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Why do private firms hold less cash than public firms?
International evidence on cash holdings and borrowing costs
Abstract
We contend, with aid of a stylized model, that high borrowing costs can overwhelm
precautionary motives and induce low cash holdings in private firms. Supportive of our
hypothesis, we find European private firms hold less cash than public firms and this pattern
reflects differential borrowing costs. Results are robust to endogeneity concerns and reveal
private firms use cash flow to pay-down existing debt instead of building cash reserves; private
firms are also less sensitive to precautionary reasons for holding cash. Further, stronger creditor
rights and debt market development lead to convergence in cash policies of public and private
firms.
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I. Introduction
This paper investigates how fundamental differences between public and private firms affect
their cash policies. Keynes (1936) predicts that cash holdings may be beneficial to firms with
limited access to external capital markets because they can be used to finance future valuable
projects or activities.1 Along these lines, Harford, Klasa, and Maxwell (2014) emphasize that
cash reserves are important in reducing refinancing risk for firms with short-term debt.
Additionally, Pinkowitz and Williamson (2001) show that public firms that borrow from banks
rather than public debt markets tend to hold more cash. This line of research suggests that private
firms should hold higher precautionary cash reserves than public firms given that private firms
have limited access to external capital markets, have more short-term debt and are subject to
monitoring by banks. However, contrary to these predictions, empirical evidence using U.S. data
documents that private firms tend to hold less cash than public firms (Farre-Mensa, 2015; Gao et
al, 2013). In this paper, we propose a borrowing cost explanation for the difference in cash
policies of public and private firms and test our hypothesis with cross-country data.
We use a stylized model of firms’ cash retention decisions to develop our hypothesis that
higher opportunity costs of holding cash for private firms can lead to a cash holdings differential
between public and private firms. The rationale is that firms face a trade-off between retaining
cash for future investment opportunities and paying down debt. By reducing debt, the firm
lowers its borrowing costs (we will interchangeably refer to these as costs of cash) – the
downside is that the firm may have less investment capital in future states when it cannot raise
sufficient external borrowing. Hence, when the current costs of debt financing are significantly
high, the costs of holding cash may outweigh the precautionary benefits of holding cash, and a
1 This benefit is described as a precautionary motive for holding cash. Opler et al. (1999), among others, provide
evidence consistent with this precautionary motive.
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firm may use funds to reduce debt rather than hold cash. Reducing debt today may also increase
future funding capacity and lower restrictive covenants, providing an additional rationale for
using cash to pay-off debt.
The existing literature documents that the cost of debt financing is higher for private
firms than for public firms (Pagano, Panetta, and Zingales, 1998; Saunders and Steffen, 2011).
Saunders and Steffen, for example, find that private firms face higher borrowing costs than
public firms because of the higher costs of information production, lower bargaining power and
the higher likelihood of shareholder-debtholder conflicts. Thus, private firms may not only face
considerable precautionary benefits of holding cash but also substantial costs of holding cash
because of high borrowing costs. This could lead to lower cash balances for private firms than
public firms.
To test our cost of cash hypothesis, we analyze cash policies of private and public
European firms. This approach allows us to make contributions to the literature on corporate
liquidity not only by utilizing a unique comprehensive sample of private firms but also by taking
advantage of cross-country variation in the development of legal and financial institutions.2
Thus, we are able to perform an in-depth analysis of the role of financing frictions in explaining
cash holdings. We start by documenting that, similar to U.S. evidence, European unlisted firms
hold significantly less cash than listed firms.3 These results are robust to alternative
specifications, including instrumental variable models that address endogeneity concerns of
public status, and hold in both matched and unmatched samples.
Next, we show that listed and unlisted firms differ in their propensity to save cash out of
cash flow. Following Acharya et al. (2007), we jointly estimate responses of firms’ cash and debt
2 Unlike U.S. firms, both public and private European firms are required to report their financial data. 3 We use the terms “listed” and “public” (and “unlisted” and “private”) interchangeably.
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policies to cash flow innovations. Listed firms display a higher propensity to save cash from cash
flow (i.e., their cash flow sensitivity of cash is significantly higher than that of unlisted firms). In
contrast, unlisted firms are more likely to use cash flow to pay off their debt (i.e., their cash flow
sensitivity of debt is significantly higher than that of listed firms). These results hold in both a
matched sample and a sub-sample of firms that change status from listed to unlisted. These
findings are consistent with the hypothesis that holding cash is relatively costlier for private
firms.
We then explore the determinants of the differential in cash holdings across listed and
unlisted firms to provide additional insight into the economic forces that drive corporate
liquidity. Our cross-sectional analysis shows that the differential in cash holdings across listed
and unlisted firms is related to the degree of debt market development. Consistent with our
model’s predictions, in countries with less developed debt markets and high cost of debt
financing, unlisted firms retain significantly less cash than their counterparts in countries with
more developed debt markets. Furthermore, our time-series analysis shows that variation in the
cost of loans within a country is negatively related to cash holding of unlisted firms, and this
relation is stronger in countries with less developed debt markets. As debt markets develop and
the cost of debt financing is reduced, unlisted firms increase their cash holdings and the
differential in cash holdings between unlisted and listed firms shrinks significantly. We note that
using these country-level proxies for the cost of debt financing helps mitigate endogeneity
concerns in firm-level regressions.
Our findings support the hypothesis that private firms retain relatively little cash because
of the high opportunity costs implicit in the steep borrowing costs they face. While the traditional
(perfect market) approach suggests that cash should be viewed as negative debt, this is not
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necessarily the case in the presence of market imperfections.4 As our findings illustrate, firms’
preference for higher cash versus lower debt depends on borrowing costs as indicated, for
instance, by whether the firms are listed or unlisted.
Our results are robust to controlling for agency costs (Gao et al, 2013) and unlikely to be
due to the difference in selective disclosure between public and private firms (Farre-Mensa,
2015). We also show that the cash holdings differential between private and public firms is
unlikely to be due to the differences in firms’ hedging needs (Acharya et al (2007)). Our results
highlight the role of financing frictions in affecting firms’ liquidity management decisions. Most
importantly, they suggest that the relation between financing frictions and cash holdings is non-
monotonic. On the one hand, firms with the greatest access to the capital markets, such as large
U.S. public firms with high credit ratings, hold relatively little cash because the benefits are low
(Opler et al., 1999). On the other hand, firms that lack access to public equity markets and face
high cost of debt financing, such as private firms, hold relatively little cash because the
opportunity costs of holding cash are high.
Another important difference in cash policies across listed and unlisted firms that we
observe is related to the precautionary motive for holding cash. Prior literature shows that public
firms with high cash flow volatility (e.g., Opler et al., 1999; Bates, Kahle and Stulz, 2009) are
more likely to hold cash. The precautionary motive predicts that cash holdings allow these firms
to deal with adverse shocks when access to capital markets is costly. While we also find that
listed firms with high cash flow volatility hold more cash, this does not appear to be the case for
unlisted firms. Similarly, we find that the cash differential between listed and unlisted firms is
greater in industries with better growth opportunities: while listed firms increase cash holdings in
4 Acharya et al. (2007), offers a model in which cash flow is stochastic to show that cash is not equivalent to
negative debt. Our model provides another context in which cash is not equivalent to negative debt.
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response to an improvement in growth opportunities, unlisted firms do not. While the
precautionary motive suggests that firms with better growth opportunities hold more cash (Bates
et al., 2009), this does not appear to be the case for private firms.5 Costly debt financing may
override the benefits to private firms from accumulating cash for precautionary reasons.
The remainder of the paper is organized as follows. Section II presents our model and
develops the cost of cash hypothesis. Section III describes the data and sample selection
procedure. Section IV examines the differences in cash holdings between listed and unlisted
firms. Section V investigates the cash flow sensitivity of debt and cash for listed and unlisted
firms. Section VI examines the determinants of the differences in cash holdings between listed
and unlisted firms. Section VII investigates the precautionary motives for holding cash by listed
and unlisted firms. Section VIII concludes the paper.
II. Costly External Financing and Cash Holdings
Costly external financing affects both the costs and benefits of holding cash (Almeida et
al., 2004). The extant research emphasizes the benefits of holding cash when the cost of external
capital is high due to asymmetric information and agency problems. Holding cash is beneficial
because firms can invest in valuable future projects and activities without the substantial costs
and uncertainty associated with raising new external capital. The literature refers to this as the
precautionary motive for holding cash. Existing empirical literature provides evidence that
factors related to the precautionary benefits of cash such as growth opportunities and cash flow
volatility positively impact cash holdings (e.g., Opler et al., 1999).
5 Firms with better growth opportunities hold more cash because adverse shocks and financial distress are costlier
for them.
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The opportunity cost of cash for financially constrained firms has received less attention
in the literature than have the benefits of holding cash. Firms’ executives, however, emphasize
costs of holding cash when the cost of external capital is high (Lins, Servaes, and Tufano, 2010).
Lins et al, in a survey of chief financial officers from 29 countries, find that factors related to the
cost of cash are the second most important determinant of cash holdings. As we would expect,
managers recognize that holding cash entails an incremental cost that is equal to the cost of debt
financing (or interest paid on debt) minus the rate of return that the firm earns on its cash
holdings.6 Thus, a firm with a high cost of debt could find it optimal to use funds to pay down
debt.7 Also, paying down debt can enhance the firm’s future debt capacity, especially if the
retired debt included restrictive covenants.
The downside of not retaining cash, however, is that in some future states of the world
the firm may find it costly or difficult to raise capital, e.g., if it lacks sufficient collateral, and
may find itself largely dependent on its cash holdings to undertake investment opportunities. We
illustrate this trade-off using a simple stylized model. We develop testable implications of the
model and, in particular, explore the implications of debt market and banking sector
development on firms’ cash holdings.
A. A Simple Model of Cash Holdings and Cost of Debt
In this sub-section, we propose a simple stylized model to illustrate the tradeoff firms can
face between cash retention and debt pay-down when borrowing is costly and there is the
6 The rate of return that a firm earns on its cash holdings is the risk free rate minus a carry cost (Bolton et al (2011).
