Conglomerate Discount and Financial Constraints: A Novel View to an Old Puzzle

Andriy Bodnaruk* Massimo Massa** Lei Zhang***

Abstract We study the conglomerate discount from a novel perspective. We argue that the discount – measured in terms of Tobin’s Q – far from being a sign of lower value is, instead, a sign of higher value for the conglomerate. This can be explained as conglomerates being less financial constrained than single segments firms. A financially constrained firm is forced to select only the high return investments and therefore its Q will be very high. Given that single segment firms are more likely to be financially constrained than multiple segment ones, they should also display a Tobin’s Q higher than the (less constrained) equivalent division of the conglomerate. Therefore, the conglomerate discount measures not so much the lower “value” of the conglomerate firm as the higher constrains faced by the single-segments firms used as benchmark. We empirically test this hypothesis by employing a novel – and purely exogenous – measure of financial constraints that is based on the relative financial constraints between bond and bank capital supply in the region in which the firm is located. We focus on the US corporations from 1996 to 2004 and we show that firms characterized by higher financial constraints have higher Q as they only select the more profitable investments. We then provide evidence that the lower the degree of financial constraints, the more likely it is that the firm is a conglomerate. Finally, we show a positive correlation between the size of the conglomerate discount and the degree of excess financial constraints of the conglomerate. One-standard deviation increase in the difference of financial constraints between the single-segment firms used as a benchmark and the conglomerate raises the conglomerate discount by 5.38% which corresponds to 33.39% of unconditional mean. Our findings suggest a reinterpretation of the standard intuition about conglomerates in which the conglomerate discount gauges the lower degree of financially constraints of the conglomerate.

JEL Classification: G12, G3, G32

Keywords: conglomerate discount, financial constraints, Tobin’s Q.

∗ Mendoza College of Business, University of Notre Dame; ∗∗, ∗∗ ∗ Finance Department, INSEAD. Please address all correspondence to Massimo Massa, INSEAD, Boulevard de Constance, 77300 Fontainebleau France, Tel: +33160724481, Fax: +33160724045 Email: massimo.massa@insead.edu.

Introduction

One interesting puzzle in finance is the conglomerate discount. The literature has argued that multiple

segment firms – i.e., conglomerates – are worth less than the equivalent single segment ones. The

measure of value has been Tobin’s Q. The lower value has been justified in terms of negative

externalities related to the creation of a conglomerate. These externalities have been identified in the

lower ability of the CEO to oversee different lines of business, in inefficient transfers within the

conglomerate, in power struggles between the divisional managers and so on. The common underlying

assumption is that creating a conglomerate makes the individual segments of the conglomerate or their

sum less efficients and this reduces value, hence the definition of “conglomerate discount”. We propose

an alternative intuition.

We argue that the difference in value between a conglomerate and the corresponding single-segment

firms is far from being a sign of lower value, but, on the contrary, a sign of higher value for the

conglomerate. This is due to the way value is measured. As a standard financial textbook shows, Tobin’s

Q is a very imperfect measure of value. Indeed, a firm with higher growth will have a higher Tobin’s Q.

However, a higher Tobins’ Q is also a signal that, all else equal, the firm is more financially constrained.

Indeed, a financially constrained firm is forced to select only the high return investments and therefore

its marginal Q will be lower than its average Q. We argue that that this second facet of Tobin’s Q and

the related inability to finance all the investment opportunities and the forced selection of only the more

profitable ones creates a positive relationship between financial constraints and Q. That is, the firm’s Q

is positively related to the degree of financial constraints of the firm.

Single segment firms are more likely to be financially constrained than multiple segment ones; the

reason being that conglomerates are better able to reduce their constraints. Indeed, as Stein (1997)

argues, conglomerates are born to overcome financial constraints and limited access to capital. However,

if single segment firms are more constrained, they should also display a higher Tobin’s Q than the (less

constrained) equivalent division of the conglomerate. This implies that the conglomerate discount is

being based on the difference between the Tobin’s Q of the firm and that of the sum of the single-

segment firms should measure not so much the lower “value” of the conglomerate firm as the higher

constrains faced by the single-segments firms used as a benchmark. If this is the case, the interpretation

of the discount as a sign of wealth distruction created by the process of conglomeration has to be

reversed. Indeed, creating a conglomerate, far from destroying value does actually help the firm to

overcome its financial constraints and helps it to increase its firm value.

The test of this hypothesis is particularly difficult as it requires a measure of financial constraints that

is not directly related to Tobin’s Q or its determinants. The standard measures of financial constraints –

Kaplan and Zingales (KZ) Index, Whited and Wu (WW) Index, Almeida, Campello and Weisbach

(2005) availability of cash – while correct measures of financial constraints, are either directly based on

1

Tobin’s Q or based on some of its main determinants such as leverage, dividend payout, cash flows and

cash holdings. All of these variables have been found to be directly related to Q. This makes it

observationally impossible to use the standard measures of financial constraints to test the

abovementioned assumption.

For example, if the difference between single-segment firms and multiple-segments firms is just due

to differences in leverage, this can be interpreted as conglomerates having higher leverage as well as

higher leverage firms end up being conglomerates. In any case, it would be difficult to draw any positive

interpretation as leverage is endogenous with respect to being a conglomerate and to the firm value as

opposed to just being one of its determinants. Moreover, all the variables entering the standard measures

of financial constraints have been already employed by the literature as control variables in the analysis

of the conglomerate discount. It is therefore necessary to find a purely exogenous determinant of

financial constraints that affects Tobin’s Q, but it is not affected by it.

In this paper, we employ a novel – and purely exogenous – measure of financial constraints that is

based on the contraints induced by an imbalance between bond and bank capital supply in the region in

which the firm is located. We use it to test the intuition that part of the conglomerate discount can be

attributed to the single segment firms being more financially constrained than the conglomerates.

We test these hypotheses focusing on the US corporations from 1997 to 2004. We construct a

measure of financial constraints based on the location of the firm and the relative availability of bond

and bank-financing. We rely on the findings of Massa and Zhang (2009) to define this measure as a

measure of financial constraints. This measure of financial constraints exploits the information

contained in the local bank deposits and the bond holdings by institutional investors located in a

particular area. Higher financial constraints are due to the fact that either the local banking sector is not

able to absorb a reduction in bondholder appetite by granting new loans or that the local bond market is

less able to replace bank financing.

We start by showing that our measure of financial constraints is related to higher Tobin’s Q. Firms

characterized by higher financial constraints have higher Q. An increase of one standard deviation in the

degree of financial constraints of the firm corresponds to an increase in the firm Tobin’s Q by 0.07

which corresponds to 4.31% relatively to unconditional mean. The result is robust to controlling for

variables proxying for the expected rate of growth of the firm as well as for a host of other firm specific

characterstics. This is consistent with the liteature showing that financial constraints do actually increase

Q (Kaplan and Zingales, 2000, Whited and Wu, 2006).

We then show that financial constraints help to forecast both the probability that a firm is a

conglomerate as well as its conglomerate discount. In particular, a probit model on the probability that

the firm is a conglomerate shows that the probability that the firm is a conglomerate is negatively related

to its financial constraints. That is, if we consider a sample of firms and we want to predict which firm is

2

a conglomerate, the degree of financial constraints is a valid predictor. The lower the degree of financial

constraints the more likely it is that the firm is a conglomerate. The fact that conglomerates are less

financially constrained is also consistent with Stein (1997) theory. Indeed, if conglomerates are created

to reduce financial constraints, we expect that after the conglomerate has been created, the financial

constraints should be lower. That is, conglomerates should, on average, have lower degree of financial

constraints that single segment firms.

We then relate financial costraints to the firm’s excess value – i.e., the difference between the firm

Q and the firm’s imputed Q (the weighted average of the single segments Qs the firm operates in). In

particular, we relate excess value to the difference between the degree of financial constraints of the

conglomerate firm and the degree of financial constraints of the single segments firms used as a

benchmark. The results strongly support our working hypothesis. There is a positive correlation between

the degree of financial constraints of the conglomerate and the degree of financial constraints of the

benchmark single segment firms. A one-standard deviation increase in the difference of financial

constraints between the single-segment firms used as a benchmark and the conglomerate reduces the

conglomerate discount by 5.38% or 33.39% relatively to unconditional mean. The results are robust to

alternative specifications as well as to different ways of accounting for the selection bias based on either

Heckman selection model or simultaneous equation system. The results are always robust to variables

controlling for the expected rate of growth of the firm as well as a host of other firm specific

characterstics.

The results are conditional on the firm having a positive leverage. As expected, firms with no debt

are not subject to this type of constraint. It is important to notice that while our measure of financial

constraints helps to explain why firm do to take up debt, still it is not its main determinant. It is

important to relate our findings to those of Mansi and Reeb (2002). They argue that the conglomerate

discount can be explained in terms of corporate diversification. They hypothesize that while

diversification reduces shareholder value, it enhances bondholder value due a reduction in firm risk.

"Lowering firm risk should lower shareholder value and increase bondholder value, with the value

effects to shareholders depending on the amount of leverage in the firm" (Mansi and Reeb, 2002). This

implies that excess value is decreasing in diversification and most pronounced for the firm with above

average level of debt, while there is no effect for equity firms. We therefore share with them the

empirical observation that most of the action to explain the conglomerate discount is in the levered

firms. However, our channel is quite different. Indeed, it is not diversification that conglomeration

brings about, but reduction of financial constraints. This implies that the channel is not a lower wealth

transfer from bondholders, but a higher ability to invest. The latter increases the value of the

conglomerates, but lowers its Tobin’s Q. Indeed, the results on Tobin’s Q and the evidence showing that

in the case of more financially constrained firms investment increases Tobin’s Q more provides a direct

3

support to this interpretation. Our results provide an alternative interpretation that is not relying on the

potentially very small effects related to the wealth transfer between bondholders and equityholders.

It is also important to note that our results are robust to the treatment of econometric selection isues

previsouly advocated as a source of the conglomerate discount (e.g., Whited, 2001; Campa and Kedia,

2002; Villalonga 2004a). Indeed, we control for the probability that the firm is a conglomerate using

both Heckman and simultaneous equation system approaches.

