agriregionieuropa A CCOUNTING FOR MULTIPLE IMPACTS OF THE C OMMON A GRICULTURAL P OLICIES IN RURAL AREAS : AN ANALYSIS USING A B AYESIAN NETWORKS APPROACH Sardonini L. 1, Viaggi D. 1 and Raggi M. 2 1 Department of Agricultural Economics and Engineering, University of Bologna, Italy 2 Department of Statistics, University of Bologna, Italy 122 nd European Association of Agricultural Economists Seminar Evidence-Based Agricultural and Rural Policy Making Methodological and Empirical Challenges of Policy Evaluation February 17 th – 18 th, 2011, Ancona (Italy) associazioneAlessandroBartola studi e ricerche di economia e di politica agraria Centro Studi Sulle Politiche Economiche, Rurali e Ambientali Università Politecnica delle Marche

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy)  Objective  Background  Methodology: Bayesian Networks (BNs)  Results from a farm/household survey in 9 EU countries  Discussion Outline

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Objective  Discuss the potential use of Bayesian Networks to represent the multiple determinants and impacts of CAP in rural areas across Europe: – Analysis of stated intention to farming in 9 EU countries (micro level data)

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Background 1/2 Tools for evaluating effects of CAP are wide and heterogeneous: − high number of drivers − high number of potential dimensions (economic, social and environmental issues) − complex behaviour

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Background 2/2 Problems due to the complexity of relationships: − non-linear − too many variables − correlations among explanatory variables − multiple variables outcome − missing data

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Bayesian Networks (BNs) Some application fields: – Artificial Intelligence (first field): NASA, NOKIA – Sociology: Rhodes 2007 – Medical diagnoses: Kahn et al – Environment: species conservation (Marcot et al. 2006), water (Zorrilla et al. 2010) – Land Use (Bacon et al. 2002)

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Bayesian Networks (BNs)  Simple and useful tools for modelling predictions and aiding resource managment decision making  Direct Acyclic Graph (DAG) where the nodes are random variables and the arcs represent direct connections between them (under conditional dependence assumptions)

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Bayesian Networks (BNs)  Example from Charniak 1996 Family-out Bowel problem Dog out Hear bark Light on outcome child node causal link Input parent nodes

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) BNs: advantages  Graphical construction interface  Incomplete database  Learn from data  Prior information  No linear relation  Could combine empirical data and expert judgement  Multiple outcomes

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) BNs: methodology  Assuming a set explanatory variables pa(x)  Computation of P(x i |pa(x))  Estimation using EM alghorithm: – Maximization of the log-likelihood – Iterative process – Update the posterior probability Bayes theorem

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Case study  Around 2000 farm-households interviews in 9 EU countries (telephone, face-to-face, postal)  European project CAP-IRE “Assessing the multiple Impacts of the Common Agricultural Policies (CAP) on Rural Economies”, 7th FP (SSH )  Questions about farm and household (social characteristics, structural aspects and future intentions )  Policy scenarios: – CAP after 2013, No-CAP after 2013

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) BNs: Application 1/2  Variables used in the network: – Current farm/household characteristics  Multiple outcomes in terms of: VariableLabel INTENTIONReaction to the hypothetical policy scenarios CHANGE_LEGAL_STATUSChanging in legal status PESTICIDESChanging in use of pesticides CHANGE_SELLOUTPUTSChanging who sells output LAND_OWNEDChanging in farm size (land owned) MACHINERYChanging in machinery endowment INNOVATION_01Adoption of at least one innovation CREDITChanging in use of credit HH_LAB_INChanging in household labour on farm

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) BNs: Application 2/2

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Net description  The causal relationships derive by WPs results and economic theory  INTENTION is a key node  Current characteristics influence the INTENTION and all the outcome nodes Outcome child nodesParent nodes INTENTION Cap, farm size, rent, income from farm, age, fulltime hh, country CHANGE_LEGAL_STATUSSFP per ha and advisory assistant PESTICIDESFarm size, specialization, SFP per ha, advisory assistant CHANGE_SELLOUTPUTSFarm size and innovation LAND_OWNEDFarm size, rent, altitude, SFP per ha, fulltime hh MACHINERYFarm size, rent, SFP per ha, fulltime hh, innovation INNOVATION_01SFP per ha, educational level, advisory assistant, age CREDITFarm size, SFP per ha, fulltime hh, rent, innovation HH_LAB_IN Educational level, fulltime hh, SFP per ha, specialization, income from farm, rent, innovation

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) BNs: Result (CPTs) Future stated plan to:  Adopt at least one INNOVATION_01: – young with a degree or old with high level of SFP and education  Increase the LAND_OWNED: – medium and medium-large farm size, rented-in already land and with at least two fulltime household members  Increase in MACHINERY: – increase in land and adopt at least one innovation  Increase in PESTICEDES: – livestock and mixed specialisation, SFP in the class 150-|500€ and increase the land  CHANGE_SELLOUTPUT – increase in land and adopt at least one innovation

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) BNs: Results Effect of scenario (Cap/No-Cap) – Exit frequency increases in No-Cap (from 21% to 30.6%) – The adoption of at least one innovation decreases in No-Cap (from 28.9% to 25.5%) – The increasing in land size decreases in No-Cap (from 19.2% to 17.2%) – The increasing in the fulltime household decreases in No-Cap (from to 18.1%)

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) BNs: Accurancy  Error rates: percentage of missclassified between observed and predicted VariableError rate Intention1.037% Land owned8.019% Innovation5.226% Pesticides18.05% Machinery14.85% Change_sell_output22.37% Change_legal status11.07% Credit24.19% Hh_lab_in10.33%

agriregionieuropa 122 nd EAAE Seminar, February 17 th – 18 th, 2011, Ancona (Italy) Discussion  Results – Coherence between the outcomes and the expectations – The older show a larger likelihood to quit farming activity – Good fit of the net in terms of low error rates  Further developments – Policy simulation: simulate the multiple outcomes from farming under different exogenous conditions

agriregionieuropa Thank for the attention