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Policy Uncertainty and Corporate Debt Maturity
Dung T.T. Tran
The Robert J. Manning School of Business,
University of Massachusetts Lowell,
1 University Avenue,
Lowell, MA 01854, United States.
Email: [email protected]
Tel.: +1 (857) 222 6154
Hieu V. Phan
The Robert J. Manning School of Business,
University of Massachusetts Lowell,
1 University Avenue,
Lowell, MA 01854, United States.
Email: [email protected]
Tel.: +1 (978) 934 2633
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Policy Uncertainty and Corporate Debt Maturity
October 2017
Abstract
This study examines the relation between government economic policy uncertainty and
debt contracting of U.S. public firms. We find that policy uncertainty is negatively related to
corporate debt maturity and positively related to the cost of debt and the number of restrictive
debt covenants. These relations are driven by firms with non-investment grade ratings or no
ratings, indicating that external creditors are concerned about the borrowing firms’ payment
ability during the periods of high policy uncertainty, leading them to impose shorter term debt
with less favorable terms and charge higher risk premiums. Further analysis indicates that the
negative relation between policy uncertainty and corporate investments documented by previous
research is stronger for firms with poor credit ratings or no ratings. Overall, our evidence
suggests that policy uncertainty increases the refinancing risk and the cost of debt for firms with
poor credit ratings, leading to their investment delays.
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1. Introduction
Government economic policies can alter the business environment in which firms operate.
The process of debating and adopting new policies usually takes considerable time and the
outcomes could be unpredictable. Policy uncertainty is harmful for firm operation. Previous
studies document that policy uncertainty adversely affects corporate investments (Gulen and Ion,
2016) and costs of capital (Gilchrist, Sim, and Zakrajsek, 2011; Pastor and Veronesi, 2012). While
the structure of debt maturity, i.e., the use of short-term versus long-term debt, is an important
factor when considering a firm’s financial policy, no previous studies have examined the relation
between policy uncertainty and corporate debt maturity.
In this study, we examine the relation between policy uncertainty and corporate debt
maturity structure of U.S. firms. We present two opposing views about the relation between policy
uncertainty and corporate debt maturity. The supply-side hypothesis is developed based on the
willingness of suppliers of capital to favor lending short-term over long-term when policy
uncertainty is high. Because creditors are exposed to the borrowers’ higher default risks associated
with long-term debt, they can more easily exercise monitoring when lending in short-term (Stulz,
2000; Rajan and Winton, 1995). Alternatively, the demand-side hypothesis is based on the need of
firms to reduce the refinancing risks associated with short-term debt by increasing long-term
borrowing. As short-term debt exposes firms to higher refinancing needs, and thus, higher liquidity
risk (Diamond, 1991; Guedes and Opler, 1996; Custódio et al., 2013), firms should be more
reluctant to borrow in short-term amid high policy uncertainty.
We employ the monthly policy uncertainty index developed by Baker, Bloom, and Davis
(2016; hereinafter labeled BBD index) and construct the annual policy uncertainty measure using
the equal-weighted average of the BBD index of the last six months of the year for analysis. The
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BBD index comprises of three components: the frequency of news media references to economic
policy uncertainty, the number of federal tax code provisions set to expire in future years, and the
extent of forecaster disagreement over future inflation and federal government purchases. We use
new bond issues data from SDC Platinum to construct our primary variable of debt maturity. The
sample includes 6,433 firm-year observations of 1,051 unique firms over the period 1985-2015.
To test our hypotheses, we first regress the maturity of new debt issue sample on the annual
policy uncertainty measure. We find robust evidence of a negative relationship between policy
uncertainty and debt maturity. The effect of policy uncertainty on debt maturity is also
economically important. Moreover, our finding is consistent with the supply-side explanation but
inconsistent with the demand-side explanation. In particular, firms borrow shorter term debt amid
high policy uncertainty because creditors are not willing to lend long-term debt.
To gain further insight into the relation between policy uncertainty and corporate debt
maturity, we split the samples into two subgroups with investment grade ratings or poor ratings
based on S&P credit ratings or Moody credit ratings, then we compare the differences between the
effects of policy uncertainty on debt maturity structure of the subsample firms. We find that the
negative relation between policy uncertainty and debt maturity structure is more pronounced for
firms with lower credit ratings or without credit ratings, which suggests that financially constrained
firms borrow shorter term debt because creditors might be concerned about these firms’ payment
ability amid high policy uncertainty, leading to their imposition of short-term debt.
To ensure the robustness of our results, we repeat the analysis using different ways to
construct the policy uncertainty measure, such as using the equal-weighted BBD index over the
last three months or twelve months or the value-weighted index over the last three months, six
months, or twelve months of a given year. Our findings are qualitatively unchanged. We also
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perform analysis with the news-based component of the policy uncertainty index and find that this
component has stronger effects on the maturity of new debt issues than the overall BBD index.
We further conduct similar analyses using alternative measures of short-term debt based on
balance sheet data obtained from Compustat but our findings persist.
We investigate the relation between policy uncertainty and the number of restrictive debt
covenants made to U.S. borrowing firms using the private loan data obtained from the Thomson
Reuter LPC’s Dealscan database. Our loan covenant data include 14,913 firm-year observations
of 4,970 unique firms from 1988 to 2012. Our results indicate that the number of debt covenants
is positively related to policy uncertainty, suggesting less favorable loan terms by banks during
periods of high policy uncertainty. Additional subgroup analysis indicates that the relation between
policy uncertainty and debt covenants is more pronounced for financially constrained firms, i.e.,
those with poor credit ratings or no ratings. Collectively, our evidence suggests that financially
constrained firms are faced with not only difficulty in borrowing long term debt but also stricter
debt terms amid high policy uncertainty.
It is possible, although less likely, that firms with poor credit ratings choose to borrow
short-term debt rather being imposed upon by external creditors during periods of high policy
uncertainty. To explore this possibility, we perform a complementary analysis of the relation
between policy uncertainty and the cost of debt using the generalized method of moments (GMM)
method. The results indicate that yield spread is positively related to policy uncertainty and this
relation is driven by borrowing firms with non-investment grade ratings. Moreover, our estimation
results also indicate a negative relation between the yield spread and bond maturity for non-
investment grade bonds, implying that it is less costly for firms with poor credit ratings to borrow
long-term debt. We note that this finding is consistent with the theoretical prediction of a
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downward-sloping credit yield curve for risky bonds by Merton (1974), Jarrow, Lando, and
Turnbull (1997), and Longstaff and Schwartz (1995), and the empirical findings of Sarig and
Warga (1989) and Fons (1994). Taken together, our evidence supports the supply side hypothesis
that creditors are concerned about the adverse effects of policy uncertainty on the payment ability
of firms with poor credit ratings, leading them to lend shorter term debt and charge higher risk
premiums.
To the extent that policy uncertainty makes it more difficult for firms, particularly
financially constrained ones, to raise long-term debt financing, it is expected to adversely affect
the level of investment of these firms (Graham and Harvey, 2001; Almeida, Campello, Laranjeira,
and Weisbenner, 2012). We further examine the link between the financing and investment effects
of policy uncertainty. We first replicate the baseline regressions of Gulen and Ion (2016). Our
results confirm the negative effect of policy uncertainty on corporate investment measured by the
ratio of capital expenditures to book value of assets, indicating that high policy uncertainty results
in investment delays. Moreover, when we examine the investment effect of policy uncertainty for
two subgroups of firms sorted on credit ratings, we find that this effect is significantly stronger for
firms with poor credit ratings. This evidence suggests financing, particularly debt maturity
structure, as a possible channel through which policy uncertainty negatively affects corporate
investments.
Our research contributes to the literature in three ways. First, to the best of our knowledge,
this is the first study that establishes the link between policy uncertainty and corporate debt
maturity structure. We show that policy uncertainty is an important determinant of corporate debt
maturity, an integrated component of corporate financial policy. Second, to the extent that
corporate debt maturity structure affects corporate investments, our finding that financially
7
constrained firms are screened out of the long end of the maturity spectrum during the high policy
uncertainty periods offers a plausible explanation for the negative real effects of policy uncertainty
documented by previous research. Finally, our paper provides important implications for policy
makers, corporate managers, and investors given the tremendous policy uncertainty they are
facing.
The rest of our paper proceeds as follows. Section 2 presents literature review and develops
our testable hypotheses. Section 3 provides data, sample, and variable description. Section 4
presents empirical results and discussions. Section 5 presents robustness checks and Section 6
concludes the paper.
2. Literature Review and Hypothesis Development
The economic consequences of policy uncertainty has recently been a topic of increased
interest. One focus of the debate in prior research is the effect of policy uncertainty on corporate
investments. Bernanke (1983) and Rodrik (1991) find that, when investment is irreversible,
uncertainty increases the value of waiting for new information about the profitability of the
projects. Julio and Yook (2012) examine the cycles of firm-level corporate investment associated
with the timing of national elections and report that political uncertainty negatively affects
corporate investment expenditures. Gulen and Ion (2016) document a negative relation between
policy-related uncertainty and both firm- and industry-level investments. Jens (2017) studies the
link between political uncertainty and firm investment using U.S. gubernatorial election as an
exogenous variation in uncertainty and find evidence of investment declines before elections.
Nguyen and Phan (2017) investigate how policy uncertainty influences mergers and acquisitions
8
(M&As) and find a negative relation between policy uncertainty and firm acquisitiveness as well
as a positive relation between policy uncertainty and the completion time of M&A deals.
Previous studies report that policy uncertainty affects corporate financing decisions.
