Tests of H1 based on samples matched on both propensity score and capital

allocation activeness

The matching algorithm discussed in section 4.3.2 is based solely on propensity score for

the existence of internal control weaknesses. Even thought the algorithm has been relatively

successful in eliminating differences in characteristics commonly associated with internal control

quality, the matched non-ICW firms are systematically different from ICW firms in terms of

capital allocation activeness. To produce a more consistent matching sample, I conduct an

alternative matching procedure based on both capital allocation activeness and the previously

used propensity score. To generate the match firms, I first require the value of DLC of the ICW

25

firm and the non-ICW candidates to be within 0.03 of each other.19 From the set of candidate

firms meeting the preceding requirement, I choose a non-ICW firm that is closest in propensity

score for each ICW firm. The match algorithm produces 117 pairs of firms corresponding to a

total 703 firm-year observations (373 for ICW firms and 340 for non-ICW firms).

Panel A of Table 5 indicates that the matched non-ICW firms have a similar level of

capital allocation activeness as that of the ICW firms. The other firm characteristics are

statistically indistinguishable between the matched non-ICW firms and the ICW firms, except

that the matched non-ICW firms are less inventory-intensive (statistically significant at the 10%

level based on t-tests).

Panel B of Table 5 presents estimates of equation (4). For the matched non-ICW firms,

capital allocation activeness is not significantly associated with future industry-adjusted

operation performance (column 1). However, results reported in column (4) suggest that, after

controlling for firm characteristics, active capital allocation results in more positive future

industry-adjusted operation performance (the relationship is statistically significantly at the 10%

level) for the matched non-ICW firms. Consistent with H1, the association between capital

allocation activeness and future operating performance is significantly negative for ICW firms

(columns 2 and 5), and it is also significantly more negative for ICW firms than for the matched

non-ICW firms (columns 3 and 6).

4.3.4. Tests of causality

So far I have argued that the direction of causality is from internal capital allocation to

firm performance. This makes sense because the analysis relates future firm performance to past

19 Using alternative distance requirements such as 0.01, 0.02, 0.04 and 0.05 yields qualitatively similar results.

26

capital allocation. Still, it is possible that firms increase the activeness of capital allocation in

anticipation of weak future performance. However, as documented in previous sections, the

negative relationship between capital allocation activeness and future firm performance exists

only for the ICW firms, but not for the matched non-ICW firms that are similar in all observable

characteristics including the level of capital allocation activeness. To attribute the results to

reverse causality, one has to argue that the reverse causality exists for ICW firms but not for non-

ICW firms that are similar in characteristics, but it is not clear why this would be the case.

Nevertheless, I attempt to further mitigate the concern about reverse causality by directly testing

the direction of causality using Granger causality tests.20 Specifically, I include both past and

future DLC in estimating equation (4).

INDADJ_ROA = β0 + β1 DLC_future + β2 DLC + Controls + ε (7)

As defined earlier, DLC is a measure of capital allocation activeness over a three-year

period preceding the year in which industry adjusted ROA is measured. DLC_future is defined

in the same way as DLC except that it is measured over a three-year period following the year in

which industry adjusted ROA is measured.21 A significantly negative β1 would be consistent

with poor financial performance causing firms to increase the activeness of internal capital

allocation. The control variables are the same ones that are included in the preceding analysis.

The results are presented in Table 6. The coefficient on DLC_future is statistically

insignificant for either the ICW subsample, or the non-ICW subsample, or the pooled sample.

The insignificant relationship between firm operation performance and future capital allocation

20 Examples of studies also using this method to test for direction of causality include Beaver et al. (2006), Lennox and Park (2006) and McNichols and Stubben (2008).

21 I measure DLC_future over a three-year period to be consistent with the definition of DLC. The results remain unchanged if the measure period is one year instead.

27

activeness does not support the contention that firm performance may the level of capital

allocation activeness. Instead, internal capital allocation is more likely to be determined by firms’

long-term capital budgeting policies, as economic theories suggest. The coefficient on past

capital allocation activeness is still significantly negative for the ICW firms and insignificant for

the non-ICW firms, even after controlling for any association between future allocation

activeness and current financial performance.

To summarize, the combined evidence in Tables 3 through 6 suggests that active internal

capital allocation results in poorer financial performance where internal control quality is low

and firms have to rely on low quality information in making internal capital allocation decisions.

