School of Economics and Finance - Massey …econfin.massey.ac.nz/school/documents/seminarseries... · Web viewTherefore, it is argued that state ownership relinquishment is the key

State ownership and stock return volatility: New Evidence from China’s secondary privatisation

Feng Xiea1, Jing Chia, and Jing Liaoa

Abstract

The study adds new evidence of the impact of state ownership on firm performance uncertainty followed by the Non-tradable Share (NTS) Reform. Using hand collected data of state ownership in Chinese listed firms, we first find a negative relationship between state ownership and stock return volatility. This result indicates that state ownership can mitigate corporate performance uncertainty when the overall corporate governance is weak. Second, we add new evidence on the determinants of residual state ownership. As such, government is more likely to retain the ownership in high labour intensity, industry-leading, larger size, and highly leveraged firms. Our results are robust when controlling for endogeneity issues. Our results are very much relevant to Chinese investors and policy makers, since all the state-owned shares of Chinese listed firms were gradually converted into tradable after the NTS reform launched in 2005.

Keywords: Financial markets uncertainty, State ownership, Stock return volatility, China

JEL Classification: G28, G32

1a All authors are from the School of Economics and Finance, Massey University, New Zealand. Corresponding Author: Feng Xie. School of Economics and Finance, Massey University (Manawatu Campus), Private Bag 11-222, Palmerston North 4442, New Zealand, Tel: +64-6-3505799 ext. 85635; Email: [email protected]

1. Introduction

This study investigates the impact of state ownership on firm performance uncertainty after

the Non-tradable Share (NTS) Reform in China. The NTS reform is one of the most important

reforms of Chinese financial markets, which transfer non-tradable shares into tradable

shares. Non-tradable shares are held mainly by state agencies and state-owned enterprises

(SOEs), therefore, the NTS reform is also referred to China’s secondary privatisation (Liao,

Liu & Wang, 2014).

To the best of our knowledge, our study is the first to explore the impact of state ownership

in China on stock return volatility. Our results indicate that state control can mitigate

corporate performance uncertainty when the overall corporate governance is weak. Second,

we provide empirical evidence to support the state-owned enterprises reform statement

“Guiding Opinions of the Central Committee of the Communist Party of China and the State

Council on Deepening State-Owned Enterprise Reform” announced on 24 August, 2015 (The

Central Committee of the Chinese Communist Party, the State Council, 2015), where the

significant role of state ownership in political foundations and economic development is

repeatedly declared. It is documented in the Guiding Opinions that

“Unswervingly consolidate and develop state-owned economy and encourage,

support and guide the development of the non-state economy. Insist on the principal

status of state ownership, give play to the dominant role of the State-owned

economy, actively promote the cross-shareholding and mutual integration of the

State-owned capital, collective capital and non-public capital ……”

Moreover, we add new evidence to the determinants of the privatisation decision-making

process. That is, the Chinese government maintains the ownership and control in

economically important firms and sectors. Our study provides important understanding of

China’s secondary privatisation and SOE reform, as well as the goal of the government

during the reform transition and post-NTS reform period.

There is considerable controversy regarding the role of state ownership in listed firms. One

strand of literature argues that governments use SOEs to pursue their political and

economic benefits, for example, to achieve excess employment, election support, or private

benefits diverted from insider expropriation (Dinc & Gupta, 2011; Liu, Sun, & Woo, 2006; Liu

& Tian, 2012; Shleifer & Vishny, 1994). Another strand of literature finds that governments

provide political and financial back-up, which in turn improves firm performance (Blanchard

& Shleifer, 2001; Qian, 2003), lowers risk-taking (Boubakri, Cosset, & Saffar, 2013; Khaw,

Liao, Tripe, & Wongchoti, 2016), and offers effective monitoring, especially when legal

investor protection is weak (Chen, Li, Su, & Sun, 2011; Perotti, 2003).

China deserves special attention in the debate on the role of state ownership in listed firms,

given the fact that the Chinese government controls and owns a significant proportion of

shares in listed firms. Previous studies examine the impact of state ownership on firm

performance, but those studies use either the non-tradable state ownership or a mixture of

the non-tradable/tradable ownership to address this issue. In this study, we use hand

collected data of tradable state ownership to examine the role of state ownership on stock

return volatility.

Utilizing the data of 1,860 listed firms in China whose state shares are fully tradable over the

time period between 2007 and 2014, we first explore the impact of state ownership on

stock return volatility. Second, in order to investigate whether the government ownership

can serve as a stabiliser of the financial markets, we analyse the government motivation of

privatisation when state shares become tradable. Given the fact that state-owned shares

were purchased based on the net asset value of the firms which is much lower than the

market trading prices (Liao et al., 2014), asking the question of whether the state intends to

sell these tradable state-owned shares to make huge capital gains or keep them to reduce

the uncertainty of the financial markets is very much crucial. Following Li and Yamada

(2015)2, we examine the determinants of tradable state ownership after the NTS reform

from both political and economic perspectives. Third, we adopt the Cox-hazard model to

investigate the impact of state control on the speed of converting non-tradable shares to

tradable share. Our results show that state ownership can significantly reduce the volatility

of stock return. In addition, the Chinese government still retains the ownership and control

in economically important firms and sectors, such as high labour intensity, larger size, highly

leveraged, and industry-leading firms. We argue that these results show the government’s

incentive to stabilise the financial markets, which in turn can stabilise the Chinese economy.

Moreover, the Cox-hazard model results confirm that the government tends to take longer

time to finish the whole reform process in these economically important firms and sectors.

The remainder of the paper proceeds as follows. Section 2 is the literature review and

hypothesis development. Section 3 describes the sample and variable measurements.

Section 4 presents the analyses and results’ discussion. Section 5 is the conclusion.

2 Li and Yamada (2015) investigate the political and economic motivations of the Chinese government in privatisation following the Share Issue Privatisation (SIP) in China from 1998 to 2007. Our study focuses on the time period after all state shares become tradable from 2007 to 2014.

2. Literature review and hypothesis development

2.1 Institutional background

A unique characteristic of Chinese capital markets before the NTS reform was a dual share

structure where almost two thirds of listed firms’ outstanding shares were non-tradable

shares which were mainly held by the state and legal persons, while the remaining shares

can be traded freely in the markets and are mainly held by individual and institutional

investors (Firth, Fung, & Rui, 2007).

A large body of literature has documented the problems caused by the partial trading and

partial privatisation features. Liao et al. (2014) state that the split share structure leads to a

poor corporate governance system due to incentive disparity between controlling

shareholders and minority shareholders, which in turn leads to weak legal investor

protection. In addition, the partial trading and partial privatisation jeopardise the

development of the Chinese stock markets. Cai, Li, Xia, and Zhang (2012) point out that the

dual class ownership structure constrains the financing function of the Chinese stock

markets in that non-tradable shares tend to be severely undervalued, and the equity

financing is mainly realised through tradable shares. Moreover, investors speculate in the

stock markets for short term returns, rather than aiming at long term returns, since only one

third of total shares outstanding are traded and it is relatively easy to manipulate the stock

prices (Liu & Tian, 2012).

