Silence Is Golden: Political Connection and Corporate Disclosure of Government Subsidies

ABSTRACT

This paper examines the association between firms’ political connection and their voluntary disclosure on government subsidies using a sample of Chinese non-state-owned enterprises. For the sample period 2007~2016, we find that politically connected subsidy-receiving firms provide a lower amount of subsidy disclosures through annual reports than unconnected ones. This association is not due to the general lower disclosure incentives of connected firms, is mainly driven by connected firms whose subsidies are difficult to justify, is stronger for firms registered in provinces with a higher level of corruption and firm with higher media attention, and becomes weaker following the anti-corruption campaign starting in late 2012. These findings suggest that politically connected subsidy recipients tend to withhold subsidy information to reduce the costs for the politicians they connect to and themselves from the public scrutiny of subsidies granted through relationships. Consistent with this interpretation, we further show that politically connected firms are granted more subsidies and this association is stronger in provinces with a higher level of corruption and becomes weaker following the anti-corruption campaign. JEL Classification: H2; M4; P16 Keywords: Government subsidies; Political connection; Corporate disclosure; Corruption

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1. Introduction

Government expenditures on subsidies, including those granted to private businesses,

remain high in many countries, often amounting to several percentage points of GDP (Schwartz

and Clements, 1999). Buigues and Sekkat (2011) report that the share of GDP represented by

subsidies to business ranges from a high of 3% (Austria) to a low of 0.2% (Greece) in the period

1998-2002. While subsidies are ideally used to address some market failures and correct for

social inequalities, the misuse of subsidies due to government failures have been an important

public concern in many countries (Schwartz and Clements, 1999). Thus, government subsidies

to business are usually scrutinized and monitored intensively by the public and other interest

groups (Pappas et al., 2018; Huang, 2018). Given the controversial nature of government

subsidies and the resulting public monitoring, it is of importance to understand how subsidized

firms disclose information about the subsidies they receive.

In this study, we examine how political connection is associated with subsidized firms’

disclosure of subsidy information using a sample of Chinese public firms. The Chinese setting

is unique in that Chinese listed firms are required to disclose the subsidies they received in

annual reports and the government is not required to disclose information on subsidy programs.

As the rule for subsidy disclosure is largely principles-based and not all aspects of subsidies

are required to be disclosed, firms have considerable discretion in their disclosure practices,

leading to substantial cross-sectional variation in disclosure levels. In contrast, the U.S.

government is required to disclose detailed information on subsidies granted to business and

subsidized firms are not required to disclose subsidy information.1 U.S. firms rarely disclose

subsidy information voluntarily, presumably due to the mandatary disclosure by the

                                                               1 In U.S., the Federal Funding Accountability and Transparency Act (FFATA) requires federal contract, grant, loan, and other financial assistance awards of more than $25,000 to be displayed on a searchable and publicly accessible website (USAspending.gov). Federal agencies are required to report the name of the entity receiving the award, the amount of the award, the recipient’s location, the place of performance location, as well as other information (Huang, 2018).

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government. In addition, China’s economy is characterized with weak legal enforcement and

pervasive connection-based transactions. Thus, the influence of political connections is likely

to be more pronounced in China than in other market-oriented economies.

As subsidy is a major form of wealth redistribution, firms receiving subsidies are subject

to greater public scrutiny, especially when they are politically connected. Prior research has

shown that politically connected firms obtain more government subsidies in both U.S. and

China (Abodia, Koester, and Petacchi, 2018; Jin and Zhang, 2017). In a country with weak

legal institutions and more relationship-based wealth transfers like China, subsidies received

by connected firms may be perceived as benefits accruing from political ties or even “under-

the-table” dealings. Thus, firms obtaining extra or even normal subsidies through political

connections have incentives to conceal their subsidy information (Leuz and Oberholzer-Gee,

2006). This disclosure strategy will minimize the costs associated with adverse public scrutiny

for the firms and also shield the government officials they connect to from potential

reputational and other costs (Ramanna and Roychowdhury, 2010). Firms have incentives to

help their connected officials because it will consolidate their relationships with the officials

from which they can extract future benefits (Ramanna and Roychowdhury, 2010; Pappas et al.,

2018). We therefore hypothesize that politically connected subsidy recipients would disclose

less subsidy information than unconnected ones.

The forgoing prediction is not obvious ex ante. Guedhami et al. (2014) show that

politically connected firms have incentives to increase transparency to signal to the market that

they are not engaged in self-dealing. Thus, connected subsidy recipients may also want to be

transparent with subsidy information. The connected recipients who do not obtain relationship-

based subsidies may even have incentives to disclose more information in order to signal their

innocence and distinguish themselves from connected firms that do obtain relationship-based

subsidies. In addition, because government subsidies, especially the extra amount obtained,

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generally increase firm value, recipient firms may want to disclose more information in order

to positively influence the capital market and other stakeholders.

To measure the level of subsidy information disclosure, we manually collect the detailed

information provided in the subsidy footnote of the annual report. We construct a

comprehensive disclosure measure that captures the extents to which the subsidy source (i.e.,

government agency granting the subsidy), the policy basis for the subsidy, and the subsidy type

are disclosed in the annual report. 3 We measure political connection with two indicator

variables: whether a firm’s board chairman or CEO is a current or former government or

military official (officer connection, hereafter, e.g., Fan, Wong, and Zhang, 2007; Chen, Ding,

and Kim, 2010),4 and whether a firm is registered in the hometown of the incumbent provincial

political leaders (the party secretary or the governor) or in a city in which he/she had the longest

part of his/her work experience (regional bias, hereafter, e.g., Faccio and Parsley, 2009; Cao et

al., 2018).5

Our empirical analysis is based on a sample of listed non-state-owned enterprises

(NSOEs). We focus on NSOEs because they are subject to much stricter public scrutiny with

respect to extracting economic benefits through political connections than state-owned

enterprises (SOEs). SOEs, by definition, are owned by the government on behalf of the nation

(taxpayers). Thus, wealth transfer from the government to SOEs is not generally viewed as a

transfer and is subject to lesser public scrutiny. For the sample period 2007-2016, we first show

descriptive evidence on subsidy disclosures for NSOEs that receive subsidies. We find that

subsidy type is disclosed for 96% of subsidies, consistent with the mandatory disclosure

                                                               3 Section 4.2 and Appendix A describe the measure in detail. It ranges between 0.2 and 1 with a higher value meaning more disclosure. We do not include the issuance of subsidy-related interim announcements into our subsidy disclosure measure because the information in the interim announcements may overlap with that in the annual reports. In Section 4.6.1, we show that our finding is robust to incorporating interim announcements into the subsidy disclosure measure, and using other alternative subsidy disclosure measures. 4 In China, military officials are also very powerful and are able to influence the decisions of government officials (Fan, Wong and Zhang, 2007). 5  In China, a firm’s registration city is the same as the city of its main office (Chen, Hung, and Wang, 2018). 

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requirement for subsidy type, while subsidy source and policy basis are disclosed only for 42%

and 22% of subsidies, respectively, consistent with firms’ general reluctance to disclose such

information when the disclosure is not mandated.

Next, we show a negative association between political connection and the level of

subsidy disclosure among subsidized firms. The effect is not only statistically significant, but

also economically meaningful. A subsidy recipient with officer connection (regional bias) has

a lower value of the subsidy disclosure measure by 0.016 (0.024), which accounts for 8.2%

(12.3%) of the standard deviation of the measure. These findings are consistent with our

prediction that connected subsidy recipients conceal subsidy information to lower the costs

from the public scrutiny. We further show that this result is not due to the general lower

disclosure incentives for connected firms as documented by Hung et al. (2018):6 using the

same sample, we find that our political connection measures are not negatively associated with

general corporate disclosures, measured with the frequency of management earnings forecasts

and the length of MD&A in the annual report.

One concern about our analysis is that political connection is endogenous and our findings

could be due to economic factors that are associated with both political connection and subsidy

disclosure. We argue that while officer connection is presumably a firm choice (Faccio, 2010),

regional bias is arguably not a function of firm characteristics because it is mainly driven by

the geographical coincidence between a firm and the provincial political leaders. Nevertheless,

we conduct extensive analyses to address the potential endogeneity issue and provide

additional evidence.

First, we examine whether the negative association between political connection and

subsidy disclosure is more pronounced in provinces or periods in which government officials

                                                               6  Hung et al. (2018) find that politically connected firms provide fewer disclosures, measured with management earnings forecasts, and attribute this effect to connected firms’ weaker capital market incentives and/or lower litigation risk. 

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are more corrupt and quid-pro-quo exchanges are more pervasive. We expect that when the

government is more corrupt, subsidies are more likely to be granted through connections (Fang

et al., 2018) and thereby politically connected subsidy recipients are more like to withhold

subsidy information. Consistent with this prediction, we find that the association between

political connection and subsidy disclosure is significantly more negative in higher corruption

provinces and becomes weaker after China’s far-reaching anti-corruption campaign launched

at the end of 2012. This evidence increases our confidence that the documented negative

connection-disclosure relation is due to politically connected subsidy recipients reducing

subsidy disclosure to avoid related public scrutiny.

Second, we examine whether the negative association between political connection and

subsidy disclosure is stronger for firms subject to greater public scrutiny. Measuring public

scrutiny with media attention, we find that the connection-disclosure association is

significantly more negative for firms that have more media coverage. This evidence further

supports that connected subsidy recipients disclose less subsidy information to avoid related

costs due to public scrutiny.7 Third, we show that the negative association between political

connection and subsidy disclosure is mainly driven by politically connected firms whose

subsidy granting is difficult to justify, measured as whether the firm’s industry is the focus of

government support based on the country’s current Five-Year Plan.8 In addition, we construct

a measure of subsidy granting due to political connection and show that it is negatively

associated with subsidy disclosure. This evidence suggests that the connection-disclosure

association we document is primarily due to unjustifiable subsidies granted through

                                                               7  This evidence also helps rule out the alternative explanation that our result is due to the general lower disclosure incentives of connected firms proposed by Hung et al. (2018). If our finding is driven by political connections provide political protection and/or easier access to financing, the negative association between connection and subsidy disclosure should become weaker for firms with greater media attention, not stronger as we document, because media attention will deter firms from extracting benefits from political connections.  8  China’s Five-Year Plans are a series of social and economic development initiatives issued since 1953. See Section 4.4.4. for details. 

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connections.

Fourth, if the associations between our political connection measures and subsidy

disclosure and their variation with corruption are due to politically connected subsidy recipients’

concern about the public scrutiny related to the subsidies obtained through relationship, we

expect that the political connection measures are positively associated with the amount of

subsidy granting and the associations are stronger in higher corruption provinces and become

weaker following the anti-corruption campaign. Our evidence is consistent with these

predictions. Fifth, we show that our main finding is robust to using a model with firm fixed

effects and to using matched samples based on propensity score matching (PSM). Finally, we

repeat our main analyses for SOEs and generally do not find similar results. As we argue above,

SOEs are subject to less public scrutiny for their political connections and related wealth

transfers, and therefore connected SOEs receiving subsidies may have no or weaker incentives

to hide their subsidy information. Thus, finding no or weaker results for SOEs provides

additional support for our argument that politically connected NSOEs that receive subsidies

reduce subsidy disclosure to avoid related public scrutiny.

Our study contributes to the literature on corporate disclosure. To our best knowledge, it

is the first to provide evidence concerning firms’ disclosure on government subsidies. We

identify political connection as an important factor that drives subsidy disclosure and provide

evidence on how and when political connection affects subsidy disclosure. Given the mixed

findings in the literature regarding connected firms’ preference for transparency (Chaney et al.,

2011; Guedhami et al., 2014; Hung et al., 2018),9 one cannot simply extrapolate the findings

of the prior literature to our paper.

We also add to the literature on how political incentives affect corporate financial

                                                               9   Guedhami et al. (2014) show that connected firms have greater incentives to improve transparency, while Chaney et al. (2011) and Hung et al. (2018) find that connected firms provide lower quality accounting number and fewer disclosures.  

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reporting and disclosure (e.g., Ramanna and Roychowdhury, 2010; Piotroski, Wong, and Zhang,

2015). By showing that politically connected subsidy recipients withhold subsidy information,

our study broadens the understanding of the relation between political factors and corporate

disclosure. While there has been a long literature on how political factors affect financial

reporting, dated back to the political cost hypothesis proposed by Watts and Zimmerman (1978,

1986), relatively few studies have examined corporate disclosure in this area (Miller and

Skinner, 2015).

