A manual for measuring total factor productivity in...

A manual for measuring total factor productivity in Australian agriculture Yu Sheng and Tom Jackson

Research by the Australian Bureau of Agricultural

and Resource Economics and Sciences

ABARES Technical Report 15.2

October 2015

© Commonwealth of Australia 2015

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Sheng, Y & Jackson, T 2015, A manual for measuring total factor productivity in Australian agriculture, ABARES technical report 15.2, Canberra, October. CC BY 3.0.

ISSN 189-3128 ISBN 978-1-74323-255-2 ABARES project 43510

Internet

A manual for measuring total factor productivity in Australian agriculture is available at agriculture.gov.au/abares/publications.

Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES)

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Acknowledgements

The authors thank interview and survey participants for their input. Thanks also to Alistair Davidson and Peter Gooday for their support during the project and in preparing this manual.

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Manual for measuring TFP in Australian agriculture ABARES

3

Contents

Summary 5

1 Introduction 6

2 Agricultural TFP measurement in Australia: a brief literature review 8

3 The agricultural production account in Australia 11

3.1 Output 11

3.2 Input 14

4 Gross output, gross input and total factor productivity 23

4.1 Gross output and gross input 25

4.2 Industry-level TFP growth: technological progress versus resource reallocation 27

4.3 Slowdown in TFP growth 31

5 Robustness check 32

6 Conclusion 33

Appendix A: Data collection and compilation for the agricultural production account 34

Outputs 34

Inputs 35

Glossary 38

References 39

Tables

Table 1 Gross output, input and TFP indexes, 1948–49 and 2013–14 23

Table 2 Decomposition of TFP among outputs and inputs 25

Table 3 Short-term output, input and TFP growth, 1948–49 to 2013–14 27

Table 4 Comparison of agricultural TFP estimates with previous studies 32

Table A1 Definition of crop outputs 34

Table A2 Definition of livestock outputs 35

Table A3 Definition of land inputs 35

Table A4 Definition of capital inputs 36

Table A5 Definition of labour inputs 36

Table A6 Definition of materials and services inputs 37


4

Figures

Figure 1 Price index and implicit quantity of crop output, 1948–49 to 2013–14 12

Figure 2 Price index and implicit quantity of livestock output, 1948–49 to 2013–14 13

Figure 3 Rental prices for capital items by group, 1948–49 to 2013–14 15

Figure 4 Capital service inputs by capital item group, 1948–49 to 2013–14 16

Figure 5 Land service quantity input and rental price, 1948–49 to 2013–14 17

Figure 6 Unit land price, 1948–49 to 2013–14 18

Figure 7 Crop and pasture land input (million hectares), 1948–49 to 2013–14 18

Figure 8 Wages of hired and self-employed workers, 1948–49 to 2013–14 20

Figure 9 Hours worked by hired and self-employed workers, 1948–49 to 2013–14 20

Figure 10 Implicit quantities of materials and services input by type, 1948–49 to

2013–14 22

Figure 11 Price index of materials and services input by type, 1948–49 to 2013–14 22

Figure 12 Gross output index, 1948–49 to 2013–14 25

Figure 13 Gross input index, 1948–49 to 2013–14 26

Figure 14 TFP index, 1948–49 to 2013–14 28

Figure 15 Capital-labour ratio and materials and services-labour ratio, 1948–49 to

2013–14 29

Figure 16 Proportion of crop products in gross output value, 1948–49 to 2013–14 30

Figure 17 Output, input and TFP growth by decade, 1948–49 to 2013–14 31


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Summary This manual describes a newly proposed method and dataset used by ABARES to measure

output, input and total factor productivity in the Australian agriculture industry. We apply

recently developed statistical methods to a novel dataset, compiled from Australia’s national

account statistics and farm surveys. The results show agricultural productivity grew at 2.0 per

cent a year on average between 1948–49 and 2013–14. The results provide a unique measure of

the productivity performance of Australia’s agriculture industry over time, giving insight into

trends in farm performance and the effects of policy reforms.

The method used in this manual is an outcome of collaboration between ABARES and other

agencies interested in agricultural productivity, namely the US Department of Agriculture’s

Economic Research Service, Agriculture and Agri-Food Canada and the European Commission’s

Directorate-General for Agriculture and Rural Development. It provides a thorough description

of input, output and productivity for the Australian farm sector. This provides domestic

policymakers and other stakeholders an improved evidence base for understanding trends in

productivity and profitability on farms. Other outcomes from this project include technical

reports describing the methods that underlie the data and comparing capital use across a

number of developed countries (Ball et al. 2015; Sheng, Jackson & Zhao 2014).

Key findings

Productivity growth is a major driver of the long-run competitiveness and profitability of Australian farmers. It helps to offset the effects of adverse climatic conditions and allows farmers to increase output despite having limited resources such as land.

Between 1948–49 and 2013–14 total factor productivity (TFP) in Australia’s agriculture industry grew at 2.0 per cent a year on average, accounting for more than 80 per cent of output growth over this period.

Productivity growth on individual farms has been driven by technological progress. In particular, adoption of new technologies and management practices has increased production efficiency, including through facilitating substitution of capital and intermediate inputs for labour.

The reallocation of resources from farms with relatively low efficiency to those with higher efficiency has also been a significant driver of industry-level productivity growth.

Agricultural TFP growth has slowed in the past 20 years, dropping from 2.6 per cent a year between 1948–49 and 1999–2000 to 0.9 per cent a year since the late 1990s. Potential reasons for this are considered in the paper, and include prolonged drought and changes in expenditure on research and development.


6

1 Introduction Increasing productivity has long been recognised as the most important long-term source of

growth in output and profits in the Australian farm sector. Previous studies have generally found

that productivity growth is responsible for more than two-thirds of agricultural output growth

and almost all growth in farm profits in Australia during the post-war period (Mullen 2010;

Productivity Commission 2011). Agricultural productivity growth also plays an important role in

maintaining the international competitiveness of Australian farmers and helps to offset the

negative influence on farm profit of factors such as declines in the terms of trade, increasing

climate variability and tightening constraints on natural resource use.

Domestic policy settings influence agricultural productivity by shaping farmers’ incentives and

their capacity to innovate. Recognising this, Australian governments have employed a range of

macroeconomic, microeconomic and industry-specific reforms to reduce regulatory burden,

restore market competition and improve the efficiency of the rural research and development

(R&D) system (Commonwealth of Australia 2015; Gray, Oss-Emer & Sheng 2014). Although past

reforms facilitated structural adjustment in Australian agriculture and provided a more

favourable environment for farming, it remains difficult to link these reforms to changes in

industry-level productivity. A significant reason for this is a lack of data that measure the long-

run productivity performance of the agriculture industry as a whole.

The total factor productivity (TFP) index is widely used to measure productivity because it

provides a measure of how efficiently farmers combine all inputs to produce output. To measure

TFP, researchers usually aggregate various outputs (for example, crop and livestock products)

into an index of total output and compare this with an index of total input that aggregates

various inputs such as land, labour, capital, and materials and services. Agricultural productivity

changes are identified when the ratio of total output to total input changes over time. Two

Australian Government agencies—ABARES and the Australian Bureau of Statistics—use this

approach to regularly produce agricultural TFP estimates.

ABARES uses a gross output model and farm survey data to derive ‘bottom-up’ TFP measures for

the Australian broadacre agriculture and dairy industries. These industries cover around 70 per

cent of all agricultural activities in Australia and the series are available from 1977–78. Although

the survey data do not cover all agricultural industries, these estimates are useful because they

provide detailed measures of productivity performance for individual sectors and regions.

The Australian Bureau of Statistics (ABS) measures TFP for the agriculture, forestry and fishing

industry as a whole. Using national accounts data and two models—the value-added model and

the gross output model—the ABS derives ‘top-down’ TFP measures, which are available from

1988–89 for the value-added estimates and from 1994–95 for the gross output estimates. The

value-added estimates are primarily suited to comparing productivity growth across market

sectors of the economy, but cannot describe long-term changes in agricultural productivity

because of the limited time series. In addition, these estimates are potentially biased by the

absence of quality adjustments for inputs such as land and labour.


