13 November 2015, Rome
Food and Agriculture Organization of the United Nations (FAO)
•
New work on methodologically robust Agricultural Capital Stock statistics
funded by One of Five FAO Strategic Objectives (SO) for the 2014-2015
biennium :
To Enable inclusive and efficient agricultural and food systems (SO4)
•
•
Output/Activity required from Statistics Division:
•
To develop and implement methodologies for the measurement of capital stock
and mechanization
•
To develop, collect/compile, validate, and disseminate data on annual capital
stock estimates
Work and approach supported by FAO Legislated Bodies (Committee on Agriculture,
African Commission on Agricultural Statistics, Asia-Pacific Commission on Agricultural
Statistics, Latin America-Caribbean-FAO Working Group on Agricultural Statistics)
a) Capital Stock related variables
Total economy
Agriculture, Forestry
& Fishery (AFF)
Agriculture (Ag)
✔
✔
✘
✔
✘
✔
✘
✔
b) Macro-economic variables not in a)
✔³
✔
Value added¹
✘
✔
Gross Output²
✘
✔
Operating surplus, gross
✔
✘
Operating surplus, net
✘
✔
Compensation of employees²
✘
✔
Employment²
c) Other labour variables (for OECD countries only)
Gross fixed capital formation
Net capital stock
Gross capital stock
Consumption of fixed capital
Wages and salaries, N. of employees,
Self-employed, Full-time equivalents total engaged & employees, Hours
worked - total engaged & employees
✘
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
¹ necessary for calculating the investment ratio and capital stock
² supporting variables in the calculation capital stock
³ it refers to the Gross Domestic Product (GDP)

