Governmentsubsidyandfirmsatahighriskofdelisting:evidencefromChina

Zhaohua Li*

August 28, 2016

Abstract

This paper examines who receives direct government subsidy when a firm faces delisting risk and how such subsidy affects a firm’s market valuation, profitability, and labor intensity. I find that subsidies are more likely to be granted to firms that have high risk of delisting, political connections and big size, regardless whether they are state owned or private. Even for state-owned enterprises, having political connections are twice as likely to receive subsidy than non-connected state firms. I also find that the receipt of a subsidy is not only endogenously related to the characteristics of recipients, but also increases firm value and profitability and significantly reduces its employment. The subsidized firms have better profitability and lower labour intensity than unsubsidized firms. Our results imply that it is with whom the firm has connections (i.e., political connections) that matters in the receipt of capital allocation when confronting delisting risk, not what it is (i.e., state owned).

Keywords: delisting risk, government subsidy, political connection, state owned

JEL classification: G30, G38, K0,

* Faculty of Agribusiness and Commerce, Lincoln University, Christchurch 7647, Canterbury, New Zealand. Tel: +64 4 230221. Email address: Zhaohua.li@lincoln.ac.nz. Thanks to Ji Wu, Baiding Hu, Takeshi Yamada, Creg Clydesdale, Gillis Mcleans and seminar participants at Lincoln University for comments. Research funding from Lincoln University is acknowledged. I thank research assistants Qing Liang and Yanfu Li for great research assistance and Eric Scott for technical copy editing.

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1. Introduction

Financial news from the General Motors bailout in 2009 by the U.S. government’s $50 billion bailout plan stresses the importance of government assistance as a way of resolving distress. Financial distress, to some extent, leads to bankruptcy and/or delisting. As suggested by Shleifer and Vishny (1992) and Asquith et al. (1994), the liquidation value of a firm’s assets will be less than the market value of the firm’s equity. As a result, controlling shareholders have incentives to seek a government subsidy to restructure the firm to emerge from financial distress. Delisting is generally viewed as a first step toward potential bankruptcy. Hence, delisting pressure, especially when being a listed firm is regarded as a privilege in an economy, imposes substantial financial and reputational risks on distressed firms.

The potential influence of shareholders in the firms at high delisting risk suggests that the effects and the reasons for obtaining government subsidy in high delisting risk firms can be very different from those for healthy firms. In particular, Lee et al. (2014) find there are mixed responses among interviewees over the question whether the rescue of distressed firms is one objective of governments in offering subsidies. As one interviewee (also a government official) suggested, the fear of bankruptcy and the substantial influence on local social stability through unemployment can be a determining factor in granting a subsidy to a distressed firm on a case by case basis.

There are two competing hypotheses concerning who gets a government subsidy: social and political. The social view is based on the economic theory of institutions. It suggests that, in an imperfect market, a government measure like a subsidy improves welfare. In a similar line, state-owned enterprises (SOEs) are created to address market failures. According to this view, SOEs are more likely to get a government subsidy in high delisting risk firms. In contrast, theories on the politics of government ownership (Shleifer and Vishny, 1994) suggest that politicians use SOEs as a mechanism to pursue individual goals such as transferring resources to their supporters in order to win votes. The political view suggests that a firm with political connections is more likely to get a government subsidy.

The theories cannot be differentiated by looking at the receipt of a subsidy; it is not clear whether firms receive a subsidy to perform a social objective or to meet politicians’ wishes. In this paper, China’s unique delisting framework allows me to address those problems. Instead of looking at the overall subsidy performance, I focus on the nature of firms that receive a subsidy. China’s delisting effects are closely linked to the Initial Public Offering (IPO) features. It is widely documented that

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China’s IPO features are characterised by a quota system, bribery, corruption, local government screening, earning management, etc. (Du and Xu, 2009; Francis et al., 2009; Wan and Yuce, 2007). It can be reasonably expected that listed firms have already incurred a huge cost to get a ticket to enter the IPO market but the quality of listed firms does not necessarily mean sustained performance. Therefore, it is crucial to set a good delisting requirement to refresh the pool of listed firms. Nevertheless, China’s delisting rules, to a large extent, are based on an accounting criterion: the profitability. As long as a firm does not have consecutive losses for three years, it can stay in the stock market. Technically, the three year loss window is equivalent to the delisting risk window. The delisting window exposes firms at risk of delisting to tremendous pressures and introduces some interesting incentives into the dynamics between government and firms. Firms are strongly motivated to seek assistance from the government and being a SOE certainly has an innate advantage, but to what extent SOEs obtain benefits from such a relationship remains unknown. Being a private firm under such stressful scenario is not the end of the world. To establish and expand political connections can be a pathway to a government subsidy. On the other hand, governments that have financial means, have choices of granting subsidies to both healthy and distressed firms. All these factors make China a suitable laboratory to examine the impact of subsidies on firms’ outcomes and why some firms receive subsidies when in distress. I collected details of all firms at high delisting risk in China’s stock market between 1998 and 2012. I trace data back three years before and after the delisting window. The study examines the following two questions:

First, I examine why firms receive a subsidy. By using the treatment effect model, I find that subsidies are more likely to be granted to firms with a high delisting risk, political connections, and big size, regardless of whether the recipients are SOEs. The result echoes the connections in a crisis hypothesis in Acemoglu et al. (2016) where the value of political connections matter a great deal in turbulent times. The result also shows that for SOEs, having political connection two times more likely to receive a subsidy than non-connected state firms. I claim that this result supports the political view of government intervention.

Second, I examine the effect of a subsidy on firm valuation, profitability and employment. The results show that the subsidy has positive relevant to Tobin’s q and profitability. The receipt of subsidy also lowers labor intensity compared with an unsubsidized firm.

The study’s findings have important implications for firms, regulators, analysts and academics. For firms, this study documents that in distressed times political connections are more valuable assets than the nature of state ownership because this

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reflects an ability to extract ‘rent’ from a relationship with politicians. Thus, this study identifies a value driver specific to firms at delisting risk that cannot be ignored by financial analysts. This value driver is also apparent in a country with strong overall institutions such as U.S. (Acemoglu et al., 2016). To regulators, the accounting based delisting rule induces firms look for political connections as a pathway to get government subsidies. Allowing the continued trading of companies that are engaging in rent seeking through personal ties with high-level government officials harms potential investors and exchange reputation. I call for a change in delisting rules.

This study is different from the literature on four grounds. First, compared with the volume of IPO studies, there are relatively few studies on delisting. Much less is known about the impact of delisting rules on firms and how other factors affect delisting. Compared with the abundance of papers on subsidies, much is unknown about subsidies to a specific group of firms at high delisting risk. Combining the subsidy and delisting factors at such a critical and stressful moment, numerous incentives emerge from local governments, firms at delisting risk and politicians. The dynamics of relationships offer a great opportunity to examine how each factor works, or does not work, to address high delisting risk.

Secondly, state-owned firms and/or political connections are correlated concepts and some publications use them interchangeably. One should not link all state-owned enterprises as politically connected. The political connection study examines to what extent a firm can extract value from such a relationship (Francis et al., 2009). State firms without strong political connections do not necessarily get more benefits than non-connected State firms. My study considers them separately.

Thirdly, two schools of thought have different views on the role of government. The ‘grabbing hand’ model believes that politicians use political power in some places to direct resource allocation for their own benefit. Hence a government subsidy can be used by politicians as a way to achieve private benefit. The helping hand model believes government intervention helps to improve welfare when there is market failure. Hence a government subsidy would help firms ease the delisting risk. Utilizing the delisting risk window, this study differentiates the two competing hypotheses.

Fourthly, a government decision government to grant subsidies to firms reflects various incentives. It introduces a selection bias in the observed economic outcome. By appropriate modelling, this study shows that any estimation method that does not account for this selection issue is likely to yield biased estimates.

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The rest of the paper is organized as follows. Section 2 is a literature review and hypothesis development. Section 3 describes sample selection and data collection. Section 4 describes the methodology and that is followed by Section 5 which presents the results. Finally, Section 6 summarizes and concludes the paper.

2. Literature review and hypothesis development

This study is related to two literature streams. The first is in law and finance looking at market regulation and development, delisting procedures, delisting standards, and the impact of delisting on investors. La Porta et al. (2006), using data from 49 countries, find securities laws matter in stock market development because they facilitate private contracting. Macey et al. (2008) find the current delisting process is flawed in the U.S. because it has few benefits accruing to stock exchanges and the large costs are borne by firms and investors. Jiang and Wang (2008) specifically examine the delisting criteria in China. Using a novel research design, they find that earnings-based delisting requirements are misconstrued. They drive financially healthy firms out of stock market and induce listed firms to engage in earnings manipulation to avoid delisting.

