USING AGLINK AND THE POSITIVE
MATHEMATICAL PROGRAMMING TO
ASSESS THE EFFECT OF THE CAP:
THE CASE OF RICE SUPPLY IN ITALY
Piero Conforti
INEA - National Institute of Agricultural Economics
Rome, Italy
Introduction
• Aim of the exercise: obtain an ex-ante assessment
of the effects of the CAP that takes into account
both the EU-wide perspective and local supply
response
• The idea is to generate results at the EU-level
with AGLINK (OECD, 2001), and use them in
Positive Mathematical Programming (PMP)
models (Paris e Howitt, 1998; Paris and Arfini, 1995;
Arfini, 2001) run on Italian provinces with farmlevel data.
EU rice market in AGLINK
• Direct payments only affect area, and not yields (a
proxy for partial “decoupling”)
• Market price (PP) is separate from intervention
price (PI), and is related (also) to stocks
but:
• there are no cross-price effects on other crops;
• trade is exogenous;
• total area limitation is not modelled;
• one single “rice” instead of indica and
japonica
EU rice market in AGLINK
Efforts toward:
• separating indica and japonica types of rice;
• including alternative crops;
• improving policy representation (especially the
removal of intervention, and set-aside);
PMP on Italian rice-growing provinces
A “positive” programming approach that reproduces
observed land distribution and simulates the effect of a policy
change.
3 stages:
1. Solves a Linear Programming (LP) problem (obj =
gross revenue) under constraint given by resources and
observed production; this yields marginal costs (from
the dual solution);
2. Estimates the underlying total cost function;
3. Simulates the effect of a policy change starting from
the estimated cost function.
PMP on Italian rice-growing provinces
• Yields are fixed: direct payments are modelled as
a fully “coupled” transfer;
• Price transmission is considered uniform among
provinces;
• FADN data are not always representative.
Two models together: possible advantages
• Italian agriculture in general is highly diversified.
For several CAP products it is useful to simulate the
effects of alternative policies on local production
patterns, and on specific farm types (e.g. dairy; durum
wheat).
• In perspective, CAP provisions may become
increasingly subject to national and local fine-tuning
(e.g. hypotheses of direct payments modulation;
environmental cross-compliance).
Two models: possible advantages
• Policy provisions that are not included in AGLINK
(due to aggregation) can be taken into account by
PMP models (e.g. the national distribution of direct
payments and of set-aside payments).
• PMP models can be used in for more realistic
simulation exercises, especially concerning the effects of
policies on market price (e.g. the reduction or removal of
intervention).
Two models: possible drawbacks
• A “small region” hypothesis is required: no single
province modelled with the PMP should influence
prices at the EU level.
• Time-frame consistency: PMP is short run, while
AGLINK is medium-term; AGLINK current
baseline is 2000-06, while the latest FADN data is
1999.
• (specific for this application) AGLINK EU module
does not take into account cross-price effects for
rice, while PMP is short-run, and does take into
account cross-price effects.
First results: AGLINK, standard baseline
price
stocks
270
700
250
600
230
Base
210
Base
190
reform
170
500
reform
400
300
150
2000 2001 2002 2003 2004 2005 2006
2000 2001 2002 2003 2004 2005 2006
3a
3b
supply
consumption
2000
1900
1800
Base
1700
reform
1600
1500
2000 2001 2002 2003 2004 2005 2006
3c
2450
2400
2350
2300
2250
2200
2150
2100
2050
Base
reform
2000 2001 2002 2003 2004 2005 2006
3d
First results: AGLINK modified baseline
prices
340
stocks
700
320
600
300
280
normal baseline
260
indica
240
japonica
220
500
400
300
normal baseline
200
japonica
indica
100
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
00
20
01
20
02
20
03
20
04
20
05
20
06
200
4a
4b
supply
consumption
1900
1700
normal
baseline
indica
1500
1300
1100
900
japonica
06
20
04
05
20
02
03
4b
20
20
20
01
20
20
00
700
500
2300
2100
1900
1700
1500
1300
1100
900
normal baseline
indica
japonica
20
00
20
01
20
02
20
03
20
04
20
05
20
06
2100
4d
First results: the PMP model
% changes
provinces
rice supply (tonnes)
scenario A scenario B
total crop production
scenario A scenario B
Vercelli
Pavia
Novara
Alessandria
Torino
Biella
Milano
Mantova
Verona
Rovigo
Ferrara
Oristano
Grosseto
-14.63
-12.09
-11.96
-14.77
-23.97
-15.05
-20.33
-25.39
-16.90
-19.22
-20.17
-100.00
-14.17
-19.98
-16.62
-18.02
-24.59
-38.23
-21.86
-41.42
-56.85
-28.26
-32.37
-33.68
-100.00
-23.68
-19.84
-18.68
-20.41
-16.91
-7.76
-18.69
-13.99
-4.38
-8.23
-8.56
-9.98
2.96
-11.26
-26.32
-24.97
-30.36
-23.76
-7.79
-24.92
-18.97
-3.97
-8.94
-9.18
-11.40
2.96
-14.78
Total
-15.32
-23.24
-13.94
-17.71
gross margins (all crops)
scenario A scenario B
-14.29
-11.74
-14.09
-12.12
-5.13
-11.55
-10.97
-5.22
-6.21
-6.84
-7.70
-3.52
-6.56
-20.94
-18.22
-24.90
-16.77
-5.19
-17.10
-13.22
-5.27
-6.57
-7.18
-8.49
-2.04
-9.20