1. The GTAP Data Base and the EU IO tables Presented by Terrie Walmsley Csilla Lakatos, Badri Narayanan and Robert McDougall

2. Motivation for GTAP Increasing demand for quantitative analysis of global trade issues: e.g. WTO-Doha Round, NAFTA, EU integration, Kyoto Protocol, Chinas 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: –fully documented; –publicly available (free to contributors); –easy to use (education); and –accessible to non-modelers.

3. 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. –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)

4. I-O Structure Requirements 4 Primary Imported Domestic Intermediate usage Final Intermediate usage Final Value added UF (tax-free) - Import duty MF Primary Imported Domestic Intermediate usage Final Intermediate usage Final Value added UP (tax-paid) Indirect taxes OP

5. 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

6. 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

7. Common Problems faced by contributors Splitting Domestic and imported use matrix Building commodity tax matrices Trade and transport margins Dwellings Re-exports Concordances Negative capital stocks 7

8. 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 8

9. Unusual Shares 9 SupplyUseENTROPY Share in Representati ve Table IO -Share Oil SeedsVegetable Oils0.380.140.87 Oil and GasExports0.240.270.00 Sugar 0.230.310.01 Vegetable OilsVegetable oils0.150.180.00 Financial services 0.150.140.52 Forestry 0.140.070.36 Other minerals 0.130.260.03 TradeOil and gas0.120.180.01 Business servicesConsumption0.110.010.17 Other animal products Cattle meat0.110.130.00

10. 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 I- O 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. 10

11. Composite Regions: Rules 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. 11 Country GDP (USD B) GDP per cap. (USD) Best match afg19.0697lka npl5.6227bgd

12. Volumes and Prices: IEA energy data sets MAcMap (CEPII and David Laborde (IFPRI) and UNCTAD). Domestic Support (OECD PSE/CSE), Agreement on Textiles and Clothing (Francois and Worz), Export subsidies (Aziz Elbehri) protection data sets International Data Sets: 226 Countries Goods (COMTRADE and Mark Gehlhar Services (Nico van Leeuwen and Arjan Lejour, CPB and IMF) trade data sets C, I, G, POP: World Bank macro data set 12 IMF Income and Factor Taxes Ag Production targeting and Ag IO (EUROSTAT, Hans Grinsted Jensen (FOI) and Hsin Huang (OECD)) agricultural data set

13. Construction Process 13 I-O Tables Fitted I-O Tables GTAP Data Base International Data Sets FIT Assemble Eliminate changes in stocks Reconcile with international data sets: Adjust the IO tables to match the macro datasets Entropy theoretic approach

14. Data Assembly 14 GTAP Data Base Assemble Parameters Primary Factor Splits FIT 'ed I-O Tables Income / Factor Taxes

15. 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? 15

16. Satellite Datasets Energy volumes CO2 and non-CO2 emissions Land use by Agro zone Migration and remittances Foreign income payments and receipts 16

17. Future Directions (v8 & 9) IO tables –Commodity Taxes –Dwellings –More programs Skill shares Domestic margins 17

18. 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. 18