59 © 2018 Conscientia Beam. All Rights Reserved. AUDIT FEES AND AUDIT QUALITY: A STUDY OF LISTED COMPANIES IN THE DOWNSTREAM SECTOR OF NIGERIAN PETROLEUM INDUSTRY ALIU Momodu Mohammed1+ Okpanachi Joshua2 Mohammed Nma Ahmed3 1Department of Accountancy, School of Business Studies, Auchi Polytechnic, Nigeria 2,3Department of Accounting & Management, Faculty of Arts and Social Sciences, Nigerian Defence Academy (NDA) Kaduna, Nigeria (+ Corresponding author) ABSTRACT Article History Received: 4 July 2018 Revised: 3 August 2018 Accepted: 16 August 2018 Published: 28 August 2018 Keywords Audit fee Audit quality Theory of inspired confidence. This study examined the relationship between audit fees and audit quality of listed companies in the downstream sector of the Nigerian petroleum industry. In order to achieve this objective, a total of nine (9) listed companies in the downstream sector of Nigerian Petroleum Industry were selected. Secondary data used for the study was extracted from the annual reports of the selected companies for eight (8) financial years (2007-2014). Audit quality which is the dependent variable was regressed on audit fees alongside leverage and age as control variables using the binary logit regression method. Finding shows that audit fee has a negative significant relationship with audit quality, while leverage also has an inverse relationship but was not significant. Firm age, on its part, had a positive sign and significantly associated with audit quality. It was therefore concluded that high audit fees have the likelihood of compromising auditors’ independence, thereby, resulting in lower audit quality. The study recommends that regulators of the auditing practice should adopt measures that would regulate and monitor the audit pricing process in order to strike a balance that would curtail over-charging and or under-charging which evidence shows could impair the independence of the auditor, thereby affect audit quality. 1. INTRODUCTION The issues regarding audit quality and the factors that can influence it have dominated the accounting literature in recent times. The reasons are not far-fetched, especially when one considers the magnitude of the incessant corporate scandals that rocked several already-established firms in the onset of the 21st century. Both international and indigenous researchers have thus, beamed their search-light on the factors that could impair audit quality. One of the factors that have remained recurrent in the audit quality discuss is the independence of the auditor (Karsemeijer, 2012). There are two popular professional accounting bodies in Nigeria as it stands, they include: ICAN (Institute of Chartered Accountants of Nigeria) and ANAN (Association of National Accountant of Nigeria). Among the core mandate of these accounting bodies is to regulate professional accounting practices (including Auditing) in the country. The Companies and Allied Matters Acts [CAMA] of 2004 stipulates that all listed companies in Nigeria shall engage the services of an independent (external) auditor. The apparent onus of this demand is for the external auditor, in expressing his independent professional opinion on the “true and fair view” of the information contained in the financial report; lends credence on the reliability of the said information for the confidence-reassurance of the stakeholders. Humanities and Social Sciences Letters 2018 Vol. 6, No. 2, pp. 59-73 ISSN(e): 2312-4318 ISSN(p): 2312-5659 DOI: 10.18488/journal.73.2018.62.59.73 © 2018 Conscientia Beam. All Rights Reserved. https://orcid.org/orcid-search/quick-search?searchQuery=AJAYI%20Boboye%20L. https://orcid.org/orcid-search/quick-search?searchQuery=AJAYI%20Boboye%20L. https://orcid.org/orcid-search/quick-search?searchQuery=AJAYI%20Boboye%20L. https://www.doi.org/10.18488/journal.73.2018.62.59.73 Humanities and Social Sciences Letters, 2018, 6(2): 59-73 60 © 2018 Conscientia Beam. All Rights Reserved. In providing such an important service, the external auditor is entitled to a certain fee chargeable to the client (the auditee) as remuneration for the auditing endeavors. This fee is called “Audit fees”. According to Oladipupo and Monye-Emina (2016) the audit firms are at freedom to charge what they consider fit as audit fees. In other words, the amount charged as audit fees could be discretional. Thus, the fees charged by an audit firm or eventually paid by the auditee (client) for audit services could be higher or even lower with respect to what another auditor may accept within a particular sector. Previous researchers (see Al-Khoury et al. (2015)) have expressed concerns concerning how audit fees could affect audit quality; with majority contesting that audit quality can be strongly influenced by the fees paid to the auditor. At the core of such speculations, several schools of thoughts exist. For example; Karsemeijer (2012) argue that “the higher the audit fees, the more important a client is to the firm and so, independence and therefore the quality of the audit could be compromised”. Conversely, Ettredge et al. (2007) opined that when a client (auditee) pays lower audit fees comparable with what other companies in the same industry are paying, there is every likelihood that the client becomes loyal to the audit firm which might lead to the auditor overlooking material misstatement and or allowing management to engage in aggressive income smoothing. On the other hand, Ettredge et al. (2007) equally argue that financial satisfaction (as a result of high audit fees paid an auditor) “may increase the professionalism and the effort exerted by the auditor which will enhance the