Furthermore, our study complements Aobdia et al. (2018) and Jin and Zhang (2017) and

shows that politically connected firms obtain more government subsidies than unconnected

firms and the relation is stronger when the government is more corrupt. Our findings based on

regional bias also contribute to the literature on regional favoritism, a phenomenon that some

political leaders choose policies that mainly benefit their preferred regions (Holder and

Raschky, 2014). We show that regional bias, as we define, is an economically meaningful

measure of political connection in China, which we suggest to be used in future studies because

it is less subject to the endogeneity issue.10

Our findings also have some policy implications. To the extent that subsidy recipients

have incentives to withhold subsidy information, especially when the subsidies are obtained

through connections, the voluntarily disclosed subsidy information may be below the socially

optimal level. This provides a theoretical ground for regulators to mandate the disclosure of

subsidy information. Our finding that subsidy source and policy basis are not disclosed for the

majority of subsidy recipients, especially politically connected firms, is consistent with China’s

mandating such disclosures in 2017. In addition to the mandatory disclosure requirement in

China, Financial Accounting Standards Board (FASB) of U.S. has recently proposed to require

                                                               10 Our evidence is consistent with the finding of a concurrent study by Cao et al. (2018), which shows that firms enjoy more favorable tax treatments if their registration city is the incumbent provincial leader’s hometown.

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companies to disclose detailed information about subsidy awards (FASB, 2015). However, we

caveat that the findings from China may not be equally applied to U.S. due to the differences

in institutions and legal environments.

The rest of the paper proceeds as follows. Section 2 provides the institutional background

of government subsidies in China and the related financial reporting requirements. Section 3

reviews the related literature and develops the main hypothesis. Section 4 presents the

empirical analyses. Section 5 concludes.

2. Government Subsidies and Disclosure Requirement in China

Chinese government grants a large amount of subsidies to business. According to The Wall

Street Journal, approximately 14% of listed nonfinancial firms’ profits are attributable to

government subsidies. 11 During our sample period from 2007 to 2016, on average, the

government grants subsidies of 106 billion CNY (approximately 15.5 billion U.S. dollars) to

public firms based the subsidy information disclosed by firms. The government provides

subsidies to businesses in various forms, including direct cash payments (cash subsidies), free

or low-cost loans (credit subsidies), reductions of tax liabilities (tax subsidies), provisions of

goods and services at below-market prices (in-kind subsidies), and purchases of goods and

services at above-market prices (procurement subsidies). The government can also subsidize

business implicitly by implementing policies favoring certain industries. For instance, it can

increase market entry barriers to suppress supply and enable incumbent firms in the industry

to charge a higher price and enjoy higher profit. This implies that the observed subsidies may

comprise only a fraction of the full extent of subsidies. To the extent that it is nearly impossible

to know the full extent, the available subsidy data have usually been confined to what can

readily be observed and quantified. We follow this approach in our study.

                                                               11 Source: https://www.wsj.com/articles/in-trumps-china-industrial-subsidies-loom-large-1479270824.

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Both central and local governments in China are entitled to award subsidies. Although the

local governments are guided by the central government policies, they are allowed considerable

discretion in subsidy allocation due to their informational advantage with respect to the local

developmental needs. The central government evaluates the performance of local government

officials partly based on the economic and social performance of local firms within their

jurisdictions. The performance evaluation, along with the delegation of authority to local

governments in subsidy granting, incentivizes local officials to boost local economic growth

by taking advantage of subsidies.

Like many other countries, China uses government subsidies to fix market failures, achieve

economies of scale in production, and accomplish social policy objectives, which is evident in

subsidies granted to firms in certain industries, such as agriculture, public utilities, and high-

tech industries, and firms with certain characteristics, such as loss-making firms and firms that

provide a larger number of jobs (Chen, Lee, and Li, 2008). However, mounting evidence

suggests that government officials may abuse their power and grant subsidies to their favored

firms (Fang et al., 2018; Jin and Zhang, 2017). In China, decisions to grant subsidies are made

by individual government officials, rather than peer reviewers and expert panels, as in most

western nations (Fang et al., 2018). Thus, subsidies granting is more likely to be influenced by

political connection and corruption. The Chinese media have reported many cases in which

officials in charge of subsidies granting exchange subsidies for bribes and complained that

government subsidies have been abused.12

Chinese listed firms have been required to disclose subsidy information in their annual

reports since 2001. Specifically, the Enterprise Accounting System issued by the Ministry of

Finance (MOF) in December 2000 for the first time clearly defined the scope of accounting

                                                               12 For instance, China Youth Daily, a major newspaper in China, reported on January 3, 2014 that one official of the Ministry of Finance (MOF) took bribe of around 24.5 million CNY during the period from 2001 to 2011 in exchange for government subsidies. See http://zqb.cyol.com/html/2014-01/03/nw.D110000zgqnb_20140103_7-01.htm.

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treatment for government subsidies. During 2001 through 2006, listed firms were required to

report subsidies as a separate line item in income statement. Since the issuance of Chinese

Enterprise Accounting Principles No. 16-Government Subsidies in 2007, public firms have

been reporting subsidies in “other income” and required to disclose in the notes of the annual

reports the amount and type (what the subsidy is about, such as innovation support, export

support, and so on) of subsidies received from various government agencies.13 We provide in

Appendix A two examples of such disclosures. Beyond this requirement, firms may voluntarily

disclose additional information, such as the source and policy basis of the subsidy. Moreover,

firms are encouraged to issue interim announcements voluntarily upon receiving subsidies. In

our sample, 21% of subsidy recipients have issued such announcements.

3. Related Literature and Hypothesis Development

3.1. Related literature

Our study relates to the literature on the relation between political forces and corporate

financial reporting and disclosure. Politicians have the power to affect upon wealth

redistribution in various ways, including through corporate taxes, regulations, and subsidies

(Stigler, 1971; Peltzman, 1976). Early works by Watts and Zimmerman (1978, 1986) propose

the political cost hypothesis which argues that the fear of government scrutiny and its

associated costs or the desire for wealth transfers in the regulatory process incentivizes firms

to manage their reported earnings downwards (Miller and Skinner, 2015). Prior studies have

provided evidence consistent with the political cost hypothesis (e.g., Jones, 1991; Cahan, 1992;

Key, 1997; Han and Wang, 1998; Grace and Leverty, 2010; Ramanna and Roychowdhury,

2010). While the literature has shown that the political process provides managers with

                                                               13  While “subsidy type” is usually used to mean cash subsidy, tax break, and so on, we follow the language used in Chinese Enterprise Accounting Principles No. 16-Government Subsidies and use “type” to mean what the subsidy is about.  

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incentives to manage reported earnings, it remains largely unknown whether and how managers

attempt to manage their firms’ information environments through other ways such as voluntary

disclosures. As Miller and Skinner (2015) notes, it remains relatively undeveloped how

political and regulatory incentives affect firms’ disclosure choices.

Using the Chinese setting, a recent study by Piotroski, Wong, and Zhang (2015) shows

that politicians and their affiliated firms, namely, firms operating in their jurisdictions,

temporarily suppress negative information in response to political incentives related to two

visible political events ─ meetings of the National Congress of the Chinese Communist Party

and promotions of high-level provincial politicians. More recently, Lee, Walker, and Zeng

(2017) examine the influence of government subsidies on voluntary corporate social

responsibility (CSR) disclosure of Chinese listed firms. They find that government subsidies

are positively associated with firms’ tendency to issue CSR reports and such effect is

concentrated among NOSEs rather than SOEs. Huang (2018) shows that U.S. firms receiving

government subsidies provide more disclosure of general corporate information as well as

subsidy-goaled related information. Our study adds to this line of literature by providing

evidence on how firms’ disclosure of subsidies, an outcome of the political process, is shaped

by political factors.

Several recent studies directly examine how political connections are associated with firm

transparency in a cross-country setting and produce mixed findings. Guedhami et al. (2014)

argue that politically connected firms have a greater incentive to improve information

transparency to convince outside investors that they refrain from self-dealing. Consistent with

this argument, they show that connected firms are more likely to appoint Big N auditors.

However, Chaney et al. (2011) and Hung et al. (2018) find that connected firms provide lower

quality accounting number and fewer disclosures. In particular, Hung et al. (2018) show that

politically connected firms provide fewer disclosures, measured with management earnings

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forecasts, and attribute this effect to connected firms’ weaker capital market incentives and/or

lower litigation risk. They argue that because connected firms have better access to credits and

obtain privileged loans from banks that are influenced by politicians, they have lesser need to

raise capital from the market and thus have lower disclosure incentives. In addition, because

connected firms enjoy political protection and have lower litigation risk from withholding

information, they have lower incentives to avoid lawsuits. Given these mixed findings, one

cannot simply extrapolate the findings of the prior literature to our study. Notably, none of these

studies include Chinese firms in the sample.

Our study is also related to the literature on economic determinants of government

subsidies. Chen, Lee, and Li (2008) find that Chinese local governments provide subsidies to

help firms boost their earnings to circumvent the central government’s regulation on rights

offering and delisting. A recent study by Jin and Zhang (2017) documents evidence that firms

with political connections through independent directors are granted more subsidies and

granting subsidies through relationship lead to capital misallocation. Similarly, Aobdia, Koester,

and Petacchi (2018) find that U.S. firms’ political connections are positively associated with

goverment subsidies and the quid-pro-quo behavior in the state subsidy award process results

in a less efficient allocation of government resources. Fang et al. (2018) show that the anti-

corruption campagn in China beginning in late 2012 and the departures of local government

officials responsible for innovation programs strenghtened the relatioship between firms’

historical innovation efficiency and subsequent subsidy awards, and depressed the influence of

their corruption-related expenditure. Our study is based on the presumption that politically

connected firms are awarded more government subsidies and the relation is stronger when the

government is more corrupt, which we confirm empirically. In this sense, our study

complements this line of literature.

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3.2. Hypothesis development

Subsidy recipients have incentives to disclose subsidy information due to several related

benefits. First, such disclosure increases firm transparency in general. It has been shown that

firms enjoy various benefits from a more transparent information environment, such as lower

costs of capital and enhanced liquidity (Beyer et al., 2010). Second, because government

subsidies generally increase firm value (Desai and Hines, 2008; Girma et al., 2009; Lee, Walker,

and Zeng, 2014), disclosing subsidy information can positively influence the capital market

and other stakeholders. Prior studies have shown that subsidies can stimulate R&D activities,

improve firm profitability, enhance competitiveness, and reduce capital constraints (e.g.,

Davidson and Segerstrom, 1998; Girma et al., 2009; Claro, 2006). Consistent with these

benefits, Lee et al. (2014) show that subsidies for Chinese listed firms are positively associated

with firm value. The aforementioned benefits are larger when the subsidy information is more

important for the market, for instance, when the firm relies more on subsidies and when the

subsidy amount is larger.

As we discuss in Sections 1 and 2, mounting evidence suggests that government subsides

could be granted through political connections in China (Fang et al., 2018; Jin and Zhang,

2017). Based on the interview outcomes of Lee et al. (2014), the personal connection (or

guangxi) between entrepreneurs and government officials is an important reason for subsidy

granting in China, in addition to business and industry characteristics. Chinese listed firms,

especially NSOEs, have strong incentives to compete for subsidies for several reasons. First,

the China Security Regulatory Committee (CSRC) imposes strict delisting rules for firms that

continuously report losses.14 Thus, listed firms have strong incentives to compete for subsidies

to avoid trading restrictions and delisting because subsidies are recorded into net income (Chen,

                                                               14  If a listed firm reports losses in two consecutive years, its stock will be classified as specially treated (ST) and face many trading and financial restrictions (Lee et al., 2014). It will be suspended from trading after three consecutive years of losses and be delisted for four consecutive years of losses. 

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Lee, and Li, 2008). Second, NSOEs have competitive disadvantages relative to SOEs because

the latter enjoy greater government supports, including subsidies and preferential bank loans

(Allen, Qian, Qian, 2005; Lee et al., 2014). This provides NSOEs with additional incentives to

seek wealth transfers from the government through political connections.

In a country with weak legal institutions and more relationship-based wealth transfers like

China, subsidies received by politically connected firms may be perceived as benefits accruing

from political ties or even “under-the-table” dealings and thus are subject to public scrutiny

and monitoring. Given the strict public scrutiny for subsidies, especially those received by

politically connected firms, disclosing subsidy information may impose substantial costs for

the recipient firms and government officials they connect to if the subsidies are obtained

through relationships. The disclosed information may trigger further investigation by the public

and media, which may lead to the ultimate discovery of the quid-pro-quo exchanges.

