Government intervention and corporate M&A … Government intervention and corporate M&A transactions: Evidence from China Qigui Liu, Tianpei Luo, Gary Gang Tian1 School of Accounting,

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Government intervention and corporate M&A transactions: Evidence

from China

Qigui Liu, Tianpei Luo, Gary Gang Tian1

School of Accounting, Economics and Finance, University of Wollongong, Australia

Department of Finance, Deakin University, Australia

Abstract

This study examines the impact of government intervention on mergers and acquisitions

(M&A) decisions and post-M&A performance of Chinese listed firms. We documents that

government intervention through majority ownership distorts M&A decisions in SOEs. In

order to fulfill its social responsibilities, SOEs conduct more socially desired M&As, i.e., they

are more likely to acquire local and politically connected targets and targets controlled state.

These M&As reduce firm value which are indicated by the worse market reactions around the

announcement of M&As. Our results further indicate that government strengthens its control

power over SOEs by appointing politically affiliated managers in SOEs. The impact of

government intervention is enhanced when local governments have a stronger intervening

motivation. Overall, our results illustrate that the ‘grabbing hand’ of government reduces firm

value in listed SOEs in China. The M&As is one of the possible channels through which

government extracts resources from listed SOEs to accomplish social and political objectives.

Keywords: Mergers and acquisitions, Political connections, Vertical mergers, Government

intervention, Corruption

1 Gary Tian is corresponding author, his email is [email protected]. Qigui Liu’s email address is

[email protected]. Tianpei Luo’s email address is [email protected].

mailto:[email protected]

mailto:[email protected]

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1. Introduction

M&A decisions are among the most important forms of corporate investment because these

investments tend to significantly affect future corporate performance and shareholder wealth

(Gaspar et al., 2005). Whether M&A performance create value to shareholders and what drives

the M&A performance has long been interest in the corporate finance. The empirical results

generally show that mergers and acquisitions do not necessarily bring value to acquiring firms,

but produce mixed results2. Another strand of literature focuses on how M&A performance is

influenced by factors such as the acquiring and target firms’ characteristics, merger type,

payment method and managers’ personal characteristics, such as stress or overconfidence

(Moeller et al., 2005; Moeller, 2005; Fan and Goyal, 2006; Dong et al., 2006; Rhodes-Kropf

et al., 2005; Masulis et al., 2007; Malmendier and Tate, 2008; Cai and Sevilir, 2012; Ishii and

Xuan, 2014).

What has been less explored in the literature is how firms’ M&A decision and performance are

influenced by the government intervention. Heavy government intervention is a common

business feature in most countries. Previous studies argue that the major objectives of

interventionist government are rent seeking, extraction of private benefits and protection of

local industries from competition3. As the majority shareholder in state-owned enterprises

(SOEs), governments exert intervention by using their voting rights to influence firm decisions

(Beuselinck et al., 2015), but such influences may be detrimental to firm value. By appointing

a connected manager, interventionist government extracts resources from listed firms under

their control to accomplish the social objectives rather than to maximize firm value (Fan et al.,

2007; Wu et al., 2010; Chen et al., 2011b). Empirical evidences reveal that government

2 See Dodd (1980), Franks and Harris (1989), Bradley et al. (1983), Land et al., 1991, and Masulis et al. (2007),

Firth (1980), Asquith (1983), Malatesta (1983), Franks et al (1991) and Agrawal et al. (1992). 3 See Stigler (1971), Peltzman (1979), McChesney (1987), De Soto (1991), Shleifer and Vishny (1998) and Fan

et al. (2007).

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intervention leads to a low government quality (Ginka et al., 2012), investment inefficiency

(Chen et al., 2011) and worse post-IPO stock return (Fan et al., 2007), which results less

economic efficiency (Qian and Roland, 1998 and Wang et al., 2008).

In the current study, we examine the impact of government intervention on M&A decisions

and post-M&A performance in SOEs. Previous studies has confirmed that government

intervention in SOEs hinders corporate performance, however, there still not enough evidence

to indicate whether and how government intervention in SOEs constitutes a friction when they

make M&A decisions, which lead to worse post-M&A performance in China. To understand

the impact of government intervention on firms’ M&A decisions and performance, this study

aims to provide empirical evidence to answer the following questions: Does government

intervention affect their M&A decisions and post-M&A performance in SOEs? How does

government intervention influence M&A decisions and performance in SOEs? Does the impact

of government intervention on M&A performance in SOEs differ under different level of

institutional environment and different level of local SOEs’ political and social burden from

the local government?

Given the significantly less developed law and institutional environment in China, including

investor protection system, quality of government and corporate governance and

underdeveloped capital markets (Allen et al., 2005), the theoretical argument predicts that the

government intervention will negatively affect firm performance. This negative relationship is

more pronounced in SOEs due to ambiguous clarification of the ultimate property rights in

SOEs. This paper examines the role of government intervention in SOEs’ M&A decisions and

post-M&A performance in China. We hypothesize that the government intervention alters

SOEs’ M&A decisions to help local government pursue social desirable and political objectives

which destroy firm value.

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In this study, we firstly measure the government intervention through the government

ownership in listed SOEs. We test whether listed SOEs will be more likely to conduct M&As

for social welfare maximization rather than for shareholders’ interests. In China, the

governments have a conflicted dual role in SOEs. As the majority shareholders and owner of

SOEs, they are supposed to maximize firm performance and shareholder wealth. However, the

non-transferability of state owned shares and assets create thorny incentive problems among

both government and firm managers (Fan et al., 2007 and Shleifer and Vishny, 1986). On the

other hand, as an administrator of social welfare, government officials are motivated to

influence firm’s decisions and undertake projects which pursue social desirable objectives

rather than firm value maximization. Moreover, the tournament-style promotion system based

on regional economic performance and social stability creates a stronger incentives for local

government leaders to exert their influence over SOEs for their promotion potential (Cao et al.,

2015). Therefore, strong government intervention usually leads to poor firm performance

(Chen et al., 2011). In this study, we conjecture that government intervention distorts M&A

decisions in SOEs through the majority ownership of government, which have worse post-

M&A performance than non-SOEs.

The politically connected managers in SOEs are argued that to be the channel for government

intervention over listed SOEs in China (Fan et al., 2007) and are expected to be better

accomplishing social and political goals in contrast to those unconnected managers. Firstly, the

government still retained the decision right on the appointment of CEOs in listed SOEs and

preferred to appoint affiliated managers on listed SOEs to implement its interventions. These

connected managers therefore, have a stronger incentive to pursue political objectives which

may run counter to corporate productivity. In addition, the poor corporate governance arising

from the multilayered principal-agent framework in SOEs, managers of SOEs are less

effectively monitored by the government, which makes the first type of agency issue severe in

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SOEs. In other words, managers of SOEs have a strong incentive to pursue their private benefit

through empire-building or even bribe-taking, which further harms the interest of shareholders.

The incentive to pursue private benefit is stronger when managers have political connections

because these help them to retain their positions when the firm suffers a bad performance (You

and Du, 2012). Therefore, we further test the impact of government intervention by examining

the influence of political connections on M&A transactions in SOEs. Following Fan et al.

(2007) we classify the connected SOEs are those which their chairman or CEO is a current or

former government officials. We conjecture that in SOEs, politically connections will

negatively affect post-M&A performance in contrast to non-connected SOEs.

This study mainly focus on the impact of government intervention on M&A transactions in

SOEs in China but also uses non-SOEs are a control sample to reveal the variations in this

relationship. In contrast, non-SOEs have a simpler goal structure of value maximization, we

expect that political connections in non-SOEs will bring benefits to connected firms because,

rather than being nominated by the government, connected managers of non-SOEs are

generally sought and employed by the board, or even the controlling shareholders, to maximize

shareholder interests. So the main purpose for employing those connected managers is to

overcome financial barriers and obtain external financial resources (Faccio, 2006). We predict

that connected managers in non-SOEs will pursue more value-adding M&As because they are

able to obtain government support to acquire quality target firms.

As the largest emerging market, the capital market in China provides an ideal institutional

environment to conduct our analysis. First, as a controlling shareholder in SOEs, Chinese

government plays a crucial role in its operation activities. During the Chinese decentralization

in 1980s, the local governments in China at all levels have obtained authority and responsibility

for their own local economies and be entailed devolution of the supervision power of SOEs

from the central governments (Qian and Roland, 1998). This encourages fiscal competitions

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among local governments and motivates them to intervene and prey local SOEs for local

economic growth and social objectives. The prohibition of selling government owned shares

in SOEs and the social welfare responsibility of SOEs make it difficult to imagine the

government would either grant these firms discretion over staffing levels or subject them to

truly enterprise-threatening competition in the market (Megginson and Netter, 2001).

Moreover, it has been observed increasing government policies favoring the state sector in

recent years. Therefore, the government ownership and policies are likely to continue to

influence Chinese SOEs’ corporate decisions.

Secondly, in contrast to the western market, a distinguishing feature of the M&A market in

China is government intervention at the various levels of this process (Liu et al., 2013b)

especially for SOEs. Although central government has largely granted operation decisions

rights to SOE managers during the corporatization process, the government retained the

ultimate decision rights conceding the M&As in SOEs (Qian, 1995). Therefore, M&As in

SOEs could be a possible channel through which government exert its controlling power over

SOEs. Like the IPO process, M&A deals in SOEs can only be conducted if approved by the

Expert Advisory Committee for the Merger, Acquisition and Restructuring Listed Companies,

which is an affiliated unit of China Securities Regulatory Commission, an authority of the

Chinese government.

Thirdly, in China, the government maintains its control over listed SOEs by appointing as the

top managers of SOEs mainly current or former government officials or bureaucrats. This

further enhances the power of government intervention in the decision making process of

SOEs. Therefore, M&A activities in SOEs are mainly the result of government intervention,

which is facilitated and strengthened by connected managers. Overall, the significant

government intervention in SOEs in China together with the vital role of politically connected

managers provides us an opportunity to conduct our study.

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Using a sample of 514 acquisition announcements in Chinese publicly listed firms from 2005

to 2011, we conduct a series of empirical analyses to provide evidence for our hypotheses. Our

findings confirm that the government intervention distorts M&As behaviors in SOEs and

reduce firm value which is indicated by a worse M&A performance in SOEs as compared to

non-SOEs. Moreover, political connections further reduce the post-M&A performance in SOEs

which is which is measured by the cumulative abnormal return around the M&A

announcement. This result consists with the argument that politically connected managers are

the channel to exert government intervention over SOEs. We future test whether SOEs are

more likely to conduct social desired M&As through examining the type of targets which

acquired by SOEs. We find that in contrast to non-SOEs, SOEs are more likely to acquire local

targets, firms controlled by local governments and politically connected targets. They pay

significantly high M&A premium in these deals. These M&As consequently receive

significantly worse market reaction and run counter to the shareholders’ interest. The negative

effects of government intervention on M&A performance are more severe in politically

connected SOEs in contrast to non-connected SOEs. However, we find that politically affiliated

non-SOEs receive better market reaction in M&A transactions than unaffiliated counterparts,

which supports the ‘Helping hand’ effect of government on firm performance in private firms

(Shleifer and Vishny, 1998).

