Accounting Research Letters
Article in Press
Volume X, Issue X s

The Accounting and Economic Association of Japan

202X
pp. XX-XX

Do Linguistic and Accounting Reporting
Complexity Affect Audit Fee?
Peijun Liu
Osaka University
Submitted: February 5, 2024; Revised March 6, 2024; Accepted: March 11, 2024;
Available online XXXX XX, 202X
Abstract
Measuring audit clients’ complexity attracts scholars’ attention as quantifying linguistic complexity in textual disclosure and accounting reporting complexity in financial statements becomes available with automatic tools.
Previous research identifies three categories of complexity measures including operational, linguistic, and accounting reporting complexity, while only operational complexity has been studied in Japanese audit market.
This study aims to explore whether auditors in Japan responds to complexity of textual disclosure in annual reports and of financial statement. Using a large sample comprising 14,389 firm-year observations from 2014 to
2021, this paper finds a positive association between linguistic and accounting reporting complexity with audit
fees. This result is robust when taking file size as alternative measure of linguistic complexity. In addition, thispaper reports a positive relationship between audit fees and linguistic complexity in a special section of annual
report, Business Risks. These findings suggest that Japanese auditors increase their audit efforts when their clients are more complex in terms of textual disclosure and financial statements.
Keywords: audit fees, accounting complexity, XBRL, textual disclosure, Japan audit market

I am grateful for the valuable advice from Professor Atsushi Shiiba and anonymous referees. I appreciate the helpful commentsfrom Professor Tomomi Takada at the early stage of this paper. I also thank comments in seminars at Osaka University. Special
thanks to the Center for Mathematical Modeling and Data Science (MMDS) of Osaka University for making Refinitiv available
for this study.
Corresponding Author: Peijun Liu, 1-7, Machikaneyama, Toyonaka, Osaka 560-0043, Japan© 2024 The Accounting and Economic Association of Japan.
All rights reserved.

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Accounting Research Letters, Volume X, Issue X (202X) XX-XX

1 Introduction
This paper aims to investigate how the level of various sources of clientʼs complexity affect audit
pricing behavior. The traditional empirical research suggests that audit fee is mainly driven by theefforts and risks that auditors take, which could be reflected through the attributes of clientʼs financial reporting system (Simunic, 1980; Yazawa, 2011). Accordingly, prior research usually incorporates operational complexity measures as proxies of the level of clientʼs complexity (Hay,
Knechel and Wong, 2006). It is usually assumed that the more complex the clientʼs operation is,
the more time and effort is required for auditors to complete audit test and prepare audit report.
However, recent studies suggest instead of operational complexity, a more comprehensive measure of complexity should be considered when investigating the determinants of audit fees, since
operational complexity reflects only one aspect of audit clientsʼ characteristics. (Hoitash andHoitash, 2018; Hossain et al., 2019; Blanco et al., 2021; Malik, Shan and Tong, 2022). Following thesearguments, this paper highlights two types of complexity measures: (1) linguistic complexity, and(2) accounting reporting complexity in determining audit fees. It is hypothesized that a higher
level of linguistic and accounting reporting complexity is associated with higher level of audit fees.
To test hypotheses, this paper uses a large sample of 14,389 firm-year observations from Japaneselisted companies over the period 2014-2021. The empirical model in this paper follows the work ofFukukawa (2012) and different complexity measures are replaced and tested in order. Specifically,
the linguistic complexity is proxied by length, readability, and file size (Li, 2008; Loughran andMcdonald, 2014), while accounting reporting complexity is measured by the total numbers of
unique monetary XBRL tags used in financial statements (Hoitash and Hoitash, 2018; Jin and Okumura,2022). The results find that both alternative measures have economically and statisticallysignificant effects on audit fees.
This paper extends the current literature in three important ways. First, to the authorʼs best
knowledge, the discussion on relationship between narrative disclosure and audit fee is limited inJapan. This study contributes to audit literature by examining the role of linguistic attributes inaudit pricing behavior. Second, following the study of Kim and Fukukawa (2013), this paper provide evidence on the importance of business risk disclosure in explaining audit efforts and fees.
Third, this paper contributes to studies on accounting reporting complexity in Japan. While Jin
and Okumura (2022) finds accounting reporting complexity affects investorsʼ information processing costs and therefore, shapes stock market reaction to earnings announcement, this paper extend the understanding of accounting reporting complexity to the field of auditing. This study–2–

