Corporate Control and Enterprise Reform in China: An Econometric Analysis of Block Share Trades [1 ed.] 3790820199, 9783790820195

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Corporate Control and Enterprise Reform in China

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Christian Büchelhofer

Corporate Control and Enterprise Reform in China An Econometric Analysis of Block Share Trades

Physica-Verlag A Springer Company

Series Editors Werner A. Müller Martina Bihn Author Christian Büchelhofer [email protected]

ISBN 978-3-7908-2019-5

e-ISBN 978-3-7908-2020-1

DOI 10.1007/978-3-7908-2020-1 Contributions to Economics ISSN 1431-1933 Library of Congress Control Number: 2007940711 © 2008 Physica-Verlag Heidelberg zzgl. Diss., Univ. München, 2007 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Physica-Verlag. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Production: LE-TEX Jelonek, Schmidt & Vöckler GbR, Leipzig Cover design: WMX Design GmbH, Heidelberg Printed on acid-free paper 987654321 springer.com

Preface

The completion of this research would not have been possible without the generous support of many individuals and institutions. These are specific obligations which I wish to acknowledge here. I would like to express my gratitude to my thesis supervisors. I am highly indebted to Professor Franz Waldenberger for his invaluable help and encouragement from the very beginning of this research project. Not only this research but I personally greatly benefited from his guidance and supervision over the years. I am also indebted to Professor Ralf Elsas for his support and detailed comments which significantly improved this research. At the University of Munich, I would further like to thank Professor Joachim Winter and Dr. Florian Heiss for their patience and effort in increasing my understanding of the econometric methods used in this study. Perhaps even more important I am indebted to them because of their contribution in shaping my fascination about micro-econometrics in general. For making my research stay at China Center for Economic Research (CCER), Peking University, one of the highlights of this research project I owe a special debt of gratitude to Professor Hu Dayuan, Deputy Director of CCER. I am especially grateful to Ms. Jenny Liu and Ms. Wendy Tong for facilitating my research stay at CCER and initiating the contact to data sources and interview partners. At Peking University, I would further like to thank Professor Shen Minggao and Professor Tian Lihui for their time, expertise and willingness to talk to a research student. Thanks to Mr. Wang Chao and Ms. Yangjie Yu for valuable research assistance. I also thank Mr. Xiong Peng, managing director of SinoFin Information Services, for his willingness to share his knowledge and expertise. I am grateful for helpful comments received from Dr. Stephen Green, senior economist at the Standard Chartered Bank in Shanghai, and Professor Ba Shusong, senior research fellow of the Development Research Center of the State Council. I would also like to thank Dr. Suixin Zhang, Mr. Ye Wen and Mr. Chen Tong for kind hospitality and overall support during

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Preface

my stay in Peking. My warm thanks go to Mr. Ming Heng for numerous karaoke events but mostly for his extended friendship. My greatest debt, however, is to my mother, Theres, and my father, Robert, who made it possible for me for some years to regard as my main task the completion of this research.

Munich, October 2007

Christian Büchelhofer

Contents

VIII

Contents

List of abbreviations

X

List of abbreviations

Abbreviation

Description

QFII

Qualified Foreign Institutional Investor Framework

RE

Random Effects

RMB

Renminbi

ROA

Return of Assets

SAMB

State Asset Management Bureau

SASAC

State-owned Assets Supervision and Administration Commission

SCSC

State Council Securities Commission

SD

Standard Deviation

SE

Standard Errors

SETC

State Economic and Trade Commission

SHSE

Shanghai Stock Exchange

SIC

State Information Center

SIP

Share Issue Privatization

SOE

State-owned Enterprise

SPC

State Planning Commission

ST

Special Treatment

SZSE

Shenzhen Stock Exchange

The table contains a list of the most commonly used abbreviations in this study.

List of tables

1 Introduction

The benefits and costs of control over corporate resources have attracted much attention among financial economists. The debate was originally initiated by Adam Smith’s legendary warning in his 1776 published book “An Inquiry into the Nature and Causes of the Wealth of Nations” about the “negligence and profusion” that will result when those who manage enterprises are “rather of other people’s money than of their own”.1 A century and a half later Adolf Berle and Gardiner Means (1932) returned to the theme of diffuse stock ownership. They argue in their seminal work “The Modern Corporation and Private Property“ that modern enterprises face “the dissolution of the old atom of ownership [i.e. small organizations in which the owners where also the managers] into its component parts, control and beneficial ownership”.2 This notion of the separation of ownership and control has had a profound impact on economists. Michael C. Jensen and William H. Meckling (1976) modelled the effects of the dispersion of ownership and control by emphasizing “the general problem of agency”.3 As economists started to employ this agency perspective, it was mainly in the context of diffuse shareholders and professional managers.4 Research on corporate governance around the world, however, revealed that ownership is rather concentrated than dispersed in most countries. 1 2 3 4

Adam Smith (1776); reprinted by Modern Library, New York (1937, p. 700). Berle and Means (1932, p. 8). Jensen and Meckling (1976, p. 309). Morck et al. (1988, p. 301) suggest that at low levels of ownership, increases in managerial ownership help to align the interests of managers and shareholders. At higher levels of ownership, however, additional ownership by insiders leads to entrenchment. McConnell and Servaes (1990, p. 607) only confirm the findings for low levels of inside ownership. The most important theoretical objection has been put forward by Demsetz and Lehn (1985, p. 1174). Basically, they argue that in a competitive market environment market forces will make sure that every company chooses its value maximizing ownership structure. As a consequence, an endogeneity problem arises because ownership structure and firm value are determined simultaneously. Himmelberg et al. (1999, p. 360) note that panel data have certain advantages in addressing these issues.

2

1 Introduction

This not only challenged the Berle and Means picture of the prevalence of diffuse ownership in modern corporations. But it also shifted the nature of the agency problem. The widely cited paper by La Porta, Lopez-de-Silanes and Shleifer (1999) suggests that “[…] the theory of corporate finance relevant for most countries should focus on the incentives and opportunities of controlling shareholders to both benefit and expropriate the minority shareholders”.5 This statement indicates two very different effects of blockholders on firm value and minority shareholder wealth. The first effect arises because a blockholder compared to small shareholders has stronger incentives and more opportunities to monitor management and improve firm performance. This monitoring activity of the large shareholder can lower agency costs in the firm and increase cash flows for all shareholders. Shleifer and Vishny (1986) are among the first to stress this view of shared benefits of control.6 The second and opposite effect occurs when a controlling shareholder can capture monetary or other private benefits of control, often at the expense of minority shareholders.7 In fact, blockholders’ activities may be at the same time value-increasing and value-decreasing. Both sides are well documented in numerous studies, though the net impact on firm value remains disputed.8 The importance of blockholder control is now widely recognized.9 However, a central issue pertains to the dynamics of control allocation. Circumstances may require that control changes hands, and the question arises whether such transfers are efficient. Much attention has been given to control transactions in developed economies characterized by firms with dispersed ownership and a well developed legal system protecting the rights

5 6

7

8

9

La Porta et al. (1999, p. 474). In particular, Shleifer and Vishny (1986, p. 471) argue that the presence of a large shareholder provides a partial solution to the free-rider problem in takeovers. Burkart et al. (1997, p. 716) challenge the view that the reduction of managerial discretion by large shareholders is purely beneficial. Rather the authors stress that ownership concentration involves a trade-off between control and initiative. See Denis and McConnell (2003, pp. 9-19) for an international review of the conflicting effects of controlling blockholders on firm value. Holderness (2003) surveys the US literature; Becht and Röell (1999) examine blockholdings in Europe based on the research initiative by the European Corporate Governance Network (ECGN).

1 Introduction

3

of minority investors.10 In contrast, the economics of control transfers involving firms with an existing dominant blockholder in the legal and regulatory environment of emerging market economies are less well understood. This study examines the frequency, causes and consequences of changes in corporate control in Chinese listed firms over an 11-year period (1996 to 2006) thereby contributing to the research on the efficiency of corporate control allocation. Three main questions are asked: First, what factors determine changes in control in Chinese listed firms? Second, what types of operational and corporate governance changes take place after changes in control? And third, does firm performance improve after changes in control? This study contributes to the literature in several ways. First, previous evidence on ownership changes almost exclusively refers to developed economies. For instance, Bethel et al. (1998), Denis and Sarin (1999), and Denis and Kruse (2000) examine changes in control in the market-based US economy; Köke (2004) studies changes in control in the bank-based economy of Germany. This study examines China, the biggest emerging market economy. China has many of the typical characteristics of an emerging market economy: low legal protection of creditors and investors, a relatively inefficient banking system and significant involvement of political authorities in firm governance.11 The administrative governance approach present in China fostered the fast growth of the Chinese stock market, but at the same time hinders the emergence of a more effective governance system.12 The initial intention of developing the Chinese stock market was to gain an additional financing source for state-owned enterprises (SOE) and at the same time improve their operating efficiency. The Chinese stock markets have been highly regulated during the 1990s and control transfers could only take place upon administrative approval. Because of the unique institutional background of the Chinese capital market, the control rights market is very different from that of other countries. 10

Grossman and Hart (1980) focus on firms with dispersed ownership; Shleifer and Vishny (1986) and Burkart et al. (1997) model a single controlling shareholder; Burkart et al. (2000) analyze transfers of control with a minority block shareholder and otherwise dispersed ownership; Bennedsen and Wolfenzon (2000) focus on firms in which corporate policy is the result of interaction among multiple large shareholders. 11 For detailed comparison of China’s creditor rights, shareholder rights, law enforcement and legal system with the La Porta et al. (1998) sample see Allen et al. (2005, pp. 65-69). 12 For an introduction to China’s stock market see e.g. Green (2004).

4

1 Introduction

Assessing the effectiveness of changes in control for Chinese listed firms increases the understanding of the role of control transfers in emerging markets in general. Second, most studies on corporate governance in China exclude the market for control transfers. Some state that this market is inexistent; others only attribute a minor role to it.13 However, if the unique channels of control transfers in China’s stock markets are considered, it becomes evident that transfers of control took place quite frequently. This study is the first to provide systematic evidence on the causes and consequences of control changes. The large sample size used in this study allows to apply panel estimation and thereby to control for unobserved heterogeneity. To control for endogeneity in the reform process in China seems to be of particular importance. Third, this study identifies changes in ultimate firm ownership based on individual shareholder data. This is significant because Chinese corporations are often owned through pyramids or cross-ownership structures, making changes in direct ownership less meaningful.14 It improves on Cai and Chen (2004) whose work is based on changes in direct ownership covering only the 1997 to 1999 period. It extends the work of Chen et al. (2007) who focus on a specific type of transaction in the 1996 to 2000 period. Fourth, mass privatization around the world brought special focus to the role of the State as a controlling shareholder and the benefits of control in the process of privatization.15 The analysis of the unique channels of control transfers in Chinese listed firms not only provides insights into the motives and constraints of the key players involved in governance practises in China. But it also enhances the understanding of a rather silent privatization process in a communist country with potentially interesting implications for other emerging markets.

13

See e.g. Fan et al. (2007, p. 4), Chen et al. (2006, p. 432); Kato and Long (2006, p. 799). 14 Liu and Sun (2005) classify shareholdings of Chinese listed firms on the basis of the principle of ultimate ownership. This significantly increased the understanding of ultimate control structures of China’s listed firms. 15 Megginson and Netter (2001) provide an exhaustive review of over 225 studies regarding various economic aspects surrounding privatization.

1 Introduction

5

The principal empirical results of this study are the following: The analysis of the causes of changes in control revealed that poorly performing firms are the predominant targets of changes in control. In addition, private parties were likely to purchase blocks of shares leading to changes in control in more, rather then less concentrated firms. This suggests that changes in control take place in firms that offer the greatest opportunities for value improvement. These results are consistent with the view that the market for control transfers in China helped to reverse inefficient control structures. The results on the consequences of changes in control indicate that restructuring activities take place after control transfers. It further appears that firm performance increases following changes in control. Taken as a whole, this evidence suggests that the Chinese market for control transfers identifies and rectifies problems of poor corporate performance. The remainder of this study is organized as follows. Section 2 provides a short description of China’s economic reforms, details the main features of China’s stock markets and includes a review of prior research on corporate governance in Chinese listed firms. The aim is to formulate testable hypothesis. Section 3 describes the data, the sample selection procedure, the concept of control to identify changes in ultimate control and provides evidence on the frequency of control changes in Chinese listed firms. The empirical evidence on the causes of changes in control is presented in Section 4, and the consequences of changes in control are documented in section 5. Section 6 summarizes the main findings and concludes the study.

2 China’s reform process, stock market development, and testable hypotheses

Milton Friedman states in the 2002 preface of his classic “Capitalism and Freedom” that “there is no doubt that the residents of China are freer and more prosperous than they were under Mao – freer in every dimension except the political”.16 This statement rests on the fact that China is uniformly ruled by the Communist Party of China (CPC). Nevertheless, to fully understand the factors that shape economic policy-making in China, the incentives and constraints among the interest groups within China’s political structure need to be considered. In general, opposition has taken place within a three-level state structure: The senior leadership, central government leaders and local government leaders.17

2.1 China’s approach to economic reforms China officially initiated its economic reform process at the third Plenum of the eleventh Central Committee of the CPC in December 1978. China’s approach to economic reform is best illustrated in a metaphor attributed to Deng Xiaoping as “crossing the river by feeling the stones under the feet”.18 Not only did this strategy lead China to take a gradualist approach in reforming its centrally planned economy. It also enabled policy makers to implement a reform process without a large scale privatization program.19 Over the last decade, economic growth has been an impressive 9 per cent per annum. China’s enterprise reforms have been far reaching and the scale

16

Friedman (2002, p. ix). For details regarding the organizational structure of the CPC, see Shambaugh (2000, pp. 168-173) and regarding the structure of power within the CPC, see Lieberthal and Oksenberg (1990, pp. 135-168). 18 Cited after Prasad (2004, p. 2). 19 See Yusuf et al. (2006, p. 116). 17

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2 China’s reform process, and testable hypotheses

and scope of the internal transformation are impressive.20 The Chinese economy is now characterised more by private rather than public ownership. For the economy as a whole, the private share of gross domestic product (GDP) has risen from 50 per cent of value added in 1998 to 59 per cent in 2003.21 The private sector accounts at year-end 2003 for 76.4 per cent of total employment in the non-farm business sector. The share in employment of the public sector has fallen from 30.0 per cent in 1998 to 16.4 per cent in 2003. While in 1998 only 3.6 per cent of employment in the non-farm business sector has been attributed to the listed sector, the share of the listed sector in employment grew until 2003 to 7.2 per cent. Restructuring has been most rapid in the industrial sector. Since 1999, the number of SOE has been reduced dramatically and now they account for only 15 per cent of all national, industrial enterprises.22 The decline in SOE’ share in total industrial assets has been much less dramatic. SOE still account for more than half of total assets. Already larger than the average non-state enterprise, SOE have grown much faster over the period 1999 to 2004 – 2.5 times, versus 1.4 times for non-state enterprises. One notable achievement of enterprise reforms has been the improved profitability of the SOE sector measured by return on state-owned assets. Nonetheless, state enterprises in the industrial sector compare poorly with private companies in terms of productivity. Moreover, as of October 2004 about 40 per

20

The following section on the current structure of the Chinese economy is derived from an OECD survey of China’s business sector and a World Bank survey on China’s ownership transformation. See OECD (2005a, pp. 81 and 135) and Garnaut et al. (2005, pp. 7-8). 21 Value added is defined as gross output minus intermediate consumption and equals the sum of employee compensation, net operating surplus and depreciation of capital assets. Firms are separated by the type of controlling shareholder. Private sector includes both domestic and overseas private firms and investment. The public sector includes state controlled (directly or indirectly) and collectively controlled enterprises. The non-farm business sector excludes agriculture, government and non-profit service sectors. It was equivalent to 76 per cent of GDP in 1998 and 79 per cent in 2003. See OECD (2005a, pp. 81 and 135). 22 Total number of national enterprises covers all state-owned industrial enterprises and enterprises with annual sales of over RMB5million. Return on assets is calculated as operating surplus divided by fixed assets plus inventories. Unless stated otherwise, reported data is for October 2004. See Garnaut et al. (2005, p. 7).

2.1 China’s approach to economic reforms

9

cent of SOE were making losses, compared with 18 per cent for the nonstate sector.23 Considering the above data presentation on the transformation in the Chinese economy, a natural question arises: What are the reasons for this remarkable transformation? The major component of this success story in economic growth is the huge economic restructuring of SOE accompanied by the emergence of private business. In particular, economic policy making in China aimed at stimulating market mechanisms and at introducing modern management systems. The reform process advanced in several phases.24 During the economic reforms of the 1980s, the Chinese government initiated a program that decentralized managerial decision rights of SOE from the central government down to the local firm level. The decentralization was motivated by the central government’s desire to promote markets and to gradually phase out its central planning function.25 In the 1990s, the reform was deepened by increased political support for privatization of small SOE and opening the door for ownership restructuring of large SOE. This policy of “grasping the big and letting go of the small” was officially adopted at the Chinese Communist Party’s 15th Party Congress in 1997.26 The government has used various mechanisms to let go of small enterprises, including the restructuring into shareholding companies, and the sale and consolidation through entering into alliances and encouraging mergers and acquisitions (M&A).27 A large amount of evidence shows that the major players behind the rise in privatization of medium and small SOE are local governments, especially those at municipal and county lev-

23

Total factor productivity of directly controlled state firms is less than half that of privately controlled firms based on OECD (2005a, pp. 97-98) estimates using comprehensive industrial micro data. 24 This section is mostly based on two OECD surveys on domestic policy challenges and enterprise governance in China. See OECD (2002, pp. 12-18) and OECD (2005b, pp. 309 and 312-13). Naughton (1995) provides a detailed discussion on China’s earlier enterprise reforms from a historic perspective. 25 In particular, the “contract responsibility system” implemented performance contracts between the government and firm management. Shirley and Xu (2001) analyse this institutional arrangement. 26 Cited after Tenev et al. (2002, p. 28). 27 See Garnaut et al. (2005, pp. 2-7) for a detailed discussion of the numerous ways that Chinese firms have been exited and restructured.

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els.28 Several explanations have been put forward to explain the incentives and motives of local government officials in encouraging local business.29 The most recent and unifying theory considers the pay-off structure of local bureaucrats.30 This pay-off structure consists of two parts: concerns about the political career path and private benefits derived from the control of local SOE. Liu, Sun, and Woo (2006) argue that career concerns of local officials are shaped by the cadre evaluation system which was put in place as a response to the evolving institutional environment during the reform era. According to this view, central government officials reward and punish local officials primarily on the basis of the economic performance of each leader’s jurisdiction. This evaluation criterion motivates local officials to promote their local GDP growth.31 The authors further stress that local officials are required to boost the profitability of local firms to achieve impressive growth records. This reasoning is justified by the decentralization of decision and income rights to local governments, the intensified crossregional competition and the hardened budget constraints of local governments as a result of the fiscal reform in the 1990s and the ongoing banking credit centralization.32 As a consequence of intensified competition and hardened budget constraints lower firm profitability in general reduced the incentives of local governments to maintain control.33 The notion of grasping the big in turn involves two related sets of reforms. First, the government is creating a number of large enterprise groups with 28

See Tenev et al. (2002, p. 20); Garnaut et al. (2005, p. 33); OECD (2005a, p. 97) report that “87 per cent of the decline in the number of state held industrial firms in the period 1998-2003 came from the prefecture and county level”. 29 Several studies relate the incentives given to local officials to different economic factors, such as fiscal decentralization, emergence of local government ownership, or interregional competition in product markets; see Qian and Weingast (1997), Che and Qian (1998), and Li, Li, and Zhang (2000) respectively. 30 The following section is based on Liu, Sun, and Woo (2006, pp. 2020-2026). 31 Li and Zhou (2005, p. 1760) analyze turnover data of top provincial leaders between 1979 and 1995 and find that the likelihood of promotion of provincial leaders increases with their economic performance. 32 The second half of the 1990s witnessed considerable centralization and commercialization of the banking system. Reforms imposed strict targets on China’s four state-owned banks to control their non-performing loans. Coupled with the ongoing banking reform is the largely completed fiscal reform, which has institutionalized the division of tax revenues between the central and local governments. For details on the central-local government fiscal relations and the banking reform see OECD (2002, pp. 53-58). 33 See Liu, Sun, and Woo (2006, pp. 2022-2023).

