The International Zero-Leverage Phenomenon

Wolfgang Besslera, Wolfgang Drobetzb,

Rebekka Hallerc, and Iwan Meierd*

This draft: August 2012

Abstract Extreme debt conservatism is an international phenomenon which has increased over time. While only 5% of the firms in our international sample maintained a zero-leverage policy in 1989, this fraction rose to roughly 15% by 2010. The increasing prevalence of zero-leverage firms is related to the different IPO waves and the accompanying changes in industry composition. In addition, we attribute the higher propensity to maintain a zero-leverage policy throughout all size and age groups to the increasing asset volatility during our sample period. Country-specific factors also affect extreme debt conservatism. Countries with a market-oriented financial system, higher creditor protection, and a classical tax system exhibit the highest percentage of zero-leverage firms. Analyzing the supply-side of firms’ financing choices, we show that only a small number of profitable firms with high payout ratios deliberately maintain zero-leverage. In contrast, most zero-leverage firms are constrained by their debt capacity; they tend to be smaller, riskier, and less profitable, and they are the most active equity issuers. With respect to the demand-side of financing choices, we document that firms which follow a zero-leverage policy only for a short period of time seek financial flexibility. After abandoning zero-leverage, these mostly unconstrained firms jump to higher leverage ratios, make higher investments, and reduce their cash holdings by a larger amount compared to constrained zero-leverage firms, which remain debt-free for longer periods of time. Keywords: Capital structure, debt conservatism, financial constraints, financial flexibility

JEL classification codes: G32

a Wolfgang Bessler, Center for Banking and Finance, Justus-Liebig-University Giessen, Licher Straße 74, 35394

Giessen, Germany. Mail: wolfgang.bessler@wirtschaft.uni-giessen.de. b Wolfgang Drobetz, Institute of Finance, University of Hamburg, Von-Melle-Park 5, 20146 Hamburg, Germany.

Tel.:+49-40-42838-5506; fax: +49-40-42838-4627; mail: wolfgang.drobetz@wiso.uni-hamburg.de. Correspond-ing author.

c Rebekka Haller, Institute of Finance, University of Hamburg, Von-Melle-Park 5, 20146 Hamburg, Germany. Mail: rebekka.haller@wiso.uni-hamburg.de.

d Iwan Meier, HEC Montréal, 3000 Chemin de la Côte-Sainte-Catherine, Montréal (Québec), Canada, H3T 2A7. Mail: iwan.meier@hec.ca.

* We would like to thank Yakov Amihud, András Danis, Dave Denis, Espen Eckbo, Miguel Ferreira, Dimitrios Gounopolos, Ulrich Hofbaur, Philipp Immenkötter, Peter Limbach, and participants at a research seminar at the University of Hannover, the 2011 Financial Management Association (FMA) Europe Conference in Porto, the 2011 Swiss Finance Association Conference in Zurich, the 2011 Symposium for Banking, Finance, and Insur-ance in Karlsruhe, the 2012 WHU Campus for Finance Conference, and the 2012 European Financial Manage-ment Association (EFMA) Conference in Barcelona for helpful comments.

The International Zero-Leverage Phenomenon

This draft: August 2012

Abstract Extreme debt conservatism is an international phenomenon which has increased over time. While only 5% of the firms in our international sample maintained a zero-leverage policy in 1989, this fraction rose to roughly 15% by 2010. The increasing prevalence of zero-leverage firms is related to the different IPO waves and the accompanying changes in industry composition. In addition, we attribute the higher propensity to maintain a zero-leverage policy throughout all size and age groups to the increasing asset volatility during the sample period. Country-specific factors also affect extreme debt conservatism. Countries with a market-oriented financial system, higher creditor protection, and a classical tax system exhibit the highest percentage of zero-leverage firms. Analyzing the supply-side of firms’ financing choices, we show that only a small number of profitable firms with high payout ratios deliberately maintain zero-leverage. In contrast, most zero-leverage firms are constrained by their debt capacity; they tend to be smaller, riskier, and less profitable, and they are the most active equity issuers. With respect to the demand-side of financing choices, we document that firms which follow a zero-leverage policy only for a short period of time are seeking financial flexibility. After abandoning zero-leverage, these mostly unconstrained firms jump to higher leverage ratios, make higher investments, and reduce their cash holdings by a larger amount com-pared to constrained zero-leverage firms, which remain debt-free for longer periods of time. Keywords: Capital structure, debt conservatism, financial constraints, financial flexibility

JEL classification codes: G32

2

1. Introduction

Major listed firms in the Standard & Poor’s 500 index, such as Apple, Yahoo, Texas Instru-

ments, Bed Bath & Beyond or Urban Outfitters, all have something in common: they are debt-

free. These firms are examples for the stylized fact in corporate finance that the proportion of

zero-leverage firms has increased over time. The zero-leverage puzzle is not only a phenome-

non in the United States, but it is also observable on other major financial markets. While only

5.14% of all firms in our international sample renounced the use of debt in 1989, the percent-

age of zero-leverage firms rose to 14.97% by 2010. In the presence of market frictions, there

are two main theories of capital structure: the trade-off theory (Kraus and Litzenberger, 1973)

and the pecking order theory (Myers and Majluf, 1984). Both theories advocate the use of debt

because of either tax benefits or lower costs of asymmetric information. The empirical litera-

ture is undecided which theory better describes firms’ financing decisions (Frank and Goyal,

2008).1 Perhaps most troubling, neither the static trade-off theory nor the pecking order theory

is able to explain why so many firms in our international sample follow a zero-leverage policy.

This study contributes to our understanding of a zero-leverage policy in three ways. First, we

offer explanations for the upward trend in the fraction of zero-leverage firms. On the one hand,

the increasing prevalence of zero-leverage is related to the different IPO waves in our sample

countries. A related explanation is that there are industry effects due to changes in industry

composition toward sectors where extreme debt conservatism is more commonly adopted. On

the other hand, the zero-leverage phenomenon is driven by a vintage effect, where newly listed

firms in the later years of the sample period are more likely to a pursue a zero-leverage policy.

We attribute this increasing propensity to maintain a zero-leverage policy throughout all size

and age groups to firms’ increasing asset volatility (or business risk) over the sample period.

Second, the zero-leverage puzzle extends to major financial markets outside the United States,

although there are differences across countries. Our country-level analysis reveals that coun-

tries with capital-market-oriented financial systems, common law origins, high creditor protec-

tion, and classical tax systems exhibit the highest percentage of zero-leverage firms.

A third contribution of our study is that we look at both demand- and supply-side explanations

for zero-leverage. Most zero-leverage firms in our sample are financially constrained and have 1 Most empirical studies either focus on identifying factors that affect firms’ capital structures (Rajan and Zin-

gales, 1995; Frank and Goyal, 2009; Gungoraydinoglu and Öztekin, 2011; Fan et al., 2012) or directly test theories of capital structure (Shyam-Sunder and Myers, 1999; Frank and Goyal, 2003; Bessler et al., 2011).

3

no other option than renouncing the use of debt (supply-side effect). These firms are smaller,

younger, riskier, and less profitable; they are the most active equity issuers and hoard the larg-

est cash holdings of all sample firms. Constrained firms do not possess sufficient debt capacity

and thus (have to) maintain a zero-leverage policy for longer periods of time, on average. In

contrast, there is a small subsample of firms that deliberately choose to adopt a zero-leverage

policy. These financially unconstrained firms are more profitable, distribute higher dividends,

and are older as well as larger than their constrained zero-leverage peers.

When we restrict our international sample to established firms with a minimum of 15 years of

historical data in order to analyze the time-series variation in leverage, we show evidence for

the importance of flexibility in capital structure decisions (Graham and Harvey, 2001). Firms

that maintain a zero-leverage policy only for a short period of time preserve their debt capacity

for use in the near future (demand-side effect).2 After abandoning zero-leverage, these uncon-

strained zero-leverage firms jump to higher leverage ratios, make higher investments, and re-

duce cash holdings by a larger amount compared to their constrained zero-leverage peers. Un-

constrained zero-leverage firms have the flexibility to issue larger amounts of debt and use it

together with their cash holdings to invest in future periods when good opportunities arise. In

contrast, constrained firms lack sufficient debt capacity and are unable to issue as much debt;

they follow a zero-leverage policy for longer periods of time, are more dependent on internal

funds and thus are less flexible in their investments.

The zero-leverage puzzle is neither compatible with the standard capital structure theories nor

with more recent variations of them. While not all capital structure theories predict an optimal

leverage ratio, none is able to explain why firms abstain from using debt. For example, based

on the static trade-off theory, Leland (1994) forecasts an average debt ratio of approximately

60%. More recent simulation studies based on dynamic trade-off theories use contingent claim

analysis and derive minimum leverage ratios as low as 10% (Morellec, 2003; Ju et al., 2005).

Goldstein et al. (2001) and Hennessy and Whited (2005) also assume a dynamic framework.

These models imply that firms, in the presence of adjustment costs or adverse selection costs,

become debt-free in order to avoid either forgoing future investment or financing them with

new risky securities. The model of DeAngelo et al. (2011) predicts that ex ante optimal finan-

2 Debt capacity is usually defined as a “sufficiently high” debt ratio so that the costs of financial distress curtail

further debt issues (Shyam-Sunder and Myers 1999; Chirinko and Singha 2000).

4

cial policies preserve the ability to access the capital markets ex post in the event of shocks to

investment opportunities or after unexpected earnings shortfalls. The option to issue debt later

at comparable terms is valuable, and the opportunity cost of raising debt implies that target

capital structures tend to be more conservative than those produced by standard trade-off mod-

els. DeAngelo and Roll (2012) analyze leverage stability and document that it arises only in-

frequently and then largely when firms have low leverage; nevertheless, very low leverage is

almost always temporary. Leverage peaks and troughs are not exogenous shocks that induce

rebalancing to some target leverage ratio. Instead, managers chose to pay down debt when

attaining a leverage trough and then issue debt to fund expansion.

In contrast to variants of the trade-off theory, the pecking order theory does not imply a well-

defined target leverage. Myers (1984) argues that a firm’s capital structure reflects the accu-

mulation of its past financial requirements. When information asymmetry is temporarily low,

firms with sufficient internal funds have less incentive to use external financing (Autore and

Kovacs, 2009; Bessler et al., 2011). However, even a dynamic pecking order theory is unable

to explain why firms with little or no debt tend to rely heavily on equity and are not willing to

exhaust all internal funds (including large cash balances) prior to obtaining external financing.

Our study follows along a growing literature on debt conservatism and zero-leverage. Graham

(2000) develops interest-benefit deduction functions by estimating marginal tax rates and re-

ports that large and profitable firms with high cash holding and low expected distress costs

could significantly increase their value if they used the optimal amount of debt. Minton and

Wruck (2001) analyze the low leverage phenomenon and show that financial conservatism is

not an industry-specific phenomenon, even though conservative firms are more prone to indus-

tries related to high financial distress. DeAngelo and Roll (2012) document that the incidence

of low leverage firms in recent years is due to a surge in listings by young growth firms that

have little or no debt. A few recent studies explicitly analyze the prevalence of zero-leverage

firms. One strand of this literature investigates firm-level fundamentals. Byoun et al. (2011),

Devos et al. (2012), Strebulaev and Yang (2012), and Dang (2011) report that zero-leverage

firms tend to be smaller, accumulate higher cash reserves, and exhibit higher market-to-book

ratios as well as higher payout ratios. These findings are closely related to studies on cash

holding that document a negative relationship between leverage and corporate liquidity (Opler

et al., 1999; Drobetz et al., 2010). However, taken together, it is difficult to reconcile the ob-

5

served characteristics of zero-leverage firms with standard capital structure theories. Another

strand of this literature explores the quality of firm-level corporate governance mechanisms

and managerial entrenchment as a potential explanation for zero-leverage. Using data from the

United States, Strebulaev and Yang (2012) suggest that managerial and governance character-

istics are important determinants for the decision to maintain a zero-leverage policy. In con-

trast, Devos et al. (2012) and Byoun et al. (2011) report that zero-leverage firms do not suffer

from weaker internal or external governance mechanisms. As an example, Devos et al. (2012)

document that the debt initiation decisions of zero-leverage firms are not enhanced by shocks

to their entrenchment, such as takeover threats or activist block holders. While poor firm-level

corporate governance does not explain the zero-leverage puzzle, their results indicate that fi-

nancial constraints contribute to extreme debt conservatism. In another approach, Lambrecht

and Pawlina (2012) attribute the zero-leverage puzzle to human capital intensive industries.

They develop a theoretical model in which the asymmetry between physical capital and trans-

ferable human capital induces negative net debt.3

Only a few studies analyze low-leverage policies as a part of the intertemporal investment and

financing decisions. Minton and Wruck (2001) show that firms with conservative debt policies

increase leverage when they are faced with lower internal funds and higher investments. More

recently, Marchica and Mura (2010) analyze the dynamics of low-leverage policies by using a

sample from the United Kingdom. Based on DeAngelo’s et al. (2011) arguments, they docu-

ment that firms incur higher capital expenditures and higher abnormal investments following a

period of low leverage, and that these investments are likely to be financed through the issu-

ance of debt. Most important, there is a measurable effect from financial flexibility in the form

of untapped borrowing reserves. Similarly, De Jong et al. (2012) document that US firms with

high unused debt capacities and thus more financial flexibility make higher future investments

than firms with less financial flexibility. Furthermore, more flexible firms are in a position to

reduce investment distortions in constrained times because they have better access to the debt

markets in these times. De Jong et al. (2012) conclude that firms save debt capacity for more

constrained periods in the future.

3 Instead of assessing explanations for the prevalence of zero-leverage, Lee and Moon (2011) test the long-run

equity performance of zero-leverage firms. Based on calendar-time portfolio regressions, they show that zero-leverage firms tend to outperform. Contradicting the results in Strebulaev and Yang (2012), they suggest that zero-leverage firms are more conservative, self-disciplined, and prudent in making decisions.

6

While most of the related studies focus on debt conservatism in general, we explicitly analyze

debt-free firms and provide several explanations for the increasing prevalence of zero-levera-

ge, such as IPO and industry effects as well as increasing asset risk. Moreover, we are the first

to show that zero-leverage is an international phenomenon, albeit country-specific factors ex-

ert strong influences. Our study further contributes to the literature by analyzing both supply-

and demand-side explanations for the zero-leverage puzzle. While there have been attempts to

differentiate between constrained and unconstrained zero-leverage firms (Byoun et al., 2011;

Devos et al., 2012), our framework is novel in that it entails a detailed comparison between the

two groups of zero-leverage firms based on alternative measures of financial constraints. Fi-

nally, we examine the investment and cash holding decisions of previously unlevered firms.

Most important, the financing decisions of firms that maintain zero-leverage only for a short

period of time are attributable to their preference for financial flexibility; these firms strategi-

cally use their debt capacity and start to issue debt when good investment opportunities arise.

The remainder of our study is structured as follows: Section 2 provides descriptive statistics

and presents stylized facts about the zero-leverage puzzle across countries. Section 3 analyzes

firm- and country-level determinates for the decision to maintain zero-leverage. Section 4 and

Section 5 provide an analysis of the capital supply-side and demand-side explanations for ze-

ro-leverage, respectively. Finally, Section 6 concludes and provides an outlook for further re-

search.

2. Data Description and Stylized Facts

2.1 Definition of Variables and Descriptive Statistics

In order to analyze firms that maintain a zero-leverage policy, we collect annual balance sheet

and market data of listed firms from the United States, Canada, the United Kingdom, Germa-

ny, and Japan from the Compustat Global database over the time period from 1989 to 2010.

Our sample consists of active and inactive publicly traded industrial firms and avoids a survi-

vorship bias. Given the specific nature of their businesses, financial and utility firms (with SIC

6000-6999 and 4900-4949) are omitted from the sample. Firms that do not have an industrial

sector- or a country code in the database as well as firms with non-consolidated balance sheets

are excluded. Within this basic specification, our sample consists of 13,897 consolidated firms

(8,692 active and 5,205 inactive) with 176,907 firm-year observations for the whole sample.

7

Tables A1 and A2 provide an overview of all variables used in our empirical analyses together

with a detailed description of their construction principles. We exclude firm-year observations

with missing information on total assets and market value. Furthermore, following Frank and

Goyal (2003), we recode deferred taxes, purchase of treasury shares, and preferred stock to

zero if firm-year observations are missing. All constructed variables are winsorized at the 1%

and the 99% tails in order to reduce outlier observations. After these additional data cleaning

steps, our final panel includes 13,309 industrial firms with a total of 151,638 firm-year obser-

vations. Furthermore, we follow Devos et al. (2012) and Strebulaev and Yang (2012) and de-

fine book leverage as the ratio of the sum of short- and long-term liabilities to total assets. Ze-

ro-leverage observations are firm-year observations without leverage.

