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Unformatted text preview: essarily errors in the ﬁnancial statements. The
auditor collected all the differences identiﬁed during the audit regardless of magnitude to
consider the potential aggregate effect of all identiﬁed differences.15 These were the actual
differences the auditor discussed with client management. When DIFF is a dependent variable, we expect the MI coefﬁcient to be negative, suggesting that a weak MI assessment is
associated with more audit differences.
Equation (5) examines the impact of MI on the probability of discovering current period
errors without consideration of EXTENT, TIMING, or PERSUASIVENESS. To better assess
the incremental impact of MI on discovering error, we control for MI’s inﬂuence on audit
planning. We examine Equation (6) where we include EXTENT, TIMING, or PERSUASIVENESS in the model.
Table 1 provides descriptive statistics on our dependent and independent variables.
Mean MI is 1.40 and only 9 (18) of the 60 ﬁrms have MI scores of 0 (1). Hence, the
median ﬁrm in the sample has high management integrity and, on average, auditors assessed
client integrity as moderate. This result is consistent with ﬁndings from the client acceptance
literature that suggests auditors screen out very high-risk clients (Johnstone and Bedard
2003). The mean RMM of 1.50 suggests that auditors on average assessed the risk of
material misstatement as low.
The mean of PERSUASIVENESS (1.62) suggests that on average auditors sought information from a mix of internal and external sources, but relied more heavily on internal
sources. The mean of TIMING (79.67) suggests on average a majority of the procedures
were performed at year-end. Auditors identiﬁed an average of $180,973 in audit differences,
but over two-thirds of the clients (69 percent) did not have any audit differences. The
frequency of differences is consistent with prior research (for a summary see Eilifsen and
Table 2 presents the correlation between all the variab...
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- Spring '10