How Does Government Debt Affect Corporate Financial and
Investment Policies? *
John R. Graham
Fuqua School of Business, Duke University and NBER
Mark T. Leary
Olin School of Business, Washington University
Michael R. Roberts
The Wharton School, University of Pennsylvania and NBER
Preliminary Draft
First Draft: September 23, 2011
Current Draft: March 15, 2014
*
We thank Andy Abel, Effi Benmelech, Joao Gomes, Boyan Jovanovich, Stefan Nagel, Josh Rauh, Ken Singleton,
Amir Sufi, and Jules Van Binsbergen; seminar participants at Duke University, London Business School, London
School of Economics, Miami University, Notre Dame, MIT, Oklahoma University, Stanford University, University
of British Columbia, University of Chicago, University of Colorado, University of Illinois, University of
Pennsylvania, University of Utah, Vanderbilt University, Yale University; and conference participants at the SITE
for helpful comments. We also thank Ahn Dong and Jeremy Yoo for research assistance. Roberts gratefully
acknowledges financial support from the Jacobs Levy Equity Management Center for Quantitative Financial
Research. A portion of this work was completed while Roberts was visiting the Graduate School of Business at
Stanford University, to whom he is grateful for their hospitality. Graham: (919) 660-7857, john.graham@duke.edu,
Leary: (314) 935-6394, leary@wustl.edu, Roberts: (215) 573-9780, mrrobert@wharton.upenn.edu.
How Does Government Debt Affect Corporate Financial and
Investment Policies?
Abstract:
Using a novel dataset of accounting and market information that spans most publicly traded
firms over the last century, we find an economically large and robust negative relation between
government debt and corporate debt and investment. An additional dollar of treasury debt is
associated with a $0.04 reduction in corporate debt issuances, a $0.05 increase in cash
accumulation, no significant change in corporate equity issuances, and a $0.09 reduction in
corporate investment. These relations are more pronounced in larger, less risky firms whose debt
is a closer substitute for treasuries. The channel through which this effect operates is financial
intermediaries, whose balance sheets reveal a substitution between lending to the federal
government and lending to the corporate sector. The relations between government debt and
corporate policies, as well as the substitution between government and corporate lending by
intermediaries, are stronger after 1970 when increasing competition from foreign investors
increased the demand and price for treasuries.
There is mounting evidence suggesting that government debt impacts the corporate
sector. Theoretically, fluctuations in the supply of government debt can impact investor demand
for alternative securities, the relative prices of those securities, and, consequently, firms’
incentives to issue different securities (e.g., Friedman (1978). The presence of market
imperfections, such as information frictions, can in turn link variation in financing decisions to
variation in investment decisions (Modigliani and Miller (1958)). Empirically, recent evidence
has shown that variation in the supply of treasuries influences corporate bond spreads
(Krishnamurthy and Vissing-Jorgensen (2013)), bond returns (Greenwood, Hansen, and Stein
(2013) and Greenwood and Vayanos (2013)), the maturity structure of corporate debt
(Greenwood, Hansen, and Stein (2013) and James and Badoer (2013), and corporate leverage
ratios (Graham, Leary, and Roberts (2014)).
We build on this evidence by investigating the impact of government debt on corporate
capital structure and investment policy, as well as the channels linking government to the
corporate sector. To do so, we use a unique, comprehensive dataset containing accounting and
market information for U.S. nonfinancial publicly traded firms over the last century. Our data
enable us to exploit cross-sectional variation at both the sector and firm-level, which we show is
critical for uncovering and understanding the link between government financing and corporate
behavior.
Our results show a significant and robust negative relation to between government debt
and corporate debt for unregulated firms, i.e., firms outside the transportation and utility
industries. Specifically, we find that a one-dollar increase in treasuries is associated with a fourcent decrease in corporate debt. When we examine debt net of cash and liquid assets, the
magnitude of the relation more than doubles to over eight cents reflecting the buildup of liquid
assets during periods of large government debt accumulation.
These findings are robust, found in both level and first difference specifications and after
controlling for known capital structure determinants, such as profitability, asset tangibility, the
market-to-book ratio and tax incentives. The relation also persists after controlling for
government expenditures and other macroeconomic factor, such as inflation, the level of interest
rates, bond yield spreads, and real GDP growth. Subsample analysis reveals that the negative
relation between government and corporate debt policies is present throughout the last 100 years
and is not solely a consequence of the large government deficit of World War II or fiscal policy
during recessions.
In contrast, we find no robust relation between government debt policy and corporate
equity policy. Likewise, we find no significant relation between government and corporate debt
policies for regulated firms, whose leverage is largely flat over the last century and whose asset
size has shrunk significantly over the last 100 years relative to unregulated firms (Graham,
Leary, and Roberts (2013)).
Turning to investment, we find a significant and negative association between
government net debt issuances and corporate capital expenditures. An additional dollar of
treasury debt is associated with a nine-cent decline in investment, consistent with the
approximate nine-cent decline in net debt noted above. Like leverage and debt policy, the
investment relation is robust to a host of controls including government expenditures and
macroeconomic factors, as well as firm characteristics such as the market-to-book ratio,
profitability, cash holdings, and lagged investment. The investment relation is also larger in
magnitude after 1968, though the estimate is statistically insignificant.
While these findings are consistent with government debt policy influencing corporate
behavior, they cannot rule out the possibility that variation in government debt policy is instead
capturing latent or mismeasured corporate investment opportunities. An alternative interpretation
of our findings is that the government issues debt in bad economic times during which
investment opportunities and the demand for credit are low. The observable control variables and
differential response of corporate debt and equity policies narrow the scope for this alternative
but they do not eliminate it. In an effort to address this ambiguity and shed further light on
precisely how government debt policy is influencing corporate behavior, we examine crosssectional and longitudinal variation in the sensitivity of corporate policies to government debt,
and the channels linking government and corporate financial polices.
We find that larger, less risky, and less financially constrained firms’ debt, cash, and
investment policies are on average more sensitive to variation in government debt. It is these
firms for which corporate debt is a relatively close substitute for treasuries. It is also these firms
whose financial and investment policies should be less sensitive to variation in the business
cycle, suggesting that our conditioning variables are eliminating much of this confounding
variation form the government debt measure.
2
We also find that the asset portfolios of financial intermediaries responsible for a
significant portion of corporate lending – commercial banks, insurance companies, and public
pension funds – display a strong sensitivity to government debt policy. Increases in government
debt are met with an increase in treasury holdings and a reduction in corporate loans, bonds, and
commercial paper by all of these intermediaries. Thus, intermediaries substitute lending to the
public and private sector.1
Finally, we investigate the impact on our results of the increasing foreign holdings of
domestic debt that began in 1970. We find that the magnitude of the relations between
government debt and corporate policies are larger after 1969 than before. For example, a $1
increase in treasuries pre-1969 is associated with a $0.03 reduction in corporate debt, $0.04
increase in cash, and $0.08 reduction in investment. After 1968, the estimates for debt and
investment increase to $0.09 and $0.14, though the latter estimate is statistically noisy. We also
find a significant negative coefficient in our leverage regressions on the interaction between
government debt and foreign holdings, implying that increases in foreign holdings amplify the
negative association between corporate debt and government debt.
These results suggest that rather than relaxing a lending constraint on domestic
intermediaries, the increase in foreign holdings of US treasuries can be viewed as an increase in
competition for safe assets. This interpretation is consistent with recent evidence in
Krishnamurthy and Vissing-Jorgensen (2013) showing that the convenience yield on treasuries
increased during the 20th century leading to higher prices for treasuries. In effect, financial
intermediaries had to spend more to satisfy their demand for safe assets, which exacerbated their
substitution between government and corporate lending.
As mentioned above, our study is related to a growing literature investigating the impact
of government debt on the corporate sector. However, our study differs in several important
ways. First, we focus on quantities. Previous studies by Krishnamurthy and Vissing-Jorgensen
(2013), and Greenwood and Vayanos (2013a, 2013b) emphasize the implications for prices.
