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Article

The High-Speed Railway Opening and Audit Fees: Evidence from China

by
Dongshan Ma
1,
Shengqiang Zhang
2,* and
Jiayu Zhao
2,*
1
Business School, Liaoning University, Shenyang 110136, China
2
Sun Wah International Business School, Liaoning University, Shenyang 110136, China
*
Authors to whom correspondence should be addressed.
Sustainability 2022, 14(20), 13353; https://doi.org/10.3390/su142013353
Submission received: 10 September 2022 / Revised: 6 October 2022 / Accepted: 14 October 2022 / Published: 17 October 2022
Versions Notes

Abstract

:
By constructing a staggered difference-in-differences model, we examined the effect of the high-speed railway opening on audit fees and its mechanism. The high-speed railway opening significantly reduces audit fees, and the inhibitory effect is more pronounced in firms located in non-central cities and small- and medium-sized audit firms. Furthermore, the high-speed railway opening mainly affects the audit fees by intensifying the competition in the audit market, but information asymmetry has no significant impact, indicating that the high-speed railway opening mainly reduces the audit fees by reducing the audit costs. In addition, the high-speed railway opening can improve the audit quality, which further shows that the high-speed railway opening can promote reasonable competition in the audit market.

1. Introduction

In 2008, China’s first high-speed railway (hereafter HSR), the Beijing-Tianjin Intercity High-Speed Railway, was opened, marking China’s entry into the era of HSR. By the end of 2018, China’s HSR operation mileage had reached 29,000 km, making China the country with the longest HSR operation mileage, the highest transportation density, and the most complex network operation scene in the world [1]. HSR has changed Chinese people’s trip mode [2], lifestyle [3], and production mode [4,5], and also promoted the comprehensive, coordinated, and sustainable development of China’s economy and society [6,7]. For enterprises, the HSR accelerates the flow of resources and production factors between cities [8], especially the flow of labor and information [9,10,11], thus affecting the behavior of enterprises. Auditing is a complex activity, requiring auditors to visit the audited unit for on-site investigation and face-to-face communication with managers to collect sufficient and appropriate auditing evidence [12]. In this process, transportation plays an important role in auditor–client relationships [13]. Does the HSR opening promote the flow of auditors and information between the audit firm and client and affect the audit fees? We aimed to enhance the understanding of this issue.
The mechanism of the impact of the HSR opening on audit fees can be explained from the perspective of audit market competition and information asymmetry. (1) From the perspective of audit market competition, the HSR opening can promote the flow of auditors, reduce the audit cost of non-local auditors, inhibit auditors’ geographical preferences [14], promote enterprises to choose non-local auditors, and strengthen the competition in the audit market. The fiercer the competition in the audit market, the lower the audit fees [15,16]. (2) From the perspective of information asymmetry, the HSR opening can promote the flow of information and reduce information asymmetry, which helps auditors easily obtain information about the audited unit and its customers, suppliers, and other stakeholders. The low degree of information asymmetry enables auditors to know more information, thus reducing audit risks [17] and audit costs [18]. Moreover, the HSR opening can alleviate information asymmetry and reduce the supervision cost of investors [19], which helps investors fully understand enterprise information. Thus, it strengthens the supervision of management and reduces the agency problem of enterprises. The lower the agency problem of enterprises, the lower the audit fees [20]. It can be seen that the HSR opening can reduce audit fees by intensifying the competition in the audit market and alleviating information asymmetry.
Based on the above analysis, we selected A-share non-financial listed companies from 2003 to 2017 as the research sample and investigated the relationship and mechanism between the HSR opening and audit fees by constructing a staggered difference-in-differences model. We found that the HSR opening significantly reduces audit fees, and the grouped regression based on regional heterogeneity and auditor heterogeneity suggests that the negative effect is more pronounced in non-central cities and small- and medium-sized audit firms. Furthermore, from the perspective of audit market competition and information asymmetry, this paper verifies the mechanism of the impact of the HSR opening on audit fees and indicates that the HSR opening affects audit fees by intensifying the audit market competition. In addition, we investigate the relationship between the HSR opening and audit quality and find that the HSR opening improves the audit quality, indicating that the HSR opening will not bring vicious competition in the audit market. This study highlights the impact of the HSR opening on the audit market and provides empirical evidence for the certified public accountant industry to promote audit market competition through infrastructure construction and standardized audit fees.
Our study makes three major contributions. First, this paper enriches the literature on the impact of the HSR opening on microeconomic consequences. The existing research is mainly about the impact of the HSR opening on corporate investment and financing behavior [21,22,23,24], corporate social responsibility [25], corporate fraud [26], and the impact on the capital market [27,28]. Regarding the impact of the HSR opening on auditor behavior, Liu (2021) studied its impact on audit quality [13], but prior literature has paid little attention to its impact on the audit market from the perspective of audit fees. Second, our study contributes to the literature on the effective mechanism of audit fees. Prior studies have examined factors that affect audit fees, such as the auditor’s effort, customer audit risk [18], and the relative bargaining power between auditors and customers [29]. In this study, we investigate the impact of infrastructure construction on audit fees from the perspective of the external environment. Third, our path analysis suggests that the HSR opening affects audit fees by intensifying the competition in the audit market. Previous literature has studied the impact of competition on audit fees [30]; however, the research results are greatly affected by endogeneity. We employed a quasi-exogenous shock to alleviate the self-selection problem. Additionally, this paper reveals the mechanism of the HSR opening to reduce audit fees and improve audit quality. It provides empirical evidence for giving full play to the positive effect of the HSR opening on intensifying audit market competition for emerging markets.
The remainder of the paper proceeds as follows. The second part describes the theoretical analysis and research assumptions. The research design is discussed in the third. The empirical results and analysis are presented in the fourth section. The following section discusses further research, and the final section concludes the paper.

2. Theoretical Analysis and Research Hypothesis

2.1. The HSR Opening on Audit Fees

The mechanism of the impact of the HSR opening on audit fees can be explained from the perspectives of audit market competition and information asymmetry.