After fully realising the problems in SOEs and the Chinese stock markets caused by the split

share structure, in 2005 the Chinese government launched the Non-tradable Share Reform.

This reform converted non-tradable shares to tradable shares, with negotiated

compensation paid from the non-tradable shareholders to tradable shareholders. Unlike the

previous SOE reform, the NTS reform allows individual firms to have their own reform

proposals rather than a one-size-fits-all solution, and leaves the final decision to

shareholders, especially tradable shareholders. In addition, in order to avoid a sudden

increase of the stock supply and to stabilise the stock markets, the Chinese government

mandates a compulsory lock-up period of at least 12 months for non-tradable shares after

the reform plan’s effective day, and non-tradable shareholders are not allowed to sell more

than 5% (10%) of their outstanding shares within 12 (24) months after the lock-up period. By

the end of 2007, 97% of the firms trading on the Chinese A-share markets had completed

the NTS reform (Li, Wang, Cheung, & Jiang, 2011).

2.2. The impact of the NTS reform

Prior studies, such as Liao et al. (2014) and Chi, Liao, and Li (2014), document that the NTS

reform boosts SOEs output, profitability, and employment. It is stated that the NTS reform

in China has achieved greater success on firm financial and operating performance in

comparison with the SIPs, especially on profitability (Chi et al., 2014). In terms of the

determinants of the performance improvement, some studies argue that the better

alignment of the interests between the controlling and minority shareholders is the main

reason behind the performance improvements. Jiang, Laurenceson, and Tang (2008) argue

that, due to the mandated lock-up period, the potential large capital gain provides

controlling shareholders more incentive to pressure firm management to focus more on

profit maximisation. Moreover, Liao et al. (2014) state that the improvements of SOE

performance after the NTS reform are positively related to government agents’

privatisation-led incentive of increasing share value.

Several studies have also explored the impacts of the NTS reform on corporate governance,

and find that the reform creates an incentive alignment between the controlling and

minority shareholders, and strengthens the corporate governance of Chinese listed firms in

a weak investor protection environment (Liao et al., 2014). Liu and Tian (2012) and Jiang and

Habib (2012) find the NTS reform significantly reduces earnings management measured by

absolute discretionary accruals, and tunnelling practices through pledging bank loans and

related party transactions.

To summarise, studies so far show evidence that the NTS reform provides some solutions

for issues caused by split structure ownership in Chinese listed firms, better aligns the

interests between controlling and minority investors, and improves firm performance and

corporate governance.

2.3. The role of state ownership

For decades, academics have argued that state ownership is the source of corporate

inefficiency; many governments view privatisation as the key method of reducing

government interference in the market and promoting economic efficiency. Shleifer and

Vishny (1994) argue that SOEs are inefficient due to the interest disparity between the

government and private investors. It is argued that governments aim to achieve both

political and economic objectives at the same time (Shleifer & Vishny, 1998). A number of

studies have documented that privatised firms enjoy significant firm performance

improvements (Boubakri & Cosset, 1998; D’Souza & Megginson, 2000; Megginson, Nash, &

Randenborgh, 1994). Therefore, it is argued that state ownership relinquishment is the key

solution of firm efficiency and economic development (Megginson & Netter, 2001).

However, privatisation is not a panacea. Privatisation has not been successful in the former

Soviet Union, and both Argentina and Malaysia have ended up with re-nationalisation.

Indeed, the positive roles of state ownership in partially privatised firms and economic

development are found in recent studies. Perotti (1995) adopts a theoretical model and

concludes that governments retaining control in privatised firms is to increase the credibility

of the privatisation programmes, which in turn can achieve both political and economic

goals. In addition, Mok and Hui (1998) argue that state ownership lowers the uncertainty of

the stock market because state ownership itself is a signal of the government’s confidence

in the company, and also provides a business guarantee to individual investors. To

summarise, the positive impacts of state ownership have been documented in these three

aspects; firm performance, legal investor protection environment, and corporate risk-taking.

2.3.1. State ownership and firm performance

Megginson (2005) conducts a comprehensive survey of the impact of privatisation on firm

performance in developed and developing countries, and states that transferring from state

control to private control increases firm performance. It is suggested that state ownership is

detrimental to firm performance (Megginson & Netter, 2001; Shleifer & Vishny, 1998).

However, Boubakri, Cosset, and Guedhami (2005) and D’Souza, Megginson, and Nash (2005)

conduct two similar studies to examine potential determinants of performance

improvements in divested firms of developed and developing countries respectively, and

suggest that there appear to be differences in the potential sources of these performance

improvements. In developed countries, the relinquishment of government control and the

presence of foreign ownership have the most significant impact on post-privatisation

performance improvement. While, in developing countries, environmental factors such as

economic growth, and institutional factors such as stock market development and the

extent of legal protection, are major determinants of post-privatisation performance

improvements. Therefore, the conclusion of state ownership decreasing firm performance is

arbitrary.

Studies including Sun and Tong (2003), Wang (2005), Quan and Huyghebaert (2004), and

Chen, Firth, and Rui (2006) document decreased profitability following the Share Issue

Privatisation (SIP), the first round of privatisation in China. Therefore, state ownership

relinquishment might not increase firm performance in China. Sun, Tong, and Tong (2002)

argue that the Chinese government supports SOEs in three ways. First, partial privatisation

may signal the government’s confidence in the future growth of firms. Second, the

government offers a free hand in efficient monitoring when legal investor protection is weak

in China. Third and last, the government can provide both political and financial back-up,

such as licensing and subsidisation.

It is expected that the interests between state owners and minority shareholders aligns

better after the NTS reform, as the value of state shares is linked with corporate market

performance, which provides an incentive for the government to boost firm performance

(Liao et al., 2014). Liao et al. (2014) find that the NTS reform has significantly increased firm

performance in 1,032 listed firms in China, and the performance improvement of SOEs is

more pronounced due to the political and financial back-up from the government.

Therefore, state ownership is expected to play important and supportive roles for firm

performance improvement, especially after the NTS reform.

2.3.2. State ownership and investor legal protection environment

Apart from the role state ownership plays at the firm specific level, state ownership also

stabilises the institutional environment and the whole financial market. Perotti (2003)

answers the question of why mixed ownership structure is the dominant engine of financial

market development. He argues that partial privatisation remains the most desirable

solution as “state ownership is seen as justified when explicit regulation is hard to

implement because of non-verifiable contingencies” (Perotti, 2003, p.11). Indeed, state

ownership completes contracting and legislation, especially in developing countries with

insufficient legal protection for private property rights, such as in China. Chen et al. (2011)

argue that the property rights of private firms in China are not well-protected by formal

institutions, which gives them a disadvantage in terms of obtaining resources and exposure

to larger corporate performance volatility. They also find that private firms with political

connections out-perform those without political connections. This suggests that political

connections or state shares can be considered as a substitute for formal legal investor

protection. In addition, Vaaler and Schrage (2009) conduct a theoretical model to examine

the impact of residual state ownership on stability and financial performance, and find that

shareholder returns are positively related to residual state ownership. Therefore, in the

absence of a competitive property rights market and a well-functioning legal framework,

state ownership can send positive signals to investors that the government is fully

committed and confident of the companies’ economic fate, which in turn reduces corporate

performance uncertainty and stabilises the whole financial market.