In order to address the potential endogeneity issue, we first examine whether the influence of

political connections on the M&A decisions of SOEs differs between regions with different

levels of government intervention, and our results confirm that SOEs with connected managers

pursue more value-destroying M&As in regions with more government interventions. In

addition, we provide evidence that SOEs with connected managers conduct more value-

destroying M&A when the local unemployment rate is higher, suggesting that social welfare

becomes an important concern for politically connected SOEs when local government is facing

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more pressures for social stability. Taken together, our findings suggest that, through majority

ownership in SOEs and the appointment of politically connected managers, government

intervention is distort M&A decisions for social and political objectives in SOEs and destroy

firm value. This intervention is more severe in region with high level of government

intervention and large social pressures.

This study contributes to the literature in the following ways. First, our evidence enrich the

extent of literature in M&As. Previous studies in this area primarily focus on the impact that

different factors, such as the size of the acquiring firm, payment methods, corporate governance

and social ties (Moeller et al., 2004; Faccio and Masulis, 2005; Dong et al., 2006; Rhodes-

Kropf et al., 2005; Masulis et al., 2007; Cai and Sevilir, 2012; Ishii and Xuan, 2014), have on

post-M&A performance in developed market. Our findings reveal that in emerging markets

such as China, listed firms’ M&A decisions and performance are seriously affected by

government intervention.

Secondly, our findings suggests that SOE’s M&A decisions can act as a channel through which

government ownership and political connections affect the value of listed firms in China.

Moreover, we find that local governments are more likely to ‘grabbing’ listed firms’ resources

when they have stronger intervening motivations. Of course, M&A transaction is only one of

many channels which interventionist government may use to transfer resources from listed

firms for their political goals. The government can expropriate listed firms by forcing them to

undertake value destroyed related party transactions, accept affiliated appointment and conduct

inefficient investments (Chen et al., 2011; Cheung et al., 2010; Fan et al., 2007).

Finally, our analysis may also help to enhancing the understanding the different value of

political connections between SOEs and non-SOEs in China. In particular, previous studies

mainly focus on the effect of political capital on firm value and access to the financial market

(Fisman, 2001; Johnson and Mitton, 2003; Khwaja and Mian, 2005; Faccio, 2006; Fan et al.,

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2007; Chen et al., 2011) and their results are mixed. Our results reveal that the different roles

by political connected managers depend on the different controlling shareholders in China.

The remainder of the paper is organized as follows. Section 2 develops our hypotheses. Section

3 introduces our data, sample, variables and the empirical model employed. Section 4 presents

the empirical results and interpretations. Section 5 summarizes and concludes this paper.

2. Institutional environment and hypothesis development

2.1. Institutional environment: mergers and acquisitions in China

Prior to the economic reform in China, the Chinese firms were fully controlled by government

and the M&A decisions were made based on the macro-economic situation and government

needs. After the economic reform, especially after the establishing of the Shanghai and

Shenzhen stock exchanges, M&A deals increased significantly and became more market-

oriented, but the central and local governments still retained the ultimate right of decision about

mergers and acquisition in SOEs (Fan et al., 2007). Moreover, the main purpose of acquisitions

in SOEs was still largely to rescue and support poorly performing local SOEs to fulfill social

and political objectives, such as regional development, social stability and personal promotion.

This value-destroying government intervention is enhanced by the appointment of current or

former government bureaucrats as firms’ executives (Chen et al, 2011b). On the other hand,

the M&A deals conducted by non-SOEs are more market-oriented, without being much

influenced by governmental objectives. Given the close ties between the wealth of controlling

shareholders and firm performance, non-SOEs tend to maximize firm value through seeking

profitable investment projects, which include quality target firms. However, valuable

investment opportunities are scarce in China. This motivates non-SOEs seeking and

establishing political connections to obtain quality M&A target firms.

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Under this unique institutional environment, firms’ merger and acquisition decisions are very

likely to be influenced by managers’ political connections, both in SOEs and non-SOEs.

Anecdotal evidence from recent media reports has shown that politically connected managers

in SOEs (non-SOEs) are more likely to conduct value-destroying (value-adding) M&As. For

instance, a recent ten billion RMB acquisition deal conducted by China Resources, which was

the 18th largest SOE by sales in 2012, has drawn much attention in China4. This abnormal

acquisition deal was initiated and supported by the Chairman of China Resources, Mr. Song

Lin, who held an equivalent rank as vice-ministerial level government official and the secretary

of the People’s Political Consultative Conference. In February 2010, China Resources and its

affiliates agreed to acquire the Jinye Coking Group, which was facing significant financial

distress and significant decrease in profits. In this acquisition contract, China Resources and its

affiliates agreed to pay 10.3 billion RMB for 80% of equity of the Jinye Group, which mainly

included three coal mines and seven related companies, and the acquisition price was actually

much higher than the assessed fair value of the assets (5.2 billion RMB). Moreover, the

exploration licenses of three coal mines, which were the most valuable assets in this

acquisition, expired before this deal. The other seven related companies neither generated

profits nor started operation after this deal. Under this situation, however, Mr. Song still forced

China Resources and its affiliates to conduct the acquisition. Overall, China Resources lost

billions of RMB in this acquisition and Mr. Song and other senior executives were dismissed

in 2014.

2.2 Hypothesis development

2.2.1 Government intervention and acquisition performance

4 The detailed information can be viewed at http://www.yicai.com/news/2013/03/2562209-0_1.html.

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As discussed in above section, government may seek rents from listed SOEs by utilizing their

political powers over these firms, especially in countries where institutional constraints are

weak (Sheifer and Vishny, 1994; Sheifer and Vishny, 1998; Fan et al., 2007; Cheung et al.,

2010; Chen et al., 2011). In China, the government has the conflicting dual roles in SOEs, the

administrator of social welfare and ultimate controlling shareholder. This motives the

interventionist government to accomplish social and political objectives such as social stability,

regional economy development and regional employment. Thus the government intervention

will change SOE’s objective function to that is preferred by government, which consequently

reduce firm productivity and value (Lin et al., 1998). The prohibition of selling government

owned shares in SOEs enhance the incentive of the government leaders and firms’ managers

to expropriate listed SOEs for social benefits by exercising their intervention (Megginson and

Netter, 2001). Since the government still retains the ultimate decision rights of M&A in SOEs,

M&A could be the channel through which government exerts it intervention. It is therefore

reasonable to expect that the M&As announced by SOEs would receive a worse market

reactions than non-SOEs. Thus, we hypothesize that:

H1a: In contrast to non-SOEs, the post-M&A performance is worse in SOEs due to the


Although we argue that government intervention has important impact on SOE’s M&A

decisions, the establishing the system of state-owned business groups which is so called ‘Multi-

layer Legal Person system’ makes it more difficulty for government to intervene listed SOE’s

decision making process (Fan et al., 2013). However, the appointments of current and former

government officials as top managers are expected to enhance the government intervention in

listed SOEs. Therefore, the politically connected managers are viewed as a channel though

which government leaders grab resources from SOEs (Fan et al., 2007). Moreover, since the

social benefits based promotion system, the politically connected managers are also more

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willing to perform government agendas and political objectives for their future promotion. This

phenomenon is strengthened by low pay-performance sensitivity in SOEs (Firth et al., 2006).

Thus, we expected that in contrast to unconnected SOEs, the politically connected SOEs are

able to better fulfill government intervention thought conducting M&A and destroy firm value.

We hypothesize that:

H1b: The post-M&A performance is worse in SOEs with politically connected managers than

unconnected SOEs.

2.2.2 Government intervention, M&A decisions and post-M&A performance

If, as expected, the M&A is a channel through which government exerts its intervention over

SOEs for the purpose of maximizing social, political and personal objectives rather than

maximizing firm value, we should further expect that a positive relationship between

government intervention and value-destroying M&As in SOEs. In this section, we investigate

how government intervention affects M&A decisions in SOEs.

One direct channel to investigate how government intervention affects post-M&A performance

is through examining the M&A premium paid by SOEs. Due to the strong government

intervention, the government may expropriate SOEs by requiring a high M&A premium in

selling these firms under their control. Moreover, the self-interest government officials and

managers may even extract private benefits, such as stolen cash received in bribes, by

employing their political power over SOEs to force them pay high M&A premium for personal

benefits. This situation becomes worse if the SOEs have politically connected managers. The

China Resource case in section 2.1 provides the anecdotal evidence on this argument. In

addition, due to the lower pay-performance sensitivity, the managers in SOEs are less likely

make efforts on the analysis of the true value of target firms. This further increases the

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likelihood of overpaying in M&As for SOEs. Therefore, we expect that due to the government

intervention, SOEs will pay higher M&A premium than non-SOEs. Thus, we hypothesize that:

H2a: SOEs will pay a higher M&A premium in contrast non-SOEs due to the strong


The local SOEs in China are usually accused of operating inefficiently because they have to

take more responsibility for local social obligations, such as tax delivery, employment, and

GDP growth. Therefore, when local firms suffer from financial distress which may risk the

local social stability and economy growth, local government are more likely to exercise its

controlling power over SOEs, especially for these politically connected SOEs and force them

to rescue these distressed firms from going bankruptcy. Connected managers in SOEs also have

incentive to acquire other local firms not only because of concerns over future promotion but

also to make private benefits, such as kickbacks and bribery, due to the poor corporate

governance and lack monitoring by the large shareholders. Consequently, these social and

personal benefits distort the M&A decision making process and harm firm performance. Thus,

we expected that government intervention increase the incentive of conducting local M&As in

SOEs, but receive a worse market reaction around the announcement. We therefore

hypothesize that:

H2b: In SOEs, government intervention increases the likelihood of conducting local M&As,

which reduce the post-M&A performance in those firms in contrast to non-SOEs.

Based on the foregoing arguments, we suggest that government officials may expropriates

listed SOEs through M&As for social and political objectives. This government intervention

in M&As could be enhanced and more easily applied if the target firm is also controlled by

state. As the controlling shareholder of acquiring and target firms, bureaucrats utilize their

control right over both firms to fulfill social and event personal objectives. The government

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can force listed SOEs to acquire a poorly performed SOE or sell a SOE to the listed firm at a

price higher than the market value. However, such M&A transactions are more difficult to be

completed between SOEs and non-SOEs. Especially after the enactment of 2007 Property Law,

private firms are given more protections against the potential expropriation of their assets

(Berkowitz et al., 2015). Cheung et al. (2010) find that the related party transactions between

local SOEs reduce firm value which in the line with the ‘grabbing hand’ of government. Thus,

we expect that government intervention increase the incentive of listed SOEs to acquire a SOE

target, but such M&As have poor post-M&A performance. We therefore hypothesize that:

H2c: In SOEs, government intervention increases the likelihood of acquiring a SOE target,

which has a negative impact on the post-M&A performance in those firms as compared with

non-SOEs.

Politically connected managers are suggested as a channel though which government exerts its

intervention in SOEs (Fan et al., 2007). As discussed above, these politically connected

managers are expected to be better fulfilled social objectives such as regional economy

development, employment and social stability, either because of their political duty or personal

benefits. We therefore, expect that the degree of government intervention will be higher if the

acquiring and target firms both have top managers who are current or former government

officials. This enhanced government intervention further distorts listed SOEs M&A behavior

and reduce the post-M&A performance. We hypothesize that:

H2d: In SOEs, government intervention increases the likelihood of SOEs to acquire a

politically connected target firm, but reduces those firms post-M&A performance in contrast

with non-SOEs.

