Debt Capacity of Tangible Assets: What is Collateralizable in the
Debt Market?
Erasmo Giambona
∗
Armin Schwienbacher
**
November 17, 2007
Abstract
Starting with Titman and Wessels (1988) and Rajan and Zingales (1995), capital structure studies
have consistently found a positive relationship between an aggregate measure of tangible assets, including
land, buildings and equipments, and leverage. Consistent with our theoretical predictions, we find that
tangibility and leverage are directly linked only for credit constrained firms. This link subsists however
only for "hard" tangible assets, namely land and buildings, after any specific source of endogeneity is
carefully addressed, which is consistent with the view that tangible assets differ in terms of redeployability,
contractibility and speed of depreciation. We also find that tangibility and leverage are separate decisions
for credit unconstrained firms.
Keywords: land, buildings, machineries & equipments, leverage, simultaneity, endogeneity.
JEL Classification: G32
∗ Contact
author: Assistant Professor, Finance Group, University of Amsterdam, Roetersstraat 11, 1018 WB Amsterdam,
The Netherlands, +31 20 525 5321, e.giambona@uva.nl. ** Assistant Professor, Finance Group, University of Amsterdam,
Roetersstraat 11, 1018 WB Amsterdam, The Netherlands, +31 20 525 4344, a.schwienbacher@uva.nl and Catholic University
of Louvain. Acknowledgment: we are grateful for insightful comments to Tim Riddiough and seminar participants at the
University of Amsterdam (brown bag series), University of Lille, Catholic University of Leuven, University of Maastricht and
University of Georgia. Any errors are our own responsibility.
1
Introduction
“Ford Motor is considering offering its factories as security to lenders as part of a drive by the
troubled Detroit-based carmaker to bolster cash reserves in the face of massive losses.” — Financial
Times, 24 October 2006
Many corporate capital structure studies have documented a positive relationship between collateral
(measured as the fraction of property, plant, and equipment to total assets) and firm leverage (see, e.g.,
Titman and Wessels, 1988, Rajan and Zingales, 1995, MacKay and Phillips, 2005, Bharath, Pasquariello and
Wu, 2006, Faulkender and Petersen, 2006; and very recently Wald and Long, 2007, Kale and Shahrur, 2007,
Lemmon and Zender, 2007 just to cite some). This is largely explained by the fact that tangible assets can
be pledged as collateral to lenders and thus allow companies to raise debt.
The empirical approach followed in aforementioned studies consists of including as explanatory variable
an aggregate measure of tangibility in a leverage regression for large samples of public companies. This
approach implicitly assumes that all types of tangible assets (e.g., land, buildings, and machineries & equipments) posses the same degree of debt capacity with no loss of information associated with the use of an
aggregate measure of tangibility (that does not distinguish among the different types of tangible assets).
More importantly, by conducting the analysis on the "average" U.S. public firm, these studies miss to explain that tangible assets are not important to create debt capacity for firms that have otherwise already
wide access to the debt market - e.g., credit unconstrained firms - while a direct positive link between leverage and tangible assets is likely to exist only for firms with limited access to the debt market (e.g., credit
constrained firms). This distinction between firms based on whether or not they face credit constraints raises
another salient issue, which has not been uncovered in previous empirical studies. In fact, we cannot rule
out the possibility that, confronted with limited access to the debt market, firms might endogenously choose
an investment structure with more tangible assets to increase their debt capacity. This argument relies on
the assumption that the proportion of tangible assets is not exclusively dictated by the type of business but
is a “discretionary” decision variable for each firm within an industry at least to some extent.
2
The aim of this paper is to address each of these issues. We depart empirically from previous studies
in that we use a different tangibility ratio for each different type of tangible assets. This allows us to test
whether different assets posses the same propensity to generate collateral in the financing process. We tackle
the potential endogeneity of the tangibility ratio in the leverage regression by modelling the tangibility ratio
of the different types of tangible assets as a function of several carefully identified exogenous instruments.
Finally, we develop a theoretical model showing that a strict positive relation between tangibility and leverage
exists only for firms that do not have otherwise access to the debt market (e.g., credit constrained) while
tangibility and leverage are separate decisions for credit unconstrained firms. We provide strong empirical
support for this prediction.
Our paper closely relates to a recent study by Faulkender and Petersen (2006). The argument underlying
their paper is that firms might be "rationed by their lenders" when trying to raise debt financing. The
source of capital (in their study, whether or not a firm has access to the public debt market), as they show,
can mitigate these frictions in the debt market. We follow a similar approach to theirs in that we also
explicitly recognize that frictions in the supply-side of the credit market might create credit rationing. We
focus however on the role of collateral as a means for firms to increase their debt capacity.
Under the assumptions of Modigliani-Miller, collaterals do not matter. They merely affect the risk level
of debt but do not create additional corporate value. However, they may enhance the value of firms in the
presence of financial frictions such as moral hazard - either risk-shifting (Jensen and Meckling, 1976) or
underinvestment (Myers, 1977) - and adverse selection (asymmetric information). In these cases, collateralizable assets can be pledged to debtholders in order to mitigate inefficiency costs. This in turn increases
debt capacity of credit constrained firms. In contrast, collaterals have no effect on firm value if the firm
is financially unconstrained (e.g., because substantial parts of the investment can be financed with internal
resources or the firm owns other assets with significant liquidation value).
Our theoretical framework relies on the model of Almeida and Campello (2007). We consider the case
of a firm that needs external sources of finance in the form of debt and equity to implement an NPV
maximizing investment strategy. This investment strategy comprises an operationally efficient combination
3
of intangible and tangible assets, where the latter are further distinguishable into “soft” tangibles and
“hard” tangibles depending on their propensity to create collateral. In fact, “hard” tangibles are easily
redeployable, contractible and depreciate slowly, which are all desirable qualities in the event the financier
has to liquidate the assets to recover the loan amount. These qualities, we argue, make real estate — namely,
land and buildings — valuable collaterals, while it is likely to be less the case, on average, for machineries &
equipments.
Because there are issuance costs associated with raising equity, the firm would like to finance the entire
investment with debt. However, in the repudiation framework of Hart and Moore (1994) followed in this
paper, the manager is the only depositary of the technical skills necessary to operationalize the investment.
Hence, she might threaten to leave the company in order to force the creditors to renegotiate the terms of
the loan. As a result, firms will only be able to raise an amount of debt not exceeding the liquidation value
of “hard” tangibles, while the remaining part must be raised by issuing equity. Of course, this threat is
effective only if debt financing exceeds the liquidation value of “hard” tangibles. This constraint does not
apply if the firm posses other sources of collateral. In the case this additional collateral is equal to or exceeds
the value of the desired investment level, then the firm is credit unconstrained and can entirely finance the
investment by issuing debt.
From the discussion in the above paragraph, it is clear that in our framework there is a strictly positive
relation between leverage and “hard” tangibles for credit constrained firms. A firm that purchases a higher
fraction of “hard” tangibles is able to raise more debt. Other types of assets, such as machineries & equipments, might not impact leverage directly because they are less suitable as collateral. Rather, the effect is
reversed in this case with more debt allowing to purchase more machineries & equipments, which can then
be used as an "additional" but only “secondary” source of collateral.
Following Almeida and Campello (2007), we assume that the proportion of tangible assets in the investment structure is dictated by the "nature of the operations" (i.e., exogenously given). However, we cannot
rule out empirically that some firms, confronted with important lending constraints, might choose to shift
towards an investment structure with more tangible assets in order to increase their debt capacity. This
4
choice might still be desirable, if the cost savings associated with issuing debt exceed in absolute value the
additional operating costs due to the loss of efficiency.1 This implies that leverage and tangibility are then
potentially set simultaneously in equilibrium.
We check for this possibility in our data. If "tangibility" is dictated by the type of business, then we
should observe that most of the variation in our tangibility ratio should occur between industries. Following
a similar methodology as in MacKay and Phillips (2005), we regress our firm-level tangibility ratio on the
4-digit SIC industry-mean tangibility ratio lagged 1-period. We find that industry variation only explain
part of the overall variation and that there is significant within-industry variation left, especially for "harder"
tangible assets, such as land and buildings. This calls for the possibility that indeed tangibility might be
endogenous in the leverage regression.
This division of firms based on whether they are credit constrained raises another prominent issue,
which has been overlooked in previous empirical studies. Almeida and Campello (2007) constitute a notable
exception in the context of investment-cash flow sensitivity. More specifically, they examine the effects of
imperfection in the debt market on real investments. In their empirical part, they show that investment-cash
flow sensitivities increase with asset tangibility only when firms are financially constrained. However, since
they focus on the investment-cash flow sensitivity, only the impact on levels of investments are examined but
not on the firm’s capital structure. We extend this distinction between credit constrained and unconstrained
firms into the capital structure context.
Our OLS evidence from regressing leverage on different measures of tangibility and several control variables for our full sample of COMPUSTAT firms does not support our claim that different tangible assets
might have a different debt capacity depending on their degree of "hardness". But OLS fails to captures the
full extent of the relation between tangibility and leverage. However, using a fixed-effect panel regression approach, which enhances our model specification, we find that land and buildings incorporate a higher degree
of debt capacity although other tangible assets (e.g., machineries & equipments) still affect debt capacity
directly but their effect is economically less important. Yet, if there remain some unobserved characteris1 This
could be the case, for instance, for small firms if there are economies of scale associated with issuing equity.
5
tics that are changing within each firm while being related to leverage, then endogeneity could still bias
fixed-effect regression estimates. To control for this possibility we combine fixed-effects with a two-stage
instrumental variables approach. We find that the coefficients on land and buildings increase in economic
significance, implying that debt capacity of real estate is higher after possible sources of endogeneity have
been carefully addressed. Moreover, none of the other tangible assets - e.g., machineries & equipments - seem
to affect leverage directly. Their coefficients are economically much smaller compared to the coefficients on
either land or buildings while now they are no longer statistical significant in the leverage regression. This is
consistent with our expectation that the sign of the relation between "softer" tangibles and leverage might
actually work from leverage to ("soft") tangible assets after endogeneity has been properly addressed.2 Most
importantly, when we replicate the same results for different sub-samples that we classify a priori as either
credit constrained or credit unconstrained, we find strong support for the prediction of our theoretical model
that "hard" tangible assets only increase debt capacity for the first group of firms while tangibility and
leverage decisions are separate choices for credit unconstrained firms.
The remainder of this paper is structured as follows. The next section discusses related literature. Section
3 offers a theoretical discussion and derives empirical hypotheses. Section 4 presents data and summary
statistics. The empirical analysis is done in Section 5. Section 6 concludes.
2
Related Literature
This study relates to several strands of literature on capital structure decision documenting a positive
relationship between asset tangibility and firm leverage. These studies aim at testing several capital structure
theories. For instance, a series of recent studies reconsider the empirical relevance of the pecking order theory
(e.g., Shyam-Sunder and Myers, 1999, Fama and French, 2002, Frank and Goyal, 2003, Bharath, Pasquariello
2 This
result suggests that we should use caution in interpreting evidence reported in previous studies, which rely on an
aggregate measure of tangibility. The evidence that an aggregate measure of tangibility enters the leverage regression with a
positive coefficient does not imply that all tangible assets are suitable collaterals. We have found indeed that only real estate
enhances debt capacity.
6
and Wu, 2006, Jimenez, Salasa, Saurina, 2006, and Lemmon and Zender, 2007). Titman and Wessels (1988)
evidence, among other things, that smaller firms that entail more risk are more prone to use short-term debt,
consistent with theory. Their main contribution however is methodological in that they use a “factor-analytic
technique” that better controls for the use of proxy variables. Rajan and Zingales (1995) provide a crosscountry study of different capital structure theories. Bharath, Pasquariello and Wu (2006) introduces a new
measure of asymmetric information to see the extent to which asymmetric information drives leverage. All
these studies include a measure of asset tangibility, though only as a control variable. Another cross-country
analysis by Braun (2002) shows that tangible assets are more important in countries with weak financial
systems (i.e., where external financial contractibility is problematic and thus requires more collaterals). This
study primarily focuses on legal factors and uses industry-level data, while our analysis uses firm-level data.
Other international studies relate to legal and institutional factors (e.g., Qian and Strahan, 2006, and Liberti
and Mian, 2005) and highlight the importance of non-specific assets when agency risk is important.
The paper that is closest to ours is by Faulkender and Petersen (2006). They show that the source of
capital may also affect leverage, in particular whether firms have access to the bond market. Therefore, the
extent to which firms can access debt finance can shape the way investment opportunities are financed. While
their focus is very different (since we examine the composition of firm’s tangible assets), our methodology of
analysis is similar to theirs.
Almeida and Campello (2007) constitute a notable exception linking the impact of tangible assets to
the distinction between firms based on whether or not they face credit constraints. More specifically, they
examine the effect of imperfection in the debt market on real investments. In their empirical part, they
show that investment-cash flow sensitivities increase with asset tangibility only when firms are financially
constrained. However, since they focus on the investment-cash flow sensitivity, only the impact on levels
of investments are examined but not on the firm’s capital structure. We extend this distinction between
credit constrained and unconstrained firms into the capital structure context. Moreover, we use disaggregate
measures of tangibility in order to capture the underlying mechanisms that cannot be observed with an
aggregate measure.
7
Eisfeldt and Rampini (2007) investigate the role of leasing on the debt capacity of firms. They show that
compared to secured lending, leasing provides additional debt capacity, since repossessing leased assets is
easier than seizing secured assets.
Inderst and Müller (2006) develop a theoretical framework in which collaterals may improve arm’slength financing. Collaterals help to mitigate inefficient credit decisions when soft information is critical,
since it makes debt less sensitive to cash flow variations. A greater competition from transaction lenders
then increases collateral requirements. Shleifer and Vishny (1992) examine in a theoretical framework the
redeployability and endogeneity of liquidation value of assets in an industry equilibrium context. They show
that the value becomes endogenously determined if the assets is industry-specific, since it is likely that
liquidation hits many participants of the same industry at the same time. This limits the redeployability
of assets and thus reduces liquidation value. Rajan and Winton (1995) develop a model that indicates that
collaterals, along with covenants, provide incentives to lenders to monitor borrowers to avoid default, since
they enhance the value of intervention. These analyses consider collaterals as generic and thus do not help
us in disentangling "soft" from "hard" tangible assets directly. However, they provide convincing rationales
for why collaterals in general may impact financial decisions of firms.