Due to the public scrutiny costs, firms obtaining extra or even normal subsidies through

political connections have incentives to conceal their subsidy information. This disclosure

strategy will minimize the costs associated with adverse public scrutiny for the firms and also

shield the government officials they connect to from potential reputational and other costs

(Ramanna and Roychowdhury, 2010). Firms have incentives to help their connected officials

because it will consolidate their relationships with the officials from which they can extract

future benefits (Ramanna and Roychowdhury, 2010; Pappas et al., 2018). This notion is

consistent with the finding in the literature that in a country with higher levels of corruption in

which politicians and connected firms are more likely to engage in illegal wealth transfers,

connected firms are likely to reduce transparency to help politicians hide negative information

that can potentially hurt their reputation and careers (e.g., Leuz and Oberholzer-Gee, 2006;

Piotroski et al., 2015). We therefore hypothesize that politically connected subsidy recipients

would disclose less subsidy information than unconnected ones.

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We expect the above prediction to mainly apply to NSOEs, because unjustifiable subsidies

granted to NSOEs are more likely to be viewed as a wealth transfer from tax payers to wealthy

individuals. SOEs, especially those with very high state ownership, by definition, are owned

by the government on behalf of the nation (taxpayers). Wealth transfer from the government to

SOEs is not generally viewed as a transfer. Moreover, unlike NSOEs whose profits are more

likely to be retained in the hands of wealthy private shareholders (compared to the government

as a shareholder), SOEs are more generous in redistributing their profits to the public in the

form of dividends paid to the government shareholder (Lee et al., 2017). Thus, SOEs are subject

to less strict public scrutiny with respect to extracting economic benefits through political

connections than NSOEs.

Hypothesis: Politically connected NSOEs that receive government subsidies provide less

disclosure on subsidies than unconnected ones.

We note that political connection may positively relate to firms’ subsidy disclosure for

several reasons. First, due to public scrutiny, politically connected firms hiding subsidy

information may hurt the reputations of both the firm and the politicians (Huang, 2018). Thus,

connected subsidy recipients may also want to be transparent with subsidy information. This is

especially true for connected firms who do not obtain relationship-based subsidies.15 They

may even have incentives to disclose more information in order to signal their innocence and

distinguish themselves from connected firms that do obtain relationship-based subsidies

(Guedhami et al. 2014).16 In addition, because government subsidies, especially the extra

amount obtained, generally increase firm value, recipient firms may want to disclose more

information in order to positively influence the capital market and other stakeholders,

                                                               15Not all politically connected firms obtain extra unjustifiable subsidies through relationship. Some may not obtain extra subsidies at all; some may obtain extra subsidies due to the information advantage resulting from the connection, not favor from the politicians.    16  Guedhami et al. (2014) argue that politically connected firms have a greater incentive to improve information transparency to convince outside investors that they refrain from self-dealing. Consistent with this argument, they show that connected firms are more likely to appoint Big N auditors.  

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especially when the extra amount is obtained through connection-based information advantage,

not through corruption.

As we discuss in Section 3.1, Hung et al. (2018) find that politically connected firms

provide fewer disclosures, measured with management earnings forecasts, and attribute this

effect to connected firms’ weaker capital market incentives and/or lower litigation risk. If one

views subsidy disclosure as one aspect corporate disclosure, Hung et al.’s (2018) finding also

suggests that political connections are negatively associated with subsidy disclosure. However,

this is an alternative explanation we want to rule out (see Sections 4.3 and 4.4.3).

4. Empirical Analyses

4.1. Measurement of subsidy disclosure and political connection

To measure the level of subsidy information disclosure, we manually collect the detailed

information provided in the subsidy footnote of the annual report. We then construct a

comprehensive disclosure measure (Sub_disclosure) that captures the following three

dimensions: i) the percentage of subsidies whose sources (i.e., the government agency or the

level of government that grants the subsidy) are disclosed in the annual report (Source), ii) the

percentage of subsidies for which the policy basis is disclosed in the annual report

(Policy_basis), and iii) the percentage of subsidies for which the subsidy type (Type) are

disclosed in the annual report.

We sort Source, Policy_basis and Type into quintiles, with 0.2 for the lowest quintile and

1 for the highest one. Sub_disclosure is then calculated as the average quintile of the three

measures, ranging between 0.2 and 1.17 A larger value indicates a greater level of subsidy

disclosure. In our data, more than 20% of the values of Source and Policy Basis are

                                                               17 Calculating the composite disclosure measure based on the quartiles, instead of the actual values, of the three underlying variables has the advantage of not treating the raw values of two variables (e.g., Source and Policy basis) equally in terms of the disclosure level. Our results are robust to calculating the measure as the simple average of the three underlying variables (see Section 4.6.1.).

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concentrated at 0. For these two variables, we code the 0 group as the lowest quintile and

partition the remaining observations into quartiles and code the first (second, third, fourth)

quartile as the second (third, fourth, fifth) quintile for the full sample. Similarly, because more

than 20% of the values of Type are concentrated at 1, we code the 1 group as the highest quintile

and partition the remaining observations into quartiles and code the first (second, third, fourth)

quartile as the first (second, third, fourth) quintile for the full sample. 18 We provide in

Appendix A two examples on the calculation of Sub_disclosure.

We use two measures of political connection that have been used in the literature. First,

we measure the political connectedness of the CEO and the Chairman of the board based on

their career history. We classify a Chairman (CEO) as being politically connected if she is

currently serving or has formerly served in the government or the military (Fan, Wong, and

Zhang, 2007; Faccio, 2006).19 We create a dummy variable, Officer Connect, that equals one

if a firm has a politically connected Chairman and/or CEO, and zero otherwise. Our second

measure of political connection is based on the geographical coincidence between the firm and

the incumbent provincial political leaders (Faccio and Parsley, 2009; Cao et al., 2018). We

define political leaders in a province as the provincial party secretary and the governor, because

they are the two most powerful politicians in a province.20 Politicians tend to choose policies

in favor of the geographically connected firms, such as firms operating in their hometowns, a

phenomenon referred to as regional favoritism (Holder and Raschky, 2014). We define a

dummy variable, Regional Bias, which equals one if a firm is registered in the birthplace of the

                                                               18 Although the distributions of the three underlying variables are concentrated at 0 or 1, using quintiles defined in this way still has the advantage of creating a finer ranking for observations whose values are not concentrated at 0 or 1 than using quartiles, terciles, or sample median. 19 Fan et al. (2007) consider only CEOs’ connections. We also consider the Chairman because in China the Chairman is the legal representative of the company under corporate law, as well as the highest authority of the company. Some prior studies treat the Chairman rather than CEO as the top manager of a firm in China (e.g., Firth, Fung and Rui, 2006). Faccio’s (2006) measure is even broader, including connections of large shareholders and all top officers (CEO, president, vice-president, chairman, or secretary). 20 In China, the party secretary is the de facto “first-in-command” official and is empowered with decision-making rights on key political and economic matters (e.g., Persson and Zhuravskaya, 2009; Kung and Chen, 2011).

18    

current provincial party secretary or governor or a city in which the current provincial party

secretary or governor spent longest time during her career, and zero otherwise.

4.2. Data and descriptive statistics

Our sample includes all NSOEs listed on the Shenzhen and Shanghai Stock Exchanges

that received government subsidies during the period 2007-2016. We begin the sample in 2007

to ensure a consistent disclosure requirement. As we describe in Section 2, Chinese Enterprise

Accounting Standards No. 16-Government Grants was enacted in 2007, which requires that

public firms report subsides in “other income” and disclose in the notes of the annual reports

the type and amount of subsidies. We manually collect the subsidy information from the annual

reports.21 The information on provincial political leaders is manually collected from various

sources including local government websites, news release, People’s Daily Online

(www.people.com.cn), and other public announcements. We also manually searched IPO

prospectuses and annual reports to confirm whether the CEOs and the Chairman of the board

are politically connected. We source the information on media coverage from Chinese Research

Data Services (CNRDS), and financial and stock market information from the CSMAR

database.

We condition our analyses on firm-years that receive subsidies because unsubsidized firms

have no subsidy information to disclose. After removing financial firms and requiring the

availability of control variables used in the main analysis, our final sample consists of 7,874

firm-year observations for 1,017 unique firms. Table 1 presents descriptive statistics for the

sample firms. An average firm has market capitalization of 5,117 million Chinese Yuan

(roughly 743 million U.S. dollars) and a leverage ratio (Lev) of 40.4%. Around 13% of firms

                                                               21 According to the Accounting Standards for Enterprises No.16 (Government Grants), government subsidies are “monetary or non-monetary assets obtained free by an enterprise from the government, but excluding the capital invested by the government as the owner of the enterprise”. Subsidies, including the type and amount, should be recorded in a firm’s financial statements as “non-operating income”.

19    

make a loss (Loss) and the average return on assets (ROA) is 4.3%.  

For 33.3% of firm-years in the sample, the CEO or Chairman is a current or former

government or military official (Officer Connect). The mean of the Reginal Bias variable

indicates that 29.5% of firm-years are for firms that are registered in the birthplace of the

current provincial party secretary or governor or a city in which the current provincial party

secretary or governor spent longest time during her career. This evidence is consistent with the

pervasive political connection in China. On average, subsidy recipients disclose in annual

reports the subsidy sources (Source) for 41.5%, subsidy policy bases (Policy Basis) for 22.1%,

and subsidy types (Type) for 95.6% of subsidies received. The mean of Type being close to 1

and the relatively low means for Source and Policy Basis are consistent with the mandatary

disclosure requirement of subsidy type and the absence of such requirement for subsidy source

and policy basis. The average value of the subsidy disclosure measure, Sub_disclosure, is 0.583,

which is slightly different from its theoretical value 0.6 because the distribution of each

disclosure component is concentrated at 0 or 1.22

 

4.3. Political connection and subsidy disclosure: Baseline results

To examine the effect of corporate political connection on subsidy disclosure, we estimate

the following ordinary least squared (OLS) model:

Sub_disclosureit=α+β×POLICONit+∑Controlsit+Industry FE+Province FE

+Year FE+εit, (1)

where Sub_disclosure is the composite measure of subsidy disclosure as defined in Section 4.1,

POLICON is one of our two measures of political connection, namely, Officer Connect and

Regional Bias. We expect a negative coefficient on the political connection measure (β).

Controls refer to a set of control variables. Since no prior studies have directly examined

                                                               22 If the distribution of each component is not concentrated and the quartiles are well defined, by construction, the mean of Sub_disclosure should be 0.6. 

20    

economic factors that are associated with firms’ disclosure of government subsidies, we rely

on existing studies on general corporate disclosure to select our control variables (e.g.,

Bourveau et al., 2018; Guay et al., 2016; Francis et al., 2008; Chen et al., 2008). Specifically,

we control for the following firm characteristics: the natural logarithm of total assets (Size),

leverage ratio (Lev), the natural logarithm of total number of employees (Employee), the

market-to-book ratio (MTB), return on assets (ROA), a dummy variable for loss firms (Loss),

the number of analysts following the firm (Analyst), the percentage of institutional shareholding

(Institutional), annual stock return (Return), the standard deviation of sales scaled by average

sales over a rolling three-year window (σSale), and the standard deviation of annual stock

returns over a rolling 36-month window (σReturn). Detailed definitions of these variables are

provided in Appendix B.

In addition to these commonly used control variables, we also control for the natural

logarithm of one plus subsidy amount (Subsidy).23 This is an important control because if the

amount is larger, subsidy information is more material and the firm is more likely to provide

detailed disclosure. In addition, To the extent that receiving subsidies is perceived positively

by the capital market and other stakeholders of the firm, we expect that firms receiving more

subsidies are likely to provide more subsidy disclosures. We further include industry and

province fixed effects to control for time-invariant industry and regional characteristics that are

likely to be associated with subsidy disclosure policy, respectively, and year fixed effects to

account for year-specific factors that affect subsidy disclosure for all sample firms, such as

macroeconomic conditions. We cluster standard errors by firm to account for possible within-

firm dependence of the error terms.

Table 2 reports the results of estimating equation (1). In columns 1 and 2, we separately

                                                               23  While all firm-years in our sample are subsidy recipients, we measure Subsidy as the natural logarithm of one plus the subsidy amount, instead of the natural logarithm of subsidy amount, to be consistent with the measurement in Table 7, for which we include firms receiving no subsidies.  

21    

include Officer Connect and Regional Bias to measure political connections. In both columns,

the coefficients on the political connection measure are significantly negative. In column 3, we

include both political connection measures and find that the coefficients on both variables are

significantly negative and their magnitudes are equal to the corresponding estimates in columns

1 and 2. These results indicate that politically connected firms provide less subsidy disclosure,

consistent with our prediction. The estimated effects of political connection are also

economically meaningful. The coefficient estimate for Officer Connect in column 3, -0.016,

suggests that the subsidy disclosure level is lower by 0.016, which accounts for 8.2% of the

standard deviation of the disclosure measure (0.195), for firms whose Chairman or CEO is a

current or former government or military official than other firms. The estimated effect of

Regional Bias is even larger in magnitude (-0.024), accounting for 12.3% of the standard

deviation of the disclosure measure.

