The Relative Importance of Auditor Characteristics Versus Client Factors in Explaining Audit Quality

Abstract

The U.K. listed firms are used to investigate whether auditor attributes (fixed effects for audit firms, audit offices, and audit partners) add incrementally to baseline models with client controls in explaining audit quality. We document that accounting firm fixed effects add significantly to baseline models. To the extent an accounting firm can standardize its audits, there should be no differences across engagements. However, we find significant interoffice differences, and also significant inter-partner differences within offices. R² analyses, hierarchical linear models, LASSO (least absolute shrinkage and selection operator) regressions, and R² decomposition analyses all show that partners are the most important auditor-related characteristic. To better understand the cause of partner variation, we test a set of partner demographic variables (in lieu of partner fixed effects), but we find that they explain little variation, once we control for firm and office differences. We conclude that partner variation is important in explaining audit quality, but understanding the causes requires going beyond existing publicly available demographic data.

Keywords

accounting firms audit offices engagement partners earnings quality going concern reports restatements

Introduction

This study investigates the importance of auditor characteristics in explaining audit quality, after controlling for client factors. The audit quality is assessed through the quality of audited earnings, proxied by abnormal accruals and financial restatements, and the likelihood an auditor issues a going concern audit report. The construct of earnings quality can be measured in multiple ways, and we focus on two of the most commonly used metrics: abnormal accruals and restatements (Dechow et al., 2010). Audit quality is inferred from higher quality audited earnings (smaller accruals and fewer restatements) and more going concern audit reports, all else equal. However, also the neutral term “audit outcomes” is used throughout the article to refer to these empirical observables (DeFond & Zhang, 2014; Francis, 2011).¹

In contrast to prior research, our study adopts a more comprehensive approach to assess the relative importance of clients and auditors. To do this, we begin with a reduced-form baseline model of client factors (only) and then conduct a series of econometric analyses to determine whether models with auditor characteristics add incrementally to the explanatory power of the reduced-form baseline model. Specifically, we start by introducing sequentially three levels of auditor variables: audit firm fixed effects, engagement office fixed effects, and individual partner fixed effects. Prior studies have examined in a more piecemeal manner the effects of audit firm characteristics, typically large versus small firms (Big 4/non–Big 4), the effects of interoffice differences, and more recently the effects of individual partner characteristics using publicly observable demographic variables. However, this body of research has not systematically investigated the incremental effects of firms, offices, and partners in the kind of nested analysis as in our study. Instead, prior studies typically focus on just one factor at a time such as certain accounting firm differences (Lennox & Pittman, 2011), or certain engagement office differences (Francis & Yu, 2009), or the effects of certain partner characteristics (Kallunki et al., 2019; Lo et al., 2019), while ignoring other auditor characteristics that may also be important.

The research design in our study requires multi-year panel data for accounting firms, audit offices, and engagement partners. Data on accounting firms and the locations of the engagement offices that issue audit reports are typically available in most countries and are obtained from audit reports. However, engagement partner data are relatively new in most countries. The United Kingdom adopted a partner identification disclosure rule beginning in 2009, which provides the necessary partner-level panel data, and for this reason our study uses a U.K. sample of listed firms for the period 2009–2015.²

In audit research there is a limited set of empirically observable auditor characteristics that has been used to measure accounting firm, audit office, and engagement partner differences. For this reason, we test auditor characteristics more broadly with a fixed effects framework. A set of fixed effect variables, such as accounting firm indicator variables, will generally have greater explanatory power than the use of individual variables using available data such as accounting firm size or industry expertise. Such variables are typically incomplete and can omit important characteristics that might not be observable empirically, which is why fixed effects are a standard econometric control for omitted or unobservable variables. A good example is cross-country research in financial economics where country-level fixed effects typically explain more cross-country variance than country-specific variables such as gross domestic product (GDP) and institutional structures (Doidge et al., 2004).

Following the standard designs in the auditing literature which takes an audit production perspective, our research question is whether auditor-related fixed effects add significantly to baseline models with client controls in explaining audit outcomes.³ We begin by testing whether accounting firm fixed effects add significantly to the baseline models. This is the logical starting point for the identification of auditor characteristics because an accounting firm develops a standardized testing approach and implements a quality control system designed to ensure compliance with the firm’s policies and a consistent testing approach across all of its engagements. The first null hypothesis is that there are no interfirm differences, that is, accounting firm fixed effects do not add significantly to the reduced-form baseline model of client factors.

We next test whether there are systematic interoffice differences, incremental to the control for accounting firm fixed effects. This design determines whether there are “within firm” differences across an audit firm’s engagement offices. Accounting firms develop standardized testing approaches and implement quality control systems to ensure consistent audit quality across engagements. However, accounting firms are organized as networks of decentralized offices for the delivery of audits to clients, which also means that an accounting firm’s testing procedures and control systems are implemented at the decentralized office level (albeit with oversight). The decentralized office structure means there is the potential for systematic interoffice variation in the firm’s audits. Thus, the second null hypothesis is that there are no interoffice differences within firms, after controlling for accounting firm fixed effects.

Finally, the third null hypothesis is that there are no significant inter-partner differences on audit quality, after controlling for the effects of accounting firm and office fixed effects. A partner typically leads an engagement team out of a specific office to service the clients of that office. The test determines whether there is inter-partner variation within an office of an accounting firm.⁴

An F-test analysis of changes in the “R²-within” of our models provides evidence that accounting firm fixed effects add significantly to the reduced-form baseline model.⁵ We also find significant interoffice differences which appear to play a larger role in explaining audit outcomes than do interfirm differences. Finally, we report significant inter-partner variation and document that partner effects explain more of the variation in audit quality than the combined effect of firms and offices.

To assure that these findings are not just driven by the mere introduction of new independent variables at each stage, we investigate our research question employing additional econometric techniques. First, we use hierarchical linear modeling (HLM) that explicitly controls for the nested structure of the accounting firm data in our study, that is, partners are located in specific offices, and specific offices are part of a specific accounting firm and obtain consistent results (Chang et al., 2018; Nakagawa & Schielzeth, 2013). Next we use a penalized regression approach, more precisely LASSO (least absolute shrinkage and selection operator) regressions, which perform both variable selection and regularization to enhance the prediction accuracy and interpretability of the statistical model it produces.⁶ This analysis indicates once more that audit partners are the most important auditor-related characteristic in explaining audit quality. We also obtain similar results when analyzing Big 4 clients and non–Big 4 clients separately under all of the econometrics approaches employed.

