The existing literature about determinants of audit fee finds that those characteristics of a firm that conveys the “high quality” signal to the market can obtain higher audit fees. These studies ignore the differences among auditors, which are contradicted with individual auditor behavioral literature that showing different characteristics of auditors influence audit quality. Therefore, this paper hypothesizes that different auditors obtain different audit fees. Using the data of listed companies in China from 2010 to 2015, this paper constructs the regression model of the audit fees at individual auditor level and finds that age, gender, educational background, industry specialization, position and busyness all have significantly correlations with the audit fees. The results illustrate that audit client considers at individual auditor level when choosing audit services and pays different level of audit fees, which provide empirical evidences to selection and cultivation of auditors.
Audit fee is the economic remuneration for auditors who provide audit services, which are an agency fee according to certain standards. The audit fee includes the total cost of audit through the overall audit work, the risk compensation and the profit demand. During the actual audit work, the audit fee influences not only audit quality, but also the development of accounting firms and audit industry.
Therefore, audit fee is always the research focus of domestic and foreign scholars. Simunic [
However, these studies are based on an implied assumption that determinants of audit fees only exist at the firm level and there is no difference of audit quality and audit fees at individual auditor level, which is significantly contrary to existing auditor behavior literature. Some scholars have found that there is no audit premium for firm’s industry specialization when controlling auditor’s industry specialization [
Based on the above theories, this paper studies the determinants of audit fees at individual auditor level, including seven characteristics of the auditor’s (the characteristics of the population and profession). In our research design, we assign an indicator variable to each auditor who signs audit reports for multiple clients for multiple years. We then estimate an audit-fee model by including these indicators, and control for audit client, audit firm, year and industry effects that could possibly affect audit fees. The results find that demographic and professional characteristics of auditors have significant influence on audit fees: age and gender have significant positive correlations with audit fees, while education background, industry specialization, position, number of audit year and busyness all have positive correlations with audit fees. These results suggest that individual auditors differ to a notable extent in terms of audit fees.
We conduct two additional tests to examine the robustness of these findings. In one test, we measure the explanatory variables in another way, by combining the two auditor features for the year into an integrated variable. In another test, we expand the test year length to measure whether the relationship between auditor characteristics and the audit fees is influenced by the external policy. The robustness results are almost consistent and the explanatory variables still have significant effects on the explanatory variables in these two robustness tests.
This paper contributes to two areas theoretically and practically. Firstly, this paper contributes to the broad literature examining links between individual auditors’ characteristics and audit fees. We study the determinants of the audit fees from the perspective of individual audit level, rather than merely from the perspectives of audit clients and audit firms. The results show that audit clients consider not only at audit firm’s level but at individual auditor’s level as well, reflecting the “people-oriented” feature in the audit industry. In sum, this paper provides a more detailed research perspective from individual level and enriches the audit fee researches.
Secondly, this paper also has some practical significance for the development of China’s audit market. From 2014, Chinese regulators have changed the audit charging mode from mandatory government guidance price to market price, which arises doubts that Chinese audit market is imperfect and not ready to execute market price. In this paper, we find that the individual characteristics of the auditor that deliver the signal of “high quality” can be recognized by the audit clients and obtain higher audit fees, which reveals that Chinese audit fee market is ordered because the price can follow the principle of marketization. In addition, this paper provides suggestions to China’s Ministry of Finance, China Institute of Certified Public Accountants and other institutions to develop accounting personnel training program to strengthen the younger generation of auditor’s professional skills education.
The remainder of this paper is organized as follows. The literature review is discussed in the second section. The third section discusses the underlying theory and research hypothesis. The forth section discusses the research design and the fifth section presents the empirical results. Some concluding comments are offered in the final section.
The existing literature of audit fees is mainly based on two perspectives, the perspective of auditees and auditors.
1) Audit client
From the perspective of audit client, Simunic [
Several scholars try to find the correlation between company governance and audit fees. Collier and Gregory [
Besides, some scholars find corporate risk influence audit fees. Bell et al. [
2) Audit firm
From the perspective of auditors, majority of scholars consider the factors at firm level. Simunic [
Besides, some scholars find firm’s industry specialization influence the audit fees. Craswell et al. [
It can be seen from the above literature that the determinants of the audit fees mostly at audit client level, and the studies from the perspective of auditor level are mostly studied at firm level, without considering the difference among individual auditor level. Therefore, this paper which studies the determinants of the audit fees at individual auditor level is of theoretical importance.