2.1 China’s approach to economic reforms

11

extensive cross-ownership by encouraging mergers in core industries. To oversee the largest and centrally owned non-financial SOE, the Stateowned Assets Supervision and Administration Commission (SASAC) was created in 2003. The fundamental idea underpinning SASAC is to exercise ownership rights in a centralized and unified manner. The SASAC and its regional and local affiliates have the objective to separate ownership from management and to focus on investment returns.34 Second, the government is promoting the public issuance of SOE equities and the development of stock markets to increase ownership diversification and to raise funding for restructuring. These share issue privatizations (SIP) in the 1990s became a very important strategy in vitalizing large and medium sized SOE. Due to the ideology of the socialist market economy, privatization was only partial with the state remaining the largest single blockholder in most of the listed firms. In fact, the official term used in China is not “privatization” but “share ownership scheme” as this term better conforms to the communist public ownership principle.35 China’s senior leadership has successfully implemented incentive structures at various levels of government to foster the development of competitive markets. This development, however, also caused significant social costs. In this sense, growing unemployment is a structural by-product of China’s ongoing transformation into a market economy and can be regarded as one of the major constraints faced by government officials in reforming SOE.36 Moreover, China faces significant inequality of social welfare distribution between rich and poor, and also between regions. In fact, a feature of reform in the past 20 years has been the differential pace of regional economic growth. The correction of income disparity between regions is a focus of present government policy in China.37 34

The SASAC is under direct authority of the State Council. The priorities for action by the SASAC include the creation and enhancement of the role of boards in SOE, the establishment of a well structured and transparent board nomination process and the restriction of irregular behavior by the state as a controlling shareholder. For a detailed definition of its duties refer to Ling (2004, p. 3) and Chen (2004, p. 2). 35 See Sun and Tong (2003, p. 187). 36 In particular, excess labour is generated in rural areas by productivity gains in the agricultural sector and in urban areas by the downsizing of SOE; see OECD (2002, p. 24) for details. 37 The inter-provincial divergence of GDP per capita has risen dramatically. Most central and western provinces have lost substantially in comparison to 1980. In Gansu province, for instance, per capita income fell from 84% of national average in 1980 to 56% in 1999. See Taube and Ögütcü (2002, p. 3).

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Overall, the preceding analysis implies the following insights on China’s approach to economic reform. First, the progress of the transformation is gradual but steady, with a mixture of full and partial privatization arrangements. Second, development of competition without large scale privatisation is a typical feature of the Chinese enterprise reform. Third, concerns about potential unemployment have delayed further restructuring.

2.2 The development of stock markets in China “As for securities and the stock market, are they finally good or bad? […] Are they things that only capitalism has or can socialism also make use of them?”38 Deng Xiaoping, Shenzhen, 1992 From their beginning in the early 1990s, the stock markets in Shanghai and Shenzhen were designed primarily to help SOE raise capital and reduce their debt burden. At year-end 2006, 1445 firms were listed on the Shanghai and Shenzhen stock exchanges. 2.2.1 The setup and main features of China’s stock markets The restructuring process of SOE in China involves the carving out of operational units and reorganizing them as limited liability companies with share capital.39 A frequent approach has been to restructure SOE into two parts: an enterprise group parent and a subsidiary. Typically, the subsidiary acquired the productive assets and was incorporated as a joint-stock company and listed. The parent retained most of the non-productive assets, including redundant staff and most of the debt. Usually the parent remained a traditional SOE. Upon the initial public offering (IPO) the parent firm receives legal person (LP) shares in return for the assets it inserted. LP shares make up about one-third of the total capital stock of the listing firm. Another third are state shares mostly held by local governments and the central government. Prior to 2002 the State Economic and Trade Commission (SETC) and the CPC Enterprise Work Commission managed the state shareholdings at the central government level. Local government shareholdings were often under the responsibility of State Asset Management Bureau (SAMB). Since 2003 the management of state shareholdings has 38 39

Cited after Green (2004, p. 7). The description of the typical listing process is based on Green and Liu (2005, pp. 113-118).

2.2 The development of stock markets in China

13

been centralized and now the SASAC manages state shareholdings both at the central government level and through their local branches at the local government level. The shares held by the state and legal entities are not tradable on the two exchanges. The remaining one-third is publicly issued and traded by individuals and institutions. A-shares refer to the sum of public shares and are freely traded by mainland private individuals and institutions and to some extent by foreign institutions via the qualified foreign institutional investor framework (QFII).40 About 10 per cent of listed firms have also issued shares to foreigners (called B-, H-, and N-shares). All shares, tradable and non-tradable, carry the same voting and dividend rights.41 Chinese share issue privatizations (SIP) – in fact, only the partial privatization of one-third of a firm’s equity – can be characterized as being highly politicized. At the core of the politically determined selection for firms to be listed was the quota system. The quota system operated from 1993 to 2000. In this period, the State Planning Commission (SPC) determined each year’s volume of equity issuance and local governments were then given the responsibility of selecting SOE for listing.42 As well as controlling issuance volume and types of companies, the government also controlled the IPO price. During 1993-99, a price/earnings (P/E) ratio of 15 was set as standard for all new issues, regardless of industry, or the value of projected cash flows. Thus, the central government used the quota system to determine stock market growth and to influence trading sentiment.43 Pistor and Xu (2005b) argue that the quota system helped China to jumpstart its stock market in the early stages of development. According to the authors, the quota system functioned as a major governance device by substituting non-existent standard legal governance with administrative governance at the IPO stage. Since 2000 the CSRC relied increasingly on list40

A-shares are the most actively traded shares in China; see Shirai (2004, p. 1470) for details. 41 B-shares are held by foreign individuals and institutions and since March 2001 by domestic individuals in China. H- and N-shares refer to listings in Hong Kong and New York, respectively. Other overseas shares include “red chips”. This term refers to Hong Kong registered holding companies into which Chinese assets have been injected; see Wang, Xu, and Zhu (2004, pp. 470-474). See Appendix A.1: List of share categories of China’s stock market, p. 105. 42 See Groenewold et al. (2005, p. 5). 43 Since secondary market P/E multiples were commonly higher, share prices would usually rocket on the first trading day. This created huge incentives for the take-up of IPOs and thus fulfilled the governments need to create sufficient IPO demand. See Green (2003, pp. 162-163).

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2 China’s reform process, and testable hypotheses

ing requirements established in the Company Law, its own listing requirements and information available in financial data.44 So far, privately owned firms have had an extremely difficult time in winning a public listing place. By October 2002, only 67 private firms had succeeded in winning a listing in their own right. Most of these private firms had good political connections and state-affiliated organs among their owners.45 As a result of this IPO selection process, the Chinese stock market is still largely dominated by former SOE. Perhaps the most distinguishing feature of firms listed on China’s stock exchanges is the dominance of state ownership and control. The designation of shares into state, LP and individual is enshrined in China’s Company Law. However, most legal entities holding LP shares are ultimately owned by the state. Liu and Sun (2005) trace the ultimate owners of all listed firms and find that the state is in the ultimate and absolute control of 81.6 per cent of all publicly quoted firms by the end of 2001. They identify two control patterns: (1) direct government control (government agencies) of 9 per cent of the quoted companies and (2) indirect government control (state-controlled legal entities) of 72.6 per cent of listed firms via stock pyramids. Next to the high degree of ultimate state control, ownership is highly concentrated with the largest shareholder holding on average 45 per cent of total share outstanding.46 Jones et al. (1999) show in a study of SIP in 59 countries that it is common for governments to retain a controlling stake in newly privatized SOE after the IPO.47 However, a feature that China’s listed firms have in common and that is rather unique is the non-tradability of the majority of shares outstanding. Since 1997 an active market for the transfer of LP and state shares has evolved in China’s stock market. Given the overwhelming presence of non-tradable shares in the entire ownership structure, the transfer of non-tradable shares is the only feasible way of gaining control of a company. This two step procedure – first a sale of a minority stake and a 44

See Pistor and Xu (2005b, p. 7). These firms are usually also required to issue non-tradable shares to their founding owners. The average float of their total equity at year-end 2002 was 31 per cent; see Green (2003, p. 152). 46 State-controlled legal entities include publicly listed firms, SOE, unlisted firms and academic institutions. Liu and Sun (2005, p. 48) apply the concept of ultimate control of La Porta et al. (1999, pp. 476-477) to the context of Chinesestyle stock pyramids. 47 See Jones et al. (1999, p. 230). 45

2.2 The development of stock markets in China

15

listing, then an off-exchange transfer of control – represents a very unique feature of China’s stock market. The theoretical model of Perotti (1995) points out that the privatization of policy-sensitive SOE will vary depending on the governments economic objectives and the political conditions.48 The senior leadership’s initial intention to restrict the tradability of shares served the main purpose of keeping the control of SOE firmly in government’s hands. In fact, the senior leadership soon recognized that the predominance of non-tradable shares constitutes a major problem for market liquidity. Huang and Xu (2005) report large price differentials of tradable and non-tradable shares in the pricing of block trade transactions and attribute the negative price differential to a substantial liquidity discount of non-tradable shares.49 Chinese authorities have tried to deal with the problem of non-tradable shares on several occasions. In the first attempt in August 2000, auctions activities of LP shares have been encouraged. This experiment was called off due to the concern that auction activities would grow out of government control. A second attempt was initiated in June 2001, when the government announced its plan to reduce its presence in company ownership. This was followed by a market crash with a loss of almost one third of market valuation because the proposal envisaged an equal pricing for tradable and non-tradable shares. The government had to call of its plan in October 2001.50 The government has learnt some lessons from past failures and its latest program takes into account both trading conditions on the secondary market and the interests of the owner of tradable shares. On September 5th, 2005, the China Securities Regulatory Commission (CSRC) issued the “Measures on administration of split share structure reform of listed companies”. This is the first official document providing details about the implementation of the share structure reform. The main difference between the 2005 share structure reform and earlier attempts is that the new reform invites non-tradable and tradable shareholders to bargain over the conditions by which non-tradable shares are to be converted into tradable 48

Perotti (1995, pp. 855-856) argues that initial sales are likely to be smaller so that the governments could signal to investors that policy reversals are unlikely because the government will bear redistribution costs associated with regulatory interference. 49 See Huang and Xu (2005, p. 12). 50 See Green (2003, p. 196).

16

2 China’s reform process, and testable hypotheses

shares.51 Such flexibility seemed to work well. At year-end 2006, almost all listed firms had initiated the share structure reform. Given the gradual implementation of the reform, the share structure reform is estimated to be completed by 2010.52 2.2.2 The regulatory framework of China’s stock markets As part of the reform process, China has written new commercial and securities laws, introduced accounting and disclosure standards, and built regulatory agencies. Rather than developing institutions from scratch, China used existing bureaucracies as initial regulators and monitors of financial markets, including the state-owned stock exchanges, the People’s Bank of China (PBC), and the State Council Securities Commission (SCSC).53 The presence of three regulatory agencies led to confusion over the roles of the regulators since they had overlapping duties. As a result, many of the rules formulated by central government were manipulated, undermined or simply ignored by local officials.54 The centralisation of regulatory functions was a response to failures of this governance structure. In 1998, the state reorganized the regulatory agencies into one ministry rank unit, the China Securities Regulatory Commission (CSRC). With the new monitoring mechanisms in place, the space for opportunist defection by the leaders of the stock exchanges in support of local interests has been greatly reduced. Moreover, since the CSRC has gained an exclusive mandate to oversee equity policy, the space for inter-bureau dispute has been reduced.55 The CSRC is now in charge of local securities, the monitoring of listed firms, approval of share issuance applications as well as regulatory respon51

After agreement over the conversion price has been reached, a 12 month lockup period is established for the holders of tradable shares. In the two years after expiration of the lock-up, a holder of non-tradable shares with more than 5% of the total issued share capital of the listed company is further prohibited from trading on the stock exchange more than 5% (10%) of the company’s total share capital within 12 (24) months. See CSRC (2005) for further details. 52 See Inoue (2005, pp. 45-53). 53 Chen and Shih (2002, pp. 62-68) and Green (2004, pp. 137-157) provide extensive discussion on the rise of the CSRC. 54 Wedeman (1999, pp. 118-119) provides a review of the different channels used by local leaders to defect from central government guidelines. 55 Huang (1996, p. 656) discusses the administrative control structure among central and local leaders.

2.2 The development of stock markets in China

17

sibility over securities. According to the CSRC, the objective of the legal framework is to protect the interests of the investors based on the principles of transparency, fairness, and justice.56 The Securities Law, together with the Company Law, the Securities Investment Fund Law and the Labour Law, form the legislative basis for listed firms in China. The Company Law was first implemented in 1993. The National People’s Congress Standing Committee passed an amendment of the Company Law taking effect on January 1st, 2006. The new Company Law relaxes the requirements for company incorporation and introduces new rules to better protect investor interests.57 On July 1st, 1999, the Securities Law came into force, marking the end of a seven year drafting process and establishing the most definitive regulatory structure China’s securities markets have had to date. The Securities Law vests the CSRC with the primary power to regulate markets, yet allows it to delegate decisions to the stock exchanges.58 In mature equity markets, like in the United States (US) and the United Kingdom (UK), mergers and acquisitions (M&A) regulation tends to be focused on the protection of minority investors. In China more emphasis is placed on the need for administrative approval to protect the interests of the Chinese state.59 The first set of rules for the transfer of state assets governs the transfer of shares themselves. This is based on the nature of the shares (LP or state), on the identity of the buyer and seller (public or private entity), and on the administrative level of the firm in question (central or local government). Before 2003, the transfer of state-owned LP shares held by provincial-level firms was authorized at the local level; those held by central entities were authorized by the central authorities. The system changed in 2003 with the creation of the SASAC. Now central-level authorization is required for LP shares owned by state entities at both the central and local levels. This requirement is based on the nature of the owner of the shares rather than by the nature of the shares themselves. If non-state-owned LP shares are to be transferred, then the local Industrial and Commercial Bureau is the only organ that needs to grant permission. The share transfer is then simply registered with the settlement and clearing company at the relevant stock exchange. The second set of rules gov-

56

For an extensive summary of the legal framework see OECD (2002, pp. 49-53). See Wilson and Tao (2006, p. 30). 58 See Walter et al. (2003, p. 67). 59 This section is primarily based on Green and Liu (2005, pp. 135-137) and Green (2004, pp. 66-74) 57

18

2 China’s reform process, and testable hypotheses

erns the restructuring of listed companies.60 In particular, in September 2002 the CSRC published the “Listed Company Merger and Acquisition Management Measure”. This regulation requires that all deals involving a change in ownership of more than 30 per cent of a firm’s assets had to seek authorization by the CSRC. In addition, deals need to be authorized by the firm’s board of directors and the shareholders’ meeting. In May 2002 the CSRC established a ‘restructuring authorization committee’ to oversee applications by current and new shareholders for any restructuring involving more than 50 per cent of a firm’s assets.61 Thus, in an emerging capital market like China’s issuance approval, listing quotas, and the transfer of large share blocks are still strictly controlled. 2.2.3 The corporate governance of China’s listed firms In China, the development of an effective corporate governance system is indivisible from the restructuring process of SOE. The Chinese system of corporate governance can be best described as a control-based model, in which the state as the controlling shareholder employs all feasible governance mechanisms to tightly control the firm.62 Most importantly, such a model provides the controlling state shareholders with plenty of discretion to engage in self-dealing and expropriate minority shareholders. As a consequence, it is difficult to separate business from politics in China.63 In fact, almost every corporate governance practise in China is directly or indirectly related to the rent-seeking incentives of politicians. Hence, it is not surprising that the “China Corporate Governance Report 2003” identified a

60

According to OECD (2005b, p. 316) this framework is oriented towards protecting investors from ‘fake’ acquisitions since some deals are aimed at manipulating market prices. 61 The CSRC’s M&A regulations include a framework for tender offers. According to Green (2004, p. 70) these rules were largely redundant. There were few incentives for launching a market-based takeover since the majority shareholder controlled a much larger stake than could be accumulated on the market. 62 This section is based on Liu (2006, pp. 418-419). The author provides a first review of the evolving research on corporate governance in China. 63 Wong, Opper, and Hu (2004, pp. 33 and 40) report that Chinese Communist Party committees retain considerable decision-making power in listed firms over personnel decisions. Furthermore, 56% of listed firms retain ties with the government and ministries.

2.2 The development of stock markets in China

19

number of issues concerned primarily with limiting abuse by controlling shareholders and with improving minority rights.64 In general, governance mechanisms are broadly characterized as being internal or external to the firm. The internal mechanisms of primary interest are the board of directors, incentive contracts such as executive stock ownership, and the ownership structure of the firm. The primary external mechanisms include a well-functioning capital market enabling the transfer of corporate control and a viable legal and regulatory system.65 The board of directors primarily exists to hire, fire, monitor, and compensate management. Economic theory suggests that the board’s effectiveness in its monitoring function is determined by its independence, size and composition.66 Fan, Wong, and Zhang (2007) report that the level of board independence is rather weak in China. The authors state that a typical corporate board in China has about nine directors, 24 per cent of whom are current or former government bureaucrats and 33 per cent of whom are senior managers of the company. These percentages suggest a tight link between the listed firm and the controlling shareholder. There are few professionals (lawyers, accountants, or finance experts) on Chinese boards and almost no representatives of minority shareholders. Moreover, Fan et al. (2007) find a negative relation between politician presence and professionalism of boards. They argue that in such firms professionalism is in low demand, also because professionalism may reveal information that can jeopardize the firms’ rent-seeking activities.67 Bai et al. (2004) add that more than one third of CEOs are also either the chairman or a vice chairman of the board of directors. This severely impedes the board from playing an effective monitoring role. The authors further find that the CEO being the chairman or vice chairman of the board of directors has statistically significant and negative effects on Tobin’s Q.68 In 2001, the CSRC released the “Guide Opinion on Establishing Independent Director System by Listed Companies” to overcome the difficulty of internal personnel control of many listed firms. This guideline requires listed firms to employ in64

This report was issued by the CSRC and the State Economic and Trade Commission (SETC); see OECD (2005b, p. 308). 65 For general surveys of corporate governance see Denis and McConnell (2003) and Shleifer and Vishney (1997). For a review of corporate governance in Asia see Claessens and Fan (2002). 66 See Kose and Senbet (1998, p. 379). 67 See Fan et al. (2007, pp. 20-24). 68 See Bai et al. (2004, p. 613).

