Cost Economies and Market Power in U.S. Beef Packing

Industrial Organization, Livestock Production/Industries,

THOUGHTS ON PRODUCTIVITY, EFFICIENCY AND CAPACITY UTILIZATION MEASUREMENT FOR FISHERIES

Productivity Analysis, Resource /Energy Economics and Policy,

TECHNOLOGIES AND LOCALIZED TECHNICAL CHANGE

Heterogenous Technologies, Transformation Function, Localized Technical Change, Production Economics, Research Methods/ Statistical Methods, Q12, O33, C35,

Distinguishing Different Industry Technologies and Localized Technical Change

This contribution is based on the notion that different technologies are present in an industry. These different technologies result in differential “drivers” of economic performance depending on the kind of technology used by the individual firm. In a first step different technologies are empirically distinguished. Subsequently, the associated production patterns are approximated and the respective change over time is estimated. A latent class modelling approach is used to distinguish different technologies for a representative sample of E.U. dairy producers as an industry exhibiting significant structural changes and differences in production systems in the past decades. The production technology is modelled and evaluated by using the flexible functional form of a transformation function and measures of first- and second-order elasticities. We find that overall (average) measures do not well reflect individual firms’ production patterns if the technology of an industry is heterogeneous. If there is more than one type of production frontier embodied in the data, it should be recognized that different firms may exhibit very different output or input intensities and changes associated with different production systems. In particular, in the context of localized technical change, firms with different technologies can be expected to show different technical change patterns, both in terms of overall magnitudes and associated relative output and input mix changes. Assuming a homogenous technology would result in inefficient policy recommendations leading to suboptimal industry outcomes.Heterogenous Technologies, Transformation Function, Localized Technical Change, Production Economics, Q12, O33, C35,

Corporate Social Responsibility and Economic Performance

We describe some perspectives on corporate social responsibility (CSR), in order to provide a context for considering the strategic motivations and implications of CSR. Based on this framework, which is based on characterizing optimal firm decision making and underlies most existing work on CSR, we propose an agenda for further theoretical and empirical research on CSR. We then summarize and relate the articles in this special issue to the proposed agenda.

The association between cardiorespiratory fitness and cardiometabolic risk in children is mediated by abdominal adiposity: the HAPPY study

Background: It is unclear whether cardiorespiratory fitness (CRF) is independently linked to cardiometabolic risk in children. This study investigated a) the association between CRF level and presence of cardiometabolic risk disorders using health-related cut points, and b) whether these associations were mediated by abdominal adiposity in children. Methods: This was a cross-sectional design study. Anthropometry, biochemical parameters and CRF were assessed in 147 schoolchildren (75 girls) aged 10-14 years. CRF was determined using a maximal cycle ergometer test. Children were classified as ‘fit’ or ‘unfit’ according to published thresholds. Logistic regression was used to investigate the odds of having individual and clustered cardiometabolic risk factors according to CRF level and whether abdominal adiposity mediated these associations. Results: Children classified as unfit had increased odds of presenting individual and clustered cardiometabolic risk factors (p 0.05). Conclusions: This study suggests that the association between CRF and cardiometabolic risk is mediated by abdominal adiposity in 10-14 year-old children and that abdominal adiposity may be a more important determinant of adverse cardiometabolic health in this age group