The closest to our findings is the recent evidence of Maksimovic and Philips (2002) who argue that

the conglomerate discount can be related to difference in productivities between single-segment firms

and equivalent units inside the conglomerate. Our results – even if not estimated with Census Data

unavailable to us – are consistent with theirs and explain them in terms of financial constraints.

Our findings relate to different streams of research. First, we relate to the literature on internal capital

markets. This has explained the lower value of the conglomerates with lower firm value (Lang and

Stulz, 1994, Berger and Ofek, 1995, Servaes, 1996, Lamont, 1997, Shin and Stulz, 1998, Lins and

Servaes, 1999, Denis, Denis and Yost, 2002, Lamont and Polk, 2002). A number of theories have been

put forward to explain this result. At the firm level, the existence of private benefits of control and/or

manager’s aversion to idiosyncratic risk might imply overinvestment through wasteful diversification

(e.g. Jensen, 1986, Stulz, 1990, Matsusaka and Nanda, 1997, Denis et al., 1997, Aggarwal and Samwick,

2003). Also, econometric problems (e.g Whited, 2001, Campa and Kedia, 2002, Villalonga 2004a), data

issues (e.g. Villalonga 2004b), differences in productivity (Maksimovic and Philips, 2002), valuation

(Graham et al., 2002) and capital structure (Mansi and Reeb, 2002) between single-segment and multi-

segment firms have been variably used as explanations.

We contribute to this literature by showing that the standard measure of difference in value between

the conglomerate and the single segment firms, far from capturing a lower value for the conglomerate,

instead, represents a higher degree of constraints for the single segment firms.

Second, we relate to the literature on financial constraints (Kaplan and Zingales, 2000, Whited and

Wu, 2006, Alemeida, Campello and Weisbach, 2005). Unlike all the existing measures of financial

constraints – e.g., KZ index – our measure is not dependent on firm specific actions or corporate policies

– e.g., dividend policy, ratings – and is instead based on the characteristics of the market in which the

firm is, with the advantage of being a considerably less endogenous variable. We contribute to the

literature by finding direct empirical support for the theory that financial constraints have a positive

impact on Tobin’s Q (Whited and Wu, 2006, Gomes, Yaron and Zhang, 2006).

Third, we relate to the literature on geography and proximity investment (e.g., Coval and Moskowitz,

1999, 2001). This literature has mostly focused on the equity side, showing that investors (households

and institutional investors) tend to hold the stocks of firms located nearby and showing that this has

implications in terms of the value of the stocks (Hong et al., 2005). We focus on the debt side and we

4

show that this has equally important implications for the value of the firm. In the case of the equity side,

the identification between institutional investors and local investors is in general a first type

approximation, as we do not have information about where the individual investors invest. In the case of

bonds, instead, the results are potentially more telling, as corporate bonds are mostly held by

institutional investors. This makes our estimates more accurate than one would hope in the equity side.

Finally, we relate to the literature on industrial clustering. Almazan et al., (2005) show that being

located within an industry cluster increases opportunities to make acquisitions and induces firms to have

lower debt ratios and larger cash balances than their industry peers located outside clusters. We

complement their results as we focus on the geographical clustering of financing.

The remainder of the paper is articulated as follows. Section 2 describes the sample, the variables we

use as well as our measures of financial constraints. Section 3 tests whether finacial constraints are

related to higher firm’s Tobin’s Q. Section 4 analyzes the relationship between conglomerates and

financial constraints. Section 5 tests whether the conglomerate discount can be explained in terms of

financial constraints. A brief conclusion follows.

2. Data and Definition of the Main Variables

2.1 Sample construction

Our primary data sources are the annual CRSP-Compustat Merged files (containing firm-level

accounting data) and the Compustat Segment files (containing segment assets, investment and cash-flow

data). We keep in the sample multiple-segment firms that have non-missing segment SIC codes, CRSP

share codes equal to 10 or 11, positive values for book equity and sales higher or equal to 20 million

USD (Berger and Ofek, 1995). We remove firms whose sum of segment sales is more than 1 percent

away from total firm sales reported in Compustat, as well as financial companies and utilities (Berger

and Ofek, 1995). We winsorize all variables at the 1% level.

The second data source is information on bond and bank lending. For the bond market, we use

Lipper’s eMAXX fixed income database. It contains details of fixed income holdings for nearly 20,000

U.S. and European insurance companies, U.S., Canadian and European mutual funds, and leading U.S.

public pension funds. It provides information on quarterly ownership of more than 40,000 fixed-come

issuers with $5.4 trillion in total fixed income par amount from the first quarter of 1998 to the second

quarter of 2005. For the bank market, we use information from the FDIC’s Summary of Deposits (SOD)

database. It contains deposit data for more than 89,000 branches/offices of FDIC-insured institutions.

The Federal Deposit Insurance Corporation (FDIC) collects deposit balances for commercial and

savings banks as of June 30 of each year starting from 1994. The data are collected annually.

Information on the geographical location of the institutions investing in bonds (ZIP codes) is from the

5

Lipper’s eMAXX fixed income database. We obtain joint bond investor and bank branch coordinates

(longitudes and latitudes) by merging the ZIP codes with the Gazetteer Files of Census 2000.

2.2 Construction of the Proxy for Financial Constraints

We use a new measure of financial constraints that relies on the local segmentation of the borrowing

market. If the debt market is locally segmented and bond and bank debt are not close substitutes (Massa

et al., 2005), a regional market with a high financial constraints in the availability of bond and bank

financing will make it harder/more costly for firms to replace one source of debt financing with the other

when it is needed. This higher cost is related to the need to borrow outside of its local market, where it is

less known, the information asymmetry with the distant lenders is higher and transaction costs are

steeper.

For example, let us consider a firm whose source of debt financing is located in a specific financial

habitat – defined in terms of the potential bond-holders and lenders – in which it is possible to issue

bonds, but the ability to replace them with bank debt is scarce. If the bond market becomes unreliable

and fickle, the firm will face constraints in substituting its bond financing with bank financing. The firm

may then try to borrow outside of its local market, but then higher information asymmetry and steeper

transaction costs would make the cost of doing it potentially very high. This would induce the firm

either to scale down its activity or to switch to equity. Therefore, given the limited substitutability

between debt and equity financing, the “imbalance” between bond and bank financing does in fact act as

a financial constraint.

Massa and Zhang (2009) show that, for a fixed capacity of the bank and bond sector, debt imbalance

effectively constrains the firm’s ability to buffer shocks and to finance its investment opportunities and

shifts its financing towards equity. Imbalance makes the firm “rationed” in the debt market and increases

its cost of debt financing. Imbalance raises the yield spread on the firm’s bonds and increases their

sensitivity to aggregate yield shocks. The higher the imbalance, the more a change in Treasury bond

yields increases the bond yield of the firm and its bond spread.

Imbalance behaves as a measure of financial constraints meeting all the tests the literature has

devised to identify financial constraints. Firms characterized by a higher imbalance have a higher

sensitivity of investment to Tobin’s Q (Baker, Stein and Wurgler, 2005) and to cash flows (Fazzari,

Hubbard and Petersen, 1988) as well as higher sensitivity of cash holdings to cash flows (Almeida,

Campello and Weisbach, 2005). They are also more likely to hold cash (Acharya, Almeida and

Campello, 2007) and less likely to pay dividends (Fazzari, Hubbard and Petersen, 1988) or use cash as

method of payments in M&As.

Higher imbalance is related to lower leverage. Firms characterized by higher imbalance resort more

to equity and less to debt, increasing SEOs and lowering leverage. The impact on leverage is enhanced

in case the firm faces higher cash flow uncertainty, as a firm with more stable cash flows, instead, will

6

be able to use them to finance its investment. Overall, imbalance increases the firm’s value reaction to

firm specific and market shocks.

In order to construct our measure of financial constraints, we proceed as follows. We start by

clustering firms according to location. Our data on the geographical location (ZIP codes) and deposits of

bank branches are from the FDIC’s Summary of Deposits (SOD) database. We use partitioning

clustering analysis to simplify the location structure of bond investors and bank branches. More

specifically, we first cluster the set of bond investors based on their geographical distances with each

other and set the number of clusters to be 10. Then, we independently partition the set of bank branches

into 10 bank clusters according to their geographical distances. By this procedure for each year we set

up 10 bond clusters and 10 bank clusters. We repeat our methodology from 1997 to 2004 for both bond

and bank clustering after backfilling the location structures.1

Next, we decide on the bond and bank cluster each firm belongs to. Our data on firm locations come

from the historical Compustat location files. For example, we first calculate the average distance of firm

i to investors at bond cluster j. Then, we pick up the bond cluster with the smallest distance and assign

firm i to it. We do analogously for the distance of firm i to bank branches: we pick up the bank cluster

with the smallest distance and assign firm i to it.

We define “Financial constraints” at the level of the local bank/bond cluster. In particular, for firm i,

we first calculate the average distance of firm i to investors at bond cluster j and denote it as ijd . Then,

we pick up the bond cluster j* with the smallest ijd and assign firm i to j* (we denote j* as V*(i)). By

the same token, for firm i, we first calculate the average distance of firm i to bank branches at bank

cluster k and denote it as .ikd Then, we pick up the bank cluster k* with the smallest ikd and assign

firm i to k* (we denote k* as D*(k)). Let be the average holdings of bond investor j during year t,

be the deposits of bank branch k at year t, then for firm i our measure of bank/bond imbalance is:

jtV

ktD

∑∑

∑∑

∈∈

∈∈

+

−

=

)()(

)()(it

**

**

Imbalance

iDkkt

iVjjt

iDkkt

iVjjt

DV

DV.

In building this measure of financial constraints, we made two key assumptions. First, we assumed

that the constraint exists either because bank financing is less than bond financing as well as vice versa.

