Benefits of Downward Earnings Management and Political Connection: Evidence from Government Subsidy and Market Pricing

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Benefits of Downward Earnings Management and Political Connection: Evidence from Government Subsidy and Market Pricing Haiyan Jianga, , Yuanyuan Hub, Honghui Zhangc, Donghua Zhouc ⁎

a

Department of Accounting, Waikato Management School, University of Waikato, Private Bag, 3105, New Zealand School of Accountancy, Massey Business School, Massey University, New Zealand c School of Accounting, Jiangxi University of Finance and Economics, Jiangxi 330013, China b

ARTICLE INFO

ABSTRACT

Keywords: Government subsidy Earnings management Political connection Market pricing China

This paper examines the role of downward earnings management and political connection on the receipt of government subsidies and market pricing of subsidies. Using subsidies data handcollected from Chinese listed firms over the period 2004–2014, the results show a significantly positive association between downward earnings management and the receipt of government subsidies. The results also reveal that a firm's political connection is conducive to securing subsidies for poor performers, but not for good performers. Market pricing analyses demonstrate that share markets value subsidies positively in general, but the effect is ameliorated in firms conducting downward earnings management. No discernible difference is found between the market pricing of subsidies received by firms with political connections and those without.

1. Introduction Government subsidies are a common phenomenon in both developed and emerging economies and are often tactically used by governments to offset market imperfections, develop economies of scale, and meet social policy objectives (Schwartz & Clements, 1999). At the country or industry level, a strand of economics and finance literature investigates the effect of government subsidy on operational efficiency (Dube, 2003; Kebede, 2006), production capacity (Cotti & Skidmore, 2010), research and development activities (Gorg & Strobl, 2006; Liu & Shieh, 2005), employment (Girma, Gorg, Strobl, & Walsh, 2008; Wang & Wang, 2013), and export competitiveness (Desai & Hines, 2008). At firm level, government subsidies are found to influence firm value (Bar-Yosef & Landskroner, 1988; Lee, Walker, & Zeng, 2014), firm performance (Balsari & Ucdogruk, 2008; Tzelepis & Skuras, 2004), and firm productivity (Bergström, 2000; Harris & Trainor, 2005). Compared to their consequences, the determinants of subsidies are considerably under-researched, so we know little about why some firms are subsidized more, or more often, than others. Given the discretionary nature of government subsidies, studying their determinants should be insightful. To this end, our paper proposes and empirically tests two determinants of subsidy receipt—downward earnings management and political connection. We also investigate how the market pricing of subsidies is affected by these two factors. We conduct the investigations using Chinese data for the following reasons. First, fiscal decentralization in China induces local governments to engage in an inter-jurisdictional competition to offer subsidies, since the economic performance of subsidy recipients within their jurisdictions is paramount to local economies and, therefore, influences the bureaucratic ranks of local politicians (Li,

⁎

Corresponding author. E-mail addresses: [email protected] (H. Jiang), [email protected] (Y. Hu).

https://doi.org/10.1016/j.intacc.2018.11.001

0020-7063/ © 2018 University of Illinois. All rights reserved.

Please cite this article as: Jiang, H., International Journal of Accounting, https://doi.org/10.1016/j.intacc.2018.11.001

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1998). Although policy on subsidies is centralized and promulgated by the Ministry of Finance (MOF) and the central government stipulates the recipients (e.g. firms, sectors, regions, etc.) and the utility of these subsidies, local governments have the power to determine their timing and amount (Wu & Cheng, 2011). Due to the discretionary nature of subsidy, the amount received by firms varies considerably. Hence, investigation using Chinese data guarantees rich insight. Second, subsidy is widespread and is crucial to business success in China. Compared to the United States, there is wide coverage of Chinese firms that have received government subsidies.1 With a large hand-collected dataset, we aim to provide valuable evidence informative for other countries where subsidies are granted at governments' discretion but for which the empirical investigation is constrained by the lack of machine-downloadable archival data.2 We posit two firm-level factors determining discretionary subsidy decisions: downward earnings management and political connection (hereafter PCON). Gaining government support is alleged to be an incentive for downward earnings management (Jones, 1991; Lim & Matolcsy, 1999; Navissi, 1999), as it assists firms' efforts to conceal certain levels of profitability and “reserves” accruals to increase future performance. Downward earnings management increases the opacity of earnings and may, therefore, mislead government subsidy decisions. This is particularly prominent in the Chinese political economy, where local governments engage in judicial competition (Xu, 2011), facing pressure to rescue low-performing firms in the region. Therefore, we explore whether the conduct of downward earnings management is indeed related to an increase in subsidy receipt. Meanwhile, literature provides rich evidence on PCON's function in facilitating scarce political resource allocation and influencing firm performance. We thus incorporate this perspective into our investigation of subsidy determinants. Receiving government subsidies has strong implications on firms' future values. Grants are required to be recognized systematically as income over the period necessary to match the related costs they are intended to compensate (IAS 20, 1983). Similarly, Chinese Accounting Standards define government subsidies as the benefits provided by the government to an enterprise in the form of monetary assistance or non-monetary assets but exclude capital invested by the government as a partial owner of the enterprise (Chinese Accounting Standard, 2006). These grants are recognized as non-operating income. The primary goal of government subsidies in China is to promote the development of strategic industry sectors such as agriculture, public utilities, and high-tech industries (Chen, Lee, & Li, 2008). Subsidies are also provided for firms to alleviate capital constraints and to support firms in financial difficulties (Claro, 2006; Lee et al., 2014). As a result, subsidy recipients have a boost in income and cash flow—and often, a strong signaling effect on the market. Therefore, this paper also examines the market pricing of subsidies. Lee et al. (2014) examine the value relevance of government subsidies for Chinese listed firms and conclude that subsidies are related positively to firm value in general, but less so for distressed firms; the value relevance of a subsidy also depends on the purpose for which subsidies are used and the channel through which they are granted. This study suggests the capability of market participants to untangle the implications of subsidies, even in an emerging market. Extending this line of inquiry, our study investigates the market pricing of subsidies in general, as well as the effect of subsidies on stock price, conditional on downward earnings management and PCON. Using a large sample set of subsidies data hand-collected over the period 2004–2014, we conducted univariate and multivariate analyses to investigate the proposed determinants and market pricing effect of subsidies. The analyses provide strong support for the proposition that downward earnings management, using both accruals and real activities, is positively associated with the size of subsidies received. This result holds after we conduct a batch of tests to alleviate the concerns for possible self-selection bias and reverse causality between downward earnings management and subsidies. Then, we find that the PCON established by poor performers is associated positively with subsidies, whereas the same relationship does not exist for good performers, suggesting PCON's non-monotonic function in securing subsidies for firms with varying levels of operating efficiency. Furthermore, market pricing analyses reveal that although the market places a strong price premium on subsidies in general, subsidies received by firms conducting downward earnings management by using accruals is discounted in value. However, we do not find the same ameliorating effect of PCON on the market pricing of subsidies. This study contributes in several ways. First, although the financial economics literature has made great strides in understanding the consequences of government subsidies at the country and industry level, accounting literature is scant. That is somewhat surprising, as one specific international accounting standard—IAS 20—is dedicated to this issue, and government subsidy is a common economic transaction practiced by many governments. We propose, and test empirically, managerial financial reporting practices as an antecedent of receiving a government subsidy. Thus, this study is one of the few to examine the determinants of subsidy at the firm level and adds to the government subsidy literature from a financial reporting perspective. Furthermore, although both accrual and real earnings management practices are conducted by firms to secure subsidies, the market pricing analysis highlights investors' discount on subsidies obtained by firms conducting accrual earnings management, while being indifferent to real earnings 1 For instance, our sample shows that 70% of Chinese listed firms in 2013 received government subsidies. This is much higher than the 10% in Pappas, Walker, Liang, and Zeng (2017) using U.S. data. Specifically, Pappas et al. (2017) start the sample with 59,285 firm-year observations, but only approximately 10% of the initial sample, 5878 firm-year observations, has received subsidies. 2 Subsidy information following IFRS is disclosed in the financial statements either separately or under a general heading such as, “other income.” Alternatively, subsidies are directly deducted in reporting the related expense. Detailed information on the subsidy can only be found in the notes to financial statements. Therefore, subsidy data must be manually collected. In the United States, explicit guidance for government assistance received by business entities is lacking, although the FASB issued a proposed Accounting Standards Update in 2015 for feedback, which is much consistent with those required by IFRS. The absence of clear guidelines of disclosures in accounting standards has resulted in a diversity of practices and the lack of useful information about subsidy. Although subsidy information can be sourced from the Subsidy Tracker in the United States, such information does not match the Compustat database. Hence, a manual check is necessary to ensure the accuracy of subsidy data (Pappas et al., 2017).

