4.6 Robustness Tests
4.6.1 Alternative Audit Quality Measure
are possibly differentiated in their service quality in the presence of competition. This finding leads us to accept Hypothesis 4 with low confidence, violating my Hypothesis above.
In summary, robustness checks do not support my hypothesis and hence highlight the importance of the measurement of audit quality used.
Variables
Full Sample DISACC
(1)
Full Sample DISACC*BIG4
(2)
BIG4 Sample DISACC
(3)
NB4 Sample DISACC
(4)
MRKT_COMP 0.0305*** 0.0320 0.0320*** 0.0234
(2.704) (1.445) (2.745) (1.027)
SIZE -0.0104*** -0.0104*** -0.00632*** -0.0253***
(-7.826) (-7.846) (-5.874) (-8.874)
CA 0.0476*** 0.0471*** 0.0405*** 0.0599***
(4.369) (4.328) (3.411) (3.214)
INVREC 0.0239* 0.0253* -0.00402 0.0383*
(1.729) (1.834) (-0.286) (1.709)
LEV 0.0298*** 0.0297*** 0.0384*** 0.0137
(3.465) (3.459) (4.874) (0.866)
ROA 0.216*** 0.217*** 0.180*** 0.253***
(18.32) (18.34) (9.988) (17.69)
LOSS -0.0327*** -0.0323*** -0.0391*** -0.0220***
(-9.417) (-9.302) (-9.817) (-3.824)
CR 0.000951 0.000955 0.00114 0.00159
(1.283) (1.285) (1.185) (1.324)
BTM 0.0106*** 0.0106*** 0.0164*** 0.00550*
(5.571) (5.600) (8.137) (1.807)
GROWTH -0.00500 -0.00506 -0.00556 0.00166
(-1.295) (-1.311) (-1.218) (0.249)
BUSSEG 0.0123*** 0.0126*** 0.0125*** 0.000685
(3.073) (3.145) (3.393) (0.0599)
GEOSEG 0.00719*** 0.00720*** 0.00780*** 0.00762
(2.688) (2.694) (2.829) (1.299)
ISSUE 0.00825** 0.00816** 0.00283 0.0216***
(2.541) (2.509) (0.907) (3.904)
EXORD 0.0140*** 0.0143*** 0.0131*** 0.0129*
(4.685) (4.774) (4.612) (1.790)
FOREIGN -0.0117*** -0.0116*** -0.0134*** -0.00430
(-3.325) (-3.291) (-3.512) (-0.687)
PENSIONS -0.00442 -0.00432 0.00305 -0.00546
(-0.683) (-0.667) (0.396) (-0.600)
NAT SPEC 0.00727 0.00576 0.00719 0
(0.984) (0.778) (0.937) (0)
CITY SPEC 0.00689* 0.00675* 0.00458 -0.00999
(1.883) (1.838) (1.367) (-0.993)
JOINT SPEC -0.00300 -0.00129 -0.00666 0
(-0.271) (-0.116) (-0.639) (0)
LIT 0.00668* 0.00680* 0.0120*** 0.00265
(1.772) (1.810) (3.381) (0.356)
IC_WEAK -0.0103* -0.0102* 0.00127 -0.0221***
(-1.915) (-1.913) (0.237) (-2.822)
OFFICE_SIZE -0.00446** -0.00734*** 0.00242 -0.00553
(-2.482) (-3.209) (0.898) (-1.128)
INFLUENCE -0.00698 -0.00955 0.0223** -0.0184
(-0.856) (-1.171) (2.532) (-1.272)
FEE_RATIO 0.00612 0.00363 -0.00442 0.0296
(0.719) (0.425) (-0.496) (1.384)
Constant 0.101*** 0.137*** -0.00947 0.144**
(3.724) (4.189) (-0.227) (2.146)
Observations 11,974 11,974 8,060 3,914
R-squared 0.281 0.282 0.227 0.372
Notes: ***, **, * indicate 1%, 5%, and 10% significance levels, respectively (two-tailed). t-Values are in parentheses.
Standard errors are clustered by client firm. Fixed effects for years and industries are included but not tabulated for reasons of brevity. The dependent variable in the regressions is the natural logarithm of Audit Fees. Variables are defined in the appendix.
