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Government-background customers, audit risk and audit fee

Government-background customers, audit risk and audit fee CHINA JOURNAL OF ACCOUNTING STUDIES 2019, VOL. 7, NO. 3, 385–406 https://doi.org/10.1080/21697213.2019.1703391 ARTICLE a b c Chao Dou , Man Yuan and Xiao Chen a b Business School, Central University of Finance and Economics, Beijing, China; School of Accountancy, Capital University of Economics and Business, Beijing, China; School of Economics and Management, Tsinghua University, Beijing, China ABSTRACT KEYWORDS Government-background Based on the 2007–2015 customer data of listed firms, this paper customer; audit risk; audit studies the impact in China of customers with a government back- fee; supply chain ground on the audit fee from the perspective of supply chain. It is claimed to be the first time this impact has been studied. The results indicate that the presence of a government-background customer helps to reduce the audit fee significantly. Moreover, the relationship is more evident when the customer is stable or comes from the central government, and we can also observe a stronger relationship under high financial constraints. Finally, the paper also shows that the exis- tence of a government-background customer can effectively alleviate an enterprises’ audit risk, thus reducing the audit fee. 1. Research background The difference in companies’ audit fees is an interesting issue in both academia and industry. In 2017, for example, more than a quarter of the audit fees paid by listed companies in China’s A-shares market was attributed to only 1% of the companies. Previous literature mainly put an emphasis on the enterprise characteristics, such as company size, business complexity, corporate governance, and litigation risk, and the audit firm’s characteristics, such as audit firm size, reputation, and industry audit speciality (Ball, Jayaraman, & Shivakumar, 2012), from which we can get a reasonable explanation about a companies’ audit fees. However, a study of the audit fees from the view of the supply chain was merely the start. Although there have been a few achievements, they have mainly focused on overall characteristics such as customer concentration (Xiongyuan, Peng, & Jinping, 2014), in which analysis has not gone deep into the concrete information about the customer. Nowadays, as the relationship between supply and demand is getting closer, corporations and their large customers tend to become inter- dependent. From the view of customer characteristics, this paper analyses whether corporations’ dependence on some particular customers influences their audit fees. Customers could not only provide positive support, but also exert negative pressure on corporations. In China, government-background customers represented by government CONTACT Man Yuan yuanm.14@sem.tsinghua.edu.cn School of Accountancy, Capital University of Economics and Business, Beijing, China Paper accepted by Xi Wu. This article has been republished with minor changes. These changes do not impact the academic content of the article. © 2019 Accounting Society of China 386 C. DOU ET AL. and state-owned enterprises are particularly special. So, what special influence can government-background customers exert on corporations and how can this influence affect the audit fees are interesting questions that should attract attention. Influenced by the traditional western economic perspective, affecting the economy through the government’s ‘visible hand’ tends to be criticised. In particular, the phenom- enon of so-called ‘state advanced and private retreat’ in China caused controversy regarding the roles that government and state-owned enterprises should play in a market economy. However, the great achievements made by China in the last 40 years of reform and open policy showed the success of the socialist market economy, which indicated the rationality of government intervention. Previous literature has mainly focused on tradi- tional government interventions such as government subsidy and tax preference (Hetong & Haopin, 2009), and there was little attention paid to government procurement. At the 19th CPC National Congress, President Xi Jinping emphasised that the government should play a better role in economic development. Hence, a study on government- background customers would help to promote understanding about how government interventions influenced the capital market and the enterprise operation. Specifically, apart from government department, state-owned enterprises played an important role in the economy under the direction of government in China. On one hand, state-owned enterprises were directly leaded by the relevant SASAC (State-owned Assets Supervision and Administration Commission). On the other hand, state-owned enterprises had differ- ent kinds of social responsibility (Wencheng & Shihui, 2014), such as maintaining national employment rate and economy stability etc. Hence, it was inevitable for state-owned enterprises to make operating decisions that accorded with policy orientation. Admittedly, government departments and state-owned enterprises had different natures and responsibilities. However, this paper defines, in the government-dominated market in China, both government departments and state-owned enterprises as government-back- ground customers for a more comprehensive study of how the government as a whole influences enterprises in the market through government procurement. Different from firm customers, the behaviour of government-background customers reflects government policy, so that orders from government-background customers not only meet demand, but are also influenced by policy factors. Thus, government procure- ment was a special government intervention in a form somewhere between market transactions and government direct support, which not only supported the enterprises but also met social need, and it was not just due to political connections or client relations. Based on state credit and fiscal power, government-background customers not only had stable and enormous demand, but also offered much lower risk compared with ordinary customers. Such demand could provide safety and reliability for the whole supply chain through the mechanism of risk transfer, by which the uncertainty faced by enterprises would be reduced. As is known, risk premium and workload are determinants of audit fees (Songsheng & Zhili, 2019). Thus, government-background customers could effectively reduce the audit risks of the relevant enterprises, thus influencing the audit fees further. According to statistical data, a significant improvement was made in recent years regard- ing government procurement, concerning the procurement scale, procurement scope and policy effect. However, compared with the developed countries in Europe and America, there were still gaps with regard to the size of orders and the procurement CHINA JOURNAL OF ACCOUNTING STUDIES 387 amount as a share of GDP. In other words, there was great potential for government- background customers to influence economic growth in China. Based on the 2007–2015 customer data of listed firms, this paper finds that the existence of government-background customers helps to reduce audit fees for listed firms. In addition, if the government-background customers had greater stability and were higher in political level, the audit fees of listed firms would be much lower. And such a relationship was especially evident concerning listed firms with tight financial constraints. Finally, this paper studied how such relationships form. It finds that the existence of government-background customers helps to reduce the audit risks, so reducing audit fees. In conclusion, this paper helps us have better knowledge of the influence of government-background customers on firms. The main contributions of this paper mainly lie in the following four aspects: first, different from the literature that focused on customer concentration and political connection, this paper is thought to be the first to study the influence of government-background customers on audit fees. This paper also provides a new viewpoint for studies about govern- ment procurement. Second, this paper focuses on government-background custo- mers’ nature and reveals its relationship with audit fees. In particular, this paper discusses how the stability and political level of government-background customers helps to reduce the audit fees. Third, as the auditing risk path to show how the existence of government background-customers influences audit fees, it is an impor- tant complement to the literature on supply chain finance. Meanwhile, it offers a reference for the development of the Chinese capital market in this ‘new normal’ economy. This paper offers enlightenment in investors’ interpretation about listed companies obtaining orders from government-background customers. Finally, it also reveals the mechanism of how micro policy would influence micro participants in the market. 2. Literature review and research hypothesis Thereisawiderangeoffactors thatinfluence audit fees, which have been discussed in related literature. Representative examples, such as Wu Lina (2003), concluded that the audit fee was mainly decided by audit cost, risk cost and the normal profitofthe accounting firm. Similarly, Wang Xiongyuan et al. (2014) pointed out that the audit fee was mainly decided by the auditor’s workload and audit risk. They revealed that audit fees would be lower when the risk of the enterprise was low and the workload of auditor was light. The rest of the literature mostly followed the model developed by Simunic (1980). Therefore, such studies were mostly conducted from aspects of the client and audit firms. On onehand, studiesthatfocused on the clientfound that asset size and numbers of subsidiaries were the two main factors that decided the audit fees. Meanwhile, content of the financial report, corporate governance, internal control, media attention and competitive environment were also important factors that influenced the audit fees (Jifu, 2007; Lina, Chunfei, & Zhengfei, 2012;Liquan& Hanwen, 2013;Qiliang,Hui,Chao, Yigang,&Hanwen, 2014;Yanheng, 2011; Yixia, 2011). On the other hand, studies that focused on audit firms discussed the pricing mechanism of audit fees from the viewpoint of audit opinion, regional factor, 388 C. DOU ET AL. reputation mechanism, penalty and macro-environment (Tianshu & Jun, 2013; Xiaoxia, 2013; Yixia, 2011). And they found there did exist relationships. With the rapid development of supply chain finance and relevant theories in recent years, how the benefit-related parties of the firm influenced the audit fee, especially the customers of the audited firm, aroused academia’s interest. As the most important resource of revenue, firms’ customer relations had a direct effect on their business activities, cost structure and profitability (Gosman, Kelly, Olsson, & Warfield, 2004; Kim & Wemmerlöv, 2015). The interaction relationship between firms and their customers has been perceived by investors long ago. Although a firm’s customers do not influence the firm’s audit fee directly, they do have an influence on the firm’s risk through transactions with each other, which will finally influence the audit fee. Customer relations could strengthen the supply chain integration to reduce the audit fee, but might also absorb risk to increase the audit fee as well. Thus, ordinary customer relations played the role of a double-edged sword for the development of a firm; specific consideration should be taken of specific conditions to verify the effect of ordinary customer relations. However, in China, government-background customers, as special major clients, would influence audit fees in their own peculiar way. A study of this could provide more cognition about markets in China. On one hand, when the government plays a role in the development of firms as customers through department- or state-owned enterprises, the firms might benefit from the cooperation with such major customers in its supply chain, which is called the ‘income effect’. Good relationships with the government helped the firm reduce its risk in operation and development. Previous research has shown, as the interdependence between members in the supply chain was enhanced, two different conditions would appear among suppliers and customers: cooperation-dominated and competition-domi- nated, which resulted in two opposite perspectives in favour of ‘income effect’ and ‘risk effect’ respectively. ‘Income effect’ claimed that cooperation promoted information shar- ing, collaboration and credit within the supply chain, which increased the potential value of the firm. Specifically, customers could prompt the firm to improve management efficiency, reduce selling expenses and concentrate on long-term performance, which brought healthy cash flow and stable revenue for the firm. Hence, it reduced the risk faced by the company (Johnson, Kang, & Yi, 2010; Patatoukas, 2012). Under such a perspective, the existence of major customers helped the firm to stabilise its supply chain, and the firm with the most major customers could gain higher and more stable revenue. However, the ‘risk effect’ claimed that major customers’ bargaining power might mean the company was exploited, which could result in its bad financial performance and poor cash flow. Therefore, firms that kept long relationships with only one or a few major customers or suppliers might sacrifice a lot for this. And such firms would face higher risk in decision- making and operating (Kale & Shahrur, 2007; Wang, 2012). Different from firm customers, government-background customers act as one of the government’s interventions, which encourage firms to produce high-level products to meet the real demand from society. It is more than a simple trading relationship but a particular kind of government interven- tion in a form somewhere between market transactions and government direct support. Meanwhile, based on the state credit, demand from government-background customers was stable and had low risk (Cohen & Li, 2016; Dan, Scott, Matthew, & Sarah, 2016), which provided stable and high revenue for the firm to develop. The aim of government- CHINA JOURNAL OF ACCOUNTING STUDIES 389 background customers was to promote the development of the economy and firms in the market, so that they would not just focus on short-term benefit. Hence, the ‘risk effect’ seldom appeared when concerning government-background customers, compared with traditional firm customers. Instead, the firm could gain much more support due to its fine relationship with government-background customers, which of course should be regarded as the ‘income effect’. On the other hand, as government procurement was considered as one of the most important means for the government to intervene in the economy, the object and allocation of government procurement could reflect strong policy orientation, aimed to provide resources for firms’ healthy and steady development. Such could be considered the ‘support effect’. So far, a lot of research has focused on the influence of the govern- ment’s traditional intervention on the economy. And there had been sharp conflicts over whether the policies such as government subsidies worked well. A significant part of the research found that a government subsidy could not promote the development of firms effectively but resulted in a series of potential problems (Bergström, 2000; Dong, Gao, Li, & Dan, 2012; Tzelepis & Skuras, 2004; Xiao & Jing, 2001). Different from traditional ways of government support, government procurement was not merely support by money or policy (Aihua & Han, 2018; Fangwang, 2015; Jinghuan, Xiao, & Baoshun, 2013; Zheng, Can, Jian, & Fei, 2010). As a means of government intervention from the supply side, govern- ment procurement would not only follow the policy orientation but also meet the real demand for products. Therefore, high quality was acquired in the process of production and the finished product. Meanwhile, measures such as open tender and regular assess- ment were taken to ensure the implementation. Such procurement was not only support but also a spur for the firm, through which the problems of so-called ‘one-way support’ and ‘zombie firms’ were prevented (Can, Zhuquan, Deming, & Wei, 2015; Xiongyuan et al., 2014). From such help, the firm could keep developing in a positive cycle. Moreover, as the government-background customers whose orders were commonly based on state credit had low risk and huge demand (Dan et al., 2016; Yun, Zhe, Yijie, & Xuanting, 2017), relevant firms’ revenue in the future and their security of operation would be assured. As a result, it was expected that, government procurement tended to be a kind of indirect support. The firms were stimulated to keep making progress by government-background customers’ orders. And the adverse effect of traditional government intervention was avoided, so that the ‘support effect’ could take better effect and the uncertainty in firms’ operation was reduced. In conclusion, firms that had government-background customers could not only benefit from the relevant policies through the ‘support effect’, but also benefit from government-background customers through the ‘income effect’, so that the risks and uncertainty faced by the firms were reduced. As a result, the audit risk was reduced and the audit fees would reduce accordingly. Therefore, our first hypothesis is proposed as follows. Hypothesis 1: The existence of government-background customers helps to reduce firms’ audit fees. This paper also investigates how the stability and compliance of government-back- ground customers influenced the audit fees. Li Xinzi (2015) classified government subsidy 390 C. DOU ET AL. as accidental-form, policy-guided-form and project-form, and found that government subsidies’ influence on firms’ profitability was different if they were of different persis- tence. Similarly, orders from government-background customers might vary in its stabi- lity. Some firms could gain stable orders from government-background customers owing to the high quality of their products and good relationship with the government, while others could only gain such orders occasionally. In other words, stable orders from government-background customers meant that the firms’ revenue was mainly influenced by such customers, which also meant that the government-background customers could exert a continuous influence on the relevant firms. By contrast, occasional orders could not provide stable sales for the firms, which meant that such government-background customers could not exert continuous influence on the relevant firms. Gosman et al. (2004) found that stable customers helped to stabilise the supply chain, so that the relevant firms could have high revenue and low risk. In fact, stable government-back- ground customers helped to reduce relevant firms’ fluctuations in revenue, so that the stable profitability improved the prospect of the firms. Meanwhile, the government- background customers could also influence the relevant firms on their supervision and governance. Opportunism of the management would be reduced and earnings manage- ment would also be reduced, so that the quality of the firms’ profit would be improved. Hence, stable relationships with customers helped to ensure financial performance and reduce risk. This is Hypothesis 2a. Hypothesis 2a: Stable relationships with government-background customers helps to reduce audit fees compared with occasional orders. Meanwhile, different administrative levels differ in patterns of governance in China, such as central government and local government. In past literature, such a difference was confirmed. Xiao and Jing (2001) showed how local government influenced firms’ financial performance. Jian, Chuanming, and Junhua (2012) found that there exists a difference in the hope of local government and central government for the development of the firms, which influenced the behaviour of the entrepreneur who had relevant political connec- tions. After distinguishing the level of political connections, Jian and Chuanming (2013) found that connections with local government had more positive effects on debt maturity structure than connections with central government. Tang song and Sun Zheng (2014) found that the problem of over-payment to management was more serious in central state-owned enterprises than local ones. Shu and Xiaoyan (2014) also find that connection with local government had a more positive effect on firms obtaining long-term loans than the connection with central government. Generally speaking, the studies above showed that different levels of political connection would result in different economic conse- quences in firms’ development. Under the enormous system of government, it was inevitable that policies such as subsidy and government procurement would be influenced by interests and political relationship. Therefore, various aspects were taken into account in the process of policy making by all levels of government. As far as government procurement was concerned, central government should have an overview and macro control of the economy, while it usually faced more attention and supervision. Hence, procurement from central govern- ment was fairer and more transparent. By contrast, the supervision system in local CHINA JOURNAL OF ACCOUNTING STUDIES 391 governments was not perfect and their vision was also limited. So, procurement from local government was more likely to be shortsighted and tied to various interests. Thus, relationships and interests were considered more. In recent years, there appeared many cases of chaos in local government procurement, such as corruption and arrears. As a result, compared with central government-background customers, there was more risk in payment will and policy implementation for local government-background customers, which increased the audit risk and resulted in higher audit fees. Based on such phenom- enon, we propose Hypothesis 2b. Hypothesis 2b: Compared with local government-background customers, relationships with central government-background customers helped reduce more audit fees. As China came into the new normal economy stage, debt default and financing difficulty occurred in many firms. The problem of financing constraints was serious for the whole market, which influenced the risk of firm and stabilisation of the economy directly. Kaplan and Zingales (1997) proposed that financing constraints was caused by the incompleteness of the market (information asymmetry, agent cost etc.), which resulted in the obvious difference between the costs of outside and inside financing. Usually, when a firm faced severe financing constraints, it was always stuck in a bad financial condition (Kebin & Haijian, 2014). Such a firm would have greater risk and challenges in its operation and development (Dingyu & Cong, 2016; Jia, Shu, & Yu, 2014). The audit fee was decided by various factors, which not only reflected the expected audit resources invested by the auditor, but also reflected the risk compensation for potential audit risk. Beatty (1993) found that the worse the financial condition the audit client was in, the more risk of litigation the auditor would take, so that a higher audit fee the auditor would acquire. Hence, when the firm was experiencing tight financing con- straints, the auditor usually would charge a higher audit fee, considering the risk. In such a situation, the existence of a government-background customer was a key support for the relevant firm. The government-background customer provided stable demand to relieve the operating risk of the relevant firm, so that the audit fee of such a firm could be reduced accordingly. Obviously, such an effect was more important to firms experiencing tighter financing constraints compared with those with light financing constraints. Based on such logic, there is hypothesis 2c from the viewpoint of firms’ characteristic. Hypothesis 2c: Compared with the firms with light financing constraints, relationships with government-background customers help reduce audit fees for those with tight financing constraints. 3. Research design 3.1. Research data Since there is no policy to force listed companies to disclose specific customer names in their annual reports, and most companies chose not to disclose detailed customer information before 2007, the sample covers from 2007 to 2015. By screening the top five customers’ information disclosed in the A-share listed company’sannualreport, we 392 C. DOU ET AL. combine the company’s shareholder capital contribution information and corporate property rights data disclosed by the national corporate credit information disclosure system to accurately trace the state-owned holding company and government depart- ment information among the company’s customers, and then match government-back- ground customers. Other financial data come from the CSMAR database. In order to ensure the integrity and reliability of the data, the research object must meet the following six requirements: (1) only list companies that disclose the top five customers’ name information in the annual report; (2) the shareholding structure of the listed company’s customers is clear, and the controlling shareholder can be traced; (3) delete thesamplewhosetop five customers’ concentration is less than 1%; (4) complete financial data and audit fees in 2007–2015; (5) remove 1% of the maximum value from above and below. The reason for removing the sample of the top five customers with a concentration less than 1% of is that, in the sample of manufacturing-oriented compa- nies, the top five customers of listed companies that disclose customer name informa- tion account for more than 1% of sales. Moreover, it is also difficult to measure the impact of a single type of large customer on the sales of the company when the corporate customers are too dispersed (such as Wanke Corp., whose customers are extremely dispersed). Through the above data screening process, this paper finally manual-finished a sample of 1496 listed companies, a total of 6586 annual observations. Table 1 illustrates the sample distribution. From Panel A of Table 1, we can find that there are 774 samples of government procurement orders, accounting for about 12% of the total sample, while government-background customer samples account for about two-thirds of the total sample. In view of the fact that many companies have both government-background customers and other types of customers, there are 6155 entries in all samples with orders from non-government-background customers. From the perspective of the duration of the government-background customer procurement orders in Panel B of Table 1, the government-background customer procurement orders show a relatively stable trend, with 56.08% of orders lasting for 4 to 6 years, and even 13.71% of orders lasting more than 7 years, indicating that the impact of government-based procurement orders on specific companies is often stable. Table 1. Sample distribution. Customer type Number Proportion Panel A. Customer Type Distribution Government customer 774 11.75% Government-background customer 4394 66.71% (Government department, state-owned enterprise) Others (foreign enterprises, private enterprises) 6155 93.46% Total sample (firm-year) 6586 Order duration (year) Number Proportion Panel B. Stability distribution [1,3] 452 30.21% [4,6] 839 56.08% [7,9] 205 13.71% Total sample (firm) 1496 100% Since the customers of some enterprises include the government departments, state-owned enterprises and private enterprises, the sum of the statistics in Panel A of Table 1 exceeds 100%. CHINA JOURNAL OF ACCOUNTING STUDIES 393 3.2. Research model To test the impact of a government-background customer on the corporate audit fee, we draw on the audit cost analysis model of Wang Xiongyuan et al. (2014)and Lili and Yuanyuan (2018), and estimate the following regression model to test H1–H3: Auditfee ¼ β0 þ β1Procurement þ β2Size þ β3Lev þ β4Sale þ β5Opi i;t i;t i;t i;t i;t i;t þ β6Current þ β7ARInv þ β8Return þ β9Sig þ β10Roa i;t i;t i;t i;t i;t þ β11Big4 þ β12Sub þ β13SOE þ ε i;t i;t i;t The dependent variable, Auditfee , measures the natural logarithm of the annual audit i,t fee of firm i in year t, and the independent variable, Procurement ,isthe ratioofthe i,t government-based procurement order amount to the total sales of firm i in year t. Under the special economic system and social background of the country, the national policy and development plan are usually led by the government, while the state- owned enterprises cooperate and support. Therefore, in the regression analysis, Procurement is represented by the government departments (Govper)and thegovern- ment-based department (Stateper). Since government-basedprocurementandgov- ernment subsidy are important means for the government to intervene in the economy, many companies that receive government-based procurement orders often receive a large amount of government subsidy at the same time. In order to avoid the endogenous impact of the two on a company’s business development, we add government subsidy (Sub)of firm i during year t in the control variables. Other control variables include corporate financial indicators, such as company size (Size), debt level (Lev), sales revenue (Sale), current ratio (Current), accounts receivable and inventory ratio (ARInv) and return on assets (Roa), and market reaction indicators, such as stock return (Return) and stock return volatility (Sig), and audit indicators, such as whether it is audited by Big Four (Big4) and audit opinion (Opi), and corporate property indicator (SOE). All regression results are clustered at the company level, and the variable definitions are given in Table 2. 