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management. Thus, the findings enrich the real earnings management literature from the perspective of government subsidies. Lastly, our paper addresses a topic that is of current concern to governments internationally, in that understanding a firm's reporting practices and motives is essential in making economically justifiable decisions at the time of allocating subsidies. Specifically, the finding of increased government fiscal support to downward earnings manipulators calls for governments' scrutiny of the earnings quality of subsidy recipients. Although the country of study chosen is China, subsidies are commonly granted by governments in many countries, so our findings should have resonance in other countries. The rest of the paper is organized as follows. Section 2 discusses the related literature and develops testable hypotheses. Section 3 describes our sample section and research design. Section 4 presents empirical results. Section 5 concludes the paper, recapping the contributions of the study. 2. Literature survey and the development of hypotheses 2.1. Government subsidies and earnings management Government subsidy is widespread in China for specific political and economic reasons. During China's economic liberalization process, the political system was highly decentralized, with local governments having autonomous policymaking powers within their jurisdictions (You & Du, 2012). The decentralization gives local governments strong motivations for inter-jurisdictional competition (Xu, 2011). Politicians' promotion and demotion are primarily dependent on local economic prosperity, as measured by regional GDP growth and unemployment rates (Li & Zhou, 2005). As local revenue is predominantly derived from listed firms within their jurisdictions, local governments are incentivized to prop up local firms. To this end, local governments not only attract capital by providing tax incentives and offering preferential tax rates, but also grant subsidies to improve local listed firms' reported earnings (Chen et al., 2008). As a scarce resource, government subsidies are not available to all listed firms, and the amount of subsidy granted often varies significantly between subsidized firms (Lee et al., 2014). For example, 70% of Chinese listed firms in 2013 received government subsidies. Among them, the maximum amount reached approximately CNY 10.38 billion, while the minimum was only CNY 311. Both the size and the recipients of subsidies allocated are at the discretion of local governments, who set and change eligibility criteria based on their objectives (Aschhoff, 2008). Government subsidies come in a variety of forms, including treasury direct subsidies, value-added tax (VAT) refunds, corporate income tax refunds, technology and innovation grants, etc. Our study focuses on direct subsidies, for example, cash payments and loan guarantees, because they are non-tax based subsidies allocated in a more discretionary manner by local governments (Lee et al., 2014).3 Using Chinese data, a few studies provide valuable insights into the firm-level consequences of government subsidies in China. Chen et al. (2008) report that local governments provide subsidies to help firms boost their earnings for rights issues and avoid delisting.4He (2016) examines how government subsidies affect firms' accruals earnings management practices before IPOs and finds a low level of earnings management before IPOs in firms receiving more financial subsidies or income tax benefits granted by local governments because such fiscal support helps firms achieve their desired earnings targets for IPO purposes. We take a different angle from these subsidy consequence studies, in an attempt to identify the factors that determine the level of government subsidies. Firms have strong incentives to seek subsidies. Receipt of government subsidies not only has strong implications on firms' future values but also helps firms overcome capital constraints (Claro, 2006; Lee et al., 2014). Subsidies boost firms' net income and cash flow and, thereby, have positive implications for the stock price of subsidy recipients (Lee et al., 2014). From the customers' perspective, receipt of subsidies implies government endorsement of a firm's projects, which has positive implications for firms' future growth and promotes sales of products (Lee et al., 2014, p.153). We argue that firms may have incentives to conduct negative earnings management in order to qualify for subsidies. Jones (1991), Lim and Matolcsy (1999), and Navissi (1999) argue that one of the incentives of firms conducting negative earnings management is to obtain favoritism from government bodies. As aforementioned, Chinese local governments have strong motivations for inter-jurisdictional competition to improve their economic development (Xu, 2011). Many listed firms are state-owned enterprises (SOEs) owned by local governments, and therefore, those firms are crucial to their local economies as a source of fiscal revenue (Chen et al., 2008; Chen & Wang, 2004). If local governments do not provide troubled firms with subsidies, those firms may suffer from severe financial problems in the short run (Wang & Wang, 2013), which will eventually trigger a level of instability in the local economy resulting from unemployment and firm bankruptcy. We posit that knowing their importance for the local economy and social 3 A VAT refund is less likely to be manipulated, as it is not subject to local government administration. The tax-based subsidies aim to encourage the development of certain industries, businesses, and prioritized regions (He, 2016; Lee et al., 2014). Only firms located in special economic zones, or those operating in industries prioritized by government, are entitled to receive the tax-based subsidies. Previous literature (Chen et al., 2008; He, 2016; Lee et al., 2014) suggests tax refunds (and technology and innovation grants) provide little discretion for local governments. 4 The China Securities Regulatory Commission (CSRC) uses bright-line regulatory benchmarks to grant approvals for IPOs and rights offerings and to initiate performance-related delisting. The CSRC has set and changed the standards required for listed firms to issue right shares quite frequently over the years. In 1993, firms were required to have only two consecutive years of profits before they could issue rights. In September 1994, the CSRC specified, for the first time, that a firm must have an average ROE of > 10% in the previous three years before it could issue rights. In January 1996, the CSRC toughened this requirement, stating that a firm must have > 10% ROE for each of the previous three years. CSRC then lowered the standard in March 1999, requiring that firms have an average ROE above 10% in the past three years but not lower than 6% in any of these years. In March 2001, CSRC further lowered the standard, stating that firms must have an average ROE above 6% in the past three years. Chinese listed firms have an incentive to manage reported earnings to meet these specific performance benchmarks (Piotroski & Wong, 2012).

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stability, listed firms may be motivated to manipulate earnings downward in the hope of obtaining subsidies from governments. In addition, Chinese firms and entrepreneurs tend to conceal their wealth through negative earnings management to avoid public attention. He, Rui, and Xiao (2016) find that egalitarianism within Chinese Confucian culture, i.e., resentment against the rich, penalizes the flaunting of wealth. The ‘social sanction’ is manifested as increased government monitoring, reduced government subsidies, and falling market values observed in the years following the publication of firms and their entrepreneurs on the Hurun Rich List.5 Meanwhile, as accruals reverse, the negative accruals realized in the current year reverse in the following years, resulting in a seemingly improved performance. This, in turn, sends a signal to the granting government that justifies the profit-enhancing function of subsidies and is conducive to securing future subsidies. In addition, local governments may not be able to make completely correct judgements about corporate profitability and social commitments and, thus, fail to recognize earnings management due to information asymmetry between management and government agencies. Taken together, the unique Chinese institutional setting where the listed firms operate provides strong incentives for corporate managers to conduct downward earnings management for the purpose of acquiring government subsidies. Thus, the first testable hypothesis proposes a positive association between firms' downward earnings management and the magnitude of subsidies received. H1. Firms' downward earnings management is positively associated with government subsidies.

2.2. Government subsidies, PCON, and firm profitability Receipt of government subsidies through negative earnings management is not without cost. Downward earnings management depresses a firm's performance, adversely affecting executives' performance evaluation and compensation. Therefore, as far as the cost of using downward earnings management to obtain subsidies is concerned, corporate managers may consider alternatives, for example, employing political connection to increase the likelihood of winning subsidies (Lee et al., 2014). There is a growing body of literature on the various roles of PCON in creating value and enhancing the competitive advantages of the business. PCON literature suggests that political connection can bring about a range of benefits, including relaxed regulatory oversight on the connected companies or stiffer regulatory oversight on the rivals of connected firms (De Soto, 1989; Stigler, 1971), preferential access to credit markets and bank loans (Dinc, 2005), as well as favorable regulatory conditions (De Soto, 1989), government subsidies (Johnson & Mitton, 2003), and tax discounts (De Soto, 1989)—all ultimately resulting in increases in a firm's value (e.g., Faccio, 2006; Hillman, 2005).6 PCON is especially important in transitional economies, where property rights protection and the enforcement of contracts are weak, and government interference is substantial (Allen, Qian, & Qian, 2005; Faccio, 2006; Fan, Wong, & Zhang, 2007; He, Wan, & Zhou, 2014; Shleifer & Vishny, 1998). Similarly, managers of Chinese listed firms tend to develop and maintain an extensive network of connections at various levels of government for the aforementioned special benefits they bring. Political ties increase firm values in China (Ding, Jia, Wu, & Zhang, 2014; Sheng, Zhou, & Li, 2011). Lee et al. (2014) interview a group comprising government officials, accountants, entrepreneurs, academics, and analysts. Their interview-based evidence suggests that one important consideration influencing whether or not firms receive subsidies is the existence of personal connections (or guanxi) between entrepreneurs and officials. Wu and Cheng (2011) argue that managers with PCON may influence the government subsidy decision-making process by lobbying policymakers. In addition, managers with prior political experience are familiar with government procedures and preferences and, thus, are able to predict government resource allocation priorities. This insight, in turn, provides the connected firms with greater opportunities to receive government funding than their counterparts without PCON. Despite the implied relationship between PCON and subsidies, we argue that the effect of PCON on subsidy receipt is not monotonic. On the one hand, You and Du (2012) find that politically connected CEOs are less likely to be dismissed, and there is a positive relationship between the political connections of retained CEOs and future firm performance. However, this relation exists only when firm profitability is below the industry median. These findings suggest that the value of PCON is contingent on a firm's operating performance and that the benefits of PCON may outweigh their costs when firms do not meet their profitability targets. Analytically, Shleifer and Vishny (1994) demonstrate that the motivation for the more common bribes from managers to politicians is the hope for government support in promoting restructuring and raising subsidies for the firm. The analysis results suggest that privatization of very unprofitable firms results in continued subsidies but little restructuring. In contrast, potentially profitable firms tend to undertake restructuring by laying off people and changing product lines. This analytical model suggests that the effect of PCON on seeking subsidies is shown only in firms with poor performance. On the contrary, Wu and Cheng (2011) find a positive relation between PCON and receiving subsidies only when managerial 5 The Hurun Report is widely recognized as the foremost authority in tracking the rapid changes of wealth among China's high-net-worth individuals. The report was established in 1999 by Rupert Hoogewerf, the ‘godfather’ of the China Rich List. Hoogewerf was awarded the New Weekly magazine's prestigious Person of the Year award in 2002 for his contribution to the understanding of wealth in China. In 2004, he was named one of the “100 Top Influencers in China's Globalization” by Global Entrepreneur magazine. 6 PCON literature provides inconclusive evidence for its effect on the firm performance, finding either a positive (Francis et al., 2009; Goldman, Rocholl, & So, 2009; Li, Meng, Wang, & Zhou, 2008; Su & Fung, 2013) or a negative effect (Bliss & Gul, 2012; Fan et al., 2007; Shleifer & Vishny, 1998). The uncertainty of PCON's impact on firm values leads to investor skepticism.