7

Box 1 Gross output versus value added models

Statistical agencies generally use two types of model for measuring TFP: the gross output model and the value-added model. The key difference between the two models is that they measure output and input in different ways. Specifically, the value-added model excludes intermediate inputs (such as materials, energy and services used in production) when measuring outputs and inputs, while they are included in the gross output model. TFP growth estimated when using the value-added model is usually higher than that estimated when using the gross output model, although both are valid. Usually, the gross output model is considered to give a better indication of the full extent of disembodied technological change.

This manual provides a detailed discussion of the method and data proposed by ABARES to

estimate agricultural TFP from 1948–49 to 2013–14. This allows ABARES to produce estimates

for an extended time period using data from national accounts and farm surveys, compiled using

international best practice methods. Two features distinguish this work from other studies.

First, this study is the first attempt to measure gross output based TFP for the agricultural sector

as a whole (that is, not only for the broadacre and dairy sectors and separate from the forestry

and fishing sectors). Second, the estimates take into account quality differences in land, labour

and some intermediate inputs.

The results show that agricultural TFP in Australia grew at an average rate of 2.0 per cent a year

between 1948–49 and 2013–14. This growth accounts for more than 80 per cent of output

growth during this period. Industry-level productivity growth has been driven by a number of

factors related to technological progress, such as changes in the output and input mix of farms,

and resource reallocation between farms and industries. For example, the adoption of new

technology and management techniques has facilitated the substitution of capital and

intermediate inputs for labour. It has also contributed to the movement of resources from small

to large farms over the past six decades. However, the rate of growth has not been constant.

Agricultural TFP growth has slowed over the past 20 years, falling to 0.9 per cent a year since

the late 1990s. This finding is consistent with previous analysis of trends in TFP for the

broadacre agriculture sector (Sheng, Mullen & Zhao 2010).

Section 2 of this paper provides a brief review of the literature on measuring agricultural TFP in

Australia. Section 3 describes how each of the outputs and inputs included in the agricultural

production account were measured. Section 4 contains a brief description of the aggregate

output and input series and presents the estimates of agricultural TFP between 1948–49 and

2013–14. Section 5 presents the robustness checks against other estimates, and conclusions are

drawn in Section 6.


8

2 Agricultural TFP measurement in Australia: a brief literature review

While the concept of agricultural TFP is reasonably simple, it remains challenging to measure it

in practice. Generally, statistical agencies prefer to use the growth accounting based index

method to measure agricultural TFP (Fuglie & Wang 2012; OECD 2001). Studies that have used

this approach differ from each other in three aspects: the choice of aggregation method, the

definition of the production account and the method used to compile data. This section provides

a comprehensive review of studies that have measured agricultural TFP in Australia, including

those by Knopke (1988), Mullen and Cox (1996), Zhao, Sheng and Gray (2012), Powell (1974),

Productivity Commission (2005) and ABS (2007; 2000). The purpose of this review is to inform

the choice of method and data in our study.

Jorgenson and Griliches (1967) were the first to use the growth accounting based index to

measure TFP at the national level. More recently, the approach has been used to measure

agricultural TFP in the United States (Ball 1985; Ball et al. 1997a; ERS 2009) and worldwide

(Fuglie & Wang 2012). In Australia, this approach has been used in a small number of studies

since the 1970s. These studies can generally be classified into one of two categories depending

on how they construct the production account and the data they use.

Studies in the first category use farm survey data to measure agricultural TFP at the sector or

farm level. For example, using data from the Australian Sheep Industry Survey, Lawrence and

McKay (1980) used a gross output model to estimate TFP growth in the Australian sheep

industry between 1952–53 and 1976–77. Over this period, TFP growth was estimated to be

2.9 per cent a year on average, mainly driven by the capital deepening process (whereby inputs

such as labour are progressively replaced by capital).

In another study of this kind, Knopke (1988) estimated dairy industry TFP growth using data

from the Australian Dairy Industry Survey. After netting out the effects of protection and

regulation (namely a price subsidy), the study showed the average TFP growth of dairy farms

was 1.5 per cent a year between 1967–68 and 1982–83, and that this rate varied significantly

across regions. Although these studies were innovative in terms of methodology and data, they

were criticised for focusing only on subsectors, rather than the agriculture industry as a whole.

To measure TFP growth for the entire non-irrigated (broadacre) agriculture industry, Knopke,

Strappazzon and Mullen (1995) and Mullen and Cox (1996) used data from the Australian

Agricultural and Grazing Industry Survey for the periods 1977–78 to 1993–94 and 1952–53 to

1993–94, respectively. In particular, these authors examined agricultural TFP and its sensitivity

to different index formula. In both studies a weighting strategy was employed to correct

aggregation bias that may arise when compiling inputs and outputs because of differences in

sample representativeness across industries and survey regions. The results showed that

average TFP growth for the Australian broadacre industry was 2.4–2.6 per cent a year between

1952–53 and 1993–94. A similar method was applied by Knopke, O’Donnell and Shepherd

(2000) to measure TFP growth in the Australian grains industry over the period 1977–78 to

1998–99.

Recently, Zhao, Sheng and Gray (2012) reviewed previous studies and developed a general

empirical framework (which is currently used by the OECD as a standard farm-level analytical

tool) for using farm survey data to measure TFP and its growth in the Australian broadacre and

dairy industries. The results show that, between 1977–78 and 2010–11, the average annual


9

growth rate of broadacre TFP was 1.4 per cent a year, while dairy industry productivity growth

was 1.6 per cent a year from 1978–79 to 2010–11. This study represents the first good measure

of long-term productivity performance in Australia’s broadacre and dairy industries.

Although these studies have generated useful insights into the productivity of Australian farms,

there are some limitations associated with using farm survey data to measure agricultural TFP.

First, the limited industry coverage of the surveys mean that TFP estimates are only available for

the broadacre and dairy industries, and therefore do not provide insight into the productivity

performance of the agriculture industry as a whole. Second, most farm surveys have only been

conducted since 1977–78, which means the statistics on investment that are required to

accurately estimate the capital input are not available and instead a simplified (current

inventory) method must be used. This treatment neglects the effects of differences in the vintage

of productive capital, reducing the accuracy with which the capital input is measured

(Yotopoulos 1967). Third, a suitable adjustment is not made for differences in the quality of

inputs such as land, labour and some materials and services.

The second category of studies that estimate agricultural TFP use national accounts data to

measure industry-level output and input. When using these data, the production account is

constructed somewhat differently to the survey-based studies. For example, to derive output

and input quantities using national account statistics, researchers follow the accounting rule

which states that the gross value of output should be equal to the value of inputs, and both are

derived as the sum of quantities multiplied by the corresponding prices.

In an early example of this type of study, Powell (1974) used national accounts data to measure

agricultural TFP growth in Australia between 1920–21 and 1969–70. In this study, the

agriculture industry comprises the agriculture, forestry and fishing sectors, and the production

account is defined using the value-added model. Output is measured as value-added deflated by

a producer price index and total input is measured by deflating value series for three types of

capital and three types of labour by appropriate price indexes. The results showed that

Australian agricultural productivity increased at around 2.0 per cent a year over the period of

1920–21 to 1969–70, mainly driven by technological progress.

The first official attempt to use national accounts statistics to measure agricultural TFP growth

was a study by the Australian Bureau of Statistics (ABS 2000). In this study, the agriculture

industry is defined to include the agriculture, forestry and fishing sectors. Agricultural TFP

growth was estimated to be around 3.0 per cent a year between 1984–85 and 2013–14 using the

value-added model and 1.2 per cent a year between 1994–95 and 2012–13 using the gross

output model. The large difference between these two estimates mainly reflects differences in

the treatment of intermediate inputs in these two models (Basu & Fenald 2002). The estimated

TFP series based on the value-added model was later extended by the Productivity Commission

(2005) to cover the period 1970–71 to 2002–03. The Productivity Commission (2005) also used

a more complex perpetual inventory method to measure the capital input. Recently, some

further extensions, including those by ABS (2007) and Wei, Howe and Zhang (2012), further

improved this method by introducing a quality adjustment procedure for labour.

