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1
State Ownership, Soft-Budget Constraint and Cash Holdings:
Evidence from China’s Privatized Firms
William L. Megginson
Price College of BusinessThe University of Oklahoma
Zuobao Wei
College of Business AdministrationUniversity of Texas at El Paso
Current draft: November 14, 2012
Abstract
We study the relation between state ownership and cash holdings in China’s share-issue privatized firmsfrom 1993 to 2007. We find that the level of cash holdings declines as state ownership increases. Thisnegative relation is attributable to the soft-budget constraint (SBC) inherent in state ownership. TheChinese financial system is dominated by the state-owned banks, an environment very conducive for theSBC effect. We further examine and quantify the effect of state ownership on the value of cash and findthat the marginal value of cash declines as state ownership increases. The next RMB added to cash
reserves of the average firm is valued at RMB 0.94 by the market. The marginal value of cash in firmswith zero state ownership is RMB 0.33 – RMB 0.47 higher than in firms with majority state ownership.The SBC effect exacerbates agency problems inherent in state-controlled enterprises, contributing to thelower value of cash.
JEL Classification: G32, G34, G38Keywords: Privatization, State Ownership, Soft Budget Constraint, Cash Holdings, China
Please address correspondence to:
Zuobao WeiDepartment of Economics and FinanceCollege of Business AdministrationThe University of Texas at El PasoEl Paso, Texas 79968Tel: (915)747-5381; Fax: (915) 747-6282e-mail: [email protected]
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1
State Ownership, Soft-Budget Constraint and Cash Holdings:
Evidence from China’s Privatized Firms
Abstract
We study the relation between state ownership and cash holdings in China’s share-issue privatized firms
from 1993 to 2007. We find that the level of cash holdings declines as state ownership increases. This
negative relation is attributable to the soft-budget constraint (SBC) inherent in state ownership. The
Chinese financial system is dominated by the state-owned banks, an environment very conducive for the
SBC effect. We further examine and quantify the effect of state ownership on the value of cash and find
that the marginal value of cash declines as state ownership increases. The next RMB added to cash
reserves of the average firm is valued at RMB 0.94 by the market. The marginal value of cash in firms
with zero state ownership is RMB 0.33 – RMB 0.47 higher than in firms with majority state ownership.
The SBC effect exacerbates agency problems inherent in state-controlled enterprises, contributing to the
lower value of cash.
JEL Classification: G32, G34, G38
Keywords: Privatization, State Ownership, Soft Budget Constraint, Cash Holdings, China
November 14, 2012
We thank Jarrad Harford, Ivalina Kalcheva, Sifei Li, Karl Lins, Yixin Liu, Yu Liu, Neslihan Ozkan, Meijun Qian,Valerity Sibilkov, and Feixue Xie for helpful comments and suggestions. We would also like to thank the Universityof Oklahoma’s Price College of Business and George Lynn Cross Research Professorship, as well as the Universityof Texas at El Paso’s College of Business Administration for providing financial support for this research. We thank Chen Liu, our discussant at the 2012 FMA in Atlanta, Georgia for helpful comments.
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State Ownership, Soft-Budget Constraint and Cash Holdings:
Evidence from China’s Privatized Firms
1. Introduction
The efficient management of liquidity is essential to the success of any business. Over the past
decade or so, financial economists have devoted much attention to the causes and consequences of the
large and increasing amount of liquid assets on corporate balance sheets around the world. Scholars have
developed various explanations for the cross-section, cross-country and time-series variations in corporate
cash holdings. 1 These explanations include the motives for holding cash, the tradeoff theory between the
marginal costs and benefits of holding cash, the degree of financial constraints, corporate governance and
agency theory, and country level law and institution development. In this paper, we employ a well-
established theory in economics, the soft-budget constraint (SBC) theory, to explain cash holdings in
share-issue privatized (SIP) firms, an important group of publicly traded firms that has received limited
attention so far in the growing body of literature on corporate cash holdings.2 We center our study on the
relation between state ownership and cash holdings in China’s SIP firms.
Share-issue privatized firms differ from other publicly traded firms (the de novo private firms) in
one very important aspect: the privatizing governments continue to wield substantial influence or control
in the SIP firms through retained equity ownership and/or “golden shares.” In the case of China, most of
the publicly traded companies are former state-owned enterprises (SOE) that were privatized through
share offerings starting in the early 1990s. The Chinese government retains an average (median) of 30.5%
1 See Kim, Mauer and Sherman (1998), Harford (1999), Opler, Pinkowitz, Stulz and Williamson (1999), Dittmar,Mahrt-Smith and Servaes (2003), Almeida, Campello and Weisbach (2004), Pinkowitz, Stulz and Williamson(2006), Faulkender and Wang (2006), Dittmar and Mahrt-Smith (2007), Kalcheva and Lins (2007), Harford, Mansiand Maxwell (2008), Bates, Kahle and Stulz (2009), Liu and Mauer (2011), and many others.
2 Over the past four decades, governments around the world have undertaken massive privatization programs(Megginson and Netter, 2001; Megginson, 2005; Bortolotti and Megginson, 2012). The cumulative proceeds raised by privatizing governments since 1977 now exceeds $2.5 trillion. Privatization has taken place in developed anddeveloping economies and across all political persuasions. Many governments chose share-issue privations (SIP), asopposed to direct asset sales. Measured by the amount of proceeds raised, SIPs are by far the largest share offeringsin history. The share issue privatization of China’s state-owned Industrial and Commercial Bank of China (ICBC) in2006 raised a staggering US$22 billion (McGuiness and Keasey 2010). The biggest IPO of all time by a U.S.company, the November 2010 IPO of General Motors after it emerged from bankruptcy, was also a privatization inthat the federal government sold roughly half of the stake it acquired in GM in the 2008 rescue.
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(32.18%) equity ownership in our sample firms. Other privatizing governments also retain ownership in
the SIP firms. Jones, Megginson, Nash and Netter (1999) study share-issue privatization programs in 59
countries and find evidence of continued political interventions. The governments in their sample sell an
average (median) of 43.9% (35.0%) of the SOE’s capital in initial offers and 22.7% (18.1%) in
subsequent seasoned issues, suggesting that the governments retain significant equity ownership in the
SIP firms. Though these governments may have surrendered the day-to-day management of the firm post-
privatization, many retain effective control by employing a variety of measures. One of the commonly
used techniques is the creation of a “golden share” that gives the privatizing government veto power on
major corporate decisions, such as top personnel decisions, mergers and acquisitions, and foreign
takeovers.
State ownership has long been linked to inefficiency and underperformance (Shleifer and Vishny,
1994; Boycko, Shleifer and Vishny, 1996; Megginson and Netter, 2001). A major cause is that SOEs are
more likely than private firms to suffer from the soft-budget constraint (SBC) effect first formulated by
Kornai (1979, 1980). In a nutshell, the SBC theory predicts that an organization with a budget constraint
(the BC-organization) can always count on a supporting organization (the S-organization) to bail it out
when its budget constraint is persistently breached (a brief discussion of the SBC theory is presented in
Section 2). The SBC theory in economics has a direct parallel in the financial constraint literature in
corporate finance. These two strands of literature are closely related in that they are both concerned with
the firm’s liquidity position and access to credit. Almeida, Campello and Weisbach (2004) use five
variables to measure firm level financial constraint; the dividend payout ratio, asset size, whether or not a
firm’s debt is rated, whether or not a firm’s commercial paper is rated, and the KZ index developed by
Kaplan and Zingales (1997). Several recent studies have shown that financially constrained firms hold
more cash than unconstrained firms (Almeida, Campello and Weisbach 2004, Faulkender and Wang 2006,
Denis and Sibilkov 2010). The SBC theory suggests that firms with high state ownership tend to have
softer budget constraints. State-owned enterprises in China have better access to credit in state-owned
banks and can expect to receive financial help in times of distress (Lin and Tan 1999, Cull and Xu 2000
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and 2003). A firm with a SBC does not have the need to hold high levels of cash since credit is readily
available. Therefore, we expect cash holdings and state ownership to be negatively related.
The second main objective of this study is to examine and quantify the effect of state ownership
on the value of cash to shareholders. Faulkender and Wang (2006) show that the value of the next dollar
added a firm’s cash reserves depends on the firm’s current level of cash and the current level of debt,
among other firm characteristics. Several recent studies further show that high agency costs are associated
with low marginal values of cash (Pinkowitz, Stulz and Williamson, 2006; Dittmar and Mahrt-Smith,
2007; Kalcheva and Lins, 2007; and Maurer and Liu, 2011). State ownership has been linked to agency
problems due to ineffective monitoring mechanisms and poor managerial incentives in state-owned firms,
among other reasons (Shleifer and Vishny 1994; Boycho, Shleifer and Vishny 1996; Megginson and
Netter 2001; Sun and Tong 2003). Agency theory suggests that those who control the firm use corporate
resources to further their own interests (Jensen 1986). One type of resources, liquid assets, provides a
particularly attractive opportunity for managers to expropriate, since liquid assets are readily accessible
by managers and do not attract much scrutiny from capital markets or other outside stakeholders. The
inherent SBC effect associated with state ownership further exacerbates agency problems due to moral
hazard (Kornai 2001). Therefore, we expect a negative relation between state ownership and the marginal
value of cash.
China’s share-issue privatized firms and the institutional environment within which they operate
provide an excellent setting to examine the dynamic relationships among state ownership, soft-budget
constraint and cash holdings. First, there is a wide cross-sectional variation of state ownership in China’s
SIP firms, ranging from zero to almost 90% in our sample firms. Second, the SIP firms operate in a
financial system dominated by state-owned banks (a brief description of the Chinese financial system is
presented in Section 2). Such an environment provides fertile ground for the SBC effect to take hold (Lin
and Tan 1999). Third, cash holdings in China’s publicly traded firms are large and increasing. At the end
of 2007, the total amount of liquid assets held by China’s publicly traded non-financial firms was
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approximately RMB 1.51 trillion (about US$204 billion).3 In fact, the cash ratio increased steadily over
our sample period from a median of 5.4% in 1993 to 17.4% in 2007.4 These numbers show that, as in U.S.
corporations, investment in liquid assets is important to Chinese companies. However, scholars thus far
have paid very little attention to the causes and consequences of cash holdings in China’s listed sector,
even though China has the world’s second largest economy and (some years) the second largest stock
market by market capitalization (Allen, Qian and Qian, 2005). In this regard, our study contributes to the
cash holding literature by filling the gap with the first comprehensive empirical evidence from China.
We first examine the relation between state ownership and cash holdings while controlling for
other variables. We find that state ownership and cash holdings are negatively related, consistent with our
hypothesis that high state ownership leads to softer budget constraints, and therefore less need to hold
high levels of cash. We further find that smaller, more profitable and high growth firms hold more cash,
and that debt and net working capital are negatively related to cash holdings, consistent with findings in
U.S. and other international firms. In the second part of the paper, we examine and quantify the effect of
state ownership on the marginal value of cash to shareholders, and find that the marginal value of cash
declines as state ownership increases. For the average firm in our sample, the next RMB of cash added to
its cash reserves is valued at RMB 0.94, whereas the next RMB of cash is valued at RMB 0.33 – RMB 0.47
higher in firms with zero state ownership than in firms with majority state ownership. As the government
reduces its stake in SIP firms, agency problems are mitigated, leading to higher marginal value of cash. A
related explanation is that as state ownership declines, access to credit in state-owned banks becomes
harder, SO the budget constraint hardens. Firms with hard budget constraint invest their funds more
efficiently and receive higher returns on capital, leading to higher marginal values of cash (Kornai 2001,
Denis and Sibilkov 2010).
