Internal and External Capital Markets - INSEAD · Internal and External Capital Markets Urs C. Peyer * Department of Finance INSEAD April 25, 2002 Abstract – This study tests the

Internal and External Capital Markets

Urs C. Peyer *

Department of Finance INSEAD

April 25, 2002

Abstract – This study tests the proposition that firms that make efficient use of their internal capital markets can lower the cost of transacting in the external capital market. Using a large panel data set of diversified firms from 1980–1998, I show that diversified firms with an efficient internal capital allocation display a higher propensity to use external capital relative to comparable single segment firms. This result is robust to including other controls, such as measures of information asymmetry, capital needs, relative valuation and firm size. Further, a higher use of external capital by diversified firms relative to single segment firms is associated with a higher excess value, but only for efficient internal capital market users. I also demonstrate the robustness of these findings by employing a sample of firms that experience an increase in expected investment outlays. My findings support predictions from theoretical models, such as Stein (1997), and are consistent with the interpretation that diversified firms with an efficient internal capital market benefit from lower-cost access to external capital by alleviating information asymmetry problems between managers and investors. For helpful discussions and comments, I would like to thank Anil Shivdasani, Jennifer Conrad, Claudio Loderer, Henri Servaes, Steve Slezak and Marc Zenner, Mike Cliff, Eitan Goldman, Maria Nondorf, David Ravenscraft, Jeffrey Wurgler and seminar participants at Arizona State University, Boston College, Darden, Emory, Illinois, INSEAD, London Business School, University of Maryland, University of Miami, University of North Carolina, University of Pittsburgh, University of Toronto and Virginia Tech, the 2001 Young Scholar Conference at the College of William and Mary, the 2001 WFA meetings and the 14th Australasian Finance and Banking Conference. An earlier version of this paper received the 2001 Trefftz Award. *Urs Peyer, Department of Finance, INSEAD, Boulevard de Constance, 77305 Fontainebleau, France. Tel +33 (0)1 6072 4178; Fax +33 (0)1 6072 4045; email: [email protected]

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Internal and External Capital Markets

This paper examines the interaction between internal and external capital markets. For the

purpose of this paper, I define an internal capital market as the mechanism by which headquarters

allocates capital to the various divisions of the firm. If headquarters allocates investment to the

divisions with the highest marginal return, then the firm uses its internal capital market

efficiently. The primary question in this study is whether and how a firm’s internal allocation is

related to its transactions with the external capital market.

Answering this question can help us to better understand how firms finance their

investments. Specifically, are there differences between single segment and diversified firms in

the sources of financing? What characteristics of diversified firms lead to more or less use of

external capital? The answers are important in the light of theories that try to explain the benefits

and costs of diversification.1 A potential benefit of diversification is to establish an internal

capital market (ICM). Creating an ICM can have at least two advantages. First, internal resource

allocation can be more efficient than allocation performed by the external capital market. This

issue is the focus of recent theoretical and empirical research investigating whether diversified

firms use their ICMs to efficiently reallocate capital. Theoretical models and arguments

predicting an efficiency gain from internal capital allocation are found in Weston (1970),

Williamson (1970, 1986), Gertner et al. (1994) and Stein (1997). Alternative models based on

agency conflicts emphasizing the drawbacks of internal allocation are developed by Scharfstein

and Stein (2000) and Rajan et al. (2000). Empirical tests of these models by Lamont (1997), Shin

and Stulz (1998), Scharfstein (1998) and Rajan et al. (2000), among others, suggest that capital is

reallocated internally, but that, on average, the reallocation is inefficient. On the other hand,

Maksimovic and Phillips (2001) and Khanna and Tice (2001) conclude that internal capital

markets are working efficiently by reallocating capital away from low productivity to high

productivity factories or stores.

A second potential advantage of an ICM is its effect on transactions with external capital

markets (ECM). Stein (1997) theoretically analyzes the interaction between the efficiency of

internal capital allocation, the size of the ICM (number of divisions and their correlation in

investment opportunities), the use of external capital and firm value. With information

asymmetries and agency problems between managers and outside investors, firms can be

financially constrained. Potentially, information asymmetry problems can be reduced through an

ICM. Take an external investor who bases her decision about how much to lend to a firm on her

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estimate of the firm’s value-maximizing investment needs. According to the law of large

numbers, the precision of the estimate of the optimal amount of capital increases with the number

of projects in the firm if the projects’ capital needs are imperfectly correlated. The same logic

applies to single segment firms. However, the ICM allows HQ to reallocate capital to the highest

marginal return divisions. Investment in single segment firms cannot be reallocated across

divisions, which exacerbates the under- and overinvestment problem. Thus, lending to a

headquarters that oversees a portfolio of projects with imperfectly correlated capital needs, i.e., a

diversified firm, is different from lending to a portfolio of single segment firms in that

information asymmetry problems are less important. Therefore, diversified firms that allocate

capital efficiently in the ICM and firms with larger ICMs, i.e., firms with more divisions and

lower correlation of divisional investment opportunities, should be able to use more external

capital. On the other hand, HQ of a more diversified firm might loose the ability to efficiently

reallocate capital because HQ itself becomes less informed about all the possible investment

opportunities. Thus the impact of size of the ICM on a firm’s ability to raise external capital

should differ by its monitoring technology, i.e., the efficiency of internal capital allocation.

Diversified firms that are able to alleviate some of the information asymmetry and agency

problems in transacting with the external capital market should have a higher value because they

underinvest less and thus can raise external capital at a lower cost than single segment firms.

However, inefficient ICM users should not be able to access more external capital because they

do not have or do not use superior inside information about their projects. Thus, an external

investor should not be willing to invest more capital in such firms, since headquarters does not

allocate the capital in a value-maximizing fashion.

The above arguments suggest interesting cross-sectional relationships between the efficiency

of the internal capital allocation, the size of the ICM, the use of external capital and firm value.

Analyzing these interactions will further our understanding of the potential costs and benefits of

an internal capital market and hence diversification.

I test the above arguments in two ways. First, I employ a panel of diversified Compustat

firms from 1980–1998. In this sample, diversified firms use, on average, less external capital than

comparable single segment firms. However, the analysis indicates a significantly positive

correlation between the efficiency of internal capital allocation, as proxied by the relative value

added by allocation (RVA) of Rajan et al. (2000), and a firm’s use of external capital. Moreover,

diversified firms with a larger ICM use more external capital only if their internal capital

allocation is efficient. Firms with a large ICM allocating capital inefficiently use significantly less

external capital. In addition, the analysis also suggests that firms that allocate capital more

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efficiently can reduce the impact of information asymmetry problems (e.g., Myers and Majluf,

1984) when raising capital externally. Further, I find a significant relationship between a firm’s

ICM characteristics, its use of external capital and excess value measures (Berger and Ofek,

1995; Lang and Stulz, 1994). Consistent with Rajan et al. (2000), it emerges that firms with a

more efficient internal capital allocation display a higher excess value, and firms with a higher

diversity in their investment opportunities (a proxy for the size of an ICM and potential agency

conflicts) display a lower excess value. A new finding is that firms that use more external capital

have a lower excess value. The important exceptions are firms with an efficient internal capital

allocation and firms with both, an efficient internal capital allocation and a large ICM – these

firms are valued significantly higher if they use relatively more external capital. On average, such

firms even have a positive excess value. The inferences from the first part of the paper, using

panel data on all diversified firms, strongly support the predictions from Stein’s (1997) model that

firms with an efficient internal capital allocation and firms with larger, but still efficient ICMs can

raise more external capital and that doing so increases firm value.

I try to alleviate concerns of endogeneity and simultaneity using different econometric

techniques. However, causality is difficult to establish in this framework. Therefore, I employ a

second approach. Many theoretical models investigating the use of external capital rely on the

assumption that a new, positive-NPV project arrives unexpectedly and that the entrepreneurs’

wealth and/or the firm’s internal funds are insufficient to cover the investment (e.g., Myers

(1977), Myers and Majluf (1984), Li and Li (1996), Stein (1997)). In order to mimic more closely

the setting in which these models are specified, I select a sample of diversified firms that operate

in industries that receive a positive shock to investment opportunities, proxied by industry median

q. To ensure that the change in q does not merely reflect a surprise in current cash flow, I require

that the industry’s median cash flow remain constant. This setting provides a natural experiment

to investigate whether diversified firms that receive an unexpected valuable project use more or

less external financing than comparable single segment firms.2 Consistent with the findings of the

panel data study, diversified firms use more external capital if their internal capital allocation is

more efficient. Also, diversity has a positive effect only if the internal allocation is efficient;

otherwise diversity negatively affects a firm’s use of external capital. As expected from

arguments such as Myers and Majluf (1984), firms with more information asymmetries use less

external capital. This relation is alleviated only if firms internally allocate capital efficiently. In

this setting, firms that are able to raise more external capital should find a profitable investment

opportunity. Nevertheless, the use of external capital is only significantly positively related to

excess value for firms with an efficient internal capital allocation and those with both, a higher

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diversity and a more efficient internal capital allocation. In addition, changes in capital

expenditures at the firm and segment level suggest that the external capital raised is used to

increase investment.

Taken together, the evidence from a sample of firms that experience an exogenous shock to

investment opportunities suggests that ICM characteristics are important determinants of a

diversified firm’s ability to capture new growth opportunities by allowing the firm to use more

external capital. These findings highlight an additional, related advantage for firms with an

efficient ICM, namely easier access to external capital.

Prior empirical research on the interaction between internal and external capital markets

includes Comment and Jarrell (1995), Billett and Mauer (2002), Hadlock, Ryngaert and Thomas

(2001), and Fee and Thomas (1999). The study most similar to mine is Comment and Jarrell

(1995), who test Williamson’s (1970, 1975, 1986) argument that firms with ICMs transact less in

the external capital market. They find that, on average, diversified firms raise less external capital

but return more to their outside investors, and they conclude that there is no clear evidence that

diversification leads to less reliance on external capital markets. However, basing the conclusion

purely on average comparisons between diversified and single segment firms is problematic in the

light of findings by Rajan et al. (2000) and Scharfstein (1998) who show that firms are on average

allocating capital ineffic iently and by Berger and Ofek (1995) who find a valuation discount for

the average diversified firm relative to single segment firms. I extend Comment and Jarrell’s

analysis in several ways. First, I investigate the effects of ICM characteristics such as size and

efficiency on a firm’s use of external capital. Second, I compare diversified firms to their single

segment peers. Third, I control for other factors that may influence the use of external capital.

Fourth, I relate the use of external capital to firm value.

Berger and Ofek (1995) find that diversified firms use more debt, but conclude that the

difference is economically insignificant. Hadlock et al. (2001) find a less negative announcement

return to equity offerings for diversified firms than for single segment firms. Fee and Thomas

(1999) show that diversified firms have lower measures of information asymmetry. They link

those measures directly to excess value and find a negative relationship. My findings suggest that

efficient ICM users can reduce the cost of information asymmetry. Therefore, besides the direct

effect on pricing, there should also be an indirect effect through the firm’s ability to raise more

external capital.

Billett and Mauer (2002) find that diversified firms can increase firm value if capital is

transferred to segments with above-industry-average return on assets that would be financially

constrained if the divisions were single segment firms. However, their study does not analyze

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whether the transfers were made due to relaxed credit constraints at the firm level or whether free

cash flow from other divisions was reallocated.

The remainder of the paper is organized as follows. The next section briefly reviews

underlying theories for my tests. Section 2 describes the sample, the tests, and the results for the

panel data set. In Section 3, I describe tests and show results for the industry shock sample.

Conclusions follow.

1 Underlying Theories

In a world with perfect markets, it does not matter whether investment is funded by internal

or by external capital markets. However, the source of financing can matter in the presence of

informational asymmetry and agency problems. Stein (1997) considers a model where managers

have better information about their projects’ success than external investors and use this

information efficiently to allocate capital to the divisions with the highest marginal return.

Furthermore, he assumes that managers derive private benefits that increase with the resources

under their control and that their tendency to overinvest is costly, such that the external capital

market may impose credit rationing. Under these assumptions, Stein shows that diversified firms

can sometimes raise more external capital than single segment firms and that doing so increases

firm value. A numerical example can help to illustrate the reasoning.

Assume that there are two projects. These two projects can be owned individually by two

single segment firms or a diversified firm can own both projects. Managers know which of the

projects (if any) are going to be successful. External investors, however, only have an ex ante

expectation about the probabilities of each project’s success. Assume that the expected

probability of a good outcome is p = 0.5 and that of a bad outcome is (1 – p) = 0.5. Investment in

the project can be either 1 or 2 units. In the bad state, investing 1 unit in a project results in a

verifiable gross return (y1) of 1.1, and investing 2 units results in y2 = 1.9. Therefore, the optimal

level of investment in the bad state is 1 unit. In the good state, I assume that the project’s return is

scaled up by a factor, θ = 1.4, such that θ (y2 – y1) > 1, which implies that the optimal investment

per project is 2 units in the good state. Without a revelation scheme, investment cannot be made

state-contingent and external investors have no means of telling which projects are good or bad.

Thus, the question an external investor faces is whether to invest 1 or 2 units per project when

projects are organized as single segment firms. Investing 1 unit in a single project firm provides

an expected NPV of pθ y1 + (1 – p)y1 – 1 = 0.32. Investing 2 units in a single project firm results

in an expected NPV of pθ y2 + (1 – p)y2 – 2 = 0.28. Therefore, external investors optimally invest

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only 1 unit in single segment firms. Information asymmetries and agency problems thus result in

external capital constraints.

If a diversified firm owns both projects, the external investor determines the optimal

investment by computing the expected NPV per project for 2, 3 or 4 units of total capital raised.

Under the assumption that outcomes are independent across projects, the value of having an ICM

can be easily computed. If the external investor invests 2 units, the expected NPV is the

probability weighted average of the projects’ returns. The assumption that headquarters allocates

funds to the project with the highest marginal return, i.e., ICM efficiency, is now important in

determining the expected NPV, which is 2(1 – p)2y1 + 2p2 θ y1 + 2p(1 – p)θ y2 –2 = 0.65. Per

project, the expected NPV is 0.325, which is larger than the 0.32 that could be expected from a

single segment firm realizing a project.3 If the external investor invests 3 units, the expected NPV

is (1 – p)2(y1 + y2) + p2 θ (y1 + y2) + 2p(1 – p)(y1 + θ y2) – 3 = 0.68. Per project, the expected NPV

has increased to 0.34. The additional increase in value is due to the fact that, on average, more

positive NPV projects can be realized and only in one instance (both projects in the bad state) is

there more overinvestment compared to the previous scenario. As long as the expected benefit

from realizing more positive NPV projects is bigger than the cost of overinvesting, a diversified

firm can relax credit constraints relative to single segment firms. If the diversified firm was

allowed to raise 4 units, however, the expected NPV would drop to 0.56, which results in an

expected NPV per project of only 0.28. In this case, no reallocation occurs because each project is

always investing 2 units and a diversified firm is not more valuable than two separate single -

project firms.