audit quality”. This dominating linkage of audit fees as a significant factor in determining auditor independence viz-à-vis audit quality is apparent in prior literatures where the former (audit fees) is repeatedly applied as a proxy for audit quality (e.g. DeFond and Zhang (2014)) and auditor independence (see Okolie (2014); Babatolu et al. (2016); Maria (2016)). From the foregoing, it looks agreeable that audit fees (whether abnormally high or incredibly low) can influence auditor independence and by implication, audit quality. However, limited indigenous empirical evidences are available to that effect. The few existing studies all showed conflicting outcomes. For example: on one side, Oladipupo and Monye-Emina (2016) find that audit fees do not significantly affect audit quality in Nigerian quoted firms; the findings of Yuniarti (2011) using CPA firms in Indonesia equally towed the same line. On the other hand, Karsemeijer (2012) using US listed companies, finds that high audit fees are significantly associated with low audit quality; while a recent study by Babatolu et al. (2016) and that of Okolie (2014) equally find that audit fee is significantly related to audit quality. The conflicting evidence(s) continues. It is on this premise that this study derived its core objective to examine the relationship between audit fees and audit quality among listed companies in the downstream sector of Nigerian petroleum industry. To achieve this objective the study Hpothesize that: HO: Auditor fees have no significant positive relationship on audit quality. 2. CONCEPTUALIZING AUDIT QUALITY AND AUDIT FEES Based on available literature, audit quality is a multi-dimensional construct that has proved quite difficult to quantify and measure. Until now, it appears there is still no uniform definition of audit quality. Riyatno (2007) as cited in Yuniarti (2011) supports this assertion as he portrays “audit quality as something that is abstract, difficult to measure and can only be perceived by the users of audit services”. However, the definition of audit quality by DeAngelo (1981) that “audit quality measures the probability that an auditor will ascertain and straightforwardly report material errors, falsification and exclusion discovered in a client’s accounting system”, appears to be the most desired definition used by previous researchers. To other researchers such as Baothem and Ussahawanitchkit (2009) “audit quality is the probability that an auditor will not issue an unqualified report for financial statements containing material errors”. To this researcher, audit quality represents the willingness to uncover any material misstatements and unethical accounting practices in the financial statement, and conveying such information appropriately without bargain. Several proxies are usually adopted in measuring audit quality including: discretionary accruals, the use of a Big4 audit firms, as well as audit fees among others. Humanities and Social Sciences Letters, 2018, 6(2): 59-73 61 © 2018 Conscientia Beam. All Rights Reserved. Audit fees, on its part, represent the amount charged by the auditor for an audit process performed for the accounts of an enterprise (Walid, 2012). As earlier mentioned, listed companies are statutorily required to have their accounts audited by an external auditor without compromising the quality of audit, it is expected that they would want the fees they pay to be reasonable. On the side of the auditors, they would also expect to receive adequate fees for their services in order to maintain their services at a satisfactory level. In addition to companies and auditors, the public in general and shareholders may equally be concerned that the audit fee is not set at such a level - either too high or too low, in order not to undermine the confidence of the audit opinion (Walid, 2012). According to Jusoh et al. (2013) the reputation of most audit firms and the quality of their audit services are often related to the amount paid for the audit functions. According to Okolie (2014) higher audit fees are reflected in higher costs resulting from greater audit quality. Francis (2004) as cited in Karsemeijer (2012) contested that “higher audit fees imply higher audit quality, ceteris paribus, because the higher audit fees are imposed because of either greater effort or more specialized auditors”. Moizer (1997) also asserts that audit fee is associated with higher audit quality resulting in higher reputation of the auditors. Thus, since larger audit firms receive larger audit fees than smaller audit firms as previous studies such as Copley (1991) and Wooten (2003) have shown, which ultimately is expected to translate to higher audit quality; why has majority of the crisis-ridden firms in recent past been audited by the top-cadre audit firms. In fact, Dabor and Dabor (2015) report that “the entire failed banks in Nigeria in the last decade had wonderful audited financial reports; most of the banks even declared huge profits but went under few months after such declarations”. 