5 Conclusion
This study examines the effect of local audit market competition on audit pricing and audit quality. The motivation behind this paper is to address regulators concerns about the potential lack of competition in audit markets, which are thought to create impediments on the practice (European Commission, 2010; FRC, 2018; GAO, 2003, 2008). This paper analyses a comprehensive sample of US audit engagements spanning across 2011 to 2019, at the metropolitan statistical area, and employs a relatively uninvestigated measure of market competition to comment on the debate surrounding the impact of local market competition on the audit fee and audit quality. To control for the effect of audit complexity on local market competition, a separation is made between Big-4 and NB4 auditors, as NB4 auditors generally audit clients with lower complexity. Results from the analysis indicate that local market competition is insignificant11 and negatively associated with audit fees, inferring that firms situated in competitive local markets offer price discounts in order to entice customers to attain their service, relieving regulators concerns. Furthermore, test scores suggest that local market competition has no impact on audit quality, when using restatements as a proxy for audit quality12. Therefore, results indicate that audit firms situated in competitive environments do not reduce audit quality, in response to lower audit fees. Lastly, the differential effect between Big-4 and NB4 auditors, shows no significant variation between the segments. The additional analysis of the size of client is provided to further address the challenge of complexity.
Evidence is found, that SME clients situated in competitive counties are offered price discounts, compared to SBE clients and large clients who are not. This highlights the importance of separating audits of complexity.
Overall, this paper addresses a relevant research topic that few others have attempted to investigate. Specifically, this paper provides evidence on the impact of local market competition on audit fees and audit quality, of which is important to both regulators and audit market participants GAO (2003, 2008). Secondly, results of this paper indicate that audit firms compete on audit fee pricing, especially amongst SME clients, thus inferring that competitive markets, can exist simultaneously within highly concentrated areas (Keune, et al., 2016).
Finally, my results confirm the importance of measuring at a local level rather than a national
11 (P value = 0.127) although insignificant, enough to infer a casual relationship.
12 Robustness tests do not concur and thus results should be taken lightly.
level (Kallapur, Sankaraguruswamy, & Zang 2010; Boone, Khurana, & Raman, 2012; Ye, &
Zhang, 2018; Eshleman, & Lawson, 2017; Numan et al., 2012)
This paper is not without limitations. This paper, ultimately does not discuss whether all counties are competitive, rather, when they are competitive the effect it has on audit fees and audit quality. Therefore, it is reasonable to recommend that future papers look at the actual number of MSA’s that are classed as competitive than those that are not. Secondly, my results are generally biased towards audit of higher complexity, as is the norm for a large sample of mandatory audits. To overcome this issue in future papers, I recommend focusing on certain market segments and evaluating the effects, as done in the additional analysis. Another drawback of my results is that an alternative conclusion is found when using an alternative method for audit quality measurement. This highlights the importance of looking at multiple measures of audit quality to ensure a sound comment. Therefore, for future papers I recommend using multiple measures of audit quality to infer a relationship between local market competition and audit quality. Lastly, a drawback of my results includes the fact local market competition levels are not weighted according to their measurement. This creates a possible impediment on the inference of my results, and therefore these results should be taken lightly.
This remains a limitation and I suggest future papers address the possible impact of this effect.
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APPENDIX A
Variable Definitions with Compustat Mnemonics
Variable Definition
AFEE = the natural logarithm of audit fees (in $ thousands).
MRKT_COMP = Market share instability, measured as the sum of the absolute values of the annual percentage-point changes in market shares of all audit firms in a client’s local industry audit market. We use standardized values of this measure in our regression analyses.
LOC_COMP = Market share instability, measured as the sum of the absolute values of the annual percentage-point changes in market shares of all audit firms in a client’s local audit market. We use standardized values of this measure in our regression analyses.
