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UOP - ITALYFilippo Arfini (University of Parma. Italy). Michele Donati (University of Parma. Italy)

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The Impact of CAP reform on the Employment Levels in Rural Area

CARERA

WP3

Assessing the Structural Impact of CAP Reform in the Farming Sector

Creta 18 / 03/ 2006

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Objectives and main issues of WP3 (1)

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The objective of this WP is to analyse and assess the Common Agricultural Policy impacts on land use and economic variables across some specific EU regions in five countries: Greece, Italy, UK, Germany, Sweden.

This phase of the project should examine the specificity and the characteristics of agricultural holdings at regional level.

The assess of CAP is based on the methodology of Positive Mathematical Programming (PMP) in its most recent development.

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Objective of WP3 (2)

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In detail: Analysis of the impact of different mix of CAP measures

on:• the economic performance of agricultural

holdings • the potential developments in incomes and

changes in land use. • We should identify policy-induced changes in some key

variables at farm level. • We should describe and assess to what extent the

characteristics of each region change as a consequence of the implementation of different CAP policy scenario.

• We should provide information and data useful for other statistical methodologies and further analysis.

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Objective of WP3 (3)

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In terms of data, the main input for the regional models will be the EU-FADN data .

These data pertain to crop land use, yields, total variable costs, output prices and subsidies and are

structured per farm typology in each EU region.

FADN database is enough for the purpose of FADN database is enough for the purpose of CARERA ? CARERA ?

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Objective of WP3 (4)

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The PMP regional models will allow to assess the main relevant effects of each of the different scenarios envisaged by the implementation of different policy scenario both on:

• the supply side (land allocation and total output per crop)

• the economic variables (gross saleable production, total subsidies, total variable costs, gross margins and marginal land values)

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Description of the work

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Will be taken into account farm modelsfarm models who describe the agricultural sector of each rural area.

The models will be then improved to deal with labour effects at farm holding level

This Work Package contains three (3) different research tasks as following:

Task 3.1: Organisation of the FADN Database (all partners)

Task 3.2: Calibration of PMP Models (UOP)

Task 3.3: Prediction Phase of PMP Models (UOP)

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Description of the work

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Task 3.1: Organisation of the FADN Database

This task is linked to Work Package 2 and its scope is to organise the FADN data in the form required by PMP.

The information, collected from FADN in some relevant case study-areas, by interviews and focus groups, will lead to identify specific case studies that will represent farms’ types on which the reaction to the new scheme of the agricultural policy will be evaluated.

Assessing the supply and demand allocation and income effects under “static” conditions, identifying structural changes and other secondary items will be other goals of this task.

Impacts on rental values of land and quota and income distribution between producer and factor owners will be analyzed.

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Description of the work

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Task 3.1: Organisation of the FADN Database

This task is linked to Work Package 2 and its scope is to organise the FADN data in the form required by PMP.

The information collected from FADN in some relevant case study-areas, by interviews and focus groups, will lead to identify specific case studies that will represent farms’ types on which the reaction to the new scheme of the agricultural policy will be evaluated.

Assessing the supply and demand allocation and income effects under “static” conditions, identifying structural changes and other secondary items will be other goals of this task.

Impacts on rental values of land and quota and income distribution between producer and factor owners will be analyzed.

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Description of the work

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Task 3.2: Calibration of PMP Models

Individual PMP Models will be calibrated and tested under consistent and comparable scenarios using common databases and assumptions.

The models also will provide other partners with information on land use organisation, shadow prices of land, farm income, and use of specific inputs (labour, land, quota, rights).

Some problems for detect the amount of work per crop and Ha

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Description of the work

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Task 3.3: Prediction Phase of PMP Models

Once the models are calibrated and describe the character of the agricultural sector of each region, policy scenario will be applied.

The information generated will be used by other activities in the project, that is for

(a) quantitative assessment of economic impacts by regional input-output models;

(b) exploration of alternative options and Pillar-2 compliance;

(c) evaluation of the sociological and structural impacts of different policies.