1 The formula to calculate distances is the first order approximation to the great circle distance:

2lon1)] - (lon2* 53.0[ 2lat1)] - (lat2* [69.1 + , where lat1, lat2, lon1, and lon2 are latitude and longitude values in degrees. The backfilling procedure is as follows. We assume there is no big shift on the locations of insurance fund families and pension fund families so for these investors we use the location as of 1998. For mutual fund families, we focus on the ones matched with CRSP mutual fund database from 1991 to 1997. For bank clustering from 1991 to 1993, we use the same location structure of bank branches as of 1994.

7

That is, the constraint can equally well come from the bank side of the supply as well as from the bond

side. Second, we assumed that the bank lending decision takes place at the branch level as opposed to

the headquarters one. It may be argued that, while for the case of small-medium sized firm, the lending

decision takes place at the headquarters level, for big firms loan are granted directly from the bank

headquaters. To address these two issues, we construct two alternative measures. The first is similar to

the previous Bank/Bond Imbalance, but splits it into its bond and its bank component. That is, there are

two measures: the first (ImbalanceBond) is defined as Imbalance if Imbalance is positive and zero

otherwise. The second (ImbalanceBank) is defined as the negative of Imbalance if Imbalance is negative

and zero otherwise. The intuition is that ImbalanceBond (ImbalanceBank) captures the situations in

which there is more/excessive supply of bond (bank) financing.

ImbalanceHeadQ is constructed the same way as Imbalance with the following alteration: for the

companies with market capitalization above median in a given year the distance from the provider of

bank financing is the distance between the firm and the Headquarters of the bank. For companies with

market capitalization below median the distance from the provider of bank financing is the distance

between the firm and the local branch of the bank.

We report the descriptive statistics of our proxies of financial constraints in Table I. In Panel A, we

see that the mean (median) Imbalance, averaged over the 4183 firm-year observations, is 0.22 (0.20).

Let us suppose the overall debt available in the region is 1 dollar with 0.61 dollar of bonds and 0.39

dollar of bank debts. Imbalance measures the bank/bond mismatches (0.61-0.39) per dollar of (overall)

debt available in the region (bank and bond cluster the firm is belonging to). The mean (median) value

of ImbalanceBond and ImbalanceBanks are, respectively, 0.07 (0.00) and 0.15 (0.07). The mean

(median) value of ImbalanceHeadQ is 0.28 (0.20). Taken together, these statistics show that imbalance

is really driven by ImbalanceBank. That is, in general the binding constraint is the availability of bond

capital as opposed to bank capital.

We then use our proxies of financial constraints to define the difference between the degree of

financial constraints (imbalance) of the conglomerate (multi-segment company) and the segment- sales-

weighted average financial constraints (imbalance) of single-segment firms operating in the same two-

digit industries. We define these “Excess Imbalance”, “Excess ImbalanceBank”, “Excess

ImbalanceBond”, and “Excess ImbalanceHeadQ”. Panel B of Table 1 demonstrates that multiple-

segment companies have on average lower financial constraints then single-segment companies as

proxied by measures of excess imbalance.

3.3 Other Variables and Measures of Conglomerate Excess Value and Other Variables

We employ as control for alternative hypotheses the set of other variables used in previous papers, such

as the percentage growth in firm sales (Compustat item 12) in the past year (Sales Growth), the firm’s

number of business lines reported in Compustat segment data (Number of Segments), the ratio between

8

market value and book value of assets (Tobin’s Q, defined as in Villalonga, 2004) and a measure of

diversity in investment opportunities between segments (Diversity), defined (Rajan et al., 2000) as

( )∑

∑∈

∈−−

=j

j

Ni jssi

Ni jssiji

ssiji

NQ

NQwQw 1Diversity

2

,,.

Tobin’s Q is defined as the ratio between market value and book value of assets (e.g. Villalonga,

2004). The numerator of Tobin’s Q (Fama and French, 2002) is equal to liabilities (item 181) minus

deferred taxes and investment credit (item 35) plus preferred stock (item 10, or 56, or 130, in that order)

plus market value of equity (item 25 times item 199). The denominator is total assets (item 6). Segment

Q, denoted Qiss, is the average Q of all single-segment firms in segment i’s SIC code. We use 4-digit SIC

codes if there are at least 3 single-segment firms in that SIC code; if not, we use 3-digit, and so forth.

We also calculate the firm’s weighted sum of the Qs of the individual segments (Imputed Q) by

imputing to each segment the average Tobin’s Q of all single-segment firms in segment i’s SIC code

(denoted Qiss):2

∑∈=≡

jNissij,i QwQ̂Q Imputed

where Nj is the set of segments belonging to firm j, wi,j is the weight of segment i on the firm j total

assets, and Qiss is the average Q of all single-segment firms in segment i’s SIC code. The difference

between the firm’s Q and the Imputed Q captures the diversification discount. We call it Excess Value.

We consider three alternative ways of defining Excess Value based on Villalonga (2004), Lang and

Stulz (1994) and asset based measure of Berger and Ofek (1995). For the details in constructing the

excess value measures we refer to the corresponding articles.

Among the other variables, Assets is the company’s total assets (Compustat item 6). Long-Term

Debt is long-term liabilities (item 9) over total assets. Institutional Ownership is the fraction of shares

outstanding pertaining to institutional investors (estimated from Spectrum 13f). Dividend Payer is a

dummy which takes a value of 1 if the company paid a dividend in the previous year, 0 otherwise.

Dispersion Analyst Forecasts is a standard deviation of 1 year EPS forecasts by the analysts (estimated

from I/B/E/S). NYSE Traded is a dummy variable which takes a value of 1 if a company trades on

NYSE, 0 otherwise. Age is the number of years since the company entered the CRSP database.

Conglomerate dummy is a dummy variable which takes a value of 1 if the company has more then one

business segment, 0 otherwise.

Table 1 presents summary statistics. The average (median) firm in our sample has more than 3.5

$Bn. (1.0$Bn) in assets and operates in 2.51 (2.0) business segments. The conglomerate discount is

2 To calculate the average Q of single-segment firms, we use 4-digit SIC codes as long as there are at least 3 single-segment firms in that SIC code; if not, we use 3-digit, and so forth.

9

patent in the sample, with multi-segment firms exhibiting an average (median) discount of 16.1%

(14.5%) relative to single-segment firms. The average (median) firm age is 23 (18) years, revealing that

firms in our sample are rather mature, established firms.

2.4 Dealing with the Potential Endogeneity of Financial Constraints

Imbalance is already a very exogenous variable by construction. However, there may be some

residual endogeneity due to the fact that asset managers may locate themselves in area with higher

concentration of firms and/or the size of their asset under management may depend on the wealth of the

local economy and therefore, indirectly, on the profitability and success of the firms in the area. To

address this issue, we identify some exogenous determinants of the degree of financial constraints. They

are variables that explain the size of local asset under management that is invested in bonds as well as

variables that explain the size of bank lending availability in the local region.

The variables that explain the the size of local assets under management that is invested in bonds are

related to the changes in the Prudent-Investor Law in the state. This rule states that under common law,

each security in the portfolio has to be individually a prudent investment. This has a direct impact on the

portfolio managed by an institutional investor subject to such rule (e.g., insurance firms, banks, pension

funds (Badrinath, Kale and Ryan, 1996, Del Guercio, 1996, Chen, Yao and Yu, 2007). We rely on the

existing literature (Schanzenbach and Sitkoff, 2007) showing that changes in such a regulation has

increased the amount invested in equities at the expense of corporate bonds. In particular, we use the

number of years since the state in which the company is incorporated has adopted changes to the

Prudent-Investor Law based on Uniform Prudent Investor Act (UPIA) (see Schanzenbach and Sitkoff

(2007) for more details). Similarly, we use the number of years since the state in which the company is

incorporated has adopted changes to the Prudent-Investor Law not based on UPIA. We call the former

“UPIA Based Statute” and the latter “non-UPIA Based Statute”.

The variables that explain the amount of local lending are related to changes in legislation about

interstate branching and bank mergers. They are: “State-wide Branching” and “MBHC Activity”. State-

wide Branching is defined as the number of years from 1965 where statewide bank branching is allowed

in the state where the firm is located. MBHC Activity is defined as the number of years from 1965

where multi-bank holding company activity is allowed in the state the firm belongs to. We also use

state-level dummies to control for state-specific fixed effects.

Earlier adoption of state-wide branching and multi-bank holding company activity increased amount

of bank financing available. We therefore expect them to result in lower financial constraints.

Introduction of amendments to Prudent-Investor Laws created incentives for trusts and other assets

managers to shift their portfolios from corporoate bonds to equities reducing the demand and availability

of public debt. We anticipate that variables related to these regulatory changes should affect financial

constraints in an adverse way.

10

In Table 2, we report the regressions results relating our measures of financial constraints to these

instruments. As expected, earlier adoption of state-wide branching and multi-bank holding company

activity result in lower imbalance. Earlier introduction of changes to Prudent-Investor Laws led to

increase in levels of imbalance.

The results also show that the instruments explain a high percentage of the imbalance measures. Not

only the statistical significance of the instruments is high, but the instruments explain quite a high

fraction of Imbalance (Incremental Adjusted R2 of 62.78%), ImbalanceBond and ImbalanceEquity

(respectively Adjusted R2 of 76.48% and Adjusted R2 of 68.07%) and ImbalanceHeadQ (Adjusted R2 of

50.91%). The F-test of the joint significance of the instruments rejects the null of weak instruments

(Staiger and Stock, 1997). Overall these findings suggest that the instruments meet the first required

criterion: they are related to the variable of interest. To assess whether the second criterion is met – i.e.,

the instruments do not affect the dependent variables in the second stage through other channels than the

instrumented variables – in all the specifications, we report the Hansen’s test of overidentification. They

will always fail to reject the null, providing supporting evidence in favor of the quality of our

instruments.

Finally, it is also worth mentioning that less than 4.5% firms do move their headquarters in our

sample period. This further reduces the concern for endogeneity of our financial constraints proxy.

