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Dynamic capital structure, speed of adjustment, macroeconomic conditions
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Electronic copy available at: http://ssrn.com/abstract=1101664
Macroeconomic Conditions and Capital Structure Adjustment Speed
Douglas O. Cook and Tian Tang*
Abstract
Studies show that capital structure choice varies over time and across firms and that macroeconomic conditions are important factors in analyzing firms’ financing choices. However, studies have largely ignored the impact of macroeconomic conditions on the adjustment speed of capital structure toward targets. Hackbarth et al. (2006) develop a contingent model for analyzing the impact of macroeconomic conditions on dynamic capital structure choice. Allowing for dynamic capital structure adjustments, their model predicts that firms should adjust their capital structure faster in booms than in recessions. We employ U.S. data over a 30 year sample period to test the relationship between macroeconomic conditions and capital structure adjustment speed using both two-stage and integrated partial adjustment dynamic capital structure models. We find evidence supporting the prediction from Hackbarth et al’s theoretical framework that firms adjust to target leverage faster in good states than in bad states, where states are defined by term spread, default spread, GDP growth rate, and market dividend yield. Our results also support the pecking order theory in that under-levered firms adjust faster than firms that are over-levered. We find evidence favoring the market timing theory implication that under-levered firms have less incentive to adjust toward target leverage when stock market performance is good, as measured by dividend yield on the market and price-output ratio. Robustness tests demonstrate that our speed of capital structure adjustment cannot be simply explained by firm size, the degree of deviation from target, or by the definition of debt ratio. Our results are also robust to potential boundary issues. JEL classifications: G11; G18; G23 Keywords: Dynamic capital structure, speed of adjustment, macroeconomic conditions
* Both authors are from the Culverhouse College of Business, University of Alabama, Tuscaloosa, AL 35487-0224. Cook: [email protected], (205) 348-8971. Tang: [email protected], (205) 239-5671. We thank Xudong Fu for helpful suggestions. Cook gratefully acknowledges financial support from the Ehney A. Camp, Jr. Chair of Finance and Investments.
Electronic copy available at: http://ssrn.com/abstract=1101664
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1. Introduction
Although there are corporate capital structure theories explaining firms’ financing
decisions, little is known about how macroeconomic conditions affect the adjustment
speed of capital structure towards target leverage. In this paper, we use two dynamic
partial adjustment capital structure models to test the relationship between
macroeconomic conditions and the adjustment speed of capital structure.
The primary existing theories of corporate capital structure explaining firms’
financing decisions can be categorized as the tradeoff, pecking order, and market timing
theories. In the tradeoff theory, firms select target leverage ratios based on an exchange
between the benefits and costs of increased leverage (Modigliani and Miller, 1963,
Jensen and Meckling, 1976, Myers, 1977, Stulz, 1990, Hart and Moore, 1995, and Ross,
1977). In the absence of any adjustment cost, firms would continuously offset deviations
from target. The presence of large adjustment costs would likely slow down the
adjustment time.1
The pecking order theory suggests that investments are first financed by internal
funds, then external debt, and, as a last resort, external equity (Myers and Majluf, 1984).
According to this theory, firms do not have a strong incentive to rebalance their capital
structures. It suggests a very slow adjustment speed towards a target debt ratio.
Baker and Wurgler (2002) propose the market timing theory of capital structure,
arguing that current capital structure is the cumulative outcome of past attempts to time
the market. In this theory, there is no optimal capital structure and market valuation has a
1 Myers (1984) points out that large adjustment costs could force firms into long excursions away from their initial debt ratios. Fisher, Heinkel and Zechner’s (1989) dynamic tradeoff model in the presence of recapitalization costs indicates that firms’ actual leverage ratios deviate away from target ratios but firm characteristics explain some of the cross-sectional deviations.
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persistent impact on capital structure. However, Leary and Roberts (2005) provide
evidence contradicting the implications of market timing theory. They show that the
persistent effect of shocks on leverage is more likely due to the presence of adjustment
costs than to an indifference towards capital structure.
Numerous papers suggest that firms’ financing decisions nudge them towards
target leverage ratios, consistent with the tradeoff theory, but the evidence regarding
adjustment speed is mixed. Survey results presented by Graham and Harvey (2001)
indicate that about 80 percent of the CFOs in their sample affirm having a target range or
“strict” debt-equity ratio target. In addition, managers express concern about the costs
and advantages associated with debt financing. Shyam-Sunder and Myers (1999) test the
static tradeoff theory against the pecking order theory and show that the latter has greater
time series explanatory power. Their result suggests a slow adjustment speed toward
target leverage. Other articles, such as Fama and French (2002), Baker and Wurgler
(2002), Welch (2004), and Hovakimian (2006), also provide evidence of slow adjustment
in capital structure.
Several recent papers provide evidence of faster adjustment speed than in
previous studies. Flannery and Rangan (2006) find a much faster adjustment speed after
controlling for firm fixed effects. Leary and Roberts (2005) show that the impact of
shocks on leverage is appropriately rebalanced away over the subsequent two to four
years. Alti (2006) suggests that the impact of market timing activities in IPOs on
leverage vanishes completely in two years. He concludes that the long-run market timing
effect on leverage is limited and firms’ financing decisions in the long-run are largely
consistent with leverage targets.
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Lemmon et al. (2008) find that although there is some convergence toward the
mean over time, cross-sectional variation in firms’ leverage ratios are closely related to
initial leverage ratios even prior to firm IPOs. They also find that a significant portion of
a firm’s capital structure is explained by firm fixed effects. Cook and Kieshnick (2008)
relax the assumptions in Lemmon et al. that the conditional expectation functions of their
proportional leverage measures should be linear functions of the explanatory variables.
They show that the conditional expectation function is consistent with a sigmoidal
function and that the convergence and persistence patterns observed by Lemmon, Roberts,
and Zender can be explained by the two inflection points and characteristics of this
sigmoidal function. They also show that the importance of firm fixed effects is reduced
after controlling for the non-linear nature of the conditional expectation function,
Studies show that capital structure choice varies over time and across firms and
that macroeconomic conditions are important factors in analyzing firms’ financing
choices (e.g. Choe, Musulis, and Nanda, 1993; Gertler and Gilchrist, 1994; Korajczyk
and Levy, 2003).2 However, studies on the adjustment speed of capital structure derived
from analyzing traditional capital structure theories as well as studies on the role of
macroeconomic factors in capital structure choice have largely ignored the impact of
macroeconomic conditions on the adjustment speed of capital structure toward targets.
Hackbarth et al. (2006) develop a contingent model for analyzing the impact of
macroeconomic conditions on dynamic capital structure choice. Allowing for dynamic
2 Choe, Musulis, and Nanda (1993) find equity issuance relative to the market value of bonds to be positively correlated with previous stock returns and various business cycle variables. Gertler and Cilchrist (1994) show that small firms have relatively more stable short-term debt over the business cycle than large firms. Korajczyk and Levy (2003) examine the impact of macroeconomic conditions on capital structure choice for financially constrained and unconstrained firms and find evidence that target leverage is counter-cyclical for financially constrained firms, while pro-cyclical for financially unconstrained firms.
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capital structure adjustments, their model predicts that firms should adjust their capital
structure faster in booms than in recessions. The only related empirical study is by
Drobetz et al. (2006), who document a positive correlation between the business cycle
and speed of capital structure adjustment for a sample of 91 Swiss firms.
We employ U.S. data over a 30 year sample period to test the relationship
between macroeconomic conditions and capital structure adjustment speed using both
two-stage and integrated partial adjustment dynamic capital structure models. We find
evidence supporting the prediction from Hackbarth et al’s (2006) theoretical framework
that firms adjust to target leverage faster in good states than in bad states, where states are
defined by term spread, default spread, GDP growth rate, and market dividend yield. Our
results also support the pecking order theory in that under-levered firms adjust faster than
firms that are over-levered. We find evidence favoring the market timing theory
implication that under-levered firms have less incentive to adjust toward target leverage
when stock market performance is good, as measured by dividend yield on the market
and price-output ratio. Robustness tests demonstrate that our speed of capital structure
adjustment cannot be simply explained by firm size, the degree of deviation from target,
or by the definition of debt ratio. Our results are also robust to potential boundary issues.
The rest of the paper is as follows. Section 2 discusses the dynamic partial-
adjustment capital structure model used in this study and the specifications of variables.
Section 3 describes the data and sample for the empirical analysis. Section 4 provides the
empirical analysis results. Section 5 presents a series of robustness tests. Conclusions
are in Section 6.
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2. The model and specifications
2.1. Two models
Since recent literature contains two distinct partial adjustment models, we employ
both a two-stage and an integrated dynamic partial-adjustment capital structure model.
2.1.1. Two-stage dynamic partial-adjustment capital structure model
We utilize a dynamic partial-adjustment capital structure model (Hovakimian et
al., 2001; Drobetz and Wanzenried, 2006), which allows target debt ratios to vary both
across firms and over time, and implies that deviations from targets are not necessarily
quickly offset. Following previous studies on capital structure (e.g. Fama and French,
2002; Kayhan and Titman, 2007), we estimate the adjustment speed of capital structure
towards target using two-stage estimations based on target leverage proxy from the first-
stage regression. The model is as follows:
Stage 1:
Di,t*= γMacrot-1 + βXi,t-1 (1)
Although previous studies obtain the fitted value of Equation (1) as the proxy for
target leverage using linear regression models, Papke and Wooldridge (1996) point out
that there are methodological problems using linear models for fractional data. To
manage such problems, they develop a quasi-likelihood method with a fractional
dependent variable. Thus, we follow Papke and Wooldridge (1996) and use the quasi-
maximum likelihood estimation method (QMLE) to estimate the fitted value of Equation
(1) as the proxy for target leverage.
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In a frictionless world, firms would move quickly back to their target level, which
is the level chosen by firms in the absence of any adjustment costs (Hovakimian, Opler
and Titaman, 2001; De Miguel and Pindado, 2001). However, in the presence of
adjustment costs, firms may adjust partially back to their desired leverage ratio over
multiple periods. In the second stage, we use the standard partial adjustment model in the
literature (Hovakimian, Opler and Titaman, 2001; De Miguel and Pindado, 2001) as
follows:
Stage 2:
Di,t - Di,t-1 = δ (Di,t* - Di,t-1) + εi,t (2)
where δ represents the proportion of deviation away from the firm’s target leverage,
closed by the firm from period t-1 to period t. In other words, the negative coefficient
estimate before the lagged debt ratio captures the adjustment speed back toward target
leverage, which is the main focus of this study. δ=1 indicates that firms fully adjust for
any deviation away from their targets. In the presence of adjustment costs, as in this
study, δ is expected to be less than 1. We estimate Equation (2) using standard OLS with
robust t-statistics from standard errors corrected for heteroskedasticity.
2.1.2 Integrated dynamic partial-adjustment capital structure model
Evaluating the two-stage estimation procedure that is commonly used in the
literature, Flannery and Rangan (2006) show that the partial adjustment speed reflected
by the coefficient on target leverage from first-stage regressions is abnormally smaller
than theory would predict and that the long-term elasticity of the observed debt ratio
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relative to its target is significantly different from unity. Thus, following Flannery and
Rangan (2006), we estimate the impact of macroeconomic conditions on the capital
structure adjustment speed by including the partial adjustment and firm fixed effects in
one integrated capital structure model. Specifically, we model the target debt level of
firm i in period t (Di,t) as a linear function of a set of lagged macroeconomic variables
(Macrot-1) and firm characteristic variables (Xi,t-1), which are the same as in Equation (1).
The standard partial adjustment model is equivalent to Equation (2). Then, substituting
(1) into (2) and rearranging yields the following:
Di,t = (1- δ) Di,t-1 + δ βXi,t-1 + δ γMacrot-1 + εi,t (3)
We estimate the speed of capital structure adjustment from Equation (3) across
good and bad macroeconomic states, respectively. During model estimation, we control
for firm fixed effects since Flannery and Rangan (2006) find that this increases
adjustment speed. We do not include year dummy variables in the subsequent panel
regression since these may absorb the time-varying influence of macroeconomic
conditions on capital structure.