Finally, our study relate to recent findings on the impact of exogenous asset value shocks on leverage
and cost of debt. Changes in asset value directly impact the value of collaterals and thus debt capacity of
firms. For instance, Chaney, Sraer and Thesmar (2006) examine shocks on land holding. Although they
focus on land, they have little to say about how land differs from other assets. They merely use land as
laboratory for testing shocks on collaterals. They find that collaterals can have significant value in that it
allows to issue more debt. Similarly, Gan (2006) studies the impact of collaterals on corporate investments
The shock considered is the land market collapse in Japan. The channel through which losses in collateral
value reduces debt capacity is through the loss of banking relationships. This makes it harder for borrowers
to obtain loans in the future.
8
3
Theory on Collaterals
Under the assumptions of Modigliani-Miller, collaterals do not matter. They merely affect the risk level
of debt but do not create additional corporate value. However, they may enhance the value of firms in
the presence of financial frictions such as moral hazard (either risk-shifting (Jensen and Meckling, 1976) or
underinvestment (Myers, 1977)) and adverse selection (asymmetric information). In these cases, collateralizable assets can be pledged to debtholders in order to mitigate inefficiency costs. This in turn increases
debt capacity of credit constrained firms.
The next two subsections extend the framework of Almeida and Campello (2007) to show that a strictly
positive link between tangibility and leverage only exists for credit constrained firms.
3.1
The Relation between Leverage and Asset Tangibility
In this section, we extend the theoretical framework of Almeida and Campello (2007) to derive empirical
implications on the equilibrium leverage ratio.3 It largely builds on the model of inalienability of human
capital and managerial repudiation presented by Hart and Moore (1994). Consider a firm with an investment
opportunity that produces cash flows (CF1 ) of f (I) from realized investment I > 0 in one-period time. The
firm needs outside financing to undertake this project.
Let us denote by B the amount of debt the firm raises. Creditors are assumed to be risk neutral and the
risk-free rate is normalized to zero. Suppose further that the firm has additional pledgeable risk-free assets W
(these can also be cash that the firm uses to partially finance the investment, as in Almeida and Campello,
2007; here we use this alternative interpretation merely for convenience). We depart from Almeida and
Campello’s framework by assuming that the firm also has access to equity financing to raise the remaining
investment amount, which we denote by E. However, equity financing involves issuance costs that make
debt financing strictly preferable to raising equity. We suppose for simplicity that a fraction ε > 0 goes to
the underwriter as issuance costs while the firm gets the rest, i.e., (1 − ε) E. To keep the analysis simple,
3 Note
that other theoretical approaches yield similar results. For instance, Faulkender and Petersen (2006) use the credit
rationing framework of Stiglitz and Weiss (1981) to also derive a positive relationship between asset tangibility and leverage.
9
let us suppose that ε is infinitely small so that we do not carry this parameter further; however it makes
management to strictly prefer debt over equity (Alternatively, one could introduce tax shield benefits to
ensure that the manager strictly prefers debt over equity, everything else being equal. This would require
introducing corporate taxes into the model.)
The parameter 0 ≤ τ ≤ 1 denotes the fraction of investment I that is tangible and thus can be recovered
by the creditors in case of liquidation (in addition to W ). We assume τ is exogenous4 and thus independent of
the amount of investment made. Within the framework of Hart and Moore (1994), managers may repudiate
contracts by withdrawing their critical human capital right after the money has been invested (and potentially
triggering liquidation). This would force lenders to renegotiate contracts. Assuming for simplicity that
management has all the bargaining power at the renegotiation stage, the creditors will obtain the liquidation
value only, i.e., W + τ I. Given these expectations on the renegotiation outcome, creditors lend to the firm
an amount no larger than the liquidation value of the firm; i.e.,
B ≤ W + τI
Under this constraint on the access to credits, the net present value (N P V ) of this investment opportunity
is
N P V = f (I) − I
subject to I = E + B and B ≤ W + τ I. This NPV goes to the current shareholders. When the second
condition is binding, the first one becomes I = E + W + τ I. We then obtain E = (1 − τ ) I − W , while assets
W are entirely pledged to creditors.
The first best is achieved for
If b ∈ max f (I) − I
With respect to the optimal capital structure, it implies that the firm should raise as much debt as possible,
which here equals the investment amount If b . This involves highest possible leverage ratio and no issuance
costs for equity. However, this may not be achievable if τ < 1 and W is "too low", since then the firm
4 In
the empirical analysis, we control for the possibility τ is endogenously determined.
10
cannot issue more debt than W + τ If b . Otherwise, the firm is not constrained and it will make its financing
decisions independently from its investment decisions.
3.2
The Impact of Financial Constraints on Debt Issuance
We consider two cases: (1) where the firm can raise enough debt to finance the project so that E = 0 (this
occurs here when W is sufficiently large to avoid new equity issuance), and (2) when the firm requires issuing
some equity E > 0 to raise enough capital for the project.
Case 1 (E = 0): Let us calculate the leverage ratio (i.e., the ratio debt over firm value) for this simple
case:5
leverage ratio =
If b
W + f (If b )
which is clearly independent of the degree of asset tangibility τ .
Case 2 (E > 0): Recall that this is the constrained case as the firm will not be able to fund the project
entirely with debt, since W + τ If b < If b . The rest, namely (1 − τ ) If b − W , is raised through new equity E.
In this case, the equilibrium leverage ratio equals6
leverage ratio =
W + τ If b
W + f (If b )
It is easy to see that leverage is now unambiguously increasing in the tangibility ratio τ .
Proposition 1 summarizes these empirical implications on leverage.
Proposition 1 The relation between leverage and asset tangibility evolves as follows: (i) for low levels of
tangibility (for any τ < (If b − W ) /If b ), the relation is strictly positive, but (ii) for any higher level, there
is no relationship.
5 Derivation:
¡ ¢
B = If b ; E = 0; V = B + E + E old = If b + 0 + W + N P V = W + f If b , where V represents total firm value
(sum of debt and equity (new and old), essentially the value of existing assets plus the present value of new investments).
¡ ¢
6 Derivation: B = W + τ I ; E = I
old = W + NP V ; V = B + E + E old = W + f I
fb
f b − B = (1 − τ ) If b − W ; E
fb .
11
The proposition states that there is only a relationship when the firm cannot raise as much debt as it
wants and thus is credit constrained (in terms of debt financing).7 In this case, it first needs to raise equity
up to the amount (1 − τ ) If b − W and invest in assets needed for the project in order to acquire enough
collateral to raise the amount of debt W + τ If b . In the firm does not first raise this amount of equity,
creditors will not be ready to lend as much. In equilibrium, the firm invests the total amount If b .
Out theory implicitly assumes that a strictly positive relation only exists between "hard" tangible assets
and leverage. The theory, however, does not specify what "hard" tangible assets are. But, indeed, “hard”
tangibles are easily redeployable, contractible and depreciate slowly, which are all desirable qualities in the
event the financier has to liquidate the assets to recover the original loan amount.8 While we argue that
these qualities make real estate - namely, land and buildings - valuable collateral, we address this mainly
as an empirical issue, which we tackle by utilizing a different tangibility measure for each different type of
tangible assets, which constitutes a departure from previous empirical studies.
Following Almeida and Campello (2007), we start by assuming that the proportion of tangible assets in
the investment structure is dictated by the "nature of the operations"; i.e., τ is an exogenous parameter.
However, we cannot rule out empirically that some constrained firms might choose to shift towards an
investment structure with more tangible assets in order to increase their debt capacity. This choice might
still be desirable if the cost savings associated with issuing debt exceed in absolute value the additional
7 As
in Faulkender and Petersen (2006), we distingiush between financial constraints and credit constraints. In our context,
firms are credit constrained since they are not able to raise as much debt as they want. However, they are not financially
constrained since they raise the remainder through equity issuance. Thus, the context studies here is similar to Faulkender and
Petersen (2006).
8 The
concept of redeployability was developed by Shleifer and Vishney (1992) who argued that more standardized assets,
with a well-developed secondary market, are more easily redeployable to other users in the event of liquidation. This makes
easily redeployable assets more suitable collateral than firm-specific assets. Contractible assets, in particular assets that are
unmovable, also ensure higher value in the event of liquidation given that control over such assets can more easily be transferred
to creditors (Hart and Moore, 1994). Slow depreciation is also a desirable property for collaterals. While a computer quickly
depreciates, a building depreciates very slowly and may even increase in value over time. Accounting-wise, commercial buildings
depreciate over 39 years in the US, while land cannot be depreciated (only improvements to land such as roads and fences can).
12
operating costs due to the loss of efficiency.9 This implies that leverage and tangibility are then potentially
set simultaneously in equilibrium. This argument relies on the assumption that the proportion of tangible
assets is not exclusively dictated by the type of business but is a “discretionary” decision variable for each
firm within an industry at least to some extent. Empirically we are able to to disentangle this issue by
modeling a firm choice on the proportion of each different tangible asset type as a function of carefully
selected instrumental variables.
Moreover, in our framework, the equilibrium outcome is only a function of a few variables. Empirically
we control for a variety of factors that are now routinely used in the capital structure literature.
4
Sample Selection and Descriptive Statistics
4.1
Data and Variables
Our sample consists of active and inactive firms reported in COMPUSTAT with main operations in the
U.S.. We base our analysis on industrial firms (SIC code 2000-5999) excluding therefore real estate industry,
financial services, public and primary sectors. The capital structure literature has used the ratio of property, plant and equipment net of accumulated depreciation to the book value of total assets as a proxy for
tangibility. We claim in this study that some tangible assets, such as buildings and land, might be more
valuable as collaterals compared for instance to machineries & equipments. COMPUSTAT provides a breakdown of property, plant and equipment (PPE) into land, buildings, machineries and equipments, capitalized
leases, construction of plants and equipments in progress and other tangible assets that are not otherwise
classifiable into any of the preceding groups. We build a different tangibility measure for each component of
property, plant and equipment. For comparability with the extant capital structure literature, these different
measures of tangibility are based on values net of accumulated depreciation. Our data ends in 1996 because
COMPUSTAT no longer reports net values for the components of property, plant and equipment starting
from January 1997.
9 This
could be the case, for instance, for small firms if there are economies of scale associated with issuing equity.
13
We match the COMPUSTAT data with several data files used as instruments in several instrumental
variable regressions that we perform to tackle the simultaneity link between leverage and the firm’s decision
on how much property, plant and equipment to own. We model this latter decision as a function of industry
characteristics, development of real estate and equipments markets, real estate supply restrictions and regulations, performance of real estate market and economy conditions, and geo-climatic characteristics of the
firm headquarter state. Using these instrument sets limits the starting sample period to 1984. Our sample
period ranges therefore from 1984-1996.Our main results are however qualitatively similar if we use proxies
for tangibility based on gross values for land, buildings, machineries and equipments, capital leases, plants
and equipments in progress and other tangible assets over the period 1984-2005.
Leverage is the ratio of total debt to market value of total assets. Total debt is defined as the sum of
long-term debt — COMPUSTAT data item 9 - and current liabilities — COMPUSTAT data item 34. Market
value of total assets is the sum of book value of total assets — COMPUSTAT data item 6 — minus book value
of equity — COMPUSTAT data item 60 - plus market value of equity, which is the product between the share
price — COMPUSTAT data item 199 — and total number of shares outstanding — COMPUSTAT data item
54. Tangibility: Land is the ratio of net book value of land — COMPUSTAT data item 158 — to book value
of total assets. Tangibility: Buildings is the ratio of net book value of buildings — COMPUSTAT data item
155 — to book value of total assets. Tangibility: Machineries & Equipments is the ratio of net book value
of machineries & equipments — COMPUSTAT data item 156 — to book value of total assets. Tangibility:
Capital Leases is the ratio of net book value of capitalized leases — COMPUSTAT data item 159 — to book
value of total assets. Tangibility: Plants & Equipments in Progress is the ratio of net book value of plants
& equipments in progress — COMPUSTAT data item 73 — to book value of total assets. Tangibility: Others
is the ratio of net book value of all remaining tangible assets — COMPUSTAT data item 250 — to book value
of total assets. Tangibility: Total is the ratio of the sum of net book value of land, building, machineries
and equipments, capital leases, plants and equipments in progress, and other tangible assets to book value of
total assets. Tangibility: Total is the proxy for tangible assets used in the extant capital structure literature.
Market-to-Book Ratio is the ratio of market value of total assets to book value of total assets. Firm size is
14
the market value of total assets (measured in millions of 1996 dollars10 ). We use the natural logarithm of
firm size in our regression models. Profitability is the ratio of earnings before interest, taxes, depreciation
and amortization — COMPUSTAT data item 13 — to book value of total assets. Volatility is the ratio of the
standard deviation of earnings before interest, taxes, depreciation and amortization using at least 4 years
of consecutive future observations to the average book value of total assets over the same time horizon.
Earnings Growth is the ratio of the change in income before extraordinary items — COMPUSTAT data item
20 — from t to t+1 to the market value of equity. Investment Tax Credit Dummy is a dummy variable equal
to 1 if investment tax credit — COMPUSTAT data item 51 — is positive and zero otherwise. Net Operating
Loss Carryforward Dummy is a dummy variable equal to 1 if operating loss carry forward — COMPUSTAT
data item 52 — is positive and zero otherwise. Bond Market Access is a dummy variable that takes the value
of 1 if the firm has either a bond rating — COMPUSTAT data item 280 — or a commercial paper rating —
COMPUSTAT data item 283 — and zero otherwise. This is the same definition as used by Faulkender and
Petersen (2006) to proxy access to public bond markets. Firm Age is the number of years since the firm
first appeared in COMPUSTAT. We use the natural logarithm of firm age in our regressions. Regulated
Firm Dummy is a dummy variable taking the value of 1 for regulated firms (SIC code 4900-4939) and zero
otherwise.