Second, we emphasize the implications for the debt-equity margin. Previous studies by
Greenwood, Hansen, and Stein (2010) and Badoer and James (2013) focus on the maturity
implications. Though not the focus of their study, Graham, Leary, and Roberts (2013) also
1
We also find a negative relation between government debt policy and intermediaries holdings of agency debt,
mortgages, and consumer credit but no relation to corporate equity, the majority of which is held by mutual funds.
3
document a negative correlation between corporate leverage and government debt. Our financial
policy results build on their work to quantify the effect, address the issue of government
spending, and highlight the heterogeneity of the effect in the cross-section of firms. Third, we
provide evidence on the implications for corporate assets, namely cash and investment. Finally,
we provide evidence on the channel through which these relations occur.
More broadly, our study is related to a large macroeconomic literature investigating
crowding out (e.g., Elmendorf and Mankiw (1998) and Hubbard (200?). This literature has
focused largely on the relation between interest rates and government debt. Our results are
suggestive of a crowding out effect. However, our focus on unregulated firms precludes us from
dismissing the possibility that the decline in investment is associated with a reallocation to other
firms in the economy.
The remainder of the paper proceeds as follows. Section 1 discusses our data and presents
summary statistics. Section II discusses the theoretical motivation for why government debt
might affect the corporate sector. We also outline our empirical framework and highlight the
identification challenges. Section III presents our results relating government debt to corporate
financial and investment policy. Section IV investigates the role of financial intermediaries in
transmitting variation in government debt onto corporate financial policy. Section V investigates
the implications of integrating credit markets for our findings. Section VI discusses the potential
interpretations of our findings. Section VII concludes.
I.
Data and Summary Statistics
Our primary data is that used in Graham, Leary, and Roberts (2013). The sample begins
with all firms listed in the Center for Research in Security Prices (CRSP) monthly stock files.
This sample includes all firms listed on the New York Stock Exchange (NYSE) since 1925, all
firms listed on the American Stock Exchange (AMEX) since 1962, and all firms listed on the
NASDAQ since 1972. For these firms, stock market data comes from CRSP. Accounting data is
obtained from two sources: Standard and Poor’s (S&P) Compustat database and data handcollected from Moody’s Industrial and Railroad manuals. We exclude financial firms from all of
our analysis. The end result is an unbalanced firm-year panel beginning in 1920 and ending in
2012.
4
As in Graham, Leary and Roberts (2013), we distinguish between two sectors of the
economy that we loosely refer to as regulated (utilities, railroads, and telecommunications) and
unregulated (all other nonfinancial industries) because of different institutional environments.
We recognize that regulatory status is dynamic, heterogeneous, and extends beyond our
classification (e.g., airlines). Thus, we emphasize that these are merely labels to identify a
division in our data that acknowledges the different mechanisms determining financial policy
across these sectors, and that is consistent with previous research on corporate financial policy
(e.g., Graham and Leary (2010)). For the most part, we focus our attention on the unregulated
sector but examine the regulated sector in robustness tests. Figure 1 highlights the relative size of
these two sectors, in terms of total assets, during our sample period.
We supplement this data with a number of macroeconomic time-series. Together, the
corporate data and macroeconomic data form two samples. The first is an annual time-series
containing aggregate corporate measures and macroeconomic factors. The aggregate corporate
measures are constructed by summing across firms each year. For ratios, we sum separately the
numerator and denominator before taking the ratio. The second sample is a firm-year panel also
containing information on firm balance sheets and income statements. Specific details regarding
data sources and variable construction are described in Appendix A.
Table 1 presents summary statistics for these samples. Panel A examines the aggregate
time-series, Panel B the panel data. We present results for the primary measures used throughout
our analysis and which are defined in Appendix A. However, in our analysis below we
investigate a host of alternative measures to ensure the robustness of our findings. Focusing on
panel A we see that most corporate series are highly persistent. Aggregate leverage, defined as
the ratio of total interest bearing debt to the book value of total assets, is quite volatile with an
annual standard deviation of just under 7%. Measuring debt net of liquid assets amplifies this
volatility. On average, firms are net security issuers, where we measure net issuance as a fraction
of assets as of the start of the period. The different magnitudes between debt and equity reflect
differences in firm selection: smaller firms tend to issue equity. In aggregate, debt is the primary
source of external financing (Gorton and Winton (2003)).
Like corporate financial policy, fiscal policy is quite volatile. We define government
leverage as the ratio of federal debt help by the public to total assets of the unregulated sector to
maintain consistency with our measures of leverage and investment. This measure of federal debt
5
includes holdings by the Federal Reserve, but excludes intergovernmental holdings such as those
by the Social Security Administration. We investigate alternative definitions below. The flow of
government credit is measured by the change in federal debt from year t-1 to year t divided by
assets in year t-1. We focus on federal debt because it represents majority of total government
debt, and is responsible for most of its variation over time.2 Finally, government expenditures are
the flow of federal expenditures during the year scaled by assets as of the start of the year. In
general, we normalize stock variables by contemporaneous assets, and flow variables by lagged
assets.
Panel B presents similar statistics for the firm-year panel sample. We winsorize each ratio
at the upper and lower one percentiles to address possible data-coding errors and mitigate the
influence of outliers on our results. These statistics provide a useful benchmark for comparison
with more recent studies of capital structure (e.g., Frank and Goyal (2005), Lemmon, Roberts,
and Zender (2008)).
II.
Theoretical Motivation and Identification Challenge
This section discusses the theoretical motivation for our study. To synthesize the
motivating theory, we follow closely Taggart (1985) and refer interested readers to his and other
papers for further details.3 Taggart begins with a three-sector economy consisting of households,
nonfinancial corporations and financial institutions. Financial assets are in net zero supply so that
the economy-wide balance sheet consists of tangible assets and household net worth. The role of
the financial system is to reconcile the return stream generated by tangible assets with the
planned consumption path of the household sector. That is, the securities issued by corporations
and the services provided by financial intermediaries are designed to transform the timing and
certainty of the cash flow streams generated by the economy’s physical assets to meet household
demands. Thus, aggregate capital structure is determined by households’ demand for asset
characteristics, the corporate sector’s financial transformation technology, and competition over
transformation services among the sectors.
2
See Figure 1 in Appendix A of Graham, Leary, and Roberts (2014).
Taggart (1985) extends the aggregate model of Miller (1977). For other theories of aggregate corporate capital
structure, see McDonald (1983) and Benninga and Talmor (1988).
3
6
Figure 2 presents two figures from Taggart (1985). On the horizontal axis of each figure
is the aggregate quantity of corporate debt (B), on the vertical axis the risk-adjusted return on
debt (r*D) and equity (r*E). The marginal corporate tax rate is denoted by tC. Using returns on the
y-axis instead of prices implies that the slopes of the supply and demand curves will be reversed.
Investment is held fixed so that movements along the horizontal axis correspond to substitutions
between debt and equity.
In equilibrium, aggregate leverage will depend on the interaction of corporations’
willingness to supply debt, and investor demands to hold debt at different yields. The elasticities
of these supply and demand curves reflect the willingness of firms and investors, respectively, to
substitute between debt and equity securities. Panel A presents the aggregate supply and demand
curves under the perfect markets assumptions of Modigliani and Miller (1958). These
assumptions imply that both supply and demand curves are infinitely elastic, as firms and
investors can costlessly transform streams of cash flows.4 Thus, investors are unwilling to accept
any yield differential between debt and equity and corporate capital structure is indeterminate.
The presence of market frictions can alter both the level and slope of the supply curve.
Specifically, the tax deductibility of interest expenses shift up the supply curve by reducing the
cost of debt for firms, while agency (Jensen and Meckling (1976)) and bankruptcy (Robichek
and Myers (1965)) costs reduce the elasticity of the supply curve. Further, because the tax shield
is based on nominal interest payments, an increase in inflation is expected to increase firms’
desire to supply debt and lead to higher equilibrium leverage.
Frictions affecting investors’ abilities to transform cash flows impact the demand curve.
