W ork i ng P a pe r S eri e s
Working Paper 1/3, Work Package 8, December 2001
Documentation of the Idara Agricultural Sector
Simulation Model for CEE Countries (i-Sim )
DRAFT VERSION 01/2002
Kai Bauer
Institute for Agricultural Policy (IAP), Bonn University
This Research Project is Financed by the EU Commission's 5th Framework Programme
(QLRT-1526):
Quality of Life and Management of Living Resources
Key Action 5 - Sustainable Agriculture, Fisheries and Forestry, and Integrated Development
of Rural Areas Including Mountain Areas
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Table of Contents
1
INTRODUCTION
6
2
THE ANALYTICAL CORE OF THE MODEL
8
2.1 Background
8
2.2 Supply Side
2.2.1
Technology
2.2.2
Activity Levels
2.2.3
Determining the shift factor
2.2.4
Scaling factor to enforce land balance
2.2.5
Set Aside
2.2.6
Introduction of shadow revenues
2.2.7
Lower bound on activity revenue arguments in double log functions
2.2.8
Input demand
8
8
9
9
10
10
10
11
12
2.3 Processing
12
2.4 Final demand
13
2.5 Exogenous components of market balance
14
2.6 Price transmission and political instruments
14
2.7 Market Clearing
15
2.8 Iterated solution
17
3
THE DATA BASE
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3.1 Making the ABTA operational
19
3.2 Data Conversion
3.2.1
Technical checks, EURO and units conversion of the idara data files
3.2.2
Conversion from Excel to Gams/tab.file
3.2.3
Consistency Checks
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22
24
25
3.3 Data Aggregation
3.3.1
3-year averages
3.3.2
New Or Old EAA Concept
3.3.3
Standard Aggregation of the data
3.3.4
Distinguishing Male and Female Calves
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29
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3.4 Producer Prices
32
3.5 Prices for processed goods (excl. Milk Products)
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3.6 Prices of milk products
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4.1 World Market Prices
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4.2 Consumer Prices
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4.3 Consumer Expenditure
38
4.4 Political Variables for the base year
4.4.1
Converting the OECD PSE data
4.4.2
Calculating market price support (TARR)
4.4.3
Final preparations
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39
40
41
4.5 Political Variables for the reference / simulation year
4.5.1
Tariffs
4.5.2
Premiums
4.5.3
Quotas
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42
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43
4.6 Exogenous Yields
4.6.1
Trend estimation
4.6.2
Expert opinions
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48
4.7 Elasticities
48
4.8 Others
48
5
TECHNICAL INFRASTRUCTURE AND SOFTWARE
49
5.1 Excel data collection files
49
5.2 Fortran Programme files
49
5.3 Table Files for data storage
50
5.4 Gams Files
53
6
CONCLUSIONS AND FURTHER RESEARCH NEEDS
55
7
REFERENCES
57
8
ANNEX
58
8.1 List of Parameters and Variables
8.1.1
Parameter definitions for the model
8.1.2
Variable definition in the model
8.1.3
Define the Equations
58
58
60
61
8.2 Codes of the idara data base (Excel Sheets)
63
8.3 Codes of the Aggregated Database for i-sim
63
8.4 Political Variables
8.4.1
Political Variables Base Year 1998 per product (EURO/t)
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Political Variables Base Year 1998 per activity (EURO/ha or hd)
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8.5 Matrix of elasticities
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8.6 Exchange Rates
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8.7 Important Groups of Products
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List of Tables
Table 1. List of Activities with Shadow Revenues in the base period
Table 2. The different types of market clearing in the i-sim model (sets_i-sim.gms)
Table 3. Selected use activities
Table 4. Raw Products and corresponding derived products of i-sim
Table 5. Unit re-definitions performed by VBA Macro “transfermacro_xls”
Table 6. Aggregated i-sim column codes and corresponding codes from the original idara data sheets
Table 7. Aggregated i-sim row codes and corresponding codes from the original idara data sheets
Table 8. Political Variables in the base year 1998 per production activity (EURO/ha or hd)
Table 9. Political Variables in the base year 1998 per product (EURO/t) for selected products
Table 10. Scalars that define the thresholds for trend estimates
Table 11. Overview of i-sim file types
Table 12. Executable Programmes of the i-sim system
Table 13. Content of table file mfss-bas.tab
Table 14. Contents of table file mfss-pol.tab
Table 15. Contents of table file mfss-sim.tab
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16
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22
30
31
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42
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50
50
51
52
List of Figures
Figure 1. Screenshot of Datasheet Output.xls
Figure 2. Data Conversion Step 1: Corrections and Conversion to EURO
Figure 3. Data flow from Excel GAMS to tab.file storage: Consistency and aggregation
Figure 4. Data flow from Excel GAMS to tab.file storage: Trend estimation and storage
Figure 5: Overview of Consitency Relations
Figure 6. Calculation of Relative World Market Prices for the base year 1998 and simulation year 2006
Figure 7. Deriving political variables for i-sim
Figure 8. Transfer of political variables and world market prices to the tab file
Figure 10. Calculation of trends and transfer of trend data to tab file
Figure 9. Example of non-linear trend estimate
Figure 11. GAMS file infrastructure of data aggregation
Figure 12. GAMS file infrastructure of the core model
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1 Introduction
The idara Agricultural Sector Simulation Model (i-sim) has been developed in the
framework of the idara project in order to simulate the effects of agricultural policy changes in
Central and Eastern European Countries. The model aims at providing a first assessment of
the effects on the agricultural sector in terms of production levels, income and trade. In view
of the central importance of the agricultural sector in the rural areas of the CEEC countries
the model provides essential information to evaluate and develop integrated strategies for
rural development and agriculture in the CEEC countries. Besides the direct application of
the model in the project context it is envisaged to be used by the CEEC partners for further
analysis during the negotiation process for EU accession of their respective countries.
In order for the modelling system to fulfil these requirements it needs to have the
following characteristics:

transparency

user friendliness

relatively low data requirements

comparability with EU-15 model CAP-Sim/MFSS99
In view of these requirements and the time frame of three years of the project, which is
relatively short for the development of a full blown simulation model, it was an almost obvious
choice to use an existing model as a “blue print” to develop the new model for the CEECs.
For this purpose the above mentioned MFSS99 Model was chosen since it best fitted the
requirements.
In the MFSS99 model the production structures of all EU-15 countries are subjected to
the same changes in policy instruments and compared with one common agricultural policy
in the reference run. This technique could obviously not be used for policy simulations of the
three CEE countries in the idara model. The political variables differ between the three
countries in the reference run according to the specific agricultural policy applied in the base
year 1998 in each individual country. A simple enlargement of the MFSS99 and an
incorporation of the accession countries into the model for the reference run as well as for
the simulations would thus have various disadvantages:
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
The model would have been too complex and nontransparent. This would lead to less
user-friendliness and make the model difficult to handle.

The possibilities for the project partners from Poland, Czech Republic and Hungary to
adjust model parameters and exogenous variables according to their country’s production
characteristics would be limited, since the model specification would be mainly done in
Bonn.

