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8159.0 - Experimental Estimates for the Manufacturing Industry, 2009-10  
Latest ISSUE Released at 11:30 AM (CANBERRA TIME) 12/12/2011  Final
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Contents >> Concepts and Methods >> Methodology

METHODOLOGY

The experimental estimates in this release were produced using a combination of Economic Activity Survey (EAS) data collected directly by the ABS and BAS data obtained from the ATO. The methodology used was essentially the same as in previous releases (i.e. 2006-07, 2007-08, and 2008-09).


EAS Collection Design

In order to decrease the statistical reporting load placed on providers, the collection strategy for the EAS is to use directly collected data from a sample of businesses, in combination with BAS data sourced from the ATO.

Businesses in the Profiled and Non-profiled populations which have employment of 300 or more, or are deemed to be 'significant', are completely enumerated via directly collected survey data.

Other businesses are available for random sample selection only if their business is identified as being an employing business (based on ATO records) or their turnover exceeds a threshold level. Turnover thresholds are set for each ANZSIC class so that the contribution of surveyed businesses accounts for approximately 97.5% of total industry class turnover as determined by BAS data. Data for businesses selected from this part of the population are obtained via direct collection.

Businesses which meet neither of these criteria are referred to as 'micro non-employing businesses' and are not eligible for selection in the EAS sample. For these units, BAS data are obtained and added to the directly collected estimates (with minimal modelling applied).

More detailed information about the EAS collection design can be found in Australian Industry, 2009-10 (cat. no. 8155.0), Explanatory Notes paragraphs 36-38.


The Experimental Manufacturing Estimates Model

The estimation method used to create the experimental estimates makes use of observed linear relationships between data collected from businesses in the EAS and auxiliary information available from BAS data. Where the auxiliary information is strongly correlated with data items collected in the EAS, this information has been used to create predicted values for non-profiled businesses and small profiled businesses that were not selected in the survey. The auxiliary variables used to create predicted values were:

  • BAS total sales (to model sales and service income)
  • BAS wages and salaries (to model wages and salaries, industry value added and employment).

Modelling was used on the BAS data rather than substituting it directly as the BAS data items did not map directly to their corresponding EAS data item definitions.

The ANZSIC class experimental estimates for 2009-10 were created subject to the constraint of being additive to national ANZSIC subdivision estimates, as published in the 2009-10 issue of Australian Industry (cat. no. 8155.0). This is also true for state/territory experimental estimates: the state/territory estimates within an ANZSIC subdivision were constrained to sum to the Australian Industry subdivision estimate. This means that the aggregate of state/territory experimental estimates for a given subdivision aligns with the national subdivision estimate published in Australian Industry. However, individual state/territory by ANZSIC subdivision experimental estimates were not constrained to add to the state/territory by ANZSIC division level estimates published in Australian Industry. Consequently, for each state and territory, there are minor differences between the division level experimental estimates and those published in Australian Industry.

For the purpose of compiling experimental ANZSIC class estimates, for Division C Manufacturing in this publication, data for businesses are sourced via one of three categories (or 'streams') in accordance with significance and collection-related characteristics. The diagram below illustrates the ways in which the data streams contribute to the experimental estimates for the manufacturing industry.





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