The number of firms included in our sample differs across countries. For example, we observe

a strong increase in the number of German firms over the time. The Compustat Global data-

base includes only 100 German firms in the year 1989, whereas by the year 2010 the number

of firms increases to 461. The major reason for this sharp increase in the number of listed

firms and coverage is the strong European IPO activity during our sample period (Giudici and

Roosenboom, 2004; Bessler and Seim, 2012; Vismara et al., 2012). The number of Japanese

firms also increases strongly, from 944 firms in 1989 to 2,535 firms in 2010. In contrast, the

number of sample firms in the Anglo-Saxon countries, i.e., the United States, Canada, and the

United Kingdom, rises at a lower rate, from 2,669 in 1989 to 3,450 in 2010.

Table I provides descriptive statistics for all variables. Most important, the median book lever-

age ratio (the ratio of interest bearing debt to total assets) is 19.1% for the full sample. Japan

boasts the highest median book leverage ratio (23.2%), followed by Canada (19.7%) and the

United States (18.6%). Germany (15.8%) and the United Kingdom (14.3%) exhibit the lowest

book leverage ratios. These differences in cross-country leverage are similar to those reported

by Rajan and Zingales (1995), Gungoraydinoglu and Öztekin (2011), and Fan et al. (2012).

[Insert Table I here]

2.2 Stylized Facts about the International Zero-Leverage Phenomenon

As shown in Figure 1 and Table II, the large number of zero-leverage firms and the sharp in-

crease in the percentage of firms without debt is an international phenomenon. On average,

12.25% of all firm-year observations are classified as zero-leverage during our sample period.

8

Nevertheless, cross-country analyses reveal substantial differences. While about 14% of all

firm-year observations in the Anglo-Saxon countries are zero-leverage, on average, this frac-

tion is 8.62% and 5.63% in Germany and Japan, respectively. Most important, Figure 1 and

Table II document a strong increase in the percentage of zero-leverage firms over time. Using

all observations in the sample, only 5.14% of the firms are classified as zero-leverage in 1989,

but this value increases to 14.97% by 2010. The increase is most pronounced in the Anglo-

Saxon countries. For example, the percentage of zero-leverage firms in the United States and

the United Kingdom increases to above 20% by the end of our sample period. These cross-

country differences are explored in Section 3.3 in more detail.

[Insert Table II and Figure 1 here]

In order to examine whether size is an important determinant for the decision to follow a zero-

leverage policy, we sort all firm-year observations into size quartiles. Figure 2 depicts the per-

centage of zero-leverage firms in each size group over time. As expected, small firms are most

likely to renounce the use of debt. At the end of the sample period, more than 27% of all firms

in the smallest size quartile (Q1), which Fama and French (2008) refer to as micro caps, pur-

sue a zero-leverage policy. This finding is consistent with capital structure theories that incor-

porate motives related to agency costs and/or asymmetric information (see Section 3.2). Nev-

ertheless, the zero-leverage phenomenon is not confined to the smallest firms, and there is a

pronounced upward trend in other size quartiles as well. While the fraction of zero-leverage

firms in the largest size quartile (Q4) increases slightly but never rises above 5%, it goes up to

more than 10% (Q3) and 15% (Q2) in the two middle size quartiles.

[Insert Figure 2 here]

DeAngelo and Roll (2012) argue that the increased incidence of low leverage firms in recent

years is due to a surge in listing by young growth firms that have little or no debt. In fact, our

sample period is characterized by varying activity on national IPO markets, and a main reason

for the sharp increase in the number of listed firms and coverage is the strong IPO activity.

Fama and French (2001) suggest that the change in the characteristics of newly listed firms is

attributable to a decline in the cost of equity, which allowed firms with remote cash flow ex-

pectations to raise public equity. Therefore, we analyze if a new listing vintage effect is able to

explain the large increase in the number of firms which tap the public equity market and re-

9

nounce the use of debt.4 Four listing groups are defined according to a firm’s IPO date. The

first group comprises all firms listed before 1989; the second group includes all firms listed

between 1989 and 1993; the third group between 1994 and 2003; and the fourth group be-

tween 2004 and 2010. These four groups roughly represent the aggregate IPO cycles during

our sample period. Figure 3 depicts the evolution of the percentage of zero-leverage firms in

the four different listing groups for the full sample and the individual countries. In fact, the

zero-leverage phenomenon is more pronounced in the more recent vintage periods. While the

fraction of zero-leverage firms in the pre-1989 listing group exhibits little variation over time,

each vintage group starts with a higher percentage of zero-leverage firms. Moreover, the frac-

tions of zero-leverage firms in the more recent listing groups are strongly increasing over time.

Although the percentage of zero-leverage firms decreases in the most recent years in all vin-

tage groups, the ordering of the different vintage groups remains unchanged.5 An exception to

this is the United States, where the fraction of zero-leverage firms in the last vintage group is

lower than that in the preceding one. A potential explanation is the sharp decline in IPO activi-

ty in the United States following the technology bubble burst in 2000 (Ritter et al., 2012). An

alternative explanation could be an increasing debt capacity during the up-markets just before

the outbreak of the financial crisis in 2008. In contrast, Canada and Germany experienced a

second IPO wave between 2004 and 2007, potentially explaining the higher fraction of zero-

leverage firms in the last vintage group. Overall, these results indicate that the upward trend in

the percentage of zero-leverage firms is partly driven by firms in more recently listed groups,

thus by new IPO firms entering the sample.

[Insert Figure 3 here]

In a related analysis, we investigate whether our findings for vintage effects are directly relat-

ed to firm age. Firm age is measured as the difference between the actual calendar year and a

firm’s IPO date. The IPO date is obtained from merging the available IPO dates in the Com-

pustat Global and the Thomson One databases. We classify a firm as an IPO firm if it was

4 Related to this conjecture, Byoun et al. (2011) document that several years prior to becoming debt-free zero-

leverage firms rely mainly on external equity in order to reduce debt when the stock market valuation is high. Custódio et al. (2011) use a vintage approach to analyze the declining debt-maturity of US firms.

5 In results not reported, we use different (fixed-length) listing periods with similar outcomes. In a robustness check for the United Kingdom, we exclude all IPOs from our sample that are listed on the Alternative Invest-ment Market or AIM (Espenlaub et al., 2009). Again, our results remain qualitatively unchanged.

10

listed over the preceding three years (and as established otherwise).6 Figure 4 depicts the evo-

lution of the fraction of zero-leverage firms in three different age groups for the full sample

and the individual countries: (i) the oldest firms listed before 1989; (ii) IPO firms (not older

than three years and listed 1989 or later); (iii) non-IPO firms (older than three years and listed

1989 or later). Young IPO firms exhibit the highest fraction of zero-leverage firms, and it is

increasing sharply over time from roughly 6% in 1989 to almost 25% in 2007. Nevertheless,

the percentage of zero-leverage firms is increasing in the subsample of non-IPO firms as well.

Taken together, the results on firm size and age indicate that a large number of zero-leverage

firms are small and young. According to Hadlock and Pierce (2010), size and age are particu-

larly useful predictors for the level of a firm’s financial constraints. Therefore, in Section 4 we

use their constraint measure (SA-index) in order to analyze capital supply-side effects and

differentiate between financially constrained and unconstrained zero-leverage firms.

[Insert Figure 4 here]

Our stylized facts so far suggest that changes in the sample composition explain a large part of

the increase in the percentage of zero-leverage firms. However, as we do not observe that new-

ly listed firms in each vintage year start debt-free and then initiate using debt as they mature,

the increase in the percentage of zero-leverage firms cannot be fully captured by listing vin-

tage and age effects. Therefore, we analyze if changes in the overall industry composition also

contribute to explaining the increasing percentage of zero-leverage firms in the more recent

listing groups. If riskier industries have become relatively larger because of newly-listed IPO

firms, an increase in newly-listed technology firms in the more recent years may explain the

vintage effect and contribute to the increase in the percentage of zero-leverage firms.

In a first step, we assign firms to the 10 Fama and French (1997) industries based on their

four-digit SIC codes.7 Figure 5 shows the evolution of the percentage of zero-leverage firms in

main industrial sectors (excluding financial and utility firms) for both the full sample and the

individual countries. Zero-leverage firms are not limited to certain industries. Nevertheless, a

common observation across all countries is that zero-leverage firms are concentrated in the 6 As a robustness check, we use a five year period in the secondary market to classify IPO firms, and the results

remain qualitatively unchanged. 7 See http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/Data_Library/det_10_ind_port.html. If SIC codes

are not available in Compustat, we use the GICS (Global Industry Classifications Standard) codes to assign a firm to an industry sector.

11

healthcare and the information technology sectors. In the full sample, the percentage of zero-

leverage firms in these two sectors increases to approximately 32% and 37%, respectively, by

the end of the sample period. This high concentration of zero-leverage firms is consistent with

Wessel and Titman’s (1988) conjecture that firms with unique or specialized products (having

large research and development expenditures and high selling expenses) are likely to impose

higher costs on their customers, workers, and suppliers in the event of liquidation, and thus

choose extremely conservative debt ratios.8 The pattern also follows along Shleifer and Vish-

ny’s (1992) conjecture that non-redeployability of assets (and their lower ex post liquidation

values) creates ex ante incentives to reduce leverage in order to mitigate the possibility of

forced sales at prices below “value in best use”. Moreover, there are several country-specific

industry effects. For example, the percentage of zero-leverage firms in the telecommunication

sector is high in Germany, which is attributable to privatization efforts in this sector and firms’

going public during the late 1990s. In both the United Kingdom and Canada, the zero-leverage

phenomenon is pronounced in the energy and materials sectors. This increase in the percent-

age of zero-leverage firms is accompanied by significant IPO activity in these two sectors in

both countries. For example, in our sample the materials sector accounts for 34% of all Cana-

dian IPOs in 2003, 48% in 2004, and 43% in 2005 (in terms of the number of issues), and, as

such, this heavy IPO activity in the materials sector accounts for the increase in the percentage

of zero-leverage firms (to more than 30%) during these years. Similar patterns are observable

for the materials and the energy sectors in the United Kingdom. All in all, the country-specific

industry effects seem to be driven to a large extent by the IPO waves in the different countries.

[Insert Figure 5 here]

In a second step, we compare the actual percentage of zero-leverage firms with the percentage

of zero-leverage firms when holding the industry weights constant over time. Using the 1989

market capitalization weights, Figure 6 plots the yearly percentage of zero-leverage firms

against the value-weighted average across industries over time. The two lines for the full sam-

ple start to diverge in the early 1990s, when the actual percentage of zero-leverage firms in-

creases more than that based on the constant 1989 weights. This difference increases to rough- 8 Froot’s (1992) case study of Intel Corporation is an early example from the information technology sector. In

the late 1980s and early 1990s, Intel chose a policy of zero net-debt given its highly competitive and techno-logically risky environment. Similarly, firms in the pharmaceutical sector are potentially threatened by exces-sive damages claims, and thus they follow extremely conservative leverage policies.

12

ly five percentage points by 2000. Nevertheless, there is a strong increase in the percentage of

zero-leverage firms in both groups. If industry composition effects were able to fully capture

the zero-leverage phenomenon, the line comprising the 1989 market capitalization weights

should not exhibit a strong upward trend. As a result, the international zero-leverage phenom-

enon cannot be purely driven by IPO firms shifting to industries where extreme debt conserva-

tism is more commonly adopted.

[Insert Figure 6 here]

With increasing business risk, the prevalence of extreme debt conservatism is more likely to

increase. In addition, if there has been a trend for increasing business risk over time, this effect

contributes to explaining the zero-leverage phenomenon. A zero-leverage firm’s stock return

volatility reflects its business risk; thus stock return volatility equals asset volatility. For all

other (debt-financed) firms, we follow Frank and Goyal (2009) and compute their asset vola-

tility by unleveraging the annual volatility of stock returns (see Table A1). Annual standard

deviations are derived from monthly stock returns (matched from Thomson Datastream). Fig-

ure 7 depicts the evolution of asset volatility over the four vintage periods (as defined in Sec-

tion 2.2) for both zero-leverage and debt-financed firms. There are two observations here:

First, asset risk is substantially higher in the zero-leverage group in comparison with the group

of debt-financed firms. And second, there is a general trend of increasing asset return volatility

over time (Campbell, et al., 2010; Wei and Zhang, 2006); thus the vintage effect seems strong-

ly related to this change in asset risk.9 Therefore, in addition to IPO and industry effects, high-

er asset return risk is another driver of the increasing propensity to maintain a zero-leverage

policy over time and in the different vintage periods.

Firms with higher business risks are more likely to engage in risk management. In addition to

their risk management choices on the asset side of the balance sheet, their capital structure

constitutes another layer of corporate risk management (Stulz, 1996). By reducing the amount

of debt in its capital structure (or even completely deleveraging), a firm reduces shareholders’

total risk exposure because equity represents a residual claim and offers an all-purpose risk

cushion against losses. Equity provides protection against risks that are difficult to anticipate

or measure (Meulbroek, 2002). In this sense, our findings with regards to asset risk are con- 9 We use all months in a vintage group to compute asset return volatility. The effect is even more pronounced if

we use only the first three years of return data for each vintage group.

13

sistent with our earlier observation that zero-leverage firms tend to be concentrated in the

more opaque information technology and healthcare sectors.

[Insert Figure 7 here]

Taken together, these stylized facts offer explanations (albeit incomplete and interrelated ones)

for the strong increase of zero-leverage firms over time. The percentage of zero-leverage firms

changes with firm size, age, and industry. The vintage effect, changes in industry composition

and higher business risk further contribute to explaining the surprising increase in the percent-

age of zero-leverage firms across all sample countries. However, the fraction of zero-leverage

firms is increasing in the older vintage groups and in the sample based on the (constant) 1989

industry composition as well, and thus there remain unexplained parts of the puzzle.

3. Firm- and Country-level Determinants of Zero-Leverage

3.1 A Propensity Model Using Standard Capital Structure Variables

This section starts by quantifying the roles of changing firm characteristics and the potentially

increasing propensity to follow a zero-leverage policy in explaining the zero-leverage puzzle.

We adopt the approach used by Fama and French (2001), Bates et al. (2009), and Denis and

Osobov (2008). In a first step, we run yearly logistic regressions using the full sample to esti-

mate the probability of firms exhibiting zero-leverage during a 1989-1993 base period. The

dependent binary variable is set equal to 1 for a firm adopting a zero-leverage policy in year 𝑡

(and 0 otherwise). Our explanatory variables are the four standard capital structure variables

(Rajan and Zingales, 1995; Frank and Goyal, 2009): profitability, market-to-book ratio, size,

and tangibility (see Table A1). In a second step, we calculate the probability for each firm to

maintain a zero-leverage policy based on these characteristics in each year (starting in 1994)

using the average annual coefficients estimates from the base period. The expected percentage

of zero-leverage firms is obtained by averaging the individual probabilities across firms in

each year and multiplying the result by 100. As the probabilities associated with firm charac-

teristics are fixed at their base period values, variation in the expected percentage of zero-

leverage firms after 1993 is attributable to the changing firm characteristics. Any difference

between the expected percentage and the actual percentage of zero-leverage firms measures

the firms’ propensity to maintain a zero-leverage policy.

14

Table III shows the results of our out-of-sample logistic regression. Controlling for the chang-

es in firm characteristics, changes in the unexpected proportion of zero-leverage firms reflect

changes in the propensity to follow extreme debt conservatism. At the beginning of the fore-

casting period, the difference between the actual and the expected percentage is small, indicat-

ing that the coefficients obtained from the base period are good predictors for the expected

fraction of zero-leverage firms. However, the actual percentage of zero-leverage firms is high-

er than the expected percentage, and the difference increases over time. This observation sug-

gests that there is an increasing propensity to follow a zero-leverage policy.

[Insert Table III here]

Moreover, the expected values barely change over time, indicating that the four standard capi-

tal structure variables would not allow for more zero-leverage observations. However, the ac-

tual percentage of zero-leverage firms is increasing sharply over time. Using the estimated

coefficients from the base period regression on firm characteristics in any given sample year

after the base period systematically underestimates the actual fraction of debt-free firms. These

findings suggest considering additional variables to explain the zero-leverage phenomenon.

3.2 A More Comprehensive Analysis of Firm Fundamentals

Table IV reports the evolution of the percentage of zero-leverage firms for a large set of firm

characteristics over three-year subperiods. For each variable, we divide firms into three groups

using the 30th and 70th percentiles as breakpoints. We also test whether there is a significant

time trend in the different subperiods. The discussion is structured along major determinants

of capital structure theories, such as taxes, agency problems and asymmetric information.