3 Liquid assets (or cash reserves) include cash and marketable securities at the end of 2007 across all non-financialfirms in our database. The Chinese currency is called renminbi (people’s money), or RMB. The exchange rate at theend of 2007 was RMB7.36718/US$ (IMF International Financial Statistics).
4 The cash ratio is defined as cash reserves over total book assets minus cash reserves.
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The rest of the paper is organized as follows. Section 2 discusses the soft-budget constraint theory
and provides a snapshot of the Chinese financial system. Section 3 examines the determinants of cash
holdings, while Section 4 is devoted to the analysis of the marginal value of cash. Section 5 provides
concluding remarks.
2. Soft-Budget Constraint Theory and the Chinese Financial System
This section discusses the theoretical foundation of our paper, the soft-budget constraint theory
pioneered by János Kornai (1979, 1980). We discuss the SBC theory in the context of the Chinese
financial system within which China’s privatized firms operate.
2.1. The Soft-budget constraint theory
János Kornai observed in the 1970s that the chronic loss-making Hungarian state-owned
enterprises were never allowed to fail during that country’s experiment with market reforms (Kornai,
1979, 1980). These firms were always rescued, or bailed out, by government subsidies or other
arrangements. Kornai dubbed this phenomenon the SBC effect. The SBC effect has an inter-temporal
nature: a firm in financial distress expects to be rescued and this expectation in turn affects its behavior.
Since Kornai (1979, 1980), the SBC theory has been formally modeled and empirically tested in the
context of transition economies by Dewattripont and Maskin (1995), Schaffer (1998), Berglöf and Roland
(1998), Lin, Cai and Li (1998), Frydman, Gray, Hessel and Rapaczynzky (1999), among others. The
SBC theory has been a very important workhorse for economists involved in studying and formulating
economic policy for the post-socialist and transition economies (Kornai, Maskin and Roland 2003).
The SBC effect involves the dynamics between a pair of organizations, the budget-constraint
organization (BC-organization) that expects to be bailed out when in financial trouble and the supporting
organization (S-organization) that comes to its rescue. A BC-organization is constrained by its liquidity
position, access to credit, or debt burdens. A BC-organization is said to face a hard-budget constraint
(HBC) if it does not receive support from S-organizations when deficit occurs. It must reduce costs or
restructure, or it will cease operations if the deficit persists. The SBC phenomenon occurs if an S-
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organization is ready to cover all or part of the deficit. Kornai, Maskin and Roland (2003) list five pairs
of “BC-organization and S-organization” dynamics. The first is the dynamics between state-owned
enterprises (BC-organizations) in transition economies and governments (S-organizations). Most SBC
research focuses on this pair.
The SBC effect is also observed in banks and other financial intermediaries, though the term
“SBC effect” is rarely used in the media or finance literature. In the modern history of financial crises,
and as recently as the 2008-09 great recession, large banks or financial intermediaries (e.g. AIG) are
rarely allowed to fail, so “too big to fail” in this case meets the criterion of the SBC effect perfectly. The
other three types of BC-organizations that also potentially suffer from the SBC effect are non-profit
organizations, indebted local governments and national economies. If the troubled non-profit organization
provides a vital service, the government or other non-profits will come in and provide support. A
troubled local government has a SBC because it can either rely on the central government for rescue or
raise taxes to deal with the deficits. National economies can also suffer from SBC effect. Countries that
have become insolvent due to persistent deficits and untenable debt burdens can expect to be bailed out
by international financial agencies or the international financial community (Fischer 1999). The ongoing
Euro crisis and the bailouts of the Greek and other Euro-zone economies provide new evidence that the
SBC effect can strike national economies.
The motives for a troubled BC-organization to request subsidy or other assistance are self-evident.
The livelihoods, privileges, and prestige of leaders of the BC-organizations are dependent upon their
leadership positions. Hence, leaders of the troubled BC-organizations can be expected to do everything in
their power to fight for survival. The motives for an S-organization to bail out a troubled BC-
organization require more elaboration. Kornai, Maskin and Roland (2003) list six potential motives for
an S-organization to bail out a distressed BC-organization: its own best business interest, paternalism,
political, reputational, spillover effects, and corrupt influences.
In cases where the S-organization is a profit-maximizing bank or an investor (a hedge fund or a
private equity fund) that has made invested substantial investments in the troubled BC-organization, it
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may be in their best business interest to extend more credit or invest more capital in hope of recouping
past investment (Dewatripont and Maskin 1995). When a state-owned enterprise (SOE) is in financial
trouble and failing, government officials may feel protective and responsible for its failure. Paternalism
may motivate the government officials to bailout the troubled SOE (Kornai, 1980). Politicians in
democratic societies need to win elections, while politicians in non-democratic societies desire job
security and legitimacy. In either case, high employment levels are crucial to their political future
(Shleifer and Vishny, 1994). When a SOE or private firm operating in the politician’s district is failing,
he or she is politically motivated to use his influence to secure extra credit, subsidy or other arrangements
to insure the SOE’s survival (Shleifer and Vishny 1994).
When a BC-organization is operating under multi-level hierarchical control, leaders at the top
may have reputational incentives to prevent financial failure at the lower level. A bailout is most likely
when a failure will be so spectacular that it brings substantial bad publicity on the entire enterprise.
Spillover effects can be a major concern if a large enterprise fails. The 2009 U.S. auto bailout is a good
example. Politicians in both parties recognized that if General Motor and Chrysler were allowed to fail,
the spillover effects on the peripheral industries could be enormous. Concern for the potential spillover
effects was a major reason Congress approved the auto bailout loans. Examples of corrupt influences
include cronyism and outright bribery.
An S-organization can deploy one of three types of means or instruments to soften a budget
constraint: fiscal means, credits, or indirect supports (Kornai, Maskin and Roland 2003). Fiscal means
include subsidies and tax concessions granted to a distressed BC-organization. State-owned banks under
pressure from politicians may offer loans to financially distressed SOEs that would not have been eligible
for credit if normal lending standards applied. Banks or other creditors can also relax the terms of
existing loans to save a failing BC-organization. Some of the oft-used indirect methods include erecting
barriers to entry for foreign competitors and mandating that certain government agencies purchase
products from the troubled firm. Kornai (2001) observes that a government usually deploys fiscal means
first. When fiscal means are restricted, then credit methods, such as soft loans, will follow.
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There is a large empirical literature on SBC theory, most of which focuses on transition
economies (Maskin 1999, Kornai, Maskin and Roland 2003). Quoted below is a paragraph that is highly
relevant to our study:
“Specifically, researchers have asked: Is a state-owned enterprise more likely to count on abailout than a private firm? Does a privatized firm have better chances of state rescue than a denovo private firm? Do privatization and bolstering the private sector reinforce the trend toward hardening the budget constraint? Affirmative answers come from a succession of studies: Giles Alfandari, Qimaio Fan and Lev Freinkman (1996); EBRD (2000); James Anderson, GeorgesKorsun and Murrell (2000); Roman Frydman et al. (2000); and Schaffer (1998). It is alsoshown that demonopolization helps harden the budget constraint (Lubomir Lizal, Miroslav Singer and Jan Svejnar, 2001)” (Kornai, Maskin and Roland 2003, pp. 1100)
The evidence cited above provides the empirical foundation for our study: Firms with high state
ownership have softer budget constraints and therefore suffer from a more severe case of the SBC effect
than do de novo private firms.
2.2. A snapshot of the Chinese financial system 5
The People’s Republic of China (PRC) was founded in 1949. Soon thereafter, private ownership
of business was outlawed and private businesses were nationalized in the subsequent decade. Between
1950 and 1978, the year the economic reform started, the People’s of Bank of China (PBOC) was the
Chinese financial system. PBOC was owned by the central government and managed by the Ministry of
Finance. It served both as China’s monetary authority (central bank) and its sole commercial bank.
Through its nationwide branches, PBOC controlled about 93% of China’s total financial assets and almost
all of the financial transactions at the onset of the 1978 economic reform (Allen et al. 2011).
The first decade of the reform was characterized by two major initiatives, the agriculture reform
and the financial reform. The agriculture reform abolished the people’s communes and allowed farmers
to keep some of the profits derived from farming in their assigned lots ( fen dian dao hu 分田到户 ). The
financial reform separated the central banking and commercial banking functions of PBOC. The PBOC
keeps its central banking role as the monetary authority but the “big four” state-owned banks were
established or reassigned to take over the commercial lending and transaction activities. The Bank of
5 Ayyagari, Demirgu-Kunt and Maksimovic (2010) and Allen, Qian, Zhang and Zhao (2011) provide excellentreviews of the institutional details of the Chinese financial system.
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China (BOC) was given the business focus of dealing with foreign exchange markets and international
trades. The Construction Bank of China (CBC) was given the task of financing capital investments. The
Agriculture Bank of China (ABC) was formed to handle financial transactions in rural areas. And lastly,
the Industrial and Commercial Bank of China (ICBC) was set up to handle the remaining industrial and
commercial financing activities of the PBOC.
The financial reform was a prerequisite for the state-owned enterprise (SOE) reform that started
in earnest in the late 1980s and culminated by the establishment of China’s two stock exchanges in 1990,
the Shanghai Stock Exchange (SSE) and the Shenzhen Stock Exchange (SZSE). Almost all of the listed
firms in the two stock exchanges are former large SOEs that were restructured or carved out prior to
selling shares to the public. The “big four” state-owned banks have also become publicly traded
companies with shares listed on Hong Kong Stock Exchange (HKSE, H-shares) and/or SSE (A-shares).
CBC was listed on HKSE in 2005 and ICBC and BOC were listed on both HKSE and SSE in 2006. ABC
completed its listing on both HKSE and SSE in 2010. Though all “big four” have become listed banks,
the central government, through the China Investment Corporation (CIC, China’s sovereign fund) and the
Ministry of Finance, retains majority ownership in all of these banks (McGuiness and Keasey, 2010).
They all remain firmly under the state’s control. Since the establishment of SSE and SZSE, the stock
market has grown exponentially in terms of the number of listed firms and total market capitalization.
However, the bond market, especially the corporate bond market, is severely underdeveloped. Table 1
summarizes the growth and relative size of the main components of the Chinese financial system.
**** Insert Table 1 about here ****
At the end of 2007, the stock market had 1530 firms listed on either SSE or SZSE and a market
cap of over RMB 32.7 (about US$4.44) trillion. The bond market is dominated by treasury bonds and
policy bonds (similar to municipal bonds in the U.S.) and had about RMB 8.6 (US$1.17) trillion bonds
outstanding in 2007. However, the corporate bond market is very small compared to the stock market: it
had about RMB 0.77 (US$0.10) trillion corporate bonds outstanding in 2007, about 2.4% of the size of the
stock market.
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Table 1 also shows that bank loans dominate the Chinese financial system. In 2007, there were
over RMB 26 (US$3.55) trillion bank loans outstanding, approximately 33 times the size of the corporate
bond market. This suggests that most of the debt financing in China comes from bank loans, not the bond
market. This feature is important and highly relevant to our study. If a distressed firm must go through
the scrutiny of the bond market to get credit, its budget constraint would be hardened, ceteris paribus. If
instead it expects and receives credit from state-owned banks that have both the motives and means to
come to the rescue, this leads to a softened budget constraint (La Porta, Lopez-de-Silanes and Shleifer
2002). This dynamic has two implications for our study. First, managers in firms with high state
ownership do not feel the need to hold high level of cash because they can always count on easy credit
from state-owned banks in time of trouble. In fact, Cull and Xu (2000) document that state-owned banks
in China give preferential treatment to distressed state-owned firms when allocating credits, while
tolerating late or omitted payments on existing loans. Brandt and Li (2003) also provide direct evidence
that private firms in China are required to provide more collateral than state-owned firms to obtain the
same amount of credit. Several empirical studies also show that bank credits have become the principal
means of softening the budget constraints in many post-socialist countries (Coricelli and Djankov 2001;
Claessens and Djankov 1998; and Schaffer 1998).