Due to the restricted model handling and application in CEEC, the transfer of know-how
and skills in modelling and policy impact analysis would be limited.
Because of these reasons the project partners agreed at the modelling workshop in Bonn
in December 2000 on developing three closely related country models (Poland, Hungary,
Czech Republic) for the reference
Model System
Pattern Model
Reference Run
Accession
Scenarios
run
Accession
Scenarios
and
individual
country
simulations. This structure not only
allows
for
country
specific
reference runs, but also enables
MFSS99
Hungary
MFSS99
MFSS99
Poland
MFSS99
EU15+
HU, PL, CZ
the CEEC partners to handle their
country model individually and to
analyse the impact of different
policy changes on their agricultural
sectors independently. Since the i-
MFSS99
Czech R.
sim data base is fully compatible
with the MFSS99/CAPSIM Model
it will be easily possible to include Czech Republic, Hungary and Poland into the EU model.
This “EU-18” Model will however be beyond the scope of this research project
This paper is intended to serve as a thorough documentation of all aspects of the i-sim
system. It thus not only describes the key model equations, parameters and variables, but
also describes in some detail the construction of the underlying data base, the data checks
involved and also contains an important section on the exogenous parameters and how they
are derived. Finally, in Chapter 5, the software and file infrastructure of the whole system is
documented.
While the current version of this document reflects the up-to-date status of the model, it
lies in the nature of the modelling effort that changes will occur until the end of the project
lifetime. These changes will be documented in subsequent version of this document and
posted on the projects’ web site.
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2 The analytical core of the model
2.1 Background
The idara simulation model (i-sim) is based on the Institute of Agricultural Policy's CAPSIM1 system (W ITZKE, VERHOUK, ZINTL, 2001), which was designed to be a transparent
modelling tool to be applied on the CAP by policy makers and their staff who require a first
quantitative assessment of conceivable policy scenarios in a short period of time without
lengthy consultation of scientific research teams. For this purpose, transparency of the
modelling system is crucial. Transparency, together with limited resources, requires a
number of simplifications and ad hoc procedures. These could only be avoided in research
projects with a longer term perspective.
Consequently i-sim is a purely comparative static modelling tool, abstracting from any
dynamics
arising
from
the
livestock
sector
or
from
the
formation
of
(price-)
expectations.While CAPSIM is driven by a set of elasticities taken from other models or from
the literature, for idara we use the CAPSIM elasticities as a starting point an replace
elasticities where available in literature. Finally the new matrix was calibrated according to
microeconomic theory.2 Due to the limited availability of time series it was not possible to
estimate elasticities from the idara data base. More sophisticated procedures, such as
econometric estimations, could only be ventured in longer term research projects. Regarding
the international environment, i-sim has to rely on exogenous assumptions, either for trade
quantities or for world market prices.
2.2 Supply Side
2.2.1 Technology
Supply is assumed to originate in a fixed yield technology3:
1
Formerly: MFSS99
Witzke:
3 The equations reproduced in this chapter correspond to the crucial equations in the GAMS code.
Abbreviations used there have been incorporated here with few changes to assist readers who try to
read the GAMS code at some point of time.
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___
PRDx = a (YLDa,x * LVLa )
( 2-1)
Where
PRD
___ x = production of good x
YLDa,x = exogenous yield of good x in activity a
LVLa = activity level (usually ha or hd) of activity a
The exogenous yield is a fairly simplistic assumption but will
contribute to the
transparency this model is trying to introduce A serious empirical investigation of this issue
would go beyond the scope of the idara project. However, other studies have shown that
intensity changes play a minor role as compared to changes in activity levels as far as
explaining supply response is concerned.4
2.2.2 Activity Levels
The activity levels stem from a double log behavioural equation relating them to prices of
relevant outputs and inputs:
___
LOG( LVLl) = SPLl +CFAC1+ 1 (LOG(PPv + PRETv + PREMl/YLDl,v ) * LELASl,v)
where
LVLl
SPL
___l
YLDl,v
PPv
PRETv
=
=
=
=
=
PREMl
LELASl,v
CFAC1
= premium per unit (ha or hd) of activity l
= elasticity of activity l with respect to price of good v
= "scaling factor" to model ceilings on premiums
activity level (usually ha or hd) of price dependent activity l
shift parameter in level equation l
exogenous yield of good v in activity l
market price at producer level of variable input/output v
premium per t of good v
2.2.3 Determining the shift factor
The Shift factor SPL1 is determined by the following equation:
4
Reference?!
( 2-2)
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= Log1 (LVLB) - 1 Log(REVSB*LELASREV)
( 2-3)
in order to reproduce the observed activity levels in the base period.
2.2.4 Scaling factor to enforce land balance
It was found out that the WATSIM modelling team at the Institute has solved similar
problem, to enforce an energy balance on feed, using a simple scaling factor to be set by
GAMS. This scaling factor introduces a difference between "planned" levels, which may
violate the land balance, and actual levels, which will be scaled proportionally upwards or
downwards until the land balance is met. The scaling factor is usually close to one, i.e. it
involves only a small correction of "planned" levels.
The advantage of the scaling factor is mainly simplicity. The use of the multinomial logit
model
would make the program more complicated though it has some theoretical
advantage. .
2.2.5 Set Aside
For the reference run there are no set aside products, since in Hungary, Czech Republic
and Poland this instrument is not applied so far. For the simulation run, however, it is
necessary to model the Set Aside system. The details of the specification still need to be
determined.
2.2.6 Introduction of shadow revenues
In the current agricultural policies of CEEC countries production quotas play a minor role.
For the reference run, there will thus be no modelling of quota regimes necessary. However,
for the simulation run, when it will be assumed that the CEECs adopt some form of the
Common Agricultural Policy, it will be necessary to model the effects of a quota regime on
the domestic markets. In order to provide room for the introduction of quotas, while working
with the same elasticity set, shadow revenues are used to drive the behavioural equations
(Witzke et.al., 2001: xxx). Thus, for the reference run, shadow revenues are forced to equal
actual revenues for most products. Only for activities with exogenous levels (grassland,
olives, wine, industrial crops, fallow land, and "other animals"), the shadow revenue is a free
variable to take on any value necessary to comply with the price independent levels.
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With the shadow revenues, it will be possible on the introduction of a quota system in
CEEC countries to model for example the direct effect on the levels of suckler cows which
are likely to be substitutes to dairy cows on the supply side. In the present version of i-sim,
activities that become restricted by a quota system have to be added to the set of activities
with free shadow revenues (set "SREV").
Table 1. List of Activities with Shadow Revenues in the base period
Activity Code
Activity Description
OLIV
INDU
OANI
GRAS
OFOD
WINE
Olives
Industrial Crops
Other Animals
Grassland
Other Fodder Crops
Wines
Quota/Exogenous
Levels (Q/E)
E
E
E
E
E
E
2.2.7 Lower bound on activity revenue arguments in double log
functions
In order to restrict the model solutions to plausible values a bound is introduced on
activity revenues. Since prices and yields are positive the bound is set at >0.
For cattle activities, a calves balance provides a similar safeguard against technically
infeasible results:
___
___
LVLdcow * YLDdcow,calv + LVLscow * YLDscow,calv = LVLcalf + LVLbull + LVLheif
where
LVLdcow
LVLscow
LVLcalf
LVLbull
LVL
___ heif
YLD
___ dcow,calv
YLDscow,calv
=
=
=
=
=
=
=
activity level of dairy cows (hd/year)
activity level of suckling cows (hd/year)
activity level of calfes for fattening (hd)
activity level of fattening male cattle (hd)
activity level of fattening female cattle (hd)
calves born per dairy cow
calves born per suckling cow
( 2-4)
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Note that this is a static calves balance which imposes feasibility regarding calves in a
new equilibrium but which need not guarantee a "reasonable" dynamic behaviour of the
cattle population. This requirement would be beyond the scope of a purely static model.
2.2.8 Input demand
Input demand for feed is be specified dependent on (livestock) quantities:
LOG( INPf) = SPFl + 1 (LOG(PPg * LELASl,v)) + 1 (LOG(PRDm* FELASl,v))
where
INPf
PRDm
PPg
LELAS
FELAS
( 2-5)
= input demand for feed f
= production of animal output m
= market price at producer level of feed input g
= Input Demand Elasticity with respect to input price
= Input Demand Elasticity with respect to Production Level
These quantity dependent demands follow from a cost function description of the
technology with appropriate separability assumptions. For other variable inputs (chemicals,
overheads...), a price dependent specification is currently implemented, similar to the output
side. Given that MFSS99 is not designed to be relevant for environmental questions (fixed
yields, no regional differentiation) it appears reasonable to aggregate these other inputs
considerably for the sake of simplicity.
2.3 Processing
For processing we have adopted a specification which proved useful in the CAPRI
project:
LOG( PRCx) = SPPx + y (LOG(PPy) * PELASx,y)
where
PRCx
SPPx
PPy
= processing of good x
= shift parameter in processing equation x
= market price at producer level of good y
( 2-6)
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= elasticity of processing of x with respect to raw product price of good y
Processing takes place only at the MARKET level (MAR00). The raw products and their
corresponding processed goods are:
POTA.STAR
OCER.RICE
SUGB.MOLA
RAPE.RAPO
SUNF.SUNO
SOTH.SOYO
OLIV.OLIO
RAPE.RAPC
SUNF.SUNC
SOTH.SOYC
OLIV.OLIC
SUGB.SUGA
2.4 Final demand
Final demand is specified based on a double log function with elasticities still to be
derived from a review of recent literatures. The current set of elasticities has been taken
from the old SPEL system (MFSS99's predecessor).
LOG(CNSx) = SPDx + y (LOG(FPy * DELASx,y)) + y (LOG(EXP* DELASx,exp))
where
CNSx
SPDx
FPy
DELASx,y
=
=
=
=
final demand for good x
shift parameter in demand equation x
final demand (consumer) price of good y
elasticity of demand x with respect to price of good y
DELASx,exp
=
elasticity of demand x with respect to total consumer expenditure
( 2-7)
It has to be mentioned that the procedure used to derive consumer prices from the
expenditure data on food aggregates in EU member states rests on a number of ad hoc
assumptions which require reappraisal and updating. It is up to the idara research team to
come up with an applicable technique to derive consumer prices for CEEC countries.
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2.5 Exogenous components of market balance
Certain positions of minor importance will be linked to production by fixed relationships:
____
LNKx = PRDx * LFACx
where
PRDx
( 2-8)
= gross production of good x
LNK
____x = use of good x linked to production (SPEL codes: PSEE+SEEP+PLOF+PCOF)
LFACx = fixed relation of uses linked to gross production for good x (derived from base
period)
2.6 Price transmission and political instruments
Common market prices derive from international prices where net trade is allowed to
adjust freely:
PTEx = PWx * (1+ TARRx ) + TARRx +FLEVx
where
PTEx
PWx
TARRx
TARRx
FLEVx
=
=
=
=
=
( 2-9)
EU market price (for EU intra trade)
Exogenous world market price
ad valorem tariff
specific tariff (fixed ammount per t)
flexible levy / export restitution
and
FLEVx = PWx * (1+ TARRx ) + TARRx - PADMx
( 2-10)
where
PADMx
= Administered EU price
The prices for milk products derive with zero profit condition from price of fat, protein and
processing cost
PTE( MLKPRO ) 
 PPCNT
MLKCNT
MLKCNT
where
* CNTMLKPRO,MLKCNT
( 2-11)
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= EU Market price of milk products
= Producer price of milk contents
= Milk contents: fat, protein, processing costs
= % content fat, protein and processing costs (the later are of course 1)
EU member state producer prices follow EU market prices according to a fixed
proportional relationship derived from the base period:
__ ___
PPx = PTEx * PPx / PTEx
where
PP
__ x
PP
x
___
PTEx
( 2-12)
= producer price in EU member state
= producer price in EU member state in base period
= EU market price in base period
Final demand prices are related to market prices at the producer level by a fixed
marketing margin derived from the base period:
__ __
PFx = PPx + PFx - PPx
where
PF
__ x
PFx
( 2-13)
= final demand price in EU member state
= final demand price in EU member state in base period
2.7 Market Clearing
In the model, markets clear either with a fixed price or with fixed trade quantities. In the
first case the price is fixed by the world market (plus tariffs), by domestic policy (flexible
levies) or for a number of products (group "FXPTE", see table below), by assumption. Apart
from the inputs, the latter group mainly includes certain products of limited importance with
very uncertain information on elasticities or world market prices.
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Table 2. The different types of market clearing in the i-sim model (sets_i-sim.gms)
Fixed Price
Fixed Trade
Group
-
Group FXPTE
Group FXTRD
MSBAL
Description
Exogenous World
Market Price +
Tariffs + Domestic
Policy
Exogenous fixed
product price (no
world market price
available)
Exogenous trade
volume for the EU
with free trade
among MS
Trade is fixed at the
member state level
Products
All other products
OLIV, INDU, WINE,
OCRO, OANI,
COWO, RICE,
OLIO, OLIC, IPLA,
IGEN, REST,
VEGE, FRUI,
OFOD, GRAS
MOLA, STAR,
OMPR, SUGB,
POTA, EGMI,
YCAM, YCAF
Not used for the
single country
models
Source: Own table
For market clearing with fixed trade, there are again two varieties, the most common
being an introduction of exogenous trade volume for the EU with free trade among MS.
Currently trade is fixed at base year values but at least in principle there is the possibility of
introducing any other quantity as exogenous .
The less common case is that trade is fixed at the member states level, as is assumed
for fodder (OFOD, GRAS), and calves (YCAM, YCAF). Though there is some trade of
calves between MS there will not be wide changes in the trade flows of young animals.
Treating calves, as non-tradables will be
a safeguard against this kind of unexpected
results. However, as every constraint, fixing the trade structure may even work like a straight
jacket and inhibit the model's feasibility. If this decision is to be reversed in the future, this
may be done quickly by changing the membership in-group "MSBAL" (in program sets_isim.gms)
Market clearing may be expressed in a simplified form, neglecting export quotas and
sales to intervention authorities, as follows:
NETTRDx = SUPx - DEMx
where
( 2-14)
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x
DEMx = INPx(PP) + CNSx(PF) + PRCx(PP)
and elements (possibly zero) of the market balance are
NETTRDx
= net exports of good x
SUPx
= supply of good x
DEMx
= demand of good x
PRDx
LNKx
PRCx
PRC _ Y
INPx
CNSx
x
=
=
=
=
=
=
gross production of good x
uses linked to production of good x
processing of raw product x
fixed final product yield of good x per ton of corresponding raw product
input use of good x
final consumption of good x
The model calculates net trade as a residual quantity if it is allowed to vary. If net trade is
fixed politically (by export quotas) or by assumption of exogeneity (for example for pork) and
if in addition intervention sales are limited, the model calculates the EU market price PTE x
consistent with predetermined net exports.
2.8 Iterated solution
MFSS99 computes simulation results starting from the base year situation to which is
calibrated. It responds to changes in exogenous variables, possibly after translation through
identities, as the elasticity sets prescribe and adjusts endogenous variables, for example
prices, to make them compatible with given behavioural functions and the changed set of
exogenous variables.
However this search for a solution value may be too difficult for the solver if large
changes, say deviations beyond 20% from the base year, are tried in one step. Instead it
turned out frequently indispensable to divide such a large move in smaller steps, each time
starting with the solution values of the last small step. Because the solver usually finds the
solution the faster the closer the solution is to the initial situation, this kind of iterated
approaching of the desired simulation does not increase the overall solution time seriously
when the number of iterations is set to 20. This fairly high value should guarantee feasibility