Regarding the effects of control variables, we find that the amount of subsidy (Subsidy)

is positively associated with the extent of subsidy disclosure, which is consistent with our

expectation. Large firms (Size) disclose less information regarding government subsidies,

consistent with large firms being subject to stricter public scrutiny with respect to subsidy

granting. The subsidy disclosure level increases with stock return (Return) and decreases with

return volatility (σReturn), consistent the finding in the literature that better performing firms

provide more disclosure while riskier firms provide less disclosure (e.g., Ajinkya et al., 2005).

The subsidy disclosure level is negatively associated with institutional ownership

(Institutional), possibly because subsidies received by firms with high institutional ownership

are subject to stricter public scrutiny. In addition, we find that the subsidy disclosure level is

negatively associated with a firm’s leverage ratio (Lev) and number of employees (Employee).

We next address the concern that the finding in Table 2 could be due to connected firms’

lower disclosure incentives in general, as argued in Hung et al. (2018). We repeat the analyses

22    

in Table 2 using as the dependent variables two measures of firms’ general disclosures: the

natural logarithm of one plus the frequency of management earnings forecasts (Freq) and the

natural logarithm of the length of MD&A (the number of words) in the annual report

(MD&A).24 We use the same sample as in Table 2 to explore how political connections relate

to general corporate disclosures specifically in this sample. The results reported in Table 3

indicate that the association between each political connection measure and each general

disclosure measure is insignificant. Thus, it is unlikely that our results in Table 2 are due to the

general lower disclosure incentives for connected firms.

4.4. Political connection and subsidy disclosure: Cross-sectional analyses

One concern about our analysis in Section 4.3 is that political connection is endogenous

and our finding could be due to economic factors that are associated with both political

connection and subsidy disclosure. We note that this concern is mitigated by the fact that

regional bias is arguably not a function of firm characteristics because it is mainly driven by

the geographical coincidence between a firm and the provincial political leaders. Nevertheless,

we conduct extensive cross-sectional tests to address the potential endogeneity issue. In

particular, we show that the negative association between political connection and subsidy

disclosure is stronger for firms registered in higher corruption provinces and firm with higher

media attention, and becomes weaker following the anti-corruption campaign started in late

2012. In addition, the negative connection-disclosure association is mainly driven by connected

firms whose subsidies are difficult to justify. To the extent that any alternative explanation of

our baseline finding should also be consistent with these cross-sectional findings, these tests

                                                               24   Hung et al. (2018) also use the frequency of management earnings forecasts as their major measure of disclosure. However, their study does not include China. Chinese regulators mandate earnings forecasts when managers’ earnings expectations meet bright-line thresholds (e.g., when earnings are expected to increase or decrease by at least 50% from the prior year) and allow voluntary forecasts in other circumstances (Huang et al., 2018). In our analysis, we manually exclude forecasts that are mandated.  

23    

help increase our confidence that our result is due to politically connected firms withholding

subsidy information to reduce public scrutiny.

4.4.1. The effect of provincial corruption level

If the negative association between political connection and subsidy disclosure we

document is due to politically connected subsidy recipients’ incentives to avoid public scrutiny

of subsidies granted through relationships, we expect the association to be more pronounced

for firms operating in a more corrupt region, because subsidies are more likely to be granted

through relationships or even bribes when the government is more corrupt (Fang et al., 2018).

To test this prediction, we estimate the following equation:

Sub_disclosureit=α + β1×Connectit + β2×Connectit × High-Corruptit + ∑Controlsit

+ Industry FE + Province FE + Year FE + εit, (2)

where High-Corrupt is a dummy variable that equals one if the corruption level (Corrupt) of

the province a firm is registered in is above the sample median, and zero otherwise, and all

other variables are as defined in equation (1). We measure the provincial corruption level with

the number of government officials of vice county-division rank or above that are convicted

due to corruption during our sample period.25 We expect a negative estimated coefficient of

Connect×High-Corrupt (β2). We do not include High-Corrupt separately in equation (2) due

to the inclusion of province fixed effects.

Table 4 reports the results of this analysis. We omit the results of control variables for

brevity. Column 1 reports the results for Officer Connect as the measure of political connection.

We find a negative and significant coefficient on Officer Connect×High-Corrupt (-0.024, t-

statistic=-1.95) and a negative but insignificant coefficient on Officer Connect (-0.005, t-

                                                               25 In China, the rank for government officials is in accordance with the administrative level: state level (guojia ji); provincial-ministerial level (shengbu ji); prefecture-bureau level (tingju ji); county-division level (xianchu ji); and township-section level (xiangke ji). We follow prior literature and focus on officials of vice county-division rank or above (Cole, Elliott, and Zhang, 2009; Xu and Yano, 2017).

24    

statistic=-0.53). Similarly, in column 2, when political connection is measured with Regional

Bias, the coefficient on Regional Bias×High-Corrupt is positive and significant (-0.037, t-

statistic=-2.23), and the coefficient on Regional Bias is negative but insignificant (-0.007, t-

statistic=-0.64). We find qualitatively similar results when including both Officer Connect and

Regional Bias and their interactions with High-Corrupt into the regression (column 3). These

results suggest that, as predicted, the negative association between political connection and

subsidy disclosure mainly exists and is more pronounced for firms operating in a higher

corruption region.

The coefficient estimates in Table 4 indicate a lager economic effect of political

connection on subsidy disclosure for firms in high corruption regions than in Table 2 for the

full sample. For firms in high corruption regions, firms with officer connections have a lower

subsidy disclosure level by 0.028 (|-0.006-0.022|) and firms with regional bias have a lower

disclosure level by 0.042 (|-0.007-0.035|), which account for 14.4% and 21.5% (vs. 8.2% and

12.3% in the full sample), respectively, of the standard deviation of the disclosure measure

(0.195).

4.4.2. The effect of the 2012 anti-corruption campaign

The Political Bureau of the Communist Party of China (CPC) announced the Eight-point

Regulation on December 4, 2012, with an intention to curb bureaucracy, extravagance, and

corruption of party members. The anti-corruption campaign has led over one hundred thousand

convictions, including the fall of a number of “tigers” (government officials with very high

ranks). Research has shown that the anti-corruption campaign is generally effective in deterring

corruption (e.g., Griffin, Liu, and Shu, 2017; Lin et al., 2018; Fang et al., 2018). For instance,

Griffin, Liu, and Shu (2017) show that the campaign has reduced the entertainment

expenditures of targeted corrupt firms. Fang et al. (2018) find that the campaign strengthens

25    

the relationship between firms’ innovation efficiency and subsequent subsidy awards.

We expect that firms are less able to reap benefits, including subsidies, from government

officials through relationship following the anti-corruption campaign, and therefore the public

concerns on relationship-based subsidy awards are substantially mitigated. Accordingly, we

predict that the negative relation between political connection and subsidy disclosure becomes

weaker following the campaign.26 To test this prediction, we estimate the following equation:

Sub_disclosureit=α + β1×Connectit + β2×Connectit × Postit + ∑Controlsit

+ Industry FE + Province FE + Year FE + εit, (3)

where Post is a dummy variable that equals one for the post-campaign periods (i.e., 2013-2016),

and zero otherwise (i.e., 2007-2012). We expect a negative estimated coefficient of

Connect×Post (β2).

Columns 1 to 3 of Table 5, Panel A report the results of estimating equation (3). We include

the two political connection measures separately in columns 1 and 2, and together in column

3. Across all three columns, we find a negative and significant coefficient on each political

connection measure and a positive and significant coefficient on its interaction with the Post

variable. Further, the sum of these two coefficients is close to zero and insignificant. These

results suggest that the negative association between political connection and subsidy

disclosure becomes weaker following the 2012 anti-corruption campaign and it mainly exists

in the pre-campaign period, reinforcing our inference that the negative association we

document in Table 2 is due to politically connected firms’ concerns of public scrutiny of

relationship-based subsidies. As expected, the economic effect of political connection on

subsidy disclosure is larger in magnitude in the pre-campaign period than in the full sample (-

0.026 vs. -0.016 for Officer Connect and -0.049 vs. -0.024 for Regional Bias based on the

                                                               26  While we believe that the reduction in corruption after the campaign will weaken the negative relation between political connection and subsidy disclosure, the higher likelihood and costs of corruption being detected in the post-campaign period could strengthen the relation.  

26    

estimates in column 3).

One alternative explanation for the above results is that they may simply reflect a random

time trend in the association between political connection and subsidy disclosure. To address

this concern, we conduct a placebo analysis using 2011, 2012, 2014, and 2015 as the pseudo

first year of the campaign. Specifically, for each pseudo year t, we include into the analysis the

years t-2, t-1, t and t+1 and define Post as 1 for t and t+1 and 0 otherwise. We then repeat the

analysis in column 3 of Table 5, Panel A for each pseudo year and report the results in Panel B.

For comparison, we also report the results based on the actual first year of campaign (2013)

using the sample period 2011~2014.

We find that for the pseudo years 2011, 2012, 2014, and 2015, the coefficients on the

interaction terms between the political connection measures and Post are all insignificant

except that for 2012 the coefficient on Regional Bias×Post is positive and significant (column

2). In contrast, for the actual first year of campaign, we find positive and significant coefficients

on both Officer Connect×Post and Regional Bias×Post (column 3). This evidence suggests

that our findings in Panel A are unlikely to be due to a random time trend. We note that the

significantly positive coefficient on Regional Bias×Post in column 2 (2012 as the pseudo first

year of the campaign) is not inconsistent with this conclusion for two reasons. First, the period

with Post equal to 1 (2012 and 2013) overlaps with the actual post-campaign period (2013 is

in the post-campaign period). Second, provincial political leaders (party secretaries and

governors) might have been informed of the campaign before it was officially announced in

December 2012.

4.4.3. The effect of media attention

Next, we examine how the negative association between political connection and subsidy

disclosure we document varies with firms’ media attention. If the association is due to

27    

politically connected subsidy recipients’ incentives to avoid public scrutiny of subsidies

granted through relationship, we expect it to be more pronounced for firms with higher median

attention (Huang, 2018). To test this prediction, we create an indicator variable High-Media

Attention for firms whose media coverage is above the sample median, and estimate equation

(2) with High-Corrupt replaced with High-Media Attention. We also include High-Media

Attention because it is not absorbed by the province fixed effects as in the case for High-

Corrupt. We expect a negative coefficient on the interaction term of High-Media Attention with

Officer Connect (Reginal Bias). We do not have a directional prediction for the coefficient of

High-Media Attention because media attention could be positively associated with subsidy

disclosure due to the market’s positive perception of government subsides, whereas it could

also reduce subsidy disclosure if the subsidy is unjustified.

Table 6 reports the results of this analysis. Column 1 reports the results using Officer

Connect as the measure of political connection. We find a negative and significant coefficient

on Officer Connect×High-Media Attention (-0.021, t-statistic = -2.16), while the coefficient of

Officer Connect becomes insignificant (-0.006, t-statistic=-0.70). Similarly, when political

connection is measured with Regional Bias in column 2, the coefficient on Regional

Bias×High-Media Attention is negative and significant (-0.019, t-statistic=-1.88), while the

coefficient on Regional Bias is insignificant (-0.014, t-statistic=-1.39). We find qualitatively

similar results when including both Officer Connect and Regional Bias and their interactions

with High-Media Attention in column 3. Collectively, these results suggest that, as expected,

the negative association between political connection and subsidy disclosure is more

pronounced for firms with higher media attention. The estimated coefficient of High-Media

Attention is consistently positive and significant, suggesting that on average firms provide more

subsidy disclosure when their media coverage is higher, consistent with the notion that the

market generally perceives government subsidies positively.

28    

The results in Table 6 also help rule out the general lower disclosure incentives of

connected firms proposed by Hung et al. (2018) as an alternative explanation of our main

finding. If our finding is driven by political connections provide political protection and/or

easier access to financing, the negative association between connection and subsidy disclosure

should become weaker for firms with greater media attention, not stronger as we document,

because media attention will deter firms from extracting benefits from political connections.

4.4.4. The effect of subsidy justifiability

The central argument of our paper is that politically connected subsidy recipients reduce

subsidy disclosure to hide their relationship-based dealings with government officials. Thus,

political connection is negatively associated with subsidy disclosure presumably only when the

connected firms obtain subsidies through relationship. To provide evidence on this prediction,

we decomposed connected firms into two groups based on whether their subsidies are

justifiable based on the current Five-Year Plan (or Five-Year Guideline).27 China’s Five-Year

Plans are a series of social and economic development initiatives issued since 1953. Planning

is a key characteristic of socialist economies, and one plan established for the entire country

normally contains detailed economic development guidelines for all its regions. In particular,

each plan specifies a set of industries that will be the focus of government support during the

five-year period. We use this information to measure the degree of justifiability for a firm’s

subsidy.