Thus far, the example has shown that a diversified firm with an efficient ICM can use more

external capital and increase firm value. The increase in firm value has two sources. First, the

ability to transfer funds to the highest marginal return project (winner picking) is valuable. The

expected NPV per project increases from 0.32 for a single segment firm to 0.325 for a diversified

firm with an efficient ICM purely by combining two projects. Second, combining two projects

under the supervision of one headquarters can result in lower costs of information asymmetries.

In the example, the expected NPV per project of 0.325 in a diversified firm with 2 units of

investment increases to an expected NPV per project of 0.34 if the diversified firm receives 3

units of investment. This increase in value reflects a reduction in the cost of information

asymmetry.4 Note that this benefit only exists if the firm is using its ICM efficiently. Only then

can the external investor benefit from headquarters’ superior information by delegating the

investment allocation decision to management. To see this, assume the CEO has a pet project in

which she always invests 2 units, regardless of the project’s outcome. If the firm could raise 3

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units of investment, then the expected NPV of the firm would be 0.60. Per project that is an

expected NPV of 0.30, which is lower than for a single project firm getting only 1 unit of

investment per project. Hence, efficient internal allocation is an important characteristic of an

ICM with respect to transactions in the external capital markets.

Allowing headquarters to increase the number of projects under its control makes it even

easier for outside investors to invest in the diversified firm. Assume an extreme case in which a

diversified firm owns 100 projects and each project’s outcome is independent. According to the

law of large numbers, an external investor would now expect roughly 50 projects in the good state

and 50 in the bad state. She would be willing to invest almost at the first-best level of, on average,

150 units. Therefore, a firm with a larger ICM should be able to use more external capital.

However, this prediction is based on the assumption that HQ can monitor many divisions without

a decrease in the quality of allocational efficiency. It also leads to the counterfactual prediction

that one huge firm could maximize value by making information asymmetry issues unimportant.

Extending the model, Stein (1997) shows that if monitoring becomes harder the more divisions a

firm accumulates, then allocational efficiency decreases with the size of the ICM. This suggests

that the size of an ICM might be non-linearly related to a firm’s ability to use more external

capital. Combining the two characteristics of an ICM, size and allocational efficiency, the model

predicts that firms with an efficient internal capital allocation and a large ICM use the most

external capital. Firms with a large ICM but inefficient allocation should use the least. Similarly,

the use of external capital should have a positive effect on firm value if the firm has an efficient

internal capital allocation or both an efficient internal capital allocation and a large ICM. A

negative relation between the use of external capital and size of the ICM is predicted if the

internal allocation is inefficient.

While this example is clearly a simplified version of investment allocation, it serves to

highlight that firms with efficient and larger ICMs (more divisions, and divisions with less-

correlated outcomes) should be able to relax some of the credit constraints otherwise imposed on

single segment firms and reduce the cost of information asymmetries. It also shows that the per-

project value of the diversified firm that raises more external capital should be higher than that of

both a diversified firm that does not raise more external capital and a single project firm.

Other papers, such as Stulz (1990), Froot et al. (1993) and Li and Li (1996) argue that if

diversification reduces cash flow volatility, the likelihood of over- and underinvestment is

reduced, and cash flows are more certain to cover the existing debt. Therefore, newly raised

external funds are less likely to be used to pay existing debt. This implies that a diversified firm

8

that has to finance a new, positive-NPV project with external capital before the existing debt is

due is more likely to receive external financing than is a single segment firm for a similar project.

Fluck and Lynch (1999) show that firms acquire marginally profitable single segment firms

that, because of agency problems, cannot find external financing as stand-alone firms. Within a

diversified firm, however, the conglomerate can raise funds sufficient to finance the marginally

profitable segment. Thus, diversified firms should be able to raise more external financing than

comparable single segment firms, and this should be value enhancing, even though diversified

firms might trade at a discount relative to their industry median peers.

Matsusaka and Nanda (2001) model a firm’s need to raise external capital for different levels

of internal resources. They assume a fixed deadweight cost of external capital, independent of

whether a diversified or single segment firm raises capital. In their model, an ICM is valuable

because it allows the diversified firm to avoid external financing in more instances than single

segment firms. However, there are cases where internal capital is insufficient and diversified

firms raise more external capital than comparable single segment firms, and doing so is valuable.

Matsusaka and Nanda conclude that efficient ICM firms do not necessarily access external capital

markets less often. Their analysis, however, holds properties of the internal capital market

constant, and does not address interactions between ICM and ECM for different organizational

forms. Important for this study is their finding that the level of internal capital available is a

significant determinant of external capital use.

In summary, the tests focus on the following predictions: (i) Firms with an efficient internal

capital allocation should be able to use more external capital. (ii) While more diversified firms

will face more difficulty in allocating capital efficiently, those that are efficient should have a

positive correlation between the size of the ICM and the use of external capital. (iii) Information

asymmetry problems will drive a wedge between the cost of internal and external capital, as in

Myers and Majluf (1984). These costs can be reduced if internal capital allocation is performed

efficiently. Therefore, measures of information asymmetry should be negatively correlated with a

firm’s use of external capital, although, less so for firms with an efficient internal capital

allocation, i.e., firms with better monitoring.

With respect to firm value, the model predicts: (iv) a positive correlation between efficiency

of internal capital allocation and firm value5 (v) a positive relation between the use of external

capital and value for efficient allocators, and for large diversified firms with an efficient internal

capital allocation.

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2 Panel Data Sample

2.1 Sample Selection

I use all firms listed on Compustat’s industry segment files (including research files) for

1980–1998. Firms with incomplete segment information on sales, assets or capital expenditures

are dropped, as are firms with segments in the one-digit SIC codes of 0, 6 or 9.6 Firms with sales

less than $10 million are also excluded.7 Following Berger and Ofek (1995), I require the sum of

the segment sales to be within 1% of the net sales for the firm and the sum of the segment assets

to be within 25% of the firm assets. I apply a multiple to the remaining segment assets, such that

the sum of the recomputed segment assets adds up to total assets. I further restrict the sample to

firms with complete information on market value of equity and cash flow statement items.

Diversified firms are also dropped from the sample if imputed values for the segments are

missing. Imputed values are computed at the 3-digit SIC code level using only single segment

firms (at least five) with available data to compute the industry median.8 Additionally, firms are

excluded if their one year lagged value(s) of the variables described below is (are) missing. In

effect, this limits my sample to firms that survive any two-year period and have complete data

available in both years. Furthermore, to reduce endogeneity concerns, I use lagged values as a

proxy for ICM efficiency; therefore, I require that the lagged number of segments be at least two.9

Finally, I require that the firms have daily stock returns available on CRSP for at least 30 days in

the previous year in order to compute return volatilities.10 Imposing all of the data requirements

results in a sample of 8,538 diversified firm-years spread over the period 1981–1998. Over the

same time period, there are 34,065 single segment firms that pass the same screening process.

The number of diversified firms is fairly evenly distributed over time (not shown). There are

4,983 firm-years with 2 segments, 2,341 with 3 segments, 903 with 4 segments and 312 with 5 or

more segments.

2.2 Determinants of the Use of External Capital

According to Stein (1997), the key drivers of a firm’s use of external capital are the

efficiency of the internal capital allocation, the size of the ICM, and the degree of information

asymmetry. In addition, use of external capital can be affected by a firm’s need for capital and its

relative valuation.11 I estimate the following cross-sectional regressions for firm i and year t:

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. value)(relative need) (capital

)efficiency ICM asymmetry on (informati asymmetry)n informatio(

)efficiency ICM size (ICM )efficiency ICM size (ICM

)efficiency (ICMsize) (ICM size) (ICM

capital externalnet Excess

1 ,9 ,8

1 ,71 ,6

21,51 ,4

1 ,32

1,21 ,1

,

−

−−

−−

−−−

++

×++

+×+×+

+++

=

titi

titi

titi

tititi

ti

γγ

γγ

γγ

γγγα

(1)

The subsequent sections describe the proxies used for the above variables and their

univariate statistics. Detailed definitions for all variables are given in Appendix 1.

2.2.1 Dependent Variable: Use of External Capital

I compute a measure of net external capital raised by diversified firms in excess of that

raised by single segment firms as follows. First, I compute net external capital as the proceeds

from the sale of debt, common and preferred stock minus the amount of debt retired and common

and preferred stock repurchased. To make the use of external capital comparable between

diversified and single segment firms, I compute an imputed value of net external capital based on

the median of single segment firms in the same 3-digit SIC code as the divisions of the diversified

firm. I match the median ratio of net external capital to sales to the individual segments of the

diversified firms according to year and industry. Then I multiply each segment’s imputed ratio by

segment sales and add up all of the imputed net external capital values to form a firm-level

imputed net external capital amount. I call the final measure Excess Net External Capital (EEC)

and compute it as follows:

assets of book value Laggedcapital externalnet Imputed capital externalNet

−

=capital external Excess net

A positive EEC implies that a diversified firm raises more net external capital than do

comparable single segment firms in its industries.

Table 1 shows that the median and mean EEC for diversified firms are significantly below

the median and mean EEC for single segment firms. The median for diversified firms is

–0.00549 and is significantly different from zero, which is the median of single segment firms, by

construction. The mean for diversified firms is 0.02966 and is significantly different from zero at

the 1% level, but this average is still significantly below the single segment mean, which is

0.04442. Consistent with Williamson (1975) these numbers suggest that diversified firms use

external capital markets less extensively than single segment firms. However, the use of external

capital should depend on a firm’s ICM characteristics. Thus a multivariate analysis is needed.

Further, the univariate test statistics should be interpreted with caution because the panel data

observations are not independent.

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2.2.2 Measures of the Size of the ICM

The measure of size of the ICM has three basic aspects. The first is the number of different

operations or divisions; the second is the correlation of investment opportunities between these

divisions; the third is the size differences between the divisions. A measure of ICM size that

encompasses all three aspects is diversity, used by Rajan et al. (2000). Diversity is defined as the

standard deviation of the segment asset-weighted imputed q divided by the equally weighted

average imputed segment q. There is a significantly positive median for diversity of 0.286 (Table

1). A higher value of the variable can indicate more divisions, less dependence in the segments’

investment opportunities, and/or segments of more equal size. Thus, according to Stein (1997), a

higher value of diversity should be positively related to EEC if and only if a firm is internally

allocating capital efficiently.

Alternative measures that capture individual aspects of ICM size are the number of business

segments a firm reports or the inverse of the Herfindahl Index. For more detailed definitions see

Appendix 1.

2.2.3 Measures of ICM Efficiency

Stein (1997) shows that it is critical that headquarters be good at distinguishing between

good and bad projects and that the internal allocation be efficient. An ICM is considered efficient

if investment is allocated to the projects/segments with the highest marginal return.

I use two proxies to measure the allocational efficiency of the ICM. The first measure is the

relative value added by allocation (RVA) introduced by Rajan et al. (2000). I compute RVA as

follows:

( ) 11

−−−−= ∑∑

==

n

jss

ss

j

jjss

ss

j

jn

jj

j

j

j

j

j

BA

Capex

BA

Capexw

BA

Capex

BA

Capexqq

BA

BARVA ,

where BA is book value of assets of the firm, BAj is the book value of assets of segment j,

Capex is the firm’s capital expenditures, ss

j

ss

jBACapex is the asset-weighted average ratio of

single segment firms in the same industry as the segment of the diversified firm, wj is the ratio of

segment j assets to firm assets, qj is the asset-weighted Tobin’s q of single segment firms

operating in the same three-digit SIC industry as segment j, and q is the segment sales-weighted

qjs of the firm. BA, qj and wj are beginning-of-the-period values.

The expression ss

ss

j

j

j

j

BA

Capex

BA

Capex− is a proxy for transfers made between segments of a

diversified firm. It compares the segment’s investment ratio to the asset-weighted average

12

investment ratio of single segment firms in the same industry. The latter serves as a proxy for

what a segment’s investment ratio would have been were it a stand-alone entity.

∑=

−

n

jssj

ssj

j

jj BA

Capex

BA

Capexw

1 is a proxy for the overall funds available to a diversified firm relative

to its single segment peers. This term is subtracted from the industry-adjusted investment ratio to

correct for potential differences in availability of total capital that should not count as transfers.

( )qq j − identifies segments within a firm that have better-than-firm-average investment

opportunities. Thus, a firm with an efficient ICM should have a positive RVA because it transfers

capital to segments with better-than-firm-average investment opportunities and invests more than

single segment peers do in those segments.12

A second measure of the efficiency of internal allocation, used by Peyer and Shivdasani

(2001), is q-sensitivity of investment. q-sensitivity is defined as follows:

SalesFirmSalesFirm

CapexFirm

SalesCapex

qqSalesn

j jjj∑

=

−

×−×1

)( ,

where qj is the imputed Tobin’s q of segment j and q is the segment sales-weighted qjs of

the firm. Capex is the capital expenditures of the segment, and Firm Capex is the capital

expenditures of the firm. This measure is positive if a segment with a q above the firm’s average

q has an above-firm-average investment ratio (capital expenditures/sales) and a segment with

below-average q has a below-firm-average investment ratio. Therefore, q-sensitivity indicates

whether headquarters has invested relatively more in the high-q segments of the firm and

relatively less in the low-q segments based on the firm’s available resources.

A third measure is based on Maksimovic and Phillips (2001) and Schoar’s (2001) analysis of

the effect of differences in segment productivity. These papers suggest that a firm is efficiently

allocating capital if more investment is allocated to divisions with above average productivity. As

a proxy for segment productivity, Maksimovic and Phillips (2001) show that segment cash flow

can be used. I construct a measure called cash flow-sensitivity as in Peyer and Shivdasani (2001),

where the expression ( )qq j − in q-sensitivity is replaced with ( )cfcf j − . cfj is the cash flow to

sales ratio of segment j and cf is the average cash flow to sales ratio of the firm. This measure

also serves as a robustness check because it does not rely on imputed values but rather on

individual segment level information. 13

The measures of ICM efficiency use capital expenditures to proxy for segment investment.

Because the amount of capital expenditures is, in part, determined by a firm’s use of external

13

capital, the proxies for ICM efficiency are potentially simultaneously determined with my proxy

for a diversified firm’s use of external capital. To alleviate this problem, I use lagged values of

the measures of ICM efficiency as instruments.

Table 1 shows univariate statistics RVA, q-sensitivity and cf-sensitivity. RVA (cf-sensitivity)

has a median of –0.000005 (0) and a mean of –0.0004 (–0.00008) that is significantly negative at

the 10% level.14 Q-sensitivity has a median of 0 and a significantly positive mean of 0.001. For

single segment firms, these measures are always zero by definition.

