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: dcook@cba.ua.edu, (205) 348-8971. Tang: ttang@cba.ua.edu, (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