Uncertainty is positively related to the costs of external financing by increasing the risk of default
(Gilchrist, Sim, and Zakrajsek, 2011) and increasing risk premia required by investors (Pástor and
Veronesi, 2012; Kelly, Pástor, and Veronesi, 2016). Francis, Hasan, and Zhu (2013) report a ten
basis point increase in U.S. bank loan spread as a result of a one standard deviation increase in
political uncertainty. The increased cost of external financing can help explain the negative relation
between uncertainty and investment. Cao, Duan, and Uysal (2013) investigate the role of political
uncertainty in firms’ intertemporal capital structure and find a negative relation between political
uncertainty and leverage ratios. They also show that when firms are uncertain about the general
political conditions, they prefer to stay underleveraged for extended periods, suggesting that firms
are more concerned about maintaining financial flexibility during periods of increased political
uncertainty. These authors also report that firms that have access to public debt markets, i.e.,
having bond ratings, are less affected by changes in political uncertainty in their capital structure
decisions. This finding supports the role of having public debt market access in increasing firms’
flexibility in borrowing (Cao et al., 2013). Colak, Flannery, and Öztekin (2014) examine how
uncertainty influences transaction costs and leverage adjustment and find that political uncertainty
increases financial intermediation costs for both new equity issuances and new debt issuances,
which slows down the leverage adjustment process. Moreover, higher adjustment costs due to
political uncertainty also reduce the frequency and volume of new capital issues to alter the capital
structure. Jens (2017) finds that firms delay debt and equity issuances that are tied to investments
during higher political uncertainty. Colak, Durnev, and Qian (2017) study the effects of political
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uncertainty on initial public offering and document fewer IPOs at states that are scheduled to have
an election, suggesting that when facing high political uncertainty, firms tend to delay their
financing activities. They also find political uncertainty leads to higher cost of capital for firms,
consistent with the earlier argument of Pástor and Veronesi (2012, 2013). The foregoing discussion
suggests that policy uncertainty can affect corporate financing decisions through both the demand
and supply channels.
The maturity structure of corporate debt, i.e., the use of short-term versus long-term debt,
is an integrated part of a firm’s financial policy. Under the corporate governance perspective, short-
term debt can be an effective tool to reduce managers’ incentives to increase risk (Barnea, Haugen,
and Senbet, 1980), mitigate the agency problem of free cash flows (Jensen, 1986), reduce agency
costs associated with underinvestment or asset substitution (Myers, 1977; Jensen and Meckling,
1976; Leland and Toft, 1996), provide a powerful monitoring tool for creditors (Stulz, 2000), and
provide flexibility to creditors in monitoring managers (Rajan and Winton, 1995). From the
investment perspective, short-term debt allows firms to avoid underinvestment induced by the debt
overhang problems (Myers, 1977; Diamond and He, 2014), and increase financial flexibility by
allowing firms to preserve debt capacity for investment in future growth opportunities (Graham
and Harvey, 2001). However, from the liquidity perspective, short-term debt financing exposes
firms to higher liquidity risk because it requires more frequent renegotiations, exposing the
borrowing firms to the risk of insolvency if they fail to honor their debt payment obligations
(Jensen, 1986).
The choice of debt maturity can be driven by firm characteristics. Barclay and Smith
(1995), among others, find that larger firms or firms with fewer growth options use more long-
term debt. Analyzing the trade-off between the borrower’s preference for short-term debt due to
10
an expectation for future credit rating improvement and liquidity risk, Diamond (1991) argues
theoretically that firms with high credit ratings prefer to borrow short-term debt, firms with
somewhat lower ratings prefer to use long-term debt, and firms with lowest ratings can only borrow
short-term debt. These findings suggest that firms’ choice of debt maturity signals private
information to outside investors (Diamond, 1991; Barclay and Smith, 1995).
Furthermore, debt maturity structure has been shown to influence investment decisions.
Aivazian, Ge, and Qiu (2005) documents a negative relation between the percentage of long-term
debt in total debt and investment for firms with high growth opportunities. Almeida et al. (2011)
report that the structure of debt maturity had important real effects for industrial firms during the
2007-2008 financial crisis. In particular, they find that firms with a significant portion of long-
term debt maturing right after the third quarter of 2007 experience a drop in their investment level.
We develop two opposing views regarding the potential effects of policy uncertainty on
debt maturity structure: the supply-side hypothesis and the demand-side hypothesis.
The Demand-side Hypothesis
Theoretical explanations for debt maturity, such as the agency costs, signaling and liquidity
risk, and asymmetric information, largely originate from the demand-side factors. Short-term debt
helps reduce agency costs of debt (Myers, 1977) and asset substitution problems (Jensen and
Mecklings, 1976), serves as a useful mechanism to discipline managers (Brockman, Martin, and
Unlu, 2010), and signals private information to outside investors (Flannery, 1986; Diamond, 1991;
Barclay and Smith, 1995).
The demand-side explanations for how policy uncertainty affects debt maturity structure
relate to the firms’ willingness and ability to borrow rather than the creditors’ willingness and
ability to lend. Liquidity risk is one of the disadvantages of short-term debt financing as suggested
11
by Diamond (1991), Guedes and Opler (1996), and Custódio et al. (2013). Relying on short-term
debt financing means more frequent refinancing needed. Short-term debt creates liquidity risk
when the borrowing firm is unable to refinance, prompting the lenders to liquidate firm assets to
recover their loans. Thus, firms that use short-term debt tend to be more negatively affected by
credit supply shocks and face more financial constraints (Custódio et al., 2013). Therefore, during
the periods of increased policy uncertainty when cash flow volatility increases and it is more
challenging to refinance short-term loans, firms are more likely to reduce short-term debt and
increase long-term debt to mitigate the refinancing risk. A recent study by Alfaro, Bloom, and Lin
(2016) builds a model that shows a negative relation between uncertainty shocks and short-term
debt and suggest that firms reduce short-term debt when uncertainty is high. Following the
foregoing discussions, we state our demand side hypothesis as follows:
H1: Corporate debt maturity is positively related to policy uncertainty.
The Supply-side Hypothesis
Custódio, Ferreira, and Laurean (2013) report a secular decrease in corporate debt maturity
of U.S. firms during the period 1976-2008, which was mainly caused by firms with higher
information asymmetry and newly listed firms in the 1980s and 1990s. They argue that it is the
supply-side factors, rather than the demand-side factors, which drive the documented trend of
corporate debt maturity. In particular, these authors do not find evidence consistent with the
explanations of the agency costs of debt, maturity matching, taxes, signaling or liquidity risk, or
macroeconomic factors for debt maturity. However, they find that credit supply conditions, i.e.,
investor demand, have significant influence on the evolution of debt maturity. Other earlier studies
also find evidence consistent with the supply-side effects on debt financing. Faulkender and
Petersen (2006) show that firms with access to external debt markets (i.e., having credit ratings)
12
can raise more debt. Leary (2009) studies the change in bank credit supply caused by the 1961
emergence of the market for certificates of deposit and the 1966 credit crunch and reports that
following an expansion (contraction) in the availability of bank loans, bank-dependent firms
increase (decrease) their leverage ratios significantly compared to firms with bond market access.
Lemmon and Roberts (2010) take into account the supply shock in the junk bond market caused
by the collapse of Drexel Burnham Lambert and the subsequent regulatory changes in 1989 and
document that limited substitution to bank debt and alternative sources of capital makes net
investment to decline one-for-one with the reduction in net debt issuance. They also emphasize
that even large firms with access to public credit markets are vulnerable to the volatility in the
capital supply.
We argue that creditors may prefer to lend shorter term rather than longer term debt during
the periods of high policy uncertainty for the following reasons. Policy uncertainty can increase
the borrowing firms’ operating risk, leading to more volatile future cash flows that adversely affect
their debt payment ability. Because short-term debt helps creditors to better monitor firm
management (Stulz, 2000) and adds more flexibility to creditors in monitoring managers (Rajan
and Winton, 1995), lending short-term debt is less risky for creditors, particularly during the
periods of high policy uncertainty. In addition, long-term debt typically exposes creditors to higher
default risks than short-term debt does. Therefore, creditors may be less willing to lend long-term
debt when the economic conditions are highly uncertain. Also, the normal upward-sloping yield
curve in the U.S. debt market indicates that bond yields rise as maturity increases. The upward-
sloping yield curve reflects creditor expectations for increasing inflation in the future. To account
for future inflation, creditors demand higher risk premium for debt with longer maturity,
suggesting higher debt cost for longer term debt. Even though borrowing long-term debt reduces
13
the borrowing firms’ refinancing and default risks, the creditors’ unwillingness to supply long-
term debt amid high policy uncertainty may leave the borrowing firms with only the short-term
debt choice. Following these discussions, we state our supply side hypothesis as follows:
H2: Corporate debt maturity is negatively related to policy uncertainty.
3. Data and Sample
Previous studies largely rely on short-term debt ratios calculated from balance sheet data,
which may include the short-term portion of long-term debt that will soon mature, as a proxy for
debt maturity. Using balance sheet short-term debt ratios might raise a concern that short-term debt
ratios are based on some arbitrary cutoff points. In addition, since firms do not recapitalize
frequently (Leary and Roberts, 2005), their leverage ratios and debt maturity could be the result of
their past financing decisions. Thus, to avoid the potential drawback from using balance sheet data,
we focus our analysis on the relation between policy uncertainty and debt maturity structure using
the new debt issue data; however, we also use the debt maturity measures from balance sheet data
to verify the robustness of our results.
We obtain the sample of non-convertible and private new debt issues annually from the
Securities Data Company (SDC) and exclude all asset-backed, mortgaged-backed, and federal
credit agency issues. The annual firm-level accounting data and S&P long-term debt ratings are
obtained from the COMPUSTAT database. The daily and monthly stock returns are obtained from
CRSP database. Following prior literature, we exclude firms from the utility and financial
industries with the four-digit SIC codes from 4,900-4,999 and from 6,000-6,999 respectively
because these firms are highly regulated.
14
We use the U.S. monthly policy uncertainty index developed by Baker, Bloom, and Davis
(2016) as a measure of policy uncertainty in our analysis. The sample period is from January 1985
to December 2015 during which the data on the BBD index are available. The unconsolidated new
debt issues sample consists of 6,433 deals belonging to 1,051 unique firms over the sample period.
Because firms may issue multiple tranches of debt in a given year, we construct two alternative
consolidated samples by aggregating issue-level data to a firm-level data. The equal-weighted
consolidated sample contains 3,474 firm-year observations. The issue size-weighted sample has
3,466 firm-year observations. In addition, the balance sheet sample consists of 77,832 firm-year
observations belonging to 9,783 unique firms.
Measuring Policy Uncertainty
Previous research uses different measures of policy uncertainty which can be classified
into election related and non-election related categories. The election-based policy uncertainty
measures reflect the election-driven uncertainty prevalent before the national elections
(Boutchokova, Doshi, Durnev, and Molchanov, 2012; Durnev, 2012; Julio and Yook, 2012; Colak
et al., 2014). The non-election-based measures capture the government ability to implement its
promised policies (Colak et al., 2014). In addition, recent literature widely adopts the news-based
non-election policy uncertainty index of Baker, Bloom, and Davis (2016), which captures the
policy uncertainty reported in the news media (Cao et al., 2013; Colak et al., 2014; Gulen and Ion,
2016).
