Were regulatory changes in reporting "abnormal items" justified? : evidence of intra-period classificatory earnings management practices in Australia

Purpose Managers generally have discretion in determining how components of earnings are presented in financial statements in distinguishing between ‘normal’ earnings and items classified as unusual, special, significant, exceptional or abnormal. Prior research has found that such intra-period classificatory choice is used as a form of earnings management. Prior to 2001, Australian accounting standards mandated that unusually large items of revenue and expense be classified as ‘abnormal items’ for financial reporting, but this classification was removed from accounting standards from 2001. This move by the regulators was partly in response to concerns that the abnormal classification was being used opportunistically to manage reported pre-abnormal earnings. This study extends the earnings management literature by examining the reporting of abnormal items for evidence of intra-period classificatory earnings management in the unique Australian setting. Design/methodology/approach This study investigates associations between reporting of abnormal items and incentives in the form of analyst following and the earnings benchmarks of analysts’ forecasts, earnings levels, and earnings changes, for a sample of Australian top-500 firms for the seven-year period from 1994 to 2000. Findings The findings suggest there are systematic differences between firms reporting abnormal items and those with no abnormal items. Results show evidence that, on average, firms shifted expense items from pre-abnormal earnings to bottom line net income through reclassification as abnormal losses. Originality/value These findings suggest that the standard setters were justified in removing the ‘abnormal’ classification from the accounting standard. However, it cannot be assumed that all firms acted opportunistically in the classification of items as abnormal. With the removal of the standardised classification of items outside normal operations as ‘abnormal’, firms lost the opportunity to use such disclosures as a signalling device, with the consequential effect of limiting the scope of effectively communicating information about the nature of items presented in financial reports.

Severity of driver injury and vehicle damage in traffic crashes at intersections: A Bayesian hierarchical analysis

Most crash severity studies ignored severity correlations between driver-vehicle units involved in the same crashes. Models without accounting for these within-crash correlations will result in biased estimates in the factor effects. This study developed a Bayesian hierarchical binomial logistic model to identify the significant factors affecting the severity level of driver injury and vehicle damage in traffic crashes at signalized intersections. Crash data in Singapore were employed to calibrate the model. Model fitness assessment and comparison using Intra-class Correlation Coefficient (ICC) and Deviance Information Criterion (DIC) ensured the suitability of introducing the crash-level random effects. Crashes occurring in peak time, in good street lighting condition, involving pedestrian injuries are associated with a lower severity, while those in night time, at T/Y type intersections, on right-most lane, and installed with red light camera have larger odds of being severe. Moreover, heavy vehicles have a better resistance on severe crash, while crashes involving two-wheel vehicles, young or aged drivers, and the involvement of offending party are more likely to result in severe injuries.

Knowledge management

This paper describes the background and methodology developed and employed in undertaking research developing a Knowledge Management Strategy for a key construction focused government agency. This paper reviews this methodology and examines a likely Knowledge Management Strategy.