2.3.3. State ownership and corporate risk-taking

Several studies have documented that state ownership is negatively related to corporate

risk-taking. Boubakri et al. (2013) investigate the impact of state ownership in corporate

risk-taking using 381 privatised firms from 26 emerging markets and 31 industrialised

countries over the 1981 to 2007 period. They measure corporate risk-taking in two ways.

One is the volatility of a firm’s earnings over four-year overlapping years; and the other is

the difference between maximum and minimum of ROA over four overlapping years. It is

argued that a powerful government is more likely to be conservative when undertaking risky

investments, in order to stabilise social benefits and employment (Boubakri et al., 2013).

Similarly, Khaw et al. (2016) also document that state ownership is negatively associated

with corporate risk-taking by using Chinese setting over the period of 1999 to 2010. To

achieve stable stock returns and reduce the uncertainty of the stock markets, the

government tends to take less risky investments (Khaw et al., 2016). Although corporate

risk-taking is a fundamental driver of firm performance and development (Faccio, Marchica,

& Mura, 2011; John, Litov, & Yeung, 2008), Khaw et al. (2016) find a negative relationship

between corporate risk-taking and firm performance, as high risk-taking in a poor legal

protection environment is detrimental to firm value enhancement (John et al., 2008).

Therefore, state ownership in countries with poor legal protection and law enforcement,

such as in China, is able to reduce corporate risk-taking, which stabilises corporate

performance fluctuation.

2.3.4. Hypothesis development

The NTS reform converts non-tradable shares to tradable shares, and opens the gate for the

second-round of privatisation in China. It is of high concern whether the Chinese

government will sell a large number of state shares after they can be freely traded in the

markets and cause instability in the financial markets. As an investor whose shares were

purchased at the book value of the firm assets, which is much lower than the market value

of tradable shares, and therefore the Chinese government is offered a good opportunity to

obtain large capital gains following the NTS reform (Cai et al., 2012; Liao et al., 2014).

However, the government’s previous attempts in state ownership sales did not end well

when the markets reacted adversely and the stock market dropped sharply. The Shanghai

and Shenzhen Composite Indexes dropped by 7.3% and 6.8% respectively in 1999, and 31%

and 32.9% respectively in 2001, when the government attempted to privatise state

ownership to raise capital (Liao et al., 2014).

Overseeing the privatisation programmes in China, from the SIP to the NTS reform, it is not

hard to tell that the government is very cautious when it comes to state ownership

relinquishment. Unlike the massive privatisation approach adopted by the Eastern

European nations, the Chinese government uses a partial privatisation strategy, aiming to

improve the SOEs performance by establishing market-oriented incentives while

maintaining state ownership and control in economically important sectors and enterprises

(Liu et al., 2006). In addition, the Chinese government has emphasised repeatedly the

importance of state ownership in economic development. It is stated that instead of

relinquishing state ownership in listed firms, the SOE reform should aim to improve SOEs’

management systems and increase the pace of marketisation, as the SOE is the pillar of the

national economy (The Communist Party of China, 1999, 2012). Therefore, it is expected

that the Chinese government may choose to retain their shares and control for social

stability purpose, even after the state shares become tradable.

Moreover, given the positive roles of state ownership in reducing corporate risk-taking,

improving firm performance, and providing a positive signal in building investors’ confidence

in the financial markets, especially when legal institutions are not in place and law

enforcement is weak, we expect that the state ownership can reduce the uncertainty in the

stock markets followed by the NTS reform. We therefore, have the following hypothesis:

H1: State ownership in China is negatively related to the stock return volatility.

3. Data and variables

3.1. Data description

To investigate the impact of tradable state ownership on corporate performance

uncertainty after the NTS reform, we include firms with state ownership listed on the

Shanghai and Shenzhen Stock Exchanges. The performance data are collected from the

CSMAR China Stock Market Financial database. The firm-level data are collected from the

CSMAR China Listed Firms’ Corporate Governance Research database. Particularly, we hand

collect the tradable state ownership data from Sina Finance (http://finance.sina.com.cn).

The time period of our sample is from the year that the firm become fully tradable to 2014.

We first manually collect the dates that the state shares become fully tradable on for each

individual firms from the CSRC official information disclosure website cninfo

(www.cninfo.com.cn). Then we manually collect the tradable state ownership from Sina

Finance (http://finance.sina.com.cn), which is the sum of the top ten tradable state

ownership. To investigate the impact of the state ownership on stock return volatility after

the NTS reform, we collect the data of the state ownership after the lock-up period expires,

given the complicated state share compensation and reimbursement process during the

lock-up period. We use the observation year as the same year of the announcement of all

the state shares becoming fully tradable if the announcement is made before July. For

example, a firm’s state shares became fully tradable on 11 October, 2006, so the starting

year for this firm is 2007. Our sample period is from 2007 to 2014.

The de-listed firms and the firms that do not implement the NTS reform during the sample

period are excluded. After filtering the outliers at the 1% and 99% levels, the final sample in

our study contains 1,860 listed firms that consist of 6,682 firm-year observations.

Panel A of Table 1 shows the distribution of the sample firm-year observations by year. The

increasing trend of the firm-year observations from 28 in 2007 to 1,412 in 2014

demonstrates that the reformed firms in China gradually achieve full tradability of the state

shares. Panel B of Table 1 shows the distributions of sample firm-year observations by

industry. The industry classification is based on the 2012 CSRC industrial classification of

listed companies and includes 16 industries. In our sample period, 66.06% are in

manufacturing; 9.08% are in wholesale and retail; 5.67% are in electric power, heat, gas and

water; 5.42% are in real estate; and the rest of the firms are from agriculture and forestry

industry, mining, construction, transport, storage and postal services, accommodation,

information transmission, software and information, leasing and commercial service, water

conservancy, environment and public facility management, culture, sports and

entertainment, financial industry and others. Panel C shows the statistical summary of state

ownership from 2007 to 2014. The increasing trend of observations each year illustrates

that the state shares become tradable as time goes by. The average tradable state

ownership in 2007 and 2008 is 8.93% with 28 firm-year observations, and 22.45% with 160

firm-year observations, respectively. From 2009 onwards, the average tradable state

ownerships are all above 30%. It is indicated that firms tend to take a long time to unfreeze

their non-tradable state shares.

[Insert Table 1 about here]

3.2. Stock return volatility measures

Following Pan, Wang, and Weisbach (2015), Irvine and Pontiff (2009), and Li et al. (2011), we

use realised return volatility and idiosyncratic return volatility to measure stock return

volatility. Realised return volatility includes the standard deviation of monthly stock returns

and the standard deviation of market-adjusted monthly stock returns. To estimate

idiosyncratic return volatility, we calculate monthly volatility of residual stock return of

market model regression.

rit = αi + β rmt + εit

Idiosyncratic return volatility is defined as √Var(ε it ) from the above equation.