3. Financial Constraints and Firm’s Value

We start by focusing on the relationship between financial constraints and Tobin’s Q. The literature

(Kaplan and Zingales, 2000, Whited and Wu 2006) has argued that, all else equal, more financially

contrained firms have higher Tobin’s Q. The idea is that financially constrained firms are forced to

select only the most profitable investment opportunies and therefore the Tobins’ Q of financially

constrained firms are higher. We start by relating Tobin’s Q to financial constraints. We regress Q on

the different measures of imbalance as well as a set of control variables. These are: log(Assets,

Long_term Debt, CAPEX, Sales Growth, Institutional Ownership, Dividend Payer dummy, Dispersion

of Analyst Forecasts, NYSE traded dummy and Diversity. We report the results in Table 2.

Specifications 1 and 5 provide the results of OLS regressions. Specifications 2-4 and 6-8 present the

results of IV-regressions. The instruments are defined as above. We include industry and time fixed

effects and, following Petersen (2007), we cluster standard errors at the firm level.

The results show a strong positive correlation between Q and financial constrais. Firms characterized

by high financial constrains have high Q. This holds across the different specifications and is robust to

the alternative set of control variables. This is also economically relevant. One standard deviation higher

degree of financial constraints is related to an increase in Tobin’s Q by 0.07 which corresponds to 4.31%

increase relatively to the unconditional mean. The results are also robust to the instrumentation.

11

Long-term debt of the company appears to have the strongest effect on Tobin’s q. One standard

deviation increase in long-term debt decreases Tobin’s Q by 0.21. Variation in other statistically and

economically significant determinants has effect on Tobin’s Q similar to that of Imbalance. One

standard deviation increase in Log(Assets) (Capex, Sales Growth, Institutional Ownership) increases

Tobin’s Q by 0.12 (0.06, 0.10, 0.06). An increase in Dispersion of Analyst Forecasts by one standard

deviation is related to a decline in Q by 0.07.

4. Financial Constraints and Conglomerates

We move on to consider conglomerates. Standard theory (e.g., Stein, 1997) posits that financially

constrained firms have an incentive to create conglomerates in order to reduce their constraints.

Therefore, conglomerates should in general be less financially constrained than single-segment firms.

This provides both a testable restriction and a source of econometric concern as the sample of

conglomerates would be structurally characterized by lower financial constraints. We therefore have to

control for it when we relate the difference in value between conglomerates and single segment firms

and financial constraints. We will consider these two aspects separately. We start by relating

conglomerates to financial constraints.

We estimate a probit regression in which the dependent variable is a dummy taking the value of one

if the firm is a conglomerate and zero otherwise. The explanatory variables include our proxies for

financial constraints as well as a set of control variables. We include industry and time fixed effects and,

following Petersen (2007), we cluster at the firm level.

We report the results in Table 3. Panel A reports the results for Imbalance. Specifications 1 and 3

use actual level of Imbalance. Specifications 2 and 4 use instrumented Imbalance. Panel B reports the

results for the alternative measures of imbalance. All the specifications provide the results of IV-

regressions. The results show a strong and statistically significant negative relationship between

imbalance and the likelihood that the firm is a conglomerate. This is robust across different

specifications and controlling for different sets of variables. A one standard devition higher Imbalance

(ImbalanceBond, Imbalance Bank, ImbalanceHeadQ) reduces the likelihhod that the firm is a

conglomerate by 2.63% (5.31%, 3.53%, 3.76%) or 4.68%(9.45%, 6.29%, 6.70%) relatively to the

sample mean. This suggests that we need to proper control for this selection issue in the next step, to

assess the relationship between financial constraints and conglomerate discount.

As expected, conglomerates are more likely to be larger and more mature companies with lower

institutional ownership. Conglomerates also have lower levels of investment and lower dispersion of

analyst earnings forecasts. Higher Implied Tobin’s is negatively related to the likelihood of a company

being a conglomerate, which is consistent with Stein (1997).

12

5. Financial Constraints and Conglomerate Discount

We now study whether financial constraints affect the conglomerate excess value. We regress the firm

excess value on the firm’s excess imbalance and a set of control variables. As we mentioned, excess

value is the difference between the firm’s Q and Imputed Q, while excess imbalance is the difference

between the firm’s imbalance and its “imputed” imbalance. We use three definitions of excess value, the

first is based on Villalonga (2004), the second is based on Lang and Stulz (1994) and the third is based

on Berger and Ofek (1995).

We consider three main specifications. The first is the unconditional one. The second specification

takes into account that financial contraints may also affect the fact that the firm is a conglomerate and

that conglomerates, being born to overcome financial constraints, are less constrained. The third

specification is also controlling for this selection bias, but using a simultaneous equation system. The

Heckman specification relies on the probit described in the previous section. In particular, we use the

residuals from the calculation of the Probit as described above to construct the Heckman’s Lambda and

we include it in the specification. We use company age as identifying variable – i.e., variable that is used

in the Probit estimation, but does not enter excess value - excess imbalance regression.

As additional robustness check, we also consider a simultaneous equation framework. The first part

of the simultaneous system involves a firm’s decision to become a conglomerate. The second part of our

joint framework relates a conglomerate discount to excess financial constraints.

In all the specifications, we include industry and time fixed effects; standard errors are clustered

either at year or firm level. The results are reported in Table 4. Panel A provides the results of simple

pooled regressions. Panel B provide the results of Heckman selection model. Panel C provides the

results of a simultaneous equation system. Panels A-C use Villalonga’s Excess Value as a dependent

variable. Panel D provides the results of Heckman selection model with Lang-Stulz and Berger-Ofek

measures of excess value as dependent variables. In Specifications 1 and 5 provide the results with

excess imbalance estimated from actual variables. Specifications 2-4 and 6-8 present the results of with

excess imbalance estimated from instrumented measures of imbalance. The instruments as well as the

other control variables are defined as above.

The results show a strong positive correlation between the difference in financial constraints and

excess value. These findings are robust across different specifications and controlling for different sets

of variables. On average, financial constraints defined in terms of Imbalance (ImbalanceBond,

Imbalance Bank, and ImbalanceNew) explain 33.39% (43.63%, 25.49%, and 27.61%) of the excess

value. If we just focus on Imbalance, we see that an increase of one standard deviation in the degree of

financial constraints corresponds to a 0.054 absolute decrease in the average conglomerate discount,

equivalent to a 33.39% (35.14% and 37.86%) decrease for Villalonga’s (Lang and Stulz’s and Berger

and Ofek’s) definition of excess value. These results also hold in the instrumented specification.

13

Regarding the other variables, Implied Q and Long-term Debt are negatively related to excess value, the

latter result being consistent with Mansi and Reeb (2002).

In Panel E of Table 4 we relate excess value to excess financial constraints with control variables

measured in excess of values implied by single-segments firms. We calculate the difference between the

company assets, long-term debt, capital expenditures, growth of sales, institutional ownership, dividend

payer dummy, dispersion of analyst forecasts, and NYSE traded dummy of the conglomerate (multi-

segment company) and the segment- sales-weighted averages of these variables of single-segment firms

operating in the same two-digit industries. We define these “Excess Assets”, “Excess LongTerm-Debt”,

“Excess Capex”, Excess Sales Growth”, “Excess Institutional Ownership”, “Excess Dividend Payer”,

“Excess Dispersion of Analysts Forecasts”, and “Excess NYSE Traded” and use them as controls

variables. As in Panels A-D the results reveal strong positive relation between excess financial

constraints and excess value.

Overall, these findings are consistent with our working hypothesis that the conglomerate discount

can be explained in large part in terms of the differences in financial constraints between the

conglomerate and the equivalent single segment firms.

One way of assessing whether we are in the presence of spurious correlation and our measures of

financial constraints just pick up some spuriously related effects, is to condition on the degree of

leverage of the firm. The more a firm relies on leverage, the more our debt-based measure of financial

constraints should matter. We therefore reestimate the previous specification relation excess value to

excess imbalance, conditioning on whether the company has long-term debt. That is, we interact our

measures of financial constraints with two dummies: the first takes the value of 1 if the firm has debt

and zero otherwise, while the second dummy takes the value of 1 if the firm does not have debt and zero

otherwise. Control and instrumental variables are defined as above.

We report the results in Table 5. Panel A provides the results of Heckman selection model, while

Panel B provides the results of the second stage of the system of simultaneous equations. We use

Villalonga’s Excess Value as a dependent variable. Specifications 1 and 5 provide the results with

excess imbalance estimated from actual variables. Specifications 2-4 and 6-8 present the results of with

excess imbalance estimated from instrumented measures of imbalance.

The results confirm the previous ones and show that the impact of financial constraints is

concentrated in the firms with debt. Scarce or no impact is there in the case of firms with no debt. This

can explained in two ways. In the presence of high imbalance the firm does not issue debt at all. This

means that the impact of imbalance is concentrated in determining whether the firm has debt.

Alternatively, the fact that the firm does not have debt is not due to imbalance, but still the lack of debt

makes the firm less sensitive to the imbalance between alternative sources of debt.

14

Overall these findings support our working hypothesis, showing a direct impact of financial

constraints on the conglomerate discount.

Conclusion

We study the conglomerate discount from a novel perspective. We argue that the discount – measured in

terms of Tobin’s Q – far from being a sign of lower value is, on the contrary, a sign of higher value for

the conglomerate. Indeed, while Q captures the value of growth of the firm, it also signals that, all else

equal, that the firm is more financially constrained. A financially constrained firm is forced to select

only the high return investments and therefore its marginal Q will be lower than its average Q. We argue

that this explains the conglomerate disscount.

Given that single segment firms are more likely to be financially constrained than multiple segment

ones, they should also display a higher Tobin’s Q than the (less constrained) equivalent division of the

conglomerate. This implies that the conglomerate discount being based on the difference between the

Tobin’s Q of the firm and that of the sum of the divisions of the conglomerate should measures not so

much the lower “value” of the conglomerate firm as the higher constrains faced by the single-segments

firms used as benchmark.

We test this hypothesis by employing a novel – and purely exogenous – measure of financial

constraints that is based on the relative financial constraints between bond and bank capital supply in the

region in which the firm is located and we use it to test the intuition that part of the conglomerate

discount can be attributed to the single segment firms being more financially constrained than the

conglomerate ones.