To assess the effect of policy uncertainty on the maturity structure of corporate debt, we
use the policy uncertainty index developed by Baker et al. (2016). The BBD index is constructed
as a weighted average of three components that reflect the frequency of news media references to
economic policy uncertainty, the number of federal tax code provisions set to expire in future
15
years, and the extent of forecaster disagreement over future inflation and federal government
purchases.
The first component is a count of search results in 10 large newspapers containing the
following triple: “uncertainty” or “uncertain”; “economic” or “economy”; and “congress”,
“legislation”, “white house”, “regulation”, “federal reserve”, or “deficit” (including variants like
“uncertainties”, “regulatory” or “the Fed”). To meet the criteria, an article must contain terms in
all above three categories. Baker et al. (2016) use 10 leading newspapers: USA Today, Miami
Herald, Chicago Tribune, Washington Post, Los Angeles Times, Boston Globe, San Francisco
Chronicle, Dallas Morning News, New York Times, and Wall Street Journal and search the digital
archives of each paper from January 1985 to get the monthly count of policy uncertainty articles.
The total number of counted articles in each newspaper in each month is normalized by the total
number of articles in that newspaper to adjust for the changing volume of news throughout time.
The second component of the index relates to uncertainty about expiration of tax code
provisions in the future. Based on reports by the Joint Committee on Taxation (JCT), Baker et al.
(2016) get the sum of the discounted number of tax code expirations to obtain an index value for
each January and hold it constant during the calendar year.
The third component of the index captures the uncertainty related to monetary policy and
government spending, using data from the Federal Reserve Bank of Philadelphia’s Survey of
Professional Forecasters. This component measures the forecast dispersion for the CPI and for
purchases of goods and services by state, local, and the federal governments. The dispersion
measures include the interquartile range of each set of inflation rate forecasts in each quarter, and
the ratio of nominal federal purchases to nominal GDP, which is ratio of the interquartile range of
four-quarter-ahead forecasts by the median four-quarter-ahead forecast multiplying by a 5-year
16
backward-looking moving average. The values of the forecast disagreement measures are held
constant within each calendar quarter.
The above three components weights are 1/2, 1/6, 1/3, respectively, in the overall BBD
index. In our regressions, we construct the annual policy uncertainty index of year t as the natural
logarithm of the equal-weighted average of the monthly BBD index of the last three, six, or twelve
months of the year, denoted by PU3, PU6, and PU12. In addition, we compute value-weighted
averages of the index in the last three months, six months, and twelve months of year t, denoted
by PU3W, PU6W, and PU12W, and give more weights to the more recent month. In addition to
the overall index, we examine the news-based component of the BBD policy uncertainty index
separately and construct the variables in a similar manner. To address potential endogeneity
concern, we lag the policy uncertainty measures by one period in our baseline analysis.
Measuring Debt Maturity Structure
We measure the maturity of new debt issues, denoted by Bond Maturity, as the natural
logarithm of the years to maturity of new debt issues in the unconsolidated sample. In the first
consolidated sample, we use the equal-weighted average maturity of multiple debt issues as the
proxy for debt maturity. In the second consolidated sample, we use the issue size-weighted average
maturity of multiple debt issues as the proxy for debt maturity, in which the consolidated maturity
is computed as the weighted average of the total proceeds of multiple debt issues by a firm in a
given year.
To measure the debt maturity of the balance sheet sample, we follow Custódio et al. (2013),
Brockman, Martin, and Unlu (2010) and Dang and Phan (2016) in using the proportions of a
company’s total debt that mature within one, two, three, four, or five years or less, denoted by ST1,
ST2, ST3, ST4, and ST5, respectively, as measures of short-term debt. We also include the ratio of
17
debt in current liabilities, but exclude the current portion of long-term debt, to total debt, denoted
by STNP, as an alternative measure of short-term debt. Total debt is calculated as the sum of debt
in current liabilities and long-term debt.
Control Variables
Following the debt maturity literature, we control for firm characteristics in our debt
maturity model, which include firm size (Scherr and Hulburt, 2001; Guedes and Opler, 1996), the
square of firm size, market leverage, asset maturity, managerial ownership, market-to-book ratio
(Barclay and Smith, 1995), term structure of interest rates, abnormal earnings, asset return
standard deviations or asset volatility, z-score dummy for firms with a high Altman (1977) Z-score,
and rating dummy for firms with S&P credit ratings (Brockman et al., 2010). The Appendix
provides detailed description of the control variables.
3.3. Descriptive Statistics
Table 1 reports the summary statistics of our new debt issues sample. We winsorize all
variables at the 1st and 99th percentiles to reduce the effects of outliers that can bias our results.
The average maturity of new debt issues is 12.07 years. Our sample firms have leverage, asset
maturity, market-to-book, abnormal earnings, asset volatility, and R&D comparable to those in
previous debt maturity studies, such as Brockman et al. (2010) and Dang and Phan (2016), but
larger firm sizes.
[Insert Table 1 about here]
We also generate the descriptive statistics of the variables in the balance sheet sample.
Similar to Brockman et al. (2010), we set all observations of short-term debt ratios that exceed one
to equal one, and set all negative observations of short-term debt ratios to equal zero. The proxy
18
of short-term debt, ST3, has a mean value of 54.8%, which is higher than the mean value of 40%
reported by Brockman et al. (2010), probably due to different sample periods (the descriptive
statistics for the balance sheet sample are not reported for brevity but are available from the
authors).
Figure 1 plots the BBD index measured as the natural logarithm of the equal-weighted
average of the monthly BBD index of the last six months of the year with the equal-weighted
average years to maturity of new debt issues over the sample period 1985-2015. The graph shows
the opposite trends of debt maturity and policy uncertainty as the average years to maturity of new
debt issues increase when the BBD index decreases.
4. Empirical Models, Results, and Discussions
4.1. Policy Uncertainty and Debt Maturity – Baseline Regressions
We begin our analysis by examining the relation between policy uncertainty and the
maturity of the new debt issues while controlling for factors shown to explain debt maturity in
previous studies. Our baseline regression model is as follows:
Bond Maturity i,t = αt + ß PUt-1 + δ Xi, t-1 + ɛi,t (1)
The dependent variable is the natural logarithm of the years to maturity of new debt issues
in the unconsolidated sample. The test variable is the natural logarithm of the average monthly
BBD index over the last six months of a given year. Xi,t-1 is a vector of firm-level control variables.
We control for industry fixed effects by including industry dummies (using two-digit SIC codes).
We control for issuer type fixed effects by adding issuer type dummies that equal to one for R144D
issue and zero otherwise (Brockman et al., 2010), and issue type fixed effects by including issue
type dummies that equal to one for investment grade bonds, and zero otherwise. We note that,
19
since policy uncertainty data are a time-series, which is the same for all firms in a given year, we
do not control for year fixed effects to preserve the explanatory power of policy uncertainty.
[Insert Table 2 about here]
The debt maturity regression results reported in Panel A of Table 2 show that the
coefficients on policy uncertainty are all negative, ranging from -0.094 to -0.07, and statistically
significant at the 1% and 5% levels in all specifications, suggesting that policy uncertainty is
negatively related to debt maturity. The coefficients are economically significant: using the results
reported in column 5 for calculation, we find that a one standard deviation increase in BBD index
is associated with 1.026 years decrease in debt maturity. These results provide preliminary support
for the supply side hypothesis that creditors lend shorter term debt amid high policy uncertainty.
The results of other control variables are generally consistent with those reported in the
literature (e.g., Johnson, 2003; Brockman et al., 2010). For example, firm size is negatively related
to debt maturity in our sample, but square of firm size has a stronger positive relation with maturity,
suggesting a non-monotonic relation between size and maturity of bonds (Diamond, 1991). As the
underinvestment hypothesis argues that firms match the maturity of assets and liabilities, we
observe a positive relation between asset maturity and debt maturity in our sample. The results
also show that market-to-book is negatively related to debt maturity, consistent with Myers’ (1977)
prediction that firms with greater growth opportunities face higher underinvestment problems so
they tend to use more short-term debt.
We conduct several robustness checks for our baseline results. Panel B of Table 2 reports
the results of the firm-level debt maturity regressions. The coefficients on policy uncertainty are
statistically and economically significant across model specifications for both equal-weighted and
20
issue size-weighted maturities. This evidence indicates that our results are robust to firm-level
measures of debt maturity.
We further try alternative measures of policy uncertainty. In panel A of Table 3, we
substitute the six-month policy uncertainty with three-month, twelve-month equal-weighted
average BBD index, or the three-month, six-month, and twelve-month value-weighted average
BBD index in the regressions with industry and issuer type fixed effects. The results are
qualitatively similar. In panel B of Table 3, we perform a similar analysis using the news-based
component of the BBD index. The direction of the relation between news-based policy uncertainty
and debt maturity is in line with our results using the overall policy uncertainty index.
[Insert Table 3 about here]
We further investigate the robustness of our results using the balance sheet sample and
report the results in Table 4. The dependent variable is the ratio of notes payable and short-term
debt maturing in one, two, three, four, or five years or less to total debt. The test variable is the
lagged policy uncertainty index measured as the natural logarithm of the equal-weighted average
of the BBD index in the last six months of a given year. We use the same set of control variables
as in our baseline regressions. We find a positive and statistically significant relation between most
of the alternative measures of short-term debt and policy uncertainty, which lends additional
support for a negative relation between debt maturity and policy uncertainty. While we do not
observe significant results for ST2 and ST3, it is noted that using short-term debt ratios calculated
from the balance sheet may bias the analysis results due to arbitrary cutoff points and these ratios
may simply reflect historical financing decisions.
[Insert Table 4 about here]
21
In summary, based on both new debt issues and balance sheet data, we find consistent
evidence of a negative relationship between policy uncertainty and the maturity structure of
corporate debt. This finding is consistent with the supply-side explanation but is inconsistent with
the demand-side hypothesis. Our results indicate that creditors may be reluctant to lend long-term
debt in uncertain policy environment, leading to borrowing firms’ using more short-term debt
financing.
4.2. Policy Uncertainty, Debt Maturity, and Financial Constraints
To assess how financial constraints moderate the relation between policy uncertainty and
debt maturity structure, we split the new debt issues sample into two subgroups based on credit
ratings (Faulkender and Petersen, 2006; Campello, Graham, and Harvey, 2010). The investment
grade subgroup includes issues with S&P ratings equal to or better than BBB- and a Moody ratings
equal to or better than Baa3. The high yield subgroup consists of issues having S&P ratings equal
to or lower than BB+ and a Moody rating equal to or lower than Ba1.1 Out of 6,433 new bond
issues from 1985 to 2015, 5,302 issues have investment grades and 1,131 are high yield bonds.