Farm structures, including major improvements to other buildings and structures

Transport equipment

Machinery and equipment

Communications equipment, office machinery and computers

Produced intangible fixed assets, e.g. software, R&D

Major improvements to tangible non-produced assets, e.g. land

Livestock

Trees
Share of equipment and structures in NCS and GFCF in the USA
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
1947 1950 1953 1956 1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010
Equipment-NCS
Structures-NCS
Equipment-Inv
Structures-Inv
1. Current FAOSTAT database , with data up to 2007, based on aggregation of
separate calculations of asset categories;
2. A new national-accounts based Capital Stock database.
countries
item
Denmark
Land Development
Denmark
2000
2003
2005
2007
98.0
98.0
98.0
98.0
Livestock (Fixed Assets)
100.0
100.0
100.0
100.0
Denmark
Livestock (inventory)
100.0
100.0
100.0
100.0
Denmark
Machinery & Equipment
87.5
87.5
87.5
87.5
Denmark
Plantation Crops
95.5
95.5
95.5
95.5
Denmark
Structures for Livestock
95.5
95.5
95.5
95.5
Denmark
Capital Stock + (Total)
94.8
94.8
94.9
94.9
Ecuador
Land Development
98.0
98.0
98.0
98.0
Ecuador
Livestock (Fixed Assets)
100.0
100.0
100.0
100.0
Ecuador
Livestock (inventory)
100.0
100.0
100.0
100.0
Ecuador
Machinery & Equipment
87.5
87.5
87.5
87.5
Ecuador
Plantation Crops
95.5
95.5
95.5
95.5
Ecuador
Structures for Livestock
95.5
95.5
95.5
95.5
Ecuador
Capital Stock + (Total)
97.8
97.9
97.9
97.9
countries
item
France
Land Development
France
2000
2003
2005
2007
98.0
98.0
98.0
98.0
Livestock (Fixed Assets)
100.0
100.0
100.0
100.0
France
Livestock (inventory)
100.0
100.0
100.0
100.0
France
Machinery & Equipment
87.5
87.5
87.5
87.5
France
Plantation Crops
95.5
95.5
95.5
95.5
France
Structures for Livestock
95.5
95.5
95.5
95.5
France
Capital Stock + (Total)
93.6
93.5
93.7
93.6
Niger
Land Development
98.0
98.0
98.0
98.0
Niger
Livestock (Fixed Assets)
100.0
100.0
100.0
100.0
Niger
Livestock (inventory)
100.0
100.0
100.0
100.0
Niger
Machinery & Equipment
87.5
87.5
87.5
87.5
Niger
Plantation Crops
95.5
95.5
95.5
95.5
Niger
Structures for Livestock
95.5
95.5
95.5
95.5
Niger
Capital Stock + (Total)
98.9
98.9
99.0
99.0
Source: FAOSTAT
Sources: OECD and FAO SST
Gross capital stock, millions 2005 USD
Gross capital stock, millions 2005 USD
GCS
Countries
item
Denmark
Denmark
Denmark
Denmark
Denmark
Denmark
Land Development
Livestock (Fixed Assets)
Livestock (inventory)
Machinery & Equipment
Plantation Crops
Structures for Livestock
Denmark
Ecuador
Ecuador
Ecuador
Ecuador
Ecuador
Ecuador
Ecuador
2005
DK/ EC
2,993
3,693
652
0.4
0.6
0.6
4,296
24
9.4
0.006
877
1.8
Capital Stock + (Total)
Land Development
Livestock (Fixed Assets)
Livestock (inventory)
Machinery & Equipment
Plantation Crops
Structures for Livestock
12,535
0.6
Capital Stock + (Total)
19,358
63,065
Gross output
Value added
DK/ EC
DK/ EC
1.8
0.9
7,030
6,108
1,078
456
4,199
486
4,820
GCS for Ecuador is calculated from reported values of GFCF
Source: FAOSTAT
Sources: OECD and FAO SST
Gross capital stock, millions 2005 USD
Gross capital stock, millions 2005 USD
GCS
Countries
item
France
France
France
France
France
France
Land Development
Livestock (Fixed Assets)
Livestock (inventory)
Machinery & Equipment
Plantation Crops
Structures for Livestock
France
Ecuador
Ecuador
Ecuador
Ecuador
Ecuador
Ecuador
Ecuador
2005
FR/ EC
19,050
18,783
3,315
2.7
3.1
3.1
40,590
3,695
89.1
0.9
10,724
22.1
Capital Stock + (Total)
Land Development
Livestock (Fixed Assets)
Livestock (inventory)
Machinery & Equipment
Plantation Crops
Structures for Livestock
96,156
5.0
Capital Stock + (Total)
19,358
227,347
7,030
6,108
1,078
456
4,199
486
4,820
Gross output
Value added
FR/ EC
FR/ EC
17.3
13.0
Gross Capital Stock 2005, millions 2005 USD
FAOSTAT
Australia
Austria
Belgium
Canada
Czech Republic
Denmark
Estonia
Finland
France
Germany
Hungary
Iceland
Ireland
Israel
Italy
119,275
15,412
6,912
100,954
11,852
12,535
2,171
12,265
96,156
84,125
11,370
985
20,470
2,421
85,127
OECD (ISIC
Rev.3:01)
74,345
59,601
17,349
68,212
14,859
63,065
796
30,403
227,347
290,611
31,888
991
19,766
5,020
426,349
Net Capital Stock, millions 2005 USD
FAOSTAT /OECD
1.6
0.3
0.4
1.5
0.8
0.2
2.7
0.4
0.4
0.3
0.4
1.0
1.0
0.5
0.2
FAOSTAT
OECD (ISIC
Rev.3:01)
115,979
14,017
6,623
94,596
11,133
11,897
1,987
11,101
90,052
76,597
10,766
898
19,573
2,316
78,328
FAOSTAT ≤ 50% of reported GCS
FAOSTAT ≥ 150% of reported GCS
61,417
46,344
8,056
39,678
7,167
33,992
596
15,219
120,560
146,516
18,203