Also interesting are the univaria te statistics in Panel B of Table 1. Mean and median EEC are

reported for firms stratified by RVA and diversity. Consistent with Stein’s (1997) predictions,

diversified firms with positive RVA and a measure of diversity larger than the sample median

diversity display the highest EEC. Firms with negative RVA and a measure of diversity larger

than the median display the lowest EEC indicating that ICM size can have very different effects

on a firm’s use of external capital depending on the efficiency of internal capital allocation.

2.2.4 Measures of Information Asymmetry

I use several measures for the degree of information asymmetry. First, I use the lagged ratio

of intangibles to total assets, expecting it to be negatively related to EEC. The advantage of this

ratio is that it is not affected by prices set in the external capital market, i.e., using this proxy, it

should be possible to identify the degree of information asymmetry that exists between managers

and outside investors.15

A second set of proxies is based on prices. Following Dierkens (1991) and Fee and Thomas

(1999), I compute residual variance and total variance of the daily stock returns over a calendar

year prior to the fiscal year-end. I use a market model to extract daily residual returns and

compute the variance over all of the available daily residual returns. The CRSP value-weighted

index, including dividends, is used as a proxy for the market return. As shown in Table 1, the

median daily residual variance (total variance) for diversified firms is 0.00057 (0.00063) and the

median for single segment firms is 0.00094 (0.00101).

As a third set of proxies, I use IBES analysts’ forecasts about a firm’s earnings per share. I

construct a standardized measure of analysts’ forecast dispersion using the standard deviation of

the one-year-ahead forecast of earnings per share standardized by the absolute value of the

average forecast. A higher value of this measure is expected to indicate greater information

asymmetry because it reflects a wider range of forecasts about the future earnings of a company.

The standardized analysts’ forecast dispersion could be computed for only 4,021 firm-years. For

3,370 firm-years, IBES information is missing. Another 1,147 observations are lost because only

one analyst’s forecast is available, and no standard deviation can be computed.

14

To test the argument made in section 1, that a diversified firm with an efficient internal

capital allocation can alleviate the effect of information asymmetry on the firm’s use of external

capital, I compute the following interaction variable. I create a dummy variable (RVADUMt–1)

that is equal to one if RVA at the beginning of the year (t-1) is greater than or equal to zero and

interact it with a proxy for information asymmetry.

2.2.5 Measures of Capital Need

Stein’s (1997) theory is based on the assumption that an entrepreneur has to raise external

capital for his projects because the financing needs exceed his personal wealth. Internally

generated cash flow from previous years is exogenous to the model. Matsusaka and Nanda (2001)

show that higher levels of internal capital reduce the need for costly external capital. As a proxy

for internal capital available to the firm, I compute excess internal cash flow. Internal cash flow is

defined as net cash flow from operations minus dividends. Excess internal cash flow is computed

in a similar way as EEC.16 I expect a firm with more excess internal cash flow to cover more of

its capital needs with internal capital. Hence a negative relation between excess internal cash flow

and EEC is expected, holding everything else constant. Table 1 shows that the median of excess

internal cash flow is significantly positive for diversified firms. Further, both median and mean

excess internal cash flow are higher for diversified firms than for single segment firms.

The need for capital is also determined by the available growth opportunities. As a proxy for

growth opportunities, I use the firm’s Tobin’s q at the beginning of the period. I expect firms with

a higher q to be in greater need of capital, holding everything else constant. One complication in

using q is that it might also be a proxy for information asymmetry. If so, one would expect a

negative relation between q and EEC.

2.2.6 Measures of Relative Valuation

Myers & Majluf (1984) show that firms that are overvalued are more likely to issue risky

new securities. Findings by Lucas and McDonald (1990), Asquith and Mullins (1986), Mikkelson

and Partch (1986), and Jung et al. (1996) confirm that firms are more likely to issue new

securities when their relative valuation is high. I use the stock return over the prior fiscal year and

lagged excess value as proxies for relative valuation. I follow Berger and Ofek (1995), and define

excess value as follows:

Excess value =

)(

logVIV , and ( )[ ]MSi

ni i SalesVMSalesVI /)( 1 ×= ∑ = ,

where V is the sum of market value of equity and book value of assets less the book value of

equity and deferred taxes, I(V) is the imputed firm value, Salesi is segment i’s sales, Mi(V/Sales)MS

15

is the sales multiplier (calculated as the median of the single segment firms in the same 3-digit

SIC code industry), and n is the number of segments per firm. An alternative way to compute

excess value is developed by Lang and Stulz (1994). They compute excess value as the difference

between Tobin’s q of the diversified firm and the segment asset weighted average of imputed

segment qs. Their imputed q is the average of the single segment firms’ qs. I compute the log of

the ratio of the firm’s Tobin’s q to the sum of segment sales-weighted imputed qs. The imputed

qs are median qs of single segment firms in the same 3-digit SIC code industry. A positive

relation to EEC is expected if higher stock returns and excess values indicate higher relative

valuations.

2.3 Results

Table 2 reports the regression results of equation (1) with EEC as the dependent variable.

Since the predictions relate to cross-sectional differences, Table 2 reports time-series averages of

coefficients of cross-sectional regressions run year-by-year. The reported t–statistics are based on

the time-series variation in the coefficients. This procedure is similar to that of Fama and

MacBeth (1973) and is also used in Rajan et al. (2000).17

Models 1–4 show results using different measures of the size of the ICM . In model 1 the

coefficient on diversity is significantly negative (–0.003). However, the interaction term with

allocational efficiency, RVADUM, is significantly positive (0.005). This supports the predictions,

and is consistent with the univariate statistics in Panel B of Table 1. The results still hold even

after introducing diversity squared as shown in model 2. Only firms with an efficient internal

capital allocation display a significantly positive relation with EEC. Similar inferences can be

drawn from the other two proxies of ICM size, the number of segments and the inverse of the

Herfindahl Index. For example, model 3 shows that the coefficient on the number of segments is

significantly negative (–0.026) but the interaction with RVADUM is significantly positive (0.040).

Increasing the number of segments from one to two, i.e. diversifying, decreases EEC by 0.026,

unless the firm has a positive measure of RVA, in which case, EEC increases by an overall 0.014

(0.040–0.026). Given the average difference in EEC between single segment and diversified firms

of 0.015, it seems that a change in the number of segments has an economically significant

impact on a firm’s use of external capital.

The coefficients on the proxies for ICM efficiency are always positive and significant at the

5% level. In model 1, the coefficient on RVA is 0.879. The point estimate suggests that a one-

standard deviation increase (0.0195) in RVA increases EEC by 0.017. Again, this corresponds to

about the average difference in EEC between single segment and diversified firms. Model 5 uses

16

q-sensitivity instead, and finds a significant positive coefficient. Firms with a more efficient

internal capital allocation use more external capital, consistent with Stein (1997). This conclusion

is also supported by model 6 where the coefficient on cf-sensitivity is significantly positive

(0.019). Thus, firms that allocate more investment to divisions with above firm average

productivity use more external capital.18

The measures of information asymmetry are expected to be negatively related to EEC. Of

specific interest for the tests in this paper is whether diversified firms with a more efficient ICM

display a less negative sensitivity to information asymmetry. Model 1 reports that the coefficient

on the lagged ratio of intangibles to total assets (–0.176), and the coefficient on residual variance

(–6.544) are significantly negative supporting Myers and Majluf (1984). Moreover, the

coefficient on the interaction variable between the lagged ratio of intangibles to total assets and

RVADUMt–1 is significantly positive (0.159). None of the implications change if total variance is

used instead of residual variance, as shown in model 7. Model 8 shows that the standardized

analysts’ forecast dispersion is significantly negatively related to EEC, with a coefficient of

–0.009.19 The coefficient on the interaction variable between the standardized analysts’ forecast

dispersion and RVADUMt–1 is significantly positive (0.003). The finding that the interaction

variables display a positive correlation with EEC is consistent with the notion that firms with an

efficient internal capital allocation can overcome some of the information asymmetry problems in

transacting with the external capital markets.

The proxies for need for capital are excess internal capital and beginning-of-the-year

Tobin’s q. Excess internal capital is significantly negatively related to EEC in every model. The

coefficient of –0.725 in model 1 suggests that a firm that has one dollar more internal capital than

a comparable single segment firm will use about 72.5 cents less external capital than its single

segment peers. Note that this coefficient is also significantly different from one, thus further

supporting the notion that market frictions make internal and external capital imperfect

substitutes. Tobin’s q is significantly positively related to EEC.

The coefficients on the measures of relative valuation are significantly positive in all the

models (in three cases, excess value is significant only at the 10% level). In model 1, the lagged

annual stock return has a coefficient of 0.037, and is significant at the 1% level. The coefficient

on lagged excess value is 0.009, and significant at the 5% level. Model 9 shows that the

coefficient on lagged excess value computed according to Lang and Stulz (1994) is also

significantly positive (0.037). These findings are consistent with the interpretation that firms are

more likely to issue new securities when their relative valuation is high.

17

Overall, the regression results show that firms with more divisions and with more

independent divisions use more external capital relative to their single segment peers only if the

firm is allocating capital efficiently in its ICM. Also, such firms can alleviate the impact of

information asymmetry problems when accessing external capital markets and use more external

capital. These findings suggest that the use of external capital significantly depends on the

characteristics of the ICM.

2.4 Robustness

In this section I examine the robustness of the findings presented in Table 2 by investigating

issues of supply and demand of external capital, including single segment firms in the analysis,

employing a different econometric method and by using different definitions of the dependent

variable. The exact definitions of the alternative dependent variables are given in Appendix 1.

Table 1 reports their univariate statistics.

2.4.1 Supply and Demand of External Capital

In essence, equation (1) is a reduced form of a supply equation and a demand equation for

external capital. Thus, a priori, it is not clear whether the coefficients on ICM size and efficiency

reflect supply side effects, as Stein’s (1997) model would imply. I perform the following test to

address the question of whether changes in the ICM characteristics affect the supply of external

capital holding demand constant.

I select a sample of diversified firms where the demand for external capital is held constant

but ICM characteristics are allowed to vary. Thus, holding demand constant, the coefficients

should reflect the effects of changes in the supply of external capital. This sample consists of

firms where the change in Tobin’s q, as a proxy for future investment opportunities, and the

change in internal cash flow between two consecutive years is within plus or minus 5%. 330 firms

pass this screen. Table 3 reports OLS regression results testing the following equation:

ies)opportunit investment( flow)cash internal(

)efficiency ICM asymmetry n informatio( asymmetry)n informatio()efficiency ICM size ICM(

)efficiency ICM( size) ICM(

76

5

43

21

∆+∆

+∆×∆+∆+∆×∆

+∆+∆+=∆

ββ

βββ

ββαEEC

(1')

In Table 3, all three models display significantly positive coefficients on the change in

diversity (0.035 in model 1) and the change in RVA (0.439 in model 1). Further, the coefficient

on the change in residual variance is marginally significantly negative (–1.466 in model 1)

indicating that firms with an increase in information asymmetry experience a reduction in the

supply of external capital unless they improve their allocational efficiency (coefficient of 1.122

18

on the interaction variable between the change in residual variance and a dummy variable equal to

one if RVA has increased). Models 2 and 3 additionally control for the changes in the demand for

external capital. With the exception of the coefficient on annual return (0.069), no demand side

coefficient is significantly different from zero. Furthermore, the increase in R-squared from

adding all the controls for the demand of external capital is only a marginal 0.018 from 0.182

(model 1) to 0.200 (model 2). In summary, the results reported here are at least not contradicting

the notion that the market’s supply of external capital is dependent on ICM characteristics.

2.4.2 Including Single Segment Firms

Even though I have defined an ICM as the mechanism by which HQ can allocate capital to

the different divisions, one could also argue that a single segment firm’s management allocates

capital in much the same way but just to different projects within the firm. Thus, the question

arises whether single segment firms are really different from diversified firms in their ability to

reduce the impact of information asymmetry on the use of external capital based on their

allocational efficiency.

The main measure employed thus far to proxy for the efficiency of internal capital

allocation, RVA, is zero by definition for all single segment firms. To allow for cross-sectional

variation among focused firms I employ a measure called the absolute value added by allocation

(AVA is defined in Appendix 1) used by Rajan et al. (2000).

Table 4 shows the results. As indicated by the coefficient on the multi-segment dummy of

–0.011, diversified firms, on average, use less external capital than comparable single segment

firms. This is consistent with the findings in the univariate analysis.

AVA is significantly positively related to EEC with a coefficient of 0.559. However, the

interaction variable between AVA and the multi-segment dummy is not significantly different

from zero.20 More interestingly, for single segment firms with a positive AVA there is no

significant reduction in the impact of information asymmetry on the use of external capital. This

is in stark contrast to the positive and significant coefficient on the interaction variable between

AVADUM, the multi-segment dummy and the measure of information asymmetry.

The results suggest that there are differences in the effect of internal capital allocation

between single segment and diversified firms.21 Only in diversified firms do we observe a

significant reduction in the impact of information asymmetry. The finding that AVA is positively

related to EEC is probably less surprising because firms that invest more than the median single

segment firm in the industry probably also use more external capital to finance their investment

than the median firm. Such an almost mechanical relation is not present in the RVA measure, but

it is precisely the reason why RVA is zero for all single segment firms. Thus, it is all the more

19

surprising that single segment firms cannot relax information asymmetry problems in the same

way that diversified firms can.22 Having shown that adding single segment firms has no effect on

the inferences drawn solely based upon the cross-sectional analysis of diversified firms, I

concentrate on diversified firms only.

2.4.3 Econometric Methodology

As an alternative to reporting time-series averaged coefficients from year-by-year cross-

sections, model 1 of Table 5 shows results using firm fixed-effects regressions. The coefficients

and their significance levels are very similar to those reported in Table 2. None of the above

conclusions are affected. However, the coefficients on Tobin’s q and excess value are now

insignificant, and the coefficient on residual variance decreases (significant at the 10% level).

2.4.4 Excess Net External Capital with Dividends and Interest

EEC, as defined thus far, does not consider dividends and interest payments as a decrease in

external capital. In model 2 of Table 5, the regressions are re-estimated using EEC including

interest and dividends as the dependent variable. None of the coefficients are significantly

different from the base case in model 2 of Table 2. Note that in this model, the definition of

excess internal capital is altered to include interest and dividend payments.23

2.4.5 Excess Net External Capital with Asset Sales

EEC does not consider proceeds from asset sales as an increase in external capital. There are

several reasons for this. First, determinants of asset sales are likely to be quite different from

determinants of new debt and equity issues. For example, Shleifer and Vishny (1992) show that

selling assets in a depressed industry can lead to relatively low sales prices because asset markets

become very illiquid. Schlingemann et al. (2001) find evidence that asset market liquidity is an

important determinant of which division is sold. In addition, Gertner et al. (1994) demonstrate

that diversified firms, especially those with efficient internal capital allocation, can redeploy

poorly performing assets internally and therefore reduce their transactions in the asset market.