of the model's solution, at least for standard cases.
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3 The Data Base
The Activity Based Table of Accounts (ABTA) serves as methodological background for
the generation of a consistent data base. It enables the simultaneous consideration of
agricultural outputs, inputs and prices at the sectoral level with differentiation into various
production activities. The agricultural sector is defined according to EU standards, i.e. the
Economic Accounts for Agriculture (EAA).
3.1 Making the ABTA operational
Since the structure of the ABTA is difficult to handle during the data collecting process, it
was decided at the First Meeting that the Bonn team will develop EXCEL sheets to make the
data input and synthesis easier and more transparent.
Figure 1. Screenshot of Datasheet Output.xls
In a first step two EXCEL files were created, one for the output and one for the input
items of the ABTA. The differentiation of products, input items and activities and their
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corresponding four letter code has been taken from the SPEL/MFSS99 base system and
was modified according to the production structure of our partner countries. In a second step
connections to existing data systems were drawn. In co-operation with ASA-Institute Bonn,
the input item definitions were adopted according the EAA. Partners who had already filled
the ASA data sheets for the EAA generation can easily import this data into the IDARA
sheets. This will avoid double work and give the possibility to benefit from existing
databases. As a further measure to ensure compatibility to EUs Systems, the data entries
were sorted according to the 5-digit codes of EUROSTATs NewCronos data base.
The sheet “output.xls” is a list of all entries in the top two quadrants of the Activity Based
Table of Accounts, i.e. Output Generation and Output Use. The sheet contains all items
listed according to product groups. This means that both generation and use of a particular
product are grouped together. Where several products exist within each production activity,
the level of each production activity is listed with its main product. The sheet covers 37 raw
products, 7 fodder products, 15 processed products and 19 animal products of which 7 are
young animals (piglets, calves etc.). These products are produced in 49 production activities
and can be used in the three groups of demand activities: on farm use (7), domestic market
(7) or trade (8). Each data point can be written as value in national currency or as physical
quantity in tons with the corresponding price. One has to fill at least two of the three numbers
to calculate the one missing (see Figure 2). In order to generate a transparent data base it is
necessary to document the source of the data. A source code for all sources used in the
individual countries has been developed and enables all project partners and users of the
data base to trace every single information.
The sheet for input data is structured in such a way that input use and input generation
are listed per activity. At the bottom of the list are the sectoral aggregates. According to the
new EAA methodology, the input sheets cover 12 variable (intermediate) input items such as
fertilisers, fodder and repairs and 9 fix input items such as depreciation, wages, rents,
interest and taxes. The data is entered in the same way as in the “output” sheet. For
reference of the positions and codes used in the sheets please refer to Annex 1.
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Some positions that need further explanation:
Table 3. Selected use activities
Code
PIND
Description
Industrial Use
PPRO
Processing on Market
PCOM
Human Consumption on
Market
Details
Industrial use of agricultural products. These quantities go
into uses that are not for human consumption, e.g. wheat
starch, washing powders, cosmetics
Processing of the well defined products in the model that
have corresponding derived products that are also
considered in the model. See table below with products and
their derivatives.
This involves all agricultural products that can be consumed
by humans in whatever state of processing. For example the
quantities of Wheat (SWHE) that are used for bread will be
stored under this position. However, the MILK that goes to
Butter (BUTT), Milk Powder (MIPO) and other milk products
(OMPR) will NOT appear here, but under the position PPRO
(see above).
Table 4. Raw Products and corresponding derived products of i-sim
Raw
Product
CODE
Raw Product
Description
Idara Derivative
Code
Idara Derivative Description
POTA
OCER
SUGB
RAPE
Potatoes
Other Cereals
Sugar Beet
Rape Seed
STAR
RICE
MOLA
RAPO, RAPC
SUNF
Sunflower Seed
SUNO, SUNC
SOTH
Soya beans and
other oil seeds
SOYO, SOYC
SUGA
OMPR
Potato Starch
Milled Rice
Molasses
Vegetable Fats and Oils –
Rape, Oilcakes – Rape
Vegetable Fats and Oils –
Sunflowers, Oilcakes –
Sunflowers
Vegetable Fats and Oils – Soya
beans and other oil seeds,
Oilcakes – Soya beans and
other oil seeds
Sugar
Other Milk Products
BUTT, MIPO,
OMPR
Butter, Skimmed Milk Powder
and Other Milk Products
SUGB
EGMI
(MUTM)
MILK
Ewes and goat milk
Raw Milk
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3.2 Data Conversion
After the data files are filled it is necessary to convert the data from Excel files to a
GAMS compatible format, since GAMS is the programming language which is used to run
the i-sim modelling system. The Data Conversion is performed in two steps.
3.2.1 Technical checks, EURO and units conversion of the idara
data files
In a first step the idara data sheets XX_input.xls and XX_output.xls5 are checked for
typing mistakes and residual prices/values are calculated where only quantities and either
prices or values are given. The resulting new tables are saved under the new names:
XX_input_NC.xls and XX_output_NC.xls. The macro that performs this task is called
"Correct". This macro and all others that do data conversions, are to be found in the file
XX_transfermacro.xls". This file also contains a sheet named "control panel", Figure 2. The
blue arrows in this figure are linked to the macros for most intuitive use.
Apart from some checks concerning misspelled code, this first step also changes some
unit definitions. The changes performed here are described in the following table:
Table 5. Unit re-definitions performed by VBA Macro “transfermacro_xls”
Data Group
UNIT Original Data
Sheets
UNIT idara
database
Values
Quantities
Mio NC
Tonnes (t) or heads
(hds)
EURO/t
kg/ha or kg/hd
Mio EURO
1000 t or 1000 hds
kg/1000 hds
kg/1000 hds
Kg/hd
hds/hd
hds/hd
hds/hd
kg/1000 hds
kg/1000 hds
kg/1000 hds
hds/hd
hds/hd
hds/hd
Prices
Input/Output
Coefficients
Exceptions:
POUL.POUL
HENS.POUL
HENS.EGGS
DCOW.CALV
SCOW.CALV
HENS.CHIC
5
EURO/t
kg/ha or kg/hd
XX denotes the country codes HU, PL, CZ respectively
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MUTM.LAMB
RCAL.BULL
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hds/hd
hds/hd
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hds/hd
hds/hd
hds/hd
As figure 1 shows, the created files can then be used to fill the idara_abta.xls file for
consistency checks using the macros "ABTAOutput" and "ABTAInput" (to be found in
abtamacro.xls). In step 2 the corrected datasheets with values and prices in national
currency are then converted to EURO using the exchange rates in xrs.xls.
The resulting data in EURO is saved to new files called XX_output_EURO.xls and
XX_input_EURO.xls. However, for the procedure to derive the political variables, only the file
with data in national currencies is used in order to be able to compare the values with the
PSE database which is also in national currencies.
Figure 2. Data Conversion Step 1: Corrections and Conversion to EURO
Original Data File
input.xls/output.xls
Step 1:
Correct
Database
Idara Data File
input_NC.xls
output_NC.xls
Write input_NC to
idara_abta.xls
Idara Data File
idara_abta.xls
Write output_NC
to idara_abta.xls
Exchange Rates
XRS.xls
Step 2:
convert to EURO
Idara Data File
Input_EURO.xls
Output_EURO.xls
Correct
input.xlsa.xls
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3.2.2 Conversion from Excel to Gams/tab.file
Since the original data for the idara data base is collected in EXCEL sheets and has a
slightly differing data structure, it was necessary to develop a tool to convert the data to
GAMS files in the required format and structure. GAMS files are standard formatted files that
can be used as input by the GAMS modelling system. Furthermore, there are a few routines
of data preparation running in GAMS such as the aggregation of products and activities to
the final model structure and the estimation of trends. After these aggregation steps have
been completed the results are stored as a tab-file in the Tab File Data Management where
they are ready for the model run . The Tab File Data Management System is the IAP’s own
software solution for data storage and management.
Figure 3. Data flow from Excel GAMS to tab.file storage: Consistency and aggregation
Idara Data
Files
input_EURO.xls
Inputprices.GMS
Visual
Basic Macro
Input.GMS
sets.GMS
MakeGMS.xls
Conversion of data to
GAMS format
Output.GMS
output_EURO.xls
Consistency Checks
i_A3.gms
Corrections and
Recalculations
BMDAT.GMS
Export of Consistent
Data to ACCESS
i_correct.gms
Idaradata.gms
i_consist.
gms
Idara
Diagnosis
Visual Basic
GAMS
aggregation of
products
and activities, new
allocation
of young animals,
corrections
I_consist.LST
Files
Aggregation of data
base for modelling
ex-post
diagnosis
Gams files
Flat files
I_prices.GMS
Excel/
Access files
MS Excel/ACCES
BASAGG.GMS
BASAGG.SDA
GAMS and SDA Files
with aggregated
data base
GAMS
Source: Own figure
The application of the MFSS99 data management routines showed that not all routines
are needed for the preparation of the idara data base. These FORTRAN routines, which are
somewhat difficult to handle were condensed and simplified.
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Figure 4. Data flow from Excel GAMS to tab.file storage: Trend estimation and storage
MFSSBAS.TAB
SETSTRD.GMS
BASAGG.GMS
BASAGG.EXE
BASAGG.SDA
store aggregation
results in TAB-file
TRDDAT.GMS
Visual Basic
Do trend estimations
Type:
BAEM (yearly)
1990 - 1998
GAMS
Fortran
BASTRD.SDA
BASTRD.EXE
store trend results in TAB-file
Fortran files
Gams files
Flat files
Trends
of type:
BTRM
1993 - 2006
Excel files
TAB files
GAMS
Tab File Data Management
3.2.3 Consistency Checks
What Consistency Checks should do
Consistency checks are important tools to prepare the data base for modelling. As was
described in Chapter 2, the analytical core of the i-sim model contains a number of balances
that ensure that the model produces plausible results in the reference and simulations runs.
However, if the base year data doesn’t comply with these balances the solver not be able to
find a solution. It is thus necessary to check for consistency problems beforehand.
During the modelling workshop in Bonn December 4-7 2000 it was decided that each
country will take care of the necessary consistency checks individually. They will be
performed in the ABTA table. However, during the first attempts to prepare the base run in
summer 2001 it became clear that it will be necessary to have a centralised set of
consistency checks that is performed before the data base is linked to the i-sim model in
order to avoid tedious search for infeasibilities of the solver. Together with the Hungarian
partners the consistency checks were programmed in GAMS in order to have rapid results
(i_consist_XX.gms). At the same time the necessary corrections of the data to fulfil the
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consistency requirements were collected in another GAMS file (i_correct_XX.gms). In further
consultations with the Partners from Czech Republic and Poland the i_consist_XX.gms
programme was further refined and developed.
List of Consistency Checks
Figure 5 gives an overview of some of the core relations in the idara database that will be
used for consistency checks in the ABTA tables. For a description of the codes, please refer
to the detailed description of the consistency checks below and the code lists in the annex.
Figure 5: Overview of Consitency Relations
Output Generation on Farm
YIELD x
LEVL
=
PROP
PLOF PCOF SEEP
FEEP
PROF
PCSF
TRAP
PIMT
MAPR
Output Use on Market
PDOM
PLOS
PEXT
PCSM
PCOM PFEE PSEE PIND PPRO
Consistency of physical production
Physical production volumes (PROP) are data derived from the EAA. The average yields
however are collected from different sources. One crucial consistency check is thus to see if
YIELD coefficients, production levels (LEVL) and production quantities fit together.
PROPAGRO 
YLD
PACT , AGRO
*LVLPACT
( 3-1)
PACT
where:
PROPAGRO = Gross Production of agricultural product AGRO
YLDPACT,AGRO = Output coefficient of activity PACT, product AGRO (kg/ha)
LVLPACT = production activity level of activty PACT (in ha or heads)
On Farm balance
On farm balance is a parameter indicating the balance between the gross output at the
farm level and other farm level activities which make use of the output produced. In other
words it is the balance indicating the flow of output from the gross output to the market. The
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farm level activities making use of the final output are: losses on farm, own final
consumption, seed on farm, feeding stuffs on farm and change in stocks on farm.
TRAPAGRO  PROPAGRO  ONFARM AGRO
( 3-2)
where:
TRAP = Sales on Farm
PROP = Physical Production
ONFARM = Uses on farm (losses, consumption, seed, processing, stock changes)
AGRO = Index for agricultural products
Market balance
The market balance is a parameter indicating the balance between supply to the market,
which is composed of imports and sales on farm and the use made of these supplies.
PIMTAGRO + TRAPAGRO = PLOSAGRO + PCOMAGRO + PFEEAGRO + PSEEAGRO + PINDAGRO +
PPROAGRO + PEXTAGRO + PCSMAGRO
( 3-3)
Where:
PIMT = Imports
TRAP = Sales on Farm
PLOS = Losses on Market
PCOM = Consumption on Market
PFEE = Feed use on Market
PSEE = Seed use on Market
PIND = Industrial Use
PPRO = Processing on Market
PEXT = Exports
PCSM = Stock changes on Market
Sectoral Aggregates for Inputs
This is a parameter indicating the balance between the supply and demand of inputs.
The supply component includes the amount of seed input produced on the farm and sold in
the market as input, imported inputs and part of the production that is used for feed on the
farm.
TRAPINPUT  PIMTINPUT  FEEPINPUT 
 INPCOEF
PACT , INPUT
PACT
* LVLPACT
( 3-4)
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Area Balance
In order to ensure that the sum of hectares of all land bound production activities does
not exceed the total agricultural land available, the following balance is introduced in the
model.
LANDREG 
 LVL
( 3-5)
CACT
CACT
where:
LANDREG = Total agricultural land in Region REG
LVLCACT = Activity level of crop production activity CACT
Calves Balance
The parameter calves balance indicates the balance of young animals, where the supply
should be equivalent to that of the demand. The supply part of this equation represents
activities that supply young animals, namely dairy cows and sucker cows. And it is a factor of
the level and the yield of those activities. The other part of the balance represents activities
that demand young animals (ADEM), these include the activities bulls, heifers and calves
fattening.
YLD
ASUP,CALV
* LVLASUP 
ASUP
 LVL
ADEM
( 3-6)
ADEM
Where:
ASUP= Activities supplying young animals (DCOW,SCOW)
ADEM= Activities demanding young animals (BULL, HEIF, CALF)
YLD = Yield coefficient of Activity ASUP providing product calves
LVL = Activity level of activity ASUP or ADEM
3.3 Data Aggregation
The data aggregation is a necessary step to allow for easier handling of the model,
especially with view to data and time limitations. As soon as the model is up and running one
can consider desegregating certain product groups in order to investigate these market
segment. At this stage, however, the priority lies with the evaluation of sector wide effects
and in order to simulate these effects the aggregation is extremely useful. Technically this
step is performed by a GAMS programme called bmdat_xx.
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3.3.1 3-year averages
In order to avoid strong influences of specific conditions in a given year and in order to
be able to integrate the production activities that entail several years, 3-year averages are
calculated before further calculations begin. The 3 step procedure is as follows:
3
QUANTITYA3 
 QUANTITY
t
t 1
( 3-7)
3
3
VALUEA3 
PRICE A3 
VALUE
t
t 1
( 3-8)
3
VALUEA3
QUANTITYA3
( 3-9)
where:
QUANTITY = Quantity data in 1000t
VALUE = Values in 1000 EURO
PRICES = Prices in EURO
T = years
A3 = Code for 3-year averages
By
this
procedure
it
is
ensured
that
the
consistency
condition
QUANTITY*PRICE=VALUE is met in the 3-years average data, too. All the calculations for
the 3-years-averages can be found in the file i_A3_xx.gms, which is included in the
bmdat_xx.gms files (see Figure 3, page 19).
3.3.2 New Or Old EAA Concept
The new EAA concepts are not yet fully implemented in the i-sim system. This is
because idara ex post data mostly corresponds to old definitions and the investigation of
differences is still under way. However, the i-sim gross concept (column PROP) closely
matches new EAA definitions, as intrasectoral feed use is included and simplification of the
model suggested eliminating certain intrasectoral transactions, which are not included in the
new EAA: Some of the changes are:

Nutrients from manure have been eliminated

Milk produced by suckler cows and sheep which is immediately fed to young animals is
not counted as output (and input)
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Consequently, the i-sim market balance reflects quite well the new concepts. However,
because the new basic prices and revised accounting of subsidies and taxes are not yet
available, the output value (PROV) differs to some extent from these new concepts. Only
when looking at NVAF we may expect a close resemblance of i-sim and new EAA concepts
because the different categories of “product related” and “other” subsidies/taxed are
aggregated again. Thus the calculated NVAF will at least closely match new EAA definitions
and is suitable as a relevant indicator of sectoral income in agricultural policy.
For more detailed technical information on the calculation process the comments in the
associated GAMS program (bmdat.gms) should provide sufficient insight.
3.3.3 Standard Aggregation of the data
The standard approach of the modelling effort was to collect original data as detailed as
possible and then aggregate the original data to satisfy the needs of the i-sim model. It was
thus necessary to aggregate the original idara data to a aggregated data base that provides
the aggregates used in the i-sim model. Table 5 and 6 give an overview of which positions
were aggregated. It needs to be stressed that the current aggregations are identical with the
ones used in the original EU model MFSS99. This allows for direct comparison of the results.
However, should certain aggregations prove inadequate, they can be changed at a later
stage.
Table 6. Aggregated i-sim column codes and corresponding codes from the original idara data
sheets
Columns (activities and other items) _ digits 1 to 4 of 8 digit codes
i-sim
OCER
SOTH
INDU
VEGE
FRUI
WINE
OCRO
OFOD
PORK
SHEE
In idara datasheets
corresponding to:
RYE, OATS, OCER, RICE
SOYA, OOIL
FLAX,TOBA,OIND,HOPS
CAUL,TOMA,OVEG
APPL
(APPE,PEAC,PEAR),
OFRU,TAGR,OGRA
TWIN,OWIN
NURS,FLOW,OCRO
OROO, SILA
PORK, PIGL
MUTM, MUTT
Description
Other cereals
Soya beans and other oilseeds
Industrial crops
Vegetable
Fruits
Wine
Other final crop products
Other fodder
Pig fattening
Sheep and goat fattening
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Table 7. Aggregated i-sim row codes and corresponding codes from the original idara data
sheets
Rows (Products and other items) _ digits 4 to 8 of 8 digit codes
i-sim
OCER
SOTH
INDU
VEGE
FRUI
WINE
OCRO
OFOD
IGEN
IPLA
In idara datasheets
corresponding to:
RYE, OATS, OCER, RICE
SOYA, OOIL
FLAX,TOBA,OIND,HOPS
CAUL,TOMA,OVEG
APPL,APPE,PEAR,PEAC,
OFRU,TAGR,OGRA
TWIN,OWIN
NURS,FLOW,OCRO
OROO,
SILA,DHAY,STRA,OFOR,
FMAI
IPHA,REPV,ENEV, INPV,
AGSE, OGSE
TFER
(NITF,PHOF,POTF,CAOF,
OFER), PLAP, SEEP
DEPM
DEPM, DEPP, DEPO
SOYO
SOYC
SOYO,OTHO
SOYC,OTHC
Description
Other cereals
Soya beans and other oilseeds
Industrial crops
Vegetables
Fruits
Wine
Other final crop products
Other fodder
General cost items
Fertiliser and other inputs
specific for plant production
Depreciation machines and
others
Vegetable fats and oils soya/other oil seeds
Oilcakes - soya
3.3.4 Distinguishing Male and Female Calves
Another major deviation from original idara data is the distinction of balances on male
and female calves, more precisely "young cattle", because these are consolidated flows of
young animals between the net producing activities DCOW and SCOW on the one hand, the
"consuming" activities BULL, HEIF, FCAM, FCAF on the other and finally the outside world.
This distinction could rely to a large extent on hard statistical information. Exceptions are the
unknown composition of calves slaughtering and trade according to the two sexes. However,
calves raised per cow and stock changes were calculated largely as residual items implying
that any errors in the statistical information i-sim aggregation procedures are likely to be
collected here.
In the disaggregation of calves we relied again on the maximum entropy approach:
Adhere to any hard information given, for example the given level of male plus female calves
fattening, while refraining as much as possible to use the available degrees of freedom to
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deviate from a priori expectations in the disaggregation. We considered the following
information sufficiently hard to preclude any deviations from it:

activity levels DCOW, SCOW, HEIF, BULL and the sum FCAM+FCAF

calves requirements of HEIF, BULL, FCAM, and FCAF (equal to 1 or 0 depending on the
sex)

net output coefficients of SCOW and DCOW as implied by the gross calves production
and own requirements according to expert assumptions when combined with the
assumption that 50% of all born calves are male while the other half is female.

total imports of young cattle (regardless of the sex)
We had clearly imprecise priori expectations on the following items:

Activity levels FCAM and FCAF

Sex composition of imports of young cattle

Stock changes of male and female young cattle
To the latter variables we assigned therefore the solution values of the maximum entropy
problem.
The relevant positions for the disaggregation are shown below. The table name “IDARA”
denotes the pre-aggregation/disaggregation data base and the table name “DATA” denotes
the modified data.
3.4 Producer Prices
The idara data base contains two different prices: Internal Use Prices (PRIN) for uses
within the agricultural sector and Farm Gate Prices (PRIC), which are used for valuing the
use activities corresponding to the EAA definition of the production value. The following onfarm use activities are involved for this (EUROSTAT 1995: 149):

Sales on farm (TRAP)

Stock Changes on Farm (PCSF)

