Productivity, Profitability and Industry Good Activities

Productivity, Profitability and Industry Good Activities

Report to Dairy Insight

2 February 2007

2 February 2007

Preface

NZIER is a specialist consulting firm that uses applied economic research and analysis to provide a wide range of strategic advice to clients in the public and private sectors, throughout New Zealand and Australia, and further afield.

NZIER is also known for its long-established Quarterly Survey of Business Opinion and Quarterly Predictions.

Our aim is to be the premier centre of applied economic research in New Zealand. We pride ourselves on our reputation for independence and delivering quality analysis in the right form, and at the right time, for our clients. We ensure quality through teamwork on individual projects, critical review at internal seminars, and by peer review at various stages through a project by a senior staff member otherwise not involved in the project.

NZIER was established in 1958.

Authorship

This report has been prepared at NZIER by Brian Speirs and reviewed by Dr Jagadish Guria. The assistance of David Wright (Dairy Insight) and Dan Coup (Burleigh Evatt) is gratefully acknowledged:

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Executive Summary

General Summary • Productivity is a simple concept, but:

− partial productivity measures can be misleading

− total factor productivity measures are difficult to understand

− it is questionable whether productivity growth of 4 per cent per annum over the last 10 years would be physically achievable

• Profitability is the central goal of farm businesses

• Productivity is only one component of profitability

• Industry good activities are only partially focussed on industry profitability

• Even where profit is the focus of industry good activities, the linkage is loose – farmers must “adopt” the technology

• Industry good activities are best measured as a portfolio of projects

Industry Good Activities

Industry good activities generally fall into two categories:

• Activities that are beneficial to the industry, but whose benefits cannot be captured by those who fund or provide the activity

• Long term investments in the industry made with the expectation of accelerating delivery of better technology and products to the industry.

Productivity

What is productivity?

• Productivity is a measure of efficiency

• Partial productivity (e.g. milksolids per hectare) measures are easy to calculate and understand but do not provide the full picture. For example, improved productivity in one area may be offset by lower productivity elsewhere

• Total factor productivity (TFP) captures all components but users do not understand the calculation or the numbers produced.

The McKinsey Target

• The language used by McKinsey’s suggested a value based productivity measure. However, the underlying meaning of “real” measurement is that values are being converted to quantities

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• While McKinsey’s suggested a 4 per cent target, in practice it appears that this was broken into 3 separate targets (cows, land, and costs), each of 4 per cent

• The measurement of productivity in each of these areas appears to use a series of partial productivity measures.

Productivity measures

• The Dexcel TFP calculation is a measure of physical efficiency. This physical efficiency does not have a close relationship with the profit imperative inherent in commercial operations such as dairy farms

• The setup of the Centre of Excellence (COE) envisaged use of a value based productivity measure

• Calculation of a value based productivity measure showed greater volatility than the physical productivity measure calculated by Dexcel.

Inclusion of capital into TFP calculations

• Inclusion of capital as an input into TFP calculations poses serious challenges

• In particular, land values are correlated with output values. Therefore, an increase in output values will tend to automatically increase input values

• Land is an asset in its own right, and may change value for reasons totally unrelated to the production process

• A generalised methodology for calculating a value-based productivity measure is proposed, but not explored. Other options are to exclude land from productivity calculations completely, or to consider land productivity as a separate measurement.

The 4 Per Cent Productivity Target

• Productivity growth of 4 per cent per annum over 10 years implies that outputs need to increase by 48 per cent over and above any increase in inputs

• Production of milksolids per hectare have increased at an annual rate of 2.4 per cent over the last 10 years, but costs per hectare have increased faster (+3.1% p.a.)

• If the overall aim of productivity gains is to reduce the unit costs of milk production, then a 4 per cent productivity gain over the last 10 years implies that milksolids per hectare in 2004-05 would have needed to be 51 per cent higher than the highest recorded figure (2003-04) without any increase in costs over those actually incurred in 2004-05

• It is questionable whether the current land area and fertiliser inputs would be sufficient to meet the energy demands of producing this extra volume of milk. Is it physically possible to achieve this level of productivity?

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The On-Farm Situation • The farm actually encompasses several “business units” with each of

these having different outputs. Profit is a key output, but has varying degrees of importance to each business unit

• Profit cannot be targeted directly – it is achieved by targeting its primary components (revenue and expenditure) and sub-components

• Productivity is explicitly identified as a sub-component of profit generation

• Revenue is strongly affected by the exchange rate. About half of the change in dairy payout can be explained by movements in the exchange rate as measured by the Trade Weighted Index

• Dairy industry good activities are largely targeted at a number of areas identified as being of relevance to the ultimate generation of profit. Some of these are “active” measures (e.g. new technology) some are “insurance” functions (e.g. bio-security), while others are “enabling” (e.g. industry training).

Measuring On-Farm Performance • A set of characteristics of ideal performance measures is defined

• It is unlikely that one measure will answer all questions

• A set of performance measures is proposed. Each performance measure was a “partial” measure meaning that, on its own, it does not capture the whole picture. However, taken as a group, a reasonable picture emerges

• The measures do not include any measurement of capital growth.

Performance of Industry Good Activities • Not all industry good activities directly affect on-farm performance

• The benefits of industry good activities have to be voluntarily adopted by industry participants

• There are difficulties associated with the measurement of the benefits arising from industry good activities

• Measurement of the worth of activities should be project based

• Ideally, all projects should be measured. This will provide the range of successes achieved by industry good activities. Performance can then be assessed on the basis of the whole portfolio

• Failure of individual projects is to be expected. However, excessive failure rates would be cause for examination of project selection and monitoring processes

• Insurance functions should be assessed on their costs relative to the protection given to the industry

• Other activities should also be assessed on their individual costs and benefits.

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Contents 1. Introduction...............................................................................................1

2. Industry Good ...........................................................................................1

2.1 What are Industry Good Activities ..........................................................1

2.2 What are the Dairy Industry Good Activities? .........................................3

3. Productivity...............................................................................................5

3.1 What is Productivity? ..............................................................................5

3.2 The McKinsey Productivity Target ..........................................................5

3.3 The Dexcel TFP Calculation...................................................................6

3.4 The Centre of Excellence TFP Model.....................................................9

3.5 What is driving the industry? ................................................................13

3.6 The Heart of the Issue ..........................................................................16

3.7 The 4 Per Cent Productivity Target ......................................................18

4. The On-Farm Situation ...........................................................................20

4.1 What is Important On-Farm? ................................................................20

4.2 Drivers of profitability ............................................................................21

4.3 Industry Good Activities and Profit Drivers ...........................................24

5. Measuring farm performance ................................................................25

6. Measuring Industry Good Activity ........................................................29

6.1 Industry Good and On-Farm Performance ...........................................29

6.2 General Measurement Difficulties.........................................................29

6.3 Measuring On-Farm Related Activities .................................................31

6.4 Measuring Non-Farm Related Activities ...............................................32

7. Conclusions ............................................................................................33

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Figures Figure 1 Outcomes Hierarchy for New Zealand Dairy Industry.......................... 4

Figure 2 Profit Maximisation and Productivity .................................................... 8