[Insert Table 5 about here]
Columns 1 and 2 of Table 5 report the regression results for the two subgroups. We find
that while policy uncertainty has a significant negative effect on debt maturity of issuers with non-
investment grade ratings, it does not affect the debt maturity of issuers with investment grade
ratings. This result suggests that the negative effect of policy uncertainty on debt maturity is driven
by firms with poor credit ratings, i.e., financially constrained firms. Columns 3 and 4 of Table 5
examine the two subgroups of firms in our balance sheet sample, using ST3 as the dependent
1 We use SDC Platinum’s issue types that are based on the same classification.
22
variable. We use S&P’s long-term debt ratings to classify firms into two subgroups of which the
investment grade subgroup includes issuers with S&P ratings equal to or better than BBB- and the
high yield subgroup includes firms with S&P ratings lower than BB+. We find even more revealing
results compared to the new debt issues sample. The coefficient on policy uncertainty is positive
and statistically significant for high yield firms, indicating that these firms increase short-term debt
amid high uncertainty. More importantly, the coefficient on policy uncertainty is negative and
statistically significant for investment grade firms, suggesting that these firms reduce (increase)
short-term (long-term) debt amid high policy uncertainty.
We interpret our results as evidence that creditors are less willing to lend long-term debt
when policy uncertainty is high because long-term debt is associated with higher default risks
while short-term debt allows creditors to monitor firm management more easily (Stulz, 2000). The
default risk is significantly higher for financially constrained firms that lack financial flexibility
and are in poor financial conditions. Creditors should be more concerned about the payment ability
of these firms amid high policy uncertainty, leading them to lend short-term debt. Thus, the lack
of access to long-term debt forces these firms to rely more on short-term debt financing amid high
policy uncertainty. On the other hand, creditors may still be willing to provide long-term debt
financing to financially unconstrained firms, i.e., firms with good credit ratings, when policy
uncertainty is high because their healthy financial condition can guarantee debt repayment. This
argument is consistent with Cao et al.’s (2013) finding that capital structure decisions of firms with
better access to public debt are less sensitive to changes in political uncertainty.
4.3. Policy Uncertainty, Financial Constraints, and Debt Covenants
23
We expect that during periods of high policy uncertainty, consistent with the supply side
hypothesis, lenders are not only less willing to lend in long-term but are also more likely to impose
more loan covenants. We use the number of covenants in commercial loan contracts reported in
the Thomson Reuters LPC’s Dealscan database to examine the effect of policy uncertainty on the
number of bank loan covenants made to U.S. firms. Our covenant data covers the period from
1988 to 2012. Our sample includes 14,913 firm-year observations of 4,970 unique firms. Our
covenant regression model has the following form:
NUMBER OF COVENANTSi,t = αt + ß PUt-1 + δ Xi,t-1 + ɛi,t (4)
The dependent variable is the sum of loan covenants imposed by lenders on each deal for
each year. The policy uncertainty variable is the natural logarithm of the six-month equal-weighted
average BBD index. We follow previous studies on loan covenants to include size, leverage,
market-to-book ratio, standard deviation of stock returns, asset tangibility, cash flow volatility,
and deal size as measured by the natural logarithm of deal amount as the control variables
(Demiroglu and James, 2010; Hertzel and Officer, 2012). We include loan purpose fixed effects
in all regressions to control for the cross-sectional variation in the purposes of getting the loans.
We also control for firm fixed effects in the second specification. To examine how the effect of
policy uncertainty on debt covenants differ between financially constrained firms and financially
unconstrained firms, we divide the sample into two subgroups based on S&P long-term credit
ratings. The investment grade subgroup includes issuers with S&P ratings equal to or better than
BBB- and the high yield subgroup includes firms with S&P ratings lower than BB+.
[Insert Table 6 about here]
Columns 1 and 2 of Table 6 present the results of the effect of policy uncertainty on debt
covenants for all firms in the sample. The results indicate that policy uncertainty is positively
24
related to debt covenants as the coefficients on policy uncertainty are positive (0.143 when
controlling for only loan purpose fixed effects, and 0.130 when controlling for both loan purposes
fixed effects and firm fixed effects) and statistically significant at the 1% level.
We then examine how the relation between policy uncertainty and debt covenants is
affected by financial constraints by dividing the debt covenants sample into two subgroups based
on credit ratings. Similarly to previous sections, the investment grade subgroup includes issues
with S&P ratings equal to or better than BBB- and the high yield subgroup consists of issues having
S&P ratings equal to or lower than BB+. There are 11,437 firm-year observations belonging to
financially constrained firms and 2,653 firm-year observations belonging to financially
unconstrained firms. The larger proportion of financially constrained firms suggests that firms with
poor credit ratings are more likely to rely on bank loans while firms with good credit ratings are
more likely to tap the public debt market with more favorable debt terms.
Columns 3 to 6 of Table 6 report the regression results for the two subgroups. We find that
the effect is stronger for firms with non-investment grade ratings or no ratings in both regression
specifications. The findings suggest that financially constrained firms are likely to face more
restrictive covenants on their loans amid high policy uncertainty, probably due to the lenders’
concern about these firms’ payment ability.
4.4. Policy Uncertainty, Debt Maturity, and Cost of Debt
Previous studies find that uncertainty increases cost of external financing due to increased
risk of default (Gilchrist et al., 2011) and increased equity risk premium (Pastor and Veronesi,
2012). In this section, we examine the relation between policy uncertainty and the costs of debt.
25
As policy uncertainty affects the maturity structure of corporate debt, we expect that it also has a
significant effect on debt costs. We estimate the following cost of debt model:
SPREADi,t = αt + ß LMATt + δ PUt-1 + γ Xi,t-1 + λ Zi,t +ɛi,t (3)
Since cost of debt and maturity of debt are likely to be endogenous and jointly determined,
we examine the relationship between cost of debt and policy uncertainty by running the GMM
regressions with instruments, similar to Brockman et al. (2010) and Dang and Phan (2016). 2
Motivated by these studies, we use firm size and the square of firm size as instruments for bond
maturity. We use the yield spread, measured as the daily difference between the corporate bond’s
yield-to-maturity and the linearly interpolated benchmark Treasury bond yield, as a measure for
the cost of debt. We calculate the benchmark Treasury yields based on 1-, 2-, 3-, 5-, 7-, 10-, 20-,
and 30-year constant maturity series. The test variable is the natural logarithm of the lagged equal-
weighted average six-month policy uncertainty index. Xi,t-1 is a vector of firm-level control
variables, which include profitability, leverage, and interest coverage. Zi,t is a vector of issue-level
and market-level control variables, including stock return volatility, bond rating average, coupon
rate, bond issue size, and yield curve slope. The detailed calculation of control variables are
presented in the Appendix. We also include industry and issuer types fixed effects by including
industry dummies and issuer type dummies in the regressions.
[Insert Table 7 about here]
The second-stage GMM regression results reported in column 1 of Table 7 indicate that
the cost of debt increases in policy uncertainty. We present the results for the new debt issue sample
as well as for two separate types of bond issues. While previous research contends that the positive
relation between uncertainty and cost of external financing is driven by the increase in the risk of
2 We also estimate the two-stage least squares regressions and the results are similar to the GMM regressions.
26
default (Gilchrist, Sim, and Zakrajsek, 2011) or the increase in the equity risk premium (Pastor
and Veronesi, 2011), our evidence suggests that the positive relation arises from the increase in
the use of shorter term debt financing, which becomes costlier when policy uncertainty increases.
However, this result only holds for the non-investment grade issues as policy uncertainty is
positive and statistically significant in column 3, but it is negative and statistically significant in
column 2. These results imply that for firms with non-investment ratings, borrowing shorter term
(longer term) debt is costlier (cheaper). Short-term debt also requires more frequent refinancing,
and thus, exposes the borrowing firms to higher liquidity risks. The results further indicate that
creditors might be concerned about these firms’ payment ability and liquidity risks amid high
policy uncertainty, leading them to charge higher risk premium during periods of high policy
uncertainty.
We note that the results in column 1 of Table 7 also indicate that bond maturity is
negatively related to yield spread, which is inconsistent with the normal yield curve of the U.S.
debt market. However, as we investigate the relation between policy uncertainty and cost of debt
for the two separate subgroups sorted on credit ratings, we find a positive relation between yield
spread and bond maturity for investment grade issues, which is consistent with a positive slope of
the yield curve, but a negative relation between yield spread and debt maturity for non-investment
grade issues. The latter finding is consistent with the theoretical arguments for a downward-sloping
credit yield curve for risky bonds (Merton, 1974; Jarrow, Lando, and Turnbull, 1997; Longstaff
and Schwartz, 1995) and previous empirical findings (Sarig and Warga, 1989; Fons, 1994).
4.5. Policy Uncertainty, Financial Constraints, and Corporate Investment
27
To complete our analysis, we investigate the financing-based explanation for Gulen and
Ion’s (2016) findings of a negative relationship between policy uncertainty and capital
investments. Specifically, we examine whether policy uncertainty has different effects on the
investments of financially constrained versus unconstrained firms. Using annual data, we estimate
the following regression model, which is similar to the baseline model in Gulen and Ion (2016):
INVESTMENTi,t = αt + ß PUt-1 + δ Xi,t-1 + λ Zi,t + ɛi,t (5)
The dependent variable is capital investment, calculated as capital expenditures divided by
the beginning of the period total assets. The policy uncertainty variable is the natural logarithm of
the six-month equal-weighted average BBD index. Similar to Gulen and Ion (2016), we control
for three firm-level financial variables (Xi,t-1) and two macroeconomic variables (Zi,t). Tobin’s q is
calculated as the ratio of the market value of equity plus the book value of assets minus book value
of equity plus deferred taxes, to the book value of assets. Cash Flow is measured by the ratio of
net operating cash-flow to lagged total assets. Sales Growth is the year-on-year change in annual
sales. GDP Growth is the year-on-year growth in real GDP. Election Indicator is a dummy variable
that equals one if a presidential election is hold in the current calendar year, and zero otherwise.