4.4. Tests of H2: The mitigating role of external signals for segment investment

opportunities

Table 7 reports estimates of equation (5) using the sample including both ICW firms and

non-ICW firms. In equation (5), the three-way interactive terms between measures of external

signal informativeness, DLC and ICW are used to infer the effect of external signal

informativeness on internal capital allocation efficiency. I first examine the impact of the two

proxies for external signal noisiness one at a time in columns (1)-(2). Note that a higher value

of IOS_DIFF (IOS_IQR) stands for more (less) informative external signals on divisional

investment opportunities. In columns (1) and (2), the coefficient estimates on

DLC*ICW*IOS_DIFF and DLC*ICW*IOS_IQR are statistically significant and the signs are

consistent with the prediction under H2 that more informative external signals mitigate the effect

of weak internal controls on internal capital allocation efficiency. Lastly, I test the predictions

on both IOS_DIFF and IOS_IQR simultaneously in column (3). The inferences are unchanged;

28

the coefficients estimates on both DLC*ICW*IOS_DIFF and DLC*ICW*IOS_IQR are

statistically significant in the predicted directions.22

4.5. Tests of H3: Improvement in internal control and changes in internal capital allocation

efficiency

To test the hypothesis that relates changes in the efficiency of internal capital allocation

to improvement in internal control quality, I use the sample of ICW firms over the period 1999-

2008. Panel A of Table 8 reports the descriptive statistics for the variables used in the regression

analysis. 24.5% of the firm-year observations are after remediation of internal control

weaknesses is reported. Compared to descriptive statistics presented in Table 1, which are for

the sample including both ICW and non-ICW firms, Panel A of Table 8 indicates that the ICW

firms have relatively low operating performance on average.

Panel B of Table 8 reports estimates of equation (6). Consistent with the results shown

previously in the paper, column (1) indicates that, for firms with low internal control quality,

active internal capital allocation is negatively associated with future operating performance prior

to ICW remediation. Column (2) shows that the negative relationship between active capital

allocation and future performance disappears after ICWs are remediated. Column (3) reports

that the change in the association between DLC and future operating performance from the pre-

remediation subsample and the post-remediation subsample, as measured by the coefficient on

DLC*REMED, is statistically significant at the 10% level. Regressions as reported in columns

(4)-(6) further include controls for firm characteristics; the inferences are unchanged. In

22 As shown in columns (2) and (3), the coefficient estimates on DLC*ICW turns positive after the three-way interactive term DLC*ICW*IOS_IQR is included in the regression. The change in the sign of the coefficient estimates on DLC*ICW is due to the fact that IOS_IQR always takes positive values and that the coefficient on the three-way interactive term DLC*ICW*IOS_IQR is negative.

29

summary, the results reported in Panel B of Table 8 are consistent with the prediction of H3 that

improvement in internal control quality, which presumably results in higher quality information

being used in capital allocation decisions, is associated with an increase in internal capital

allocation efficiency.

5. Conclusions

While numerous economic theories suggest a critical role for information quality in the

workings of internal capital markets, there exists little direct empirical evidence possibly due to

the difficulty in measuring internal information quality. Internal control quality as reported

under the Sarbanes Oxley Act provides a unique opportunity to investigate the role that internal

accounting information quality plays in the capital allocation decisions of internal capital

markets. Using internal control quality to proxy for the quality of internal accounting

information used in internal capital allocation decisions, I find evidence consistent with

predictions by theoretical studies such as Harris et al., (1982), Antle and Eppen (1985), Harris

and Raviv (1996), Harris and Raviv (1998), Scharfstein and Stein (2000), Rajan et al. (2000) and

Bernardo et al., (2001). Empirical analysis shows that the efficiency of internal capital allocation

is positively associated with the quality of internal accounting information. Specifically, for

firms where internal control quality is low and presumably low quality information is used to

guide allocation of investment capital across different business divisions, active internal

allocation of capital is associated with lower future operating performance. However, for firms

where internal control quality is high and presumably high quality information is used to guide

allocation of investment capital across different business divisions, active internal allocation of

capital does not result in lower future operating performance.

30

I continue to show that the documented association between internal control quality and

internal capital allocation efficiency is not explained by characteristics commonly associated

with firms that report internal control weaknesses. Further analysis indicates that the positive

association between internal capital allocation efficiency and internal accounting information

quality is driven neither by parametric assumptions about functional forms imposed in regression

analysis nor by reverse-causality. The detrimental effect of low accounting information quality

on internal capital allocation efficiency is mitigated when there exist relatively informative

external signals for divisional investment opportunities that may be used in corporate

headquarters’ internal capital allocation decisions. Lastly, internal capital allocation becomes

more efficient when internal accounting information quality is improved, as indicated by

remediation of internal control weaknesses.