2.1.1. Audit Market Competition Perspective

The HSR opening brings compression of time and space, promotes the circulation of auditors and improves circulation efficiency, intensifies competition in the audit market, and thus reduces audit fees. Service and knowledge-intensive industries are more sensitive to the HSR opening because they depend more on personnel and information flow [31,32]. The HSR is passenger-specific and time-sensitive, which could promote labor mobility [33,34,35], especially high-skilled labor [36,37]. Du and Peng (2017) argue that the HSR opening can promote the flow of senior talents in enterprises [38]. As auditors are high-skilled talents, the HSR opening will also have an impact on their mobility.
China’s audit market has regional/geographical particularities [39]. The competition in the audit market is mainly concentrated among the provinces. Specifically, China’s audit market has geographical preferences. Thanks to the advantages of accessible information, convenient transportation, frequent communication, and social networks brought by the geographical location of local auditors, it is easy for the local auditor to win over local clients. Thus, such regional/geographical characteristics have resulted in a monopoly of local auditors in the local audit market. The regional characteristics of the audit market also make most audit firms set up headquarters or branches in various regions of the country to serve the regional audit market.
The HSR opening has accelerated the circulation of auditors to a certain extent, weakened the geographical advantage, and intensified the competition in the audit market. On the one hand, the HSR opening can accelerate the circulation of auditors, save audit costs, and intensify regional audit market competition. HSR has the advantages of time-saving and cost-effective performance in medium- and long-distance transportation [40,41]. China’s HSR network has improved the accessibility between cities in Northeast China, Southwest China, and cities along the eastern coast from Shanghai to Guangzhou [42], which can promote the flow of non-local auditors between cities in different regions. Thus, it could reduce the transportation costs, information costs, communication costs, and supervision costs of non-local auditors [43], thereby saving audit costs and creating conditions for non-local auditors to enter the local audit market and participate in the competition. On the other hand, the HSR opening expands the scope of auditor services in other places and intensifies the competition in the audit market. The HSR opening can deepen the openness between regions [44]. In addition to the reduction in audit costs, auditors have the motivation to enter the non-local audit market and participate in the competition. Furthermore, the HSR opening brings a space and time compression effect, shortens the time for auditors to travel to different places for business, and improves the circulation of auditors. Therefore, the service scope and service capacity of audit firms would be widened, geographical preferences for auditors’ selection would be weakened [43], and the competition in the audit market would be strengthened. Additionally, competition in the audit market reduces audit fees [15,16], as the HSR opening could accelerate the circulation of audit talents, intensify competition in the audit market, and thus reduce audit fees.

2.1.2. Information Asymmetry Perspective

The HSR opening can accelerate the flow of information, reduce information asymmetry, and thus reduce audit fees. For auditors, the HSR opening could promote the flow of information, reduce information asymmetry [19,22,45], and help auditors obtain information about clients and their customers, suppliers, and stakeholders. The reduction in information asymmetry helps auditors know more information, thereby reducing audit risks [17] and audit fees [18]. For clients, the HSR opening can promote the flow of information, alleviate information asymmetry, help investors fully understand enterprise information, reduce investors’ supervision costs [46,47], strengthen the supervision of management, and reduce enterprises’ agency costs. The lower the enterprise agency costs, the lower the audit fees [48]. It shows that the HSR opening can reduce audit fees by strengthening the competition in the audit market and alleviating information asymmetry.
Hypothesis 1 (H1).
The HSR opening can reduce audit fees.

2.2. The HSR Opening and Audit Fees: The Impact of Regional Heterogeneity

Most audit firms in China have headquarters or branches in regional centers, and provincial capitals and cities specifically designated in the state plan are often regarded as regional centers. According to the China Securities Regulatory Commission, as of March 2019, 40 audit firms across the country have practicing licenses in business related to securities and futures, and their headquarters are located in 11 provincial capital cities and one prefecture-level city (Wuxi City). Among them, there are 22 in Beijing, 5 in Shanghai, 2 in Tianjin, 3 in Jiangsu (2 in Nanjing and 1 in Wuxi), 2 in Zhejiang (Hangzhou), and 1 each in Fujian (Fuzhou), Guangdong (Guangzhou), Hubei (Wuhan), Sichuan (Chengdu), Shandong (Jinan), and Shaanxi (Xi’an) [49]. As for their branches, they are mainly distributed in provincial capital cities and cities specifically designated in the state plan. It can be seen that audit firms with securities and futures business qualifications are mainly located in provincial capitals and cities specifically designated in the state plan. According to the national urban system planning (2006–2020) of China, cities are divided into three categories: national central cities, regional central cities, and non-central cities. Among them, national central cities include Beijing, Shanghai, Guangzhou, Shenzhen, Chongqing, and Tianjin. Regional central cities are provincial capitals except for the aforementioned cities, as well as Qingdao, Ningbo, Dalian, and Xiamen. Other cities are non-central cities. Audit firms are mainly located in national central cities and regional central cities.
From the perspective of audit market competition, the HSR opening can improve the mobility of auditors, save audit costs, expand the service capacity and scope of audit firms, and inhibit the geographical preferences of auditors’ selection [43]. Intense competition ensues as clients tend to opt for non-local audit firms. As China’s audit firms are mainly located in national central cities and regional central cities, the audit market competition in these regions is fierce, and it is difficult for audit firms (including branches) located in non-central cities to carve up the market. On the contrary, audit firms located in national and regional central cities could take advantage of the space and time compression effect brought by the HSR opening to actively grow business in non-central cities, thereby intensifying the competition in the audit market in non-central cities and reducing audit fees.
From the perspective of information asymmetry, the HSR opening can accelerate the flow of information and alleviate information asymmetry. For national central cities and regional central cities, their various infrastructures are relatively sound, the information flow is fast, and there is little information asymmetry. For non-central cities, infrastructures are relatively weak, the information flow is slow, and the degree of information asymmetry is relatively high. The HSR opening has a limited effect on auditors to gain more information about clients and their stakeholders located in national and regional central cities, as well as enterprise investors’ understanding of enterprise information. In non-central cities, the infrastructure is insufficient, and most auditors and investors are located in central cities. The HSR opening can compress the space and time between central cities and non-central cities, promote auditors and enterprise investors to understand the situation of enterprises located in non-central cities, alleviate information asymmetry, and reduce audit fees. Therefore, we believe that the effect of the HSR opening to reduce audit fees is significant in non-central cities, but not significant in national and regional central cities.
Hypothesis 2 (H2).
There is regional heterogeneity in the inhibitory effect of the opening of high-speed railway on audit fees. This inhibitory effect is significant in non-central cities, but not in national central cities and regional central cities.

2.3. The HSR Opening and Audit Fees: The Impact of Auditor Heterogeneity

From the perspective of audit market competition, the HSR opening strengthens the audit market competition and affects the supply and demand of the non-local audit market. For the non-local audit market, high quality and low price are the two competitive strategies adopted by audit firms [50]. Large-sized audit firms (measured by the “top ten” in this paper) generally believe that they have high audit quality [51]. For reputation, they generally adopt high-quality strategies. Clients are also willing to choose large-sized audit firms to transmit high-quality accounting information to stakeholders [52,53]. Although the HSR opening has intensified the audit market competition and promoted the saving of audit costs, considering the impact of the reputation mechanism, large audit firms will not reduce audit fees. For small- and mid-sized audit firms, to win in the fierce competition, they often adopt a low-price strategy. The cost-saving effect of the HSR opening will help small- and mid-sized audit firms reduce audit fees and obtain clients in the fierce audit market competition.
From the perspective of information asymmetry, the HSR opening can alleviate information asymmetry and reduce audit fees. For large-sized audit firms, they have relatively high information acquisition ability. There is little information asymmetry between large audit firms and their clients [54], and the opening of high-speed railway has a limited effect on alleviating information asymmetry. On the contrary, small- and mid-sized audit firms have weak access to information, and the HSR opening can alleviate information asymmetry, thereby reducing audit fees. Therefore, there is auditor heterogeneity in the impact of the HSR opening on audit fees. The negative effect in non-top 10 audit firms is significant, but not in the top 10 audit firms.
Hypothesis 3 (H3).
There is auditor heterogeneity in the inhibitory effect of the HSR opening on audit fees, which is significant in the non-top 10 audit firms, but not in the top 10 audit firms.

