TECHNICAL NOTE FINER LEVEL MANUFACTURING INDUSTRY ESTIMATES

INTRODUCTION

1 The 'Manufacturing industry' data cube contains finer level estimates for the Australian manufacturing industry for the 2014-15, 2015-16 and 2016-17 reference periods. The estimates used a combination of data directly collected in ABS surveys and Business Activity Statement (BAS) data sourced from the Australian Taxation Office (ATO).


ESTIMATION METHODOLOGY

2 The estimation method used to create the manufacturing estimates made use of observed linear relationships between data collected from businesses in the EAS and auxiliary information available from BAS data. Where the auxiliary information was strongly correlated with data items collected in the EAS, this information was 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).

PRODUCING ESTIMATES

3 The following diagram illustrates the ways in which Division C Manufacturing businesses contributed to the estimates to the finer level estimates for the manufacturing industry.

DATA STREAMING

4 For the purpose of compiling the estimates in this publication, data for businesses as recorded on the ABS Business Register (ABSBR) contributed via one of three categories (or 'streams') in accordance with significance and collection-related characteristics.

The Survey Stream:

5 The survey stream consisted of businesses with directly collected EAS data.

The Modelled Stream:

6 The modelled stream included all businesses not selected in the EAS (the survey stream) whose turnover, in aggregate, was above the bottom 2.5 percentile of BAS sales for that industry, or were identified as employing businesses (based on ATO information).

7 Modelled data were created through the use of robust, trimmed regression estimators, which used survey data regressed against BAS data. The BAS data were found to have a high correlation with corresponding data from the EAS. The regression factors were obtained by utilising units from the survey stream and comparing their reported survey data with their matching BAS data. These regression factors were created at the ANZSIC subdivision level. Sales and service income was modelled using BAS total sales as the auxiliary variable; wages and salaries, employment and IVA were modelled using BAS wages and salaries. Modelling of employment also took into account the business type (i.e. type of legal organisation) using a factor created at the ANZSIC division level. Modelled data for units in the modelled stream were created by multiplying their BAS data by the calculated regression factors.

Business Activity Statement (BAS) stream:

8 The BAS stream comprised data for those non-employing businesses whose turnover, in aggregate, was below the bottom 2.5 percentile of BAS sales for that ANZSIC subdivision.

9 Data for the BAS stream was produced using a technique that used BAS turnover to model income from sales of goods and services and BAS non-capitalised purchases to model purchases. The modelling parameters were based on the relationship between BAS data and reported data for small businesses in the direct collect sample over 3 years and were defined at the industry level. Wages and salaries were modelled as 0. Industry value added was derived based on modelled values of sales and service income and purchases. Employment was based on the business type of (legal) structure.

10 Initial national ANZSIC class and state/territory ANZSIC subdivision estimates for the manufacturing industry were produced by aggregating the contributing data streams.


STATE AND TERRITORY ANZSIC SUBDIVISION ESTIMATES

11 Additional rules were applied to produce state/territory ANZSIC subdivision estimates:

• for businesses (from any stream) operating in only a single state or territory, their initial estimates contributed to the relevant state or territory and ANZSIC subdivision estimates.
• for businesses (from the survey stream) operating in more than one state or territory, their initial estimates (i.e. directly collected EAS data) contributed to the states and territories in alignment with the EAS methodology.
• for businesses (from the modelled stream) operating in more than one state or territory, their initial estimates were prorated across the states and territories in which they operated, based on a factor calculated at the ANZSIC division level from surveyed multi-state units of similar size. These modelled multi-state businesses accounted for only a small proportion of the estimates.

12 The ANZSIC class manufacturing estimates for 2016-17 were created subject to the constraint of being additive to national ANZSIC subdivision estimates produced from the EAS. This was also true for state/territory estimates: the state/territory estimates within an ANZSIC subdivision were constrained to sum to the EAS estimate. This meant that the aggregate across all state/territory estimates for a given subdivision aligned with the EAS national subdivision estimate. However, the aggregate across all ANZSIC subdivision estimates for a given state/territory were not constrained to add to the state/territory by ANZSIC division level EAS estimates. Consequently, for each state and territory, there are minor differences between the division level estimates contained in this data cube and EAS estimates presented in the other data cubes in this release.


ASSUMPTIONS IN THE MODEL

13 The quality of estimates depends on the validity of the following assumptions underpinning the modelling:

• the national ANZSIC subdivision estimates and state/territory division estimates produced from the EAS were of sufficient quality to warrant disaggregation, respectively, at ANZSIC class level and state/territory level
• it was valid to distribute the difference between EAS national subdivision estimates and the initial subdivision estimates, based on the size of the modelled stream
• the relationship between the EAS data items and the BAS data items was meaningful and consistent. Analysis supports this assumption, with the correlation being of consistent quality to produce reliable estimates
• the auxiliary (BAS) data was of high quality
• the industry coding was accurate on the ABS maintained Business Register.

Users should consider the suitability of these assumptions when interpreting the estimates.