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Politicians and Privatization Decisions in China*
Jing Liao School of Economics & Finance
College of Business Massey University, New Zealand Phone: +64 6 356 9099 Ext.84051
Email: [email protected]
Chris Malone School of Economics & Finance
College of Business Massey University, New Zealand Phone: +64 6 356 9099 Ext.84034 Email: [email protected]
Martin Young School of Economics & Finance
College of Business Massey University, New Zealand Phone: +64 6 356 9099 Ext.84062 Email: [email protected]
August 2014
* We thank helpful comments from Dr Philip Sinnadurai and participants at the 2014 AFAANZ Conference in Auckland.
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Politicians and the Privatization Decisions in China
ABSTRACT
This paper examines the impact of rent-seeking politicians on privatization decisions during the Non-
Tradable Share (NTS) reform in China, which aims at converting non-tradable shares mainly held by
state agencies or by State Owned Enterprises into tradable shares. Our results show that privatizations
were delayed in firms with politically connected CEOs, and delayed in firms where the local
government has higher incentives to seek rent from the business sector. We draw attention to
policymakers that gains of privatization may not be achieved if firms continue to be sensitive to
political preference. Even though the NTS reform opened up the gate to further privatizing listed
State-owned Enterprises in China, reducing political interference is critical to establishing efficient
corporate governance in the privatized firms. A further defining characteristic of the timing of the
conversions is that firms with a higher proportion of management owned shares tend to be selected in
the early stage of the reform and tend to complete the reform earlier than others. This indicates that
managers are keen to capitalise on the opportunities the NTS reform created.
Keywords: Privatization; Rent Seeking; Management; China
JEL codes: G34, G38
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1. INTRODUCTION
This paper examines the impact of politicians on privatization decisions during the Non-
Tradable Share (NTS) reform in China, which was officially initiated by the China Securities
Regulatory Commission (CSRC) in April 2005. The key objective of the NTS reform was to
convert non-tradable shares, which are held mainly by state agencies and state-owned
enterprises (SOEs), into tradable shares. By the end of 2007, 97% of the firms trading on the
Chinese A-Share Market had completed the reform. There were two initiatives in the reform,
first, there was the granting of legitimate trading rights to the untraded shares held by state-
owned listed SOEs. Second, there was a further stage in the privatization process whereby the
State’s ownership in the SOEs was further reduced (Liao, Liu and Wang, 2014). The NTS
reform provides a unique opportunity to examine the incentives of politicians during the
privatization process. Although the reform was initiated by the government, the
commencement and timing of the adjustment process was left up to individual firms. Firms
had three years in which to complete the reform process so there was considerable latitude for
firms as to when they would initiate the process of reform1.
The large-scale privatization programme in China was started through listing SOEs on the
Shanghai Stock Exchange (SHSE) and Shenzhen Stock Exchange (SZSE) in the early 1990s.
However, China’s listed firms continued to be characterized as State owned and controlled
after the Share Issue Privatization (SIP) (Sun and Tong, 2003). State ownership is perceived
to be associated with low efficiency and expropriation (Berkman, Cole and Fu, 2009; Liu and
Tian, 2012). In addition, the non-tradable share policy was seen to create serious governance
issues particularly in regard to the weak protection for tradable shareholders (Li, Wang,
1 For the reform at least two-thirds of non-tradable shareholders, which are typically state agencies and SOEs, must agree to the process.
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Cheung and Jiang, 2011). The NTS reform was expected to mitigate corporate governance
problems through the elimination of non-tradable shares. However, rent seeking politicians2
may have a significant impact on the privatization decisions. Shleifer, Boycko and Vishny
(1996) indicate that the critical agency problem in SOEs is the agency problem with
politicians rather than that with managers. Politicians are unwilling to give up control rights
after privatization because control brings them both political benefits and bribes (Shleifer and
Vishny, 1994). We expect that politicians have strong incentive to delay the NTS reform in
China.
Utilizing a sample of 531 firms that went public from 1996 to 2004 and carried out the
NTS reform by the end of 20063, we find that during the first step of the reform (the
announcement of implementing the reform), firms with politically connected CEOs were
more likely to commence the NTS reform later than firms without politically connected
CEOs. In the second step of the reform (the negotiation upon how to compensate tradable
shareholders by non-tradable shareholders), privatization was delayed in firms with
politically connected CEOs, and delayed in firms where the local government has higher
incentives to seek rents from the business sector.
This study contributes to the literature with two perspectives. First, we draw attention to
policymakers that gains of privatization may not be achieved if firms continue to be sensitive
to political preference and interference. Even though the NTS reform facilitated the further
privatization of listed SOEs in China, a change in ownership alone at the microeconomic
level may not be sufficient to guarantee greater enterprise efficiency after privatization.
2 Tullock (1988) indicates that rent seeking is essentially the use of resources for the purpose of obtaining rents for people where the rents themselves come from something which has a negative social value. 3 According to Li, Wang, Cheungand Jiang (2011), over 97% of the Chinese A-share market capitalization had completed the reform by the end of 2007. More than 95% of our initial sample firms completed the reform by the end of 2006. We exclude the firms implemented the reform in 2007 and 2008 as they tend to be outliers in our initial sample.
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Megginson (2005) suggests that a very important step in a successful SOE privatization is
known as commercialization, which means not only converting the objective of the enterprise
from maximizing social welfare to value maximization, but also developing new private-
sector operating procedures and policies. However, the appointment of current or former
politicians onto boards is a common phenomenon in Chinese listed firms (Fan, Wong and
Zhang, 2007). About 22.2% of CEOs are politically connected in our sample and our analysis
indicates that this subsample of firms tended to delay the reform. Moreover, government
bureaucrats are under pressure to reach provincial GDP targets and personal achievements,
therefore they may use SOEs under their influence as a channel to seek rents and to achieve
such targets. In support of this view, our results show that firms tended to delay the reform
when the government bureaucrats were in control of the organisation. We argue that political
interference is likely to be behind these results and reducing such political interference is
critical in establishing efficient corporate governance within privatized firms. Our results are
in line with Liao, Liu and Wang (2014), which test the success of the NTS reform and find
that the reform had a privatization effect that improved SOE output and profits, but did not
boost SOE corporate governance and operating efficiency.
Second, we draw attention to policymakers that although management shareholding is
expected to align the interests of managers with shareholders, firm managers may collude
with controlling shareholders to expropriate minority shareholders during a privatization
process. Our results show that managers had incentives to increase their shareholdings during
the implementation of the NTS reform. Management shares (non-tradable shares) accounted
for less than 1% of total shares in Chinese listed firms before the NTS reform but
management ownership increased and reached 6.78% of total shares in 2010 after the reform.
