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The GTAP Data Base and the EU
IO tables
Presented by
Terrie Walmsley
Csilla Lakatos, Badri Narayanan and
Robert McDougall
Motivation for GTAP
• Increasing demand for quantitative analysis of global trade
issues: e.g. WTO-Doha Round, NAFTA, EU integration, Kyoto
Protocol, China’s WTO accession.
• Historically analysis was done “in-house” in a few agencies:
OECD, World Bank, FAO; and at a few university research
centers.
• Combines the advantages of Agency and University approaches.
• Publicly funded project, based in academia, which supports a
global economic data base and model which are:
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fully documented;
publicly available (free to contributors);
easy to use (education); and
accessible to non-modelers.
GTAP Data Base
• Philosophy: Find the best person in the world to do the job
and sell them on it!
• GTAP establishes standards, coordinates the work and
brings it together into ONE useable data base.
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Global coverage: 112 regions (vs. 13 in version 1)
Sectoral detail: 57 sectors (vs. 37 in version 1)
2004 base year
Bilateral trade data/shipping margins: USDA, CPB
Protection data: UNCTAD, CEPII, WB, OECD…
National data bases: national collaborators
Physical data limited to energy sectors (IEA)
I-O Structure Requirements
Value added
Imported
Intermediate
usage
Primary
Final
Primary
Intermediate
usage
Final
Intermediate
usage
Domestic
MF
Imported
- Import duty
Intermediate
usage
Final
Domestic
UP (tax-paid)
Final
UF (tax-free)
Value added
Indirect taxes
OP
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AgroSAM Project
• IPTS – EU JRC, Marc Mueller with Ignacio Domínguez and
Hubertus Gay
• Objectives
– AgroSAMs for EU-27 – late 2009 with a Disaggregated
Agricultural Sector (AgroSAM)
– EU IO tables for GTAP v7.0 and 7.1
– The number of agricultural sub-sectors should allow:
• the incorporation of datasets from already existing economic models (e.g.
CAPRI);
• the reusability by other modelling systems (e.g. GTAP);
• the utilisation of readily available datasets from statistical departments (e.g.
EuroStat, FAOSTAT).
– Other
• A transparent and automatised routine for updating AgroSAMs
• For GTAP an automated routine for converting SAMs to IO format
AgroSAM Outputs
• SUPPLY & USE tables
– SUPPLY – Basic prices
• Commodity taxes (vectors)
• Trade & Transport Margins (vectors)
– USE – Purchaser prices
• Intermediate and final demands
• Factor use
• Activity taxes
• ‘Missing’
– Imports USE matrices
– Commodity tax matrices
– Margin matrices
Common Problems faced by
contributors
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Splitting Domestic and imported use matrix
Building commodity tax matrices
Trade and transport margins
Dwellings
Re-exports
Concordances
Negative capital stocks
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I-O Tables Requirements
• Sectoral classification:
– Full 57 sectors not required
– Separate food and agriculture, energy, other
• Sign conditions: no negative flows except in changes in
stocks
• Sectoral balance condition: Sales = Costs
• Unusual Shares
– Entropy-theoretic technique
– 'flags' strange shares
• Reject and Chopping bloc
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Unusual Shares
Supply
Use
ENTROPY
Oil Seeds
Oil and Gas
Sugar
Vegetable Oils
Financial services
Forestry
Other minerals
Trade
Business services
Other animal
products
Vegetable Oils
Exports
Sugar
Vegetable oils
Financial services
Forestry
Other minerals
Oil and gas
Consumption
Cattle meat
0.38
0.24
0.23
0.15
0.15
0.14
0.13
0.12
0.11
0.11
Share in
Representati
ve Table
0.14
0.27
0.31
0.18
0.14
0.07
0.26
0.18
0.01
0.13
IO -Share
0.87
0.00
0.01
0.00
0.52
0.36
0.03
0.01
0.17
0.00
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Clean, Disaggregate, Synthesize
• Disaggregate
– Of the 113 regions in GTAP 7: only 36 I-O tables have all 57
sectors; no disaggregation needed
– 40 tables need agricultural disaggregation; use agricultural IO data set.
– 17 tables need non-agricultural disaggregation; use
representative table.
• Agricultural Production Targeting
– (EUROSTAT: Hans Grinsted Jensen (FOI) and Hsin Huang
(OECD))
• Synthesize
– Create 19 composite regions.
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Composite Regions: Rules
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Match each member country to a primary region.
Match is by per capita GDP.
Match is only within geographic regions.
Composite region I-O table is linear combination of
primary region I-O tables.
Country
afg
npl
GDP
(USD B)
19.0
5.6
GDP per
cap. (USD)
697
227
Best
match
lka
bgd
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International Data Sets: 226 Countries
Ag Production
targeting and Ag IO (EUROSTAT, Hans
Grinsted Jensen (FOI) and Hsin
Huang (OECD))
IMF
agricultural data set
C, I, G, POP: MAcMap (CEPII and David Laborde
(IFPRI) and UNCTAD).
Goods
(COMTRADE
and Mark Gehlhar
Bank
IncomeWorld
and Factor
Taxes
Support
(OECD
PSE/CSE),
ServicesDomestic
(Nico van
Leeuwen
and Arjan
Agreement
on Textiles and Clothing
Lejour,
and IMF)
macro data
setCPB
(Francois and Worz),
Export
subsidies (Aziz Elbehri)
trade data
sets
Volumes and Prices: IEA
protection data sets
energy data sets
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Construction Process
I-O Tables
• Eliminate changes in
stocks
• Reconcile with
international data sets:
Adjust the IO tables to
match the macro datasets
• Entropy theoretic
approach
FIT
Fitted I-O
Tables
International
Data Sets
Assemble
GTAP Data
Base
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Data Assembly
FIT'ed
I-O Tables
Parameters
Primary Factor
Splits
Income / Factor
Taxes
Assemble
GTAP Data Base
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Checks and Comparisons
• Across Versions
– Comparison programs: aimed at highlighting large
differences between the datasets associated with
large flows.
– Entropy-theoretic measure and successive rescaling
– Highlighted:
• improved treatment of domestic margins in EU
• Problems with dwellings
• Countries
– How much did a countries IO table change during
construction?
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Satellite Datasets
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Energy volumes
CO2 and non-CO2 emissions
Land use by Agro zone
Migration and remittances
Foreign income payments and receipts
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Future Directions (v8 & 9)
• IO tables
– Commodity Taxes
– Dwellings
– More programs
• Skill shares
• Domestic margins
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Theoretical Background
FIT Module
• Bacharach, M. (1970), Biproportional matrices
and input-output change, Cambridge.
• James, M. and R. McDougall (1993), “FIT: An
input-output data update facility for SALTER”,
SALTER working paper 17, Australian Industry
Commission.
• Theil, H. (1967), Economics and information
theory, North-Holland, Amsterdam.
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