3. Methodology and measurement of variables

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3.1. Sample

The sample used in this paper consists of M&A deals conducted by publicly listed firms on the

Shanghai and Shenzhen stock exchanges from 2005 to 2011. We use the CSMAR China Listed

Firm’s M&A Database to obtain announcement dates, information on acquiring and target

firms and M&A financial information for completed deals in our sample period. We also collect

other information from a series of datasets from the CSMAR database. These include the China

Stock Market Financial Statement Database from 2005 to 2011; the China Listed Firm’s

Corporate Governance Research Database from 2005 to 2011; the China Stock Market Trading

Database from 2004 to 2011. The CSMAR database is one of the most important and widely

used databases in research on the Chinese capital market.

Following previous studies in acquisitions, we require M&A deals to meet the following

criteria. We require that the acquiring firm obtains at least 51% of the target shares and omit

M&A deals in which the acquiring firm already holds at least 51% of the target before the deal

(Malmendier and Tate, 2008). Moreover, we exclude small transactions in which the deal value

is less than 1% of the acquirer’s market capitalization (Cai and Sevilir, 2012 and Masulis et al.,

2007). We exclude the announcement if the acquiring firm announces two or more M&A deals

within three months. We require the acquirer to make annual financial statement information

available (three years prior to acquisition announcements and three years post these

announcements) and stock return data (250 trading days prior to M&A announcements) from

CSMAR databases. Finally, we exclude deals in which acquiring or target firm information,

announcement date and financial data are missing. After meeting these criteria, our final sample

yields 514 M&A cases among a total of 10,586 firm year observations.

Tables 1 and 2 provide the distribution of our 514 M&A deals by year and industry,

respectively. Panel A of table 1 demonstrates that the M&A deals significantly increase during

our sample period, especially after 2006. This result is consistent with the view that the Chinese

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non-tradable share reform facilitates firms to conduct mergers and acquisitions. Our results

indicate that almost 47 percent of M&A are conducted by SOEs. The munber of M&As

conducted by non-SOEs increase significantly after 2007. In panel B, we find that around 39

(63) percent of M&As in SOEs (non-SOEs) are conducted by connected (non-connected)

managers in both SOE and non-SOE subsamples. While the percentage of M&A conducted by

connected managers increases in non-SOEs from 18.2 percent in 2005 to 39.4 percent in 2011,

but there is no great variation in the SOE subsample.

<Table 1>

Table 2 presents the distribution of M&A by industry. Almost 50% of M&A deals are

conducted in the manufacturing industry. There is a cross-industry variation in the likelihood

of having an acquisition conducted by politically connected managers. Acquisitions in the

infrastructure and public utility sectors, such as construction, real estate, electricity, mining and

gas and hot water services, are more likely to be conducted by politically connected managers.

<Table 2>

3.2. Measurement of variables

3.2.1. Government intervention

The reliability of the measurement of government intervention is critical in this study. We test

our research questions by measuring the government intervention at two different levels.

Firstly, we measure the government intervention by examining whether a firm is controlled by

the government. We define a listed firm is a SOEs which the dummy variable ‘SOE’ equals 1

if the firm’s ultimate controlling shareholder is central or local governments, any government

departments or a SOEs. Secondly, we further measure the government intervention through the

firm’s political affiliation. Following Fan et al. (2007), we define a firm as having political

connections if either the CEO or chairman of the board satisfies any one of following three

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criteria. The CEO or chairman of the board is: (1) a current or former government official; (2)

a current or former member of the People’s Political Consultative Conference; (3) a current or

former member of the People’s Congress.

The corporate political connections data is collected manually from the profile of the CEO and

chairman of board. The CSMAR corporate government database provides detailed

biographical information about top managers. For those who have not been recorded, we

collected this information from the firm’s annual report. We employ the dummy variable (PC)

to measure the acquiring firm’s political connections, which equals 1 if the CEO or the

chairman of director satisfies the three criteria and 0 otherwise.

3.2.2. Other variables

A series of variables are constructed to measure firms’ M&A decisions and post-M&A

performance. We first define a dummy variable ‘M&A’, which is equal to 1 if the firm conducts

an M&A in a given year, in order to measure the likelihood of conducting an M&A.

Furthermore, we employ several stock and accounting-based measures to evaluate the post-

M&A performance of the Chinese listed firms in our sample. The stock performance measures

are the three-, five- and 11-days post-acquisition cumulative abnormal market-adjusted stock

returns (CARs). We use the Capital Asset Pricing Model (CAPM) to find the expected stock

returns during event windows for adjustment in all our stock performance analyses. The market

value-weighted market index of both the Shanghai and Shenzhen stock exchanges are

employed as market return in this model. The estimate window is 250 trading days, which start

from 280 trading days prior to the announcement. Following Huang et al. (2014), total M&A

premium is defined as the different between M&A price and the fair value of the target firm.

To make our results more robust, we measure the M&A premium using both the relative value

of the premium (PREMIUM 1), which is the total premium relative to fair value of the target

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firm, and the absolute premium (PREMIUM 2), which is the natural logarithm of the total

premium.

We also use three long-term performance measures, which are the change in Tobin’s q (Growth

in Q), the change in ROA (Growth in ROA) and the growth in earnings (Growth in Earning).

The Tobin’s q is calculated as the market value divided by replacement value. We calculate

ROA as net income divided by total assets. Consistent with previous literature (Fan et al.,

2007), we use the pre-M&A accounting figures as a benchmark to evaluate change of

accounting performance in the post-M&A period. We calculate the Growth in Q and Growth

in ROA by subtracting the average Tobin’s q and ROA in the three years prior to the M&A

announcement from the three years of annual Tobin’s q and ROA after the M&A

announcement. The Growth in Earning is the percentage change of the average of annual

earnings from three years before the M&A to three years after.

In this study, we manually collect target firm information from the M&A announcement. We

collect location data for the target firm and include a dummy variable (LOCAL) which equals

1 if the acquiring and target firms are located in the same province and 0 otherwise We measure

the target ownership structure by the dummy variable ‘TARGET SOE’ which equals 1 if the

target firm is controlled by state. We also collect target’s political capital data as ‘TARGET

PC’, which equals 1 if the target firm’s managers satisfy the previously mentioned three

political connection criteria and 0 otherwise. We also collect information about the industry in

which target firm operated. If the acquiring firm conducts a vertical merger, we include a

dummy variable (VERTICAL) that equals 1, if the industry sector of the target firm is upstream

or downstream of the acquiring firm’s industry sector.

To conduct our regression analysis, we also include various control variables in our regression

models to control for factors which may affect M&A performance. The definitions of these

variables are reported in detail in Appendix A.

19

3.3. Regression model

To examine the effect of political connections on the likelihood of a firm conducting M&A and

on post-M&A performance, we employ the following equation as the baseline regression

model.

𝑀&𝐴 𝐷𝑢𝑚𝑚𝑦/𝑃𝑜𝑠𝑡 − 𝑀&𝐴 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒/𝑀&𝐴 𝑐ℎ𝑎𝑟𝑎𝑐𝑡𝑒𝑟𝑖𝑠𝑡𝑖𝑐𝑠

= 𝛼0 + 𝛽1 × 𝐺𝑜𝑣𝑒𝑟𝑛𝑚𝑒𝑛𝑡 𝐼𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛𝑖,𝑡 + 𝛽2 × 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝑖,𝑡 + 𝛽3

× 𝑌𝑒𝑎𝑟 𝑎𝑛𝑑 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐷𝑢𝑚𝑚𝑦 + 𝜀 ①

In this equation, the key dependent variables are M&A dummy, stock and accounting post-

M&A performance, and M&A characteristics. When the dummy variable is used as a

dependent variable, the model becomes a logistic model. Post-M&A performance is measured

by CARs, as discussed above. M&A characteristics are variables to proxy the following

characteristics of acquisitions: (1) whether the target firm is a SOE; (2) whether the target firm

is a local firm; (3) whether the target firm has a politically connected manager; (4) whether the

M&A is a vertical merger;. The key independent variable is government intervention, which is

measured by the firm’s government ownership and political connections. The year and industry

dummies are also included in our regression models to control for the effect of year and

industry. The key independent variables may interact with other variables when necessary. All

variables are defined in Appendix A.

4. Empirical results

4.1. Summary statistics and univariate tests

Table 3 presents the summary statistics for our main variables. The results show that the

proportion of connected firms accounts for 25% of our firm year observations, while 53% of

our firm year observations are SOEs which are controlled by the government or a government

agency. For our M&A deals, our results show that the acquiring firm’s shareholders earn

20

slightly positive returns from conducting M&As. The one-, two- and five-day CARs are around

2% in our sample. The acquiring firms are willing to pay up to 81% more than the fair value

of target firms to gain controlling rights, which is shown by the positive value (0.81) of

PREMIUM1. Compared with acquiring firms, the size of target firms is relatively small, at

about 22% of the acquirer’s market value. In our study, 18% of target firms have political

connections (TARGET PC) and vertical mergers and acquisitions (VERTICAL MERGE) are

about 29% of the 514 M&A deals.

<Table 3>

In table 4, we present the univariate test for stock performance of M&A conducted by SOEs

and non-SOEs. The results indicate that the overall market performance which is measured by

the three-, five- and 11-days post-M&A CARs, is worse for SOEs than for non-SOEs.

Moreover, we find that the SOEs with connected managers underperform firms without

political connections, in terms of post-M&A market performance. These differences are

statistically significant. These results support our arguments that government intervention

distorts M&A performance in SOEs through its controlling rights in listed SOEs and politically

connected managers. In contrasted to the results for SOEs, connected non-SOEs perform

significantly better than firms without political connections which support the argument that in

the counties with weak legal protection and less developed capital market, politically connected

managers provide various benefits to these connected private. Moreover, our results on the

M&A premium reveal that politically connected non-SOEs pay significantly lower premium

than their counterparts. The results from other long-term performance measures in Table 5

confirm the above arguments. We find the post-M&A Tobin’s q, ROA and earnings are worse

for connected SOEs, but improve in connected non-SOEs. These differences are also

statistically and economically significant. Thus, these results confirm our hypotheses H1a and

H1b.

21

<Table 4>

<Table 5>

4.2 Political connections and post-M&A performance

Before we examine the implications of government intervention on M&A performance, we

first estimate the effect of government intervention on the likelihood of a firm conducting an

M&A in listed SOEs. The results presented in Table 6 suggest that SOEs are inactively in the

M&A market. This result suggests that managers in listed SOEs have less incentive to bear

risks from M&A in order to avoid losses. However, connected SOEs are more likely to conduct

M&A. The coefficient of PC is positive and statistically significant at the 1% level of

significance in column 2. It indicates that since the government has more direct intervention

power over connected SOEs, these firms are forced to undertake more M&As which is for

accomplishing social or political objectives, in contrast non-connected SOEs.

