Evidence on Auditor Error Correction Decisions

EVIDENCE ON AUDITOR ERROR CORRECTION DECISIONS ROBINSON & FERTUCK

Evidence on Auditor Error Correction Decisions

Chris Robinson York University

Len Fertuck University of Toronto

A bstract A unique sample of auditor error correction decisions

made in practice is investigated to see what factors influenced the decisions. A general model of auditor hehaviour is postulated, in which the auditors are expected to respond to materiality requirements, legal liability considerations, and pressures from managers to avoid disclosing material items. The results indicate that all three factors influence the correction decision.

Rksumk Cette iiude porte sur un Pchantillon unique de

dicisions de correction d’erreurs effectuies par des vhrificateurs; elle vise a determiner les facteurs influencant ces dicisions. On a postule un mod6le gtnkral de comportement des virlficateurs, selon lequel ils sont supposis avoir a prendre en compte les exigences de 1 Tmportance relative, les considkrations de responsabiliti face h. la loi et les pressions exerctes sur eux par les dirigeants afin d’iviter la rtvklation d’iliments significatifs. Les risultats obtenus indiquent que ces trois facteurs influencent les dicisions de correction.

INTRODUCTION

Businesses, governments and individuals use audited financial statements for a wide variety of important decisions, including investments, capital budgeting, mergers and acquisitions, regulation, taxation, dividend payouts and remuneration of senior management. Without the credibility given to these statements by the auditors, these decision makers would not be able to rely upon them, and other means of providing the information would be needed. The judgement practices of auditors are thus of considerable importance to the entire business community.

In this paper we present Canadian empirical evidence of actual auditor decisions, made in a normal practice environment, on an important question. The study uses a unique data base to examine which errors discovered on a substantive audit are corrected, and which are left

The authors are grateful to the Canadian Certified General Accountants’ Research Foundation for financial support and to the three Canadian public accounting firms which provided the data used. Joel Amernic. Jacob Birnberg, David Foot. Michael Gibbins, Myron Gordon. Joshua Ronen, Dan Simunic. Dan Thornton, two anonymous referees and participants in workshops at the University of Alberta and University of Waterloo provided valuable comments.

uncorrected. Auditors must make error correction decisions constantly in their work. Evidence of what decisions they make in practice may give useful insights into which factors are important in their judgments.’

Auditors are subject to a variety of pressures in their work. The size and nature of the errors, the financial and legal position of the client, and conflicts between managerial and outside capital interests may all affect their decisions. We hypothesize several factors which likely have a significant effect on auditor decisions and develop observable proxies so we can test the hypotheses using our data.

The unique database consists of information related to 610 errors discovered on 61 year-end audits. Three Canadian audit firms collected the da ta for us retrospectively. This makes the study non-obtrusive, and gives the work considerable external validity.

Three field studies have looked at auditor decisions revealed in published U.S. statements and one has examined actual auditor working papers. Bernstein [ 19671, Neumann [ 19681, and Frishkoff [ 19701 used publicly available information to examine items that were judged to be material. They had no access to items that were judged to be immaterial. Waters [1971]

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EV\DENCE ON AUDITOR ERROR CORRECTION DECISIONS ROBINSON & FERTUCK

obtained actual cases from auditors, but did not examine immaterial items. Many other studies (cited in the next section) used a clinical methodology with subjects making decisions on fictional cases. Ours is the first study to examine the complete set of actual errors discovered on each audit and the auditors’ decisions, and it is the first field study to use Canadian data.

In the next section we discuss the factors which might affect auditor error correction decisions and we suggest appropriate proxies to use in examining the data. Later sections describe the data, the empirical tests which were performed, and the conclusions of the study.

FACTORS AFFECTING THE AUDITOR’S DECISIONS

An examination of the ‘agency theory and the human information processing literature reveals three broad factors that are expected to influence an auditor’s decision to require that management correct an error before a statement will be signed. They can be categorized into professional, personal, and pressure reasons.

Professional reasons-related to materiality-and personal reasons-related to the self-interest of avoiding professional and legal liability-provide an incentive to correct errors. Pressures from management, arising from an agency relationship, may provide incentives to avoid corrections.