From the perspective of auditor’s individual characteristics, this paper refers to the demographic research method to examine whether audit clients will consider at individual auditor level when choosing audit services and pay audit fee premiums to auditors who convey “high quality” signals.
It can be seen from the existing literature of audit fee determinants that the determinants of the audit fees can be measured from the audit client and auditor level. From the perspective of the relationship between the firm and the audit fees, it can be found that firms which deliver “high quality” signals to market tend to receive audit fee premiums. For example, “Big 4” are found to receive higher audit fees than other firms. It is worth noting that these studies are based on an implicit assumption that auditors at the firm provide the same audit quality and receive the same audit fees. This is contrary to the conclusions drawn by the existing literature of individual auditor’s characteristic, which reveal that there are differences among auditors and their individual characteristics act directly on the cognitive styles and decision-making behaviors and thereby affecting audit quality.
On the other hand, because of lacking information and knowledge, external financial statement users form opinions of audit quality mainly basing on broad measures of audit quality, such as the firm’s reputation and firm’s size. But this assumption ignores another group of important users of audit service, the managers of audit clients. Unlike external financial reporting users, managers have more close contacts and communication with auditors and attempt to get more services from auditors, such as getting advice on the internal control, even if these services do not influence audit quality perceived by external users of financial statements. It has been found that auditors will choose auditors based on different needs: the motivation for small firms to choose auditors is to improve corporate governance while the motivation for large firms to choose auditors is to obtain external financing advice [
1) Age
Age usually affects the tendency of risks. Some scholars believe that elder people tend to be more mature and don’t like risks [
H1: Auditor’s age has a significant positive correlation with the audit fees.
2) Gender
Cognitive psychology and marketing theory believe that gender may affect the individual’s judgment, and that women are good at dealing with complex tasks because women have better abilities to distinguish differences and integrate decision-making clues than men. Therefore, when dealing with tasks, women deal with information more accurately and efficiently than men. Based on this hypothesis, Chung and Monroe [
A series of experimental studies have examined the attitudes towards the risk between men and women, and find that in most cases, women are more likely to avoid risk than men. Women usually choose less risky and more secure behavior than men [
H2: Gender has a significant negative correlation with the audit fees.
3) Education Background
Spence [
H3: Education background of auditors has a significant positive correlation with the audit fees.
4) Industry Specialization
The audit industry expertise is a unique industry knowledge arisen from the continuous services of auditors in the same industry [
Specifically, this expertise is based on the auditors’ deep understanding of industry operating characteristics and operational risks, which should be cultivated by experience. This industry expertise helps auditors to better and more effectively identify the financial risks of the industry’s clients, help formulate more appropriate audit plans and implement appropriate auditing procedures, and ultimately produce reasonable audit opinions. Thus, audit expertise generally means higher audit quality [
H4: Auditor’s industry specialization has a significant positive correlation with the audit fees.
5) Position
A series of studies find that firm partners are more cautious than other auditors [
On the other hand, “partner” is the top of auditor profession, which usually represents sufficient professional competence and a sense of professional ethics, and delivers “high audit quality” signal to the market. Therefore, audit clients are likely to pay higher audit fees to partners. Thus, we make the hypothesis:
H5: “Partner” position has a significant positive correlation with the audit fees.
6) Number of Audit Year
DeAngelo [
H6: The number of audit year has a significant positive correlation with the audit fees.
7) Busyness
Whether the auditor’s busyness will affect the auditor’s decision-making and work is argued by scholars at home and abroad. On the one hand, too many audit tasks will distract the auditors’ attention, weaken the auditors’ energy in each project, and influence audit decisions, resulting in the decline of audit quality [
H7: Audit busyness has a significant positive correlation with the audit fees.
1) Variables Definition
In this paper, we refer to the method of Chin and Chi [
In terms of measurement of busyness, this paper refers to the method of Goodwin and Wu [
Referring to existing audit fee literature, this paper controls other factors that affect audit fees, including audit opinion, big 10, whether to switch the firm (Switch), Earnings Management (Em), the percentage of inventory and account receivable on total assets(Complexity), asset-liability ratio (Lev), company annual revenue growth rate (Growth), company size (Size), whether the company is punished (Punish) and whether the company’s net profit for the year is negative(Loss).
2) Empirical Models
Based on the above theoretical analysis and assumptions, this paper establishes the following models.