2.1. Theoretical Framework The Limperg’s theory of Inspired Confidence of 1985 provides an underlying theoretical basis for this study. Although the DeAngelo (1981) economic theory of auditor independence which implies that audit fees create very different incentives for an auditor and have therefore opposing effects on audit quality also forms a direct link. The auditors’ theory of inspired confidence also offers a linkage between stakeholders’ requirement for credibly audited reports and the capacity of the audit processes to meet those needs. The theory of inspired confidence posits that the auditor, as a confidential agent, derives his broad function from the need for expert and independent assessment plus the need for an expert and independent judgment supported by evidence. Minimizing the risk of undetected material misstatements implies that the accountant is under a duty to conduct his work in a manner that does not betray the confidence which he commands before the rational person even if the accountant may not produce what is greater than the expectation of the stakeholders (Limperg, 1985). The import of the theory of inspired confidence is that the duties of the auditors derive from the confidence that are bestowed by the public on the success of the audit process and the assurance which the opinion of the accountant conveys. Since this confidence determines the existence of the process, a betrayal of the confidence logically means a termination of the process or function. Many companies seek tenders for audit services with a focus on audit quality. They correctly focus on matters such as expertise and experience of the engagement team, industry knowledge, the availability of specialist skills to deal with complex issues and auditor independence. However, some tenders focus on reducing fees and saving costs, inappropriately assuming that audit quality is only an issue for the audit firm. While there may be some instances where an effective but more efficient audit is obtained, there could be pressures in some audit firms to limit the impacts on margins. Both the auditee and the audit firm are expected to act rationally whilst trying to maximize their own utility which might not always be perfectly aligned between the two parties (Ross, 1973). The question that arises wherefrom is; how can the audit firm ensure that they acts in the best interest of the stakeholders and in commensurate with the huge amounts the clients pay for audit services in order not to compromise the confidence bestowed in them? Humanities and Social Sciences Letters, 2018, 6(2): 59-73 62 © 2018 Conscientia Beam. All Rights Reserved. 2.2. Review of Empirical Studies on Audit Fees and Audit Quality The table 2.1 below shows summarized empirical studies related to this study. Table-2.1. Summary of Recent Empirical Studies Author(s)/ Year Variables Methodology Country of Research Major Finding(s) on Audit Fees Babatolu et al. (2016) Audit firm tenure, audit fee and audit firm rotation; against Audit Quality. Secondary data (7 banks from Nigeria Stock Exchange) 2009-2013 Nigeria A positive insignificant relationship exists between audit fee and audit quality Maria (2016) Audit Fees (Auditor independence), Audit firm Rotation; against Audit Quality Secondary data (2604 companies from New York Stock Exchange) 1997 – 2015 United States Positive significant relationship between Audit fees and Audit Quality Oladipupo and Monye- Emina (2016) Abnormal audit fees against Audit Quality Secondary data (50 companies quoted on the Nigeria Stock Exchange) 2005-2012 Nigeria Abnormal audit fees does not have significant effect on Audit quality Hossain et al. (2015) Audit Team Composition, Audit fees, audit firm size; against Audit Quality Secondary data (1,080 year-firm observations) 2008-2012 Japan Audit fees are based on the size of an audit team; and has a positive association with audit quality Khan and Haq (2015) Abnormal (excess) audit fees and Audit quality Secondary data (150 non- financial firms) 2007-2011 Pakistan The quality of audit is not impaired when auditors are paid extra (excess) audit fee Al-Khoury et al. (2015) Audit fees, audit tenure and mandatory rotation; against Auditor Independence Primary data administered on 85 Auditors and Public Accountants Jordan There is a negative significant relationship between Audit fees and Auditor Independence Jacob et al. (2015) Big4, audit fees; against Audit quality Secondary data (495 BSE firms) 2000-2013 India Large audit firms earn significantly higher abnormal fees; such abnormal fees are not associated with reduction in the quality of audit and reported earnings Moraes and Martinez (2015) Audit tenure, audit fees; against Audit quality Secondary data (300 firms) 2009-2012 Brazil Audit firms that charge less audit fees tend to be more relaxed regarding earnings management by their client Okolie (2014) Auditor Independence (Audit fees) and audit tenure; against Discretionary Accruals Secondary data (57 companies listed in NSE) 2006 – 2011 Nigeria Higher audit fee is likely to result in impairment of auditor independence and could create greater opportunities for accrual manipulation. Kasai (2014) financial institutions’ shareholdings, audit quality; against Audit Quality Secondary data (1,720 Japanese companies) 2004- 2007 Japan Higher audit fees are likely to compromise auditors’ independence, thereby, lowering audit quality. Suseno (2013) Auditor independence, audit fees; on Audit Quality Primary data from 73 Public Accountant offices Indonesia Audit fees significantly influences the auditing quality Karsemeijer (2012) Non-audit fees and Audit fees; against Earnings Management (proxy for Audit Quality) Secondary data (2,568 US listed companies) 2010 only United States Positive significant association between audit fees and the absolute value of discretionary accruals (meaning that high fees are associated with low audit quality). Yuniarti (2011) Audit firm size and Audit fees; against Audit Quality Primary data from 37 Certified Public Accountants and External Auditors Indonesia Audit fee significantly affects the quality of audit. Choi et al. (2010) Abnormal audit fees against Audit Quality Secondary data (7,061 companies) 2000-2003 Hong-Kong