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2 China’s reform process, and testable hypotheses

dependent directors, whose total number should account for one-third of the board of directors before June 2003.69 Kato and Long (2006) study CEO turnover in Chinese listed firms over the period 1998 to 2002. They report that the median number of independent director exceeded 2 by the end of 2002. They find that the introduction of independent directors increases the link between stock return and CEO turnover. However, they find no such link between CEO turnover and accounting measures of performance.70 Chen et al. (2006) study whether certain board characteristics have an effect on the propensity to commit fraud in China. The authors report that firms with a large portion of outside directors commit less fraud.71 In sum, the boards of Chinese listed firms are characterized by strong bureaucratic influence, weak governance and low professionalism. The CEOs of Chinese listed firms are expected to serve the interests of the controlling state shareholder more than those of the listed firm or outside minority investors. Managerial insider ownership is very small in China and executive stock option schemes are very rare.72 Thus, incentive pay is unlikely to be a primary corporate governance mechanism at work in Chinese listed firms. Ownership structure, i.e. the identities of a firm’s equity holders and the sizes of their positions, is a potentially important element of corporate governance. Most theoretical arguments concerning state ownership rely on the assumption that politicians use firms to pursue political and social objectives (e.g. correct market failures, reduce income and regional inequality, or provide excessive employment) with negative consequences for firm performance.73 However, political control over the firms’ decision 69

According to the guidelines, an individual must meet the following conditions to be considered independent. First, neither the individual nor his or her relatives may work for the listed firm or its subsidiaries. Second, the individual may not directly or indirectly own more than 1 per cent of the listed firm. Third, neither the individual nor his or her relatives may be among the largest 10 shareholders of the listed firm. Fourth, neither the individual nor his or her relatives may work for a company that owns more than 5 per cent of the stock of the listed firm. Fifth, neither the individual nor his or her relatives may work for one of the largest 5 shareholder companies; see World Bank (2002, pp. 83-99). 70 Kato and Long (2006, pp. 806 and 811). 71 Interestingly, they find that internationally well-reputed audit firms do not deter corporate fraud any more than other auditors. See Chen et al. (2006, pp. 441 and 445). 72 Bai et al. (2004, p. 608) report average managerial holdings of only 0.1 per cent. 73 See Shleifer and Vishny (1994, p. 996) and Boycko et al. (1996, p. 317).

2.2 The development of stock markets in China

21

making may as well improve firm performance by securing the access to scarce resources or bank finance in a quasi-market economy. Moreover, given agency and expropriation issues between managers, shareholders, and various levels of government organs, political control might mitigate incentive problems.74 Xu, Zhu and Lin (2005) argue that the increased decision-making autonomy of Chinese managers after the ownership reform potentially reduces political costs but may as well increase agency costs especially when other mechanisms normally used to counter managerial moral hazard are weak or simply not present.75 Chang and Wong (2004) argue that even though politicians have non-profit maximising objectives, they have an incentive to prevent controlling shareholders and managers from engaging in miss-behaviour simply because miss-behaviour reduces the amount of resources over which politicians have discretion. It follows that the net effect of political control depends on the balance between political costs and incentive problems of managers and shareholders.76 Several researchers have studied the relationship between ownership structure and firm performance in China. Most of this research addressed the relation between direct state ownership and various measures of firm performance. Existing empirical evidence on this topic is mixed. Xu and Wang (1999) report that Chinese firms’ accounting performance is unambiguously negatively related to the level of state ownership.77 Tian (2001) argues that the impact of state ownership on stock market valuation follows a U-shaped relation. With increasing control rights of the government, the probability of expropriation increases and corporate value decreases. However, with a sufficiently large stake state ownership increases firm value by providing monitoring and preferential treatments. This nonlinearity is attributed to the state’s varying interest alignment with other shareholders when state ownership increases.78 Contrary to the common belief, Sun, Tong and Tong (2002) suggest that partial government ownership has a positive impact on firm performance. They describe the relationship between government ownership and firm performance as an inverted 74

Chang and Wong (2004, p. 619) and Xu, Zhu, and Lin (2005, p. 2) call the incentive problems of managers ‘agency costs’ and the incentive problems of shareholders ‘expropriation’. 75 However, politicians often maintained formal authority over key personnel and investment decisions and, in particular, over labour decisions; see Xu, Zhu, and Lin (2005, p. 5). 76 See Chang and Wong (2004, p. 618). 77 See Xu and Wang (1999, p. 88). 78 See Tian (2001, pp. 29-30).

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2 China’s reform process, and testable hypotheses

U-shape pattern.79 These conflicting results might be due to different sample selections and periods of observations. The most serious concern with the above studies, however, is that direct shareholdings do not account for the ultimate control structure of Chinese listed firms. Some authors suggest that the distinction between state and LP ownership can be significant.80 The aforementioned study by Liu and Sun (2005), however, revealed that most LP shareholders are ultimately owned by the state.81 Therefore, a simple separation of state shares from LP shares can create considerable ambiguity about the identity of the ultimate controller. A simple pooling of state and LP shares into one class of shares causes problems since LP shareholders can be state-controlled entities or non-state-controlled firms. Thus, it is crucial that studies of corporate control and firm performance in China consider the ultimate control structure rather than direct shareholdings. Besides these cross-sectional studies, some authors examined the impact of political decision making power and firm performance. Chang and Wong (2004) show that the decision making power of local party committees relative to the power exercised by the largest shareholder is associated positively with firm performance. This suggests that political control mitigates expropriation problems. They also find that the decision making power of local party committees relative to managers is negatively associated with firm performance. This suggests that managers are more concerned with profits than are politicians.82 Xu, Zhu, and Lin (2005) examine the trade-off between political costs and agency costs. By separating firm autonomy into two categories, namely autonomy in labour decisions and all other decisions, the authors provide evidence that firm performance is affected positively by autonomy in labour decisions but negatively by autonomy in all other decisions. They suggest that labour decisions are dominated by political costs and that other decisions are dominated by agency costs.83 Finally, Cheung et al. (2005) study a sample of connected transactions between Chinese publicly listed firms and their controlling SOE shareholders. They find that political connections are detrimental for minority shareholders and suggest that state shareholdings in China appear to create conflicts of interest between controlling SOE and public shareholders.84 79

See Sun et al. (2002, p. 23). See e.g. Xu and Wang (1999, p. 92); Wei et al. (2005, p. 98); Sun et al. (2002, p. 22). 81 See Liu and Sun (2005, p. 52). 82 See Chang et al. (2004, pp. 619 and 632). 83 See Xu et al. (2005, pp. 18 and 22). 84 See Cheung et al. (2005, pp. 25 and 30). 80

2.2 The development of stock markets in China

23

When internal governance mechanisms fail to a large degree, there is incentive for outside parties to seek control of the firm. In this regard, an active market for corporate control is considered to be essential for the efficient allocation of resources. This is expressed by Jensen’s (1993) doubt that any other governance mechanism might be as effective as the takeover market or even be able to substitute its activity.85 For market based economies, the disciplinary role of control changes is well documented.86 UK or US corporate control markets are mainly composed of mergers and tender offers, whose transaction terms are determined by the market. Because of the unique institutional background of the Chinese capital market, the control rights market is very different from that of other countries. Most studies on corporate governance in China exclude the market for corporate control. Some state that this market is inexistent; others only attribute a minor role to it.87 However, if the unique channels of control transfers in China’s stock markets are considered, it becomes evident that transfers of control took place quite frequently in the non-tradable share market of the exchanges.88 Existing research on control transfers in China is rather limited. Cai and Chen (2004) study a sample of ownership changes based on changes in large shareholders for the period 1997 to 1999. The authors report that changes in the largest shareholder are associated with moderate performance improvements but only when firms experienced asset restructuring after the ownership change.89 Chen et al. (2007) examine ownership transfers over the 1996 to 2000 period. They restrict their sample to a certain type of transaction and find positive performance effects when control is passed to a private entity.90 These studies are subject to several concerns. First, the selection of certain types of transactions restricts the results to a certain group of firms. Second, without a concept of control based on ultimate share ownership, it is hard to know whether changes in large shareholders affect the control structure of firms. Third, the short time periods of these studies do not allow to apply panel estimation and thereby to control for unobserved firm heterogeneity.

85

See Jensen (1993, pp. 850-852). See Jensen and Ruback (1983) for the US and Franks and Mayer (1995) for the UK. 87 See e.g. Fan et al. (2007, p. 4), Chen et al. (2006, p. 432); Kato and Long (2006, p. 799). 88 See section 3.3.1, pp. 38-41 for details on the means of control transfers in the Chinese stock market. 89 See Cai and Chen (2004, p. 79). 90 See Chen et al. (2007, p. 20). 86

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2 China’s reform process, and testable hypotheses

La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1998) hypothesize that the legal system is a fundamentally important corporate governance mechanism. They argue that the extent to which a country’s laws protect investor rights and the extent to which those laws are enforced are the most basic determinants of the ways in which corporate finance and corporate governance evolve in that county.91 Over the last decade a remarkably developed legal infrastructure for financial markets has been created in China. This does, however, not by itself secure proper enforcement. Private enforcement of investor rights has been virtually absent in China so far given weak protection of minority investors and the dominant position of ultimate state control of listed firms.92 The level of public enforcement in China is a matter of political priorities. The CSRC’s attitude to loss making firms illustrates that more progressive ruling is often undermined by political interference. In 1998, the CSRC established the categories Special Treatment (ST) and Particular Transfer (PT) to place loss-making firms in separate categories of the exchanges.93 However, local governments heavily oppose the de-listing. By December 2001, a compromise was reached. After being de-listed, a firm could apply for its shares to be traded on an over the counter (OTC) basis at the branches of a number of large securities firms.94 According to Pistor and Xu (2005a), over the period 1998 to March 2004 only 22 per cent of all enforcement actions resulted in fines with the remaining ones being subject to warnings or informal reprimands. Between 1999 and 2005, a total of 21 firms were delisted nationwide. The authors identify the level of incomplete law and the insufficient independence of the judiciary as the key conditions undermining law enforcement in China.95 Overall, public law enforcement in the form of fines or other sanctions imposed by the CSRC has been weak. Given the relatively weak law enforcement in China, alternative nonstandard governance mechanisms could play a role in China’s system of corporate governance. In this respect, Allen (2005) argues that competition appears to be a particularly important factor driving corporate governance 91

See La Porta et al. (1998, p. 1151). See OECD (2005b, p. 318). 93 A company is placed in ST category if (1) it has negative net profits for two consecutive fiscal years; (2) the shareholder’ equity is lower than the registered capital; (3) a firm’s operations have been stopped; or (4) if the firm is involved in a damaging lawsuit or arbitration. If a firm is unable to turn around within a certain period, it will be further downgraded to PT and may face de-listing; See Bai et al. (2002, pp. 7-9). 94 See Green (2003, pp. 188-9). 95 See Pistor and Xu (2005a, pp. 190-196 and 204). 92

2.3 Block trading in China and testable hypotheses

25

in China. In many industries there is fierce competition both from domestic and overseas firms.96 A second plausible governance mechanism could rest in the role of state-owned banks. Although banks are prohibited from holding shares directly, the generally high leverage of listed firms suggests a tight link between banks and listed firms. In particular, banks are quite vigilant regarding the firms’ restructuring moves because they have been given a strict target on controlling their own non-performing loans.97 In sum, while great progress has been made to better understand the corporate governance issues in China, many questions remain unaddressed. For example, the causality and dynamics between current governance practices and various institutional constraints have yet to be mapped out. This requires considering the motives and constraints of the key players involved in governance practises. Thus, the analysis of corporate governance issues in China needs to take into account the very unique institutional structure of the Chinese economy.

2.3 Block trading in China and testable hypotheses The hypotheses basically say when firms are likely to experience a change in control and what the consequences of a control change are likely to be. The first four hypotheses are related to the causes of changes in control and the fifth hypothesis is concerned with the consequences of changes in control. In the formulation of hypothesis particular attention is paid to key characteristics of the Chinese system of corporate governance. In China the market for corporate control is thought to be not very active. In fact, hostile takeover attempts in China have been virtually absent. While the market for corporate control seems to be less active, block trades are very common. These transactions take place in the non-tradable share market of the exchanges since this is the only feasible way of gaining effective control. In several cases these block trades led to changes in control. For market-based economies, like the US, Jensen (1988) and Scharfstein (1988) argue that the market for corporate control improves 96 97

See Allen (2005, p. 174) Allen, Qian, Qian (2005, p. 81) report that around 30 per cent of listed firms’ funding comes from bank loans, and that this ratio has been very stable despite the fast growth of the stock markets. See Allen et al. (2005, pp. 76-78 and 8083) for details of the banking reform and the importance of the banking system in financing the domestic economy.

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2 China’s reform process, and testable hypotheses

efficiency by enabling a third party to take control of a firm which fails to use corporate resources under the current control structure.98 Indeed, international evidence suggests that changes in control take place in the case of poor firm performance.99 It remains to be exploited whether this reasoning applies to the Chinese stock market. H.1:

Poorly performing firms are more likely to experience a change in control.

Among the various governance mechanisms, control concentration in the hands of large shareholders appears to be the favoured mechanism in most countries for resolving the collective action problem among shareholders.100 The high degree of ownership concentration in China seems to be both a reflection of the state’s reluctance to let go of its control of former SOE and a response of privately controlled firms to the weak protection for outside investors. The effect of ownership concentration on the likelihood of a change in control is not obvious. Shleifer and Vishny (1986) develop a model to demonstrate that a certain degree of ownership concentration is desired for the takeover market to work more effectively.101 A beneficial effect might be expected from blockholders in markets with overall low share concentration as they increase the probability of a takeover by overcoming the free-rider problem. Köke (2004), however, finds that in the highly concentrated share market of Germany shareholder control works as a substitute for changes in control.102 Thus, transfers of control might become more difficult in markets with overall high share concentration because incumbent large shareholders must be willing to sell their blocks. This negative relation between shareholder concentration and the probability of a change in control should be particularly prominent in China because ownership is highly concentrated.

98

Jensen (1988, p. 23) views the market for corporate control as a major component of the managerial labour market in which alternative management teams compete for the right to manage corporate resources. Scharfstein (1988, p. 185) interpretes the takeover mechanism as an indirect means for shareholders to renegotiate their contract with management. 99 See e.g. Bethel et al. (1998, p. 624) for the US and Holmstrom and Kaplan (2001) for a review of the US literature; Köke (2004, p. 68) provides empirical evidence for Germany. 100 See La Porta et al. (1999, p. 498). 101 See Shleifer and Vishny (1986, p. 464). 102 See Köke (2004, p. 75).

2.3 Block trading in China and testable hypotheses

H.2:

27

Ownership concentration reduces the likelihood of a change in control.

Creditors, such as banks, are also large and potentially active monitors. They typically have a variety of control rights especially when firms default and in part because they typically lend short-term, so borrowers have to come back at regular intervals for more funds.103 In China, the Commercial Bank Law prevents banks from owning shares in their name. Nevertheless, state-owned banks have a strong incentive to monitor listed firms in China given the generally high leverage of listed firms due to historical debt burdens. This suggests a close link between listed firms and banks due to strong lending relationships. Assuming that Chinese banks are mostly interested in securing the repayment of outstanding debt, it is expected that banks enhance the probability of a change in control to reverse inefficient control structures in highly leveraged firms. H.3:

Changes in control are more likely in highly leveraged firms.

Given the control-based model of corporate governance in China, political influence can be expected to play a large role in determining the likelihood of changes in control. In this respect, one of the top priorities of central government officials is to maintain social stability.104 This particularly concerns employment lay-offs in the restructuring process. In this study, the labour intensity of production is used as a measure of the importance of labour in determining the likelihood of a change in control.105 Given the generally high importance of employment in a country with overpopulation and underdeveloped social security, it is expected that firms with a higher labour intensity of production are less likely to experience a change in control.

103

Shleifer and Vishny (1997, p. 757). See Edin (2003, p. 39). 105 Liu et al. (2006, p. 2029) suggest the average compensation cost per worker as a measure of the importance of labour in the Chinese privatization process. Such data, however, was not available. 104

28

H.4:

2 China’s reform process, and testable hypotheses

Firms with a higher labour intensity of production are less likely to experience a change in control.

If changes in control increase firm efficiency, operational changes are expected to take place after a change in control.106 In particular, it is expected that turnover rates for the management board increase after a change in control. Moreover, restructuring activities like asset sales or lay-offs should accompany changes in control. Ultimately, it is expected that firm performance increases after changes in control took effect. H.5:

Changes in corporate control are followed by corporate restructuring and improvements in performance.

Governance structures are evolving as the Chinese government, private parties and markets seek to strengthen firm competitiveness. A rather silent but nevertheless strongly in importance increasing governance mechanism is competition in product market. Over the last decade, several important rules and legislations have been introduced that increased competition in product markets faced by Chinese listed firms.107 This incremental marketdriven process is likely to affect the probability of changes in control because intensified competition changes incentive structures of local government officials. Given that no adequate proxy for product market competition could be determined due to limited data availability, it is assumed that the effect of product market competition is captured by ownership and performance variables.108

106

Jensen (1986, p. 328) argues that takeovers occur in response to breakdowns of internal control processes in firms and that management is likely to invest free cash flow in inefficient projects of monitoring is not strict enough. Bringing the firm back on track requires to revert these investments through asset sales or lay-offs. 107 This includes essentially price liberalizations and the removal of entry barriers; see Guo and Yao (2005, p. 216). 108 In particular, industry-level data, like total output per industry, value added per industry, or market share of the five largest producers per industry, was not available.

3 Data, concept of control, and summary statistics on changes in control

This study examines the frequency, causes and consequences of control changes using a sample of Chinese listed firms for the years 1996 to 2006. To identify changes in control, a concept of control that is based on ultimate share ownership is applied. This is significant because given the trading restrictions of different share classes and the historical relationship between listed and unlisted parent companies Chinese listed firms are in their majority ultimately controlled by local governments through pyramids or cross-ownership structures. This makes changes in direct ownership less meaningful.

3.1 Data sources The data sources used in this study are the China Stock Market and Accounting Research Database (CSMAR) developed by Shenzhen GTA Information Technology Company, the Block Trade and Financial Reporting Database by SinoFin Information Services (SinoFin) and the Financial Database constructed by Shanghai based WIND Information Corporation (WIND). Sun and Tong (2003), Bai et al. (2004), Fan et al. (2007) used the CSMAR Database for their research and Kato and Long (2006) the SinoFin Database. So far no reference in the academic literature can be found in which authors use the WIND Database.109 A very unique part of this dataset is the classification of the buyers and sellers involved in block trades in ultimate controller categories (see section 3.2 for details). This classification has exclusively for this research project been carried out by China Economic Information Network (CEInet) Data Company, a national information network sponsored jointly by the State Information Center (SIC) and the National Development and Reform Commission (NDRC).

109

WIND is not the primary source of this study and has primarily been used for consistency checks of other data sources.

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3 Data, concept of control, and summary statistics on changes in control

Fan et al. (2007) use regional economic data provided by CEInet in their research. Most of the data has been collected in 2006 during a research stay at China Center for Economic Research (CCER) of Peking University. During this period, several interviews have been conducted primarily with academics of Peking University and professionals active in the Chinese securities market industry.

3.2 Sample selection, and concept of ultimate control As a starting point of the sample selection key ownership characteristics have been collected for the full population of publicly listed firms in China. Changes in ultimate share ownership are identified by analyzing the impact of block trade transactions on the ownership structure of firms. Data on block trade transactions has been collected for the period 1996 to mid-2006. In the analysis of the causes of control changes, firm characteristics are lagged by one calendar-year to control for potential endogeneity issues and are thus collected over the period 1995 to 2005.110 The sample selection is subject to the following restrictions: First, all B-share firms were excluded due to limited data availability. For instance, for the year 2005 a total of 23 firms, which issued B-shares only, have been excluded. Second, all firm years of delisted firms were dropped from the whole sample period 1995 to 2005. This is due to the absence of ownership data for these firms. This restriction, however, should not introduce a potential survivorship bias given that over the period 1995 to 2005 only 21 firms have been delisted by the CSRC. Third, a total of 10 financial firms were excluded to prevent the distortion of the sample by the unique financial reporting standards of this particular industry. Finally, all firms for which several key variables could not be determined have been dropped. In 2005, this involved 18 firms. For the year 2005, the sample selection resulted in a total number of 1,327 firms. The total sample contains over the whole sample period 1995 to 2005 a total of 10,070 firm years. Table 3.1 illustrates the sample selection from the universe of listed firms in Mainland China for year-end 2005.

