Consumption of welfare-friendly food products in Great Britain, Italy and Sweden, and how it may be influenced by consumer attitudes to, and behaviour towards, animal welfare attributes

The interest in animal welfare and welfare-friendly food products has been increasing in Europe over the last 10 years. The media, highlighting traditional farming methods and food scares such as those related to salmonella, bovine spongiform encephalopathy/variant Creutzfeldt-Jakob disease (BSE) and avian influenza, have brought the methods of animal farming to public attention. Concerns about farm animal welfare are reflected in the increase in the number of vegetarians and vegans and an increase in consumers wishing to purchase food which is more animal welfare-friendly. This paper considers consumers’ attitudes to animal welfare and to marketing practices, such as product labelling, welfare grading systems and food assurance marks using comparative data collected in a survey of around 1500 consumers in each of Great Britain, Italy and Sweden as part of the EU-funded Welfare Quality research project. The findings suggest a need for the provision of improved consumer information on the welfare provenance of food using appropriate product labelling and other methods.

A structural time series approach to modelling multiple and resurgent meat scares in Italy

This paper exploits a structural time series approach to model the time pattern of multiple and resurgent food scares and their direct and cross-product impacts on consumer response. A structural time series Almost Ideal Demand System (STS-AIDS) is embedded in a vector error correction framework to allow for dynamic effects (VEC-STS-AIDS). Italian aggregate household data on meat demand is used to assess the time-varying impact of a resurgent BSE crisis (1996 and 2000) and the 1999 Dioxin crisis. The VEC-STS-AIDS model monitors the short-run impacts and performs satisfactorily in terms of residuals diagnostics, overcoming the major problems encountered by the customary vector error correction approach.