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reputation is high and when firms' past performance is superior to others. As the sample included in Wu and Cheng (2001) includes only 233 firms over the period 2002–2004, our paper intends to provide an empirically robust test. Despite the controversy, we rely on a strong theoretical perspective in developing H2, arguing that PCON facilitates the receipt of subsidies only in underperforming firms, but not in firms with superior performance. The testable hypothesis is stated as follows. H2. PCON is associated positively with government subsidies in underperforming firms but not in outperforming ones. 2.3. Market pricing of government subsidies Government subsidy is disclosed below operating income as one of the below-the-line items on the Statement of Financial Performance for investors, to facilitate investors' understanding of core-earnings and non-recurring items (Chen & Wang, 2004). Hence, information on subsidies should have a bearing on stock price. Items such as cash subsidies from the treasury, tax refunds, and technology and innovation grants directly or indirectly boost the receiving firm's current and future cash flow, which is translated into high stock price based on the discounted cash flow model. Chen and Wang (2004) investigate the value relevance of four belowthe-line items—investment income, government subsidy, non-operating revenues, and non-operating expenses—that are separately reported after operating income by Chinese listed firms, as required by accounting standards. They find that below-the-line items are more value relevant than operating income. Furthermore, financial analysts interviewed by Lee et al. (2014) assert that the information on subsidy acquisition of the firms they follow serves as a useful input in their earnings forecasts, setting target prices, and making stock recommendations. Therefore, information on subsidies is continuously disseminated to investors in capital markets via financial analysts. Collectively, we posit a positive market pricing of subsidies. H3. The market price is associated positively with government subsidies. We then make further inquiry into whether the market pricing of subsidies differs between subsidy recipients who conduct downward earnings management and utilize PCON and those who do not employ these two means. Lee et al. (2014)’s empirical analyses show that subsidies are significantly and positively related to firm value, but less so for distressed firms and for subsidies granted through non-tax channels, implying a conditional market effect of subsidies on receiving firms' financial strength and on the type of subsidy received. Earnings management practices obfuscate information that investors use for financial statement analysis and stock valuation. Prior studies find that managed earnings are less credible than unmanaged ones and, thereby, investors would place less value on the former (He, 2016; Holthausen & Verrecchia, 1988; Teoh & Wong, 1993). Using Chinese data, Haw, Qi, Wu, and Wu (2005) provide similar evidence that investors are able to differentiate quality earnings from the contaminated ones to make investment decisions. In addition, since accruals reverse, earnings manipulated for the sake of receiving subsidies would vary across years, rendering a low earnings persistence. Earnings volatility increases investors' perception of risk, which leads to discounted share prices. Taken together, the discussion above leads to the hypothesis formulated as follows. H3a. Market pricing of government subsidies differs between firms conducting downward earnings management and their counterparts. Although the market may react positively to information on subsidies in general, there are reasons for PCON to mitigate or accentuate this effect. On the one hand, the market may discount subsidies to firms with political ties for the following reasons. First, if the government grants a firm subsidy because of its political ties, rather than making an economically fair decision on the basis of project viability, the subsidies obtained in this way are unlikely to give a positive indication of future firm value. Furthermore, Wu, Wu, Zhou, and Wu (2012) demonstrate that among state-owned enterprises, subsidies obtained on the basis of political ties are not likely to be utilized efficiently, because SOEs with PCON are more concerned about meeting local government goals instead of acting in the best interests of public shareholders.7 In addition, there is uncertainty about the timing and size of subsidies that connected firms may receive (Chen, Ding, & Kim, 2010). Also, the benefits of PCON are implicit and are neither recurring nor cyclical due to a number of factors, including the changes in policies and politician appointments, availability of financial resource of the government, and even connected politicians' wellbeing (Chen et al., 2010; Kim, Pantzalis, & Park, 2012; Lee et al., 2014). Fisman (2001) finds that the share price of companies connected to Indonesia's then-President Suharto plummeted upon news of his declining health. Lee et al. (2014) reveal that non-taxbased subsidies, including cash grants, loan guarantees, and debt concessions, received by politically connected Chinese firms are less persistent than operating income. Therefore, the high uncertainty of receiving subsidies through political favoritism can obscure the time-series pattern of reported earnings of the connected firms and thereby reduce analyst earnings forecast accuracy (Chen et al., 2010). Earnings volatility also increases investors' perception of riskiness, which is translated into a low share price of stocks. Lastly, firms with PCON tend to have high financial reporting opacity to conceal the political rent-seeking activities (Chaney, Faccio, & Parsley, 2011), resulting in information asymmetry between investors and managers. Following this line of argument, the informativeness of subsidies may be low in firms with political ties. On the other hand, subsidies obtained by PCON firms may be perceived as beneficial by investors, as they are strong indicators of government support. Chaney et al. (2011) examine the impact of political connections on the cost of debt from the perspective of bondholders. Their evidence suggests that poor earnings quality does not translate into higher debt financing costs for politically 7

However, they also find that private firms with politically connected managers outperform those without such managers. 5

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connected firms, implying that these firms are not penalized by debtholders. This may be due to the perceived benefits of PCON, as aforementioned. More directly, Boubakri, Guedhami, Mishra, and Saffar (2012) argue that because politically connected firms have softer budget constraints and are less sensitive to competition and market pressure than comparable non-connected firms, the overall exposure to market-wide risk is lower for politically connected firms, especially during economic downturns, thus driving the cost of capital down for these firms. Using a total sample comprised by 1248 firm-year observations from 26 countries over the period 1997–2001, they find that politically connected firms enjoy a lower cost of equity capital than their non-connected peers. A lower cost of capital is translated into a higher share price of PCON firms compared to their counterparts. Taken together, this line of argument indicates that subsidies obtained by PCON firms may be favorably perceived by investors, rendering a positive relation between subsidies and share price in PCON firms. Due to the existence of competing viewpoints, the testable hypothesis is developed in non-directional form. H3b. Market pricing of government subsidies differs between firms with and without PCON. 3. Sample selection and research design 3.1. Sample and data Our sample consists of all publicly traded Chinese firms listed on the Shanghai and Shenzhen stock exchanges from 2004 to 2014. 2004 is selected as the starting year because executives' curriculum vitae used to measure PCON were not fully disclosed in annual reports before then. We obtain all financial and stock market data from the China Stock Market & Accounting Research (CSMAR) database, and manually collected government subsidy information and political connection data (PCON) from the annual reports. Panel A of Table 1 shows the sample selection procedure. We started with a large sample. After excluding 6283 observations having no disclosure on subsidy information, and a further 523 observations that lack data to measure the rest of the variables, our final sample contains 14,124 firm-year observations for the subsidy determinant test of H1 and H2. For market pricing analyses (H3s), our sample is further reduced to 13,480 observations due to unavailable share prices, although those observations have been included in the regression analyses for H1 and H2 testing. Panel B of Table 1 presents the distribution of sample observations by industry. The statistics show that 15.4% of our sample is clustered in the Machinery, Equipment, Instrument industry, and only 1% of our sample is from the Transmitting and Culture industry. The distribution of our final sample by industry is similar to that in the CSMAR database, which indicates that our sample is unbiased. We provide a summary of subsidies granted to listed firms over the years in Panel C of Table 1. The statistics show that in general, subsidies granted are increasing, from 0.38 billion in 2004 to around 100 billion in 2014. Roughly, 539 billion Chinese Yuan was granted over 11 years. In accordance with this trend, the number of firms receiving subsidies increased from 60 in 2004 to 1240 in 2014, highlighting the increasing trend and the importance of subsidies in the Chinese economy. 3.2. Model design 3.2.1. Variable measurement 3.2.1.1. Government subsidy (GS). Government subsidy (GS) is measured as total subsidy income in a financial year deflated by total assets and is used as the dependent variable in subsidy determinant tests. The subsidy is reported as non-operating income on the Statement of Financial Performance according to Chinese accounting standards. The following are the major types of subsidies disclosed by Chinese firms: (1) treasury direct subsidy, (2) value-added tax (VAT) refund, (3) corporate income tax refund, and (4) technology and innovation grants. As the VAT refund is not subject to local government administration, it is less likely to be manipulated and, thus, is excluded from the total subsidy income for our empirical analysis. For the market pricing test investigating stock price reaction to subsidies, we deflate the total subsidy income firms received in a financial year by the number of outstanding shares, to be consistent with other independent variables in the market pricing model. 3.2.1.2. Downward earnings management (DEM). This study uses three alternative measures of downward earnings management—DEM1 and DEM2 are measures of negative discretionary accruals, while DEM3 measures income-decreasing real earnings management. Downward earnings management (DEM1) is dummy variable taking the value of 1 if a firm has negative discretionary accruals (DAC). Discretionary accruals are estimated using the modified Jones model, controlling for firm performance (Dechow, Sloan, & Sweeny, 1995; Kothari, Leone, & Wasley, 2005). We estimate the following equation for all firms in the same industry (using the two-digit industry code) with at least eight observations in an industry in a particular year:

ACCt / TAt

1

=

0 (1/ TAt 1)

+

1 [(

SALESt

RECEIVABLEt )/ TAt 1] +

2 (PPEt / TAt 1)

+

3 (ROAt 1)

+

t

(1)

where ACC is total accruals, calculated as earnings before extraordinary items and discontinued operations, minus operating cash flows; TA is total assets in year t − 1; ΔSALES is a change in sales from year t − 1 to year t; ∆RECEIVABLE is a change in accounts receivable from year t − 1 to year t; PPE is gross property plant & equipment; ROA is return on assets, measured as earnings before extraordinary items and discontinued operations for the preceding year, divided by total assets for the same year. The coefficient estimates from Eq. (1) are used to estimate the non-discretionary component of total accruals (NDAC) for each sample observation. The discretionary accruals (DAC) are then the residual from Eq. (1). We also measure an alternative DAC using Dechow's (1995) model, which is highly similar to Eq. (1) except for eliminating ROA as a control variable. If DAC is negative using the Dechow et al. 6

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Table 1 Sample selection and industry distribution. Panel A: Selection criteria. Original non-financial sample observations from CSMAR

20,930

exclude: Observations with missing subsidy data Observations with missing data for computing other variables Sample observation used for subsidy determinant test – H1 and H2 exclude: Observations with missing market data Sample observation used for market pricing test – H3s

6283 523 14,124 644 13,480

Panel B: Industry distribution Industry

Observations

Percentage

A: Farming, Forestry, Animal Husbandry & Fishery B: Mining and Quarrying C0: Food and Beverage C1: Textile, Clothing, Fur C3: Papermaking, Printing C4: Petroleum, Chemical, Rubber, Plastic C5: Electronic C6: Metal, Nonmetal C7: Machinery, Equipment, Instrument C8: Medicine, Biologic Products C9: Other manufacturing D: Production & Supply of Power, Gas & Water E: Construction F: Transportation, Storage G: Information Technology Industry H: Wholesale and Retail Trades J: Real Estate K: Social Services L: Transmitting, Culture Industry M: Integrated Total

350 319 608 585 284 1474 487 1227 2171 911 148 679 292 660 837 994 827 413 137 721 14,124

0.025 0.023 0.043 0.041 0.020 0.104 0.034 0.087 0.154 0.065 0.010 0.048 0.021 0.047 0.059 0.070 0.059 0.029 0.010 0.051 1.000

Panel C: Subsidies granted to listed firms over sample period Year

Amount (in billions)

Number of firms receiving subsidies

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Total

0.383 0.6273 0.3712 24.87 97 31.99 49.26 66.63 85.16 83.23 99.51 539.03

60 57 64 911 1011 1081 1116 1163 1220 1232 1240 9155a

a

9155 are the firm-year observations with non-zero subsidy values.