2.2. Definitions of Leverage
There is no consensus on whether book- or market-valued debt ratios should be
used in capital structure studies. Some argue that leverage should be computed using the
book value of capital because book ratios are independent of factors that are not under the
direct control of firms (Fama and French, 2002; Thies and Klock, 1992). Others prefer
market debt ratios. For example, Welch (2004) provides evidence that market leverage
better reflects the agency problems between creditors and equity holders and can serve as
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an indispensable input into WACC computations. Since it is highly possible that some
firms have book value rather than market value targets and vice versa, we use both book
and market leverage measures. Specifically, our book debt ratio is:
BDi,t = ti
titi
TALDSD
,
,, + (4)
where SDi,t + LDi,t is the sum of firm i’s short-term and long-term book value of interest-
bearing debt at time t, and TAi,t denotes the book value of total assets.
We use the following market debt ratio:
MDi,t=titititi
titi
PSLDSDLDSD
,,,,
,,
++
+ (5)
where SDi,t + LDi,t is the sum of firm i’s short-term and long-term book value of interest-
bearing debt at time t, and Si,tPi,t denotes the product of the number of common shares
outstanding and the stock price per share at time t, which denotes the market value of
firm i’s equity. We estimate our two models using both BDi,t and MDi,.
2.3. Determinants of leverage
2.3.1. Macroeconomic target determinants
There is evidence that macroeconomic variables can affect target leverage through
the aggregated distribution of wealth between managers and outside shareholders
(Kiyotaki and Moore, 1997; Levy, 2001). Korajczyk and Levy (2003) argue that
corporate profits and equity performance influences managers’ compensation. Therefore,
following Korajczyk and Levy, we use three proxies for the aggregate distribution effect:
CPG, VRMR, and CPSPREAD where CPG represents the two-year aggregate domestic
non-financial corporate profit growth, obtained from the annual Flow of Funds database
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on the Federal Reserve website; VRMR equals the two-year value-weighted market
return of stocks traded on the NYSE/AMEX/NASDAQ, extracted from CRSP; and
CPSPREAD is the commercial paper spread, computed from the spread between the
annualized rate of three-month commercial paper and the three-month Treasury bill.3
2.3.2. Firm characteristics determinants
We follow the literature and use a standard set of firm determinants of leverage
(Ranjan and Zingales, 1995; Hovakimian, 2003; Hovakimian et al., 2001; Fama and
French, 2002; Flannery and Rangan, 2006).
MB is the ratio of market value to book value of total assets. There is mixed
evidence on the relationship between MB and leverage ratio. For example, higher MB
could be viewed as a sign of greater future investment opportunities which firms may try
to protect by restraining their leverage (e.g. Hovakimian et al., 2004; Flannery and
Rangan, 2006). On the other hand, a simple version of the pecking order theory implies
that leverage increases when investment exceeds retained earnings (Drobetz et al., 2006).
TANG is the ratio of gross property, plant and equipment to total assets. Firms
with greater tangible assets, potentially collateralized, are likely to have relatively lower
bankruptcy costs, and thus, higher debt capacity (Titman and Wessels, 1998; Hovakimian
et al,. 2004).
3 We obtain the aggregate domestic non-financial corporate profit growth rate from the Annual Flow of Funds database on the Federal Reserve Board’s web page at http://www.federalreserve.gov/releases. The commercial paper rate and the Treasury bill rate are from the Federal Reserve Board’s web page at http://www.federalreserve.gov/releases.
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EBIT is the ratio of earnings before interest and taxes to total assets. Firms with
higher earnings per asset dollar tend to operate with lower leverage ratios because high
retained earnings reduce the need to issue debt.
DEP equals the ratio of depreciation to total assets. Firms with higher
depreciation expenses are less likely to issue debt for tax shield purposes. LNTA is the
natural logarithm of total assets, which we use as a proxy for firm size. Larger firms tend
to have higher leverage ratios because they have lower cash flow volatility, better access
to financial markets, and are less likely to become financially distressed (Rajan and
Zingales, 1995; Hovakimian et al., 2004).
We use the variables RD, RDD, and SE to proxy firm uniqueness. RD is the ratio
of R&D expenses to firm book assets. RDD is a dummy variable that takes the value of 1
if firms report R&D expenses and 0, if otherwise. SE equals selling expenses scaled by
total sales. Firms with higher R&D expenses and higher selling expenses tend to have
unique assets and develop unique products, which may indicate higher bankruptcy costs
(Titman, 1984; Hovakimian et al., 2004). Thus, firms with higher R&D and selling
expenses are more likely to protect themselves with lower leverage ratios. In order to
control for industry characteristics which may not be captured by other independent
variables, we include the firm’s industry median debt ratio, where the industry is
identified by using the Fama and French 49 industry definition.
To test the effect of current leverage levels relative to target, we construct
LEVDUMMY, which takes the value of one if a firm-year observation is over-levered, i.e.
when (Di,t-1 – Di,t-1*) is greater than zero, but otherwise takes the value of zero.4
4 Since Lemmon et al. (2008) argue that most of the variation in firms’ leverage ratios is closely related to their initial leverage ratios, we add initial leverage ratios to our models’ explanatory variables. We find
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2.4. Macroeconomic factors
In order to test the impact of macroeconomic conditions on the speed of capital
structure adjustment, it is important to analyze the macroeconomic factors that define
macroeconomic conditions. We employ such a set of factors, commonly used in the
literature as indicators of macroeconomic conditions. These factors include term spread,
default spread, GDP growth rate, market dividend yield, and the price-output ratio.
We measure term spread as the difference between the twenty-year government
bond yield series and the three-month Treasury-bill rate series. High term spread is
viewed as a strong predictor for a good economy (Stock and Watson, 1989; Estrella and
Mishkin, 1998). Thus, we expect faster adjustment speed in good macroeconomic
conditions as predicted by a high term spread.5
Following Korajczyk and Levy (2003) and Fama and French (1989), we define
default spread as the difference between the average yield of bonds rated Baa and the
average yield of bonds rated Aaa, each rated by Moody’s and with a maturity between
20 and 25 years. Tracking long-term business cycle conditions, this indicator is higher
during recessions and lower during expansions (Fama and French, 1989). Thus, we
expect that firms will adjust capital structure faster when default spreads are lower.6
that although the adjustment speed estimates from our models are reduced by about 20 percent in magnitude in each scenario, the signs and significance of the key regressors are consistent with those obtained from estimating models without the initial leverage ratio. In other words, the impact of macroeconomic conditions on firm adjustment speed remains significant even after considering the initial leverage ratio. We do not report these results in the tables. 5 Drobetz (2006) uses the three month money market interest rate as a macroeconomic factor but Estrella and Hardouvelis (1991) argue that the slope of the yield curve has more predictive power than the short-term interest rate. 6 Similar measures in the literature include the yield difference between AAA rated corporate bonds and government bonds (Drobetz, 2006), the yield difference between high yield corporate bonds and AAA
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Since an economic recession is traditionally defined as a decline in real Gross
Domestic Product (GDP) for two or more successive quarters of a year, we use the real
GDP growth rate over quarters in a year as a direct indicator of macroeconomic
conditions. We expect a faster adjustment speed in good macroeconomic conditions as
indicated by a higher contemporaneous real GDP growth rate.
Although these three macroeconomic factors are unambiguous predictors of
adjustment speed, the pecking order theory suggests that under-levered firms should
adjust faster than over-levered firms due to the preference of issuing new debt compared
to issuing new equity. In order to test this effect, we measure the impact of leverage level
relative to target on adjustment speed.
We also employ two stock market performance-related macroeconomic factors:
market dividend yield and price-output ratio. Market dividend yield equals total dividend
payments on the value-weighted NYSE/AMEX/Nasdaq portfolio over year t-1 divided by
the current value of the portfolio at time t. Since dividend levels tend to be sticky, a high
dividend yield indicates low stock prices, which are more likely in economic contractions
(Drobetz, 2006). Therefore, we anticipate that the adjustment speed of capital structure
will be higher when the dividend yield is lower.
As an indicator of future stock market performance, we use the price-output ratio,
calculated as the S&P stock price index in January in a given year scaled by GDP from
the previous year.7 This price-output ratio has been shown to track a substantial fraction
of variation in both expected returns and excess returns on the aggregate stock market,
corporate bonds, and the difference between the high yield corporate bond rate and the rate of 10-year Treasury bonds (Gertler and Lown, 1999). 7 The S&P stock price index is available from Robert Shiller’s hompage at http://www.econ.yale.edu/~shiller/data.htm while GDP data is available from the website of the U.S. Department of Commerce at http://www.bea.gov/bea.
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capturing a larger fraction of this variation than price-earnings and price-dividend ratios
(Rangvid, 2006).8 The mean reversion in the price-output ratio implies that expected
returns are high if current stock prices are low relative to current GDP. Thus, ceteris
paribus, we expect the adjustment speed of capital structure to be higher when the price-
output ratio is lower.9
However, if firms time equity issuance (Baker and Wurgler, 2002), they would be
reluctant to issue equity when stock prices are low. The confluence of these two effects
makes prediction difficult. For example, suppose the firm is over-levered and the price-
output ratio is low. The firm wishes to issue equity to move toward the leverage target
but is reluctant to do so because stock prices are low. However, if the firm is under-
levered, then it could issue debt and move towards its target ratio. Due to these
conflicting influences, we analyze the effect of being over-levered and under-levered on
adjustment reactions.
2.5. Defining good and bad states of macroeconomic conditions
Since we intend to examine the impact of macroeconomic conditions on capital
structure adjustment speed by estimating and comparing the adjustment speed across
good and bad macroeconomic states, it is necessary to identify the good and bad
macroeconomic states based on the macroeconomic factors discussed in the previous
section. We proceed by dividing the 30 year sample period from 1976 to 2005 into
quintiles based on the order of each macroeconomic factor. For divisions based on the
8 Korajczyk and levy (2003) use the three-month CRSP value-weighted equity market returns as a proxy of stock market performance. 9 In consideration of the fact that the price-output ratio may predict stock returns farther into the future, we also re-estimate the price-output ratio lagged an additional year. These results, not reported, are consistent with the results obtained using the price-output ratio.
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term spread and GDP growth rate factor, we equate good macroeconomic states with the
highest quintile factor years, moderate macroeconomic states with the mid-three quintile
factor years, and bad macroeconomic states with the lowest quintile factor years since, as
discussed in Section 2.4, good states are defined as higher past term spreads and higher
current GDP growth rate. For divisions based on default spread, dividend yield, and
price-output ratio, we equate good macroeconomic states with the lowest quintile factor
years, moderate macroeconomic states with the mid-three quintile factor years, and bad
macroeconomic states with the highest quintile factor years because good
macroeconomic conditions are defined in terms of lower past default spread, lower
dividend yield, and lower past price-output ratio.
3. Data and sample
We obtain the primary sample of firm-year observations used in this study from
Compustat’s Industrial Annual Database over the sample period 1976 to 2005.
Consistent with earlier studies, we exclude financial firms (6000-6999) and utilities
(4900-4999) from the sample because they are usually regulated and special factors might
be incorporated into their capital structure decisions (Fama and French, 2002; Frank and
Goyal, 2003; and Korajczyk and Levy, 2003). In order to be included in the sample, the
firm must have complete data available in two adjacent years. We exclude observations
with leverage levels that fall outside the outlier leverage levels of [0,1]. Our final sample
consists of 127,665 firm-year observations for analysis based on the book-valued debt
ratio and 129,936 firm-year observations for analysis based on the market-valued debt
ratio.
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We extract the cash dividend amount and market capitalization from CRSP to
compute the dividend yield on the market. We obtain other relevant macroeconomic
variable information from the website of the Federal Reserve Board, U.S Department of
Commerce and Robert Shiller’s homepage.10
4. Empirical Analysis
4.1. Summary Statistics
Table 1 Panel A reports the mean, median and standard deviation of the debt
ratios over the sample period, 1976 to 2005. Consistent with the argument in the
literature that book-valued debt ratios are less subject to non-controllable firm factors, we
find that the book debt ratio fluctuates less over the sample period than the market debt
ratio. Both the market-based and book value-based debt ratios, are relatively low in the
stock market expansion periods of the 1990s, increase slightly during the internet crash at
the millennium, and then decrease as the stock market begins its recovery. This is
consistent with the view that firms time their equity issuance and have less incentive to
issue debt when the stock market performs well. Table 1 Panel B demonstrates the
substantial actual and absolute levels of deviation from target leverage. For example, the
actual book (market) debt ratio deviation from target ranges from -.2259 (-.2710) to .3026
(.3598) across quintiles.
