Industry Policy and Analysis Branch
Industry Innovation Division
Department of Industry, Innovation, Science, Research and Tertiary Education
Canberra
The author thanks Dean Parham (ANU Visiting Research Fellow) and Dr Hui Wei (The Australian Bureau of
Statistics) for their reviewing the draft paper and providing expert comments. The author also thanks many colleagues, in particular Richard Snabel, for their reading the draft paper and contributing helpful comments.
The views expressed in this paper are the author’s and do not necessarily reflect those of DIISRTE or the
Australian Government.

This paper provides an overview of the productivity measurement framework under which Australian productivity statistics are provided. It is intended to convey current knowledge of productivity measurement to general readers (non-experts) by offering a jargon-free narrative with many examples to help better grasp key concepts surrounding productivity. Important productivity measurement issues and assumptions are also examined in relation to their implications on industry policy. The paper is expected to increase public awareness of the technical aspects of productivity debates, and can be regarded as an introductory material for those who are interested in productivity. ii

Introduction to Productivity Measurement Framework .................................................................................................... i
Abstract ...................................................................................................................................................................... ii
Contents..................................................................................................................................................................... iii
Introduction to Productivity Measurement Framework ................................................................................................... 1
1.
Issues about productivity .................................................................................................................................. 1
1.1 Productivity debate ........................................................................................................................................... 1
1.2 Our intent and approach ................................................................................................................................... 1
1.3
Productivity statistics .................................................................................................................................... 2
2. Key concepts related to productivity ...................................................................................................................... 1
2.1 Inputs to production .......................................................................................................................................... 1
2.1.1 Single inputs .................................................................................................................................................. 1
2.1.1.1 Labour and Capital inputs ........................................................................................................................... 1
2.1.1.2 Intermediate inputs ..................................................................................................................................... 2
2.1.1.3. ‘Technology’ inputs .................................................................................................................................... 2
2.1.2 Combined inputs ........................................................................................................................................... 3
2.2. Output from production .................................................................................................................................... 4
3. The concept of productivity .................................................................................................................................... 5
3.1 Labour productivity ........................................................................................................................................... 5
3.2 Multifactor productivity (MFP) ........................................................................................................................... 6
3.2.1 MFP concept ................................................................................................................................................. 6
3.2.2 Embodied and disembodied ‘technology’ (progress) ..................................................................................... 7
3.3. Productivity growth (rate) ................................................................................................................................. 8
3.4. Indexation ........................................................................................................................................................ 9
3.5. Labour productivity decomposition ................................................................................................................ 10
4. Productivity measurement framework .................................................................................................................. 11
4.1 Measuring quantity ......................................................................................................................................... 11
4.2 Two types of productivity measurements ....................................................................................................... 11
4.3 Capital input ................................................................................................................................................... 12
4.4 Labour input ................................................................................................................................................... 13
4.5 Factor incomes ............................................................................................................................................... 13
5. Challenges to the ABS MFP measurement program ........................................................................................... 14
5.1 Labour quality ................................................................................................................................................. 14
iii

5.2 Capital input and capital quality ...................................................................................................................... 14
5.3 Constant returns to scale (CRS) ..................................................................................................................... 15
5.4 Capital utilisation adjustment .......................................................................................................................... 15
5.5 Capital and labour shares ............................................................................................................................... 16
5.6 Intangible input and output ............................................................................................................................. 16
6. Concluding remarks ............................................................................................................................................. 17
References............................................................................................................................................................... 19
Appendix .................................................................................................................................................................. 21
iv

One of the key agendas in Australia has always been how to increase productivity in order to maintain sustainable economic growth. Productivity debates come and go, in tandem with the economic situation at a time. Most recently, the debate about Australian productivity has intensified, with a measured decline in productivity in the past years. Extended discussions about this productivity decline will be included in a companied paper (Productivity Concepts and Policy
Directions). Most productivity debates occur in the policy area. They are often fuelled by the media, statistics, research papers and political views. However, public awareness of what productivity is remains poor. In particular, when referring to multifactor productivity, confusions are significant, exacerbated by the complex nature of its measurement. For this reason, communications conveying concepts across different groups in the non-experts domain is difficult, jeopardising a healthy public debate around productivity.
One key factor contributing to the ambiguity surrounding a productivity debate has been identified as the technicality of productivity. A variety of technical issues are intimately built into the definition and measurement of productivity. On the other hand, productivity debate participants often have inadequate awareness of the complexity surrounding productivity. More often than not, productivity statistics are literally taken to interpret the Australian economy. In many cases, this is oversimplified.
This paper aims to take an initiative to address the ambiguity surrounding productivity debates to improve communication by offering a narrative about the productivity measurement framework.
The audience is non-experts, though experts may find the paper useful. Clearly, to bring technical issues such as definitions, assumptions and measurements to non-experts remains a great challenge to us. Moreover, we will have to go across the boundary by examining or even exploring the implications of productivity measurement details for public policy.
To narrate what a concept or an assumption means, simple examples and example-based analyses are proven to be helpful. But we are mindful of possible unintended consequences of using examples for describing economic concepts. Arguments based on examples can be vulnerable and subjective to criticism. In addition, the narrative approach itself has limitations in conveying technicality.
1 Despite these, narrative with rich examples is still the only approach suitable for non-technical readers. Therefore, by appreciating the challenging task ahead, we ask
1
Reminiscent to communicating science to the public, the technicality of some concepts may never be possibly precisely described in laymen language, and examples to convey popular science can sometimes be accused of ‘not precise’ by experts.

for some understanding from readers, experts or laymen alike, when attempting to strike a balance between rigor and readability.
The paper is expected to not only provide educational material for non-experts who wish to understand better productivity, but also help policy makers gain an analytical insight into productivity debates.
The Australian Bureau of Statistics (ABS) is the central statistical agency in Australia. It regularly publishes productivity statistics. To assist in better use of these statistics, the ABS also publishes many helpful technical notes and research papers related to productivity.
2 However, unlike many other widely-referenced official statistics such as inflation, GDP and unemployment rate, productivity statistics were relatively less used. Over the years the ABS has made a great effort to make the productivity concept and measurement issues accessible to users. But even so, many non-experts and policy makers still encounter significant conceptual difficulties from time to time when they attempt to utilise the rich source of information about productivity. This barrier is mainly due to the technical nature of the productivity concept and its measurement.
The ABS publishes productivity statistics under the ABS productivity measurement framework.
This measurement framework was developed in line with other statistics frameworks adopted by the ABS National Accounts. It is also consistent with OECD recommendations (OECD 2001) for the best practice of measuring productivity, which is widely adopted by many other countries.
Producing official statistics requires the application of economic theory and at the same time making assumptions, as well as many other practical considerations such as feasibility and data availability. Economic theories and related assumptions become the foundations underpinning the statistics provided. They can sometimes change the interpretations of statistics significantly. This is no different for productivity statistics. Therefore, sufficient understanding of the productivity measurement framework is essential for better use and interpretation of productivity statistics.
The labour productivity statistics are the main statistics the ABS publishes regularly.
In comparison to the ABS multifactor productivity (MFP) statistics, which is an indirect measure, the ABS labour statistics are much better measured.
How to better measure multifactor productivity (MFP) is still an active research subject and some key issues are continuously being investigated (Zheng 2005). As recognised by the ABS, the MFP statistics published since 1985 and onwards reflect a great achievement by the ABS. However, the
MFP measurement framework is not in its maturity (see Wei 2011). This caution highlights the challenges the ABS faces with productivity measurement. Yet, undeniably, each assumption incorporated in the MFP measurement framework can have ramifications unseen by policy makers.
2 See ABS publications cat. no. 5229.0, 5234.0, 5204.0 and 5260.0.55.002
2