3.3. Descriptive statistics Table 3 provides descriptive statistics on the variables, from which we can find that state- owned enterprises occupy the majority among the sample that actively disclose customer information. According to the statistics of the order type, in the sample of government procurement orders, the average proportion of direct orders from government depart- ments in the sample exceeds 13%. If we further consider the purchase of state-owned enterprises, this proportion will be nearly 25%, indicating that the government-back- ground customer has a major impact on the company’s operational development, which affects the perception of the capital market. In addition, audit fee (Auditfee), litigation risk (L_litAmount) and operational risk (Varroa) also have large differences in distribution, which provides potential feasibility for studying how government-background customers influence the audit fee and audit risk. 394 C. DOU ET AL. Table 2. Variable definition. Symbol Name Definition Dependent variable Auditfee Audit Fee The natural logarithm of the annual audit fee of firm i in year t. it Amount of Lawsuit The ratio of the amount of lawsuit of firm i divided by operate income in L_litAmount it year t. Varroa Business Risk ROA variance of firm i in year t. it Independent variable Gov Government customer A dummy variable that is set to 1 if there are government customers it (including party, government, military departments and government institutions at all levels) in top five customers of firm i in year t, and 0 otherwise. State Government-background A dummy variable that is set to 1 if there are government-background it customer customers (including party, government, military departments, government institutions and state-owned enterprises at all levels) in top five customers of firm i in year t, and 0 otherwise. Govper Government customer order Proportion of government procurement (including party, government, it ratio military departments and government institutions at all levels) to total sales of firm i in year t. Stateper Government-background Proportion of government-based procurement (including party, it customer order ratio government, military departments, government institutions and state- owned enterprises at all levels) to total sales of firm i in year t. Control variable Size Company size Natural logarithm of total assets at the end of year t. it Lev Leverage ratio Ratio of total liabilities to total assets at the end of year t. it Sale Sales revenue Natural logarithm of total sales revenue in year t. it Opi Audit opinion A dummy variable that is set to 1 if firm i is issued a non-standard it unqualified audit opinion in year t, and 0 otherwise. Current Current ratio Ratio of current assets to current liabilities at the end of year t. it ARInv Accounts receivable and The sum of accounts receivable and inventory divided by total assets at it inventory ratio the end of year t. Return Stock return ratio Stock return of firm i in year t. it Sig Stock return volatility Standard deviation of daily stock return in year t. it Roa Return on assets Ratio of net profit to total assets balance in year t. it Big4 Big 4 audit A dummy variable that is set to 1 if firm i is audited by the Big Four in year it t, and 0 otherwise. SOE State-owned enterprise A dummy variable that is set to 1 if firm i belongs to state-owned it enterprise (according to the actual controller property) in year t, and 0 otherwise. Sub Government subsidy Total government subsidy divided by total assets at the end of the year t. it Table 3. Descriptive statistics. Variable Mean Number Standard Deviation 25% Median 75% Auditfee 13.117 6586 0.603 12.854 13.080 13.552 it L_litAmount 0.001 6586 0.007 0 0 0.009 it Varroa 0.053 6586 0.037 0.028 0.046 0.653 it Gov 0.118 6586 0.322 0 0 0 it State 0.667 6586 0.471 0 1 1 it Govper (%) 13.101 774 16.192 2.857 6.483 16.370 it Stateper (%) 24.174 4394 22.549 7.954 15.456 33.262 it Sub 0.011 6586 0.020 0.002 0.005 0.013 it Size 23.851 6586 1.999 22.480 25.133 27.349 it Lev 0.468 6586 0.353 0.337 0.476 0.582 it Sale 19.328 6586 1.614 17.425 19.615 19.996 it Opi 0.956 6586 0.240 1 1 1 it Current 1.398 6586 1.180 1.119 1.241 1.858 it ARInv 0.285 6586 0.152 0.170 0.267 0.391 it Return 0.396 6586 1.494 −0.327 0.005 1.217 it Sig 0.252 6586 0.212 0.094 0.209 0.326 it Roa 0.037 6586 0.081 0.004 0.032 0.070 it Big4 0.050 6586 0.179 0 0 0 it SOE 0.598 6586 0.506 0 1 1 it CHINA JOURNAL OF ACCOUNTING STUDIES 395 4. Regression analysis 4.1. Government-background customer and the audit fee In order to test the impact of government-background customer on the audit fee, Table 4 performs the corresponding regression analysis according to Model 1. As shown in the table, the first and second columns introduce the dummy variables Gov and State from the perspective of the presence or absence of government (government-based) customer order, and it is found that there are negative correlations between government customer and audit fee (β = −0.136), and there are negative correlations between government- background customer and audit fee (β = −0.118). Both are significant at the 10% level, which indicates that companies with government (government-based) customer orders have significantly lower audit fees than those without such customers. The third and fourth columns introduce continuous variables from the perspective of the proportion of government (government-based) procurement orders to total sales. It is found that the government procurement orders are negatively correlated with audit fee (β = −0.354), Table 4. Government-background customer and audit fee. Auditfee Auditfee Auditfee Auditfee it it it it Gov −0.136* it (−1.74) State −0.118* it (−1.69) Govper −0.354** it (−2.25) Stateper −0.309*** it (−3.16) Sub 1.146 0.818 1.115 0.962 it (1.26) (1.25) (1.23) (1.10) Size 0.298*** 0.275*** 0.349*** 0.285*** it (5.24) (6.39) (5.41) (4.72) Lev −0.048 −0.057 −0.064 −0.072 it (−0.52) (−0.62) (−0.49) (−0.54) Sale 0.201*** 0.182*** 0.164*** 0.153*** it (3.66) (3.32) (3.86) (3.83) Opi 0.106*** 0.095*** 0.143*** 0.121*** it (3.35) (3.73) (3.77) (3.47) Current −0.007 −0.006 −0.005 −0.007 it (−1.06) (−0.95) (−0.90) (−0.96) ARInv −0.034 −0.030 −0.044 −0.032 it (−0.69) (−0.60) (−0.53) (−0.68) Return −0.028** −0.027* −0.024** −0.026** it (−2.17) (−1.75) (−2.00) (−1.96) Sig 0.852 0.815 0.834 0.942 it (0.81) (1.04) (0.90) (0.79) ROA −0.345 −0.305 −0.308 −0.292 it (−1.08) (−1.23) (−1.09) (−1.04) Big4 0.731*** 0.987*** 0.795*** 0.932*** it (6.41) (4.85) (5.51) (6.07) SOE −0.065*** −0.048*** −0.056*** −0.064*** it (−2.87) (−4.31) (−4.31) (−2.91) Constant 5.977** 7.375** 5.659** 5.341*** (2.10) (2.33) (2.39) (2.74) Industry fixed effect Yes Yes Yes Yes Year fixed effect Yes Yes Yes Yes N 6586 6586 6586 6586 R-squared 0.629 0.617 0.648 0.652 ***, **, * Indicate statistical significance at the 0.01, 0.05, and 0.10 levels, respectively. 396 C. DOU ET AL. and the results are significant at the 5% level, meanwhile, the government-based pro- curement orders are also negatively correlated with the audit fee (β = −0.309), and the results are significant at the 1% level, indicating that the government (government-based) procurement account for a higher proportion of total sales, and the audit fee is signifi- cantly lower than those with a low proportion. It is worth noting that the results of the government (government-based) customer in Table 4 are weaker than the government (government-based) procurement orders, indicating that the purchase amount ratio is more reasonable. Therefore, in the subse- quent analysis and research, we mainly use the ratio of government (government-based) purchase account to total sales as the main research variable. In addition, the coefficient of Sub is positive, although the results are not significant, but it shows that government subsidies play a role in increasing audit fee. Among the control variables, similar to the findings in the existing literature, the company may get a higher audit fee when the size is larger, the sales revenue is higher, and obtaining standard audit opinions from the big four; and the results are significant at the 1% level. In summary, the government (govern- ment-based) customers can bring the impact of the audit fee reduction to the enterprise, which is consistent with the expectation of Hypothesis 1. 4.2. Sub-sample tests Based on the above research findings, we start with a series of sub-sample tests based on the characteristics of the government-background customers and the company itself, focusing on factors’ influences such as order duration, procurement level and the degree of financing constraints faced by the company. Table 5 illustrates the corresponding regression results, and the orders are divided into four categories according to the duration. The longest group is the stable order, and the shortest group is the sporadic order. The results show that for the stable customer relationship, the government custo- mers have a significant negative correlation with audit fee (β = −0.368), while the coefficient between government-background customers and audit fee is also negatively correlated (β = −0.317), and the results are all significant at the 1% level. In sharp contrast, for sporadic orders, only the government procurement orders are negatively correlated with audit fee (β = −0.326), and the results are only significant at the 10% level, while the government-based procurement orders do not achieve significant results. Although the results of the two samples show a negative correlation, the absolute value of the regres- sion coefficients of the stable order are larger (−0.368<-0.326, −0.317<-0.298), and the significant levels are higher. A further Chow test of the coefficients in the two groups of samples also shows the difference between the two samples, from the results of the last row in the table, it can be seen that the coefficient differences between Govper and Stateper are significant at the level of 5% and 10%, respectively. These statistical results strongly explain that stable government-based procurement orders may effectively bring down the audit fee, in line with Hypothesis 2a. Table 6 considers the impact of the political hierarchy. Based on the previous studies, we divide the government customers into the central government (central enterprise) level and the local government (local state enterprise) level for comparative study. As shown in the table, when the control variables are not considered, the central level of government procurement (Govper-Central) is negatively correlated with audit fee CHINA JOURNAL OF ACCOUNTING STUDIES 397 Table 5. Government-background customer and audit fee. Sporadic Stable Sporadic Stable Auditfee Auditfee Auditfee Auditfee it it it it (Sporadic vs. Stable) Govper −0.326* −0.368***   it   (−1.67) (−2.84)   Stateper −0.298 −0.317*** it   (−1.52) (−3.07) Sub 1.187 1.217 0.905 0.992 it (1.16) (1.24) (0.93) (0.99) Size 0.363*** 0.302*** 0.295*** 0.342*** it   (5.76) (6.11) (4.95) (5.01) Lev −0.053 −0.067 −0.067 −0.062 it (−0.70) (−0.62) (−0.57) (−0.51) Sale 0.206*** 0.157*** 0.164*** 0.173*** it (2.78) (3.66) (4.03) (3.42) Opi 0.124*** 0.143*** 0.100*** 0.096*** it (3.23) (3.58) (3.16) (3.35) Current −0.005 −0.005 −0.006 −0.007 it (−1.10) (−1.07) (−1.09) (−1.05) ARInv −0.031 −0.036 −0.038 −0.033 it (−0.49) (−0.42) (−0.64) (−0.55) Return −0.027* −0.024** −0.028** −0.028** it (−1.86) (−2.01) (−2.24) (−2.20) Sig 1.015 0.797 1.006 1.026 it (0.99) (0.71) (1.04) (0.90) ROA −0.279 −0.301 −0.295 −0.267 it (−0.92) (−1.11) (−0.86) (−0.93) Big4 1.015*** 0.740*** 0.887*** 1.042*** it (4.68) (5.40) (5.35) (6.02) SOE −0.054*** −0.066*** −0.060*** −0.064*** it (−4.20) (−3.27) (−2.94) (−3.73) Constant 5.595*** 5.531** 7.185* 6.867*   (2.62) (2.12) (1.89) (1.94) Industry fixed effect Yes Yes Yes Yes Year fixed effect Yes Yes Yes Yes N 1652 1648 1652 1648 R-squared 0.639 0.664 0.652 0.670 Govper/Stateper:Sporadic = Stable[p-value] 0.029 0.076 ***, **, * Indicate statistical significance at the 0.01, 0.05, and 0.10 levels, respectively. (β = −0.238), and local level government procurement (Govper-Local) is negatively corre- lated with audit fee (β = −0.098), and the results are significant at the 1% and 10% levels. Similarly, the central level of government-based procurement (Stateper-Central) is nega- tively correlated with audit fee (β = −0.219), local level government-based procurement (Stateper-Central) is negatively correlated with audit fee (β = −0.100), and the results are significant at the 1% and 10% levels, respectively. We find the absolute value of the regression coefficient at the central level is larger than the absolute value at the local level, and the significance is higher, which illustrates that compared with the local level, the central level government (government-based) procurement could reduce the audit fee more effectively. After the introduction of control variables, the central level government procurement and audit fee are still negatively correlated (β = −0.225), and the results are significant at the 5% level, while the local level government procurement and audit fee are not significantly negative correlated (β = −0.146). Similarly, central level government-based procurement is negatively correlated with audit fee (β = −0.187), and local level govern- ment-based procurement is negatively correlated with audit fee (β = −0.102), and the 398 C. DOU ET AL. Table 6. Government-background customer and audit fee at different levels of government- background (central vs. local). Auditfee Auditfee Auditfee Auditfee it it it it Govper-Central −0.238*** −0.225** it (−2.88) (−2.48) −0.098* −0.146 Govper-Local it (−1.70) (−1.64) Stateper-Central −0.219*** −0.187** it (−3.01) (−2.29) −0.100* −0.102* Stateper-Local it (−1.67) (−1.86) Sub 1.054 0.941 it (1.27) (0.95) 0.353*** 0.356*** Size it (7.26) (4.38) Lev −0.065 −0.044 it (−0.77) (−0.76) 0.141** 0.224*** Sale it (2.44) (4.14) Opi 0.114*** 0.130*** it (3.16) (3.54) −0.006 −0.006 Current it (−0.83) (−0.67) ARInv −0.036 −0.035 it (−0.45) (−0.68) −0.021** −0.031* Return it (−2.30) (−1.77) Sig 1.078 0.824 it (0.89) (1.03) −0.345 −0.229 ROA it (−1.22) (−1.46) Big4 0.914*** 0.667*** it (4.57) (3.95) −0.057*** −0.062*** SOE it (−3.34) (−3.81) Constant 13.010*** 12.998*** 7.693*** 5.023** (2.98) (2.77) (2.92) (2.03) Industry fixed effect Yes Yes Yes Yes Year fixed effect Yes Yes Yes Yes N 6586 6586 6586 6586 R-squared 0.068 0.061 0.655 0.661 ***, **, * Indicate statistical significance at the 0.01, 0.05, and 0.10 levels, respectively. results are significant at the 5% and 10% levels, respectively. The comparison shows that the absolute value of the regression coefficient at the central level is larger than the absolute value of at the local level, and the level of significance is higher, indicating that whether it is government procurement or government-based procurement, compared with the local level, the central level government customers have a greater impact on audit fee, consistent with the expectations of Hypothesis 2b. For the sake of robustness, we also test whether the influence of central level govern- ment-background customers on corporate audit fee is greater than that of local level government-background customers by calculating the standardised regression coefficients of various variables, the regression results are shown in columns 1 and 3 of Table 6.Inthe regression analysis, the standardised regression coefficients of Govper-Central (Govper- Local)are −0.028 (−0.010) and −0.024 (−0.013), respectively, and the coefficients of Govper-Central are significantly larger than the Govper-Local, which indicates that govern- ment customers at the central level have greater impact. In the regression analysis of CHINA JOURNAL OF ACCOUNTING STUDIES 399 columns 2 and 4 of Table 6, the standardised regression coefficients for Stateper-Central (Stateper-Local)are −0.020 (−0.009) and −0.015 (−0.008), respectively, the coefficient of Stateper-Central is also significantly larger than the Stateper-Local in terms of absolute value, indicating that the central level government-background customers have a greater impact. The above results are consistent with the regression conclusions in Table 6. Table 7 divides the degree of financial constraints faced by the enterprise into high and low categories to test the listed companies in the face of extreme difficulties. The regression results show that when the enterprises face higher financial constraints, the government (government-based) procurement ratio and audit fee show a more signifi- cant negative correlation, and the coefficient and significance level are more significant than for the sample with lower financial constraints. The Chow test of the two sets of sample coefficients in the last row also supports the existence of this difference. This illustrates that, compared with the enterprises with a low degree of financial constraints, the margin of influence exerted by the government (government-based) customers with higher degree of financial constraints is more effective, which proves Hypothesis 2c. Table 7. Government-background customer and audit fee. High Low High Low Auditfee Auditfee Auditfee Auditfee it it it it (High degree of financing constraints vs. low) Govper −0.388*** −0.314*   it (−2.76) (−1.78)   Stateper −0.335*** −0.276* it (−2.80) (−1.92) Sub 1.043 1.054 0.870 0.921 it (1.38) (1.40) (0.96) (1.23) Size 0.264*** 0.403*** 0.342*** 0.332*** it (5.76) (4.49) (7.03) (7.37) Lev −0.058 −0.045 −0.048 −0.064 it (−0.64) (−0.73) (−0.57) (−0.55) Sale 0.228*** 0.230*** 0.208*** 0.201*** it (4.10) (3.17) (2.92) (2.85) Opi 0.100*** 0.146*** 0.117*** 0.142*** it (3.73) (4.85) (3.77) (3.04) Current −0.008 −0.005 −0.007 −0.005 it (−0.74) (−0.99) (−1.20) (−1.13) ARInv −0.039 −0.041 −0.040 −0.030 it (−0.54) (−0.56) (−0.57) (−0.71) Return −0.035* −0.021** −0.023** −0.034* it (−1.78) (−2.20) (−2.00) (−1.94) Sig 1.033 0.734 0.969 1.006 it (0.84) (0.82) (0.91) (0.70) ROA −0.245 −0.286 −0.327 −0.333 it (−1.07) (−1.45) (−1.37) (−0.97) Big4 1.051*** 1.042*** 0.987*** 0.759*** it (4.12) (3.90) (5.40) (5.74) SOE −0.047*** −0.039*** −0.063*** −0.062*** it (−3.52) (−3.02) (−4.67) (−3.36) Constant 6.231** 6.167** 5.277* 4.705** (2.39) (2.49) (1.93) (2.15) Industry fixed effect Yes Yes Yes Yes Year fixed effect Yes Yes Yes Yes N 3293 3293 3293 3293 R-squared 0.667 0.616 0.638 0.633 Govper/Stateper:High = Low[p-value] 0.010 0.023 ***, **, * Indicate statistical significance at the 0.01, 0.05, and 0.10 levels, respectively. 