**** Insert Table 1 about here ****
Second, SBCs exacerbate agency problems inherently related to state ownership. Because
managers in firms with SBC expect credit to be readily available in time of trouble, they may be
motivated to invest in politically expedient projects, as opposed to NPV maximizing ventures. The return
on investment from politically motivated projects should be lower than that from NPV maximizing ones.
Table 2 presents the nonperforming loans (NPL) to total loans ratios for selected countries in 2000-2007.
This shows that the Chinese banking sector has a significantly higher fraction of bad loans compared to
both the major developed nations and the other large developing countries. The average ratio of NPLs to
total loans in China over 2000-2007 is 17%, more than twice the level in India, the next highest country.
Allen, Qian and Qian (2005) document that a large fraction of these bad loans resulted from lending
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decisions based on political considerations or corrupt influences, rather than on project merits. In terms
of its relative size to the stock market, China’s banking system plays a much bigger role in
enterprise financing than in countries described in La Porta, Lopez-de-Silanes, Shleifer, and Vishny
(hereafter LLSV 1997, 1998) and Levine (2002). Allen, Qian and Qian (2005) also find that China’s law
and institutions--including investor protection, corporate governance, accounting standards, and quality of
government, are much less developed than most of the countries in LLSV (1997, 1998) and Levine (2002).
The overall institutional environment in China is very conducive for the SBC effect to take hold, even
after three decades of reform.
**** Insert Table 2 about here ****
3. The Determinants of Cash Holdings
This section examines the determinants of cash holdings with a focus on state ownership as an
explanatory variable. Data and summary statistics are presented in section 3.1, while univariate test
results are presented in section 3.2. Multivariate regression results and robustness tests are presented in
sections 3.3 and 3.4, respectively.
3.1. Data
The variables for our cash holding analysis are calculated from balance sheet and income
statement items in the CSMAR database (China Security Market and Accounting Research), which
contains accounting data and stock market data for all firms listed on Shanghai Stock Exchange (SSE) or
Shenzhen Stock Exchange (SZSE) since their establishment in 1990 and 1991, respectively.
We start with all firms listed on the two stock exchanges at any time during 1990-2007. We
follow customary practices to exclude financial firms, firm years with negative book equity, firm years
with negative net assets (book assets minus cash and equivalents), firm years with negative dividends, and
firm years that do not have the required stock returns or accounting data. We require a firm to have a
minimum of three consecutive years of data because we use the average sales growth rate over a three –
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year period to control for growth opportunities. The final sample consists of 9,862 firm years over the
period 1993-2007.
Summary statistics are presented in Table 3. Firm level ratios are winsorized at the top and
bottom 1%. The key variables in Table 3 are calculated as follows. Cash is cash plus equivalents. Net
assets is book assets less cash and equivalents. Real size is net assets scaled to 2007 values using China’s
CPI from the IMF website. Market-to-book ratio is book assets minus book equity, plus market value of
equity, all divided by book assets. Book leverage is short-term debt plus long-term debt over book assets.
Net working capital is current assets net of cash and equivalents minus current liabilities. Cash flow is net
income plus depreciation minus cash dividends paid to common stockholders. Capital expenditures is the
change in net fixed assets over the fiscal year plus depreciation for that year. The 3-yr growth rate is the
average of two sales growth rates over the previous three-year period. Industry sigma is the standard
deviation of an industry’s mean cash flows over the sample period from 1993-2007. Industries are
China’s 2-digit SIC equivalent B classifications defined by China Security Regulatory Commission
(CSRC). Payout ratio is cash dividends over net assets. State ownership is the fraction of total shares
retained by the state after share-issue privatization. Finally, Institutional ownership is the fraction of
shares owned by legal entities, such as mutual funds, insurance companies, pension funds, or another
firm. Many of these are controlled or owned by various levels of governments.
**** Insert Table 3 about here ****
Table 3 shows that the median firm has cash holdings of approximately 13% of net assets,
translating into about RMB 203 million, US$28 million in 2007 value. The median firm has net assets of
RMB 1564 million, or about US$212 million in 2007 value. Mean and median state ownership are 30.5%
and 32.2%, respectively.
Figure 1 illustrates the median cash holding of China’s privatized firms from 1993-2007. Figure 1
shows secular increases in cash holdings from 1993-2001, slight decreases from 2002-2004, and a marked
uptrend from 2005 to 2007. One possible explanation for this time trend is that the two Chinese stock
exchanges, the Shanghai Stock Exchange and the Shenzhen Stock Exchange, were established in 1990
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and 1991, respectively. All of the listed companies were relatively recent IPO firms, so the secular
increases in cash holdings from 1994-2001 coincides with a higher concentration of IPOs during that
period, relative to the period 2001-2007. Bates, Kahle and Stulz (2009) document that IPO firms hold
more cash than non-IPO firms in the United States, so we argue that the more recently a firm has its IPO,
the more cash it holds, all else equal. In our robustness checks, we control for firm age, defined as the
number of years since the IPO, and expect cash holdings to be negatively related to firm age.
**** Insert Figure 1 about here ****
3.2. Univariate tests
Table 4 presents univariate comparisons of firm characteristics across quartiles of cash holdings,
measured as cash-to-net assets. The cash quartiles are constructed each year. These comparisons provide a
first look at whether or not there are significant variations in firm characteristics between firms with high
cash holdings (4th quartile) and firms with low cash holdings (1st quartile). The last column in Table 4
presents t-tests of the significance of mean differences in firm characteristics between the first and fourth
quartiles.
**** Insert Table 4 about here ****
Firms in the fourth quartile differ significantly from firms in the first quartile of cash holdings, at
better than the 1% level, for all variables except for net working capital and industry cash flow volatility
(industry sigma). The univariate tests show that high-cash firms have lower state ownership. However,
simple univariate tests are not sufficient to draw conclusions concerning the determinants of corporate
cash holdings, so we proceed to perform multivariate regressions.
3.3. Multivariate regression results
State ownership is the focus of our analysis and the main explanatory variable. SBC theory
suggests that state ownership is inherently related to soft-budget constraints: the higher the state
ownership, the softer the budget constraint. If a firm can expect credit to be readily available when in
financial distress, it has less need to hold high levels of cash. Therefore, we expect a negative relation
between cash holdings and state ownership. We also include institutional ownership as a control variable
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for its potential effect on cash holdings. Shleifer and Vishny (1986) argue that large shareholders help
mitigate free rider problem and reduce managerial opportunism by forcing managers to disgorge excess
cash. Large shareholders also act to promote their own interest, which can differ from that of other
shareholders (Shleifer and Vishny 1997). Empirically, Harford, Mansi and Maxwell (2008) find no
significant relation between cash holdings and institutional ownership after controlling for lagged cash
holdings. In the case of China, many of the institutional owners are owned or controlled by various levels
of government, and they also have a profit maximizing motive (Wei, Xie and Zhang 2005). These two
characteristics point to a negative relation between institutional ownership and cash holdings.
In choosing other control variables for our multivariate models, we follow Opler, Pinkowitz,
Stulz, and Williamson (1999), Almeida, Campello and Weisbach (2004), and Kalcheva and Lins (2007).6
We control for firm size with a financial constraint proxy used in Almeida, Campello and Weisbach
(2004). As shown in Table 1, bank loans dominate debt financing in China. Most listed firms do not have
bonds outstanding, therefore bond or commercial paper ratings are not available as proxies for firms being
financially constrained. To receive bank loans, firms must use fixed assets as collateral or secure third
party guarantees (Allen, Qian and Qian 2005). Because small firms have less acceptable collaterals than
large firms, they are likely to be more financially constrained. Therefore, we expect smaller firms to hold
higher levels of cash to cope with unforeseen future liquidity shocks.
We also control for firm growth opportunities, industry cash flow volatility, capital expenditures,
debt, net working capital, and dividends. Firms with more growth opportunities are expected to hold
more cash because cautious managers are aware of the costs associated with underinvestment in positive
NPV projects if cash shortfalls occur. Firms that operate in an industry with more volatile cash flows are
expected to hold more cash to hedge against unforeseeable cash shortfalls. Firms expecting high capital
spending in the coming year are expected to hold more cash in the current year to avoid the costs
associated with being unable to make scheduled capital investments. Debt and working capital can serve
6 Fan, Wong and Zhang (2007) use hand collected data and find that about 27% of the CEOs in China’s listed firmsare politically connected. They find that politically connected firms underperform unconnected firms. Politicalconnectedness would be a logical control variable for this study, but for lack of data, it is not included in our model.
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as cash substitutes. Therefore, debt and working capital are expected to be negatively related to cash
holdings. The prediction of the effect of dividends on cash holdings is less clear. On the one hand,
dividend-paying firms can always cut or eliminate dividends to meet cash shortfalls. If this is the case,
dividend-paying firms are expected to hold less cash than non-paying firms. On the other hand, firms
rarely cut dividends, much less eliminate them, unless they are in deep financial distress. Dividend-
paying firms may hold more cash in anticipation of the dividends to be paid in the coming period.
The empirical results are presented in Table 5. Models in columns (1) and (2) employ only the
common determinants of cash holdings found in other studies (Opler, et al., 1999; Ozkan and Ozkan,
2004; and Kalcheva and Lins, 2007). Models columns (3) and (4) add state ownership and institutional
ownership as explanatory variables. Across all four models in Table 5, several consistent findings are
evident. Real firm size, leverage and net working capital are negatively and significantly related to cash
holdings at the one percent level, indicating that smaller firms hold a higher level of cash and that debt
and working capital may be treated as cash substitutes. Table 5 also shows that the cash ratio is
significantly positively related to firm profitability, growth opportunities, capital expenditures, and
industry cash flow volatility (industry sigma). These findings are consistent with our predictions and with
empirical evidence for U.S. firms (Opler, et al., 1999 and Hartford, 1999), and other international studies
(Dittmar, Mahrt-Smith and Servaes, 2003; Ozkan and Ozkan, 2004; and Kalcheva and Lins, 2007). Table
5 shows that dividend-paying firms hold a higher level of cash, contrary to findings in the studies cited
above, perhaps suggesting that dividend-paying firms seldom cut dividends even during adverse periods.
As discussed in Section 2, it is relatively more difficult to raise funds from the severely underdeveloped
corporate bond market and the seasoned equity market in China.7 Dividend-paying firms in China hold
more cash to avoid a situation in which they have to raise outside funds to support their dividend policies.
**** Insert Table 5 about here ****
7 The Chinese Security Regulatory Commission (CSRC) uses return of equity (ROE) as a criterion for rightsofferings and seasoned new issues. This criterion has changed over time. The latest requirement is that a firm musthave a three-year average ROE greater than 6% to be eligible for rights offerings and seasoned new issues (Liu andLu 2007).
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Columns (3) and (4) show that state ownership is negatively related to corporate cash holdings.
This finding is significant at the one percent level and consistent with the predictions of the SBC theory.
Institutional ownership is also negatively related to cash holdings, significant at the one percent level.