The engine of national income is production. In a more general sense, production processes must include producing goods and services. However, for illustrative purposes, only goods production needs to be considered.
3
The concept of productivity is built on many different economic concepts related to production such as inputs and outputs. They appear to be simple, but are often the sources of potential confusion. A proper scrutiny of these basic concepts is beneficial before being able to grasp the concept of productivity.
In modern production processes, tools and equipment are required to accompany labour. Therefore, production requires a variety of other inputs besides the basic labour input. Inputs can have many different forms or components, such as the time spent on production, the number of people participating in production, the number of tools or equipment used for production, the size of the workplace, or even the (intangible) technical advice. Furthermore, inputs can be represented at different aggregation levels.
In a way, production may be visualised as an input-output responsive system, in which inputs drive outputs. Treating production in such a ‘mechanic’ manner helps understand why outputs can be modelled by inputs. Realistically, not all inputs can be identified or measured. But the more inputs are identified, potentially the more accurate for modelling outputs.
4
In the following, a critical review of inputs and output is conducted to lay down the basis for introducing labour productivity and multi-factor productivity.
Different inputs can be grouped into different groups according to similarity. Each group may be regarded as a single input. In the neoclassic economic framework, labour and capital are the two fundamental groups (essential to any production process).
5 In this framework, (gross) output is driven by two different single inputs - labour and capital.
3 Whether the output is goods or services (or both) is largely reflected by the nature of the industry. It is noted that except specific situations (such as discussing data availability issues), explaining the productivity concept does not need to treat goods and services separately. Using examples of goods production only is sufficient in introducing the concept of productivity. This may be regarded as a convention which will be adopted throughout the paper.
4 Readers with a basic econometric knowledge may find this easy to understand. An econometric model with known exogenous (independent) variables can be constructed to predict endogenous (dependent) variables, but there are always hidden variables which cannot be explicitly included in the model. These unknown variables can be treated as random and collectively have no correlation with any known exogenous variable if the model is a good one.
5 In the economic theory, the simplest production function (e.g. Cobb-Douglas type) would have only two independent variables – labour and capital.

Labour and capital inputs are easy to conceptualise. Only when their measurements are considered, can they become complicated.
The conventional measure of labour inputs is straightforward - the total hours put into a production in relation to measurable output. Note that labour hour alone does not reflect labour quality, but in a diverse labour force with a distribution of different labour quality, it is an effective and convenient measure of labour inputs.
Measurements of capital inputs are more complicated (for example, OECD 2001) as the role capital plays for output is more complex. Sometimes, capital inputs can be hard to quantify. For example, in producing chickens by a farmer, capital at least includes land and the hut accommodating chickens during the night. There can be more to add in order to take a full account of capital. To measure the capital for producing chickens, an aggregation of production-involved capital components is required.
In general, National Accounts from a statistical agency provides the standard accounting rules for capital inputs – what and how to account. Of course, a detailed account of capital has to involve considering depreciation and even intangibles, as will be discussed later.
Grouping inputs into labour input or capital input is consistent with neoclassical economics in describing (gross) output in production processes. In modern approaches, however, intermediate inputs are separated from all inputs.
Intermediate (goods) inputs or producer (goods) inputs are inputs in the production of other goods inputs, such as partly finished goods inputs.
6 Also, they are inputs used in production of final output. A firm may make intermediate goods then use them, or make them then sell, or purchase and then use them. In production processes, intermediate goods either become part of the final product, or are changed beyond recognition in the process. For example, the chicken foods can be defined as intermediate inputs for producing chickens as final output.
It is necessary to point out that output can be either modelled by labour and capital only, or by labour, capital and intermediate inputs. They represent two different perspectives (models) for the same production process. The two different models may require different methods accounting for capital. For example, the chicken foods consumed for raising chickens are intermediate inputs, but they can be simply counted as capital inputs when only labour and capital are considered.
As will be discussed later, production can either be treated as gross output or value added. In principle, intermediate needs not to be specifically isolated in a value added process. Intermediate inputs are not counted in GDP (value added) to avoid double counting, as the final product should only be counted.
7
Apart from labour, capital and intermediate inputs, one particular input to the production processes drew economists’ great attention in late 20 century, though it is not directly measurable. US data showed a steady economic growth seen in the last few decades (50s to 90s), and output was not driven by labour participation and capital alone (see Barro & Sala-i-Martin 2004). Something else more important, later coined as ‘technology’, was there in driving the economic growth.
6 The use of the term ‘intermediate goods’ can be slightly misleading since in advanced economies about half of the value of intermediate inputs consist of services.
7 Readers are reminded that the concept of ‘technology’ is introduced in the simplest way, but not necessarily following the economics literature. For more details, refer to Jorgenson’s (1996) paper,
Technology and
Growth , and associated discussions.
2

Therefore, ‘technology’ is a broad but rather vague concept in the economic domain. It is different from the true technology conventionally referred to ‘the collection of such tools, machinery, modifications, arrangements and procedures’. In economics, ‘technology’ is more associated with something other than labour, capital and intermediate inputs, but improves the efficiency of utilizing them.
8 A typical example is entrepreneurship and innovation which contribute to
‘technology’ without necessarily requiring increasing labour and capital input. In general, ‘technology’ is more the nomenclature of combined inputs such as human capital, entrepreneurship, management and innovation. Each component can contribute to economic growth quite differently.
It is im portant to note that the definition of ‘technology’ is vulnerable and can lead to confusion. The biggest problem about the ‘technology’ input which confronted economists is how to measure it.
‘Technology’ could only be measured indirectly.
There are deta iled questions one will inevitably ask. If ‘technology’ can be measured, would the measured ‘technology’ be the same thing as defined? Moreover, it would be too simple to treat
‘technology’ as an independent input, as it interacts with labour and capital and so on, often in complex ways. In practice, the measured ‘technology’ or ‘technological’ progress may include more than is intended to measure, known or unknown, including measurement errors. Moreover, different economic models mean different measured ‘technology’. Nevertheless, regardless of how imprecise ‘technology’ is defined, it is of great conceptual simplicity and convenience in the development of modern economics.
Numerous efforts have been made in the productivity research area to calculate more accurately the impact of ‘technology’ on output. One believes that if data is attainable a more detailed identification of different inputs will help better measure this effect. Along this line, the OECD (2001) proposed to adopt models which separate intermediates (I) (materials (M), energy (E) and services (S)) from all inputs.
The ABS has also begun considering this model (Wei 2011).
Inputs to production can be decomposed, and so can they be combined. The concept of a combined input deserves special attention because it will directly affect multifactor productivity.
One major difficulty in understanding multifactor productivity among many non-experts originates from a lack of understanding of what a combined input really means.
In the simplest case, labour and capital inputs can be combined. By doing so, output can be regarded as being driven by the combined labour and capital input, irrespective if it is labour or capital. Similarly, labour, capital and intermediate inputs can be combined, which effectively drive gross output.
One obvious question is how to combine labour and capital, given the difference between them. For example, one working hour (labour) is not equal to one square metre of land (capital). What does a combined labour and capital input actually mean in this example? In fact, the efficient market provides the answer. The market value of labour is the wage (or more accurately the Compensation to Labour in
National Accounts which includes Fringe Benefits etc.). The market value of capital (strictly any tangible and intangible asset which provides economic rent over more than one period) is the rent to consume the capital for production over a given period of time. The total wages paid to labour and total expenditures on capital rents can be added up. In total, this reflects a combined cost of labour and capital for production.
The next question is what a combined input means economically. In the context of a gross output, this relates to the share of labour or capital inputs when they are combined, or what proportions of labour
8 However, ‘technology’ in the economic context is closely related to the status of true technology. For example, the emergence of computers revolutionized production and increased economic growth.
3

