The Value of Financial Flexibility and Payout Policy
Marc Steffen Rappa , Thomas Schmidb , Daniel Ludwig Urbanc
a
Institute of Management Accounting, School of Business Administration and Economics, Philipps-Universität
Marburg, Am Plan 1, D-35032 Marburg
b
Department of Financial Management and Capital Markets, Center for Entrepreneurial and Financial Studies
(CEFS), Technische Universität München, Arcisstrasse 21, D-80333 München
c
Department of Financial Management and Capital Markets, Technische Universität München, Arcisstrasse 21,
D-80333 München
Abstract
We propose a novel empirical measure for a firm’s value of financial flexibility and examine its
impact on payout decisions. Studying listed firms from 23 countries over the 1998-2008 period,
we find convincing evidence that the value of financial flexibility is an important determinant of
payout policy. Specifically, firms with a high value of financial flexibility tend to limit or even
avoid payouts. If such firms decide to disgorge cash, they prefer share repurchases to dividends.
Overall, our results are consistent with the view that financial flexibility considerations determine
the pecking order of payouts.
JEL classification: G32, G35
Keywords: Payout policy, financial flexibility, dividends, share repurchases
Email addresses: msr@m-s-rapp.de (Marc Steffen Rapp), thomas.schmid@cefs.de (Thomas Schmid),
daniel.urban@wi.tum.de (Daniel Ludwig Urban)
Working paper
June 11, 2012
One of the central challenges in financial economics is the quest to understand the payout
behavior of firms. While it is well known that payout decisions are irrelevant in perfect capital
markets (see Miller and Modigliani (1961)), intense discussions among managers, shareholders,
researchers, and other commentators suggest that payouts do matter. Moreover, survey results suggest that (financial) managers (i) attribute substantial weight to financial flexibility considerations
when they decide on their capital structure (e.g., Graham and Harvey (2001)) and (ii) prefer repurchasing shares to paying cash dividends, because they perceive repurchases as a more flexible
means of payout (e.g., Brav, Graham, Harvey, and Michaely (2005)).
In this paper, we combine these two views. In particular, we argue that – besides market
imperfections1 – a firm’s value of financial flexibility is a major determinant of its payout behavior. Thus, we put forward a financial flexibility perspective of payout behavior. Under this view,
firms attributing a high value to financial flexibility are expected to limit or even avoid payouts.
The key rationale for this view is the observation that ceteris paribus payouts reduce internal financing opportunities and raising external capital comes along with substantial costs (e.g., Jensen
and Meckling (1976), Myers and Majluf (1984), and Ritter (1987)). Furthermore, we expect that
payout decisions follow a pecking order according to which firms with a high value of financial
flexibility are expected to prefer share repurchases to dividends when they decide to distribute
earnings to their shareholders. This reasoning is in line with previous research indicating that dividends are often considered as an ongoing commitment, while share repurchases can be omitted
or reduced more easily (e.g., Guay and Harford (2000)). Thus, if firms rely on share repurchases
instead of dividends, they are supposed to face fewer financial constraints as a result of dividend
expectations. Overall, we expect that a firm’s pecking order of payouts depends on its value of
1
Subsequent to Miller and Modigliani (1961), researchers have relaxed the rather strict assumption of perfect
capital markets and investigated whether, among others, taxes (e.g., Black (1976), Auerbach (1979), and Poterba and
Summers (1984)) or agency conflicts (e.g., Jensen (1986), La Porta, Lopez-de Silanes, Shleifer, and Vishny (2000),
and Brockman and Unlu (2009)) impact the corporate payout decision.
2
financial flexibility.
Unfortunately, the value a firm attributes to its financial flexibility is not directly observable.
To overcome this problem, we construct a novel proxy for a firm’s financial flexibility, the value of
financial flexibility. For this, we combine two well-established approaches in the literature. First,
Gamba and Triantis (2008) argue that a firm’s financial flexibility is determined by five factors:
growth opportunities, profitability, costs of holding cash, costs of external financing, and the reversibility of capital. A straightforward but rather simple approach to approximate a firm’s value
of financial flexibility would thus be to construct an index that measures the rank of a company
along these five dimensions. However, this approach suffers from arbitrary weights assigned to its
components. Moreover, they do not reflect the market’s assessment of a firm’s financial flexibility.2
Hence, we rely on the estimation approach by Faulkender and Wang (2006) – which they used in
the context of the marginal value of cash – and extend it with empirical proxies of the determinants of financial flexibility as identified by Gamba and Triantis (2008) to construct a measure for
a firm’s value of financial flexibility. To illustrate the validity of our measure, we conduct two
event studies related to (i) the presentation of the Troubled Assets Relief Program (TARP) and (ii)
its (unexpected) rejection by the House of Representatives. As expected, we find that firms with
high a (low) value of financial flexibility generate positive (negative) excess returns around the
presentation and negative (positive) announcement returns around the rejection of the program.
To test the impact of financial flexibility considerations on payout policy, we construct a global
sample covering listed firms from 23 countries over the 1998-2008 period. Our panel dataset
includes (i) accounting and capital market data from Compustat/CRSP for U.S. companies and
Worldscope/Datastream for all other countries in the sample, (ii) country- and year-specific, handcollected tax data on corporate and individual taxation, and (iii) country-specific corporate gov2
Nevertheless, we construct such an index as a robustness test. The results are in line with those obtained by our
main estimation methodology for the value of financial flexibility.
3
ernance data related to creditor or shareholder protection.3 In total, our sample covers more
than 20,000 individual firms from both developed (e.g., the U.S., the UK, Japan) and developing economies (e.g., China, India, Brazil).
Our empirical tests reveal that (i) both the likelihood and amount of dividends and share repurchases decrease with the value of financial flexibility and that (ii) firms with a high value of
financial flexibility are less likely to initiate but more likely to omit dividends. With respect to the
payout channel choice, we find that (iii) firms with a low value of financial flexibility are more
likely to rely on dividends than share repurchases, whereas firms with a high value of financial
flexibility prefer share repurchases to dividends.
In particular, we show that the likelihood of a dividend payment decreases from 89.92% to
47.07% as the value of financial flexibility rises from the 5% to the 95% percentile. Multinomial
logit analysis also suggests that the likelihood of dividends decreases by about 69% from 40.65%
to 12.79% as the value of financial flexibility increases from the lowest to the highest decile.
Moreover, the likelihood of mixed payouts (i.e., both share repurchases and dividends) declines
from 43.16% to 8.15%. Finally, the likelihood of share repurchases rises from 4.68% to 20.34%
as the value of financial flexibility increases from the lowest to the highest decile, suggesting that
firms with a high value of financial flexibility substitute dividend payments with share repurchases
in order to avoid ongoing dividend commitments. It seems that payout policy follows a pecking
order driven by financial flexibility considerations. Firms with a high value of financial flexibility
tend to avoid payouts. If these firms decide to disgorge cash, for whatever reason, they prefer share
repurchases to dividends as a more flexible means of payout.
These results are consistent with the financial flexibility perspective of payout behavior. Furthermore, they indicate that financial flexibility is a major determinant of payout decisions, even
3
By combining Compustat and Worldscope data, we are able to ensure both a high data quality and a large sample
size.
4
after controlling for tax and corporate governance-based explanations of payout policy. Our results
are also robust to another specification of our financial flexibility measure and numerous estimation
procedures.
This paper contributes to the literature along three dimensions. First, we propose a novel empirical measure for a firm’s value of financial flexibility and demonstrate the validity of this proxy.
Second, this is – to the best of our knowledge – the first large-scale empirical study on the impact
of financial flexibility on payout decisions. Third, using a global sample allows us to compare the
economic impact of the value of financial flexibility relative to taxation and corporate governance
quality. Furthermore, this sample ensures that the identified effect is of general importance and
alleviates concerns that it may be restricted to certain countries. Overall, our analysis contributes
to a better understanding of firms’ payout behavior.
The remainder of this paper is organized as follows. In Section I, we develop our hypotheses.
The dataset is described in Section II. In Section III, we summarize the empirical results. In Section
IV, we examine the robustness of these results. In Section V, we conclude by summarizing the
results and commenting on their implications for both regulators and researchers.
I. Theoretical Framework
In this section of the paper, we motivate why financial flexibility might have an impact on
payout decisions and develop testable hypotheses. Furthermore, we explain the idea behind our
measure for the value of financial flexibility as well as its construction.
A. Motivation and Hypotheses
The rationale behind financial flexibility and its possible impact on payout policy can be best
explained by considering the benefits and costs of payouts. On the one hand, the distribution of
cash among shareholders has potential benefits for the firm. In particular, distributing cash may
5
signal good earnings prospects to equity investors.4 Furthermore, undistributed cash may be used
by managers to increase their own utility, possibly at the expense of the owners. In this context,
payouts reduce agency conflicts of equity (e.g., Jensen (1986)). On the other hand, payouts come
at a cost. Payouts reduce the firm’s ability to (internally) finance its future investments and hence
increase its probability of financial distress.
However, the relative importance of costs and benefits of payouts may vary across firms. In this
paper, we argue that a firm’s financial flexibility reflects this trade-off. Financial flexibility refers
to the ability of a firm to avoid financial distress when it has to deal with negative cash flow shocks
and to fund profitable investment opportunities when they arise (Gamba and Triantis (2008), p.
2263). If, for instance, a firm has high growth opportunities, it is expected to pay considerable
attention to its financial flexibility, resulting in a high value of financial flexibility.
Under this financial flexibility perspective of payout behavior, we expect that both the probability and amount of both dividends and share repurchases are lower in firms that attribute a high
value to financial flexibility. Furthermore, we also expect a negative relation between a firm’s
value of financial flexibility and the likelihood of dividend initiations and a positive relation with
the likelihood of dividend omissions. Finally, firms with a high value of financial flexibility are
supposed to follow a pecking order when making payout decisions. In general, they are expected
to avoid payouts. However, if these firms decide to disgorge cash, for whatever reason, they should
prefer share repurchases to dividends. This is because share repurchases can be reduced or omitted
more easily than dividends, because, in contrast to dividends, they are not regarded as ongoing
commitments.
• Hypothesis H1: Firms with a high value of financial flexibility have lower payouts (proba4
See, among others, Bhattacharya (1979), Miller and Rock (1985), and John and Williams (1985) on signaling
theories of payout policy.
6
bility and amount).
• Hypothesis H2: Firms with a high value of financial flexibility are less likely to initiate and
more likely to omit a dividend.
• Hypothesis H3: Firms with a high value of financial flexibility prefer no payouts to share
repurchases and share repurchases to dividends.
B. The Empirical Measure for the Value of Financial Flexibility
Unfortunately, financial flexibility cannot be observed directly. Hence, we propose a novel
empirical measure for financial flexibility by combining two approaches that have been well established in the literature. In particular, we use the factors influencing the value of financial flexibility
as described by Gamba and Triantis (2008) and apply the methodology of Faulkender and Wang
(2006) to calculate an empirical proxy.5
Faulkender and Wang (2006) look at how an additional unit of cash in a firm will be distributed
among its investors. Based on this perspective, the authors hypothesize that the marginal value of
cash decreases both with larger cash holdings and higher leverage, since an additional $1 of cash
should be worth less to the firm if the firm already disposes of a large amount of cash and because
a higher leverage increases the probability that the additional $1 will end up in the hands of the
creditors, resulting in a lower value to the shareholders.6 The authors calculate the marginal value
of cash using a pooled ordinary least squares (OLS) approach. They regress the annual excess stock
return of a firm on changes in firm characteristics over the fiscal year, paying special attention to
changes in a firm’s cash position. Put differently, the authors estimate the market’s response to
5
In a recently published paper, Liu and Mauer (2011) also employ the approach of Faulkender and Wang (2006)
to determine the marginal value of cash. In particular, they add vega to the Faulkender and Wang (2006) model to
analyze the impact of CEO risk-taking incentives on the value of cash.
6
This reasoning goes back to the contingent claim analysis by Black and Scholes (1973).
7
changes in cash over the most recent year. The excess market return is defined as the one-year
stock return relative to the return of its benchmark portfolio. Each stock is assigned to one of the
25 Fama and French (1993) value-weighted size and book-to-market portfolios based on its size
and book-to-market ratio.
Instead of looking at the distribution of cash among investors, we extend this approach by
valuing a firm’s financial flexibility. Therefore, we include the factors determining the value of
financial flexibility described by Gamba and Triantis (2008) in the Faulkender and Wang (2006)
model. Since Gamba and Triantis (2008) do not describe empirical measures for the factors, we
identify measurable variables for each factor. The factors and their measurements are as follows:
• Growth opportunities: Gamba and Triantis (2008) argue that growth opportunities affect
the value of financial flexibility. Higher growth opportunities are expected to increase the
value of financial flexibility, since a firm with many profitable growth opportunities is expected to face unexpected cash flow shocks more often, making financial flexibility more
valuable. We approximate growth opportunities by a firm’s Tobin’s, TobQi,t , defined as the
sum of total assets and market capitalization less the book value of common equity deflated
by total assets.
• Profitability: In order to measure the profitability of a firm, we employ the ratio of a firm’s
operating cash flow, OCFi,t , to its lagged market capitalization. Firms with a higher profitability should have a lower value of financial flexibility, since they should be able to dispose
of a higher amount of cash, all other things being equal.
• Costs of holding cash: Gamba and Triantis (2008) show that the effective costs of holding
cash, T , affect the value of financial flexibility. These costs can be estimated by comparing
8
the taxation of interest at the individual level to interest taxation at the corporate level:
T=
TC
,
TI
(1)
where TC is the tax rate applicable to corporate interest income and T I is the tax rate applicable to individual interest income. If T were greater than one, for instance, this would
imply that interest income is taxed more heavily at the corporate level, resulting in higher
effective costs of holding cash at the corporate level. In other words, it is more beneficial to
shareholders if they hold cash instead of the company. Hence, higher costs of holding cash
decrease a firm’s value of financial flexibility.
• Costs of external financing: A firm with higher costs of external financing is expected to
have a higher value of financial flexibility, since it is more costly for it to raise new capital.