Table 2 Panels A and B present the univariate tests of leverage variables across
good and bad states of macroeconomic conditions defined by different macroeconomic
factors. For each factor (i.e. the criteria dividing macroeconomic conditions into states
10 The Federal Reserve Board’s website is at http://www.federalreserve.gov/releases, the U.S. Department of Commerce’s website is at http://www.bea.gov/bea, and Robert Shiller’s homepage is at httpe://www.econ.yale.edu/~shiller/data.htm.
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including term spread, default spread, GDP growth rate, dividend yield, and price-output
ratio), we report the means and medians in good and bad states, and the differences in
means and medians between good and bad states. We also report P-values assuming
unequal variances in variables between good and bad state sub-samples.
The result shows that debt ratios are generally significantly higher in bad states
than in good states, regardless of how the debt ratio is measured. This counter-cyclical
feature for debt levels is consistent with theories developed and evidence provided in the
literature.11 The median debt ratios across good and bad states exhibit the same pattern as
the mean debt ratios.
Table 2 Panel C shows that there is a smaller percentage of over-leveraged firms
than under-leveraged firms in each state regardless of the division criterion. This is
consistent with the modified pecking order story (Myers, 1984) that indicates firms are
concerned less about excessively low leverage than they are about excessively high
leverage.
4.2. Adjustment speed estimates
In this section, we estimate capital structure adjustment speed based on the
integrated dynamic partial adjustment model and the two-stage dynamic partial
adjustment model and illustrate results in Table 3 and Table 4, respectively.
Macroeconomic factors are term spread, default spread, GDP growth rate, dividend yield
11Theoretically, Levy (2001) develops an agency model in which the optimal amount of leverage is increased to realign manager’s incentives with those of shareholders in recessions. Hackbarth et al’s (2006) framework for analyzing the impact of macroeconomic conditions on dynamic capital structure choice predicts that leverage ratios should be countercyclical. Empirically, Choe et al. (1993) and Bayless and Chaplinsky (1996) present evidence that equity issuance increases during expansions due to the counter-cyclical variation in adverse selection costs. Korajczyk and Levy (2003) also provide evidence of the counter-cyclical feature of the debt level.
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and price-output ratio. To detect pecking order effects, we analyze the impact on
adjustment speed of a firm being over or under-levered relative to target on the first three
factors. To detect timing effects, we observe the net effect on adjustment speed of
macroeconomic factors and market timing as well as the interaction of over- or under-
leverage with the dividend yield and price-output ratio. We measure leverage using both
book value and market value.
4.2.1. Integrated dynamic partial adjustment capital structure model
We control for firm fixed effects and report the results from estimating Equation 3
in Table 3, Panels A through E. For each panel, columns 2 through 4 present regression
results for the good, bad and pooled sub-samples when debt ratio is computed on a book
value basis. Columns 5 through 7 present these same results when debt ratio is computed
on a market value basis.
In order to compare the difference in the speed of capital structure adjustment
towards target between good and bad states, we include an interaction term, computed by
the product of the lagged debt ratio and the good state dummy variable, which takes the
value of 1 if the firm-year observation belongs to a good state and takes the value of 0 if
otherwise. Panel A presents estimation results for Equation (3), when the states of
macroeconomic conditions are defined by term spread. The results show that, for both
book- and market-valued debt ratios, firms adjust their capital structure back to target
leverage faster in good states than in bad states. Specifically, for the book-value debt
ratio, firms close in one year about 79.1% (since 1-δ=20.9%) of the gap between the
actual and target debt ratio in good states, while they only correct about 65.2% of
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deviation away from target in bad states. The negative coefficient estimate on the
interaction term between the lagged debt ratio and the good state dummy in the pooled
regression is further evidence that adjustment is faster in good states than in bad states.12
The positive coefficient on the interaction term between the lagged debt ratio and
the leverage dummy variable provides supporting evidence for the pecking order theory
as underleveraged firms with little reluctance to issue external debt adjust faster than
over-levered firms.13 The same patterns occur when debt ratios are measured on a
market-value basis, in which case firms adjust in one year about 79.3% of the deviation
from target leverage in good states, while they only adjust 68.5% back to target in bad
states. Again, the difference in adjustment speed estimates across the two states is
significant and the signs of the interaction terms are identical.
Panels B and C report results when the determinants of states depend on default
spread and GDP growth rate. All results show that the adjustment speed of capital
structure is faster in good states than in bad states, from both a book and market debt ratio
perspective. This is consistent with the prediction of Hackbarth et al’s (2006) model. As
in Panel A, these results also contain support for the evidence of pecking order effects.
Panels D and E illustrate the regression results from estimating Equation (3),
when the states of macroeconomic conditions are defined by market dividend yield and
price-output ratio. As discussed in Section 2.4, market timing can act as a confounding
effect when macroeconomic conditions are defined by these two stock market
performance related factors. Thus, the measure of a firm’s adjustment speeds toward
12 The negative interaction coefficient implies faster adjustment because the coefficient on the lagged debt ratio is 1-δ, where δ is the proportion of deviation from target leverage closed from period t-1 to period t. 13 The positive coefficient on the leverage dummy enhances the ratio 1-δ, resulting in a slower adjustment, δ, for over-levered firms and a faster adjustment for under-levered firms.
21
target observed from the estimations on separate good and bad state sub-samples is not
sufficiently informative. Therefore, we include the interaction term GOODDUMMY *
LEVDUMMY * BDt-1/MDt-1 in the pooled sample to capture additional evidence
regarding the timing effect on adjustment speed. The resulting mostly significantly
negative signs for this new interaction term suggests that in good states, defined as good
stock market performance, over-levered firms tend to adjust faster than under-levered
firms. This is consistent with predictions from the market timing theory since firms
that are timing the market are inclined (reluctant) to issue equity (debt) in periods with
good stock market performance.
4.2.2. Two-stage dynamic partial adjustment capital structure model
Table 4, Panels A through E, report the results from estimating the capital structure
adjustment speed using the two-stage model when good and bad states are defined by
term spread, default spread, GDP growth rate, dividend yield and price-output ratio,
respectively. We do not report the results from the first-stage regressions since the
coefficient estimates of firm characteristics and macroeconomic target determinants are
generally consistent with previous studies.
We find that firms tend to adjust faster towards target leverage in good states than
in bad states when states are determined by term spread, default spread, and GDP growth
rate. These results are consistent with the adjustment pattern under the integrated
regression method is used in Table 3. The negative coefficient estimates on the
interaction terms between the lagged debt ratios and the good state dummies in the
22
pooled regressions are further evidence that adjustment is faster in good states than in bad
states.14
Similar to Roberts (2002) and Flannery and Rangan (2006), we find that the
magnitude of adjustment speed is relatively smaller in the two-stage model. For example,
in Table 4 Panel A where states are determined by term spread, the coefficient on the
lagged debt variable indicates that firms close about 62.7% of the deviation from target
leverage in good states and close about 53.7% in bad states when book-value based debt
is used, while the counterpart results from integrated regression suggest 79.1% in good
states and 65.2% in bad states.15
The positive coefficient on the interaction term between the lagged debt ratio and
the leverage dummy variable provides supporting evidence for the pecking order theory
since over-levered firms adjusting slower is consistent with the pecking order reluctance
to issue equity versus external debt.16
Results for states determined by the market dividend yield and price-output ratio
are consistent with those obtained from the integrated dynamic partial adjustment model
estimation. The negative coefficients from mixing the leverage dummy variables with
the interactions between the lagged debt ratios and the good state dummy variables
suggests that over-levered firms adjust faster than under-levered firms in good states, for
both book-valued and market-valued debt ratios. This provides evidence supporting the
14 The negative coefficient enhances the magnitude of the negative coefficient on the lagged debt ratio, which equals –δ. 15 From equation 2 in the two-stage model, the coefficient on the lagged debt ratio represents the negative of the proportion of deviation from target leverage closed from period t-1 to period t not 1 minus this proportion as in equation 3 from the integrated model. 16 Being over-levered reduces the adjustment speed because the magnitude of the negative coefficient on the lagged debt ratio (–δ) is reduced by the positive interaction coefficient.
23
market timing hypothesis since over-levered firms have more incentive than under-
levered firms to adjust toward target when stock prices are high.
5. Robustness
5.1. Boundary issues
Cook, Kieschnick, and McCullough (2008) address the specification error that
arises when the decision of whether to issue a type of financing is assumed to be
equivalent to the decision on how much of that financing to use. The application for this
paper is that including zero-debt issuance firms may cause a bias in our adjustment speed
estimate.
Thus, we re-estimate the adjustment speed using the integrated and two-stage
dynamic partial adjustment models on sub-samples but leaving out the zero-debt issuance
firm-year observations. We report only the coefficient estimates before the key
regressors from the integrated (two-stage) model in Table 5 (6). We find that the
adjustment speed estimates from both models on the new sub-samples are consistent with
those in the original sub-samples with firms adjusting faster toward targets in good states
compared to bad states when states are determined by term spread, default spread, and
GDP growth rate. Consistent with the pecking order theory, the positive coefficients on
the interaction term between the lagged debt ratios and the leverage dummy variables
indicate that under-levered firms with little reluctance to issue external debt adjust faster
than over-levered firms. Consistent with market timing, we find that over-levered firms
tend to adjust faster than under-levered firm when states are defined by the market
24
dividend yield and price-output ratio. Thus, our results on adjustment speed are not
affected significantly by including zero-debt issuance firm-year observations.
5.2. Firm size impact
Drobetz (2006) argues that large firms should be able to more easily correct
deviations from debt targets because they have better assess to public debt markets and
have relatively lower adjustment costs. This suggests that there may be a positive
relationship between firm size and the speed of capital structure adjustment. Therefore,
our realized state-dependent faster adjustment speed may be attributable to the larger size
of firms in those states rather than to macroeconomic factors. Therefore, we examine the
differences in the mean logarithm of total assets, a proxy for firm size, across good and
bad states as defined by our five macroeconomic factors. The results, shown in Table 7,
indicate that, although the differences in mean firm size between good and bad states are
generally significant, regardless of the definition used to define states, the signs of the
differences are mixed. In other words, there is no strong pattern showing firm size to be
larger in the states where faster adjustment speed is observed. Thus, since there is no
significant distinction in firm size across good and bad states, the faster adjustment speed
found in good states is not attributable to the larger size of firms in those states.
5.3. Distance away from target
Since it is documented that firms farther away from target leverage adjust faster
(Drobetz, 2006), another possible explanation for our results is that firms tend to deviate
more from their target debt level in good states when states are defined by term spread,
25
default spread, GDP growth rate, and market dividend yield, and tend to deviate less in
good states when states are defined by the price-output ratio.17 Therefore, this relative
deviation rather than the impact of macroeconomic conditions may lead to a faster
adjustment of capital structure. We examine the difference in mean absolute value of
deviation from the target level between good and bad states to test whether firms tend to
deviate further in good states than in bad ones. We measure this deviation from the target
debt ratio as the distance between the actual and target debt ratio as follows,
DISi,t = ti, ti, D - *D (6)
where Di,t*= γMacrot-1 + βXi,t-1, Macro is a set of macroeconomic target variables and X
is the vector of firm characteristics determining the target debt level, and D could be
either the book-value debt ratio or the market-value debt ratio. We follow Papke and
Wooldrige (1996) and use the quasi-maximum likelihood estimation method (QMLE) to
estimate the fitted value of Equation (1) as the proxy for target leverage and report the
results in Table 8. The mean difference in distance between actual and target debt ratios
relative to good and bad states, where states are based on term spread, default spread,
GDP growth rate, dividend yield and price-output ratio are reported, respectively. The
results do not support firms being consistently farther away from their target in good
states compared to bad states. Therefore, the faster adjustment speed of capital structure
in good states cannot be attributed to the fact that the distance between actual and target
debt ratios tends to be greater in those states.