The final analysis substitutes publicly available partner demographic variables for partner fixed effects to better understand specific factors that may drive partner differences. Our analysis examines demographic variables commonly used in previous audit partner literature such as gender, type of degree, years of experience (which may also proxy for age), and busyness of the auditor (number of clients) as well as variables not previously investigated such as the quality of the university attended. The amount of variance explained by the set of specific partner variables in our study is quite small, much less than that explained by partner fixed effects, and this suggests that other (unknown) partner characteristics drive the inter-partner differences in our study.⁷

The results of our study should be of interest to accounting firms and regulators as both groups are interested in audit quality and gaining a better understanding of the drivers of audit quality. The findings are strongly suggestive that inter-partner variation is the most important audit factor that affects audit quality. However, our findings also indicate the insufficiency of using solely publicly available partner demographic variables such as gender, age, and experience. The point is not to criticize such studies, but rather to emphasize the need to go beyond such data to understand what it is about partners that matter.

One potentially useful direction to better understand the causes of inter-partner variation would be to draw on findings in the organizational behavior literature on job performance. This research finds that the main driver of job performance is a person’s cognitive ability. The only auditing study of cognitive ability we are aware of is Kallunki et al. (2019) who find that IQ data from mandatory military service are correlated with audit quality for a sample of male Swedish auditors. In addition to ability, personality characteristics also have an important influence on job performance (Schmitt, 2014). Factors in the standard “big five” model of personality traits are openness to experience, conscientious, extraversion, agreeableness, and neuroticism or emotional stability (Barrick & Mount, 1991). It is also possible that audits are affected by engagement partners’ attitudes to risk and their risk-taking behavior (Morgeson et al., 2010).

Another aspect of job performance that could be important in the auditing context is the effect of audit partner leadership behaviors on audit engagements. Studies in the organizational behavior literature have established an association between leadership and performance (e.g., Burke et al., 2006), as well as a link between personality and leadership (Church et al., 2019). In addition, there is emerging team science research that may be relevant to understanding the performance of partner-led engagement teams (Driskell et al., 2018; Kozlowski, 2018).

The above discussion makes clear that the way forward in understanding partner effects is to learn more about audit partners and the organizational structures in which they operate. There is some emerging research that points in this direction. For example, Bol et al. (2018) combine an instrument on tacit knowledge with proprietary accounting firm data and document that accounting firms place a value on tacit knowledge, as evidenced by higher promotability assessment, better annual assessments, and payment of cash bonuses. Tacit knowledge is important in building the human capital of individual auditors and audit firms and should lead to higher quality audits. Grabner et al. (2020) extend this research with proprietary data from a Big 4 accounting firm, and a survey of its partners, to document that tacit knowledge of partners is positively associated with the firm’s own internal assessment of audit quality. In another study, Hardies et al. (2020) use an instrument to measure individual partner personality traits for a partner sample from a large accounting firm and then link these traits to auditor skepticism and the auditor’s propensity for skeptical behaviors. Professional skepticism is emphasized in audit standards and is considered by regulators to be a driver of audit quality. As these studies illustrate, gaining a deeper understanding of audit partner behaviors will benefit from the support and participation of accounting firms.⁸

The remainder of the study proceeds as follows. The next section reviews prior literature and develops the study’s hypotheses. The research design and sample are then discussed, followed by the empirical results and the study’s conclusions.

Hypothesis Development and Methods

Testing Framework Using Nested Models

Auditing standards and legal/regulatory requirements in most countries require accounting firms to have firm-wide policies to control and monitor the quality of audits (Bedard et al., 2008). The Public Company Accounting Oversight Board (PCAOB) regulates audit quality for listed companies in the United States through its Statements on Quality Control, denoted QC in the PCAOB standards. In countries that have adopted standards of the International Accounting and Auditing Assurance Board, such as the U.K. sample we use, the quality control standards are qualitatively similar to the broad principles of the PCAOB and are set out in International Standard on Quality Control 1.⁹

Auditing standards and audit regulations set a minimum standard for accounting firms, but there is considerable evidence of systematic interfirm differences in audits. The main findings from the past literature suggest that larger firms (Big 4) and auditors with greater industry expertise do better audits (e.g., Balsam et al., 2003; Cahan & Sun, 2015; Craswell et al., 1995). Despite the importance of accounting firm characteristics documented in the prior literature, extant research has not asked the explicit question we investigate, which is whether an expanded model with accounting firm fixed effects adds significantly to a reduced-form baseline model of client controls. Moreover, there is a very limited number of specific observable accounting firm attributes and characteristics that have been tested, and for this reason we believe accounting firm fixed effects can provide a better assessment of the degree of interfirm variation. This discussion leads to the first hypothesis, stated in null form as follows:

Hypothesis 1 (H1): Accounting firm fixed effects do not add significantly to a baseline model of client controls in explaining audit quality.

To test H1 for the existence of systematic interfirm differences, we empirically estimate the baseline model in Equation (1) for observable proxies for audit quality, denoted as Y:

Y = vector of client controls .

(1)

Next, we then estimate an expanded model in Equation (2) which allows for the possibility of significant interfirm differences (non-zero fixed effect coefficients):

Y = vector of client controls + accounting firm fixed effects .

(2)

An F test determines whether the expanded model with accounting firm fixed effects adds significantly to the reduced-form model in Equation (1). If accounting firm differences do not exist, then the fixed effect coefficients are zero and the model in Equation (2) collapses to the reduced-form model in Equation (1).¹⁰

The second research question asks whether there are interoffice differences within an accounting firm that affect audit outcomes. Existing studies document that office characteristics such as office size and the office’s city-specific industry expertise are important in explaining audit quality (e.g., Choi et al., 2010; Ferguson et al., 2003; Francis et al., 2014; Francis & Yu, 2009). However, these studies have typically used restricted samples of Big 4 auditors and do not explicitly control for accounting firm fixed effects which means that it is possible that the interoffice effects are simply capturing the effect of omitted accounting firm variables (e.g., Choi et al., 2010; Francis et al., 2017; Francis & Michas, 2013). This discussion leads to the second hypothesis, which is stated in null form as follows:

Hypothesis 2 (H2): An expanded model with audit office fixed effects does not add significantly to a reduced-form model with client controls and accounting firm fixed effects in explaining audit quality.

We formally test H2 by assessing whether the expanded model in Equation (3) with office fixed effects adds to the model in Equation (2). By controlling for accounting firm fixed effects, H2 tests whether there are interoffice differences within an accounting firm:

Y = vector of client controls + accounting firm fixed effects + office fixed effects .

(3)

An F test determines whether the expanded model in Equation (3) with audit office fixed effects adds significantly to the reduced-form nested model in Equation (2). If an accounting firm’s control system achieves consistency across all of its engagements, the coefficients of audit office fixed effects will be zero and we would be unable to reject the null hypothesis in H2.