110

The collection of firm characteristics begins with the initial public offering (IPO) and records one observation for each firm characteristic at calendar yearend. For firms listed prior to 1995 (a total of 158 firms) the data collection starts in 1995.

3.2 Sample selection, and concept of ultimate control

31

Table 3.1 Sample selection from listed firms in Mainland China for year-end 2005

This table illustrates the sample selection from the population of listed firms for the year 2005. The restrictions of the sample selection process are as follows: First, B-share firms have been dropped. Second, non-financial A-share firms and third, firms for which several key variables could not be determined have also been dropped. In addition, all delisted firms have been excluded. The SHSE and the SZSE represent the stock markets of Mainland China in its entirety.

To enable an analysis of the causes and consequences of changes in ultimate share ownership information on the shareholding structure, the percentage holdings of the top ten shareholders and in particular the identity of the ultimate controller is required. Being aware of the difficulties of obtaining accurate corporate ownership data in general and with respect to emerging markets in particular, special care has been taken in the construction of the dataset. The identification of block trade transactions, that convey a change in ultimate control, involved the careful and detailed examination of the impact of each block trade on the ownership structure of the target firm. This step is crucial for this research project and hence deserves special attention. The CSMAR, WIND and SinoFin block trade databases provide extensive information on the block trade transactions. In particular, the datasets contain information on the parties involved in the transaction, such as the English name of the target firm and the Chinese names of the buyers and sellers, details on the pricing of the trades, including the price per share paid for the block, the number of shares transferred, information on the transaction type, and the date of announcement and closing of the transaction. According to all three data providers, the information contained in the data-

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3 Data, concept of control, and summary statistics on changes in control

bases are directly collected from the two stock exchanges and supplemented by press articles in the “Securities Times” and “Shanghai Securities Daily”. The raw versions of the CSMAR, SinoFin and WIND block trade datasets report 2,701 transactions for 1998 to 2004, 3,174 transactions over the period 1990 to mid-2006 and 4,743 transactions for the 1994 to 2004 period respectively. However, not all of these transactions led to change in ultimate control. To identify these changes in ultimate share ownership a concept of ultimate control has been applied. This involved three main steps. First, the CSMAR, SinoFin and WIND block trade datasets have been merged into one single dataset. This step involved the careful validation of every transaction by comparing the details of the block trade characteristics. This process significantly increased the confidence in the accurateness, consistency and completeness of observations included in the dataset. However, this step also involved the use of some own judgement in determining whether an observation was included, especially when the datasets contained conflicting details on some transactions. The resulting dataset contains information on 2,446 block trades for the period 1996 to mid-2006. Second, the attention was restricted to block trades which resulted in a change of the largest shareholder. To examine whether a change in the largest shareholder occurred, the impact of each single block trade on the top ten shareholders and the shareholding structure of the respective target firm has been researched. This procedure is primarily based on a change in the identity of the largest shareholder in the next reporting year following the year in which the block trade took place. Additionally changes in the shareholding structure of different share categories have been examined and whenever applicable attributed to the respective block trade transaction. This step resulted in the identification of 708 changes in the largest shareholders over the period 1996 to 2006. The third and final step investigates whether a change in the largest shareholder led to a change in ultimate control. To conduct this analysis, information on the ultimate controller of Chinese listed firms and information on the ultimate controller of the buyers and sellers involved in the block trade transaction is required. The identity of the ultimate controller of Chinese listed firms has been obtained by the SinoFin classification of ultimate control.111 This classification provides a separation of the ultimate 111

Kato and Long (2006, p. 803) also use the ultimate controller classification of SinoFin in their study.

3.2 Sample selection, and concept of ultimate control

33

controller in state-controlled firms, private firms or individuals, foreign firms or individuals, collective groups and non-profit social associations.112 The SinoFin database only provides information on the ownership type of the ultimate controller and does not contain the percentage of shares owned by the ultimate shareholder directly or indirectly. The classification of the ultimate controllers of the buyers and sellers has been carried out by CEInet, as detailed in table 3.2. Table 3.2 Classification scheme of buyers and sellers

This classification scheme of buyers and sellers involved in block trade transactions has been developed for this specific research project.

112

To the best of my knowledge, the classification of the ultimate controller by SinoFin is the most comprehensive database on ultimate control in Chinese listed firms. WIND’s database on ultimate control only ranges from 2004 to 2006. Liu and Sun (2005) trace the chain of control for 1,105 listed firms and calculate the percentage shareholdings of their ultimate controllers. However, their information stops in 2001 and is not yet publicly available.

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3 Data, concept of control, and summary statistics on changes in control

Each buyer and seller has been assigned to one of the following ultimate control categories: (1) SOE controlled at the central government level; (2) SOE controlled at the provincial or local government; (3) firms privately controlled by Chinese citizens or Chinese corporate entities; (4) collectively owned enterprises (COE); and (5) foreign-invested enterprises (FIE). Accordingly, each block trade involving a change of the largest shareholder has been compared with changes in the SinoFin classification of ultimate control. In case a change in ultimate control occurred in the same or the following year of the block trade transaction, the classification of ultimate control by CEInet has been used to verify that the change in ultimate control has been triggered by the respective block trade. This is the case if the classification of the ultimate controller of the buyer and seller matches the classification of the new ultimate controller in the SinoFin classification. Although this analysis has been taken out with special care, there is one important restriction. This restriction is caused by the fact that the SinoFin classification of ultimate control does not allow to track changes in control within certain ownership types. For instance, an effective change in ultimate control caused by a transfer from a central government organ to a local government entity is not traceable in the SinoFin classification of ultimate control. To avoid a selection bias towards control transfers across ownership types, every block trade involving a change of the largest shareholder taking place within ownership types is included in the sample as long as there are no conflicts between the SinoFin and CEInet classification of ultimate control. According to this concept of control there have been a total of 517 changes in ultimate share ownership over the period 1996 to mid-2006. Section 3.3 provides a more detailed description of these changes in ultimate share ownership. The concept of ultimate control developed for this study allows the construction of two sub-samples of firms of the total sample. The first subsample (hereafter, “firms without change in control”) is comprised of those firms without a change in the largest shareholder or the ultimate owner over the entire sample period. The second sub-sample (hereafter, “firms with change in control”) includes all firm years for those firms experiencing a change in ultimate ownership at one or several points of time over the sample period. Table 3.3 summarizes the sample selection from Chinese listed firms over the period 1995 to 2005 and provides details on the two sub-samples of firms.

3.2 Sample selection, and concept of ultimate control

35

Table 3.3 The sample selection

This table presents information on the sample selection procedure from the population of listed Chinese firm over the period 1995 to 2005. Columns 2 and 3 report the number of firms included in the total sample and the percentage of the listed firms’ population. Columns 4 and 5 report the number and percentage of the total sample on the sub-sample of firms that did not experience a change in ultimate control over the entire sample period. Columns 6 and 7 report the number and percentage of the total sample on the sub-sample of firms that experienced a change in ultimate control.

As reported in this table, the total sample is fairly representative for the universe of publicly listed firms in China. Taking the number of all listed Chinese firms as a benchmark, the average coverage over the years 1995 to 2005 is 90 per cent, with lowest coverage in 1995 (67 per cent) and almost perfect coverage in 2004 (95 per cent). Firms without a change in ultimate control in any of the years contain 688 firms at year-end 2005 and over the entire sample period a total of 4,283 firm years. Firms with a change in control, in turn, include 474 firms at year-end 2005 and a total of 4,234 firm years over the period 1995 to 2005. Table 3.3 also shows that both sub-samples of firms combined represent about 83 per cent of the total sample. The remaining 17 per cent of the total sample is comprised of firms that experience a change in the largest shareholder but no subsequent change in the ultimate controller. These firm years have subsequently been excluded from the analysis involving the loss of a total of 1,553 firm years. This loss of observations, however, is justified by the gain in the clearest possible cut between firms with and without changes in control.

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3 Data, concept of control, and summary statistics on changes in control

Table 3.4 Summary of key ownership characteristics

Table shows the mean of ownership characteristics for the total sample and the sub-sample of firms with a change in control by year (panel A) and by industry (panel B). Panel C contains statistics for the entire sample period 1995 to 2005. RATIO is the percentage of nontradable to total shares outstanding. TOP1 is the percentage holding of the largest shareholder.

3.2 Sample selection, and concept of ultimate control

37

Table 3.4 (cont.) TOP5 are the cumulative holdings of the five largest shareholders. STATE assumes the value of one if the state ultimately controls the firm. The industry definition follows the one-digit industry classification of the CSRC. SD is standard deviation, MIN the minimum and MAX the maximum. MIN, MAX, and SD are not reported for STATE.

Table 3.4 provides information on key ownership characteristics for the total sample and the sub-sample of firms that experience a change in ultimate control. The ownership characteristics detailed in this table include the percentage of non-tradable shares to total shares outstanding (RATIO), the percentage holding of the largest shareholder (TOP1), the cumulative holdings of the five largest shareholders (TOP5), and an indicator for ultimate state control (STATE) which assumes the value of one if the Chinese state (central or local government) is the ultimate controller of firms. The average ratio of non-tradable shares to total shares is about 62 per cent for the total sample over the 11-year period. This ratio decreased from about 65 per cent in 1996 to on average 58 per cent in 2005. Thus, the ratio of nontradable to total shares remained largely stable at about two-thirds of the firm’s equity over the years. TOP1 and TOP5 indicate that ownership is very concentrated in Chinese listed firms. The largest shareholder holds on average around 44 per cent of total shares outstanding, while the cumulative holdings of the five largest shareholders account for on average 59 per cent over the 11-year period. Firms with a change in ultimate control exhibit a slightly lower ownership concentration than the average firm included in the total sample. For instance, the percentage holding of the largest shareholder is on average 39 per cent, which decreases from on average 41 per cent in 1995 to on average 35 per cent in 2005. The Chinese state, both at the central and local government level, is on average in absolute and ultimate control of 71 per cent of all firms included in the total sample over the 11-year period. However, ultimate state control decreased from 75 per cent in 1996 to 63 per cent in 2005. The table further shows that state control is highly concentrated in industries like Mining with 98 per cent, Utilities (82 per cent), Transportation (80 per cent), and Social Services (78 per cent). Measured by the cumulative holdings of the five largest shareholders, ownership is also most concentrated in these industries. Ultimate state control is lowest in Conglomerates (54 per cent), IT (59 per cent), and Wholesale & Retail (65 per cent). The ownership characteristics presented in table 3.4 are largely in line with prior research. Although coverage ratios of listed firms included in samples and periods of observation differ across studies, Kato and Long (2006, p. 805) and Bai et al. (2004, p. 609) report similar results on the average

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3 Data, concept of control, and summary statistics on changes in control

percentage holding of the largest shareholder. Considering that the coverage ratio of all publicly listed firms in China for the year 2001 is 93 per cent in this study (see table 3.3), the results on ultimate state control are largely in line with the evidence provided by Liu and Sun (2005, p. 48).

3.3 Descriptive statistics of changes in ultimate share ownership This section determines the frequency of changes in ultimate share ownership and provides details on the types of buyers and sellers of control blocks, the size of traded blocks and the pricing of control blocks. In particular, the frequency of changes in ultimate share ownership is examined for the different means of control transfers and the occurrence rate of control changes. 3.3.1 Frequency of changes in ultimate control The trade of a large block of shares does not need to be associated with a change in control over the entire firm. An example of this is a new shareholder purchasing a large block, while the majority of shares remain in the hands of the ultimate owner. According to the applied concept of control, there have been 708 changes in largest shareholders and 517 changes in ultimate control in Chinese listed firm over the period 1996 to mid-2006. Table 3.5 shows that on average 73 per cent of all changes in largest shareholders involve a change of the ultimate controller. Hence, the frequency of control changes is somewhat lower than suggested by the changes in largest shareholders. It follows that not all block trades resulting in a change of the largest shareholder lead to a change in ultimate control but that a large part does.

3.3 Descriptive statistics of changes in ultimate share ownership

39

Table 3.5 Changes in largest shareholders versus changes in control

Column (2) shows the number of changes in largest shareholders on a yearly basis. Column (3) details the number of changes in ultimate control on a yearly basis and Column (4) shows the percentage of changes in largest shareholders resulting in changes of ultimate control.

Table 3.5 also shows that changes in ultimate control grew steadily over the sample period and were highest in 2002 and 2003 with a total of 91 changes in control in both years. After 2003 the number of control changes declined. The drastic drop from 74 control changes in 2004 to only 29 control changes in 2005 is largely due to the capital market reform initiated by the CSRC in 2005. In 2006 the number of changes in control dropped further down to only 2. This is again partly due to the still ongoing reform of capital markets but also a result of data availability for the year 2006. In particular, data on block trade transactions was only available until July 2006. Rather unique features of block trading in China are the different means of control transfer.113 The control transfer of Chinese listed firms is primarily carried out in three ways: negotiated ownership transfers, administrationdominated ownership transfers, and court-forced ownership transfers. Under negotiated ownership transfer, the seller and buyer bargain over the transaction terms of the control block through face-to-face negotiation. Thus, this type of trade is not settled through the public trading system of 113

See also Green and Liu (2005, pp. 133-134) and Cai and Chen (2004, p. 69).

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3 Data, concept of control, and summary statistics on changes in control

the stock exchanges. Administrative transfers often happen between government agencies and state-owned enterprises subject to the same level of government, or between government agencies subject to the same or different levels of government. These trades are largely driven by the reorganisation of responsibilities of parts of the government bureaucracy. Such transfers usually do not involve a payment. Court-forced transfers are mostly the result of a court judgement involving an insolvent LP shareholder being forced to sell its LP shares or a firm which has used its shares as collateral for a bank loan going into default and the shares being transferred to a party nominated by the bank. Since the Commercial Bank Law prevents banks from owning shares in their own name, banks use nominees who take legal trust of the shares, pass dividends onto the bank and represent it on the board. Table 3.6 separates changes in ultimate share ownership in the different types of control transfers and provides statistics on the occurrence rate of changes in ultimate ownership. Panel A of table 3.6 shows that negotiated transfers are the most common form of transferring control, accounting for 62 per cent (322 out of 517) of all changes in ultimate control. Forced transfers sum-up to 62 cases (12 per cent of all control transfers) and administrative transfers involve a total of 133 cases (26 per cent of all cases), respectively. Despite the general growth trend in control transfers already revealed in table 3.5, there appears to be not much variation in the relative breakdown of transfer methods over the sample period 1996 to 2006. Panel B of table 3.6 details that in 36 cases the ultimate controller changed twice in one firm. Thus, several changes in ultimate control in sequence occur but are not very likely.

3.3 Descriptive statistics of changes in ultimate share ownership

41

Table 3.6 Changes in ultimate control by transfer method and occurrence rate

This table shows the number of changes in ultimate control on a yearly basis separately by transfer method (panel A) and by occurrence rate (panel B). Panel A details changes in ultimate control separately for negotiated transfers (column 2), administrative transfers (column 3) and forced transfers (column 4). The occurrence rate (panel B) refers to the number of changes in ultimate control within one firm over the entire sample period 1996 to 2006. Occurrence rate can be single (column 2), or double (column 3).

Table 3.7 shows the number of changes in ultimate share ownership by industry and province.

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3 Data, concept of control, and summary statistics on changes in control

Table 3.7 Changes in ultimate control by industry and province

This table shows the number of changes in ultimate control over the period 1996 to 2006 separately by industry (panel A) and by province (panel B). Column (2) lists the number of changes in ultimate control by industry (province), column (3) shows the number of control changes in an industry (province) divided by the total number of control changes.

3.3 Descriptive statistics of changes in ultimate share ownership

43

Table 3.7 (cont.) Column (4) shows the number of control changes in an industry (province) as a percentage of the number of listed firms in that industry (province) for year-end 2005. The industry classification follows the one-digit CSRC industry classification. Listed firms are assigned to provinces by the registered address of their headquarters. Panel B shows the ten provinces with the highest number of control transfers; “Others” include (in descending order of the number of control transfers in the particular province) the provinces Chongqing, Fujian, Jilin, Hainan, Heilongjiang, Tianjin, Yunnan, Guangxi, Xinjiang, Henan, Gansu, Qinghai, Guizhou, Innermenggu, Jiangxi, Sanxi, Shanxi, Hebei, Anhui, Ningxia, and Xizang.

The industry classification follows the one-digit CSRC industry classification and firms are assigned to one of the 31 provinces of China by the registered address of their headquarters. Panel A of table 3.7 indicates that the majority of control transfers (57 per cent of all control changes) took place in the industry Manufacturing, followed by 11 per cent of all control transfer in the industry Wholesale and Retail. Taking the number of listed firms at year-end 2005 as a benchmark, 62 per cent of all firms operating in the industry Wholesale and Retail at year-end 2005 experienced at least once a change in control over the period 1996 to 2006. Surprisingly, 90 per cent of firms operating in the industry Media experienced a change in control during the 11-year period. However, this finding only states that ultimate share ownership changed at some point in time, but does not imply that ownership changed from public to private. All 9 control transfers in the industry Media happened between government agencies (not reported). Overall, 39 per cent of all firms listed on the SHSE and the SZSE experienced a change in ultimate control over the period 1996 to mid-2006. Panel B of table 3.7 reveals that 73 changes in ultimate control or 14 per cent of all control transfers occurred in firms registered in Guangdong province. Comparing the number of changes in control with the number of listed firms in the respective province at year-end 2005 shows that 61 per cent of all firms registered in Sichuan province changed their ultimate controller in the 11-year period. Overall, 33 per cent of all changes in ultimate share ownership affected firms with registered headquarters in the provinces Guangdong, Shanghai and Sichuan. 3.3.2 Identity of buyers and sellers of control blocks To see whether a particular type of ultimate controller dominates control changes, the transaction partners are more closely examined. Table 3.8 classifies the buyers and sellers involved in block trades leading to changes in ultimate control according to the ultimate control classification of

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3 Data, concept of control, and summary statistics on changes in control

CEInet. The table reveals that the 517 changes in ultimate control have been caused by a total of 679 block trade transactions. This means that some changes in ultimate control involved more than one block trade. There are several reasons for this: In some cases the buyer purchased several blocks of shares from different sellers until a change in ultimate control occurred. In most cases of involving more than one block trade to trigger a change in ultimate control, however, the transaction parties registered the total amount of shares transferred in more than one block trade at the stock exchanges. Table 3.8 The identity of buyers and sellers

This table classifies the buyers and sellers involved in block trade transactions according to the CEInet classification of ultimate control. Each buyer and seller is assigned to one of the following ultimate control categories: (1) central government, (2) local government, (3) private firm or individual, (4) collectively-owned enterprise (COE), or (5) foreign-invested enterprise (FIE). For definitions of all categories see table 3.2.

Local governments are the most active parties involved in block trade transactions. On the one hand, local governments are the sellers in 501 block trades representing roughly 74 per cent of all block trades. On the other hand, local governments are also active buyers. The number of trades involving local governments as the buyer and the seller amounts to 212 cases or 31 per cent of all trades. Table 3.8 also shows that private firms are the most active buyers accounting for 326 block trades or 48 per cent of all trades. The number of block trades involving foreign-invested enterprises (FIE) is rather low. In 11 (15) cases a FIE acts as the seller (buyer). The least active type of controller, however, are collectively-owned enterprises (COE). This is mostly due to the difficulty of identifying this particular type of ultimate controller. CEInet stated that this difficulty is the result of the restructuring and registration of many COE as private firms in the 1990s. Overall, private parties are the net buyers, local governments the net sellers and there has been an active inter-government market.

