A market-consistent framework for the fair evaluation of insurance contracts under Solvency II

Decisions in Economics and Finance - The entry into force of the Solvency II regulatory regime is pushing insurance companies in engaging into market consistence evaluation of their balance sheet,...     

Integrated bio-economic models as tools to support land-use decision making: a review of potential and limitations

Bio-economic modelling has become a useful tool for anticipating the outcomes of policies and technologies before their implementation. Advances in mathematical programming have made it possible to build more comprehensive models. In an overview of recent studies about bio-economic models applied to land-use problems in agriculture and forestry, we evaluated how aspects such as uncertainty, multiple objective functions, system dynamics and time have been incorporated into models. We found that single objective models were more frequently applied at the farm level, while multiple objective modelling has been applied to meet concerns at the landscape level. Among the objectives, social aspects are seldom represented in all models, when being compared to economic and environmental aspects. The integration of uncertainty is occasionally a topic, while stochastic approaches are more frequently applied than non-stochastic robust methods. Most multiple-objective models do not integrate uncertainty or sequential decision making. Static approaches continue to be more recurrent than truly dynamic models. Even though integrating multiple aspects may enhance our understanding of a system; it involves a tradeoff between complexity and robustness of the results obtained. Land-use models have to address this balance between complexity and robustness in order to evolve towards robust multiple-objective spatial optimization as a prerequisite to achieve sustainability goals.     

Social and organisational implications of CAD usage: a grounded theory in a fashion company

This paper focuses on the process of Computer Aided Design (CAD) diffusion into a group engaged in the development of innovative products. Adopting an ethnographic approach, we build a grounded theory for interpreting CAD usage in terms of the interplay between variables such as management orientation, training, actors' specialisation and deskilling, availability of shared archives, technological discontinuities between organisational areas.     

Understanding the Economic Factors Influencing Farm Policy Preferences

A survey conducted in Mississippi, Texas, Indiana, and Nebraska elicited producers' preferences for various farm policy changes. This permitted examination of the diversity of preferences that single-state studies have not allowed. Five policy choices, including deficiency payments, loan programs, crop insurance, export programs, and disaster payments were examined. Logit model results predicting producer preferences for each of the five dichotomous policy choices are reported. Explanatory variables based on expected utility theory such as risk aversion, price and yield variability, and price–yield correlation are significant in various models.     

Analysis of Beef Producers' Risk Management Perceptions and Desire for Further Risk Management Education

Beef cattle producers were surveyed in Texas and Nebraska to investigate perceptions of sources of risk, the effectiveness of risk management strategies, and interest in further risk management education, particularly production risk, using probit analysis. Important decision variables identified are age, prior use of risk management tools, previous attendances of risk management education, and risk aversion. Severe drought and cattle price variability are identified as primary risk factors with potential to affect farm income. Extremely cold weather and disease are of less importance. Understocking pasture and storing hay are perceived most effective as risk management options.     

Estimating the effectiveness of extension information systems using farm trials and subjective probabilities

A method of combining survey data and Dairy Herd Improvement Association (DHIA) records to achieve low cost farm trials is presented. Farm trials and surveys of current practices and production responses are useful to identify yield gaps between expectations predicted from experimental findings and actual field results. Different management schemes can be ranked using subjective probabilities and stochastic dominance to enhance successful implementation of research findings and to increase the feedback between researchers, extension workers, and producers. A survey of current masitis control practices and expected milk yield response is the example. Combining survey results with DHIA records allowed estimating the relationship between somatic cell counts (SCC) and milk yield. Eliciting beliefs about the relationship between SCC and milk yield showed that producers agreed with predictions from the statistical model. Subjective probabilities about SCC and mastitis control practices showed that our sample of experts and producers consistently ranked the different practices but extension agents had no consensus about the the most or least effective ones.