Specifically, we decompose the variable Officer Connect into two variables: Officer

Connect-Justified, an indicator variable for a firm which has an officer connection and whose

subsidy is justifiable using the current Five-Year Plan, and Officer Connect-Unjustified, an

                                                               27  In order to more accurately reflect China’s transition from the planned economy to a socialist market economy, the name of the 11th five-year program of 2007-2010 was changed to “guideline.”  

29    

indicator variable for a firm which has an officer connection and whose subsidy is unjustifiable

using the current Five-Year Plan. We then include both measures into the regression and expect

that the negative effect of Officer Connect is mainly driven by Officer Connect-Unjustified. We

conduct a similar decomposition for the variable Regional Bias (into Regional Bias-Justified

and Regional Bias-Unjustified).28

Table 7 reports the results of this analysis. Columns 1 and 2 report the results for each

political connection measure separately and column 3 for the two measures together. We find

negative and significant coefficients for Officer Connect-Unjustified and Regional Bias-

Unjustified, whereas the coefficients of Officer Connect-Justified and Regional-Bias-Justified

are very close to zero and insignificant. In addition, the difference in the coefficients of Officer

Connect-Unjustified and Officer Connect-Justified (Regional Bias-Unjustified and Regional

Bias-Justified) is significant. These results are consistent with our prediction that political

connection is negatively associated with subsidy disclosure only when the subsidies are

difficult to justify.

4.5. Political connection and subsidy granting

A premise of the negative relation between political connection and subsidy disclosure we

hypothesize is that political connection allows firms to reap more government subsidies. In this

section, we explicitly test this assumption by regressing the variable Subsidy (the natural

logarithm of one plus the subsidy amount) on Officer Connect and Regional Bias and a set of

control variables, as well as the industry, year, and province fixed effects. We include the

following control variables (e.g., Chen et al., 2008; Jin and Zhang, 2017): the natural logarithm

of total assets (Size), leverage ratio (Lev), the natural logarithm of total number of employees

                                                               28  The means of Officer Connect-Justified, Officer Connect-Unjustified, Regional Bias-Justified, and Regional Bias-Unjustified are 0.097, 0.236, 0.094, and 0.201, respectively, proving reasonable variation for the analyses.  

30    

(Employee), the market-to-book ratio (MTB), return on assets (ROA), a dummy variable for

loss firms (Loss), an indicator variable for high-tech industry (High-tech), and

the standard deviation of sales over a rolling three-year window (σSale). Detailed definitions

of these variables are provided in Appendix B.

For this analysis, we use the full sample of listed NSOEs, including both subsidized and

unsubsidized ones, during the same period as in the analysis of subsidy disclosure (2007~2016).

The sample size increases to 9,425 from 7,874 in the previous analyses. Panel A of Table 8

presents the summary statistics for this analysis. The means of Officer Connect and Regional

bias are 0.316 and 0.263, respectively, which are slightly lower than the corresponding numbers

in Table 1 for subsidized firms (0.333 and 0.295, respectively). These differences are consistent

with subsidized firms being more likely to have political connections.

Panel B of Table 8 reports the regression results. We separately include Officer Connect

and Regional Bias in columns 1 and 2, respectively, and include both measures in column 3. In

line with our assumption that political connection is positively associated with the amount of

subsidy awards, across all columns, we find a positive and significant coefficient for each

political connection measure. Furthermore, the coefficient on Regional Bias is larger than that

on Officer Connect, 0.533 vs. 0.305 in column 3. This pattern, combined with the finding in

column 3 of Table 2 that the estimated effect of Regional Bias on subsidy disclosure is larger

in magnitude than that of Officer Connect (-0.024 vs. -0.016), suggests that the negative effect

of a form of political connection on subsidy disclosure is more pronounced when its positive

effect on subsidy granting is stronger. With respect to control variables, we find that subsidy

granting is positively associated with firm size, the number of employees, profitability, and is

negatively related to market-to-book ratio and sales volatility. In addition, firms in high-tech

industries tend to obtain more subsidies.

Our cross-sectional analyses in Sections 4.4.1 and 4.4.2 are based on the assumption that

31    

political connection is more positively associated with subsidy granting in more corrupt

provinces and before the anti-corruption campaign. We also explicitly test these assumptions

by adding to the regressions in Panel B of Table 7 the interaction terms of each political

connection measure and the variable High-Corrupt or Post, and report the results in Panel C.

As expected, we find positive and significant coefficients on Officer Connect×High-Corrupt

and Regional Bias×High-Corrupt, and significantly negative coefficients on Officer

Connect×Post and Regional Bias×Post. These results, combined with the findings in Tables 3

and 4, further suggest that the negative effect of political connection on subsidy disclosure is

more pronounced when its positive effect on subsidy granting is stronger. This finding further

increases our confidence that the negative association between political connection and subsidy

disclosure we document is due to politically connected subsidy recipients reducing subsidy

disclosure to avoid related public scrutiny on relationship-based subsidies.

4.6. Robustness tests

4.6.1. Alternative disclosure measures

Our analyses are based on the composite disclosure measure calculated using the

quintiles of the variables Source, Policy Basis, and Type. In this section, we consider several

alternative measures. First, we separately examine how political connection is associated with

Source, Policy Basis, and Type. The results reported in columns 1 to 3 of Table 9, Panel A

indicate that both Officer Connect and Regional Bias are negatively associated with each

measure, and these effects are significant except for the effect of Officer Connect on Source

and the effect of Regional Bias on Policy Basis. Second, we calculate an alternative composite

measure by calculating the simple average of Source, Policy Basis, and Type, denoted as

Sub_discosure1. Our main results are robust to this alternative measure (column 4 of Table 8,

Panel A). Third, because the variation of Type is very small (the mean is 95.9% as reported in

32    

Table 1), we recalculate the variable Sub_disclosure by removing Type. Using this measure,

denoted as Sub_discloseure2, we find similar results (column 5 of Table 9, Panel A).

Finally, we consider firms’ voluntary issuance of interim announcements for the subsidies

as another dimension of subsidy disclosure. We manually collect information on all interim

announcements and calculate the percentage of subsidies that are disclosed in interim

announcements (Interim).29 We estimate equation (1) using Interim as the dependent variable

and find that the estimated coefficient on Regional Bias is significantly negative and that on

Officer Connect is negative but insignificant (column 6, Panel A of Table 9). We further include

Interim into the calculation of the quintile-based composite disclosure measure

(Sub_disclosure3) and find that it is negatively and significantly associated with both Officer

Connect and Regional Bias (column 7, Panel A of Table 9). Overall, the results in Table 9, Panel

A indicate that our main finding is robust to various alternative measures of subsidy disclosure.

4.6.2. Alternative model specification and treatment variable

Next, we consider an alternative model specification by replacing industry fixed effects

in equation (1) with firm fixed effects. By controlling for time-invariant characteristics across

firms, a model with firm fixed effects explores how the within-firm change in political

connection is associated with the within-firm change in subsidy disclosure and helps address

the endogeneity issue. We continue to find negative and significant coefficients on Officer

Connect and Regional Bias (columns 1 to 3 of Table 9, Panel B). However, the results are

weaker both economically and statistically than those in Table 2. These weaker results are

expected for two reasons. First, political relationship in China tends to be sticky due to the

guanxi network. In the case of officer connect, even after a connected CEO leaves the position,

the current CEO may still maintain the political connection through the former CEO. In the

                                                               29  The mean of Interim is 0.155 (untabulated).  

33    

case of regional bias, even if the provincial party secretary or governor retires from the position,

she may still have substantial political influences using her guanxi network. Second, the within-

firm variation of regional bias is due to position change of the provincial party secretary or

governor. They may have similar political influences before being appointed as the party

secretary or governor, because they need to climb over the political ladder gradually to reach

that position, and may have even greater powers after leaving the position if they are promoted

to the central government.

We further consider a measure of subsidies due to political connection (Subsidy-PC)

calculated using the subsidy determinant model in Section 4.5. Specifically, we regress Subsidy

on Officer Connect and Regional Bias and their inactions with High-Corrupt and Post, as well

as the control variables and province, industry, and year fixed effects. Subsidy-PC is calculated

as

Subsidy-PC=β1Officer Connect + β2Regional Bias + β3Officer Connect×High-Corrupt

+ β4Regional Bias×High-Corrupt + β5Officer Connect×Post+ β6Regional Bias×Post, (4)

where β1 to β6 are the corresponding estimated coefficient from the subsidy determinant model.

This approach is similar to the one used by Core et al. (1999) to measure executive

compensation that is driven by corporate governance variables. We then estimate equation (1)

by replacing the political connection variable with Subsidy-PC. We find a negative and

significant coefficient on Subsidy-PC (column 4 of Table 9, Panel B), and the result is robust

to replacing industry fixed effects with firm fixed effect (column 5). This evidence further

supports our argument that firms that obtain subsidies through political connection are more

likely to withhold subsidies information.

4.6.3. Propensity score matching analysis

In a further attempt to control for differences in observable characteristics between

34    

connected and unconnected firms, we create a propensity score matched sample and rerun the

main analyses. Specifically, in the first-stage logistic regression model, we predict the

likelihood of a firm being politically connected based on the control variables and various fixed

effects in equation (1). 30 Since our main independent variables are Officer Connect and

Regional Bias, we conduct matching for the two variables separately. Robert and Whited (2013,

p.556) suggest researchers choose as many matches as possible without sacrificing accuracy.

Following their suggestion as well as other prior studies (e.g., Li and Yang, 2015; Utke, 2017),

we match firms using the caliper technique (with replacement) with a radius of 0.01.

The above matching procedure ensures that the treatment (connected) firms and control

(unconnected) firms are largely indistinguishable along a set of firm characteristics (the

matching variables) except the presence of political connection. This is confirmed by the

covariate balance test reported in Table 10, Panel A. In Panel B, we repeat the main regressions

by using the sample matched on Officer Connect (columns 1-3) and Regional Bias (columns

4-6), respectively. We find results consistent with those from the main regressions in Tables 2-

4. In an untabulated analysis, we rerun these regressions by including both political connection

proxies. Once again, our main inferences remain unaffected.

4.7. Analyses based on SOEs

In the main analyses we focus on NSOEs because they are subject to much stricter public

scrutiny with respect to extracting economic benefits through political connections than SOEs.

Because SOEs are owned by the government on behalf of the nation (taxpayers), wealth

transfer from the government to SOEs is not generally viewed as a transfer and is subject to

lesser public scrutiny. Therefore, connected SOEs receiving subsidies may have much weaker

                                                               30 Core (2010) and Shipman et al. (2016) advocate the use of the same set of control variables in both PSM and the associated regression model to avoid the appearance of post hoc model specification.

35    

incentives to hide their subsidy information. Thus, finding no or weaker results for SOEs

provides additional support for our argument that politically connected NSOEs receiving

subsidies reduce subsidy disclosure to avoid related public scrutiny.

We repeat our main analyses for a sample of subsidized SOEs during the same sample

period (2007-2016) and report the results in columns 1-3 of Table 11. We find that the

coefficients on Regional Bias and Officer Connect are both insignificant. Thus, there is no

conclusive evidence that political connection is associated with subsidy disclosure for SOEs.

We further explore how the association varies with the provincial corruption level (columns 4-

6) and the anti-corruption campaign (columns 7-9). We find that the coefficients on Officer

Connect×High-Corrupt and Regional Bias×High-Corrupt are statistically insignificant,

whereas the coefficients on Officer Connect×Post and Regional Bias×Post are positive and

marginally significant. Thus, there is no conclusive evidence that the connection-subsidy

association varies with the provincial corruption level, while there is some evidence that the

association becomes more positive following the anti-corruption campaign. Overall, we

conclude that we generally do not find similar results for SOEs.

5. Conclusion

This paper examines the association between firms’ political connection and their

disclosure on government subsidies using a sample of Chinese non-state-owned enterprises.

We hypothesize that politically connected subsidy recipients have incentives to avoid

disclosing detailed information on the subsidies they receive in order to lower the costs from

the public scrutiny related to subsidies obtain through connections. Consistent with this

prediction, for the sample period 2007~2016, we find that politically connected subsidy-

receiving firms provide a lower amount of subsidy disclosures through annual reports than

unconnected ones. This result is not due to the general lower disclosure incentives of connected

36    

firms. We further show that this association is mainly driven by connected firms whose

subsidies are difficult to justify, and is stronger for firms registered in higher corruption

provinces and firm with higher media attention, and becomes weaker following the anti-

corruption campaign started in late 2012. We generally do not find similar results for state-

owned enterprises. In addition, we find that politically connected firms are granted more

subsidies and this association is stronger in higher corruption provinces and become weaker

following the anti-corruption campaign. These findings further support our interpretation that

connected subsidy recipients reduce subsidy disclosures to reduce public scrutiny of

relationship-based subsidies.