[Insert Table IV here]

3.2.1 Taxes

The tax system is a primary factor for capital structure choices (de Jong et al., 2008; Fan et al.,

2012). As tax deductions are to a large extent generated by interest payments, it is not surpris-

ing that many zero-leverage firms exhibit high tax payments, as shown in Panel A of Table IV.

Moreover, even their non-debt tax shields are smaller compared to leveraged firms. This find-

ing is hard to explain, because non-debt tax shields are the only possibility for zero-leverage

firms to reduce their tax obligations (De Angelo and Masulis, 1980).

15

3.2.2 Signaling

In the presence of information asymmetry, Ross (1977) argues that investors should choose

larger levels of debt as a signal of higher quality and that profitability and leverage are posi-

tively related. An alternative way to incorporate signaling is through abnormal earnings. Bar-

clay et al. (1995) show that firms with higher abnormal earnings carry more secured debt in

order to control for the underinvestment problem, and thus zero-leverage firms should exhibit

low abnormal earnings.10 As shown in Panel A of Table IV, the mean percentage of zero-

leverage firms is 11.0% in the group of low abnormal earning firms and 9.0% in the group of

high abnormal earning firms. Moreover, the mean percentage of zero-leverage firms is 16.1%

in the group of low profitability firms, and with 13.8% it is only slightly lower in the group of

high profitability firms. This observation is inconsistent with the notion that leverage and prof-

itability are positively related and that firms choose higher debt levels as a signal of high

quality. Overall, the decision to follow a zero-leverage policy does not appear to be driven by

signaling considerations.

3.2.3. Asymmetric Information

Based on adverse selection costs, the pecking order theory suggests that firms prefer internal

funds to external funds and debt to equity. In contrast, the trade-off theory predicts lower lev-

erage under high asymmetric information; with pronounced information asymmetries, the ex-

pected costs of financial distress increase and firms thus choose lower leverage ratios. We use

tangibility as a proxy variable for the degree of asymmetric information (Wessels and Titman,

1988).11 Panel A of Table IV indicates that zero-leverage firms feature lower tangibility. Re-

search and development (R&D) expenses serve as an alternative proxy variable for asymmet-

ric information. We classify firms whose R&D-to-assets ratio is above the 70th percentile (be-

low the 30th percentile) in a given sample year as high R&D firms (low R&D firms). While the

percentage of zero-leverage firms is only 10.3% (5.6%) for the low (medium) R&D group, on

average, the high R&D group consists of 16.7% zero-leverage firms.

10 Abnormal earnings are defined as the ratio of the difference between the income before extraordinary items in

time 𝑡 and 𝑡 − 1 to the firms’ market value of equity at time 𝑡 − 1. 11 However, high tangibility does not necessarily imply that industry-specific assets are highly liquid and rede-

ployable during turbulent times (Shleifer and Vishny, 1992; Pulvino, 1998).

16

Firms with a low or no credit rating at all are expected to suffer from a higher degree of infor-

mation asymmetry. Following Faulkender and Petersen (2006), we use a debt capacity varia-

ble (see Section 4 for more details) to proxy for the degree of asymmetric information and

expect firms with low debt capacity to be more likely to pursue a zero-leverage policy. Indeed,

the average percentage of zero-leverage firms is almost six times larger for firms with a low

debt capacity (17.5%) than for firms with a high debt capacity (3.6%).

Overall, we observe cross-sectional variation in zero-leverage firms that is consistent with the

asymmetric information hypothesis. The relationship between zero-leverage and the firm char-

acteristics tangibility, R&D expenses as well as credit ratings supports findings in related stud-

ies, such as Dang (2011) and Byoun et al. (2011). Most important, as indicated by the magni-

tude of the estimated time trend coefficients, the increase in the fraction of zero-leverage firms

is partly attributable to groups of firms with highly pronounced information asymmetry.

3.2.4. Agency Costs

Lower leverage minimizes the agency costs of debt, such as underinvestment (Myers, 1977)

and asset substitution (Leland and Toft, 1996). If zero-leverage firms attempt to mitigate un-

derinvestment problems by following an extremely conservative debt policy, we expect them

to exhibit high growth options, high payouts, and to rely heavily on external equity financing

in order to retain their growth options. While the respective time trends in Panel A of Table IV

are all estimated significantly, high market-to-book firms contain the highest fraction of zero-

leverage firms (20.4%), on average. High asset growth firms have a higher percentage of debt-

free firms (12.6%) than small asset growth firms (11.7%), albeit the difference is only margin-

al. Therefore, growth opportunities exert a positive (albeit weak) influence on the percentage

of zero-leverage firm.

In the absence of interest and amortization payments, dividend payouts are the only way to

smooth the earnings of zero-leverage firms. Debt conservative firms that do not pay dividends

may be prone to free cash-flow problems (Jensen, 1986). Alternatively, when coupled with

high equity issuances, higher dividends may work as a means of establishing a reputation for

moderation in expropriating wealth from shareholders (Byoun et al., 2011). Using a payout

dummy variable that takes the value of 1 if a firm pays dividends in year 𝑡 (and 0 otherwise),

Table IV shows that the percentage of zero-leverage firms is higher among non-payers. In con-

trast, when only considering dividend-paying firms, both high and low payout firms exhibit

17

higher percentages of zero-leverage firms (14.9% and 17.7%) than firms in the medium range

(6.6%). As a large proportion of zero-leverage firms do not pay dividends at all or pay low

dividends, it seems that zero-leverage firms taken as a whole do not have a strong tendency to

substitute dividend payments for leverage.12 Finally, Panel A of Table IV confirms the hy-

potheses that zero-leverage firms rely heavily on external equity financing. On average, the

percentage of zero-leverage firms is highest in the group of firms with the highest equity issu-

ances (20.0%), and it strongly increases over time (from 10.8% to 25.9%).

3.2.5. Multivariate Analysis

So far, our analysis provides evidence that a vintage effect, changes in industry composition,

and higher business risk contribute to explaining the increase in the fraction of zero-leverage

firms. Supporting the evidence of DeAngelo and Roll (2012) that the existence of low leverage

firms in recent years is due to a surge in listings by young growth firms, we document that the

majority of the zero-leverage firms in our sample carry these attributes. As expected, our pro-

pensity model reveals that the standard capital structure variables are not able to capture this

strong increase, and thus there seems to be an increasing propensity to adopt a zero-leverage

policy. In order to examine if additional capital structure variables are able to explain the in-

creasing propensity to follow a zero-leverage policy, we analyze all firm fundamentals in a

multivariate setup and run logistic regressions. The dependent binary variable takes the value

of 1 if firm 𝑖 pursues a zero-leverage strategy in year 𝑡, and 0 otherwise. All explanatory vari-

ables are lagged by one year. Based on the stylized facts in Section 2.2, we control for industry

effects by including 2-digit SIC code dummy variables (Faulkender and Petersen, 2006).

Table V presents the results of the logistic regressions. The negative intercepts in all columns

imply that zero-leverage firms are less common than debt firms. Column 1 shows the results

when we only include the standard capital structure variables. Similar to the coefficients from

the base period regression in the propensity model, size and tangibility decrease and the mar-

ket-to-book ratio and profitability increase the probability to adopt a zero-leverage policy. In

Column 2 we add all firm fundamentals from Table IV, including asset risk and dummy varia-

bles for the four vintage periods (see Section 2). Comparing the explanatory power of the lo-

12 This result contradicts the findings in Dang (2011) and Byoun et al. (2011) that zero-leverage firms exhibit

higher payout ratios than their matching firms (either to address the agency costs of free-cash flow or to build reputation for addressing the agency problem of expropriation of outside shareholders; see section 4).

18

gistic regressions in Columns 1 and 2, the pseudo R-square increases from 15.1% to 25.7%,

thus indicating that the additional firm fundamentals are predictors for the probability to main-

tain zero-leverage over and above the standard capital structure variables.

The vintage dummy variables exhibit increasing coefficients, indicating that the probability of

a firm to pursue a zero-leverage policy is higher for younger vintage periods (see Figure 3). As

expected, the coefficients on asset risk and taxes are significantly positive. The coefficient on

the payout dummy variable is significantly negative. Together with the positive coefficient on

the payout ratio, this result indicates that there are two different kinds of zero-leverage firms:

non-payout and high payout zero-leverage firms (Strebulaev and Yang, 2012). Conditional on

being a payer firm, the positive coefficient indicates that a higher payout ratio increases the

probability of maintaining a zero-leverage policy. Consistent with this positive payout coeffi-

cient, zero-leverage firms have significantly higher equity issuances than debt firms. In com-

bination with their higher asset risk, this finding is consistent with the conjecture that the riski-

est firms are constrained to use equity (Bolton and Freixas, 2000).

[Insert Table V here]

3.3 Country-Level Regression Analysis

The financial systems of our sample countries can be roughly divided into bank-oriented and

capital-market-oriented systems. According to Allen and Gale (2001), the continental Europe-

an countries and Japan are classified as bank-oriented financial systems, whereas the United

States, Canada, and the United Kingdom are considered capital-market-oriented financial sys-

tems. Petersen and Rajan (2002) and Djankov et al. (2007) note that the most important aspect

of corporate lending is information. If lenders know more about borrowers, their credit history,

or other lenders to the firm, they are less concerned about the lemons problem and extend

more credit. Relationship lending in bank-based countries implies that banks (as natural moni-

tors) have privileged access to information (Leland and Pyle, 1977), and thus the asymmetric

information problem will be less pronounced. Therefore, it is expected that leverage is lower

and the percentage of zero-leverage firms is higher in countries with a market-oriented finan-

cial system in comparison to countries with a bank-based financial system.

La Porta et al. (1998, 2002) argue that the common law system provides better external inves-

tor protection than the civil law system, resulting in better access to external finance and high-

19

er security values. Presumably, weaker legal systems and public enforcement of laws are asso-

ciated with less external equity, and, all else being equal, this implies that firms from common

law countries will use more outside equity. Fan et al. (2012) show that a country’s legal sys-

tem explains a large proportion of the variation in leverage, with common law systems being

associated with lower debt ratios. Following this conjecture, we assume that common law

countries tend to have a higher proportion of zero-leverage firms than civil law countries.

Harris and Raviv (1993) argue that the principles of a country’s bankruptcy law play an im-

portant role in determining the leverage ratio that creditors are willing to accept. As shown in

Table A4, there are large variations in the insolvency procedures across the countries in our

sample (Djankov et al. 2007). The Creditor Protection Score (CPS) from La Porta et al. (1998)

incorporates four different aspects of creditor protection in bankruptcy.13 The scores indicate

that the United Kingdom and Germany have the highest creditor protection, and the scores of

Canada and the United States point to equity-friendly bankruptcy codes.14 In equity-friendly

countries there is an explicit bankruptcy code that specifies and limits the rights and claims of

creditors and facilitates the reorganization of an ongoing business. In contrast, in debt-friendly

countries without bankruptcy codes or weakly enforced codes, creditors hastily claim the col-

lateral by liquidating distressed firms without seeking reorganization (Davydenko and Franks,

2008). Fan et al. (2012) suggest that the existence of an explicit bankruptcy code is associated

with higher debt ratios. Acharya et al. (2011) document that managers choose to reduce lever-

age in countries with stronger creditor rights.15 All else equal, we expect countries with high

creditor protection (such as the United Kingdom and Germany) to have more zero-leverage

firms than countries with low creditor protection (such as the United States and Canada).

The tax system is another crucial country-specific factor for capital structure choices (de Jong

et al., 2008; Fan et al., 2012). Under the classical tax system dividend payments are taxed at

both corporate and personal levels, whereas interest payments are tax-deductible corporate

expenses. This tax system prevails in the United States, Japan and the United Kingdom (post- 13 The four aspects in the CPS are: no automatic stay on assets, rights of secured creditors, restrictions for going

into reorganization, and management control in reorganization. A value of 1 is added to the score when the country’s laws and regulations provide each of these aspects to secured lenders. Accordingly, the CPS ranges from 0 to 4, where 0 indicates a very low and 4 a very high creditor protection.

14 Japan falls into the middle of the two systems. Given the relatively low CPS score points, we assign it to the equity-friendly regime in our logistic regression analysis.

15 This demand-side effect outweighs any supply-side effects, e.g., strong creditor rights increase the supply of credit, which would raise overall leverage if demand for borrowing is unaffected.

20

2000). In contrast, the goal of the various forms of the dividend imputation tax system is to tax

corporate profits only once. Firms can deduct interest payments, but the domestic shareholders

of a corporation receive a tax credit for the taxes paid by the corporation. During our sample

period, this system was in place in Canada, Germany, and the United Kingdom (pre-2001).

The proportion of corporate tax available as a tax credit under an imputation system varies

from country to country. Nevertheless, given the larger tax benefits from leverage, we expect a

lower percentage of zero-leverage firms in countries with a classical tax system.

Panel B of Table IV provides evidence that firms from countries with capital-market-oriented

financial systems, a common law origin, and high creditor protection exhibit the highest prob-

ability to maintain zero-leverage. The differences between the percentages of zero-leverage

firms in the subgroups are small at the beginning of our sample period but strongly increase

over time. The results of logistic regressions involving the set of country-level variables are

shown in Column 4 of Table V. Following Fan et al. (2012), we use variables for the financial

system, the legal origin, creditor protection, the tax system, GDP per capita growth, and the

inflation rate as explanatory variables in our logistic regression (see Table A1 for variable de-

scriptions). The coefficient on the financial system dummy variable is significantly positive,

indicating that the probability of a firm maintaining a zero-leverage policy is higher in coun-

tries with a market-based financial system. Given perfect correlation in our sample, this result

supports the conjecture that common law countries tend to have a higher proportion of zero-

leverage firms. Moreover, the probability of firms following a zero-leverage policy is higher in

countries with a high creditor protection, as indicated by the positive and significant coeffi-

cient on the bankruptcy code dummy variable. The impact of the tax system is negative, im-

plying that the probability of a firm having zero-leverage is higher in countries with a classical

tax system. An explanation is that multinational firms are able to shift their leverage into coun-

tries with the most favorable tax regime, and thus the country of origin’s tax code is not re-

strictive in their choice of leverage (Desai and Dharnapal, 2004; Huizinga et al., 2008).

The estimated coefficients on the variables GDP per capita growth and inflation are negative,

supporting the result of Djankov et al. (2007) that firms are more likely to carry higher lever-

age ratios in countries with a better legal environment and more stable and healthier economic

conditions. The variable deposit, defined as the ratio of liquid liability (M3) to GDP, measures

the degree of a country’s financial intermediation (or financial depth; Beck et al., 2000). We

21

find a negative relationship between the likelihood of a firm being classified as zero-leverage

and the relative size of the deposits in the country of origin (Booth et al., 2001). Finally, in

order to validate the robustness of our results, we include all firm fundamentals and country-

specific variables into a comprehensive logistic regression model. As shown in Column 5 of

Table V, all variables maintain their direction of influence and their statistical significance,

while the pseudo R-square increases only slightly from 25.9% to 26.4%. Countries with a capi-

tal-market-oriented financial system, high creditor protection, and a classical tax system exhib-

it the highest percentage of zero-leverage firms.

4. The Impact of Supply-Side Effects

According to Stiglitz and Weiss (1981), firms are sometimes rationed by lenders. In fact, one

of their major concerns is being shut out of the capital markets during market downturns (Gra-

ham and Harvey, 2001). Faulkender and Petersen (2006) argue that when estimating a firm’s

target leverage, empirical analyses should not only include the determinants of a firm’s pre-

ferred leverage (demand side) but also those factors that measure the constraints on its ability

to increase leverage (supply side). They propose that a firm’s ability to issue public (rated)

debt can be interpreted as an indicator of a large debt capacity. Firms with a credit rating have

easier access to the debt markets than those without a rating, and thus rated firms will hold

more leverage. This result can occur either directly through a quantity channel (lenders are

willing to lend more) or a price channel (firms with access to a cheaper source of capital will

borrow more). Following this notion, a novel approach to better understand the incompatible

characteristics of zero-leverage firms is to distinguish between firms that deliberately choose

to pursue a zero-leverage policy and firms that have no other option than renouncing the use of

debt. Zero-leverage firms that have no other option are either unable to issue debt or have no

access to external financing at all.

