capital structure and business cycles

Capital structure and business cycles

Shumi Akhtar

Australian National University, School of Finance, Actuarial Studies, and Applied Statistics,College of Business and Economics, Canberra, ACT 0200, Australia

Abstract

This study investigates the relationship between business cycles and capitalstructure. Specifically, it extends the work of Lemmon et al. (2008), by incorpo-rating the effect of four different stages of the business cycle – peak, contraction,trough and expansion – on the relative importance of the unobserved permanentcomponent of the capital structure. Results indicate that business cycles play animportant role in explaining the unobserved permanent component of leverageratios after controlling for firm fixed effects. In particular, the model becomesmuch stronger in explaining the variation in leverage ratios after accounting forbusiness cycle phases.

Key words: Capital structure; Business cycle; Firm fixed effects; Unobservedpermanent component

JEL classification: G37, F22, H21

doi: 10.1111/j.1467-629X.2011.00425.x

1. Introduction

This study investigates the relationship between business cycles and capitalstructure.1 Specifically, it extends the work of Lemmon et al. (2008), by incorpo-rating the effect of four different stages of the business cycle – peak, contraction,trough and expansion – on the relative importance of the unobserved permanentcomponent of the capital structure.

I thank Professor Tom Smith and Dr Barry Oliver for valuable comments. This projectwas funded by the CIGS/FASIGS SA10 and RF10.

1 The terms capital structure, leverage ratios and debt ratios are used interchangeably inthe paper.

Received 24 March 2011; accepted 5 May 11 by Robert Faff (Editor).

� 2011 The AuthorAccounting and Finance � 2011 AFAANZ

Accounting and Finance

Currently, research shows that after accounting for firm fixed effects, there areno additional variables with significant explanatory power to explain capitalstructure. For example, Chang and Dasgupta (2011)2 show that firm fixed effectscontribute as much as 95 per cent of the explained variation. They also confirmthe findings of Lemmon et al. (2008), who find that variables such as size, mar-ket-to-book, profitability, initial leverage, industry median, tangibility and cashflow volatility (hereafter referred to as the extant determinants of capital struc-ture) fail to completely capture the variation in leverage ratios when firm fixedeffects are considered. Lemmon et al. (2008) suggest that the majority of the vari-ation in leverage is determined by an unobserved, time invariant effect (alterna-tively known as the unobserved permanent component). These findings raise theconcern that previous models, which have ignored the time invariant factor, arelikely to be misspecified. Although Lemmon et al. (2008) state that identifyingthe factors behind the long-lived satiability feature of leverage ratios is veryimportant, a detailed investigation of these factors was outside the scope of theirstudy. Therefore, the current study extends the work of Lemmon et al. (2008) byinvestigating whether the inclusion of business cycle variables strengthens theexplanatory power of the model after accounting for firm fixed effects.The business cycle may explain the unobserved time invariant feature of lever-

age ratios for two reasons. First, different phases of the business cycle experiencea partial time invariant feature, which indicates the possible existence of comove-ments between leverage ratios and business cycles. This means that they couldtrend together on a partial basis towards a long-run steady-state equilibrium.Hence, investigating the common partial comovement feature in a regression set-ting can reveal if the inclusion of variables capturing phases of the business cycle,in addition to the extant determinants, enhances the explanatory power of themodel, after accounting for firm fixed effects. Second, the business cycle is a

2 Chang and Dasgupta (2011) highlight how challenging it is to do capital structure-related research – especially when it comes to modelling target debt ratios. According totheir study, the most important challenges are as follows. First, debt ratios have a non-standard data generating process (e.g. debt ratios are bounded in the unit interval).Second, capital structure data often involve several unbalanced panels, comprising manyyoung firms with only a few years of data, and large firms that have survived for longperiods. Third, there are difficulties in determining from the evolution of the debt ratioswhether a given firm-specific variable affects the debt ratio because it affects the amountof financing deficits and retentions, or whether it is a determinant of a desired capitalstructure, i.e., the target debt ratios. While the current study is aware of these challenges,the following counter arguments justify my research. First, the measure of target debtratio is controversial as Welch (2011) and Chang and Dasgupta (2011) themselvesacknowledge. Given that there is as yet no consensus about the ‘appropriate’ measure oftarget debt ratios, the non-standard data generating process issue therefore seem less rele-vant. Second, the unbalanced panel does not seem to be a problem in my study given thatI use the firm fixed effect technique. Third, the lagged-dependent variables should over-come any issues associated with financing deficit and retentions versus determining desiredcapital structure.

2 S. Akhtar/Accounting and Finance


time-related factor, and based on the attempt by Lemmon et al. (2008) to solvethe capital structure issue linked to time-related issues, it seems logical toexplore business cycle phases as a possible solution to the mystery of capitalstructure determinants.Lemmon et al. (2008) argue that the estimated associations between leverage

and extant determinants are highly sensitive to changes in model specification.These associations experience an average decrease in magnitude of 86 per cent(65 per cent) in the book (market) leverage regression after controlling for firmfixed effects (accounting for the permanent component of leverage ratios) andserially correlated errors (accounting for the transitory component of leverageratios). They suggest that the majority of the variation in leverage is determinedby an unobserved time invariant effect. In addition, they find that leverage ratiosare remarkably stable over time. More specifically, firms with relatively high(low) leverage tend to maintain relatively high (low) leverage for periods of20 years or more. This stability is considered an unobserved permanent compo-nent of the leverage ratios. To explain this unobserved permanent component,they use adjusted R squared from traditional leverage regressions. They statethat using various model specifications, the adjusted R squared ranges between18 and 29 per cent. However, in contrast, the adjusted R squared from a regres-sion of leverage ratios using firm fixed effects (in their words, statistical ‘stands-in’ for the permanent component of leverage) is 60 per cent, which implies thatthe majority of variation in leverage ratios in a panel of firms is time invariantand is largely unexplained by previously identified determinants.By including the effect of business cycle stages on the relative importance of

this unobserved time invariant factor, this study addresses the following ques-tion: does the relative importance of the time invariant factor, compared toextant determinants, change or remain unaffected when different phases of thebusiness cycle are considered?This study contributes to the literature on the impact of business cycles on cap-

ital structure. Although previous studies have investigated the effect of macro-economic conditions on capital structure (Gertler and Gilchrist, 1993; Korajczykand Levy, 2003; Amdur, 2009), this study is the first attempt at examining theimpact of phases of the business cycle on capital structure, incorporating boththe time invariant factor and the previously identified determinants. Moreover,this study provides a more comprehensive analysis of corporate capital structureunder different business cycle conditions. Further, I investigate whether businesscycles have a variable impact on long-term debt, whereas other studies mostoften employ aggregate measures of leverage (i.e. leverage measures based ontotal debt).My sample consists of all US firms featured in the Compustat data file

between 1950 and 2010. The business cycle data are obtained from the NationalBureau of Economic Research (NBER). Results indicate that business cyclesplay an important role in explaining the unobserved permanent component ofleverage ratios after controlling for firm fixed effects. In particular, the model

S. Akhtar/Accounting and Finance 3


becomes much stronger in explaining the variation in leverage ratios afteraccounting for business cycle phases. These results are robust across various sen-sitivity tests.The remainder of the paper is structured as follows. Section 2 discusses the

previous studies that have been conducted in this area and develops the hypothe-ses. Section 3 describes the data selection and model development. Section 4analyses the results. Section 5 presents conclusions.