(1995) model, downward earnings management (DEM2) takes the value of 1, and 0 otherwise. In addition, we also measure a firm's real earnings management practice, because the opportunistic reporting behavior of firms is not limited to AEM alone.8 To manipulate short-term reported earnings artificially, managers also manipulate the timing or structure of real operations (Roychowdhury, 2006). We follow prior literature in developing our REM proxies (Cohen & Zarowin, 2010; Garcia Lara, Osma, & Penalva, 2009; Gunny, 2010; Roychowdhury, 2006; Zang, 2012) as the sum of abnormal levels of cash flow from operations (ACFO), abnormal production costs (APROD), and abnormal discretionary expenses (ADISX). ACFO is computed by estimating the following regression model within each two-digit SIC industry and year:

8

For a review of AEM literature, please refer to Dechow, Ge, and Schrand (2010). 7

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H. Jiang et al.

CFO/ TAt

1

=

(1 /TAt 1) +

0

1

(SALES / TAt 1) +

2

(2)

( SALES / TAt 1) +

where CFO is cash flows from operations. ACFO is the residual of Eq. (2). We multiply the residuals from the estimation model by −1 so that higher values of ACFO indicate income-increasing REM.9 To estimate the abnormal production cost (APROD), we follow Roychowdhury (2006) and use the following model:

PROD /TAt

1

=

(1/ TAt 1) +

0

1

(SALES / TAt 1) +

2

( SALES /TAt 1) +

3

(3)

( SALESt 1/ TAt 1) +

where PROD is production cost, measured as the sum of the cost of goods sold and change in inventory. We use the residual from Eq. (3) as our measure of APROD. A high value of APROD indicates higher REM, as production costs are abnormally high when managers use overproduction opportunistically to lower the cost of goods sold. To compute abnormal discretionary expenses (ADISX), we estimate the following regression and use its residual value to measure ADISX:

DISX / TAt

1

=

0

(1 /TAt 1) +

1

(4)

(SALESt 1/TAt 1) + ,

where DISX is discretionary expenses (advertising expense, R&D, and SG&A expenses). We multiply the residuals from the estimation Eq. (4) of DISX by −1 so that higher values of ADISX indicate income-increasing REM. Based on the aggregate measure of REM, we proxy the downward earnings management (DEM3) as a dummy variable taking the value of 1 if a firm has negative REM, and 0 otherwise. 3.2.1.3. Political connection (PCON). Following Faccio (2006) and extant Chinese PCON literature, we measure political connections to capture whether the chairman or CEO has political ties in their incumbent position and prior career. Political ties exist if the chairman or CEO (1) is a member of the National People's Congress (NPC), (2) is a member of the National Committee of the Chinese People's Political Consultative Conference (CPPCC), or (3) has served as a government official in provincial, municipal, or countylevel government before becoming chairman or CEO of the listed firm. PCON takes a value of 1 when the chairman or CEO meets any one of the above criteria and 0 otherwise. This measure is a broad definition of political connection. It enables us to capture the strength of political connection and is consistent in essence with the measures of Fan et al. (2007) and Francis, Hasan, and Sun (2009). Our second measure of PCON is a continuous variable, PCON_Intensity_CEO, measured as a score of the chairman's or CEO's political connection intensity. Scores of 5, 4, 3, 2, and 1 are assigned when chairman or CEO was an official of the central government (5), provincial government (4), municipal government (3), county-level government (2), or the lowest level government (i.e., district) (1). A chairman or CEO without political ties is granted a score of 0. The higher the score, the greater the intensity of the chairman's or CEO's political connection. It is an aggregate score taking account of both the chairman's and the CEO's scores. 3.2.2. Model testing the determinants of subsidies To test whether firms conduct downward earnings management (H1) and establish PCON (H2) for the purpose of receiving government subsidies, Eq. (5) is defined as follows.

GSit =

0DEMi,t

+

+

9CFOi,t

1PCONi,t

+

+

2 SOE i,t

+

3SIZE i,t

+

4 GROWTH

+

5LEVi,t

+

6ROAi,t

+

7 CAPIi,t

+

2 STAFFi,t

+

8SOE i,t

(5)

10 GDPi,t

where government subsidy (GS) is the sum of subsidies received by firm i in year t deflated by total assets; downward earnings management, DEM, is a dummy variable taking a value of 1 if the firm has negative discretionary accruals or negative measures of real earnings management activities. DEM is measured in three alternative ways as detailed in Section 3.2.1. PCON stands for political connection, which is a dummy variable taking a value of 1 when the chairman or CEO is politically connected and 0 otherwise. Two PCON measures are used for H2 testing. We also control for a batch of variables that may be determinants of subsidies. SOEs are controlled, as Wang and Wang (2013) find that local SOEs receive more government subsidies in China. Firm size (SIZE) is measured as the log of total assets and is controlled as a common firm-specific factor. If large firms receive more subsidies, we would observe a positive coefficient on size, whereas a negative sign on the coefficient will be observed if subsidies are channeled to small firms. Leverage (LEV) is measured as long-term liability deflated by total assets. It is controlled because Tzelepis and Skuras (2004) find that subsidies are provided to reduce the liquidity of the firms, indicating that firms suffering liquidity problems are more likely to be subsidized. We then control for firm performance using return on assets (ROA), measured using net income divided by total assets, because capital subsidization is positively correlated with firm profitability (Tzelepis & Skuras, 2004). In addition, capital intensity (CAPI) is controlled and measured as the ratio of total fixed assets to total assets, because Lee (1996) finds that industry capital growth is positively related to government–industry incentives using tax refunds and allowances in Korea. STAFF stands for the natural log of the number of employees, which is a proxy for government rent-seeking by forcing connected firms to provide more job opportunities. According to capture theory (Peltzman, 1976; Stigler, 1971), government grants and loans are given to firms that can provide more rents to it. This would predict that government facilitation is more likely for firms that can provide more employment which, in turn, helps to maintain political and social stability. Growth (GROWTH) is measured as the market to book value of equity and is controlled, as government subsidization is often used to support high growth potential (Hart, McGuiness, O'Reilly, & Gudgin, 2000). Cash flow from operations (CFO) is controlled because firms' cash flow deficiency may affect subsidy decisions. GDP per capita 9 Roychowdhury (2006, p. 341) suggests that price discounts to boost sales, along with overproduction, reduce abnormal CFO, while cutting discretionary expenditures increases abnormal CFO. The net effect on abnormal CFO, therefore, is ambiguous.

8

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in the province where a firm is domiciled is also controlled, as Bergström (2000) finds government subsidy decisions are largely affected by local economic development, and rich provinces have more fiscal income for distribution in the form of subsidies. Continuous variables are winsorized at their 1st and 99th percentiles. We first estimate Eq. (5) using the regression on the full sample of observations to test H1, and then test H2 using firm-year observations with high profit margins vs. those with low profit margins. The median of operating margin is used to partition the high vs. low profit margin sub-samples. 3.2.3. Model testing for market pricing of subsidies In H3, we propose positive market pricing of information about subsidies received by firms. Following Barth and Clinch (1998), we begin by using share price as a summary measure of information relevant to investors and investigate the ability of investors to recognize financial statement amounts including government subsidies. The baseline model is presented in Eq. (6).

PRICEi, t =

0

+

1 GSPSi, t

+

2 EPSNETi, t

+

3 BVPSi, t

+

(6)

i, t

where GSPS is government subsidy, measured as the sum of subsidy received deflated by the number of outstanding shares, in order to be consistent with the rest of the independent variables, which are all measured on a per-share basis. EPSNET is the earnings per share minus the subsidy per share, because the subsidy is disclosed as non-operating income and, thus, is summarized into firms' total earnings on the financial statement. BVSP is book value of equity per share. Then, to test H3a and H3b on whether the market prices subsidies differently for firms conducting downward earnings management (establishing PCON) and their counterparts, Eq. (7) is formulated as follows.

PRICEi, t =

0

+

1 GSPSi, t

+

2 DEM

+

3 GSPS

DEM +

4 EPSNETi, t

+

5 BVPSi, t

+

i, t

(7)