5.4. Alternative measurements of debt ratio
17 In states defined by the price-output ratio, the market timing effect on adjustment speed dominates the macroeconomic conditions effect.
26
Since the definition of “leverage” varies across capital structure studies, we
analyze whether our results are robust to different definitions of debt ratio. We employ
three alternative forms of market-valued debt ratios commonly used in the literature and
re-estimate the two-stage and integrated partial adjustment models.
The alternative market-valued debt ratios are defined as follows:
MD1i,t=titititi
titi
PSBETALDSD
,,,,
,,
+−
+ (7)
MD2i,t=tititi
ti
PSTLTL
,,,
,
+ (8)
MD3i,t=tititititi
ti
PSBECLTALD
,,,,,
,
+−− (9)
where SDi,t + LDi,t is the sum of the book value of firm i’s short-term and long-term
interest-bearing debt at time t; Si,tPi,t represents the product of the number of common
shares outstanding and stock price per share at time t, which is the market value of firm
i’s equity; TAi,t denotes the book value of firm i’s total assets at time t; BEi,t is the book
value of firm i’s equity; TLi,t represents the book value of firm i’s total liabilities at time t;
CLi,t denotes current liabilities of firm i at time t, and BEi,t is the book value of equity of
firm i at time t.
For brevity, we do not report the results in tables. However, consistent with our
previous results we find that, regardless of the debt ratio definition, firms tend to adjust
faster towards target leverage in good states than in bad states when states are defined by
term spread, default spread, and GDP growth rate, and dividend yield. When market
dividend yield and price-output ratio are used as the criterion to distinguish between good
and bad states, the results provide strong evidence of the market timing theory.
27
Altogether, the results from the estimations when alternative market-valued debt ratios
are used are consistent with the previous results when primary book and market debt
ratios are analyzed.
6. Conclusion
We study the impact of macroeconomic conditions on the speed of capital
structure adjustment by analyzing U.S data over the sample period from 1976 to 2005.
We find that firms adjust faster toward target leverage in good states than in bad ones,
when states are defined by term spread, default spread, GDP growth rate, and market
dividend yield, a finding that is consistent with the prediction from Hackbarth et al.
(2006)’s theoretical model. Our results also support the pecking order theory in that
firms that are under-levered adjust faster than firms that are over-levered.
We find evidence favoring the market timing theory implication that under-
levered firms have less incentive to adjust toward target leverage when stock market
performance is good,.
We also find evidence consistent with predictions from the market timing theory.
When good states are defined as the dividend yield on the market and price-output ratio,
which captures the variation of expected aggregate stock market returns, under-levered
firms are less likely to adjust their leverage ratio towards target than over-leveraged firms.
In other words, we find slower adjustment speed for under-leveraged firms in good
macroeconomic conditions, indicated by lower dividend yield and lower past price-output
measured by lower dividend yield and lower past price-output ratio.
28
Since it is possible that the faster speed of capital structure adjustment found is
due to firms being larger, or to firms deviating farther from target leverage in those states,
or to the definition of leverage, we show that our results are robust across these
characteristics. Our results are also robust to possible boundary issues.
29
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33
Table 1 Summary statistics of leverage Panel A presents the annual mean, median and standard deviation of leverage variables from 1976 to 2005. Panel B reports the actual and absolute deviation values from target leverage by quintiles over the sample period. We calculate target leverage according to equation (1) and employ the quasi-maximum likelihood estimation method to fit values. The sample includes all Industrial Compustat firms with complete data for two adjacent years. The debt ratios are defined as follows: BD is the book-valued debt ratio computed by (long-term book debt + short-term book debt)/total book assets. MD is the market-valued debt ratio computed by (long-term book debt + short-term book debt)/(long-term book debt + short-term book debt + stock price* number of shares outstanding), Long- and short-term debt and total assets numbers are in book values. We report overall numbers of observations. Panel A. Actual leverage statistics
BD MD Mean Median Std obs Mean Median Std obs
1976 0.2545 0.2379 0.1745 3140 0.3680 0.3451 0.2543 31321977 0.2578 0.2401 0.1713 3080 0.3722 0.3592 0.2483 30781978 0.2638 0.2502 0.1705 2994 0.3666 0.3511 0.2395 29891979 0.2736 0.2600 0.1720 3048 0.3709 0.3517 0.2434 30481980 0.2737 0.2567 0.1813 3334 0.3472 0.3103 0.2529 33351981 0.2681 0.2451 0.1840 3397 0.3397 0.3043 0.2543 34051982 0.2691 0.2442 0.1959 3861 0.3448 0.3076 0.2654 38911983 0.2544 0.2204 0.1984 3902 0.2735 0.2164 0.2407 39421984 0.2557 0.2271 0.1973 3986 0.2932 0.2431 0.2453 40051985 0.2653 0.2353 0.2037 4094 0.2892 0.2404 0.2442 40991986 0.2724 0.2449 0.2093 4026 0.2852 0.2315 0.2467 40571987 0.2753 0.2514 0.2087 4168 0.2934 0.2390 0.2480 41421988 0.2775 0.2511 0.2126 4274 0.3031 0.2508 0.2547 42211989 0.2853 0.2571 0.2213 4123 0.2990 0.2370 0.2590 41041990 0.2832 0.2574 0.2214 4063 0.3371 0.2841 0.2812 40471991 0.2663 0.2399 0.2166 4037 0.3043 0.2353 0.2748 40441992 0.2456 0.2173 0.2081 4100 0.2650 0.1932 0.2556 40941993 0.2290 0.1978 0.1981 4327 0.2282 0.1597 0.2300 42861994 0.2267 0.1987 0.1960 4593 0.2258 0.1669 0.2208 45651995 0.2343 0.2098 0.1974 4774 0.2336 0.1705 0.2304 48291996 0.2324 0.2032 0.2024 5201 0.2297 0.1579 0.2349 52981997 0.2411 0.2086 0.2138 5669 0.2292 0.1535 0.2391 58081998 0.2577 0.2275 0.2227 5591 0.2683 0.1950 0.2637 57651999 0.2596 0.2361 0.2190 5399 0.2771 0.2007 0.2727 56192000 0.2518 0.2219 0.2218 5292 0.2958 0.2072 0.2924 55422001 0.2463 0.2124 0.2240 5157 0.2903 0.1934 0.2936 54602002 0.2385 0.2002 0.2216 4853 0.2954 0.2064 0.2930 51882003 0.2239 0.1908 0.2114 4589 0.2371 0.1514 0.2583 48942004 0.2090 0.1703 0.2045 4454 0.1960 0.1206 0.2245 4708
34
2005 0.2017 0.1579 0.2015 4139 0.1887 0.1157 0.2186 4341Overall 0.2517 0.2257 0.2065 127665 0.2819 0.2176 0.2592 129936 Panel B. Actual and absolute deviations from target leverage
BD Quintile1 Quintile2 Quintile3 Quintile4 Quintile5 Actual deviations from target leverage
-.2259
-.1244
-.0324
.0801
.3026
Absolute deviations from target leverage
.0262
.0804
.1334
.1926
.3361
MD
Quintile1 Quintile2 Quintile3 Quintile4 Quintile5 Actual deviations from target leverage
-.2710
-.1429
-.0516
.0820
.3598
Absolute deviations from target leverage
.0305
.0924
.1535
.2288
.4020
35
Table 2 Summary statistics of leverage across states This table presents differences in means and medians of leverage variables across states over the sample period 1976 to 2005 in Panel A and Panel B. Panel C presents the percentage of over-leveraged firms across states over the same sample period. We determine states using five macroeconomic factor indicators. These five macroeconomic factor indicators are as follows: (1) Term spread: measured as the difference between the twenty-year government bond yield series and the three-month Treasury-bill rate series; (2) Default spread: defined as the difference between the average yield of bonds rated BAA by Moody’s and the average yield of bonds with Moody’s rating of AAA; (3) GDP growth rate: defined as the average real GDP growth rate over quarters in a year; (4) Dividend yield on the market: defined as total dividend payments on the value-weighted NYSE/AMEX/Nasdaq portfolio over year t-1 divided by the current value of the portfolio at time t; (5) Price-output ratio: computed as the S&P stock price index in January in a given year scaled by GDP from the previous year. We divide the 30 years in the sample periods into macroeconomic quintiles based on each macroeconomic factor. Sorting by term spread or GDP growth rate factor places years in the highest macroeconomic quintile -- good state (lowest macroeconomic quintile – bad state) when term spread and GDP growth rate are in the highest (lowest) quintile. Sorting by default spread, dividend yield or price-output ratio places years in the highest macroeconomic quintile -- good state (lowest quintile – bad state) when default spread, dividend yield or price-output ratio are in the lowest (highest) quintile. We report p-values. Panel A. Summary statistics of book-valued debt ratio across states
Term Spread Default Spread GDP Growth
Rate Dividend Yield Price-output
Ratio Mean Median Mean Median Mean Median Mean Median Mean Median
Good 0.233 0.200 0.243 0.215 0.258 0.233 0.223 0.189 0.266 0.244Bad 0.265 0.241 0.265 0.237 0.267 0.242 0.268 0.241 0.245 0.212 G vs. B -0.032 -0.041 -0.022 -0.022 -0.009 -0.009 -0.045 -0.052 0.021 0.031p-value <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 Panel B. Summary statistics of market-valued debt ratio across states