We exclude those firm-year observations for which the value of total assets or net sales is less than 1
million. To control for the presence of outliers we delete observations if property, plant and equipment is
more than 100% of total assets, land is more than 100% of total assets (similarly for buildings, machineries
and equipments, capital leases, plants and equipments in progress, and other tangible assets), the marketto-book ratio is larger than 10, and earnings growth or profitability are larger than 3 in absolute value. Our
results remain qualitatively similar if we winsorize the remaining continuous variables at the 1%. This entails
replacing values below (above) the 1st (99th) percentile with the 1st (99th) percentile.
We further exclude from our sample those firm-year observations showing large changes in business
fundamentals. In particular, we exclude those firm-year observations characterized by an increase in size
1 0 We
use the Producer Price Index (PPI) published by the U.S. Department of Labor as the deflator.
15
or sales of more than 100% compared to the previous year. Moreover, we exclude firms involved in major
restructuring, bankruptcy or merger activities.
Our final sample includes 1,684 firms with complete data to run our main regression models for at
least one year over the sample period 1984-1996. The resulting unbalanced panel includes 8,963 firm-year
observations meaning that each firm appears in our sample on average 5.3 years with a range from 2 to 12
years.
4.2
Summary Statistics
Table 1 presents summary statistics of our dataset. Overall, our sample (first column) shows that companies
have on average a leverage of 20.47% in market value. This is about similar to other studies (e.g., Faulkender
and Petersen, 2006). About 36% of total assets are tangible assets, where land accounts for 7% of total
tangible assets (or 2.58% of all assets) while buildings for 27% (or 9.67% of all assets). Real estate (i.e.,
land + buildings) account for 34% of all tangible assets of US companies. About half of tangible assets are
composed of machineries & equipments. Interestingly, the standard deviation of each decomposed measure is
much larger (if compared to the sample mean) than the standard deviation of the aggregate measure of total
tangible assets (mean of 0.3565 and standard deviation of 0.1712, which yields a ratio mean over standard
deviation of 2.08). For instance, the standard deviation of "Land" is almost twice as large as the sample
mean of "Land". This already indicates that some components are likely to be negatively correlated (e.g.,
due to the substitutability of some assets like "Machineries & Equipment" and "Capital Leases") so that on
an aggregate level the variation is smaller.
Evidence on the correlation between the different components of tangible assets is provided in Table 3.
"Land" and "Building" are strongly posivitely correlated (at 0.461 and significant at the 1% level), which
is intuitive. It suggests that much of the land purchased is in connection with the acquisition of buildings,
and vice versa. However, all other correlations are smaller and, most interestingly, many are negative. For
instance, "Machineries & Equipment" is negatively correlated with both "Land" and "Building". A possible
explanation for these results is that land and buildings are purchased occasionally only (though typically
16
together at the same time) so that their correlation with other tangible assets is close to zero, since the stock
of other tangible assets changes more regularly (even gross values). This generates a negative correlation,
notably due to the common denominator.
5
5.1
Results
Empirical Strategy
Our main model regresses leverage on our proxies for tangibility, namely land, buildings, machineries & equipments, capital leases, plants & equipments in progress and others, and several control variables (unrestricted
model). For comparability with previous capital structure studies, we estimate all regressions reported in
this study using also Tangibility: Total, which is simply the sum of the tangibility proxies described in
the previous sentence. This is equivalent to estimating a leverage regression model with the coefficients on
the different proxies for tangibility restricted to be equal (restricted model). For all regressions, reported
p-values are based on White heteroskedasticity consistent errors adjusted for the residuals correlation across
observations of a given firm (White, 1980; Rogers, 1993). All regressions also include year dummies. Table
4 reports regression results for both the restricted and unrestricted models using OLS, our base case. To
tackle the issue of simultaneity between the choice of tangible assets and leverage ratio, Table 5 reports
the estimates of both regression models including firm fixed-effects. More importantly, Table 7 reports the
(second stage) results from estimating the same models using an instrumental variables approach, while the
first stage estimation results are reported in Table 6. Finally, Table 8 reports the (second stage) results from
the estimation of the unrestricted model using the IV approach applied to different sub-samples of credit
constrained and credit unconstrained firms.
5.2
Basic OLS Regressions (Restricted and Unrestricted Tangibility Measure)
Our benchmark regression is shown in Table 4. The first regression provides results in the traditional setting
using a pooled tangibility measure, which is our benchmark for all our extensions. This confirms previous
17
findings of a positive correlation between tangibility and leverage. This supports the view that tangible
assets can be used as collateral and thus can be more easily purchased with debt. When decomposing the
tangibility measure into its main components (Regression 2), it is interesting to see that the effect is different
between the various components, suggesting indeed that not all tangible assets have the same capacity to
provide collateral for debt financing. Perhaps most surprisingly however, "Land" is not significant. On
the other hand, coefficients of "Buildings" and "Machineries & Equipment" (as well as "Others") have the
expected sign and are statistically significant at the 1% level.
Most of the control variables enter the leverage regressions with the expected sign. We provide a more
complete discussion of the control variable results in section 5.3.3.
The message provided by these estimations is somewhat unclear although the overall pattern indicates a
positive effect for all types of tangible assets except Plants and Equipment in Progress. However, the fact
that Land is not significant is inconsistent with our expectation regarding asset hardness. This result is only
seemingly puzzling. OLS regressions indeed fail to capture the complexity of the relation between leverage
and different tangible assets.
5.3
The Endogeneity of Tangible Assets in the Leverage Regression Model —
FE Regressions
Regressions presented in Table 4 are estimated using ordinary least square (OLS). But OLS regression
requires our different measures of tangibility to be determined exogenously; otherwise, their estimates could
be biased. We argue in the theory section that firm facing credit constraints might endogenously choose
an operating structure with proportionally more "harder" assets to enhance their debt capacity. We check
for this possibility in our data. If "tangibility" is dictated by the type of business, then we should observe
that most of the variation in our tangibility ratio should occur between industries. Following a similar
methodology as in MacKay and Phillips (2005), we regress our firm-level tangibility ratio on the 4-digit SIC
industry-mean tangibility ratio lagged 1-period. The Adj − R2 of this regression is about 55%. This implies
that a bit more than half of the variation in the tangibility ratio is between-industry variation, which is
18
consistent with the expectation that the "type of business" is the main determinant of the proportion of
tangible assets a firm needs to run its business. However, this by far is not the only determinant. The
Adj − R2 from running the same regression but including now firm dummies increases to 87%. This increase
implies that 32% of the variation in the tangibility ratio is within-industry variation, while the remaining 13%
is within-firm variation. This evidence calls for the possibility that indeed tangibility might be endogenous
in the leverage regression.11 We address this issue thoroughly in the two next subsections by controlling
first for firm fixed-effects and finally by combining firm fixed-effects with an instrumental variable approach.
Taken together, this discussion implies that firms with credit constraints might set leverage and tangibility
simultaneously in equilibrium. One way to handle part of this problem is to use a fixed-effects (FE) panel
regression to control for those unobserved characteristics that are fixed within each firm but changing between
firms (see Verbeek and Nijman, 1992a, b, and Vella, 1998).
Including firm-fixed effects in the regressions provides partial insights into the puzzling results for "Land"
(see Table 5). Tangibility variables are now in line with empirical predictions on asset hardness, where
"Land" has the strongest effect on leverage. Perhaps surprisingly, the "Total tangibility" measure in the
pooled regression is only slightly affected in economic terms. However, decomposed measures show the
need for considering different groups of tangible assets separately as they vary according to their degree of
"hardness".
1 1 We
also assess the source of variation for the different type of tangible assets. Following a similar methodology as for the
aggregate measure of tangibility, we find that about 58% of the variation of the tangibility ratio for real estate (e.g., land and
buildings) is within-industry variation in this case, while between-industry variation is only 31%. The remaining 12% is withinfirm variation. If assets do differ in their degree of “hardness”, as it is likely to be the case for land, buildings and machineries
& equipments, then we cannot exclude that a firm might choose an investment structure which includes a higher proportion of
"harder" assets to increase its debt capacity. This can only be disentangled using disaggregate measures of tangibility — one for
each asset type - combined with an instrumental variables approach, which is what we do in this study.
19
5.3.1
More on Endogeneity of Tangible Assets — Instrumental Variables
Fixed-effect regressions control for unobserved heterogeneity that is fixed within firm though allowed to vary
across firms. If there remain some unobserved characteristics that are changing within each firm while being
related to leverage, then fixed-effect regression could still result in biased estimates. To control for this
possibility we combine FE with a two-stage instrumental variables (IV) approach.
The first stage of this instrumental variable approach consists of estimating each of the six measures of
tangibility (the ratios of land, buildings, machineries & equipments, capital leases, plants & equipments in
progress and other tangible assets to total assets) as a function of all the variables in the leverage regression
plus several carefully selected instruments. These estimated values (instead of their observed counterparts)
are finally used in a second stage regression for leverage. Because these values are estimated using instruments
capturing how firms decide upon the amount and composition of their tangible assets while they must be
unrelated to leverage, this will break the simultaneity link between tangibility and leverage providing unbiased
estimates.
Identification within an instrumental variable approach requires that for each endogenous variable we
have at least one exogenous instrument that is related to the endogenous variable but unrelated to leverage.
In our case, identification requires that we have at least six valid instruments. That is, one for each of our
measures of tangibility.
In searching for good instruments our aim is to identify those factors that will help isolate how each firm
combines different types of tangible assets to conduct its operations while they do not depend on managerial
discretion. We tackle this issue thoroughly by conducting whenever possible interviews with consulting
companies on the role of commercial real estate for end-users, rating companies on the role of corporate
tangible assets for rating quality, corporate (real estate) executives on the insights of the decision process for
the amount and composition of different corporate tangible assets, and federal U.S. institutions and regulators
(Real Property Disposal Division - General Services Administration (GSA), Department of Commerce and
the Fed) on the regulatory restrictions for the supply of land and other real estate assets as well as the
20
use of such assets as collaterals. We are able to identify 5 distinct groups of exogenous institutional factors
that might explain the observable variation in the composition of the tangibility structure across firms. To
keep the discussion of instrumental variables sufficiently short, we relegate to Appendix II a more detailed
discussion of how the variables are constructed and their data sources.
We report summary statistics for these factors in Table 2.
The first group includes “Industry Characteristics” (Panel A — Table 2). The rationale for choosing
industry characteristics as instruments is that within each industry there is a standard practice for how
different types of tangible assets have to be combined to conduct business operations while managers have
no control upon them. To capture this industry standard practice, we use industry-year averages for the
ratios of land, buildings, machineries & equipments, capital leases, plants & equipments in progress and other
tangible assets to total assets. The variables constitute our first six instruments. This panel also includes
the average industry age as an additional instrument. The rationale for including this latter variable is that
the overall tangibility structure of a firm might be changing as the industry becomes more mature.
The second group includes factors related to the “Development of Real Estate and Equipments Markets”
(Panel B — Table 2). The rationale for choosing this second group of instruments is that if the leasing
markets for real estate properties and equipments in a state are well developed and efficient, then this will
make companies with their headquarter in that state exogenously less likely to own real estate properties and
equipments (and therefore more likely to rent). This argument implicitly assumes that each firm is not large
enough within its headquarter-state to affect the degree of development and efficiency of these markets. This
logic was suggested to us by discussions we had with corporate real estate executives and commercial real
estate consultants. To proxy for the efficiency of the leasing market for commercial properties (e.g., offices
and industrial buildings) we compute for each year-state in our sample the number of Equity Real Estate
Investment Trusts (REITs) weighted by the state population size relative to the entire U.S. population and
match it with each sample firm. We develop similar proxies for the development of non-REIT Commercial
real estate lessors by state, Commercial real estate developers by state, and machineries & equipment leasing
companies by state.
21
The third group includes factors related to “Real Estate Supply Restrictions and Regulations” (Panel C
— Table 2). The argument underlying the choice of these factors is that firms operating in real estate markets
with looser real estate supply restrictions and regulations are less likely to own as well as develop (and more
likely to rent) real estate properties because this will favor the development of a local market for renting
real estate properties. We use the number of real estate disposals by the Federal Government as a proxy for
real estate supply restrictions, where states with larger numbers of real estate disposals as weighted by the
state population size relative to total U.S. population are considered to have looser supply restrictions for
land and real estate. We match it by firm-year using the headquarter state. As an additional proxy for real
estate supply restrictions, we gather from the webpage of the Fraser Institute data on the Index of Economic
Freedom by state for each year in our sample period.
The fourth group includes factors related to “Past Performance of Real Estate Markets and Economy
Conditions” (Panel D — Table 2). The argument underlying this choice is that firms operating in those
commercial real estate markets that have experienced significant past performance after controlling for
inflation and volatility in the rental market and soundness of the state economy might own (as opposed to
rent) on average more real estate properties. We focus on the performance of industrial buildings and plants
because our sample includes only industrial firms, which mainly own production facilities. We compute the
rate of return as the internal rate of return over a three year period starting from the reference year. We
compute the average rate of return over each five-year period ending one year prior to each year from 1984
to 1996 (our sample period) for all geographical areas. We also expect that firms operating in real estate
markets characterized by strong growth and volatility of rental prices will be more likely to own (as opposed
to rent) their production facilities. To control for this eventuality we compute the average and standard
deviation for the growth rate in the rental rate of primary residence over each five-year period ending one
year prior to each year from 1984 to 1996 (our sample period) for all geographical areas. We also include
in this fourth group data on state unemployment rate over each five-year period ending one year prior to
each year from 1984 to 1996 (our sample period) for all geographical areas, which we match it by firm-year
using the headquarter state. We use the state past unemployment rate as an exogenous proxy for prospects
22
of growth for firms operating in that state.
Finally, our fifth group includes factors related to the “Geo-Climatic Characteristics” (Panel E — Table 2)
of the state of the company headquarter. The rationale for this choice is that the geo-climatic characterization
of a state will have an impact on the firm decision to own (as opposed to lease) tangible assets depending
on the frequency/geographical concentration and severity of the damage caused by geo-climatic phenomena.