We expect the market's reaction to subsidies will differ between firms conducting downward earnings management and their counterparts. In this case, we may observe a moderating effect of DEM on the association between GSPS and PRICE (H3a). We apply the same reasoning to firms establishing PCON, and test H3b by replacing DEM in Eq. (7) with PCON. Based on this argument, we expect a significant γ3 if the market reacts to subsidies differently for firms conducting downward earnings management (establishing PCON) and their counterparts. All our equation analyses are conducted by controlling industry and year fixed factors and by clustering standard errors by firms. This is in order to control for potential heteroskedasticity and autocorrelation problems and to provide robust standard error estimation with reliable t-statistics (Gow, Ormazabal, & Taylor, 2010; Petersen, 2009). 4. Empirical results 4.1. Descriptive statistics Table 2, Panel A reports the descriptive statistics of variables. Raw values of government subsidies are reported showing a mean of CNY 34.8 million and a standard deviation of CNY 491 million. The median raw value of the subsidy is CNY 1.785 million, suggesting the existence of large amounts of subsidies to a few firms.10 Other variables show standard deviations that are in line with previous studies. In addition, 66.8% of sample observations come from SOEs. We report the raw count of STAFF and GDP per capita in order to provide meaningful insight. The average GDP per capita is 40,000, with a high variability of CNY 24,000 between provinces. For the regression analyses, the subsidy is deflated by total assets in the determinant analyses, and by the number of outstanding shares in the market pricing analyses. Due to the high variations, GDP raw data is standardized. All continuous variables are then winsorized at their 1st and 99th percentiles. Panel B of Table 2 reports the correlation matrix. Downward earnings management (DEM1) and political connection (PCON) are positively correlated with government subsidies (GS). Profitability (ROA), capital intensity (CAPI), employee numbers (STAFF), and gross domestic product (GDP) are positively correlated with subsidies, while growth (GROWTH) shows a negative correlation with subsidies. Therefore, the positive correlations between DEM1/PCON and GS provide preliminary evidence for the positive effect of downward earnings management and political connection on receiving subsidies. Among the variables used for the market pricing test, share price (PRICE) shows a positive correlation with subsidies, earnings per share (EPS_EXSUB), and book value per share (BVPS), as expected. In Panel C of Table 2, we firstly show the univariate analysis results of comparing subsidies received by firms conducting downward earnings management and firms with PCON with the subsidies received by their counterparts. The results show significantly higher values of GS received by firms conducting downward earnings management and having PCON than are received by their counterparts. The t-statistics are 6.559 and 2.257 (significant at better than the 0.01 and 0.05 levels, respectively). Furthermore, to examine whether the downward management of earnings is a proxy for opportunistic reporting and thus is associated with poor earnings quality, we compare two common measures of earnings quality, earnings smoothness and earnings comparability, between firm-year observations with downward earnings management (DEM = 1) and their counterparts (DEM = 0). We expect a lower earnings smoothness and comparability for firms that conduct downward earnings management in comparison to those that do not. 10 Further sensitivity analysis is conducted to exclude 710 observations with the highest amounts of subsidies (above the 90th percentile of subsidy raw values). Regression analysis with the trimmed sample shows highly consistent results with our main findings. We therefore conclude that our findings are not driven by extremely large values of subsides received by big firms.

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The results reported in Panel C of Table 4 show that firm-year observations for those conducting downward earnings management have less smooth and comparable earnings than their counterparts. The t-statistics of mean comparisons for earnings smoothness and comparability between the two sub-sample groups are −2.15 and − 2.23 (significant at better than the 0.05 and 0.01 levels, respectively). 4.2. Multivariate analysis 4.2.1. Downward earnings management and PCON as the determinants of subsidies Using OLS, we conduct a series of regression analyses on the determinants of subsidy receipt and the market pricing of subsidies. Table 3 reports determinant analysis results on the effect of downward earnings management on subsidies as proposed in H1. Three Table 2 Descriptive statistics and correlation matrix. Panel A: Descriptive statistics Variable

Obs

Mean

Std. Dev.

25th percentile

Median

75th percentile

GS_raw (in million CNY) GS DAC_ Kothari DAC_ Dechow REM_ Roychowdhury PCON_CEO SOE SIZE GROWTH LEV ROA CAPI STAFF (raw count) GDP_raw (in CNY) PRICE GSPS EPS_EXSUB BVPS

14,124 14,124 14,124 14,124 14,124 14,124 14,124 14,124 14,124 14,124 14,124 14,124 14,124 14,124 13,480 13,480 13,480 13,480

34.800 0.003 −0.006 0.009 −0.693 0.177 0.668 21.820 0.208 0.536 0.023 0.280 5852 40,000 10.713 0.028 0.297 3.450

491.00 0.007 0.113 0.136 0.726 0.381 0.471 1.365 0.606 0.207 0.081 0.192 20,000 24,000 7.838 0.059 0.571 2.245

0 0.000 −0.058 −0.049 −0.888 0.000 0.000 20.899 −0.039 0.392 0.008 0.127 817 20,000 5.710 0.000 0.028 1.988

1.785 0.001 −0.006 0.002 −0.532 0.000 1.000 21.719 0.113 0.542 0.027 0.245 2035 34,000 8.270 0.005 0.199 3.095

13.200 0.003 0.043 0.054 −0.279 0.000 1.000 22.616 0.288 0.677 0.054 0.411 4792 57,000 12.940 0.028 0.498 4.484

Panel B: Correlation matrix

GS (1) DEM1 (2) PCON_CEO (3) SOE (4) SIZE (5) GROWTH (6) LEV (7) ROA (8) CAPI (9) STAFF (10) CFO (11) GDP (12) PRICE (13) EPS_EXSUB (14) BVPS (15)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

(15)

1.000 0.054 0.019 0.008 −0.002 −0.020 0.010 0.033 0.023 0.049 0.004 0.153 0.074 −0.115 −0.038

1.000 0.010 −0.015 −0.018 −0.008 0.041 −0.022 −0.013 0.016 0.441 0.005 −0.008 −0.039 −0.023

1.000 −0.033 0.113 0.018 0.017 0.043 0.002 0.078 0.016 0.018 0.040 0.064 0.062

1.000 0.241 −0.027 −0.006 0.030 0.167 0.237 0.068 0.008 0.004 0.059 0.168

1.000 0.046 0.130 0.213 0.050 0.656 0.077 0.299 0.118 0.387 0.646

1.000 0.021 0.205 −0.044 −0.019 0.071 −0.037 0.112 0.198 0.085

1.000 −0.367 0.000 0.056 −0.167 −0.038 −0.101 −0.199 0.317

1.000 −0.076 0.138 0.299 0.088 0.335 0.647 0.119

1.000 0.233 0.270 −0.235 −0.143 −0.113 −0.067

1.000 0.172 0.026 0.116 0.252 0.401

1.000 −0.067 0.163 0.290 −0.004

1.000 0.105 0.106 0.146

1.000 0.545 0.319

1.000 0.467

1.000

Panel C: Univariate analysis to compare subsidies and earnings quality received by firms conducting downward earnings management (establishing PCON) with those received by their counterparts Mean of GS

Mean of Earnings Smoothness

Mean of Earnings Comparability

Group DEM1 = 1 DEM1 = 0

Obs. 7516 6608

Mean 0.0034 0.0027

t-stat 6.559***

Obs. 6249 5093

Mean 0.498 0.510

t-stat −2.15**

Obs. 6522 5497

Mean −0.067 −0.065

t-stat −2.23***

PCON_CEO = 1

2494

0.0034

2.257**

̶

̶

̶

̶

̶

̶

(continued on next page) 10

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Table 2 (continued) Panel C: Univariate analysis to compare subsidies and earnings quality received by firms conducting downward earnings management (establishing PCON) with those received by their counterparts Mean of GS PCON_CEO = 0

11,630

0.0030

Mean of Earnings Smoothness

Mean of Earnings Comparability

̶

̶

̶

̶

̶

̶

Variable definitions: GS is government subsidy, measured as the sum of subsidies deflated by total assets; DEM1 and DEM2 are dummy variables taking the value of 1 if discretionary accruals are negative. Discretionary accruals are estimated using Kothari et al.'s (2005) performance-adjusted Jones model (DAC_ Kothari) and Dechow et al.'s (1995) modified Jones model (DAC_ Dechow) respectively. DEM3, as a dummy variable, represents negative real earnings management, measured as the sum of abnormal cash flow, abnormal production costs, and abnormal discretionary expense (REM_ Roychowdhury). PCON is political connection, measured as (1) a dummy variable, PCON_CEO, taking the value of 1 if chairman and CEO have political ties and 0 otherwise; and (2) a continuous variable, PCON_INS, political connection intensity, measured as a score of the chairman or CEO's political connection intensity. Scores of 5, 4, 3, 2, and 1 are assigned when the chairman or CEO was an official of central government (5), provincial government (4), municipal government (3), county-level government (2), or the lowest level of government (i.e., district) (1). Chairman or CEO without political ties is granted with 0. The higher the score, the greater the intensity of the chairman's or CEO's political connection is. It is an aggregate score taking into account of both the chairman's and the CEO's scores. State-owned enterprises (SOEs) take the value of 1 if a firm is controlled by state; firm size (SIZE) is measured as the log of total assets; leverage (LEV) is measured as long-term liability deflated by total assets; firm performance is measured as return on assets (ROA), being net income divided by total assets; capital intensity (CAPI) is measured as the ratio of total fixed assets to total assets; STAFF stands for the natural log of the number of employees; growth (GROWTH) is the market to book value of equity; cash flow from operations (CFO) is cash flow from operating activities; GDP is GDP per capita in the province where a firm domiciles. Earnings smoothness is calculated following Tucker and Zarowin (2006). Earnings smoothness measures the correlation between the change in discretionary accruals and the change in pre-discretionary accrual income using the current and past four years' observations. The reversed fractional ranking of income smoothing of each firm-year observation within its industry-year is used following prior studies. Earnings comparability is measured as the degree to which firms' earnings outputs covary more with industry peers, following De Franco et al. (2011). The bold figures in Panel B are significant at better than 5% levels. In Panel C, *** and **represent a statistical significance level of 1% and 5% (onetailed test). Table 3 Downward earnings management and government subsidies – H1 testing DV = GS

Predicted sign

VARIABLES Constant

+

DEM

+

PCON_CEO

?

SOE

+

SIZE

?