3.3 Descriptive statistics of changes in ultimate share ownership

45

Given the relative importance of different types of ultimate controllers and to overcome the difficulty with respect to classifying COE, all changes in ultimate control were assigned to one of the following two transaction party categories: (1) private transfers and (2) state transfers. In particular, private transfers include all trades in which a private firm or individual buys a controlling stake from a state entity (local or central government level), transfers of control between private firms or individuals, and the transfer of a controlling stake involving a COE as the buying entity. State transfers involve transfers of control between government entities at different (local or central) government levels or at the same (local or central) government level, and control transfers involving a state entity buying from a private entity or a COE. Table 3.9 summarizes the outcome of the classification of changes in ultimate control in private and state transfers. Table 3.9 Changes in ultimate control by transaction parties

This table shows the number of changes in ultimate control separately for private transfers (column 2) and state transfers (column 3). Column (4) details the percentage of private transfers in which a private firm or individual buys a controlling stake from a state entity (local or central government level). Private transfers include trades in which a private firm or individual buys a controlling stake from a state entity (local or central government level), transfers of control between private firms or individuals, and the transfers of a controlling stake involving a COE as the buying entity. State transfers involve transfers of control between government entities at different (local or central) government levels or at the same (local or central) government level, and control transfers involving a state entity buying from a private entity or a COE.

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3 Data, concept of control, and summary statistics on changes in control

According to table 3.9 about 47 per cent (245 out of 517) of all changes in ultimate control are classified as private transfers. A total of 272 changes in ultimate share ownership, in turn, are classified as state transfers. Out of the 245 private transfers, a total of 75 per cent involve the transfer of control from a state entity to a private firm or individual. In other words, roughly every third change in ultimate control of all 517 changes in ultimate control entailed that a private firm became the new ultimate owner of a formerly state controlled enterprise. 3.3.3 Pricing and size distribution of control blocks It is essential to suggest that control transfers of listed firms in China have a distinguishable non-market trait, which is also shown in the pricing of deals. In a well developed capital market, bidding prices are often based on observable stock prices before the transaction.114 In China, however, the only type of transaction that involves a bargaining between the transactions parties over the transfer price are negotiated transfers. Nevertheless, in some cases also administrative transfers and in most cases court-forced transfers involve a payment and thus report transfer prices. There are, however, important administrative restrictions on how low prices can go. In 1997, the Ministry of Finance issued the policy “Regulatory Opinion on State Ownership Rights Implementation by Owners in Joint Stock Companies”.115 Because the transfer of control involves mostly non-tradable shares, this policy requires that the transfer price is based on the net asset value (NAV) per share. Thus, the transfer price of block shares in China has little to do with the market price of tradable shares. With the NAV per share as the official floor, parties bargain over the size of the premium. Table 3.10 reports the pricing of block trades resulting in changes of ultimate control, separately for all transfers, private transfers and state transfers. For the purpose of comparison, panel A of table 3.10 reports the pricing of block trades relative to the share prices on the announcement date of these trades. This pricing method is based on a total of 477 block trades, in 114

Regardless of the price paid in the transaction, Barclay and Holderness (1989 and 1991) report that block transfers in the US are accompanied by positive cumulative abnormal returns surrounding the announcement date of these trades. Wang and Zhang (2004, p. 9) examine the stock price reactions after 587 negotiated block transfers in the SHSE and SZSE for the 1996 to 2000 period. They generally confirm that announcement dates of block trades are associated with positive cumulative abnormal returns. 115 See Green and Liu (2005, p. 134).

3.3 Descriptive statistics of changes in ultimate share ownership

47

which a transfer price and a share price could be determined. Relative to share prices block transfers are priced at a substantial discount of on average 76 per cent over the period 1996 to mid-2006. This discount remains relatively stable over the first three sub-periods 1996 to 1998, 1999 to 2001, and 2002 to 2004. In the last sub-period 2005 to 2006, however, the discount slightly diminished to on average 55 per cent. There is no obvious difference in the pricing of block trades relative to share prices between private transfers and state transfers. Table 3.10 The pricing of block trades

Table shows the pricing of block trades for the period 1996 to mid-2006, separately for private transfers, state transfers and all transfers. For each category the mean and median are reported for the entire sample period and separately for four sub-periods. Panel A reports the pricing of block trades based on share prices. The premiums/discounts are calculated as the transfer price per share divided by the share price on the announcement date of the transaction minus one. Panel B reports the pricing of block trades based on net asset values (NAV) per share. NAV per share is calculated as the firm’s book value of equity divided by the total number of shares outstanding (tradable and non-tradable) for the year preceding the block trade. The discounts/premiums are calculated as the transfer price per share divided by the NAV per share minus one. A definition of private and state transfers can be found in table 3.9.

Panel B of table 3.10 reports the pricing of block trades relative to the NAV per share. Over the entire sample period 1996 to mid-2006, this pricing method is based on 485 block trades for which a transfer price and a

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3 Data, concept of control, and summary statistics on changes in control

NAV per share could be determined. In contrast to transfer prices relative to share prices, the transfer prices relative to NAV per share are priced at a premium of on average 16 per cent. The median premium over the 11-year period is 4 per cent. Premiums paid on NAV per share are particularly high for the sub-periods 1996 to 1998 and 2002 to 2004. Over the entire sample period 1996 to mid-2006 state transfers are priced slightly higher than private transfers with an on average 19 per cent premium for state transfers and an on average 14 per cent premium for private transfers. To put these results into perspective, Huang and Xu (2005) report an average discount of transfer prices to share prices of 72 per cent for the years 2002 and 2003. They, however, restrict their sample to negotiated transfers regardless of the impact of these trades on the ownership structure of firms. The authors do not provide information on transfer prices relative to the NAV per share.116 Table 3.11 shows the size distribution of block trades, separately for all transfers, private transfers and state transfers. The size of block trades is measured as the number of shares transferred divided by the total number of shares outstanding (tradable and non-tradable). Table 3.11 Size distribution of blocks purchased

Table shows the size distribution of block purchased for the period 1996 to mid-2006, separately for private transfers, state transfers and all transfers. Block size is measured as the number of shares traded in the respective block trade divided by the total number of shares outstanding (tradable and non-tradable). Block size is observed at the direct level of ownership, block ownership is determined at the ultimate level applying the concept of control. Therefore, purchases of blocks refer to changes in ultimate ownership of direct share blocks. Column (2) shows the total number of blocks purchased. Columns (3), (5), (7), and (9) disaggregate these purchases into four size classes. Columns (4), (6), (8), and (10) relate the disaggregate number of blocks to the number of all blocks purchased (column 2).

116

See Huang and Xu (2005, p. 23)

3.3 Descriptive statistics of changes in ultimate share ownership

49

According to this table, in 16 per cent of all block trades the amount of shares transferred was between 50 per cent and 100 per cent of the firms’ equity. The majority of all trades (34 per cent of all trades) involved the transfer of shares representing between 25 per cent and 50 per cent of total share capital. Table 3.11 also reveals that private transfers generally involve the transfer of smaller ownership stakes. For example, while only 22 per cent of state transfers include the trade of ownership stakes between 10 per cent and 25 per cent of total share capital, 42 per cent of all trades involving private transfers fall into this size category.

4 Causes of changes in ultimate share ownership

The purpose of the section is to provide first-hand evidence on the determinants of changes in ultimate share ownership from an unbalanced panel of cross-sectional data of Chinese listed firms over an eleven-year period (1996 to 2006). The aim is to test hypotheses one to four, as outlined in section 2.3.

4.1 Methodological approach, data, and univariate results To examine the ex-ante determinants of control changes, a univariate and a multivariate analysis are applied. The univariate analysis compares key firm characteristics of firms with a change in control and firms that do not experience a change in control. The multivariate analysis investigates the causes of control changes in a more systematic fashion using a conditional fixed effect (FE) logistic estimation. 4.1.1 Definition of variables and univariate results The general approach in the multivariate analysis is to run a regression of a binary dependent variable indicating a change in ultimate share ownership on a set of pre-event firm characteristics. Firm characteristics explaining the likelihood of changes in ultimate control are lagged by one year to control for potential endogeneity issues. The definitions of all variables used in the empirical analysis of section 4 can be found in table 4.1. The dependent variable (UCT) is the result of the applied concept of ultimate control. UCT is of binary outcome and assumes the value of one if a change in ultimate share ownership occurred. More specifically, the dependent variable takes the value of one if one or more block trades occurred in a calendar year leading to a change in ultimate control and is zero otherwise.

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Table 4.1 Definition of variables

4.1 Methodological approach, data, and univariate results

53

Table 4.1 (cont.)

The table contains the definitions of all variables used in the empirical analysis of section 4.

To test hypothesis H.1 concerning the influence of firm performance on the likelihood of a change in control, several performance measures are constructed. The main measure of firm performance used in this study is return on assets (ROA). ROA is calculated as earnings before interest and tax (EBIT) over the book value of total assets. There are two main reasons why ROA is used as the benchmark measure of performance. First, the pricing of block trades in China is based on NAV rather than on the share price. Thus, if performance matters as a determinant of changes in ultimate control this should be reflected in the operating performance of firms rather than their observable market value on the exchanges. Second, the subsequent analysis of the consequences of changes in control is mainly concerned with potential operating performance improvements after a change in ultimate share ownership. Thus, ROA is used since it is known as a measure of the operating performance of firms. Nevertheless, to test the robustness of the results alternative measures of firm performance are constructed. The first alternative measure of firm performance is the yearly growth in sales revenue (SALES_G). The second alternative measure (LOSS) is a indicator variable that takes the value of one if earnings per share (EPS) in a firm are negative or zero at year-end and is zero otherwise. To include a market-based measure of firm performance Tobins’q (TOBINSQ) is used. One difficulty with this performance measure is that a

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large proportion of shares of the listed firms in China are non-tradable and hence do not have market prices. No consensus exists in the Chinese based corporate governance literature about how to compute the total market value of firms with a substantial percentage of non-tradable shares. One approach is to use the price of tradable shares as a proxy for the price of the non-tradable shares.117 However, this method clearly overstates the market valuation of the firm because non-tradable shares have a lower price than the tradable ones due to a substantial liquidity discount.118 In this study, the variable TOBINSQ is calculated as the sum of the market value of tradable equity and the book value of total debt divided by the book value of total assets. Thus, TOBINSQ is based solely on the market value of tradable shares.119 Hypothesis H.2 is concerned with the influence of the concentration of ownership on the likelihood of a change in control. Ownership concentration is proxied by the Herfindahl index (see table 4.1 for a definition of the construction of this concentration measure). The benchmark concentration measure is the Herfindahl index of the five largest shareholders (HTOP5). It is, however, examined whether results are robust to alternative measures of ownership concentration. These alternative measures include the Herfindahl index of the three (HTOP3) and ten (HTOP10) largest shareholders and the percentage holding of the largest shareholder (TOP1). Apart from the concentration of ownership, other ownership characteristics of firms are likely to influence the probability of a change in control. The possibly most important factor in the Chinese case is the identity of owners. Therefore, the indicator variable STATE is added to all estimations to control for aspects related to the influence of ultimate state control. The dummy variable STATE assumes the value of one if the Chinese state (central or local government) ultimately controls the firm. In addition, the variable RATIO is included in all estimations.120 RATIO is calculated as the percentage of non-tradable shares to total shares outstanding. This 117

For instance, Wei et al. (2005, p. 93) follow this approach. Bai et al. (2004, pp. 608 and 611) define two alternative measures of Tobins’q by taking a 70 per cent and 80 per cent discount for non-tradable shares compared to the price of tradable shares. Their results are, however, not affected by the different measures of Tobins’q. 119 This approach might under-estimate the total market value of firms. However, it has the advantage of only using market prices where a market price really exits and avoids assumptions about the relative prices of tradable and non-tradable shares. 120 Huang and Xu (2005, p. 11) were among the first to suggest this variable.

118

4.1 Methodological approach, data, and univariate results

55

variable controls for liquidity constraints attached to the non-tradability of shares and the extent of market exposure of firms since both the liquidity of shares and the market exposure increase with the percentage of tradable shares in the open market. H.3 deals with the impact of creditors on the likelihood of a change in control. The influence of creditor is measured by the variable LEVERAGE and calculated as the book value of total debt divided by the book value of total assets. To avoid the distortion of results by excessive outliers, observations with debt-asset ratios larger than one have been dropped. This involved a total of 147 cases for the full sample of 10,070 firm years. To examine whether the influence of creditors on the likelihood of a change in control particularly affects loss-making firms an interaction term consisting of an indicator for financial distress and a proxy for the default risk on bank credit is needed. The indicator for financial distress is the variable LOSS, as defined above. The proxy for the default risk on bank credit is the variable CREDITOR, calculated as the book value of short-term interest bearing debt to the book value of total debt. CREDITOR is a proxy for the leverage structure of firms and in particular measures the exposure of firms to short-term leverage.121 Hypothesis H.4 is concerned with the influence of labour constraints on the probability of a change in ultimate share ownership. To measure the influence of labour constraints, the variable LABOUR serves as a proxy for the labour intensity of production. LABOUR is calculated as the book value of fixed assets to the total number of employees. Data on number of employees was only available over the period 1998 to 2005. Note that a higher ratio of fixed assets to number of employees implies a lower labour intensity of production. Other variables are standard measures used in the corporate finance literature to control for heterogeneity across firms.122 Among these, the variable 121

Other potential proxies for the default risk on bank credit include the relative position of bank credit to total debt and the interest coverage ratio. Unfortunately, data on net interest and bank credit was not available for the universe of publicly listed firms in China. The corporate bond market in China is, however, very small. Therefore the debt positions of firms are mostly related to bank financing. See OECD (2005a, pp. 158-160) for a description of the corporate bond market in China and Guo and Yao (2005, p. 223) for alternative measures of the default risk on bank credit used in the privatization literature. 122 See e.g. Barclay and Holderness (1989, pp. 385-386).

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SIZE is a general proxy for firm heterogeneity. SIZE is calculated as the natural logarithm of the book value of total assets. The variable CASH is a proxy for the liquidity position of firms and measured as the book value of cash and marketable securities over the book value of total assets. The variable INTANGIBILITY measures the relative weight of intangible assets on the balance sheet of firms and is calculated as the book value of intangible assets over the book value of total assets. Finally, yearly time dummies are included in all estimations with 1995 as the reference category. Table 4.2 compares firms based on whether they experience a change in ultimate ownership and provides summary statistics of key firm characteristics over the entire sample period 1995 to 2005. The table also provides tests for the statistical differences in these measures. The p-values for differences in means are form a standard t-test and those for medians are from a Wilcoxon rank-sum test.123 The results of table 4.2 indicate that firms experiencing a change in ultimate control are significantly smaller measured by the book value of total assets than firms without a change in control. Firms with a change in control have average book values of total assets of RMB0.9billion compared with average book values of total assets of RMB1.5billion for firms without a change in control. The average debt-asset ratio of firms with a change in control is about 58 per cent over the eleven-year period. This is significantly higher than the average debt-asset ratio of 46 per cent of firms without a change in control. Firms with a change in control also have significantly higher proportions of short-term debt to total debt, averaging 35 per cent and 31 per cent for firms with and without changes in control, respectively. Moreover, firms with a change in control are significantly less liquid measured by the cash holdings to total assets but have a significantly higher ratio of intangible assets to total assets compared with firms that do not change the ultimate controller.

123

The Wilcoxon rank-sum test is a non-parametric alternative to the two-sample t-test. See Greene (2003, p. 106) for details of the t-test; Sun and Tong (2003, p. 195) apply the Wilcoxon rank-sum test. The STATA code for the t-test is “ttest” and for the Wilkoxon rank-sum test “ranksum”.

4.1 Methodological approach, data, and univariate results

57

Table 4.2 Firm characteristics by firms with and without change in control

This table provides differences of the means (medians) of key firm characteristics for all firms with and without a change in control over the sample period 1995 to 2005. SIZE is the natural logarithm of total assets. LEVERAGE is total debt divided by total assets. CREDITOR is short-term debt over total debt. CASH is marketable securities and cash divided by total assets. INTANGIBILITY is intangible assets over total assets. ROA is EBIT divided by total assets. LOSS is an indicator that assumes the value of one if EPS are zero or negative. RATIO is non-tradable shares divided by the number of total shares outstanding. TOP1 is the percentage holding of the largest shareholder. HTOP5 is the Herfindahl index of the holdings of the five largest shareholders.

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Table 4.2 (cont.) STATE is an indicator that equals one if the Chinese state (central or local government) is the ultimate controller. LABOUR is fixed assets divided by total number of employees. The total number of observations for LABOUR is slightly reduced because employee data is only available since 1998. The p-values for differences in means are from a standard t-test; those for medians are from a Wilcoxon rank-sum test. Medians are not reported for indicator variables.

Both performance measures, ROA and LOSS, indicate that firms experiencing a change in control are relatively less profitable compared with firms that do not experience a change in control. For instance, the average ROA for firms with a change in control is 9.1 per cent compared to 11.5 per cent for firms without a change in control. With respect to the ownership structure, table 4.2 reveals that firms with a change in control have a significantly lower proportion of non-tradable shares to total shares and a significantly lower concentration of ownership, measured as both the Herfindahl index of the five largest shareholders and the percentage holding of the largest shareholder. The indicator variable of ultimate state control is significantly lower for firms with a change in control. Finally, there appears to be no statistically significant difference in the labour intensity of production measured by the ratio of fixed assets to total number of employees between firms with and without changes in ultimate share ownership. 4.1.2 Model specification The multivariate analysis examines the influence of certain firm characteristics on the likelihood of a change in ultimate share ownership and takes into account that different variables can simultaneously affect the probability of a change in control. One crucially important factor that determines the choice of the model is the importance to control for endogeneity in the reform process in China. The literature on ownership restructuring in China demonstrates this importance, both because of the political decision process in China and because results that seem robust in the cross section are more ambiguous when fixed effects estimation are applied.124 In this respect, the most serious concern is that one of the explanatory variables is endogenously determined by some unobservable factor. For instance, the unobservable ability of the management in terms of receiving political support might influence the accessibility of the firm to bank credit. 124

For a discussion of this literature see Jefferson and Su (2006, p. 152).

4.1 Methodological approach, data, and univariate results

59

Then, one of the right-hand-side regressors, in this example the leverage, is correlated with some unobservable firm-specific heterogeneity. This leads to an inconsistent estimator due to an omitted variable bias. To control for unobserved heterogeneity, a conditional fixed effects (FE) logit model as implemented in the statistical software STATA is applied.125 To illustrate the main virtues and shortcomings of this model, consider first the structure of the data used in this study.126 In particular, an unbalanced panel data set of N firms is observed. For each firm up to T yearly observations are recorded. At each observation the dependent variable yit records whether a change in ultimate share ownership occurred, where i indexes firms and t refers to the year of observation. The probability of an event, i.e. a change in ultimate control, is the function of one-year lagged explanatory variables, xit. The unobservable firm-specific heterogeneity is defined as αi, which is assumed to be constant over time. Then, a way to model the relationship between the event of a change in ultimate share ownership and pre-event firm characteristics is as follows: P(yit = 1 | αi, xit) =

e (α i + xit β )

1 + e (α i + xit β )

; i = 1, …, N; t = 1, …, T.

(4.1)

This model makes the following general assumptions: first, conditional on xit, the independent variable yit has the probability given by Eq. (4.1); second, P(yit = 1) depends on xit through the logistic function; third, P(yit = 1) is governed by a vector of k common (“structural”) parameters and a firm-specific (“incidental”) parameter αi.127 One way to estimate the parameters of this model is to include a dummy variable for each firm and then to maximize the unconditional likelihood function. The unconditional maximum-likelihood (ML) estimator of the incidental parameters is consistent as T converges to infinity for fixed N but inconsistent as N converges to infinity for fixed T. In essence, the inconsistency arises because the number of incidental parameters increases

125

Version 9.0 of the statistical software STATA offers two codes “clogit” and “xtlogit, fe” to perform a conditional FE logistic estimation. 126 The following section is based on Wooldridge (2002, pp. 482-492), Cameron and Trivedi (2005, pp. 795-797), and Greene (2003, pp. 695-701). 127 See Katz (2001, pp. 379-380).