The third research question asks whether there are inter-partner differences, after controlling for both interfirm and interoffice differences. Recent studies find evidence that partner characteristics may have an effect on audit outcomes (Cahan & Sun, 2015; Kallunki et al., 2019; Knechel et al., 2015; Lo et al., 2019). Audit partner research has emerged in recent years due in part to the growing availability of partner data in many countries (Lennox & Wu, 2018). These studies find evidence that publicly available partner demographic data such as gender, age, experience, workload or busyness, industry expertise, risk preferences, and partner tenure with the client are associated with audit quality.¹¹ However, given the significant findings in the literature on the effects of both accounting firms and practice offices, it is clearly necessary to control for these effects in order to isolate the incremental effect (if any) of audit partners in explaining audit quality. However, the extant partner literature does not typically control for systematic interfirm or interoffice differences, which makes it difficult to make unambiguous inferences about the idiosyncratic effect of audit partners.¹² This leads to the third hypothesis, stated in null form as follows:

Hypothesis 3 (H3): An expanded model with engagement partner fixed effects does not add significantly to a reduced-form model with client controls plus accounting firm and audit office fixed effects in explaining audit quality.

We formally test H3 by assessing whether the expanded model in Equation (4) with partner fixed effects adds to the reduced-form nested model in Equation (3) reported above that includes client controls plus accounting firm and audit office fixed effects:

Y = vector of client controls + accounting firm fixed effects + office fixed effects + engagement partner fixed effects .

(4)

By controlling for both accounting and office fixed effects, H3 explicitly tests whether there are inter-partner differences within an office of an accounting firm. An F test determines whether the expanded model in Equation (4) adds significantly to the reduced-form nested model in Equation (3). This testing approach rules out that omitted variables for accounting firms and/or engagement offices explain cross-sectional differences in audit outcomes by individual signing partners.

Sample and Variables

The study uses U.K. panel data for listed firms from two primary databases: FAME and DataStream. The time frame is the 7-year period from 2009, that is, the first year the U.K. regulation required disclosure of the audit engagement partner, through the 2015 fiscal year. The variables needed for the empirical models (financial accounting data, accounting firm name, and audit signing partner) come from FAME.¹³ If a company has missing values in the FAME database, we use data from DataStream, if available. DataStream is also used to identify restatements, as that information is not available on FAME. The name and location of the audit firm, engagement office, and signing partner are obtained from the audit report in the client’s annual report and publicly available online. When we could not find the online report, we use FAME as it also provides the original annual reports of companies, including the audit report.

To construct the sample, an accounting firm must have at least two unique offices, with the further requirement that an office must have at least two unique audit partners. These are the minimal conditions for estimating audit office fixed effects and individual partner fixed effects. The initial sample is 879 unique partners for 1,091 unique clients. Once we exclude firms missing the necessary financial data as well as those accounting firms and offices that did not meet the above screens, the final sample is 5,411 client firm-year observations, consisting of 1,025 unique clients that are audited by 15 accounting firms with 135 unique offices and 665 unique signing partners. In the subsample of Big 4 clients, there are 3,579 firm-year observations, consisting of 628 unique clients that are audited by 70 unique offices and 450 unique signing partners. The non–Big 4 subsample has 1,832 firm-year observations, 397 unique clients audited by 11 accounting firms with 65 unique offices and 215 unique signing partners.¹⁴

Table 1 has detailed information on the accounting firms in the study. Big 4 (non–Big 4) firms have a median of 200 (26) listed clients in 17 (3) engagement offices, with a median of 5 (3) unique signing partners per office, and a median of 2 (2) clients per signing partner.

Table 1.

Descriptive Statistics for U.K. Accounting Firms, Audit Offices, and Engagement Partners (2009–2015).

Variable	M	Q1	Median	Q3	SD
Panel A: Big 4 clients
Number of engagement offices per accounting firm	17.750	12.500	17.000	23.000	6.291
Number of listed clients per accounting firm	183.750	146.000	200.000	221.500	54.243
Number of listed clients per engagement office	11.592	3.000	5.000	9.000	21.446
Number of signing partners per accounting firm	124.500	90.000	124.500	138.000	32.588
Number of signing auditors per engagement office	8.648	3.000	5.000	8.000	13.627
Number of unique listed clients per signing partner	3.009	1.000	2.000	4.000	2.241
Panel B: non–Big 4 clients
Number of engagement offices per accounting firm	6.000	2.000	3.000	9.000	6.277
Number of listed clients per accounting firm	42.182	5.000	26.000	53.000	52.579
Number of listed clients per engagement office	7.788	2.000	4.000	9.000	11.826
Number of signing partners per accounting firm	21.182	4.000	11.000	27.000	24.807
Number of signing partners per engagement office	4.470	2.000	3.000	5.000	5.359
Number of unique listed clients per signing partner	3.431	1.000	2.000	4.000	3.303

Empirical Models

The main research design uses a fixed effects framework adapted from the managerial style analysis in Bertrand and Schoar (2003). As explained above, our first statistical approach uses a series of nested models to assess the sequential and incremental fixed effects of accounting firms, offices, and engagement partners in explaining audit quality. The dependent variables in the study are observable audit outcomes from which audit quality is inferred: the quality of audited earnings (EQ) and the auditor’s going concern reporting (GC). Better audits are inferred from higher quality audited earnings and more going concern reports, all else equal.

The empirical models in the study, including fixed effects for accounting firms, audit offices, and engagement partners, are specified as follows:

\begin{matrix} EQ = α + β_{1} Siz e_{it} + β_{2} Le v_{it} + β_{3} Δ Le v_{it} + β_{4} Cf o_{it} + β_{5} Ro a_{it} + β_{6} Los s_{it} + β_{7} Re t_{it} \\ + β_{8} Newfi n_{it} + Σ γ_{k} Yea r_{k} + Σ k_{l} Industr y_{l} + Σ λ_{m} AccountingFir m_{m} \\ + Σ δ_{n} Offic e_{n} + Σ χ_{o} Partne r_{o} + ε_{it} . \end{matrix}

(5)

\begin{matrix} GC = α + β_{1} Siz e_{it} + β_{2} Le v_{it} + β_{3} Δ Le v_{it} + β_{4} Cf o_{it} + β_{5} Ro a_{it} + β_{6} Los s_{it} \\ + β_{7} Re t_{it} + β_{8} Newfi n_{it} + β_{9} Investmen t_{it} + β_{10} Bankruptcy_Ris k_{it} \\ + β_{11} Ag e_{it} + β_{12} National_MKT_Lea d_{it} + β_{13} Local_MKT_Lea d_{it} \\ + β_{14} Aud_Off_Siz e_{it} + β_{15} Client_Influenc e_{it} + Σ γ_{k} Yea r_{k} \\ + Σ k_{l} Industr y_{l} + Σ λ_{m} AccountingFir m_{m} + Σ δ_{n} Offic e_{n} + Σ χ_{o} Partne r_{o} + ε_{it} . \end{matrix}

(6)

Variables are defined in the appendix.