The second literature stream is about government subsidies and examines the purposes, benefits and cost of government subsidies. Economists believe, under the condition of a perfectly competitive market, nothing can justify a subsidy. Introducing a subsidy, or other government intervention (such as a tax or quotas), within a perfect market will be inefficient and welfare-diminishing. But, if the market assumption is relaxed, situations arise where government intervention such a subsidy improves welfare; an efficient subsidy program can correct a market failure. The main stated objectives of governments for using subsidies include industrial development, innovation and support for national champions, environment related objectives, to certain sectors inherent in nature (such as agriculture and energy) and redistribution (World trade report, 2006). In addition to these generally accepted reasons, there are government subsidies or assistance to troubled firms or banks, although it remains controversial to justify government intervention in those scenarios.

Faccio (2006) analyses the likelihood of government bailouts of 450 politically connected firms from 35 countries and finds politically connected firms are significantly more likely to be bailed out than similar non-connected firms. Yet those bail-out firms, exhibit significantly worse financial performance than their non-connected peers at the time of and following the bailout. This evidence suggests that the inefficient use of capital and bailouts of politically connected firms are especially

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wasteful. Such rent-seeking behaviour in the public sector is not new. In the seminal work by Krueger (1974), she argues that government intervention often leads people to compete for economic rents. She showed the total value of rents was 7% of GDP in India in 1964. The rents from import licenses was 15% of GNP in Turkey in 1968. Shleifer and Vishny (1994) present a bargaining model between politicians and managers. Subsidies to public enterprises and bribes from managers to politicians all emerge in the model. Several empirical studies have attempted to quantify the value (positive or negative) or the consequences of political connection. Using data from 43 countries, Dinç (2005) finds government owned banks increase lending in election years more than private banks, which is a sign that politicians use government owned entities to distribute rents to their supporters. Fan et al. (2007) find in China that politically connected CEOs underperform non-connected CEOs by 18% based on three-year post-IPO stock returns and have poorer post-IPO earnings growth and sales growth. More recently, using U.S. data, Alfonso (2016) finds analysts have greater difficulty forecasting the earnings of politically connected firms than those of non-connected firms because politicians often grant political favours to firms in an unpredictable manner.

This study contributes to the literature by exploring a specific mechanism (i.e., delisting) through which listed firms may be able to use political influence to extract economic rents (i.e., subsidies) from the public sector when such firms confront delisting risk. I develop the hypotheses below.

2.1 Government subsidy and delisting risk

Listed firms have delisting avoidance incentives. One key factor is the huge cost of trading. Using a proprietary data set from Pink Sheets, Inc., Macey et al. (2008) find on the first day of trading on the Pink Sheet for stocks delisted from NYSE, the average percentage spread is 39.32%. This spread varies across firm size. The smallest 23 stocks had a spread of 51.28%, whereas the largest 12 stocks had a spread of 4.02%. Further analysis reveals that a large part of the price decline is because of the loss of a share trading venue.

Another key cause in firms avoiding delisting is the sunk cost of the IPO stage; this is especially relevant to studies of delisting in China. The regulatory framework in an IPO has an indirect impact on delisting because of the special stock issuing system in China. At the inception of stock market in 1993, the China Securities Regulatory Commission (CSRC) determined the aggregate number of new shares (quota) that may be issued each year. Shares were then distributed to individual provinces and ministries (Du and Xu, 2009; Francis et al., 2009) and eventually to companies.

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Chinese authorities controlled who could be listed and how the issue price was to be determined (Cheung et al., 2009)1. The quota system was officially in place from 1993 to 2000, but it actually governed financial markets till 2003. IPO issuing then changed to an approval-based system. Despite the new IPO issuing system, in essence, CSRC controls who lists and the timing and pricing of the new listing2. The approval-based IPO system creates rents for companies to seek and incur a sunk cost that is hard to measure. This cost can be offset only when they get listed and remain in the stock market. Francis et al. (2009) find issuing firms with political connections reap significant preferential benefits in going public in China. Local governments are the key players in the process because they recommend companies in their jurisdiction to CSRC. Du and Xu (2009) show that in the pre-listing stage regional governments tend to choose better-performing state-owned enterprises to go public. Chen et al. (2008) find that local government provides subsidies to help firms boost their earnings above the regulatory threshold of rights offering and delisting. Therefore, when companies face the delisting risk, they are highly likely to seek and be granted subsidies by governments to avoid delisting because the delisting rule in China is accounting-based that can record a subsidy as revenue to the firm. As governments recommend firms in the IPO process, there is no reason for firms not to seek assistance from governments in the face of a delisting risk.

Governments have several incentives to help listed firms to avoid delisting. First, the promotion of provincial leaders hinges on the local economy’s performance. Li and Zhou (2005) find that the likelihood of the promotion of provincial leaders increases with their economic performance and the likelihood of termination decreases with their economic performance. Second, getting an IPO approved in a province is viewed as a sign of political power and a delisting brings great disgrace to a politician’s reputation (Chen et al., 2008). Third, if a listed firm is delisted, a company is highly likely to file bankruptcy and becomes a burden to local government and also accrues reputational cost. In view of these, I develop hypothesis 1 as follows:

Hypothesis 1: It is highly likely that a firm will get a government subsidy when it is facing delisting risk.

2.2. The social versus the political view of government intervention

1 According to Cheung et al. 2009, initially (1999), a formula that multiplied a pre-set P/E ratio between 13 and 15 to the average of the company’s past three years’ earnings was used in setting the issue price. Several reforms attempted to address the setting of the issue price including the cumulative auction method, cumulative price inquiry, and institutional investor method. 2 In 2016, CSRC aims a transition to U.S. style of registration based system.

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Shleifer and Vishny (1998) present an alternative theory (‘grabbing hand’ model) to the ‘helping hand’ model or interventionist policies (van Brabant, 2000). The political view of government considers politicians use political power in some places to direct resource allocation for their benefit. Fisman (2001) estimates the value of political connections using Indonesian data at the firm level. When President Suharto’s health worsened, the stock returns of Suharto dependent firms dropped significantly more than those of independent firms. That study concludes that a considerable percentage of connected firms’ value comes from political connections and it inspired much research in this field. Studies related to China find political connections give politically connected firms favourable terms such as in their IPO (Francis et al., 2009), in lending terms and firm performance (Li et al., 2008), in compensation and performance premium (Conyon et al., 2015), in mergers and acquisitions (Liu et al., 2016), positive abnormal returns (He et al., 2014), higher cash dividends (Su et al., 2014). Cull et al. (2015) find state firms with political connections are associated with fewer financial constraints. As delisting exposes listed firms under extreme distress and it ruins the reputation of politicians if delisting occurs, I hypothesise that:

Hypothesis 2a: Politically connected firms are more likely to receive a government

subsidy.

The helping hand model holds a different view of the role of government. It is believes that government intervention helps to improve welfare when there is market failure, thus justifying state ownership of the corporation (Atkinson and Stiglitz, 1980). A large volume of research has examined the role of state ownership in various of issues such as privatization (Megginson and Netter, 2001), firm performance and bank lending. For studies related to China, Groves et al. (1994) find that the productivity of state owned enterprises improved because of the incentives introduced into the autonomy of firms. Gan et al. (2014) find a significantly negative market response to bank loan announcements granted by state owned banks disappears since a series of reforms in the Chinese banking system. It is also found that government assists firms in imminent delisting risk to engage in earning management (Chen et al., 2008; Liu and Lu, 2007). In view of this, I develop hypothesis 2b as follows: Hypothesis 2b: State owned firms are more likely to receive a government subsidy.

Hypotheses 2a and 2b were developed based on competing theories, hence they compete with each other and are subject to testing with the data.

2.3. The impact of a subsidy on firms’ outcomes

There are many studies that assess the impact of R&D incentives on innovation. Most examine the R&D subsidies effect on a firm’s innovation output, e.g., investment in

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R&D tangible assets, employment (see Becker (2015) for a survey paper). Only a few studies examine the effect of R&D subsidies on a firm’s innovation output, e.g., patents (Bronzini and Piselli, 2016; Jaffe and Le, 2015) There are even fewer studies examining the impact of a subsidy on a firm’s performance measures. Griliches (1979) uses the production function approach to study the returns (total factor productivity) to R&D. Griliches and Regev (1998) use the production function to study private firm performance of government funded R&D in Israeli firms. They find a positive relationship between a subsidy and benefits to the firm. Lee et al. (2014) examine the value relevance of government subsidies for Chinese companies using seemingly unrelated regressions. They find subsidies are positively related to stock price. Unlike Lee et al. (2014), in this study, I explicitly regard a subsidy as an endogenous variable and use Tobin’s q and ROA as a firm’s outcome measures. I hypothesise that:

Hypothesis 3a: The receipt of subsidy increases a firm’s valuation and profitability.