Human Consumption on Farm (PCOF)
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However, in the model equations there is no differentiation between PRIC and PRIN and
thus some sort of average price is needed. This unit value (UVAL) is calculated as follows6
UVALAGRO 
X
PI , AGRO
 PRIN PI , AGRO   X PX , AGRO  PRIC PX , AGRO
PI
X
PX
( 3-10)
PIX , AGRO
PIX
where:
UVAL
AGRO
PI
PX
PIX
PRIN
PRIC
X
Unit Value in EURO/t
Agricultural Products
Farm Balance Positions that are valued with Internal Use Price
Farm Balance Positions that are valued with Farm Gate Price
All Farm Balance Positions
Internal Use Price
Farm Gate Price
Physical Quantity in 1000 t
3.5 Prices for processed goods (excl. Milk Products)
Since the agricultural databases in CEEC countries don’t usually include data on
processed goods it was necessary to find an additional source of information on this group
of products. The FAO data base was an alternative that could provide supply balances and
trade data, but no prices for the following products:
Description
I-SIM Code
Starch
Milled Rice
Molasses
Rape Seed Oil
Sunflower Oil
STAR
RICE
MOLA
RAPO
SUNO
Other Oils (incl.
Soya Oil)
SOYO
Olive Oil
Cake of Rape
seed
Cake of sunflower
seeds
Cakes of other oil
seeds
OLIO
RAPC
6
SUNC
SOYC
Gams Code: i_correct.gms
FAOSTAT Code
n.a.
n.a.
n.a.
Oil of Rape Seed
Oil of Sunflower
Seed
Oil of maize, Oil
of linseed, oil of
Soya beans
Oil of Olives
Cake of Rape
seed
Cake of sunflower
seeds
Cake of maize,
cake of linseed,
cake of Soya
beans
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Cake of Olives
As an estimate for the internal market prices, the unit value was calculated from the data on
values and quantities of exports (f.o.b) of the these products.
3.6 Prices of milk products
As a first step to derive the milk product prices the same methodology is applied as for
the other processed goods. The values and quantities for exports are extracted from the
FAOSTAT data base. The mapping of the FAO and IDARA categories is done in the
following manner:
Table 8. i-sim positions that are derived from FAOSTAT
Description
I-SIM Code
FAOSTAT Positions
Butter
Skimmed Milk
Powder
Other Milk
Products
BUTT
MIPO
Butter
Dry Skim Cow Milk
OMPR
Yoghurt, Whole Milk condensed and evaporated,
Whey, Processed Cheese, Dry Whole Milk, Dry
Whey, Dry Buttermilk, Fresh Cream, Cheese and
Curd, Whole Milk Cheese, Buttermilk
The domestic market price for Butter and Skimmed Milk Powder is estimated by
calculating the unit value for these products from the FAOSTAT trade data on exports:
UVALMLKPRO, REG 
XVMLKPRO, REG
XQMLKPRO, REG
where:
UVAL = Unit Value
MLKPRO = Set of milk products
REG = Set of regions (HU, CZ, PL)
XV = Value of Exports
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XQ = Quantity of Exports
The price for the aggregate “Other milk products” (OMPR) in the model represents a
serious aggregation problem, since it contains such divers products as processed milk,
cottage cheese and all kinds of other cheese, which vary widely in terms of prices and
density. In order to solve this problem a theoretical price for the milk contents fat and protein
as well as the processing costs are estimated by means of a maximum entropy approach. As
a starting value for this estimation has served the weighted average of the unit value of the
OMPR aggregation, which is shown in the above table.
3.7 EAA Positions
Description
I-SIM Code
Total Input Use in “PROV”.TOIN
the sector
Total Input Use
“PROV”.INPUT
per input position
Total Input Use
PACT.”TOIN”
per Activity
Unit
Mio EUR
1000 EUR
1000 EUR
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4 Exogenous Model Parameters
4.1 World Market Prices
OECD Reference Prices for Czech Republic, Hungary and Poland are used as
exogenous variables World Market Prices. In the Excel file XX-wp.xls7 the ratio between
these reference prices and the market prices (XX000.UVAL) of each country are calculated
both for the base year 1998 as well as for the projection year 2006. In order to calculate the
relative prices for the base year the bmdat_XX writes out a file called i_prices_XX.gms (see
Figure 3, page 24) which contains all prices in the idara data base and which is then
imported into XX-wp.xls in order to calculate this price relation (see Figure 6). The OECD
reference prices are extracted from the OECD’s 20/20 PSE database and first stored as
XX_OECDprices.xls. All relevant data is then brought together in the XX-wp.xls file.
Figure 6. Calculation of Relative World Market Prices for the base year 1998 and simulation
year 2006
I_prices.GMS
XX_OECD Prices.xls
XRS.xls
XX-WP.xls
Task 2: Edit world market prices
in relative terms
Relative World Market
Prices for 1998 and 2006
MFSSPOL.TAB
Political
variables
World market
prices
7
As always in this paper, XX denotes the country codes CZ, HU and PL respectively
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The OECD prices which are in national currency are recalculated in EURO using the
exchange rates in xrs.xls and the data in the sheet “i-sim prices” from the idara data base.
Since the producer prices that were used by the OECD are not identical to the idara producer
prices, the ratio between OECD Reference Price and OECD Producer Price is calculated
and used to approximate the world market prices by multiplying it with the observed idara
prices from the idara data base.
WO.PRIC BAS , AGRO  MAR.UVAL. BAS , AGRO *
WO.UVAL. BAS , AGRO 
WO.OREF BAS , AGRO
MAR.OPRO BAS , AGRO
WO.PRIC BAS , AGRO
MAR.UVALBAS , AGRO
( 4-1)
( 4-2)
In order to calculate the simulation year prices the WATSIM Model8 results were used in
the following way:
WO.UVAL. AGROSIM  WO.UVAL. AGROBAS * (1  ir  watsim) 8
( 4-3)
where:
WO.OREF = OECD Reference Price of Product AGRO
MAR.OPRO = OECD Producer Price for Region MAR
WO.UVAL = Relative World Market Prices for Region MAR
WO.PRIC = Idara Reference Price
MAR.UVAL = Market Prices in CEEC Country (from idara Data Base)
BAS = Base Year 1998
SIM = Simulation Year 2006
AGRO = Index of Agricultural Outputs
Watsim = Percentage annual price changes as estimated by the WATSIM
simulation model
4.2 Consumer Prices
Consumer prices are generally derived in the GAMS programme DOCPRI.gms from the
relation between producer and consumer prices in EU member states. This is a highly
unsatisfying solution, since it can be assumed that this is one of the areas where the
8
LAMPE, Martin von: Modelling Long-Term Prospects of Agricultural World Markets and the Impacts of
the Macroeconomic and Political Environment. In: Agrarwirtschaft, Bd. 49 Heft 9/10:
September/Oktober 2000, S. 319-335
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difference between EU and CEECs is still very large. This is certainly one of the part of the isim data base where more input is needed in order to find more adequate data.
For secondary products the consumer prices are derived from the unit values in the FAO
database. The necessary calculations of unit values is performed in XX_SECOTRD.xls and
saved as SECOCPRI.gms
4.3 Consumer Expenditure
Consumer expenditure is estimated from OECD data (OECD 2001b: 267-269) on GDP
and the percentage of GDP that is used for investments. It is assumed that the residual must
be consumption by the household and the state9. (or using the share of food expenditure in
total expenditure + the OECD outlook for GDP development)
EXPEREG = GDPREG * (1 – INVESTREG)
( 4-4)
Where:
EXPEREG = Consumer Expenditures in Region REG
GDPREG = Gross Domestic Product in Region REG
INVESTREG = Percentage of GDP invested in Region REG
4.4 Political Variables for the base year
At the 1st progress meeting in Budapest the modelling team of the idara project has
discussed a paper presented by the Bonn team on the use of the OECD PSE database to
derive political variables for the idara simulation model i-sim (BAUER, 2000). It was agreed
that the proposed procedure could be used as a pragmatic way to derive the political
variables and get started on trials with the model. Essentially, it was suggested that two
different methodologies be applied: one for Market Price Support measures and another for
all other measures. The OECD figure on market price support is the difference between the
level of production valued at domestic prices and the level of production valued at border
(reference) prices. Since the OECD domestic prices are not identical to the domestic prices
observed in the idara data base, it was decided to use the above calculated i-sim world
market prices to calculate the market price support in the same way as the OECD does, but
with the i-sim domestic prices prices. All other measures of support were merely converted to
support per activity level instead of total support in the sector.
9
These calculations are performed in the excel sheet xx_macrodata.xls
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For further details on the theoretical background, please consult the idara Working Paper
"Agricultural Policies in Hungary 1998". This section will focus on the technical
implementation of the procedure.
4.4.1 Converting the OECD PSE data
The PSE data base is stored in the file pseXX1998.xls. It contains five sheets, the first
one is called pseXX1998 and contains the original data from the OECD database. The
second sheet "polvaXX" is the first working step in deriving the political variables and it takes
into account only the main positions from the original data, like Level of production, Value of
production or Market Price Support. In the heading of the table there are now the i-sim
product codes (row 2) and the corresponding i-sim activity codes (row 3). Also the OECD
types of support are attributed to idara categories of subsidies: payments based on output
becomes SUBS, payments based on area planted/number of animals becomes PREM and
finally payments based on input use becomes APO1. The button below the table activates
the macro "combineData" in file polvamacro.xls, which imports the necessary data on level of
production, value of production and activity levels from the idara data base file
XX_output_NC.xls. This file needs to be open for the procedure to work.
This macro not only imports the named positions, but also disaggregates the OECD
position beef and veal to two separate positions according to their respective activity levels
as found in the idara data base.
In order to use the PSE data in the idara simulation model the PSE data which denotes
support per product for the whole sector, needs to be re-calculated per unit of product in
case of subsidies related to products and per activity level in case of subsidies based on
activity levels (such as payments based on input use). This recalculation is done in the sheet
"polvaXX.2". For more transparency the formula for these calculations are in the excel cells,
not in the macro.
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Figure 7. Deriving political variables for i-sim
OECD2006.xls
I_prices.GMS
XX_OECD Prices.xls
fapri2006.xls
Idara Data
File
XRS.xls
XX_output_NC.
xls
XX-WP.xls
TARR
Positions
positions.xls
pseXX1998.xl
s
TA
RR
PSE Data
(OECD)
.Y
POLVA.xls
Task 1: Edit political
variables
Poltical Varibales
BASE, REF and SIM
RUN for 1998 and 2006
SPEL-POL.TAB
Political variables World
market prices
4.4.2 Calculating market price support (TARR)
Due to the entity in the price transmission equation, the difference between the world
market prices and the market prices is determined by the political variables TARA, TARR,
FLEV as well as TIMPL in case of fixed products. For the reference situation (with national
policies for PL, HU and CZ) there is only TARA (OECD 2001a: 82-85) In order for these
entities to fit exactly the market prices and world market prices they are derived in xx-wp.xls
and then transferred to the xxPSE1998.xls file (sheet “imp.TARA”), where the other political
variables are derived. In xx-wp.xls the difference between the world market prices and the
national prices is calculated and this is written to the TARA variable.
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4.4.3 Final preparations
The final step in this file is performed automatically in sheets "pola.bas" and "polp.bas"
when pressing the button "Update!". These buttons activate the macros called
"writePOLVA_polp" and "writePOLVA_pola" which convert the data from sheet polvaXX.2 to
EURO using the OECD exchange rates stored in xrs.xls. Furthermore the EURO data is then
written in the required format to the sheets pola.bas and polp.bas. From these sheets the
data can simply be copied via clipboard to the MFSS systems input file polva.xls. (See Figure
8). All political variables are collected in this file polva.xls
CODE
Description
Unit
QUTS
PADM
MADM
TARA
TARR
SUBS
EXPQ
IMPQ
quota on sales
administrative price
maximum quantities bought at administered price
tariff absolute
tariff relative
producer subsidy per ton
export quota
import quota
1000 t
EUR
1000 t
EUR
%
EUR/t
1000 t
1000 t
Figure 8. Transfer of political variables and world market prices to the tab file
MFSS99
POLVAR.XLS
Clipboard
MFSSPOL.TAB
1. Edit political variables
Political
variables
World market
prices
Preparation
EU-WP.XLS
2. Edit world market prices
in relative terms
3. Core MFSSModel
MODEL.CSV
Visual Basic
POLVAR.CSV
SOL.SDA
Run the model
MARKETDEF.GMS
MARKETITER.GMS
MARKETRES:GMS
GAMS
Fortran
Fortran files
Gams files
Flat files
Excel files