Miller (1977) introduces personal taxes and restrictions on tax arbitrage generate an upward
sloping demand curve in which investors are arrayed along the demand curve in order of
ascending tax rates. Greenwood, Hansen, and Stein (2010) rely on heterogeneous preferences for
different corporate securities in combination with limited arbitrage capital to generate an upward
sloping demand curve.5 Finally, Krishnamurthy and Vissing-Jorgensen (2013) assume that
treasury securities embed a “convenience” feature comprised of high liquidity and safety that
4
Demand would still be perfectly elastic if households could not costlessly perform transformation services but
instead financial intermediaries could.
5
Greenwood, Hansen, and Stein (2010) focus on short-term versus long-term debt; however, these notions can be
replaced with debt and equity.
7
cannot be replicated by investors. In conjunction with a decreasing marginal benefit of
convenience, their assumptions also generate an upward sloping demand curve.
Government debt operates as a substitute for corporate debt in investor’s portfolios.
Increases in government debt shift the demand curve inward, leading to increases in the cost of
debt and a reduction in the equilibrium quantity of debt. Indeed, this is what Greenwood,
Hansen, and Stein (2010) and Greenwood and Vayanos (2013) find; increases in the supply of
treasuries increase the cost of debt relative to other securities.
The implications for investment from these theories follow almost immediately from
Modigliani and Miller (1958). Each of the above theories are predicated on violations of the
irrelevance proposition. Thus, there is a role for financial policy to impact investment policy and
firm value.
II.A Empirical Strategy and Identification Challenges
The discussion above motivates our empirical approach and highlights the identification
challenges. We estimate a reduced-form asset demand equations for corporate debt (and equity):
(1)
QtC
QG
= α + β t + ΓX t + ε t .
At
At
where QtC is the quantity of the corporate debt, QtG is the quantity of government debt, At is the
book value of total assets, and Xt is a vector of control variables, and ε t is an idiosyncratic error
term that is potentially serially correlated. All test-statistics are estimated using Newey-West
standard errors with two lags to address serial correlation in the errors. To address spurious
regression concerns (Granger and Newbold (1956)), we incorporate a linear trend in all levels
specifications and estimate equation (1) in first difference form. Finally, we normalize all
quantities by the book value of assets to address scale differences, though we investigate the
effect of alternative normalizations in our robustness tests.
We address confounding forces with a set of observable, contemporaneous controls
motivated by the theoretical discussion above and existing empirical evidence. We address the
issue of government demand by including a measure of federal expenditures. This ensures that
any identifying variation is driven by the government’s choice of financing – debt versus tax
8
raises.
We include the market-to-book ratio and real GDP growth to capture corporate
investment opportunities. The yield spread between a BAA-rated corporate bond and the tenyear treasury bond is a proxy for the cost of corporate debt, i.e., expected returns. The return on a
three-month treasury bill captures the level of interest rates and the financing and investment
environment. Inflation is included because all corporate debt is in nominal terms. We also
include the return on assets, EBIT / Assets, and a measure of asset intangibility – two important
empirical determinants in the capital structure literature.6
Our investment specification follows closely that found in Philippon (2009):
G
⎛ QtG − Qt−1
⎞
I tC
=α +β⎜
(2)
⎟ + ΓX t + ε t .
At−1
⎝ At−1 ⎠
where I tC is corporate investment and QtG , At, Xt and ε t are as defined in equation (1). We address
serial correlation in ε t and spurious regression concerns in a manner identical to that proposed
for equation (1). We normalize investment and the net flow of government debt by the book
value of total assets as of the start of the period, t-1.
The control variables include a proxy for marginal q, the market-to-book ratio. We also
include the return on assets, the one period lagged stock of cash and liquid assets, and lagged
investment as additional control variables identified by existing work as being relevant for
corporate investment (e.g., Fazarri, Hubbard, and Petersen (1988), Eberly, Rebello ??). As in our
debt specification, we include several additional macroeconomic factors to address concerns
about mismeasured investment opportunities and a shifting supply of corporate debt.
Specifically, we include the return on three-month treasury bills, the BAA-ten-year treasury yield
spread, inflation, and federal government expenditures. All flow variables are contemporaneous
with investment, stock variables and interest rates are lagged one period.
The discussion of the previous section highlights the empirical challenges in estimating
equation (1) consistently. In particular, the ideal experiment would randomly shock the supply of
treasuries, holding fixed the supply of corporate debt, the investment opportunities of the
corporate sector, and the investment demand of the government. Lacking such a shock any
correlation between government debt and corporate behavior may still be consistent with
Modigliani and Miller’s (1958) irrelevance theorem. For example, a negative relation between
6
Many studies (e.g., Rajan and Zingales (1995) and Frank and Goyal (2008)) show that these two firm
characteristics are robust determinants of corporate capital structure.
9
government debt and corporate debt (and investment) could reflect the econometrician’s inability
to adequately hold fixed corporate investment opportunities. In this interpretation, the
government issues debt in bad times when investment opportunities are poor and firms’ demands
for credit is low because their desire to invest is low. In other words, variation in QtG may capture
variation in latent or mismeasured investment opportunities.
Our empirical strategy is twofold. The first approach is to control for confounding
variation by way of observables. Recognizing the limitations of this approach, we also exploit
heterogeneity in the estimated effect. In particular, we allow the parameter of interest from
equations (1) and (2), β , to vary over time with observables. We will also exploit the crosssectional variation of our data by estimating panel versions of equations (1) and (2) in which β
can vary cross-sectionally. While none of these empirical strategies can provide a level of
confidence in our inferences equal to that provided by a clear source of exogenous variation in
QtG , in concert they can provide strongly suggestive evidence that limits the scope for alternative
interpretations.
III.
The Relation Between Government Debt and Corporate Policies
IV.A Corporate Financial Policy
Figure 3 illustrates the relation between government and corporate leverage. During the
last century, government debt experienced several notable transitions beginning with a dramatic
expansion after the Great Depression to fund World War II. From its peak of 109% of GDP in
1946, government debt as a share of income fell steadily until 1972 when it leveled off at
approximately 25% of GDP. The 1980s saw a renewed increase in public sector leverage that
was spawned by the Reagan-era military buildup, and that persisted until the mid-1990s. In 2008,
public debt-to-GDP began another dramatic increase in response to the most recent recession and
financial crisis.
Turning to corporate leverage, a negative relation with government leverage is apparent.
As government leverage increased sharply from 1917 to 1945, corporate leverage experienced a
less severe but nonetheless significant decline from 17% to 11% over this same period. From
1945 to 1970, as government debt fell, corporate leverage increased more than threefold to 35%.
10
After little change during the 1970s, corporate leverage increased sharply in the mid-1980s in
conjunction with the leveraged buyout boom (Kaplan and Stromberg (2009)) before trending
downward over the next thirty years. As shown in Graham, Leary, and Roberts (2013), the other
measures of leverage examined in Table 1 show similar patterns.
Table 2 presents ordinary least squares (OLS) regression results for several specifications
of equation (1). The estimates reveal the following inferences. First, government leverage and
corporate leverage are strongly negatively related. This relation is robust to the inclusion of both
macroeconomic and firm characteristic control variables. This relation is also found in both
levels and first differences. In fact, the coefficient estimate in the levels specification, column
(3), is only modestly larger in magnitude to that in the first difference specification, column (6).
Column (6) of Table 2 indicates that a one dollar increase in the supply of treasuries is
associated with a $0.04 decrease in the demand for corporate debt. (We refer to the “demand” for
corporate debt only to ease the exposition, not to imply a clear delineation between supply and
demand.) Several other estimates are worth mentioning. Real GDP growth exhibits a clear
negative correlation with leverage, consistent with previous research emphasizing countercyclical leverage among nonfinancial firms (e.g., Korajczyk and Levy (2003)). The yield spread
is insignificant in the levels specification, but significantly negative in the difference
specification implying that as the rate of change in the yield spread increases the rate of change
in leverage ratios tends to decrease. Finally, we see a strong negative association between
profitability and leverage, consistent previous evidence (e.g., Titman and Wessels (1988)) and
firms’ reliance on internal equity for financing.
