1
SOURCES AND METHODS:
WORLD AGRICULTURE STATISTICS 1995 – 2015
NATIONAL ACCOUNTS-BASED STRUCTURAL ANALYSIS DATABASES
Jan Karlsson, 21 February 2017
Purpose
There are two main reasons for setting up the present databases:
(i)
to provide measures of levels and growth of the world and regional agriculture
industries with respect to output, value added, investment, employment and capital
stock; and
(ii)
to facilitate undertaking of KLEMS growth accounting and the calculation of total factor
productivity.
The present paper describes the data sources for the databases, how reported data from countries
and international organizations have been processed and how missing data have been imputed. It
also presents the methods and assumptions made for deriving capital stock data.
Data are presented in two partly overlapping databases:
1. World Agriculture Statistics, which covers over 200 countries.
2. Agriculture Statistics OECD countries, which covers about 30 OECD countries.
Part I: Introduction and overview
I.1
Industry coverage
Data cover the industries for Crop and animal production, hunting and related service activities and
the aggregate Agriculture, forestry, fishing. The former industry is a subindustry of the latter. All
data are expressed in the international standard industry classification ISIC Revision 4, which has the
following codes for the two industries: ISIC Rev.4: A01 and ISIC Rev.4: A01-03, respectively. Note
that last year’s databases were expressed in ISIC Rev.3.
I.2
Data sources
All input data to the present database have the following sources:
1. National Accounts Official Country Data (OCD) submitted to and disseminated by the United
Nations Statistics Division (UNSD).
2. National Accounts Estimates (NAE) disseminated by the United Nations Statistics Division
(UNSD).
3. The OECD National Accounts database.
4. The Economic Research Service of the US Department of Agriculture.
2
I.3
Bridging of Classification
Countries reporting data in ISIC Rev. 4, somewhat below 100 countries, normally stopped recording
data in ISIC Rev. 3 in 2008. However, data in the two classifications of ISIC are usually available for
several years before 2008 which implies that “bridges” can be constructed between the two
classifications. Data in ISIC Rev. 4 can therefore be scaled backwards to years before ISIC Rev.4 data
were available, in some case as far back as to 1970. Data for those countries that are still reporting
in ISIC Rev 3 are converted into ISIC Rev. 4 using average bridge ratios of the former countries, taking
into account the particular category of country when selecting bridge ratio. For further details see
part II.
I.4
Variables included in the databases and their units of measurement
The variables in the database are expressed in national currencies, often referred to as local
currency unit (LCU), in current prices and in either constant 2005 or 2010 prices (in the case of the
database Agriculture Statistics OECD countries). Some variables are also expressed in US dollars
(USD) in current prices and constant 2005 prices. For those variables that are recorded neither in
USD and nor in volume measures the deflators and exchange rates included in the database World
Agriculture Statistics can easily be applied for this purpose. It should also be noted that Gross
output and Value added are measured in Basic prices.
The tables below on the following pages show the variables and their unit of measure which are
covered by the two databases.
I.6
Length of time series
Data are compiled for the period 1970 - 2015 but are only disseminated only as from 1995. The
reason for this is that national accounts data for many countries are just rough estimates before
1990 and for several newly created countries more reliable data are only available as from 1995.
However, for interested users the full database as from 19970 can be made available, having taken
note of the above-mentioned restrictions on reliability
I.7
Imputations and calculations of missing data
As far as possible reported country data are used. When data are missing for Gross output, Value
added and Gross fixed capital formation various imputation methods are applied, see parts II and III.
Gross and Net capital stock and Consumption of fixed capital are calculated based on standard
statistical methods with certain assumptions concerning length of service life of assets and
depreciation rate, see part III.
For other variables no imputations of missing data are made – only reported data are shown.
3
Variables covered in the database World Agriculture Statistics
Primary data sources: UNSD National Accounts Official Country Data, UNSD National Accounts Estimates and OECD
National Accounts Database
GROSS OUTPUT, millions of current national currency and USD
GROSS OUTPUT, millions of national currency and USD, constant 2005 prices
VALUE ADDED, millions of current national currency and USD
VALUE ADDED, millions of national currency, constant 2005 prices
GROSS FIXED CAPITAL FORMATION, millions of current national currency and USD
GROSS FIXED CAPITAL FORMATION, millions of national currency and USD, constant 2005 prices
CONSUMPTION OF FIXED CAPITAL (reported), millions of current national currency and USD
CONSUMPTION OF FIXED CAPITAL (derived), millions of current national currency and USD
CONSUMPTION OF FIXED CAPITAL (reported) , millions of national currency and USD, constant 2005 prices
CONSUMPTION OF FIXED CAPITAL (derived) , millions of national currency and USD, constant 2005 prices
GROSS CAPITAL STOCK, millions of current national currency and USD
GROSS CAPITAL STOCK, millions of national currency and USD, constant 2005 prices
NET CAPITAL STOCK, millions of current national currency and USD
NET CAPITAL STOCK, millions of national currency and USD, constant 2005 prices
EMPLOYMENT, TOTAL, number of persons
GROSS OPERATING SURPLUS AND MIXED INCOME, millions of current national currency
NET OPERATING SURPLUS AND MIXED INCOME, millions of current national currency
COMPENSATION OF EMPLOYEES, millions of current national currencies
Variables only for Crops and animal production, ISIC Rev.4:A01
AGRICULTURE LABOR (persons economically active in agriculture, +15 years, male+female). Source:
USDA_ERS / FAOSTAT
AGRICULTURAL LAND, 1000 Ha of Rainfed Cropland Equivalents. Source: USDA-ERS / FAOSTAT
AGRICULTURE MACHINERY: GROSS FIXED CAPITAL FORMATION OR APPARENT CONSUMPTION , million
current national currency
Deflators, exchange rates and GDP per capita
Agriculture, forestry, fishing (ISIC Rev. 4:A01-03): VALUE ADDED deflator, local currency unit
Agriculture, forestry, fishing (ISIC Rev. 4:A01-03): VALUE ADDED deflator, USD
Total economy: GROSS FIXED CAPITAL FORMATION deflator, USD
Total economy: GROSS FIXED CAPITAL FORMATION deflator, local currency unit
GROSS FIXED CAPITAL FORMATION (total economy) over GDP
Per capita GDP, USD, current prices
Exchange rate AMA
Exchange rate IMF
4
Variables covered in the database Agriculture Statistics OECD countries
Primary data source: OECD National Accounts Database
GROSS OUTPUT, millions national currencies, current prices
VALUE ADDED, millions national currencies, current prices and constant 2010 prices
CONSUMPTION OF FIXED CAPITAL, millions national currencies, current prices and constant 2010 prices
COMPENSATION OF EMPLOYEES, millions national currencies, current prices
WAGES AND SALARIES, millions national currencies, current prices
GROSS OPERATING INCOME AND MIXED INCOME, millions national currencies, current prices
NET OPEARING SURPLUSE AND MIXED INCOME, millions national currencies, current prices
GROSS FIXED CAPITAL FORMATION, millions national currencies, current prices and constant 2010 prices
GROSS FIXED CAPITAL FORMATION IN MACHINERY AND EQUIPMENT, millions national currencies, current prices
and constant 2010 prices
GROSS FIXED CAPITAL FORMATION IN CULTIVATED BIOLOGICAL ASSETS, millions national currencies, current
prices and constant 2010 prices
TOTAL EMPLOYMENT, number of 1,000 persons
TOTAL EMPLOYMENT, number of 1,000 jobs
TOTAL EMPLOYMENT, million of number of hours worked
EMPLOYEES, number of 1,000 persons
EMPLOYEES, number of 1,000 jobs
EMPLOYEES, million of number of hours worked
SELF-EMPLOYED, 1,000 persons
SELF-EMPLOYED, 1,000 jobs
SELF-EMPLOYED, million of hours worked
GROSS CAPITAL STOCK, million national currencies, current prices and constant 2010 prices
GROSS CAPITAL STOCK, MACHINERY AND EQUIPMENT, million national currencies, current prices and constant
2010 prices
GROSS CAPITAL STOCK, CULTIVATED BIOLOGICAL ASSETS, million national currencies, current prices and constant
2010 prices
NET CAPITAL STOCK, million national currencies, current prices and constant 2010 prices
I.8
Statistical contra Analytical databases
The database Agriculture Statistics OECD countries is almost exclusively is based on country data
as reported to the OECD. It can be considered as a Statistical database. On the other hand, as a
substantial number of data points have been bridged and/or imputed in the database World
Agriculture Statistics it should be considered as an Analytical database.
I.9
Overlap between the two databases
For the OECD countries data on Gross output, Value added, Gross fixed capital formation,
Consumption of fixed capital and Gross and Net operating surplus, expressed in LCU, are generally
the same in the two databases. The volume measures differ because of the use of different base
year and of deflators, see I.11 below. The data for Gross and Net capital stock in the two databases
might also differ for the following reasons:

In World Agriculture Statistics all measures are based on calculations from the double
declining balance method using 25 years as length of service life and a depreciation rate of
0.08 for all countries, see part III.
