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Sardonini L. 1 , Viaggi D. 1 and Raggi M. 2

Accounting for multiple impacts of the Common Agricultural Policies in rural areas: an analysis using a Bayesian networks approach. Sardonini L. 1 , Viaggi D. 1 and Raggi M. 2 1 Department of Agricultural Economics and Engineering, University of Bologna, Italy

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Sardonini L. 1 , Viaggi D. 1 and Raggi M. 2

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  1. Accounting for multiple impacts of the Common Agricultural Policies in rural areas: an analysis using a Bayesian 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 122nd European Association of Agricultural Economists Seminar Evidence-Based Agricultural and Rural Policy Making Methodological and Empirical Challenges of Policy Evaluation February 17th – 18th, 2011, Ancona (Italy) Centro Studi Sulle Politiche Economiche, Rurali e Ambientali associazioneAlessandroBartolastudi e ricerche di economia e di politica agraria Università Politecnica delle Marche

  2. Outline • Objective • Background • Methodology: Bayesian Networks (BNs) • Results from a farm/household survey in 9 EU countries • Discussion

  3. 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. 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. 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. 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. 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. Bayesian Networks (BNs) • Example from Charniak 1996 Family-out Bowelproblem Input parent nodes Dog out Light on child node causal link Hearbark outcome child node

  9. 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. BNs: methodology • Assuming a set explanatory variables pa(x) • Computation of P(xi|pa(x)) • Estimation using EM alghorithm: • Maximization of the log-likelihood • Iterative process • Update the posterior probability Bayes theorem

  11. 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. BNs: Application 1/2 • Variables used in the network: • Current farm/household characteristics • Multiple outcomes in terms of:

  13. BNs: Application 2/2

  14. Net description • The causal relationships derivebyWPsresults and economictheory • INTENTION is a key node • Currentcharacteristicsinfluence the INTENTION and all the outcomenodes

  15. 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. 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. BNs: Accurancy • Error rates: percentage of missclassified between observed and predicted

  18. 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. Thank for the attentionlaura.sardonini@unibo.it

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