First, all the auditor’s individual characteristics and control variables are incorporated into the equation to establish model (1).
Then, single individual characteristic is put into the equation respectively to establish model (2).
Category | Code | Definition | |
---|---|---|---|
Explained Variable | Fee | Audit fee, measured by the natural logarithm of the company’s audit fee | |
Explaining Variables | Spe | Industry Specialization, measured by the cumulative number of signature of auditor i in industry k before year t | |
Gender | Gender, is 1 for male auditor and 0 otherwise | ||
Age | Auditor’s age | ||
Edubg | Auditor’s education background, is 1 for bachelor degree or above, and 0 otherwise | ||
Partner | Whether the auditor is partner, is 1 for partner, and 0 otherwise | ||
Exp | Number of audit year, measured by the time of year i deducting the year of registration of CPA | ||
Busyness | Auditor busyness, measured by the total audit tasks of auditor i in year t | ||
Control Variables | Firm’s Characteristics | Opinion | Audit Opinion, is 1 for modified audit opinion, and 0 otherwise |
Big 10 | Top 10 firms in year i, is 1 for the company which is audited by “big 10”, and 0 otherwise | ||
Switch | Whether switch the firm, is 1 if the company switch the firm, and 0 otherwise | ||
Auditee’s Characteristics | Em | Earnings management, extraordinary item/ absolute value of profit for the year | |
Complexity | Complexity of economic business, the sum of inventory and account receivables/total asset | ||
Lev | Solvency, asset-liability ratio | ||
Grow | Development capacity, revenue growth rate | ||
Size | Asset size, natural logarithm of total assets at year end | ||
Punish | Governance risk, is 1 if the company is punished by regulators, and 0 otherwise | ||
Loss | Financial risk, iss 1 if profit for the year is positive, and 0 otherwise |
1) Sample and data
We first collect data of Chinese listed companies at China Stock Market and Accounting Research database (CSMAR), and cross-check the identities of signing auditors against the enquiry system compiled by the CICPA (available at http://cmis.cicpa.org.cn, in Chinese). Data on individual auditors’ demographic information are also obtained from this source. We manually input each auditor’s full name into the relevant search fields and match the search results with the audit firm and individual auditor data collected from companies’ annual reports. We start our sample period at fiscal 2010 to mitigate the possible effects of the promulgation and the implementation of 2010 audit standards. And we drop publicly listed companies in financial sector and observations with missing value, resulting in a total of 22,728 observations from 2010 to 2015 in our final sample.
2) Descriptive Statistics
Variable | Obs | Mean | Median | Std.Dev. | Min | Max |
---|---|---|---|---|---|---|
Fee | 22,728 | 13.47 | 13.38 | 0.590 | 12.30 | 15.32 |
Age | 22,728 | 41.17 | 41 | 6.725 | 27 | 60 |
Gender | 22,728 | 0.694 | 1 | 0.461 | 0 | 1 |
Edubg | 22,728 | 0.741 | 1 | 0.438 | 0 | 1 |
Spe | 22,728 | 9.195 | 5 | 11.20 | 1 | 57 |
Partner | 22,728 | 0.594 | 1 | 0.491 | 0 | 1 |
Exp | 22,728 | 11.70 | 12 | 5.339 | 1 | 25 |
Busyness | 22,728 | 3.111 | 2 | 2.375 | 1 | 16 |
Big10 | 22,728 | 0.488 | 0 | 0.500 | 0 | 1 |
Em | 22,728 | 0.0105 | 0.0046 | 0.0192 | 2.20e−07 | 0.142 |
Complexity | 22,728 | 0.274 | 0.249 | 0.175 | 1.00e−05 | 0.892 |
Switch | 22,728 | 0.0780 | 0 | 0.268 | 0 | 1 |
Opinion | 22,728 | 0.0260 | 0 | 0.159 | 0 | 1 |
Lev | 22,728 | 0.444 | 0.441 | 0.216 | 0.0409 | 0.899 |
Size | 22,728 | 9.540 | 9.474 | 0.536 | 8.287 | 11.20 |
Punish | 22,728 | 0.179 | 0 | 0.383 | 0 | 1 |
Loss | 22,728 | 0.0878 | 0 | 0.283 | 0 | 1 |
Grow | 22,728 | 0.468 | 0.039 | 8.940 | −40.35 | 52.42 |
years old. For auditor’s education background, 74.1% auditor has bachelor degree or above, indicating that auditor degree is dominated by bachelor degree. For industry specialization, the mean is 9.20, the minimum is 1 while the maximum is 57, indicating there is a big gap of industry specialization among auditors. For position, the number of firm partner accounts for 59.4% of total auditors. For number of audit year, the mean is 11.70, indicating that auditors need audit experience. For busyness, the mean is 3.11, and the maximum is 16 which is a rarely case. And there is no significant difference between mean and medium, so this paper uses OLS regression.