Panel B of Table 2 presents the results of a host of additional robustness tests. The
baseline model for this analysis is the same as that presented in columns (3) and (6) in Panel A.
The dependent variable is corporate leverage, measured contemporaneously with the covariates
unless otherwise specified. We modify this baseline specification in a variety of ways, as
indicated by each row. The figures in each row correspond to the coefficient estimates (and tstatistics in parentheses) on the government leverage variable. The first column corresponds to
specifications in levels of all of the variables, the second column first differences.
The first four rows explore alternative measures of corporate leverage, our dependent
variable. The first row defines corporate leverage as the ratio of “net debt” (debt – cash holdings
and short-term investments) to assets. The marginal effect of government leverage on net debt
11
leverage is significantly larger in magnitude due to the positive correlation between corporate
cash holdings and government leverage (Graham, Leary, and Roberts (2012)). This inflation is
highlights the strong positive relation between government debt and corporate cash and liquid
assets. The second and third rows replace the numerator of our leverage measure with long-term
debt and short-term debt, respectively. We see similar negative associations for both measures in
the first difference specifications. The magnitude is somewhat smaller than the $0.04 in Panel A
reflecting the weak correlation between long- and short-term debt policies.
The next two rows alter the independent variables, X. Inclusion of the corporate tax rate –
unreported – has virtually no impact on the estimated relation. However, the tax incentive to
issue debt created by corporate taxes is mitigated by the presence of personal taxes. As such, in
unreported analysis we include a debt-tax incentive variable defined as:
(3) Debt Tax Incentive =
taxCorporate − taxPersonal
(1 − taxPersonal )
.
The relation between corporate and government leverage is unaffected.
Rows six through nine examine different subperiods to see investigate time variation in
the estimated effect. With only 90 observations, statistical power is limited. Nonetheless, (lack
of) variation in the magnitude of the coefficient on government leverage can still be informative.
For all of these tests, we rely on our baseline specifications found in columns (3) and (6) in Panel
A.
Excluding the years during and just after World War II has little affect on the economic
magnitude or statistical significance of the government leverage coefficient. In fact, the
magnitude of the coefficient in both the level and difference specification is dramatically larger
without this period. Focusing on just the first half of the sample, 1926 to 1968, reveals
statistically significant estimates in both the levels and difference specification, though the
magnitudes are slightly smaller than those in Panel A. Estimates from the second half of the
sample are quite a bit noisier but shed additional light on the results from column (6). The level
specification is actually positive, but statistically insignificant, because of the strong trend effect.
The coefficient in the first difference specification is negative, highly statistically significant and
more than twice as large in magnitude as the estimate over the entire sample period. Together,
columns (6) through (8) suggest that the sensitivity of corporate debt to government debt has
12
increased markedly over the last century. Row (9) shows that the sensitivity is not just occurring
during recession years.
The last two rows examine alternative samples aimed at addressing concerns about
sample turnover. The annual exit rate from our sample, due to bankruptcies, mergers,
acquisitions, and buyouts is approximately 8% per annum. However, turnover also occurs
because of sample expansion, particularly in 1962 and 1972 when AMEX and NASDAQ listed
firms joined our sample. Row (10) defines the sample as the 500 largest firms each year. The
turnover in this groups is less than 2% per annum. Row (11) just examines NYSE-listed firms.
The results are almost identical to those found in columns (3) and (6) in Panel A suggesting that
sample composition effects are not behind our results.
In order to distinguish between debt and equity policies, Figure 4 presents the time series
of corporate net debt (Panel A) and net equity (Panel B) issuances against government net debt
issuances. All variables are scaled by start of period assets. To ease inspection, the large spikes
in government debt during World War II are truncated at 10%. Panel A reveals a clear negative
association between the flows of credit in the two sectors. Panel B reveals a somewhat less clear
relation between net corporate equity issuance and net debt government debt issuance.
We formally investigate these relations with the following regressions
C
⎛ QtG − QtG ⎞
QtC − Qt−1
=
α
+
β
(4)
⎜ A
⎟ + ΓX t + ε t .
At−1
⎝
⎠
t−1
where the variables are as defined before. All stock and price variables are lagged one period,
flow variables are contemporaneous with the dependent variable. Serial correlation in the error
term of equations (4) and (5) is addressed by Newey-West standard errors assuming a two-period
lag structure.
Table 3 presents the regression results. As a fraction of assets, net debt issuances are
significantly negatively related to government net debt issuances. The magnitude of the
coefficient is of similar size as that found in Table II. As a fraction of investment, we again see
large and significant negative associations between net debt issuances and net government debt
issuances. In contrast, the results for net equity issuances are less robust and statistically
significant once we include control variables. Equity market conditions are the most important
determinants of net equity issuances.
13
IV.C Corporate Investment
Figure 5 plots corporate investment against government net debt issuances. The former is
defined as the change in net physical plant, property, and equipment scaled by lagged assets, the
latter as the change in federal debt held by the public divided by lagged assets. Much like the net
debt issuance relation, investment appears negatively correlated with the net flow of government
debt. A negative relation is not surprising in light of our previous findings and the flow of funds
identity. In Panel B of Table II, we documented a decline in debt net of cash equal to
approximately $0.08 per dollar of treasury issuance. Table III shows that net equity is largely
unchanged implying an approximate decline in investment of approximately $0.08, unless
dividends adjust. However, dividends are notoriously “sticky” (Brav et al. (2008)) and, indeed,
we find no relation between corporate dividends and government debt policy in unreported
results.
Table IV presents OLS estimates of equation (2). The results show an economically large
and statistically significant negative relation between corporate investment and government net
debt issuances. Looking at column (3), we see that the magnitude of this effect is just under
$0.09 per one dollar of treasury issuance – consistent with the discussion above. Firms are
issuing less debt, accumulating internal equity and not offsetting this decline in funding with an
increase in external equity issuances or decrease in distributions. Consequently, investment
declines. We also note that both profitability and the market-to-book ratio are significantly
positively correlated with investment, as are the level of interest rates and inflation, consistent
with previous research (e.g., Philippon (2009)).
IV.
Cross-Sectional Heterogeneity
Our analysis thus far has documented a robust negative relation between corporate and
government debt – both stocks and flows – and between corporate investment and government
debt. Interpretation of this result has thus far been limited by our reliance on aggregate data and
the ability to control for confounding effects with observables. In this section we attempt to
buttress our evidence by exploiting cross-sectional variation provided by our panel data. In
particular, we ask: which firms’ financial and investment policies are more (less) sensitive to
14
variation in government debt? In classifying firms, we focus on financial health, broadly defined.
The motivation is two-fold. First, if corporate debt is a substitute for government debt in
investors’ portfolios, then the debt of more credit-worthy firms is a closer substitute than that of
less credit-worthy firms (Friedman (1978)).
Second, this classification provides a useful falsification test to held address identification
concerns. In particular, one concern with interpreting our results above is the counter-cyclical
nature of government debt and deficits. This correlation with the business cycle suggests that our
estimates above may capture the effect of variation in aggregate investment opportunities that are
not adequately controlled for with observables. Increases in government debt coincide with
economic downturns when investment opportunities are poor and firms require less external
capital – consistent with our results above. However, if this explanation is behind our results,
then the association between corporate policies and government debt should be larger for
financially weaker and more constrained firms as shown by Gertler and Gilchrist (1994) and
Gertler and Himmelberg (1995).
Likewise, this analysis can help alleviate concerns about contemporaneous shifts in the
supply curve of corporate debt due to variation in its determinants, such as expected default costs
and agency costs. Specifically, financially constrained firms are characterized as such precisely
because they face greater frictions, such as expected default costs, information asymmetry, or
agency conflicts. If variation in government debt is just capturing variation in these frictions then
we would expect that these frictions are exacerbated in bad times. For example, default costs are
higher in bad times when secondary asset markets are depressed and less liquid and the
likelihood of default is greater. Thus, if government debt is capturing contemporaneous shifts in
the supply curve then we would expect the relation between corporate policies and government
debt to be larger for more financially constrained firms.