The quality of audited earnings (EQ) in Equation (5) is tested with two variables: (a) performance-adjusted abnormal working capital accruals (ABWC), with controls for the firm’s contemporaneous performance using return on assets (Kothari et al., 2005)¹⁵ and (b) restatements (RESTATE), which is an indicator variable coded 1 if a firm-year financial statement is subsequently restated. The going concern model is estimated with an indicator variable coded 1 if the auditor issues a going concern report (GC).

All empirical models in the study include a set of client control variables for other factors that can affect earnings quality and going concern reporting. All models have common control variables for firm size, company leverage, operating cash flow, accounting return on assets (profitability), stock market returns, the presence of losses, new external financing, and dummy variables for year and industry (Dechow et al., 1995; Gul et al., 2013; Johnson et al., 2002; Kothari et al., 2005).

The going concern model includes additional firm-level controls for the age of the firm (younger firms are riskier), the level of investments (a proxy for liquidity), and client bankruptcy risk (Zmijewski, 1984). We also control for auditor characteristics based on prior research. The auditor’s national-level and office-level industry leadership controls for systematic differences in audits conducted by industry experts (Krishnan, 2003). Audit office size is included to control for its impact on audit quality (Choi et al., 2010; Francis & Yu, 2009). Finally, a variable that measures the relative size of each client to an office is included to take into account the relative importance of a client to an office, and the incentives and risks based on relative client size (Carcello & Li, 2013). As a sensitivity analysis, we drop these auditor controls because they may cause the understatement of accounting firm and/or engagement office fixed effects. However, the results are not affected by their inclusion or exclusion.

All models are estimated using ordinary least squares (OLS) regression with fixed effects and, given the use of panel data, we use robust standard errors that are clustered by unique audit client to correct for heteroskedasticity and serial dependence associated to panel data. The restatement and going concern models have a dichotomous-dependent variable but are estimated using linear probability models (OLS), in line with other studies that have used fixed effect approach (Gul et al., 2013).¹⁶ After we add incrementally the audit firm, audit office, and partner fixed effects, we measure the increase in the R²-within of the models and perform an F test.

To assure our findings are not simply the mechanical result of the inclusion of additional independent variables at each stage, we adopt two additional econometric approaches. First, we repeat the tests described above for Equations (1) through (4) using HLM which explicitly controls for the nested structure of the underlying data through random effects for accounting firms, audit offices, and engagement partners that are unexplained by OLS (Nakagawa & Schielzeth, 2013). These models are reported later in the article and the results are consistent with the OLS estimations. Furthermore, we use a penalized regression approach, in particular a LASSO regression model, to assess the value that individual audit firm, office, and partner fixed effects potentially add to a baseline model, using Equation (1) as the starting point. The results, explained and reported later in the article, provide corroborative support to our findings from OLS and HLM.¹⁷

Results

Descriptive Statistics

Table 2 reports detailed descriptive statistics for all variables separately for the Big 4 and non–Big 4 subsamples. For Big 4 (non–Big 4) clients, the mean of ABWC is 6.6 (11.2) percent of lagged total assets. There are 31% of Big 4 client-years with subsequent restatements, compared with 24.6% for non–Big 4 clients. The restatement rates are similar to other U.K. studies (Campa & Donnelly, 2016). Going concern reports are issued for 5.6% of Big 4 client-years, and 13.1% of non–Big 4 client-years. Control variables are comparable with other U.K. studies (Carcello & Li, 2013). Big 4 clients are larger, more highly leveraged, have lower bankruptcy risk, and report fewer losses compared with non–Big 4 clients.¹⁸

Table 2.

Firm-Year Descriptive Statistics for the U.K. Big 4 and Non–Big 4 Samples (2009–2015).

Variables	N	M	Q1	Median	Q3	SD
Panel A: Big 4 clients
ABWC	3,579	0.066	0.015	0.036	0.077	0.098
RESTATE	3,579	0.310	0.000	0.000	1.000	0.463
GC	3,579	0.056	0.000	0.000	0.000	0.230
Size	3,579	12.646	11.241	12.630	14.070	2.069
Lev	3,579	0.442	0.276	0.432	0.574	0.250
ΔLev	3,579	−0.003	−0.037	−0.004	0.029	0.115
Cfo	3,579	0.057	0.025	0.080	0.137	0.184
Roa	3,579	−0.008	−0.004	0.040	0.081	0.250
Loss	3,579	0.262	0.000	0.000	1.000	0.440
Ret	3,579	0.172	−0.152	0.116	0.402	0.533
Newfin	3,579	0.921	1.000	1.000	1.000	0.270
Investment	3,579	0.253	0.086	0.160	0.313	0.251
Bankruptcy_Risk	3,579	−1.790	−2.891	−2.016	−1.098	2.086
Age	3,579	22.231	8.000	12.000	25.000	24.620
Nation_MKT_Lead	3,579	0.357	0.000	0.000	1.000	0.479
Local_MKT_Lead	3,579	0.054	0.000	0.000	0.000	0.226
Aud_Off_Size	3,579	19.116	16.854	19.254	21.596	2.714
Client_Influence	3,579	0.117	0.014	0.051	0.147	0.174
Inventory	3,579	0.087	0.003	0.031	0.135	0.119
AccRec	3,579	0.124	0.020	0.094	0.182	0.125
Subsid	3,579	5.770	2.646	4.359	6.928	5.108
BusySeason	3,579	0.704	0.000	1.000	1.000	0.457
Panel B: non–Big 4 clients
ABWC	1,832	0.112	0.024	0.060	0.122	0.158
RESTATE	1,832	0.246	0.000	0.000	0.000	0.431
GC	1,832	0.131	0.000	0.000	0.000	0.338
Size	1,832	9.713	8.743	9.860	10.772	1.615
Lev	1,832	0.404	0.163	0.327	0.522	0.361
ΔLev	1,832	0.006	−0.045	−0.002	0.050	0.211
Cfo	1,832	−0.081	−0.111	0.013	0.094	0.341
Roa	1,832	−0.204	−0.214	−0.008	0.056	0.537
Loss	1,832	0.520	0.000	1.000	1.000	0.500
Ret	1,832	0.121	−0.335	0.000	0.407	0.663
Newfin	1,832	0.849	1.000	1.000	1.000	0.358
Investment	1,832	0.291	0.079	0.191	0.413	0.277
Bankruptcy_Risk	1,832	−1.042	−3.191	−2.163	−0.764	4.151
Age	1,832	22.411	7.000	13.000	25.000	25.571
Nation_MKT_Lead	1,832	0.001	0.000	0.000	0.000	0.033
Local_MKT_Lead	1,832	0.005	0.000	0.000	0.000	0.070
Aud_Off_Size	1,832	14.857	13.633	14.631	17.118	1.981
Client_Influence	1,832	0.182	0.039	0.090	0.202	0.243
Inventory	1,832	0.065	0.000	0.005	0.078	0.116
AccRec	1,832	0.127	0.003	0.089	0.204	0.138
Subsid	1,832	2.484	1.414	2.449	3.162	1.780
BusySeason	1,832	0.628	0.000	1.000	1.000	0.483