400 C. DOU ET AL. 4.3. The impact channel of government-background customer on audit fee Compared with corporate customers, government-background customers are supported by national credit and national finance, so that they have fewer risks and bankruptcy possibilities. This, therefore, can overcome the potential drawbacks of customer concen- tration, and the audit risk is relatively low. Despite the negative influence of traditional government interventions on risk (Deqiu, Sifei, & Cong, 2011; Jun & Lishuai, 2014; Min, Lijun, & Sheng, 2012; Yanyan & Danglun, 2012), the government intervention in the form of government procurement does not directly interfere with the production and opera- tion of enterprises. It is an organic combination of product demand and policy orientation, so that enterprises which meet production requirements and policy guidelines can be fully and stably developed, which is undoubtedly beneficial to reducing the audit risk. However, audit risk is the main path for government-background customers to influence audit pricing, and lower audit risk can effectively reduce the audit fee. Therefore, the existence of government-background customers may reduce the audit fee by reducing the audit risk. We attempt to further explore the influence path of government-back- ground customers on audit fee from the perspective of risk. Most of the mainstream literature subdivides audit risk into business risk (Liquan & Hanwen, 2013; Nikkinen & Sahlstrom, 2004), fraud risk (Lyon & Maher, 2005) and litigation risk (Seetharaman et al., 2002). The higher the above risks are, the more are the audit fees. The domestic research represented by Songsheng and Zhili (2019) agrees with this view. Table 8 draws on the methods of Chen Zhenglin (2016), and Fu Chao and Ji Li (2017)to measure the audit risk of different dimensions by using the amount of the lawsuit and the volatility of the ROA, while referring to the research of Wanfa and Xiaobo (2018)to construct a corresponding mediation effect model for analysis and test the mediating effect. Panel A of Table 8 illustrates the regression result of the intermediary variable, audit risk, and the government customers. The first column indicates that the proportion of government procurement is negatively correlated with the company’s lawsuit amount (β = −0.005), and the result is significantatthe 5% level. Column 3 shows that the proportion of government procurement is negatively correlated with the volatility of the ROA (β = −0.071), and the results are also significant at the 5% level. Similarly, in the second and fourth columns, the proportion of government- based procurement is negatively correlated with the two indicators of audit risk (β = −0.003, β = −0.062), and the results are all significant at the 5% level. The above results indicate that enterprises with government customers face lower operational risks and litigation risks, thus effectively reducing the audit risks faced by enterprises. In Panel B of Table 8, the regression results of government-back- ground customers and audit risk indicate that under the two different measurement methods of the government background, the regression coefficient of the govern- ment-based procurement ratio and audit risk are not significantly zero. This shows that audit risk is a partial intermediary variable that affects audit fee. On this basis, we also perform the Sobel test and we find that the Z value is also significantly larger than the critical value of 0.97 (Govper:3.11, Stateper: 3.35), which indicates that audit risk is indeed an important intermediary for the effect of government- background customers on audit fee. CHINA JOURNAL OF ACCOUNTING STUDIES 401 Table 8. Analysis of the mediating effect of audit risk. Panel A. Government-background customer and Audit Risk L_litAmount L_litAmount Varroa Varroa it it it it Govper −0.005** −0.071** it (−2.24) (−2.09) Stateper −0.003** −0.062** it (−2.51) (−2.17) Control Variables Yes Yes Yes Yes Industry fixed effect Yes Yes Yes Yes Year fixed effect Yes Yes Yes Yes N 6586 6586 6586 6586 R-squared 0.093 0.084 0.076 0.082 Panel B. Government-background customer, Audit Risk and Audit Fee Auditfee Auditfee Auditfee Auditfee it it it it Govper −0.361* −0.376* it (−1.90) (−1.68) Stateper −0.325* −0.340* it (−1.81) (−1.95) L_litAmount 22.009** 14.556*** it (2.18) (2.98) Varroa 0.305** 0.338** it (2.49) (2.27) Control Variables Yes Yes Yes Yes Industry fixed effect Yes Yes Yes Yes Year fixed effect Yes Yes Yes Yes N 6586 6586 6586 6586 R-squared 0.650 0.667 0.659 0.664 ***, **, * Indicate statistical significance at the 0.01, 0.05, and 0.10 levels, respectively. 5. Robustness tests 5.1. International guidelines Since 1 January 2007, Chinese listed companies have officially implemented new risk-oriented accounting standards, which basically achieved the convergence of Chinese accounting standards and international accounting standards. Studies have shown that the enforcement of IFRS significantly affects the audit fee; meanwhile, considering the sample size in 2007 is the smallest, it only accounts for 1% of the total sample. Therefore, we delete the 2007 sample for regression, and list the regression results in the first and second columns of Table 9.Itisfound that the proportion of government procurement is negatively correlated with audit fee (β = −0.327), and the proportion of government-based procurement is also negatively correlated with audit fee (β = −0.302), and the results are significant at the 5% and 1% levels respectively, indicating that having a customer relationship with the government department can reduce the audit fee, which is consistent with the expectation of H1. 5.2. Full sample regression When the customers are too dispersed, it is difficult to measure the impact of a single type of customer on sales. Therefore, in the previous data processing, we remove the top five customer concentration with less than 1%. In order to ensure the reliability of the results, we use the whole sample for regression; the regression results are listed in the third and fourth columns of Table 9. It is found that the proportion of government procurement is negatively correlated with the audit fee (β = −0.245), the proportion of 402 C. DOU ET AL. Table 9. Government-background customer and audit risk. Auditfee Auditfee Auditfee Auditfee Auditfee Auditfee it it it it it it Govper −0.359** −0.245* −0.323** it (−2.14) (−1.72) (−2.06) −0.306*** −0.215* −0.269* Stateper it (−2.92) (−1.84) (−1.92) Control Variables Yes Yes Yes Yes Yes Yes Industry fixed effect Yes Yes Yes Yes Yes Yes Year fixed effect Yes Yes Yes Yes Yes Yes N 6511 6511 7178 7178 1548 8788 R-squared 0.657 0.679 0.583 0.598 0.621 0.636 ***, **, * Indicate statistical significance at the 0.01, 0.05, and 0.10 levels, respectively. government-based procurement is also negatively correlated with audit fee (β = −0.215), and the results are all significant at the 10% level, indicating that even within the full sample, the conclusion is that the more government procurement orders are available, the lower the audit fee. 5.3. Propensity score matching In order to control the endogenous problems of the government procurement, we also use the propensity score matching method (PSM) to match each enterprise that has a government procurement order to an enterprise that does not have an order, and do regression tests separately according to government procurement and government- based procurement. The regression results are listed in the fifth and sixth columns of Table 9. It is found that the proportion of government procurement is negatively corre- lated with the audit fee (β = −0.359), and the proportion of government-background purchases is also negatively correlated with audit fee (β = −0.36), and the results are all significant. This result also illustrates that companies that receive government procure- ment orders have a significantly lower audit fee than those that do not receive orders. 5.4. Self-selection Considering voluntary disclosure of listed companies, the conclusions may be affected by the self-selection of the sample. In response to this problem, we examine the relationship between the disclosure of the top five customers’ information (measured by the variable, Disclosure, it is set to 1 if the company discloses, 0 otherwise) and audit fee. Table 10 illustrates the regression results, regardless of whether the control variables are consid- ered or not; although the coefficients are positive, the regression results of the company’s disclosure and audit fee are not significant. This result indicates that whether the com- pany discloses customer information does not affect the audit fee and, to some extent, the problem of sample self-selection is ruled out. 5.5. Customer concentration Since the research data is from the top five customers in the annual report, our conclusions will inevitably be affected by customer concentration. In response to this problem, we consider the government-background customer and customer concentration, and design a CHINA JOURNAL OF ACCOUNTING STUDIES 403 Table 10. Corporate disclosure of customer informa- tion and audit fee. Auditfee Auditfee it it Disclosure 0.105 0.086 it (0.98) (1.13) Control Variables No Yes Industry fixed effect No Yes Year Fixed effect No Yes N 12602 12602 R-squared 0.071 0.670 ***, **, * Indicate statistical significance at the 0.01, 0.05, and 0.10 levels, respectively. Table 11. Customer concentration, government-background customer and audit fee. Customer concentration Low High Difference Govper Low 13.309 13.006 0.303*** High 12.983 12.695 0.288*** Difference 0.326*** 0.311*** Stateper Low 13.294 13.010 0.284*** High 12.974 12.721 0.253*** Difference 0.320*** 0.289*** ***, **, * Indicate statistical significance at the 0.01, 0.05, and 0.10 levels, respectively. comparative study in Table 11. The results are shown in the table; according to the customer concentration, the enterprises with high proportion of government-based procurement have significantly lower audit fee than those with less government-based procurement. In turn, after dividing the government-based procurement orders into two categories, the difference between the low customer concentration and the high customer concentration is also significant, which is basically consistent with the results of Wang Xiongyuan et al. (2014). The effect of reducing the audit fee of customer concentration is not inconsistent with the audit fee reduction effect of government-based procurement orders. 5.6. Business complexity Table 12 divides the observations into four categories according to business complexity, and compares the difference between the regression coefficients of the most complex and simplest two groups of samples. The results strongly explain that the more compli- cated the business, the higher the potential business risk and audit risk faced by the enterprise. The presence of government-background customers may reduce the audit fee more effectively, which proves the core hypothesis of this paper. 6. Research conclusions Based on the 2007–2015 customer data of listed firms, we study the impact of govern- ment-background customers on the audit fee in China from the perspective of the supply chain, for what is claimed to be the first time. The results indicate that the presence of a government-background customer helps to reduce the audit fee significantly. Moreover, 404 C. DOU ET AL. Table 12. Government-background customer and audit risk (business complexity). Simple Complex Simple Complex Auditfee Auditfee Auditfee Auditfee it it it it Govper −0.300** −0.404***   it   (−2.06) (−2.96)   Stateper −0.308* −0.373*** it   (−1.88) (−3.16) Control Variables Yes Yes Yes Yes Industry fixed effect Yes Yes Yes Yes Year fixed effect Yes Yes Yes Yes N 1656 1646 1656 1646 R-squared 0.657 0.640 0.658 0.646 Govper/Stateper: Complex = Simple [p-value] 0.006 0.038 ***, **, * Indicate statistical significance at the 0.01, 0.05, and 0.10 levels, respectively. the relationship is more evident when the customer is consistent or comes from the central government, and we can observe a stronger relationship under high financial constraints as well. At the same time, the paper also shows that the existence of a government-back- ground customer can effectively alleviate enterprises’ audit risk, so as to reduce their audit fee. This is an important supplement to the literature on supply chain finance research. It has important implications for investors who need to interpret the information that listed companies have obtained government-background customers, and reveals the channels of influence of macro policies on market intermediaries. Since listed companies rarely disclose order data in detail, the relevant laws and regulations do not mandate listed companies to disclose specific customer information. The research has inevitable defects in the scope of the sample, and it cannot cover all listed companies. Moreover, the sales from the top five customers cannot represent the overall sales of the company. In addition, the government departments and state-owned enterprises have differ- ences in their responsibilities, they cannot be completely equalised. Therefore, we are currently unable to conduct a full-scale study on the impact of government-background customers, and it is difficult to consider the impact of specific customer structures in extreme situations. 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Accounting Research, 11,23–29. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png China Journal of Accounting Studies Taylor & Francis

Government-background customers, audit risk and audit fee

China Journal of Accounting Studies , Volume 7 (3): 22 – Jul 3, 2019

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Taylor & Francis