2. Literature review and hypothesis development

2.1. Existing literature

The question of how firms choose their capital structure is a widely researchedarea in finance. Bradley et al. (1984) study numerous determinants of the capitalstructure of a firm, including non-debt tax shields, firm cash flow volatility,advertising and research and development expenditure both with and withoutcontrolling for industry effects. They find evidence supporting a strong industryinfluence on firms’ leverage ratios. Titman and Wessels (1988) conduct a morecomprehensive study, extending the range of theoretical determinants of capitalstructure by analysing the importance of the tangibility value of assets, non-debttax shields, growth, uniqueness, industry classification, size, volatility and profit-ability as the determinants of capital structure choices. Their evidence indicatesa relationship between uniqueness, size and profitability, and capital structurechoices by firms. Rajan and Zingales (1995) conduct a similar study in an inter-national setting and identify tangibility, market-to-book, size and profitabilityas the main determinants of capital structure and show that these determinantsare relevant for all major industrialised countries, in addition to the UnitedStates.More recently, capital structure and its determinants are considered by

Graham and Harvey (2001), Baker and Wurgler (2002), Flannery and Rangan(2006), Banerrje et al. (2008), Byoun (2008), Huang and Ritter (2009), Grahamand Leary (2011), Chang and Dasgupta (2011) and Welch (2011). Althoughsome of the identified determinants do impact firms’ leverage ratios, these studiesfail to consider the influence of firm fixed effects on capital structure and theimpact of the business cycle. Lemmon et al. (2008) study fixed effects and capitalstructure and show that fixed firm effects generate an adjusted R squared of 60per cent as opposed to an adjusted R squared of 18–29 per cent for the extantdeterminants. They also show that leverage ratios characterised by both a transi-tory and permanent component tend to show a significant amount of conver-gence in the short run (transitory effect), but leverage ratios that are very stablein the long run, i.e. firms with high (low) leverage, tend to remain as such fornearly 20 years (permanent effect). Thus, they conclude that the most prevalentdeterminants of capital structure, both individually and collectively, seem tocontain less information about leverage compared to the time invariant factor.



Previous studies have led us to conclude that both the time invariant factorand the extant determinants influence capital structure over time. A reasonablequestion that emerges from this is: what happens to these unobserved permanentcomponents (or the time invariant factor) of capital structure with changes in thevarious stages of the business cycle? In other words, what is the effect that busi-ness cycles have on firms’ leverage ratios as explained by both the time invariantfactor and the extant determinants?It is possible that the time variation in business cycles leads to changes in the

relative pricing of asset classes, which can lead a given firm to choose differentcapital structures at different points in time, if all else is constant. On the basis ofthe market timing theory, Baker and Wurgler (2002) argue that firms are morelikely to issue equity when their market values are high, relative to book and pastvalues, and repurchase equity when their market values are low. They maintainthat this behaviour is consistent with capital structure behaviour, that is, firmsissue debt when market values are low relative to their book values (low marketvalues are observed during contraction and trough periods). However, they failto account for firms being financially constrained (smaller firms) and financiallyunconstrained (larger firms). Given this omission, I argue that their findings areconsistent with those of Gertler and Gilchrist (1993), Choe et al. (1993) andKorajczyk and Levy (2003), to the extent that constrained firms issue debtduring good times (expansion and peak), whereas unconstrained firms issue debtduring contraction and trough periods, as explained below.Korajczyk and Levy (2003) find that macroeconomic conditions have an influ-

ence on firms’ leverage ratios. This impact is greater for unconstrained firms thanfor constrained firms, with unconstrained firms showing counter-cyclical varia-tion in aggregate leverage with macroeconomic conditions and constrained firmsshowing pro-cyclical variation in aggregate leverage ratios. This indicates thatunconstrained firms (larger firms) issue more debt during contraction and troughstages of the business cycle (equivalent to macroeconomic contraction and reces-sion periods), while constrained firms (smaller firms) do not follow this pattern.Instead, they might issue debt pro-cyclically, during expansion and peak stagesof the business cycle. Similarly, Choe et al. (1993) and Gertler and Gilchrist(1993) have shown that aggregate net debt issues (private and public) increasefor unconstrained firms during recession (trough) associated with a monetarycontraction. Further, Gertler and Gilchrist (1994) show that aggregate net short-term debt is fairly stable over the business cycle. Hackbarth et al. (2004) andDrobetz et al. (2007) find evidence to support the view that there is a linkbetween business cycles and capital structure choices. Most recently, Amdur(2009) develops a business cycle model with financial frictions and uses it toexplain some stylised facts regarding aggregate United States (US) debt andequity flows. However, this body of literature on the influence of macroeconomicconditions on firm capital structure choices fails to incorporate the time invarianteffect. Furthermore, it does not consider the change in the relative importanceof this time invariant factor affecting capital structure with changes in



macroeconomic conditions. Thus, the novelty of the current study lies in itsattempt to provide a comprehensive analysis of the effect of business cycles oncapital structure, incorporating both the unobserved permanent component andthe previously identified determinants.For the purpose of illustration, consider the scenario of a booming economy.

As mentioned earlier, researchers have shown that for a constrained firm, a boom(duration of expansion to peak phase of the business cycle) in the economy mightlead to an increase in leverage. Further, it is reasonable to expect that for a con-strained firm, a boom in the economy might lead to an increase in firm size.Because firm size has been identified as one of the determinants of capital struc-ture and as the time invariant factor is by definition fixed, this implies that in abooming economy, the relative importance of the time invariant factor mightdecrease and the relative importance of size, as a previously identified determi-nant, might increase (for constrained firms). Stated differently, for a constrainedfirm, the adjusted R squared of firm fixed effects might decrease in magnitudeduring expansion and peak periods, and the adjusted R squared of size mightincrease in magnitude during the same period (all other things remainingconstant). The reverse might be true for contraction and trough periods. Thus,the current study explores whether the stability in capital structures observed byLemmon et al. (2008) holds true during different stages of the business cycle.

2.2. Core capital structure theory and testable hypothesis

The existing primary theories of corporate capital structure explaining firms’financing decisions can be categorised as trade-off, pecking order, managerialentrenchment and market timing theories. First, in the trade-off theory, firmsselect target leverage ratios based on an exchange between the benefits and costsof increased leverage (Modigliani and Miller, 1958, 1963; Jensen and Meckling,1976; Myers, 1977; Stulz, 1990; Hart and Moore, 1995; Baker and Wurgler,2002). The trade-off theory determines an optimal capital structure by addingvarious imperfections, including taxes, costs of financial distress and agencycosts, but retains the assumptions of market efficiency and symmetric informa-tion. Some of the imperfections that lead to an optimal trade-off are as fol-lows: higher taxes on dividends indicate more debt (Modigliani and Miller,1963; Miller and Scholes, 1978); higher non-debt tax shields indicate less debt(DeAngelo and Masulis, 1980); higher costs of financial distress indicate moreequity and less debt; short of bankruptcy, senior debt can force managers toforgo profitable investment opportunities (Myers, 1977); agency problems cancall for more or less debt; too much equity can lead to free cash flow and con-flicts of interest between managers and shareholders (Jensen 1986); and too muchdebt can lead to asset substitution and conflicts of interest between managersand bondholders (Fama and Miller, 1972; Jensen and Meckling, 1976).Second, 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, there is no optimal capital structure.To be more precise, if there is an optimum, the cost of deviating from it is insig-nificant in comparison to the cost of raising external finance. Raising externalfinance is expensive because managers have more information about the firm’sprospects than external investors and because investors are aware of this. InMyers and Majluf (1984), external investors rationally discount the firm’s stockprice when managers issue equity instead of riskless debt. To avoid this discount,managers avoid equity whenever possible. The model proposed by Myers andMajluf (1984) predicts that managers will follow a pecking order, using up inter-nal funds first, then using up risky debt, and finally resorting to equity. In theabsence of investment opportunities, firms retain profits and build up financialslack to avoid having to raise external finance in the future. According to thistheory, firms do not have a strong incentive to rebalance their capital structures.Third, according to the dynamic theory of capital structure based on manage-