Several advantages are associated with using data from the national accounts rather than data

from farm surveys to estimate agricultural TFP. Most important of these is that the

measurement of the capital input is superior. However, the studies that have been conducted

using national accounts data suffer from a number of shortcomings. First, data constraints mean

that long-term TFP estimates for the agriculture industry alone (that is, separate from the

forestry and fishing industries) cannot be produced. Second, these studies use a non-transparent

output price index to deflate the gross output value (or value added), resulting in few details


10

about the output side of the production account being able to be examined. Third, there is no

quality adjustment for land and intermediate inputs, which biases the measure of agricultural

TFP.

In this paper we seek to resolve shortcomings of previous studies by using data from both the

national accounts and farm surveys, and a suite of recently developed methods, to develop a

gross output based agricultural production account from 1948–49 to 2013–14.


11

3 The agricultural production account in Australia

In this manual, the agricultural production account is constructed using data from the national

accounts statistics and farm surveys conducted by the ABS and ABARES. A gross output model

has been employed in which outputs are categorised into three groups and inputs are

categorised into four groups. A detailed discussion of the data sources for each output is

included in Appendix A.

3.1 Output

Gross output is defined as the aggregated volume of all commodities and services that are

produced on farms or that are related to on-farm activities. For data organisation purposes,

these commodities and services are classified into around 72 relatively homogeneous products

and product categories. These products are aggregated into seven subgroups, then into one of

three broad output categories—crops, livestock and other on-farm output. Each of these three

categories is discussed in more detail in this chapter.

A three-step index aggregation process was used to calculate the quantity of gross output. A

chained Fisher index was first applied to commodity-level prices and quantities to calculate

price indexes at the subgroup level. The implicit output quantity of each subgroup is then

derived as the output value divided by the aggregate price index. The subgroup price indexes

and implicit quantities are then used to calculate price indexes and implicit quantities for the

crop, livestock and other on-farm output categories. Finally, the price indexes and quantities of

these three categories are further aggregated to obtain the gross output price and quantity.

3.1.1 Crops

In total, there are around 60 crop commodities and commodity groups, which are grouped into

eight subgroups—grains and forage (eight items), oilseeds (seven items), vegetables and melons

(21 items), fruit and nuts (17 items), horticulture and nursery products (three items), cotton

and tobacco, sugar cane and crops not included elsewhere.

Output quantities for most crop commodities were directly sourced from the ABARES Australian

commodity statistics database. In cases where data were missing, implicit quantities (defined as

the production value divided by a corresponding market price or price index) were used as an

approximation. Value statistics were obtained from the Australian National Accounts, National

Income and Expenditure (ABS 2000).

Prices of individual crop commodities are defined as real market prices and are obtained from

ABARES Australian commodity statistics database. When price data are not available, either the

unit value (defined as the production value divided by the quantity) or the farmers’ receipt price

index (from ABARES database of farm receipts and prices paid) is used to impute the time trend

relative to the base year. For crops not classified elsewhere, a price index for all crops is used to

approximate the relevant prices and the implicit quantity is derived as the gross output value

divided by this price index.

Figure 1 shows the price index and implicit quantity of the crops output category between

1948–49 and 2013–14. For most of this period total crop output has been increasing steadily, at

3.7 per cent a year. However, this strong growth has slowed in the most recent decade. Since

reaching a historical peak of 6.4 per cent a year in the late 1990s, the annual growth rate of the


12

total crop output index has been largely stagnant. In particular, with severe droughts occurring

in 2002–03 and 2006–07, the crop output index has been more variable and experienced only

mild growth since 1999–2000.

Figure 1 Price index and implicit quantity of crop output, 1948–49 to 2013–14

Source: ABARES constructed agricultural production account

3.1.2 Livestock

Commodities classified in the livestock category include four meat products, four poultry and

egg products, wool, milk and other dairy products, honey and beeswax, and other livestock

products. For most of these commodities, production data are directly available from the

ABARES Australian commodity statistics database or from Meat & Livestock Australia.

Quantities of beef, sheep meat and pork are measured on an estimated dressed weight basis.

This involves multiplying the average dressed weight for various types of cattle, sheep and pigs

by the number of each type slaughtered each year. Except for the ‘yearling’ category (where

prices are for export quality stock), prices of all other categories are estimated as the monthly

average of prices in each major state market, weighted by the proportion of each output that is

produced in the respective state.

The quantity of chicken produced (including farmed chickens and chickens culled from the

laying flock) is measured on a live weight equivalent basis. Price estimates are formed by

indexing from the December quarter 2007 price for fresh whole chickens. The total value of

poultry also includes the value of the duck and drakes and other fowl output categories. For

these two groups of products, the price indexes are defined over broilers, farm chickens and

turkeys and imputed using the farm receipt price index. Implicit quantities are derived as the

total value divided by a corresponding price index. In addition, eggs are measured by number

and the price is defined as cents per egg.

The quantity of wool is defined as the amount marketed plus stock changes. The marketing

statistics are obtained from ABARES Australian commodity statistics database. Stock changes

include both private stocks (that is, stocks held on farms and unsold wool held by brokers) and

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

0

5 000

10 000

15 000

20 000

25 000

30 000

1948–49

1955–56

1962–63

1969–70

1976–77

1983–84

1990–91

1997–98

2004–05

2011–12

Pri

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–06

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implicit quantity

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13

official stocks held by the Australian Wool Board, the Australian Wool Corporation and the

Australian Wool Realisation Commission between 1971–72 and 1992–93. The price of wool is

measured as the Eastern Market Indicator (Livestock and Livestock Products, ABS 2000).

The quantity of milk is measured in million litres. Quantities of butter and cheese are measured

in kilotonnes and converted into the corresponding amount of milk equivalent. The price of milk

is measured as a weighted average of the manufacturing price (net of market support) and the

market milk price. All data for these variables are obtained from the ABS Principal Agricultural

Commodities. For other livestock products, the treatment is similar to that used for other crops.

Figure 2 shows the price index and implicit quantity of livestock output between 1948–49 and

2013–14. The quantity of total livestock products has increased, but at a much slower rate than

that of total crop products. Between 1948–49 and 2013–14 the annual growth rate of total

livestock output was 1.3 per cent a year, less than half that of total crop output. Changes in total

livestock output and prices have not moved together consistently over time, as they have in the

case of crop products.

Figure 2 Price index and implicit quantity of livestock output, 1948–49 to 2013–14

Source: ABARES All Agricultural TFP Database

3.1.3 Other on-farm output

In addition to crop and livestock output, gross agricultural output also includes three other

components:

receipts from land rental and the provision of capital and equipment services

non-separable secondary activities such as on-farm packing and marketing and forestry activities

other farm receipts not included elsewhere.

Statistics on quantities and prices of other on-farm outputs are not available from the national

accounts. Instead, data obtained from the ABARES Australian Agricultural and Grazing Industry

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

0

5 000

10 000

15 000

20 000

25 000

1948–49

1955–56

1962–63

1969–70

1976–77

1983–84

1990–91

1997–98

2004–05

2011–12

Pri

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Imp

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–06

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Year

implicit quantity

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14

Survey were used to estimate the ratio of the value of other on-farm output to the value of crop

and livestock outputs between 1977–78 and 2013–14. The ratio was then multiplied by the

gross value of crop and livestock outputs to approximately estimate the gross value of other on-

farm output. On average, other on-farm output has accounted for 2.0 to 2.5 per cent of total

agricultural output between 1948–49 and 2013–14. The price of this output category is

approximated using the price index of all crop and livestock products. The implicit quantity is

defined by dividing the value of other on-farm output by this price index.