We measure the costs of external financing by the volatility of a firm’s total shareholder
returns, because a higher stock price volatility signals higher risk, reflecting higher debt and
equity financing costs, resulting in a higher value of financial flexibility. In this context,
PVi,t denotes the two-year stock price volatility for firm i in year t, based on the monthly
total shareholder returns in year t and t − 1.
• Reversibility of capital: The value of financial flexibility is also affected by the reversibility
of a firm’s capital. For instance, a firm able to sell its assets quickly and with a low discount
should be more flexible, all other things being equal. We approximate the reversibility of
capital by a firm’s tangibility, Tangi,t , defined as tangible assets deflated by total assets, since
tangible assets can be sold more easily compared to intangible assets. Consequently, a higher
reversibility of capital is expected to decrease the value of financial flexibility.
Instead of using benchmark portfolio returns for the calculation of the dependent variable,
9
we employ a Fama and French (1993) three factor asset pricing model to calculate cumulative
abnormal returns.7 In addition, we also include industry, country, and year dummies, denoted by
the vector Zi,t , resulting in the following equation for the value of financial flexibility:
∆Ci,t
∆Ei,t
∆NAi,t
∆RDi,t
∆Ii,t
∆Di,t
+ γ2
+ γ3
+ γ4
+ γ5
+ γ6
Mi,t−1
Mi,t−1
Mi,t−1
Mi,t−1
Mi,t−1
Mi,t−1
NFi,t
OCFi,t
Ci,t−1
+ γ8 Li,t + γ9
+ γ10 TobQi,t + γ11
+ γ12 T i,t + γ13 PVi,t
+ γ7
Mi,t−1
Mi,t−1
Mi,t−1
Ci,t−1 ∆Ci,t
∆Ci,t
∆Ci,t
+ γ14 Tangi,t + γ15
·
+ γ16 Li,t ·
+ γ17 TobQi,t ·
Mi,t−1 Mi,t−1
Mi,t−1
Mi,t−1
OCFi,t ∆Ci,t
∆Ci,t
∆Ci,t
∆Ci,t
+ γ18
·
+ γ19 T i,t ·
+ γ20 PVi,t ·
+ γ21 Tangi,t ·
Mi,t−1 Mi,t−1
Mi,t−1
Mi,t−1
Mi,t−1
ri,t − Ri,t = γ0 + γ1
+ γ22 Zi,t + i,t ,
(2)
where ri,t − Ri,t is the cumulative abnormal return of firm i in year t.8 All firm-specific factors
except leverage, Tobin’s Q, stock price volatility, and tangibility are deflated by the lagged market
capitalization of the firm, Mi,t−1 . Ci,t is cash and short-term investments. Ei,t is earnings before
interest, taxes, depreciation, and amortization (EBITDA). NAi,t is total assets minus cash. RDi,t ,
which is set to zero if missing, is research and development expense. Ii,t is interest expense. Di,t
is cash dividends. NFi,t is net cash flow from financing. ∆ denotes the one-year absolute change
of a variable. Li,t is leverage defined as total debt deflated by the sum of total debt and market
capitalization. The additional factors in the equation above, which were taken from the original
model by Faulkender and Wang (2006), are supposed to control for other determinants of abnormal
returns; namely, a firm’s financial structure (Ii,t , Di,t , Li,t , and NFi,t ), its investment policy (RDi,t and
NAi,t ), and its profitability (Ei,t ).
7
See Appendix B for an exact description of the calculation of the abnormal returns.
By regressing cumulative abnormal returns on changes in cash holdings, we implicitly assume that expected cash
holdings at the end of year t are equal to the cash holdings at year t − 1. In Section IV, we show that our results are
robust to two different approaches of adjusting for expected changes in cash.
8
10
Based on the estimated regression coefficients for
∆Ci,t
Mi,t−1
and the interaction effects, we calculate
the value of financial flexibility of firm i in year t, VOFFi,t , as follows:
VOFFi,t = γ1 + γ15
Ci,t−1
OCFi,t
+ γ16 Li,t + γ17 TobQi,t + γ18
+ γ19 Ti,t + γ20 PVi,t + γ21 Tangi,t .
Mi,t−1
Mi,t−1
(3)
In summary, we use a three-step procedure to analyze the relation between financial flexibility and
payout policy:
1. In equation (2), we regress annual cumulative abnormal returns on changes in firm characteristics. The cumulative abnormal returns were obtained using a Fama and French (1993)
three factor model.
2. Based on the regression coefficients for
∆Ci,t
Mi,t−1
and the interaction terms, we calculate the value
of financial flexibility.
3. Finally, we regress various payout variables on the value of financial flexibility in order to
analyze the influence of financial flexibility considerations on payout decisions.
Compared to previous research on financial flexibility and payout policy, our approach benefits
from a direct estimation of the value of financial flexibility. To this end, we combine the estimation
methodology of Faulkender and Wang (2006) with the factors determining the value of financial
flexibility found by Gamba and Triantis (2008). This allows us to obtain an empirical proxy for
financial flexibility, taking multiple firm characteristics into account simultaneously. In particular,
the weights of the single components of financial flexibility reflect the market’s opinion. This
approach allows us to directly estimate the relation between financial flexibility and payout policy.
11
II. Data
In order to test our hypotheses on financial flexibility, we obtain accounting and return data.
We also retrieve corporate governance and tax data to examine the relative impact of investor
protection and taxes on payout decisions.
A. Description of Sample
Our sample comprises all publicly traded, non-financial and non-utility firms resided in 23
countries over the 1998-2008 period. We obtain data for 17 European countries9 , the BRIC countries, Japan, and the United States. This firm list is either based on Compustat for U.S. firms or
Thomson Worldscope for all other countries in the sample. While most of the previous literature
employs Compustat data in U.S. samples, Worldscope is more widely used in international samples. Following more recent research (e.g., Shaver, Mitchell, and Yeung (1997), Aggarwal, Erel,
and Stulz (2009), and Favara, Schroth, and Valta (2010)), we use two different data sources in order
to improve both the size and the integrity of our global sample.
We provide a detailed overview of our sample generation process in Appendix A and Table I.
Table II provides an overview of the firm distribution across the 23 countries and over the 19982008 period. About a fourth of all firms in our final sample is resided in the U.S. Another large
fraction of firms is located in China, India, Japan, or the UK. Based on the adjusted and harmonized data, we calculate all necessary variables and ratios following our specifications in Section
I. Moreover, we winsorize all ratios at the 1 st and 99th percentiles in order to reduce the effect of
outliers. Finally, we balance all variables employed as interaction terms at their means in order to
decrease the effects of variance inflation.
9
In particular, the European sample includes firms from Austria, Belgium, Denmark, Finland, France, Germany,
Greece, Ireland, Italy, Luxembourg, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, and the United
Kingdom.
12
– Table I about here –
– Table II about here –
In addition, we also generate annual cumulative abnormal returns, the dependent variable in
our financial flexibility regression (equation (2)). In order to do so, we use a Fama and French
(1993) three factor model based on monthly total shareholder returns obtained from the Center for
Research in Security Prices (CRSP) for U.S. firms or Thomson Datastream. A detailed overview
of how we arrive at the estimation of these abnormal returns can be found in Appendix B.
In Table III, we provide summary statistics for the 1998-2008 period. Panel A refers to our
financial flexibility regressions, while Panel B refers to our payout regressions. The first five
variables in Panel B in Table III are payout variables we use as dependent variables in our payout
regressions to estimate the effects of corporate governance, taxation, and financial flexibility on
payout decisions. These variables are defined as follows:
• PayerDi,t is a dummy variable that is set to one if firm i pays cash dividends in year t and
zero otherwise.
• DIi,t is the ratio of cash dividends to net income. It is set to zero if net income is negative
and if net income is negative and cash dividends are zero. It is set to one if dividends exceed
net income.10
• PayerRi,t is a dummy variable that is set to one if firm i repurchases shares in year t and zero
otherwise.
10
Alternatively, one could also gauge a firm’s dividend payout ratio by the ratio of dividends to sales. In our
opinion, however, the payout ratio based on net income is a more appropriate measure, since net income approximates
roughly the amount of cash a firm can freely dispose of.
13
• RIi,t is the ratio of share repurchases to net income. It is set to zero if net income is negative and if net income is negative and share repurchases are zero. It is set to one if share
repurchases exceed net income.
• RTPi,t is share repurchases to total payout.
– Table III about here –
The other variables in Panel B in Table III are control variables that are known to influence
payout decisions. These control variables have been taken from Brockman and Unlu (2009). REi,t
is retained earnings deflated by total assets, TEi,t is total common equity deflated by total assets,
ROAi,t is net income divided by total assets, SGRi,t is logarithmic sales growth where sales is
denominated in millions of $US, Logsizei,t is the natural logarithm of total assets in millions of
$US, and Cashi,t is cash and short-term investments scaled by total assets.
B. Corporate Governance and Tax Variables
We also look at the relation between taxes, shareholder and creditor protection, and payout
policy in order to analyze the economic significance of financial flexibility considerations and its
importance relative to classical determinants of payout decisions.
First, differences in the taxation between share repurchases and dividends may affect payout
decisions (e.g., Poterba and Summers (1984), Chetty and Saez (2005), and Moser (2007)). When
dividends, for example, are taxed more heavily than share repurchases, firms may prefer share repurchases to cash dividends, all other things being equal. In order to measure the relative taxation
of dividends and share repurchases and its impact on payout policy, we collected data on corporate
and individual taxation for the 23 countries in our sample over the 1998-2008 period. Our primary
data source were global and European tax guides by the International Bureau of Fiscal Documen14
tation (IBFD). In the case of ambiguous or insufficient information, we also obtained tax data from
Ernst & Young Ltd. and PricewaterhouseCoopers Ltd.11
Using these data, we calculate aggregate tax rates, taking both corporate and individual dividend or capital gains taxation and imputation regimes into account. We assume that shareholders
are resided in the same country as the distributing firm and that they meet minimum holding period
requirements when calculating the tax rates. Moreover, shareholders are not supposed to qualify
as substantial shareholders.
Based on these tax rates, we calculate the country- and year-specific dividend tax penalty as
defined in Poterba and Summers (1984) in order to measure the relative taxation of capital gains
and dividends. The tax penalty indicator, δDiv , is given by:
T
− T CG
,
δDiv = Div
1 − T CG
(4)
where T Div is the aggregate tax rate taking both corporate income tax rates, individual dividend
tax rates, and, if applicable, imputation rates into account and T CG is the aggregate tax rate of
corporate income tax rates, individual capital gains tax rates into account, and, if applicable, imputation rates. Annual values for δDiv across the 23 countries in the sample are given in Table
IV.12
– Table IV about here –
In most of the countries, the dividend tax penalty is positive, indicating that, after corporate
and personal taxes, dividends are tax penalized relative to capital gains or share repurchases. Some
11
We graciously thank Ernst & Young and PricewaterhouseCoopers Ltd. for granting us access to both worldwide
corporate and individual tax guides for the 1998-2008 period.
12
Detailed tax data is available upon request.
15
countries also tried to reduce taxation differences between the two payout types. In the U.S., for
instance, δDiv decreased from 0.10 in the 1990s to almost zero in the 2000s.
In order to quantify the level of shareholder protection, we use the anti-director rights index
by Djankov, La Porta, de Silanes, and Shleifer (2008), denoted by ADi . It measures the strength
of control rights granted by country law to minority shareholders. This variable is defined as the
sum of six dummy variables that indicate whether certain requirements with regard to shareholder
protection are met.13
In line with the shareholder rights outcome hypothesis by La Porta, Lopez-de Silanes, Shleifer,
and Vishny (2000) and subsequent literature (e.g. Sawicki (2009) and Jiraporn, Kim, and Kim
(2011)), we expect a positive relation between the level of shareholder protection and earnings
distributions, since better protected shareholders should be able to force managers more easily to
pay out cash dividends or to repurchase shares.
We employ the creditor rights index proposed by Djankov, McLiesh, and Shleifer (2007), CRi,t ,
as a measure of creditor protection. Similar to the definition of the anti-director rights index, the
creditor rights index is composed of four dummy variables that indicate whether a country’s law
system meets certain requirements with regard to creditor protection. Unfortunately, we have
to drop Luxembourg from our sample, because Djankov, McLiesh, and Shleifer (2007) do not
provide data for this country. In addition, annual observations for the creditor rights index are only
available for the 1998-2003 period. As our sample period extends to the year 2008, we assume
that the creditor rights index does not change after the year 2003.14
According to the creditor rights substitute hypothesis by Brockman and Unlu (2009), there
13
Spamann (2010) provides a more recent version of the anti-director rights index. His anti-director rights index,
however, is not available for all the countries in the sample. In particular, there are missing values for China, Luxembourg, and Russia. Thus, we rely on the shareholder rights index by Djankov, La Porta, de Silanes, and Shleifer
(2008).
14
During the 1998-2003 period, the creditor rights index varies only in two of the 22 countries. In each of the cases,
the difference is only one point. Thus, the creditor rights index seems to be very constant over time.
16
should be a positive relation between creditor rights quality and share repurchases and dividends.
Debt claimants, for example, may demand additional covenants when they cannot be sure to recover their interest in a firm during bankruptcy. These covenants, which become more important
when creditor protection is low, may relate to financing and investment decisions (Nini, Smith, and
Sufi (2009)), but also to a firm’s payout policy. Thus, both managers and shareholders may agree
to payout restrictions as a substitute governance mechanism in order to improve the accessibility
to debt in countries with bad investor protection, resulting in the hypothesized relation between
creditor protection and payouts.
III. Empirical Results
In this section, we empirically test our hypotheses formulated in Section I. After calculating
the empirical measure for the value of financial flexibility in Subsection A, we provide evidence
for its empirical validity in Subsection B. The impact of the value of financial flexibility on payout
behavior is analyzed in Subsection C.
A. Estimation of the Value of Financial Flexibility
In this part of the paper, we calculate the value of financial flexibility based on equation (3).
Pooled OLS, fixed effects, and random effects regression results with yearly cumulative abnormal
returns as the dependent variable for the 1998-2008 period can be found in Table V. In each model,
we use a set of country, industry, and year dummies and firm-level clustered standard errors. We
form 12 industry portfolios following the definition by Kenneth R. French by assigning each firm
to one of the 12 portfolios based on its 4-digit SIC code.15
Overall, the regression results for the value of financial flexibility in Table V are in line with
our expectations and the theoretical considerations of Gamba and Triantis (2008), because in each
15
See http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/.
