Audit Office Size and Audit Quality: The Influence of City-Level Industry Specialists andAudit Firm Tenure

Yingwen Guo*
Nanjing University
E-mail: yingwguo2-c@my.cityu.edu.hk

Phyllis Lai-lan Mo
City University of Hong Kong
E-mail: p.mo@cityu.edu.hk* Corresponding author.

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Audit Office Size and Audit Quality: The Influence of City-Level Industry Specialists andAudit Firm Tenure

Abstract
Prior studies suggest that audit quality is positively related to audit office size, i.e. thesize of a local practice office within an audit firm. Based on a sample of U.S. listed companies
over the period 2004 - 2013, this study shows that the positive association between audit officesize and audit quality is weaker for firms audited by city-level industry specialists and longtenured audit offices. These findings suggest that city-level industry specialization (long officelevel auditor tenure) leads to deep industry (client-specific) knowledge, which tends to bebeneficial in small audit offices where the accounting professionals has less experience related tothe audits of listed companies.

Key Words: Office Size; City-level Industry Specialist; Audit Firm Tenure; Audit Quality

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Audit Office Size and Audit Quality: The Influence of City-Level Industry Specialists andAudit Firm Tenure

I. Introduction
In recent years, a growing number of studies show that audit quality is affected by officelevel auditor characteristics. This line of research is motivated by the notion that audits areadministrated by the engagement offices located in the same city as the clients‟ headquarters (e.g.
Penno and Walther 1996; Ferguson, Francis and Stokes 2003; Francis 2004). Hence, humancapitals and economic dependence on individual clients tend to be office-specific phenomenon,
leading to quality differentiation across practice offices within the same audit firm.
Existing literature shows that the size of the audit engagement office (i.e. audit office size)
is one of the determinants of audit quality (e.g., Reynolds and Francis 2000; Francis and Yu
2009; Choi, Kim, Kim and Zang 2010). “A Big 4 accounting firm is not so big when we shift tothe office level of analysis” (Francis 2004 pp. 355). For example, based on the sample of U.S.listed companies used for this study, we observe a cross-sectional variation in office size within a
Big 4 audit firm. In 2004, nearly 24% of PwC‟s audit fee revenues were earned by the largest
office in New York, while the smallest offices individually earned less than 1% of the audit firm‟
audit fee revenues. The literature provides two interpretations for heterogeneous quality acrosspractice offices. Francis et al. (2009) explain this finding from the perspective of auditors‟
3competence. Larger audit offices have more in-house experience related to the audits of publiccompanies. That is, accountants have more opportunities to accumulate knowledge or experience
if they work in larger audit offices with larger clientele bases; in addition, they can consult with
their peers within the same offices, leading to an increased likelihood of detecting financial
misstatement and hence higher audit quality. Put differently, larger offices supply higher-quality
audits due to abundant human capitals. Choi et al. (2010) raises an explanation related to theargument of DeAngelo (1981), i.e., larger offices have a greater number of clients, thus earning
more client-specific quasi-rents that serve as collateral against auditors‟ opportunistic behaviors.
As a result, auditors in larger audit offices have less incentive to compromise their independence.
In this study, we first examine how the association between audit office size and audit
quality is affected by city-level industry specialization. Prior studies have documented thepositive effect of city-level industry leadership on audit quality (e.g. Ferguson et al. 2003;
Francis, Reichelt and Wang 2005), suggesting that deep industry knowledge is limited to specificoffices. We expect that the shortage of human capitals in small audit offices can be mitigated by
the accumulation of industry-specific knowledge. In addition, as stated previously, small offices
are more economically dependent on particular clients, thus they are more likely to acquiesce. Ifthey are city-level industry specialists, we expect the likelihood of auditor acquiescence could be
reduced, as industry specialists have incentives to protect their reputation (Reichelt and Wang
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2010). Based on the discussions above, we argue that city-level industry leadership mitigates theassociation between audit office size and audit quality.
Next, we investigate how office-level auditor tenure affects the association between audit
office size and audit quality. Prior studies show that auditors can accumulate more client-specificknowledge and supply higher-quality audits, as the length of auditor tenure increases (e.g.
Johnson, Khurana, and Reynolds 2002; Myers, Myers and Omer 2003; Gul, Fung and Jaggi
2009). The aforementioned studies focus on auditor tenure at firm level. We extend this line ofresearch to the office level. We argue that client-specific knowledge resides in the engagement
offices, thus the longer-tenured audit offices can accumulate more client-specific knowledge,
thus weaken the lack of in-house experience in small offices, i.e., weaken the linkage between
small office size and poor audit quality.
Using the data of U.S. listed companies from 2004 to 2013, we empirically test the twoquestions and have the following findings. First, audit office size is positively associated withaudit quality as measured by absolute discretionary accruals. Second, this positive association is
weakened for city-level industry specialists. Third, this association is also mitigated if a company
is audited by an office with longer auditor tenure (e.g. at least 3 years). To summarize, either
city-level industry specialization or increased office-level auditor tenure alleviates the positive
association between audit office size and audit quality.
5This study contributes to current literature in the following aspects. Firstly, prior studies
present evidence of lower-quality audits supplied by smaller offices, due to the lack of humancapitals or lower auditor independence (Francis et al. 2009; Choi et al. 2010). Our paper makes
contributions to this line of research, by identifying other office-level auditor characteristics (i.e.,city-level industry specialization and audit office tenure) that mitigate heterogeneous quality
across audit offices of various sizes. Secondly, our study adds to current literature in the effect ofcity-level industry specialization on audit quality (e.g. Ferguson et al. 2003; Francis et al. 2005;
Reichelt et al. 2010). Our findings suggest that city-level industry specialization play animportant role in small audit offices. Thirdly, to the best of our knowledge, our paper is one offew studies that pay attention to auditor tenure at office level. Current literature generally focuses
on auditor tenure at firm level, and suggests the lack of client-specific knowledge in the earlyyears of audit engagements. We apply this argument to office-level analysis, based on the notionthat client-specific knowledge is held by engagement offices and is not easily shared amongaudit offices within an audit firm.
The reminder of this paper is organized as follows. Section I reviews relevant literatureand develops the hypotheses. Section III introduces the research design. Section IV describes the
sample and presents descriptive statistics. Section V reports the results of multivariate analysis.
In Section VI, we draw the conclusions.
6II. Literature Review and Hypothesis Development
Audit Office Size and Audit Quality
The literature generally suggests that Big 4 audit firms supply audits of higher quality
than do Non-Big 4 audit firms (e.g. DeAngelo 1981; Dye 1993; Lennox 1999). However, “a Big
4 accounting firm is not so big when we shift to the office level of analysis” (Francis 2004). In
recent years, a body of research concludes that audit quality is positively related to the size ofengagement office, namely audit office size (Reynolds et al. 2000; Francis et al. 2009; Choi et al.
2010); hence there is quality differentiation across practice offices within the same audit firm.
This finding is consistent with the argument of DeAngelo (1981), i.e., in larger offices, auditors
can earn more aggregate client-specific quasi-rents which serve as collateral against
opportunistic behaviors; in other words, auditors in larger offices have “more to lose” in case ofaudit failures; they have less incentives to compromise their independence, and hence, provides
audits of higher quality (Reynolds et al. 2000; Choi et al. 2010). Francis et al. (2009) provides analternative explanation. That is, in larger offices with larger clientele base, the accountants canaccumulate more “in-house” experience related to the audits of public companies, leading to“collective human capital”. Moreover, larger offices can provide auditors with greater support7

networks for consultation, i.e., auditors may consult with their peers when meeting withproblems. As a result, the accountants working in larger offices tend to be more competent.