3. Research Design

3.1. Sample Data

We selected A-share listed companies from 2003 to 2017 as the initial sample, and after excluding ST companies, financial companies, and samples with missing data, a total of 26,054 samples were obtained. The opening time of the HSR, the cities through which it passes, the design, and the initial operating speed were obtained from http://www.china-railway.com.cn/ (accessed on 10 July 2022) and http://www.nra.gov.cn/ (accessed on 10 July 2022), which are manually collated. Corporate financial data, corporate governance, and other data were from the China Stock Market and Accounting Research (CSMAR) database. The data on the road, railway, and water passenger turnover and the number of people surfing the internet were obtained through http://www.stats.gov.cn/ (accessed on 10 July 2022). Whether there is an airport in the listed company’s office comes from http://www.caac.gov.cn/ (accessed on 10 July 2022). The number of airports within 100 km of the listed company’s office is obtained by manual collection. The ranking data of audit firms comes from https://www.cicpa.org.cn/ (accessed on 10 July 2022). To avoid the interference of extreme values on the estimation results, we performed winsorization on all continuous variables at 1% and 99% quantiles.

3.2. Model Design and Variable Definition

3.2.1. Model Design

Referring to Beck (2010) [55], the model is constructed as follows:
Feei,c,t = α0 + α1HSRc,t × Postc,t + δXi,t + Firmi + Cityc + Yeart + εi,c,t
In Model (1), i refers to the enterprise, c refers to the city where the listed company’s office is located, t refers to the year, and the explained variable Feei,c,t refers to the audit cost of the enterprise firm i in city c in year t, which is measured by the natural logarithm of the annual audit fees of the enterprise. Among explanatory variables, HSR × Post is the interaction term of whether the HSR is opened in the city where the listed company’s office is located (HSR: if the city where the listed company is located opens HSR, it is included in the treatment group and equals 1; otherwise, it is 0) and the opening time of the high-speed railway in the city where the company’s office is located (Post: if the time is after the HSR opening time, it is 1; otherwise, it is 0), measuring the opening effect of the HSR. X is the control variable that changes with time at the enterprise level. According to Simunic (1980) [18] and Maher et al. (1992) [30], we control the enterprise size (Size), return on total assets (Roa), financial leverage (Lev), profitability (Loss), the proportion of inventory and accounts receivable (Rip), the growth rate of main business income (Growth), quick ratio (Quick), ownership concentration (Top1), the proportion of independent directors (Outdir), management shareholding ratio (Con), board size (Board), and property rights (Soe). We also include control variables measured at audit firm size and brand (Big4), auditor change (Switch), and audit opinion (Opinion). The variable definitions are shown in Table 1. We control the firm (Firm), city (City), and year (Year) fixed effects.

3.2.2. Variable Definition

  • HSR opening effect (HSR × Post). According to The Code for Design of High-speed Railway (Trial) (2009) and Regulations on the Administration of Railway Safety, a railway with a speed of 250 km per hour (including reserved) or more and an initial speed of 200 km per hour or more is defined as HSR. The audit work of listed companies is in the first half of the year, and the first half of the year is to audit the financial statements of the previous year. The audit fees of the previous year have generally been determined before the audit, and the audit fees of the current year will be determined by referring to the audit situation of the previous year. We believe that the HSR opening in the first half of the year will have an impact on the audit fees of the current year. Similarly, the HSR opening in the second half of the year will essentially affect the audit fees for the next year. Therefore, we define the HSR opening in the first half of the year as the HSR opening in the city where the listed company’s office is located in the current year. If the HSR opening is in the second half of the year, it would be defined as opening in the next year. Some studies suppose that the HSR opening should be defined by the actual opening time [13]. However, most of the HSRs opened at the end of December, which has little impact on audit firms. Therefore, in the robustness test, we define the HSR opening in the city where the listed company’s office is located from January to November as the opening of the current year, and the HSR opening in December as the opening of the next year.
  • Regional heterogeneity. According to The National Urban System Planning 2006–2020, Chinese cities are divided into three categories: national central city (NCC), regional central city (RCC), and non-central city (Non_CC). The specific classification assumptions are mentioned in Hypothesis 2 and will not be repeated here.
  • Auditor heterogeneity. Using the Top 100 information of Comprehensive Evaluation of Audit Firms published by the Chinese Institute of Certified Public Accountants every year (since 2003), the top 10 audit firms are identified as the big 10 (Big10), and the rest are identified as non-big 10 (Non_Big10).
  • Audit market competition (AMC). According to Numan and Willekens (2012) [15], we use the market share of current audit firms of enterprises in the same industry, region (province), and year to measure AMC.
  • Information asymmetry (IA). Referring to Hutton et al. (2009) [56], we use the sum of the absolute value of discretionary accruals in the past three years and the dummy variables for the degree of information asymmetry constructed by median grouping to measure the information asymmetry (IA).

4. Empirical Results and Analysis

4.1. Describe Statistical Results and Analysis

Table 2 presents the descriptive statistics of the main variables. The average audit fee (Fee) during the sample period is 13.524, the maximum value is 16.200, the minimum value is 12.206, and the standard deviation is 0.733, indicating that the audit fees of different listed companies during the sample period are quite different. The average HSR opening effect (HSR × Post) during the sample period is 0.434, indicating that about 43.4% of the samples during the sample period will be affected by the HSR opening.
Table 3 shows the impact of the HSR opening on audit fees for the whole sample, different types of cities, and different types of audit firms. In column (1), the coefficient of HSP × Post is significantly negative at the level of 5%, indicating that the HSR opening can reduce the audit fees of the listed companies. Hypothesis 1 is verified. Columns (2)–(4) show the impact of the HSR opening in different types of cities on audit fees. In column (4), the coefficient of HSP × Post is significantly negative at the level of 5%, and in columns (2) and (3), the coefficient of HSP × Post is not significant, which indicates that the HSR opening accelerates the flow of labor and information in non-central cities, strengthens the competitiveness of the audit market, reduces information asymmetry, and thus curbs audit fees. The impact on national and regional central cities is not significant, because most audit firms have set up headquarters or branches in national and regional central cities, resulting in fierce competition before the HSR opening. Moreover, the infrastructure of central cities is complete, the flow of information is fast, and the degree of information asymmetry is low. Therefore, the HSR opening has little impact on it. Due to geographical distance, some non-local audit firms are reluctant to undertake business for clients in non-central cities, especially remote cities, considering audit costs (traffic costs, opportunity costs, etc.). Additionally, there is less competition in the audit market in non-central cities. Most audit firms’ headquarters and branches are located in central cities, while listed companies may be located in non-central cities, making it more difficult for audit firms to obtain client information in non-central cities. The degree of information asymmetry between audit firms in non-central cities and clients is large, and the HSR opening can improve the audit market environment to a certain extent, strengthen the competition in the audit market in non-central cities, alleviate information asymmetry between audit firms and clients in non-central cities, and thus reduce audit fees. Hypothesis 2 is verified. Columns (5) and (6) list the impact of the HSR opening on audit fees in different types of audit firms. In column (6), the coefficient of HSP × Post is significantly negative at the 1% level, and in column (5), the coefficient of HSP × Post is not significant, indicating that the HSR opening can reduce the audit fees of the non-big 10 audit firms, and the impact on the big 10 audit firms is not significant. It shows that after the HSR opening, small- and mid-sized audit firms (Non_Big10) are the main force to reduce the audit fees, the audit firms with high audit quality are less affected by the HSR opening, and Hypothesis 3 is verified.