Figure 3 Land Price and Profitability Trends.................................................... 13

Figure 4 Relative Shares of TFP Inputs........................................................... 14

Figure 5 Elements of Profit .............................................................................. 22

Figure 6 Milksolid Prices and Exchange Rates................................................ 23

Figure 7 Partial Productivity Measures ............................................................ 27

Figure 8 Trends in Production Costs ............................................................... 27

Figure 9 Trends in Revenue and Expenditure ................................................. 28

Figure 10 Establishing a Baseline.................................................................... 30

Tables Table 1 Value Based Productivity Calculation ................................................. 11

Table 2 Value Based Productivity Calculation (2) ............................................ 12

Table 3 Effect of 4 Per Cent Productivity Growth............................................. 19

Table 4 Outputs of Business Components ...................................................... 20

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1. Introduction In October 2006, the Fonterra Shareholders Council called for a review of Industry Good Activities in the dairy industry. The expressed rationale for the review was that “the current structure is not reaching targeted levels of performance”1. Specifically, “on-farm productivity gains over recent years have been less than 1% - well below the industry target of 4%”.

The origin of the 4 per cent productivity target was in a study of the dairy industry by McKinsey & Co in the late 1990s. This subsequently led to the target being adopted by the industry and incorporated into their Strategic Framework2.

This paper looks at the rationale for industry good activities, and provides a brief overview of the industry good activities undertaken in the dairy industry.

As productivity has been identified as the key issue in the rationale for the review, the concept of productivity is examined. Several approaches to measuring productivity are examined with particular emphasis given to the problems associated with including capital as an input to the productivity measure. Finally, the implications of 4 per cent productivity growth was examined in terms the performance of the industry over the last 10 years.

The relationship between profit and productivity is examined, followed by an examination of the drivers of profit on-farm. The characteristics of a good performance measure are then considered, and a series of performance measures are proposed.

Finally, the issue of measurement of industry good activities is then considered.

2. Industry Good

2.1 What are Industry Good Activities

General economic theory starts from the premise that firms have profit maximisation as their primary motivation. A firms actions are then dictated by the degree of profit they can realise from that action. A key component of this model is the necessity for firms to be able to capture the benefits of their actions. For example, an application of fertiliser will grow more grass for a farmer. 1 “Council Calls for Review of Industry Good”, Press Release, Fonterra Co-operative Group Ltd, 24

October, 2006. http://www.scoop.co.nz/stories/BU0610/S00451.htm 2 http://www.dairyinsight.co.nz/downloads/Strategic_Framework_II_2005_2015_v1_13.pdf

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Expanding on this, there are investments that would be worthwhile for firms, but are simply too large for a small business to contemplate. In this situation, firms can club together to jointly provide the service to themselves. For example, artificial breeding services are beyond the capabilities of any individual dairy farmer, but with all dairy farmers grouped together, the investment makes good economic sense. Note that in this example, the acquisition of the benefits only comes through participation in the scheme, so there is still a linkage between individual costs and benefits.

There is another category of investment faced by small businesses in a large industry. These are investments where there is no direct linkage between those making the investment and those capturing the benefit. For example, collection and dissemination of information about the industry is seen as an essential industry function. However, the direct benefits to each individual firm are small, and left to their own devices, the individual business owners would not invest in the activity despite their acknowledgement that the function is necessary for the industry as a whole to prosper.

This type of investment is categorised as an “Industry Good” because the benefits are spread across the entire industry irrespective of who bears the costs. The fundamental problem here is “free riding” – if payment for these activities is voluntary then some people will choose not to pay. In turn, this means that either other people will need to pay more, or the service will not be provided at all. This is why industry good activities are usually funded by means of a compulsory levy – albeit with the requirement that levy payers periodically approve the levy payment.

Industry good in New Zealand industries often have another type of activity – research. The objective of this activity is to provide the basic scientific knowledge which can then be developed into products beneficial to the industry. This is a long-term investment in the future of the industry. The industry funds this research because the science is not sufficiently developed for a commercial firm to invest in it – but it is of potential future value to the industry. By developing the basic scientific knowledge, the industry hopes to provide the benefits of that knowledge sooner than would occur without the research investment.

To summarise, industry good activities are:

• activities that are beneficial to the industry, but that would not otherwise be carried out because the benefits cannot be captured by those who fund the activity

• long-term investments in the industry made with the expectation of accelerating delivery of better technology and products to the industry.

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2.2 What are the Dairy Industry Good Activities?

Dairy Insight is the funder of Industry Good activities for the dairy industry in New Zealand. Its role is to set out the strategy for industry good activities, and then utilise the levy it obtains from dairy farmers to fund appropriate activities.

The overall strategy defined by Dairy Insight is contained in the document “Strategic Framework for New Zealand’s Future Dairy Farming and Industry: 2005-2015”3. This defines the strategic areas which Dairy Insight works in. These are:

• Production

• Business and human resources

• Community interface

• Product value

• Operational capability

Figure 1 overleaf shows an outcomes hierarchy for a NZ dairy farm. The key requirement in this hierarchy is assumed to be profit on the dairy farm. This is then decomposed into functional areas. The shaded areas are those where Dairy Insight is funding activities. These activities are covered by the first four strategic areas outlined above. The fifth strategic area operates largely at the industry level rather than the farm level.

Following the hierarchy down each chain, it can be seen there are several areas where Dairy Insight is not active. These are: climate, exchange rates, and input prices. Given that Dairy Insight would not be expected to have any success in influencing these areas, this lack of activity is to be expected.

3 http://www.dairyinsight.co.nz/downloads/Strategic_Framework_II_2005_2015_v1_13.pdf


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Figure 1 Outcomes Hierarchy for New Zealand Dairy Industry

Profit

Revenue Expenditure

Production Prices

Feed AnimalGenetics

Biosecurity PlantGenetics Climate

ProductValue

OffshorePrices

ExchangeRate

InputQuantities

InputPrices

Technology ManagementSkills

BiosecurityEnvironment

Animal Welfare

ResourceAvailability

GeneticsFarm systemsMilk harvesting

Business planningHuman resources

Human resourcesInfrastructure

Source: NZIER


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3. Productivity

3.1 What is Productivity?

At its simplest level, productivity is the relationship of the outputs of a process to the inputs to the process. This may also be termed efficiency.

Typical expressions of productivity are of so many units of output per unit of input. Therefore, milksolids per cow or milksolids per hectare are expressions of productivity.

Technically, such expressions are termed partial productivity measures because they measure the output in terms of only one input. Use of partial productivity measures may give false impressions of industry improvement as one input gets substituted for another.

For example, a farm that grazes its heifers and dry cows on sheep and beef farms, and/or purchases significant quantities of feed, can have substantially higher milksolids per hectare figures than a farm that does not follow these practices. The farm following those practices has actually used more inputs to generate its outputs, but those inputs have not been included in the productivity measure used.

To provide a complete measure of productivity, total factor productivity (TFP) is calculated. This assesses the efficiency of producing all outputs against the usage of all inputs used in producing the outputs.

