have been linked to certain pesticides); (3) WTP to reduce effects on the reproductive system; and (4) WTP to reduce antibiotic resistance.
For some phenomena, it is not clear whether more data are needed or whether existing data need to be more thoroughly analyzed or interpreted. For example, one participant asked whether more research is needed on populations that tend to be excluded from research studies. Steven Wing responded that, in some cases, “we have a lot of data, but we are not paying attention to the data that we have.” The greater challenge, in his opinion, is the question(s) being asked. He mentioned a long history of research on questions that are of economic interest to producers and a short history of research on questions related to the health and environmental impacts of production systems. “So do we need more [data]? Maybe we do,” he said. But the lack of information is also a result of “who is at the table.” As another example, a participant questioned the call for more scientific evidence on the association between antimicrobial use in food animals and antimicrobial resistance in humans. He implored that scientists have known about the cost of antimicrobial resistance for decades, since the 1969 Swann Committee report (Swann et al., 1969). Yet, the demand for more data persists. Why? He called for more discussion on the political nature of the debate about antimicrobial use in food animals.
Early on during the workshop, an audience member commented on the complexity of the food system and its wide range of effects and asked whether there was a way to ensure that all costs and benefits have actually been measured. He said, “What if I miss something? … The ultimate answer would be just wrong.” Even if the focus is on marginal costs, not total costs, still the dimensions of that margin need to be known. “To some extent,” he said, “I have sort of despaired listening to this conversation.” John Antle expressed similar concern about the wide range of effects, noting that policies that fail to consider important consequences “really mess things up.”
Given what she characterized as “squishiness” from a lack of data and problems with analyzing those data, Katherine Smith questioned the intention of tallying up all costs and benefits to derive an estimate of the total “true” cost of food. She suggested evaluating the effect of public policy on one “dimension” or on the trade-offs between a couple of dimensions of the food system, instead of calculating total cost. Jayson Lusk and others agreed that analyzing the costs and benefits of specific interventions might be a more feasible research strategy than estimating the “true” cost of food.