4.2. Robustness Test

4.2.1. Parallel Trend Test

The staggered difference-in-differences model needs to meet the assumption of a parallel trend. For this reason, according to Cornaggia et al. (2015) [57] and Chen et al. (2018) [58], we build the following variables: three years or more before HSR opening (Before3+), two years before its opening (Before2), one year before its opening (Before1), the opening year (Cur), one year after its opening (After1), two years after its opening (After2), and three years and above after its opening (After3+). Specifically, Before3+ represents three years or more before the HSR opening in the city where the listed company’s office is located is 1; otherwise, it is 0. The definitions of other variables are similar and will not be repeated. One year before its opening is taken as the comparison benchmark. Then, these variables are multiplied by HSR, and HSR × Before3+, HSR × Before2, HSR × Cur, HSR × After1, HSR × After2, and HSR × After3+ replace HSR to estimate Model (1). The estimated results are shown in Table 4. In columns (1), (3), and (6) of Table 4, the coefficients of HSR × Before3+ and HSR × Before2 are not significant, and the coefficients of HSR × After1, HSR × After2, and HSR × After3+ are significantly negative, indicating that the staggered difference-in-differences test performed in Table 3 conforms to the assumption of parallel trend.

4.2.2. Placebo Test

To verify the robustness of the impact of the HSR opening on audit fees, the placebo test is used to re-estimate Model (1). We select the samples that were not affected by the HSR opening from 2003 to 2008, assume the opening time of HSR (Post) one year (Postt−1), two years (Postt−2), and three years (Postt−3) earlier than the actual opening time, and establish an interaction term with whether the HSR is opened in the city where the listed company’s office is located (HSR × Postt−1, HSR × Postt−2, HSR × Postt−3). The estimated results are shown in Table 5. In Table 5, the coefficients of HSR × Postt−1, HSR × Postt−2, and HSR × Postt−3 are not significant, indicating that the HSR opening effect of the pseudo constructed cannot reduce the audit fees in the full sample, non-central cities, and non-top 10 samples. This shows that our conclusion is robust.

4.2.3. Control the Impact of Other Transportation Infrastructure, City Categories, and the Internet

The staggered difference-in-differences model could inhibit the influence of other confounding factors on the regression results. However, other transportation modes and the internet will also affect the auditors’ travel mode and information communication mode, thus intensifying the audit market competition and reducing information asymmetry. Additionally, the city categories would have an impact on investigating the relationships between HSR opening and the audit fees, given that the firms in big cities have much more resources and information. Therefore, we control road, rail, and water passenger turnover, the city categories, and internet access. In 2017, the number of internet users in all provinces was missing. According to the data of 2017, we calculated the growth rate of the number of internet users in all provinces in 2016 based on the number of internet users in 2015 and 2016 and approximated it to the growth rate of 2017 to calculate the number of internet users in all provinces in 2017. In the control variable, the natural logarithm of 1 plus passenger turnover of the highway, railway, and water transportation and the number of internet users is used to measure the impact of other transportation infrastructure and the internet on the study. In addition, for auditors, aviation is also an important way of travel. We control aviation by using Airport (if there is an airport in the city where the listed company’s office is located, it is 1; otherwise, 0) and Airport_Num (the number of airports within 100 km of the listed company’s office). Since the auditors’ original transportation mode was mainly flights, we also further investigated the incremental effect of the HSR opening on flights. The samples were grouped and regressed according to whether there is an airport in the city where the listed company’s office is located and the mean and median of the number of airports within 100 km of the listed company’s office. The regression results show that the opening of the high-speed railway can reduce audit costs regardless of whether there is an airport or the number of airports, and the opening of the high-speed railway has an incremental effect on flights. Considering the impact of other transportation modes and the internet, Model (1) is re-estimated. The estimated results are shown in Table 6, and the conclusions are consistent with the above.

4.2.4. Replace the Measurement Method of HSR Opening

Some studies posit that the HSR opening should be defined by the actual opening time [13]. However, most of the HSRs opened in December, which has little impact on audit firms. Therefore, the HSR opening from January to November is defined as the opening of the current year, and the HSR opening in December is defined as the opening of the next year. The estimated results are shown in Table 7, and the conclusion is consistent with the main results.

4.2.5. Exclude the Impact of Differences in Accounting Standards on the Research

There are great differences between the accounting standards implemented in 2007 and the previous ones. Because most of the control variables used in this paper come from the financial statements of listed companies, the differences in accounting standards will lead to inconsistency between the data after 2007 and the data before 2007, thus affecting the reliability of the research results. For this reason, the data before 2007 are excluded, and Model (1) is re-estimated. The estimated results are shown in Table 8, and the conclusion is still robust.

4.2.6. Replace the Estimation Method

We used OLS regression to re-estimate Model (1) and adjusted the standard error of the regression coefficient at the company level. The estimated results are shown in Table 9, and the conclusion is consistent with the previous text.

4.2.7. Replacement of the Explanatory Variables

We construct a dummy variable (HSR_dummy, if the city where the listed company is located opens the HSR in the current year, it equals 1; otherwise, it is 0) to compare the effects before and after the opening of the high-speed railway. The results are shown in Table 10 and our conclusion is still robust.

5. Further Study

5.1. Analysis of the Impact Mechanism

We believe that the HSR opening can reduce audit fees by intensifying the competition in the audit market and alleviating information asymmetry. To verify our assumptions, we construct Models (2) and (3) by introducing audit market competition (AMC) and information asymmetry (IA).
Feei,c,t = ρ0 + ρ1HSRc,t × Postc,t + ρ2AMCi,t + ρ3HSRc,t × Postc,t × AMCi,t + πXi,t + Firmi + Cityc + Yeart + ωi,c,t
Feei,c,t = γ0 + γ1HSRc,t × Postc,t + γ2IAi,t + γ3HSRc,t × Postc,t × IAi,t + ηXi,t + Firmi + Cityc + Yeart + μi,c,t
Model (2) examines the audit market competition mechanism, and Model (3) examines the information asymmetry mechanism. We focus on the coefficient of ρ3 and γ3. The definitions of other variables are the same as those of Model (1) and will not be repeated.

5.1.1. Audit Market Competition Mechanism

Table 11 shows the impact of audit market competition on the relationship between the HSR opening and audit fees. We use the following three methods to measure the audit market competition (AMC). (1) The opposite number of the proportion of the audit fees of the company’s current audit firm to that of all firms in the same industry, region, and year (AMC_AF). (2) The opposite number of the proportion of the client asset scale of the company’s current audit firm to that of all firms in the same industry, region, and year (AMC_CAS). (3) The inverse number of the proportion of the clients of the company’s current audit firm to the total number of clients of all firms in the same industry, region, and year (AMC_NC). In columns (1)–(9), the coefficients of HSR × Post × AMC are significantly negative, indicating that the impact of the HSR opening on audit fees is affected by the audit market competition. The inhibitory effect of the HSR opening on audit fees is enhanced with the strengthened audit market competition, and the audit market competition mechanism has been verified.