Term Spread Default Spread GDP Growth
Rate Dividend Yield Price-output
Ratio Mean Median Mean Median Mean Median Mean Median Mean Median
Good 0.236 0.163 0.245 0.172 0.299 0.247 0.223 0.148 0.328 0.290Bad 0.317 0.259 0.305 0.255 0.324 0.269 0.307 0.248 0.272 0.184 G vs. B -0.081 -0.095 -0.060 -0.082 -0.025 -0.022 -0.084 -0.100 0.056 0.106p-value <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 Panel C. Percentage of over-leveraged firms across states
Term Spread Default Spread GDP Growth
Rate Dividend Yield Price-output
Ratio BD MD BD MD BD MD BD MD BD MDGood 41.70% 35.24% 43.16% 37.48% 44.76% 41.81% 40.23% 34.03% 44.69% 43.40%Bad 43.39% 43.01% 43.57% 41.04% 43.29% 43.33% 45.08% 43.73% 42.71% 40.57%
36
G vs. B -1.69% -7.77% -0.41% -3.56% 1.47% -1.52% -4.85% -9.70% 1.98% 2.83%p-value 0.0001 <.0001 0.3408 <.0001 0.0011 0.0006 <.0001 <.0001 <.0001 <.0001
37
Table 3 Regression results for adjustment speed estimates from the integrated dynamic partial adjustment capital structure model This table reports the results of estimating Equation (3): Di,t = (1- δ) Di,t-1 + δ βXi,t-1 + δ γMacrot-1 + εi,t by controlling for firm fixed effects across good and bad states. Columns 2, 3 and 4 in each panel present the estimation results when the book-value debt ratio is used, computed as (long-term book debt + short-term book debt)/total book assets. Columns 5, 6, and 7 in each panel report the estimation results when market-value debt ratio is used, computed by (long-term book debt + short-term book debt)/(long-term book debt + short-term book debt + stock price* number of shares outstanding). The independent variables are as follows: CPG represents two-year aggregate domestic nonfinancial corporate profit growth, which is obtained from the Annual Flow of Funds database on the Federal Reserve website. VRMR represents the two-year value-weighted market return of stocks traded on NYSE/AMEX/NASDAQ, which is extracted from CRSP. CPSPREAD is the commercial paper spread, computed from the annualized rate of three-month commercial paper less the three-month Treasury bill. MB equals the ratio of market to book value. TANG equals the ratio of gross property, plant and equipment to total assets; EBIT is the ratio of earnings before interest and tax to total assets. DEP is depreciation expenses as a fraction of total assets. RDD is a dummy variable that takes the value of 1 if firms report R&D expenses and takes the value of 0, if otherwise. SE equals selling expenses scaled by net sales. LNTA is the natural log of total assets. RD equals the ratio of R&D expenses to total assets. IND_BD/MD is the median book/market debt ratio of the firm’s industry, where the industry categorization is based on the Fama and French 49 industry definition. LEVDUMMY takes the value of 1 if the firm is over-levered, defined as (Di,t-1 – Di,t-1
*) being greater than zero, and takes the value of 0, if otherwise. GOODDUMMY takes the value of 1 if the firm year observation belongs to a good state and the value of 0, if otherwise. We create interaction terms between the lagged debt ratio, the leverage dummy variable and the good state dummy variable. Panels A through E report the estimation results for the good, bad, and pooled-state sub-samples as defined by term spread, default spread, GDP growth rate, dividend yield, and price-output ratio. These five macroeconomic indicators define the good and bad states as follows: (1) Term spread is the difference between the twenty-year government bond yield series and the three-month Treasury-bill rate series. (2) Default spread is the difference between the average yield of bonds rated BAA by Moody’s and the average yield of bonds with Moody’s rating of AAA. (3) GDP growth rate is defined as average real GDP growth rate over quarters in a year. (4) Dividend yield on the market equals total dividend payments on the value-weighted NYSE/AMEX/Nasdaq portfolio over year t-1 divided by the current value of the portfolio at time t. (5) Price-output ratio is the S&P stock price index in January in a given year scaled by GDP from the previous year. We divide the 30 years in the sample periods into macroeconomic quintiles based on each macroeconomic factor. Sorting by the term spread or GDP growth rate factor places years in the highest macroeconomic quintile -- good state (lowest macroeconomic quintile – bad state) when the term spread and GDP growth rate are in the highest (lowest) quintile. Sorting by default spread, dividend yield or price-output ratio places years in the highest macroeconomic quintile -- good state (lowest quintile – bad state) when
38
default spread, dividend yield or price-output ratio are in the lowest (highest) quintiles. We report coefficient estimates in the tables (t-statistics are in parenthesis) with *, **, and *** indicating significance at the 10%, 5%, and 1% levels, respectively. We also report the R-squared statistic and number of observations. Panel A. Results from regressions when states are determined by term spread BD MD Good Bad G vs. B Good Bad G vs. B BDt-1/MDt-1 0.209 0.348 0.326 0.207 0.315 0.319
(24.21) *** (38.96) *** (53.61) *** (28.75) *** (37.95) *** (59.70) *** VRMR 0.011 -0.002 0.005 0.022 0.005 0.016
(3.10) *** (-0.69) (2.13) ** (5.71) *** (1.20) (6.18) *** CPSPREAD -1.837 -0.265 -0.235 -0.865 -0.337 -0.155
(-5.13) *** (-2.38) ** (-2.64) *** (-2.22) ** (-2.48) ** (-1.52) CPG 0.002 -0.006 0.002 -0.001 -0.015 -0.004
(0.85) (-0.89) (1.19) (-0.18) (-1.84) * (-1.94) * MB 0.000 0.000 0.000 0.000 -0.001 0.000
(4.85) *** (-0.26) (2.12) ** (0.21) (-8.02) *** (-2.28) ** TANG 0.070 0.070 0.074 0.059 0.082 0.070
(9.34) *** (9.29) *** (16.18) *** (7.67) *** (9.37) *** (13.91) *** EBIT 0.003 -0.005 0.000 0.000 -0.002 0.000
(3.41) *** (-2.87) *** (0.17) (0.91) (-1.90) * (2.25) * DEP 0.019 0.035 0.003 0.015 -0.008 0.002
(2.45) ** (2.05) ** (0.56) (2.82) *** (-0.62) (2.42) ** RD 0.000 0.000 0.000 0.000 0.000 0.000
(-1.11) (0.82) (-0.01) (-0.94) (0.21) (-0.53) RDD -0.016 -0.016 -0.017 -0.021 -0.026 -0.025
(-4.41) *** (-5.51) *** (-8.42) *** (-5.60) *** (-7.20) *** (-11.05) *** SE 0.000 0.000 0.000 0.000 0.000 0.000
(-0.10) (0.65) (3.13) *** (0.88) (2.10) ** (4.37) *** LNTA 0.004 0.007 0.005 0.010 0.012 0.013
(3.60) *** (6.59) *** (7.56) *** (8.72) *** (9.17) *** (17.68) *** IND_BD/MD 0.235 0.166 0.214 0.237 0.081 0.151
(9.51) *** (8.25) *** (15.69) *** (12.40) *** (5.19) *** (15.63) *** LEVDUMMY 0.180 0.153 0.168 0.160 0.190 0.176
(56.47) *** (43.96) *** (80.61) *** (48.11) *** (52.03) *** (81.38) *** LEVDUMMY*BDt-1/MDt-1 0.115 0.097 0.110 0.232 0.166 0.209
(10.90) *** (8.82) *** (16.19) *** (26.48) *** (17.24) *** (36.49) *** GOODDUMMY 0.008 0.005
(4.65) *** (2.79) *** GOODDUMMY*BDt-1/MDt-1 -0.051 -0.085
(-11.63) *** (-21.54) ***
Fix-effect Yes Yes Yes Yes Yes Yes Obs 25629 25210 50839 26385 25756 52141 R-Square 0.9041 0.9177 0.8793 0.9145 0.9285 0.8993
39
Panel B. Results from regressions when states are determined by default spread BD MD Good Bad G vs. B Good Bad G vs. B BDt-1/MDt-1 0.170 0.191 0.282 0.131 0.202 0.260
(21.83) *** (20.97) *** (43.94) *** (18.02) *** (26.16) *** (45.92) *** VRMR 0.034 0.010 0.024 0.022 0.062 0.053
(5.94) *** (2.68) *** (9.92) *** (3.40) *** (15.63) *** (19.00) *** CPSPREAD 0.773 -0.186 -0.091 0.776 -0.755 -0.352
(2.37) ** (-2.42) ** (-1.27) (2.14) ** (-7.98) *** (-4.03) *** CPG -0.012 0.008 0.004 -0.055 0.016 0.007
(-1.78) * (3.42) *** (1.73) * (-7.27) *** (5.71) *** (2.81) *** MB 0.000 -0.001 -0.001 0.000 -0.007 0.000
(-1.69) * (-3.19) *** (-4.13) *** (-0.13) (-15.02) *** (-3.40) *** TANG 0.081 0.080 0.080 0.079 0.083 0.073
(10.92) *** (10.03) *** (17.44) *** (10.10) *** (9.34) *** (14.54) *** EBIT -0.004 -0.039 -0.005 0.000 -0.016 0.000
(-2.69) *** (-9.16) *** (-4.29) *** (-1.99) ** (-5.91) *** (-1.67) * DEP 0.035 -0.040 -0.001 0.025 0.061 0.024
(2.80) *** (-7.51) *** (-0.36) (2.84) *** (3.08) *** (3.05) *** RD 0.000 0.000 0.000 0.000 0.000 0.000
(-0.42) (0.90) (0.20) (1.31) (0.72) (0.15) RDD -0.006 -0.012 -0.013 -0.021 -0.021 -0.019
(-1.58) (-3.60) *** (-6.25) *** (-5.15) *** (-5.52) *** (-8.58) *** SE 0.000 0.000 0.000 0.000 0.000 0.000
(0.94) (0.28) (0.79) (4.64) ** (-0.73) (3.50) *** LNTA 0.013 0.022 0.009 0.026 0.038 0.019
(9.56) *** (12.52) *** (12.08) *** (18.20) *** (19.04) *** (22.93) *** IND_BD/MD 0.329 0.207 0.239 0.265 0.134 0.162
(11.47) *** (7.54) *** (15.62) *** (12.34) *** (9.24) *** (14.50) *** LEVDUMMY 0.196 0.162 0.184 0.181 0.192 0.192
(74.80) *** (49.92) *** (95.25) *** (69.00) *** (57.81) *** (97.96) *** LEVDUMMY*BDt-1/MDt-1 0.039 0.103 0.070 0.222 0.127 0.170
(4.24) *** (9.66) *** (10.69) *** (27.08) *** (14.73) *** (30.01) *** GOODDUMMY 0.002 -0.008
(0.91) (-3.71) *** GOODDUMMY*BDt-1/MDt-1 -0.039 -0.010
(-6.89) *** (-1.99) **
Fix-effect Yes Yes Yes Yes Yes Yes Obs 31226 23407 54633 31883 23483 55366 R-Square 0.8915 0.8867 0.8734 0.8997 0.9092 0.8913
40
Panel C. Results from regressions when states are determined by GDP growth rate BD MD Good Bad G vs. B Good Bad G vs. B BDt-1/MDt-1 0.267 0.315 0.332 0.250 0.323 0.321