GROWTH

+

LEV

+

ROA

+

CAPI

+

STAFF

+

CFO

−

GDP

+

Year FE Industry FE Observations (N) Adjusted R2

(1)

(2)

(3)

DEM1

DEM2

DEM3

14.530⁎⁎⁎ (5.93) 0.630⁎⁎⁎ (4.13) 0.103 (0.44) 0.497⁎⁎ (2.45) −0.816⁎⁎⁎ (−7.02) −0.093 (−0.84) 1.840⁎⁎⁎ (3.57) 5.604⁎⁎⁎ (4.74) 2.780⁎⁎⁎ (4.12) 0.376⁎⁎⁎ (3.54) −1.760 (−1.42) 0.569⁎⁎⁎ (3.91) Included Included 14,124 0.12***

14.547⁎⁎⁎ (5.96) 0.695⁎⁎⁎ (4.21) 0.101 (0.43) 0.494⁎⁎ (2.44) −0.816⁎⁎⁎ (−7.03) −0.105 (−0.95) 1.820⁎⁎⁎ (3.53) 6.039⁎⁎⁎ (4.98) 2.711⁎⁎⁎ (4.04) 0.380⁎⁎⁎ (3.59) −1.992 (−1.60) 0.572⁎⁎⁎ (3.93) Included Included 14,124 0.12***

14.495⁎⁎⁎ (5.88) 0.575⁎⁎ (2.54) 0.113 (0.48) 0.481⁎⁎ (2.38) −0.820⁎⁎⁎ (−7.02) −0.118 (−1.06) 1.941⁎⁎⁎ (3.74) 4.866⁎⁎⁎ (4.25) 2.515⁎⁎⁎ (3.81) 0.356⁎⁎⁎ (3.31) 0.135 (0.13) 0.557⁎⁎⁎ (3.83) Included Included 14,124 0.12***

Note: t values are presented in parentheses. ***, **, and * represent a significance level of 1%, 5%, and 10%. Variables are winsorized at the 1% level. GS is the ratio of the amount of subsidy to total assets, which shows a small value. For easy interpretation, we use GS multiplied by 1000 in the regression analysis where GS is the dependent variable. GSit = α0DEMi,t + α1PCONi,t + α2SOEi,t + α3SIZEi,t + α4GROWTH + α5LEVi,t + α6ROAi,t + α7CAPIi,t + α2STAFFi,t + α8SOEi,t + α9CFOi,t + α10GDPi,t (5) 11

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measures of downward earnings management are used for the robustness check. The results show strong evidence of the positive association between subsidies received (GS) and the three measures of downward earnings management (coefficients 0.630, 0.695, and 0.575; t-statistics 4.13, 4.21, and 2.54, respectively, for DEM1, DEM2, and DEM3), with significant levels at better than 1% when DEM1 and DEM2 are the variables of interest, and 5% when DEM3 is used. The strong positive coefficients on the three DEM measures suggest that both accrual earnings management and real earnings management are positively associated with subsidies. In this analysis with the full sample, PCON does not show a discernible association with subsidies (GS). State control (SOE) is positively associated with subsidies, in line with the common expectation that government subsidies are a channel used to prop up statecontrolled enterprises more than private firms. Firms tend to receive more subsidies when they have high leverage (LEV), profitability (ROA), great capital intensity (CAPI), and when they hire more employees (STAFF) and operate in regions where GDP per capita is high. The governments in rich provinces with high GDP tend to have fiscal surpluses and, thus, have more resources for distribution in the form of subsidies. In addition, subsidies tend to be allocated to small firms, as firm size (SIZE) is negatively associated with subsidies received. Regression analyses control for year and industry fixed effects, and standard errors are clustered by firm to control for potential heteroskedasticity and autocorrelation problems. While we examine the effect of financial reporting quality on the government subsidy decision making, it is also possible that earnings management is driven by other firm-level characteristics, which may also explain the magnitude of subsidies firms received. This poses potential endogeneity problem due to self-selection bias. In addition, reverse causality presents if managers manipulate earnings downward to hide the government subsidies granted in order to avoid public attention and rent-seeking activities. To alleviate the concern for endogeneity caused by self-selection and reverse causality, we conduct robustness tests using the Heckman two-stage regression, propensity score matching (PSM) methodology, GMM Arellano-Bond linear estimation, and a regression analysis using lagged DEM. The results are reported in Table 4. First, we employ a Heckman two-stage test, and the results are reported in Panel A of Table 4. In the first stage, probit model is used to regress the dummy variable, DEM1, on a series of firm-level control variables. The results show that the coefficient on BIG10 is positive and significant (coefficient 0.075, z-statistic 2.31, p < .05), which may be due to the fact that auditors are mainly concerned about upward earnings management and, thus, restrict positive abnormal accruals. Some control variables also show significant associations with DEM1. The inverse Mills ratio generated from the first stage probit model is then used in the second stage regression explaining the receipt of subsidies (GS), along with the variable of interest, DEM, and the remaining controls. Second stage regression analysis reveals the coefficient on DEM1 to be positive and significant (coefficient 0.634, t-statistic 4.13, p < 0.01), a result that is consistent with the baseline model results reported in Table 3. We repeat the Heckman test for DEM2 and DEM3 as the measures of downward earnings management, and the results largely resemble each other (untabulated for brevity). Another commonly employed technique for mitigating selection bias is propensity score matching (PSM) methodology, which controls for self-selection by matching sample firms with control firms having similar characteristics according to a function of covariates (Rosenbaum & Rubin, 1983, 1985). Panel B of Table 4 presents the analysis results. After employing a nearest neighbor (NN) matching without replacement using a small caliper in order to ensure effective matching, we compare the control variables between the treatment group (DEM = 1) and the matched control group (DEM = 0). Section I of Panel B shows that the matching has effectively reduced the differences in a set of control variables measuring firm-level characteristics, and thus the matched sample is ready for regression testing on the effect of downward earnings management on subsidies. The regression results as reported in Section II show that both DEM1 and DEM2 still hold strong power in explaining the magnitude of subsidies (coefficients 0.601 and 0.950, t-statistics 3.11 and 4.61, p < 0.01), although the positive effect of DEM3 is only marginally significant (t-statistic 1.92, p < 0.10). Panel C of Table 4 shows the results of the Arellano-Bond linear estimation. The estimation is a GMM dynamic panel data regression, which takes into account the unobserved panel-level effects correlated with the lags of the dependent variable (Arellano & Bond, 1991). Given that subsidy granting and a firm's earnings management may be ‘sticky’, we employ this dynamic estimation to control for the effect of prior years' subsidies and earnings management decisions on subsidies received in current year. The results suggest that the positive effect of DEM holds strongly for DEM1 and DEM3 estimations, despite a weaker finding on the DEM2. Lastly, we regress the subsidies firms received on the lagged DEM and control variables. The results, presented in Panel D of Table 4, are again consistent with our findings showing that downward earnings management in the prior year is also positively associated with government subsidies in the current year. In all, the batch of tests suggests that endogeneity and reverse causality cannot explain away our main findings, and thereby, the evidence supports H1. To test H2 on the effect of PCON on subsidy receipt in firms with high vs. low levels of operating efficiency, we partition the full sample into two sub-samples of high performers and low performers based on the median operating margin. Poor performers, such as those with low profit margins, are expected to have stronger PCON–subsidy associations than good performers because of the strong motivation poor performers have to establish PCON for the sake of securing government subsidies. The results reported in Table 5, Panel A show that PCON_CEO is significantly and positively related to subsidies for poorly performing firms (coefficients 0.550, 0.544, and 0.553; t-statistics 2.17, 2.15, and 2.17; p < .05 for DEM1, DEM2 and DEM3 analysis, respectively), whereas this effect is insignificant for good performers. Therefore, the results lend support to H2. Interestingly, state control (SOE) significantly explains the receipt of subsidies in poorly performing firms, but its effect is insignificant among good performers, suggesting a role for government in propping up poor performers using subsidies when they have large shares at stake. Other control variables show highly consistent results with the baseline analysis reported in Table 3. Panel B of Table 4 shows similar but weaker evidence when political connection intensity (PCON_INS) is used as the variable of interest (coefficients on PCON_INS are marginally significant, p < 0.05, for poor performers). Although our main analyses have controlled for industry fixed effect, we further test whether the effects of downward earnings 12

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Table 4 Tests to alleviate concerns for endogeneity between downward earnings management and subsidies – H1 testing. Panel A: Heckman two-stage test on the effect of downward earnings management on subsidies First stage Probit

DV = DEM1

Second stage OLS

DV = GS

VARIABLES

Coefficient (z-statistic)

VARIABLES

Coefficient (t-statistic)

Constant

1.379*** (5.36) 0.075** (2.31) −0.079*** (−5.44) −0.091*** (−4.26) 0.730*** (10.79) −3.867*** (−20.59) −1.407*** (−17.11) −0.017 (−1.28) 13.413*** (57.03) −0.000 (−0.01)

Constant

15.020⁎⁎⁎ (5.95) 0.634⁎⁎⁎ (4.13) 0.104 (0.44) 0.440⁎ (1.92) −0.793⁎⁎⁎ (−6.01) −0.128 (−0.98) 1.788⁎⁎⁎ (3.31) 5.336⁎⁎⁎ (3.97) 2.687⁎⁎⁎ (3.86) 0.316⁎ (1.79) −2.760 (−1.18) 0.571⁎⁎⁎ (3.93) −1.237 (−0.51) Included Included 14,124 0.12***

BIG10 SIZE GROWTH LEV ROA CAPI STAFF CFO SOE

DEM1 PCON_CEO SOE SIZE GROWTH LEV ROA CAPI STAFF CFO GDP IMR

Year FE Industry FE Observations (N) Pseudo R2

Included Included 14,124 0.25***

Year FE Industry FE Observations (N) Adjusted R2

Panel B: Propensity score-matched (PSM) technique Section I: Covariate matching Variable

Treated group

Control groups

t-statistic

PCON_CEO SOE SIZE GROWTH LEV ROA CAPI STAFF CFO GDP

0.181 0.681 21.871 0.221 0.531 0.024 0.288 7.596 0.045 0.001

0.171 0.686 21.867 0.195 0.530 0.026 0.288 7.570 0.046 0.021

0.94 −0.44 0.08 1.60 0.11 −0.87 0.05 0.64 −0.29 −0.75

Section II: Regression result of the effect of downward earnings management on subsidies using PSM matched samples.