5.1.2. Information Asymmetry Mechanism

Table 12 shows the impact of information asymmetry on the relationship between the HSR opening and audit fees. Information asymmetry (IA) is measured by the following two methods. (1) The sum of the absolute value of discretionary accruals in the past three years (IA_SUM). (2) Assign a value of 1 to the samples that are greater than the median sum of the absolute value of discretionary accruals in the past three years, assign a value of 0 to the samples that are less than the median, and construct a dummy variable of the degree of information asymmetry (IA_Med). In columns (1)–(6), the coefficient of HSR × Post × IA is not significant, indicating that the inhibition of the HSR opening on audit fees is not significantly affected by information asymmetry, and the information asymmetry mechanism is not established. It can be seen that the audit market competition is the main mechanism that affects the relationship between the HSR opening and audit fees. The HSR opening intensifies the audit market competition and reduces audit fees.

5.2. The HSR Opening and Audit Quality

It can be seen from the above conclusion that the HSR opening can intensify the competition in the audit market of non-central cities and small- and mid-sized audit firms. Given the fierce competition in the audit market, will there be vicious competition behavior of low-balling, thereby reducing the audit quality? According to Chio et al. (2010) [59], we use the absolute value of discretionary accruals (DA) to measure the audit quality, construct Model (4), and investigate the impact of the HSR opening on audit quality in the full sample, the non-central cities sample, and the small- and mid-sized audit firms sample. We use the modified DD method to estimate a proxy for accrual quality to measure audit quality according to Dechow and Dichev (2002) [60], McNichols (2002) [61], and Francis et al. (2005) [62]. We apply conservatism in accounting to measure audit quality [63,64].
DAi,c,t/DDi,c,t/Basui,c,t = φ0 + φ1HSRc,t × Postc,t + χX`i,t + Firmi + Cityc + Yeart + ψi,c,t
In Model (4), HSR × Post represents the HSR opening. DA/DD/Basu is the audit quality, and X’ represents the control variables. Referring to Chio et al. (2010) [59] and Huang et al. (2016) [65], we control the enterprise size (Size), return on total assets (Roa), financial leverage (Lev), profitability (Loss), the proportion of inventory and accounts receivable (Rip), the growth rate of main business income (Growth), quick ratio (Quick), market value book ratio (MB), and the natural logarithm of the number of days between the date of the financial statement and the date of issuance of the audit report (Delay) while controlling the company, city, and year fixed effect.
Table 13 shows the estimated results of the impact of the HSR opening on audit quality. In column (1), the coefficient of HSR × Post is significantly negative at the level of 1%, indicating that the HSR opening improves the audit quality. It is mentioned above that the reduction in audit fees has not affected the audit quality, which means that the HSR opening can promote reasonable competition in the audit market. In column (2), the coefficient of HSR × Post is significantly negative at the level of 1%, indicating that for non-central cities, the HSR opening can improve the audit quality. Combined with the above conclusions, in non-central cities, the HSR opening strengthens the audit market competition, while audit firms do not have the behavior of reducing the audit quality by low-balling. In addition, in column (3), the coefficient of HSR × Post is significantly negative at the level of 1%, indicating that the HSR opening can improve the audit quality for small- and mid-sized audit firms. It shows that in small- and mid-sized audit firms, there is no low-balling to reduce the audit quality. Additionally, after replacing the measurement of audit quality, the results in columns (4)–(9) verify our assumptions. It can be seen that the HSR opening has intensified the competition in the audit market, but it will not lead to low-balling or reduce the audit quality.

6. Conclusions and Suggestions

The impact of the HSR opening on the capital market and enterprise behavior is a key area of interest in the academic community, but the existing research rarely involves the impact of the HSR opening on audit fees. From the perspective of audit market competition and information asymmetry, in this paper, we explain the impact of the HSR opening on audit fees and argue that the HSR opening can reduce audit fees by intensifying audit market competition and alleviating information asymmetry. After theoretical analysis, using the data of A-share non-financial listed companies from 2003 to 2017 in China, we verified the impact of the HSR opening on audit fees by constructing a staggered difference-in-differences model. The results show that the HSR opening can reduce audit fees, and this inhibitory effect is significant in the samples of non-central cities and non-top 10 audit firms. After a series of robustness checks—that is, parallel trend test, placebo test, controlling the impact of other transportation infrastructure and the internet, changing the measurement method of HSR opening, excluding the impact of differences in accounting standards on the research, and changing the estimation method—the conclusion is still robust. Furthermore, the regulatory effect test based on competition in the audit market and information asymmetry shows that competition in the audit market can significantly enhance the negative correlation between the HSR opening and audit fees, and information asymmetry has no significant effect on the HSR opening to inhibit audit fees, indicating that the HSR opening affects audit fees by intensifying the competition in the audit market. In addition, for audit firms in non-central cities and for small- and mid-sized audit firms, the HSR opening can improve the audit quality, indicating that the HSR opening will not lead to low-balling behavior of the audit firms while intensifying the competition in the audit market. Therefore, the audit industry can use the impact of infrastructure construction on the competition in the audit market to standardize the audit fees and improve audit quality.
Based on the above conclusions, we offer the following suggestions. (1) HSR construction can intensify the competition in the audit market to a certain extent, which is of positive significance for breaking the regional monopoly of the audit market and expanding the non-local audit market. Therefore, for non-central cities with a low degree of competition in the audit market, the government should actively respond to the call of the state and increase investment in high-speed railway construction to promote competition in the audit market. (2) In different types of firms, the impact of the HSR opening on their audit fees is different, and the inhibitory effect is more pronounced in small- and mid-sized audit firms. Due to the fierce competition in the audit market, small- and mid-sized audit firms may choose the low price strategy. However, the HSR opening will not lead to low-balling behavior or reduce the audit quality. Therefore, our findings suggest that the audit industry promotes the rationalization of audit fees while improving audit quality thanks to the HSR opening. (3) The HSR opening can promote the circulation of audit talents, thus strengthening the competition in the audit market. Therefore, the authorities should actively take advantage of the impact of the HSR opening on audit talents, and encourage non-local firms to participate in local audit market competition to promote and improve the local audit market environment.

Author Contributions

Conceptualization, D.M. and S.Z.; methodology, D.M.; software, J.Z.; validation, D.M., J.Z., and S.Z.; formal analysis, D.M.; investigation, D.M.; resources, S.Z.; data curation, J.Z.; writing—original draft preparation, D.M. and J.Z.; writing—review and editing, D.M. and J.Z.; visualization, S.Z.; supervision, S.Z.; project administration, D.M.; funding acquisition, S.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Liaoning Social Science Planning Fund (grant numbers L20AJY005, L19AGL001) and Liaoning Revitalization Talents Program (grant number XLYC1907159).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All the data and sources are given in this paper.

Conflicts of Interest

The authors declare no conflict of interest.