We focus on the US corporations from 1996 to 2004. We show that financial constraints are related

to higher Tobin’s Q. Firms characterized by higher financial constraints have higher Q. An increase of

one standard deviation in the degree of financial constraints of the firm corresponds to an increase the

firm Tobin’s Q by 0.07 or 4.31% relatively to the sample mean.

We then show that financial constraints help to forecast both the probability that a firm is a

conglomerate as well as its conglomerate discount. The lower the degree of financial constraints the

more likely it is that the firm is a conglomerate. We then relate financial costraints to the firm’s excess

value – i.e., the difference between the firm Q and the firm’s imputed Q (the weighted average of the

single segments Qs the firm operates in).

We show a positive correlation between the degree of financial constraints of the conglomerate and

the degree of financial constraints of the benchmark single segment firms. The parameter estimates

show that a one-standard deviation increase in the difference of financial constraints between the single-

segment firms used as a benchmark and the conglomerate raises the conglomerate discount by 5.38% (or

33.39% relatively to the sample mean).

15

Our findings suggest a reinterpretation of the standard intuition about conglomerates. We show that

the standard measure of difference in value between the conglomerate and the single segment firms, far

from capturing a lower value for the conglomerate, instead, represents a higher degree of constraints for

the single segment firms.

16

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Table 1: descriptive statistics This table presents summary statistics for the sample used in this study. The sample includes firms in the merged CRSP-Compustat Segments universe for the period 1996-2004 that have non-missing segment SIC codes, and non-missing values for market capitalization, segment sales, segment assets, diversity variables, and manager biographical data. We remove securities with share codes different from 10 or 11, as well as financial companies and utilities. We also remove firms with negative book equity, with sales lower than 20 million USD, and whose sum of segment sales are more than 1 percent away from total firm sales reported in Compustat. Imbalance is defined in the following way for firm i, we first calculate the average distance of firm i to investors at bond cluster j and denote it as ijd . Then, we pick up the bond cluster j* with the smallest ijd and assign firm i to j* (we denote j* as V*(i)). By the same token, for firm i, we first calculate the average distance of firm i to bank branches at bank cluster k and denote it as .ikd Then, we pick up the bank cluster k* with the smallest ikd and

assign firm i to k* (we denote k* as D*(k)). Let be the average holdings of bond investor j during year t,

be the deposits of bank branch k at year t, then for firm i our measure of bank/bond imbalance is: jtV

ktD

∑∑

∑∑

∈∈

∈∈

+

−

=

)()(

)()(it

**

**

Imbalance

iDkkt

iVjjt

iDkkt

iVjjt

DV

DV.

ImbalanceBond is defined as Imbalance if Imbalance is positive and zero otherwise. ImbalanceBank is defined as the negative of Imbalance if Imbalance is negative and zero otherwise. ImbalanceHeadQ is constructed the same way as Imbalance with the following alteration: for the companies with market capitalization above median in a given year the distance from the provider of bank financing is the distance between the firm and the Headquarters of the bank. For companies with market capitalization below median the distance from the provider of bank financing is the distance between the firm and the local branch of the bank. Tobin’s Q is defined as the ratio between market value and book value of assets (e.g. Villalonga, 2004). The numerator of Tobin’s Q (Fama and French, 2002) is equal to liabilities (item 181) minus deferred taxes and investment credit (item 35) plus preferred stock (item 10, or 56, or 130, in that order) plus market value of equity (item 25 times item 199). The denominator is total assets (item 6). Segment Q, denoted Qi

ss, is the average Q of all single-segment firms in segment i’s SIC code. We use 4-digit SIC codes as long as there are at least 3 single-segment firms in that SIC code; if not, we use 3-digit, and so forth. Imputed Q is the weighted sum of the Q of the individual segments

∑∈=≡

jNissij,i QwQ̂Q Imputed

where Nj is the set of segments belonging to firm j, wi,j is the weight of segment i on the firm j total assets, and Qi

ss is the average Q of all single-segment firms in segment i’s SIC code. Assets is the company’s total assets (COmpustat item 6). SalesGrowth is the percentage growth in sales (Compustat item 12) from the past year. Long-Term Debt is long-term liabilities (item 9) over total assets. Institutional Ownership is the fraction of shares outstanding pertaining to institutional investors (estimated from Spectrum 13f). Dividend Payer is a dummy which takes a value of 1 if the company paid a dividend in the previous year, 0 otherwise. Dispersion Analyst Forecasts is a standard deviation of 1 year EPS forecasts by the analysts (estimated from I/B/E/S). NYSE Traded is a dummy variable which takes a value of 1 if a company trades on NYSE, 0 otherwise. Age is the number of years since the company entered the CRSP database. Conglomerate dummy is a dummy variable which takes a value of 1 if the company has more then one business segment, 0 otherwise. Diversity is a diversity in investment opportunities between segments defined (Rajan et al., 2000) as

( )∑

∑∈

∈−−

=j

j

Ni jssi

Ni jssiji

ssiji

NQ

NQwQw 1Diversity

2

,,.

State-wide branching is defined as the number of years from 1965 where statewide bank branching is allowed in the state where the firm is located. MBHC activity is defined as the number of years from 1965 where multi-bank holding company activity is allowed in the state the firm belongs to. UPIA Based Statute (non-UPIA Based Statute) is the number of years since the state in which the company is incorporated has adopted changes to the

20

21

Prudent-Investor Law based (not based) on Uniform Prudent Investor Act (UPIA) (see Schanzenbach and Sitkoff (2007) for more details). Excess Imbalance (Excess Imbalance Bank, Excess Imbalance Bond, Excess Imbalance HeadQ) is the difference between a multi-segment company imbalance measure and segment sales weighted average imbalance of one-segment companies operating in the same two-digit industries. Excess Value is the difference between the firm’s Q and Imputed Q. We use three definitions of excess value, the first is based on Villalonga (2004), the second is based on Lang and Stulz (1994) and the third is an assets based measuare of Berger and Ofek (1995).

Panel A: cross-section of firms N Mean Std. Dev. Q1 Median Q3 Imbalance 4183 0.218 0.146 0.089 0.201 0.274 Imbalance Bank 4183 0.145 0.167 0.000 0.074 0.233 Imbalance Bond 4183 0.073 0.120 0.000 0.000 0.089 Imbalance HeadQ 4031 0.276 0.139 0.166 0.297 0.362 N of business segments 4183 2.509 1.748 1.000 2.000 4.000 Tobin’s Q 4183 1.729 1.145 1.109 1.398 1.922 Assets 4183 3529.410 7929.940 449.586 1028.500 2887.100 Imputed Q 4183 1.763 0.893 1.210 1.516 2.041 Long-Term Debt 4183 0.270 0.160 0.153 0.257 0.366 Capex 4183 0.066 0.061 0.029 0.048 0.080 SalesGrowth 4183 0.161 0.422 -0.006 0.075 0.203 Inst. Ownership 4183 0.603 0.211 0.475 0.637 0.762 Dividend Payer 4183 104.812 244.606 11.601 31.800 88.100 Disp. An. Forecasts 3739 0.143 0.285 0.027 0.054 0.122 NYSE traded 4183 0.060 0.237 0.000 0.000 1.000 Age 4183 23.094 19.515 7.000 18.000 32.000 Conglomerate Dummy 4183 0.526 0.499 0.000 1.000 1.000 Diversity 2059 0.244 0.172 0.117 0.203 0.329 State Branching 3940 2.529 0.587 2.079 2.565 2.944 MBHC Activity 3940 2.438 0.837 2.398 2.639 3.045 Non-UPIA Based Statute 3940 6.02 5.183 1.000 5.000 10.000 UPIA Based Statute 3940 2.181 1.977 1.000 1.000 3.000 Panel B: excess imbalance and measures of conglomerate discount N Mean Std. Dev. Q1 Median Q3 Ex. Imbalance 1106 -0.017 0.167 -0.098 0.000 0.054 Ex. Imbalance Bank 1106 -0.016 0.174 -0.091 0.000 0.044 Ex. Imbalance Bond 1106 -0.002 0.128 -0.031 0.000 0.000 Ex. Imbalance HeadQ 1106 -0.005 0.154 -0.074 0.000 0.081 Villalonga’s Excess Value 1106 -0.161 0.870 -0.521 -0.145 0.215 Lang and Stulz ExVal 1078 -0.153 0.897 -0.542 -0.144 0.220 Berger and Ofek ExVal 975 -0.142 0.384 -0.322 -0.116 0.231

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Table 2: Dealing with Potential Endogeneity of Measures of Financial Constraints We address potential endogeneity of measures of imbalance by relating them to variables explaining the size of local asset under management that is invested in bonds and variables that explain the size of bank lending availability in the local region. These include State Branching, MBHC Activity, Non-UPIA Based Statute, UPIA-based statute as well as state-level dummies. Our list of control variables includes Log(Assets), Long-Term Debt, Capex, SalesGrowth, Institutional Ownership, Dividend Payer dummy, year and industry fixed effects. Standard errors are clustered at firm level. We report adjusted R2 for the full regressions and for the regressions including only the instrumental variables. F-test for the joint significance of instruments is also reported. All variables are described in Table 1.