<Table 6>

Table 7 presents the regression results for the first hypothesis, which predict that due to

government intervention, listed SOEs have a worse market reaction as compared to listed non-

SOEs and the politically connected SOEs are underperformed than unconnected SOEs around

the announcements of M&As. From columns 1 to 3, we use the full sample to test whether

government intervention which is measured by the government ownership leads to a worse

post-M&A performance in listed SOEs in contrast to non-SOEs. We find the coefficients on

SOE significantly negatively relates to the three-day, five-day, and 11-day cumulative

abnormal return indicating a worse market reaction when SOEs make M&A announcements.

These results are consistent with our argument that government utilizes their controlling power

over listed SOEs to intervene their M&A behaviors for social and political goals. These M&A

announcements usually receive negative market reactions and reduce firm value. In columns 4

22

to 5, we further test the impact of political connection on the post-M&A performance in listed

SOEs. The significantly negative coefficients of PC suggest that the politically connected SOEs

receive significantly worse market reactions when they conduct M&As as compared to non-

connected listed SOEs. Although politically connected SOEs may receive preferential

treatments in M&As, but our results reveals that the negative effect of government intervention

offsets these benefits. Thus, the findings confirm our expectation that the interventionist

government appoints current or former bureaucrats as the top managers to ensure the control

over SOEs and strength their intervention. Overall, the findings in table 7 support our

hypothesis H1 and suggest that government intervention reduces the M&A performance in

SOEs by utilizing their control rights over SOEs and appointment politically connected

managers.

For other control variables, consistent with previous literature, we find that relative size

(RELATIVE SIZE) is positive and significant, which indicates that acquiring a larger target

generates higher returns. Larger firms receive better post-M&A performance which is indicated

by the positive coefficient on SIZE. However, cash-financed M&A perform worse than stock

or mixed-finance deals, which is consistent with the results of Moeller et al. (2004) and Travlos

(1987).

<Table 7>

In table 8, we further provide the regression results regarding the impact of political

connections on the post-MA long-term accounting performance of SOEs. This result confirms

the above market performance results. Overall, the results from tables 7 and 8 indicate that the

government intervention in acquiring firms are associated with lower market and long-term

accounting performance in SOEs, which confirms our hypotheses H1a and H1b. We argue that

the possible explanations on this negative impact of government intervention on SOEs’ M&A

23

performance is the heavy social responsibility of SOEs. The following sections will provide

more evidence to support our arguments.

<Table 8>

4.3 How government intervention decreases post-M&A performance in SOEs?

We have provided evidence that government intervention results in worse post-M&A

performance in SOEs. In this section, we aim to provide the answer for the question how

government intervention affects M&A decision making in SOEs.

4.3.1 Government intervention and M&A premium

In this section, we investigate the impact of government intervention on M&A premium in

SOEs. Table 9 presents the regression results. As expected, the coefficient on SOE is

significantly positive to the M&A premium. This result confirms our expectation that local

government may expropriate listed SOEs by asking a higher price than the fair value of the

target firms. However, we find that the political connections in SOEs do not have significant

impact on M&A premium. We note that the sample size is quite small due to the missing data

in the fair value and trading value of the target firms in the CSMAR database. In contrast, we

find the politically connected managers reduce the M&A premium in non-SOEs. The detail

analyses for this finding will be reported in the following section in our study. Overall, we find

that SOEs pay higher in M&As than non-SOEs in China.

<Table 9>

4.3.2 Government intervention, local acquisitions and post-M&A performance

As results in previous sections have shown, we find SOEs perform poorly compared to private

firms in M&As. Over the year of decentralization in China, local governments have to take the

responsibility of local economy and compete with other provinces for the limited resources.

Therefore, they have strong incentives to seek helps and expropriate listed SOEs under their

24

jurisdiction to support the local economy and social benefits. We argue that listed SOEs may

conduct more M&As to support local economy or buyout financial distressed local firms, no

matter whether those mergers create value for shareholders or not. In order to provide empirical

evidence for this argument, in this section we examine whether SOEs with political connections

are more likely to acquire local firms and whether such acquisitions harm firm value.

Table 10 presents the logistic regression results regarding the impact of government

intervention on the likelihood of local M&A deals. In column 1, the coefficient on SOE is

statistically positive to the likelihood of acquire a local firm. Moreover, we find the politically

connected SOEs conduct more local M&As than its non-connected counterparts. These

findings support our above arguments that the government officials exert intervention to

encourage SOEs conduct M&As under their jurisdiction in order to support local economy.

More importantly, in panel 1 of table 11, we find the coefficient on the interaction between

SOE and LOCAL is statistically significantly negative to cumulative abnormal return over the

event windows, which indicate that the SOEs’ post-M&A performance are significantly lower

when they acquire a local target. This finding is confirmed by the regression results for the

impact of political connection in SOEs and local M&As on the post-M&A performance in

panel B of table 11. We find that in contrast to non-connected SOEs, the connected SOEs

receive worse market reaction when they conduct a local M&A. Our findings support our

hypothesis H2c and suggest that interventionist governments are motived to expropriate local

SOEs by forcing them undertake local M&As and this government intervention are more

pronounced if the firms have connected managers. This result is consistent with the argument

that local governments have stronger incentives to intervene SOEs for social and political goals

which harms investment efficiency (Chen et al., 2011).

<Table 10>

<Table 11>

25

4.3.3. Government intervention, target firm ownership structure and post-M&A

performance

In the previous section, we find that politically connected SOEs are more likely to acquire a

local target in order to support local economy. In this section, we further investigate whether

SOEs are prefer to acquire the targets which also controlled by the state. These results are

tabulated in table 12. Consistent with our expectation, we find that the SOEs are more likely to

acquire the SOEs target as compared to non-SOEs which is shown by the significantly positive

coefficient on the SOE in column 1 of table 12. Moreover, our results indicate that politically

connected SOEs are bale to acquire more SOEs target than non-connected SOEs. One possible

explanation for this result is that SOEs can easily obtain valuable sate owned assets compared

to private firm. If this argument is correct, consequently, this M&A will receive positive market

reactions and increase firm value. On the other hand, due to the government intervention, the

M&A decision may be distorted in SOEs and they have to acquire certain poorly performed

SOEs for social benefits which harms firm value. In table 13, we present the regression results

for the impact of government intervention and target ownership structure on the post-M&A

performance. We find the coefficient of the interaction term SOE*TARGET SOE and

PC*TARGET SOE are statistically negatively related to the post-M&A performance. These

results suggest that acquiring a SOEs target reduce firm value in SOEs which support the

government intervention argument.

<Table 12>

<Table 13>

4.3.4. The effect of target firm political connections on post-M&A performance

Asa shown in previous sections, our results reveal that politically connected managers are the

channel through which government exert intervention over SOEs. We therefore, expect that

26

the degree of government intervention will be strengthened if the deal involving a politically

connected target. We argue that listed SOEs are more likely to acquire a politically connected

target in order to better accomplish social and political goals, but these M&A will harm firm

value. The empirical results are tabulated in table 14 and 15.

From table 14, we find that government intervention which is measured by the government

ownership and political connected manager in SOEs increases the likelihood of acquiring a

politically connected target in listed SOEs. More importantly, our results in table 15 shows that

the coefficients on the interaction term ‘SOE*TARGET PC’ and PC*TARGET PC’ are

significantly negative to the post-M&A performance, which indicate that acquiring a politically

connected target reduces firm value in listed SOEs. This result confirms that target political

connections enhance the effectiveness of government extraction of resources from listed SOEs

in pursuit of social and private gains. Overall, the results in table 14 and 15 support our

hypothesis H2d.

<Table 14>

<Table 15>

4.4. The political connections, M&A decisions and post-M&A performance in non-SOEs

In the previous sections, our results indicate that government intervention distort M&A diction

making behaviors in SOEs. The interventionist government and self-interest politically

connected managers utilizes the control power over listed SOEs to pursuer social, political and

personal objectives, such as employment, regional economic development, social stability and

promotion. We find that through the government ownership and politically connected

managers, government intervention reduce the M&A performance in SOEs. In contrast to firms

with intervention, SOEs are more likely to acquire local and state controlled firms, especially

for politically connected SOEs. The politically connected target firms strengthen the effect of

27

government intervention in M&As in listed SOEs. However, these distorted M&A decisions

have poor market reactions and reduce firm value. Our results are in the line with the grabbing

hand argument proposed by Sheleifer and Vishny (1994 and 1998) and confirms the previous

empirical results that government expropriates listed SOEs for social welfare (Fan et al., 2007;

Chueng et al., 2010; Chen et al., 2011).

In contrast to the negative influence of government intervention on M&A performance of listed

SOEs, we use the non-SOEs as a control sample and investigate the impact of political

connections on the M&A decisions and performance in non-SOEs. As discussed in previous

section, the private firms in China are comparable to the firms in developed countries, which

have a simple value maximization goal structure. They will seek political connections only if

these politically affiliated managers provide economic benefits. Therefore, we expect that

private firms seek political ties with government in order to obtain quality targets in M&As.

These connected managers paly a ‘help hand’ role and shareholders gain from the close ties

with government (Fisman, 2001; Faccio, 2006; Chen et al., 2011b; Boubakri et al., 2012).

In table 16 tabulates the impact of political connections on the M&A decision in private firms

in China. We find the politically connected non-SOEs are more acquisitive than their non-

connected counterparts, which is shown by the significantly positive coefficient on PC. It

suggests that in an emerging market with substantial government intervention, politically

connected firms have more chances to obtain investment opportunities and actively involve in

M&As due to their strong ability to communicates with the government. In contrast to SOEs,

we do not find that politically connected non-SOEs have the preference to acquire a local target

or a target with politically connected managers. However, our result reveals that the politically

connected non-SOEs are more likely to acquire a target which is controlled by the state, which

is shown by the significantly positive coefficient on PC in column 3 of table 16. This result is

consistent with the argument that politically connected managers would help firms to obtain

28

preferential treatment from state owned enterprises and use their political influence to seek

investment opportunities under their control (Khwaja and Mian, 2005; Li et al., 2008).

Table 17 presents the results for the impact of political connection on the post-M&A

performance in non-SOEs. We find the coefficient of PC is significantly positively related to

the post-M&A performance, which is consistent with our expectation that politically affiliated

managers help connected firms to obtain quality target and results a better post-M&A

performance than non-connected private firms. Moreover, being connected with local

government, the acquiring non-SOEs could have superior knowledge of the value of target

firms and reduce the information asymmetry between acquiring and target firm. Furthermore,

with the weak institutional environment and poor protection of property rights, connected non-

SOEs are able to acquire a target firm at a relatively low price since local governments could

use their political power and force the target firm’s managers and shareholders to accept the

price offered by politically connected firms. Therefore, we expect that the political connection

reduce the M&A premium in non-SOEs. The significantly negative coefficients on PC in

column 4 and 5 of table 17 confirm our expectation. These results also support our previous

findings that political connection increase M&A performance in non-SOEs. Avoiding paying

too much in M&A would always be advantageous and would represent more value captured

for the acquiring firms (Harford et al, 2012; Cai and Sevilir, 2012). Overall, our results in table

16 and 17 are consistent with the ‘helping hand’ argument of political connection. The

connected non-SOEs seek rents from government through politically affiliated managers when

they conduct M&As. These value-added deals increase shareholder wealth in private firms.