5

In Agriculture Statistics OECD countries each country has reported data based on their
own method for calculating Gross and Net capital stock. Length of service life and
depreciation rates can, as will be seen in part III, vary significantly between countries.
1.10 Data on Land and employment
Land: Data on land has been downloaded from an ERS-USDA database on Measuring International
Agricultural Total Factor Productivity (TFP) Growth. These data originate from FAOSTAT land
database but further processed by ERS-USDA. In order to account for the contributions to growth
from different land types, irrigated cropland, rain-fed cropland, and permanent pastures are
converted into 1000 Ha of "rainfed cropland equivalents" based on their relative productivity. For
further details see Annex. Data are available up to and including 2013. As land data tend to change
rather slowly it seems reasonable to bring forward 2013 data to 2015.
Employment: Two complementary sets of employment data are presented:
1. Employment in Agriculture, forestry, fishing (ISIC Rev4:A01-03) and in Crop and animal
production (ISIC Rev 4:A01), as reported in UNSD National Accounts Official Country Data,
and
2. Agricultural Labor, persons economically active in agriculture, +15 years, male and female,
downloaded from ERS-USDA database on Measuring International Agricultural Total Factor
Productivity (TFP) Growth, which is largely based on FAOSTAT labour data.
For some countries it seems as if employment data are less accurate, probably as a result of not
applying measurement to the right activity and occupation definitions. In several cases obvious
errors have been spotted (and when possible also corrected). In some cases it can be suspected that
data refer rather to the rural population than to the employment in agriculture. Having two
measures of labour input facilitates benchmarking. A second reason for including the ERS-USDA
data is to be able to use the same input variable in growth accounting exercises as the one applied
by ERS-USDA.
For the OECD countries very detailed employment data are available covering not only total
employment but also, number of employees, self-employed in number of persons and jobs. Data
are also available on hours worked.
I.11
Deflators and Exchange rates
In the database World Agriculture Statistics data in constant 2005 prices, measured in Local
Currency Units (LCU) and in US dollars (USD), are derived using the following deflators:
a) Total economy GFCF deflators, for LCU and USD, implicitly derived from UNSD National
Accounts Estimates. In the absence of specific industry deflators these deflators are used for
deflation of GFCF in Agriculture, forestry, fishing and for Crops and animal production as well
for Consumption of fixed capital (CFC) and Gross and Net capital stock in these industries.
6
b) Value Added deflators for Agriculture, forestry, fishery (ISIC Rev.4:A01-03), for LCU and
USD, implicitly derived from UNSD National Accounts Estimates. In the absence of Gross
output deflators these deflators are also used to deflate Gross output.
The exchange rates applied are those disseminated in the UNSD National Accounts Estimates.
Data in the Agriculture Statistics OECD countries are only expressed in local (national) currency.
Furthermore, the volume measures are expressed in constant 2010 prices (and not in 2005 prices as
in World Agriculture Statistics) and the deflators applied are those by the respective country. The
implicit deflators are therefore specific to the two industries covered in the database which of
course implies that volume measures are more accurately measured.
The OECD volume measures have not been converted to 2005 prices as the rebasing might not
properly reflect the underlying weights of the price deflators set up by countries. Of course the user
of this database can easily do such rebasing by him/her self.
7
Part II:
II.1
Further details on the data processing
Bridging
Series-bridging. Data for one and the same variable in OCD are presented in different series
numbers which refer to data in different versions of the System of National Accounts methodology,
different currencies, fiscal years, or by other reasons of differences. Data in different series are not
directly comparable. Fortunately, most of the series are overlapping for one or several years which
mean that it is possible to bridge older series into more resent ones, in order to create long data
series.
ISIC Revisions-bridging. Data in ISIC Rev.3 are bridged into ISIC Rev. 4 when data in the two series
overlap for one or several years. When there is no overlap the ISIC Rev. 3 data are multiplied by an
average conversion factor derived from similar countries having both Rev 3 and Rev 4 data.
It should be noted that in the bridging process there might be a loss in the reliability of data if the
bridges are not stable. For instance, in bridging between ISIC Rev. 4 and Rev. 3 the bridges might for
some countries vary significantly from one year to another. In this case an average of bridge years is
applied, which of course still enters an error of unknown magnitude.
II.2 Imputations of missing data points in time series of Value added, Gross
output and Gross fixed capital formation
Introduction
The basis for imputations is investment ratios (GFCF/VA) based on available reported data from
countries (OCD), which is then applied to Value added from NAE. When data are missing for Gross
output for certain years, ratios of available reported data on Value added/Gross output are used in
order to impute missing data through trend calculations.
Gross and Net capital stock are calculated based on reported and/or imputed series of GFCF applied
to the double-declining balance method, which is described in detail in part III below.
For Crop and animal production (ISIC Rev.4: A01) missing data, including capital stock data that are
not reported by countries, are imputed using the ratio of value added for Crops and animal
production (ISIC Rev.4:A01) over Value added in Agriculture, forestry, fishery (ISIC Rev.4: A01-03).
This ratio is denoted VA_Crop / VA_AFF (for a vast majority of countries this ratio exceeds 0.8 – on a
world basis it amounts to 0.9), see section II.3 below for details. Exceptions are smaller island
nations and forest-dense countries, e.g. in north Europe.
Imputations of individual data cells can sometimes get astray and give strange values. Such values
are, however, normally spotted in the validation process. Overall, however, the imputations seem to
give a reasonable good estimate of the order of magnitude.
Moreover, as reported data are available for almost all major producer countries regional aggregates
and world total in particular are fairly reliable.
8
Procedures for individual variables
Value added (VA): If OCD data (or in a few cases when OECD data are missing for OECD countries)
are missing then NAE data are used. This implies that we can get complete VA time series for the
period 1970-2015.
Gross output (GO): As the ratio value added over gross output (VA/GO) is very stable over time, see
data on the standard deviation in table 1 below, this ratio can be used for imputing missing data
points in the series. Table 1 also shows that the ratio VA/ GO is around 0.8 or more in developing
countries whilst in developed countries it is in the range 0.3 – 05. 1 The imputing is done either by
interpolation of gaps or by fore- or back casting through linear regression. The difference between
gross output and value added is intermediate consumption.
Gross fixed capital formation (GFCF): The first step is to calculate the investment ratio, that is, GFCF
over VA as far as data are available from OCD. As this ratio is fairly stable over time (but less stable
than the value added/gross output ratio) it can be used for interpolating gaps and for fore- or back
casting through linear regression.
When reported country data on Consumption of fixed capital (CFC) are available these can be used
as a proxy GFCF, when the latter are missing, as the averages of CFC and GFCF over a medium term
period are normally of the same magnitude. At the end of this paper graphs, for a selection of
countries, show the close correlation between GFCF and CFC, although there might be a time lag
between the two series. The amplitude is usually of the same magnitude
Value added in OCD and in NAE: When calculating indicators, such as the investment ratio - which
form the bases for the imputation of GFCF - it is important that the nominator and denominator
originate from the same series numbers of the Official Country Data, i.e. that they are measures in
the same “frame”. For this reason Official Country Data are used as far as possible. National
Accounts Estimates are used only when the former data are missing.
II.3 Imputations when there are no country data on Gross output and Gross
fixed capital formation
(a) Agriculture, forestry, fishing (ISIC Rev.4: A01-03)
Availability of reported country data: Gross output (GO) and Value added (VA) data are available for
about 160 and 170 countries, respectively. For those countries which we do not have report value
added data NAE data are used, giving the result that value added data are available for practically all
countries in the world (Taiwan, Province of China being the only major exception).
Gross output (GO): Analysis of the GO and VA data shows that countries with similar economic level
and structure have VA/GO ratios of roughly the same magnitude. Therefore, when there are no data
on gross output available for a country they are imputed from a linear regression in which the ratio
value added over gross output (VA / GO) is regressed against GDP/capita and the ratio of exports of
1
Calculated from data in the 2016 version of World Agriculture Statistics database, which included data up to
2014. Similar results would certainly be obtained for 2015 from the presented updated database.
9
agriculture products over value added (EXP / VA). It should be noted that for almost all major
producer countries data on gross output are available.