City-level Industry Specialists, Audit Office Size and Audit Quality
An office is defined as a city-level industry specialist if it has the largest market shares in
an industry in a particular city and in a particular year. Francis, Stokes and Anderson (1999) findthat a national market leader is not always a city-level market leader. Ferguson et al. (2003) andFrancis et al. (2005) show that joint national and city-level industry specialists can earn audit fee
premium; while national industry leadership alone does not generate any fee premium. Reichelt
et al. (2010) presents evidence of higher audit quality supplied by joint national and city-levelindustry specialists, as well as city-specific industry specialists alone (i.e., without being national
industry specialists). These studies imply that deep industry-specific knowledge is specificallyheld by city-based individual accountants in local engagement offices; there exists no “positive
network externalities” and the city-level industry expertise cannot be easily distributed to otherpractice offices (Ferguson et al. 2003; Francis 2004)
Based on the literature, we expect that city-level industry specialization can help small
audit offices to conduct effective audits, through the accumulation of deep industry-specificknowledge, thus the linkage between small offices and poor audit quality is alleviated. Moreover,8

small offices are more likely to acquiesce to clients‟ demand for favorable reporting, as they tend
to be more economically dependent on individual clients. However, if they are city-basedindustry experts, they enjoy greater reputational capitals than do non-specialists. The incentive
for reputation protection could outweigh economic dependence to affect audit quality (e.g.
Reynolds et al. 2000; DeFond, Raghunandan and Subramanyam 2002; Li 2009). Based on thediscussions above, we argue that the effect of audit office size and audit quality tends to be
mitigated by city-level industry specialists. Therefore, we develop the first hypothesis as follows:
H1: City-level industry specialization weakens the positive association between audit
office size and audit quality, ceteris paribus.

Audit Office Tenure, Audit Office Size and Audit Quality
A stream of literature suggests that shorter auditor tenures at firm level lead to lower
audit quality, as auditors lack client-specific knowledge in the early years of audit engagement(e.g. Johnson, Khurana, and Reynolds 2002; Myers, Myers and Omer 2003; Ghosh and Moon
2005; Chen, Lin and Lin 2008; Gul, Fung and Jaggi 2009). As the length of auditor tenure
increases, auditors can develop more client-specific knowledge and hence are more likely todetect financial misstatement and conduct effective audits. So far, there is little empiricalresearch that shifts the analysis of auditor tenure to the office level.
9Similar to industry-specific knowledge, we expect that client-specific knowledge are
specifically held by city-based local engagement offices and cannot be easily transferred to otheroffices. Therefore, the office-level auditor tenure should a fundamental determinant of audit
quality. Moreover, we expect the client-specific knowledge acquired by long-tenured audit
offices tend to be more beneficial in small offices that have less in-house experience. In the earlyyears of audit engagements, audit partners lack client-specific knowledge; they more likely torely on the existing office-based support network. As audit office tenures increase, enhanced
client-specific knowledge could mitigate the lack of in-house experience in small offices. Even if
the clients switch audit partners within the same office, the client-specific knowledge acquiredby the predecessor partners can be shared within the office, contributing to the local support
network. To summarize, the audit office size – audit quality association is alleviated for longtenured audit offices.
To empirically test this issue, we develop the second hypothesis as below:
H2: Long office-level auditor tenure weakens the positive association between audit
office size and audit quality, ceteris paribus.

III. Research Design
Measurement of Audit Quality10

We use the absolute values of discretionary accruals to measure audit quality. Based onprior studies (e.g., Choi et al. 2010; Choi, Kim, Qiu and Zang 2012), we use two proxies of
discretionary accruals. The first one is based on the model in Ball and Shivakumar (2006):
TACC jt TA jt 1   0  1 (1 TA jt 1 )   2 [(REV jt  REC jt ) TA jt 1 ]   3 ( PPE jt TA jt 1 )
  4 CFO jt TA jt 1   5 DCFO jt   6 CFO jt TA jt 1  DCFO jt   jt

(1)

Where, the subscript j and t (t-1) represent firm j in year t (t-1). TACC denotes total
accruals, i.e., income before extraordinary items minus cash flow from operations; TA denotes
year-end total assets;  REV and  REC represent the changes in net revenue and the changes inreceivables, respectively; PPE denotes gross property, plant and equipment. CFO refers to cash
flows from operation; DCFO is an indicator variable equal to 1 if CFO is negative and 0
otherwise. We estimate Eq. (1) for each two-digit SIC code industry and every year with at least
20 observations. The discretionary accruals, denoted as |DA1|, are equal to the residuals
estimated from Eq. (1).
The second proxy is the performance-matched discretionary accruals developed by
Kothari, Leone and Wasley (2005). We estimate the modified Jones model in Eq. (2) and denotethe unadjusted discretionary accruals (i.e. the residuals from Eq. (2)) as DA. Then we match eachobservation of DA with the one that has the closest return on assets (ROA) in the same two-digit

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SIC code industry and in the same year. The performance-adjusted discretionary accruals,
denoted as DA2, equal to DA minus the matched firm‟s DA.
TACC jt TA jt 1   0  1 (1 TA jt 1 )   2 [(REV jt  REC jt ) TA jt 1 ]
  3 ( PPE jt TA jt 1 )   jt

(2)

Model for Testing Hypothesis
To test hypothesis H1, we construct the empirical model specified as follows:| DA1 | jt or | DA2 | jt   0  1OFFICE _ SIZE jt   2OFFICE _ SIZE jt  INDSPEC jt  3 INDSPEC jt   4 LONG jt   5 BIG4 jt   6 LNTA jt   7CFO jt  8 STD _ CFO jt   9 LEV jt  10 LOSS jt  11 ISSUE jt  12 MTB jt(3)

 13 SALES_GROW TH jt  14 SALES_VOL jt  15 IND_GROWTH jt 16 LAGACC jt   jt
Where, for firm j in year t, |DA1| and |DA2| denotes the absolute values of DA1 and DA2,
respectively. OFFICE_SIZE represents audit office size as measured by natural logarithm of an
office‟s aggregate audit fee revenues (Francis et al. 2009). INDSPEC is an indicator variableequal to 1 for city-level industry specialists and 0 otherwise. Existing literature suggests that bothlarger audit office size and city-level industry specialization lead to higher audit quality (e.g.
Francis et al. 2009; Reichelt et al. 2010), we thus expect negative coefficients on OFFICE_SIZE
and INDSPEC. As predicted by H1, city-level industry specialists mitigate the association