3.3. Control variables

In analysing the relationship between state ownership and stock return volatility, we control

for several variables that have been previously identified, including firm performance and

governance variables. Sales growth, calculated as the growth rate of annual sales, is

included to measure the firm growth opportunity, which is expected to be positively related

to performance uncertainty (Boubakri et al., 2013). We use return on assets (ROA), calculate

as the ratio of net income to total assets, to measure accounting performance. Shan, Taylor,

and Walter (2014) state that firms with lower ROA are expected to have higher stock return

fluctuation. In addition, we use the valued-weighted annual stock return to measure stock

performance, and Tobin’s Q to represent corporate market performance. The previous

literature supports the positive trade-off between stock returns and stock return volatility

(French, Schwert, & Stambaugh, 1987; Ludvigson & Ng, 2007; Lundblad, 2007). In terms of

the relationship between Tobin’s Q and stock return volatility, we expect a positive

relationship between Tobin’s Q and stock return volatility, as the higher the corporate value

the higher the growth opportunities, which leads to greater fluctuation in stock returns

(Shan et al., 2014; Pan et al., 2015).

In terms of the corporate governance and firm characteristic variables, we include board

independence, board size, leverage, the ratio of intangible assets to total assets, firm size,

and firm age. Board independence (calculated as the ratio of the number of independent

directors to the total number of directors on the board) and board size (calculated as the

natural log of the total number of directors in the board) are expected to be negatively

associated with stock return volatility. Firms with a larger board size and more independent

board are less likely to make extreme decisions, and therefore, lead to less variable

corporate performance (Cheng, 2008). Moreover, higher leverage (the ratio of total debt to

total assets), a higher ratio of intangible assets to total assets, smaller and younger firms are

riskier; therefore, these firms could have higher stock return volatility (Shan et al., 2014).

The detailed description of each variable is shown in Appendix A. Table 2 shows the

summary statistics of the. The average ROA of sample firms is 3.89% with a maximum ratio

of 31.13%. The average leverage ratio is 47.92% and the maximum value reaches 93.83%.

The stock return shows a quite big variance with a minimum ration of -81.8% and a

maximum return of 853.74%. This suggested that we may need to further drop the extreme

values for robustness check.


Table 3 reports the pairwise correlation matrix of the key variables. The correlation matrix

of the independent variables shows no serious multicollinearity concerns.


4. Results and discussion

In this section, we present and discuss the results of the impact of state ownership on stock

return volatility. We also present the results of the robustness checks and additional tests.

4.1. State ownership and stock return volatility

To investigate the impact of state ownership on stock return volatility after the NTS reform

in China, we use multivariate regression of panel data with clustered standard errors by

industry, controlling for firm and year fixed-effects, which is a common method to control

for omitted variables in a panel dataset (Massa, Zhang, & Zhang, 2014). The initial regression

specification is as follows3:

Stock return volatilityit = α + β1Stateit + β2Sales growthit + β3ROAit + β4Stock returnit +

β5Tobin’s Qit + β6Board independenceit + β7Board sizeit + β8Leverageit +

β9Intangible/assetsit + β10Firm sizeit + β11Firm ageit + Ɛit

Table 4.1 reports the results of the above regression model. The state ownership variable is

significantly and negatively associated with stock return volatility in Models 2 and 3

3 We control the effect of institutional ownership on stock return volatility by adding the share percentage of fund investors and Qualified Foreign Institutional Investor (QFII) as the proxies of institutional ownership. We find institutional ownership is not significantly related to the stock return volatility. Thus the results are not shown.

consistently at the 1% level, and the effect is significant at the 5% level in Model 1.

Moreover, the state ownership is economically significant with magnitudes of -0.0873, -

0.1262, and -0.13094 in each of the three models, respectively. That is, a 1% decrease in

state ownership increases by 0.0873%, 0.1262%, and 0.1309% the standard deviation of

monthly stock returns, standard deviation of market-adjusted monthly stock returns, and

standard deviation of idiosyncratic returns, respectively. It is revealed that the state

ownership after the NTS reform can effectively reduce the volatility of stock return, which in

turn stabilises the Chinese stock markets.

In addition, ROA is significantly and negatively associated with stock return volatility in the

three models. This is consistent with the findings of Dutt and Humphery-Jenner (2013) and

Cheng (2008), which indicate that firms with better accounting performance tend to have

less market performance volatility. We also find that stock return is significantly and

positively related to stock return volatility at the 10% level. This finding is supported by the

positive return and risk trade-off theory (French et al., 1987; Ludvigson & Ng, 2007;

Lundblad, 2007). Other variables are not statistically significant in the three models.

[Insert Table 4.1 about here]

Further, we separate the full sample into SOEs and non-SOEs based on whether the ultimate

controller is the state or a non-state entity. The results are shown in Table 4.2 and Table 4.3.

The state ownership in SOEs can significantly reduce the stock return volatility at 1%

significant level in all three models shown in Table 4.2. While, state ownership in non-SOEs

has no significant impact on the stock return volatility.

4 We use the formula; standard deviation of the independent variable multiplied by the coefficient of the independent variable divided by the standard deviation of the dependent variable; to calculate the economic significance of an independent variable.



4.2. Endogeneity

The negative relationship between state ownership and stock return volatility may be

subject to endogeneity bias. Previous studies including Boubakri, Cosset, Guedhami, and

Saffar (2011) and Li and Yamada (2015) find that governments are more likely to retain state

shares in big and less risky firms. We therefore use a system dynamic panel Generalised

method of moments (GMM) model, introduced by Arellano and Bover (1995) and Blundell

and Bond (1998), to address the possible endogeneity issue. The GMM test includes one lag

of the dependent variable as covariates and contains unobserved panel-level effects, fixed

at both the firm and year levels. The results of the dynamic panel GMM model are shown in

Table 5. State ownership variables are still significant at the 10% level in all three models.

The results indicate that state ownership is negatively associated with stock return volatility,

which stabilises the financial markets. This confirms that our findings are robust to the

causality concern.

Stock returns are positively significant in all three models, which implies the positive

relationship between risk and return. We also find Tobin’s Q is positively related to stock

return volatility at the 1% significance level in both Model 2 and Model 3. This is consistent

with the findings of Shan et al. (2014) and Pan et al. (2015). Similarly, firm size is positively

and significantly related to stock return volatility, which indicates that large firms are more

likely to have large stock return variation. In addition, firm age is found to have a negative

relationship with stock return volatility at the 1% significance level in both Model 2 and

Model 3. It is indicated that younger firms are risker, which is consistent with the findings of

Cheng (2008) and Shan et al. (2014).