rial entrenchment by Zwiebel (1996), high valuations and good investmentopportunities facilitate equity finance, but at the same time allow managers tobecome entrenched. They may then refuse to raise debt to rebalance in later peri-ods. This has a market-timing flavour, because managers issue equity when valu-ations are high and do not subsequently rebalance, but in this case, any markettiming aspect has a very different interpretation. Managers are not attempting toexploit new investors. Rather, they are exploiting existing investors ex post bynot rebalancing. According to Baker and Wurgler (2002), both views might bevalid.Fourth, Baker and Wurgler (2002) propose the market timing theory of capital

structure, arguing that current capital structure is the cumulative outcome ofpast attempts to time the market. In this theory, there is no optimal capital struc-ture, and market valuation has a persistent impact on capital structure. How-ever, Leary and Roberts (2005) provide evidence contradicting the implicationsof market timing theory. They show that the persistent effect of shocks on lever-age is more likely owing to the presence of adjustment costs than an indifferencetowards capital structure.No single theory of capital structure is capable of explaining all of the time ser-

ies and cross-sectional patterns that have been documented (Parson and Titman,2009; Chang and Dasgupta, 2011; Graham and Leary, 2011). The relative impor-tance of these explanations has varied in different studies. Generally, the peckingorder theory received greatest attention and credibility in the 1990s, but it hasrecently fallen out of favour (Huang and Ritter, 2010). Similarly, the market tim-ing theory of Baker and Wurgler (2002) has challenged both static trade-off andpecking order theories. However, a number of papers have, in turn, challengedthe evidence provided by Baker and Wurgler (2002) that the securities have long-lived effects on capital structure. Evidently, literature so far provides no explana-tion of the relationship between the business cycle and the core theories of capitalstructure. In this study, I consider this issue and develop possible theoriesconnecting core theories of capital structure and the business cycle.



A trade-off model would imply pro-cyclical leverage since during expansions(when the equity market is performing well, expected bankruptcy costs are lower,firms are more likely to have taxable income to shield and firms have more freecash) debt should be relatively more attractive for unconstrained firms (Jensenand Meckling, 1976; Gertler and Hubbard, 1993; Zwiebel, 1996). A peckingorder theory would be consistent with these macroeconomic patterns becausefirms prefer using internally generated funds to finance investments and havingmore internal funds during expansion and peak periods. Baker and Wurgler’s(2002) market timing theory would also be aligned with this argument.Korajczyk and Levy (2003) suggest that unconstrained firms are substantiallymore sensitive to variations in macroeconomic conditions than constrainedfirms, consistent with arguments that unconstrained firms can deviate from theirtarget capital structure to time their issues to periods when market conditionsare most favourable. They find that after correcting for a range of firm-specificvariables, the macroeconomic variables help explain some of the counter-cyclicalleverage patterns for the unconstrained firms. This result is consistent with Levy(2001). Levy (2001) develops an agency model in which debt aligns managers’interests, which include private benefit extraction, with those of the externalshareholders. In recessions, leveraged managers’ wealth is reduced relative toexternal shareholders. This shift in relative wealth exacerbates the agency prob-lem and increases the optimal amount of leverage to realign managers’ incentiveswith those of the shareholders. This leads to counter-cyclical leverage for firmsthat are not severely constrained. The literature, which often uses size as a proxyfor the level of financial constraints, generally agrees with the proposition thatfirms facing greater financial constraints find it difficult to borrow to smoothcash flows following negative shocks to the economy. Gertler and Gilchrist(1993) find that aggregate net debt issues, following recessions, increase for largefirms but remain stable for small firms that rely on private debt.It is not known what impact the business cycle has across all these theories in

explaining capital structure variation. Arguably, different phases of the businesscycle and different phases of the macroeconomy might show similar patterns,which could lead firms to make similar financial decisions. Ultimately, it is anempirical question. Therefore, I examine the null and alternative hypotheses:

Null Hypothesis: Different stages of the business cycle have no impact onthe relative importance of the unobserved permanent component of capitalstructure.

Alternate Hypothesis: Different stages of the business cycle conditions have animpact on the relative importance of the unobserved permanent component ofcapital structure.

The above hypothesis is investigated for long-term book leverage ratios andmarket leverage ratios. For comparison purposes, I use both ordinary leastsquares (OLS) regressions and fixed effects specifications.



3. Data selection and variable measurements

3.1. Data

The initial sample consists of all US non-financial firms’ year observationsfrom the annual fundamentals Compustat database between 1950 (which is thefirst year for which annual data were reported by Compustat) and 2010. Thisincludes data on annual company fundamentals as well as the market. The USmarket is selected for this project for a number of reasons. First, because mostprevious studies have been conducted using data from the US, using the samecountry provides a comparability advantage. Second, because the US economyis the largest in the world, the business cycle effect would be most clearly andpersuasively observed in that setting. Third, because a full business cycle typicallyoccupies many years, conducting a thorough analysis would require the maxi-mum number of years possible. The US data offer the widest time frame.From the original sample, I exclude observations with missing or negative val-

ues of book assets because most variables are calculated by scaling the values bythe corresponding values of book assets. In addition, I have eliminated anyobservations with missing data for the full set of variables used in the analysis.The analysis also requires leverage, both book and market, to lie in the closedunit interval or within the range (0, 1). The final sample consists of 225,717observations across 24,102 different firms.

3.2. Variable measurement

3.2.1. Measures of leverage

There are different definitions of leverage used in the literature. For the pur-pose of this study, I have used the definition of market and book leverage ratioconsidered by Lemmon et al. (2008) as a long-term debt measure. This measureis consistent with previous studies such as Titman and Wessels (1988) and Frankand Goyal (2009).

3.2.2. Determinants of leverage

To examine the relative importance of determinants of leverage ratios underfour different stages of the business cycle (peak, contraction, trough and expan-sion), I have examined a list of capital structure determinants that are commonin the studies of Rajan and Zingales (1995), Lemmon et al. (2008) and Frankand Goyal (2009). These determinants are size, market-to-book, profitability,tangibility, industry median, cash flow volatility and dividend paying status.Collectively, these variables accommodate the trade-off, pecking order, manage-rial entrenchment and market timing theories. Data on all these variables areobtained from the Compustat data file. Because the construction of the specified



determinants has been discussed in the prior literature, a detailed discussion isnot provided here. Table 1 summarises the construction of the variables.

3.3. Summary statistics

Table 2 presents summary statistics for all sample firms. Following Lemmonet al. (2008), all the variables are winsorised at the upper and lower one-percen-tiles to mitigate the effect of outliers and to reduce the impact of errors in thedata. The table shows that, on average, the book value of leverage and marketvalue of leverage are 0.26 and 0.28, respectively. This is consistent with Lemmonet al. (2008), among others. On average, US firms’ market-to-book value ratiosuggests that firms are valued relatively higher in the market than their book val-ues. The average and the median ratio of asset tangibility (0.34 and 0.29), medianindustry leverage (0.24 and 0.24) and cash flow volatility (0.11 and 0.07) are verysimilar to Lemmon et al. (2008). Table 3 presents a correlation matrix for thefull sample. It shows that correlations between the variables are relatively low,and so multicollinearity should not be a major problem for this study.Table 4 presents a breakdown of descriptive statistics by decade for the depen-

dent and independent variables, showing how the sample changes over time. Itshows that both book and market measures of leverage were noticeably higherprior to the 1980s, but they gradually decreased after this period. Similarly, theproportion of dividend payers was higher in the early periods and especially inthe 1950s and 1960s, when almost all firms paid dividends. It has graduallydeclined over time, and the proportion of firms that pay dividends is at its lowestlevel in the current decade. This downward trend in dividends is consistent withthe disappearing dividends phenomenon documented by Fama and French(2002).