and capital inputs should contribute to the combined labour and capital input. Again the efficient market slew the dragon. Combined input must be optimal for price takers in responding to an efficient market.
Some further explanations to optimal combined input are useful. According to basic economic theory, different combinations of labour and capital can be selected because they are substitutes. But there is only one combination which eventuates, as a producer intends to achieve optimal output. This means that a producer responsible for production optimally allocates labour and capital to maximize profit, given a targeted cost for the combined labour and capital input.
9 For example, a farmer could get a business loan of $100,000 for three years to raise chickens. This amount will be spent on the combined cost of labour and capital. In theory, he has the best market information to allocate labour and capital for optimal production. He may therefore be only interested in how to efficiently utilise the combined input ($100,000) to maximise the number of chickens produced.
In summary, a combined labour and capital input can be regarded as a ‘single’ input to drive output.
The same applies to combining more than labour and capital inputs. Therefore, identifying a combined input (or multiple inputs combined) is conceptually no different from identifying a single input such as the labour input or the capital input. Understanding of this concept is very helpful before introducing multifactor productivity as will be discussed later.
Like inputs, the concept of output can be simple in the economic context. In the simplest production unit such as a farmer raising chickens, output can be easily measured, for example, by counting the number of chickens produced over a period of time. However, in a slightly more complicated production unit with multiple products as output (such as raising both chickens and cattle), the simplicity of output measure vanishes. One chicken does not equal one bull/cow, but they have to be ‘added together’ to measure output.
Again, the definition of output for a more complicated production unit has to rely on market values in order to add them up, similar to input aggregation. The existence of a competitive and efficient goods and services market is almost always assumed in order to define the market values for all outputs.
To determine the value for any single output, its market price has to be known. There are two different prices
– nominal (current) price and constant price. The latter is the nominal price at a point in time.
There are different implications for selecting different prices. Using nominal price means to measure output by value, and using constant price means to measure output by volume. They can make a big difference. For example, if suddenly the market price for chickens rises due to external reasons, the production by volume measure remains the same. The production by value measure, however, will increase.
Official output statistics published by the ABS contain both volume and value measures on the basis of either utilising constant price or current price. The latter is more related to GDP. And the former is more related to productivity, as shall be discussed below. Productivity is defined as a volume measure but not a value measure.
Strictly speaking, in aggregating volume based output a mathematical model will be required so that value based output can be inferred from value based output measure. This will be further discussed when introducing an indexation method. The same method applies to aggregating volume based input.
9
Or equivalently a producer minimises the combined labour and capital input cost, given a targeted output.
4

The concept of productivity builds entirely on the concepts of input and output.
Productivity normally means the rate of production of goods (or services).
10 In the economic context, however, productivity is given a more specific meaning. It is more about the efficiency of production of goods in a production unit which is responsible for the main production. (The production unit is arbitrary ranging from a person, a family, a small business, and up to industry or total national production.) A meaningful measure of the efficiency of generating output is against something, preferably against input. To paraphrase it, output is compared to a particular input. For example, output can be measured against time, the number of employees, labour hours, or some capital inputs. Theoretically, there are many different kinds of measures of output in terms of inputs, and therefore many kinds of productivity.
Different measures of productivity represent different perspectives of efficiency. Production may be efficient with respect to one input but may be inefficient with respect to another.
The generic form of definition of productivity is rather simple (OECD 2001):
Productivity

Output
Input
(1)
For the same output, according to (1), different types of input define different types of productivity. For convenience, the types of productivity can be categorised according to whether the input used is single input or combined input.
11
Productivity defined in such a way has the dimension of output for every unit of input. For example, the productivity dimension can be the number of chickens per unit input, either single-input unit or multipleinput unit.
Although (1) has great simplicity, it does not deal with the output in-homogeneity with respect to the input. For example, suppose the input is time. If measuring in days, productivity can be highly variable.
But, if it is measured over a period of one year, covering sufficiently the domain of in-homogeneity of production with respect to time, the fluctuations of these short term productivity measures disappear.
Such a measure of rate of production more accurately reflects the true production. In general, after the in-homogeneity of production with respect to the input considered is sufficiently covered, (1) is better defined. It represents an ‘average’ measure of productivity.
12
The most important single-input productivity is labour productivity (LP) in which the input unit is simply labour hours:
( LP )

Output
Labour ( hours )
. (2)
Labour productivity measures the efficiency of production
– ‘average’ output per hour of labour input.
To correctly understand what ‘average’ means here is important. In general, there must be a varying
10 It needs to be reminded that only goods production is considered for discussion. This is consistent with the convention adopted for explaining concepts related to productivity.
11 When one single input is used to define productivity, it may be regarded as ‘single-input productivity’.
Likewise, when a combined input is used, it may be regarded as
‘multiple-input productivity’.) This convention will be used throughout the paper.
12
A similar example is the introduction of average cost. Cost for producing unit output varies, but a more realistic measure of true cost is to look at the average cost over a period.
5