3) Regression Results
Specially, auditor age is significantly negatively correlated with the audit fees at 1% level, which is contrary to our expectation, the reason of which might be that younger auditors have better learning skills and energy than elder auditors and can adapt to changing standards and busy audit work. Therefore, the younger auditors are more welcomed by audit clients. The auditor’s gender is significantly negatively correlated with the audit fees at the 1% level, indicating that audit clients are more willing to provide audit fee premiums for female auditors because the female auditor is more cautious in the audit process and has a higher practice ethics. There is a significant positive correlation between the auditor’s educational background and the audit fee at 1% level, indicating that the audit clients can identify the “high quality” signal conveyed by the educational background. Audit industry specialization is significantly positively correlated with the audit fees at 10% level, indicating that audit industry specialization is one of the “high quality” signals. Whether the auditor is partner significantly positively correlates with the audit fee at 1% level, indicating that the audit clients believe that the position affects audit quality. There is a significant positive correlation between the number of audit year and audit fees at 1% level, indicating that the audit clients prefer auditors with longer audit year. Auditor’s busyness has a significant positive correlation with audit fee at 5% level, indicating that those busier auditors have a higher reputation and audit quality and are favored by audit clients. These results indicate that the audit clients not only consider the firm factors, but also consider the individual characteristics of the auditors when choosing audit service, and willing to pay audit premium to characteristics that convey “high quality” signals.
The regression results of columns (2)-(8) of
Variables | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) |
---|---|---|---|---|---|---|---|---|
Age | −0.0065*** | −0.0041*** | ||||||
(−11.71) | (−10.60) | |||||||
Gender | −0.0298*** | −0.0249*** | ||||||
(−5.25) | (−4.43) | |||||||
Edubg | 0.0281*** | 0.0449*** | ||||||
(4.64) | (7.56) | |||||||
Spe | 0.0006* | 0.0006** | ||||||
(1.81) | (2.25) | |||||||
Partner | 0.0196*** | 0.0118** | ||||||
(2.92) | (2.22) | |||||||
Exp | 0.0031*** | −0.0008 | ||||||
(3.88) | (−1.57) | |||||||
Busyness | 0.0031** | 0.0040*** | ||||||
(2.37) | (3.63) | |||||||
Big10 | 0.1062*** | 0.1075*** | 0.1132*** | 0.1114*** | 0.1131*** | 0.1137*** | 0.1121*** | 0.1131*** |
(19.72) | (19.93) | (21.04) | (20.69) | (21.02) | (21.10) | (20.84) | (21.01) | |
Em | 2.3665*** | 2.3792*** | 2.3677*** | 2.3782*** | 2.3742*** | 2.3697*** | 2.3795*** | 2.3762*** |
(16.76) | (16.81) | (16.69) | (16.78) | (16.73) | (16.70) | (16.80) | (16.75) | |
Complexity | 0.0887*** | 0.0887*** | 0.0926*** | 0.0905*** | 0.0934*** | 0.0928*** | 0.0915*** | 0.0922*** |
(4.73) | (4.72) | (4.92) | (4.81) | (4.96) | (4.93) | (4.87) | (4.90) | |
Switch | −0.0722*** | −0.0795*** | −0.0793*** | −0.0791*** | −0.0777*** | −0.0797*** | −0.0799*** | −0.0769*** |
(−7.43) | (−8.21) | (−8.17) | (−8.16) | (−7.96) | (−8.20) | (−8.26) | (−7.89) | |
Opinion | 0.1753*** | 0.1741*** | 0.1761*** | 0.1726*** | 0.1754*** | 0.1757*** | 0.1742*** | 0.1760*** |
(10.30) | (10.21) | (10.30) | (10.11) | (10.26) | (10.28) | (10.21) | (10.30) | |
Lev | −0.0752*** | −0.0807*** | −0.0846*** | −0.0823*** | −0.0853*** | −0.0848*** | −0.0849*** | −0.0835*** |
(−4.72) | (−5.05) | (−5.29) | (−5.15) | (−5.32) | (−5.29) | (−5.32) | (−5.21) | |
Size | 0.8386*** | 0.8412*** | 0.8420*** | 0.8396*** | 0.8422*** | 0.8418*** | 0.8422*** | 0.8432*** |
(136.08) | (136.69) | (136.55) | (136.06) | (136.54) | (136.40) | (136.88) | (136.58) | |
Punish | 0.0280*** | 0.0259*** | 0.0253*** | 0.0259*** | 0.0247*** | 0.0246*** | 0.0246*** | 0.0247*** |
(4.08) | (3.76) | (3.66) | (3.75) | (3.57) | (3.56) | (3.57) | (3.57) | |