Based on our findings in Section 2.2 and in contrast to related studies (Devos et al., 2012;

Byoun et al., 2011; Dang, 2011), we hypothesize that not all zero-leverage firms face supply-

side constraints. Specifically, we assume that there are two different groups of zero-leverage

firms that are either financially constrained or unconstrained. In order to sort out the different

types of zero-leverage firms, we construct two measures of financial constraints: (i) estimated

debt capacity and (ii) the SA-index (Hadlock and Pierce, 2010). Based on Bolton and Freixas’

(2000) extended pecking order model, Lemmon and Zender (2010) argue that a firm’s ability

22

to issue public (rated) debt indicates a large debt capacity.16 While the presence (or absence)

of rated debt provides an indication of the extent to which a firm has access to relatively low-

cost borrowing on the public bond market and suggests a relatively large (or small) debt ca-

pacity, the use of the actual presence or absence of a bond rating as a measure of debt capacity

may present a problem. Firms without bond ratings might have chosen to fully rely on equity

financing for reasons outside of the pecking order despite having the capacity to issue rated

debt. Identifying such firms as being constrained in their debt capacity may lead to biased re-

sults. Therefore, we use a predictive model of whether a firm has a public bond rating in a

given year as the primary indication for the extent of its debt capacity. We run a logistic re-

gression using the full sample over the 1989-2010 time period to assess whether a firm is like-

ly able to access debt markets. The dependent binary variable in the logistic model takes a

value of 1 if firm 𝑖 in year 𝑡 has a long-term credit rating, and 0 otherwise.17 As in Faulkender

and Petersen (2006) and Lemmon and Zender (2010), the predicting firm characteristics are

the following: tangibility, size, market-to-book ratio, EBIT to sales ratio, R&D expenses, age,

volatility, and industry dummy variables for all 2-digit SIC codes in the sample.18 In order to

divide the sample into constrained and unconstrained firms, we insert the estimated coeffi-

cients into the logistic regression model and compute the estimated probabilities that a given

firm would be able to obtain a bond rating in each sample year during. The levels of these

probabilities are used as an indicator for the debt capacity of a given firm.

Our second financial constraints measure is the SA-index, introduced by Hadlock and Pierce

(2010). They develop a financial constraints index based on firm size and age. The index load-

ings are obtained using ordered logit regressions, where the dependent variable ranges from 1

(least constrained) to 5 (most constrained) and is assigned based on manual inspections of the

firms’ annual reports and 10-K filings. Hadlock and Pierce (2010) suggest that the SA-index 16 Consistent with the pecking order theory, Lemmon and Zender (2010) report that if external funds are re-

quired, debt appears to be preferred to equity if there are no concerns about debt capacity. Denis and Sibilkov (2010) also use the existence of a bond rating as one of their measure of financial constraints.

17 In contrast to Compustat US, Compustat Global does not include rating information. We use the RatingXpress historical rating files from S&P to determine whether a firm has a long-term credit rating. These files contain historical ratings for all rating levels (entities, maturities, and issues) and rating types (long- and short-term, local, and foreign currency). Coverage of the RatingXpress historical files differs among the countries groups: 34% of the firms in the United States, the United Kingdom, and Canada, 14% in Germany, and 9% in Japan.

18 We follow Faulkender and Petersen (2006) and exclude leverage as an explanatory variable because we sort firms into zero-leverage and non-zero-leverage firms. The results of our full-sample predictive model are pre-sented in Table A3. As a robustness check, we also estimate the model for US data, and the signs of the coef-ficients are similar as in Lemmon and Zender (2010) and Faulkender and Petersen (2006).

23

provides a better measure of financial constraints than other indices, such as the Kaplan and

Zingales (1997) index or the Whited and Wu (2006) index. The SA-index is calculated as:

(1) 𝑆𝐴 − 𝑖𝑛𝑑𝑒𝑥 = −0.737 × 𝑆𝑖𝑧𝑒 + 0.043 × 𝑆𝑖𝑧𝑒2 − 0.040 × 𝐴𝑔𝑒,

where 𝑆𝑖𝑧𝑒 is the natural logarithm of total assets, and 𝐴𝑔𝑒 denotes the number of years a firm

is contained with a non-missing stock price in Compustat Global. A higher (lower) SA-index

value is consistent with greater (smaller) financial constraints.

In order maintain the panel structure of our dataset, firms are grouped in quintiles according to

their mean value of a constraint measure during the sample period. In a first step, we calculate

quintiles of the cross-sectional distribution for each of the two alternative constraint measures

in each sample year and assign all firm-year observations to one of these quintiles. In a second

step, we compute the (rounded) average quintile of a given firm over time and assign all its

observations to this average quintile.19 This procedure leaves us with five portfolios for each

constraint measure in which all sample firms are sorted according to their average quintile. For

the debt capacity measure, firms in quintile 5 and 4 (Q5 and Q4; highest rating probability) are

considered as unconstrained, and firms in quintile 2 and 1 (Q2 and Q1; lowest rating probabil-

ity) as constrained; the reverse order holds for the alternative SA-index. In order to avoid mis-

classifications in the middle quintile, we exclude all firms in Q3 from our analysis. Moreover,

we construct a debt capacity dummy variable (which takes a value of 1 if a firm’s debt capaci-

ty is in Q4 and Q5, and 0 if it is in Q1 and Q2) and add it to the logistic regression model in

Column 2 of Table V. As expected, the probability of a firm to maintain a zero-leverage policy

increases conditional on being classified as debt constrained (with statistical significance at the

10% level). The signs of all other estimated coefficients remain unchanged compared to the

initial model in Column 2 of Table V.

Table VI shows a comparison of the mean characteristics of constrained and unconstrained

zero-leverage firms with each other as well as with all other sample firms. Panels A and B use

the debt capacity measure and the SA-index, respectively. For each fundamental variable, we

compute the mean across each of the three subsamples (debt-financed firms, constrained zero-

leverage firms, and unconstrained zero-leverage firms) and test whether there are differences 19 This procedure has the disadvantage that a firm belongs to one portfolio (quintile) over the full sample period.

As a robustness check, we group firms according to the yearly median debt capacity across firms, but the re-sults (not reported) remain qualitatively unchanged.

24

in means (based on a two-sample t-test). A first finding based on the number of firm-year ob-

servations in the different subsamples is that there are far more constrained than unconstrained

zero-leverage firms. This result seems to suggest that zero-leverage is not so much a deliberate

strategy. In fact, most zero-leverage firms are characterized as financially constrained, indicat-

ing that they have no other option than renouncing the use of debt. As shown in Section 2.2,

firms classified as debt constrained tend to be small and young (and vice versa). Therefore, all

our results are qualitatively similar for both approaches, and we focus in our discussion on the

difference in mean tests based on the debt capacity constraint measure in Panel A of Table

VI.20 In order to confirm that our two financial constraint measures are able to distinguish be-

tween constrained and unconstrained zero-leverage firms, we compute Almeida’s et al. (2004)

cash flow sensitivity of cash. They document that the effect of financial constraints is captured

by a firm’s propensity to save cash out of cash flows, and we thus apply their baseline empiri-

cal model (with changes in cash holdings as the dependent and cash flow, Tobin’s Q, and size

as the independent variables) to our international sample of zero-leverage firms (not reported).

As expected, for all constraint measures the constrained zero-leverage firms exhibit a signifi-

cantly positive cash flow sensitivity of cash, underlining their limited ability to access capital

markets. In contrast, unconstrained zero-leverage firms have a negative cash flow coefficient,

thus corroborating that our constraint measures adequately classify zero-leverage firms.21

[Insert Table VI here]

Almeida et al. (2011) suggest that constrained firms hold more cash than unconstrained firms;

their model predicts that concerns about future financing abilities are a major determinant of

cash holdings. Denis and Sibilkov (2010) also report that the value of cash increases with the

degree of financing constraints. Consistent with this argument, Opler et al. (1999) and Byoun

(2011) document that cash holdings and leverage are negatively related to leverage. Our anal- 20 As the SA-index has been calculated for U.S. data and we use an international sample, we conduct an addi-

tional robustness check for the suitability of this constraint measure. Following Bharath et al. (2009), we use principal component (PCA) analysis to derive the common informational component of firm size and age. Similar to our other constraint measures, portfolios are constructed where firms in quintiles 1 and 2 (Q1 and Q2) are taken as constrained, and firms in quintile 4 and 5 (Q4 and Q5) as unconstrained. Using this alterna-tive PCA-measure to recur the analysis in Table VI, the results (not reported) remain qualitatively unchanged.

21 As another robustness check, we use Fazzari’s et al. (1988) investment-cash flow sensitivities to differentiate between constrained and unconstrained zero-leverage firms. However, Kaplan and Zingales (1997) and Had-lock and Pierce (2010) report that in some cases the most constrained firms exhibit low investment-cash flow sensitivities, casting doubt on this approach. In fact, we also observe that constrained zero-leverage firms ex-hibit lower investment-cash flow sensitivities (albeit partially insignificant) than their unconstrained peers.

25

ysis in Table VI shows that constrained zero-leverage firms even hoard significantly higher

cash reserves than unconstrained zero-leverage firms and all other debt-financed firms in our

sample. Constrained zero-leverage firms possess the lowest debt capacity, and thus they ac-

cumulate higher cash reserves to avoid being forced to reject positive net present value pro-

jects. As constrained firms will suffer from more pronounced information asymmetries, this

finding is further consistent with Opler et al. (1999) and Drobetz et al. (2010) notion that firms

with higher adverse selection costs hold more cash due to a precautionary motive.

An observation that is hard to explain in our univariate analysis is the high payout ratio of ze-

ro-leverage firms in the absence of an unambiguous relationship between zero-leverage and

profitability (Panel A of Table IV). This seemingly incompatible result can be reconciled by

distinguishing between constrained and unconstrained zero-leverage firms. Table VI indicates

that constrained zero-leverage firms are less profitable than all other debt-financed firms. In

contrast, unconstrained zero-leverage firms tend to be the most profitable ones (and even more

profitable than all other sample firms). This higher profitability of unconstrained zero-leverage

firms is consistent with their high debt capacity, which is also substantiated by their potentially

high interest coverage ratio. Therefore, a small subsample of highly profitable zero-leverage

firms exist that deliberately chooses an extremely conservative debt strategy. These financially

unconstrained firms are more profitable, pay out more dividends, and are larger as well as old-

er than their constrained zero-leverage peers. Unconstrained zero-leverage firms exhibit lower

growth opportunities compared to their constrained zero-leverage peers, as indicated by their

lower R&D expenses and market-to-book ratios. Agency costs of free cash flow may be a

main rational for the high payout ratios of unconstrained zero-leverage firms. This conjecture

is supported by the observation that unconstrained zero-leverage firms tend to issue less equi-

ty, while making higher capital expenditures (and boasting higher changes in property, plant,

and equipment) than their constrained zero-leverage peers. Given their profitability together

with their high cash holdings, they simply have no need to raise external equity.

In sharp contrast, constrained zero-leverage firms are the most active equity issuers of all sam-

ple firms, supporting Lemmon and Zender’s (2010) notion that concerns over debt capacity

largely explain the use of new external equity financing. Accompanying their high equity issu-

ance activities, constrained zero-leverage firms prefer payouts as a means to signal their good

quality. As expected, constrained zero-leverage firms also exhibit higher asset volatility than

26

both unconstrained zero-leverage and debt-financed firms. Finally, constrained zero-leverage

firms are different from their unconstrained peers with respect to the length of a debt-free pe-

riod before jumping up to debt financing. Table VI contains the variable labeled zero-leverage

duration; it refers to the ratio of a firm’s longest zero-leverage period divided by the number of

years the firm is contained in our sample. As expected, financially constrained firms have no

debt capacity and thus maintain zero-leverage for significantly longer periods of time.

All in all, there seem to be two different types of firms that adopt a zero-leverage policy. First,

most zero-leverage firms are financially constrained and have no other option than renouncing

the use of debt, as indicated by the observation that they maintain zero-leverage for longer

periods of time. These firms tend to be smaller, younger, and riskier; and they are also the

most active equity issuers in our sample. Moreover, constrained zero-leverage firms are char-

acterized by higher growth opportunities, but lower profitability and lower total payout ratios.

The observation that they also hoard higher cash reserves is in line with Simutin’s (2010) find-

ing that high excess cash firms invest more in the future. He interprets this evidence as con-

sistent with the hypothesis that excess cash holdings proxy for risky growth options.22 Second,

there is a rather small subsample of firms that deliberately chooses to maintain a zero-leverage

policy. These financially unconstrained firms are more profitable, pay out higher dividends,

and are older as well as bigger than their constrained zero-leverage peers. While the first group

of firms is debt constrained and simply unable to raise debt, this second group is unconstrained

and would, in principle, have access to debt markets. Apple and Google are two very promi-

nent examples of firms that are assigned to this latter group over extended periods during our

sample period.

5. The Impact of Demand-Side Effects

Separating zero-leverage firms into financially constrained and unconstrained firms explains a

large part of the inconsistent results from our univariate analyses. However, both constrained

and unconstrained zero-leverage firms exhibit significantly lower capital expenditures, which

is in contrast to their higher market-to-book ratios and higher R&D expenses. Financial flexi-

22 In a related analysis, Palazzo (2012) similarly documents that high cash firms have high betas, higher growth

opportunities, low current profitability, and small size.

27

bility may be the missing link to fully capture the zero-leverage phenomenon.23 For example,

Arslan et al. (2011) suggest that low leverage and high cash holdings are the two components

of flexibility, and thus flexible firms have a greater capacity to pursue growth opportunities.

The model of DeAngelo et al. (2011) predicts that ex ante optimal financial policies preserve

the ability to access the capital markets ex post in the event of shocks to investment opportuni-

ties (or after unexpected earnings shortfalls). The option to issue debt is valuable, and the op-

portunity cost of borrowing implies that target capital structures are more conservative than

those produced by standard trade-off models (which omit the value lost when a firm is unable

to preserve the option to borrow later at comparable terms). In line with this prediction, Mar-

chica and Mura (2010) show that low-leverage UK firms attempt to maintain financial flexibil-

ity by preserving debt capacity and starting to issue debt as soon as they are able to exploit

their growth opportunities.24

Byoun (2011) hypothesizes that developing firms that are in the phase of building up financial

flexibility choose low leverage ratios. In contrast, growth firms that are in the next phase of

their lifecycle and that utilize financial flexibility to fund growth opportunities have high lev-

erage ratios, while mature firms that are in the phase of recharging financial flexibility carry

moderate leverage. Developing firms are small, with large cash holdings, low capital expendi-

tures, and a low rating probability. In addition, given their low debt capacity, they prefer using

equity over debt. Simutin (2010) reports a positive relationship between excess cash holdings

and future stock returns, which he interprets as evidence that excess cash holdings are a proxy

for risky growth options. The marginal value of cash is higher for developing firms with un-

certain future investment opportunities; these firms have lower internal funds and face higher

financial constraints. Hennessy and Whited (2005) argue that firms become debt-free in order

to prepare for large capital expenditures in the near future or to exploit future investment op-

portunities. Developing firms are expected to keep capital expenditures low in order to be able

23 Financial flexibility refers to a firm’s ability to respond in a timely and value-maximizing manner to unex-

pected changes in a firm’s cash flows or investment opportunity set. Graham and Harvey’s (2001) survey re-sults indicate that financial flexibility is the most important determinant of corporate capital structure.

24 On the other hand, Denis and McKeon (2012) report that firms substantially increase their total debt even at a time when their debt ratios are at least 10% above the estimated target leverage. They interpret these findings as consistent with De Angelo et al.’s (2011) conjecture that a firm’s leverage ratio consists of both a perma-nent and a transitory component; the former reflects a firm’s long-run target and the latter the evolution of its cash flows and operating needs.

28

to exploit future investment opportunities.25 It is against the backdrop of these studies that our

findings so far also suggest a relationship between the choice to initiate a zero-leverage policy

and financial flexibility.

In order to better understand financial flexibility in the form of untapped reserves of borrow-

ing, we analyze the evolution of the variables associated with financial flexibility in a dynamic

framework. Specifically, we identify all jumps from a zero-leverage policy to a book leverage

ratio greater than zero (jump-ups). As already shown in Table VI, financially constrained firms

maintain a zero-leverage policy for longer periods of time before they eventually raise debt (as

indicated by the variable labeled zero-leverage duration). Rather than building up flexibility by

preserving debt capacity, these firms are unable to tap the debt markets. In our attempt to ana-

lyze the time-series dynamics of leverage and the demand for financial flexibility, we follow

DeAngelo and Roll (2012) and exclude all firms with a history in Compustat Global of less

than 15 years.26 Table VII is related to DeAngelo and Roll’s (2012) framework and reports the

mean and median values of book leverage, changes in property, plant, and equipment, capital

expenditures, and cash holdings from four years before 𝑡 = −4 to three years after 𝑡 = +3 the

jump to a debt policy. We specify 𝑡 = 0 as the event year, i.e., it denotes the last year during

which a firm maintains zero-leverage just before issuing debt, and 𝑡 = +1 is the first year after

the jump. Statistical significance levels are reported for the differences between the variables’

average values between 𝑡 = 0 and 𝑡 = +1. Firms in Panel A of Table VII exhibit exactly one

year of zero-leverage before they eventually decide to issue debt. Similarly, firms have had

exactly two years of zero-leverage before the jump in Panel B, exactly three years in Panel C,

exactly four years of in Panel D, and at least five years of zero-leverage in Panel E. By defini-

tion, the mean and median book leverage ratio is zero in 𝑡 = 0; they are also zero in 𝑡 = −4 to

𝑡 = −1 depending on the model specification.