Constant DEM All variables in main test Year FE Industry FE Observations (N) Adjusted R2

(1)

(1)

(1)

DEM1 13.624⁎⁎⁎ (4.45) 0.601⁎⁎⁎ (3.11) Included Included Included 5332 0.13***

DEM2 14.013⁎⁎⁎ (4.65) 0.950⁎⁎⁎ (4.61) Included Included Included 4744 0.14***

DEM3 12.163⁎⁎ (2.29) 0.889⁎ (1.92) Included Included Included 904 0.17***

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Table 4 (continued) Panel C: GMM Arellano-Bond linear estimation

Constant GS_t-1 GS_t-2 DEM DEM_t-1 DEM_t-2 All variables in main test Year FE Industry FE Observations (N) Wald Chi-square

(1)

(1)

(1)

DEM1 20.598⁎⁎⁎ (2.84) 0.162⁎⁎⁎ (9.65) 0.061⁎⁎⁎ (4.16) 3.297⁎⁎⁎ (4.58) 1.086⁎ (1.95) −0.500 (−0.81) Included Included Included 9669 1086***

DEM2 24.452⁎⁎⁎ (3.86) 0.161⁎⁎⁎ (8.73) 0.068⁎⁎⁎ (4.87) 1.003* (1.82) 0.206 (0.31) −0.815 (−1.20) Included Included Included 9669 1152***

DEM3 20.362⁎⁎⁎ (2.93) 0.167⁎⁎⁎ (12.19) 0.071⁎⁎⁎ (5.20) 3.382⁎⁎ (2.02) 4.127⁎⁎⁎ (2.62) −1.300⁎ (−1.74) Included Included Included 9669 1832***

Panel D: Regression analysis with lagged downward earnings management measures DV = GS

Constant LAGDEM All variables in main test Industry FE Year FE Observations (N) Adjusted R2

(1)

(1)

(1)

LAGDEM1

LAGDEM2

LAGDEM3

15.532 (10.22) 0.362⁎⁎⁎ (3.035) Included Included Included 12,604 0.06***

15.639 (10.30) 0.2702⁎⁎ (2.234) Included Included Included 12,604 0.06***

15.514⁎⁎⁎ (10.12) 0.416⁎ (1.65) Included Included Included 12,604 0.06***

⁎⁎⁎

⁎⁎⁎

Note: t values are presented in parentheses. ***, **, and * represent a significance level of 1%, 5%, and 10%. Variables are winsorized at the 1% level. For brevity, we report the regression results using DEM1. Heckman tests are also performed with DEM2 and DEM3 as the dependent variable of the probit model at the first stage and the variable of interest at the second stage. IMR, Inverse Mills Ratio, calculated using the first stage regression estimates, is controlled at the second stage regression. Note: t values are presented in parentheses. ***, **, and * represent a significance level of 1%, 5%, and 10%. Variables are winsorized at the 1% level. For brevity, we report the regression results using nearest neighbor matching technique. Covariate matching comparisons in section I are made when DEM1 is used as the outcome variable during matching. Note: z values are presented in parentheses. ***, **, and * represent a significance level of 1%, 5%, and 10%. Variables are winsorized at the 1% level.

management and political connection are different between the high-tech industry and industries providing public services and social function. To this end, Eq. (5) is tested respectively with sample firms from (1) utilities (industry code D) and social services (industry code K), and (2) information technology industry (industry code G). We find the DEM is positive and significant for both subsamples—the significance level of DEM is 99% for the utilities and social service subsample, and the level of significance is 95% for the high-tech industry subsample (untabulated).11 In addition, we test whether the effect of downward earnings management on subsidy is affected by IFRS adoption in China from 2007 because of the increase in earnings management activities after IFRS adoption suggested by extant literature (Cang, Chu, & Lin, 2014). Interestingly, we find that the effect of downward earnings management using DEM1 and DEM2 is insignificant over the period 2004–2006 (with 3769 observations), but it is much more significant for post-IFRS period (2007–2014) using 10,355 observations. However, the positive effect of DEM3, the real earnings management measure, remains the same for both pre- and post-IFRS periods. The results suggest that it is the accruals that have been actively manipulated by firms post_IFRS in their pursuit of subsidies. Furthermore, we divide SOE sample firms into central vs. local SOEs and test Eq. (5). We find that the effect of downward earnings management is positive and significant for both subsamples, but the level of significance is greater for local SOEs than central SOEs. This is consistent with our argument that local governments often 11 We also exclude utilities and social service sample firms from the full sample and test H1 on the remaining sample. We find that the positive effect of downward earnings management on subsidy holds.

14

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Table 5 PCON and subsidies (high vs. low performer) – H2 testing. Panel A: PCON_CEO is used as the measure of PCON DV = GS

Constant DEM PCON_CEO SOE SIZE GROWTH LEV ROA CAPI STAFF CFO GDP Year FE Industry FE Observations (N) Adjusted R2

DEM1

DEM1

DEM2

Poor Performer

Good Performer

19.395 (9.32) 0.877⁎⁎⁎ (4.30) 0.550⁎⁎ (2.17) 1.011⁎⁎⁎ (5.41) −1.141⁎⁎⁎ (−10.11) −0.303⁎ (−1.71) 2.455⁎⁎⁎ (4.35) 7.526⁎⁎⁎ (5.30) 3.003⁎⁎⁎ (4.73) 0.533⁎⁎⁎ (5.45) −2.145 (−1.17) 0.750⁎⁎⁎ (6.00) Included Included 6991 0.14***

8.630 (4.59) 0.265* (1.83) −0.131 (−0.85) −0.077 (−0.49) −0.422⁎⁎⁎ (−4.69) 0.094 (0.72) 0.417 (0.86) 3.837⁎⁎ (2.15) 2.014⁎⁎⁎ (4.18) 0.203⁎⁎ (2.52) 0.682 (0.50) 0.361⁎⁎⁎ (3.67) Included Included 7133 0.12***

⁎⁎⁎

⁎⁎⁎

DEM2

DEM3

Poor Performer

Good Performer

Poor Performer

Good Performer

19.553 (9.42) 0.886⁎⁎⁎ (4.19) 0.544⁎⁎ (2.15) 1.009⁎⁎⁎ (5.40) −1.146⁎⁎⁎ (−10.17) −0.325⁎ (−1.83) 2.455⁎⁎⁎ (4.37) 8.028⁎⁎⁎ (5.47) 2.845⁎⁎⁎ (4.54) 0.538⁎⁎⁎ (5.49) −2.213 (−1.20) 0.746⁎⁎⁎ (5.97) Included Included 6991 0.14***

8.609 (4.61) 0.320⁎⁎ (2.04) −0.133 (−0.85) −0.078 (−0.50) −0.421⁎⁎⁎ (−4.69) 0.090 (0.69) 0.400 (0.82) 4.059⁎⁎ (2.26) 2.005⁎⁎⁎ (4.20) 0.206⁎⁎ (2.57) 0.498 (0.38) 0.366⁎⁎⁎ (3.72) Included Included 7133 0.12***

19.393 (9.20) 0.774⁎⁎ (2.16) 0.553⁎⁎ (2.17) 0.979⁎⁎⁎ (5.26) −1.148⁎⁎⁎ (−10.18) −0.343⁎ (−1.93) 2.605⁎⁎⁎ (4.62) 6.699⁎⁎⁎ (4.81) 2.630⁎⁎⁎ (4.25) 0.508⁎⁎⁎ (5.14) 0.713 (0.47) 0.745⁎⁎⁎ (5.95) Included Included 6991 0.14***

8.543⁎⁎⁎ (4.49) 0.318* (1.87) −0.123 (−0.79) −0.081 (−0.52) −0.421⁎⁎⁎ (−4.66) 0.084 (0.64) 0.437 (0.90) 3.436⁎⁎ (1.97) 1.890⁎⁎⁎ (4.02) 0.192⁎⁎ (2.39) 1.420 (1.28) 0.352⁎⁎⁎ (3.60) Included Included 7133 0.12***

⁎⁎⁎

⁎⁎⁎

DEM3

⁎⁎⁎

Panel B: PCON_INS is used as the measure of PCON DV = GS

Constant DEM PCON_INS SOE SIZE GROWTH LEV ROA CAPI STAFF CFO GDP Year FE Industry FE Observations (N) Adjusted R2

DEM1

DEM1

DEM2

DEM2

DEM3

DEM3

Poor Performer

Good Performer

Poor Performer

Good Performer

Poor Performer

Good Performer

19.382⁎⁎⁎ (9.31) 0.877⁎⁎⁎ (4.29) 0.095⁎ (1.85) 1.006⁎⁎⁎ (5.38) −1.140⁎⁎⁎ (−10.08) −0.300⁎ (−1.69) 2.467⁎⁎⁎ (4.38) 7.525⁎⁎⁎ (5.30) 2.983⁎⁎⁎ (4.71) 0.533⁎⁎⁎ (5.44) −2.152 (−1.17) 0.750⁎⁎⁎ (6.01) Included Included 6991 0.14***

8.687⁎⁎⁎ (4.61) 0.264** (1.98) −0.003 (−0.11) −0.071 (−0.45) −0.425⁎⁎⁎ (−4.72) 0.093 (0.72) 0.416 (0.85) 3.829⁎⁎ (2.15) 2.011⁎⁎⁎ (4.17) 0.202⁎⁎ (2.51) 0.692 (0.51) 0.364⁎⁎⁎ (3.69) Included Included 7133 0.12***

19.539⁎⁎⁎ (9.41) 0.885⁎⁎⁎ (4.19) 0.093⁎ (1.81) 1.004⁎⁎⁎ (5.37) −1.145⁎⁎⁎ (−10.14) −0.322⁎ (−1.81) 2.468⁎⁎⁎ (4.39) 8.026⁎⁎⁎ (5.47) 2.825⁎⁎⁎ (4.52) 0.538⁎⁎⁎ (5.49) −2.216 (−1.20) 0.746⁎⁎⁎ (5.98) Included Included 6991 0.14***

8.665⁎⁎⁎ (4.63) 0.318⁎⁎ (2.03) −0.004 (−0.13) −0.072 (−0.46) −0.424⁎⁎⁎ (−4.72) 0.089 (0.68) 0.399 (0.82) 4.051⁎⁎ (2.26) 2.003⁎⁎⁎ (4.18) 0.205⁎⁎ (2.56) 0.509 (0.38) 0.369⁎⁎⁎ (3.73) Included Included 7133 0.12***

19.381⁎⁎⁎ (9.19) 0.776⁎⁎ (2.16) 0.096⁎ (1.86) 0.974⁎⁎⁎ (5.23) −1.147⁎⁎⁎ (−10.15) −0.340⁎ (−1.91) 2.617⁎⁎⁎ (4.65) 6.699⁎⁎⁎ (4.81) 2.610⁎⁎⁎ (4.22) 0.508⁎⁎⁎ (5.13) 0.704 (0.47) 0.745⁎⁎⁎ (5.96) Included Included 6991 0.14***

8.591⁎⁎⁎ (4.51) 0.322* (1.69) −0.003 (−0.10) −0.076 (−0.49) −0.424⁎⁎⁎ (−4.68) 0.083 (0.64) 0.436 (0.89) 3.430⁎⁎ (1.97) 1.888⁎⁎⁎ (4.01) 0.191⁎⁎ (2.37) 1.422 (1.28) 0.355⁎⁎⁎ (3.62) Included Included 7133

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Table 5 (continued) Panel B: PCON_INS is used as the measure of PCON DV = GS

DEM1

DEM1

DEM2

DEM2

DEM3

DEM3

Poor Performer

Good Performer

Poor Performer

Good Performer

Poor Performer

Good Performer 0.12***

Note: This table reports the results of regression using Eq. (5) to test PCON's function on receiving subsidies for poor vs. good performers as proposed in H2. Panel A reports the analysis results using PCON_CEO, and Panel B shows the analysis results using PCON_INS (political connection intensity). Variable definitions are in Table 2. Continuous variables are winsorized at their 1st and 99th percentiles. All specifications include year and industry fixed effects. Robust t-statistics are in brackets. ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels, respectively (twotailed test). Table 6 Market pricing of government subsidies, downward earnings management, and PCON. PRICEi, t =

0

+

PRICEi, t =

0

+

DV = PRICE

1 GSPSi, t 1 GSPSi, t

+

2 EPSNETi, t 2 DEM

Predicted sign

Constant

+

GSPS

+

DEM

−

GSPS*DEM

−

PCON

+

GSPS*PCON

?