This study provides rare insights into the workings of internal capital markets. By

highlighting the effect of internal accounting information quality on internal capital allocation

efficiency, this study extends the research on the resource-allocation role of accounting

information to internal capital markets.

31

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Appendix: Variable definitions

Variable Definition

ICW

An indicator variable that equals one if the firm reports any material

internal control weaknesses during the years 2004, and zero otherwise

INDADJ_ROA Industry-adjusted ROA

DLC

A measure of the activeness of internal capital allocation, defined as the

deviation from lagged capital allocation

SIZE The log of total assets

LOGAGE The log of firm age

FOREIGNSALES

An indicator variable that equals one if the firm has a non-zero foreign

currency translation, and zero otherwise

INVENT Inventory divided by total assets

MA

An indicator variable that equals one if the firm has at least one merger

or acquisition activity in any of previous three years (including current

year), and zero otherwise

RESTRUCT

An indicator variable that equals one if the firm reports a restructuring

charge in any of previous three years (including current year), and zero

otherwise

SALESGROW The log of sales growth averaged over the past three years.

37

NSEG The total number of the firm’s operating and geographic segments

LOSS

An indicator variable that equals one if the firm reports negative income

in any of previous three years (including current year), and zero

otherwise

ZSCORE The decile rank of Altman (1968) z-score

ROA Income before extraordinary items, divided by total assets

IOS_DIFF

A measure of within-firm (among different segments) variation of

Tobin’s Q

IOS_IQR

A measure of within-industry (among different peer firms) variation in

Tobin’s Q

REMED

An indicator variable that equals one if the year is after reported

remediation of internal control weaknesses, and zero otherwise

38

Table 1: Descriptive statistics

The sample includes all firm-year observations for both ICW and non-ICW firms from 1999 to 2003 with

available data. Variable definitions are in the appendix.

Variable Mean Median Std. Dev. 25% 75% N

ICW 0.180 0.000 0.384 0.000 0.000 2308 INDADJ_ROA 0.003 0.008 0.080 -0.020 0.039 2308 DLC 0.127 0.104 0.097 0.054 0.175 2308 SIZE 6.904 6.813 1.615 5.824 8.005 2308 LOGAGE 3.046 3.332 0.859 2.485 3.738 2250 FOREIGNSALES 0.188 0.000 0.390 0.000 0.000 2250 INVENT 0.146 0.135 0.113 0.058 0.208 2245 MA 0.335 0.000 0.472 0.000 1.000 2250 RESTRUCT 0.338 0.000 0.473 0.000 1.000 2250 SALESGROW 0.104 0.057 0.254 -0.006 0.149 2250 NSEG 6.820 6.000 2.586 5.000 8.000 2250 LOSS 0.364 0.000 0.481 0.000 1.000 2250 ZSCORE 5.500 6.000 2.872 3.000 8.000 2033 ROA 0.025 0.034 0.061 0.002 0.063 2308 IOS_DIFF 0.139 0.058 0.194 0.000 0.214 2308 IOS_IQR 0.641 0.610 0.250 0.484 0.777 2308

39

Table 2: Correlations

This table reports the Pearson pair-wise correlations. The sample includes all firm-year observations for both ICW and non-ICW firms from 1999

to 2003 with available data. Variables are defined in the appendix. All continuous variables are winsorized at the 1st and 99th percentiles.

Correlation coefficients that are significant at 5% or better are in bold.

Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1 ICW

2 INDADJ_ROA -0.10

3 DLC -0.02 -0.05

4 SIZE -0.09 0.04 -0.23

5 LOGAGE -0.02 0.10 -0.04 0.25

6 FOREIGNSALES 0.04 -0.04 -0.06 0.09 0.03

7 INVENT 0.08 0.04 0.17 -0.33 -0.05 -0.02

8 MA 0.01 0.03 0.10 0.07 -0.04 0.00 0.01

9 RESTRUCT -0.04 -0.06 -0.05 0.13 0.01 0.13 0.03 -0.03

10 SALESGROW 0.01 -0.02 0.14 0.07 -0.14 -0.11 -0.10 0.25 -0.15

11 NSEG 0.04 -0.04 0.03 0.29 0.15 0.24 -0.04 0.07 0.13 -0.04

12 LOSS 0.08 -0.31 0.08 -0.16 -0.15 0.07 0.01 -0.09 0.16 -0.06 0.03

13 ZSCORE 0.02 -0.36 -0.04 0.21 -0.01 0.03 -0.33 -0.01 0.06 -0.03 0.14 0.33

14 ROA -0.08 0.48 -0.08 0.10 0.14 -0.07 0.03 0.02 -0.18 0.07 -0.04 -0.52 -0.52

15 IOS_DIFF -0.05 0.09 0.05 0.02 0.16 -0.03 0.08 0.07 -0.08 0.04 0.15 -0.04 -0.15 0.14

16 IOS_IQR 0.05 0.02 0.06 -0.12 -0.07 0.10 0.17 0.10 -0.02 0.04 0.05 0.06 -0.19 0.05 0.19

40

Table 3: The efficiency of internal capital allocation and internal control weaknesses