Some studies examine the relationship between subsidy and employment. To create jobs via subsidies is a commonly used policy tool. Kaldor (1936) advocates the use of subsidies as a remedy to unemployment. As expenditure on labour causes deadweight loss, Layard and Nickell (1980) propose the use of marginal employment subsidies given to additional jobs created. To subsidize other aspects of firm activities such as R&D, capital investment, and exporting, can be viewed as marginal employment subsidies (Girma et al., 2008). Despite the use of marginal employment subsidies, analysis of the job additions is limited. Girma et al. (2008) provide evidence of additional jobs through subsidies in Ireland for manufacturing. The caution in Girma et al. (2008) result is that most subsidies are given to other aspects of firms such as R&D and capital acquisition, rather than direct employment. Fuest and Huber (2000) compare investment subsidy and employment subsidy and provide evidence that 90% of the expenditure is investment subsidies, whereas programmes directly promoting employment are almost negligible. The link between subsidy and employment is relatively unestablished in the literature and awaits further exploration. As our sample consists of firms at high delisting risk, profitability is the key factor by which firms get relief from delisting pressure. It is essential to lower the labor intensity in such firms to reduce the labor expenditure. It is unlikely that the subsidy recipient firms will have additional employment. Hence, I hypothesise that: Hypothesis 3b: A government subsidy will lower labor in firms confronting high

delisting risk.

3. Data collection and sample selection.

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This study samples all high delisting risk firms on the Shanghai (SHSE) and Shenzhen Stock Exchanges (SZSE) between 1998 and 2012. The classification of firms at high delisting risk is in the guidelines introduced by the China Security Regulatory Commission (CSRC) in 1999. The sample includes 3251 firm year observations.

The five sources of information employed in this study are:

a. China Stock Market and Accounting Research Database (CSMAR). This database was used to collect various data such as subsidy information, the resumes of top management and board of directors, stock price, accounting data, shareholder information, employment data, and industry classification.

b. China Statistical Yearly Books. Provincial GDP data in a given year for each

province were collected from this book series.

c. Economic Policy Uncertain Index was obtained from the Economic Policy Uncertainty website3.

d. China Securities and Futures Statistical Yearbooks. The number of delisting

firms for mainland China in a given year were collected from this book series.

e. Hong Kong Exchange Fact Books. The number of delisting firms for Hong Kong in a given year were collected from this book series.

3.1 Identification of firms at a high delisting risk

The sample of firms at high delisting risk was collected from CSMAR China Stock Special Treatment and Particular Transfer Research Database. This database starts in 1998 because there was no regulation on delisting till 1998, when the CSRC issued the Special Treatment (ST) and Particular Transfer (PT) regulations. Several revisions of the ST and PT regulations have been made, but the core delisting rule is accounting based and remains unchanged. A listed company will be designated an “ST” if it reports a net loss for two consecutive years and a delisting requirement if it suffers a net loss for three consecutive years after being labelled as an “ST” firm4. Overall, a firm has only two years (ST1 and ST2) to work itself out of trouble once it is labelled as ST. If a firm does not show profit at the third year (ST3) then it is delisted. The rigid accounting based standard is simple to use. The extreme pressure of delisting is real, but companies eventually delisted are rare. On average, less than 0.5% of 3 Data can be accessed from http://www.policyuncertainty.com/china_monthly.html. Accessed in 2014. The methodology of the index is available from Baker, S.R., Bloom, N., Davis, S.J., 2015. Measuring economic policy uncertainty, National Bureau of Economic Research.. 4 There are other criteria for determining a ST status such as failure to get an unreserved audit report. Such cases are usually rare. The majority of firms get ST status for two consecutive losses. The special treatment is: the daily stock price movement is restricted to not more than 5% in either direction, and the company’ s semi-annual report must be audited, which is not expected of the other Chinese listed firms. Additionally, the ST status prohibits a listed firm from raising additional equity capital on the stock market.

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companies get delisted per year in mainland China, which is in sharp contrast to the fact that 2.2% of companies get delisted per year from the Hong Kong Stock Exchange (Table 1). Undoubtedly, the delisting rule in China is unique5. ST firms use all kinds of ways to emerge from ST status, including seeking help from government, earning management, restructuring, and debt forgiveness. On average, ST firms emerge from ST status after 3.2 years. Companies usually remain in ST status slightly longer than the regulatory frame of 3 years because of lengthy restructuring. If companies fail to produce a profit at the end of restructuring, then they will be delisted and drop out of the sample. Thus a company is defined as a high delisting risk firm if it has been designated as “ST”. In this study, variable (ST) is a dummy variable. It equals 1 if the firm is listed as ST and 0 otherwise. Then I collect data from two years before and two years after its ST status. By way of example, the timeline of data collection for a particular ST firm is as follows:

___|________|__________|________|_______|________|__________|___

Pre-ST Pre-ST ST ST ST post-ST post-ST

t(-2) t( -1) t(1 ) t(2) t(3) t(4) t(5)

I classify sample firms into three periods: pre-ST (pre distress time), during ST (distress time), and post ST (post distress time) periods. For example, when company 00005 was labelled as ST in 2003, it then went through restructuring till 2007 and eventually recovered. I include firm information for 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, and 2009. I classify 00005 into three periods: pre-ST period (2001-2002), during ST period (2003-2007), and post ST period (2008-2009).

This study has unbalanced panel data. Some variables have slightly fewer than 3251 observations because of the unavailability of the data. The sample and summary statistics are presented in Table 3 of section 5.

[Insert Table 1 here]

3.2. Identification of politically connected firms and state firms

I am primarily concerned about criteria for the classification of politically connected firms and state firms since they appear similar but are qualitatively different. A state firm is directly or indirectly controlled by a government or a government agency. Thus the definition is based on control rights. I have classified a firm as a state firm (State) if the government or a nominal agent controlled by the government is the 5 NYSE’s reasons for delisting are share price below minimum, market cap below minimum, bankruptcy, net tangible assets or net income below minimum, other. NASDAQ delisting requirements are: bid price below minimum, net income below minimum, bankruptcy, market cap below minimum, insufficient public float, public interest concern, other.

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largest shareholder. Specifically, I followed Li and Yamada (2015) definition. The shareholder information is obtained from the company's annual report downloaded from CSMAR. I examined the background of the largest shareholder or the background of the ultimate controller. If there is not sufficient information to identify the background of the ultimate controller, I exclude the company. On average, the largest shareholder has 34.8% of ownership and the largest government shareholder has 39.4% of ownership (see Table 3). It is acknowledged that the largest shareholder has strong control rights and the nature of ownership (state vs private) can be very important.

A politically connected firm is not defined by control rights and ownership but by the networking the company has. A number of studies (Boubakri et al., 2008; Dinç, 2005; Faccio, 2006; Fan et al., 2007; Leuz and Oberholzergee, 2006) examine the extent and impact of political connection on various issues at the firm level. The consensus is that a good measure of political connection needs a way to link a specific person who currently works or worked in government to the higher level of the corporate ladder in a firm. In view of this, I consider a company is politically connected if at least one member of its board of directors works or worked in the government or military. The data are from the CSMAR China Corporate Figure Characteristic Database. To further measure the strength of political connection (PoliticalConn), I calculate the percentage of the Board of Directors who are working or have prior work experience in government or the military6.

Table 2 reports the sample distribution based on the political connection and state firm criteria. In this sample, the state and private firms' proportions are 53% and 47%, respectively. The politically connected firms' proportion is 76% and 24% for non-politically connected firms. Hence, political connection is perceived to be a valuable resource for many firms, especially private firms.

[Insert Table 2 here]

3.3. Government subsidy

The information on subsidies are from the CSMAR China Stock Market Financial Database—Statement Notes. Specifically, the information is available in a footnote to the Non-operating Income Account in the annual consolidated report. A search is then conducted based on the key words "government" or "subsidies", or "government subsidy". After text filtering to verify the accuracy of the data, the nature of the disclosed footnote is subject to human reading.

6 Data start from 2008.

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In essence, the measurement used in this study captures direct government subsidy. It does not include government subsidy in indirect form or in grey areas such as land rights transfer, or interest expense exemption to a distressed company. The variable Subsidy measures the amount of subsidy received by a firm in period t. The variable Receivesub is a dummy variable equal to 1 when a firm receives a subsidy from government and 0 otherwise.

3.4. Outcome variables

I measure the effect of a government subsidy on three aspects: firm valuation (Tobin’s q), profitability (ROA) and employment (Labor). I compute Tobin's q as the sum of the market value of assets and debt divided by ending total assets; where there is non-negotiable equity, I use book value. This measure is winsorized at 1% because there are extreme values because of restructuring of distressed firms. Labor is defined as the number of employees in an enterprise divided by its total assets, then scaled by 1,000,000. ROA is measured by net income divided by the total assets and then winsorised at 1%.

3.5 Other variables

Size: natural log of total assets.

Ln(K/S): natural log of Property, Plant and Equipment divided by sales (it measures the tangibility of a firm).

LnPPE: natural log of Property, Plant and Equipment.

Leverage: ratio of total liabilitiesy over total assets.