(27.76) *** (34.32) *** (52.79) *** (30.99) *** (37.60) *** (57.76) *** VRMR 0.017 -0.001 0.008 0.022 0.035 0.041
(3.17) *** -(0.21) (3.56) *** (3.62) *** (6.33) *** (14.52) *** CPSPREAD -0.734 -0.475 -0.324 2.665 -0.570 0.130
(-1.86) * (-4.48) *** (-4.25) *** (5.79) *** (-4.44) *** (1.45) CPG 0.006 0.010 0.003 -0.019 0.007 0.013
(1.08) (1.84) * (1.26) (-2.61) *** (1.03) (3.82) *** MB 0.000 0.000 0.000 0.000 -0.002 -0.001
(0.22) (-1.69) * (-2.26) ** (-2.98) *** (-7.12) *** (-10.58) *** TANG 0.094 0.067 0.080 0.081 0.085 0.089
(12.18) *** (8.74) *** (16.67) *** (9.25) *** (9.33) *** (16.20) *** EBIT 0.002 -0.007 -0.004 0.000 -0.012 -0.001
(0.88) (-3.63) *** (-3.66) *** (-0.40) (-7.00) *** (-4.78) *** DEP 0.007 0.023 0.001 -0.021 0.001 -0.010
(2.60) *** (0.97) (0.30) (-1.87) * (0.03) (-1.13) RD 0.000 0.000 0.000 0.000 0.000 0.000
(0.27) (-1.18) (0.95) (-1.62) (0.33) (-0.72) RDD -0.018 -0.020 -0.018 -0.023 -0.026 -0.023
(-6.73) *** (-6.98) *** (-9.73) *** (-7.00) *** (-7.47) *** (-10.57) *** SE 0.000 0.000 0.000 0.000 0.000 0.000
(1.07) (0.03) (1.05) (1.39) (-0.47) (1.60) LNTA 0.006 0.007 0.008 0.013 0.013 0.015
(5.38) *** (6.11) *** (11.57) *** (10.01) *** (9.55) *** (17.93) *** IND_BD/MD 0.167 0.159 0.166 0.117 0.060 0.106
(7.62) *** (7.50) *** (12.21) *** (8.90) *** (3.78) *** (11.84) *** LEVDUMMY 0.171 0.140 0.162 0.191 0.195 0.195
(51.98) *** (38.25) *** (74.44) *** (54.36) *** (48.97) *** (84.76) *** LEVDUMMY*BDt-1/MDt-1 0.047 0.136 0.085 0.139 0.128 0.143
(4.15) *** (11.91) *** (11.95) *** (14.86) *** (12.79) *** (23.95) *** GOODDUMMY 0.006 -0.005
(4.05) *** (-2.60) *** GOODDUMMY*BDt-1/MDt-1 -0.031 -0.040
(-7.22) *** (-10.52) ***
Fix-effect Yes Yes Yes Yes Yes Yes Obs 24952 23848 48800 25398 24181 49579 R-Square 0.9125 0.9195 0.8844 0.9255 0.9313 0.9037
41
Panel D. Results from regressions when states are determined by dividend yield BD MD Good Bad G vs. B Good Bad G vs. B BDt-1/MDt-1 0.206 0.284 0.311 0.191 0.283 0.294
(26.14) *** (30.30) *** (44.73) *** (28.96) *** (31.90) *** (46.20) *** VRMR 0.001 0.015 0.006 0.013 0.080 0.031
(0.37) (2.41) ** (2.21) ** (3.74) *** (11.46) *** (10.60) *** CPSPREAD 1.320 -0.121 -0.071 2.177 -0.516 -0.414
(2.72) *** (-1.14) (-0.84) (4.23) *** (-4.13) *** (-4.34) *** CPG 0.004 0.004 0.003 -0.006 0.003 -0.011
(1.35) (0.93) (1.30) (-1.64) (0.53) (-4.63) *** MB 0.000 0.000 0.000 0.000 -0.001 0.000
(5.22) *** (-0.45) (1.02) (0.19) (-3.74) *** (-2.40) ** TANG 0.073 0.059 0.067 0.051 0.049 0.054
(9.99) *** (7.87) *** (14.78) *** (6.87) *** (5.72) *** (11.13) *** EBIT 0.003 -0.008 -0.001 0.000 -0.006 0.000
(3.74) *** (-3.82) *** (-0.94) (0.87) (-3.44) *** (1.89) * DEP 0.020 -0.006 -0.004 0.016 0.065 0.002
(2.80) *** (-0.32) (-0.59) (3.24) *** (3.22) *** (1.78) * RD 0.000 0.000 0.000 0.000 0.000 0.000
(-1.11) (-2.90) *** (-1.99) ** (-0.36) (0.63) (-0.58) RDD -0.021 -0.014 -0.017 -0.028 -0.021 -0.023
(-5.76) *** (-3.85) *** (-7.92) *** (-7.42) *** (-5.30) *** (-9.81) *** SE 0.000 0.000 0.000 0.000 0.000 0.000
(-0.71) (2.46) ** (2.46) ** (0.38) (2.94) *** (3.23) *** LNTA 0.004 0.004 0.003 0.011 0.017 0.013
(3.45) *** (3.38) *** (5.03) *** (10.23) *** (11.98) *** (18.56) *** IND_BD/MD 0.271 0.194 0.211 0.204 0.210 0.168
(10.87) *** (8.20) *** (14.46) *** (12.09) *** (12.29) *** (15.95) *** LEVDUMMY 0.179 0.172 0.173 0.155 0.190 0.170
(59.03) *** (50.76) *** (85.04) *** (51.95) *** (53.87) *** (82.40) *** LEVDUMMY*BDt-1/MDt-1 0.097 0.124 0.111 0.227 0.194 0.231
(9.82) *** (11.12) *** (14.44) *** (28.32) *** (19.42) *** (33.86) *** GOODDUMMY 0.004 0.003
(2.39) ** (1.69) * GOODDUMMY*BDt-1/MDt-1 -0.072 -0.067
(-8.62) *** (-8.96) *** GOODDUMMY*LEVDUMMY*BDt-1/MDt-1 0.011 -0.016
(1.43) (-2.33) **
Fix-effect Yes Yes Yes Yes Yes Yes Obs 26202 25474 51676 26888 25774 52662 R-Square 0.8994 0.9001 0.8764 0.9125 0.9178 0.8965
42
Panel E. Results from regressions when states are determined by Price-output ratio BD MD Good Bad G vs. B Good Bad G vs. B BDt-1/MDt-1 0.248 0.128 0.174 0.225 0.069 0.144
(25.59) *** (17.00) *** (26.76) *** (27.37) *** (9.80) *** (23.45) *** VRMR -0.006 0.004 0.006 -0.045 0.012 0.010
(-0.71) (1.83) * (3.31) *** (-4.84) *** (4.92) *** (4.52) *** CPSPREAD -0.736 -0.832 -0.342 -2.383 1.367 -0.702
(-6.72) *** (-1.64) (-3.82) *** (-18.39) *** (2.30) ** (-6.53) *** CPG 0.023 -0.006 0.002 0.082 -0.061 -0.001
(6.45) *** (-1.37) (0.88) (19.40) *** (-11.92) *** (-0.31) MB -0.003 -0.001 -0.001 -0.006 0.000 0.000
(-3.75) *** (-6.80) *** (-8.45) *** (-8.31) *** (-1.42) (-2.25) ** TANG 0.085 0.086 0.086 0.097 0.093 0.083
(9.89) *** (11.24) *** (17.56) *** (9.80) *** (11.42) *** (15.13) *** EBIT -0.050 -0.003 -0.004 -0.040 0.000 0.000
(-9.46) *** (-4.18) *** (-4.91) *** (-7.29) *** (-0.39) (-0.22) DEP -0.092 -0.009 -0.010 -0.007 0.023 0.016
(-3.44) *** (-1.49) (-1.73) * (-0.26) (5.61) *** (4.14) *** RD -0.001 0.000 0.000 0.000 0.000 0.000
(-0.79) (-0.28) (-0.07) (0.25) (-0.59) (-0.46) RDD -0.014 -0.016 -0.016 -0.017 -0.020 -0.020
(-5.46) *** (-4.15) *** (-8.15) *** (-5.61) *** (-4.49) *** (-8.44) *** SE 0.000 0.000 0.000 0.000 0.000 0.000
(-0.13) (1.31) (1.06) (1.73) * (2.15) ** (2.41) ** LNTA 0.016 0.004 0.004 0.036 0.024 0.016
(9.07) *** (3.48) *** (5.84) *** (17.42) *** (18.08) *** (18.92) *** IND_BD/MD 0.158 0.279 0.234 0.208 0.221 0.234
(6.60) *** (11.62) *** (17.60) *** (15.12) *** (13.89) *** (23.60) *** LEVDUMMY 0.134 0.193 0.177 0.174 0.179 0.190
(39.04) *** (69.26) *** (85.15) *** (44.67) *** (63.92) *** (86.98) *** LEVDUMMY*BDt-1/MDt-1 0.105 0.101 0.146 0.121 0.282 0.282
(9.34) *** (11.25) *** (19.84) *** (13.16) *** (35.72) *** (41.61) *** GOODDUMMY -0.027 -0.030
(-10.96) *** (-10.43) *** GOODDUMMY*BDt-1/MDt-1 0.199 0.159
(20.82) *** (18.71) *** GOODDUMMY*LEVDUMMY*BDt-1/MDt-1 -0.153 -0.183
(-19.19) *** (-25.29) ***
Fix-effect Yes Yes Yes Yes Yes Yes Obs 21232 30746 51978 21303 32282 53585 R-Square 0.8951 0.8916 0.8804 0.9157 0.9012 0.8961
43
Table 4 Regression results for adjustment speed estimates from a two-stage dynamic partial adjustment capital structure model This table reports the results of estimating a two stage model pertaining to Equations (1) and (2). We estimate the first-stage using QMLE by Papke and Wooldrige (1996) and the second-stage using standard OLS with t-statistics reflecting a standard error correction for heteroskedasticity. This table reports only the results from the second-stage. Columns 2, 3 and 4 in each panel present the estimation results when book-value debt ratio is used, computed as (long-term book debt + short-term book debt)/total book assets. Columns 5, 6, and 7 in each sub-table report the estimation results when market-value debt ratio is used, computed by (long-term book debt + short-term book debt)/(long-term book debt + short-term book debt + stock price* number of shares outstanding). The independent variables in the second-stage are: BDt-1/MDt-1 is the lagged value of debt ratio, BDt* equals the target debt ratio obtained as the fitted value from the first-stage regression. LEVDUMMY takes the value of 1 if the firm is over-levered, defined as (Di,t-1 – Di,t-1
*) being greater than zero, and takes the value of 0, if otherwise. GOODDUMMY takes the value of 1 if the firm year observation belongs to a good state and the value of 0, if otherwise. We create interaction terms between the lagged debt ratio, the leverage dummy variable and the good state dummy variable. Panels A through E report the estimation results for the good, bad, and pooled-state sub-samples as defined by term spread, default spread, dividend yield, GDP growth rate and price-output ratio. These five macroeconomic indicators define the good and bad states as follows: (1) Term spread is the difference between the twenty-year government bond yield series and the three-month Treasury-bill rate series. (2) Default spread is the difference between the average yield of bonds rated BAA by Moody’s and the average yield of bonds with Moody’s rating of AAA. (3) GDP growth rate is defined as average real GDP growth rate over quarters in a year. (4) Dividend yield on the market equals total dividend payments on the value-weighted NYSE/AMEX/Nasdaq portfolio over year t-1 divided by the current value of the portfolio at time t. (5) Price-output ratio is the S&P stock price index in January in a given year scaled by GDP from the previous year. We divide the 30 years in the sample periods into macroeconomic quintiles based on each macroeconomic factor. Sorting by the term spread or GDP growth rate factor places years in the highest macroeconomic quintile -- good state (lowest macroeconomic quintile – bad state) when the term spread and GDP growth rate are in the highest (lowest) quintile. Sorting by default spread, dividend yield or price-output ratio places years in the highest macroeconomic quintile -- good state (lowest quintile – bad state) when default spread, dividend yield or price-output ratio are in the lowest (highest) quintile. We report coefficient estimates in the tables (t-statistics are in parenthesis) with *, **, and *** indicating significance at the 10%, 5%, and 1% levels, respectively. We also report the R-squared statistic and number of observations.