Table 1. Ratio Value added over Gross output in 2014, mean of the 5 most recent years and the standard deviation
Country or Area
Mozambique
Rwanda
Chad
Gambia
Bhutan
Ghana
Burkina Faso
Central African Republic
South Sudan
Turks and Caicos Islands
Niger
Papua New Guinea
Mauritania
Madagascar
Tanzania - Mainland
Bahamas
Democratic Rep. of the Congo
Gabon
Kenya
Burundi
Zambia
Guinea
Sierra Leone
Dominica
Algeria
Saint Kitts and Nevis
Cameroon
Egypt
Lebanon
Cabo Verde
India
Pakistan
Cote d'Ivoire
Senegal
Lesotho
Benin
Togo
Santa Lucia
Tonga
Saudi Arabia
Tunisia
Cook Islands
Malaysia
Myanmar
Thailand
VA/GO Mean STDEV
2014 VA/GO VA/GO
0.95
0.93
0.91
0.90
0.89
0.88
0.86
0.86
0.85
0.85
0.84
0.84
0.83
0.83
0.83
0.81
0.81
0.81
0.81
0.81
0.81
0.81
0.81
0.80
0.80
0.80
0.79
0.79
0.79
0.78
0.77
0.77
0.77
0.77
0.77
0.77
0.77
0.76
0.75
0.75
0.75
0.74
0.74
0.74
0.74
0.89
0.95
0.91
0.90
0.90
0.91
0.89
0.86
0.85
0.83
0.90
0.90
0.86
0.02
0.02
0.00
0.02
0.01
0.00
0.01
0.82
0.81
0.83
0.01
0.01
0.02
0.80
0.80
0.81
0.03
0.05
0.00
0.81
0.77
0.81
0.75
0.77
0.76
0.79
0.78
0.78
0.78
0.77
0.77
0.83
0.77
0.76
0.78
0.79
0.75
0.75
0.75
0.01
0.02
0.01
0.08
0.02
0.03
0.01
0.03
0.02
0.02
0.02
0.01
0.05
0.04
0.02
0.03
0.05
0.01
0.02
0.04
0.80
0.79
0.03
0.03
0.00
0.01
0.04
0.04
0.02
Country or Area
Swaziland
Montserrat
British Virgin Islands
Nigeria
Guatemala
Botswana
VA/GO Mean
2014 VA/GO
0.74
0.74
0.73
0.73
0.73
0.73
Indonesia
0.72
Philippines
0.72
Bahrain
0.72
Djibouti
0.72
Iran (Islamic Republic of)
0.72
Sri Lanka
0.72
Bangladesh
0.71
Oman
0.71
Saint Vincent and the Grenadines 0.71
Albania
0.71
Kuwait
0.71
Kosovo
0.71
Iraq
0.71
Peru
0.70
Libya
0.70
Bermuda
0.69
Kiribati
0.69
Micronesia (Fed. States of)
0.69
Sao Tome and Principe
0.69
Solomon Islands
0.69
Palau
0.69
Timor-Leste
0.69
Vanuatu
0.69
Paraguay
0.68
Armenia
0.68
Bolivia (Plurinational State of)
0.68
Suriname
0.66
Antigua and Barbuda
0.66
Colombia
0.66
Mongolia
0.66
Namibia
0.66
Morocco
0.65
Mauritius
0.64
Dominican Republic
0.64
El Salvador
0.64
Montenegro
0.64
Panama
0.64
Qatar
0.64
Nicaragua
0.63
STDEV
VA/GO
0.75
0.75
0.73
0.70
0.75
0.75
0.01
0.01
0.01
0.02
0.02
0.04
0.72
0.72
0.00
0.01
0.74
0.75
0.78
0.71
0.71
0.71
0.74
0.71
0.79
0.70
0.70
0.67
0.02
0.03
0.04
0.02
0.01
0.02
0.09
0.66
0.67
0.67
0.67
0.67
0.67
0.68
0.61
0.72
0.66
0.70
0.71
0.72
0.65
0.62
0.67
0.74
0.76
0.58
0.64
0.62
0.64
0.02
0.03
0.03
0.03
0.03
0.03
0.02
0.08
0.04
0.04
0.03
0.03
0.05
0.03
0.03
0.02
0.05
0.06
0.04
0.06
0.01
0.03
0.03
0.04
0.03
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Table 1. Ratio Value added over Gross output in 2014, mean of the 5 most recent years and the standard deviation
Country or Area
Brunei Darussalam
United Arab Emirates
Belize
Syrian Arab Republic
Greenland
Uruguay
Zimbabwe
Fiji
Venezuela
Honduras
Mexico
Jamaica
Ecuador
Argentina
Seychelles
Italy
Cayman Islands
Cuba
Republic of Korea
The form. Yugoslav Rep. of Macedonia
Kazakhstan
Spain
Brazil
Greece
Azerbaijan
Georgia
Croatia
Republic of Moldova
Yemen
Russian Federation
Sweden
Finland
Cyprus
Costa Rica
Barbados
Slovakia
Slovenia
State of Palestine
Iceland
Japan
Romania
Faeroe Islands
Serbia
Austria
Switzerland
VA/GO Mean STDEV
2014 VA/GO VA/GO
0.63
0.62
0.61
0.61
0.60
0.59
0.59
0.59
0.58
0.58
0.58
0.57
0.57
0.56
0.56
0.56
0.55
0.55
0.54
0.54
0.54
0.54
0.54
0.53
0.53
0.53
0.52
0.52
0.51
0.51
0.51
0.50
0.49
0.49
0.49
0.49
0.48
0.48
0.48
0.48
0.47
0.46
0.46
0.45
0.45
0.63
0.70
0.62
0.73
0.61
0.67
0.60
0.79
0.59
0.59
0.65
0.56
0.77
0.54
0.66
0.59
0.55
0.52
0.66
0.49
0.56
0.57
0.60
0.60
0.66
0.07
0.06
0.01
0.08
0.02
0.07
0.05
0.04
0.02
0.02
0.04
0.02
0.12
0.03
0.05
0.03
0.01
0.02
0.26
0.03
0.05
0.03
0.04
0.05
0.09
0.49
0.48
0.61
0.52
0.62
0.53
0.54
0.56
0.49
0.42
0.47
0.50
0.55
0.58
0.51
0.45
0.47
0.49
0.46
0.02
0.08
0.06
0.01
0.07
0.03
0.04
0.05
0.05
0.02
0.09
0.04
0.05
0.03
0.04
0.01
0.03
0.02
Country or Area
VA/GO Mean STDEV
2014 VA/GO VA/GO
Israel
0.44
New Zealand
0.44
France
0.43
Belarus
0.43
Bosnia and Herzegovina
0.43
Ukraine
0.42
United Kingdom
0.41
United States
0.41
South Africa
0.41
Hungary
0.40
Poland
0.40
Chile
0.40
Estonia
0.40
Jordan
0.39
Netherlands
0.39
Norway
0.38
Czech Republic
0.38
Canada
0.38
Germany
0.37
Malta
0.36
Lithuania
0.36
Aruba
0.35
Liechtenstein
0.35
Luxembourg
0.35
Curaçao
0.34
New Caledonia
0.34
Bulgaria
0.34
Turkey
0.34
Sint Maarten
0.33
Portugal
0.32
Netherlands Antilles
0.31
China, Hong Kong Special Adm. Region
0.31
China, Macao Special Adm. Region 0.31
Singapore
0.31
Kyrgyzstan
0.30
Tajikistan
0.30
Latvia
0.29
San Marino
0.28
Belgium
0.27
Denmark
0.27
Ireland
0.26
Trinidad and Tobago
0.23
Germany, Federal Republic of
Sudan (up to 2011)
Yemen Arab Republic [former]
0.45
0.46
0.46
0.42
0.46
0.46
0.44
0.44
0.48
0.39
0.42
0.47
0.40
0.42
0.41
0.50
0.40
0.43
0.43
0.46
0.43
0.49
0.03
0.03
0.03
0.06
0.02
0.10
0.02
0.03
0.06
0.02
0.02
0.05
0.03
0.12
0.04
0.07
0.05
0.02
0.04
0.06
0.05
0.05
0.49
0.37
0.50
0.43
0.43
0.33
0.65
0.39
0.36
0.09
0.06
0.06
0.06
0.06
0.10
0.17
0.09
0.05
0.34
0.42
0.47
0.35
0.25
0.36
0.39
0.35
0.41
0.48
0.79
0.79
0.04
0.09
0.11
0.04
0.07
0.05
0.07
0.08
0.07
0.03
0.02
0.06
11
Gross fixed capital formation (GFCF): For some 100 countries and areas there are no data at all on
GFCF in Agriculture, forestry, fishery (ISIC Rev.4: A01-03). In these cases data on investment ratios
are imputed from regression equations (linear and logarithmic) in which the endogenous variable is
the investment ratio (GFCF / VA) and the exogenous variables GDP/capita and Exports of agriculture
products over value added (EXP / VA). The parameters of the equation are estimated based on the
data from just under 100 countries from which data are available (a few outlier countries have been
excluded). The countries are classified into two strata: below and over $10’000 GDP/capita,
In developed countries, the total economy investment ratio (GFCF/GDP) is normally smaller that the
corresponding investment ratio in Agriculture, forestry, fishery (GFCF-AFF/VA-AFF) indicating that
agriculture is more capital intensive than the economy as a whole. In the developing countries it is
very much the reverse – agriculture is much less capital intensive than the economy as a whole. This
is one reason why GFCF total economy /GDP cannot be taken as a proxy for GFCF-AFF/VA-AFF.