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between audit office size and audit quality, which translates to a positive coefficient on
OFFICE_SIZE×INDSPEC.
We include a number of other control variables expected to affect discretionary accruals.
LONG is an indicator variable equal to 1 if office-level audit tenure is larger than 3 years (Gul etal. 2009). BIG4 captures the effects of audit firm size on audit quality (e.g. DeAngelo 1981; Dye
1993). LNTA is the natural logarithm of total assets and is expected to be negatively associatedwith |DA1| and |DA2|, as larger client firms have more stable operations and tend to report
smaller discretionary accruals (e.g. Dechow and Dichev 2002). CFO denotes cash flows fromoperations deflated by total assets. There should be a negative correlation between cash flows
and abnormal accruals (Frankel, Johnson and Nelson 2002). STD_CFO represents the standard
deviation of CFO over the last three fiscal years (Hribar and Nichols 2007). LEV is total
liabilities deflated by total assets. On one hand, highly leveraged firms are more likely to violate
debt covenants and hence have more incentives to overstate reported earnings (e.g., DeFond andJiambalvo 1994). On the other hand, high leverage may restrict managers‟ ability to manipulateearnings (e.g. Ke 2001; Iturriaga and Hoffmann 2005). Therefore, we do not predict the sign ofthe coefficient on LEV. Teoh, Wong and Rao (1998) finds that firms that are raising capitals havegreater incentives to manipulate earnings, thus we use the indicator variable ISSUE to capture
this effect. LOSS is an indicator equal to 1 if a firm reports negative net income and 0 otherwise;13

it captures the effect of financial distress on earnings management. We also include
SALES_GROWTH (sales growth) and MTB (market-to-book ratio) to control for firm growth(Ashbaugh, Lafond and Mayhew 2003; Gul et al. 2009). Yet empirical evidence regarding therelations between discretionary accruals and these variables are mixed. We also control for thevolatility of sales as measured by the standard deviation of sales in the past three years (Hribar etal. 2007). IND_GROWTH is the sales growth in a two-digit SIC code industry and is predicted tohave a positive relation with discretionary accruals (Myers, Myers and Omer 2003). Moreover,
we use LAGACC (lagged total accruals) to capture the reversals of accruals over time (Ashbaugh
et al. 2003; Choi et al. 2010). All the variables are defined in details in Appendix I.
We construct the regression model in Eq. (4) to test H2:| DA1 | jt or | DA2 | jt   0  1OFFICE _ SIZE jt   2OFFICE _ SIZE jt  LONG _ TENURE jt  3 INDSPEC jt   4 LONG jt   5 BIG4 jt   6 LNTA jt   7CFO jt 8 STD _ CFO jt   9 LEV jt  10 LOSS jt  11 ISSUE jt  12 MTB jt(4)

 13 SALES_GROW TH jt  14 SALES_VOL jt  15 IND_GROWTH jt 16 LAGACC jt   jt
Eq. (4) is identical to Eq. (3), except that the interaction term is replaced by
OFFICE_SIZE×LONG. H2 predicts that increased office-level auditor tenure can alleviate thenegative relation between audit office size and discretionary accruals; thus we expect the positivecoefficient on OFFICE_SIZE×LONG. The control variables in Eq. (4) are the same as those in
Eq. (3), so there is no repeated interpretation here.
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When estimating Eq. (3) and (4), we include year dummies and industry dummies into
the model, and cluster the standard errors at firm level.

VI. Data Collection and Descriptive Statistics
Data Collection
We retrieve auditor-related information from Audit Analytics database, and then merge
the data with the financial data extracted from Compustat database. The initial sample consists of
81,455 firm-year observations. First, 10,957 observations are excluded since their auditors arelocated outside the U.S. Second, we eliminate 63 observations of U.S. auditors that are not
located in any of metropolitan statistical areas (MSAs) delineated by the Office of Management
and Budget (OMB) in 2013. 1 These procedures leave 70,495 observations. Next, we delete theobservations without complete data required to compute the variables in the regression models,or the observations that belong to the financial industry (SIC codes: 6000 - 6999). In the final
sample, there are 33,469 observations over the period 2004 - 2013, representing 6,067 uniquefirms audited by 1,133 audit practice offices of 558 audit firms located in 158 MSAs. The sample
selection is reported in Table 1.

1

Following Francis et al. (2005), we identify the MSAs from the website of U.S. census:

http://www.census.gov/population/metro/

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[Insert Table 1 about Here]

Univariate Analysis
Table 2 reports the descriptive statistics for absolute discretionary accruals (|DA1| and|DA2|), audit office size (OFFICE_SIZE), and the other independent variables, based on thesample of 33,469 observations. The mean of |DA1| (|DA2|) is 0.238 (0.288), with the standard
deviation of 0.652 (0.754). OFFICE_SIZE has the mean (median) value of 16.442 (17.073). The
mean of INDSPEC and LONG is 0.377 and 0.617 respectively, i.e., on average, 37.7% of thefirm-year observations are audited by city-level industry specialists, and 61.7% of the firm-years
have been audited by the same office for at least three consecutive years. Big 4 audit firms (BIG4)
account for 63.9% of the observations. In addition, client size (LNTA) has the mean value of
5.426, i.e., the mean client assets are approximately 227.239 million dollars. Most financial
variables are highly skewed with large standard deviations. We will winsorize the continuous
variables at the bottom 1% and the top 1% in the regression analyses.
[Insert Table 2 about Here]
Table 3 represents the correlation matrix among the variables used in Eq. (3) and Eq. (4).
First, office size (OFFICE_SIZE), city-level industry specialists (INDSPEC) and long-tenured
office (LONG_TENURE) have significantly negative correlations with both |DA1| and |DA2|,16

suggesting that larger offices, city-level industry specialization and longer office-level auditor
tenure might restrict earnings management. Second, |DA1| and |DA2| are negatively correlatedwith the variables such as BIG4, LNTA, CFO, MTB and LAGACC, and are positively correlatedwith STD_CFO, LEV, LOSS, ISSUE, SALES_GROWTH, SALE_VOL and IND_GROWTH. Thatis, the correlation coefficients between discretionary accruals and the control variables are
significant with predicted signs. Third, there is no serious problem of multicollinearity, as mostindependent variables are not highly correlated with each other, except the correlation between
OFFICE_SIZE and BIG4 (LNTA) of 0.771 (0.634), as well as the correlation between BIG4 and
LNTA of 0.663.
[Insert Table 3 about Here]
Next, we plots the mean values of |DA1| for each quintile of audit office size for citylevel industry specialists and non-specialists respectively, 2 to further examine whether the
association between audit quality and office size differs between the two sub-samples. As shownin Figure 1, as audit office size increases, the magnitudes of discretionary accruals decline forboth sub-samples. In the 1st quintile of audit office size, the mean |DA1| for non-specialists issubstantially higher than that of city-level industry specialists. The mean |DA1| of the two subsamples converges in the 5th quintile. That is, the magnitudes of |DA1| decline more quickly for2

The plots of mean |DA2| for each quintile of audit office size are similar.