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without bound, while the amount of information about each incidental parameter remains fixed.128 Chamberlain (1980, p. 228) offers an estimator of the structural parameters that is consistent even in the presence of incidental parameters. This estimator is obtained by conditioning the likelihood function on minimal sufficient statistics for the incidental parameters and then maximizing the conditional likelihood function. In the logit case, this minimal sufficient statistics are given by ∑t yit . In particular, Chamberlain (1980) observed that the conditional likelihood function, n  T  LC = ∏ P  yi1 ,..., yiT ∑ yit  , i =1  i =1 

(4.2)

is free of the incidental parameters αi. The joint distribution of yi ≡ (yi1,…,yiT)’ conditional on xit, αi, and ∑t yit does not depend on αi. Therefore, standard conditional ML estimation can be used to consistently estimate the ’s.129 Following the notation in Cameron and Trivedi (2005, p. 796), the set of Bc = {di| ∑t d it = ∑t yit = c} is defined as the set of all possible sequences of 0s and 1s for which the sum of T binary oucomes is ∑t yit = c . Then, if it is conditioned on ∑t yit = c , αi is eliminated and (4.1) becomes P(yi| ∑ t yit = c, xi, ) =

(( y x ' ) β ) e ∑ t it it

(( ∑ d x ' ) β ) ∑ d ∈Bc e t it it

.

(4.3)

In other words, the joint probabilities for firms are conditioned on ∑ t yit in order to sweep out the fixed effect αi.130 Thus, the main virtue of the conditional FE logit model is that it controls for unobservable firm heterogeneity 128

See Greene (2003, pp. 695-697) for a detailed technical report on the unconditional FE model with a binary dependent variable and a description of the incidental parameters problem. 129 Wooldridge (2002, p. 491) points out that the fact that this conditional distribution does not depend on αi is a feature of the logit functional form. The same argument does not work for the probit case. 130 See Chamberlain (1980, pp. 228-230) and Greene (2003, pp. 696-698); for a detailed deviation of this estimator see Baltagi (1995, pp. 178-180).

4.1 Methodological approach, data, and univariate results

61

in firm-specific effects that are constant over time. In addition, the model allows the inclusion of year dummies. This controls for time-specific timeinvariant effects, like the macroeconomic environment. Nevertheless, the virtue of the control of firm-specific and time-specific time-invariant effects comes with some limitations. First, as it is usually the case in FE estimation, no coefficients for time-constant variables can be estimated with this model. However, once firm-specific effects are controlled for the effects of time-constant variables, like industry and location effects, are also controlled for. In addition, conditional FE estimation does not contain a constant term. The time-invariant FE are eliminated by conditioning on xit and the sum of possible outcomes of yit. In this procedure, the constant term becomes essentially part of the FE and is therefore also eliminated. Second, all observations without any within-firm variation in the dependent variable, i.e. firms that do not experience a change in control in any of the years, drop out of the maximum likelihood expression. In conditional FE estimation it is not possible to condition on ∑ t yit = 0, since this can only occur if all yit = 0, and similarly for ∑ t yit = T.131 This involves the loss of a considerable number of observations. In other words, the sample size is reduced to firms which experienced at least once a change in ultimate share ownership. Third, since the FE are not estimated, it is not possible to compute probabilities or marginal effects with the estimated coefficients. This would require plugging in a value for αi. Because the distribution of αi is unrestricted – in particular, E(αi) is not necessarily zero – it is hard to know what to plug in for αi. However, the FE logit estimator immediately provides the effect of each element of xt on the logodds ratio.132 To sum-up, the hallmark of the conditional FE model is that it controls for unobserved firm heterogeneity, which is assumed to be constant over time. Since this is a particularly important factor in the Chinese case, it is therefore argued that this feature outweighs the restrictions associated with the model.133

131

See Cameron and Trivedi (2005, pp. 796-797) for a more technical illustration of this point. 132 See Greene (2003, p. 699) and Wooldridge (2002, p. 492). 133 See Appendix A.2: Hausman test, p. 106-107 for a comparison of random effects versus fixed effects estimations.

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Applied to the data used in this study, the general and simplified conditional FE specification to test H.1 is as follows: P(UCTit=1) =

Λ( 1SIZEit + 2LEVERAGEit + 3CASHit + 4INTANGIBILITYit + 5ROAit + 6RATIOit + 7STATEit + Dt).

(4.4)

UCTit is the binary dependent variable assuming the value of one if a change in ultimate share ownership occurred, Λ(·) is the logistic cumulative distribution function with Λ(z) = ez/(1+ez), ROAit is the performance measure and variable of interest, SIZEit, LEVERAGEit, CASHit, INTANGIBILITYit, RATIOit, and STATEit are control variables, Dt is a vector of year dummies, are the coefficients to be estimated, and is a vector of coefficients on the year dummies. To show that the results obtained for H.1 are robust to the inclusion of additional variables, equation (4.4) is augmented by the variable HTOP5 which provides a test of H.2. For a more specific test of H.3 the variables LEVERAGE and ROA will be replaced by the variables CREDITOR and LOSS. In addition the interaction term CREDITOR × LOSS is added to the model. To test H.4 the same model used for testing H.2 is applied with the modification of adding the variable LABOUR.

4.2 Multivariate results and interpretation The multivariate analysis is based on the sample of firms which experienced a change in control at least once over the eleven year sample period. Results are presented for the full sample and two sub-samples. The subsamples separate control transfers in private and state transfers. To put the results into perspective the interpretation compares findings to evidence on the small and medium enterprise sector in China.

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63

4.2.1 Econometric results The results of conditional FE models predicting changes in control by various firm characteristics are presented in table 4.3. Standard errors (SE) are heteroscedasticity-robust SE by using the Huber/White sandwich estimator for the variance – covariance matrix.134 Model (1) of table 4.3 indicates that poorly performing firms are more likely to experience a change in control. The coefficient on ROA is negative and statistically significant at the 5 percent level. The odds-ratio of ROA indicates that an increase of ROA by one percentage point decreases the odds of a change in control versus no change in control by 2.2 per cent holding all other variables constant.135 The finding that poor performance increases the probability of a change in control remains present in all four specifications of the conditional FE models predicting changes in control. This supports H.1. Model (2) of table 4.3 adds the variable HTOP5 as a measure of the concentration of ownership to the specification of model (1). The coefficient on HTOP5 is positive and statistically significant at the 5 per cent level. This suggests that shareholder control, measured by ownership concentration, serves as a complementary governance device by increasing the probability of a change in control. Hence, H.2 is rejected. Given the dominance of ultimate state control in Chinese listed firms, it comes at no surprise that the variable STATE is statistically significant at the 1 per cent level in all four specifications of the conditional FE models.

134

Wooldridge (2002, p. 57) contains a general description of the Huber/White estimator and Greene (2003, pp. 519-521) details the Huber/White estimator in ML estimation. Table 4.3 contains the goodness-of-fit measure pseudo Rsquared as suggested by McFadden and the p-value of the model measured by the Wald-statistic. See Wooldridge (2002, p. 465) for the definition of McFadden’s likelihood ratio index and Greene (2003, pp. 676-678) for a discussion of the Wald-test. 135 The interpretation of the odds-ratio is as follows: If the variable increases by one unit then the odds of a change in control versus no change in control (P(y = 1)/P(y = 0)) change by the factor of the odds-ratio (OR). Since a one-unit increase for the variable ROA is equal to 100 per cent, a change in the variable ROA by one percentage point is: 0.108973^(1/100) = 0.9780 or 2.2 percent holding all other variables constant.

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Table 4.3 Causes of changes in ultimate share ownership

4.2 Multivariate results and interpretation

65

Table 4.3 (cont.) Table reports results of conditional FE models predicting changes in control by various firm characteristics. All variables are defined in table 4.1. All models include time dummies (not reported). Robust standard errors (reported in parenthesis) are calculated using the White/Huber sandwich estimator for the variance – covariance matrix. Odds-ratios are reported in brackets. Pseudo R2 is based on McFadden’s likelihood ratio index. *, ** and *** indicate that individual coefficients are statistically significant at the 0.10, 0.05 and 0.01 level, respectively.

Models (1) and (2) of table 4.3 indicate that firms with high debt-asset ratios are more likely to experience a change in control. The variable LEVERAGE is positive and statistically significant at the 5 per cent level in both specifications. Model (3) investigates the impact of creditors on the probability of a change in control more closely. The coefficient of CREDITOR is positive and statistically significant at the 5 per cent level, suggesting that a higher default risk on bank credit increases the likelihood of a change in control. Thus, it appears that creditors enhance the probability of a change in control. The indicator variable LOSS is positive and statistically significant at the 1 per cent level. The coefficient on the interaction term of CREDITOR and LOSS is negative but not statistically significant. Hence, creditors seem to increase the likelihood of a change in control, providing support for H.3. There is no evidence, however, that creditors behave differently when firms have a high default risk on bank credit and very poor performance. Model (4) tests the impact of the labour intensity of production on the likelihood of a change in control. The variable LABOUR is negative and statistically significant at the 5 per cent level. This suggests that firms with a higher labour intensity of production are more rather than less likely to experience a change in control. Hence, H.4 is rejected. The results of table 4.3 further reveal that SIZE and CASH are powerful predictors of the probability of a change in control. Both coefficients on SIZE and CASH are negative and statistically significant at the 1 per cent level in all four specifications of the conditional FE models. The variables INTANGIBILITY and RATIO do not affect the likelihood of a change in control. The year dummies (not reported) are individually and jointly significant at the 1 per cent level. The concept of ultimate control applied in this study enables a separation of control changes in private transfers and state transfers. Private transfers include trades in which a private firm or individual buys a controlling stake from a state entity (local or central government), transfers of control be-

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tween private firms or individuals, and the transfers of a controlling stake involving a COE as the buying entity. State transfers involve transfers of control between government entities at different (local or central) government levels or at the same government level, and control transfers involving a state entity buying from a private entity or a COE. Estimations of models (2) to (4) of table 4.3 have been repeated for the two sub-samples of private and state transfers. The results are presented in table 4.4. Models (1) to (3) of table 4.4 re-estimate the results of the full sample for private transfers and models (4) to (6) for state transfers. ROA is no longer significant in private transfers while it remains negative and statistically significant at the 5 per cent level in state transfers. Models (2) and (5) of table 4.4 reveal that the LOSS indicator remains statistically significant for private and state transfers. In private transfers, however, LOSS is only statistically significant at the 10 per cent level. This suggests that poor firm performance increases the likelihood of a change in control, but only if the firms’ financial difficulties are severe. In summary, firm performance has a strong impact on the likelihood of a change in control in state transfers and affects private transfers only when firm performance is very poor. Table 4.4 also shows that ownership variables are no longer significant in state transfers. The only exception is the variable HTOP5 in model (6) with a positive coefficient and a 10 per cent significance level. The variable STATE looses its explanatory power in state transfers mostly because of the generally very high level of ultimate state control in these transfers. In private transfers, however, ownership variables are highly significant. The concentration of ownership remains positive and is statistically significant at the 1 per cent level in all three specifications of private transfers. The coefficient of the variable RATIO turns out to be negative and statistically significant at the 5 per cent level in models (1) and (2) of table 4.4. Moreover, the variable STATE remains positive and statistically significant at the 1 per cent level in all three specifications of private transfers. In sum, the results indicate that the initially expected negative relation between ownership concentration and the probability of a change in control actually appears to run in the opposite direction. The variable LEVERAGE looses its significance in state transfers, while it remains positive and statistically significant at the 10 per cent level in model (1) of private transfers. By contrast, the variable CREDITOR is no longer significant in private transfers, but remains positive and statistically significant at the 5 per cent level in state transfers. Thus, creditors generally appear to enhance the likelihood of a change in control.

4.2 Multivariate results and interpretation

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Table 4.4 Causes of changes in ultimate share ownership by transaction parties

Table reports results of conditional FE models predicting changes in control by various firm characteristics separately for private transfers and state transfers. All variables are defined in table 4.1. All models include time dummies (not reported). Robust standard errors (reported in parenthesis) are calculated using the White/Huber sandwich estimator for the variance – covariance matrix. *, ** and *** indicate that individual coefficients are statistically significant at the 0.10, 0.05 and 0.01 level.

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Models (3) and (6) of table 4.4 show that the variable LABOUR is no longer significant in private transfers, but remains negative and statistically significant at the 5 per cent level in state transfers. Hence, contrary to the initial conjecture firms with a higher labour intensity of production are more likely to experience a change in control and this seems to be particularly the case in state transfers. With the exception of model (3), the variable SIZE remains negative and statistically significant at the 1 per cent level in both private and state transfers. The variable CASH looses some of its significance but remains negative and statistically significant at the 5 per cent level in most specifications of table 4.4. The year dummies (not reported) remain jointly significant at the 1 per cent level. 4.2.2 Interpretation of the results To interpret the findings of table 4.3 and 4.4 and to put the results into perspective, the motives of buyers and sellers involved in control transfers are considered. As shown in table 3.8, the most active sellers of control blocks in Chinese listed firms are local governments.136 To explain the motives of local governments in privatizing medium and small enterprises, Liu and Sun (2006) argue that local leaders are primarily concerned with their political career path and private benefits from control.137 The political career path, in turn, is determined by the cadre evaluation system which puts the primary focus of future promotions of local leaders on local GDP growth. The hardened budget constraints and intensified cross-regional competition faced by local governments increased the incentives of local governments to restructure SOE. Given that the promotion of local leaders is primarily based on the local GDP growth in their jurisdiction, this provided incentives to restructure especially poorly performing firms. Some of these arguments seem to carry over to the listed sector. Several studies report that the performance of listed firms in China declined after the IPO. For instance, Sun and Tong (2003) find that both return on sales

136

See section 3.3.2, p. 44. See Liu et al. (2006, p. 2024) and section 2.1, pp. 9-10 for details and further references.

137

4.2 Multivariate results and interpretation

69

and earnings on sales decline after listing.138 Wang, Xu, and Zhu (2004) report that the listing of SOE is associated with a significant drop in operating performance measured by return on assets.139 Thus, the generally poor performance of SOE after listing coupled with hardened budget constraints altered the incentives for local governments to sell their controlling stakes. The tendency observed in the non-listed sector that low firm profitability reduces the government’s incentives to maintain control seems to carry over to the listed sector. This is reflected in the results of table 4.3 which indicate that poorly performing firms are more likely to experience a change in control. The empirical evidence further suggests that the liquidity position of firms is significantly negatively related to the probability of a change in control. The variable CASH is statistically significant in all specifications presented in the tables 4.3 and 4.4. Hence, it appears that local governments are not willing to relinquish control in firms with large cash holdings. This can be interpreted as evidence that control transfers are less likely in firms with potentially high private benefits from control since large amounts of cash facilitate the payments of generous compensations or other perquisites and ensure that firms pay dividends. The significance of firm size in explaining the likelihood of changes in control suggests that the initial official policy approach to economic reform adopted in China and literally expressed in the notion of “grasp the large and let the smaller go”, is not only present in the whole economy but carries over to the listed sector.140 The above reasoning deals with the supply side of the Chinese market for control transfers. Table 3.8 further revealed that the two most common types of buyers are again local governments or other government-owned entities and private firms or individuals. In particular, table 3.9 showed that every third change in ultimate control involved a private party buying a controlling stake from a local or central government.141 Anecdotal evidence suggests that the acquisition of a listed firm can be thought of as an indirect way to go public for private enterprises.142 This 138

See Sun and Tong (2003, p. 215). See Wang et al. (2004, p. 485). 140 An alternative interpretation concerns wealth constraints of investors. Shleifer and Vishny (1992, pp. 1362-1364) show that the market for corporate control is less liquid as firm size increases. 141 See section 3.3.2, p. 45. 142 See Cai and Chen (2004, p. 69). 139

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seems to be a particularly important motive of privately owned firms since going public by an IPO is very difficult to achieve for them. The empirical results of table 4.4 show that ownership variables are among the most important predictors of changes in control in private transfers. In particular, the concentration of ownership is positive and statistically significant at the 1 per cent level in models (1) to (3) of table 4.4. The variable RATIO has a negative coefficient and is statistically significant at the 5 per cent level in models (1) and (2) of table 4.4. Hence, it appears that changes in control are more rather than less likely in concentrated firms. This contrasts the finding of Köke (2004) who reports a substitutive relation between shareholder control and the probability of a change in control in the concentrated German share market.143 The most distinguishing difference between concentrated ownership structures in China and other markets is the dominant position of the state. The positive relation between ownership concentration and the likelihood of a change in control observed in the Chinese stock market might be interpreted as indicating that the potential improvements through better monitoring are likely to be higher in firms with a dominant position of the state as both the largest shareholder and the ultimate controller.144 In addition, private firms regard the listing place itself as a major asset and the potential benefits of buying a controlling stake may be especially significant if the new ultimate owner can influence corporate policy so as to improve firm performance.145 Autonomy in corporate policy, in turn, may be higher for private parties if concentrated holdings of the state can be bought out by a change in control.146 Moreover, the results suggest that the probability of a change in control increases with the market exposure of firms, i.e. a lower ratio of non-tradable to total shares, in private transfers. This might indicate that firms with an overall lower frac143

See Köke (2004, p. 75). Wei, Xie, and Zhang (2005, p. 106) report that when government ownership decreases and other blockholder become dominant, firm value increases. Bai et al. (2004, p. 610) support this result. 145 If the herfindahl index of the second to tenth largest shareholder and the percentage holding of the largest shareholder (TOP1) enter the estimation of model (2) of table 4.3 simultaneously, it turns out that the former is not statistically significant while TOP1 is positive and statistically significant at the 10 per cent level (not reported). This suggests that the largest shareholder, i.e. the state, is rather autonomous in determining the likelihood of a change in control. 146 Alternatively one could argue that potential private benefits from control are higher in more concentrated firms increasing the probability of a change in control. In this respect, Dyck and Zingales (2004, p. 572) show in a cross-country study that ownership is more concentrated in countries in which private benefits of control are larger.

144

4.3 Robustness of the multivariate results

71

tion of non-tradable shares entail lower liquidity constraints making a change in control more attractive for private firms. Green and Liu (2005) suggest that government-owned entities buy listed firms in order to preserve them to ensure that the listing place is not lost, employees are not laid off and important assets are not lost to firms from rival provinces.147 These motives are partly visible in the empirical results presented in tables 4.3 and 4.4. The evidence on state transfers revealed that firm performance is an important predictor of the likelihood of a change in control. In addition, the proxy for the default risk of bank credit is positive and statistically significant at the 5 per cent level in model (5) of table 4.4. The results of model (6) of table 4.4 further indicate that firms with a higher labour intensity of production are more likely to experience a change in control. These findings can be interpreted as indicating that reallocations of corporate control between government owned entities or different levels of government become more likely in firms with high leverage, poor firm performance and a higher importance of labour in the production process. This suggests that changes in control represent an important mechanism to deal with inefficient control structures even within the government bureaucracy. In this respect, state transfers can be regarded as essentially rescue actions of politically sensitive firms which face the threat of de-listing or even insolvency. Overall, the results on the causes of changes in control in Chinese listed firms suggest that (1) firm performance matters, (2) the ex-ante control structure of firms is of importance, and (3) control transfer are largely driven by interests of the Chinese state.