The concomitance of frequency/geographical concentration and extent of damages are used as a condition
to argue that these phenomena might determine how firms decide upon the amount of tangible assets to
own. We are able to identify two factors - earthquakes and hurricanes — that might be associated with large
damages to properties with possible implications on the amount of tangible assets a firm will decide to own.
We decide to only include California earthquakes in our final sample because all the earthquakes in the other
states during our sample period had low impact. We match the number of California earthquakes lagged
1 year by firm-year using the headquarter state. As for hurricanes that occurred in the U.S. during the
sample period 1984-1996, we only focus on category-3 or higher hurricanes because these are the only one
associated with “extensive damage” in the Saffir/Simpson Hurricane Scale (see Blake, Rappaport, Landesa,
Miami, 2007). We match each state-year hurricane event lagged 1 year by firm-year using the headquarter
state.
5.3.2
Determinants of Asset Structure — 1st Stage Regression
Table 6 reports the first stage results of the instrumental variable regressions12 for our entire sample respectively for our pooled measure of tangibility — “Tangibility: Pooled” (Column 2) — and starting from Column
3 for each of its constituencies, namely “Land”, “Buildings”, “Machineries & Equipments”, “Capital Leases”,
“Plants and Equipments in Progress” and “Others”. Panel A, Table 6 shows that in choosing the relative
proportion of each type of tangible assets the industry practice plays a very determinant role both from a
statistical as well as form an economic perspective. In particular, the proportions of land, buildings, ma1 2 In
the interest of space we only report regression results for the excluded instruments. The complete set of results with the
included instruments is readily available from the authors.
23
chineries and equipments, capital leases, plants and equipments in progress and other tangible assets to total
assets are directly increasing in their industry averages. If we assume that the industry averages are a proxy
for the industry standard practice and we use as an example the case of land — Column 3, Table 6 — then the
positive coefficient on “Tangibility: Land — Industry Average” means that firms operating in those industries
requiring a more intensive use of land will use more land compared to otherwise similar firms operating in
industries where the land component is less important. We can interpret similarly the positive coefficients
on the other industry average instruments for the buildings regression, the machineries and equipments
regression, the capital leases regression, the plants and equipments in progress regression, and the other
tangible assets regression. Panel A, Table 6 presents some other interesting results. The positive coefficient
for “Tangibility: Machineries and Equipments — Industry Average” in the building regression — Column 4,
Table 6 — shows that firms operating in those industries making heavier usage of machineries and equipments
will need consequently a higher proportion of buildings such as plants and factories. The positive coefficient
for the natural logarithm of “Industry Age” in the same regression shows that companies tend to own more
real estate as their industry becomes more mature. This might happen because as the industry becomes
more mature, productivity shocks are perhaps less frequent and smoother making companies more willing
to own a slowly depreciating asset such as real estate (Tuzel, 2007). “Industry Age” does not enter any
the other first stage regressions with a statistical significant coefficient, although its coefficient is very close
to threshold of 10% significance for the other tangible assets regression (Column 8, Table 6). Similarly, we
can interpret the positive coefficient on “Tangibility: Buildings — Industry Average” for the machineries and
equipments regression (Column 5, Table 6). The negative coefficient on “Tangibility: Plants and Equipments
in Progress — Industry Average” in the same regression shows that firms operating in industries with intense
in-house development of equipments will use a lower proportion of machineries and equipments. Finally,
the negative coefficient on “Tangibility: Capital Leases — Industry Average” for plants and equipments in
progress regression — Column 7, Table 6 — shows that firms operating in those industries relying more heavily on leasing equipments or plants from specialized suppliers engage less in internal development of such
hardware. This might be the case for instance of nuclear plants, which are so specialized that companies
24
have to rely necessarily to outside vendors for example by means of leasing.
Panel B, Table 6 reports the regression results for our instruments intended to capture the degree of
development and efficiency of the leasing market for real properties as well as machineries and equipments.
The coefficient on the natural logarithm for the number of REIT companies operating in the state of the
companies headquarter enters the land, buildings and plants and equipments in progress regression with the
expected negative sign although it is not significant for the building regression. This evidence is consistent
with the expectation that companies operating in well functioning leasing markets for real estate properties
are less likely to own or engage in the development real estate properties. This is also consistent with the
prediction in Tuzel (2007) that there might be a premium associated with owning real estate due to its
slow depreciation, which makes companies more vulnerable to productivity shocks. The number of REITs
variable enters the machineries and equipments regression with a negatively significant coefficient. While this
result might at first look surprising, it is possible that the variable is a more general proxy for the efficiency
of overall leasing market in the state including that for machineries and equipments. This would explain
that companies operating in more developed leasing markets will own less (and lease more) machineries and
equipments. Of the other two variables that we use as proxies for the efficiency of the of the leasing market
for real estate properties, the natural logarithm for the number of Non-REIT commercial real estate lessors in
the state of the company headquarter is never significant in any of the regressions in Table 6. This is perhaps
because our prior proxy for the degree of sophistication of the leasing market for real estate properties based
on the number of REITs captures already the efficiency and development of that market, which might not be
surprising given that REITs are largely recognized to be the most sophisticated structure for providing real
estate services (include references). The natural logarithm for number of commercial real estate developers
in the state of the company headquarter enter the land, buildings, and plants and equipments in progress
regressions with the expected negative sign but it is statistically significant only for the buildings regression.
A well-functioning market for developing commercial real estate properties is indirect evidence that firms will
be able to find in the rental market state-of-art facilities for offices or production purposes, which reduces
the necessity for owning real estate properties. One could also expect that companies with less real estate
25
will use capital leases more intensively. Unfortunately, none of the three variables enter the capital leases
regression with a significant coefficient. This of course might be because our capital leases variable pools
equipments leases to real estate completely ignoring the effect of operating leases.
Panel B, Table 6 also report the coefficient estimates on the natural logarithm for the number of leasing
companies for machinery and equipments in the headquarter state. Our expectation before running the
regressions was that firms operating in those states with many such leasing companies would be less like
to own (and develop) machineries and equipments and perhaps more likely to lease. The variable is not
significant in either the machineries and equipments or capital leases regression but significant in the plants
and equipments regression though with the wrong sign. One possible explanation for this lack of significance
is that the number of leasing companies in a state is not a good proxy for the efficiency of the underlying
leasing market for machineries and equipments in that state because unlike real estate rental services, which
have to be provided in loco, leasing services might be supplied to out-of-state companies.
In addition to the proxies described in the above paragraph measuring directly the degree of sophistication
of the leasing markets for real estate properties, we also attempt to control for the degree of institutional
restrictions that might affect the supply of real estate. Think for instance of the case of a local real estate
market with relatively few real estate operators but showing strong signs of deregulation. Controlling for
the number of REIT and non-REIT operators alone will not suffice the purpose of explaining real estate
ownership and development in this case, because companies in this market might still own less real estate
relative to otherwise similar companies based on a reasonable expectation that the market will soon become
efficient. Panel C, Table 6 reports the regression results for our two proxies of real estate supply restrictions
and regulations. The coefficient on the natural logarithm for the number of real estate disposals by the federal
government in the headquarter state as expected is significantly negative in the buildings regression but not
however in the land and plants and equipments in progress regressions. As a complement to this result, we
find that this same variable enter the capital leases regression with a significantly positive coefficient meaning
that these companies might be substituting ownership with real estate renting. As a high number of real
estate disposals is a proxy of a decreases “interference” of the federal government with the local real estate
26
market, this evidence shows that firms operating in those real estate markets expected to gain efficiency in
the near future will own less (and therefore rent more) building facilities. The coefficient on the natural
logarithm of the state index of economic freedom enters the land, building and plants and equipments in
progress regressions with the expected negative sign but it is statistically significant at the 9.4% only for
the last regression. This result is further evidence that as the interference of all levels of government into
business activity (including real estate) decreases making the surge of a sophisticated market for real estate
leasing likely, then companies are less likely to engage in development of real estate properties in house.
Panel D, Table 6 reports regression results for our measures of average performance, average rental inflation rate and its volatility, as well as average unemployment rate all measured over the five-year period
ending one year prior to the sample year and matched by year-firm by means of the headquarter state. The
coefficient on “Commercial Real Estate Rate of Return by State” is insignificant for either land or buildings regressions disproving our expectation that firms in those commercial real estate markets experiencing
stronger past performance might invest more heavily in real estate. However, the fact that the variable enters
the capital leases regression with a significantly negative coefficient shows that firms operating in real estate
markets that have experienced higher past performance prefer to lease a lower proportion of their tangible
assets. The coefficient on “Housing Rental Rate Growth by State” is as expected positive and economically
large, which, despite being statistically significant only at the 8.3% level, is evidence that firms own more
real estate properties in markets that have experienced an upsurge of rental prices over the past five years.
However, the fact that the coefficient on “Housing Rental Rate Growth Volatility by State” is not significant
in the buildings regression shows that the volatility of rental prices is not important for the decision to own
real estate properties. Further, these variables are statistically insignificant for both the capital leases and
the plants and equipments in progress regressions, although “Housing Rental Rate Growth Volatility by
State” enters the machineries and equipments regression with a significantly negative coefficient. Finally,
the “Unemployment Rate by State” variable enters the plants and equipments in progress regression with
the expected negatively significant coefficient corroborating the prediction that high unemployment reduces
development activities.
27
Panel E, Table 6 report the coefficient estimates for our “Geo-Climatic Characteristics” variables. The
coefficient on our lagged proxy for major earthquakes enters the machineries and equipments regression
with a significantly positive coefficient. This might be because companies operating in those states affected
by a major seismic event replace in the aftermath damaged equipments with new ones, which causes a
temporary upsurge in their level relative level of machineries and equipments. Consistently, the variable
enters the capital leases regression with a positive coefficient although with a p-value of only 5.6% showing
that companies could also replace damaged equipments leasing new ones. Somewhat surprisingly our lagged
proxy for major hurricanes enters the capital leases regression with a negative coefficient but the coefficient
is estimated with a level of precision to be only close to the minimum required threshold of statistical
acceptance of 10%. Both variables however enter the plants and equipments in progress regression with
negative coefficients - respectively with p-values of 5.9 and 5.2 percent - consistent with the notion that
the damages created by major geo-climatic events might have the effect of slowing development activities.
Interestingly neither variable enter the land or building regressions with a statistically significant coefficient
consistent with the expectation that the impact of earthquakes and hurricanes on land and buildings is not
large at a point to cause replacement especially for companies located in those areas where these events are
frequent.
Column 2, Table 6 also reports the first stage regression results when we pool all tangible assets. Results
shows that almost all “Industry Characteristics” variables in Panel A with the exception of “Others —
Industry Average” and the natural logarithm of industry age are statistically significant. Our proxies for
number of REITs and number of developers in Panel B are also statistically significant. Several instruments
however loose their statistical significance. This comes at not surprise because by using a pooled variable
we undermine the ability of our instruments to isolate the heterogeneity of different tangible assets.
We now turn to the issue of validity of the instruments used in our first stage regressions. Using instruments that are only weakly correlated with the endogenous variables would make our IV estimates biased
toward OLS estimates (Bound, Jaeger and Baker, 1995; Staiger and Stock, 1997). The evidence reported in
Table 6 allows us to strongly reject this hypothesis in favor of its alternative of unbiased IV estimates. First
28
of all, the F-statistic reported at the bottom of Table 6 shows that we can reject the null hypothesis that our
instruments are jointly equal to zero at the 1% level of statistical significance or higher in all but the other
tangible assets first stage regression. This latter result is unsurprising because our “Tangibility: Others” is a
rather heterogeneous variable pooling all types of tangible assets that are not otherwise classifiable as land,
buildings, machineries & equipments, capital leases, and plants & equipments in progress. As expected, this
heterogeneity makes the identification of good instruments extremely arduous.
Table 6 further reports the Shea’s (1997) Partial R2 (see for instance, MacKay and Phillps, 2005) which
measures the overall relevance of our instruments after accounting for their intercorrelation therefore correcting for possible redundancy of instruments. The Shea’s R2 is large in absolute value with a maximum
value of 12.9% for the land regression as well relative to the total R2 in all first stage regressions, though
less so for the “Tangibility: Others” regression where however the partial R2 is high (e.g., 2.8%) relative to
the total R2 of 3.6% implying a high explanatory power of the excluded instruments compared to all other
regressors used in this first stage regression. Finally, the Hansen (1982) J-test of overidentifying restrictions
reported in Tables 7 and 8 shows that we can never reject the joint null hypothesis that our instruments
are uncorrelated with the residuals in the leverage regression and the excluded instruments are correctly
excluded from the second stage regression. Therefore, we conclude that the leverage regression is correctly
specified and the instruments are adequately orthogonal to the residuals.
5.3.3
Determinants of Debt Capacity — 2nd Stage Regression
Table 7 shows estimations of the restricted and unrestricted model. These estimations represent the secondstage regressions. They also include firm-fixed effects and standard deviations are clustered across observations of a given firm (White, 1980, and Rogers, 1993).
Compared to the results in Table 5 (fixed-effects estimations only), the difference between real estate
assets (i.e., Land and Buildings) and the other tangible assets has become larger. Even more interesting,
only real estate assets now serve as valuable collateral for debt finance. Other types of assets do not impact
leverage directly. This evidences that tangibility variables are indeed endogenous and particularly on the
29
disaggregate level. This indicates that the direct effect of some of these assets on leverage is stronger than
evidenced in previous studies on capital structure, while others are less. The actual composition of tangible
assets is therefore critical.
However, this does not mean that other tangible assets such as machineries & equipments are not used as
collaterals, but that the causality does the other way around. Debt that is being raised is used to purchase
machines & equipments that are pledged as collateral, but existing machines & equipments do not enable
a firm to raise even more debt. In contrast, there is a direct effect of real estate assets on the capability of
firms to raise more debt. We identify, however, this causal relationship only for real estate.
The coefficient of Tangibility: Plants & Equipments in Progress is also highly significant and positive. Its
value (0.383) is in-between the coefficients of Buildings (0.499) and Machineries & Equipments (0.175). This
is fully consistent with our prediction on the pecking-order of tangible assets, given that "Tangibility: Plants
& Equipments in Progress" is a combination of buildings and machineries & equipments. It is therefore also
not surprising that it is statistically significant, given the results obtained for "Buildings" and "Machineries
& Equipments".