This table reports estimates of the following equation:

INDADJ_ROA = β0 + β1 DLC*ICW + β2 DLC + β3 ICW

+ β4 SIZE + β5 LOGAGE + β6 FOREIGNSALES + β7 INVENT + β8 MA

+ β9 RESTRUCT + β10 SALESGROW + β11 NSEG + β12 LOSS + β13 ZSCORE

+ β14 ROA

+ Year fixed effects + ε (4)

All explanatory variables are lagged by one year relative to the dependent variable. The main test

variables are in bold. The sample includes all firm-year observations for both ICW and non-ICW firms

from 1999 to 2003 with available data. Variable definitions are in the appendix. All continuous variables

are winsorized at the 1st and 99th percentiles. Intercepts are omitted for brevity. All coefficient estimates

are multiplied by 100. T-statistics, reported in absolute values in parentheses below the coefficient

estimates, are based on heteroskedasticity-consistent Huber-White sandwich standard errors that allow for

firm-level clustering. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively,

based on two-tailed tests.

41

Table 3 (continued)

Variable Predicted

sign

(1)

Non-ICW

firms

(2)

ICW

firms

(3)

Pooled

sample

(4)

Non-ICW

firms

(5)

ICW

firms

(6)

Pooled

sample

DLC*ICW - -16.25** -15.19**

(1.97) (2.14)

DLC - -1.68 -17.92*** -1.67 2.34 -18.00** 1.48

L2 (0.74) (2.62) (0.73) (1.17) (2.57) (0.76)

ICW ? -0.09 0.91

(0.10) (1.06)

SIZE ? 0.26* -0.39 0.11

(1.71) (1.29) (0.80)

LOGAGE ? -0.27 1.16** 0.03

(1.05) (2.11) (0.15)

FOREIGNSALES ? -0.17 1.67* 0.01

(0.35) (1.94) (0.02)

INVENT ? -2.84 1.68 -2.08

(1.54) (0.54) (1.31)

MA ? 0.45 1.12 0.54

(1.32) (1.08) (1.63)

RESTRUCT ? -0.20 -1.09 -0.25

(0.46) (1.01) (0.63)

SALESGROW ? -5.39*** 1.73 -3.86**

(2.88) (0.61) (2.42)

NSEG ? -0.12 0.23 -0.05

(1.41) (1.42) (0.63)

LOSS - -0.69 0.27 -0.63

(1.48) (0.32) (1.56)

ZSCORE - -0.50*** -0.42** -0.48***

(5.18) (2.27) (5.70)

ROA + 51.61*** 47.47*** 51.00***

(7.98) (5.95) (9.53)

Number of obs. 1893 415 2308 1657 373 2030

Adjusted R2 -0.00 0.04 0.02 0.27 0.24 0.27

42

Table 4: The efficiency of internal capital allocation and internal control weaknesses

- samples matched on propensity score

The sample includes all firm-year observations from 1999 to 2003 for 117 ICW firms and 117 non-ICW

firms matched on propensity score. The propensity score is the predicted probability of reporting internal

control weakness using a model that include SIZE, LOGAGE, FOREIGNSALES, INVENT, MA,

RESTURCT, LOSS, ZSOCRE, SALESGROW and NSEG as explanatory variables. Panel A compares firm

characteristics between the ICW firms and the matched non-ICW firms. Panel B reports estimates of the

following equation:

INDADJ_ROA = β0 + β1 DLC*ICW + β2 DLC + β3 ICW

+ β4 SIZE + β5 LOGAGE + β6 FOREIGNSALES + β7 INVENT + β8 MA

+ β9 RESTRUCT + β10 SALESGROW + β11 NSEG + β12 LOSS + β13 ZSCORE

+ β14 ROA

+ Year fixed effects + ε (4)

43

Table 4 (continued)

Panel A: comparison between ICW firms and matched non-ICW firms

§ ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, for difference in means

(or median) between non-ICW firms and ICW firms based on the t-tests (the Wilcoxon rank sum tests).