4.3 Robustness of the multivariate results To test the sensitivity of the results, a number of robustness checks are conducted. First, it is examined whether the results depend on the choice of the performance measure and the degree of ownership concentration. Second, the conditional FE models predicting changes in control are repeated for the sub-samples of different means of control transfers. Third, it is examined whether multiple changes in control affect the results. 147

See Green and Liu (2005, p. 130).

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4 Causes of changes in ultimate share ownership

4.3.1 Alternative measures of explanatory variables To test whether the results depend on the measures of explanatory variables, model (2) of table 4.3 is used as the benchmark. The results of this robustness test are reported in table 4.5. Models (1) to (3) of table 4.5 examine whether the results depend on the choice of the performance measure and replace the variable ROA of the benchmark model with the alternative measures of firm performance SALES_G, the indicator variable LOSS and the variable TOBINSQ, respectively. The variable SALES_G is negative and statistically significant at the 5 per cent level and LOSS is positive and statistically significant at the 1 per cent level. The coefficient on TOBINSQ is negative but not statistically significant.148 Given the NAV-rule of determining the prices of large share blocks in China, this finding indicates that the operating performance of firms serves as an indicator of the probability of a change in control while share prices do generally not. In sum, the results are robust to alternative measures of accounting based firm performance. Model (4) to (6) of table 4.5 replace the variable HTOP5 used in the benchmark model with the alternative measures of ownership concentration HTOP3, HTOP10 and TOP1, respectively. The coefficient of the herfindahl index of the three largest shareholders (HTOP3) is positive and statistically significant at the 1 per cent level. The concentration of ownership among the ten largest shareholders (HTOP10) is positive and statistically significant at the 5 per cent level and the percentage holding of the largest shareholder (TOP1) is positive and statistically significant at the 10 per cent level. Thus, it appears that the result of model (2) of table 4.3 is robust to alternative measures of ownership concentration. Overall, results seem to be rather robust to alternative measures of explanatory variables.

148

The construction of the variable TOBINSQ does not affect this result. Regardless of the assumptions on the price of non-tradable and tradable shares, the variable remains insignificant.

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73

Table 4.5 Causes of control changes – alternative measures

Table reports results of conditional FE models predicting changes in control by various firm characteristics. All variables are defined in table 4.1.

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4 Causes of changes in ultimate share ownership

Table 4.5 (cont.) All models include time dummies (not reported). Robust standard errors (reported in parenthesis) are calculated using the White/Huber sandwich estimator for the variance – covariance matrix. *, ** and *** indicate that individual coefficients are statistically significant at the 0.10, 0.05 and 0.01 level, respectively.

4.3.2 Different means of control transfers As a further robustness test the sample of firms with a change in control is separated into the different means of control transfers. The first sub-sample is comprised of negotiated transfers and the second sub-sample of administrative and court-forced transfers. The second sub-sample includes both administrative transfers and court-forced transfers to guarantee that enough observations are included in this sub-sample. This ensures the precise estimation of the parameters of the model. In addition, both administrative and court-forced transfers take in their majority place between state entities. In particular, 94 per cent of administrative transfers involve solely state entities and about 80 per cent of court-forced transfers happen between government entities.149 Negotiated transfers, in contrast, involve in 64 per cent of all cases private firms and in the remaining 36 per cent of cases transfers between government entities. To avoid identification problems, firms with multiple changes in control and different means of transfer are excluded from the analysis.150 Estimations are repeated for models (2) to (4) of table 4.3. The results are reported in table 4.6. Models (1) to (3) of table 4.6 refer to the sub-sample of negotiated transfers. There are three major findings for this sub-sample of firms. First, the results on firm performance are in line with the findings for private transfers. Poor firm performance increases the likelihood of a change in control but only if firm performance is very poor. Second, ownership structure appears to be an important explaining factor in these types of transfers. Both variables HTOP5 and STATE remain positive and statistically significant. In addition the variable RATIO is negative and statistically significant at the 5 per cent level in model (1) of table 4.6. Third, the coefficient of LABOUR is negative and statistically significant at the 1 per cent level. The remaining variables provide no additional insights. 149

The remaining 6 per cent of administrative transfers involve COE. If, for instance, a firm experiences a change in control in year 1999 and 2001 and the change in control in 1999 was identified as a court-forced transfer but the change in control in 2001 as a negotiated transfers, then this firm has been excluded from the analysis.

150

4.3 Robustness of the multivariate results

75

Table 4.6 Causes of changes in ultimate share ownership by means of transfer

Table reports results of conditional FE models predicting changes in control by various firm characteristics separately for negotiated transfers and administrative/court-forced transfers. Variables are defined in table 4.1. All models include time dummies (not reported). Robust standard errors (reported in parenthesis) are calculated using the White/Huber estimator for the variance – covariance matrix. *, ** and *** indicate that individual coefficients are statistically significant at the 0.10, 0.05 and 0.01 level.

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4 Causes of changes in ultimate share ownership

The results for the sub-sample of administrative and court-forced transfer are reported in models (4) to (6) of table 4.6. In line with the evidence found for state transfers in models (4) to (6) of table 4.4, firm performance is one of the most important factors in explaining the likelihood of a change in control in these transfers. The coefficient on ROA is negative and statistically significant at the 5 and at the 1 per cent level in models (4) and (6) of table 4.6, respectively. There is, however, one additional insight which seems to be a unique factor in explaining the causes of changes in control in these particular transfer methods. Interestingly, the interaction term of the variables LOSS and CREDITOR is negative and statistically significant at the 5 per cent level. The most distinguishing difference between these types of transfers and negotiated transfers is that they generally do not involve a transfer price. Hence, it appears that creditors use their influence in the firm to oppose a change in control if the payments associated with the transfers do not compensate for the high default risk on bank credit. This result is, however, only present in these particular means of transfers and does not hold for the full sample of firms with a change in control. 4.3.3 Multiple changes in control The conditional FE estimations of the causes of changes in control treat each change in control as a separate event regardless of whether one firm is subject to multiple changes in control. Note that only cases where no change in control of the same firm occurred within at least two years before the consecutive change in control are included in the sample.151 This avoids that multiple changes in control of one firm lead to overlapping event windows. However, one potential concern associated with this methodology is that some firm characteristics could systematically differ between the first and second change in control. This concern particularly affects the ownership structure of Chinese firms. Given the institutional requirement regarding the division of shares into tradable and non-tradable shares, the typical ownership structure of listed firms is roughly divided into one-third of tradable shares, and two thirds of non-tradable shares. Non-tradable shares are again initially split half in half into state shares 151

Consider that a given firm has experienced two changes in control, one in 1998 and one in 1999. Then there is in fact only one observation reflecting pre-event characteristics, i.e. 1997. Variables in 1998 would be affected by the change in control event in the same year.

4.3 Robustness of the multivariate results

77

and legal person shares.152 Thus, the concern arises whether the ownership structure of firms systematically differs between the first change in control with the initial set-up of the ownership structure in place and consecutive changes in control. To address this concern, models (2) to (4) of table 4.3 are re-estimated for two sub-samples of firms. The first sub-sample includes only firms with a single change in control over the 11-year sample period. The second sub-sample of firms includes only firms with a second change in control. In particular, this sub-sample of firms only consists of firm years after the first change in control. For instance, if firm i experienced a change in control in year t and in year t+3 then observations for this firm are only recorded after year t+1. The results are presented in table 4.7. First note that the results are fairly robust for the first sub-sample of firms, as shown in models (1) to (3) of table 4.7. All coefficients keep their signs, although in some exceptional cases coefficients loose some of their significance. Models (4) to (6) of table 4.7 report the results for the second sub-sample of firms. With the exception of the variable ROA which stays negative and statistically significant at the 5 per cent level, most other variables loose their significance. This also includes the ownership variables, although they keep the signs of previous estimations. These results are due to the very small sample size, which casts serious doubts on the precise estimation of the parameters of the model. The average sample size is only about 200 observations with a total of 36 second changes in ultimate control. As such no definite conclusion can be drawn from these results.

152

See section 2.2.1, pp. 12-13 for details on the typical listing process of SOE.

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Table 4.7 Causes of changes in ultimate share ownership by occurrence rate

Table reports results of conditional FE models predicting changes in control by various firm characteristics separately for single and second change in control. Variables are defined in table 4.1. All models include time dummies (not reported). Robust standard errors (reported in parenthesis) are calculated using the White/Huber estimator for the variance – covariance matrix. *, ** and *** indicate that individual coefficients are statistically significant at the 0.10, 0.05 and 0.01 level.

4.3 Robustness of the multivariate results

79

Without being able to entirely reject the above concern due to the limited sample size of sub-sample two, the following needs to be considered: The initial division of non-tradable shares into state and LP shares does not imply that these shares are held in indivisible blocks of one-third’s of the firm’s equity. Rather these share categories exhibit substantial variations in their degree of concentration among firms already before changes in control occur. This is shown in the standard deviation of ownership characteristics in table 3.4.153 Moreover, changes in control in Chinese listed firms take place in the non-tradable share market and involve the exchange of LP and state shares or both. Note that these shares remain non-tradable after the change in control and perhaps even more important remain in their majority classified as either LP or state shares after the change in control. In this respect, table 3.4 reported that the ratio of non-tradable to total shares remained rather stable over the 11-year period. In this study, changes in the ownership structure of firms are observed at the ultimate control level and not at direct shareholding levels. Thus, it is rather unlikely that the division of shares into tradable and non-tradable shares and the division of nontradable shares into LP and state shares systematically differs between the first and a consecutive change in ultimate share ownership.

153

See section 3.2, p. 36.

5 Consequences of changes in ultimate share ownership

The results so far indicate that changes in control are most likely to occur in poorly performing firms – firms that offer the greatest opportunities for value improvement. If changes in control increase firm efficiency, changes in corporate control should be followed by corporate restructuring and ultimately lead to improvements in firm performance. To analyze the consequences of control changes, a univariate and a multivariate analysis are applied. The aim is to test H.5, as outlined in section 2.3.

5.1 Univariate analysis of the consequences of control changes The univariate analysis compares restructuring and performance measures of firms that experience a change in ultimate share ownership with firms that do not experience such a change in any of the sample years. 5.1.1 Definition of restructuring and performance measures Regarding corporate restructuring, the following measures are used: turnover of the chief executive officer (CEO), change in fixed assets, growth rate of employment, change in number of subsidiaries, change in registered firm names and change in the one-digit industry classification of the CSRC. A definition of the all measures used in the empirical analysis of section 5 can be found in table 5.1. CEO turnover is measured as an indicator variable which takes the value of one if the CEO changes from year t-1 to year t. Change in fixed assets is calculated as the yearly growth rate of the book value of fixed assets. This variable serves as a proxy for asset divestures.154 Growth rate of employ154

Data on capital expenditure or sales of corporate assets was not available.

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ment is calculated as the yearly growth in total number of employees. Change in the number of subsidiaries is calculated as the yearly growth in total number of subsidiaries. Name changes are captured by a dummy variable assuming the value of one if the firm changed its registered firm name at the SHSE or SZSE from year t-1 to year t. The indicator variable identifying industry changes equals one if the CSRC changed the one-digit industry classification of the respective firm from year t-1 to year t. Table 5.1 Definition of corporate restructuring and performance measures

Table contains a description of the construction of all measures of corporate restructuring and performance used in section 5.

The analysis of the impact of changes in control on corporate performance is based on the following performance measures: return on assets, growth in sales revenue, an indicator for earnings loss, and an indicator of the classification of firms in special treatment (ST) or particular transfer (PT) by the CSRC.

5.1 Univariate analysis of the consequences of control changes

83

The definition of the first three measures of firm performance is equivalent to the definition of these variables in the empirical analysis of section 4. The last performance measure captures a rather unique characteristic of China’s stock market. In 1998, the CSCR created the categories ST and PT to place loss-making firms in separate categories of the exchanges. Firms which are classified as either ST or PT face the most serious risk of being de-listed.155 This indicator variable takes the value of one if firms are classified as either ST or PT by the CSRC in a given year and is 0 otherwise. 5.1.2 Univariate results The univariate analysis compares firms with a change in control and firms not experiencing a change in control. For firms with a change in control, the mean of restructuring and performance measures are calculated for the year of the change in ultimate share ownership (year 0), the following years (year 1, year 2), and the year prior to the change (year -1). For firms without a change in control, statistics refer to the whole period of observation.156 To test for any significance in the median values of unmatched data, the Wilcoxon rank-sum test is applied. Results of the univariate analysis measuring the impact of control changes on corporate restructuring are reported in table 5.2. The results reveal that CEO turnover increased among firms with a change in control from 28.2 per cent in the year prior to the change in control to 30.3 per cent in the year of the change in control from. This CEO turnover rate is significantly above the average 26.8 per cent CEO turnover for firms without a change in control over the 11-year period. Firms with a change in control experience a significant increase in the book value of fixed assets following changes in control. The growth rate of fixed assets increases from 16.2 per cent one year before the change in control to 23.5 per cent two years after the change in control. Firms not experiencing a change in control exhibit a fixed assets growth rate of 26.0 per cent over the entire period of observation. Thus, while fixed assets increase following changes in control, this increase is significantly lower than the average growth rate of fixed assets of firms without a change in control.

155 156

See section 2.2.3, p. 24 for details. Köke (2004, pp. 71-73) uses a similar methodology.

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Table 5.2 Univariate results of corporate restructuring following control changes

Restructuring activities of firms that experience a change in ultimate ownership in year 0 compared with restructuring activities of firms that do not experience such a change in any year. Firms are compared at the median of each firm characteristic. For firms with a change in control, statistics are calculated for the year preceding the change (year -1), the year of change (year 0), and the two years following the change (year 1 and year 2). For other firms statistics refer to the whole period of observation. The test statistics are Wilcoxon rank-sum tests for any significant change in the median values of unmatched data. *, **, *** indicate significance at the 0.10, 0.05, and 0.01 level respectively.

The average growth rate of employment of firms not experiencing a change in control is about 6.3 per cent over the 11-year period. This is significantly higher than the growth rate of employment of firms with changes in control. For firms with a change in control the growth rate of

5.1 Univariate analysis of the consequences of control changes

85

employment drops from 2.5 per cent in the year preceding the change in control to 1.1 per cent in the year of the change in control. Employment growth is again increasing following the change in control year. Firms with a change in control show a large increase in the growth rate of the number of subsidiaries from one year prior to the change in control (17.6 per cent) to the year of the change in control (37.8 per cent). After the second year of the change in control the growth rate of the number of subsidiaries still amounts to 35.2 per cent for firms with a change in control. Firms not experiencing a change in control exhibit an even higher growth rate of subsidiaries of 41.9 per cent over the 11-year period. Table 5.2 further reveals that firms with a change in control change their official firm name and one-digit industry classification significantly more often than firms without a change in control. For instance, only 0.3 per cent of firms not experiencing a change in control switched to a different one-digit industry classification over the 11-year period. This compares to 1 per cent of firms with a change in control in the second year after the control event. Overall, the univariate results on restructuring activities after changes in control suggest that new ultimate owners of firms use their control rights to influence operational decisions and governance. A question that follows is whether operational and governance changes were accompanied by improvements in firm performance. Table 5.3 reports the univariate results of corporate performance following changes in control. According to the univariate analysis, the mean operating performance, measured as ROA, shows no substantial improvements following changes in control but remains rather stable at about 8.8 per cent. Firms not experiencing a change in control have a significantly higher ROA of 11.5 per cent over the 11-year period than firms with a change in control. The performance measure growth in sales revenue, in turn, indicates that sales are increasing following changes in control from 13.1 per cent one year prior to the change in control to 21.6 per cent two years after the control event. Sales growth in the second year after the change in control even exceeds the average growth in sales revenue of 20.4 per cent for firms not experiencing a change in control.

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Table 5.3 Univariate results of performance following control changes

Performance of firms that experience a change in ultimate ownership in year 0 compared with characteristics of firms that do not experience such a change in any year. Firms are compared at the median of each performance measure. For firms with a change in control, statistics are calculated for the year preceding the change (year -1), the year of change (year 0), and the two years following the change (year 1 and year 2). For other firms statistics refer to the whole period of observation. The test statistics are Wilcoxon rank-sum tests for any significant change in the median values of unmatched data. *, **, *** indicate significance at the 0.10, 0.05, and 0.01 level respectively.

Table 5.3 further reveals that the fraction of loss-making firms decreases from 20.9 per cent one year before the control change to 13.3 per cent two years after the change in control. Despite this decrease, the fraction of lossmaking firms two years after the control event is still twice as large for firms with a change in control compared to firms not experiencing a change in control. In addition, while only 1.7 per cent of all firms without a change in control have been classified as ST or PT category over the 11year period, 4.8 per cent of firms with a control change are still classified as either ST or PT category two years after the change in control. Overall, the univariate results provide strong support for increased restructuring activities following control events. At this point of the analysis,

5.2 Multivariate analysis of the consequences of control changes

87

however, it is less clear whether these restructuring activities – apart from reducing the fraction of loss-making firms – lead to performance improvements.

5.2 Multivariate analysis of the consequences of control changes The multivariate analysis examines the consequences of changes in control more systematically by applying the Arellano-Bond generalised method of moments (GMM) estimator as implemented in STATA.157 5.2.1 Model specification The objective of the ex-post analysis is to determine whether changes in control affect restructuring activities of firms and ultimately lead to improvements in firm performance. This objective is inherently dynamic because operational changes and firm performance are compared at the time of the change in control and at points in time after the change in control. Moreover, it is of particular interest to compare the impact of changes in control for private transfers and state transfers. Hence, one has to compare the impact of a change in control for firms experiencing such an event to firms that do not experience a change in control and for private transfers to state transfers. The general approach to achieve this objective is to run a regression of the variable of interest, i.e. the restructuring or performance measure, on a set of dummy variables indicating the event of a change in control. To explore the impact of changes in control at several points in time, the dummy variables for the changes in control are expanded to reflect the corresponding post-event period. The most important consideration in the construction of such a model is the detachment of the impact of a change in control from factors that might as well influence the variable of interest. This can be achieved by using panel estimation and including the lag of the dependent variable as one of the right-hand side regressors. With this lagged dependent variable any measured influence on the performance or restructuring measure is condi157

Version 9.0 of the statistical software STATA offers the code “xtabond” to implement the Arellano-Bond GMM estimator.

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tioned on the entire history of the right-hand side variables.158 In the econometric literature and in the context of panel data, such models are usually referred to as dynamic panel data models. A general way to write such a model is as follows: yit = yi,t-1 + xit' β + αi + µit , i = 1, …., N, t = 1, …., T.

(5.1)

The , and are parameters to be estimated. xit is a 1× k vector of strictly exogenous covariates, αi are the firm specific effects, and µit is the independent and identically distributed (i.i.d.) error term with variance σ µ2 . Standard panel models like fixed-effects or random effects models are biased in this case, since the lagged dependent variable is correlated with the error term uit. This is even the case if it is assumed that uit is not itself autocorrelated.159 The general approach to estimate such a model relies on Arellano and Bond (1991) which suggest a GMM estimator using instrumental variables techniques.160 By taking first differences the heterogeneity can be swept from the model. Accordingly equation (5.1) becomes yit - yi,t-1 = (yi,t-1 - yi,t-2) + (xit − xi ,t −1 )' β + (µit - µi,t-1).