Our study contributes to the literature on how political incentives affect corporate financial

reporting and disclosure (e.g., Ramanna and Roychowdhury, 2010; Piotroski et al., 2015), as

well as the economic impact of political connection (e.g., Aobdia et al., 2018). It is also of

interest to policy makers with respect to the mandatory disclosure requirement of subsidy

information. To the extent that subsidy recipients have incentives to withhold subsidy

information, especially when the subsidies are obtained through connections, the voluntarily

disclosed subsidy information may be below the socially optimal level.

37    

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Table 1 Descriptive Statistics

Variables Obs. Mean Std. Dev. 25% Median 75%

Subsidy disclosure measures

Sub_disclosure 7,874 0.583 0.195 0.467 0.533 0.733

Source 7,874 0.415 0.420 0 0.252 0.919

Policy Basis 7,874 0.221 0.356 0 0 0.414

Type 7,874 0.959 0.103 0.985 1 1

General disclosure measures

Freq (before taking log) 7,874 1.834 1.825 0.000 1.000 4.000

Freq (in log) 7,874 0.810 0.700 0.000 0.693 1.609

MD&A (before taking log) 7,874 7,105 4,184 3,845 6,584 9,590

MD&A (in log) 7,874 8.634 0.881 8.255 8.793 9.169

Political connection measures

Officer Connect 7,874 0.333 0.471 0 0 1

Regional Bias 7,874 0.295 0.456 0 0 1

Firm characteristics

Subsidy 7,874 15.804 1.668 14.936 15.930 16.873

Log (Market Cap) 7,874 22.385 45.027 3.067 8.287 21.278

Size 7,874 21.631 1.074 20.874 21.528 22.272

Market Cap (in millions) 7,874 5116.917 11380.630 1162.360 2235.428 4707.262

Lev 7,874 0.404 0.217 0.231 0.391 0.553

Employee 7,874 7.383 1.148 6.664 7.366 8.106

MTB 7,874 2.647 2.187 1.218 1.967 3.337

ROA 7,874 0.043 0.060 0.017 0.041 0.071

Loss 7,874 0.128 0.334 0 0 0

Analyst 7,874 1.601 1.112 0.693 1.609 2.565

Institutional 7,874 0.052 0.065 0.007 0.032 0.076

Return 7,874 0.185 0.618 -0.213 -0.002 0.384

σSale 7,874 0.225 0.201 0.095 0.168 0.285

σReturn 7,874 1.125 6.615 -0.223 1.514 2.381

This table reports the descriptive statistics for the variables used in the main empirical analysis. The sample consists of all listed non-state-owned enterprises (NSOEs) that received government subsides during the period 2007-2016. All variables except the dummy variables are winsorized at the 1% and 99% levels. Variable definitions are in Appendix B.

43    

Table 2 Political Connections and Disclosure of Subsidies

Dependent Variable: Sub_disclosure (1) (2) (3)

Officer Connect -0.016*** -0.016*** (-2.602) (-2.632) Regional Bias -0.024*** -0.024*** (-2.794) (-2.812) Subsidy 0.012*** 0.012*** 0.012*** (5.659) (5.695) (5.697) Size -0.014** -0.014*** -0.013** (-2.516) (-2.600) (-2.444) Lev -0.042** -0.039** -0.040** (-2.558) (-2.370) (-2.465) Employee -0.015*** -0.016*** -0.016*** (-3.889) (-3.970) (-3.985) MTB 0.000 0.000 0.000 (0.039) (0.016) (0.040) ROA -0.039 -0.044 -0.042 (-0.705) (-0.797) (-0.755) Loss 0.001 0.001 0.001 (0.126) (0.137) (0.121) Analyst 0.002 0.003 0.003 (0.693) (0.834) (0.908) Institutional -0.106*** -0.103*** -0.108*** (-2.660) (-2.587) (-2.722) Return 0.009* 0.009** 0.009** (1.947) (1.968) (1.982) σSale 0.011 0.012 0.010 (0.780) (0.867) (0.728) σReturn -0.001* -0.001* -0.001* (-1.735) (-1.780) (-1.748) Industry FE Yes Yes Yes Year FE Yes Yes Yes Province FE Yes Yes Yes N 7,874 7,874 7,874 Adj. R2 0.064 0.064 0.066

This table reports the OLS regression results for the association between political connection and firms’ subsidy disclosure. The sample consists of all listed non-state-owned enterprises (NSOEs) that received government subsides during the period 2007-2016. The dependent variable Sub_disclosure is an aggregated measure of subsidy disclosure (see Appendix A for details). The independent variables of interest are Officer Connect, a dummy variable that equals 1 if the CEO or board Chairman is currently serving or has formerly served in the government or the military, and Regional Bias, a dummy variable that equals 1 if the firm’s registration city is the incumbent provincial leader’s (the party secretary or governor) birthplace or former workplace where he/she had the longest part of his/her work experience. The definition of control variables are in Appendix B. All variables except the dummy variables are winsorized at the 1% and 99% levels. t-statistics (in the parentheses) are calculated using robust standard errors corrected for heteroscedasticity and clustered at the firm level. ***, **, and * indicate significance at the 1%, 5% and 10% levels, respectively.

44    

Table 3 Political Connections and General Corporate Disclosures

This table reports the OLS regression results for the association between political connection and firms’ general disclosures, measured with the natural logarithm of one plus the frequency of management earnings forecasts (Freq) and the length (the natural logarithm of the number of words) of MD&A in the annual report (MD&A). The sample consists of all listed non-state-owned enterprises (NSOEs) that received government subsides during the period 2007-2016. The independent variables of interest are Officer Connect, a dummy variable that equals 1 if the CEO or board Chairman is currently serving or has formerly served in the government or the military, and Regional Bias, a dummy variable that equals 1 if the firm’s registration city is the incumbent provincial leader’s (the party secretary or governor) birthplace or former workplace where he/she had the longest part of his/her work experience. The definition of control variables are in Appendix B. All variables except the dummy variables are winsorized at the 1% and 99% levels. t-statistics (in the parentheses) are calculated using robust standard errors corrected for heteroscedasticity and clustered at the firm level. ***, **, and * indicate significance at the 1%, 5% and 10% levels, respectively.

Dependent Variable: Freq MD&A

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

Officer Connect -0.005 -0.005 0.004 0.005 (-0.259) (-0.256) (0.188) (0.196) Regional Bias 0.014 0.014 0.036 0.036 (0.525) (0.524) (1.022) (1.024) Control Variables Yes Yes Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Province FE Yes Yes Yes Yes Yes Yes

N 7,874 7,874 7,874 7,874 7,874 7,874 Adj. R2 0.203 0.203 0.203 0.412 0.412 0.412

45    

Table 4 Political Connections and Disclosure of Subsidies: Effect of Provincial Corruption Level

Dependent Variable: Sub_disclosure

(1) (2) (3) Officer Connect -0.005 -0.006 (-0.527) (-0.660) Regional Bias -0.007 -0.007 (-0.638) (-0.678) Officer Connect×High-Corrupt -0.024* -0.022* (-1.951) (-1.757) Regional Bias× High-Corrupt -0.037** -0.035** (-2.229) (-2.131) Control Variables Yes Yes Yes Industry FE Yes Yes Yes Year FE Yes Yes Yes Province FE Yes Yes Yes

N 7,874 7,874 7,874

Adj. R2 0.065 0.065 0.067

This table reports the OLS regression results of the association between political connection and firms’ subsidy disclosure, conditioned on the provincial corruption level. The sample consists of all listed non-state-owned enterprises (NSOEs) that received government subsides during the period 2007-2016. The dependent variable Sub_disclosure is an aggregated measure of subsidy disclosure (see Appendix A for details). The independent variables of interest are Officer Connect, a dummy variable that equals 1 if the CEO or board Chairman is currently serving or has formerly served in the government or the military, and Regional Bias, a dummy variable that equals 1 if the firm’s registration city is the incumbent provincial leader’s (the party secretary or governor) birthplace or former workplace where he/she had the longest part of his/her work experience. The level of corruption (Corrupt) is measured as the provincial number of officials of vice county-division rank or above investigated in the recorded cases of corruption. High-Corrupt is a dummy variable that equals 1 if Corrupt is above the sample median, and 0 otherwise. The results for control variables are omitted for brevity. t-statistics (in the parentheses) are calculated using robust standard errors corrected for heteroscedasticity and clustered at the firm level. ***, **, and * indicate significance at the 1%, 5% and 10% levels, respectively.

46    

Table 5 Political Connections and Disclosure of Subsidies: Effect of Anti-Corruption Campaign

Panel A: Main Results

Dependent Variable: Sub_disclosure

(1) (2) (3) Officer Connect (β11) -0.025*** -0.026*** (-2.899) (-3.068) Regional Bias (β12) -0.048*** -0.049*** (-4.554) (-4.635) Officer Connect×Post (β21) 0.017* 0.018* (1.700) (1.867) Regional Bias×Post (β22) 0.049*** 0.050*** (4.187) (4.289) p-value for β11+ β21=0 0.295 0.317 p-value for β12+ β22=0 0.907 0.886 Control Variables Yes Yes Yes Industry FE Yes Yes Yes Year FE Yes Yes Yes Province FE Yes Yes Yes

N 7,874 7,874 7,874

Adj. R2 0.064 0.067 0.069

47    

Panel B: Placebo Tests

Dependent Variable: Sub_disclosure

First year for Post=1

2011 2012 2013 2014 2015

(1) (2) (3) (5) (6) Officer Connect -0.009 -0.018 -0.030*** -0.018* -0.004 (-0.681) (-1.490) (-2.926) (-1.939) (-0.419) Regional Bias -0.046*** -0.060*** -0.040*** -0.019 -0.016 (-2.857) (-4.268) (-3.274) (-1.631) (-1.186) Officer Connect×Post -0.020 -0.001 0.024** 0.005 -0.004 (-1.334) (-0.095) (2.181) (0.439) (-0.340) Regional Bias×Post 0.008 0.043*** 0.031** 0.011 0.012 (0.544) (3.090) (2.298) (0.876) (1.032) Control Variables Yes Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Province FE Yes Yes Yes Yes Yes

N 2,966 3,566 3,920 4,327 4,123

Adj. R2 0.077 0.068 0.085 0.074 0.053

This table reports the OLS regression results of the association between political connection and firms’ subsidy disclosure, conditioned on the period before and after the anti-corruption campaign. The sample consists of all listed non-state-owned enterprises (NSOEs) that received government subsides during the period 2007-2016. The dependent variable Sub_disclosure is an aggregated measure of subsidy disclosure (see Appendix A for details). The independent variables of interest are Officer Connect, a dummy variable that equals 1 if the CEO or board Chairman is currently serving or has formerly served in the government or the military, and Regional Bias, a dummy variable that equals 1 if the firm’s registration city is the incumbent provincial leader’s (the party secretary or governor) birthplace or former workplace where he/she had the longest part of his/her work experience. In Panel A, Post is a dummy variable that equals 1 for observations after the anti-corruption campaign (i.e., 2013-2016), and 0 otherwise (i.e., 2007-2012). Panel B presents the results of placebo tests using 2011, 2012, 2014, and 2015 as the pseudo first year of the campaign. Specifically, for each pseudo year t, we include into the analysis the years t-2, t-1, t and t+1 and define Post as 1 for t and t+1 and 0 otherwise. For comparison, we also report the results based on the actual first year of campaign (2013) using the sample period 2011~2014. The results for control variables are omitted for brevity. t-statistics (in the parentheses) are calculated using robust standard errors corrected for heteroscedasticity and clustered at the firm level. ***, **, and * indicate significance at the 1%, 5% and 10% levels, respectively.