All specifications include firm fixed effects. We obtain accounting data from Compustat from
January 1985 to December 2015 period, and winsorized all variables at 1% and 99%. We examine
firms in both the new debt issue sample as well as in the Compustat universe. The debt issue
sample includes 3,423 firm-year observations, and the Compustat universe sample consists of
136,500 firm-year observations. We sort the sample firms into investment grade and high yield
subgroups.
[Insert Table 8 about here]
28
Table 8 presents the results of the relationship between capital investment and policy
uncertainty based on subgroup analysis for the new debt issue sample and the balance sheet sample.
The results indicate that policy uncertainty has more pronounced negative effect on investment of
firms with high yield debt ratings in both samples. This finding is consistent with the argument
that firms with investment grade ratings are financially unconstrained and have better access to
debt market, so they are more likely to have sufficient resources to support investments during
periods of high policy uncertainty. Thus, policy uncertainty has weaker effect on these firms’
investments. On the other hand, firms with non-investment grade ratings or no ratings, i.e.,
financially constrained firms, face greater difficulty in borrowing during the high policy
uncertainty periods. Even if financially constrained firms are successful in raising external debt
financing, they would be reluctant to use the shorter term debt that they can borrow during the
periods of high policy uncertainty to finance long-term investment projects due to the refinancing
risk, leading to more investment delays. This evidence suggests financing constraint, particularly
short-term debt financing, as a possible channel through which policy uncertainty adversely affects
corporate investments.
5. Robustness Checks
In addition to using alternative measures of debt maturity and policy uncertainty in Section
4.1, we conduct additional tests to further confirm the robustness of our results.
Quarterly data, length of the effect, and extreme policy uncertainty
We check for the robustness of our results by rerunning the short-term debt regressions
using quarterly balance sheet data. The quarterly balance sheet data provides only data for the
proportion of debt maturing in one year, but do not report data for the proportion of debt maturing
29
in two, three, four, or five years. We calculate the quarterly policy uncertainty index as the equal-
weighted average of the monthly policy uncertainty in a quarter. Using the similar regression
specifications as in Table 4 for quarterly data, we find that the coefficient on quarterly short-term
debt ratio is positive (0.033) and significant at the 1% level. This result indicates that our finding
of a negative relation between policy uncertainty and debt maturity is robust to quarterly data.
In the next analysis, we examine the evolution of the effect of policy uncertainty on debt
maturity structure by running 12 separate quarterly regressions that includes one of the lags 1-12
of quarterly policy uncertainty measures. We plot the coefficients of policy uncertainty in Figure
2. We find that the effect of policy uncertainty lasts up to three quarters, which is shorter than the
effect of policy uncertainty on firm investment documented by Gulen and Ion (2016) that persists
up to eight quarters. Firms are likely to raise financing first before they can commit to investment,
which could explain the timing gap between the effect of policy uncertainty on debt maturity
structure and investment.
Next, we investigate the effect of extreme policy uncertainty on debt maturity structure by
rerunning regressions with quarterly balance sheet data using a dummy variable for extreme policy
uncertainty. We define extreme policy uncertainty as one if the quarterly policy uncertainty index
belongs to the highest tercile and zero otherwise. We run 12 separate regressions using lags 1-12
of the extreme policy uncertainty dummy. Our unreported result indicates a positive and
statistically significant coefficient of extreme policy uncertainty and that the effect of extreme
policy uncertainty persists up to three periods, which is consistent with the result reported above.
Control for Investment Opportunities
Our results could be subject to an endogeneity concern due to the omitted variable problem.
The reason is that both policy uncertainty and debt maturity might be correlated with unobserved
30
investment opportunities. Therefore, we include proxies for investment opportunities as additional
controls in our analysis.3 The first proxy is the expected GDP growth generated using one-year-
ahead GDP forecasts from the Philadelphia Federal Reserve’s biannual Livingstone survey. We
compute the expected GDP growth as the percentage change between mean GDP forecast and the
current GDP level. The second proxy is the Michigan Consumer Confidence Index from the
University of Michigan to control for consumers’ expectations about future economic prospects.
The third proxy is the monthly Investor Sentiment Index from Baker and Wurgler (2007) to control
for expectations by equity-market participants. The results in Table 1A of the Internet Appendix
indicate that the effect of policy uncertainty on debt maturity is qualitatively unchanged,
confirming the robustness of our results.
Control for Economic Uncertainty
It is possible that policy uncertainty simply picks up the effects of general economic
uncertainty rather than the effect of policy-related uncertainty. To address this concern, we include
measures of macroeconomic uncertainty as suggested by Bloom (2009) as control variables. First,
we use the uncertainty about future economic growth obtained from the Livingstone survey
mentioned above, calculated every June and December as the coefficient of variation in GDP
forecasts obtained from the survey. The second variable is the uncertainty about future
profitability, proxied by the within-year cross-sectional standard deviation of firm-level profit
growth, as the year-on-year change in net profit divided by average sales. The third variable is the
uncertainty perceived by the equity markets, proxied by the monthly cross-sectional standard
deviation of stock returns and the VXO (implied volatility) index from the Chicago Board Options
Exchange. We include these variables in our baseline specifications in addition to the three proxies
3 Gulen and Ion (2016) also control for the forth variable, the Conference Board’s monthly Leading Economic Index
as a proxy for future GDP; however, we have not added this variable due to the availability of data at this time.
31
for investment opportunities variables above. The results reported in Table 1A in the Internet
Appendix indicate that our results continue to hold.
IV Regression
We further use instrumental variable (IV) regression to address potential endogenous
relation between policy uncertainty and debt financing. Similar to Gulen and Ion (2016), we use
partisan polarization in the U.S. House of Representatives and Senate as an instrument for policy
uncertainty. McCarty, Poole and Rosenthal (1997) develop the DW-NOMINATE scores to
measure a legislator’s ideological positions over time. We use the first dimension of the DW-
NOMINATE scores, which relates to the legislator’s positions on government intervention in the
economy, to calculate a polarization measure. The polarization measures are calculated separately
for the House of Representatives and Senate as the differences between the Republican and
Democratic party averages in the first dimension of the DW-NOMINATE scores. The polarization
variable can satisfy the conditions of an instrument for two reasons. First, it is correlated with the
policy uncertainty index. The higher the level of polarization in the House or Senate, the more
disagreement between politicians, the higher the level of uncertainty related to policy decisions,
holding everything else constant. Second, polarization is unlikely to be directly correlated with
debt financing.
To perform the instrument variable analysis, we cannot use the usual two-stage least
squares methodology because both policy uncertainty variable and the instrument are cross-
sectionally invariant. Instead, in the first stage, we run a time-series regression of the policy
uncertainty index on the instrument and control variables. Control variables are the same as those
in our baseline regressions. Then, we use the fitted values from this regression as a surrogate for
policy uncertainty in the baseline regressions.
32
[Insert Table 9 about here]
Columns 1 and 2 of Table 9 presents the first-stage and second-stage results of the debt
maturity IV regression. We find that the negative relationship between policy uncertainty and debt
maturity is virtually unchanged.
Canadian Policy Uncertainty
One might concern that the BBD index does not necessarily proxy for policy-related
uncertainty, but rather reflects non-policy economic uncertainty, which leads to potential
measurement error bias. Gulen and Ion (2016) argue that since the United States and Canadian
economies are tightly linked, the shocks that affect the general economic uncertainty in the U.S.
are likely to have an impact on the general economic uncertainty in Canada. To isolate the policy
uncertainty part of the BBD index, we follow a two-step regression approach. We first run a time-
series regression of the U.S. BBD index on the Canadian BBD index. The control variables in this
time-series regression include the cross-sectional average of the firm-level variables (Tobin’s q,
Cash flow, Sale Growth) used in the investment specifications in Section 4.4, economy-wide
average of the investment irreversibility and dependence on government spending variables used
in Gulen and Ion (2016), election year indication, real GDP growth, and the investment
opportunities variables used in Section 5.1. Then, we obtain the residuals from this regression,
which should represent only U.S. policy-related uncertainty. We use the residuals as a proxy for
U.S. policy uncertainty in our regressions. The results reported in column 3 of Table 9 indicate
that this new proxy for policy uncertainty is negatively related to debt maturity, which is consistent
with our earlier finding.
6. Conclusion
33
Using the policy uncertainty index by Baker, Bloom, and Davis (2016), our study examines
the relation between policy uncertainty and corporate debt maturity structure of U.S. public firms
during the period 1985-2015. We find strong and robust evidence of a negative relation between
policy uncertainty and debt maturity and the finding holds for both maturities of new bond issues
obtained from SDC Platinum as well as short-term debt ratios calculated from the balance sheet
data. The results are robust to several alternative measures of policy uncertainty and debt maturity.
Moreover, policy uncertainty is positively related to the number of debt covenants in loan
contracts. We find that these relations are stronger for financially constrained firms, i.e., those with
non-investment grade credit ratings, as these firms face creditors’ unwillingness to lend long-term
debts when policy uncertainty is high. In addition, our analysis on the cost of debt shows that yield
spread increases amid higher uncertainty for the new debt issue sample, and the result is
concentrated on non-investment grade borrowing firms. We investigate the link between policy
uncertainty and debt maturity and the relation between policy uncertainty and corporate
investment, and find that investment of financially constrained firms is more affected by policy
uncertainty, possibly due to their lack of external debt financing and unfavorable debt terms. Our
finding suggests that the effects of policy uncertainty on debt contracting as one of the channels
through which policy uncertainty adversely affects real investments.
34
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Appendix
Table A. Variable Definition
Variable Calculation Data sources
Abnormal earnings The ratio of the difference between the income before
extraordinary items, adjusted for common or ordinary
stock (capital) equivalents at time t and t–1, to the market
value of equity.
COMPUSTAT
Asset maturity Property, plant, and equipment over depreciation times the
proportion of property, plant, and equipment in total
assets, plus the ratio of current assets to the cost of goods
sold times the proportion of current assets in total assets.
COMPUSTAT
Asset volatility The standard deviation of the stock return (during the
fiscal year) times the market value of equity, all divided
by the market value of assets.
COMPUSTAT
Average rating Number between 1 and 19 (1 for CCC and 19 for AAA),
using the average of Standard and Poor’s and Moody’s
ratings (if only 1 rating is available, we use that bond
rating)
SDC Platinum
Coupon The coupon rate of the specified bond SDC Platinum
Interest coverage Logarithmic transformation of the pre-tax interest
coverage ratio.