17the sub-sample non-specialists, suggesting the effect of audit office size on audit quality tends toweaker for the companies audited by city-level industry specialists.
[Insert Figure 1 about Here]We also plot the mean of |DA1| for each quintile of audit office size for long-tenured
audit offices (> 3 years) and short-tenured audit offices (at most 3 years) respectively. As
illustrated in Figure 2, short-tenured audit offices tend to be associated with higher |DA1| than dolong-tenured audit offices in the 1st quintile of audit office size; while the means of |DA1| forlong-tenured and short-tenure offices gradually converge as audit office size increases. Figure 2
supports our prediction that the audit office size – audit quality association is mitigated by longer
office-level auditor tenure.
[Insert Figure 2 about Here]V. Multivariate Analysis
Results of Regression Analysis for H1 and H2
We estimate Eq. (3) to test H1 and report the results in Table 4 Panel A. Section (1) andSection (2) reports the regression results when using |DA1| and |DA2| as the dependent variable,
respectively. The coefficients on OFFICE_SIZE are significantly negative, and the interaction
term OFFICE_SIZE ×INDSPEC is positively associated with |DA1| and |DA2|, i.e., city-level18

industry specialists mitigates the positive association between accrual quality and audit officesize, supporting H1. The variable INDSPEC also has significant coefficients with the negative
sign, suggesting that accruals quality tends to be higher if a firm is audited by a city-levelindustry specialist.
[Insert Table 4 about Here]The coefficients on the other control variables have the expected signs. The coefficient on
LONG is negative, reflecting that longer audit office tenure leads to higher audit quality. Client
size (LNTA) and cash flows from operation (CFO) are negatively associated with |DA1| and|DA2|, and cash flow volatility (STD_CFO) has positive association with |DA1| and |DA2|. The
coefficients on LEV and LOSS are positive suggesting that earnings quality tends to be lower inthe firms with more debts or negative earnings. ISSUE has a positive association with |DA1| and|DA2|, reflecting that the companies that are raising funds are more likely to manage earnings.
The coefficients on MTB are negative, and the coefficients on SALES_GROWTH and
SALES_VOL are significantly positive. The positive (negative) coefficients on IND_GROWTH(LAGACC) are consistent with prior research (e.g. Myers et al. 2003; Choi et al. 2010).
Table 4 Panel B reports the results of testing H2, with Section (1) and Section (2)reporting the results of using |DA1| and |DA2|, respectively. Similarly, OFFICE_SIZE isnegatively related to |DA1| and |DA2|, and the coefficients on OFFICE_SIZE×LONG are positive.
19

These results are consistent with H2, i.e. longer office-level auditor tenure weakens the effect ofaudit office size on audit quality. The coefficients on the other control variables are qualitatively
similar to those reported in Panel A of Table 4, thus we do not interpret them repeatedly

Sensitivity Tests
We conduct a variety of sensitivity tests. First, we use signed discretionary accruals as thealternative measure of audit quality. We estimate Eq. (3) and Eq. (4) based on the sub-samples of
positive and negative discretionary accruals. Table 5 Panel A present the results of Eq. (3). For
the sub-samples of positive discretionary accruals (DA1>0 or DA2>0), the coefficients on
OFFICE_SIZE are negative and the coefficients on OFFICE_SIZE ×INDSPEC are positive. In
contrast, OFFICE_SIZE and OFFICE_SIZE ×INDSPEC have positive and negative association
with negative discretionary accruals (DA1<0 or DA2<0), respectively. These results suggest thatlarger offices can effectively restrict both positive and negative discretionary accruals, and thiseffect could be alleviated by city-level industry leadership.
Table 5 Panel B reports the results of Eq. (4) using signed discretionary accruals as theindependent variable. Using the sub-samples of positive discretionary accruals, we derive
negative (positive) coefficients on OFFICE_SIZE (OFFICE_SIZE ×LONG). When we estimate
Eq. (4) using the sub-samples of negative discretionary accruals, the coefficients on
20

OFFICE_SIZE and OFFICE_SIZE ×LONG are positive and negative, respectively. In summary,increased audit office tenure mitigates the relation between audit office size and signed
discretionary accruals.
[Insert Table 5 about Here]Third, we re-estimate Eq. (3) using the alternative measures of city-level industry
specialists based on Reichelt et al. (2010), i.e., an auditor is defined as a city-level industry
specialists if it has the largest market share in a two-digit SIC category in a particular city and ina particular year, and its market share is at least 10% higher than the second largest industry
leader, with the results presented in Table 6. The use of the alternative measure does not
qualitatively alter our main findings.
[Insert Table 6 about Here]To make sure that the results for testing H2 are driven by office-level audit tenures rather
than firm-level audit tenures, we conduct a sensitivity test as follows: first, we replace thevariable LONG in Eq. (4) by OFFICE_CHG, i.e., an indicator equal to 1 if the company change
engagement office with the same audit firm in current fiscal year and 0 otherwise; second, we
estimate Eq. (4) using the sub-samples of companies do not switch audit firms during the sample
period. The insignificant (negative) coefficients on OFFICE_SIZE (OFFICE_SIZE ×

21

OFFICE_CHG) suggest that the effect of audit office size on audit quality is more pronounced in
the year of intra-audit firm office change.
[Insert Table 7 about Here]VI. Conclusions
This study tests how city-level industry specialists and office-level auditor tenure affectthe relation between audit office size and audit quality. First, we find that audit office size ispositively associated with audit quality as measured by absolute discretionary accruals. Thisfinding is consistent with prior studies (Francis et al. 2009; Choi et al. 2010). Second, we findthat this association is weakened by city-level industry specialization and longer auditor tenure atoffice level. Our findings suggest that city-level industry specialists (long-tenured audit offices)
have accumulated deep industry-specific (client-specific) knowledge, thus mitigate the lack of“in-house” experience related to the audits of public companies in smaller offices. Consequently,
the effect of audit office size on audit quality is weaker if a company is audited by a city-levelindustry specialists or a long-tenured audit office.
In the past decade, the collapse of Enron, which represents more than 35% of Arthur
Andersen‟s revenues in Huston office (Francis 2004), suggests the importance of office-levelauditing research. Extant literature has presented the evidence of heterogeneous quality across22practice offices within the same audit firm. In this study, we demonstrate two factors that could
mitigate the heterogeneous quality at office level, i.e. city-level industry leadership and audit
office tenure. Our study provides regulators and audit firms with insight into the influence ofaudit office characteristics on audit quality. Based on this study, we suggest that audit firms may
pay particular attention to the audit quality of small offices that are not industry specialists or in
the early years of audit engagement. Stated differently, if audit firms adopt certain strategies, e.g.
to specialize in certain industries or to conduct strict audit procedures in the first few years ofaudit engagements, we expect that audit firms are likely to achieve „uniform quality‟ acrosspractice offices of various sizes.

