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LABOUR ACCOUNT METHODS
There are a number of methods used to benchmark flow data, depending on the type of data to be benchmarked. The method used the majority of the time, due to its accuracy and ease of implementation, is the ‘Proportional Denton Method’. This method preserves the movement of the quarterly data by minimising the absolute difference in the relative adjustments of two neighbouring quarters (i.e. keeping the benchmarked data to indicator data ratio as constant as possible over the time series), under the constraint that the sum of the quarters is equal to the annual data for each benchmark year.
The Australian Labour Account uses a modified Proportional Denton Method to benchmark the Quarterly Business Indicators Survey (QBIS) industry data to the annual industry data from the Economic Activity Survey (EAS).
For more information regarding the Proportional Denton Method, refer to paragraph 7.40 in the Australian System of National Accounts: Concepts, Sources and Methods (cat. no. 5216.0).
ANNUALISING AUSTRALIAN LABOUR ACCOUNT DATA
Data in the Australian Labour Account are compiled with quarterly estimates as the primary level of data compilation, with annual estimates subsequently produced from quarterly data. The method used to annualise data varies for each quadrant, depending on whether data are stock or flow estimates.
The Jobs and Persons quadrants contain stock data, which are data that measure certain attributes at a point in time. Data in these quadrants are annualised using a simple arithmetic average of the four quarterly estimates. While these average annual levels are not true stock values, in the sense that they are not measured at a specific point in time, the purpose of presenting annual estimates as an arithmetic average is to minimise issues with using any particular quarterly observation to represent an annual stock, as any particular quarterly observation may under or over represent “usual” stock levels for a particular year. This is particularly relevant for industries which exhibit strongly seasonal employment levels, for example retail trade.
For example, consider the example in Table 6.1 below of two industries which exhibit the following patterns in employed persons over a one year period.
Table 6.1 – Annualising stocks example
The annual average stock level for 2015-16 for Industry A is 123 thousand employed persons. The choice of using an annual average, an end of year stock level (of 130 thousand employed persons) or a mid-point stock level (of 120 thousand employed persons) for this industry does not significantly change the annual level of employed persons.
For Industry B, which shows a strong cyclical increase in employed persons each December, the choice of annual stock level is more significant. If an annual average stock level (of 243 thousand employed persons in 2015-16) or end of year stock level (of 220 thousand employed persons) were chosen, a much lower annual stock level would result than if a mid-point stock level (of 300 thousand employed persons) were used.
The Labour Volume and Labour Payments quadrants contain flow data, which represent a measure of activity over a given period. Data in these quadrants are annualised as the sum of the four quarterly estimates.
Any original time series can be thought of as a combination of three broad and distinctly different types of behaviour, each representing the impact of certain types of real world events on the information being collected: systematic calendar related events, short-term irregular fluctuations and long-term cyclical behaviour.
Seasonal adjustment is a statistical technique that attempts to measure and remove the effects of systematic calendar related patterns including seasonal variation to reveal how a series changes from period to period. Seasonal adjustment does not aim to remove the irregular or non-seasonal influences, which may be present in any particular data series. This means that movements of the seasonally adjusted estimates may not be reliable indicators of trend behaviour.
The ABS software for seasonal adjustment is the SEASABS (SEASonal analysis, ABS standards) package, a knowledge based seasonal analysis and adjustment tool. The seasonal adjustment algorithm used by SEASABS is based on the X-11 Variant seasonal adjustment software from the U.S. Census Bureau.
In cases where the removal of only the seasonal element from an original series (resulting in the seasonally adjusted series) may not be sufficient to allow identification of changes in its trend, a statistical technique is used to dampen the irregular element. This technique is known as smoothing, and the resulting smoothed series are known as trend series.
Smoothing, to derive trend estimates, is achieved by applying moving averages to seasonally adjusted series. A number of different types of moving averages may be used; for quarterly series a seven term Henderson moving average is generally applied by the ABS. The use of Henderson moving averages leads to smoother data series relative to series that have been seasonally adjusted only. The Henderson moving average is symmetric, but asymmetric forms of the average may be applied as the end of a time series is approached. The application of asymmetric weights is guided by an end weight parameter, which is based on the calculation of a noise-to-signal ratio (i.e. the average movement in the irregular component, divided by the average movement in the trend component). While the asymmetric weights enable trend estimates for recent periods to be produced, they result in revisions to the estimates when subsequent observations are available.
Revisions to trend series may arise from:
For more information about ABS procedures for deriving trend estimates and an analysis of the advantage of using them over alternative techniques for monitoring trends, see Information Paper: A Guide to Interpreting Time Series - Monitoring Trends (cat. no. 1349.0).
In the Australian Labour Account, quarterly tables are produced in original, seasonally adjusted and trend terms.
For the purpose of deriving the annual average level from quarterly stocks of jobs and employed persons using an arithmetic average, original quarterly series are used.
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