Lower audit fee(s) is not significantly associated with audit quality; abnormally high audit fees are negatively associated with audit quality. Source: Fieldwork (2016) 3. METHODOLOGY The population of this study consists of ten (10) listed companies in the downstream sector of the Nigerian petroleum industry. However, one of the companies (Seplat Petroleum PLC) was inevitably excluded from the sample due to incomplete data, having been listed in 2012. Finally, nine (9) of the companies formed the sample size (see appendix for the list of the sampled companies) and was thus used for the analysis for a period of eight (8) Humanities and Social Sciences Letters, 2018, 6(2): 59-73 63 © 2018 Conscientia Beam. All Rights Reserved. financial years (2007 – 2014). Cross sectional data was gathered from the annual reports of all ten listed companies in the downstream sector of Nigerian petroleum industry. In analyzing the relationship between auditor fees and audit quality, the binary probit model estimation technique was utilized considering that dependent variable (audit quality) is binary (1 and 0). Thus, the ordinary least squares (OLS) multiple regression model cannot yield reliable coefficients and inference statistics where the dependent variable is dichotomous in nature. The model developed for the study basically relates auditor fee with audit quality measured, in line with previous literatures, as 1 if firm i is audited by a Big4 audit firm at year t and 0 otherwise. The Big4 audit firms includes; Akintola Williams Deloitte, KPMG, PricewaterhouseCoopers and Ernst & Young. Studies like Skinner and Srinivasan (2012) provide both theoretical and empirical justification for the use of big audit firms as a proxy for audit quality. Two (2) other variables (leverage and age) were included as control variables in line with previous studies such as Bergstresser and Philippon (2006). The age of the company was included as older companies would likely wish to preserve their reputation and ensure high quality reports. Leverage was equally included to control for the effect of financial policies adopted by the company on audit quality outcome. The econometric analysis was conducted using Eviews 8.0 computer software. Several diagnostic assumption tests such as VIF, serial-correlation, heteroscedaticity and normality assumption tests were conducted prior to the regression estimation. 3.1. Model Specification and Measurement of Variables The general expression of the model goes as: Audit Quality = f(Audit fees) ………………………………………….Equ (1) Infussing the two (2) control variables, we have: Audit Quality = f(Audit fees, Leverage, Age)…………………………….Equ (2) Expressing the model in econometric form: AQit = =0 + 1LnAFEEit + 2LEVit + 3AGEit + et…………………..Equ (3) Where: β0 = Intercept; β1-3 = Unknown Coefficients AQ = AUDIT QUALITY = measured by the likelihood that a sampled firm employs the services one of the big audit firms earlier listed. A dummy value of 1 is assigned if the firm uses any of the big4 and 0 if otherwise. LnAFEE = AUDIT FEES = measured using natural logarithm of total fees paid by company i in year t for audit services. LEV = LEVERAGE = measured as total debt scaled by total assets AGE = COMPANY AGE = measured as difference between current year and company’s year of incorporation E = Error term The apriori expectations were predicted as: 1 > 0; 2 < 0; and 3 > 0 The descriptive statistics table above provides information about the sample characteristics. AQ showed a mean value of 0.597 with a with a minimum and maximum of 0 and 1 respectively, implying that over half of the sampled companies are audited by the Big4 audit firms. Also from the result, the average audit fee cumulatively paid by the sampled firms during the period studies was N3,299,033 (in millions). The lowest audit fee paid during the period was N5500 (in millions) while the highest was N29,977,000. More so, leverage (LEV) has a mean value of 0.743 implying that majority of the sampled companies depend on external financing in financing their assets. The average age of the sample companies is 24 years. It was also noted that the probability values of the Jarque-Bera statistics are low for all the series, signifying an evenly distributed data set. Humanities and Social Sciences Letters, 2018, 6(2): 59-73 64 © 2018 Conscientia Beam. All Rights Reserved. 4. DATA ANALYSES AND INTERPRETATION Table-4.1. Descriptive Statistics AQ AUDFEE LEV AGE Mean 0.597222 3299033. 0.743446 23.83333 Median 1.000000 34782.00 0.816845 22.00000 Maximum 1.000000 29977000 4.338958 58.00000 Minimum 0.000000 5500.000 -4.32867 2.000000 Std. Dev. 0.493899 6919491. 1.245341 13.62578 Skewness -0.39646 2.429015 -0.5064 0.877564 Kurtosis 1.157177 7.833991 8.661229 3.733639 Jarque-Bera 12.07411 140.9037 99.22583 10.85611 Probability 0.002389 0.000000 0.000000 0.004392 Sum 43.00000 2.38E+08 53.52814 1716.000 Sum Sq. Dev. 17.31944 3.40E+15 110.1121 13182.00 Observations 72 72 72 72 Source: Researchers Computation (2016) Table-4.2. Correlations Matrix Covariance Analysis: Ordinary Probability AQ LNAFEE LEV AGE AQ 1.000000 ----- ----- LNAFEE -0.219878 1.000000 -1.885785 ----- 0.064* ----- LEV -0.121422 -0.059825 1.000000 -1.023466 -0.501434 ----- 0.3096 0.6176 ----- AGE 0.337301 0.494675 -0.151473 1.000000 2.997739 4.762233 -1.282109 ----- 0.004** 0.000** 0.2040 ----- Source: Eviews 8.0 (2016) **. Correlation is significant at the 0.01 level *. Correlation is significant at the 0.10 level The correlation matrix in table 4.2 portrays how the variables are associated with each other. As portrayed, a negative correlation exists between LnAFEE and AQ (r = -0.22); and also between AQ and LEV (r = -0.12). This suggests that audit fees and audit quality moves in opposite direction, just as leverage and audit quality. thus, an increase in one will ultimately lead to a decrease in the other. However, while the association between audit fee and AQ is fairly-strong at 10%, that of LEV and AQ is not significant at any level. Also, AGE appeared to correlate positively with AQ and Audit fees with r=0.34 and r = 0.49 respectively. Both associations was equally statistically strong at 1% levels (on both ends) suggesting that older firms are likely associated with higher audit fees and high audit quality. It was also observed that there was no issue of high-correlation; the highest correlation was between AGE and LnAFEE (0.495). This suggests that multicollinearity problem would not occur in the series. The VIF test below further re-affirms that. Table-4.3. The Variance Inflation Factors (VIF) test for Multicollinearity Coefficient Uncentered Centered Variable Variance VIF VIF C 0.047165 19.48683 NA LNAFEE 0.000401 24.75875 1.324393 LEV 0.001620 1.393808 1.023799 AGE 1.79E-05 5.541097 1.350642 Source: Eviews 8 (2016) Humanities and Social Sciences Letters, 2018, 6(2): 59-73 65 © 2018 Conscientia Beam. All Rights Reserved. The test for multicollinearity was performed using the Variance Inflation Factors (VIF). From the result, all the VIF values are very close to the value of 1 which suggests that there is no multi-collinearity problem between the variables. The highest centered VIF as seen above is 1.350642, this shows the fitting appropriateness of the model of the study. Figure-4.1. Normality Test Source: Eviews 8.0 (2016) The output in figure 4.1 checks for the normality of the residuals of a regression line. As shown in the result, which a combination of the entire 72 observations of the study, the Jargue Bera statistic stood at 1.82 with a corresponding probability value of 0.4017 (40.2%). Since the p-value is far beyond the benchmark of 5%, we cannot reject the null hypothesis. This implies that the population residual (u) is normally distributed and fulfills the assumption of a good regression line. Table-4.4. Result of the Heteroskedasticity Test Heteroskedasticity Test: Breusch-Pagan-Godfrey F-statistic 0.295221 Prob. F(3,68) 0.8287 Obs*R-squared 0.925704 Prob. Chi-Square(3) 0.8192 Scaled explained SS 0.629915 Prob. Chi-Square(3) 0.8896 Source: Eviews 8.0 (2016) The result presented in table 4.4 shows that the p-value (0.8192 or 81.9%) of the corresponding observed chi- square value is greater than 5%. Hence, we cannot reject the null hypothesis. This means that the error variance is not serially correlated. Hence, the null hypothesis of homoskedastic error term (which is desirable) can be assumed. Table-4.5. Result of the Breusch-Godfrey Serial Correlation LM Test Breusch-Godfrey Serial Correlation LM Test: F-statistic 1.689840 Prob. F(2,66) 0.1924 Obs*R-squared 3.507324 Prob. Chi-Square(2) 0.1731 Source: Eviews 8.0 (2016) From the Breusch-Godfrey serial correlation (LM) test result in table 4.5, the p-value of the observed R square value is 17.3% which is far greater than the critical values at 5% significant level. Hence, the null hypothesis of no serial correlation is thereby accepted accordingly. 0 2 4 6 8 10 12 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 S e r i e s : R e s i d u a l s S a m p l e 1 7 2 O b s e r v a t i o n s 7 2 Mean -2.52e-16 Median 0.068925 Maximum 0.751339 Minimum -0.997172 Std. Dev. 0.408535 Skewness -0.309460 Kurtosis 2.525763 Jarque-Bera 1.823887 Probability 0.401743 Humanities and Social Sciences Letters, 2018, 6(2): 59-73 66 © 2018 Conscientia Beam. All Rights Reserved. Table-4.6. Result of the Binary Probit Estimation Dependent Variable: AQ Method: ML - Binary Probit (Quadratic hill climbing) Variable Coefficient Std. Error z-Statistic Prob. C 2.105809 0.789910 2.665885 0.0077 LnAFEE -0.279669 0.076224 -3.669065 0.0002 LEV -0.149627 0.098610 -1.517367 0.1292 AGE 0.076335 0.018016 4.236999 0.0000 McFadden R-squared 0.279182 Mean dependent var 0.597222 S.D. dependent var 0.493899 S.E. of regression 0.412313 Akaike info criterion 1.082950 Sum squared resid 11.56016 Schwarz criterion 1.209431 Log likelihood -34.98619 Hannan-Quinn criter. 1.133302 Deviance 69.97238 Restr. deviance 97.07355 Restr. log likelihood -48.53678 LR statistic 27.10118 Avg. log likelihood -0.485919 Prob(LR statistic) 0.000006 Obs with Dep=0 29 Total obs 72 Obs with Dep=1 43 Source: Eviews 8.0 (2016) Table 4.6 shows the outcome of the binary probit regression technique adopted for the study. From the table, the McFadden R-squared value, which shows the combined explanatory effect of the independent variables on the dependent variable (AQ), stood at 0.279 showing that the model has an explanatory power of about 28%. What this portends is that about 72% of the systematic variation in the dependent variable (AQ), proxied here using the Big4, was not accounted for in the model and have been contained by the error term. On the overall significance level of the model, the model passed the significance test even at 1% level with LR statistic (goodness-of-fit test) and corresponding probability value of 27.101 and 0.000006 respectively. Thus, the explanatory variables were capable of explaining the variations in the dependent variable (AQ). An evaluation of the slope coefficients of the explanatory variables and the corresponding Z-statistics values revealed that audit fees (LnAFEE) has a negative (sign) significant relationship with audit quality (AQ). This was depicted by the slope coefficient of -0.27967; and the z-Statistics (-3.669) and probability value of 0.0002 which are statitically significant at 0.01 (1%) levels. Thus, a unit increase in audit fee (LnAFEE) will ultimately cause a significant decrease in audit quality (AQ) by up to 27.97%. Similarly, the variable of leverage (LEV), which acts as a control variable in the study, also showed an inverse relationship with audit quality (AQ) in agreement with the apriori expectation. However, unlike the variable of audit fee (LnAFEE), the relationship between leverage and audit quality is not statically significant because the p-value of 0.1292 exceeds the 0.05 benchmark. The last control variable, company age (AGE) is positively associated with audit quality (AQ) and passed the significance test at 1% levels. This suggests that the older a firm becomes, the more likely that the audit quality will increase significantly. 5. DISCUSSION OF FINDINGS Based on the outcome of the results, audit fees showed an inverse significant relationship with audit quality. It can therefore be interpreted that higher audit fees may likely lead to a decline in audit quality. This results is in tandem with those obtained by Al-Khoury et al. (2015) in Jordan market, Okolie (2014) using Nigerian data; and Kasai (2014) using Japanese data. The implication of this result may erupt divided perceptions, considering that large audit firms are practically associated with higher audit fees for which high quality audit service is envisaged. More so, Veronica and Anggraita (2016) and Okolie (2014) also support this position when they argued that paying higher audit fees paid to an external auditor is likely to increase the economic bond between the auditor and the auditee, thereby impairing the auditor’s independence. On this submission, our result appears to have alligned with the underlying expectation relying on the assumption that an impaired auditor independence will likely leads to an auditor allowing for aggressive earnings management which will cause the quality of audit to plummet. The case of Humanities and Social Sciences Letters, 2018, 6(2): 59-73 67 © 2018 Conscientia Beam. All Rights Reserved. Africa Petroleum (now Forte Plc), as reported by Aliyu and Ishaq (2015) where about 24 billion Naira credit facilities were not disclosed in the financial statement is a typical instance of how earnings management could be condoned due to impaired auditor independence. The findings of Maria (2016); Moraes and Martinez (2015) and Gupta et al. (2009) which find that audit quality declined when the audit fee is abnormally low but higher when the audit fee was astronomically high as the auditors who earned excess fees will be mindful of the perceived threat to their independence while discharging their duties and thus, take necessary steps to preserve their reputation capital. The two control variables of leverage and age displayed a negative and positive relationship with audit quality respectively. However, the former is not significant (p=0.129) while the latter (AGE) passed the significance test at 1% (p=0.0000). The slope coefficient signs of the two variables (LEV and AGE) aligned with the expectation, because the basic assumption is that older firms have more to protect including reputation and are most likely not to compromise; while highly levered firms may be tempted to save costs and engage in lowering audit fees which may negatively affect the audit quality. Ashbaugh et al. (2003) supports that higher amount of debt ratio is generally associated with lower earnings quality. 6. CONCLUSION AND RECOMMENDATIONS This study basically examined the relationship between audit fees on audit quality in Nigeria. The major research question was to find out if audit fees (whether high or low) have significant influence in determining audit quality. to address this fundamental question, cross sectional data was gathered from the annual financial reports and statements of nine (9) out of the ten (10) oil and gas companies in the Downstream Sector of Nigerian Petroleum Industry listed on the floor of Nigeria Stock Exchange for 8 financial years. Audit quality was taken as the dependent variable is measured as a dummy variable by assigning the value of 1 if the company was audited by one of the Big4 audit firms in a particular year, and 0 if not. Audit fees, on its part, was taken as the independent variable, along two other control variables – leverage and age. The audit fee variable was measured as the natural log of total fees paid recorded as auditor remuneration in the financial reports assessed. In all, the data set amounted to panel of 72 observations which was analyzed using descriptive statistics, correlation and binary logit estimation technique. Based on the outcome of the results, it can be concluded that a dominating majority of the sampled companies employ the services of one of the Big4 audit firms and over 50% of the firms are highly levered. On the major research question of the study, the result showed that higher audit fees is associated with lower audit quality, thereby supporting the assumption that “higher audit fees are likely to compromise auditors’ independence and, thereby, result in lower audit quality”. It was also evident from the correlation result that older firms are most likely to pay more audit fees and are also associated with higher audit quality (see table 4.2). On the variable of leverage and firm age, is was ascertained that audit quality is invariant to firm leverage, while firm age is a significant factor in explaining variations in both audit fees and audit quality. It is recommended that regulators of the auditing practice should adopt measures to regulate and monitor the audit pricing process in order to strike a balance and reduce over-charging and under-charging which several school of thoughts suggest could be used to impair the independence of the auditor. Funding: This study received no specific financial support. Competing Interests: The authors declare that they have no competing interests. Contributors/Acknowledgement: All authors contributed equally to the conception and design of the study. REFERENCES Al-Khoury, A.F., M. Al-Sharif, J. Hanania, I.A. Al-Malki and M. Jallad, 2015. Auditor independence and mandatory auditor rotation in Jordan. International Business Research, 8(4): 73-82. 