44
Panel A. Results from regressions when states are determined by term spread BD MD Good Bad G vs. B Good Bad G vs. B CONSTANT -0.051 -0.045 -0.050 -0.038 -0.046 -0.039 (-24.44) *** (-19.85) *** (-27.15) *** (-23.54) *** (-23.17) *** (-24.84) *** BDt-1/MDt-1 -0.627 -0.537 -0.570 -0.634 -0.580 -0.571
(-59.95) *** (-52.64) *** (-72.84) *** (-82.23) *** (-68.26) *** (-91.32) *** BD*/MD* 0.447 0.393 0.421 0.388 0.362 0.351
(39.21) *** (35.11) *** (52.01) *** (44.47) *** (42.87) *** (59.42) *** LEVDUMMY 0.170 0.168 0.170 0.163 0.212 0.186
(46.55) *** (44.40) *** (64.64) *** (49.59) *** (65.51) *** (79.19) *** LEVDUMMY*BDt-1/MDt-1 0.145 0.085 0.112 0.226 0.158 0.192
(11.34) *** (6.89) *** (12.61) *** (24.10) *** (17.19) *** (29.02) *** GOODDUMMY 0.002 0.002
(1.38) (1.56) GOODDUMMY*BDt-1/MDt-1 -0.023 -0.054
(-4.15) *** (-12.27) *** Obs 25629 25210 50839 26385 25756 52141 R-Square 0.4564 0.406 0.4315 0.5004 0.4782 0.4874 Panel B. Results from regressions when states are determined by default spread BD MD Good Bad G vs. B Good Bad G vs. B CONSTANT -0.041 -0.034 -0.040 -0.034 -0.018 -0.033 (-24.56) *** (-11.19) *** (-22.01) *** (-26.50) *** (-10.31) *** (-23.15) *** BDt-1/MDt-1 -0.597 -0.543 -0.577 -0.662 -0.574 -0.628
(-64.87) *** (-53.03) *** (-76.96) *** (-84.42) *** (-68.43) *** (-102.41) *** BD*/MD* 0.400 0.365 0.403 0.389 0.307 0.375
(43.99) *** (27.70) *** (53.80) *** (53.33) *** (36.07) *** (64.71) *** LEVDUMMY 0.180 0.173 0.178 0.201 0.204 0.205
(58.62) *** (47.29) *** (75.94) *** (75.07) *** (64.22) *** (100.58) *** LEVDUMMY*BDt-1/MDt-1 0.104 0.068 0.087 0.215 0.106 0.159
(9.48) *** (5.38) *** (10.54) *** (24.13) *** (12.01) *** (25.39) *** GOODDUMMY -0.004 -0.002
(-2.80) *** (-1.72) * GOODDUMMY*BDt-1/MDt-1 0.002 -0.010
(0.43) (-2.22) ** Obs 31226 23407 54633 31883 23483 55366 R-Square 0.4484 0.4318 0.4426 0.5103 0.494 0.5029
45
Panel C. Results from regressions when states are determined by GDP growth rate BD MD Good Bad G vs. B Good Bad G vs. B CONSTANT -0.046 -0.043 -0.047 -0.028 -0.031 -0.031 (-18.10) *** (-16.32) *** (-24.48) *** (-17.56) *** (-15.64) *** (-19.84) *** BDt-1/MDt-1 -0.543 -0.526 -0.528 -0.597 -0.578 -0.566
(-50.41) *** (-49.72) *** (-65.76) *** (-73.70) *** (-65.04) *** (-86.36) *** BD*/MD* 0.405 0.376 0.397 0.336 0.316 0.312
(33.40) *** (30.52) *** (47.15) *** (43.64) *** (36.46) *** (50.80) *** LEVDUMMY 0.163 0.158 0.161 0.209 0.210 0.209
(46.61) *** (38.90) *** (60.64) *** (67.76) *** (60.79) *** (90.18) *** LEVDUMMY*BDt-1/MDt-1 0.083 0.098 0.091 0.131 0.140 0.134
(6.62) *** (7.55) *** (10.15) *** (14.67) *** (14.87) *** (20.34) *** GOODDUMMY 0.002 0.002
(1.55) (1.57) GOODDUMMY*BDt-1/MDt-1 -0.015 -0.029
(-2.78) *** (-7.10) *** Obs 24952 23848 48800 25398 24177 49575 R-Square 0.4096 0.3863 0.3991 0.5011 0.4616 0.4736 Panel D. Results from regressions when states are determined by dividend yield BD MD Good Bad G vs. B Good Bad G vs. B CONSTANT -0.043 -0.049 -0.041 -0.030 -0.051 -0.029 (-22.310 ** (-18.87) *** (-21.30) *** (-20.55) *** (-22.89) *** (-18.87) *** BDt-1/MDt-1 -0.614 -0.586 -0.601 -0.631 -0.608 -0.605
(-59.82) *** (-54.27) *** (-60.94) *** (-86.37) *** (-66.17) *** (-72.77) *** BD*/MD* 0.408 0.415 0.409 0.350 0.396 0.347
(36.24) *** (35.04) *** (50.97) *** (41.09) *** (42.35) *** (58.44) *** LEVDUMMY 0.159 0.184 0.172 0.153 0.213 0.182
(43.09) *** (50.63) *** (66.42) *** (48.99) *** (67.40) *** (80.92) *** LEVDUMMY*BDt-1/MDt-1 0.147 0.107 0.142 0.237 0.172 0.224
(11.68) *** (8.33) *** (13.61) *** (26.48) *** (17.51) *** (26.66) *** GOODDUMMY -0.007 -0.009
(-4.55) *** (-6.97) *** GOODDUMMY*BDt-1/MDt-1 -0.003 -0.011
(-0.27) (-1.12) GOODDUMMY*LEVDUMMY*BDt-1/MDt-1 -0.026 -0.044 (-2.44) ** (-4.77) *** Obs 26202 25474 51676 26888 25774 52662 R-Square 0.4455 0.4366 0.4449 0.5039 0.4817 0.4962
46
Panel E. Results from regressions when states are determined by price-output ratio BD MD Good Bad G vs. B Good Bad G vs. B CONSTANT -0.027 -0.046 -0.047 -0.007 -0.044 -0.044 (-8.15) ** (-28.64) *** (-32.23) *** (-3.68) (-31.17) *** (-34.84) *** BDt-1/MDt-1 -0.491 -0.630 -0.636 -0.553 -0.671 -0.672
(-45.27) *** (-68.91) *** (-73.61) *** (-67.23) *** (-89.68) *** (-94.00) *** BD*/MD* 0.327 0.416 0.425 0.256 0.384 0.386
(23.45) *** (46.46) *** (56.27) *** (31.78) *** (56.18) *** (67.68) *** LEVDUMMY 0.141 0.182 0.171 0.180 0.206 0.205
(36.68) *** (53.56) *** (65.95) *** (47.83) *** (73.76) *** (90.57) *** LEVDUMMY*BDt-1/MDt-1 0.070 0.143 0.169 0.107 0.260 0.265
(5.26) *** (12.82) *** (17.71) *** (11.36) *** (30.72) *** (34.32) *** GOODDUMMY -0.011 -0.015
(-6.68) *** (-9.94) *** GOODDUMMY*BDt-1/MDt-1 0.154 0.133
(12.85) *** (14.45) *** GOODDUMMY*LEVDUMMY*BDt-1/MDt-1 -0.158 -0.188 (-16.13) *** (-22.72) *** Obs 21232 30746 51978 21303 32282 53585 R-Square 0.3834 0.451 0.4308 0.463 0.5046 0.4983
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Table 5 Robustness check of boundary issue using the integrated dynamic partial adjustment model This table reports the results from re-estimating Equation (3) using the integrated dynamic partial adjustment model but omitting the zero-debt issuance firm-year observations. Panels A through E report the key estimation results for the good, bad, and pooled-state subsamples defined by term spread, default spread, dividend yield, GDP growth rate and price-output ratio. We report coefficient estimates for only the key regressors including the lagged value of debt ratio (BDi,t-1/MDi,t-1), LEVDUMMY which takes the value of 1 if the firm year observation is defined as over-levered and the value of 0, if otherwise, the interaction term between LEVDUMMY and the lagged debt ratio (LEVDUMMY * BDi,t-1/MDi,t-1), Good dummy which takes the value of 1 if the firm year observation belongs to a good state and the value of 0, if otherwise, and the interaction term between the lagged debt ratio and good dummy variable: (GOODDUMMY* BDi,t-
1/MDi,t-1). In panels D and E, we also report the interaction term between the leverage dummy variable, good dummy variable, and the lagged debt ratio (GOODDUMMY* LEVDUMMY * BDi,t-1/MDi,t-1). The five macroeconomic indicators used to define the good and bad states as follows: (1) Term spread is the difference between the twenty-year government bond yield series and the three-month Treasury-bill rate series. (2) Default spread is the difference between the average yield of bonds rated BAA by Moody’s and the average yield of bonds with Moody’s rating of AAA. (3) GDP growth rate is defined as average real GDP growth rate over quarters in a year. (4) Dividend yield on the market equals total dividend payments on the value-weighted NYSE/AMEX/Nasdaq portfolio over year t-1 divided by the current value of the portfolio at time t. (5) Price-output ratio is the S&P stock price index in January in a given year scaled by GDP from the previous year. We divide the 30 years in the sample periods into macroeconomic quintiles based on each macroeconomic factor. Sorting by the term spread or GDP growth rate factor places years in the highest macroeconomic quintile -- good state (lowest macroeconomic quintile – bad state) when term spread and GDP growth rate are in the highest (lowest) quintile. Sorting by default spread, dividend yield or price-output ratio places years in the highest macroeconomic quintile -- good state (lowest quintile – bad state) when default spread, dividend yield or price-output ratio are in the lowest (highest) quintile. Coefficient estimates are reported in the tables (with t-statistics in parenthesis) *, **, and *** indicate significance at 10%, 5%, and 1% level, respectively. We report the R-squared statistic and number of observations.
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Panel A. Regression results when states are determined by term spread BD MD Good Bad G vs. B Good Bad G vs. B BDt-1/MDt-1 0.212 0.322 0.313 0.208 0.314 0.323 (20.75) *** (32.87) *** (45.54) *** (25.32) *** (34.49) *** (54.46) *** LEVDUMMY 0.168 0.135 0.153 0.155 0.185 0.170
(47.17) *** (37.06) *** (67.83) *** (42.61) *** (47.58) *** (72.93) *** LEVDUMMY*BDt-1/MDt-1 0.109 0.124 0.123 0.216 0.158 0.199
(9.03) *** (10.61) *** (16.54) *** (22.30) *** (15.25) *** (31.94) *** GOODDUMMY 0.008 0.004
(3.88) *** (1.99) ** GOODDUMMY*BDt-1/MDt-1 -0.050 -0.084
(-10.00) *** (-18.97) *** Fix effect Yes Yes Yes Yes Yes Yes Obs 22288 22935 45223 23075 23476 46551 R-Square 0.8952 0.9139 0.8692 0.9098 0.9250 0.8928 Panel B. Regression results when states are determined by default spread BD MD Good Bad G vs. B Good Bad G vs. B BDt-1/MDt-1 0.138 0.177 0.263 0.125 0.204 0.258 (15.70) *** (18.01) *** (37.34) *** (15.47) *** (24.92) *** (41.98) *** LEVDUMMY 0.175 0.150 0.168 0.173 0.189 0.186
(60.67) *** (44.64) *** (81.09) *** (60.62) *** (54.98) *** (89.24) *** LEVDUMMY*BDt-1/MDt-1 0.064 0.109 0.089 0.214 0.114 0.162
(6.44) *** (9.70) *** (12.51) *** (23.95) *** (12.73) *** (26.76) *** GOODDUMMY 0.005 -0.008
(2.11) ** (-3.55) *** GOODDUMMY*BDt-1/MDt-1 -0.045 -0.007
(-7.41) *** (-1.20) Fix effect Yes Yes Yes Yes Yes Yes Obs 27501 21839 49340 28155 21944 50099 R-Square 0.8823 0.8818 0.8639 0.8940 0.9059 0.8851
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Panel C. Regression results when states are determined by GDP growth rate BD MD Good Bad G vs. B Good Bad G vs. B BDt-1/MDt-1 0.259 0.288 0.308 0.252 0.316 0.318 (24.60) *** (28.63) *** (44.84) *** (29.24) *** (34.18) *** (53.01) *** LEVDUMMY 0.155 0.127 0.145 0.183 0.190 0.187
(44.13) *** (32.97) *** (63.57) *** (49.50) *** (45.80) *** (77.50) *** LEVDUMMY*BDt-1/MDt-1 0.062 0.159 0.108 0.136 0.121 0.140
(5.11) *** (13.08) *** (14.29) *** (13.79) *** (11.47) *** (22.14) *** GOODDUMMY 0.007 -0.004
(4.19) *** (-2.21) ** GOODDUMMY*BDt-1/MDt-1 -0.034 -0.042
(-7.15) *** (-10.19) *** Fix effect Yes Yes Yes Yes Yes Yes Obs 22750 21835 44585 23200 22178 45378 R-Square 0.9074 0.9138 0.8776 0.9219 0.9283 0.8984 Panel D. Regression results when states are determined by dividend yield BD MD Good Bad G vs. B Good Bad G vs. B BDt-1/MDt-1 0.201 0.283 0.304 0.180 0.306 0.307 (21.53) *** (27.25) *** (38.37) *** (24.13) *** (31.63) *** (42.98) *** LEVDUMMY 0.162 0.160 0.159 0.148 0.185 0.164
(48.03) *** (44.86) *** (71.89) *** (45.04) *** (49.98) *** (73.47) *** LEVDUMMY*BDt-1/MDt-1 0.103 0.125 0.119 0.220 0.168 0.213
(9.20) *** (10.36) *** (14.20) *** (24.78) *** (15.69) *** (28.53) *** GOODDUMMY 0.003 0.004
(1.74) * (1.88) * GOODDUMMY*BDt-1/MDt-1 -0.067 -0.079
(-7.05) *** (-9.40) *** GOODDUMMY* LEVDUMMY*BDt-1/MDt-1 0.006 -0.005 (0.76) (-0.68) Fix effect Yes Yes Yes Yes Yes Yes Obs 22501 23240 45741 23217 23574 46791 R-Square 0.8897 0.8937 0.8653 0.9076 0.9148 0.8905
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Panel E. Regression results when states are determined by price-output ratio BD MD Good Bad G vs. B Good Bad G vs. B BDt-1/MDt-1 0.229 0.107 0.156 0.223 0.076 0.154 (22.41) *** (12.33) *** (21.20) *** (25.93) *** (9.41) *** (22.38) *** LEVDUMMY 0.124 0.170 0.158 0.171 0.170 0.183
(35.21) *** (55.02) *** (70.85) *** (43.10) *** (55.08) *** (77.65) *** LEVDUMMY*BDt-1/MDt-1 0.116 0.124 0.162 0.110 0.264 0.263
(9.88) *** (12.43) *** (20.30) *** (11.58) *** (30.10) *** (35.80) *** GOODDUMMY -0.031 -0.031
(-11.15) *** (-9.37) *** GOODDUMMY*BDt-1/MDt-1 0.187 0.139
(17.99) *** (15.03) *** GOODDUMMY* LEVDUMMY*BDt-1/MDt-1 -0.130 -0.162 (-15.67) *** (-21.14) *** Fix effect Yes Yes Yes Yes Yes Yes Obs 19997 26554 46551 20078 28052 48130 R-Square 0.8899 0.8814 0.8708 0.9124 0.8946 0.8887
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Table 6 Robustness check of boundary issue using the two-stage dynamic partial adjustment model This table reports the results from re-estimating Equations (1) and (2) using a two-stage dynamic partial adjustment model but omitting the zero-debt issuance firm-year observations. Panels A through E report the key estimation results for the good, bad, and pooled-state subsamples defined by term spread, default spread, dividend yield, GDP growth rate and price-output ratio. We report coefficient estimates for only the key regressors including the lagged value of debt ratio (BDi,t-1/MDi,t-1), LEVDUMMY which takes the value of 1 if the firm year observation is defined as over-levered and the value of 0, if otherwise, the interaction term between LEVDUMMY and the lagged debt ratio (LEVDUMMY * BDi,t-1/MDi,t-1), Good dummy which takes the value of 1 if the firm year observation belongs to a good state and the value of 0, if otherwise, and the interaction term between the lagged debt ratio and good dummy variable: (GOODDUMMY* BDi,t-
1/MDi,t-1). In panels D and E, we also report the interaction term between the leverage dummy variable, good dummy variable, and the lagged debt ratio (GOODDUMMY* LEVDUMMY * BDi,t-1/MDi,t-1). The five macroeconomic indicators used to define the good and bad states as follows: (1) Term spread is the difference between the twenty-year government bond yield series and the three-month Treasury-bill rate series. (2) Default spread is the difference between the average yield of bonds rated BAA by Moody’s and the average yield of bonds with Moody’s rating of AAA. (3) GDP growth rate is defined as average real GDP growth rate over quarters in a year. (4) Dividend yield on the market equals total dividend payments on the value-weighted NYSE/AMEX/Nasdaq portfolio over year t-1 divided by the current value of the portfolio at time t. (5) Price-output ratio is the S&P stock price index in January in a given year scaled by GDP from the previous year. We divide the 30 years in the sample periods into macroeconomic quintiles based on each macroeconomic factor. Sorting by the term spread or GDP growth rate factor places years in the highest macroeconomic quintile -- good state (lowest macroeconomic quintile – bad state) when term spread and GDP growth rate are in the highest (lowest) quintile. Sorting by default spread, dividend yield or price-output ratio places years in the highest macroeconomic quintile -- good state (lowest quintile – bad state) when default spread, dividend yield or price-output ratio are in the lowest (highest) quintile. Coefficient estimates are reported in the tables (with t-statistics in parenthesis) *, **, and *** indicate significance at 10%, 5%, and 1% level, respectively. We report the R-squared statistic and number of observations.
