Salecker, Jan

[["dc.bibliographiccitation.artnumber","103944"],["dc.bibliographiccitation.journal","Journal of Economic Dynamics and Control"],["dc.bibliographiccitation.volume","117"],["dc.contributor.author","Kopp, Thomas"],["dc.contributor.author","Salecker, Jan"],["dc.date.accessioned","2020-12-08T08:09:28Z"],["dc.date.available","2020-12-08T08:09:28Z"],["dc.date.issued","2020"],["dc.description.abstract","Marketing channel choices in agricultural trade networks affect the networks\\’ overall performance and influence rural livelihoods. This study identifies key determinants of these choices among natural rubber traders in Indonesia to evaluate four policy scenarios and their potential effects on rural incomes. Since traders\\’ marketing decisions are based on past interactions, resulting trade networks are formed in recursive processes and can be understood as complex adaptive systems. Due to inherent endogeneity in these systems, process-based approaches such as agent-based modelling (ABM) can be effective in understanding them. Using a self-gathered primary dataset from Jambi Province, Indonesia, we implement and parameterise an ABM to simulate the formation of the rubber trading network and analyse the effects on rural livelihoods of four hypothetical policy scenarios: improved micro-credit availability, increased access to education, better infrastructure and transportation capacity, and market information availability. The model is calibrated through a genetic algorithm which maximises the similarity between the simulated network and the actual network observed in the data. Results indicate that sellers\\’ decisions on a buyer are primarily determined by debt obligations and past peer-interactions. The most influential sellers have a similar level of formal education as their peers and live in close physical proximity. Results of the policy scenario analysis suggests that policies aimed at reducing sellers\\’ dependence on credit from buyers and increasing education are the most effective policies for improving value chains and reducing poverty in the region under consideration."],["dc.identifier.doi","10.1016/j.jedc.2020.103944"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/69458"],["dc.language.iso","en"],["dc.relation.issn","0165-1889"],["dc.relation.orgunit","Abteilung Ökosystemmodellierung"],["dc.subject.gro","Agent-based modelling"],["dc.subject.gro","Complex adaptive systems"],["dc.subject.gro","Networks"],["dc.subject.gro","Rubber"],["dc.subject.gro","Indonesia"],["dc.subject.gro","Agricultural trade"],["dc.title","How traders influence their neighbours: Modelling social evolutionary processes and peer effects in agricultural trade networks"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]