To test these hypotheses, we examine the sensitivity of corporate policies to government
debt as a function of firms’ financial health, broadly defined. We use four proxies that capture
financial health and credit-worthiness: firm size, earnings volatility, the Hadlock-Pierce (HP)
index of financial constraints, and an estimated probability of default based on the model of
Merton (1970). Each year, we classify firms into four buckets based on quartiles for each proxy.
For our analysis, we focus on the lower and upper quartiles. For example, small firms are defined
as those firms falling in the lowest quartile of the distribution of assets, while large firms are
15
those firms falling in the highest quartile. We then run three regressions corresponding to three
different dependent variables – corporate leverage, corporate net debt issuance, and corporate
investment – for each subsample, for each proxy.
Each regression includes firm fixed effects to eliminate concerns about time-invariant
cross-sectional heterogeneity. The control variables – not reported – are identical to those found
in in column (3) of Panel A, Table II (corporate leverage), column (2) of Table III (corporate net
debt issuance), and column (3) of Table IV (corporate investment).
The results are presented in Table V, with t-statistics in parentheses and firm-year
observation counts (Obs). Standard errors are clustered by year because the source of identifying
variation in the coefficient of interest – government debt – is at the year level. Columns (1) and
(2) present the results using firm size as a proxy. The first set of results show that the coefficient
estimate in the regression of corporate leverage on government leverage (and control variables)
equals -0.062 when estimated on the subsample of small firms, and -0.117 when estimated on the
subsample of large firms. The coefficient on the change in government debt divided by lagged
assets in the corporate net debt issuance regression is -0.006 for small firms, and -0.061 for large
firms. Finally, the investment regression reveals a coefficient on the government debt variable
equal to -0.040 for small firms and -0.058 for large firms. These results highlight that the
corporate policies of large firms are more sensitive to government debt than those of small firms.
Moving across the table to columns (3) through (8) reveals similar findings in that
financially unconstrained, more credit-worthy firms with less volatile earnings exhibit financial
and investment policies that are more sensitive to variation in government debt. One exception is
earnings volatility in which the differences are less apparent. It is difficult to reconcile these
findings with the alternative interpretation of our findings that government debt is merely a
proxy for aggregate investment opportunities or the determinants of supply of corporate debt,
such as expected default costs and agency costs.
V.
The Channel: Financial Intermediaries
To understand the channel through which government debt influences corporate policy,
we turn to an investigation of the financial intermediaries responsible for the majority of
corporate debt holdings. Figure 6 presents the distribution of corporate bond holdings across
16
investor types, as reported in the U.S. Flow of Funds. Domestic financial institutions have
historically held the large majority of corporate bonds. Insurance companies are the largest
investors, with life insurers holding a significantly larger fraction, on average, than Property and
Casualty companies. However, state and local pensions and commercial banks also hold a
significant fraction.
Since 1970, this distribution has experienced two significant changes. The first began in
the 1970s with the increase in foreign holdings, categorized as “Other.” The second began in the
1980s with the increase in mutual fund holdings, also categorized as “Other.” In conjunction
with bank lending, commercial banks, insurance companies, and state and local pension funds
have owned the lion’s share of corporate debt during the last 70 years. As an aside, the spike in
household share of direct ownership in 1945 is not a transitory spike. Rather, much corporate
debt was directly held by households and traded on exchanges before World War II.
Figure 6 highlights that any linkage between government debt and the policies of the nonfinancial corporate sector are likely to be found in the portfolios of financial intermediaries. As
such, Figure 7 examines the asset allocations of these intermediaries over time from the Flow of
Funds. To ease the presentation, we focus on the allocation across credit market instruments.
Panels A, B, and C present the results for commercial banks, insurance companies, and state and
local pension funds, respectively. A few results standout. First, treasuries as a share of
intermediaries’ portfolios declined dramatically between 1945 and 1970 when the government
retired outstanding bonds used to finance World War II. Second, there appears to be a negative
association between treasury and corporate debt holdings, though the strength is unclear.
In Table VI, we examine this asset allocation tradeoff and its association with
government debt using a simple regression. Specifically, we estimate the following regression
⎛ QG ⎞
⎛L⎞
(5) Δ ⎜ t ⎟ = α + βΔ ⎜ t ⎟ + ΓΔX t + ηt
⎝ At ⎠
⎝ At ⎠
where the dependent variable is the change in the ratio of intermediary corporate lending to total
assets, ΔGD is the change government leverage (i.e., the ratio of federal debt held by the public
to GDP). The control variables in each specification include contemporaneous changes in: the
return on the three-month treasury bill, the BAA-ten year treasury bond yield spread, inflation,
equity market return, real GDP growth, corporate return on assets, asset intangibility, and the
market-to-book ratio.
17
The table presents the estimated coefficient on the government debt variable and tstatistic in parentheses. Serial correlation in the error term of both equations is addressed with
Newey-West standard errors assuming a two-period lag structure. The top panel presents the
results where the dependent variable is lending by intermediaries to corporations (i.e., the
fraction of intermediary assets allocated to corporate debt). Columns (1) and (2) present the
results using the balance sheet data of commercial banks, where corporate lending is defined as
the sum commercial and industrial (C&I) loans and corporate bonds. Columns (3) and (4)
present the results for insurance companies – property and casualty and life where corporate
lending is defined as corporate bonds and private placements. Finally, columns (5) and (6)
present the results for state and local pension funds, where corporate lending is defined as the
sum of corporate bonds and commercial paper. The bottom panel presents results where the
dependent variable is the fraction of assets allocated to US treasuries.
The top panel shows a statistically significant and robust negative relation between
corporate lending and variation in government debt. In other words, when the government
increases its borrowing corporate lending by financial intermediaries decreases as a fraction of
assets. Panel B shows that this is, in part, a consequence of increased intermediary lending to the
federal government. Thus, intermediaries are substituting between lending to the government and
to the nonfinancial corporate sector.
VI.
Conclusion
We show that government debt crowds out corporate debt and investment – an effect we
refer to as financial crowding out. In particular, when the government increases the supply of
treasuries, financial intermediaries reallocate their assets to absorb the treasuries by reducing
their lending to the nonfinancial corporate sector. Further, this effect is concentrated among the
largest, most credit-worthy and financially unconstrained firms whose debt is a closer substitute
for treasuries than that of smaller, riskier firms. We hope future research can provide greater
resolution on the mechanisms that provide a role for government debt to influence corporate
behavior.
18
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22
Appendix A: Variable Definitions
This appendix provides details on the data sources, sample construction, and variable
construction. We use the acronym GFD for Global Financial Database, a source for many
macroeconomic series.
A.1 Government debt
Government leverage in our analyses is defined as the ratio of Federal debt held by the
public to GDP. We focus on Federal debt because it comprises the majority of total government
debt, and is responsible for most of its variation over time. This fact is made apparent in Figure
A.4, which presents a stacked area chart of government debt divided by GDP. In fact, the
estimates of state and local debt are somewhat misleading. A significant fraction of state and
local assets consists of U.S. Treasuries (on average $0.5 trillion between 2000 and 2010). Thus,
state and local governments can act as a pass through for Federal debt by issuing their own debt
claims against these assets. Focusing on the debt held by the public avoids “double counting”
since a significant fraction of U.S. Treasuries outstanding are held by other government entities,
such as the social security administration.
A.2 Variable definitions
Gross Domestic Product Implicit Price Deflator: Source = GFD, Series = USGDPD, Annual data
from 1947 to 2010.
United States Annualized Exports of Goods and Services: Source = GFD, Series = USEXPGSQ,
Annual data from 1947 to 2010.
United States Annualized Exports of Goods and Services: Source = GFD, Series = USIMPGSQ,
Annual data from 1947 to 2010.