Finally, and perhaps surprisingly, the coefficient of total tangible assets in the restricted model (first
regression in Table 7) did not change dramatically compared to the estimations obtained in the OLS (Table
4) and firm-fixed effects (Table 5) regressions. One possible reason may be the fact that this aggregate
measure simply averages out the different effects. However, if this is true, it does not allow to understand
the underlying effects of the different types of tangible assets and thus is likely to miss important effects.
Our analysis on diaggreate measures shows that the the impact disproportionately affects assets such as real
estates and that the underlying effects are endogenous with respect to asset tangibility. Existing studies
have missed to control for these effects.
“Tangibility: Total” is the sum of land, buildings, machineries & equipments, capital leases, plants &
equipments in progress, and others. This means that the “Restricted Model” reported in Table 4 is the
restricted version of the “Unrestricted Model”, where the coefficients on Land, Buildings, and the other
tangibility variables are restricted to be equal.
30
One important prediction throughout this study is that different tangible assets vary in their degree
of hardness. Some assets, such as land and buildings, are more suitable as collaterals increasing therefore
the amount of capital a firm can borrow. The implication of this argument is that the unrestricted model
is preferable from a theoretical perspective to the restricted model used so far in the capital structure
literature. Nevertheless, we further check the validity of the restrictions from a statistical viewpoint. Our
testing mechanism is as follows. We estimate first a model with two different regressors for tangible assets,
one pooling all real estate assets — namely land and buildings — and the other pooling all other remaining
tangible assets — namely machineries & equipments, capital leases, plants & equipments in progress and
other tangible assets. Under the null hypothesis that the restrictions are valid (e.g., the coefficient on the
real estate regressor is equal to the coefficient on the remaining tangible assets), the resulting Wald test
statistic follows a Chi-square distribution (see Judge et al., 1988, pp.: 832-834).
The Wald test for the full sample in the case of the instrumental variable regression is equal to 6.22 with
a p-value of 1.26%, which suggests that we can strongly reject the null hypothesis that the restricted model
is nested in the unrestricted model. We reach a qualitatively similar conclusion for the model with firm-year
fixed effects, but we cannot reject the null hypothesis that restricted model is nested into the unrestricted
model for the model without firm fixed effects, which is consistent with our argument that this model fails
to capture the simultaneity between leverage and tangible assets.13
Turning now to the control variables we find that most of them enter the leverage regressions with the
expected sign. The coefficient on the market-to-book ratio is as expected negative and significant. This
finding is consistent with Myers’ (1977) and Hart’s (1993) prediction that firms with significant growth
1 3 As
an additional robustness check, we also test the null hypothesis that the coefficients on all tangibility regressors are
equal. This is a weaker test because it also imposes the restriction that the coefficient on “land” is equal to the coefficient on
“buildings”. Given that the coefficients on these two real estate regressors are indeed possibly equal, this will make it more
difficult to reject the restricted model. Nonetheless, the Chi-square test for the two alternative instrumental variable regressions
in Table 7 is equal to 10.99, which means that we can reject the null hypothesis that the restricted model is nested in the
unrestricted model with a p-value of 5.15%. The p-value for the test is equal to 6.16% for the two models reported in Table 4
using OLS without fixed-effects, but it is only equal to 10.81% for the two models reported in Table 5.
31
opportunities will use lower leverage to avoid the under-investment problem. Firm size enters the regression
models in Tables 5 and 7 with the expected positive sign because larger firms have a higher reputation. The
coefficient is not however statistically significant. The variable however is highly significant and negative in
Table 4, which uses OLS as the estimation technique. The latter evidence might depend on the effects of
the endogeneity of firm size in the leverage regression. If firm size is a proxy for the borrower quality and
high quality borrowers display lower leverage ratios, then OLS is simply capturing the negative correlation
between size and leverage. Fixed-effect regressions as those reported in Tables 5 and 7 mitigate the effect
of this endogeneity, which allows to uncover the causational relation between size and leverage. Johnson
(2003) reports a similar pattern in the relation between size and leverage when going from OLS to fixedeffects regressions. Consistent with Myers’ (1984) pecking order theory, highly profitable firms use lower
leverage. More volatile cash flows induce higher costs of financial distress for leveraged firms so that we
should expect lower leverage. As predicted, the volatility variable enters the leverage regressions with the
expected negative sign but is statistically significant only for the OLS regressions reported in Table 4. Firms
predicting abnormal earnings growth can raise leverage as a device to signal this expectation (Ross, 1977).
Consistent with this prediction, earnings growth enter all regressions with the expected positively significant
coefficient. Consistent with the prediction that firms with alternative tax shields might use lower leverage,
the investment tax credit dummy enters all our regressions with a negatively significant coefficient. However,
contrary to the expectation, the coefficient on the net operating loss carryforward dummy is positive and
significant in all regression. Barclay, Marx and Smith (2003) and Johnson (2003) find the same alternate
pattern for the two variables. Johnson (2003) explains that the positive sign on the net operating loss
carryforward dummy can be justified because carryforwards imply that the firm has incurred some equity
losses, which will cause leverage to increase. Consistent with the argument and finding in Faulkender
and Petersen (2006) that firms with access to the public debt market are less opaque and can therefore
borrow more, we find that our bond market access dummy enters all regressions with a positively significant
coefficient. A better access to the bond market lowers the cost of debt, providing incentives to use more
debt. We also find that firm age enters the leverage regressions in Tables 5 and 7 with a positively significant
32
coefficient, with the exception of the unrestricted model in Table 7 where the variable is only close to the
10% threshold of statistical significance. The variable however enters the OLS regressions in Table 4 with a
negative but only marginally significant coefficient. Similarly to what we argue for the negative coefficient
on firm size reported in Table 4 for the OLS regressions, a negative OLS coefficient for age might depend
on the effects of the endogeneity of age in the leverage regression. If firm age is a proxy for the borrower
quality and high quality borrowers display lower leverage ratios, then OLS is simply capturing the negative
correlation between age and leverage. Fixed-effect regressions as those reported in Tables 5 and 7 mitigate
the effect of this endogeneity, which allows to uncover the causational relation between age and leverage.
Finally, the positively significant coefficient on the regulated firm dummy reported in Table 4 is consistent
with the argument in Smith (1986) and the evidence in Johnson (2003) and Faulkender and Petersen (2006)
(just to cite a few) that regulation mitigates the agency problems between the firm and its financiers.
5.4
The Importance of Tangible Assets for Credit Constrained and Unconstrained Firms
One important prediction of our theoretical model is that collateralizable assets are particularly useful to
enhance the borrowing capacity of credit constrained companies but not of unconstrained ones. Therefore,
we re-estimate our regression models for different sub-sets of our sample firms that can be classified ex-ante
as credit constrained.
We rely on the three classification schemes that are most widely used in the investment literature (see, for
instance, Almeida, Campello and Weisbach, 2004; Almeida and Campello, 2007; Erickson and Withed, 2000),
namely firm size, whether or not a firm has access to the bond market and dividend payout ratio. Erickson
and Whited (2000) argue that smaller firms are most likely younger and are therefore confronting higher
information asymmetry in the debt market. We can therefore expect that smaller firms face higher financial
constraints.14 We proceed similar to Erickson and Whited (2000) and Almeida and Campello (2007) to
1 4 Erickson
and Whited (2000) suggest convincingly that firm size is a rather genuine classification scheme for financial
constraints because it cannot be endogenously determined by the management in the short- to medium-term.
33
separate firms into financially constrained and unconstrained. In particular, in each sample year from 19841996 we rank firms according to their market size and we classify as financially constrained (unconstrained)
those firms with a market size in the bottom (top) three deciles of the yearly size distribution for at least six
years (e.g., half our total sample period) or for the entire period the firm appears in our dataset whichever
is shorter.
Whited (1992) suggests that whether or not a firm has access to the debt market can also be used as a
proxy for credit constraints. This is so because to obtain a bond rating firms undergo “a great deal of public
scrutiny”, which should lower the level of information asymmetry that they will face in the debt market. We
can therefore expect that firms without a bond rating face higher financial constraints. Following a similar
approach as for market size, in each sample year from 1984-1996 we rank firms according to whether they
have a bond rating and we classify as financially constrained (unconstrained) those firms without (with) a
bond rating for at least six years or for the entire period the firm appears in our dataset whichever is shorter.
Fazzari, Hubbard and Petersen (1998) argue that a firm dividend payout ratio is also a proxy for whether
a firm is financially constrained. The rationale is that firms decide to pay out dividend only after investment
and financing decisions have already been made. If therefore a firm decides not to pay out dividends, then
it must be the case that it is facing financial constraints otherwise debt borrowing could have been used to
pay dividends. We can therefore expect that firms paying low dividends are facing high financial constraints.
We define the dividend payout as the ratio of dividends plus stock repurchases to operating income. In
each sample year from 1984-1996 we rank firms according to their dividend payout ratio and we classify as
financially constrained (unconstrained) those firms with a dividend payout ratio in the bottom (top) three
deciles of the yearly dividend payout ratio distribution for at least six years or for the entire period the firm
appears in our dataset whichever is shorter.15
1 5 Our
results hold if we do not include those firms that are present in out dataset for less than six years. Results are also
robust to other classification schemes. In particular, results also hold if in each sample year from 1984-1996 we rank firms
according to their market size and we classify as financially constrained (unconstrained) those firms with a market size in the
bottom (top) three deciles of that year size distribution. Results hold if we follow similar classification schemes for bond market
access and dividend payout.
34
The evidence reported in Table 8 is strikingly consistent with our theoretical predictions. Real estate
is a suitable source of collateral for all three sub-samples of financially constrained companies, namely firms
in the bottom 3 deciles of size and payout and without access to the public debt market. In particular,
the coefficient on “Tangibility: Buildings” is positive and significant for all three sub-samples of financially
constrained companies and also economically larger, compared to the coefficient of 0.499 reported in Table 7
for the full sample, for firms without a bond market access and in the bottom 3 deciles of payout but smaller
for firms in the bottom 3 deciles of size. The coefficient on “Tangibility: Land” is positively significant and
economically larger, compared to the coefficient of 0.577 reported in Table 7 for the full sample, for firms in
the bottom 3 deciles of size and without a bond market access. The coefficient is however not significant for
the sub-sample of firms in the bottom 3 deciles of payout, although in this case “Tangibility: Machineries
and Equipments” enter the leverage regression with a significantly positive coefficient. Moreover, Table 8
shows that none of the proxies for collateralizable assets enter the leverage regressions with a significantly
positive coefficient for the sub-sample of financially unconstrained companies, namely firms in the top 3
deciles of size and payout and with access to the public debt market. Overall, results reported in Table 8
bring strong support to our prediction that tangible assets are valuable as collaterals only for financially
constrained firms.
5.5
Robustness
The analysis presented 5.3.2 has shown the relevance and validity of the instruments used in our IV regressions. Nevertheless, because we face the rather formidable task for a corporate finance study of dealing
with the potential endogeneity in the structural leverage regression of six regressors, we use special care in
assessing further the robustness of our results to other identification schemes.
Lewbel (2007) has shown that the presence of heteroskedasticity of the errors in the first stage regressions
can be exploited as a viable source of identification in instrumental variable regressions. This is particularly
valuable especially in those cases when otherwise “outside” ordinary instruments are not available to the
researcher. Because we are able to identify 19 ordinary instruments, we only apply the methodology proposed
35
by Lewbel (2007) as a further check for the robustness of our findings.
Lewbel (2007) shows that, under the condition of heteroskedasticity, all products (covariances) between
the residuals from the first stage regressions and each (or a subset) of the exogenous regressors centered about
their sample mean can be used as proper instruments to achieve identification. In our case, this requires that
we obtain the residuals from each of the six first-stage regressions including only the included instruments
estimated via OLS. Because identification is achieved through heteroskedasticity, we next test for the presence
of heteroskedasticity. The modified Wald test for heteroskedasticity shows that heteroskedasticity is strong
in all six first-stage regression models. The next step is to multiply the predicted residuals from each firststage regression by each of the 9 exogenous regressors centered about their sample mean (excluding the
year dummies). This gives us 54 instruments (e.g., 6*9) that we use as a means of identification for our
IV regressions. We use this methodology to re-estimate all the results reported in Tables 7 and 8. Our
results are qualitatively unchanged, although usually the coefficients on the land and building regressors are
economically somewhat smaller for the financially constrained sub-samples compared to those reported in
Table 8.16 We also re-run all IV regressions including now our original 19 ordinary instruments in addition
to the 54 instruments obtained following Lewbel (2007) finding again that our results hold. We conclude
that our IV estimates are robust to different sources of identification.
We also re-estimate all of instrumental variables regressions via an instrumental variables generalized
method of moments (IV-GMM). The IV-GMM estimator provides an efficiency gain over the standard IV
estimator by allowing for the form of heteroskedasticity to be unknown as well as for arbitrary intra-cluster
correlation in the sample (see MacKay and Phillips, 2005; Baum, Schaffer, Stillman, 2002).17 . All our results
hold to this alternative estimation technique.
1 6 It
is also worth notice that the coefficient on the land regressor for firms in the “Top 3 Deciles of Size” is now economically
smaller but significant at the 9.5% level.
Erickson and Whited (2000) suggest convincingly that firm size is a rather genuine classification scheme for financial constraints because it cannot be endogenously determined by the management in the short- to medium-term.
1 7 Hayashi
(2000) emphasize the poor properties of the IV-GMM estimator in small sample. Given the size of our sample this
is not a concern in this study.
36
The panel structure of our data might introduce serial correlation in the residuals across observations of
a given firm. Petersen (2007) has provided simulation evidence showing that regressions with firm clustering
result in standard errors that are corrected for the effect of these types of serial correlation. For this reason,
all regression results discussed so far in this paper are adjusted for clustering. Nevertheless, recent papers
(see, for instance, Kayhan and Titman, 2007, Wald and Long, 2007) have proposed block-bootstrapping from
the original sample as an alternative way of handling the potential serial correlation across observations of
a given firm. We further check the robustness of our results to this alternative methodology.