Imbalance Imbalance Bank Imbalance Bond Imbalance HeadQ estimate t-stat estimate t-stat estimate t-stat estimate t-stat State-wide Branching -0.132 (-12.91) 0.006 (0.81) -0.137 (-13.77) -0.078 (-9.44) MBHC Activity -0.051 (-6.76) -0.039 (-6.52) -0.012 (-1.76) -0.061 (-5.78) log(Non-UPIA Based Statute) 0.080 (4.92) 0.021 (2.59) 0.058 (4.77) 0.080 (5.15) log(UPIA Based Statute) 0.024 (2.66) -0.008 (-1.65) 0.032 (4.61) 0.018 (2.24) State Dummies Yes Yes Yes Yes Controls Yes Yes Yes Yes Time FE Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Clustering Firm Firm Firm Firm Adj R2 0.6547 0.7951 0.7369 0.5419 Adj R2 of Instruments 0.6278 0.7648 0.6807 0.5091 N 4220 4220 4220 4220 F-test 122.79 51.33 132.01 188.73 0.01 0.01 0.01 0.01

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Table 2: Tobin’s q and imbalance We relate company Tobin’s q to the measures of imbalance. Specifications 1 and 5 provide the results of OLS regressions. Specifications 2-4 and 6-8 present the results of IV-regressions. Imbalance measures are instrumented with State-wide Branching, MBHC Activity, Non-UPIA Based statute, UPIA Based Statue and state level dummies. All variables are described in Table 1. (1) (2) (3) (4) (5) (6) (7) (8) estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat Imbalance 0.510 (2.45) 1.161 (3.52) 0.570 (2.57) 1.307 (3.68) Imbalance Bank 1.344 (3.87) 1.501 (4.07) Imbalance Bond 0.180 (0.53) 0.136 (0.37) Imbalance HeadQ 1.729 (4.43) 1.933 (4.60) Log(Assets) 0.082 (2.74) 0.090 (2.93) 0.086 (2.85) 0.083 (2.78) 0.057 (1.77) 0.064 (1.95) 0.058 (1.80) 0.060 (1.87) Long-Term Debt -1.288 (-7.32) -1.222 (-6.76) -1.214 (-6.77) -1.215 (-6.78) -1.374 (-7.23) -1.304 (-6.69) -1.297 (-6.70) -1.291 (-6.65)Capex 1.025 (2.48) 0.737 (1.79) 1.058 (2.57) 0.612 (1.57) 0.800 (1.87) 0.451 (1.07) 0.790 (1.88) 0.321 (0.80) SalesGrowth 0.235 (2.93) 0.229 (2.69) 0.233 (2.75) 0.200 (2.38) 0.207 (2.57) 0.192 (2.29) 0.197 (2.36) 0.166 (1.98) Inst. Ownership 0.293 (2.05) 0.288 (2.06) 0.292 (2.09) 0.289 (2.07) 0.171 (1.07) 0.151 (0.97) 0.161 (1.03) 0.154 (0.99) Dividend Payer 0.000 (0.50) 0.000 (0.20) 0.000 (0.03) 0.000 (0.34) 0.000 (0.64) 0.000 (0.35) 0.000 (0.19) 0.000 (0.48) Disp An. Forecasts -0.236 (-4.18) -0.235 (-4.14) -0.231 (-4.10) -0.225 (-3.89)NYSE traded -0.060 (-0.95) -0.068 (-1.04) -0.064 (-1.01) -0.028 (-0.41) Time FE Yes Yes Yes Yes Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Clustering firm firm firm firm firm firm firm firm R2 0.1932 0.1886 0.2025 0.1786 0.1995 0.1939 0.2108 0.1825N 4183 3940 3940 3951 3739 3517 3517 3527 Hansen 33.88 29.55 33.44 33.99 28.61 33.01 (0.57) (0.73) (0.54) (0.56) (0.77) (0.56)

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Table 3: Likelihood of being a conglomerate and imbalance We provide the results of probit regressions of the relation between measures of imbalance and the likelihood of being a conglomerate. Panel A reports the results for Imbalance. Specifications 1 and 3 use actual level of Imbalance. Specifications 2 and 4 use instrumented Imbalance. Panel B reports the results for alternative measures of imbalance. All specifications provide the results of IV-regressions. Instruments are described in Table 2. All variables are described in Table 1. Panel A: (1) (2) (3) (4) estimate t-stat ME estimate t-stat ME estimate t-stat ME estimate t-stat ME Imbalance -0.454 (-3.94) -0.180 -0.952 (-6.80) -0.378 -0.531 (-5.24) -0.210 -0.987 (-7.63) -0.391Log(Assets) 0.351 (8.00) 0.139 0.331 (9.00) 0.131 0.376 (11.64) 0.149 0.351 (10.73) 0.139 Implied Tobin Q -0.128 (-3.11) -0.051 -0.119 (-2.73) -0.047 -0.159 (-3.39) -0.063 -0.156 (-3.26) -0.062Long-Term Debt -0.054 (-0.47) -0.021 -0.138 (-1.32) -0.055 0.072 (0.55) 0.029 -0.016 (-0.13) -0.006Capex -1.435 (-2.71) -0.569 -1.497 (-2.99) -0.594 -1.558 (-2.86) -0.617 -1.654 (-3.07) -0.656SalesGrowth -0.002 (-0.03) -0.001 0.014 (0.40) 0.006 -0.003 (-0.05) -0.001 0.008 (0.17) 0.003 Inst. Ownership -0.301 (-3.25) -0.120 -0.231 (-2.62) -0.092 -0.204 (-2.11) -0.081 -0.133 (-1.38) -0.053Dividend Payer -0.001 (-4.50) 0.000 -0.001 (-4.55) 0.000 -0.001 (-4.30) 0.000 -0.001 (-4.32) 0.000 Log(Age) 0.370 (19.06) 0.147 0.370 (15.29) 0.147 0.335 (14.98) 0.133 0.338 (10.99) 0.134 Disp.An. Forecasts -0.298 (-3.08) -0.118 -0.278 (-2.87) -0.110NYSE traded 0.110 (1.16) 0.043 0.058 (0.60) 0.023 Time FE Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Clustering Year Year Year Year R2 0.2389 0.2411 0.2522 0.2545 N 4371 4157 3906 3710 Hansen 41.86 41.65 (0.31) (0.31)

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Panel B (1) (2) (3) (4) estimate t-stat ME estimate t-stat ME estimate t-stat ME estimate t-stat ME Imbalance Bank -0.921 (-7.06) -0.366 -0.954 (-6.76) -0.378 Imbalance Bond -0.844 (-2.85) -0.335 -0.882 (-5.43) -0.350 Imbalance HeadQ -0.942 (-4.97) -0.374 -1.019 (-6.38) -0.404Log(Assets) 0.332 (9.03) 0.132 0.332 (9.11) 0.132 0.352 (10.82) 0.139 0.352 (10.80) 0.140 Implied Tobin Q -0.119 (-2.59) -0.047 -0.118 (-2.77) -0.047 -0.157 (-3.17) -0.062 -0.154 (-3.29) -0.061Long-Term Debt -0.122 (-1.16) -0.048 -0.110 (-1.03) -0.043 0.001 (0.01) 0.000 0.012 (0.09) 0.005 Capex -1.572 (-3.21) -0.624 -1.666 (-3.26) -0.661 -1.729 (-3.34) -0.685 -1.821 (-3.37) -0.722SalesGrowth 0.011 (0.30) 0.004 0.012 (0.31) 0.005 0.004 (0.09) 0.002 0.005 (0.10) 0.002 Inst. Ownership -0.232 (-2.65) -0.092 -0.222 (-2.46) -0.088 -0.135 (-1.40) -0.053 -0.121 (-1.23) -0.048Dividend Payer -0.001 (-4.57) 0.000 -0.001 (-4.52) 0.000 -0.001 (-4.33) 0.000 -0.001 (-4.29) 0.000 Log(Age) 0.370 (15.36) 0.147 0.373 (15.31) 0.148 0.337 (11.09) 0.134 0.339 (10.80) 0.135 Disp.An. Forecasts -0.279 (-2.83) -0.111 -0.286 (-2.89) -0.113NYSE traded 0.058 (0.61) 0.023 0.063 (0.64) 0.025 Time FE Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Clustering year year year year R2 0.2410 0.2399 0.2544 0.2535 N 4157 4157 3710 3710 Hansen 44.32 42.01 46.82 42.17 (0.29) (0.31) (0.28) (0.31)

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Table 4: Conglomerate Discount and Excess Imbalance We report the result on the relation between measures of excess imbalance and conglomerate discount. Panel A provides the results of OLS regressions. Panel B provides the results of Heckman selection model. Panel C provides the results of treatment regressions. Panel A-C use Villalonga’s Excess Value as a dependent variable. Panel D provides the results of Heckman selection model. Speicification 1-4 use with Lang and Stulz excess value as a measure of conglomerate discount. Specifications 5-6 use Berger-Ofek asset based excess value as a measure of conglomerate discount. Panel E reports the results of the relation between Villalonga’s Excess Value and excess financial constraints with control variables measured in excess of values implied by single segment firms. Specifications 1 and 5 provide the results with excess imbalance estimated from actual variables. Specifications 2-4 and 6-8 present the results of with excess imbalance estimated from instrumented measures of imbalance. Instruments are described in Table 2. All variables are described in Table 1. Panel A: Villalonga’s conglomerate discount and excess imbalance (1) (2) (3) (4) (5) (6) (7) (8) estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat Ex. Imbalance 0.322 (2.46) 0.451 (3.12) 0.329 (2.45) 0.488 (3.25) Ex. Imbalance Bank 0.464 (3.67) 0.470 (3.56) Ex. Imbalance Bond 0.366 (1.71) 0.506 (2.56) Ex. Imbalance HeadQ 0.410 (1.72) 0.432 (1.85) Log(Assets) 0.117 (3.15) 0.118 (3.11) 0.120 (3.19) 0.112 (2.97) 0.132 (3.35) 0.133 (3.34) 0.133 (3.40) 0.125 (3.16) Implied Tobin Q -0.303 (-5.63) -0.319 (-4.79) -0.325 (-4.93) -0.335 (-4.91) -0.316 (-5.23) -0.335 (-4.54) -0.342 (-4.71) -0.353 (-4.71)Long-Term Debt -1.126 (-7.03) -1.105 (-5.78) -1.079 (-6.03) -1.044 (-6.01) -1.187 (-7.97) -1.178 (-6.48) -1.142 (-6.67) -1.115 (-6.43)Capex 0.447 (0.65) 0.436 (0.78) 0.450 (0.82) 0.521 (0.96) 0.218 (0.38) 0.100 (0.22) 0.090 (0.20) 0.178 (0.39) SalesGrowth 0.046 (0.85) 0.086 (1.07) 0.092 (1.13) 0.079 (1.00) 0.054 (0.94) 0.102 (1.42) 0.106 (1.47) 0.089 (1.20) Inst. Ownership -0.092 (-1.27) -0.030 (-0.42) -0.064 (-0.95) -0.080 (-1.13) -0.149 (-1.19) -0.061 (-0.51) -0.098 (-0.83) -0.145 (-1.06)Dividend Payer 0.000 (-0.32) 0.000 (-1.26) 0.000 (-1.32) 0.000 (-1.04) 0.000 (-0.51) 0.000 (-1.20) 0.000 (-1.22) 0.000 (-1.05)Disp.An. Forecasts -0.195 (-1.71) -0.185 (-1.71) -0.189 (-1.71) -0.188 (-1.79)NYSE traded 0.030 (0.44) -0.008 (-0.11) -0.006 (-0.08) -0.010 (-0.13)Diversity -0.279 (-1.62) -0.267 (-1.18) -0.270 (-1.19) -0.264 (-1.16) Time FE Yes Yes Yes Yes Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Clustering year year year year year year year year

