Production risk,risk aversion and the determination of risk attitudes among Spanish rice producers

Agricultural production is subject to risk and the attitudes of producers toward risk will influence input choices insofar as these affect production risk. Risk attitudes in turn may be affected by certain socioeconomic characteristics of producers. Using 2004 survey data from a cross‐section of 130 Spanish rice farms, we estimate risk‐aversion coefficients of farmers and investigate the influence of a series of socioeconomic variables on their risk attitudes. Our results show that farmers exhibit risk‐averse behavior and that risk attitudes are related to a series of socioeconomic characteristics. In particular, the belief that the farm will continue after the producer retires is found to increase the degree of risk aversion, while age is found to have nonlinear effects on risk aversion. Off‐farm income, especially from nonagricultural activities, is found to reduce risk aversion. Neither the educational level of the producer nor the presence of dependents on the household is found to have an effect on risk preferences. Regarding the production technology, we find that land, labor, and fitosanitary products are risk‐reducing inputs, whereas capital, seeds, and fertilizer all increase risk.     

EU Policies on Bioenergy and their Potential Clash with the WTO

The paper outlines EU policy on bioenergy, including biofuels, in the context of its policy initiatives to promote renewable energy to combat greenhouse gas emissions and climate change. The EU’s Member States are responsible for implementing EU policy: thus, the UK’s Renewables Obligation on electricity suppliers and its Renewable Transport Fuel Obligation and road‐fuel tax rebates are examined. It is unlikely that EU policy is in conflict with the WTO Agreement on Agriculture or that on Subsidies and Countervailing Measures, but its provisions on environmental sustainability criteria could be problematic.     

If You Provide It,Will They Read It? Response Time Effects in a Choice Experiment

Substantial effort is expended in the design of surveys, including the amount and type of information they contain. However, we often do not know how involved respondents are in reading and processing the informational content of a survey and making choices, and whether different levels of involvement result in systematic differences in estimated models. To address this issue, we recorded response times for each respondent of an internet‐based choice experiment for stream restoration. Response times per survey section and for the entire survey were used as proxies for the amount of involvement in reading information provided or answering choice questions. Response times per survey section fell rapidly, possibly signaling learning, use of heuristics, or attempts to quickly dispel with the survey. Response times were found to be independent of demographics and attitudes. Log‐likelihood ratio tests failed to reject the null hypotheses of equal coefficients and scale parameters across response time‐partitioned data. However, there exists an association between response times and the increasing learning curve or difficult choice trade‐offs, suggesting a heuristic response. Additional research on response time effects and survey design is needed, especially with the rise in electronic surveying media. D'énormes efforts sont investis dans la conception de sondages, notamment pour déterminer la quantité et le type d'information présentée. Toutefois, nous ne savons pas combien de temps les répondants consacrent à la lecture et au traitement de cette information et au choix des réponses, ni si les divers degrés de participation entraînent ou non des différences systématiques dans les modèles estimés. Pour s'attaquer à cette question, nous avons chronométré les personnes qui ont répondu à un sondage en ligne sur la restauration des cours d'eau. Nous avons utilisé le temps de réponse pour chaque section et pour le sondage au complet comme mesure approximative de l'effort des participants pour lire l'information et répondre aux questions. Pour chaque section, le temps de réponse diminuait rapidement, soit en raison des connaissances heuristiques des répondants, soit en raison de leur désir d'effectuer le sondage le plus rapidement possible. Le temps de réponse s'est révélé indépendant des caractéristiques démographiques et des attitudes des répondants. Des tests du rapport de vraisemblance n'ont pas rejeté les hypothèses nulles de coefficients égaux et de paramètres d'échelle de l'ensemble des données cloisonnées. Toutefois, il existe un lien entre le temps de réponse et la courbe d'apprentissage croissante ou la difficulté des choix, ce qui laisse supposer une réponse heuristique. Il faudrait effectuer davantage de recherche sur les effets du temps consacré pour répondre à un sondage et pour le concevoir, en raison du nombre croissant de sondages en ligne.     

BANKING LICENCES, BAILOUTS AND REGULATOR ABILITY

I analyse a model in which it is socially optimal for banks to manage depositor funds but in which concerns about fraud discourage depositing and justify regulation. The regulator screens bankers and decides the level of charter value which they will receive as incentive to prevent fraud. She can also encourage deposits by insuring them. The optimal policy depends upon the regulator's screening ability: high ability regulators rely upon charter value and low ability regulators rely upon deposit insurance. I relate these findings to the regulation of transition economy banks, to operational risk management, and to banking competition policy.     

Higher Intensity,Higher Profit? Empirical Evidence from Dairy Farming in New Zealand

Over the last two decades there have been significant and well‐documented changes in the nature and structure of New Zealand dairy production. One particular feature has been a marked shift in the ‘input intensity’ of dairy farming systems through increased use of supplementary feed. These changes have generated debate about the impact of dairy farm intensity on the performance of farm businesses and the competitiveness of the New Zealand dairy sector. Using a novel econometric approach, we assess statistically the impact of three types of dairy farming systems on milk production and financial performance, using farm business data provided by DairyBase^®. Our empirical results show that higher input systems perform significantly better physically than lower input systems, but not financially. The disaggregated analysis suggests that the average treatment effects differ by region and performance quantiles.