G20 host Australia faces hard truths of multinational profit shifting

The G20 Finance Ministers have the opportunity this weekend to endorse the initial recommendations of the OECD on how to address the global problem of multinational tax avoidance. The work of the OECD on the issue to date is substantial. Most notable is the adoption by many nations, including Australia, of the Common Reporting Standard for the automatic exchange of tax information. This standard will allow significant inroads to be made into tax avoidance, particularly by individuals sheltering money offshore. This is the first step in an ambitious tax reform program. There is a long way to go if we are to end the issue now known as Base Erosion and Profit Shifting (BEPS). This week’s release of the first of the OECD recommendations contains some positive signs that further advances will be made. It also recognises some hard truths.

Fair values and audit fees: Evidence from asset revaluations in Australia

This paper examines the association between the level of audit fees paid and asset revaluations, one use of fair value accounting. This Australian study also investigates attributes of asset revaluations and the association with the level of audit fees paid. We find that firms choosing the revaluation model incur higher audit fees than those that chose the cost model; asset revaluations made by directors lead to the firm incurring higher audit fees than for those made by external independent appraisers; and revaluation of investment properties leads to lower audit fees. The findings suggest that asset revaluations can result in higher agency costs and audit fees vary with the reliability of the revaluations and the class of assets being revalued.

Weather variability, tides, and Barmah Forest Virus Disease in the Gladstone region, Australia

In this study we examined the impact of weather variability and tides on the transmission of Barmah Forest virus (BFV) disease and developed a weather-based forecasting model for BFV disease in the Gladstone region, Australia. We used seasonal autoregressive integrated moving-average (SARIMA) models to determine the contribution of weather variables to BFV transmission after the time-series data of response and explanatory variables were made stationary through seasonal differencing. We obtained data on the monthly counts of BFV cases, weather variables (e.g., mean minimum and maximum temperature, total rainfall, and mean relative humidity), high and low tides, and the population size in the Gladstone region between January 1992 and December 2001 from the Queensland Department of Health, Australian Bureau of Meteorology, Queensland Department of Transport, and Australian Bureau of Statistics, respectively. The SARIMA model shows that the 5-month moving average of minimum temperature (β = 0.15, p-value < 0.001) was statistically significantly and positively associated with BFV disease, whereas high tide in the current month (β = −1.03, p-value = 0.04) was statistically significantly and inversely associated with it. However, no significant association was found for other variables. These results may be applied to forecast the occurrence of BFV disease and to use public health resources in BFV control and prevention.

Cross-correlations between weather variables in Australia

The information on climate variations is essential for the research of many subjects, such as the performance of buildings and agricultural production. However, recorded meteorological data are often incomplete. There may be a limited number of locations recorded, while the number of recorded climatic variables and the time intervals can also be inadequate. Therefore, the hourly data of key weather parameters as required by many building simulation programmes are typically not readily available. To overcome this gap in measured information, several empirical methods and weather data generators have been developed. They generally employ statistical analysis techniques to model the variations of individual climatic variables, while the possible interactions between different weather parameters are largely ignored. Based on a statistical analysis of 10 years historical hourly climatic data over all capital cities in Australia, this paper reports on the finding of strong correlations between several specific weather variables. It is found that there are strong linear correlations between the hourly variations of global solar irradiation (GSI) and dry bulb temperature (DBT), and between the hourly variations of DBT and relative humidity (RH). With an increase in GSI, DBT would generally increase, while the RH tends to decrease. However, no such a clear correlation can be found between the DBT and atmospheric pressure (P), and between the DBT and wind speed. These findings will be useful for the research and practice in building performance simulation.