Non-ICW firms (N=335)

ICW firms (N=373)

Difference in means§

Difference in medians§

Variable Mean Median Mean Median DLC 0.138 0.112 0.126 0.108 * ** SIZE 6.687 6.699 6.555 6.527 LOGAGE 3.133 3.434 3.089 3.258 FOREIGNSALES 0.257 0.000 0.236 0.000 INVENT 0.165 0.154 0.172 0.147 MA 0.337 0.000 0.343 0.000 RESTRUCT 0.304 0.000 0.316 0.000 SALESGROW 0.071 0.040 0.092 0.060 ** NSEG 7.367 7.000 7.097 7.000 LOSS 0.430 0.000 0.426 0.000 ZSCORE 5.994 6.000 5.633 6.000 ROA 0.017 0.030 0.017 0.024

44

Table 4 (continued)

Panel B: regression analysis

All explanatory variables are lagged by one year relative to the dependent variable. The main test

variables are in bold. Variable definitions are in the appendix. All continuous variables are winsorized at

the 1st and 99th percentiles. Intercepts are omitted for brevity. All coefficient estimates are multiplied by

100. T-statistics, reported in absolute values in parentheses below the coefficient estimates, are based on

heteroskedasticity-consistent Huber-White sandwich standard errors that allow for firm-level clustering.

***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, based on two-tailed

tests.

Variable Predicted

sign

(1)

Non-ICW

firms

(2)

ICW

firms

(3)

Pooled

sample

(4)

Non-ICW

firms

(5)

ICW

firms

(6)

Pooled

sample

DLC*ICW - -21.85** -19.72**

(2.03) (2.40)

DLC - 3.03 -18.35** 3.61 6.61 -17.99** 4.74

L2 (0.46) (2.44) (0.55) (1.29) (2.57) (1.01)

ICW ? 1.68 1.33

(1.16) (1.25)

SIZE ? 0.57 -0.39 0.02

(1.35) (1.29) (0.08)

LOGAGE ? -0.17 1.16** 0.53

(0.29) (2.11) (1.40)

FOREIGNSALES ? 1.42 1.67* 1.21*

(1.28) (1.95) (1.74)

INVENT ? -1.39 1.69 0.82

(0.46) (0.54) (0.37)

MA ? 1.67* 1.12 1.19*

(1.68) (1.08) (1.68)

RESTRUCT ? -0.75 -1.08 -0.91

(0.58) (1.01) (1.09)

SALESGROW ? -4.13 1.74 -0.81

(0.76) (0.61) (0.26)

NSEG ? -0.40** 0.23 -0.07

(2.09) (1.42) (0.56)

LOSS - 0.08 0.27 -0.24

(0.07) (0.32) (0.36)

ZSCORE - -0.64*** -0.42** -0.52***

(3.55) (2.26) (4.27)

ROA + 50.22*** 47.52*** 47.59***

(3.42) (5.96) (6.01)

Number of obs. 335 373 708 335 373 708

45

Adjusted R2 -0.00 0.04 0.02 0.28 0.24 0.25

Table 5: The efficiency of internal capital allocation and internal control weaknesses

- samples matched on propensity score and activeness of internal capital allocation

The sample includes all firm-year observations from 1999 to 2003 for 117 ICW firms and 117 non-ICW

firms matched on both propensity score and activeness of internal capital allocation. The propensity score

is the predicted probability of reporting internal control weakness using a model that include SIZE,

LOGAGE, FOREIGNSALES, INVENT, MA, RESTURCT, LOSS, ZSOCRE, SALESGROW and NSEG as

explanatory variables. Panel A compares firm characteristics between the ICW firms and the matched

non-ICW firms. Panel B reports estimates of the following equation:

INDADJ_ROA = β0 + β1 DLC*ICW + β2 DLC + β3 ICW

+ β4 SIZE + β5 LOGAGE + β6 FOREIGNSALES + β7 INVENT + β8 MA

+ β9 RESTRUCT + β10 SALESGROW + β11 NSEG + β12 LOSS + β13 ZSCORE

+ β14 ROA

+ Year fixed effects + ε (4)

Panel A: comparison between ICW firms and matched non-ICW firms

§ ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, for difference in means

(or median) between non-ICW firms and ICW firms based on the t-tests (the Wilcoxon rank sum tests).