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© 2019 Accounting Society of China
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2169-7221
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2169-7213
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10.1080/21697213.2019.1703391
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Abstract

CHINA JOURNAL OF ACCOUNTING STUDIES 2019, VOL. 7, NO. 3, 385–406 https://doi.org/10.1080/21697213.2019.1703391 ARTICLE a b c Chao Dou , Man Yuan and Xiao Chen a b Business School, Central University of Finance and Economics, Beijing, China; School of Accountancy, Capital University of Economics and Business, Beijing, China; School of Economics and Management, Tsinghua University, Beijing, China ABSTRACT KEYWORDS Government-background Based on the 2007–2015 customer data of listed firms, this paper customer; audit risk; audit studies the impact in China of customers with a government back- fee; supply chain ground on the audit fee from the perspective of supply chain. It is claimed to be the first time this impact has been studied. The results indicate that the presence of a government-background customer helps to reduce the audit fee significantly. Moreover, the relationship is more evident when the customer is stable or comes from the central government, and we can also observe a stronger relationship under high financial constraints. Finally, the paper also shows that the exis- tence of a government-background customer can effectively alleviate an enterprises’ audit risk, thus reducing the audit fee. 1. Research background The difference in companies’ audit fees is an interesting issue in both academia and industry. In 2017, for example, more than a quarter of the audit fees paid by listed companies in China’s A-shares market was attributed to only 1% of the companies. Previous literature mainly put an emphasis on the enterprise characteristics, such as company size, business complexity, corporate governance, and litigation risk, and the audit firm’s characteristics, such as audit firm size, reputation, and industry audit speciality (Ball, Jayaraman, & Shivakumar, 2012), from which we can get a reasonable explanation about a companies’ audit fees. However, a study of the audit fees from the view of the supply chain was merely the start. Although there have been a few achievements, they have mainly focused on overall characteristics such as customer concentration (Xiongyuan, Peng, & Jinping, 2014), in which analysis has not gone deep into the concrete information about the customer. Nowadays, as the relationship between supply and demand is getting closer, corporations and their large customers tend to become inter- dependent. From the view of customer characteristics, this paper analyses whether corporations’ dependence on some particular customers influences their audit fees. Customers could not only provide positive support, but also exert negative pressure on corporations. In China, government-background customers represented by government CONTACT Man Yuan yuanm.14@sem.tsinghua.edu.cn School of Accountancy, Capital University of Economics and Business, Beijing, China Paper accepted by Xi Wu. This article has been republished with minor changes. These changes do not impact the academic content of the article. © 2019 Accounting Society of China 386 C. DOU ET AL. and state-owned enterprises are particularly special. So, what special influence can government-background customers exert on corporations and how can this influence affect the audit fees are interesting questions that should attract attention. Influenced by the traditional western economic perspective, affecting the economy through the government’s ‘visible hand’ tends to be criticised. In particular, the phenom- enon of so-called ‘state advanced and private retreat’ in China caused controversy regarding the roles that government and state-owned enterprises should play in a market economy. However, the great achievements made by China in the last 40 years of reform and open policy showed the success of the socialist market economy, which indicated the rationality of government intervention. Previous literature has mainly focused on tradi- tional government interventions such as government subsidy and tax preference (Hetong & Haopin, 2009), and there was little attention paid to government procurement. At the 19th CPC National Congress, President Xi Jinping emphasised that the government should play a better role in economic development. Hence, a study on government- background customers would help to promote understanding about how government interventions influenced the capital market and the enterprise operation. Specifically, apart from government department, state-owned enterprises played an important role in the economy under the direction of government in China. On one hand, state-owned enterprises were directly leaded by the relevant SASAC (State-owned Assets Supervision and Administration Commission). On the other hand, state-owned enterprises had differ- ent kinds of social responsibility (Wencheng & Shihui, 2014), such as maintaining national employment rate and economy stability etc. Hence, it was inevitable for state-owned enterprises to make operating decisions that accorded with policy orientation. Admittedly, government departments and state-owned enterprises had different natures and responsibilities. However, this paper defines, in the government-dominated market in China, both government departments and state-owned enterprises as government-back- ground customers for a more comprehensive study of how the government as a whole influences enterprises in the market through government procurement. Different from firm customers, the behaviour of government-background customers reflects government policy, so that orders from government-background customers not only meet demand, but are also influenced by policy factors. Thus, government procure- ment was a special government intervention in a form somewhere between market transactions and government direct support, which not only supported the enterprises but also met social need, and it was not just due to political connections or client relations. Based on state credit and fiscal power, government-background customers not only had stable and enormous demand, but also offered much lower risk compared with ordinary customers. Such demand could provide safety and reliability for the whole supply chain through the mechanism of risk transfer, by which the uncertainty faced by enterprises would be reduced. As is known, risk premium and workload are determinants of audit fees (Songsheng & Zhili, 2019). Thus, government-background customers could effectively reduce the audit risks of the relevant enterprises, thus influencing the audit fees further. According to statistical data, a significant improvement was made in recent years regard- ing government procurement, concerning the procurement scale, procurement scope and policy effect. However, compared with the developed countries in Europe and America, there were still gaps with regard to the size of orders and the procurement CHINA JOURNAL OF ACCOUNTING STUDIES 387 amount as a share of GDP. In other words, there was great potential for government- background customers to influence economic growth in China. Based on the 2007–2015 customer data of listed firms, this paper finds that the existence of government-background customers helps to reduce audit fees for listed firms. In addition, if the government-background customers had greater stability and were higher in political level, the audit fees of listed firms would be much lower. And such a relationship was especially evident concerning listed firms with tight financial constraints. Finally, this paper studied how such relationships form. It finds that the existence of government-background customers helps to reduce the audit risks, so reducing audit fees. In conclusion, this paper helps us have better knowledge of the influence of government-background customers on firms. The main contributions of this paper mainly lie in the following four aspects: first, different from the literature that focused on customer concentration and political connection, this paper is thought to be the first to study the influence of government-background customers on audit fees. This paper also provides a new viewpoint for studies about govern- ment procurement. Second, this paper focuses on government-background custo- mers’ nature and reveals its relationship with audit fees. In particular, this paper discusses how the stability and political level of government-background customers helps to reduce the audit fees. Third, as the auditing risk path to show how the existence of government background-customers influences audit fees, it is an impor- tant complement to the literature on supply chain finance. Meanwhile, it offers a reference for the development of the Chinese capital market in this ‘new normal’ economy. This paper offers enlightenment in investors’ interpretation about listed companies obtaining orders from government-background customers. Finally, it also reveals the mechanism of how micro policy would influence micro participants in the market. 2. Literature review and research hypothesis Thereisawiderangeoffactors thatinfluence audit fees, which have been discussed in related literature. Representative examples, such as Wu Lina (2003), concluded that the audit fee was mainly decided by audit cost, risk cost and the normal profitofthe accounting firm. Similarly, Wang Xiongyuan et al. (2014) pointed out that the audit fee was mainly decided by the auditor’s workload and audit risk. They revealed that audit fees would be lower when the risk of the enterprise was low and the workload of auditor was light. The rest of the literature mostly followed the model developed by Simunic (1980). Therefore, such studies were mostly conducted from aspects of the client and audit firms. On onehand, studiesthatfocused on the clientfound that asset size and numbers of subsidiaries were the two main factors that decided the audit fees. Meanwhile, content of the financial report, corporate governance, internal control, media attention and competitive environment were also important factors that influenced the audit fees (Jifu, 2007; Lina, Chunfei, & Zhengfei, 2012;Liquan& Hanwen, 2013;Qiliang,Hui,Chao, Yigang,&Hanwen, 2014;Yanheng, 2011; Yixia, 2011). On the other hand, studies that focused on audit firms discussed the pricing mechanism of audit fees from the viewpoint of audit opinion, regional factor, 388 C. DOU ET AL. reputation mechanism, penalty and macro-environment (Tianshu & Jun, 2013; Xiaoxia, 2013; Yixia, 2011). And they found there did exist relationships. With the rapid development of supply chain finance and relevant theories in recent years, how the benefit-related parties of the firm influenced the audit fee, especially the customers of the audited firm, aroused academia’s interest. As the most important resource of revenue, firms’ customer relations had a direct effect on their business activities, cost structure and profitability (Gosman, Kelly, Olsson, & Warfield, 2004; Kim & Wemmerlöv, 2015). The interaction relationship between firms and their customers has been perceived by investors long ago. Although a firm’s customers do not influence the firm’s audit fee directly, they do have an influence on the firm’s risk through transactions with each other, which will finally influence the audit fee. Customer relations could strengthen the supply chain integration to reduce the audit fee, but might also absorb risk to increase the audit fee as well. Thus, ordinary customer relations played the role of a double-edged sword for the development of a firm; specific consideration should be taken of specific conditions to verify the effect of ordinary customer relations. However, in China, government-background customers, as special major clients, would influence audit fees in their own peculiar way. A study of this could provide more cognition about markets in China. On one hand, when the government plays a role in the development of firms as customers through department- or state-owned enterprises, the firms might benefit from the cooperation with such major customers in its supply chain, which is called the ‘income effect’. Good relationships with the government helped the firm reduce its risk in operation and development. Previous research has shown, as the interdependence between members in the supply chain was enhanced, two different conditions would appear among suppliers and customers: cooperation-dominated and competition-domi- nated, which resulted in two opposite perspectives in favour of ‘income effect’ and ‘risk effect’ respectively. ‘Income effect’ claimed that cooperation promoted information shar- ing, collaboration and credit within the supply chain, which increased the potential value of the firm. Specifically, customers could prompt the firm to improve management efficiency, reduce selling expenses and concentrate on long-term performance, which brought healthy cash flow and stable revenue for the firm. Hence, it reduced the risk faced by the company (Johnson, Kang, & Yi, 2010; Patatoukas, 2012). Under such a perspective, the existence of major customers helped the firm to stabilise its supply chain, and the firm with the most major customers could gain higher and more stable revenue. However, the ‘risk effect’ claimed that major customers’ bargaining power might mean the company was exploited, which could result in its bad financial performance and poor cash flow. Therefore, firms that kept long relationships with only one or a few major customers or suppliers might sacrifice a lot for this. And such firms would face higher risk in decision- making and operating (Kale & Shahrur, 2007; Wang, 2012). Different from firm customers, government-background customers act as one of the government’s interventions, which encourage firms to produce high-level products to meet the real demand from society. It is more than a simple trading relationship but a particular kind of government interven- tion in a form somewhere between market transactions and government direct support. Meanwhile, based on the state credit, demand from government-background customers was stable and had low risk (Cohen & Li, 2016; Dan, Scott, Matthew, & Sarah, 2016), which provided stable and high revenue for the firm to develop. The aim of government- CHINA JOURNAL OF ACCOUNTING STUDIES 389 background customers was to promote the development of the economy and firms in the market, so that they would not just focus on short-term benefit. Hence, the ‘risk effect’ seldom appeared when concerning government-background customers, compared with traditional firm customers. Instead, the firm could gain much more support due to its fine relationship with government-background customers, which of course should be regarded as the ‘income effect’. On the other hand, as government procurement was considered as one of the most important means for the government to intervene in the economy, the object and allocation of government procurement could reflect strong policy orientation, aimed to provide resources for firms’ healthy and steady development. Such could be considered the ‘support effect’. So far, a lot of research has focused on the influence of the govern- ment’s traditional intervention on the economy. And there had been sharp conflicts over whether the policies such as government subsidies worked well. A significant part of the research found that a government subsidy