Our results also show that firms with a higher proportion of management owned shares tend
to announce and complete the reform earlier than others. This indicates that managers are
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keen to capitalise on the opportunities the NTS reform created. Megginson and Netter (2001)
indicate that privatization improves performance by transferring the firms to private
ownership, but preferably to outside shareholders rather than managers. The evidence in
Central and Eastern Europe indicates that insider control has been the most important
impediment to effective privatization (Frydman, Pistor and Rapaczynski, 1996).
The following section introduces the privatization programme in China. Section three
discusses the impact of politicians on privatization decisions during the NTS reform. Section
four describes the data and methodology we use, as well as the main results along with
robustness analysis. Section five concludes the study.
2. THE PRIVATIZATION PROGRAMME IN CHINA
2.1. PRIVATIZING SOEs THROUGH PUBLIC SHARE OFFERINGS
The reform of China’s SOEs officially started with the third Plenum of the 11th Central
Committee of the Communist Party of China (CPC) in December 1978. In October 1992,
after the 14th Party Congress, a new strategy was announced focusing on constructing a
socialist market economy and establishing a modern corporate system. Under this strategy,
the SHSE and SZSE were established in December 1990 and April 1991, respectively, to
transform medium and large SOEs into publicly listed companies. At this point, the reform
entered a new stage – privatizing SOEs through public share offerings, which is known as
SIP.
Initially, China’s SIP was suggested to be far from successful for several reasons that
include, first, many privatized firms are still characterized as being under state control after
the SIPs and this is perceived to be inefficient at the firm level because state shareholders
pursue social objectives instead of firm value maximization (Shleifer and Vishny, 1994).
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Second, non-tradable shareholders, who initially owned two-thirds of shares outstanding on
average, acted as a dead-weight on efforts to improve firm performance (Liao, Liu and Wang,
2014). The state council created three categories of shares for restructuring SOEs into
shareholding companies including state shares, legal person shares, and individual shares4.
Individual shares are tradable shares that initially represented only about one-third of the total
number of shares on issue. State shares and legal person shares are not publicly traded on the
stock exchanges. Non-tradable shareholders are entitled to exactly the same voting and cash
flow rights as tradable shareholders. But non-tradable shares are valued based on the book
values of firm assets rather than the share market. In this sense, the interests of tradable and
non-tradable shareholders can be quite different, particularly if non-tradable shareholders
receive private benefits (Liao, Liu and Wang, 2014). Third, the split-share structure creates a
significant cross-shareholding structure of ownership in listed firms, which in turn leads to
weak protection for tradable shareholders (Cao, Pan and Tian, 2011). An important detail to
note is that a “good assets/bad assets” model was generally utilized to restructure SOEs
before their initial public offering. Under this approach, the original company is divided into
two parts: a share holding company and a parent company. The good assets go to the share
holding company to go public, while the money-losing assets are left to the parent company.
This splitting was expected to help the original SOE to streamline its business structure and
relocate its valuable resources to the listed company to pursue a faster development. However,
the parent companies (controlling shareholders) have been found to engage in expropriation
through related party transactions, which consequently hurts the ‘good’ firm performance
(Chen, Jian and Xu, 2009). The split-share structure largely facilitated this process. In
addition, the split-share structure has been recognised as a major reason for excessive
4 The Interim Regulations on the Management of State-owned Assets of Shareholding Companies, launched on 27th July, 1992
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speculation and overtrading in Chinese stock markets (Li, et al., 2011; Liao, Liu and Wang,
2014). This is a problem that could affect optimal capital allocation decision making.
2.2.THE NON-TRADABLE SHARE (NTS) REFORM IN CHINA
On April 29 2005, China Securities Regulatory Commission (CSRC) announced the “Notice
on the Trial Implementation of Measures to Tackle Segregation of Share Capital in Listed
Companies and Related Questions” (the “Notice”, is also referred to the NTS reform). The
objective of the “Notice” was to make all non-tradable shares tradable. As the major holders
of non-tradable shares are state agencies and SOEs, the reform also opened up the possibility
for further privatizing listed state-owned enterprises in China (Liao, Liu and Wang, 2014).
A typical process under this reform begins with the firm choosing a date within the three
year window allowed to initiate the reform process. When ready they give notice to the stock
exchange and at this point an announcement is made of the commencement of the reform for
the firm. The trading in the firm’s securities is suspended on the same day. The government
has the authority to manage and monitor the NTS reform, but the commencement of the
reform for individual firms should be agreed by at least two-thirds of non-tradable
shareholders. The second step of the reform is the negotiation upon how to compensate
tradable shareholders by non-tradable shareholders since non-tradable shares are priced
basing on the book values of firm assets rather than the market value of the firm. It is
reported in Table 2 of this study that in more than three quarters of the case, the market value
of the tradable shares was higher than the book value per share. This difference creates
significant interest in the negotiation process regarding compensating tradable shareholders,
as it is expected that after the reform, the share price will fall for existing tradable
shareholders. A Shareholders’ meeting will be organised to vote on the reform proposal
especially on the compensation scheme. Finally, the proposed compensation scheme will be
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announced to the public when an agreement is made and the trading will be resumed. The
time taken in the negotiation process varies. The compensation approaches employed by
reform firms include cash payments to tradable shareholders, paying stock dividends to
tradable shareholders, transferring shares from non-tradable shareholders to tradable
shareholders, issuing new share capital only to tradable shareholders, and etc., among which,
cash payments and paying stock dividends to tradable shareholders are most popular
approaches (Li, et al., 2011). Non-tradable shareholders face a lock-up period when they are
prohibited from selling converted shares through the stock exchanges or through transfer
arrangements for a period of 12 months after the conversion to maintain the overall stability
of the stock market (Liu and Tian, 2012). Firms will usually be fully tradable 36 months after
the reforms are completed5.
The NTS reform was implemented gradually in batches, 417 firms were selected to carry
out the reform in 2005 and 872 firms were selected in 2006. By the end of 2007, over 97% of
the Chinese A-share market capitalization had completed the reform (Li et al., 2011). With
the reform the proportion of tradable shares in listed firms has changed considerably. Figure I
to Figure III reports the time trends in ownership structure of approximately 2,200 Chinese
listed firms for the time period from 1999 to 2010.