Non-ICW firms (N=340)

ICW firms (N=373)

Difference in means§

Difference in medians§

Variable Mean Median Mean Median DLC 0.124 0.103 0.126 0.108 SIZE 6.703 6.588 6.555 6.527 LOGAGE 3.039 3.384 3.089 3.258 FOREIGNSALES 0.241 0.000 0.236 0.000 INVENT 0.155 0.151 0.172 0.147 * MA 0.347 0.000 0.343 0.000 RESTRUCT 0.276 0.000 0.316 0.000 SALESGROW 0.088 0.053 0.092 0.060 NSEG 7.000 7.000 7.097 7.000 LOSS 0.388 0.000 0.426 0.000 ZSCORE 5.944 6.000 5.633 6.000 ROA 0.019 0.033 0.017 0.024

46

Table 5 (continued)

Panel B: regression analysis

All explanatory variables are lagged by one year relative to the dependent variable. The main test

variables are in bold. Variable definitions are in the appendix. All continuous variables are winsorized at

the 1st and 99th percentiles. Intercepts are omitted for brevity. All coefficient estimates are multiplied by

100. T-statistics, reported in absolute values in parentheses below the coefficient estimates, are based on

heteroskedasticity-consistent Huber-White sandwich standard errors that allow for firm-level clustering.

***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, based on two-tailed

tests.

47

Variable Predicted

sign

(1)

Non-ICW

firms

(2)

ICW

firms

(3)

Pooled

sample

(4)

Non-ICW

firms

(5)

ICW

firms

(6)

Pooled

sample

DLC*ICW - -16.91* -20.34**

(1.74) (2.52)

DLC - -1.38 -18.35** -1.50 8.81* -17.99** 4.97

L2 (0.30) (2.44) (0.33) (1.92) (2.57) (1.27)

ICW ? 1.57 1.97*

(1.23) (1.96)

SIZE ? 0.41 -0.39 -0.14

(0.72) (1.29) (0.51)

LOGAGE ? -0.33 1.16** 0.37

(0.53) (2.11) (1.03)

FOREIGNSALES ? -0.64 1.67* 0.10

(0.61) (1.95) (0.18)

INVENT ? -7.78 1.69 -3.25

(1.65) (0.54) (1.27)

MA ? 1.05 1.12 1.16*

(1.06) (1.08) (1.70)

RESTRUCT ? 1.30 -1.08 0.10

(1.30) (1.01) (0.15)

SALESGROW ? -4.28 1.74 -1.57

(1.31) (0.61) (0.78)

NSEG ? -0.16 0.23 0.07

(0.59) (1.42) (0.47)

LOSS - -1.51 0.27 -0.90

(1.61) (0.32) (1.50)

ZSCORE - -0.46* -0.42** -0.39***

(1.73) (2.26) (2.95)

ROA + 43.50*** 47.52*** 47.28***

(2.92) (5.96) (6.46)

Number of obs. 340 373 713 340 373 713

Adjusted R2 -0.00 0.04 0.02 0.19 0.24 0.21

48

Table 6: Granger causality tests

This table reports estimates of the following equation:

INDADJ_ROA = β0 + β1 DLC_future + β2 DLC + Controls + ε (7)

All explanatory variables are lagged by one year relative to the dependent variable, except that

DLC_future is measured over a three-year period following the year in which the dependent variable is

measured. The main test variables are in bold. The sample includes all firm-year observations for both

ICW and non-ICW firms from 1999 to 2003 with available data. Variable definitions are in the appendix.

All continuous variables are winsorized at the 1st and 99th percentiles. Intercepts are omitted for brevity.

All coefficient estimates are multiplied by 100. T-statistics, reported in absolute values in parentheses

below the coefficient estimates, are based on heteroskedasticity-consistent Huber-White sandwich

standard errors that allow for firm-level clustering. ***, **, and * indicate significance at the 1%, 5%,

and 10% levels, respectively, based on two-tailed tests.

49

Variable Predicted

sign

(1)

Non-ICW

firms

(2)

ICW

firms

(3)

Pooled

sample

(4)

Non-ICW

firms

(5)

ICW

firms

(6)

Pooled

sample

DLC_future ? -1.86 1.44 -1.30 -0.28 -2.01 -0.10

(0.73) (0.24) (0.52) (0.13) (0.28) (0.04)

DLC*ICW - -16.26** -15.19**

(1.97) (2.13)

DLC - -0.91 -18.51** -1.14 2.44 -17.28** 1.52

L2 (0.40) (2.33) (0.48) (1.11) (2.08) (0.71)

ICW ? -0.09 0.91

(0.09) (1.05)

SIZE ? 0.25 -0.41 0.11

(1.64) (1.37) (0.77)

LOGAGE ? -0.27 1.19** 0.03

(1.05) (2.11) (0.15)

FOREIGNSALES ? -0.17 1.66* 0.01

(0.35) (1.92) (0.02)