48    

Table 6 Political Connections and Disclosure of Subsidies: Effect of Media Attention

Dependent Variable: Sub_Disclosure (1) (2) (3) Officer Connect -0.006 -0.005 (-0.695) (-0.647) Regional Bias -0.014 -0.014 (-1.392) (-1.390) Officer Connect×High-Media Attention -0.021** -0.023** (-2.160) (-2.300) Regional Bias×High-Media Attention -0.019* -0.020** (-1.884) (-1.987) High-Media Attention 0.015** 0.014** 0.021*** (2.500) (2.313) (3.168) Control Variables Yes Yes Yes Industry FE Yes Yes Yes Year FE Yes Yes Yes Province FE Yes Yes Yes N 7,874 7,874 7,874 Adj. R2 0.065 0.065 0.067

This table reports the OLS regression results of the association between political connection and firms’ subsidy disclosure, conditioned on firms’ media attention. The sample consists of all listed non-state-owned enterprises (NSOEs) that received government subsides during the period 2007-2016. The dependent variable Sub_disclosure is an aggregated measure of subsidy disclosure (see Appendix A for details). The independent variables of interest are Officer Connect, a dummy variable that equals 1 if the CEO or board Chairman is currently serving or has formerly served in the government or the military, and Regional Bias, a dummy variable that equals 1 if the firm’s registration city is the incumbent provincial leader’s (the party secretary or governor) birthplace or former workplace where he/she had the longest part of his/her work experience. High-Media Attention is a dummy variable that equals 1 if the number of press articles covering a firm in a given year is larger than the annual median and 0 otherwise. The results for control variables are omitted for brevity. t-statistics (in the parentheses) are calculated using robust standard errors corrected for heteroscedasticity and clustered at the firm level. ***, **, and * indicate significance at the 1%, 5% and 10% levels, respectively.

49    

Table 7 Political Connections and Disclosure of Subsidies: Effect of Subsidy Justifiability

Dependent Variable: Sub_Disclosure (1) (2) (3) Officer Connect-Justified (β11) 0.004 0.000 (0.406) (0.028) Officer Connect-Unjustified (β12) -0.025*** -0.024*** (-3.467) (-3.318) Regional Bias-Justified (β21) 0.001 -0.002 (0.073) (-0.173) Regional Bias-Unjustified (β22) -0.035*** -0.034*** (-3.728) (-3.605) p-value for β11 = β12 0.011 0.037 p-value for β21 = β22 0.003 0.010 Control Variables Yes Yes Yes Industry FE Yes Yes Yes Year FE Yes Yes Yes Province FE Yes Yes Yes N 7,874 7,874 7,874 Adj. R2 0.065 0.066 0.068

This table reports the OLS regression results of the association between political connection and firms’ subsidy disclosure separately for connected firms whose subsidies are justifiable and connected firms whose subsidies are unjustifiable. The sample consists of all listed non-state-owned enterprises (NSOEs) that received government subsides during the period 2007-2016. The dependent variable is Sub_disclosure, an aggregated score of subsidy disclosure quality (for details, see Appendix A). Officer Connect-Unjustified (Regional Bias-Unjustified) is a dummy variable that equals 1 if Officer Connect (Regional Bias) is equal to 1 and the firm operates in an industry that is not the focus of government support according to the current Five-Year Plan, and 0 otherwise. Officer Connect-Justified (Regional Bias-Justified) is a dummy variable that equals 1 if Officer Connect (Regional Bias) is equal to 1 and the firm operates in an industry that is the focus of government support according to the current Five-Year Plan, and 0 otherwise. Officer Connect is a dummy variable that equals 1 if the CEO or board Chairman is currently serving or has formerly served in the government or the military and 0 otherwise. Regional Bias, a dummy variable that equals 1 if the firm’s registration city is the incumbent provincial leader’s (the party secretary or governor) birthplace or former workplace where he/she had the longest part of his/her work experience, and 0 otherwise. The results for control variables are omitted for brevity. t-statistics (in the parentheses) are calculated using robust standard errors corrected for heteroscedasticity and clustered at the firm level. ***, **, and * indicate significance at the 1%, 5% and 10% levels, respectively.

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Table 8 The Impact of Political Connections on Subsidies Panel A: Summary statistics

Obs. Mean Std. Dev. P25 Median P75

Subsidy 9,425 11.522 7.202 9.616 15.253 16.547 Officer Connect 9,425 0.316 0.465 0 0 1 Regional Bias 9,425 0.263 0.440 0 0 1 Size 9,425 21.437 1.084 20.723 21.348 22.076 Lev 9,425 0.421 0.243 0.236 0.403 0.573 Employee 9,425 7.196 1.249 6.494 7.219 7.981 MTB 9,425 2.674 2.347 1.179 1.942 3.331 ROA 9,425 0.041 0.064 0.015 0.040 0.070 High-tech 9,425 0.094 0.292 0 0 0 Loss 9,425 0.091 0.288 0 0 0 σSale 9,425 0.243 0.228 0.100 0.178 0.302

Panel B: Political connections and subsidies

Dependent Variable:Subsidy (1) (2) (3) (4) Officer Connect 0.303* 0.305* (1.785) (1.795) Regional Bias 0.531** 0.533** (2.360) (2.372) Size 1.355*** 1.335*** 1.339*** 1.319*** (9.600) (9.412) (9.400) (9.214) Lev -0.031 0.003 -0.048 -0.014 (-0.066) (0.006) (-0.103) (-0.031) Employee 0.542*** 0.544*** 0.548*** 0.550*** (5.218) (5.238) (5.266) (5.285) MTB -0.110** -0.112** -0.111** -0.113** (-2.211) (-2.244) (-2.223) (-2.256) ROA 4.001*** 3.965*** 3.995*** 3.958*** (2.809) (2.786) (2.801) (2.778) Loss 0.124 0.131 0.136 0.143 (0.435) (0.461) (0.479) (0.505) High-tech 0.440** 0.431** 0.440** 0.431** (2.081) (2.039) (2.082) (2.040) σSale -1.587*** -1.565*** -1.573*** -1.550*** (-4.093) (-4.041) (-4.064) (-4.011) Industry FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes Province FE Yes Yes Yes Yes N 9,425 9,425 9,425 9,425 Adj. R2 0.232 0.232 0.233 0.233

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Panel C: Political connections and subsidies: The effects of anti-corruption campaign and provincial corruption level Dependent Variable: Subsidy (1) (2) (3) (4) (5) (6) (7) Officer Connect -0.264 -0.231 1.234*** 1.243*** 0.713** (-1.110) (-0.967) (5.295) (5.351) (2.486) Regional Bias -0.119 -0.108 1.144*** 1.154*** 0.496 (-0.388) (-0.350) (4.057) (4.118) (1.375) Officer Connect ×High-Corrupt 1.275*** 1.168*** 1.203*** (3.961) (3.614) (3.761) Regional Bias×High-Corrupt 1.546*** 1.429*** 1.351*** (3.640) (3.362) (3.174) Officer Connect×Post -1.608*** -1.620*** -1.657*** (-5.636) (-5.699) (-5.875) Regional Bias×Post -1.142*** -1.170*** -1.079*** (-3.438) (-3.544) (-3.260) Control Variables Yes Yes Yes Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Province FE Yes Yes Yes Yes Yes Yes Yes N 9,425 9,425 9,425 9,425 9,425 9,425 9,425 Adj. R2 0.234 0.234 0.236 0.235 0.234 0.236 0.239

This table reports results of estimating the impact of political connection on subsidy granting. The sample consists of all listed non-state-owned enterprises (NSOEs) during the period 2007-2016. Panel A reports summary statistics. Panel B reports the OLS results of estimating the effect of political connection on subsidy granting. Panel C reports the results for how the effect of political connection on subsidy granting varies with the provincial corruption level and the anti-corruption campaign. Subsidy is the natural logarithm of one plus subsidy amount. Officer Connect is a dummy variable that equals 1 if the CEO or board Chairman is currently serving or has formerly served in the government or the military and 0 otherwise. Regional Bias, a dummy variable that equals 1 if the firm’s registration city is the incumbent provincial leader’s (the party secretary or governor) birthplace or former workplace where he/she had the longest part of his/her work experience, and 0 otherwise. The level of corruption (Corrupt) is measured as the provincial number of officials of vice county-division rank and above investigated in the recorded cases of corruption. High-Corrupt is a dummy variable that equals 1 if Corrupt is above the sample median. Post is a dummy variable that equals 1 for the period 2013-2016, and 0 for 2007-2012. Other variables are defined in Appendix B. All variables except the dummy variables are winsorized at the 1% and 99% levels. t-statistics (in the parentheses) are calculated using robust standard errors corrected for heteroscedasticity and clustered at the firm level. ***, **, and * indicate significance at the 1%, 5% and 10% levels, respectively.

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Table 9 Robustness Tests

Panel A: Alternative disclosure measures Dependent Variable: Source Policy Basis Type Sub_discosure1 Sub_discosure2 Interim Sub_disclosure3 (1) (2) (3) (4) (5) (6) (7) Officer Connect -0.019 -0.029*** -0.005* -0.016** -0.017** -0.003 -0.013** (-1.431) (-2.702) (-1.709) (-2.239) (-2.223) (-0.298) (-2.558) Regional Bias -0.048*** -0.019 -0.008* -0.025** -0.026** -0.020* -0.021*** (-2.695) (-1.263) (-1.800) (-2.465) (-2.509) (-1.932) (-3.073) Control Variables Yes Yes Yes Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Province FE Yes Yes Yes Yes Yes Yes Yes N 7,874 7,874 7,874 7,874 7,874 7,874 7,874 Adj. R2 0.042 0.084 0.078 0.064 0.064 0.128 0.079

Panel B: Alternative model specification and treatment variable Dependent Variable: Sub_disclosure (1) (2) (3) (4) (5) Officer Connect -0.016** -0.016** (-2.191) (-2.217) Regional Bias -0.017* -0.017* (-1.729) (-1.762) Subsidy-PC -0.023*** -0.025*** (-5.463) (-6.226) Control Variables Yes Yes Yes Yes Yes Firm FE Yes Yes Yes No Yes Industry FE No No No Yes No Year FE Yes Yes Yes Yes Yes Province FE No No No Yes No N 7,874 7,874 7,874 7,874 7,874 Adj. R2 0.290 0.290 0.290 0.069 0.294

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This table reports robustness tests. The sample consists of all listed non-state-owned enterprises (NSOEs) that received government subsides during the period 2007-2016. Panel A reports the results based on the following alternative subsidy disclosure measures. Source is the percentage of subsidies whose sources are disclosed in the annual report. Policy Basis is the percentage of subsidies whose policy bases are disclosed in the annual report. Type is the percentage of subsidies whose types are disclosed in the annual report. Interim is the percentage of subsidies that are announced in the interim reports. Sub_disclosure1 is the average of Source, Policy Basis and Type. Sub_disclosure2 is the average of the quintiles of Source and Policy Basis. Sub_disclosure3 is the average of the quintiles of Source, Policy Basis, Type and Interim. Panel B reports the results based on alternative model specifications (firm fixed effects) and an alternative treatment variable, Subsidy-PC, calculated as 0.713× Officer Connect +0.496× Regional Bias-1.657× Officer Connect×Post -1.079×Regional Bias×Post+1.203× Officer Connect ×High-Corrupt +1.351×Regional Bias×High-Corrupt, where the coefficients are from column 7 of Table 7, Panel C. Sub_disclosure is an aggregated measure of subsidy disclosure (see Appendix A for details). Officer Connect is a dummy variable that equals 1 if the CEO or board Chairman is currently serving or has formerly served in the government or the military. Regional Bias is a dummy variable that equals 1 if the firm’s registration city is the incumbent provincial leader’s (the party secretary or governor) birthplace or former workplace where he/she had the longest part of his/her work experience. High-Corrupt is a dummy variable that equals 1 if Corrupt is above the sample median, and 0 otherwise, where Corrupt is measured as the provincial number of officials of vice county-division rank and above investigated in the recorded cases of corruption. Post is a dummy variable that equals 1 for the period 2013-2016, and 0 for 2007-2012. t-statistics (in the parentheses) are calculated using robust standard errors corrected for heteroscedasticity and clustered at the firm level. ***, **, and * indicate significant differences at the 1%, 5% and 10% levels, respectively.