Peijun Liu, Do Linguistic and Accounting Reporting Complexity Affect Audit Fee?

suggests that higher accounting reporting complexity also increases the processing costs and efforts of auditors and audit fee accordingly.
The structure of this paper is organized as follows. Section 2 provides a review on related literature and hypotheses development. Section 3 presents research design, including definition of variables, regression model and sample selection. Then the results are provided in Section 4. Section 5
is about conclusion.

2 Literature Review and Hypotheses Development
Studies on audit fees have demonstrated that client size, complexity, and risk are three leading
determinants of audit fees (Simunic, 1980; Yazawa, 2011). The relationship between audit pricing
and clint attributes is summarized as formula below (Yazawa, 2011, p. 31):
Audit Fee=f｛ Audit Ef fort(Size, Complexity), Audit Risk( financial Risk)｝(1)

Specifically, client complexity is widely accepted by previous literature as an important driver oflevels of audit fees, since it represents decentralization and diversification of clientʼs business andtherefore increases the efforts of auditor (Simunic, 1980). This association between client complexity and audit fee has been demonstrated by extant empirical studies worldwide, including Japan(Hay et al., 2006). To be specific, the results of prior Japanese literature document a positive relationship between complexity of financial reporting entity and audit fees (Yazawa, 2011; Fukukawa,
2012; Takada, 2017). They suggest more complex client requires more labor and procedure for auditors, and thus auditor usually find it more difficult and time-consuming to conduct tests to more
complex financial report of its clients.
Previous studies suggest that auditors price their services by analyzing all client-specific information (Simunic, 1980; Hossain et al., 2019; Malik et al., 2022). In recent years, there is increasing
number of studies reporting that narrative disclosure also have impact on audit fees, including
business risk items disclosed in annual report (Kim and Fukukawa, 2013), disclosure properties
such as readability (Hoitash and Hoitash, 2018; Hossain et al., 2019; Blanco et al., 2021), and tones(Greiner, Patelli and Pedrini, 2020; Malik et al., 2022). These findings suggests that narrative disclosure also has impact on audit fees as a source of complexity. In addition, a recent approach proposed by Hoitash and Hoitash (2018) shows a higher level of accounting reporting complexity
tends to result in poor reporting quality and ultimately, lead to increases in audit efforts and fees.
However, in the studies mentioned above, the measures of client complexity in Japanese audit–3–

Accounting Research Letters, Volume X, Issue X (202X) XX-XX

market tend to be limited to the proxy of clientʼs operation.
This study relates to previous literature on audit fees by investigating the role of various types ofcomplexity measures on the pricing decision of auditors. First, this study considers how linguistic
complexity and accounting reporting complexity affects audit fees in Japan. Li (2008) documentsthat more lengthy and less readable annual reports signal unfavorable and complex disclosure andpoor performance, due to the intension of management to hide adverse information by strategically structuring narrative reports. In terms of audit fees, the textual information should be concerned because (1) compared with numbers, textual disclosure provides forward-looking information on the risks of clientʼs operation (Hossain et al., 2019); (2) textual disclosure is supposed toreveal the communication patterns of the management and other stakeholders, in which auditors
may develop an understanding of their clientsʼ performance and risks (Malik et al., 2022). A more
complex annual report may require auditors to increase audit efforts or lead to higher audit risk.
Therefore, following the formula of Yazawa (2011), the first hypothesis of this paper is as follows:
Hypothesis 1: Linguistic complexity is positively associated with audit fees.
In addition to linguistic complexity, there is increasing evidence indicating that accounting reporting complexity measure also contributes to explaining audit fees. Hoitash and Hoitash (2018) reports more complex financial statement increases the likelihood of misstatements and results inless reliable financial reports. Thus, in order to monitor complex accounting concepts and avoid
possible errors in clientʼs annual reports, auditors may increase their efforts, resulting in longer
audit lags (Zhou, 2020) and higher audit fees (Hoitash and Hoitash, 2018). In Japan, it is found thathigher accounting reporting complexity, measured by accounting concepts in earnings announcement, is associated with higher information processing costs of investors (Jin and Okumura, 2022).
However, to the authorʼs best knowledge, there is no evidence showing the association between
accounting reporting complexity and audit fees in Japanese context. Thus, this paper presents thesecond hypothesis as follows:
Hypothesis 2: Accounting reporting complexity is positively associated with audit fees.