4.5 Endogeneity issue

We have documented that government intervention has a significantly negative effect on M&A

performance in Chinese SOEs. However, these results could be biased by potential endogeneity

issues. This means that the post-M&A performances are caused by other unobserved factors

29

rather than managers’ political connections. In order to address this issue, we adopt the

following three methods: (1) We examine the effect of political connections on post-IPO

performance in regions with different levels of government intervention. In regions with more

interventions, SOEs with connected managers are more likely to be influenced by the

government to push them to conduct M&As for maximum social welfare, and in the meantime

there are more opportunities for connected managers to pursue private benefit. Thus we expect

that if it is government intervention that causes the poor performance in M&As in SOEs, we

should observe that the effect of government intervention through politically connected

managers on post-M&A performance should be more pronounced in regions with more

government interventions. (2) In order to provide further evidence on the question whether

M&A are more likely to be used to pursue social welfare in SOEs, we investigate the impact

of political connection on post-M&A performance in provinces with high and low employment

rates. In provinces with a high unemployment rate, the local government is more likely to force

politically connected SOEs to acquire poorly performing local firms, which prevents further

increases in the local unemployment rate and maximizes social welfare. Thus, it is expected

that the negative impact of political connection on post-M&A performance would be more

pronounced in provinces with a high unemployment rate.

4.5.1 Political connections, government intervention and post-M&A performance in

SOEs

The literature on the Chinese economy suggests that the level of government intervention varies

between different regions, and that governments in regions with less developed markets have

a more significant and stronger influence on the local economy and tend to have more power

of control over the firms in their regions (Qian and Weingast, 1997 and Lau et al., 2000 and

Chen et al., 2011a). In this section, we employ the widely used market-oriented index, which

is calculated as the inverse of government control over the economic resources, to measure

30

government interventions (Chen et al., 2011a). This index is a relative ranking index compiled

annually by the National Economic Research Institute under the auspices of the China Reform

Foundation (Fan et al., 2010). To facilitate understanding, we multiply this index by -1 to

measure the strength of market intervention.

Table 18 tabulates the results for the effect of regional government intervention (GOV) on the

relationship between political connections and post-M&A performance in SOEs. The

coefficient of interaction between PC and GOV is significantly negative. This result indicates

that connected SOEs have significantly worse post-M&A performance if the regional

government intervention is strong. Therefore, our finding is consistent with our expectation,

suggesting that the negative influence of government intervention on M&A performance in

SOEs is more pronounced in regions with stronger intervention.

<Table 18>

4.5.2 Political connections, unemployment rate and post-M&A performance in SOEs

Previous studies argue that, if the local government suffers from high unemployment, they

would be under more pressure to intervene in firms’ decisions to solve this social problem,

even it will incur poor firm performance (Fan et al., 2013). In particular, the politically

connected managers in SOEs are the channel through which government exerts its

interventions. Therefore, in provinces with high unemployment, the politically connected

SOEs are more likely to conduct M&A to reduce local unemployment, but this may result in

poor post-M&A performance. In this section, we collected the provincial unemployment rates

in China from 2005 to 2011 and identify a province as having high unemployment if its

unemployment rate is higher than the median unemployment. We expect that the negative

impact of political connections in SOEs would be more pronounced in provinces burdened with

high unemployment. These results are presented in table 19.

31

We find that political connections have a significantly negative effect on post-M&A

performance when the province suffers from a high unemployment problem in SOEs. However,

it becomes insignificant in provinces without this social problem. This result confirms our

expectations and previous results that social welfare is an important concern when politically

connected SOEs conduct M&A deals. The influence of government intervention on SOEs

becomes stronger when local society and economy need more supports from SOEs.

<Table 19>

5. Conclusion

This study investigates the effect of government intervention on M&A decisions and post-MA

performance in Chinese listed firms from 2005 to 2011. Our empirical results suggest that

through government ownership in SOEs and politically connected managers, government

intervention reduces the firms’ incentive to conduct M&As. More importantly, our results

reveal that the post-M&A performances are significantly worse in SOEs than non-SOEs. Our

further evidence illustrates SOEs are more likely to acquire a local target or target which is also

controlled by local governments. The politically connected managers in target firms facilitate

the government intervention dominated M&As in SOEs. Moreover, these distorted M&A

decisions have poor market reactions and reduce firm value. The politically connected

managers further reduce the M&A performance in SOEs. We further provide evidence that the

effect of the value-destroying acquisitions made by SOEs increases in regions with high level

of government intervention and require supports from listed SOEs.

Overall, our results are in the line with the ‘grabbing hand’ of government argument and

provide new evidence on the literature that examines the impact of government intervention on

firm performance. The empirical evidence suggests that in country with weak institutional

environments, such as China, M&As are one of the possible channels through which

32

interventionist government expropriate listed SOEs for social or political objectives. These

distortional M&A decisions and behaviors are due to the strong controlling power of local

government officials over SOEs. Moreover, the poor corporate governance and multilayered

principal–agent framework, politically connected managers become the channel though which

government exert intervention in SOEs and extract resources for social benefits. Therefore, our

study may enhance the understanding of effect of government intervention on recent

government dominated M&A deals in China and other countries that are characterized by

strong local governments.

33

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39

Table 1 Number of acquisitions by year

Panel A Number of M&A deals by year in firms with and without political connections

Year

Full sample SOEs Non-SOEs

Number % of Total Sample by

Year Number

% of Total Sample by

Year Number

% of Total Sample by

Year

2005 31 6.00% 20 8.20% 11 4.07%

2006 49 9.50% 26 10.66% 23 8.52%

2007 96 18.70% 44 18.03% 52 19.26%

2008 77 15.00% 38 15.57% 39 14.44%

2009 72 14.00% 40 16.39% 32 11.85%

2010 80 15.60% 38 15.57% 42 15.56%

2011 109 21.20% 38 15.57% 71 26.30%

Total 514 100.00% 244 100.00% 270 100.00%

Panel B Number of M&A deals by year in SOEs and non-SOEs with and without political connections

Year

SOE Non-SOE

Political connected Non-Political Connected Political connected Non-Political Connected

Number Percentage Number Percentage Number Percentage Number Percentage

2005 9 45.0% 11 55.0% 2 18.2% 9 81.8%

2006 9 34.6% 17 65.4% 8 34.8% 15 65.2%

2007 18 40.9% 26 59.1% 20 38.5% 32 61.5%

2008 16 42.1% 22 57.9% 16 41.0% 23 59.0%

2009 14 35.0% 26 65.0% 16 50.0% 16 50.0%

2010 15 39.5% 23 60.5% 10 23.8% 32 76.2%

2011 14 36.8% 24 63.2% 28 39.4% 43 60.6%

Total 95 38.9% 149 61.1% 100 37.0% 170 63.0%

40

Table 3 Summary statistics

VARIABLE NAME No. Mean St. dev. 25th percentile Median 75th percentile

SOE 10586 0.53 0.50 0.00 1.00 1.00

PC 10586 0.25 0.43 0.00 0.00 1.00

SIZE 10586 21.59 1.33 20.73 21.43 22.26

Q 10586 1.73 1.19 1.06 1.34 1.90

OPCFTA 10586 0.04 0.15 0.00 0.04 0.09

LEVERAGE 10586 0.51 0.38 0.31 0.49 0.64

BOARDSIZE 10586 9.13 1.91 8.00 9.00 9.00

BOARDIND 10586 0.36 0.05 0.33 0.33 0.38

OWNERSHIP 10586 0.04 0.11 0.00 0.00 0.00

CAR_1 514 0.02 0.08 -0.03 0.01 0.05

CAR_2 514 0.02 0.11 -0.04 0.01 0.06

CAR_5 514 0.03 0.14 -0.05 0.01 0.08

RELATIVE SIZE 514 0.22 0.96 0.02 0.04 0.10

CASH PAYMENT 514 0.84 0.36 1.00 1.00 1.00

Growth in Q 353 0.46 1.58 -0.08 0.23 0.69

Growth in ROA 352 0.01 0.11 -0.02 -0.00 0.03

Growth in Earning 353 0.96 8.81 -0.63 0.52 2.08

PREMIUM1 194 0.81 3.64 -0.01 0.00 0.00

PREMIUM2 194 23.03 0.03 23.03 23.03 23.03

TARGET PC 514 0.18 0.39 0.00 0.00 0.00

VERTICAL MERGE 514 0.29 0.46 0.00 0.00 1.00

41

Table 4 The univariate tests for post-M&A performance around announcement date

This table presents the cumulative abnormal return upon acquisition announcement and takeover premium for acquiring firms. It reports the CARs for the three-day event

window (CAR [-1, +1]), five-day event window (CAR [-2, +2]) and 11-day event window (CAR [-5, +5]). ‘PREMIUM1’ is defined as the ratio of total M&A premium to the

fair value of the target firm and ‘PREMIUM2’ is defined as the natural logarithm of the total M&A premium, which is the difference between trading value of the target and

the target fair value.

CARs Full sample Political connection Non-political connection Difference test

Mean Median Mean Median Mean Median t value z value

CAR_1 0.02 0.01 0.02 0.01 0.02 0.01 0.00 0.00

CAR_2 Full sample 0.02 0.01 0.03 0.01 0.02 0.01 0.01 0.00

CAR_5 0.03 0.01 0.03 0.00 0.03 0.01 0.00 -0.01

PREMIUM1 0.80 0.00 0.31 0.00 1.13 0.00 -0.82 0.00

PREMIUM2 23.03 23.02 23.02 23.03 23.03 23.03 -0.01 0.00

CAR_1 0.01 0.00 -0.01 -0.01 0.03 0.01 -0.04*** -0.02***

CAR_2 SOE 0.01 0.00 -0.02 -0.02 0.03 0.02 -0.05*** -0.04***

CAR_5 0.02 0.01 -0.01 -0.03 0.04 0.03 -0.05*** -0.06***

PREMIUM1 0.87 0.00 0.55 0.00 1.11 0.00 -0.56 0.00

PREMIUM2 23.03 23.03 23.03 23.03 23.03 23.02 0.00 0.01

CAR_1 0.03 0.01 0.05 0.02 0.01 0.01 0.04*** 0.01***

CAR_2 Non-SOE 0.03 0.01 0.07 0.05 0.01 0.00 0.06*** 0.05***

CAR_5 0.03 0.01 0.08 0.05 0.01 0.00 0.070*** 0.05***

PREMIUM1 0.72 0.00 -0.03 0.00 1.17 0.00 -1.20* 0.00*

PREMIUM2 23.03 23.02 23.02 23.02 23.03 23.03 -0.01 -0.01*

42

Table 5 The univariate tests for changes in long-term performance around announcement year

This table presents the changes in long-term accounting performance around announcement year. The Growth in Q is measured as the difference between the average annual

Tobin’s q of the three years after the merger announcements and the three years before the merger announcement. The Growth in ROA is measured as the difference between

the average annual ROA of the three years after the merger announcements and the three years before the merger announcement. The Growth in Earning is the growth rate of

earning from average annual earning of the three years before the merger announcement to that after the three years after.