Note. ABWC = performance-adjusted abnormal working capital accruals; RESTATE = restatement; GC = going concern.

Pooled Sample OLS Results

The reduced-form baseline models in Equation (1) are estimated with client controls but without any auditor-related fixed effects. Untabulated results indicate that all models are significant at p < .01, with an R²-within ranging between 2% and 12%. Results for individual variables are consistent with prior research.

H1 tests whether accounting firm fixed effects add significantly to the reduced-form baseline model with client controls. The incremental effect of accounting firm fixed effects is reported in subpanel 1 of Table 3. The F-ratios range in value from 2.46 to 42.02 and are significant at p < .01 for all three models, which rejects the null hypothesis that the set of fixed effect coefficients is not significantly different from zero. We conclude that accounting firm fixed effects add significantly to the baseline model, although the changes in the R²-within of models are small, less than one percentage point.

Table 3.

Sequential Testing of the Incremental Significance of Accounting Firms, Engagement Offices, and Partner Fixed Effects Over Baseline Models.

	(A)	(B)	(C)
Dependent variable	ABWC	RESTATE	GC
R²-within	3.08%	11.82%	2.28%
Obs.	5,411	5,411	5,411
1. Incremental significance of audit firm fixed effects (incremental to baseline models)
ΔR²-within_AF	0.99%	0.31%	0.58%
[F-Stat]	[2.89***]	[42.02***]	[2.46***]
2. Incremental significance of engagement office fixed effects (incremental to audit firm fixed effects)
ΔR²-within_EO	4.11%	2.26%	3.51%
[F-Stat]	[351.21***]	[1,743.05***]	[2,415.05***]
3. Incremental significance of individual partner fixed effects (incremental to audit firm and office fixed effects)
ΔR²-within_IP	13.55%	12.36%	13.62%
[F-Stat]	[4.2 × 10⁹ ***]	[1 × 10⁹ ***]	[6.4 × 10⁵ ***]
Total R²-within with audit firm, engagement office, and individual partner fixed effects plus controls
R²-within	21.73%	26.75%	19.99%

Note. All models are estimated using ordinary least squares with fixed effects and robust standard errors clustered by unique firm. ABWC = performance-adjusted abnormal working capital accruals; RESTATE = restatement; GC = going concern; AF = audit firm; EO = engagement office; IP = individual partner.

***

Significance at the 1% level.

H2 tests whether audit office fixed effects add significantly to the reduced-form baseline model with client controls plus accounting firm fixed effects. This is reported in subpanel 2 of Table 3. The F-ratios are quite large and significant at p < .01 in the three models which rejects the null hypothesis. The changes in R²-within are still small, though ranging between 2 and 4 percentage points.

H3 tests whether partner fixed effects add significantly to the reduced-form baseline model with client controls plus the fixed effects of accounting firms and engagement offices and is reported in subpanel 3 of Table 3. The F-ratios are very large and are significant at p < .01 in all models, which rejects the null hypothesis. The large F-ratio values are suggestive that partner fixed effects are relatively more important than either accounting firm or office fixed effects. The changes in R²-within are around 13 percentage points.

Separate Big 4 and Non–Big 4 Model Estimations

Table 4 reports the models in Table 3 estimated separately for the Big 4 and non–Big 4 subsamples. The Big 4 results (Panel A) and non–Big 4 results (Panel B) in Table 4 are broadly consistent with the full sample findings in Table 3. In Panel A for the Big 4 sample, the F tests fail to reject the null hypothesis that the accounting firm fixed effect coefficients are not significantly different from zero in any of the three models and therefore do not add significantly to the reduced-form baseline model without accounting firm fixed effects. These results suggest there are no systematic differences, on average, in audit quality across the Big 4 firms. However, results of the incremental tests of audit offices and engagement partners indicate they add significantly to baseline models and indicate the presence of interoffice differences within Big 4 firms, and inter-partner differences within Big 4 offices, as in Table 3 for the full sample estimations.

Table 4.

Big 4 and Non–Big 4 Tests of Incremental Significance of Accounting Firms, Engagement Offices, and Individual Partner Fixed Effects.

	(A)	(B)	(C)
Dependent variable	ABWC	RESTATE	GC
Panel A: Big 4 clients
R²-within	2.21%	13.09%	3.51%
Obs.	3,579	3,579	3,579
1. Incremental significance of Big 4 audit firm fixed effects (incremental to baseline models)
ΔR²-within_AF	0.05%	0.02%	0.31%
[F-Stat]	[0.51]	[0.16]	[1.91]
2. Incremental significance of Big 4 engagement office fixed effects (incremental to audit firm fixed effects)
ΔR²-within_EO	2.50%	1.85%	3.21%
[F-Stat]	[7,105.89***]	[7,685.94***]	[207.74***]
3. Incremental significance of Big 4 individual partner fixed effects (incremental to audit firm and office fixed effects)
ΔR²-within_IP	15.76%	13.02%	12.92%
[F-Stat]	[15.501.29***]	[1,367.91***]	[2.3 × 10⁷ ***]
Total R²-within with Big 4 audit firm, engagement office, and individual partner fixed effects plus controls
R²-within	20.52%	27.98%	19.95%
Panel B: Non–Big 4 clients
R²-within	5.55%	10.84%	10.28%
Obs.	1,832	1,832	1,832
1. Incremental significance of non–Big 4 audit firm fixed effects (incremental to baseline models)
ΔR²-within_AF	1.10%	0.55%	0.71%
[F-Stat]	[2.40***]	[7.14***]	[1.29]
2. Incremental significance of non–Big 4 engagement office fixed effects (incremental to audit firm fixed effects)
ΔR²-within_EO	2.08%	3.62%	2.42%
[F-Stat]	[24.09***]	[64.88***]	[22.47***]
3. Incremental significance of non–Big 4 individual partner fixed effects (incremental to audit firm and office fixed effects)
ΔR²-within_IP	11.64%	11.26%	12.57%
[F-Stat]	[1,456.03***]	[234.95***]	[5,809.95***]
Total R²-within with non–Big 4 audit firm, engagement office, and individual partner fixed effects plus controls
R²-within	20.37%	26.27%	25.98%

Note. All models are estimated using ordinary least squares with fixed effects robust standard errors clustered by unique firm. ABWC = performance-adjusted abnormal working capital accruals; RESTATE = restatement; GC = going concern; AF = audit firm; EO = engagement office; IP = individual partner.