Insert Figure I here
Figure I reports the the time trends of the proportion of tradable versus non-tradable shares.
Each of the categories refers to the proportion of the category compared to the total amount
of shares outstanding. It shows that tradable shares represented less than 40% of the total
shares in 1999. From 2005 the proportion of tradable shares started to increase gradually and
5 The selling of more than 5% (10%) of the total issued share capital of the listed company is further prohibited in 12 (24) months after the expiration of the “12-month lock-up period.
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reached about 70% in 2010. Clearly, the problem of domination by non-tradable shares has
been addressed with the implementation of the NTS reform.
Insert Figure II here
Figure II summarizes the components of non-tradable shares of Chinese listed firms from
1999-2010. It shows state shares represented a constant percentage of about 35% of total
shares listed from 1999 to 2004. But this ownership started to drop from 2005 and accounted
for about 10% of total shares in 2010, directly as a result of the NTS reform.6 Legal person
shares accounted for about 15% percentage in total for the time period from 1999-2005 and
have had a slight decrease since 2006. Foreign shares, employee shares and management
shares have relatively low proportions of the total. But managers appear to have incentives to
increase their non-tradable shareholdings since the NTS reform. Since 2008 the proportion of
management shares has risen, increasing to 6.78% of the total shares in 2010. As discussed,
all non-tradable shares will be fully tradable, when the lockup period has expired.
Insert Figure III here
Figure III summarizes the ownership concentration of Chinese listed firms from 1999 to
2010. It shows that the ownership structure of Chinese listed firms is highly concentrated
throughout this whole time period, but this concern is mitigated slightly by the
implementation of the NTS reform. In 1999 largest shareholders, on average, held 45.50% of
the total shares in listed firms, while the average largest shareholding dropped to 36.50% in
2010. We also trace the identities of the largest shareholders. It is shown that in 1999, about
74% of the largest shareholders were state, but the ratio dropped to about 21% in 2010. This
result indicates that state control is reduced due to the implementation of the NTS reform.
In summary, the ownership structure of Chinese listed firms experienced change since
2005 with the NTS reform. The proportion of non-tradable shares dropped significantly, as
6 State shares are those held by government agencies and by some types of corporatized SOEs (Berkman, Coles and Fu, 2009).
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have the state control shares. In addition, managers have significantly increased their
shareholdings in non-tradable shares, which can be freely traded in the stock markets after the
lockup period.
3. The DECISION TO PROVATIZE, IMPACTS FROM POLITICIANS AND
MANGERS
3.1. POLITICIANS AND PRIVATIZE DECISIONS
Rent-seeking politicians are perceived to have little incentive to pursue meaningful reform
because they can use SOEs to favour their political supporters through excessive employment,
regionally targeted investments and deliberate under-pricing of products or over-pricing of
purchased inputs (Shleifer and Vishny, 1994). Dinc and Gupta (2011) find that privatization
of Indian state-owned firms is delayed in the regions where there is keen competition for
voter support between the governing and opposition parties. They argue that politicians tend
to delay privatization because privatization is politically costly for them. Chinese government
is utilizing two approaches to maintain its influence over privatized enterprises, through
appointing politically related CEOs and through holding shares in the listed firms. Even
though privatization is not subject to election competition in China, we expect the benefits
from NTS reform will be delayed in the presence of rent seeking politicians.
3.1.1. PRIVATIZATION AND POLITICALLY CONNECTED CEOs
Shleifer and Vishny (1994) create a theoretical framework that politicians extract rents from
firms in order to fulfil their social or political goals such as regional fiscal health and social
stability. In an examination of this theory, Faccio (2006) using the data of 20,202 listed firms
in 47 countries finds that politically connected firms underperform their peers although they
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benefit from easier access to debt financing, lower taxation, and stronger market power.
Faccio argues that the underperformance of politically connected firms is too large to be
entirely due to rent–seeking activities by politicians, and that political connections have
distorted the allocation of funds and investment decisions, and therefore the long–term
growth of these firms.
The appointment of current or former government officials on firm boards is a common
phenomenon in Chinese listed firms. Fan, Wang and Zhang (2007) find that about 27% of the
CEOs of Chinese privatized firms are former or current government bureaucrats, in addition
firms led by politically connected CEOs are more likely to appoint other bureaucrats to the
board of directors rather than directors with relevant professional backgrounds. Hung, Wong
and Zhang (2012) find that Chinese SOEs with politically connected CEOs are more likely to
pursue overseas listing than non-politically connected firms. They suggest this is not because
the politically connected CEOs have better past performance but because they take overseas
listing as a means for private benefits, given that they are more likely to get political
promotion after their firms are listed overseas. Chen, Sun, Tang and Wu (2011) report that
investment efficiency is significantly lower in Chinese firms with political connections
compared to those without political connections. They argue that this low efficiency is due to
the tunnelling effect, and that government bureaucrats extract resources from firms.
In summary, the value maximization objective is likely to be distorted when politically
connected CEOs pursue their own personal political goals or pursue private benefits. In this
research we expect that the NTS reform will be delayed in firms with politically connected
CEOs, for reform will reduce the ability of politically connected CEOs in pursuing private
benefits.
3.1.2. PRIVATIZATION AND PROVICIAL BUREAUCRATS
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Fan, Huang, Morck and Yeung (2014) suggest that, by western standards, politically linked
private benefit taking is rampant in many parts of China. They find that firms whose
managers have closer connections with bureaucrats are more vertically integrated, since
vertical integration has been used to overcome impediments to doing business with
bureaucratic governments. Chen, Feng, and Cao (2014) suggest that private benefit seeking
behaviour in the coal industry is common in China. Provincial governments commonly
license coal enterprises without due consideration to safety qualifications. Cheng and Ngo
(2014) study the performance of Chinese tobacco industry and find that private benefit
seeking local politicians took precedence over state plans of national interests. Although
governments are supposed to benefit from privatization through revenues from the sale of
SOEs, the reduction in government subsidies, and improved incentives for the firms (Dinc
and Gupta, 2011; Megginson and Netter, 2001), government bureaucrats, who are under
pressure to meet provincial GDP targets and pursue personal achievements, may have
incentives to delay the privatization. In this paper we expect the NTS reform will be delayed
in firms where the provincial bureaucrats have higher incentives to seek rent from the
business sector.