A carry cost may arise from a difference between the corporate and personal income tax rates: Corporations
generally face higher tax rates on interest earned on its cash holdings than individuals (Graham, 2000; Faulkender
and Wang, 2006) Also, a carry cost may arise when a firm retains some portion of its cash holdings in non-interest
bearing accounts (Azar et al, 2016) These carry costs are not related directly to the degree of financial constraints.
We investigate the opportunity cost of holding cash that is directly related to the degree of financial constraints 7 Further, paying off debt and decreasing leverage may increase borrowing capacity in the future and reduce
financial constraint.
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possibility of being constrained in future periods. The model applies most directly to the case of
firms that are largely dependent on external debt financing.
The model has three relevant dates 𝑡 = 0, 1 & 2. A firm decides on its cash retention and
debt pay down decisions on date 0, prior to learning about its investment opportunities. On date
1, the firm learns of its opportunities and makes its investment decision using its retained cash
and additional borrowing to finance investment. Date 2 is the terminal date on which project cash
flows are realized and debt is repaid. The firm begins with cash holdings of 𝐶0 on date 0. The
cash holdings are the result of cash inflow from past investments and the firm’s prior cash
holdings. On this date, the firm has pre-existing debt with a face value of 𝐵0. The debt is due on
date 2. All market participants are taken to be risk-neutral.
There are two sources of friction in the model that constrain borrowing and affect the
firm’s choice of cash that is retained from date 0 to date 1. The first friction is the cost of
borrowing 𝑔. This is the borrowing cost associated with asset verification and monitoring of
loans and is incremental to the time-value-of-money, which we normalize to zero for simplicity.
The borrowing costs are taken to be proportional to the loan amount i.e., the cost of borrowing is
𝑔𝐵, if 𝐵 is the loan amount. Since our analysis is centered on the cash retention/pay-down
decision on date 0, we simplify exposition by assuming that the cost 𝑔 applies only to debt that is
held between date 0 and 1. Further, it is assumed that 𝐵0 > 𝐶0, so that the firm is unable to fully
payoff its outstanding debt on date 2 in the absence of new and profitable investment. As a
result, if existing debt cannot be junior to new debt issued on date 1, the firm will be unable to
raise additional capital unless lenders are persuaded that the firm has a sufficiently profitable
investment opportunity.
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The second friction we consider is the possibility that, even when the firm has a positive
NPV investment, it may be unable to raise additional capital because potential lenders/banks are
unable to verify the existence of a profitable opportunity. The likelihood that a firm with good
investments is unable to raise external financing is denoted by 𝛼, and provides a measure of
banking and debt market development (or, more specifically, its absence) in the environment in
which the firm operates.8 We assume that the cost of borrowing 𝑔 is an increasing function of 𝛼
and of a firm specific ‘opaqueness’ measure denoted by 𝜃, which is intended to capture the cost
of firm information acquisition and monitoring by lenders. Hence, the cost of borrowing can be
expressed as a function 𝑔(𝜃, 𝛼). The advantage of separate parameters, 𝛼 and 𝜃, to represent
debt-market development and firm specific transparency is that it allows us to develop
predictions on the interaction between firm specific transparency and debt-market development
on cash policies e.g., the possibly differential effect of debt market development on public versus
private firms.
To develop our predictions, we assume a plausible functional form for borrowing costs:
𝑔(𝜃, 𝛼) = 𝑀𝑎𝑥{𝜃𝛽𝛼𝛾, 𝜎} where 𝛽 > 1, 𝛾 > 1 and where 𝜎 denotes some minimum cost of
borrowing that all firms face. For now, we will assume that 𝜃𝛽𝛼𝛾 > 𝜎 and that 𝑔(𝜃, 𝛼) = 𝜃𝛽𝛼𝛾;
we will explicitly take note of the minimum cost 𝜎 in the context of firms with rather low
monitoring and information costs e.g., firms that are listed and well established. The advantage
of a Cobb-Douglas-type functional form 𝜃𝛽𝛼𝛾 is that it implies that 𝜕𝑔
𝜕𝜃> 0,
𝜕𝑔
𝜕𝛼 > 0 and the
cross-partial 𝜕2𝑔
𝜕𝜃𝜕𝛼> 0. The positive cross-partial captures the notion that, when the debt
market/banking sector is less developed (i.e., α is larger), there is greater divergence in the
8 For simplicity, we assume that 𝛼 is reflective of financial market development and unaffected by firm
characteristics 𝜃. Assuming 𝛼 to be a function of 𝜃 leads to more involved expressions but does not affect our
predictions, as long as the effect of 𝜃 on 𝛼 is sufficiently small.
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overall borrowing costs between more and less transparent firms. In other words, public firms,
with their greater transparency and greater monitoring by, say, institutional investors are
impacted less by weaker creditor rights and debt market development than private firms. An
assumption of this nature is necessary to obtain specific predictions – that we subject to
empirical tests – on the interaction between firm-specific transparency and debt market
development.
In making its cash retention decision on date 0, the firm takes account of future capital
needs to fund investment on date 1. It is assumed that there are no productive investments on
date 0 and, further, that the firm does not distribute cash in the form of dividends/repurchases
since it faces the risk of being cash constrained on date 1. Hence, on date 0, the firm limits its use
of cash 𝐶0 to paying down its outstanding debt, while retaining the rest. The amount of cash used
to pay down debt is denoted by 𝛥, with 𝐶0 − 𝛥 representing the cash retained. The benefit of
paying down debt is the reduction in the borrowing costs by 𝑔𝛥 between dates 0 and 1.
We next turn to the investment opportunity available to the firm on date 1. We assume
that there is a probability 𝜇 > 0 that the firm has a profitable investment opportunity, while with
probability 1 − 𝜇 it has no positive NPV investment. While the firm knows whether it has such
an opportunity, this is not necessarily apparent to outside providers of capital. As indicated
above, a source of friction is that even if the firm has a profitable investment opportunity, there is
still a probability 𝛼 that it will be unable to raise additional financing; while with probability 1 −
𝛼 outside investors will be able to verify the opportunity and provide sufficient funds to invest
optimally. The profitable opportunity is taken to be such that an investment of 𝐼 produces a cash
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payoff of 𝑓(𝐼) on date 2, where 𝑓′ > 0, 𝑓′′ < 0 and 𝑓′′′ ≤ 0. With this investment opportunity,9
the optimal investment level is 𝐼∗, where 𝑓′(𝐼∗) = 1.
We analyze the firm’s cash retention decision on date 0 by examining the payoff
implications in the three possible states on date 1: (1) With probability 𝜇(1 − 𝛼), the firm has a
project and can invest optimally; (2) with probability 𝜇𝛼, the firm has a project but is unable to
raise additional financing: this is the state in which cash retention proves valuable; and (3) the
firm has no project and cannot raise additional financing.
In state (1), the firm is able to raise sufficient funds to invest optimally. Hence, its payoff
on date 2, assumed to be positive, can be expressed as:
𝜋1 = 𝑓(𝐼∗) − (𝐵0 − 𝛥)(1 + 𝑔) + 𝐶0 − 𝛥 − 𝐼∗ (1)
= 𝑓(𝐼∗) − (𝐵0)(1 + 𝑔) + 𝐶0 + 𝑔𝛥 − 𝐼∗ > 0 (1’)
In expression (1) above, the debt pay-down on date 0 is 𝛥; 𝐶0 − 𝛥 is the retained cash, while
(𝐵0 − 𝛥)(1 + 𝑔) is the portion of the date 0 debt that will be repaid on date 2. To invest 𝐼∗, the
firm raises external financing of 𝐼∗ − 𝐶0 + 𝛥. As noted earlier, for expositional ease we have
assumed that there is no incremental borrowing cost between dates 1 and 2. As expression (1’)
makes evident, the advantage of paying down 𝛥 is that it results in a saving of 𝑔𝛥 in terms of
payoff on date 2.
In state (2), lenders are unable to verify that the firm has a profitable project. Given our
assumption that 𝐵0 > 𝐶0, lenders will not make new loans without being able to verify that the
firm has access to a profitable project (since they would not expect to be repaid otherwise). The
firm is, therefore, able to invest only its available resources 𝐶0 − 𝛥 in the investment
opportunity. We will assume that the firm’s payoff on date 2 is positive after investing in the
9 Note that the optimal investment level 𝐼∗is unaffected by the borrowing cost 𝑔 on account of our simplifying
assumption that the borrowing costs apply only to existing debt between dates 0 and 1.
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project, since it would otherwise have no incentive to invest. The firm’s payoff can be expressed
as:
𝜋2 = 𝑓(𝐶0 − 𝛥) − (𝐵 − 𝛥)(1 + 𝑔) (2)
Finally, in state (3), the firm has no investment opportunity and its payoff on date 2 is 0 since its
existing cash holdings are below its date 2 obligations.
We can now determine the firm’s optimal choice of 𝛥 on date 0, assuming that the firm
(or manager) seeks to maximize its expected value as of date 2. The date 0 objective function can
be expressed as:
𝑉 = 𝜇(1 − 𝛼)[𝑓(𝐼∗) − 𝐵(1 + 𝑔) + 𝐶0 + 𝑔 𝛥 − 𝐼∗] +
𝜇 𝛼 [𝑓(𝐶0 − 𝛥) − (𝐵 − 𝛥)(1 + 𝑔)] (3)
In expression (3) above, the two terms represent the probabilities × [expected payoffs] in state 1
and state 2 respectively. The payoffs in the two states are as indicated in equations (1’) and (2).
To determine the optimal value of 𝛥 that maximizes the objective function (3), we obtain
the first-order-condition:
𝛿𝑉
𝛿𝛥= 0 = 𝜇(1 − 𝛼)[𝑔 ] + 𝜇𝛼[−𝑓′(𝐶0 − 𝛥) + (1 + 𝑔)], (4)
which gives,
[1−𝛼
𝛼] 𝑔 + (1 + 𝑔) = 𝑓′(𝐶0 − 𝛥), (5)
or,
(1
𝛼− 1) 𝑔 + (1 + 𝑔) = [
𝑔
𝛼] + 1 = 𝑓′(𝐶0 − 𝛥). (5’)
Substituting for the function 𝑔(𝜃, 𝛼) = 𝜃𝛽𝛼𝛾, we can express (5’) as:
𝜃𝛽𝛼𝛾−1 + 1 − 𝑓′(𝐶0 − 𝛥) = 0. (6)
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Equation (6) represents the trade-off between cash retention and debt pay-down: A unit
of cash holdings imposes an expected marginal cost of 𝜃𝛽𝛼𝛾−1, and yields an expected positive
value of 𝑓′(𝐶0 − 𝛥) − 1.