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Table 10 Regression Using Propensity Score Matched Sample

Panel A: Covariate balance test

Officer Connect-match Regional Bias-match

Officer Connect =1

Officer Connect =0

t-stat Regional Bias

=1 Regional Bias

=0 t-stat

Subsidy 15.881 15.875 0.13 15.833 15.859 -0.41 Size 21.728 21.706 0.71 21.688 21.662 0.67 Lev 0.402 0.401 0.13 0.420 0.433 -1.50 Employee 7.487 7.485 0.05 7.334 7.310 0.58 MTB 2.391 2.398 -0.12 2.694 2.645 0.60 ROA 0.047 0.047 0.48 0.041 0.041 -0.02 Loss 0.105 0.109 -0.46 0.137 0.131 0.55 Analyst 1.696 1.689 0.25 1.662 1.672 -0.25 Institutional 0.051 0.051 0.01 0.052 0.051 0.28 Return 0.198 0.193 0.28 0.201 0.178 1.05 σ Sale 0.212 0.210 0.31 0.235 0.237 -0.25 σ Return 1.072 1.170 -0.51 1.139 1.035 0.42

Panel B: Analyses based on the matched samples

Dependent Variable: Sub_disclosure Officer Connect PSM Regional Bias PSM (1) (2) (3) (4) (5) (6) Officer Connect -0.017*** -0.006 -0.025*** (-2.70) (-0.62) (-2.96) Regional Bias -0.026*** -0.007 -0.047*** (-2.97) (-0.64) (-4.06) Officer Connect× High-Corrupt

-0.024*

(-1.92) Regional Bias× High-Corrupt

-0.040**

(-2.35) Officer Connect×Post 0.017* (1.69) Regional Bias×Post 0.043*** (3.04) Control Variables Yes Yes Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Province FE Yes Yes Yes Yes Yes Yes N 7,650 7,650 7,650 6,410 6,410 6,410 Adj. R2 0.065 0.065 0.065 0.062 0.064 0.064

This table reports the results of the main analyses based on propensity score matched (PSM) samples. The full sample (before matching) consists of all listed non-state-owned enterprises (NSOEs) that received government subsides during the period 2007-2016. We use all control variables, as well as industry, year, and province fixed effects in equation (1) as the matching variables. We match politically connected firms with unconnected firms using the caliper method (with replacement) with a radius of 0.01. We conduct matching for Officer Connect and Regional Bias separately and thus create two matched samples. Panel A reports the covariate balance tests. In Panel B, we repeat the main regressions using the matched samples. Sub_disclosure is an aggregated measure of subsidy disclosure (see Appendix A for details). Officer Connect

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is a dummy variable that equals 1 if the CEO or board Chairman is currently serving or has formerly served in the government or the military. Regional Bias is a dummy variable that equals 1 if the firm’s registration city is the incumbent provincial leader’s (the party secretary or governor) birthplace or former workplace where he/she had the longest part of his/her work experience. High-Corrupt is a dummy variable that equals 1 if Corrupt is above the sample median, and 0 otherwise, where Corrupt is measured as the provincial number of officials of vice county-division rank and above investigated in the recorded cases of corruption. Post is a dummy variable that equals 1 for the period 2013-2016, and 0 for 2007-2012. t-statistics (in the parentheses) are calculated using robust standard errors corrected for heteroscedasticity and clustered at the firm level. ***, **, and * indicate significant differences at the 1%, 5% and 10% levels, respectively.

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Table 11 Analyses Based on SOEs

Dependent Variable: Sub_disclosure (1) (2) (3) (4) (5) (6) (7) (8) (9) Officer Connect -0.005 -0.006 -0.005 -0.006 -0.014 -0.015 (-0.713) (-0.781) (-0.561) (-0.683) (-1.495) (-1.561) Regional Bias -0.013 -0.013 -0.004 -0.005 -0.020** -0.021** (-1.516) (-1.554) (-0.440) (-0.482) (-2.100) (-2.152) Officer Connect×High-Corrupt -0.000 0.002 (-0.009) (0.136) Regional Bias× High-Corrupt -0.020 -0.020 (-1.234) (-1.222) Officer Connect×Post 0.021* 0.021* (1.833) (1.859) Regional Bias×Post 0.019* 0.019* (1.686) (1.689) Control Variables Yes Yes Yes Yes Yes Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Province FE Yes Yes Yes Yes Yes Yes Yes Yes Yes N 7,594 7,594 7,594 7,594 7,594 7,594 7,594 7,594 7,594 Adj. R2 0.127 0.128 0.128 0.126 0.128 0.128 0.127 0.128 0.128

This table reports the results of the main analyses based state-owned enterprises (SOEs) that received subsidies for the period 2007-2016. Sub_disclosure is an aggregated measure of subsidy disclosure (see Appendix A for details). Officer Connect is a dummy variable that equals 1 if the CEO or board Chairman is currently serving or has formerly served in the government or the military. Regional Bias is a dummy variable that equals 1 if the firm’s registration city is the incumbent provincial leader’s (the party secretary or governor) birthplace or former workplace where he/she had the longest part of his/her work experience. High-Corrupt is a dummy variable that equals 1 if Corrupt is above the sample median, and 0 otherwise, where Corrupt is measured as the provincial number of officials of vice county-division rank and above investigated in the recorded cases of corruption. Post is a dummy variable that equals 1 for the period 2013-2016, and 0 for 2007-2012. t-statistics (in the parentheses) are calculated using robust standard errors corrected for heteroscedasticity and clustered at the firm level. ***, **, and * indicate significant differences at the 1%, 5% and 10% levels, respectively.

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Appendix A: Examples of Subsidy Disclosure in Annual Reports and Calculation of the Disclosure Measure Example 1: Dongcheng Pharmaceutical, 2014 Dongcheng Pharmaceutical Co., Ltd, the largest pharmaceutical API manufacturers in China, received eight government subsidies in 2014, amounting to approximately 4.4 million RMB (about 0.63 million US dollars). In its 2014 annual report, the company disclosed the subsidy information as follows:

Type of subsidy

Subsidy amount

(in thousand RMB)

Subsidy source Policy basis

Shandong special funds for self-innovation and achievement transformation

1,100

Finance Bureau of Yantai Economic and Technological Development Area (YEDA)

Circular YEDA No.2/71 [2014]

Science and technology grant funds 1,000 Finance Bureau of YEDA  Circular YEDA No.266 [2014]

Municipal foreign economic and trade development policy funds

530.4 Finance Bureau of YEDA  Circular YEDA No.9/50 [2014]

Support funds for enhancing the international competitiveness of export bases

500 Finance Bureau of YEDA  Circular YEDA No.17/99 [2014]

Special funds for technical transformation

500 Finance Bureau of YEDA  Circular YEDA No.148 [2014]

Funds for export credit insurance 448 Finance Bureau of YEDA  Circular YEDA No.17/99 [2014]

Special funds for science and technology development plan

300 Finance Bureau of YEDA  Circular YEDA

No.10/106 [2014] Provincial foreign economic and trade development policy funds

3 Finance Bureau of YEDA  Circular YEDA No.1/99 [2014]

Total 4,381.4

The measure of subsidy disclosure, Sub_disclosure, for the company is calculated as follows: 1) The company disclosed the sources for all subsidies. The percentage of subsidies whose

sources are disclosed (Source) is 100%, which is in the fifth quintile of the distribution of Source. Thus, we assign a score of 1 for this disclosure dimension.

2) The company disclosed the types for all subsidies. The percentage of subsidies whose types are disclosed (Type) is 100%, which is in the fifth quintile of the distribution of Type. Thus, we assign a score of 1 for this disclosure dimension.

3) The company disclosed the policy bases for all subsidies. The percentage of subsidies whose policy bases are disclosed (Policy Basis) is 100%, which is in the fifth quintile of the distribution of Policy Basis. Thus, we assign a score of 1 for this disclosure dimension.

 Consequently, the composite subsidy disclosure measure, Sub_disclosure, is calculated as (1+1+1)/3=1.   

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Example 2: Tianshou Technology & Development, 2007 Inner Mongolia Tianshou Technology & Development Co. Ltd is a public company principally engaged in the processing, production and sales of textile products. In 2007, this company received government subsidies amounting to approximately 9 million RMB (about 1.4 million US dollars). In its 2007 annual report, the company disclosed the subsidy information as follows:

Type of subsidy Subsidy amount

(in 10 thousand RMB) Subsidy source Policy basis

Economic Development Guidance Fund

500 None None

Award for Progress in Science and Technology

136 None None

Standard Subsidy Fund 250 None None Export Fair Subsidy 12 None None Other 26.6 None None Total 924.6 None None

The measure of subsidy disclosure, Sub_disclosure, for the company is calculated as: 1) The company did not disclose any sources for the subsidies. The percentage of subsidies

whose sources are disclosed (Source) is 0, which is in the first quintile of the distribution of Source. Thus, we assign a score of 0.2 for this disclosure dimension.

2) The company disclosed the types for most subsidies. The percentage of subsidies whose types are disclosed (Type) is 97.12%, which is in the third quintile of the distribution of Type. Thus, we assign a score of 0.6 for this disclosure dimension. Note that due to the concentration of Type at the value 1, we assign all observations with Type=1 as the top quintile and split the remaining observations evenly into four groups as the first to fourth quintiles.

3) The company did not disclose the policy basis for any subsidies. The percentage of subsidies whose policy bases are disclosed (Policy Basis) is 0, which is in the first quintile of the distribution of Policy Basis. Thus, we assign a score of 0.2 for this disclosure dimension.

 As a result, the composite subsidy disclosure measure, Sub_disclosure, is calculated as (0.2+0.6+0.2)/3=0.333.    

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Appendix B: Variable Definitions Variable Definition Source Sub_disclosure An aggregated score of subsidy disclosure level (for details, see Appendix A). Hand collection Source The percentage of subsidies whose sources are disclosed Hand collection Policy Basis The percentage of subsidies whose policy bases are disclosed Hand collection Type The percentage of subsidies whose types are disclosed Hand collection Interim The percentage of subsidies that are announced in the interim reports Hand collection Sub_disclosure1 The average of Source, Policy Basis and Type Self-constructed Sub_disclosure2 The averaged quintile value of Source and Policy Basis Self-constructed Sub_disclosure3 The averaged quintile value of Source, Policy Basis, Type and Interim. Self-constructed Freq The natural logarithm of one plus the frequency of management earnings forecasts issued during a

fiscal year. WIND

MD&A The natural logarithm of the number of words in the MD&A section of the annual report. CNRDS Officer Connect A dummy variable that equals 1 if the CEO or board Chairman is currently serving or has formerly

served in the government or the military, and 0 otherwise. Hand collection

Regional Bias A dummy variable that equals 1 if the firm's registered city is the incumbent provincial leader’s birthplace or former workplace where he/she spent longest in the past work experience, and 0 otherwise.

Hand collection

Subsidy Natural logarithm of one plus subsidy value. Hand collection Subsidy-PC 0.713× Officer Connect +0.496× Regional Bias-1.657× Officer Connect×Post-1.079×Regional

Bias×Post+1.203×Officer Connect×High-Corrupt +1.351×Regional Bias×High-Corrupt, where 0.713, 0.496,-1.657,-1.079, 1.203 and 1.351 are the coefficient estimates in Column (7) of Panel C Table 7, respectively.

Self-constructed

Size Natural logarithm of total assets CSMAR Lev The ratio of total liabilities to total assets. CSMAR Employee Natural logarithm of total number of employees CSMAR MTB The market-to-book ratio computed as market value divided by shareholders’ equity. CSMAR ROA Return on assets, calculated as net income divided by total assets CSMAR Loss A dummy variable that equals 1 if the annual operating profit is negative, and 0 otherwise. CSMAR Analyst The number of analysts issuing at least one earnings forecast about the firm. CSMAR Institutional The percentage of institutional shareholding. CSMAR Return Buy and hold return over the twelve months prior to April 30. CSMAR σ Sale The standard deviation of a firm's annual net sales over a rolling three-year window CSMAR σ Return The standard deviation of a firm's annual stock returns over a rolling 36 months window. CSMAR High-tech A dummy variable that equals 1 if the firm is officially classified as high-tech firm, and 0 otherwise. Hand collection

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Corrupt The provincial number of officials of vice county-division ranks and above investigated in the registered cases on corruption.

Hand collection

High-Corrupt A dummy variable that equals 1 if Corrupt is above the sample median, and 0 otherwise. Hand collection Post A dummy variable that equals 1 for the period 2013-2016, and 0 for 2007-2012. Self-constructed High-Media Attention A dummy variable that equals 1 when the number of news covering the firm is larger than its yearly

median and 0 otherwise CNRDS

Officer Connect -Justified

A dummy variable that equals 1 if the CEO or board Chairman is currently serving or has formerly served in the government or the military, and the firm operates in a key industry that is supported by the government according to the five-year plans, and otherwise 0.

Self-constructed

Officer Connect -Unjustified

A dummy variable that equals 1 if the CEO or board Chairman is currently serving or has formerly served in the government or the military, but the firm operates in industries that are not supported by the government according to the five-year plans, and otherwise 0.

Self-constructed

Regional Bias-Justified

A dummy variable that equals 1 if the firm's registered city is the incumbent provincial leader’s birthplace or former workplace where he/she spent the longest part of his/her work experience, and the firm operates in a key industry that is supported by the government according to the five-year plans, and otherwise 0.

Self-constructed

Regional Bias-Unjustified

A dummy variable that equals 1 if the firm's registered city is the incumbent provincial leader’s birthplace or former workplace where he/she spent the longest part of his/her work experience, but the firm operates in industries that are not supported by the government according to the five-year plans, and otherwise 0.

Self-constructed