Thus, a firm that allocates capital efficiently is expected to raise more external capital in the

financial markets but raise less capital by transacting in the asset markets. I use the sum of the

Compustat items ‘sale of property, plant and equipment’ and ‘sale of investment’ as a proxy for

asset sales, add it to net external capital raised, re-compute EEC and show the regression results

in model 3 of Table 5. The main difference from the regression using the base definition of EEC

is that the coefficient on RVA and its significance decrease. Thus, the asset market appears to be

used differently by firms with an efficient internal capital allocation. However, a complete

evaluation of these differences is beyond the scope of this paper.

20

2.4.6 Equity versus Debt Transactions

The measure of the use of external capital does not differentiate between transactions in the

debt and equity markets. According to Myers and Majluf (1984), however, one would expect that

information asymmetry problems have a more significant influence on raising equity than debt.

Thus, if allocational efficiency can reduce the cost of information asymmetry, the coefficient on

the interaction variable between information asymmetry and RVADUM should be especially

significant in a regression with new equity issued as the dependent variable. However, as long as

debt is also risky, information asymmetry will also affect a firm’s use of debt.

Excess increase in equity and excess increase in debt are used as dependent variables in

models 4 and 5 of Table 5 to study the effect of the size of the ICM and the allocational

efficiency. RVA is an important determinant of a firm’s use of equity as well as debt. Both

sources of external financing are also significantly negatively affected by information asymmetry

problems, and information asymmetry costs are reduced for firms with an efficient internal capital

allocation raising equity and debt. However, the coefficients are larger and more significant in the

equity regression than in the debt regression, indicating that the importance of allocational

efficiency is higher in the equity market than in the debt market.

Taken together, the robustness tests support the results shown in Table 2. Firms with an

efficient internal capital allocation and larger ICMs are able to alleviate some of the credit

constraints faced by single segment firms in transacting with the external capital markets.

2.5 Excess Value and Excess Net External Capital

In the previous section, I have found that ICM characteristics, such as the size and efficiency

of capital allocation are significant determinants of firm’s use of external capital. The question

now is whether the use of external capital has any impact on firm valuation – and, if so, what is

the direction? As demonstrated in section 1, Stein (1997) predicts a positive correlation between a

firm’s use of external capital and value for diversified firms with an efficient internal capital

allocation and firms with a large and efficient ICM.

I use excess value as a proxy for firm value relative to comparable single segment firms.24

The median (mean) excess values for diversified firms are reported in Table 1 as –14.87%

(–16.34%) using Berger and Ofek’s sales multiplier method and –15.54% (–13.37%) using Lang

and Stulz’s method. These means and medians are all significantly negative at the 1% level.25

Table 1, Panel C shows univariate statistics for excess value for firms stratified by RVA, EEC

and diversity. Interestingly, firms with positive RVA, high diversity and high EEC are firms that

display a positive median excess value of 3%. Firms with negative RVA, high diversity and high

21

EEC have a negative median excess value of 33%. Given the criticism by Graham et al. (2002),

Campa and Kedia (2000) and others about the validity of inferring value destruction due to

diversification from average excess value measures, the following tests exploit cross-sectional

differences rather than absolute levels.

The tests are based on the following model using firm i and year t:

1 ,81 ,7

1 ,6

1 ,51 ,4

1,31 ,21 ,1

,

value)excess(size) ICM of measure EEC(

size) ICM of measure EEC(

) size ICM of measure(size) ICM of measure(

) EEC(RVA)( EEC)(

valueExcess

−−

−

−−

−−−

+××+

×+

×++

×++++

=

titi

ti

titi

titititi

ti

RVADUM

RVADUM

RVADUM

γγ

γ

γγ

γγγβα

(2)

where iα is the firm-fixed effects, tβ is the year-fixed effects, EEC is the excess net external

capital and RVA is the relative value added by allocation. Beginning-of-the-period values of the

independent variables are used as instruments for the contemporaneous values to alleviate

endogeneity and simultaneity issues. Section 3 below presents results using an exogenous event

to further investigate issues of causality that clearly arise from equations (1) and (2).

Lamont and Polk (2001) find that the change in excess value is negatively related to the

lagged level of excess value indicating mean reversion in excess value. To control for this, I

include lagged excess value as an additional independent variable.

Table 6 reports firm fixed-effects regressions of equation (2). I use firm fixed-effects to

alleviate concerns that the status of diversification is endogenous and to control for other

unobservable cross-sectional heterogeneity (e.g., Campa and Kedia, 2000; Graham et al., 2002;

Fluck and Lynch, 1999).

In model 1 of Table 6, the coefficient on EEC is 0.068 but not significant.26 RVA is

positively related to excess value, consistent with the findings in Rajan et al. (2000). The

coefficient on RVA is 0.278, and is significant at the 5% level. Furthermore, the interaction

variable between EEC and RVADUM has a significantly positive coefficient, indicating that firms

with an efficient internal capital allocation that use more external capital are those with higher

valuation. The coefficients on diversity and the interaction between EEC and diversity are both

negative, albeit only the former significantly. This finding is consistent with Rajan et al. (2000)

and Lamont and Polk (2002) who find that higher diversity is associated with lower value. More

interestingly, the interaction between EEC, RVADUM and diversity is marginally significantly

positive with a coefficient of 0.069. Taken together, the data provide support for the argument

that firms that allocate capital efficiently, have a larger ICM and use more external capital are

22

also valued higher, i.e., interactions between internal and external capital markets do seem to

affect valuation. However, the insignificant coefficient on EEC is inconsistent with Stein (1997)

and seems to suggest that inefficiently allocating firms that use more external capital are not

significantly valued less.

In model 2, the number of segments rather than diversity is used as a measure of ICM size.

The results remain basically unchanged. One exception is that the coefficient on the number of

segments is not significantly negatively related to excess value, whereas diversity in model 1 is.

However, this finding is consistent with Lang and Stulz (1994), who show that excess value does

not decrease significantly beyond two segments.

There are two major concerns with this test of equation (2). First, Nickell (1981) shows that

using fixed-effect regressions in conjunction with dynamic panel data, e.g., panel data with a

lagged dependent variable, provides biased and inconsistent estimates. Second, Graham et al.

(2002) find that excess value, on average, decreases from before to after a merger. This decrease

is, to a large extent, caused by the target, which already has a negative excess value before the

merger. To control for this second problem, I re-estimate the models excluding firms that change

their number of segments.27 However, the inferences from regressions excluding firm-years

where the number of segments changed are qualitatively similar to the results discussed below,

and are omitted for brevity.

To address the first problem, and following Arellano and Bond (1991), I use GMM to

estimate equation (2) in first differences. Using first differences also eliminates the impact of a

firm fixed-effect. I employ lagged differences as instruments because the first differences of the

independent variables are still correlated with the residuals. In order for the lagged differences to

be valid proxies, the second order autocorrelation in residuals needs to be insignificant. Table 6

reports tests of the first and second order autocorrelations. The tests cannot reject the null

hypothesis of no second order autocorrelation in the residual.

When employing GMM, there is not much guidance on the optimal number of lags that

should be used as instruments. However, the Sargan test of over-identifying restrictions, reported

in Table 6, serves as a test of whether the set of instruments as a whole is uncorrelated with the

error term. The results reported use one lag only to keep the sample size as large as possible. For

those regressions, the p-value of the Sargan test is never below 0.1, confirming the validity of the

specification.28

Model 3 of Table 6 reports the coefficients using the Arellano and Bond technique.

Compared to the fixed-effect estimates, the number of observations has decreased from 8,538 to

6,368 because lagged differences are required as instruments. Note that the coefficient on EEC

23

changes its sign, and the magnitude and significance levels of all the coefficients are different.

The coefficient on EEC is now –0.102 and significant at the 1% level, indicating that firms that

raise more external capital without being efficient internal capital market users display lower

excess values. The impact of RVA is also estimated to be much larger with a coefficient that

increases from 0.278 to 1.089. The statistical significance in both cases is at the 5% level. More

importantly, the coefficient on the interaction variable between EEC, RVADUM and diversity has

increased from 0.069 to 0.116, and is significant at the 5% level. These results are consistent with

the predictions of Stein (1997), and emphasize that a firm that uses more external capital is valued

significantly higher if it has a large ICM and allocates capital efficiently in its ICM.

The coefficient on the lagged dependent variable is 0.530 and significant, consistent with the

finding of Lamont and Polk (2001). Relative to the fixed-effect model, the increase in the

coefficient is also consistent with the bias documented by Arellano and Bond (1991).

Model 4 shows Arellano and Bond regression results replicating model 2, which employs the

number of segments instead of diversity as a measure of ICM size. The coefficients and their

significance are similar to those in model 3. Finally, model 5 uses the Lang and Stulz (1994)

excess value measure. This change does not materially affect any of the inferences.

To summarize, the data suggest that firms with a la rger ICM and a more efficient internal

capital allocation raise more external capital, and doing so is associated with higher value,

consistent with the interpretation that interactions between internal and external capital markets

are important and reflected in firm valuation.

3 Industry Shock Sample

In this section I attempt to address the issue of causality of the results shown thus far by

studying firm behavior in the following situation. Theoretical models by Stein (1997), Li and Li

(1996) and others are based on the assumption that a new, positive-NPV project needs financing,

but the entrepreneurs’ wealth and/or the firm’s internal resources are insufficient to cover the

initial investment. In this section, I use a smaller sample that more closely mimics the setting in

which the models are specified. In this framework, firms are likely to underinvest if they cannot

access external capital markets to finance new investment projects. This implies that firms that

raise more external capital should increase investment, and a higher use of external capital should

lead to higher firm valuation. The question is whether ICM characteristics are an important

determinant of a firm’s ability to raise additional external capital in order to realize the growth

opportunity.

24

3.1 Sample Selection

The aim of the sample selection procedure is to choose firms that receive an exogenous,

positive shock to their investment opportunities. Such firms/divisions should display an increase

in Tobin’s q, holding everything else constant.29 However, Tobin’s q is not observable at the

segment level. Therefore, a sample of firms with operations in industries that have experienced a

significant increase in Tobin’s q is selected. Since industry q could increase as a result of

unexpected changes in industry cash flow, industry cash flow is required to remain constant.30

In order to select industries, only data on single segment firms are used. Industries are

defined at the 3-digit SIC code level. To make changes in industry q comparable across

industries, the change in the standardized industry median q between two consecutive years is

computed. The standardized industry median q is defined as follows:

Standardized industry median q =

−

q

σ

qqt ,

where qt is the industry median q at time t, q is the time-series average of industry median

qs, and qσ is the standard deviation of the time-series of industry median qs. As a control for the

industry cash flow, a measure of standardized industry median cash flow is defined as:

Standardized industry median cash flow =

−

cf

σ

cfcft ,

where cft is the industry median cash-flow-to-assets ratio at time t, cf is the time-series

average of the industry median cash-flow-to-assets ratio and, cfσ is the standard deviation of the

time-series of industry median cash-flow-to-assets ratios.

An industry is determined as having experienced a positive q shock if the change in the

standardized industry median q exceeds 1.25, and the change in the standardized industry median

cash flow is between –0.25 and +0.25.31 The rationale for using industry level qs rather than firm-

level qs is that changes in firm q could reflect the market’s view of idiosyncratic changes, such as

manageria l mistakes, which do not generally alter the set of investment opportunities. Industry-

level changes should better reflect changes in industry investment opportunities and industry cash

flow, thus allowing for a better control for expected capital needs. Using this procedure, 59 three-

digit SIC code industries with a positive q shock during 1980–1998 are obtained. Appendix 2 lists

all the sample-industries by event year and the change in their standardized industry q and cash

flow. One concern with this sample selection procedure is whether new firms entering the sample

could be responsible for the large increase in industry q. Appendix 2 shows that this is unlikely

25

because the number of single segment firms used to compute the annual standardized values is

very stable.

The final sample consists of diversified firms that have a segment in at least one of the

industries that experience a positive q shock. The selected diversified firms are also required to

have at least one segment in the industry with a positive q shock one year prior to the shock. In

addition, the sample selection criteria in section 2 are also observed. This results in a sample of

390 diversified firms with 497 segments in one of the selected industries. Appendix 2 shows the

number of segments per industry-year.

First, I investigate whether characteristics of a firm’s ICM help to explain the use of external

capital, given the exogenous shock to investment opportunities and controlling for differences in

the availability of internal cash flow. Next, the relation between the use of external capital and the

change in excess firm value is examined.

3.2 Determinants of the Use of External Capital

The research design in this section focuses on changes in the use of external capital by

diversified firms relative to comparable single segment firms. Changes are measured as the

difference between the values at t–1, the year before the industry shock, and t, the end of the year

in which the industry shock occurred.

3.2.1 Factors, Proxies and Predicted Effect

An increase in a segment’s investment opportunities should lead headquarters to allocate

more resources to that segment (Stein, 1997; Shin and Stulz, 1998). Firms that have an efficient

internal capital allocation and a larger ICM should be able to raise more external capital in order

to capture the new opportunities. Information asymmetry is again expected to have less of a

negative impact on a firm’s ability to use external capital if the firm is efficiently allocating

capital.

For the main part of the analysis I use independent variables as of t–1, the year-end before

the industry shock. Results using simultaneous changes of the independent variables between t–1

to t are also shown. However, the latter imposes a look-ahead bias because investors do not have

a measure of RVA at t at the time they have to make the decision of whether or not to supply

capital, and how much to supply. On the other hand, the exogenous change in at least one of the

segment’s investment opportunities requires a reallocation of the resources given that single

segment firms also change their investment strategy (not shown). If single segment firms are

credit constrained and diversified firms reallocate efficiently, RVA should increase due to the

26

exogenous shock to industry q, and thus establish causality.32 Given the trade-off, both results are

shown.

In the following analysis, I re-estimate a version of equation (1) with the change in EEC as

the dependent variable. I add a control for the relative size of the segment that operates in the

shocked industry, defined as the ratio of the segment assets in the positive q shock industry to

total assets (‘hit-size’). A positive coefficient on hit-size would indicate that firms that have a

larger fraction of their assets in one of the positive q shock industries (i.e., a diversified firm that

is more like a single segment firm) display a greater increase in their use of external capital than

diversified firms with only few assets in a positive q shock industry.

3.2.2 Results

Table 7, panel A shows univariate statistics. At t–1, the year before the industry shock, the

median EEC is positive for firms with an efficient internal capital allocation, i.e., those with

RVADUMt-1 equal to one, and negative for firms with an inefficient internal capital allocation.