To obtain the effect of the firm opaqueness and difficulty of monitoring (𝜃) on the firm’s
cash retention choice we take the derivative of equation (6) with respect to 𝜃 (keeping 𝛼 fixed):
𝛽𝜃𝛽−1𝛼𝛾−1 + 𝑓′′(𝐶0 − 𝛥). (𝜕𝛥
𝜕𝜃) = 0. (7)
Since 𝑓′′(𝐶0 − 𝛥) < 0, it follows that (𝜕𝛥
𝜕𝜃) > 0. This leads to our first prediction:
Prediction 1: An increase in firm opaqueness and cost of monitoring, will be associated with a
greater pay-down of debt 𝛥 and lower cash retention 𝐶0 − 𝛥.
We next consider the effect of debt market and banking development on the cash
retention decision by firms. In particular, we are interested in the differential effect that debt
market and banking sector development can have on firms that are more or less transparent. We
do this by showing that 𝑠𝑖𝑔𝑛 [𝜕2𝛥
𝜕𝜃𝜕𝛼] > 0 under our assumptions. The positive sign implies that
the lack of debt market development will have a greater marginal impact on the reduction in the
cash holdings of more opaque firms. Or, conversely, stronger debt market development will have
a greater marginal effect on the cash holdings of less transparent firms.
We proceed by taking the derivative of equation (7) with respect to 𝛼:
𝛽. (𝛾 − 1). 𝜃𝛽−1𝛼𝛾−2 + 𝑓′′(𝐶0 − 𝛥). (𝜕2𝛥
𝜕𝜃𝜕𝛼) − 𝑓′′′(𝐶0 − 𝛥)
𝜕𝛥
𝜕𝜃
𝜕𝛥
𝜕𝛼 = 0 (8)
Since the project is assumed to be such that: 𝑓′′ < 0 and 𝑓′′′ ≤ 0, it follows that (𝜕2𝛥
𝜕𝜃𝜕𝛼) > 0.
We can state:
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Prediction 2: An increase in debt market and banking sector development will result in a
relatively greater increase in the cash holdings by more opaque and harder-to-monitor firms,
relative to cash holdings by more transparent and easier-to-monitor firms.
We turn next to the case of firms that have low monitoring costs and their borrowing
costs are such that 𝑔 = 𝜎 (i.e., where borrowing costs are such that 𝜃𝛽𝛼𝛾 < 𝜎). It follows that
equation (5’) in this case becomes:
𝜎𝛼−1 + 1 − 𝑓′(𝐶0 − 𝛥) = 0. (5′′)
Taking derivatives with respect to 𝛼, we obtain:
−𝜎𝛼−2 + 𝑓′′(𝐶0 − 𝛥). (𝜕𝛥
𝜕𝛼) = 0. (9)
Since, 𝑓′′ < 0, it follows from equation (9) that for firms with low monitoring and borrowing
costs, we can have (𝜕𝛥
𝜕𝛼) < 0. In other words, for these firms an improvement in the debt market
and banking-sector development (i.e., lower 𝛼) can lead to less cash retention. This is the
opposite of the effect for firms that are costly to monitor and have relatively large borrowing
costs. The reason is that as the likelihood of the firm being able to finance its project increases
(i.e., 1 − 𝛼 increases), it tends to hold less cash since its need for precautionary cash-holding
decreases; while its borrowing costs are relatively insensitive to debt market development. We
can state:
Prediction 3: For firms that are more transparent and have relatively low monitoring and
borrowing costs, an increase in debt market and banking sector development can lead to a
decrease in the firms’ cash holdings.
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B. Discussion of the Model and Implications for Private and Public Firms
In our empirical analysis, we investigate whether the cost of borrowing can explain firms’
cash policies and, in particular, whether it can account for the differences in cash holdings
between public and private firms. We refer to this interchangeably as the cost of cash or the cost
of borrowing hypothesis.
Given the information production and monitoring functions of the stock market (Dow and
Gorton, 1997; Subrahmanyam and Titman, 1999; and Edmans, 2009), private firms are likely to
have higher ‘opaqueness’ measure, 𝜃, than public firms and thus, according to our model, higher
borrowing costs. Existing empirical literature, indeed, suggests that the cost of debt financing is
higher for private firms than for public firms (Pagano, Panetta, and Zingales (1998) and Saunders
and Steffen (2011)). Saunders and Steffen (2011), for example, find that private firms face
higher borrowing costs than public firms because of the higher costs of information production,
lower bargaining power and the higher likelihood of shareholder-debtholder conflicts. Then,
from Prediction 1, we would expect private firms to have lower cash holdings and to be more
likely to apply cash flow to pay down existing debt.
There is also literature that indicates that lower levels of creditor protection and debt
market development are associated with higher cost of debt (Qian and Strahan (2007) and Bae
and Goyal (2009)), which is captured in our model by parameter α. Interestingly, our model
predicts that creditor protection and debt market development can have a differential effect on
the cash policies of public and private firms. In particular, debt market development is expected
to have a relatively greater impact on the cash holdings of less transparent firms with high
borrowing costs such as private firms (Prediction 2). For highly transparent firms with relatively
low borrowing costs such as public firms, debt market development may actually lead to a
reduction in their level of cash holdings (Prediction 3). Hence, while private firms are expected
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to increase cash holdings when the debt market is more developed — the impact on cash
holdings of public firms will be less positive and could even be negative. Overall, we expect a
convergence in the cash polices of public and private firms when creditor rights are stronger and
the debt market is more developed.
We note that our model is consistent with the precautionary arguments for accumulating
cash, as it follows directly from the model that a firm with high growth opportunities will hold
more cash. A more productive investment means that 𝑓′(𝐶0 − 𝛥) becomes larger. In
equilibrium (see equation 6, for instance) this implies that firms will choose a lower 𝛥 i.e., there
will be more cash retention by all firms. However, it also follows from the model that the effect
will be muted when borrowing costs are high (as is the case for private firms) that they have a
strong incentive to reduce borrowing costs.10
III. Data Description
In this section, we describe our data and sample selection procedure.
A. The Sample Selection
Our primary data source is the 2011 version of Amadeus, by Bureau van Dijk. This
database provides balance sheet and income statement items for a set of European firms from
1996 to 2011. An important advantage of Amadeus is that it includes data for a comprehensive
set of public and private firms. This advantage is made possible because European law requires
10 To see this, compare a private firm that retains cash 𝐶𝐿 with an otherwise equivalent public firm that retains 𝐶𝐻,
where 𝐶𝐻 > 𝐶𝐿 . Given strict concavity of the investment payoff function we have 𝑓′(𝐶𝐿) > 𝑓′(𝐶𝐻). Now, assume
that the both firms experience a similar increase in marginal productivity of 𝜏 > 0 (i.e., the marginal outputs are
shifted up by a constant). In equilibrium (see equation 6), this will lead to an increase in cash retention for both firms
such that: 𝑓′(𝐶𝐿 + 𝛿𝐿) = 𝑓′(𝐶𝐿) + 𝜏 and 𝑓′(𝐶𝐻 + 𝛿𝐿) = 𝑓′(𝐶𝐻) + 𝜏. Given strict concavity, it follows that 𝛿𝐻 > 𝛿𝐿. This is since, given that 𝑓′(𝐶𝐿) > 𝑓′(𝐶𝐻), a larger change in 𝐶𝐻 has a similar marginal effect as a smaller
change in 𝐶𝐿 .
17
both public and private firms to report financial statements. The data are collected from each
nation’s official public body in charge of collecting the annual financial statements in its country,
and always come from the officially filed and audited accounts.
The Amadeus dataset is divided into three parts. The first contains the largest firms in the
database, the second contains the next largest, and the third contains the remaining firms. Our
sample comes from the first part of the dataset – the largest firms. The dataset includes a flag for
whether the company is listed on a major stock exchange. All other firms are classified as
unlisted. The dataset reports only contemporaneous information rather than historical
information for this variable. We use historical Amadeus DVDs to track changes in listing status
over time. We use the variable “Legal Form” to exclude unlimited partnerships, sole
proprietorships, cooperatives, foreign companies, foundations, and government enterprises.
As in Giannetti (2003), we exclude Eastern European economies since the quality of the
accounting data provided for these economies is poor. We exclude firm-years with total assets
less than 10 million U.S. dollars, and exclude financial and miscellaneous firms (US SIC-
equivalent codes 60-69 and 89). With a few exceptions, we set a variable to missing if its
observations are within its top or bottom percentile, to avoid the effect of outliers. The
exceptions are standard deviation of cash flows and balance sheet items, such as cash scaled by
total assets. In these cases, we set a variable to missing if its values are non-positive or its
observations are within the top percentile. These filters result in 1,004,674 firm-year
observations.
We complement firm-level data with country indexes of financial and legal development.
We measure debt market development using private credit to GDP from Djankov, McLeish and
Shleifer (2007) and the index of creditor rights from La Porta, Lopez-de-Silanes, Shleifer, and
18
Vishny (1998). To gauge the degree of shareholder rights, we use the anti-self-dealing index
from Djankov et al. (2008), which measures how difficult it is for minority shareholders to
thwart the consumption of private benefits by controlling parties. Djankov et al. (2008), argue
that self-dealing is the central problem of corporate governance in most countries. Following
Dermiguc-Kunt and Levine (1996) and Love (2003), we compute an index of stock market
development that equals the sum of standardized market capitalization to GDP, total value traded
to GDP, and turnover (total value traded to market capitalization). We obtain each of the
elements of this index from the World Bank.
B. The Matching Procedure
Only 2.5% of the firm-year observations in our sample are listed firms. All other firms
are unlisted. To make the samples of listed and unlisted firms more comparable in size, we match
listed firms to unlisted firms based on country, industry code, and total assets. We keep our
matching criteria simple to allow for comparisons between public and private firms across
multiple characteristics.