R2 0.1972 0.2123 0.2094 0.2117 0.2068 0.2273 0.2242 0.2267N 1106 999 1009 982 1006 899 908 883

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Panel B: Villalonga’s conglomerate discount and excess imbalance: accounting for selection (Heckman) (1) (2) (3) (4) (5) (6) (7) (8) estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat Ex. Imbalance 0.317 (2.44) 0.423 (3.15) 0.332 (2.53) 0.469 (3.39) Ex. Imbalance Bank 0.436 (3.75) 0.450 (3.74) Ex. Imbalance Bond 0.327 (1.52) 0.481 (2.39) Ex. Imbalance HeadQ 0.393 (1.70) 0.413 (1.84) Log(Assets) 0.121 (3.11) 0.140 (3.72) 0.143 (3.82) 0.141 (3.87) 0.129 (2.97) 0.148 (3.57) 0.150 (3.65) 0.149 (3.63) Implied Tobin Q -0.305 (-5.65) -0.329 (-4.89) -0.336 (-5.04) -0.348 (-4.99) -0.315 (-5.23) -0.341 (-4.61) -0.349 (-4.79) -0.363 (-4.80)Long-Term Debt -1.129 (-7.17) -1.112 (-5.84) -1.087 (-6.12) -1.053 (-6.14) -1.185 (-8.18) -1.186 (-6.49) -1.151 (-6.71) -1.127 (-6.54)Capex 0.412 (0.55) 0.236 (0.37) 0.234 (0.37) 0.241 (0.39) 0.240 (0.36) -0.034 (-0.06) -0.056 (-0.10) -0.039 (-0.07)SalesGrowth 0.044 (0.81) 0.069 (0.86) 0.074 (0.91) 0.057 (0.69) 0.056 (0.95) 0.090 (1.27) 0.093 (1.32) 0.071 (0.94) Inst. Ownership -0.092 (-1.27) -0.030 (-0.42) -0.065 (-0.95) -0.081 (-1.13) -0.150 (-1.21) -0.058 (-0.48) -0.095 (-0.79) -0.140 (-1.00)Dividend Payer 0.000 (-0.32) 0.000 (-1.19) 0.000 (-1.24) 0.000 (-0.94) 0.000 (-0.52) 0.000 (-1.16) 0.000 (-1.19) 0.000 (-1.00)Disp.An. Forecasts -0.195 (-1.68) -0.188 (-1.71) -0.192 (-1.72) -0.191 (-1.81)NYSE traded 0.029 (0.42) -0.006 (-0.08) -0.004 (-0.06) -0.008 (-0.10)Diversity -0.217 (-1.33) -0.176 (-0.84) -0.185 (-0.88) -0.172 (-0.82) Heckman λ 0.031 (0.36) 0.163 (1.92) 0.175 (2.11) 0.221 (2.19) -0.020 (-0.18) 0.113 (1.09) 0.122 (1.14) 0.175 (1.53) Time FE Yes Yes Yes Yes Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Clustering year year year year year year year year R2 0.2102 0.2131 0.2103 0.2132 0.2224 0.2277 0.2246 0.2276N 1106 999 1009 982 1002 899 908 883

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Panel C: Villalonga’s conglomerate discount and excess imbalance: simultaneous equation system (1) (2) (3) (4) (5) (6) (7) (8) estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat Ex. Imbalance 0.514 (2.87) 0.757 (2.95) 0.552 (2.91) 0.796 (2.91) Ex. Imbalance Bank 0.843 (3.30) 0.862 (3.19) Ex. Imbalance Bond 0.568 (1.74) 0.586 (1.62) Ex. Imbalance HeadQ 0.766 (2.48) 0.836 (2.54) Log(Assets) -0.102 (-2.68) -0.063 (-1.71) -0.063 (-1.71) -0.066 (-1.79) -0.121 (-2.94) -0.080 (-2.02) -0.080 (-2.05) -0.083 (-2.11) Implied Tobin Q -0.140 (-2.31) -0.207 (-3.44) -0.208 (-3.45) -0.206 (-3.41) -0.131 (-2.03) -0.202 (-3.15) -0.203 (-3.15) -0.202 (-3.13) Long-Term Debt -1.047 (-5.57) -0.975 (-5.41) -0.977 (-5.44) -0.982 (-5.46) -1.090 (-5.35) -1.027 (-5.21) -1.026 (-5.22) -1.037 (-5.29) Capex 1.385 (3.31) 1.213 (3.21) 1.234 (3.23) 1.310 (3.50) 1.275 (2.99) 1.083 (2.85) 1.084 (2.83) 1.171 (3.11) SalesGrowth 0.352 (4.39) 0.305 (4.00) 0.308 (4.03) 0.310 (4.01) 0.352 (4.28) 0.299 (3.93) 0.302 (3.96) 0.304 (3.93) Inst. Ownership 0.465 (3.04) 0.439 (3.06) 0.427 (2.98) 0.425 (2.95) 0.311 (1.83) 0.302 (1.89) 0.288 (1.81) 0.279 (1.75) Dividend Payer 0.000 (2.45) 0.000 (1.95) 0.000 (1.94) 0.000 (1.91) 0.000 (2.47) 0.000 (1.96) 0.000 (1.95) 0.000 (1.91) Disp.An. Forecasts -0.202 (-2.54) -0.181 (-2.35) -0.181 (-2.37) -0.177 (-2.31) NYSE traded 0.032 (0.33) 0.065 (0.68) 0.064 (0.68) 0.065 (0.69) Conglomerate Dummy 1.244 (12.09) 1.107 (8.88) 1.093 (8.51) 1.105 (8.78) 1.306 (12.91) 1.170 (9.65) 1.155 (9.28) 1.170 (9.54) time fe Yes Yes Yes Yes Yes Yes Yes Yes industry fe Yes Yes Yes Yes Yes Yes Yes Yes clustering firm firm firm firm firm firm firm firm 1st age estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat Imbalance -0.491 (-2.20) -0.853 (-2.36) -0.554 (-2.49) -0.930 (-2.60) Imbalance Bank -0.829 (-2.31) -0.891 (-2.53) Imbalance Bond -1.301 (-2.81) -1.411 (-3.03) Imbalance HeadQ -0.741 (-1.78) -0.870 (-2.09) Log(age) 0.213 (5.02) 0.230 (4.88) 0.236 (4.88) 0.234 (4.91) 0.212 (5.09) 0.226 (4.97) 0.233 (4.99) 0.230 (5.01) Controls, FE, clustering Yes Yes Yes Yes Yes Yes Yes Yes λ -0.912 (-11.70) -0.814 (-8.66) -0.806 (-8.37) -0.815 (-8.60) -0.961 (-12.38) -0.863 (-9.27) -0.854 (-8.99) -0.864 (-9.20) Nobs 3294 3091 3102 3102 2943 2754 2764 2764

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Panel D: Lang-Stulz and Berger-Ofek conglomerate discounts and excess imbalance (1) (2) (3) (4) (5) (6) (7) (8) estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat Ex. Imbalance 0.293 (2.29) 0.390 (2.87) 0.154 (3.15) 0.184 (1.85) Ex. Imbalance Bank 0.421 (3.51) 0.171 (1.71) Ex. Imbalance Bond 0.194 (0.93) 0.126 (0.89) Ex. Imbalance HeadQ 0.306 (1.88) 0.089 (1.11) Log(Assets) 0.121 (3.36) 0.142 (3.67) 0.147 (3.73) 0.149 (3.72) 0.054 (4.06) 0.061 (3.39) 0.063 (3.54) 0.057 (4.56) Implied Tobin Q -0.302 (-6.44) -0.328 (-4.81) -0.334 (-4.98) -0.337 (-5.06) -0.105 (-3.34) -0.115 (-4.41) -0.118 (-4.59) -0.123 (-3.77) Long-Term Debt -1.182 (-7.88) -1.161 (-5.56) -1.137 (-5.78) -1.145 (-6.10) -0.535 (-6.44) -0.516 (-5.84) -0.509 (-5.67) -0.527 (-6.51) Capex 0.741 (0.76) 0.529 (0.78) 0.538 (0.80) 0.526 (0.76) 0.632 (1.71) 0.501 (1.73) 0.500 (1.73) 0.699 (2.08) SalesGrowth 0.022 (1.32) 0.043 (0.53) 0.048 (0.59) 0.044 (0.54) 0.005 (0.17) 0.023 (0.38) 0.027 (0.45) 0.039 (1.17) Inst. Ownership -0.092 (-1.79) -0.046 (-0.58) -0.089 (-1.16) -0.086 (-1.09) -0.006 (-0.08) 0.022 (0.22) 0.001 (0.00) -0.028 (-0.39) Dividend Payer 0.000 (-0.46) 0.000 (-0.89) 0.000 (-0.95) 0.000 (-0.96) 0.000 (0.41) 0.000 (-0.23) 0.000 (-0.26) 0.000 (0.40) Heckman λ -0.019 (-0.19) 0.114 (1.13) 0.129 (1.31) 0.157 (1.54) 0.077 (0.44) 0.094 (0.10) 0.076 (1.24) 0.050 (0.92) Time FE Yes Yes Yes Yes Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Clustering year year year year year year year year R2 0.2091 0.2124 0.2102 0.2081 0.2268 0.2335 0.2281 0.2163 N 1104 997 1007 1007 1002 904 914 946