This difference is significant at the 5% level. Similarly, in year t, efficient ICM firms have a

significantly higher median EEC than inefficient ICM firms. More importantly, those that

efficiently allocate capital in their ICM (measured by RVADUMt-1=1) increase EEC significantly

between t and t–1. The change in EEC for the other firms (RVADUMt-1=0) is insignificantly

different from zero. Similar results obtain if means are used and if the classification of efficient

versus inefficient internal capital allocation is based on the change in RVA rather than RVA at t–

1. The univariate analysis suggests an important effect of allocational efficiency on a firm’s use

of external capital. The following paragraph describes tests of whether these effects are still there

even after controlling for other determinants of a firm’s use of external capital.

Table 8 reports OLS regressions using the change in EEC as the dependent variable. Model 1

uses lagged values of the independent variables; model 2 uses the contemporaneous changes. In

model 1, the coefficient on diversity is significantly negative. The square term is not significantly

different from zero. The coefficient on RVA and the interaction between diversity and RVADUM

are significantly positive. Firms that allocate capital more efficiently raise more external capital,

and a larger ICM seems to help in doing so. Also, firms with more information asymmetry do not

increase their use of external capital, except if the internal capital allocation is efficient, as

indicated by the positive coefficient on the interaction variable between the intangible to total

assets and RVADUM in model 1 and standardized analysts’ forecast dispersion and RVADUM in

model 3. This suggests that diversified firms with more information asymmetry problems can

benefit from an efficient internal capital allocation by reducing the impact of information

problems on raising external capital.

27

The coefficient on internal cash flow at t–1 is marginally significantly positive. A positive

coefficient supports the notion that cash flow is used as collateral to raise new external capital. It

is also consistent with an interpretation that these firms are more profitable and, given the shock

to investment opportunities, should be given more capital to invest relative to less profitable or

less productive firms (Maksimovic and Phillips, 2002).

Measures of relative valuation are insignificant in this sample. The reduced importance of

lagged annual stock returns can be interpreted as supporting the sample selection. Here, the

reason for accessing external capital does not seem to be overvaluation but rather increased

investment opportunities. Further supporting this notion are the following statistics. Panel B of

Table 7 shows increased investment at the firm level (measured as the ratio of capital

expenditures to sales ratio minus the imputed ratio), more so in firms with an efficient internal

capital allocation and those that increase their use of external capital. Even more pronounced is

this pattern in the industry shock segments. Firms, classified as efficient internal capital allocators

increase the segment capital expenditures to sales ratio by 0.03 compared to inefficiently

allocating firms, with an increase of 0.01. The difference is highly significant. Moreover, the

change in segment investment (net of imputed capital expenditure to sales ratio) for firms with a

positive RVA at t–1 is significantly positive (0.014), while for firms with a negative RVA it is

significantly negative (–0.015). The univariate statistics also show median capital expenditure to

sales ratios (and adjusted ratios by imputed capital expenditures to sales ratios) for firms stratified

by the change in EEC. Firms with an increase in EEC increase investment; significantly more so

than firms that decrease EEC. These statistics are consistent with the notion that investment

opportunities increase, followed by an increase in investment, especially by firms with easier

access to external capital markets.

Model 2 of Table 8 shows the results using contemporaneous changes in the independent

variables. The change in diversity is negatively related to the change in EEC, unless the firm

increases its allocational efficiency measured by a dummy variable equal to one if the change in

RVA is positive, i.e., DUM( ∆ RVA ≥ 0). The coefficient on information asymmetry is negative,

but the interaction variable with DUM( ∆ RVA ≥ 0) is positive. This finding is in line with model 1

and suggests that firms, which improve their allocational efficiency due to the exogenous change

in investment opportunities, are less affected by changes in information asymmetry when they

transact with the external capital markets.

28

3.3 Changes in Excess Value and Changes in Excess Net External Capital

The dependent variable in this specification is the change in excess value between t–1 and t

computed, following Berger and Ofek (1995).33 The main variables of interest are EEC and its

interaction variables with ICM size and allocational efficiency.

Table 7, panel A shows the univariate statistics for excess value. Excess values are, on

average, negative for the sample as a whole in both years t and t–1. However, firms with an

efficient internal capital allocation display a significantly higher excess value in t and t–1 as well

as a significant increase between t and t–1. For firms classified as inefficient internal capital

allocators, excess value decreases. These results are generally consistent with Rajan et al. (2000)

and Cocco and Mahrt-Smith (2001) but a multivariate analysis has to show the inter-relation with

accessing external capital to finance growth on firm value.

Table 9 displays the OLS regression results.34 Model 1 shows that the change in EEC is

positively but insignificantly related to the change in excess value. However, the coefficients on

the interaction variables of EEC with RVADUM and diversity (0.828) and separately with

RVADUM only (0.223) are both significantly positive. On the other hand, neither the coefficient

on diversity (–0.013) nor on the interaction between the change in EEC and diversity (–0.200) is

significantly negative. The data are consistent with the interpretation that raising external capital

in situations where new growth opportunities require new investment is less harmful to

shareholders even for large, diversified firms with a relatively inefficient internal capital

allocation. However, relatively speaking, shareholders of large, diversified firms with an efficient

internal capital allocation benefit significantly more from an increase in external capital. This

argument is supported by the significance of the interaction variable between all three variables,

EEC, diversity and RVADUM.

In model 2 EEC at t–1 is used as an instrument for the change in EEC. The inferences are

similar to those from model 1 and support the notion that raising external capital is an important

determinate of firm value in this sample. Finally, model 3 shows results if the contemporaneous

change in RVA is used rather than the lagged value of RVA. Here, the contemporaneous change

in EEC and RVA are not significantly related to the change in excess value, and the interaction

variables are generally smaller and less significant than the coefficients in model 1.

As an additional test I also employ a two-stage procedure. The first stage regression to

predict the change in EEC is based on model 1 of Table 8. In the second stage regression, this

predicted change in EEC was used as an instrument. Even though the statistical significance of

the coefficients and the R-squared of the second stage regression decrease, none of the inferences

are affected (not tabulated).

29

In summary, diversified firms that have a larger ICM, allocate capital relatively more

efficiently and use more external capital can increase firm value. This finding is from a sample of

diversified firms that are likely to be in need of external capital in order to alleviate

underinvestment problems. The contribution of the industry shock sample analysis is twofold.

First, it shows that easier access to external capital markets can be achieved by diversified firms

with certain ICM characteristics and that a higher use of external capital is providing the firm

with the opportunity to capture new growth opportunities. Second, the sample selection procedure

should alleviate concerns about causality in the relations documented.

4 Conclusions

This study examines the interaction between internal and external capital markets. I find that

ICM characteristics are important determinants of a firm’s use of external capital. While firms

with a larger ICM, on average, use less external capital, firms with a larger ICM and a more

efficient internal capital allocation use significantly more external capital. In addition, the

analysis suggests that a more efficient internal capital allocation can help a firm to reduce the

impact of information asymmetry problems when raising external capital. More importantly,

firms using more external capital are valued lower, unless they have an efficient internal capital

allocation and a large ICM. Consequently, there is an additional benefit of diversification for

firms with an efficient internal capital allocation, namely, lower cost for external capital.

These findings are robust to different regression techniques and proxies for use of external

capital. The conclusions drawn are also unaffected if the sample is restricted to diversified firms

that have a division in an industry with a positive q shock, thus mimicking more closely a setting

in which a firm needs external financing to realize new, positive-NPV projects. This suggests a

partial answer to Zingales’s (2000) question about the factors that determine a firm’s ability to

capture new growth opportunities. This study shows the importance of a firm’s ICM

characteristics in financing, and thus capturing, new growth opportunities.

Taking a broader view, this study adds to research about decisions made in hierarchies

(ICMs) and markets (ECMs), surveyed in Stein (2001). Coase (1937) argues that firms exist

because transactions are less costly if made internally than externally, and Rajan et al. (2000)

conclude that there are important differences between hierarchies and markets. The results here

demonstrate that there are significant feedback effects from hierarchies to markets. Firms that

choose to transact in a hierarchy also change their ability to transact in markets.

30

Appendix 1 Description of Variables

MEASURES OF USE OF EXTERNAL CAPITAL Excess Net External Capital

(EEC) Excess net external capital = (net external capital – imputed net external capital) / lagged book value of assets [or standardized by lagged market value = market value of common equity plus book value of assets minus book value of common equity minus deferred taxes]. Net external capital = net common and preferred stock issued (#108-#115) plus net long-term debt issued (#111–#114) plus changes in short-term debt (#301). Numbers with #, refer to Compustat items. Imputed net external capital is computed as the segment sales (or asset) weighted sum of the median net external capital to sales (assets) ratio of single segment firms in the same 3-digit SIC code industry as the segment of the diversified firm. Sales-weighting is the standard.

Excess Net External Capital Including Dividends and Interest

Net external capital is defined as net common and preferred stock issued (#108–#115) plus net long-term debt issued (#111–#114) plus changes in short-term debt (#301) minus cash dividends (#127) minus interest paid (#15). Excess net external capital is computed as above.

Excess Net External Capital Including Asset Sales

Net external capital is defined as net common and preferred stock issued (#108–#115) plus net long-term debt issued (#111–#114) plus changes in short-term debt (#301) plus sale of PP&E (#107) plus sale of investments (#109) plus change of short-term investments (#309) plus sales of investing activities (#310) plus increases in other financing activities (#312). Excess net external capital is computed as above.

Excess Increase in Equity Increase in equity is defined as common and preferred stock issued (#108). Computing excess increase in equity follows the same steps as EEC.

Excess Increase in Debt Increase in debt is defined as long-term debt issued (#111). Computing excess increase in debt follows the same steps as EEC.

MEASURES OF ICM SIZE 1/Herfindahl Index

Inverse of the Herfindahl Index. ∑ ∑= =

=

n n

jjj SalesSales

1j

2

1 Index Herfindahl ,

where n is the number of segments and j refers to the segment. Number of Segments The number of segments a firm reports. Firms reporting more than five segments

are assigned the value five. Diversity

( )n

q

n

wqqw

n

jjn

j

j∑

∑ =

= −

− 1

1

2j

1, where wj is segment j’s share of total assets, qj is

imputed q, n is the number of segments and wq is the average asset weighted qj. wj and qj are beginning-of-the-period values.

MEASURES OF ICM EFFICIENCY Relative Value Added by

Allocation (RVA)

( ) BABA

Capex

BA

Capexw

BA

Capex

BA

Capexqq

n

jss

ss

j

jjss

ss

j

j

jj

j

j

j

j

−−−− ∑∑

== 1

n

1j BA , where

Capex is capital expenditures, qj is the asset-weighted average Tobin’s q of single-segment firms that operate in the same 3-digit SIC industry of segment j, n is the number of segments, BA is firm assets, BAj is segment assets and Capexj

ss/BAjss is the asset-weighted average Capex/asset ratio for the single

segment firms in the corresponding industry of segment j. wj is the ratio of segment assets to firm assets. BA, wj and qj are beginning-of-the-period values.

Absolute Value Added by Allocation (AVA)

( )

−−∑

=ss

ss

j

jn

jj

j

j

j

BA

Capex

BA

Capexq

BA

BA

1

1 ,

where Capex is capital expenditures, qj is the asset-weighted average Tobin’s q of single-segment firms that operate in the same 3-digit SIC industry of segment j, n is the number of segments, BA is firm assets, BAj is segment assets and Capexj

ss/BAjss is the asset-weighted average Capex/asset ratio for the single

segment firms in the corresponding industry of segment j. wj is the ratio of segment assets to firm assets. BA, wj and qj are beginning-of-the-period values.

31

Q-sensitivity ∑

=

−

×−×

n

j jj

j

SalesFirm

CapexFirm

SalesCapex

qqSalesFirm

Sales

1

)( , where Capex is

capital expenditures, qj is beginning-of-the-period median Tobin’s q of single segment firms that operate in the same 3-digit industry as segment j, q is the segment-asset-weighted average of the segment qs for the firm and n is the number of segments.

Cash Flow-sensitivity Cf-sensitivity is q-sensitivity except that qj is replaced with segment j’s cash flow to sales ratio and q is replaced with the median cash flow to sales ratio of single segment firms operating in the same 3-digit industry as segment j. Cash flow is defined as operating income plus depreciation.

MEASURES OF CAPITAL NEED Tobin’s q Tobin’s q is the market-to-book ratio, where market value is computed as

the market value of common equity plus book value of assets minus book value of common equity minus deferred taxes.

Excess Internal Cash Flow For firms with Compustat cash flow statements (#318=7), Internal cash flow is net cash flow from operations (#308) minus cash dividends (#127). For firms reporting a working capital statement, a cash statement by source and use of funds or a cash statement by activity (#318=1,2,3), internal cash flow is total funds from operations (#110) minus working capital change (#236) minus cash dividends (#127). Excess internal cash flow = (internal cash flow – imputed internal cash flow) / lagged book value of assets. Imputed internal cash flow is computed as the segment sales (assets) weighted sum of the median internal cash flow to sales (assets) ratio of single segment firms in the same 3-digit SIC code industry as the segment of the diversified firm.

Excess Internal Cash Flow Including Dividend and Interest

For firms with Compustat cash flow statements (#318=7), internal cash flow is defined as net cash flow from operations (#308) plus interest (#15). For firms reporting a working capital statement, a cash statement by source and use of funds or a cash statement by activity (i.e., #318=1,2,3), Internal cash flow is total funds from operations (#110) minus working capital change (#236) plus interest (#15). Excess internal cash flow is computed as above.

MEASURES OF INFORMATION ASYMMETRY Residual Variance Residual variance is computed over a calendar year by using daily returns

and a market model with the value-weighted CRSP index, including dividends as the market return. Variance is not annualized.

Total Variance Total variance is computed over a calendar year using the daily stock returns, including distributions. Variance is not annualized.

Intangible Assets / Total Assets Intangible assets (#33) divided by total assets (#6). Standardized Analysts’ Forecast

Dispersion The numerator is computed as the standard deviation of analysts’ forecasts of the firm’s one-year ahead fiscal year-end earnings per share (stdev). The denominator is the absolute value of the mean of the forecasts (meanest). Variables are from IBES.

MEASURES OF RELATIVE VALUATION Excess Value

(Berger and Ofek, 1995) Excess value =

)(log VIV , where ( )[ ]MSi

n

i i SalesVMSalesVI /)( 1 ×= ∑ = ,

where V is the sum of market value of equity and book value of assets less the book value of equity and deferred taxes, I(V) is the imputed firm value, Salesi is the segment i’s sales, Mi(V/Sales)MS is the sales multiplier (calculated as the median of the single segment firms in the same 3-digit SIC code industry), and n is the number of segments per firm.