3.4. Empirical specification

To test the proposed hypotheses, I have employed five models. Model 1 is inOLS form, while Model 2 accounts for firm fixed effects in the regression:

Leveragei;t ¼ b0 þ b1

XXi;t�1 þ b2Constraini;t�1

þ b3Unconstraini;t�1 þ mt þ /t þ ei;t ð1Þ


XXi;t�1 þ b2Constraini;t�1

þ b3Unconstraini;t�1 þ gi þ mt þ /t þ ei;t ð2Þ

where, i indexes firms, t indicates years, Leveragei,t is the leverage for firm i inyear t, X is a set of control variables, mt is a year fixed effect, /t is industryfixed effect and ei,t is the error term. The control variables are the extant



Table 1

Construction of variables

Variable

abbreviation

Variable

description Variable construction

Dependent variables

LEV_LTB Long-term book

leverage

Long-term debt/total assets.

LEV_LTM Long-term market

leverage

Long-term debt/(Long-term debt + market value of equity).

Independent variables

Size Total assets Ln (Total assets).

MB Market value of

equity to book

value of equity

Market value of equity/Book value of equity.†

PROF Accounting

profitability

Operating income before depreciation/Total book assets.

TANG Tangibility of assets (Net property, plant and equipment)/total book assets.

LEV_IMed Industry median

leverage

2 digit Global Industry Classification standard codes are

used to identify each industry. There are ten industries

under this classification.

CFVol Cash flow

variation

The standard deviation of historical operating income,

requiring at least 3 years of historical data.

DivPayer Whether a

firm pays

dividends

Equals to 1 if firm paid out dividends during a fiscal year

and 0 otherwise.

BCyc Four phases of

business cycle

Business cycle is classified into four phases: peak,

contraction, trough and expansion. To identify each

business cycle, I rely on NBER.‡ For example, in any

given year when a phase occurs, it takes a value of 1,

otherwise 0. It is worth noting that multiple phases of

the business cycle can occur in a given year. For example,

according to NBER categorisation in 1981, all four phases

of the business cycle were observed. In some years, 2 phases

of the business cycle are observed; however, the phases do

not necessarily occur in an orderly sequence (e.g. an

expansion may not be followed by a peak).

Cons Financially

constrained

firms

Firms are defined financially constrained following

Korajczyk and Levy (2003) (e.g. firms do not pay

dividends, do not have a net equity or debt purchase and

have market to book value >1). Firms are then ranked

from low to high based on their total assets. Firms that

belong to the first quartile take a value of 1, otherwise 0.

Uncons Financially

unconstrained

firms

Financially unconstrained firms are those firms that are not

in the category of financially constrained, and firms are

ranked from low to high based on their total assets. Then,

firms that belong to the fourth quartile take a value of 1,

otherwise 0.

†Market value of equity is calculated as stock price times the shares outstanding (which is used to

calculate EPS). ‡http://www.nber.org.



determinants of capital structure (see Data and Sample Selection section fordetails).Note that the error term in Model 1 is assumed to be possibly heteroskedastic

and correlated within firms, while in Model 2, the errors are homoskedastic as gicontrols for firm fixed effects.Both Models 1 and 2 are used as baseline cases for comparison purposes (to

confirm the key previous study (Lemmon et al., 2008) and for drawing a direct

Table 2

Summary statistics for full sample

Variable Mean Median Std. Dev. Min Max

LEV_LTB 0.26 0.24 0.23 0.00 0.98

LEV_LTM 0.28 0.25 0.19 0.00 0.97

Size 5.11 5.09 2.06 1.22 12.02

MB 1.63 1.11 1.87 0.02 8.34

PROF 0.09 0.12 0.20 )4.13 7.90

TANG 0.34 0.29 0.22 0.00 0.95

LEV_IMed 0.24 0.24 0.05 0.02 0.53

CFVol 0.11 0.07 1.53 0.01 5.69

DivPayer 0.41 0.00 0.50 0.00 1.00

Cons 0.23 0.00 0.34 0.00 1.00

Uncons 0.21 0.00 0.41 0.00 1.00

BCyc_P 0.15 0.00 0.35 0.00 1.00

BCyc_C 0.27 0.00 0.45 0.00 1.00

BCyc _T 0.11 0.00 0.31 0.00 1.00

BCyc_E 0.72 1.00 0.28 0.00 1.00

Tables 2 and 3 present summary statistics and a correlation matrix of the final Compustat sample

comprising 225,717 observations across 24,102 firms from the years 1950 to 2010, while Table 4

presents a further breakdown of the summary statistics across variables by decade. The variables are

defined as follows. Long-term book leverage (LEV_LTB) is the ratio of long-term debt to total

assets. Market leverage (LEV_LTM) is the ratio of long-term debt divided by the sum of total debt

and market value of equity. Size (Size) is the natural log of Total assets. Market-to-book (MB) ratio

is the ratio of market value of equity to total book value of equity. Profitability (PROF) is the ratio

of operating income before depreciation to total book assets. Tangibility (TANG) is the ratio of net

property, plant and equipment divided by total book assets. Industry Median Leverage (LEV_IMed)

is the median of the book (or market) value of the relevant type of debt by two digit GIC sector code

and by year. Cash flow volatility (CFVol) is measured as the standard deviation of historical operat-

ing income, requiring at least 3 years of historical data. Dividend Payer (DivPayer) is a dummy vari-

able equal to one if the firm paid dividends, otherwise zero. Financially constrained firms (Cons) are

deemed financially constrained within the spirit of Korajczyk and Levy (2003) (e.g. firms do not pay

dividends, do not have a net equity or debt purchase and have market to book value >1). Firms are

then ranked from low to high based on their total assets. Firms that belong to the first quartile take

a value of 1, otherwise 0. Financially unconstrained firms (Uncons) are those firms that are not in the

category of financially constrained (as defined above), and firms are ranked from low to high based

on their total assets. Then, firms that belong to the fourth quartile take a value of 1, otherwise 0.

Business Cycle BCyc_P, BCyc_C, BCyc_T and BCyc_E indicates Peak, Contraction, Trough and

Expansion and is a dummy variable equal to one if the year corresponds to a particular phase of the

business cycle in a given year and 0 otherwise.



comparison with Models 3 and 4), in particular, showing the importance of busi-ness cycle effects on the unobserved permanent component of the capital struc-ture after controlling for firm fixed effects.To examine the effect of the stages of the business cycle on the relative impor-

tance of the unobserved permanent component of long-term leverage ratios,Model 3 (OLS) and Model 4 (firm fixed effects) are proposed:


XXi;t�1 þ b2Constraini;t�1 þ b3Unconstraini;t�1

þ b4Peakt þ b5Contractiont þ b6Trought þ b7Expansiont

þ mt þ /t þ ei;t ð3Þ



þ b4Peakt þ b5Contractiont þ b6Trought þ b7Expansiont

þ gi þ mt þ /t þ ei;t: ð4Þ

The coefficients b4 to b7 and the adjusted R square in Model 4 are of main inter-est in this project. They will be compared with Model 2 to examine whether thebusiness cycle modelling has any major bearing on the relative importance of thefixed firm effects. A comparison between Models 3 and 1 and between Models 3and 4 will also provide further insights. Finally, I propose Model 5 that is anextension and modification of Model 2, for which there are four versions, associ-ated with each phase of the business cycle. For example, in the case of the peakphase:

Table 3

Correlation matrix for the full sample

[A] [B] [C] [D] [E] [F] [G] [H] [I] [J] [K] [L] [M] [N]

LEV_LTB [A] 1

LEV_LTM [B] 0.81 1

Size [C] 0.13 0.17 1

MB [D] )0.36 )0.18 )0.12 1

PROF [E] 0.01 0.01 0.03 )0.16 1

TANG [F] 0.28 0.29 0.02 )0.14 0.18 1

LEV_IMed [G] 0.24 0.18 )0.02 )0.15 0.19 0.33 1

CFVol [H] )0.07 )0.05 )0.11 0.17 )0.27 )0.11 )0.10 1

DivPayer [I] 0.08 0.06 0.32 )0.11 0.23 0.21 0.30 )0.10 1

Cons [J] )0.11 )0.10 )0.33 0.12 )0.30 )0.14 0.01 0.17 )0.22 1

Uncons [K] 0.16 0.12 0.36 )0.06 0.15 0.15 )0.01 )0.09 0.26 )0.29 1

BCyc_P [L] 0.03 0.05 0.02 )0.02 )0.01 0.07 0.03 0.09 0.01 )0.01 0.01 1

BCyc_C [M] 0.07 0.01 0.05 )0.08 0.08 0.05 0.04 0.01 0.04 )0.03 0.05 0.67 1

BCyc _T [N] 0.05 0.02 )0.03 )0.02 )0.01 0.03 0.11 )0.01 0.04 0.02 )0.03 0.44 0.57 1

BCyc_E [O] )0.04 0.01 )0.10 0.07 0.02 0.05 0.12 )0.03 0.04 0.06 )0.08 0.12 )0.49 0.11



Tab

le4

Summarystatistics

partitioned

bydecad

e

Periods

Stat

LEV_

LTB

LEV_

LTM

SIZ

EMB

PROF

TANG

LEV_

IMed

CFVol

DivPayer

Cons

Uncons

BCyc_P

BCyc_C

BCyc_T

BCyc_E

1950–1960

Mean

0.27

0.27

4.97

1.55

0.13

0.38

0.25

0.05

0.90

0.22

0.29

0.42

0.41

0.30

0.55

Med

0.24

0.20

4.66

1.14

0.16

0.34

0.26

0.03

1.00

0.00

0.00

0.00

0.00

0.00

1.00

Max

0.81

0.95

9.77

7.89

6.56

0.87

0.27

2.27

1.00

1.00

1.00

1.00

1.00

0.00

1.00

Min

0.00

0.00

1.73

0.39

)0.07

0.04

0.22

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

Std

0.15

0.18

1.77

1.12

0.08

0.19

0.02

0.14

0.30

0.47

0.39

0.49

0.49

0.00

0.00

1961–1970

Mean

0.28

0.30

4.78

1.58

0.11

0.38

0.25

0.05

0.90

0.21

0.28

0.40

0.40

0.00

0.37

Med

0.24

0.20

4.68

1.14

0.16

0.34

0.25

0.03

1.00

0.00

0.00

0.00

0.00

0.00

1.00

Max

0.79

0.84

9.77

7.89

7.76

0.87

0.27

3.17

1.00

1.00

1.00

1.00

1.00

0.00

1.00

Min

0.00

0.00

1.95

0.34

)0.07

0.04

0.20

0.07

0.00

0.00

0.00

0.00

0.00

0.00

0.00

Std

0.15

0.18

1.77

1.11

0.08

0.20

0.02

0.14

0.30

0.46

0.39

0.49

0.49

0.00

0.00

1971–1980

Mean

0.30

0.33

4.84

1.62

0.10

0.36

0.26

0.06

0.84

0.20

0.27

0.10

0.32

0.11

0.89

Med

0.23

0.29

4.59

0.74

0.17

0.32

0.26

0.04

1.00

0.00

0.00

0.00

0.00

0.00

1.00

Max

0.76

0.96

10.81

6.70

3.62

0.95

0.53

4.69

1.00

1.00

1.00

1.00

1.00

1.00

1.00

Min

0.00

0.00

1.22

0.13

)0.94

0.01

0.23

0.09

0.00

0.00

0.00

0.00

0.00

0.00

0.00

Std

0.16

0.24

1.83

1.15

0.10

0.19

0.02

0.24

0.37

0.47

0.38

0.30

0.47

0.32

0.61

1981–1990

Mean

0.25

0.28

5.18

1.37

0.10

0.36

0.24

0.10

0.74

0.21

0.27

0.10

0.19

0.10

1.00

Med

0.21

0.22

5.13

0.92

0.15

0.32

0.24

0.05

1.00

0.00

0.00

0.00

0.00

0.00

1.00

Max

0.97

0.97

11.99

7.86

5.15

0.92

0.28

4.43

1.00

1.00

1.00

1.00

1.00

1.00

1.00

Min

0.00

0.00

1.67

0.04

)3.43

0.00

0.19

0.10

0.00

0.00

0.00

0.00

0.00

0.00

1.00

Std

0.17

0.22

2.06

1.23

0.14

0.21

0.02

0.77

0.44

0.44

0.41

0.29

0.40

0.30

0.00

1991–2000

Mean

0.24

0.26

5.14

1.95

0.07

0.30

0.22

0.18

0.46

0.26

0.24

0.12

0.07

0.07

0.75

Med

0.19

0.14

5.06

1.23

0.13

0.25

0.22

0.07

0.00

0.00

0.00

0.00

0.00

0.00

1.00

Max

0.78

0.87

12.02

8.34

7.90

0.82

0.32

3.18

1.00

1.00

1.00

0.00

1.00

1.00

1.00

Min

0.00

0.00

1.92

0.05

)4.13

0.00

0.09

0.06

0.00

0.00

0.00

0.00

0.00

0.00

0.00

Std

0.19

0.22

2.07

2.51

0.22

0.22

0.05

1.09

0.50

0.45

0.41

0.00

0.25

0.25

0.00

2001–2010

Mean

0.20

0.24

5.72

1.70

0.06

0.24

0.18

0.21

0.36

0.27

0.24

0.25

0.34

0.14

0.90

Med

0.18

0.12

5.72

1.21

0.10

0.17

0.17

0.09

0.00

0.00

0.00

0.00

0.00

0.00

1.00

Max

0.98

0.73

11.89

7.65

4.93

0.91

0.27

5.69

1.00

1.00

1.00

1.00

1.00

1.00

1.00

Min

0.00

0.00

2.23

0.02

)2.59

0.00

0.02

0.02

0.00

0.00

0.00

0.00

0.00

0.00

0.00

Std

0.20

0.23

2.10

1.57

0.22

0.22

0.05

1.97

0.48

0.40

0.46

0.43

0.48

0.35

0.30





þ b4Peakt þ b5Peakt �X

Xi;t�1 þ b6Peakt � Constraini;t�1

þ b7Peakt �Unconstraini;t�1 þ gi þ mt þ /t þ ei;t: ð5Þ

In Model 5, the interaction terms, defined as the product between the givenbusiness cycle dummy and each leverage determinant, are designed to capturethe direct role that each business cycle phase has in differentially enhancingthe explanatory power of the leverage model. Specifically, the coefficients b5to b7 and the adjusted R squared are of key interest and will be comparedwith the results in Model 2. In Models 3, 4 and 5, the significance of thebusiness cycle coefficients and the improved adjusted R squared will provideevidence of whether business cycle modelling is important in explaining therelative importance of the unobserved permanent component of long-termdebt.