United States Gross Federal Debt Held by the Public (Bil. of $, NA), Source = GFD, Series =
USFYGFDPUBA, Annual data from 1938 to 2010. This series is extended back in time by
23
assuming that total Federal debt is equal to Federal debt held by the public. Pre-1938 Federal
debt
data
is
obtained
from,
http://www.usgovernmentspending.com/Federal_state_local_debt_chart.html.
Corporate Income Tax Rate: This rate corresponds to the top corporate income tax rate. Source =
“Corporation Income Tax Brackets and Rates, 1909-2002”, http://www.irs.gov/pub/irssoi/02corate.pdf. Annual data from 1909 to 2010.
United States M1 Money Stock: Source = GFD, Series = USM1W, Year-end monthly data from
1929 to 2010.
United States M2 Money Stock: Source = GFD, Series = USM2W, Year-end monthly data from
1947 to 2010.
United
States
State
and
Local
Debt:
Source
=
US
government
(http://www.usgovernmentspending.com/Federal_state_local_debt_chart.html),
spending
Annual
data
from 1902 to 2010.
United States Nominal GDP: Source = GFD, Series = GDPUSA, Year-end annual data from
1790 to 2010.
United States Unemployment Rate: Source = GFD, Series = UNUSAM, Year-end annual data
from 1890 to 1928. Year-end monthly data from 1929 to 2010
International Holdings of US Debt: Source = Flow of Funds, Series = Foreign Holdings of U.S.
Treasuries. Annual data from 1945 to 2010. Prior to 1945 we assume that there are no foreign
holdings of US Treasuries.
USA Government 90-day T-Bills Secondary Market: Source = GDP, Series = ITUSA3D, Yearend monthly data from 1920 to 2010.
24
USA 10-year Bond Constant Maturity Yield: Source GFD, Series, IGUSA10D, Year-end
monthly data from 1790 to 2010.
United States BLS Consumer Price Index NSA: Source GFD, Series, IGUSA10D, Annual data
from 1820 to 1874. Monthly data from 1875 to 2010 collapsed to an annual series by averaging
within years.
Moody's Corporate AAA Yield: Source GFD, Series, MOCAAAD, Year-end monthly data from
1857 to 2010.
Moody's Corporate BAA Yield: Source GFD, Series, MOCBAAD, Year-end monthly data from
1919 to 2010.
Variable Construction
Inflation = [CPI(t) – CPI(t-1)] / CPI(t) where CPI(t) is the consumer price index in year t
computed as the average monthly CPI for the year.
US Net exports = [US exports – US imports] / US GDP
GDP growth = [GDP(t) – GDP(t-1)] / GDP(t-1) where GDP(t) is US gross domestic product in
year t.
Government Leverage = US public debt held by the public in year t / GDP(t)
Net Debt Issuances by the US Government = Change in US public debt held by the public from
year t-1 to t / GDP(t-1)
Book Leverage = Total Debt / Total book value of assets
Market leverage = Total Debt / (Total Debt + Equity Market Capitalization)
25
Net Debt leverage = (Total Debt – Cash) / Total book value of assets
Net Debt Issuance = [Total Debt(t) – Total Debt(t-1)] / Total book value of assets(t-1)
Net Equity Issuance = [Equity issues(t) – Equity repurchases(t)] / Total book value of assets(t-1)
Market-to-Book Equity Ratio = Equity Market Capitalization / Book Equity
Profitability = operating income before depreciation / total book value of assets
Tangibility = net plant property and equipment / total book value of assets
Intangible Assets = [Total Assets – (Net PP&E + cash and marketable securities + accounts
receivable + inventories)] / Total Assets
Asset growth = [Total book value of assets(t) - Total book value of assets(t-1)] / Total book
value of assets(t)
26
Figure 1
Regulated vs. Unregulated Assets
The figure presents annual time series of the fraction of total assets owned by unregulated firms defined
Assets of Unregulated Firms / Total Firm Assets (%)
50
60
70
80
90
100
as those firms not in the utility, railroad, or transportation industries.
1920
1930
1940
1950
1960 1970
Year
1980
1990
2000
2010
Figure 2
Supply and Demand for Corporate Debt
The figure shows the theoretical demand and supply curves for corporate debt under different assumptions about the relevant market frictions. On the horizontal axis of each figure is the aggregate quantity
∗
∗
of corporate debt (B), on the vertical axis the risk-adjusted return on debt (rD
) and equity (rE
). Panel
A presents the case of perfect markets as in Modigliani and Miller (1958). Panel B presents the general case in which firms face costs in transforming cash flow streams (e.g., corporate taxes, bankruptcy
costs, agency costs), generating a downward sloping supply curve, and investors and intermediaries face
costs in transforming cash flow streams (e.g., personal taxes, heterogeneous expectations), generating
an upward sloping demand curve.
Panel A: Modigliani & Miller Irrelevance
r*D, r*E D, S B Panel B: General Case
r*D, r*E D r*E S B* B Figure 3
Corporate and Government Leverage
The figure presents annual time series of government leverage and corporate leverage. Government
leverage is the ratio of federal debt held by the public to total assets of the unregulated sector. Corporate
0
10
20
Corporate Debt / Assets (%)
15
20
25
30
Corporate Leverage
Government Leverage
40 60 80 100 120 140 160 180 200
Government Debt / Assets (%)
35
leverage is the ratio of all interest bearing debt to total assets.
1920
1930
1940
1950
1960 1970
Year
1980
1990
2000
2010
Figure 4
Corporate and Government Net Security Issuances
The figure presents annual time series of for the net flow of securities. Government net debt issuances
is the annual change in federal debt held by the public divided by total assets of the unregulated sector
at the start of the year. Corporate net debt issuances is the annual change in all interest bearing debt
divdied by total assets at the start of the year. Corporate net equity issuances is the annual issuance of
equity, net of repurchases, divdied by total assets at the start of the year. The corporate series represent
unregulated corporations. Values greater than 10% in magnitude are truncated to ease the presentation.
Net Issuances / Assets (%)
−5
0
5
10
Panel A: Corporate Net Debt
−10
Corporate Net Debt Issuances
Government Net Debt Issuances
1920
1930
1940
1950
1960 1970
Year
1980
1990
2000
2010
Net Issuances / Assets (%)
−5
0
5
10
Panel B: Corporate Net Equity
−10
Corporate Net Equity Issuances
Government Net Debt Issuances
1920
1930
1940
1950
1960 1970
Year
1980
1990
2000
2010
Figure 5
Corporate Investment and Government Net Debt Issuances
The figure presents annual time series of for the net flow of securities. Government net debt issuances
is the annual change in federal debt held by the public divided by total assets of the unregulated sector
at the start of the year. Corporate investment the annual change net physical plant, property, and
equipment divdied by total assets at the start of the year. Values greater than 10% in magnitude are
10
−10
Corporate Investment
Government Net Debt Issuances
1920
1930
1940
1950
1960 1970
Year
1980
1990
2000
2010
−10
−5
0
5
Government Net Debt Issuances
Corporate Investment / Assets (%)
−5
0
5
10
15
20
truncated to ease the presentation.
Figure 6
Distribution of Corporate Bond Holdings
The figure presents the distribution of corporate and foreign bond holdings from the Flow of Funds. The
Funds category includes: Money market mutual funds, Mutual funds, Closed-end funds, and Exchangetraded funds. The Other category includes: Federal, State, and Local Governments, Federal Retirement,
Foreign holdings, Government-sponsored enterprises, Finance companies, Real estate investment trusts,
Security brokers and dealers, Holding companies, and Funding corporations.
100% 90% 80% Other 70% Funds 60% Banks 50% Pensions 40% 30% Insurance 20% Households 2009 2005 2001 1997 1993 1989 1985 1981 1977 1973 1969 1965 1961 1957 1953 1949 0% 1945 10% Figure 7
Financial Intermediary Asset Allocation Among Credit Market Instruments
The figure presents the annual portfolio allocations across credit instruments for US commercial banks,
US Life and Property & Casualty Companies, and US State and Local Pension Funds.