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Table
Table 1. Variable definitions.
Table 1. Variable definitions.
VariablesDefinitions
FeeNatural logarithm of annual audit fees of enterprises.
HSR × PostThe interaction term of whether the HSR is opened in the city where the listed company’s office is located (HSR) and after the opening time of the HSR in the city where the listed company’s office is located (Post).
NCC/RCC/Non_CCCities in China are divided into national central cities (NCC), regional central cities (RCC), and non-central cities (Non_CC).
Big10/Non_Big10Using the Top 100 Information of Comprehensive Evaluation of Audit Firms published by the Chinese Institute of Certified Public Accountants from 2003 to 2017, the top 10 CPA firms are identified as “big 10” CPA firms (Big10), and the rest are identified as “non-big 10” CPA firms (Non_Big10).
AMCThe market share of the current audit firms in the same industry, region (province), and year.
IAThe sum of the absolute value of discretionary accruals in the past three years (IA_SUM). If the IA is higher than the median (IA_Med), the variable is 1; otherwise, it is 0.
SizeThe natural logarithm of total assets at the end of the year.
RoaThe ratio of net profit to total assets at the end of the year.
LevThe ratio of total liabilities to total assets at the end of the year.
LossIf the net profit of the company is negative, it is 1; otherwise, it is 0.
RipThe ratio of the sum of inventory and accounts receivable to the total assets.
GrowthThe growth rate of main business income.
QuickThe ratio of current asset inventory to current liabilities.
Top1The shareholding ratio of the largest shareholder.
OutdirThe proportion of independent directors on the board.
ConThe shareholding ratio of the management.
BoardThe total number of directors.
SoeThe state-owned enterprise is 1; otherwise, it is 0
Big4The big four audit firms (PwC, DTT, KPMG, and EY) is 1; otherwise, it is 0
SwitchThe change in the listed company’s CPA firm is 1; otherwise, it is 0.
OpinionIf the annual audit opinion of the company is a non-standard audit opinion, it is 1; otherwise, it is 0.
CityThe dummy variable of the city where the listed company’s office is located—266 dummy variables are set.
YearYear dummy variable—14 dummy variables are set.
Table
Table 2. Descriptive statistics of main variables.
Table 2. Descriptive statistics of main variables.
VarNameObsMeanSDMaxMinP25MedianP75
Fee2605413.5240.73316.20012.20613.01713.43013.892
HSR × Post260540.4340.4961.0000.0000.0000.0001.000
Size2605421.8681.28625.76819.11420.95821.72422.607
Roa260540.0400.0610.225−0.2130.0130.0360.068
Lev260540.4600.2201.1360.0510.2920.4590.617
Loss260540.9020.2971.0000.0001.0001.0001.000
Rip260540.2720.1730.7620.0050.1410.2490.374
Growth260540.2190.5573.943−0.646−0.0130.1230.303
Quick260541.6972.21914.8030.1270.6131.0031.767
Top1260540.3630.1550.7520.0900.2400.3400.475
Outdir260540.3670.0530.5710.2500.3330.3330.400
Con260540.0900.1760.6700.0000.0000.0000.062
Board260548.9201.82915.0005.0008.0009.0009.000
Soe260540.4840.5001.0000.0000.0000.0001.000
Big4260540.0610.2391.0000.0000.0000.0000.000
Switch260540.1530.3601.0000.0000.0000.0000.000
Opinion260540.0460.2091.0000.0000.0000.0000.000
Table
Table 3. The HSR opening and audit fees.
Table 3. The HSR opening and audit fees.
VarName(1)(2)(3)(4)(5)(6)
Full SampleNCCRCCNon_CCBig10Non_Big10
HSR × Post−0.026 **0.004−0.014−0.037 **0.016−0.042 ***
(−2.528)(0.208)(−0.771)(−2.123)(1.094)(−3.054)
Size0.296 ***0.294 ***0.308 ***0.276 ***0.332 ***0.272 ***
(33.201)(18.833)(17.954)(18.969)(22.131)(24.042)
Roa0.124 *0.1390.1160.097−0.0100.170 **
(1.762)(1.015)(0.938)(0.979)(−0.087)(2.254)
Lev0.069 **0.0420.112 *0.0440.0130.082 **
(2.074)(0.623)(1.884)(0.978)(0.265)(2.032)
Loss−0.023 ***−0.016−0.014−0.033 ***−0.009−0.026 **
(−2.657)(−0.866)(−0.926)(−2.674)(−0.775)(−2.407)
Rip−0.0500.001−0.149 **0.051−0.013−0.040
(−1.277)(0.010)(−2.392)(0.828)(−0.210)(−0.924)
Growth−0.003−0.006−0.016 **0.0060.003−0.013 ***
(−0.657)(−0.881)(−2.081)(0.799)(0.364)(−2.762)
Quick−0.003−0.001−0.003−0.004−0.007 **−0.001
(−1.237)(−0.327)(−0.797)(−1.252)(−2.307)(−0.430)
Top1−0.045−0.023−0.090−0.043−0.143 *−0.052
(−0.833)(−0.223)(−0.919)(−0.511)(−1.793)(−0.796)
Outdir0.0620.2220.071−0.0940.0820.024
(0.798)(1.435)(0.548)(−0.870)(0.803)(0.264)
Con−0.078−0.1480.029−0.000−0.0920.002
(−1.469)(−1.380)(0.317)(−0.007)(−1.606)(0.031)
Board0.008 **0.0040.010 *0.0060.012 **0.007 **
(2.401)(0.584)(1.836)(1.327)(2.517)(1.973)
Soe−0.015−0.0020.029−0.059 *−0.0350.008
(−0.714)(−0.047)(0.810)(−1.829)(−1.087)(0.362)
Big40.297 ***0.223 ***0.412 ***0.298 ***0.293 ***0.108
(7.351)(4.260)(4.931)(3.489)(5.621)(1.464)
Switch−0.022 ***−0.037 ***−0.020 **−0.014 *−0.042 ***−0.011 *
(−4.462)(−4.092)(−2.241)(−1.786)(−4.381)(−1.777)
Opinion0.080 ***0.101 ***0.079 ***0.066 ***0.069 ***0.064 ***
(5.346)(3.312)(3.290)(2.831)(2.688)(3.750)
FirmYesYesYesYesYesYes
CityYesYesYesYesYesYes
YearYesYesYesYesYesYes
Constant6.181 ***7.139 ***5.583 ***7.730 ***7.068 ***7.156 ***
(30.532)(13.970)(15.440)(23.410)(19.464)(26.846)
N26,05483148021971910,62915,425
Adj. R20.7000.6270.7140.6770.6250.690
Note: T-value in brackets is the robust standard error. *** means the significance level is 1%, ** means the significance level is 5%, and * means the significance level is 10%.
Table
Table 4. Parallel trend test.
Table 4. Parallel trend test.