Industry dummies: Commerce, Conglomerates, Finance, Industrials, Public Utility, Manufacturing. The baseline is manufacturing.

First shareholding: the percentage shareholding of the largest shareholder.

EM (Earning management): industry-median-adjusted-accruals (IAACC) that follow following Liu and Lu (2007)

NUM (number of listed firms): the number of all listed firms per year in a province where the headquarters of a listed firm are found. For example, in 1998, Guangdong had 110 listed firms located in the province, so all correspond to 110 for 1998.

GDP rank: rank of the provincial GDP in a given year. The higher the number, the higher of GDP. For example, Guangdong was the richest province in 1998, so

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the GDP rank of Guangdong is 31. All firms located in Guangdong are coded as 31 in 1998.

Z Score: this is the Altman score following Altman (2002).

Cash flow: The net income before extraordinary items and depreciation divided by the replacement value of capital stock at the beginning of the year. This measure follows Goyal and Yamada (2004).

PUI: Policy Uncertainty Index.

4. Methodology

4.1. Sample selection issue and model

I have data on firm characteristics and whether the firm received a government subsidy. A naïve way to estimate the effect of a subsidy on a firm’s outcome (Tobin’s q, ROA and Labor) is to estimate a regression of a firm’s outcome variables on firm characteristics plus a dummy variable that is defined as Receivesub=1 for the receipt of a government subsidy and 0 otherwise. The problem with this regression model lies in the nature of Receivesub. This specification treats the dummy variable Receivesub as exogenous when Receivesub is not exogenous. The dummy variable is endogenous and should be modelled directly; otherwise, the firm’s outcome regression estimating the impact of the receipt of a government subsidy will be biased.

There is large amount of work discussed in literature on sample selection. The sample selection model (such as the Heckman two stage model) and treatment effect model are commonly used to estimate samples that: (1) are not generated randomly, (2) a binary explanatory variable was endogenous and (3) sample selection must be considered in the evaluation of the impact of such a dummy variable (Guo and Fraser, 2014). However, there is an important difference between the sample selection model and the treatment effect model: the former analyses outcome data observed only for Receivesub=1, and the treatment effect model analyses outcome data for both Receivesub=1 and Receivesub=0 7 . In the sample, one can observe Tobin’s Q, employment and change in cash flow regardless of whether the firm receives a government subsidy. Hence, I use the treatment effect model in this study.

The treatment effect model used in this study is expressed in two equations:

7 The classical example of the sample selection model is considered in Heckman, J., 1974. Shadow Prices, Market Wages, and Labor Supply. Econometrica 42, 679-694. under the context of shadow price. One can only observe the wage when a women joins the labor force (when D=1). One cannot observe the wage when a woman stays at home (when D=0)

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Selection equation: 𝑅𝑒𝑐𝑒𝑖𝑣𝑒𝑠𝑢𝑏)* = 𝑏 + 𝛾𝑍)* + 𝑢)* (1)

Outcome equation: 𝑌)* = 𝑎 + 𝛽𝑋)* + 𝑑𝑅𝑒𝑐𝑒𝑖𝑣𝑒𝑠𝑢𝑏)* + 𝜀)* (2)

where 𝑌)* is Q, and Labor separately. 𝑋)* is matrix of variables that determine 𝑌)*. Β is the vector of coefficients. Receivesub is the dummy variable that equals 1 when the firm receives a government direct subsidy in period t. 𝑍)* is a matrix of variables that determine𝑅𝑒𝑐𝑒𝑖𝑣𝑒𝑠𝑢𝑏)*, and γ is the vector of coefficients to 𝑍)*. The error terms 𝜀)*

and 𝑢)* are bivariate normal with mean zero and covariance matrix 𝜎78 𝜌𝜌 1 . The

covariates 𝑋)* and 𝑍)* are unrelated to the error terms.

4.2. Model specification and variables for selection equation

For the specification of the selection equation, I use the following variables:

Zit: Stateit, PoliticalConnit, STit, First shareholdingit, Sizeit, NUMit, GDP rankit, , Ln(>

?)AB, Z Scoreit, EMit, PUIt, Industry dummiesi, Yeart.

The two variables of interest in the selection equation are State and PoliticalConn. State captures the nature of firm’s ownership and PoliticalConn captures with whom the firm is connected. The coefficients of these two variables are used to test hypothesis 1. I also include ST to capture how the delisting risk affects the government subsidy grant decision. The ST coefficient ST is used to test hypothesis 2.

The identification strategy I use in this study is to find variables in 𝑍)* correlated to Receivesub, but unrelated to 𝑌)* for any other reason. Specifically, to identify the model, the variables that uniquely explain the government’s choice (Receivesubsidy) are included in Eq.(1), but are excluded from the outcome [Eq.(2)]. If the instruments in vector 𝑍)* have sufficient explanatory power, they should help identify the selection equation from outcome equation. I include Num and GDP Rank in the selection equation. Most government subsidies are from local government, the more listed firms in a given province, the tighter are the budget constraints of the government (Chen et al., 2008). The higher GDP rank of a province, the richer the local government, hence it is more able to provide a government subsidy. However, richer provinces are more likely to use the market mechanism (such as a merger or acquisition) to deal with distressed firms rather than government intervention such as a subsidy. It is an empirical question to test this effect. Earning management is an included factor. Firms have high incentives to conduct earning management to avoid being delisted according to the accounting based delisting rule in China. Chen et al. (2008) find that local government provides subsidies to help firms boost their earnings above the delisting regulatory threshold. Thus I include EM to control for this effect.

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In addition, I use industry dummies as important variables to capture the government’s decision on a subsidy. Li and Yamada (2015) and Tian and Estrin (2008) find that the government keep a shareholding in certain industries for national security and development, hence it is likely that government will provide financial assistance to them when they are facing delisting.

In addition to the above identification variables, I have some control variables. Size is to capture any firm size effect. First shareholding is to control the effect of a controlling shareholder. Z Score measures the overall financial distress factor. Ln(K/S) is to control for the proportion of tangible assets.

4.3. Model specification and variables for outcome equation

Though I have only one selection model, I have two different outcome equation specifications tailored for Q, ROA and Labor because each has different determinants, and requires different matrices of 𝑋)* . Both Tobin’s q and ROA measure firm performance. Tobin’s q is a market based measure free from problems associated with accounting based measures that often arise from accounting number manipulation. It is a forward looking measure that represents investors’ expectations for firms. However, it is more subject to market sentiment and investors’ preferences. Hence, I use an accounting measure, ROA, as a complement. ROA is a static measure that shows how profitable a company’s assets are in generating revenue now. Thus Tobin’s q and ROA are good complements.

For the specification of Tobin’s q and ROA equations, I use the following variables:

XABD,FGH : Receivesubit, STit, Stateit, Sizeit, Leverageit, Ln(

>?)AB , Cash flowit, PUIt,

Industry dummiesi, Yeart

Receivesub is simultaneously estimated from selection model (2). It is a key variable in this study. It measures the impact of a government subsidy on a firm. The coefficient on this variable is used to test hypothesis 3.

ST is a time dummy. It captures how the ST stage affects a firm’s performance. State is an ownership dummy. Sun and Tong (2003) find state ownership has a negative impact on a firm’s performance. Thus State allows me to control for this effect. The inclusion of Size controls for the market power. Leverage is used to reflect the impact of capital structure on firm performance. Li and Yamada (2015) find that leverage has

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a significant impact on Tobin’s q and ROA for both state owned and private firms. The impact of debt is an empirical question in China. On one hand, over borrowing may have a negative impact on profitability, whereas the disciplining role of debt on management could improve its performance. Variable Ln(K/S) is used to measure the extent of the agency problem because tangible assets are easier to monitor and can be used as good collateral. The variable Cash flow is included in the outcome equation. Free cash flow hypothesis by Jensen (1986) states that high cash flow leads to a free cash problem, hence is detrimental to firm performance. Given our sample is about firms in delisting risk, the higher a firm’s cash flow in this sample, the better it is for firm performance. PUI is to control for changes in tax and regulatory policy. It is a measure of riskiness. I also include year and industry dummy variables to control for annual and industrial fixed effects in all equations.

For the labour intensity equation, I use the following explanatory variables:

XABIJKLM: Receivesubit, STit, Stateit, Sizeit, Leverageit, LnPPEit, PUIt, Industry dummiesi, Yeart

𝐿𝑛𝑃𝑃𝐸)* is to measure the effect of capital intensity. Leverage measures the effect of financial conditions on employment. (Li and Yamada, 2015) find a positive effect of leverage on employment in China. Carvalho (2014) finds a positive relationship between state owned bank lending and employment in Brazil.

5. Results

5.1. Descriptive Statistics

In two panels, Table 3 presents the sample’s descriptive information. Panel A provides the means, standard deviation, minimum, and maximum values for all variables in total sample. Panel B groups the sample into pre-ST, ST, and post-ST periods. Significance tests for differences were conducted using the t-statistic.