In order to match each listed firm to an unlisted firm, we first consider all listed firms in
2008, choosing this year because it contains the largest number of firms for any given year in our
sample. We then exclude the largest listed companies (total assets of the company exceeds total
assets of the largest unlisted company in the country by 20 million U.S. dollars or more) as these
companies are likely to have easy access to international financial markets and are less likely to
be subject to the constraints imposed by domestic markets (see Giannetti, 2003). Next, we
require exact matches on country and industry code and the closest possible match on total
assets, measured as of 2008. The matching is done without replacement. Our matched sample
includes only the largest private corporations. We perform most of the tests using the matched
19
sample. To the extent that the largest private companies are more likely to behave like public
companies, this procedure may bias our results towards finding no differences between public
and private firms. For robustness, we also perform some of the tests using the full (unmatched)
sample.
C. Descriptive Statistics
We present descriptive statistics in Table 1 for the matched sample of listed and unlisted
firms. The table contains total assets in millions of USD and various balance sheet items as
percentage of total assets. As indicated, unlisted firms hold significantly less cash and cash
equivalents than listed firms: unlisted firms hold 9% in cash as a proportion of total assets, while
listed firms hold 14%. This difference of 5% is statistically significant, and is consistent with
Prediction 1, that private firms will hold less cash because their borrowing costs are higher. We
confirm this univariate result in various tests below. Interestingly, unlisted firms also hold higher
short-term debt, a variable typically associated with higher levels of cash (see Falato,
Kadyrzhanova and Sim, 2013; Harford, Klasa, and Maxwell, 2014). We also note unlisted firms
are somewhat smaller than their listed counterparts even in our matched sample.
In table 2, we present cash holdings across countries. Unlisted firms hold less cash in
most countries, and this difference is statistically significant in 11 out of 16 countries. This
pattern is also very consistent over time. In Figure 1 we plot average cash holdings over time for
listed and unlisted firms, and find that unlisted firms hold less cash than listed firms for every
year in the sample period.
20
IV. Differences in Cash Holdings between Public and Private Firms
A. Baseline Results
In this section, we test whether public firms hold less cash than private firms using
regression analysis. We consider the following model:
Cash Holdingsit = β*Listedit + δ*Xit + εit , (10)
where Cash Holdings is cash and cash equivalents divided by total assets. Listed is an indicator
variable for the firm being listed on a major stock exchange in the country. We include a set of
firm-level control variables, as well as country, industry, and year dummy variables (X). We
include country dummies to ensure we are measuring within-country differences between listed
and unlisted firms as well as controlling for unobserved time-invariant country effects. We also
include industry and year dummies to control for industry wide factors and time trends that may
affect cash holdings. Standard errors are clustered at the firm level.
The set of firm-level controls includes variables that have been found to determine cash
holdings in earlier studies (e.g., Opler et al., 1999, and Bates et al., 2009). Leverage is measured
as total debt divided by the sum of total debt and shareholder funds. Total debt is the sum of
long-term debt plus short-term loans. Size is measured as the log of total assets, where assets are
in USD. Decreasing marginal benefits to holding cash would predict a negative relation between
size and cash. We use sales growth to proxy for growth opportunities. Later in the paper, we
consider alternative proxies for growth opportunities. Firms with growth opportunities may
prefer to hold more cash for precautionary reasons to avoid foregoing growth opportunities due
to financing difficulties. Cash flow to assets is operating cash flow divided by lagged assets.
Firms with high cash flow may be able to accumulate more cash.
21
We compute the standard deviation of cash flows from the current and the past four
values of annual cash flow to assets ratio. If two or more cash flow to assets ratios are missing,
then the variable is set to missing. We expect firms with high cash flow risk to hold more cash
for precautionary reasons. Investment in tangible assets is the change in tangible fixed assets
divided by lagged assets. To the extent that investment in tangible assets increases debt capacity,
it may reduce the demand for cash. Investment in intangible assets is the change in intangible
fixed assets divided by lagged assets. Investment in intangible assets tends to be associated with
higher levels of information asymmetry, and greater difficulty accessing external capital markets.
As a result, it may increase demand for cash retention. Net working capital is current assets
minus current liabilities minus cash divided by lagged assets. Net working capital consists of
assets that substitute for cash, and thus we expect a negative relation between cash and net
working capital. Finally, firm age is observation year minus year of incorporation. More mature
firms typically have more stable cash flows and lower growth opportunities and require less
cash.
Results of the analysis are presented in Panel A of Table 3. We continue to find that
unlisted firms hold less cash than listed firms when controlling for other determinants of cash
holdings. For the matched sample, the coefficient is 0.033 and is statistically significant at the
1% level, indicating that unlisted firms hold 3% less of their assets in cash than listed firms. This
is economically significant since it indicates that cash holdings of listed firms are more than one
third larger than those of unlisted firms that hold about 9% of their assets in cash. This result is
similar for the unmatched sample. The coefficients on the firm-level control variables are as
expected and consistent with previous studies.
22
We should note that the analysis in Panel A of Table 3 does not control for the dividend
policies of listed and unlisted firms. This is because Amadeus excludes dividend variables.
Previous research, such Opler et al. (1999), however, shows that firms that pay dividends hold
less cash. To investigate whether differences in dividend policies explain the difference in cash
holdings between listed and unlisted firms, we obtain data on dividend payouts for listed firm
using Osiris, another dataset distributed by Bureau van Dijk with extended coverage of financial
data for listed firms. Panel B of Table 3, compares cash holdings of listed firms that pay
dividends to cash holding of unlisted firms. If higher propensity of unlisted firms to pay
dividends explains our results in Panel A, then we should find no difference in cash holdings
between listed firms that pay dividends and unlisted firms. This is not the case however: both
listed firms that pay dividends and listed firms that do not pay dividends hold more cash than
unlisted firms.11
Our results also suggest that low cash holdings of unlisted firms are not due to the
inability of the private firms to generate enough cash from operations because we control for
cash flow. This is different from Denis and Sibilkov (2010), who show that some public firms
with limited access to external markets exhibit low cash holdings because of persistently low
cash flows.
B. Instrumental Variable Models
Our matched sample alleviates concerns that our results are driven by the endogeneity of
the listing status. However, matching can only be done on observable characteristics, and private
and public firms may differ along unobservable characteristics. To address this concern, we
employ a two-stage least squares regression and a treatment effects model. Our instrumental
11 See Michaely and Roberts (2012), for a detailed discussion of dividend policies of private firms.
23
variable is the percentage of firms in a country-region that are listed.12 A rationale is that the
presence and preferences of local investors could affect firms’ decision to go public (investors
often exhibit a ‘home bias’ i.e., have a preference to invest locally). We believe the instrument to
be valid since it is expected to be positively related to the likelihood of a firm being listed
(relevance), while there is no clear reason for the instrument to be directly related to cash policy
(exclusion restriction).
Table 4, Panel A presents results for the two-stage least squares regression using the
matched sample. In the first stage, we estimate a linear probability model in which the dependent
variable is an indicator variable for the firm being listed. As indicated, the proportion of listed
firms in the region within a country is estimated with a positive and significant coefficient at the
1% level. The second stage shows that listed firms tend to hold more cash. The coefficient on the
instrumented listed variables is 0.079 and is statistically significant at the 1% level. The
coefficient suggests that a listed firm holds on average 8% more cash as a percentage of assets
than an unlisted firm. This coefficient is about twice as large as the coefficient on comparable
OLS regression.
To investigate further robustness of our results, in Table 4, Panel B, we estimate the
treatment effects model using the two-step Heckman procedure. In the first step, we estimate a
probit model for the probability of being public, and use the proportion of listed firms in the
region as an instrument. In the second step, we estimate the determinants of cash holdings, while
accounting for possible self-selection bias regarding the choice of private/public status. We
continue to find that unlisted firms hold less cash than listed firms when controlling for other
determinants of cash holdings.
12 We obtain the region of the firm’s location from Amadeus. Regions are territorial units that comprise a country.
24
In un-tabulated results we use, in the spirit of Saunders and Steffen (2011), distance to
the country’s main stock exchange as an alternative instrument for the choice of being public.
Stock exchanges are located in the country’s financial capital, and proximity should facilitate
access to public capital markets in that it facilitates how firms match with investors and
underwriters during the going public process. In addition, it is plausible that the instrument
satisfies the exclusion restriction since we would not expect distance to a country’s financial
capital to have a direct effect on cash holdings. To reduce skewness, we use log(distance). We do
this for both the instrumental variables and the treatment effects models. We obtain results that
are qualitatively similar to the results discussed above, with listed firms retaining greater cash
holdings.
As we have discussed, the positive cash holdings differential between listed and unlisted
firms would not be expected within Keynes’s (1936) framework that predicts that cash holdings
serve a precautionary function for firms with limited access to external capital markets (such as
unlisted firms) because cash holdings can help those firms seize valuable projects or activities in
the future. Opler et al. (1999), among others, provide evidence consistent with the precautionary
motive for holding cash among listed firms. Our results indicate, however, that the precautionary
motive tends to be dominated by the high costs of debt for unlisted firms inducing them to hold
little cash compared to listed firms (Prediction 1). In the sections below, we provide more direct
evidence consistent with the cost of borrowing hypothesis.
25
V. The Cash Flow Sensitivity of Debt and Cash for Public and Private
Firms
In this section we provide further support for our cost of borrowing hypothesis. Our
hypothesis implies that private firms will use cash flow to pay off debt, in lieu of accumulating
cash, because private firms bear a higher cost of debt than public firms. To test this hypothesis
directly, we investigate the cash flow sensitivity of debt and cash for listed and unlisted firms.
We expect the cash flow sensitivity of debt to be higher for unlisted firms than for listed firms,
and the cash flow sensitivity of cash to be lower for unlisted firms.