23

References
Ashbaugh, H., R. Lafond, and B. Mayhew. 2003. Do non-Audit services compromise auditor
independence? Further evidence. The Accounting Review 78 (3): 611-639.
Ball, R., and L. Shivakumar. 2006. The role of accruals in asymmetrically timely gain and loss
recognition. Journal of Accounting Research 44 (2): 207-242.
Balsam, S., J. Krishnan, and J. S. Yang. 2003. Auditor industry specialization and earnings
quality. Auditing: A Journal of Practice and Theory 22 (2): 71–97.
Chaney, P., D. C. Jeter, and P. E. Shaw. 2003. The impact on the market for audit services ofaggressive competition by auditors. Journal of Accounting and Public Policy 22(6): 487516.
Chen, Chi-Ying, C. J. Lin, and Y. C. Lin. 2008. Audit partner tenure, audit firm tenure, anddiscretionary accruals: does long auditor tenure impair earnings quality? Contemporary
Accounting Research 25 (2): 415-445.
Choi, J. H., F. Kim, J. B. Kim, and Y. Zang. 2010. Audit office size, audit quality and audit
pricing. Auditing: A Journal of Practice and Theory 29 (1): 73-97.
DeAngelo, L. 1981. Auditor size and audit quality. Journal of Accounting and Economics 3 (3):
183–199.
Dechow, P., and I. Dichev. 2002. The quality of accruals and earnings: The role of accrual
estimation errors. The Accounting Review 77 (Supplement): 35–59.
DeFond, M., and J. Jiambalvo, 1994. Debt covenant violations and manipulations of accruals.
Journal of Accounting and Economics 17 (1-2): 145-176.

24

DeFond, M., Raghunandan, K., Subramanyam, K., 2002. Do non-audit service fees impair
auditor independence? Evidence from going concern audit opinions. Journal of
Accounting Research 40 (4), 1247–1274.
Dye, R. A., 1993. Auditing standards, legal liabilities and auditor wealth. Journal of Political
Economy 101 (5): 887–914.
Ferguson, A., J. R. Francis, and D. J. Stokes. 2003. The effects of firm-wide and office levelindustry expertise on audit pricing. The Accounting Review 78 (2): 429-449.
Francis, J. R. 2004. What do we know about audit quality? The British Accounting Review 36:
345-368.
Francis, J. R., K. Reichelt, and D. Wang. 2005. The pricing of national and city-specificreputation for industry expertise in the U.S. audit market. The Accounting Review 80 (1):113-136.
Francis, J. R., D. J. Stokes, and D. J. Anderson. 1999. City markets as a unit of analysis in audit
research and the re-examination of Big 6 market shares. Abacus 35: 185–206.
Francis, J. R., and D. Yu. 2009. Big 4 office size and audit quality. The Accounting Review 84(5): 1521–1552.
Frankel, R. M., M. F. Johnson, and K. K. Nelson. 2002. The Relation between Auditors‟ Fees for
Non-audit Services and Earnings Management. The Accounting Review 77 (Supplement):
71-105.
Ghosh, A., and D. Moon. 2005. Auditor tenure and perceptions of audit quality. The Accounting
Review 80 (2): 585–612.
Gul, F. A., S. Fung, and B. Jaggi. 2009. Earnings quality: some evidence on the role of auditor
tenure and auditors' industry expertise. Journal of Accounting and Economics 47 (3): 265– 287.
25

Hribar, P., and D. C. Nichols. 2007. The use of unsigned earnings quality measures in tests ofearnings management. Journal of Accounting Research 45 (5): 1017-1053.
Iturriaga, F. J. L., and P. S. Hoffmann. 2005. Managers discretionary behavior, earnings
management and internal mechanisms of corporate governance: empirical evidence from
Chilean firms. Corporate Ownership & Control 3 (1): 17- 29.
Johnson, V. E., I. K. Khurana, and J. K. Reynolds. 2002. Audit-firm tenure and the quality offinancial reporting. Contemporary Accounting Research 19 (4): 637–60.
Ke, B. 2001. Why do CEOs of publicly traded firms prefer reporting small increases in earnings
and long duration of consecutive earnings increases? SSRN Working Paper, Nanyang
Technological University.
Kothari, S. P., A. J. Leone, and C. E. Wasley. 2005. Performance matched abnormal accrual
measures. Journal of Accounting and Economics 39 (1): 163-197.
Krishnan, G. 2005. The association between Big 6 auditor industry expertise and the asymmetrictimeliness of earnings. Journal of Accounting, Auditing, & Finance 20 (3): 209-228.
Lennox, C. 1999. Audit quality and auditor size: An evaluation of reputation and deep pockets
hypotheses. Journal of Business Finance and Accounting 26 (7-8): 779-805.
Li, C. 2009. Does client importance affect auditor independence at the office level? Empiricalevidence from going-concern opinions. Contemporary Accounting Research 26 (1): 201–
230.
Mansi, S. A., W. F. Maxwell, and D. P. Miller. 2004. Does auditor quality and tenure matterto
investors? Evidence from the bond market. Journal of Accounting Research 42 (4): 755–
93.

26

Myers, J., L. Myers, and T. Omer. 2003. Exploring the term of the auditor-client relationship andthe quality of earnings: A case for mandatory auditor rotation? The Accounting Review 78(3): 779-799.
Penno, M., and B. Walther. 1996. The concentration of local markets: A study of accounting,advertising and law. Accounting Horizon 10 (2): 88-99.
Reichelt, K., and D. Wang. 2010. National and office-specific measures of auditor industry
expertise and effects on audit quality. Journal of Accounting Research 48 (3): 647 – 686.
Reynolds, J. K., and J. R. Francis. 2000. Does size matter? The influence of large clients on
office level ?auditor reporting decisions. Journal of Accounting and Economics 30 (3):375-400.
Teoh, S. H., T. J. Wong, and G. R. Rao. 1998. Are accruals during initial public offerings
opportunistic? Review of Accounting Studies 3 (March): 175-208.