There are two plausible explanations for this. First, institutional investors such as mutual funds and
pension funds have the power to force firms to disgorge excess cash holdings and, second, many
institutional owners are state-owned or state-controlled and therefore have easy access to credit in state-
owned banks. The latter seems more plausible given that firms operate in a credit environment conducive
to the SBC effect.
3.4. Robustness checks 8
Table 6 provides two robustness checks, since some variables in Table 5 may be determined for
each firm jointly with its cash holdings. The static tradeoff theory suggests that firms choose leverage,
cash holdings, and investment level simultaneously, so to alleviate this concern, we follow Opler, et al.
(1999) and re-estimate the models in Table 6 after omitting the capital expenditures, leverage, and
dividend variables. The results, presented in Panel A of Table 6, largely support the same conclusions as
those in Table 5 for the non-omitted explanatory variables. In particular, our main conclusion that cash
holdings and state ownership are negatively related remains intact.
**** Insert Table 6 about here ****
Another concern regarding regression results in Table 5 is that some of the cash holdings may be
transitory because firms might have raised cash to spend on capital projects in the next period. To control
for this situation, we add as an explanatory variable the interaction between next year’s change in cash
holdings and next year’s capital expenditures. If firms hold transitory funds to be spent in the following
year on capital projects, this interaction term should be negatively related to current year’s cash holdings.
A control for firm age measured as number of years since IPO is also included. Following Bates, Kahle
and Stulz (2009), we argue that the more removed (older) a firm is from its IPO year, the lower its cash
8 For another robustness check, we use logarithm of the ratio of cash plus marketable securities over sales as analternative measure of cash holdings and re-estimate the models in Table 5. The main results are largely the same asthose in Table 5. These regression results are not included for the purpose of brevity but are available upon request.
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holdings, so firm age should have a negative coefficient. Adding the aforementioned interaction term and
firm age as additional control variables, we re-estimate the models in Table 5. The results are presented
in Panel B of Table 6 and are consistent with those in Table 5. Also, our main finding regarding the
relation between state ownership and cash holdings remains intact.
4. State Ownership and the Value of Cash
This section is devoted to analyzing the relation between state ownership and the marginal value
of cash in China’s privatized firms. Section 4.1 discusses the methodology employed in the analysis,
while section 4.2 discusses data and summary statistics for the value of cash analyses, and section 4.3
presents empirical results and robustness checks.
4.1. Methodology
To estimate the marginal value of cash, we employ the Faulkender and Wang (2006) model with
modifications: we add state ownership and the interaction between state ownership and the change in
cash to their original specifications. 9 Our model is as follows:
1,
,
4
1,
,
3
1,
,
2
1,
,
10,,
t i
t i
t i
t i
t i
t i
t i
t i B
t it i MVE
INT
MVE
NA
MVE
EBIT
MVE
CASH Rr
1,
,
1,
1,
8,7
1,
1,
6
1,
,
5
t i
t i
t i
t i
t i
t i
t i
t i
t i
MVE
CASH
MVE
CASH MLEV
MVE
CASH
MVE
DIV
t i
t i
t i
t it i
t i
t i
t i MVE
CASH STATE STATE
MVE
CASH MLEV ,
1,
,
,11,10
1,
,
,9
(1)
The dependent variable is excess annual stock return defined as B
t it i Rr ,, , where t ir , is firm i’s
monthly-compounded annual stock return in year t (fiscal year-end) and Bt i R , is stock i’s benchmark
portfolio return in year t . Faulkender and Wang (2006) use the 25 Fama and French (1993) portfolios
9 Our model does not include R&D and net new financing due to lack of data. Dittmar and Mahrt-Smith (2007) alsoemploy a modified version of the Faulkender and Wang (2006) model in their study of governance and value of cashstudy.
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formed on size and book-to-market ratio as their benchmark portfolios. Since there are no such portfolios
readily available for China’s stock market, we follow Fama and French (1993) and construct our own
benchmark portfolios on size and book-to-market ratios.10 Wang and Xu (2004) employ the Fama-French
three-factor model to study the Chinese stock market and find the explanatory power of three-factor
model increases from 81 to 90 percent of stock returns in the cross section when the fraction of tradable
shares is added to the model.
The independent variables in equation (1) are firm specific factors similar to those in Faulkender
and Wang (2006). They are defined as follows. CASH i,t is cash holdings for firm i at time t. INTi,t is
interest expenses. MLEVi,t is market value leverage and is calculated as short-term debt plus long-term
debt divided by the sum of short-term debt, long-term debt and market value of equity. NAi,t is net assets.
EBITi,t is operating earnings before interest and taxes. STATEi,t is state ownership. ΔXi,t indicates the
unexpected change in variable X for firm i from time t-1 to time t, Xi,t – Xi,t-1. We initially use the
realized change, assuming the expected change is zero, and then conduct various robustness tests using
alternative estimates of expected change in cash to calculate the unexpected change in cash holdings.
Since annual stock return is (MVEi,t-MVEi,t-1) divided by MVEi,t-1, and all the explanatory
variables except market leverage are scaled by lagged market value of equity, MVE i,t-1, this
standardization allows interpretation of the estimated coefficients as the dollar change in value for a one-
dollar change in the corresponding explanatory variable. Following Faulkender and Wang (2006), we
include two interaction terms,1,
,
1,
1,
t i
t i
t i
t i
MVE
CASH
MVE
CASH and
1,
,
,
t i
t i
t i MVE
CASH MLEV . We want to test what
effects cash levels and debt levels have on the marginal value of cash in China’s privatized firms. For U.S.
based firms, the coefficients of these two interaction terms are significantly negative, indicating the
marginal value of cash declines with larger cash holdings and higher leverage.
10 The steps of constructing our benchmark portfolios are detailed in Appendix A. We choose to construct 16 benchmark portfolios for the Chinese market instead of the 25 benchmark portfolios in Fama and French (1993).This is because in the early years of the Chinese stock market, the number of listed firms was relatively small (seeTable 1).
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We add a third interaction term to the model, the interaction between state ownership and change
in cash holdings,1,
,
,
t i
t i
t i MVE
CASH STATE . This interaction term captures the relationship between state
ownership and change in cash holding and is one of the main contributions of the paper. State ownership
has been linked to agency problems due to ineffective monitoring and poor incentives (Shleifer and
Vishny 1994, Boycko, Shleifer and Vishny 1996). The SBC effect inherent in state ownership
exacerbates these agency problems. Recent studies show that the marginal value of cash declines with
higher agency costs and poor corporate governance (Dittmar, Mahrt-Smith, and Servaes, 2003; Pinkowitz,
Stulz and Williamson, 2006; Kalcheva and Lins, 2007; Dittmar and Mahrt-Smith, 2007; Hartford, Mansi
and Maxwell, 2008; Liu and Mauer, 2011). In addition, Managers in firms with SBC may invest in
politically expedient projects, as opposed to NPV maximizing projects, leading to lower return on capital.
Therefore, we expect a negative coefficient for the interaction term,1,
,
,
t i
t i
t i MVE
CASH STATE .
4.2. Data for the value of cash analysis
We obtain accounting and stock return data for the value of cash analysis from the CSMAR
(China Security Market and Accounting Research) database. Details regarding the overall data and
sample selection are discussed in Section 3.1, while Table 7 below presents only the summary statistics
for data used in the value of cash analysis.
**** Insert Table 7 about here ****
All independent variables except book leverage are scaled by lagged market value of equity
(MVEt-1), thereby allowing us to interpret the coefficient of an independent variable as the dollar change
in value for a one-dollar change in the associated variable. The median one-year excess return is -3.27%
while the mean one-year excess return is a slightly negative -0.28%, consistent with the distribution of
abnormal returns being right-skewed. All of the independent variables are also right-skewed or slightly
right-skewed. The mean change of cash holdings (CASHt) is 1.55% of lagged market value of equity,
while the median change is 0.39%. The mean cash holding at the beginning of the year (CASHt-1) is
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11.27%, and the median is 7.40%. The mean change in operating income (EBITt) is 1.02% of lagged
market equity and the median change is 0.36%. The mean change in net assets ( NAt) is 8.36% of lagged
market equity while the median is 5.14%. The mean change in cash dividends (DIVt) is close to zero
and the median is zero. The mean market leverage (MLEV t) is 17.45% and median is 13.70%. The
distributional characteristics of these variables are similar to the U.S. based data as found in Faulkender
and Wang (2006) and others, although the magnitude is different for some variables. While American and
Chinese data are not directly comparable, it is helpful to gain a sense of what the China data looks like.
4.3. Empirical results
In this section, our main objective is to quantify the effect of state ownership on the value of the
next RMB cash raised or retained by the firm. We also wish to measure the marginal value of cash for the
average firm in our sample. Our third objective is to test whether or not the marginal value of cash
declines with higher level of cash and with higher level of debt, as is the case in U.S. studies (Faulkender
and Wang, 2006 and Dittmar and Mahrt-Smith, 2007). Section 4.3.1 reports the regression results for the
full sample, while robustness checks for the full sample are reported in Section 4.3.2. We then divide the
full sample into subsamples by state ownership and re-estimate equation (1), and report results in Section
4.3.3.
4.3.1. Full sample regression results
The full sample regression results are reported in Table 8. In column (1), the coefficient for
change in cash (ΔCASHt) is 0.783, indicating that one additional RMB added to cash holdings is valued at
RMB 0.78. The model in column (1) does not take into account the interactions between change in cash
and level of cash, or between change in cash and leverage. The regression reported in column (2) allows
these interactions. The coefficients for the interactions terms, ΔCASHt x CASHt-1 and ΔCASHt x MLEVt ,
are negative and significant at the one percent level, suggesting that the marginal value of cash declines
with higher levels of cash and with higher leverage. These findings are consistent with evidence found in
U.S. firms (Faulkender and Wang, 2006 and Dittmar and Mahrt-Smith, 2007). How the market values an
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extra RMB of cash depends not only on the amount of cash a firm already holds, but also on the amount of
debt it holds. For instance, consider two otherwise identical firms: Firm A has high debt and firm B has
low debt. An extra dollar raised is valued more highly in firm B than in firm A, since an extra dollar
raised will more likely be used to pay interest to creditors in firms with a high debt levels. Conversely, an
extra dollar raised in a low debt firm will more likely be invested in positive NPV projects or in other
productive activities. After allowing for these interactions, the coefficient corresponding to a change in
cash (ΔCASHt) increases dramatically, from 0.783 in column (1) to 1.328 in column (2). This result
suggests that, for an all-equity firm (MLEVt=0) that has zero cash holdings (CASHt-1=0), an extra RMB is
valued by shareholders at RMB 1.33. However, the average firm in our sample is not an all-equity
company with zero cash holdings but instead has 11.27% of lagged market equity in cash holdings and a
market leverage of 17.45%. Therefore, for the average sample firm, an extra RMB is valued at RMB 0.95,
as shown in Panel B of Table 8.11
A key prediction by the SBC theory is that state ownership has a negative effect on the market
valuation of corporate cash holdings. To test this prediction, we add state ownership (STATE t) and the
interaction between state ownership and change in cash (ΔCASHt STATEt ) as additional explanatory
variables, as shown in column (3) of Table 8. The coefficient corresponding to the change in cash
(ΔCASHt) is 1.448, suggesting that, for an all-equity firm (MLEV t=0) with zero cash holdings (CASHt-
1=0) and zero state ownership (STATEt=0), an extra RMB of cash added to its cash holdings is valued at
RMB1.45. For the average firm, an extra RMB is valued at RMB 0.94, as shown Panel B of Table 8.
