6302.0 - Average Weekly Earnings, Australia, Nov 2014 Quality Declaration
ARCHIVED ISSUE Released at 11:30 AM (CANBERRA TIME) 26/02/2015
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CHANGES IN THIS ISSUE
SEASONALLY ADJUSTED ESTIMATES
In 2012, as part of the transition from a quarterly to a biannual frequency, the ABS conducted an assessment of seasonality in the biannual AWE series. At the time, it was determined that moving to a biannual frequency eliminated seasonality for most AWE series and for these series the seasonally adjusted estimate was exactly equal to the original estimate.
A recent review into the seasonality of biannual AWE series has reassessed which series are displaying seasonality. Seasonal factors are now applied to additional series while other series are no longer displaying seasonality. For these series the seasonally adjusted estimate will now equal the original estimate. These changes are applied to the entire published series (i.e. commencing with May 2012). Relevant series are annotated in the time series spreadsheets available from the Downloads tab of this issue.
For further details on seasonally adjusted estimates in AWE, please refer to paragraphs 56 to 59 in the Explanatory Notes.
PRIVATISATION OF MEDIBANK PRIVATE LIMITED
Medibank Private Limited was privatised on 25 November 2014. For the purposes of ABS statistics this change from public sector to private sector is effective from the December quarter 2014. For Average Weekly Earnings, this means the change is reflected in the November 2014 estimates. Any impact from the privatisation on the measurement of change between Average Weekly Earnings statistics for the May and November 2014 issues, at the Sector and State by Sector levels, is not statistically significant and within current released standard errors for each series.
COMPARABILITY WITH WAGE PRICE INDEX
For information on comparability between AWE and WPI, refer to the feature article Average Weekly Earnings and Wage Price Index - What do they measure?.
TREATMENT OF SURVEY OUTLIERS
Prior to May 2014, surprise outliering was used as the sole methodology to identify and reduce the impact on the estimates of a business whose weighted survey response is an outlier i.e significantly different to businesses in a group with similar characteristics (based on employment size, sector, state and industry). Surprise outliering involves treating the identified outlier as if it were the only extreme unit in the group's population. The outlier is given a weight of one and the weights of the other units in the group are adjusted upwards accordingly. From the May 2014 issue, winsorisation methodology was introduced as the primary method to treat outliers in AWE. Winsorisation moderates the impact of an outlier business without the harsh impact of the surprise outliering approach. This improved methodology will provide more stable time series estimates. Surprise outliering will continue to be used for a small number of extreme values that may not be sufficiently moderated by the winsorisation method. For further information, see paragraphs 37 to 39 of the Explanatory Notes.
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