4. Analysis and results

4.1. Effect of business cycle on leverage

Table 5 shows the results for the baseline OLS (Model 1) and fixed effectsregressions (Model 2) in explaining the long-term market leverage ratios.3 Theresults presented in Column 1 and 2 are largely consistent with those of Lemmonet al. (2008): in particular, the magnitude, direction of the determinants, statisti-cal significance and the adjusted R squared values. The results for Models 3 and4 in Columns 3 and 4 of the same table show the significance of incorporatingbusiness cycle phases in explaining capital structure. Most importantly, thesefindings support the hypothesis that the business cycle plays a significant role inexplaining the unobserved time invariant effect of leverage ratios.The results shown from Model 4 (in comparison to Models 1, 2 and 3) support

the hypothesis in four key ways. First, the incorporation of the four phases ofthe business cycle shows that these factors are important. That is, we see that thebusiness cycle phases have a significant role in both the OLS and the fixed effectsmodel. Specifically, the contraction (BCyc_C: t-test = 2.40 in OLS andt-test = 3.14 in fixed effects) and trough (BCyc_T: t-test = 4.22 in OLS andt-test = 4.49 in fixed effects) phases of the business cycle are both highly signifi-cant in explaining the capital structure variation.Second, it is notable that when business cycle factors are incorporated into

the fixed effects regression, the explanatory power of the model increases by 0.14

3 Results for book leverage measures are very similar to the market leverage case. As such,results for the book versions are suppressed to conserve space. Details are available fromthe author upon request.



Table 5

The effect of business cycles on leverage ratios

Model 1: OLS Model 2: Firm FE

Model 3:

OLS (BC)

Model 4:

Firm FE (BC)

Coeff t-test Coeff t-test Coeff t-test Coeff t-test

Constant 0.0521 1.75* 0.0663 1.14 0.0299 1.55 0.2115 1.18

Size 0.0672 8.32*** 0.2471 9.12*** 0.0332 10.38*** 0.1836 24.83***

MB )0.0891 )26.13*** )0.0876 )22.19*** )0.0681 )26.12*** )0.1121 )2.93***PROF )0.1003 )3.12*** )0.1075 )2.96*** )0.1021 )3.47*** )0.1390 )8.64***TANG 0.1782 10.18*** 0.1642 8.07*** 0.1522 10.18*** 0.7471 8.01***

LEV_IMed 0.8324 11.17*** 0.7844 7.08*** 0.7665 11.77*** 0.0222 5.77***

CFVol )0.0741 )5.31*** )0.0932 )5.14*** )0.0191 )4.98*** )0.0478 )5.33***DivPayer )0.0532 )4.05*** )0.0433 )4.02*** )0.0431 )3.88*** )0.0321 )3.29***Cons )0.0765 1.12 )0.1539 )1.54 )0.1315 )1.11 )0.1198 )1.25Uncons 0.1042 1.98** 0.0895 3.15*** 0.1243 2.11** 0.1432 2.47**

BCyc_P )0.2417 )1.61 )0.2076 )1.59BCyc_C 0.2539 2.40** 0.0532 3.14***

BCyc _T 0.5148 4.22*** 0.5125 4.49***

BCyc_E )0.2241 )0.19 )0.01 )0.07Year fixed effect Yes Yes Yes Yes

Industry fixed effect Yes Yes Yes Yes

Adj R2 0.31 0.68 0.37 0.82

This table presents the results of estimating four regression models using market leverage as the

dependent variable. The final Compustat sample comprising 225,717 observations across 24,102 firms

from the years 1950 to 2010 is used for the analysis. The OLS and fixed effect specifications are

discussed in the text – refer to equations (1)–(4). Year fixed effects denote that the calendar year fixed

effects are included in the specification. Industry fixed effect indicates the inclusion of dichotomous

variables, indicating membership of one of the nine industries, as assigned by the global industrial

classification standard. Market leverage (LEV_LTM) is the ratio of long-term debt divided by the

sum of total debt and market value of equity. Size (Size) is the natural log of Total assets. Market-

to-book (MB) ratio is the ratio of market value of equity to total book value of equity. Profitability

(PROF) is the ratio of operating income before depreciation to total book assets. Tangibility

(TANG) is the ratio of net property, plant and equipment divided by total book assets. Industry

Median Leverage (LEV_IMed) is the median of the book (or market) value of the relevant type of

debt by two digit GIC sector code and by year. Cash flow volatility (CFVol) is measured as the stan-

dard deviation of historical operating income, requiring at least 3 years of historical data. Dividend

Payer (DivPayer) is a dummy variable equal to one if the firm paid dividends, otherwise zero. Finan-

cially constrained firms (Cons) are deemed financially constrained within the spirit of Korajczyk and

Levy (2003) (e.g. firms do not pay dividends, do not have a net equity or debt purchase and have

market to book value >1). Firms are then ranked from low to high based on their total assets. Firms

that belong to the first quartile take a value of 1, otherwise 0. Financially unconstrained firms

(Uncons) are those firms that are not in the category of financially constrained (as defined above),

and firms are ranked from low to high based on their total assets. Then, firms that belong to the

fourth quartile take a value of 1, otherwise 0. Business Cycle BCyc_P, BCyc _C, BCyc _T and BCyc

_E indicates Peak, Contraction, Trough and Expansion and is a dummy variable equal to one if the

year corresponds to a particular phase of the business cycle in a given year and 0 otherwise. ***, **,

* indicate statistical significance at the level of 1, 5 and 10%, respectively.



(i.e. the difference in adjusted R squared between Model 2 versus Model 4). Thisfinding reinforces the conjecture that the business cycle plays a significant role inexplaining the unobserved time-invariant effect. Also, fixed effects capturesubstantially more of the variation in leverage than does the OLS counterpart(Model 3 versus Model 4). More specifically, OLS produces an adjusted Rsquared of only 37 per cent, whereas the fixed effects counterpart more thandoubles this to 82 per cent.Third, it is also noteworthy that the increase in explanatory power from

including the business cycle effects in the OLS model of 0.06 (0.31–0.37, Model 1versus Model 3) is clearly dominated by the increase of 0.14 in the fixed effectscase (0.68–0.82, Model 2 versus Model 4). That is, while business cycle modellingis clearly important regardless of the estimation setting, its value becomes espe-cially evident in the fixed effects regression.Fourth, to test the combined qualitative effect of the inclusion of all four busi-

ness cycle phases in explaining the unobserved time invariant component of theleverage ratios in the firm fixed effects regression, I conduct a likelihood ratio testbetween Model 2 (restricted model) and Model 4 (unrestricted model) in Table 5.The likelihood ratio test statistic reveals a value of 314.22 which easily exceedsthe 1 per cent chi-square cut-off value of 13.28, thus supporting the need toinclude business cycle effects in the model. Collectively, these four findings sup-port the proposition that business cycle phases have a significant impact on therelative importance of the unobserved permanent component of capital structurevariation.The theoretical interpretation of the above findings suggests that in a shrinking

economy, when businesses are experiencing a contraction phase of the businesscycle, long-term market leverage tends to increase. This is consistent with themacroeconomic factors justification (Levy, 2001). Similarly, a positive and signif-icant relationship is also observed between market leverage and the trough phaseof the business cycle. Specifically, in a ‘trough’ phase, the model suggests that themarket value of leverage will increase. This suggests that during an economicrecession or bearish market, firms tend to seek or rely more on external debtrelative to peak or boom periods. These results suggest that, on average, themean intercept leverage ratio of 0.21 will increase by a magnitude of 5.32 percent in a contraction phase and 51.25 per cent in a trough phase of the businesscycle. This is intuitively appealing, as during ‘tough’ economic times, firms mightstruggle to generate sufficient profits to fund new investment from internally gen-erated cash flows.Finally, regarding the general role played by the extant determinants of lever-

age, the following observations can be made based on the general outcomeacross all four models. First, the directions and the magnitude of the coefficientsin the extant variables are found to be relatively similar to Lemmon et al. (2008)and Frank and Goyal (2009). Second, the statistical significance of the variablesis also found to reasonably comparable to Lemmon et al. (2008) for both OLSand firm fixed effect models.