3 Research Design
3.1 Measures of Complexity
This study uses three groups of distinct complexity measures, including (1) operational complexity variables for baseline model, (2) linguistic complexity variables to test hypothesis 1 and (3) ac–4–

Peijun Liu, Do Linguistic and Accounting Reporting Complexity Affect Audit Fee?

counting reporting complexity variables to test hypothesis 2. Specifically, this section mainly elaborates the latter two measures of interests.
Regarding linguistic complexity, it is reported that lengthier and less readable annual reports indicate more complex financial disclosure (Li, 2008; Dyer, Lang and Stice-Lawrence, 2017). Thus, thefirst proxy, Length, the natural log of total number of words in annual report is included. In addition, following the paper of Kim, Yazawa and Ito (2022), this paper measures Japanese readability
with the method proposed by Li (2016). The formula of this readability score is provided in Table
1. In contrast with Fog index, the method of Li (2016) assign lower value for more complex texts.
In other words, in contrast with other variables, a negative relationship is predicted between thisproxy, Readability, and audit fees.
Further, as of work of Kim and Fukukawa (2013) suggests a positive relationship between thebusiness risk items and audit efforts, this study attempts to extend this finding by including thenatural log of total words in the section of Business Risks in annual report, Length_Risk, and its
readability, Readability_Risk, as two alternative measures for linguistic complexity.
In addition, since there are conflicting views regarding whether traditional readability measures
are reliable or not, this paper includes the annual report document file size, F_Size, as another alternative proxy for linguistic complexity. This proxy does not require specific knowledge and assumption to interpret the information contained in annual reports, and therefore, is believed freefrom such biases (Loughran and Mcdonald, 2014).
As for accounting reporting complexity, I define the accounting reporting complexity, ARC, using
the measure proposed by Hoitash and Hoitash (2018). It is assumed that financial statement become more complex when the number of accounting concepts disclosed in it increases. To be specific, ARC is measured by the total number of distinct monetary XBRL tags in financial statements(Hoitash and Hoitash, 2018; Jin and Okumura,2022).
3.2 Empirical Model
In order to test hypotheses in Section 2, this study adopts following regression model, based on thework of Fukukawa (2012):
lnFEEi,t= α0 +βkCOMPLEXi,t,k +α1lnASSETSi,t +α2Quicki,t +α3 ROAi,t+ α4 DEi,t +α5 Lossi,t +α6Tokyoi,t +α7YEi,t +α8 BIG4 i,t + Yeart+ Industryi+ εi,t(2)–5–