Proxy variables were identified for each factor. These variables are expected to be related to the dichotomous dummy variable CORRECT. CORRECT has a value of 1 if the auditor required a correction and 0 otherwise. The variables and their expected signs are identified in Table I . A positive sign for a coefficient implies that a higher value for the proxy variable is associated with a higher probability that the auditor required a correction of the error.

Professional Materiality Factors According to a large body of empirical and normative

research, the materiality of an error should be the most important factor in determining whether the error should be corrected. Auditors are supposed to correct all material errors, but do not have to correct immaterial ones. Materiality is defined by what statement users consider material. However, no standards exist to tell auditors exactly what errors are material. They rely on their professional judgement and ethical sense to determine what they should do to maintain a clear conscience.

Existing research identifies three factors that serve as criteria for correcting an error: earnings, the size of the financial statement line, and working capital. ERROR/ ASSET, ERROR/ LINE, and ERROR/ WORKCAP were identified as proxies for these three factors.

ERROR/ ASSET is a proxy for earnings, which has been found to be a significant factor in every materiality

study.2 ERROR/ ASSET is defined as the absolute value of the after-tax error divided by total assets. Assets was used instead of income because income can become zero or negative, causing numeric problems. Assets should be proportional to long-run expected income and should be a stable positive number. The sign of the coefficient should be positive because the allowable error should increase as the assets of the firm increase3.

ERROR/ LINE is a proxy for the size of the line item ~ o r r e c t e d . ~ The requirement for detailed disaggregation under GAAP suggests that the line items, and errors in them, affect the decisions of users. It is difficult to determine which side of the double entry is the more important line item. Without any a priori reason to use either line item, we assumed that the error in the smaller of the possible line items was the most important. The sign is expected to be positive because the allowable error should increase as the size of the line item increases.

ERROR1 WORKCAP is a proxy for the effect of an error on working capital. Working capital receives a lot of attention in the literature.* When the firm did not segregate current items, or an error did not affect working capital, then ERROR/ WORKCAP was set to 0. The sign is expected to be positive because the allowable error should increase as the working capital increases.

Additional variables - like effects on income trends, revenue, or net worth - have been suggested in the literature. We do not report results using them because they are highly correlated with ERROR/ ASSET and do not add new insights.

All of these factors provide an incentive for the auditor to require corrections when the user of the financial statement would want corrections. These factors provide no conflict between the users’ interests and the auditors expected actions.

Personal Liability Factors The auditor has a personal incentive to require the

correction of errors to avoid legal liability for the errors. An uncorrected error is costly if the error is later detected and the auditor is sued or charged with a legal or professional offence. The auditor has the greatest incentive to correct errors when a third party is known to rely on the statements, when there is a risk of corporate failure, and when the error can be objectively verified. RELY, DEBT/ ASSET, and OBJECTIVE are proxy variables for these situations.

RELY is coded 1 when a third party was relying on a report and 0 otherwise. Reliance is most likely to occur when the third party is contemplating taking control or has a contract that gives access to inside information. Uncorrected errors are most likely to be discovered and acted upon under these circumstances. The sign is expected to be positive, indicating that the auditor is more likely to correct errors when a third party is relying. Since the sample contained only two firms in this situation, the effect is unlikely to be statistically significant.

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DEBTJASSET is a proxy for the likelihood of corporate failure which would again raise the threat of third party intervention. The sign is expected to be positive, indicating more corrections when the risk of failure increases.

OBJECTIVE is coded as 1 if the error is objectively verifiable, like cash or inventory counts, and 0 otherwise. Objective errors are easily verified and are not subject to dispute on accounting principles, so they are very hard to defend in court.6 Thus the auditor is more likely to correct them to prevent a later dispute, leading to an expected positive sign.

These factors provide an incentive to correct errors that may otherwise not need to be corrected. The incentives provided by these factors serve the self-interest of the auditor without harming the interests of the users. The user is never harmed by a correction. The user may still be harmed by a correction avoided, such as a non- objective error. None of these factors provides an incentive to correct such errors.