4.2. Further analysis

Table 6 presents the interaction effects between each business cycle phase(Model 5) and the leverage determinants. The aim of this interaction testingapproach is to assess whether any particular variable has additional significance,in any given business cycle phase, in explaining the variation of leverage ratios.In all cases, the business cycle factor becomes significant, indicating a mean shiftin the leverage ratios. Essentially, the results in Table 6 reinforce the importanceof the business cycle in explaining the relative importance of the unobserved per-manent component of the capital structure. This is indicated by the significantbusiness cycle phase dummy, interaction coefficients and the high adjusted Rsquared, compared with results in Table 5. The adjusted R squared values inTable 6 range between 0.71 and 0.74, and this is noticeably higher than theadjusted R squared value of 0.68 for Model 2 in Table 5. Further, holdingall else constant, firms, on average, tend to have less long-term leverage duringpeak and expansion phases (peak: t-test = )3.34, expansion: t-test = )3.76),while they tend to hold significantly higher debt ratios during contraction(t-test = 2.15) and trough (t-test = 4.71) phases. These results suggest thatthere is a significant mean (intercept) shift in long-term leverage ratios for firmsduring the business cycle. The economic significance of these results can beobserved in the magnitude of the business cycle coefficients. The dichotomouscoefficient of peak and trough phases of the business cycle shows that, on aver-age, the leverage ratios decrease during peak ()3.90 per cent) and expansion()27.03 per cent) and expand during contraction (5.81 per cent) and trough(32.18 per cent), as compared to the mean intercept leverage ratios of 0.01,)0.01, 0.02 and )0.37 during peak, expansion, contraction and trough period,respectively (Model 4 in Table 5). These results are consistent with prior studies(Gertler and Gilchrist, 1993; Levy, 2001; Korajczyk and Levy, 2003).In addition, unconstrained firms appear to borrow more long-term debt dur-

ing trough and contraction periods (based on the positive and significant coeffi-cients on the interaction terms ‘BCyc_D*Uncons’ in these cases), while incontrast, constrained firms borrow more long-term debt during peak and expan-sion periods (based on the positive and significant coefficients on the interactionterms ‘BCyc_D*Cons’ in these cases).Are the extant determinants sensitive to each of the phases of business cycle in

explaining the variation of leverage ratios? The market-to-book interaction coef-ficient is positive and statistically significant in explaining the market value oflong-term debt during peak and expansion phases of the business cycle (peak:t-test = 3.21, and expansion: t-test = 9.17). Interestingly, this evidence contra-dicts Baker and Wurgler’s (2002) market timing theory (e.g. when the equityvaluation is high, managers issue more equity and are reluctant to issue debt)but is consistent with the counter-cyclical pattern arguments of Korajczyk andLevy (2003). A possible explanation may be that when a firm is highly valued inthe market, it becomes relatively cheaper (i.e. faces relatively favourable costs)



Table 6

The interaction effect of business cycles and firm’s characteristics on leverage ratios

Peak Contraction Trough Expansion

Coeff t-test Coeff t-test Coeff t-test Coeff t-test

Constant 0.0126 1.22 0.0183 0.80 )0.0112 )0.37 )0.3710 )5.57***Size 0.0249 4.90*** 0.0141 3.84*** 0.0090 5.31*** 0.0531 2.54**

MB )0.0493 )16.84*** )0.0475 )15.58*** )0.0646 )18.24*** )0.1745 )7.39***PROF )0.1082 )3.43*** )0.1011 )3.09*** )0.1095 )3.33*** )0.1433 )3.90***TANG 0.1431 9.15*** 0.1295 8.90*** 0.1447 9.16*** 0.1294 9.14***

LEV_IMed 0.8573 11.37*** 0.8762 9.34*** 0.7481 9.35*** 0.6760 8.81***

CFVol )0.0135 )2.28** )0.0096 )2.36** )0.0119 )2.76** )0.0105 )2.29**DivPayer )0.0281 )1.61 )0.0232 )1.26 )0.0305 )1.78* )0.1003 )0.90Cons 0.1317 3.25*** 0.0755 2.24** )0.0903 )3.25*** 0.2904 2.24**

Uncons 0.1131 2.72** 0.0831 3.55** 0.1062 2.72** 0.0802 1.55

BCyc_D )0.0390 )3.34*** 0.0581 2.15** 0.3218 4.71*** )0.2703 )3.76***BCyc_D*Size 0.0412 0.25 0.0104 0.69 0.0198 0.84 )0.0311 1.68*

BCyc_D*MB 0.0622 3.21*** )0.0620 )1.18 )0.0334 )2.46** 0.2447 9.17***

BCyc_D*PROF )0.1573 )10.11*** 0.2690 1.09 0.4011 0.98 )0.1532 )3.69***BCyc_D*TANG 0.0224 1.13 0.0846 1.62 0.0945 1.16 0.0783 1.11

BCyc_D*LEV_Imed 0.2091 1.47 0.4615 1.43 0.2096 1.31 0.1225 1.15

BCyc_D*CFVol )0.0244 )1.47 )0.0095 )1.57 )0.0311 )2.17** )0.0219 )1.34BCyc_D*DivPayer 0.0197 3.19*** )0.0321 )0.48 )0.1159 )1.16 0.0690 2.61**

BCyc_D*Cons 0.0488 3.02*** 0.0223 1.51 0.0231 1.62 0.0275 2.88***

BCyc_D*Uncons 0.0265 1.41 0.0214 3.17*** 0.0127 2.71** 0.0208 1.57

Year and industry FE Yes Yes Yes Yes

Industry fixed effect Yes Yes Yes Yes

Adjusted R2 0.71 0.71 0.72 0.74

This table presents the results of four sets of parameter estimates using market leverage as a dependent vari-

able. The final Compustat sample comprising 225,717 observations across 24,102 firms from the years 1950 to

2010 is used for the analysis. The firm fixed effect specification for Model 5 is discussed in the text. Year fixed

effects denote that the calendar year fixed effects are included in the specification. Industry fixed effect indicates

the inclusion of dichotomous variables, indicating membership of one of the nine industries, as assigned by the

global industrial classification standard. Market leverage (LEV_LTM) is the ratio of long-term debt divided by

the sum of total debt and market value of equity. Size (Size) is the natural log of Total assets. Market-to-book

(MB) ratio is the ratio of market value of equity to total book value of equity. Profitability (PROF) is the ratio

of operating income before depreciation to total book assets. Tangibility (TANG) is the ratio of net property,

plant and equipment divided by total book assets. Industry Median Leverage (LEV_IMed) is the median of

the book (or market) value of the relevant type of debt by two digit GIC sector code and by year. Cash flow

volatility (CFVol) is measured as the standard deviation of historical operating income, requiring at least