VarName(1)(2)(3)(4)(5)(6)
Full SampleNCCRCCNon_CCBig10Non_Big10
HSR × Before3+−0.007−0.021−0.0120.007−0.0240.009
(−0.611)(−0.972)(−0.628)(0.376)(−0.746)(0.642)
HSR × Before2−0.011−0.012−0.0250.002−0.0410.002
(−1.329)(−0.679)(−1.625)(0.162)(−1.556)(0.236)
HSR × Cur−0.007−0.0080.009−0.0070.020−0.015
(−0.838)(−0.445)(0.649)(−0.545)(1.039)(−1.425)
HSR × After1−0.025 **−0.022−0.014−0.013−0.000−0.026 **
(−2.557)(−1.154)(−0.802)(−0.825)(−0.009)(−2.075)
HSR × After2−0.037 ***−0.006−0.025−0.043 **0.008−0.049 ***
(−3.232)(−0.223)(−1.174)(−2.411)(0.699)(−3.131)
HSR × After3+−0.059 ***−0.002−0.062 **−0.063 **0.009−0.079 ***
(−3.797)(−0.067)(−2.148)(−2.476)(0.391)(−3.855)
ControlsYesYesYesYesYesYes
FirmYesYesYesYesYesYes
CityYesYesYesYesYesYes
YearYesYesYesYesYesYes
Constant6.192 ***7.145 ***5.634 ***7.747 ***7.099 ***7.183 ***
(30.656)(13.943)(15.588)(23.482)(19.276)(27.045)
N26,05483148021971910,62915,425
Adj. R20.7010.6260.7150.6770.6250.690
Note: T-value in brackets is the robust standard error. *** means the significance level is 1%, ** means the significance level is 5%.
Table
Table 5. Placebo test.
Table 5. Placebo test.
VarName(1)(2)(3)(4)(5)(6)
Full SampleNCCRCCNon_CCBig10Non_Big10
Panel A: One year
HSR × Postt−1−0.0090.014--−0.014−0.008
(−0.354)(0.415)--(−0.219)(−0.311)
ControlsYesYesYesYesYesYes
FirmYesYesYesYesYesYes
CityYesYesYesYesYesYes
YearYesYesYesYesYesYes
Constant7.536 ***8.250 ***7.227 ***9.119 ***7.585 ***8.845 ***
(16.751)(12.173)(10.655)(15.651)(5.979)(25.059)
N648219852117238010765406
Adj. R20.2370.1970.2820.2530.2560.186
Panel B: Two years
HSR × Postt−1−0.007−0.0140.0010.0530.017−0.007
(−0.351)(−0.420)(0.039)(0.649)(0.247)(−0.333)
ControlsYesYesYesYesYesYes
FirmYesYesYesYesYesYes
CityYesYesYesYesYesYes
YearYesYesYesYesYesYes
Constant7.538 ***8.223 ***7.227 ***9.109 ***7.603 ***8.844 ***
(16.769)(12.171)(10.649)(15.619)(6.017)(25.008)
N648219852117238010765406
Adj. R20.2370.1970.2810.2530.2560.186
Panel C: Three years
HSR × Postt−10.0090.0020.009−0.0040.021−0.006
(0.638)(0.067)(0.318)(−0.095)(0.464)(−0.397)
ControlsYesYesYesYesYesYes
FirmYesYesYesYesYesYes
CityYesYesYesYesYesYes
YearYesYesYesYesYesYes
Constant8.468 ***8.588 ***7.178 ***9.122 ***7.522 ***8.541 ***
(23.194)(12.582)(10.555)(15.630)(5.905)(22.919)
N648219852117238010765406
Adj. R20.2370.1970.2820.2530.2570.186
Note: T-value in brackets is the robust standard error. *** means the significance level is 1%.
Table
Table 6. Control the impact of other transportation infrastructure, the city categories, and the internet.
Table 6. Control the impact of other transportation infrastructure, the city categories, and the internet.
VarName(1)(2)(3)(4)(5)(6)
Full SampleNCCRCCNon_CCBig10Non_Big10
Panel A: Control Variable Increase Road, Rail, Water, Internet, Airport, NCC, RCC
HSR × Post−0.026 **−0.021−0.009−0.035 **0.019−0.042 ***
(−2.477)(−1.133)(−0.481)(−1.987)(1.237)(−3.042)
ControlsYesYesYesYesYesYes
FirmYesYesYesYesYesYes
CityYesYesYesYesYesYes
YearYesYesYesYesYesYes
Constant6.176 ***7.259 ***5.464 ***7.882 ***4.974 ***7.092 ***
(29.213)(12.263)(14.100)(22.710)(9.767)(24.744)
N25,75282427941956910,51815,234
Adj. R20.7100.6370.7240.6910.6260.707
Panel B: Control variable increase Road, Rail, Water, Internet, Airport_Num, NCC, RCC
HSR × Post−0.025 **−0.021−0.010−0.035 **0.017−0.041 ***
(−2.452)(−1.136)(−0.545)(−1.979)(1.132)(−3.023)
ControlsYesYesYesYesYesYes
FirmYesYesYesYesYesYes
CityYesYesYesYesYesYes
YearYesYesYesYesYesYes
Constant6.651 ***7.242 ***6.864 ***6.872 ***6.778 ***7.676 ***
(30.776)(12.681)(16.751)(18.693)(15.376)(26.336)
N25,75282427941956910,51815,234
Adj. R20.7100.6370.7240.6920.6250.707
Note: T-value in brackets is the robust standard error. *** means the significance level is 1%, ** means the significance level is 5%.
Table
Table 7. Replace the measurement method of HSR opening.
Table 7. Replace the measurement method of HSR opening.
VarName(1)(2)(3)(4)(5)(6)
Full SampleNCCRCCNon_CCBig10Non_Big10
HSR × Post−0.021 **−0.007−0.003−0.032 *0.026−0.040 ***
(−2.037)(−0.419)(−0.159)(−1.818)(1.631)(−2.962)
ControlsYesYesYesYesYesYes
FirmYesYesYesYesYesYes
CityYesYesYesYesYesYes
YearYesYesYesYesYesYes
Constant6.176 ***7.152 ***5.565 ***7.666 ***6.683 ***7.147 ***
(30.532)(14.003)(15.458)(23.116)(20.850)(26.875)
N26,05483148021971910,62915,425
Adj. R20.7000.6270.7140.6760.6250.690
Note: T-value in brackets is the robust standard error. *** means the significance level is 1%, ** means the significance level is 5%, and * means the significance level is 10%.
Table
Table 8. Excluding the impact of differences in accounting standards on the research.
Table 8. Excluding the impact of differences in accounting standards on the research.
VarName(1)(2)(3)(4)(5)(6)
Full SampleNCCRCCNon_CCBig10Non_Big10
HSR × Post−0.022 **0.004−0.008−0.037 **0.005−0.030 **
(−2.405)(0.230)(−0.545)(−2.367)(0.365)(−2.561)
ControlsYesYesYesYesYesYes
FirmYesYesYesYesYesYes
CityYesYesYesYesYesYes
YearYesYesYesYesYesYes
Constant6.752 ***7.170 ***6.743 ***7.131 ***6.359 ***8.115 ***
(26.466)(12.563)(15.240)(17.735)(17.802)(28.438)
N21,800702666168158996811,832
Adj. R20.6800.6170.6910.6450.6190.677
Note: T-value in brackets is the robust standard error. *** means the significance level is 1%, ** means the significance level is 5%.
Table
Table 9. Replacement of the estimation method.
Table 9. Replacement of the estimation method.
VarName(1)(2)(3)(4)(5)(6)
Full SampleNCCRCCNon_CCBig10Non_Big10