***

Significance at the 1% level.

Panel B of Table 4 reports the non–Big 4 results and these results mirror the full sample results in Table 3. F tests for all of the auditor fixed effects are significant (except for audit firm fixed effects when we investigate GC), with the largest F ratios for the test of partner fixed effects.

Additional Analyses

HLM

HLM is an alternative estimation approach used when data are nested. In our test setting, auditor data are nested as follows: individual audit partners are nested in a specific office, and offices are nested within a specific accounting firm. Chang et al. (2018) use HLM with Taiwanese data and test for the effect of an accounting firm variable (accounting firm tenure) and an audit partner variable (lead partner engagement tenure). While the data structure in Chang et al. (2018) is not a classic nested data set, compared with our study, they do document that OLS results are not fully consistent with their HLM estimations. Given their findings, and because our data are classically nested, we re-estimate the models in Equations (1) through (4) using HLM that explicitly recognizes and controls for the nested structure of the data. Specifically, HLM controls for the variance from random effects of the nested data that are not explained by OLS methods (Nakagawa & Schielzeth, 2013). The HLM results are reported in Table 5.

Table 5.

Hierarchical Linear Model Tests of Incremental Significance of Accounting Firms, Engagement Offices, and Partner Fixed Effects.

	(A)	(B)	(C)
Dependent variable	ABWC	RESTATE	GC
Panel A: Pooled sample
1. Incremental significance of audit firm fixed effects (incremental to baseline models)
Chi-square	55.65***	55.65***	32.16***
[p-value]	[.000]	[.000]	[.000]
2. Incremental significance of engagement office fixed effects (incremental to audit firm fixed effects)
Chi-square	236.63***	236.63***	198.88***
[p-value]	[.000]	[.000]	[.000]
3. Incremental significance of individual partner fixed effects (incremental to audit firm and office fixed effects)
Chi-square	1,100.96***	864.33***	850.85***
[p-value]	[.000]	[.000]	[.000]
Panel B: Big 4 clients
1. Incremental significance of audit firm fixed effects (incremental to baseline models)
Chi-square	1.84	0.66	11.38**
[p-value]	[.399]	[.882]	[.023]
2. Incremental significance of engagement office fixed effects (incremental to audit firm fixed effects)
Chi-square	178.88***	90.60***	130.78***
[p-value]	[.000]	[.008]	[.000]
3. Incremental significance of individual partner fixed effects (incremental to audit firm and office fixed effects)
Chi-square	565.60***	589.30***	526.66***
[p-value]	[.000]	[.000]	[.000]
Panel C: Non–Big 4 clients
1. Incremental significance of audit firm fixed effects (incremental to baseline models)
Chi-square	21.44**	11.45	14.51
[p-value]	[.011]	[.323]	[.151]
2. Incremental significance of engagement office fixed effects (incremental to audit firm fixed effects)
Chi-square	41.36	76.45***	50.53
[p-value]	[.368]	[.000]	[.146]
3. Incremental significance of individual partner fixed effects (incremental to audit firm and office fixed effects)
Chi-square	250.25***	260.59***	287.87***
[p-value]	[.000]	[.000]	[.000]

Note. ABWC = performance-adjusted abnormal working capital accruals; RESTATE = restatement; GC = going concern.

**,***

Significance at the 5% and 1% level, respectively.

Since HLM is a maximum likelihood model, it is a chi-square test that compares Equations (1) through (4) to determine whether the likelihood ratio of expanded models is significantly larger than the reduced-form baseline models. Panel A of Table 5 presents the results for the full sample. The HLM results are consistent with the ordinary least squares (OLS) evidence in Table 3. All of the chi-square tests are significant at p < .01, which indicates the expanded models add significantly to the reduced-form models. Specifically, in subpanel 1 of Table 5, the chi-square tests indicate there are significantly larger likelihood ratios for the expanded models with accounting firm fixed effects compared with the reduce-form baseline model with only client controls.

Similarly, in subpanel 2 of Table 5 the chi-square tests show significantly larger likelihood ratios in the expanded model with audit office fixed effects, compared with the reduced-form model including firm-level controls and accounting firm fixed effects. In subpanel 3, we again observe significantly larger likelihood ratios in the expanded model with engagement partner fixed effects compared with reduced-form models controlling for firm-level factor as well as audit firm and audit office fixed effects. Note also that the chi-square values are greatest when adding partner fixed effects (subpanel 3) and are smallest when adding accounting firm fixed effects in subpanel 1. This is the same pattern observed in Table 3 for the F ratios and is consistent with partner fixed effects adding relatively more in explaining audit outcomes than either audit offices or accounting firms. The same analysis, separately for Big 4 and non–Big 4 clients, is presented in Panels B and C of Table 5. The results consistently highlight significantly larger likelihood ratios in the expanded models when engagement partner fixed effects are added to reduced-form models controlling for firm-level factors, audit firm, and audit office fixed effects.

LASSO regression

To make sure that the increase in the R²-within after the introduction of partner fixed effects is not just due to the large number of independent variables added at that stage, we employed a different statistical approach than those reported above, which focuses on variable selection rather than on the change in the R²-within after the inclusion of new variables. In particular, we use a penalized regression model, called LASSO regression, which selects the best set of explanatory variables from a range of potential covariates (James et al., 2013).¹⁹ More specifically, this approach reduces the number of variables in a regression model, keeping only those that have a strong effect on the dependent variable. In our research setting that includes audit firm, audit office, and audit partner fixed effects, we implement a penalized regression model that keeps only those auditor-related indicators that contribute more significantly to explain the audit outcome variables. Accordingly, using the LASSO regression command provided in STATA 16, we estimate a LASSO regression for all audit outcomes, imposing that the client-level controls must always be kept as the independent variables of the model, that is, our Equation (1), and setting, instead, all auditor-related fixed effects as part of the pool of covariates from which the LASSO regression has to select from and include in a potential extended model. The result of this selection is reported in Table 6.