COMPUSTAT
Issue size Natural logarithm of the total proceeds from the bond
issue (in $ million).
SDC Platinum
Leverage The ratio of total debt to the sum of market equity and
total debt, in which market equity is calculated as the
closing stock price times common shares outstanding.
COMPUSTAT
LMAT The time, measured in years, from the date of issuance to
the date of maturity in natural logarithm.
SDC Platinum
Market-to-book ratio Market value of the firm divided by the book value of
total assets
COMPUSTAT
Profitability Operating income before depreciation scaled by sales. COMPUSTAT
S&P credit ratings
dummy
A dummy variable that equals one if a firm has an S&P
rating on long-term debt, and zero otherwise
COMPUSTAT
Size The natural log of (market value of equity + book value of
assets – book value of equity) for firm i in year t
COMPUSTAT
Short-term debt 1 (ST1) The ratio of debt in current liabilities to total debt. COMPUSTAT
Short-term debt 2 (ST2) The ratio of debt in current liabilities plus debt maturing in
two years to total debt.
COMPUSTAT
Short-term debt 3 (ST3) The ratio of debt in current liabilities plus debt maturing in
two or three years to total debt.
COMPUSTAT
39
Short-term debt 4 (ST4) The ratio of debt in current liabilities plus debt maturing in
two, three, or four years to total debt.
COMPUSTAT
Short-term debt 5 (ST5) The ratio of debt in current liabilities plus debt maturing in
two, three, four, or five years to total debt.
COMPUSTAT
Short-term debt NP
(STNP)
The ratio of debt in current liabilities without the current
proportion of long-term debt to total debt.
COMPUSTAT
Term structure of
interest rate
Yield on 10-year government bonds subtracted from the
yield on 6-month government bonds at the year end
FRED at the
Federal Reserve
Bank of St. Louis
Treasury rate Treasury rate that corresponds most closely to the specific
bond’s maturity
FRED at the
Federal Reserve
Bank of St. Louis
Yield curve slope The difference between 10-year and 2-year Treasury rates FRED at the
Federal Reserve
Bank of St. Louis
Z-score Bankruptcy
dummy
A dummy variable that equals one if Altman’s Z-score is
greater than 1.81 and zero otherwise
COMPUSTAT
41
Figure 2: Coefficients of Policy Uncertainty on Quarterly Short-term Debt Ratio with 95%
Confidence Interval
-0.05
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
0.04
0.05
1 2 3 4 5 6 7 8 9 10 11 12
Upper bound Coefficient on Policy Uncertainty Lower bound
42
Table 1: Descriptive Statistics
This table reports the descriptive statistics of the variables. The sample consists of new bond issues by
sample firms over the period 1985-2015. Bond Maturity is the natural logarithm of the years to maturity of
new debt issues. Equal-weighted Bond Maturity is the equal-weighted average of the maturities of
multiple debt issues throughout a year. Issue size-weighted Bond Maturity is the weighted average of
the maturities of multiple debt issues throughout a year based on issue size. Measures of policy
uncertainty include the annual policy uncertainty index of year t as the natural logarithm of the
equal-weighted average of the monthly BBD index of the last three, six, or twelve months of the
year t-1, denoted by PU3, PU6, and PU12, or a value-weighted average of the BBD index over the
same periods. News-based policy uncertainty measures are the news-based component of the
overall index and are constructed similarly. Variables are defined in Table A.1 in Appendix.
Variables are winsorized at the top and bottom 1% to reduce the impact of outliers.
Variable N Mean Std. dev. 1st Quartile Median 3rd Quartile
Years to Maturity 6,433 12.070 9.923 6.086 10.144 10.503
Bond Maturity 6,433 2.250 0.652 1.806 2.317 2.352
Equal-weighted Bond Maturity 3,474 2.426 0.447 2.153 2.411 2.582
Issue-size weighted Bond Maturity 3,466 2.325 0.493 2.031 2.317 2.501
PU3 6,433 4.696 0.292 4.462 4.685 4.944
PU3 (news-based) 6,433 4.748 0.346 4.542 4.780 5.014
PU6 6,433 4.672 0.298 4.435 4.618 4.887
PU6 (news-based) 6,433 4.717 0.329 4.425 4.645 4.985
PU12 6,433 4.647 0.268 4.396 4.657 4.790
PU12 (news-based) 6,433 4.680 0.265 4.421 4.684 4.927
PU3W 6,433 4.690 0.288 4.479 4.707 4.875
PU3W (news-based) 6,433 4.742 0.340 4.526 4.692 5.048
PU6W 6,433 4.688 0.295 4.466 4.613 4.922
PU6W (news-based) 6,433 4.739 0.335 4.450 4.780 5.011
PU12W 6,433 4.659 0.278 4.456 4.604 4.838
PU12W (news-based) 6,433 4.699 0.290 4.461 4.698 4.917
Size (in $ million) 6,433 47,525 83,807 5,240 16,189 48,563
Size 6,433 9.412 1.455 8.421 9.548 10.702
Size squared 6,433 3.058 0.245 2.902 3.090 3.271
Leverage 6,433 0.166 0.123 0.076 0.137 0.223
Asset maturity 6,433 12.455 9.438 4.867 10.343 18.231
Market-to-book 6,433 1.928 0.928 1.294 1.642 2.255
Term structure 6,433 0.016 0.013 0.004 0.017 0.027
Abnormal earnings 6,433 0.002 0.077 -0.009 0.005 0.015
Asset volatility 6,433 0.049 0.025 0.031 0.043 0.061
43
R&D 6,433 0.018 0.030 0.000 0.000 0.024
R&D dummy 6,433 0.419 0.493 0.000 0.000 1.000
Z-Score dummy 6,433 0.787 0.409 1.000 1.000 1.000
Rating dummy 6,433 0.923 0.267 1.000 1.000 1.000
44
Table 2: Policy Uncertainty and Maturities of New Debt Issues: Baseline Regressions
The table presents the estimates of OLS regressions of maturities of new debt issues. Panel A is based on an unconsolidated sample of new debt
issues. The dependent variable in Panel A is the natural logarithm of years to maturity of the newly issued debt. Panel B is based on a consolidated
sample at the firm-year level. The dependent variable are the natural logarithm of the annual equal-weighted average of the years to maturity of the
newly issued debt and the natural logarithm of the annual issue size-weighted average of the years to maturity of the newly issued debt. In both
panels, policy uncertainty is the natural logarithm of the equal-weighted average of the BBD index in the last six months of the prior year. Control
variables are defined in the Appendix. Heteroscedasticity-robust standard errors are given in parentheses. *, **, and *** indicate significance levels
of the coefficients at the 10%, 5%, and 1% levels, respectively.
Panel A: Unconsolidated Sample (Transaction Level)
Variable (1) (2) (3) (4) (5) (6) (7)
Policy Uncertainty -0.081** -0.070** -0.094*** -0.085** -0.081** -0.070**
(0.035) (0.035) (0.035) (0.035) (0.035) (0.035)
Size -0.244** -0.242** -0.450*** -0.187* -0.388*** -0.185* -0.372***
(0.105) (0.105) (0.109) (0.105) (0.108) (0.104) (0.108)
Size Squared 1.403** 1.411** 2.592*** 1.046* 2.184*** 1.035* 2.071***
(0.614) (0.614) (0.632) (0.612) (0.630) (0.604) (0.624)
Leverage -0.235*** -0.225*** -0.225** -0.148* -0.131 -0.110 -0.043
(0.084) (0.084) (0.090) (0.086) (0.092) (0.091) (0.097)
Asset Maturity 0.007*** 0.007*** 0.005*** 0.007*** 0.005*** 0.007*** 0.005***
(0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)
Market-to-Book -0.031*** -0.035*** -0.022* -0.033*** -0.019 -0.032*** -0.017
(0.011) (0.011) (0.012) (0.011) (0.012) (0.011) (0.012)
Term Structure 0.118 1.195 0.870 1.077 0.684 1.266* 0.929
(0.615) (0.757) (0.755) (0.755) (0.753) (0.757) (0.755)
Abnormal Earnings 0.073 0.067 0.108 0.069 0.110 0.076 0.126
(0.081) (0.080) (0.080) (0.080) (0.080) (0.080) (0.080)
Asset Volatility -1.118*** -0.998*** -1.641*** -0.757** -1.378*** -0.816** -1.455***
(0.327) (0.329) (0.347) (0.335) (0.351) (0.335) (0.350)
R&D 0.488 0.479 0.943** 0.426 0.891** 0.478 0.966**
(0.349) (0.348) (0.405) (0.349) (0.405) (0.349) (0.405)
45
R&D Dummy 0.051*** 0.050*** 0.075*** 0.047** 0.071*** 0.050*** 0.076***
(0.019) (0.019) (0.023) (0.019) (0.023) (0.019) (0.023)
Z-Score Dummy 0.048** 0.048** 0.041 0.043* 0.035 0.043* 0.031
(0.024) (0.024) (0.026) (0.024) (0.026) (0.024) (0.026)
Rating Dummy 0.036 0.038 0.046* 0.039 0.047* 0.039 0.047*
(0.026) (0.026) (0.027) (0.026) (0.026) (0.026) (0.026)
Issuer-type Dummy -0.106*** -0.130***
(0.021) (0.022)
Issue-type Dummy 0.081*** 0.128***
(0.019) (0.020)
Constant 0.219 0.536 -1.132 1.183 -0.328 1.058 -0.411
(0.892) (0.899) (0.923) (0.900) (0.920) (0.886) (0.911)
Industry fixed effects No No Yes No Yes No Yes
Issuer type fixed effects No No No Yes Yes No No
Issue type fixed effects No No No No No Yes Yes
Observations 6,433 6,433 6,433 6,433 6,433 6,433 6,433
Adj. R-squared 0.018 0.019 0.051 0.021 0.054 0.020 0.054
Panel B: Consolidated Sample (Firm-Year Level)
Variable Equal-weighted Bond Maturity Issue size-weighted Bond Maturity
(1) (2) (3) (4) (5) (6)
Policy Uncertainty -0.105*** -0.128*** -0.106*** -0.113*** -0.136*** -0.113***
(0.034) (0.035) (0.034) (0.034) (0.035) (0.034)
Constant 1.155 1.826 1.894* 0.937 1.618 1.726
(1.186) (1.159) (1.148) (1.201) (1.174) (1.170)
Industry fixed effects Yes Yes Yes Yes Yes Yes
Issuer type fixed effects No Yes No No Yes No
Issue type fixed effects No No Yes No No Yes
Observations 3,474 3,474 3,474 3,466 3,466 3,466
Adj. R-squared 0.094 0.102 0.105 0.097 0.105 0.108
46
Table 3: Policy Uncertainty and Debt Maturity – Alternative Measures of Policy Uncertainty
The table presents the estimates of OLS regressions of maturity of new debt issues, as the natural logarithm of the years to maturity of the newly
issued debt, on the alternative measures of policy uncertainty index, as the natural logarithm of the equal-weighted average of the BBD index in the
last three months, twelve months, the value-weighted average of the BBD index in the last three months, six months, and twelve months of the prior
year, the similar variables constructed for news-based components of the BBD index, and the control variables. Control variables are defined in the
Appendix. Heteroscedasticity-robust standard errors adjusted for firm-level clustering are given in parentheses. *, **, and *** indicate significance
levels of the coefficients at the 10%, 5%, and 1% levels, respectively.