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Panel E: Villalonga’s conglomerate discount, excess imbalance and excess control variables (1) (2) (3) (4) (5) (6) (7) (8) estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat Ex. Imbalance 0.421 (2.43) 0.487 (3.17) 0.470 (2.34) 0.567 (3.05) Ex. Imbalance Bank 0.414 (2.59) 0.572 (3.37) Ex. Imbalance Bond 0.462 (1.69) 0.703 (2.55) Ex. Imbalance HeadQ 0.274 (2.24) 0.363 (2.57) Ex. Log(Assets) -0.010 (-1.26) 0.013 (2.15) -0.009 (-1.16) -0.004 (-0.48) -0.019 (-1.79) 0.005 (0.57) 0.007 (0.62) -0.016 (-1.17)Implied Tobin Q -0.214 (-4.14) -0.229 (-3.48) -0.215 (-4.12) -0.239 (-4.32) -0.229 (-3.86) -0.241 (-3.32) -0.251 (-3.50) -0.257 (-4.15)Ex. Long-Term Debt -0.841 (-5.29) -0.871 (-5.34) -0.848 (-5.40) -0.842 (-5.30) -0.938 (-5.27) -0.979 (-5.40) -0.924 (-4.90) -0.970 (-5.21)Ex. Capex 1.945 (2.72) 1.811 (2.69) 1.923 (2.76) 2.047 (2.93) 1.858 (2.54) 1.688 (2.41) 1.632 (2.27) 1.966 (2.79) Ex. SalesGrowth 0.070 (1.57) 0.131 (2.50) 0.070 (1.55) 0.121 (2.06) 0.097 (1.58) 0.154 (2.52) 0.152 (2.36) 0.139 (1.97) Ex. Inst. Ownership 0.016 (0.36) 0.088 (1.17) 0.020 (0.48) -0.015 (-0.29) -0.110 (-1.29) -0.020 (-0.19) -0.040 (-0.44) -0.165 (-1.77)Ex. Dividend Payer 0.000 (-1.45) 0.000 (-2.62) 0.000 (-1.55) 0.000 (-1.89) 0.000 (-0.38) 0.000 (-1.36) 0.000 (-1.43) 0.000 (-0.49)Ex. Disp.An. Forecasts -0.162 (-1.42) -0.147 (-1.34) -0.150 (-1.34) -0.127 (-1.24)Ex. NYSE traded 0.034 (0.34) -0.014 (-0.14) 0.006 (0.06) 0.029 (0.27) Heckman λ -0.581 (-4.10) -0.424 (-3.44) -0.577 (-4.10) -0.514 (-3.34) -0.645 (-4.34) -0.485 (-3.95) -0.476 (-3.75) -0.577 (-3.54) Time FE Yes Yes Yes Yes Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Clustering year year year year year year year year R2 0.198 0.2003 0.1981 0.1912 0.2135 0.2144 0.2106 0.2081N 1098 999 1096 1034 978 886 892 920

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Table 5: Conglomerate discount and excess financial constraints: the effect of leverage We report the result on the relation between measures of excess imbalance and conglomerate discount conditioning on whether the company has long-term debt or not. Panel A provide the results of Heckman selection model. Panel B provides the results of treatment regressions. We use Villalonga’s Excess Value as a dependent variable. Specifications 1 and 5 provide the results with excess imbalance estimated from actual variables. Specifications 2-4 and 6-8 present the results of with excess imbalance estimated from instrumented measures of imbalance. Instruments are described in Table 2. All variables are described in Table 1. Panel A: Villalonga’s measure: Heckman selection model (1) (2) (3) (4) (5) (6) (7) (8) estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat Ex. Imbalance * debt 0.320 (2.56) 0.252 (2.06) 0.337 (2.69) 0.283 (2.40) -- * no debt 0.256 (0.40) 1.068 (1.56) 0.225 (0.37) 0.983 (1.36) Ex. Imbalance Bank * debt 0.386 (4.16) 0.400 (4.25) -- * no debt 1.532 (1.48) 1.385 (1.30) Ex. Imbalance Bond * debt 0.307 (1.50) 0.462 (2.42) -- * no debt 2.045 (0.93) 1.625 (0.72) Ex. Imbalance HeadQ * debt 0.259 (1.97) 0.273 (1.86) -- * no debt 2.055 (1.37) 1.921 (1.48) Log(Assets) 0.121 (3.12) 0.130 (3.09) 0.143 (3.84) 0.147 (3.75) 0.129 (2.97) 0.145 (3.11) 0.150 (3.66) 0.155 (3.59) Implied Tobin Q -0.305 (-5.66) -0.335 (-5.35) -0.335 (-4.96) -0.337 (-5.03) -0.315 (-5.25) -0.350 (-5.15) -0.349 (-4.72) -0.351 (-4.80) Long-Term Debt -1.128 (-7.21) -1.087 (-5.11) -1.085 (-5.69) -1.092 (-6.03) -1.185 (-8.19) -1.183 (-5.63) -1.146 (-6.17) -1.170 (-6.54) Capex 0.408 (0.56) 0.362 (0.75) 0.267 (0.43) 0.266 (0.41) 0.232 (0.36) 0.092 (0.21) -0.021 (-0.04) 0.005 (0.01) SalesGrowth 0.045 (0.81) 0.086 (1.12) 0.065 (0.83) 0.061 (0.76) 0.056 (0.96) 0.087 (1.21) 0.085 (1.27) 0.078 (1.14) Inst. Ownership -0.091 -1.24 -0.050 -0.43 -0.069 (-0.96) -0.076 (-0.94) -0.150 (-1.20) -0.124 (-0.83) -0.098 (-0.78) -0.124 (-0.92) Dividend Payer 0.000 (-0.31) 0.000 (-1.30) 0.000 (-1.23) 0.000 (-1.25) 0.000 (-0.51) 0.000 (-1.29) 0.000 (-1.18) 0.000 (-1.26) Disp.An. Forecasts -0.195 (-1.69) -0.211 (-2.10) -0.192 (-1.71) -0.196 (-1.78) NYSE traded 0.029 (0.40) 0.036 (0.55) -0.002 (-0.03) 0.001 (0.01) Diversity -0.212 (-1.27) -0.171 (-0.80) -0.175 (-0.83) -0.174 (-0.84)

Heckman λ 0.031 (0.38) 0.112 (1.44) 0.164 (1.95) 0.196 (2.21) -0.019 (-0.18) 0.124 (1.95) 0.112 (1.05) 0.146 (1.38)

Time FE Yes Yes Yes Yes Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Clustering year year year year year year year year

R2 0.2102 0.1788 0.2113 0.2097 0.2225 0.1924 0.2225 0.2236 N 1106 999 1009 1009 1002 899 1002 908

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Panel B: Villalonga’s measure: simultaneous equation system (1) (2) (3) (4) (5) (6) (7) (8) estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat estimate t-stat Ex. Imbalance * debt 0.513 (2.83) 0.611 (2.42) 0.561 (2.92) 0.653 (2.42) -- * no debt 0.526 (0.90) 0.678 (0.95) 0.399 (0.69) 0.563 (0.78) Ex. Imbalance Bank * debt 0.706 (2.81) 0.731 (2.75) -- * no debt 0.560 (0.73) 0.437 (0.55) Ex. Imbalance Bond * debt 0.256 (0.90) 0.245 (0.78) -- * no debt 0.078 (0.04) -0.063 (-0.03) Ex. Imbalance HeadQ * debt 0.613 (2.04) 0.668 (2.08) -- * no debt 1.110 (1.14) 0.976 (1.00) Log(Assets) -0.102 (-2.69) -0.065 (-1.73) -0.066 (-1.77) -0.067 (-1.78) -0.121 (-2.95) -0.082 (-2.03) -0.083 (-2.08) -0.084 (-2.08) Implied Tobin Q -0.140 (-2.31) -0.207 (-3.42) -0.208 (-3.43) -0.207 (-3.41) -0.131 (-2.02) -0.202 (-3.12) -0.203 (-3.12) -0.202 (-3.11) Long-Term Debt -1.047 (-5.58) -0.975 (-5.39) -0.978 (-5.42) -0.977 (-5.43) -1.091 (-5.36) -1.024 (-5.18) -1.028 (-5.21) -1.027 (-5.22) Capex 1.385 (3.31) 1.337 (3.35) 1.435 (3.58) 1.417 (3.59) 1.274 (2.98) 1.208 (3.01) 1.299 (3.23) 1.280 (3.21) SalesGrowth 0.352 (4.40) 0.318 (4.02) 0.323 (4.07) 0.322 (4.03) 0.353 (4.28) 0.313 (3.96) 0.318 (4.00) 0.317 (3.96) Inst. Ownership 0.465 (3.04) 0.445 (3.07) 0.435 (3.00) 0.432 (2.97) 0.311 (1.84) 0.311 (1.92) 0.299 (1.85) 0.291 (1.80) Dividend Payer 0.000 (2.46) 0.000 (1.95) 0.000 (1.95) 0.000 (1.92) 0.000 (2.47) 0.000 (1.96) 0.000 (1.96) 0.000 (1.93) Disp.An. Forecasts -0.202 (-2.54) -0.180 (-2.34) -0.180 (-2.36) -0.177 (-2.32) NYSE traded 0.031 (0.33) 0.043 (0.47) 0.047 (0.51) 0.046 (0.49) Conglomerate Dummy 1.244 (12.10) 1.105 (8.67) 1.100 (8.62) 1.100 (8.59) 1.306 (12.93) 1.167 (9.42) 1.162 (9.34) 1.163 (9.36) time fe Yes Yes Yes Yes Yes Yes Yes Yes industry fe Yes Yes Yes Yes Yes Yes Yes Yes clustering firm firm firm firm firm firm firm firm Nobs 3294 3091 3102 3102 2943 2754 2764 2764

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