INVENT ? -2.84 1.84 -2.08

(1.54) (0.58) (1.31)

MA ? 0.45 1.13 0.54

(1.32) (1.07) (1.63)

RESTRUCT ? -0.20 -1.14 -0.25

(0.46) (1.05) (0.63)

SALESGROW ? -5.39*** 1.75 -3.86**

(2.88) (0.62) (2.42)

NSEG ? -0.12 0.24 -0.05

(1.37) (1.41) (0.61)

LOSS - -0.68 0.30 -0.63

(1.47) (0.35) (1.55)

ZSCORE - -0.50*** -0.43** -0.48***

(5.17) (2.27) (5.69)

ROA + 51.71*** 47.30*** 51.08***

(8.01) (5.87) (9.56)

Number of obs. 1893 415 2308 1657 373 2030

Adjusted R2 -0.00 0.04 0.02 0.27 0.24 0.27

50

Table 7: The mitigating role of external signals

This table reports estimates of the following equation:

INDADJ_ROA = β0 + β1 DLC*ICW + β2 DLC + β3 ICW

+ β4 DLC*ICW*IOS_DIFF + β5 DLC*ICW*IOS_IQR

+ β6 DLC*IOS_DIFF + β7 DLC*IOS_IQR

+ β8 ICW*IOS_DIFF + β9 ICW*IOS_IQR + β10 IOS_DIFF + β11 IOS_IQR

+ β12 SIZE + β13 LOGAGE + β14 FOREIGNSALES + β15 INVENT + β16 MA

+ β17 RESTRUCT + β18 SALESGROW + β19 NSEG + β20 LOSS + β21 ZSCORE

+ β22 ROA

+ Year fixed effects + ε (5)

All explanatory variables are lagged by one year relative to the dependent variable. The main test

variables are in bold. The sample includes all firm-year observations for both ICW and non-ICW firms

from 1999 to 2003 with available data. Variable definitions are in the appendix. All continuous variables

are winsorized at the 1st and 99th percentiles. Intercepts are omitted for brevity. All coefficient estimates

are multiplied by 100. T-statistics, reported in absolute values in parentheses below the coefficient

estimates, are based on heteroskedasticity-consistent Huber-White sandwich standard errors that allow for

firm-level clustering. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively,

based on two-tailed tests.

51

Table 7 (continued)

Variable Predicted sign (1) (2) (3)

DLC*ICW*IOS_DIFF + 54.39** 58.83**

(2.06) (2.26)

DLC*ICW*IOS_IQR - -52.36** -55.23**

(2.01) (2.17)

DLC*ICW ? -21.23** 23.02 18.44

(2.38) (1.42) (1.22)

ICW*IOS_DIFF ? -11.41*** -11.68***

(3.36) (3.55)

DLC*IOS_DIFF ? -19.86* -20.68*

(1.75) (1.80)

IOS_DIFF ? 4.34*** 4.46***

(3.35) (3.33)

ICW*IOS_IQR ? 5.81 6.54*

(1.63) (1.91)

DLC*IOS_IQR ? 1.42 3.33

(0.13) (0.31)

IOS_IQR ? 0.11 -0.38

(0.08) (0.29)

DLC ? 4.21* 0.39 1.92

(1.69) (0.06) (0.29)

ICW ? 2.26** -3.31 -2.43

(2.11) (1.46) (1.13)

SIZE ? 0.12 0.08 0.10

(0.94) (0.62) (0.76)

LOGAGE ? -0.01 0.07 0.01

(0.05) (0.28) (0.06)

FOREIGNSALES ? -0.05 -0.06 -0.11

(0.12) (0.13) (0.27)

INVENT ? -1.95 -2.13 -2.00

(1.25) (1.34) (1.27)

MA ? 0.56* 0.56* 0.60*

(1.68) (1.71) (1.80)

RESTRUCT ? -0.18 -0.23 -0.16

(0.45) (0.58) (0.39)

SALESGROW ? -3.88** -3.80** -3.85**

(2.46) (2.47) (2.52)

NSEG ? -0.07 -0.05 -0.06

(0.83) (0.60) (0.80)

LOSS - -0.75* -0.68* -0.80**

(1.88) (1.72) (2.04)

ZSCORE - -0.46*** -0.46*** -0.44***

(5.52) (5.25) (5.12)

ROA + 50.55*** 50.36*** 49.80***

(9.43) (9.32) (9.21)

Number of obs. 2030 2030 2030

Adjusted R2 0.27 0.27 0.27

52

Table 8: Remediation of internal control weaknesses and changes in internal capital

allocation efficiency

The sample includes all firm-year observations for ICW firms from 1999 to 2008 with available data.