Pressure Factors Managers may want the auditor to avoid correcting

an error that should really be corrected. Managers can exert pressure by dismissing the auditor, withholding consulting contracts, or refusing to make referrals. These can all be costly and they are most costly when the client is a large firm. The proxy variables PUBLIC, MANAGER%, RELEASE, ASSET, and DEBT/ ASSET are chosen to detect these pressures, if any.

PUBLIC is coded 1 if the firm is a publicly traded company and 0 otherwise. Companies with publicly traded debt are more likely to exert pressure because ownership and management are more separated. They have more kinds of securities, allowing more opportun- ities for one group to expropriate another. These clients are more desirable because of their size, visibility, and potential consulting needs. The expected sign of the coefficient is negative because the auditor is less likely to ask for corrections from public firms who are good clients.

MANAGER% measures the manager’s share of the equity. In the Jensen and Meckling scenario, as the manager’s share of the equity declines, the incentive to consume perquisites increases. So should the pressure to keep the auditor from reporting that consumption. The expected sign is positive because increasing manager equity leads to less pressure to misreport and to more error corrections.

RELEASE is coded 1 when there was an early release of financial results to non-management persons and 0 otherwise. Financial results are often released before the auditor has signed the report. If the auditor then discovers errors that need correction, the manager is placed in an undesirable position. The manager may lose credibility even if the errors are the result of honest mistakes. The expected sign is negative because prior release may lead to situations where the manager will exert pressure to avoid corrections.

ASSETis a proxy for the size of the audit fee. Current and future fee data are not available, but larger clients are expected to pay larger fees and therefore provide a larger incentive to keep the client. Thus the coefficient should have a negative sign. Frishkoff [1970] used this as one possible explanation of a significant effect from assets in his study.

DEBTJASSET is a measure of financial leverage. As it increases, the shareholders’ incentive to expropriate bondholders increases.7 An owner-manager may exert pressure on the auditor to avoid correcting errors to hide evidence of expropriation. The sign is expected to be negative because larger debt would lead to fewer corrections. This is the opposite of the positive sign that was expected for this term in the section above. As a result, the expected signs oppose each other and it is not possible to predict which effect should dominate.

The presence of these factors would suggest a conflict between the interests of the auditors and the interests of the users. If pressure succeeds in reducing the number of corrections, then the interests of the user are not being served.

THE DATA

Three large Canadian public accounting firms provided data on 610 errors discovered on 61 audit engagements performed during the years 1978-80.8 All errors and their disposition were included. These audits were the prime responsibility of 15 partners. The firms’ staff recorded the data on our coding sheets and we reviewed the data for reasonableness as a check for coding errors. The errors and omissions we found were rectified. This sample of error correction decisions is thus non-obtrusive and should represent actual practice.

A broad spectrum of industries and company sizes are included, so we have no reason to suspect a selection bias. It would be difficult for the firms to introduce a deliberate bias, since they did not know how we were going to analyze the data. The firms had to make the selection themselves for reasons of confidentiality, so we cannot be absolutely sure that it is unbiased. If there is a bias, there is no way of knowing the nature or direction of the bias. This is a unique data base and there are no other similar studies or data bases with which to compare this one.

The companies covered a wide range of sizes from small businesses with less than $1 million in assets to large businesses with over $500 million in assets. Eight had losses both before and after taxes and extraordinary items, while nine had net income over $10 million. Return on shareholder’s equity was also widely spread. There were eight loss companies, 1 1 low profit, 34 moderate to good profits and eight with over 30% return on equity. Ten firms had working capital deficits. At least one company appeared in every one-digit Standard Indus- trial Code, but we had too few in any one industry group to search for industry effects.


EVIDENCE ON AUDITOR ERROR CORRECTION DECISIONS

TABLE 1

Variables and Expected Signs

Variable

Professional factors: ERROR/ ASSET ERROR/ LINE ERROR/ WORKCAP

Personal factors: RELY OBJECTIVE DEBT/ ASSET

Pressure factors: PUBLIC MANAGER% RELEASE ASSET DEBT/ ASSET

Expected Sign

+ + +

+ + +

Dummy variables coded 1 for yes, 0 for no.