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Description of the work

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DeliverablesThe present Work Package will produce two kinds of

scientific results:(1) Modeling of an Experimental approach for the

Assessment of the Structural Changes in Farming Sector (D6)

(2) A Report on the Effects of CAP Reform on the Farming Activities and Employment Levels (D9)

Milestones and Expected Results:(1) M3.1 Analysis of FADN database(2) M3.2 Model calibration(3) M3.3 Prediction of changes in the economic

structure of farming activities

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Description of the work

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Wp3 Timing

Work Packages and Tasks

0-3 4-6 7-9 10-12 13-15 16-18 19-21 22-24

3.1 Organisation of the FADN Database

3.2 Calibration of Positive Mathematical Programming Models

3.3 Prediction Phase of Positive Mathematical Programming Models

1.      Modeling of an Experimental approach for the Assessment of the Structural Changes in Farming Sector (D6)A Report on the Effects of CAP Reform on theFarming Activities and Employment Levels(D9)

Month

WP3: Assessing the Structural I mpact of CAP Reform in the Farming Sector

TASK

Deliverables

Data for the models;

Model organisation;

Estimation of variable costs;

Integrated approach with PMP and MSA?

Open issues:

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Open issues: data for the models

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Some comments about EU FADN

FADN is an instrument for evaluating the income of agricultural evaluating the income of agricultural holdingsholdings and the impacts of the Common Agricultural Policy.

The survey does not cover all the agricultural holdingsnot cover all the agricultural holdings in the Union but only those which due to their size could be considered commercial. Which FT we should consider? Problem of representativity.

FADN doesn’t provide information about crops variable costcrops variable cost

From a theoretical point of viewFrom a theoretical point of view. FADN is “the ideal” instrument for all researchers. It contains all the necessary information for the construction of an agricultural policy analysis model.

From a practical point of viewFrom a practical point of view. FADN in fact presents great limits that influence its use … the lack of representativity of land allocation especially at sub regional level

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Open issues: data for the models

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FADN is good for provide data for sector models (by FT),

but…

Sector model are good for represent agricultural sector at

regional (rural) level ?

How we can built a good regional model in term of representativity of farm holdings considering also others sectors present in the rural area?

Should we consider also Local Work Systems?

Our proposal is to merge EU FADN with others statistical source relevant at NUTS II level (ie REGIO)

Information for PMP models

FADNFADN Databank:•Crop yields•Prices•Subsidies•Total Costs•Labour

REGIOREGIO Databank:•Land harvested for different crops and AWU

New DatabaseNew Database to be used with PMP

models

PMP modelsPMP models

Estimation of variables (costs) and adjustments to produce information adapted to the quantitative tool

The two database will be merged with respect to the Fadn stratification criteria (Nuts2, Altitude, Class of Size). Some problems . . .

YEAR 2002 YEAR 2002

Information collected

Land use: hectars cultivated for each cropLand use: hectars cultivated for each crop Production: quantity producted for each cropProduction: quantity producted for each crop PricePrice SubsidySubsidy Total cost: totale specific cost per farmTotal cost: totale specific cost per farm

FADN Archive – year 2002FADN Archive – year 2002

Land harvested for different cropsLand harvested for different crops Total production for each cropTotal production for each crop Yields (tons/ha)Yields (tons/ha) Labour: family labour and external labourLabour: family labour and external labour

REGIO Databank – year 2002REGIO Databank – year 2002

Information lacks and merging problems

The information collected by the FADN FADN archive provide very few information about the amount of subsidies per crop.

Considering the information from REGIO, REGIO, we encounter more difficulties in order to merging the two databank:

We have not a reference on the Altitude level nor for the Classes of Size;

The land use is not completely mapped (A2crop); i.e. there are not information about forage crops and the possible infromation conintained in the table A2efarm provide information only upto year 2000.

Organisation of the model

NUTS2

2ˆNUTSQ

Altitude 1

Altitude 2

Altitude 3

Size1

Size2

Size3

Size4

Size5

Size6

Size7

NUTS2 Regions

One Q matrix for each NUTS2 Region

The model provides simulations for each class of size, for each altitude area and for each NUTS2 region.