3.2 Input

Estimating inputs is more complex than estimating outputs. This is mainly because for the inputs

of capital and land, service flows need to be derived from stocks because only the flow of

services enter the production process. OECD (2001) includes a detailed discussion of this issue.

In addition, quality adjustments need to account for heterogeneity in land, labour and some

intermediate inputs, which affects the efficiency of production.

3.2.1 Capital

The capital input group contains six items: non-dwelling buildings and structures, plant and

machinery, transportation vehicles, inventory, livestock and other cultivated biological

resources, and intellectual property (or intangible assets). Accounting for capital inputs in the

form of livestock and other cultivated biological resources and intellectual property is an

important contribution of this paper because the role of these inputs in agricultural production

has been increasing over time (Diewert 2005).

Estimating the input of capital services involves three steps. First, the productive capital stock is

estimated by applying the perpetual inventory method to historical data on investment. Second,

a formula based on the price of new investment, the rate of physical decay and an appropriate

rate of return on capital is used to estimate the rental rate (or price) of capital items. The capital

services input is then derived by multiplying the rental price and the productive capital stock.

Measuring the input of non-dwelling buildings and structures, plant and machinery and

transportation vehicles begins with calculating the stock of productive capital for each asset.

This is done using the perpetual inventory method, whereby the stock of capital is the sum of all

past investments (at constant prices), weighted by the relative efficiencies of capital goods of

each age. Long time series of investment data are required. For the period 1959–60 to 2013–14

data required for this calculation are obtained directly from the National accounts data cube,

data for 1900–01 to 1919–20 are from ‘Technology progress in Australian agriculture’ (Butlin

1975; Powell 1974) and for 1919–20 to 1959–60 data are from ‘A preliminary annual database:

1900–01 to 1973–74’ (Williams 1990). For the period 1869–70 to 1900–01, data are compiled

from statistics of private and public capital published in various government bulletins.

Rental prices of capital goods are derived by analysing the investment behaviour of farmers.

Investment is assumed to occur until the rate of return on additional capital is equal to the

opportunity cost of capital. This opportunity cost varies over time depending on the real rate of

return, which is measured as the smoothed nominal yield on government bonds less the rate of

inflation (Ball et al. 2010).

For inventory, livestock and other cultivated biological resources and intellectual property, the

estimation procedure is similar to that described in previous paragraphs, except that the

productive capital stock is defined as the total value of these capital items in the corresponding

year. This treatment reflects that, unlike depreciable fixed assets (non-dwelling buildings and


15

structures, plant and machinery and transportation vehicles), the opportunity cost of these

assets is equal to the present value of the return obtained from their capital services into the

future (Diewert 2005).

Figure 3 and Figure 4 present rental prices and capital service estimates for each capital group

from 1948–49 to 2013–14. For simplicity, livestock and other cultivated biological resources

and inventory are combined into one group. Figure 3 shows that the rental prices of most capital

groups have followed an inverse U-shape over time, largely reflecting trends in real interest

rates. Specifically, the rental prices of most capital items peaked in the 1990s or 2000s and have

remained steady or declined since. Despite this, Figure 4 shows the value of capital service flows

from non-dwelling buildings and structures, plant and machinery and transportation vehicles

(these account for more than 90 per cent of total capital services) have maintained an increasing

trend over time. Capital service flows from livestock and other cultivated biological resources

and inventory have also increased in the long run, but with more variation. The finding is

consistent with ABS estimates and indicates that more capital services have been used in

agricultural production over time.

Figure 3 Rental prices for capital items by group, 1948–49 to 2013–14


0.000

0.020

0.040

0.060

0.080

0.100

0.120

0.140

0.160

0.180

0.200

1948–49 1955–56 1962–63 1969–70 1976–77 1983–84 1990–91 1997–98 2004–05 2011–12

Imp

licit

pri

ce (

20

05

–06

= 1

.0)

Year

Buildings and structures

Plant and machinery

Transport vehicles

Inventory


16

Figure 4 Capital service inputs by capital item group, 1948–49 to 2013–14


3.2.2 Land

The land service input is measured as the land stock multiplied by a rental price. The land stock

is defined as the total area of land (in hectares) operated by farms. The rental price is obtained

by multiplying the real rate of return by the average price of land used for agricultural

production. Land prices are derived as the unit value of unimproved land, with an adjustment

for quality difference across regions and over time. A hedonic regression is used to eliminate the

variation in agricultural land prices that is caused by macroeconomic factors not related to

production.

It is widely argued that farm land prices are influenced by many factors unrelated to agricultural

production, such as the distance to major cities and amenity values. In addition, spatial

differences in soil type and quality mean it is difficult to compare land prices directly across

regions. To address these problems we estimate the price of land in two steps. First, the price of

land in the base year (2005–06) is estimated using the hedonic regression method, whereby the

price of land used for agricultural production is revalued based on a bundle of characteristics

associated with its use for agriculture and other purposes (Ball et al. 1997b). Second, assuming

that the proportion of total land value that is associated with its agricultural characteristics is

constant over time, an aggregate price index is used to impute the time trend. This index also

incorporates differences in the flow of services provided by land across Australian states and

land classes.

Land price data are sourced for the 32 regions included in the ABARES farm survey program.

Land characteristics are sourced from the USDA World Soil Resources Office, based on the work

of Eswaren, Beinroth and Reich (2003) and Sanchez, Palm and Buol (2003). GIS mapping is used

to overlay the regions with data describing 18 environmental attributes of land, including soil

acidity, salinity, moisture stress and others. To increase sample size, time series observations for

the 32 survey regions have been used and time dummies are included in the regression to

eliminate year-specific effects.

0

1 000

2 000

3 000

4 000

5 000

6 000

7 000

8 000

1948–49 1955–56 1962–63 1969–70 1976–77 1983–84 1990–91 1997–98 2004–05 2011–12

Imp

licit

qu

anti

ty (

A$

mill

ion

at

20

05

–06

pri

ces)

Year

Buildings and structures

Plant and machinery

Transport vehicles

Inventory


17

Agricultural land is divided into two types: crop (including fallow) and pasture. Land areas are

collected for each type at the state level from 1948–49 onwards. From 1977–78 onwards, land

prices are sourced directly from ABARES farm surveys. For earlier years, prices by land type and

state are imputed using average annual growth rates.

Figure 5 illustrates trends in the value of land service flows over time. The input of land services

was stable at a low level between 1949–50 and 1979–80 before increasing rapidly throughout

the 1980s, reaching a peak in 1989–90. This growth was driven by significant increases in land

prices and real interest rates. The land services input has declined since the early 1990s as

interest rates have fallen, despite ongoing increases in land prices.

Figure 5 Land service quantity input and rental price, 1948–49 to 2013–14


The overall trend in land use masks significant differences in the price and use of different types

of land. Specifically, between 1948–49 and 2013–14 the price of crop land grew faster than that

of pasture land (Figure 6). At the same time, the area of land used for cropping activities

increased, while that used for livestock activities decreased particularly after the mid 1970s

(Figure 7).