Table 2 shows the disposition of the errors and the sign of the effect of income errors. Just under 1 / 3 of the errors were corrected and just over 1 / 2 of the income errors decreased income. The income errors were 80% of the total and 3/4 of the errors were objectively verifiable.

Table 2 provides two pieces of evidence consistent with some external pressure on the auditors.

First, 27% of income errors were corrected, but 46% of non-income errors were corrected. According to all the literature, users are most often concerned with errors that affect income, so these should be more likely to be corrected. Of course, this depends upon the relative sizes of the income and non-income errors. Since they have different characteristics we cannot investigate this idea further by comparing them directly.

Second, only 31% of the errors were corrected. The easiest way for the auditor to be sure of acting in the user’s interest is to correct every error, since the greater degree of accuracy can never hurt the user. The fact that the auditors did not correct every error may imply some pressure to leave errors uncorrected, even though correction is usually a simple, inexpensive process.

EMPIRICAL TESTS

In this section we present evidence that the hypothesized pressures do appear t o affect auditors’ error correction decisions. We report the following analyses: comparison of public and private firms; multivariate

ROBINSON & FERTUCK

logistic regression using the variables in Table 1 ; holdout samples; correlation between independent variables; and, cumulative materiality.

Comparison of Public and Priwate Firms ERROR/ ASSET is significantly larger for private

firms at the 99% confidence level for income errors, and at the 94% level for non-income error^.^ This makes it impossible to use PUBLIC as a variable in any regression-type analysis. Private firm errors are much more likely to be corrected, and this would make PUBLIC significant and negative in any regression. However, this would not mean that larger errors remain uncorrected in public firms, merely that the auditors on average find smaller errors in their examinations of public firms. l o

Table 3 compares the error correction decisions for public and private companies using ERROR/NI, E R R O R / A S S E T , E R R O R / WORKCAP and ERROR/ LINE. In general, relatively more large errors go uncorrected for public firms. The often-quoted rules- of-thumb for ERRORiNI are that errors below 5% are immaterial, errors from 5--10% may be material, and those over 10% definitely are. Only 37% of the public company errors over 5% were corrected, while 67% of the private firm errors over 5% were corrected.

We have arbitrarily chosen cutoffs for ERROR/ ASSET, ERROR/ WORKGAP and ERROR/ LINE. In each case, the percentage of errors corrected above that cutoff in private firms is substantially higher. For ERROR/ ASSET, the percentage of corrections actually declines as the size of the error increases for public firm errors.

TABLE 2

Error Statistics

Income Income All Cases Affected Not Affected

(%I (%I (%)

Total 610 (100) 489 (80) 121 (20)

CORRECT:

Yes 189 (31) 133 (27) 56 (46) No 421 (69) 356 (73) 65 (54)

OBJECTIVE:

Yes 49 (74) 338 (69) 1 1 1 (92) No 161 (26) 151 (31) 10 (8)

Sign of Income Effect

Increase 219 (45) Decrease 270 (55)

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The previously-cited literature says that users should have some interest in non-income errors and in the effect of errors on working capital and the specific line item. Table 3 shows a pattern of less likely correction of larger errors for public firms. This is consistent with managerial pressure to avoid error corrections when there is owner- manager separation.

Multivariate Tests Logit was used in multivariate tests. It is more robust

to deviations from the multivariate normality assump- tion than multiple discriminant analysis. It is also less sensitive to extreme values (Press and Wilson [ 1978]).11

The result of the Logit analysis for errors that affect income is shown in Table 4. PUBLIC has not been included because of the problem discussed in the previous section. The appropriate test is a Chi-square test. Column P shows the significance level for the test that the coefficient is not different from zero. The confidence level for the intercept and DEBT/ ASSET is two-tailed, because there was no prior expectation for the sign. The confidence level is one-tailed for the other variables because an expected sign was specified for each one. P values less than or equal to .05 are marked with an * to indicate that the unstandardized coefficients are significant. All significant coefficients have the expected sign.