degree of labour quality. Different labour hours with different quality affect output in-homogenously. If ignoring labour quality, the labour productivity (2) will represent an ‘average’ labour productivity.
An interesting question can arise if labour quality needs to be adjusted or not, when labour quality differs significantly. This largely depends on what one intends to measure, as will be discussed further.
Theoretically, LP in (2) varies with labour quality if it is not adjusted. After making a quality adjustment,
LP no long varies with labour quality.
13 This is reminiscent of a seasonally adjusted unemployment rate. A seasonally adjusted unemployment rate is free from seasonal impact. But a non-seasonally adjusted unemployment rate represents an actual measure, and therefore varies with season.
Seasonally adjusted and non-seasonally adjusted unemployment rates convey different messages.
In practice, both labour input and output need to be aggregated. The labour productivity may then be specifically named as Aggregated Labour Productivity (ALP).
Capital productivity can naturally be introduced as a different type of single input productivity, and can be explained in a similar way.
When output is measured against all identifiable inputs such as labour, capital and intermediates (in the context of gross output) combined, it is called total factor productivity (TFP) or multifactor productivity (MFP).
14 It is intended to measure how those ‘unidentifiable inputs’ (or ‘other inputs’) affect the efficiency of production with respect to identifiable inputs. Obviously, MFP is meant to measure
‘technology’. Depending on the number of inputs identified, and if output is gross output or value added output, the socalled ‘all other inputs’ can vary. For example, we can either combine labour and capital inputs when value added output is considered, or combine labour, capital and intermediate inputs when gross output is considered. Different ways of combining inputs lead to different types of MFP.
The most widely used MFP in line with the neoclassic production (function) is defined only when labour and capital inputs are combined (OECD 2001):
Output
Multifactor Productivity (MFP) =
Combined labour & capital input
. (3)
MFP thus defined measures ‘average’ (gross) output per unit of combined input. It ignores the inhomogeneity of output with respect to the combined input. In some parts of production, the combined labour and capital inputs may contribute more to output than others. In other words, the quality of combined inputs can vary. The MFP defined by (3) ignores the quality of different combined input, by taking an ‘average’ approach. MFP defined by (3) varies with the quality of combined input.
Combined input can also be adjusted by its quality, almost identical to the LP case. Similarly, adjusting input quality or not, in large part, depends what one wishes to measure. With quality adjusted combined input, the measured MFP will be independent of input quality. This will be discussed further.
13 Quality adjustment of LP effectively means to define labour productivity by using an effective labour,
L
  q
1 q
2
L ( q ) w ( q ) dq
, where q denotes labour quality and w denotes the quality adjustment function.
Therefore, quality adjusted LP does not vary with labour quality.
14 This means that those intangibles or ‘technology’ is excluded from measurable inputs. Also note that there might be argument that TFP differs from MFP. But such a fine technical debate does not add value for understanding MFP.
6

When the combined labour and capital input is measured by their market (dollar) values (or total expenditure on labour and capital), MFP effectively measures the ‘average’ output for unit expenditure on labour and capital combined.
15
More generally, MFP is defined as
MFP

All
Output
' non
 techno log y ' inputs
(4)
What message does the MFP defined by (3) or (4) carry? Effectively, MFP is a measure of the efficiency of allocating combined inputs for output. By (4), it implies that the ‘technology’ leads to the efficiency. Since MFP depends on the quality of combined inputs, partly it measures the quality of combined inputs. This will be further discussed.
To consolidate the concept of MFP, let us look at the previous hypothetical chicken-raising example again. Suppose the farmer produced 1,000 chickens in the first year by spending $7,000 on combined labour and capital input. Also assume that he adopted an innovative approach (as a technological progress) to raise chickens next year, and produced 1,200 chickens without increasing expenditure on combined labour and capital. The quality of spending the same $7,000 in the next year is increased.
Clearly the farmer’s MFP increased by 20% over a period of one year. (In this example, MFP increases because of adopting innovation.)
In the neoclassical economics framework, as discussed previously, the only input excluded in the MFP definition is ‘technology’. Therefore, MFP measures ‘technology’.
16
In contemporary debates, what exactly constitutes ‘technology’ is often a significant ambiguity. From the measurement point of view, ‘technology’ is model dependent. Even for the same ‘technology’, different production models would lead to different interpretations of ‘technology’.
‘Technology’ can be independent of measurable inputs, and can also coexist with them because of their interactions. For example, to take advantage of technological progress in digital mobile telecommunication, costs for acquiring equipment are normally associated with such things as purchasing cell phones. So it requires labour to adopt this new technology, through training and operation. The situation is drastically different if ‘technology’ is due to an innovation consisting of a bright idea of using existing equipment to increase output. This type of ‘technology’ (innovation) can be independent of capital investment.
For the above reason, in productivity research, ‘technology’ (progress) as measured by MFP can be decomposed into two different components:
 embodied MFP;
 disembodied MFP.
15 Using expenditure as measured input is mainly for illustrative purposes. It does not imply that input must be measured by its expenditure. Practically, litter needs to be concerned when adopting input index for calculating
MFP, as will be discussed later.
16 In contrast, in the case of labour productivity, it measures all non-labour inputs – capital and ‘technology’ combined. Accordingly, without ‘technological progress’, labour productivity can still increase as a result of more capital investment under a constant MFP.
7

The embodied MFP component is dependent on measurable inputs, but the disembodied MFP component is independent of them. The relative magnitudes between these two components are determined by the production process in consideration, but to recognise their difference is important.
(For the embodied MFP component, it can further be decomposed into the labour and capital parts respectively.)
There is an important association between embodied MFP and input quality.
Input quality increase is related to ‘technological’ progress. For example, education and training as human capital increases the quality of labour. This labour quality change can equally be interpreted as
‘technological’ progress. As a result, in theory once inputs are adjusted by quality, the measured MFP only represents the disembodied ‘technology’.
In most situations, a simple assumption is made that ‘technology’ impacts labour and capital the same way.
17 The ‘technology’ increases the same efficiency for using labour and capital. This assumption is convenient for analysis, but can fail.
OECD (2001) recommended a type of MFP - KLEMS multifactor productivity, where capital, labour, energy, material and services are all combined for input. Theoretically, KLEMS MFP is expected to be more accurate in measuring impact of ‘technology’ on output because more inputs are taken into account.
18
How ‘other inputs’ impacts on productivity over time can simply be determined by working out the difference in productivity. Usually, the productivity growth rate or the percentage change of productivity over time (often each year) is calculated.
Often little distinction has been made between productivity and productivity growth in many discussions around productivity. But making a clear distinction between them is essential. However, in many situations, productivity growth or productivity growth rate are mentioned interchangeably.
A general form of productivity growth can be derived from its definition (see Appendix):
 
, (5) where O , C and P denote output, input and productivity respectively. The symbol ‘^’ denotes the growth rate. For labour productivity growth, C is the total labour hours input, and for MFP growth rate, C is the total expenditure on labour and capital combined.
In the case of MFP, the above can be further expressed as
    s l
 s k
, (6) where, s l
 wL / C
and s k
 rK / C represent the share of labour ( L ) costs and capital ( K ) costs respectively, therefore add to unity. Note that w and r are wages and capital rental costs paid for production. The results are derived based on the assumption that wages and capital rental costs are competitive. As seen from the above relationship (6), MFP growth is the residual of the output growth subtracting the cost growth of labour and capital combined.
It should cause no confusion among readers that (6) is usually derived in the literature (see, for example, Zheng 2005) by adopting a production function under the (economic) growth framework. This approach was also used in the DIISRTE Consolidated Report (2009) – Innovation Metrics Framework for deriving TFP. When constant returns to scale (CRS) is assumed, C=O (for example, Chiang 1974),
17 This is regarded as ‘Hicks neutral’. The optimal allocation of labour and capital is independent of ‘technology’.
18 The ‘Hicks neutral’ component of ‘technological’ progress is more accurately determined.
8

a case that labour and capital costs exhaust output.
19 In principle, the two approaches should make no difference. One advantage of deriving MFP from its definition, though, is consistency – deriving MFP growth, irrespective of the form of production function.
The growth relationship (6) offers a direct interpretation of MFP growth. If output grows the same proportion as combined input grows, i.e., CRS, MFP has no growth. Only the extra MFP growth above
CRS captures the ‘technological’ progress. This is why this extra MFP growth is coined as ‘residual growth’.
It is important to reiterate that production can either be referred to as gross output or value addition, depending on the economic model chosen. MFP measured, if representing disembodied ‘technology’, should be the same in theory. This is clearly not the case, which will be further mentioned in the next section.
The ABS publishes or investigates two different kinds of labour productivity (LP) and MFP growth calculations. The following table summarises the two different kinds of labour productivity (LP) and two different kinds of MFP (ABS 2012).
Table 1: Classification of different productivity
Productivity
LP1
LP2
MFP1
MFP2
Output
Gross output
Value added
Gross output
Value added
Input
Labour
Labour
Labour, capital and intermediate
Labour and capital
Inputs and outputs are measured discretely, while the definition of productivity, (3) or (4), considers continuous input and output. Indexation of input and output is used to bridge the gap between theory and discrete measurement.
An index of a quantity is defined by its current value (period 1 with subscript 1) over its past referenced value (period 0 with subscript 0). Then input index and output index can be defined as
I input