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

0

1 000

2 000

3 000

4 000

5 000

6 000

1948–49 1955–56 1962–63 1969–70 1976–77 1983–84 1990–91 1997–98 2004–05 2011–12

Pri

ce in

dex

(2

00

5–0

6 =

1.0

)

Imp

licit

qu

anti

ty (

A$

mill

ion

at

20

05

–06

pri

ces)

Year

implicit quantity

price index


18

Figure 6 Unit land price, 1948–49 to 2013–14


Figure 7 Crop and pasture land input (million hectares), 1948–49 to 2013–14


3.2.2 Labour

Total labour use is aggregated from two types of workers: hired and self-employed. Similar to

the United States and Canada, self-employed workers in Australia provide more than half of the

total labour input used in agricultural production. As the owners of farms, self-employed

workers generally derive their payment for labour from farm profits so there are no statistics on

their wages. In previous studies (Powell 1974; Zhao, Sheng & Gray 2012) the average wage for

0

500

1 000

1 500

2 000

2 500

1948–49 1955–56 1962–63 1969–70 1976–77 1983–84 1990–91 1997–98 2004–05 2011–12

Lan

d p

rice

(A

$ p

er h

ecta

re)

Year

pasture land

crop land

0

5

10

15

20

25

30

35

0

100

200

300

400

500

600

1948–49 1955–56 1962–63 1969–70 1976–77 1983–84 1990–91 1997–98 2004–05 2011–12

Cro

p la

nd

(m

illio

n h

ecta

res)

Pas

ture

lan

d (

mill

ion

hec

tare

s)

Year

pasture land

crop land


19

hired workers is used to approximate the wage of self-employed workers. However, this

treatment does not accurately reflect the nature of the input provided by self-employed workers

or the real return they receive. To deal with this problem, we endogenise self-employed

workers’ wages by treating them as the ‘residual claimants’ of the production account. This

means we assume that the compensation these workers receive for their labour and

entrepreneurship is the profit that remains each year once payments to all other inputs have

been made.

The quantity of labour used is defined as the total number of hours worked. This is estimated by

multiplying the number of hired, self-employed and unpaid family workers employed in

agriculture by the average number of hours worked per week by people in each of these

categories. In performing this estimation, the ABS labour quality adjustment index for

agriculture has also been used to account for differences in gender, age and education levels

across the three types of labour (Wei, Howe & Zhang 2011). The treatment provides an

additional adjustment for quality differences across the three types of labour used in agricultural

production.

The wage for hired labour is derived by dividing the total payment to hired labour by the total

number of hours worked. The total payment to these workers is sourced from ABS National

Income and Expenditure (cat. 5204.0). The compensation for self-employed labour is measured

as the gross value of output less the costs of capital, land services and intermediate inputs. The

real wage for self-employed workers is thus defined as the real compensation paid to these

workers, divided by the number of hours worked.

Between 1948–49 and 1979–80 the estimated wages of hired and self-employed workers

increased smoothly over time and were similar (Figure 8). However, from the early 1980s hired

workers’ wages continued to increase, while those of self-employed workers declined. A possible

explanation for this is that increased investment on farms in the late 1970s and early 1980s

created a high level of debt, which when combined with high interest rates eroded the income of

farm owners (Lewis & Kirby 1988). This trend continued until the late 1990s, when self-

employed workers’ wages started to catch up with those of hired workers.

The data also suggest that the wage gap between hired and self-employed workers has

influenced the composition of the agricultural labour force over time. In particular, the trend in

the number of hours worked by hired and self-employed workers diverged in the latter part of

the study period, particularly during the 1980s, although both have declined overall (Figure 9).


20

Figure 8 Wages of hired and self-employed workers, 1948–49 to 2013–14


Figure 9 Hours worked by hired and self-employed workers, 1948–49 to 2013–14


3.2.2 Materials and services

The materials and services input represents eight subgroups: fuel, lubricants and electricity;

fertiliser; chemicals and medicines; seed, fodder and livestock purchases; marketing; plant hire;

repairs and maintenance; and other purchased inputs. Data describing these inputs are sourced

from ABARES farm surveys from 1978–79 onwards. For the period before 1978–79, statistics

0

5

10

15

20

25

30

35

40

1948–49 1955–56 1962–63 1969–70 1976–77 1983–84 1990–91 1997–98 2004–05 2011–12

Wag

e (A

$ p

er h

ou

r)

Year

hired workers

self-employed

0

100

200

300

400

500

600

700

800

900

1948–49 1955–56 1962–63 1969–70 1976–77 1983–84 1990–91 1997–98 2004–05 2011–12

Ho

urs

wo

rked

(m

illio

n h

ou

rs)

Year

hired workers

self-employed


21

are obtained from ‘Historical Trends in Australian Agricultural Production, Exports, Incomes and

Prices: 1952–53 to 1978–79’ (ABARES 1980) and ‘Australian Rural Production, Exports, Farm

Income and Indexes of Prices Received and Paid by Farmers: 1949–50 to 1970–71’ (ABARES

1972).

Fuel, lubricants and electricity

This group includes petrol, diesel, gas and electricity used in agricultural production. Quantity

and price data are sourced from the ABARES Australian commodity statistics. An overall price

index for these inputs is constructed using the quantities of each item used in production as

weights. The implicit quantity is then derived as total expenditure (excluding direct subsidies)

divided by the price index.

Fertiliser, chemicals and medicines

The price of this group is approximated using an overall index of prices paid by farmers. The

implicit quantity used is then calculated as total expenditure divided by the price index.

Seed, fodder and livestock purchase

The price of this group is approximated using the index of prices paid by farmers. The implicit

quantity used is derived as total expenditure on these items divided by the price index. An

adjustment was made to account for changes in the quality of these inputs over time using an

index obtained from the Food and Agriculture Organization of the United Nations (FAO).

Marketing, plant hire and repairs and maintenance

These are the main services used in agricultural production. The price and quantity of these

services is approximated by the index of prices paid by farmers. The implicit quantity of these

inputs used in production was derived as total expenditure on these items divided by the price

index.

Other purchased inputs

These collectively account for around 20 per cent of the total value of intermediate inputs. This

category includes items such as customised machinery services and irrigation water purchases.

The price of this group is approximated using the index of prices paid by farmers. The implicit

quantity is derived as total expenditure divided by the price index.

Generally, the use of materials and services in Australian agriculture has increased over time,

although the two oil crises in the 1970s create a significant break in the series (Figure 10).

Relatively slow growth in the price of materials and services compared with other inputs

(namely land, capital and labour) is likely to be an important factor behind the overall upward

trend in the use of these inputs (Figure 11). Similarly, a rapid increase in price of materials and

services since 2005 is likely to have contributed to slower growth in the use of these inputs.


22

Figure 10 Implicit quantities of materials and services input by type, 1948–49 to 2013–14


Figure 11 Price index of materials and services input by type, 1948–49 to 2013–14


0

500

1 000

1 500

2 000

2 500

3 000

3 500

4 000

4 500

5 000

1948–49 1955–56 1962–63 1969–70 1976–77 1983–84 1990–91 1997–98 2004–05 2011–12

Imp

licit

qu

anti

ty (

A$

mill

ion

at

20

05

–06

pri

ces)

Year

fuel and lubricants fertiliser

chemicals and medicine seed and fodder

marketing repairs and maintenance

plant hire other materials and services

0.0

0.5

1.0

1.5

2.0

2.5

1948–49 1955–56 1962–63 1969–70 1976–77 1983–84 1990–91 1997–98 2004–05 2011–12

Pri

ce in

dex

(2

00

5–0

6 =

1.0

)

Year

fuel and lubricants fertiliser

chemicals and medicine seed and fodder

marketing repairs and maintenance

plant hire other materials and services


23

4 Gross output, gross input and total factor productivity

The procedures and data described in chapter 3 have been used to estimate implicit quantities of

each of the outputs and inputs that comprise the agricultural production account. These implicit

quantities are then used as weights to construct aggregate indexes of input and output prices.

Implicit gross output and input quantities are derived by dividing the value of agricultural

output and input (from the national accounts statistics) by the relevant aggregate price index.

Total factor productivity is measured as the quantity of gross output divided by the quantity of

gross input. Results are shown in Table 1 and Table 2.