In the example given above, the input of purchased feed would be included as an input into the milk production process, as would the feed cost of grazing the heifers and dry cows off farm. This would enable productivity to be calculated across the overall production process.

3.2 The McKinsey Productivity Target1

McKinsey suggested adoption of a “4% real productivity target across entire value chain”. The rationale for this target was that this was enough to stay ahead of real price declines and competitors, that other countries were achieving this growth in dairy, and that other industries were exceeding this level of productivity growth.

There are a number of ambiguities in the McKinsey presentation:

“Real productivity” is not defined. Two constructions of this can be envisaged: 1 Information in this section comes from a McKinsey PowerPoint presentation “Driving On-Farm

Productivity” dated 18 June 1999


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• The price effect can be taken out of each individual component using a deflator specific to the component. This is the approach taken by the Dexcel TFP model.

• The price effect can be taken out of the aggregated components using an overall deflator. This expresses the inputs and outputs in constant dollars, which – provided the deflator is appropriate – could be said to be an aggregate volume measure

It is not clear whether there is a major difference between the above constructions, but the second approach gives scope to use a deflator that is unrelated to the inputs or outputs being deflated. Overall, it is clear that either construction returns inputs and outputs to quantitative terms – even if they are expressed in value terms.

McKinsey split productivity into three components – cow productivity, land productivity, and cost productivity. Their international comparisons used these split measures of productivity and did not try to amalgamate these to a single measure of overall productivity.

Further, the examples of the “highest” productivity gain for these measures each came from a different country. Argentina had the highest productivity growth for cows, California for land, and Brazil for costs. It is unclear whether any single country exhibited uniformly high rates of productivity growth across all three measures.

The methodology to measure the productivity growth in each of these areas was not defined. However, the “economic levers” for each of these measures consisted of a number of partial productivity measures such as milksolids per cow, cows per hectare, labour costs per cow, and inputs per cow.

In summary:

• While the language used suggested a value based productivity measure, the underlying meaning of “real” measurement is that values are being converted to quantities

• While McKinsey’s suggested a 4 per cent target, in practice it appears that this was broken into 3 separate targets (cows, land, and costs), each of 4 per cent

• The measurement of productivity in each of these areas appears to use a series of partial productivity measures.

3.3 The Dexcel TFP Calculation

As Dexcel is accountable for the industry target of 4 per cent productivity growth (as written into the Dexcel Deed of Trust), Dexcel commissioned a consultant to create a software package that would allow Dexcel to calculate the actual level of productivity and productivity growth in the industry.


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In general terms, the model works as follows:

• The primary inputs into the model are the results of the Dexcel Economic Survey of New Zealand Dairy Farmers. This information is supplemented by cost and price indices from Quotable Value NZ and Statistics NZ.

• Inputs of capital and labour are calculated from the data collected.

• The output and input items are ‘deflated’ to constant dollar terms. This is a critical step in the overall process, as the intention and effect of this action is to convert the inputs to a QUANTITY basis.

• The derived quantity series are then indexed to a nominated base year.

• The indexes are then weighted together to produce an overall index of outputs, and an overall index of inputs.

• The ratio of the output index to the input index is the productivity index, and the percentage change between one year and the next in the productivity index is the productivity growth.

The above shows that the calculation of the productivity index is a relatively simple process. Nevertheless, there are a number of concerns about the process. These concerns can generally be grouped into two categories:

• general lack of understanding of the calculation and the result

• specific concerns (whether justified or not) about the way certain inputs are measured and valued in the index.

This paper will only concern itself with the general issues, as any specific issues are beyond the scope of this paper.

Much of the general lack of understanding of the model stems from the deflation of inputs and outputs to constant dollar terms. This step was highlighted as being critical in the process outlined above.

Some of the implications of this step are (in general):

• Changes in the milk price have no direct impact on productivity (but there could be indirect effects if the price change affects the quantity of inputs used or the duration of milking)

• Increases in land value have no impact2 on productivity

• Increases in dairy company share values3 should have no impact4 on productivity. However, increases in numbers of shares should be counted as an increased capital input.

2 In the long run, high land prices will encourage efficient utilisation of the land resource. However,

in the short run, a change in land price does not impact on farmer behaviour. 3 This is a confusing concept for capital because both quantity and price can be expressed in the

same terms (dollars). For dairy company shares, it is best to think of the quantity as the number of shares and the price as the share price. This makes it clear that a change in the share price has not increased the number of shares (quantity of share capital) owned by the dairy farm.


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The underlying principle in these implications is that the productivity index is calculating a ratio of the physical outputs of dairy farming relative to the physical inputs. Changing the price of an input or output does not in itself change the physical quantity of that input or output.

This highlights one of the major problems with the current productivity index in relation to the on-farm situation – that is prices, and therefore profitability, are fundamentally disconnected from productivity.

Further, using some basic economic analysis, we can show that profit maximising behaviour will always be acting in a manner contrary to productivity maximisation. This is shown in the Figure below:

Figure 2 Profit Maximisation and Productivity

Out

put V

alue

/ In

put C

ost

Input QuantityIaIb

Oa

Ob

Productionfunction

CC

A

B Input cost

Source: NZIER

The production function shows the relationship between the quantity of a given input (e.g. fertiliser) and the value of the output produced. The essential characteristic of this function is that the additional quantity (hence value) of the output produced declines as more of that input is used. This is referred to as the law of diminishing returns.

4 Originally, the deflator used in the Dexcel model was inadequate for this task and allowed some of

the value change to be counted as a quantity change. It is not clear whether this has been corrected in the latest publication or not.


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The theory of profit maximisation says that a firm will continue to increase its use of any input until such time as the marginal (additional) revenue generated by the use of that input is equal to the marginal cost of using that input.

Profit is maximised where the slope of the total revenue curve (represented by the production function) is equal to the slope of the input cost line (line CC has this slope). The slopes of the two lines are equal at point A on the production function, indicating that profit maximisation occurs at input quantity Ia, and output value Oa.

Consider now whether point A represents the most efficient use of the input. If we compare point A with point B, we can see that point B uses less than half the input quantity, but produces about 80 per cent of the output value relative to point A. Clearly, point B is a much more efficient use of the input than point A.

We could repeat this process using another lower level of input, and would conclude that each lower input level is more productive than using a higher level of inputs. Note this is a partial analysis of productivity where all other inputs remain fixed. In practice, this fixing of other inputs may not occur, but the analysis remains useful to demonstrate principles.

This demonstrates the fundamental disconnect between productivity – the most efficient use of resources in quantitative terms – and profitability – what businesses actually aim to achieve.

3.4 The Centre of Excellence TFP Model

3.4.1 Value Based Productivity

The current productivity target was set during the restructuring of the dairy industry in 2000. The papers of the time that set up the Centre of Excellence (COE) defined the productivity measure to use. This was:

inputspriceoutputspriceTFPValue ×

×=

If we express the actual calculation of TFP used by Dexcel in this manner, it would be:

inputsoutputsTFPQuantity =

The Dexcel calculation is strictly a measure of physical efficiency – the quantity of outputs as a function of the quantity of inputs. In contrast, the measure actually set out by the documents setting up the COE envisaged a VALUE based measure of efficiency.