Excess Value (Lang and Stulz, 1994)

Log of the ratio of the firm’s actual Tobin’s q at the end of the year to the sum of segment sales-weighted imputed qs.

Annual Stock Return Total return over a calendar year.

32

Appendix 2 Description of the Industry Shock Sample

Industries are defined at the 3-digit SIC code level using single segment firms only. Industries are selected based on the change in the standardized industry median q and the change in the standardized industry median cash flow, which are defined as follows:

Standardized industry median q =

−

q

σ

qq t ,

where qt is the industry median q at time t, q is the time-series average of industry median qs and qσ is

the standard deviation of the time-series of industry median qs.

Standardized industry median cash flow =

−

cf

σ

cfcf t ,

where cft is the industry median cash flow to assets ratio at time t, cf is the time-series average of the

industry median cash flow to assets ratio and cfσ is the standard deviation of the time-series of industry

median cash flow to assets ratios. An industry is selected to have experienced a positive q shock if the change in the standardized industry median q exceeds + 1.25, and the change in the standardized industry median cash flow is between –0.25 and +0.25. Diversified firms are selected into the sample if they have at least one segment in one of the selected industries in the year of the shock and have at least one segment in that respective industry prior to the shock. The year of the shock is indicated by t. The final sample consists of 390 diversified firms. Year of Shock

3-digit SIC

Change in standardized industry q, t–1 to t

Mean standardized industry q, t

Change in standardized industry cash flow, t–1 to t

Mean standardized industry cash flow, t

Number of single segment firms, t

Number of single segment firms, t–1

Number of segments in diversified firms

1982 221 1.36 0.61 0.07 -0.91 7 7 10 1982 333 1.26 0.49 -0.11 -1.25 4 4 1 1983 131 2.99 0.75 -0.14 -0.49 68 54 55 1983 138 1.85 0.83 -0.16 -0.42 20 21 42 1985 104 1.38 0.56 0.06 0.27 7 7 5 1985 225 1.65 0.80 0.10 -0.79 13 10 0 1985 245 1.51 -0.16 0.00 -0.28 14 15 4 1985 262 2.02 1.07 0.18 -0.01 7 6 3 1985 273 2.25 1.67 -0.16 -0.49 4 6 14 1985 539 2.31 1.16 -0.03 -0.88 4 6 1 1985 581 1.63 0.49 -0.05 -1.18 65 55 23 1986 262 1.39 2.46 0.17 0.16 7 7 3 1986 349 1.27 1.11 -0.21 0.01 5 8 13 1986 571 1.41 0.71 0.10 -0.05 12 10 1 1987 331 2.94 2.43 0.24 0.67 14 14 10 1987 333 2.56 1.25 -0.07 0.30 6 7 3 1988 232 1.47 0.47 -0.16 -0.02 12 11 2 1988 267 1.90 1.22 0.16 -0.12 9 10 10 1988 349 3.98 2.82 -0.17 0.54 6 3 9 1988 369 1.27 0.35 -0.20 -0.39 13 12 8 1988 473 1.91 -0.11 -0.18 -0.66 5 6 0 1988 509 2.23 1.14 0.21 -0.03 9 8 5 1988 512 1.27 0.48 0.03 -0.11 8 9 8 1988 531 2.13 1.16 0.14 -0.98 15 16 0 1988 599 1.86 1.13 0.17 -0.18 6 8 5

33

Year of Shock

3-digit SIC

Change in standardized industry q, t–1 to t

Mean standardized industry q, t

Change in standardized industry cash flow, t–1 to t

Mean standardized industry cash flow, t

Number of single segment firms, t

Number of single segment firms, t–1

Number of segments in diversified firms

1988 794 1.32 0.46 -0.19 0.27 7 7 2 1990 353 1.62 0.56 0.20 0.14 12 12 28 1990 373 1.81 -0.22 -0.05 -0.24 5 6 2 1991 205 3.34 0.78 0.06 0.26 5 5 2 1991 273 2.54 2.07 0.13 0.23 6 6 5 1991 282 1.57 -0.19 0.00 -0.47 6 6 10 1991 333 1.55 0.28 -0.07 -0.62 5 6 5 1991 346 1.33 0.27 -0.19 -0.63 6 6 2 1991 354 3.16 2.28 -0.13 -0.87 9 12 16 1991 363 3.41 1.77 -0.21 1.04 8 6 3 1991 371 2.36 0.87 0.19 -0.51 32 29 23 1991 736 1.91 0.78 0.12 -0.55 14 18 9 1991 738 2.12 1.60 -0.08 0.40 19 19 9 1991 808 1.34 1.04 0.00 0.00 11 6 3 1993 104 2.58 2.47 -0.01 0.25 32 33 2 1993 205 1.98 1.16 0.23 0.99 6 5 1 1993 282 1.99 1.55 -0.23 -1.05 6 6 13 1993 331 1.91 1.78 0.09 0.30 28 30 11 1993 871 1.27 0.64 -0.20 -0.50 19 19 12 1994 346 2.13 1.79 -0.03 -0.20 7 6 5 1994 473 1.28 0.21 -0.22 -0.71 7 6 1 1995 394 1.73 0.15 0.14 -0.96 28 23 8 1995 484 1.96 0.85 -0.24 -0.27 15 12 5 1995 489 2.32 0.99 0.01 0.03 7 6 4 1995 492 2.23 -0.16 -0.25 -0.47 34 35 40 1995 505 1.38 0.60 -0.20 0.02 6 5 5 1995 874 2.67 2.39 -0.25 -0.20 7 8 8 1996 230 1.60 0.50 0.14 0.12 6 6 1 1996 245 1.67 1.00 0.03 -0.30 10 8 1 1996 501 1.82 -0.11 0.00 -1.01 12 9 4 1996 591 1.45 0.84 -0.02 -0.80 12 14 3 1997 805 1.80 1.05 0.02 -0.87 17 21 3 1998 399 2.53 1.42 0.19 -0.02 8 10 9 1998 781 1.59 0.73 0.02 -0.55 10 16 7

Mean 1.95 0.97 -0.02 -0.26 12.92 12.58 8.42 Sum 762 742 497

34

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37

Table 1

Summary Statistics Univariate Statistics using data from 1980 to1998. Variables are defined in Appendix 1. Single segment firms are those with only one segment reported on the Compustat segment file. Diversified firms are those with more than one segment reported. t–1 indicates that the one-year lagged value of the variable is used. Medians and means of the panel data are reported. Significance levels are indicated by *, **, *** corresponding to a 10%, 5%, 1% significance level. Panel A reports univariate statistics by classifying firms as either single segment or diversified based upon the number of segments a firm reports. Panels B and C report univariate statistics for diversified firms only. Panel A: Single Segment and Diversified Firms Variable Median

Single Segment

Median Diversified

Mean Single Segment

Mean Diversified

Standard Deviation Diversified

Excess Net External Capital (EEC) 0 -0.00549*** 0.04442*** 0.02966*** 0.24803 Excess Net External Capital Including

Dividends and Interest 0 -0.00880*** 0.04198*** 0.02415*** 0.24712

Excess Net External Capital Including Asset Sales

0 -0.00645*** 0.04840*** 0.03316*** 0.24776

Excess Increase in Equity 0 -0.00510*** 0.03210*** 0.02091*** 0.20233 Excess Increase in Debt 0 0.0155*** 0.04551*** 0.0505*** 0.26495 Excess Internal Cash Flow 0 0.00394*** -0.00336*** -0.00389** 0.13096 Excess Internal Cash Flow Including Dividends and Interest

0 0.00314*** -0.00725*** -0.00386** 0.13287

1/Herfindahl Index Based on Sales 1 1.77810*** 1 1.89100*** 0.68418 Diversity 0 0.28617*** 0 0.31481*** 0.18982 Residual Variance 0.00094*** 0.00057*** 0.00186*** 0.00129*** 0.00377 Total Variance 0.00101*** 0.00063*** 0.00192*** 0.00135*** 0.00379 Intangible Assets / Total Assets 0 0.00906** 0.04561*** 0.06147*** 0.10985 Annual Stock Return 0.05101*** 0.08191*** 0.15464*** 0.14838*** 0.56826 Excess Value (Berger and Ofek, 1995, with sales multiplier)

0 -0.14866*** -0.00913 -0.16339*** 0.70348

Excess Value (Lang and Stulz, 1994) 0 -0.15542*** 0.03193*** -0.13374*** 0.59810 Relative Value Added by Allocation (RVA) 0 -0.000005 0 -0.0004* 0.01950 Absolute Value Added by Allocation (AVA) 0.000 -0.000489*** 0.00104*** -0.00110*** 0.00879 Q-sensitivity 0 0 0 0.00117** 0.02797 Cf-sensitivity 0 0 0 -0.00008* 0.00411 Number of Observations 34065 8538 34065 8538 8538

Panel B: Excess Net External Capital by Diversity and Allocational Efficiency for Diversified Firms Excess Net External Capital RVA ≥ 0 at t–1 RVA < 0 at t–1 Diversity ≥ Median Diversity Median 0.005 -0.016 (Mean) (0.054) (-0.013) Diversity < Median Diversity Median 0.001 -0.009 (Mean) (0.025) (-0.006) Panel C: Excess Value, Allocational Efficiency and Excess Net External Capital for Diversified Firms Excess Value (Berger and Ofek, 1995) RVA ≥ 0 at t–1 RVA < 0 at t–1 Diversity ≥ Median Diversity EECt–1 ≥ Median EEC Median 0.030 -0.330 (Mean) (0.045) (-0.359) EECt–1 < Median EEC Median -0.066 -0.169 (Mean) (-0.129) (-0.190) Diversity < Median Diversity EECt–1 ≥ Median EEC Median 0.022 -0.277 (Mean) (0.031) (-0.319) EECt–1 < Median EEC Median -0.106 -0.200 (Mean) (-0.121) (-0.200)

38

Table 2

Excess Net External Capital

Time-series averaged coefficients of cross-sectional OLS regressions on a year-by-year basis are reported (Fama-MacBeth, 1973). All the data are for the period 1980–1998. The dependent variable is excess net external capital. The t-statistics are based on the time-series standard deviation of the coefficients and are reported underneath the coefficients. R-squared are time-series averages. The Herfindahl Index is based on sales. The number of segments range from 1 to 5, where 5 includes firms with 5–10 segments. RVADUMt–1 = 1 if RVA ≥ 0 at t–1. The addition, t–1, means that the one-year lagged value of the variable is used. In Model 5, RVADUMt–1 = 1 if q-sensitivity ≥ 0 at t–1. In Model 6, RVADUMt–1 = 1 if cf-sensitivity ≥ 0 at t–1. For other variable definitions see Appendix 1. The significance levels are indicated by *, **, *** corresponding to 10%, 5%, 1% significance levels. Model 8 only includes firms with available information on the standard deviation of analysts’ forecasts reported in IBES. Excess Net External Capital

Model (1) (2) (3) (4) (5)

Measures of Size of ICM Diversity, t–1 -0.003**

(2.064) -0.009** (2.184)

-0.010** (2.119)

(Diversity, t–1)2 -0.002 (1.057)

-0.002 (1.054)

Number of Segments, t–1 -0.026** (2.154)

(Number of Segments, t–1)2 -0.000 (0.243)

1/Herfindahl Index , t–1 -0.015** (2.101)

(1/Herfindahl Index, t–1)2 -0.001 (0.483)

Measures of ICM Efficiency Relative Value Added by Allocation

(RVA), t–1 0.879** (2.339)

0.887** (2.375)

0.865** (2.279)

0.875** (2.353)

Q-sensitivity, t–1 0.318** (2.850)

(Size of ICM, t–1) × (RVADUMt–1) 0.005*** (3.609)

0.017** (2.781)

0.040** (2.467)

0.044** (2.568)

0.019*** (2.902)

(Size of ICM, t–1)2 × (RVADUMt–1) 0.001 (0.802)

0.004 (1.244)

0.008** (2.763)

0.002 (0.972)

Measures of Information Asymmetry Residual Variance -6.544***

(3.200) -6.782*** (3.293)

-6.269*** (3.033)

-5.963** (2.884)

-6.998*** (3.422)

Intangible Assets / Total Assets, t–1 -0.176*** (5.804)

-0.182*** (5.814)

-0.191*** (4.922)

-0.194*** (5.040)

-0.182*** (5.472)

(Intangible Assets / Total Assets, t–1) × (RVADUMt–1)

0.159** (2.269)

0.153** (2.133)

0.119** (2.216)

0.122** (2.338)

0.168** (2.338)

Measures of Capital Need Tobin’s q (Beginning of Year) 0.024***

(3.514) 0.024*** (3.420)

0.024*** (3.474)

0.024*** (3.381)

0.026*** (3.506)

Excess Internal Cash Flow -0.725*** (14.259)

-0.725*** (14.261)

-0.730*** (14.272)

-0.726*** (14.268)

-0.724*** (14.095)

Measures of Relative Valuation Excess Value (Berger and Ofek), t–1 0.009**

(2.154) 0.009** (2.174)

0.008* (1.947)

0.009** (2.435)

0.009** (2.165)

Annual Stock Return, t–1 0.037*** (4.405)

0.037*** (4.388)

0.037*** (4.384)

0.0375*** (4.364)

0.036*** (4.187)

Number of Observations 8538 8538 8538 8538 8538 Average R-squared 0.266 0.271 0.272 0.294 0.276

39

Table 2 (continued)

Excess Net External Capital Excess Net External Capital

Model (6) (7) (8) (9)

Measures of Size of ICM Diversity, t–1 -0.006**

(2.013) -0.009** (2.198)

-0.016* (1.832)

-0.014** (1.960)

(Diversity, t–1)2 -0.002 (0.965)

-0.002 (1.067)

-0.008 (0.945)

-0.007 (1.064)

Measures of ICM Efficiency Relative Value Added by Allocation

(RVA), t–1 0.888**

(2.381) 0.867** (2.259)

0.845** (2.156)

Cf-sensitivity, t–1 0.019** (1.998)

(Size of ICM, t–1) × (RVADUMt–1) 0.017** (2.460)

0.017** (2.747)

0.021*** (3.693)

0.012** (2.013)

(Size of ICM, t–1)2 × (RVADUMt–1) 0.001 (0.893)

0.001 (1.001)

0.002 (0.668)

0.001 (0.521)

Measures of Information Asymmetry Residual Variance -6.464***

(2.921) -6.999***

(3.286) Total Variance -6.299***

(3.160)

Intangible Assets / Total Assets, t–1 -0.146*** (4.115)

-0.182*** (5.817)

-0.191*** (5.748)

(Intangible Assets / Total Assets, t–1) × (RVADUMt–1)

0.163** (2.154)

0.152** (2.128)

0.150** (2.098)

Standardized Analysts’ Forecast Dispersion

-0.009** (2.745)

Standardized Analysts’ Forecast Dispersion × (RVADUMt–1)

0.003** (2.103)

Measures of Capital Need Tobin’s q (Beginning of Year) 0.027***

(3.268) 0.024*** (3.423)

0.016* (1.868)

0.008 (1.395)

Excess Internal Cash Flow -0.603*** (12.371)

-0.724*** (14.057)

-0.644*** (10.560)

-0.697*** (13.343)

Measures of Relative Valuation Excess Value (Berger and Ofek), t–1 0.011**

(2.362) 0.009** (2.184)

0.010* (1.876)

Excess Value (Lang and Stulz), t–1 0.037*** (4.197)

Annual Stock Return, t–1 0.038*** (4.065)

0.037*** (4.392)

0.039*** (3.107)

0.047*** (4.636)

Number of Observations 8538 8538 4021 8538 Average R-squared 0.300 0.271 0.272 0.255

40

Table 3

Robustness Tests

Changes in Excess Net External Capital With Constant Demand for Capital OLS regressions using the sample of firms where the change in Tobin’s q and the change in cash flow between two consecutive years are within plus/minus 5%. The data are from 1980–1998. Year dummies are not reported. The dependent variable is the change in excess net external capital (∆ EEC). All the changes are measured between t–1 and t, where t is the end of the year for which Tobin’s q and cash flow are constant. Excess value is computed according to Lang and Stulz’s (1994) method. DUM(∆ RVA ≥ 0) is a dummy variable equal to one if the change in the RVA is positive. Cash flow is operating income before depreciation. For other variable definitions, see Appendix 1. On the first line, the coefficients are reported with their significance level indicated by *, **, *** corresponding to 10%, 5% and 1% significance level based on White-adjusted standard errors. In brackets, the absolute values of the t-statistics are reported.

