1. 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

2. 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

3. 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)

4. 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

5. 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

6. 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. 1997 – Environment: species conservation (Marcot et al. 2006), water (Zorrilla et al. 2010) – Land Use (Bacon et al. 2002)

7. 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)

8. 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

9. 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

10. 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

11. 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-216672)  Questions about farm and household (social characteristics, structural aspects and future intentions )  Policy scenarios: – CAP after 2013, No-CAP after 2013

12. 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

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

14. 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

15. 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

16. 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 19.35 to 18.1%)

17. 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%

18. 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

19. agriregionieuropa Thank for the attention laura.sardonini@unibo.it