Table 6.

Category of the Auditor-Related Variables Selected by a Least Absolute Shrinkage and Selection Operator Regression (in Addition to Firm-Level Controls).

	(A)	(B)	(C)
Dependent variable	ABWC	RESTATE	GC
Panel A: Pooled sample
Audit firms	3%	0%	8%
Engagement offices	24%	0%	52%
Individual partners	73%	100%	40%
Panel B: Big 4 clients
Audit firms	0%	0%	0%
Engagement offices	0%	0%	17%
Individual partners	100%	100%	83%
Panel C: Non–Big 4 clients
Audit firms	0%	0%	17%
Engagement offices	0%	0%	66%
Individual partners	100%	100%	17%

Note. ABWC = performance-adjusted abnormal working capital accruals; RESTATE = restatement; GC = going concern.

Panel A of Table 6 reports the results for the full sample. Column A of Table 6 indicates that for abnormal accruals, 73% of the auditor-related fixed effects retained in the LASSO regression are partner fixed effects. The results for restatements are even stronger since the LASSO regression excludes all audit firm and office fixed effects, keeping only partner fixed effects. Results are weaker when we examine going concern reporting since a LASSO regression keep all types of auditor-related fixed effects in a potential extended model, the majority of them being office effects. Partner fixed effects, however, still represent 40% of the auditor-related fixed effects selected by the LASSO regression.

Panels B and C of Table 6 report separately the results for Big 4 and non–Big 4 clients. Results for abnormal accruals and restatements are similar between Big 4 and non–Big 4 clients and highlight the importance of engagement partners in explaining audit quality. In these subsample analyses, only partner fixed effects are selected by a LASSO regression. For going concern reporting, we observe some differences between Big 4 and non–Big 4 clients. For Big 4 clients, 83% of the auditor-related fixed effects kept by a LASSO regression are linked to individual partners. In contrast, for non–Big 4 clients the majority of the auditor-related fixed selected by the LASSO regression are audit offices. Overall, the LASSO regressions indicate that partners are consistently the strongest predictor of audit outcomes, compared with audit firms and offices.

Analysis of partner demographic variables

Given that partner fixed effects are the dominant auditor factor in explaining audit quality in Tables 3 through 6, to better understand what drives the partner differences, we extend the analysis by exploring a set of specific partner demographic variables based on publicly available data commonly used in partner studies (e.g., Cahan & Sun, 2015). To do this, we collect the following publicly available partner information: gender, quality of the university attended, type of degree, and the year they became a Chartered Accountant (to calculate years of experience). These data come from membership records of the U.K. professional bodies (i.e., Institute of Chartered Accountants in England and Wales, Institute of Chartered Accountants of Scotland, Chartered Accountants of Ireland, Association of Chartered Certified Accountants), Bloomberg, the website “Company House” (available at beta.companieshouse.gov.uk), websites of the accounting firms, and LinkedIn. We also use the listed company data in the sample to measure partner busyness (number of listed clients). Missing data results in a reduced sample of 544 partners compared with the full sample of 665: 365 partners in Big 4 firms, and 179 in non–Big 4 accounting firms.

Using these characteristics, we added to the baseline models the variables for partner gender, rank of the university attended, whether a partner has a major in accounting, as well as variables that measure partner experience and busyness and we test whether they add whether they increase their explanatory power relative to baseline models. This analysis is reported in Table 7.

Table 7.

Incremental Significance of the Set of Auditor Characteristics Over and Above Accounting Firm and Office Fixed Effects, Versus the Incremental Significance of Partner Fixed Effects.

	(A)	(B)	(C)
Dependent variable	ABWC	RESTATE	GC
Panel A: Pooled sample (n = 4,658)
ΔR²-within from individual partner characteristics	0.25%	0.05%	0.08%
[F-Stat]	[1.01]	[0.48]	[0.48]
ΔR²-within from partner fixed effects	13.27%	11.76%	17.82%
[F-Stat]	[3,893.15***]	[3.5 × 10⁸ ***]	[1.9 × 10⁸ ***]
Panel B: Big 4 clients (n = 3,070)
ΔR²-within from individual partner characteristics	0.29%	0.08%	0.09%
[F-Stat]	[1.23]	[0.53]	[0.44]
ΔR²-within from partner fixed effects	14.11%	11.90%	18.09%
[F-Stat]	[1.1 × 10⁶ ***]	[18,478.65***]	[1.7 × 10⁸ ***]
Panel C: Non–Big 4 clients (n = 1,588)
ΔR²-within from individual partner characteristics	0.61%	0.00%	0.90%
[F-Stat]	[0.86]	[1.21]	[2.16*]
ΔR²-within from partner fixed effects	11.88%	11.24%	13.17%
[F-Stat]	[274.07***]	[420.74***]	[3.6 × 10⁵ ***]

Note. All models are estimated using ordinary least squares with fixed effects and robust standard errors clustered by unique firm). ABWC = performance-adjusted abnormal working capital accruals; RESTATE = restatement; GC = going concern.

,***Significance at the 10% and 1% level, respectively.

The sample in Table 7 is smaller than that used in Tables 3 and 4 due to the lack of demographic data for some partners. The results show that the set of partner variables does not add significantly to reduced-form baseline models with client controls plus fixed effects for accounting firms and audit offices. The F tests are all insignificant at the .10 level for the pooled sample and for the separate Big and non–Big 4 samples. Using this smaller sample, for the sake of comparability, we also test the incremental effect of partner fixed effects, rather than partners’ individual characteristics, and the F tests are all significant and consistent with the full sample estimations in Tables 3 and 4. We conclude that a set of partner demographic variables does not add significantly to models over and above the reduced models with controls for the fixed effects of accounting firms and offices.²⁰

The partner literature does not typically control for either accounting firm or audit office fixed effects. While our study does not include every partner characteristics in the existing partner literature, for the set of publicly available partner variables we examine, the evidence is suggestive the partner variables may not have much explanatory power over and above the fixed effects of accounting firms and audit offices. Accordingly, the findings raise questions about the interpretation and importance of partner variables in prior studies, and it is unclear how incrementally significant the demographic variables are in these studies.²¹

In conclusion, we have evidence that partner fixed effects explain far more than individual audit partner variables. This is also consistent with other fields such as financial economics which shows that country fixed effects have far greater explanatory power than the country-specific variables (Doidge et al., 2004). In the econometrics literature, fixed effects are a classic control for omitted variables. In our context, the implication is that inter-partner variation is largely driven by factors other publicly observable demographic variables such as age, gender, and experience. At this stage, we do not know what the significant drivers of the inter-partner variation are, but our study is important because it points to the need for more in-depth research that moves beyond publicly available demographic data.²²

