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ABOUT PRODUCTIVITY STATISTICS
Q. What are the different measures of labour input?
A. The three common methods of measuring labour input are number of employed persons, hours worked and quality adjusted hours worked. The ABS publishes productivity statistics on both an hours worked basis and quality adjusted hours worked basis. These statistics are derived from estimates of hours actually worked, obtained from the Labour Force Survey. Indexes of hours worked are preferred to employment numbers because hours worked captures changes in overtime, standard weekly hours, leave, and part-time work. Quality adjusted hours worked further captures changes in the education and experience of the workforce.
A quality adjusted labour index (QALI) measures both changes in hours worked and changes in quality (that is, changes in educational achievement and experience). Aggregate QALI indexes have grown faster than the corresponding unadjusted hours worked indexes, implying that labour quality has been increasing. Assuming that higher wages reflect a higher marginal product of labour, labour quality will increase when the high wage rate groups of workers increase their hours worked faster than the low wage rate groups. Aggregate QALI indexes for the market sector and twelve selected industries are compiled using Australian Census data. Inter-census periods are interpolated so care should be taken interpreting year on year changes in labour composition.
Q. What is KLEMS?
A. The ABS published the experimental estimates of industry level KLEMS MFP in March 2016. The term KLEMS represents the five inputs categories - capital (K), labour (L), energy (E), materials (M), and services (S). KLEMS provides, through a more detailed statistical decomposition, more information on the contributions to output growth, and production efficiency. KLEMS also provides a suitable tool for evaluating the effects of changes in the input mix, such as the role of labour hours and composition relative to capital services or intermediate inputs in accounting for observed industry output growth. For more information see Experimental Estimates of Industry Level KLEMS Multifactor Productivity (cat. no. 5260.0.55.003 and cat. no. 5260.0.55.004).
Q. Are productivity statistics revised?
A. Yes. 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 analysis of current economic conditions. 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.
Q. What is a growth cycle?
A. A useful method of examining changes in productivity over an extended period involves identifying and dividing the data into productivity growth cycles. Productivity growth cycle peaks are determined by comparing the annual MFP estimates with their corresponding long-term trend estimates. The peak deviations between these two series are the primary indicators of a growth cycle peak, although general economic conditions at the time are also considered. The purpose is to minimise the effects of cyclical factors that may cause the year-to-year changes in MFP to deviate from its conceptual definition. In this way, most of the effects of variations in capacity utilisation and much of the random error are removed. By averaging between peaks, it is assumed that these peaks represent similar levels of capacity utilisation, allowing more like-for-like comparisons of MFP between different growth cycles.
Q. Which industries are covered?
A. Ideally, MFP measures should cover all economic activities, but this is only possible if all of the necessary data are available. The market sector comprises sixteen industries under the Australian and New Zealand Standard Industrial Classification, 2006 (ANZSIC06); that is, from ANZSIC06 Divisions A to N, plus Divisions R and S. The detailed industries included in the market sector are as follows:
Until 2009-10, the market sector consisted of twelve industries (Divisions A to K and P of Australian and New Zealand Standard Industrial Classification 1993 (ANZSIC93)). The current market sector definition improves relevance in two key respects: it reflects the growing contribution of services industries in the economy; and improves economic coverage. The current estimates are not directly comparable to those published prior to the adoption of ANZSIC06 due to significant changes in coverage.
Q. Why do some industries not have productivity statistics?
A. While measures of labour productivity are published for the non-market sector (cat. no. 5206.0), non-market industries (ie. Divisions O, P and Q) are currently excluded from ABS MFP productivity estimates. The industries included in the non-market sector are:
Non-market industries are those industries in which the majority of output is provided free of charge or at prices which are not economically significant (in that there is only a weak relationship between price and the supply and demand for the good or service). Output measures for the non-market industries are typically derived using input costs and so by definition there is no productivity growth. Ownership of dwellings is also excluded from the market sector because no employment is associated with it.
Q. What is growth accounting?
A. Growth accounting involves decomposing gross output growth into contributions from growth in labour, capital and intermediate inputs and MFP. This framework provides an analytical tool to identify the underlying drivers of growth. ABS MFP statistics are compiled on the basis of the standard growth accounting framework, which is widely adopted by leading statistical agencies and recommended by the OECD. Growth accounting allows us to better understand the contribution of productivity growth to output growth, as well as the other drivers of output growth. In the growth accounting framework, growth in labour productivity can be decomposed into growth in capital deepening (the ratio of capital to labour), growth in labour quality and growth in MFP.
INTERPRETING PRODUCTIVITY RESULTS
Q. How is productivity data used?
A. Productivity statistics are useful performance indicators for the formulation and evaluation of policies involving long-term growth, efficiency and competitiveness. Labour productivity is widely used for making historical, inter-industry and inter-country growth comparisons. Furthermore, labour productivity is often regarded as an indicator of improvements in living standards as growth in labour productivity has a close long term relationship with growth in labour earnings.
Q. How do I interpret productivity results?
A. The interpretation of productivity indexes depends on how output and inputs are measured. Ideally, the output indexes will measure all output produced from the input which is measured by the input indexes. Caution is required when interpreting productivity statistics due to the various inputs and output measurement issues and the complexity of the production processes. The ABS measures of productivity growth reflect a mix of factors, including:
In practice, both output and inputs can be difficult to measure and, because productivity is estimated as a residual, the timing of output and input affects productivity indexes. For example, when production takes longer than a year, inputs will be measured before the corresponding output leading to a decline in measured productivity. For the Australian economy, examining MFP movements over growth cycles is a common approach for interpreting productivity performance over time, due to the short-term volatility of annual estimates.
Q. What are some limitations of productivity analysis?
A. Productivity estimates are subject to limitations in measurement, as not all inputs and outputs to a production process can be measured accurately. This may be due to inherent measurement difficulties, or because including that input or output is out of scope of the analysis. In either case, changes in an unmeasured input or output will affect productivity measurement and how it is interpreted. Examples of difficult to measure and usually unmeasured inputs include the weather, water, natural resources, intangibles such as organisational and social capital, and public capital, such as government provided infrastructure. They can have a significant bearing on how inputs are transformed into outputs, but are outside the current scope of ABS models.
Q. How can I get more information on productivity?
A. Free access to all published productivity data is available on the ABS website (https://www.abs.gov.au). If you require more detailed information, or would like to speak to someone about productivity estimates, please email email@example.com.
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