TAB files
FORTRAN.GMS
SELAS.GMS
DELAS.GMS
CPRI.GMS
4. Deviation of MFSS99 results
to base data
5. Deviation of MFSS99 results
to reference run
6. Exploitation tables
for the MFSS99 results
MFSSBAS.TAB
Type:
BAEM
(3 year average)
1990 - 1999
Trends
of type:
TRXX
1985 - 2006
MFSSSIM.TAB
Simulations
of type:
BRXX, RRXX
SRXX 2006
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give an overview of the resulting political variables for selected
products and production activities. For the full set of political variables used in the model,
please see Annex. It is most striking that the political variables per activity are relatively low.
Of course the payments per ha or head are much higher than the calculated figures. But
since they are not granted to every unit of production the average in the whole sector is
rather low. Since it can be assumed that the effect of highly selective measures of this kind
will also be low when looking at the whole sector it seems justified to use this approximation.
Table 9. Political Variables in the base year 1998 per production activity (EURO/ha or hd)
PREM
PL SWHE
HIST
PRET
CEIL
SETR
SETA
APO1
0.00114
PL DCOW
CZ SWHE
0.02375
-
0.00678
0.00114
CZ DCOW
0.02375
0.00678
Table 10. Political Variables in the base year 1998 per product (EURO/t) for selected products
QUTS
HU SWHE
HU MILK
PADM
MADM TARA
-
TARR
SUBS
EXPQ
IMPQ
25.25
110.67
3.67
PL SWHE
PL MILK
CZ SWHE
CZ MILK
25.59
61.89
4.46
65.95
4.5 Political Variables for the reference / simulation year
4.5.1 Tariffs
In economic terms the accession of the CEE Countries to the EU represents them joining
a customs union with no internal trade barriers, but common external tariffs. Since the i-sim
model works for each country seperately it can be assumed that they do not influence the
common market price level. Of course if 10 CEEC join at the same time there will be
repercussions on the EU market. But this cannot be modelled with the current formulation of
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the model, but will have to wait until the CEECs are integrated in the CAPSIM framework for
example.
Since it would be tedious and extremely problematic to find the right tariffs that would be
effective for CEECs in case of accession and since from producer and consumer point of
view the relevant prices will simply be the EU prices, the method chosen for i-sim calculates
the tariffs from exogenous world market prices and exogenous EU market prices for the
simulation year. The source for the world market prices is the FAPRI simulation model as
portrayed above. The source for the EU market prices is the OECDs agricultural outlook for
2006. This source was chosen since it represents a well known and well tested forecasting
tool and has been approved by EU experts within the OECD framework. The following
formula is used to calculate the resulting relative tariff that satisfies these assumptions.
TARRY , EU  PTEY , EU / PW Y  1
These calculations are performed in excel for more transparency (xx-wp.xls) and then
transferred to the mfss-pol.tab file by i-sim.exe via polva.xls.
4.5.2 Premiums
Premiums are the explicit policies that are still being discussed and are very much open
at the moment. For the principal scenarios the EU and CEEC positions were used as
described in the issues paper of the EU Commission. The information that was extracted
from the commission paper is to be found in the excel sheet positions.xls. For modelling
purposes we distinguish two types of premiums: Group Premiums (PRET) and Premiums for
individual activities (PREM). The latter is calculated using historical yields (HIST).
Xxx insert table with scenario assumptions
See also Working Paper: Scenarios
4.5.3 Quotas
For the base year reproduction and the reference run there were no quota restrictions
taken into account for the CEE countries. However, as they access the common market a
number of quota restrictions apply, most importantly for sugar and milk. In order to model
these quotas the relevant products are equipped with shadow revenues that are free
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variables to be determined by the model in order to fit the fixed production quantities that are
determined by the quota. As the quota for sugar is stated in tonnes of white sugar, the
relevant quota quantities for sugar beet is determined as follows:
PRD.FX SUGB  QUTS SUGA / PRC _ Y
4.6 Exogenous Yields
As has been described in Chapter 2, the fixed yield assumption of the i-sim model
assumes that the exogenous yields do not change with the level of production or intensity of
production. Thus, for the production function the fixed yields need to be estimated for the
simulation year. For this purpose there are two general approaches foreseen in the i-sim
model: trend estimation and expert knowledge.
4.6.1 Trend estimation
The trend estimation is the default option to determine exogenous yields in the i-sim
system. After
all the preparations on the data base are finalised, OLS-estimations are
calculated to establish trends for the ex-ante period. The results are stored in "TR"-structure
tables in the TAB-file data management system.
The procedure described below is carried out after the data base is completed and the
consistency is checked. Please note that all comments in the programme code that begin
with # are not relevant for idara, but are for the EU model and can be ignored. The
description is based on the trend estimation procedure developed by Heinz-Peter Witzke at
the IAP and amended by the idara team.
For details of the trend projections please refer to the listing of the GAMS Programme.
The standard procedure is to calculate linear trends on the original, non -transformed
variables based on ex post data 1993-1998, this being considered a reasonable compromise
between degrees of freedom and a sufficient weight for recent data. However when applied
to thousands of time series it is quite clear that due to outliers or particularly strong recent
trends the procedure will generate a certain percentage of unreasonable projections, say for
2006. Given insufficient resources to check and modify all projections case by case, possibly
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using more sophisticated statistical procedures to detect outliers in the ex-post data, we
introduced a robust security device for the trend projections: In case of linear trend
projections exceeding base year values (1994) by more than 200%, the projection is
repeated using a non-linear transformation of the variables which imposes an asymptotic
value of +230% of the base year value on the projection line (and similar for negative trends).
After transformation, the estimation may be performed using the OLS formula, which is used
in the corresponding GAMS program.10
Figure 9. Calculation of trends and transfer of trend data to tab file
MFSSBAS.TAB
SETSTRD.GMS BASAGG_00.GMS
I_BASAGG.EXE
BASAGG.SDA
store aggregation
results in TAB-file
Type:
BAEM (yearly)
1990 - 1998
TRDDAT_XX.GMS
Visual Basic
Do trend estimations
GAMS
Fortran
BASTRD.SDA
I_BASTRD.EXE
store trend results in TAB-file
Fortran files
Trends
of type:
TRXX
1998 - 2006
Gams files
Flat files
Excel files
TAB files
Technical Implementation
The calculations are performed by a GAMS programme,
called trddat-XX.gms. A
number of sets that are used for the calculations are taken from separate files and included
in trddat-idara.gms. These are: setstrd.gms and sets_i-sim.gms. The estimation results are
written to a sda file in the end. For the results of the trend estimations to be used by the
model, they must be written back into the tab file, with the aggregated idara data, called
10 Witzke, H.P. (2000): Establishing the model base. IAP, Bonn. Unpublished Working Paper
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mfss-bas.tab. The executable basagg.exe does the job of transferring the trends from the
gams output in sda format to the tab file.
This paragraph gives an overview of what happens in the core programme file
trddat_XX.gms.
Various GAMS programme options are set for convenience, which are
rather technical details that are of minor relevance at this point. For reference of the options
chosen in the gams file, please refer to the GAMS manual.
The gams file basagg.gms contains the idara data after it was aggregated to the MFSS
aggregates. This file is now included in trddat-idara.gms. The 'putclose con' statement is
used to show the string "data input" on the screen while the programme is running.
Estimate Trends on Activity Levels and Yields
Firstly, parameters are defined that are used for the OLS estimator. TRENDVAL is the
name of the table in which the estimation results will be written.
The putclose statement is used to show a text on the screen that indicated the start of
the estimation procedure.
Scalars k1 to k 6 are defined and set at a certain level. These scalars define thresholds
for different trend estimators. They can be used to adopt the trend estimators to country
specific circumstances. gives an overview of their significance. K1 and K3 define the upper
and lower threshold for the linear trend. K1 set at 0.8 means that the data considered for the
trend estimate must not be more than 80% below the standard year defined in the
programme as 1994.
Table 11. Scalars that define the thresholds for trend estimates
Scalar
K1
K2
K3
K4
K5
K6
Value
0.8
0.7
2
2.3
1.5
0.67
Significance
Lower threshold for linear trend
Lower Asymptotic Value
Upper Threshold for linear trend
Upper Asymptotic Value
Upper Threshold to define Outliners
Lower Threshold to define Outliners
K2 and K4 define the upper and lower asymptotic value. These values are used to
generate a non-linear trend the range defined by K1 and K3 for the linear trend is not met by
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the data. illustrates how non-linear trends are estimated. The assumption behind this
procedure is 'decreasing returns to time'. This assumption holds certainly true for the EU,
but it is open to debate if this assumption is equally valid for the CEEC countries where we
have observed for example in the case of sugar beet yields rather steep linear trends.
K5 and K6 define the thresholds for possible outliners. If one data point is out of these
boundaries, it will not be taken into account for the trend estimations.
After the thresholds are defined the core calculations are performed. First a loop over
regions, columns and rows is done provided that data for 1994 is available. Then a loop over
years greater than 1993 is performed. After possible outlines are excluded the loop runs the
OLS estimation by itself.
9500
9000
8500
8000
7500
7000
6500
6000
5500
5000
BAEM
05
03
01
99
97
95
93
91
89
BTRM
87
85
BL.SWHE.SWHE [kg/ha]
Figure 10. Example of non-linear trend estimate
If the thus calculated trend estimates for 2006 differ from the 1998 'standard' year by
more than what was defined in K1 and K3, then non-linear trends are calculated by using
the pre-defined asymptotic values K2 and K4 and
a log function. The details of this
calculation are not essential at this stage, the effect can be seen in Figure 10.
Data Output
Finally, after all the necessary calculations are completed, the results are written to an
sda file. This procedure is performed in an external file called flattrend.gms.
After the gams file trddat-idara.gms has estimated the trends and stored it in an sda file,
the user can start the executable file bastrd.exe that will write the trend estimation results
back in the MFSS-BAS.tab under the type "BTRM".
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4.6.2 Expert opinions
Since the economic environment in the CEEC countries is still very dynamic it is obvious
that for certain products the trends calculated from the available data don’t reflect the current
knowledge on the developments in the sector. It is thus crucial to open the possibility for
expert knowledge to be included in the model. All parameters based on expert opinion will be
documented in order to sustain a high degree of transparency.
4.7 Elasticities
In terms of use of elasticities, where possible existing elasticities for the CEEC countries
were used, otherwise EU elasticities taken from the MFSS99 model were implemented. See
Annex for details.
4.8 Others
Parameter
Necessary Adjustments
Where
DCOW.DCOW,
HEIF.DCOW,
HEIF.BULL etc
Remonte Rate
calculate from data
I_correct.gms
Set at 0.2
I_correct.gms
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5 Technical infrastructure and software
The idara data base is principally stored in two systems, one of which is an MS Access
97 data base for various analysis and documentation purposes11. This widely used data
management system will be a solid basis for studies on the data without using the MFSS
Modelling System.
Table 12. Overview of i-sim file types
File Extension
EXCEL Sheets
FORTRAN
Programmes
FLAT Files
GAMS Files
TABLE Files
.xls
.exe
.sda
.gms
.tab
Dark Blue
Purple
beige
light green
Light blue
National Data
Collection
Data transfer
from flat files to
tab files
Colour Code
Purpose
Preparation of
political
variables and
world market
prices
Intermediate All
Data format calculations,
for transfer
optimisation,
from GAMS
Data viewing and format to tab
data
file for
management
storage
(DAOUT.exe)
Data storage
Some VBA
User friendly
Macros that