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The papers further envisaged that an analysis of specified factors would accompany the calculation of the productivity index. Factors specified in those papers were:

− inflation

− exchange rate and milk prices

− land price on the capital input costs and the Weighted Average Cost of Capital (WACC) used

− climate (drought or other extreme event) on milk production and production costs

− other extreme events (disease outbreak)

Once again, the first three points identified in this list clearly suggest use of a value based productivity index – and as noted previously, none of those three factors will (directly) influence the current productivity index.

3.4.2 Calculating TFP on a Value Basis

In the sections that follow, productivity is discussed in terms of both value and quantity. The identifiers (V) and (Q) are used to make it clear which measure is being discussed.

The following table shows the results of the productivity calculation using the methodology5 outlined in the papers setting up the COE. The source data for this calculation is the Economic Survey of New Zealand Dairy Farms published by Dexcel. This is supplemented by interest rate data from the Reserve Bank of New Zealand.

The calculations shown exclude non-farm items from income, expenditure, and assets. However, it includes non-dairy activities carried out on the farm. This is because the TFP (V) calculation “is targeted at the ‘Farm Business’ not just the dairy farming enterprise”.

Most of these numbers can be lifted directly from the Economic Survey. The only number that requires any significant calculation is the ‘Cost of Capital’. In 2004-05, this was calculated using a total capital figure of $3.2 million at a WACC6 of 9.9 per cent.

Calculated in this manner, inputs into the dairy farm are significantly higher than are recorded in the farm accounts. This is because input values are

5 Except that Weighted Average Cost of Capital is calculated using the pre-tax cost of debt rather

than an after-tax figure. This is because the WACC (using after tax cost of debt) is normally used to discount cash flows coming out of firm. In this case, we are using the WACC to calculate an input cost internal to the firm, therefore the cost of debt should be the pre-tax cost.

6 See previous footnote.


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attributed to both equity and owner labour – neither of which appears in the farm accounts.

Table 1 Value Based Productivity Calculation Owner-Operators: Excluding capital growth

2000-01 2001-02 2002-03 2003-04 2004-05

Total Farm Outputs 457,576 475,722 402,696 446,473 476,359

InputsOperating expenses 146,571 152,511 166,813 183,755 200,175Overheads 86,745 86,001 93,346 104,185 109,358Labour (Owner) 57,065 56,605 60,403 59,623 56,885Cost of Capital 179,456 162,159 234,046 249,596 315,310Total Farm Inputs 469,837 457,276 554,608 597,159 681,728

"Profit" -12,261 18,446 -151,912 -150,686 -205,369

Productivity (V) 0.974 1.040 0.726 0.748 0.699Productivity % change 22.2% 6.8% -30.2% 3.0% -6.5%5-Year rolling average 11.6% 13.0% 2.0% 2.2% -2.3%

Published productivity (Q) % change -1.3% 1.8% 1.5% 1.7% -3.2% Source: NZIER

On this basis, the average owner-operator dairy farm has made “losses” from 2002-03 onwards – that is, the value of the farm outputs have been less than the total value of its inputs. Consequently, the productivity (V) index figure has been less than 1.0 from 2002-03 onwards.

More importantly, the productivity (V) change for 2004-05 was measured at -6.5 per cent, and the rolling 5-year average productivity change was -2.3 per cent.

Comparison of the annual productivity (V) change calculated in Table 1 with the published productivity (Q) change shows significant variation. In 2000-01, the value-based productivity figure increased while the published (physical) productivity figure declined. This situation reversed in 2002-03, while in other years in the series, both figures went in the same direction. The magnitude of the numbers is significantly different.

Overall, it is best to consider these productivity changes as “unrelated”. They measure different things and give different answers.

3.4.3 Inclusion of capital growth

Some papers published at the time the COE was set up indicated that the change in net worth of the farming business would be part of the productivity measurement. Other material makes no mention of this.

If capital growth was included in the productivity (V) measurement, then the productivity calculation could be as shown in Table 2:


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Table 2 Value Based Productivity Calculation (2) Owner-Operators: Including capital growth

2000-01 2001-02 2002-03 2003-04 2004-05

Farm outputs 457,576 475,722 402,696 446,473 476,359Capital growth 240,920 197,544 51,776 233,356 554,321Total outputs 698,496 673,266 454,472 679,829 1,030,680

Farm inputs 469,837 457,276 554,608 597,159 681,728

"Profit" 228,659 215,990 -100,136 82,670 348,952

Productivity (V) 1.487 1.472 0.819 1.138 1.512Productivity % change 48.3% -1.0% -44.3% 38.9% 32.8%5-Year rolling average 28.5% 27.6% 9.3% 4.2% 8.6%

Published productivity (Q) % change -1.3% 1.8% 1.5% 1.7% -3.2% Source: NZIER

Examination of this table reveals several points:

• Capital growth forms a large part of the total outputs in the period shown. In fact, in the 2004-05 year, capital growth is the dominant output

• The farm makes a “profit” in all but one of the years shown

• Individual year productivity (V) fluctuates significantly

• The 5-year productivity (V) change reduces (but does not eliminate) this volatility

• The most recent 5-year productivity (V) change shows average productivity growth of 8.6 per cent.

This final point highlights the dichotomy of farming in general – if growth in asset values is excluded, farm productivity (V) is poor (-2.3% over the most recent 5 years). On the other hand, if the increase in asset values is included, then productivity (V) growth is good (+8.6%).

In turn, this highlights the divergence of dairy land prices from the underlying profitability of the dairy farming business. This is shown in Figure 3.

This chart has not been adjusted for inflation. Overall, it shows that dairy farm profit per hectare in 2004-05 was little different than it was in 1988-89, even in nominal terms. However, in the same period, land prices per hectare increased to 3.7 times their 1988-89 values.


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Figure 3 Land Price and Profitability Trends 1988-89 = 1000

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

1988-89 1992-93 1996-97 2000-01 2004-05

Profit index

Land priceindex

Source: NZIER

Based on Dexcel data

This divergence poses a number of questions:

• Why did the dairy industry expand so much during this period if profit (per hectare) was not increasing?

• What has driven the land price increase if not profit?

• How can the industry achieve the targeted productivity (V) gains if the land price keeps increasing?

These questions will be considered in the following section.

3.5 What is driving the industry?

3.5.1 Why has the dairy industry expanded?

The dairy industry has expanded for two fundamental reasons. Firstly, while its underlying profitability was not increasing, it was more profitable than the alternative land uses. Therefore, farms were converted from other land uses into dairy production.

Secondly, farms became larger, meaning that the profit per farm (and per farm family) was increasing, even if profit per hectare was remaining (relatively) stable. This was made possible by improved milking technology (along with the accumulation of knowledge on managing larger herds and farms), allowing more cows to be milked per person.

This second point is interesting in the context of the issues surrounding productivity. Total factor productivity (V)(excluding capital growth) measures are little different in 2004-05 from their 1988-89 levels, but profits per farm are well up (+73%).


2 February 2007

This raises the question of whether farm owners themselves have been operating on a partial productivity basis – i.e. focusing on profit per farm rather than profit as a function of total resource usage.