Our main interests in column (3) of Table 8 are the coefficients of state ownership (STATE t) and
the coefficient of the interaction term (ΔCASHt STATEt ). Column (3) shows that the coefficient of
state ownership (STATEt) is negative and significant at the 10% level, suggesting that the level of state
ownership has a negative effect on the marginal value of cash. Furthermore, the coefficient of the
interaction term between state ownership and change in cash, ΔCASHt STATEt , is -0.426, significant at
11 The marginal value of cash calculation: 1.328+(-0.701*0.1127)+(-1.687*0.1745)= RMB 0.95.
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the 5% level, suggesting that an extra RMB raised is valued at RMB 0.43 higher in firms with zero state
ownership than in firms with 100% state ownership, ceteris paribus. This finding is consistent with the
prediction by SBC theory that state ownership has a negative effect on the marginal value of cash. A firm
with high state ownership has a soft-budget constraint and more agency problems. Therefore, managers
in high state ownership firms are more likely to misuse the extra RMB of cash raised for personal perks or
invest in politically expedient projects, as opposed to NPV maximizing projects.
**** Insert Table 8 about here ****
4.3.2. Alternative measures of unexpected change in cash: Robustness checks 12
We use the entire realized change in cash in the econometric models in Table 8, implicitly
assuming the entire change is unexpected. Since the dependent variable is annual excess stock return, the
expected portion of the change in cash may already be incorporated in the benchmark portfolio return. If
stock prices quickly reflect unexpected new information, excess stock return should be a function of only
the unexpected portion of the change in cash holdings. In our first robustness check, we follow
Faulkender and Wang (2006) and use two measures of unexpected change in cash holdings.
First, we use the average change in cash in the corresponding benchmark portfolio as the
expected change in cash. Thus, our first measure of unexpected change is the realized change in cash
minus the average change in cash in the benchmark portfolio. We re-estimate equation (1) and the results,
presented in columns (1) and (2) in Table 9, are consistent with those in Table 8. Our main prediction
remains intact that the marginal value of cash declines as state ownership increases. For the average firm
in our sample, an extra RMB of cash is valued by shareholders at RMB 1.01 – 1.03, slightly higher than
that in Table 8.
12 As another robustness check, we also follow Dittmar and Mahrt-Smith (2007) and Kalcheva and Lins (2007) andemploy market-to-book ratio (dependent variable) in our analysis. We find that the coefficient of the interactionterm between state ownership and excess cash is significantly negative. The results are not included for brevityreason and are available upon request.
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For the second measure of unexpected change in cash, we use a modified Almeida, Campello and
Weisbach (2004, ACW hereafter) model to estimate the expected change in cash. The model is as
follows:
1,
1,
2
1,
1,
10
1,
,
t i
t i
t i
t i
t i
t i
MVE
CAPEX
MVE
NWC
MVE
CASH
t it it i
t i
t i Size yr Growth MVE
STDBT ,1,51,4
1,
1,
3 3
(2)
Where Δ NWC is change in net working capital, CAPEX is capital expenditures, and ΔSTDBT is change
in short-term debt, all lagged and scaled by lagged market equity. Growth3yr is average sales growth rate
over the previous three years and SIZE is natural logarithm of book assets.13
We use a two-step OLS to
estimate equation (2). The first step is to estimate the coefficients in equation (2) for each industry, while
the second is to use the coefficients estimated in the first step to calculate the predicted (expected) change
in cash for each individual firm in that industry. The unexpected change in cash is therefore the realized
change in cash minus the change in cash estimated from equation (2). We proceed to re-estimate equation
(1) using the ACW measure of unexpected change in cash holdings, and the regression results are
reported in columns (3) and (4) of Table 9. These results are largely consistent with those in Table 8, in
that the marginal value of cash declines with higher leverage and higher state ownership. Again, our
main prediction remains robust in that state ownership has a significant and negative effect on the
marginal value of cash in China’s SIP firms. In extreme cases, an extra RMB is valued RMB 0.61 higher
in firms with zero state ownership than in firms with 100% state ownership, ceteris paribus. For the
average firm in our sample, an extra RMB of cash is valued at RMB 0.78 – RMB 0.81, as shown in Panel
B of Table 9.
**** Insert Table 9 about here ****
13 In the original ACW model, lagged Tobin’s Q is used instead of sales growth rate, while Δ NWC, CAPEX andΔSTDBT are scaled by lagged book assets instead of lagged market equity. R&D and acquisitions are included intheir original model. We do not include R&D and acquisitions in our model due to lack of data.
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4.3.3. State ownership and the value of cash: subsample regression results
The results from the full sample regressions show that state ownership has a negative effect on
the marginal value of cash, as shown in Tables 8 and 9. State ownership ranges from zero to almost 90%
in our sample. In the full sample of 9837 firm years, 2625 firm years have zero state ownership, 4275
firm years have between zero and 50% state ownership, and 2937 firm years have 50% or higher state
ownership. Studies have shown that ownership and firm value have a nonlinear relation (Demsetz and
Lehn 1985, McConnell and Servaes 1990, and Wei, Xie and Zhang 2005). The SBC theory predicts that
the relative softness of the budget constraint should manifest itself in the marginal value of cash across
different ranges of state ownership. To perform this robustness check, we divide the full sample into four
groups. Group 1 consists of 2625 firm years with zero state ownership and group 4 consists of 2937 firm
years with 50% or higher state ownership.14 The remaining firm years are sorted by state ownership and
then divided about equally into two groups: group 2 has 2137 firm years with 0.0 – 31.6% state
ownership and group 3 has 2138 firm year with 31.6 – 50.0% state ownership. We then re-estimate
Equation (1) across the four groups of firm years, excluding state ownership (STATEt) and the interaction
term (ΔCASHt STATEt) as explanatory variables. These stepwise regressions serve two purposes. They
provide another robustness check on the SBC prediction concerning the marginal value of cash across
different ranges of state ownership and, more importantly, they eliminate any potential endogeneity
between state ownership and excess stock returns, since state ownership is excluded as an explanatory
variable in the regression model. The subsample regression results are reported in Table 10.
**** Insert Table 10 about here ****
The main results in Table 10 are consistent with those in Table 8. Our focus is the marginal value
of cash across these four groups. The coefficients corresponding to change in cash (ΔCASHt) are 1.69 in
group 1, 1.44 in group 2, 1.07 in group 3, and 0.96 in group 4. These findings suggest that, for an all-
equity firm (MLEVt=0) that currently holds no cash (CASHt-1=0), an extra RMB of cash raised is valued
14 Ayyagari, Demirgüç-Kunt and Maksimovic (2010) also use 50% as the cutoff point for state-owned vs non-stateowned firms. Though there is no “golden share” in China, the government can often control a privatized firm withless than 50% directownership though other state-controlled institutional owners.
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at RMB 1.69 in group 1, RMB 1.44 in group 2, RMB 1.07 in group 3, and RMB 0.96 in group 4. For the
average firm in each group, an extra RMB is valued at RMB 1.15 in group 1, RMB 1.04 in group 2, RMB
0.81 in group 3, and RMB 0.78 in group 4, as shown in Panel B of Table 10. These results suggest that an
extra RMB is valued RMB 0.37 higher for the average firm in group 1 (zero state ownership) than for the
average firm in group 4 (50% or higher state ownership). This difference is statistically and economically
significant.
Like those in Table 8, the regression models in Table 10 implicitly assume that the entire realized
change in cash is unexpected. For the same reasons discussed in Section 4.3.2, we conduct robustness
checks for our subsample regressions, employing the same two alternative measures of unexpected
change in cash holdings, and re-estimating the models in Table 10 across the four groups of firm years
sorted by state ownership. The first alternative measure uses the benchmark portfolio average change in
cash as the expected change in cash. The results are reported in Table 11. The second alternative
measure uses the modified ACW model to estimate the expected change in cash. The results are reported
in Table 12.
**** Insert Tables 11 and 12 about here ****
The results in Table 11 are consistent with those in Table 12. The coefficients of change in cash
(ΔCASHt) suggest that, for all-equity firms with zero cash holdings, an extra RMB of cash raised is
valued at RMB 1.83 in group 1, RMB 1.43 in group 2, RMB 1.12 in group 3, and RMB 1.01 in group 4.
For the average firm in each group, an extra RMB of cash is valued at RMB 1.27 in group 1, RMB 1.03 in
group 2, RMB 0.90 in group 3, and RMB 0.83 in group 4. It can be inferred from these results that one
extra RMB is valued RMB 0.44 higher in the average firm in group 1 than in the average firm in group 4.
The results in Table 12 show a nonlinear relation between state ownership and the marginal value of cash.
For the average firm in each group, an extra RMB is valued at RMB 1.02 in group 1, RMB 1.05 in group 2,
RMB 0.42 in group 3, and RMB 0.69 in group 4. These results suggest that an extra RMB is valued RMB
0.33 higher in firms with zero percent state ownership than in firms with 50% or more state ownership.
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Again, our overall finding that state ownership is negatively related to the marginal value of cash remains
intact.
5. Conclusions
This study examines the relation between state ownership and corporate cash holdings in China’s
share-issue privatized firms from 1993 to 2007. We find that cash holdings and state ownership are
negatively related, consistent with the prediction by the soft-budget constraint theory first formulated by
Kornai (1979, 1980). The level of cash holdings is also negatively related to institutional ownership,
possibly due to the fact that many of the institutional owners are owned or controlled by various levels of
governments. We also control for other variables and find that smaller, more profitable and higher
growth firms hold more cash and that debt and net working capital are negatively related to cash holdings,
suggesting that debt and working capital may be treated as cash substitutes. These findings are consistent
with evidence found in U.S. and international firms.
We further examine the relation between state ownership and the value of cash and find that the
marginal value of cash declines as state ownership increases. For the average firm in our sample, the next
RMB raised is valued at RMB 0.94 by shareholders. Furthermore, the marginal value of cash in firms with
zero state ownership is RMB 0.33 – RMB 0.47 higher than that in firms with majority state ownership. We
attribute this negative relation to the SBC effect inherent in state ownership. China’s share-issue
privatized firms operate in a financial system largely dominated by state-owned banks. The majority of
debt financing does not go through the scrutiny of capital markets, but instead comes from banks. On the
one hand, the technocrats or politicians who control the banking sector have the motives and means to
soften the budget constraint of distressed state-controlled firms. On the other hand, self-interested
managers operating under a SBC might misuse the raised funds for personal perks or invest in politically
expedient projects, as opposed to NPV maximizing projects. This quintessential SBC effect help explain
the negative relation between the marginal value of cash and state ownership.
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The contributions of this study are as follows. This is the first study to examine the causes and
consequences of cash holdings in share-issue privatized (SIP) firms, an important group of firms that has
received inadequate attention in the growing literature on cash holdings. Our paper is also the first to
employ the well-established SBC theory from economics to study the effect of state ownership on the
level of cash holdings and on the marginal value of cash. SIP firms with retained state ownership are
more likely to suffer from the SBC effect and are less financially constrained, whereas de novo private
firms are likely to have hard budget constraints (HBC). Our findings that the level of cash holdings and
the marginal value of cash are both negatively related to state ownership are new additions to the existing
empirical literature.