We also employ a difference-in-difference approach to address the endogeneity bias. In

Table 6, it can be seen that state ownership change variable is negatively and significantly

related to stock return volatility at the 1% level in the three models. That is, the decrease of

state ownership by 1% leads to a 0.0048%, 0.0086%, and 0.0087% increase in standard

deviation of monthly stock returns, standard deviation of market-adjusted monthly stock

returns, and standard deviation of idiosyncratic returns, respectively. The change of sales

growth ratio is significantly and positively related to stock return volatility at the 1% level. It

is shown that firms with increased growth opportunity are more likely to have increased

stock return volatility. In addition, the changes of Tobin’s Q and firm size are both positively

related to the change of stock return volatility, which illustrates a positive relationship

between corporate market value and stock return volatility. The changes of board

independence and board size both have negative relationships with stock return volatility

change at the 5% or 10% significance level, which is consistent with Cheng (2008)’s findings

that more independent boards and larger boards can lower variability of corporate

performance.


4.3. The government decision on privatisation

To further examine whether it is the Chinese government’s intension to maintain state

shares in privatised firms after the NTS reform, we also conduct a logistic test to find the

determinants of privatisation decision-making. The initial regression is as follows:

State controlit = α + β1Labourit + β2Weighted MCit + β3Sizeit + β4Leverageit + β5ROAit +

β6Non-tradableit + β7MBit + β8Tax to salesit + β9RPTit + β10GDP per capitalit + β11GDP

growth rateit + Ɛit

The dependent variable “State control” is a dummy variable, which equals one if the state is

the ultimate controller of a firm, and otherwise zero. In terms of the independent variables,

we follow Li and Yamada (2015) by using labour intensity (Labour) as one of the proxies for

social stability. Li and Yamada (2015) argue that the social security system for workers

outside the SOEs is weak in China. In order to maintain social stability, the government has

to control SOEs to keep the employment level high. Therefore, labour intensity is expected

to have a positive relationship with state control. We also use industry-weighted market

capitalisation (Weighted MC), firm size (size), ROA, and firm leverage (Leverage) as the

proxies of social stability, following Cosset, Durnev, and Santos (2015). It is argued that

governments tend to retain high ownership in industry-leading firms to stabilise the

business environment (Ng, Yuce, and Chen, 2009). In addition, bigger firms, under-

performing firms, and highly leveraged firms tend to be controlled by the government for

reasons of social stability (Boubakri et al., 2011; Cosset et al., 2015). Therefore, it is

suggested that the expectation sign of industry-weighted market capitalisation, firm size,

and firm leverage are positive, and that of ROA is negative. For the control variables, we use

the percentage of non-tradable shares, MB ratio, tax to sales ratio, and a related party

transaction (RPT) dummy variable, which equals one if firms have related party transactions

with local governments, bureaus of state-owned assets and departments of finance, and

otherwise zero. In addition, we control for macroeconomic factors by using GDP per capita

and GDP growth rate.

Table 7 represents the logistic test results, showing that the government is less likely to

relinquish the state shareholding in high labour intensity, large, and under-performing firms.

The variables Labour, Size, and ROA are all significantly associated with State control. The

results are consistent with the findings of Boubakri et al. (2011) and Li and Yamada (2015).

In addition, the negative relationship between GDP per capital and state control indicates

that the government tends to retain the state ownership in firms in less-developed

provinces. Industries like electric power, heat, gas and water; transport, storage and postal

services; and water conservancy, environment and public facility management are strategic

industries that the government holds shares in for reasons of national security, which is

consistent with the study of Li and Yamada (2011). All in all, the results reveal that the

government has a strong motivation to maintain the social stability after the NTS reform,

which in turn stabilises the Chinese economy.


To gain further understanding of the motivation of the Chinese government in maintaining

residual state ownership after the NTS reform, we replace the dummy variable state control

with the residual state ownership after all state shares become tradable in individual firms.

The multivariate test results are shown in Table 8. In Model 1, we use the full sample with

6,411 firm-year observations. Models 2 and 3 are subsample analyses based on whether the

ultimate controller is the state or a non-state entity. The weighted MC variables are

positively significant in three models; firm size is positively significant at the 1% level in

Models 1 and 2. The results are robust in supporting our findings that the government tends

to retain shareholding in industry-leading and large firms to reduce uncertainty in the

Chinese stock markets.


4.4. The government decision on reform speed

As well as the analysis of the effects of the government’s incentives on the degree of

privatisation along with the reform, we also adopt Cox-hazard regression analysis to

investigate the determinants of the speed of the NTS reform, as the time period of the

reform could demonstrate the government’s desire for privatisation. The initial regression

specification for the Cox-hazard regression is as follows:

h(t) = h0(t) exp (β1x1 + β2x2 +· · ·+βkxk)

The dependent variable h(t) is the number of days, starting from the reform date of

individual firms, to the announcement date when all the reform shares become tradable.

The Cox-hazard model results are displayed in Table 9. The dummy variable state control is

significantly and negatively associated with the number of days that a firm becomes fully

tradable. This suggests that firms with state as the ultimate controller are more likely to

take a longer time to finish the full reform process. Moreover, firms with higher labour

intensity, large size, and highly leverage tend to have a longer reform process. The results

reveal that the government does have concerns regarding economy stability when carrying

out the NTS reform.


5. Conclusion

In this paper, we reply on a manually collected unique database where the state ownership

is fully tradable. Our results show that tradable state ownership can significantly reduce the

stock return volatility, which is measured by monthly stock return volatility, monthly

market-adjusted stock return volatility, and idiosyncratic return volatility. Our results hold

not only for firm-year fixed effects with standard errors clustered by industry, but also for

endogeneity tests, by using the system dynamic GMM test and difference-in-difference

approach. Second, we find that the government tends to retain control and shares in

economically strategic firms, such as high labour intensity, industry-leading, large, and

under-performing firms. Finally, our results indicate that it takes the longer time to convert

non-tradable shares to tradable shares in firms controlled by the state, and in high labour

intensity, industry-leading, and large firms, in order to maintain economic stability.

The findings suggest that, when facing the opportunity of trading state shares to potentially

gain large price appreciation, the Chinese government chooses to retain the state shares

and control in economically strategic firms to stabilise the financial markets. In addition, the

positive role of state ownership in reducing the volatility of stock return provides broad

implications for policy makers’ decisions on further privatisation strategies.