3 years of historical data. Dividend Payer (DivPayer) is a dummy variable equal to one if the firm paid divi-

dends, otherwise zero. Financially constrained firms (Cons) are deemed financially constrained within the spirit

of Korajczyk and Levy (2003) (e.g. firms do not pay dividends, do not have a net equity or debt purchase and

have market to book value >1). Firms are then ranked from low to high based on their total assets. Firms

that belong to the first quartile take a value of 1, otherwise 0. Financially unconstrained firms (Uncons) are

those firms that are not in the category of financially constrained (as defined above), and firms are ranked

from low to high based on their total assets. Then, firms that belong to the fourth quartile take a value of 1,

otherwise 0. Business Cycle BCyc_P, BCyc _C, BCyc _T and BCyc _E indicates Peak, Contraction, Trough

and Expansion and is a dummy variable equal to one if the year corresponds to a particular phase of the busi-

ness cycle in a given year and 0 otherwise. Business Cycle (BCyc)_D is a dummy variable equal to one if the

year corresponds to a particular phase of the business cycle in a given year and 0 otherwise. (BCyc)_D times

the leverage determinants are the interactions variables. ***, **, * indicate statistical significance at the level of

1, 5 and 10%, respectively.



for managers to raise debt. However, the estimated coefficient on the interactionterm with profitability is negative and significant for the peak and expansioncases (peak: t-test = )10.11, expansion: t-test = )3.69), and the dividend payerinteraction coefficient is positive and significant for the same phases (peak:t-test = 3.19, expansion: t-test = 2.75). These results are in agreement withpecking order theory, managerial entrenchment and market timing theory. Forinstance, during the peak and expansion stages of the business cycle, firms per-form relatively better in generating profits. These profits can be sufficient for aregular dividend, and the remaining internally generated funds can be utilised tofinance positive net present value investment projects.Cash flow volatility is negatively significant in determining long-term market

leverage ratios only during trough times (t-test = )2.17). In all other phases, ithas no statistical importance. This result is consistent with trade-off theory.Specifically, during a trough period, a firm’s cash flows may be relatively morevolatile and, as a result, may lead firms to be financially stretched or distressed.Higher costs associated with financial distress attract more equity and less debt.

4.3. Robustness analysis

4.3.1. Long-run and short-run effects of the business cycle

If managers are concerned only about changes in the long-run equilibrium levelof leverage determinants (Lemmon et al., 2008), the results of Model 2 could beunreliable. This is because Model 2 assumes that the effect on leverage of pre-determined variables (X) is delayed by one period and is ‘complete’, with no per-sistent effects. However, if managers are slow to respond to changes in (X) or ifmanagers gradually adjust leverage to changes in X, Model 2 provides an incom-plete description of the leverage data generating process. Alternatively, if manag-ers ignore short-term or transitory fluctuations in the factors that determineleverage, Model 2 still provides an incomplete description of capital structure.To examine these alternatives, I rely on Lemmon et al. (2008) to estimate a dis-

tributed lag model of long-term leverage ratios, essentially expanding the tradi-tional specification in Model 2 to incorporate deeper lags of the leveragedeterminants.4 To assess the appropriate lag length, I carry out two specificationsearches using the Akaike Information Criterion (AIC) and the Bayesian Infor-mation Criterion (BIC). Both searches indicate an appropriate lag length of tenperiods. Results for short-run impacts and long-run impacts are estimated.Results remain very similar across the short-run (maximum lag of 2) and long-run (lag >3 inclusive), and, most importantly, the business cycle factors direc-tional signs remain similar (especially for lag one and two) while the significance

4 Results are qualitatively similar to Table 5 and therefore are not presented in tabularform.



level of the business cycle coefficients disappears as deeper lags are considered(lags three to ten).To summarise, when deeper lags are taken, the impact of the business cycle in

explaining long-run variation of leverage ratios diminishes gradually, and theeffective impact of the business cycle is evidence for short-run variation in lever-age ratios. This indicates that each phase of the business cycle has a distinctimpact and is relatively instantaneous (lag of one or two) on leverage ratios butloses its intensity as higher order lags are considered. This could be a matter offirms’ financial characteristics being more simultaneously and contemporaneouslyaligned with the business cycle and leverage ratios. In relation to the relativeimportance of the business cycle in explaining the unobserved time invariant com-ponent of leverage ratios, the results (not reported) indicate that when deeper lagsfor (X) are analysed, the business cycle phases gradually lose their significance.Also, a reduction in adjusted R squared is observed in the fixed effect model,when more than 2 lags are considered. In total, these results suggest that businesscycle phases explain short-run variation of leverage, but not long-run variation.

4.3.2. Business cycle and persistence

Powell et al. (2009) show that if the dependent variable and independent vari-ables are persistent over time, then this persistence in both variables leads to spu-rious results with overstated R-squared, coefficients and t- statistics. Powell et al.(2009) investigate this issue using a monthly sampling interval. They show thatonce the independent variable persistence is accounted for, the significance ofmost of the coefficients in the regressions disappears. They present an economet-ric procedure for the detection of persistence.5 I employ the Powell et al. (2009)procedure and examine all four stages of the business cycle to identify persis-tence. The test results indicate that the persistence problem is unlikely. It is possi-ble that persistence is observed more in high frequency monthly data than inyearly data (as used in this study).

4.3.3. Validity of the model

To test the robustness of the models, residuals, goodness of fit and drop indeviance tests are conducted. The residuals from models tend to accord with the

5 The procedure requires the calculation of transparent probability by using a transitionmatrix (it is four by four in this case since there are four phases of business cycle). In par-ticular, the authors provide a benchmark to compare the transition matrix’s probabilityoutcome (q) with a value of 0.50. The interpretation of their proposed value is that if qequals 0.5, then the first-order autocorrelation of the test variable is zero (no autocorrela-tion). If the q value falls within a range of ±0.20 around 0.50, it is still considered to beinsignificant autocorrelation. Test results indicate that the transition probability value qfalls well within the acceptable range of 0.40–0.66.



standard normal distribution, and values outside the range of ±3 standarddeviations from the mean are considered potential outliers. An inspection sug-gests no evident outliers. In addition, no pattern of heteroskedasticity is evidentfrom the residual plots, suggesting independence of the data. A residuals analysisof the goodness-of-fit test is performed for the models, resulting in no cause forconcern. In addition, a range of alternative proxies for size, cash flow volatilityand growth measures produce robust findings. Further, as argued by Ramseyand Schafer (2002), it is often difficult to make strong conclusions solely on basisof the goodness-of-fit test. Therefore, the overall significance of the models istested using the drop in deviance test (maximum likelihood), and the results aresupportive.

5. Conclusion

Overall, the results of this study are consistent with those of Lemmon et al.(2008), who show that leverage ratios have a permanent component to them,and that the extant determinants explain little of the variation in leverage whenfirm fixed effects are taken into consideration. However, the results becomesomewhat different when business cycle phases are incorporated into the fixedeffect model. The unobserved permanent component of the leverage ratios varia-tion is well explained by the incorporation of the four stages of a business cycle:peak, contraction, trough and expansion. This is because of the fact that thebusiness cycle phases carry significant explanatory power, which is important inthe relative significance of the unobserved permanent component of the leverageratios. This is primarily evidenced by the relatively significant improvement inthe fixed effect model, captured by a high adjusted R squared after the inclusionof the business cycle phases and high significant coefficients for the business cyclephases themselves. In particular, I find that business cycle phases become muchmore statistically significant in explaining variation in leverage after accountingfor fixed firm effects. This is the first study to provide insight into the capitalstructure decisions of firms during different stages of the business cycle after con-trolling for firm fixed effects. By examining the relative importance of the perma-nent component and the extant determinants during different stages of thebusiness cycle, this research may help regulators (including regulators of interestrates and tax rates) to target different business cycle period policies accordingly.Further, this may also assist institutions and lenders in aligning their decisionswith capital structure choices for firms that are constrained and unconstrained.

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