Future research on cash holdings of Chinese firms should focus on how cash holdings are
impacted by firm level bank lending relations, lines of credit, and bond ratings. Though the current
Chinese corporate bond market is still underdeveloped relative to the size of its economy and stock
market, it is growing rapidly (Allen, Qian, Zhang, and Zhao 2011). As the government continues to
divest from the banking sector, more and more firms will have to go through the capital market to receive
credit. Therefore, we can expect the SBC effect to be mitigated as the Chinese financial system becomes
more efficient. Also, a survey of firm CFOs in China similar to Lins, Servaes, and Tufano (2010) will
help scholars understand the determinants of corporate liquidity in China’s listed firms. A third potential
topic for future research is to follow Denis and Sibilkov (2010) and examine returns on investment
projects made by low state ownership firms and by high state ownership firms. Finally, we hope our paper
will serve as a starting point for more research on liquidity management in privatized firms in other post-
socialist countries.
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Figure 1. Median cash holdings, 1993-2007. Cash-to-net assets is calculated as cash plus marketable securities plus marketable securities.
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
C a s h - t o - n e t a s s e t s
Year
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Table 1: The major components of the Chinese financial markets 1991-2007The table presents descriptive statistics of the major components of the Chinese financial market from 1991-2007: the stock m banking sector. The last column shows the RMB/US$ exchange rates for comparison purposes. Policy bonds are bonds issueTreasury Department for specific infrastructure projects.
Stock market Number of Market cap Bond market ( RMB billion) Bank loans
Year listed firms ( RMB billion) Total T-bonds Policy bonds Corp bonds ( RMB billion)
1991 14 11 151 106 12 33
1992 53 105 225 128 14 82
1993 183 354 245 154 11 80
1994 291 369 306 229 10 68 3998
1995 323 347 566 330 171 65 5054
1996 530 984 747 436 251 60 6116
1997 745 1753 966 551 363 52 7491
1998 851 1951 1356 777 512 68 8652
1999 947 2647 1777 1054 645 78 9373
2000 1086 4809 2192 1367 738 86 9937
2001 1154 4352 2516 1562 853 101 11231
2002 1223 3833 3072 1934 1005 133 13129
2003 1285 4246 3595 2260 1165 169 15900
2004 1373 3706 4182 2578 1402 202 17820
2005 1378 3243 5061 2877 1782 402 19469
2006 1421 8940 5999 3145 2301 553 22535
2007 1530 32714 8635 4874 2993 768 26169 Data sources: the stock market statistics are from Shanghai Stock Exchange Fact Book 2008 and Shenzhen Stock Exchange Fact Book 200
statistics are from Statistical Yearbook of China 1990-2010 (National Bureau of Statistics of China, Beijing, China). The RMB/US
International Financial Statistics various issues.
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Table 2: Bank nonperforming loans to total loans of selected countries (%)This table presents the percentage of nonperforming loans to total loans for selected countries. Included are major developed the U.K. and Canada), major developing countries (the BRICS countries – Brazil, Russia, India, China, and South Africa, pluAsian economies (South Korea, Hong Kong).
2000 2001 2002 2003 2004 2005 2006 2007 Mean (2000-2007)
China 22.4 29.8 26 20.4 15.6 8.6 7.1 6.2 17.01 United States 1.1 1.3 1.4 1.1 0.8 0.7 0.8 1.4 1.08 Japan 5.3 8.4 7.2 5.2 2.9 1.8 1.5 1.4 4.21 Germany 4.7 4.6 5 5.3 4.9 4.0 3.4 2.6 4.31 Canada 1.3 1.5 1.6 1.2 0.7 0.5 0.4 0.7 0.99 United Kingdom 2.5 2.6 2.6 2.5 1.9 1.0 0.9 0.9 1.86 Hong Kong 7.3 6.5 5 3.9 2.3 1.4 1.1 0.8 3.54 Korea 8.9 3.3 2.4 2.6 1.9 1.2 0.8 0.7 2.73 Brazil 8.3 5.6 4.8 4.8 3.9 3.5 3.5 3.0 4.68 Russia 7.7 6.2 5.6 5 3.3 2.6 2.4 2.5 4.41 India 12.8 11.4 10.4 8.8 6.6 5.2 3.3 2.5 7.63 South Africa -- 3.1 2.8 2.4 1.8 1.5 1.1 1.4 2.01 Chile 1.7 1.6 1.8 1.6 1.2 0.9 0.7 0.8 1.29 Mexico 5.8 5.1 4.6 3.2 2.5 1.8 2.0 2.7 3.46 Data source: IMF Financial Stability Report various issues.
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Table 3: Summary statistics for cash holdings analysis
This table presents summary statistics on key variables for our sample of firms from 1993-2007 on CSMAR (ChinaSecurity Market and Accounting Research) available through WDRS. Cash is cash plus marketable securities. Netassets is assets less cash plus marketable securities. Real size is net assets deflated using the CPI into 2007 RMB.China’s CPI is obtained through the IMF website. Market-to-book ratio is book assets minus book equity, plusmarket value of equity, all divided by book assets. Book leverage is short-term debt plus long-term debt over book assets. Net working capital is current assets less cash and equivalents minus current liabilities. Cash flow is netincome plus depreciation minus common cash dividends. Capital expenditures is change of net fixed assets plusdepreciation. 3-yr growth rate is the average sales growth rate over the previous three years period. Industry sigmais the standard deviation of an industry’s cash flow over our sample period. Industries are China’s 2-digit SICequivalent B classifications defined by CSRC (China Security Regulatory Commission). Payout ratio is cashdividends over net assets. State ownership is the fraction of total shares retained by the state after share-issue privatization. Institutional ownership is the fraction of shares owned by non-state affiliated legal entities.
25th 75th
mean percentile median percentile std N
Cash/net assets 0.1896 0.0692 0.1301 0.2353 0.1995 9862
Real size ( RMB, millions) 3000.5 884.3 1563.8 3053.4 6062.0 9862
market-to-book ratio 2.4911 1.3762 1.9120 2.8698 1.8501 9862
book leverage 0.2319 0.1212 0.2296 0.3339 0.1433 9862 Net working capital/net assets 0.1681 0.0295 0.1651 0.3102 0.2101 9862
Cash flow/net assets 0.0692 0.0324 0.0596 0.0961 0.1328 9862
Capital expenditures/net assets 0.0695 0.0174 0.0766 0.1737 0.3680 9862
3-yr growth rate 0.2491 0.0224 0.1564 0.3322 0.5116 9862
Industry sigma 0.1718 0.0689 0.0867 0.1181 0.2514 9862
Dividend dummy 0.4770 0 0 1 0.4995 9862
Payout ratio 0.02323 0 0 0.0269 0.0637 9862
State ownership 0.3050 0 0.3218 0.5269 0.2534 9862
Institutional ownership 0.2385 0.0016 0.1546 0.4420 0.2448 9862
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Table 4: Firm characteristics by cash holding quartiles: univariate tests
This table presents univariate comparisons of means and medians of the key variables of our sample of 9862 firmyears from 1993-2007. Cash quartiles are calculated each year. Median values are in the brackets. Cash is cash plusmarketable securities. Net assets is assets less cash plus marketable securities. Real size is net assets deflated usingthe CPI into 2007 RMB. China’s CPI is obtained through the IMF website. Market-to-book ratio is book assetsminus book equity, plus market value of equity, all divided by book assets. Book leverage is short-term debt pluslong-term debt over book assets. Net working capital is current assets less cash and equivalents minus currentliabilities. Cash flow is net income plus depreciation minus common cash dividends. Capital expenditures ischange of net fixed assets plus depreciation. 3-yr growth rate is the average sales growth rate over the previousthree years period. Industry sigma is the standard deviation of an industry’s cash flow over our sample period.Industries are China’s 2-digit SIC equivalent B classifications defined by CSRC (China Security RegulatoryCommission). Payout ratio is cash dividends over net assets. State ownership is the fraction of total shares retained by the state after share-issue privatization. Institutional ownership is the fraction of shares owned by non-stateaffiliated legal entities. Dividend dummy equals one if a firm pay cash dividends and zero otherwise. Firm age isyears since IPO. t-test is for the mean difference between the first quartile and the fourth quartile. p-values are in the parentheses below the t-statistics.
First Second Third Fourth t-stat
quartile quartile quartile quartile ( p-value)
Cash/net assets 0.0394 0.1014 0.1804 0.4372 -78.01[0.0388] [0.1004] [0.1727] [0.3543] (0.0001)
Real size 3364.83 3332.66 2787.71 2517.35 4.34
[1327.70] [1827.53] [1653.12] [1487.72] (0.0001)
Market-to-book ratio 2.4568 2.2636 2.4630 2.7811 -5.68
[1.8986] [1.8009] [1.9044] [2.0657] (0.0001)
Book leverage 0.2702 0.2583 0.2272 0.1720 24.25
[0.2684] [0.2569] [0.2257] [0.1552] (0.0001)
Net working capital/net assets 0.1607 0.1772 0.1833 0.1512 1.53
[0.1450] [0.1689] [0.1848] [0.1649] (0.1263)
Cash flow/net cash 0.03077 0.0530 0.0749 0.1190 -20.25
[0.0431] [0.0533 [0.0634] [0.0839] (0.0001)
Capital expenditures/net assets 0.0891 0.1144 0.0781 -0.0035 7.45[0.0652] [0.0884] [0.0834] [0.0713] (0.0001)
3-yr growth rate 0.1980 0.2420 0.2766 0.2798 -5.35
[0.1083] [0.1522] [0.1870] [0.1738] (0.0001)
Industry sigma 0.1701 0.1742 0.1670 0.1761 -0.81
[0.0839] [0.0852] [0.0868] [0.0875] (0.4186)
Dividend dummy 0.3339 0.4653 0.5271 0.5816 -18.01
[0] [0] [0] [1.00] (0.0001)
Payout 0.0127 0.0173 0.0238 0.0392 -15.27
[0] [0] [0] [0.0137] (0.0001)
Firm age 6.59 6.60 6.33 5.84 8.57
[6.00] [6.00] [6.00] [5.00] (0.0001)
State ownership 0.3012 0.3218 0.3128 0.2842 2.32[0.3112] [0.3500] [0.3354] [0.2785] (0.0202)
Institutional ownership 0.2600 0.2240 0.2308 0.2391 2.96
[0.1973] [0.1239] [0.1354] [0.1440] (0.0031)
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Table 5: Regression results of cash holding determinants, 1993-2007
The dependent variable is natural logarithm of cash holdings measured as cash plus marketable securities over netassets. Net assets is assets less cash plus marketable securities. Real size is net assets deflated using the CPI into2007 RMB. China’s CPI is obtained through the IMF website. Market-to-book ratio is book assets minus book equity, plus market value of equity, all divided by book assets. Book leverage is short-term debt plus long-term debtover book assets. Net working capital is current assets less cash and equivalents minus current liabilities. Cash flowis net income plus depreciation minus common cash dividends. Capital expenditures is change of net fixed assets plus depreciation. 3-yr growth rate is the average sales growth rate over the previous three years period. Industrysigma is the standard deviation of an industry’s cash flow over our sample period. Industries are China’s 2-digit SICequivalent B classifications defined by CSRC (China Security Regulatory Commission). Dividend dummy equalsone if a firm pays cash dividends and zero otherwise. State ownership is the fraction of total shares retained by thestate after share-issue privatization. Institutional ownership is the fraction of shares owned by non-state affiliatedlegal entities. White heteroscedastic-consistent t-stats are in the parentheses (White, 1980).