17
specification, all interaction terms supposed to measure a firm’s value of financial flexibility exhibit the expected sign and are significant at the 1% level. The coefficient for ∆Ci,t is positive and
significant at the 1% level. According to the negative and significant coefficients for Ci,t−1 · ∆Ci,t
and Li,t ·∆Ci,t , firms with higher lagged cash holdings and a higher leverage have a lower value of financial flexibility. The value of financial flexibility is also higher in firms with more growth opportunities (TobQi,t · ∆Ci,t ). In addition, firms with higher profitability, approximated by OCFi,t · ∆Ci,t ,
have a lower value of financial flexibility. Furthermore, the negative and significant coefficient for
T i,t · ∆Ci,t indicates that the value of financial flexibility decreases as the opportunity costs of holding cash in the firm increase. The value of financial flexibility is higher for firms with high external
financial costs, as indicated by the positive coefficient for the stock price volatility, PVi,t · ∆Ci,t .
Finally, firms with a better reversibility of capital, measured by Tangi,t , have a lower value of financial flexibility, because a firm should, in general, be able to sell tangible assets more easily than
intangible assets in the case of unexpected cash needs.
– Table V about here –
Based on the regression coefficients reported in Table V, we are now able to calculate the
empirical measure for the value of financial flexibility, VOFFi,t , as defined in equation (3). In the
following, we only use estimates for the value of financial flexibility based on the OLS regressions
in model I of Table V. The (unreported) results based on fixed and random effects estimation are
similar.
B. Do we Really Measure Financial Flexibility?
Next, we investigate the empirical validity of our measure for the value of financial flexibility.
In order to do so, we perform two event studies related to the announcement (September 19, 2008)
and the temporary and unexpected rejection (September 29, 2008) of the Troubled Asset Relief
18
Program (TARP) in the U.S. TARP is a program designed by the U.S. government meant to attenuate the effects of the global financial crisis, especially after the collapse of Lehman Brothers on
September 15, 2008. It was first announced on September 16, 2008, and surprisingly rejected by
the House of Representatives by a vote of 228-205 on September 29, 2008. On the same day, the
Dow Jones Industrial Average closed 778 points or almost 7% lower – its biggest point loss ever
and the biggest percentage loss since September 17, 2001, the first trading day after 9/11.
For September 19, 2008, we expect positive abnormal returns for firms with a high value of
financial flexibility, because the announcement of TARP suggested that banks under trouble would
be bailed out, resulting in a stronger financial sector and therefore improved borrowing opportunities. This is of special importance for firms that have a high value of financial flexibility. On the
other hand, abnormal returns should be negative for firms with a low value of financial flexibility,
since the acceptance of TARP would improve the situation of financially restricted competitors.
Following the same reasoning, our expectations for the rejection of TARP on September 29, 2008,
are vice versa.
Since TARP is a program mainly related to the U.S. economy, we restrict our event studies to
all U.S. firms in the sample. The estimation period for both events is based on 250 trading days
ending 10 tradings before the collapse of Lehman Brothers (September 15, 2008). We set the event
period to the three trading days centered around the respective event days in order to mitigate the
effects of confounding events (e.g., the bankruptcy of Lehman Brothers).
The results for both event studies can be found in Table VI. Panel A shows mean three-day
cumulative abnormal returns for the lowest and highest annual deciles of the value of financial
flexibility based on a market model, while Panel B refers to a Fama and French (1993) three
factor model. In line with our expectations, we find positive cumulative abnormal returns for firms
with a high value of financial flexibility on September 19, 2008, and negative cumulative abnormal
19
returns for firms with a low value of financial flexibility. This holds true in both specifications. The
mean difference between the three-day cumulative abnormal returns for those two deciles in the
Fama and French (1993) specification is 4.33% and it is statistically different from zero at the 1%
level. Around September 29, 2008, firms with a low value of financial flexibility yielded a slightly
positive cumulative abnormal return, while the mean cumulative abnormal return of firms with a
high value of financial flexibility is negative. Again, the differences are statistically significant at
least at the 5% level. Overall, these findings suggest that our measure indeed captures the effects
of financial flexibility.
– Table VI about here –
C. Impact of Financial Flexibility on Payout Policy
Having shown that our financial flexibility measure approximates a firm’s value of financial
flexibility, we examine its impact on payout decisions. In Table VII, we present means of several
variables across annual deciles for the value of financial flexibility during the 1998 to 2008 period.
This allows us to gain first insights into the relation between financial flexibility and payout decisions. Interestingly, both the likelihood and the amounts of dividends decrease with a higher value
of financial flexibility, which is in line with the financial flexibility perspective of payout policy
(Hypothesis H1). For instance, the mean likelihood of a dividend payment is 0.8413 in the lowest
financial flexibility decile, while it amounts to only 0.2557 in the highest decile. Similarly, DIi,t
decreases from 0.4690 in the first decile to 0.0999 in the highest decile, suggesting that firms with
a low value of financial flexibility pay out almost 50% of their net income via dividends, while
firms with a high value of financial flexibility disgorge only 10% of their net income by means
of dividends. In addition, the value of financial flexibility appears to be negatively related to the
likelihood and the amounts of share repurchases, which is also in line with Hypothesis H1. This relation, however, is not clearly monotonic. The ratio of share repurchases to total payouts increases
20
as the value of financial flexibility ranges from its lowest to its highest decile, indicating that firms
prefer share repurchases to dividends when they attribute more value to their financial flexibility.
This is consistent with Hypothesis H3 as share repurchases are assumed to be more flexible as
dividends. These preliminary findings already suggest that financial flexibility has a major impact
on payout decisions. Using these analyses as a starting point, we now systematically investigate
the relation between a firm’s value of financial flexibility and its payout policy.
– Table VII about here –
C.1. Dividends
We start with examining the relation between the value of financial flexibility and dividend
payouts. Table VIII shows pooled logit and tobit regression results with the dependent variable
being either the dividend dummy, PayerDi,t (Panel A), or the ratio of dividends to net income, DIi,t
(Panel B), for the 1998-2008 period with firm-level clustered standard errors in parentheses. In
model I in Panel A, we employ VOFFi,t as the sole explanatory variable. Its coefficient amounts to
-3.265 and it is significantly different from zero at the 1% level. Thus, firms with a higher value
financial flexibility are less likely to pay dividends, which is in line with the financial flexibility
perspective of payout policy (Hypothesis H1). In models II and III, we add our corporate governance measures, control variables, and industry and year dummies, resulting in our full regression
model (model III in Table VIII):
PayerDi,t = β0 + β1 REi,t + β2 TEi,t + β3 ROAi,t + β4 SGRi,t + β5 Logsizei,t + β6 Cashi,t
+ β7 ADi + β8 CRi,t + β9 δDivi,t + β10 VOFFi,t + β11 Yi,t + i,t .
(5)
where ADi is the anti-director rights index of Djankov, La Porta, de Silanes, and Shleifer (2008),
CRi,t is the creditor rights index of Djankov, McLiesh, and Shleifer (2007), and δDivi,t is the
21
country- and year-specific dividend tax penalty indicator.16 Yi,t is a vector of industry and year
dummies.
– Table VIII about here –
In the full model, the coefficient for VOFFi,t decreases to -2.614 but remains significant at
the 1% level. In models II and III, the coefficients for CRi,t and ADi are positive and significant
at the 1%, which is in line with the creditor rights substitute hypothesis by Brockman and Unlu
(2009) and the outcome hypothesis by La Porta, Lopez-de Silanes, Shleifer, and Vishny (2000).
The coefficient for δDivi,t is negative and significant at the 1% level, indicating that firms are less
likely to pay dividends when corporate and individual taxes penalize dividends relative to share
repurchases.
The coefficients for REi,t , TEi,t , ROAi,t , and Logsizei,t are positive, suggesting that larger firms
with high retained earnings, equity ratios and high profitability are more likely to pay dividends.
Moreover, the negative coefficients for SGRi,t and Cashi,t indicate that firms with strong growth
opportunities and high cash holdings have lower payouts. The negative coefficient for the cash
variable is in line with DeAngelo, DeAngelo, and Stulz (2006) who argue that firms maintain high
cash holdings in order to fund profitable investment opportunities, resulting in a negative coefficient
for Cashi,t .
Next, we analyze the economic significance of shareholder and creditor protection, taxation,
and the value of financial flexibility. In order to do so, we follow the same methodology as in
Brockman and Unlu (2009). The results can be found in Figure 1. The four graphs show predicted
probabilities of cash dividends for increasing percentiles of VOFFi,t and δDivi,t as well as varying
16
Note that by using this tax incentive indicator, we implicitly assume that a firm is owned only by individual
shareholders who do not qualify as substantial shareholders, but who are supposed to meet minimum holding period
requirements.
22
levels of ADi and CRi,t . The predicted probabilites are based on the coefficients obtained from the
logit regression in model III of Table VIII.17
– Figure 1 about here –
As indicated by the upper graph in Figure 1, the probability of paying dividends drops from
89.92% to 47.09% as the value of financial flexibility increases from the 5% to the 95% percentile,
suggesting that a firm with a relatively low value of financial flexibility is almost twice as likely
to pay a dividend than a firm with a high value of financial flexibility. In the second graph, we
look at the probability of dividends as a function of the dividend tax penalty. The observed slope
is substantially flatter, indicating that the overall impact of taxes on dividend decisions is weaker
compared to financial flexibility considerations. Figure 1 also shows that the likelihood of dividends increases by 62% as the shareholder rights index ranges from its lowest to its highest value.
The variation in the likelihood of dividend payouts arising from a variation in the creditor rights
index is comparably low, since the probability of dividends rises only by 20% as the creditor rights
index increases from zero to four. Overall, the evidence in Figure 1 suggests that both financial
flexibility and shareholder protection have a strong influence on dividend decisions while tax issues
and creditor rights are only of minor importance.
In Panel B of Table VIII, we use DIi,t as the dependent variable. Again, the coefficient for
VOFFi,t is negative and highly significant. Summing up, Table VIII and Figure 1 provide strong
evidence for the financial flexibility perspective of payout policy (hypothesis H1).
C.2. Share Repurchases
We also analyze the relation between financial flexibility and both the likelihood and the
amount of share repurchases. Table IX provides logit and tobit regression results for the value of
All explanatory variables except VOFFi,t , δDivi,t , ADi , and CRi,t are evaluated at the sample median. The year is
set to 2008 and the industry to the manufacturing industry.
17
23
financial flexibility, VOFFi,t , with the dependent variable being either the share repurchase dummy,
PayerRi,t (Panel A), or the ratio of share repurchases to net income, RIi,t (Panel B). The evidence in
Table IX suggests that firms with a high value of financial flexibility are less likely to repurchase
shares, as indicated by the negative and highly significant coefficients for VOFFi,t , which is again
in line with the financial flexibility perspective of payout policy (Hypothesis H1). In addition,
repurchase amounts are also negatively related to the value of financial flexibility (Panel B).
– Table IX about here –
The coefficient for CRi,t is negative and significant at the 1% level, suggesting that firms are
less likely to repurchase shares in countries with better creditor protection, which is in contrast to
the positive sign for CRi,t found in the dividend regressions. One possible explaination for this is
that debt providers might not be aware of share repurchases, because outside the U.S., share repurchases have only recently gained in importance.18 Therefore, covenants do not necessarily cover
share repurchases, making it easier for firms to disgorge money by repurchasing shares in countries with weak creditor rights, because dividends are expected to be subject to creditor restrictions
in countries with bad creditor protection, as suggested by the positive coefficient for CRi,t in the
dividend regressions. In contrast, creditors resided in countries with strong protection could be
able to prevent firms from repurchasing shares without having to rely on covenants. In line with
the outcome hypothesis by La Porta, Lopez-de Silanes, Shleifer, and Vishny (2000), there is a positive and significant relationship between ADi and share repurchases. The positive and significant
coefficient for δDivi,t indicates that both the likelihood and the amounts of share repurchases are
higher in countries where share repurchases carry tax advantages relative to dividends.
18
In 1998, for example, European and BRIC firms only repurchased shares for $18 and $0.4 billion. These numbers
increased to $104 and $5 billion in 2008, respectively.
24
In Figure 2, we again evaluate the economic significance of these results. Similar to Figure 1,
we plot share repurchase probabilities against increasing percentiles of VOFFi,t and δDivi,t as well
as varying levels of ADi and CRi,t . Going from the 5th to the 95th VOFFi,t percentile results in a
decline in the likelihood of share repurchases by about 17%. Thus, in contrast to the steep slope
in the upper graph in Figure 1, the relation between financial flexibility and the likelihood of share
repurchases is rather flat. Compared to the high variation in the likelihood of share repurchases as
a result of differences in shareholder and creditor protection, the impact of taxation on the decision
to repurchase shares is also relatively weak.
– Figure 2 about here –
C.3. Initiation and Omission of Dividends
In the following, we also look at the decision to omit or to initiate dividends. Therefore, we
regress two dummy variables, OMITi,t and INITi,t , on the tax, corporate governance, and financial
flexibility measures. These dummy variables are defined as follows:
• OMITi,t , the dividend omission dummy, is set to one if a firm stops paying a dividend in year
t and zero if the firm continues to pay a dividend.
• INITi,t , the dividend initiation dummy, is set to one if the firm pays a dividend in year t and
if cash dividends have been zero in year t − 1. It is set to zero if the firm continues not to pay
dividends in year t.
The results for the dividend omission dummy can be found in Panel A of Table X. First of all,
the coefficient for VOFFi,t is positive and significant at the 1% level in each model, indicating that
firms with a high value of financial flexibility are more likely to omit dividend payments in order
to remain financially flexible, which is in line with Hypothesis H2. In addition, the coefficient
25
for the anti-director rights variable, ADi , is negative and significant at the 1% level, providing
evidence for the shareholder rights outcome hypothesis. Finally, there is a positive relation between
creditor protection and the likelihood of dividend omissions, although the coefficient for CRi,t is
not significant in model III.