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C 3.657341 1.416037 2.582801 0.0098 LnAFEE -0.474943 0.136971 -3.467461 0.0005 LEV -0.242980 0.167408 -1.451426 0.1467 AGE 0.126117 0.031361 4.021513 0.0001 McFadden R-squared 0.278011 Mean dependent var 0.597222 S.D. dependent var 0.493899 S.E. of regression 0.412371 Akaike info criterion 1.084528 Sum squared resid 11.56337 Schwarz criterion 1.211010 Log likelihood -35.04302 Hannan-Quinn criter. 1.134881 Deviance 70.08604 Restr. deviance 97.07355 Restr. log likelihood -48.53678 LR statistic 26.98751 Avg. log likelihood -0.486709 Prob(LR statistic) 0.000006 Obs with Dep=0 29 Total obs 72 Obs with Dep=1 43 Dependent Variable: AQ Method: ML - Binary Probit (Quadratic hill climbing) Covariance matrix computed using second derivatives Variable Coefficient Std. Error z-Statistic Prob. C 2.105809 0.789910 2.665885 0.0077 LnAFEE -0.279669 0.076224 -3.669065 0.0002 LEV -0.149627 0.098610 -1.517367 0.1292 AGE 0.076335 0.018016 4.236999 0.0000 McFadden R-squared 0.279182 Mean dependent var 0.597222 S.D. dependent var 0.493899 S.E. of regression 0.412313 Akaike info criterion 1.082950 Sum squared resid 11.56016 Schwarz criterion 1.209431 Log likelihood -34.98619 Hannan-Quinn criter. 1.133302 Deviance 69.97238 Restr. deviance 97.07355 Restr. log likelihood -48.53678 LR statistic 27.10118 Avg. log likelihood -0.485919 Prob(LR statistic) 0.000006 Obs with Dep=0 29 Total obs 72 Obs with Dep=1 43 https://scholar.google.com/scholar?hl=en&q=Do%20abnormal%20audit%20fees%20matter%20in%20Nigerian%20audit%20market? https://scholar.google.com/scholar?hl=en&q=The%20economic%20theory%20of%20agency:%20The%20principal’s%20problem https://scholar.google.com/scholar?hl=en&q=The%20economic%20theory%20of%20agency:%20The%20principal’s%20problem https://scholar.google.com/scholar?hl=en&q=Audit%20quality%20and%20auditor%20reputation:%20Evidence%20from%20Japan http://dx.doi.org/10.2308/accr-50198 https://scholar.google.com/scholar?hl=en&q=An%20empirical%20analysis%20of%20auditor%20independence%20and%20audit%20fees%20on%20audit%20quality https://scholar.google.com/scholar?hl=en&q=Impact%20of%20Abnormal%20audit%20fee%20to%20audit%20quality:%20Indonesian%20case%20study https://scholar.google.com/scholar?hl=en&q=Determinants%20of%20audit%20fees:%20Evidence%20from%20Lebanon https://scholar.google.com/scholar?hl=en&q=Determinants%20of%20audit%20fees:%20Evidence%20from%20Lebanon http://dx.doi.org/10.5539/ibr.v5n11p136 https://scholar.google.com/scholar?hl=en&q=Research%20about%20audit%20quality Humanities and Social Sciences Letters, 2018, 6(2): 59-73 70 © 2018 Conscientia Beam. All Rights Reserved. Descriptive Statistics AQ AUDFEE LEV AGE Mean 0.597222 3299033. 0.743446 23.83333 Median 1.000000 34782.00 0.816845 22.00000 Maximum 1.000000 29977000 4.338958 58.00000 Minimum 0.000000 5500.000 -4.328674 2.000000 Std. Dev. 0.493899 6919491. 1.245341 13.62578 Skewness -0.396456 2.429015 -0.506401 0.877564 Kurtosis 1.157177 7.833991 8.661229 3.733639 Jarque-Bera 12.07411 140.9037 99.22583 10.85611 Probability 0.002389 0.000000 0.000000 0.004392 Sum 43.00000 2.38E+08 53.52814 1716.000 Sum Sq. Dev. 17.31944 3.40E+15 110.1121 13182.00 Observations 72 72 72 72 Correlation Matrix Covariance Analysis: Ordinary Included observations: 72 Correlation t-Statistic Probability AQ LNAFEE LEV AGE AQ 1.000000 ----- ----- LNAFEE -0.219878 1.000000 -1.885785 ----- 0.0635 ----- LEV -0.121422 -0.059825 1.000000 -1.023466 -0.501434 ----- 0.3096 0.6176 ----- AGE 0.337301 0.494675 -0.151473 1.000000 2.997739 4.762233 -1.282109 ----- 0.0038 0.0000 0.2040 ----- Variance Inflation Factors Included observations: 72 Coefficient Uncentered Centered Variable Variance VIF VIF C 0.047165 19.48683 NA LNAFEE 0.000401 24.75875 1.324393 LEV 0.001620 1.393808 1.023799 AGE 1.79E-05 5.541097 1.350642 Normality Test 0 2 4 6 8 10 12 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 Series: Residuals Sample 1 72 Observations 72 Mean -2.52e-16 Median 0.068925 Maximum 0.751339 Minimum -0.997172 Std. Dev. 0.408535 Skewness -0.309460 Kurtosis 2.525763 Jarque-Bera 1.823887 Probability 0.401743 Humanities and Social Sciences Letters, 2018, 6(2): 59-73 71 © 2018 Conscientia Beam. All Rights Reserved. Auto-Correlation Test Breusch-Godfrey Serial Correlation LM Test: F-statistic 1.689840 Prob. F(2,66) 0.1924 Obs*R-squared 3.507324 Prob. Chi-Square(2) 0.1731 Test Equation: Dependent Variable: RESID Method: Least Squares Included observations: 72 Presample missing value lagged residuals set to zero. Variable Coefficient Std. Error t-Statistic Prob. C 0.059832 0.217998 0.274460 0.7846 LNAFEE -0.003873 0.019984 -0.193788 0.8469 LEV -0.015204 0.040700 -0.373568 0.7099 AGE -6.52E-05 0.004184 -0.015572 0.9876 RESID(-1) 0.171021 0.123800 1.381433 0.1718 RESID(-2) 0.121720 0.123967 0.981875 0.3297 R-squared 0.048713 Mean dependent var -2.52E-16 Adjusted R-squared -0.023354 S.D. dependent var 0.408535 S.E. of regression 0.413278 Akaike info criterion 1.150261 Sum squared resid 11.27269 Schwarz criterion 1.339983 Log likelihood -35.40939 Hannan-Quinn criter. 