Land is a non-produced asset and is thus not included in the valuation of capital formation. On the
other hand, land improvements, trees and orchards are included. It goes without saying that land is
important when making productivity analysis in agriculture. Non-produced assets, such as land, are
therefore included in the income accounts.
Furthermore, the SNA advocates having supplementary accounts to the standard accounts could
which display the implicit services provided by non-financial assets. The contribution of labour input
to production is recognized in compensation of employees. By also associating estimates of capital
services with the standard breakdown of value added, the contributions of both labour and capital
to production can be portrayed in a form ready for use in the analysis of productivity in a way
entirely consistent with the accounts of the SNA.
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 for many countries have incomplete information on how
large share of GFCF is machinery and equipment and how large share is structures and other assets.
For some OECD countries national accounts data on GFCF in machinery and equipment are available.
For instance, in the United States machinery accounts for over 80% of total GFCF and for about 50%
of net capital stock (NCS) - the difference in the two shares are explained by difference in length of
service lives and rates of depreciation, see figure 1 and figure 2 in part III of this document. In the
euro-area the ratio of agriculture machinery investments in total investments is about 60% and just
below 70% in Canada.
For non OECD countries the database contain estimates of GFCF in machinery and equipment by
calculating apparent consumption of agriculture machinery which of course is not the same as GFCF
but can serve as a very rough proxy.
In view of what has been stated above, the user should be aware of the fact that for certain
countries data are just imputations and should only be used as an indication of order of magnitude
and used only in an analytical context and as an input to regional and world aggregates.
(b) Crop and animal production (ISIC Rev.4: A01)
Availability of reported country data: Gross output (GO) and Value added (VA) are available for 113
and 122 countries, respectively. For GFCF only some 60 countries have reported data.
12
Figure 1: Investments in Machinery / Total investments (GFCF) in
Agriculture, forestry, fishing (ISIC Rev.4:A01-03)
1.00
0.90
0.80
0.70
0.60
Australia
0.50
EU-€
0.40
0.30
0.20
Canada
USA
0.10
0.00
Value added imputation: For the aggregate industry Agriculture, forestry, fishing (ISIC Rev.4: A0103), we have value added data for 208 countries and/or territories. In order to impute the value
added for the 86 countries and territories for which data are missing in the sub-industry Crops and
animal production (ISIC Rev. 4:A0=1), the following approach has been taken:
1. We calculate the value added ratio of Crops and animal production over of Agriculture,
forestry, fishing, VA_Crop/VA_AFF for the 122 countries for which data are available.
2. The same countries are then classified according to geographical, climate and topological
characteristics and for each category the mean VA_Crop/VA_AFF ratio is calculated, see
table 2 below.
3. The 86 countries for which VA_Crops data are missing are matched with the closest category
in table 2 and assigned the corresponding VA_Crop/VA_AFF ratio.
Imputations of gross output and gross fixed capital formation: For those countries where no
reported data available, the VA_Crop/VA_AFF ratio is applied to reported or imputed data for the
same variables for Agriculture, forestry, fishing.
As the dataset for Crop and animal production (ISIC Rev4:A01) contains significantly more imputed
data points than Agriculture, forestry, fishing (ISIC Rev.4: A01-03) it consequently has more data with
lower reliability.
13
Table 2: Ratio of Value added (Crops) / Value added (Agriculture, forestry, fishing)
COUNTRY CHARACTERISTICS
Mean Stdev Max Min
COSTAL COUNTRY
Agriculture; some fishing activity, marginal forestry activity
0.89 0.09 1.04 0.72
Agriculture; some (modest) fishing and forestry activity
0.86 0.11 1.03 0.52
Agriculture; substantial forestry activity, marginal fishing activity
0.62 0.14 0.76 0.44
Substantial forest activity (e.g. Sweden) or forestry & fishing (Norway)
0.31 0.10 0.45 0.25
Gulf State
0.73 0.21 0.95 0.55
SMALL ISLAND COUNTRY
Dominant fishing activity, marginal agriculture activity, no forestry (Island)
0.19
Small islands dominated by tourism ("garden agriculture)
0.63 0.23 0.94 0.17
LANDLOCKED COUNTRY
Agriculture (prairie, step, desert); marginal forest
0.89 0.14 1.00 0.62
Agriculture; some forest activity
0.88 0.09 1.00 0.84
II.4 Reliability of data
The database World Agriculture Statistics contains reported country data as well as a substantial
amount of imputed and calculated data (note that capital stock data for all countries, except for
some OECD countries, are calculated).
IT SHOULD THEREFORE BE NOTED THAT THE DATABASE IS AN ANANYTICAL DATABASE WHICH
MAY CONTAIN DATA THAT DIFFERS FROM OFFICIALLY REPORTED COUNTRY DATA.
However, as far as possible all available reported country data are used. In some cases, country data
have been rejected as they are obviously wrong, at least in the present context. In some other cases,
errors in reported country data disseminated by the UNSD have been corrected, e.g. because of
errors of factors of 100 or 1000. Still, the present database includes some reported country data,
disseminated by the UNSD, that seem strange or unreliable. As there is no way of correcting them
they have still been included.
It should be noted that there are two United Nations Statistics Division (UNSD) time series on Value
added in Agriculture, Forestry and Fishing (ISIC Rev.3: A-B): (i) Value added, National Accounts
Estimates (VA_NAE) and (ii) Value added, National Accounts Official Country Data (VA_OCD). The
first type of data source covers about 220 countries and territories. The second data source, which
is input to the National Accounts Estimates, covers about 170 countries. In most cases the two value
added sources coincide or show only marginal differences. In a few cases, however, the differences
are very significant. In this database both the Value added measures are presented.
Explanations to Inconsistent or strange data
In some cases reported country data for sub-industry Crop and animal production (ISIC Rev.4:A01)
are, for some or several years, equal or even slightly exceed those of Agriculture, forestry, fishery
(ISIC Rev. 4: A01-03. There can be several explanations to this fact:
14






Differences between Value added Official Country Data and Value added National Accounts
Estimates. In most cases they coincide but for some countries they differ - normally
marginally but for a few countries significantly.
The conversion of data from ISIC Rev. 3 to ISIC Rev.4 which introduces errors.
The mixing of data sources.
Because of imputation factors, e.g. the investment ratio GFCF/VA, which are averages over
several years. For an individual year averages might not work that well.
In many of the countries that were part of former USSR and former Yugoslavia data before
1995 often seem somewhat strange, in particular when they are expressed in current USD.
In extreme case there can be classification explanations why a sub-industry can be larger
than the aggregated industry to which it is a member.
For some countries there are doubts about the reliability of the applied implicit deflators of the UN
National Accounts Estimates. For instance, data for Somalia and Sudan, expressed in current USD,
show large jumps from one year to another as a result corresponding fluctuation in the deflators.
15
PART III
Calculations of Gross and Net Capital Stock
and Consumption of Fixed Capital
III.1 Capital stock concepts
Gross capital stock is measured as the cumulative flow of investments, corrected for the retirement
pattern. It does not correct for the loss of efficiency. The gross fixed capital stock is therefore the
value, at a point in time, of assets held by producers with each asset valued at “as new” prices – i.e.
at the prices for new assets of the same type - regardless of the age and actual condition of the
assets. The “as new” prices are obtained by revaluing assets acquired in earlier periods using price
indices for the relevant types of assets.
Net or wealth capital stock is the stock of assets surviving from past periods and corrected for
depreciation. The net stock is valued as if the capital good (used or new) were acquired on the date to
which a balance sheet relates, that is, assets are valued at their market prices. The net capital stock
is thus the value at a point in time of assets at the prices for new assets of the same type less the
cumulative value of consumption of fixed capital accrued up to that point.
Consumption of fixed capital is the difference between successive market values of assets and can
be obtained indirectly by using age-efficiency profiles to obtain the age-price profiles of assets and
then subtracting successive values of the assets. More commonly consumption of fixed capital is
estimated directly by applying depreciation functions to the gross value of assets. Several different
depreciation functions are available and each implies a different age-efficiency profile. Consumption
of fixed capital can thus be seen as the decline, during the course of the accounting period, in the
current value of the stock of fixed assets owned and used by a producer as a result of physical
deterioration (or wear and tear), normal obsolescence or normal accidental damage.
Depreciation is used as a synonym for consumption of fixed capital.
Age-efficiency and age-price profiles, asset prices and depreciation. The age-efficiency profile of an
asset describes the change (usually the decline) in the efficiency of an asset as it ages. Efficiency in
this context refers to the asset's ability to produce a quantity of capital services for a given amount
of inputs.
The age-price profile of an asset describes the change (usually the decline) in the price of an asset as
it ages. The age-efficiency profile is relevant to the measurement of capital services, while the ageprice profile is relevant to the measurement of the net capital stock and consumption of fixed
capital.