EPSNET

+

BVPS

+

Year FE Industry FE Observations (N) Adjusted R2

+

+

+

3 BVPSi, t

3 GSPS

+

DEM +

(6)

i, t

4 EPSNETi, t

+

5 BVPSi, t

+

(7)

i, t

(1)

(2)

(3)

(4)

(5)

(6)

Baseline

DEM1

DEM2

DEM3

PCON_CEO

PCON_INS

3.314*** (7.29) 14.779*** (9.10)

3.435*** (7.35) 22.178*** (8.14) −0.151 (−1.15) −10.787*** (−3.63)

3.394*** (7.25) 21.625*** (8.69) −0.092 (−0.69) −10.921*** (−4.04)

3.970*** (8.09) 11.599** (2.07) −0.721*** (−3.45) 3.457 (0.62)

3.312*** (7.28) 14.978*** (8.84)

3.293*** (7.22) 14.937*** (8.94)

5.110*** (20.86) 0.538*** (8.78) Included Included 13,480 0.46***

5.089*** (20.89) 0.539*** (8.79) Included Included 13,480 0.46***

5.143*** (20.85) 0.550*** (8.94) Included Included 13,480 0.46***

0.017 (0.08) −0.990 (−0.27) 5.122*** (20.73) 0.549*** (8.92) Included Included 13,480 0.46***

0.033 (0.66) −0.218 (−0.24) 5.117*** (20.74) 0.549*** (8.91) Included Included 13,480 0.46***

5.122*** (20.77) 0.549*** (8.92) Included Included 13,480 0.46***

Note: This table presents the results of the regression analyses on investors' pricing of government subsidies (model specification 1), market pricing of subsidies conditional on the effect of the downward earnings management (model specifications 2, 3 and 4), and market pricing of subsidies conditional on political connection (model specifications 5 and 6). The dependent variable is PRICE, which is share price immediately after the filing date. EPSNET is the earnings per share minus subsidy per share because subsidy is summarized into firms' total earnings on financial statements. BVSP is book value of equity per share; GSPS is government subsidies per share because other variables have been measured on a per share basis. All other variables are defined in Table 2. Continuous variables are winsorized at their 1st and 99th percentiles. All specifications include year and industry fixed effects. Robust t-statistics are in brackets. ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels, respectively (two-tailed test).

contest economic success inter-jurisdictionally, and thus, SOEs controlled by local governments have strong incentives to conduct downward earnings management in their attempt to secure subsidies. 4.2.2. Market pricing of subsidies To test the market pricing of subsidies, we regress share price immediately after the annual report filing date on subsidies received, earnings, and book value of equity using Eq. (6). Further tests on the conditional effects of subsidies on downward earnings management and PCON using Eq. (7) are conducted. All independent variables are measured on a per share basis. The OLS analysis results are reported in Table 6. Model specification (1) is a baseline model testing price reaction to subsidies after controlling earnings per share and book value per share. The result shows a positive coefficient on GSPS (coefficient 14.779, t-statistic 9.10, p < 0.01), supporting H3. This effect is also economically significant, in that one standard deviation increase, i.e., 6 cents, in GSPS raises the share price by CNY 0.88 16

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(0.06 × 14.779). EPSNET and BVPS are both significantly and positively associated with share price, in accordance with prior pricing studies. Thus, H3 is supported. We then test whether the market pricing of subsidies differs for firms conducting downward earnings management, and these results are reported in model specifications (2), (3), and (4). The results show a significant negative coefficient on GSPS*DEM (coefficients −10.787 and − 10.921, t-statistics −3.63 and − 4.04, both p < .01 when DEM1 and DEM2 are used as measures of DEM). Therefore, we find that when firms conduct negative earnings management using accruals, firms' subsidies are less favorably priced by investors, perhaps due to their awareness of accrual manipulation. In general, H3a is supported. However, although the market does penalize firms with negative earnings management via manipulating real activities (coefficient − 0.721, tstatistic −3.45, p < 0.01), those firms' subsidies are not discounted, as evidenced by the insignificant positive coefficient on GSPS*DEM3 (coefficient 3.457, t-statistic 0.62). Because real earnings management, such as giving sales discounts and overproduction, are more deceptive to investors (Chi, Lisic, & Pevzner, 2011; Zang, 2012), investors may perceive downward real earnings management as ‘real’ bad news, rather than it being strategically used as a tool to secure government subsidies. Although investors can see through and discount subsidies obtained via conducting negative abnormal accruals, the subsidies received via downward real earnings management (GSPS*DEM3) have not received the same market reaction. The last two model specifications, (5) and (6), focus on the moderating effect of PCON on the market pricing of subsidies. The analyses do not support our proposition in H3b that the market prices subsidies differently for firms with PCON from their counterparts. Both measures of PCON_CEO and PCON_Intensity_CEO fail to show a significant effect on GS*PCON in the analyses, suggesting that political connection does not affect investors' perception of subsidies. In addition, receipt of subsidies is still consistently positively priced by investors, with a significant price premium, across the model specifications. All regression analyses report a high adjusted R-squared value, suggesting fitness of the models. 5. Conclusion This study proposes two firm-specific factors shaping government subsidy receipt and investigates the market pricing effect of subsidies. The analyses demonstrate that downward earnings management using both accruals and real activities is used strategically by firms to gain government subsidies. This result is robust to several tests, alleviating concerns for endogeneity and reverse causality between downward earnings management and subsidy. We also reveal that established PCON is conducive to securing subsidies by poor performers but not by good performers. Market pricing analyses show a strong price premium on subsidies in general, but subsidies received by firms conducting downward earnings management using accruals are discounted, indicating investors' awareness of, and concern for, the conduct of downward earnings management using accruals. In addition, the results do not suggest a moderating effect of PCON on the market pricing of subsidies. Our paper makes several contributions in the following respects. First, we propose and empirically test two factors potentially determining government subsidy. Thus, this study is one of the few to examine the determinants of subsidy at firm level and adds to the government subsidy literature. In addition, this study advances the accounting literature on subsidy by proposing financial reporting practices that have real economic implications, in this case, being opportunistically employed by firms to secure government subsidies. Meanwhile, our investigation covers both accrual and real earnings management practices, and the findings of market pricing analysis pinpoint investors' discount on (indifference to) subsidies obtained by firms conducting accrual (real) earnings management. Thus, the results indicate investors' inability to detect opportunistic real earnings management. The findings should be informative to governments internationally, highlighting the importance of censoring subsidy recipients' financial reporting quality (e.g., discretionary accruals, earnings smoothness, comparability of earnings to other firms, etc.). To the extent that firms reveal similar earnings patterns to secure subsidies, our findings may have policy implications for governments in other countries. Appendix A. Appendix A.1. Variable definition Variable

Definition

Subsidy determinant analysis: GS Government subsidy, measured as the sum of subsidies received by a firm, deflated by total assets. DEM Downward earnings management, measured as a dummy variable. It takes a value of 1 if the firm has negative discretionary accruals or negative measures of real earnings management activities and 0 otherwise. DEM is measured in three alternative ways as detailed in Section 3.2.1. PCON Political connection, which is a dummy variable taking a value of 1 when a firm is politically connected and 0 otherwise. PCON is measured in two ways, as detailed in Section 3.2.1. SOE State-owned enterprises, taking the value of 1 if a firm is controlled by the state and 0 otherwise. SIZE Firm size, measured as the natural logarithm of total assets. LEV Leverage, measured as long-term liabilities divided by total assets. ROA Firm performance, measured as net income divided by total assets. CAPI Capital intensity, measured as the ratio of total fixed assets to total assets. STAFF The natural logarithm of the number of employees. GROWTH Growth, measured as the market to book value of equity. CFO Net cash flow from operations, retrieved from the Statement of Cash Flow.

17

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GDP per capita in the province where a firm is domiciled, retrieved from China's National Bureau of Statistics atwww.stats.gov.cn/english/ Auditor quality, a dummy variable taking the value of 1 if a firm's auditor is one of the top 10 auditors in China, ranked based on audit revenues, and 0 otherwise. Earnings smoothness Earnings smoothness, measured as the correlation between change in discretionary accruals and change in pre-discretionary accrual income using the current and past four years' observations. The reversed fractional ranking of income smoothing of each firm-year observation within its industry-year is used following Tucker and Zarowin (2006). Earnings comparEarnings comparability, measured as the degree to which firms' earnings outputs covary more with industry peers, following De Franco, ability Kothari, and Verdi (2011). Market pricing of subsidy analysis: PRICE The closing share price of next trading day after the release of annual report. GSPS Government subsidy per share, measured as the amount of government subsidy divided by the number of outstanding shares. EPSNET Net earnings per share, measured as earnings per share net of subsidy per share. BVSP Book value of equity per share, measured as the total shareholders' equity divided by the number of outstanding shares.

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