CARs Full sample Political connection Non-political connection Difference test

Mean Median Mean Median Mean Median t value z value

Growth in Q 0.50 0.23 0.50 0.17 0.43 0.29 0.07 -0.12

Growth in ROA Full sample 0.01 0.00 0.02 -0.01 0.00 0.01 0.02 -0.02

Growth in Earning 0.96 0.51 0.68 0.55 1.13 0.50 -0.45 0.03

353 132 221

Growth in Q 0.20 0.18 0.11 0.09 0.25 0.23 -0.14* -0.19**

Growth in ROA SOE 0.01 -0.01 -0.01 -0.01 0.01 0.00 -0.02** -0.01**

Growth in Earning 1.41 0.50 -0.33 0.21 2.56 0.55 -2.89* -0.34

183 73 110

Growth in Q 0.74 0.36 0.98 0.39 0.61 0.35 0.37 0.05

Growth in ROA Non-SOE 0.03 0.01 0.05 0.02 0.01 0.00 0.04* 0.02*

Growth in Earning 0.47 0.71 1.93 1.37 -0.30 0.33 2.23** 1.03**

170 59 111

43

Table 6 The impact of government intervention on the likelihood of mergers and

acquisitions

This table presents logistic regression results for the impact of government intervention on the likelihood of

mergers and acquisitions. The dependent variable is binary, where 1 signifies that the firm makes at least one

merger bid that is eventually successful in a given year. The independent variable ‘SOE’ is a dummy variable

equals 1 if the controlling shareholder of the listed firm is government or SOEs. The independent variable PC is

a dummy variable taking value of 1 if a firm’s chairman or CEO is politically connected. Control variables are

defined in Appendix A. Column 1 reports the results for the full sample. Columns 2 and 3 report logit regression

results for SOEs and non-SOEs, respectively. P-values are displayed in brackets. *, ** and *** indicate

significance at the 10%, 5% and 1% levels, respectively.

SOEs vs. Non-SOEs SOE: PC vs. Non-PC

MA M&A

SOE -0.38***

(0.00)

PC 0.84***

(0.00)

SIZE -0.00 0.02

(0.93) (0.72)

Q 0.00 0.02

(0.86) (0.27)

OPCFTA 0.79* -0.26

(0.09) (0.74)

LEVERAGE -0.15 -0.66*

(0.19) (0.05)

BOARDSIZE -0.02 -0.05

(0.45) (0.21)

BOARDIND -0.33 -2.05

(0.72) (0.15)

OWNERSHIP -2.83*** -23.31

(0.00) (0.19)

CONST -2.09** -2.09

(0.03) (0.11)

YEAR YES YES

INDUSTRY YES YES

N 10586 5630

pseudo R-sq 0.03 0.04

44

Table 7 The impact of government intervention on post-M&A performance

This table presents regression results for the impact of government intervention on the post-M&A performance.

The dependent variable is post-M&A performance measured by CARs for three-day, five-day and 11-day window

(CAR_1, CAR_2 and CAR_5). The independent variable ‘SOE’ equals 1 if the firm that are ultimately controlled

by the states. The independent variable PC is a dummy variable taking the value of 1 if a firm’s chairman or CEO

is politically connected. Control variables are defined in Appendix A. P-values are displayed in brackets. *, **

and *** indicate significance at the 10%, 5% and 1% levels, respectively.

SOEs vs. Non-SOEs SOEs: PC vs. Non-PC

CAR(-1,1) CAR(-2,2) CAR(-5,5) CAR(-

1,1)

CAR(-

2,2)

CAR(-

5,5)

SOE -0.02** -0.03** -0.02

(0.01) (0.01) (0.13)

PC -0.04*** -0.05*** -0.05***

(0.00) (0.00) (0.00)

RELATIVE SIZE 0.01*** 0.02** 0.02*** 0.06*** 0.09*** 0.09***

(0.01) (0.01) (0.00) (0.00) (0.00) (0.00)

CASH PAYMENT -0.08*** -0.10*** -0.11*** -0.08*** -0.10*** -0.10***

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

SIZE 0.01* 0.01 0.01 0.02*** 0.02*** 0.02**

(0.09) (0.12) (0.15) (0.00) (0.00) (0.03)

Q 0.00 0.01** 0.01*** 0.01 0.01 0.01

(0.16) (0.04) (0.01) (0.18) (0.15) (0.15)

OPCFTA 0.02 0.03 0.02 0.07 0.02 0.00

(0.67) (0.50) (0.70) (0.23) (0.77) (1.00)

LEVERAGE -0.01 -0.01 -0.01 -0.02 -0.02 -0.06

(0.48) (0.55) (0.54) (0.28) (0.50) (0.11)

BOARDSIZE -0.00 -0.00 -0.00 -0.00 -0.00 -0.00

(0.25) (0.67) (0.62) (0.55) (0.88) (0.84)

BOARDIND -0.01 0.00 -0.06 -0.15* -0.15 -0.23

(0.90) (0.98) (0.57) (0.08) (0.21) (0.12)

OWNERSHIP 0.03 0.04 -0.01 -0.14 -0.95 -2.06

(0.49) (0.55) (0.90) (0.89) (0.50) (0.25)

CONST 0.02 0.00 -0.02 -0.15 -0.23 -0.15

(0.76) (0.98) (0.86) (0.16) (0.12) (0.41)

YEAR YES YES YES YES YES YES

INDUSTRY YES YES YES YES YES YES

N 514 514 514 244 244 244

adj. R-sq 0.20 0.19 0.17 0.36 0.32 0.22

45

Table 8 The impact of political connections on firms’ long-term performance in SOEs. This table presents regression results for the impact of political connections on firms’ long-term performance. The

dependent variable is growth in Tobin’s q, ROA and earnings. The independent variable PC is a dummy variable

taking the value of 1 if a firm’s chairman or CEO is politically connected. Control variables are defined in

Appendix A. P-values are displayed in brackets. *, ** and *** indicate significance at the 10%, 5% and 1% levels,

respectively.

SOE: PC vs. Non-PC

Growth in Q Growth in ROA Growth in Earning

PC -0.18* -0.01* -2.06

(0.07) (0.05) (0.22)

RELATIVE SIZE 0.12 -0.01 15.33***

(0.63) (0.53) (0.00)

CASH PAYMENT 0.18 -0.02** -0.47

(0.22) (0.04) (0.85)

SIZE -0.16*** -0.01*** 0.25

(0.00) (0.00) (0.77)

OPCFTA 0.51 -0.07 -7.78

(0.41) (0.13) (0.48)

LEVERAGE -0.40* 0.03* -1.58

(0.08) (0.08) (0.70)

BOARDSIZE 0.02 0.00 0.29

(0.54) (0.14) (0.56)

BOARDIND -1.76* 0.09 8.43

(0.07) (0.19) (0.62)

OWNERSHIP 23.23 -2.44 43.69

(0.42) (0.24) (0.93)

CONST 4.47*** 0.23*** -10.95

(0.00) (0.00) (0.54)

YEAR YES YES YES

INDUSTRY YES YES YES

N 183 182 183

adj. R-sq 0.34 0.11 0.14

46

Table 9 The impact of government intervention on takeover premium

This table presents the regression results for the impact of political connections on the takeover premium. The

dependent variable ‘PREMIUM1’ is defined as the ratio of total M&A premium to the fair value of the target

firm. The dependent variable ‘PREMIUM2’ is defined as the natural logarithm of the total M&A premium, which

is the difference between the trading value of the target and the target fair value. The independent variable ‘SOE’

equals 1 if the firm that are ultimately controlled by the states. The independent variable PC is a dummy variable

taking the value of 1 if a firm’s chairman or CEO is politically connected. Control variables are defined in

Appendix A. P-values are displayed in brackets. *, ** and *** indicate significance at the 10%, 5% and 1% levels,

respectively.


PREMIUM1 PREMIUM2 PREMIUM1 PREMIUM2

SOE 0.72 0.01**

(0.21) (0.03)

PC -0.56 -0.01

(0.50) (0.40)

RELATIVE SIZE 0.57 0.01 3.15*** 0.05***

(0.12) (0.11) (0.01) (0.00)

CASH PAYMENT 0.21 -0.01 1.18 -0.00

(0.77) (0.12) (0.37) (0.89)

SIZE -0.11 -0.01** -0.09 0.00

(0.69) (0.04) (0.81) (0.33)

Q -0.40 -0.00 -0.40 -0.00

(0.12) (0.16) (0.29) (0.31)

OPCFTA 0.16 0.01 -1.34 -0.00

(0.95) (0.68) (0.81) (0.98)

LEVERAGE -0.12 0.01 -0.84 -0.02

(0.92) (0.53) (0.68) (0.32)

BOARDSIZE 0.01 0.00 0.09 0.00

(0.94) (0.52) (0.73) (0.82)

BOARDIND 2.64 0.02 15.08 0.01

(0.60) (0.71) (0.10) (0.86)

OWNERSHIP -1.01 -0.00 -125.83 -1.39

(0.82) (0.96) (0.42) (0.34)

CONST 0.43 23.13*** -4.66 22.96***

(0.95) (0.00) (0.62) (0.00)

YEAR YES YES YES YES

INDUSTRY YES YES YES YES

N 196 196 108 108

adj. R-sq 0.12 0.10 0.22 0.23

47

Table 10 The impact of political connections on the likelihood of conducting a local

merger and acquisition

This table present the logistic regression results for the impact of political connections on the likelihood of

conducting a local merger. The dependent variable LOCAL is defined as 1 if the acquirer and target firms are

located in the same province and the target firm is controlled by the state. The dependent variable ‘SOE’ is a

dummy variable equals 1 if the listed firm is controlled by state. The independent variable PC is a dummy variable

taking the value of 1 if a firm’s chairman or CEO is politically connected. P-values are displayed in brackets. *,

** and *** indicate significance at the 10%, 5% and 1% levels, respectively. Columns 1 and 2 report logit

regression results for SOEs and non-SOEs, respectively.


LOCAL LOCAL

SOEs 0.37*

(0.08)

PC 0.59*

(0.05)

SIZE 0.02 -0.02

(0.86) (0.89)

Q -0.06 0.18

(0.44) (0.29)

OPCFTA -1.02 -4.03**

(0.32) (0.05)

LEVERAGE -0.17 -0.47

(0.61) (0.53)


(0.16) (0.74)

BOARDIND -2.88 1.87

(0.11) (0.54)

OWNERSHIP -0.22 -84.85

(0.87) (0.22)

CONST 1.42 0.53

(0.54) (0.88)

YEAR YES YES

INDUSTRY YES YES

N 512 240


48

Table 11 The impact of government intervention and local M&As on the post-M&A

performance

This table presents the regression results for the impact of government intervention and local

M&As on the post-M&A performance. The dependent variable is post-M&A performance

measured by CARs for three-day, five-day and 11-day window (CAR_1, CAR_2 and

CAR_5). The independent variable ‘SOE’ equals1 if the firm that are ultimately controlled by

the states. The independent variable PC is a dummy variable taking the value of 1 if a firm’s

chairman or CEO is politically connected. The dependent variable ‘LOCAL’ equals 1 if the

acquirer and target firms are located in the same province. The ‘SOE*LOCAL’ is the

interaction between ‘SOE’ and ‘LOCAL’. The ‘PC*LOCAL’ is the interaction term between

‘PC’ and ‘LOCAL’. Control variables are defined in Appendix A. P-values are displayed in

brackets. *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively.