The purpose is to identify age-efficiency profiles which seem plausible on a priori grounds and which
generate age-price profiles which are consistent with the empirical evidence that age-price profiles
are usually downward sloping and with some convexity towards the origin.
Estimates of consumption of fixed capital can be obtained by applying a depreciation function to the
gross value of assets. Several different depreciation functions are available and each implies a
different age-efficiency profile. Clearly the depreciation function selected should be at least broadly
consistent with the age-efficiency profile used in calculating volume indices of capital services. Often
16
geometric depreciation is chosen as it implies that the age-efficiency profile is also approximately
geometric (and will be exactly geometric for assets with infinite service lives). Geometric
depreciation is therefore appropriate for assets whose efficiency declines by the largest absolute
amount in the first year of its service life.
When a pattern of constant percentage decline in asset values is chosen for the age-price profile
(“geometric pattern”), a simple method to obtain geometric coefficients is the double-declining
balance method – a method used here for calculating the capital stock statistics.
Effect of different assumptions of length of service life. The level of the gross stock changes in the
same direction as the changes to service lives. Consumption of fixed capital, however, generally
changes in the opposite direction; that is, increasing the service lives reduce the amount of
consumption of fixed capital. This happens because, with longer service lives, each asset is written
off over a longer period and this outweighs the increase due to the fact that longer service lives
mean that there are more assets in the stock.
Net capital stock is obtained by deducting accumulated consumption of fixed capital from the
gross stock. Since longer service lives will always increase the gross capital stock and will usually
decrease consumption of fixed capital, the net capital stock will tend to increase when longer
service lives are used. Moreover, the increase in net capital stock as service lives are lengthened will
be relatively larger than in the case of the gross capital stock.
A final conclusion is that growth rates of gross and net stocks and of consumption of fixed
capital become less volatile as service lives are lengthened. With longer service lives any lumpiness
in investment flows into and out of the stock tends to be dampened by the larger size of the stock.
To summarise:





Longer service lives always increase the size of the gross capital stock.
Longer service lives usually reduce consumption of fixed capital.
Longer service lives usually increase the size of the net capital stock and by relatively more
than in the case of the gross capital stock.
Longer service lives have an unpredictable effect on growth rates.
Longer service lives reduce the volatility over time in the growth of stocks and capital
consumption.
III.2 Service lives, mortality functions and depreciation assumptions
Depreciation levels vary considerably among countries, even among rather homogenous countries
like those in the EU. It may also vary within countries. In the United States, for instance, BLS use
longer service lives than BEA because the former calculates the productive capital stock instead of
net capital stock which is calculated by BEA. The use of longer service lives for productive capital
stock is justified because the rate of deterioration is slower than the rate of depreciation and fall less
rapidly in the early part of the service life of an asset. For Farm tractors and Agriculture machinery
except tractors the service lives used by BEA are 9 and 14 years, respectively. The BLS service lives
are 14 and 17 years, respectively, for the same assets. 1/
17
However, there seems to be no unique economic factor to explain these differences between
countries concerning service lives. Methodological differences exist, but the causality is not clearcut. To improve the possibilities of explanation, it would be helpful to apply more standardised
methods. A harmonised asset and industry breakdown will improve the possibilities to separate
structural from methodological influences. An open question will remain for the harmonisation of
service life assumptions. While it must be conceded that presently differences in service life
assumptions across countries might have historical origins, it should also be kept in mind that the
service life of an asset is determined by economic factors. These might be different across
countries. Depending on the type of asset, service lives might differ.
Another aspect to consider is that the service life of capital in industrialised countries is shrinking.
In the USA the average service life of capital fell in the period 1948-1987 from about 30 years to
under 25 years, mainly as a result of expansion of high technology assets. 2/ This has direct
implications on the depreciation. Looking at the overall depreciation rate it rose from 4.1% in 1988
to 5.4% in 2000. In Germany, the increase at this aggregation level was barely visible. For
equipment goods and immaterial assets the increase in the USA was from 13.6% to 18.3%. In
Germany the depreciation rate rose from 15.1% in 1991 to 16.6% in 1999, illustrating above what
was said about expansion of high technology goods. It is also important to note that for new
investment goods the change will necessarily have been more intense in both countries.2/
In the calculation of total factor productivity (TFP), depreciation is one of the most important
variables for assessing the contribution of capital to overall productivity growth. An empirical
comparison of the magnitude of depreciation or, as it is also called, consumption of fixed capital
(CFC), in the EU countries can help to understand the different significance of CFC in the European
countries. The relationship between depreciation and net domestic product at factor costs varies
considerably between the EU countries. It is low with an average of 11 per cent in Greece and more
than twice as high at 24 per cent in Finland. In most countries, the ratio is, however, quite stable
over time.
If CFC is taken as a proxy for capital services in Agriculture, forestry, fishing (ISIC Rev.4:A01-03), then
there are two major explanations, why CFC ratios might vary across countries: differences in (i)
Production technology and in (ii) Market structures.
Different production technology can have multiple causes.
Geographic factors: Mild or rough climate, mountainous or flat surface, kind of borders, the length
of seacoasts, etc..
Demographic factors: Population density, Composition of the population by age, employment, etc.
Different production functions for the aggregates can also result from different degrees of economic
specialisation:
 Large-scale grain production which requires more capital (services) than fruit and vegetable
production.
 Small countries may be more specialised with respect to certain product varieties than
bigger ones.
18
Even if the need for capital services is different across countries the amount of investment should
be at least in a similar order, which data in the present database has demonstrated. A relationship
between depreciation levels and current investment levels would be expected; at least if such
figures are averaged for a longer period (see the graphs in the annex of the present paper). This
relation would indicate the growth rate of investment in the past. In a stationery economy,
depreciation would equal investment. For growing economies, depreciation values would be
expected to be below those of investments.
There is a variety of approaches for estimating service lives: experts’ advises, administrative data
(e.g. for cars), surveys of service lives and use of other countries’ estimates. Know surveys of service
lives ask data for more than 200 different types of assets (in Germany tables for tax service lives
cover more than 2’000 types of assets).
Ideally assets should be broken down in homogeneous groups by industries. Unfortunately very few
countries have data for such groups.
Choice of discard (or mortality) functions. A PIM2 model based on a discard function is used by most
of the EU 15 countries when calculating capital stock and CFC. The discard function chosen by most
countries is bell-shaped, except in Spain, where a delayed linear function is used. Austria and
Sweden apply the BEA method. Still it can be shown that the specific shape of the discard function
does not have so much impact on the outcome.
Depreciation schedules. Most countries apply linear depreciation. Some, e.g. Austria, Sweden and
the United States apply a geometric depreciation method. There are a number of reasons which
suggest that geometric depreciation may be a more adequate depreciation schedule. It is argued, for
instance, on the basis of empirical and theoretical evidence that depreciation, e.g. the loss of value
over time, is higher at the beginning than at the end of the service life of an asset. This may
especially be true for assets which are exposed to keen competition and rapid changes in technology
and/or taste. 3/, 4/
On the other hand, for other types of assets, the contrary can be argued. Mainly for assets with a
long service life, investors are frequently not motivated by an expected short-term return on capital,
but by the expected increase in value of the asset. Investments in dwellings are a typical example
where this kind of investment behaviour can be observed and explained theoretically.
2
PIM = Perpetual Inventory Method.
19
III.3 Method for calculating Net and Gross capital stock
Data on Net (or Wealth) Capital Stock (NCS) and Gross Capital Stock (GCS) are available only for
some 20 and 40 countries, respectively, to a large extent from some OECD countries and included in
the OECD databases. For Consumption of Fixed Capital (CFC) some data (complete data series or
just a few data points) are available for about 100 countries. For all other countries, NCS, GCS and
CFC have been calculated by using the Double Declining Balance Method, see below, which is
advocated both by the SNA and the OECD Manual on Measuring Capital. 3 In applying this method,
assumptions have to made about two of the following three mutually related parameters:

the depreciation rate, denoted by δ,

the length of service life of assets, denoted by T, and

the balancing rate, denoted by R.
It should be noted that capital stock statistics, whether they are reported by countries or calculated,
are not result of measureable transactions - hence the database on capital stock statistics is not a
statistical database but rather an Analytical Database.
Calculation of net capital stocks. The simplest computational approach towards the measurement
of depreciation and net stocks is by using a constant, age-independent rate of consumption of fixed
capital (geometric rate). It dispenses from the need to specify extra parameters for a retirement
profile and it permits to formulate a straight forward link between capital stocks, investment and
consumption of fixed capital:
WtE = WtB + It – δ (It/2+WtB) + Xt = It (1 - δ/2) + WtB (1 – δ) + Xt
In the formula above 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, and Xt is other
changes in volumes of the group of assets. All variables are valued at average prices of a reference
period which could be year t.
