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State Ownership, Soft-Budget Constraint and Cash Holdings:

Evidence from China’s Privatized Firms

William L. Megginson

Price College of BusinessThe University of Oklahoma

[email protected]

Zuobao Wei

College of Business AdministrationUniversity of Texas at El Paso

[email protected]

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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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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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).


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.


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.


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.


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



15

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.



16

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.


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).



17

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.


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.



18

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.



19

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).



20

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.


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



21

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



22

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.



23

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.


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.



24

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.


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.



25

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.


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.



26

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.



27

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.



28

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.



29

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33

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



34

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.



35

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.



36

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



37

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)



38

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.



39

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)


(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)


(-6.99) (-7.17)

State ownership -0.3095*** -0.3241***

(-4.15) (-4.34)




N 9862 9862 9862 9862

Adj. R 2 0.1572 0.1589 0.1620 0.1639




40

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)


(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)


(-7.84) (-7.93)

State ownership -0.4062*** -0.4131***

(-5.29) (-5.38)




N 8486 8486 8486 8486

Adj. R 2 0.2204 0.2211 0.2265 0.2273




41

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



42

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


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



43

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



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



44

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).


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



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



45

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).



(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



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



46

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).



(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



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



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

china cash 2013 safrica

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