Appendix A defines the variables used in this study.Variable DefinitionStandard deviation of monthly stock return Standard deviation of monthly stock returnStandard deviation of market-adjusted monthly stock return

Standard deviation of the difference between monthly stock return and average market stock return

Standard deviation of Idiosyncratic return volatility Standard deviation of residuals from a market model regression

State Total tradable state ownershipState control A dummy variable which equals one if the ultimate

controller of a firm is the stateSales growth The growth rate of annual salesROA The ratio of net income to total assetsBoard independence The ratio of the number of independent directors to

the total number of directors on the boardBoard size The natural log of the total number of directors in

the boardLeverage The ratio of total debt to total assetsStock return The annual stock returnsTobin’s Q The ratio of the sum of market equity and total

book liability to the total assets; Intangible/assets is the ratio of total intangible assets to total assets

Intangible/assets The ratio of intangible assets to total assetsFirm size The natural log of the total market capitalisation of

the firmFirm age The natural log of the number of years from the

establishment of the firm to the year of observationLabour The total number of workers divided by total asset,

scaled by 106

Weighted MC The industry weighted market capitalisationNon-tradable

MB

The ratio of number of non-tradable shares to total number of sharesThe market to book equity ratio

Tax to sales The ratio of tax expense to total salesRPT A dummy variable which equals to one if firms have

related party transactions with local governments, bureaus of state-owned assets and departments of finance, otherwise zero

GDP per capita The provincial GDP per capita based on the location of the headquarters of a firm

GDP growth rate The provincial GDP growth rate based on the location of the headquarters of a firm

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Table 1. Distribution of sample firmsThis table reports the distribution of sample over the sample period from 2007 to 2014. Panel A reports the distribution of 6682 sample firm-year observations by year. Panel B reports the distribution of 6682 sample firm-year observations by Industry. Panel C shows the time trend of the firms included into the sample.Panel A: By yearYear Firm-year observation Percentage (%)2007 28 0.422008 160 2.392009 447 6.692010 882 13.202011 1139 17.052012 1263 18.902013 1351 20.222014 1412 21.13Total 6682 100Panel B: By industry5

Industry Firm-year observation Percentage (%)Agriculture, forestry 81 1.21Mining 120 1.80Manufacturing 4414 66.06Electric power, heat, gas and water 379 5.67Construction 102 1.53Wholesale and retail 607 9.08Transport, storage and postal services 246 3.68Accommodation 25 0.37Information transmission, software and information technology services 83 1.24Financial 16 0.24Real estate 362 5.42Leasing and commercial service 33 0.49Water conservancy, environment and public facility management 57 0.85Culture, sports and entertainment 60 0.90Others 97 1.45Total 6682 100

Panel C: The time trend of sample firms included into the sample.Year Observation Mean Min Max2007 28 0.0893 0.0027 0.43862008 160 0.2245 0.0025 0.79102009 447 0.3185 0.0022 0.79002010 882 0.3006 0.0022 0.79452011 1139 0.3018 0.0023 0.7974

5 The industry classification is based on the 2012 CSRC industrial classification of listed companies with 15 industries. For more details please refer to CSRC, 2012. Beijing: The Guidelines for the Industrial Classification of Listed Companies (No. 31).

2012 1263 0.3047 0.0022 0.79742013 1351 0.3177 0.0023 0.79712014 1412 0.3001 0.0022 0.7971

Table 2. Descriptive statisticsThis table reports the summary statistics of the variables included in the analysis for a sample of 1860 listed firms that consists 6682 firm-year observations. The description of each variable is summarized in Appendix A.Variables Observation Mean Standard deviation Min MaxStandard deviation of monthly stock return 6682 0.1267 0.0822 0.0268 3.6307Standard deviation of market-adjusted monthly stock return 6682 0.0981 0.0682 0.0187 3.3207Standard deviation of idiosyncratic return 6682 0.0980 0.0682 0.0185 3.3216Sales growth 6682 0.2079 0.6719 -0.9777 14.3528Board Independence 6682 0.3672 0.0535 0.1429 0.7143Board size 6682 2.1959 0.1987 1.3863 3.0910ROA 6682 0.0389 0.0500 -0.6515 0.3113Leverage 6682 0.4792 0.2089 0.0478 0.9383Stock return 6682 0.2044 0.6009 -0.8187 8.5374Tobin's Q 6682 1.7577 1.1644 0.3184 15.0649Intangible/assets 6682 0.0484 0.0726 0.0000 0.8950Firm size 6682 22.3386 1.0039 19.8477 27.8925Firm age 6682 2.6834 0.3952 0.6931 3.5553

Table 3. Correlation matrix of the identified variablesThe correlation matrix of the variables for the sample of 1,860 listed firms with 6,682 firm-year observations are presented in this table. The description of each variable is summarized in Appendix A.

Standard deviation of monthly stock return

Standard deviation of market-adjusted monthly stock return

Standard deviation of idiosyncratic return State

Sales growth

Board independent

Board size ROA Leverage

Stock return

Tobin's Q Intangible/assets

Firm size

Firm age

Standard deviation of monthly stock return 1.0000Standard deviation of market-adjusted monthly stock return 0.7620 1.0000Standard deviation of idiosyncratic return 0.7619 0.9991 1.0000

State -0.0430 -0.0321 -0.0313 1.0000

Sales growth -0.0020 0.0006 0.0006 0.0098 1.0000Board independent -0.0082 -0.0066 -0.0067

-0.0041

-0.0085 1.0000

Board size -0.0609 -0.0555 -0.0532 0.2014 0.0001 -0.3563 1.0000

ROA -0.0209 -0.0081 -0.0073-

0.0438 0.0001 -0.0283 0.0080 1.0000

Leverage 0.0105 0.0170 0.0201 0.2329 0.0102 0.0184 0.1358-

0.3893 1.0000

Stock return 0.3670 0.4116 0.4167-

0.0025-

0.0034 -0.0069-

0.0158 0.0525 0.0389 1.0000

Tobin's Q 0.0955 0.1750 0.1755-

0.0534-

0.0071 0.0042-

0.1015 0.1646 -0.1970 0.2707 1.0000

Intangible/assets -0.0247 -0.0219 -0.0223 0.0457 0.0097 -0.0210 0.0202-

0.0170 -0.0472-

0.0062 0.0537 1.0000

Firm size -0.0350 0.0286 0.0305 0.2793-

0.0094 0.0745 0.2500 0.2574 0.1450 0.2117 0.0342 -0.01501.000

0

Firm age -0.0543 0.0173 0.0176 0.0992 0.0195 -0.0247 0.0327-

0.1050 0.2635 0.0159 0.0421 0.02690.064

9 1.0000This table reports the correlation matrix of variables for the sample with 6,682 firm-year observations over the sample period from 2007 to 2014.

Table 4.1. State ownership and stock return volatility (full sample)(Clustered by standard error of industry)This table presents the results of the relationship between state ownership and volatility of corporate market performance of the Chinese listed firms from 2007 to 2014. The regression model is as below:

Stock return volatilityit = α + β1Stateit + β2Sales growthit + β3ROAit + β4Stock returnit + β5Tobin’s Qit + β6Board independenceit + β7Board sizeit + β8Leverageit + β9Intangible/assetsit + β10Firm sizeit + β11Firm ageit + Ɛit

The definitions of all the variables are shown in Appendix A. A superscript *, ** or *** denotes significance at the 10%, 5% or 1%, respectively. All models are fixed at firm and year level with standard error of industry clustered.Dependent variable Expected

signsStandard

deviation of monthly stock

return

Standard deviation of market-adjusted

monthly stock return

Standard deviation of idiosyncratic

return

Independent variables (1) (2) (3)

Intercept 0.0218(0.07)

0.0212(0.07)

0.0149(0.05)

State - -0.0311**(-2.73)

-0.0373***(-3.62)

-0.0387***(-4.02)

Sales growth + -0.0000(-0.36)

0.0000(0.24)

0.0000(0.43)

ROA - -0.0414*(-1.83)

-0.0432***(-3.33)