could not promote the development of firms effectively but resulted in a series of potential problems (Bergström, 2000; Dong, Gao, Li, & Dan, 2012; Tzelepis & Skuras, 2004; Xiao & Jing, 2001). Different from traditional ways of government support, government procurement was not merely support by money or policy (Aihua & Han, 2018; Fangwang, 2015; Jinghuan, Xiao, & Baoshun, 2013; Zheng, Can, Jian, & Fei, 2010). As a means of government intervention from the supply side, govern- ment procurement would not only follow the policy orientation but also meet the real demand for products. Therefore, high quality was acquired in the process of production and the finished product. Meanwhile, measures such as open tender and regular assess- ment were taken to ensure the implementation. Such procurement was not only support but also a spur for the firm, through which the problems of so-called ‘one-way support’ and ‘zombie firms’ were prevented (Can, Zhuquan, Deming, & Wei, 2015; Xiongyuan et al., 2014). From such help, the firm could keep developing in a positive cycle. Moreover, as the government-background customers whose orders were commonly based on state credit had low risk and huge demand (Dan et al., 2016; Yun, Zhe, Yijie, & Xuanting, 2017), relevant firms’ revenue in the future and their security of operation would be assured. As a result, it was expected that, government procurement tended to be a kind of indirect support. The firms were stimulated to keep making progress by government-background customers’ orders. And the adverse effect of traditional government intervention was avoided, so that the ‘support effect’ could take better effect and the uncertainty in firms’ operation was reduced. In conclusion, firms that had government-background customers could not only benefit from the relevant policies through the ‘support effect’, but also benefit from government-background customers through the ‘income effect’, so that the risks and uncertainty faced by the firms were reduced. As a result, the audit risk was reduced and the audit fees would reduce accordingly. Therefore, our first hypothesis is proposed as follows. Hypothesis 1: The existence of government-background customers helps to reduce firms’ audit fees. This paper also investigates how the stability and compliance of government-back- ground customers influenced the audit fees. Li Xinzi (2015) classified government subsidy 390 C. DOU ET AL. as accidental-form, policy-guided-form and project-form, and found that government subsidies’ influence on firms’ profitability was different if they were of different persis- tence. Similarly, orders from government-background customers might vary in its stabi- lity. Some firms could gain stable orders from government-background customers owing to the high quality of their products and good relationship with the government, while others could only gain such orders occasionally. In other words, stable orders from government-background customers meant that the firms’ revenue was mainly influenced by such customers, which also meant that the government-background customers could exert a continuous influence on the relevant firms. By contrast, occasional orders could not provide stable sales for the firms, which meant that such government-background customers could not exert continuous influence on the relevant firms. Gosman et al. (2004) found that stable customers helped to stabilise the supply chain, so that the relevant firms could have high revenue and low risk. In fact, stable government-back- ground customers helped to reduce relevant firms’ fluctuations in revenue, so that the stable profitability improved the prospect of the firms. Meanwhile, the government- background customers could also influence the relevant firms on their supervision and governance. Opportunism of the management would be reduced and earnings manage- ment would also be reduced, so that the quality of the firms’ profit would be improved. Hence, stable relationships with customers helped to ensure financial performance and reduce risk. This is Hypothesis 2a. Hypothesis 2a: Stable relationships with government-background customers helps to reduce audit fees compared with occasional orders. Meanwhile, different administrative levels differ in patterns of governance in China, such as central government and local government. In past literature, such a difference was confirmed. Xiao and Jing (2001) showed how local government influenced firms’ financial performance. Jian, Chuanming, and Junhua (2012) found that there exists a difference in the hope of local government and central government for the development of the firms, which influenced the behaviour of the entrepreneur who had relevant political connec- tions. After distinguishing the level of political connections, Jian and Chuanming (2013) found that connections with local government had more positive effects on debt maturity structure than connections with central government. Tang song and Sun Zheng (2014) found that the problem of over-payment to management was more serious in central state-owned enterprises than local ones. Shu and Xiaoyan (2014) also find that connection with local government had a more positive effect on firms obtaining long-term loans than the connection with central government. Generally speaking, the studies above showed that different levels of political connection would result in different economic conse- quences in firms’ development. Under the enormous system of government, it was inevitable that policies such as subsidy and government procurement would be influenced by interests and political relationship. Therefore, various aspects were taken into account in the process of policy making by all levels of government. As far as government procurement was concerned, central government should have an overview and macro control of the economy, while it usually faced more attention and supervision. Hence, procurement from central govern- ment was fairer and more transparent. By contrast, the supervision system in local CHINA JOURNAL OF ACCOUNTING STUDIES 391 governments was not perfect and their vision was also limited. So, procurement from local government was more likely to be shortsighted and tied to various interests. Thus, relationships and interests were considered more. In recent years, there appeared many cases of chaos in local government procurement, such as corruption and arrears. As a result, compared with central government-background customers, there was more risk in payment will and policy implementation for local government-background customers, which increased the audit risk and resulted in higher audit fees. Based on such phenom- enon, we propose Hypothesis 2b. Hypothesis 2b: Compared with local government-background customers, relationships with central government-background customers helped reduce more audit fees. As China came into the new normal economy stage, debt default and financing difficulty occurred in many firms. The problem of financing constraints was serious for the whole market, which influenced the risk of firm and stabilisation of the economy directly. Kaplan and Zingales (1997) proposed that financing constraints was caused by the incompleteness of the market (information asymmetry, agent cost etc.), which resulted in the obvious difference between the costs of outside and inside financing. Usually, when a firm faced severe financing constraints, it was always stuck in a bad financial condition (Kebin & Haijian, 2014). Such a firm would have greater risk and challenges in its operation and development (Dingyu & Cong, 2016; Jia, Shu, & Yu, 2014). The audit fee was decided by various factors, which not only reflected the expected audit resources invested by the auditor, but also reflected the risk compensation for potential audit risk. Beatty (1993) found that the worse the financial condition the audit client was in, the more risk of litigation the auditor would take, so that a higher audit fee the auditor would acquire. Hence, when the firm was experiencing tight financing con- straints, the auditor usually would charge a higher audit fee, considering the risk. In such a situation, the existence of a government-background customer was a key support for the relevant firm. The government-background customer provided stable demand to relieve the operating risk of the relevant firm, so that the audit fee of such a firm could be reduced accordingly. Obviously, such an effect was more important to firms experiencing tighter financing constraints compared with those with light financing constraints. Based on such logic, there is hypothesis 2c from the viewpoint of firms’ characteristic. Hypothesis 2c: Compared with the firms with light financing constraints, relationships with government-background customers help reduce audit fees for those with tight financing constraints. 3. Research design 3.1. Research data Since there is no policy to force listed companies to disclose specific customer names in their annual reports, and most companies chose not to disclose detailed customer information before 2007, the sample covers from 2007 to 2015. By screening the top five customers’ information disclosed in the A-share listed company’sannualreport, we 392 C. DOU ET AL. combine the company’s shareholder capital contribution information and corporate property rights data disclosed by the national corporate credit information disclosure system to accurately trace the state-owned holding company and government depart- ment information among the company’s customers, and then match government-back- ground customers. Other financial data come from the CSMAR database. In order to ensure the integrity and reliability of the data, the research object must meet the following six requirements: (1) only list companies that disclose the top five customers’ name information in the annual report; (2) the shareholding structure of the listed company’s customers is clear, and the controlling shareholder can be traced; (3) delete thesamplewhosetop five customers’ concentration is less than 1%; (4) complete financial data and audit fees in 2007–2015; (5) remove 1% of the maximum value from above and below. The reason for removing the sample of the top five customers with a concentration less than 1% of is that, in the sample of manufacturing-oriented compa- nies, the top five customers of listed companies that disclose customer name informa- tion account for more than 1% of sales. Moreover, it is also difficult to measure the impact of a single type of large customer on the sales of the company when the corporate customers are too dispersed (such as Wanke Corp., whose customers are extremely dispersed). Through the above data screening process, this paper finally manual-finished a sample of 1496 listed companies, a total of 6586 annual observations. Table 1 illustrates the sample distribution. From Panel A of Table 1, we can find that there are 774 samples of government procurement orders, accounting for about 12% of the total sample, while government-background customer samples account for about two-thirds of the total sample. In view of the fact that many companies have both government-background customers and other types of customers, there are 6155 entries in all samples with orders from non-government-background customers. From the perspective of the duration of the government-background customer procurement orders in Panel B of Table 1, the government-background customer procurement orders show a relatively stable trend, with 56.08% of orders lasting for 4 to 6 years, and even 13.71% of orders lasting more than 7 years, indicating that the impact of government-based procurement orders on specific companies is often stable. Table 1. Sample distribution. Customer type Number Proportion Panel A. Customer Type Distribution Government customer 774 11.75% Government-background customer 4394 66.71% (Government department, state-owned enterprise) Others (foreign enterprises, private enterprises) 6155 93.46% Total sample (firm-year) 6586 Order duration (year) Number Proportion Panel B. Stability distribution [1,3] 452 30.21% [4,6] 839 56.08% [7,9] 205 13.71% Total sample (firm) 1496 100% Since the customers of some enterprises include the government departments, state-owned enterprises and private enterprises, the sum of the statistics in Panel A of Table 1 exceeds 100%. CHINA JOURNAL OF ACCOUNTING STUDIES 393 3.2. Research model To test the impact of a government-background customer on the corporate audit fee, we draw on the audit cost analysis model of Wang Xiongyuan et al. (2014)and Lili and Yuanyuan (2018), and estimate the following regression model to test H1–H3: Auditfee ¼ β0 þ β1Procurement þ β2Size þ β3Lev þ β4Sale þ β5Opi i;t i;t i;t i;t i;t i;t þ β6Current þ β7ARInv þ β8Return þ β9Sig þ β10Roa i;t i;t i;t i;t i;t þ β11Big4 þ β12Sub þ β13SOE þ ε i;t i;t i;t The dependent variable, Auditfee , measures the natural logarithm of the annual audit i,t fee of firm i in year t, and the independent variable, Procurement ,isthe ratioofthe i,t government-based procurement order amount to the total sales of firm i in year t. Under the special economic system and social background of the country, the national policy and development plan are usually led by the government, while the state- owned enterprises cooperate and support. Therefore, in the regression analysis, Procurement is represented by the government departments (Govper)and thegovern- ment-based department (Stateper). Since government-basedprocurementandgov- ernment subsidy are important means for the government to intervene in the economy, many companies that receive government-based procurement orders often receive a large amount of government subsidy at the same time. In order to avoid the endogenous impact of the two on a company’s business development, we add government subsidy (Sub)of firm i during year t in the control variables. Other control variables include corporate financial indicators, such as company size (Size), debt level (Lev), sales revenue (Sale), current ratio (Current), accounts receivable and inventory ratio (ARInv) and return on assets (Roa), and market reaction indicators, such as stock return (Return) and stock return volatility (Sig), and audit indicators, such as whether it is audited by Big Four (Big4) and audit opinion (Opi), and corporate property indicator (SOE). All regression results are clustered at the company level, and the variable definitions are given in Table 2. 