Dependent Variable

Change in Excess Net External Capital

Model (1) (2) (3)

Measures of Size of ICM Change in Diversity 0.035**

(2.210) 0.035** (2.211)

0.035** (2.212)

Measures of ICM Efficiency Change in Relative Value Added by

Allocation 0.439** (2.294)

0.421** (2.001)

0.594*** (2.531)

Change in Diversity × DUM(∆ RVA ≥ 0) 0.051** (2.412)

0.046** (2.339)

0.050** (2.408)

Measures of Information Asymmetry Change in Residual Variance -1.466*

(1.701) -1.410* (1.655)

-1.535* (1.812)

Change in Residual Variance × DUM(∆ RVA ≥ 0)

1.122** (2.419)

1.023** (1.981)

1.099** (2.293)

Intangible Assets / Total Assets, t–1 0.041 (0.512)

Measures of Capital Need Change in Excess Internal Cash Flow -0.014

(0.118) -0.015 (0.121)

Change in Cash Flow -0.000 (0.912)

-0.000 (1.000)

Change in Tobin’s q -0.013 (0.363)

-0.013 (0.366)

Measures of Relative Valuation Change in Excess Value (Lang and Stulz) -0.015

(0.933) -0.014 (0.892)

Annual Return 0.069** (2.164)

0.069** (2.170)

Number of Observations 330 330 330 Adjusted R-squared 0.182 0.200 0.198

41

Table 4

Excess Net External Capital and Single Segment Firms

Time-series averaged coefficients of cross-sectional OLS regressions on a year-by-year basis are reported. The t-statistics are based on the time-series standard deviation of the coefficients and are reported, in brackets, underneath the coefficients. R-squared are time-series averages. The Multi-segment dummy is one if the firm reports more than one segment, and zero otherwise. AVADUMt–1 = 1 if AVA ≥ 0 at t–1, where t–1 indicates that the one-year lagged value of the variable is used. Other variables are defined in Appendix 1. Model 2 only includes observations for which the standard deviation of analysts’ forecasts, as reported by IBES, is available. Dependent Variable Excess Net External Capital

Model (1) (2)

Multi-segment Dummy -0.011*** (3.211)

-0.009** (2.528)

Measures of Size of ICM Diversity, t–1 -0.012**

(2.243) -0.013*** (4.451)

(Diversity, t–1)2 -0.003 (0.364)

-0.004 (0.995)

Measures of ICM Efficiency Absolute Value Added by Allocation (AVA) , t–1 0.559***

(7.642) 0.421*** (7.140)

Absolute Value Added by Allocation (AVA) , t–1 × (Multi-segment Dummy)

-0.041 (0.716)

-0.036 (0.877)

(Diversity, t–1) × (AVADUMt–1) 0.005** (2.161)

0.013** (2.110)

(Diversity, t–1)2 × (AVADUMt–1) 0.001 (0.722)

0.001 (0.471)

Measures of Information Asymmetry Residual Variance -2.577**

(2.334)

Intangible Assets / Total Assets , t–1 -0.309*** (4.129)

(Intangible Assets / Total Assets, t–1) × (AVADUMt–1) 0.090 (1.045)

(Intangible Assets / Total Assets, t–1) × (AVADUMt–1) × (Multi-segment Dummy)

0.211** (2.855)

Standardized Analysts’ Forecast Dispersion -0.013** (2.468)

Standardized Analysts’ Forecast Dispersion × (AVADUMt–1) 0.003 (0.865)

Standardized Analysts’ Forecast Dispersion × (AVADUMt–1) × (Multi-segment Dummy)

0.010** (1.958)

Measures of Capital Need Tobin’s q (Beginning of Year) 0.001

(0.106) 0.001 (0.113)

Excess Internal Cash Flow -0.619*** (9.407)

-0.600*** (6.726)

Measures of Relative Valuation Excess Value (Berger and Ofek), t–1 0.029***

(9.544) 0.036*** (8.103)

Annual Stock Return, t–1 0.045*** (8.360)

0.057*** (8.760)

Number of Observations 42603 20331 Average R-squared 0.221 0.205

42

Table 5

Robustness Tests

Model 1 is estimated using firm fixed-effects with year dummies (not reported). Models 2-5 report time-series averages of coefficients of cross-sectional regressions using different dependent variables. Excess increase in equity is the difference between common and preferred stock issued (Compustat item 108) by the diversified firm minus the imputed equity issued, standardized by lagged book value of assets. Excess increase in debt is the difference between long-term debt issued (Compustat item 111) by the diversified firm minus the imputed long-term debt issued, standardized by lagged book value of assets. RVADUMt–1 = 1 if RVA ≥ 0 at t–1, where t–1 indicates that the one year lagged value of the variable is used. For other variable definitions see Appendix 1. In models 2, 4 and 5, excess internal cash flow includes interest and dividends (see Appendix 1 for a more detailed definition). For model 1, the absolute values of the heteroskedasticity robust t-statistics are reported in brackets. For models 2-5, t-statistics are based upon time-series standard deviations of the coefficients (Fama-MacBeth, 1973). The R-squared reported are time-series averages except for model 1. The significance levels are indicated by *, **, *** corresponding to 10%, 5%, 1% levels, respectively. All the data are for the period 1980–1998. Dependent Variable Excess Net

External Capital Fixed-Effects

Excess Net External Capital Including Interest and Dividends

Excess Net External Capital with Asset Sales

Excess Increase in Equity

Excess Increase in Debt

Model (1) (2) (3) (4) (5)

Measures of Size of ICM Diversity, t–1 -0.008**

(2.164) -0.009** (2.155)

-0.007* (1.874)

-0.012** (2.268)

0.005 (0.766)

(Diversity, t–1)2 0.001 (0.227)

-0.000 (0.149)

-0.001 (1.094)

0.004 (1.061)

0.001 (1.089)

Measures of ICM Efficiency Relative Value Added by

Allocation (RVA) , t–1 0.308*** (7.711)

0777** (1.960)

0.674* (1.790)

0.455*** (5.225)

0.145** (2.413)

(Diversity, t–1) × (RVADUMt–1) 0.016** (2.742)

0.017*** (3.091)

0.012** (2.167)

0.010*** (2.983)

0.012** (2.111)

(Diversity, t–1)2 × (RVADUMt–1) 0.001 (0.096)

0.001 (0.437)

-0.000 (0.751)

0.001 (0.705)

0.001 (0.624)

Measures of Information Asymmetry Residual Variance -1.023*

(1.645) -8.307*** (3.886)

-5.022** (2.490)

-0.745 (0.739)

-0.196 (0.112)

Intangible Assets / Total Assets, t–1

-0.132** (2.689)

-0.156*** (4.887)

-0.108** (2.475)

(Intangible Assets / Total Assets, t–1) × (RVADUMt–1)

0.213** (6.867)

0.171** (2.231)

0.081* (1.794)

Standardized Analysts’ Forecast Dispersion

-0.071*** (5.910)

-0.034*** (3.611)

Standardized Analysts’ Forecast Dispersion × (RVADUMt–1)

0.065** (2.056)

0.010* (1.682)

Measures of Capital Need Tobin’s q (Beginning of Year) -0.000

(0.306) 0.025*** (3.392)

0.022*** (3.543)

0.026*** (3.964)

0.003 (0.904)

Excess Internal Cash Flow -0.649*** (29.400)

-0.687*** (15.780)

-0.783*** (17.358)

-0.326*** (4.857)

-0.406*** (4.517)

Measures of Relative Valuation Excess Value (Berger and Ofek),

t–1 0.004 (0.614)

0.004 (0.835)

0.014*** (3.070)

-0.002 (0.795)

-0.003 (0.546)

Annual Stock Return, t–1 0.027*** (6.175)

0.041*** (4.709)

0.032*** (3.779)

0.028*** (4.159)

0.033*** (4.197)

Number of Observations 8538 8538 8538 4021 4021 R-squared 0.172 0.257 0.222 0.261 0.121

43

Table 6

Excess Value and Excess Net External Capital

Models 1 and 2 use firm fixed-effects with year dummies (not reported). Models 3-5 use the Arellano-Bond (1991) technique. These regressions are estimated in first differences. In addition, the lagged (differenced) dependent variable is instrumented by the second lagged difference, thus reducing the sample size to 6,368. The dependent variable is excess value computed either according to Berger and Ofek’s (1995) sales multiplier method or according to Lang and Stulz’s (1994) method. RVADUM t–1 = 1 if RVA ≥ 0 at t–1, where t–1 indicates that the one-year lagged value of the variable is used. NA signifies not available. For other variable definitions, see Appendix 1. The absolute values of the heteroskedasticity robust t-statistics are reported in brackets. The significance levels are indicated by *, **, *** corresponding to 10%, 5%, 1% levels, respectively. All the data are for the period 1980–1998.

Dependent Variable Excess Value According to:

Berger and Ofek

Lang and Stulz

Model (1) (2) (3) (4) (5)

M ethod FE FE Arellano-Bond

Arellano-Bond

Arellano-Bond

Excess Net External Capital (EEC), t–1

0.068 (1.500)

0.071 (1.611)

-0.102*** (3.381)

-0.096*** (3.109)

-0.078** (2.460)

Relative Value Added by Allocation (RVA), t–1

0.278** (2.080)

0.276** (2.065)

1.089** (2.304)

1.097** (2.321)

0.398** (2.445)

EEC, t–1 × RVADUMt–1 0.145** (2.497)

0.141** (2.410)

0.152** (2.274)

0.158** (2.660)

0.105** (2.572)

Diversity, t–1

-0.032** (2.406)

-0.041* (1.799)

-0.036* (1.731)

(Diversity, t–1) × RVADUMt–1 0.022* (1.826)

0.026 (1.219)

0.023 (1.297)

EEC, t–1 × (Diversity, t–1) -0.033 (1.108)

-0.085** (1.987)

-0.055** (2.092)

EEC, t–1 × RVADUMt–1 × (Diversity, t–1)

0.069* (1.714)

0.116** (2.138)

0.188** (2.379)

Excess Value (Berger and Ofek), t–1 0.400*** (36.446)

0.416*** (37.195)

0.530*** (20.343)

0.532*** (20.396)

0.369*** (19.794)

Number of Segments – 1, t–1

-0.002 (0.241)

-0.018 (1.011)

(Number of Segments – 1, t–1) × RVADUMt–1

0.013 (0.922)

0.026 (1.693)

EEC, t–1 × (Number of Segments – 1, t–1)

-0.059** (2.432)

-0.101** (2.123)

EEC, t–1 × RVADUMt–1 × (Number of Segments – 1, t–1)

0.098** (2.002)

0.113** (1.984)

Number of Observations 8538 8538 6368 6368 6368

R-squared 0.583 0.580 NA NA NA

Sargan test: prob > chi2 NA NA 0.19 0.20 0.13

H0: no autocorrelation in first order (p-value)

NA NA 0.00 0.00 0.00

H0: no autocorrelation in second order (p-value)

NA NA 0.22 0.24 0.22

44

Table 7

Summary Statistics for the Industry Shock Sample Univariate statistics of diversified firms, which have at least one segment in the industry classified as experiencing a positive q shock. An industry is determined as having experienced a positive q shock if the change in the standardized q is greater or equal to 1.25 and the change in the standardized industry cash flow is between – 0.25 and + 0.25. For a more detailed description of the sample selection procedure, see Appendix 2. For definitions of the variables, see Appendix 1. Panel A reports medians on the first line, and means on the second line in brackets. Panel B reports medians only. Here Capex is the ratio of capital expenditures to sales. Imputed Capex is the median capital expenditures to sales ratio of single segment firms operating in the same industry as the segment. At the firm level imputed Capex is the segment sales-weighted sum of segment imp uted Capex. The significance levels for means and medians are indicated by *, **, *** corresponding to 10%, 5% and 1%, respectively. The p-values are based on mean comparison t-tests and Wilcoxon rank sign tests for medians. All the data are for the period 1980–1998. There are 390 firms in this sample with 497 segments in one of the shocked industries. Panel A: Univariate Statistics of Use of External Capital and Excess Value Variables Median

(Mean) RVA = 0

t–1 RVA < 0

t–1 p-value

difference ∆ RVA

= 0 ∆ RVA

< 0 p-value

difference -0.002 0.003 -0.009 0.05 0.000 -0.004 0.49 Excess Net External Capital,

t–1 (0.012) (0.022) (-0.003) (0.08) (0.013) (0.010) (0.91) 0.001 0.015 -0.011 0.00 0.017 -0.019 0.00 Excess Net External Capital,

t (0.029) (0.054) (-0.009) (0.00) (0.076) (-0.021) (0.00) 0.003 0.010 -0.002 0.01 0.016 -0.014 0.03 Change in Net External

Capital (0.017) (0.032) (-0.006) (0.03) (0.063) (-0.031) (0.01)

-0.087 -0.019 -0.168 0.01 -0.072 -0.090 0.63 Excess Value (Berger and Ofek), t–1 (-0.095) (-0.031) (-0.191) (0.02) (-0.076) (-0.103) (0.76)