27

Appendix I
Variable Definitions
Dependent Variables
DA1= Discretionary accruals measured by Ball and Shivakumar (2006)‟s model;= Firm-performance discretionary accruals measured by Kothari et al.
DA2(2005).
Independent Variables= The size of a practice office within an audit firm, which is measured by
OFFICE_SIZE
national logarithm of an office‟s aggregate client audit fees.
= 1 if a firm is audited by a city-level industry specialist and 0 otherwise.
City-level industry specialist is defined as an auditor the auditor whose
INDSPEC
market share is greater than 50% in a two-digit SIC category (in a particular
city and a particular year).
LONG_TENURE = 1 if the audit office tenure is at least 4 years and 0 otherwise.
= 1 if a firm is audited by one of the Big 4 accounting firms and 0
BIG4otherwise.
LNTA= Natural logarithm of total assets at the end of fiscal years.
= Cash flows from operations scaled by total assets at the end of fiscal
CFO
years.
= Cash flow volatility as measured by the standard deviation of CFO for the
STD_CFO
most recent three fiscal years.
LEV= Total liabilities scaled by total assets.
LOSS= 1 if a firm reports negative net income and 0 otherwise.
= 1 if the sum of debt or equity issued in the past 3 years are more than 5%
ISSUEof the total assets and 0 otherwise.
MTB= Market-to-book ratio,= Client-specific sales growth rate, i.e. changes in sales revenues deflated
SALES_GROWTH
by sales revenue in prior year.
= Sales volatility as measured by the standard deviation of sales for the
SALES_VOL
recent three fiscal years.
J

IND_GROWTHLAGACC=  Salesj ,tj 1J Salesj 1

j ,t 1by two-digit SIC codes, where J is the number ofcompanies in an industry.
= Lagged total accruals in the last fiscal year. Total accruals are measured
by income before extraordinary items minus cash flow from operations.

28

Table 1
Sample Selection Process
Initial Sample
Step 1: Excluding observations audited by auditors located outside the U.S.
Step 2: Excluding observations audited by U.S. auditors not located in any MSA
Step 3: Excluding observations with incomplete data required to calculate thevariables in the regression models
Step 4: Excluding observations in the financial industry
Final Sample (2004– 2013)

29

81,455(10,957)(63)(33,690)(3,276)33,469

Table 2
Descriptive Statistics

Variable
|DA1|
|DA2|
OFFICE_SIZEINDSPEC
LONG
BIG4
LNTA
CFO
STD_CFO
LEV
LOSS
ISSUE
MTB
SALES_GROWTH
SALES_VOL
IND_GROWTHLAGACC

N
33469
33469
33469
33469
33469
33469
33469
33469
33469
33469
33469
33469
33469
33469
33469
33469
33469

Mean
0.238
0.288
16.642
0.377
0.617
0.639
5.426
-0.364
1.012
4.059
0.396
0.832
0.751
1.209
0.199
1.058
-1.660Standard
Deviation
0.652
0.754
2.221
0.485
0.486
0.480
2.760
10.50926.467162.017
0.489
0.374
0.992
71.1900.997
0.127
66.368

1st
percentile
0.001
0.002
11.002
0.000
0.000
0.000
-2.254-5.197
0.002
0.044
0.000
0.000
0.000
-0.9640.000
0.693-8.559Median0.080
0.110
17.073
0.000
1.000
1.000
5.640
0.066
0.048
0.518
0.000
1.000
0.445
0.069
0.088
1.065
-0.06299th
percentile
3.243
3.663
20.587
1.000
1.000
1.000
10.7820.354
7.044
20.755
1.000
1.000
4.000
4.947
1.697
1.349
0.535

Notes: This table reports descriptive statistics for the sample firms. The sample consists of
33,468 firm-year observations, representing 6,067 unique companies from 2004 to 2013. Allvariables are defined in Appendix I.

30

Table 3
Correlation Coefficient Matrix
1.
1. |DA1|

1.000

2. |DA2|

0.896(0.000)-0.261(0.000)-0.098(0.000)-0.108(0.000)-0.255(0.000)-0.404(0.000)-0.089(0.000)0.060(0.000)0.124(0.000)0.232(0.000)0.047(0.000)-0.132(0.000)0.027(0.000)0.120(0.000)0.037(0.000)-0.072(0.000)3. OFFICE_SIZE
4. INDSPEC
5. LONG
6. BIG4
7. LNTA
8. CFO
9. STD_CFO
10. LEV
11. LOSS
12. ISSUE
13. MTB
14. SALES_GROWTH
15. SALES_VOL
16. IND_GROWTH
17. LAGACC

2.

3.

4.

5.

6.

7.

8.

1.000
-0.246(0.000)-0.095(0.000)-0.108(0.000)-0.242(0.000)-0.381(0.000)-0.075(0.000)0.063(0.000)0.107(0.000)0.216(0.000)0.045(0.000)-0.133(0.000)0.010(0.057)0.105(0.000)0.041(0.000)-0.069(0.000)

1.000
0.082
(0.000)0.234(0.000)0.771(0.000)0.634(0.000)0.065(0.000)-0.050(0.000)-0.034(0.000)-0.242(0.000)0.060(0.000)0.088(0.000)-0.013(0.020)-0.108(0.000)0.015(0.006)0.032(0.000)

1.000
0.102
(0.000)0.300(0.000)0.283(0.000)0.011(0.055)-0.015(0.006)-0.012(0.027)-0.159(0.000)0.032(0.000)0.127(0.000)-0.002(0.765)-0.036(0.000)-0.010(0.058)0.013(0.015)

1.000
0.287
(0.000)0.258(0.000)0.019(0.001)-0.030(0.000)-0.001(0.866)-0.136(0.000)-0.108(0.000)0.052(0.000)-0.002(0.760)-0.032(0.000)-0.084(0.000)0.023(0.000)

1.000
0.663
(0.000)0.052(0.000)-0.045(0.000)-0.029(0.000)-0.296(0.000)0.076(0.000)0.118(0.000)-0.015(0.007)-0.103(0.000)-0.015(0.007)0.031(0.000)

1.000
0.134
(0.000)-0.060(0.000)-0.074(0.000)-0.461(0.000)0.085(0.000)0.231(0.000)-0.012(0.029)-0.163(0.000)-0.050(0.000)0.049(0.000)1.000-0.033(0.000)-0.148(0.000)-0.054(0.000)-0.012(0.027)0.028(0.000)0.001(0.905)-0.090(0.000)-0.003(0.543)0.018(0.001)

Notes: p-value in parentheses(Continued)

31

Table 3 (Continued)
9.
9. STD_CFO

1.000

10. LEV

0.012(0.033)0.036(0.000)0.013(0.020)-0.022(0.000)0.009(0.086)0.023(0.000)-0.002(0.755)-0.396(0.000)

11. LOSS
12. ISSUE
13. MTB
14. SALES_GROWTH
15. SALES_VOL
16. IND_GROWTH
17. LAGACC

10.

11.

12.

13.

14.

15.

16.