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Organisation of the model

GrossMargin

(1)

GrossMargin

(2)

GrossMargin

(3)

GrossMargin

(n)

Resource(1)

Resource(2)

Resource(3)

Resource(n)

MilkProduc .

(1)

MilkProduc .

(2)

MilkProduc .

(3)

MilkProduc .

(n)

Nuts2region 1

Nuts2region 2

Nuts2region 3

Nuts2region n

ObjectiveFunction

Structuralcostraints(Land Use)

QuotaCostraint

Regional GrossMargin(PROFT)

Sub-regionalstructurecapacity (bn)

Regional MilkQuota

Information scheme

Regio tablesRegio tables Fadn dataFadn data

GAMS-GDX Routines

PMP DatasetPMP Dataset

Database Processor

Tables for PMP modelsTables for PMP models

Spreadsheet automatic generator

Integrated Database

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For lack of representativity became necessary to find new data source.

Integration of FADN with REGIO can provide unique regional data and help modelers for policy propose.

The integration of the two databases with PMP allows the later aggregation of the models, moving the policy analysis from sub-regional, to regional and to national level.

This approach is characterized by high level of flexibility, which allows to join together sub-regions with similar administrative and climatic characteristics.

Organisation of the model

Estimation of specific variable costs

The lack in specific variable cost information at crop level doesn’t permit to derive completely the cost function parameters to use in the prediction phase. So, we have to implement an approach to derive this kind of cost different respect to the traditional PMP.

In particular we can proceed substituting the first phase of PMP model in the follow alternative ways:

1) Derive the parameters for the cost function by an estimation using the Maximun Entropy approach (Leon et alt., 2000);

2)Derive the marginal cost associated to each crop imposing directly the first order optimality conditions in the first phase of PMP (Heckelei, 2003).

Phases I nputs Approach Outputs

Phase 1 Use of micro-economic, macro-economic, social and demographic indicators.

PMP, Multivariate Spatial Analysis

Model Calibration; identifi cation of homogeneous areas and corresponding clusters; (NUTS 2)

Phase 2 Policy Analysis PMP Agricultural Policy Analyses and I ndexes of Elasticity

Phase 3 Updating macro-economic indicators using the indexes of elasticity

MSA New re-organisation of homogeneous areas corresponding to the new clusters

Phase 4 Policy recommendation

The integrated model for CARERA Analysis

The integrated model for CARERA Analysis

Group IndicatorsMain social and demographic indicators and territorial morphology

% change in population 1990-2001; female activity ratio, dependency ratio, ageing index, population density, % hilly areas, % mountain areas.

Economic structure Employees per km2: agriculture, industry, services; ratio of industry employees; ratio of services employees; per capita GDP; unemployment ratio.

Agricultural structure Avg. UAA per farm; % UAA of farms under 2 ha; % UAA of farms above 50 ha; tractors per 100 ha UAA; AWU per 100 ha UAA; % UAA on total agricultural surface; % change in number of farms; % change in UAA.

Agricultural activities and land useCrops as UAA %Animal production: heads/UAA

Soft wheat; durum wheat; barley; maize; rice; dried leguminous vegetables; potatoes; beet; sunflower; soya beans; horticulture; grapes; oilseeds; apples; pears; peaches; kiwi; chestnuts; feeding crops; meat; milk; woods; pastures.

Productivity of agriculture

Standard gross margin per ha of UAA; marginal price of land; gross saleable production per ha (vegetables), GSP per ha (animal production); variable costs for saleable production per ha; variable costs for re-used production per ha; variable costs for animal production per ha.

Integration with food industry

% food firms on total manufacturing firms; employees per food firm; employees in the food sector per km2.

We propose

1.Use intrgrated PMP/MSA approach for all the all the EU NUTSII RegionsEU NUTSII Regions (D6).(D6).

2.Develop a specific software able to merge data and integrate PMP/MSA for the purpose of regional analysis.

3.Soft analysis for all the 15 (or more) EU countries

4.Deep analysis for specific sub-regions in five Country where take in consideration the effect of Rural Development Plan (D9).(D9).

Open questions

What need to others WP in terms of data?

Which regions we have to consider?

How to consider Rural Development Plan?

Which policies we have to take in consideration ?