Although the difference in leverage between 𝑡 = 0 and 𝑡 = +1 is always statistically signifi-

cant at the 1% level in Table VII, there are notable differences in the levels of leverage across 25 DeAngelo et al. (2011) similarly argue that firms with low retained earnings tend to be in the capital infusion

stage; these firms are likely to be developing firms with a higher need for financial flexibility. In fact, Table V indicates that firms with high retained earnings are more likely to follow a zero-leverage policy.

26 Following this approach, we exclude a large part of the constrained zero-leverage firms (mostly IPO firms and firms from the later vintage groups). However, age and vintage effects are not able to fully capture the zero-leverage phenomenon. In what follows, we analyze the time-series dynamics of leverage together with firms’ investment and cash holding behavior. DeAngelo and Roll (2012) sequentially compare cross-sections of lev-erage ratios and document that cross-sectional migration is pervasive.

29

model specifications. While firms that follow a zero-leverage policy for exactly one year jump

to a median (mean) leverage ratio of 5.7% (11.0%) in Panel A, this ratio is decreasing with the

length of the preceding zero-leverage period. Firms that maintain a zero-leverage policy for at

least five years switch to median leverage ratio of only 2.2% (5.2%) in Panel E. This observa-

tion further supports our conjecture that constrained zero-leverage firms have no debt capacity

and thus (have to) maintain a zero-leverage policy for longer periods of time. Apparently, they

even issue less debt once they are able to tap the debt markets.

[Insert Table VII here]

Recognizing the relationship between debt constraints and the duration of a zero-leverage pol-

icy before abandoning it, we further analyze changes in property, plant, and equipment, capital

expenditures, and cash holdings; these variables are taken as indicators for financial flexibility

(Arslan et al., 2011; Byoun, 2011). Firms’ investments in property, plant, and equipment sig-

nificantly increase after the jump in Panels A-C (with firms in Panel A having the largest dif-

ference between 𝑡 = 0 and 𝑡 = +1). In contrast, investments in property, plant, and equipment

are smaller in Panel D, and they are even slightly lower after the jump compared to the preced-

ing zero-leverage periods in Panel E. The findings are similar for capital expenditures, all sug-

gesting that unconstrained zero-leverage firms build up financial flexibility and use their flexi-

bility after switching to a debt policy when investment projects arise. In contrast, constrained

zero-leverage firms (with a longer duration of zero-leverage) tend to make even lower invest-

ments after the jump than before. Finally, our results are even more pronounced for the evolu-

tion of cash holdings. Unconstrained firms in Panel A of Table VII with only one year of zero-

leverage tend to be the most profitable ones (see Table VI), thus increasing their cash holdings

before the jump and reducing them when they invest. In contrast, constrained firms in Panel E

with at least five years of zero-leverage tend to be the least profitable ones in our sample.

However, for precautionary motives they hoard the largest amount of cash in the years prior to

the jump (albeit slightly declining), and they even slightly increase cash holdings in the subse-

quent years. All these observations are consistent with our earlier findings in Table VI.

Another important robustness check is to consider negative net debt ratios instead of zero book

leverage. Net debt is calculated as the ratio of the difference between total book leverage and

cash holdings to total assets. Acharya et al. (2007) and Berk et al. (2010) argue that cash hold-

ings should be interpreted as negative debt and are thus part of capital structure decisions ra-

30

ther than an explanatory variable.27 Byoun (2011) and DeAngelo et al. (2011) also show that

firms which build up or maintain financial flexibility hoard large cash holdings. Using nega-

tive net debt ratios instead of zero book leverage allows us to examine cash holding and lever-

age decisions in an integrated framework. Table A5 replicates Table VII based on negative net

debt ratios. Our findings remain qualitatively unchanged, indicating that even firms that do not

follow a zero-leverage policy but instead maintain a negative net debt ratio tend to build up

financial flexibility.

Taken together, unconstrained zero-leverage firms have the flexibility to issue larger amounts

of debt and use it together with their cash holdings to invest as profitable investment projects

arise. In contrast, constrained firms lack sufficient debt capacity and are not in the position to

issue as much debt; thus they are not as flexible to invest. In addition, they hoard the largest

amount of cash as they fear that they may not be able to issue further debt in the near future.

As a further robustness check, Table VIII validates our results on financial flexibility using a

dynamic panel regression approach. Following Marchica and Mura (2010), we estimate a Q-

model of investment as follows:28

(2) 𝐶𝑎𝑝𝑒𝑥𝑖𝑡 = 𝛼 × 𝐶𝑎𝑝𝑒𝑥𝑖𝑡−1 + 𝛽1 × 𝐶𝐹𝑖𝑡−1 + 𝛽2 × 𝑄𝑖𝑡 + 𝛽3 × 𝑍𝐿 − 𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛𝑖𝑡 +

+𝛽4 × (𝐶𝐹𝑖𝑡−1 × 𝑍𝐿 − 𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛𝑖𝑡) + 𝜂𝑖 + 𝜂𝑡 + 𝜐𝑖𝑡,

where 𝐶𝑎𝑝𝑒𝑥𝑖𝑡 denotes capital expenditures; 𝑄𝑖𝑡 is Tobin’s Q (computed as the ratio of market

value to book value of assets); 𝐶𝐹𝑖𝑡−1 the one period lagged cash flow; and 𝑍𝐿 − 𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛𝑖𝑡

a zero-leverage duration dummy variable. As an example, in the one-year specification in Ta-

ble VIII the latter dummy variable is set equal to 1 in 𝑡 = 0 if a firm maintains zero-leverage

for exactly one year prior to the jump (and 0 otherwise); the other specifications are defined

accordingly. Most important, (𝐶𝐹𝑖𝑡−1 × 𝑍𝐿 − 𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛𝑖𝑡) denotes an interaction term with

cash flow that tests whether firms with a shorter zero-leverage duration are more flexible and

benefit from their enhanced investment ability as well as a lower investment-cash flow sensi-

tivity. Finally, 𝜂𝑖 denotes an entity fixed effect; 𝜂𝑡 a time-specific effect; and 𝜐𝑖𝑡 a disturbance

term (serially uncorrelated with mean zero). The dynamic panel model in equation (1) is esti-

27 Lambrecht and Pawlina (2012) argue that net debt contains more information than the traditional left censored

book leverage ratio. Hennessy and Whited (2005) and Acharya et al. (2007) simultaneously analyze financing and cash holding decisions.

28 See Cleary (1999), Alti (2003), or Brown and Petersen (2009) for details on the Q-model.

31

mated using three different GMM-techniques to control for endogenity and fixed effects. Spe-

cifically, we use the Arellano and Bond (1991) difference GMM-estimator in Panel A of Table

VIII, the Blundell and Bond (1998) system GMM-estimator in Panel B, and the DPF-estimator

with a fractional dependent variable (Elsas and Florysiak, 2011) in Panel C.29

In line with Marchica and Mura (2010), we observe a significantly positive relationship be-

tween capital expenditures and Tobin’s Q, consistent with the prediction that growth opportu-

nities play an important role in investment decisions. The coefficient on cash flow is always

positive and significant, indicating that the presence of capital market imperfections may result

in firms relying on internal funds to invest (Fazzari et al., 1988; Gatchev et al., 2010). Support-

ing our descriptive analysis, the zero-leverage duration dummy variables exerts a positive im-

pact on capital expenditures, indicating that zero-leverage firms invest more after switching to

a debt policy. Most important, both the magnitude of the estimated coefficients and their sig-

nificance levels tend to decline for dummy variables that indicate longer zero-leverage dura-

tions. Firms that maintain a zero-leverage policy for longer periods of time tend to be con-

strained and unable to invest as much as firms with a shorter duration of zero-leverage. More-

over, the interaction term between the zero-leverage duration dummy and cash flow is nega-

tive for firms that deliberately choose a zero-leverage policy over a shorter period of time.

These firms possess a high debt capacity, are financially flexible and in a position to raise ex-

ternal funds for investment, and thus they are less dependent on internal funds. In contrast,

firms that maintain a zero-leverage policy for longer periods of time are constrained in their

debt capacity and exhibit insignificant coefficients on the interaction term.

[Insert Table VIII here]

6. Conclusion

This study investigates why a surprisingly large number of firms maintain a zero-leverage pol-

icy, a behavior that cannot be explained based on the standard theories of capital structure. We

analyze the zero-leverage puzzle by using a large international sample. Although country-level

differences are important, the increasing percentage of zero-leverage firms represents an inter-

national phenomenon. While only 5% of all firms in our sample followed a zero-leverage poli-

cy in 1989, this percentage increases to roughly 14% by 2010. We start our analysis with styl- 29 Drobetz and Schilling (2012) provide a detailed discussion of these estimators.

32

ized facts about the zero-leverage phenomenon. First, there is an IPO effect, and thus a large

proportion of the upward trend in the percentage of zero-leverage firms is generated by firms

that went public in the more recent sample years. In fact, the observed increase in the percent-

age of zero-leverage firms is closely related to the different IPO waves across our sample. Se-

cond, there is an industry effect due to changes in industry composition toward sectors where

extreme debt conservatism is more commonly adopted. The IPO effect and the industry effect

are interrelated, and both effects are linked up with the attempt of zero-leverage firms to build

up and maintain financial flexibility to respond in a timely and value-maximizing manner to

unexpected changes in cash flow and/or investment opportunities (demand side-explanation).

Third, the zero-leverage phenomenon is driven by a more general vintage effect, where newly

listed firms in the later years of the sample period are more likely to maintain a zero-leverage

policy. The increasing asset volatility over the sample period offers an explanation for this

increase in the percentage of zero-leverage firms over the listing groups. Exploiting the cross-

sectional information from our international sample, we show that countries with a capital-

market-oriented financial system, a common law origin, high creditor protection, and a classi-

cal tax system exhibit the highest percentage of zero-leverage firms.

Another novel aspect of our study is that we focus on the supply-side of financing choices by

dividing the full sample of zero-leverage firms into financially constrained and unconstrained

ones. Only a small number of very profitable firms with high payout ratios deliberately pursue

a zero-leverage policy. In contrast, most zero-leverage firms are constrained by their debt ca-

pacity. They tend to be smaller, riskier, and less profitable, and they are the most active equity

issuers. Zero-leverage firms accumulate higher cash holdings than all other sample firms, pre-

sumably in an attempt to build up financial flexibility. In particular, firms that maintain zero-

leverage for only a short period of time are seeking financial flexibility. After abandoning their

zero-leverage policy, they switch to higher book leverage ratios, make higher investments, and

reduce their cash holdings by a larger amount compared to constrained firms that (have to)

maintain zero-leverage for longer periods of time.

33

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39

Figure 1: Distribution of Zero-leverage Firms over Time

Figure 2: Percentage of Zero-leverage Firms by Size Quartile

0%

5%

10%

15%

20%

25%Full Sample United States

Canada United Kingdom

Germany Japan

0%

5%

10%

15%

20%

25%

30%

35%Size Quartile 1 (small)Size Quartile 2Size Quartile 3Size Quartile 4 (large)

40

Figure 3: Percentage of Zero-leverage Firms by Vintage Group

Full Sample United Kingdom

United States Germany

Canada Japan

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35%Pre 1989

1989-1993

1994-2003

2004-2010

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45%Pre 19891989-19931994-20032004-2010

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45% Pre 19891989-19931994-20032004-2010

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25%Pre 19891989-19931994-20032004-2010

41

Figure 4: Percentage of Zero-leverage Firms by Age Group

Full Sample United Kingdom

United States Germany

Canada Japan

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5%

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30%listed pre 1989listed 1989 and later (older than 3 years)IPO firm (not older than 3 years)

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listed 1989 and later (older than 3 years)

IPO firm (not older than 3 years)

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IPO firm (not older than 3 years)

42

Figure 5: Percentage of Zero-leverage Firms by Industry

Full Sample United Kingdom

United States Germany

Canada Japan

0%

5%

10%

15%

20%

25%

30%

35% Energy MaterialsManufacturing Consumer durablesConsumer non-durables HealthcareInformation technology Telcommunication

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50% Energy MaterialsManufacturing Consumer durablesConsumer non-durables HealthcareInformation technology Telcommunication

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60% Energy MaterialsManufacturing Consumer durablesConsumer non-durables HealthcareInformation technology Telcommunication

0%

5%

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15%

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30%Energy MaterialsManufacturing Consumer durablesConsumer non-durables HealthcareInformation technology Telcommunication

43

Figure 6: Percentage of Zero-leverage Firms and Changing Industry Composition

Figure 7: Evolution of Median Asset Risk over Vintage Periods

0%

5%

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25%Percentage of zero-leverage firms with 1989 industry weightsActual percentage of zero-leverage firms

0%

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40%

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50%

Pre-1989 1989-1993 1994-2003 2004-2010

Median asset risk of zero-leverage firms p.a.

Median asset risk of debt-financed firms p.a.

44

Table I: Descriptive Statistics The descriptive statistics show the mean, the standard deviation (S.D.), the median, the number of firm-year observations (N), as well as the minimum (Min.) and maximum (Max.) value of each variable. The sample consists of 13,309 industrial firms from the United States, Canada, the United Kingdom, Germany, and Japan. The sample period is from 1989 to 2010. Annualized data is obtained from the Compustat Global database. All variables are defined in Table A1. Apart from debt capacity and age, firm-level variables are winsorized at the upper and lower one percentile.

Variable Mean S.D. Median Min. Max. N Mean Median N Mean Median N Mean Median N Mean Median N Mean Median N

Book leverage 0,227 0,214 0,191 0,000 2,384 151.638 0,232 0,186 63.479 0,220 0,197 9.381 0,179 0,143 22.665 0,195 0,158 8.560 0,252 0,232 47.553Size 5,650 1,900 5,597 -1,301 12,035 151.638 5,715 5,657 63.479 5,484 5,476 9.381 4,688 4,549 22.665 5,352 5,111 8.560 6,107 5,949 47.553Age 13,031 13,950 8,000 -18,000 61,000 151.638 8,324 7,000 63.479 7,063 6,000 9.381 6,167 5,000 22.665 6,337 5,000 8.560 24,967 22,000 47.553Market-to-book 1,760 1,493 1,272 0,257 10,000 151.630 2,127 1,518 63.474 1,912 1,367 9.379 1,904 1,400 22.665 1,598 1,228 8.559 1,202 1,044 47.553Asset growth 0,136 0,562 0,056 -0,856 17,998 140.935 0,161 0,059 59.690 0,324 0,099 8.727 0,193 0,060 20.238 0,090 0,042 7.662 0,048 0,044 44.618Payout ratio 0,022 0,046 0,006 0,000 0,587 110.712 0,027 0,004 57.139 0,019 0,000 8.778 0,022 0,011 19.180 0,016 0,004 6.779 0,011 0,006 18.836Equity issue 0,070 0,186 0,003 -0,019 1,768 110.051 0,071 0,005 61.844 0,098 0,006 9.146 0,092 0,001 20.627 0,044 0,000 6.743 0,019 0,000 11.691Tangibility 0,292 0,219 0,251 0,000 0,992 151.513 0,273 0,210 63.357 0,466 0,453 9.379 0,284 0,231 22.664 0,235 0,210 8.560 0,299 0,286 47.553Asset risk 0,492 0,339 0,399 0,066 3,845 149.063 0,556 0,457 62.535 0,515 0,417 9.241 0,495 0,387 22.058 0,502 0,373 8.376 0,397 0,349 46.853R&D 0,033 0,088 0,000 0,000 1,159 151.638 0,056 0,002 63.479 0,027 0,000 9.381 0,022 0,000 22.665 0,018 0,000 8.560 0,011 0,001 47.553Abnormal earnings -0,002 0,329 0,011 -5,788 20,302 128.549 -0,002 0,012 59.250 0,013 0,014 8.569 -0,007 0,012 19.530 0,008 0,012 7.564 -0,004 0,007 33.636Profitability 0,019 0,210 0,052 -3,178 0,544 151.043 0,015 0,072 63.281 -0,002 0,054 9.285 -0,004 0,067 22.473 0,016 0,049 8.541 0,041 0,037 47.463Taxes 0,019 0,030 0,014 -0,188 0,440 151.172 0,022 0,016 63.367 0,015 0,008 9.323 0,017 0,014 22.469 0,020 0,013 8.541 0,018 0,014 47.472Non-debt tax shield 0,045 0,040 0,037 0,000 0,836 140.485 0,049 0,041 63.182 0,052 0,044 9.285 0,045 0,036 22.139 0,061 0,048 8.488 0,032 0,028 37.391Cash holdings 0,175 0,192 0,111 0,000 0,982 151.638 0,193 0,095 63.479 0,143 0,053 9.381 0,161 0,088 22.665 0,152 0,083 8.560 0,169 0,139 47.553Capital expenditures 0,056 0,066 0,036 0,000 0,814 131.716 0,061 0,041 62.651 0,109 0,066 9.106 0,055 0,036 20.748 0,055 0,039 6.704 0,034 0,024 32.507Retained earnings -0,280 2,440 0,130 -70,310 1,134 150.723 -0,543 0,115 62.639 -0,495 0,064 9.309 -0,523 0,102 22.665 -0,108 0,017 8.558 0,192 0,182 47.552Debt capacity 0,147 0,217 0,047 0,000 0,990 133.453 0,185 0,068 56.792 0,167 0,053 7.779 0,118 0,022 18.523 0,146 0,038 7.196 0,107 0,043 43.163Deposits 1,208 0,624 0,845 0,604 2,422 151.638 0,677 0,672 63.479 0,965 0,780 9.381 1,085 1,076 22.665 0,941 0,983 8.560 2,072 2,032 47.553GDP per capita growth 0,014 0,020 0,019 -0,056 0,053 151.638 0,016 0,020 63.479 0,015 0,018 9.381 0,016 0,021 22.665 0,014 0,015 8.560 0,011 0,013 47.553Inflation rate 0,013 0,018 0,018 -0,025 0,077 151.638 0,023 0,022 63.479 0,021 0,023 9.381 0,028 0,028 22.665 0,011 0,010 8.560 -0,007 -0,009 47.553

JapanFull Sample United States Canada United Kingdom Germany

45

Table II: Distribution of Zero-leverage Firms over Time This table summarizes the distribution of zero-leverage firms over time by presenting the absolute numbers and the percentages of firms that maintain a zero-leverage (ZL) policy. A firm is classified as zero-leverage if it has no long-term and short-term debt in a given year 𝑡.