On average, there is RMB 5 million (equivalent to US$760,000) per firm granted as a direct government subsidy each year. Looking at time, the size of the subsidy increases from an average of RMB 2 million (US$304,300) at the pre-ST stage to RMB 5 million at the ST stage and reaches to nearly RMB 9 million (US$1,369,000) at the post-ST stage. The differences between the subsidy sizes across the three stages are significant. Some firms get nothing, whereas other firms get as much as RMB 1 billion (US$152,000,000) in subsidy. In our sample, 22.4% of firms received a government subsidy; the rest got nothing. The proportion of firms receiving a subsidy

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increased from 16% at the pre-ST stage to 24% at the ST stage and reaches 27.8% at the post-ST stage.

On average, 16% of firms have political connections. The popularity of having a political connection increases from 13% at the pre-ST stage to 17% at the ST stage and get to 19.2% at post-ST stage. Variable State shows that stated-owned enterprises account for 53.3% of our sample; the proportion decreases from 59.9% at the pre-ST stage to 49% at the post-ST stage. That is, about 10% of firms in our sample converted to private firms after the ST cycle. The opposite trends for Politicalconn and State show the different underlying values derived from firms at delisting risk.

Tobin’s q has a mean of 2.513 for the overall sample. The highest mean of 3.243 is at the ST-stage. That reflects the highest risk level, hence the highest firm valuation to compensate for the risk. ROA has a mean of -0.094 for the overall sample. Not surprisingly, it is worst at the pre-ST stage, -0.14, improving to 0.011 at the post-ST stage. The average number of workers in the sample is 2119. The mean is 2724 workers at the pre-ST stage, drops to 1687 at the ST-stage, and then rises to 2178 workers at the post-ST stage. It is not surprising that the number of workers drops at the most distressed time. When I scale the number of employees by a firm’s total assets, the labour intensity is highest at the ST-stage with a mean of 2.132. This is because the total assets shrink faster than the number of employees. The differences for all outcome variables are statistically significant across the three stages.

The Z Score has a mean of -1.12. The lower the Z Score, the higher chance of bankruptcy (financial distress). The mean Z Score is lowest (-2.344) at the ST stage, and improves to 0.054 at the post-ST stage. EM has a mean of -0.097. It has a negative accrual of -0.133 at the pre-ST stage and gradually converges to -0.001 at the post-ST stage. The degree of earning management decreases along the ST cycle. This provides preliminary evidence that a firm is subject to higher degree of earning management at the pre-ST and during the ST stage.

5.2. Selection model

Table 4 gives the estimation results from the selection model for all equations. First, ρ is the estimated rho in the variance-covariance matrix, which is the correlation between the error uit of the selection equation (1) and the error εit of the outcome equation (2). In column (1) for the Tobin’s q equation, ρ=-0.847, which is estimated by Stata. Because the treatment effect model assumes that the level of correlation between the two error terms is non-zero and because violation of that assumption can

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lead to estimation bias, it is often necesary to test Ho:ρ=0. Given χ2=48.1 (p<0.01), I can reject the null hypothesis at a statistically significant level and conclude that ρ is not equal to 0. This suggests that applying the treatment effect model is appropriate. Similar arguments apply to columns (2) and (3).

Second, the coefficients of ST are significant for all columns. This suggests that when a firm is facing the delisting risk, it is more likely to receive a government subsidy. This supports hypothesis 1. This result needs caution when one compares it with Lee et al. (2014) results that finds government subsidies are less value relevant for firms in distress. The difference between their study and this one is that they use the Altman Z score for the distressed firm sample, I focus on the sample of delisting risk. Technically, the distressed risk and delisting risk are two distinct risk factors. In the Table 4, one can observe that coefficient of Z Score is significant only in the ROA equation, but coefficients on ST are significant for all equations, reflecting the different features of the two variables.

Third, the coefficients of PoliticalConn are significant for all equations; in contrast, the coefficients for State are insignificant in all equation. This is a clear indication that hypothesis 2a is supported and 2b is not supported. Having a political connection in a firm greatly increases the chance of receiving a government subsidy. It is clearly documented that, at the firm level, the rent-seeking activities via personal relationships extract value for companies at delisting risk; this is a supplement to Acemoglu et al. (2016) that shows, at the aggregate market level, the market generates abnormal returns to Timothy Geithner connected firms when the U.S. was in turbulent times.

Lastly, among other variables that affect the receipt of government subsidy, I find large firms with a higher degree of earning management, engaged in industrial and public utility industries, located in a province with more listed firms are more likely to receive a government subsidy. Firms with higher one shareholder’s ownership and located in a richer province are less likely to receive a government subsidy.

5.3. Amount of subsidy of connected and non-connected firms

In Table 4, one can see that political connections are important in receiving a government subsidy, but state ownership is not so important. In this section, Table 5 compares subsidy received by connected firms versus non-connected firms in the ST cycle. It can be seen that within the state-owned firms, politically connected firms (group A), on average, receive RMB 8.64 million (US$1,300,000) in the ST cycle. Non-connected state firms (group B) receive RMB 5.95 million (US$ 890,000).

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Group A received 1.5 times more than group B; the difference is significant. For private firms, the politically connected ones (group C) received RMB 4.11 million (US$ 620,000) government subsidy. Non-connected private firms (group D) only receive RMB 2.31 million (US$360,000). Group C received twice as much as group D; the difference is significant.

Not surprisingly, and consistent with Table 4, Table 5 shows that politically connected firms received 1.5 to 2 times more than non-connected firms; the differences are greatest in the Pre-St and during the ST stage, when firms need financial assistance the most. When one looks at the time dimension, the subsidy size granted to firms increases from t(-2) to t(5), which shows that once firms start to receive a subsidy, they increasingly seek subsidies from government, even after they pass the difficult time.

5.4. Firm valuation, profitability and labor intensity

In this section, I look at the impact of a government subsidy on a firm’s outcomes. Panel A, Table 6, examines the outcome model for firm performance. Taking Tobin’s q as an example, in the first column of (1) for Tobin’s q equation, the reported χ2=175.03 (p<0.01) from Table 6 is a Wald test of all coefficients in the regression model (except the constant) being zero. This method gauges the goodness of fit of the model. With p<0.001, one can conclude that the covariates in the regression model may be appropriate, and at least one of the covariates has an effect not equal to zero.

The estimated treatment effect is an indicator of the subsidy impact net of observed selection bias. The coefficient of Receivesub in Tobin’s q equation suggests that the subsidy recipients have a mean Tobin’s q that is 3.649 units higher than non-recipients. Similarly, subsidy recipients have a mean ROA 0.449 units higher than non-recipients. Overall, this shows that receiving a subsidy has a positive impact on the firm performance of recipients. This supports hypothesis 3a that subsidies are positively related to firm value and profitability. It also suggests that the Chinese capital market recognizes that government subsidies enhance corporate value, hence the result provides a valuation insight to analysts to correctly value a business that benefits from subsidies versus one without subsidies. Among other factors, when a firm is in the ST stage, there is a negative impact on firm performance, which is consistent with our expectation. Size has negative impact on Tobin’s q. Leverage impacts Tobin’s q and ROA differently. The difference is due to the nature of the two measures. Tobin’s q is a forward looking measure looking into the future. The higher leverage, the riskier a firm is, thus it gets a higher firm valuation. The higher the leverage, the higher is interest expense, thus lower profitability. Cash flow has

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positive impact on both Tobin’s q and ROA. Firms engaged in industrial and public utilities have a lower ROA.

Panel B, Table 6, shows the results for labour intensity. In the first column of (1), the reported model has a χ2=1821.32 (p<0.01). One can conclude that the covariates in the regression model are appropriate. The coefficient of Receivesub suggests that subsidy recipients have a mean Labor -1.392 units lower than non-recipients, which is consistent hypothesis 3b. This result echoes the findings in Agrawal and Matsa (2013) where they find that higher worker unemployment risk, measured by above median labor intensity, is associated with higher unemployment insurance benefits in the U.S. labor market. Given that government subsidies in my sample can be viewed as an alternative to labor insurance to existing workers, the receipt of a subsidy helps managers to lower the labor intensity of a firm. The ST stage has positive impact on labor intensity. Note that in panel B, Table 3, the number of workers actually drops to its lowest level during the ST stage but, after scaling this number by firm’s total assets to get the labor intensity measure, labor is highest in the ST stage. Thus the positive significant coefficient on ST in the labor equation is due to the scaling effect. Size has a negative impact on labor. Leverage has positive impact on labor. The result is consistent with the studies by Carvalho (2014) and Li and Yamada (2015). LnPPE has a positive impact on labor. The higher the capital intensity of a firm, the higher is its labor intensity. Finally, firms in industrial and public utilities have higher labor intensity.