interface for rapid
convert the excel simulations
sheets to GAMS
files
5.1 Excel data collection files
The excel data collection files were already described in chapter 3 on national data.
5.2 Fortran Programme files
In order to use the i-sim modelling system, however, it is necessary to transfer the idara
data base to the IAP's own data storage software. This software consists of three basic
programmes:
11
This data base will be provided as soon as a fairly reliable set of data has been prepared
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Table 13. Executable Programmes of the i-sim system
Name of the programme
Function
Documentation
mfss.exe
steer the model
See below
basagg.exe
transfer the aggregated
data base in basagg.sda to
the tab-file mfss-bas.tab
Simply press the button
bastrd.exe
transfer the trend data
estimated by trddat.gms
and stored preliminarily to
mfss-bas.tab
Simply press the button
daout.exe
view the data
help file included
daserv.exe
edit the structure of the
data base
help file included
5.3 Table Files for data storage
The data is stored in files called tab-files. The following tab-files are used in the i-sim
system:
Table 14. Content of table file mfss-bas.tab
Content
Regions
Periodicity12 Years
Types
Description
13
base data (from
excel sheets
input.xls and
output.xls)
HU
CZ
PL
12
00,A3
96-99
BAEM
Idara Base Data
00
96-06
TRHU
Trends (exogenous variables)
00
96-06
T3HU
00,A3
95-98
BAEM
BAEM Data as trend for base
year reproduction
Idara Base Data
00
95-06
TRCZ
Trends (exogenous variables)
00
96-06
T3CZ
00,A3
90-98
BAEM
BAEM Data as trend for base
year reproduction
Idara Base Data
00
90-06
TRPL
Trends (exogenous variables)
00
90-06
T3PL
A3 BAEM Data as trend for base
year reproduction
00 means all data is for the single year stated. A3 for example would indicate that 3 year averages
were used
13 Types are different sets of data.
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Table 15. Contents of table file mfss-pol.tab
Regions
Periodicity Years Types Short Description
14
HU
CZ
PL
14
A3, 00
98
BAHU
BASE RUN
A3, 00
98
BPHU
BASE RUN
00
06
RAHU
REFERENCE RUN
00
06
RPHU
REFERENCE RUN
00
06
1PHU
SCENARIO RUN 1
00
06
1AHU
SCENARIO RUN 1
00
06
2PHU
SCENARIO RUN 2
00
06
2AHU
SCENARIO RUN 2
A3
98
BACZ
BASE RUN
98
BPCZ
BASE RUN
06
RACZ
REFERENCE RUN
06
RPCZ
REFERENCE RUN
06
1PCZ
SCENARIO RUN 1
06
1ACZ
SCENARIO RUN 1
06
2PCZ
SCENARIO RUN 2
06
2ACZ
SCENARIO RUN 2
98
BAPL
BASE RUN
98
BPPL
BASE RUN
06
RAPL
REFERENCE RUN
06
RPPL
REFERENCE RUN
06
1PPL
SCENARIO RUN 1
06
1APL
SCENARIO RUN 1
06
2PPL
SCENARIO RUN 2
06
2APL
SCENARIO RUN 2
A3
Description
15
BASE RUN Political
Variable per Activity
BASE RUN Political
Variable per Product
REFERENCE RUN Political
Variable per Activity
REFERENCE RUN Political
Variables per Product
SCENARIO RUN 1 Political
Variables per Product
SCENARIO RUN 1 Political
Variables per Activity
SCENARIO RUN 2 Political
Variables per Product
SCENARIO RUN 2 Political
Variables per Activity
BASE RUN Political
Variable per Activity
BASE RUN Political
Variable per Product
REFERENCE RUN Political
Variable per Activity
REFERENCE RUN Political
Variables per Product
SCENARIO RUN 1 Political
Variables per Product
SCENARIO RUN 1 Political
Variables per Activity
SCENARIO RUN 2 Political
Variables per Product
SCENARIO RUN 2 Political
Variables per Activity
BASE RUN Political
Variable per Activity
BASE RUN Political
Variable per Product
REFERENCE RUN Political
Variable per Activity
REFERENCE RUN Political
Variables per Product
SCENARIO RUN 1 Political
Variables per Product
SCENARIO RUN 1 Political
Variables per Activity
SCENARIO RUN 2 Political
Variables per Product
SCENARIO RUN 2 Political
Variables per Activity
00 means all data is for the single year stated. A3 for example would indicate that 3 year averages
were used
15 Types are different sets of data.
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Table 16. Contents of table file mfss-sim.tab
Regions
Perio Years Types Short Description
17
dicity
Description
16
HU
CZ
PL
16
A3
A3
98
06
BRHU
RRHU
BASE
REFERENCE RUN
A3
06
RIHU
REFERENCE RUN
A3
06
RWHU
REFERENCE RUN
A3
06
RTHU
REFERENCE RUN
A3
06
1SHU
SCENARIO RUN
A3
06
2SHU
SCENARIO RUN
A3
A3
98
06
BRCZ
RRCZ
BASE
REFERENCE RUN
A3
06
1SCZ
SCENARIO RUN
A3
06
2SCZ
SCENARIO RUN
A3
A3
98
06
BRPL
RRPL
BASE
REFERENCE RUN
A3
06
1SPL
SCENARIO RUN
A3
06
2SPL
SCENARIO RUN
Base Year Reproduction
Results of the Reference
Run
Results of the Reference
Run (only Inflation changed)
Results of the Reference
Run (only world market
prices changed)
Results of the Reference
Run (only trends changed)
Results of the Scenario Run
EU Position
Results of the Scenario Run
Hungarian Position
Base Year Reproduction
Results of the Reference
Run
Results of the Scenario Run
EU Position
Results of the Scenario Run
Czech Position
Base Year Reproduction
Results of the Reference
Run
Results of the Scenario Run
EU Positions
Results of the Scenario Run
Polish Position
00 means all data is for the single year stated. A3 for example would indicate that 3 year averages
were used
17 Types are different sets of data.
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5.4 Gams Files
Most calculations and certainly the modelling operations itself are conducted by the
General Algebraic Modelling System (GAMS). While it would theoretically be possible to
have all the relevant data, set assignments, equations and model statements in one file this
would be highly unpractical and impossible to steer. For the purpose of breaking up the
model code into manageable smaller bits that are related in their tasks the GAMS language
offers include statements. The GAMS compiler reads the included files into the memory and
treats them as if it was one file.
Figure 11 shows that the GAMS file bmdat_xx.gms, which is responsible for the
aggregation of the original idara database to the i-sim database, contains a number of sub
files for sets, data input etc.
Figure 11. GAMS file infrastructure of data aggregation
BMDAT_XX.GMS Data aggregation file
Sets
Data Input files
SETS_XX.GMS
SETS_i-sim.GMS
Set definitions
file for country
specific sets
Set definitions file
for almost all sets
used in i-sim
SETS_IDARA.GMS
OUTPUT_XX.gms
INPUT_XX.gms
Country Data Input
from output.xls
Country Data Input
from input.xls
i_A3_XX.GMS
Set definitions file for
the original data base
Recalculation to 3year averages
i-CORRECT_XX-.GMS
Correction file: Corrects mistakes, calculates
residuals, calculates missing data from other sources
Writes out IDARADATA_XX.gms for further use of
data base
FLATDATA.GMS
Writes out aggregation
results to SDA Data file for
transfer to tab file
GAMS Files
basagg.GMS
i_prices_xx.GMS
Aggregated data
Prices to calculate relative world
market prices and political variables
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Figure 12. GAMS file infrastructure of the core model
MFSS99.GMS
Sets
SETS_XX.GMS
SETS_i-sim.GMS
Set definitions
file for country
specific sets
Set definitions file
for almost all sets
used in i-sim
Data Input files
CPRI.gms
FORTRAN.gms
Data Input on
Consumer
Prices
Data Input
including political
variables
SETS_IDARA.GMS
Set definitions file for
the original data base
Model Core
i-MARKETDEF-.GMS
Define the model - Core Model Equations
SELAS.GMS
DELAS.GMS
Data input file of supply
elasticities
Data input file of
demand elasticitiesy
i-MARKETTIER.GMS
i-MARKETRES.GMS
FLATFILE.GMS
Iterated Simulation of
the Market Model
Compiles Output for
listing and for TAB-file
from simulation results
Output for TAB-file from
simulation results
GAMS Files
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6 Conclusions and further research needs
The time frame needed for the development of a full fledged agricultural simulation
model is beyond the scope of a three year project. This is even more true if a consistent
data base needs to be set up virtually from scratch. In order to come up with a feasible
working version of an agricultural sector model for the CEEC countries we have used an
existing model designed to analyse changes in EU policy for the CEEC. Apart from the
obvious advantage of reducing the time needed to come up with the first simulations, the
advantage of this approach was to have results that can easily be compared with the results
from the EU model.
First tests suggest that the model will be able to shed some light on the effects of
different (EU) policies on the CEEC countries. However, in order to achieve robust results
which allow for a high degree of differentiated analysis, it will be necessary to work on the
following sets of parameters during the course of the last project year.
Consumer prices
Consumer prices are at this stage derived from the ratio between consumer and raw
product prices in the EU. This is not an adequate assumption since this ratio can be
expected to be different in the EU given the known difference in productivity and
competitiveness between EU and CEEC up- and downstream industries.
Elasticities
The limited number of elasticities that could be taken from literature for this project
clearly portrays the need for further research in this direction. Studies that shed further light
on the behaviour of economic agents in the CEEC are desperately needed, but can not be
delivered in the scope of this project.
Political Variables
The political variables used for the reference run can still be refined. Especially for
Poland and Hungary where small but nonetheless significant amounts of government support
is channelled through market price support systems other than simple tariffs as is assumed
for the most part in our present political variables.
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The next important step in our project will be to define the political variables for the
simulation run. This can now be done parallel to the further refinement of the reference run.
Constant Yield Assumption
Especially for CEEC countries it can be assumed that the yields depend on level of
production
Yields might even be different with different policy scenarios. Pitlik pointed out that the
with EU accession one can expect more investment in technical innovations and thus higher
yields.
Subsidies get passed on to the up and downstream sectors by market pressure
More accurate modelling of the production cost side
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7 References
BAUER, K. (2000): Agricultural Policy in Hungary 1998. Idara Working Paper Series
N° 1/2, IAP, Bonn
EUROSTAT (1995): SPEL System - Methodological documentation (Rev. 1), Vol. 1,
Luxemburg
LAMPE, M. von (2000): Modelling Long-Term Prospects of Agricultural World Markets
and the Impacts of the Macroeconomic and Political Environment. In:
Agrarwirtschaft, Bd. 49 Heft 9/10, S. 319-335
OECD (2001a): Agricultural Policies in OECD Countries 2000. OECD, Paris
OECD (2001b): Main Economic Indicators. OECD, Paris
WITZKE, H.P. (2000): Establishing the model base. Annex 7 to Contract dossier
9635002. EUROCARE, Luxemburg - Bonn. Unpublished Working Paper
WITZKE, H.P. / VERHOOG, D. / ZINTL, A. (2001): Agricultural Sector Modelling: A New
Medium-term Forecasting and Simulation System (MFSS99). Eurostat,
Luxemburg
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8 Annex
8.1 List of Parameters and Variables
8.1.1 Parameter definitions for the model
PRC_Y(REG,X)
PRCOSTSB
Processing relation per ton of raw product
processed
processing cost per ton of sugar beet
PBAS(REG,X,PRICEP)
PW (X)
WGT (X)
Base year prices
Uniform middle atlantic ocean price
Trade weights used to disaggregate some
world market prices
PRDB(REG,X)
LVLB(REG,PACT)
REVB(REG,PACT)
REVSB(REG,PACT)
PRETFACB(REG,PACT)
Production quantities base year
Activity level base year
Activity revenue base year
Activity shadow revenue base year
Scaling factor to implement ceilings on group
premiums in base period
Scaling factor to implement ceilings on
special supplements in base period
Farm use linked to production
Yields base year
Yields simulation year
PREMFACB(REG,PACT)
LNKB(REG,X)
YLDB(REG,PACT,X)
YLD(REG,PACT,X)
CNSB(REG,X)
INPB(REG,X)
INDB(REG,X)
IND (REG,X)
STCB(REG,X)
SUPB(REG,X)
DEMB(REG,X)
Consumer quantities base year
Input quantities in the base year
Industrial use in the base year
Industrial use in the simulation year
Stock changes in the base year (farm +
market)
Stock changes in the simulation year (farm +
market)
Domestic supply in the base year
Domestic demand in the base year
PRCB(REG,X)
CNTB(MLKPRO,MLKCNT)
Quantities processed in the base year
Content of protein & fat of milk products in
STC (REG,X)
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base year
NETTRDB(X)
Net trade in base period
LELASREV(REG,PACT,PAC1)
LELASPPI(REG,PACT,X)
LELASPPF(REG,PACT,X)
IELASREV(REG,X,PACT)
IELASPPI(REG,X,Y)
FELASLVL(REG,X,PACT)
FELASPPF(REG,X,Y)
PELAS(AGRO)
SETPAR(REG,*)
Activity elasticities wrt revenues
Activity elasticities wrt industrial input prices
Activity elasticities wrt feed prices
Input elasticities wrt revenues
Input elasticities wrt industrial input prices
Feed elasticities wrt production activities
Feed elasticities wrt feed prices
Processing elasticities
Parameters for fallow land equation (ela &
const & slope)
Parameters for MS net trade equation
Demand elasticities
NETPAR(REG,X)
DELAS(REG,X,*)
SPL(REG,PACT)
SPI(REG,X)
SPP(REG,X)
SPD(REG,X)
SPF(REG,X)
TASTE(REG,X)
Shift parameter of double log functions level