– Table X about here –
In Panel B of Table X, we analyze dividend initiations. In each model in Panel B, the coefficient for VOFFi,t is negative and significant, indicating that firms are less likely to start paying a
dividend when they have a high value of financial flexibility, as predicted by Hypothesis H2. This
finding suggests that firms with a high value of financial flexibility try to avoid dividend initiations.
Moreover, the coefficient for ADi is positive and significant at the 1% level. The positive and significant coefficient for CRi,t indicates that firms are less likely to initiate dividends when creditor
protection is low, which is in line with the substitute hypothesis of Brockman and Unlu (2009).
C.4. Is there a Pecking Order of Payout Policy?
So far, we have only looked at dividend and share repurchase decisions independently of each
other. Now, we use a multinomial logit model to analyze the dividend and share repurchase decision simultaneously. This model is estimated based on the full regression model including our
measure for financial flexibility, VOFFi,t , as well as corporate governance, tax, and firm-specific
control variables and industry and year dummies (equation (5)). The dependent variable is a categorical variable, Pi,t . Pi,t is set to zero if firm i does not pay dividends or repurchase shares in year
t. It is set to one if the firm repurchases shares, but does not pay cash dividends. It is set to two
if the firm pays cash dividends but does not repurchase shares. It is set to three if the firm both
repurchases shares and pays cash dividends.
26
Table XII shows mean predicted probabilities for the four payout categories across increasing
deciles of VOFFi,t , estimated by the pooled multinomial logit model. The results for the multinomial logit model can be found in Table XI.19 Overall, the results in Table XII suggest that payout
decisions follow a pecking order, as predicted by Hypothesis H3.
– Table XI about here –
– Table XII about here –
While firms from the lowest VOFFi,t decile do not disgorge cash with a likelihood of 11.51%,
firms with the highest value of financial flexibility are more than 400% more likely not to distribute
money to its shareholders. At the same time, the likelihood of dividends decreases by about 69%
from 40.65% to 12.79% as the value of financial flexibility increases from the lowest to the highest
decile. Moreover, the likelihood of both share repurchases and dividends declines from 43.16% to
8.15%, which corresponds to a reduction by 80%. Hence, in the highest VOFFi,t decile, a mixed
payout is even less likely than a dividend payment, while this payout type constitutes the most
likely payout decision in firms with a low value of financial flexibility. Finally, the likelihood of
share repurchases rises slightly from 4.68% to 20.34% as the value of financial flexibility increases
from the lowest to the highest decile, suggesting that firms with a high value of financial flexibility
substitute dividend payments by share repurchases in order to avoid ongoing dividend commitments. While the likelihood of share repurchases alone is overall relatively low, the probability of
share repurchases even exceeds the likelihood of dividends in the highest VOFFi,t decile.
– Figure 3 about here –
In addition to Table XII, Figure 3 illustrates the economic impact of financial flexibility on
payout decisions. It shows that both the likelihood of dividends and mixed payouts decrease with
19
As indicated in the table, the coefficients for VOFFi,t are significant at the 1% level.
27
an increasing value of financial flexibility while the probability of share repurchases and, more
importantly, the likelihood of no distributions at all increase with VOFFi,t . Thus, firms with a high
value of financial flexibility are, in general, reluctant to disgorge cash to their shareholders. However, if these firms decide to distribute money, for whatever reason, they prefer share repurchases to
dividends. This is in line with our financial flexibility perspective of payout policy, because share
repurchases are generally considered as being more flexible than dividends. To validate this result,
we perform a second empirical test in which we directly analyze the impact of financial flexibility
on the payout method for all firms that either pay dividends or repurchase shares. For this, we
employ the value of share repurchases divided by the value of total payouts as the dependent variable. The results are reported in Table XIII. As can be seen, the VOFFi,t has positive impact on
the fraction of share repurchases. Again, this indicates that firms with a high VOFFi,t prefer share
repurchases over dividends if they decide to distribute cash.
– Table XIII about here –
IV. Robustness Tests
In this section, we present several robustness tests. First, we examine whether our results hold
true if we use unexpected changes in cash instead of actual changes for the estimation of the value
of financial flexibility. Second, we replace corporate governance and tax variables with country
dummies in the payout regressions. Third, we employ anothermethodology to estimate the value
of financial flexibility. In particular, we construct an index based on the five factors by Gamba
and Triantis (2008) that determine a firm’s value of financial flexibility. Please note that we do not
report all regression results for these approaches. Instead, we only show how the results found in
Figure 3 change if these alternative estimation methodologies are applied (Figures 4, 5, and 6).20
20
Detailed results can be obtained from the authors upon request.
28
Finally, we perform several subsample regressions and take advantage of our international sample.
By doing so, we are able to show that the relation between financial flexibility holds both globally
and for various economic regions.
A. Unexpected Changes in Cash
In our base model following equation (2), we regress cumulative abnormal returns on changes
in cash holdings in order to estimate the value of financial flexibility. This implicitly assumes that
expected cash holdings at the end of year t are equal to the cash holdings at year t − 1. In order to
analyze the robustness of our results to a measure of unexpected changes in cash, we now employ
two approaches following Almeida, Campello, and Weisbach (2004). They propose a methodology
to approximate unexpected changes in cash holdings, defined as the difference between realized
and expected change in cash. According to the first approach by Almeida, Campello, and Weisbach
(2004), expected cash holdings can be estimated by the following equation:
∆CashHoldingsi,t = α0 + α1
Ei,t−1 − Di,t−1
+ α2 TobQi,t−1 + α3 Logsizei,t−1 + i,t ,
TAi,t−1
(6)
where TAi,t is the market value of total assets. Similar to Faulkender and Wang (2006), we assume
that the market has only access to information related to a firm’s last fiscal year when estimating
expected cash and we therefore use lagged explanatory variables. In line with the second approach
by Almeida, Campello, and Weisbach (2004), we also add capital expenditures, the change in net
working capital, and the change in short-term debt, all deflated by the lagged market value of total
assets, to equation (6).
Based on the coefficients obtained from an OLS estimation including firm effects we calculate
expected and unexpected changes in cash holdings and then estimate the value of financial flexibility according to our specification in equation (2). All in all, our results are robust to those two
29
approximations of unexpected changes in cash holdings, both in terms of the size of the coefficients and their significance. Firms with a high value of financial flexibility have lower dividends
and share repurchases. Moreover, they are less likely to initiate, but more likely to omit a dividend.
Finally, the pecking order of payout policy corresponding to our financial flexibility perspective
(Hypothesis H3) still holds, as suggested by Figure 4 that is based on annual deciles of the value
of financial flexibility incorporating the second approach by Almeida, Campello, and Weisbach
(2004). While most of the firms with a low value of financial flexibility pay dividends or disgorge
cash both by repurchasing shares and paying dividends, firms with high a high value of financial
flexibility disgorge no cash in more than 50% of the cases or repurchase shares in about 20% of
the cases.
– Figure 4 about here –
B. Country Dummies
Next, we also re-run all our regressions using country dummies instead of our corporate governance and tax variables. By doing so, we are able to show that our financial flexibility results are
robust to controlling for effects related to cross-country differences. Again, firms with a high value
of financial flexibility are less likely to disgorge money to their shareholders, they are less likely
to initiate dividends, and more likely to omit them. Figure 5 also indicates that the likelihood of
dividends and mixed payouts declines considerable for increasing deciles of the value of financial
flexibility, while the probabilities of share repurchases and no payouts rise, which is in line with
our financial flexibility perspective of payout policy.
– Figure 5 about here –
30
C. Financial Flexibility Index
In this subsection, we employ another empirical measure for the value of financial flexibility.
So far, we relied on the estimation methodology of Faulkender and Wang (2006), extended by the
financial flexibility determinants described by Gamba and Triantis (2008). As argued above, this
measure entails the advantage that the weights of its five components reflect the market’s view on
a firm’s financial flexibility determinants. Nevertheless, we additionally construct an alternative
index for the value of financial flexibility. Therefore, we split the five determinants of the value of
financial flexibility into annual deciles. Firms within the highest (lowest) decile for a variable (e.g.
Tobin’s Q) are assigned ten (one) point(s). Then, we sum the points and divide them by 50, the
highest possible value. This procedure results in a firm-specific financial flexibility index ranging
from 0.1 to 1, whereby a higher value indicates a higher value of financial flexibility.
21
Next, we use the financial flexibility index – instead of VOFFi,t – in our payout regressions.
Figure 6 shows the results for the multinomial logit model with the financial flexibility index as
empirical proxy for a firm’s value of financial flexibility. As can be seen, the results are comparable
to the results from Section III. Results for the dividend and share repurchase regressions yield
similar results. Consequently, we argue that our results hold true if we use this alternative approach
to estimate a firm’s financial flexibility.
– Figure 6 about here –
D. Subsample Regressions
Finally, we test the robustness of our results by examining the relation between the value of
financial flexibility and payouts for the U.S., Europe, as well as the BRIC countries and Japan. In
order to so, we estimate the value of financial flexibility separately for each of these regions using
21
For a comparable approach in the context of corporate opacity, see Anderson, Duru, and Reeb (2009).
31
OLS estimation with industry and year dummies. Then, we regress our payout variables on the
value of financial flexibility based on the region-specific estimates. Tables XIV and XV provide
results for dividends and share repurchase, respectively. Table XIV indicates that there is a negative and significant relation between the value of financial flexibility and both the likelihood and
the amount of dividends across the three economic regions. Although the regression coefficients
declined in magnitude, the effect is nevertheless of economic significance. In Europe, where the
magnitude of the coefficient for VOFFi,t is lowest, there is still a decline of 14% in the likelihood of
paying dividends as the value of financial flexibility increases from the 5% to the 95% percentile.
– Table XIV about here –
In Table XV, we look at the impact of financial flexibility on share repurchases across the three
economic regions. Again, we find a significant and negative relationship between VOFFi,t and both
the likelihood and the amount of share repurchases. The impact of financial flexibility on share
repurchase decisions seems to be larger in the U.S. as well as in the BRIC countries and Japan as
suggested by Table IX. However, for the 17 European countries in our sample, the relation between
our financial flexibility measure and share repurchases is insignificant, which may be explained by
the fact that share repurchases have only recently gained in importance in Europe as they have long
been abolished in several European countries. Furthermore, results not shown in this paper also
indicate that the pecking order also holds within the three subsamples.
– Table XV about here –
All in all, the analyses in this section provide sound evidence that our financial flexibility
estimations obtained in Section III are robust to various empirical specifications.
32
V. Conclusion
In this paper, we analyze how a firm’s financial flexibility affects its payout decisions. Unfortunately, financial flexibility cannot be directly observed. Hence, we construct a novel empirical proxy for a firm’s financial flexibility, the value of financial flexibility, by combining two
approaches well established in the literature. In particular, we combine the determinants of financial flexibility found by Gamba and Triantis (2008) with the estimation methodology for a firm’s
marginal value of cash as proposed by Faulkender and Wang (2006). An event study exploiting
the quasi-experimental opportunity of the announcement of TARP and its unexpected rejection
provides evidence for the empirical validity of our proxy.
Using a global sample covering 23 both developed (e.g., the U.S., the UK, Japan) and developing (e.g., China, India, Brazil) countries over the 1998-2008 period, we find that a firm’s value of
financial flexibility has a strong impact on its payout policy. In line with our hypotheses, both the
probabilities and the amounts of dividends and share repurchases decrease as the value of financial
flexibility increases. Furthermore, we find that firms with a high value of financial flexibility are
more likely to omit but less likely to initiate a dividend.
Multinomial logit analysis also reveals that payout policy follows a pecking order. While firms
in the lowest value of financial flexibility decile do not distribute earnings to their shareholders
with a likelihood of only 11.51%, firms with the highest value of financial flexibility are more than
five times as likely not to disgorge cash to their shareholders. As the value of financial flexibility
increases from the lowest to the highest decile, the likelihood of dividends also declines by about
69%. At the same time, the likelihood of share repurchases increases from 4.68% to 20.34%. This
suggests that firms with a high value of financial flexibility substitute dividend payments by share
repurchases if they want to disgorge cash in order to avoid ongoing dividend commitments.
Several robustness tests ensure the validity of these results. For example, we show that neither
33
the estimation methodology (by replacing our main methodology with an index approach and
by using unexpected instead of actual changes in cash) nor the specific geographic region (by
performing sub-sample regressions for the U.S., Europe, and BRIC/Japan) has a major impact on
the results.
Our findings have several implications, both for academics and policymakers. First, financial
flexibility has largely been ignored in the empirical literature thus far. One of the potential reasons
for this may be empirical difficulties, since financial flexibility is not directly observable. Our
results, however, clearly suggest that a firm’s value of financial flexibility should be taken into
account, at least with regard to payout policy. Second, the findings indicate that the value of
financial flexibility impacts the behavior of firms. Hence, capital market regulators may want to
think about ways to ensure that growth is not hindered in firms for which financial flexibility is of
high importance.
Of course, there are several avenues for future research. The strong results for payout policy suggest that the value of financial flexibility might also influence other corporate policy decisions such as the capital structure choice. Furthermore, it would be interesting to investigate how
firm-specific corporate governance quality as well as ownership structures influence the impact of
financial flexibility on payout policy.
34
Appendix A. Accounting Data
In this section, we describe in detail how we obtain our accounting variables. With respect
to our U.S. sample, we download data on all active and inactive firms denominated in $US from
Compustat North America for the 1997-2009 period (see Panel A in Table I for an overview of
the sample generation process using Compustat data). First, we exclude all observations whose
Foreign Incorporation Code or Location Code is not equal to ”USA”. We then drop firms not listed
on the NYSE, AMEX, or NASDAQ. Moreover, we eliminate financial (SIC code between 6000
and 6999) and utility (SIC code between 4900 and 4949) firms or firms with missing SIC from
the sample. We also create a calendar year variable to control for differences in reporting periods
across the firms. Therefore, we match fiscal year t + 1 of firm i with calendar year t if a firm’s
fiscal year ends in the first six months of year t + 1. Otherwise, we match fiscal year t of firm i with
calendar year t.
After having downloaded the U.S. data, we perform some additional data checks. In order
to obtain market data required for our screening procedure, we link the Compustat annual file to
the Center for Research in Security Prices (CRSP) database. In addition, we also eliminate firms
whose shares are not traded as ordinary common stock by removing observations whose CSRP
Share Code is missing or not equal to 10 or 11. After having performed these steps, our U.S.
sample consists of 6,270 firms.