Accounting Research Letters, Volume X, Issue X (202X) XX-XX

Where

and

are Firm and Year subscripts, respectively. COMPLEX

are the k variables of in-terests, including linguistic complexity, Length, Readability, Length_Risk, Readability_Risk, F_Size, andaccounting reporting complexity, ARC. In addition, to compare with complexity measures in previous studies, COMPLEXi,t,k also includes operational complexity proxies such as SUBS, CATA, BUSSEG,GEOSEG (Simunic, 1980; Stanley, 2011; Yazawa, 2011; Kim and Fukukawa, 2013; Takada, 2015).
This study adopts control variables for firm size, (lnASSETS), and audit risks (Quick, ROA, DE andLOSS). In addition, following Fukukawa (2012), a dummy variable indicating if a firmʼs headquarter is located in Tokyo is included (Tokyo), as well as a dummy variable indicating the year-end(YE). BIG4 shows the premium of Big 4 auditors in their compensation (Hu, 2014). This model also
considers year and industry fixed effect. The definition of variables used in regression formula (2)is shown in Table 1.
Table 1 Definition of Variables
Variables
Dependent Variable
lnFEE
Complexity Proxies
SUBS
CATA
BUSSEGGEOSEG
LengthReadability
Length_Risk
Readability_Risk
ARC
F_SizeControl Variables
lnASSETS
Quick
ROA
DE
Loss
Tokyo
YE
BIG4

Definitions
The natural logarithm of audit fee (in yen)
The square foot of the numbers of subsidiaries
Current assets divided by total assets
The natural logarithm of the total numbers of business segments
The natural logarithm of the total numbers of geographic segments
The natural logarithm of total numbers of words in annual reports
The measure of readability of all textual sections of annual reports, defined as (number ofwords per sentence * -0.056 + proportion of kango * -0.126 + proportion of wago * -0.042 +proportion of verbs * -0.145 + proportion of auxiliaries * -0.044 + 11.724), (Li, 2016)
The natural logarithm of total numbers of words in Business Risk section of annual reports
The measure of readability of Business Risk section of annual reports
The natural log of the total number of distinct monetary XBRL tags in financial statementsof the 10-K filings (Hoitash and Hoitash, 2018)
The natural logarithm of the size of XBRL file submitted to EDINET, in megabytes
The natural logarithm of total assets
Current assets/current liabilities
Earnings before interests and taxes/total assets
Long-term liabilities/total assets
The indicator variable that takes 1 if there is net loss during the last 3-year period, and 0
otherwise
The indicator variable that takes 1 if the firmʼs headquarter is located in Tokyo, and 0 otherwise
The indicator variable that takes 1 if the end of fiscal year is on 31st March, and 0 otherwise
The indicator variable that takes 1 if the auditor is a member of Big4, and 0 otherwise

3.3 Sample Selection
This section provides the sample collection process. First, the XBRL formatted annual reports arecollected from eol database. Second, firmsʼ fundamentals and segments data are obtained fromNEEDS Financial Quest. Finally, audit-related data, including audit fees and auditorʼs details, are–6–

Peijun Liu, Do Linguistic and Accounting Reporting Complexity Affect Audit Fee?

obtained from Refinitiv. The final sample consists of 14,389 firm-year observations from 2014 to 2021.
Observations are selected from 2014 because textual disclosure is mandated by FSA to be incorporated in XBRL files after the year end of 2013. The sample selection process starts with 20,604
firm-year observations using Japan GAAP and with non-missing financial fundamentals fromNEEDS Financial Quest database. Then, 286 observations of financial firms are excluded. Matchingwith XBRL files for calculating ARC and textual features (Length, Readabilty, Length_Risk,
Readability_Risk) decreased the sample to 16,558. Finally, another 2,169 observations are excluded
due to missing audit fee and auditor details in Refinitiv database.

4 Results
4.1 Descriptive Statistics
Table 2 and 3 show the summary statistics and correlation matrix of key variables. Especially, inTable 3, the univariate correlation between lnFEE and Length is 0.6 and significant in 0.01 significant level, revealing a strong positive association between linguistic complexity with audit fee1.
These statistics are consistent with prior research (Malik et al., 2022). In addition, the correlation
between Readability and lnFEE is negative, showing more readable annual reports will decreaseaudit efforts. The correlation coefficients of length and readability in Business Risk are similar to
those shown above. These results support the arguments in H1. Moreover, the coefficients of
F_Size and ARC are positive, as predicted in H2.
Table 2 Summary of Statistics
VARIABLES
lnFEE
SUBS
CATA
BUSSEGGEOSEG
LengthReadability
Length_Risk
Readability_Risk
ARC
F_SizelnASSETS
Quick
ROA
DE
Loss
Tokyo
YE
BIG4