MRKT_CONCEN = Industry audit market concentration, calculated as (∑𝑖=1∑ [𝐼𝑛(𝐴𝑠𝑠𝑒𝑡𝑠𝑖)×𝐷𝑖
𝑖=1[𝐼𝑛(𝐴𝑠𝑠𝑒𝑡𝑠𝑖) )2, where Di is an audit office in an audit market and i is market share in an audit market industry based on the natural logarithm of assets. Audit markets are defined as Industry-MSA-years. This variable is calculated based on the auditor’s Industry &
MSA and using all available client-years in Audit Analytics. If the client is audited by a Big-4 auditor in a given year, then
IND_MSA_CONCEN is based only on the market shares of the Big-4 audit firms within the MSA.
LOC_CONCEN = audit market concentration, calculated as (∑𝑖=1[𝐼𝑛(𝐴𝑠𝑠𝑒𝑡𝑠𝑖)×𝐷𝑖
∑𝑖=1[𝐼𝑛(𝐴𝑠𝑠𝑒𝑡𝑠𝑖) )2, where Di is an audit office in an audit market and i is market share in an audit market based on the natural logarithm of assets. Audit markets are defined as MSA-years. This variable is calculated based on the auditor’s MSA and using all available client-years in Audit Analytics.
If the client is audited by a Big-4 auditor in a given year, then
MSA_CONCEN is based only on the market shares of the Big-4 audit firms within the MSA.
BIG4 = 1 if the auditor is KPMG, EY, Deloitte, or PricewaterhouseCoopers, 0 otherwise.
NB4 = 1 if the auditor is not KPMG, EY, Deloitte, or PricewaterhouseCoopers, 0 otherwise.
SIZE = the natural log of the firm’s total assets (AT).
CA = the ratio of current assets (ACT) to total assets (AT).
INVREC = inventory (INVT) plus receivables (RECT), all scaled by total assets (AT).
LEV = total debt (DLTT + DLC) divided by total assets (AT).
ROA = income before extraordinary items (IB) divided by total assets (AT).
LOSS = 1 if income (IB) is negative, 0 otherwise.
CR = current ratio, calculated as current assets (ACT) divided by current liabilities (LCT).
BTM = book equity (SEQ) divided by market value of equity (PRCC_F x CSHO).
CHGSALE = current year sales (SALE) less prior year sales scaled by prior year sales.
BUSSEG = the natural log of the number of business segments the firm reports. If segment data are missing, then we assume the firm has one segment.
GEOSEG = the natural log of the number of geographic segments the firm reports.
If segment data are missing, then we assume the firm has one segment.
ISSUE = 1 if the sum of long-term debt issues (DLTIS) and equity issues (SSTK) during the past three years is greater than 5 percent of total assets (AT), 0 otherwise.
EXORD = 1 if the firm reports extraordinary items (XIDO), 0 otherwise.
FOREIGN = 1 if the firm pays foreign income taxes (TXFO), 0 otherwise.
PENSION = 1 if the firm reports a pension or a post-retirement plan (XPR), 0 otherwise.
NAT_SPEC = 1 if the auditor has a market share in the firm’s industry exceeding 30 percent, 0 otherwise.
CITY_SPEC = 1 if the auditor has a market share in the firm’s industry in the firm’s city exceeding 50 percent, 0 otherwise.
JOINT_SPEC = 1 if the auditor is both a national leader and a city specialist (i.e., if NAT_SPEC = 1 and CITY_SPEC = 1), 0 otherwise.
LIT = An indicator variable equal to 1 if the firm operates in the following risk-of-litigation industries (by SIC code): 2833–2836, 3570–3577, 3600–3674, 5200– 5961, or 7370.
IC_WEAK = An indicator variable equal to 1 if the firm reported a material weakness under Section 302 or 404.
Additional Variables Used in the Audit Quality Analysis
RESTATEMENT = An indicator variable equal to 1 if the firm’s financial statements in year t were restated due to a GAAP failure, as identified in the Audit Analytics Restatements file.
OFFICE_SIZE = The log of total audit fees charged to all audit clients within an auditor office i.
AUDIT_FEES = The log of the audit fees a firm paid in year t.
FEE_RATIO = The ratio of the firm’s payments for nonaudit fees to audit fees plus non-audit fees in year t.
INFLUENCE = The percentage of an audit office’s total fees derived from the client.
Q_SIZE = The log of the firm’s sales.
GROWTH = The change in sales from year t-1 to year t divided by lagged sales.