Next, we filter and obtain data for the 22 other countries in our sample using the Worldscope
database (Panel B in Table I). This time, we start by generating a firm list obtained from Thomson
Financial comprising all active and inactive, publicly traded, and non-ADR firms for the 1995-2009
period. We drop firms whose primary identifier is unknown to the Worldscope database and firms
resided in tax havens by looking at the ISIN country code.22 We also exclude financial and utility
22
In particular, these tax havens comprise Antigua and Barbuda, Bermuda, Cayman Islands, Falkland Islands,
35
firms from our list. Using this firm list, we download firm-level data denominated in $US from
Thomson Worldscope for the 1995-2009 period. In addition, we drop observations with missing
fiscal year end date or observations where the company changes its fiscal year end date. Similar
to above, we calculate a calendar year variable based on a firm’s reporting date. Furthermore, we
remove observations whose country code indicates that the corresponding firm is not resided in
one of the 22 countries in our sample. Finally, we drop observations with unknown share type
or whose equity is not traded as ordinary common stock. After this step, the Worldscope sample
consists of 15,497 firms.
Next, we merge the Compustat and the Worldscope sample. In order to do so, we have to match
our variables according to their definitions in both databases. After having merged Compustat and
Worldscope data, we remove observations where the calendar year of the observation is equal or
higher than the year of the firm’s inactive date (Panel C in Table I). Moreover, we drop observations
with inconsistent data (for instance, observations with negative sales). Furthermore, we restrict our
sample period from 1998 to 2008, resulting ultimately in a sample of 20,325 firms.
We also correct for inflation by converting the data to real $US, setting the base year to 2005.
Consumer price indexes were obtained from the Organisation for Economic Co-operation and
Development (OECD).
Appendix B. Generation of Cumulative Abnormal Returns
In this section, we explain how we derive annual cumulative abnormal returns, the dependent
variable in our financial flexibility regressions (equation (2)). In order to calculate cumulative
abnormal returns, ri,t − Ri,t , we use a Fama and French (1993) three factor model. According to
this specification, we obtain risk-free interest rates, the Fama and French (1993) factors, and firm
Guernsey, Jersey, Isle of Man, and Virgin Islands.
36
return data.23
To begin with, we obtain monthly firm-level return data for the period from January 1, 1992,
to January 1, 2009. Similar to above, we use two data sources. For the U.S. firms in our sample,
we acquire CRSP return data, while we draw return data from Thomson Datastream for the other
22 countries.
First, we download monthly U.S. return and stock price data denominated in $US from CRSP.
We drop observations with missing return or observations whose lagged stock price is less than $1
in order to reduce the effects of penny stocks (Ince and Porter (2006)).
Second, we obtain monthly total shareholder returns and price data denominated in local currency24 from Datastream, using a firm’s ISIN assigned to its primary ordinary common stock as
Datastream identifier. According to Datastream, monthly total shareholder returns are defined ”as
the theoretical growth in value of a shareholding over a specified period, assuming that dividends
are re-invested to purchase additional units of an equity or unit trust at the closing price applicable
on the ex-dividend date.”25 In order to overcome well-known Datastream integrity problems, we
adjust our data, following the approach by Ince and Porter (2006). First, Datastream continues
to report a firm’s last stock price when it becomes inactive, resulting in zero returns. Since these
returns are no real returns, we drop observations at the end of a stock’s return series that are zero.
Moreover, we drop any pair of returns if one of the returns exceeds 300% and their compound
return is less than 50% in order to account for data errors. Finally, we set returns to missing if its
23
We also use the return data obtained in this subsection to calculate the two-year stock price volatility in equation
(2).
24
When we calculate the cumulative abnormal returns, we assume that a shareholder is resided in the same country as the corresponding firm and that the investor tries to maximize his return denominated in his home currency.
Therefore, we do not convert return data into $US.
25
In the CRSP database, dividends are re-invested at month-end. Thus, the definition of monthly returns is not
similar in both databases. Therefore, we also aggregate daily CRSP returns to obtain monthly CRSP returns comparable to monthly Datastream returns. We find that the correlation coefficient between yearly abnormal returns based
on monthly CRSP returns and aggregated daily CRSP returns amounts to 0.9985. Hence, we rely on monthly CRSP
returns in the following.
37
lagged unadjusted stock price, denominated in local currency, is less than unity. By doing so, we
mitigate the effects of penny stocks.
After having adjusted the firm-level return data, we obtain short-term risk-free interest rates for
different economic regions, assuming that investors only have access to local capital markets and
hence a local risk-free interest rate:
• Brazil: Interbank Certificates of Deposit, available only from October 10, 1994.
• China: One-month Interbank Offered Rate, available only from January 9, 2002.
• Europe without UK: Frankfurt Interbank Offered Rate and starting December 30, 1998, the
one-month Euro Interbank Offered Rate.26
• India: One-month Mumbai Interbank Offered Rate, available only from December 1, 1998.
• Japan: One-month Gensaki Treasury Bill Rate (cf. Griffin (2002)), available only from
March 1, 1993.
• Russia: 8-30 days Russia Interbank Offered Rate, available only from September 1, 1994.
• USA: One-month U.S. Treasury Bill Rate (source: Ibbotson Associates).
• UK: One-month London Interbank Offered Rate.
Finally, we obtain Fama and French (1993) factors to calculate cumulative abnormal returns.
Recent research (e.g. Griffin (2002)) indicates that domestic pricing models can explain much
more time-series variation in stock returns than global pricing models, resulting in lower pricing
26
At this point, we assume that Europe (without UK) is one economic region. We treat the United Kingdom
separately, since its capital market is much more developed compared to other European countries. Moreover, the
number of UK firms in our sample is large compared to the number of European firms, making it possible to calculate
region-specific Fama and French (1993) factors based on a sufficiently large sample (cf. Table II).
38
errors. Thus, we use specific Fama and French (1993) factors for the economic regions defined
above. For, the U.S., we obtain monthly Fama and French (1993) factors, comprising the excess
market return, a Small Minus Big (SMB) factor and a High Minus Low (HML) factor, directly
from Kenneth R. French’s webpage.27 For the other regions, however, no Fama and French (1993)
factors are available. Therefore, we estimate our own monthly Fama and French (1993) factors
for each of the regions mentioned above. These calculations are based on all stocks in our sample
assigned to the corresponding region. To ensure comparability with the original Fama and French
(1993) factors, we also add utility firms, since Fama and French (1993) do not exclude these firms.
Fama and French (1993) construct their SMB and HML factors using all non-financial NYSE,
AMEX, and NASDAQ stocks and portfolio breakpoints based on NYSE stocks. For example,
they use the median size of all NYSE stocks to form two portfolios consisting of larger and smaller
NYSE, AMEX, and NASDAQ stocks. As we obtain different Fama and French factors for different
economic regions, we do not use NYSE breakpoints as defined in Fama and French (1993). Instead,
we use breakpoints based on all stocks in an economic region in order to take regional differences
into account. This procedure may, however, result in deviations from the original approach by
Fama and French (1993), since the size breakpoints based on the full sample are expected to be
smaller compared to NYSE-only breakpoints, because NYSE firms tend to be larger compared to
AMEX and NASDAQ stocks. In this regard, Schmidt, von Arx, Schrimpf, Wagner, and Ziegler
(2011) find that the mean (median) of the NYSE breakpoint for size is the 0.81 (0.81) quantile
for all NYSE, AMEX, and NASDAQ stocks during the 07/1986-12/2008 period. Thus, we also
construct size portfolios based on the 80% breakpoint in order to investigate the robustness of our
Fama and French (1993) factors.28
27
See http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data library.html.
In addition, Schmidt, von Arx, Schrimpf, Wagner, and Ziegler (2011) find that the book-to-market breakpoints
based on all stocks do not differ much from NYSE-only breakpoints. Thus, we use in both cases the 30% and 70%
quantiles for the book-to-market portfolios as in Fama and French (1993).
28
39
After having obtained our own Fama and French (1993) factors, we are able to calculate annual
cumulative annual returns by performing monthly time-series regressions. For each month in the
period from 1998 to 2008, we regress monthly excess stock returns, defined as the stock return
minus the risk-free rate of return, on a constant, the excess market return, and the SMB and HML
factors using a five year observation window. We require that there have to be at least 36 monthly
return observations available during the 60 month observation window. Based on the regression
coefficients, we calculate monthly abnormal returns. Using these monthly abnormal returns, we
calculate yearly cumulative abnormal returns as the sum over 12 monthly abnormal returns for a
given firm. We find that the correlation coefficient between yearly cumulative abnormal returns
based on the two types of Fama and French factors amounts to 0.9931. In the following, we hence
employ cumulative abnormal returns based on Fama and French (1993) factors using the median
size breakpoint, since some of the six portfolios used to calculate the Fama and French (1993)
factors remain empty when we use 80% size breakpoint, which would result in a lower number of
observations.
40
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43
Table I: Sample generation process.
This table provides a brief description of the sample generation process. Data sources are Compustat for U.S. firms
(Panel A) and Worldscope for non-U.S. firms (Panel B).
Step
Description
Firms
Panel A: Compustat sample
1
2
3
4
5
All active and inactive firms with data between 1997 and 2009 have been downloaded from
Compustat North Americas
Observations whose LOC (ISO Country Code - Headquarters) or FIC (Foreign Incorporation
Code) is not equal to ”USA” have been removed from the sample.
Observations whose EXCHG (Compustat Stock Exchange Code) is not equal to 11 (NYSE),
12 (AMEX), 14 (NASDAQ), or whose EXCHG is missing have been removed from the sample.
Observations whose SICH (Historic SIC Code) is between 4900 and 4949 (utilities), 6000
and 6999 (financials), or whose SICH is missing have been removed from the sample.
Observations whose SHRCD (CSRP Share Code) is not equal to 10 or 11 (i.e. ordinary
common shares) or whose SHRCD is missing have been removed from the sample.
20,952
15,938
9,477
6,329
6,270
Panel B: Worldscope sample
1
2
3
4
5
6
7
A firm list containing all active and inactive firms resided in 17 European countries, Japan, or
the BRIC countries during the 1997-2009 period has been drawn from Thomson Financial.
Firms whose primary identifier (i.e. the Thomson Entity Key) was unknown to Thomson
Worldscope have been removed from the firm list.
Firms resided in tax havens (e.g., Virgin Islands or Guernsey) have been removed from the
firm list.
Financials and utilities amd firms with missing SIC code have been removed from the firm
list. Using this firm list, accounting data have been downloaded from Thomson Worldscope.
Observations with missing fiscal year end date or where the company changes its fiscal year
end date have been removed from the sample.
Observations where the Thomson Financial country code is not equal to AUT, BEL, BRA,
CHE, CHN, DEU, DNK, ESP, FIN, FRA, GBR, GRC, IND, IRL, ITA, JPN, LUX, NLD,
NOR, PRT, RUS, or SWE have been removed from the sample.
Observations with unknown share type or whose equity is not traded as common stock have
been removed from the sample.
23,766
23,672
23,289
17,455
16,669
16,631
15,479
Panel C: Merged sample
1
2
3
4
The Compustat and Worldscope samples have been merged.
Observations where the year of the observation is equal or higher than the year of the firm’s
inactive date have been removed from the sample.
Observations with inconsistent data (sales lower than zero, total common equity lower than
zero, cash dividends greater than sales, cash dividends negative) have been removed from the
sample.
The sample period has been set to the 1998-2008 period.
44
21,749
21,726
21,378
20,325
45
JPN
2,810
2,896
3,138
3,211
3,316
3,421
3,525
3,548
3,550
3,514
3,424
3,972
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Total
68
70
75
72
64
63
62
61
62
60
60
101
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Total
Year
AUT
Year
17
16
20
20
21
19
22
26
26
25
24
31
LUX
98
100
93
93
94
97
108
107
109
105
98
145
BEL
136
138
124
120
118
118
117
115
110
106
94
171
NLD
42
78
81
72
72
94
117
118
121
120
119
152
BRA
118
116
110
114
115
119
142
164
175
181
165
242
NOR
131
985
1,023
1,044
1,214
1,313
1,364
1,342
1,381
1,503
1,608
1,709
CHN
63
62
66
61
56
52
51
49
49
43
41
81
PRT
110
107
105
102
102
98
105
116
116
117
114
151
DNK
21
20
23
27
38
61
94
157
184
184
173
195
RUS
114
115
116
114
114
113
113
113
111
112
111
142
FIN
112
111
110
108
106
103
105
104
103
101
98
147
ESP
710
738
712
682
674
663
654
665
653
631
584
1,001
FRA
223
257
259
262
260
265
307
351
371
390
360
490
SWE
565
584
615
583
552
550
568
610
610
585
557
849
DEU
146
154
169
166
167
168
176
178
181
174
169
225
CHE
174
214
250
258
257
258
269
261
265
262
243
324
GRC
1,002
986
1,051
1,060
1,068
1,148
1,255
1,338
1,345
1,326
1,159
2,104
UK
301
352
390
394
472
618
693
1,765
1,882
1,914
1,895
2,089
IND
3,919
3,795
3,640
3,606
3,571
3,533
3,495
3,407
3,337
3,215
2,993
5,634
USA
49
51
50
51
50
54
59
68
67
63
57
87
IRL
11,097
12,132
12,410
12,413
12,700
13,127
13,613
14,884
15,027
14,945
14,350
20,325
Total
168
187
190
193
199
199
212
221
219
214
204
283
ITA
This table presents the number of firms across years and countries. A firm has to meet the data requirements given in Section II to appear in the
sample.
Table II: Sample composition.
Table III: Summary statistics for the 1998-2008 period.
This table provides summary statistics for the variables used in the financial flexibility regressions (Panel A) and
the payout regressions (Panel B) for the 1998-2008 period. ri,t − Ri,t is the cumulative abnormal return obtained
from a Fama and French (1993) model (cf. Appendix B). Ci,t is cash and short-term investments to lagged market
capitalization. Ei,t is EBITDA to lagged market capitalization. NAi,t is total assets minus cash holdings to lagged
market capitalization. RDi,t is research and development expense to lagged market capitalization. It is set to zero if
missing. Ii,t is interest expense to lagged market capitalization. Di,t is cash dividends to lagged market capitalization.