In our empirical analysis, we follow Acharya, Almeida, and Campello (2007), and
endogenize debt and cash policies. Specifically, we estimate the following system of equations
using three-stage least squares:
ΔDebtit = α1Listedit + α2CashFlowit + α3CashFlowxListedit + α4ΔCashHoldingsit
+ a5ΔDebtit-1 + δ*Xit + εit , (11)
ΔCashHoldingsit = α1Listedit + α2CashFlowit + α3CashFlowxListedit + α4ΔDebtit
+ a5ΔCashHoldingsit-1 + δ'*Xit + ε'it , (12)
where Listed is an indicator variable for the firm being listed. ΔDebt is change in total debt scaled
by beginning of the period assets. ΔCashHoldings is change in cash scaled by initial of period
assets. CashFlow is cash flow scaled by lagged assets. We also include a set of firm-level control
variables such as firm size, growth opportunities as measured by sales growth, and country,
industry and year dummies (X). Standard errors are clustered at firm-level.
26
Results are presented in Table 5, Panel A. They are consistent with our predictions. In the
regressions of changes in debt we find that higher levels of cash flow are associated with debt
reductions for unlisted firms. This effect is muted for listed firms, as evidenced by the positive
coefficient on the interaction variable that is of similar magnitude to the negative coefficient on
cash flow. For unlisted firms, debt drops by 1.28% for a one standard deviation increase in cash
flow. For listed firms, debt increases by a meager 0.01% for a one standard deviation increase in
cash flow.13
In the regressions of changes in cash, we find that high cash flows are associated with
increases in the cash account for both listed and unlisted firms, and this effect is more
pronounced for listed firms, as evidenced by the positive coefficient on the interaction variable.
Cash holdings of unlisted firms increase by 1.35% for a one standard deviation increase in cash
flows, while the cash holdings of listed firms increase by an additional 0.58% for a total of
1.93%.
In Table 5, Panel B we examine a sample of firms that change listing status to investigate
how a firm’s propensity to use cash flow to pay off debt varies for a firm as it changes its own
listing status. The sample contains firms that switch status from unlisted to listed and vice-versa.
We re-estimate equations 11 and 12, but include firm and year fixed effects. Standard errors are
clustered at the firm level. This analysis allows us to examine the effects of listing status while
keeping firm time-invariant characteristics constant, and mitigates any concerns that our results
are explained by unobservable characteristics. Our results are consistent with previous findings.
We find that firms that switch their status from unlisted to listed and vice-versa, are more likely
13 Cash flow scaled by total assets has a mean of 0.081 and a standard deviation of 0.105. The economic effect for
listed firms is computed as the sum of the coefficients on the cash flow relative to total assets, -0.122 and its
interaction with the indicator variable for listed, 0.123 multiplied by 1 standard deviation of cash flow scaled by
assets.
27
to use cash flow to pay off debt when they are unlisted, and are more likely to use cash flow to
increase their cash holdings when they are listed. These results suggest that propensity to use
cash flow to pay off debt changes with the change in listing status.
In sum, we find that public firms are more likely to use cash flow to accumulate cash,
while private firms are more likely to pay down debt. These results are consistent with our cost
of cash hypothesis.
VI. Cross-Country and Time-Series Variation in Cash Held by Public and
Private Firms
In this section, we explore cross-country differences in the development of legal and
financial institutions and their effect on cash holdings of public and private firms to test the
predictions of our model. In addition, we explore time variation in the cost of loan financing
within countries to provide further support for our hypothesis. We also discuss alternative
explanations for the cash holding differential.
A. Creditor Protection, Loan Market Development and the Differences in Cash Holdings
We first investigate the effect of creditor protection and loan market development on the
difference in cash holdings between public and private firms. Our cost of borrowing hypothesis
predicts that as the cost of private debt financing declines and the cost of holding cash is reduced,
private firms will increase cash holdings and the difference in cash holdings between public and
private firms is likely to shrink (Prediction 2). The latter condition is because private firms with
their greater opaqueness and limited monitoring by, say, institutional investors are more
impacted by weaker creditor rights and debt market development than are public firms.
28
Results of the analysis are tabulated in Table 6. We proxy for loan market development
and the cost of loan financing at the country level with the amount of private credit as a
percentage of GDP from Djankov et al. (2007), and the creditor rights index from La Porta et al.
(1998). In panel A, we partition the sample in countries with private credit to GDP above the
median and countries with private credit to GDP below the median. In panel B, we partition the
sample in firms with the creditor rights index above the median and below the median. For the
sake of brevity, we do not tabulate the results for firm-level control variables. Control variables
are the same as in Table 3.
Results in Panel A show that listed firms hold more cash in both subsamples; however,
this differential is significantly larger when private credit to GDP is low and the cost of loan
financing is high. Listed firms hold 1.3% more cash than unlisted firms when private credit to
GDP is high; and 5.2% more cash when private credit to GDP is low. These coefficients are
statistically different at the 1% level. Results in Panel B are similar. Listed firms hold more cash
in both sub-samples, but listed firms hold relatively more cash in countries with low creditor
rights. Listed firms hold 1.3% more cash in countries where creditor rights are above the median,
and 4.5% more cash where creditor rights are below the median. As with private credit to GDP,
these coefficients are statistically different at the 1% level. Overall, these findings suggest that
cost of cash is an important economic factor that explains corporate liquidity. To that end, they
are consistent with Azar et al (2016) who find that the carry cost of cash, calculated as a product
of rate of return on Treasury bills and a share of firm’s cash holding kept in zero interest rate
accounts, may explain dynamics of corporate cash holdings over time.
29
B. Country-level Variables and Cash Holdings of Public and Private Firms
In this sub-section, we study the determinants of cash holdings of private and public
firms separately to investigate whether debt market development has a positive effect on the cash
holdings of private firms (Prediction 2), and an insignificant or even negative effect on the cash
holdings of public firms (Predictions 2 and 3). These tests allow us to further understand what
drives the cross-country differences-in-differences in cash holdings that we document in the
previous section, and provide additional support for the cost of cash hypothesis.
Results are presented in Panels A and B in Table 7. Columns 1 and 2 report results for the
sub-sample that includes unlisted firms only, while columns 3 and 4 report results for the sub-
sample that includes listed firms. In addition to proxies for the cost of debt financing, in
regressions 2 and 4 we include a proxy for managerial agency problems at the country-level and
a proxy for stock market development in our regressions. We proxy for managerial agency
problems with anti-self-dealing index from Djankov et al. (2008), which measures how difficult
it is to prevent the consumption of private benefits by controlling parties. We measure stock
market development following Dermiguc-Kunt and Levine (1996) and Love (2003). This
measure is based on trading activity and size of the country’s stock market (see section III for
details). Each regression also includes the firm-level determinants of cash holdings reported in
Table 3, year and industry dummies, and the log of gross domestic product as an additional
control variable for cross-country differences. Standard errors are clustered at the firm level.
In Panel A we proxy for cost of debt using private credit to GDP, while in Panel B we use
creditor rights. We find that debt market development is associated with a higher level of cash
holdings for unlisted firms and lower levels of cash holdings for listed firms. In Panel A, the
coefficient on Private-Credit-to-GDP for unlisted firms is 0.022 (statistically significant at the
1% level), while for listed firms, the coefficient is -0.014 (also statistically significant at the 1%
30
level). The results are consistent with private firms finding it expensive to accumulate cash in
countries where private credit availability is scarce and consequently the cost of debt is high,
while public firms will accumulate more cash when debt market development is low for
precautionary reasons. Our results are robust when we use creditor rights index to proxy for the
cost of debt. In Panel B, the coefficient on Creditor Rights is 0.028 for unlisted firms
(statistically significant at the 1% level), and -0.012 for listed firms (also statistically significant).
In regressions 2 and 4 we include two additional control variables: a proxy for managerial
agency problems (anti-self-dealing index), and a proxy for stock market development. The use of
the anti-self-dealing index is motivated by previous research that links managerial agency
problems and cash holdings, though the direction of the relationship is ambiguous. Self-
interested managers may hold excess cash rather than paying it out to shareholders because cash
reduces firm risk and increases managerial discretion (Gao et al., 2013). However, findings in
Dittmar and Mahrt-Smith (2007) and Hartford, Mansi and Maxwell (2008), suggest that firms
with high degree of managerial agency problems actually have smaller cash reserves because
managers dissipate cash quickly.
The use of the stock market development proxy is motivated by the literature that
emphasizes the precautionary benefits of holding cash. Specifically, the precautionary motive
suggests that firms hold more cash to better cope with adverse shocks when external capital
markets are difficult to tap. As the access to external capital markets, such as the stock market,
improves, firms should reduce cash holdings due to a decrease in the benefits of holding cash.
Each regression also includes firm-level determinants of cash holdings reported in Table 3, year
and industry dummies, and the log of gross domestic product as an additional control variable for
cross-country differences.
31
We continue to find that debt market development is associated with a higher level of
cash holdings for unlisted firms. In regression 2 of Panel A, the coefficient on Private-Credit-to-
GDP is 0.025 and statistically significant at the 1% level. For listed firms, the coefficient on
Private-Credit-to-GDP is 0.001 and is no longer statistically significant (regression 4). The
difference in the coefficients between listed and unlisted firms is statistically significant at the
1% level (untabulated). Our results are robust when we use creditor rights index to proxy for the
cost of debt (see Table 7, Panel B).
The anti-self-dealing index and stock market development have an effect on the cash
holdings of listed firms only. This is expected, since managerial agency problems should be a
bigger concern for public firms due to their more dispersed ownership, and only public firms are
directly affected by the stock market development. Similar to Dittmar et al. (2003), we find that
the anti-self-dealing index is negatively related to the cash holdings of listed firms. Additionally,
we find that stock market development is negatively related to the cash holdings of listed firms.
This result is consistent with the precautionary motive for holding cash, and is broadly consistent
with Prediction 3 from our model.
C. Time-variation in the Cost of Debt Financing and Cash Holdings
In this sub-section, we further investigate whether cash holdings of private firms are
related to the cost of debt financing. We explore time-variation in the cost of loan financing.