Table 1 Gross output, input and TFP indexes, 1948–49 and 2013–14

Financial year Output index Input index TFP index

1948–49 27.6 70.2 19.4

1949–50 24.8 70.8 17.5

1950–51 46.7 70.8 33.0

1951–52 32.4 69.5 22.5

1952–53 36.7 72.7 26.7

1953–54 36.2 75.6 27.3

1954–55 35.5 77.3 27.5

1955–56 36.9 80.3 29.6

1956–57 37.1 79.9 29.7

1957–58 34.5 82.1 28.3

1958–59 40.9 84.4 34.5

1959–60 39.4 85.3 33.6

1960–61 39.5 88.3 34.9

1961–62 42.1 93.1 39.1

1962–63 39.9 95.8 38.2

1963–64 40.4 98.9 39.9

1964–65 42.2 99.1 41.9

1965–66 38.9 99.3 38.7

1966–67 45.6 101.1 46.1

1967–68 41.5 98.3 40.8

1968–69 47.3 103.7 49.1

1969–70 47.5 101.1 48.0

1970–71 45.9 101.1 46.4

1971–72 48.4 100.6 48.7

1972–73 42.7 102.1 43.7

1973–74 50.0 99.6 49.8

1974–75 48.6 96.7 47.0

1975–76 49.9 95.3 47.6


24

1976–77 52.1 92.7 48.3

1977–78 50.2 90.9 45.7

1978–79 53.3 96.8 51.6

1979–80 57.5 90.7 52.1

1980–81 64.4 88.5 57.0

1981–82 71.8 89.4 64.2

1982–83 57.4 93.1 53.4

1983–84 73.6 92.9 68.4

1984–85 74.7 91.9 68.6

1985–86 76.0 93.7 71.2

1986–87 76.7 94.1 72.1

1987–88 74.4 95.0 70.7

1988–89 75.4 94.1 71.0

1989–90 78.1 97.4 76.1

1990–91 85.1 97.1 82.7

1991–92 78.2 98.7 77.2

1992–93 87.7 98.7 86.6

1993–94 90.6 100.6 91.2

1994–95 76.5 99.9 76.4

1995–96 83.5 106.7 89.2

1996–97 89.7 110.3 98.9

1997–98 93.4 108.7 101.5

1998–99 99.7 108.4 108.1

1999–2000 102.9 110.3 113.5

2000–01 92.3 107.6 99.3

2001–02 92.5 108.8 100.7

2002–03 78.5 100.2 78.6

2003–04 93.6 102.0 95.5

2004–05 93.5 101.6 95.1

2005–06 100.0 100.0 100.0

2006–07 84.6 99.0 83.7

2007–08 87.3 100.6 87.8

2008–09 90.2 103.1 93.0

2009–10 89.3 102.3 91.4

2010–11 99.8 101.1 100.9

2011–12 109.4 100.4 109.9

2012–13 111.4 97.3 108.4

2013–14 103.0 100.5 103.5

Note: The input, output and TFP indexes reported in this table are estimated using data currently available from the ABS national account statistics and could change when data are updated.


25

Table 2 Decomposition of TFP among outputs and inputs

Item name Growth rate (%) Value share (%) Contribution to TFP (%)

Outputs

- crops 3.73 42.9 1.60

- livestock 1.29 55.1 0.71

- other outputs 2.16 2.0 0.04

Total – 100.0 2.36

Inputs

- land –0.20 9.3 –0.02

- capital 2.39 20.3 0.49

- labour –2.70 27.8 –0.75

- materials and services 1.45 42.7 0.62

Total – 100.0 0.33

4.1 Gross output and gross input

Gross agricultural output in Australia has increased steadily over the long term, although

substantial fluctuations between years and decades mean that short-term changes are highly

variable. Between 1948–49 and 2013–14 the volume of agricultural gross output increased at an

average annual growth rate of 2.4 per cent a year. However, the growth rate is not stable over

time (Figure 12). For example, average annual output growth from 1989–90 to 1999–2000 was

3.8 per cent a year, while from 1999–2000 to 2009–10 it was –1.4 per cent a year.

Figure 12 Gross output index, 1948–49 to 2013–14


Two significant causes of these fluctuations are demand shocks in international markets and

unfavourable seasonal conditions. For example, a shift in export market focus from England to

0

20

40

60

80

100

120

1948–49 1955–56 1962–63 1969–70 1976–77 1983–84 1990–91 1997–98 2004–05 2011–12

Gro

ss o

utp

ut

ind

ex (

20

05

–06

= 1

00

)

Year


26

the United States and Japan in the 1970s contributed to output growth falling to 0.7 per cent a

year during that decade, down from 4.5 per cent a year in the 1950s and 3.3 per cent a year in

the 1960s (Gruen 1998). In addition, droughts occurred in 1982–83, 1994–95, 2002–03 and

2006–07, significantly reducing agricultural output in the corresponding year and retarding

gross output growth in subsequent years.

Gross output growth has been accompanied by substantial change in the output mix. From

1948–49 to 2013–14 the share of livestock products in gross output declined from 63.7 per cent

to 43.6 per cent, while the share of crop products increased from 34.1 per cent to 54.7 per cent.

In the short run, movement of resources away from livestock and towards cropping can be

explained by changes in relative prices. In the long run, this trend is more likely to reflect faster

technological progress in the cropping industry (Sheng, Jackson & Davidson 2015).

Gross input use in Australian agriculture has increased over time, but the growth rate has been

slower than that of gross output and is characterised by cyclical behaviour (Figure 13). Between

1948–49 and 2013–14 the volume of total input increased at 0.4 per cent a year. Total input

grew at 2.0 per cent and 1.6 per cent a year in the 1950s and 1960s, respectively; however, a

rapid increase in the price of materials and services during the 1970s significantly increased

input costs and led to gross input declining by 1.1 per cent a year during this decade.

Throughout the 1980s and 1990s strong output growth led to greater demand for inputs and as

a result gross input grew at 0.7 per cent a year in the 1980s and 1.5 per cent a year in the 1990s.

From the early 2000s strong growth in input prices contributed to declining input use (at –0.8

per cent a year).

Figure 13 Gross input index, 1948–49 to 2013–14


Similar to outputs, there has also been change in the composition of inputs over time. For

example, between 1948–49 and 2013–14, the share of labour in total costs declined from 58.9

per cent to 28.3 per cent. In contrast, the shares of capital and intermediate inputs have

increased from 5.2 per cent and 32.6 per cent to 22.7 per cent and 44.2 per cent of total inputs,

respectively. This suggests that Australian agriculture has benefited from labour-saving

0

20

40

60

80

100

120

1948–49 1955–56 1962–63 1969–70 1976–77 1983–84 1990–91 1997–98 2004–05 2011–12

Gro

ss in

pu

t in

dex

(2

00

5–0

6 =

10

0)

Year


27

technological progress, which has led to greater use of capital and intermediate inputs in place

of labour. The role of land services has been declining over time as land prices have increased

and the total area of land used for agricultural production has declined. Overall, from 1948–49

to 2013–14 the input of land services has declined at an average rate of 0.2 per cent a year.

4.2 Industry-level TFP growth: technological progress versus resource reallocation

Agricultural TFP in Australia has grown rapidly over the past six decades (Table 3, Figure 14).

From 1948–49 to 2013–14 agricultural TFP grew at 2.0 per cent a year on average. Growth in

TFP has mainly been driven by significant output growth (2.4 per cent a year), which exceeded

input growth (0.3 per cent a year).

Table 3 Short-term output, input and TFP growth, 1948–49 to 2013–14

Time period Output growth rate Input growth rate TFP growth rate

1948–49 to 1959–60 2.44 4.46 2.02

1959–60 to 1969–70 1.68 3.29 1.61

1969–70 to 1979–80 2.83 0.73 –1.10

1979–80 to 1989–90 2.63 3.37 0.74

1989–90 to 1999–2000 2.28 3.76 1.48

1999–2000 to 2009–10 –0.70 –1.40 –0.80

2009–10 to 2013–14 3.95 3.21 –0.70


28

Figure 14 TFP index, 1948–49 to 2013–14


Ongoing technological progress within farms and resource reallocation between farms are the

main drivers of agricultural TFP growth. We discuss each of these drivers in turn. First, TFP

growth is a function of many factors related to technological progress on individual farms. These

factors include advances in knowledge and farm practices, diffusion of appropriate technologies

and improvements in the organisation of production (Dahl, Leith & Gray 2013). In addition, the

diffusion of new technologies among farms has contributed to an increase in average farm size

and has facilitated a trend towards greater specialisation as farmers have increasingly

substituted labour for larger and more efficient plant and equipment and higher-quality

fertilisers and chemicals (Dunlop, Turner & Howden 2004; Nossal et al. 2009). This technology-

driven shift in the mix of inputs used in production has led to increases in the capital-labour

ratio and the materials and services-labour ratio of 12.7 times and 6.5 times, respectively,

between 1948–49 and 2013–14 (Figure 15).