All ZL % All ZL % All ZL % All ZL % All ZL % All ZL %1989 3.713 191 5,144 1.992 145 7,279 284 27 9,507 393 7 1,781 100 4 4,000 944 8 0,8471990 4.006 215 5,367 2.072 161 7,770 301 29 9,635 530 16 3,019 130 4 3,077 973 5 0,5141991 4.206 267 6,348 2.199 200 9,095 315 35 11,111 564 23 4,078 128 1 0,781 1.000 8 0,8001992 4.621 338 7,314 2.481 247 9,956 328 43 13,110 611 29 4,746 155 8 5,161 1.046 11 1,0521993 5.183 433 8,354 2.779 313 11,263 392 59 15,051 630 37 5,873 182 10 5,495 1.200 14 1,1671994 5.912 474 8,018 2.983 332 11,130 444 70 15,766 677 37 5,465 203 8 3,941 1.605 27 1,6821995 6.435 535 8,314 3.170 352 11,104 467 74 15,846 763 53 6,946 225 11 4,889 1.810 45 2,4861996 7.086 631 8,905 3.380 416 12,308 467 69 14,775 1.036 80 7,722 291 15 5,155 1.912 51 2,6671997 7.373 690 9,358 3.383 437 12,918 469 72 15,352 1.181 103 8,721 307 16 5,212 2.033 62 3,0501998 7.609 720 9,462 3.388 452 13,341 494 66 13,360 1.205 108 8,963 366 23 6,284 2.156 71 3,2931999 8.080 792 9,802 3.518 467 13,275 507 63 12,426 1.148 135 11,760 486 39 8,025 2.421 88 3,6352000 8.496 975 11,476 3.618 526 14,538 498 69 13,855 1.228 205 16,694 592 49 8,277 2.560 126 4,9222001 8.419 1.095 13,006 3.412 558 16,354 476 70 14,706 1.254 241 19,219 626 63 10,064 2.651 163 6,1492002 8.236 1.143 13,878 3.277 567 17,302 474 71 14,979 1.231 234 19,009 559 59 10,555 2.695 212 7,8662003 8.072 1.246 15,436 3.127 593 18,964 486 78 16,049 1.218 260 21,346 526 68 12,928 2.715 247 9,0982004 8.168 1.391 17,030 3.073 617 20,078 503 101 20,080 1.310 314 23,969 503 72 14,314 2.779 287 10,3272005 8.279 1.390 16,789 2.962 599 20,223 503 104 20,676 1.432 320 22,346 507 64 12,623 2.875 303 10,5392006 8.311 1.421 17,098 2.844 559 19,655 474 102 21,519 1.487 364 24,479 546 72 13,187 2.960 324 10,9462007 8.087 1.345 16,632 2.690 505 18,773 422 84 19,905 1.429 337 23,583 584 82 14,041 2.962 337 11,3772008 7.670 1.192 15,541 2.537 463 18,250 381 77 20,210 1.283 296 23,071 560 70 12,500 2.909 286 9,8322009 7.230 1.134 15,685 2.387 466 19,522 358 67 18,715 1.150 263 22,870 523 79 15,105 2.812 259 9,2112010 6.446 965 14,971 2.207 454 20,571 338 57 16,864 905 205 22,652 461 62 13,449 2.535 187 7,377

Number of observations

151.638 18.583 12,25 63.479 8.509 13,40 9.381 1.363 14,53 22.665 3.199 14,11 8.560 738 8,62 47.553 2.675 5,63

JapanGermanyUnited States Canada United KingdomFull SampleYear

46

Table III: Propensity Model This table reports the out-of sample estimates from logistic regressions for the difference between the expected percentage and the actual percentage of zero-leverage firms in the full sample (in rows [5]-[7]). In a first step, yearly logistic models are used to estimate the probabilities that firms with given characteristics (profitability, market-to-book, size, and tangibility) maintain a zero-leverage policy during the 1989-1993 base period. In a second step, the probability for each firm to follow a zero-leverage policy is calculated based on the characteris-tics in each year (after 1993), using the average annual coefficients from this base period. The expected percent-age of zero-leverage firms is obtained by averaging the individual probabilities across firms in each year and multiplying the result by one hundred. Rows [1]-[4] show the average values of the firm fundamentals in each year (after 1993).

[1] [2] [3] [4] [5] [6] [7]

Year Profitability Market-to-book Size Tangibility Actual % Expected %Expected

−Actual %

1994 0,049 1,683 5,654 0,332 8,018 6,140 -1,877

1995 0,051 1,906 5,617 0,326 8,314 6,787 -1,527

1996 0,051 1,893 5,565 0,321 8,905 6,841 -2,064

1997 0,042 1,900 5,543 0,319 9,358 6,776 -2,583

1998 0,019 1,834 5,582 0,321 9,462 6,293 -3,169

1999 0,015 2,296 5,642 0,304 9,802 7,614 -2,188

2000 0,002 1,867 5,598 0,286 11,476 6,366 -5,110

2001 -0,037 1,649 5,480 0,290 13,006 5,758 -7,248

2002 -0,026 1,391 5,500 0,290 13,878 5,404 -8,475

2003 0,000 1,814 5,598 0,280 15,436 6,485 -8,951

2004 0,016 1,889 5,635 0,267 17,030 6,785 -10,245

2005 0,019 1,978 5,596 0,255 16,789 7,249 -9,540

2006 0,013 1,888 5,658 0,248 17,098 6,744 -10,354

2007 0,008 1,706 5,766 0,249 16,632 6,093 -10,539

2008 -0,007 1,266 5,761 0,263 15,541 5,216 -10,325

2009 0,003 1,459 5,856 0,264 15,685 5,470 -10,215

2010 0,032 1,538 6,084 0,264 14,971 5,312 -9,659

1,950 0,239 -0,549 -1,480

Average logistic regression coefficients 1989-1993 (base period):

47

Table IV: Percentage of Zero-leverage Firms by Group of Firms This table reports the percentage of zero-leverage firms by groups of firms. The breakpoints for the small, medi-um, and large groups are the annual 30th and 70th percentiles of each firm characteristic. The sample period is from 1989 to 2010. All variables are defined in Table A1. Apart from debt capacity and age, firm-level variables are winsorized at the upper and lower one percentile.

Panel A: Firm-level Fundamentals

89-90 91-93 94-96 97-99 00-02 03-05 06-08 09-10 89-10

SizeSmall 0,129 0,167 0,184 0,192 0,236 0,298 0,281 0,262 0,219 1,306 ***

Medium 0,029 0,051 0,061 0,076 0,115 0,148 0,164 0,156 0,100 0,911 ***Large 0,007 0,012 0,016 0,026 0,038 0,052 0,049 0,042 0,030 0,253 ***

Market-to-bookSmall 0,047 0,040 0,049 0,056 0,108 0,106 0,135 0,122 0,083 0,629 ***

Medium 0,029 0,038 0,042 0,055 0,077 0,121 0,112 0,098 0,072 0,565 ***Large 0,089 0,156 0,176 0,189 0,215 0,281 0,264 0,259 0,204 1,339 ***

Asset growthSmall 0,054 0,068 0,062 0,100 0,132 0,164 0,182 0,176 0,117 1,011 ***

Medium 0,053 0,058 0,077 0,073 0,097 0,136 0,137 0,114 0,093 0,548 ***Large 0,038 0,088 0,105 0,106 0,142 0,182 0,169 0,180 0,126 0,872 ***

Payout dummyPayer 0,055 0,070 0,067 0,066 0,073 0,105 0,108 0,104 0,081 0,322 ***

Non payer 0,108 0,131 0,150 0,156 0,184 0,233 0,242 0,241 0,181 1,103 ***Payout ratio

Small 0,111 0,128 0,146 0,148 0,175 0,231 0,242 0,236 0,177 1,082 ***Medium 0,041 0,047 0,056 0,056 0,066 0,077 0,094 0,091 0,066 0,319 ***

Large 0,091 0,124 0,114 0,126 0,136 0,183 0,187 0,230 0,149 0,838 ***Equity issue

Small 0,078 0,088 0,084 0,081 0,099 0,138 0,166 0,177 0,114 0,676 ***Medium 0,051 0,072 0,082 0,082 0,097 0,139 0,144 0,166 0,104 0,692 ***

Large 0,108 0,142 0,167 0,184 0,218 0,261 0,265 0,259 0,200 1,311 ***Tangibility

Small 0,096 0,142 0,164 0,190 0,260 0,316 0,309 0,294 0,221 1,897 ***Medium 0,030 0,045 0,056 0,063 0,085 0,117 0,122 0,113 0,079 0,581 ***

Large 0,039 0,045 0,042 0,045 0,053 0,075 0,076 0,067 0,055 0,225 ***Asset risk

Small 0,050 0,064 0,059 0,066 0,084 0,110 0,128 0,112 0,084 0,475 ***Medium 0,049 0,061 0,064 0,081 0,107 0,158 0,165 0,161 0,106 0,865 ***

Large 0,063 0,101 0,137 0,140 0,197 0,224 0,199 0,186 0,156 1,033 ***R&D

Small 0,045 0,064 0,068 0,077 0,118 0,157 0,157 0,142 0,103 0,774 ***Medium 0,032 0,026 0,026 0,036 0,061 0,093 0,089 0,088 0,056 0,456 ***

Large 0,074 0,107 0,131 0,153 0,191 0,224 0,232 0,219 0,167 1,166 ***Abnormal earnings

Small 0,060 0,069 0,099 0,084 0,109 0,163 0,160 0,139 0,110 0,688 ***Medium 0,079 0,112 0,121 0,125 0,136 0,176 0,176 0,179 0,138 0,659 ***

Large 0,050 0,074 0,060 0,063 0,101 0,118 0,129 0,127 0,090 0,531 ***Profitability

Small 0,072 0,088 0,100 0,124 0,207 0,243 0,237 0,220 0,161 1,420 ***Medium 0,023 0,031 0,041 0,050 0,065 0,094 0,094 0,082 0,060 0,439 ***

Large 0,072 0,117 0,124 0,126 0,129 0,176 0,181 0,181 0,138 0,671 ***

Time Trend×100

48

Panel A: continued

Panel B: Country-level Characteristics

89-90 91-93 94-96 97-99 00-02 03-05 06-08 09-10 89-10

TaxesSmall 0,065 0,082 0,095 0,113 0,173 0,235 0,223 0,208 0,149 1,305 ***

Medium 0,021 0,031 0,040 0,048 0,081 0,094 0,098 0,086 0,062 0,479 ***Large 0,082 0,123 0,131 0,141 0,143 0,185 0,192 0,188 0,148 0,694 ***

Non-debt tax shieldSmall 0,101 0,152 0,149 0,143 0,180 0,233 0,246 0,236 0,180 1,037 ***

Medium 0,049 0,074 0,075 0,080 0,098 0,137 0,136 0,123 0,096 0,526 ***Large 0,057 0,057 0,065 0,074 0,105 0,121 0,114 0,109 0,088 0,442 ***

Cash holdingsSmall 0,016 0,015 0,015 0,017 0,031 0,035 0,033 0,031 0,024 0,124 ***

Medium 0,026 0,037 0,047 0,048 0,063 0,095 0,097 0,089 0,063 0,428 ***Large 0,124 0,183 0,203 0,238 0,310 0,386 0,386 0,361 0,274 2,655 ***

Capital expendituresSmall 0,105 0,128 0,140 0,137 0,167 0,227 0,226 0,217 0,168 0,951 ***

Medium 0,070 0,082 0,091 0,102 0,108 0,138 0,143 0,129 0,108 0,462 ***Large 0,059 0,085 0,098 0,090 0,103 0,121 0,116 0,115 0,098 0,307 ***

Retained earningsSmall 0,052 0,081 0,097 0,125 0,191 0,239 0,237 0,215 0,155 1,438 ***

Medium 0,017 0,031 0,040 0,045 0,063 0,084 0,085 0,070 0,054 0,373 ***Large 0,099 0,124 0,130 0,133 0,148 0,195 0,196 0,203 0,154 0,749 ***

Debt capacity Small 0,100 0,137 0,148 0,152 0,184 0,238 0,229 0,216 0,175 0,970 ***

Medium 0,039 0,058 0,065 0,079 0,115 0,149 0,157 0,156 0,102 0,837 ***Large 0,010 0,013 0,018 0,028 0,045 0,061 0,059 0,057 0,036 0,327 ***

Time Trend×100

89-90 91-93 94-96 97-99 00-02 03-05 06-08 09-10 89-10

DepositsSmall 0,078 0,097 0,106 0,132 0,160 0,197 0,189 0,200 0,145 0,999 ***

Medium 0,058 0,089 0,107 0,101 0,132 0,171 0,176 0,152 0,123 0,844 ***Large 0,007 0,027 0,033 0,033 0,055 0,097 0,112 0,074 0,055 0,574 ***

GDP per capita growthSmall 0,071 0,066 0,084 0,037 0,073 0,138 0,166 0,132 0,096 0,785 ***

Medium 0,067 0,086 0,076 0,121 0,145 0,175 0,170 0,194 0,129 1,088 ***Large 0,010 0,071 0,097 0,125 0,143 0,170 0,153 0,135 0,113 0,899 ***

Inflation rateSmall 0,008 0,077 0,025 0,048 0,064 0,100 0,104 0,088 0,064 0,646 ***

Medium 0,073 0,071 0,114 0,110 0,134 0,193 0,179 0,139 0,127 0,835 ***Large 0,064 0,074 0,100 0,114 0,158 0,201 0,193 0,207 0,139 1,160 ***

Tax systemImputation system 0,050 0,074 0,091 0,101 0,132 0,161 0,164 0,158 0,116 0,771 ***

Classical system 0,053 0,074 0,082 0,093 0,127 0,165 0,164 0,153 0,114 0,762 ***Bankruptcy code High creditor protection 0,027 0,048 0,064 0,090 0,155 0,200 0,207 0,200 0,124 0,616 ***Low creditor protection 0,057 0,079 0,088 0,097 0,120 0,154 0,151 0,140 0,111 1,375 ***

Financial systemMarket-based 0,069 0,096 0,111 0,124 0,164 0,204 0,206 0,206 0,148 0,653 ***

Bank-based 0,010 0,014 0,026 0,038 0,069 0,105 0,111 0,093 0,058 1,049 ***Law system

Civil law 0,010 0,014 0,026 0,038 0,069 0,105 0,111 0,093 0,058 1,049 ***Common law 0,069 0,096 0,111 0,124 0,164 0,204 0,206 0,206 0,148 0,653 ***

Time Trend×100

49

Table V: Firm- and Country-level Logistic Regression This table reports the results from firm- and country-level logistic regressions. The dependent variable takes the value of 1 if a firm has zero debt in a given year 𝑡 (and 0 otherwise). A firm is classified as zero-leverage if it has no long-term and short-term debt in year 𝑡. All country- and firm-specific characteristics are described in Table A1. All explanatory variables are lagged by one period, and all logistic regressions use additional industry dummy variables based on two-digit SIC codes (unreported). The listing dummy variables denote the four list-ing groups, which are defined according to a firm’s IPO date (the pre-1989 dummy variable is omitted). ***, **, and * indicate statistical significance at the 1, 5, and 10 percent level, respectively.