TABLE 3

Error Correction Decisions for Public and Private Companies

Variable Relative Size of Error

Public Private

ERROR/ N I 5%* (income errors only) 10%

ERROR/ ASSET .5%

errors only) (non-income 1 %

ERROR/ 1 %*

(all errors) 5% WORKCAP

ERROR/ LINE lo%* (all errors) 25%

% of Errors Corrected (Total No. of Material and Immaterial Errors)

* The group with the lower error bound includes the group with the higher error bound.

Of the variables which are related to professional materiality judgments, ERROR/ ASSET and ERROR/ WORKCAP are both significant. These results are consistent with the expectation that auditors consider materiality as an important criterion in correcting errors.

Of the variables which are related to a personal interest in avoiding legal liability, OBJECTIVE is significant with the expected sign. It is significant at the 99% level, with the largest Chi-square of any variable except the intercept. This is consistent with the view that auditors consider legal liability in making correction decisions.

Of the variables which are related to pressure from management, MANAGER%, ASSET, and DEBT/ ASSET are significant with the expected sign. The sign of DEBT/ ASSET indicates that the effect of management pressure is stronger than the personal interest in reducing legal risk. This is consistent with owner- managers pressuring the auditor to conceal information from bondholders.

TABLE 4

Hypothesis Test, Income Errors

Variable Coefficient Chi-Sq.

intercept 2.479 33.42 Professional factors: ERROR/ ASSET 257.363 14.58

ERROR/ 9.795 4.69 WORKCAP

Personal factors: RELY - 8.035 .28 OBJ ECTlV E 1.746 26.82 Pressure factors: MANAGER% .009 4.9 1 RELEASE -.269 .85 ASSET -.005 3.89 DEBT/ ASSET -2.262 3.83 Whole Model 134.42

R2 = ,203

ERROR/ LINE .002 .21

Classification Table Predicted Corrections

No Yes

P

o.ooo*

o.ooo* 0.644 0.030*

0.625 o.ooo*

0.027* 0.357 0.049* 0.050* o.ooo*

Total

335 21

No 1 80 53 1 ::t Yes

Total 415 74 489

Actual Corrections

Classification Success Rate: Corrected: 39.8% Not corrected: 94.146 Overall: 79.3%

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The result of the test on non-income errors is weaker and is not shown here. The complete model is highly significant, but DEBT/ ASSET is the only significant variable, with a negative coefficient and a confidence level of 98%. The overall success rate in classification is 70.2% and the R2 for the equation is .082. This is not surprising, since only 12 I da ta points were available for this test.

One reason why DEBT/ ASSET might be so important for non-income errors is the nature of the interest in such e r ro r s . They a r e only going t o affect t h e categorization of assets, not the total. Lenders are the financial statement users most affected by categorization, since it relates t o their security and their measurements of solvency a n d liquidity unde r debt covenants. Therefore, the major incentive for managers t o misreport arises when DEBTiASSET is high and managers might have some covenant problems. The negative sign on DEBT/ ASSET implies that auditors respond to this pressure by not correcting some non-income errors.

These results are consistent with auditors behaving in response to external pressures. A finding that one or two pressure variables were statistically significant might be the result of chance or misspecification. The high significance levels and correct signs for three pressure variables, as specified, is strong evidence against the hypothesis that auditors consider only materiality when making error correction decisions. We can think of no rational story about user criteria that could account for this result.

The Effect of Multicollinearity Some of the independent variables are correlated. We

checked the correlation matrix, ran stepwise regressions, and ran regressions with one member of any pair of correlated independent variables omitted. The stepwise regressions produced t h e s a m e results, and t h e coefficients on the variables stayed stable when other variables were omitted. We conclude that multicollinearity is not affecting the results.

There is a n interaction between OBJECTIVE and larger errors. Using the 5% of net income rule of thumb to define 'larger' income errors, there were 71 'larger' errors in the sample. In these the correction rate for the objective errors was 75% (33 of 44) and for the non- objective errors, 33% (9 of 27). This difference is consistent with the hypothesis that when errors get large enough to matter, auditors prefer t o correct the ones that are more defensible in court. This supports the result in Table 4.