HSR × Post−0.038 ***0.014−0.034 *−0.071 ***−0.007−0.044 ***
(−3.263)(0.650)(−1.743)(−3.393)(−0.347)(−2.888)
ControlsYesYesYesYesYesYes
FirmYesYesYesYesYesYes
CityYesYesYesYesYesYes
YearYesYesYesYesYesYes
Constant5.560 ***5.462 ***6.199 ***5.831 ***4.976 ***6.254 ***
(32.758)(17.851)(21.048)(23.373)(22.330)(29.183)
N26,05483148021971910,62915,425
Adj. R20.7160.7250.6910.7050.7480.638
Note: The table shows the OLS estimation results of the difference-in-differences model, the t-value is in brackets, and cluster adjustment has been made at the enterprise level. *** means the significance level is 1%, and * means the significance level is 10%.
Table
Table 10. Replacement of the explanatory variables.
Table 10. Replacement of the explanatory variables.
VarName(1)(2)(3)(4)(5)(6)
Full SampleNCCRCCNon_CCBig10Non_Big10
HSR_dummy−0.015 **0.004−0.012−0.016 *−0.000−0.011
(−2.364)(0.249)(−1.038)(−1.706)(−0.018)(−1.370)
ControlsYesYesYesYesYesYes
FirmYesYesYesYesYesYes
CityYesYesYesYesYesYes
YearYesYesYesYesYesYes
Constant6.176 ***7.139 ***7.030 ***7.701 ***7.062 ***7.111 ***
(17.707)(38.399)(29.948)(53.534)(22.124)(22.358)
N26,05483148021971910,62915,425
Adj. R20.6610.5720.6740.6280.5330.635
Note: T-value in brackets is the robust standard error. *** means the significance level is 1%, ** means the significance level is 5%, and * means the significance level is 10%.
Table
Table 11. Mechanism test of HSR opening affecting audit fees: audit market competition.
Table 11. Mechanism test of HSR opening affecting audit fees: audit market competition.
VarNameAMC_AFAMC_CASAMC_NC
(1)(2)(3)(4)(5)(6)(7)(8)(9)
Full
Sample		Non_
CC		Non_Big10Full
Sample		Non_
CC		Non_Big10Full
Sample		Non_
CC		Non_Big10
HSR × Post−0.048 ***−0.100 ***−0.078 ***−0.042 ***−0.088 ***−0.065 ***−0.050 ***−0.098 ***−0.076 ***
(−3.755)(−5.840)(−4.389)(−3.328)(−5.243)(−3.687)(−3.658)(−5.454)(−3.965)
AMC−0.198 ***−0.208 ***−0.159 ***−0.049 ***−0.055 ***−0.028−0.039 *−0.041 **−0.004
(−10.588)(−11.025)(−6.451)(−2.818)(−3.198)(−1.161)(−1.942)(−2.040)(−0.152)
HSR × Post × AMC−0.079 ***−0.106 ***−0.160 ***−0.059 **−0.066 *−0.097 **−0.087 ***−0.104 **−0.135 ***
(−3.006)(−2.588)(−3.540)(−2.352)(−1.811)(−2.377)(−3.010)(−2.537)(−2.876)
ControlsYesYesYesYesYesYesYesYesYes
FirmYesYesYesYesYesYesYesYesYes
CityYesYesYesYesYesYesYesYesYes
YearYesYesYesYesYesYesYesYesYes
Constant7.353 ***5.900 ***6.301 ***7.465 ***5.879 ***6.299 ***7.363 ***5.806 ***6.228 ***
(20.978)(50.435)(29.584)(19.130)(49.551)(29.091)(18.742)(49.776)(28.831)
N25,767956915,24925,767956915,24925,767956915,249
Adj. R20.7170.7160.6520.7110.7100.6450.7110.7100.645
Note: T-value in brackets is the robust standard error. *** means the significance level is 1%, ** means the significance level is 5%, and * means the significance level is 10%.
Table
Table 12. Mechanism test of HSR opening affecting audit fees: information asymmetry.
Table 12. Mechanism test of HSR opening affecting audit fees: information asymmetry.
VarNameIA_SUMIA_Med
(1)(2)(3)(4)(5)(6)
Full SampleNon_CCNon_Big10Full SampleNon_CCNon_Big10
HSR × Post−0.028 **−0.050 **−0.034 **−0.029 **−0.044 **−0.051 ***
(−2.050)(−2.271)(−1.998)(−2.326)(−2.207)(−3.212)
IA0.003−0.0200.0050.002−0.004−0.002
(0.170)(−0.652)(0.226)(0.290)(−0.437)(−0.219)
HSR × Post × IA0.0140.066−0.0420.0080.0160.013
(0.409)(1.031)(−1.091)(0.726)(0.906)(0.905)
ControlsYesYesYesYesYesYes
FirmYesYesYesYesYesYes
CityYesYesYesYesYesYes
YearYesYesYesYesYesYes
Constant7.528 ***7.788 ***6.629 ***7.521 ***7.743 ***6.641 ***
(18.007)(21.356)(25.100)(18.029)(21.370)(25.323)
N22,340811413,35422,340811413,354
Adj. R20.7080.6790.7040.7080.6780.704
Note: T-value in brackets is the robust standard error. *** means the significance level is 1%, ** means the significance level is 5%.
Table
Table 13. The HSR opening and audit quality.
Table 13. The HSR opening and audit quality.
VarNameDADDBasu
(1)(2)(3)(4)(5)(6)(7)(8)(9)
Full SampleNon_CCNon_Big10Full SampleNon_CCNon_Big10Full SampleNon_CCNon_Big10
HSR × Post−0.010 ***−0.010 ***−0.013 ***−0.006 *−0.003−0.008 *0.0040.026 **0.009
(−5.125)(−3.220)(−4.937)(−1.657)(−0.504)(−1.682)(0.712)(2.179)(1.123)
ControlsYesYesYesYesYesYesYesYesYes
FirmYesYesYesYesYesYesYesYesYes
CityYesYesYesYesYesYesYesYesYes
YearYesYesYesYesYesYesYesYesYes
Constant−0.0030.0620.055−0.530 ***0.0220.1451.262 ***1.088 ***0.627 ***
(−0.073)(1.260)(1.010)(−3.293)(0.243)(0.772)(13.088)(6.549)(3.351)
N17,027626010,25417,027626010,25417,027626010,254
Adj. R20.0710.0590.0740.0560.0890.1110.0220.0110.028
Note: T-value in brackets is the robust standard error. *** means the significance level is 1%, ** means the significance level is 5%, and * means the significance level is 10%.
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Ma, D.; Zhang, S.; Zhao, J. The High-Speed Railway Opening and Audit Fees: Evidence from China. Sustainability 2022, 14, 13353. https://doi.org/10.3390/su142013353

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Ma D, Zhang S, Zhao J. The High-Speed Railway Opening and Audit Fees: Evidence from China. Sustainability. 2022; 14(20):13353. https://doi.org/10.3390/su142013353

Chicago/Turabian Style

Ma, Dongshan, Shengqiang Zhang, and Jiayu Zhao. 2022. "The High-Speed Railway Opening and Audit Fees: Evidence from China" Sustainability 14, no. 20: 13353. https://doi.org/10.3390/su142013353

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