3.3. Descriptive statistics Table 3 provides descriptive statistics on the variables, from which we can find that state- owned enterprises occupy the majority among the sample that actively disclose customer information. According to the statistics of the order type, in the sample of government procurement orders, the average proportion of direct orders from government depart- ments in the sample exceeds 13%. If we further consider the purchase of state-owned enterprises, this proportion will be nearly 25%, indicating that the government-back- ground customer has a major impact on the company’s operational development, which affects the perception of the capital market. In addition, audit fee (Auditfee), litigation risk (L_litAmount) and operational risk (Varroa) also have large differences in distribution, which provides potential feasibility for studying how government-background customers influence the audit fee and audit risk. 394 C. DOU ET AL. Table 2. Variable definition. Symbol Name Definition Dependent variable Auditfee Audit Fee The natural logarithm of the annual audit fee of firm i in year t. it Amount of Lawsuit The ratio of the amount of lawsuit of firm i divided by operate income in L_litAmount it year t. Varroa Business Risk ROA variance of firm i in year t. it Independent variable Gov Government customer A dummy variable that is set to 1 if there are government customers it (including party, government, military departments and government institutions at all levels) in top five customers of firm i in year t, and 0 otherwise. State Government-background A dummy variable that is set to 1 if there are government-background it customer customers (including party, government, military departments, government institutions and state-owned enterprises at all levels) in top five customers of firm i in year t, and 0 otherwise. Govper Government customer order Proportion of government procurement (including party, government, it ratio military departments and government institutions at all levels) to total sales of firm i in year t. Stateper Government-background Proportion of government-based procurement (including party, it customer order ratio government, military departments, government institutions and state- owned enterprises at all levels) to total sales of firm i in year t. Control variable Size Company size Natural logarithm of total assets at the end of year t. it Lev Leverage ratio Ratio of total liabilities to total assets at the end of year t. it Sale Sales revenue Natural logarithm of total sales revenue in year t. it Opi Audit opinion A dummy variable that is set to 1 if firm i is issued a non-standard it unqualified audit opinion in year t, and 0 otherwise. Current Current ratio Ratio of current assets to current liabilities at the end of year t. it ARInv Accounts receivable and The sum of accounts receivable and inventory divided by total assets at it inventory ratio the end of year t. Return Stock return ratio Stock return of firm i in year t. it Sig Stock return volatility Standard deviation of daily stock return in year t. it Roa Return on assets Ratio of net profit to total assets balance in year t. it Big4 Big 4 audit A dummy variable that is set to 1 if firm i is audited by the Big Four in year it t, and 0 otherwise. SOE State-owned enterprise A dummy variable that is set to 1 if firm i belongs to state-owned it enterprise (according to the actual controller property) in year t, and 0 otherwise. Sub Government subsidy Total government subsidy divided by total assets at the end of the year t. it Table 3. Descriptive statistics. Variable Mean Number Standard Deviation 25% Median 75% Auditfee 13.117 6586 0.603 12.854 13.080 13.552 it L_litAmount 0.001 6586 0.007 0 0 0.009 it Varroa 0.053 6586 0.037 0.028 0.046 0.653 it Gov 0.118 6586 0.322 0 0 0 it State 0.667 6586 0.471 0 1 1 it Govper (%) 13.101 774 16.192 2.857 6.483 16.370 it Stateper (%) 24.174 4394 22.549 7.954 15.456 33.262 it Sub 0.011 6586 0.020 0.002 0.005 0.013 it Size 23.851 6586 1.999 22.480 25.133 27.349 it Lev 0.468 6586 0.353 0.337 0.476 0.582 it Sale 19.328 6586 1.614 17.425 19.615 19.996 it Opi 0.956 6586 0.240 1 1 1 it Current 1.398 6586 1.180 1.119 1.241 1.858 it ARInv 0.285 6586 0.152 0.170 0.267 0.391 it Return 0.396 6586 1.494 −0.327 0.005 1.217 it Sig 0.252 6586 0.212 0.094 0.209 0.326 it Roa 0.037 6586 0.081 0.004 0.032 0.070 it Big4 0.050 6586 0.179 0 0 0 it SOE 0.598 6586 0.506 0 1 1 it CHINA JOURNAL OF ACCOUNTING STUDIES 395 4. Regression analysis 4.1. Government-background customer and the audit fee In order to test the impact of government-background customer on the audit fee, Table 4 performs the corresponding regression analysis according to Model 1. As shown in the table, the first and second columns introduce the dummy variables Gov and State from the perspective of the presence or absence of government (government-based) customer order, and it is found that there are negative correlations between government customer and audit fee (β = −0.136), and there are negative correlations between government- background customer and audit fee (β = −0.118). Both are significant at the 10% level, which indicates that companies with government (government-based) customer orders have significantly lower audit fees than those without such customers. The third and fourth columns introduce continuous variables from the perspective of the proportion of government (government-based) procurement orders to total sales. It is found that the government procurement orders are negatively correlated with audit fee (β = −0.354), Table 4. Government-background customer and audit fee. Auditfee Auditfee Auditfee Auditfee it it it it Gov −0.136* it (−1.74) State −0.118* it (−1.69) Govper −0.354** it (−2.25) Stateper −0.309*** it (−3.16) Sub 1.146 0.818 1.115 0.962 it (1.26) (1.25) (1.23) (1.10) Size 0.298*** 0.275*** 0.349*** 0.285*** it (5.24) (6.39) (5.41) (4.72) Lev −0.048 −0.057 −0.064 −0.072 it (−0.52) (−0.62) (−0.49) (−0.54) Sale 0.201*** 0.182*** 0.164*** 0.153*** it (3.66) (3.32) (3.86) (3.83) Opi 0.106*** 0.095*** 0.143*** 0.121*** it (3.35) (3.73) (3.77) (3.47) Current −0.007 −0.006 −0.005 −0.007 it (−1.06) (−0.95) (−0.90) (−0.96) ARInv −0.034 −0.030 −0.044 −0.032 it (−0.69) (−0.60) (−0.53) (−0.68) Return −0.028** −0.027* −0.024** −0.026** it (−2.17) (−1.75) (−2.00) (−1.96) Sig 0.852 0.815 0.834 0.942 it (0.81) (1.04) (0.90) (0.79) ROA −0.345 −0.305 −0.308 −0.292 it (−1.08) (−1.23) (−1.09) (−1.04) Big4 0.731*** 0.987*** 0.795*** 0.932*** it (6.41) (4.85) (5.51) (6.07) SOE −0.065*** −0.048*** −0.056*** −0.064*** it (−2.87) (−4.31) (−4.31) (−2.91) Constant 5.977** 7.375** 5.659** 5.341*** (2.10) (2.33) (2.39) (2.74) Industry fixed effect Yes Yes Yes Yes Year fixed effect Yes Yes Yes Yes N 6586 6586 6586 6586 R-squared 0.629 0.617 0.648 0.652 ***, **, * Indicate statistical significance at the 0.01, 0.05, and 0.10 levels, respectively. 396 C. DOU ET AL. and the results are significant at the 5% level, meanwhile, the government-based pro- curement orders are also negatively correlated with the audit fee (β = −0.309), and the results are significant at the 1% level, indicating that the government (government-based) procurement account for a higher proportion of total sales, and the audit fee is signifi- cantly lower than those with a low proportion. It is worth noting that the results of the government (government-based) customer in Table 4 are weaker than the government (government-based) procurement orders, indicating that the purchase amount ratio is more reasonable. Therefore, in the subse- quent analysis and research, we mainly use the ratio of government (government-based) purchase account to total sales as the main research variable. In addition, the coefficient of Sub is positive, although the results are not significant, but it shows that government subsidies play a role in increasing audit fee. Among the control variables, similar to the findings in the existing literature, the company may get a higher audit fee when the size is larger, the sales revenue is higher, and obtaining standard audit opinions from the big four; and the results are significant at the 1% level. In summary, the government (govern- ment-based) customers can bring the impact of the audit fee reduction to the enterprise, which is consistent with the expectation of Hypothesis 1. 4.2. Sub-sample tests Based on the above research findings, we start with a series of sub-sample tests based on the characteristics of the government-background customers and the company itself, focusing on factors’ influences such as order duration, procurement level and the degree of financing constraints faced by the company. Table 5 illustrates the corresponding regression results, and the orders are divided into four categories according to the duration. The longest group is the stable order, and the shortest group is the sporadic order. The results show that for the stable customer relationship, the government custo- mers have a significant negative correlation with audit fee (β = −0.368), while the coefficient between government-background customers and audit fee is also negatively correlated (β = −0.317), and the results are all significant at the 1% level. In sharp contrast, for sporadic orders, only the government procurement orders are negatively correlated with audit fee (β = −0.326), and the results are only significant at the 10% level, while the government-based procurement orders do not achieve significant results. Although the results of the two samples show a negative correlation, the absolute value of the regres- sion coefficients of the stable order are larger (−0.368<-0.326, −0.317<-0.298), and the significant levels are higher. A further Chow test of the coefficients in the two groups of samples also shows the difference between the two samples, from the results of the last row in the table, it can be seen that the coefficient differences between Govper and Stateper are significant at the level of 5% and 10%, respectively. These statistical results strongly explain that stable government-based procurement orders may effectively bring down the audit fee, in line with Hypothesis 2a. Table 6 considers the impact of the political hierarchy. Based on the previous studies, we divide the government customers into the central government (central enterprise) level and the local government (local state enterprise) level for comparative study. As shown in the table, when the control variables are not considered, the central level of government procurement (Govper-Central) is negatively correlated with audit fee CHINA JOURNAL OF ACCOUNTING STUDIES 397 Table 5. Government-background customer and audit fee. Sporadic Stable Sporadic Stable Auditfee Auditfee Auditfee Auditfee it it it it (Sporadic vs. Stable) Govper −0.326* −0.368***   it   (−1.67) (−2.84)   Stateper −0.298 −0.317*** it   (−1.52) (−3.07) Sub 1.187 1.217 0.905 0.992 it (1.16) (1.24) (0.93) (0.99) Size 0.363*** 0.302*** 0.295*** 0.342*** it   (5.76) (6.11) (4.95) (5.01) Lev −0.053 −0.067 −0.067 −0.062 it (−0.70) (−0.62) (−0.57) (−0.51) Sale 0.206*** 0.157*** 0.164*** 0.173*** it (2.78) (3.66) (4.03) (3.42) Opi 0.124*** 0.143*** 0.100*** 0.096*** it (3.23) (3.58) (3.16) (3.35) Current −0.005 −0.005 −0.006 −0.007 it (−1.10) (−1.07) (−1.09) (−1.05) ARInv −0.031 −0.036 −0.038 −0.033 it (−0.49) (−0.42) (−0.64) (−0.55) Return −0.027* −0.024** −0.028** −0.028** it (−1.86) (−2.01) (−2.24) (−2.20) Sig 1.015 0.797 1.006 1.026 it (0.99) (0.71) (1.04) (0.90) ROA −0.279 −0.301 −0.295 −0.267 it (−0.92) (−1.11) (−0.86) (−0.93) Big4 1.015*** 0.740*** 0.887*** 1.042*** it (4.68) (5.40) (5.35) (6.02) SOE −0.054*** −0.066*** −0.060*** −0.064*** it (−4.20) (−3.27) (−2.94) (−3.73) Constant 5.595*** 5.531** 7.185* 6.867*   (2.62) (2.12) (1.89) (1.94) Industry fixed effect Yes Yes Yes Yes Year fixed effect Yes Yes Yes Yes N 1652 1648 1652 1648 R-squared 0.639 0.664 0.652 0.670 Govper/Stateper:Sporadic = Stable[p-value] 0.029 0.076 ***, **, * Indicate statistical significance at the 0.01, 0.05, and 0.10 levels, respectively. (β = −0.238), and local level government procurement (Govper-Local) is negatively corre- lated with audit fee (β = −0.098), and the results are significant at the 1% and 10% levels. Similarly, the central level of government-based procurement (Stateper-Central) is nega- tively correlated with audit fee (β = −0.219), local level government-based procurement (Stateper-Central) is negatively correlated with audit fee (β = −0.100), and the results are significant at the 1% and 10% levels, respectively. We find the absolute value of the regression coefficient at the central level is larger than the absolute value at the local level, and the significance is higher, which illustrates that compared with the local level, the central level government (government-based) procurement could reduce the audit fee more effectively. After the introduction of control variables, the central level government procurement and audit fee are still negatively correlated (β = −0.225), and the results are significant at the 5% level, while the local level government procurement and audit fee are not significantly negative correlated (β = −0.146). Similarly, central level government-based procurement is negatively correlated with audit fee (β = −0.187), and local level govern- ment-based procurement is negatively correlated with audit fee (β = −0.102), and the 398 C. DOU ET AL. Table 6. Government-background customer and audit fee at different levels of government- background (central vs. local). Auditfee Auditfee Auditfee Auditfee it it it it Govper-Central −0.238*** −0.225** it (−2.88) (−2.48) −0.098* −0.146 Govper-Local it (−1.70) (−1.64) Stateper-Central −0.219*** −0.187** it (−3.01) (−2.29) −0.100* −0.102* Stateper-Local it (−1.67) (−1.86) Sub 1.054 0.941 it (1.27) (0.95) 0.353*** 0.356*** Size it (7.26) (4.38) Lev −0.065 −0.044 it (−0.77) (−0.76) 0.141** 0.224*** Sale it (2.44) (4.14) Opi 0.114*** 0.130*** it (3.16) (3.54) −0.006 −0.006 Current it (−0.83) (−0.67) ARInv −0.036 −0.035 it (−0.45) (−0.68) −0.021** −0.031* Return it (−2.30) (−1.77) Sig 1.078 0.824 it (0.89) (1.03) −0.345 −0.229 ROA it (−1.22) (−1.46) Big4 0.914*** 0.667*** it (4.57) (3.95) −0.057*** −0.062*** SOE it (−3.34) (−3.81) Constant 13.010*** 12.998*** 7.693*** 5.023** (2.98) (2.77) (2.92) (2.03) Industry fixed effect Yes Yes Yes Yes Year fixed effect Yes Yes Yes Yes N 6586 6586 6586 6586 R-squared 0.068 0.061 0.655 0.661 ***, **, * Indicate statistical significance at the 0.01, 0.05, and 0.10 levels, respectively. results are significant at the 5% and 10% levels, respectively. The comparison shows that the absolute value of the regression coefficient at the central level is larger than the absolute value of at the local level, and the level of significance is higher, indicating that whether it is government procurement or government-based procurement, compared with the local level, the central level government customers have a greater impact on audit fee, consistent with the expectations of Hypothesis 2b. For the sake of robustness, we also test whether the influence of central level govern- ment-background customers on corporate audit fee is greater than that of local level government-background customers by calculating the standardised regression coefficients of various variables, the regression results are shown in columns 1 and 3 of Table 6.Inthe regression analysis, the standardised regression coefficients of Govper-Central (Govper- Local)are −0.028 (−0.010) and −0.024 (−0.013), respectively, and the coefficients of Govper-Central are significantly larger than the Govper-Local, which indicates that govern- ment customers at the central level have greater impact. In the regression analysis of CHINA JOURNAL OF ACCOUNTING STUDIES 399 columns 2 and 4 of Table 6, the standardised regression coefficients for Stateper-Central (Stateper-Local)are −0.020 (−0.009) and −0.015 (−0.008), respectively, the coefficient of Stateper-Central is also significantly larger than the Stateper-Local in terms of absolute value, indicating that the central level government-background customers have a greater impact. The above results are consistent with the regression conclusions in Table 6. Table 7 divides the degree of financial constraints faced by the enterprise into high and low categories to test the listed companies in the face of extreme difficulties. The regression results show that when the enterprises face higher financial constraints, the government (government-based) procurement ratio and audit fee show a more signifi- cant negative correlation, and the coefficient and significance level are more significant than for the sample with lower financial constraints. The Chow test of the two sets of sample coefficients in the last row also supports the existence of this difference. This illustrates that, compared with the enterprises with a low degree of financial constraints, the margin of influence exerted by the government (government-based) customers with higher degree of financial constraints is more effective, which proves Hypothesis 2c. Table 7. Government-background customer and audit fee. High Low High Low Auditfee Auditfee Auditfee Auditfee it it it it (High degree of financing constraints vs. low) Govper −0.388*** −0.314*   it (−2.76) (−1.78)   Stateper −0.335*** −0.276* it (−2.80) (−1.92) Sub 1.043 1.054 0.870 0.921 it (1.38) (1.40) (0.96) (1.23) Size 0.264*** 0.403*** 0.342*** 0.332*** it (5.76) (4.49) (7.03) (7.37) Lev −0.058 −0.045 −0.048 −0.064 it (−0.64) (−0.73) (−0.57) (−0.55) Sale 0.228*** 0.230*** 0.208*** 0.201*** it (4.10) (3.17) (2.92) (2.85) Opi 0.100*** 0.146*** 0.117*** 0.142*** it (3.73) (4.85) (3.77) (3.04) Current −0.008 −0.005 −0.007 −0.005 it (−0.74) (−0.99) (−1.20) (−1.13) ARInv −0.039 −0.041 −0.040 −0.030 it (−0.54) (−0.56) (−0.57) (−0.71) Return −0.035* −0.021** −0.023** −0.034* it (−1.78) (−2.20) (−2.00) (−1.94) Sig 1.033 0.734 0.969 1.006 it (0.84) (0.82) (0.91) (0.70) ROA −0.245 −0.286 −0.327 −0.333 it (−1.07) (−1.45) (−1.37) (−0.97) Big4 1.051*** 1.042*** 0.987*** 0.759*** it (4.12) (3.90) (5.40) (5.74) SOE −0.047*** −0.039*** −0.063*** −0.062*** it (−3.52) (−3.02) (−4.67) (−3.36) Constant 6.231** 6.167** 5.277* 4.705** (2.39) (2.49) (1.93) (2.15) Industry fixed effect Yes Yes Yes Yes Year fixed effect Yes Yes Yes Yes N 3293 3293 3293 3293 R-squared 0.667 0.616 0.638 0.633 Govper/Stateper:High = Low[p-value] 0.010 0.023 ***, **, * Indicate statistical significance at the 0.01, 0.05, and 0.10 levels, respectively. 