3.2. MANAGEMENT OWNERSHIP AND PRIVATIZE DECISIONS
Blanchard and Aghion (1999) suggest that privatization benefits would proceed faster if
governments decided to privatize to insiders. They also show that insider privatization may
lead to efficiency gains because it aligns control and property rights. However, there is
extensive evidence that insider privatization has been a failure throughout the former Soviet
Union, which indicates the importance of bringing in new management under the
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privatization (Megginson and Netter 2001). As for the NTS reform in China, we propose that
existing managers have the incentive to speed up the reform if there is a potential to capture
and monetise wealth gains in the form of purchasing non-tradable management shares in the
presence of superior information.
4. DATA, METHODOLOGY AND EMPIRICAL RESULTS
4.1. DATA
The initial sample for this study includes 1,197 companies that went public on the SHSE and
SZSE Stock exchanges from 1994 to 2004. Of these, 985 firms had state shareholdings post
the IPO. We then select the 860 firms that completed the NTS reform over 2005 and 2006.
After filtering out the firms with missing information on key variables, such as background
information of the CEOs and provincial economic data, the final sample for analysis consists
of 531 firms that went public from 1996 to 2004 and carried out the NTS reform by the end
of 2006. The NTS reform data is collected from the Wind Financial Database. The IPO Data
is collected from the CSMAR China Stock Market Initial Public Offering Research Database.
Other firm-specific data is collected from the CSMAR China Listed Firm’s Corporate
Governance Research Database and from the CSMAR China Stock Market Financial
Database. The provincial economic data is collected from the National Bureau of Statistic of
China database. We collect by hand the background information of the CEOs of the sample
firms through SINA Finance (http://finance.sina.com.cn/stock/). This information, including
the directors’ work experience, is provided under the ‘Corporate Governance’ section of each
listed company.
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4.2. RENT-SEEKING INCENTIVE MEASURES
In this paper, we use political connectedness to proxy the rent seeking incentives of CEOs.
Following Fan, Wong and Zhang (2007), we identify a CEO’s political connections by
examining whether s/he is currently or was formerly an officer within the central or local
government, or within the military. PCEO refers to a dummy variable equals 1 when a firm
has a politically connected CEO; otherwise, it equals 0. Following Chen, Li, Su and Sun
(2011), we conduct two measures to proxy the rent seeking incentives of provincial
bureaucrats based on the local fiscal conditions. Expenditure refers to the ratio of a local
government’s expenditure to its revenue. Provincial governments will have higher incentives
to extract rents from business sectors if they face a large fiscal deficit. The second proxy to
capture the rent seeking incentives of provincial bureaucrats is Revenue, which refers to the
ratio of a local government’s administrative revenue to total fiscal revenue. This proxy
measures the arbitrary collection of revenue by a local government (Chen et al., 2011).
4.3. MANAGEMENT INCENTIVE MEASURES
We use two measures to proxy managers’ incentive toward speeding up the reform.
Management refers to the ratio of the number of Management Shares to the total number of
shares. BM refers to the book to market ratio. Firms that have a higher proportion of
management owned shares will have stronger incentives to be early reformers because they
are keen to capitalise on the opportunities the NTS reforms created. Moreover, firms that
16
have lower book to market ratios have better growth opportunities and have more likelihood
of producing capital gains in the reform process.
4.4. DESCRIPTIVE STATISTICS
Table 1 presents the descriptive statistics on the 531 sample firms. Panel A describes the
sample by the year of IPO. Panel B breaks down the sample by the year for completing the
NTS reform. Panel C groups the sample by the political connection of CEOs of the firms.
PCEO refers to a firm with a politically related CEO. Panel D reports the sample by industry.
Insert Table 1 here
About 29% of the sample firms were reformed in 2005 and 71% completed the reform in
2006. Firms with political related CEOs account for 22.22% of our sample firms. More than
60% of the sample firms are from manufacturing industries and about 14% of the firms
belong to conglomerate sector.
4.5. THE DETERMONANTS OF THE NTS REFORM DECISIONS
Following Dinc and Gupta (2011), we use the Cox proportional hazard model to test factors
that influence the timing of the sample firms to implement the reform. The initial regression
specification for the Cox proportional hazards regression is as follows:
h(t) = h0(t) exp (β1x1 + β2x2 +· · ·+βkxk)
Throughout the paper, we report the coefficients rather than the hazard ratios from the
estimations. Moreover, the robust standard errors are clustered by location (firms located in
Yunnan, Xinjiang, Tibet, Shaanxi, Sichuan, Qinghai, Ningxia, Inner Mongolia, Guangxi,
17
Guizhou, Gansu, Chongqing are recognised as western area). Although China has achieved
impressive economic growth, it is one of the countries with the sharpest imbalance in
development among different regions. In 2000, the Chinese government launched the Great
Western Development Strategy. The strategy covers 6 provinces (Gansu, Guizhou, Qinghai,
Shaanxi, Sichuan, and Yunnan), 5 autonomous regions (Guangxi, Inner Mongolia, Ningxia,
Tibet, and Xinjiang), and 1 municipality (Chongqing). The major objective of the strategy is
to steer state investment, outside expertise, foreign loans and private capital into the regions7.
As the strategy has a significant impact on China’s overall economy, the strategy is expected
to affect the NTS reform as well.
The dependent variable h(t) is the batch number that the sample firm announcing its NTS
reform. Appendix A reports the reform calendar of sample firms. Two sample firms were
selected as the first batch on 9th of May 2005, and the reform was expanded gradually to
listed firms at roughly a weekly base. 66 batches consist of 531 sample firms completed the
reform by the end of 2006.
We control firm-specific factors that influence the privatization decisions. Dinc and Gupta
(2011) argue that information costs are lower for larger firms that privatized. They also argue
that larger firms are also more likely to be privatized in the early stages of a privatization
programme in order to avoid the cost of adverse selection. Gupta, Ham, and Svejnar (2008)
and Dinc and Gupta (2011) argue that governments may prefer to privatize well performing
firms in an effort to build public support for the programme. To accommodate these views we
include lagged measures of Firm Size and Profitability in the specification. Firm size is
measured as the natural logarithm of total sales, and Firm profitability is calculated as net
income divided by total assets (ROA). We expect the Chinese government will encourage the
privatization of larger and more profitable firms first to facilitate a successful reform and to
7 Source: http://en.wikipedia.org/wiki/China_Western_Development
18
encourage social stability during the NTS reform. In addition, we measure the proportion of
non-tradable shares in each firm one year before the reform and expect firms with more non-
tradable shares might be reformed earlier. Six industry/sector dummies are also accounted for
in the estimation.
Insert Table 2 here
Table 2 presents the summary statistics for key variables for the 2005 to 2006 time period.