Li,t is leverage defined as total debt deflated by the sum of total debt and market capitalization. NFi,t is net cash flow
from financing to lagged market capitalization. TobQi,t is Tobin’s Q defined as the sum of total assets and market
capitalization less the book value of common equity deflated by total assets. OCFi,t is operating cash flow to lagged
market capitalization. T i,t measures the relative taxation of interest at the corporate and individual level (equation (1)).
PVi,t is the two-year volatility of monthly total shareholder returns. Tangi,t is tangibility, defined as tangible assets
deflated by total assets. PayerDi,t is a dummy variable that is set to one if firm i pays cash dividends in year t and zero
otherwise. DIi,t is the ratio of cash dividends to net income. It is set to zero if net income is negative and if net income
is negative and cash dividends are zero. It is set to one if dividends exceed net income. PayerRi,t is a dummy variable
that is set to one if firm i repurchases shares in year t and zero otherwise. RIi,t is the ratio of share repurchases to net
income. It is set to zero if net income is negative and if net income is negative and share repurchases are zero. It is set
to one if share repurchases exceed net income. RTPi,t is share repurchases to total payout. REi,t is retained earnings
deflated by total assets. TEi,t is total common equity deflated by total assets. ROAi,t is net income divided by total
assets. SGRi,t is logarithmic sales growth where sales is denominated in millions of $US (2005 = 100). Logsizei,t is
the natural logarithm of total assets in millions of $US (2005 = 100). Cashi,t is cash and short-term investments scaled
by total assets. ∆ denotes the one-year absolute change of a variable. All ratios are winsorized at the 1% level.
Variable
N
Mean
1 st Quartile
Median
3rd Quartile
SD
0.2402
0.0479
0.0530
0.2070
0.0000
0.0031
0.0023
0.3175
0.4242
0.0525
1.8705
0.1655
0.8500
0.1679
0.9980
0.4295
0.1620
0.2144
0.5558
0.0178
0.0210
0.0125
0.3240
0.2491
0.2595
1.6423
0.2233
0.1570
0.0756
0.1576
1.0000
0.4138
1.0000
0.4945
0.3487
0.4721
Panel A: Financial flexibility regressions
ri,t − Ri,t
∆Ci,t
∆Ei,t
∆NAi,t
∆RDi,t
∆Ii,t
∆Di,t
Ci,t−1
Li,t
NFi,t
TobQi,t
OCFi,t
T i,t
PVi,t
Tangi,t
87,290
118,983
115,327
118,969
119,355
113,207
109,645
119,268
129,236
111,810
129,431
111,910
146,698
110,330
139,824
0.0310
0.0105
0.0211
0.0798
0.0011
0.0004
0.0005
0.2527
0.2561
0.0136
1.7477
0.1037
0.7285
0.1361
0.9032
-0.2141
-0.0376
-0.0308
-0.0652
0.0000
-0.0019
0.0000
0.0535
0.0282
-0.0615
0.9552
0.0136
0.6500
0.0839
0.8814
0.0083
0.0017
0.0091
0.0400
0.0000
0.0000
0.0000
0.1412
0.1871
-0.0093
1.2408
0.0787
0.6700
0.1170
0.9810
Panel B: Payout regressions
PayerDi,t
DIi,t
PayerRi,t
131,599
131,525
91,872
0.5740
0.2702
0.3354
0.0000
0.0000
0.0000
1.0000
0.1169
0.0000
Continued on next page.
46
Table III: Summary statistics for the 1998-2008 period (continued).
Variable
N
Mean
1 st Quartile
Median
3rd Quartile
SD
RIi,t
RTPi,t
REi,t
TEi,t
ROAi,t
SGRi,t
Logsizei,t
Cashi,t
91,939
59,596
131,696
143,990
143,803
132,925
144,026
143,901
0.1571
0.2934
-0.3023
0.4845
-0.0040
0.1099
5.2021
0.1773
0.0000
0.0000
-0.0505
0.3155
-0.0036
-0.0440
3.9462
0.0427
0.0000
0.0017
0.2339
0.4682
0.0278
0.0833
5.1141
0.1109
0.0455
0.6517
0.5894
0.6531
0.0651
0.2195
6.3895
0.2387
0.3280
0.4022
2.5229
0.2244
0.1737
0.3371
1.9377
0.1911
47
48
AUT
0.2500
0.2500
0.2500
0.2500
0.2500
0.2500
0.2500
0.2500
0.2500
0.2500
0.2500
JPN
0.1216
0.1216
0.1216
0.1216
0.1216
0.1216
0.0000
0.0000
0.0000
0.0000
0.0000
Year
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Year
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
-0.4459
-0.4459
-0.4459
-0.3419
-0.2520
-0.2520
-0.2520
-0.2520
-0.2519
-0.2517
-0.2517
LUX
0.1676
0.1676
0.1676
0.1676
0.1643
0.1627
0.1627
0.1627
0.1500
0.1500
0.1500
BEL
0.6000
0.6000
0.6000
0.2500
0.2499
0.2499
0.2499
0.2499
0.2500
0.2499
0.2499
NLD
-0.1111
-0.1111
-0.1111
-0.1111
-0.1111
-0.2500
-0.2500
-0.1765
-0.1765
-0.1765
-0.1765
BRA
0.0000
0.0000
0.0000
0.1100
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
NOR
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.2000
CHN
0.1749
0.1749
0.2000
0.2500
0.2500
0.2500
0.2000
0.2000
0.2000
0.2000
0.2000
PRT
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
DNK
0.0000
0.0000
0.0000
0.1954
-0.0805
-0.0805
-0.0805
-0.0460
-0.0460
-0.0460
-0.0460
RUS
-0.3889
-0.3889
-0.4085
-0.4085
-0.4085
-0.4085
-0.4085
-0.1672
-0.1167
-0.1167
-0.1167
FIN
0.2488
0.0900
0.0900
0.1122
0.1122
0.0941
0.0941
0.0941
0.0941
0.0000
0.0000
ESP
0.1272
0.1360
0.1470
0.1380
0.1323
0.0674
0.0368
0.0307
0.0307
0.0301
0.0028
FRA
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
SWE
0.1464
0.2368
0.2003
0.0078
0.2557
0.2558
0.2373
0.2216
0.2216
0.2373
0.2374
DEU
0.4243
0.4244
0.4204
0.4153
0.4100
0.4037
0.4037
0.4036
0.4037
0.4037
0.4037
CHE
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
GRC
-0.2500
-0.2500
-0.2500
-0.2500
-0.2500
-0.2500
-0.2500
-0.2500
-0.2500
-0.2500
0.0854
UK
-0.1250
0.0110
0.1333
0.2290
0.0418
0.0416
0.0449
0.1402
0.1402
0.1700
0.1700
IND
0.0909
0.1158
0.1137
0.1005
0.1045
0.0335
-0.0032
0.0128
0.0139
0.0174
0.0686
USA
0.1771
0.3250
0.3250
0.2750
0.2750
0.2750
0.2750
0.2750
0.2750
0.2624
0.2624
IRL
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
ITA
This table presents the tax disadvantage of dividends relative to share repurchases across 23 countries over the 1998-2008 period. The definition
of the tax disadvantage follows Poterba and Summers (1984) and can be found in equation (4).
Table IV: Tax disadvantage of dividends relative to share repurchases across 23 countries over the 1998-2008 period.
Table V: Regression results for the value of financial flexibility.
This table presents the results of regressing annual cumulative abnormal returns, ri,t −Ri,t , on changes in firm characteristics over the 1998-2008 period. ri,t − Ri,t is the cumulative abnormal return obtained from a Fama and French (1993)
model (cf. Appendix B). Ci,t is cash and short-term investments to lagged market capitalization. Ei,t is EBITDA
to lagged market capitalization. NAi,t is total assets minus cash holdings to lagged market capitalization. RDi,t is
research and development expense to lagged market capitalization. It is set to zero if missing. Ii,t is interest expense
to lagged market capitalization. Di,t is cash dividends to lagged market capitalization. Li,t is leverage defined as total
debt deflated by the sum of total debt and market capitalization. NFi,t is net cash flow from financing to lagged market
capitalization. TobQi,t is Tobin’s Q defined as the sum of total assets and market capitalization less the book value of
common equity deflated by total assets. OCFi,t is operating cash flow to lagged market capitalization. T i,t measures
the relative taxation of interest at the corporate and individual level (equation (1)). PVi,t is the two-year volatility of
monthly total shareholder returns. Tangi,t is tangibility, defined as tangible assets deflated by total assets. ∆ denotes
the one-year absolute change of a variable. All ratios are winsorized at the 1% level. All variables used as interaction
terms are balanced at their means. Model I represents the results obtained from an OLS regression. Model II represents a fixed effects regression whereas model III represents a random effects regression. Models I and III have been
estimated using a set of industry, country, and year dummies. Standard errors are clustered at the firm-level and given
in parentheses. Statistical significance at the 1%, 5%, or 10% level is indicated by ***, **, or *, respectively.
Variable
I
II
III
Intercept
0.008
(0.010)
0.467***
(0.033)
0.246***
(0.011)
0.055***
(0.005)
0.142
(0.110)
-1.001***
(0.131)
1.952***
(0.121)
0.100***
(0.006)
-0.306***
(0.007)
0.094***
(0.011)
0.080***
(0.003)
0.364***
(0.012)
-0.01
(0.007)
1.033***
-0.021
(0.036)
0.498***
(0.032)
0.208***
(0.011)
0.018***
(0.005)
-0.013
(0.111)
-0.725***
(0.132)
1.136***
(0.120)
0.333***
(0.012)
-0.796***
(0.017)
0.141***
(0.012)
0.137***
(0.005)
0.335***
(0.014)
0.012
(0.007)
1.536***
-0.002
(0.010)
0.462***
(0.032)
0.237***
(0.011)
0.044***
(0.004)
0.076
(0.110)
-0.946***
(0.129)
1.706***
(0.119)
0.154***
(0.007)
-0.384***
(0.008)
0.112***
(0.011)
0.094***
(0.003)
0.378***
(0.012)
-0.006
(0.007)
1.163***
∆Ci,t
∆Ei,t
∆NAi,t
∆RDi,t
∆Ii,t
∆Di,t
Ci,t−1
Li,t
NFi,t
TobQi,t
OCFi,t
T i,t
PVi,t
Continued on next page.
49
Table V: Regression results for the value of financial flexibility (continued).
Variable
Tangi,t
Ci,t−1 · ∆Ci,t
Li,t · ∆Ci,t
TobQi,t · ∆Ci,t
OCFi,t · ∆Ci,t
T i,t · ∆Ci,t
PVi,t · ∆Ci,t
Tangi,t · ∆Ci,t
R2
Adjusted R2
N
I
II
III
(0.036)
0.008
(0.012)
-0.139***
(0.022)
-0.182***
(0.041)
0.114***
(0.022)
-0.119***
(0.032)
-0.145***
(0.022)
1.397***
(0.231)
-0.318***
(0.114)
(0.050)
-0.246***
(0.030)
-0.151***
(0.023)
-0.137***
(0.041)
0.080***
(0.022)
-0.103***
(0.033)
-0.143***
(0.021)
1.162***
(0.227)
-0.303***
(0.110)
(0.038)
-0.021
(0.014)
-0.138***
(0.022)
-0.167***
(0.041)
0.104***
(0.021)
-0.119***
(0.032)
-0.147***
(0.022)
1.408***
(0.230)
-0.321***
(0.112)
0.216
0.215
70,465
0.264
0.263
70,465
70,465
50
Table VI: The value of financial flexibility and the global financial crisis.
This table presents the results of two event studies related to the announcement (September 19, 2008) and the rejection
(September 29, 2008) of the Troubled Asset Relief Program (TARP) in the U.S. The event studies are based on all
U.S. firms in the sample, an estimation window based on 250 trading days ending 10 tradings before the collapse of
Lehman Brothers (September 15, 2008), and an event period of three trading days centered around the respective event
days. Panel A shows mean three-day cumulative abnormal returns for the lowest and highest deciles of the value of
financial flexibility (VOFF) based on a market model, while Panel B refers to a Fama and French (1993) three factor
model. Statistical significance at the 1%, 5%, or 10% level is indicated by ***, **, or *, respectively.
Event
VOFF decile 1
VOFF decile 10
Difference
Panel A: Market model
September 19, 2008
September 29, 2008
-0.81%
0.03%
0.63%
-1.36%
1.44%***
-1.39%***
Panel B: Fama and French (1993) three factor model
September 19, 2008
September 29, 2008
-1.63%
0.45%
2.70%
-0.37%
51
4.33%***
-0,82%**
52
PayerDi,t
PayerRi,t
DIi,t
RIi,t
RTPi,t
REi,t
Ci,t−1
Li,t
TobQi,t
OCFi,t
T i,t
PVi,t
Tangi,t
VOFF
decile
0.8413
0.4762
0.4690
0.1717
0.1530
0.2957
0.7000
0.5199
0.8801
0.2515
1.9864
0.0880
0.9824
1
0.8673
0.5394
0.4421
0.1877
0.1816
0.3386
0.3823
0.3687
0.9433
0.1536
1.8656
0.0943
0.9773
2
0.8165
0.4812
0.3903
0.1697
0.1979
0.2645
0.2940
0.3368
1.0605
0.1470
1.7157
0.1097
0.9678
3
0.7548
0.3973
0.3626
0.1639
0.2185
0.1331
0.2330
0.3251
1.1484
0.1360
1.3895
0.1139
0.9497
4
0.6948
0.3798
0.3315
0.1769
0.2700
0.1345
0.1829
0.2873
1.2360
0.1196
1.1675
0.1171
0.9263
5
0.6332
0.3894
0.2941
0.1849
0.3151
0.1069
0.1674
0.2429
1.3582
0.1056
1.0707
0.1259
0.8941
6
0.5621
0.3842
0.2476
0.1940
0.3549
-0.0427
0.1507
0.2067
1.5100
0.0879
1.0209
0.1366
0.8531
7
0.4734
0.3711
0.2033
0.1952
0.4049
-0.2838
0.1430
0.1597
1.7414
0.0665
1.0022
0.1519
0.8154
8
0.3746
0.3381
0.1532
0.1888
0.4574
-0.6851
0.1385
0.1062
2.2010
0.0420
0.9865
0.1780
0.7992
9
0.2557
0.2624
0.0999
0.1534
0.4878
-1.9573
0.1240
0.0478
4.2487
-0.0043
0.9922
0.2295
0.8201
10
This table presents means of several variables for annual VOFFi,t deciles for the period from 1998 to 2008. VOFFi,t deciles are increasing from
1 to 10. VOFFi,t is the value of financial flexibility. PayerDi,t is a dummy variable that is set to one if firm i pays cash dividends in year t and
zero otherwise. PayerRi,t is a dummy variable that is set to one if firm i repurchases shares in year t and zero otherwise. DIi,t is the ratio of cash
dividends to net income. It is set to zero if net income is negative and if net income is negative and cash dividends are zero. It is set to one if
dividends exceed net income. RIi,t is the ratio of share repurchases to net income. It is set to zero if net income is negative and if net income
is negative and share repurchases are zero. It is set to one if share repurchases exceed net income. RTPi,t is share repurchases to total payout.