5.5. Differences in firms outcome before and after adjustments to sample selection

One observe eight statistically significant variables in selection model, which indicates the presence of selection bias and underscores the importance of explicitly considering selection when modelling firm’s outcomes. In this section, I summarizes the results from different approaches.

Table 7 tabulates the differences in outcomes before and after adjustments to sample selection. Taking Tobin’s q as an example, the data show the mean Tobin’s q for the subsidy group is 2.527; that for the non-subsidy group is 2.780. The unadjusted mean difference between groups was -0.253. Using an OLS regression to adjust for covariates, the adjusted mean difference is -0.13 (coefficient of Receivesub in panel A of Table 6). In other words, the non-subsidy group is 0.13 units lower than the subsidy group; the difference is not statistically significant. The data suggest that the involvement of government has a negligible effect on Tobin’s q. However, consider a different analytical approach. The treatment effect model adjusts for the heterogeneity of a subsidy by considering covariates affecting selection bias in the subsidy granting

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decision. The results show that the subsidy group has 3.649 units higher firm valuation than the non-subsidy group. This suggests that both the unadjusted (found by independent t test) and the adjusted mean difference (found above OLS regression) are biased because of selection bias. ROA and labor intensity have similar issues as found in Tobin’s q.

5.6. Government subsidy and economic efficiency

In this section, I examine the firm outcomes of firms stratified by government subsidy and political connection in ST cycle. This analysis bears upon the question of government intervention. That is, government intervention is justified in market failure, yet whether the intervention leads to an improvement in firm performance is so far unknown. In considering the efficiency of government subsidy, I ask whether subsidized firms are better or worse than the non-subsidized group. In this regard, I examine the relative efficiency of corporate welfare.

To address this question, I examine Tobin’s q, ROA, and leverage of firms before and after the ST stage and I ask whether the performance and labor intensity of subsidized politically connected firms (group A) are different from those of non-subsidized politically connected firms (group B). I also compare the firms’ outcomes of subsidized non-connected firms (group C) and non-subsidized, non-connected firms (group D) from t(-2) to t(5).

The results are in Table 8. The statistic of greatest interest is the difference in firms’ outcomes between groups. There are not many differences when one looks at them year by year, probably because a firm receives a subsidy non continuously in the ST cycle. A firm may receive a subsidy in t(-1), then t(2), and then t(4). Thus evaluating the efficiency of government subsidy in the overall results is more useful. First, from column (8), panel B, subsidized firms, both politically connected and not connected, outperform non-subsidized firms in ROA in the ST cycle. Also, the ROA steadily increases from t(-2) to t(5). This result provides direct evidence that a government subsidy greatly helps firms to improve their profitability, hence lowers their delisting risk because ROA is the core measure of the delisting criteria.

Second, from column (8), panel C, subsidized firms, both politically connected and not connected, have lower labor intensity than non-subsidized firms. Labor intensity is also lower in the post ST years than the ST years. The results suggest that the subsidy helps distressed firms to restructure the labor force and lower the labor intensity.

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When a firm is facing the delisting risk, lenders, customers, suppliers and other stakeholders are likely to change their incentives to engage with it. Government assistance may be one solution, but whether assistance leads to a resurgence in the economic performance of those companies is unknown. Results in Table 8 seem to justify government subsidies in the corporate delisting scenario in China since subsidized firms have a significantly better ROA and lower labor intensity than unsubsidized firms. However, there are no differences between the groups with Tobin’s q. This raises an issue relevant to Jiang and Huang’s (2008) study that documents that earnings based delisting criteria are misconstrued. Such a policy induces firms into remnant earning management in order to avoid delisting. Therefore, it is very likely the improvement in ROA in this study is because of the policy effect, and that is why the relative efficiency gain is not shown in Tobin’s q between groups.

6. Conclusions

This paper examines a particular form of government support for firms at high risk of delisting. I show that a government subsidy is given to firms at high risk of delisting to help the firms avoid delisting. Further analysis reveals that political connections lead to a favourable subsidy receipt. Though there are anecdotal hints of the possibility, my study empirically evaluates this relationship. For a sample of 3251 firm observations, I document that politically connected firms received up to twice as much in subsidies as non-connected firms, regardless the nature of ownership. Because of the strong linkage between political connections and subsidy receipt, the political connection percentage increased from 13% in the pre-ST stage to 19% in the post ST stage. The result indicates that personal ties with political officers matters a great deal more than the commonly believed state ownership status for companies in distress times.

The impact of a subsidy on firm outcomes is examined. I find a subsidy increases Tobin’s q, ROA and lower labor intensity net of selection bias. I also find subsidised firms have a higher ROA and lower labor intensity than unsubsidized firms in the 7 year ST cycle.

Overall, the findings suggest that an accounting based delisting rule unintentionally induces firms at high delisting risk to engage in rent seeking activities via political connections as a pathway to obtain government subsidies. This paper calls for a review and reform of the delisting regulations. The tiny percentage of firms being

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delisted in China each year seriously restricts the market self-regulating process. It signals to the market that once a firm gets into the IPO stage, it can remain in the stock market forever to enjoy various financing and reputation privileges. This self-reinforcing cycle further lures companies to engage in rent-seeking activities in the delisting stage. The CSRC is currently considering broadening the scope of reasons for delisting to include voluntary delisting, a minimum total revenue requirement, a minimum trading volume, fraudulent and disclosure. This study is timely to provide strong support for much needed policy change and analysis.

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Table 1 Firm delisting from the Shanghai Stock Exchange, Shenzhen Stock Exchange and Hong Kong Stock Exchanges, 1998-2014

Shanghai Stock Exchange Shenzhen Stock Exchange Hong Kong Stock Exchange

Number of firms delisted

Total number of firms

Percentage delisted

Number of firms delisted

Total number of firms

Percentage delisted

Number of firms delisted

Total number of firms

Percentage delisted

(1) (2) (3) (4) (5) (6) (7) (8) (9)

1998 0 438 0 0 414 0 0 0 0 1999 0 484 0.00% 1 465 0.22% 13 701 1.85% 2000 0 572 0.00% 0 516 0.00% 10 736 1.36% 2001 2 646 0.31% 3 514 0.58% 11 756 1.46% 2002 1 715 0.14% 7 509 1.38% 8 812 0.99% 2003 2 780 0.26% 2 507 0.39% 15 852 1.76% 2004 4 837 0.48% 7 540 1.30% 11 892 1.23% 2005 7 834 0.84% 5 547 0.91% 28 934 3.00% 2006 5 842 0.59% 7 592 1.18% 25 975 2.56% 2007 7 860 0.81% 3 690 0.43% 16 1048 1.53% 2008 2 864 0.23% 0 761 0.00% 30 1087 2.76% 2009 3 870 0.34% 3 848 0.35% 16 1145 1.40% 2010 5 894 0.56% 0 1169 0.00% 20 1244 1.61% 2011 2 931 0.21% 1 1411 0.07% 53 1326 4.00% 2012 3 954 0.31% 1 1540 0.06% 66 1368 4.82% 2013 2 953 0.21% 5 1536 0.33% 32 1451 2.21% 2014 1 997 0.10% 2 1618 0.12% 47 1548 3.04%

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Table 2 Summary on political connections and state owned, 1998-2012

Table 2 reports the number of firm observations stratified by political connection and state owned nature for 1998 to 2012. A firm is coded as a state firm if the government is the largest shareholder or a nominal agent controlled by the government is and is coded as non-state firm otherwise. A firm is considered to have political connections if at least one member on the Board of Directors is working or has prior work experience in government or the military. A firm is considered without political connections if none of its Board of Directors is working or has prior work experience in government or the military. The data for political connection are available only from 2008.

