Key MoniQA Output: Scryer

While there is a strong demand for the evaluation of the specific impacts of food safety regulations and a wide body of quantitative research has been proposing methods for monetisation of policy impacts (see Ragona and Mazzocchi, 2008 and references therein), complete and reliable cost-benefit analyses (CBA) are an exception rather than the rule . There are many reasons for this, and the most apparent ones are (a) poor data availability, especially for some key impacts (public health, administrative burdens, etc.); (b) difficulty in isolating confounding factors (e.g. market forces, weather, etc.); (c) probabilistic outcome of some actions, as food hazards may still occur with lower risks; (d) uncertainty in compliance levels; (e) different timing in the occurrence and discounting of costs and benefits (e.g. short-term costs for SMEs vs. long-term health outcomes). Hence, the main obstacles to an effective ex-ante evaluation of food policies depend on the difficulties in quantifying and monetising their impacts. An alternative approach to CBA is multi-criteria decision analysis (MCDA), which can deal with both qualitative and quantitative assessments, and is not constrained by the need of monetisation. Furthermore, impact assessments are also subject to a number of uncertainty factors, which may arise from different sources. Within the MCDA arena a distinction between internal and external uncertainty is provided (Stewart, 2005), where the former stems from difficulties and inconsistencies in judgements or problem structuring, and the latter from the probabilistic nature of external factors (e.g. climate, third party behaviours, etc.). These issues were thoroughly explored within the MoniQA project, under the priority of developing a transparent and flexible approach (named SCRYER) to support the impact assessment of food safety policies. To this purpose, a framework was developed to account for the following aspects: (a) identification of 14 potential impacts, with different levels of detail and aggregation, and a breakdown into sub-impact where needed; (b) explicit consideration of uncertainty in the qualitative assessment of these impact; (c) Inclusion of an element of ‘proportionate level of analysis’ to identify those impacts for which quantification (and possibly monetisation) is needed, feasible and affordable; (d) The possibility of evaluating simultaneously qualitative and quantitative assessments; (e) Targeting an appropriate balance between flexibility (and ease of use) and transparency. Policy ranking in SCRYER is based on fuzzy multicriteria analysis. The approach draws from the discrete multicriteria method by Munda et al. (1995) and clarifies the specific assumptions and steps to make it operational for the MoniQA evaluation toolbox.