On Productivity: The Influence of Natural Resource Inputs

By Topp, Vernon; Kulys, Tony | International Productivity Monitor, Fall 2014 | Go to article overview

On Productivity: The Influence of Natural Resource Inputs


Topp, Vernon, Kulys, Tony, International Productivity Monitor


MULTIFACTOR PRODUCTIVITY (MFP), which is measured as a residual (the growth in the volume of output not explained by the growth in the volume of labour and capital inputs), reflects other sources of change in the productive capacity of an industry or economy as well as technical change. This article looks at the effect of one of these other possible sources of change, namely natural resource inputs.

Many natural resource inputs are not directly measured in the national accounts, yet changes in their use in production or changes in their quality can affect measured value added and hence MFP estimates. in recent years, there have been sustained periods of strongly negative MFP growth in three important Australian industries--mining, agriculture, forestry and fishing (AFF or agriculture for short), and utilities (electricity, gas, water and waste services) (chart 1). changes in natural resource inputs appear to have been a major contributor. This article draws heavily on two research studies undertaken by the Australian Productivity Commission that looked at the productivity performance of the mining industry (Topp et al., 2008) and the utilities industry (Topp and Kulys, 2012).

For natural resource inputs to affect MFP growth in an industry they must be changing, and they must be a significant input for the industry. That is, the production of output must depend on the availability and/or quality of the resource input. The most straightforward example of industry reliance on a natural resource input is rainfall in AFF. Rainfall is not included in the measures of inputs to production when MFP is estimated for this industry although changes in rainfall have a direct influence on agricultural output each year. As a result, rainfall variability shows up as variability of output and hence measured MFP, rather than as variability in the total quantity of inputs used. MFP growth in AFF was negative at times during the last decade or so, not because farmers became less technically efficient, but because it did not rain as much.

Recent periods of slow or negative MFP growth in all three industries mentioned can be attributed, at least in part, to large reductions in the quantities (or qualities) of natural resource inputs being used in production. If the quality or quantity of unmeasured inputs is declining over time relative to measured inputs, estimates of MFP growth will understate technical progress. Conversely, if the relative quality or quantity of natural resource inputs increases, estimates of MFP growth will overstate technical progress, giving an impression that an industry has achieved greater technical progress than is actually the case.

Declines in MFP growth that are the result of a decline in the availability or quality of a natural resource input do, however, reflect a real increase in the costs of production. (2) Hence, while this decline in MFP does not reflect technical regress, it does reflect a decline in the output that can be produced by the economy (all else equal). This can be interpreted as a loss in productivity that is not caused by a loss in productive efficiency. Rather it is caused by a decline in natural resource inputs.

There are three main reasons why the quality or quantity of natural resources available as inputs to production can change: natural variability; (3) depletion through use or natural processes; and diversion to competing uses. Whether any of these have a material impact on MFP estimates is dependent on the particular situation. The three industries provide some good examples of the contingent nature of this issue.

Box 1 Multifactor Productivity Growth Measurement

The Australian Bureau of Statistics (ABS) generates estimates of
industry MFP using a conventional growth accounting framework
outlined in ABS (2007, 2012a) and Zheng (2005), and recommended by
the OECD (2001). Underlying the approach is an assumed production
function:

[Y. … 

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