(5.2)

In a next step, equation (5.2) can be estimated using the instrumental variable estimator with yi,t-2 as an instrument for (yi,t-1 - yi,t-2). This is a valid instrument, since yi,t-2 is not correlated with (µit - µi,t-1) assuming the errors µit are serially uncorrelated. Furthermore, yi,t-2 is a good instrument since it is correlated with (yi,t-1 - yi,t-2). But as Arellano and Bond (1991, p. 280) have shown, even more efficient estimation is possible by using additional lags of the dependent variable as instruments. For example, both yi,t-2 and yi,t-3 might be used as instruments. The model is then over-identified and consistent estimation is possible by panel GMM.161 The use of this estimator requires at least four observations for each firm since the first three observations are lost due to lags and differencing. In addition, this model cru158

The following section is based on Greene (2003, pp. 307-334), and Cameron and Trivedi (2005, pp. 763-768). 159 See Cameron and Trivedi (2005, pp. 764-765) for a illustration of the bias present in standard panel estimators when a lagged dependent variable is included as one of the right-hand side regressors. 160 See Arellano and Bond (1991, p. 279); Wooldridge (2002, pp. 421-436) provides a general introduction to GMM estimation and Greene (2003, pp. 303307) illustrates instrumental variables estimation in the context of the random effects model. 161 Over-identified in the sense that there are more instruments than endogenous regressors.

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89

cially hinges on the assumption that there is no presence of second-order autocorrelation in the differenced residuals. The presence of first-order autocorrelation in the differenced residuals does, in turn, not imply that the estimates are inconsistent. In their seminal paper, Arellano and Bond (1991) provide specification tests for both the validity of the overidentifying restrictions (known as the Sargan-test) and the required lack of second order autocorrelation in the differenced residuals.162 A detailed discussion of this estimator including a description of the instrument matrix can also be found in Bond (2002).163 To test H.5 in a systematic fashion, the following basic specification is estimated: yit =

∑

j =1,2

jyi,t-j

+

0Dτ=0

+

1Dτ=1

+

2Dτ=2

+φ0(Dτ=0PRIVATE) +

(5.3)

φ1(Dτ=1PRIVATE) + φ2(Dτ=2PRIVATE) + η1SIZE + αi + νt + µit

According to equation (5.3), yit is regressed on a set of explanatory variables, where yi,t-j is a lagged dependent variable, and , , φ, and η are coefficients to be estimated. The set of dummy variables Dτ indicate the event of a change in control in event time. Three different time periods are considered with τ = 0 for the year of the change in control, τ = 1 for the first year after the change in control, and τ = 2 for the second year after the change in control. In addition, an interaction term of the indicator of a change in control and the variable PRIVATE is included. PRIVATE is itself an indicator variable and assumes the value of one if the control transfer has been classified as a private transfer (see table 3.9, p. 45 for details). Hence, Dτ × PRIVATE measures the difference of the impact of a change in control on y between private and state transfers of control. Moreover, the panel specification consists of a firm specific effect αi, and (T-1) calendar year dummies νt. Finally, SIZE controls for general firm heterogeneity. The empirical design of equation (5.3) enables a dynamic analysis and takes into account that adjustment processes occur over time, firms are observed at several points in time, and the existence of multiple changes in control.164 162

See Arellano and Bond (1991, pp. 281-283) for a discussion of these points and a description of the test statistics to deal with the validity of these assumptions. 163 See Bond (2002, pp. 146-148) and Greene (2003, pp. 309-311) for the deviation of the instrument matrix. 164 The empirical design is closely related to the methodology used in Elsas (2007, pp. 19-20).

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5.2.2 Econometric results Demsetz and Lehn (1985) argue that investors have incentives to purchase blocks of shares when the expected benefits outweigh the expected costs.165 To realize potential benefits from the purchase of poorly performing firms, operational changes are expected to take place. After restructuring activities took effect, it is further expected that firm performance increases. To test hypothesis H.5, dynamic panel regressions using ROA, growth in sales revenue, change in fixed assets and growth rate of employment as dependent variables are performed. Results are reported in table 5.4. Panel A of this table shows results for a comparison of firms with and without changes in control regardless of the identity of the involved transaction parties. Panel B of table 5.4 estimates equation (5.3) as outlined above. For ease of exposition, the table shows coefficient estimates for the change in control event dummies only. The last two columns of table 5.4 report specification tests for the dynamic panel data model. In all estimations the Sargan test does not reject the null hypothesis that the over-identifying restrictions are valid. The null hypothesis of no first-order autocorrelation in the differenced residuals is rejected (not reported), but the null hypothesis of no second-order autocorrelation is not rejected. Thus, specification tests support the overall validity of the model. Panel A of table 5.4 provides evidence for performance improvements following changes in control. For the regression of ROA as the dependent variable, the coefficients on the change in control dummies Dτ=1 and Dτ=2 are both positive and significantly different from zero at the 1 per cent level. Hence, in the first and second year after the change in control an increase in the operating performance measure ROA occurred (the reference group are firms not experiencing a change in control).

165

See Demsetz and Lehn (1985, pp. 1159-1169).

5.2 Multivariate analysis of the consequences of control changes

91

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5.2 Multivariate analysis of the consequences of control changes

93

The results further indicate an increase in sales growth following changes in control. The coefficients of the change in control indicators are positive and statistically significant in all three event periods. The impact on sales growth is largest in the first year after the change in control with a coefficient of 0.24 and a statistical significance at the 1 per cent level. If change in fixed assets is used as the dependent variable, the results show a significant increase in the growth rate of fixed assets in the year of the change in control. The coefficient on the control event dummy Dτ=0 is positive and statistically significant at the 1 per cent level. There are no significant differences in the growth rate of employment for firms with and without changes in control. Panel B of table 5.4 reveals that there are large differences in the impact of control changes on performance and restructuring measures between private and state transfers. The regression using ROA as the dependent variable indicates a positive and significant impact of changes in control on ROA for state transfers in the first and second year after the change in control (the reference group is again firms not experiencing a change in control). The coefficients of the event indicators for private transfers are, however, insignificant in all three event periods. Moreover, the results reveal that the overall positive impact of changes in control on sales growth observed in Panel A of table 5.4 is largely driven by private transfers. In particular, the coefficients on the change in control indicators are positive and statistically significant at the 5 per cent level for private transfers in the first and second year after the change in control. Panel B of table 5.4 provides further evidence for a different impact of changes in control on the growth rate of fixed assets between private and state transfers. In the year of the change in control the coefficient of the control change indicator is positive for state transfers and negative for private transfers. Both coefficients are statistically significant at the 1 per cent level. Moreover, two years after the change in control the coefficient on the event dummy is positive and statistically significant at the 5 per cent level for private transfers. Finally, neither private transfers nor state transfers were systematically associated with a statistically significant impact on the growth rate of employment compared with firms not experiencing a change in ultimate share ownership. These finding may be interpreted as indicating that depending on the identity of the new ultimate owner (i.e. state versus private) different restructuring activities take place after changes in control. Given the large fraction of highly indebted and loss-making firms in state transfers, the primary objective for the new ultimate state owner might be to boost the

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profitability of the newly acquired firm in order to prevent the de-listing. It appears that one of the favored methods to achieve this objective is the injection of new assets. It further appears that these newly injected assets exhibit a potentially high contribution to ROA. In private transfers, in contrast, an increase in sales growth is accompanied by the divestment of potentially marginally profitable assets in the year of the control event. This can be interpreted as being consistent with the argument that private parties secure value enhancing changes in corporate policies by focusing on long-term asset restructuring rather than short-term profitability increases. To investigate this argument further a longer time series of data would be required since adjustments in firm’s behavior are not instantaneous following changes in control. Instead the implementation of restructuring activities may take several years to be effective. In this regard, the results may further indicate that longer time horizons are required to fully capture employment effects. Overall, there is clear evidence that changes in control are followed by asset restructurings. Moreover, it appears that changes in control are followed by improvements in firm performance. This provides support for H.5. 5.2.3 Robustness of the results To test the sensitivity of the results, a number of robustness checks are performed. The results of these robustness tests are reported in Table 5.5. Panel A of table 5.5 estimates equation (5.3) by two-step GMM but includes the variable LEVERAGE as an additional right-hand side variable. The variable LEVERAGE is calculated as book value of total debt to book value of total assets. Comparing the estimates to those obtained in Panel B of table 5.4 shows that explicitly controlling for the leverage of firms has virtually no effect on the estimated coefficients and their statistical significance.166

166

Furthermore, the inclusion of other control variables, like e.g. CREDITOR or CASH, does not affect the reported results.

5.2 Multivariate analysis of the consequences of control changes

95

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5 Consequences of changes in ultimate share ownership

5.2 Multivariate analysis of the consequences of control changes

97

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5 Consequences of changes in ultimate share ownership

To address the concern that extreme outliers are driving the results, Panel B of table 5.5 reports results of the two-step GMM estimator after winsorizing the dependent variables. In particular, winsorizing involves assigning to outliers beyond the 1st and 99th percentiles a value equal to the value of the 1st and 99th percentile in order to avoid undue influence of outliers.167 This procedure has the advantage of not loosing observations but reducing the measured impact of extreme outliers. With some minor changes in the relative size of some estimated coefficients, the significance pattern remains unaffected for the regressions of ROA, growth in sales revenue and change in fixed assets compared to the results in Panel B of table 5.4. For the regression of growth rate of employment the null hypothesis of no second-order autocorrelation in the differenced residuals is now rejected at the 5 per cent significance level. Panel C of table 5.5 reports results of the one-step Arellano-Bond GMM estimator with heteroscedasticity-robust standard errors. In fact, Arellano and Bond (1991) and Bond (2002) suggest the one-step estimator rather than the two step estimator for inference on coefficients. This is partly because simulation studies have suggested very modest efficiency gains from using the two-step version, even in the presence of considerable heteroscedasticity. But more importantly because two-step standard errors tend to be biased downward in small samples.168 The comparison of Panel C of table 5.5 and Panel B of table 5.4 reveals that reported results are largely unaffected by the use of the one-step or two-step estimator. Therefore, heteroscedasticity seems to be not a major problem here. An additional potential concern with the Arellano-Bond estimator is the “weak instruments” problem of instrumental variable estimators.169 Since the Sargan-test of the two-step GMM estimates does not reject the null hypothesis that the over-identifying restrictions are valid, this problem is unlikely to affect the results. However, to explicitly address this potential concern, equation (5.3) is re-estimated using a fixed effects panel regres167

See McNeil et al. (2004, p. 77) or Cebenoyan and Strahan (2004, p. 26) for a similar approach. 168 See Arellano and Bond (1991, pp. 291 and 293) and Bond (2002, p. 147). The reason for not reporting the Sargan-test in the one-step case with heteroscedasticity robust standard errors is that only in the case of a homoscedastic error term does the Sargan test have an asymptotic chi-squared distribution. In fact, Arellano and Bond (1991, p. 291) found evidence that the one-step Sargan-test over-rejects in the presence of heteroscedasticity. 169 See Wooldridge (2002, pp. 101-104) for an illustration of the “weak instruments” problem.

5.2 Multivariate analysis of the consequences of control changes

99

sion including lagged dependent variables.170 As noted previously, the fixed effects model including a lagged dependent variable leads to biased coefficients. However, the model may nevertheless serve as a benchmark because for t going to infinity the model is consistent. Estimation results are reported in Panel D of table 5.5. The coefficients on the dummies and the significance pattern remain similar to the results reported in Panel B of table 5.4.171 In sum, the results are rather robust to (1) the inclusion of additional controls, (2) adjustment for outliers, (3) heteroscedasticity, and (4) the “weak instruments” problem.

170

The following robustness tests are based on the description in Elsas (2007, p. 37). As a further robustness test, a simple fixed effects regression where the error term is assumed to follow an AR1 process has been estimated (not reported). The potential improvement is that the large number of potentially weak instruments is avoided. The significance patterns remain similar to the results reported in table 5.4 of Panel B. This robustness check is available from the author upon request.

171

6 Conclusion

Using data from an unbalanced panel of Chinese listed firms for the period 1996 to 2006, this study examines the frequency, causes and consequences of changes in ultimate share ownership. In contrast to the common perception, China has an active market for control transfers. The most active sellers of control blocks are Chinese local governments and the most active buyers are private entities. In addition, there was an active inter-government market for control transactions. The results on the causes of control changes indicate that poor firm performance makes a change in control more likely. This finding is largely in line with international evidence and suggests that changes in control take place in firms that offer the greatest opportunities for value improvements.172 Firms under strong creditor influence are generally more likely to experience a change in control. This suggests that creditor control and control changes are complementary governance devices. In administrationdominated transfers, however, this relation is reversed. Since administrative transfers do usually not involve payments, it appears that creditors are only enhancing changes in control structures as long as the payments associated with the transfers guarantee a reduction in the default risk of bank credit. The results on the causes of control changes further reveal that depending upon the identity of the transaction parties different factors influence the probability of a change in control. In this regard, severe financial difficulties and employment considerations particularly affect state transfers. In particular, firms with a high labour intensity of production are more likely to experience a change in control. This finding is interpreted as evidence that changes in control are an important mechanism to deal with ex-ante inefficient control structures in politically sensitive firms. In private transfers, in turn, the ownership structure of firms appears to be an 172

For instance, Bethel et al. (1998, p. 631) find that activist investors in the US typically target poorly performing firms and Köke (2004, p. 68) reports that German listed firms making earnings losses are more likely to experience a change in control.

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important explaining factor. This finding is interpreted as indicating that the pre-event control structure of firms is an important determinant of future autonomy in corporate policy. Taken together, these findings illustrate that control transfers in Chinese listed firms are largely driven by the interests of the Chinese state. The results on the consequences of changes in control indicate that changes in control are followed by asset restructuring, increases in sales revenue and operating performance improvements. The specific restructuring strategy seems to depend on the identity of the new ultimate controller. While improvements in firm performance in state transfers appear to be mostly driven by large asset injections, increases in sales revenue in private transfers are accompanied by the divestment of assets in the year of the control change and followed by investments in fixed assets two years after the control event. These findings should, however, be interpreted with some caution. It is hard to rule out the possibility that profitability improvements in state transfers are mainly due to the injection of assets with high contributions to ROA rather than overall efficiency improvements. Increases in sales growth observed in private transfers might be due to the consolidation of income statements of the acquired firm and already existing businesses of the acquiring private party. Whether or not these changes in control lead to long-term profitability improvements remains therefore an open question. It would be interesting to explore this question more extensively by collecting several additional years of data. Given the rather short history of China’s stock market, this task must be left to future research efforts. Taken as a whole and apart from the above mentioned concern, the evidence reported in this study suggests that the Chinese market for control transfers identifies and rectifies problems of poor corporate performance. The central finding along the allocation of control rights dimension is that the quiet and mostly successful evolution of the control transfer market in China facilitates the gradual privatization of China’s state-controlled listed firms. The question remains whether this is the best way of reforming China’s large enterprises. Organizing the privatization of large firms as a two-step process – first the listing of a minority stake and then the offexchange transfer of control – has a number of advantages. It allows the clarification of property rights at a later date when privatization of control

6 Conclusion

103

might be politically more feasible.173 In addition, one-to-one negotiations allow for particular issues, like excess debt and overemployment, to be agreed in detail. However, organizing ownership transfers in this way also has the significant disadvantage of forcing the private sector to finance the restructuring of the state industry. It is rather questionable whether this is economically efficient. In fact, the premia on NAV paid by private parties can be regarded as an access fee to the stock market. In addition, the acquisition process might be subject to abuse. Deals negotiated behind closed doors tend to be vulnerable to corruption.174 Going forward, the dynamics of corporate control allocation in China’s stock market are likely to by affected by the future completition of the share structure reform. Progress in equity market reform, notably the reduction in the overhang of non-tradable shares, is one of the top policy priorities for both the CSRC and the SASAC. The ongoing share structure reform is also key to the successful creation of a more market-driven corporate control market in China. With the majority of shares being freely tradable on the exchanges, the market exposure of firms will inevitably increase. Together with the implementation of market supportive laws, regulations and enforcement this is likely to give rise to an investor protection environment that is conducive to the supply of liquidity.175 Increased market liquidity should further encourage investments and capital market development.176 In such an environment, the establishment of a transparent auction or tender process together with the removal of the NAV price-floor rule might not only maximise the proceeds of the sale of state assets but at the same time might be effective in the avoidance of side payments to facilitate deals. This will ultimately allow the costs and benefits associated with corporate control to be determined by market forces. 173

Remarkable to note that the recently completed National People’s Congress in March this year enacted the first law to protect private property explicitly. See the East Asia and Pacific Update of the World Bank (2007, pp. 12-14 and 39) for more details. 174 See Green and Liu (2005, pp. 138-141). The authors provide case-study evidence on corruption in the organization and pricing of control transfers in China. 175 Brockman and Chung (2003, p. 935) posit that strong investor protection reduces the liquidity costs associated with asymmetric information. 176 Shleifer and Wolfenzon (2002, pp. 5 and 19) develop a market equilibrium model of corporate finance in the environment of weak investor protection. Their model predicts that firms are more valuable, ownership concentration is lower and stock markets are more developed in countries with better protection of shareholders.

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Following this line of thought, it is probably true that in the long run changes in both internal and external governance fostered by the creation of market supporting institutions are necessary conditions for improving the performance of firms in emerging markets. Nevertheless, the results of this study suggest that at least during a transition period changes in corporate control structures based on one-to-one negotiations without a formal legal system in place can be effective in improving firm performance. It is these one-to-one negotiations that facilitated the quiet and mostly successful evolution of the control transfer market in China. This importance of market building in bringing about efficient re-allocations of corporate resources is a useful implication for emerging markets around the world that have weak legal systems and weak property rights protection.

Appendix

A.1 List of share categories of China’s stock market

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Appendix

Table A.1 (cont.) Table contains a description of the different share categories of China’s stock market. *There are subcategories under this definition. Source: Allen et al. (2005, p. 28), Green (2004, p. 29).

A.2 Hausman test It is argued in the model specification of section 4 that it is of particular importance to control for firm-specific heterogeneity in the analysis of the causes of changes in ultimate share ownership. In general terms, random effects (RE) and FE logit models with a binary dependent variable can be distinguished by the assumptions made on the relation between the unobserved firm-specific heterogeneity αi and the idiosyncratic error term µit.177 The RE model with a binary dependent variable crucially relies on the assumption that all explanatory variables, xit, are strictly exogenous conditional on αi: once αi is conditioned on, only the explanatory variables, xit, appear in the response probability at time t. This assumption places a restriction on the distribution of the firm-specific heterogeneity in the RE model. ML estimation of the RE logit model has no closed-form solution and it is standard to compute it numerically using quadrature methods.178 In the FE model, in turn, the distribution of the αi is unrestricted. To illustrate the importance of using FE, the hausman test is applied. The Hausman test is based on the difference between the RE and FE estimates. Since FE is consistent when αi and xit are correlated, but RE is inconsistent, a statistically significant difference is interpreted as evidence against the RE assumptions. As pointed out by Wooldridge (2002), correlation between xis and uit for any s and t causes both FE and RE to be inconsistent. This assumption of strict exogeneity is maintained under the null and the alternative in the Hausman test.179

177

See Greene (2003, pp. 690-694), Wooldridge (2002, p. 490), and Cameron and Trivedi (2005, pp. 795-796) for a detailed description of the RE logit model with a binary dependent variable. 178 See Cameron and Trivedi (2005, p. 796) for details. 179 See Wooldridge (2002, pp. 288-291) for a detailed description of the Hausman test.

A.2 Hausman test

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In particular, model (2) of table 4.3 was estimated using a RE logit model and a FE logit model.180 The resulting test statistic of the Hausman test is 47.52. The critical value from the chi-squared distribution with 18 degrees of freedom is 34.81, which is far smaller than the test value. Hence, the hypothesis that the firm-specific effects are uncorrelated with the other regressors in the model is rejected at the 1 per cent significance level.181 Therefore, FE estimation seems to be more promising.

180

The random effects model was implemented by the STATA code „xtlogit, re“ and includes time, regional, and industry controls. The conditional FE logit model is estimated as defined in section 4.1.2. The hausman test is implemented in STATA by the code “hausman”. More details are available from the author upon request. 181 Restricting the observations used in the RE model to the same observations used in the conditional FE model does not affect this result.

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