Variable definitions are in the appendix.

Panel A: descriptive statistics

This panel reports descriptive statistics of the variables used in the regressions of Panel B.

Variable Mean Median Std. Dev. 25% 75% N

REMED 0.245 0.000 0.430 0.000 0.000 797

INDADJ_ROA -0.019 -0.002 0.115 -0.042 0.033 797

DLC 0.125 0.105 0.097 0.050 0.180 797

SIZE 6.678 6.603 1.576 5.711 7.763 797

LOGAGE 3.042 3.135 0.774 2.565 3.689 787

FOREIGNSALES 0.291 0.000 0.455 0.000 1.000 787

INVENT 0.172 0.150 0.136 0.073 0.236 783

MA 0.314 0.000 0.464 0.000 1.000 787

RESTRUCTURING 0.435 0.000 0.496 0.000 1.000 787

SALESGROW 0.122 0.076 0.247 0.005 0.182 787

NSEG 7.269 7.000 2.843 5.000 9.000 787

LOSS 0.468 0.000 0.499 0.000 1.000 787

ZSCORE 5.501 6.000 2.872 3.000 8.000 731

ROA 0.013 0.029 0.094 -0.012 0.064 797

53

Table 8 (continued)

Panel B: regression analysis

This panel reports estimates of the following equation:

INDADJ_ROA = β0 + β1 DLC*REMED + β2 DLC + β3 REMED

+ β4 SIZE + β5 LOGAGE + β6 FOREIGNSALES + β7 INVENT + β8 MA

+ β9 RESTRUCT + β11 SALESGROW + β12 NSEG + β13 LOSS + β14 ZSCORE

+ β14 ROA

+ Year fixed effects + ε (6)

All explanatory variables are lagged by one year relative to the dependent variable. The main test

variables are in bold. The sample includes all firm-year observations for ICW firms from 1999 to 2008

with available data. Variable definitions are in the appendix. All continuous variables are winsorized at

the 1st and 99th percentiles. Intercepts are omitted for brevity. All coefficient estimates are multiplied by

100. T-statistics, reported in absolute values in parentheses below the coefficient estimates, are based on

heteroskedasticity-consistent Huber-White sandwich standard errors that allow for firm-level clustering.

***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, based on two-tailed

tests.

54

Variable Pred.

sign

(1)

Pre-

remediation

(2)

Post-

remediation

(3)

Pooled

sample

(4)

Pre-

remediation

(5)

Post-

remediation

(6)

Pooled

sample

DLC*REMED - 28.51* 24.25*

(1.85) (1.94)

DLC - -20.24** 8.27 -20.24** -23.66*** 15.08 -20.89**

(2.36) (0.62) (2.36) (2.80) (1.40) (2.48)

REMED ? 0.79 -1.69

(0.38) (0.95)

SIZE ? -0.12 1.69** 0.21

(0.36) (2.09) (0.63)

LOGAGE ? 1.46** -3.56*** 0.31

(2.15) (2.90) (0.46)

FOREIGNSALES ? 1.00 1.19 1.31

(1.02) (0.69) (1.42)

INVENT ? 0.75 -7.61 -1.88

(0.19) (1.45) (0.55)

MA ? 0.75 -2.05 -0.29

(0.69) (1.22) (0.31)

RESTRUCT ? -1.16 -2.69 -1.48

(1.01) (1.58) (1.53)

SALESGROW ? 1.17 -5.00 0.94

(0.77) (0.98) (0.61)

NSEG ? 0.24 0.15 0.20

(1.38) (0.58) (1.36)

LOSS - -1.44 -2.76 -2.09**

(1.48) (1.30) (2.30)

ZSCORE - -1.10*** -1.30*** -1.14***

(4.04) (3.04) (4.81)

ROA + 15.86 -3.46 11.89

(1.19) (0.24) (1.14)

Number of obs. 602 195 797 547 180 727

Adjusted R2 0.04 0.00 0.04 0.21 0.23 0.20

55

Figure 1: External signals for investment opportunities.

This figure illustrates the measurement of the informativeness of external signals for the investment

opportunity sets of two segments (A and B) of a conglomerate firm, as are observed by the headquarters.

The observed external signals are the probability density distribution of investment opportunities (IOS)

inferred from single-segment peer firms operating in the same industries as A and B, respectively. The X

axis stands for the investment opportunities, and the Y axis stands for the probability density.

Panel A

56

Figure 1 (continued)

Panel B: an example of relatively informative external signals

Panel C: an example of relatively uninformative external signals