5260.0.55.003 - Information Paper: Experimental Estimates of Industry Level KLEMS Multifactor Productivity, 2015  
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INTRODUCTION

PURPOSE OF PAPER
This paper presents the first ABS experimental estimates of industry level KLEMS multifactor productivity (MFP) for the 16 market sector industries. The purpose of these estimates is to facilitate more in–depth analysis of the determinants of growth at the industry level.

In recent years, key users of macroeconomic performance indicators have advocated increasing the availability of more detailed productivity statistics as the main way to improving their overall fitness for purpose. In particular, expanding growth accounts to the industry level to meet the need for more granular measures of economic performance. In response to this, the ABS commenced research into developing estimates of industry level KLEMS MFP.

Moreover, the approach to compiling KLEMS MFP estimates follows the initiatives being developed and advocated by other national statistical organisations including USA, Canada, Japan, Korea, the EU, India, China, Brazil and Argentina. The term KLEMS represents the five input categories – Capital (K), Labour (L), Energy (E), Materials (M) and Services (S).

There are a number of challenging conceptual and measurement issues involved in compiling productivity statistics. The results in this paper should be considered experimental and hence, interpreted with reference to the qualifying discussions.

The paper is structured as follows. Chapter 2 discusses the methodology and interpretation of productivity measures. Chapter 3 presents a brief summary of results. Chapter 4 focuses on the analysis of individual industry estimates. Several areas still require further research and provide a basis for future work in this field. These topics are summarised in the concluding chapter.

The ABS plans to publish industry level KLEMS MFP estimates on an annual basis and welcomes user feedback regarding the results and methodology. An industry level KLEMS MFP data cube will become available on the ABS website at www.abs.gov.au in early 2016. Please forward any comments to Khanh Hoang (khanh.hoang@abs.gov.au).

BACKGROUND
The ABS has been producing aggregate MFP statistics since 1985. The ABS produces annual indexes of labour and multifactor productivity (MFP) for the “market sector”, and since 2007, for each industry division within the market sector. The market sector consists of industries which predominantly produce goods and services which are sold at “market prices”, that is, prices which determine the quantity of goods produced and sold and which over the business cycle cover the cost of production.

The annual productivity measures for the market sector are published in Australian System of National Accounts (cat. no. 5204.0), and annual industry level productivity indexes in Estimates of Industry Multifactor Productivity (cat. 5260.0.55.002). The ABS also produces quarterly estimates of labour productivity (i.e. GDP per hour worked) for the market sector and for the whole economy. These are published in Australian National Accounts: National Income, Expenditure and Product (cat. no. 5206.0).

Table 1 provides an overview of the types of productivity measures currently published by the ABS including the new experimental KLEMS MFP estimates. The productivity indexes produced by the ABS are produced for each of two output measures: gross output (GO) and gross value added (GVA), and for one or more of the primary input measures: labour input (L) measured as either hours worked (H) or quality adjusted labour inputs (QALI), capital services (K), and intermediate inputs (II). The new experimental KLEMS MFP estimates separates intermediate inputs into energy (E), materials (M) and services.


Table 1: Summary of Productivity measures published by the ABS

Productivity Type
Output
Inputs


GO
GVA
Labour
Capital Services
Intermediate inputs

Labour Productivity
Y
Y
Capital Productivity
Y
Y
Value added MFP
Y
Y
Y
Gross output MFP
Y
Y
Y
Y
KLEMS MFP
Y
Y
Y
Y

WHY KLEMS
KLEMS is a useful tool in addressing the challenge of developing more detailed industry performance indicators for the formulation and evaluation of policies involving long–term growth, efficiency and competitiveness. It provides, through a more detailed statistical decomposition, more information on the inputs contributing to output growth, and production efficiency. This helps policy makers and economists to identify factors associated with economic growth, such as structural changes in industry’s input mix, particularly with regards to the relative contribution from the intermediate inputs. This also facilitates a more disaggregated analysis of the industry origins of aggregate productivity growth, such as changes in the relative importance of input components over time.

Within intermediate inputs, the classification into energy (E), materials (M) and services (S) is beneficial in that they have distinctively different roles in the production process. This helps in evaluating trends in the way industries interact. One key interaction is that the intermediate input components reflect renting, hiring and out-sourcing between industries. An industry’s reliance on primary inputs relative to intermediate inputs may change due to changes in leasing and hiring arrangements rather than the productive process itself. When capital is rented under an operational lease arrangement from a firm in another industry, the use of the capital is classified as an intermediate input of the lessee. For example, a construction company may lease a crane from the rental and hiring industry, which is recorded as a service component in the intermediate inputs of the lessee and as capital services by the lessor in the rental and hiring industry.