1.225790 F-statistic 0.675936 Durbin-Watson stat 1.942034 Prob(F-statistic) 0.643152 Heteroskedasticity Test Heteroskedasticity Test: Breusch-Pagan-Godfrey F-statistic 0.295221 Prob. F(3,68) 0.8287 Obs*R-squared 0.925704 Prob. Chi-Square(3) 0.8192 Scaled explained SS 0.629915 Prob. Chi-Square(3) 0.8896 Test Equation: Dependent Variable: RESID^2 Method: Least Squares Included observations: 72 Variable Coefficient Std. Error t-Statistic Prob. C 0.219977 0.108127 2.034432 0.0458 LNAFEE -0.001361 0.009969 -0.136528 0.8918 LEV -0.002602 0.020041 -0.129815 0.8971 AGE -0.001564 0.002104 -0.743329 0.4598 R-squared 0.012857 Mean dependent var 0.164582 Adjusted R-squared -0.030693 S.D. dependent var 0.204722 S.E. of regression 0.207840 Akaike info criterion -0.250145 Sum squared resid 2.937423 Schwarz criterion -0.123664 Log likelihood 13.00523 Hannan-Quinn criter. -0.199793 F-statistic 0.295221 Durbin-Watson stat 1.749637 Prob(F-statistic) 0.828716 Humanities and Social Sciences Letters, 2018, 6(2): 59-73 72 © 2018 Conscientia Beam. All Rights Reserved. Appendix Two (Data) COMPANIES Year AQ Audfee LEV AGE LNAFEE Beco Petroleum PLC 2007 1 3660000 0.89579 21 15.112974 Beco Petroleum PLC 2008 1 3500000 0.833324094 22 15.068274 Beco Petroleum PLC 2009 0 4000000 0.543206476 23 15.201805 Beco Petroleum PLC 2010 1 5000000 0.171231764 24 15.424948 Beco Petroleum PLC 2011 0 4177456 0.269045129 25 15.245213 Beco Petroleum PLC 2012 0 5220000 0.286521686 26 15.468008 Beco Petroleum PLC 2013 0 5550000 0.196236421 27 15.529308 Beco Petroleum PLC 2014 0 5689000 0.177332117 28 15.554045 Conoil Plc 2007 1 14000 0.340462334 18 9.5468126 Conoil Plc 2008 1 16000 0.512350454 19 9.680344 Conoil Plc 2009 1 16500 0.469970204 20 9.7111157 Conoil Plc 2010 1 18000 1.776312563 21 9.798127 Conoil Plc 2011 0 19500 1.573890811 22 9.8781697 Conoil Plc 2012 1 21000 0.708086783 23 9.9522777 Conoil Plc 2013 0 25000 0.305817622 24 10.126631 Conoil Plc 2014 0 27500 0.566726398 25 10.221941 Eterna Oil & Gas Plc 2007 1 5500 0.961815899 18 8.6125034 Eterna Oil & Gas Plc 2008 0 6000 1.671180067 19 8.6995147 Eterna Oil & Gas Plc 2009 1 8000 1.26953124 20 8.9871968 Eterna Oil & Gas Plc 2010 1 7890 1.300886781 21 8.9733514 Eterna Oil & Gas Plc 2011 1 8000 0.68594409 22 8.9871968 Eterna Oil & Gas Plc 2012 1 10000 0.864877974 23 9.2103404 Eterna Oil & Gas Plc 2013 1 11000 0.760001226 24 9.3056506 Eterna Oil & Gas Plc 2014 1 12000 0.816403803 25 9.3926619 Forte Oil (Formerly AP) 2007 1 22000 0.872141631 23 9.9987977 Forte Oil (Formerly AP) 2008 0 28000 0.892326858 24 10.23996 Forte Oil (Formerly AP) 2009 1 32000 0.898603377 25 10.373491 Forte Oil (Formerly AP) 2010 1 53956 0.91961 26 10.895924 Forte Oil (Formerly AP) 2011 0 33828 0.789714908 2 10.429044 Forte Oil (Formerly AP) 2012 0 41273 0.834470088 3 10.627964 Forte Oil (Formerly AP) 2013 0 48841 0.913622707 4 10.796325 Forte Oil (Formerly AP) 2014 0 65345 0.833305853 5 11.087436 JAPAUL OIL 2007 0 700000 0.726121897 13 13.458836 JAPAUL OIL 2008 0 1200000 0.749360393 14 13.997832 JAPAUL OIL 2009 0 2500000 0.86191 15 14.731801 JAPAUL OIL 2010 0 3500000 0.52134782 16 15.068274 JAPAUL OIL 2011 0 4000000 0.16033138 17 15.201805 JAPAUL OIL 2012 0 4500000 1.115106 18 15.319588 JAPAUL OIL 2013 0 4650000 0.351775533 19 15.352378 JAPAUL OIL 2014 0 4800000 0.438140779 20 15.384126 Mobil Oil 2007 1 8349 0.881988356 29 9.0298971 Mobil Oil 2008 0 8349 0.095626211 30 9.0298971 Mobil Oil 2009 1 11736 -4.328673818 31 9.3704163 Mobil Oil 2010 1 11678 -3.006806104 32 9.365462 Mobil Oil 2011 1 12365 -2.055455543 33 9.4226252 Mobil Oil 2012 1 12940 -1.796748878 34 9.4680786 Mobil Oil 2013 1 23823 0.093154018 35 10.078407 Mobil Oil 2014 1 28177 3.270297519 36 10.246261 Mrs OIL (formerly Texaco, Chevron) 2007 1 8400 3.752664657 30 9.035987 Mrs OIL (formerly Texaco, Chevron) 2008 1 9000 3.413469154 31 9.1049799 Mrs OIL (formerly Texaco, Chevron) 2009 0 10500 4.25058446 2 9.2591305 Mrs OIL (formerly Texaco, Chevron) 2010 0 13500 4.338957661 3 9.510445 Mrs OIL (formerly Texaco, Chevron) 2011 1 12500 0.154361768 4 9.4334839 Mrs OIL (formerly Texaco, Chevron) 2012 1 17114 0.203187141 5 9.7476521 Mrs OIL (formerly Texaco, Chevron) 2013 1 24914 0.791850504 6 10.123185 Mrs OIL (formerly Texaco, Chevron) 2014 1 24914 0.76784 7 10.123185 OANDO (Unipetrol, AGIP) 2007 1 35736 0.834314516 15 10.483914 OANDO (Unipetrol, AGIP) 2008 1 55200 0.843219434 16 10.918718 Humanities and Social Sciences Letters, 2018, 6(2): 59-73 73 © 2018 Conscientia Beam. All Rights Reserved. OANDO (Unipetrol, AGIP) 2009 0 86700 0.843068244 17 11.370209 OANDO (Unipetrol, AGIP) 2010 0 135000 0.802335551 18 11.81303 OANDO (Unipetrol, AGIP) 2011 0 130100 0.81728719 19 11.776059 OANDO (Unipetrol, AGIP) 2012 1 164956 0.783580563 20 12.013434 OANDO (Unipetrol, AGIP) 2013 1 169802 0.89996 21 12.042388 OANDO (Unipetrol, AGIP) 2014 1 171000 0.139012191 22 12.049419 Total Nig Plc 2007 1 17000000 0.857609488 51 16.648724 Total Nig Plc 2008 0 15000000 0.93713042 52 16.523561 Total Nig Plc 2009 1 19000000 0.883301609 53 16.75995 Total Nig Plc 2010 1 20900000 0.862137636 54 16.85526 Total Nig Plc 2011 1 22990000 0.844706141 55 16.95057 Total Nig Plc 2012 1 22990000 0.90045 56 16.95057 Total Nig Plc 2013 1 25289000 0.859383115 57 17.04588 Total Nig Plc 2014 1 29977000 0.759511289 58 17.215941 Views and opinions expressed in this article are the views and opinions of the author(s), Humanities and Social Sciences Letters shall not be responsible or answerable for any loss, damage or liability etc. caused in relation to/arising out of the use of the content.