0

20

40

60

80

100

120

1948–49 1955–56 1962–63 1969–70 1976–77 1983–84 1990–91 1997–98 2004–05 2011–12

TFP

ind

ex (

20

05

–06

= 1

00

)

Year


29

Figure 15 Capital-labour ratio and materials and services-labour ratio, 1948–49 to 2013–14


Rapid technological progress in Australian agriculture is mainly driven by ongoing public and

private investment in R&D and extension activities. In 2008–09 total public funding for

agricultural R&D and extension was approximately $1.5 billion (equivalent to about 3.3 per cent

of the gross value of agricultural output), of which Australian governments contributed more

than two-thirds (Mullen 2010). Recent analysis (Sheng et al. 2013) shows that domestic public

investment in agricultural R&D, plus foreign spill-ins, has accounted for around two-thirds of

agricultural productivity growth over the past five decades.

Second, industry-level agricultural TFP growth has also been driven by continuing resource

reallocation from farms or industries with relatively low efficiency to those with higher

efficiency. Since the 1970s this process has been supported by economy-wide reforms to

increase the flexibility of input markets and to restore the role of market mechanisms in

determining the movement of resources between farms and industries (Gray, Oss-Emer & Sheng

2014).

For the agriculture industry, the most significant benefit of these reforms is that farms with

relatively high productivity levels and growth are more likely to be able to increase their

production scale than those which have relatively low productivity levels and growth. In turn,

this process facilitates increases in the proportion of output produced by the largest and most

efficient farms, increasing the efficiency of the industry as a whole. Sheng, Jackson and Davidson

(2015) used farm survey data to show that resource reallocation between farms associated with

this industrial structure adjustment accounted for around half of productivity growth in the

Australian broadacre agriculture industry between 1977–78 and 2009–10 and that this

contribution has been increasing over time. Technological progress and resource reallocation

interact, such that their individual contributions to agricultural productivity growth overlap.

In addition to cross-farm effects, resource reallocation among subsectors of the agriculture

industry has also contributed to aggregate TFP growth. Between 1948–49 and 2013–14 crop

output increased at 3.3 per cent a year, 2.7 times faster than the growth in livestock output over

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

1948–49 1955–56 1962–63 1969–70 1976–77 1983–84 1990–91 1997–98 2004–05 2011–12

Rat

io

Year

Capital/Labour ratio

Materials and services/Labour ratio


30

the same period. As a consequence, crop outputs now account for 64 per cent of total

agricultural output, significantly more than the 35 per cent share they held in 1948–49 (Figure

16). The level and growth of TFP in the crop sector are much higher than in the livestock sector

(Dahl, Leith & Gray 2013), so the change in output structure towards crop production has

contributed to aggregate TFP growth.

Figure 16 Proportion of crop products in gross output value, 1948–49 to 2013–14


Analysis of long-term trends in productivity performance can be usefully augmented by

considering short-term trends. Figure 17 compares the average growth rates of input, output

and TFP over decade-long sub-periods between 1948–49 and 2013–14. Agricultural TFP growth

has generally fluctuated between 1 per cent and 3 per cent a year and rapid output growth has

been the major driver of TFP growth. This finding is consistent with the long-term results.

0

10

20

30

40

50

60

70

1948–49 1955–56 1962–63 1969–70 1976–77 1983–84 1990–91 1997–98 2004–05 2011–12

Pro

po

rtio

n o

f gr

oss

ou

tpu

t va

lue

(%)

Year


31

Figure 17 Output, input and TFP growth by decade, 1948–49 to 2013–14


4.3 Slowdown in TFP growth

A significant slowdown in agricultural TFP growth occurred during the 2000s. Between 1999–

2000 and 2009–10 agricultural TFP grew at –0.2 per cent a year, a much slower rate than the

average of other sub-periods of 2.3 per cent a year. Although the growth rate subsequently

increased to 1.1 per cent a year between 2010–11 and 2013–14, this is a relatively short period

over which to estimate TFP growth, so relatively little emphasis can be placed on this estimate.

To ensure that the finding of a slowdown in agricultural TFP is not an artefact of short-term

fluctuations or measurement error, we have performed a statistical analysis (namely, CUSUQ

test) on the TFP series to identify the presence of a structural break. The results indicate that

between 1948–49 and 2013–14 the only break point occurred in 1999–2000. This result is

consistent with the results of a similar analysis of the trend in TFP for the broadacre agriculture

industry (Sheng, Mullen & Zhao 2010).

The productivity slowdown in recent years is largely the result of severe drought, although that

is not the only cause. ABARES research has shown that reduced public investment in agricultural

R&D since the late 1970s also contributed to the slowdown (Sheng, Mullen & Zhao 2010).

Another possibility is that the gains from advances in technology and market reforms in the

1980s and 1990s have now largely been realised, so new breakthroughs are now required to

restore productivity growth to its long-run trend.

-2

-1

0

1

2

3

4

5

1948–49 to 1959–60

1959–60 to 1969–70

1969–70 to 1979–80

1979–80 to 1989–90

1989–90 to 1999–00

1999–00 to 2010–11

2010–11 to 2013–14

Gro

wth

rat

e (%

)

Year

TFP

Output

Input


32

5 Robustness check As a robustness check, we compare the TFP estimates derived in this study with those obtained

from other work. Although there are differences in methodology and data between studies

(which may contribute to differences in the results), we would expect to see some consistency

across different estimates of Australia’s agricultural productivity performance.

Table 4 presents annual TFP growth rates for various time periods that have been generated in

previous studies, and comparable estimates derived from the series generated in this study. The

results show that the growth rate of agricultural TFP found in this paper is lower than the

estimates generated by the ABS value-added model for the agriculture, forestry and fishing

industry and lower than ABARES estimates for the broadacre agriculture industry. However, the

growth rate is similar to, or slightly higher than, estimates obtained by Coelli and Rao (2005)

and Fuglie (2010) for the agriculture industry when using FAO data.

Table 4 Comparison of agricultural TFP estimates with previous studies

Study

TFP growth in literature

(%)

TFP growth in

this paper (%)

Industrial coverage

Data source Time period covered

ABS (value added)

2.8 1.6 agriculture, forestry and fishing

ABS National accounts

1984–85 to 2010–11

PC (value added) 2.5 1.9 agriculture, forestry and fishing


1974–75 to 2007–08

ABS (gross output)

1.2 1.5 agriculture, forestry and fishing


1994–95 to 2009–10

ABARES 1.3 1.8 broadacre agriculture

ABARES farm survey

1977–78 to 2009–10

Mullen and Cox (1996)

2.4–2.6 2.2 broadacre agriculture

ABARES farm survey

1952–53 to 1993–94

Knopke (1995) 2.7 2.3 broadacre agriculture

ABARES farm survey

1977–78 to 1994–95

Coelli and Rao (2005)

1.8 2.1 Australian agriculture

FAO data 1980 to 2000 (1980–81 to 2000–01)

Fuglie (2010) 1.4 1.6 Australian agriculture

FAO data 1961 to 2006 (1961–62 to 2006–07)

Variations in TFP estimates between studies are likely to be caused by differences in industry

coverage and the treatment of various outputs and inputs. Table 4 also shows that TFP estimates

tend to converge as the period of time that is covered increases.