Or are they counting on the long-term appreciation of land prices over and above the rate of inflation (coupled with the fact that this gain is tax-free) to ultimately compensate them for the low rate of cash generation relative to asset size?

3.5.2 What has driven the land price increase?

Land price is fundamentally driven by the demand for land ownership, especially in an environment where the supply of land is fixed or declining. However, this is not a particularly useful answer.

The total factor productivity analysis actually gives us some guidance here. Table 1 groups the farm inputs into 4 broad areas – working expenses, overheads, labour7, and capital. The relative size of these 4 groups is shown in the following chart:

Figure 4 Relative Shares of TFP Inputs Per cent

0%

10%

20%

30%

40%

50%

60%

1988-89 1992-93 1996-97 2000-01 2004-05

Operating expensesOverheadsLabour (Owner)Cost of Capital

Source: NZIER

In general terms, operating expenses have increased their share of farm inputs, labour has declined, while overheads have remained stable. Capital usage has fluctuated – it was high in the late 1980s due to high interest rates, and fell in the early 1990s as interest rates declined. Capital usage increased in the mid-1990s as land prices and interest rates increased, and then fell in the late 1990s as interest rates fell again. Finally, capital usage has increased in the 2000s as land prices and interest rates have increased.

7 Note that working expenses still retains employed labour. The labour element identified here is

only the owner labour.


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The key point to note from this pattern is that on average, capital has made up around 45 per cent of the inputs into the TFP (V) calculation (when calculated using a WACC basis) over the last 16 years. Even if farmers do not explicitly think of capital inputs in this manner, their implicit behaviour supports a capital input of around 45 per cent into their farming operation. If the capital proportion exceeds this (either due to increases in land prices or interest rates, or decreases in other inputs due to declining output prices), then land price will remain stable or decline. If the proportion of capital in the inputs is less than average (due to reductions in interest rates, or increases in other inputs due to increasing output prices), then land prices will tend to increase.

In short, farmers have an implicit view of the amount of capital required in their business relative to other inputs. If the cost of capital declines, then farmers will pay more for land until the capital share of inputs into their farming operation approximates that implicit view.

Following this theory, the WACC was 18.76 per cent in 1988-89, and fell to a low of 8.76 per cent in 2003-04. On its own, this would support land prices more than doubling over the period, before any account is taken of increased usage of other inputs, increased labour efficiency (both affecting input shares), and any other factors affecting land prices (such as demand for lifestyle blocks).

Capital made up 48 per cent of inputs in 2004-05. This suggests that land prices would have started to come under some downward pressure in 2004-05, as this was over the historic average usage of capital. This pressure will have increased further in 2005-06 and 2006-07 due to further increases in interest rates. A return to lower interest rates would relieve some of this pressure.

3.5.3 Effect of Land Price on Productivity Targets

At this point, it is worthwhile returning to first principles – a productivity (V) increase will be observed when the value of the farm outputs increase at a faster rate than the value of the inputs. This statement assumes that we are using a value based productivity measure.

Capital (land) simply represents one of the inputs into the productivity (V) measure, while the productivity (V) measure relates all outputs to all inputs. Therefore, we would normally8 expect an increase in land prices to flow through as an increase in the value of total inputs.

On this basis, we would expect increases in land value to be reflected in lower productivity (V) measures, unless output values increase to compensate. 8 However, if interest rates are falling, then this may not occur.


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3.6 The Heart of the Issue

The inclusion of capital as an input into the productivity (V) measure poses some serious challenges.

The key issues are that:

• the price of land has significant correlations to the value of the farm outputs, and to interest rates in the economy

• land is not simply an input into the production process, but an asset in its own right.

3.6.1 Land price correlated with other productivity factors

If the price of milk goes up, land prices will tend to increase as well. This is on the basis that higher milk prices will generate higher profits, making land more affordable.

The productivity (V) consequences of this relationship are that an increase in the value of the farm outputs will automatically generate an increase in the value of the farm inputs. Depending on the size of the change in land prices, observed productivity (V) could go up, remain the same, or go down.

Contrast this with a factory producing an industrial product. If the price of the product goes up, the value of the factory outputs would increase. But we would not expect an automatic increase in the land value of the factory (or the rental equivalent). We would, however, expect the value of the firm to increase (i.e. share value or goodwill).

This neatly identifies the problem faced by the dairy farm productivity measures – the land price encapsulates two components. The first is a genuine input into the farming operation, while the second is a valuation of the farm business.

Of course, a business is still expected to make a financial return on its valuation, but the calculation of that return does not involve itself with the minutiae of the production process. Typically, the measure used is profit (or some variation thereof) expressed as a percentage of assets or equity.

This type of measure can be viewed as an “external” measure of the business profitability. In contrast, the productivity (V or Q) measures are “internal” measures of the business process efficiency.

3.6.2 Land as an Asset

There are many classes of assets. Productive assets and financial assets are expected to make a measurable return on their value in each accounting period.


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Some assets such as precious metals, art and jewellery act as stores of wealth. However, these classes of asset can also change their value significantly.

Land is usually considered an asset class in its own right. It has characteristics from both of the two groups of asset classes just described. It is expected to make a measurable return on its value due to its nature as a necessary input into primary production processes. However, it also acts as a store of wealth, and can change or retain its value for reasons totally unrelated to its ability to generate revenue.

3.6.3 Productivity Implications

These characteristics of land mean that:

• increases in output values will not be fully reflected in productivity (V) measures because of the circularity between output values and land prices. Indeed, productivity (V) may go down – especially when output values subsequently fall while land prices remain at their higher level

• land price changes that are unrelated to farming activity will cause productivity (V) to fluctuate for reasons totally beyond the control of the farmer or any industry organisation.

This means that while measurement of productivity (V) is a good idea, the inclusion of land values in the calculation brings a large number of problems with it.

Choices available at this stage are:

• exclude land completely from the productivity (V) calculation

• exclude land from the productivity (V) calculation, but consider land productivity as a separate measure (as implied by the McKinsey presentation)

• devise a way to sensibly include land into the productivity (V) calculation.

One method of including land in the productivity (V) calculation could be:

• Land should be recognised as having 3 separate values – an input into the production process, a business value, and a “pure asset” value that is independent of the production process and the business.

• Proxies should be developed to estimate the input and business values of the land. The “pure asset” value could then be the residual of the actual land value after deduction of the input and business values.

• The input value could then be applied to the productivity (V) calculation.

• The business value could be applied to a measure of return on assets or equity.


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• Changes in business profitability would be expected to be reflected in a change in the business valuation of land.

• Changes in land values over and above what can be accounted for by changes in the business value would be a function of the pure asset component.

Expanding further on this is beyond the scope of this paper, but gives some ideas for devising a more sensible productivity (V) measure for the dairy industry.

It should be noted in passing that the issues surrounding land values are not unique to the dairy or pastoral industries. Similar increases in land values have occurred in the commercial and residential property markets. This has had the effect of reducing yields in the commercial property market, and reducing housing affordability in the residential market.

3.7 The 4 Per Cent Productivity Target

Assuming that industry participants can agree on exactly what productivity measure should be used, there remains the issue of whether a target of 4 per cent productivity growth is reasonable.