Bootstrapping consists of sampling with replacement from the original sample. To preserve the dependence structure of the errors in the original sample, Kayhan and Titman (2007), Wald and Long (2007) and
Petersen (2007) propose sampling firm clusters rather than firm-year observations.18 We obtain 1,000 random draws each including T firm clusters from the original sample and each time we estimate the regression
coefficients. For instance, in replicating the results in Table 7, each of the 1,000 draws from the original
sample will include 1,684 firm clusters but because we are sampling with replacement each sample might
include repeated firm-clusters, while some firms might not appear in the draw.
Replicating all our analysis using this block-bootstrap procedure shows that most of our results are
qualitatively similar though in general with larger standard errors, consistent with the simulation results
reported by Petersen (2007) and the empirical evidence in Kayhan and Titman (2007). The only two
exceptions are the coefficients on "Land" for the unrestricted model in Table 7 and the firms in the bottom
3 deciles of firm size (Column 2, Table 8).
There is an ongoing debate in the finance literature on the choice between a book measure of leverage
advocated among others by Graham and Harvey (2001), Frank and Goyal (2003) and Shyam-Sunder and
Myers (1999) and a market measure of leverage advocated by Welch (2004) and widely used in most of
the empirical capital structure literature. More important, Welch (2004) points out that, because of the
accounting rules, the book value of equity decreases as fixed assets depreciate, which would explain in turn
why tangible assets appear an important determinant of leverage. In light of the fact that our main focus
1 8 See
Horowitz (2003) for a description of the block-bootstrap procedure. A classic reference on bootstrapping is Efron (1979).
37
is on the debt capacity of tangible assets, the results that we have presented so far are based on the most
widely used market leverage. Nevertheless, we check the robustness of our results to a book measure of
leverage. We find that our results are qualitatively similar. The only exceptions are the regression reported
in the Column “Bottom 3 Deciles of Size”, Table 8, where the coefficient on land is still highly significant
while the coefficient on buildings looses it marginal significance and the regression reported in the Column
“Bottom 3 Deciles of Payout”, Table 8, where now neither the coefficients on buildings or machinery and
equipments are longer statistically significant.
Some of the coefficients may be biased for the following reason. We believe that our estimated coefficients of "Land" and "Buildings" are likely to be conservative. While debt maturity often matches the life
expectancy of machineries and equipment, it is not the case for "Land" and "Buildings" that only depreciates
very slowly or not at all. In market value terms, these assets may in fact even increase in value. This then
allows firms to "recycle" these assets as collaterals in that they can be used a second time to raise further
loans. In this case, coefficients reported in Table 4 (and any follow-up tables) are potentially too low if firms
hold these assets for a very long time and repaid much of the loan. In addition, coefficients of other types of
tangible assets are most likely inflated, since the recycling of real estate assets will allow firms to finance a
larger fraction of machineries and equipment with debt than without real estate. Other tangible assets than
real estate typically depreciate within a few years, and thus do not have the same recycling property.
6
Conclusions
Starting with Titman and Wessels (1988) and Rajan and Zingales (1995), many capital structure studies
have documented a positive relationship between tangibility (measured as the ratio of property, plant, and
equipment to total assets) and firm leverage. This is largely explained by the fact that tangible assets can be
pledged as collateral to lenders and thus allow companies to raise debt. There has not been empirical evidence
to our knowledge on whether different types of tangible assets, namely land, buildings, and machineries &
equipments, posses the same propensity to generate collaterals in the financing process. Mostly importantly,
38
previous studies miss to explain that tangible assets do not create debt capacity for firms that have otherwise
already wide access to the debt market - e.g., credit unconstrained firms - while a direct positive link between
leverage and tangible assets is likely to exist for firms with limited access to the debt market (e.g., credit
constrained firms). This distinction between firms based on whether or not they face credit constraints
raises another salient issue, which has not been uncovered in previous empirical studies. We cannot rule out
the possibility that, confronted with limited access to the debt market, firms might endogenously choose an
investment structure with more tangible assets to increase their debt capacity.
This paper contributes to the extant literature on capital structure and tangibility in two main respects.
First, we show theoretically that tangibility increases debt capacity only for credit constrained companies
while tangibility and leverage are independent decisions for credit unconstrained firms. Empirically we
provide strong support for this prediction. Second, we provide evidence that, after controlling for possible
sources of endogeneity, only "hard" tangible assets - land and buildings - increase debt capacity. This result
suggests that we should use caution in interpreting evidence reported in previous studies, which rely on an
aggregate measure of tangibility. The evidence that an aggregate measure of tangibility enters the leverage
regression with a positive coefficient does not imply that all tangible assets are suitable collaterals. We have
found indeed that only real estate enhances debt capacity.
39
A
Appendix I - Numerical Example
In this appendix we provide a numerical example of the theoretical model presented in Section 3.2. Let us
suppose that the optimal investment level (If b ) equals 5, the collateralizability ratio (τ ) equals 0.5 and the
present value of the investment opportunity (f (If b )) equals 10. This means that the N P V = 10 − 5 = 5 of
this investment opportunity. Note that τ represents an average collateral quality of purchased assets, and
we assume it constant, regardless the level of investment level (I). For instance, if the project requires that
for each dollar invested 25% are for real estate, 50% in machineries and 25% in intangible assets, then the
resulting τ employed in the calculation (namely, equal to 0.5) represents a weighted average (with 25% - 50%
- 25% as weights) of the τ ’s of each asset type. In other words, a firm with a large fraction of real estate
assets would have a larger τ , if real estate assets are “harder”. In our example, we suppose that this yields
τ = 0.5. For simplicity, existing assets have a present value of 10, while their liquidation value is denoted by
W.
Let us consider two different cases:
A.1
The firm is financially unconstrained: W = 10
Given existing asset W , the firm has collaterals after investment equal to 10 +
1
2
5, i.e., 12.5. This means
that it can borrow the entire needed amount of 5 in form of debt, since the loan amount is smaller than the
total value of the firm’s collaterals. Thus, the loan B = 5 and new equity E = 0 so that firm value V = W +
“present value of the investment opportunity” = 20. This yields a leverage ratio of B/V = 5/20 = 0.25.
Note that the leverage ratio would be the same for any W ≥ 8.5.
A.2
The firm is credit constrained: W = 1
In this scenario, the firm has significantly less current assets in place that can be pledged to lenders. In
fact, for I = 5, the total amount of collaterals that the firm can pledge is equal to 1 +
1
2
5, i.e., 3.5. This
is less than the required investment amount (I). The remainder therefore needs to be raised through equity
40
issuance, E = 1.5. In this case, the 1.5 is invested in the project, which then contributes to the generation of
collaterals up to 3.5 (since then the firm is able to invest I = 5). However, part is supported by shareholders.
The firm value remains the same, namely V = 10 + 10 = 20. However, the leverage ratio is now lower:
B/V = 3.5/20 = 0.175.
A.3
The firm is credit constrained but its assets have higher collateral value:
W = 1 but τ = 0.6
In this case, existing assets have the same collateral value as in Case (A.2); however the new assets have
higher collateral value as their τ is greater. This means that the firm will require less equity finance, since
every dollar of investment yields more collaterals, which in turn allows to borrow more. Total collateral value
of the firm’s assets after investment into the new project is 1 + 0.6 * 5, i.e., 4. This is also the amount that
the firm can borrow. However the firm is still required to raise equity finance for the remaining amount.
Thus, E = 1. This yields a leverage ratio of B/V = 4/20 = 0.20. Compared to Case (A.2), the increased
leverage ratio is the result of higher collateral value of the new assets put in place through the new project
(i.e., the value of τ ).
Comparing the three cases ((A.1), (A.2) and (A.2.1)), this implies that for credit constrained firms,
we obtain a positive relationship between collateralizability ratio (τ ) and leverage, but no relationship for
unconstrained firms. In empirical studies, τ is measured by the tangibility ratio (i.e., the fraction of total
firm assets that are tangible) times the estimated coefficient of the regression.
B
Appendix II — Instrumental Variables
In this Appendix, we describe in more details how the different instrumental vairables are constructed.
The first group includes “Industry Characteristics” (Panel A — Table 2). Each year we compute the
industry average for the ratios of land, buildings, machineries & equipments, capital leases, plants & equipments in progress and other tangible assets to total assets. As for the average industry age, we calculate the
41
average age of all firms operating in the industry, where each firm age is measured as the number of years
since the firm first appeared in the COMPUSTAT database.
The second group includes factors related to the “Development of Real Estate and Equipments Markets”
(Panel B — Table 2). To proxy for the efficiency of the leasing market for commercial properties (e.g., offices
and industrial buildings) we compute for each year-state in our sample the number of Equity Real Estate
Investment Trusts (REITs) weighted by the state population size relative to the entire U.S. population and
match it with each sample firm.19 We gather our REIT data from SNL Data Sources, to our knowledge
the most comprehensive database on commercial real estate. We obtained population data by state and for
the entire U.S. for each sample year from U.S. Census Bureau, Population Division. We use this weighted
measure of number of REITs operating in a state as a proxy for efficiency and development of local real estate
market. Our expectations are that industrial companies operating in states with a dynamic and efficient
leasing market for real estate properties are less likely to own real estate properties. Similarly, we expect that
these companies are less likely to engage in the development of real estate properties such as office buildings
to be used for administrative purposes or production plants.
Although some states might be characterized by few commercial real estate operators adopting the REIT
structure, this is not per se evidence of scarce development of the leasing market for commercial real estate
properties because real estate companies in some states might have adopted a different structure for a variety
of reasons that we are not necessarily able to observe. To control for this possibility, for each year-state in
our sample we gather from COMPUSTAT data on the number of Non-REIT commercial real estate lessors
(e.g. companies operating in the two industries with SIC codes 6510 and 6512). Given that this variable
is a proxy for the efficiency of the leasing market for real estate properties similar to the number of REITs
discussed above, we expect it to be negatively related to real estate ownership (e.g., land and buildings)
and development (e.g., plants & equipments in progress). As a proxy for the efficiency of the real estate
1 9 We
use the number of REITs rather than size of all REITs operating in a state because data on total assets are more
sparsely available on either SNL Data Sources or COMPUSTAT in the early years of our sample period. This of course reduces
the power of our instruments but allows us to conserve the entirety of the sample size.
42
development market, for each year-state in our sample we gather from COMPUSTAT data on the number of
commercial real estate developers (e.g. companies operating in the SIC code 1540). Finally, as a proxy for the
efficiency of the leasing market for machineries and equipments we collect data on the number of Machinery
& Equipment Leasing companies from COMPUSTAT (e.g., companies operating in the two industries with
SIC codes 7350 and 7359). As for the number of REIT companies, each of the other three variables in this
second group described in the above paragraphs is weighted by the state population size relative to the U.S.
population and matched with each sample firm.
The third group includes factors related to “Real Estate Supply Restrictions and Regulations” (Panel C
— Table 2). Unfortunately availability of indexes on land and real estate supply restrictions is only available
for the most recent years (see for instance Gyourko, Saiz and Summers, 2006), which do not cover our sample
period. Therefore, we talked to several real estate experts in both industry and academia to gain insights
on which factors might be used as indicators for real estate supply restrictions. This search was fruitful. We
found out that there exists an agency in the US — called the General Services Administration (GSA) — that
manages and disposes federal land and buildings. We were able to obtain from the Real Property Disposal
Division of the agency under the Disclosure of Information Act the number of disposals of land and other
federal real estate properties for each state from the year 1984 to present. We use the number of real estate
disposals by the Federal Government as a proxy for real estate supply restrictions, where states with larger
numbers of real estate disposals as weighted by the state population size relative to total U.S. population
are considered to have looser supply restrictions for land and real estate. We match it by firm-year using
the headquarter state. Our expectation is the “Number of Real Estate Disposals by Federal Government”
and the ratios of land, buildings and development of plants to total assets will be negatively related because
looser real estate supply restrictions favor an active rental market for real estate properties making ownership
and in-house development of such properties less stringent for the firm. Conversely, we expect leasing of real
estate properties to be higher in those states with looser real estate supply restrictions. As an additional
proxy for real estate supply restrictions, we gather from the webpage of the Fraser Institute data on the
Index of Economic Freedom by state for each year in our sample period. The index measures in a scale from
43
1 to 10 the interference of federal, state and other local governments on the freedom with which one can
purse any economic activity (including real estate), with an index of 1 indicating strong interference and 10
low interference. Our expectations are that higher economic freedom will favor the growth of a vivid market
for real estate rentals, making it less necessary for a company to own and/or develop (as opposed to lease)
real estate facilities.
The fourth group includes factors related to “Past Performance of Real Estate Markets and Economy
Conditions” (Panel D — Table 2). We gather our data on performance for commercial real estate properties
from the NCREIF IRR Partition and Attribution Analysis database. To our knowledge the NCREIF database is the only source containing performance data on different types of commercial properties going back
to the seventies for the 8 main US geographical areas.20 We focus on the performance of industrial buildings
and plants because our sample includes only industrial firms, which mainly own production facilities. One
major source of concern when one uses real estate performance data is that they are obtained from appraisal
values, which suffer from smoothing and lagging (see for instance Case and Quigley, 1991; Clapp and Giaccotto, 1992). These problems arise because appraisals are done by humans who will tend to be cautious
before making sharp adjustment in property prices to reflect market conditions. The result will be that the
appraisal price will be lagging with the respect to the “true” underlying market price, though it will converge
to it in the medium term. We are able to mitigate this lagging effect because NCREIF provides appraised
value for all industrial properties for each year from 1978. Therefore, we compute the rate of return as the
internal rate of return over a three year period starting from the reference year. For instance, if we want to
calculate the rate of return for the year 1990, we compute the internal rate of return using as initial value the
appraised value in 1990 and as final value the appraised value in 1992. This will mitigate the lagging effect
because by the year 1992 it is more likely that appraisers will have impounded into the price the underlying market conditions. We compute the average rate of return over each five-year period ending one year
prior to each year from 1984 to 1996 (our sample period) for all geographical areas. Finally, we assign this
2 0 These
areas include the north-east, mid-east, pacific, mountain, south-west, south-east, east north-central, west north-
central.