The average ratio of a local government’s expenditure to its revenue is 74.01% with a
maximum number of 186.76%. The mean suggests that most local governments generally
operate at a fiscal surplus, and fewer than 25% overspend—which would create a concern
that rent seeking incentives may be necessary. On average, about 8.15% of local government
revenue comes from administrative income. This ratio measures the extent to which local
government officials can charge the enterprises within their jurisdiction (Chen, et al., 2011).
On average, managers hold 0.29% of shares outstanding of sample firms while the maximum
management shareholding reached a ratio of 41.29%. Appendix B defines the variables used
in the study.
4.5.1. FACTORS THAT INFLUENCE THE COMMITMENT OF THE NTS REFORM
In this section, we examine the factors that influence the commitment of the NTS reform. The
dependent variable is the batch numbers that the sample firms launch into the NTS reform.
The independent variables are all lagged 1 year.
Insert Table 3 here
Table 3 presents the results of the Cox proportional hazard regression of the 531 firms that
went public from 1996 to 2004 and completed the NTS reform by the end of 2006. Consistent
with the findings of Dinc and Gupta (2011), the results of Model 1 show that larger and well
19
performing firms are significantly more likely to be privatized at the early stage of the NTS
reform. We add the management incentive proxies, Management and BM, into Model 2, and
the results show that firms with higher management ownership are more likely to be
privatized at the early stages of the reform, while firms with higher book to market ratio are
significantly more likely to be privatized later. These results are consistent our expectations
and indicate that managers have incentives to initiate early reform when they own non-
tradable shares and when the firm commands a value premium.
In Model 3 to Model 5, we add the rent seeking proxies, PCEO, Expenditure and Revenue,
into the regression, respectively. The results show that the estimated coefficient on PCEO is
negative and statistically significant at the 1% level, indicating that the NTS reform is
significantly delayed in firms with a politically related CEO. This result in line with the
modelling of Shleifer and Vishny (1994) that posits that politicians are unwilling to give up
control rights because control gives them better opportunities to extract rents from the firms
they manage for fulfilling their social or political goals. The estimated coefficients on
Expenditure and Revenue are not statistically significant.
4.5.2. FACTORS THAT INFLUENCE THE NEGOTIATIONS FOLLOWING THE
ANNOUNCEMENT OF THE NTS reform
In this section, we conduct tests to further explore the determinants of the timing of the NTS
reform. When the reform is announced, there will be negotiations between different parties.
Tradable shareholders are keen to protect their interests, but they have little bargaining power
in the reform process. Politically connected CEOs and provincial bureaucrats have incentive
to delay the reform, while managers tend to speed up the reform if they have shareholdings in
the firm, particularly if it is a growth firm. We use the lag between the date of announcing the
20
reform and the date of completing the negotiation as the dependent variable to further test the
incentives of different parties towards the reform. Table 3 reports that on average, firms spent
72 days on the negotiation process, with a minimum of 24 days and a maximum of 1248 days.
This indicates that some factors do influence the process which leads to a big variance of the
timing.
Insert Table 4 here
Table 4 presents the results when the lag between the starting and completing the reform is
utilized as the dependent variable. The coefficients on PCEO, Expenditure and Revenue are
negative and statistically significant at the 1% level, 5% level and 1% level, respectively. Our
results indicate that firms with politically connected CEOs and firms located in the provinces
where the local governments have higher incentives to seek rent from business sectors spent
more time on the negotiation. This is in line with our hypothesis that privatisation is delayed
in firms with rent seeking politicians. Moreover, well performing firms and firms with higher
management ownership are more likely to complete the negotiation earlier. Opposite to the
results in Table 3, the coefficients on BM turn to positive, which indicates that firms with
higher book to market ratio tend to complete the negotiation earlier than firms have lower
book to market ratio. This result is likely related to the need for increased bargaining over
what a fair value for the growth firms’ shares should be as by definition there is a wide
difference between book value per share and market value per share.
4.6.REBUSTNESS TESTS
4.6.1. THE INTERACTION BETWEEN POLITICALLY CONNECTED CEOs AND
MANAGEMENT OWNERSHIP
21
Politically connected CEOs have strong incentives to delay the reform to fulfil political goals,
while managers are keen to capitalise on the opportunities the NTS reforms created. In the
following section, we explore the bargaining power between politically connected CEOs and
managers by conducting two interaction variables, PCEO × Management and PCEO × BM.
Our expectation is that the interaction measures will help identify the relative importance of
the PCEO factor versus the management ownership factor, or book to market factors. If we
see a negative coefficient we would support the view the political connectedness factor is the
more important influence.
Insert Table 5 here
Table 5 reports the results when the two interaction variables are added into the Cox
proportional hazards regression. The dependent variable is the lag between the starting and
completing the NTS reform. The coefficient on PCEO is significantly negative, while the
coefficients on Management and BM are both positive. Interestingly, the estimated
coefficients on PCEO × Management and PCEO × BM are both significantly negative at the
1% level. This result indicates that politically connected CEOs have more bargaining power
compared to managers when it comes to the timing of the reform process.
4.6.2. PRIVATIZATION AND DEBT FINANCING
Liu and Tian (2012) argue that State shareholder dominated firms in China will often borrow
excess debt and use tunnelling, through inter-corporate loans and related party transactions, to
transfer funds out of firms. Higher leverage allows the controlling shareholders to control
more resources without diluting their control over the corporation (Stulz, 1988). Harvey, Lins
and Roper (2004) further note that in emerging markets, banks are generally controlled by
governments therefore State shareholders can use bank loans for their own purposes. Faccio,
22
Lang and Young (2010) confirm the above argument and further indicate that controlling
shareholders typically have an incentive to use more debt to commence tunnelling where
creditor protection is weak. Literature already shows that politically connected firms in China
take advantage of borrowing on preferential terms from State-owned banks, and receive help
from government sponsors when they are in distress (Bai, Lu and Tao, 2006). Firth, Lin and
Wong (2008) also find there is a negative relation between leverage and investment in China,
and this negative relation is weaker in firms with a higher level of state shareholding than in
firms with a lower level of state shareholding. They argue that state-owned banks treat state-
owned firms more favourably in their lending decisions.
In the following section, we test the impact of leverage on the NTS reform decision, and
we expect that firms with higher leverage ratios have stronger incentives to delay the NTS
reform because the NTS reform reduces state shareholders’ tunnelling possibilities.