Panel A SOEs vs. Non-SOEs

(1) (2) (3) (4) (5) (6)

CAR_1 CAR_1 CAR_2 CAR_2 CAR_5 CAR_5

SOE -0.02** 0.00 -0.02** -0.00 -0.02 -0.00

(0.02) (0.77) (0.02) (0.74) (0.17) (0.82)

LOCAL -0.02*** -0.01 -0.02*** -0.01 -0.02** -0.01

(0.00) (0.49) (0.01) (0.45) (0.03) (0.36)

SOE*LOCAL -0.03** -0.03* -0.02

(0.01) (0.08) (0.34)

RELATIVE SIZE 0.01** 0.01*** 0.01** 0.02** 0.02*** 0.02***

(0.01) (0.01) (0.02) (0.01) (0.00) (0.00)

CASH PAYMENT -0.08*** -0.08*** -0.11*** -0.11*** -0.11*** -0.11***

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

SIZE 0.01* 0.01* 0.01 0.01 0.01 0.01

(0.08) (0.10) (0.11) (0.13) (0.14) (0.15)

Q 0.00 0.00 0.01** 0.01** 0.01*** 0.01***

(0.15) (0.17) (0.04) (0.04) (0.01) (0.01)

OPCFTA 0.01 0.00 0.03 0.02 0.02 0.01

(0.79) (0.93) (0.59) (0.68) (0.78) (0.84)

LEVERAGE -0.01 -0.01 -0.01 -0.01 -0.01 -0.01

(0.44) (0.42) (0.52) (0.50) (0.51) (0.50)

BOARDSIZE -0.00 -0.00 -0.00 -0.00 -0.00 -0.00

(0.17) (0.17) (0.56) (0.56) (0.53) (0.53)

BOARDIND -0.02 -0.00 -0.01 0.00 -0.08 -0.07

(0.72) (0.96) (0.87) (0.97) (0.47) (0.54)

OWNERSHIP 0.03 0.03 0.04 0.04 -0.01 -0.01

(0.50) (0.51) (0.55) (0.57) (0.89) (0.88)

CONST 0.02 0.02 0.02 0.01 -0.02 -0.02

(0.77) (0.81) (0.88) (0.91) (0.90) (0.89)



N 514 514 514 514 514 514

49

adj. R-sq 0.22 0.23 0.20 0.20 0.18 0.18

Panel B SOEs: PC vs. Non-PC

(1) (2) (3) (4) (5) (6)


PC -0.03*** -0.01 -0.04*** -0.02 -0.04*** 0.00

(0.00) (0.49) (0.00) (0.42) (0.00) (0.95)

LOCAL -0.03*** -0.02** -0.04*** -0.02 -0.03* -0.00

(0.00) (0.04) (0.00) (0.15) (0.05) (0.80)

PC*LOCAL -0.03* -0.04 -0.07**

(0.06) (0.12) (0.03)

RELATIVE SIZE 0.06*** 0.06*** 0.08*** 0.08*** 0.09*** 0.09***

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

CASH PAYMENT -0.09*** -0.08*** -0.10*** -0.09*** -0.10*** -0.10***

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

SIZE 0.01*** 0.01*** 0.02*** 0.02*** 0.02** 0.02**

(0.00) (0.00) (0.00) (0.00) (0.04) (0.04)

Q 0.01 0.00 0.01 0.01 0.01 0.01

(0.21) (0.28) (0.18) (0.22) (0.17) (0.24)

OPCFTA 0.03 0.03 -0.02 -0.02 -0.04 -0.03

(0.61) (0.59) (0.81) (0.82) (0.73) (0.74)

LEVERAGE -0.03 -0.02 -0.03 -0.02 -0.07* -0.06

(0.19) (0.26) (0.40) (0.49) (0.09) (0.14)

BOARDSIZE -0.00 -0.00 -0.00 -0.00 -0.00 -0.00

(0.45) (0.33) (0.80) (0.65) (0.79) (0.59)

BOARDIND -0.13 -0.13 -0.12 -0.12 -0.21 -0.21

(0.14) (0.13) (0.29) (0.29) (0.16) (0.15)

OWNERSHIP -0.30 -0.12 -1.11 -0.91 -2.21 -1.83

(0.77) (0.91) (0.42) (0.52) (0.22) (0.31)

CONST -0.10 -0.10 -0.17 -0.17 -0.10 -0.11

(0.36) (0.35) (0.23) (0.23) (0.57) (0.56)



N 244 244 244 244 244 244

adj. R-sq 0.40 0.41 0.34 0.35 0.23 0.25

Table 12 The impact of political connections of acquiring firms on the likelihood of

acquiring a SOE target.

This table presents the logistic regression results for the impact political connections on the likelihood

of acquiring a SOE target. The dependent variable TARGET SOE is defined as 1 if the target firm is

controlled by the state. The independent variable PC is a dummy variable taking the value of 1 if a

firm’s chairman or CEO is politically connected. P-values are displayed in brackets. *, ** and ***

indicate significance at the 10%, 5% and 1% levels, respectively. The column 1 report the regression

50

results for pooled sample. Columns 2 and 3 report logit regression results for SOEs and non-SOEs,

respectively.


TARGET SOE TARGET SOE

SOE 2.47***

(0.00)

PC 0.74**

(0.01)

SIZE -0.06 -0.04

(0.60) (0.78)

Q 0.08 0.02

(0.30) (0.89)

OPCFTA 0.49 -1.52

(0.72) (0.44)

LEVERAGE 0.42 0.19

(0.26) (0.80)


(0.98) (0.90)

BOARDIND -0.87 -0.41

(0.69) (0.89)

OWNERSHIP -1.26 31.29

(0.61) (0.45)

CONST -1.16 -0.01

(0.67) (1.00)

YEAR YES YES

INDUSTRY YES YES

N 512 240


Table 13 The impact of political connections and target firms’ ownership structure on

post-M&A performance.

This table presents the regression results on the impact of political connections and target firms’ ownership

structure on post-M&A performance. The dependent variable is post-M&A performance measured by CARs for

three-day, five-day and 11-day window (CAR_1, CAR_2 and CAR_5). The independent variable ‘SOE’

equals1 if the firm that are ultimately controlled by the states. The independent variable PC is a

dummy variable taking the value of 1 if a firm’s chairman or CEO is politically connected. The TARGET SOE is

defined as 1 if the target firm is controlled by the state. The ‘SOE*TARGET SOE’ is the interaction term between

‘SOE’ and ‘TARGET SOE’. The PC*TARGET SOE is the interaction term between PC and TARGET SOE.

Controlling variables (the same as the results in Table 7 and 8) are included but not reported to save space. P-

values are displayed in brackets. *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively.

Panel A SOEs vs. Non-SOEs

51

(1) (2) (3) (4) (5) (6)


SOE -0.02* -0.01 -0.02** -0.01 -0.02 -0.00

(0.06) (0.56) (0.04) (0.36) (0.22) (0.76)

TARGET SOE -0.01 0.02 -0.01 0.02 -0.00 0.03

(0.45) (0.15) (0.57) (0.23) (0.75) (0.26)

SOE*TARGET SOE -0.04** -0.04* -0.05

(0.02) (0.07) (0.11)

CONST 0.03 0.02 0.00 -0.01 -0.02 -0.03

(0.74) (0.84) (0.96) (0.95) (0.87) (0.80)



N 514 514 514 514 514 514

adj. R-sq 0.20 0.21 0.19 0.19 0.17 0.17

Panel B SOE: PC vs. Non-PC

(1) (2) (3) (4) (5) (6)


PC -0.04*** -0.02 -0.05*** -0.02 -0.05*** -0.02

(0.00) (0.27) (0.00) (0.33) (0.00) (0.41)

TARGET SOE -0.01 0.00 -0.00 0.01 -0.00 0.01

(0.28) (0.85) (0.77) (0.40) (0.80) (0.53)

PC*TARGET SOE -0.03* -0.05* -0.05

(0.07) (0.07) (0.15)

CONST -0.14 -0.14 -0.23 -0.22 -0.15 -0.15

(0.18) (0.19) (0.12) (0.12) (0.42) (0.43)



N 244 244 244 244 244 244

adj. R-sq 0.36 0.37 0.31 0.32 0.22 0.22

52

Table 14 The impact of government intervention on the likelihood of acquiring a

politically connected target.

This table presents the logistic regression results for the impact political connections on the likelihood

of acquiring a SOE target. The TARGET PC is a dummy variable which takes the value of 1 if

the target firm’s Chairman or CEO is a current or former government official, a deputy in NPC

or a member of the CPPCC. The independent variable ‘SOE’ equals1 if the firm that are

ultimately controlled by the states. The independent variable PC is a dummy variable taking the

value of 1 if a firm’s chairman or CEO is politically connected. P-values are displayed in brackets. *,

** and *** indicate significance at the 10%, 5% and 1% levels, respectively.


TARGET PC TARGET PC

SOE 0.70**

(0.01)

PC 0.97***

(0.01)

SIZE 0.13 0.11

(0.32) (0.54)

Q 0.00 -0.29

(0.96) (0.18)

OPCFTA 1.78 3.08

(0.21) (0.20)

LEVERAGE -0.10 -1.20

(0.84) (0.25)

BOARDSIZE 0.04 -0.11

(0.58) (0.30)

BOARDIND 1.78 0.14

(0.43) (0.96)

OWNERSHIP 0.75 25.80

(0.69) (0.54)

CONST -6.94** -2.79

(0.02) (0.50)

YEAR YES YES

INDUSTRY YES YES

N 512 226


Table 15 The impact of government intervention and target firm on post-M&A

performance for SOEs and non-SOEs.

This table presents the regression results for the impact of political connections in acquiring firms and target firms

on post-M&A performance for our SOE sample. The dependent variable is post-M&A performance measured by

CARs for three-day, five-day and 11-day window (CAR_1, CAR_2 and CAR_5). The independent variable

‘SOE’ equals1 if the firm that are ultimately controlled by the states. The independent variable PC

53

is a dummy variable taking the value of 1 if a firm’s chairman or CEO is politically connected. The TARGET PC

is a dummy variable which takes the value of 1 if the target firm’s Chairman or CEO is a current or former

government official, a deputy in NPC or a member of the CPPCC. The ‘SOE*TARGET PC’ is the

interaction term between ‘SOE’ and ‘TARGET PC’. PC*TARGET PC is the interaction between PC

and TARGET PC. Controlling variables (the same as the results in Table 7 and 8) are included but not reported

to save space. P-values are displayed in brackets. *, ** and *** indicate significance at the 10%, 5% and 1%

levels, respectively.