During our sample period, European countries made a significant effort to integrate capital
markets. Findings in Bekaert et al. (2007), and Gupta and Yuan (2009), suggest that market
integration helps reduce financial constraints and is likely to reduce the cost of external financing
for private firms. To proxy for the cost of loan financing, we use country-level interest rates
32
reported by the European Central Bank.14 These interest rates are calculated using all outstanding
loans with a maturity of 1 year or less made by monetary financial institutions in a given country to
non-financial corporations. We recognize that a country-level measure is a noisy proxy for the
cost of debt financing at the firm-level; however, in its favor, a country-level measure is less
susceptible to concerns of endogeneity. As before, we expect that the effect of the cost of debt on
cash holdings is stronger for private firms compared to public firms (Prediction 2). One reason is
that the firms likely to benefit the most from an improvement in credit market conditions are the
firms that are least transparent and hardest to monitor (e.g., private firms) (Bae and Goyal
(2009)). This is consistent with the well-recognized pattern that a slowing economy tends to
widen credit spreads (e.g., between investment grade and speculative bonds), while spreads
narrow in a growing economy.
Results are reported in Table 8. In Panel A, we regress cash holdings on loan interest
rates, the set of firm-level control variables included in Table 3, and year and firm fixed effects.
We continue to find evidence that cash holdings of private firms are negatively related to the cost
of debt financing. In Panel B we include an interaction variable, to capture the effect of the cost
of loans when creditor rights are high. We interact the cost of loans with an indicator variable for
the country’s creditor rights being above median. We find that the interaction is positive and
significant for private firms, suggesting that the cost of loans becomes less important for
determining cash holdings when creditor rights are high. For public firms, the interaction is
insignificant. Results are generally consistent with prediction 2 from our model.
14 The link to the data is: http://sdw.ecb.europa.eu/browse.do?node=9484266
33
D. Alternative Explanations
In this sub-section, we first investigate whether differences in the hedging needs between
public and private firms explain the difference in cash holdings we document. Acharya et al
(2007) show that firms with high hedging needs may benefit by hoarding cash, while firms with
low hedging needs may benefit by building debt capacity. High/low hedging needs arise when
investment opportunities tend to arrive in low/high cash flow states.
We follow Acharya et al (2007) to measure firms’ hedging needs. For each firm-year in
the sample we compute the correlation between investment opportunities and the firm’s cash
flow. To proxy for investment opportunities, we compute the median three-year-ahead sales
growth rate in the firm’s three-digit SIC industry. This approach relies on the premise that firms’
perceptions of investment opportunities are related to estimates of future sales growth in their
industries and that those estimates, on average, coincide with the ex-post observed data. We also
create two dummy variables: Low hedging needs, which takes the value of one when the
correlation between investment opportunities and the firm’s cash flow is above 0.20, and High
hedging needs, which takes the value of one when the correlation between investment
opportunities and the firm’s cash flow is below - 0.20. Appendix 1 presents the results of the
analysis. We run a specification similar to the ones presented in Table 3 additionally controlling
for the hedging needs. We continue to find that listed firms hold more cash than unlisted.
In a contemporaneous paper to ours, Farre-Mensa (2015) argues that in the US private
firms hold less cash than public firms because of differences in disclosure requirements. In the
US, private firms can selectively disclose confidential information to private investors, while
public firms cannot. Farre-Mensa contends that these differences in disclosure requirements
make public firms susceptible to mispricing, which results in public firms hoarding more cash to
34
optimize the timing of equity issues. Private firms are not susceptible to the same miss-valuation
waves, and do not need to hoard as much cash.
We highlight that in Europe (1) both private and public firms have an obligation to report
financial statements, resulting in a narrower gap in disclosure between private and public firms,
and (2) the difference in disclosure requirements for private and public firms does not vary
across the countries in our study. As a result, our cross-country evidence that private firms hold
less cash in countries with less developed private debt markets is unlikely to be explained by
differences in disclosure requirements. Nor are disclosure requirements likely to explain the
finding that the difference in cash holdings between private and public firms is wider for
countries with less developed private debt markets.
Overall, the results in this section highlight the fact that financing frictions play an
important role in explaining firms’ cash policies. The previous literature demonstrates that public
firms that are financially constrained accumulate more cash than financially unconstrained public
firms because the precautionary benefits of holding cash are lower for unconstrained firms (e.g.,
Opler et al., 1999). Our results, however, suggest that in extreme cases, when external financing
is significantly costly, firms accumulate relatively little cash because the opportunity cost of
holding cash is high. Thus, the relation between cash holdings and financing frictions is non-
linear. Private firms with limited access to external markets and with a high cost of external
financing behave similarly to financially unconstrained public firms with low financing costs,
and both accumulate relatively little cash, albeit for different reasons, while constrained public
firms accumulate relatively high cash reserves. The identification of this non-monotonic relation
highlights the benefits of an expanded sample such as ours.
35
VII. Precautionary Motive for Holding Cash and Access to Public Equity
Markets
In this section, we take a closer look at the ability of private firms to accumulate cash for
precautionary reasons. As we have noted, an important benefit of cash holdings is that they allow
firms to better cope with adverse shocks and finance valuable projects or activities in the future.
High costs of cash may, however, impair the ability of private firms to respond to this
precautionary motive for holding cash.
The precautionary motive would suggest that high levels of cash flow volatility should be
associated with higher levels of cash since these types of firms are more likely to suffer from
cash shortfalls. We run specifications similar to those in Table 3 separately for listed and unlisted
firms, and compare the coefficients on cash flow volatility between these two types of firms. We
present results in Table 9. In Panel A, we document a notable difference in the coefficients for
cash flow volatility for listed and unlisted firms. Consistent with the precautionary motive, we
find that the coefficient on cash flow volatility is positive and highly statistically significant for
listed firms. This coefficient, however, is insignificant for unlisted firms suggesting such firms
are less sensitive to the precautionary benefits of holding cash. A plausible explanation is that the
incremental borrowing costs outweigh the benefits of cash holdings for precautionary reasons.
We then take a closer look at the relation between cash holdings and growth
opportunities. Previous research suggests that high growth firms would benefit from
accumulating cash for precautionary reasons (Opler et al., 1999, and Bates et al., 2009). Cash
holdings help these firms avoid missing out on valuable growth opportunities due to difficulties
with external financing. Results using sales growth suggest that growth opportunities are
positively related to cash holdings in both listed and unlisted firms. Sales growth, however, is
36
likely to capture not only growth opportunities but also cash flow from assets in place. Thus, we
consider two additional proxies for growth opportunities: industry market-to-book and global PE
ratios from Bekaert et al., (2007).15 Results are presented in specifications 3-6 in Table 9, Panel
A. We find that these proxies for growth opportunities are positively related to cash holdings
only for listed firms.
Further, we investigate the effect of equity dependence on cash policies of public and
private firms to provide additional insights into the ability of private firms to accumulate cash for
precautionary reasons. Equity-dependent firms are rich in growth opportunities and in need of
external financing (Rajan and Zingales, 1998). Results are presented in Table 9, specifications 7
and 8. We compute a firm’s external equity dependence as in Rajan and Zingales (1998), to
measure the industry’s demand for external equity. While we find that external equity
dependence is positively related to the cash holdings in listed firms, this is not the case for
unlisted firms. The coefficient is statistically insignificant.
In Table 9, Panel B we run regressions for unlisted firms for sub-samples of high and low
creditor rights to investigate whether the insensitivity of private firms to the precautionary
motive varies with debt market development. We find that private firms do not respond to the
precautionary motive regardless of the degree of debt market development. The coefficients on
the cash flow volatility and industry market-to-book are statistically insignificant in both
countries with strong and weak creditor rights.16 Our results suggest that when debt market
15 We obtain the annual global PE ratios for each industry from DataStream and manually match the DataStream
industry codes to 3-digit SIC codes available in Amadeus. 16 In untabulated analysis, we find that our results are robust to alternative proxies for growth opportunities.
37
development is high the incremental borrowing costs continue to outweigh the benefits of cash
holdings for precautionary reasons.
In Table 10, we investigate the combined effect of equity dependence and creditor rights
on the difference in cash holdings between listed and unlisted firms. Based on our findings so
far, we expected the difference in cash holdings between listed and unlisted firms to be at its
highest in countries with low creditor rights (when cash holdings for unlisted firms are relatively
low) and industries with high dependence on external equity (when cash holdings for listed firms
are relatively high). In turn, the difference in cash holdings should be at its lowest in countries
where creditor rights are high (when cash holdings for unlisted firms are somewhat higher) and
industries where external equity dependence is low (when cash holding for listed firms are
relatively low). We partition the sample four-ways: firms from countries with creditor rights
above and below median, and firms from industries with equity dependence above and below
median. We run a specification identical to that in Table 3, model 1, and cluster standard errors
at firm level. As anticipated, we find that the difference in cash holdings is the highest for low
credit rights countries in industries with high equity dependence. This difference is 0.06
compared to 0.03 in the full matched sample. The difference in cash holdings is the lowest in
countries with high creditor rights and industries with low equity dependence. In fact, in this
setting the difference in cash holdings between listed and unlisted firms is indistinguishable from
zero.
Overall, our results suggest that private firms are less sensitive to the precautionary
benefits of holding cash than public firms, consistent with the cost of cash argument.
38
VIII. Summary and Conclusions
In this paper, we emphasize that financing frictions lead not only to considerable
precautionary benefits of holding cash but also to substantial costs of holding cash. We propose a
simple model of cash retention policy and hypothesize that private firms, with higher opportunity
costs of holding cash, will hold significantly less cash than public firms. Using a comprehensive
sample of private and public firms, we provide evidence consistent with this cost of cash
hypothesis. The results are shown to be robust to endogeneity concerns. We further show that
private firms tend to use cash flow to redeem existing debt rather than to accumulate cash.
Consistent with our model’s predictions, we show that in countries with less developed
debt markets and higher costs of borrowing, private firms retain significantly less cash than their
counterparts from countries with more developed loan markets. We also show that time-series
variation in the country-level cost of loans is negatively related to cash holdings of unlisted
firms, and this relation is stronger when the country’s debt market is less developed. Overall,
stronger creditor-rights and debt-market development is associated with a convergence in the
cash holdings by unlisted and listed firms.
By analyzing private firms, we are able to show that the relation between financing
frictions and cash holdings is non-linear. The prior literature demonstrates that financially
constrained firms accumulate more cash than unconstrained firms. Our results, however, suggest
that in extreme cases, when external financing is significantly costly, firms accumulate relatively
little cash due to the high cost of holding cash.