Insert Table 6 here
Table 6 presents the results when leverage is used as a proxy to capture a firm’s attitude
towards privatization. We create two variables to measure leverage. Leverage refers to debt
ratio (total debt to total assets). Excess Leverage refers to the difference between a firm’s
leverage and the average leverage level of all Chinese listed firms in a specific year (Liu and
Tian, 2012). The dependent variable is the lag between starting and completing the reform.
Table 6 shows that the estimated coefficients on Leverage and Excess Leverage are all
statistically negative in Model 1 to Model 48. These results indicate that firms with higher
leverage ratios are more likely to delay the NTS reform. Moreover, the estimated coefficients
on PCEO are statistically negative in Module 3 and Model 4 when PCEO is added into the
8 The results of using the batch number as dependent variables confirm that the coefficients on Leverage and Excess Leverage are all statistically negative. The results are not presented and are available on request.
23
regression. We also included Expenditure and Revenue into the regression to instead of
PCEO, The estimated coefficients on Expenditure and Revenue are all significantly negative9.
4.6.3. PRIVATIZATION AND STATE OWNERSHIP
In this section, we use state ownership as a proxy to capture the rent seeking incentive of state
shareholders. Largest refers to the proportion of shares held by the largest shareholder.
Bureaucrat is a dummy if the largest shareholder is a government agency or a SOE.
Insert Table 7 here
Table 7 reports the results when the two variables Largest and Bureaucrat, are added into
the estimation. The dependent variable is the lag between starting and completing the
reform10. Due to the high correlation between Largest and Non-tradable, Non-tradable is
excluded from the regression. The estimated coefficients on Largest are significantly positive
but the State dummies are statistically negative at the 1% level in Model 1 to Module 3. The
estimated coefficient on PCEO, Expenditure and Revenue are all significantly negative. The
results indicate that state shareholders have incentive to delay the reform. This is consistent
with our expectation.
4.6.4. OTHER ROBUSTNESS CHECK
We also do other tests to further check whether our results are robust11. Our sample includes
3 firms from the financial section which may bias the results. Therefore we exclude the
financial firms and re-run the regressions. The results are robust. We group the 531 firms into
9 The results are not presented and are available on request. 10 The results of using the batch number as dependent variable are not presented and are available on request. 11 All the results are not presented and are available on request.
24
firms that were reformed in 2005 and were reformed in 2006 to do subsample analysis. All
the results are quantitatively similar to the results reported.
5. CONCLUSION
Using a sample of 531 firms that went public from 1996 to 2004 and completed the NTS
reform by the end of 2006, we examine the factors that influenced the NTS reform decisions.
We first test the factors that influence the timing of the announcements of the reform for
individual firms. Second, we explore the factors that affect the negotiation between tradable
and non-tradable shareholders after the reform is announced.
Our results show that the privatisations were delayed in firms with politically connected
CEOs, and delayed in firms where the local government has higher incentives to seek rent
from the business sector. We also find that firms that have a higher proportion of
management owned shares and growth firms tended to be selected into the early stages of the
reform. In terms of the negotiation following the announcement of the reform, firms subject
to rent seeking politicians spent more time to complete the negotiation. Growth firms also
required more negotiation time as these firms faced a larger differential between sharemarket
value and book value per share.
We conduct two interaction variable tests, PCEO × Management and PCEO × BM, to test
the bargaining power between politically connected CEOs and managers during the
negotiation process. The significantly negative coefficients on PCEO × Management and
PCEO × BM indicate that politically connected CEOs are more likely to win in the
negotiation process. We also use the level of leverage and state ownership to proxy the
incentive of state shareholders to tunnel from the firms. The results confirm that firms subject
25
to rent-seeking incentives (firms with a higher leverage ratio or firms with a higher state
ownership) are more likely to delay the NTS reform.
Overall, our results support the theoretical model of Shleifer and Vishny (1994) and
suggest that rent-seeking parties are reluctant to cede control in the firms and avoid the early
stages of privatization because corporate governance improvements associated with
privatization may limit their ability to engage in rent seeking. We argue that reducing
political interference is critical to establishing more efficient corporate governance in
privatized firms.
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27
Appendix A The Reform Calendar of Sample Firms Appendix B presents the reform calendar of the 531 sample firms that went public on Shanghai or Shenzhen Stock exchanges from 1996 to 2004 and completed the NTS reform by the end of 2006 2006. Start Date of Share Reform refers to the date that the government announced the implementation of the NTS reform. Batch refers to the batch number that the firm is selected to implement the NTS reform. Start Date of Share Reform
Batch Number of Sample Firms Selected
Start Date of Share Reform
Batch Number of Sample Firms Selected
9/05/2005 1 2 05/06/2006 37 1020/06/2005 2 12 12/06/2006 38 1312/09/2005 3 11 19/06/2006 39 719/09/2005 4 8 26/06/2006 40 1626/09/2005 5 7 03/07/2006 41 1110/10/2005 6 11 10/07/2006 42 317/10/2005 7 7 17/07/2006 43 524/10/2005 8 8 24/07/2006 44 231/10/2005 9 10 31/07/2006 45 307/11/2005 10 7 07/08/2006 46 414/11/2005 11 6 14/08/2006 47 521/11/2005 12 12 21/08/2006 48 228/11/2005 13 5 28/08/2006 49 305/12/2005 14 9 04/09/2006 50 212/12/2005 15 5 11/09/2006 51 519/12/2005 16 9 18/09/2006 52 323/12/2005 17 18 25/09/2006 53 531/12/2005 18 12 09/10/2006 54 105/01/2006 19 2 16/10/2006 55 216/01/2006 20 7 23/10/2006 56 123/01/2006 21 19 30/10/2006 57 113/02/2006 22 19 06/11/2006 58 320/02/2006 23 15 13/11/2006 59 627/02/2006 24 25 20/11/2006 60 66/03/2006 25 23 27/11/2006 61 113/03/2006 26 12 04/12/2006 62 220/03/2006 27 10 11/12/2006 63 227/03/2006 28 19 18/12/2006 64 23/04/2006 29 14 25/12/2006 65 310/04/2006 30 6 30/12/2006 66 217/04/2006 31 10 24/04/2006 32 14 8/05/2006 33 13 15/05/2006 34 9 22/05/2006 35 14
29/05/2006 36 10 Total 531
28