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Panel A. Regression results when states are determined by term spread BD MD Good Bad G vs. B Good Bad G vs. B BDt-1/MDt-1 -0.642 -0.563 -0.591 -0.643 -0.594 -0.583 (-59.30) *** (-54.87) *** (-73.33) *** (-86.98) *** (-73.53) *** (-96.84) *** LEVDUMMY 0.155 0.153 0.155 0.151 0.201 0.174
(41.50) *** (39.72) *** (57.67) *** (45.21) *** (62.02) *** (73.16) *** LEVDUMMY*BDt-1/MDt-1 0.159 0.110 0.133 0.235 0.165 0.201
(12.14) *** (8.83) *** (14.65) *** (25.66) *** (18.77) *** (31.30) *** GOODDUMMY 0.000 0.000
(-0.24) (0.03) GOODDUMMY *BDt-1/MDt-1 -0.022 -0.051
(-3.51) *** (-10.94) *** Obs 22288 22935 45223 23075 23476 46551 R-Square 0.4462 0.4069 0.4268 0.4931 0.4768 0.4829 Panel B. Regression results when states are determined by default spread BD MD Good Bad G vs. B Good Bad G vs. B BDt-1/MDt-1 -0.633 -0.577 -0.610 -0.698 -0.608 -0.663 (-67.33) *** (-57.85) *** (-81.09) *** (-92.21) *** (-75.54) *** (-112.93) *** LEVDUMMY 0.164 0.157 0.163 0.188 0.193 0.194
(52.55) *** (42.77) *** (68.19) *** (70.94) *** (60.94) *** (95.68) *** LEVDUMMY*BDt-1/MDt-1 0.136 0.099 0.118 0.238 0.130 0.181
(12.24) *** (8.02) *** (14.31) *** (27.82) *** (15.40) *** (30.48) *** GOODDUMMY -0.005 -0.002
(-3.00) *** (-1.83) * GOODDUMMY *BDt-1/MDt-1 0.002 -0.013
(0.39) (-2.82) *** Obs 27501 21839 49340 28155 21944 50099 R-Square 0.4513 0.4396 0.4471 0.5140 0.5034 0.5112
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Panel C. Regression results when states are determined by GDP growth rate BD MD Good Bad G vs. B Good Bad G vs. B BDt-1/MDt-1 -0.576 -0.557 -0.560 -0.629 -0.579 -0.607 (-52.71) *** (-51.29) *** (-67.63) *** (-79.09) *** (-72.90) *** (-96.38) *** LEVDUMMY 0.148 0.142 0.146 0.196 0.202 0.196
(41.36) *** (34.51) *** (53.98) *** (63.44) *** (57.51) *** (84.47) *** LEVDUMMY*BDt-1/MDt-1 0.114 0.128 0.121 0.154 0.145 0.160
(8.88) *** (9.65) *** (13.29) *** (17.61) *** (16.39) *** (25.52) *** GOODDUMMY 0.001 0.002
(0.48) (1.31) GOODDUMMY *BDt-1/MDt-1 -0.015 -0.030
(-2.53) ** (-7.01) *** Obs 22750 21835 44585 23200 22176 45376 R-Square 0.4159 0.3888 0.4029 0.5079 0.4655 0.4829 Panel D. Regression results when states are determined by dividend yield BD MD Good Bad G vs. B Good Bad G vs. B BDt-1/MDt-1 -0.631 -0.608 -0.626 -0.647 -0.638 -0.610 (-59.69) *** (-55.91) *** (-62.99) *** (-89.93) *** (-72.92) *** (-82.49) *** LEVDUMMY 0.144 0.168 0.156 0.140 0.201 0.171
(38.18) *** (45.42) *** (59.35) *** (44.12) *** (64.20) *** (74.96) *** LEVDUMMY*BDt-1/MDt-1 0.164 0.129 0.164 0.253 0.181 0.227
(12.73) *** (9.92) *** (15.87) *** (28.43) *** (19.63) *** (29.93) *** GOODDUMMY -0.010 -0.011
(-4.98) *** (-6.57) *** GOODDUMMY *BDt-1/MDt-1 0.009 -0.017
(0.67) (-1.84) * GOODDUMMY* LEVDUMMY*BDt-1/MDt-1 -0.033 -0.034 (-3.16) *** (-4.06) *** Obs 22501 23240 45741 23217 23574 46791 R-Square 0.4348 0.4345 0.4394 0.4990 0.4745 0.4922
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Panel E. Regression results when states are determined by price-output ratio BD MD Good Bad G vs. B Good Bad G vs. B BDt-1/MDt-1 -0.532 -0.649 -0.658 -0.588 -0.678 -0.681 (-49.02) *** (-67.07) *** (-72.47) *** (-74.16) *** (-92.65) *** (-97.77) *** LEVDUMMY 0.126 0.167 0.155 0.168 0.194 0.193
(32.74) *** (48.07) *** (59.18) *** (44.57) *** (68.29) *** (84.77) *** LEVDUMMY*BDt-1/MDt-1 0.106 0.160 0.188 0.132 0.263 0.268
(7.98) *** (13.88) *** (19.34) *** (14.61) *** (31.56) *** (35.74) *** GOODDUMMY -0.011 -0.015
(-5.77) *** (-8.74) *** GOODDUMMY *BDt-1/MDt-1 0.145 0.119
(11.74) *** (13.50) *** GOODDUMMY* LEVDUMMY*BDt-1/MDt-1 -0.142 -0.170 (-14.98) *** (-22.10) *** Obs 19997 26554 46551 20078 28052 48130 R-Square 0.3968 0.4404 0.4267 0.4733 0.4935 0.4942
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Table 7 Robustness check on firm size impact This table analyzes the firm size impact across good and bad states over the sample period from 1976 to 2005. We measure firm size as the logarithm of total assets (LNTA). We report mean differences in firm size across good and bad states for debt ratios measured on both a book- and market-value basis. The book- and market- value debt ratios are as follows: BD is the book-value debt ratio computed by (long-term book debt + short-term book debt)/total book assets, MD is the market-value debt ratio computed by (long-term book debt + short-term book debt)/(long-term book debt + short-term book debt + stock price* number of shares outstanding). Column 2 and 3 examine the mean difference in LNTA between good and bad states as defined by term spread, which is the difference between the twenty-year government bond yield series and the three-month Treasury-bill rate series. Columns 4 and 5 analyze the mean difference in LNTA between good and bad states as defined by default spread, which is the difference between the average yield of bonds rated BAA by Moody’s and the average yield of bonds with a Moody’s rating of AAA. Columns 6 and 7 examine the mean difference in LNTA between good and bad states as defined by GDP growth rate, which is the average real GDP growth rate over quarters in a year. Columns 8 and 9 examine the mean difference in LNTA between good and bad states as defined by dividend yield on the market, measured as total dividend payments on the value-weighted NYSE/AMEX/Nasdaq portfolio over year t-1 divided by the current value of the portfolio at time t. Columns 10 and 11 analyze the mean difference in LNTA between good and bad states as defined by price-output ratio, computed as the S&P stock price index in January in a given year scaled by GDP from the previous year. Term Spread Default Spread GDP Growth Rate Dividend Yield Price-output Ratio LNTA-BD LNTA-MD LNTA-BD LNTA-MD LNTA-BD LNTA-MD LNTA-BD LNTA-MD LNTA-BD LNTA-MDGood 18.456 18.334 18.388 18.107 18.290 18.238 18.512 18.402 18.394 18.380Bad 18.374 18.299 18.122 18.331 18.458 18.403 18.131 18.087 18.550 18.375 G vs. B 0.082 0.035 0.266 -0.224 -0.168 -0.165 0.381 0.315 -0.156 0.005p-value <.0001 0.104 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.792
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Table 8 Robustness check of the distance of actual debt ratio deviation from target This table analyzes the distance between actual and target debt ratios across good and bad states over the sample period from 1976 to 2005. We measure the distance as: DISi,t = ti, ti, D - *D , where Di,t
*= γMacrot-1 + βXi,t-1, Macro is a set of macroeconomic target variables, and X is the vector of firm characteristics determining the target debt level. The debt ratios are book- and market-value debt ratios defined as follows: BD is the book-value debt ratio calculated as (long-term book debt + short-term debt)/total book assets. MD is the market-value debt ratio computed as (long-term book debt + short-term book debt)/(long-term book debt + short-term book debt + stock price* number of shares outstanding), Columns 2 and 3 show the mean difference in DIS between good and bad states as defined by term spread, measured as the difference between the twenty-year government bond yield series and the three-month Treasury-bill rate series. Columns 4 and 5 report the mean difference in DIS between good and bad states as defined by default spread, the difference between the average yield of bonds rated BAA by Moody’s and the average yield of bonds with a Moody’s rating of AAA. Columns 6 and 7 examine the mean difference in DIS between good and bad states defined by GDP growth rate, average real GDP growth rate over quarters in a year. Columns 8 and 9 in DIS illustrate the mean difference between good and bad states as defined by dividend yield on the market, measured as total dividend payments on the value-weighted NYSE/AMEX/Nasdaq portfolio over year t-1 divided by the current value of the portfolio at time t. Columns 10 and 11 report the mean difference in DIS between good and bad states as defined by price-output ratio, computed as the S&P stock price index in January in a given year scaled by GDP from the previous year.. Term Spread Default Spread GDP Growth Rate Dividend Yield Price-output Ratio DIS-BD DIS-MD DIS-BD DIS-MD DIS-BD DIS-MD DIS-BD DIS-MD DIS-BD DIS-MDGood 0.154 0.170 0.154 0.170 0.150 0.184 0.152 0.167 0.143 0.190Bad 0.155 0.193 0.152 0.181 0.156 0.196 0.161 0.190 0.161 0.183 G vs. B -0.002 -0.024 0.003 -0.011 -0.005 -0.012 -0.009 -0.023 -0.018 0.007p-value 0.1684 <.0001 0.008 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001