39
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42
Table 1. Descriptive Statistics: Balance Sheet Data
The table presents balance sheet data for the firms in the matched sample in 2008. Details of the
matching procedure are provided in the text. The data are from the 2011 version of Amadeus.
The sample includes non-financial firms from Western European countries. Accounting items are
scaled by total assets. ***, **, * denote statistical significance at the 1%, 5% and 10% levels,
respectively.
Listed Unlisted
Mean N Mean N Diff. in
means
Assets Fixed assets (of which)
0.50 2,897 0.44 2,886 0.05***
Intangible 0.19 2,893 0.06 2,782 0.14*** Tangible 0.23 2,897 0.25 2,810 -0.02** Current assets 0.50 2,897 0.56 2,887 -0.05*** Cash and cash equiv. 0.14 2,734 0.09 2,428 0.05*** Total assets ($ mill.) 1,745 2,898 1,267 2,898 478** Liabilities Shareholders’ funds 0.47 2,833 0.38 2,685 0.09*** Non-current liabilities (of which)
0.21 2,873 0.24 2,846 -0.04***
Long-term debt 0.14 2,766 0.16 2,641 -0.02*** Current liabilities (of which)
0.34 2,898 0.43 2,888 -0.09***
Loans 0.08 2,789 0.13 2,756 -0.06***
43
Table 2. Descriptive Statistics: Cash Holdings across Countries
The table presents mean cash holdings of listed and unlisted firms across counties for the
matched sample over the 1996-2011 period. Details of the matching procedure are provided in
the text. We present pooled sample means and sample size for each country. The data are from
the 2011 version of Amadeus. The sample includes non-financial firms from Western European
countries. ***, **, * denote statistical significance at the 1%, 5% and 10% levels, respectively.
Cash/Total Assets
Listed Unlisted
Mean N Mean N Diff. in
means
Austria 0.09 151 0.08 152 0.01
Belgium 0.14 785 0.13 771 0.01
Denmark 0.11 344 0.08 356 0.03***
Finland 0.11 732 0.07 610 0.04***
France 0.18 3,095 0.10 2,943 0.08***
Germany 0.15 3,178 0.11 2,107 0.04***
Greece 0.07 1,807 0.07 1,636 0.00
Ireland 0.21 233 0.09 148 0.13***
Italy 0.10 1,486 0.06 1,266 0.04***
Netherlands 0.12 966 0.10 757 0.02***
Norway 0.14 907 0.11 960 0.03***
Portugal 0.04 212 0.04 169 0.00
Spain 0.09 869 0.10 802 0.00
Sweden 0.17 945 0.09 966 0.08***
Switzerland 0.12 249 0.08 211 0.04***
United Kingdom 0.14 7,302 0.10 7,005 0.04***
44
Table 3. Differences in Cash Holdings across Public and Private Firms: Regression
Analysis
The table presents results of OLS regressions for the matched and unmatched samples (Panel A),
and for listed and unlisted firms where listed firms are partitioned into firms that have paid a
dividend in that year and firms that have not paid a dividend in that year (Panel B). The
dependent variable is cash and cash equivalents divided by total assets. Listed is an indicator
variable for the firm being listed on a major stock exchange. Leverage is measured as total debt
divided by the sum of total debt and shareholder funds. Total debt is the sum of long term debt
plus short term loans. Sales growth is computed as the one-year change in sales divided by
beginning-of-period sales. Standard deviation of cash flows is the standard deviation of current
and the past four cash flows to assets. If two or more cash flows to assets are missing, then the
variable is set to missing. Investment in tangible assets is the one-year change in the value of
tangible fixed assets divided by lagged assets. Investment in intangible assets is the one-year
change in the value of intangible fixed assets divided by lagged assets. Net working capital is
current liabilities minus current assets minus cash divided by lagged assets. Firm age is years
since incorporation. The estimation procedures correct standard errors for clustering at the firm
level. ***, **, * denote statistical significance at the 1%, 5% and 10% levels, respectively.
Panel A.
Matched (1)
Unmatched (2)
Listed 0.033*** 0.032***
Leverage -0.132*** -0.128***
Log(total assets) -0.009*** -0.011***
Sales growth 0.016*** 0.011***
Cash flow/total assets 0.261*** 0.286***
Standard deviation of cash flows 0.147*** 0.055***
Investments in tangible assets -0.161*** -0.181***
Investments in intangible assets 0.053* 0.009
Working capital (net of cash) -0.138*** -0.165***
Firm age -0.000*** -0.000***
Country dummies Yes Yes
Year dummies Yes Yes
Industry dummies Yes Yes
N 16,377 379,065
Adjusted R2 0.2175 0.2176
45
Table 3. (continued)
Panel B.
Listed Firms
that pay
dividends (1)
Listed firms that
do not pay
dividends (2)
Diff.
Listed 0.036*** 0.031*** 0.005
Firm-level controls Yes Yes
Country dummies Yes Yes
Year dummies Yes Yes
Industry dummies Yes Yes
N 12,991 10,408
Adjusted R2 0.2112 0.1927
46
Table 4. Differences in Cash Holdings across Public and Private Firms: Instrumental
Variables Approaches
The table presents results of the two-stage least squares estimation for the matched sample (Panel
A), and the treatment effects model for the unmatched sample (Panel B). The dependent variable
is cash and cash equivalents divided by total assets. Listed is an indicator variable for the firm
being listed on a major stock exchange. Leverage is measured as total debt divided by the sum of
total debt and shareholder funds. Total debt is the sum of long term debt plus short term loans.
Sales growth is computed as the one-year change in sales divided by beginning-of-period sales.
Standard deviation of cash flows is the standard deviation of current and the past four cash flows
to assets. If two or more cash flows to assets are missing, then the variable is set to missing.
Investment in tangible assets is the one-year change in the value of tangible fixed assets divided
by lagged assets. Investment in intangible assets is the one-year change in the value of intangible
fixed assets divided by lagged assets. Net working capital is current liabilities minus current
assets minus cash divided by lagged assets. Firm age is years since incorporation. Proportion of
listed firms in a region is used as an instrument. The estimation procedures correct standard
errors for clustering at the firm level. ***, **, * denote statistical significance at the 1%, 5% and
10% levels, respectively.
Panel A.
Dependent variable
Listed (1)
Cash/total assets (2)
Listed 0.079***
Leverage -0.129*** -0.126***
Log(total assets) 0.023*** -0.011***
Sales growth 0.019** 0.014***
Cash flow/total assets -0.258*** 0.273***
Standard deviation of cash flows 0.184*** 0.135***
Investments in tangible assets 0.218*** -0.171***
Investments in intangible assets 1.804*** -0.028
Working capital (net of cash) -0.001 -0.138***
Firm age 0.002*** -0.000***
Proportion of listed firms 3.558***
Country dummies Yes Yes
Year dummies Yes Yes
Industry dummies Yes Yes
N 16,292 16,292
Adjusted R2 0.1387 0.1963
47
Panel B.
Dependent variable
Listed (1)
Cash/total assets (2)
Listed 0.043***
Leverage -0.399*** -0.128***
Log(total assets) 0.343*** -0.011***
Sales growth 0.038*** 0.011***
Cash flow/total assets -0.295*** 0.286***
Standard deviation of cash flows 0.956*** 0.054***
Investments in tangible assets -0.181***
Investments in intangible assets 0.007
Working capital (net of cash) -0.165***
Firm age 0.004*** -0.000***
Proportion of listed firms 14.899***
Country dummies Yes
Year dummies Yes
Industry dummies Yes
N 378,022 378,022
48
Table 5. Cash Flow, and Changes in Cash Holdings and Debt
The table presents results of the 3SLS regressions. Panel A presents results for the matched
sample. Panel B presents results for the sub-sample of firms that changed listing status. Change
in Debt is change in total debt scaled by beginning of period assets. Total debt is the sum of long
term debt plus short term loans. Change in cash is change in cash scaled by beginning of period
assets. Listed is an indicator variable for the firm being listed on a major stock exchange. Sales
growth is computed as the one-year change in sales divided by beginning-of-period sales. We
include country, industry and year dummies. The estimation procedures correct standard errors
for clustering at the firm level. ***, **, * denote statistical significance at the 1%, 5% and 10%
levels, respectively.
Panel A.
Change in Debt (1)
Change in Cash (2)
Listed -0.012*** 0.001
Cash flow/total assets -0.122*** 0.129***
Listed x cash flow/total assets 0.123*** 0.055***
Sales growth 0.041*** 0.006*
Log(total assets) 0.005*** -0.002***
Change in cash 0.159
Change in leverage 0.161**
Lagged leverage -0.013***
Lagged cash
-0.060***
Country dummies Yes Yes
Year dummies Yes Yes
Industry dummies Yes Yes
N 24,771 24,771
49
Table 5. (continued)
Panel B.
Change in Debt (1)
Change in Cash (2)
Listed -0.027*** -0.007**
Cash Flow/total Assets -0.078** 0.151***
Listed x cash flow/total assets 0.159*** 0.065***
Sales growth 0.044*** 0.016***
Log(total assets) 0.080*** 0.013***
Change in cash -0.113* -0.044
Change in leverage
Lagged leverage -0.066* Lagged cash
-0.353***
Year dummies Yes Yes
Firm dummies Yes Yes
N 6,233 6,233
50
Table 6. Differences in Cash Holdings of Listed and Unlisted Firms and Private Debt
Market Development
The table presents results of OLS regressions for the matched sample. We run regressions on two
subsamples of firms: firms in countries with above median debt market development and firms in
countries with below median debt market development. In Panel A, we measure debt market
development with Private Credit/GDP, which we get from Djankov et al. (2007). In Panel B, we
use an index on creditor rights from La Porta, et al. (1998). The dependent variable is cash and
cash equivalents divided by total assets. Listed is an indicator variable for the firm being listed
on a major exchange. Each regression includes the same set of control variables that is included
in Table 3. Each regression includes country, year and industry dummies. The estimation
procedures correct standard errors for clustering at the firm level. We test for the null hypothesis
that the coefficients are equal across the two models using seemingly unrelated estimation. ***,
**, * denote statistical significance at the 1%, 5% and 10% levels, respectively.
Panel A.
Private Credit/GDP
Above median (1)
Below median (2)
Diff.