Independent Variables

1989-1993 listing dummy 0,157 *** 0,160 *** 0,235 ***

1994-2003 listing dummy 0,245 *** 0,248 *** 0,286 ***

2004-2010 listing dummy 0,284 *** 0,289 *** 0,450 ***

Size -0,331 *** -0,336 *** -0,321 *** -0,333 ***

Market-to-book 0,141 *** 0,010 * 0,011 * 0,019 ***

Asset growth -0,253 *** -0,259 *** -0,244 ***

Payout Dummy -0,124 *** -0,126 *** -0,003

Payout 3,825 *** 3,842 *** 3,485 ***

Equity issue 0,565 ** 0,565 *** 0,668 ***

Tangibility -3,007 *** -1,289 *** -1,294 *** -1,277 ***

Asset Risk 0,759 *** 0,762 *** 0,696 ***

R&D 0,244 * 0,229 * -0,012

Abnormal earnings -0,131 *** -0,131 *** -0,101 **

Profitability 0,729 *** 0,743 *** 0,734 *** 0,636 ***

Taxes 4,264 *** 4,256 *** 4,575 ***

Non-debt tax shield -0,759 ** -0,739 ** -0,531 *

Cash holdings 4,247 *** 4,245 *** 4,199 ***

Capital expenditures -0,795 *** -0,795 ** -0,816 ***

Retained earnings 0,055 *** 0,054 *** 0,067 ***

Debt capacity -0,065 * -0,111 ***

Deposits -0,129 *** -0,205 ***

GDP per capita growth -5,913 *** -7,731 ***

Inflation rate -1,207 -4,764 ***

Tax system -0,414 *** -0,240 ***

Bankruptcy code 0,097 *** 0,249 ***

Financial system 0,389 *** 0,410 ***

Intercept -0,367 *** -0,967 *** -1,016 *** -1,500 *** -1,549 ***

Number of observations

Pseudo-R2

[1]

77.460

0,1512

[2]

77.460

0,2565

[3]

77.460

0,2595

[4]

77.460

[5]

0,2637

77.460

0,0682

50

Table VI: Constrained and Unconstrained Zero-leverage Firms This table compares the mean characteristics of constrained and unconstrained zero-leverage (ZL) firms with debt-financed firms for the full sample using a two-sample t-test. A firm is classified as zero-leverage if it has no long-term and short-term debt in a given year 𝑡. Following Lemmon and Zender (2010), the proba-bility of a firm to have a public debt rating (debt capacity) is used to divide the sample into constrained and unconstrained firms (see Appendix 3) in Panel A. Alternatively, based on Hadlock and Pierce (2010), the SA-index is used to divide the sample into constrained and unconstrained firms in Panel B. The index is computed as (−0.737 × 𝑆𝑖𝑧𝑒) + (0.043 × 𝑆𝑖𝑧𝑒2) − (0.040 × 𝐴𝑔𝑒), where size is the log of total assets, and age is the number of years the firm is contained in Compustat Global with a non-missing stock price. Firms are sorted into quintiles based on the average quintile of a given firm over time. For the debt capacity measure, firms in the upper two quintiles are considered as unconstrained, and firms in the lower two quintiles as constrained; the reverse holds for the SA-index. All country- and firm-specific characteristics are defined in Table A1. ***, **, and * indicate statistical significance at the 1, 5, and 10 percent level, respectively.

Panel A: Debt Capacity

Panel B: SA-Index

Variable

Age 1,5763 *** 5,6854 *** 4,1091 ***Size 2,9716 *** 2,2874 *** -0,6842 ***Market-to-book -0,0245 -0,9517 *** -0,9272 ***Asset growth 0,0207 -0,0365 *** -0,0572 ***Payout ratio 0,0194 *** -0,0051 *** -0,0245 ***Equity issue -0,0789 *** -0,0920 *** -0,0131 ***Tangibility 0,0543 *** 0,1595 *** 0,1053 ***Asset risk -0,1026 *** -0,2395 *** -0,1369 ***R&D -0,0375 *** -0,0539 *** -0,0163 ***Abnormal earnings -0,0106 * -0,0170 *** -0,0064Profitability 0,1748 *** 0,1155 *** -0,0593 ***Taxes 0,0173 *** -0,0006 ** -0,0179 ***Non-debt tax shield -0,0010 0,0082 *** 0,0092 ***Cash holdings -0,0814 *** -0,2646 *** -0,1832 ***Capital expenditures 0,0079 *** 0,0151 *** 0,0071 ***Retained earnings 1,3637 *** 1,0825 *** -0,2812 ***Zero-leverage duration -0,0777 *** -0,3667 *** -0,2889 ***

∆ Property, plant, and equipment

0,0188 *** 0,0144 *** -0,0043 *

Number of observations

ZL unconstrained− ZL constrained

Debt-financed−ZL constrained

Debt-financed−ZL unconstrained

ZL unconstrained: 3,014ZL constrained: 10,902

Debt-financed: 131,627ZL constrained: 10,902

Debt-financed: 131,627ZL unconstrained: 9,648

Variable

Age 9,1746 *** 7,4651 *** -1,7095 ***Size 2,8875 *** 2,2170 *** -0,6705 ***Market-to-book -0,4498 *** -1,0574 *** -0,6075 ***Asset growth 0,0909 *** -0,0768 *** 0,0140 *Payout ratio 0,0296 *** -0,0025 *** -0,0322 ***Equity issue -0,1411 *** -0,1155 *** 0,0256 ***Tangibility 0,0596 *** 0,1523 *** 0,0927 ***Asset risk -0,1934 *** -0,2642 *** -0,0708 ***R&D -0,0463 *** -0,0542 *** -0,0080 ***Abnormal earnings -0,0048 -0,0144 *** -0,0097 *Profitability 0,2029 *** 0,1279 *** -0,0750 ***Taxes 0,0210 *** 0,0011 *** -0,0199 ***Non-debt tax shield -0,0009 0,0089 *** 0,0098 ***Cash holdings -0,1246 *** -0,2748 *** -0,1502 ***Capital expenditures 0,0014 0,0102 *** 0,0116 ***Retained earnings 1,7302 *** 1,1452 *** -0,5850 ***Zero-leverage duration -0,1493 *** -0,3904 *** -0,2411 ***

∆ Property, plant, and equipment

0,0160 *** 0,0001 *** 0,0161 *

Number of observations

ZL unconstrained− ZL constrained

Debt-financed−ZL constrained

Debt-financed−ZL unconstrained

ZL unconstrained: 2,665ZL constrained: 12,438

Debt-financed: 133,055ZL constrained: 12,438

Debt-financed: 133,055ZL unconstrained: 2,665

51

Table VII: Jump-up Decisions This table shows the means and medians of the variables that are related to financial flexibility surrounding the abandonment of a zero-leverage policy for a sample of industrial firms with at least 15 years of data history in the Compustat Global database. 𝑡 = 0 and 𝑡 = 1 denote the years of and immediately after the jump to a debt policy, respectively. All other event years from 𝑡 = −4 to 𝑡 = +3 are defined accordingly. Each panel refers to a different duration of the zero-leverage policy prior to the switch, e.g., the firms in Panel A maintained a zero-leverage policy for exactly one year and those in Panel E for at least five years immediately before they switch to a debt policy. A firm is classified as zero-leverage if it has no long-term and short-term debt in a given sam-ple year 𝑡. All variables are defined in Table A1. ***, **, and * indicate a significant difference between the mean value of a variable in the year after a jump (𝑡 = +1) and the year of the jump (𝑡 = 0) at the 1, 5, and 10 percent level, respectively.

Panel A: One Year Zero-leverage before the Jump

Panel B: Two Years Zero-leverage before the Jump

-4 -3 -2 -1 0 2 3Mean 0,126 0,113 0,106 0,069 0,000 0,110 *** 0,123 0,123Median 0,069 0,048 0,033 0,023 0,000 0,057 0,066 0,066N 275 294 324 335 392 392 373 373Mean 0,038 0,016 0,010 0,019 0,028 0,103 *** 0,048 0,048Median 0,004 0,003 0,004 0,001 0,004 0,029 0,016 0,016N 262 283 303 327 353 392 373 373Mean 0,057 0,057 0,060 0,056 0,062 0,073 *** 0,066 0,066Median 0,036 0,037 0,038 0,034 0,035 0,039 0,040 0,040N 248 268 305 322 365 374 358 358Mean 0,191 0,190 0,201 0,213 0,257 0,210 *** 0,207 0,207Median 0,145 0,140 0,153 0,150 0,194 0,153 0,160 0,160N 275 294 324 335 392 392 373 373

Event year relative to zero-leverage in year 0:

Book leverage

Capital expenditures

Cash holdings

1

∆ Property, plant, and equipment

-4 -3 -2 -1 0 2 3Mean 0,136 0,123 0,098 0,000 0,000 0,104 *** 0,121 0,121Median 0,066 0,042 0,025 0,000 0,000 0,043 0,054 0,054N 155 162 170 204 204 204 194 194Mean 0,011 0,013 0,010 0,014 0,040 0,082 ** 0,036 0,036Median 0,004 0,002 0,001 0,002 0,008 0,023 0,007 0,007N 150 156 165 179 204 204 194 194Mean 0,051 0,056 0,054 0,051 0,053 0,064 ** 0,052 0,052Median 0,042 0,035 0,033 0,035 0,037 0,040 0,033 0,033N 142 153 159 192 193 192 186 186Mean 0,223 0,220 0,237 0,292 0,272 0,220 *** 0,228 0,228Median 0,180 0,189 0,205 0,236 0,211 0,147 0,166 0,166N 155 162 170 204 204 204 194 194

Book leverage

Capital expenditures

Cash holdings

1Event year relative to zero-leverage in year 0:

∆ Property, plant, and equipment

52

Panel C: Three Years Zero-leverage before the Jump

Panel D: Four Years Zero-leverage before the Jump

Panel E: At least Five Years Zero-leverage before the Jump

-4 -3 -2 -1 0 2 3Mean 0,123 0,087 0,000 0,000 0,000 0,083 *** 0,119 0,119Median 0,056 0,027 0,000 0,000 0,000 0,034 0,050 0,050N 127 133 149 149 149 149 142 142Mean 0,021 0,020 0,010 0,023 0,020 0,079 *** 0,035 0,035Median 0,002 -0,002 0,002 0,005 0,010 0,026 0,011 0,011N 119 129 136 149 149 149 142 142Mean 0,055 0,053 0,049 0,053 0,052 0,061 ** 0,052 0,052Median 0,035 0,034 0,034 0,036 0,034 0,040 0,043 0,043N 122 128 144 143 143 146 140 140Mean 0,250 0,270 0,320 0,338 0,305 0,221 *** 0,223 0,223Median 0,175 0,223 0,254 0,270 0,255 0,158 0,181 0,181N 127 133 149 149 149 149 142 142

Capital expenditures

1

Cash holdings

Book leverage

Event year relative to zero-leverage in year 0:

∆ Property, plant, and equipment

-4 -3 -2 -1 0 2 3Mean 0,116 0,000 0,000 0,000 0,000 0,078 *** 0,100 0,100Median 0,034 0,000 0,000 0,000 0,000 0,027 0,050 0,050N 71 87 87 87 87 87 82 82Mean 0,022 0,028 0,038 0,035 0,031 0,083 *** 0,025 0,025Median -0,001 0,007 0,006 0,012 0,016 0,030 0,012 0,012N 64 74 87 87 87 87 82 82Mean 0,064 0,065 0,066 0,054 0,051 0,054 0,050 0,050Median 0,043 0,040 0,039 0,037 0,034 0,032 0,027 0,027N 66 77 81 82 81 81 78 78Mean 0,274 0,336 0,345 0,327 0,297 0,231 *** 0,263 0,263Median 0,214 0,286 0,283 0,276 0,217 0,186 0,218 0,218N 71 87 87 87 87 87 82 82

1Event year relative to zero-leverage in year 0:

Book leverage

Capital expenditures

Cash holdings

∆ Property, plant, and equipment

-4 -3 -2 -1 0 2 3Mean 0,000 0,000 0,000 0,000 0,000 0,052 *** 0,061 0,061Median 0,000 0,000 0,000 0,000 0,000 0,022 0,035 0,035N 57 57 57 57 57 57 54 54Mean 0,040 0,036 0,022 0,031 0,039 0,073 ** 0,021 0,021Median 0,007 0,016 0,010 0,005 0,004 0,026 0,008 0,008N 48 56 56 56 56 56 54 54Mean 0,054 0,054 0,055 0,061 0,054 0,051 0,051 0,051Median 0,031 0,037 0,038 0,036 0,030 0,032 0,032 0,032N 55 55 55 56 55 55 53 53Mean 0,356 0,376 0,348 0,345 0,305 0,235 *** 0,281 0,281Median 0,317 0,359 0,295 0,297 0,235 0,175 0,240 0,240N 57 57 57 57 57 57 54 54

1Event year relative to zero-leverage in year 0:

∆ Property, plant, and equipment

Capital expenditures

Cash holdings

Book leverage

53

Table VIII: GMM Regression for Jump-ups This table shows the GMM regression results for the Q-model of investment (as specified in equation 2). The model includes zero-leverage (ZL) duration dummy variables that refer to the duration of a zero-leverage period immediately before the jump to a debt policy. A firm is classified as zero-leverage if it has no long-term and short-term debt in a given year 𝑡. The dependent variable is capital expenditures in year 𝑡. As an example, in the one-year specification the ZL-duration dummy variable is set equal to 1 in 𝑡 = 0 if a firm maintains zero-lever-age for exactly one year prior to the jump (and 0 otherwise); the other specifications are defined accordingly. Most important, (ZL-duration dummy×Cash flow) is an interaction term with cash flow that tests whether firms with a shorter zero-leverage duration are more flexible and benefit from their higher investment ability as well as lower investment-cash flow sensitivity. Panel A reports the results obtained by estimating the model using the Arellano and Bond (1991) difference GMM-estimator, Panel B the Blundell and Bond (1998) system GMM-estimator, and Panel C a model with a fractional dependent variable (DPF-estimator; Elsas and Florysiak, 2010). GMM regression coefficients are estimated using heteroskedasticity robust standard errors. All variables are defined in Table A1. ***, **, and * indicate statistical significance at the 1, 5, and 10 percent level, respectively.