Depreciation rates. The first step towards computing the net stock above is to select a rate of
consumption of fixed capital, δ. In absence of good information about the rates of depreciation, δ
can be set by reference to other countries’ depreciation rates of similar types of assets or other
countries’ service lives of similar types of assets. A common way of estimating δ is the declining
balance method with δ=R
/ TA where TA is the average service life of an asset, and R is a
parameter around 2. Because service lives tend to be influenced by institutional and climatic
conditions, it is preferable to use parameters from similar countries rather than from very different
countries. The table 3 below provides some rough-and-ready points of reference for average
depreciation rates for relatively broad classes of assets.
3
Main source: Measuring Capital. OECD Manual, Second edition, 2009.
20
Initial stocks. Once a selection for δ has been made, a starting stock for some period t0 has to be
computed. For the computation of the initial stock, there are several avenues: using capital survey
information or making a plausible estimate for the long-run growth rate of volume investment. In
addition, a simple approximation can be used when geometric age-efficiency or age-price profiles
apply. In this case, the productive (or net) stock at the beginning of the benchmark year t0 can
approximately be written as the cumulative, depreciated investment of previous years:
Wt0 (geometric) ≈ [It0-1 + (1-δ) It0-2 + (1-δ)2 It0-3 + …]
Table 3. Examples of benchmarks for rates of consumption of fixed capital, by broad type of asset
Machinery and equipment
Non-residential and residential structures
Declining balance parameter R
Average service
life TA
1.5
2
Declining balance parameter R
Average service
life TA
1
1.5
40
3.8%
5%
10
15%
20%
50
3.0%
4%
15
10%
13.3%
60
2.5%
3.3%
20
7.5%
10%
70
2.1%
2.9%
25
6%
8%
80
2.1%
2.5%
The next step is to make a plausible assumption about the long-run growth of volume of investment
– the simplest possibility may be to set it equal to the long-run growth rate of volume GDP, or in our
case value added in agriculture, for which there may be empirical estimates, and call this long-run
growth rate θ. By assumption, one has It=It-1(1+θ). This relation can be inserted into the
expression above for the initial capital stock:
[It0-1 + (1-δ) It0-2 + (1-δ)2 It0-3 + …]
= It0-1[1 + (1-δ)(1+θ) + (1-δ)2(1+θ)2 + …]
= It0-1(1+θ) / (δ+ θ)
= It0 / (δ+ θ)
21
It is now possible to approximate the initial capital stock at the beginning of period to by the
product of the level of investment expenditure in period to (the first period for which there is
information on investment expenditure) and a combination of parameters for depreciation and
long-term investment or GDP growth, or as in this case, long term growth in value added in
agriculture forestry and fishery growth.
The first period in time for which information on GFCF is available will determine the date for which
this initial stock Wt0B can be calculated. Even if time series of volume GFCF are not available
directly, it is worth attempting to estimate a series of investment data for at least some years into
the past so as to place the necessarily inaccurate estimate for the initial stock as far as possible into
the past. Measurement errors of the initial stock will then matter much less for the most recent
estimates.
For example, a functional relationship between the volume growth of GFCF and GDP could be
established on the basis of those periods for which information exists. Under the assumption that
this relationship is stable over time, and given GDP series that date further back in history, a set of
volume GFCF series can be estimated and then be used, together with an estimate for the initial
stock, to build up stock measures WtE for recent years.
As has been shown above δ will vary depending on the type of asset so we need to break down
GFCF into as many categories as possible. At the minimum it would be important to separate
machinery and equipment from structures and buildings. As was mentioned before, this has not
been successful when it concerns non-OECD countries. We are therefore confined to using only
total GFCF in Agriculture, forestry, fishery (ISIC Rev.4: A01-03).
The assumed declining balance rate R together with the service life assumption T determines the
depreciation rate δ which is in turn relevant for the size of the calculated NCS and the level of CFC.
Although the maximum service life of a geometrically depreciated asset tends towards infinity, the
number of years after which an asset has lost 50%, 90% or 99% of its value can easily be calculated.
However, it should be noted that service life in the context of geometric depreciation has another
understanding than for e.g. life expectancy of a population. For other depreciation models,
however, there is more of concordance between life expectancy of cohorts of population and of
assets.
III.4 Assumptions and simulations of depreciation rates (δ), length of service
life (T) and balancing rate (R) – benchmarking exercises
III.4.1 Previous assumptions
In the World Agriculture Statistics prepared in 2015 (covering data up to 2013 and inclusive) it was
assumed, somewhat arbitrarily but in absence of any other estimates, that the depreciation rates
ranged from 0.03, in the least developed countries, to 0.08 in the most developed economies. In
22
other words, the depreciation rate was set to depend on the economic level of the respective
country. It goes without saying that there was a large degree of arbitrariness in applying these
deprecation rates.
Based on empirical analysis and literature surveys (see below) it was assumed in In the World
Agriculture Statistics, prepared in 2016 (covering data up to 2014 and inclusive), that the
depreciation rate was 0.056 for all countries and the service live 25 years.
When calculating the initial stock the long terms growth rates of value added in Agriculture, forestry,
fishery (ISIC Rev.3: A+B) were used.
III.4.2 Explicit and implicit analysis of country data on length of service life and
depreciation rates
Three complementary approaches and sources have been applied in order to get more hard facts on
which values to choose for the parameters δ,
T and R.
1. WIOD/KLEMS/OECD-STAN databases:4 Implicitly derived δ and T from
WIOD/KLEMS/OECD-STAN databases. Data are, however, only available up 2011, at the
latest, and in ISIC Rev.3. Data in Rev, 4 and up to 2013 are now available but have not yet
been analysed.
2. Literature survey: A thorough literature survey of country practices in selecting the
parameters. Country data are available only up to early 2000 and concerns mainly industry
as a whole in ISIC Rev.3.
3. OECD National Accounts database: Implicitly derived δ and T from the OECD National
Accounts database. Data are available up to 2015 and in ISIC Rev 4 for Agriculture, forestry,
fishing and for Crop and animal production.
The results of the three approaches are shown below.
II.4.3 WIOD/KLEMS/OECD-STAN databases
(a) Implicitly derived depreciation rates δ
For 39 countries, of which some are not OECD countries (China Peoples’ Republic of, Indonesia,
India, Russian Federation, Romania, Bulgaria and Brazil, Latvia and Malaysia), reported data are
available on both GFCF and NCS. Assuming that NCS has been calculated by the double-declining
balance method then δ can derived from the formula shown above. Table 4 below shows the
derived depreciation rate δ for 34 countries. Some interesting conclusions can be drawn from this
table:
1. 27 countries have depreciation rates in the range 0.042 – 0.083. On the other end, 11
countries showed depreciation rates in the range 0.102 - 0.123, of which two countries had
rates close to 0.2 (Poland with 0.174 and Malaysia with 0.196).
4
World Input Output Database (WIOD), World KLEMS and EU KLEMS Databases.(KLEMS stands for
Capital, Labour, Energy, Material and Services and are used in growth accounting).
23
2. These depreciation rates are for the particular countries very stable over time (the standard
deviation is, as can be seen from Table 4, very low).
3. The ratio of NCS to GCS varies from 0.3 in Poland (a result of a high depreciation rate) to
0.86 in Australia. For all other countries, except Austria and Canada, the ratio varies around
0.5.
4. The ratio NCS/GCS is very stable over time which is illustrated by the low standard deviation
(see Table 4).
5. Table 4 also partly illustrates a fact which is more evident when analysing the present
Structural Database as a whole, namely that:
 Developed countries have a much higher investment ratio in agriculture than the
developing countries;
 While the investment ratio in agriculture is higher than in the economy as a whole in
developed countries the reverse is noticed in the developing countries.
 Developed countries have a much lower ratio VA/GO, generally around 0.4, than
developing countries, generally 0.8 and above (discussed above).
 The “outlier” investment ratio of 1.3 in Luxembourg should be noted (Denmark also
had investment ratios exceeding 1 in the period 2007-2009. This was a result of a
surge in intermediate consumption, which had the effect that value added fell,
whilst at the same time GFCF were stable). At the other “outlier” end is Mexico and
Malaysia with investment ratios of only 0.02 and 0.096, respectively.
As countries in most cases have used other methods for calculating depreciation, e.g. linear
depreciation, these derived depreciation rates, based on assumed geometric depreciation, can only
give an idea of the order of magnitude of the behind-lying actual depreciation rates. However, when
comparing reported CFC with those derived from the applied double-declining balance method and
the implicit depreciation rates surprisingly good fit is obtained (not always for individual years but
certainly for averages over years).