State Firms Non-State Firms Total Firms with political connection 1130 1096 2226

Firms without political connection 428 289 717

Total 1558 1385 2943

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Table 3 Descriptive data of Chinese firms at high risk of delisting

This dataset comprises 3251 firm year observations for Chinese firms at high risk of delisting between 1998 and 2012. The sample includes the Pre-ST, ST and post-ST periods. Subsidy is the amount of subsidies received per firm year, denominated in Chinese Yuan. Receivesub is a dummy variable equal to 1 when the firm receives subsidies from government and 0 otherwise. ST is a dummy variable equal to 1 if the firm is listed as Special Treatment Status, and 0 otherwise. PoliticialConn is the percentage of the Board of Directors who work or have prior work experience in government or the military (these data start from 2008). State is a dummy variable equal to 1 if the government or a nominal agent controlled by the government is the largest shareholder, and 0 otherwise. Tobin’s q is the sum of the market value of assets and debt divided by the year-end total assets; when there is non-negotiable equity, book value is used (this variable is winsorized at 1%). ROA is measured by net income divided by the total assets, then winsorized at 1%. Workers is the number of employees per firm per year. Labor is defined as the number of employees in an enterprise divided by its total assets, then scaled by 1,000,000. First shareholding is the percentage of shareholdings held by the largest shareholder. Size is the natural log of total assets. Leverage is the ratio of total liabilities over total assets. Ln(K/S) is natural log of Property, Plant, and Equipment divided by sales, winsorized at 1%. LnPPE is the natural log of Property, Plant, and Equipment. NUM is the number of all listed firms in a province in a given year where the headquarter of a listed firm is situated. GDP rank measures the provincial GDP rank in a given year. The higher the number, the higher the GDP rank. Z score is the Altman Z score calculated as Z = 1.2WC_TA + 1.4RE_TA+ 3.3EBIT_TA + 0.6MV_BV + 0.99S_TA, where WC_TA: working capital/total assets, RE_TA: retained earnings/total assets, EBIT_TA: earnings before interest and taxes/total assets, MV_BV: market value of the equity/book value of total liabilities, S_TA: sales/total assets. The Z score is winsorized at 1%. Cash flow is measured as net income before extraordinary items and depreciation divided by the replacement value of capital stock at the beginning of the year, winsorized at 1%. PUI is the policy uncertainty index. EM is the industry median adjusted accruals, winsorized at 1%; Accruals is defined as the difference between net income and cash flows from operating activities divided by total assets. Panel A provides the means for the entire sample. Panel B shows the means of the key variables for the Pre-ST, ST and post-ST periods. Columns 4 and 5 report the difference of the means test. The significance tests use the two tailed t-statistic.

Panel A Variable Obs Mean Std. Dev. Min Max Subsidy(amount) 3236 5,090,709 40,500,000 0 1,290,000,000 Receivesub 3251 0.224 0.417 0.000 1.000 ST 3251 0.461 0.499 0.000 1.000 PoliticalConn 2943 0.164 0.149 0 0 State 3251 0.533 0.499 0.000 1.000 Tobin’s q 3158 2.513 3.876 0.650 31.090 ROA 3155 -0.094 0.4 -2.9 0.71 Workers 3033 2119 4514 1 79196 Labor 3033 1.812 3.770 0 143.595 First shareholding 3030 0.348 0.163 0.022 0.894 Size 3155 20.553 1.295 10.840 25.440 Leverage 3155 1.503 16.511 0.000 877.260 Ln(K/S) 2975 -0.569 1.569 -5.242 3.980 LnPPE 3129 18.892 1.756 8.42 24.99 NUM 3201 73.948 56.549 8 291 GDP rank 3201 20.015 8.619 1.000 31.000 Z Score 3057 -1.12 5.05 -36.97 3.29 Cash flow 3145 -0.103 1.052 -22.320 20.450 PUI 3251 114.04 49.025 55.687 244.4 EM 3107 -0.097 0.397 -2.722 -0.001

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Table 3 Descriptive data of Chinese firms at high risk of delisting (continued)

Panel B Pre-ST

times (Pre-ST)

Distress Times (ST)

Post-distress time (Post-ST)

t-test statistic (1)-(2)

t-test statistic (3)-(2)

(1) (2) (3) (4) (5) Subsidy(amount) 2,186,039 5,112,120 8,927,105 -2.163* 1.799 Receivesub 0.161 0.240 0.278 -4.937** 1.938* ST 0.000 1.000 0.000 - - PoliticalConn 0.131 0.171 0.192 -6.654** 2.896** State 0.599 0.510 0.490 4.413** -0.896 Tobin’s q 1.645 3.243 2.288 -9.504** -5.54** ROA -0.140 -0.116 0.011 -1.322 8.198** Workers 2724 1687 2178 5.396** 2.479** Labor 1.707 2.132 -1.342 -2.738** -5.237** First shareholding 0.371 0.322 0.369 7.215** 6.597** Size 20.860 20.164 20.878 13.686** 11.773** Leverage 0.684 2.494 0.717 -2.322* -1.968* Ln(K/S) -0.408 -0.510 -0.886 1.344 -4.568** LnPPE 19.383 18.509 19.383 12.338** 5.308** NUM 64.150 78.660 77.279 -6.364** -0.509 GDP rank 19.966 20.063 19.981 -0.271 -0.210 Z Score -0.316 -2.344 0.054 9.068** 9.027** Cash flow -0.324 -0.127 0.214 -7.53** 12.52** PUI 99.200 115.030 132.113 -9.9662** 7.289** EM -0.133 -0.122 -0.001 -0.591 6.27** ** and * indicate significance at the 0.01 and 0.05 levels, respectively.

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Table 4 Results from selection equation

This table presents estimates of the factors in the selection equation of treatment effect model (1). ST is a dummy variable equal to 1 if the firm is listed as Special Treatment Status, and 0 otherwise. PoliticialConn is the percentage of the Board of Directors who work or have prior work experience in government or the military (these data start from 2008). State is a dummy variable equal to 1 if the government or a nominal agent controlled by the government is the largest shareholder, and 0 otherwise. First shareholding is the percentage of shareholdings held by the largest shareholder. Size is the natural log of total assets. Ln(K/S) is natural log of Property, Plant, and Equipment divided by sales, winsorized at 1%. NUM is the number of all listed firms in a province in a given year where the headquarters of a listed firm is situated. GDP rank measures the rank of provincial GDP in a given year. The higher the number, the higher the GDP rank. Z score is the Altman Z score calculated as Z = 1.2WC_TA + 1.4RE_TA+ 3.3EBIT_TA + 0.6MV_BV + 0.99S_TA, where WC_TA: working capital/total assets, RE_TA: retained earnings/total assets, EBIT_TA: earnings before interest and taxes/total assets, MV_BV: market value of the equity/book value of total liabilities, S_TA: sales/total assets. Z score is winsorized at 1%. Cash flow is measured as net income before extraordinary items and depreciation divided by the replacement value of capital stock at the beginning of the year, winsorized at 1%. PUI is the policy uncertainty index. EM is industry median adjusted accruals, winsorized at 1%; Accruals is defined as the difference between net income and cash flows from operating activities divided by total assets. The industry dummy effects are measured relative to the manufacturing industry. Statstics in parentheses are the robust standard error adjusted for clusters in companies.

Variables Tobin’s q ROA Labor ST 0.282** 0.365** 0.353** (4.27) (5.53) (4.30) PoliticalConn 0.839** 0.680** 1.613** (3.26) (2.99) (4.70) State 0.072 0.076 0.131 (0.82) (1.02) (1.34) First shareholding -0.832** -1.014** -1.638** (4.05) (4.10) (5.29) SIZE 0.371** 0.035 0.285** (7.48) (0.59) (5.44) NUM 0.004** 0.002* 0.006** (3.38) (2.16) (4.70) GDP rank -0.023** -0.016** -0.045** (4.15) (3.08) (6.36) Ln(K/S) -0.002 0.021 -0.011 (0.07) (0.76) (0.28) Z Score -0.005 0.179** 0.040 (0.36) (4.29) (1.83) EM 0.009** 0.003** 0.020 (8.03) (4.79) (0.16) Commerce -0.064 0.154 -0.038 (0.32) (0.69) (0.13) Conglomerates 0.129 0.194 0.303 (0.92) (1.30) (1.62) Finance 0.284 0.307 0.441 (1.52) (1.55) (1.73) Industrials 0.440** 0.592** 0.719** (3.62) (5.09) (4.87) Public_utility 0.510** 0.705** 0.627** (2.86) (3.69) (3.16) Constant -8.456** -1.450 -6.652** (8.35) (1.12) (5.92) ρ -0.847 0.840 0.45 χ2 for (H0:ρ=0) 48.1 40.3 7.35 Prob> χ2 0 0 0

** and * indicate significance at the 0.01 and 0.05 levels, respectively.

29

Table 5 Subsidies received by politically connected firms, non-connected firms, state firms and non-state firms in the Special Treatment cycle 1998-2012

Table 5 gives the mean subsidy for connected state firms versus counter parties in the ST cycle. The p-values for difference in means between groups A and B, and of groups C and D are based on t-tests for independent samples. The last column reports the mean subsidy for the entire ST cycle for each group.