X
1
X
0
, I output

O
O
0
1
. (7)
They represent the percentage of input and output over their reference values respectively.
Consequently, the productivity index can be defined as
I productivi ty

P
1
P
0

I output
.
I input
(8)
Because all reference values are constant (do not change once defined), input index, output index and productivity index are equivalent to input, output and productivity.
The formula (8) implies that the productivity index can be derived from the input and output indices.
The growth relationship (5) or (6) is equally applicable to the indices. Little confusion should arise when using indices to replace real variables. This is the approach adopted by government statistical
19
CRS: if both labour and capital increases a proportion output will increase the same proportion. Due to CRS, the total output is exhausted by labour and capital costs.
9

agencies such as the ABS (see ABS cat. no. 5260.0.55.001). Note also that the OECD (2001) defined productivity using indices.
There is a great advantage in using indices. Indexation facilitates aggregate inputs and outputs, which are necessary not only in dealing with multi-inputs and multi-outputs but also from aggregating firmlevel production to industry sector production (or even to the whole economy production). Different ways of aggregation define different types of indices. For productivity measurement purposes, the
Tornqvist index 20 is widely used, because of its mathematical convenience for productivity growth calculation. This is the approach adopted by the ABS.
An additional advantage in using indices is that they are dimensionless. Using different units in measuring inputs or outputs should not alter the indices.
There are two different kinds of output indices – volume index and value index. In calculating a productivity index, volume index is used. However, due to practical reasons, volume data can be hard to obtain. In this case, volume has to be inferred by using price data, which is sometimes available. To obtain a volume index, a constant price is used.
A particular important concept related to calculating input and output indices is the weight used in aggregation. In arriving at a final input index or output index, individual inputs and outputs contribute to their final indices very differently. This is reflected by different weights for different inputs or outputs.
How to determine the weights is largely a technical issue. However, it is not trivial for productivity statistics users to understand that different indexation methods mean different ways of calculating weights.
21 Whether the weights are based on volume or based on value can have a marked difference on the productivity index, and affect interpretations of the productivity index. Moreover, different indexation methods require different data. One of the painstaking jobs the ABS is undertaking is to obtain quality input and output data so that an accurate productivity index can be calculated.
As discussed previously, labour productivity (LP or ALP) varies with inputs other than labour. Typically, it contains MFP information. If labour quality is not adjusted, LP will also contain labour quality, or embodied ‘technology’.
Measuring MFP can be carried out by decomposing labour productivity. Research of this kind has been conducted in this area. Nota bly, the ABS’ most recent research (Wei & Zhao 2012) is of particular interest. This work has significant industry relevance. On the basis of the decomposition framework
(Stiroh 2002), value added labour productivity growth can be decomposed into three basic components:
Labour productivity growth = Industry contributions +
Reallocation of materials +
Reallocation of hours.
The first part originates from a summation of individual productivity contributions weighted by the relative value added among industries – related to MFP. The second component represents the sum of all individual industry changes in the allocation of intermediate inputs. The last component reflects the sum of the change in labour in all industries. The above decomposition enables us to assess and interpret different impacts on labour productivity. In particular, the ‘reallocation of hours is of great importance, as it is associated with structure change. This has been found quite important in affecting labour productivity (Wei & Zhao 2012).
20 Tornqvist indices are based on geometric mean of all individual components.
21
The weight used in Tornquist index is conventionally determined by the average weight obtained in two period
0 and period 1 to ensure accuracy.
10

The decomposition method is robust for many other different uses, for example, seeking understanding of material reallocation effect (across industries) on labour productivity.
Once productivity is defined on theoretical grounds, a framework to measure it is required.
Measurement of productivity is more a practical issue once the theoretical foundation is set up. In the following, each pillar supporting productivity measurement is introduced.
Consistent with the OECD recommendation, the ABS measures volume or quantity for productivity measurement. Volume measure is less affected by market price, and it more reflects the intrinsic production capacity. In contrast, measuring value is more related to profit. (Profit differs from productivity.)
In doing so, inputs and outputs need to be aggregated using the constant price rather than current price. Indices developed in such a way should be regarded as volume indices.
Practically, a statistics agency can have sound value data but poor volume data. Volume could only be inferred by utilising value data. This requires collecting price data. How this is done has implications on the accuracy of input and output index calculations.
Understanding the necessity of using volume indexation for productivity purposes is of vital importance.
For example, a firm can increase profitability while losing productivity. This can be due solely to an increase of market price for its output. 22 Equally, a firm can decrease profit while increasing productivity due to a price drop.
As discussed previously, the ABS investigated two different types of productivity measures
– value added (VA) productivity and gross output (GO) productivity. Since 1974-1975, the ABS has started to publish value added productivity. Publishing value added MFP commenced in 1985-1986. It was only later in 1994-95 that the ABS began to produce gross output LP and MFP.
Note that unlike VA MFP, GO MFP is based on Input-Output tables, which are essentially results published by the ABS National Accounts.
VA MFP can differ from GO MFP, as the two models for measuring MFP are different. At the aggregate level, the two different measures of MFP growth rate are related by a simple relationship (see Zheng
2005), assuming that ‘technological’ progress yields the same output value increase, irrespective of how it is measured. In reality, however, this is unlikely the case. GO MFP growth is systematically smaller than VA MFP and less variable, confirmed by the ABS through a comparison between the available GO MFP and VA MFP between 1994-95 and 2000-01. However, the differences between the two measures vary significantly for different industry sectors. For some sectors, such as mining and manufacturing, the discrepancies are very pronounced up to about 200% for the manufacturing and about 50% for the mining sector at 2000-01 (Zheng 2005).
As validation for the value-added approach, the ABS tested the consistency: if the industry level results can be added up to match that derived from the National Accounts’ aggregate input and output for the entire period 1990-01 to 2001-01. The comparison shows a significant agreement.
22 The research paper, ‘What is the difference between productivity and profit’ by Arthu Ha, Loris Strappazzon and William Fisher (2001) specifically address the relationship between profit and productivity.
11