Panel A: Commercial Banks
100% 90% 80% Other 70% 60% Consumer Credit 50% Mortgages 40% Loans 30% Agency & Muni 20% Treasuries 0% 1945 1949 1953 1957 1961 1965 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005 2009 10% Panel B: Life and Property & Casualty Insurance Companies
100% 90% 80% 70% Loans 60% Mortgages 50% Bonds & CP 40% 30% Agency & Muni 20% Treasuries 0% 1945 1949 1953 1957 1961 1965 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005 2009 10% Panel C: Public Pension Funds
100% 90% 80% 70% 60% Mortgages 50% Bonds & CP 40% Agency & Muni 30% Treasuries 20% 0% 1945 1949 1953 1957 1961 1965 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005 2009 10% Table I
Summary Statistics;
Panel A presents summary statistics for the aggregate time series. Panel B presents summary statistics
for the firm-level panel data. All variables are presented as percentages, except the market-to-book asset
ratio. All variables are formally defined in Appendix A.
Panel A: Aggregate Time Series
Obs
Mean
SD
Min
Max
AR(1)
93
19.36
6.82
8.41
30.31
0.99
(Debt - Cash) / Assets
93
8.08
9.80
-16.06
21.47
0.98
Net Debt Issuance / Assets
93
1.76
1.69
-1.61
5.60
0.60
Net Equity Issuance / Assets
87
1.43
1.69
-0.93
8.94
0.66
Investment / Assets
93
2.91
2.27
-3.48
6.97
0.70
EBIT / Assets
93
10.14
3.05
1.86
18.34
0.81
Market-to-Book Asset Ratio
88
1.27
0.25
0.57
1.90
0.79
Cash / Assets
93
11.28
3.87
5.97
24.47
0.94
Intangible Assets / Assets
93
16.11
9.85
5.99
36.53
1.02
93
3.61
2.96
0.02
14.30
0.90
Corporate Financial & Investment Policy
Debt / Assets
Firm Characteristics
Macroeconomic Variables
3-Month T-Bill Return
BAA - 10 Year Treasury Yield Spread
93
2.13
1.04
0.39
6.16
0.68
Inflation
92
2.69
4.22
-11.46
16.66
0.59
Equity Market Return
87
11.58
20.31
-44.36
57.50
0.01
Real GDP Growth
92
3.31
5.16
-13.93
16.99
0.37
Federal Debt / Assets
85
45.63
34.51
13.38
184.80
0.96
Net Debt Issuance / Assets
85
3.42
8.57
-12.34
50.45
0.76
Government Expenditures / Assets
83
20.71
10.40
2.61
56.39
0.92
Government Variables
Panel B: Panel Data
Obs
Mean
SD
Min
Max
222,940
21.73
20.09
0.00
88.35
Corporate Financial & Investment Policy
Debt / Assets
(Debt - Cash) / Assets
222,779
5.40
33.00
-86.09
81.93
Net Debt Issuance / Assets
200,667
3.57
15.81
-31.42
89.20
Net Equity Issuance / Assets
194,044
9.95
35.46
-16.59
252.49
Investment / Assets
199,991
1.11
60.53
-291.90
339.36
EBIT / Assets
220,042
3.14
21.85
-112.62
36.68
Market-to-Book Asset Ratio
213,323
1.76
1.61
0.36
10.74
Cash / Assets
222,791
16.37
19.39
0.04
88.28
Intangible Assets / Assets
218,850
-1.96
5.89
-38.59
0.93
Firm Characteristics
Table II
Corporate and Government Leverage
The table presents OLS coefficient estimates and t-statistics in parentheses. Panel A presents OLS
estimates and t-statistics in parentheses from time series regressions of aggregate corporate leverage
on macroeconomic factors and firm characteristics. Columns (1) through (3) present results using the
level of all variables, columns (4) through (6) using first differences. Panel B presents the results of a
series of robustness tests. Each row represents a different OLS estimation. Column (1) presents the
estimated coefficient on the government leverage variable from a levels specification, column (2) from a
first difference specification. Each specification includes as controls, all of the macroeconomic variables
and firm characteristics presented in columns (3) and (6) of Panel A. A lienar trend is included in each
levels specification. Rows (1) through (3) change the definition of the dependent variable, corporate
leverage. Rows (4) and (5) change the right hand side variables. Rows (6) through (9) excludes different
periods from the estimation sample. Rows (10) through (11) use different samples of firms. All right
side variables are contemporaneous with the dependent variable and are formally defined in Appendix
A. Newey-West standard errors assuming two non-zero lags are used to compute all t-statistics (in
parentheses). Statistical significance at the 10%, 5% and 1% levels are indicated by “*”, “**”, and
“***”, respectively.
Panel A: Dependent Variable = Corporate Debt / Assets
Levels
Gov Debt / Assets
First Differences
(1)
(2)
(3)
(4)
(5)
(6)
-0.046***
-0.082***
-0.053*
-0.025**
-0.036**
-0.039**
( -5.900)
( -3.147)
( -1.902)
( -2.389)
( -2.375)
( -2.179)
Macroeconomic Variables
T-Bill Return
BAA - T-Bond Spread
Inflation
Equity Market Return
Log Real Growth of GDP
Gov Exp / Assets
0.358
0.260
-0.031
-0.010
( 1.423)
( 1.071)
( -0.380)
( -0.108)
-0.117
-0.274
0.286**
0.303*
( -0.247)
-0.032
( -0.518)
0.055
( 1.978)
-0.016
( 1.841)
0.020
( -0.424)
( 0.725)
( -0.580)
( 0.578)
-0.003
-0.017
0.004
-0.001
( -0.280)
( -1.392)
( 0.999)
( -0.138)
-0.206***
-0.112
-0.087***
-0.061**
( -3.252)
( -1.541)
( -3.150)
( -2.241)
0.184**
0.086
0.003
0.004
( 2.190)
( 0.976)
( 0.100)
( 0.113)
Firm Characteristics
EBIT / Assets
Intangible Assets / Assets
Market-to-Book Asset Ratio
-0.539**
-0.195**
( -2.500)
( -1.996)
-0.235
-0.071
( -1.631)
( -0.416)
0.260
0.676
( 0.145)
( 0.701)
Trend
Yes
Yes
Yes
No
No
No
Obs
85
82
82
84
81
81
Panel B: Robustness Tests
Levels
First Differences
(1)
(2)
(1) (Debt - Cash) / Assets
-0.119***
-0.082***
( -2.725)
( -3.064)
(2) Corp LT Debt / Assets
-0.054***
-0.024*
( -2.917)
( -1.870)
-0.016
-0.020***
( -1.518)
( -3.163)
(4) Corporate Tax Rate
-0.068***
-0.039**
( -2.581)
( -2.142)
(5) Additional Macro Variables
-0.057***
-0.038**
( -3.262)
( -2.192)
-0.137***
-0.089***
( -5.514)
( -3.087)
-0.031***
-0.031*
( -3.093)
( -1.804)
Alternative Measures of Corporate Leverage
(3) Corp ST Debt / Assets
Changes to the X-Variables
Subperiods
(6) Excluding Years 1942-1955
(7) Pre-1967
(8) Post-1968
(9) No Recession Years
0.134
-0.099***
( 1.422)
( -2.827)
-0.035
-0.046**
( -1.463)
( -2.080)
-0.050*
-0.038**
( -1.792)
( -2.088)
Alternative Samples
(10) 500 Largest Firms
(11) NYSE Firms
-0.052*
-0.040**
( -1.935)
( -2.150)
Obs
82
85
82
24.383
( 0.797)
1.060
( 0.857)
Market-to-Book Asset Ratio
1.865**
( 2.467)
-4.340
( -1.338)
0.023
( 1.353)
( 0.675)
( 0.296)
1.595
( 0.333)
( -0.531)
0.022
0.630
( -0.289)
( -0.223)
-0.015
-0.083
( -0.340)
( 2.397)
-0.001
-0.444
0.074**
-7.353
( -1.031)
-0.279
( -1.101)
( 1.938)
( 3.763)
( -2.029)
0.026
( 0.929)
85
( -2.221)
-1.939**
(4)
4.423*
( -1.492)
( -5.216)
-1.595**
(3)
0.209***
-0.042
-0.056***
(2)
% Investment
Intangible Assets / Assets
EBIT / Assets
Firm Characteristics
Gov Exp / Assets
Log Real Growth of GDP
Equity Market Return
Inflation
BAA - T-Bond Spread
T-Bill Return
Macroeconomic Variables
Gov Net Debt Issuances / Assets
(1)
% Assets
Net Debt Issuance
the 10%, 5% and 1% levels are indicated by “*”, “**”, and “***”, respectively.
