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

Motivation for GTAP • Increasing demand for quantitative analysis of global trade issues: e. 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: – – 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 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)

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

Agro. SAM Project • IPTS – EU JRC, Marc Mueller with Ignacio Domínguez and Agro. SAM Project • IPTS – EU JRC, Marc Mueller with Ignacio Domínguez and Hubertus Gay • Objectives – Agro. SAMs for EU-27 – late 2009 with a Disaggregated Agricultural Sector (Agro. SAM) – EU IO tables for GTAP v 7. 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. Euro. Stat, FAOSTAT). – Other • A transparent and automatised routine for updating Agro. SAMs • For GTAP an automated routine for converting SAMs to IO format

Agro. SAM Outputs • SUPPLY & USE tables – SUPPLY – Basic prices • Agro. SAM 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 • • Splitting Domestic and imported use matrix Building 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

I-O Tables Requirements • Sectoral classification: – Full 57 sectors not required – Separate 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

Unusual Shares Supply Use ENTROPY Oil Seeds Oil and Gas Sugar Vegetable Oils Financial 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. 14 0. 13 0. 12 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 9

Clean, Disaggregate, Synthesize • Disaggregate – Of the 113 regions in GTAP 7: only 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. 10

Composite Regions: Rules • • Match each member country to a primary region. Match 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. Country afg npl GDP (USD B) 19. 0 5. 6 GDP per cap. (USD) 697 227 Best match lka bgd 11

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

Construction Process I-O Tables • Eliminate changes in stocks • Reconcile with international data 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 13

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

Checks and Comparisons • Across Versions – Comparison programs: aimed at highlighting large differences 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

Satellite Datasets • • • Energy volumes CO 2 and non-CO 2 emissions Land Satellite Datasets • • • Energy volumes CO 2 and non-CO 2 emissions Land use by Agro zone Migration and remittances Foreign income payments and receipts 16

Future Directions (v 8 & 9) • IO tables – Commodity Taxes – Dwellings Future Directions (v 8 & 9) • IO tables – Commodity Taxes – Dwellings – More programs • Skill shares • Domestic margins 17

Theoretical Background FIT Module • Bacharach, M. (1970), Biproportional matrices and input-output change, Cambridge. Theoretical Background FIT Module • Bacharach, M. (1970), Biproportional matrices and input-output change, Cambridge. • James, M. and R. Mc. Dougall (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