Panel B SOEs vs. Non-SOEs

(1) (2) (3) (4) (5) (6)


SOE -0.02** -0.01 -0.03** -0.02 -0.02 -0.01

(0.02) (0.27) (0.01) (0.14) (0.15) (0.68)

TARGET PC -0.00 0.02* -0.01 0.02 -0.01 0.03

(0.61) (0.08) (0.64) (0.21) (0.65) (0.14)

SOE*TARGET PC -0.05*** -0.05** -0.07**

(0.01) (0.04) (0.02)

CONST 0.00 -0.00 -0.01 -0.01 -0.04 -0.04

(0.98) (1.00) (0.95) (0.93) (0.76) (0.74)



N 514 514 514 514 514 514

adj. R-sq 0.20 0.21 0.19 0.19 0.17 0.18

Panel A SOE: PC vs. Non-PC

(1) (2) (3) (4) (5) (6)


PC -0.03*** -0.02** -0.04*** -0.03** -0.05*** -0.02

(0.00) (0.02) (0.00) (0.03) (0.00) (0.16)

TARGET PC -0.02 0.01 -0.01 0.01 -0.02 0.02

(0.12) (0.61) (0.29) (0.49) (0.25) (0.42)

PC*TARGET PC -0.05** -0.06** -0.08**

(0.02) (0.03) (0.02)

CONST -0.15 -0.16 -0.23 -0.25* -0.15 -0.18

(0.16) (0.12) (0.12) (0.09) (0.41) (0.33)



N 244 244 244 244 244 244

adj. R-sq 0.36 0.38 0.32 0.33 0.22 0.24

Table 16 The impact of government intervention on M&A decisions

This table presents the regression results for the impact of political connection on M&A

decisions in non-SOEs. The dependent variable ‘MA’ is binary, where 1 signifies that the

firm makes at least one merger bid that is eventually successful in a given year. The

54

dependent variable ‘LOCAL’ equals 1 if the acquirer and target firms are located in the same

province. The dependent variable ‘TARGET SOE’ equals 1 if the target firm is controlled by

the state. The TARGET PC is a dummy variable which takes the value of 1 if the target

firm’s Chairman or CEO is a current or former government official, a deputy in NPC or a

member of the CPPCC. The independent variable ‘PC’equals1 if the target firm’s Chairman

or CEO is a current or former government official, a deputy in NPC or a member of the

CPPCC. Control variables are defined in Appendix A. P-values are displayed in brackets. *,


Non-SOEs: PC vs. Non-PC

M&A LOCAL TARGET SOE TARGET PC

PC 0.36** 0.30 0.87* 0.46

(0.01) (0.28) (0.06) (0.29)

SIZE 0.01 0.05 -0.13 0.13

(0.83) (0.77) (0.60) (0.57)

Q -0.00 -0.03 0.11 0.09

(0.91) (0.71) (0.27) (0.38)

OPCFTA 1.26** 0.51 2.25 1.60

(0.04) (0.68) (0.27) (0.43)

LEVERAGE -0.08 -0.15 0.50 0.29

(0.40) (0.70) (0.30) (0.60)

BOARDSIZE 0.00 -0.09 0.01 0.30**

(0.91) (0.30) (0.92) (0.02)

BOARDIND 1.30 -7.83*** -4.26 3.17

(0.33) (0.00) (0.32) (0.44)

OWNERSHIP -2.51*** 0.75 -0.99 2.20

(0.00) (0.62) (0.71) (0.30)

CONST -3.67** 1.41 2.56 -22.68

(0.02) (0.71) (0.66) (0.98)

YEAR YES YES YES YES

INDUSTRY YES YES YES YES

N 4867 265 257 261

pseudo R-sq 0.06 0.06 0.12 0.14

55

Table 17 The impact of political connections on post-M&A performance

This table presents regression results for the impact of political connections on the cumulative abnormal return.


(CAR_1, CAR_2 and CAR_5). The dependent variable ‘PREMIUM1’ is defined as the ratio of total M&A

premium to the fair value of the target firm. The dependent variable ‘PREMIUM2’ is defined as the natural

logarithm of the total M&A premium, which is the difference between the trading value of the target and the target

fair value. The independent variable PC is a dummy variable taking the value of 1 if a firm’s chairman or CEO is

politically connected. Control variables are defined in Appendix A. P-values are displayed in brackets. *, ** and

*** indicate significance at the 10%, 5% and 1% levels, respectively.

Non-SOEs: PC vs. Non-PC

CAR_1 CAR_2 CAR_5 PREMIUM1 PREMIUM2

PC 0.03*** 0.05*** 0.06*** -1.77** -0.00*

(0.01) (0.00) (0.00) (0.04) (0.07)

RELATIVE SIZE 0.01** 0.01* 0.02* -0.02 -0.00

(0.02) (0.09) (0.06) (0.97) (0.42)

CASH PAYMENT -0.05*** -0.06*** -0.07** -1.55 -0.00

(0.01) (0.00) (0.01) (0.15) (0.36)

SIZE 0.00 0.00 0.01 1.26** 0.00

(0.84) (0.85) (0.29) (0.01) (0.45)

Q 0.00 0.01* 0.02** 0.08 0.00

(0.43) (0.10) (0.02) (0.84) (0.17)

OPCFTA -0.01 0.02 0.02 -1.64 -0.01

(0.75) (0.76) (0.76) (0.67) (0.18)

LEVERAGE -0.00 -0.00 0.01 -1.46 0.00

(0.91) (0.84) (0.81) (0.41) (0.78)

BOARDSIZE -0.00 -0.00 -0.00 -0.41 0.00

(0.38) (0.70) (0.95) (0.21) (0.66)

BOARDIND 0.03 0.02 0.01 -2.35 -0.03

(0.78) (0.88) (0.96) (0.79) (0.27)

OWNERSHIP 0.03 0.03 -0.00 0.25 0.01

(0.63) (0.64) (1.00) (0.96) (0.69)

CONST 0.05 0.02 -0.20 -20.08* 23.02***

(0.71) (0.90) (0.39) (0.06) (0.00)

YEAR YES YES YES YES YES

INDUSTRY YES YES YES YES YES

N 270 270 270 86 86

adj. R-sq 0.17 0.19 0.20 0.16 0.05

Table 18 The impact of regional government intervention on post-M&A performance of

SOEs.

This table presents the regression results for the impact of political connections on the cumulative abnormal return.


56

(CAR_1, CAR_2 and CAR_5). The independent variable PC is a dummy variable taking the value of 1 if a firm’s

chairman or CEO is politically connected. GOV is the government intervention which is measured by Fan’s

marketization index. We multiply the marketization index by -1 for convenience. Controlling variables (the same

as the results in Table 7 and 8) are included but not reported to save space. P-values are displayed in brackets. *,


(1) (2) (3) (4) (5) (6)


PC -0.04*** -0.11*** -0.05*** -0.17*** -0.05*** -0.17**

(0.00) (0.01) (0.00) (0.00) (0.00) (0.01)

GOV -0.00 0.00 -0.00 0.00 0.00 0.01

(0.50) (0.74) (0.31) (0.76) (0.85) (0.27)

PC*GOV -0.01* -0.02** -0.02*

(0.06) (0.02) (0.06)

CONST -0.14 -0.12 -0.21 -0.18 -0.16 -0.12

(0.18) (0.25) (0.14) (0.21) (0.40) (0.51)



N 244 244 244 244 244 244

adj. R-sq 0.36 0.36 0.32 0.33 0.22 0.23

Table 19 The impact of provincial unemployment rate on the effect of government

intervention on post-M&A performance of SOEs

This table presents the regression results for the impact of local unemployment rate on the effect of political

connections on post-M&A performance in SOEs. The dependent variable is post-M&A performance measured

by CARs for three-day, five-day and 11-day windows (CAR_1, CAR_2 and CAR_5). The independent variable

PC is a dummy variable taking the value of 1 if a firm’s chairman or CEO is politically connected. The column

‘High’ represents the M&A deals conducted by SOEs located in the provinces with unemployment which is at a

rate higher than the annual median unemployment rate. The column ‘Low’ represents the M&A deals conducted

in provinces with an unemployment rate which is lower than the median. Control variables (like the results in

Table 9 and 10) are included but not reported to save space. P-values are displayed in brackets. *, ** and ***

indicate significance at the 10%, 5% and 1% levels, respectively.

High Low High Low High Low


PC -0.04*** -0.02* -0.06*** -0.02 -0.06*** -0.02

(0.00) (0.08) (0.00) (0.24) (0.01) (0.42)

RELATIVE SIZE -0.00 0.09*** -0.02 0.13*** -0.04 0.17***

(1.00) (0.00) (0.65) (0.00) (0.42) (0.00)

CASH PAYMENT -0.09*** -0.08*** -0.10*** -0.11*** -0.14*** -0.08**

(0.00) (0.00) (0.00) (0.00) (0.00) (0.01)

CONST -0.03 -0.06 -0.13 -0.08 0.14 -0.23

(0.83) (0.69) (0.51) (0.65) (0.62) (0.35)



N 115 129 115 129 115 129

adj. R-sq 0.35 0.44 0.34 0.46 0.22 0.31

57

Appendix A. Variable definitions

Variable name Detailed definition

M&A The dummy equals 1 if the firm announced a merger and acquisition, and 0

otherwise.

CAR_1 The cumulative abnormal return over a three-day event window from one day

prior to the M&A announcement to one day post the announcement.

CAR_2 The cumulative abnormal return over a five-day event window from the two

days prior to the M&A announcement to the two days post the announcement.

CAR_5 The cumulative abnormal return over an eleven-day event window from the

five days prior to the M&A announcement to the five days post the

announcement.

PREMIUM 1 The ratio of total M&A premium to the fair value of the target firm.

PREMIUM 2 The natural logarithm of the total M&A premium, which is the difference

between trading value of the target and the target fair value.

VERTICAL A dummy variable which equals 1 if the announced merger is vertically related.

PC A dummy variable that equals 1 if the acquirer's current CEO and chairman of

the board are current or former officers of the government or military or a

deputies of the People's Congress or People's Political Consultative

Conference.

TARGET PC A dummy variable that equals 1 if target's current CEO and chairman of the

board are current or former officers of the government or military or deputies of

the People's Congress or People's Political Consultative Conference.

Growth in Q This is calculated by subtracting the average Tobin’s q in the three years prior

to the merger and acquisition announcement from the three years of annual

Tobin’s q after the merger and acquisition announcement.

Growth in ROA This is calculated by subtracting the average ROA in the three years prior to the

merger and acquisition announcement from the three years of annual ROA after

the merger and acquisition announcement.

Growth in earnings This is the percentage change in the average of annual earnings before the

merger and acquisition announcement to the three years after the merger and

acquisition.

SOE A dummy variable which equals 1 if the controlling shareholder of the listed

firm is government or government agency, and 0 otherwise.

SIZE The natural logarithm of book value of total assets.

Q Market value/replacement value.

OPCFTA Total operation cash flow scaled by total assets.

LEVERAGE The ratio of total debt to total assets

BOARDSIZE The total number of board of directors

BOARDIND The ratio of the number of independent directors on the board to the total

number of directors on the board .

OWNERSHIP Percentage of shares owned by managers

RELATIVE SIZE The ratio of the acquisition trading value to acquiring firm’s total assets.

CASH PAYMENT A dummy variable equal to 1 if the merger and acquisition is financed entirely

by cash and 0 otherwise.

Documents

Government intervention and corporate M&A … Government intervention and corporate M&A transactions: Evidence from China Qigui Liu, Tianpei Luo, Gary Gang Tian1 School of Accounting,