(1)
N
14,38914,38914,38914,38914,38914,38914,38914,38914,38914,38914,38914,38914,38914,38914,38914,38914,38914,38914,389(2)mean
17.754
3.773
0.569
3.093
0.505
10.8490.744
7.273-0.3425.042
1.015
11.052
0.414
0.056
0.464
0.183
0.521
0.705
0.754

(3)
sd
0.840
2.596
0.192
2.115
1.517
0.223
0.610
0.648
0.593
0.118
0.237
1.562
0.188
0.064
0.188
0.387
0.500
0.456
0.430

(4)
min
16.396
1.000
0.112
0.000
0.000
10.385-1.6435.714-1.862
4.691
0.531
7.466
0.050
-0.225
0.098
0.000
0.000
0.000
0.000

(5)
max
20.42826.4200.946
12.0009.000
11.444
1.810
8.740
1.592
5.288
1.653
15.062
0.862
0.253
0.874
1.000
1.000
1.000
1.000

All continuous variables are winsorized at 1 and 99 percentile level.
1

To address possible multicollinearity issue, this paper estimates variance inflation factors (VIFs) in all regressions with values
about 2. The values are far below the threshold of 10 in previous studies (Hossain et al., 2019)–7–

Accounting Research Letters, Volume X, Issue X (202X) XX-XX

Table 3 Correlation Matrix(1) lnFEE(2) SUBS(3) CATA(4) BUSSEG(5) GEOSEG(6) Length(7) Readability(8) Length_Risk(9) Readability_Risk(10) ARC(11) F_Size

(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
1
0.76***
1-0.19*** -0.20***10.26*** 0.33*** -0.12***1
0.13*** 0.14*** 0.03*** -0.34***1
0.60*** 0.56*** -0.17*** 0.28*** 0.04***1-0.10*** -0.06*** 0.13*** -0.08*** 0.11*** -0.13***1
0.13*** 0.13*** 0.10*** 0.02*** -0.03*** 0.40*** 0.08***1-0.06*** -0.07*** 0.07*** -0.08*** 0.06*** -0.11*** 0.11*** -0.13***10.43*** 0.44*** -0.26*** 0.28*** 0.05*** 0.43*** -0.08*** -0.07*** -0.03***
0.59*** 0.56*** -0.25*** 0.30*** 0.07*** 0.73*** -0.23*** 0.09*** -0.09***

(10)

(11)

1
0.50***

1*** represents statistical significance at the 0.01 level.

4.2 Regression Results
Table 4 presents estimation results of audit fee models. Column (1) shows the results of baselinemodel, without controlling complexity of audit clients. This result is consistent with previous audit
studies (e.g. Fukukawa, 2012; Hu, 2014; Takada, 2015). To compare each complexity measure, thisstudy regressed the model with different groups of test variables, including operational complexity
proxies in Column (2), length and readability of annual reports in Column (3), length and readability of Business Risks in Column (4), and file size and accounting reporting complexity in Column (5).
Column (3) and (4) present positive association between audit fee and linguistic complexity as
suggested in hypothesis 1. First, the coefficients of Length and Length_Risk are 0.786 and 0.158 respectively. The positive, and economically and statistically significant coefficients indicate that on
average, auditors charge higher prices when clientsʼ financial reports are lengthier. It is noted thatthe association between length and audit fee exhibits larger economic significance, in contrast tooperational complexity measures in Column (2). Furthermore, by examining linguistic characteristics of business risk disclosure in Column (4), the results support Kim and Fukukawaʼs (2013)
argument that auditors respond to clientʼs business risk. However, the readability measure,
Readability and Readability_Risk, are either economically, or statistically insignificant. These insignificant coefficients may result from the misspecification of readability measures (Loughran and Mcdonald, 2014), and are in line with the results in Hoitash and Hoitash (2018). Taken together, theseresults support H1.
Column (5) shows the estimated results for hypothesis 2, in which the coefficients of accounting
reporting complexity, ARC, is positive and significant as expected. In addition, in response to thecriticism against readability measurement, the alternative variable, F_Size is found positively asso–8–