Subsidy (amount in millions)

Pre-ST ST (distress) Post-ST (normal) Mean

Sample t(-2) t(-1) t(1) t(2) t(3) t(4) t(5) Connected and State firms (A) 2.77 5.98 20.32 3.41 4.16 9.28 14.55 8.64 Non-connected and State firms (B) 1.57 3.16 8.02 2.30 1.93 8.45 16.23 5.95 Difference (A)-(B) 1.20 2.82 12.30 1.11 2.23 0.83 -1.69 2.69 p-value difference 0.36 0.41 0.15 0.48 0.20 0.93 0.92 0.08 Connected and non-State firms (C) 0.48 1.24 1.55 5.33 1.57 4.24 14.40 4.11 Non-connected and non-State firms (D) 0.05 0.80 2.01 0.62 7.84 0.27 4.55 2.31 Difference (C)-(D) 0.43* 0.44 -0.46 4.71* -6.26 3.97** 9.85 2.31* p-value difference 0.03 0.48 0.70 0.03 0.26 0.01 0.19 0.02

** and * indicate significance at the 0.01 and 0.05 levels, respectively.

30

Table 6 Results from the outcome equation

This table presents estimates of the factors in the outcome equation of treatment effect model (2) and the OLS regression. Receivesub is a dummy variable equal to 1 when the firm receives subsidies from government and 0 otherwise. ST is a dummy variable equal to 1 if the firm is listed as Special Treatment Status, and 0 otherwise. State is a dummy variable equal to 1 if the government or a nominal agent controlled by the government is the largest shareholder, and 0 otherwise. Tobin’s q is the sum of the market value of assets and debt divided by total assets; when there is non-negotiable equity, book value is used (this variable is winsorized at 1%). ROA is measured by net income divided by the total assets, then winsorized at 1% level. Labor is defined as the number of employees in an enterprise divided by its total assets, then scaled by 1,000,000. First shareholding is the percentage of shareholdings held by the largest shareholder. Size is the natural log of total assets. Leverage is the ratio of total liabilities over total assets. Ln(K/S) is natural log of Property, Plant, and Equipment divided by sales, winsorized at 1%. Cash flow is measured as net income before extraordinary items and depreciation divided by the replacement value of capital stock at the beginning of the year, winsorized at 1%. PUI is the policy uncertainty index. Panel A provides results from outcome equations for Tobin’s q and ROA. Panel B provides results from outcome equations for Labor. Panel A Tobin’s q ROA Variables Treatment

Effect OLS

Treatment

Effect OLS

Receivesub 3.649** -0.130 0.449** -0.004 (11.54) (0.70) (10.12) (0.20) ST -0.663** -0.408** -0.041* 0.001 (4.71) (3.33) (2.01) (0.09) State -0.077 0.031 0.022 0.013 (0.45) (0.20) (1.30) (0.89) Size -1.685** -1.499** 0.001 0.035** (8.29) (8.39) (0.08) (3.05) Leverage 0.027** 0.028** -0.003** -0.003** (2.96) (2.98) (5.06) (4.25) Ln(K/S) 0.065 0.026 0.006 0.003 (0.88) (0.40) (1.00) (0.57) Cash flow 0.265** 0.363** 0.302** 0.344** (3.59) (4.56) (8.33) (12.44) PUI -0.002 0.002 -0.000* -0.000 (1.93) (1.49) (2.30) (1.94) Commerce 0.345 0.400 0.021 0.048 (0.90) (1.12) (0.57) (1.93) Conglomerates -0.058 0.300 -0.040 0.002 (0.20) (0.99) (1.28) (0.07) Finance -0.586 -0.318 0.007 -0.017 (1.43) (0.67) (0.19) (0.24) Industrials -0.327 0.180 -0.088** -0.011 (1.57) (0.87) (3.03) (0.51) Public_utility -0.004 0.472 -0.134** -0.010 (0.01) (1.42) (3.20) (0.26) Year 0.181** 0.234** 0.001 0.003 (6.09) (7.33) (0.38) (1.31) Constant -327.069** -436.957** -2.039 -6.671 (5.73) (7.12) (0.40) (1.47) Adjusted R2 - 0.40 - 0.36 χ2 (14) 175.03 - 256.18 - Prob> χ2 0 - 0 - Statstics in parentheses are robust standard errors adjusted for clusters in companies.

** and * indicate significance at the 0.01 and 0.05 levels, respectively.

31

Table 6 Results from outcome equation (continued)

Panel B Labor Variables Treatment Effect OLS Receivesub -1.392* 0.259 (2.22) (1.61) ST 0.285* 0.117 (2.11) (1.12) State 0.369* 0.329* (2.40) (2.23) Size -0.643** -0.746** (4.73) (5.99) Leverage 0.156** 0.154** (35.68) (27.47) LnPPE 0.271** 0.273** (5.39) (5.61) Policy uncertainty -0.000 0.000 (0.07) (0.39) Commerce -0.008 0.018 (0.02) (0.06) Conglomerates 0.168 0.016 (0.70) (0.07) Finance 0.369 0.569 (0.56) (0.89) Industrials 0.956** 0.693** (3.90) (2.93) Public Utility 0.892* 0.652 (2.21) (1.39) Year -0.103** -0.132** (3.94) (5.19) Constant 216.382** 275.283** (4.12) (5.41) Adjusted R2 - 0.67 χ2 (13) 1821.32 - Prob> χ2 0 - Statstics in parentheses are robust standard error adjusted for clusters in companies.

** and * indicate significance at the 0.01 and 0.05 levels, respectively.

32

Table 7 Differences in Outcomes before and after adjustment of the sample selection

Table 7 tabulates the differences in outcomes before and after adjustment of sample selection; the sample consists of the pre-ST and ST stages. Independent t tests on mean differences or t tests on regression coefficients are reported.

Group and Comparison Outcome Measures Tobin’s q ROA Labor

Firms who received subsidy 2.527 -0.063 1.511 Firms who did not subsidy 2.780 -0.144 2.098 Unadjusted mean difference -0.253 0.081** -0.587** OLS Regression-adjusted mean difference -0.13 -0.004 0.259 Adjusted mean difference controlling sample selection

3.649** 0.449** -1.392*

** and * indicate significance at the 0.01 and 0.05 levels, respectively.

33

Table 8 The effect of government subsidy to Chinese firms in Special Treatment and economic efficiency

Table 8 reports the Tobin’s q, ROA, and labor stratified by government subsidy and political connectedness in the ST cycle. The p-values for difference in means between groups A and B, and groups C and D are based on t-tests for independent samples. The last column reports the mean Tobin’s q, ROA and Labor for the entire ST cycle for each group and for each panel.

Panel A Tobin’s q Pre-ST ST (distress) Post-ST (normal) Mean t(-2) t(-1) t(1) t(2) t(3) t(4) t(5) (1) (2) (3) (4) (5) (6) (7) (8) Subsidised and connected firms (A) 2.2 2.25 2.43 2.36 2.75 2.04 1.91 2.25 Non subsidised and Connected firms (B) 1.39 1.81 2.2 2.37 3.3 2.37 1.72 2.07 Difference (A)-(B) 0.81** 0.44* 0.23 -0.01 -0.55 -0.33 0.19 0.18 p-value difference 0 0.04 0.49 0.99 0.40 0.23 0.25 0.13 Subsidised and non-connected firms (C) 1.87 2.09 2.34 2.72 2.17 2.47 2.89 2.36 Non subsidized and non-Connected firms (D) 1.5 1.53 1.94 2.46 2.95 2.65 2.98 2 Difference (C)-(D) 0.37 0.56 0.4 0.26 -0.78 -0.18 -0.09 0.36 p-value difference 0.2 0.07 0.25 0.75 0.47 0.85 0.95 0.11

Panel B ROA

Pre-ST ST (distress) Post-ST (normal) Mean

Subsidised and connected firms (A) -0.06 -0.15 -0.02 -0.02 -0.07 0.04 0.03 -0.03 Non subsidised and Connected firms (B) -0.05 -0.24 -0.2 -0.1 -0.08 0.03 0.05 -0.1 Difference (A)-(B) -0.01 0.09** 0.18** 0.08 0.01 0.01 -0.02 0.07** p-value difference 0.22 0 0 0.08 0.96 0.53 0.12 0 Subsidised and non-connected firms (C) -0.07 -0.28 -0.03 -0.06 -0.12 0.08 0.04 -0.08 Non subsidized and non-Connected firms (D) -0.07 -0.25 -0.24 -0.17 -0.2 0.03 0.03 -0.15 Difference (C)-(D) 0 -0.03 0.21** 0.11 0.08 0.05 0.01 0.07*

p-value difference 0.87 0.83 0 0.17 0.59 0.15 0.2 0.05

Panel C Labor intensity

Pre-ST ST (distress) Post-ST (normal) Mean

Subsidised and connected firms (A) 1.25 1.33 1.42 1.51 1.56 1.1 1.06 1.3 Non subsidised and Connected firms (B) 1.39 1.89 2.45 2.18 2 1.32 1.15 1.88 Difference (A)-(B) -0.14 -0.56** -1.03 -0.67* -0.44 -0.22 -0.09 -0.58**

p-value difference 0.36 0.00 0.07 0.02 0.17 0.16 0.55 0 Subsidised and non-connected firms (C) 0.95 1.41 1.43 1.26 1.5 1.2 1.78 1.39 Non subsidized and non-Connected firms (D) 1.79 2.24 1.81 2 5.03 1.58 1.4 2.05 Difference (C)-(D) -0.84** -0.83** -0.38 -0.74 -3.53 -0.38 0.38 -0.66**

p-value difference 0 0 0.21 0.15 0.19 0.31 0.7 0 ** and * indicate significance at the 0.01 and 0.05 levels, respectively.

34

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