Listed 0.013** 0.052*** -0.039***
Firm-level controls Yes Yes
Country dummies Yes Yes
Year dummies Yes Yes
Industry dummies Yes Yes
N 8,439 7,938
Adjusted R2 0.2338 0.2143
Panel B.
Creditor Rights
Above median (1)
Below median (2)
Diff.
Listed 0.013** 0.045*** -0.032***
Firm-level controls Yes Yes
Country dummies Yes Yes
Year dummies Yes Yes
Industry dummies Yes Yes
N 7,245 9,132
Adjusted R2 0.2384 0.2148
51
Table 7. Cash Holdings of Listed and Unlisted, and Legal and Financial Development
The table presents results of OLS regressions for listed and unlisted firms for the matched
sample. In Panel A we control for debt market development with Private Credit/GDP from
Djankov, McLeish, and Shleifer (2007), and in Panel B with Creditor rights index from La Porta,
et al. (1998). The dependent variable is cash and cash equivalents divided by total assets. The
anti-self-dealing index is from Djankov et al. (2008). The stock market development index is
constructed from World Bank data following Dermiguc-Kunt and Levine (1996). GDP is from
the World Bank. Each regression includes the same set of control variables that is included in
Table 3. The estimation procedures correct standard errors for clustering at the firm level. ***,
**, * denote statistical significance at the 1%, 5% and 10% levels, respectively.
Panel A.
Unlisted Listed
(1) (2) (3) (4)
Private credit/GDP 0.022*** 0.025*** -0.014*** 0.001
Anti-self-dealing 0.001 -0.030***
Stock market development -0.006 -0.024**
Log(GDP) -0.005 0.049***
Firm-level controls Yes Yes Yes Yes
Year dummies Yes Yes Yes Yes
Industry dummies Yes Yes Yes Yes
N 7,022 7,022 9,355 9,355
R2 0.1622 0.1624 0.2619 0.2682
Panel B.
Unlisted Listed
(1) (2) (3) (4)
Creditor rights index 0.028*** 0.031*** -0.012** 0.001
Anti-self-dealing -0.003 -0.031***
Stock market development -0.006 -0.026**
Log(GDP) -0.012 0.049***
Firm-level controls Yes Yes Yes Yes
Year dummies Yes Yes Yes Yes
Industry dummies Yes Yes Yes Yes
N 7,022 7,022 9,355 9,355
R2 0.1641 0.1642 0.2682 0.2682
52
Table 8. Cash Holdings of Listed and Unlisted, and Cost of Loans
This table presents results of OLS regressions for listed and unlisted firms for the matched
sample. The dependent variable is cash and cash equivalents divided by total assets. Cost of
loans is country-level interest rates from the European Central Bank calculated using all
outstanding loans with a maturity of 1 year or less made by monetary financial institutions in a
given country to non-financial corporations. Each regression includes the same set of firm-level
control variables that is included in Table 3. The estimation procedures correct standard errors
for clustering at the firm level. ***, **, * denote statistical significance at the 1%, 5% and 10%
levels, respectively.
Panel A.
Unlisted
(1) Listed
(2)
Cost of loans -0.008*** 0.002
Firm-level controls Yes Yes
Year dummies Yes Yes
Firm dummies Yes Yes
N 6,465 9,051
R2 0.7897 0.7620
Panel B.
Unlisted
(1) Listed
(2)
Cost of loans -0.014*** 0.004
Cost x (D=1 if high cr. rights) 0.005** -0.001
Firm-level controls Yes Yes
Year dummies Yes Yes
Firm dummies Yes Yes
N 6,465 9,051
R2 0.7899 0.7620
53
Table 9. Precautionary Benefits and Cash Holdings
The table presents results of OLS regressions for the matched sample. Panel A includes separate regressions for listed and unlisted
firms, while panel B presents regressions for the sub-sample of unlisted firms only. The dependent variable is cash and cash
equivalents divided by total assets. Listed is an indicator variable for the firm being listed on a major stock exchange. Leverage is
measured as total debt divided by the sum of total debt and shareholder funds. Total debt is the sum of long term debt plus short term
loans. Sales growth is computed as the one-year change in sales divided by beginning-of-period sales. Standard deviation of cash
flows is the standard deviation of current and the past four cash flows to assets. If two or more cash flows to assets are missing, then
the variable is set to missing. Investment in tangible assets is the one-year change in the value of tangible fixed assets divided by
lagged assets. Investment in intangible assets is the one-year change in the value of intangible fixed assets divided by lagged assets.
Net working capital is current liabilities minus current assets minus cash divided by lagged assets. Firm age is years since
incorporation. We compute an industry’s external equity dependence as in Rajan and Zingales (1998). Creditor rights index is from La
Porta, et al., (1998). Firm-level controls in Panel B are the same as those in Panel A and in Table 3. We test for the null hypothesis that
the coefficients are equal across the two models using seemingly unrelated estimation. The estimation procedures correct standard
errors for clustering at the firm level. ***, **, * denote statistical significance at the 1%, 5% and 10% levels, respectively.
54
Table 9. (continued)
Panel A.
Listed Unlisted Listed Unlisted Listed Unlisted Listed Unlisted
(1) (2) (3) (4) (5) (6) (7) (8)
Leverage -0.192*** -0.097*** -0.187*** -0.105*** -0.183*** -0.102*** -0.194*** -0.097***
Log(total assets) -0.005*** -0.012*** -0.006*** -0.012*** -0.004*** -0.012*** -0.004*** -0.014***
Sales growth 0.010* 0.016*** 0.004 0.016* 0.011** 0.015** 0.015*** 0.019***
Cash flow/total assets 0.291*** 0.207*** 0.291*** 0.221*** 0.321*** 0.203*** 0.273*** 0.190***
Std. Dev. of cash flows 0.290*** -0.008 0.263*** 0.023 0.250*** 0.005 0.285*** -0.008
Inv. in tangible assets -0.158*** -0.154*** -0.121*** -0.138*** -0.165*** -0.152*** -0.156*** -0.158***
Inv. in intangible assets 0.072** -0.104* 0.056 -0.044 0.104*** -0.117* 0.041 -0.100
Working capital (net of cash) -0.130*** -0.150*** -0.145*** -0.148*** -0.122*** -0.148*** -0.115*** -0.145***
Firm age -0.000*** 0.000 0.000*** 0.000** 0.000*** 0.000 -0.003*** -0.000**
Market-to-Book Industry
0.016*** -0.009 Global PE ratio
0.0004** 0.000
External equity dependence
0.021*** 0.005
Country dummies Yes Yes Yes Yes Yes Yes Yes Yes
Year dummies Yes Yes Yes Yes Yes Yes Yes Yes
Industry dummies Yes Yes Yes Yes Yes Yes No No
N 9,355 7,022 3,807 2,808 6,900 5,788 8,517 6,373
Adjusted R2 0.2835 0.1666 0.2927 0.1733 0.2889 0.1649 0.2851 0.1524
55
Table 9. (continued)
Panel B.
Creditor Rights
Above median (1)
Below median (2)
Diff.
Std. dev. of cash flows -0.061 0.117 -0.178
Market-to-book industry -0.022 0.003 -0.025
Firm-level controls Yes Yes
Country dummies Yes Yes
Year dummies Yes Yes
Industry dummies Yes Yes
N 1,555 1,253
Adjusted R2 0.2245 0.1340
56
Table 10. External equity dependence, Creditor rights, and Cash Holdings
The table presents results of OLS regressions for the matched sample. The dependent variable is
cash and cash equivalents divided by total assets. Listed is an indicator variable for the firm
being listed on a major stock exchange. We compute an industry’s external equity dependence as
in Rajan and Zingales (1998). Creditor rights index is from La Porta, et al. (1998). Firm-level
controls are the same as those in Panel A and in Table 3. We test for the null hypothesis that the
coefficients are equal across the two models using seemingly unrelated estimation. The
estimation procedures correct standard errors for clustering at the firm level. ***, **, * denote
statistical significance at the 1%, 5% and 10% levels, respectively.
External equity dependence
Above median (1)
Below median (2)
Diff.
Creditor Rights above median
Listed 0.028*** -0.010 0.038***
Firm-level controls Yes Yes
Country dummies Yes Yes
Year dummies Yes Yes
Industry dummies Yes Yes
N 3,146 3,479
Adjusted R2 0.2375 0.2325
Creditor Rights below median
Listed 0.059*** 0.035*** 0.024**
Firm-level controls Yes Yes
Country dummies Yes Yes
Year dummies Yes Yes
Industry dummies Yes Yes
N 3,113 5,152
Adjusted R2 0.2271 0.2253
57
Figure 1. Cash Holdings for Listed and Unlisted Firms across Time
The figure contains average cash holdings to total assets ratio for listed and unlisted firms from 1997
through 2010.
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
19971998 1999 2000 2001 2002 20032004 2005 2006 2007 20082009 2010
Cas
h h
old
ings
/ T
ota
l ass
ets
Year
Unlisted
Listed
58
Appendix 1. Cash holdings and Hedging Needs
The table presents results of OLS regressions for the matched sample. The dependent variable is
cash and cash equivalents divided by total assets. Listed is an indicator variable for the firm
being listed on a major stock exchange. Hedging needs is the correlation between investment
opportunities and the firm’s cash flow, where investment opportunities is the median three-year-
ahead sales growth rate in the firm’s three-digit SIC industry. Low hedging needs is a dummy
variable that takes the value of one when the correlation between investment opportunities and
the firm’s cash flow is above 0.20, and zero otherwise. High hedging needs is a dummy variable
that takes the value of one when the correlation between investment opportunities and the firm’s
cash flow is below -0.20, and zero otherwise. Each regression includes the same set of firm-level
control variables that is included in Table 3. The estimation procedures correct standard errors
for clustering at the firm level. ***, **, * denote statistical significance at the 1%, 5% and 10%
levels, respectively.
(1) (2)
Listed 0.032*** 0.032***
Hedging needs 0.003
Low hedging needs 0.001
High hedging needs -0.001
Firm-level controls Yes Yes
Country dummies Yes Yes
Year dummies Yes Yes
Industry dummies Yes Yes
N 14,910 14,910
Adjusted R2 0.2116 0.2175