Panel A: Overall Policy Uncertainty Index
Variable (1) (2) (3) (4) (5) (6)
PU3 -0.108***
(0.034)
PU12 -0.080*
(0.043)
PU3W -0.108***
(0.033)
PU6W -0.102***
(0.035)
PU12W -0.088**
(0.039)
Controls Yes Yes Yes Yes Yes
Industry fixed effects Yes Yes Yes Yes Yes
Issuer type fixed effects Yes Yes Yes Yes Yes
Observations 6,433 6,433 6,433 6,433 6,433
Adj. R-squared 0.055 0.054 0.055 0.054 0.054
Panel B: News-based Policy Uncertainty Index
Variable (1) (2) (3) (4) (5) (6)
PU3 (news-based) -0.127***
(0.026)
PU6 (news-based) -0.133***
47
(0.029)
PU12 (news-based) -0.203***
(0.045)
PU3W (news-based) -0.132***
(0.026)
PU6W (news-based) -0.138***
(0.028)
PU12W (news-based) -0.167***
(0.035)
Controls Yes Yes Yes Yes Yes Yes
Industry fixed effects Yes Yes Yes Yes Yes Yes
Issuer type fixed effects Yes Yes Yes Yes Yes Yes
Observations 6,433 6,433 6,433 6,433 6,433 6,433
Adj. R-squared 0.056 0.056 0.056 0.057 0.057 0.056
48
Table 4: Policy Uncertainty and Short-term Debt – Balance Sheet Data
The table presents the estimates of OLS regressions of various measures of short-term debt, as measured by the proportion of notes payable in total
debt, the proportion of debt maturing in one, two, three, four, and five years or less (ST1-ST5), on the six-month policy uncertainty measure, as the
natural logarithm of the equal-weighted average of the BBD index in the last six months of the prior year, and the control variables. Control variables
are defined in the Appendix. Heteroscedasticity-robust standard errors adjusted for firm-level clustering are given in parentheses. *, **, and ***
indicate significance levels of the coefficients at the 10%, 5%, and 1% levels, respectively.
Variable STNP
(1)
ST1
(2)
ST2
(3)
ST3
(4)
ST4
(5)
ST5
(6)
Policy Uncertainty 0.011** 0.013** 0.005 0.007 0.016*** 0.022***
(0.005) (0.005) (0.006) (0.006) (0.006) (0.006)
Size 0.063*** 0.064*** 0.066*** 0.047*** 0.028*** 0.005
(0.008) (0.008) (0.009) (0.009) (0.009) (0.008)
Size Squared -0.404*** -0.464*** -0.517*** -0.416*** -0.290*** -0.131***
(0.039) (0.036) (0.041) (0.041) (0.038) (0.036)
Leverage -0.560*** -0.758*** -0.719*** -0.607*** -0.450*** -0.354***
(0.015) (0.015) (0.016) (0.016) (0.015) (0.015)
Asset Maturity -0.000 -0.000*** -0.000** -0.000** -0.000** -0.000**
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Market-to-Book 0.000 -0.002* -0.001 0.000 -0.000 0.001
(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)
Term Structure -0.682*** -0.523*** -0.173 0.101 0.077 0.003
(0.081) (0.093) (0.113) (0.114) (0.110) (0.100)
Abnormal Earnings -0.014*** -0.025*** -0.023*** -0.016*** -0.015*** -0.012***
(0.002) (0.003) (0.003) (0.003) (0.002) (0.002)
Asset Volatility -0.273*** -0.011 0.132*** 0.131*** 0.130*** 0.108***
(0.027) (0.026) (0.028) (0.026) (0.025) (0.022)
R&D -0.144*** 0.009 0.103*** 0.099*** 0.074*** 0.035**
(0.021) (0.020) (0.022) (0.020) (0.019) (0.017)
R&D Dummy -0.001 -0.005 -0.002 0.000 0.001 0.005
(0.005) (0.005) (0.005) (0.005) (0.005) (0.005)
Z-Score Dummy -0.060*** -0.096*** -0.086*** -0.071*** -0.057*** -0.046***
49
(0.004) (0.004) (0.004) (0.004) (0.004) (0.004)
Rating Dummy -0.002 -0.039*** -0.103*** -0.148*** -0.175*** -0.179***
(0.004) (0.005) (0.006) (0.006) (0.006) (0.007)
Constant 0.550*** 1.745*** 1.769*** 1.620*** 1.397*** 1.160
(0.046) (0.051) (0.051) (0.051) (0.047)
Industry fixed effects Yes Yes Yes Yes Yes Yes
Observations 77,780 77,832 65,079 64,643 64,375 63,008
Adj. R-squared 0.140 0.252 0.317 0.317 0.288 0.262
50
Table 5: Policy Uncertainty, Debt Maturity, and Financial Constraints
The table presents results of the baseline regressions estimated for two subsamples based on credit ratings. In column (1) and (2), the dependent
variable is the maturities of new debt issues, measured by the natural logarithm of the years to maturity of the newly issued debt. New debt issues
are classified into investment grade issues and high yield issues based on both Moody’s and S&P long-term debt ratings. In column (3) and (4), the
dependent variable is the proportion of debt maturing in three years or less (ST3). Firms are classified into investment grade firms and high yield
firms based on S&P long-term debt ratings. In both panels, the main independent variable is the six-month policy uncertainty index, proxied by the
natural logarithm of the equal-weighted average of the BBD index in the last six months of the prior year. Control variables are defined in the
Appendix. Heteroscedasticity-robust standard errors adjusted for firm-level clustering are given in parentheses. *, **, and *** indicate significance
levels of the coefficients at the 10%, 5%, and 1% levels, respectively.
Variable
New Debt Issues Sample Compustat Sample
Investment Grade
(1)
High Yield
(2)
Investment Grade
(3)
High Yield
(4)
Policy Uncertainty
-0.053 -0.114*** -0.054*** 0.024***
(0.041) (0.033) (0.011) (0.007)
Size -0.497*** -0.008 0.206*** -0.034***
(0.190) (0.131) (0.021) (0.010)
Size Squared 2.777** 0.082 -1.164*** -0.076*
(1.142) (0.728) (0.107) (0.045)
Leverage 0.053 -0.031 -0.556*** -0.592***
(0.168) (0.071) (0.053) (0.016)
Asset Maturity 0.008*** -0.002* 0.000 -0.000***
(0.002) (0.001) (0.000) (0.000)
Market-to-Book -0.008 0.057*** -0.004 0.001
(0.014) (0.015) (0.004) (0.001)
Term Structure 0.906 0.941 0.189 0.028
(0.920) (0.642) (0.219) (0.130)
Abnormal Earnings 0.292 0.022 -0.019* -0.016***
(0.206) (0.050) (0.011) (0.003)
Asset Volatility -1.654*** -0.574** -0.137 0.160***
(0.482) (0.283) (0.136) (0.027)
51
R&D 1.295*** -1.111** 0.154 0.098***
(0.442) (0.544) (0.119) (0.021)
R&D Dummy 0.094*** 0.003 -0.030** 0.009
(0.030) (0.019) (0.012) (0.006)
Z-Score Dummy 0.002 0.017 -0.067*** -0.072***
(0.039) (0.018) (0.011) (0.005)
Rating Dummy 0.100** -0.019 -0.030 -0.158***
(0.041) (0.019) (0.019) (0.007)
Issuer type Dummy -0.221*** -0.035**
(0.044) (0.017)
Constant -1.389 2.311** 2.197*** 1.001***
(1.716) (0.986) (0.150) (0.057)
Industry fixed effects Yes Yes Yes Yes
Issuer type fixed effects Yes Yes Yes Yes
Observations 5,302 1,131 10,645 54,277
Adj. R-squared 0.064 0.113 0.171 0.299
52
Table 6: Policy Uncertainty, Debt Covenants, and Financial Constraints
The table presents results of the regressions of debt covenants on policy uncertainty estimated for all firms and two subsamples based on credit
ratings. The dependent variable is the number of debt covenants. The main independent variable is the six-month policy uncertainty index, proxied
by the natural logarithm of the equal-weighted average of the BBD index in the last six months of the prior year. Firms are classified into investment
grade and high yield subsamples based on S&P long-term debt ratings. Control variables are defined in the Appendix. Heteroscedasticity-robust
standard errors adjusted for firm-level clustering are given in parentheses. *, **, and *** indicate significance levels of the coefficients at the 10%,
5%, and 1% levels, respectively.