Shift parameter of double log function inputs
Shift parameter of double log functions
processing
Shift parameter of double log functions
demand
Shift parameter of double log functions feed
demand
Taste shifter for double log functions demand
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8.1.2 Variable definition in the model
NETTRD(X)
NETMS(MS,X)
Net exports of Market to world market
Net exports of MS to other "Market" and
ROW
PRD(REG,X)
LVL(REG,PACT)
CFAC(REG)
REV(REG,PACT)
REVS(REG,PACT)
LNK(REG,X)
Gross production
Activity level
Scaling factor to enforce land balance
Activity revenue
Activity shadow revenue
Farm use linked to production
CNS(REG,X)
PRC(REG,X)
INP(REG,X)
SUP(REG,X)
DEM(REG,X)
Final consumption on market
Processing to secondary products
Input use
Domestic supply
Domestic demand
ITS(X)
FLEV(X)
TIMPL(X)
Intervention sales
Flexible levy
Implied tariff supplement in case of
exogenous trade
PP(REG,X)
PF(REG,X)
Market price raw product stage
Market price final products
PPCNT(MLKCNT)
PTE(X)
PRETFAC(REG,PACT)
Price of milk contents at raw product stage
Price for goods traded inside of "Market"
Scaling factor to implement ceilings on group
premiums
Scaling factor to implement ceilings on
special supplements
PREMFAC(REG,PACT)
CNT(MLKPRO,MLKCNT)
Content of protein & fat of milk products
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8.1.3 Define the Equations
Supply & demand definition for MS
SUP(REG,X)
DEM(REG,X)
Supply definition
Demand definition
Price transmission equations
PTE(X)
PTP(REG,X)
REV(REG,PACT)
REVS(REG,PACT)
PRETFAC(REG,PACT)
PREMFAC(REG,PACT)
PTF(REG,X)
PTM(MLKPRO)
PTS
PTMOLA
Price transmission uniform world to Market
price
Price transmission member state raw product
price to Market price
Definition of activity revenues
Equality of shadow and ordinary activity
revenues where applicable
Scaling factor to implement ceilings on group
premiums
Scaling factor to implement ceilings on
special supplements
Price transmission raw product price to final
demand
Price transmission milk products
Price transmission sugar beet products
Price transmission molasses
Political instruments on Market level
FLEV(X)
ITS(X)
SEX(X)
Definition flexible levies or export restitutions
Intervention sales to Market authorities
Subsidized exports
Behavioural equations
PRD(REG,X)
LNK(REG,X)
LVL(REG,PACT)
PRC(REG,X)
INP(REG,X)
Production equation for raw products
Farm use linked to production:
SEEP+PSEE+PLOF+PCOF
Activity level equation for raw products
Processing equation for raw products
Input demand equation for intermediate
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FED(REG,X)
SET(REG)
CNS(REG,X)
MCCNT(MLKPRO)
Kai Bauer
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demand
Feed demand equation for feedstuffs
Fallow land equation (for set aside)
Human consumption on market
Marginal processing cost function for milk
products
Balances
MBAL(X)
LBAL(REG)
MSBAL(MS,X)
Market market balances
Land balance
MS market balance for national "FXTRD"
products
NETMS(MS,X)
MLKBAL(MLKCNT)
MS net trade for national "FXTRD" products
Balance on milk fat and protein
Objective
OBJ
Objective: minimise cost to Market
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8.2 Codes of the idara data base (Excel Sheets)
8.3 Codes of the Aggregated Database for i-sim
8.4 Political Variables
8.4.1 Political Variables Base Year 1998 per product (EURO/t)
QUTS
HU OILS
HU SWHE
HU BARL
HU MAIZ
HU OCER
HU SUNF
HU MILK
HU BEEF
HU VEAL
HU PORK
HU MUTT
HU EGGS
HU POUL
PL SWHE
PL BARL
PL MAIZ
PL OCER
PL RAPE
PL SOTH
PL MILK
PL BEEF
PL PORK
PL POUL
CZ SWHE
CZ BARL
CZ OCER
CZ RAPE
CZ SOTH
CZ MILK
CZ BEEF
CZ VEAL
CZ PORK
CZ EGGS
CZ POUL
CZ SUGA
PADM
MADM
TARA
-25.2477299
-15.2172021
-23.9183728
-54.6285056
16.6022477
110.665345
89.1216289
1169.76502
55.297609
-242.292804
515.783496
133.107364
25.5858262
15.2192746
24.3534272
17.1986609
8.74900622
10.1276829
61.8861238
224.557163
38.8362752
350.051171
4.46444984
2.81726963
2.8498259
-8.25661216
-8.35172855
65.9510676
38.8781663
38.8781663
-75.1935101
301.94694
20.3027156
2.57543681
TARR
SUBS
0
0
0
0
0
3.66976849
16.5794345
217.613197
21.4356852
30.858879
16.2213645
11.4951924
EXPQ
IMPQ
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8.4.2 Political Variables Base Year 1998 per activity (EURO/ha or hd)
PREM
PL SWHE
PL BARL
PL OCER
PL RAPE
PL DCOW
PL BULL
PL PORK
PL HENS
PL POUL
CZ SWHE
CZ BARL
CZ OCER
CZ RAPE
CZ DCOW
CZ BULL
CZ PORK
CZ HENS
CZ POUL
0.00163
0.02375
0.04262
0.00163
0.02375
0.04262
-
HIST
PRET
CEIL
SETR
SETA
APO1
0.00114
0.00072
0.02377
0.00137
0.00678
0.00605
0.00019
0.00004
0.00001
0.00114
0.00072
0.02377
0.00137
0.00678
0.00605
0.00019
0.00004
0.00001
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8.5 Matrix of elasticities
table ELAD( *,*,*)
*
S0
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
S0000.SWHE
PL000.SWHE
PL000.DWHE
PL000.BARL
PL000.MAIZ
PL000.OCER
PL000.POTA
PL000.BEEF
PL000.VEAL
PL000.PORK
PL000.MUTT
PL000.EGGS
PL000.POUL
PL000.SUGA
PL000.RAPO
PL000.SUNO
PL000.SOYO
PL000.OLIO
PL000.MIPO
PL000.BUTT
PL000.OMPR
PL000.VEGE
PL000.FRUI
PL000.REST
PL000.INC
PL000.SWHE
PL000.DWHE
PL000.BARL
PL000.MAIZ
PL000.OCER
PL000.POTA
PL000.BEEF
PL000.VEAL
PL000.PORK
PL000.MUTT
PL000.EGGS
PL000.POUL
PL000.SUGA
PL000.RAPO
PL000.SUNO
PL000.SOYO
PL000.OLIO
PL000.MIPO
PL000.BUTT
PL000.OMPR
PL000.VEGE
PL000.FRUI
PL000.REST
PL000.INC
PL000.SWHE
PL000.DWHE
PL000.BARL
PL000.MAIZ
PL000.OCER
PL000.POTA
PL000.BEEF
PL000.VEAL
PL000.PORK
PL000.MUTT
PL000.EGGS
PL000.POUL
PL000.SUGA
PL000.RAPO
PL000.SUNO
PL000.SOYO
PL000.OLIO
PL000.MIPO
PL000.BUTT
PL000.OMPR
PL000.VEGE
PL000.FRUI
SWHE
DWHE
BARL
MAIZ
OCER
POTA
BEEF
-0.42216
0.00134
0.00055
0.00267
0.02621
-0.00149
-0.28722
-0.00081
-0.00001
-0.00013
-0.0011
0.01294
0.00105
-0.00352
-0.28218
0.00135
-0.00013
0.00207
0.01292
0.00104
-0.00324
0.08738
-0.33378
-0.00012
0.00747
0.01287
0.00103
-0.0035
-0.00081
-0.00001
-0.25036
0.01286
0.01287
0.00103
-0.00352
0.00151
0.00008
0.00153
-0.28663
0.01293
0.00105
0.05708
0.01306
0.0002
0.00212
0.01788
-0.36013
0.00363
-0.00952
-0.00218
-0.00003
-0.00036
-0.00299
-0.00122
-0.4401
-0.0054
-0.00953
0.00079
-0.00952
0.00068
-0.00124
-0.00218
0.00018
-0.00218
0.00016
-0.00074
-0.00076
0.0008
-0.00009
-0.00002
-0.00006
-0.00007
-0.01282
-0.00017
-0.00017
0.00018
-0.00002
0
-0.00002
-0.00002
-0.00296
-0.00002
-0.00003
0
-0.00003
0
-0.0002
-0.00036
0.00003
-0.00036
0.00003
-0.00169
-0.00299
0.00025
-0.00299
0.00021
-0.00048
-0.00122
0.00017
-0.00122
0.00036
-0.00221
-0.01197
-0.00157
0.04991
0.02137
-0.00005
-0.00003
-0.00003
0.00003
0
0
0
0
-0.00051
-0.00023
-0.00024
0.00025
-0.00003
0
-0.00002
-0.00002
-0.00403
0.00007
0.00007
0.00017
0.00019
-0.00001
0.02263
0.02263
-0.0035
0.03183
0.03112
-0.00157
0.02775
-0.00278
-0.00431
-0.00431
-0.00877
0.00043
0.00042
0.0004
0.0004
0.00043
0.00367
-0.01097
0.00008
0.00008
0.00008
0.00008
0.00008
0.00026
-0.00087
0.00038
0.00038
0.00038
0.00038
0.00038
0.00021
-0.00182
0.00036
0.00036
0.00036
0.00036
0.00036
0.00125
0.01719
0
0
0
0
0
0
0.00082
-0.35027
0.04751
-0.0028
-0.01096
0.02831
0.00236
-0.42805
-0.00011
-0.00088
0.00156
-0.001
-0.00182
-0.70641
-0.00182
0.00353
-0.00076
-0.00412
-0.00054
-0.45024
0.00734
0.00079
0.00081
0.00114
0.00082
-0.19951
0.03741
0.0345
-0.0028
0.03692
-0.00625
-0.00975
-0.00975
-0.01651
0.00232
0.00231
-0.00011
0.00202
-0.0002
-0.00032
-0.00032
-0.00068
0.0032
0.00317
0.0595
0.00338
0.07359
-0.00131
-0.00131
-0.00234
0.01093
0.01085
-0.00054
0.00953
-0.00096
-0.00149
-0.00149
-0.00314
0.00203
0.00202
0.00113
0.00188
0.00155
0.00007
0.00007
-0.00093
0
0
0
0
0
0
0
0.0001
0
0
0
0.00015
0.00002
0.00002
0.00002
0.00002
0.00002
0
-0.00008
0.00001
0.00001
0.00001
0.00001
0.00001
0
0.00642
0.00539
0.00538
0.00535
0.00535
0.00538
0.00425
-0.01065
0.00577
0.00577
0.00575
0.00575
0.00577
0.06275
-0.00321
0.00008
0.0001
0.00007
0.0001
0.00013
0.00011
0.00016
0.00011
0.00016
0.0002
-0.00004
-0.00008
0.00256
-0.00008
0.00015
0.00582
0.00638
0.00552
0.00639
0.00944
-0.00609
-0.01066
0.51751
-0.01065
0.03641
-0.00131
-0.00322
0.0004
-0.00322
0.00687
-0.57324
0.00157
0.00007
0.00175
0.00011
0.00011
0.00011
0.00249
-0.37659
0.00011
-0.00002
0.00016
0.00016
0.00016
0.00014
0.00014
-0.76704
0.00015
0.0032
-0.00006
-0.00006
0.14104
-0.00128
0.00552
-0.38367
0.00745
0.00734
0.00734
0.03858
0.03662
0.52139
0.03497
-0.33927
-0.00642
-0.00642
0.02205
0.02133
0.0004
0.02029
-0.00007
-0.44103
-0.00185
0
Idara
Working
Paper
Kai Bauer
*
* SUPPLY Elasti cities
*
TABLE LELASREV
*
SWHE
DWHE
BARL
MAIZ
OCER
PULS
POTA
MAR00.SWHE
0.13684
-0.00042
-0.00625
-0.00506
-0.01032
-0.00232
-0.00318
MAR00.DWHE
-0.00217
0.09387
-0.00163
-0.00101
-0.00271
-0.00768
-0.00161
MAR00.BARL
-0.01083
-0.00055
0.06947
-0.00516
-0.01356
-0.01158
-0.00311
MAR00.MAIZ
-0.01237
-0.00048
-0.00728
0.13268
-0.00733
-0.001
-0.00336
MAR00.OCER
-0.04124
-0.0021
-0.03124
-0.01197
0.21201
-0.00102
-0.00206
MAR00.PULS
-0.01323
-0.00849
-0.03817
-0.00234
-0.00145
0.1966
-0.01073
MAR00.POTA
-0.00693
-0.00068
-0.00391
-0.003
-0.00112
-0.00409
0.15867
MAR00.SUGB
-0.01727
-0.00337
-0.00982
-0.00713
-0.00436
-0.00305
-0.00799
MAR00.RAPE
-0.0168
-0.00125
-0.04844
-0.00753
-0.01917
-0.01162
-0.00843
MAR00.SUNF
-0.00631
-0.00593
-0.00788
-0.01291
-0.0079
-0.00105
-0.00101
MAR00.SOTH
-0.03592
-0.00685
-0.01378
-0.07347
-0.01004
-0.001
-0.00176
MAR00.OLIV
-0.00101
-0.00077
-0.00102
-0.00105
-0.00097
-0.00069
-0.00102
MAR00.INDU
-0.02263
-0.00234
-0.00409
-0.00918
-0.0039
-0.00688
-0.001
MAR00.VEGE
-0.0019
-0.00145
-0.00042
-0.0008
-0.00035
-0.00049
-0.00184
MAR00.FRUI
-0.00155
-0.00021
-0.00074
-0.00318
-0.00047
-0.00019
-0.0005
MAR00.WINE
-0.00232
-0.00224
-0.00072
-0.00195
-0.00032
-0.00156
-0.00058
MAR00.OCRO
-0.00838
-0.00161
-0.00479
-0.00342
-0.00208
-0.00146
-0.00383
MAR00.DCOW
MAR00.BULL
MAR00.FCAF
MAR00.FCAM
MAR00.SCOW
MAR00.HEIF
MAR00.PORK
MAR00.SHEE
MAR00.HENS
MAR00.POUL
MAR00.OANI
MAR00.OFOD
-0.01165
-0.00087
-0.00642
-0.0032
-0.00149
-0.00589
-0.00285
MAR00.GRAS
-0.01279
-0.00049
-0.00148
-0.0011
-0.00064
MAR00.FALL
-0.03302
-0.01541
-0.00381
-0.00543
-0.04133
+
SUGB
RAPE
SUNF
SOTH
OLIV
INDU
VEGE
MAR00.SWHE
-0.00496
-0.00338
-0.00095
-0.00102
-0.00027
-0.0019
-0.00262
MAR00.DWHE
-0.005
-0.0013
-0.00463
-0.001
-0.00106
-0.00101
-0.0103
MAR00.BARL
-0.00488
-0.01692
-0.00206
-0.00068
-0.00047
-0.0006
-0.001
MAR00.MAIZ
-0.005
-0.00371
-0.00476
-0.00508
-0.00068
-0.00188
-0.0027
MAR00.OCER
-0.00499
-0.01542
-0.00476
-0.00114
-0.00103
-0.00131
-0.00194
MAR00.PULS
-0.005
-0.01338
-0.00091
-0.00016
-0.00105
-0.0033
-0.00385
MAR00.POTA
-0.005
-0.0037
-0.00033
-0.00011
-0.00059
-0.00018
-0.00554
MAR00.SUGB
0.19226
-0.00351
-0.00263
-0.00051
-0.00463
-0.00146
-0.02381
MAR00.RAPE
-0.005
0.32735
-0.00079
-0.00014
-0.00106
-0.00188
-0.00555
MAR00.SUNF
-0.005
-0.00106
0.19007
-0.00019
-0.00103
-0.00178
-0.00657
MAR00.SOTH
-0.00513
-0.001
-0.001
0.24545
-0.001
-0.00164
-0.06166
MAR00.OLIV
-0.005
-0.00081
-0.00059
-0.00011
0.0565
-0.00032
-0.001
MAR00.INDU
-0.005
-0.0045
-0.00319
-0.00055
-0.00101
0.18831
-0.001
MAR00.VEGE
-0.00496
-0.00081
-0.00072
-0.00127
-0.00019
0.15113
MAR00.FRUI
-0.00524
-0.00022
-0.00016
-0.0002
-0.00029
-0.00104
MAR00.WINE
-0.005
-0.00124
-0.00163
-0.0012
-0.00033
-0.0001
-0.00103
MAR00.OCRO
-0.00258
-0.00168
-0.00126
-0.00024
-0.00222
-0.00076
-0.01148
MAR00.DCOW
MAR00.BULL
MAR00.FCAF
MAR00.FCAM
MAR00.SCOW
MAR00.HEIF
MAR00.PORK
MAR00.SHEE
MAR00.HENS
MAR00.POUL
MAR00.OANI
MAR00.OFOD
-0.00468
-0.00455
-0.00068
-0.00012
-0.00103
-0.00037
MAR00.GRAS
-0.00252
-0.00359
MAR00.FALL
-0.00435
-0.03019
-0.02362
-0.00328
-0.01782
+
FRUI
WINE
OCRO
DCOW
BULL
FCAF
FCAM
MAR00.SWHE
-0.00145
-0.0019
-0.01007
MAR00.DWHE
-0.00101
-0.0095
-0.01002
MAR00.BARL
-0.00121
-0.00103
-0.00997
66
Kai Bauer
Idara
Working
Paper
67
8.6 Exchange Rates
YEAR
89
90
91
92
93
94
95
96
97
98
99
2000
Exchange Rates
CZ000 (CZK/EURO) HU000 (HUF/EURO) PL000 (PLN/EURO)
15.8150
55.4075
0.1882
18.6421
76.7386
1.2056
36.4400
92.4598
1.3074
36.5459
102.1992
1.7631
34.1335
107.6112
2.1235
34.0807
124.6738
2.6961
34.6928
164.3137
3.1699
34.4543
193.6548
3.4213
35.9410
211.5646
3.7154
36.1074
239.7092
3.9060
35.1882
241.2004
4.0326
35.6129
260.1843
4.6083
8.7 Important Groups of Products
Fixed Price
Fixed Trade
Group
-
Group FXPTE
Group FXTRD
MSBAL
Description
Exogenous World
Market Price +
Tariffs + Domestic
Policy
Exogenous fixed
product price (no
world market price
available)
Exogenous trade
volume for the EU
with free trade
among MS
Trade is fixed at the
member state level
Products
All other products
OLIV, INDU, WINE,
OCRO, OANI,
COWO, RICE,
OLIO, OLIC, IPLA,
IGEN, REST,
VEGE, FRUI
MOLA, STAR,
OMPR, SUGB,
POTA, EGMI,
OFOD, GRAS,
YCAM, YCAF
Not used for the
single country
models
Group Info
Inputs
Code
Description
FEED
Feeding stuffs
NOFEED
Aggregates of industrial inputs
Products
POTA,SUGB,RAPE,SUNF,S IGEN, IPLA, REST
OTH,OLIV,INDU,FRUI,VEGE,
FOTI,SUGA,WINE,
MILK,EGMI,MIPO,BUTT,OMP
R, MOLA,STAR,FENI, OFOD,
GRAS,
PULS,RAPO,SUNO,SOYO,R
APC,SUNC,SOYC,OLIC,FPRI
,
Idara
Working
Paper
Kai Bauer
68
SWHE,DWHE,BARL,MAIZ,O
CER,RICE
Table 17. Important Activity Groups in i-sim
Group Code
Description
Elements
GCOP
Activities with group
premiums
Oil Seeds
Activities with shadow
revenues
Cereals
Crop Activities
SWHE,DWHE,BARL,OCER,MAIZ,RAPE,SUNF,S
OTH,PULS,FALL
RAPE, SUNF, SOTH
OLIV,INDU,WINE,FALL,GRAS
OILS
SREV
CERE
CACT
SWHE , DWHE , BARL , MAIZ , OCER
SWHE,DWHE,BARL,MAIZ,OCER,PULS,POTA,S
UGB,RAPE,SUNF,SOTH