(1) (2) (3) (4)
Intercept -0.4921** -0.4954** -0.0675 -0.0668
(-2.07) (-2.09) (-0.28) (-0.28)
Real size -0.0734*** -0.0755*** -0.0844*** -0.0867***
(-6.63) (-6.82) (-7.56) (-7.77)
Leverage -1.4001*** -1.4033*** -1.4200*** -1.4241***(-18.00) (-18.06) (-18.30) (-18.37)
3-yr growth rate 0.0561*** 0.0556*** 0.0609*** 0.0605***
(2.92) (2.89) (3.18) (3.16)
Industry sigma 0.1479** 0.1486** 0.1603** 0.1613**
(2.19) (2.20) (2.38) (2.40)
Cash flow/net assets 1.9447*** 1.9317*** 1.9394*** 1.9258***
(20.52) (20.39) (20.54) (20.41)
Net working capital/net assets -0.4822*** -0.4820*** -0.4800*** -0.4798***
(-9.54) (-9.54) (-9.53) (-9.54)
Capital expenditures/net assets 0.0879*** 0.0971*** 0.0862*** 0.0959***
(2.63) (2.90) (2.58) (2.87)
Dividend dummy 0.3466*** 0.3417*** 0.3514*** 0.3463***(15.96) (15.73) (16.24) (16.00)
Institutional ownership -0.6319*** -0.6439***
(-8.52) (-8.68)
State ownership -0.4152*** -0.4285***
(-5.75) (-5.93)
Year dummies y y y y
Industry dummies y y y y
Firm fixed effect n y n y
N 9862 9862 9862 9862
Adj. R 2 0.2119 0.2134 0.2180 0.2197
***, ***, and * indicate significant level at the 1%, 5%, and 10%, respectively.
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Table 6: Regression results for cash holding analysis: Robust test
The dependent variable is natural logarithm of cash holdings measured as cash plus marketable securities over netassets. Net assets is assets less cash plus marketable securities. Real size is net assets deflated using the CPI into2007 RMB. China’s CPI is obtained through the IMF website. Market-to-book ratio is book assets minus book equity, plus market value of equity, all divided by book assets. Book leverage is short-term debt plus long-term debtover book assets. Net working capital is current assets less cash and equivalents minus current liabilities. Cash flowis net income plus depreciation minus common cash dividends. Capital expenditures is change of net fixed assets plus depreciation. 3-yr growth rate is the average sales growth rate over the previous three years period. Industrysigma is the standard deviation of an industry’s cash flow over our sample period. Industries are China’s 2-digit SICequivalent B classifications defined by CSRC (China Security Regulatory Commission). Dividend dummy equalsone if a firm pays cash dividends and zero otherwise. State ownership is the fraction of total shares retained by thestate after share-issue privatization. Institutional ownership is the fraction of shares owned by non-state affiliatedlegal entities. Cash difference is change in cash holdings in the following period. White heteroscedastic-consistent t-statistics are in the parentheses (White, 1980).
Panel A: Reduced form regressions
(1) (2) (3) (4)
Intercept -1.6090*** -1.6148*** -1.2292*** -1.2306***
(-6.95) (-6.98) (-5.20) (-5.21)
Real size -0.0330*** -0.0353*** -0.0437*** -0.0461***(-3.10) (-3.31) (-4.06) (-4.28)
3-yr growth rate 0.0623*** 0.0625*** 0.0671*** 0.0673***
(3.15) (3.16) (3.40) (3.41)
Industry sigma 0.1115* 0.1111 0.1214* 0.1212*
(1.60) (1.59) (1.74) (1.74)
Cash flow/net assets 2.4815*** 2.4502*** 2.4823*** 2.4504***
(31.15) (30.68) (31.20) (30.73)
Net working capital/net assets -0.0817* -0.0827* -0.0748 -0.0757
(-1.71) (-1.73) (-1.57) (-1.59)
Institutional ownership -0.5365*** -0.5496***
(-6.99) (-7.17)
State ownership -0.3095*** -0.3241***
(-4.15) (-4.34)
Year dummies y y y y
Industry dummies y y y y
Firm fixed effect n y n y
N 9862 9862 9862 9862
Adj. R 2 0.1572 0.1589 0.1620 0.1639
***, ***, and * indicate significant level at the 1%, 5%, and 10%, respectively.
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Panel B: Regressions taking into account firm age and change in cash holdings
(1) (2) (3) (4)
Intercept -0.5437*** -0.5427*** -0.0198 -0.0142
(-2.19) (-2.19) (-0.08) (-0.06)
Real size -0.0656*** -0.0678*** -0.0768*** -0.0792***
(-5.61) (-5.79) (-6.52) (-6.71)Leverage -1.0565*** -1.0579*** -1.0727*** -1.0746***
(-12.80) (-12.82) (-13.03) (-13.06)
3-yr growth rate 0.0371* 0.0369* 0.0393** 0.0390**
(1.85) (1.84) (1.96) (1.91)
Industry sigma 0.0616 0.0616 0.0621 0.0622
(0.59) (0.59) (0.60) (0.60)
Cash flow/net assets 3.2523*** 3.2531*** 3.2398*** 3.2405***
(24.36) (24.38) (24.36) (24.38)
Net working capital/net assets -0.5038*** -0.5032*** -0.5030*** -0.5023***
(-9.53) (-9.52) (-9.55) (-9.54)
Dividend dummy 0.2795*** 0.2773*** 0.2808*** 0.2784***
(12.27) (12.17) (12.38) (12.27)Cash difference x(capital expenditure/net assets) -3.3814*** -3.4181*** -3.3976*** -3.4370***
(-11.44) (-11.56) (-11.54) (-11.67)
Firm age -0.0771*** -0.0721*** -0.1004*** -0.0954***
(-3.43) (-3.20) (-4.43) (-4.20)
Institutional ownership -0.6140*** -0.6209***
(-7.84) (-7.93)
State ownership -0.4062*** -0.4131***
(-5.29) (-5.38)
Year dummies y y y y
Industry dummies y y y y
Firm fixed effect n y n y
N 8486 8486 8486 8486
Adj. R 2 0.2204 0.2211 0.2265 0.2273
***, ***, and * indicate significant level at the 1%, 5%, and 10%, respectively.
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Table 7: Summary statistics for value of cash analysis
This table provides summary statistics for the variables in our sample firm years from 1993-2007. B
t it i Rr ,, is
excess return, where t ir , is firm i’s monthly-compounded annual stock return in year t (fiscal year-end) and B
t i R , is
stock i’s benchmark portfolio return at year t . All variables except excess return and MLEV are deflated by laggedmarket value of equity MVEt-1. ΔXt =Xt – Xt-1. CASH is cash plus cash equivalents. EBIT is operating income before interest and taxes. NA is book assets less cash and cash equivalents. INT is interest expenses. DIV is cash
common dividends. MLEV is market leverage. Subscript (t-1) means the beginning of fiscal year t .
mean 25th percentile median 75th percentile STD N B
t it i Rr ,, -0.0028 -0.1959 -0.0327 0.1415 0.5917 9837
ΔCASH 0.0155 -0.0192 0.0039 0.0367 0.1063 9837
CASH -1 0.1127 0.0355 0.0740 0.1444 0.1213 9837
ΔEBIT 0.0102 -0.0081 0.0036 0.0217 0.0740 9837
Δ NA 0.0836 -0.0117 0.0514 0.1547 0.3907 9837
ΔINT 0.0024 -0.0010 0.0010 0.0044 0.0089 9837
ΔDIV 0.0006 0.0000 0.0000 0.0027 0.0129 9837
MLEV 0.1745 0.0663 0.1370 0.2514 0.1405 9837
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Table 8: Value of cash regression results: Full sampleThis table presents regression results of excess return (dependent variable) on changes in firm characteristicsover the fiscal year. All variables except excess return, MLEV and STATE are deflated by lagged marketvalue of equity MVEt-1. ΔXt =Xt – Xt-1. CASH is cash plus cash equivalents. EBIT is operating income beforeinterest and taxes. NA is book assets less cash and cash equivalents. INT is interest expenses. DIV is cashcommon dividends. MLEV is market leverage. STATE is fraction of state ownership. Subscript (t-1) meansthe beginning of fiscal year t . White heteroscedastic-consistent t-statistics are in the parentheses (White, 1980).
Panel A: Regression results (1) (2) (3)
ΔCASHt 0.7831*** 1.3280*** 1.4476***
(13.67) (13.97) (12.81)
ΔEBITt 1.7861*** 1.7926*** 1.7942***
(22.49) (22.60) (22.59)
Δ NAt 0.151*** 0.1493*** 0.1489***
(9.59) (9.50) (9.46)
ΔINTt 1.826** 1.6786** 1.7191**
(2.68) (2.47) (2.52)
ΔDIVt -0.3531 -0.335 -0.3248
(-0.80) (-0.76) (-0.74)
CASHt-1 0.8964*** 0.9031*** 0.9145***(16.12) (16.26) (16.27)
MLEVt -0.5206*** -0.4834*** -0.4823***
(-10.19) (-9.41) (-9.35)
ΔCASHt x CASHt-1 -0.7008*** -0.6973***
(-2.96) (-2.92)
ΔCASHt x MLEVt -1.6867*** -1.7030***
(-5.67) (5.72)
STATEt -0.0426*
(-1.84)
ΔCASHt STATEt -0.4264**
(-1.97)
Intercept 0.0194 0.0112 0.0125
(0.24) (0.14) (0.25)
N 9837 9837 9837
Adj. R 2 0.1238 0.1285 0.1291
***, ***, and * indicate significant level at the 1%, 5%, and 10%, respectively.
Panel B: The marginal value of cash
Mean value
CASHt-1 0.1127 0.1127
MLEVt 0.1745 0.1745
STATEt 0.3188
Value of RMB1.00 0.95 0.94
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Table 9: Regression results using alternative definitions of the expected change in cashThis table presents regression results of excess return (dependent variable) on changes in firm characteristics over the fiscal year. All variables except excess return, MLEV and STATE are deflated by lagged market value of equityMVEt-1. ΔX represents change of X from t to (t-1), Xt – Xt-1. CASH is cash plus cash equivalents. EBIT isoperating income. NA is book assets less cash and cash equivalents. INT is interest expenses. DIV is cash commondividends. MLEV is market leverage. STATE is fraction of state ownership. White heteroscedastic-consistent t-statistics are in the parentheses (White, 1980). Columns (1) and (2) use the benchmark portfolio average as thenormal change in cash holdings and Column (3) and (4) use the Almeida, Campello and Weisbach (1994) (ACW,
hereafter) method to estimate the normal change in cash holdings.