Holdout Tests The R2 for the income errors model is only 20%. The

explained variance does not matter so much in a prediction model if the decisions are correctly classified. In this sample, the classification errors are strongly skewed toward predicting that items which were actually corrected, were not corrected. In effect, since the signs on all the relative size variables are correct, this result

means that 80 relatively small errors were nonetheless corrected. The auditor variables entered into the Logit regression did not contribute to an explanation of them. The overall success rate of 79.3% indicates that the model performs better than pure chance, which would explain 50%.

A classification table which is classifying errors that were included in the model-building process is biased towards a high success rate. One method of verifying the validity of the model is t o build the model on a subset of the observations and then predict the remaining holdout subset. The error rate on the subset should be an unbiased measurement. Results using a random holdout subset showed no significant differences in error rates, and are not presented here.

Since the estimated function is skewed to missing a large number of quite small errors which the auditors corrected, one might think that these errors were simply corrected for bookkeeping convenience. We found that was not the case. Often, larger e r rors remained uncorrected on the audit. We also asked the auditors to identify the companies for which they were also doing the bookkeeping. We eliminated them for another test (not shown) with no significant change in the results.

Cumulative Materiality We should consider the cumulative effect of several

errors which affect the same accounts in the same audit. It is not clear whether each error should be considered individually or in aggregate with all others affecting the same account. If several errors are corrected because their cumulative effect on one account is material, the other side of the entry may leave uncorrected errors in another account. These may amount to a material error, where before they were offset in aggregate t o a negligible sum. Very little has been said about this cumulative problem in the literature. Most of the clinical studies avoided the issue by experimenting with independent errors.

Leslie [ 19771 discussed the problem and claimed that a considerable number of auditors make their decisions on a n item by item basis, which he argued is suboptimal. He presented n o data to support his statements. Pattillo [ 19761 interpreted his results as showing that auditors used cumulative measures while users preferred item- by-item consideration. The cumulative rule contained in the American Institute of Certified Public Accoun- tants Statement on Auditing Standards No. 47 has no formal counterpart in Canada , which means that Canadian auditors have n o specific guideline.

An auditor who considers only materiality will start with the largest error and work downwards when choos ing which e r ror (s ) to co r rec t t o rectify a cumulatively material total error. This procedure would result in E R R O R / A S S E T being the only significant variable in our results. If other factors influence the auditor, they will be revealed by the choice of individual corrections. For example, in the previous section we saw that many small errors were being corrected while

RCSAI CJAS 33 DECEMBRE/ DECEMBER 1989


some quite large ones were not. This observation might be partly explained by cumulative materiality.

We examined the cumulative effect of errors on net income to the degree permitted by the sample size. We omitted firms in which the total cumulative income error was under 5%, since these should not require correction on a cumulative basis. We note that many quite small errors were corrected in these cases. In two cases, errors with a cumulative effect greater than the cumulative effect of all errors combined were corrected. These reversed the sign of the cumulative errors which were left uncorrected. The most likely reason is that one or more lines were significantly affected, even though income was not.

There seems little disagreement that a cumulative income error over 10% would be material. Seventeen of the 24 firms in the sample had errors accumulating to more than 10%. Of these, 12 made corrections which reduced the cumulative error to less than 10%. The other five cases appear to be inconsistent with a cumulative materiality decision rule, because the uncorrected cumulative error ranged from 10-20% of net income.

None of the errors were corrected for five of the remaining seven companies whose cumulative error totals were between 5% and 10% of net income. This evidence indicates some tendency to leave cumulatively larger errors uncorrected. Since no absolute materiality guideline exists, we cannot be sure there was any pressure to prevent corrections. However, this evidence justifies further studies to determine the reason for leaving large errors uncorrected.

CONCLUSIONS

of corrections, then the interests of the user are not being served. The significance of ASSET, MANAGER%, and DEBT/ ASSET in the model implies that user interests could be compromised. It does not prove that users were hurt by the decisions. The auditors might be acting in their own interests without hurting the users.