-0.100 0.030 -0.248 0.00 -0.009 -0.168 0.01 Excess Value (Berger and Ofek), t (-0.109) (0.033) (-0.322) (0.00) (0.003) (-0.209) (0.02)

-0.010 0.058 -0.077 0.00 0.055 -0.063 0.01 Change in Excess Value (Berger and Ofek) (-0.014) (0.064) (-0.131) (0.00) (0.079) (-0.105) (0.01)

Panel B: Univariate Statistics of Firm and Segment Investment before and after the Industry Shock Variables Median RVA = 0

t–1 RVA < 0

t–1 p-value

difference ∆ EEC = 0

∆ EEC < 0

p-value difference

Firm Capex-Imputed Capex, t–1

-0.003 0.004 -0.009 0.00 0.001 -0.005 0.19

Firm Capex-Imputed Capex, t

-0.004 0.008 -0.015 0.00 0.009 -0.016 0.00

Change in (Firm Capex-Imputed Capex)

-0.001 0.004 -0.006 0.02 0.008 -0.011 0.01

Segment Capex-Imputed

Capex, t–1 -0.001 0.005 -0.006 0.08 0.001 -0.004 0.23

Segment Capex-Imputed Capex, t

-0.002 0.019 -0.021 0.00 0.018 -0.021 0.01

Change in (Segment Capex-Imputed Capex)

-0.001 0.014 -0.015 0.00 0.012 -0.016 0.00

Segment Capex, t–1

0.062 0.066 0.055 0.00 0.063 0.060 0.50

Segment Capex, t

0.088 0.097 0.069 0.00 0.099 0.066 0.00

Change in Segment Capex

0.018 0.030 0.010 0.00 0.033 0.005 0.00

45

Table 8

Changes in Excess Net External Capital

OLS regressions using the industry shock sample for the period 1980–1998. Year dummies are not reported. The dependent variable is the change in excess net external capital (∆ EEC). All the changes are measured between t–1 and t, where t is the end of the year in which the industry shock occurred. Excess Value is computed according to Berger and Ofek’s (1995) sales multiplier method. RVADUM is a dummy variable equal to one if RVA is not negative, and zero otherwise. DUM(∆ RVA ≥ 0) is a dummy equal to one if the change in RVA between t–1 and t is not negative. Hit-size is the ratio of segment(s) assets (segments that belong to the shocked industry) to total assets of the firm. For other variable definitions see Appendix 1. On the first line the coefficients are reported with their significance level indicated by *, **, *** corresponding to 10%, 5% and 1% significance based on White-adjusted standard errors. The absolute values of the t-statistics are reported in brackets underneath.

Dependent Variable

Change in Excess Net External Capital

Model (1) (2) (3)

Measures of Size of ICM Diversity, t–1

-0.016** (2.308)

-0.018** (2.176)

Diversity2, t–1

0.000 (0.180)

-0.003 (1.595)

Change in Diversity

-0.020** (2.051)

Measures of ICM Efficiency Relative Value Added (RVA), t–1

2.640** (2.144)

2.543*** (2.933)

Change in RVA

1.830* (1.696)

(Diversity, t–1) × RVADUMt–1

0.026** (2.239)

0.023** (2.482)

(Diversity, t–1)2 × RVADUMt–1

-0.003 (1.263)

0.002 (0.409)

Change in Diversity × DUM( ∆ RVA ≥ 0)

0.056** (2.533)

Measures of Information Asymmetry Intangible Assets / Total Assets, t–1

-0.207*** (3.565)

-0.321** (2.600)

Intangible Assets / Total Assets, t–1 × RVADUMt–1

0.137** (2.761)

Intangible Assets / Total Assets, t–1 × DUM( ∆ RVA ≥ 0)

0.149** (2.234)

Standardized Analysts’ Forecast Dispersion, t–1

-0.014** (2.263)

Standardized Analysts’ Forecast Dispersion, t–1 × RVADUMt–1

0.009* (1.788)

Measures of Capital Need Excess Internal Cash Flow, t–1

0.264* (1.926)

0.274* (1.901)

Change in Excess Internal Cash Flow

-0.325** (2.002)

Measures of Relative Valuation Annual Return, t–1

0.012 (0.523)

0.004 (0.208)

0.016 (0.694)

Measure of Size Hit-size

0.047 (1.177)

0.011 (0.229)

-0.012 (0.306)

Number of Observations 390 390 229 Adjusted R-squared 0.382 0.185 0.231

46

Table 9

Changes in Excess Value and Changes in Excess Net External Funds

OLS regressions using the industry shock sample for the period 1980–1998. Year dummies are not reported. The dependent variable is the change in excess value between t–1 and t. Excess Value is computed according to Berger and Ofek’s (1995) sales multiplier method. The variable ∆ EEC is the change in excess net external capital. RVADUM is a dummy variable equal to one if RVA is not negative and zero otherwise. DUM(∆ RVA ≥ 0) is a dummy equal to one if the change in RVA between t–1 and t is not negative, and zero otherwise. Hit-size is the ratio of segment(s) assets (segments that belong to the shocked industry) to total assets of the firm. For other variable definitions see Appendix 1. On the first line the coefficients are reported with their significance level indicated by *, **, *** corresponding to 10%, 5% and 1% significance based on White-adjusted standard errors. The absolute values of the t-statistics are reported in brackets underneath.

Dependent Variable

Change in Excess Value

Model (1) (2) (3) Change in Excess Net External Capital

( ∆ EEC) 0.126 (0.818)

0.148 (1.036)

Relative Value Added by Allocation (RVA), t–1

1.702** (2.553)

1.803** (2.725)

Change in Relative Value Added by Allocation ( ∆ RVA)

0.098 (0.361)

Diversity, t–1 -0.013 (1.562)

-0.012 (1.231)

-0.016* (1.718)

(Diversity, t–1) × RVADUM t–1 0.042*** (3.214)

0.041** (2.213)

(Diversity, t–1) × DUM(∆ RVA ≥ 0) 0.038** (2.749)

( ∆ EEC) × RVADUM t–1 0.223** (2.016)

( ∆ EEC) × (Diversity, t–1) -0.200 (0.620)

-0.420 (1.643)

( ∆ EEC) × (Diversity, t–1)× RVADUM t–1 0.828** (2.139)

( ∆ EEC) × DUM(∆ RVA ≥ 0) 0.147 (0.535)

( ∆ EEC) × (Diversity, t–1)× DUM(∆ RVA ≥ 0)

0.600** (2.336)

Excess Net External Capital (EEC), t–1 0.189 (1.323)

(EEC, t–1) × RVADUM t–1 0.255** (2.250)

(EEC, t–1) × (Diversity, t–1) -0.230 (0.899)

(EEC, t–1) × (Diversity, t–1)× RVADUM t–1 0.630** (2.261)

Excess Internal Cash Flow, t–1 0.002 (0.011)

0.035 (0.133)

0.185 (0.657)

Excess Value (Berger and Ofek), t–1 -0.358*** (3.882)

-0.364*** (3.868)

-0.362*** (3.928)

Hit-size 0.119 (0.926)

0.073 (0.579)

0.082 (0.684)

Number of Observations 390 390 390 Adjusted R-squared 0.479 0.430 0.371

47

Endnotes

1 For an overview, see Campa and Kedia (2000). 2 Cocco and Mahrt-Smith (2001) also study how diversified firms react to industry shocks and find that the

option to re-allocate capital in the ICM is most often abused by discounted conglomerates in the event of high industry returns.

3 For simplicity, I do not include an effort dilution cost which might exist because headquarters can appropriate some of the divisional managers’ private benefits (see Grossman and Hart (1986) and Stein (1997)). Including such an additional cost of diversification can reduce the benefits documented below.

4 The numerical values in this example are chosen to show the main determinants of the interactions between internal and external capital markets. Allowing for correlation in projects’ outcome (i.e., capital needs), differences in projects’ returns between the good and bad outcome and different ex ante outcome probabilities will affect the probability that the diversified firm can raise more external capital relative to single segment firms, unless the diversified firm can own more than two projects. Note that if the ex ante expectation of the good state occurring is high enough that overinvestment is cheaper than underinvestment, external investors will not impose credit constraints on single project firms. Under this assumption, a diversified firm might receive less external capital by increasing the number of projects under one roof.

5 This prediction is not unique to Stein (1997). Rajan et al. (2000) also predict this relation and empirically find support for it.

6 This is consistent with Lamont and Polk’s (2002) sample selection process. SIC 9 contains mostly nonoperating divisions. SIC 0 contains agriculture operations with an average of only about 40 single segment firms per year, which is insufficient to compute imputed values. SIC 6 contains financial firms, where the market-to-book ratio is difficult to interpret and many cash flow statement variables are not available.

7 I convert all dollar values to their 1990 level by applying a GDP deflator. The $10 million size limit mainly eliminates single segment firms.

8 The analysis has also been done at the 2-digit SIC level without changing any of the conclusions. 9 Results are also shown if only diversified firms with the same number of segments in two consecutive

years are used. 10 Only 19 firms have less than 127 trading days; 42 have less than 200 but more than 30. The mean and

median of the residual variance are not significantly different between the firms with less than 128 trading days and the firms with more than 127 trading days. None of the results changes significantly if the limit is set either at 127 or at 200 trading days.

11 Note that agency problems are not treated as a separate determining factor, although, they might have an indirect impact through other factors, such as ICM efficiency (e.g., Rajan et al., 2000; Scharfstein and Stein, 2000).

12 Chevalier (2000) shows that in her sample the ranking of imputed q and firm q only correspond in about 60% of the cases using the 3-digit SIC level to impute q. This is a potential problem in using RVA. Note, however, that the value added due to reallocation is higher when the differences in divisional investment opportunities are higher. Thus, in situations where the divisional qs are close, the ranking might be incorrect, but then investment should also not differ substantially.

13 Scharfstein (1998) uses segment cash flow for this reason. 14 Rajan et al. (2000) find a significantly negative mean RVA of –0.0012. Using only data up to 1993, as

Rajan et al. (2000), the mean RVA in my sample is a significantly negative –0.001. 15 Proxies based on prices can be lower either because the firm is well enough diversified such that the cost

of information asymmetry is reduced or because information asymmetry actually is lower. 16 For a detailed description, see Appendix 1. Note that none of the variables used to compute EEC is also

used to compute excess internal capital. Section 2.4 reports regression results using alternative definitions.

17 A robustness test with respect to the estimation technique, using firm fixed-effects, is shown in Table 5. 18 As noted before, the findings are robust to limiting the sample to the 7,035 firms that report the same

number of segments in two consecutive years. 19 Note that residual variance is not used in model 8 because residual variance itself might be determined, in

part, by analysts’ forecasts. However, qualitatively similar results obtain if residual variance is included.

48

20 AVA for single segment firms is defined using the industry-median q. Using the firms’ own q there is a

significant difference in the coefficient on AVA between single segment firms and diversified firms. The coefficient is higher for single segment firms.

21 Ackerman (1968) compares resource allocation in integrated and conglomerate companies. 22 Replacing diversity by the number of different 4-digit SIC codes reported by Compustat as a measure of

the size of the ICM, does not lead to a significant coefficient for single segment firms. Results for diversified firms are similar to the ones reported in model 3 of Table 2 (omitted for brevity).

23 The main reason for excluding dividends is that they are a strong commitment, and changes are very costly (e.g., Shyam-Sunder and Myers, 1999). This potential complication will become important in testing the relation between EEC and firm value since firms with higher dividends would display a lower EEC. Miller and Rock (1985) demonstrate that an increase in dividends is viewed as a positive signal regarding firm value. Denis et al. (1994) empirically confirm such a relationship. Thus, firms with higher dividends are expected to be valued higher. If dividends are used to signal rather than being viewed as an external capital market transaction, they potentially induce a negative correlation between EEC and firm value that is unrelated to the tests of interest in this study.

24 Graham et al. (2001), Campa and Kedia (2000) and Villalonga (2000) show that an average negative excess value does not imply that diversification per se destroys value. Rather, firms might endogenously choose to diversify and/or acquire targets that are significantly discounted even as stand alone firms. I address these issues in three ways, described in more detail below. First, as suggested by Campa and Kedia firm-fixed effect regressions are used. Second, lagged excess value, as in Lamont and Polk (2002), is added as a control and a consistent panel data technique (Arellano-Bond, 1991) is employed to estimate the regression in first differences and using lagged differences as instruments. Third, the industry shock sample with an exogenous event, described in section 3 below, is used to study changes around the event, holding other things constant.

25 Following Berger and Ofek (1995) in excluding observations where excess values are less than –1.386 or more than 1.386, neither the univariate nor the regression results change significantly. Results using the Berger and Ofek asset multiplier excess value are qualitatively similar and are not reported here.

26 In untabulated regressions, similar results obtain if sales growth, log of assets and EBIT/sales are included (e.g., Berger and Ofek, 1995).

27 As Graham et al. (2001) show, increases in the number of segments are caused by merger & acquisition activities in about two-thirds of the cases in their sample.

28 The Sargan test rejects the null with p < 0.05 if more than two lags are used. However, as shown in Arellano and Bond (1991), the Sargan test rejects the null too often in situations with lagged dependent variables and with a larger number of instruments.

29 That is, if the market sees a positive chance that the project will be realized or sold. 30 Lamont (1997), Blanchard et al. (1994) and Harford and Haushalter (2000) employ event study

methodologies to investigate the effect of shocks to cash flow on firms’ use of funds. Blanchard et al. (1994) and Harford and Haushalter (2000) analyze how firms transact with the ECM after the shock. However, they do not explore whether differences exist between single segment and diversified firms.

31 The cut-off values of 1.25, –0.25 and 0.25 are arbitrary. If a normal distribution of q is assumed, my standardization procedure computes a standard normal variable, where the value 1.25 corresponds to the 10th percentile using a one-tailed test. However, since the change between two standardized values is used, the probability is path dependent. To test whether the procedure inadvertently picks up industries that recover from a very low realization of the standardized q, the standardized industry median q and cash flow in the year of the shock, t, are shown in Appendix 2. The average standardized industry median q across all industries in the year of the shock is 0.97, and no industry has a standardized q below –0.22.

32 The predictions of Maksimovic and Phillips’ (2002) neoclassical model are that divisions with relatively lower productivity should decrease their size, given a positive demand shock, and higher productivity divisions should increase their size. Using cf-sensitivity rather than RVA, the conclusions drawn from such a measure of allocational efficiency are not different from those presented (not shown).

33 Qualitatively similar results are obtained if the Lang and Stulz’s (1994) method is used (not shown). 34 Since the industry shock sample includes no firm in two consecutive years, tests in this section are less

likely to be influenced by possible estimation bias introduced by uncontrolled time-series correlation nor is there a problem of using a lagged dependent variable.