754
11,104
3,208
217,319
FAOSTAT/OECD
1.9
0.3
0.8
2.4
1.6
0.4
3.3
0.7
0.7
0.5
0.6
1.2
1.8
0.7
0.4
Gross Capital Stock 2005, millions 2005 USD
FAOSTAT
Japan
Luxembourg
Netherlands
Norway
Poland
Portugal
Republic of Korea
Slovakia
Slovenia
Spain
Sweden
Switzerland
United Kingdom
United States of
America
293,689
496
12,057
9,157
74,080
14,785
16,332
6,513
2,861
83,135
14,662
8,535
49,011
617,633
OECD (ISIC
Rev.3:01)
477,191
2,071
84,645
18,079
83,994
14,691
92,978
8,285
3,729
117,695
30,878
33,961
77,269
727,166
Net Capital Stock, millions 2005 USD
FAOSTAT /OECD
0.6
0.2
0.1
0.5
0.9
1.0
0.2
0.8
0.8
0.7
0.5
0.3
0.6
0.8
FAOSTAT
OECD (ISIC
Rev.3:01)
266,445
445
11,591
8,345
67,304
13,976
15,318
6,249
2,588
78,239
13,656
7,945
45,891
577,252
FAOSTAT ≤ 50% of reported GCS
FAOSTAT ≥ 150% of reported GCS
309,337
1,027
48,290
14,612
23,597
6,976
61,129
3,127
1,450
86,362
18,808
17,208
37,062
397,855
FAOSTAT/OECD
0.9
0.4
0.2
0.6
2.9
2.0
0.3
2.0
1.8
0.9
0.7
0.5
1.2
1.5
LAND DEVELOPMENT
“Land development = ∑ {(arable land) x (unit price) + (irrigated land) x (unit price)}
Plantation crops = ∑ (land under permanent crop) x (unit price)”
AGRICULTURE MACHINERY
“Machinery and equipment = ∑ {(number of machinery for i) x (unit price of machinery for i) +
(economically active population in agriculture) x US $35)}
Where i stands for tractor, harvester & threshers and milking machine. US $ 35 has been
taken from 1995 series after adjusting for price rises.”
•
Global database on Agricultural Capital Stock and related structural statistics,
•
Covering 223 countries and territories for 1970-2013, with supporting
metadata/documentation
•
Industrial Coverage: Total Economy; Agriculture, Forestry & Fishing (AFF), ISIC
Rev.3:A+B; Agriculture (Ag) subsector, ISIC Rev.3:01
•
Variables
•
•
Capital Stock related variables: GFCF, Net Capital Stock (NCS), Gross Capital Stock (GCS),
Consumption of Fixed Capital (CFC)
•
Other SNA variables: Value-Added¹, Gross Output², Gross Operating Surplus, Net Operating
Surplus, Compensation of Employees², Employment²
•
Other Employment Variables (for OECD countries only): Wages and salaries, Number of
employees, Number of self-employed, Full-time equivalents - total engaged, Full-time
equivalents – employees, Hours worked - total engaged, Hours worked – employees
Other indicators: Investment Ratio (IR), and Agriculture Orientation Index (AOI)
¹ necessary for calculating the investment ratio and capital stock
² supporting variables in the calculation capital stock
Gross Fixed Capital Formation in Agriculture, forestry and fishery by region,
constant 2005 USD, 2000-2013
140
120
2005 USD (billions)
100
Africa
Asia & Pacific
Europe
Latin America & Caribbean
Northern America
Other Developed
80
60
40
20
0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
*Other Developed includes Australia, Japan and New Zealand
• Agriculture, forestry
and fishery GFCF in
2013 estimated at $363
billion globally (2005
USD), up 5% from 2012
and 50% from 2000.
• Asia showed highest
level of agricultural
investment in 2013,
with GFCF of $131
billion (2005 USD) in
2013, surpassing
Europe which led until
2008.
• Note: Analysis begins in
2000 due to missing or
unreliable data for many
countries prior to 2000
• In developed countries,
the GFCF Agriculture
Orientation Index (AOI)
usually exceeds 1,
indicating a higher level
of investment in AFF
relative to its share of
the economy.
Agricultural Orientation Index (AOI) in Agriculture, forestry and fishery by
region, constant 2005 USD, 2000-2013
2.5
2.0
Africa
Asia & Pacific
Europe
Latin America & Caribbean
Northern America
Other Developed
1.5
• Developing countries
have a GFCF Agriculture
Orientation Index (AOI)
less than 1, indicating a
lower share of
agricultural investment
relative to its economic
share.
1.0
0.5
0.0
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
*Other Developed includes Australia, Japan and New Zealand
2011
2012
2013
1. Data Source: selection varies by country and variable
•
•
•
UNSD: National Accounts Estimates (UNSD : NAE), Official Country Data (UNSD : OCD)
OECD: Structural Analysis database (OECD-STAN), National Accounts Database (OECD-NA)
World Input Output Database (WIOD)
2. Data Bridging
•
•
•
Bridging across data series within a single data sources
Bridging across ISIC revisions 3. and 4.
Bridging across data sources
Note: Bridging was essential to create a long time series for capital stock related variables
3. FAO Estimation/imputation
•
•
Imputation/estimation missing observations in an existing series