REi,t is retained earnings deflated by total assets. Ci,t−1 is lagged cash and short-term investments to lagged market capitalization. Li,t is leverage
defined as total debt deflated by the sum of total debt and market capitalization. TobQi,t is Tobin’s Q defined as the sum of total assets and market
capitalization less the book value of common equity deflated by total assets. OCFi,t is operating cash flow to lagged market capitalization. T i,t
measures the relative taxation of interest at the corporate and individual level (equation (1)). PVi,t is the two-year volatility of monthly total
shareholder returns. Tangi,t is tangibility, defined as tangible assets deflated by total assets. All ratios are winsorized at the 1% level.
Table VII: Mean firm characteristics for VOFF deciles, 1998-2008.
Table VIII: Financial flexibility and dividends.
This table presents the pooled logit and tobit regression results with firm-level clustered standard errors for the period
from 1998 to 2008. The dependent variable is either PayerDi,t (Panel A) or DIi,t (Panel B). PayerDi,t is a dummy
variable that is set to one if firm i pays dividends in year t and zero otherwise. DIi,t is the ratio of cash dividends to
net income. It is set to zero if net income is negative and if net income is negative and cash dividends are zero. It is
set to one if dividends exceed net income. REi,t is retained earnings deflated by total assets. TEi,t is total common
equity deflated by total assets. ROAi,t is net income divided by total assets. SGRi,t is logarithmic sales growth where
sales is denominated in millions of $US (2005 = 100). Logsizei,t is the natural logarithm of total assets in millions
of $US (2005 = 100). Cashi,t is cash and short-term investments scaled by total assets. CRi,t and ADi are creditor
and shareholder rights indexes from Djankov, McLiesh, and Shleifer (2007) and Djankov, La Porta, de Silanes, and
Shleifer (2008). δDivi,t is a tax variable indicating the preferability of dividends over capital gains following the
definition by Poterba and Summers (1984). VOFFi,t is the value of financial flexibility. It has been estimated using
industry, country, and year dummies, and a pooled OLS model. All ratios are winsorized at the 1% level. Standard
errors are in parentheses. Statistical significance at the 1%, 5%, or 10% level is indicated by ***, **, or *, respectively.
Panel A: PayerDi,t
Panel B: DIi,t
Variable
I
II
III
IV
V
VI
Intercept
1.426***
(0.026)
-0.310***
(0.073)
0.400***
(0.005)
0.056***
(0.020)
-3.265***
(0.074)
0.437***
(0.020)
0.045**
(0.020)
-0.182*
(0.107)
-3.079***
(0.075)
-2.525***
(0.133)
0.578***
(0.054)
0.426***
(0.105)
4.520***
(0.229)
-0.222***
(0.043)
0.286***
(0.013)
-0.492***
(0.120)
0.437***
(0.021)
0.186***
(0.021)
-1.306***
(0.134)
-2.614***
(0.086)
-0.925***
(0.019)
0.068***
(0.005)
0.044***
(0.005)
-0.205***
(0.030)
-0.872***
(0.018)
-0.107***
(0.032)
0.096***
(0.008)
-0.051**
(0.024)
-0.032
(0.044)
-0.251***
(0.013)
0.040***
(0.003)
-0.215***
(0.032)
0.053***
(0.005)
0.051***
(0.005)
-0.490***
(0.033)
-0.651***
(0.020)
0.117
91,188
No
0.148
91,145
No
0.073
91,160
No
0.088
91,117
No
REi,t
TEi,t
ROAi,t
SGRi,t
Logsizei,t
Cashi,t
ADi
CRi,t
δDivi,t
VOFFi,t
Pseudo R2
N
Dummies
0.303
85,186
Industry /
Year
53
0.143
85,186
Industry /
Year
Table IX: Financial flexibility and share repurchases.
This table presents pooled logit and tobit regression results with firm-level clustered standard errors for the period from
1998 to 2008. The dependent variable is either PayerRi,t (Panel A) or RIi,t (Panel B). PayerRi,t is a dummy variable
that is set to one if firm i repurchases shares in year t and zero otherwise. RIi,t is the ratio of share repurchases to
net income. It is set to zero if net income is negative and if net income is negative and share repurchases are zero.
It is set to one if share repurchases exceed net income. REi,t is retained earnings deflated by total assets. TEi,t is
total common equity deflated by total assets. ROAi,t is net income divided by total assets. SGRi,t is logarithmic sales
growth where sales is denominated in millions of $US (2005 = 100). Logsizei,t is the natural logarithm of total assets
in millions of $US (2005 = 100). Cashi,t is cash and short-term investments scaled by total assets. CRi,t and ADi are
creditor and shareholder rights indexes from Djankov, McLiesh, and Shleifer (2007) and Djankov, La Porta, de Silanes,
and Shleifer (2008). δDivi,t is a tax variable indicating the preferability of dividends over capital gains following the
definition by Poterba and Summers (1984). VOFFi,t is the value of financial flexibility. It has been estimated using
industry, country, and year dummies, and a pooled OLS model. All ratios are winsorized at the 1% level. Standard
errors are in parentheses. Statistical significance at the 1%, 5%, or 10% level is indicated by ***, **, or *, respectively.
Panel A: PayerRi,t
Panel B: RIi,t
Variable
I
II
III
IV
V
VI
Intercept
-0.125***
(0.025)
-0.399***
(0.068)
-0.218***
(0.013)
-0.140***
(0.037)
-1.086***
(0.062)
0.299***
(0.019)
-0.491***
(0.019)
0.580***
(0.092)
-1.051***
(0.061)
-4.407***
(0.135)
0.061***
(0.013)
1.622***
(0.098)
1.509***
(0.137)
-0.546***
(0.041)
0.297***
(0.011)
0.223**
(0.105)
0.582***
(0.024)
-0.483***
(0.021)
1.778***
(0.126)
-0.494***
(0.072)
-0.340***
(0.030)
0.103***
(0.010)
-0.264***
(0.010)
0.265***
(0.048)
-0.340***
(0.029)
-1.574***
(0.060)
0.029***
(0.005)
0.509***
(0.045)
-0.131**
(0.057)
-0.411***
(0.022)
0.120***
(0.005)
0.227***
(0.051)
0.193***
(0.011)
-0.239***
(0.010)
0.619***
(0.057)
-0.065**
(0.032)
0.014
68,709
No
0.048
68,678
No
0.004
68,784
No
0.030
68,753
No
REi,t
TEi,t
ROAi,t
SGRi,t
Logsizei,t
Cashi,t
ADi
CRi,t
δDivi,t
VOFFi,t
Pseudo R2
N
Dummies
0.132
63,595
Industry /
Year
54
0.071
63,671
Industry /
Year
Table X: Financial flexibility and dividend omissions and initiations.
This table presents the pooled logit regression results with firm-level clustered standard errors for the period from
1998 to 2008. The dependent variable is either OMITi,t (Panel A) or INITi,t (Panel B). OMITi,t is set to one if the firm
stops paying a dividend in year t and zero if the firm continues to pay a dividend. INITi,t is set to one if the firm pays
a dividend in year t and if cash dividends have been zero in year t − 1. It is set to zero if the firm continues not to pay
dividends in year t. REi,t is retained earnings deflated by total assets. TEi,t is total common equity deflated by total
assets. ROAi,t is net income divided by total assets. SGRi,t is logarithmic sales growth where sales is denominated
in millions of $US (2005 = 100). Logsizei,t is the natural logarithm of total assets in millions of $US (2005 = 100).
Cashi,t is cash and short-term investments scaled by total assets. CRi,t and ADi are creditor and shareholder rights
indexes from Djankov, McLiesh, and Shleifer (2007) and Djankov, La Porta, de Silanes, and Shleifer (2008). δDivi,t
is a tax variable indicating the preferability of dividends over capital gains following the definition by Poterba and
Summers (1984). VOFFi,t is the value of financial flexibility. It has been estimated using industry, country, and year
dummies, and a pooled OLS model. All ratios are winsorized at the 1% level. Standard errors are in parentheses.
Statistical significance at the 1%, 5%, or 10% level is indicated by ***, **, or *, respectively.
Panel A: OMITi,t
Panel B: INITi,t
Variable
I
II
III
IV
V
VI
Intercept
-3.154***
(0.029)
-2.081***
(0.080)
-1.622***
(0.032)
-2.544***
(0.121)
0.909***
(0.080)
-0.326***
(0.022)
0.105***
(0.026)
0.813***
(0.084)
1.412***
(0.187)
-0.079***
(0.024)
-2.570***
(0.156)
-4.455***
(0.294)
-0.003
(0.121)
-0.343***
(0.015)
0.322
(0.218)
-0.413***
(0.025)
0.033
(0.024)
0.949***
(0.096)
-2.070***
(0.089)
0.192***
(0.039)
0.111***
(0.030)
-1.937***
(0.091)
-2.817***
(0.204)
0.290***
(0.044)
-0.735***
(0.134)
3.045***
(0.322)
0.104
(0.071)
0.034**
(0.016)
0.277**
(0.140)
0.202***
(0.042)
0.226***
(0.032)
-1.255***
(0.122)
0.007
56,503
No
0.017
56,476
No
0.041
33,639
No
0.050
33,624
No
REi,t
TEi,t
ROAi,t
SGRi,t
Logsizei,t
Cashi,t
ADi
CRi,t
VOFFi,t
Pseudo R2
N
Dummies
0.146
52,485
Industry /
Year
55
0.105
31,764
Industry /
Year
Table XI: Estimation of the impact of financial flexibility on payout policy using a multinomial logit model.
This table presents the pooled multinomial logit regression results with firm-level clustered standard errors for the
period from 1998 to 2008. The dependent variable Pi,t is a categorical variable that is set to zero if the firm does
not pay dividends or repurchase shares in year t. It is set to one if the firm repurchases shares, but does not pay
cash dividends. It is set to two if the firm pays cash dividends but does not repurchase shares. It is set to three if
the firm both repurchases shares and pays cash dividends. REi,t is retained earnings deflated by total assets. TEi,t is
total common equity deflated by total assets. ROAi,t is net income divided by total assets. SGRi,t is logarithmic sales
growth where sales is denominated in millions of $US (2005 = 100). Logsizei,t is the natural logarithm of total assets
in millions of $US (2005 = 100). Cashi,t is cash and short-term investments scaled by total assets. CRi,t and ADi are
creditor and shareholder rights indexes from Djankov, McLiesh, and Shleifer (2007) and Djankov, La Porta, de Silanes,
and Shleifer (2008). δDivi,t is a tax variable indicating the preferability of dividends over capital gains following the
definition by Poterba and Summers (1984). VOFFi,t is the value of financial flexibility. It has been estimated using
industry, country, and year dummies, and a pooled OLS model. All ratios are winsorized at the 1% level. The model in
this table has been estimated using a set of industry and year dummies. Standard errors are in parentheses. Statistical
significance at the 1%, 5%, or 10% level is indicated by ***, **, or *, respectively.
Variable
Repurchase (Pi,t = 1)
Dividend (Pi,t = 2)
Dividend & repurchase
(Pi,t = 3)
Intercept
-2.458***
(0.181)
0.031***
(0.010)
1.201***
(0.137)
1.444***
(0.140)
-0.550***
(0.055)
0.282***
(0.016)
0.543***
(0.132)
0.120***
(0.031)
-0.511***
(0.025)
1.176***
(0.156)
-0.279***
(0.086)
-2.632***
(0.187)
0.352***
(0.039)
-0.043
(0.137)
4.652***
(0.269)
-0.346***
(0.054)
0.345***
(0.017)
-0.424***
(0.162)
0.409***
(0.034)
0.191***
(0.030)
-1.218***
(0.177)
-2.030***
(0.115)
-7.176***
(0.214)
0.873***
(0.102)
1.524***
(0.154)
6.287***
(0.386)
-0.852***
(0.069)
0.593***
(0.019)
-0.514***
(0.175)
1.014***
(0.037)
-0.241***
(0.029)
0.727***
(0.174)
-2.712***
(0.132)
REi,t
TEi,t
ROAi,t
SGRi,t
Logsizei,t
Cashi,t
ADi
CRi,t
δDivi,t
VOFFi,t
Pseudo R2
N
0.0.219
62,244
56
Table XII: Financial flexibility and payout decisons.
This table presents mean predicted probabilities for certain payout types across increasing deciles of VOFFi,t estimated
from the pooled multinomial logit model (Table XI). The dependent variable Pi,t in this model is a categorical variable
that is set to zero if the firm does not pay dividends or repurchase shares in year t. It is set to one if the firm repurchases
shares, but does not pay cash dividends. It is set to two if the firm pays cash dividends but does not repurchase shares.
It is set to three if the firm both repurchases shares and pays cash dividends. VOFFi,t is the value of financial flexibility.