44
five-year performance to each year-state according to the geographical area where it belongs and we match
it by firm-year using the headquarter state.21 The effect of smoothing is less important to us because it has
implications for the volatility of the property prices, which we do not use in this study.22 Our expectation is
that firms operating in those industrial property markets with higher past performance will be more likely
to own and less likely to rent their plants and production facilities. We also expect that firms operating in
real estate markets characterized by strong growth and volatility of rental prices will be more likely to own
(as opposed to rent) their production facilities. To control for this eventuality we obtained from the webpage
of the Bureau of Labor Statistics data on the inflation rate for rental rates of primary residence in major
urban areas from 1978. We use these data to compute the average and standard deviation for the growth
rate in the rental rate of primary residence over each five-year period ending one year prior to each year
from 1984 to 1996 (our sample period) for all geographical areas. Finally, we assign these five-year average
and standard deviation rental growth rates to each year-state according to the geographical area where it
belongs and we match it by firm-year using the headquarter state. We also include in this fourth group data
on state unemployment rate over each five-year period ending one year prior to each year from 1984 to 1996
(our sample period) for all geographical areas. The webpage of the Bureau of Labor Statistics provides data
on unemployment rate by state starting from 1976. Therefore, we are able to assign these five-year average
unemployment rates to each year-state and we match it by firm-year using the headquarter state. We use
the state past unemployment rate as an exogenous proxy for prospects of growth for firms operating in that
state. Our expectation is that firms operating in those states that have experienced higher unemployment
rates in the recent past will engage in lower development activities and therefore we should find a negative
relation between the unemployment rate and our measure of “Plants & Equipments in Progress”.
Our fifth group includes factors related to the “Geo-Climatic Characteristics” (Panel E — Table 2) of
the state of the company headquarter. The concomitance of frequency/geographical concentration and
2 1 We
gather information on which states belong to any of the 8 main geographical areas from the webpage of the U.S. Census
Bureau, Geography Division.
2 2 Unlike
market price changes, which might be erratic, appraisers will be likely to smooth or round the prices of the appraised
properties. This of course will reduce volatility.
45
extent of damages are used as a condition to argue that these phenomena might determine how firms decide
upon the amount of tangible assets to own. We focus on earthquakes and hurricanes. We obtain data
on earthquakes and hurricanes from the webpage of the National Oceanic & Atmospheric Administration
(NOAA) — National Geographic Data Center (NGDC), U.S. Department of Commerce. We are able to
identify 34 earthquakes occurred in the U.S. during the sample period 1984-1996, of which 18 are located
in California. The database also reports the Modified Mercalli Intensity (MMI) index, which provides an
empirical indication for the severity of the earthquake based on its impact on properties and people. The
index ranges from 1, which indicates that the earthquakes in almost imperceptible, to 12, which indicates that
the earthquake is tremendously devastating for people and properties. One of the criteria that we consider
to argue that the earthquake might influence the amount of tangible assets in a firm overall asset structure
is the extent of the damage. Therefore, we only decide to include those earthquakes for those state-years
with at least an earthquake with an MMI of 8 or higher because eight represents the benchmark from which
even specially designed structure, which are most likely the types of structures that firms own, might suffer
damages. This leaves only with California earthquakes in our final sample because all the earthquakes in
the other states during our sample period had an MMI lower than 8. We match the number of California
earthquakes lagged 1 year by firm-year using the headquarter state.
We obtain data on hurricanes from the webpage of the National Oceanic & Atmospheric Administration
(NOAA) — National Hurricane Center (NHC), U.S. Department of Commerce. We are able to identify 15
category-3 or higher hurricanes occurred in the U.S. during the sample period 1984-1996. We only focus on
category-3 or higher hurricanes because these are the only one associated with “extensive damage” in the
Saffir/Simpson Hurricane Scale (see Blake, Rappaport, Landesa, Miami, 2007). We match each state-year
hurricane event lagged 1 year by firm-year using the headquarter state.
Our expectation is that the proportion of machineries and equipments will increase in the year following a
major geo-climatic event as companies will tend to replace damaged equipments with new ones. Similarly, we
expect that leasing of new machineries and equipments will increase in the aftermaths of a strong hurricane or
major earthquake. On the contrary, we expect that the development in-house of new plants and equipments
46
might be slowed down following a major geo-climatic event because the company might have lost temporarily
the capacity to continue its development activity. It is less clear on the other hand whether and how these
events will have any significant impact on the amount of lands and buildings in the aftermaths of hurricanes
and earthquakes because land and buildings are less likely to be damaged at a point when a replacement is
necessary.
47
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52
Table 1
Summary Statistics of Firm Characteristics
The table provides summary statistics of the variables used in several regressions throughout the paper. Each variable is
defined in detail in the text. Our final sample includes 1,684 firms over the sample period 1984-1996. The resulting
unbalanced panel includes 8,963 firm-year observations.
Leverage
Tangibility: Total
Tangibility: Land
Tangibility: Buildings
Tangibility: Machineries &
Equipments
Tangibility: Capital Leases
Tangibility: Plants and Equipments
in Progress
Tangibility: Others
Market-to-Book Ratio
Firm Size
(in billions)
Profitability
Volatility
Earnings Growth
Investment Tax Credit Dummy
Net Operating Loss Carryforward
Dummy
Bond Market Access
Firm Age
Regulated Firm Dummy
Number of Observations
Mean
St. Dev.
Min
Max
0.2047
0.3565
0.0258
0.0967
0.1843
0.1743
0.1712
0.0432
0.0875
0.1212
0.0000
0.0000
0.0000
0.0000
0.0000
0.9923
0.9786
0.6653
0.9135
0.9012
0.0354
0.0120
0.0753
0.0317
0.0000
0.0000
0.6030
0.9612
0.0023
1.5925
1.3367
0.0221
1.0034
6.9670
0.0000
0.2684
0.0009
0.5742
9.9533
240.5943
0.1120
0.0888
0.0195
0.2476
0.2495
-0.1582
0.0834
-0.2955
0.4316
0.4327
2.8499
0.0011
2.9251
0.0000
0.0000
0.8364
1.9330
2.8914
1.0000
1.0000
0.1698
25.9690
0.0522
8,963
0.3755
10.3147
0.2225
8,963
0.0000
1.0000
0.0000
8,963
1.0000
46.0000
1.0000
8,963
53
Table 2
Summary Statistics of Excluded Instruments
The table provides summary statistics of the instruments that we use in the first stage of several instrumental variable regressions
throughout the paper to model how firms choose different tangible assets. Each instrument is defined in detail in the text. Our final
sample includes 1,684 firms over the sample period 1984-1996. The resulting unbalanced panel includes 8,963 firm-year observations.
Panel A: Industry Characteristics
Tangibility: Land – Industry Average
Tangibility: Buildings – Industry Average
Tangibility: Machineries & Equipments – Industry
Average
Tangibility: Capital Leases – Industry Average
Tangibility: Plants and Equipments in Progress –
Industry Average
Tangibility: Others – Industry Average
Industry Age
Mean
St. Dev.
Min
Max
0.0245
0.0947
0.1851
0.0220
0.0436
0.0854
0.0000
0.0000
0.0000
0.3913
0.4352
0.8642
0.0375
0.0111
0.0535
0.0139
0.0000
0.0000
0.3567
0.2179
0.0026
27.5826
0.0072
4.4149
0.0000
17.0000
0.1315
44.0000
5.3127
4.3464
7.5354
4.8045
0.0000
0.0000
47.5038
19.4404
0.5629
0.7969
0.0000
2.9597
4.0076
5.4273
0.0000
19.5517
5.1503
7.2336
0.0000
67.8805
6.7052
0.4983
5.2000
8.2000
0.0803
0.0475
-0.0113
0.1738
0.0488
0.0156
0.0200
0.0852
0.0104
0.0047
0.0024
0.0219
0.0671
0.0164
0.0264
0.1366
0.1802
0.0306
8,963
0.7169
0.1722
8,963
0.0000
0.0000
8,963
5.0000
1.0000
8,963
Panel B: Development of Real Estate and
Equipments Markets
Number of REITs by State
Number of Non-REIT Commercial Real Estate
Lessors by State
Number of Commercial Real Estate Developers by
State
Number of Machinery&Equipment Leasing
Companies by State
Panel C: Real Estate Supply Restrictions and
Regulations
Number of Real Estate Disposals by Federal
Government by State
Index of Economic Freedom by State
Panel D: Past Performance of Real Estate
Market and Economy Conditions
Commercial Real Estate Rate of Return by State
(previous 5 years average ending one year prior to
sample year)
Housing Rental Rate Growth by State (previous 5
years average ending one year prior to sample year)
Housing Rental Rate Growth Volatility by State
(previous 5 years ending one year prior to sample
year)
Unemployment Rate by State (previous 5 years
average ending one year prior to sample year)
Panel E: Geo-Climatic Characteristics
Number of Earthquakes
Number of Hurricanes
Number of Observations
54
Table 3
Pair-wise Correlation of Tangibility Measures
This table presents the pair-wise correlation coefficients between our different tangibility measures. *** (**; *) indicate
respectively that the correlation coefficient is statistically different from zero at the 1% (5%; 10%) level.
Land
Buildings
Machineries & Equipments
Capital Leases
Plants and Equipments in
Progress
Others
Land
Buildings
Machineries
&
Equipments
1
0.4613***
-0.0665***
0.0243**
1
-0.1157***
-0.1620***
1
-0.0265**
1
0.0143
-0.0137
0.0146
-0.0396***
0.0643***
-0.0342***
-0.0339***
-0.0130
55
Capital
Leases
Plants &
Equipments
in Progress
Others
1
-0.0046
1
Table 4
Leverage Regressions: OLS Estimation
The dependent variable is the ratio of book value of total debt to the market value of total assets. Tangibility: Total is the
ratio of the sum of net book value of land, building, machineries and equipments, capital leases, plants and equipments in
progress, and other tangible assets to book value of total assets. Both models are estimated using OLS and include year
dummies. p-values reported in parentheses are based on White heteroscedastic consistent errors adjusted for clustering
across observations of a given firm (Rogers, 1993 and White, 1980). Our final sample includes 1,684 firms over the sample
period 1984-1996. The resulting unbalanced panel includes 8,963 firm-year observations. *** (**; *) indicate respectively that
the regression coefficient is statistically different from zero at the 1% (5%; 10%) level.
Restricted Model
Tangibility: Total
0.192
(0.000)***
Tangibility: Land
Tangibility: Buildings
Tangibility: Machineries & Equipments
Tangibility: Capital Leases
Tangibility: Plants and Equipments in Progress
Tangibility: Others
Market-to-Book Ratio
-0.063
(0.000)***
-0.008
(0.001)***
-0.137
(0.000)***
-0.174
(0.000)***
0.017
(0.043)**
-0.024
(0.000)***
0.058
(0.000)***
0.070
(0.000)***
-0.012
(0.098)*
0.063
(0.003)***
0.324
(0.000)***
8,963
0.271
Ln of Firm Size
Profitability
Volatility
Earnings Growth
Investment Tax Credit Dummy
Net Operating Loss Carryforward Dummy
Bond Market Access
Ln (1+Firm Age)
Regulated Firm Dummy
Constant
Number of Observations
R2
56
Unrestricted Model
0.178
(0.129)
0.239
(0.000)***
0.173
(0.000)***
0.219
(0.000)***
-0.029
(0.763)
0.300
(0.001)***
-0.062
(0.000)***
-0.007
(0.003)***
-0.138
(0.000)***
-0.173
(0.000)***
0.016
(0.052)*
-0.024
(0.000)***
0.057
(0.000)***
0.069
(0.000)***
-0.012
(0.089)*
0.069
(0.006)***
0.321
(0.000)***
8,963
0.2739
Table 5
Leverage Regressions: Fixed-Effects Estimation
The dependent variable is the ratio of book value of total debt to the market value of total assets. Tangibility: Total is the
ratio of the sum of net book value of land, building, machineries and equipments, capital leases, plants and equipments in
progress, and other tangible assets to book value of total assets. To control for firm fixed-effects, each variable is demeaned
by the firm-specific means. Both models also include year dummies. p-values reported in parentheses are based on White
heteroscedastic consistent errors adjusted for clustering across observations of a given firm (Rogers, 1993 and White, 1980).
Our final sample includes 1,684 firms over the sample period 1984-1996. The resulting unbalanced panel includes 8,963
firm-year observations. *** (**; *) indicate respectively that the regression coefficient is statistically different from zero at the
1% (5%; 10%) level. The reported R2 does not include the effect of the firm dummies.
Restricted Model
Tangibility: Total
Unrestricted Model
0.218
(0.000)***
Tangibility: Land
Tangibility: Buildings
Tangibility: Machineries & Equipments
Tangibility: Capital Leases
Tangibility: Plants and Equipments in Progress
Tangibility: Others
Market-to-Book Ratio
-0.0458
(0.000)***
0.0048
(0.489)
-0.122
(0.000)***
-0.0362
(0.555)
0.0182
(0.002)***
-0.018
(0.000)***
0.036
(0.000)***
0.050
(0.000)***
0.055
(0.035)**
n.a.
0.005
(0.955)
8,963
0.230
Ln of Firm Size
Profitability
Volatility
Earnings Growth
Investment Tax Credit Dummy
Net Operating Loss Carryforward Dummy
Bond Market Access
Ln (1+Firm Age)
Regulated Firm Dummy
Constant
Number of Observations
R2 (within)
57
0.401
(0.004)***
0.286
(0.000)***
0.147
(0.000)***
0.231
(0.001)***
0.1727
(0.012)**
0.209
(0.380)
-0.045
(0.000)***
0.0047
(0.462)
-0.125
(0.000)***
-0.033
(0.597)
0.018
(0.003)***
-0.018
(0.000)***
0.036
(0.000)***
0.049
(0.000)***
0.050
(0.053)*
n.a.