400 C. DOU ET AL. 4.3. The impact channel of government-background customer on audit fee Compared with corporate customers, government-background customers are supported by national credit and national finance, so that they have fewer risks and bankruptcy possibilities. This, therefore, can overcome the potential drawbacks of customer concen- tration, and the audit risk is relatively low. Despite the negative influence of traditional government interventions on risk (Deqiu, Sifei, & Cong, 2011; Jun & Lishuai, 2014; Min, Lijun, & Sheng, 2012; Yanyan & Danglun, 2012), the government intervention in the form of government procurement does not directly interfere with the production and opera- tion of enterprises. It is an organic combination of product demand and policy orientation, so that enterprises which meet production requirements and policy guidelines can be fully and stably developed, which is undoubtedly beneficial to reducing the audit risk. However, audit risk is the main path for government-background customers to influence audit pricing, and lower audit risk can effectively reduce the audit fee. Therefore, the existence of government-background customers may reduce the audit fee by reducing the audit risk. We attempt to further explore the influence path of government-back- ground customers on audit fee from the perspective of risk. Most of the mainstream literature subdivides audit risk into business risk (Liquan & Hanwen, 2013; Nikkinen & Sahlstrom, 2004), fraud risk (Lyon & Maher, 2005) and litigation risk (Seetharaman et al., 2002). The higher the above risks are, the more are the audit fees. The domestic research represented by Songsheng and Zhili (2019) agrees with this view. Table 8 draws on the methods of Chen Zhenglin (2016), and Fu Chao and Ji Li (2017)to measure the audit risk of different dimensions by using the amount of the lawsuit and the volatility of the ROA, while referring to the research of Wanfa and Xiaobo (2018)to construct a corresponding mediation effect model for analysis and test the mediating effect. Panel A of Table 8 illustrates the regression result of the intermediary variable, audit risk, and the government customers. The first column indicates that the proportion of government procurement is negatively correlated with the company’s lawsuit amount (β = −0.005), and the result is significantatthe 5% level. Column 3 shows that the proportion of government procurement is negatively correlated with the volatility of the ROA (β = −0.071), and the results are also significant at the 5% level. Similarly, in the second and fourth columns, the proportion of government- based procurement is negatively correlated with the two indicators of audit risk (β = −0.003, β = −0.062), and the results are all significant at the 5% level. The above results indicate that enterprises with government customers face lower operational risks and litigation risks, thus effectively reducing the audit risks faced by enterprises. In Panel B of Table 8, the regression results of government-back- ground customers and audit risk indicate that under the two different measurement methods of the government background, the regression coefficient of the govern- ment-based procurement ratio and audit risk are not significantly zero. This shows that audit risk is a partial intermediary variable that affects audit fee. On this basis, we also perform the Sobel test and we find that the Z value is also significantly larger than the critical value of 0.97 (Govper:3.11, Stateper: 3.35), which indicates that audit risk is indeed an important intermediary for the effect of government- background customers on audit fee. CHINA JOURNAL OF ACCOUNTING STUDIES 401 Table 8. Analysis of the mediating effect of audit risk. Panel A. Government-background customer and Audit Risk L_litAmount L_litAmount Varroa Varroa it it it it Govper −0.005** −0.071** it (−2.24) (−2.09) Stateper −0.003** −0.062** it (−2.51) (−2.17) Control Variables Yes Yes Yes Yes Industry fixed effect Yes Yes Yes Yes Year fixed effect Yes Yes Yes Yes N 6586 6586 6586 6586 R-squared 0.093 0.084 0.076 0.082 Panel B. Government-background customer, Audit Risk and Audit Fee Auditfee Auditfee Auditfee Auditfee it it it it Govper −0.361* −0.376* it (−1.90) (−1.68) Stateper −0.325* −0.340* it (−1.81) (−1.95) L_litAmount 22.009** 14.556*** it (2.18) (2.98) Varroa 0.305** 0.338** it (2.49) (2.27) Control Variables Yes Yes Yes Yes Industry fixed effect Yes Yes Yes Yes Year fixed effect Yes Yes Yes Yes N 6586 6586 6586 6586 R-squared 0.650 0.667 0.659 0.664 ***, **, * Indicate statistical significance at the 0.01, 0.05, and 0.10 levels, respectively. 5. Robustness tests 5.1. International guidelines Since 1 January 2007, Chinese listed companies have officially implemented new risk-oriented accounting standards, which basically achieved the convergence of Chinese accounting standards and international accounting standards. Studies have shown that the enforcement of IFRS significantly affects the audit fee; meanwhile, considering the sample size in 2007 is the smallest, it only accounts for 1% of the total sample. Therefore, we delete the 2007 sample for regression, and list the regression results in the first and second columns of Table 9.Itisfound that the proportion of government procurement is negatively correlated with audit fee (β = −0.327), and the proportion of government-based procurement is also negatively correlated with audit fee (β = −0.302), and the results are significant at the 5% and 1% levels respectively, indicating that having a customer relationship with the government department can reduce the audit fee, which is consistent with the expectation of H1. 5.2. Full sample regression When the customers are too dispersed, it is difficult to measure the impact of a single type of customer on sales. Therefore, in the previous data processing, we remove the top five customer concentration with less than 1%. In order to ensure the reliability of the results, we use the whole sample for regression; the regression results are listed in the third and fourth columns of Table 9. It is found that the proportion of government procurement is negatively correlated with the audit fee (β = −0.245), the proportion of 402 C. DOU ET AL. Table 9. Government-background customer and audit risk. Auditfee Auditfee Auditfee Auditfee Auditfee Auditfee it it it it it it Govper −0.359** −0.245* −0.323** it (−2.14) (−1.72) (−2.06) −0.306*** −0.215* −0.269* Stateper it (−2.92) (−1.84) (−1.92) Control Variables Yes Yes Yes Yes Yes Yes Industry fixed effect Yes Yes Yes Yes Yes Yes Year fixed effect Yes Yes Yes Yes Yes Yes N 6511 6511 7178 7178 1548 8788 R-squared 0.657 0.679 0.583 0.598 0.621 0.636 ***, **, * Indicate statistical significance at the 0.01, 0.05, and 0.10 levels, respectively. government-based procurement is also negatively correlated with audit fee (β = −0.215), and the results are all significant at the 10% level, indicating that even within the full sample, the conclusion is that the more government procurement orders are available, the lower the audit fee. 5.3. Propensity score matching In order to control the endogenous problems of the government procurement, we also use the propensity score matching method (PSM) to match each enterprise that has a government procurement order to an enterprise that does not have an order, and do regression tests separately according to government procurement and government- based procurement. The regression results are listed in the fifth and sixth columns of Table 9. It is found that the proportion of government procurement is negatively corre- lated with the audit fee (β = −0.359), and the proportion of government-background purchases is also negatively correlated with audit fee (β = −0.36), and the results are all significant. This result also illustrates that companies that receive government procure- ment orders have a significantly lower audit fee than those that do not receive orders. 5.4. Self-selection Considering voluntary disclosure of listed companies, the conclusions may be affected by the self-selection of the sample. In response to this problem, we examine the relationship between the disclosure of the top five customers’ information (measured by the variable, Disclosure, it is set to 1 if the company discloses, 0 otherwise) and audit fee. Table 10 illustrates the regression results, regardless of whether the control variables are consid- ered or not; although the coefficients are positive, the regression results of the company’s disclosure and audit fee are not significant. This result indicates that whether the com- pany discloses customer information does not affect the audit fee and, to some extent, the problem of sample self-selection is ruled out. 5.5. Customer concentration Since the research data is from the top five customers in the annual report, our conclusions will inevitably be affected by customer concentration. In response to this problem, we consider the government-background customer and customer concentration, and design a CHINA JOURNAL OF ACCOUNTING STUDIES 403 Table 10. Corporate disclosure of customer informa- tion and audit fee. Auditfee Auditfee it it Disclosure 0.105 0.086 it (0.98) (1.13) Control Variables No Yes Industry fixed effect No Yes Year Fixed effect No Yes N 12602 12602 R-squared 0.071 0.670 ***, **, * Indicate statistical significance at the 0.01, 0.05, and 0.10 levels, respectively. Table 11. Customer concentration, government-background customer and audit fee. Customer concentration Low High Difference Govper Low 13.309 13.006 0.303*** High 12.983 12.695 0.288*** Difference 0.326*** 0.311*** Stateper Low 13.294 13.010 0.284*** High 12.974 12.721 0.253*** Difference 0.320*** 0.289*** ***, **, * Indicate statistical significance at the 0.01, 0.05, and 0.10 levels, respectively. comparative study in Table 11. The results are shown in the table; according to the customer concentration, the enterprises with high proportion of government-based procurement have significantly lower audit fee than those with less government-based procurement. In turn, after dividing the government-based procurement orders into two categories, the difference between the low customer concentration and the high customer concentration is also significant, which is basically consistent with the results of Wang Xiongyuan et al. (2014). The effect of reducing the audit fee of customer concentration is not inconsistent with the audit fee reduction effect of government-based procurement orders. 5.6. Business complexity Table 12 divides the observations into four categories according to business complexity, and compares the difference between the regression coefficients of the most complex and simplest two groups of samples. The results strongly explain that the more compli- cated the business, the higher the potential business risk and audit risk faced by the enterprise. The presence of government-background customers may reduce the audit fee more effectively, which proves the core hypothesis of this paper. 6. Research conclusions Based on the 2007–2015 customer data of listed firms, we study the impact of govern- ment-background customers on the audit fee in China from the perspective of the supply chain, for what is claimed to be the first time. The results indicate that the presence of a government-background customer helps to reduce the audit fee significantly. Moreover, 404 C. DOU ET AL. Table 12. Government-background customer and audit risk (business complexity). Simple Complex Simple Complex Auditfee Auditfee Auditfee Auditfee it it it it Govper −0.300** −0.404***   it   (−2.06) (−2.96)   Stateper −0.308* −0.373*** it   (−1.88) (−3.16) Control Variables Yes Yes Yes Yes Industry fixed effect Yes Yes Yes Yes Year fixed effect Yes Yes Yes Yes N 1656 1646 1656 1646 R-squared 0.657 0.640 0.658 0.646 Govper/Stateper: Complex = Simple [p-value] 0.006 0.038 ***, **, * Indicate statistical significance at the 0.01, 0.05, and 0.10 levels, respectively. the relationship is more evident when the customer is consistent or comes from the central government, and we can observe a stronger relationship under high financial constraints as well. At the same time, the paper also shows that the existence of a government-back- ground customer can effectively alleviate enterprises’ audit risk, so as to reduce their audit fee. This is an important supplement to the literature on supply chain finance research. It has important implications for investors who need to interpret the information that listed companies have obtained government-background customers, and reveals the channels of influence of macro policies on market intermediaries. Since listed companies rarely disclose order data in detail, the relevant laws and regulations do not mandate listed companies to disclose specific customer information. The research has inevitable defects in the scope of the sample, and it cannot cover all listed companies. Moreover, the sales from the top five customers cannot represent the overall sales of the company. In addition, the government departments and state-owned enterprises have differ- ences in their responsibilities, they cannot be completely equalised. Therefore, we are currently unable to conduct a full-scale study on the impact of government-background customers, and it is difficult to consider the impact of specific customer structures in extreme situations. 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Journal

China Journal of Accounting StudiesTaylor & Francis

Published: Jul 3, 2019

Keywords: Government-background customer; audit risk; audit fee; supply chain

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