-0.0410***(-3.13)

Stock return + 0.0486(1.63)

0.0585*(2.05)

0.0585*(2.05)

Tobin’s Q + -0.0007(-0.19)

0.0013(0.37)

0.0014(0.40)

Board independence - -0.0141(-1.09)

-0.0031(-0.18)

-0.0020(-0.12)

Board size - -0.0098(-1.29)

-0.0121(-1.15)

-0.0116(-1.09)

Leverage + 0.0083(0.34)

0.0020(0.10)

0.0026(0.12)

Intangible/assets + 0.0274(1.38)

0.0291(1.66)

0.0301(1.22)

Firm size - 0.0057(0.37)

0.0064(0.40)

0.0061(0.38)

Firm age - -0.0090(-0.69)

-0.0174(-1.12)

-0.0133(-0.86)

Observation 6682 6682 6682Firm fixed effect Yes Yes YesYear fixed effect Yes Yes YesR-squared 0.2938 0.2596 0.2642

35

Table 4.2. State ownership and stock return volatility (SOEs)(Clustered by standard error of industry)This table presents the results of the relationship between state ownership and volatility of corporate market performance of the Chinese listed firms from 2007 to 2014. The regression model is as below:



signsStandard


return




return


Intercept 0.5901(1.01)

0.6113(1.03)

0.6272(1.06)

State - -0.0534***(-5.03)

-0.0551***(-4.57)

-0.0055***(-4.65)

Sales growth + 0.0000(0.72)

0.0000**(2.21)

0.0000**(2.53)

ROA - -0.0484(-1.25)

-0.0506(-1.33)

-0.0483(-1.28)

Stock return + 0.0758(1.71)

0.0817*(1.86)

0.0818*(1.86)

Tobin’s Q + -0.0040(-0.86)

-0.0007(-0.16)

-0.0007(-0.16)


0.0006(0.02)

0.0043(0.17)

Board size - -0.0106(-0.88)

-0.0106(-1.19)

--0.0100(-1.16)

Leverage + 0.0034(0.07)

-0.0161(-0.37)

-0.0158(-0.36)

Intangible/assets + 0.0753(1.55)

0.0785(1.38)

0.0805(1.44)

Firm size - -0.0103(0.39)

-0.0106(-0.37)

-0.0108(-0.38)

Firm age - -0.0235(-1.24)

-0.0617***(-4.38)

-0.0620***(-4.44)


36

Table 4.3. State ownership and stock return volatility (Non-SOEs)(Clustered by standard error of industry)This table presents the results of the relationship between state ownership and volatility of corporate market performance of the Chinese listed firms from 2007 to 2014. The regression model is as below:



signsStandard


return




return


Intercept -0.1913***(-3.44)

-0.2388***(-6.45)

-0.2429***(-6.09)

State - 0.0092(0.41)

0.0047(0.43)

0.0039(0.37)


0.0010***(4.80)

0.0010***(4.47)

ROA - -0.0449(-1.50)

-0.0572***(-3.33)

-0.0547***(-3.27)

Stock return + 0.0123***(5.99)

0.0310***(17.12)

0.0305***(17.54)

Tobin’s Q + 0.0038***(3.98)

0.0035**(2.94)

0.0036***(3.10)


0.0137(1.38)

0.0102(1.10)

Board size - -0.0237***(-4.27)

-0.0344***(-5.89)

-0.0343***(-5.60)

Leverage + 0.0105(1.27)

0.0153**(2.61)

0.0163**(2.75)

Intangible/assets + -0.0343**(-2.24)

-0.0323(-1.32)

-0.0315(-1.28)

Firm size - 0.0218***(12.59)

0.0210***(17.99)

0.0209***(16.47)

Firm age - -0.0204***(-4.78)

-0.0229***(-5.72)

-0.0161***(-3.49)


37

Table 5. Endogeneity test of the state ownership and stock return volatility: Dynamic GMM test (full sample) This table represents the dynamic-panel GMM results of the relationship between state ownership and volatility of corporate market performance of the Chinese listed firms from 2007 to 2014. The definitions of all the variables are shown in Appendix A. A superscript *, ** or *** denotes significance at the 10%, 5% or 1%, respectively. All models are fixed at firm and year level.Dependent variable

Expected signs

Standard deviation of




Standard deviation of idiosyncratic return

Independent variables

(1) (2) (3)

Intercept -0.2167** -0.1061 -0.1158(-2.35) (-1.06) (-1.16)

Dependent variable-1

0.0129(0.99)

0.0076(0.60)

0.0056(0.45)

State - -0.0245*(-1.84)

-0.0318*(-1.85)

-0.0306*(-1.79)


0.0007(0.47)

0.0006(0.46)

ROA - -0.0212(-0.82)

0.0109(0.42)

0.0124(0.48)

Stock return + 0.0361***(15.78)

0.0394***(16.67)

0.0392***(16.67)

Tobin’s Q + 0.0022(1.53)

0.0042***(2.87)

0.0043***(2.99)

Board independence

- -0.0237(-0.76)

-0.0304(-0.97)

-0.0282(-0.91)

Board size - -0.0106(-0.89)

-0.0045(-0.37)

-0.0034(-0.28)

Leverage + 0.0091(0.72)

0.0087(0.68)

0.0095(0.76)

Intangible/assets + -0.0431(-1.17)

-0.0304(-0.97)

-0.0149(-0.41)

Firm size - 0.0171***(4.90)

0.0153****(4.10)

0.0158***(4.25)

Firm age - -0.0150(-0.83)

-0.0456***(-2.56)

-0.0474***(-2.66)

Observation 4615 4615 4615Firm fixed effect Yes Yes YesYear fixed effect Yes Yes Yes

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Table 6. Endogeneity test of the state ownership and stock return volatility: Difference in difference approach (full sample) This table represents results of the difference in difference regression analysis. The regression model is as below:

∆Stock return volatilityit = α + β1∆Stateit + β2∆Sales growthit + β3∆ROAit + β4∆Stock returnit + β5∆Tobin’s Qit + β6∆Board independenceit + β7∆Board sizeit + β8∆Leverageit + β9∆Intangible/assetsit + β10∆Firm sizeit + β11∆Firm

ageit + Ɛit

The definitions of all the variables are shown in Appendix A. A superscript *, ** or *** denotes significance at the 10%, 5% or 1%, respectively. All models are fixed at industry and year level with standard error of firm clustered.Dependent variable Expected

signs∆Standard


return

∆Standard deviation of

market-adjusted monthly stock

return

∆Standard deviation of idiosyncratic

return


Intercept -0.4533*** -0.7242*** -0.7579***(-12.31) (-9.33) (-10.09)

∆State - -0.0048***(-3.26)

-0.0086***(-3.80)

-0.0087***(-3.88)

∆Sales growth + 0.0003***(2.87)

0.0004**(2.05)

0.0005**(2.17)

∆ROA - -0.0022(-1.23)

-0.0040(-1.44)

-0.0039(-1.41)

∆Stock return + -0.0002(-0.31)

-0.0006(-0.44)

-0.0005(-0.40)