(b) Derived length of service life T
From reported data on GCS, NCS, CFC and GFCF (for CFC there are two sets of data namely:
CFCreported and CFCderived , the later derived from applying the double-declining balance method) we
can calculate T from 4 different approaches assuming simultaneous exit of assets (so called “one
hoss shay”, or rectangular mortality function), straight line depreciation and finally geometric
depreciation when derived CFC are used. Table A1 in the annex summarizes the calculations from
the four methods. As all methods are “backwards” calculations and cannot consider the particular
country assumptions it is not surprising that they do not give exactly the same results although for
one and the same country the results are fairly stable. On the other hand, the table shows large
differences in implicit service life T between countries, from


10 – 20 years in Australia, Estonia, France, Israel, Luxembourg, Malaysia, Slovenia and the
United Kingdom, to
30 years or more in Germany, Hungary, Italy, Republic of Korea (which every 10 years used
to do complete inventories of capital stocks), Slovak Republic and Spain.
24
25
III.4.4 Literature survey of country practices in selecting depreciation rates (δ), length of service
life (T) and balancing rate (R)
Annex tables A2 – A8 summarize the results of a literature survey of country practices and of surveys
carried out by the OECD, UNECE and Eurostat. These tables are rather detailed and for this reason
the results are further summarized in table 5 below giving averages for the various asset categories.
For farm buildings and structures the service lives is just under 40 years with a depreciation rate of
0.027. Cultivated assets has an average service life of just under 25 years while for agriculture
machinery, including transport equipment it is about 12 years with a depreciation rate of 0.135.
If we now combine the three asset categories with assumed shares of 40, 40 and 20%, respectively,
we can approximate the service life for all assets in agriculture, forestry, fishery – we do
unfortunately not have GFCF broken done by asset categories - to just under 25 years and a
depreciation rate 0.056..5
Looking at time series data for the United States on GFCF and NCS with breakdown on structures
and machinery it can be seen from figure 2 that while GFCF fluctuates between 70 and 90% its share
of NCS is around 50% structure account for the other 50% but for only between 10 and 30% of GFCF.
5
In the 2013 Structural Analysis Database an attempt was made to estimate agriculture machinery investment
from apparent consumption of agriculture machinery using COMTRADE and UNIDO INDSTAT databases.
The result was very unreliable mainly as a result of the instability of the COMTRADE databases. In some cases
there were negative apparent consumption – in other cases apparent consumption of agriculture machinery
exceeded total GFCF.
26
Implicit service lives in 15 EU countries
Implicit service lives have been calculated in 15 EU countries showing that there is a stable group of
countries in the EU with service lives at around 30 years (the Netherlands, the United Kingdom,
Ireland, Finland; Germany, France, Spain and Sweden), see table 6 below 3/.
Extremely low values seem to be applied in Luxembourg, Denmark, Portugal and Belgium.
Comparatively high values were found for Italy, Austria and Greece.
Even if the outlier Greece and the countries with extremely low service lives are ignored, the
difference between a low service life of 25 years, e.g. for the Netherlands, and a high service life of
39 years, e.g. for Austria, is still quite high. The impact of this difference on capital stock estimates
for these countries will be proportional to the relationship between service lives. This will exert a
severe impact on productivity and profitability estimates.
27
Figure 2. Shares of agriculture machinery & equipment and structues in NCS and
GFCF in the USA, Agiculture, forestry, fishing
1.00
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
Equipment-NCS
Structures-NCS
Equipment-Inv
Structures-Inv
Source: BEA, United States.
III.4.5 OECD National Accounts database – implicitly derived depreciation rates and length of
service lives.
Table 7 summarizes results when deriving implicit depreciation rates and length of service lives for
22 OECD countries with national accounts data up to 2015 in ISIC Rev.4. As can be seen from the
table the depreciation rates vary from 0.04 to 0.17 with a mean and median value of 0.08. The
length of service lives vary from 13 years to 39 years with median of 25 and a mean of 27 years.
III.4.6 Conclusion
Both the benchmarking exercises and the literature survey have showed that there is a large
variation between countries concerning depreciation rates and service lives. There are also
differences in the selection of depreciation and mortality functions. These differences are to a large
part a result of structural differences with respect to, for instance, share of agriculture vis-à-vis
forestry and fishery or share of fruits and vegetables production compared to grain.
In absence of country information on service life, depreciation rates and depreciation and mortality
function it seems reasonable, in view of the surveys shown above, to apply the double-declining
balance method with a fixed δ = 0.08 for all countries for which official capital stock data are not
available. The average service life of assets is set to 25 years. In last year’s World Agriculture
Statistics the depreciation rate was set to δ = 0.056 which was suggested by the above-mentioned
literature survey. The main reason for changing the depreciation rate is that the basis for the
literature survey is rather old compared with the benchmarking of OECD national accounts data,
which include data for 2015. In any case whatever depreciation rate which is chosen it is just gives
28
calculation examples. It is suggested that the database user he or she herself simulate various
depreciation rates depending on analytical purpose for using the data.
Table 7: Implicit depreciation rates (δ) and length of service lifes (n) in selected OECD countries for capital stock in
Agriculture, forestry, fishery (ISIC Rev.4 :A)
Country
Poland
Israel
France
Chile
Finland
Australia
United States
Estonia
Slovenia
Belgium
Greece
Netherlands
Czech Republic
Norway
Italy
Germany
Republic of Korea
Denmark
Austria
Sweden
Slovakia
Hungary
Median δ
Median n
δ
δ sd
Year Service life,
Mean: Stan. dev:
δ
years (n),
Service
Service most mean
most recent life, years life, years recent
year
(n)
(n)
year
2012
2014
2015
2013
2014
2013
2015
2013
2013
2013
2013
2014
2014
2014
2014
2013
2014
2014
2013
2013
2013
2013
0.08
25
25
26
1
18
21
2
25
13
25
11
0
1
19
22
19
28
22
25
19
23
22
26
22
26
2
1
2
2
0
0
34
33
34
34
1
1
30
39
30
37
2
2
39
40
38
41
2
1
Mean δ
Mean n
0.17
0.11
0.10
0.10
0.09
0.09
0.09
0.08
0.08
0.08
0.08
0.08
0.08
0.07
0.06
0.06
0.06
0.05
0.04
0.04
0.04
0.04
0.16
0.13
0.10
0.08
0.08
0.10
0.07
0.12
0.07
0.08
0.08
0.08
0.08
0.07
0.06
0.06
0.10
0.07
0.05
0.05
0.04
0.04
0.02
0.01
0.01
0.02
0.00
0.01
0.01
0.04
0.01
0.02
0.01
0.00
0.01
0.00
0.00
0.00
0.02
0.01
0.00
0.00
0.00
0.01
δ (depreciation Length of service
rate)
life, years
increasing
decreasing
decreasing
increasing
increasing
decreasing
Increasing
increasing
decreasing
decreasing
increasing
increasing
decreasing
increasing
increasing
decreasing
slowly decreasing slowly increasing
increasing
decreasing
increasing
decreasing
increasing
increasing
decreasing
increasing
decreasing
decreasing
increasing
decreasing
increasing
slowly decreasing slowly increasing
increasing slowly
decreasing
increasing
increasing
decreasing
0.08
27
As for the application of the double-declining balance method, benchmarking has been made by
comparing reported OECD country data with those obtained when calculating NCS with abovementioned method, using δ = 0.08 (which is the average depreciation rate used in the World KLEMS
database for structure and machinery). The difference between reported and calculated data
differed between some countries – for some countries the differences were marginal whilst for
other they are more substantive but still of the same order of magnitude
29
Sources:
1/ Bureau of Labor Statistics, Multifactor Productivity, July 26, 2006: Overview of Capital Inputs for
the BLS Multifactor Productivity Measures.
2/ Ulf von Kalckreuth and Jürgen Schröder: Monetary Transmission in the New Economy: Service
Life of Capital, Transmission Channels and the Speed of Adjustment. Discussion paper 16/2,
Economic Research Centre of the Deutsche Bundesbank
3/Bernard Görzig, DIW Berlin, April 2007: depreciation in EU Member States: Empirical and
Methodological Differences.
4/ UNECE: Survey of National Practices in Estimating service Lives of Capital Assets (2004).
5/ International Comparison Program: Estimating Government Stock of Fixed Capital (2012).
30
ANNEX TABLES AND GRAPHS
31
Figure 1. Gross fixed capital fromation over Value added (GFCF/VA) and Consumption of fixed capital over Value added
(CFC/VA) in selected countries
0.120
Botswana
0.100
0.080
0.060
GFCF/VA
CFC/VA
0.040
Linear (GFCF/VA)
Linear (CFC/VA)
0.020
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
1978
1977
1976
1975
1974
0.000
Jordan
0.350
0.300
0.250
0.200
GFCF/VA
0.150
CFC/VA
0.100
0.050
0.000
2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995
Kuwait
0.250
0.200
0.150
GFCF/VA
CFC/VA
0.100
0.050
0.000
2013201220112010200920082007200620052004200320022001200019991998199719961995199419931992
32
Figure 1. Continued.