One explanation to the difference is the assumption that different industries pay for the same labour wages is not likely to hold.
The ABS MFP statistics have been improved recently. Service sectors used to be more problematic in measuring output, prohibiting from measuring their MFP. The ABS measured MFP for 14 industry sectors in the past, and it now measures 16 industry sectors’ MFP due to data improvement for some service sectors.
23 Education and Health still remain unmeasured for MFP. In the MFP context, the ABS defines ‘market-sector’ as the aggregation of all those industry sectors which have MFP measurement.
The ‘market-sector’ today has a size of well over seventy per cent of GDP.
The appropriate measure of capital input is ‘capital service’ in the context of productivity analysis. It reflects the combined amount of ‘service’ each asset provides over a period. Different assets are bundled together to be modelled for their contributions to capital service.
There are capital items which do not directly contribute to production, although they can be listed in a company’s balance sheet. For example, the additional property asset a company possesses may have nothing to do with production, and it should not provide capital service. These capital assets need to be excluded for MFP measurement. However, non-working capitals contribute to a company capital stock.
To determine each asset’s productive capital, the so-called ‘Perpetual Inventory Method’ (PIM) is adopted (cf. for example, Jorgenson, Gollop and Fraumeni 1999). The idea is that past investment on each asset contributes to current productive capital stock, and they are accumulated over time, with the two important elements being:

Asset age-efficiency and when it becomes ineffective – jointly a measure of how new or old assets contribute to working capital differently;

Real investment expenditure on asset type.
There are costs for holding capital, and the costs differ for different assets. To determine an asset’s capital cost, the ABS considers corporate income tax, tax depreciation allowances, investment tax credits and indirect taxes.
The capital (Tornqvist) index can then be constructed by aggregating all assets’ contributions (see, for example, Zheng 2005).
The same method of capital input formulation is applied to each industry sector.
The ABS notes the limitation related to the capital estimate. This includes data quality and various assumptions built into the capital input method.
The adoption of the PIM implies that capital services to be modelled are utilised fully. In fact, after an investment is made, utilisation of an asset for its capital service may lag behind what the model assumes. Capitals can therefore be underutilised. To make such a correction, detailed data related to
23
Currently sixteen industries are Agriculture, Forestry and Fishing, Mining, Manufacturing, Electricity, Gas,
Water and Waste Services, Construction, Wholesale Trade, Retail Trade, Accommodation and Food Services,
Transport, Postal and Warehousing, Information, Media and Telecommunications, Financial and Insurance
Services, Rental, Hiring and Real Estate Services, Professional, Scientific and Technical Services,
Administrative and Support Services, Arts and Recreation Services, Other Services. Previously fourteen industries are Agriculture, Forestry and Fishing, Mining, Manufacturing, Electricity, Gas, Water and Waste
Services, Construction, Wholesale Trade, Retail Trade, Accommodation and Food Services, Transport and
Storage, Communication Services, Financial and Insurance, Cultural and Recreational Services. Data source:
Experimental Estimates of Industry Multifactor Productivity 2009-10 (ABS Cat. No. 5260.0.55.002)
12

capital use must be required. This is difficult, however. For this reason, the ABS’ capital service and net capital stock estimates are not adjusted for the rate of capital utilisation (Wei 2005).
Construction of the labour input index is relatively straightforward. Data of hours worked basically come from the ABS Labour Force Survey. At the broad level, little confusion will arise, as annual hours worked can be aggregated and used for LP or MFP estimates. At the industry level, the ABS publishes total hours worked. At the ‘market-sector’ level, the ABS adopts the fixed weight Laspeyres index to aggregate all industry annual hours worked (Zheng 2005).
24
As indicated earlier, labour input does not have to be adjusted by labour quality. At the aggregate level, this may not be an issue. By selecting a weight calculated by the share of total wage cost rather than the share of total hours worked, the quality of aggregate labour input can be partially adjusted (Zheng
2005). Higher wages pay for higher-skilled workers. The ABS attempted to produce experimental quality adjusted labour input (QALI) for the aggregate market-sector many years ago. Currently, the
ABS publishes both MFP estimates based on labour and quality-adjusted labour.
The MFP index based on quality adjusted labour tends to be smaller (Wei 2005), as discussed earlier.
Part of embodied ‘technology’ on labour has been removed after adjusting labour quality. The labour quality improvement can be a significant part of the embodied MFP growth.
Under the growth accounting framework (6), the proportions of labour and capital growth as measured by output contribute to MFP growth separately. These contributions are weighted by the labour income and capital income shares (compared to total income). More broadly, the three types of shares (if gross output is measured) need to be calculated: 25

Labour input share;

Capital input share;

Intermediate inputs share (applicable to gross output measure only).
For the intermediate input, its share can be directly obtained by the current price measures of gross outputs and intermediate inputs in the ABS supply-use tables.
For the labour and capital inputs shares, however, they are not directly obtainable.
The ABS calculates the labour and capital input shares by counting the expenditure/incomes items
(Zheng 2005). To maintain the integrity of the MFP statistics, detailed accounting for labour and capital items need to be consistent with the National Accounts.
There are various expenditure/income items in the current price measure of value added, as listed below:

Compensation of employees;

Other taxes less subsidies (other net taxes) on production and imports;

Gross operating surplus;

Gross mixed income.
24 Laspeyres index is calculated based on aggregation using arithmetic mean. For whatever index used, it is more a matter convenience.
25 For illustrative purposes, MFP measurement on the basis of gross output is not discussed in detail.
13

There are significant technical details involved in measuring the labour and capital shares. For a detailed description, interested readers should refer to the research paper by Zheng (2005).
The ABS MFP measurement framework underpins the Australian official MFP statistics. As described previously, various assumptions must be made in relation to the methods adopted. All these underpin the productivity measurement framework. The complexity associated with this framework has been indicated in numerous ABS research papers (e.g. Wei 2011). Caution is necessary when users attempt to interpret the MFP statistics.
Recent ABS MFP statistics fuelled the productivity debate. Sometimes, the ABS results are used literally without taking a cautious approach. Limitation on the ABS productivity measures is often ignored. Clearly, this poses a challenge to the ABS for further improvement.
In the following part of the paper, some aspects relating to the current MFP measurement framework will be explored, highlighting the potential policy ramifications of each assumption adopted in the MFP measurement framework.
Labour quality is generally not considered in defining productivity. But quality adjustment has a noticeable effect (Wei 2011), showing that labour quality also drives output. At the present stage, the
ABS publishes productivity statistics with and without quality adjusted labour.
Correctly aggregating heterogeneous labour input requires disaggregating hours worked by education, work experience etc. Moreover, specific knowledge at firm level needs to be considered as well. In practice, this can be hard to do, especially at the industry level. This is still an area of active research
(see Wei 2011).
When labour quality is adjusted, the labour-embodied MFP component is removed. Without adjusting labour quality, the MFP includes this component. The same argument applies to adjusting capital quality, as will be discussed next. Therefore, adjusting labour quality enables us to measure disembodied MFP more accurately, consistent with the ABS’ strategic direction towards providing better MFP statistics.
Capital input aggregation is apparently more complicated than labour input aggregation. How to more accurately measure capital input for MFP estimates is an active research topic. It is desirable if different methods of estimating capital for different industry sectors are used. Adopting one model for all industry sectors has shortcomings. At the moment, it is difficult to examine every assumption about capital input built into the ABS MFP framework. This issue is currently under investigation in the ABS
(Wei 2011).
Similar to labour quality, capital quality also contributes to output growth. To make (disembodied) MFP measurement more accurate, capital quality adjustment is equally important. To adjust capital quality, the complexity of capital inputs aggregation must be addressed. And data availability for this improvement is also a significant issue. Only limited discussion is available on this aspect in the literature (see Jorgenson, Ho and Stiroh 2005).
14