Loss | 0.0507*** | 0.0499*** | 0.0489*** | 0.0488*** | 0.0490*** | 0.0490*** | 0.0498*** | 0.0492*** |
(5.25) | (5.15) | (5.03) | (5.03) | (5.05) | (5.05) | (5.14) | (5.07) | |
Grow | −0.0006** | −0.0006** | −0.0007** | −0.0006** | −0.0007** | −0.0006** | −0.0007** | −0.0006** |
(−2.11) | (−2.21) | (−2.26) | (−2.20) | (−2.24) | (−2.22) | (−2.24) | (−2.21) | |
Year/Ind | Control | Control | Control | Control | Control | Control | Control | Control |
Constant | 5.5120*** | 5.4469*** | 5.2887*** | 5.2621*** | 5.2679*** | 5.2658*** | 5.2790*** | 5.2466*** |
(87.24) | (88.13) | (88.47) | (88.32) | (88.32) | (88.25) | (88.41) | (87.48) | |
Obs. | 22,728 | 22,728 | 22,728 | 22,728 | 22,728 | 22,728 | 22,728 | 22,728 |
R-squared | 0.57 | 0.57 | 0.56 | 0.56 | 0.56 | 0.56 | 0.56 | 0.56 |
r2_a | 0.567 | 0.565 | 0.563 | 0.564 | 0.563 | 0.563 | 0.562 | 0.563 |
F | 785.4 | 923.1 | 916.5 | 919.2 | 915.4 | 915.4 | 917.8 | 916.0 |
t statistics in parentheses *p < 0.05, **p < 0.01, ***p < 0.001.
For control variables, there is a significant positive correlation between “Big 10” accounting firms and the audit fee. The level of earnings management is significantly positively related to the audit fees. The complexity of economic business is significantly positively correlated with the audit fees. Whether to switch the firm is significantly negatively correlated with the audit fees. There is a significant positive correlation between the size of the auditee and the audit fees. Whether the company is punished is significantly positively related to the audit fees and the loss for the year has a significant positive correlation with the audit fees. The company’s growth is positively correlated with the audit fees. Contrary to the expectation that the company’s asset-liability ratio is significantly negatively correlated with the audit fee, indicating that the higher the asset-liability ratio, the lower the audit fees. The descriptive statistics shows that lev of the auditee is at a reasonable level. The higher of reasonable lev, the more power owned by creditors, which means managers must improve earnings quality, reduce the possibility of financial frauds, and reduce the likelihood of significant financial risks of the company, thereby reducing audit fees [
4) Robustness Tests
This paper carries out the robustness tests on the empirical results by 2 methods. In the first robustness test, we measure the explanatory variables in another way, by combining the two auditor features for the year into an integrated variable; and the other variables in the model do not change. The definitions of variable of robustness test 1 are presented in
In the second robustness test, we expand the year length, starting from 2007 to measure whether the relationship between auditor characteristics and audit fees is influenced by external policy. It is found from
Code | Definition |
---|---|
Age | is 1 if both auditors’ ages are above medium of age for the year, and 0 otherwise |
Gender | is 1 if both auditors are men, and 0 otherwise |
Edubg | is 1 if both auditors have bachelor degrees or above, and 0 otherwise |
Spe | is 1 if both auditors’ industry specializations are above medium of industry specialization for the year, and 0 otherwise |
Partner | is 1 if both auditors are partners, and 0 otherwise |
Exp | is 1 if both auditors’ number of audit year is above medium of number of audit year for the year, and 0 otherwise |
Busyness | is 1 if both auditors’ busyness is above medium of busyness for the year, and 0 otherwise |
(9) | (10) | |
---|---|---|
Age | −0.0947*** | −0.0056*** |
(−10.38) | (−12.09) | |
Gender | −0.0128 | −0.0272*** |
(−1.64) | (−5.34) | |
Edubg | 0.0266*** | 0.0276*** |
(3.11) | (5.07) | |
Spe | 0.0072 | 0.0003 |
(0.84) | (1.26) | |
Partner | 0.0190** | 0.0258*** |
(2.08) | (4.34) | |
Exp | 0.0295*** | 0.0021*** |
(3.22) | (3.09) | |
Busyness | 0.0138* | 0.0036*** |
(1.73) | (3.11) | |
Big10 | 0.1049*** | 0.1308*** |
(13.38) | (25.59) | |
Em | 2.3223*** | 2.1035*** |
(11.39) | (20.66) | |
Complexity | 0.0803*** | −0.1181*** |