Decile
1
2
3
4
5
6
7
8
9
10
No payout
Repurchase
Dividend
Dividend &
repurchase
0.1151
0.1312
0.1810
0.2472
0.2695
0.2838
0.3120
0.3647
0.4384
0.5872
0.0468
0.0554
0.0737
0.1006
0.1178
0.1314
0.1442
0.1670
0.1883
0.2034
0.4065
0.3717
0.3591
0.3485
0.3426
0.3280
0.3081
0.2692
0.2230
0.1279
0.4316
0.4417
0.3862
0.3037
0.2701
0.2568
0.2358
0.1991
0.1503
0.0815
57
Table XIII: Trade-off between share repurchases and dividends.
This table presents the pooled tobit regression results with firm-level clustered standard errors for the period from
1998 to 2008. The dependent variable is RTPi,t . RTPi,t is share repurchases to total payout. REi,t is retained earnings
deflated by total assets. TEi,t is total common equity deflated by total assets. ROAi,t is net income divided by total
assets. SGRi,t is logarithmic sales growth where sales is denominated in millions of $US (2005 = 100). Logsizei,t is
the natural logarithm of total assets in millions of $US (2005 = 100). Cashi,t is cash and short-term investments scaled
by total assets. CRi,t and ADi are creditor and shareholder rights indexes from Djankov, McLiesh, and Shleifer (2007)
and Djankov, La Porta, de Silanes, and Shleifer (2008). δDivi,t is a tax variable indicating the preferability of dividends
over capital gains following the definition by Poterba and Summers (1984). VOFFi,t is the value of financial flexibility.
It has been estimated using industry, country, and year dummies, and a pooled OLS model. All ratios are winsorized
at the 1% level. Standard errors are in parentheses. Statistical significance at the 1%, 5%, or 10% level is indicated by
***, **, or *, respectively.
Variable
I
II
III
Intercept
-0.109***
(0.012)
0.763***
(0.049)
0.716***
(0.039)
-0.105***
(0.013)
-0.235***
(0.012)
0.203***
(0.057)
0.532***
(0.036)
-0.093
(0.067)
-0.054***
(0.007)
0.528***
(0.047)
-0.663***
(0.085)
-0.177***
(0.025)
0.033***
(0.005)
0.456***
(0.064)
-0.013
(0.013)
-0.219***
(0.012)
0.669***
(0.067)
0.568***
(0.038)
0.016
49,390
No
0.074
49,365
No
0.113
45,699
Industry / Year
REi,t
TEi,t
ROAi,t
SGRi,t
Logsizei,t
Cashi,t
ADi
CRi,t
δDivi,t
VOFFi,t
Pseudo R2
N
Dummies
58
Table XIV: Financial flexibility and dividends for various economic regions.
This table presents the pooled logit and tobit regression results with firm-level clustered standard errors for the period
from 1998 to 2008 for three economic regions. The dependent variable is either PayerDi,t (Panel A) or DIi,t (Panel B).
PayerDi,t is a dummy variable that is set to one if firm i pays dividends in year t and zero otherwise. DIi,t is the ratio of
cash dividends to net income. It is set to zero if net income is negative and if net income is negative and cash dividends
are zero. It is set to one if dividends exceed net income. REi,t is retained earnings deflated by total assets. TEi,t is
total common equity deflated by total assets. ROAi,t is net income divided by total assets. SGRi,t is logarithmic sales
growth where sales is denominated in millions of $US (2005 = 100). Logsizei,t is the natural logarithm of total assets
in millions of $US (2005 = 100). Cashi,t is cash and short-term investments scaled by total assets. CRi,t and ADi are
creditor and shareholder rights indexes from Djankov, McLiesh, and Shleifer (2007) and Djankov, La Porta, de Silanes,
and Shleifer (2008). δDivi,t is a tax variable indicating the preferability of dividends over capital gains following the
definition by Poterba and Summers (1984). VOFFi,t is the value of financial flexibility. It has been estimated using all
observations from the respective subsamples, industry and year dummies, and a pooled OLS estimation. All ratios are
winsorized at the 1% level. All models in this table have been estimated using a set of industry and year dummies.
Standard errors are in parentheses. Statistical significance at the 1%, 5%, or 10% level is indicated by ***, **, or *,
respectively.
Panel A: PayerDi,t
Panel B: DIi,t
Variable
USA
EU17
BRIC/JPN
USA
EU17
BRIC/JPN
Intercept
-1.474***
(0.159)
0.230***
(0.049)
-0.464**
(0.199)
2.672***
(0.371)
-0.777***
(0.091)
0.352***
(0.024)
-1.320***
(0.242)
-1.630***
(0.179)
-3.972***
(0.179)
0.586***
(0.088)
0.373**
(0.177)
6.862***
(0.401)
-0.818***
(0.067)
0.416***
(0.023)
-1.180***
(0.204)
-0.860***
(0.140)
-4.074***
(0.317)
2.322***
(0.231)
3.138***
(0.234)
-0.169
(0.616)
0.056
(0.121)
0.358***
(0.027)
-1.174***
(0.281)
-1.307***
(0.164)
-0.276***
(0.084)
0.047***
(0.009)
-0.326***
(0.070)
-0.092
(0.090)
-0.317***
(0.035)
0.090***
(0.008)
-0.449***
(0.083)
-0.550***
(0.063)
0.178***
(0.036)
0.111***
(0.014)
-0.027
(0.041)
0.505***
(0.067)
-0.348***
(0.019)
0.056***
(0.004)
-0.407***
(0.052)
-0.267***
(0.031)
0.432***
(0.048)
0.230***
(0.003)
0.278***
(0.032)
-2.277***
(0.166)
-0.223***
(0.024)
0.011***
(0.003)
-0.184***
(0.046)
-0.331***
(0.034)
0.241
27,056
0.336
26,969
0.311
31,203
0.144
27,056
0.133
26,969
0.115
31,203
REi,t
TEi,t
ROAi,t
SGRi,t
Logsizei,t
Cashi,t
VOFFi,t
Pseudo R2
N
59
Table XV: Financial flexibility and share repurchases for various economic regions.
This table presents the pooled logit and tobit regression results with firm-level clustered standard errors for the period
from 1998 to 2008 for three economic regions. The dependent variable is either PayerRi,t (Panel A) or RIi,t (Panel
B). PayerRi,t is a dummy variable that is set to one if firm i repurchases shares in year t and zero otherwise. RIi,t is
the ratio of share repurchases to net income. It is set to zero if net income is negative and if net income is negative
and share repurchases are zero. It is set to one if share repurchases exceed net income. REi,t is retained earnings
deflated by total assets. TEi,t is total common equity deflated by total assets. ROAi,t is net income divided by total
assets. SGRi,t is logarithmic sales growth where sales is denominated in millions of $US (2005 = 100). Logsizei,t is
the natural logarithm of total assets in millions of $US (2005 = 100). Cashi,t is cash and short-term investments scaled
by total assets. CRi,t and ADi are creditor and shareholder rights indexes from Djankov, McLiesh, and Shleifer (2007)
and Djankov, La Porta, de Silanes, and Shleifer (2008). δDivi,t is a tax variable indicating the preferability of dividends
over capital gains following the definition by Poterba and Summers (1984). VOFFi,t is the value of financial flexibility.
It has been estimated using all observations from the respective subsamples, industry and year dummies, and a pooled
OLS estimation. All ratios are winsorized at the 1% level. All models in this table have been estimated using a set of
industry and year dummies. Standard errors are in parentheses. Statistical significance at the 1%, 5%, or 10% level is
indicated by ***, **, or *, respectively.
Panel A: PayerRi,t
Panel B: RIi,t
Variable
USA
EU17
BRIC/JPN
USA
EU17
BRIC/JPN
Intercept
-1.433***
(0.163)
0.061***
(0.013)
0.766***
(0.142)
1.820***
(0.180)
-0.763***
(0.067)
0.294***
(0.017)
0.269*
(0.143)
-0.837***
(0.111)
-3.971***
(0.179)
0.104***
(0.034)
0.114
(0.182)
2.857***
(0.326)
-0.591***
(0.074)
0.273***
(0.018)
0.797***
(0.194)
-0.103
(0.150)
-4.067***
(0.317)
0.225
(0.153)
2.268***
(0.235)
-1.660**
(0.703)
-0.351***
(0.112)
0.168***
(0.030)
0.591*
(0.310)
-2.371***
(0.261)
-0.657***
(0.088)
0.038***
(0.007)
0.240***
(0.079)
-0.004
(0.086)
-0.532***
(0.040)
0.138***
(0.008)
0.286***
(0.081)
-0.404***
(0.060)
-1.887***
(0.090)
0.050***
(0.013)
-0.018
(0.091)
0.501***
(0.113)
-0.359***
(0.041)
0.115***
(0.008)
0.550***
(0.100)
-0.056
(0.075)
-1.084***
(0.099)
0.038*
(0.021)
0.627***
(0.060)
-2.999***
(0.257)
-0.156***
(0.040)
0.043***
(0.008)
0.236***
(0.081)
-0.478***
(0.074)
0.096
24,456
0.125
21,221
0.093
17,520
0.046
24,458
0.076
21,272
0.077
17,543
REi,t
TEi,t
ROAi,t
SGRi,t
Logsizei,t
Cashi,t
VOFFi,t
Pseudo R2
N
60
Probability of dividends
1.0
0.90
0.88
0.84
0.9
0.77
Probability
0.8
0.68
0.7
0.57
0.6
0.47
0.5
0.4
0.3
0.2
0.1
5%
10%
25%
50%
75%
Percentiles of VOFF
90%
95%
0.71
0.70
25%
50%
75%
90%
Percentiles of the dividend tax penalty indicator
95%
Probability of dividends
1.0
0.9
0.82
0.80
0.77
Probability
0.8
0.77
0.74
0.7
0.6
0.5
0.4
0.3
0.2
0.1
5%
10%
Probability of dividends
0
1.0
0.86
0.9
Probability
0.8
0.73
0.63
0.7
0.6
0
0.80
0.53
0.5
0.4
0.3
0.2
0.1
1
2
3
Shareholder rights
4
5
0.80
0.83
3
4
Probability of dividends
0
1.0
0
0.9
Probability
0.8
0.69
0.77
0.73
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1
2
Creditor rights
Figure 1: The four graphs0 visualize predicted probabilities of dividends for increasing0 percentiles of the value of
financial flexibility (VOFFi,t ), increasing percentiles of the tax penalty indicator (δDivi,t ), varying levels of shareholder
rights (ADi ), and varying levels of creditor rights (CRi,t ). The predicted probabilites are based on the coefficients
obtained from the logit regression in model III in Table VIII. All explanatory variables except VOFFi,t , δDivi,t , ADi ,
and CRi,t are set to the sample median. The year is set to 2008 and the industry is set to the manufacturing industry.
In doing so, we follow the same methodology as in Brockman and Unlu (2009).
61
0
0
Probability of share repurchases
1.0
0.9
Probability
0.8
0.7
0.58
0.6
0.58
0.56
0.54
0.52
0.50
0.48
50%
75%
Percentiles of VOFF
90%
95%
0.64
0.65
25%
50%
75%
90%
Percentiles of the dividend tax penalty indicator
95%
0.5
0.4
0.3
0.2
0.1
5%
10%
25%
Probability of share repurchases
1.0
0.9
Probability
0.8
0.7
0.61
0.54
0.6
0.5
0.43
0.43
5%
10%
0.54
0.4
0.3
0.2
0.1
Probability of share repurchases
0
1.0
0
0.9
0.74
Probability
0.8
0.7
0.61
0.6
0.47
0.5
0.33
0.4
0.3
0.22
0.2
0.1
1
2
3
Shareholder rights
4
5
Probability of share repurchases
0
1.0
0
0.9
Probability
0.8
0.7
0.66
0.54
0.6
0.5
0.42
0.4
0.31
0.3
0.22
0.2
0.1
0
1
2
Creditor rights
3
4
0
Figure 2: The four graphs0 visualize predicted probabilities of share repurchases for increasing
percentiles of the
value of financial flexibility (VOFFi,t ), increasing percentiles of the tax penalty indicator (δDivi,t ), varying levels of
shareholder rights (ADi ), and varying levels of creditor rights (CRi,t ). The predicted probabilites are based on the
coefficients obtained from the logit regression in model III in Table IX. All explanatory variables except VOFFi,t ,
δDivi,t , ADi , and CRi,t are set to the sample median. The year is set to 2008 and the industry is set to the manufacturing
industry. In doing so, we follow the same methodology as in Brockman and Unlu (2009).
62
0
0
Payout probabilities
0.7
Probability
0.6
0.5
0.4
0.3
0.2
0.1
0.0
1
No paypout
2
3
4
5
6
VOFF decile
Share repurchases
Dividends
7
8
9
10
Dividends & repurchases
Figure 3: The graph visualizes mean predicted payout probabilities by increasing VOFFi,t deciles. VOFFi,t is the
value of financial flexibility. The predicted probabilites are based on the multinomial logit model in Table XI.
0
63
0
Payout probabilities
0.7
Probability
0.6
0.5
0.4
0.3
0.2
0.1
0.0
1
No paypout
2
3
4
5
6
VOFF decile
Share repurchases
Dividends
7
8
9
10
Dividends & repurchases
Figure 4: The graph visualizes mean predicted payout probabilities by increasing VOFFi,t deciles. VOFFi,t is the
value of financial flexibility. It has been estimated incorporting the approach by Almeida, Campello, and Weisbach
(2004) to adjust for expected changes in cash holdings in model (2).
0
64
0
Payout probabilities
0.7
Probability
0.6
0.5
0.4
0.3
0.2
0.1
0.0
1
No paypout
2
3
4
5
6
VOFF decile
Share repurchases
Dividends
7
8
9
10
Dividends & repurchases
Figure 5: The graph visualizes mean predicted payout probabilities by increasing VOFFi,t deciles. VOFFi,t is the
value of financial flexibility. In contrast to figure 3, the predicted payout probabilities are based on a multinomial logit
model including country dummies instead of corporate governance and tax variables.
0
65
0
Payout probabilities
0.7
Probability
0.6
0.5
0.4
0.3
0.2
0.1
0.0
1
No paypout
2
3
4
5
6
VOFF decile
Share repurchases
Dividends
7
8
9
10
Dividends & repurchases
Figure 6: The graph visualizes mean predicted payout probabilities by increasing deciles for the alternative financial
flexibility measure as defined in Section IV.
0
66
0