Peijun Liu, Do Linguistic and Accounting Reporting Complexity Affect Audit Fee?

ciated with audit fees. The results in Column (5) strongly support hypothesis 2 and provide further evidence for hypothesis 1, which indicates that auditors require more compensation whenprocessing more complex financial statements.
Table 4 Regression Results
VARIABLES
SUBS(1)Model1CATA
BUSSEGGEOSEG
Length(2)Model20.105***(41.24)0.214***(6.14)
0.014***(7.49)
0.017***(5.86)(3)
Model3(4)
Model4

0.786***(34.32)0.073***(11.89)Readability
Length_Risk

0.158***(24.62)-0.004(-0.65)

Readability_Risk
ARC
F_SizelnASSETS
Quick
ROA
DE
Loss
Tokyo
YE
BIG4
Constant
Year & Industry
Observations
Adj. R-squared
Robust(5)Model50.458***(132.26)
0.245***(9.71)-0.281***(-3.74)0.428***(19.31)0.158***(13.82)0.153***(20.37)-0.072***(-8.61)0.346***(42.04)12.050***(272.50)
YES
14,3890.744

0.313***(74.91)-0.031(-0.82)-0.313***(-4.82)0.249***(12.42)0.116***(11.14)0.130***(18.67)-0.038***(-5.10)0.345***(44.48)13.304***(284.43)
YES
14,3890.793

0.397***(104.60)
0.212***(8.82)-0.233***(-3.24)0.315***(15.03)0.132***(11.98)0.120***(16.62)-0.062***(-7.79)0.325***(40.84)4.288***(18.78)YES
14,3890.768

0.451***(136.05)0.170***(6.90)-0.354***(-4.80)0.340***(15.54)0.139***(12.26)0.146***(19.92)-0.056***(-6.84)0.328***(40.31)11.083***(184.43)
YES
14,3890.755

0.250***(6.34)
0.401***(16.97)0.413***(104.31)0.273***(11.06)-0.191***(-2.59)0.350***(15.98)0.158***(13.96)0.143***(19.22)-0.074***(-9.07)0.351***(42.87)10.906***(58.15)
YES
14,3890.752

statistics in Table 4 are calculated from Whiteʼs (1980) method. *** represent significance at the 0.01 level.

Furthermore, it can be noticed that the changing incremental explanatory power in each model, in
which different groups of complexity measures are substituted in order. Compared with the baseline model in Column (1), adding operational complexity proxies in Column (2) still contributes
the most explanatory power of 4.9%, followed by linguistic complexity proxies in Column (3) and(4) of 2.4% and 1.1%, and accounting reporting complexity proxies in Column (5) of 0.8%. Howev–9–

Accounting Research Letters, Volume X, Issue X (202X) XX-XX

er, given the high adjusted R-squares in audit fee models, this change supports the argument that
linguistic complexity and accounting reporting complexity helps to explain changes in audit fees.

5 Conclusion
Recent literature indicates that auditors consider various aspects of their clientsʼ complexity level
when pricing their service (Hoitash and Hoitash, 2018; Malik et al., 2022). This paper finds thatexcept traditional proxies of complexity in empirical audit fee models, linguistic and accounting reporting complexity also have a significant explanatory power on audit fee pricing. The findings ofthis paper highlight the usefulness of linguistic attributes to external stakeholders such as auditors. In addition, this paper also highlights the importance of business risk disclosure in audit pricing, which extends the finding in previous literature (Kim and Fukukawa, 2013). However, thisstudy also has limitations. Firstly, due to the lack of mature measure of Japanese financial text,
this paper does not control other properties of narrative disclosure, such as tone, stickiness, etc.
Secondly, it is found that readability is not significantly associated with audit fee. This finding may
result from the misspecification of readability formula (Loughran and Mcdonald, 2014). To sum up,this paper suggests auditors also respond to level of complexity of textual disclosure and financial
statements, and it encourages a more comprehensive measure when considering the impact of clientsʼ complexity in audit fee model for future studies.
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