Panel A: Difference GMM-estimator

Panel B: System GMM-estimator

Panel C: DPF-estimator

Capital expenditure (t−1) 0,044 *** 0,042 *** 0,042 *** 0,043 *** 0,043 ***Cash flow (t−1) 0,039 *** 0,040 *** 0,040 *** 0,040 *** 0,041 ***Tobin's Q 0,004 *** 0,004 *** 0,004 *** 0,004 *** 0,004 ***ZL-duration dummy 0,019 *** 0,023 *** 0,025 *** 0,009 *** 0,015 ***ZL-duration dummy×Cash flow -0,021 *** -0,037 *** -0,013 ** -0,002 -0,003Constant 0,042 *** 0,042 *** 0,042 *** 0,042 *** 0,042 ***Number of observations 52.482 52.482 52.482 52.482 52.482

One year ZL Two years ZL Three year ZL Four years ZL At least

five years ZL

Capital expenditure (t−1) 0,168 *** 0,167 *** 0,168 *** 0,168 *** 0,168 ***Cash flow (t−1) 0,027 *** 0,027 *** 0,027 *** 0,027 *** 0,027 ***Tobin's Q 0,003 *** 0,003 *** 0,003 *** 0,003 *** 0,003 ***ZL-duration dummy 0,010 *** 0,012 *** 0,007 ** 0,005 ** 0,004 **ZL-duration dummy×Cash flow -0,008 ** -0,027 * -0,017 -0,003 -0,003Constant 0,028 *** 0,028 *** 0,028 *** 0,028 *** 0,028 ***Number of observations 56.541 56.541 56.541 56.541 56.541

One year ZL Two years ZL Three year ZL Four years ZL At least

five years ZL

Capital expenditure (t−1) 0,530 *** 0,529 *** 0,529 *** 0,531 *** 0,530 ***Cash flow (t−1) 0,032 *** 0,033 *** 0,033 *** 0,033 *** 0,033 ***Tobin's Q 0,003 *** 0,003 *** 0,003 *** 0,003 *** 0,003 ***ZL-duration dummy 0,017 *** 0,017 *** 0,020 *** 0,007 ** 0,009 *ZL-duration dummy×Cash flow -0,001 ** -0,025 * -0,010 -0,011 -0,019Constant 0,017 *** 0,017 *** 0,017 *** 0,017 *** 0,017 ***Number of observations 56.541 56.541 56.541 56.541 56.541

One year ZL Two years ZL Three year ZL Four years ZL At least

five years ZL

54

Table A1: Definition of Variables

Variable Definition Construction

Panel A: Firm-level variables

Book leverage Ratio of long- and short-term debt to total book assets (𝑑𝑙𝑡𝑡 + 𝑑𝑙𝑐)/𝑎𝑡 Size Natural logarithm of total book assets 𝑙𝑜𝑔(𝑎𝑡) Age Difference between the actual year and the firms’ IPO date Market-to-book Market-to-book ratio (𝑎𝑡 − 𝑐𝑒𝑞 + 𝑚𝑘𝑣𝑎𝑙)/𝑎𝑡 Asset growth Assets in event year t minus assets in year 𝑡 − 1, divided by assets in 𝑡 − 1 (𝑎𝑡𝑛 − 𝑎𝑡𝑛−1)/𝑎𝑡𝑛−1 Payout ratio Ratio of the sum of cash dividends and share repurchases to book assets (𝑟𝑝 + 𝑑𝑖𝑣)/𝑎𝑡 Equity issue Ratio of total equity issues to book assets 𝑠𝑠𝑡𝑘/𝑎𝑡 Tangibility Ratio of fixed asset to book assets 𝑝𝑝𝑒𝑛𝑡/𝑎𝑡 Asset risk Unlevered annualized volatility of the logarithmic monthly stock returns 𝑆𝐷(𝑟𝑡) × (𝑚𝑘𝑣𝑎𝑙/(𝑎𝑡 − 𝑐𝑒𝑞 +𝑚𝑘𝑣𝑎𝑙)) R&D Firm’s research and development expenses 𝑥𝑟𝑑/𝑎𝑡 Abnormal earnings Ratio of difference between the income before extraordinary items for

time 𝑡 and 𝑡 − 1 over the market value of equity ∆𝑜𝑖𝑏𝑑𝑝/𝑚𝑘𝑣𝑎𝑙

Profitability Ratio of operating income before depreciation to book assets 𝑒𝑏𝑖𝑡/𝑎𝑡 Taxes Ratio of the income taxes paid to total book assets 𝑡𝑥𝑡/𝑎𝑡 Non-debt tax shield Ratio of depreciation to total assets 𝑑𝑝/𝑎𝑡 Cash holdings Ratio of cash holdings to book assets 𝑐ℎ𝑒/𝑎𝑡 Capital expenditure Ratio of capital expenditure to book assets 𝑐𝑎𝑝𝑥/𝑎𝑡 Retained earnings Ratio of retained earnings to book assets 𝑟𝑒/𝑎𝑡

55

Cash flow Ratio of cash flow to book assets 𝑐𝑓𝑙/𝑎𝑡 Tobin’s Q Ratio of market to book value of assets (𝑎𝑡 + 𝑚𝑘𝑣𝑎𝑙 − 𝑐𝑒𝑞 − 𝑡𝑥𝑑𝑐)/𝑎𝑡 ∆ Property, plant, and equipment

Ratio of difference between the net property plant &equipment for time 𝑡 and 𝑡 − 1 over book assets

∆𝑝𝑝𝑒𝑛𝑡/𝑎𝑡

Debt capacity Dummy variable for estimated debt capacity of a firm in a given year 𝑡 (based on rating probability)

see Table A3

Panel B: Country-level variables

Deposits Ratio of a country’s deposits (liquid liability) to GDP. (Source: World Bank)

GDP per capita growth Annual real GDP growth rate of each country. (Source: World Bank)

Inflation Annual change of Consumer Price Index of each country. (Source: World Bank)

Legal system A dummy that equals 1 for countries with a common law system (United States, Canada, United Kingdom), and 0 for countries with a civil law system (Germany, Japan). (Source: Fan et al., 2012)

Financial system A dummy variable that equals 1 if the country’s financial system is market-based (United States, Canada, United King-dom), and 0 if it is bank-based (Germany, Japan). (Source: Demirgüç-Kunt and Levine, 1999, 2001)

Tax system A dummy variable that equals 1 if the country has a dividend imputation tax system (Germany, Japan, United Kingdom ≤ 2000), and 0 if the country has a classical tax system (United States, Japan, United Kingdom ≥ 2001) during the sample period. (Source: Fan et al., 2012)

Bankruptcy code A dummy variable that equals 1 if the country has a high creditor protection (high CPS; United Kingdom, Germany), and 0 if the country has low creditor protection (low CPS; United States, Canada, Japan). (Source: Djankov et al., 2007)

56

Table A2: Description of Compustat Abbreviations Variable Description United States, Canada United Kingdom Japan Germany at Assets - total at at at at capx Net capital expenditure capx (f.c. 1, 3, 5, 7) capx (f.c. 7, 10, 11, 12)

capxfi (f.c. 12) capx (f.c. 10, 11) capx (f.c. 10, 11)

cfl Cash flow cfl cfl cfl cfl che Cash and equivalents che che che che div Cash dividend dv (f.c. 1, 3, 5, 7) dv (f.c.7, 10, 11)

eqdivp (f.c.12) dv (f.c.10, 11) dv (f.c.10, 11)

dlc Short-term debt dlc dlc dlc dlc dltt Long-term debt dltt dltt dltt dltt dp Depreciation expenses dp dp dp dp ebit Earnings before interest and taxes ebit ebit ebit ebit intan Intangibles intan intan intan intan lt Liabilities – total lt lt lt lt mkval Market value mkval mkval mkval mkval oibdp Op. income bf. depreciation & amortization oibdp oibdp oibdp oibdp ppent Property, plant, and equipment (net) - total ppent ppent ppent ppent pstk* Preferred stock – total pstk* pstk* pstk* pstk* re Retained earnings re re re re rp Purchase of common and preferred stocks prstkc

(f.c. 1, 3, 5, 7) prstkc (f.c.7, 11, 12)

prstkc + purtshr* (f.c. 10)

prstkc (f.c.11) prstkc + purtshr*

(f.c. 10)

prstkc (f.c.11) prstkc + purtshr*

(f.c. 10) sale Sales/Turnover sale sale sale sale seq Shareholders’ equity – Total seq seq seq seq sstk Sale of common and preferred stock sstk sstk sstk sstk txdc* Deferred taxes txdc* txdc* txdc* txdc* txt Total taxes txt txt txt txt xint Interest expense xint xint xint xint xopr Operating expense xopr xopr xopr xopr xrd* Research and development expense xrd xrd xrd xrd * Missing observations are replaced by zero. f. c. refers to the format code and identifies the format of a firm’s Flow of Funds Statement in Compustat Global.

57

Table A3: Logistic Regression predicting Debt Capacity This table reports the results from logistic regressions to predict debt capacity. The dependent variable is a dummy variable that equals 1 if a firm has a public bond rating in the RatingXpress historical file from Standard and Poors (S&P) in a given year 𝑡 (and 0 otherwise). All variables are described in Table A1. Explanatory vari-ables are lagged by one period. The model uses additional dummy variables for each two-digit SIC code (unre-ported). Our classification uses the coefficients from the full sample. The subsample results for the United States are for comparison with Faulkender and Petersen (2006). ***, **, and * indicate statistical significance at the 1, 5, and 10 percent level, respectively.

Tangibility 0,3263 *** 0,0174

Size 1,2018 *** 1,2472 ***

Market-to-book 0,0482 *** -0,0982 ***

Profitability/Sale -0,0008 -0,0011 *

R&D/Sale -0,0036 0,0047 *

Age -0,0414 *** 0,0261 ***

Volatility -0,1658 *** -0,6468 ***

Intercept -10,3019 *** -10,2089 ***

Number of observations

Pseudo R2 0,3746

United States

133.488

Full Sample

56.809

0,4345

58

Table A4: Bankruptcy Laws This table summarizes the bankruptcy procedures in the sample countries. The last line reports the Creditor Protection Scores (CPS) based on La Porta et al. (1998). This score ranges from 0 to 4, where 0 indicates very low and 4 very high creditor protection.

United States1 “Bankruptcy Code“

Canada2 “Bankruptcy Act“

United Kingdom3

“Insolvency-Act” Germany4 “Insolvenzverfahren“

Japan5 “Kaisha Seiri“

Super-priority financing Yes Yes No Yes -

Automatic stay on assets Unlimited Unlimited No 3 months Unlimited

Secured creditors first paid Yes Yes Yes Limited Yes

Restrictions for going into reorganization No No Yes Yes No

Management control in bankruptcy Management stays in control; supervision by court

Insolvency adminis-trator; appointed by court

Secured creditors Insolvency adminis-trator; appointed by court

Neutral administrator

CPS 1 1* 4 3 2**

1 The exact procedure can be found in Chapter 11 of the “United States Codes“. 2 The exact procedure can be found in the “Bankruptcy and Insolvency Act“. 3 The exact procedure can be found in the “Insolvency-Act” and in the “Enterprise-Act”. 4 The exact procedure can be found in the “Deutsche Insolvenzordnung”. 5 The exact procedure can be found in the “Kaisha kôsei hô”. * Change from 2 to 1 in 1992 caused by an amendment to the „Bankruptcy and Insolvency Act”. In this amendment the act was broadened to provide ways for insolvent

debtors to avoid bankruptcy by negotiating reorganizations. (Djankov et al. 2007). ** Change from 3 to 2 in 2000 as a result of the “Corporate Reorganization Law”. This law prohibits the enforcement of collateral rights outside the reorganization process.

(Djankov et al. 2007).

59

Table A5: Jump-up Decisions for Zero Net-Debt This table shows the means and medians of the variables that are related to financial flexibility surrounding the abandonment of a zero net-debt policy for a sample of industrial firms with at least 15 years of data history in the Compustat Global database. t = 0 and t = 1 denote the years of and immediately after the jump to a net debt policy, respectively. All other event years from t = −4 to t = +3 are defined accordingly. Each panel refers to a different duration of the zero net-debt policy prior to the switch, e.g., the firms in Panel A maintained a zero net-debt policy for exactly one year and those in Panel E for at least five years immediately before they switched to a net debt policy. A firm is classified as having zero net-debt if the difference between total debt and cash holdings is zero or below zero in a given year t. All variables are defined in Table A1. ***, **, and * indicate a significant difference between the mean value of a variable in the year after a jump (t = +1) and the year of the jump (t = 0) at the 1, 5, and 10 percent level, respectively.

Panel A: One Year Zero Net-Debt before the Jump

Panel B: Two Years Zero Net-Debt before the Jump

-4 -3 -2 -1 0 2 3Mean 0,134 0,128 0,118 0,108 0,000 0,104 *** 0,121 0,121Median 0,082 0,085 0,075 0,059 0,000 0,060 0,073 0,073N 756 805 857 902 1.157 1.157 1.097 1.097Mean 0,106 0,122 0,113 0,097 0,175 0,232 ** 0,140 0,140Median 0,057 0,057 0,058 0,059 0,079 0,128 0,072 0,072N 718 777 832 870 1.008 1.157 1.097 1.097Mean 0,062 0,062 0,064 0,062 0,063 0,077 *** 0,069 0,069Median 0,045 0,044 0,046 0,042 0,044 0,051 0,047 0,047N 635 684 756 803 971 983 941 941Mean 0,118 0,116 0,107 0,099 0,189 0,109 *** 0,116 0,116Median 0,082 0,084 0,082 0,078 0,161 0,084 0,089 0,089N 756 805 857 902 1.157 1.157 1.097 1.097

∆ Property, plant, and equipment

Event year relative to zero-leverage in year 0:1

Book leverage

Capital expenditures

Cash holdings

-4 -3 -2 -1 0 2 3Mean 0,131 0,125 0,109 0,000 0,000 0,099 *** 0,116 0,116Median 0,092 0,089 0,065 0,000 0,000 0,060 0,080 0,080N 361 384 407 551 551 551 530 530Mean 0,100 0,094 0,093 0,158 0,139 0,243 *** 0,131 0,131Median 0,046 0,055 0,047 0,088 0,088 0,131 0,071 0,071N 346 368 391 467 551 551 530 530Mean 0,062 0,064 0,062 0,057 0,070 0,078 *** 0,064 0,064Median 0,044 0,045 0,043 0,042 0,049 0,050 0,042 0,042N 307 337 357 423 439 446 430 430Mean 0,126 0,121 0,109 0,213 0,196 0,110 *** 0,121 0,121Median 0,089 0,091 0,084 0,182 0,168 0,088 0,095 0,095N 361 384 407 551 551 551 530 530

Capital expenditures

Cash holdings

∆ Property, plant, and equipment

Event year relative to zero-leverage in year 0:1

Book leverage

60

Panel C: Three Years Zero Net-Debt before the Jump

Panel D: Four Years Zero Net-Debt before the Jump

Panel E: At least Five Years Zero Net-Debt before the Jump

-4 -3 -2 -1 0 2 3Mean 0,106 0,091 0,000 0,000 0,000 0,083 *** 0,088 0,088Median 0,058 0,047 0,000 0,000 0,000 0,045 0,048 0,048N 206 215 298 298 298 298 289 289Mean 0,104 0,109 0,153 0,149 0,170 0,203 * 0,135 0,135Median 0,034 0,043 0,072 0,085 0,115 0,119 0,092 0,092N 196 208 242 297 297 297 288 288Mean 0,056 0,055 0,050 0,061 0,072 0,074 ** 0,063 0,063Median 0,045 0,041 0,039 0,044 0,052 0,052 0,043 0,043N 176 185 229 233 239 248 242 242Mean 0,110 0,099 0,211 0,217 0,195 0,117 *** 0,136 0,136Median 0,084 0,093 0,186 0,201 0,170 0,099 0,111 0,111N 206 215 298 298 298 298 289 289

∆ Property, plant, and equipment

1

Book leverage

Capital expenditures

Cash holdings

Event year relative to zero-leverage in year 0:

-4 -3 -2 -1 0 2 3Mean 0,105 0,000 0,000 0,000 0,000 0,080 *** 0,095 0,095Median 0,064 0,000 0,000 0,000 0,000 0,047 0,055 0,055N 151 213 213 213 213 213 207 207Mean 0,059 0,183 0,134 0,158 0,108 0,158 ** 0,090 0,090Median 0,040 0,086 0,062 0,100 0,082 0,106 0,043 0,043N 147 177 213 213 213 212 206 206Mean 0,063 0,056 0,062 0,058 0,066 0,072 ** 0,053 0,053Median 0,047 0,039 0,036 0,040 0,044 0,048 0,036 0,036N 126 169 176 182 185 186 183 183Mean 0,097 0,234 0,235 0,207 0,176 0,104 *** 0,111 0,111Median 0,089 0,189 0,197 0,173 0,151 0,088 0,092 0,092N 151 213 213 213 213 213 207 207

Cash holdings

∆ Property, plant, and equipment

Event year relative to zero-leverage in year 0:1

Book leverage

Capital expenditures

-4 -3 -2 -1 0 2 3Mean 0,000 0,000 0,000 0,000 0,000 0,057 *** 0,104 0,104Median 0,000 0,000 0,000 0,000 0,000 0,060 0,072 0,072N 171 171 171 171 171 171 168 168Mean 0,120 0,148 0,161 0,164 0,123 0,162 * 0,130 0,130Median 0,078 0,076 0,091 0,063 0,086 0,120 0,057 0,057N 125 171 171 171 171 171 168 168Mean 0,055 0,060 0,055 0,059 0,060 0,063 * 0,053 0,053Median 0,041 0,042 0,039 0,045 0,041 0,038 0,037 0,037N 134 137 142 151 153 156 157 157Mean 0,242 0,254 0,255 0,229 0,204 0,110 *** 0,125 0,125Median 0,187 0,214 0,210 0,196 0,164 0,090 0,103 0,103N 171 171 171 171 171 171 168 168

Book leverage

Capital expenditures

Cash holdings

∆ Property, plant, and equipment

Event year relative to zero-leverage in year 0:1