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Introduction

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.

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Introduction

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  1. 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

  2. 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 farm-level data. Introduction

  3. 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

  4. Efforts toward: separating indica and japonica types of rice; including alternative crops; improving policy representation (especially the removal of intervention, and set-aside); EU rice market in AGLINK

  5. 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

  6. 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. PMP on Italian rice-growing provinces

  7. 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 together: possible advantages

  8. 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 advantages

  9. 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. Two models: possible drawbacks

  10. First results: AGLINK, standard baseline

  11. First results: AGLINK modified baseline

  12. First results: the PMP model

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