∆Tobin’s Q + 0.0357*(1.85)

0.0735**(2.32)

0.0729**(2.29)

∆Board independence - -0.1206*(-1.71)

-0.2230**(-1.99)

-0.1991*(-1.79)

∆Board size - -0.3996*(-1.79)

-0.7503**(-2.27)

-0.7437**(-2.27)

∆Leverage + 0.0293(1.03)

0.0564(1.25)

0.0549(1.21)

∆Intangible/assets + -0.0030*(-1.69)

-0.0044**(-1.97)

-0.0044*(-1.90)

∆Firm size - 10.1288***(14.91)

18.7660***(9.96)

18.8161***(17.47)

∆Firm age - 0.1955(0.97)

-0.2015(-0.68)

-0.0713(-0.23)

Observation 4408 4408 4408Industry fixed effect Yes Yes YesYear fixed effect Yes Yes YesR-squared 0.1829 0.1734 0.1766

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Table 7. The determinants of privatisation decision making: logistic test (Full sample) This table represents the logistic regression results of the determinants of the privatisation decision-making process in the Chinese listed firms from 2007 to 2014. The regression model is as below:

State controlit = α + β1Labourit + β2Weighted MCit + β3Sizeit + β4Leverageit + β5ROAit + β6Non-tradable + β7MBit + β8Tax to salesit + β9RPTit + β10GDP per capitalit + β11GDP growth rateit + Ɛit

The dependent variable is a dummy variable, which equals 1 if a firm’s ultimate controlling shareholder is the state, and zero otherwise. The definitions of all the variables are shown in Appendix A. A superscript *, ** or *** denote the significance levels of 10%, 5% or 1%, respectively.

Independent variables State control

Intercept -4.0781***(-2.85)

Labour 0.0711*(1.69)

Weighted MC 2.7473(1.37)

Size6 0.4776***(12.96)

Leverage 0.1710(0.86)

ROA -2.4089***(-3.48)

Control variables

Non-tradable

MB

-2.1388***(-16.59)0.0057(0.43)

Tax to sales -0.8772(-0.67)

RPT 0.6064**(2.22)

GDP per capita -0.6259***(-6.68)

GDP growth rate 0.5207(0.40)

Agriculture, forestry 0.6806*(1.92)

Mining -0.2162(-0.60)

Manufacturing -0.6309**(-2.22)

Electric power, heat, gas and water 1.0338***(4.00)

Construction 0.1550(0.49)

Wholesale and retail 0.4081(1.35)

Transport, storage and postal services 1.6694***(5.49)

Accommodation 1.8637***(2.75)

Information transmission, software and information 0.1938

6 The results are very similar when using the natural logarithm of total market capitalisation instead of total assets; the only difference is that the MB ratio becomes negatively significant at the 1% level.

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technology services (0.59)

Financial 0.1621(0.28)

Real estate 0.1405(0.56)

Leasing and commercial service 1.7295**(2.19)

Water conservancy, environment and public facility management

2.1822***(3.85)

Culture, sports and entertainment -1.1900***(-2.99)

Year fixed effect YESIndustry fixed effect YESObservation 6411Log likelihood -3345.1812Pseudo R-square 0.2143

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Table 8. The determinants of privatisation decision-making, robustness analysisThis table represents the results of the determinants of the privatisation decision-making process in the Chinese listed firms from 2007 to 2014. We include the firms with fully tradable state ownership. The regression model is as below:

Stateit = α + β1Labourit + β2Weighted MCit + β3Sizeit + β4Leverageit + β5ROAit + β6Non-tradable + β7MBit + β8Tax to salesit + β9RPTit + β10GDP per capitalit + β11GDP growth rateit + Ɛit

The dependent variable is the tradable state ownership. Model 1 shows the full sample results. Model 2 shows the results of sample with state as the ultimate controller. Model 3 shows the results of the sample with controller from the private sector. The definitions of all the variables are shown in Appendix A. A superscript *, ** or *** denote the significance levels of 10%, 5% or 1%, respectively. All models are fixed at industry and year level with standard error of firm clustered.

Independent variables State(full sample)

(1)

State(state controller)

(2)

State(non-state controller)

(3)Intercept -0.7299*** -0.5449*** -0.3168

(-3.59) (-2.76) (-1.08)Labour -0.0014

(-0.36)0.0045*

(1.67)-0.0085(-0.74)

Weighted MC 0.9787***(3.69)

0.5831**(2.49)

1.7387***(3.95)

Size7 0.0430***(7.69)

0.0388***(7.20)

-0.0048(-0.49)

Leverage 0.0464(1.46)

-0.0167(-0.53)

0.1329***(2.79)

ROA -0.1260(-1.28)

-0.0925(-0.94)

0.0289(0.21)

Control variables

Non-tradable

MB

-0.1150***(-5.62)-0.0005(-0.25)

-0.0606**(-2.34)-0.0007(-0.33)

-0.0254(-1.06)-0.0009(-0.36)

Tax to sales 0.3439*(1.72)

0.5749***(3.09)

0.0640(0.22)

RPT 0.0373(1.57)

-0.0029(-0.13)

0.1247**(2.05)

GDP per capita -0.0041(-0.28)

-0.0022(-0.16)

0.0530***(2.65)

GDP growth rate -0.1390(-0.88)

0.0639(0.43)

-0.5928***(-2.67)

Year fixed effect YES YES YES

Industry fixed effect YES YES YES

Observation 6411 3974 2437

R-square 0.1224 0.1357 0.0663

Table 9. The speed of the NTS reform: Cox-hazard model 7 The results are very similar when using the natural logarithm of total market capitalisation instead of total assets; the only difference is that the MB ratios become negatively significant at the 1% level in both the full sample and state-owned sample.

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The table represents the Cox-hazard model results of the speed of the privatisation decision-making process in the Chinese listed firms from 2007 to 2014. The figures in the brackets are z-values of the independent variables. We include the firms with fully tradable state ownership. The dependent variable is the number of days from the date of the reform announcement to the date that state ownership becomes fully tradable. The definitions of all the variables are shown in Appendix A. A superscript *, ** or *** denote the significance levels of 10%, 5% or 1%, respectively.

Dependent variables: The number of days between the reform announcement date and the fully tradability dateIndependent variables Hazard ratio

State control 0.8498**(-2.30)

Labour 0.9188***(-3.35)

Weighted MC 3.8707(1.29)

Size8 0.8486**(-2.54)

Leverage 1.7054**(2.24)

ROA

Non-tradable

MB

0.5167(-0.92)1.0915(1.28)

0.9601**(-2.07)

Tax to sales 0.2014(-1.64)

RPT 1.2275(0.57)

GDP per capita 1.0000**(-2.43)

GDP growth rate 0.3773(-1.10)

Observation 1018

Log likelihood -6018.3960

8 The results are very similar when using a natural logarithm of total market capitalisation instead of total assets.

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Documents

School of Economics and Finance - Massey …econfin.massey.ac.nz/school/documents/seminarseries... · Web viewTherefore, it is argued that state ownership relinquishment is the key