Variable All firms Investment Grade High Yield
(1) (2) (3) (4) (5) (6)
Policy Uncertainty
0.143***
0.130**
0.031
0.181**
0.110*
0.193***
(0.053) (0.051) (0.078) (0.073) (0.064) (0.070)
Size -0.265*** -0.341*** -0.182*** -0.419*** -0.203*** -0.298***
(0.018) (0.038) (0.041) (0.061) (0.021) (0.047)
Leverage 0.988*** 0.721*** 0.900*** 0.376 0.782*** 0.627***
(0.106) (0.163) (0.287) (0.363) (0.115) (0.181)
Market-to-book 0.043*** 0.070*** -0.078*** 0.034 0.040*** 0.067**
(0.011) (0.021) (0.025) (0.045) (0.015) (0.028)
Return Volatility 0.401** 0.272 2.995*** 1.440*** 0.227 0.214
(0.175) (0.172) (0.550) (0.485) (0.194) (0.176)
Asset Tangibility -0.227*** -0.815*** -0.279*** -0.867*** -0.187*** -0.646***
(0.043) (0.127) (0.077) (0.205) (0.049) (0.152)
Log(Deal amount) 0.120*** 0.097*** -0.091** -0.036 0.139*** 0.117***
(0.020) (0.021) (0.042) (0.040) (0.021) (0.026)
Constant 0.531 2.870*** 4.873*** 5.830*** 0.031 0.836
(0.382) (0.461) (0.682) (0.879) (0.436) (0.519)
Firm fixed effects No Yes No Yes No Yes
Loan purpose
fixed effects
Yes Yes Yes Yes Yes Yes
Observations 14,913 14,913 2,653 2,653 11,437 11,437
Pseudo R-squared 0.1716 0.0469 0.2558 0.1383 0.0939 0.0621
53
Table 7: Policy Uncertainty, Debt Maturity, and Cost of Debt
The table reports the second-stage of the GMM estimates of the regressions of the bond yield spread,
measured by the daily difference between the corporate bond’s daily yield-to-maturity and the linearly
interpolated benchmark Treasury bond yield, on the maturity of new debt issues, the six-month policy
uncertainty index, and the control variables. Control variables are defined in the Appendix. We use firm
size and firm size squared as instruments for bond maturity. C-statistics are the statistics from testing if the
variable being tested must be treated as endogenous. F-statistics are the statistics from the F-test of joint
significance of instruments in the (untabulated) first-stage regressions. J-statistics are the statistics from the
Hansen test of validity of the instruments under the null of non-overidentification. Heteroscedasticity-robust
standard errors adjusted for firm-level clustering are given in parentheses. *, **, and *** indicate
significance levels of the coefficients at the 10%, 5%, and 1% levels, respectively.
Variable All debt issues
(1)
Investment Grade
(2)
High Yield
(3)
Bond Maturity
-0.032***
0.010***
-0.038***
(0.008) (0.003) (0.013)
Policy Uncertainty 0.005*** -0.002*** 0.015***
(0.001) (0.001) (0.002)
Return Std. Dev. -0.034 0.096*** 0.004
(0.022) (0.012) (0.012)
Average Return -0.231 -0.358** -0.090
(0.191) (0.154) (0.170)
Average Bond Rating -0.002*** -0.001*** -0.002***
(0.000) (0.000) (0.000)
ROS 0.007*** -0.001 0.001
(0.002) (0.001) (0.002)
Interest Coverage -0.006*** -0.003*** -0.000
(0.002) (0.001) (0.001)
Leverage -0.010*** 0.012*** -0.004
(0.004) (0.003) (0.002)
Coupon Rate 0.659*** -0.039 0.492***
(0.097) (0.052) (0.028)
Issue Size 0.004*** 0.000 0.004***
(0.001) (0.000) (0.001)
Yield Slope 0.441*** 0.324*** 0.192***
(0.038) (0.022) (0.074)
Issuer-type Dummy -0.002 0.003*** 0.003***
(0.001) (0.001) (0.001)
Constant 0.022** 0.005 0.002
(0.009) (0.004) (0.030)
Issuer-type effect Yes Yes Yes
Observations 6,692 5,426 1,266
C-statistics 25.141*** 14.596*** 5.346***
F-statistics 9.420*** 11.027*** 6.409***
J-statistics 53.751*** 36.472*** 14.238***
54
Table 8: Policy Uncertainty, Financial Constraints, and Corporate Investment
The table presents the estimates of OLS regressions of the firm-level annual investment, as measured by
the ratio of capital expenditure over lagged total assets, on the six-month policy uncertainty index, measured
by the natural logarithm of the equal-weighted average of the BBD index in the last six months of the prior
year, and the control variables. The first panel reports results for firms in Compustat universe, the second
panel reports results for firms in the new debt issue samples in this study. Classifications of firms into
investment grade and high yield bonds/deals are based on Moody’s and S&P long-term debt ratings. Control
variables are defined in the Appendix. Heteroscedasticity-robust standard errors adjusted for firm-level
clustering are given in parentheses. *, **, and *** indicate significance levels of the coefficients at the 10%,
5%, and 1% levels, respectively.
Variable
New Debt Issues Sample Compustat Sample
Investment Grade
(1)
High Yield
(2)
Investment Grade
(3)
High Yield
(4)
Policy Uncertainty
-0.007**
-0.038**
-0.004***
-0.012***
(0.003) (0.016) (0.001) (0.001)
Tobin’s q 0.096*** 0.047 0.096*** 0.015***
(0.032) (0.074) (0.015) (0.002)
Cash Flow 0.010*** 0.076*** 0.009*** 0.005***
(0.003) (0.013) (0.001) (0.000)
Sale Growth 0.003 0.007 0.004* 0.004***
(0.010) (0.015) (0.002) (0.000)
GDP Growth 0.003*** 0.002 0.002*** 0.003***
(0.001) (0.002) (0.000) (0.000)
Election Indicator 0.003* 0.020** 0.001*** 0.003***
(0.002) (0.010) (0.000) (0.000)
Constant 0.069*** 0.165** 0.052*** 0.098***
(0.016) (0.078) (0.007) (0.005)
Firm fixed effect Yes Yes Yes Yes
Observations 2,427 895 17,592 118,908
Adj. R-squared 0.101 0.212 0.086 0.053
Difference of
Coefficients on PU
P-value = 0.061
55
Table 9: Policy Uncertainty and Debt Maturity: IV Model and Two-Step Error-in-
Measurement Correction Model
The table presents the results of the two-stage IV regression and the second-step error-in-measurement
correction model. The dependent variable is the natural logarithm of years to maturity of the newly issued
debt. In the IV model, polarization is a measure of partisan polarization in the U.S. Senate used as the
instrument for policy uncertainty. In the error-in-measurement model, policy uncertainty is measured by
the OLS residuals obtained from regressing the U.S. policy uncertainty index in on the Canadian policy
uncertainty index, and on cross-sectional average of the control variables for investment, investment
opportunities, and investment irreversibility as in Gulen and Ion (2016). Control variables are defined in
the Appendix. Heteroscedasticity-robust standard errors adjusted for firm-level clustering are given in
parentheses. *, **, and *** indicate significance levels of the coefficients at the 10%, 5%, and 1% levels,
respectively.
Variable
IV Model Two-step Error-in-Measurement
Correction Model
(3)
First-stage
(1)
Second-stage
(2)
Polarization
0.398***
(0.022)
Instrumented PU -0.341*
(0.179)
PU (Residuals) -0.301***
(0.060)
Size 0.034 -0.352** -0.439***
(0.037) (0.152) (0.109)
Size Squared -0.151 2.055** 2.514***
(0.218) (0.876) (0.633)
Leverage 0.159*** -0.092 -0.137
(0.030) (0.131) (0.092)
Asset Maturity -0.000 0.005** 0.005***
(0.000) (0.002) (0.001)
Market-to-Book -0.011*** -0.037* -0.020*
(0.003) (0.021) (0.012)
Term Structure 13.088*** 4.054 -0.123
(0.289) (2.559) (0.623)
Abnormal Earnings 0.007 0.104 0.113
(0.029) (0.097) (0.080)
Asset Volatility 0.903*** -1.012** -1.251***
(0.110) (0.492) (0.348)
R&D -0.098 0.937 0.951**
(0.129) (0.599) (0.410)
R&D Dummy -0.008 0.066* 0.074***
(0.007) (0.040) (0.024)
56
Z-Score Dummy -0.005 0.034 0.033
(0.008) (0.034) (0.026)
Rating Dummy 0.023** 0.048 0.036
(0.009) (0.031) (0.026)
Issuer-type Dummy -0.039*** -0.150*** -0.102***
(0.007) (0.028) (0.022)
Constant 4.509*** 0.942 -1.112
(0.323) (1.445) (0.895)
Industry fixed effects Yes Yes Yes
Issuer type fixed effects Yes Yes Yes
Observations 6,077 6,077 6,253
Adj. R-squared 0.642 0.058 0.060
57
INTERNET APPENDIX
Table A1: Policy Uncertainty and Debt Maturity: Control for Investment Opportunities
and Economic Uncertainty
The table presents estimation results of our baseline regressions using several alternative macroeconomic
proxies for investment opportunities and economic uncertainty. The dependent variable is the maturities of
new debt issues at the transaction level, measured by the natural logarithm of years to maturity of the newly
issued debt. The main independent variable is the six-month policy uncertainty, proxied by the natural
logarithm of the equal-weighted average of the BBD index in the last six months of the prior year. Column
(1) additionally controls for investment opportunities variables. Column (2) additionally controls for
economic uncertainty variables. Control variables are defined in the Appendix. Heteroscedasticity-robust
standard errors adjusted for firm-level clustering are given in parentheses. *, **, and *** indicate
significance levels of the coefficients at the 10%, 5%, and 1% levels, respectively.
Bond Maturity
Panel A: Control for
investment opportunities
(1)
Panel B: Control for
economic uncertainty
(2)
Policy Uncertainty -0.137*** -0.125***
(0.042) (0.045)
Expected GDP Growth -0.342 -0.531
(0.806) (0.884)
Consumer index -0.000 -0.001
(0.002) (0.002)
Investor sentiment index -0.047*** -0.040**
(0.015) (0.016)
GDP forecast dispersion -0.017
(0.018)
Profit growth SD -0.099***
(0.019)
VXO -0.006***
(0.002)
Return SD -1.077*
(0.598)
Size -0.387*** -0.310***
(0.109) (0.108)
Size Squared 2.166*** 1.813***
(0.631) (0.626)
Leverage -0.112 -0.089
(0.092) (0.092)
Asset Maturity 0.005*** 0.005***
(0.001) (0.001)
Market-to-Book -0.016 -0.011
(0.012) (0.012)
Term Structure 0.361 -0.433
(1.020) (1.039)
58
Abnormal Earnings 0.105 0.097
(0.080) (0.079)
Asset Volatility -1.160*** -0.904**
(0.352) (0.360)
R&D 0.837** 0.622
(0.405) (0.405)
R&D Dummy 0.071*** 0.069***
(0.023) (0.023)
Z-Score Dummy 0.033 0.032
(0.026) (0.026)
Rating Dummy 0.049* 0.035
(0.026) (0.026)
Issuer-type Dummy -0.125*** -0.084***
(0.022) (0.023)
Constant -0.024 0.735
(0.945) (0.942)
Industry fixed effects Yes Yes
Issuer type fixed effects Yes Yes
Observations 6,433 6,433
Adj. R-squared 0.056 0.062