Estimations of capital stock related variables, and variables required for these estimations
(Value-Added, Gross Output)
Note: No estimation or imputation was conducted for other variables
INDUSTRIES COVERED
ISIC Rev 3
AGRICULTURE, FORESTRY, FISHERY
A+B
AGRICULTURE HUNTING AND RELATED SERVICIES
A01
No. of countries with data
Total economy
A+B
A01
I. NATIONAL ACCOUNTS ESTIMATES (UNSD)
GDP (current and fixed prices, LCU and $)
GFCF (current and fixed prices, LCU and $)
GFCF deflator
GDP/capita (current prices, LCU and $)
Value added ISIC Rev3: A+B (current and fixed prices, LCU and $)
Value added deflator
II. OFFICIAL COUNTRY DATA (UNSD)
OUTPUT
Less Intermediate consumption
VALUE ADDED, GROSS
COMPENSATION OF EMPLOYEES
OPERATING SURPLUS, GROSS
MIXED INCOME, GROSS
Less CONSUMPTION OF FIXED CAPITAL
OPERATING SURPLUS, NET
MIXED INCOME, NET
GROSS FIXED CAPITAL FORMATION
CLOSING STOCK OF FIXED ASSETS
EMPLOYMENT
>200
>200
>200
>200
>200
>200
148
98
157
126
134
25
98
104
15
87
23
70
105
81
78
18
56
55
11
54
22
50
Data for additional countries from STAN, OECD-NA and WIOD
•
For OECD countries , by order of preference:
1.
2.
3.
4.
•
OECD: Structural Analysis database (OECD-STAN)
OECD - National Accounts Database (OECD-NA)
UNSD : Official Country Data
World Input Output Database (WIOD)
For all other countries, by order of preference:
1.
2.
3.
UNSD : Official Country Data
non-OECD countries covered in OECD-STAN and OECD-NA
World Input Output Database (WIOD)
1. WHAT IS THE BRIDGING ?
It is a process that connects two or more :
• fully specified data stores
• for a limited time or on an ongoing basis
N.B.: Before the bridging is undertaken, we “rebase” the constant values using the
2005 as a base year. Moreover, only values in LCU are bridged.
2. WHY WE USE THE BRIDGING ?
In order to have long data series, as data in different series / ISIC Revisions /
Sources are not comparable.
3. WHEN WE USE THE BRIDGING ?
i.
Series-bridging
This involves UNSD - Official Country Data, as data are reported in different series.
Different series numbers are used to store different time-series versions of national accounts
statistics, e.g. different SNA national accounts methodology, different currencies, fiscal years,
or by different sources.
• NB: Priority is given to the most recent series
ii. ISIC Revisions-bridging
The present dataset reports data in ISIC Rev.3, as most of the countries still report
information in ISIC Rev.3.
The bridging of various revisions of ISIC involves the UNSD - Official Country Data
& the OECD – STAN database. If no data for a certain year is available in ISIC Rev.3,
but it is available in ISIC Rev.4, then the latter will be bridged into ISIC Rev.3.
iii. The sources-bridging
In order to expand the time series, different sources are bridged.
How do we use the sources ?
1. OECD (STAN and National Accounts) : it is used as the primary source for OECD
countries (the dbs include also some non-OECD countries, e.g. China, Brazil)
NB: data from OECD-NA are in ISIC Rev.4 only - converted into ISIC Rev.3.
2. UNSD (Official Country Data) : it is usually used for non-OECD countries.
3. WIOD : it is used when no other source is available.
4. HOW THE BRIDGING IS UNDERTAKEN ?
The bridging is possible only when there is overlap between at least two data for the
same year.
Once the two series to be bridged have been identified, we calculate their conversion
factor (as a ratio of the two series):
• We have used the last available year or the last 3-years average, depending on the trend of the
series: if the trend is more or less stable, then we assume that the last available year is
sufficient, otherwise if the values varies significantly over the years, then an average has been
taken.
The bridging is not recommended when the two series differ significantly, e.g. when
the conversion factor is greater than 1.5 or is smaller than 0.5.
Series-bridging
Gross Fixed Capital Formation, ISIC Rev.3:A+B
The bridging is
possible
• No overlap
• Different currency
 Bridging not possible
a. If reported data are available only for a limited number of years :
the missing years' data are imputed based on data on the most recent, or an average of the
most recent, available investment ratio (GFCF/VA).
b. If there are no data at all :
• imputation for GFCF in Ag are based on (1) value-added ratio for Ag over AFF to GFCF in AFF;
• imputation for GFCF in Ag & GFCF in AFF are based on (2) regression equations (linear and