85
( -2.015)
-0.037**
(5)
82
( 3.663)
5.371***
-0.072*
( -1.784)
( -0.072)
-0.008
( -1.028)
-0.067
( -0.796)
-0.028
( 3.139)
0.017***
( 0.311)
0.011
( 1.370)
0.255
( 0.953)
0.060
( 0.876)
0.044
(6)
% Assets
85
( -4.963)
-3.329***
(7)
82
( 3.083)
149.833***
-1.584
( -1.295)
( -1.243)
-5.901
( -0.956)
-1.658
( 1.369)
2.205
( 1.710)
0.384*
( -0.313)
-0.632
( 0.713)
5.709
( 0.551)
1.169
( -1.119)
-1.600
(8)
% Investment
Net Equity Issuance
in Appendix A. Newey-West standard errors assuming two non-zero lags are used to compute all t-statistics (in parentheses). Statistical significance at
variables are contemporaneous with the dependent variable; stock variables and interest rates are lagged one period. All variables are formally defined
Equity Issuances are normalized by lagged assets and contemporaneous investment as indicated at the top of the columns. All flow and first difference
The table presents OLS coefficient estimates and t-statistics in parentheses. The dependent variable is indicated at the top of the columns. Net Debt and
Corporate Net Security Issuances and Government Net Debt Issuances
Table III
Table IV
Corporate Investment and Government Net Debt Issuance
The table presents OLS coefficient estimates and t-statistics in parentheses. The dependent variable
is the corporate investment in period t divided by total assets in period t-1. All flow variables are
contemporaneous with the dependent variable; stock variables and interest rates are lagged one period.
Newey-West standard errors assuming two non-zero lags are used to compute all t-statistics (in parentheses). Statistical significance at the 10%, 5% and 1% levels are indicated by “*”, “**”, and “***”,
respectively.
Gov Net Debt Issuances / Assets
(1)
(2)
(3)
(4)
-0.105***
-0.125***
-0.089***
-0.052**
( -4.310)
( -4.302)
( -3.506)
( -2.432)
Macroeconomic Variables
T-Bill Return
0.157*
0.254***
0.162**
( 1.760)
( 3.376)
( 2.075)
BAA - T-Bond Spread
-0.780***
( -2.813)
-0.071
( -0.290)
-0.071
( -0.361)
Inflation
0.156***
0.062
0.146***
( 3.171)
( 0.931)
( 2.636)
0.042
0.014
-0.018
( 1.266)
( 0.479)
( -0.730)
0.414***
0.234***
( 5.827)
( 2.900)
Gov Exp / Assets
Firm Characteristics
EBIT / Assets
Market-to-Book Asset Ratio
1.534*
1.684**
( 1.710)
( 2.127)
Cash / Assets
0.085
( 1.282)
Lag Investment / Assets
0.380***
( 4.125)
Trend
Yes
Yes
Yes
Yes
Obs
84
82
82
82
Obs
Gov Net Debt Issuances / Assets
47,324
( -3.931)
( -4.034)
45,408
-0.058***
-0.040***
46,329
( -1.939)
44,786
( -0.264)
Obs
-0.061*
-0.006
Gov Net Debt Issuances / Assets
45,551
( -8.210)
( -4.604)
44,280
-0.117***
-0.062***
(2)
(1)
Obs
Gov Debt / Assets
Big
Small
Firm Size
(5)
31,961
( -7.138)
-0.107***
45,527
( -7.879)
-0.120***
44,265
( -3.029)
-0.041***
(6)
Const.
HP Index
Unconst.
Corp Total Debt / Assets
(4)
High
32,651
( -5.138)
-0.064***
32,067
( -3.182)
-0.084***
46,308
( -1.409)
-0.043
33,061
( -8.503)
-0.072***
47,005
( -4.723)
-0.066***
Investment / Assets
32,546
( -1.040)
-0.031
45,279
( -4.409)
-0.051***
44,756
( 0.169)
0.005
Corp Net Debt Issuances / Assets
31,383
( -6.488)
-0.099***
(3)
Low
Earn Vol
51,676
( -5.262)
-0.057***
50,637
( -2.061)
-0.058**
49,681
( -8.072)
-0.111***
(7)
Low
47,729
( -3.909)
-0.047***
46,965
( -0.646)
-0.016
45,785
( -7.486)
-0.098***
(8)
High
Default Pr.
all t-statistics (in parentheses). Statistical significance at the 10%, 5% and 1% levels are indicated by “*”, “**”, and “***”, respectively.
To ease the presentation we present only the coefficient on the government debt variable. Standard errors cluster at the year level are used to compute
specifications are identical to that found in column (2) of Table III. The investment specifications are identical to that found in column (3) of Table IV.
on each strata for each dependent variable. The leverage specifications are identical to that found in column (3) of Table II. The net debt issuance
Vol), the Hadlock-Pierce (HP) index of financial constraints, and an estimated probability of default (Default Pr.). We estimate separate OLS regression
based on the upper and lower quartiles of the credit-risk/financial constraint distribution proxy. We use four proxies: firm size, earnings volatility (Earn
their corresponding results - leverage, net debt issuances, and investment. For each dependent variable, we stratify the sample into two subsamples
The table presents OLS coefficient estimates and t-statistics in parentheses of firm fixed effects regressions. The dependent variables are indicated above
Cross-Sectional Heterogeneity
Table V
Table VI
Intermediary Asset Composition and Government Leverage
The table presents OLS coefficient estimates and t-statistics in parentheses. Each panel presents results
from six different regressions. The dependent variable is indicated by the panel title and is measured
relative to the total financial assets of the financial institution, denoted at the top of the columns. For
example, the first panel, denoted Corporate Loans, Bonds, & Commercial Paper, presents the estimates
from regressions of ratio of loans, corporate bonds, and commercial paper asset holdings to total financial
assets on the ratio of federal debt held by the public to total assets of the unregulated sector. Columns
(1) and (2) present the results for US chartered depository institutions. Columns (3) and (4) present the
results for US insurance companies. Columns (5) and (6) present the results for state and local pensions.
Odd numbered columns correspond to univariate regressions, even numbered columns correspond to
multivariate regressions with the following controls: three month t-bill rate, BAA-Treasury bond spread,
inflation, equity market return, real GDP growth, corporate EBIT / assets, corporate intangible assets
/ assets, and corporate market-to-book ratio. All right hand side variables are contemporaneous with
the dependent variable. All variables are in first differnces. Newey-West standard errors assuming two
non-zero lags are used to compute all t-statistics (in parentheses). Statistical significance at the 10%,
5% and 1% levels are indicated by “*”, “**”, and “***”, respectively.
Change in (Corporate Lending / Assets)
Banks
(1)
Insurance
(2)
(3)
(4)
Public Pensions
(5)
(6)
Corporate Loans, Bonds & Commercial Paper
Gov Debt / Assets
Obs
-0.054***
-0.055***
-0.041*
-0.039*
-0.127*
-0.088
( -3.437)
( -3.276)
( -1.871)
( -1.932)
( -1.776)
( -1.469)
72
72
75
75
65
65
Treasuries
Gov Debt / Assets
Obs
0.180***
0.195***
0.183***
0.200***
0.065
0.082
( 4.045)
( 3.724)
( 9.758)
( 10.344)
( 1.267)
( 1.622)
76
76
75
75
65
65