1.000
0.025
(0.000)0.005(0.357)-0.016(0.004)0.001(0.931)
0.081(0.000)0.006(0.296)-0.010(0.059)

1.000
0.062
(0.000)-0.072(0.000)0.006(0.279)0.071(0.000)-0.018(0.001)-0.028(0.000)Notes: p-value in parentheses

32

1.000
-0.071(0.000)0.004(0.444)0.007(0.198)0.031(0.000)-0.009(0.111)1.000-0.009 1.000(0.085)-0.049 0.006
1.000(0.000) (0.252)-0.060 0.001 -0.023 1.000(0.000) (0.922) (0.000)
0.017 -0.004 -0.011 0.000(0.002) (0.418) (0.055) (0.952)

Figure 1
Distribution of Absolute Discretionary Accruals Categorized by Audit Office Size: City-level
Industry Specialists vs. Non-Specialists
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1

2

3

Non-specialists

4

5

Office-level Industry Specialists

Notes: In Figure 1, based on the sub-sample of office-level industry specialists and nonspecialists respectively, we divide the observations of audit office size (OFFICE_SIZE) into five
percentile categories; we then plot the mean of |DA1| for the observations that belong to each of
the five groups. The mean values of |DA1| are in the vertical axis and the group indicators ofaudit office size are in the horizontal axis.

33

Figure 2
Distribution of Absolute Discretionary Accruals Categorized by Audit Office Size: Short-tenured
Offices vs. Long-tenured Offices

0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1

2

3

4

Short-Tenured Offices

5Long-Tenured Offices

Notes: In Figure 2, based on the sub-sample of short-tenured and long-tenured audit offices
respectively, we divide the observations of audit office size (OFFICE_SIZE) into five percentile
categories; we then plot the mean of |DA1| for the observations that belong to each of the fivegroups. The mean values of |DA1| are in the vertical axis and the group indicators of audit officesize are in the horizontal axis.

34

Table 4Main Results of Testing Hypotheses
Panel A: Association Among Audit Quality, Audit Office Size and Office-level Industry
Specialists
Section (1)|DA1|VARIABLES

Predicted
Sign

Coefficient

P value

Section (2)|DA2|Coefficient

P value

OFFICE_SIZE-0.0070**0.013-0.0094***
0.002
OFFICE_SIZE ×INDSPEC+
0.0111***
0.003
0.0124***0.002
INDSPEC-0.1750***
0.008-0.2019***
0.004
LONG
-0.0061
0.352-0.01130.111
BIG4-0.0057
0.552-0.00200.849
LNTA-0.0290***0.000-0.0298***
0.000
CFO
-0.0844***0.000-0.0922***0.000
STD_CFO+
0.0477***0.000
0.0604***
0.000
LEV
?
0.0303***0.000
0.0289***
0.000
LOSS
?
0.0315***0.000
0.0283***
0.000
ISSUE
+
0.0391***0.000
0.0400***
0.000
MTB
?
-0.0115***0.000-0.0174***
0.000
SALES_GROWTH?
0.0408***0.000
0.0382***0.000
SALES_VOL+
0.1129***0.000
0.1218***0.000
IND_GROWTH+
0.0545**0.026
0.0750***
0.006
LAGACC-0.0762***0.000-0.0730***
0.000
Constant
0.5708***0.000
0.7145***0.000
Year Dummies
Included
IncludedIndustry Dummies
Included
Included
Observations
33,46933,469Adjusted R2
0.403
0.372
Notes: ***, ** and * indicates statistical significance at 1%, 5% and 10% level, respectively. All
the variables are defined in Appendix I.

35

Table 4 (Continued)
Panel B: Association Among Audit Quality, Audit Office Size and Office-level Auditor Tenure
Section (1)|DA1|VARIABLES

Predicted
Sign

Coefficient

P value

Section (2)|DA2|Coefficient

P value

OFFICE_SIZE-0.0102***
0.001-0.0145***
0.000
OFFICE_SIZE ×LONG+
0.0127***0.000
0.0170***0.000
INDSPEC
0.0115**0.030
0.00780.181
LONG-0.2118***
0.000-0.2877***
0.000
BIG4
-0.0106
0.245-0.0076
0.441
LNTA-0.0288***0.000-0.0297***
0.000
CFO
-0.0843***
0.000-0.0920***
0.000
STD_CFO+
0.0478***0.000
0.0605***
0.000
LEV
?
0.0305***0.000
0.0292***
0.000
LOSS
?
0.0326***0.000
0.0296***
0.000
ISSUE
+
0.0382***0.000
0.0388***
0.000
MTB
?
-0.0114***0.000-0.0172***
0.000
SALES_GROWTH?
0.0411***0.000
0.0386***
0.000
SALES_VOL+
0.1122***0.000
0.1207***0.000
IND_GROWTH+
0.0497**0.043
0.0685**0.012
LAGACC-0.0757***
0.000-0.0723***
0.000
Constant
0.6275***0.000
0.8059***0.000
Year Dummies
Included
IncludedIndustry Dummies
Included
Included
Observations
33,46933,469Adjusted R2
0.403
0.372
Notes: ***, ** and * indicates statistical significance at 1%, 5% and 10% level, respectively. All
the variables are defined in Appendix I.

36

Table 5
Alternative Measures of Audit Quality: Signed Discretionary Accruals
Panel A: Association Among Audit Quality, Audit Office Size and Office-level Industry Specialists

VARIABLES
OFFICE_SIZEOFFICE_SIZE ×INDSPECINDSPEC
LONG
BIG4
LNTA
CFO
STD_CFO
LEV
LOSS
ISSUE
MTB
SALES_GROWTH
SALES_VOL
IND_GROWTHLAGACC
Constant
Year Dummies
Industry Dummies
Observations
Adjusted R2

(1)
DA1>0
Coefficient-0.0065***
0.0083***-0.1295**-0.0031-0.0045-0.0268***-0.1192***
0.0327***-0.0000-0.0111*0.0357***-0.0103***
0.0345***0.1328***0.0624***-0.0142*0.5954***
Included
Included
18,427
0.399

P value
0.006
0.006
0.015
0.591
0.605
0.000
0.000
0.000
0.990
0.068
0.000
0.000
0.000
0.000
0.006
0.056
0.000

(2)
DA1<0
Coefficient
P value(3)
DA2>0
Coefficient

0.0077**-0.0133***
0.2220***0.00680.01020.0228***0.0253-0.0239**-0.0339***-0.0817***-0.0368***
0.0126***-0.0361***-0.1182***-0.04430.0943***-0.5132***
Included
Included
15,0370.473-0.0066***
0.0061*-0.0949*-0.0063-0.0033-0.0269***-0.1248***0.0419***0.0032-0.0280***0.0277***-0.0168***
0.0348***0.1342***
0.0982***-0.0147*
0.6328***
Included
Included
18,563
0.382

0.029
0.005
0.009
0.393
0.388
0.000
0.166
0.036
0.000
0.000
0.000
0.000
0.000
0.000
0.175
0.000
0.000

P value
0.009
0.061
0.098
0.319
0.720
0.000
0.000
0.000
0.453
0.000
0.000
0.000
0.000
0.000
0.000
0.068
0.000

(4)
DA2<0
Coefficient
0.0106**-0.0163***0.2748***0.00520.01060.0247***0.0269-0.0334***-0.0323***-0.0920***-0.0492***
0.0161***-0.0274***-0.1229***-0.04220.0886***-0.7032***
Included
Included
14,906
0.417

P value
0.011
0.003
0.005
0.557
0.443
0.000
0.166
0.005
0.000
0.000
0.000
0.000
0.007
0.000
0.224
0.000
0.000

Notes: ***, ** and * indicates statistical significance at 1%, 5% and 10% level, respectively. All the variables are defined in Appendix
I.