A production unit is assumed to produce at the maximum rate as described by the implicit efficient production process (or production function). At the aggregate level, it is assumed to be operating in a competitive market, and CRS is therefore a legitimate assumption. CRS means that a unit output increases in the same proportion as labour and capital expenditure increases. This output increase is not due to ‘technological’ progress, as it only reflects an increase of production scale. They have to be removed for measuring MFP growth. 26
Apart from the output growth due to increasing production size, the following are all related to output growth:

Labour quality;

Capital quality;

Economies of scale;

Technical efficiency in production.
Coelli, Rao and Battese (2001) provided a lengthy discussion about MFP measurement. They decomposed the MFP growth into three components:
MFP growth index = (Technical efficiency index) x (Scale index) x
(‘Technological’ progress index).
If a production unit has CRS, the scale index becomes unity (maintaining the same technical efficiency). If it is at maximum production the technical efficiency index is unity.
The above decomposition suggests that caution needs to be exercised when interpreting the measured
MFP. If a production operates at a low technical efficiency level, the growth in the measured MFP can merely reflect an improvement in technical efficiency in production. At the aggregated level, little concern is necessary as, by definition, markets are assumed efficient and average operation has to be efficient.
However, even if the industry sector is competitive, individual firms can behave very differently. This has a significant ramification if any attempt is made to measure firm-level MFP. CRS cannot be applied at the firm-level. As a result, at the firm level, adopting the ABS measurement framework for individual firms is inadmissible.
Given a heterogeneous industry, the CRS assumption may apply better to some industry sectors than others.
An interesting question arises if CRS is valid at a time many industries are going through fundamental structure change? Research in this direction is interesting.
As discussed previously, the capital input aggregation is based on an asset’s productive capital accumulation. The capital input model is applied to many different industries. However, capital investment may lag behind and investment may not be utilised for production for a reasonable long period. However, under the current MFP measurement framework, this issue is not considered (see
Zheng 2005, Wei 2011).
On theoretical grounds, this can have significant ramifications on industry policy, especially for industries in which capital investment is not fully utilised in the short and medium term. For example, a strategic investment in a particular mining project may have to wait for many years for capital returns.
The consequence of under-utilisation of capital would mean less real productive capital for production, or productive capitals are overestimated. As a result, MFP growth may be underestimated. Could this have some relevance to the mining sector, in which returns to investment can take much a longer time
26 In the case of gross output, CRS needs to be expanded to include intermediates.
15

scale? More research to ascertain the impact of capital under-utilisation on MFP measures would definitely be very useful.
The ABS is currently reviewing its methodology for splitting gross mixed income into labour and capital components, in order to more accurately measure capital and labour shares. But it is so not clear if this would have a significant improvement on MFP results.
As a related question, we may ask if capital and labour shares have no relationship with capital and labour input. Theoretically, there is a relationship between them. However, due to a lack of price data, the capital share has to be worked out using income data published by the National Accounts.
‘Intangibles’ are perceived as important in driving economic growth. One approach to recognising the contributions of ‘intangibles’ to output is to regard ‘intangibles’ as a type of capital, consisting of nonmaterial things that contribute to the output of future goods and services. For example, investment to establish a brand name for a company by means of advertising, or establishing a training program for employees to increase their knowledge and skills (human capital) is an intangible capital input. Another example can be investment in improving entrepreneurship or management skills.
27 In general, all types of ‘research and development’ (R&D) investment should be regarded as intangible capital.
However, the ‘Perpetual Inventory Method’ (PIM), which is incorporated into calculations of productivity, is mainly tangible-asset based. Only measurable physical or tangible assets are counted as capital input in the existing productivity measurement framework. In the meantime, the National
Accounts also ignore most of the intangibles, even though R&D has now been included as intangible capital. Intangible inputs have been mainly considered as intermediate inputs, therefore not regarded as capital investment. As a result, official productivity statistics mainly exclude intangibles.
There are many issues which are still not clear. Early overseas research (Corrado et al. 2006) indicated that the inclusion of intangible assets in the accounting system makes a significant difference in the observed patterns of U.S. economic growth. The inclusion of intangible investment in the real output of the nonfarm business sector increases the growth rate of output per hour (labour productivity increase) by 10 to 20 per cent relative to the base-line case in which intangibles are ignored.
There are two main ways to evaluate intangibles
— financial market valuation (the difference between market values of firms and the value of tangible assets of firms) and direct expenditure-based measures of individual types of intangibles.
Based on the second measure, business intangibles can be classified into three main categories
(Corrado et al. 2006):

Computerised information (mainly computer software);

Innovative property (scientific R&D and non-scientific R&D);

Economic competencies (brand equity and firm-specific resources.
For the period between 1998 and 2000, all these capital expenditures represent about 12 per cent of
GDP. Its ratio to tangible capital spending is 1.2 per cent.
27
In simple terms, an entrepreneur is ‘a person who controls the policy of the firm’ as defined by the economist
Frederic Charles Courtenay Benham (1900-1962). See http://adb.anu.edu.au/biography/benham-fredericcharles-courtenay-5201 for more details.
16

These authors also investigated the values of output. In the 2000-03 period, intangible capitals accounts for 15 per cent of the total (US) national income, compared to 25 per cent of tangible capital items (the rest of 60 per cent is for labour compensation). These authors also found that intangibles contributed to annual labour productivity growth by 0.41 per cents, compared to a total labour productivity growth of 1.45 per cents.
In the Australian context, similar research has been carried out to investigate intangibles investment
(Barnes, Paula, and Andrew McClur 2009). They found there was $57 billion worth of investment in intangibles in 2005-06, of which only 20 per cent was treated as investment in the national accounts.
The intangibles investment grows over time (5 to 10 per cent since 1974-75). They argued that ignoring intangible assets as a source of capital services can result in bias in the estimates of MFP growth.
It is important to note that the OECD (2012a) began a twoyear horizontal project titled ‘New Sources of Growth: Intangible Assets’, aiming to provide evidence of the economic value of knowledge-based capital as a new source of growth and improve understanding of current and emerging challenges for policy. The project will be launched at events in early 2013, combined with a set of publications that will include a synthesis report for the 2013 OECD Ministerial Council Meeting.
Currently, the OECD is undertaking a project on New Sources of Growth - Knowledge-Based Capital
(KBC). The Working Party on Industry Analysis (WPIA) has been working on improving the measurement of knowledge based assets, mainly on investment in organisational capital and R&D, as well as on the depreciation of such assets (OECD 2012b).
While the importance of intangibles in driving economic growth is widely recognised, there are significant measurement issues. Apart from various complications involved in intangible outputs (for example, education output is difficult to measure), the main issue associated with intangibles is the difficult to determine amortisation (the rate of capital depreciation). The mortality rates for tangible capitals are relatively easy to measure, but not for the intangibles. Therefore the ‘Perpetual Inventory
Method’ (PIM) is hard to use for intangible capitals. For example, how long the investment for a skills training of a workforce could last is difficult to determine. It can be zero (if not relevant at all) to infinite
(if it is essential). Moreover, how to define and measure Organisation Capital (OC) remains quite unclear.
At the present stage, the ABS National Account 2010-11 (ABS Cat. No. 5204.0) includes intangible fixed assets such as R&D, computer software, entertainment, literary or artistic originals, and mineral exploration intended to be used for more than a year. Intangible non-produced assets includes such assets as purchased goodwill, 3G spectrum licences, patented entities and leases on land and subsoil assets. It is necessary to note that estimation of these assets is in its infancy. Currently only the value of 3G spectrum licences is included in the national and sector balance sheets. In addition to these initiatives, the ABS has been undertaking a human capital measurement program for some years now.
Including human capital into the productivity measurement fra mework is the ABS’ ultimate goal.
As emphasised at the beginning, our intention in writing this paper is primarily for improving communication on productivity issues, by recognising the existence of a gap between advanced arguments in productivity debates and their poor public awareness. The importance of improving this type of communication should not be underrated, because productivity debates are becoming increasingly more important today than ever in shaping industry and economic policies. It is legitimate to strive to place productivity debates on healthy ground, without over-interpreting the productivity statistics. The first thing we need to do is to raise awareness of what productivity is and how it is measured. Our work represents an initial step towards that goal.
Through our work, we are compelled to believe that non-experts and policy makers require sufficient awareness of technical details surrounding productivity so that caution can be exercised when interpreting productivity statistics. Well-informed productivity debates are of vital importance for developing better economic policies.
17