R² decomposition analysis

To further assess the relative importance of client factors versus auditor characteristics in explaining audit outcomes, we undertake a variance decomposition analysis, derived from the theoretical work of Shapley (1953) and Owen (1977), based on iterative combinations of sets of regressors to measure the incremental contribution of each regressor (or set of regressors in our case) in explaining model R-squares. Decomposition analysis is viewed as superior to R² comparisons to understand the importance of sets of regressors relative to each other (e.g., see Huettner & Sunder, 2012; Sastre & Trannoy, 2002).²³ The evidence (not tabulated) indicates that, for the full sample, the client control variables in the baseline model (1) are the dominant set of regressors and account for 62%–67% of the explained variance in the three models. Audit partner fixed effects are the next most important set of regressors, representing from 24% to 29% of the explained variance in the three models. Engagement office fixed effects are the third most important set of regressors, and account for 6%–9% of the explained variance. The least important set of regressors are accounting firm fixed effects which account from 1% to 4% of explained variance in most of the models. Similar evidence is observed if we separate Big 4 and non–Big 4 clients.

Other robustness tests

A potential concern is the relatively small sample size of some partner-year observations. For this reason, we impose an additional requirement that audit partners must have at least two unique clients, and partners must have also a minimum of 10 client-year observations to be in the sample. This process increases the number of partner-year observations, but it reduces the overall sample size to 3,443 firm-year observations, compared with 5,411 observations for the full sample. For this reduced sample, the results are consistent with those reported in Table 3 and 4. Based on these additional tests we have no reason to believe partner fixed effects are misestimated due to partner-year sample sizes.

Fixed effects are a strong control for unobserved time-invariant firm characteristics, but it also means that auditor-related fixed effect coefficients will have statistically significant cross-sectional variation only to the extent there are auditor changes of some type in the data, that is, when a client changes accounting firms and/or audit offices, or if the engagement partner changes. In other words, if a client has exactly the same accounting firm, same engagement office, and same audit partner over the entire sample period, then the auditor-related fixed effects will have no explanatory power over and above client fixed effects. In this respect, the test is less generalizable than tests without client fixed effects.²⁴ Therefore, we re-estimate the models in Table 3 without imposing client fixed effects. The results (not tabulated) are consistent with Table 3, although the changes in R² are much larger for the auditor characteristics, especially for audit office and engagement partner fixed effects. However, we conclude that the overall tenor of the results in Table 3 are robust to controlling for, and not controlling for, client fixed effects.

We also estimate an additional LASSO regression where we have included in the set of potential covariates the personal auditor characteristics, in addition to audit firm, audit office, and audit partner fixed effects. The LASSO regression continues to prioritize partner fixed effects and the results of this analysis are in line with those reported in Table 6.

Finally, London is the largest city-specific audit market in the United Kingdom, and to rule out the results are driven by a London effect we conduct two tests. First, we drop those observations in London, and second we add a London indicator variable to all models. Results from these two tests are consistent with the tabled results and indicate that a London effect does not drive the results.

Conclusion

Prior research has explored the effects of audit firm characteristics on audit quality, such as accounting firm size and the accounting firm’s industry expertise. Other studies have examined the effects of audit office characteristics and individual partner demographic variables on audit outcomes. These studies typically focus on just one auditor characteristic and ignore other auditor factors that might also affect audit quality. In contrast, our study takes what we view as a more systematic and comprehensive approach to studying auditor effects.

To isolate the idiosyncratic effects of audit firms, offices, and partners, we use a series of econometric tests to investigate whether auditor attributes, measured with fixed effects, add incrementally to baseline models with client controls. We find that, overall, interfirm differences, interoffice differences, and inter-partner differences are incrementally significant. However, all of our econometric approaches consistently show that the inter-partner differences are by far the most important auditor factor in explaining audit quality.

Given the importance of inter-partner differences on audits, we further analyze partner demographic characteristics and find that when we use such variables in the models in lieu of partner fixed effects, the incremental importance of partner variables (over and above the effects of accounting firms and offices) is small and mainly insignificant. Thus, our analysis concludes that the type of publicly available partner demographic data that are being used in the partner research literature may not have much explanatory power in explaining audit quality, once there is control for the fixed effects of accounting firms and audit offices.

Our findings will be of interest to accounting firms and regulators in the United Kingdom, and to similar countries in the European Union, Australia, Canada, United States, and other countries that have a well-developed accounting profession and similar institutions and regulatory oversight. While our results should apply to other developed countries with similar regulations, accounting professions, and audit markets, we acknowledge there could be something unique about the United Kingdom that may limit the study’s generalizability.

The results in our study are strongly suggestive that inter-partner variation is the single most important auditor characteristic in explaining audit quality. However, it remains for future research to identify exactly what are the most important partner characteristics that explain the inter-partner variation we observe in our study. At the beginning of the article we suggested the organizational behavior literature on job performance might provide some useful directions in advancing our understanding of audit partner differences. This will require access to accounting professions in accounting firms to go beyond the limited understanding that appears to be the case with publicly available demographic partner data.

Footnotes

Appendix

Acknowledgements

We would like to acknowledge the support and encouragement of the Editor, Bharat Sarath, the Associate Editor, Kannan Raghunandan, as well as an anonymous reviewer for her or his important comments and suggestions. Furthermore, we thank Joe Gerakos, Nick Hallman, Inder Khurana, Mike Minnis, Marleen Willekens, workshop participants at University of Technology Sydney and University of Missouri, and conference participants at the inaugural research conference of the Foundation for Audit Research (Nyenrode University, Netherlands), EIASM Workshop on Audit Quality European Accounting Association Annual Congress, and the American Accounting Association Annual Meeting.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Mara Cameran

Notes

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The Relative Importance of Auditor Characteristics Versus Client Factors in Explaining Audit Quality

Abstract

Keywords

Introduction

Hypothesis Development and Methods

Testing Framework Using Nested Models

Sample and Variables

Empirical Models

Results

Descriptive Statistics

Pooled Sample OLS Results

Separate Big 4 and Non–Big 4 Model Estimations

Additional Analyses

HLM

LASSO regression

Analysis of partner demographic variables

R2 decomposition analysis

Other robustness tests

Conclusion

Footnotes

Appendix

Acknowledgements

Declaration of Conflicting Interests

Funding

ORCID iD

Notes

References

R² decomposition analysis