Appendix B Variable definitions This appendix defines the variables used in the study. The NTS reform data are collected from the Wind Financial Database. The IPO Data are collected from the CSMAR China Stock Market Initial Public Offering Research Database. Other firm-specific data is collected from the CSMAR China Listed Firm’s Corporate Governance Research Database and from the CSMAR China Stock Market Financial Database. The macroeconomic data are collected from the National Bureau of Statistic of China database. The background information of the CEOs is hand collected through SINA Finance (http://finance.sina.com.cn/stock/). Batch = the batch number that the firm is selected to implement the NTS reform. Lag = the number of days between starting the reform and completing the reform. PCEO = 1 when a firm has a politically connected CEO; otherwise, it equals 0. Expenditure = a local government’s expenditure / its revenue. Revenue = a local government’s administration expenditure / its total fiscal revenue. Leverage = total debt / total assets Excess Leverage = a firm’s leverage minus the industrial average leverage level of all Chinese listed firms in a specific year. Management = the number of Management Shares / the total number of shares. BM = the book value of equity / the market value of equity Firm size = the natural logarithm of total assets ROA = net income / total assets Non-tradable = the number of non-tradable shares/ the total number of shares outstanding Largest = the number of shares held by the largest shareholder / the total number of shares outstanding State = 1 if the largest shareholder is a government agency or SOE
29
Figure I – III The Time trends in Chinese ownership structure of listed form from 1999 to2010
Figure I and II report the time trends in Chinese ownership structure of the 2,198 listed firms for the time period from 1999 to 2010. Figure I summarizes the the time trends of the proportion of tradable vs. non-tradable shares of Chinese listed firms from 1999 to 2010. Figure III summarizes the ownership concentration of Chinese listed firms from 1999 to 2010. Figure II summarizes the component of non-tradable shares of Chinese listed firms from 1999-2010. Tradable Shares refers to the proportion of tradable shares to the total shares outstanding. Non-tradable Shares refers to the proportion of non-tradable shares to the total shares outstanding. State Shares refers to the proportion of State shares to the total shares outstanding. Legal Person Shares refers to the proportion of domestic legal person shares to the total shares outstanding. Foreign Legal Person Shares refers to the proportion of foreign legal person shares to the total shares outstanding. Employee Shares refers to the proportion of employee shares to the total shares outstanding. Management Shares refers to the proportion of management shares to the total shares outstanding.
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Figure ITradable vs. Non‐tradable Shares
Tradable Shares
Non‐tradable Shares
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Figure IIComponent of Non‐tradable Shares
State Shares/ Total Shares
Legal Person Shares/ TotalShares
Foreign Shares/Total Shares
Employee Shares/ TotalShares
Management Shares/ TotalShares
30
Figure III The Time trends in ownership concentration of Chinese listed form from 1999 to2010
This figures report the time trends in ownership concentration of 2,198 Chinese listed firms for the time period from 1999 to 2010. Largest Shareholding refers to the proportion of shares held by the largest shareholder to the total shares outstanding. State is a dummy which equals 1 if the largest shareholding is state owned. Legal Person is a dummy which equals 1 if the largest shareholder is a legal person. Foreign is a dummy which equals 1 if the largest shareholder is a foreign legal person. Management is a dummy which equals 1 if the largest shareholder is a manager of the firm. Nature Person is a dummy which equals 1 if the largest shareholder is a nature person.
0
10
20
30
40
50
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Figure IIILaregest Shareholding
Laregest Shareholding
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
State
Legal Person
Foreign
Management
Nature Person
31
Table 1 Descriptive Statistics
Panel A: By Year of IPO Panel B: By Year of the NTS reform
Number
Percentage (%)
Number Percentage (%)
1996 100 18.83 2005 154 29.00 1997 102 19.21 2006 377 71.00 1998 56 10.551999 44 8.292000 86 16.202001 29 5.462002 36 6.782003 39 7.342004 39 7.34Total 531 100 Total 531 531
Panel C: By the political connection of the CEOs Panel D: By Industry Concentration
Number Percentage (%)
Number Percentage (%)
PCEO 118 22.22 Finance 3 0.56 Non-CEO 413 77.78 Public Utility 49 9.23
Real Estate 26 4.90 Conglomerate 74 13.94 Manufacturing 335 63.09 Commence 44 8.29
Total 531 100 Total 531 100
This table presents the descriptive statistics on the 531 sample firms. Panel A describes the sample by the year of IPO. Panel B breaks down the sample by the year for completing the NTS reform. Panel C groups the sample by the political connection of CEOs of the firms. PCEO refers to a firm with a politically related CEO. Panel D reports the sample by industry.
32
Table 2 Summary Statistics for Key Variables
N Mean Std
Deviation Min Max
Percentile 25
Median Percentile
75 Batch 531 1 66 Lag 531 72 90.7413 24 1248 28 47 625PCEO 531 0.2222 0.4161 0 1 0 0 0Expenditure 531 0.7401 0.2309 0.2948 1.8676 0.5699 0.7412 0.9085Revenue 531 0.0815 0.034 0.024 0.1873 0.0616 0.0754 0.1055Leverage 531 0.4942 0.1761 0.1141 0.9649 0.3676 0.5139 0.6248Excess Leverage
531 -0.1253 0.1838 -0.6235 0.4046 -0.2488 -0.1128 -0.0062
Management 531 0.0029 0.0241 0 0.4129 0 0 0.0002BM 531 0.7109 0.3535 0.0452 2.5782 0.4579 0.6608 0.8971Firm Size 531 20.6169 1.3763 15.1834 27.4068 19.8121 20.5579 21.3595ROA 531 0.0182 0.0711 -0.528 0.2057 0.0061 0.0243 0.0480Non-tradable 531 0.6225 0.0943 0.2377 0.8485 0.5691 0.6330 0.6945Largest 531 0.4512 16.3613 0.1093 0.8485 0.2973 0.4452 0.5986
State 531 0.7495 0.4337 0 1 0 1 1
This table reports the summary statistic for key variables on the 531 sample firms. Batch refers to the batch number that the firm is selected to implement the NTS reform. Lag refers to the number of days between starting the NTS reform and completing the NTS reform. PCEO refers to a dummy variable that equals 1 when a firm has a politically connected CEO; otherwise, it equals 0. Expenditure refers to the ratio of a local government's expenditure to its revenue. Revenue refers to the ratio of a local government's administration revenue to its total fiscal revenue. Leverage refers to the debt ratio (total debt to total assets). Excess Leverage refers to the difference between a firm’s leverage and the industrial average leverage level of all Chinese listed firms in a specific year. Management refers to the ratio of the number of management shares to the total number of shares. BM refers to the book to market ratio. Firm size refers to the natural logarithm of total sales. ROA refer to net income divided by total assets. Non-tradable refers to the number of non-tradable shares to the total number of shares outstanding. Largest refers to the proportion of shares held by the largest shareholder to the total number of shares outstanding. State refers to a dummy variable that equals 1 if the largest shareholder is a government agency or SOE.