This paper was initially intended to accompany the DIISRTE Productivity Synthesis Paper as a benchmark technical reference. It may also serve as educational material for general readers for better understanding productivity or/and better using ABS productivity statistics. The paper is complementary to many ABS technical references published over time. Readers probably should not regard our work as final. Instead, it should be thought of as an evolving research work, which will be continuously improved in the future.
18

Arthur, Ha, Loris Strappazzon, and William Fisher (2001), ‘ What is the difference between productivity and profit?
’, Department of Natural Resources and Environment, Victoria, Melbourne http://www.dpi.vic.gov.au/about-us/publications/economics-and-policy-research/2001publications/difference-between-productivity-and-profit
Australian Bureau of Statistics (1989), Development of Multifactor Productivity Estimates for Australia
1974-75 to 1987-8 , Information Paper, cat. no. 5229.0, ABS, Canberra
Australian Bureau of Statistics (1994), Australian National Accounts Multifactor Productivity, cat. no.
5234.0, ABS, Canberra
Australian Bureau of Statistics (1999), Australian System of National Accounts, cat. no. 5204.0, ABS,
Canberra
Australian Bureau of Statistics (2010-11), Australian System of National Accounts, cat. no. 5204.0,
ABS, Canberra
Australian Bureau of Statistics (2010), Experimental Estimates of Industry Multifactor Productivity
2009-10 , cat. no. 5260.0.55.002, ABS, Canberra
Barnes, Paula, and Andrew McClur (2009),
Investments in Intangible Assets and Australia’s
Productivity Growth , Staff working papers, Productivity Commission, Canberra http://www.pc.gov.au/research/staff-working/intangible-investment
Barro, Robert J. and Xavier Sala-i-Martin (2004), Economic Growth , Second edition, MIT,
Massachusetts
Corrado, Carol, Charles Hulten, and Daniel Sichel (2006), Intangible Capital and Economic Growth ,
Staff working paper, US Federal Reserve Board http://www.federalreserve.gov/Pubs/feds/2006/200624/200624pap.pdf
Chiang, Alpha C. (1967), Fundamental Methods of Mathematical Economics , Sec Edition, McGraw-Hill, p405-407, Tokyo
Coelli, T., Rao, D. S. P. and Battese, G. E. (1998), An Introduction to Efficiency and Productivity
Analysis , Kluwer Academic Publishers, The Netherland
Department of Industry, Innovation, Science, Research and Tertiary Education (DIISRTE) (2009),
Innovation Metrics Framework - Consolidated Report
Jorgenson, D.W., 1996, Technology in growth theory, in J.C. Fuhrer and J. Sneddon Little, eds,
Technology and Growth, Conference Proceedings, Federal Reserve Bank of Boston, Boston, MA,
USA, pp. 45 –89.
Jorgenson, Dale W., Frank M. Gollop, and Barbara M. Fraumeni (1999), Productivity and U.S.
Economic Growth , Harvard University Press, Cambridge, MA
Organization for Economic Co-operation and Development (OECD) (2001), OECD Productivity
Manual: A Guide to the Measurement of Industry Level and Aggregate Productivity Growth , Paris,
OECD
Organization for Economic Co-operation and Development (OECD) (2009), Measuring Capital OECD
Manual , Second Edition, Paris, OECD
Organization for Economic Co-operation and Development (OECD) (2012a), New Sources of Growth:
Knowledge-Based Capital Driving Investment and Productivity in the 21st Century, Paris, OECD http://www.oecd.org/science/innovationinsciencetechnologyandindustry/50498841.pdf
Organization for Economic Co-operation and Development (OECD) (2012b), Working Party on Industry
Analysis: Measuring knowledge based capital: initial findings, CIIE Meeting, 25-26 October 2012
19

Reilly,
R., W. Milne and S. Zhao (2005), ‘
Quality-adjusted Labour Inputs
’,
Australian Bureau of
Statistics Research Paper, cat. no. 1351.0.55.010, ABS, Canberra
Solow, Robert M. (1956), A Contribution to the Theory of Economic Growth , ‘Quarterly Journal of
Economics’, 70, February, p65-94
Solow, Robert M. (1957), Technical Change and the Aggregate Production Function , ‘Review of
Economics and Statistics’, 39, August, p312-320
Stiroh, Kevin J., (2002), Information Technology and the U.S. Productivity Revival: What Do the
Industry Data Say?, American Economic Review, American Economic Association, vol. 92(5), p 1559-
1576, December http://ideas.repec.org/e/pst41.html
Wei, Hui (2011), The ABS Productivity Measurement Program , Australian Bureau of Statistics
Research paper, ABS, Canberra
Wei, Hui and Penfei Zhao (2012), The Industry Sources of Australia’s Productivity Slowdown,
Presentation at RBA , ABS, Canberra
Zheng, Simon (2005), Estimating Industry Level Multifactor Productivity: Methods and Experimental
Results, Australian Bureau of Statistics Research paper, cat. No. 1351.055.004, ABS, Canberra
20

Assuming output
O ( X
1
, X
2
,...
X n
)
can be modelled by n inputs X , productivity measured against
X can be defined as i
P i
( X
1
, X
2
,..., X n
)

O ( X
1
, X
2
,..., X n
) / X i
(A1)
Each input X can either be single or combined input. The productivity has a unit of output per unit input
X
. i
For labour productivity, X i

L , which measures average output in labour hours – unit output per working hour. If measured by capital inputs, X i

K , the productivity becomes the ‘capital product ivity’, for example, the average number of chicken produced per square meter (over a period of time). All these measures represent a particular perspective on the efficiency of production.
When a combined input is considered, say,
C

C ( X
1
, X
2
,...
X k
), k
 n
, the productivity can be defined as
P c
( C ( X
1
, X
2
,..., X k
), X k

1
, X k

2
,..., X n
)

O ( C , X k

1
, X k

2
..., X n
) / C ( X
1
, X
2
,..., X k
) .
(A2)
Productivity thus defined measures the average output against the combined input – average output per unit combined input. This productivity is a function of other inputs.
21