33

6 Conclusion This study applies the growth accounting based index method to data obtained from national

accounts statistics and farm surveys and used the gross output model to estimate aggregate

output, input and TFP for Australia’s agriculture industry between 1948–49 and 2013–14. The

results show that productivity growth in this period has been rapid, at 2.0 per cent a year,

mainly driven by output expansion. Ongoing technology progress within farms and the resource

reallocation between farms that this has induced have been the main drivers of this industry-

level productivity growth. The results also provide evidence that there has been a slowdown in

agricultural TFP growth over the past decade, consistent with previous studies of productivity in

Australia’s broadacre agriculture industry (Sheng, Mullen & Zhao 2010).

Although the estimates presented in this paper have been derived using the best available

methods, some limitations should be noted. In particular, the absence of some necessary data

prevents the making of comprehensive adjustments for differences in the quality of some

intermediate inputs such as crop chemicals, medicines and seed. Generally, this limitation will

tend to bias estimates of productivity downward. Making further quality adjustments is an

important subject for future research.


34

Appendix A: Data collection and compilation for the agricultural production account To derive agricultural TFP estimates using the gross output based index method, the price and

quantity of each input and output used in the industry needs to be known. Agricultural outputs

include crops, livestock, vegetables, fruit and other on-farm output, while agricultural inputs

include land, labour, capital and materials and services. The data sources that we have used for

each output and input are listed in this appendix.

Outputs

1 Crops

Crop outputs are organised into seven groups: grains and forage crops, oil crops, cotton and

tobacco, fruits, vegetables, floriculture, and other crops. Table A1 lists the commodities within

each of these categories.

Table A1 Definition of crop outputs

Categories Variables to be collected

Grains and forage crops Wheat, barley, oats, rice, rye, hay and silage, maize, sorghum

Oil crops Canola, cottonseed, peanuts, soybeans, flaxseed, sunflowers

Legumes Field peas, lupins, chick peas, other legumes

Cotton and tobacco Cotton lint, tobacco

Fruits and nuts Apples, apricots, avocados, cherries, cranberries, dates, figs, grapes, grapefruit, lemons, nectarines, olives, oranges, peaches, pears, plums, prunes, tangelos, tangerines, strawberries, almonds, hazelnuts, macadamia nuts, pecans, walnuts, other fruit

Vegetables and melons Fresh asparagus, processing asparagus, fresh snap beans, processing beans, broccoli, cauliflowers, celery, fresh cucumber, processing cucumber, lettuce, fresh sweet corn, processing sweet corn, fresh spinach, processing spinach, green peas, onions, fresh tomatoes, processing tomatoes, beans (dry), peas (dry), lentils, potatoes, sweet potatoes, honeydew, watermelon

Floriculture Floriculture and other green house nursery

Other crops Sugar beet, sugar cane, maple products, mushrooms, hops, spearmint, peppermint, other green house and nursery

Others Crops not elsewhere included

Notes: The measure of grain output is gross of grain used for livestock feeding on farms. This quantity is also included as an intermediate input. All prices are measured from the producer’s perspective by adding subsidies and subtracting indirect taxes from market values. The quantities of other crops, fruit and tree nuts and vegetables and melons are constructed as implicit quantities. Sources: Data are obtained from the ABS Australian Agricultural Census and ABARES Australian commodities

2 Livestock

Livestock outputs are organised into four categories: meat animals, dairy, poultry and eggs, and

other livestock products. Table A2 lists the commodities within each of these categories.


35

Table A2 Definition of livestock outputs

Categories Variables to be collected

Meat animals Cattle and calves, pigs, sheep and lambs

Dairy Milk

Poultry and eggs Poultry, farm chickens, broilers, turkey, eggs

Other livestock products Total cash receipts for other livestock products not elsewhere specified, value of inventory change, value of home consumption

Notes: The ‘other livestock products’ category includes wool, mohair, honey, aquaculture, rabbits, and race horses, among others. The quantity of this output is derived as an implicit quantity. Specifically, a price index for total livestock is obtained by aggregating across the other livestock output categories (that is, meat animals, dairy, and poultry and eggs). The implicit quantity is then obtained by dividing the value of total cash receipts for other livestock by this price index. A price index for meat animals is constructed based on the price and quantity of sales (measured as dressed weight). This price index is used to deflate the value of cash receipts for livestock sales, net of the value of inventory change and the value of household consumption. All quantities of poultry are liveweight equivalent. Included in the total value of poultry is the value of ‘miscellaneous poultry’. The value of ‘miscellaneous poultry’ is derived using the price index for broilers, farm chickens and turkey. Farm chickens refers to chickens culled from the laying flock. In addition, dressed weights are defined as follows: ox 320–400 kg; cow 200–240 kg; before January 1996, lamb, 16–18 kg, score 3; from January 1996, lamb, 18–20 kg score 3; mutton, 18–24 kg, score 3; from July 2003, trade lamb, 18–22 kg, score 2–4; mutton, 18–24, score 2–3; from June 2006, trade lamb 18–22 kg, score 2–4; mutton 18–24 kg, score 2–3; and pigs, 60–75 kg. Sources: Data are obtained from the ABS Australian Agricultural Census, the ABS Australian Yearbook and ABARES Australian commodities

3 Other outputs

As noted in the paper, this output category includes all farm outputs, services and non-separable secondary activities not elsewhere included in the crop and livestock production accounts. The data source for this output category is the national accounts.

Inputs

1 Land

Table A3 Definition of land inputs

Categories Variables to be collected Data sources and notes

Crop land Land area operated, price of land Agricultural census, ABARES farm survey

Pasture land Land area operated, price of land Agricultural census, ABARES farm survey

Land characteristics Irrigation, aluminium toxicity, calcareous, sulfidic, moisture stress, aridic torric, leaching, waterlogging, high phosphorus, alkalinity, salinity, cryic frigid, permafrost, cracking clays, volcanic organic content, clayey topsoil, loamy topsoil, clayey subsoil, loamy subsoil, rock sandy topsoil and sandy subsoil

US Department of Agriculture, Economic Research Service

Note: Data on land areas and prices are collected at the state level.


36

2 Capital

Table A4 Definition of capital inputs


Fixed capital

- transportation vehicles and other transportation tools

Investment series in nominal terms, price index


- other plant and machinery Investment series in nominal terms, price index


- non-residential buildings and structures

Investment series in nominal terms, price index


Non-fixed assets

- inventory Inventory stock at the beginning and the end of each period


- stock livestock Inventory stock at the beginning and the end of each period


- vines, trees and other bio-capital Total value and price index ABS National accounts

Intangible assets Examples include computers and R&D investment


3 Labour

Table A5 Definition of labour inputs


Employed workers Number of workers, hours worked per week, wage paid

ABS National accounts, ABARES farm surveys

Proprietors and farm owners Number of workers, hours worked per week


Non-paid family members Number of workers, hours worked per week


Quality adjustment index Average age, education levels, etc. ABS labour force survey


37

4 Materials and services

Table A6 Definition of materials and services inputs


Fuel, lubricants and electricity Diesel, petrol, gas, lubricants and other oils and electricity

Australian Commodity Database

Fertilisers, chemicals and medicines

Fertilisers, chemicals and animal medicines


Feed and seed Silage, animal feed and seed Australian Commodity Database

Repairs and maintenance Machinery repairs, building maintenance and expenditure on land improvement


Packing, marketing and transport Costs related to on-farm packaging, marketing and transport activities


Insurance Expenditure on insurance Australian Commodity Database

Plant hire Costs related to plant hire Australian Commodity Database

Other materials and services Expenditure on materials and services not elsewhere categorised



38

Glossary ABARES Australian Bureau of Agricultural and Resource Economics and Sciences

ABS Australian Bureau of Statistics

FAO Food and Agriculture Organization of the United Nations

R&D research and development

TFP total factor productivity


39

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