0.020
(0.806)
8,963
0.233
Table 6
Determinants of Tangible Assets Ownership Structure: First Stage of IV (2SLS) Regressions
This table reports the first stage instrumental variable regression estimates of seven different regression models where the dependent variables are
respectively Tangibility: Total, Land, Buildings, Machineries & Equipments, Capital Leases, Plants & Equipments in Progress, and Others. We use as
instruments for how firms choose different tangible assets five different sets of variables, which control for industry characteristics – Panel A –
development of real estate and equipments markets – Panel B – real estate supply restrictions and regulations – Panel C – performance of real estate market
and economy conditions – Panel D – and geo-climatic characteristics of a firm headquarter state. We only tabulate coefficients on excluded instruments in
the interest of space. To control for firm fixed-effects, each variable is demeaned by the firm-specific means. All models also include year dummies. pvalues reported in parentheses are based on White heteroscedastic consistent errors adjusted for clustering across observations of a given firm (Rogers,
1993 and White, 1980). Our final sample includes 1,684 firms over the sample period 1984-1996. The resulting unbalanced panel includes 8,963 firm-year
observations. *** (**; *) indicate respectively that the regression coefficient is statistically different from zero at the 1% (5%; 10%) level. The reported R2’s
do not include the effect of the firm dummies.
Tangibility:
Total
Tangibility:
Land
Tangibility:
Buildings
Tangibility:
Machineries &
Equipments
Tangibility:
Capital Leases
Tangibility:
Plants &
Equipments
in Progress
Tangibility:
Others
0.6309
(0.027)**
0.4579
(0.000)***
0.5282
(0.000)***
0.4432
(0.000)***
0.5012
(0.000)***
-0.0017
(0.994)
0.1207
(0.318)
0.6798
(0.021)**
-0.0361
(0.417)
-0.0114
(0.479)
-0.0001
(0.997)
-0.0466
(0.314)
-0.0245
(0.766)
0.0098
(0.648)
0.1108
(0.177)
0.4046
(0.000)***
0.0327
(0.076)*
-0.0069
(0.853)
-0.2016
(0.147)
-0.0749
(0.503)
0.1404
(0.055)*
-0.1548
(0.119)
0.1116
(0.003)***
0.4778
(0.000)***
0.0340
(0.558)
-0.1762
(0.037)**
-0.0725
(0.636)
-0.0506
(0.522)
-0.0229
(0.663)
0.0291
(0.131)
0.0240
(0.029)**
0.4602
(0.000)***
0.0694
(0.212)
-0.1019
(0.295)
-0.0198
(0.653)
0.0318
(0.507)
-0.0508
(0.184)
0.0033
(0.728)
-0.0360
(0.081)*
0.8593
(0.000)***
0.0101
(0.829)
0.0240
(0.460)
-0.0137
(0.536)
-0.0005
(0.955)
0.0017
(0.705)
-0.0079
(0.189)
-0.0031
(0.730)
0.2621
(0.002)***
0.0169
(0.103)
-0.0184
(0.000)***
0.0009
(0.930)
-0.0226
(0.087)*
0.0013
(0.839)
-0.0018
(0.034)**
0.0006
(0.763)
-0.0044
(0.157)
0.0022
(0.095)*
-0.0031
(0.199)
0.0013
(0.803)
-0.0143
(0.053)*
0.0017
(0.661)
-0.0084
(0.010)***
-0.0029
(0.670)
-0.0046
(0.571)
-0.0051
(0.238)
-0.0029
(0.114)
0.0032
(0.496)
0.0029
(0.528)
-0.0028
(0.319)
-0.0024
(0.056)*
-0.0019
(0.515)
-0.0037
(0.204)
0.0050
(0.003)***
0.0001
(0.718)
0.0007
(0.433)
0.0015
(0.318)
0.0003
(0.574)
0.0018
(0.284)
-0.1614
(0.081)*
0.0003
(0.415)
-0.0119
(0.576)
-0.0017
(0.050)**
-0.0366
(0.449)
0.0023
(0.006)*
-0.0091
(0.890)
0.0012
(0.044)**
-0.0526
(0.139)
-0.0003
(0.567)
-0.0467
(0.094)*
0.0001
(0.431)
-0.0045
(0.731)
-0.0346
(0.754)
0.0102
(0.737)
-0.0293
(0.619)
0.1150
(0.117)
-0.0624
(0.115)
-0.0630
(0.099)*
-0.0051
(0.663)
0.2214
(0.440)
-0.5840
(0.198)
-0.0669
(0.360)
0.0566
(0.673)
0.2769
(0.083)*
0.1573
(0.480)
-0.0984
(0.595)
-0.7971
(0.014)**
0.0802
(0.459)
-0.1528
(0.310)
0.0046
(0.959)
0.1493
(0.196)
0.0250
(0.395)
0.0027
(0.959)
-0.3095
(0.301)
-0.0112
(0.883)
0.1243
(0.447)
-0.0576
(0.772)
-0.1484
(0.204)
-0.2158
(0.046)**
-0.0009
(0.983)
0.0041
(0.001)***
-0.0070
(0.434)
8,963
0.0003
(0.170)
-0.0001
(0.971)
8,963
0.0008
(0.253)
0.0055
(0.157)
8,963
0.0031
(0.000)***
-0.0047
(0.436)
8,963
0.0009
(0.056)*
-0.0043
(0.102)
8,963
-0.0009
(0.059)*
-0.0042
(0.052)*
8,963
-0.0002
(0.190)
0.0009
(0.439)
8,963
Panel A: Industry Characteristics
Tangibility: Land – Industry Average
Tangibility: Buildings – Industry Average
Tangibility: Machineries & Equipments – Industry
Average
Tangibility: Capital Leases – Industry Average
Tangibility: Plants & Equipments in Progress –
Industry Average
Tangibility: Others – Industry Average
Ln (1+Industry Age)
Panel B: Development of Real Estate and
Equipments Markets
Ln(1+Number of REITs by State)
Ln(1+Number of Non-REIT Commercial Real
Estate Lessors by State)
Ln(1+Number of Commercial Real Estate
Developers by State)
Ln(1+Number of Machinery&Equipment Leasing
Companies by State)
Panel C: Real Estate Supply Restrictions and
Regulations
Ln(1+Number of Real Estate Disposals by Federal
Government by State)
Ln(1+Index of Economic Freedom by State)
Panel D: Past Performance of Real Estate
Market and Economy Conditions
Commercial Real Estate Rate of Return by State
(previous 5 years average ending one year prior to
sample year)
Housing Rental Rate Growth by State (previous 5
years average ending one year prior to sample year)
Housing Rental Rate Growth Volatility by State
(previous 5 years ending one year prior to sample
year)
Unemployment Rate by State (previous 5 years
average ending one year prior to sample year)
Panel E: Geo-Climatic Characteristics
Ln(1+Lagged Num. Earthquakes)
Ln(1+Lagged Num. Hurricanes)
Number of Observations
R2
F-statistic (excluded instruments are jointly 0)
Shea’s Partial R2 (excluded instruments)
0.1128
0.1558
0.0863
0.1190
0.0764
0.1218
0.0357
10.55***
3.68***
5.79***
7.73***
4.02***
2.94***
1.36
0.091
0.1281
0.0553
0.0844
0.0607
0.1042
0.028
58
Table 7
Leverage Regressions: Second Stage of IV (2SLS) Regressions
This table reports the second stage of the instrumental variable regression models for leverage. The dependent variable is
the ratio of book value of total debt to the market value of total assets. Tangibility: Total is the predicted value from the first
stage regression reported in Table 6. Similarly, Tangibility: Land, Buildings, Machineries & Equipments, Capital Leases,
Plants & Equipments in Progress, and Others are the predicted values from the corresponding first stage regressions
reported in Table 6. To control for firm fixed-effects, each variable is demeaned by the firm-specific means. Both models
also include year dummies. p-values reported in parentheses are based on White heteroscedastic consistent errors adjusted
for clustering across observations of a given firm (Rogers, 1993 and White, 1980). Our final sample includes 1,684 firms
over the sample period 1984-1996. The resulting unbalanced panel includes 8,963 firm-year observations. *** (**; *) indicate
respectively that the regression coefficient is statistically different from zero at the 1% (5%; 10%) level. The reported R2’s do
not include the effect of the firm dummies.
Restricted Model
Tangibility: Total
0.2722
(0.000)**
Tangibility: Land
Tangibility: Buildings
Tangibility: Machineries & Equipments
Tangibility: Capital Leases
Tangibility: Plants & Equipments in
Progress
Tangibility: Others
Market-to-Book Ratio
Ln of Firm Size
Profitability
Volatility
Earnings Growth
Investment Tax Credit Dummy
Net Operating Loss Carryforward Dummy
Bond Market Access
Ln (1+Firm Age)
Regulated Firm Dummy
Number of Observations
R2
Hansen J-statistic for overidentification
Unrestricted Model
-0.0449
(0.000)***
0.0037
(0.526)
-0.1171
(0.000)***
-0.0323
(0.600)
0.0182
(0.002)***
-0.0181
(0.000)***
0.0363
(0.000)***
0.0503
(0.000)***
0.0534
(0.040)**
N.A.
8,963
0.2284
22.533
59
0.577
(0.021)**
0.499
(0.002)***
0.175
(0.064)*
0.081
(0.690)
0.383
(0.017)**
0.763
(0.458)
-0.043
(0.000)***
0.003
(0.661)
-0.119
(0.000)***
-0.028
(0.654)
0.019
(0.002)***
-0.019
(0.000)***
0.035
(0.000)***
0.048
(0.000)***
0.042
(0.104)
N.A.
8,963
0.2193
14.820
Table 8
Leverage Regressions: Second Stage of IV (2SLS) Regressions for Financially Constrained and Financially Unconstrained
Firms
This table reports the second stage of the instrumental variable regression models for leverage for different sub-groups of financially
constrained firms (e.g., firms in the bottom 3 size deciles, firms without bond market access and firms in the bottom 3 payout deciles) and
for financially unconstrained firms (e.g., firms in the top 3 size deciles, firms with bond market access and firms in the top 3 payout
deciles) as detailed defined in the text. The dependent variable is the ratio of book value of total debt to the market value of total assets.
Tangibility: Land, Buildings, Machineries & Equipments, Capital Leases, Plants & Equipments in Progress, and Others are the predicted
values from the corresponding first stage regressions (not tabulated) estimated for each sub-sample of financially constrained and
financially unconstrained firms using the same set of instruments used in Table 6 for the entire sample. To control for firm fixed-effects,
each variable is demeaned by the firm-specific means. Both models also include year dummies. p-values reported in parentheses are based
on White heteroscedastic consistent errors adjusted for clustering across observations of a given firm (Rogers, 1993 and White, 1980). ***
(**; *) indicate respectively that the regression coefficient is statistically different from zero at the 1% (5%; 10%) level. The reported R2’s
do not include the effect of the firm dummies.
Tangibility: Land
Tangibility: Buildings
Tangibility: Machineries &
Equipments
Tangibility: Capital Leases
Tangibility: Plants &
Equipments in Progress
Tangibility: Others
Market-to-Book Ratio
Ln of Firm Size
Profitability
Volatility
Earnings Growth
Investment Tax Credit
Dummy
Net Operating Loss
Carryforward Dummy
Bond Market Access
Ln (1+Firm Age)
Number of Observations
R2
Hansen J-statistic for
overidentification
Bottom 3
Deciles of Size
Top 3 Deciles
of Size
Bond Market
Access: Not
Bond Market
Access: Yes
Bottom 3
Deciles of
Payout
Top 3
Deciles of
Payout
0.8203
(0.000)***
0.4496
(0.063)*
0.2773
(0.107)
-0.0915
(0.834)
0.8944
(0.436)
1.1880
(0.234)
-0.0369
(0.000)***
-0.0224
(0.123)
-0.0489
(0.036)**
-0.1227
(0.023)**
0.0370
(0.000)***
-0.0217
(0.051)*
0.0453
(0.000)***
N.A.
1.5905
(0.249)
-0.6458
(0.359)
0.1418
(0.540)
-0.5086
(0.452)
-0.8419
(0.032)**
-6.3620
(0.075)*
-0.0272
(0.001)***
0.0015
(0.922)
-0.3881
(0.000)***
-0.0697
(0.641)
-0.0293
(0.322)
-0.0209
(0.022)**
-0.0052
(0.662)
0.0318
(0.001)***
-0.1665
(0.002)***
1,946
0.0643
12.684
0.6111
(0.024)**
0.6119
(0.001)***
0.1935
(0.081)*
0.0367
(0.889)
0.4147
(0.014)**
1.2877
(0.233)
-0.0375
(0.000)***
0.0007
(0.913)
-0.0959
(0.000)***
-0.0414
(0.514)
0.0207
(0.003)***
-0.0180
(0.001)***
0.0370
(0.000)***
N.A.
-0.8891
(0.669)
0.4770
(0.570)
-0.0811
(0.781)
-0.0867
(0.902)
-1.2154
(0.217)
-2.4003
(0.412)
-0.0545
(0.004)***
0.0211
(0.242)
-0.3977
(0.001)***
-0.0235
(0.929)
0.0354
(0.266)
-0.0142
(0.114)
0.0081
(0.542)
N.A.
0.0638
(0.032)**
6,898
0.1989
12.542
0.0574
(0.568)
1,063
0.1376
6.912
-0.0853
(0.905)
0.6546
(0.038)**
0.3137
(0.013)**
0.0680
(0.817)
0.9766
(0.019)**
1.7394
(0.173)
-0.0422
(0.000)***
-0.0076
(0.493)
-0.0218
(0.363)
-0.0990
(0.227)
0.0307
(0.000)***
-0.0300
(0.016)**
0.0322
(0.002)***
0.0907
(0.008)***
0.0758
(0.111)
2,219
0.2028
17.707
-0.0329
(0.984)
-0.1302
(0.752)
0.0055
(0.987)
0.5949
(0.676)
-0.0503
(0.920)
9.8410
(0.533)
-0.0296
(0.003)***
0.0304
(0.151)
-0.4188
(0.000)***
-0.0336
(0.830)
-0.0195
(0.728)
-0.0125
(0.238)
-0.0034
(0.805)
-0.0075
(0.710)
0.1080
(0.318)
1,039
0.1901
11.983
0.0707
(0.173)
2,057
0.2603
11.891
60