Productivity growth of 4 per cent per year implies that:

• outputs will increase by 48 per cent over a 10 year period with no increase in inputs; or

• the same output can be achieved using about two-thirds of the initial inputs; or

• both inputs and outputs will change such that the ratio of outputs to inputs will increase by a factor of 1.48 (or more).

If we use the 3-way split of productivity illustrated in the McKinsey presentation9, we can estimate the state of the industry today had we actually achieved 4 per cent productivity growth over the last 10 years. We can then compare this with the actual state of the industry. The comparison will give us some idea of the reality of the 4 per cent target. This is shown in Table 3 below.

The units in this table have been chosen so that land and cow productivity can be multiplied together to provide a measure of milksolids per hectare. Costs have been expressed on a per hectare basis so that these can then be related to the calculated milksolids per hectare to provide a real cost per unit of milksolids.

9 Note that McKinsey did not strictly identify the measurement units. Therefore, this is only one

interpretation of their intent.


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Table 3 Effect of 4 Per Cent Productivity Growth

Cows Land Costs CostsUnits Kg MS/cow Cows/ha $/ha $/kg1994-95 291 2.41 1,578.32 2.25+4% pa for 10 yrs 431 3.57 1,066.26 1.52Actual 2004-05 329 2.72 2,147.72 2.40Difference (%) -24% -24% 101% 58%Actual growth (%pa) 1.2% 1.2% 3.1% 0.6% Notes: Costs = Total farm expenses less interest less non-dairy expenses.

2004-05 costs deflated to 1994-95 level using Dairy Input Price Index. Source: NZIER

Overall, this finds that actual productivity growth has fallen well short of the target 4 per cent in each category. While there have been modest productivity gains in cow and land productivity, production costs per hectare have actually increased in real terms over the period faster than the growth in milksolids per hectare.

If the overall aim of productivity gains is to reduce the unit costs of milk production, then we can calculate what level of production is required to do this.

The actual cost per kilogram of milk in 2004-05 was $2.40 (in 1994-95 dollar terms). To achieve 4 per cent productivity growth in this measure, that cost should have been $1.52. If we assume that this is to be achieved using the actual level of inputs used in 2004-05, then milksolids per hectare would have needed to be 1,413 kilograms.

This compares with actual milksolids per hectare of 895 kg in 2004-05 (on Survey farms), and peak recorded milksolids per hectare of 936 kg in 2003-04.

This means to have achieved the 4 per cent productivity growth in production cost per kilogram, milksolids per hectare would need to be nearly 51 per cent ahead of the highest level recorded, while costs remained unchanged from the 2004-05 level. One has to question whether the energy demands of producing that much more milk could realistically be generated from the current land area and fertiliser inputs.

In short, is this level of productivity physically achievable?


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4. The On-Farm Situation

4.1 What is Important On-Farm?

While the preceding section has focused on productivity and the problems associated with measuring it, it has also allowed some useful points to be drawn out of the discussion on capital.

One of those points was that the price of land exceeds any reasonable value it has as an input into the productive process. If this point is accepted, then we can move on to ask:

• what are the businesses or activities that take place on a farm that account for the “excess” land price over and above its input value?

• what is important for those businesses or activities – both individually and jointly?

• what role do industry organisations have in providing support for those activities?

If we take the split of land value into three components as suggested in Section 3 (input value, business value, and pure asset value), then we can draw up a table of the nature of each activity and the desired outputs for those activities:

Table 4 Outputs of Business Components

Land Value Component Business Output

Input value Farming Revenue Profit

Business value Business Profit

Pure asset value Investment Speculation Store of wealth

Profit? Capital growth? Price resistance to decline?

Source: NZIER

Clearly, profit is a relevant output to each of the business activities, but it has a different importance to each activity.

From a business perspective, profit is the key output by which the business should be measured. In one sense, business is indifferent to the activity that is carried out – its only concern is that the activity produces sufficient profit to justify the investment (business value). If profit is insufficient, then the business could be sold, and the cash re-invested in another enterprise that


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provides a higher level of profit. Using the terminology used earlier, this view is “external” to processes occurring within the business.

The “farming” perspective is an internal view of the business. While profit is the ultimate goal, this is achieved by generation of revenue, and control of costs. Therefore, the focus is on the detail of the farming processes.

The pure asset component of land prices could be associated with a number of activities. Similarly, there are a number of potential outputs available for this component. While the table lists some characteristics, the importance of each of these outputs (or characteristics) is less clear. For example, if the business value is fully explained by the profit, what is the significance of profit to the pure asset component of land values? On the other hand, if profit was not being generated, the land may well be less attractive as an investment.

From an industry viewpoint, the ‘farming’ and ‘business’ perspectives outlined above are clearly of direct relevance. In contrast, it is not clear that the pure asset components of land can be influenced by industry actions, notwithstanding the fact that land values impact on profitability, and that (most of) the land owners are industry participants.

Given the importance of profit to the farming and the business perspectives, this will now be considered further.

4.2 Drivers of profitability

Profit is essentially the difference between the revenue generated by the farming operation, and the expenses incurred in producing that revenue. The revenue and expenses can then be broken down further into the factors driving those components. One such breakdown was shown in Figure 1 earlier in this paper.

Griffel-Tatjé and Lovell10 have proposed an alternative decomposition illustrated in Figure 5. Of particular relevance to the current discussion, the breakdown identifies the relationship between productivity and profit.

There are some useful points to note from this structure:

• Productivity is shown to be only one component in the profit structure

• Productivity is defined in quantity terms – not in value terms

• Productivity itself is composed of two parts – operating efficiency, and technical change

10 Griffel-Tatjé and Lovell (1996): Profits and Productivity, Wharton Financial Institutions Center.

http://knowledge.wharton.upenn.edu/papers/79.pdf


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• Other factors that contribute to profit are price, product mix, resource mix, and scale. However, product mix is not a major factor in the dairy industry.

Figure 5 Elements of Profit

Profit

Price Quantity

ProductivityActivity

TechnicalChange

OperatingEfficiencyScaleResource

MixProduct

Mix

Source: NZIER Based on Griffel-Tatjé and Lovell

These points have significant implications for the evaluation of industry success.

Firstly, if profit is the primary goal of the farm business, then productivity is not a good measure of the success of the farm business.

Secondly, the definition of productivity outlined in this profit decomposition does not line up with that envisaged by the papers setting up the Centre of Excellence. However, it does line up with the calculation of productivity in the Dexcel TFP model.

The first point will be examined further in Section 5. The second point can only be noted – it is clear that further discussion on productivity requires clear definitions of what is expected to be achieved.

In terms of the diagram shown in Figure 5, Griffel-Tatjé and Lovell provide a methodology for deriving the relative importance of each of the factors identified. Following this through is beyond the scope of this paper, but some comment on the importance of price is in order.

The following chart shows the relationship between the average milksolids price received each season and Trade Weighted Exchange Rate Index as recorded by the Reserve Bank. The TWI scale is inverted so a close relationship will show the two lines tracking together.