(2.98) | (−6.27) | |
Switch | −0.0679*** | −0.0601*** |
(−4.79) | (−7.03) | |
Opinion | 0.1686*** | 0.1735*** |
(6.91) | (13.24) | |
Lev | −0.0761*** | 0.0190*** |
(−3.32) | (18.47) | |
Size | 0.8387*** | 0.7987*** |
(94.47) | (170.19) | |
Punish | 0.0272*** | 0.0216*** |
(2.75) | (3.49) | |
Loss | 0.0534*** | 0.0613*** |
(3.84) | (7.49) | |
Grow | −0.0007* | −0.0000 |
(−1.75) | (−0.54) | |
Constant | 5.3157*** | 5.7842*** |
(61.58) | (113.60) | |
Observations | 11072 | 29094 |
R-squared | 0.57 | 0.57 |
r2_a | 0.566 | 0.572 |
F | 380.4 | 949.1 |
t statistics in parentheses *p < 0.05, **p < 0.01, ***p < 0.001.
This paper bases on the data of listed companies in China from 2010 to 2015 to conduct empirical study about the influence of auditor’s individual characteristics on audit fees, selecting age, gender, education background, position, the number of audit year and busyness as explanatory variables. The empirical results show that in addition to the factors of audit client level and accounting firm level, the auditor’s individual characteristics also have influence on audit fees. The age, gender, educational background, industry specialization, position, number of audit year and busyness of auditors all significantly related to audit fees. The auditors who are female, younger, partner and have higher degree of education, more audit experience, and higher reputation tend to gain favors of the audit clients and obtain higher audit fees.
This paper may provide the following suggestions. Firstly, audit industry should strengthen the professional abilities of the individual auditors. The empirical results show that when choosing audit services, the audit clients not only consider the characteristics of the firm, but also consider the individual characteristics of auditors, and they are willing to pay audit fee premiums for the auditors who deliver the “high quality” signals. Therefore, the audit firm should strengthen the professional education and on-the-job training of auditors, and pay attention to the cultivation of industry expertise, to improve audit quality and receive higher audit fees. To improve auditors’ education degrees, the firm should introduce outstanding graduates with highly educated backgrounds. On the other hand, the audit firm should build a reasonable incentive mechanism to encourage auditors to receive continuing education to continuously improve their own professional abilities. In terms of practical experience, audit firms need to pay attention to the experience of auditors and guide them to accumulate industrial specialization.
Secondly, the audit firm should pay attention to the disclosure of the characteristics of the individual auditors, because the firms merely disclose some concise information, such as the firm’s comprehensive ranking nowadays. However, according to the conclusion of this paper, audit clients care about individual auditors and are willing to pay different audit fees. Therefore, more disclosure of individual auditors’ information will reduce the search costs of audit clients and help clients select appropriate audit services more efficiently.
This paper proves that the individual characteristics of auditors will have an impact on audit fees. And this paper mainly measures the demographic characteristics and practicing characteristics of the auditors from several aspects. However, the individual characteristic is an abstract concept. In addition to the features used in this paper, there are many features that affect the auditor’s decision-making. For example, Environment, personal character, economic status and other factors. However, due to the difficulty of data collection, individual characteristics cannot be fully defined. Therefore, in the future research, we can include more comprehensive characteristic of auditors to examine the audit fees at individual auditor level.
Liu, S.H. (2017) An Empirical Study: Auditors’ Characte- ristics and Audit Fee. Open Journal of Accounting, 6, 52-70. https://doi.org/10.4236/ojacct.2017.62005