37

Table 5 (Continued)
Panel B: Association Among Audit Quality, Audit Office Size and Office-level Auditor Tenure

VARIABLES
OFFICE_SIZEOFFICE_SIZE ×LONG
INDSPEC
LONG
BIG4
LNTA
CFO
STD_CFO
LEV
LOSS
ISSUE
MTB
SALES_GROWTH
SALES_VOL
IND_GROWTHLAGACC
Constant
Year Dummies
Industry Dummies
Observations
Adjusted R2

(1)
DA1>0
Coefficient-0.0074***
0.0066**0.0097**-0.1106**-0.0077-0.0267***-0.1193***0.0326***0.0001-0.0104*
0.0351***-0.0103***
0.0347***0.1328***0.0606***-0.0141*0.6114***
Included
Included
18,427
0.399

P value
0.006
0.016
0.046
0.020
0.357
0.000
0.000
0.000
0.988
0.087
0.000
0.000
0.000
0.000
0.007
0.057
0.000

(2)
DA1<0
Coefficient
P value(3)
DA2>0
Coefficient

0.0104***-0.0135***-0.00350.2260***0.01660.0224***0.0251-0.0246**-0.0342***-0.0830***-0.0356***
0.0126***-0.0363***-0.1172***-0.0368
0.0935***-0.5611***
Included
Included
15,0370.473-0.0114***0.0122***0.0076-0.2044***-0.0059-0.0269***-0.1242***0.0416***0.0034-0.0272***
0.0271***-0.0167***
0.0351***0.1335***0.0954***-0.0144*0.7171***
Included
Included
18,563
0.383

0.008
0.001
0.593
0.001
0.140
0.000
0.170
0.031
0.000
0.000
0.000
0.000
0.000
0.000
0.261
0.000
0.000

P value
0.000
0.000
0.157
0.000
0.505
0.000
0.000
0.000
0.424
0.000
0.000
0.000
0.000
0.000
0.000
0.074
0.000

(4)
DA2<0
Coefficient
0.0108**-0.0108**-0.00070.1803**0.01810.0243***0.0272-0.0340***-0.0326***-0.0934***-0.0479***
0.0161***-0.0276***-0.1230***-0.03450.0878***-0.7052***
Included
Included
14,906
0.416

P value
0.014
0.011
0.935
0.014
0.169
0.000
0.163
0.004
0.000
0.000
0.000
0.000
0.007
0.000
0.320
0.000
0.000

Notes: ***, ** and * indicates statistical significance at 1%, 5% and 10% level, respectively. All the variables are defined in Appendix
I.

38

Table 6
Alternative Measures of Office-level Industry Specialists
Section (1)|DA1|VARIABLES

Predicted
Sign

Coefficient

P value

Section (2)|DA2|Coefficient

P value

OFFICE_SIZE-0.0079***
0.006-0.0103***
0.001
OFFICE_SIZE ×INDSPEC2+
0.0104***0.003
0.0122***0.001
INDSPEC2-0.1674***
0.008-0.1986***
0.004
LONG
-0.0063
0.339-0.01150.104
BIG4-0.0044
0.642-0.00160.879
LNTA-0.0288***0.000-0.0298***
0.000
CFO
-0.0845***0.000-0.0923***
0.000
STD_CFO+
0.0476***0.000
0.0603***
0.000
LEV
?
0.0302***0.000
0.0289***
0.000
LOSS
?
0.0318***0.000
0.0285***
0.000
ISSUE
+
0.0390***0.000
0.0399***
0.000
MTB
?
-0.0113***0.000-0.0172***
0.000
SALES_GROWTH?
0.0408***0.000
0.0382***0.000
SALES_VOL+
0.1129***0.000
0.1217***0.000
IND_GROWTH+
0.0562**0.021
0.0767***0.005
LAGACC-0.0762***0.000-0.0729***
0.000
Constant
0.5828***0.000
0.7269***0.000
Year Dummies
Included
IncludedIndustry Dummies
Included
Included
Observations
33,46933,469Adjusted R2
0.403
0.372
Notes: The alternative measure defines a city-level industry specialist if the auditor that has thelargest market share in a two-digit SIC category and its market share is at least 10% greater than
the second largest industry leader (in a particular city and a particular year). ***, ** and *indicates statistical significance at 1%, 5% and 10% level, respectively. All the variables aredefined in Appendix I.

39

Table 7
Association among Audit Quality, Audit Office Size and Office-level Auditor Tenure: Subsample Analysis
Section (1)|DA1|VARIABLES

Predicted
Sign

Coefficient

P value

Section (2)|DA2|Coefficient

P value

OFFICE_SIZE2-0.00160.315-0.0028
0.126
OFFICE_SIZE2 ×OFFICE_CHG+-0.0208***
0.002-0.0267***
0.001
INDSPEC
0.00460.287-0.0020
0.692
OFFICE_CHG
0.3655***0.002
0.4651***
0.001
BIG4
-0.0537***0.000-0.0552***
0.000
LNTA
-0.0157***
0.000-0.0160***
0.000
CFO
-0.0899***
0.000-0.0786***
0.000
STD_CFO+
0.0378***0.000
0.0410***
0.000
LEV
?
0.0236***0.000
0.0266***
0.000
LOSS
?
0.0309***0.000
0.0255***
0.000
ISSUE
+
0.0150***0.007
0.0159**
0.012
MTB
?
-0.0069***0.001-0.0097***
0.000
SALES_GROWTH?
0.0280***0.000
0.0255***0.000
SALES_VOL+
0.1191***0.000
0.1326***0.000
IND_GROWTH+
0.0446*0.068
0.0570**0.041
LAGACC-0.0615***0.000-0.0655***
0.000
Constant
0.5873***0.000
0.6941***
0.000
Year Dummies
Included
IncludedIndustry Dummies
Included
Included
Observations
17,651
17,651
Adjusted R2
0.347
0.316
Notes: We replace the variable LONG in Eq. (4) as OFFICE_CHG (an indicator variable equal
to 1 if the company changes audit office within the same audit firm and 0 otherwise), andestimate the equation using the sub-sample of companies that do not change audit firms during
the sample period, and replace as ***, ** and * indicates statistical significance at 1%, 5% and
10% level, respectively. All the variables are defined in Appendix I.

40