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Figure 6 Milksolid Prices and Exchange Rates Milksolids: $/kg TWI: Jun 1979 =100

0.0

1.0

2.0

3.0

4.0

5.0

6.0

1986-87 1990-91 1994-95 1998-99 2002-03

30.0

40.0

50.0

60.0

70.0

80.0

90.0

MS (LHS)

TWI (RHS)

Source: NZIER LIC RBNZ

This shows a mixed relationship between the two. There are periods when a relationship between the two seems apparent (1987-88 to 1993-94, and 1997-98 to 2002-03), while the lines diverge during other periods.

However, if allowance is made for the effects of the GATT Uruguay round completed in 1994, then the TWI explains around half of the changes in the observed milksolids price. This means that the exchange rate is a significant contributor to the milksolids price received by dairy farmers. In turn, this has a direct impact on farm revenues and profits.

Given the high level of the exchange rate during the 2004-05 and 2005-06 seasons, it is inescapable that dairy farm revenues will have been depressed to some extent. The fact that the actual payout has been better than is indicated by simple regressions against the exchange rate index provides little consolation. However, it is critical to recognise that a significant part of the lack of profitability in the 2004-05 and 2005-06 seasons comes directly from the exchange rate impact.

Over the period 1986-87 to 2005-06, the TWI averaged 59.8. In 2005-06, the actual level was 68.8. Had the TWI in 2005-06 been at the 59.8 level, then we estimate that the average payout would have been around 43 cents per kilogram higher, or $4.53 in total. While this is a simplistic analysis which does not take hedging strategies into account, it does indicate the impact of recent exchange rate levels.

Extending this further, if one were to look for industry wide evidence of financial gains over the last 5 or 10 years, it would seem necessary to make


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some adjustment for the current adversity of the exchange rate. Likewise, the benefits realised from an unexpectedly low exchange in 2000-01 and 2001-02 can hardly be attributed to industry progress. This poses some significant challenges to measuring improvements in performance in value terms.

4.3 Industry Good Activities and Profit Drivers

How do industry good activities contribute to profits on farms?

• Assisting the development of new technology to push out the production frontier illustrated in Figure 2. This is the ‘Technical change’ component identified in Figure 5, along with various boxes in Figure 1.

• Assisting the development of new technology to extract more value from industry production. This is the ‘Product value’ box in Figure 1, and part of the ‘Price’ component in Figure 5.

• Transferring the knowledge regarding the new technology to industry participants.

• Improving management practices in the industry towards best practice. This is the ‘Operating Efficiency’ component identified in Figure 5.

Industry good activities have enabling functions:

• Promotion of the industry and industry training to help ensure a future supply of industry participants

• Assistance in providing or encouraging provision of infrastructure services (e.g. broadband) to industry participants

Industry good activities also have protective (insurance) functions:

• Prevention of production losses by keeping out plant and animal pests and diseases

• Prevention of potential future trade barriers through bio-security, improving the environmental impact, and maintaining high animal welfare standards in the dairy industry

What areas do industry good activities have no impact on?

• Input prices

• Exchange rates

• Climate


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5. Measuring farm performance If productivity is not a good measure of industry success, then what is?

Before going on to look at alternative measures, it is worthwhile to look at the type of problems that beset many of the commonly used performance measures. These include:

• relationship to size. Revenue, profit, and associated measures typically increase as the farm size increases. Therefore, direct measurement of these variables is partly measuring farm size.

• effect of debt structure. A highly indebted farm will usually have lower profit than a similar farm with low debt. This makes profit a poor measure, and earnings before interest and tax (EBIT) is often used to accommodate this problem.

• effect of management labour. An owner-operator’s labour is usually not directly costed in the accounts. Therefore, an owner-operator farm will usually show a higher profit than a similar sized farm run by a manager. Therefore, adjustment to include the “cost” of the owner’s labour is desirable.

• exclusion of run-off area. Some farms have a run-off which is used for winter grazing, heifer rearing, and growing supplementary feed. It is important that this area be included in any per hectare analyses.

• effect of feed purchases. Farms that purchase significant quantities of feed can produce more milk per hectare or cow than farms that rely on own-grown feed. This effect flows through into revenue measures also.

• effect of off-farm grazing. This is similar to feed purchases. Off-farm grazing of heifers and cows increases the availability of the owned farm area for milk production, resulting in higher production figures per hectare or per cow.

From the above, it appears that an ideal benchmarking measure would have the following characteristics:

• adjust for size of farm (including run-off)

• adjust for debt structure

• include owner labour

• account for feed purchases and off-farm grazing as necessary

To this list should be added:

• simplicity

• transparency

• understandable

At this point, it is worth asking: “Can one measure achieve everything?”


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If not, the we need to determine what measures are useful. Returning to the basic principles of profit, this suggests we need some measurement of revenue generation, and some measure of the costs associated with that revenue generation.

In the dairy farm context, we know that revenue generation is closely aligned with production given the uniformity of the product and price. Therefore, we can simplify the revenue generation down to a measurement of milksolids production.

Consideration of costs immediately begs the question of which costs to include, and in particular, how to handle the cost of land. Given that no definitive answer on the input value of land was reached earlier in this paper, it may be best to leave land costs to one side for later consideration.

Given these principles, the following measurements are suggested:

• kilograms milksolids per hectare

• kilograms milksolids per cow

• kilograms milksolids per labour unit

• kilograms milksolids per kg dry matter (or kg milksolids per unit of metabolisable energy)

• production cost per kg milksolids (broken down into categories)

• total production cost as a percentage of the milksolids price.

These are all partial measures of productivity. This gives them the advantages of simplicity, transparency, and being understandable.

None of these measures provides a complete picture of the farm. However, taken together, they provide a reasonable range of measures over the farm production and value creation process.

Each of the first three measures has the potential to be biased by changing farm management practices. In particular, feed purchases and grazing dry stock off farm will lead to increases in these productivity measures.

The fourth measure (kg MS / kg DM) potentially overcomes the limitations of the first three measures because it is based around feed consumed, regardless of its source. The disadvantage of this measure is that there is insufficient data to calculate this on most individual farms, let alone at an industry level.

These measures are graphed in the Figures below:


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Figure 7 Partial Productivity Measures Trends in milk production

0

100

200

300

400

500

600

700

800

900

1,000

1988-89 1992-93 1996-97 2000-01 2004-050

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

Kg MS / haKg MS / cowKg MS / lbr unit (RHS)

Source: NZIER Dexcel

Figure 7 shows that milksolids per labour unit have increased faster (+82%) over the period than milksolids per hectare (+50%) or milksolids per cow (+30%).

Figure 8 Trends in Production Costs Dollars per kilogram milksolids

0.00

0.50

1.00

1.50

2.00

2.50

1988-89 1992-93 1996-97 2000-01 2004-05

Working expenses Overheads Finance Owner labour


Working expenses per kilogram of milksolids have increased by 100 per cent over the period, compared with increases of 49 per cent for finance, and 27 per cent for overheads. In contrast, owner labour has declined by 25 per cent.


