This release contains a data cube which provides estimates of KLEMS multifactor productivity (MFP) for individual industries in the Australian economy. The term KLEMS represents the five input categories – Capital (K), Labour (L), Energy (E), Materials (M) and Services (S). The methodology for constructing the data is outlined in the Information Paper: Experimental Estimates of Industry Level KLEMS Multifactor Productivity (cat. no. 5260.0.55.003). Commencing this issue, the experimental label has been removed.
The data cube includes measures of input, output and KLEMS MFP at the industry level from 1995-96. The 16 industries included in the data cube comprise the 'market sector', and are as follows:
|A||Agriculture, Forestry and Fishing|
|D||Electricity, Gas, Water and Waste Services|
|H||Accommodation and Food Services|
|I||Transport, Postal and Warehousing|
|J||Information, Media and Telecommunications|
|K||Financial and Insurance Services|
|L||Rental, Hiring and Real Estate Services|
|M||Professional, Scientific and Technical Services|
|N||Administrative and Support Services|
|R||Arts and Recreation Services|
Under a gross output based MFP approach, the contribution of each of the primary and intermediate inputs to output is weighted using the cost shares of each input. The cost shares for labour and capital are their respective primary incomes, divided by the current price value of gross output, while the cost shares for intermediate inputs are the expenditures on inputs, divided by the value of gross output.
Reliability and future revisions
Productivity estimates are prepared from a wide range of statistical sources, some of which are available soon after the reference period, while others only with a delay of several years. Most of the data are derived from the regular program of statistical surveys undertaken by the ABS or as a by-product of government administrative processes. The frequency, detail and timeliness of these data sources are constrained by many factors, including the other statistical purposes which they must serve. Any increase in timeliness of data is usually at the expense of detail, reliability or additional resources. Therefore, productivity estimates in recent years are particularly sensitive to revisions as improved data become available.
Revisions arise from the progressive incorporation of more up-to-date data, re-weighting of chain volume series and, from time-to-time, the introduction of new economic concepts, data analysis and improved data sources and methods. Revisions are an inevitable consequence of the compilation process, reflecting both the complexity of economic measurement and the need to provide economic policy advisers and other users with initial estimates that are timely in order to maximise their use in the analysis of current economic conditions.
For further information about these and related statistics, contact: email@example.com or the National Information and Referral Service on 1300 135 070.