Panel A: Regression results
Port - Ave ACW
(1) (2) (3) (4)
ΔCASHt 1.4109*** 1.5451*** 1.1635*** 1.3235***
(14.23) (13.07) (9.91) (9.65)
ΔEBITt 1.7638*** 1.7640*** 1.7682*** 1.7674***
(22.19) (22.19) (21.40) (21.40)
Δ NAt 0.1650*** 0.1649*** 0.1732*** 0.1728***
(10.66) (10.66) (10.74) (10.72)
ΔINTt 1.6516** 1.6703** 1.5446** 1.5519**
(2.42) (2.45) (2.14) (2.15)
ΔDIVt -0.3376 -0.3233 -0.0637 -0.0513
(-0.77) (0.73) (-0.12) (-0.10)
CASHt-1 0.9220*** 0.9230*** 0.8659*** 0.8680***
(16.32) (16.34) (14.75) (14.79)
MLEVt -0.5045** -0.5026*** -0.543*** -0.5403***
(-9.87) (-9.83) (-9.93) (-9.89)
ΔCASHt x CASHt-1 -0.7200*** -0.7290*** -0.0470 -0.0394
(-2.92) (-2.96) (-0.14) (-0.12)
ΔCASHt x MLEVt -1.7429*** -1.7479*** -1.9819*** -1.9492***
(-5.63) (-5.65) (-5.12) (-5.03)
STATEt -0.0472** -0.0539**
(-2.06) (-2.16)
ΔCASHt STATEt -0.4728** -0.6073**
(-2.10) (-2.26)
Intercept 0.0137 0.0211 0.0184 0.0261
(0.28) (0.43) (0.28) (0.40)
N 9837 9837 8886 8886
Adj. R 2 0.1299 0.1305 0.1227 0.1234
***, ***, and * indicate significant level at the 1%, 5%, and 10%, respectively.
Panel B: The marginal value of cash
Mean value
CASHt-1 0.1127 0.1127 0.1151 0.1151
MLEVt 0.1745 0.1745 0.1775 0.1775
STATEt 0.3188 0.3102
Value of RMB1.00 1.03 1.01 0.81 0.78
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Table 10. Regression results of the relation between state ownership and value of cashThis table presents regression results of excess return (dependent variable) on changes in firm characteristics over the fiscal year. All variables except excess return and MLEV are deflated by lagged market value of equity MVEt-1.ΔX represents change of X from t to (t-1), Xt – Xt-1. CASH is cash plus cash equivalents. EBIT is operating income before interest and taxes. NA is book assets less cash and cash equivalents. INT is interest expenses. DIV is cashcommon dividends. MLEV is market leverage. Firm years are divided into four groups according to state ownershipin the firm: group 1 in Column (1) has zero state ownership; group 4 in Column (4) has 50% or higher stateownership; the remaining firms years are equally divided into the middle two groups in Column (2) and Column (3).
White heteroscedastic-consistent t-statistics are in the parentheses (White, 1980).
Panel A: Regression results
State ownership range
(1) (2) (3) (4)
0.00% 0.00 – 31.6% 31.6 – 50.0% >50.0%
ΔCASHt 1.6916*** 1.443*** 1.0738*** 0.9554***
(7.52) (7.69) (5.38) (5.94)
ΔEBITt 2.6037*** 1.4111*** 1.3134*** 1.7417***
(14.55) (9.01) (7.67) (13.60)
Δ NAt 0.1188*** 0.1117*** 0.2911*** 0.0902***
(3.52) (3.32) (8.69) (3.59)
Δ
INTt -3.2894** 3.2275** 3.2299** 4.2723***(-2.09) (2.22) (2.17) (4.26)
ΔDIVt -0.8376 0.724 -1.2821 0.5365
(-0.77) (0.63) (-1.44) (0.95)
CASHt-1 0.9875*** 0.8579*** 1.1462*** 0.7183***
(7.81) (7.20) (9.32) (8.70)
MLEVt -0.5515*** -0.5374*** -0.6498*** -0.3674***
(-4.57) (-4.56) (-5.82) (-5.18)
ΔCASHt x CASHt-1 -1.0709* -0.821* 0.1988 -0.8498**
(-1.83) (-1.91) (0.38) (-1.98)
ΔCASHt x MLEVt -2.6889*** -1.6756*** -1.5657** -0.4513
(3.87) (-3.07) (-2.29) (-0.88)
Intercept -0.0443 0.0989 -0.0022 -0.1004
(-0.27) (0.50) (-0.01) (-0.79)
N 2625 2138 2137 2937
Adj R 2 0.1424 0.1081 0.1426 0.1633
***, ***, and * indicate significant level at the 1%, 5%, and 10%, respectively.
Panel B: The marginal value of cash
Mean value
CASHt-1 0.1108 0.1179 0.1170 0.1074
MLEVt 0.1573 0.1809 0.1816 0.1799
Value of RMB1.00 1.15 1.04 0.81 0.78
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Table 11: Regressions using benchmark portfolio average as the normal change in cash holdingsThis table presents regression results of excess return (dependent variable) on changes in firm characteristics over the fiscal year. All variables except excess return and MLEV are deflated by lagged market value of equity MVE(t-1). ΔX represents change of X from t to (t-1), Xt – Xt-1. CASH is cash plus cash equivalents. EBIT is operatingincome. NA is book assets less cash and cash equivalents. INT is interest expenses. DIV is cash common dividends.MLEV is market leverage. Firm years are divided into four groups according to state ownership in the firm: group 1in Column (1) has zero state ownership; group 4 in Column (4) has 50% or higher state ownership; the remainingfirms years are equally divided into the middle two groups in Column (2) and Column (3). White heteroscedastic-
consistent t-statistics are in the parentheses (White, 1980).
Panel A: Regression results
State ownership range
(1) (2) (3) (4)
0.00% 0.00 - 31.6% 31.6 – 50.0% >50.0%
ΔCASHt 1.8286*** 1.4298*** 1.1194*** 1.0113***
(7.91) (7.34) (5.22) (6.05)
ΔEBITt 2.5418*** 1.4162*** 1.2787*** 1.7218***
(14.14) (9.03) (7.46) (13.41)
Δ NAt 0.1374*** 0.1308*** 0.309*** 0.1012***
(4.16) (3.93) (9.39) (4.07)
Δ
INTt -3.2958** 3.2607** 3.3169** 4.1951***(2.08) (2.24) (2.23) (4.16)
ΔDIVt -0.784 0.7102 -1.3491 0.5628
(-0.72) (0.62) (-1.52) (1.00)
CASHt-1 1.0277*** 0.8495*** 1.2185*** 0.7183***
(7.95) (7.05) (9.55) (8.65)
MLEVt -0.5926*** -0.5471*** -0.6776*** -0.3712***
(-4.93) (-4.67) (-6.09) (-5.27)
ΔCASHt x CASHt-1 -1.0039* -0.8196* 0.3611 -0.955**
(-1.65) (-1.84) (0.68) (-2.12)
ΔCASHt x MLEVt -2.8713*** -1.7004*** -1.4385** -0.4329
(-3.99) (-2.99) (-2.01) (-0.81)
Intercept 0.0092 0.0761 0.0677 -0.043
(0.04) (0.34) (0.30) (-0.26)
N 2625 2138 2137 2937
Adj. R 2 0.1460 0.1054 0.1445 0.1649
***, ***, and * indicate significant level at the 1%, 5%, and 10%, respectively.
Panel B: The marginal value of cash
mean
CASHt-1 0.1108 0.1179 0.1170 0.1074
MLEVt 0.1573 0.1809 0.1816 0.1799
Value of RMB1.00 1.27 1.03 0.90 0.83
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Table 12: Regressions using ACW estimates as the normal change in cash holdingsThis table presents regression results of excess return (dependent variable) on changes in firm characteristics over the fiscal year. All variables except excess return and MLEV are deflated by lagged market value of equity MVE(t-1). ΔX represents change of X from t to (t-1), Xt – Xt-1. CASH is cash plus cash equivalents. EBIT is operatingearnings before interest and taxes. NA is book assets less cash and cash equivalents. INT is interest expenses. DIV iscash common dividends. MLEV is market leverage. Firm years are divided into four groups according to stateownership in the firm: group 1 in Column (1) has zero state ownership; group 4 in Column (4) has 50% or higher state ownership; the remaining firms years are equally divided into the middle two groups in Column (2) and
Column (3). White heteroscedastic-consistent t-statistics are in the parentheses (White, 1980).
Panel A: Regression results
State ownership range
(1) (2) (3) (4)
0.00% 0.00 – 31.6% 31.6 – 50.0% >50.0%
CASHt 1.6167*** 1.5519*** 0.4184* 0.8411***
(6.11) (6.49) (1.70) (4.29)
ΔEBITt 2.6061*** 1.4023*** 1.2747*** 1.7128***
(13.79) (8.65) (7.21) (12.89)
Δ NAt 0.1474*** 0.1283*** 0.3276*** 0.1041***
(4.28) (3.71) (9.63) (4.00)
Δ
INTt -4.5287*** 3.1427** 3.1797** 4.8528***(-2.67) (2.08) (2.05) (4.50)
ΔDIVt 0.2072 0.71 -1.3146 0.7917
(0.15) (0.54) (-1.18) (1.15)
CASHt-1 0.9425*** 0.8180*** 1.0897*** 0.7254***
(7.05) (6.52) (8.57) (8.28)
MLEVt -0.5899*** -0.6074*** -0.6744*** -0.4265***
(-4.57) (-4.89) (-5.77) (-5.65)
ΔCASHt x CASHt-1 -0.3464 -0.8715 1.6460** -0.0582
(-0.43) (-1.35) (2.42) (-0.11)
ΔCASHt x MLEVt -3.4406*** -2.1532*** -1.0605 -0.7761
(-3.83) (-2.83) (-1.28) (-1.22)
Intercept -0.0577 0.1306 -0.0106 -0.1182
(-0.43) (0.98) (-0.07) (-0.96)
N 2342 1989 1980 2575
Adj. R 2 0.1410 0.1034 0.1324 0.1556
***, ***, and * indicate significant level at the 1%, 5%, and 10%, respectively.
Panel B: The marginal value of cash
mean
CASHt-1 0.1130 0.1195 0.1200 0.1098
MLEVt 0.1606 0.1831 0.1841 0.1834
Value of RMB1.00 1.02 1.05 0.42 0.69
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Appendix A: Construction of 16 benchmark portfolios by size and book-to-market ratio
We follow Fama and French (1993) in constructing our benchmark portfolios. Details are as follows:(1) We construct 16 size and book-to-market benchmark portfolios for China’s listed firms from 1993-2007. We
exclude observations in 1990-1993 and construct 16 benchmark portfolios instead 25 portfolios as in Fama andFrench (1993), because the numbers of listed firms are limited in the first few years following the establishment of the Shanghai Stock Exchange in 1990 and the Shenzhen Stock Exchange in 1991. See Panel E below.
(2) Firms in China are required to use calendar year as their fiscal year.
(3)
At the end of June in year t , we rank and divide firms into quartiles according to their sizes measured by marketvalue of equity (stock price at end of June × number of shares outstanding). The four size groups are: small, 2, 3,and large.
(4) For each year t , we rank firms according to book-to-market ratio at year t-1 and divide them into four quartiles.The book-to-market quartiles are: low, 2, 3, and high.
(5) The 16 benchmark portfolios are constructed from the intersections of the four size groups and the four book-to-market groups. The benchmark portfolio returns are equally-weighted annual returns compounded monthly fromJuly of year t to June of year t+1.
(6) The mean values in the tables below are averaged across the years.
Panel A: Mean portfolio size (RMB, millions) Panel B: Mean book-to-market ratiolow 2 3 high low 2 3 high
small 983.8 988.1 943.0 861.2 0.1719 0.3339 0.4714 0.6925
2 1492.1 1515.6 1529.7 1491.2 0.1994 0.3341 0.4439 0.70843 2302.7 2382.9 2324.0 2318.0 0.2046 0.3250 0.4703 0.6916
large 7532.8 7436.6 6996.8 6978.4 0.2043 0.3441 0.4467 0.6478
Panel C: Mean number of stocks in the portfolio Panel D: Mean portfolio returnslow 2 3 high low 2 3 high
small 56.2 57.1 55.1 44.2 0.1580 0.2144 0.3222 0.4625
2 44.1 54.9 58.9 54.4 0.0738 0.1647 0.1763 0.3130
3 48.9 50.0 53.3 60.5 0.0764 0.1253 0.1812 0.2063
large 63.1 50.3 45.4 53.5 0.0828 0.1331 0.2071 0.1902