These results are further supported by an examination of the proportion of errors of different sizes and by examination of cumulative errors. Correlation tests and holdout tests indicate that the results are not likely to be a statistical artifact.

The clinical materiality studies could not discover this effect because of the lack of a realistic decision setting. The evidence of pressure indicates that a more comprehensive study should be undertaken to determine whether user interests are being compromised.

The results suggest two lines of inquiry. First, more rigorous models of auditor behaviour are needed. These should be based on realistic decision-settings, so proper empirical hypothesis testing can be done. This study has identified a number of proxy variables that may be useful in predicting error correction decisions. However, the model only explains 20% of the variance so there is a lot of room for improvement. This topic is already being pursued by many researchers.

Second, much larger samples of auditor decisions are required. The potential effects are so numerous that a very large sample size is required to capture them all in a field study. Our results suggest that auditors respond to pressure. They are not conclusive without a specific model and more data.

NOTES

The evidence we have obtained using a unique data base suggests that auditors consider professional materiality criteria, personal risk criteria, and manage-

I We say auditor error correction decisions as a convenient shorthand for corrections made by managers after auditors have discovered errors and requested correction.

2 Various measures of earninas were used or recommended in Abdel-Khalik ment pressures when they make error correction decisions in practice. This result should be expected, since auditors are rational economic agents who must balance professional standards, personal legal risks, and financial risks.

Professional materiality factors provide an incentive for the auditor to require corrections when the user of the financial statement would want corrections. There is no conflict between the users’interests and the auditors’ expected actions when these factors come into play as evidenced by the significance of ERROR/ ASSET and ERROR/ WORKCAP,

Personal risk factors provide an incentive to correct errors that may otherwise not need to be corrected. The incentives provided by these factors serve the self-interest of the auditor without harming the interests of the users. The user is not harmed by the significance of OBJEC- TIVE in the model.

The pressure factors suggest a conflict between the interests of the auditors and the interests of the users. If management pressure succeeds in reducing the number

[ 19771. Bernstein [1967], Boaisman and Robertson [1973], Dyer[1973], FASB [1975], Firth [ 19791, Frishkoff 119701, Moriarity and Barron “976, 19791, Patlillo [1976], Rose et al. [1970], Rosen [1982], Ward 119761, Waters 119711 and Woolsey [ 19541. The empirical linkage between earnings and security prices was shown by Easton [1985]. Financial analysis textbooks like Foster [ 19861 emphasize the importance of earnings.

3 We ran the tests using as the denominator net income (denoted ERROR/ NI), five-year average net income, income trend based on the previous four years and net income before taxes and extraordinary items. ERROR/ ASSET exhibited the highest significance levels, and the results for the other variables changed very little.

4 The effect on the line item was used as a factor or recommended in: Dyer [1973], FASB [1975], Pattillo [1976], Waters [ 19711 and Woolsey [1954].

3 Working capital was used or recommended as a factor in: Abdel-Khalik 119771, Boatsman and Robertson [1974], Dyer [1973], FASB [1975], Firth “9791, Rose et al. [1970], Waters [I9711 and Woolsey 119541.

6 Rosen [I9821 discovered when he debriefed his banker subjects that many of them expected all objective errors to be corrected automatically, regardless of size. Knapp [198S] hypothesized that management would be more likely to get its way in accounting disputes with the auditor when the question is not specifically resolved by an accounting standard.

7 See, for example, Jensen and Meckling 119761. 8 No significant or pervasive changes in Canadian auditing or accounting

standards, that affected the tests, occurred during this period. 9 The same result holds for ERROR/NI. An interesting question for further

research elsewhere is why this large difference appears. Hypotheses about



the relative strength of internal control and external auditor’s examinations of private and public companies could be constructed from this evidence and tested by further research

10 The distribution of E R R O R i A S S E T and E R R O R i N l appear to be homogeneous with respect to the other independent variables

1 1 Parallel runs using multiple discriminant analysis showed no significant differences

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DECEMBREI DECEMBER 1989

Documents

Evidence on Auditor Error Correction Decisions