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Figure 9 Trends in Revenue and Expenditure Dollars per kilogram milksolids

2.50

3.00

3.50

4.00

4.50

5.00

5.50

6.00

6.50

1988-89 1992-93 1996-97 2000-01 2004-05

Dairy revenueProduction costs

Notes: Includes livestock trading in revenue Includes owner labour in production costs


Total dairy revenue (including livestock) per kilogram increased by 30 per cent over the period while expenditure increased by 42 per cent.

Average “profit” has averaged 28 cents per kilogram over the period. This includes an allowance for owner labour, but no allowance for a return to capital. If the 2000-01 and 2001-02 years are excluded, this average profit margin shrinks to just 10 cents per kilogram. In nominal terms, profit per kilogram in 2004-05 is only half the 1988-89 level.

This “profit” figure can be considered the return to capital generated by the farming enterprise. Capital includes land, buildings, investments, and cows. Expressing this in terms of equity (as finance costs are included in the expenditure), 2004-05 generated a 1.2 per cent return. This return excludes any capital growth that may have occurred.

Capital growth was substantial over the period – but is this an output that should be attributed to farming? See the discussion in Section 4.1.

Equity (at open) has more than doubled over the period, from $9.53 per kilogram in 1988 to $21.66 in 2004. This increased further during the 2004-05 season to be $27.38 per kilogram by May 2005.

This analysis highlights a number of issues regarding on-farm performance. Moreover, because the measures are expressed in terms that industry participants understand, greater potential exists for action to be taken to address those issues. In contrast, the results of the TFP calculation are not understood, and little is done because the nature of the problem is not clear.


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6. Measuring Industry Good Activity

6.1 Industry Good and On-Farm Performance

Industry good activities have a range of purposes – see Sections 2 and 4.3. Some of these activities are targeted at on-farm performance. Others are not.

Even those activities targeting on-farm performance do so in an indirect way – they provide the building blocks for industry participants to use. They do not directly implement those building blocks. It is up to the industry participants themselves to implement changes to their business operation. Effectively, industry good activities influence the environment in which the businesses operate.

Industry good activities also tend to take place over a number of years. New technology takes time to move from the concept stage, through testing, to product form. It then takes time for the new technology to be adopted by the industry.

It also needs to be recognised that industry good activities are not the only cause of changes in industry performance. Recognition of poor (or good) industry performance does not necessarily demonstrate poor (or good) performance of industry good activities.

To properly measure industry good activities, the costs and benefits of the activities need to be explicitly identified. While costs are readily identified and quantified, benefits are more problematic.

6.2 General Measurement Difficulties

There are two (related) key difficulties in measuring the benefits from industry good activities. The first is establishing a “baseline” against which measurement can take place. The second is attribution of the observed difference to the various factors contributing to that difference.

Introduction of a new technology to an industry will cause change. If there are two identical firms, one of which adopts the new technology while the other does not, the impact of the new technology can be measured.

The problems at the industry level are that:

• the data does not identify firms using the new technology separate from those who are not

• only the “changed” data is available

• there are many influences on the output of the firms, not only the introduction of the new technology being examined.


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Given that only the “changed” data is available, assumptions need to be made about what would have happened had the change(s) not occurred. This is not the same as assuming a continuation of the performance that occurred prior to the introduction of the new technology. These problems are illustrated in the Figure below:

Figure 10 Establishing a Baseline

Out

put

Time

A

D

C

B

T1 T2

Source: NZIER

New technology is introduced at time T1. More new technology is introduced at time T2 which builds on the earlier technology. The observed output is on the line running to point D. What is the benefit of the original technology introduction?

Line A represents the performance prevailing at the time of the original introduction. The problem with this is that this freezes all technology changes, not just the change you are looking to identify.

Line B represents what would have happened if the new technology had not been introduced. In general, the difference between this line and line D is the benefit of the new technology.

This particular example is confused by the introduction of a second dependent technology. In this case, the benefits of the second technology are represented by the difference between lines B and C. Therefore, the benefits of the first technology are represented by the difference between lines B and D, less the difference between lines B and C.


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The primary difficulty of measurement comes in estimating the path of line B. This process is fraught with assumptions.

The second difficulty comes in isolating any other events that impact on the measurement. In this case, this is the estimation of line C. In other cases, say for provision of information services, it is isolating the effect of information from one particular source from the general mass of information services available to industry participants.

6.3 Measuring On-Farm Related Activities

6.3.1 Project based measurement

The general process (and difficulties) of measuring the effect of industry good activities on on-farm activities has been outlined in Section 6.2.

Having identified the benefits of the activity (technology) and an equivalent series of costs for developing the technology and transferring the technology to the industry, an estimate of the value of the activity can be made.

Given that the costs and benefits have occurred over a period of time, a cost-benefit analysis is the appropriate framework to use. This discounts the costs and benefits to Net Present Values (NPV) to provide an overall value to the industry in present day terms.

An appropriate extension to the NPV concept is to express the results in terms of a Benefit-Cost ratio. This gives a measure of the efficiency by which benefits have been generated.

A project with a large NPV may also have required a large investment. In this case, its benefit-cost ratio would be low, and a better outcome may have been achieved had the money been spent on a series of smaller projects with higher benefit-cost ratios. This would have resulted in a higher total NPV although each individual project would provide only small contributions.

6.3.2 Total measurement

Measuring each project individually is time consuming, and fraught with assumptions. It also does not provide a neat answer to the question: “Are industry good activities (in general) worthwhile?”

Unfortunately, there is no neat and simple answer to that question. Industry good activities have a range of outcomes, and trying to classify all activities on the basis of a single measurement is not helpful.

Ideally, all project should be assessed on their outcomes. This will identify the range of successes achieved by industry good activities, and provide a good basis for overall assessment.


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It should be noted that such an assessment is made with the benefit of hindsight. At the time that projects are commissioned, there is no certainty that any given project will generate a positive NPV – but there would be an expectation that over all projects, the benefits will exceed the costs.

Projects fail even when prospects are initially good, and even with good management. So individual project failures do not necessarily constitute a failure of the overall strategy.

However, if the proportion of projects failing is too high, then the processes involved in project selection and monitoring need to be examined. But this first requires that the projects be measured to determine success or failure.

6.4 Measuring Non-Farm Related Activities

Non-farm related activities such as bio-security need to be looked at slightly differently. These are insurance or risk-management functions – how much are we prepared to pay to prevent a poor outcome?

The first step is to quantify the amount of money going into these activities, and express that against the size of the activities that are being protected. If that level of analysis is not convincing, then more sophisticated analysis can be applied.

Other areas such as industry training require a little more thought. However, the basic principal that each activity should be measured according to its costs and benefits still applies.


2 February 2007

7. Conclusions The following points are clear from this paper:

• Productivity is a simple concept, but: − partial productivity measures can be misleading

− total factor productivity measures are difficult to understand

− it is questionable whether productivity growth of 4 per cent per annum over the last 10 years would be physically achievable

• Profitability is the central goal of farm businesses

• Productivity is only one component of profitability

• Industry good activities are only partially focussed on industry profitability

• Even where profit is the focus of industry good activities, the linkage is loose – farmers must “adopt” the technology

• Industry good activities are best measured as a portfolio of projects


Documents

Productivity, Profitability and Industry Good Activities