Netherlands
0.700
0.600
0.500
0.400
GFCF/VA
0.300
CFC/VA
0.200
0.100
0.000
2013201120092007200520032001199919971995199319911989198719851983198119791977197519731971
Poland
0.350
0.300
0.250
0.200
GFCF/VA
0.150
CFC/VA
0.100
0.050
0.000
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
Portugal
0.300
0.250
0.200
0.150
GFCF/VA
0.100
CFC/VA
0.050
0.000
2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995
33
Figure 1: Concluded
UK
0.350
0.300
0.250
0.200
GFCF/VA
0.150
CFC/VA
0.100
0.050
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
0.000
USA
0.350
0.300
0.250
0.200
GFCF/VA
0.150
CFC/VA
0.100
0.050
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
1978
1977
1976
1975
1974
1973
1972
1971
1970
0.000
South Africa
0.350
0.300
0.250
0.200
GFCF/VA
CFC/VA
0.150
0.100
0.050
0.000
2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996
34
Table A1. Derived length of service lives from 4 different approaches
Country name
Australia
Australia
Australia
Australia
Austria
Austria
Austria
Austria
Belgium
Belgium
Belgium
Belgium
Czech Republic
Czech Republic
Czech Republic
Czech Republic
Denmark
Denmark
Denmark
Denmark
Estonia
Estonia
Estonia
Estonia
Finland
Finland
Finland
Finland
France
France
France
France
Germany
Germany
Germany
Germany
Greece
Greece
Greece
Greece
Hungary
Hungary
Hungary
Hungary
Year T = GCS T= GCS(t-1) /
[NCS(t-1)
ΣGFCF
+GFCF (t) NCS(t)]
2014
2013
2012
2011
2014
2013
2012
2011
2014
2013
2012
2011
2014
2013
2012
2011
2014
2013
2012
2011
2014
2013
2012
2011
2014
2013
2012
2011
2014
2013
2012
2011
2014
2013
2012
2011
2014
2013
2012
2011
2014
2013
2012
2011
11
11
11
27
26
25
21
21
21
22
22
22
45+
45+
45+
12
12
12
26
26
26
18
18
19
36
36
36
25
26
29
45+
45+
45+
T=[GCS(t)
T=[GCS(t)
+GCS(t-1)] /
+GCS(t-1)] /
[2*CFC(t)]_
[2*CFC(t)]_
Reported] 1/ Calculated] 2/
13
13
16
36
33
34
15
15
15
15
17
18
18
14
17
14
28
18
18
20
17
15
15
17
23
18
18
20
24
16
16
16
17
26
25
27
29
16
23
16
15
16
38
38
39
22
23
22
34
34
34
35
17
17
18
15
26
25
27
24
16
15
16
18
38
38
39
47
22
22
22
21
34
34
34
34
35
Table A1. Derived length of service lives from 4 different approaches
Country name
Year
T = GCS
ΣGFCF
T= GCS(t-1) /
[NCS(t-1)
+GFCF (t) NCS(t)]
T=[GCS(t)
T=[GCS(t)
+GCS(t-1)] /
+GCS(t-1)] /
[2*CFC(t)]_
[2*CFC(t)]_
Reported] 1/ Calculated] 2/
12
13
16
13
36
39
36
32
34
14
14
13
13
10
10
Israel
2014
12
Israel
2013
13
12
Israel
2012
13
Israel
2011
12
16
Italy
2014
34
Italy
2013
37
Italy
2012
34
39
Italy
2011
34
36
Luxembourg
2014
12
Luxembourg
2013
12
14
Luxembourg
2012
14
Luxembourg
2011
12
13
Malaysia
2014
3/
10
Malaysia
2013
3/
11
Malaysia
2012
Malaysia
2011
3/
11
2/
10
Netherlands
2014
18
16
Netherlands
2013
16
10
Netherlands
2012
19
11
20
Netherlands
2011
21
20
24
25
Poland
2014
30
Poland
2013
31
18
Poland
2012
17
18
Poland
2011
35
18
16
Republic of Korea
2014
22
28
Republic of Korea
2013
22
28
28
Republic of Korea
2012
28
28
Republic of Korea
2011
21
28
27
Slovak Republic
2014
33
36
Slovak Republic
2013
33
37
38
Slovak Republic
2012
37
30
Slovak Republic
2011
32
30
41
Slovenia
2014
21
19
Slovenia
2013
21
19
19
Slovenia
2012
20
19
Slovenia
2011
21
19
19
Spain
2014
28
30
Spain
2013
31
30
Spain
2012
28
30
30
Spain
2011
28
30
30
United Kingdom
2014
United Kingdom
2013
16
16
United Kingdom
2012
16
16
16
United Kingdom
2011
16
16
16
Source data are OECD except for Malaysia for which data are from Dept. of Statistics
1/ Data on CFC are reported data.
2/ Data on CFC are derived from the double declining balance method.
3/ 2010: T = 10, 2009: T = 11 and 2007: T = 11.
36
37
38
39
40
41
42
Metadata on Land Quality,
Economic Research Service of US Department of Agriculture (ERS-USDA)
“The FAO agricultural database provides time-series estimates of agricultural land by
country and categorizes this as either permanent pasture or cropland (which is further
divided in arable and permanent crop land). It also provides an estimate of area equipped
for irrigation. The productive capacity of land among these categories and across countries
can be very different, however. For example, some countries count vast expanses of semiarid lands as permanent pastures even though these areas produce very limited agricultural
output. Using such data for international comparisons of agricultural productivity can lead
to serious distortions, such as significantly biasing downward the econometric estimates of
the production elasticity of agricultural land (Peterson, 1987).
To account for the contributions to growth from different land types, irrigated cropland,
rain-fed cropland, and permanent pastures are converted into "rainfed cropland
equivalents" based on their relative productivity. Productivity weights vary regionally. In
order not to confound the land quality weights with productivity change itself, the weights
are estimated using country-level data from the beginning of the period of study (i.e., using
average annual data from 1961-65). Let Regioni be a set of indicator variables representing
five global regions (i=1,2,…5). For each country, Regioni takes a value of either 1 if the
country is in the region and zero otherwise. Regions as (1) developed and former Soviet bloc
countries, (2) Asia-Pacific, (3) Latin America and the Caribbean, (4) West Asia and North
Africa, and (5) Sub-Saharan Africa. Define agricultural yield as total output Y divided by the
sum of cropland and pasture area. We then regress agricultural yield against the
proportions of agricultural land in rain-fed cropland (Rainfed), irrigated cropland (Irrig), and
permanent pasture (Pasture). Multiplying the land-use proportions by the regional indicator
variables allows the coefficients to vary among regions:
(10)
The coefficient vectors α, β and γ provide the quality weights for aggregating the three land
types into an aggregate land input index. Countries with a higher proportion of irrigated
land are likely to have higher average land productivity, as will countries with more cropland
relative to pasture. The estimates of the parameters in equation (10) reflect these
differences and provide a ready means of weighting the relative qualities of these land
classes.
The regression estimates show that, on average, one hectare of irrigated land was between
1.1 to 3.0 times as productive as rainfed cropland, which in turn was 10-20 times as
productive as permanent pasture. The results give plausible weights for aggregating
43
agricultural land across broad quality classes. The approach to account for land quality
differences among countries is similar to one developed by Peterson (1987), who derived
land quality weights by regressing average cropland values in U.S. states against the share of
irrigated and unirrigated cropland and long-run average rainfall. He then applied these
regression coefficients to data from other countries to derive an international land quality
index. The advantage of the present model is that it is based on international rather than
U.S. land yield data and provides results for a larger set of countries.
This adjustment for changes in different classes of land allows us to further refine the
resource decomposition of output growth in equation (6) to isolate the contribution of
irrigation apart from expansion in agricultural area to output growth. Letting X 1 be the
quality-adjusted quantity of land (and for simplicity, dropping the Region subscripts on the
land quality parameters), then a change in X1 is given by
(11)
The first two right-hand-side terms indicate the expansion in land area (with growth in
pasture area adjusted for quality to put it in comparable terms with cropland expansion).
The third term isolates the contribution of irrigation expansion:(γ-α)*100% gives the percent
augmentation to yield, holding other factors fixed, from equipping a hectare of cropland
with supplemental irrigation. Dividing equation (11) by X1 converts the expression into
percentage changes so that it shows the respective contributions of changes in rainfed
cropland, pasture area and irrigation to output growth. Combined with equation (6), the
resource decomposition expression shows the contributions to agricultural growth from
expansion of agricultural land, extension of irrigation, intensification of other inputs per
hectare, and improvements in TFP:
(12)
where θc,θp,and θw are the shares of quality-adjusted agricultural land in crops (X1c), pasture
(X1p), and irrigated area (X1w), respectively (note:X1=X1c+X1p+ X1w). The first two terms
[θcαg(X1c)+θpβg(X1p)] give the share of output growth attributable to land expansion
(holding yield fixed), while the third term [θw(γ-α)g(X1w)] indicates the share of output
growth due to the extension of irrigation (holding other inputs fixed). The fourth term of
equation (12) gives the contribution to growth of input intensification and the last term the
contribution of growth in total factor productivity.”