logarithmic) in which the endogenous variable is the investment ratio and the exogenous
variable GDP/capita.
It would have been preferable to use VA/EMPL as explanatory variable but many employment
series are missing or are of dubious quality.
The parameters of the equation are estimated based on the data from the countries from which
data are available. The R2 value of the regressions vary between 0.9 (logarithmic regression for
middle and high income countries) and 0.74 (linear regression for low income countries).
When reported country data on CFC are available these are used for estimating GFCF, when the
latter are missing, as the averages of CFC and GFCF over a medium term period are normally of
the same magnitude.
Data on Net Capital Stock (NCS), Gross Capital Stock (GCS) and Consumption of Fixed
Capital (CFC) is available only for a limited number of countries (mainly OECD countries).
For all other countries data have been calculated by FAO using the Double Declining
Balance Method.
In applying this method, assumption were made of depreciation rates which range from
0.03 to 0.08 depending on the economic level of the countries.
There is large degree of arbitrary judgements in applying these deprecation rates.
Hence, data should be interpreted with great care as they only might give an order of
magnitude and not information about the exact level.
b) Macro-economic variables not in a)
Agriculture, Forestry & Fishery (AFF)
Value added
Missing data are replaced by bridged
data of Value Added, UNSD-NAE.
If there are no Official Country Data at
all then the UNSD-NAE have been
used.
Output
Agriculture (Ag)
Missing data are replaced by bridged data of Value
Added, UNSD-NAE from those years when data are
available.
If there are no Official Country Data at all, then the
whole data series are imputed, using UNSD-NAE for
Agriculture, forestry, fishery (ISIC Rev.3:A+B) as the
basis and then multiply by the value-added ratio of
Agriculture (ISIC Rev.3:01) over Agriculture, forestry,
fishery (ISIC Rev.3:A+B) for neighbouring countries with
the same level of economic development and structure.
Missing data in country series of Gross Missing data points in country series of Gross Output,
Output, Official Country Data, are
Official Country Data, are imputed using the ratio of
imputed using the ratio of Gross
Gross Output/Value Added of adjacent years.
Output/Value Added of adjacent years.
In case the data series are empty then Gross Output is
In case the data series are empty then estimated using the ratio of Gross Output/Value Added
Gross Output is estimated using the
for neighbouring countries, with the same level of
ratio of Gross Output/Value Added for economic development and structure, applied to
neighbouring countries with the same available data on Value added, National Accounts
level of economic development.
Estimates for Agriculture, forestry, fishery (ISIC Rev.3:
A+B)
The fact that a large amount of data on GFCF is imputed or estimated is not the only
weakness of the database.
Another weakness is that we have no information on how large share of GFCF is
machinery and how large share is structures.
The present database on Gross Fixed Capital Formation is a test database. The
methodology and input data will continuously be improved - in particular when FAO data
on machinery investment become available.
Double-declining balance method (proposed by the OECD Capital Stock Manual,
the SNA 2008 and BEA)
WtE = WtB + It – δ (It/2+WtB) = It (1 - δ/2) + WtB (1 – δ)
Where
WtE and WtB are the end-year and beginning-of-the year net capital stocks,
It is gross fixed capital formation,
δ (It/2+WtB) is consumption of fixed capital,
δ is the depreciation rate
δ=R/TA where TA is the average service life of an asset, and R is a parameter around 2.
A starting stock for some period t₀ has to be computed
•
Using capital survey information or
•
Estimate for the long-run growth rate of volume investment when geometric age-efficiency
or age-price profiles apply net stock at the beginning of the benchmark year t₀ can
approximated by
Wt0 = It0 / (δ+ θ)
Θ = the long-run growth rate of volume GDP, or in our case, value added in agriculture

Depreciation rates for different groups of countries

Assumed growth rates for the initial capital stock

Deflators

Imputations when there are missing data and when there are no data at all

Stability of bridging data series (Rev 4 to Rev 3 and between different sources)

How to get reliable data on GFCF in agriculture machinery (the instability of
COMTRADE and PRODCOM data)