© european communities, 2007 background interactions between agriculture and processing industries...
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![Page 1: © European Communities, 2007 Background Interactions between agriculture and processing industries came into focus of researchers. Many of the widely](https://reader036.vdocuments.site/reader036/viewer/2022070404/56649f395503460f94c56aad/html5/thumbnails/1.jpg)
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BackgroundInteractions between agriculture and processing industries came into focus of researchers. Many of the widely used modelling systems and databases do not support such studies in the desirable detail. IPTS launched on request of DG TRADE an in-house project on the compilation of Social Accounting Matrices with a detailed representation of the agricultural sector (AgroSAM). This project combines agricultural sector data from an existing partial equilibrium model (CAPRI) with economy-wide supply and use tables provided by Eurostat.
Research ObjectiveThe main aims of this project are: to construct Social Accounting Matrices (SAMs) for the EU27 Member States which would allow analysing the economic effects of the CAP reform within and beyond agriculture. to contribute to existing tools for quantitative policy analysis that are built on SAMs, like computable general equilibrium models (CGE). to provide a data-link between agricultural sector models and multi-sector models
Datasets Used• Supply and use tables (SUT) and input output tables (IOT) from Eurostat – 59 sectors• Macro aggregates from Eurostat (NAMA) - 31 sectors• National accounts by institutional sectors (NASA) • CAPRI Database – 60 sectors
Main Challenges• While the agricultural sector is well documented, data about agricultural processing industries proved to be more difficult to obtain• SUTs are available for 24 EU Member States only• The year 2001 has the best data coverage; more recent years are not as well covered• The large number of variables underlying the AgroSAMs causes computational difficulties (compilation time)
From Partial Equilibrium to General Equilibrium Models
Linking of Databases by Means of Entropy Techniques
ContactDr. Marc Müller and Dr. Ignacio Pérez DomínguezEuropean Commission • Joint Research CentreInstitute for Prospective Technological StudiesTel. +34 954488348 • Fax +34 954488434E-mail: [email protected]
Model Flow:(1) Construction of SAMs in ESA95 Format
(2) Compilation of Priors for the AgroSAMs based on the CAPRI Database
(3) Balancing the AgroSAMs
Results:As final result, a set of balanced agricultural social accounting matrices (AgroSAMs) consistent with national supply and use tables is provided. They give a more detailed picture of the agricultural sector according to the information derived from the highly disaggregated CAPRI database. Currently the data available allow the construction of AgroSAMs for 24 EU Member States for the year 2001. The extension to 27 Member States and a more recent year is in preparation.
Use:AgroSAMs can be used as database for a variety of modelling systems currently used by analysts within European Commission: SAMs and corresponding input-output tables serve as standard format for general equilibrium models like GLOBE or GTAP (Global Trade Analysis Project), which is used for instance by DG-TRADE. Partial modelling systems (like CAPRI) which rely on sector-specific data will also benefit from the usage of the AgroSAMs because of the implicit macro-economic consistency of any derived sub-set.
1 min lns s ss
CE W W W Cross Entropy Minimand
s.t. 2 Y Y Final AgroSAM entry Y equals prior information
Y times correction factor kappa κ 3
exp s ss
W b
Kappa is defined as exponential function of bounds b and associated weights W
4 3, 1.5,0,1.5,3sb SIG Bounds b are defined as symmetric interval centred at 0; range of the interval depends on exogenously set standard deviation SIG
5 1; 0 1s ss
W W Weights W have to add up to 1 and range between 0 and 1
6 accounting identities for Y Totals of rows and columns in the AgroSAM have to be equal; associated quantities also have to be balanced
E x p e n d i t u r e s
Activities Commodities Factors
Transactions
Institutions Total
Agriculture
Industry Activities
Services
Domestic production
Agriculture
Industry Commodities
Services
Intermediate demand
Domestic consumption
Exports
Labour Factors
Capital
Payments for fixed factors
Income from abroad
Trade Trade margins Transactions
Taxes Taxes on activities
Taxes on commodities
Direct taxes
Enterprises
Households
Government
Savings-Investment
Savings
R e v e n u e s
Institutions
Rest of the world
Imports
Distribution of factor
income
Transfers
Total
• Supply-tables (purple) and Use-tables (blue) were combined with data on flows between domestic institutions (NASA, green)• Agriculture is represented only as one row and one column
• Aggregate values for agriculture from CAPRI (purple) and ESA95 (blue) deviate in some cases substantially• Need to formulate a balancing procedure to adjust agricultural data to macro-total indicated by ESA95
• Cross-entropy procedure• Final AgroSAM entry is defined as prior information times a correction factor
• Deviation from prior information depends on exogenously set standard deviation (“SIG” in left figure)• Accounting identities are imposed on all entries of the AgroSAM
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000
Intermediate demandby activities
Gross value added atbasic prices
Activity level
Domestic marketedproduction
Imports
Exports
Million Euro, current
CAPRI
ESA95