33
Table 3 Privatization Decisions in China, Rent-Seeking and Management Incentives 1 2 3 4 5
PCEO -0.0442***Expenditure -0.2125 Revenue 0.0643
Management 6.0013*** 6.0605*** 6.0220*** 5.9997***BM -0.2971*** -0.2998*** -0.2835*** -0.2976***Firm Size 0.0356** 0.0684*** 0.0694*** 0.0591*** 0.0688***ROA 5.4268*** 5.0025*** 4.9790*** 5.0052*** 5.0029***Non-tradable 0.4022 -0.0746 -0.0620 -0.0868 -0.0736
Number of firm 531 531 531 531 531Industry dummies
Yes Yes Yes Yes Yes
This table presents the results of the Cox proportional hazard regression of the 531 firms that went public from 1996 to 2004 and completed the NTS reform from 2005 to 2006. The dependent variable is the batch number that the sample firms were selected into to implement the NTS reform. The independent variables are lagged 1 year. Robust standard errors are clustered by location (firms located in Yunnan, Xinjiang, Tibet, Shaanxi, Sichuan, Qinghai, Ningxia, Inner Mongolia, Guangxi, Guizhou, Gansu, Chongqing are recognised as western area). ∗, ∗∗, and ∗∗∗ denote statistical significance at the 10%, 5%, and 1% levels, respectively.
34
Table 4 Privatization Decisions in China, Negotiation following the Announcement of the Reform 1 2 3 4 5
PCEO -0.1005***Expenditure -0.3058** Revenue -3.7913***
Management 0.4077*** 0.4588*** 0.4017** 0.3802**BM 0.1602 0.1567 0.1750** 0.2039*Firm Size 0.0366** 0.0198 0.0200 0.0102 0.0019ROA 1.2118** 1.3865*** 1.3556*** 1.3614*** 1.4645***Non-tradable 0.1450* 0.3131 0.3376* 0.2871 0.2724
Number of firm 531 531 531 531 531Industry dummies
Yes Yes Yes Yes Yes
This table presents the results of the Cox proportional hazard regression of 531 firms that went public from 1996 to 2004 and completed the NTS reform from 2005 to 2006. The dependent variable is the lag between the time of starting the NTS reform and the time of completing the NTS reform. The independent variables are lagged 1 year. Robust standard errors are clustered by location (firms located in Yunnan, Xinjiang, Tibet, Shaanxi, Sichuan, Qinghai, Ningxia, Inner Mongolia, Guangxi, Guizhou, Gansu, Chongqing are recognised as western area). ∗, ∗∗, and ∗∗∗ denote statistical significance at the 10%, 5%, and 1% levels, respectively.
35
Table 5 Interaction between PCEO and Management Incentives 1 2 3
PCEO -0.0726*** -0.0673*** -0.0332***Management 1.3782*** 0.4741*** 1.3992***BM 0.1626* 0.1667* 0.1745*
PCEO × Management -10.6957*** -10.7141***PCEO × BM -0.0490*** -0.0579***
Firm Size 0.0195 0.0200 0.0196ROA 1.3517*** 1.3530*** 1.3485***Non-tradable 0.3382* 0.3329* 0.3328*
Number of firm 531 531 531
Industry dummies Yes Yes Yes
This table presents the results of the Cox proportional hazard regression of 531 firms that went public from 1996 to 2004 and completed the NTS reform from 2005 to 2008. The dependent variable is the lag between the time of starting the NTS reform and the time of completing the NTS reform. The independent variables are lagged 1 year. Robust standard errors are clustered by location (firms located in Yunnan, Xinjiang, Tibet, Shaanxi, Sichuan, Qinghai, Ningxia, Inner Mongolia, Guangxi, Guizhou, Gansu, Chongqing are recognised as western area). ∗, ∗∗, and ∗∗∗ denote statistical significance at the 10%, 5%, and 1% levels, respectively.
36
Table 6 Privatization and Excess Debt 1 2 3 4
PCEO -0.0993*** -0.0975***Leverage -0.8129*** -0.8108*** Excess Leverage -0.6582*** -0.6544***
Management 0.1923** 0.3277*** 0.2358** 0.3757***BM 0.1053 0.1137 0.1023 0.1106Firm Size 0.0625*** 0.0545** 0.0619*** 0.0541**ROA 0.2036 0.4383 0.1892 0.4224Non-tradable 0.1289 0.1708 0.1542 0.1961
Number of firm 531 531 531 531Industry fixed effects Yes Yes Yes Yes
This table presents the results of the Cox proportional hazard regression of the 531 firms that went public from 1996 to 2004 and completed the NTS reform from 2005 to 2006. The dependent variable is the lag between starting and completing the reform. The independent variables are lagged 1 year. Robust standard errors are clustered by location (firms located in Yunnan, Xinjiang, Tibet, Shaanxi, Sichuan, Qinghai, Ningxia, Inner Mongolia, Guangxi, Guizhou, Gansu, Chongqing are recognised as western area). ∗, ∗∗, and ∗∗∗ denote statistical significance at the 10%, 5%, and 1% levels, respectively.
37
Table 7 Privatization and State Control 1 2 3
PCEO -0.0863**Expenditure -0.3153**Revenue -3.7622***
Largest 0.0052*** 0.0054*** 0.0051***State -0.0902*** -0.1011*** -0.1003**
Management 0.8458*** 0.7892*** 0.7314**BM 0.1463** 0.1685*** 0.1976**Firm Size 0.0072 -0.0032 -0.0102ROA 1.3802*** 1.3664*** 1.4650***
Number of firm 531 531 531
Industry dummies Yes Yes Yes
This table presents the results of the Cox proportional hazard regression of the 531 firms that went public from 1996 to 2004 and completed the NTS reform from 2005 to 2006. The dependent variable is the lag between starting and completing the reform. The independent variables are lagged 1 year. Robust standard errors are clustered by location (firms located in Yunnan, Xinjiang, Tibet, Shaanxi, Sichuan, Qinghai, Ningxia, Inner Mongolia, Guangxi, Guizhou, Gansu, Chongqing are recognised as western area). ∗, ∗∗, and ∗∗∗ denote statistical significance at the 10%, 5%, and 1% levels, respectively.
