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.     

The desired level of market orientation and business unit performance

Existing studies of market orientation have hypothesized that the strength of the market orientation/performance relationship depends on environmental variables such as market turbulence, technological turbulence, and competitive intensity. To date most empirical studies have failed to confirm these hypotheses; however, these studies (1) assumed that performance is a linear function of the achieved level of market orientation and (2) tested whether environmental uncertainty moderates this relationship. A complementary explanation for the impact of environmental variables on a firm’s market orientation arises from studies of organizational behavior that link the need for coordination and control to environmental uncertainty and organizational strategy. Building on this perspective, the authors argue that (1) environmental uncertainty influences the desired level of market orientation and (2) the gap between the desired and achieved levels of market orientation influence business unit performance. The authors test these hypotheses with data collected from multiple respondents in 308 US firms. The data analysis confirms that the desired level of market orientation is a function of market turbulence, competitive intensity, technological turbulence, and innovation strategy. In addition, the desired level of market orientation positively influences the achieved level. Finally, when the achieved level of market orientation is less than the desired level, business unit performance is a negative function of the gap between the desired and achieved levels of market orientation.